Gender Disparities in Financial Inclusion: the Potential of Digital Loans in Empowering Female Health Entrepreneurs in Kenya

Gender Disparities in Financial Inclusion: the Potential of Digital Loans in Empowering Female Health Entrepreneurs in Kenya | 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 Article Gender Disparities in Financial Inclusion: the Potential of Digital Loans in Empowering Female Health Entrepreneurs in Kenya Jacklyne Ashubwe, Maureen Wanyama, Joseph Siyumbu, Ines van Zuijlen, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5187398/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2025 Read the published version in Scientific Reports → Version 2 posted 4 You are reading this latest preprint version Show more versions Abstract In Sub-Saharan Africa, female entrepreneurs are well-represented in healthcare but struggle to access financial services. Digital financing technologies could help close this gap. This study assessed the readiness, perspectives and gender disparities in (digital) loan characteristics among Kenyan health Small and Medium Enterprises (SMEs). We interviewed 24 and surveyed 410 health SME owners and analyzed loan-history data from 850 health SMEs to compare traditional and digital loans, focusing on gender disparities. IDIs identified low trust in digital lenders, while survey results indicated a strong demand for loans, regardless of gender. SME owners willing to take risks, with monthly financial needs and positive perceptions of digital loans were more likely to take a digital loan. Loan-history data showed a gender difference in the initial traditional loan amount, with women-owned businesses receiving less, but this gap disappeared for digital loans. Over 50% of women-led businesses using digital loans experienced significant growth, suggesting increased digital revenues linked to these loans. This study highlights the financing gap among Kenyan health SMEs and the potential of digital loans to enhance financial inclusion. Low trust in digital lenders, especially among those without direct experience, calls for better information dissemination to build confidence in digital financial products. Health sciences/Health care Health sciences/Health care/Health care economics Figures Figure 1 Figure 2 1. INTRODUCTION In Sub-Saharan Africa (SSA), approximately 50% of all health services are delivered by the private healthcare sector, predominantly composed of small- and medium-sized enterprises (SMEs) 1 . Ensuring high-quality healthcare provision is essential for fostering human capital development and alleviating poverty 2 . Addressing the prevalent challenges faced by health SMEs in SSA - such as inadequate infrastructure, shortages of skilled medical personnel, and poor service delivery - is impossible without access to financing 3 . Supply-side healthcare expenditures are linked to improved population health outcomes in SSA, including higher life expectancy at birth and lower infant mortality rates 4 . Loans represent a significant source of capital; however, the disbursement of loans to owners of health SMEs remains challenging. Generally, health SMEs encounter obstacles in securing loans from the traditional lending institutions compared to larger firms because of a limited banking history, limited collateral, and perceived high risk of the sector 5 , 6 . The importance of SMEs businesses in SSA should not be underestimated as they are substantive contributors to the economic growth and development of countries 7 . The World Bank has documented that SME businesses contribute to 50% of global employment and account for at least 40% of the gross domestic product (GDP) in emerging economies worldwide 8 . Similar findings have been documented in SSA where SMEs contribute over 50% of the GDP 9 . Africa has a higher number of female business owners compared to male owners, accounting for 58% of the continent’s self-employed population 10 . However, despite this apparent progress, female entrepreneurs across SSA continue to earn lower profits than their male counterparts, with an average shortfall of 34% 11 . According to the Global Findex of 2021, the region holds a 12% financial gender gap 12 . Women face significant barriers in starting and growing their businesses compared to their male counterparts 13 , 14 . These barriers are multifaceted, encompassing sociocultural factors such as household and child-care responsibilities, educational deprivation and restricted autonomy 15 – 17 . Additionally, women often encounter barriers in access to finance, including limited collateral for loans due to lack of control over fixed assets, limited credit history, low financial literacy and inadequate business training 18 – 22 .These constraints significantly hinder their access to credit and negatively impact the monthly turnover of female entrepreneurs 23 , 24 . Financial inclusion has been identified as one of the factors determining the success of entrepreneurs and their businesses 25 , 26 . While some studies suggest that financial institutions may not explicitly discriminate against women in their lending practices 21 , the structural requirements of financial systems – such as collateral demands – disproportionately disadvantage women. This underscores that gender-specific barriers, rather than outright bias, are critical in shaping the financial inclusion of female entrepreneurs. These challenges are compounded by limited business networks and perpetuate a cycle of disadvantage, confining many women to smaller businesses in the informal sector. Such businesses typically require low capital investments, have limited growth potential, and yield lower returns on investments 16 , 28 . Furthermore, while there is a common agreement in the economics literature that females tend to be more risk averse than their male counterparts, a meta-analysis showed that these gender differences are not so dominant as it appeared in less than ten percent of the published papers 29 . However, the evidence in countries with a higher inherited gender-bias is homogenous, showing that women less often apply for a loan which can be driven by the belief that their credit application will be denied or their internal risk attitude which may be fed by low financial literacy 30 . The emergence of digital finance technologies opened new avenues for financial services to SMEs 32 , thereby advancing digital financial inclusion (DFI). DFI seeks to ensure that individuals and businesses, especially those underserved or excluded from traditional financial systems, have access to a comprehensive range of financial services 33 . For example, FarmDrive in Kenya delivers financial services to unbanked and underserved farmers by using mobile phone technology, alternative credit scoring, and machine learning 34 . Specifically for women, DFI facilitates economic empowerment through financial autonomy, lack of collateral requirements, protection over their finances from appropriation by others, and greater flexibility by overcoming the barriers of time and place 33 . Consequently, previously excluded rurally located SMEs now have the potential to be financially included, leading to economic growth opportunities. Mobile money is the key driver of DFI and a ground-breaking innovation in Kenya and has grown exponentially since its launch in 2007. By 2019, its user-base had expanded to 33 million users, representing 69% of the Kenyan population 15 . In 2021, over 60% of Kenyan SMEs used mobile money platform for business transactions 35 . Despite the expansion of DFI in SSA, health SMEs continue to experience growth constraints, with limited access to finance cited as the primary barrier and consequently suboptimal adoption of DFI 36 . While existing literature explores the perceptions of SME owners across various sectors regarding the use and adoption of financial services 37 , 38 , there remains scarcity of evidence on the specific impact of digital finance on SME financial inclusion in Africa. A recent study has shown that digital finance significantly enhances SME financial inclusion on the continent 39 . However, to our knowledge, no research has yet examined how digital finance has influenced financial access for health SMEs, particularly its potential benefit for female-owned health SMEs. This study seeks to address this gap by describing the digital loan landscape for health SMEs in Kenya and identifying factors that influence digital loan uptake among Kenyan health SMEs. Furthermore, we evaluate the gender distribution in real-world loan history data among 850 health SMEs in Kenya over the period 2011–2024. 2. METHODS This study comprises of two parts and utilizes both primary and secondary data, consisting of in-depth interviews (IDIs), cross-sectional quantitative surveys and administrative loan data. The primary mixed-methods approach began with an exploratory qualitative phase whose findings informed the development of the tools for the subsequent quantitative phase. The secondary administrative data includes real-world loan history data of health SMEs between 2011–2024. 2.1 General study setting This study was conducted in Kenya, a lower-middle-income-country located in East Africa. The 2022 population estimate was 55 million people 40 . The 2021 GDP per capita was approximately 2,061 US dollars 41 and the total health expenditure was 4.55% of the GDP 42 . The main revenue sources for health are the government, private health insurance, out-of-pocket expenditure, and development partners funds 43 . The inflation rate, based on consumer price indices, as of May 2020 was 5.5% rising to 6.8% in September 2023, with fluctuations in between 44 , 45 . According to a United Nations Development Program (UNDP) report, during the COVID-19 pandemic, SMEs in the country suffered negative impacts to their operations and revenues with 67.9% reporting severely reduced operations 46 . Kenya has a dual system for health service delivery. This comprises of government health facilities (46%) and the private for-profit and not-for-profit (faith-based) health facilities 47 . Supplementary file 1 provides more background on the health SME landscape in Kenya. In Kenya, the mobile money system, notably through platforms like M-Pesa, plays a significant role in financial inclusion. It allows individuals, particularly in rural or peri-urban areas, to access banking services without needing a traditional bank account. This system is widely used across different demographics, with higher adoption rates in urban areas, although rural and peri-urban areas have also seen significant uptake. Mobile money is especially popular among lower- to middle-income groups, including small business owners, youth, and women 49 . The system enables transactions such as money transfers, bill payments, and access to digital loans. In this study, digital loans are integrated into the mobile money system, where users can access small, short-term loans directly through their mobile phones, often with minimal documentation via an USSD number or an app. The service has become a key feature of financial services in Kenya, particularly for individuals with limited access to conventional banking services. 2.2 Cross-sectional mixed methods approach The following sub paragraphs elaborate on the primary data collection consisting of IDIs and a cross-sectional quantitative survey. 2.2.1 Specific study setting The study was conducted across five counties in Kenya (Nairobi, Kiambu, Kisumu, Mombasa and Makueni), highlighted in Fig. 1 . These counties were purposively selected to ensure a high number of health SMEs, geographical representativeness, and a sufficient level of mobile money accounts utilization. As such, Nairobi County was included due to the high number of health SMEs. The former Nyanza region (Kisumu County), Coast (Mombasa County), Central (Kiambu County) and the Eastern region and peri-urban (Makueni County) were selected for geographical representativeness and because they have the highest population-level utilization of mobile money accounts within their regions ranging between 85% and 94% 50 . This county distribution was retained for both the qualitative and quantitative phases of this study. Supplementary table 1 provides more information on key county attributes. 2.2.2 Study population and sampling approach Health SMEs located within the selected five study counties were sampled based on two inclusion criteria: the health SME must be private or faith-based and had been in operation for at least one year at the time of contact. The sampled health SMEs included healthcare facilities (medical clinics, dental clinics or specialist clinics), retail pharmacies, stand-alone diagnostic centers, or facilities providing any combination of these services. One health facility or pharmacy per study county that had information of the owner was selected from open national databases of licensed health facilities and pharmacies maintained by the professional regulators 47 , 51 . Subsequently, for the IDIs we applied purposive and snowball sampling to ensure the inclusion of sufficient female respondents. The anticipated sample size for the IDIs was 24 participants spread across the five study counties, with provision to continue sampling until thematic data saturation was reached. The scope of this sample covered 14 healthcare facilities (including standalone diagnostic centers) and ten pharmacies. The number of respondents allocated to each county facilitated sampling of perspectives from these respective regions. For the survey respondents, we adopted a sampling approach that ensured health SMEs were distributed proportionately across the selected study counties. We applied a sampling technique with probability proportional to size for sample allocation. Stratification was carried out based on counties and sub-counties. Researchers made phone calls to business representatives to explain the study’s objectives and schedule interviews. If a participant agreed, the participant provided the business location for an in-person interview. 2.2.3 Sampling size calculation The sample size for the study was calculated using the SurveyMonkey sample size calculator, which considers a 95% confidence level and a 5% margin of error 52 . Based on a total of 7,671 private and faith-based health SMEs, and 5,032 private pharmacies in Kenya, an initial sample size of 373 respondents was estimated. This was increased by a factor of 10% to 410 respondents to accommodate for non-response. To ensure representativeness, we pursued a proportional distribution of the 410 health SMEs between healthcare facilities and pharmacies. 2.2.4 Data collection The IDIs were conducted using a semi-structured interview guide and the overarching objective was to gain insights into reasons for (not) taking a loan. The guide was inspired by an adaptation of the PESTEL analysis framework 53 . This framework facilitated exploration of factors related to Policies, Economic climate, Social attributes, Technology, Environmental and Legal factors and has been used before to assess the external business environment for fintech providers 54 . It provided a suitable framework for categorizing key determinants that influence decisions regarding the usage of digital financial services. The interview guide was piloted (n = 2) in Nairobi and Kisumu, and since no significant changes were required, these pilot respondents were included in the study sample. The IDIs were conducted both face-to-face and via telephone. The venue for the physical interviews was the health SME premises. The interviews were carried out in English and commenced after obtaining informed consent; they lasted between 30 and 60 minutes. Upon consent, the interviews were audio recorded, and participants received a numerical code to ensure anonymity during the analyses. The survey questionnaire was subsequently developed informed by the findings of the IDIs to quantify the generated insights of the IDIs. The background characteristics included were respondents’ gender, age, education level, health SME type, location and business duration in years to capture maturity and operational experience. In addition, fed by the qualitative insights and informed by the PESTEL framework, we included e.g. level of awareness about digital loan products, financial behavior and risk-taking behaviors (adapted from FinaMetrica 55 ). Risk attitude was measured on a scale of 1 to 5 with the questions: “How willing are you to take financial risks?” and “When faced with a major financial decision, are you more concerned about the possible losses or the possible gains?” Respondents were categorized into high, medium, or low-risk takers. High-risk takers were defined as those who expressed a willingness to take significant financial risks, while low-risk takers were more cautious and concerned about potential losses. The questionnaire was digitized and administered using mobile phones through face-to-face interactions. The data collectors underwent prior training to familiarize them with the tool and ensure adherence to research ethics during data collection. Participants did not receive any incentive to participate in this study. The data collected was transmitted in real-time to a secure encrypted cloud-based database. 2.2.5 Data analysis The recorded IDIs were transcribed in Microsoft Word © and anonymized using numerical codes. A coding matrix was created corresponding to the PESTEL framework; the overarching thematic areas derived from this framework that guided the development of the interview guide were retained for the analysis. As such, deductive data analysis was used initially. This deductive approach was complemented by an inductive analysis approach whereby subthemes were allowed to emerge during the data coding process. The themes and subthemes were iteratively compared and refined. To increase the rigor and credibility of the findings, the research team held peer debriefing sessions throughout the data analysis process. Descriptive statistics of the survey data were generated for all variables of interest. Cross-tabulations were generated to gain insights into categorical variables associated with loan uptake. Chi-squared tests were conducted to determine the statistical significance of these relationships. The level for significance was set at p < 0.05. To safeguard against inflated type 1 errors, Bonferroni-corrected pairwise comparisons were applied. A logistic regression model was applied to gain insights into the factors that were associated with a digital loan uptake. We examined the robustness of the findings by evaluating the model's goodness-of-fit, examined multicollinearity using diagnostic measures, and addressed potential confounding variables. All analyses were conducted in STATA version 15.0. 2.3 Secondary data analysis on MCF loan history data The following sub-paragraphs elaborate on the secondary administrative data provided by the Medical Credit Fund (MCF). 2.3.1 Medical Credit Fund (MCF) Medical Credit Fund (MCF) is a fund dedicated to providing loans to health SMEs in SSA. MCF was founded in 2009 with the objective to overcome the access to finance challenges experienced by Health SMEs and has been active in Ghana, Kenya, Nigeria, Tanzania and Uganda. MCF combines loans with technical assistance to support healthcare providers to improve their quality of services and invest in their businesses. Since its inception, MCF has provided over 10,000 loans worth EUR 165 million to more than 2,100 health SMEs. The majority of MCF borrowers are healthcare providers like health centers, clinics, (small) hospitals and pharmacies. 2.3.2 MCF loan products Initially, MCF offered only term loans: traditional loans with security and a fixed repayment schedule provided either directly or through partnerships with local banks. Loan sizes varied between a few hundred and 2 million Euro equivalent in Kenya Shillings, with tenors from 6 months up to 96 months. The loans were used by clients to finance working capital or invest in infrastructure and equipment. With the rise in mobile money use in Kenya and further accelerated by the recent COVID-19 epidemic, MCF developed a digital loan product. To determine the health SMEs’ eligibility and maximum loan amount, MCF uses a health SME’s recent history of mobile money (M-PESA) revenues from patients (payments made by patients). The product is used to finance working capital or equipment and is typically smaller (between EUR 100 and 200,000) compared to a more traditional term loan and repaid over a shorter time span of three to six months by automatic real-time deductions from mobile money payments in the facility. The loan does not require any collateral because a share of ongoing mobile money revenues, instead of assets, are used as the basis for providing the loan. The loan product was launched in 2017 in Kenya and has been growing exponentially since then. It is currently being introduced in Ghana and Tanzania. 2.3.3 Study setting Most of Kenyan MCF clients are based in urban areas, with the large majority being in Nairobi and Kisumu. MCF aims to reach the small and medium segment of the health sector: 50% of MCF clients have less than 10 staff members and receive less than 500 patient visits per month, mostly patients from lower income groups. 2.3.4 Study sample For this study we included all term and digital loans provided in Kenya by MCF. We removed outliers by setting a threshold of EUR 800,000 for term loans, resulting in four exclusions, and EUR 200,000 for digital loans, leading to three exclusions. All financial values are expressed in EUR and the applied conversion rate was monthly (i.e. the rate at the end of the month for closed months and the end of the previous month for open months). We excluded observations where the facility owner’s gender was unknown (n = 3). 2.3.5 Variables of interest The key variable of interest is the ownership of the facilities, categorized into three options: female owned (100%), male owned (100%), or partnership (typically 50/50 male female owned). The loan type was a binary variable, either digital or traditional loans. The digital loan amount is based on the mobile money revenues from patients, and therefore we created a “growth in loan amount” variable to indicate the difference between the first and last digital loan as a proxy for growth in revenue. For facility characteristics, we considered the type of facility, categorized into levels commonly used in Kenya: level 1 (community health posts), level 2 (dental clinics, diagnostic centers, dispensaries, eye clinics, pharmacies), level 3 (health center, maternity home, outpatient center), level 4 (primary hospital). We also included the “other” category for equipment suppliers and support service companies. Additionally, the location of the facility – urban or rural – was captured. 2.3.6 Data analysis We evaluated the gender differences in the two loan types by comparing the averages in the first loan amounts, as this is an important indicator for financial inclusion. To assess statistically significant gender differences, first a log-transformation was applied to obtain a normal distribution followed by an Analysis of Variance (ANOVA) test as the comparison was between three groups. When the variable was still not normally distributed, we applied the Kruskal-Wallis test. We also assessed gender differences in the growth in loan amount . Due to an extreme leptokurtic distribution, this variable was transformed to a categorical variable so a chi 2 test could be applied to assess significant differences. A p-value < 0.05 was considered statistically significant. To ensure robustness of the gender analyses, we also conducted an ordinary least square (OLS) regression and a multinominal regression to control for facility characteristics. 2.4 Ethical Considerations For the cross-sectional mixed methods approach, ethical approval was obtained from the Strathmore University Institutional Ethics Review Committee, reference numbers SU-ISERC1616/23 and SU-ISERC1890/23. Additionally, a research permit was obtained from the National Commission for Science, Technology and Innovation (NACOSTI), permit number NACOSTI/P/23/25096. Informed consent was obtained before the in-depth interviews or surveys began. Additional consent was sought to allow the in-depth interviews to be audio-recorded. All data was anonymized of any identifier information. For the secondary data analysis on the data provided by the MCF, ethical approval was obtained from the Research Internal Review Board of the Erasmus School of Health, Policy and Management in the Netherlands and the AMREF Ethics and Scientific Review Committee in Kenya. All data were anonymized. 3. RESULTS The IDIs were conducted between May and July 2023. Two out of the 24 interviewed respondents refused to be audio recorded. Due to extensive field notes taken during the IDIs, the content of their interviews could still be included in the subsequent analyses. The survey was conducted between September and November 2023 among 439 respondents, of whom 29 respondents refused to answer a central question (whether they had taken a loan). Consequently, the survey sample comprised 410 respondents. We provide a summary of the main IDI findings first, followed by the presentation of the quantitative insights. Lastly, we analyzed real-world loan history data of 850 health SMEs including 6,350 disbursed loans between 2011–2024 (up to July). 3.1 Cross-sectional findings 3.1.1 Background characteristics of IDI respondents Of the 24 IDI participants, 39% were female. The larger proportion of these participants were aged between 30 and 40 years; more than half (63%) of their businesses were microbusinesses (2–9 employees), with 58% of these businesses being in urban areas. All participants had attained tertiary level education in health-related fields, see Table 1 . Table 1 Background characteristics of IDI participants (N = 24) Female (n = 9, 37.5%) Male (n = 15, 62.5%) Total (N = 24, 100%) n % n % n % Age 18–30 3 33.3 2 13.3 5 20.8 30–40 5 55.6 6 40.0 11 45.8 40–50 0 0 5 33.3 5 20.8 Above 50 1 11.1 2 13.3 3 12.5 Location Rural 0 0.00 0 0.0 0 0.00 Peri urban* 4 44.4 6 40.0 10 41.7 Urban 5 55.6 9 60.0 14 58.3 Education Secondary 0 0.00 0 0.00 0 0.00 Tertiary 9 100.0 15 100.0 24 100.0 Business Type Consultation & treatment 4 44.4 9 60.0 13 54.2 Diagnostic Centre 0 0.00 1 6.7 1 4.2 Pharmacy 5 55.6 5 33.3 10 41.7 Business Size Solo Entrepreneur (1) 3 33.3 2 13.3 5 20.8 Micro Business (2–9) 5 55. 6 10 66.7 15 62.5 Small Business (10–49) 1 11.1 3 20.0 4 16.7 *Peri-urban: Transition zone between urban and rural areas 56 3.1.2 Main qualitative insights Out of the 24 IDI participants, 15 had never taken a loan from a bank. Six of these participants who had not taken a bank loan were female; and 12 of these businesses were in urban areas. The participants cited a variety of reasons for not taking bank loans which included ploughing back business revenues and preferring reliance on support from family and friends. Those who expressed an aversion to bank loans cited that they considered high interest rates as a deterrent. Ten of the 24 IDI participants had ever taken a digital loan; and half of them were female. These respondents cited diverse experiences with their digital lenders. Some respondents described negative experiences with digital loans, which related to high interest rates, a lack of trust, intrusion of privacy and harassment and poor customer relations as key contributors to these negative experiences. “It’s called xxx (brand-name of digital lender) …and we said never again! They keep calling you, they keep harassing you days before the agreed date. You just have to pay. They also go to your house to take pictures… no, those ones they harass you psychologically.” (female IDI respondent). Additionally, some of these female health entrepreneurs preferred to rely on internal business revenues. Conversely, other respondents showed a business growth mindset and demonstrated a high appetite for financial information. Most of them had a positive prior loan experience, supported by the quotes below: “…you don’t have to fear of taking loans.... For you to grow you have to take a loan.” (Interviewer: Would you consider taking a loan from a digital lender?) “Respondent: yes, if they have favorable requirements…” (female IDI respondent) (Interviewer: Have you taken a loan from a digital lender?) “Yes; it was swift… (the experience) was okay…. If need be, I would take another one (digital loan).” (female IDI respondent). The key qualitative findings integrated into the survey included factors that influenced entrepreneurs’ decisions to take up loans which were risk-attitude and financial needs. 3.1.3 Background characteristics of survey respondents Table 2 outlines the background characteristics of the survey respondents. Of the 410 survey respondents, 28% were female. Most respondents were aged between 30–40 years. Slightly less than two thirds (64%) of the businesses were urban based, with microbusinesses being the most dominant. The majority (83%) reported that they systematically tracked their business revenues. Slightly more than half (51%) of the female respondents were running a pharmacy, whereas most of the male respondents (59%) provided consultation and treatment services. Table 2 Background characteristics of survey respondents (N = 410) Female (n = 115, 28.1%) Male (n = 295, 71.9%) Total (N = 410, 100%) n % n % N % Age Category 18–30 16 13.9 52 17.6 48.1 25.1 9.2 68 16.6 30–40 67 58.3 142 209 51.0 40–50 24 20.9 74 98 23.9 Above 50 8 7.0 27 35 8.5 Location Urban 67 58.3 197 66.8 264 64.4 Peri-urban 47 40.9 94 31.9 141 34.4 Rural 1 0.9 4 1.4 5 1.2 Education Level Secondary 1 0.87 1 0.34 2 0.49 Tertiary 114 99.13 294 99.66 408 99.51 Business Type* Consultation & treatment 52 45.2 174 59.0 226 55.1 Diagnostic Center 4 3.5 10 3.4 14 3.4 Pharmacy 59 51.3 111 37.6 170 41.5 Business size Solo Entrepreneur (1) 22 19.1 38 12.9 60 14.6 Micro Business (2–9) 75 65.2 201 68.1 276 67.3 Small Business (10–49) 18 15.7 56 19.0 74 18.1 Use an information system to keep track of business income and expenditures No 24 20.9 47 15.9 71 17.3 Yes 91 79.1 248 84.1 339 82.7 Financial need Never/Rarely 39 33.9 94 31.9 133 32.4 Daily/Weekly 28 24.4 76 25.8 104 25.4 Monthly 21 18.3 69 23.4 90 22.0 No specific pattern 27 23.5 56 19.0 83 20.2 Digital loan uptake No 94 81.7 249 84.4 343 83.7 Yes 21 18.3 46 15.6 67 16.3 Perception of digital loan uptake Negative 51 44.4 127 43.1 178 43.4 Neutral 32 27.8 94 31.9 126 30.7 Positive 32 27.8 74 25.1 106 25.9 Risk level assessment Low risk taker 41 35.7 90 30.5 131 32.0 Average risk taker 44 38.3 123 41.7 167 40.7 High-risk taker 30 26.1 82 27.8 112 27.3 * Statistically significant difference with p = 0.035 3.1.4 Loan history and health SMEs readiness for digital loan adoption Figure 2 shows a flowchart on the loan uptake and readiness among the entire study sample. The left arm shows that out of the 410 respondents, 39% indicated that their businesses had ever taken a loan. Bank loans were the most frequently cited source of credit among the respondents (58%) followed by digital loans (42%). Other sources of loans included savings and credit cooperative societies (14%) and informal loans from family and friends (8%) (not in figure) . Within the category of respondents who reported that they had taken a business loan, the proportion of female respondents who had taken a bank loan was 48%, whereas this proportion among male respondents was higher with 61% (p = 0.12) (not in figure). Among the 158 respondents who had taken loans, 42% had taken digital loans. The larger proportion (67%) of these respondents with a history of taking digital loans reported to be satisfied with this loan product. Additionally, slightly more than half (52%) of the respondents with a history of taking a digital loan, had this loan taken more than once. The right arm shows that among the 61% of the respondents without a loan history, almost half (44%) of the businesses experienced an acute financial need at least monthly in the year preceding the survey. Almost all respondents (86%) who had an acute business financial need at least once a month received their revenues through digital financial platforms, suggesting that they are eligible to receive digital loans. Supplementary table 2 describes the background characteristics of this particular group, which do not differ significantly from the overall study sample. Supplementary table 3 shows the gender distribution for each block of the flowchart. While we found no gender gap in overall loan uptake, we did find that among respondents with a loan history, women had more frequently taken digital loans compared to men (p = 0.3). 3.1.5 Factors associated with the uptake of digital loans Table 3 shows the logistic regression model with the uptake of digital loans as the dependent variable. We found that those with monthly financial needs were significantly more likely to take digital loans (OR of 4.22) – compared to those with ‘never/rarely’ financial needs. Regarding behavioral characteristics, we found that risk attitude and perception towards digital loans were associated with digital loan uptake. Respondents who perceived themselves to be high-risk takers had higher odds of taking a digital loan (OR of 2.32) – compared to those with a self-perceived low risk profile. Having a positive perception towards digital loans also increased the likelihood of taking a digital loan (OR of 3.14) – compared to those with a negative perception. Table 3 Logistic regression on the uptake of digital loans (N = 410) Odds ratio (95% CI) P-value Background characteristics Gender (Men) Ref Ref Women 0.77 (0.41–1.44) 0.416 Age category (18–30) Ref Ref 30–40 years 0.70 (0.32–1.54) 0.377 40–50 years 0.99 (0.38–2.56) 0.983 Above 50 years 2.72 (0.87–8.49) 0.085 Years in business 1.02 (0.97–1.07) 0.476 Location business (Urban) Ref Ref Peri-urban 1.40 (0.77–2.56) 0.269 Rural 1.86(0.16–21.28) 0.619 Business size (Solo Entrepreneur (1)) Ref Ref Micro Business (2–9 employers) 1.28 (0.57–2.84) 0.547 Small Business (10–49 employers) 0.64 (0.17–2.38) 0.507 Business type (Consultation & treatment) Ref Ref Pharmacy 1.90 (0.95–3.76) 0.068 Diagnostic Centre 1.04 (0.12–9.32) 0.970 Financial need (Never / rarely) Ref Ref Daily/weekly 2.28 (0.94–5.52) 0.067 Monthly 4.22 (1.74–10.24) 0.001** No specific pattern 1.31 (0.49–3.53) 0.591 Behaviour characteristics Risk attitude (Low risk taker) Ref Ref Average risk taker 1.54 (0.69–3.44) 0.295 High-risk taker 2.32 (0.99–5.41) 0.052* Perception towards digital loans (Negative) Ref Ref Neutral 1.52 (0.70–3.34) 0.293 Positive 3.14 (1.46–6.79) 0.004** Cons 0.03 (0.01–0.12) 0.000 Number of observations 410 Pseudo R 2 0.14 *: p < 0.05 (statistically significant) **: p < 0.01 (highly statistically significant) ***: p < 0.001 (very highly statistically significant) 3.2 Real world loan history data – Medical Credit Fund 3.2.1 Study sample description In total, we analyzed loans disbursed to 850 facilities, of which the majority were male owned (74%) (Table 4 ). More than half of the sample were level 2 facilities (dental clinics, diagnostic centers, dispensaries, eye clinics, pharmacies), and more than 80% were situated in urban areas. Overall, 70% of the facilities had taken a digital loan and 30% a term loan. Table 4 Facility characteristics (N = 850) Female-owned (19.8%, n = 168) Male-owned (74.1%, n = 630) Partnership 3 (6.1%, n = 52) Total (100%, N = 850) Facility type 1 % (n) % (n) % (n) % (n) Level 1 0.6 (1) 1.3 (8) 0 (0) 1.1 (9) Level 2 57.7 (97) 53.8 (339) 34.6 (18) 53.4 (454) Level 3 36.3 (61) 36.8 (232) 46.2 (24) 37.3 (317) Level 4 4.2 (7) 7.3 (46) 19.2 (10) 7.4 (63) Other 1.2 (2) 0.8 (5) 0 (0) 0.8 (7) Location % (n) % (n) % (n) Urban 84.5 (142) 80.6 (508) 90.4 (47) 82.0 (697) Rural 15.5 (26) 19.4 (122) 9.6 (5) 18.0 (153) Loan type * , 2 Digital 61.3 (103) 71.3 (449) 86.5 (45) 70.2 (597) Term 38.7 (65) 28.7 (181) 13.5 (7) 29.8 (253) 1 level 1 (community health posts), level 2 (dental clinics, diagnostic centers, dispensaries, eye clinics, pharmacies), level 3 (health center, maternity home, outpatient center), level 4 (primary hospital), “other” (equipment suppliers and support service companies). * p-value ≤ 0.05 2 Some facilities have both loan types 3 The majority of the partnerships are a duo-ownership of a male and female. One facility has ten owners, two facilities have five owners, three facilities have four owners, and four facilities have three owners. Table 5 describes the characteristics of loan disbursements stratified by loan type. In total 6,350 loans have been disbursed, with most of them being digital loans (n = 5,939). This distribution is uneven because by design digital loans have a shorter tenor to e.g. finance working capital and therefore customers usually come back (with an average of 8 loans per customer in this dataset). The disbursement of term loans decreased over time, as between 2011–2015 there were 275 loans disbursed while between 2021–2024 only ten loans. This can be explained by the fact that smaller term loans were replaced by digital loans since 2017, which also explains the increase in average loan amount for term loans. The large standard deviations in loan size for both loan types demonstrate there are huge variances between loans. Supplementary Figs. 1 & 2 provide an overview of the number and average first loan amount per year. Table 5 Disbursed loans between 2011–2024 by MCF (N = 6,350) Digital (N = 5,939, 93.6%) Term (N = 404, 6.4%) Total (N = 6,343, 100%) Number of loans % (n) % (n) % (N) 2011–2015 n/a 100 (275) 100 (275) 2016–2020 94.7 (2,145) 5.3 (119) 100 (2,264) 2021–2024 99.7 (3,794) 0.3 (10) 100 (3,804) Average loan amount in EUR µ (sd) µ (sd) µ (sd) 2011–2015 n/a 14,814 (28,530) 14,814 (28,530) 2016–2020 5,210 (13,307) 42,564 (63,862) 7,174 (21,184) 2021–2024 7,534 (19,210) 98,607 (103,848) 7,773 (20,379) Interest rate in % µ (sd) µ (sd) µ (sd) 2011–2015 n/a 17.5 (2.7) 17.5 (2.7) 2016–2020 23.2 (6.2) 15.4 (3.3) 22.8 (6.4) 2021–2024 34.8 (14.9) 22.6 (3.4) 34.8 (14.9) Repayment period in days µ (sd) µ (sd) µ (sd) 2011–2015 n/a 508.6 (445.4) 508.6 (445.4) 2016–2020 114.5 (175.3) 867.6 (521.6) 154.1 (267.5) 2021–2024 102.0 (132.5) 222.6 (120.0) 102.4 (132.6) 3.2.1 Gender differences in loan characteristics We examined gender differences in the first loan amount for both term and digital loans (Table 6 ). For term loans we found a clear gender gap with partnerships receiving significantly higher first loan amounts (EUR 53,799) compared to females (EUR 10,400) and males (EUR 18,321). Since facility characteristics likely influence loan size, we conducted an OLS regression on the log of the first term loan amount, controlling for facility level and location (see Supplementary table 4). After adjusting for these factors, the significant gender association remained, but small, and facility level 4 emerged as the most likely to secure the largest first term loans. No gender differences were found in the number of repeated term loans. For digital loans, there was also a significant gender difference in the size of the first loan, with partnerships receiving the largest amount (EUR 10,400) followed by males (EUR 4,671) and females receiving the lowest amount (EUR 2,793). After controlling for facility characteristics in an OLS regression (see Supplementary table 5), the significant difference between partnerships and females remained but the gender difference disappeared and here facility level 4 was also most likely to secure the largest first digital loan. We used the growth in loan amount indicator as a proxy for digital revenue growth. More than 50% of the female-owned facilities experienced high growth, which was borderline significant (p-value = 0.08) different from the other two groups. A multinominal regression (see Supplementary table 6) confirmed a stronger significant association (p-value = 0.006) after controlling for facility characteristics indicating that women were more likely to experience high growth. Table 6 Gender differences in loan characteristics Term loans (N = 404) 1 Female (n = 109, 27.0%) Male (n = 272, 67.3%) Partnership (n = 23, 5.7%) µ (sd) µ (sd) µ (sd) First loan amount** 10,400 (26,395) 18,321 (40,267) 42,187 (53,799) Repeated loans 1.7 (0.9) 1.5 (0.8) 2.6 (1.9) Digital loans (N = 5,939) 2 Female (n = 1,126, 19.0%) Male (n = 4,397, 74.0%) Partnership (n = 416, 7.0%) µ (sd) µ (sd) µ (sd) First loan amount*** 2,793 (7,187) 4,671 (14,261) 10,400 (18,691) Growth in loan amount 1,138 (5,382) 1,316 (12,255) 3,809 (24,119) Growth in loan categories* No growth (< 0), % (n) 23.8 (40) 22.4 (141) 30.8 (16) Moderate growth (0–7,499), % (n) 23.2 (39) 33.2 (209) 28.9 (15) High growth (7,500–100,527), % (n) 53.0 (89) 44.4 (280) 40.4 (21) Repeated loans 9.0 (11.6) 8.8 (11.2) 8.5 (7.9) 1 ANOVA test is performed on log-transformed value and Kruskal-Wallis test on the repeated loans indicator 2 ANOVA test is performed on log-transformed value and Chi 2 test on categorical variable * P-value = 0.08 ** P-value < 0.05 *** P-value < 0.000 but significant difference disappears after controlling for facility level and location DISCUSSION This study applied a mixed method approach to evaluate the readiness, perspectives and gender disparities in (digital) loan characteristics among health SMEs in Kenya. We interviewed 24 and surveyed 410 health SMEs owners respectively. We identified a high demand for loans among Kenyan health SMEs. Additionally, we used real-world loan history data from 850 health SMEs in Kenya to evaluate gender differences in digital and term loans. We identified a gender gap in the first disbursed term loan amount, with women-owned businesses receiving the lowest amounts and partnerships the highest. In contrast, this gender gap disappeared for digital loans. Both the survey findings and real-world loan history data demonstrate that female entrepreneurs are digitally financially included. This study adds to the evidence on health financing options for health SMEs to promote financial inclusion for female health entrepreneurs. The gender distribution in the survey and administrative loan data was comparable, suggesting that men and women have similar access to finance. However, the sample indicates that most health SMEs are male owned. This unequal gender distribution in ownership, while the health workforce is dominated by women, is likely associated with another gender gap linked to differences in career opportunities. This gender distribution of SME ownership is also documented in a 2021 survey conducted by the Kenya Bankers Association (70%) 57 , suggesting that, in terms of gender, our sample is well representative for the country. Additionally, our IDI and survey sample may appear biased due to the inclusion of only higher-educated respondents. However, the target population—health sector entrepreneurs—typically requires tertiary education to establish and manage such enterprises effectively. Thus, this reflects the population’s characteristics rather than sampling bias. The survey results identified that four in every ten health SME owners had ever taken a loan; with most opting for bank loans, while fewer than half had used digital loans. The majority of the surveyed health SME owners had not taken a loan for their businesses, while more than half of them reported an acute business financial need, at least monthly. The qualitative findings highlight poor reputation and low trust in digital lenders among health SME owners, yet the survey findings showed high satisfaction among those who had used digital loans, suggesting mistrust is stronger among those without direct experience. A study by the Consultative Group to Assist the Poor (CGAP) also found that lack of trust hindered digital loan uptake among SMEs 58 . Additionally, the survey findings also showed that high risk-takers, those with monthly acute business financial needs, and those with a positive perception of digital loans were more likely to use them. These findings demonstrate the importance of effective information dissemination by lenders to build trust. A study conducted in Kenya documented that digital loan uptake was influenced by credit information sharing, alongside financial technology and the cost of credit 59 . In the real-world loan history data, a small proportion (6%) of the included health SMEs were classified as partnerships (with the majority 50/50 male-female ownership). Our findings show that they secured the highest first loan amounts and experienced substantial growth in their digital loan journeys. A possible explanation for this finding could be that partnerships are owned by multiple stakeholders and may therefore in general dare to take more risks than single lenders. In addition, in our data 20% of partnership-owned health SMEs are classified as a level 4 facilities, compared to just 5% among female- and male-owned health SMEs which is an explanatory factor for a higher loan uptake. Another study that examined the gender gap for SMEs (not sector specific) found that firms with female ownership are less likely to use formal bank credits than firms with male owners, but this gender gap disappears when controlling for observable firm characteristics, like size 60 . This is similar to what we found in our analyses and suggests that the disparity in access to finance is driven by differences in firm characteristics rather than the owner’s gender itself and can therefore also be referred to as an unconditional gender gap. The facility characteristics (e.g. size of the business) determine the access to finance which explain why women-owned companies tend to be less likely to have access to finance because they tend to own smaller clinics. This portrays a gender gap that is not per se directly linked to access to finance. A potential explanation for the lower initial loan amounts disbursed to women is their tendency to be less risk seeking compared to men. Women could be less likely to accept the highest loan offers, opting instead for a safer, smaller (initial) loan size, while men are more inclined to take on larger, riskier loans. This gender difference in risk attitude is extensively reported in the literature 61–63 and, in this context, may be linked to the fact that women often manage multiple responsibilities, such as family care, making them less likely to take risks in their professional life. However, our findings suggest that women-owned health SMEs benefit significantly from digital loans, with over 50% experiencing substantial growth in digital revenues after taking a MCF digital loan. This suggests that digital loans may play a key role in their business success. Nevertheless, this conclusion should be interpreted cautiously, as it is based solely on digital revenue growth, without insights into the broader distribution of their cash and digital earnings. Strengths and Limitations We used IDIs to design the survey, which is a strength of the study as we have optimized context specific information. Another key strength of this study is that we analyzed unique real-world data spanning a broad time window (2011 – 2024); the loan-history data is not self-reported but drawn from administrative records, making it highly reliable. We acknowledge the following limitations. Firstly, the survey findings were self-reported which may have led to selection and information bias. Secondly, while most IDIs were conducted in person, a subset was conducted via telephone. This mode of communication may have limited the depth of responses compared to in-person interviews, potentially influencing the richness of the findings. Thirdly, while the growth in digital revenues was not the primary focus, it would have been beneficial to compare digital revenue growth with health SMEs that did not receive loans to better quantify the loan product’s impact. Lastly, we only had data on approved loans, while including data on rejection rates and requested loan size would have provided additional insights. To address this gap, we conducted surveys with health SMEs without a loan history with MCF to better understand their landscape and readiness for digital loans. Conclusions and policy implications This study highlights a significant financing gap among health SMEs in Kenya, with notable difference in loan uptake and amounts highly favoring partnership-owned health SMEs. Despite comparable access to finance between men and women, most health SMEs remain male-owned, reflecting broader gender disparities in business ownership. Expanding social support networks for female entrepreneurs could foster trust and encourage greater participation of women in the financial credit market. Given the low trust in digital lenders, especially among those without prior experience, improving information dissemination is crucial to build confidence in digital financial products. This study’s findings underscore the need for tailored loan products and strategies that account for firm characteristics, risk attitudes and gender-specific barriers to ensure equitable access to finance. Additionally, financial literacy training for female entrepreneurs could help overcome risk aversion and increase their willingness to seek external financial support during times of significant financial need. Declarations Acknowledgements We gratefully acknowledge the funding of Swedfund International AB and the Netherlands Ministry of Foreign Affairs to carry out this study. We also thank all participating healthcare facilities and data collectors for their valuable work. Author contributions CD conceptualized the study. 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Gendered Analysis of Institutional and Government Small and Medium Enterprises. https://kenya.oxfam.org/latest/publications/gendered-analysis-institutional-and-government-small-and-medium-enterprises (2022). Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYFILES.docx Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2025 Read the published version in Scientific Reports → Version 2 posted Editorial decision: Revision requested 17 Feb, 2025 Editor assigned by journal 17 Feb, 2025 Submission checks completed at journal 05 Feb, 2025 First submitted to journal 04 Feb, 2025 You are reading this latest preprint version Show more versions 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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Consultancy","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Siyumbu","suffix":""},{"id":412785532,"identity":"22f94fb1-ef6d-420d-9c85-f9112c5de317","order_by":3,"name":"Ines van Zuijlen","email":"","orcid":"","institution":"PharmAccess Foundation","correspondingAuthor":false,"prefix":"","firstName":"Ines","middleName":"van","lastName":"Zuijlen","suffix":""},{"id":412785533,"identity":"1f5f478b-ac9f-4d11-b8a4-6b5e58eb7db7","order_by":4,"name":"Nick Mutegi","email":"","orcid":"","institution":"Medical Credit Fund","correspondingAuthor":false,"prefix":"","firstName":"Nick","middleName":"","lastName":"Mutegi","suffix":""},{"id":412785534,"identity":"7d92e92a-eb03-45ee-944f-6fbd3ec38e85","order_by":5,"name":"Sabine Verschuur","email":"","orcid":"","institution":"Medical Credit Fund","correspondingAuthor":false,"prefix":"","firstName":"Sabine","middleName":"","lastName":"Verschuur","suffix":""},{"id":412785535,"identity":"0b2a4bf4-7aeb-4b92-ae3f-a271bcda1188","order_by":6,"name":"Tobias Rinke de Wit","email":"","orcid":"","institution":"PharmAccess Foundation","correspondingAuthor":false,"prefix":"","firstName":"Tobias","middleName":"Rinke","lastName":"de Wit","suffix":""},{"id":412785536,"identity":"e214d938-88bc-4c8b-a9dc-1e99192561e2","order_by":7,"name":"Dorien Mulder","email":"","orcid":"","institution":"Medical Credit Fund","correspondingAuthor":false,"prefix":"","firstName":"Dorien","middleName":"","lastName":"Mulder","suffix":""},{"id":412785537,"identity":"adc7f119-ecae-4de8-8cd0-808c0b4e3a99","order_by":8,"name":"Wendy Janssens","email":"","orcid":"","institution":"Vrije Universiteit","correspondingAuthor":false,"prefix":"","firstName":"Wendy","middleName":"","lastName":"Janssens","suffix":""},{"id":412785538,"identity":"5c695b80-5367-413b-8a56-160fd59d7e6b","order_by":9,"name":"Charlotte Dieteren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYHACgwMQmvkAMwlaEkA0WwLxWhggWngMiNPC38C88cDHHzb5/NI93x4X1NyL5m8//PDhjz8Mcub9C7BqkTjAVnBwRkKa5cw5Z7cbzzhWnDvjTJqxMW8bg7HMjQc4XMVjcJgn4bCBwY3cbdI8bAm5DTd42KQZGxgSZ0gcwK3lT8J/A/sbOc+kef4l5M6/wcP+E+gw/FoYEg4YGEjksEnztiXkbgDawgBEiTP4G7D75TDQLz1pyQYSN9LMjXn7EnI3Av0C1CthLCGBI8Tamzd/+GFjZ8A/I/nZY55vCbnzjh9++PHHHxs5CX7sDmNAigs2FOuBKAG7FiTAhsbHZcsoGAWjYBSMNAAA+qddIQu0bRMAAAAASUVORK5CYII=","orcid":"","institution":"PharmAccess Foundation","correspondingAuthor":true,"prefix":"","firstName":"Charlotte","middleName":"","lastName":"Dieteren","suffix":""}],"badges":[],"createdAt":"2024-10-01 12:40:12","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-5187398/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-5187398/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-23045-4","type":"published","date":"2025-11-11T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75896250,"identity":"f9d61f94-af28-48c8-ae8d-d31c045f33dc","added_by":"auto","created_at":"2025-02-10 10:32:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":518016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe five study counties where data was collected\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5187398/v2/3315fe05c49e70fde2516326.png"},{"id":75896252,"identity":"81729f57-fd95-4ea5-8c08-82c648df23d3","added_by":"auto","created_at":"2025-02-10 10:32:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":165796,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of loan uptake and readiness among Health SMEs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5187398/v2/f68f08a5cd793da5c1ccfeec.png"},{"id":96105114,"identity":"c7a9a91d-4d97-456a-be56-e1e5426ca8c0","added_by":"auto","created_at":"2025-11-17 16:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2687850,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5187398/v2/296fdfc2-0f91-42d8-b0aa-fc6ddf38bf79.pdf"},{"id":75896249,"identity":"4e5f689f-875f-48b8-a0cf-6f8dfc6c72f9","added_by":"auto","created_at":"2025-02-10 10:32:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":63449,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYFILES.docx","url":"https://assets-eu.researchsquare.com/files/rs-5187398/v2/aa65947430d749b60b363baa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Disparities in Financial Inclusion: the Potential of Digital Loans in Empowering Female Health Entrepreneurs in Kenya","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn Sub-Saharan Africa (SSA), approximately 50% of all health services are delivered by the private healthcare sector, predominantly composed of small- and medium-sized enterprises (SMEs) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Ensuring high-quality healthcare provision is essential for fostering human capital development and alleviating poverty \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Addressing the prevalent challenges faced by health SMEs in SSA - such as inadequate infrastructure, shortages of skilled medical personnel, and poor service delivery - is impossible without access to financing \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Supply-side healthcare expenditures are linked to improved population health outcomes in SSA, including higher life expectancy at birth and lower infant mortality rates \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Loans represent a significant source of capital; however, the disbursement of loans to owners of health SMEs remains challenging. Generally, health SMEs encounter obstacles in securing loans from the traditional lending institutions compared to larger firms because of a limited banking history, limited collateral, and perceived high risk of the sector \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe importance of SMEs businesses in SSA should not be underestimated as they are substantive contributors to the economic growth and development of countries \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The World Bank has documented that SME businesses contribute to 50% of global employment and account for at least 40% of the gross domestic product (GDP) in emerging economies worldwide \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Similar findings have been documented in SSA where SMEs contribute over 50% of the GDP \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Africa has a higher number of female business owners compared to male owners, accounting for 58% of the continent\u0026rsquo;s self-employed population \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, despite this apparent progress, female entrepreneurs across SSA continue to earn lower profits than their male counterparts, with an average shortfall of 34% \u003csup\u003e11\u003c/sup\u003e. According to the Global Findex of 2021, the region holds a 12% financial gender gap \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWomen face significant barriers in starting and growing their businesses compared to their male counterparts \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These barriers are multifaceted, encompassing sociocultural factors such as household and child-care responsibilities, educational deprivation and restricted autonomy \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Additionally, women often encounter barriers in access to finance, including limited collateral for loans due to lack of control over fixed assets, limited credit history, low financial literacy and inadequate business training \u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.These constraints significantly hinder their access to credit and negatively impact the monthly turnover of female entrepreneurs \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Financial inclusion has been identified as one of the factors determining the success of entrepreneurs and their businesses \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile some studies suggest that financial institutions may not explicitly discriminate against women in their lending practices \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, the structural requirements of financial systems \u0026ndash; such as collateral demands \u0026ndash; disproportionately disadvantage women. This underscores that gender-specific barriers, rather than outright bias, are critical in shaping the financial inclusion of female entrepreneurs. These challenges are compounded by limited business networks and perpetuate a cycle of disadvantage, confining many women to smaller businesses in the informal sector. Such businesses typically require low capital investments, have limited growth potential, and yield lower returns on investments \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Furthermore, while there is a common agreement in the economics literature that females tend to be more risk averse than their male counterparts, a meta-analysis showed that these gender differences are not so dominant as it appeared in less than ten percent of the published papers \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, the evidence in countries with a higher inherited gender-bias is homogenous, showing that women less often apply for a loan which can be driven by the belief that their credit application will be denied or their internal risk attitude which may be fed by low financial literacy \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe emergence of digital finance technologies opened new avenues for financial services to SMEs \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, thereby advancing digital financial inclusion (DFI). DFI seeks to ensure that individuals and businesses, especially those underserved or excluded from traditional financial systems, have access to a comprehensive range of financial services \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. For example, FarmDrive in Kenya delivers financial services to unbanked and underserved farmers by using mobile phone technology, alternative credit scoring, and machine learning\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Specifically for women, DFI facilitates economic empowerment through financial autonomy, lack of collateral requirements, protection over their finances from appropriation by others, and greater flexibility by overcoming the barriers of time and place \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Consequently, previously excluded rurally located SMEs now have the potential to be financially included, leading to economic growth opportunities. Mobile money is the key driver of DFI and a ground-breaking innovation in Kenya and has grown exponentially since its launch in 2007. By 2019, its user-base had expanded to 33\u0026nbsp;million users, representing 69% of the Kenyan population \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In 2021, over 60% of Kenyan SMEs used mobile money platform for business transactions \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite the expansion of DFI in SSA, health SMEs continue to experience growth constraints, with limited access to finance cited as the primary barrier and consequently suboptimal adoption of DFI \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. While existing literature explores the perceptions of SME owners across various sectors regarding the use and adoption of financial services \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, there remains scarcity of evidence on the specific impact of digital finance on SME financial inclusion in Africa. A recent study has shown that digital finance significantly enhances SME financial inclusion on the continent \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. However, to our knowledge, no research has yet examined how digital finance has influenced financial access for health SMEs, particularly its potential benefit for female-owned health SMEs. This study seeks to address this gap by describing the digital loan landscape for health SMEs in Kenya and identifying factors that influence digital loan uptake among Kenyan health SMEs. Furthermore, we evaluate the gender distribution in real-world loan history data among 850 health SMEs in Kenya over the period 2011\u0026ndash;2024.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003eThis study comprises of two parts and utilizes both primary and secondary data, consisting of in-depth interviews (IDIs), cross-sectional quantitative surveys and administrative loan data. The primary mixed-methods approach began with an exploratory qualitative phase whose findings informed the development of the tools for the subsequent quantitative phase. The secondary administrative data includes real-world loan history data of health SMEs between 2011\u0026ndash;2024.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 General study setting\u003c/h2\u003e \u003cp\u003eThis study was conducted in Kenya, a lower-middle-income-country located in East Africa. The 2022 population estimate was 55\u0026nbsp;million people \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The 2021 GDP per capita was approximately 2,061 US dollars\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and the total health expenditure was 4.55% of the GDP \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The main revenue sources for health are the government, private health insurance, out-of-pocket expenditure, and development partners funds \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The inflation rate, based on consumer price indices, as of May 2020 was 5.5% rising to 6.8% in September 2023, with fluctuations in between \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. According to a United Nations Development Program (UNDP) report, during the COVID-19 pandemic, SMEs in the country suffered negative impacts to their operations and revenues with 67.9% reporting severely reduced operations \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Kenya has a dual system for health service delivery. This comprises of government health facilities (46%) and the private for-profit and not-for-profit (faith-based) health facilities \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Supplementary file 1 provides more background on the health SME landscape in Kenya.\u003c/p\u003e \u003cp\u003eIn Kenya, the mobile money system, notably through platforms like M-Pesa, plays a significant role in financial inclusion. It allows individuals, particularly in rural or peri-urban areas, to access banking services without needing a traditional bank account. This system is widely used across different demographics, with higher adoption rates in urban areas, although rural and peri-urban areas have also seen significant uptake. Mobile money is especially popular among lower- to middle-income groups, including small business owners, youth, and women \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The system enables transactions such as money transfers, bill payments, and access to digital loans. In this study, digital loans are integrated into the mobile money system, where users can access small, short-term loans directly through their mobile phones, often with minimal documentation via an USSD number or an app. The service has become a key feature of financial services in Kenya, particularly for individuals with limited access to conventional banking services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cross-sectional mixed methods approach\u003c/h2\u003e \u003cp\u003eThe following sub paragraphs elaborate on the primary data collection consisting of IDIs and a cross-sectional quantitative survey.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Specific study setting\u003c/h2\u003e \u003cp\u003eThe study was conducted across five counties in Kenya (Nairobi, Kiambu, Kisumu, Mombasa and Makueni), highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These counties were purposively selected to ensure a high number of health SMEs, geographical representativeness, and a sufficient level of mobile money accounts utilization. As such, Nairobi County was included due to the high number of health SMEs. The former Nyanza region (Kisumu County), Coast (Mombasa County), Central (Kiambu County) and the Eastern region and peri-urban (Makueni County) were selected for geographical representativeness and because they have the highest population-level utilization of mobile money accounts within their regions ranging between 85% and 94% \u003csup\u003e50\u003c/sup\u003e. This county distribution was retained for both the qualitative and quantitative phases of this study. Supplementary table 1 provides more information on key county attributes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Study population and sampling approach\u003c/h2\u003e \u003cp\u003e Health SMEs located within the selected five study counties were sampled based on two inclusion criteria: the health SME must be private or faith-based and had been in operation for at least one year at the time of contact. The sampled health SMEs included healthcare facilities (medical clinics, dental clinics or specialist clinics), retail pharmacies, stand-alone diagnostic centers, or facilities providing any combination of these services. One health facility or pharmacy per study county that had information of the owner was selected from open national databases of licensed health facilities and pharmacies maintained by the professional regulators \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Subsequently, for the IDIs we applied purposive and snowball sampling to ensure the inclusion of sufficient female respondents. The anticipated sample size for the IDIs was 24 participants spread across the five study counties, with provision to continue sampling until thematic data saturation was reached. The scope of this sample covered 14 healthcare facilities (including standalone diagnostic centers) and ten pharmacies. The number of respondents allocated to each county facilitated sampling of perspectives from these respective regions. For the survey respondents, we adopted a sampling approach that ensured health SMEs were distributed proportionately across the selected study counties. We applied a sampling technique with probability proportional to size for sample allocation. Stratification was carried out based on counties and sub-counties. Researchers made phone calls to business representatives to explain the study\u0026rsquo;s objectives and schedule interviews. If a participant agreed, the participant provided the business location for an in-person interview.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Sampling size calculation\u003c/h2\u003e \u003cp\u003eThe sample size for the study was calculated using the SurveyMonkey sample size calculator, which considers a 95% confidence level and a 5% margin of error \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Based on a total of 7,671 private and faith-based health SMEs, and 5,032 private pharmacies in Kenya, an initial sample size of 373 respondents was estimated. This was increased by a factor of 10% to 410 respondents to accommodate for non-response. To ensure representativeness, we pursued a proportional distribution of the 410 health SMEs between healthcare facilities and pharmacies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Data collection\u003c/h2\u003e \u003cp\u003eThe IDIs were conducted using a semi-structured interview guide and the overarching objective was to gain insights into reasons for (not) taking a loan. The guide was inspired by an adaptation of the PESTEL analysis framework \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. This framework facilitated exploration of factors related to Policies, Economic climate, Social attributes, Technology, Environmental and Legal factors and has been used before to assess the external business environment for fintech providers \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. It provided a suitable framework for categorizing key determinants that influence decisions regarding the usage of digital financial services. The interview guide was piloted (n\u0026thinsp;=\u0026thinsp;2) in Nairobi and Kisumu, and since no significant changes were required, these pilot respondents were included in the study sample. The IDIs were conducted both face-to-face and via telephone. The venue for the physical interviews was the health SME premises. The interviews were carried out in English and commenced after obtaining informed consent; they lasted between 30 and 60 minutes. Upon consent, the interviews were audio recorded, and participants received a numerical code to ensure anonymity during the analyses.\u003c/p\u003e \u003cp\u003eThe survey questionnaire was subsequently developed informed by the findings of the IDIs to quantify the generated insights of the IDIs. The background characteristics included were respondents\u0026rsquo; gender, age, education level, health SME type, location and business duration in years to capture maturity and operational experience. In addition, fed by the qualitative insights and informed by the PESTEL framework, we included e.g. level of awareness about digital loan products, financial behavior and risk-taking behaviors (adapted from FinaMetrica \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e). Risk attitude was measured on a scale of 1 to 5 with the questions: \u0026ldquo;How willing are you to take financial risks?\u0026rdquo; and \u0026ldquo;When faced with a major financial decision, are you more concerned about the possible losses or the possible gains?\u0026rdquo; Respondents were categorized into high, medium, or low-risk takers. High-risk takers were defined as those who expressed a willingness to take significant financial risks, while low-risk takers were more cautious and concerned about potential losses. The questionnaire was digitized and administered using mobile phones through face-to-face interactions.\u003c/p\u003e \u003cp\u003e The data collectors underwent prior training to familiarize them with the tool and ensure adherence to research ethics during data collection. Participants did not receive any incentive to participate in this study. The data collected was transmitted in real-time to a secure encrypted cloud-based database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Data analysis\u003c/h2\u003e \u003cp\u003eThe recorded IDIs were transcribed in Microsoft Word \u0026copy; and anonymized using numerical codes. A coding matrix was created corresponding to the PESTEL framework; the overarching thematic areas derived from this framework that guided the development of the interview guide were retained for the analysis. As such, deductive data analysis was used initially. This deductive approach was complemented by an inductive analysis approach whereby subthemes were allowed to emerge during the data coding process. The themes and subthemes were iteratively compared and refined. To increase the rigor and credibility of the findings, the research team held peer debriefing sessions throughout the data analysis process.\u003c/p\u003e \u003cp\u003eDescriptive statistics of the survey data were generated for all variables of interest. Cross-tabulations were generated to gain insights into categorical variables associated with loan uptake. Chi-squared tests were conducted to determine the statistical significance of these relationships. The level for significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To safeguard against inflated type 1 errors, Bonferroni-corrected pairwise comparisons were applied. A logistic regression model was applied to gain insights into the factors that were associated with a digital loan uptake. We examined the robustness of the findings by evaluating the model's goodness-of-fit, examined multicollinearity using diagnostic measures, and addressed potential confounding variables. All analyses were conducted in STATA version 15.0.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Secondary data analysis on MCF loan history data\u003c/h2\u003e \u003cp\u003eThe following sub-paragraphs elaborate on the secondary administrative data provided by the Medical Credit Fund (MCF).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Medical Credit Fund (MCF)\u003c/h2\u003e \u003cp\u003eMedical Credit Fund (MCF) is a fund dedicated to providing loans to health SMEs in SSA. MCF was founded in 2009 with the objective to overcome the access to finance challenges experienced by Health SMEs and has been active in Ghana, Kenya, Nigeria, Tanzania and Uganda. MCF combines loans with technical assistance to support healthcare providers to improve their quality of services and invest in their businesses. Since its inception, MCF has provided over 10,000 loans worth EUR 165\u0026nbsp;million to more than 2,100 health SMEs. The majority of MCF borrowers are healthcare providers like health centers, clinics, (small) hospitals and pharmacies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 MCF loan products\u003c/h2\u003e \u003cp\u003eInitially, MCF offered only term loans: traditional loans with security and a fixed repayment schedule provided either directly or through partnerships with local banks. Loan sizes varied between a few hundred and 2\u0026nbsp;million Euro equivalent in Kenya Shillings, with tenors from 6 months up to 96 months. The loans were used by clients to finance working capital or invest in infrastructure and equipment.\u003c/p\u003e \u003cp\u003eWith the rise in mobile money use in Kenya and further accelerated by the recent COVID-19 epidemic, MCF developed a digital loan product. To determine the health SMEs\u0026rsquo; eligibility and maximum loan amount, MCF uses a health SME\u0026rsquo;s recent history of mobile money (M-PESA) revenues from patients (payments made by patients). The product is used to finance working capital or equipment and is typically smaller (between EUR 100 and 200,000) compared to a more traditional term loan and repaid over a shorter time span of three to six months by automatic real-time deductions from mobile money payments in the facility. The loan does not require any collateral because a share of ongoing mobile money revenues, instead of assets, are used as the basis for providing the loan. The loan product was launched in 2017 in Kenya and has been growing exponentially since then. It is currently being introduced in Ghana and Tanzania.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Study setting\u003c/h2\u003e \u003cp\u003eMost of Kenyan MCF clients are based in urban areas, with the large majority being in Nairobi and Kisumu. MCF aims to reach the small and medium segment of the health sector: 50% of MCF clients have less than 10 staff members and receive less than 500 patient visits per month, mostly patients from lower income groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Study sample\u003c/h2\u003e \u003cp\u003eFor this study we included all term and digital loans provided in Kenya by MCF. We removed outliers by setting a threshold of EUR 800,000 for term loans, resulting in four exclusions, and EUR 200,000 for digital loans, leading to three exclusions. All financial values are expressed in EUR and the applied conversion rate was monthly (i.e. the rate at the end of the month for closed months and the end of the previous month for open months). We excluded observations where the facility owner\u0026rsquo;s gender was unknown (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Variables of interest\u003c/h2\u003e \u003cp\u003eThe key variable of interest is the ownership of the facilities, categorized into three options: female owned (100%), male owned (100%), or partnership (typically 50/50 male female owned). The loan type was a binary variable, either digital or traditional loans. The digital loan amount is based on the mobile money revenues from patients, and therefore we created a \u003cem\u003e\u0026ldquo;growth in loan amount\u0026rdquo;\u003c/em\u003e variable to indicate the difference between the first and last digital loan as a proxy for growth in revenue. For facility characteristics, we considered the type of facility, categorized into levels commonly used in Kenya: level 1 (community health posts), level 2 (dental clinics, diagnostic centers, dispensaries, eye clinics, pharmacies), level 3 (health center, maternity home, outpatient center), level 4 (primary hospital). We also included the \u0026ldquo;other\u0026rdquo; category for equipment suppliers and support service companies. Additionally, the location of the facility \u0026ndash; urban or rural \u0026ndash; was captured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Data analysis\u003c/h2\u003e \u003cp\u003eWe evaluated the gender differences in the two loan types by comparing the averages in the first loan amounts, as this is an important indicator for financial inclusion. To assess statistically significant gender differences, first a log-transformation was applied to obtain a normal distribution followed by an Analysis of Variance (ANOVA) test as the comparison was between three groups. When the variable was still not normally distributed, we applied the Kruskal-Wallis test. We also assessed gender differences in the \u003cem\u003egrowth in loan amount\u003c/em\u003e. Due to an extreme leptokurtic distribution, this variable was transformed to a categorical variable so a chi\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test could be applied to assess significant differences. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. To ensure robustness of the gender analyses, we also conducted an ordinary least square (OLS) regression and a multinominal regression to control for facility characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Ethical Considerations\u003c/h2\u003e \u003cp\u003e For the cross-sectional mixed methods approach, ethical approval was obtained from the Strathmore University Institutional Ethics Review Committee, reference numbers SU-ISERC1616/23 and SU-ISERC1890/23. Additionally, a research permit was obtained from the National Commission for Science, Technology and Innovation (NACOSTI), permit number NACOSTI/P/23/25096. Informed consent was obtained before the in-depth interviews or surveys began. Additional consent was sought to allow the in-depth interviews to be audio-recorded. All data was anonymized of any identifier information.\u003c/p\u003e \u003cp\u003e For the secondary data analysis on the data provided by the MCF, ethical approval was obtained from the Research Internal Review Board of the Erasmus School of Health, Policy and Management in the Netherlands and the AMREF Ethics and Scientific Review Committee in Kenya. All data were anonymized.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eThe IDIs were conducted between May and July 2023. Two out of the 24 interviewed respondents refused to be audio recorded. Due to extensive field notes taken during the IDIs, the content of their interviews could still be included in the subsequent analyses. The survey was conducted between September and November 2023 among 439 respondents, of whom 29 respondents refused to answer a central question (whether they had taken a loan). Consequently, the survey sample comprised 410 respondents. We provide a summary of the main IDI findings first, followed by the presentation of the quantitative insights. Lastly, we analyzed real-world loan history data of 850 health SMEs including 6,350 disbursed loans between 2011\u0026ndash;2024 (up to July).\u003c/p\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Cross-sectional findings\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Background characteristics of IDI respondents\u003c/h2\u003e\n \u003cp\u003eOf the 24 IDI participants, 39% were female. The larger proportion of these participants were aged between 30 and 40 years; more than half (63%) of their businesses were microbusinesses (2\u0026ndash;9 employees), with 58% of these businesses being in urban areas. All participants had attained tertiary level education in health-related fields, see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBackground characteristics of IDI participants (N\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;9, 37.5%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15, 62.5%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;24, 100%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eAge\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\u003e18\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeri urban*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusiness Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsultation \u0026amp; treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusiness Size\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolo Entrepreneur (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicro Business (2\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55. 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall Business (10\u0026ndash;49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\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\"\u003e\u003cem\u003e*Peri-urban: Transition zone between urban and rural areas\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Main qualitative insights\u003c/h2\u003e\n \u003cp\u003eOut of the 24 IDI participants, 15 had never taken a loan from a bank. Six of these participants who had not taken a bank loan were female; and 12 of these businesses were in urban areas. The participants cited a variety of reasons for not taking bank loans which included ploughing back business revenues and preferring reliance on support from family and friends. Those who expressed an aversion to bank loans cited that they considered high interest rates as a deterrent.\u003c/p\u003e\n \u003cp\u003eTen of the 24 IDI participants had ever taken a digital loan; and half of them were female. These respondents cited diverse experiences with their digital lenders. Some respondents described negative experiences with digital loans, which related to high interest rates, a lack of trust, intrusion of privacy and harassment and poor customer relations as key contributors to these negative experiences.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;It\u0026rsquo;s called xxx (brand-name of digital lender) \u0026hellip;and we said never again! They keep calling you, they keep harassing you days before the agreed date. You just have to pay. They also go to your house to take pictures\u0026hellip; no, those ones they harass you psychologically.\u0026rdquo; (female IDI respondent).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eAdditionally, some of these female health entrepreneurs preferred to rely on internal business revenues. Conversely, other respondents showed a business growth mindset and demonstrated a high appetite for financial information. Most of them had a positive prior loan experience, supported by the quotes below:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026ldquo;\u0026hellip;you don\u0026rsquo;t have to fear of taking loans.... For you to grow you have to take a loan.\u0026rdquo; (Interviewer: Would you consider taking a loan from a digital lender?) \u0026ldquo;Respondent: yes, if they have favorable requirements\u0026hellip;\u0026rdquo; (female IDI respondent)\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(Interviewer: Have you taken a loan from a digital lender?) \u0026ldquo;Yes; it was swift\u0026hellip; (the experience) was okay\u0026hellip;. If need be, I would take another one (digital loan).\u0026rdquo; (female IDI respondent).\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe key qualitative findings integrated into the survey included factors that influenced entrepreneurs\u0026rsquo; decisions to take up loans which were risk-attitude and financial needs.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 Background characteristics of survey respondents\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e outlines the background characteristics of the survey respondents. Of the 410 survey respondents, 28% were female. Most respondents were aged between 30\u0026ndash;40 years. Slightly less than two thirds (64%) of the businesses were urban based, with microbusinesses being the most dominant. The majority (83%) reported that they systematically tracked their business revenues. Slightly more than half (51%) of the female respondents were running a pharmacy, whereas most of the male respondents (59%) provided consultation and treatment services.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBackground characteristics of survey respondents (N\u0026thinsp;=\u0026thinsp;410)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;115, 28.1%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;295, 71.9%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;410, 100%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeri-urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusiness Type*\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsultation \u0026amp; treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic Center\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusiness size\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolo Entrepreneur (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicro Business (2\u0026ndash;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall Business (10\u0026ndash;49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse an information system to keep track of business income and expenditures\u003c/strong\u003e\u003c/p\u003e\n \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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinancial need\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever/Rarely\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily/Weekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo specific pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital loan uptake\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerception of digital loan uptake\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk level assessment\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow risk taker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage risk taker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-risk taker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.3\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\"\u003e* Statistically significant difference with p\u0026thinsp;=\u0026thinsp;0.035\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4 Loan history and health SMEs readiness for digital loan adoption\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows a flowchart on the loan uptake and readiness among the entire study sample. The left arm shows that out of the 410 respondents, 39% indicated that their businesses had ever taken a loan. Bank loans were the most frequently cited source of credit among the respondents (58%) followed by digital loans (42%). Other sources of loans included savings and credit cooperative societies (14%) and informal loans from family and friends (8%) \u003cem\u003e(not in figure)\u003c/em\u003e. Within the category of respondents who reported that they had taken a business loan, the proportion of female respondents who had taken a bank loan was 48%, whereas this proportion among male respondents was higher with 61% (p\u0026thinsp;=\u0026thinsp;0.12) \u003cem\u003e(not in figure).\u003c/em\u003e Among the 158 respondents who had taken loans, 42% had taken digital loans. The larger proportion (67%) of these respondents with a history of taking digital loans reported to be satisfied with this loan product. Additionally, slightly more than half (52%) of the respondents with a history of taking a digital loan, had this loan taken more than once. The right arm shows that among the 61% of the respondents without a loan history, almost half (44%) of the businesses experienced an acute financial need at least monthly in the year preceding the survey. Almost all respondents (86%) who had an acute business financial need at least once a month received their revenues through digital financial platforms, suggesting that they are eligible to receive digital loans. Supplementary table 2 describes the background characteristics of this particular group, which do not differ significantly from the overall study sample. Supplementary table 3 shows the gender distribution for each block of the flowchart. While we found no gender gap in overall loan uptake, we did find that among respondents with a loan history, women had more frequently taken digital loans compared to men (p\u0026thinsp;=\u0026thinsp;0.3).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.5 Factors associated with the uptake of digital loans\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the logistic regression model with the uptake of digital loans as the dependent variable. We found that those with monthly financial needs were significantly more likely to take digital loans (OR of 4.22) \u0026ndash; compared to those with \u0026lsquo;never/rarely\u0026rsquo; financial needs. Regarding behavioral characteristics, we found that risk attitude and perception towards digital loans were associated with digital loan uptake. Respondents who perceived themselves to be high-risk takers had higher odds of taking a digital loan (OR of 2.32) \u0026ndash; compared to those with a self-perceived low risk profile. Having a positive perception towards digital loans also increased the likelihood of taking a digital loan (OR of 3.14) \u0026ndash; compared to those with a negative perception.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic regression on the uptake of digital loans (N\u0026thinsp;=\u0026thinsp;410)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\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\u003e\u003cstrong\u003eBackground characteristics\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (Men)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77 (0.41\u0026ndash;1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge category (18\u0026ndash;30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 (0.32\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.38\u0026ndash;2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove 50 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.72 (0.87\u0026ndash;8.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears in business\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (0.97\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation business (Urban)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeri-urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (0.77\u0026ndash;2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86(0.16\u0026ndash;21.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusiness size (Solo Entrepreneur (1))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicro Business (2\u0026ndash;9 employers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (0.57\u0026ndash;2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall Business (10\u0026ndash;49 employers)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 (0.17\u0026ndash;2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusiness type (Consultation \u0026amp; treatment)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90 (0.95\u0026ndash;3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04 (0.12\u0026ndash;9.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinancial need (Never / rarely)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily/weekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28 (0.94\u0026ndash;5.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.22 (1.74\u0026ndash;10.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo specific pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 (0.49\u0026ndash;3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBehaviour characteristics\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk attitude (Low risk taker)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage risk taker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54 (0.69\u0026ndash;3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-risk taker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32 (0.99\u0026ndash;5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerception towards digital loans (Negative)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRef\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52 (0.70\u0026ndash;3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14 (1.46\u0026ndash;6.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCons\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03 (0.01\u0026ndash;0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNumber of observations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e410\u003c/p\u003e\n \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\u003e\u003cem\u003ePseudo R\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (statistically significant) **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (highly statistically significant) ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (very highly statistically significant)\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Real world loan history data \u0026ndash; Medical Credit Fund\u003c/h2\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Study sample description\u003c/h2\u003e\n \u003cp\u003eIn total, we analyzed loans disbursed to 850 facilities, of which the majority were male owned (74%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). More than half of the sample were level 2 facilities (dental clinics, diagnostic centers, dispensaries, eye clinics, pharmacies), and more than 80% were situated in urban areas. Overall, 70% of the facilities had taken a digital loan and 30% a term loan.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFacility characteristics (N\u0026thinsp;=\u0026thinsp;850)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale-owned\u003c/p\u003e\n \u003cp\u003e(19.8%, n\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale-owned\u003c/p\u003e\n \u003cp\u003e(74.1%, n\u0026thinsp;=\u0026thinsp;630)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePartnership \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(6.1%, n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(100%, N\u0026thinsp;=\u0026thinsp;850)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFacility type\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% (n)\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\u003eLevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.7 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.8 (339)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.6 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.4 (454)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.3 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.8 (232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.2 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.3 (317)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.2 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e% (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e% (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e% (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.5 (142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.6 (508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.4 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.0 (697)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.5 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.4 (122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.6 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.0 (153)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoan type *\u003c/strong\u003e\u003csup\u003e,\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.3 (103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.3 (449)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.5 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.2 (597)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.7 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.7 (181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.8 (253)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026nbsp;\u003c/sup\u003e level 1 (community health posts), level 2 (dental clinics, diagnostic centers, dispensaries, eye clinics, pharmacies), level 3 (health center, maternity home, outpatient center), level 4 (primary hospital), \u0026ldquo;other\u0026rdquo; (equipment suppliers and support service companies).\u003c/p\u003e\n \u003cp\u003e* p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 \u003csup\u003e2\u003c/sup\u003e Some facilities have both loan types \u003csup\u003e\u003cstrong\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/strong\u003e\u003c/sup\u003e The majority of the partnerships are a duo-ownership of a male and female. One facility has ten owners, two facilities have five owners, three facilities have four owners, and four facilities have three owners.\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e describes the characteristics of loan disbursements stratified by loan type. In total 6,350 loans have been disbursed, with most of them being digital loans (n\u0026thinsp;=\u0026thinsp;5,939). This distribution is uneven because by design digital loans have a shorter tenor to e.g. finance working capital and therefore customers usually come back (with an average of 8 loans per customer in this dataset). The disbursement of term loans decreased over time, as between 2011\u0026ndash;2015 there were 275 loans disbursed while between 2021\u0026ndash;2024 only ten loans. This can be explained by the fact that smaller term loans were replaced by digital loans since 2017, which also explains the increase in average loan amount for term loans. The large standard deviations in loan size for both loan types demonstrate there are huge variances between loans. Supplementary Figs. 1 \u0026amp; 2 provide an overview of the number and average first loan amount per year.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDisbursed loans between 2011\u0026ndash;2024 by MCF (N\u0026thinsp;=\u0026thinsp;6,350)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDigital (N\u0026thinsp;=\u0026thinsp;5,939, 93.6%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTerm (N\u0026thinsp;=\u0026thinsp;404, 6.4%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;6,343, 100%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of loans\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% (N)\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\u003e2011\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (275)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (275)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.7 (2,145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3 (119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (2,264)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u0026ndash;2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.7 (3,794)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (3,804)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage loan amount in EUR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,814 (28,530)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,814 (28,530)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,210 (13,307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42,564 (63,862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,174 (21,184)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u0026ndash;2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,534 (19,210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98,607 (103,848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,773 (20,379)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterest rate in %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.2 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.4 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.8 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u0026ndash;2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.8 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.6 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.8 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRepayment period in days\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u0026ndash;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508.6 (445.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508.6 (445.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u0026ndash;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.5 (175.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e867.6 (521.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154.1 (267.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u0026ndash;2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.0 (132.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222.6 (120.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.4 (132.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Gender differences in loan characteristics\u003c/h2\u003e\n \u003cp\u003eWe examined gender differences in the first loan amount for both term and digital loans (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). For term loans we found a clear gender gap with partnerships receiving significantly higher first loan amounts (EUR 53,799) compared to females (EUR 10,400) and males (EUR 18,321). Since facility characteristics likely influence loan size, we conducted an OLS regression on the log of the first term loan amount, controlling for facility level and location (see Supplementary table 4). After adjusting for these factors, the significant gender association remained, but small, and facility level 4 emerged as the most likely to secure the largest first term loans. No gender differences were found in the number of repeated term loans. For digital loans, there was also a significant gender difference in the size of the first loan, with partnerships receiving the largest amount (EUR 10,400) followed by males (EUR 4,671) and females receiving the lowest amount (EUR 2,793). After controlling for facility characteristics in an OLS regression (see Supplementary table 5), the significant difference between partnerships and females remained but the gender difference disappeared and here facility level 4 was also most likely to secure the largest first digital loan. We used the growth in loan amount indicator as a proxy for digital revenue growth. More than 50% of the female-owned facilities experienced high growth, which was borderline significant (p-value\u0026thinsp;=\u0026thinsp;0.08) different from the other two groups. A multinominal regression (see Supplementary table 6) confirmed a stronger significant association (p-value\u0026thinsp;=\u0026thinsp;0.006) after controlling for facility characteristics indicating that women were more likely to experience high growth.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGender differences in loan characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTerm loans (N\u0026thinsp;=\u0026thinsp;404) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;109, 27.0%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;272, 67.3%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePartnership\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23, 5.7%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026micro; (sd)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026micro; (sd)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026micro; (sd)\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\u003eFirst loan amount**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,400 (26,395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,321 (40,267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42,187 (53,799)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRepeated loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigital loans (N\u0026thinsp;=\u0026thinsp;5,939)\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;1,126, 19.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;4,397, 74.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartnership\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;416, 7.0%)\u003c/strong\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\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026micro; (sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst loan amount***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,793 (7,187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,671 (14,261)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,400 (18,691)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrowth in loan amount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,138 (5,382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,316 (12,255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,809 (24,119)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrowth in loan categories*\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNo growth (\u0026lt;\u0026thinsp;0), % (n)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.8 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4 (141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.8 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eModerate growth (0\u0026ndash;7,499), % (n)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.2 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.2 (209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.9 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHigh growth (7,500\u0026ndash;100,527), % (n)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.0 (89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.4 (280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.4 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRepeated loans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e ANOVA test is performed on log-transformed value and Kruskal-Wallis test on the repeated loans indicator\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e ANOVA test is performed on log-transformed value and Chi\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test on categorical variable * P-value\u0026thinsp;=\u0026thinsp;0.08 ** P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e*** P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.000 but significant difference disappears after controlling for facility level and location\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION ","content":"\u003cp\u003eThis study applied a mixed method approach to evaluate the readiness, perspectives and gender disparities in (digital) loan characteristics among health SMEs in Kenya. We interviewed 24 and surveyed 410 health SMEs owners respectively. We identified a high demand for loans among Kenyan health SMEs. Additionally, we used real-world loan history data from 850 health SMEs in Kenya to evaluate gender differences in digital and term loans. We identified a gender gap in the first disbursed term loan amount, with women-owned businesses receiving the lowest amounts and partnerships the highest. In contrast, this gender gap disappeared for digital loans. Both the survey findings and real-world loan history data demonstrate that female entrepreneurs are digitally financially included. This study adds to the evidence on health financing options for health SMEs to promote financial inclusion for female health entrepreneurs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe gender distribution in the survey and administrative loan data was comparable, suggesting that men and women have similar access to finance. However, the sample indicates that most health SMEs are male owned. This unequal gender distribution in ownership, while the health workforce is dominated by women, is likely associated with another gender gap linked to differences in career opportunities. This gender distribution of SME ownership is also documented in a 2021 survey conducted by the Kenya Bankers Association (70%)\u0026nbsp;\u003csup\u003e57\u003c/sup\u003e, suggesting that, in terms of gender, our sample is well representative for the country. Additionally, our IDI and survey sample may appear biased due to the inclusion of only higher-educated respondents. However, the target population—health sector entrepreneurs—typically requires tertiary education to establish and manage such enterprises effectively. Thus, this reflects the population’s characteristics rather than sampling bias.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe survey results identified that four in every ten health SME owners had ever taken a loan; with most opting for bank loans, while fewer than half had used digital loans. The majority of the surveyed health SME owners had not taken a loan for their businesses, while more than half of them reported an acute business financial need, at least monthly. The qualitative findings highlight poor reputation and low trust in digital lenders among health SME owners, yet the survey findings showed high satisfaction among those who had used digital loans, suggesting mistrust is stronger among those without direct experience. A study by the Consultative Group to Assist the Poor (CGAP) also found that lack of trust hindered digital loan uptake among SMEs\u0026nbsp;\u003csup\u003e58\u003c/sup\u003e. Additionally, the survey findings also showed that high risk-takers, those with monthly acute business financial needs, and those with a positive perception of digital loans were more likely to use them. These findings demonstrate the importance of effective information dissemination by lenders to build trust. A study conducted in Kenya documented that digital loan uptake was influenced by credit information sharing, alongside financial technology and the cost of credit\u0026nbsp;\u003csup\u003e59\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the real-world loan history data, a small proportion (6%) of the included health SMEs were classified as partnerships (with the majority 50/50 male-female ownership). Our findings show that they secured the highest first loan amounts and experienced substantial growth in their digital loan journeys. A possible explanation for this finding could be that partnerships are owned by multiple stakeholders and may therefore in general dare to take more risks than single lenders. In addition, in our data 20% of partnership-owned health SMEs are classified as a level 4 facilities, compared to just 5% among female- and male-owned health SMEs which is an explanatory factor for a higher loan uptake. Another study that examined the gender gap for SMEs (not sector specific) found that firms with female ownership are less likely to use formal bank credits than firms with male owners, but this gender gap disappears when controlling for observable firm characteristics, like size\u003csup\u003e60\u003c/sup\u003e. This is similar to what we found in our analyses and suggests that the disparity in access to finance is driven by differences in firm characteristics rather than the owner’s gender itself and can therefore also be referred to as an unconditional gender gap. The facility characteristics (e.g. size of the business) determine the access to finance which explain why women-owned companies tend to be less likely to have access to finance because they tend to own smaller clinics. This portrays a gender gap that is not per se directly linked to access to finance.\u003c/p\u003e\n\u003cp\u003eA potential explanation for the lower initial loan amounts disbursed to women is their tendency to be less risk seeking compared to men. Women could be less likely to accept the highest loan offers, opting instead for a safer, smaller (initial) loan size, while men are more inclined to take on larger, riskier loans. This gender difference in risk attitude is extensively reported in the literature\u003csup\u003e\u0026nbsp;61–63\u003c/sup\u003e and, in this context, may be linked to the fact that women often manage multiple responsibilities, such as family care, making them less likely to take risks in their professional life. However, our findings suggest that women-owned health SMEs benefit significantly from digital loans, with over 50% experiencing substantial growth in digital revenues after taking a MCF digital loan. This suggests that digital loans may play a key role in their business success. Nevertheless, this conclusion should be interpreted cautiously, as it is based solely on digital revenue growth, without insights into the broader distribution of their cash and digital earnings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used IDIs to design the survey, which is a strength of the study as we have optimized context specific information. Another key strength of this study is that we analyzed unique real-world data spanning a broad time window (2011 – 2024); the loan-history data is not self-reported but drawn from administrative records, making it highly reliable. We acknowledge the following limitations. Firstly, the survey findings were self-reported which may have led to selection and information bias. Secondly, while most IDIs were conducted in person, a subset was conducted via telephone. This mode of communication may have limited the depth of responses compared to in-person interviews, potentially influencing the richness of the findings. Thirdly, while the growth in digital revenues was not the primary focus, it would have been beneficial to compare digital revenue growth with health SMEs that did not receive loans to better quantify the loan product’s impact. Lastly, we only had data on approved loans, while including data on rejection rates and requested loan size would have provided additional insights. To address this gap, we conducted surveys with health SMEs without a loan history with MCF to better understand their landscape and readiness for digital loans. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions and policy implications","content":"\u003cp\u003eThis study highlights a significant financing gap among health SMEs in Kenya, with notable difference in loan uptake and amounts highly favoring partnership-owned health SMEs. Despite comparable access to finance between men and women, most health SMEs remain male-owned, reflecting broader gender disparities in business ownership. Expanding social support networks for female entrepreneurs could foster trust and encourage greater participation of women in the financial credit market. Given the low trust in digital lenders, \u0026nbsp;especially among those without prior experience, improving information dissemination is crucial to build confidence in digital financial products. This study’s findings underscore the need for tailored loan products and strategies that account for firm characteristics, risk attitudes and gender-specific barriers to ensure equitable access to finance. Additionally, financial literacy training for female entrepreneurs could help overcome risk aversion and increase their willingness to seek external financial support during times of significant financial need.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the funding of Swedfund International AB and the Netherlands Ministry of Foreign Affairs to carry out this study. We also thank all participating healthcare facilities and data collectors for their valuable work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCD conceptualized the study. J.A.A., M.A. and J.S. designed the mixed methods study, collected and analyzed the data; CD analyzed the MCF data and survey data; all authors were involved in the interpretation of the data, J.A.A and C.D. wrote the initial draft manuscript with input from M.A., all authors reviewed and approved the final manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGr\u0026eacute;pin, K. A. Private sector an important but not dominant provider of key health services in low-and middle-income countries. \u003cem\u003eHealth Aff\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 1214\u0026ndash;1221 (2016).\u003c/li\u003e\n\u003cli\u003eInternational Finance Corporation. \u003cem\u003eWomen\u0026rsquo;s Leadership In Private Health Care\u003c/em\u003e. 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Country overview: Kenya. https://www.ahb.co.ke/wp-content/uploads/2021/07/Country-Overview_Kenya.pdf (2021).\u003c/li\u003e\n\u003cli\u003eKenya Institute for Public Policy Research and Analysis. \u003cem\u003eCharacteristics of Kenyan MSMEs Relevant to the Proposed Kenya Credit Guarantee Scheme\u003c/em\u003e. https://kippra.or.ke/characteristics-of-kenyan-msmes-relevant-to-the-proposed-kenya-credit-guarantee-scheme/ (2021).\u003c/li\u003e\n\u003cli\u003eOmondi, F., Onono, P. A. \u0026amp; Barasa, S. \u003cem\u003eGendered Analysis of Institutional and Government Small and Medium Enterprises.\u003c/em\u003e https://kenya.oxfam.org/latest/publications/gendered-analysis-institutional-and-government-small-and-medium-enterprises (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5187398/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5187398/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Sub-Saharan Africa, female entrepreneurs are well-represented in healthcare but struggle to access financial services. Digital financing technologies could help close this gap. This study assessed the readiness, perspectives and gender disparities in (digital) loan characteristics among Kenyan health Small and Medium Enterprises (SMEs).\u003c/p\u003e \u003cp\u003eWe interviewed 24 and surveyed 410 health SME owners and analyzed loan-history data from 850 health SMEs to compare traditional and digital loans, focusing on gender disparities. IDIs identified low trust in digital lenders, while survey results indicated a strong demand for loans, regardless of gender. SME owners willing to take risks, with monthly financial needs and positive perceptions of digital loans were more likely to take a digital loan. Loan-history data showed a gender difference in the initial traditional loan amount, with women-owned businesses receiving less, but this gap disappeared for digital loans. Over 50% of women-led businesses using digital loans experienced significant growth, suggesting increased digital revenues linked to these loans.\u003c/p\u003e \u003cp\u003eThis study highlights the financing gap among Kenyan health SMEs and the potential of digital loans to enhance financial inclusion. Low trust in digital lenders, especially among those without direct experience, calls for better information dissemination to build confidence in digital financial products.\u003c/p\u003e","manuscriptTitle":"Gender Disparities in Financial Inclusion: the Potential of Digital Loans in Empowering Female Health Entrepreneurs in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":"","date":"2025-05-20 02:24:06","doi":"","editorialEvents":[{"type":"decision","content":"Revision 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