Gender and access to bank credit around the world during the COVID-19 pandemic: The mediating role of digital transformation

Khan, Safiullah; Subramanian, Ulaganathan; Mutalib, Pg Abdul Article Gender and access to bank credit around the world during the COVID-19 pandemic: The mediating role of digital transformation Pakistan Journal of Commerce and Social Sciences (PJCSS) Provided in Cooperation with: Johar Education Society, Pakistan (JESPK) Suggested Citation: Khan, Safiullah; Subramanian, Ulaganathan; Mutalib, Pg Abdul (2024) : Gender and access to bank credit around the world during the COVID-19 pandemic: The mediating role of digital transformation, Pakistan Journal of Commerce and Social Sciences (PJCSS), ISSN 2309-8619, Johar Education Society, Pakistan (JESPK), Lahore, Vol. 18, Iss. 1, pp. 1-39 This Version is available at: https://hdl.handle.net/10419/294807 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Pakistan Journal of Commerce and Social Sciences 2024, Vol. 18(1), 01-39 Pak J Commer Soc Sci Gender and Access to Bank Credit Around the World During the COVID-19 Pandemic: The Mediating Role of Digital Transformation Safi Ullah Khan (Corresponding author) UTB School of Business, Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam Email: [email protected] Ulaganathan Subramanian UTB School of Business, Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam Email: [email protected] Pg Abdul Mutalib UTB School of Business, Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam Email: mutalib.kamallu[email protected] Article History Received: 01 Dec 2023 Revised: 24 Mar 2024 Accepted: 27 Mar 2024 Published: 31 Mar 2024 Abstract Drawing on the rich firm-level enterprise survey dataset of more than 58,000 small enterprises in 39 developing and emerging economies, this study investigates gender disparities in firms’ financial fragility, credit demand, and credit provision during the COVID-19 pandemic, and the role of digital transformation in addressing these disparities. We used a probit model with selection and an instrumental variable approach to account for the selection effects and endogeneity of the female ownership and leadership measures. Furthermore, several robustness checks are used to account for endogeneity problems caused by omitted variables and self-selection bias. These econometric tests were conducted using STATA software. We find that female-led businesses are more vulnerable to the negative effects of the pandemic and have higher demand for credit. However, they are less likely to request loans (credit self-rationing) and more likely to be denied credit when applying for bank credit. This gender bias in credit provision is exacerbated by the pre-pandemic financial constraints on female-led enterprises. This study also tested the mediating role of a firm’s technology adoption and digital transformation in credit access. The results of the mediation analysis show that female-led enterprises that adopted ecommerce and remote work technologies during the pandemic had better access to bank credit than other firms, suggesting that digital transformation significantly enhanced female-led businesses’ access to bank credit and narrowed gender disparities in the credit market in times of extreme financial and economic distress. Gender, Digital Transformation and Access to Bank Credit 2 Keywords: Digital transformation, e-commerce, gender and bank credit access, financial constraints, borrower discouragement, financial inclusion. 1. Introduction The social, economic, and financial impacts of the COVID-19 pandemic on national economies and businesses have been well-documented globally. One of the stylized facts is that female workers as well as female-led businesses suffered disproportionately due to the unprecedented, gendered implications of the crisis (e.g., Birhanu, Getachew, & Lashitew, 2022; Liu, Wei, and Xu, 2021; Njiwa et al., 2023; Elouardighi & Oubejja, 2023; Wu, 2022). Financial distress and liquidity shortfalls are important factors influencing the impact of the COVID-19 pandemic on business performance and financial vulnerability (Goldstein et al., 2022; Leng & Sun, 2024). Firms with credit access difficulties before the pandemic were more likely to have liquidity issues, sales declines (Zhang & SognGrundvåg, 2022) and restricted investments in new technologies to adjust their production and business operations (Khan, 2022). Prior studies also show that female-led businesses are disadvantaged by discriminatory “gendered ascriptions” and experience greater difficulties in accessing external finance (De Andrés et al., 2021; Marlow & Patton, 2005). Gender-based discrimination can lead to misallocation of credit and, consequently, inefficiencies in financial markets (Beck et al., 2018), which may have a negative impact on the growth and survival of small businesses, employment, and ultimately, the economy at large. However, evidence of the economic consequences of the COVID-19 crisis on female-led businesses and how this has affected their demand for and access to finance is limited and less documented. This study addressed three interrelated research questions. First, we examine gender differences in financial fragility and the demand for and supply of external financing during the COVID-19 pandemic. Women tend to have greater propensity for risk aversion (Meyll & Pauls, 2019) which may exacerbate during extreme economic uncertainty such as the COVID-19 pandemic. This may constrain their demand for bank credit during economic downturns (Cowling, Marlow, & Liu, 2020). Second, we investigate whether female-led businesses have greater tendency not to apply for bank credit due to the prototypical feminized risk aversion. Third, we investigate whether gender differences in firms’ loan demand and supply varied over time during the pandemic. The COVID-19 pandemic has exacerbated financial constraints (Balduzzi et al., 2020), affecting credit-constrained firms' investments and limiting their access to digital technologies and platforms (Khan, 2022). These are vital for ensuring continuity in business operations in the face of lockdowns, mobility restrictions, and workplace closures. The adoption of Internet technologies and digital transformation (DT) might have provided digitalized businesses in the during-COVID period with a competitive edge by positioning them for growth through access to wider segments of consumers through cost-effective supply chains (Brem et al., 2021), thus enhancing their resilience in navigating the pandemic-induced economic crisis. Digitalized small and medium-sized enterprises (SMEs) can benefit significantly from increased customer interactions and market access, Khan, Subramanian & Mutalib 3 especially those facing financial and market access restrictions (Markovic et al., 2021). Technological advancements and digital transformation have made financial resources more accessible to SMEs, lowered barriers, and improved financing and investment efficiency (Cui & Wang, 2023; Li & Xu, 2023). Thus, we also examine the mediation effect of digital transformation on women-led firms' access to bank credit during the COVID-19 pandemic. We draw on the data from the World Bank's pre-pandemic Enterprise surveys (WBES) and the ongoing follow-up "Covid-19 impact surveys (COV-FS)" covering more than 58,000 enterprises across 39 developing and emerging economies, 93% of which are SMEs. We utilized the COV-FS data for the first four waves conducted between May 2020 and August 2021. The empirical findings of this study show that female-led enterprises typically require more financing because they experience greater financial difficulties than male-led enterprises, consistent with recent empirical findings (e.g., Elouardighi & Oubejja, 2023; Njiwa, Atif, Arshad, & Mirza, 2023). They are less likely to apply for bank loans, have higher rejection rates than male-led firms, and have lower propensity for bank financing. These findings are further exacerbated by the pre-pandemic credit access difficulties experienced by female-led businesses. Furthermore, female-led enterprises have higher incidences of credit self-rationing and financial fragility. Finally, the mediation analysis shows that technology diffusion and digital transformation reduce gender disparities in financial access and fragility, resulting in better access to financial resources for digitalized female-led enterprises than for other businesses. This study contributes to the literature in three ways: First, to the best of our knowledge, this is among the first to provide new evidence of the mediating role of technology adoption and digital transformation in reducing gender gaps in SMEs' access to financial resources during the COVID-19 pandemic. Digital transformation has helped SMEs reduce the negative effects of the COVID-19 pandemic and has led to the growth of digitalized SMEs (Zia et al., 2023). Firms that leverage digital platforms through technology diffusion, digital marketing, and innovation achieve higher levels of success and profitability during crises (Rojas-García et al., 2024). Digital inclusion also reduces the gender gap in labor force participation (Mohieldin & Ramadan, 2024). Furthermore, digital transformation promotes financial inclusion by reducing the cost of financial intermediation (Cui & Wang, 2023; Skare et al., 2023). It also helps alleviate liquidity constraints by facilitating supply chain financing through digital platforms (Chen et al., 2021). This study’s findings support the notion that digital transformation benefits female-led enterprises by fighting the pandemic's negative consequences and positioning them for better access to external financing. These results imply that women-led enterprises may gain from leveraging their organizational resources to enhance access to funding by embracing digital transformation in their management, production processes, and business models to adapt to changing circumstances. Gender, Digital Transformation and Access to Bank Credit 4 Second, a broad body of literature has predominantly investigated gender differences in credit access during normal economic periods (e.g., Bertrand & Perrin, 2022; Chundakkadan, 2023; Nyarko, 2022; Wellalage & Locke, 20217). However, further studies are needed to explore gender disparities in financial access from the perspective of dual economic risks during the COVID-19 pandemic, particularly from the perspective of small businesses operating in developing and emerging markets. This study is the first to examine whether firm-level pre-pandemic external financing difficulties exacerbate the consequences of the COVID-19 crisis on women-led firms’ access to financing. Khan (2022) found that firms with pre-pandemic financing constraints were more vulnerable to greater liquidity shocks and credit risk during the COVID-19 crisis. Aristei and Gallo (2023) examined how green management practices and pre-pandemic financial conditions affected the impact of the pandemic on firms’ credit access. We contribute to this growing (but limited) strand of the literature by examining whether prior external financing constraints have a differential impact on gender disparities in financial fragility and access to finance for women-led enterprises. We consider multiple indicators of financial constraints (e.g., credit demand, loan application behavior, supply of debt finance, and bank discouragement). This study is the first to examine pre-pandemic firm heterogeneity in credit constraints and perceived financial obstacles, thereby providing robust evidence of gender disparities in the credit market in the context of adversity and exogenous economic shock. Third, in a first, this study examines the effect of gender on the demand-side credit constraints by exploring gender differences in borrower discouragement during a unique economic crisis. This is crucial in designing policies for vulnerable businesses. This study documents significant disparities in the impacts on female-led firms across regions and over time using the panel aspects of a dataset covering four rounds of COVID-19 followup survey datasets from May-2020 to August-2021. The global context provides an analysis of the "contextual" nature of the female leadership-finance access relationship, considering the economic, institutional, and cultural differences between developing and emerging countries. 2. Theory and Hypotheses 2.1 Gender, Entrepreneurship, and Finance Access Gender is a key identity marker that creates mutual understanding between human subjects (Butler 2004), with stereotypical feminine traits associated with femininity viewed as having lower values (Bowden & Mummery, 2014). In entrepreneurship, the preferred entrepreneurial profile mirrors masculine characteristics, creating a "masculinized discourse" that disadvantages women and privileges men (Marlow et al. 2008). While studies find no inherent entrepreneurial weakness attributable to gender, gendered ascriptions impede women's ability to accumulate entrepreneurial capital and legitimacy (Robb & Watson, 2012), resulting in fewer women starting businesses because of structural and tacit discrimination. Female entrepreneurs often exhibit higher risk aversion (Faccio, Khan, Subramanian & Mutalib 5 Marchica, & Mura, 2016), leading to lower demand for bank credit and a higher reluctance to take on debt (Cowling, Marlow, & Liu, 2020). This results in a scenario in which women rely on informal financing sources, whereas those seeking external financing are more cautious because of their feminine risk aversion. Banks restrict lending to marginal borrowers, particularly during financial crises, resulting in more severe credit contractions in female-led enterprises (Cesaroni et al., 2013). Previous studies have shown that women-owned firms are more credit constrained in the formal loan market than male-owned firms despite the increasing share of women-owned businesses (Faccio et al., 2016; International Labour Office, 2019). The literature proposes three main theories that describe gender-based discrimination in credit markets. This is discussed briefly below. Statistical discrimination: As the demographic characteristics of loan applicants may be related to the unobservable quality of creditworthiness (Arrow, 1973), the lender may be tempted to use the loan applicant’s gender to infer creditworthiness. Thus, if women borrowers, on average, are more likely to default on their loan, the loan officer might apply to certain women-owned businesses the average quality of funded women-led businesses in order to minimize costs pertaining to collect more borrower-specific information. Bellucci, Borisov, and Zazzaro (2010) suggest that the generally lower proportion of women-owned businesses in the economy makes the availability of information about these firms limited and less reliable, rendering access to formal credit markets for creditworthy female businesses more difficult and thus affecting the firm’s development and growth. Taste-based discrimination: Becker (1957) pioneered this theory and proposed that economic agents prefer not to engage in financial transactions with members of discriminated groups because of prejudice or bias, even at the expense of financial losses. Taste-based gender discrimination is rooted in a person’s preferences and cultural beliefs about a particular gender, which may influence financial institutions when formulating judgments about loan applications. Such discrimination will occur if loan officers responsible for approving credit inherently have antipathy towards female loan applicants (prejudiced) and prefer to avoid commercial relationships, even if they imply forgoing potentially profitable commercial transactions to avoid indulging with members of the discriminated group. In the presence of some degree of taste-based discrimination, female borrowers face higher credit constraints such as being offered less credit, experiencing higher loan rejection rates, and higher financing costs, although the circumstances may otherwise be similar for male and female borrowers. Thus, we propose the following hypothesis: ➢ H1: Financial institutions are more likely to reject loan requests by female entrepreneurs because of gender prejudice and because they typically lack the resources needed to serve as collateral for bank loans. This higher likelihood of loan rejection also increases during times of financial and economic distress such as the COVID-19 pandemic. Gender, Digital Transformation and Access to Bank Credit 6 Variations in borrower characteristics and preferences for debt financing between womenled and men-led enterprises may also play a role in the origin of gender inequalities in access to bank credit (Muravyev et al., 2009; Rizwan & Khan, 2007). Thus, gender disparities in the demand for external funding may reflect both differences in financial needs and the perception of the likelihood that a loan will be approved or denied, which may influence bank discouragement. According to a distinct stream of literature, womenled firms tend to abstain from applying for bank loans because they typically feel less confident about their capacity to negotiate loan terms with banks and financial institutions (Croson & Gneezy, 2009) and, consequently, refrain from requesting credit and exhibit discouraged borrowing behavior (Naegels, Mori, & D’Espallier, 2022). Other studies note that women-led businesses also refrain from raising funds through equity markets, as they prefer to finance investments through internal sources (Brush et al., 2018) or informal sources, such as networks of friends and family. Consequently, female-led businesses face major resource constraints compared to their male counterparts (Kogut & Mejri, 2022) because of smaller equity capital, reduced access to external equity financing (Brush et al., 2019), and bank credit (Wellalage & Thrikawala, 2021). Hence, they rely more on personal, family, and informal resources than on formal financing channels. Furthermore, negative shocks such as financial crises can impact people's risk attitudes. Risk aversion is more common among female entrepreneurs, which may limit their need for financing (Aristei & Gallo, 2016; Jetter et al., 2020; Meyll & Pauls, 2019). As the COVID-19 pandemic has disproportionately affected female-led businesses (Mustafa et al., 2021), it is expected that female entrepreneurs' propensity for risk aversion will worsen during these times (Li et al., 2021), which can influence their demand for bank credit. Drawing on the theoretical elements of risk aversion theory and resource dependency theory, we formulated the following hypothesis: ➢ H2a and H2b: Because of their generally lower risk tolerance and reluctance to incur debt finance during economic and financial distress, female entrepreneurs are more likely to have a lower demand for bank credit (H2a) and, therefore, less likely to apply for credit to meet their liquidity and financing needs (H2b). 2.2 Financing Constraints and Firm-level Financial Vulnerabilities During COVID-19 Crisis The COVID-19 pandemic has exposed firm vulnerabilities, particularly financial constraints, which have been exacerbated by the pandemic (Khan, 2023). Studies show that pre-pandemic financing constraints lead to severe liquidity problems and delinquency and hinder digital transformation efforts (Aristei & Gallo, 2023). Khan (2022) demonstrates how pre-pandemic credit constraints worsen both the credit risk and liquidity issues caused by the pandemic. Analogously, Aristei and Gallo (2023) find that pre-pandemic credit constraints exacerbate economic and financial vulnerabilities and intensify firm-level financial distress and liquidity problems. They further argue that credit-constrained businesses not only have a higher likelihood of experiencing liquidity shortfalls or having Khan, Subramanian & Mutalib 7 difficulties making repayment issues with their financial obligations but also have a harder time obtaining bank funding. Firms with better credit access before the pandemic are less likely to experience a decline in sales (Amin & Viganola, 2023). Credit constraints exacerbate economic and financial vulnerabilities, making it difficult for businesses to obtain bank financing and meet their financial obligations. Balduzzi et al. (2020) find that credit-constrained businesses have pessimistic expectations and plans to reduce investment, suggesting that financial constraints exacerbate the crisis's negative effects. Thus, we formulate the following hypothesis: ➢ H3: Pre-pandemic financing constraints worsened gender-based credit market disparities and pandemic-induced financial fragility in female-led businesses during the COVID-19 crisis. 2.3 Gender and Access to Finance: The Mediating Role of Digital Transformation Channel The digital economy has developed rapidly, leading to increased productivity, firm growth, financial performance, and corporate resilience (Xia et al., 2022) and closing gender disparities in female labor participation (Mohieldin & Ramadan, 2024). Digital transformation enables resource-constrained enterprises to reach national and international markets cost-effectively (Yu et al., 2022), facilitates efficient information flow, accelerates innovation, and assists in corporate transformation (Fitzgerald et al. 2014). However, barriers to widespread adoption include the slow diffusion of technologies, lack of financial resources, technical expertise, organizational inertia (Khan, 2022b), and understanding of technology adoption (Adomako et al., 2021). The COVID-19 crisis has prompted many SMEs to adopt digital technologies and platforms to adapt to changing customer needs and improve their resilience. Furthermore, digital transformation (DT) enhances financial inclusion by enabling efficient financial transactions and alleviating liquidity issues (Skare et al., 2023). This also increases a firm's access to working capital and short-term financial requirements (Chen et al., 2021). Thus, we formulate the following hypothesis: ➢ H4: Female-led firms that adopted DT during the COVID-19 crisis have better access to bank financing. 3. Data Description and Methodology 3.1. Data Description The empirical analysis in this study used two survey datasets: the pre-pandemic World Bank Group Enterprise Survey (WBES) and the Covid-19 Impact Follow-up Survey (COVFS), conducted in four waves after the onset of the COVID-19 pandemic using the same sample of firms from the baseline WBES. The pre-pandemic WBES collected firm-level data from registered private businesses across countries, focusing on business environment, performance, and characteristics. The COV-FS dataset evaluates the economic effects of the pandemic on private businesses using telephone interviews to calculate indicators such as business closures, operations, employment, revenue, access to external financing, and Gender, Digital Transformation and Access to Bank Credit 8 policy initiatives. Both datasets provide comparable data across countries. The COV-FS surveys were conducted in these countries over the course of three waves, between May and June 2020 and August and September 2021. The COV-FS dataset offers precise indicators of the various effects of the COVID-19 pandemic on businesses, similar to the WBES dataset for firm outcomes in the pre-pandemic period. The information acquired during these surveys was used to calculate a range of indicators, including business closures, operations, employment, revenue, workforce changes, finances, gender, policy responses, and expectations to determine the impact of the crisis on a firm's performance metrics. The WBES reflects the pre-crisis situation because it was completed before the COVID-19 pandemic began. Thus, the pre-pandemic data from the WBES served as a baseline for COV-FS indicators for comparison. The WBES collects firm-level data from a representative sample of officially registered private businesses with five or more employees in retail, manufacturing, and service industries. WBES data are comparable across countries owing to the use of a global methodology based on stratified random sampling. The WBES collects information on various components of the business environment, firm performance, and firm characteristics, including the gender of the firm's top manager and the percentage of female and male ownership. 3.2 Female Ownership Variables We constructed two variables that accounted for the gender of business owners and highestranked manager. Following Aristei and Gallo (2016), the first dummy variable (FEM-LED) equals one if the top manager of a firm is a woman and there are one or more female owners. This binary variable accounts for women’s involvement in both business ownership and management. The second dummy variable (FEMALE-OWNED) equals one if women’s ownership is 51 percent or more; hence, it measures the extent of female ownership. These variables provide a nuanced analysis of a firm's financing demand and supply by gender. Of the firms in the sample, 32.3% had at least one woman among the firm’s owners and 17.67 percent had a female top manager. 3.3 Econometric Specifications 3.3.1 Probit with Selection Model This study explores gender differences in firms' loan application decisions, financial access, and borrower bank discouragement behaviors during periods of economic uncertainty. Hence, we developed several research objectives to examine how gender affects the ability of women-owned businesses to apply for and obtain external financing during the COVID-19 pandemic. Several dependent variables were constructed, each sought to address a specific research question. First, we hypothesize that the pandemic affected financing needs differently for women-led businesses, as they have been disproportionately affected and suffered deeper financial distress (Amin & Viganola, 2023). We aim to understand the impact of gender differences on firms' external financing Khan, Subramanian & Mutalib 15 Table 2: Mean Differences in Demand for and Supply of Finance, Bank Discouragement, and Credit Constraints by Gender of Firm Ownership and Leadership Credit demand Apply for credit Applicat ion Rejected Bank discourag ed Bank financin g Equity financing Overdue in financial obligations to banks Liquidit y decrease d Panel A: Women-owned business (female ownership 51% or more) FEMALEOWNED 1 0.425 0.250 0.288 0.458 0.256 0.479 0.196 0.738 0 0.382 0.267 0.188 0.400 0.309 0.542 0.162 0.696 Mean Difference 0.043** * - 0.017* * 0.100** * 0.058*** - 0.053** * - 0.064*** 0.034*** 0.042** * Panel B: Female-ownership 51% or more, and top manager is female. FEM-LED (0, 1) 1 0.389 0.226 0.261 0.482 0.231 0.521 0.167 0 0.404 0.268 0.187 0.417 0.302 0.517 0.184 Mean Difference -0.016* - 0.042* ** 0.074** * 0.066*** - 0.071** * 0.004 -0.017*** Gender, Digital Transformation and Access to Bank Credit 16 Table 3: Demand for Finance and Bank Discouragement for Women Entrepreneurs The probit model with selection (standard errors in parentheses) for Eq. (1) – (4). FEM-LED [0, 1] equals one for firms with a female CEO and at least one woman among the firm owners. EXPORTER [0, 1] equals one if an establishment has at least 10 percent of its sales as exports. FOREIGN [0, 1] equals one if foreign ownership in the establishment is 10 percent or more. ONLINE_BUSINESS [0, 1] equals one if an establishment started or increased its e-commerce activities. WC finance is the pre-pandemic share of working capital financed through internal funding. Sale Change is the monthly percentage change in sales compared with the previous year. Demand Decrease [0, 1] equals one if the demand for the establishment’s products and services decreased compared to the previous year. The Larg firm dummy [0, 1] equals one if an establishment has 100 or more employees and zero if it has fewer than 100 employees. The Small firm dummy [0, 1] equals one if an establishment has fewer than 19 employees and zero otherwise. LIQUIDTY [0, 1] equals one if an establishment reported that liquidity and cashflow availability decreased during the COVID-19 pandemic. Govt Asst [0, 1] equals one if an establishment received financial assistance from the government during the COVID-19 pandemic. Audit account [0, 1] equals one if the establishment’s financial accounts are audited annually by independent auditors. The dependent variables for each regression are indicated at the top of each column. *** p<0.01, ** p<0.05, * p<0.10 Demand for Bank Credit Loan Application Behavior Bank Discouragement (1) (2) (3) (4) (5) (6) Credit Need Liquidity Decreased Applied Credit Need Bank Discouragement Credit Need FEM-LED 0.140*** 0.0428 -0.101** 0.0017 0.103** -0.0060 (0.0513) (0.0452) (0.0480) (0.0328) (0.0479) (0.0324) AGE 0.00216 -0.00141 0.000236 -0.0017** -0.000218 - 0.00178** (0.00151) (0.00127) (0.00128) (0.000836) (0.00128) (0.00082 3) Small firm dummy -0.00786 0.0860** - 0.240*** 0.0225 0.239*** 0.0181 (0.0421) (0.0363) (0.0387) (0.0261) (0.0386) (0.0255) Large firm dummy 0.0473 -0.122*** 0.0956* -0.0660** -0.0986* -0.0591* (0.0559) (0.0446) (0.0526) (0.0330) (0.0525) (0.0322) Foreign Ownership dummy -0.126* -0.130** -0.152** -0.260*** 0.164*** - 0.301*** (0.0667) (0.0542) (0.0627) (0.0402) (0.0629) (0.0393) Exporter dummy -0.00879 -0.111** 0.0644 -0.0117 -0.0653 -0.0178 (0.0436) (0.0388) (0.0444) (0.0286) (0.0444) (0.0283) WC finance -0.296*** 0.0775 -0.163*** -0.190*** 0.192*** -0.275*** (0.0610) (0.0522) (0.0574) (0.0387) (0.0575) (0.0365) Gov Asst 0.449*** 0.377*** 0.339*** 0.248*** -0.341*** 0.250*** Khan, Subramanian & Mutalib 17 (0.0648) (0.0570) (0.0397) (0.0254) (0.0397) (0.0252) ONLINE_BUSINESS 0.0258 0.266*** 0.125*** 0.0491** -0.125*** 0.0588** (0.0381) (0.0362) (0.0359) (0.0247) (0.0358) (0.0244) Sale Change -0.499*** -0.404*** -0.406*** (0.0624) (0.0455) (0.0451) Demand Decreased 0.868*** 0.396*** 0.395*** (0.0389) (0.0291) (0.0288) Liquidity Decreased 0.354*** 0.352*** (0.0281) (0.0278) Indebtedness 0.411*** 0.300*** -0.311*** 0.3006*** (0.0623) (0.0417) (0.0613) (0.0164) Audit account -0.00129 -0.0359 -0.0139 -0.037 (0.0385) (0.0258) (0.0375) (0.0258) Constant 1.717*** 0.688 -0.288 0.00568 0.248 0.121 (0.508) (0.436) (0.232) (0.202) (0.231) (0.201) Observations 7,875 7,875 15,808 15,808 16,124 16,124 Censored obs. 3385 Uncensored obs. 4490 Wald chi2(48) 473.35 Prob. > Chi2 0.000 Rho -0.969*** LR test of independence (rho=0): chi2(1) 211.94 Prob > chi2 = 0.0000 Industry & country dummies Yes Yes Yes Yes Yes Yes Gender, Digital Transformation and Access to Bank Credit 18 Table 4: Gender and Access to External Financing: Bank Financing Probit model with selection (standard errors are in parentheses). FEM-LED [0, 1] and other control variables are as defined in Table 3. Columns (1) and (3) present the results of the outcome equation (Eq. 3). Columns 2 (Eq. 3) and 4 (Eq. 2) present the results of the selection equations. *** p<0.01, ** p<0.05, * p<0.10 (1) (2) (3) (4) Bank Financing (2nd stage reg.) Applied (1st stage reg.) Bank Financing (2nd stage reg.) Applied (1st stage reg.) FEM-LED -0.163** -0.167*** 0.126 -0.0227 (0.0759) (0.0613) (0.105) (0.0394) FEM-LED x Credit constraint (WBES) -0.384** (0.179) AGE -0.000177 -0.00160 -0.00258 -0.0014 (0.00172) (0.00147) (0.00242) (0.00099) Small firm dummy -0.206*** -0.174*** -0.373*** -0.188*** (0.0563) (0.0478) (0.0698) (0.0313) Large firm dummy 0.153** 0.0155 0.428*** 0.00245 (0.0672) (0.0564) (0.110) (0.0387) Foreign Ownership dummy -0.352*** -0.260*** -0.176 -0.223*** (0.0851) (0.0709) (0.125) (0.0491) Exporter dummy -0.000570 0.0173 -0.0234 0.0724** (0.0589) (0.0510) (0.0798) (0.0338) WC Finance -0.498*** -0.406*** -0.0454 -0.246*** (0.0777) (0.0664) (0.121) (0.0462) Govt Asst 0.296*** 0.317*** 0.195** 0.343*** (0.0581) (0.0489) (0.0948) (0.0305) ONLINE_BUSI NESS 0.101* 0.0639 0.0807 0.0514* (0.0525) (0.0452) (0.0669) (0.0293) Sale Change -0.217*** -0.0425 (0.0585) (0.0532) Demand Decreased 0.211*** 0.165*** (0.0403) (0.0372) Liquidity Decreased 5.871 0.231*** (253.6) (0.0372) Constant -0.907*** -6.495 -0.394 -1.115*** (0.138) (253.6) (0.601) (0.259) Observations 7,391 7,391 Khan, Subramanian & Mutalib 19 The probit model with selection in Table 4 (Column 1) shows a negative and statistically significant coefficient for FEM-LED, indicating that businesses with female ownership and female CEOs tend to have a lower likelihood of accessing bank credit during the pandemic. This finding supports the notion of gender disparity in the credit market (Treichel & Scott, 2006). This result is in line with Liu et al. (2021) who identify impaired access to bank credit as one of the main channels contributing to the negative effects of the pandemic on women-led businesses. However, this result contrasts with Hewa-Wellalage et al. (2022) who found that female-owned and female-led enterprises had a marginally higher probability (2 percent points) of obtaining bank credit during the COVID-19 pandemic than male-led enterprises. Our results also contrast with those of Cowling, Marlow, and Liu (2020), who show that female businesses had a lower demand for bank credit during the 2008 Global Financial Crisis, but that female businesses that applied for loans were more likely to receive bank lending than male businesses. They argue that feminized risk aversion might have played a role in female business loan application decisions, with the result that loan applications were more likely to be submitted by conservative, less risky but stronger female businesses during periods of economic uncertainty. This positively affects the likelihood of obtaining bank loans by female businesses, given that financial institutions tend to adopt self-protective and cautious behavior in their capital allocation during periods of economic downturns. However, Cowling et al. ’s (2020) subtle, deeper analysis of the channels influencing financial institutions’ loan approval decisions reveals that female businesses’ loan applications suffer a disadvantage as financial institutions assign lower value to their collateral offered against loan security, indicating a gendered discrimination aspect in their credit supply to female businesses during the 2008 GFC. This study also investigates whether pre-pandemic financial constraints worsened gender disparities in access to external financing among women-led businesses. The results of the interaction term between pre-pandemic credit constraint and FEM-LED [FEM-LED  Credit_constraint (WBES)] in Column (3) of Table 4 show that women-owned firms with pre-pandemic tight financial conditions were less likely to have access to bank financing. In fact, our results indicate that prior credit constraints aggravated gender disparities in credit access to women-led enterprises, confirming the findings of Artistie and Gallo (2023), Khan (2022), and Zhang and Sogn-Grundvåg (2022). Recent empirical studies show that firms facing pre-pandemic credit constraints are less resilient to economic shocks (Aristei & Gallo, 2023), suffered higher sales declines and show lower resilience in navigating the economic distress induced by the COVID-19 pandemic (Amin & Viganola, 2023). Balduzzi et al. (2020) find that firms with prior financial difficulties planned to cut future investments and employment more than are unconstrained firms. Analogously, Aristei and Gallo (2023) find that prior credit constrains significantly amplify liquidity and financing problems and amplify the pandemic’s impact on firm performance and vulnerability. Our empirical results closely align with those of Khan (2022), who shows that prior credit constraints significantly exacerbated firms’ financial vulnerability, credit Gender, Digital Transformation and Access to Bank Credit 20 risk, liquidity problems, and impaired access to bank credit during the COVID-19 pandemic. Our contribution to this strand of the literature is that pre-pandemic credit constraints intensify the negative impact of the pandemic on female-led firms' financial access and vulnerability. Table 5: Instrumental Variable Probit Model (IV Probit) This table presents the results of the instrumental variable (IV) probit models (standard errors in parentheses). Columns (1) and (2) presents the results of Eq. (1) and (2), respectively. Columns (3) and (4) present the results of Eq. (3) and (4), respectively. The dependent variable for each regression is reported at the top of each column. The Gender Development Index (GDI Dummy) is an instrument for FEM-LED. This binary variable equal 1 for countries with high equality in Human Development Index achievements between men and women. *** p<0.01, ** p<0.05, * p<0.10 (1) (2) (3) (4) (5) (6) FEM-LED Credit Need FEM-LED Bank Financing FEM-LED DISC_B ANK First stage 2nd Stage First stage 2nd Stage First stage 2nd Stage FEM-LED 2.062*** -2.35*** 2.779*** (0.8049) (0.491) (0.338) AGE -0.000125 -0.00124 -0.0006*** 0.000114 -0.000412 0.00108 (0.00018) (0.000931) (0.000212) (0.00113) (0.000278) (0.00091) GDI Dummy 0.071** 0.106*** 0.0467 (0.0303) (0.0392) (0.0332) Control variables Yes Yes Yes Yes Yes Yes Observations 18,017 18,017 14,728 14,728 8,121 8,121 Wald test of exogeneity Chi2 (1) 2.75* 5.28** 4.32** Next, we exploit cross-country heterogeneity to examine whether the observed gender gaps in the credit market vary in severity depending on the most prevalent culture in each country. As in Mascia and Rossi (2017), we use the Global Gender Gap Index (GGGI) to account for cultural and institutional differences in gender-based disparities that characterize the countries in our sample. A biennial report generated by the World Economic Forum, the GGGI benchmarks national gender gaps in the economic, educational, health, and political domains. Using the 2020 Global Gender Gap rankings and the scores of the countries included in our sample, we split them into three clusters based on their relative rankings among the 153 countries covered by the index. Cluster 1, 2, and 3 consisted of countries with rankings from 1 to 50 (lowest magnitude of gender disparities), 51 to 83, and 84 to 153 (highest level of gender-based discrimination), respectively. The empirical results of the instrumental variable probit model (gender development index as an instrument for FEM-LED) in Eq. (1) – (4) for credit demand, bank Khan, Subramanian & Mutalib 21 credit, and borrower discouragement, respectively, are presented in Table 5. Again, the coefficient estimates for FEM-LED, as reported in Columns 1, 3, and 5, are in line with our previous findings that female-led businesses are more likely to have higher demand for bank credit but are more likely to be discouraged from applying and denied credit by banks and financial institutions during the COVID-19 crisis. Next, we examined the temporal heterogeneity that characterizes the panel nature of our survey dataset, covering two years from the start of the COVID-19 pandemic in 2020 and the subsequent year 2021. The first three waves of the COV-FS survey dataset were used for the empirical analysis. In our temporal heterogeneity analysis, we run our regression specifications separately for 2020 and 2021. The empirical results obtained using the COVFS survey data for 2020 and 2021 are not reported in the paper for brevity but are available upon request. According to the coefficient estimates, female-led businesses experienced same loan demand as male-led businesses in 2020. This result contrasts with the findings of Cowling et al. (2020) for the 2008 GFC. They document significant differences in loan demand between female-led and male-led businesses in the early stages of the aftermath of the 2008 GFC; women-led businesses had significantly lower application rates than maleled businesses. However, by 2021, female-led businesses ran out of their resources and had a greater need for external funding. This outcome is in line with that of Wu's (2022) finding that firms' financial conditions deteriorated as the pandemic evolved and its economic impacts materialized. Additionally, female-led businesses were less likely to request bank financing during the early stages of the pandemic in 2020. However, as the pandemic progressed, their behavior changed, and female-led businesses became more active in seeking bank funding and were more likely to request bank loans compared to their maleled counterparts. However, the gender disparity in debt financing persisted because femaleled businesses were less likely to secure bank credit than their male-led competitors were in the early and later years of the pandemic. Finally, during the later stages of the pandemic, borrowers' discouraged loan application behavior by gender and gender disparities also vanished and female businesses became active in seeking external funding. 4.3 Robustness Checks Several robustness checks were conducted to examine the validity of the empirical results. Women-led firms have been disproportionately affected by the pandemic, leading to many exiting the market and, thus, have not been covered by follow-up COF-FS surveys. Thus, our empirical analyses are based on high-quality female-led firms that have survived the pandemic, highlighting the potential understatement of gender differences in terms of financial access and fragility. To address this potential selection bias, as in Birhanu et al. (2022), we used the COV_FS question "Currently is this establishment open, temporarily closed, or permanently closed?" to estimate the likelihood of a firm exiting a market. This dummy variable, denoted by EXIT, equals one if the firm’s response is “open” to the stated question and equals zero for “permanently closed” responses. Approximately 5.23 percent Gender, Digital Transformation and Access to Bank Credit 22 of the firms were permanently closed (3,097 of 59,176 firms). Moreover, despite several efforts, the teams conducting the follow-up survey were unable to locate a sizable portion of businesses, leading to the assumption that they either left the market or were permanently closed. The follow-up survey dataset revealed that the answer to the aforementioned question was missing for such establishments, implying an exit rate of approximately 16.73 percent (9,902 of 59,176 firms). We follow Birhanu et al. (2022) and use information from the baseline WBES survey to estimate the likelihood of a firm exiting the market. We have information on both surviving and potentially exited enterprises because the follow-up surveys employed the same sample of firms from each country for which the WBES surveys were conducted the year before the COVID-19 pandemic. We construct a proxy for a firm's indebtedness from the baseline WBES dataset by taking the average of three dummy variables: overdraft facility use, line of credit or bank loan, and the owners’ personal outstanding loans. Highly indebted enterprises are more susceptible to bankruptcy during the COVID-19 pandemic. Credit defaults and exodus occur because of rising costs, a decline in revenues, and cash flow shortfalls. The second variable, Sunk Cost, represents the value of the land and buildings owned by a firm as a proportion of the total value of the land and buildings that a firm utilizes. For businesses that own the assets they use, exiting is a less desirable alternative because doing so puts a lot of risk (having to sell tangible assets during a crisis) and little benefit (in terms of rent savings) on the table. However, businesses with a high level of debt and reliance on rented capital have a strong incentive to file for bankruptcy in order to avoid paying rent and accruing interest. The probit model (dependent variable: EXIT) estimates a firm's likelihood of selection in a sample based on two key independent variables: indebtedness and sunk cost. As reported in Table 6, there are gender differences in selection into the sample, as evidenced by the negative coefficient estimates for FEM-LED and FEMALE_OWNED. The coefficient for indebtedness shows an inverse U-shaped association, suggesting that establishments are more likely to permanently close and exit the market at very high (low) levels of debt (low levels of debt may imply a firm’s credit constraints). The relationship between loan indebtedness and a firm's likelihood of selection in the sample is positively moderated by higher sunk costs. The predicted probability of selection was estimated from this model and used to construct the inverse Mills ratio, which was included in the Heckman probit model as an explanatory variable (Birhanu et al., 2022; Parker, 2018) in Equations (1)– (4). The level of indebtedness was also included in the specifications since pre-pandemic debt levels may also affect a firm’s creditworthiness during the pandemic-induced economic crisis. The results of second-stage regressions for the Heckman selection model (Table 7) show the presence of selection effects and the robustness of the results after accounting for sample selection effects. Khan, Subramanian & Mutalib 23 Second, as in Wellalage and Locke (2017), we account for the possibility that the owner/top manager’s gender is endogenous to the firm’s credit constraint using the gender development index (GDI) as an instrumental variable for FEM-LED. We construct a binary variable (GDI dummy) equal to one if a country falls in group 1, which comprises countries with high equality in Human Development Index (HDI) achievement between women and men and takes a value of zero otherwise. This analysis was conducted using an instrumental variable binomial probit model, which fits models for binary dependent variables in which at least one of the covariates is endogenous. The first stage involves estimating the predicted values of the endogenous variable (FEM-LED), using an instrument (GDI dummy) along with the control variables, which are then substituted into Equation (3) (See Seema, Seyyed, & Shehzad (2021) for further details and analysis). Finally, Propensity Score Matching (PSM) (Rosenbaum & Rubin, 1983) and Blinder– Oaxaca decomposition (BOD) (Blinder 1973; Oaxaca 1973) technique were used to analyze the impact of gender on financial access. PSM accounts for endogeneity problems caused by omitted variables and self-selection bias. Developed by Rosenbaum and Rubin (1983), PSM pairs each treated unit with one or more control units that are comparable in all observable factors except for treatment status. PSM entails stratifying the sample into treatment and control groups based on independent variables. Consequently, the impact of the independent variable can be estimated while accounting for potential confounders. PSM compares companies with female ownership and leadership to those with male ownership and leadership, assuming non-systematic variations in responses to treatment. The results (Table 8) show that the average treatment effect on treated (ATT) is negative for bank financing. Female entrepreneurs are 5.1%–7.8 percentage point less likely to have access to bank credit during the COVID-19 crisis and are 8.3% more likely to be discouraged from applying for bank credit. Gender, Digital Transformation and Access to Bank Credit 24 Table 6: Probit Model to Predict Probability of Selection in the Sample Results of the probit model (standard errors are in parentheses). The dependent variable in columns (1) and (2) is a dummy variable that equals one if an establishment is either confirmed to have closed permanently or is believed to have done so because the teams conducting the follow-up survey were unable to identify them despite numerous attempts to do so. In Columns (3) and (4), the dependent variable is a dummy variable that equals one if a firm was confirmed to be permanently closed and zero otherwise. Indebtedness, estimated using the pre-pandemic baseline WBES dataset, is the average of three dummy variables identifying whether a firm (a) used an overdraft facility, (b) had a line of credit or loan from a financial institution, or (c) the owner’s personal outstanding loans were used to finance business operations and growth. *** p<0.01, ** p<0.05, * p<0.10 (1) (2) (3) (4) selection 1: Confirmed and assumed permanently closed selection 2: Confirmed permanently closed FEM-LED -0.0467** -0.0547* (0.0224) (0.0320) FEMALE-OWNED -0.0883*** -0.0917*** (0.0233) (0.0326) Small firm -0.0914*** -0.0857*** -0.223*** -0.216*** (0.0181) (0.0186) (0.0261) (0.0268) Large firm -0.0523** -0.0584** 0.126*** 0.110*** (0.0223) (0.0231) (0.0358) (0.0369) Foreign Ownership dummy -0.0601** -0.0732** 0.0269 0.0126 (0.0281) (0.0290) (0.0453) (0.0465) Exporter dummy 0.0546*** 0.0403* 0.0732** 0.0658** (0.0211) (0.0216) (0.0324) (0.0331) Indebtedness 0.0999 0.0876 0.389*** 0.437*** (0.0797) (0.0813) (0.116) (0.118) indebtedness_squared -0.288*** -0.293*** -0.618*** -0.676*** (0.0859) (0.0881) (0.126) (0.129) Sunk Cost 0.0439* 0.0588*** 0.0647** 0.0963*** (0.0234) (0.0222) (0.0322) (0.0309) Indebtedness x Sunk Cost 0.242*** 0.234*** 0.369*** 0.304*** (0.0536) (0.0519) (0.0774) (0.0749) Constant 1.159*** 1.165*** 2.542*** 2.529*** (0.147) (0.147) (0.292) (0.292) Observations 41,502 40,308 41,177 39,990 Khan, Subramanian & Mutalib 31 An important factor in the gender gap in access to external financing is the limited access to and use of digital technologies in female-led firms. However, the gender of top managers was not associated with the utilization intensity of these online technologies (Lashitew, 2023). On the other hand, Njiwa et al. (2023) demonstrated that pre-pandemic and duringpandemic technology usage enhanced female-led businesses’ resilience to pandemicinduced economic shocks. Our results are in line with those of Torres et al. (2021), who found that women-led businesses tend to adopt digital technologies in response to the COVID-19 pandemic. This study empirically demonstrates that female-led enterprises’ use of digital technology and the digital transformation of their business models can mitigate the negative effects of female ownership and leadership on financial friction and access to bank credit. Overall, these findings imply that if there are gender gaps in access to finance, they are partially closed by women-led businesses that digitalize their operations. As a result, these businesses were better able to manage the COVID-19 challenges and were better positioned to have better access to finance. Our results are generally in line with those of Njiwa et al. (2023), who showed that gender gaps in firms’ resilience during the COVID-19 crisis were mitigated by female-led firms’ adoption of digital technologies in the pre-pandemic period, which allowed them to be better equipped to manage the negative consequences of the pandemic and remained afloat during the crisis. However, this study partially contradicts Njiwa et al.'s findings, which suggest that COVID-19 increased the negative relationship between gender and firm resilience owing to costly digital technology investments. The lack of financial resources may have pushed female-led firms to reduce their digital technology investment, causing liquidity pressure and affecting business operations. We argue that digitally transformed female-led businesses have a better chance of obtaining external financial resources, because digital transformation increased these firms’ resilience to the negative consequences of the COVID-19 pandemic and better placed them to access credit markets for external financing. Women generally have lower attitudes toward and preferences for technology adoption and utilization (Genz & Schnabel, 2023). Cai et al. (2017) revealed that the so-called “technological gender gap” persists despite recent developments in technology and its ubiquitous infiltration at both societal and organizational levels. These gender-based disparities in technology diffusion may put women-led businesses in a disadvantageous position in fighting economic challenges. 5.1 Managerial Implications This study offers several managerial and policy implications. First, female-owned, and female-run businesses suffered greater financial distress and were more vulnerable to the economic consequences of the COVID-19 pandemic. Impaired access to external financing for women entrepreneurs has been one of the main constraints inhibiting their entrepreneurial growth. The empirical analysis further revealed that gender disparities in the credit market persisted during the COVID-19 pandemic as it evolved, and its effects Gender, Digital Transformation and Access to Bank Credit 32 unfolded over an extended period. Based on the theories of discriminatory “gendered ascriptions”, we argue that female-led businesses experienced gender disparities in the credit market in times of economic and financial distress and their financial inclusion has intensified the need for more efficient policy interventions to support women’s entrepreneurship. Second, female-led businesses have shown the capacity to adapt to digital technology usage (Njiwa et al., 2023; Allison et al., 2023); however, they often face constrained access to financial resources. This can hinder their ability to access the financial services required for survival and growth. Given the findings of a consistent gender gap in credit access during the COVID-19 pandemic, our empirical results suggest that these were partially filled by female-owned and female-run businesses that digitalized their production and management operations and used digital platforms for e-commerce activities. Thus, the managerial implications of our findings is that female-led businesses that leveraged their resources in adopting digital technologies were better positioned to obtain better access to financial resources to manage liquidity shortfalls and improve their financial resilience. Recent studies have shown that female-led enterprises that adopted digital technologies both in the pre-pandemic and during-pandemic times demonstrated higher resilience to economic shocks (Njiwa et al., 2023; Santos, Liguori and Garvey, 2023). Female-led businesses face constraints and challenges in adopting digital technologies; however, they exhibited similar technology utilization intensity during the COVID-19 pandemic (Lashitew, 2023; Tønnessen, Dhir, and Flåten, 2021). This suggests that female top executives can perform equally well compared to their male counterparts in the utilization of technologies given the financial and other resources required to develope technological infrastructure in their enterprises. According to our empirical analysis, we suggest that female-led businesses should develop dynamic competencies in digital technologies and related resources to better cope with existing and future challenges and crises and position their businesses to have better access to external financial resources. Thus, technology adoption can make female businesses financially more resilient and better prepare them to face future challenges and crises. 5.2 Limitations and Future Research Exploring gender disparities in the credit market in the context of extreme financial distress such as the COVID-19 pandemic and the role of digital transformation in closing gender disparities in the credit market, this study has a few limitations that leave room for future research. First, gender bias tends to manifest more during the upside phase of the economy (Galli et al., 2020). Combining this (Galli et al., 2020) finding with our analyses, which were derived by conducting our research in times of extreme economic and financial distress, such as the COVID-19 pandemic, implies that gender differences in bank borrowing behavior follow cyclical trends. We reserve further investigation of this in the future, as it requires a longitudinal approach. Future studies are warranted to examine whether patterns of gender discrimination still exist when the pandemic’s economic Khan, Subramanian & Mutalib 33 consequences have diminished, and more comprehensive, rich, and granular datasets covering longer periods have become available for analysis. While bank funding constitutes one of the main sources of small business financing, we acknowledge and encourage the exploration of how discrimination, gendered ascriptions, and female business funding can influence female-led and female-owned businesses’ access to alternative sources of external financing during periods of high economic uncertainty such as the COVID-19 pandemic. Overall, our study demonstrates the pervasive effects of the pandemic on gender disparities in the loan market. Future empirical studies should examine how long the damaging consequences of the credit crunch persist among SMEs, even when firms have recovered in the post-pandemic period. It may also be worthwhile to conduct replication studies to determine whether gender trends in bank funding revert to their previous state after the economy achieves sustained and reliable growth. Research Funding The authors received no research grant or funds for this research study. 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