When are large female-led firms more resilient against shocks? Learnings from Indian enterprises during COVID-19 with diff-in-diff and causal forests

Working paper

In which kind of companies did the prevalence of women on corporate boards matter during the first wave of Covid-19? 2500 large Indian firms, of which only 78% initially complied with an exogenous gender quota enable a quantitative evaluation. By comparing their quarterly revenues with a Difference-in-Differences analysis, this research initially finds a significantly positive relationship between the compliance with the one-female-board-member policy and the change in revenues during the first economic shock of the Covid-19 crisis in 2020. A Triple-Difference and Causal Forest analysis indicates that this is likely endogenously driven by (self-)selection based on sectors, capital dynamics, size, independence of directors and further firm characteristics. There is no simple association of female directors and crisis revenues: The spectrum encompasses a mix of firms with a positive, a neutral and a negative association. With rare causal context for evaluating board gender diversity or other corporate governance and ESG dynamics, this piece of research illustrates the value and limitations of applying adjusted Random Forests to overcome linearity and dimensionality limitations for deeply understanding heterogeneities-within-heterogeneities as indicators of relevant distinctions.

Authors: Stein, M

• Publication status: Published
• Publisher: Centre for the Study of African Economies
• Article number: CSAE-WPS-2022-01
• Series: CSAE Working Paper Series
• Publication date: 2022-01-04
• Paper number: CSAE-WPS-2022-01
• Language: English
• Keywords: causal machine learning in double and triple difference; large firms; female leadership; generalized random forest; COVID-19; quota; women on corporate boards