While _simultaneity bias_ may overestimate the effect of being a female manager, understanding the size of this effect, particularly from the supply side (financial intermediaries), is crucial. Therefore, if OLS estimates of the female dummy in our regression capture such a supply-side bias it may not be a significant concern from the perspective of understanding the gender gaps in access to credit.

_Finally_ , our estimation may suffer from _sample selection bias_ which arises when women-led firms facing credit constraints are less likely to apply for loans even when financially constrained.<sup>14</sup> In such circumstances, only the marginally more productive and capable women are likely selected into managerial positions in formal firms with over 5 employees, implying that OLS will be an underestimation of gender gaps in credit access.

Our estimations control for several characteristics of the firm and the top manager available in WBES along with industry, country, and year fixed effects, allowing us to mitigate, at least partially, some of the potential biases. For example, women managers can self-select in certain manufacturing industries or manage firms of sizes that are more likely credit rationed (e.g., in sectors with low tangible capital that may be offered as collateral) (World Bank, 2021); gender differences in manager experience can also influence credit access and firms’ performance (Ranasinghe, 2024); controlling for exports proxies for firm capabilities and market diversification that can vary by manager’s gender and affect credit access and performance (Muzi et al., 2023); the indicator of whether a firm has its financial statements certified by external auditors is a measure of financial transparency and can potentially improve credit access (Aristei and Gallo, 2016). By including the gender of the top manager instead of the female owner, we can partially avoid the bias that arises from loan officers’ concerns that female owners might prioritize personal constraints, such as childcare or household expenses, over business needs. Lastly, to get a sense of the selection bias, we consider the underlying performance metrics of firms, such as profitability and productivity, by the gender of their top-manager, in addition to the outcome on extensive margins of loan applications.

In addition, we conduct a series of robustness checks, including an instrumental variable estimation; sensitivity to the sample period; representativeness of our sample; threshold for defining traditional countries; and alternative measures of gender and social norms. Despite these efforts, findings from our analysis should be interpreted cautiously, given the multiple sources of bias.

# **3.1.3 Base results**

Estimation results for five alternative measures of credit access are presented in Table 2. Panel A presents results for all countries, while Panel B differentiates the results between more and less traditional countries. Our results suggest that female-managed firms are equally likely to apply for credit as male-managed firms (column 1).<sup>15</sup> Moreover, these firms have lower likelihood of credit application rejection (difference of 5.3 percentage points relative to male-managed firms) and lower chances of being credit constraint (difference of 5.9 percentage points relative to male-managed firms) (columns 2 and 3), which may explain their higher likelihood of having a business debt

> 14Although the survey instrument asks for reasons for not applying for a loan, including perceptions of not requiring a loan or fearing rejection, it is challenging to distinguish facts from beliefs or the underlying reasons for such beliefs. 15This finding holds even when controlling for firm leverage.

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