That female labor force participation (FLFP) is U-shaped in per capita income is one significant stylized fact at the intersection of development and labor economics. At low levels of development, subsistence requirements render women’s work a necessity for household survival. At higher incomes, the nature of employment changes, with the growth of manufacturing jobs that tend to be staffed by men, and may accompany a contraction of agricultural labor. This, as well as social norms like purdah which encourage women’s non-participation in paid work or their association with non-household men, squeeze FLFP. Further along the industrialization growth path, these manufacturing jobs pave the way for service sector employment, for which women may exercise a comparative advantage. High wage jobs also induce higher FLFP through an opportunity cost channel. Staying home could mean a lot of foregone household income.

By no means is this law-like. Some recent empirical work, including by Gaddis and Klasen, calls into question the U-shape’s robustness, and while Heath and Jayachandran document the continued presence of the general shape, they also find the curve shifting upwards. This suggests that for a given level of real per capita income, women’s employment rates on a global basis have risen over time.

In India there’s been growing concern from various quarters that advancement in women’s labor force participation is not only stalling, but reversing. Since per capita income levels lie to the left of the U’s inflection point, then we should anticipate further declines. One peculiarity between the stylized observation and the results from India is the discrepancy in levels. For example, Heath and Jayachandran estimate a minimum FLFP of around 40%, yet we observe levels even lower than this in rural India for some rounds of the National Sample Survey’s Employment & Unemployment surveys, likely the best source of employment data available in India.


Digging deeper one layer into these employment trends reveals that employment shares by wage type have been relatively consistent across rounds, with the exception of 1987. This may have something to do with Round 43’s survey design question on primary status applying to the preceding 7 day period, whereas latter rounds used a 365 day period. In each survey year, about 40% of rural women were engaged in paid labor, with the remainder either working on an unpaid basis or ‘self-employed’ in a family business or farm operation.


We can also examine the NSS 68th Round of Employment & Unemployment data for 2011/2012, which is the most recent round for which this module was conducted. As before, I restrict the sample to rural women aged 15-65, leaving a sample size of nearly 93,000. As shown in Table 1 below, 3 out of 5 women are primarily engaged in unpaid domestic work, or that in conjunction with free collection of items like firewood and water, or activity like sewing and tailoring for household members. For ease, let’s refer to the combination of these two activities as Housework+.


The principal (primary) activity paints an incomplete picture, especially when women work part-time or seasonally and therefore likely to respond as principally involved in domestic work. An alternative is to also consider their subsidiary activity, and take the minimum value of the two. If their secondary status involves work of a non-domestic nature, a respondent is effectively pushed into an upper category. This approach works because NSS formats the values such that active labor force participation responses precede those of unpaid domestic work. As an example, a woman who reports as primarily a student and secondarily as a casual wage laborer, would be coded as the latter in Table 2.


The share of women involved in the Housework+ categories drop to below 50%, with Self-Employed and Unpaid Family Workers the largest gaining categories. While these women may primarily be engaged in domestic work, a portion of their time is also tied to the family business or farm, with no guarantee of direct wages from their labor. All the ‘working’ categories still sum up to only 35% of the population, though is a sizable improvement on the 25% when counting only reported principal activities.

Data used for the 1983-2009 analysis comes from IPUMS, which sources the NSS Employment survey data from the Ministry of Statistics and Programme Implementation, India (MOSPI). Data from the 2011 analysis comes directly from MOSPI.