In May, the Bill & Melinda Gates Foundation announced a three-year, $80 million investment towards closing the gender data gap, but I only today came across this great video on the same initiative. A portion of the funds will be directed towards improved data collection, particularly of the time use patterns of women and girls and on household asset ownership inventories. Better data means better information for policymakers conducting program evaluations (i.e., how did that recent cash transfer program differentially impact women’s and men’s employment levels?) and will enable researchers greater insight into the long-run implications of unpaid work (i.e., how does working for a household business affect children’s final education achievement?).

This will be hugely beneficial and I’m excited about the prospect of more data like this becoming available. My job market paper focuses on the impact of technological change on gender wage inequality in Indian agricultural labor markets, and in the context of the Green Revolution (starting in 1966, and to some extent still in process) I find that one effect of the Green Revolution’s productivity gains was a reduction in women’s participation in agricultural wage labor. There are potentially several reasons why this came about, but consider one strand of ethnographic research which posits that agricultural intensification expands the non-agricultural demands on women’s time, leaving less time for wage labor work. Unfortunately, there isn’t granular time use data during this period to tease out what impacted women are doing and how such changes might affect the household division of labor.

In another project with several collaborators, I use Indian time use data collected from a pilot study carried out from 1998-1999. While a country-wide survey has recently been collected, the data is not yet available which means that the best understanding anyone has about how Indians, rural and urban, spend their time is nearly 20 years old. A lot has changed since then – the country has become more urban, richer, and educated, with cell phones and computers also playing a large role in what people do, and when they do it. Making such data collection exercises more systematic and routine will offer a very helpful window not only into macro-level questions like how welfare programs affect labor decisions, but also on questions related to exercise, child-care, commuting, and leisure. This investment is welcomed news.