The gender gap in access to finance: Evidence from the COVID-19 pandemic
Introduction
Across the world, the COVID-19 pandemic highlighted the financial fragility of many businesses (Liu et al., 2021; Brown and Rocha, 2020). The sharp decline in cash inflow resulting from the temporary lockdowns during the COVID-19 pandemic has driving many firms, especially smaller ones, close to bankruptcy, unable to meet their current and ongoing financial obligation. Drawing on a survey of over 5,800 small businesses in the United States, Bartik et al. (2020) show three-quarters of respondents had just two months or less in cash in reserve during the early stages of the COVID-19 pandemic. Humphries and Ulyssea, 2020 report similar impacts. This is further exacerbated by the fact that smaller firms tend to be disproportionally disadvantaged in securing finance in periods of high uncertainty (Gompers et al., 2010; Lee et al., 2015). Evidence of lack of access to finance is presented by Brown and Rocha (2020), showing that in first quarter of 2020 small firms in China saw a 60% decline in equity investments. For the United Kingdom, Brown et al. (2020) show that entrepreneurial finance deals dropped by 30% during the pandemic.
In this paper, we examine whether female entrepreneurs faced barriers attributed to their gender in accessing debt financing—from banks and micro-finance institutions (MFIs)—and equity financing during the pandemic. Our paper is motivated by the fact that a significant portion of smaller businesses globally are female-owned/led (Bruhn et al., 2017), the importance of these firms in driving female participation in the economy, and female entrepreneurs being particularly hit hard by the pandemic (Liu et al., 2021), putting much of the progress made towards women's empowerment at risk.
The literature on gender differences in access to finance suggests that under “normal” economic conditions, female entrepreneurs are more prone to financial constraint (Cavalluzzo et al., 2002; Chaudhuri et al., 2020). Discrimination theory, widely cited in finance, helps to explain gender differences in access to finance (Aterido et al., 2013; Pham and Talavera, 2018). It focuses on prejudice or taste-based discrimination and statistical discrimination (Han, 2004). In the classical model of Becker (1957), taste-based discrimination highlights adverse and unjustified attitudes towards a specific individual stemming from cultural beliefs, such as stereotypes of female characteristics which are deemed to be of lower value than those associated with masculinity (Koenig, 2018; Ramiah et al., 2010). In a male-dominated financial system, this could result in female entrepreneurs being offered restricted access to finance and on less favourable terms (Alesina et al., 2013; Andrés de et al., 2020; Aterido et al., 2013; Bellucci et al., 2010; Cozarenco and Szafarz, 2018; Kende-Robb, 2019; Xu et al., 2016). Notwithstanding, there may be other reasons females have a lower demand for credit, reflecting their personal choices and motivation (Coleman, 2002; Cowling et al., 2020; Moro et al., 2017; Ongena and Popov, 2016). For example, they may be less inclined to apply for a loan, fearing rejection by lenders (Chaudhuri et al., 2020; Moro et al., 2017), or rely more on their own capital or from family members, which leads to less demand for external finance, including start-up loans (Andrés de et al., 2020).
A lack of information about a borrower's ability to pay leads to statistical discrimination in the debt and equity markets. Statistical discrimination is the solution to a signal extraction problem. If capital providers observe a noisy signal of borrowers’ ability and has prior information about correlates of ability (let's say a group-specific mean), then expectation of borrowers’ ability should place weight on both the signal and the mean. In such situations, group stereotypes are applied to individual borrowers (Wellalage and Locke, 2017). For example, unobservable factors of borrower's credit situation encourage lenders to use gender as a proxy for ability to pay (Andrés de et al., 2020). Gender discrimination in access to finance may worsen during uncertain periods when finance providers apply tougher conditions to less attractive borrowers (Cowling et al., 2012; Robb et al., 2013).
We contribute to the literature of gender in finance, by providing the first cross-country study examining the impact of gender in terms of accessing debt and equity financing during the COVID–19 pandemic. For a sample of 8,921 private firms from 19 (mostly developing) countries, we find no evidence suggesting that female entrepreneurs were disadvantaged, attributed to their gender, in terms of accessing finance during the pandemic. Propensity score matching and Blinder-Oaxaca decomposition tests confirm the baseline regression results. In particular, we find slight female favouritism, with female entrepreneurs up to 2 percentage points more likely to access debt financing than their male entrepreneurs during the pandemic based on their gender alone. We find no evidence of gender bias in access to equity financing. Our findings are in support of the proposition of Cowling et al. (2020), but contrary to existing prejudicial belief, that in the context of high economic uncertainty, prototypical forms of femininity become advantageous as creditor providers seek to hedge their risks by favouring more conservative lenders.
The rest of our paper is organised as follows. Section 2 presents the data and empirical method. Section 3 discusses the empirical findings, whilst Section 4 provides a number of robustness checks. Section 5 concludes.
Section snippets
Data
Our exploration of the relationship between gender and access to finance during the COVID-19 pandemic proceeds using business level data drawn from the World Bank Enterprise Surveys (WBES) and World Bank COVID-19 follow up surveys from the same organisation (World Bank, 2020). The follow-up surveys re-interview respondents of recently completed WBES to collect information about closures, changes in sales, employment and finance, government support, policy responses and expectations as a
Method
A heckprobit model (probit model with sample selection), which assumes the existence of an underlying relationship between gender and financing is constructed. This enables us to capture possible gender differences in access to debt financing—from banks and MFIs, and equity financing during the pandemic2
Empirical results
Tables 4 and 5 report the two-stage heckprobit regression results for female-owned and managed businesses, respectively.4 Reported are the marginal effects estimated around the mean point. Column I and II show that female entrepreneurs accessed debt financing (from banks and MFIs) on average by up to 2 percentage points more during the pandemic than their male counterparts, controlling for a larger
Robustness
We apply two robustness checks: propensity score matching (PSM) and Blinder–Oaxaca decomposition technique. We perform PSM to pair firms that have female ownership/leadership with other firms that have exclusive male ownership/leadership. It is then assumed that the matched firms that have non-systematic differences in response to the treatment, so they provide valid counterfactual evidence. Table 6 panels A and B provide the results for three PSM techniques: nearest neighbour matching,
Summary and conclusions
We analysed the dynamics of debt and equity financing in female-owned/led businesses for a cross-country sample of private firms during the COVID-19 pandemic. While the impact of the pandemic has been particularly severe for females, our study provides slight female favouritism, with female entrepreneurs up to 2 percentage points more likely to access debt financing than their male entrepreneurs during the pandemic based on their gender alone. This result is perhaps somewhat surprising given
CRediT authorship contribution statement
Nirosha Hewa-Wellalage: Conceptualization, Writing – original draft, Formal analysis, Methodology. Sabri Boubaker: Conceptualization, Methodology, Writing – review & editing. Ahmed Imran Hunjra: Data curation, Conceptualization, Project administration, Writing – original draft. Peter Verhoeven: Conceptualization, Methodology, Writing – review & editing.
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