where _Yict_ measures the extensive margin in access to finance for firm _i_ , in country _c_ and year _t_ according to one of the following four indicator variables: (i) credit application in the last fiscal year; (ii) rejection in most recent credit application (conditional on having applied for credit); (iii) credit constrained, defined as credit application rejection (as in ii) or not applying for credit due to interest rates not being favorable, collateral requirements being too high or the size of loan and maturity being insufficient. The indicator takes the value 0 when the firm received a loan (conditional on application) or did not apply because it did not need it; (iv) having open lines of credit. In addition, the intensive margin in access to finance is measured using the (logarithm) of the level of business debt (conditional on having open lines of credit) for firm _i_ , in country _c_ and year _t_ .

Our main variable of interest, _Fict_ , is an indicator of whether the manager of firm _i_ is a female. All estimations include firm and manager attributes, _Xict_ , that is, manager’s gender and experience, indicators of firm age, indicators of the legal status of the firm, percentage of the firm that is privately owned, indicators of firm size, an indicator of whether the firm exports and whether it has financial statements certified by external auditors. For the model where _Yict_ is an indicator of credit application rejection or being credit constrained, _Xict_ also includes firm _i_ leverage (debt to capital ratio). All estimations control for industry ( _δs_ ), country ( _φc_ ) and year ( _γt_ ) fixed effects.<sup>13</sup> Model 1 is estimated by ordinary least squares and standard errors are clustered at the country level.

To infer how the gender gaps differ based on social norms, we estimate an expanded model that includes an interaction between the gender of the firm manager, _Fict_ , and the indicator for the country being traditional using the proxy for social and cultural norms from the WVS ( _Tc_ ): In the following sub-sections, we present the results distinguishing between less traditional countries (estimate of _α_ in model 2) and more traditional countries (sum of _α_ and _θ_ in model 2).

# **3.1.2 Econometric challenges**

This reduced form model estimation has three main econometric challenges. _First_ , despite our extensive controls and fixed effects, there could still be unobserved factors influencing access to credit that correlate with the gender of the firm’s top manager. These factors include, for example, the income level of top managers, their educational background and quality, and their social networks. If the attributes of women managers related to these factors are expected to be inferior to those of male managers, then OLS will overestimate the gender gap in access to finance in equation 1 due to _omitted variable_ bias.

_Second_ , endogeneity creeps into equation 1 as women’s ability to position as firm managers may be influenced by demand and supply conditions in access to finance. For example, sensitivity of discriminatory practices by financial institutions may discourage a firm’s top manager to be a woman.

> 13Data includes 19 industries within the manufacturing sector.

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