Second, a striking feature of the Indian data is that one knows the gender composition of the workforce. Most notably, we are not aware of large-scale datasets studies that are able to unite owner’s gender, employee gender, and localized competition in the manner that this study does.<sup>5</sup> In the case of unorganized sector manufacturing and services sector establishments, one also knows the gender of the business owner. This provides exceptional details about gender-based employment patterns that stretch over a long period of time. This offers a very rare opportunity to study gender-based outcomes, especially in a developing economy setting. As establishments also have geographic and industry identifiers, one can make very precise assessments of discrimination behavior. For example, our work asks whether gender discrimination is more or less prevalent in leading or lagging regions and states. How does this relate to the local education of the workforce or the quality of physical infrastructure in these regions?

Our descriptive work suggests that there is a clear pattern of gender segmentation in both manufacturing and services. For example, in the unorganized manufacturing sector, about 90% of employees in female-owned business are female, while this share is 81% in the case of services. Further, the extent of gender segmentation in India’s female-owned as well as male-owned businesses has increased over the years. Beyond these core results, we also note that since 2001, the share of female entrepreneurs and their resulting employment has increased in the informal manufacturing sector.<sup>6</sup> For the services sector, these statistics are, however, much lower in level as well as in growth terms. Finally, although the share of female employment is slightly lower in services than in manufacturing, it has shown dynamic growth from 2001 to 2006.

These broad patterns mask the varying trends in female entrepreneurship, employment, and gender segmentation across leading versus lagging regions as well as among the industries within manufacturing and services. Our state and industry level descriptive statistics suggest that the states and industries with higher female entrepreneurship shares are also the ones with higher female participation shares in employment. Further, there is a positive association between female involvement in ownership or in employment and gender segmentation in female-led businesses. In general, the magnitude of this correlation is found to be stronger in unorganized manufacturing than in the services sector.

Among the correlates of female activity, we find that participation of women in both manufacturing and services is greatly influenced by larger working age populations and femaleto-male gender ratios in the local economy. Technology plays a vital role in reducing gender segmentation in the services industry relative to that in informal manufacturing. Across the board, we find a relatively higher importance of district level local infrastructure in determining female participation and in reducing gender segmentation among male-led plants.

> 5 Relatedly, Ghani, Kerr and O'Connell (2014) did some early parallel work in this regard with respect to female political set-asides.

> 6 Ghani, Mani and O'Connell (2013) document the substantial increase in female entrepreneurship in India’s unorganized manufacturing sector from 1994 to 2000.

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