COVID-19 Crisis and Business Resilience in Palestine: How Policy Responses Affect Firms’ Performance

COVID-19 Crisis and Business Resilience in Palestine: How Policy Responses Affect Firms’ Performance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article COVID-19 Crisis and Business Resilience in Palestine: How Policy Responses Affect Firms’ Performance Rabeh Morrar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6640456/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The COVID-19 pandemic has significantly impacted businesses worldwide, challenging firms to adapt and build resilience amidst unprecedented disruptions. This study investigates the business resilience strategies employed by Palestinian firms during the pandemic, focusing on the roles of digital transformation, labor market adjustments, and policy interventions. Drawing on panel data from the COVID-19 Business Pulse Survey (2020–2021), the study highlights the effectiveness of these responses in shaping firm performance, particularly among micro, small, and medium-sized enterprises (MSMEs). Key findings reveal a stark digital divide, with only 15.8% of SMEs adopting digital solutions compared to 44.3% of large firms, reflecting broader structural constraints in the Palestinian economy. Despite these challenges, digital adoption was associated with reduced closure days and increased business continuity, although implementation costs and infrastructural limitations hindered broader success. Labor market adjustments varied significantly across sectors, with telework benefiting higher-skilled roles while exacerbating inequities for women and informal workers. Policy measures, including the Estidama fund and labor market agreements, provided critical support but were limited in reach due to fiscal constraints and pre-existing vulnerabilities. The findings underscore the need for targeted interventions to bridge digital and resource gaps, enhance labor protections, and develop robust digital infrastructure to ensure equitable resilience. This research contributes to the understanding of crisis responses in conflict-affected developing economies, offering actionable insights for policymakers to foster sustainable business resilience in the face of future shocks. Business Resilience COVID-19 Pandemic Digital Transformation Labor Market Adjustments Telework 1. Introduction The recent COVID-19 pandemic has emerged as a serious global threat and a highly significant and unprecedented event that has changed the world with far-reaching implications for firms, investors, policymakers, and other market participants. Along with its global health impact, the pandemic has had severe economic repercussions, spilling over into major capital markets and sectors, adversely affecting the efficiency and performance of firms across all businesses. Its effect has created a business emergency that created the need for organizations to be resilient and versatile in managing the impact of the pandemic on their business operations (Khalil et al., 2022). In this context, governments all over the World imposed restrictions on movements, closed their borders, made social distance a priority, and imposed economic lockdowns during some periods of time (the great lockdown). These measurements weighted heavily the economic activities and business, putting additional pressure on both supply and demand (Organization for Economic Cooperation and Development (OECD), 2020), exacerbating the economic crises that developed and developing countries were going through before COVID. Coming on the heels of several years of an increasingly hostile political climate and feeble economic growth, with persistent social crises affecting marginal populations, vulnerable social groups, and the poor, the COVID-19 pandemic hit Palestine at its weakest (MAS, 2021). Since March 2020, the pandemic has directly affected economic activities in various sectors, negatively affecting different economic indicators, especially for micro, small and medium-sized enterprises (MSMEs). Employment rates have decreased, GDP has declined, and the fiscal capacity of the government has been reduced due to the rapid decline in revenues. The pandemic has hit sectors such as restaurant and hotel activities, construction, trade (wholesale and retail), industry, mining, and agriculture the hardest. In response to the COVID outbreak, and the disruptive changes it generated, a number of firms in Palestine have adopted a set of actions to adapt to the changes the pandemic brought to the economy (PCBS 2021, 2022). Some of these actions and policies include labor market adjustment (e.g., employees hired, reduced work hours, lower wages), financial adjustment mechanism (e.g., equity finance, loans, delay payments to suppliers or employees), as well as initiatives promoting digital adoption (e.g., remote work, use of internet, online social media networks, specialized apps, or digital platforms). A growing body of literature has emerged to assess the impact of the pandemic on the socioeconomic conditions and business activities in Palestine (MAS, 2020, 2021a, 2021b, 2021c, 2021d, 2022; PMO, 2020, ILO, 2021; UNWomen, 2021, 2022). Most of these studies used a qualitative approach to highlight the negative effects of the Pandemic (diagnostic studies) and the needed policies and interventions from different stakeholders. To the best of our knowledge, there have been no studies that evaluate the effectiveness of different policies adopted by business institutions in response to the health crisis. Using panel data from the COVID-19 Business Pulse Survey for the two years 2020 and 2021, this paper aims to study the impact of policy responses in shaping business resilience to the pandemic by analyzing firms’ performance measured by production and cash flow availability. We further document the different impacts of digital solutions and labor market and financial adjustment mechanisms among large enterprises and SMEs in both West Bank and Gaza Strip. This research fills a significant research gap in the literature, highlighting the effectiveness of policy responses that private sector adopted during the pandemic. While previous research studies in Palestine focused on qualitative approaches, this study uses quantitative approach and representative data from both West Bank and Gaza. The remainder of the study is organized as follows: the subsequent section discusses the literature review and hypotheses. Section 3 provides a brief overview of the situation in Palestine. Section 4 introduces the methodology, sample, and the variables used for the analysis. Section 5 discusses the empirical findings and Section 6 concludes with some policy recommendations. 2. Literature Review 2.1 Business Resilience and External Shocks The literature has given considerable attention to the study of business resilience in the face of external shocks (Dahles & Susilowati, 2015 ; Iborra, Safón, & Dolz, 2019 ; Ortiz-de-Mandojana & Bansal, 2016 ; Korber & McNaughton, 2018 ). Companies must possess not just the resilience to endure external shocks and bounce back from them, but also the proactivity to foresee such disruptions, plan ahead, and take proactive steps to lessen their effects. Regarding small and medium-sized businesses in particular, a lot of research has been done on their business resilience because of their presence in many industries (García, Castillo, & Durán, 2012 ) and limited resources (Petrou et al., 2020 ). Notably, the sustainability of regional and national socioeconomic systems is significantly impacted by business resilience (Adekola & Clelland, 2020 ; Hadjielias et al., 2022 ). The greatest economic slump since the Great Depression, according to the International Monetary Fund (IMF), was caused by the COVID-19 pandemic and the Great Lockdown (Gopinath, 2020 ). Moreover, an EU research found that the impact of COVID-19 on corporate performance outweighed the effect of the Great Recession (Santos, 2021). As per McKinsey & Company (2020), digital solutions have become more crucial to surmount the challenges. As per McKinsey & Company (2020), companies who made greater investments in digital technology compared to their rivals had twice the likelihood of exhibiting revenue growth. Furthermore, several studies showed that during the health crisis, innovative businesses were better able to withstand the consequences of COVID-19 than non-innovative ones (Santos, 2021). The theoretical foundation for understanding business responses to external shocks in developing economies, particularly in conflict-affected regions like Palestine, draws from multiple complementary perspectives. Dynamic Capabilities Theory (Teece, 2007) suggests that firms' ability to reconfigure resources and capabilities is crucial for adaptation during crises, particularly relevant in resource-constrained environments. This connects with the established literature on business resilience (Dahles & Susilowati, 2015 ; Iborra, Safón, & Dolz, 2019 ), which emphasizes that companies must not only endure shocks but proactively adapt their strategies, a challenge heightened in politically volatile contexts. Organizational Adaptation Theory (Cameron, 1984 ) provides the framework for understanding how firms modified their structures and processes during COVID-19. In developing economies, this adaptation faced additional constraints from institutional voids and resource limitations (Petrou et al., 2020 ). Evidence from Palestine shows these constraints were particularly acute, with firms facing dual challenges of pandemic response and ongoing political instability (MAS, 2021). 2.2 Adaptation Mechanisms During the Pandemic Various studies indicate that the COVID-19 pandemic has caused a substantial shock to businesses, prompting them to reevaluate their plans. However, these businesses can also take short-term measures to ensure business continuity (Ivanov, 2020; Ivanov and Dolgui, 2020; Mollenkopf et al., 2020; Rizou et al., 2020). Further, Klöckner et al. (2023) offered other strategies for businesses to thrive, such as operational reactions including the introduction of new goods, scaling up or down manufacturing capacity, and customer service procedures. Improved company financial position and increased liquidity are examples of financial reactions. Digital transformation emerged as a critical adaptation mechanism, though its implementation varied significantly across contexts. In addition, one of the many tactics used by firms to adjust to the mobility and social distance constraints imposed by governments was the implementation of telecommuting arrangements for the workforce (Santos, 2021). This strategy had a quick impact on how businesses operated. The use of information and communication technology (ICTs) for work done off the employer's property is known as telework agreements (the International Labour Office, 2020). This indicates that telecommuting and remote work are also included in telework, in addition to working from home. Many businesses found that telework agreements allowed them to continue operating throughout the downturn by preventing them from closing, firing employees, or cutting back on hours (Criscuolo, 2021 ). During the pandemic, firms who had previously employed this technique not only made it through, but also saw increases in sales, net income, and stock returns (Bai, 2021 ). In addition, the pandemic hastened the popularity of telecommuting (Contreras, 2021 ). The likelihood of a corporation growing was enhanced by being creative and receptive to new ideas. This may be explained by their propensity to focus on newly popular products, use cutting-edge technology in their marketing plans, enhance their online presence, and implement new business practices to accommodate societal norms and legal constraints, such as work from home policies (Santos, 2021). Research shows that firms investing more heavily in digital technology demonstrated twice the likelihood of revenue growth compared to competitors (McKinsey & Company, 2020). 2.3. Labour Market responses Another line of research focused on both temporary and permanent employment when examining how the labor market reacts to negative macroeconomic shocks. It has been demonstrated in Sweden that a negative macroeconomic shock results in a sharp surge in temporary employment after an initial decrease (Holmlund and Storrie, 2002). Adverse shocks are said to provide businesses with more motivation to hire part-timers than full-timers. Workers in these circumstances are more inclined to choose part-time work than to receive no pay at all. Kang, Park, and Suh (2020) provided similar justification for the growth in part-time employment after the shock by pointing to an increase in the labor supply for part-time work and the gradual recovery of gross wage throughout the recession. The rise in part-time work under adverse shocks can be accounted for by both non-economic and economic factors. The absence of employment during the recession is the economic driver, while the noneconomic factors include things like health issues, social responsibilities, and education (Valletta and Bengali, 2013 ; Mallier and Rosser, 1980). The primary reason for part-time occupations is thought to be involuntary part-time employment (Canon et al., 2014 ; and Valletta et al., 2020). Younger workers are more impacted than older workers when macroeconomic shocks occur, according to Verick (2009) and Campos-Vazquez (2010). Unskilled workers are also more volatile than skilled ones. The employment in the informal sector rises in comparison to the formal sector because, in particular, they are more likely to exit the labor force than older workers. Various labor market institutions give rise to the disparate responses of the labor market to the external shock. Spain, for example, has stringent worker protection laws and, as a result, less volatility in the labor market. In this instance, any external shock causes the unemployment rate to rise (Boeri, Garibaldi, and Moen, 2012). Pissarides ( 2001 ) also contends that laws pertaining to employment rights must be flexible in that the cost of termination must vary based on the state of the market. According to Galdón-Sánchez and Güell (2003), employment protections legislations raise the costs of replacements of existing workers. In addition, in addition, the existence of firing costs encourages businesses to increase wages to motivate workers and therefore employment is reduced. Nevertheless, the impact of firing restrictions vary based on the prospect of the shocks, in the sense that if businesses expect a deep recession, in this case, the employment protections legislations will discourage hiring, but less effective in discouraging firing (Chen et al. 2002). However, the efficacy of firing limits varies according to the likelihood of shocks. For example, if employers anticipate a severe recession, job protection laws would deter recruiting but will be less successful in deterring termination (Chen et al. 2002). Comparing unemployment between France and Spain, Bentolila et al. ( 2012 ) referred to the impulse of unemployment after 2008’s financial crises to the strict lax rules of temporary contracts in Spain. Likewise, Hart (2017) demonstrated that companies respond to demand shocks by shortening their working hours; however, this rule does not apply to fixed-weekly and fixed-daily hours. The shift from full-time to part-time employment by the same company accounted for the crisis's peak to trough. It is also argued that job retention via short-time patterns during recession may reduce the productive job reallocation. 2.3.1 Telework While telework was among the strategies that businesses adopted prior to the pandemic, most businesses began to depend on it during the outbreak. Regular telework rose from 31% in OECD nations before to COVID-19 to 58% during the first wave of the pandemic (Criscuolo, 2021 ). Due to the pandemic, 33% of US workers reported telecommuting (U.S. Bureau of Labor Statistics, 2021 ), while 34% of employees in the EU began teleworking (ILO, 2020 ). These statistics showed that most organizations still view telework as an innovation, even if it was around before the epidemic. Studies are currently being conducted to determine its long-term impact on employment, output levels, and company success (Criscuolo, 2021 ). Due to reduced commute times and more informal interactions among employees, telework may result in longer workdays. This might boost productivity (Criscuolo, 2021 ). Furthermore, telework allows businesses to go beyond geographical borders and draw in skilled employees regardless of where they live (Criscuolo, 2021 ). From an economic standpoint, firms were able to lower labor operating costs due to the wider range of workers and varying pay grades. Because there is less of a requirement for office space, telework also lowers leasing costs (Bloom, 2013). However, because traditional methods do not function with telework, some managerial adjustments must be made in order to take use of these benefits (Criscuolo, 2021 ). This involves making investments in the infrastructure of technology, educating managers and employees, and setting up and arranging work schedules among employees (Criscuolo, 2021 ). A number of studies have also indicated that telework might raise job satisfaction. Working from home allows teleworkers to be in more comfortable environments with better working conditions, which reduces attrition and increases employee satisfaction—all of which boost productivity (Criscuolo, 2021 ). This is in line with a previous study conducted on call center employees in China prior to COVID-19, which found that during the course of the nine-month experiment, the performance of employees who worked from home improved by 13%; 9% of this improvement was attributed to an increase in work hours, and the remaining 4% was attributed to better working conditions (Bloom, 2013). While telecommuting agreements may improve output and pleasure, there might be some unintended consequences as well. The flow of knowledge, employee creativity, and innovation may all suffer from telework. Some managers believe that informal interactions among coworkers generate many innovative ideas; nevertheless, telework diminishes this interaction, generating questions regarding the company culture and long-term employee collaboration (Criscuolo, 2021 ). Furthermore, it is important to note that working from home has a 50% reduction in the likelihood of promotion. Part of this reduction may be attributed to teleworkers' potentially lower social skills because of their reduced interactions with colleges (Bloom, 2013). In the long term, the company's culture would suffer and teleworkers' propensity to leave would rise if management were unable to control these risks. 3. A brief Overview on Palestine During the earlier stage of the pandemic, the government impose lockdowns, and non-essential activities had been forced to close to reduce infection and contain the virus spread. These events coincided with a political crisis after Palestinian National Authority refused to take clearance revenues 1 , which burden the government's budget even more (Palestine Monetary Authority (PMA), 2020 ). As a result, the economic indicators witnessed a severe contraction, recording one of the most shattering economic downturns in the last two decades during 2020, with the tourism and its related activities headed by hotels, restaurants, and trade (Palestine Monetary Authority (PMA), 2020 ) being hit the hardest. Under these circumstances, firms’ owners faced a liquidity problem; access to finance, inventory damages and limited mobility for products (importing raw materials, or exporting their products), particularly SMEs, after working under their production capacity and the drop in sales level (Palestine Economic Policy Research Institute (MAS), 2021). To deal with these condition that the pandemic imposed, firms’ reaction differ from one to another, some of them relied on digital solutions, telework, and other innovative tools, while others relied on traditional strategies (such as). Relying on digital solutions and telework arrangements depends on the extend firms had invested in its IT infrastructure such as servers, cybersecurity, and etc. (Chiara Criscuolo, 2021 ). In Palestine, reliance on these strategies was limited, as this type of work was common only among large companies (Chiara Criscuolo, 2021 ). Most small and medium companies, which represent more than 98% of the total companies operating in Palestine, did not have the capacity to shift to remote work arrangements when the pandemic hit and the government imposed the restriction measures ((MAS), 2022). Based on the Palestinian Central Bureau of Statistics survey, 82.1% of the companies surveyed did not use digital solutions, 13.7% have increased the use of digital solutions, and 4.2% have started using it recently. This suggests that the majority of companies still rely on other tools to deal with the pandemic consequences. Firms in Palestine, especially SMEs, tend to rely more on traditional strategies to deal this type of economic shocks in the absence of a written policy framework (Palestine Economic Policy Research Institute (MAS), 2021). These traditional tools include reducing operating cost by decreasing the number of workers, reducing working hours, shutting down some production lines, and seeking for credit to cover these costs and credit purchasing, which was hard to reach with high uncertainty (The Economic Research Forum, 2022 ). The Palestinian Central Bureau of Statistics survey showed that in 2021, 8.6% of the companies surveyed relied on layoffs (compared to 13% in 2020), 3.5% of them went to cut salaries (compared to 8% in 2020), and 3.1% imposed unpaid leave on the workers (compared to 11% in 2020) to solve the liquidity problem ((MAS), 2022). Therefore, labor market had been affected badly, the already high unemployment rate increased, GDP per capital fall, income and consumption levels decreased, and food insecurity started becoming a serious issue more than ever due to the increase in food prices level and decreasing incomes, according to world food program; 1.8 million people suffer from food insecurity in Palestine 2 . Under these conditions, labor market witnessed a supply shock in the early stages, as labor force participation declined sharply in the second quarter of 2020 to record its lowest level around 38.6% 3 . At the same time, many workers experienced job losses or wage cuts. However, these effects differ according to two criteria; (1) the suitability of the profession for telework, and (2) whether or not the worker could be considering an essential worker (International Labour Organization, 2020). For SMEs, workers who would be classified as informal sectors, faced higher risks of being laid off, especially among those with low working skills. In addition, women were most severely affected, due to the gender gap in labor participation rate, employment rate, and their access to finance (Palestine Economic Policy Research Institute (MAS), 2021). In addition, many women could not benefit from work-from-home arrangements due to the cultural norms, where domestic duties and childcare were her responsibilities, hence, they were more exposed to the pandemic effects (International Labour Organization, 2020). While for the suitability of the profession for telework, the occupations that were suitable for telework, whether through internet or phones, and manage to benefit from it, were able to continue their work and get paid, which represent 12% of workers in 2020. For instance, the working firms in the construction sector, one of the main sectors that don’t suit telework, recorded the highest percentage in laying off workers (23.3%) in 2021 according to Palestinian Central Bureau of Statistics survey, which was above the average (8.6%) for the same period (Palestinian Central Bureau of Statistics, 2022 ). On the other hand, one of the main sectors that managed to apply telework is education, e-learning allows teachers to continue teaching their students. However, accessibility is still a challenging issue in Palestine (The Economic Research Forum, 2022 ). First, not all teachers and students are able to connect their devices to a reliable internet at home, second, having more than one child and only one device represents an issue for the family, and last but not least, the speed of the internet is still challenging where Palestinians rely on 3G in the West Bank and 2G in Gaza Strip (Khitam Shraim, 2020 ). From the firms' point view, and referring to the Palestinian Central Bureau of Statistics survey, their respondents differ according to several factors. Starting from the size of the firms (the first factor), 44.3% of large companies relied on digital solutions in 2021, while the percentage of teleworkers from the overall workers in large companies was around 17.7% for the same period (Palestinian Central Bureau of Statistics, 2022 ). on the other hand, for small companies, the percentage of companies that rely on digital solutions represents around 15.8% only, which is less than half of the percentage for the large companies, while the percentage of teleworker was around 8.3% (Palestinian Central Bureau of Statistics, 2022 ). Taking into account that small firms represent the majority of firms in Palestine, these numbers reflect the fact that relying on digital solutions in Palestine is still in the early stages. The second factor is the nature of the economic activity, information and communication technology sector come in the forefront sectors that tend to use telework, recording 18% from the workers are a teleworker in 2020. While in the financial and insurance sector this percentage drops to 4.4% for the same period ((MAS), 2022). Worth mentioning, the low percentage in the financial and insurance sector contradicts with the worldwide studies that pointed out that knowledge-intensive services sector such as finance are more likely to use the telework (Chiara Criscuolo, 2021 ). Last but not lest factor is the employee position. manly, 69% of workers in marketing indicated to work remotely (teleworker), followed by workers in administration which was 53%, while workers in the supply chain management rely less on telework, recording only 22% ((MAS), 2022). While regarding the use of digital solutions such as internet and social media during the second period (2021) was also concentrated in the same tasks, headed by marketing tasks (83.3% of respondent firms), and administration (52.9% of firms), followed by sale, and supply chain management that recorded 37.9% and 33.5% respectively (Palestinian Central Bureau of Statistics, 2022 ). Government implemented various policies to reduce the negative impact of the pandemic in the economy. Due to the increase in the unemployment rate and the decrease income and consumption levels, the government provided cash assistance for 40202 workers who were affected by the pandemic (Palestine Economic Policy Research Institute (MAS), 2021). At the same time, the Palestine monetary authority (PMA) postpone loan repayments for four months for most sectors (which months), with exception for tourism and hotels sector which had a six-month grace period. While it prevents commotions or any additional interests on the postponement repayments. <not sure about the meaning of this sentence. On the other hand, to provide support for the supply side, government and Palestine monetary authority (PMA) lunched Estidama fund that aimed to help businesses that were affected badly during the pandemic (first wave). Later, PMA launched Monshati Platform; the first electronic platform in Palestine to provide quality services to support the financial and administrative capabilities of the entrepreneurs of micro, small, and medium enterprises, and enable them to access sources of financing, which would help in fulfilling the gap of the financial literacy in order to have a good crisis management and marketing strategy. Moreover, ministry of labor sign agreement obligated firms and business owner to pay 50% of employees' salaries, with 1000 NIS at the minimum, while paying the remaining of their obligations after the end of the crisis 4 . However, due to low number of ministry inspectors and the expansionary informal sector, controlling, monitoring and knowing to how extent firms’ owners was committed to these requirements is hard. 4. Data and Methodology 4.1 Data and summary statistics This paper uses data from COVID-19 Business Pulse Survey for the years 2020 and 2021 which was implemented by the Palestinian Central Bureau of Statistics. The aim of the COVID-19 Business Pulse Survey is to assess the dynamics of the impacts of COVID-19 on small, medium, and large establishments in Palestine for the reference period starting March to May 2020 and 2021. The survey provides a panel data by targeting the same sample (2,246 firms) for the two years 2020 and 2021. In addition to the control and general information, data also provide information about the COVID-19 impact on establishment (on employment, supply channels, demand channels), mechanism for dealing with financial problems in establishments, and required interventions (policies). Table 1 Summary Statistics in 2020 Tech: Online Presence Tech: Telework Tech: Combined No Yes No Yes None Telework Online-Pr Both # of Days Closed 43.18 43.35 42.41 42.51 46.68 43.22 47.24 30.08 46.58 Increased Production 0.06 0.06 0.06 0.06 0.06 0.06 0.07 0.05 0.06 %Change in Production 47.11 47.63 44.74 47.59 44.61 47.71 44.90 45.53 44.55 Employment Decrease 0.14 0.14 0.17 0.14 0.15 0.14 0.16 0.21 0.15 Share of workers with lower wages 0.08 0.08 0.11 0.08 0.11 0.08 0.09 0.09 0.12 Share of workers with lower hours 0.04 0.03 0.06 0.03 0.06 0.03 0.04 0.08 0.06 Share of workers who leave with wage 0.11 0.10 0.15 0.09 0.16 0.09 0.11 0.10 0.17 Share of workers who leave without wage 0.08 0.08 0.08 0.08 0.08 0.08 0.06 0.07 0.08 Note: all values are in average The figures in the table above show the variation of used variables with the adjust mechanisms (online presence, teleworking and their combination). The variations of mean values provide a preliminary description of the impact of adopted mechanisms on some key variables during the crisis period. For instance, the variations of the number of days closed show that adoption of expansion of internet use (online presence) contributed more to the alleviation of the impact of pandemic on firms in terms of closure days. Firms that focused only on expanding their Online presence were closed about 29% fewer days compared to those that did not implement any policy. In terms of production, the figures show that adoption of the two policies separately or jointly have contributed to alleviating the decrease of production caused by the pandemic crisis. This suggests that these policies were beneficial for firms to address the adverse effects of the health crisis. Otherwise, the policies were considered ineffective in reducing the loss of jobs occurred during the crisis. More specifically, adopting these policies was associated with a bit increase of leaving jobs with wages, while loss of jobs without wages seems to not be related to any of these policies. 4.2 Methodology We have adopted a methodology similar to the one proposed by Sant'Anna and Zhao (2020) to analyze how policies implemented in response to Covid have affected firms’ performance and actions. Our analysis, however, is set in a different framework from Standard DID analysis. On the one hand, there are two treatments that firms may have been affected by. First, we assume that all firms have been affected by the Pandemic. Because of this, Covid specific impact on firms’ performance cannot be identified. Second some firms decided to adopt different policies related to technology adoption. To identify the policy related effects, we impose the assumption that Covid had a homogenous impact across all firms. Otherwise, it would not be possible to differentiate effects of the technology adoption policies from long term heterogenous impact of Covid. In addition, we also assume that there are no spillover effects SUTVA (Stable Unit Treatment Values Assumption). Using Sant'Anna and Zhao (2020) framework, we consider doubly robust estimators based on the following moment conditions: $$\:E\left(\:{P}_{i}-{\Lambda\:}\left({X}_{i0}\gamma\:\right)\right)=0$$ $$\:E\left({X}_{i0}\left({y}_{it}-{y}_{i0}-{{X}_{i0}\beta\:}^{w}\right)*\frac{{\Lambda\:}\left({X}_{i0}\gamma\:\right)}{1-{\Lambda\:}\left({X}_{i0}\gamma\:\right)}E\left[\frac{1-{\Lambda\:}\left({X}_{i0}\gamma\:\right)}{{\Lambda\:}\left({X}_{i0}\gamma\:\right)}\right]|{p}_{i}=P\right)=0\:ATT-E\left[\left({y}_{it}-{y}_{i0}\right)-{X}_{i0}{\beta\:}^{w}|{p}_{i}=P\right]=0$$ The first moment condition identifies the propensity that a firm implemented a particular policy, given a set of pre-treatment characteristics \(\:{X}_{i0}\) . As described in Sant’Anna and Zhao (2020), Callaway and Sant’Anna(2021) and Cunninham(2020:Causal Effect Mixtape), it is important to include only time fixed or pretreatment covariates to avoid introducing errors with colliders or bad controls. The second moment is used to predict the potential change in the outcome of interest ( \(\:{y}_{it}-{y}_{i0}\:\) or \(\:\:\varDelta\:{y}_{i})\) using and inverse probability weight (IPW) that is based on the propensity score obtained from the first moment. This is done for the of control sample only, using the same controls as the probability model. The last outcome estimates the Average treatment effect (ATT) for firms that adopted the policies, using the predicted outcome based on the weighted Regression. This procedure is also known as an Inverse-probability-weighted regression adjustment model, but where the outcome measures changes across time, in the spirit of Sant’Anna and Zhao (2020). This is considered a doubly robust estimator, because as long as one of the two model, the propensity score or the outcome model, es correctly specified, the estimates would be unbiased. 4.3. Model Specification As recommended in the literature, we control only for pre-treatment characteristics, which were collected as part of the Baseline economic indicator. Specifically, the XX survey collected information from firms regarding employment and production activities as of 2019. Using this information our most comprehensive model controls for log(# employees) in the firm, share of workers who are unpaid workers, share of full-time workers, share of female workers, log of total production and log of total cost. As described before, we consider two Technology adoption policies: Usage or expansion in the use of digital platforms in response to Covid, and Adoption or implementation of Telework. Because many firms decided to adopt both policies, we do further sensitivity analysis differentiating between Adoption of a single policy or adopting both, and comparing firms that adopted at least one of the policies. In contrast to standard DID models, we do not have access to both pre and post treatment to many of the outcomes of interest. In the survey, however, information was collected mostly in terms of changes the firm experience during Covid. In this regard, for our paper, we consider the impact on the following variables: M1: #Days Closed during the first 3 months of the pandemic M2: Whether the Firm Declares Production increased or remain the same M3: %change in production M4: Whether total employment remain the same or increased M5: Share of workers with lower wages M6: Share of workers who work fewer hours M7: Share of workers with paid leave of absence M8: Share of workers with not-paid leave of absence 5. Results 5.1. Propensity For Strategy Adoption To understand the impact that the two policies we study had on the mitigation mechanisms firm adopted through the pandemic, we start how baseline characteristics were related to the likelihood of adopting an Expansion of internet and telecommuting. Table 3 provides the marginal effects of firm adopting a given policy using separate logit models, as well as using a joint multinomial model. The results shown in Table 2 reveal that firms with high number of employees and share of female workers are more likely to adopt a policy of expansion of internet use and telecommuting in response to restrictive public health measures. This leads to conclude that small firms were incapable to face the COVID restrictions by adopting an expansion of internet use. On the other hand, those with higher share of unpaid workers, and higher share of full-time workers were less likely to do so. On the other hand, the multinomial logit suggests that larger firms were less likely to adopt an expansion of internet use only, but preferred to adopt both policies simultaneously. When the proportion of women was large, however, firms were someone were likely to adopt internet expansion or a combination of internet expansion and telecommuting, but not telecommuting alone. Table 2 Marginal effects on Policy Adoption logit logit mlogit Internet Adoption Telecommute Neither Internet T.Commute Either Log(# Employees) 0.056 *** 0.030 *** -0.048 *** -0.008 *** 0.019 *** 0.036 *** (0.006) (0.005) (0.006) (0.002) (0.003) (0.005) % Unpaid Emp -0.026 * -0.031 ** 0.026 0.004 0.010 -0.040 *** (0.015) (0.014) (0.016) (0.006) (0.009) (0.014) % Full time -0.042 ** -0.045 *** 0.053 *** -0.011 -0.005 -0.036 ** (0.018) (0.017) (0.019) (0.007) (0.011) (0.016) % Women 0.160 *** 0.169 *** -0.165 *** 0.013 ** -0.001 0.153 *** (0.011) (0.010) (0.012) (0.005) (0.008) (0.009) Log(Tot Prod) 0.001 0.012 ** -0.005 0.006 *** -0.007 ** 0.007 (0.005) (0.005) (0.005) (0.002) (0.003) (0.004) Log(Tot Cost) 0.008 0.008 -0.010 * 0.002 0.002 0.006 (0.006) (0.006) (0.006) (0.002) (0.003) (0.005) N 10557 10557 10557 10557 10557 10557 Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 Similarly, firms with a larger proportion of full-time workers show no particular inclination to adopt either policy independently, but are less likely to adopt them simultaneously. This may be driven by frictions in the workforce, and inability of firms to make fast changes in the short run. 5.2 Effect of Policy Adoption: DID approach Using propensity scores based on the models provided in Table 3 , we use an inverse probability-regression adjustment to analyze the impact on various indicators of firm performance and Covid mitigation strategies. Table 4 provides the average treatment effects on selected indicators under three setups. The first (ATT Tech) uses each policy independently, disregarding the effect of possible combined policies (for instance, when analyzing Online presence expansion, the effect of tele-commuting is assumed to be zero). The second set (ATT Combined) compares the average treatment effects on the treated for firms that adopted a single policy, along with those that adopted both, where the control are firms that did not adopt any technology policy. Lastly, we show the impact of a combination of both Policies (ATT joint). Table 3 Treatment effects on selected indicators: 2020 (1) (2) (3) (4) (5) (6) (7) (8) ATT Tech Online Presence -2.961 *** -0.006 -2.614 *** 0.011 0.002 0.021 *** 0.005 -0.017 ** (0.898) (0.007) (0.895) (0.010) (0.008) (0.005) (0.009) (0.007) Telecommute 1.483 -0.002 -2.909 *** -0.002 -0.000 0.009 * 0.012 -0.022 *** (0.921) (0.007) (0.938) (0.010) (0.008) (0.005) (0.010) (0.007) ATT Combined Telecommute 1.613 0.007 -3.135 0.030 -0.000 0.005 0.001 -0.034 *** (1.848) (0.016) (1.985) (0.023) (0.014) (0.011) (0.017) (0.011) Online Presence -11.346 *** -0.013 -3.257 * 0.036 * -0.003 0.040 *** -0.004 -0.012 (1.820) (0.012) (1.695) (0.019) (0.012) (0.012) (0.014) (0.011) Both 0.790 -0.015 ** -3.388 *** 0.004 0.003 0.016 *** 0.010 -0.018 ** (1.108) (0.007) (1.080) (0.012) (0.007) (0.005) (0.008) (0.007) ATT Joint Either -2.458 *** -0.004 -2.694 *** 0.014 0.001 0.019 *** 0.002 -0.019 *** (0.845) (0.007) (0.850) (0.010) (0.007) (0.005) (0.008) (0.006) AVG Standard errors in parentheses 1 # of Days Closed 2 Equal or Increased Production 3 %Change in Production 4 Employment Decrease 5 Shr wrks:Lower Wages 6 Shr wrks:Lower Hours 7 Shr wrks:Leave w/ wage 8 Shr wrks:Leave w/o wage * p < 0.1, ** p < 0.05, *** p < 0.01 The first option Firms have to mitigate the impact of the Pandemic (and as an answer to the policies imposed in Palestine) was to temporary close the firm. Based on our results, the expansion of internet use policy (online presence) has a significant effect on the number of days closed. Adopting this practice is found to reduce the number of days closed outcome by almost 3 days, whereas those adopting the policy exclusively reduced the number of days closed in just over 11 days. Telecommuting, on the other hand has no significant impact on this outcome. This leads to conclude that thanks to implementing internet expansion program, firms, mainly those employing higher number of workers, succeeded in mitigating the effects of the COVID pandemic. A second option firms may have had to mitigate the impact of the pandemic was to reduce production capacity. In the survey, this is measured using two variables. Firms either say if production increased, decreased or remain the same compared to normal activities, and they also indicate how much production increased or decline. Interestingly, neither of the policies seems to have no additional effect on the probability of maintaining or increasing production, compared to firms that adopted no policy. We only see a small effect if firms adopted both policies, which lead to a higher probability (1.5pp) of reducing production during the pandemic. The second metric, how much production change, shows a different story. Among firms with similar characteristics, those who adopted the technology seem to have experienced a larger decline. In fact, our estimates suggest a decline between 2.7pp to 3.4pp depending on model specification. The third option firms have to cope with the impact of the pandemic was associated is related to their decisions regarding labor employment. Neither of the policies, or a combination, seem to have had any effect on employment change. Nevertheless, it should be considered that almost all firms (83.7) indicate that they made no changes in their labor force. We only observe a small increase in the likelihood of increasing the labor force among firms that increased their use of Internet and Online presence in general. We also see no effect on the likelihood of reducing wages among their labor force, or having to grant paid leave of absence to their workers. Unexpectedly, we do observe that firms that implemented this technology policies had to reduce hours of work for a larger share of their employees. In contrast, however, they had to grant leave of absence without pay to a smaller share of the population. As part of the efforts to follow up the performance of firms after the initial shock of the pandemic, a second survey wave, aiming to collect information on the same period, but a year after the pandemic. Unfortunately, the second round of the survey was only able to collect information for about 20% of the original cohort, due to refusal or being closed. Nevertheless, we implement a similar analysis as in Table 4 . Table 4 Treatment Effects on Selected indicators: 2021 (1) (2) (3) (4) (5) (6) (7) (8) ATT Tech OnlinePresence 5.705 *** -0.021 -0.326 0.021 0.013 0.030 ** -0.030 -0.025 ** (1.343) (0.020) (1.864) (0.017) (0.017) (0.012) (0.018) (0.012) TeleCommute 2.603 * 0.023 5.188 ** -0.049 *** 0.029 0.011 -0.016 -0.021 (1.494) (0.022) (2.075) (0.015) (0.021) (0.012) (0.020) (0.014) ATT Combined TeleCommute 1.383 0.032 8.518 ** -0.032 0.181 * -0.003 0.004 0.032 (3.720) (0.061) (4.140) (0.036) (0.095) (0.025) (0.057) (0.046) OnlinePresence 8.897 *** -0.093 ** -8.643 *** 0.106 ** 0.021 0.083 ** 0.002 -0.026 (2.777) (0.037) (3.116) (0.042) (0.032) (0.040) (0.057) (0.021) Both 4.287 ** -0.005 2.794 -0.037 ** 0.028 0.017 -0.024 -0.026 * (1.799) (0.024) (2.395) (0.018) (0.021) (0.013) (0.024) (0.016) ATT Joint Either 5.364 *** -0.016 0.705 0.016 0.032 * 0.026 ** -0.029 -0.019 (1.316) (0.020) (1.808) (0.016) (0.018) (0.012) (0.018) (0.012) N * p < 0.1, ** p < 0.05, *** p < 0.01 6. Placebo test One of the main assumptions behind the identification in difference in difference models is the parallel trends assumption, or more specifically conditional parallel trends assumption. While this assumption is at its core untestable, a usual approach is to use data before treatment was implemented to see if both treated and control groups follow similar paths. As described earlier, this is not possible with our data. Because the survey was collected after Covid hit, there is no possibility to analyze pre-policy outcome trends. An alternative for validating our results, however, is to utilize a kind of randomization/placebo analysis. The intuition of the procedure is to compare the observed estimation of the treatment effect to the effect we would estimate based randomly assigning not-treated firms into treatment. Because we should expect not to see no treatment effect among the effectively not treated, this procedure would provide an empirical approximation of how large the estimated effect could be if driven only by chance. More formally, using base-period information, we model the conditional probability that a firm has adopted a particular policy: $$\:P\left({p}_{i}=1|X\right)=\text{G}\left({x}_{i0}\delta\:\right)$$ Once the coefficients \(\:\delta\:\) have been estimated, we randomly assign not-treated units to a pseudo treatment group using the following rule: $$\:{\stackrel{\sim}{p}}_{i}=1\left({u}_{i}\le\:\text{G}\left({x}_{i0}\widehat{\delta\:}\right)\right)$$ Where \(\:{u}_{i}\) is a draw from a uniform distribution. Once the pseudo treatment groups are defined, we proceed to calculate the effects of these pseudo treatments. We repeat this process 1000 times to generate a confidence interval for the placebo assignment. If the estimated treatment effect falls within the confidence interval of the placebo assignment, it suggests Average Treatment Effect (ATT) estimated in the previous section were driven by characteristics and noise, and not by the treatment itself. On the other hand, if the treatment effect lies outside the confidence interval, it suggests that the policy was responsible for the observed treatment effect. We call this procedure a conditional placebo test because the probability of treatment depends on observed characteristics. We anticipate that this approach will be superior to the standard randomization test as it helps minimize the influence of compositional effects when estimating the treatment effects. Nevertheless, we also implement a traditional permutation test, where treatment status (policy adoption) is shuffled among all firms in the data. We consider two variants of the placebo test. The first one estimates the treatment effects given the pseudo treatment assignment using only observations who didn’t implement any of the two policies we considered here. The second one estimates the treatment effects comparing the placebo treatment units to the units that did adopt the policies of interest. In the second variant, we expect the estimated treatment effects to be similar to the ones estimated in section 5.2 , as the treated and comparison groups are more similar to each other. The permutation and placebo tests are implemented considering using the Joint Policy definition. That is comparing firms that implemented either of the technology adoption policies to those that implemented neither. This is done using 2020 and 2021 indicators. In Table 5 , we provide the ATTs estimated based on the permutation test, as well as the placebo tests. They can be compared to the ATT Joint estimates of Table 4 . Column 1 and 2, the permutation test and placebo 1, show no effect on the selected indicators. The estimated standard errors are not large enough to disregard the estimated effects for #days closed, %change in production, share of workers working fewer hours, or with leave of absence without wages. Colum 3, as it was expected as well, shows that the estimated effects have similar levels of significance and magnitude as the originally estimated effects. While the placebo tests provide no evidence regarding the parallel trend assumption required for the model identification, at the very least the placebo and permutation test suggests the estimated results are not likely to be produced at random. Table 5 Placebo Test: 2020 (1) (2) (3) Original Permutation Placebo1 Placebo2 # of Days Closed -2.458 *** 0.026 0.021 -2.519 *** (0.845) (0.911) (0.984) (0.655) Increased Production -0.004 0.000 -0.000 -0.003 (0.007) (0.007) (0.008) (0.005) %Change in Production -2.694 *** -0.005 -0.095 -2.471 *** (0.850) (0.932) (1.102) (0.727) Employment Decrease 0.014 0.000 0.000 0.010 (0.010) (0.010) (0.011) (0.008) Shr wrks:Lower Wages 0.001 0.000 -0.000 -0.002 (0.007) (0.009) (0.010) (0.006) Shr wrks:Lower Hours 0.019 *** 0.000 0.000 0.018 *** (0.005) (0.005) (0.006) (0.004) Shr wrks:Leave w/ wage 0.002 0.000 -0.001 0.001 (0.008) (0.009) (0.011) (0.007) Shr wrks:Leave w/o wage -0.019 *** -0.000 -0.000 -0.020 *** (0.006) (0.007) (0.008) (0.005) Note: Standard errors obtained using 1000 repetitions Placebo Test: 2021 (1) (2) (3) Permutation Placebo1 Placebo2 # of Days Closed -0.175 0.007 -3.893 *** (1.207) (0.766) (0.934) Increased Production 0.002 0.000 0.022 *** (0.017) (0.006) (0.006) %Change in Production 0.328 -0.022 4.575 *** (1.563) (0.816) (0.917) Employment Decrease -0.001 -0.000 0.009 (0.015) (0.009) (0.011) Shr wrks:Lower Wages 0.001 0.000 -0.010 (0.013) (0.007) (0.008) Shr wrks:Lower Hours -0.004 -0.000 0.010 (0.013) (0.005) (0.005) Shr wrks:Leave w/ wage -0.002 -0.000 0.008 (0.015) (0.008) (0.009) Shr wrks:Leave w/o wage 0.004 0.000 -0.037 *** (0.012) (0.006) (0.007) N Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 6. Discussion This paper sheds light on the ways that Palestinian firms managed to cope with the most employed policies towards embracing an entrepreneurial orientation during a crisis like COVID-19. The following section discusses these results in light of previous research and the context of Palestine. 6.1 Digital Technology adoption and business continuity The results suggest a relationship between online presence policies adopted by businesses and shorter closure periods. On average, firms with these policies are closed between three and 11 days less than comparable establishments that do not have such measures (those only dependent on an online presence cut the number of closures by 11 days). This is in agreement with McKinsey & Company, 2020 who found that during the pandemic, companies placed higher on digital ability fared better. Yet, the scale of impact in Palestine looks smaller than developed economies, probably due to limitation in infrastructure. The lower adoption rate among SMEs (only 15.8% adopt digital but 44.3% of large firms do) is consistent with the results in OECD (2020) concerning the digital divide in developing economies. Such gap is worrying considering that SMEs represent 98% of the Palestinian business sector (MAS, 2021). This is particularly low adoption rate in Palestine might be due to the infrastructure challenges highlighted by Shraim and Crompton, 2020 , which include unreliable internet connectivity and limited access to 3G/4G networks as part of education sector. Given these findings, there are numerous salient policy implications within the Palestinian context where businesses are uniquely challenged with not only operating under occupation but also doing so amidst crises (Palestine Economic Policy Research Institute [MAS], 2021). Above all, governments and regulators need to make digital infrastructure a key priority area and close the gap in angels of telecoms in the West Bank (3G) and Gaza (2G) (Shraim & Crompton, 2020 ). This may entail diplomacy to negotiate with Israeli officials for access to advanced telecommunications spectrum, as well as practical solutions for developing alternative methods of connectivity. The remarkable digital adoption gap between large firms (44.3%) and SMEs (15.8%) may be attributed to the existence of a digital divide in developing economies as indicated by OECD (2020), which calls for special policy intervention that should utilize the prevailing platforms such as the Estidama fund and Monshati Platform (PMA, 2020). The high correlation noted between firm size and the general ability of firms to adopt technology (indicated by results from the logit model) is in agreement with findings produced by the Enterprise Surveys carried out by World Bank on technology adoption in developing economies. However, the Palestinian instance illustrates especially pronounced size-based differences which may amplify existing disparities in the business community. Also, the differential effect across the two regions (West Bank and Gaza) illustrates that existing regional inequalities shaped how firms adopted and benefited from technology interventions. The geographical dimension of digital resilience this research contributes to the understanding on how constraints pertaining to location shape adaptation strategies when faced with a crisis. While our analysis suggests some evolution in policy effects between 2020 and 2021, these findings should be interpreted with substantial caution due to significant sample attrition in 2021 (retention of only 20% of the original cohort). Some policy responses appear to have led to sustained changes in business practices, such as reduced reliance on unpaid leave. However, the changing effect sizes across years could reflect either learning processes in technology implementation or shifts in the external environment. 6.2 Labor Market Adjustments and Employment Outcomes It contrasts with the traditional patterns of crisis response whereby firms would increase working hours, cut permanent or temporary staff wages and increase unpaid leave to cope with increased demand. This implies that these firms have managed the balance of employment costs and it reflects with ILO ( 2020 ) argument regarding the arrangements of work during COVID-19 in Palestine. The unique effect across employee positions (69 percent in the case of marketing but only 22 percent in supply chains for instance) is consistent with international trends outlined by the ILO ( 2020 ), which emphasize large differences even within sectors on feasibility of remote work across job functions. On the other hand, telework adoption in Palestine's financial sector (4.4%) is surprisingly low compared with global observations suggesting geo-specific barriers to be studied further. Reform of labor market policies that maintain protections for workers will be needed to address the new reality of work (International Labour Organization [ILO], 2020 ). Overall, we demonstrate that firms with digital capabilities were able to keep staff on through fewer hours rather than unpaid leave (in line with worldwide patterns by Criscuolo and Gal ( 2021 )). Nonetheless, existing policies do not specify how regulation will take shape when it comes to remote working conditions for workers and the ways their work can be monitored. There is also a need for greater capacity within the Ministry of Labor to monitor workplace conditions—particularly in an environment in which informal employment is prevalent and many women workers, who already face specific vulnerabilities, had additional challenges when adopting remote work (MAS 2021). Our finding that firms adopting technology experienced larger production declines (2.7–3.4 percentage points) requires careful interpretation. Several factors may contribute to this counterintuitive result: Technology adoption costs during implementation (Criscuolo et al., 2021) Pre-existing challenges from movement restrictions and political instability (MAS, 2021) Possible selection effects, where harder-hit firms may have been more likely to adopt technology as a survival strategy In contrast, the financial sector has a surprisingly low telework adoption rate (4.4%), which stands in stark contrast to global trends documented by McKinsey & Company (2020). Likewise, the huge loss of jobs in construction (23.3%) throughout the disaster shows that hybrid work models should be advanced on as long as it is conceivable for administrative capacities (Palestinian Central Bureau of Statistics, 2022 ). These sectoral approaches should be complemented by initiatives targeting broader parts of the business community in the development of digital skills, especially for sectors that demonstrated high rates of telework feasibility such as marketing (69%) and administration (53%), mirroring patterns evident in global studies on teleworking (International Labour Office, 2020). The placebo tests we conduct establish the robustness of the estimated policy effects thus reinforcing that what we observe are not statistical artifacts but real returns to adoption of technologies. However, there appear contextual limits in Palestine to the extent of such benefits, including: Specifically, the relative lack of digital infrastructure, especially in Gaza Economic threats which existed before as recorded by PMA (2020) Limitation of movement and prohibiting trade Low fiscal space for support programs by governments We draw on the findings in a way that contribute to wider literature on business resilience across developing economies, while drawing attention to underlying context-specific factors that may undermine potential policy effectiveness. The findings imply that while digital solutions can enhance business resilience, their effectiveness depends heavily on underlying infrastructure and institutional support. 7. Conclusion and Policy Implications Our analysis indicates that although digital technology adoption contributed to business resilience during the COVID-19 pandemic, its impact was limited and multifaceted due to contextual circumstances. Our finding that online presence was correlated with fewer days closed (3–11 days) illustrates likely benefits from digital solutions, yet stark differences in adoption rates between firms in the large categories (44.3%) and SMEs overall (15.8%) suggest how pre-existing inequality of resources/growth trajectories were reflected in ability to adapt to displacement secundum. Yet, the adoption of the digital has not been inequitable and uncovered disruptions that indicate rife structural inequalities within the Palestinian business sector. The gap in digital adoption between large firms (44.3%) and SMEs (15.8%) underscores how existing capabilities and resources driven by industry role were determinants for firm adaptation to crisis conditions. In its short time, the adjustments made by a beset labor market revealed some surprising patterns, suggesting that what we view as traditional crisis response may be changing. Firms adopting technology were more likely to cut working hours instead of using unpaid leave, indicating that firms could adapt more flexibly. The study also highlighted major differences between sectors, in which strikingly few (4.4%) of the financial sector's dual professionals took to teleworking, compared to high adopters among marketing types (69%) and people in administration (53%). But these findings suggest that sector-specific impediments and ways forward will necessitate tailored policies. Both cohorts experienced negative shocks to food insecurity, but the temporal analysis between 2020 and 2021 shows the changing nature of policy footprints — a caveat being we lost over half our sample in 2021. While some effects faded overtime, as in the case of unpaid leave reducing, this implies that part of the business adaptation due to policy responses has become persistent. This temporal dimension enriches our framework for understanding the ways that crisis responses develop, adapt and potentially become routinized over time. These contributions arise from our findings. First, they contribute to the emerging literature on business resilience in developing countries with empirical data from a context that is especially difficult. Second, they show that the effectiveness of digital solutions is highly conditional on infrastructure and institutional context. Third, they show how inequalities that are beset prior to any crisis can be magnified in response regimes and creating new modes of structural infirmity over the longer-term. Limitations of this study should be addressed by future research. However, the 2021 sample had a high attrition rate and thus limits our power to make strong inferences about longer-term impacts. Lastly, the distinct Palestinian context may render some findings less generalizable to other developing economies despite its implications for research. Moving forward, these results point to a few important areas for future work. Firstly, researching the sustainability of crisis-induced digital transformation over a longer time frame starting from a point at which economic and social costs begin to materialise in order to inform policy. Second, we believe that understanding the balance firms take when adopting digital and other resilience strategies could provide a roadmap for more holistic support programs. Ultimately, studying the way in which digital divides play a role in post-crisis recovery trajectories could assist design of more equitable support structures. These results can be significant for policymakers who target multiple, interlocking constraints on firms at the same time. The results indicate that while digital solutions can improve business resilience, their success is highly dependent upon external infrastructure and institutional capability. To this end, policy interventions cannot be applied simply as piecemeal crisis-response measures but will also need to take some of the long-term structural challenges into account. Finally, this paper contributes by developing several insights into firms' adaptation to the crisis-creating conditions of an extremely harsh environment. These results underscore the role of context-specific factors that can shape policy effectiveness and call for a multi-sector, inter-governmental and global response to build a sustainable business resilience. These insights can suggest more effective and impartial ways of continuing to bolster enterprise resilience, amidst a future of global shockwaves and transition, especially in developing economies facing multiple structural challenges. As the OECD (2020) and MAS (2021) studies also point to, international organizations and donors are uniquely positioned to help bridge this digital divide as the post-COVID normal unfolds. Support programs must strike a balance between hard infrastructure development and soft skills training (with special reference to gender gap aspects of digital work accessibility) (ILO, 2020 ). One example of this is the multiple-child, single-device problem called out in education research, which demonstrates that device access programs must go hand-in-hand with infrastructure development (Shraim & Crompton, 2020 ). Implementation needs to be phased based on existing crisis response frameworks (PMA, 2020). Short term, emergency support through existing programs of the Palestine Monetary Authority and bolstering labour monitoring capacity are indispensable. XT suggests that long-term goals form around medium-term objectives to develop complete plans for digital infrastructure improvement and sustainable financial structures for technology adoption by SMEs (OECD, 2020). Given the context of occupation, it is crucial to build resilient digital infrastructure that can function with reduced reliance on external controls (MAS, 2021) so it must be also part of a long-term objectives. These results underscore an urgent balance between crisis response and resilience (McKinsey & Company, 2020). The impact of digital solutions in helping to reduce the number of days where business close (3–11 days) shows their promise on one hand, but highlights the absolute need for supported infrastructure and institutional capacity, something that several recent studies have underscored (Criscuolo & Gal, 2021 ). While all policy options will ultimately need to be proven effective in the Palestinian context (both under occupation and with inherent weaknesses of government fiscal capacity, infrastructure etc.), they will specifically need to contend with that reality (PMA, 2020). And at the same time, the way that the crisis response needs to develop over time—we see this also in our study by comparing 2020 and 2021—there are different impacts from these years. Similar to immediate crisis response, which is important but only a temporary remedy, sustainable resilience in business requires systematic investment in the digital infrastructure and capabilities (OECD, 2020) as well as regulatory frameworks that facilitate modernisation of businesses while preserving workers’ rights (ILO, 2020 ). Our findings show short term needs but the resilient state of businesses in Palestine requires policy reform on a broader spectrum (MAS, 2021). 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I., & Sydnor, S. (2019). Does social capital pay off? The case of small business resilience after Hurricane Katrina. Journal of Contingencies and Crisis Management , 27 (2), 168–181. U.S. Bureau of Labor Statistics. (2021). Teleworking and lost work during the pandemic: New evidence from the CPS . U.S. Bureau of Labor Statistics. Valletta, R. G., & Bengali, L. (2013). What’s behind the increase in part-time work? FRBSF Economic Letter , 2013 (24), 1–6. Footnotes Palestinian National Authority refused to reserve clearance revenue from Israel after they deducted from it, knowing that clearance revenue contribute to more than half of Palestinian government revenue. https://www.wfp.org/countries/palestine?_ga=2.147480497.781717388.1663759160-545597253.1663759160 https://www.pcbs.gov.ps/portals/_pcbs/PressRelease/Press_Ar_8-8-2021-LFS-ar.pdf http://www.mol.pna.ps/news/246 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 Jul, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 12 May, 2025 First submitted to journal 11 May, 2025 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-6640456","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464080456,"identity":"ec67f807-8e10-43d0-8bd1-ddc4260b2961","order_by":0,"name":"Rabeh Morrar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFACHgZmOPsDA5AjARYkUgvjDJK1MINVEtJi3n724OMChm1y5rMPP/tsu8OOnX92A+ODt224tcicyUs2nsFw21jmXJrx7NwzycwSdw4wG87Fo0WCIcdMmofhduIMHgZj5tw2ZmaGGwls0rz4tPC/Mf8N0cL+mdmyrZ5Z/kYC+2+8WiRyzJghWniMmRnbDjMbAG1hxq/lXbI0j8FtYwkenmLG3rbjzIY3Epsl55zD57Dcg595Km7LSfCwb2b42VadLHcj+eCHN2W4tUCAAYKZDIzRBkLqUYEdacpHwSgYBaNgJAAAZ9NES8quR8gAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8808-3714","institution":"An-Najah National University","correspondingAuthor":true,"prefix":"","firstName":"Rabeh","middleName":"","lastName":"Morrar","suffix":""}],"badges":[],"createdAt":"2025-05-11 15:29:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6640456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6640456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83838132,"identity":"c10d57ac-d126-440d-9bb2-c7c3e051f81e","added_by":"auto","created_at":"2025-06-03 13:31:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1099587,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6640456/v1/5813f2c7-0a57-4081-b246-b817af581cb6.pdf"}],"financialInterests":"","formattedTitle":"COVID-19 Crisis and Business Resilience in Palestine: How Policy Responses Affect Firms’ Performance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe recent COVID-19 pandemic has emerged as a serious global threat and a highly significant and unprecedented event that has changed the world with far-reaching implications for firms, investors, policymakers, and other market participants. Along with its global health impact, the pandemic has had severe economic repercussions, spilling over into major capital markets and sectors, adversely affecting the efficiency and performance of firms across all businesses. Its effect has created a business emergency that created the need for organizations to be resilient and versatile in managing the impact of the pandemic on their business operations (Khalil et al., 2022). In this context, governments all over the World imposed restrictions on movements, closed their borders, made social distance a priority, and imposed economic lockdowns during some periods of time (the great lockdown). These measurements weighted heavily the economic activities and business, putting additional pressure on both supply and demand (Organization for Economic Cooperation and Development (OECD), 2020), exacerbating the economic crises that developed and developing countries were going through before COVID.\u003c/p\u003e \u003cp\u003eComing on the heels of several years of an increasingly hostile political climate and feeble economic growth, with persistent social crises affecting marginal populations, vulnerable social groups, and the poor, the COVID-19 pandemic hit Palestine at its weakest (MAS, 2021). Since March 2020, the pandemic has directly affected economic activities in various sectors, negatively affecting different economic indicators, especially for micro, small and medium-sized enterprises (MSMEs). Employment rates have decreased, GDP has declined, and the fiscal capacity of the government has been reduced due to the rapid decline in revenues. The pandemic has hit sectors such as restaurant and hotel activities, construction, trade (wholesale and retail), industry, mining, and agriculture the hardest.\u003c/p\u003e \u003cp\u003eIn response to the COVID outbreak, and the disruptive changes it generated, a number of firms in Palestine have adopted a set of actions to adapt to the changes the pandemic brought to the economy (PCBS 2021, 2022). Some of these actions and policies include labor market adjustment (e.g., employees hired, reduced work hours, lower wages), financial adjustment mechanism (e.g., equity finance, loans, delay payments to suppliers or employees), as well as initiatives promoting digital adoption (e.g., remote work, use of internet, online social media networks, specialized apps, or digital platforms).\u003c/p\u003e \u003cp\u003eA growing body of literature has emerged to assess the impact of the pandemic on the socioeconomic conditions and business activities in Palestine (MAS, 2020, 2021a, 2021b, 2021c, 2021d, 2022; PMO, 2020, ILO, 2021; UNWomen, 2021, 2022). Most of these studies used a qualitative approach to highlight the negative effects of the Pandemic (diagnostic studies) and the needed policies and interventions from different stakeholders. To the best of our knowledge, there have been no studies that evaluate the effectiveness of different policies adopted by business institutions in response to the health crisis.\u003c/p\u003e \u003cp\u003eUsing panel data from the COVID-19 Business Pulse Survey for the two years 2020 and 2021, this paper aims to study the impact of policy responses in shaping business resilience to the pandemic by analyzing firms\u0026rsquo; performance measured by production and cash flow availability. We further document the different impacts of digital solutions and labor market and financial adjustment mechanisms among large enterprises and SMEs in both West Bank and Gaza Strip.\u003c/p\u003e \u003cp\u003eThis research fills a significant research gap in the literature, highlighting the effectiveness of policy responses that private sector adopted during the pandemic. While previous research studies in Palestine focused on qualitative approaches, this study uses quantitative approach and representative data from both West Bank and Gaza. The remainder of the study is organized as follows: the subsequent section discusses the literature review and hypotheses. Section 3 provides a brief overview of the situation in Palestine. Section 4 introduces the methodology, sample, and the variables used for the analysis. Section 5 discusses the empirical findings and Section 6 concludes with some policy recommendations.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Business Resilience and External Shocks\u003c/h2\u003e \u003cp\u003eThe literature has given considerable attention to the study of business resilience in the face of external shocks (Dahles \u0026amp; Susilowati, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Iborra, Saf\u0026oacute;n, \u0026amp; Dolz, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ortiz-de-Mandojana \u0026amp; Bansal, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Korber \u0026amp; McNaughton, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Companies must possess not just the resilience to endure external shocks and bounce back from them, but also the proactivity to foresee such disruptions, plan ahead, and take proactive steps to lessen their effects. Regarding small and medium-sized businesses in particular, a lot of research has been done on their business resilience because of their presence in many industries (Garc\u0026iacute;a, Castillo, \u0026amp; Dur\u0026aacute;n, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and limited resources (Petrou et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, the sustainability of regional and national socioeconomic systems is significantly impacted by business resilience (Adekola \u0026amp; Clelland, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hadjielias et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe greatest economic slump since the Great Depression, according to the International Monetary Fund (IMF), was caused by the COVID-19 pandemic and the Great Lockdown (Gopinath, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, an EU research found that the impact of COVID-19 on corporate performance outweighed the effect of the Great Recession (Santos, 2021). As per McKinsey \u0026amp; Company (2020), digital solutions have become more crucial to surmount the challenges. As per McKinsey \u0026amp; Company (2020), companies who made greater investments in digital technology compared to their rivals had twice the likelihood of exhibiting revenue growth. Furthermore, several studies showed that during the health crisis, innovative businesses were better able to withstand the consequences of COVID-19 than non-innovative ones (Santos, 2021).\u003c/p\u003e \u003cp\u003eThe theoretical foundation for understanding business responses to external shocks in developing economies, particularly in conflict-affected regions like Palestine, draws from multiple complementary perspectives. Dynamic Capabilities Theory (Teece, 2007) suggests that firms' ability to reconfigure resources and capabilities is crucial for adaptation during crises, particularly relevant in resource-constrained environments. This connects with the established literature on business resilience (Dahles \u0026amp; Susilowati, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Iborra, Saf\u0026oacute;n, \u0026amp; Dolz, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which emphasizes that companies must not only endure shocks but proactively adapt their strategies, a challenge heightened in politically volatile contexts.\u003c/p\u003e \u003cp\u003eOrganizational Adaptation Theory (Cameron, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) provides the framework for understanding how firms modified their structures and processes during COVID-19. In developing economies, this adaptation faced additional constraints from institutional voids and resource limitations (Petrou et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Evidence from Palestine shows these constraints were particularly acute, with firms facing dual challenges of pandemic response and ongoing political instability (MAS, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Adaptation Mechanisms During the Pandemic\u003c/h2\u003e \u003cp\u003eVarious studies indicate that the COVID-19 pandemic has caused a substantial shock to businesses, prompting them to reevaluate their plans. However, these businesses can also take short-term measures to ensure business continuity (Ivanov, 2020; Ivanov and Dolgui, 2020; Mollenkopf et al., 2020; Rizou et al., 2020). Further, Kl\u0026ouml;ckner et al. (2023) offered other strategies for businesses to thrive, such as operational reactions including the introduction of new goods, scaling up or down manufacturing capacity, and customer service procedures. Improved company financial position and increased liquidity are examples of financial reactions.\u003c/p\u003e \u003cp\u003eDigital transformation emerged as a critical adaptation mechanism, though its implementation varied significantly across contexts. In addition, one of the many tactics used by firms to adjust to the mobility and social distance constraints imposed by governments was the implementation of telecommuting arrangements for the workforce (Santos, 2021). This strategy had a quick impact on how businesses operated. The use of information and communication technology (ICTs) for work done off the employer's property is known as telework agreements (the International Labour Office, 2020). This indicates that telecommuting and remote work are also included in telework, in addition to working from home. Many businesses found that telework agreements allowed them to continue operating throughout the downturn by preventing them from closing, firing employees, or cutting back on hours (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). During the pandemic, firms who had previously employed this technique not only made it through, but also saw increases in sales, net income, and stock returns (Bai, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, the pandemic hastened the popularity of telecommuting (Contreras, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe likelihood of a corporation growing was enhanced by being creative and receptive to new ideas. This may be explained by their propensity to focus on newly popular products, use cutting-edge technology in their marketing plans, enhance their online presence, and implement new business practices to accommodate societal norms and legal constraints, such as work from home policies (Santos, 2021). Research shows that firms investing more heavily in digital technology demonstrated twice the likelihood of revenue growth compared to competitors (McKinsey \u0026amp; Company, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Labour Market responses\u003c/h2\u003e \u003cp\u003eAnother line of research focused on both temporary and permanent employment when examining how the labor market reacts to negative macroeconomic shocks. It has been demonstrated in Sweden that a negative macroeconomic shock results in a sharp surge in temporary employment after an initial decrease (Holmlund and Storrie, 2002). Adverse shocks are said to provide businesses with more motivation to hire part-timers than full-timers. Workers in these circumstances are more inclined to choose part-time work than to receive no pay at all. Kang, Park, and Suh (2020) provided similar justification for the growth in part-time employment after the shock by pointing to an increase in the labor supply for part-time work and the gradual recovery of gross wage throughout the recession.\u003c/p\u003e \u003cp\u003eThe rise in part-time work under adverse shocks can be accounted for by both non-economic and economic factors. The absence of employment during the recession is the economic driver, while the noneconomic factors include things like health issues, social responsibilities, and education (Valletta and Bengali, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mallier and Rosser, 1980). The primary reason for part-time occupations is thought to be involuntary part-time employment (Canon et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; and Valletta et al., 2020).\u003c/p\u003e \u003cp\u003eYounger workers are more impacted than older workers when macroeconomic shocks occur, according to Verick (2009) and Campos-Vazquez (2010). Unskilled workers are also more volatile than skilled ones. The employment in the informal sector rises in comparison to the formal sector because, in particular, they are more likely to exit the labor force than older workers.\u003c/p\u003e \u003cp\u003eVarious labor market institutions give rise to the disparate responses of the labor market to the external shock. Spain, for example, has stringent worker protection laws and, as a result, less volatility in the labor market. In this instance, any external shock causes the unemployment rate to rise (Boeri, Garibaldi, and Moen, 2012). Pissarides (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) also contends that laws pertaining to employment rights must be flexible in that the cost of termination must vary based on the state of the market. According to Gald\u0026oacute;n-S\u0026aacute;nchez and G\u0026uuml;ell (2003), employment protections legislations raise the costs of replacements of existing workers. In addition, in addition, the existence of firing costs encourages businesses to increase wages to motivate workers and therefore employment is reduced. Nevertheless, the impact of firing restrictions vary based on the prospect of the shocks, in the sense that if businesses expect a deep recession, in this case, the employment protections legislations will discourage hiring, but less effective in discouraging firing (Chen et al. 2002). However, the efficacy of firing limits varies according to the likelihood of shocks. For example, if employers anticipate a severe recession, job protection laws would deter recruiting but will be less successful in deterring termination (Chen et al. 2002). Comparing unemployment between France and Spain, Bentolila et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) referred to the impulse of unemployment after 2008\u0026rsquo;s financial crises to the strict lax rules of temporary contracts in Spain.\u003c/p\u003e \u003cp\u003eLikewise, Hart (2017) demonstrated that companies respond to demand shocks by shortening their working hours; however, this rule does not apply to fixed-weekly and fixed-daily hours. The shift from full-time to part-time employment by the same company accounted for the crisis's peak to trough. It is also argued that job retention via short-time patterns during recession may reduce the productive job reallocation.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Telework\u003c/h2\u003e \u003cp\u003eWhile telework was among the strategies that businesses adopted prior to the pandemic, most businesses began to depend on it during the outbreak. Regular telework rose from 31% in OECD nations before to COVID-19 to 58% during the first wave of the pandemic (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to the pandemic, 33% of US workers reported telecommuting (U.S. Bureau of Labor Statistics, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while 34% of employees in the EU began teleworking (ILO, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These statistics showed that most organizations still view telework as an innovation, even if it was around before the epidemic. Studies are currently being conducted to determine its long-term impact on employment, output levels, and company success (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to reduced commute times and more informal interactions among employees, telework may result in longer workdays. This might boost productivity (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, telework allows businesses to go beyond geographical borders and draw in skilled employees regardless of where they live (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From an economic standpoint, firms were able to lower labor operating costs due to the wider range of workers and varying pay grades. Because there is less of a requirement for office space, telework also lowers leasing costs (Bloom, 2013). However, because traditional methods do not function with telework, some managerial adjustments must be made in order to take use of these benefits (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This involves making investments in the infrastructure of technology, educating managers and employees, and setting up and arranging work schedules among employees (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA number of studies have also indicated that telework might raise job satisfaction. Working from home allows teleworkers to be in more comfortable environments with better working conditions, which reduces attrition and increases employee satisfaction\u0026mdash;all of which boost productivity (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This is in line with a previous study conducted on call center employees in China prior to COVID-19, which found that during the course of the nine-month experiment, the performance of employees who worked from home improved by 13%; 9% of this improvement was attributed to an increase in work hours, and the remaining 4% was attributed to better working conditions (Bloom, 2013).\u003c/p\u003e \u003cp\u003eWhile telecommuting agreements may improve output and pleasure, there might be some unintended consequences as well. The flow of knowledge, employee creativity, and innovation may all suffer from telework. Some managers believe that informal interactions among coworkers generate many innovative ideas; nevertheless, telework diminishes this interaction, generating questions regarding the company culture and long-term employee collaboration (Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, it is important to note that working from home has a 50% reduction in the likelihood of promotion. Part of this reduction may be attributed to teleworkers' potentially lower social skills because of their reduced interactions with colleges (Bloom, 2013). In the long term, the company's culture would suffer and teleworkers' propensity to leave would rise if management were unable to control these risks.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. A brief Overview on Palestine","content":"\u003cp\u003eDuring the earlier stage of the pandemic, the government impose lockdowns, and non-essential activities had been forced to close to reduce infection and contain the virus spread. These events coincided with a political crisis after Palestinian National Authority refused to take clearance revenues\u003csup\u003e1\u003c/sup\u003e, which burden the government's budget even more (Palestine Monetary Authority (PMA), 2020 ). As a result, the economic indicators witnessed a severe contraction, recording one of the most shattering economic downturns in the last two decades during 2020, with the tourism and its related activities headed by hotels, restaurants, and trade (Palestine Monetary Authority (PMA), 2020 ) being hit the hardest.\u003c/p\u003e \u003cp\u003eUnder these circumstances, firms\u0026rsquo; owners faced a liquidity problem; access to finance, inventory damages and limited mobility for products (importing raw materials, or exporting their products), particularly SMEs, after working under their production capacity and the drop in sales level (Palestine Economic Policy Research Institute (MAS), 2021). To deal with these condition that the pandemic imposed, firms\u0026rsquo; reaction differ from one to another, some of them relied on digital solutions, telework, and other innovative tools, while others relied on traditional strategies (such as).\u003c/p\u003e \u003cp\u003eRelying on digital solutions and telework arrangements depends on the extend firms had invested in its IT infrastructure such as servers, cybersecurity, and etc. (Chiara Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Palestine, reliance on these strategies was limited, as this type of work was common only among large companies (Chiara Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Most small and medium companies, which represent more than 98% of the total companies operating in Palestine, did not have the capacity to shift to remote work arrangements when the pandemic hit and the government imposed the restriction measures ((MAS), 2022). Based on the Palestinian Central Bureau of Statistics survey, 82.1% of the companies surveyed did not use digital solutions, 13.7% have increased the use of digital solutions, and 4.2% have started using it recently. This suggests that the majority of companies still rely on other tools to deal with the pandemic consequences.\u003c/p\u003e \u003cp\u003eFirms in Palestine, especially SMEs, tend to rely more on traditional strategies to deal this type of economic shocks in the absence of a written policy framework (Palestine Economic Policy Research Institute (MAS), 2021). These traditional tools include reducing operating cost by decreasing the number of workers, reducing working hours, shutting down some production lines, and seeking for credit to cover these costs and credit purchasing, which was hard to reach with high uncertainty (The Economic Research Forum, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Palestinian Central Bureau of Statistics survey showed that in 2021, 8.6% of the companies surveyed relied on layoffs (compared to 13% in 2020), 3.5% of them went to cut salaries (compared to 8% in 2020), and 3.1% imposed unpaid leave on the workers (compared to 11% in 2020) to solve the liquidity problem ((MAS), 2022).\u003c/p\u003e \u003cp\u003eTherefore, labor market had been affected badly, the already high unemployment rate increased, GDP per capital fall, income and consumption levels decreased, and food insecurity started becoming a serious issue more than ever due to the increase in food prices level and decreasing incomes, according to world food program; 1.8\u0026nbsp;million people suffer from food insecurity in Palestine\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnder these conditions, labor market witnessed a supply shock in the early stages, as labor force participation declined sharply in the second quarter of 2020 to record its lowest level around 38.6%\u003csup\u003e3\u003c/a\u003e. At the same time, many workers experienced job losses or wage cuts. However, these effects differ according to two criteria; (1) the suitability of the profession for telework, and (2) whether or not the worker could be considering an essential worker (International Labour Organization, 2020).\u003c/p\u003e \u003cp\u003eFor SMEs, workers who would be classified as informal sectors, faced higher risks of being laid off, especially among those with low working skills. In addition, women were most severely affected, due to the gender gap in labor participation rate, employment rate, and their access to finance (Palestine Economic Policy Research Institute (MAS), 2021). In addition, many women could not benefit from work-from-home arrangements due to the cultural norms, where domestic duties and childcare were her responsibilities, hence, they were more exposed to the pandemic effects (International Labour Organization, 2020).\u003c/p\u003e \u003cp\u003eWhile for the suitability of the profession for telework, the occupations that were suitable for telework, whether through internet or phones, and manage to benefit from it, were able to continue their work and get paid, which represent 12% of workers in 2020. For instance, the working firms in the construction sector, one of the main sectors that don\u0026rsquo;t suit telework, recorded the highest percentage in laying off workers (23.3%) in 2021 according to Palestinian Central Bureau of Statistics survey, which was above the average (8.6%) for the same period (Palestinian Central Bureau of Statistics, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, one of the main sectors that managed to apply telework is education, e-learning allows teachers to continue teaching their students. However, accessibility is still a challenging issue in Palestine (The Economic Research Forum, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). First, not all teachers and students are able to connect their devices to a reliable internet at home, second, having more than one child and only one device represents an issue for the family, and last but not least, the speed of the internet is still challenging where Palestinians rely on 3G in the West Bank and 2G in Gaza Strip (Khitam Shraim, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom the firms' point view, and referring to the Palestinian Central Bureau of Statistics survey, their respondents differ according to several factors. Starting from the size of the firms (the first factor), 44.3% of large companies relied on digital solutions in 2021, while the percentage of teleworkers from the overall workers in large companies was around 17.7% for the same period (Palestinian Central Bureau of Statistics, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). on the other hand, for small companies, the percentage of companies that rely on digital solutions represents around 15.8% only, which is less than half of the percentage for the large companies, while the percentage of teleworker was around 8.3% (Palestinian Central Bureau of Statistics, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Taking into account that small firms represent the majority of firms in Palestine, these numbers reflect the fact that relying on digital solutions in Palestine is still in the early stages.\u003c/p\u003e \u003cp\u003eThe second factor is the nature of the economic activity, information and communication technology sector come in the forefront sectors that tend to use telework, recording 18% from the workers are a teleworker in 2020. While in the financial and insurance sector this percentage drops to 4.4% for the same period ((MAS), 2022). Worth mentioning, the low percentage in the financial and insurance sector contradicts with the worldwide studies that pointed out that knowledge-intensive services sector such as finance are more likely to use the telework (Chiara Criscuolo, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLast but not lest factor is the employee position. manly, 69% of workers in marketing indicated to work remotely (teleworker), followed by workers in administration which was 53%, while workers in the supply chain management rely less on telework, recording only 22% ((MAS), 2022). While regarding the use of digital solutions such as internet and social media during the second period (2021) was also concentrated in the same tasks, headed by marketing tasks (83.3% of respondent firms), and administration (52.9% of firms), followed by sale, and supply chain management that recorded 37.9% and 33.5% respectively (Palestinian Central Bureau of Statistics, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGovernment implemented various policies to reduce the negative impact of the pandemic in the economy. Due to the increase in the unemployment rate and the decrease income and consumption levels, the government provided cash assistance for 40202 workers who were affected by the pandemic (Palestine Economic Policy Research Institute (MAS), 2021). At the same time, the Palestine monetary authority (PMA) postpone loan repayments for four months for most sectors (which months), with exception for tourism and hotels sector which had a six-month grace period. While it prevents commotions or any additional interests on the postponement repayments. \u0026lt;not sure about the meaning of this sentence.\u003c/p\u003e \u003cp\u003eOn the other hand, to provide support for the supply side, government and Palestine monetary authority (PMA) lunched \u003cem\u003eEstidama\u003c/em\u003e fund that aimed to help businesses that were affected badly during the pandemic (first wave). Later, PMA launched \u003cem\u003eMonshati\u003c/em\u003e Platform; the first electronic platform in Palestine to provide quality services to support the financial and administrative capabilities of the entrepreneurs of micro, small, and medium enterprises, and enable them to access sources of financing, which would help in fulfilling the gap of the financial literacy in order to have a good crisis management and marketing strategy.\u003c/p\u003e \u003cp\u003eMoreover, ministry of labor sign agreement obligated firms and business owner to pay 50% of employees' salaries, with 1000 NIS at the minimum, while paying the remaining of their obligations after the end of the crisis\u003csup\u003e4\u003c/sup\u003e. However, due to low number of ministry inspectors and the expansionary informal sector, controlling, monitoring and knowing to how extent firms\u0026rsquo; owners was committed to these requirements is hard.\u003c/p\u003e"},{"header":"4. Data and Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data and summary statistics\u003c/h2\u003e \u003cp\u003eThis paper uses data from COVID-19 Business Pulse Survey for the years 2020 and 2021 which was implemented by the Palestinian Central Bureau of Statistics. The aim of the COVID-19 Business Pulse Survey is to assess the dynamics of the impacts of COVID-19 on small, medium, and large establishments in Palestine for the reference period starting March to May 2020 and 2021. The survey provides a panel data by targeting the same sample (2,246 firms) for the two years 2020 and 2021. In addition to the control and general information, data also provide information about the COVID-19 impact on establishment (on employment, supply channels, demand channels), mechanism for dealing with financial problems in establishments, and required interventions (policies).\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\u003eSummary Statistics in 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\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\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTech: Online Presence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTech: Telework\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eTech: Combined\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTelework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOnline-Pr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of Days Closed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%Change in Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e44.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of workers with lower wages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of workers with lower hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of workers who leave with wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShare of workers who leave without wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote: all values are in average\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe figures in the table above show the variation of used variables with the adjust mechanisms (online presence, teleworking and their combination). The variations of mean values provide a preliminary description of the impact of adopted mechanisms on some key variables during the crisis period. For instance, the variations of the number of days closed show that adoption of expansion of internet use (online presence) contributed more to the alleviation of the impact of pandemic on firms in terms of closure days. Firms that focused only on expanding their Online presence were closed about 29% fewer days compared to those that did not implement any policy.\u003c/p\u003e \u003cp\u003eIn terms of production, the figures show that adoption of the two policies separately or jointly have contributed to alleviating the decrease of production caused by the pandemic crisis. This suggests that these policies were beneficial for firms to address the adverse effects of the health crisis. Otherwise, the policies were considered ineffective in reducing the loss of jobs occurred during the crisis. More specifically, adopting these policies was associated with a bit increase of leaving jobs with wages, while loss of jobs without wages seems to not be related to any of these policies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Methodology\u003c/h2\u003e \u003cp\u003eWe have adopted a methodology similar to the one proposed by Sant'Anna and Zhao (2020) to analyze how policies implemented in response to Covid have affected firms\u0026rsquo; performance and actions. Our analysis, however, is set in a different framework from Standard DID analysis. On the one hand, there are two treatments that firms may have been affected by. First, we assume that all firms have been affected by the Pandemic. Because of this, Covid specific impact on firms\u0026rsquo; performance cannot be identified. Second some firms decided to adopt different policies related to technology adoption.\u003c/p\u003e \u003cp\u003eTo identify the policy related effects, we impose the assumption that Covid had a homogenous impact across all firms. Otherwise, it would not be possible to differentiate effects of the technology adoption policies from long term heterogenous impact of Covid. In addition, we also assume that there are no spillover effects SUTVA (Stable Unit Treatment Values Assumption).\u003c/p\u003e \u003cp\u003eUsing Sant'Anna and Zhao (2020) framework, we consider doubly robust estimators based on the following moment conditions:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:E\\left(\\:{P}_{i}-{\\Lambda\\:}\\left({X}_{i0}\\gamma\\:\\right)\\right)=0$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:E\\left({X}_{i0}\\left({y}_{it}-{y}_{i0}-{{X}_{i0}\\beta\\:}^{w}\\right)*\\frac{{\\Lambda\\:}\\left({X}_{i0}\\gamma\\:\\right)}{1-{\\Lambda\\:}\\left({X}_{i0}\\gamma\\:\\right)}E\\left[\\frac{1-{\\Lambda\\:}\\left({X}_{i0}\\gamma\\:\\right)}{{\\Lambda\\:}\\left({X}_{i0}\\gamma\\:\\right)}\\right]|{p}_{i}=P\\right)=0\\:ATT-E\\left[\\left({y}_{it}-{y}_{i0}\\right)-{X}_{i0}{\\beta\\:}^{w}|{p}_{i}=P\\right]=0$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe first moment condition identifies the propensity that a firm implemented a particular policy, given a set of pre-treatment characteristics \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i0}\\)\u003c/span\u003e\u003c/span\u003e. As described in Sant\u0026rsquo;Anna and Zhao (2020), Callaway and Sant\u0026rsquo;Anna(2021) and Cunninham(2020:Causal Effect Mixtape), it is important to include only time fixed or pretreatment covariates to avoid introducing errors with colliders or bad controls.\u003c/p\u003e \u003cp\u003eThe second moment is used to predict the potential change in the outcome of interest (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{it}-{y}_{i0}\\:\\)\u003c/span\u003e\u003c/span\u003eor\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\varDelta\\:{y}_{i})\\)\u003c/span\u003e\u003c/span\u003e using and inverse probability weight (IPW) that is based on the propensity score obtained from the first moment. This is done for the of control sample only, using the same controls as the probability model. The last outcome estimates the Average treatment effect (ATT) for firms that adopted the policies, using the predicted outcome based on the weighted Regression. This procedure is also known as an Inverse-probability-weighted regression adjustment model, but where the outcome measures changes across time, in the spirit of Sant\u0026rsquo;Anna and Zhao (2020).\u003c/p\u003e \u003cp\u003eThis is considered a doubly robust estimator, because as long as one of the two model, the propensity score or the outcome model, es correctly specified, the estimates would be unbiased.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Model Specification\u003c/h2\u003e \u003cp\u003eAs recommended in the literature, we control only for pre-treatment characteristics, which were collected as part of the Baseline economic indicator. Specifically, the XX survey collected information from firms regarding employment and production activities as of 2019. Using this information our most comprehensive model controls for log(# employees) in the firm, share of workers who are unpaid workers, share of full-time workers, share of female workers, log of total production and log of total cost.\u003c/p\u003e \u003cp\u003eAs described before, we consider two Technology adoption policies: Usage or expansion in the use of digital platforms in response to Covid, and Adoption or implementation of Telework. Because many firms decided to adopt both policies, we do further sensitivity analysis differentiating between Adoption of a single policy or adopting both, and comparing firms that adopted at least one of the policies.\u003c/p\u003e \u003cp\u003eIn contrast to standard DID models, we do not have access to both pre and post treatment to many of the outcomes of interest. In the survey, however, information was collected mostly in terms of changes the firm experience during Covid. In this regard, for our paper, we consider the impact on the following variables:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eM1: #Days Closed during the first 3 months of the pandemic\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM2: Whether the Firm Declares Production increased or remain the same\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM3: %change in production\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM4: Whether total employment remain the same or increased\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM5: Share of workers with lower wages\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM6: Share of workers who work fewer hours\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM7: Share of workers with paid leave of absence\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM8: Share of workers with not-paid leave of absence\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1. Propensity For Strategy Adoption\u003c/h2\u003e\n \u003cp\u003eTo understand the impact that the two policies we study had on the mitigation mechanisms firm adopted through the pandemic, we start how baseline characteristics were related to the likelihood of adopting an Expansion of internet and telecommuting. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides the marginal effects of firm adopting a given policy using separate logit models, as well as using a joint multinomial model.\u003c/p\u003e\n \u003cp\u003eThe results shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reveal that firms with high number of employees and share of female workers are more likely to adopt a policy of expansion of internet use and telecommuting in response to restrictive public health measures. This leads to conclude that small firms were incapable to face the COVID restrictions by adopting an expansion of internet use. On the other hand, those with higher share of unpaid workers, and higher share of full-time workers were less likely to do so. On the other hand, the multinomial logit suggests that larger firms were less likely to adopt an expansion of internet use only, but preferred to adopt both policies simultaneously. When the proportion of women was large, however, firms were someone were likely to adopt internet expansion or a combination of internet expansion and telecommuting, but not telecommuting alone.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMarginal effects on Policy Adoption\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\u003elogit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elogit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003emlogit\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternet\u003c/p\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTelecommute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeither\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT.Commute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEither\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog(# Employees)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.048\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.008\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Unpaid Emp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.031\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.040\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Full time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.042\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.045\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.036\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.169\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.165\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog(Tot Prod)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog(Tot Cost)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.010\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eStandard errors in parentheses\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSimilarly, firms with a larger proportion of full-time workers show no particular inclination to adopt either policy independently, but are less likely to adopt them simultaneously. This may be driven by frictions in the workforce, and inability of firms to make fast changes in the short run.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Effect of Policy Adoption: DID approach\u003c/h2\u003e\n \u003cp\u003eUsing propensity scores based on the models provided in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, we use an inverse probability-regression adjustment to analyze the impact on various indicators of firm performance and Covid mitigation strategies. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e provides the average treatment effects on selected indicators under three setups. The first (ATT Tech) uses each policy independently, disregarding the effect of possible combined policies (for instance, when analyzing Online presence expansion, the effect of tele-commuting is assumed to be zero). The second set (ATT Combined) compares the average treatment effects on the treated for firms that adopted a single policy, along with those that adopted both, where the control are firms that did not adopt any technology policy. Lastly, we show the impact of a combination of both Policies (ATT joint).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTreatment effects on selected indicators: 2020\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\u003eATT Tech\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnline Presence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.961\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.614\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.017\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.898)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.895)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTelecommute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.909\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.022\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.938)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT Combined\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTelecommute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.034\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnline Presence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.346\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.257\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.015\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.388\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.018\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT Joint\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEither\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.458\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.694\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.845)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.850)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAVG\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eStandard errors in parentheses\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\u003e1 # of Days Closed\u003c/p\u003e\n\u003cp\u003e2 Equal or Increased Production\u003c/p\u003e\n\u003cp\u003e3 %Change in Production\u003c/p\u003e\n\u003cp\u003e4 Employment Decrease\u003c/p\u003e\n\u003cp\u003e5 Shr wrks:Lower Wages\u003c/p\u003e\n\u003cp\u003e6 Shr wrks:Lower Hours\u003c/p\u003e\n\u003cp\u003e7 Shr wrks:Leave w/ wage\u003c/p\u003e\n\u003cp\u003e8 Shr wrks:Leave w/o wage\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n\u003cp\u003eThe first option Firms have to mitigate the impact of the Pandemic (and as an answer to the policies imposed in Palestine) was to temporary close the firm. Based on our results, the expansion of internet use policy (online presence) has a significant effect on the number of days closed. Adopting this practice is found to reduce the number of days closed outcome by almost 3 days, whereas those adopting the policy exclusively reduced the number of days closed in just over 11 days. Telecommuting, on the other hand has no significant impact on this outcome. This leads to conclude that thanks to implementing internet expansion program, firms, mainly those employing higher number of workers, succeeded in mitigating the effects of the COVID pandemic.\u003c/p\u003e\n\u003cp\u003eA second option firms may have had to mitigate the impact of the pandemic was to reduce production capacity. In the survey, this is measured using two variables. Firms either say if production increased, decreased or remain the same compared to normal activities, and they also indicate how much production increased or decline. Interestingly, neither of the policies seems to have no additional effect on the probability of maintaining or increasing production, compared to firms that adopted no policy. We only see a small effect if firms adopted both policies, which lead to a higher probability (1.5pp) of reducing production during the pandemic. The second metric, how much production change, shows a different story. Among firms with similar characteristics, those who adopted the technology seem to have experienced a larger decline. In fact, our estimates suggest a decline between 2.7pp to 3.4pp depending on model specification.\u003c/p\u003e\n\u003cp\u003eThe third option firms have to cope with the impact of the pandemic was associated is related to their decisions regarding labor employment. Neither of the policies, or a combination, seem to have had any effect on employment change. Nevertheless, it should be considered that almost all firms (83.7) indicate that they made no changes in their labor force. We only observe a small increase in the likelihood of increasing the labor force among firms that increased their use of Internet and Online presence in general. We also see no effect on the likelihood of reducing wages among their labor force, or having to grant paid leave of absence to their workers.\u003c/p\u003e\n\u003cp\u003eUnexpectedly, we do observe that firms that implemented this technology policies had to reduce hours of work for a larger share of their employees. In contrast, however, they had to grant leave of absence without pay to a smaller share of the population.\u003c/p\u003e\n\u003cp\u003eAs part of the efforts to follow up the performance of firms after the initial shock of the pandemic, a second survey wave, aiming to collect information on the same period, but a year after the pandemic. Unfortunately, the second round of the survey was only able to collect information for about 20% of the original cohort, due to refusal or being closed. Nevertheless, we implement a similar analysis as in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTreatment Effects on Selected indicators: 2021\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\u003eATT Tech\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnlinePresence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.705\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.025\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeleCommute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.603\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.188\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.049\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.494)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(2.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT Combined\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeleCommute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.518\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(3.720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(4.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnlinePresence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.897\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.093\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.643\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(2.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(3.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.287\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.037\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.799)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(2.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT Joint\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEither\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.364\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.808)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"6. Placebo test","content":"\u003cp\u003eOne of the main assumptions behind the identification in difference in difference models is the parallel trends assumption, or more specifically conditional parallel trends assumption. While this assumption is at its core untestable, a usual approach is to use data before treatment was implemented to see if both treated and control groups follow similar paths.\u003c/p\u003e \u003cp\u003eAs described earlier, this is not possible with our data. Because the survey was collected after Covid hit, there is no possibility to analyze pre-policy outcome trends. An alternative for validating our results, however, is to utilize a kind of randomization/placebo analysis. The intuition of the procedure is to compare the observed estimation of the treatment effect to the effect we would estimate based randomly assigning not-treated firms into treatment. Because we should expect not to see no treatment effect among the effectively not treated, this procedure would provide an empirical approximation of how large the estimated effect could be if driven only by chance.\u003c/p\u003e \u003cp\u003eMore formally, using base-period information, we model the conditional probability that a firm has adopted a particular policy:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:P\\left({p}_{i}=1|X\\right)=\\text{G}\\left({x}_{i0}\\delta\\:\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOnce the coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:\\)\u003c/span\u003e\u003c/span\u003e have been estimated, we randomly assign not-treated units to a pseudo treatment group using the following rule:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{\\stackrel{\\sim}{p}}_{i}=1\\left({u}_{i}\\le\\:\\text{G}\\left({x}_{i0}\\widehat{\\delta\\:}\\right)\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a draw from a uniform distribution. Once the pseudo treatment groups are defined, we proceed to calculate the effects of these pseudo treatments. We repeat this process 1000 times to generate a confidence interval for the placebo assignment. If the estimated treatment effect falls within the confidence interval of the placebo assignment, it suggests Average Treatment Effect (ATT) estimated in the previous section were driven by characteristics and noise, and not by the treatment itself. On the other hand, if the treatment effect lies outside the confidence interval, it suggests that the policy was responsible for the observed treatment effect.\u003c/p\u003e \u003cp\u003eWe call this procedure a conditional placebo test because the probability of treatment depends on observed characteristics. We anticipate that this approach will be superior to the standard randomization test as it helps minimize the influence of compositional effects when estimating the treatment effects. Nevertheless, we also implement a traditional permutation test, where treatment status (policy adoption) is shuffled among all firms in the data.\u003c/p\u003e \u003cp\u003eWe consider two variants of the placebo test. The first one estimates the treatment effects given the pseudo treatment assignment using only observations who didn\u0026rsquo;t implement any of the two policies we considered here. The second one estimates the treatment effects comparing the placebo treatment units to the units that did adopt the policies of interest. In the second variant, we expect the estimated treatment effects to be similar to the ones estimated in section \u003cb\u003e5.2\u003c/b\u003e, as the treated and comparison groups are more similar to each other.\u003c/p\u003e \u003cp\u003eThe permutation and placebo tests are implemented considering using the Joint Policy definition. That is comparing firms that implemented either of the technology adoption policies to those that implemented neither. This is done using 2020 and 2021 indicators.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we provide the ATTs estimated based on the permutation test, as well as the placebo tests. They can be compared to the ATT Joint estimates of Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Column 1 and 2, the permutation test and placebo 1, show no effect on the selected indicators. The estimated standard errors are not large enough to disregard the estimated effects for #days closed, %change in production, share of workers working fewer hours, or with leave of absence without wages. Colum 3, as it was expected as well, shows that the estimated effects have similar levels of significance and magnitude as the originally estimated effects.\u003c/p\u003e \u003cp\u003eWhile the placebo tests provide no evidence regarding the parallel trend assumption required for the model identification, at the very least the placebo and permutation test suggests the estimated results are not likely to be produced at random.\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\u003ePlacebo Test: 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \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\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePermutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlacebo1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlacebo2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of Days Closed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.458\u003csup\u003e***\u003c/sup\u003e\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\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.519\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(0.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.655)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.003\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 \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%Change in Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.694\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.471\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(0.850)\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(1.102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.727)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\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 \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr wrks:Lower Wages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002\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 \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr wrks:Lower Hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr wrks:Leave w/ wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\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 \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr wrks:Leave w/o wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.020\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Standard errors obtained using 1000 repetitions\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePlacebo Test: 2021\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=\"No\" id=\"Taba\" border=\"1\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePermutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlacebo1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlacebo2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# of Days Closed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.893\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(1.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.934)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%Change in Production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.575\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(1.563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.816)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.917)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\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 \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr wrks:Lower Wages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.010\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 \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr wrks:Lower Hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\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 \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr wrks:Leave w/ wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\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 \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShr wrks:Leave w/o wage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.037\u003csup\u003e***\u003c/sup\u003e\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 \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis paper sheds light on the ways that Palestinian firms managed to cope with the most employed policies towards embracing an entrepreneurial orientation during a crisis like COVID-19. The following section discusses these results in light of previous research and the context of Palestine.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Digital Technology adoption and business continuity\u003c/h2\u003e \u003cp\u003eThe results suggest a relationship between online presence policies adopted by businesses and shorter closure periods. On average, firms with these policies are closed between three and 11 days less than comparable establishments that do not have such measures (those only dependent on an online presence cut the number of closures by 11 days). This is in agreement with McKinsey \u0026amp; Company, 2020 who found that during the pandemic, companies placed higher on digital ability fared better. Yet, the scale of impact in Palestine looks smaller than developed economies, probably due to limitation in infrastructure.\u003c/p\u003e \u003cp\u003eThe lower adoption rate among SMEs (only 15.8% adopt digital but 44.3% of large firms do) is consistent with the results in OECD (2020) concerning the digital divide in developing economies. Such gap is worrying considering that SMEs represent 98% of the Palestinian business sector (MAS, 2021). This is particularly low adoption rate in Palestine might be due to the infrastructure challenges highlighted by Shraim and Crompton, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, which include unreliable internet connectivity and limited access to 3G/4G networks as part of education sector.\u003c/p\u003e \u003cp\u003eGiven these findings, there are numerous salient policy implications within the Palestinian context where businesses are uniquely challenged with not only operating under occupation but also doing so amidst crises (Palestine Economic Policy Research Institute [MAS], 2021). Above all, governments and regulators need to make digital infrastructure a key priority area and close the gap in angels of telecoms in the West Bank (3G) and Gaza (2G) (Shraim \u0026amp; Crompton, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This may entail diplomacy to negotiate with Israeli officials for access to advanced telecommunications spectrum, as well as practical solutions for developing alternative methods of connectivity. The remarkable digital adoption gap between large firms (44.3%) and SMEs (15.8%) may be attributed to the existence of a digital divide in developing economies as indicated by OECD (2020), which calls for special policy intervention that should utilize the prevailing platforms such as the Estidama fund and Monshati Platform (PMA, 2020).\u003c/p\u003e \u003cp\u003eThe high correlation noted between firm size and the general ability of firms to adopt technology (indicated by results from the logit model) is in agreement with findings produced by the Enterprise Surveys carried out by World Bank on technology adoption in developing economies. However, the Palestinian instance illustrates especially pronounced size-based differences which may amplify existing disparities in the business community.\u003c/p\u003e \u003cp\u003eAlso, the differential effect across the two regions (West Bank and Gaza) illustrates that existing regional inequalities shaped how firms adopted and benefited from technology interventions. The geographical dimension of digital resilience this research contributes to the understanding on how constraints pertaining to location shape adaptation strategies when faced with a crisis.\u003c/p\u003e \u003cp\u003eWhile our analysis suggests some evolution in policy effects between 2020 and 2021, these findings should be interpreted with substantial caution due to significant sample attrition in 2021 (retention of only 20% of the original cohort). Some policy responses appear to have led to sustained changes in business practices, such as reduced reliance on unpaid leave. However, the changing effect sizes across years could reflect either learning processes in technology implementation or shifts in the external environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Labor Market Adjustments and Employment Outcomes\u003c/h2\u003e \u003cp\u003eIt contrasts with the traditional patterns of crisis response whereby firms would increase working hours, cut permanent or temporary staff wages and increase unpaid leave to cope with increased demand. This implies that these firms have managed the balance of employment costs and it reflects with ILO (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argument regarding the arrangements of work during COVID-19 in Palestine.\u003c/p\u003e \u003cp\u003eThe unique effect across employee positions (69 percent in the case of marketing but only 22 percent in supply chains for instance) is consistent with international trends outlined by the ILO (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which emphasize large differences even within sectors on feasibility of remote work across job functions. On the other hand, telework adoption in Palestine's financial sector (4.4%) is surprisingly low compared with global observations suggesting geo-specific barriers to be studied further.\u003c/p\u003e \u003cp\u003eReform of labor market policies that maintain protections for workers will be needed to address the new reality of work (International Labour Organization [ILO], \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Overall, we demonstrate that firms with digital capabilities were able to keep staff on through fewer hours rather than unpaid leave (in line with worldwide patterns by Criscuolo and Gal (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)). Nonetheless, existing policies do not specify how regulation will take shape when it comes to remote working conditions for workers and the ways their work can be monitored. There is also a need for greater capacity within the Ministry of Labor to monitor workplace conditions\u0026mdash;particularly in an environment in which informal employment is prevalent and many women workers, who already face specific vulnerabilities, had additional challenges when adopting remote work (MAS 2021).\u003c/p\u003e \u003cp\u003eOur finding that firms adopting technology experienced larger production declines (2.7\u0026ndash;3.4 percentage points) requires careful interpretation. Several factors may contribute to this counterintuitive result:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTechnology adoption costs during implementation (Criscuolo et al., 2021)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePre-existing challenges from movement restrictions and political instability (MAS, 2021)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePossible selection effects, where harder-hit firms may have been more likely to adopt technology as a survival strategy\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the financial sector has a surprisingly low telework adoption rate (4.4%), which stands in stark contrast to global trends documented by McKinsey \u0026amp; Company (2020). Likewise, the huge loss of jobs in construction (23.3%) throughout the disaster shows that hybrid work models should be advanced on as long as it is conceivable for administrative capacities (Palestinian Central Bureau of Statistics, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These sectoral approaches should be complemented by initiatives targeting broader parts of the business community in the development of digital skills, especially for sectors that demonstrated high rates of telework feasibility such as marketing (69%) and administration (53%), mirroring patterns evident in global studies on teleworking (International Labour Office, 2020).\u003c/p\u003e \u003cp\u003eThe placebo tests we conduct establish the robustness of the estimated policy effects thus reinforcing that what we observe are not statistical artifacts but real returns to adoption of technologies. However, there appear contextual limits in Palestine to the extent of such benefits, including:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSpecifically, the relative lack of digital infrastructure, especially in Gaza\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEconomic threats which existed before as recorded by PMA (2020)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLimitation of movement and prohibiting trade\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLow fiscal space for support programs by governments\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWe draw on the findings in a way that contribute to wider literature on business resilience across developing economies, while drawing attention to underlying context-specific factors that may undermine potential policy effectiveness. The findings imply that while digital solutions can enhance business resilience, their effectiveness depends heavily on underlying infrastructure and institutional support.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion and Policy Implications","content":"\u003cp\u003eOur analysis indicates that although digital technology adoption contributed to business resilience during the COVID-19 pandemic, its impact was limited and multifaceted due to contextual circumstances. Our finding that online presence was correlated with fewer days closed (3\u0026ndash;11 days) illustrates likely benefits from digital solutions, yet stark differences in adoption rates between firms in the large categories (44.3%) and SMEs overall (15.8%) suggest how pre-existing inequality of resources/growth trajectories were reflected in ability to adapt to displacement secundum.\u003c/p\u003e \u003cp\u003eYet, the adoption of the digital has not been inequitable and uncovered disruptions that indicate rife structural inequalities within the Palestinian business sector. The gap in digital adoption between large firms (44.3%) and SMEs (15.8%) underscores how existing capabilities and resources driven by industry role were determinants for firm adaptation to crisis conditions.\u003c/p\u003e \u003cp\u003eIn its short time, the adjustments made by a beset labor market revealed some surprising patterns, suggesting that what we view as traditional crisis response may be changing. Firms adopting technology were more likely to cut working hours instead of using unpaid leave, indicating that firms could adapt more flexibly. The study also highlighted major differences between sectors, in which strikingly few (4.4%) of the financial sector's dual professionals took to teleworking, compared to high adopters among marketing types (69%) and people in administration (53%). But these findings suggest that sector-specific impediments and ways forward will necessitate tailored policies.\u003c/p\u003e \u003cp\u003eBoth cohorts experienced negative shocks to food insecurity, but the temporal analysis between 2020 and 2021 shows the changing nature of policy footprints \u0026mdash; a caveat being we lost over half our sample in 2021. While some effects faded overtime, as in the case of unpaid leave reducing, this implies that part of the business adaptation due to policy responses has become persistent. This temporal dimension enriches our framework for understanding the ways that crisis responses develop, adapt and potentially become routinized over time.\u003c/p\u003e \u003cp\u003eThese contributions arise from our findings. First, they contribute to the emerging literature on business resilience in developing countries with empirical data from a context that is especially difficult. Second, they show that the effectiveness of digital solutions is highly conditional on infrastructure and institutional context. Third, they show how inequalities that are beset prior to any crisis can be magnified in response regimes and creating new modes of structural infirmity over the longer-term.\u003c/p\u003e \u003cp\u003eLimitations of this study should be addressed by future research. However, the 2021 sample had a high attrition rate and thus limits our power to make strong inferences about longer-term impacts. Lastly, the distinct Palestinian context may render some findings less generalizable to other developing economies despite its implications for research.\u003c/p\u003e \u003cp\u003eMoving forward, these results point to a few important areas for future work. Firstly, researching the sustainability of crisis-induced digital transformation over a longer time frame starting from a point at which economic and social costs begin to materialise in order to inform policy. Second, we believe that understanding the balance firms take when adopting digital and other resilience strategies could provide a roadmap for more holistic support programs. Ultimately, studying the way in which digital divides play a role in post-crisis recovery trajectories could assist design of more equitable support structures.\u003c/p\u003e \u003cp\u003eThese results can be significant for policymakers who target multiple, interlocking constraints on firms at the same time. The results indicate that while digital solutions can improve business resilience, their success is highly dependent upon external infrastructure and institutional capability. To this end, policy interventions cannot be applied simply as piecemeal crisis-response measures but will also need to take some of the long-term structural challenges into account.\u003c/p\u003e \u003cp\u003eFinally, this paper contributes by developing several insights into firms' adaptation to the crisis-creating conditions of an extremely harsh environment. These results underscore the role of context-specific factors that can shape policy effectiveness and call for a multi-sector, inter-governmental and global response to build a sustainable business resilience. These insights can suggest more effective and impartial ways of continuing to bolster enterprise resilience, amidst a future of global shockwaves and transition, especially in developing economies facing multiple structural challenges.\u003c/p\u003e \u003cp\u003eAs the OECD (2020) and MAS (2021) studies also point to, international organizations and donors are uniquely positioned to help bridge this digital divide as the post-COVID normal unfolds. Support programs must strike a balance between hard infrastructure development and soft skills training (with special reference to gender gap aspects of digital work accessibility) (ILO, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One example of this is the multiple-child, single-device problem called out in education research, which demonstrates that device access programs must go hand-in-hand with infrastructure development (Shraim \u0026amp; Crompton, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImplementation needs to be phased based on existing crisis response frameworks (PMA, 2020). Short term, emergency support through existing programs of the Palestine Monetary Authority and bolstering labour monitoring capacity are indispensable. XT suggests that long-term goals form around medium-term objectives to develop complete plans for digital infrastructure improvement and sustainable financial structures for technology adoption by SMEs (OECD, 2020). Given the context of occupation, it is crucial to build resilient digital infrastructure that can function with reduced reliance on external controls (MAS, 2021) so it must be also part of a long-term objectives.\u003c/p\u003e \u003cp\u003eThese results underscore an urgent balance between crisis response and resilience (McKinsey \u0026amp; Company, 2020). The impact of digital solutions in helping to reduce the number of days where business close (3\u0026ndash;11 days) shows their promise on one hand, but highlights the absolute need for supported infrastructure and institutional capacity, something that several recent studies have underscored (Criscuolo \u0026amp; Gal, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While all policy options will ultimately need to be proven effective in the Palestinian context (both under occupation and with inherent weaknesses of government fiscal capacity, infrastructure etc.), they will specifically need to contend with that reality (PMA, 2020).\u003c/p\u003e \u003cp\u003eAnd at the same time, the way that the crisis response needs to develop over time\u0026mdash;we see this also in our study by comparing 2020 and 2021\u0026mdash;there are different impacts from these years. Similar to immediate crisis response, which is important but only a temporary remedy, sustainable resilience in business requires systematic investment in the digital infrastructure and capabilities (OECD, 2020) as well as regulatory frameworks that facilitate modernisation of businesses while preserving workers\u0026rsquo; rights (ILO, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings show short term needs but the resilient state of businesses in Palestine requires policy reform on a broader spectrum (MAS, 2021). It will take concerted action by government and business, as well as the international community, with appropriate regard to context-specific constraints and potential advantages.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdekola, J., \u0026amp; Clelland, D. (2020). 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The case of small business resilience after Hurricane Katrina. \u003cem\u003eJournal of Contingencies and Crisis Management\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(2), 168\u0026ndash;181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Bureau of Labor Statistics. (2021). \u003cem\u003eTeleworking and lost work during the pandemic: New evidence from the CPS\u003c/em\u003e. U.S. Bureau of Labor Statistics.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValletta, R. G., \u0026amp; Bengali, L. (2013). What\u0026rsquo;s behind the increase in part-time work? \u003cem\u003eFRBSF Economic Letter\u003c/em\u003e, \u003cem\u003e2013\u003c/em\u003e(24), 1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePalestinian National Authority refused to reserve clearance revenue from Israel after they deducted from it, knowing that clearance revenue contribute to more than half of Palestinian government revenue.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wfp.org/countries/palestine?_ga=2.147480497.781717388.1663759160-545597253.1663759160\u003c/span\u003e\u003cspan address=\"https://www.wfp.org/countries/palestine?_ga=2.147480497.781717388.1663759160-545597253.1663759160\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pcbs.gov.ps/portals/_pcbs/PressRelease/Press_Ar_8-8-2021-LFS-ar.pdf\u003c/span\u003e\u003cspan address=\"https://www.pcbs.gov.ps/portals/_pcbs/PressRelease/Press_Ar_8-8-2021-LFS-ar.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mol.pna.ps/news/246\u003c/span\u003e\u003cspan address=\"http://www.mol.pna.ps/news/246\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-industrial-and-business-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jibe","sideBox":"Learn more about [Journal of Industrial and Business Economics](https://www.springer.com/journal/40812)","snPcode":"40812","submissionUrl":"https://www.editorialmanager.com/jibe","title":"Journal of Industrial and Business Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Business Resilience, COVID-19 Pandemic, Digital Transformation, Labor Market Adjustments, Telework","lastPublishedDoi":"10.21203/rs.3.rs-6640456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6640456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe COVID-19 pandemic has significantly impacted businesses worldwide, challenging firms to adapt and build resilience amidst unprecedented disruptions. This study investigates the business resilience strategies employed by Palestinian firms during the pandemic, focusing on the roles of digital transformation, labor market adjustments, and policy interventions. Drawing on panel data from the COVID-19 Business Pulse Survey (2020\u0026ndash;2021), the study highlights the effectiveness of these responses in shaping firm performance, particularly among micro, small, and medium-sized enterprises (MSMEs). Key findings reveal a stark digital divide, with only 15.8% of SMEs adopting digital solutions compared to 44.3% of large firms, reflecting broader structural constraints in the Palestinian economy. Despite these challenges, digital adoption was associated with reduced closure days and increased business continuity, although implementation costs and infrastructural limitations hindered broader success. Labor market adjustments varied significantly across sectors, with telework benefiting higher-skilled roles while exacerbating inequities for women and informal workers. Policy measures, including the Estidama fund and labor market agreements, provided critical support but were limited in reach due to fiscal constraints and pre-existing vulnerabilities. The findings underscore the need for targeted interventions to bridge digital and resource gaps, enhance labor protections, and develop robust digital infrastructure to ensure equitable resilience. This research contributes to the understanding of crisis responses in conflict-affected developing economies, offering actionable insights for policymakers to foster sustainable business resilience in the face of future shocks.\u003c/p\u003e","manuscriptTitle":"COVID-19 Crisis and Business Resilience in Palestine: How Policy Responses Affect Firms’ Performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 13:15:18","doi":"10.21203/rs.3.rs-6640456/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-07-11T18:13:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-30T09:23:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T00:58:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Industrial and Business Economics","date":"2025-05-11T11:28:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-industrial-and-business-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jibe","sideBox":"Learn more about [Journal of Industrial and Business Economics](https://www.springer.com/journal/40812)","snPcode":"40812","submissionUrl":"https://www.editorialmanager.com/jibe","title":"Journal of Industrial and Business Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8760310e-a828-4601-b12e-d778d658eb45","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T10:57:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 13:15:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6640456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6640456","identity":"rs-6640456","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}