Venture Capital and the Survival of Entrepreneurial Firms in Crisis Periods: The Case of Covid-19.

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Marek Kacer, Nicholas Wilson, Sana Zouari, Marc Cowling This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3920888/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper investigates the survival of entrepreneurial firms during the pandemic period. Specifically, we focus on UK companies that received equity finance during their developmental stages before the onset of Covid-19. The equity finance investors in our study include venture capital and growth finance funds (both domestic and foreign), crowd funding platforms, business angels, and government venture capital funds. We build on the resource-based view (RBV) and signalling theories to develop our hypotheses. We analyse the bankruptcy processes of companies during the Covid-19 period, comparing it to the pre-Covid period. We examine various characteristics of these firms, such as their investor type, deal history (including timing, magnitude, and duration), as well as a range of financial and non-financial factors. Furthermore, we identify the equity-backed companies that utilized policy interventions in the form of guaranteed loans. We gather details about the loan contracts, lenders, and instances of loan default. This study explores the relationship between bankruptcy and loan default in relation to the firm's characteristics, investor type, investment dimensions and financial constraints. The results provide valuable insights into the link between equity financing and venture survival during crises, with important implications for policy interventions. JEL classifications G12. G33. H81. L26 Finance COVID-19 Entrepreneurial Firms Bankruptcy Government Interventions Figures Figure 1 Plain English Summary This research paper looks at how entrepreneurial firms in the UK were affected by the COVID-19 pandemic. Specifically, the study focuses on companies that received equity finance during their early stages of development before the pandemic began. The paper examines the types of investors, such as venture capital funds, growth finance funds, crowd funding platforms, business angels, and government venture capital funds. The paper is based on two theories - the resource-based view (RBV) and signalling theories - to develop hypotheses. It analyses the bankruptcy processes of firms during the pandemic and compares them to the period before COVID-19. The researchers consider various factors, including the type of investors, the history of deals (such as timing, magnitude, and duration), as well as financial and non-financial aspects of these companies. Additionally, the paper identifies the equity-backed firms that utilized policy interventions in the form of guaranteed loans. The researchers obtain details about the loan contracts, lenders, and instances of loan default. The study explores the relationship between bankruptcy and loan default, considering the characteristics of the firms, the type of investors, investment dimensions, and financial constraints. The findings of this study provide valuable insights into how equity financing impacts the survival of entrepreneurial ventures during crises like a pandemic. The implications of these findings can be useful for policymakers in designing interventions and support mechanisms for such firms. 1 Introduction This paper investigates the impact of Covid-19 on bankruptcy patterns and policy interventions, focusing on equity-financed firms in the UK corporate and Small and Medium Enterprise (SME) sector. It aims to assess the effects of the pandemic on the equity finance 'eco-system' and the outcomes for these firms at different stages of development. The pandemic disproportionately affected the supply of debt and equity finance, particularly impacting small, young, and innovative companies. Government interventions, such as loan guarantee schemes, have primarily focused on debt finance, with limited additional support for equity finance. This poses challenges for early-stage ventures heavily reliant on rounds of venture capital investments, seeking follow-on funding from existing or new investors; and for VC’s in (re)appraising the prospects of their portfolio of investments through the crisis. The unprecedented scale of policy interventions during the pandemic provided significant government-guaranteed loans to businesses (Cowling et al. 2023b). These loans were administered, by a large pool of established and new lenders, without the usual credit checks and relied on businesses' self-certification of eligibility criteria. The challenge for policymakers was to prevent widespread insolvency and job losses while also ensuring the preservation of ‘creative destruction’ (Dorr et al. 2022; Demmou, 2021). Equity finance is known as ‘patient capital’ but venture capitalists recognise that not all their ventures will succeed and, after a certain level of cumulative investment or rounds of investment in a venture, they make the decision to continue financing or close down the business (Ragozzino & Blevins, 2016). We examine whether the covid period expedited this appraisal and closure decision. For earlier stage equity-financed firms, the loan schemes presented a unique opportunity to increase liquidity and prospects i.e. to borrow to ‘ try get lucky’ and survive (Dorr et al. 2022). Indeed, the take-up rate of Covid-19 loans within our sample is 45%, and it is worth noting that a significant proportion of loan recipients applied for the maximum loan amount, indicating potential unmet borrowing demand. By utilizing a unique panel database and detailed financial and non-financial data, we examine the bankruptcies and insolvencies of firms that used Covid loans, comparing them to others during the 2020-2023 period. Additionally, we have access to a comprehensive loan portfolio database with information on borrower and lender characteristics, loan terms, and incidences of default and losses. The outcomes of equity-backed firms that utilized the loan guarantee schemes are examined in relation to loan default. Analysing a unique panel database covering the population of limited companies in the UK, data on equity finance deals, and the government guaranteed loan portfolio our paper contributes to a growing body of literature that examines the consequences of the COVID-19 crisis for the corporate and SME sector and assesses the impact of policy interventions (Gourinchas et al., 2022). We contribute to the literature on firm resilience (Lavery et al., 2023) and failure (Gourinchas et al., 2020) in periods of crises. Our analysis aligns with Door et al. (2022) examining the German context and Wang et al. (2020) for the US who also provide evidence that policy interventions lead to a backlog of insolvencies, an ‘insolvency-gap’, with consequences for economic dynamism. Uniquely our analysis focuses on equity-financed companies, an important cohort of small, growing and innovative businesses but also a subpopulation subject to ‘market failures’ in both the provision of debt (credit rationing) and equity finance. The challenges of distinguishing good and bad investments create equity-gaps (Wilson et al. 2019), and lack of early-stage funding or the ‘Valley of Death’ (Wilson et al. 2018), lack of follow on funding. Challenger banks and a pool of new alternative lenders have evolved in the UK to fill these gaps. We are interested therefore in the pattern of insolvent exits in the pandemic period of this subset of firms, their characteristics (see Cros et al. 2021) , use of debt finance, guaranteed loans, the role of new lenders and insolvency. We shed light on likely abandon decisions by investors in early-stage ventures, seeking additional finance, and examine variations in failure/survival by investor type. Finally, we explore the equity-financed firms that utilised guaranteed loans and those at the intensive margin (maximum loan) profiling the characteristics of the firm, the role of different lender types and default (hazard). Our findings provide important insights for policy intervention. The paper is structured as follows. In the next section (2), as background, we provide some analyses of the pattern of company bankruptcies, generally, and through the Covid period. We are particularly interested in policy intervention and an apparent ‘insolvency gap’ (Dorr et al. 2022) in the early phase on the pandemic. In section (3) we review relevant literature and develop hypotheses regarding our expected outcomes. In section (4) we discuss our data, variable construction, and methodology before presenting the results (5) and conclusions (6). 2 Background: Bankruptcy, equity finance investment and Covid In this section we provide some background on bankruptcy trends in the UK based on official statistics and firm level records. The latter are used to indicate the insolvent exit of each registered company in the company data panel database discussed later 1 . Over the period from the 1980’s to 2020, insolvencies are on the whole counter cyclical where increases in insolvencies coincide with, or lag periods of contraction (negative growth) as might be expected. Of course, the insolvent failures, can be viewed as part of the competitive process, ‘creative destruction’, that removes the inefficient and non-viable businesses from the economy such that resources are reallocated to the more efficient, innovative, and growing businesses that take their place. It might be expected, using the 2008 financial crisis as an anchor, that the Covid contraction would produce a comparable spike in company insolvencies. However, the pattern differs, insolvencies drop below ‘normal’ levels after the severe contraction. A sizeable ‘insolvency gap’, the survival of non-viable businesses, is apparent through 2020 and into Spring/Summer 2021 primarily coinciding with the introduction of the covid loan guarantee schemes (March 2020 to March 2021) and other policy interventions (Wilson et al. 2023 ). Regarding equity-financed ventures, because of the short-term pressures on equity finance markets (Gompers et al. 2021 ; Gompers et al. 2020 ; Cumming & Reardone, 2022) and investor uncertainty (British Business Bank, 2020 ), follow-on funding for early-stage ventures was in short supply and the Covid period saw a ‘flight to quality’. In times of economic turbulence and crisis, investors and funders are likely to shift away from risky assets to contain potential losses. We analyse data provided by Beauhurst that includes 66,748 deal-level observations over the period from 2011. In short, the pre-pandemic period up to and during 2020 equity investments showed strong growth in both the number of deals and the total investment volume. However, the number of deals and the investment volume dropped markedly in the second and third quarter of 2020, coinciding with Covid lockdowns. The seed and venture stage investments appear to be the hardest hit by the pandemic (Kacer & Wilson, 2023 ). As was anticipated, there was also a marked overall increase in the average deal value driven, predominantly, by investors focusing on later stage, higher rounds and announced investments. This shift in the pattern of investment has consequences for earlier stage equity financed companies that did not have an ‘active’ VC investor as a shareholder. These firms likely sought alternative sources of finance and had a new opportunity for financing with the roll out of guaranteed loan schemes. Loan guarantee schemes (LGS), that are implemented by governments to address market failures in the provision of debt finance for firms, have a long history in the UK and other countries. The rationale for intervention is to overcome credit rationing and create additionality (Cowling, 2010 ) by filling a credit gap, facilitating growth of the SME’s sector, and improving GDP (Panetta, 2012 ). Although some studies have highlighted negative impacts on default rates and business bankruptcy due to adverse selection by lenders (Gai et al. 2016 , 2023 ; Lelarge et al. 2010 ) and moral hazard behaviours (Myers & Majluf, 1985) by lenders and borrowers. Combined with other policy interventions, the covid loan guarantee schemes were effectively an extension of the EFG scheme but designed to help businesses cope with the uncertainty related to the Covid pandemic, avert a major business insolvency crisis, ensure the longer-term health of the corporate sector, and enable quick recovery. The UK government offered two loan guarantee schemes to smaller firms to support liquidity during the Covid-19 crisis. The BBL scheme had a loan cap of £50,000, a guarantee of 100%, and a fixed interest rate of 2.5%. The CBILS scheme had a loan cap of £5m, a guarantee of 80% and the lender set the interest rate and fees on commercial terms 2 . The focus of our analysis in the 2 main loan guarantee schemes (BBLS, CBILS). The Government-owned British Business Bank (BBB) oversaw the schemes, as guarantor. A wider range of accredited lenders were involved in processing applications and loans. This included Banks, Challenger Banks, P2P lenders, and Alternative Finance (Fintech). The BBB and the lenders rolled out the schemes at some speed, according to Browning (2023) 3 in 11 days in the case of BBLS 4 . The COVID-19 loan schemes, such as BBILS, were relatively cheap and easily accessible, potentially influencing the behaviour of loan recipients and creating moral hazard. Borrowers had incentives to use the funds for debt refinancing, replacing higher-priced debt, rather than providing ‘additionality’ in financial resource. From the lenders' perspective, established lenders had the opportunity to shift riskier portions of their loan portfolios into the loan guarantee schemes, while new entrants like Challenger Banks and Alternative Finance providers could expand their client bases by accepting a higher default rate with the assurance of government guarantees. As discussed, equity-backed firms may have faced challenges in raising further rounds of investment during the pandemic. Additionally, equity-funded firms in earlier stages of development and specific industry sectors were subject to the same cash and liquidity problems and financial constraints as other SMEs. Therefore, the loan schemes may have provided an opportunity for these firms to seek finance from mainstream banks or the new pool of specialist lenders. In a subsequent section we focus on profiling the characteristics of equity-backed firms that utilized the guaranteed loan facilities. 3 Literature review and development of hypotheses Crises should cleanse the economy of unproductive and inefficient firms facilitating the reallocation of resources to more productive uses. The pattern of insolvency differs from the crises in the past. More specifically, the failure rates dropped, due to the government measures aimed at helping businesses to overcome the coronavirus pandemic (Dorr et al. 2022 ). It is clear from the pattern of insolvencies that firms that were otherwise unviable in the immediate period pre pandemic, survived the initial covid period as a result of policy intervention. Policy intervention could have unintended consequences in that it could have created zombies – firms are sustained temporarily only because of easy access to low interest rate debt. We aim to investigate whether this is the case for equity backed firms or did the Covid period precipitate a reappraisal of their portfolio firms long term prospects? An important question is whether we observe the insolvency gap in multivariate models when we control for the relevant firm level predictors of insolvency and the type of equity investor. Our data sample of equity financed companies includes heterogeneity in terms of type of investor, stage of development (and size), the number, size, and cumulative amounts (rounds) of investment, technology, sector, and location. Some may have combinations of equity and debt in their capital structure and can be a various stage of commercialisation (income generation and profit). Equity financing is supplied by venture capital funds and private equity (VC, PE), domestic or foreign; business angels (BA), and government funding (GV). More recently crowdfunding, peer to peer lending (P2P) has provided alternative funding channels. Funding is targeted at stages of development form start-up to follow-on and growth finance and often involves syndicates of co-investors. VC’s, with a track record and associated expertise, may be regarded as the most ‘credible’ partners for new ventures (Manigart et al., 2002). Having a VC relationship helps the venture build reputation in the market, overcome the liability of newness (Ragozzino & Blevins, 2016 ). Business Angels have more credibility as an investor and are more proactive that the platform investors. In addition to providing finance, Venture capital (VC) firms play an important role in supporting and enhancing their portfolio companies and protecting VC reputation and ‘assets, value creation’. As ‘active investors’, they not only provide capital but strategic guidance, operational support, and networking opportunities to ensure the investees (value) survive and grow (Gompers & Lerner, 2016). The study suggests that VC involvement enhances a company's access to resources, knowledge, and contacts, which are crucial for growth. Buchner et al (2014) found that VC-backed companies experienced milder declines in employment and sales compared to non-VC-backed companies during the financial crisis. They attribute this resilience to the monitoring, strategic guidance, and financial support provided by VC firms. Bernstein et al (2017) examine the impact of the global financial crisis on PE-backed companies in the United Kingdom. The authors find that PE-backed companies in the UK were more resilient during the crisis compared to non-PE-backed companies due to the PE’s operational and financial support (bridging finance, equity injections, debt restructuring). Empirical papers draw on the resource-based view (RBV) of the firm, ‘dynamic capabilities’, “ the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments ” (Teece et al. 1997 ; Barney & Clark 2007 ) and ‘ resilience’ theories (REFS). Resource-based theories of the firm discuss business resilience by highlighting the importance of the firm's resources and capabilities in adapting and responding to external shocks and challenges. These theories suggest that a firm's ability to withstand and recover from disruptions is closely related to the specific resources it possesses and how effectively it can leverage them. Firms backed by established and experienced VC funds can draw on relevant business expertise and financial resource. The related literature on private equity investors also emphasises their role as ‘active investors’. Often as a major or majority shareholder, they likely have board representation and a close involvement in both strategy development and implementation, and the day-to-day monitoring of management. The investors have a pool of managerial expertise and can leverage their business networks and strong ties with banks and providers of credit to provide additional funding and resource when the investee faces challenges. In crisis periods they can provide additional injections of equity finance (Lavery et al. 2023 ) to alleviate financial difficulties. VCs oftentimes invest in loss-making enterprises because of an expectation that they will generate high returns in the future. However, VC investors are incentivized to make the best possible investment decisions, as investment outcomes determine not only the VC investor’s return but also their individual rewards (Wright & Robbie, 1998 ). When investments fail to meet initial expectations, decision makers face a liquidation dilemma: they may favour continuing projects to retain the option of improvement and escalate commitment (Guler, 2007 ) or they may decide to abandon them, resulting in the crystallization of certain losses (Li & Chi, 2013 ). As a large percentage of VC projects eventually fail (Puri & Zarutskie, 2012 ), VC investors are routinely faced with liquidation dilemmas, making them experts in abandonment decisions. The limited life of VC funds, which requires investors to exit their investments within a particular period, limits the time and incentive for poorly performing ventures with no prospect of improvement (‘living dead’ or ‘zombie’ cases) to be propped up through extending further funding i.e. they lose patience. Of course, as the development time span increases, proxied by the duration of VC involvement and the number of rounds of investment , the more likely that technologies may change, competitors emerge and the ventures’ momentum and value wane (Ragozzino & Blevins, 2016 ). In this case VCs are more likely to devote their limited attention capacity to those, more established, investments expected to generate the returns they need to satisfy their investors (and hence enable the VC to raise a subsequent fund) (Cumming & Dai, 2011). The covid period required that the VC’s re-evaluated the prospects of their portfolio firms and brought forward the ‘abandonment decision’ for those ventures that have had successive rounds of investment but without commercial success. There will be a threshold of investment level and duration of investment at which the VC reappraises whether to continue with or abandon the venture. On the other hand, the greater the VC’s ‘sunk costs’, the total amount of cumulated investment in the venture, and/or the extent of R&D, the more likely they are to be actively involved in assisting the venture to weather the crisis. For these reasons, we do not expect that VCs will be involved in propping up unviable businesses during the crisis. This leads to our first hypothesis. Hypothesis 1 VC investors will actively support the strong prospects in their portfolios through the crisis but allow non-viable businesses to fail. Consequently, we do not expect the pattern of insolvencies of equity financed firms to be different from earlier periods. Empirical studies of venture capital investment and exit decisions often draw on theories related to informational asymmetry (Akerlof, 1970 ; Gompers & Lerner, 2004 ; Ravenscraft & Scherer, 1987 ) and signalling (e.g., Spence, 1973 ; Higgins & Gulati, 2006 ; Zimmerman, 2008 ) as a framework for hypothesis development and analysis. Entrepreneurs and venture capital funds buying into entrepreneurial ventures as outsiders are faced with serious informational asymmetry problems, regarding the long-term prospects of the business and valuation, that due diligence finds difficult to uncover (Wilson et al. 2019 ; Robbie & Wright, 1995 ). Consequently, they gather and act on credible ‘signals’ of the quality of the venture and the entrepreneurs’ expertise (Higgins & Gulati, 2006 ; Lester, 2006; Zimmerman, 2008 ). Ragozzino and Bevins (2016), compile ‘ signals of quality’ in entrepreneurial firms by measuring aspects of venture capitalist firms’ previous involvements in entrepreneurial companies. The signals that are ‘ observed and costly to obtain’ (op cit. p. 993) include the presence of a VCs in the company and duration of this presence, the number of VCs that have invested in a company, the timing of their funding rounds, and the total amounts invested. Following Ragozzino and Bevins (2016), we create a number of variables relating to the history of deals for our equity-backed firms. These include investor type (VC (domestic or foreign), Business Angel, Crowd Funding, Government VC), the stage of investment (Seed, Venture, Growth, Established), whether the company had announced deals, the number of rounds of investment and cumulative amount of investment, the purpose of investment (R&D, job creation) and the period of time from the first deal and from the last deal. Following previous studies (e.g. Manigart et al. 2002) we suggest that being backed by reputable and experienced VC’s and/or Business Angels is a strong signal for additional investors and financiers. In our data this includes the domestic and foreign based VC funds. The number of rounds and cumulative investment of previous equity-backers acts as a signal of survival and future growth potential. More recent rounds are associated with more recent appraisal, due diligence, and valuation. The entrepreneurial firms with these characteristics are more likely to find support from existing investors and banking relationships in order to survive the covid shock. These characteristics can counteract the ‘liability of newness’ in relation to survival/failure companies backed by crowd funding and/or government funds and at earlier stages lack these quality signals. Of course, risk is higher for firms in consumer facing sectors. We posit, therefore that these firms are more likely to seek guaranteed loan funds to help ride the covid period and are more likely to be subject to insolvency proceedings. Our second hypothesis is: Hypothesis 2 Early-stage equity-backed companies, crowd-funded and government backed are more likely to have guaranteed loans and enter insolvency than later stage ventures with high cumulative VC investment. In our analysis we seek to understand the impact of the guaranteed loan scheme on firm survival. Of course, it is likely that firms on the ‘edge of failure’ going into the Covid period would seek finance as an opportunity to increase their chances of survival, particularly in a period of increased forbearance. The funds may be used to refinance away from more expensive existing loans. Furthermore, the pandemic impacted on all firms and therefore the competitive environment changes, at least temporarily, increasing the possibility of survival. If the company is otherwise viable and needs the funds just to overcome the temporary difficulties created by lockdowns and the decrease of economic activity, the covid loan helps to bridge the difficult period. If, on the other hand, the company is not viable, the covid loan represents a temporary easing which simply delays company failure until the funds are exhausted. Moreover, it is expected that after the external help is withdrawn (used up), these companies, having an additional burden of debt and additional creditor(s) will proceed to bankruptcy and thus the number of insolvencies will increase. Early-stage equity-backed firms, without additional equity injections are more prone to failure than other firms and the funding available through guaranteed loan schemes is unlikely to support the survival of those smaller firms that were in a poor financial position pre covid. Indeed, in the descriptive analysis that follows, we note that a high proportion of recipients reached the intensive margin in that they exhausted all their BBL borrowing capacity (loan-sales ratio = 25%) indicating that they may still have an unmet demand for borrowing and remain in a precarious position. This leads us to the formulation of the fourth hypothesis: Hypothesis 3 Equity-backed firms with a covid loan have a higher probability of failure in the covid period. Following from the previous discussion we are interested in the more detailed characteristics of our sample firms that sought and acquired covid guaranteed loans. Those equity-backed firms that do not have a VC on board are more likely to seek guaranteed loan finance. These are likely smaller, earlier stage ventures in a weaker financial position pre-covid and/or in sectors worst hit by the pandemic. Our third hypothesis concerns selection into the loan guarantee scheme: Hypothesis 4 Early-stage equity-backed companies, crowd-funded, angel and government backed are more likely to seek guaranteed loan finance than later stage and those with more rounds and higher cumulative VC investment. After analysing the characteristics of firms that seek for covid loan finance, an important question arises whether the lender type ultimately impacts the loan default patterns. Namely, due to the government financial support, there was an opportunity for established lenders to off-load the riskier parts of their loan portfolios into the loan guarantee scheme (refinance) and for challenger banks to grow their client base by accepting a high rate of default but at a low risk of losses due to the government guarantee. Hypothesis 5 Challenger banks are more likely to attract riskers SMEs and have a higher default rate 4 Data and Methods 4.1 Data To proceed with the analysis, we combine several datasets. We start with the dataset of equity funded companies. This data is provided by Beauhurst and covers equity deals from the beginning of 2011. 5 Beauhurst data covers over 90% of equity deals in the UK after 1 January 2015, both publicly announced and unannounced. Before 1 January 2015, the coverage of unannounced deals is not comprehensive. The data on unannounced deals is obtained from SH01 forms (The Return of Allotment of Shares) submitted by firms to Companies House. The remaining, less than 10%, is not covered due to incorrect filings in Companies House, etc. This dataset provides detailed information on individual deals such as deal value, stage of evolution of the company, round of funding, identity of investors, and industry sector. The resulting panel dataset covering all UK registered companies comprises financial information, details of industry sector, age, location, etc. and a long time period including the years pre- and post-covid. This data is merged, via company registration number, with a dataset that tracks all insolvent exits sourced from ONS (Office of National Statistics – the UK statistical office). We match this data to firms that have received any equity finance in the period before the Covid pandemic. Finally, we have unique access to the detailed loan information on each company from the covid loan portfolio which is drawn from the Information Management System of the Covid loan guarantee scheme. This covers all loans that were administered in the schemes. The information in this dataset covers, among others, loan amount, loan terms, lender identity and loan state. Thus, we have data on the large sample of equity funded companies, sub-sample of covid loan recipients and all recorded loan defaults and insolvent exits. Our sample selection process is detailed in Table 1 . Table 1 Sample Selection Steps Insolvent Covid Loans Panel A: Main estimation sample (covid period) Companies Companies with at least one equity deal before 31/3/2020 20,053 Less Companies without last available accounts between 1/4/2017 and 31/3/2020 -2,492 Companies that became insolvent before 31/3/2020 -392 Companies with missing values for explanatory variables -896 Companies with missing values for dependent variable (Northern Ireland) -220 Holding companies -2,009 Zero total investment -258 Final estimation sample 13,786 653 6,234 Insolvent Panel B: Historical control sample companies Companies with at least one equity deal before 31/3/2017 12,033 Less Companies without last available accounts between 1/4/2014 and 31/3/2017 -1,139 Companies that became insolvent before 31/3/2017 -199 Companies with missing values for explanatory variables -504 Companies with missing values for dependent variable (Northern Ireland) -154 Holding companies -1,326 Zero total investment -180 Final estimation sample 8,531 466 Notes: The table shows the steps involved in the preparation of the company level samples employed in the first part of the study. Panel A shows how the main covid period sample was constructed. This sample includes all eligible companies with an equity deal at the beginning of the covid period, i.e., as of 31st March of 2020. Panel B shows how the historical control sample has been constructed. The historical control sample includes all eligible companies that had an equity investor as of the 31st of March 2017. In each of these two samples, every observation corresponds to one company. The sample created by appending the two samples (the combined sample) has been used for quantification of differences in failure rates in the pre-covid and covid period. The main estimation sample was employed to quantify differences in failure rates for companies with and without a covid loan. Table 1 here We identify 20,053 firms that have had at least one round of equity finance prior to the pandemic period, more specifically, on or before the 31 March 2020. 6 Since we need the latest financial information at the start of pandemic, we exclude 2,492 companies without any financial accounts in the three-year period before the 31st of March 2020. 7 Next, in our analysis we focus on the insolvent exits in the three-year period from the beginning of April 2020 to the end of March 2023, we exclude 392 companies that became insolvent already before the 31st of March 2020. Then, we exclude 896 companies with missing values for any of the explanatory variables and 220 companies from the Northern Ireland because this region is not covered by ONS insolvency dataset. We exclude 2,009 holding companies due to different financial and assets’ structure, as well. Finally, we remove 258 companies where the total investment in all equity deals is zero. This leaves us with 13,786 firms in the covid period estimation sample. Of these, 653 are bankrupt (insolvent) in the covid period and 6,234 acquired guaranteed loans. This sample represents the main estimation sample as it includes equity funded companies that were solvent at the beginning of the covid period. We term this sample "covid period sample" in the following text. Following the study of Dorr et al. ( 2022 ) we construct a control sample for the covid period sample that includes the same type of companies from the prior (pre-covid) period. Therefore, mirroring the above-mentioned sample selection steps we construct a three-year pre-covid historical control sample of equity-backed firms starting from 2017 (Q2) comprising 12,033 firms, of which there were 466 bankruptcies during the control period after going through all sample selection steps we have 8,531 firms in the sub-sample. This sample is then appended to the covid period sample. The resulting sample contains 22,317 observations and will be termed "combined sample" in the following text. Finally, in the last part of the paper, we analyse covid loans taken by companies in the covid period sample. There are 6,234 companies that obtained one or more loans under a covid loan guaranteed scheme. This sample contains 6,936 observations (covid loans) In Table 2 we provide descriptive statistics of our main sample. A full list of constructed variables, their sources and definitions are provided in the appendix. Table 2 Descriptive statistics for the combined sample Variable N Mean SD Min Median Max Venture Capital 22,317 0.169 0.374 0 0 1 Business Angel 22,317 0.136 0.343 0 0 1 Crowd Funding 22,317 0.084 0.278 0 0 1 Government VC 22,317 0.066 0.249 0 0 1 Foreign VC 22,317 0.073 0.259 0 0 1 LN(Total Assets £m) 22,317 3,083 124,500 1 282 18,211,941 LN(Total Assets >£1m) 22,317 12.423 2.201 0.000 12.552 23.625 Working capital to total assets 22,317 -0.106 1.092 -2.988 0.209 1 Current assets to total assets 22,317 0.756 0.304 0.011 0.917 1 Current liabilities to total liabilities 22,317 0.822 0.290 0.101 1 1 Profit/loss account reserve to total assets 22,317 -1.358 1.721 -4.051 -0.734 0.983 Short and Long-term debt to total assets 22,317 0.163 0.275 0 < 0.001 0.820 Indicator of charge on assets 22,317 0.074 0.261 0 0 1 Indicator of no debt 22,317 0.497 0.500 0 0.000 1.000 Ex ante risk score 22,317 0.028 0.033 0 0.019 0.675 Missing risk score 22,317 0.044 0.206 0 0 1 Seed Stage of Investment 22,317 0.561 0.496 0 1 1 Venture Stage of Investment 22,317 0.306 0.461 0 0 1 Growth Stage of Investment 22,317 0.077 0.267 0 0 1 Established Stage of Investment 22,317 0.056 0.229 0 0 1 Number of Rounds 22,317 2.286 1.731 1 2 16 Announced Deal 22,317 0.385 0.487 0 0 1 LN(Total Investment) 22,317 12.796 1.816 6.059 12.692 20.997 Time from first deal (days) 22,317 1,277 837 0 1,131 3,377 Time from last deal (days) 22,317 778 727 0 533 3,373 Investment purpose (R&D) 22,317 0.045 0.207 0 0 1 Investment purpose (Job creation) 22,317 0.041 0.199 0 0 1 Buzzword (AI) 22,317 0.055 0.228 0 0 1 Buzzword (Fintech) 22,317 0.059 0.236 0 0 1 Sector (Media) 22,317 0.112 0.315 0 0 1 Sector (Industrial) 22,317 0.237 0.425 0 0 1 Sector (Infrastructure) 22,317 0.034 0.181 0 0 1 Sector (Retail) 22,317 0.087 0.282 0 0 1 Sector (Crafts) 22,317 0.021 0.142 0 0 1 Sector (Leisure) 22,317 0.120 0.325 0 0 1 Sector (Supply Chain) 22,317 0.026 0.160 0 0 1 Sector (Professional services) 22,317 0.431 0.495 0 0 1 Sector (Trades) 22,317 0.016 0.124 0 0 1 Sector (Personal services) 22,317 0.089 0.285 0 0 1 Sector (Technology) 22,317 0.539 0.498 0 1 1 Sector (Energy) 22,317 0.021 0.144 0 0 1 East Midlands 22,317 0.028 0.165 0 0 1 East of England 22,317 0.083 0.275 0 0 1 London 22,317 0.415 0.493 0 0 1 North East 22,317 0.026 0.159 0 0 1 North West 22,317 0.063 0.244 0 0 1 Scotland 22,317 0.062 0.242 0 0 1 South East 22,317 0.158 0.364 0 0 1 South West 22,317 0.065 0.246 0 0 1 Wales 22,317 0.026 0.159 0 0 1 West Midlands 22,317 0.039 0.193 0 0 1 Notes: The table shows descriptive statistics for the combined sample, i.e., a sample created by appending the main estimation sample and historical control sample. The variables are defined in Appendix in Table A1 . Table 2 here 4.2 Methodology In the first part of the paper, we estimate several multivariate panel binary logistic regression models (discrete time). The dependent variable is the indicator of an insolvent exit following the last available financial accounts 8 . The independent variable of interest is the indicator of the covid period (covid indicator). It is equal to unity in the covid period (financial accounts submitted from April 2017 to March 2020) and zero for the pre-covid period (accounts submitted from April 2014 to March 2017). The range of model specifications we present in each table are useful for checking the sensitivity of our main findings with respect to various control variables. The logistic regression is a conditional probability function where the probability of failure determined by a set of several covariates (vectors MIV, ITV, EDV, FR, NFV, ISI , and RI ) and the respective vectors of coefficients α k (k = 1, 2, .., 7) which measure the effect of this set of covariates on probability of failure. Subscript i represents each individual firm. $$P({y}_{i}=1|{MIV}_{i}, {ITV}_{i}, {EDV}_{i}, {FR}_{i}, {NFV}_{i}, {ISI}_{i}, {RI}_{i})=\frac{1}{1+{e}^{-\left({\alpha }_{0}+{MIV}_{i}^{T}{\alpha }_{1}+{ITV}_{i}^{T}{\alpha }_{2}+{EDV}_{i}^{T}{\alpha }_{3}+{FR}_{i}^{T}{\alpha }_{4}+{NFV}_{i}^{T}{\alpha }_{5}+{ISI}_{i}^{T}{\alpha }_{6}+{RI}_{i}^{T}{\alpha }_{7}\right)}}$$ 1 The vector MIV represents covid-related main independent variables. It is either the indicator of the covid period (models associated with hypothesis 1 ) or the indicator of the covid loan (models related to testing hypothesis 2 and 3 ). The vector ITV represents investor types variables. We generated indicators of the most frequent investor types (VC, Angel, Crowd Funding, Government VC, Foreign investor). Regarding further equity deals variables (vector EDV) we control for the stage of investment (Seed, Venture, Growth, Estab1lished), announced deals, the number of rounds, cumulative amount of investment, the purpose of investment (R&D, job creation) and the period of time from the first deal and last deal. We explore non-linear ( quadratic) relationships in relation to total investment and rounds of investment to determine the threshold at which VC’s abandon the venture. The vectors FR (financial ratios) and NFV (non-financial variables) related to firm survival. More specifically, the financial ratios represent important dimensions of firms' financial performance, i.e., liquidity (working capital to total assets, current assets to total assets), leverage (current liabilities to total liabilities, short-term and long-term debt to total assets), and profitability (profit and loss account reserve to total assets). Following the literature (Keasey & Watson, 1987 ; Altman et al. 2010 ) we employ a rich set of the non-financial characteristics 9 including company size (based on total assets) 10 , indicator of charges on assets and indicator of no debt. A crucial determinant of the insolvent exit is the ex-ante risk score at the time of last available financial year end. 11 Some companies are not risk-scored and we include the indicator for no risk score. Finally, employing industry sector indicators (vector ISI ) we control for industry top-level sector (based on the detailed descriptor in the VC database) and location (vector R - regions). Some of the estimated models include interaction terms for the covid period and investor type, or covid loan and investor type. The analysis proceeds in relation to the testing of Hypothesis 1 stating that we are unlikely to observe an insolvency-gap for the less viable equity-backed companies. Hypothesis 2 suggests that survival will be a function of investor type and stage of investment, the acquisition of maximum guaranteed loans is indicative of the venture facing financial difficulty while hypothesis 3 suggests that companies with a covid loan are more likely to fail. A further model is used to shed light on the detailed characteristics of the equity-backed companies that acquired covid loans, identify the characteristics of those that were most ‘credit constrained’ during the crisis and the investor type. Here we identify the guaranteed loan recipients and profile using set of variables discussed above, the characteristics of those firms with and without a loan. To estimate the model, we employ the probit regression 12 . The set of explanatory variables will cover those in Eq. ( 1 ) but we include additional variables (indicators of buzzwords). The dependent variables will be the indicator of covid loan. This set-up will be employed to test hypothesis 4 . In the second part of the paper, we analyse the subsample of the covid loan portfolio. We are interested in the profile of the portfolio of loans by lender types and estimate a logistic regression to profile 3 lender types: banks, challenger banks, and other lenders. The next stage is to model loan default. In line with the previous literature (Cowling et al. 2023a ) we use the Cox proportional hazard model to account for the censoring. We estimate the hazards model using the rich set of firm and loan contract variables described later. This model specification follows that of similar studies using Italian Credit Guarantee Scheme (CGS) data for Italy (Caselli et al. 2021 ) and US SBA Loan Guarantee Programme (Glennon and Nigro, 2005 ) and in other related studies concerned with new firm survival (Van Praag, 2003 ; Audretsch & Mahmood, 1995 ; Holmes et al. 2010 ). The modelling allows us to estimate the hazard risk of a loan defaulting as a function of firm characteristics, demographics, and loan contract parameters. We include lender type (vector LT ), and loan contract related variables (vector LCV ) as additional explanatory variables to those described above and employed earlier. The dependent variable is specified such that the individual loan time begins at its origination date and continues until its default date when it ends. For loans that have not defaulted by the end of the sample period (July 2023) the data are censored at this point or are continuing to progress through their term and making repayments according to the loan schedule. The hazard function is h(t) and is the risk of default at time t which is the survival time. It follows that h(t) is the hazard function which is determined by a set of several covariates (vectors LT , LCV , ITV , EDV , FR , NFV , ISI and RI ) and the respective vectors of coefficients α k (k = 1, 2, …, 8) which measure the effect of this set of covariates on hazard rate. Subscript i represents each individual firm loan contract, and t represents time. $$h\left(t\right)={h}_{0}\left(t\right)\text{e}\text{x}\text{p}({LT}_{i}^{T}{\alpha }_{1}+{{LCV}_{i}^{T}{\alpha }_{2}+ ITV}_{i}^{T}{\alpha }_{3}+{EDV}_{i}^{T}{\alpha }_{4}+{FR}_{i}^{T}{\alpha }_{5}+{NFV}_{i}^{T}{\alpha }_{6}+{ISI}_{i}^{T}{\alpha }_{7}+{RI}_{i}^{T}{\alpha }_{8})$$ 2 The vector LT represents the indicators of lender type issuing the guaranteed loan. As we have three types of lenders, the components of the vector LT are the indicators of groups of lenders - banks, challenger banks and other lenders. 13 The vector LCV represents loan contract variables. Besides loan amount, loan term and the indicator of BBLS we calculate, for each borrower, the loan to turnover ratio to identify those firms that took the maximum possible loan i.e. were at the intensive margin . We include the interaction between the indicator of BBLS and the loan to turnover ratio, too. The vectors ITV , EDV , FR , NFV , ISI and RI were defined above. We will employ this setting to test hypothesis 5 . 5 Model Specifications and Results 5.1 Equity-backed firms and the insolvency gap The results of the insolvency models employing the combined sample are presented in Table 3 . Firstly, the main variable of interest is the indicator of the covid period. As is apparent from the models, the covid period coefficient is not statistically significant in the models with a richer set of the explanatory variables. Hence there is no evidence of insolvency gap during the covid period, in other words the equity funded companies did not have a lower insolvency rate during the three-year window from April 2020 to end of March 2023, when compared with pre-coviTad period and unlike the SME sector generally. This means that government intervention in form of the covid loan schemes, relaxing of the insolvency legislation and other forms of help provided to businesses did not delay failures as there was no backlog of insolvencies of equity funded companies. This is evidence in favour of our Hypothesis 1 . Table 3 Insolvency prediction models using combined sample (1) (2) (3) (4) (5) (6) (7) (8) Insolvency Insolvency Insolvency Insolvency Insolvency Insolvency Insolvency Insolvency Covid Period Indicator -0.150** -0.147** -0.116* -0.0869 -0.0915 -0.0965 -0.0888 -0.0567 Venture Capital 0.101 -0.0834 -0.0294 -0.0142 0.00687 0.0226 0.130 Business Angel 0.204** 0.0386 0.0940 0.0605 0.0574 0.0880 0.0767 Crowd Funding 0.651*** 0.512*** 0.515*** 0.493*** 0.403*** 0.445*** 0.332** Government VC 0.286** 0.227* 0.189 0.216* 0.246** 0.0923 0.310* Foreign VC -0.181 -0.289** -0.283** -0.330** -0.232* -0.194 -0.261 Interaction Covid Period X Venture Capital -0.177 Interaction Covid Period X Business Angel 0.0290 Interaction Covid Period X Crowd Funding 0.191 Interaction Covid Period X Government VC -0.432* Interaction Covid Period X Foreign VC 0.111 Venture Stage of Investment 0.179** 0.193** 0.0820 0.0866 0.0795 0.0743 Growth Stage of Investment 0.168 0.214* 0.0774 0.0573 0.0339 0.0285 Established Stage of Investment -0.250 -0.232 -0.308* -0.366** -0.413** -0.413** Number of Rounds 0.0854*** 0.0726** 0.0591** 0.0760** 0.0783*** 0.0792*** Announced Deal -0.0307 -0.0471 -0.00850 0.0315 -0.0141 -0.0206 LN(Total Investment) 0.907*** 0.925*** 0.664** 0.696** 0.719*** 0.709*** LN(Total Investment) squared -0.0294*** -0.0295*** -0.0223** -0.0228** -0.0232** -0.0229** Time from First Deal (days) -0.000174** -0.000222*** -0.000224*** -0.000235*** -0.000240*** -0.000239*** Time from Last Deal (days) 0.0000386 0.0000192 0.0000366 0.0000555 0.0000455 0.0000462 Investment Purpose (R&D) -0.345** -0.316* -0.335** -0.256 -0.276 -0.271 Investment Purpose (Job creation) -0.0433 -0.00475 -0.0467 -0.0305 -0.0629 -0.0477 Working Capital to Total Assets -0.0910*** -0.143*** -0.132*** -0.130*** -0.130*** Current Assets to Total Assets -0.569*** -0.532*** -0.441*** -0.420*** -0.420*** Current Liabilities to Total Liabilities 0.463** 0.227 0.209 0.199 0.197 Profit/Loss Account Reserve to Total Assets -0.0444* -0.0971*** -0.121*** -0.126*** -0.125*** Short and Long-term Debt to Total Assets 1.074*** 0.411** 0.402** 0.382* 0.375* LN(Total Assets) 0.920*** 0.906*** 0.894*** 0.892*** LN(Total Assets) squared -0.0342*** -0.0341*** -0.0335*** -0.0335*** Indicator of Charge on Assets 0.179* 0.197* 0.176 0.175 Indicator of No Debt -0.328*** -0.258*** -0.237*** -0.239*** Ex-ante Risk Score 6.597*** 5.463*** 5.591*** 5.584*** Missing Risk Score 0.0310 0.152 0.178 0.177 Sector (Media) -0.311*** -0.287** -0.287** Sector (Industrial) 0.0901 0.0737 0.0695 Sector (Infrastructure) 0.00975 0.00559 0.00279 Sector (Retail) 0.0252 0.0398 0.0378 Sector (Crafts) 0.159 0.221 0.226 Sector (Leisure) 0.492*** 0.518*** 0.516*** Sector (Supply Chain) 0.366** 0.350** 0.348** Sector (Professional services) -0.193** -0.182** -0.182** Sector (Trades) 0.908*** 0.872*** 0.874*** Sector (Personal services) 0.0301 0.0450 0.0460 Sector (Technology) -0.297*** -0.294*** -0.293*** Sector (Energy) 0.421** 0.399** 0.402** East Midlands 0.279 0.285 East of England 0.137 0.137 North East 0.762*** 0.758*** North West 0.458*** 0.456*** Scotland 0.116 0.117 South East 0.140 0.139 South West 0.213 0.211 Wales 0.161 0.164 West Midlands 0.327** 0.322** Yorkshire and The Humber 0.488*** 0.491*** Constant -2.851*** -2.984*** -9.699*** -10.10*** -13.79*** -14.01*** -14.35*** -14.28*** Observations 22317 22317 22317 22317 22317 22317 22317 22317 Insolvency Events 1119 1119 1119 1119 1119 1119 1119 1119 Pseudo-R 2 0.000650 0.00855 0.0216 0.0369 0.0589 0.0730 0.0766 0.0773 Area Under ROC Curve 0.518 0.563 0.620 0.656 0.697 0.714 0.718 0.719 Notes: The table shows the estimation results for the models predicting insolvent exit using the combined sample covering both pre-covid (historical control subsample) and covid period. The dependent variable is the indicator of the insolvent exit in the 3-year period either from 1st of April 2017 to 31st of March 2020 (pre-covid historical control subsample), or from 1st of April 2020 to 31st of March 2023 (covid-period subsample). The variable of interest is the indicator of the covid period (equals one if the observation comes from the covid period subsample and zero otherwise). The models are estimated using logistic regression. The statistical significance is indicated with asterisks where the *, **, and *** denote significance at 10%, 5% and 1% significance levels. The corresponding t-statistics are computed using robust standard errors. The variables are defined in the Appendix in Table A1 . Table 3 here Secondly, we can analyse whether the patterns of insolvencies differ for specific types of investors. The results of the model with the largest set of explanatory variables (model 8) suggest that the main effects of most of the indicators for investor types are not statistically significant at 5% significance level. However, the indicator of crowd funding is positive and significant in all models, providing support for Hypothesis 2 . In terms of the magnitude of effect, the odds of failure for companies financed by crowd funding is higher by approximately 39% when compared with similar companies without such a funding. 14 On the other hand, the coefficients for the interactions between the covid period and the investor types are not statistically significant suggesting that the baseline insolvency rates attributed to specific investor types have not changed during the covid period. Thirdly, the control explanatory variables attract expected signs and magnitudes. Companies that are in the established stage or with longer time from the first deal are less likely to fail. We include a quadratic term for the total cumulative investment and find significant results. The coefficients imply that at a threshold level of c£5m of investment rounds the ventures are at higher risk of insolvency, controlling for sector and other factors. This provides evidence for ‘waning momentum’ idea suggested by Ragozzino and Blevins ( 2016 ) and the negative signal to potential investors. The crisis hastened the decision to ‘call time’ on some ventures which would leave them with difficulty raising additional rounds of investment in the VC market. Further, the larger companies are more likely to fail but the effect reverses for companies with total assets over £600,000, consistent with failure prediction model (Altman et al 2013). As expected, companies with higher liquidity and profitability are less likely to become insolvent, as are companies without debt. On the other hand, riskier companies, i.e., those with higher ex-ante risk score experience higher insolvency rates. Companies operating in the sectors of media, professional services and technology are less likely to fail while the opposite is true for companies from sectors of leisure, supply chain and trades. Finally, with respect to region, equity funded companies based in the northern regions of England (North East, North West, Yorkshire and the Humber, West Midlands) experience on average higher insolvency rates. This could be the result of insufficient equity funding because of persistent equity gaps in these regions (Wilson et al. 2019 ; Kacer & Wilson, 2023 ). 5.2 Equity-backed firms and covid period bankruptcy The results of models presented in Table 4 will shed light on the pattern of insolvencies during of the three-year period starting on the 1st of April 2020 covering the covid pandemic. Firstly, the main variable of interest is the covid loan indicator 15 to understand the role of the guaranteed covid loans in this process. The estimated coefficient is positive and statistically significant across all model specifications suggesting that companies with a covid loan are on average more likely to experience insolvent exit, supporting hypothesis 3 . The main effect is relatively strong in that, all else equal, the odds of insolvent exit is higher by about 77% for a company with covid loan (model 8, Table 4 ). However, as there are interactions between the covid loan and specific investor types, this is the case for companies without any of these investors. Table 4 Insolvency prediction models using covid period sample (1) (2) (3) (4) (5) (6) (7) (8) Variables Insolvency Insolvency Insolvency Insolvency Insolvency Insolvency Insolvency Insolvency Covid Loan Indicator 0.642*** 0.631*** 0.636*** 0.627*** 0.489*** 0.428*** 0.419*** 0.572*** Venture Capital 0.0968 -0.0474 -0.00378 -0.00459 0.0203 0.0183 -0.146 Business Angel 0.221* 0.119 0.148 0.122 0.111 0.138 0.497*** Crowd Funding 0.717*** 0.645*** 0.639*** 0.621*** 0.527*** 0.569*** 0.707*** Government VC -0.00924 0.0112 0.00276 0.0350 0.0629 -0.0205 0.329 Foreign VC -0.0204 -0.158 -0.143 -0.199 -0.131 -0.0918 -0.169 Interaction Covid Loan X Venture Capital 0.289 Interaction Covid Loan X Business Angel -0.760*** Interaction Covid Loan X Crowd Funding -0.271 Interaction Covid Loan X Government VC -0.769** Interaction Covid Loan X Foreign VC 0.232 Venture Stage of Investment 0.0730 0.117 0.0447 0.0607 0.0553 0.0530 Growth Stage of Investment 0.102 0.204 0.107 0.111 0.0900 0.0859 Established Stage of Investment -0.534** -0.449* -0.479* -0.514** -0.565** -0.550** Number of Rounds 0.0647* 0.0447 0.0419 0.0567 0.0587 0.0599 Announced Deal -0.135 -0.146 -0.110 -0.0713 -0.113 -0.0710 LN(Total Investment) 0.514 0.518 0.370 0.400 0.425 0.444 LN(Total Investment) squared -0.0139 -0.0140 -0.0109 -0.0116 -0.0121 -0.0129 Time from First Deal (days) -0.000180* -0.000226** -0.000227** -0.000243** -0.000250** -0.000255** Time from Last Deal (days) 0.0000952 0.0000821 0.0000833 0.0000932 0.0000836 0.0000874 Investment Purpose (R&D) -0.137 -0.114 -0.134 -0.0687 -0.0880 -0.0830 Investment Purpose (Job creation) -0.0664 -0.0275 -0.0378 -0.0135 -0.0311 -0.0171 Working Capital to Total Assets -0.0987** -0.151*** -0.147*** -0.146*** -0.141*** Current Assets to Total Assets -0.488*** -0.452*** -0.375*** -0.360** -0.374*** Current Liabilities to Total Liabilities 0.0123 -0.201 -0.190 -0.196 -0.201 Profit/Loss Account Reserve to Total Assets -0.0990*** -0.131*** -0.147*** -0.150*** -0.154*** Short and Long-term Debt to Total Assets 0.347 -0.111 -0.109 -0.121 -0.133 LN(Total Assets) 0.701*** 0.723*** 0.710*** 0.690*** LN(Total Assets) squared -0.0259*** -0.0270*** -0.0265*** -0.0256** Indicator of Charge on Assets -0.0258 0.0103 -0.0114 -0.00378 Indicator of No Debt -0.216** -0.171 -0.158 -0.158 Ex-ante Risk Score 5.391*** 4.102*** 4.240*** 4.210*** Missing Risk Score 0.0652 0.178 0.207 0.199 Sector (Media) -0.214 -0.183 -0.178 Sector (Industrial) 0.0893 0.0777 0.0806 Sector (Infrastructure) 0.143 0.137 0.143 Sector (Retail) 0.0860 0.0963 0.104 Sector (Crafts) 0.415* 0.462** 0.446* Sector (Leisure) 0.452*** 0.476*** 0.471*** Sector (Supply Chain) 0.292 0.276 0.303 Sector (Professional services) -0.238** -0.227** -0.221** Sector (Trades) 0.715*** 0.682** 0.679** Sector (Personal services) -0.0169 -0.000841 0.00291 Sector (Technology) -0.217** -0.217** -0.216** Sector (Energy) 0.461* 0.456* 0.476* East Midlands 0.435* 0.445* East of England 0.269* 0.262* North East 0.610** 0.624*** North West 0.401** 0.397** Scotland -0.0216 -0.0824 South East 0.226* 0.223* South West 0.116 0.113 Wales 0.243 0.211 West Midlands 0.362* 0.371* Yorkshire and The Humber 0.436** 0.423** Constant -3.338*** -3.465*** -7.638*** -7.473*** -10.50*** -10.88*** -11.21*** -11.28*** Observations 13786 13786 13786 13786 13786 13786 13786 13786 Insolvency Events 653 653 653 653 653 653 653 653 Pseudo-R 2 0.0120 0.0206 0.0304 0.0421 0.0543 0.0660 0.0692 0.0731 Area Under ROC Curve 0.579 0.612 0.645 0.670 0.695 0.707 0.711 0.716 Notes: The table shows the estimation results for the insolvency prediction models using the covid period sample. The dependent variable is the indicator of the insolvent exit in the 3-year period from 1st of April 2020 to 31st of March 2023 (equals one if the company experienced an insolvent exit during the period and zero otherwise). The variable of interest is the indicator of the covid loan (equals one if the company has a loan under any of the three covid loan guarantee schemes and zero otherwise). The models are estimated using logistic regression. The statistical significance is indicated with asterisks where the *, **, and *** denote significance at 10%, 5% and 1% significance levels. The corresponding t-statistics are computed using robust standard errors. The variables are defined in the Appendix in Table A1 . Table 5 Profile of the companies with a covid loan (1) (2) (3) (4) (5) (6) Variables Covid Loan Covid Loan Covid Loan Covid Loan Covid Loan Covid Loan Buzzword (AI) -0.244*** -0.294*** -0.264*** -0.246*** -0.153*** -0.145*** Buzzword (Fintech) -0.426*** -0.442*** -0.426*** -0.350*** -0.269*** -0.263*** Venture Capital -0.262*** -0.102*** -0.0857** -0.0765* -0.0639 -0.0645 Business Angel 0.0467 0.0829** 0.109*** 0.0801** 0.0820** 0.0964** Crowd Funding 0.251*** 0.242*** 0.249*** 0.237*** 0.182*** 0.193*** Government VC 0.0614 0.0840* 0.0683 0.0559 0.0922* 0.0685 Foreign VC -0.576*** -0.334*** -0.326*** -0.342*** -0.305*** -0.292*** Venture Stage of Investment 0.293*** 0.264*** 0.189*** 0.179*** 0.178*** Growth Stage of Investment 0.149*** 0.0825* 0.0247 -0.00885 -0.0129 Established Stage of Investment 0.0225 -0.0615 -0.0772 -0.141** -0.151*** Number of Rounds 0.0345*** 0.0458*** 0.0357*** 0.0438*** 0.0448*** Announced Deal -0.0959*** -0.101*** -0.0858** -0.0563 -0.0693* LN(Total Investment) 0.820*** 0.832*** 0.587*** 0.594*** 0.592*** LN(Total Investment) squared -0.0366*** -0.0367*** -0.0273*** -0.0271*** -0.0269*** Time from First Deal (days) -0.0000242 -0.0000236 -0.0000317 -0.0000345 -0.0000355 Time from Last Deal (days) -0.000220*** -0.000225*** -0.000208*** -0.000206*** -0.000208*** Investment Purpose (R&D) -0.199*** -0.199*** -0.217*** -0.173*** -0.179*** Investment Purpose (Job creation) 0.228*** 0.224*** 0.202*** 0.191*** 0.185*** Working Capital to Total Assets -0.000968 -0.0604*** -0.0591*** -0.0589*** Current Assets to Total Assets -0.149*** -0.0962** -0.0919** -0.0862** Current Liabilities to Total Liabilities 0.469*** 0.557*** 0.543*** 0.537*** Profit/Loss Account Reserve to Total Assets 0.0717*** 0.0452*** 0.0352*** 0.0338*** Short and Long-term Debt to Total Assets 0.858*** 0.425*** 0.410*** 0.408*** LN(Total Assets) 0.817*** 0.816*** 0.816*** LN(Total Assets) squared -0.0330*** -0.0331*** -0.0331*** Indicator of Charge on Assets 0.284*** 0.288*** 0.276*** Indicator of No Debt -0.459*** -0.436*** -0.430*** Ex-ante Risk Score 2.504*** 1.678*** 1.688*** Missing Risk Score -0.0512 -0.114* -0.109* Sector (Media) -0.107*** -0.103*** Sector (Industrial) 0.156*** 0.153*** Sector (Infrastructure) 0.0896 0.0881 Sector (Retail) 0.0997** 0.101** Sector (Crafts) 0.0335 0.0394 Sector (Leisure) 0.230*** 0.231*** Sector (Supply Chain) -0.0216 -0.0241 Sector (Professional services) 0.112*** 0.113*** Sector (Trades) 0.0868 0.0830 Sector (Personal services) 0.0349 0.0360 Sector (Technology) -0.268*** -0.267*** Sector (Energy) -0.128 -0.122 East Midlands 0.0830 East of England -0.0776* North East 0.179** North West 0.173*** Scotland -0.0940* South East 0.0584* South West 0.100** Wales 0.128* West Midlands 0.0283 Yorkshire and The Humber 0.0990 Constant -0.0291** -4.368*** -4.839*** -8.095*** -8.180*** -8.219*** Observations 13786 13786 13786 13786 13786 13786 Companies with covid loans 6234 6234 6234 6234 6234 6234 Pseudo R-squared 0.0268 0.0592 0.0713 0.115 0.127 0.129 Area under ROC curve 0.585 0.653 0.672 0.724 0.736 0.737 Notes: The table shows the estimation results for the models quantifying differences between the companies with and without covid loans using the covid period sample. The dependent variable is the indicator of covid loan (equals one if the company has a loan under any of the three covid loan guarantee schemes and zero otherwise). The models are estimated using probit regression. The statistical significance is indicated with asterisks where the *, **, and *** denote significance at 10%, 5% and 1% significance levels. The corresponding z-statistics are computed using robust standard errors. The variables are defined in the Appendix in Table A1 . Table 4 here Secondly, let us have a look at the main effects of the analysed (most frequent) investor types. The coefficients for the business angels and crowdfunding indicators are positive and statistically significant, i.e., companies without a covid loan with these investors will have higher likelihood of insolvency. The magnitude of effect is non-negligible in that the former have odds of failure higher by approximately 64%, and the latter by more than 100%, both relative to similar companies without covid loan and without the specific investor type. The situation is somewhat different for companies with a covid loan. Namely, the interaction between covid loan and business angel is negative and significant, and the same for the interaction between the covid loan and government VC. This means that a company with a covid loan and business angel investor has a lower probability of insolvent exit than a similar company with a covid loan but without a business angel investor, by about 23%. 16 Similarly, a covid loan seems to decrease the likelihood of failure for a company with angel investors by about 17%. The effect of a covid loan is similar for a company with a government VC investor; these companies with a covid loan have lower odds of insolvent exit by about 18%. The effects of the control variables are largely similar to the previous model. We find support for Hypothesis 2 in that the established companies are less likely to fail relative to earlier stage ventures and crowd funded firms are significantly more likely to fail. However, those backed by business angels and access the guaranteed loans have a lower failure risk. Business angels have good relationships and reputation with their banks and ensured their investee had the capacity to take on the debt finance. 5.3 Equity-backed firms and the covid loan scheme(s) In the previous section we examined the likelihood of failure of equity-backed firms that had covid loans and found a higher propensity to fail amongst the loan recipients, controlling for a wide range of other factors. In Table 5 we profile our sample of equity backed firms that chose (or not) to take advantage of the covid loan schemes. In order to do so we merge data from the covid loan portfolio to our database of equity backed firms and identify the firms that accessed the loan scheme. We identify 6,234 firms that acquired a loan, slightly over 45% of the main estimation sample. Estimating a binary logit model (1 = covid loan, 0 = not) we profile the characteristics of loan recipients using very similar specification as in model in Table 3 and Table 4 . First, we include the indicators of “buzzwords” AI and fintech. 17 Then, we add all the variables employed in the above models. This includes details of the main investor types, stage of investment and number of rounds of investment, whether the investee had a ‘announced’ deal, the total cumulative investment amount, and indicators of the time form the first deal and last deal per covid. We control for the stated purpose of the latest investment (R&D, Job creation), as well. Further, we include financial and non-financial characteristics pre covid – company size (assets) and financial ratios reflecting cash and liquidity, reserves, debt, and indicator of credit charges on assets. We indicate companies that have not used debt finance in the years pre covid. A pre covid insolvency risk score is an explanatory variable, too. Finally, we control for sector, and region. Table 5 here The results presented in Table 5 suggest that firms operating in AI and Fintech fields are less likely to seek for covid loan finance, possibly because of demand for higher volumes of funding that the covid loans cannot satisfy. Further, firms funded by Business Angels and Crowd Funded firms are more likely to access the preferential loan finance. 18 Crowd funded firms have dispersed shareholders and are less likely to provide additional resources during the crisis. As mentioned, Business Angels, as high net worth individuals, are likely to have strong reputations and relationships with banks, and access finance as needed. The firms backed by the foreign investors are less likely to seek loan finance 19 supporting the notion that larger foreign funds invest higher amounts of money into more developed companies and are active in supporting investees financially during difficult periods to protect their investment. On the other hand, companies funded by (domestic) VC funds and government backed ventures do not seem to have a significant impact on the use of the loan facilities. These results are qualitatively similar in all model specifications. Thus, the results are consistent with the first part of hypothesis 4 . Consistent with earlier arguments and evidence on the patterns of VC investment, during covid, firms in the established and growth phases of investment and with higher cumulative investments , 20 and recent deals are less likely to seek loans than the venture stages. This is evidence in favour of the second part of hypothesis 4 . Moreover, those using funds for R&D are less likely to seek finance than those that are growing employees. In terms of financial characteristics, the ex-ante risk score is a strong predictor of loan acquisition, suggesting firms in a more precarious financial position accessed loans. This is indicated in lower liquidity and higher working capital requirement. Although the firms appear to have higher reserves, possibly purposed for development activities, rather than trading. The effect of size is non-linear, as well. The smaller companies appear to have greater demand for covid loans and once they reach certain size (approximately £225k), the effect becomes negative. The firms accessing loans are more likely to have existing debt and charges on assets. Of course, it would be rational to replace higher priced existing loans with the cheaper covid loans and remove creditor asset charges. These firms, therefore, are not creating additional financial resources and remain prone to failure. We find variations in loan acquisition propensity by sector, based on the most common definitions in our equity finance database. Media and Communications, and technology are less likely to seek loans. However, those firms involved in industrials, supply chains, retail, leisure, and professional services acquire loans. There is some regional variation. Firms in the northern regions (NW, NE, Yorks.) the south-west, and Wales are more likely to seek funding. Again, these regions have been identified as areas where there is an ‘equity gap’ in comparison to London and other regions (Kacer & Wilson, 2023 ) indicating a shortage of funding. 5.4 Equity-backed firms and loan default In this part we analyse a sample of covid loans obtained by the 6,234 companies in the main estimation sample. There are 6,936 such loans since some companies have several loans. As of 17th of July 2023, among these loans, there were 714 defaulted loans where a government guarantee was demanded by lenders. Most of the loans in our sample are issued under BBLS and these loans represent nearly 77% of the sample. The rest of the loans are issued under either CBILS or CLBILS. We distinguish three main lender types, banks, challenger banks, and other lenders, the most frequent ones, representing nearly 84.9%, 7.4%, and 7.7%, respectively. The loan amounts range from £2,000 to £10,000,000 but nearly 51% of the loans in the sample have loan amount £50,000 which is the maximum loan amount permitted under BBLS. Similarly, loan terms range from 5 to 120 months, but majority of loans have loan term 72 months. Finally, the loan to turnover ranges from 0.007 to 0.714. The descriptive statistics are presented in Table 6 . Table 6 Descriptive statistics for the sample of covid loans Panel A. Breakdown by loan scheme and loan default Non-defaulted loans Defaulted loans Scheme Number Percentage Number Percentage Total BBLS 4,712 88.07% 638 11.93% 5,350 CBILS 1,505 95.19% 76 4.81% 1,581 CLBILS 5 100% 0 0% 5 Total 6,222 89.71% 714 10.29% 6,936 Panel B: Breakdown by lender category and loan default Non-defaulted loans Defaulted loans Lender Type Number Percentage Number Percentage Total Bank 5,323 90.39% 566 9.61% 5,889 Challenger Bank 402 78.82% 108 21.18% 510 Other Lender 497 92.55% 40 7.45% 537 Total 6,222 89.71% 714 10.29% 6,936 Panel C: Descriptive statistics of the continuous Covid loan specific variables Mean SD Min Median Max Loan Amount 108,110 295,681 2,000 50,000 10,000,000 LN(Loan Amount) 10.917 0.964 7.600 10.820 16.12 Loan Term 79 22 5 72 120 Loan to Turnover 0.206 2.130 0.007 0.143 0.714 Notes: The table shows some descriptive statistics for the sample of the covid loans taken by companies in the main estimation sample. Panel A shows frequencies and percentages of the covid loans broken down by the loan scheme and the loan default. Panel B shows frequencies and percentages of the covid loans broken down by the lender type and the loan default. Finally, Panel C shows the descriptive statistics for the continuous variables employed in the regression models. Table 6 here Next, we focus on the profiles of companies for the individual lender types. The results of the models are presented in Table 7 . Most of the estimated coefficients are not statistically significant but those which are reveal interesting information. Firstly, companies with domestic or foreign VC are indifferent to the lender type in that the estimated coefficients for both investor type are not significant in any of the models. On the other hand, business angels are more likely associated with main banks and the odds of having the main bank as a lender is higher by 44% for companies with this investor. Companies funded by government VC, on the other hand, have odds smaller by 68% that the loan provider will be a challenger bank. Table 7 Selection models for various lender categories (1) (2) (3) Variables Bank Challenger Bank Other Lender Venture Capital 0.0773 0.0656 0.0381 Business Angel 0.363** -0.407* -0.108 Crowd Funding 0.186 -0.257 0.141 Government VC 0.168 -1.125*** -0.0974 Foreign VC -0.104 0.137 0.0817 LN(Loan Amount) -0.191*** 0.289*** 0.332*** Loan Term 0.0180*** 0.00108 -0.0689*** Loan to Turnover 0.00942 0.00777 0.00656 BBLS Indicator 1.792*** 1.119*** -3.227*** Interaction BBLS X Loan To Turnover -2.406*** 3.283*** -4.871 Venture Stage of Investment -0.0547 0.0269 0.0275 Growth Stage of Investment 0.0781 -0.651** 0.173 Established Stage of Investment 0.0859 -0.213 0.118 Number of Rounds -0.0398 0.0275 0.0131 Announced Deal -0.297** 0.219 0.414* LN(Total Investment) -0.190 0.216 -0.238 LN(Total Investment) squared 0.00953 -0.00965 0.00603 Time from First Deal (days) 0.000335*** -0.000507*** -0.0000820 Time from Last Deal (days) -0.000115 0.000183 0.0000260 Investment Purpose (R&D) 0.0624 0.0323 -0.389 Investment Purpose (Job creation) -0.0685 -0.0450 0.00807 Working Capital to Total Assets -0.0916 0.0709 0.0159 Current Assets to Total Assets 0.219 -0.244 0.0401 Current Liabilities to Total Liabilities 0.147 -0.362 0.0289 Profit/Loss Account Reserve to Total Assets -0.0142 0.00640 0.0850 Short and Long-term Debt to Total Assets 0.158 -0.166 0.369 LN(Total Assets) 0.227 -0.694*** 3.502*** LN(Total Assets) squared -0.00269 0.0271*** -0.143*** Indicator of Charge on Assets 0.406*** -0.146 -0.591*** Indicator of No Debt 0.00916 0.259* -0.387** Ex-ante Risk Score -4.088*** 4.172*** 0.0952 Missing Risk Score -0.218 0.367 -0.264 Buzzword (AI) 0.209 -0.297 -0.0486 Buzzword (Fintech) -0.126 -0.146 0.886 Sector (Media) 0.153 -0.0522 -0.372 Sector (Industrial) 0.200* -0.285** -0.167 Sector (Infrastructure) -0.159 -0.214 0.666*** Sector (Retail) -0.0959 0.114 0.0145 Sector (Crafts) -0.250 0.404 0.285 Sector (Leisure) 0.140 0.271** -0.674*** Sector (Supply Chain) 0.337* -0.584 -0.270 Sector (Professional services) 0.00596 0.161 -0.277* Sector (Trades) 0.0437 0.347 -0.678* Sector (Personal services) 0.118 0.0892 -0.327 Sector (Technology) -0.0481 -0.0867 0.0997 Sector (Energy) -0.000548 -0.158 0.0716 East Midlands 0.361* -1.161*** 0.499 East of England 0.323** -0.390** -0.0406 North East 0.620*** -1.282*** 0.256 North West 0.320** -0.974*** 0.608** Scotland 1.108*** -1.420*** -0.854*** South East 0.396*** -0.629*** 0.0302 South West 0.506*** -0.902*** 0.246 Wales 0.648*** -0.779** 0.0378 West Midlands 0.695*** -0.932*** -0.0619 Yorkshire and The Humber 0.457*** -1.167*** 0.188 Constant -0.831 -2.826 -19.28*** Observations 6936 6936 6936 Number of loans (dep. var.) 5889 510 537 Pseudo R-squared 0.143 0.105 0.521 Log-likelihood -2521.4 -1629.8 -906.0 chi2 test statistic 752.6 344.5 529.7 Area under ROC curve 0.758 0.745 0.960 Notes: The table shows the estimation results for the selection models for individual lender categories. The models are estimated using binary logistic regression and the dependent variable in both models is the indicator of specific lender category. More specifically, in model 1, the dependent variable is the indicator of bank lenders, i.e., it is equal to one if the lender is a bank (main or other) and zero otherwise. In model 2, the dependent variable is the indicator of the challenger bank, and in model 3, it is the indicator of other lender. The categorisation of the lenders into lender categories is described in Appendix B1 The statistical significance of the individual estimated coefficients is based on robust standard errors and is indicated with asterisks (*, **, and *** denote statistical significance at 10%, 5%, and 1% level, respectively). Table 7 here Besides investor types, there are other interesting differences between the characteristics of companies having loans from the main bank as opposed to a challenger bank. As for the loan size distribution, banks cater for the smaller companies while the other two lender types are associated with bigger ones. Both banks and challenger banks provided more loans under BBLS while the other lenders under CBILS and CLBILS. The interaction between the loan turnover suggests that banks provide loans for companies with lower loan to turnover while challenger banks are focused more on riskier companies with higher values of the ratio. With respect to ex ante risk score, the results show that banks provide loans to less risky companies, where it is the other way around for the challenger banks. This may be because the traditional banks have sophisticated credit scoring systems in place, have a large pool of customers and provide loans to predominantly their clients. On the other hand, the challenger banks are new players and want to increase their market share. Some of them may not have sophisticated credit scoring systems or may provide loan to riskier clients because it is guaranteed by government. Another important difference is the regional distribution of clients of the lenders. While for banks it is more likely to have customers from other regions than London, majority of the challenger banks seem to be based in the London region (in the models, the London region is the reference category). This may be because the main banks have a country-wide net of branches whereas the challenger banks lack the wider net of branches and are often based in London where they also seek their clients. Table 8 and Fig. 1 here Table 8 Cox’s proportional hazard models (1) (2) (3) (4) (5) (6) (7) (8) Variables Time to Fail Time to Fail Time to Fail Time to Fail Time to Fail Time to Fail Time to Fail Time to Fail Bank Lender Indicator -0.227 -0.259 -1.024*** -1.039*** -1.054*** -1.053*** -1.078*** -1.061*** Challenger Bank Indicator 0.644*** 0.593*** -0.272 -0.296 -0.286 -0.289 -0.304 -0.280 Venture Capital -0.141 -0.129 -0.123 -0.0926 -0.101 -0.0758 -0.0772 Business Angel -0.198 -0.213 -0.195 -0.150 -0.122 -0.133 -0.113 Crowd Funding 0.579*** 0.545*** 0.532*** 0.550*** 0.508*** 0.454*** 0.468*** Government VC -0.476** -0.467** -0.421* -0.453** -0.409* -0.401* -0.413* Foreign VC -0.0808 -0.0721 -0.0185 0.00332 -0.0166 -0.00736 0.0159 LN(Loan Amount) 0.0733 0.0807 0.139** 0.160** 0.165** 0.163** Loan Term -0.00822*** -0.00817*** -0.00924*** -0.00981*** -0.0102*** -0.0103*** Loan to Turnover -0.656 -0.853 -0.979 -1.378 -1.229 -1.177 BBLS Indicator 0.847*** 0.802*** 0.849*** 0.752*** 0.779*** 0.792*** Interaction BBLS X Loan To Turnover 2.811*** 2.622** 2.456** 2.582** 2.726** 2.658** Venture Stage of Investment -0.217** -0.211** -0.147 -0.133 -0.137 Growth Stage of Investment -0.00905 -0.0236 0.133 0.151 0.134 Established Stage of Investment -0.778*** -0.811*** -0.627** -0.623** -0.619** Number of Rounds 0.0780* 0.0703 0.0700 0.0735 0.0751 Announced Deal -0.0343 -0.0194 -0.0442 -0.0306 -0.0384 LN(Total Investment) 0.605* 0.643* 0.627* 0.626* 0.630* LN(Total Investment) squared -0.0226* -0.0244* -0.0227 -0.0227 -0.0227 Time from First Deal (days) -0.000176 -0.000189* -0.000167 -0.000163 -0.000163 Time from Last Deal (days) 0.000167 0.000144 0.000160 0.000161 0.000157 Investment Purpose (R&D) -0.180 -0.170 -0.140 -0.107 -0.122 Investment Purpose (Job creation) -0.00313 0.0293 0.0318 0.0527 0.0435 Working Capital to Total Assets -0.231*** -0.165*** -0.172*** -0.173*** Current Assets to Total Assets -0.368*** -0.559*** -0.514*** -0.512*** Current Liabilities to Total Liabilities -0.439* -0.683*** -0.681*** -0.682*** Profit/Loss Account Reserve to Total Assets -0.0302 -0.00145 -0.00830 -0.00845 Short and Long-term Debt to Total Assets -0.226 -0.547** -0.522** -0.518** LN(Total Assets) 0.0811 0.0871 0.0864 LN(Total Assets) squared -0.00997 -0.00987 -0.00983 Indicator of Charge on Assets 0.0863 0.0958 0.0918 Indicator of No Debt -0.0234 -0.0113 -0.0130 Ex-ante Risk Score 5.519*** 4.762*** 4.854*** Missing Risk Score 0.130 0.185 0.198 Sector (Media) 0.0853 0.0855 Sector (Industrial) 0.152 0.149 Sector (Infrastructure) 0.353* 0.360* Sector (Retail) 0.150 0.160 Sector (Crafts) 0.139 0.126 Sector (Leisure) 0.289*** 0.288*** Sector (Supply Chain) 0.220 0.224 Sector (Professional services) -0.0749 -0.0803 Sector (Trades) 0.552** 0.540** Sector (Personal services) 0.0432 0.0443 Sector (Technology) 0.0209 0.0306 Sector (Energy) 0.127 0.140 East Midlands 0.138 East of England -0.00580 North East 0.0776 North West 0.204 Scotland -0.212 South East 0.0873 South West 0.0498 Wales -0.0576 West Midlands -0.0765 Yorkshire and The Humber 0.356* Observations 6936 6936 6936 6936 6936 6936 6936 6936 Defaulted loans 714 714 714 714 714 714 714 714 Log-likelihood -6041.4 -6021.9 -5981.7 -5970.4 -5936.5 -5919.3 -5908.2 -5904.4 Chi 2 test statistic 66.95 107.9 188.4 210.0 284.2 321.9 348.9 356.7 Pseudo-R 2 0.00467 0.00789 0.0145 0.0164 0.0220 0.0248 0.0266 0.0273 Notes: The table shows estimation results for the survival models where the dependent variable is number of days to failure (covid loan default). The models were estimated using Cox’s proportional hazard models. The statistical significance of the individual estimated coefficients is based on robust standard errors and is indicated with asterisks (*, **, and *** denote statistical significance at 10%, 5%, and 1% level, respectively). Table 8 presents Cox proportional hazard models of loan defaults. Firstly, we look at lender types. The preliminary information provided by Kaplan-Meier failure estimates in Fig. 1 suggests that loans provided by challenger banks are the riskiest, followed by other lenders, and banks. In multivariate models presented in Table 7 , the reference category is Other Lender and the results show that indeed banks do have lower hazard rates when controlled for everything else, than both challenger banks and other lenders. We find evidence supporting hypothesis 5 . The hazard of default for the covid loans provided by banks are lower by nearly 65% (model 8) when compared to other lenders. The fact that in the multivariate models the hazards of challenger banks and other lenders are not statistically different may be due to fact that higher risk of the challenger banks’ clients is explicitly accounted for. Secondly, looking at the investor types we can see that companies with crowd funding investors are more likely to default on covid loan with hazard of failure higher by about 60%. Other investor types are not associated with significantly different hazards of failure. The loan value has a positive impact on hazard of failure while loans with longer loan term are less likely to default. BBLS loans are associated with a higher hazard of failure, by about 120%. This is expected due to non-existent credit checks of the customers and 100% government coverage. The risk represented by loan to turnover ratio affects the hazard of failure only for the BBLS loans with higher ratio being riskier, as expected. The stage of evolution of a company does not have impact, apart from the established companies that are less risky, because these bigger companies usually apply for large loans provided under CBILS/CLBILS schemes where companies were screened for risk, and also on the side of lenders there is higher motivation to avoid risky clients since the government coverage is only 80%. Otherwise, the failing companies exhibit expected profile observed earlier – they have lower liquidity and are less profitable, with higher ex-ante risk score. Firms with longer term debt ratio, suggestive of collateral, have a smaller hazard rate. Thus, the new lenders that provide funds for equity-backed firms have a significantly higher default rate that the main banking sector and indicates that they were providing funds for the less viable equity-financed firms. As noted in the earlier analysis (Table 4 ) this actually accelerates their exit (failure) rather than help them survive. 6 Discussion and Conclusions Our paper examines the survival and exit of equity backed companies in the UK through the covid crisis. These are the potential high-growth companies that can have a disproportionate impact on economic growth, productivity, and the innovation spill-overs or disruptive technologies that have wider long-term benefits for the economy. Moreover, these businesses often drive the growth and development of important new and transformative sectors (e.g., recent AI advances, clean energy, financial innovation). Although there is a significant insolvency gap from 2020, we find that patterns of insolvent exit for this class of firms does not change in the covid period. The non-viable amongst equity financed firms failed without the delay apparent in the SME sector generally. Those firms backed with the credible VC funds were more likely to survive whereas crowd funded ventures had a 39% higher probability of failure. We find a quadratic relationship between failure probability and cumulative rounds of investment, those ventures reaching a c£5m threshold had a higher probability of closure than other firms. The crisis focussed investors on providing more backing for potential winners and cutting losses on waning ventures. We find higher failures rates in the sectors negatively impacted by covid. However, failures rates are higher in the northern regions that have been identified as having significant equity gaps i.e., where ventures are less likely to attract finance (see Stanbury et al. 2023). The insolvency prediction model suggests that the loan recipients have a 77% higher probability of failure. Those firms with loans backed by business angels or government VC have a lower failure rate. Clearly acquiring a loan is a symptom of financial distress and is insufficient to help the company survive. In fact, it speeds the process of failure for the smaller, earlier stage ventures. Our model profiling the characteristics of loan recipients sheds further light on this. The pre covid financial health and risk profile is a strong predictor as is sector and stage of funding. Firms funded by Business Angels and Crowd Funded firms are more likely to access the preferential loan finance. However, a significant subset of firms used the maximum available loan, suggestive of liquidity constraints with the challenger banks advancing loans to riskier companies. Our models predicting loan default show that those firms have a significantly higher default rate. Clearly there is a financing issue for the early-stage smaller businesses. The new lenders have a significantly higher default rate that the main banking sector and indicates that they were providing funds for the less viable equity-financed firms, without improving their survival chances, supporting the hypotheses. We examine a spectrum of equity financed firms by investor type and contend that the later stages ventures found support from their investors through the covid period but some ventures were re-evaluated and closed. Smaller firms, that could not raise additional equity, sought finance from the preferential loan scheme (BBL) but the amounts provided were insufficient to ensure longer term viability and the scheme likely accelerated, ‘creative destruction’, the path to insolvency. A more targeted and nuanced support scheme, tailored to potential high growth firms (and priority sectors), as distinct from SME’s, may have been more effective as would policies targeted at regions where there are disparities in the supply of entrepreneurial finance. 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The total advances amounted to over £78bn. https://researchbriefings.files.parliament.uk/documents/CBP-8906/CBP-8906.pdf Early official evaluation of the loan schemes (BBB, June 2020) concluded that the objective of unlocking credit at speed was met (£78bn of guaranteed loans) and “estimated that 0.5 million to 2.9 million jobs could have been lost ” in the absence of the loan schemes (BBB, 2020). From survey evidence the evaluation report inferred that without the scheme, “ an additional 10%-34% of BBLS borrowers (i.e., 146,000 to 505,000 businesses) and an additional 7%-28% of CBILS/CLBILS borrowers (i.e., 5,000 to 21,000 businesses) could have permanently ceased trading in 2020 ” (BBB, 2020 p10). We use the dataset as of section 3 In the UK, the first lockdown was announced by prime minister Boris Johnson on Monday 23rd of March 2020, on the 25th the related Coronavirus Act obtained royal assent and it came into force on the 26th of March. We use the cut-off point of 31st of March 2020 due to the granularity of the insolvencies data where for each company we know the quarter of the insolvent exit. If a company filed more than one set of financial accounts during the three-year period before the 31st of March 2020, we use information from the most recent one. Because insolvency is a legal process that can proceed through many steps and alternate routes it is not possible to measure the outcome (insolvency) in a ‘time to failure’ context. Hence, we use the discrete time, where the last full filing of accounts is used as the date of closure. The major reasons why it is important to employ also non-financial variables when estimating insolvency models for firms including SMEs include relaxed reporting requirements for smaller companies in the UK, unreliability of financial accounts for smaller companies (they are not audited by an external auditor), and that non-financial information are less open to manipulation (Keasey and Watson, 1987 ). We allow for non-linear relationship between the company size and insolvency. This is because such a relationship has been reported in the literature (see for instance Altman et al., 2010 ), but also because the non-monotonous relationship has been detected during (unreported) preliminary bi-variate analysis. The details of the risk score are presented in the Appendix A1, A2. We employ probit instead of logistic regression since we’ll use the results for computation of the inverse Mills ratios that are Detailed classification of individual lenders into the lender type categories is presented in Appendix B1. In model 8 in Table 3 , the coefficient for the indicator of crowd funding is equal to 0.332. It is well-known that in logistic regression, the exponentiated coefficients are interpreted as odd ratios. To arrive at the percentage change in the odds we used the formula (exp(0.332)-1)*100% = 39.4%. As a robustness check we estimate a selection model based on models reported in Table 5 and include inverse mills ratio. Also, we re-estimated the models using matched sample. The results are not materially different and are reported in our supplementary appendix To compute the total effect of a having a business angel investor, we need to consider the interaction effect with the covid loan, as well. Based on coefficients from model 8 (Table 4 ) and formula in footnote 17, the total effect of having a business angel investor is given by formula (exp(0.497–0.760*covid_loan)-1)*100%. This means, that a company having a business angel investor and a covid loan has odds of insolvency lower by 23% when compared to a company with covid loan but without a business angel investor. Our equity data provider Beauhurst, based on company description and additional analyses, provides indicators of specific “buzzwords”. We selected two most frequent ones – artificial intelligence and fintech. We use them as exclusion restrictions as they were not significant in the insolvency prediction models but they are in the covid loan selection models. The presence of business angels increases odds of having the covid loan by about 10% (exp(0.0964)-1 = 0.101). Similar, but two times greater effect is associated with crowd funded companies (exp(0.193)-1 = 0.213). For companies with a foreign investor - the odds of having a loan is smaller by about 25% (exp(-0.292)-1=-0.253). See model 6 in Table 5 for details. Although the coefficient for total investment in our model is positive and significant, suggesting the positive impact on the odds of a having a covid loan, the coefficient for its squared value is negative. Given the values of estimated coefficients, the effect becomes negative after £60k total investment. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryAppendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3920888","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271407389,"identity":"7b74b29d-3edd-494f-9b30-1eb03dc363c3","order_by":0,"name":"Marek Kacer","email":"","orcid":"","institution":"The University of Leeds, UK","correspondingAuthor":false,"prefix":"","firstName":"Marek","middleName":"","lastName":"Kacer","suffix":""},{"id":271407390,"identity":"7356cd7e-8133-4e94-9378-c5b946a24c67","order_by":1,"name":"Nicholas Wilson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYJCCAw+ABD87AxuQsgDiBIiwBD4tIDWSzWAtEsRpAasxOEysFnMGHsMDCX9s8owP85g9/MEgIcfPnv6A4UcNQ+LMBuxaLBt4DA4ktqUVmx3mMTfmYZAwlux5Y8DYc4whcTYOWwwO8G44kNhwOHEb0BZpoGsSN9zIYWDgbWBInIdPS8Kfw4mbm3nMJIEOq99/I/0B41+CWtgOJ25g5jGTADoswQCImEG24HTYYf4PIL8kzjjMVm7MYyBhOOPMG4PDMsckjHF53+B4W/KHD39sEvvbm7c9/FFhI8/fnv7w4ZsaG9kZB3BYw4xqAoQ6QCAiR8EoGAWjYBQQAABeKFgEJcRimQAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Leeds, UK","correspondingAuthor":true,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Wilson","suffix":""},{"id":271407391,"identity":"eb266777-a6ae-4e4e-ac41-92483c6e957e","order_by":2,"name":"Sana Zouari","email":"","orcid":"","institution":"The University of Leeds, UK","correspondingAuthor":false,"prefix":"","firstName":"Sana","middleName":"","lastName":"Zouari","suffix":""},{"id":271407392,"identity":"69ab0155-5a17-42a6-bec3-5899188e2a63","order_by":3,"name":"Marc Cowling","email":"","orcid":"","institution":"Oxford Brookes University","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Cowling","suffix":""}],"badges":[],"createdAt":"2024-02-02 12:42:54","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3920888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3920888/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50822415,"identity":"56898c00-a10c-40ed-918c-0391c76d377b","added_by":"auto","created_at":"2024-02-07 21:56:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52351,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier failure estimates by lender type\u003c/p\u003e\n\u003cp\u003eNotes: The figure shows the Kaplan-Meier failure estimates for the covid loans taken by companies in the main estimation sample for each lender type.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3920888/v1/acd580d82f7f7a853f2d3b2b.png"},{"id":50822840,"identity":"6b0bb61f-e121-48af-91a6-7e73a61fd170","added_by":"auto","created_at":"2024-02-07 22:04:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":602718,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3920888/v1/cad6cedb-1979-4708-ac6f-18743e105ec1.pdf"},{"id":50822416,"identity":"e79ccaa3-eea2-4780-902b-b2bbcc633bf8","added_by":"auto","created_at":"2024-02-07 21:56:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":73146,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3920888/v1/2b1bae4db7f911412fd9e489.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eVenture Capital and the Survival of Entrepreneurial Firms in Crisis Periods: The Case of Covid-19.\u003c/p\u003e","fulltext":[{"header":"Plain English Summary","content":"\u003cp\u003eThis research paper looks at how entrepreneurial firms in the UK were affected by the COVID-19 pandemic. Specifically, the study focuses on companies that received equity finance during their early stages of development before the pandemic began. The paper examines the types of investors, such as venture capital funds, growth finance funds, crowd funding platforms, business angels, and government venture capital funds. The paper is based on two theories - the resource-based view (RBV) and signalling theories - to develop hypotheses. It analyses the bankruptcy processes of firms during the pandemic and compares them to the period before COVID-19. The researchers consider various factors, including the type of investors, the history of deals (such as timing, magnitude, and duration), as well as financial and non-financial aspects of these companies. Additionally, the paper identifies the equity-backed firms that utilized policy interventions in the form of guaranteed loans. The researchers obtain details about the loan contracts, lenders, and instances of loan default. The study explores the relationship between bankruptcy and loan default, considering the characteristics of the firms, the type of investors, investment dimensions, and financial constraints. The findings of this study provide valuable insights into how equity financing impacts the survival of entrepreneurial ventures during crises like a pandemic. The implications of these findings can be useful for policymakers in designing interventions and support mechanisms for such firms.\u003c/p\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eThis paper investigates the impact of Covid-19 on bankruptcy patterns and policy interventions, focusing on equity-financed firms in the UK corporate and Small and Medium Enterprise (SME) sector. It aims to assess the effects of the pandemic on the equity finance \u0026apos;eco-system\u0026apos; and the outcomes for these firms at different stages of development. The pandemic disproportionately affected the supply of debt and equity finance, particularly impacting small, young, and innovative companies. Government interventions, such as loan guarantee schemes, have primarily focused on debt finance, with limited additional support for equity finance. This poses challenges for early-stage ventures heavily reliant on rounds of venture capital investments, seeking follow-on funding from existing or new investors; and for VC\u0026rsquo;s in (re)appraising the prospects of their portfolio of investments through the crisis. The unprecedented scale of policy interventions during the pandemic provided significant government-guaranteed loans to businesses (Cowling et al. 2023b). These loans were administered, by a large pool of established and new lenders, without the usual credit checks and relied on businesses\u0026apos; \u003cem\u003eself-certification\u003c/em\u003e of eligibility criteria. The challenge for policymakers was to prevent widespread insolvency and job losses while also ensuring the preservation of \u0026lsquo;creative destruction\u0026rsquo; (Dorr et al. 2022; Demmou, 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEquity finance is known as \u003cem\u003e\u0026lsquo;patient capital\u0026rsquo;\u003c/em\u003e but venture capitalists recognise that not all their ventures will succeed and, after a certain level of cumulative investment or rounds of investment in a venture, they make the decision to continue financing or close down the business (Ragozzino \u0026amp; Blevins, 2016). We examine whether the covid period expedited this appraisal and closure decision. For earlier stage equity-financed firms, the loan schemes presented a unique opportunity to increase liquidity and prospects i.e. to borrow to \u0026lsquo;\u003cem\u003etry get lucky\u0026rsquo;\u0026nbsp;\u003c/em\u003eand survive (Dorr et al. 2022). Indeed, the take-up rate of Covid-19 loans within our sample is 45%, and it is worth noting that a significant proportion of loan recipients applied for the maximum loan amount, indicating potential unmet borrowing demand. By utilizing a unique panel database and detailed financial and non-financial data, we examine the bankruptcies and insolvencies of firms that used Covid loans, comparing them to others during the 2020-2023 period. Additionally, we have access to a comprehensive loan portfolio database with information on borrower and lender characteristics, loan terms, and incidences of default and losses. The outcomes of equity-backed firms that utilized the loan guarantee schemes are examined in relation to loan default.\u003c/p\u003e\n\u003cp\u003eAnalysing a unique panel database covering the population of limited companies in the UK, data on equity finance deals, and the government guaranteed loan portfolio our paper contributes to a growing body of literature that examines the consequences of the COVID-19 crisis for the corporate and SME sector and assesses the impact of policy interventions (Gourinchas et al., 2022). We contribute to the literature on firm resilience (Lavery et al., 2023) and failure (Gourinchas et al., 2020) in periods of crises. Our analysis aligns with Door et al. (2022) examining the German context and Wang et al. (2020) for the US who also provide evidence that policy interventions lead to a backlog of insolvencies, an \u0026lsquo;insolvency-gap\u0026rsquo;, with consequences for economic dynamism. Uniquely our analysis focuses on equity-financed companies, an important cohort of small, growing and innovative businesses but also a \u003cem\u003esubpopulation subject to \u0026lsquo;market failures\u0026rsquo; in both the provision of debt (credit rationing) and equity finance. The challenges of distinguishing good and bad investments create equity-gaps (Wilson et al. 2019), and lack of early-stage funding or the \u0026lsquo;Valley of Death\u0026rsquo; (Wilson et al. 2018), lack of follow on funding.\u0026nbsp;\u003c/em\u003e Challenger banks and a pool of new alternative lenders have evolved in the UK to fill these gaps. \u003cem\u003eWe are interested therefore in the pattern of insolvent exits in the pandemic period of this subset of firms, their characteristics (see\u0026nbsp;\u003c/em\u003eCros et al. 2021)\u003cem\u003e, use of debt finance, guaranteed loans, the role of new lenders and insolvency. \u0026nbsp;We shed light on likely abandon decisions by investors in early-stage ventures, seeking additional finance, and examine variations in failure/survival by investor type. Finally, we explore the equity-financed firms that utilised guaranteed loans and those at the intensive margin (maximum loan) profiling the characteristics of the firm, the role of different lender types and default (hazard). Our findings provide important insights for policy intervention.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe paper is structured as follows. In the next section (2), as background, we provide some analyses of the pattern of company bankruptcies, generally, and through the Covid period. We are particularly interested in policy intervention and an apparent \u0026lsquo;insolvency gap\u0026rsquo; (Dorr et al. 2022) in the early phase on the pandemic. In section (3) we review relevant literature and develop hypotheses regarding our expected outcomes. In section (4) we discuss our data, variable construction, and methodology before presenting the results (5) and conclusions (6).\u003c/p\u003e"},{"header":"2 Background: Bankruptcy, equity finance investment and Covid","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this section we provide some background on bankruptcy trends in the UK based on official statistics and firm level records. The latter are used to indicate the insolvent exit of each registered company in the company data panel database discussed later\u003csup\u003e1\u003c/sup\u003e. Over the period from the 1980\u0026rsquo;s to 2020, insolvencies are on the whole counter cyclical where increases in insolvencies coincide with, or lag periods of contraction (negative growth) as might be expected. Of course, the insolvent failures, can be viewed as part of the competitive process, \u0026lsquo;creative destruction\u0026rsquo;, that removes the inefficient and non-viable businesses from the economy such that resources are reallocated to the more efficient, innovative, and growing businesses that take their place. It might be expected, using the 2008 financial crisis as an anchor, that the Covid contraction would produce a comparable spike in company insolvencies. However, the pattern differs, insolvencies drop below \u0026lsquo;normal\u0026rsquo; levels after the severe contraction. A sizeable \u0026lsquo;insolvency gap\u0026rsquo;, the survival of non-viable businesses, is apparent through 2020 and into Spring/Summer 2021 primarily coinciding with the introduction of the covid loan guarantee schemes (March 2020 to March 2021) and other policy interventions (Wilson et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding equity-financed ventures, because of the short-term pressures on equity finance markets (Gompers et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gompers et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cumming \u0026amp; Reardone, 2022) and investor uncertainty (British Business Bank, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), follow-on funding for early-stage ventures was in short supply and the Covid period saw a \u0026lsquo;flight to quality\u0026rsquo;. In times of economic turbulence and crisis, investors and funders are likely to shift away from risky assets to contain potential losses. We analyse data provided by Beauhurst that includes 66,748 deal-level observations over the period from 2011. In short, the pre-pandemic period up to and during 2020 equity investments showed strong growth in both the number of deals and the total investment volume. However, the number of deals and the investment volume dropped markedly in the second and third quarter of 2020, coinciding with Covid lockdowns. The seed and venture stage investments appear to be the hardest hit by the pandemic (Kacer \u0026amp; Wilson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As was anticipated, there was also a marked overall increase in the average deal value driven, predominantly, by investors focusing on later stage, higher rounds and announced investments. This shift in the pattern of investment has consequences for earlier stage equity financed companies that did not have an \u0026lsquo;active\u0026rsquo; VC investor as a shareholder. These firms likely sought alternative sources of finance and had a new opportunity for financing with the roll out of guaranteed loan schemes.\u003c/p\u003e \u003cp\u003eLoan guarantee schemes (LGS), that are implemented by governments to address market failures in the provision of debt finance for firms, have a long history in the UK and other countries. The rationale for intervention is to overcome credit rationing and create \u003cem\u003eadditionality\u003c/em\u003e (Cowling, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) by filling a credit gap, facilitating growth of the SME\u0026rsquo;s sector, and improving GDP (Panetta, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although some studies have highlighted negative impacts on default rates and business bankruptcy due to \u003cem\u003eadverse selection\u003c/em\u003e by lenders (Gai et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lelarge et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and \u003cem\u003emoral hazard\u003c/em\u003e behaviours (Myers \u0026amp; Majluf, 1985) by lenders and borrowers. Combined with other policy interventions, the covid loan guarantee schemes were effectively an extension of the EFG scheme but designed to help businesses cope with the uncertainty related to the Covid pandemic, avert a major business insolvency crisis, ensure the longer-term health of the corporate sector, and enable quick recovery. The UK government offered two loan guarantee schemes to smaller firms to support liquidity during the Covid-19 crisis. The BBL scheme had a loan cap of \u0026pound;50,000, a guarantee of 100%, and a fixed interest rate of 2.5%. The CBILS scheme had a loan cap of \u0026pound;5m, a guarantee of 80% and the lender set the interest rate and fees on commercial terms\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe focus of our analysis in the 2 main loan guarantee schemes (BBLS, CBILS). The Government-owned British Business Bank (BBB) oversaw the schemes, as guarantor. A wider range of accredited lenders were involved in processing applications and loans. This included Banks, Challenger Banks, P2P lenders, and Alternative Finance (Fintech). The BBB and the lenders rolled out the schemes at some speed, according to Browning (2023)\u003csup\u003e3\u003c/sup\u003e in 11 days in the case of BBLS\u003csup\u003e4\u003c/sup\u003e. The COVID-19 loan schemes, such as BBILS, were relatively cheap and easily accessible, potentially influencing the behaviour of loan recipients and creating moral hazard. Borrowers had incentives to use the funds for debt refinancing, replacing higher-priced debt, rather than providing \u0026lsquo;additionality\u0026rsquo; in financial resource. From the lenders' perspective, established lenders had the opportunity to shift riskier portions of their loan portfolios into the loan guarantee schemes, while new entrants like Challenger Banks and Alternative Finance providers could expand their client bases by accepting a higher default rate with the assurance of government guarantees. As discussed, equity-backed firms may have faced challenges in raising further rounds of investment during the pandemic. Additionally, equity-funded firms in earlier stages of development and specific industry sectors were subject to the same cash and liquidity problems and financial constraints as other SMEs. Therefore, the loan schemes may have provided an opportunity for these firms to seek finance from mainstream banks or the new pool of specialist lenders. In a subsequent section we focus on profiling the characteristics of equity-backed firms that utilized the guaranteed loan facilities.\u003c/p\u003e\n"},{"header":"3 Literature review and development of hypotheses","content":"\u003cp\u003eCrises should cleanse the economy of unproductive and inefficient firms facilitating the reallocation of resources to more productive uses. The pattern of insolvency differs from the crises in the past. More specifically, the failure rates dropped, due to the government measures aimed at helping businesses to overcome the coronavirus pandemic (Dorr et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is clear from the pattern of insolvencies that firms that were otherwise unviable in the immediate period pre pandemic, survived the initial covid period as a result of policy intervention. Policy intervention could have unintended consequences in that it could have created zombies \u0026ndash; firms are sustained temporarily only because of easy access to low interest rate debt. We aim to investigate whether this is the case for equity backed firms or did the Covid period precipitate a reappraisal of their portfolio firms long term prospects? An important question is whether we observe the insolvency gap in multivariate models when we control for the relevant firm level predictors of insolvency and the type of equity investor.\u003c/p\u003e \u003cp\u003eOur data sample of equity financed companies includes heterogeneity in terms of type of investor, stage of development (and size), the number, size, and cumulative amounts (rounds) of investment, technology, sector, and location. Some may have combinations of equity and debt in their capital structure and can be a various stage of commercialisation (income generation and profit). Equity financing is supplied by venture capital funds and private equity (VC, PE), domestic or foreign; business angels (BA), and government funding (GV). More recently crowdfunding, peer to peer lending (P2P) has provided alternative funding channels. Funding is targeted at stages of development form start-up to follow-on and growth finance and often involves syndicates of co-investors. VC\u0026rsquo;s, with a track record and associated expertise, may be regarded as the most \u0026lsquo;credible\u0026rsquo; partners for new ventures (Manigart et al., 2002). Having a VC relationship helps the venture build reputation in the market, overcome the \u003cem\u003eliability of newness\u003c/em\u003e (Ragozzino \u0026amp; Blevins, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Business Angels have more credibility as an investor and are more proactive that the platform investors.\u003c/p\u003e \u003cp\u003eIn addition to providing finance, Venture capital (VC) firms play an important role in supporting and enhancing their portfolio companies and protecting VC reputation and \u0026lsquo;assets, value creation\u0026rsquo;. As \u0026lsquo;active investors\u0026rsquo;, they not only provide capital but strategic guidance, operational support, and networking opportunities to ensure the investees (value) survive and grow (Gompers \u0026amp; Lerner, 2016). The study suggests that VC involvement enhances a company's access to resources, knowledge, and contacts, which are crucial for growth. Buchner et al (2014) found that VC-backed companies experienced milder declines in employment and sales compared to non-VC-backed companies during the financial crisis. They attribute this resilience to the monitoring, strategic guidance, and financial support provided by VC firms. Bernstein et al (2017) examine the impact of the global financial crisis on PE-backed companies in the United Kingdom. The authors find that PE-backed companies in the UK were more resilient during the crisis compared to non-PE-backed companies due to the PE\u0026rsquo;s operational and financial support (bridging finance, equity injections, debt restructuring).\u003c/p\u003e \u003cp\u003eEmpirical papers draw on the \u003cem\u003eresource-based view\u003c/em\u003e (RBV) of the firm, \u0026lsquo;dynamic capabilities\u0026rsquo;, \u0026ldquo;\u003cem\u003ethe firm\u0026rsquo;s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments\u003c/em\u003e\u0026rdquo; (Teece et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Barney \u0026amp; Clark \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and \u0026lsquo;\u003cem\u003eresilience\u0026rsquo;\u003c/em\u003e theories (REFS). Resource-based theories of the firm discuss business resilience by highlighting the importance of the firm's resources and capabilities in adapting and responding to external shocks and challenges. These theories suggest that a firm's ability to withstand and recover from disruptions is closely related to the specific resources it possesses and how effectively it can leverage them. Firms backed by established and experienced VC funds can draw on relevant business expertise and financial resource. The related literature on private equity investors also emphasises their role as \u0026lsquo;active investors\u0026rsquo;. Often as a major or majority shareholder, they likely have board representation and a close involvement in both strategy development and implementation, and the day-to-day monitoring of management. The investors have a pool of managerial expertise and can leverage their business networks and strong ties with banks and providers of credit to provide additional funding and resource when the investee faces challenges. In crisis periods they can provide additional injections of equity finance (Lavery et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to alleviate financial difficulties.\u003c/p\u003e \u003cp\u003eVCs oftentimes invest in loss-making enterprises because of an expectation that they will generate high returns in the future. However, VC investors are incentivized to make the best possible investment decisions, as investment outcomes determine not only the VC investor\u0026rsquo;s return but also their individual rewards (Wright \u0026amp; Robbie, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). When investments fail to meet initial expectations, decision makers face a liquidation dilemma: they may favour continuing projects to retain the option of improvement and escalate commitment (Guler, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) or they may decide to abandon them, resulting in the crystallization of certain losses (Li \u0026amp; Chi, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As a large percentage of VC projects eventually fail (Puri \u0026amp; Zarutskie, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), VC investors are routinely faced with liquidation dilemmas, making them experts in abandonment decisions. The limited life of VC funds, which requires investors to exit their investments within a particular period, limits the time and incentive for poorly performing ventures with no prospect of improvement (\u0026lsquo;living dead\u0026rsquo; or \u0026lsquo;zombie\u0026rsquo; cases) to be propped up through extending further funding i.e. they lose patience. Of course, as the development time span increases, proxied by the \u003cem\u003eduration of VC involvement\u003c/em\u003e and the \u003cem\u003enumber of rounds of investment\u003c/em\u003e, the more likely that technologies may change, competitors emerge and the ventures\u0026rsquo; momentum and value wane (Ragozzino \u0026amp; Blevins, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this case VCs are more likely to devote their limited attention capacity to those, more established, investments expected to generate the returns they need to satisfy their investors (and hence enable the VC to raise a subsequent fund) (Cumming \u0026amp; Dai, 2011).\u003c/p\u003e \u003cp\u003eThe covid period required that the VC\u0026rsquo;s re-evaluated the prospects of their portfolio firms and brought forward the \u0026lsquo;abandonment decision\u0026rsquo; for those ventures that have had successive rounds of investment but without commercial success. There will be a threshold of investment level and duration of investment at which the VC reappraises whether to continue with or abandon the venture. On the other hand, the greater the VC\u0026rsquo;s \u0026lsquo;sunk costs\u0026rsquo;, the \u003cem\u003etotal amount of cumulated investment\u003c/em\u003e in the venture, and/or the extent of R\u0026amp;D, the more likely they are to be actively involved in assisting the venture to weather the crisis. For these reasons, we do not expect that VCs will be involved in propping up unviable businesses during the crisis. This leads to our first hypothesis.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003e \u003cem\u003eVC investors will actively support the strong prospects in their portfolios through the crisis but allow non-viable businesses to fail. Consequently, we do not expect the pattern of insolvencies of equity financed firms to be different from earlier periods.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eEmpirical studies of venture capital investment and exit decisions often draw on theories related to \u003cem\u003einformational asymmetry\u003c/em\u003e (Akerlof, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Gompers \u0026amp; Lerner, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ravenscraft \u0026amp; Scherer, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) and \u003cem\u003esignalling\u003c/em\u003e (e.g., Spence, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Higgins \u0026amp; Gulati, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zimmerman, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) as a framework for hypothesis development and analysis. Entrepreneurs and venture capital funds buying into entrepreneurial ventures as outsiders are faced with serious informational asymmetry problems, regarding the long-term prospects of the business and valuation, that due diligence finds difficult to uncover (Wilson et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Robbie \u0026amp; Wright, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Consequently, they gather and act on credible \u0026lsquo;signals\u0026rsquo; of the quality of the venture and the entrepreneurs\u0026rsquo; expertise (Higgins \u0026amp; Gulati, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lester, 2006; Zimmerman, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Ragozzino and Bevins (2016), compile \u0026lsquo;\u003cem\u003esignals of quality\u0026rsquo;\u003c/em\u003e in entrepreneurial firms by measuring aspects of venture capitalist firms\u0026rsquo; previous involvements in entrepreneurial companies. The signals that are \u0026lsquo;\u003cem\u003eobserved and costly to obtain\u0026rsquo;\u003c/em\u003e (op cit. p. 993) include the presence of a VCs in the company and duration of this presence, the number of VCs that have invested in a company, the timing of their funding rounds, and the total amounts invested. Following Ragozzino and Bevins (2016), we create a number of variables relating to the history of deals for our equity-backed firms. These include investor type (VC (domestic or foreign), Business Angel, Crowd Funding, Government VC), the stage of investment (Seed, Venture, Growth, Established), whether the company had announced deals, the number of rounds of investment and cumulative amount of investment, the purpose of investment (R\u0026amp;D, job creation) and the period of time from the first deal and from the last deal.\u003c/p\u003e \u003cp\u003eFollowing previous studies (e.g. Manigart et al. 2002) we suggest that being backed by reputable and experienced VC\u0026rsquo;s and/or Business Angels is a strong signal for additional investors and financiers. In our data this includes the domestic and foreign based VC funds. The number of rounds and cumulative investment of previous equity-backers acts as a signal of survival and future growth potential. More recent rounds are associated with more recent appraisal, due diligence, and valuation. The entrepreneurial firms with these characteristics are more likely to find support from existing investors and banking relationships in order to survive the covid shock. These characteristics can counteract the \u0026lsquo;liability of newness\u0026rsquo; in relation to survival/failure companies backed by crowd funding and/or government funds and at earlier stages lack these quality signals. Of course, risk is higher for firms in consumer facing sectors. We posit, therefore that these firms are more likely to seek guaranteed loan funds to help ride the covid period and are more likely to be subject to insolvency proceedings. Our second hypothesis is:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003e \u003cem\u003eEarly-stage equity-backed companies, crowd-funded and government backed are more likely to have guaranteed loans and enter insolvency than later stage ventures with high cumulative VC investment.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn our analysis we seek to understand the impact of the guaranteed loan scheme on firm survival. Of course, it is likely that firms on the \u0026lsquo;edge of failure\u0026rsquo; going into the Covid period would seek finance as an opportunity to increase their chances of survival, particularly in a period of increased forbearance. The funds may be used to refinance away from more expensive existing loans. Furthermore, the pandemic impacted on all firms and therefore the competitive environment changes, at least temporarily, increasing the possibility of survival. If the company is otherwise viable and needs the funds just to overcome the temporary difficulties created by lockdowns and the decrease of economic activity, the covid loan helps to bridge the difficult period. If, on the other hand, the company is not viable, the covid loan represents a temporary easing which simply delays company failure until the funds are exhausted. Moreover, it is expected that after the external help is withdrawn (used up), these companies, having an additional burden of debt and additional creditor(s) will proceed to bankruptcy and thus the number of insolvencies will increase. Early-stage equity-backed firms, without additional equity injections are more prone to failure than other firms and the funding available through guaranteed loan schemes is unlikely to support the survival of those smaller firms that were in a poor financial position pre covid. Indeed, in the descriptive analysis that follows, we note that a high proportion of recipients reached the \u003cem\u003eintensive margin\u003c/em\u003e in that they exhausted all their BBL borrowing capacity (loan-sales ratio\u0026thinsp;=\u0026thinsp;25%) indicating that they may still have an unmet demand for borrowing and remain in a precarious position. This leads us to the formulation of the fourth hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003e \u003cem\u003eEquity-backed firms with a covid loan have a higher probability of failure in the covid period.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eFollowing from the previous discussion we are interested in the more detailed characteristics of our sample firms that sought and acquired covid guaranteed loans. Those equity-backed firms that do not have a VC on board are more likely to seek guaranteed loan finance. These are likely smaller, earlier stage ventures in a weaker financial position pre-covid and/or in sectors worst hit by the pandemic. Our third hypothesis concerns selection into the loan guarantee scheme:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 4\u003c/strong\u003e \u003cp\u003e \u003cem\u003eEarly-stage equity-backed companies, crowd-funded, angel and government backed are more likely to seek guaranteed loan finance than later stage and those with more rounds and higher cumulative VC investment.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eAfter analysing the characteristics of firms that seek for covid loan finance, an important question arises whether the lender type ultimately impacts the loan default patterns. Namely, due to the government financial support, there was an opportunity for established lenders to off-load the riskier parts of their loan portfolios into the loan guarantee scheme (refinance) and for challenger banks to grow their client base by accepting a high rate of default but at a low risk of losses due to the government guarantee.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 5\u003c/strong\u003e \u003cp\u003e \u003cem\u003eChallenger banks are more likely to attract riskers SMEs and have a higher default rate\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e"},{"header":"4 Data and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data\u003c/h2\u003e \u003cp\u003eTo proceed with the analysis, we combine several datasets. We start with the dataset of equity funded companies. This data is provided by Beauhurst and covers equity deals from the beginning of 2011.\u003csup\u003e5\u003c/sup\u003e Beauhurst data covers over 90% of equity deals in the UK after 1 January 2015, both publicly announced and unannounced. Before 1 January 2015, the coverage of unannounced deals is not comprehensive. The data on unannounced deals is obtained from SH01 forms (The Return of Allotment of Shares) submitted by firms to Companies House. The remaining, less than 10%, is not covered due to incorrect filings in Companies House, etc. This dataset provides detailed information on individual deals such as deal value, stage of evolution of the company, round of funding, identity of investors, and industry sector.\u003c/p\u003e \u003cp\u003eThe resulting panel dataset covering all UK registered companies comprises financial information, details of industry sector, age, location, etc. and a long time period including the years pre- and post-covid. This data is merged, via company registration number, with a dataset that tracks all insolvent exits sourced from ONS (Office of National Statistics \u0026ndash; the UK statistical office). We match this data to firms that have received any equity finance in the period before the Covid pandemic.\u003c/p\u003e \u003cp\u003eFinally, we have unique access to the detailed loan information on each company from the covid loan portfolio which is drawn from the Information Management System of the Covid loan guarantee scheme. This covers all loans that were administered in the schemes. The information in this dataset covers, among others, loan amount, loan terms, lender identity and loan state. Thus, we have data on the large sample of equity funded companies, sub-sample of covid loan recipients and all recorded loan defaults and insolvent exits. Our sample selection process is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Selection Steps\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsolvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCovid Loans\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel A: Main estimation sample (covid period)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompanies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies with at least one equity deal before 31/3/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLess\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies without last available accounts between 1/4/2017 and 31/3/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2,492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies that became insolvent before 31/3/2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies with missing values for explanatory variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies with missing values for dependent variable (Northern Ireland)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolding companies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZero total investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal estimation sample\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsolvent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel B: Historical control sample\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecompanies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies with at least one equity deal before 31/3/2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLess\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies without last available accounts between 1/4/2014 and 31/3/2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies that became insolvent before 31/3/2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies with missing values for explanatory variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies with missing values for dependent variable (Northern Ireland)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHolding companies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZero total investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal estimation sample\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe table shows the steps involved in the preparation of the company level samples employed in the first part of the study. Panel A shows how the main covid period sample was constructed. This sample includes all eligible companies with an equity deal at the beginning of the covid period, i.e., as of 31st March of 2020. Panel B shows how the historical control sample has been constructed. The historical control sample includes all eligible companies that had an equity investor as of the 31st of March 2017. In each of these two samples, every observation corresponds to one company. The sample created by appending the two samples (the combined sample) has been used for quantification of differences in failure rates in the pre-covid and covid period. The main estimation sample was employed to quantify differences in failure rates for companies with and without a covid loan.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe identify 20,053 firms that have had at least one round of equity finance prior to the pandemic period, more specifically, on or before the 31 March 2020.\u003csup\u003e6\u003c/sup\u003e Since we need the latest financial information at the start of pandemic, we exclude 2,492 companies without any financial accounts in the three-year period before the 31st of March 2020.\u003csup\u003e7\u003c/sup\u003e Next, in our analysis we focus on the insolvent exits in the three-year period from the beginning of April 2020 to the end of March 2023, we exclude 392 companies that became insolvent already before the 31st of March 2020. Then, we exclude 896 companies with missing values for any of the explanatory variables and 220 companies from the Northern Ireland because this region is not covered by ONS insolvency dataset. We exclude 2,009 holding companies due to different financial and assets\u0026rsquo; structure, as well. Finally, we remove 258 companies where the total investment in all equity deals is zero. This leaves us with 13,786 firms in the covid period estimation sample. Of these, 653 are bankrupt (insolvent) in the covid period and 6,234 acquired guaranteed loans. This sample represents the main estimation sample as it includes equity funded companies that were solvent at the beginning of the covid period. We term this sample \"covid period sample\" in the following text.\u003c/p\u003e \u003cp\u003eFollowing the study of Dorr et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) we construct a control sample for the covid period sample that includes the same type of companies from the prior (pre-covid) period. Therefore, mirroring the above-mentioned sample selection steps we construct a three-year pre-covid historical control sample of equity-backed firms starting from 2017 (Q2) comprising 12,033 firms, of which there were 466 bankruptcies during the control period after going through all sample selection steps we have 8,531 firms in the sub-sample. This sample is then appended to the covid period sample. The resulting sample contains 22,317 observations and will be termed \"combined sample\" in the following text.\u003c/p\u003e \u003cp\u003eFinally, in the last part of the paper, we analyse covid loans taken by companies in the covid period sample. There are 6,234 companies that obtained one or more loans under a covid loan guaranteed scheme. This sample contains 6,936 observations (covid loans)\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e we provide descriptive statistics of our main sample. A full list of constructed variables, their sources and definitions are provided in the appendix.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for the combined sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness Angel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrowd Funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets \u0026pound;m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18,211,941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets \u0026gt;\u0026pound;1m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking capital to total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent assets to total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent liabilities to total liabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfit/loss account reserve to total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort and Long-term debt to total assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of charge on assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of no debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx ante risk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing risk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstablished Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Rounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnounced Deal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from first deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from last deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment purpose (R\u0026amp;D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment purpose (Job creation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuzzword (AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuzzword (Fintech)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Media)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Industrial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Infrastructure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Retail)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Crafts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Leisure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Supply Chain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Professional services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Trades)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Personal services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Technology)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast of England\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLondon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScotland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22,317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe table shows descriptive statistics for the combined sample, i.e., a sample created by appending the main estimation sample and historical control sample. The variables are defined in Appendix in Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003eA1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Methodology\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the first part of the paper, we estimate several multivariate panel binary logistic regression models (discrete time). The dependent variable is the indicator of an insolvent exit following the last available financial accounts\u003csup\u003e8\u003c/sup\u003e. The independent variable of interest is the indicator of the covid period (covid indicator). It is equal to unity in the covid period (financial accounts submitted from April 2017 to March 2020) and zero for the pre-covid period (accounts submitted from April 2014 to March 2017). The range of model specifications we present in each table are useful for checking the sensitivity of our main findings with respect to various control variables.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe logistic regression is a conditional probability function where the probability of failure determined by a set of several covariates (vectors \u003cem\u003eMIV, ITV, EDV, FR, NFV, ISI\u003c/em\u003e, and \u003cem\u003eRI\u003c/em\u003e) and the respective vectors of coefficients α\u003csub\u003ek\u003c/sub\u003e (k\u0026thinsp;=\u0026thinsp;1, 2, .., 7) which measure the effect of this set of covariates on probability of failure. Subscript \u003cem\u003ei\u003c/em\u003e represents each individual firm.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$P({y}_{i}=1|{MIV}_{i}, {ITV}_{i}, {EDV}_{i}, {FR}_{i}, {NFV}_{i}, {ISI}_{i}, {RI}_{i})=\\frac{1}{1+{e}^{-\\left({\\alpha }_{0}+{MIV}_{i}^{T}{\\alpha }_{1}+{ITV}_{i}^{T}{\\alpha }_{2}+{EDV}_{i}^{T}{\\alpha }_{3}+{FR}_{i}^{T}{\\alpha }_{4}+{NFV}_{i}^{T}{\\alpha }_{5}+{ISI}_{i}^{T}{\\alpha }_{6}+{RI}_{i}^{T}{\\alpha }_{7}\\right)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe vector \u003cem\u003eMIV\u003c/em\u003e represents covid-related main independent variables. It is either the indicator of the covid period (models associated with hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) or the indicator of the covid loan (models related to testing hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The vector \u003cem\u003eITV\u003c/em\u003e represents investor types variables. We generated indicators of the most frequent investor types (VC, Angel, Crowd Funding, Government VC, Foreign investor). Regarding further equity deals variables (vector EDV) we control for the stage of investment (Seed, Venture, Growth, Estab1lished), announced deals, the number of rounds, cumulative amount of investment, the purpose of investment (R\u0026amp;D, job creation) and the period of time from the first deal and last deal. We explore \u003cem\u003enon-linear\u003c/em\u003e (\u003cem\u003equadratic) relationships\u003c/em\u003e in relation to \u003cem\u003etotal investment\u003c/em\u003e and \u003cem\u003erounds of investment\u003c/em\u003e to determine the threshold at which VC\u0026rsquo;s abandon the venture. The vectors \u003cem\u003eFR\u003c/em\u003e (financial ratios) and \u003cem\u003eNFV\u003c/em\u003e (non-financial variables) related to firm survival. More specifically, the financial ratios represent important dimensions of firms' financial performance, i.e., liquidity (working capital to total assets, current assets to total assets), leverage (current liabilities to total liabilities, short-term and long-term debt to total assets), and profitability (profit and loss account reserve to total assets). Following the literature (Keasey \u0026amp; Watson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Altman et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) we employ a rich set of the non-financial characteristics\u003csup\u003e9\u003c/sup\u003e including company size (based on total assets)\u003csup\u003e10\u003c/sup\u003e, indicator of charges on assets and indicator of no debt. A crucial determinant of the insolvent exit is the ex-ante risk score at the time of last available financial year end.\u003csup\u003e11\u003c/sup\u003e Some companies are not risk-scored and we include the indicator for no risk score. Finally, employing industry sector indicators (vector \u003cem\u003eISI\u003c/em\u003e) we control for industry top-level sector (based on the detailed descriptor in the VC database) and location (vector \u003cem\u003eR\u003c/em\u003e - regions). Some of the estimated models include \u003cem\u003einteraction terms\u003c/em\u003e for the covid period and investor type, or covid loan and investor type. The analysis proceeds in relation to the testing of Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e stating that we are unlikely to observe an insolvency-gap for the less viable equity-backed companies. Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e suggests that survival will be a function of investor type and stage of investment, the acquisition of maximum guaranteed loans is indicative of the venture facing financial difficulty while hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e suggests that companies with a covid loan are more likely to fail.\u003c/p\u003e \u003cp\u003eA further model is used to shed light on the detailed characteristics of the equity-backed companies that acquired covid loans, identify the characteristics of those that were most \u0026lsquo;credit constrained\u0026rsquo; during the crisis and the investor type. Here we identify the guaranteed loan recipients and profile using set of variables discussed above, the characteristics of those firms with and without a loan. To estimate the model, we employ the probit regression\u003csup\u003e12\u003c/sup\u003e. The set of explanatory variables will cover those in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) but we include additional variables (indicators of buzzwords). The dependent variables will be the indicator of covid loan. This set-up will be employed to test hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn the second part of the paper, we analyse the subsample of the covid loan portfolio. We are interested in the profile of the portfolio of loans by lender types and estimate a logistic regression to profile 3 lender types: banks, challenger banks, and other lenders. The next stage is to model loan default. In line with the previous literature (Cowling et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) we use the Cox proportional hazard model to account for the censoring. We estimate the hazards model using the rich set of firm and loan contract variables described later. This model specification follows that of similar studies using Italian Credit Guarantee Scheme (CGS) data for Italy (Caselli et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and US SBA Loan Guarantee Programme (Glennon and Nigro, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and in other related studies concerned with new firm survival (Van Praag, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Audretsch \u0026amp; Mahmood, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Holmes et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The modelling allows us to estimate the hazard risk of a loan defaulting as a function of firm characteristics, demographics, and loan contract parameters. We include lender type (vector \u003cem\u003eLT\u003c/em\u003e), and loan contract related variables (vector \u003cem\u003eLCV\u003c/em\u003e) as additional explanatory variables to those described above and employed earlier. The dependent variable is specified such that the individual loan time begins at its origination date and continues until its default date when it ends. For loans that have not defaulted by the end of the sample period (July 2023) the data are censored at this point or are continuing to progress through their term and making repayments according to the loan schedule.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe hazard function is h(t) and is the risk of default at time t which is the survival time. It follows that h(t) is the hazard function which is determined by a set of several covariates (vectors \u003cem\u003eLT\u003c/em\u003e, \u003cem\u003eLCV\u003c/em\u003e, \u003cem\u003eITV\u003c/em\u003e, \u003cem\u003eEDV\u003c/em\u003e, \u003cem\u003eFR\u003c/em\u003e, \u003cem\u003eNFV\u003c/em\u003e, \u003cem\u003eISI\u003c/em\u003e and \u003cem\u003eRI\u003c/em\u003e) and the respective vectors of coefficients α\u003csub\u003ek\u003c/sub\u003e (k\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip;, 8) which measure the effect of this set of covariates on hazard rate. Subscript \u003cem\u003ei\u003c/em\u003e represents each individual firm loan contract, and \u003cem\u003et\u003c/em\u003e represents time.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$h\\left(t\\right)={h}_{0}\\left(t\\right)\\text{e}\\text{x}\\text{p}({LT}_{i}^{T}{\\alpha }_{1}+{{LCV}_{i}^{T}{\\alpha }_{2}+ ITV}_{i}^{T}{\\alpha }_{3}+{EDV}_{i}^{T}{\\alpha }_{4}+{FR}_{i}^{T}{\\alpha }_{5}+{NFV}_{i}^{T}{\\alpha }_{6}+{ISI}_{i}^{T}{\\alpha }_{7}+{RI}_{i}^{T}{\\alpha }_{8})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe vector \u003cem\u003eLT\u003c/em\u003e represents the indicators of lender type issuing the guaranteed loan. As we have three types of lenders, the components of the vector \u003cem\u003eLT\u003c/em\u003e are the indicators of groups of lenders - banks, challenger banks and other lenders.\u003csup\u003e13\u003c/sup\u003e The vector \u003cem\u003eLCV\u003c/em\u003e represents loan contract variables. Besides loan amount, loan term and the indicator of BBLS we calculate, for each borrower, the loan to turnover ratio to identify those firms that took the maximum possible loan i.e. were at the \u003cem\u003eintensive margin\u003c/em\u003e. We include the interaction between the indicator of BBLS and the loan to turnover ratio, too. The vectors \u003cem\u003eITV\u003c/em\u003e, \u003cem\u003eEDV\u003c/em\u003e, \u003cem\u003eFR\u003c/em\u003e, \u003cem\u003eNFV\u003c/em\u003e, \u003cem\u003eISI\u003c/em\u003e and \u003cem\u003eRI\u003c/em\u003e were defined above. We will employ this setting to test hypothesis \u003cspan refid=\"FPar5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Model Specifications and Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Equity-backed firms and the insolvency gap\u003c/h2\u003e \u003cp\u003eThe results of the insolvency models employing the combined sample are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Firstly, the main variable of interest is the indicator of the covid period. As is apparent from the models, the covid period coefficient is not statistically significant in the models with a richer set of the explanatory variables. Hence there is no evidence of insolvency gap during the covid period, in other words the equity funded companies did not have a lower insolvency rate during the three-year window from April 2020 to end of March 2023, when compared with pre-coviTad period and unlike the SME sector generally. This means that government intervention in form of the covid loan schemes, relaxing of the insolvency legislation and other forms of help provided to businesses did not delay failures as there was no backlog of insolvencies of equity funded companies. This is evidence in favour of our Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInsolvency prediction models using combined sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovid Period Indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.150**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.147**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.116*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness Angel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.204**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrowd Funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.651***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.512***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.515***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.493***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.403***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.445***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.332**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.286**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.216*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.246**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.310*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.289**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.283**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.330**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.232*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Period X Venture Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Period X Business Angel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Period X Crowd Funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Period X Government VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.432*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Period X Foreign VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.179**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.193**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.214*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstablished Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.308*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.366**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.413**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.413**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Rounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0854***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0726**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0591**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0760**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0783***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0792***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnounced Deal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.907***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.925***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.664**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.696**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.719***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.709***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment) squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0295***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0223**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0228**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0232**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0229**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from First Deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000174**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000222***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000224***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000235***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.000240***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.000239***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from Last Deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0000366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0000455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0000462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment Purpose (R\u0026amp;D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.345**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.316*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment Purpose (Job creation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Capital to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0910***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.143***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.132***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.130***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.130***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Assets to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.569***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.532***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.441***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.420***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.420***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Liabilities to Total Liabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.463**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfit/Loss Account Reserve to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0444*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0971***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.121***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.126***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.125***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort and Long-term Debt to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.411**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.402**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.382*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.375*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.920***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.906***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.894***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.892***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets) squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0342***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0341***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0335***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0335***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of Charge on Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.179*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.197*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of No Debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.328***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.258***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.237***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.239***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-ante Risk Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.597***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.463***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.591***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.584***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing Risk Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Media)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.311***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.287**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.287**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Industrial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Infrastructure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Retail)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Crafts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Leisure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.492***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.518***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.516***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Supply Chain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.366**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.350**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.348**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Professional services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.193**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.182**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.182**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Trades)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.908***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.872***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.874***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Personal services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Technology)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.297***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.293***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.421**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.399**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.402**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast of England\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.762***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.758***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.458***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.456***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScotland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.327**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.322**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYorkshire and The Humber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.488***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.491***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.851***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.984***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.699***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13.79***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-14.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-14.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-14.28***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsolvency Events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea Under ROC Curve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eThe table shows the estimation results for the models predicting insolvent exit using the combined sample covering both pre-covid (historical control subsample) and covid period. The dependent variable is the indicator of the insolvent exit in the 3-year period either from 1st of April 2017 to 31st of March 2020 (pre-covid historical control subsample), or from 1st of April 2020 to 31st of March 2023 (covid-period subsample). The variable of interest is the indicator of the covid period (equals one if the observation comes from the covid period subsample and zero otherwise). The models are estimated using logistic regression. The statistical significance is indicated with asterisks where the *, **, and *** denote significance at 10%, 5% and 1% significance levels. The corresponding t-statistics are computed using robust standard errors. The variables are defined in the Appendix in Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003eA1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eSecondly, we can analyse whether the patterns of insolvencies differ for specific types of investors. The results of the model with the largest set of explanatory variables (model 8) suggest that the main effects of most of the indicators for investor types are not statistically significant at 5% significance level. However, the indicator of crowd funding is positive and significant in all models, providing support for Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In terms of the magnitude of effect, the odds of failure for companies financed by crowd funding is higher by approximately 39% when compared with similar companies without such a funding.\u003csup\u003e14\u003c/sup\u003e On the other hand, the coefficients for the interactions between the covid period and the investor types are not statistically significant suggesting that the baseline insolvency rates attributed to specific investor types have not changed during the covid period.\u003c/p\u003e \u003cp\u003eThirdly, the control explanatory variables attract expected signs and magnitudes. Companies that are in the established stage or with longer time from the first deal are less likely to fail. We include a quadratic term for the total cumulative investment and find significant results. The coefficients imply that at a threshold level of c\u0026pound;5m of investment rounds the ventures are at higher risk of insolvency, controlling for sector and other factors. This provides evidence for \u0026lsquo;waning momentum\u0026rsquo; idea suggested by Ragozzino and Blevins (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and the negative signal to potential investors. The crisis hastened the decision to \u0026lsquo;call time\u0026rsquo; on some ventures which would leave them with difficulty raising additional rounds of investment in the VC market. Further, the larger companies are more likely to fail but the effect reverses for companies with total assets over \u0026pound;600,000, consistent with failure prediction model (Altman et al 2013). As expected, companies with higher liquidity and profitability are less likely to become insolvent, as are companies without debt. On the other hand, riskier companies, i.e., those with higher ex-ante risk score experience higher insolvency rates. Companies operating in the sectors of media, professional services and technology are less likely to fail while the opposite is true for companies from sectors of leisure, supply chain and trades. Finally, with respect to region, equity funded companies based in the northern regions of England (North East, North West, Yorkshire and the Humber, West Midlands) experience on average higher insolvency rates. This could be the result of insufficient equity funding because of persistent equity gaps in these regions (Wilson et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kacer \u0026amp; Wilson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Equity-backed firms and covid period bankruptcy\u003c/h2\u003e \u003cp\u003eThe results of models presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e will shed light on the pattern of insolvencies during of the three-year period starting on the 1st of April 2020 covering the covid pandemic. Firstly, the main variable of interest is the covid loan indicator\u003csup\u003e15\u003c/sup\u003e to understand the role of the guaranteed covid loans in this process. The estimated coefficient is positive and statistically significant across all model specifications suggesting that companies with a covid loan are on average more likely to experience insolvent exit, supporting hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The main effect is relatively strong in that, all else equal, the odds of insolvent exit is higher by about 77% for a company with covid loan (model 8, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, as there are interactions between the covid loan and specific investor types, this is the case for companies without any of these investors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInsolvency prediction models using covid period sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInsolvency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovid Loan Indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.642***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.631***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.636***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.627***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.489***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.428***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.419***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.572***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness Angel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.221*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.497***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrowd Funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.717***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.645***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.639***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.621***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.527***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.569***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.707***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Loan X Venture Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Loan X Business Angel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.760***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Loan X Crowd Funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Loan X Government VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.769**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction Covid Loan X Foreign VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstablished Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.534**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.449*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.479*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.514**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.565**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.550**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Rounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0647*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnounced Deal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment) squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from First Deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000180*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000226**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000227**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000243**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.000250**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.000255**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from Last Deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0000833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0000932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0000836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0000874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment Purpose (R\u0026amp;D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment Purpose (Job creation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Capital to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0987**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.151***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.146***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.141***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Assets to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.488***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.452***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.375***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.360**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.374***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Liabilities to Total Liabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfit/Loss Account Reserve to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0990***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.131***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.154***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort and Long-term Debt to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.701***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.723***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.710***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.690***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets) squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0259***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0270***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0265***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0256**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of Charge on Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.00378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of No Debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.216**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-ante Risk Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.391***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.102***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.240***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.210***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing Risk Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Media)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Industrial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Infrastructure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Retail)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Crafts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.415*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.462**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.446*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Leisure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.452***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.476***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.471***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Supply Chain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Professional services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.238**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.227**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.221**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Trades)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.715***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.682**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.679**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Personal services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.000841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Technology)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.217**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.217**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.216**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.461*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.456*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.476*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.435*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.445*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast of England\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.269*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.262*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.610**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.624***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.401**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.397**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScotland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.0824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.226*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.223*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.362*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.371*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYorkshire and The Humber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.436**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.423**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.338***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.465***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.638***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.473***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-10.88***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-11.21***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-11.28***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsolvency Events\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea Under ROC Curve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eThe table shows the estimation results for the insolvency prediction models using the covid period sample. The dependent variable is the indicator of the insolvent exit in the 3-year period from 1st of April 2020 to 31st of March 2023 (equals one if the company experienced an insolvent exit during the period and zero otherwise). The variable of interest is the indicator of the covid loan (equals one if the company has a loan under any of the three covid loan guarantee schemes and zero otherwise). The models are estimated using logistic regression. The statistical significance is indicated with asterisks where the *, **, and *** denote significance at 10%, 5% and 1% significance levels. The corresponding t-statistics are computed using robust standard errors. The variables are defined in the Appendix in Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003eA1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProfile of the companies with a covid loan\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCovid Loan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCovid Loan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCovid Loan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCovid Loan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCovid Loan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCovid Loan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuzzword (AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.244***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.264***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.246***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.153***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.145***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuzzword (Fintech)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.426***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.442***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.426***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.350***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.269***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.263***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.262***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.102***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0857**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0765*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness Angel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0829**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0801**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0820**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0964**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrowd Funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.251***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.242***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.237***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.182***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.193***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0840*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0922*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.576***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.334***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.326***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.342***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.305***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.292***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.264***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.178***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0825*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstablished Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.141**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.151***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Rounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0345***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0458***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0357***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0438***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0448***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnounced Deal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0959***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.101***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0858**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0693*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.820***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.832***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.587***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.594***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.592***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment) squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0366***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0367***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0273***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0271***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0269***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from First Deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0000242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0000236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0000317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0000345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0000355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from Last Deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000220***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000225***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.000208***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000206***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000208***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment Purpose (R\u0026amp;D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.199***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.199***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.217***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.173***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.179***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment Purpose (Job creation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.228***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.224***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.202***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.191***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.185***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Capital to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0604***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0591***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0589***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Assets to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0962**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0919**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0862**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Liabilities to Total Liabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.469***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.557***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.543***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.537***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfit/Loss Account Reserve to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0717***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0452***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0352***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0338***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort and Long-term Debt to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.858***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.425***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.410***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.408***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.817***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.816***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.816***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets) squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0330***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0331***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0331***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of Charge on Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.284***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.288***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.276***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of No Debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.459***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.436***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.430***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-ante Risk Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.504***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.678***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.688***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing Risk Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.114*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.109*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Media)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.103***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Industrial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.156***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.153***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Infrastructure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Retail)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0997**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.101**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Crafts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Leisure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.230***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.231***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Supply Chain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Professional services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.112***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.113***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Trades)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Personal services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Technology)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.268***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.267***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast of England\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0776*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.179**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.173***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScotland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0940*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0584*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.100**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.128*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYorkshire and The Humber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0291**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.368***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.839***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.095***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.180***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-8.219***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompanies with covid loans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea under ROC curve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe table shows the estimation results for the models quantifying differences between the companies with and without covid loans using the covid period sample. The dependent variable is the indicator of covid loan (equals one if the company has a loan under any of the three covid loan guarantee schemes and zero otherwise). The models are estimated using probit regression. The statistical significance is indicated with asterisks where the *, **, and *** denote significance at 10%, 5% and 1% significance levels. The corresponding z-statistics are computed using robust standard errors. The variables are defined in the Appendix in Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003eA1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSecondly, let us have a look at the main effects of the analysed (most frequent) investor types. The coefficients for the business angels and crowdfunding indicators are positive and statistically significant, i.e., companies without a covid loan with these investors will have higher likelihood of insolvency. The magnitude of effect is non-negligible in that the former have odds of failure higher by approximately 64%, and the latter by more than 100%, both relative to similar companies without covid loan and without the specific investor type. The situation is somewhat different for companies with a covid loan. Namely, the interaction between covid loan and business angel is negative and significant, and the same for the interaction between the covid loan and government VC. This means that a company with a covid loan and business angel investor has a lower probability of insolvent exit than a similar company with a covid loan but without a business angel investor, by about 23%.\u003csup\u003e16\u003c/sup\u003e Similarly, a covid loan seems to decrease the likelihood of failure for a company with angel investors by about 17%. The effect of a covid loan is similar for a company with a government VC investor; these companies with a covid loan have lower odds of insolvent exit by about 18%. The effects of the control variables are largely similar to the previous model. We find support for Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e in that the established companies are less likely to fail relative to earlier stage ventures and crowd funded firms are significantly more likely to fail. However, those backed by business angels and access the guaranteed loans have a lower failure risk. Business angels have good relationships and reputation with their banks and ensured their investee had the capacity to take on the debt finance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Equity-backed firms and the covid loan scheme(s)\u003c/h2\u003e \u003cp\u003eIn the previous section we examined the likelihood of failure of equity-backed firms that had covid loans and found a higher propensity to fail amongst the loan recipients, controlling for a wide range of other factors. In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e we profile our sample of equity backed firms that chose (or not) to take advantage of the covid loan schemes. In order to do so we merge data from the covid loan portfolio to our database of equity backed firms and identify the firms that accessed the loan scheme. We identify 6,234 firms that acquired a loan, slightly over 45% of the main estimation sample. Estimating a binary logit model (1\u0026thinsp;=\u0026thinsp;covid loan, 0\u0026thinsp;=\u0026thinsp;not) we profile the characteristics of loan recipients using very similar specification as in model in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. First, we include the indicators of \u0026ldquo;buzzwords\u0026rdquo; AI and fintech.\u003csup\u003e17\u003c/sup\u003e Then, we add all the variables employed in the above models. This includes details of the main investor types, stage of investment and number of rounds of investment, whether the investee had a \u0026lsquo;announced\u0026rsquo; deal, the total cumulative investment amount, and indicators of the time form the first deal and last deal per covid. We control for the stated purpose of the latest investment (R\u0026amp;D, Job creation), as well. Further, we include financial and non-financial characteristics pre covid \u0026ndash; company size (assets) and financial ratios reflecting cash and liquidity, reserves, debt, and indicator of credit charges on assets. We indicate companies that have not used debt finance in the years pre covid. A pre covid insolvency risk score is an explanatory variable, too. Finally, we control for sector, and region.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe results presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e suggest that firms operating in AI and Fintech fields are less likely to seek for covid loan finance, possibly because of demand for higher volumes of funding that the covid loans cannot satisfy. Further, firms funded by Business Angels and Crowd Funded firms are more likely to access the preferential loan finance.\u003csup\u003e18\u003c/sup\u003e Crowd funded firms have dispersed shareholders and are less likely to provide additional resources during the crisis. As mentioned, Business Angels, as high net worth individuals, are likely to have strong reputations and relationships with banks, and access finance as needed. The firms backed by the foreign investors are less likely to seek loan finance\u003csup\u003e19\u003c/sup\u003e supporting the notion that larger foreign funds invest higher amounts of money into more developed companies and are active in supporting investees financially during difficult periods to protect their investment. On the other hand, companies funded by (domestic) VC funds and government backed ventures do not seem to have a significant impact on the use of the loan facilities. These results are qualitatively similar in all model specifications. Thus, the results are consistent with the first part of hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eConsistent with earlier arguments and evidence on the patterns of VC investment, during covid, firms in the established and growth phases of investment and with \u003cem\u003ehigher cumulative investments\u003c/em\u003e,\u003csup\u003e20\u003c/sup\u003e and recent deals are \u003cem\u003eless likely\u003c/em\u003e to seek loans than the venture stages. This is evidence in favour of the second part of hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Moreover, those using funds for R\u0026amp;D are less likely to seek finance than those that are growing employees. In terms of financial characteristics, the ex-ante risk score is a strong predictor of loan acquisition, suggesting firms in a more precarious financial position accessed loans. This is indicated in lower liquidity and higher working capital requirement. Although the firms appear to have higher reserves, possibly purposed for development activities, rather than trading. The effect of size is non-linear, as well. The smaller companies appear to have greater demand for covid loans and once they reach certain size (approximately \u0026pound;225k), the effect becomes negative. The firms accessing loans are more likely to have existing debt and charges on assets. Of course, it would be rational to replace higher priced existing loans with the cheaper covid loans and remove creditor asset charges. These firms, therefore, are not creating \u003cem\u003eadditional financial resources\u003c/em\u003e and remain prone to failure.\u003c/p\u003e \u003cp\u003eWe find variations in loan acquisition propensity by sector, based on the most common definitions in our equity finance database. Media and Communications, and technology are less likely to seek loans. However, those firms involved in industrials, supply chains, retail, leisure, and professional services acquire loans. There is some regional variation. Firms in the northern regions (NW, NE, Yorks.) the south-west, and Wales are more likely to seek funding. Again, these regions have been identified as areas where there is an \u0026lsquo;equity gap\u0026rsquo; in comparison to London and other regions (Kacer \u0026amp; Wilson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicating a shortage of funding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Equity-backed firms and loan default\u003c/h2\u003e \u003cp\u003eIn this part we analyse a sample of covid loans obtained by the 6,234 companies in the main estimation sample. There are 6,936 such loans since some companies have several loans. As of 17th of July 2023, among these loans, there were 714 defaulted loans where a government guarantee was demanded by lenders. Most of the loans in our sample are issued under BBLS and these loans represent nearly 77% of the sample. The rest of the loans are issued under either CBILS or CLBILS. We distinguish three main lender types, banks, challenger banks, and other lenders, the most frequent ones, representing nearly 84.9%, 7.4%, and 7.7%, respectively. The loan amounts range from \u0026pound;2,000 to \u0026pound;10,000,000 but nearly 51% of the loans in the sample have loan amount \u0026pound;50,000 which is the maximum loan amount permitted under BBLS. Similarly, loan terms range from 5 to 120 months, but majority of loans have loan term 72 months. Finally, the loan to turnover ranges from 0.007 to 0.714. The descriptive statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \n\u003cp\u003eTable\u0026nbsp;6\u0026nbsp;Descriptive statistics for the sample of covid loans\u003c/p\u003e\n\u003cp\u003ePanel A. Breakdown by loan scheme and loan default\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.8%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.2%\" colspan=\"2\"\u003e\n \u003cp\u003eNon-defaulted loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.2%\" colspan=\"2\"\u003e\n \u003cp\u003eDefaulted loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.8%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.8%\"\u003e\n \u003cp\u003eScheme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.8%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.8%\"\u003e\n \u003cp\u003eBBLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\"\u003e\n \u003cp\u003e4,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\"\u003e\n \u003cp\u003e88.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\"\u003e\n \u003cp\u003e638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\"\u003e\n \u003cp\u003e11.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.8%\"\u003e\n \u003cp\u003e5,350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.8%\"\u003e\n \u003cp\u003eCBILS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\"\u003e\n \u003cp\u003e1,505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\"\u003e\n \u003cp\u003e95.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\"\u003e\n \u003cp\u003e4.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.8%\"\u003e\n \u003cp\u003e1,581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.8%\"\u003e\n \u003cp\u003eCLBILS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.8%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.8%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2%\" valign=\"bottom\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.8%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ePanel B: Breakdown by lender category and loan default\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.11111111111111%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.51851851851852%\" colspan=\"2\"\u003e\n \u003cp\u003eNon-defaulted loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.592592592592595%\" colspan=\"2\"\u003e\n \u003cp\u003eDefaulted loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.777777777777779%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.11111111111111%\"\u003e\n \u003cp\u003eLender Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.925925925925926%\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.777777777777779%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003eBank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"bottom\"\u003e\n \u003cp\u003e90.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"bottom\"\u003e\n \u003cp\u003e566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e5,889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003eChallenger Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"bottom\"\u003e\n \u003cp\u003e78.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"bottom\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003eOther Lender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"bottom\"\u003e\n \u003cp\u003e92.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"bottom\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.11111111111111%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.925925925925926%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15%\" valign=\"bottom\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.777777777777779%\" valign=\"bottom\"\u003e\n \u003cp\u003e6,936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ePanel C: Descriptive statistics of the continuous Covid loan specific variables\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.217687074829932%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.217687074829932%\" valign=\"top\"\u003e\n \u003cp\u003eLoan Amount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e108,110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e295,681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e2,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e10,000,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.217687074829932%\" valign=\"top\"\u003e\n \u003cp\u003eLN(Loan Amount)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e10.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e7.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e10.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e16.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.217687074829932%\" valign=\"top\"\u003e\n \u003cp\u003eLoan Term\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.217687074829932%\" valign=\"top\"\u003e\n \u003cp\u003eLoan to Turnover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e2.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.156462585034014%\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: The table shows some descriptive statistics for the sample of the covid loans taken by companies in the main estimation sample. Panel A shows frequencies and percentages of the covid loans broken down by the loan scheme and the loan default. Panel B shows frequencies and percentages of the covid loans broken down by the lender type and the loan default. Finally, Panel C shows the descriptive statistics for the continuous variables employed in the regression models.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNext, we focus on the profiles of companies for the individual lender types. The results of the models are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Most of the estimated coefficients are not statistically significant but those which are reveal interesting information. Firstly, companies with domestic or foreign VC are indifferent to the lender type in that the estimated coefficients for both investor type are not significant in any of the models. On the other hand, business angels are more likely associated with main banks and the odds of having the main bank as a lender is higher by 44% for companies with this investor. Companies funded by government VC, on the other hand, have odds smaller by 68% that the loan provider will be a challenger bank.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection models for various lender categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChallenger Bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther Lender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness Angel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.363**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.407*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrowd Funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.125***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign VC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Loan Amount)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.191***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.332***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoan Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0180***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0689***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoan to Turnover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBBLS Indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.792***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.119***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.227***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction BBLS X Loan To Turnover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.406***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.283***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenture Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.651**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstablished Stage of Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Rounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnounced Deal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.297**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.414*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Investment) squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from First Deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000335***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000507***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0000820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime from Last Deal (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment Purpose (R\u0026amp;D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvestment Purpose (Job creation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Capital to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Assets to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Liabilities to Total Liabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfit/Loss Account Reserve to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort and Long-term Debt to Total Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.694***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.502***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLN(Total Assets) squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0271***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.143***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of Charge on Assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.406***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.591***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator of No Debt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.259*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.387**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-ante Risk Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing Risk Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuzzword (AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuzzword (Fintech)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Media)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Industrial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.200*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.285**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Infrastructure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.666***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Retail)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Crafts)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Leisure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.271**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.674***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Supply Chain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.337*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Professional services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.277*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Trades)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.678*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Personal services)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Technology)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSector (Energy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.361*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.161***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast of England\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.323**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.390**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.620***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.282***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.320**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.974***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.608**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScotland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.108***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.420***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.854***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.396***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.629***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.506***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.902***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.648***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.779**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.695***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.932***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYorkshire and The Humber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.457***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.167***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-19.28***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of loans (dep. var.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2521.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1629.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-906.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echi2 test statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e752.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea under ROC curve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe table shows the estimation results for the selection models for individual lender categories. The models are estimated using binary logistic regression and the dependent variable in both models is the indicator of specific lender category. More specifically, in model 1, the dependent variable is the indicator of bank lenders, i.e., it is equal to one if the lender is a bank (main or other) and zero otherwise. In model 2, the dependent variable is the indicator of the challenger bank, and in model 3, it is the indicator of other lender. The categorisation of the lenders into lender categories is described in Appendix B1 The statistical significance of the individual estimated coefficients is based on robust standard errors and is indicated with asterisks (*, **, and *** denote statistical significance at 10%, 5%, and 1% level, respectively).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBesides investor types, there are other interesting differences between the characteristics of companies having loans from the main bank as opposed to a challenger bank. As for the loan size distribution, banks cater for the smaller companies while the other two lender types are associated with bigger ones. Both banks and challenger banks provided more loans under BBLS while the other lenders under CBILS and CLBILS. The interaction between the loan turnover suggests that banks provide loans for companies with lower loan to turnover while challenger banks are focused more on riskier companies with higher values of the ratio. With respect to ex ante risk score, the results show that banks provide loans to less risky companies, where it is the other way around for the challenger banks. This may be because the traditional banks have sophisticated credit scoring systems in place, have a large pool of customers and provide loans to predominantly their clients. On the other hand, the challenger banks are new players and want to increase their market share. Some of them may not have sophisticated credit scoring systems or may provide loan to riskier clients because it is guaranteed by government. Another important difference is the regional distribution of clients of the lenders. While for banks it is more likely to have customers from other regions than London, majority of the challenger banks seem to be based in the London region (in the models, the London region is the reference category). This may be because the main banks have a country-wide net of branches whereas the challenger banks lack the wider net of branches and are often based in London where they also seek their clients.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\"\u003e\n \u003ctable id=\"Tab13\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCox\u0026rsquo;s proportional hazard models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(8)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to Fail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to Fail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to Fail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to Fail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to Fail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to Fail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to Fail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to Fail\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBank Lender Indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.039***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.053***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.078***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.061***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChallenger Bank Indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.644***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.593***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVenture Capital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBusiness Angel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrowd Funding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.579***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.545***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.532***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.550***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.508***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.454***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.476**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.467**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.421*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.453**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.409*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.401*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.413*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForeign VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLN(Loan Amount)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoan Term\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00822***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00817***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00924***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00981***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0102***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0103***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoan to Turnover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBLS Indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.847***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.802***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.849***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.752***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.779***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.792***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInteraction BBLS X Loan To Turnover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.811***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.622**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.456**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.582**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.726**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.658**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVenture Stage of Investment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.217**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.211**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrowth Stage of Investment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstablished Stage of Investment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.778***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.811***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.627**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.623**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.619**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Rounds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0780*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnounced Deal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLN(Total Investment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.605*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.643*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.626*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.630*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLN(Total Investment) squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0226*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0244*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime from First Deal (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000189*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.000163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime from Last Deal (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvestment Purpose (R\u0026amp;D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvestment Purpose (Job creation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking Capital to Total Assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.231***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.165***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.172***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.173***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Assets to Total Assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.368***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.559***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.514***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.512***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Liabilities to Total Liabilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.439*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.683***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.681***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.682***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfit/Loss Account Reserve to Total Assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShort and Long-term Debt to Total Assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.547**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.522**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.518**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLN(Total Assets)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLN(Total Assets) squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndicator of Charge on Assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndicator of No Debt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEx-ante Risk Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.519***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.762***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.854***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing Risk Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Media)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Industrial)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Infrastructure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.353*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.360*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Retail)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Crafts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Leisure)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.289***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Supply Chain)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Professional services)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Trades)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.552**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.540**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Personal services)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Technology)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSector (Energy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Midlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast of England\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorth West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScotland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Midlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYorkshire and The Humber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDefaulted loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog-likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6041.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6021.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5981.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5970.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5936.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5919.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5908.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5904.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e test statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e348.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e356.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eNotes:\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eThe table shows estimation results for the survival models where the dependent variable is number of days to failure (covid loan default). The models were estimated using Cox\u0026rsquo;s proportional hazard models. The statistical significance of the individual estimated coefficients is based on robust standard errors and is indicated with asterisks (*, **, and *** denote statistical significance at 10%, 5%, and 1% level, respectively).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e presents Cox proportional hazard models of loan defaults. Firstly, we look at lender types. The preliminary information provided by Kaplan-Meier failure estimates in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e suggests that loans provided by challenger banks are the riskiest, followed by other lenders, and banks. In multivariate models presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, the reference category is \u003cem\u003eOther Lender\u003c/em\u003e and the results show that indeed banks do have lower hazard rates when controlled for everything else, than both challenger banks and other lenders. We find evidence supporting hypothesis \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The hazard of default for the covid loans provided by banks are lower by nearly 65% (model 8) when compared to other lenders. The fact that in the multivariate models the hazards of challenger banks and other lenders are not statistically different may be due to fact that higher risk of the challenger banks\u0026rsquo; clients is explicitly accounted for. Secondly, looking at the investor types we can see that companies with crowd funding investors are more likely to default on covid loan with hazard of failure higher by about 60%. Other investor types are not associated with significantly different hazards of failure. The loan value has a positive impact on hazard of failure while loans with longer loan term are less likely to default. BBLS loans are associated with a higher hazard of failure, by about 120%. This is expected due to non-existent credit checks of the customers and 100% government coverage. The risk represented by loan to turnover ratio affects the hazard of failure only for the BBLS loans with higher ratio being riskier, as expected. The stage of evolution of a company does not have impact, apart from the established companies that are less risky, because these bigger companies usually apply for large loans provided under CBILS/CLBILS schemes where companies were screened for risk, and also on the side of lenders there is higher motivation to avoid risky clients since the government coverage is only 80%. Otherwise, the failing companies exhibit expected profile observed earlier \u0026ndash; they have lower liquidity and are less profitable, with higher ex-ante risk score. Firms with longer term debt ratio, suggestive of collateral, have a smaller hazard rate.\u003c/p\u003e\n\u003cp\u003eThus, the new lenders that provide funds for equity-backed firms have a significantly higher default rate that the main banking sector and indicates that they were providing funds for the less viable equity-financed firms. As noted in the earlier analysis (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) this actually accelerates their exit (failure) rather than help them survive.\u003c/p\u003e"},{"header":"6 Discussion and Conclusions","content":"\u003cp\u003eOur paper examines the survival and exit of equity backed companies in the UK through the covid crisis. These are the potential high-growth companies that can have a disproportionate impact on economic growth, productivity, and the innovation spill-overs or disruptive technologies that have wider long-term benefits for the economy. Moreover, these businesses often drive the growth and development of important new and transformative sectors (e.g., recent AI advances, clean energy, financial innovation). Although there is a significant insolvency gap from 2020, we find that patterns of insolvent exit for this class of firms does not change in the covid period. The non-viable amongst equity financed firms failed without the delay apparent in the SME sector generally. Those firms backed with the credible VC funds were more likely to survive whereas crowd funded ventures had a 39% higher probability of failure. We find a quadratic relationship between failure probability and cumulative rounds of investment, those ventures reaching a c\u0026pound;5m threshold had a higher probability of closure than other firms. The crisis focussed investors on providing more backing for potential winners and cutting losses on waning ventures. We find higher failures rates in the sectors negatively impacted by covid. However, failures rates are higher in the northern regions that have been identified as having significant equity gaps i.e., where ventures are less likely to attract finance (see Stanbury et al. 2023).\u003c/p\u003e \u003cp\u003eThe insolvency prediction model suggests that the loan recipients have a 77% higher probability of failure. Those firms with loans backed by business angels or government VC have a lower failure rate. Clearly acquiring a loan is a symptom of financial distress and is insufficient to help the company survive. In fact, it speeds the process of failure for the smaller, earlier stage ventures. Our model profiling the characteristics of loan recipients sheds further light on this. The pre covid financial health and risk profile is a strong predictor as is sector and stage of funding. Firms funded by Business Angels and Crowd Funded firms are more likely to access the preferential loan finance. However, a significant subset of firms used the maximum available loan, suggestive of liquidity constraints with the challenger banks advancing loans to riskier companies. Our models predicting loan default show that those firms have a significantly higher default rate. Clearly there is a financing issue for the early-stage smaller businesses. The new lenders have a significantly higher default rate that the main banking sector and indicates that they were providing funds for the less viable equity-financed firms, without improving their survival chances, supporting the hypotheses.\u003c/p\u003e \u003cp\u003eWe examine a spectrum of equity financed firms by investor type and contend that the later stages ventures found support from their investors through the covid period but some ventures were re-evaluated and closed. Smaller firms, that could not raise additional equity, sought finance from the preferential loan scheme (BBL) but the amounts provided were insufficient to ensure longer term viability and the scheme likely accelerated, \u0026lsquo;creative destruction\u0026rsquo;, the path to insolvency. A more targeted and nuanced support scheme, tailored to potential high growth firms (and priority sectors), as distinct from SME\u0026rsquo;s, may have been more effective as would policies targeted at regions where there are disparities in the supply of entrepreneurial finance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkerlof, G.A. (1970). The market for \u0026ldquo;lemons\u0026rdquo;: Qualitative uncertainty and the market mechanism. \u003cem\u003eQuarterly Journal of Economics\u003c/em\u003e, 84, 488\u0026ndash;500. https://doi.org/10.2307/1879431 \u003c/li\u003e\n\u003cli\u003eAltman, E., Sabato, G., \u0026amp; Wilson, N. (2010). The value of non-financial information in small and medium-sized enterprise risk management, \u003cem\u003eJournal of Credit Risk\u003c/em\u003e. 6, 1-33. https://doi.org/10.21314/JCR.2010.110 \u003c/li\u003e\n\u003cli\u003eAudretsch D.B., \u0026amp; Mahmood T. (1995). New firm survival: New results using a hazard function. \u003cem\u003eThe Review of Economics and Statistics\u003c/em\u003e, 77(1), 97-103. https://doi.org/10.2307/2109995 \u003c/li\u003e\n\u003cli\u003eBarney, J.B., \u0026amp; Clark, D.N. (2007). Resource-Based Theory Creating and Sustaining Competitive Advantages. Oxford University Press, Oxford, 327.\u003c/li\u003e\n\u003cli\u003eBernstein, S., Lerner, J., and Mezzanotti, F. (2019). Private equity and financial fragility during the crisis. The Review of Financial Studies, 32(4):1309\u0026ndash;1373. \u003c/li\u003e\n\u003cli\u003eBritish Business Bank. (2020) Small Business Equity Tracker. https://www.british-businessbank.co.uk/wp-content/uploads/2020/06/British-Business-Bank-Small-Business-Equity-Tracker-2020-Report.pdf\u003c/li\u003e\n\u003cli\u003eBuchner, A.l and Kaserer, C, and Wagner, N.F., Private Equity Funds: Valuation, Systematic Risk and Illiquidity (September 30, 2014). Available at SSRN: https://ssrn.com/abstract=1102471 or http://dx.doi.org/10.2139/ssrn.1102471\u003c/li\u003e\n\u003cli\u003eCaselli, S., Corbetta, G., Cucinelli, D., \u0026amp; Rossolini, M. (2021). A survival analysis of public guaranteed loans: Does financial intermediary matter? \u003cem\u003eJournal of Financial Stability\u003c/em\u003e, 54: 100880. https://doi.org/10.1016/j.jfs.2021.100880 \u003c/li\u003e\n\u003cli\u003eCowling, M. (2010). The role of loan guarantee schemes in alleviating credit rationing in the UK. \u003cem\u003eJournal of Financial Stability\u003c/em\u003e, 6, (1), 36-44. https://doi.org/10.1016/j.jfs.2009.05.007\u003c/li\u003e\n\u003cli\u003eCowling, M., Wilson, N., Kacer, M., \u0026amp; Nightingale, P. (2023a). 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An analysis of new firm survival using a hazard function. \u003cem\u003eApplied Economics\u003c/em\u003e, 42(2), 185-195. https://doi.org/10.1080/00036840701579234 \u003c/li\u003e\n\u003cli\u003eKacer, M., \u0026amp; Wilson, N. (2023). Supporting Innovative Start-Up and Growing Businesses: Equity Finance Provision through the Pandemic: Interim Report (March 23, 2023). \u003cem\u003eLeeds University Business School Working Paper\u003c/em\u003e, Available at SSRN: https://ssrn.com/abstract=4456252 or http://dx.doi.org/10.2139/ssrn.4456252\u003c/li\u003e\n\u003cli\u003eKeasey, K. \u0026amp; Watson, R. (1987). Non-Financial Symptoms and the Prediction of Small Company Failure: A Test of Argenti\u0026rsquo;s Hypothesis. \u003cem\u003eJournal of Business Finance \u0026amp; Accounting.\u003c/em\u003e 14\u003c/li\u003e\n\u003cli\u003eLavery, P., Megginson, W. L., \u0026amp; Munteanu, A. (2023). Growth Equity Investment Patterns and Performance .Available at SSRN: https://ssrn.com/abstract=4635307 or http://dx.doi.org/10.2139/ssrn.4635307\u003c/li\u003e\n\u003cli\u003eLelarge, C., Sraer, D., \u0026amp; Thesmar, D. (2010). Entrepreneurship and credit constraints: evidence from a French loan guarantee program. in Lerner, J. and Schoar, A. (eds), International Differences in Entrepreneurship, NBER Books\u003c/li\u003e\n\u003cli\u003eLester, R.H., Certo, S.T., Dalton, C.M., Dalton, D.R., \u0026amp; Cannella, A.A. (2006). Initial public offering investor valuations:An examination of top management team prestige and environmental uncertainty. \u003cem\u003eJournal of Small Business Management\u003c/em\u003e, 44(1), 1\u0026ndash;26. https://doi.org/10.1111/j.1540-627X.2006.00151.x \u003c/li\u003e\n\u003cli\u003eLi, Y., \u0026amp; Chi, T. (2013). 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Managerial and ownership succession and corporate restructuring: The case of management buy-ins. \u003cem\u003eJournal of Management Studies\u003c/em\u003e , 32 : 527 \u0026ndash; 550. \u003c/li\u003e\n\u003cli\u003eSpence, M.A. (1973). Job market signaling. \u003cem\u003eQuarterly Journal of Economics\u003c/em\u003e, 87, 355\u0026ndash;374. https://doi.org/10.2307/1882010 \u003c/li\u003e\n\u003cli\u003eStansbury, A.,Turner, D. \u0026amp; Balls E. (2023). Tackling the UK\u0026rsquo;s regional economic inequality: binding constraints and avenues for policy intervention, \u003cem\u003eContemporary Social Science\u003c/em\u003e, 18:3-4, 318-356, DOI: 10.1080/21582041.2023.2250745\u003c/li\u003e\n\u003cli\u003eTeece, D. J., Pisano, G., \u0026amp; Shuen, A. (1997). Dynamic capabilities and strategic management. \u003cem\u003eStrategic Management Journal\u003c/em\u003e, 18(7):509\u0026ndash;533.\u003c/li\u003e\n\u003cli\u003eVan Praag, C.M. (2003). Business survival and success of young small business owners. \u003cem\u003eSmall Business Economics\u003c/em\u003e, 21(1), 1-17. https://doi.org/10.1023/A:1024453200297 \u003c/li\u003e\n\u003cli\u003eWang, J., Yang, J., Iverson, B. C., \u0026amp; Kluender, R. (2020). Bankruptcy and the COVID-19 Crisis. \u003cem\u003eSSRN Electronic Journal\u003c/em\u003e. https://doi.org/10.2139/ssrn.3690398 \u003c/li\u003e\n\u003cli\u003eWilson, N., Kacer, M., \u0026amp; Wright, M. (2019). Equity Finance and the UK Regions: Understanding regional variations in the supply and demand of equity and growth finance for Business, BEIS Research Paper. \u003c/li\u003e\n\u003cli\u003eWilson, N., Kacer, M., \u0026amp; Cowling, M. (2023) Creative Destruction in a Crisis: Analysis of Covid Loan Schemes and Company Insolvency (September 1, 2023). Available at SSRN: https://ssrn.com/abstract=4589327 or http://dx.doi.org/10.2139/ssrn.4589327 \u003c/li\u003e\n\u003cli\u003eWright, M., \u0026amp; Robbie, K. (1998). Venture capital and private equity: A review and synthesis. \u003cem\u003eJournal of Business Finance \u0026amp; Accounting\u003c/em\u003e, 25(5-6): 521-570. https://doi.org/10.1111/1468-5957.00201\u003c/li\u003e\n\u003cli\u003eWilson N, Wright M, Kacer M. 2018. The Equity Gap and Knowledge-based Firms. \u003cem\u003eJournal of Corporate Finance.\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, pp. 626-64.\u003c/li\u003e\n\u003cli\u003eWilson N, Kacer M. 2019. \u003cem\u003eEquity Finance and the UK Regions Understanding Regional Variations in the Supply and Demand of Equity and Growth Finance for Business BEIS Research Paper Number 2019/012\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eZimmerman, M.A. (2008). The influence of top management team heterogeneity on the capital raised through an initial public offering. \u003cem\u003eEntrepreneurship Theory and Practice\u003c/em\u003e, 32(3), 391\u0026ndash;414. https://doi.org/10.1111/j.1540-6520.2008.00233.x\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Bankruptcies (or insolvencies) arise when a company is unable to pay its creditors and are categorised as liquidations (compulsory (CL) or creditors voluntary (CVL)), and receiverships. Administrations and Creditors Voluntary Arrangements (CVA).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Through the main UK pandemic period from April 2020 to July 2021 more than 971,000 loans were issued under BBL and 77,000 under CBILS. The total advances amounted to over \u0026pound;78bn.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://researchbriefings.files.parliament.uk/documents/CBP-8906/CBP-8906.pdf\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Early official evaluation of the loan schemes (BBB, June 2020) concluded that the objective of unlocking credit at speed was met (\u0026pound;78bn of guaranteed loans) and \u003cem\u003e\u0026ldquo;estimated that 0.5\u0026nbsp;million to 2.9\u0026nbsp;million jobs could have been lost\u003c/em\u003e\u0026rdquo; in the absence of the loan schemes (BBB, 2020). From survey evidence the evaluation report inferred that without the scheme, \u0026ldquo;\u003cem\u003ean additional 10%-34% of BBLS borrowers (i.e., 146,000 to 505,000 businesses) and an additional 7%-28% of CBILS/CLBILS borrowers (i.e., 5,000 to 21,000 businesses) could have permanently ceased trading in 2020\u003c/em\u003e\u0026rdquo; (BBB, 2020 p10).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;We use the dataset as of section \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;In the UK, the first lockdown was announced by prime minister Boris Johnson on Monday 23rd of March 2020, on the 25th the related Coronavirus Act obtained royal assent and it came into force on the 26th of March. We use the cut-off point of 31st of March 2020 due to the granularity of the insolvencies data where for each company we know the quarter of the insolvent exit.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;If a company filed more than one set of financial accounts during the three-year period before the 31st of March 2020, we use information from the most recent one.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Because insolvency is a legal process that can proceed through many steps and alternate routes it is not possible to measure the outcome (insolvency) in a \u0026lsquo;time to failure\u0026rsquo; context. Hence, we use the discrete time, where the last full filing of accounts is used as the date of closure.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;The major reasons why it is important to employ also non-financial variables when estimating insolvency models for firms including SMEs include relaxed reporting requirements for smaller companies in the UK, unreliability of financial accounts for smaller companies (they are not audited by an external auditor), and that non-financial information are less open to manipulation (Keasey and Watson, \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;We allow for non-linear relationship between the company size and insolvency. This is because such a relationship has been reported in the literature (see for instance Altman et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), but also because the non-monotonous relationship has been detected during (unreported) preliminary bi-variate analysis.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;The details of the risk score are presented in the Appendix A1, A2.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;We employ probit instead of logistic regression since we\u0026rsquo;ll use the results for computation of the inverse Mills ratios that are\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Detailed classification of individual lenders into the lender type categories is presented in Appendix B1.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;In model 8 in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the coefficient for the indicator of crowd funding is equal to 0.332. It is well-known that in logistic regression, the exponentiated coefficients are interpreted as odd ratios. To arrive at the percentage change in the odds we used the formula (exp(0.332)-1)*100% = 39.4%.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;As a robustness check we estimate a selection model based on models reported in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and include inverse mills ratio. Also, we re-estimated the models using matched sample. The results are not materially different and are reported in our supplementary appendix\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;To compute the total effect of a having a business angel investor, we need to consider the interaction effect with the covid loan, as well. Based on coefficients from model 8 (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) and formula in footnote 17, the total effect of having a business angel investor is given by formula (exp(0.497\u0026ndash;0.760*covid_loan)-1)*100%. This means, that a company having a business angel investor and a covid loan has odds of insolvency lower by 23% when compared to a company with covid loan but without a business angel investor.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Our equity data provider Beauhurst, based on company description and additional analyses, provides indicators of specific \u0026ldquo;buzzwords\u0026rdquo;. We selected two most frequent ones \u0026ndash; artificial intelligence and fintech. We use them as exclusion restrictions as they were not significant in the insolvency prediction models but they are in the covid loan selection models.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;The presence of business angels increases odds of having the covid loan by about 10% (exp(0.0964)-1\u0026thinsp;=\u0026thinsp;0.101). Similar, but two times greater effect is associated with crowd funded companies (exp(0.193)-1\u0026thinsp;=\u0026thinsp;0.213).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;For companies with a foreign investor - the odds of having a loan is smaller by about 25% (exp(-0.292)-1=-0.253). See model 6 in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e for details.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Although the coefficient for total investment in our model is positive and significant, suggesting the positive impact on the odds of a having a covid loan, the coefficient for its squared value is negative. Given the values of estimated coefficients, the effect becomes negative after \u0026pound;60k total investment.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"a1df510b-126e-45c5-b846-9747d3288638","identifier":"10.13039/501100000269","name":"Economic and Social Research Council","awardNumber":"ES/W010259/1","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Entrepreneurial Firms, Bankruptcy, Government Interventions","lastPublishedDoi":"10.21203/rs.3.rs-3920888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3920888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates the survival of entrepreneurial firms during the pandemic period. Specifically, we focus on UK companies that received equity finance during their developmental stages before the onset of Covid-19. The equity finance investors in our study include venture capital and growth finance funds (both domestic and foreign), crowd funding platforms, business angels, and government venture capital funds. We build on the resource-based view (RBV) and signalling theories to develop our hypotheses. We analyse the bankruptcy processes of companies during the Covid-19 period, comparing it to the pre-Covid period. We examine various characteristics of these firms, such as their investor type, deal history (including timing, magnitude, and duration), as well as a range of financial and non-financial factors. Furthermore, we identify the equity-backed companies that utilized policy interventions in the form of guaranteed loans. We gather details about the loan contracts, lenders, and instances of loan default. This study explores the relationship between bankruptcy and loan default in relation to the firm's characteristics, investor type, investment dimensions and financial constraints. The results provide valuable insights into the link between equity financing and venture survival during crises, with important implications for policy interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classifications \u003c/strong\u003eG12. G33. H81. L26\u003c/p\u003e","manuscriptTitle":"Venture Capital and the Survival of Entrepreneurial Firms in Crisis Periods: The Case of Covid-19.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-07 21:56:28","doi":"10.21203/rs.3.rs-3920888/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"226d7533-662a-400c-b22b-4bba32b16c81","owner":[],"postedDate":"February 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28623249,"name":"Finance"}],"tags":[],"updatedAt":"2024-02-07T21:56:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-07 21:56:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3920888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3920888","identity":"rs-3920888","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}