years in which a DHS survey is available. The lines connect the dots that belong to the same cohorts as they age. For easier interpretation, we group cohorts from different decades by color.

The typical inverted ‘U’ life cycle profile for employment, with the middle part of the life cycle with the highest participation, is clearly shown in Figure 5. Zooming in on the cohort of women who were born in 1970-74 (middle dark blue line in the figure), it appears that about 70 percent of these women were working at age 20-24, then this percentage increases to 80 percent when they are 25–35-year-old, and finally it declines to below 70 percent when they reach 45 years.<sup>14</sup> Figure 5 also highlights the cohort effect, namely that lines of different cohorts, when compared at the same age, display different levels of participation in the labor market. The darker shaded lines plotting labor participation for more recent cohorts lie mostly below the lighter shaded lines of their predecessors, confirming the declining trend in participation found in Figure 2 (left panel) even when the age composition is controlled for. The vertical distances between the lines of Figure 5 can be explicitly plotted as the cohort effect, and this is done in Figure 6 for Nepal and other countries in the region.<sup>15</sup> In this figure, the lines connect different cohorts observed at the same age.

In the case of Nepal, the left panel of Figure 6 shows cohorts born more recently have lower participation in the labor market at all ages. In the cases of India and Pakistan, the recent decreasing participation is confirmed by the cohort plots shown in the right panel. The exception of Bangladesh with an increase in participation for the recent cohorts is confirmed.<sup>16</sup> Going beyond this graphical examination, we applied the formal econometric Age-Period-Cohort (APC) decompositions discussed in the methods section for two countries where we have a sufficient number of evenly repeated cross section data sets. The results from the models in the APC analysis for Bangladesh and Nepal complement the conclusions from the graphical examination of these effects on female employment. Tables A5 and A6 in the Annex present the parameter estimates and their standard errors

> 14 This inverted “U” shape for the age effect, with a peak around 30-40 years of age and lower participation at the other ages, is typical for countries in South Asia as well as in other regions (see Figure A4 in the Annex).

> 15 Afghanistan is not included because it has only one round of survey in 2015.

> 16 Interestingly, the sharp rise in employment happens at the same time for the two cohorts born most recently, i.e. it seems mainly driven by data from the latest two survey rounds. Thus, what in the figure is labeled as a cohort effect, it may in fact also be a ‘year effect’. As Deaton and Paxson (1994) clearly explain, one has to make some strong assumptions to identify the age, cohort and year effects in these panel data. And clearly the graphical decomposition in Figure 7 and Figure 8 are not fully identifying these three components. For a similar analysis see also Yang, Schulhofer-Wohl and Land (2008); Fosse and Winship (2019); Tunalı, Kırdar and Dayıoğlu (2019).

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