Rwanda; Hardy and Kagy (2018), for Ghana; Islam et al. (2020) and Allison et al. (2021), for global analyses. Gaps in monthly profits, not all of them statistically significant, are summarized in Campos et al. (2019) for 14 SSA studies. Similar patterns are documented for high-income countries (e.g. Fairlie and Robb (2009); Rosa and Sylla (2016)).

These unconditional gaps typically narrow when controlling for sector and firm size (for example, Rijkers and Costa (2012); Hallward-Driemeier (2013); Gui-Diby et al. (2017); Chaudhuri et al. (2020)) because women business owners often operate in sectors where profit margins are comparatively low, and have fewer employees than their male counterparts (Amin (2011); Campos et al. (2018)). At the same time, several cross-country studies have documented that women-led businesses are more profitable and larger when they are in male-dominated sectors (Campos et al., 2019). However, firm characteristics do not explain the entirety of the gap in productivity, sales revenues, and profits. Islam et al. (2020) find that the labor productivity gap remains unchanged even with a wide range of controls. Similarly, in a study on the garment-making industry in Ghana, Hardy and Kagy (2018, 2020) find that the men-owned micro-enterprises generate significantly higher profits than women-owned micro-firms even after accounting for a wide range of firm, firm owner, and product characteristics.<sup>2</sup> Finally, Bruhn (2009), using data from Latin America and controlling for enterprise characteristics, finds gender differences in productivity for micro and small enterprises, but not for medium-sized and large firms.<sup>3</sup> Several explanations for these gaps in performance have been suggested, ranging from lower levels of business capital for women compared to male peers, less labor used by female-owned firms, and differences in the adoption of advanced business practices and innovation (Campos et al., 2019). Evidence that gender gaps in enterprise performance are related to gender norms around domestic time use and responsibilities, which greatly constrain women’s time and mobility, has also been documented (especially, but not exclusively, in the informal sector). Nordman and Vaillant (2014) show that among informal entrepreneurs in Madagascar, women are more likely than men to work from home, where unpaid domestic tasks may interfere with their productivity. Grimm et al. (2012) document similar patterns among informal entrepreneurs in seven West African countries. Kevane and Wydick (2001) argue that differences between male and female microentrepreneurs in Guatemala in the ability to respond to credit arise from the fact that women of reproductive or child-rearing age are more time-constrained than men. Finally, qualitative interviews with young women entrepreneurs in Ethiopia showed that married women are often unable to work full-time due to socially assigned responsibilities at home, which they regard as a major disadvantage for growing their business (Phororo and Verick, 2008).

In the context of the COVID-19 pandemic, the potential role of gender norms rose to the fore since it has been widely documented that women’s work (whether it be running a firm or being a wage or salaried worked) was disproportionately affected by the increased need for childcare in the home associated with lockdowns and day care and school closures. For example, female business leaders covered by the first wave

> 2The authors ascribe this pattern to the within-sector differences in firm product and different demand facing firms, more precisely, that women are making garments for women, while men produce male garments, and women garment makers operate in more ‘crowded’ markets and have more competition for a set demand.

> 3Some papers in the literature have documented a smaller gender gap in the formal sector as well. Hallward-Driemeier (2013), for example, estimates that women-owned enterprises are 6% less productive than men-owned enterprises in the formal sector, compared with a gender gap of 50% in the informal sector. Such comparisons between formal and informal sector enterprises, however, tend to draw on different data sources (comparing for example, businesses in the World Bank Enterprise Surveys to enterprises captured in multi-topic household surveys) and differences in questionnaire content, question phrasing, and survey implementation might contribute to these findings.

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