To examine the relationship between capital misallocation and credit received by firms, we go a step further and estimate an expanded version of model 1, including the logarithm of the value of debt and its interaction with the manager’s gender as a regressor.<sup>24</sup> Results in Table 5 show a statistically significant coefficient on the female dummy in more traditional countries, confirming again that on average there is a gender-driven misallocation of capital in countries with more restrictive social and cultural norms (columns 2 and 5). Importantly, a negative coefficient on the interaction of the female dummy with the debt (or the value of credit received by the firm) indicates that this association is statistically significant and stronger, especially in traditional countries (columns 2 and 5 of Table 5). Being able to borrow more can relax the credit constraint of firms and reduce capital misallocation for female-managed firms compared to male-managed firms in more traditional countries. These results underscore the importance of accessing finance to reduce gender-driven capital misallocation, particularly in traditional countries.

Table 5: Relationship between capital misallocation and gender gaps in debt

|||log(arpk)|||1+_κ_||
|---|---|---|---|---|---|---|
||All|More<br>traditional|Less<br>traditional|All|More<br>traditional|Less<br>traditional|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Female|1.061|2.026|0.217|1.069|2.079|-0.195|
||[0.550]*|[0.690]***|[0.503]|[0.683]|[0.856]**|[0.522]|
|Log(debt)|-0.0285|-0.0364|-0.0246|-0.0185|-0.00321|-0.0454|
||[0.0208]|[0.0378]|[0.0242]|[0.0193]|[0.0217]|[0.0236]*|
|Female x Log(debt)|-0.0859|-0.169|-0.0188|-0.0867|-0.175|0.0191|
||[0.0458]*|[0.0584]***|[0.0433]|[0.0563]|[0.0674]**|[0.0431]|
|Obs|3,222|1,334|1,888|3,222|1,334|1,888|
|R2|0.264|0.339|0.27|0.238|0.307|0.249|

Sources: World Bank Enterprise Surveys and World Value Surveys. Notes: The sample includes 61 countries, of which 28 are classified as more traditional and 33 as less-traditional. All models control for manager experience, indicators of firm age, legal status, size, whether the firm exports, whether it has financial statements certified by external auditors, the percentage of the firm that is privately owned, and industry, country, and year fixed effects. Standard errors are clustered at the country level. *** significant at 1%, ** at 5%, * at 10%.

# **3.3 Robustness Checks**

Our estimations illustrate gender gaps in access to credit. This section offers some robustness checks for our key results.

# **3.3.1 Considering endogeneity**

Estimating the causal effects of the gender of the top manager on a firm’s access to finance and performance is challenging in a cross-country setting. We try to address endogeneity concerns, albeit partially, by relying on information on the gender of firms’ owners available in WBES. The identifying assumption is that the gender composition of firm ownership does not directly impact decisions on access to credit in formal firms with five plus employees, and that its effect on firm

> 24To ease interpretation, we estimate separate models for more and less traditional countries instead of triple interactions.

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