(38.5 percent higher in treatment areas). For female members of productive age (15-64 years old) the results are similar (33 percent higher in treatment areas).

Next, we inspect the nature of livelihoods activity that is causing this increase. Again, field notes and project monitoring suggest that program beneficiaries are moving away from being daily wage laborers (casual labor) to self-employment, thus initiating more businesses. The survey data confirm the same. The increase in livelihoods is predominantly due to the large and significant increase in the number of self-employment livelihoods activities in farm (5.4 percent more women employed) and non-farm activities (0.7 percent more women employed) with an overall increase of 5.8 percent. Most of this increase is coming from households moving away from casual farm labor towards self-employment such as farm and non-farm businesses. Furthermore, the results indicate that there is a 8.4 percent increase in the number of farming households who have transitioned to high-value agricultural crops.<sup>15</sup> This is another indicator of improved diversification and additional source of employment opportunities. Lastly, the data also indicate a small but significant increase in the number of formal jobs within the households. It is possible that this increase could be because of the program’s focus on skills and job placement (in select areas) or because of more businesses starting and employing more people.

Last, we analyze whether the increase in the number of livelihoods and diversification has influenced women’s participation in the labor force. The survey data allow us to look at the work participation rate (WPR)<sup>16</sup> and we focus on the female household members as they are the primary beneficiaries of the program. In 2011, as per the population census data in the sample areas, there was almost no difference in the treatment and control villages(see Table A 5)<sup>17</sup> . However, in 2016-17 we estimate that there is a 5.5 percent increase in overall WPR in treatment areas and an even higher increase of 7.7 percent among women of productive age, when compared to control areas. Interestingly, the increase in the paid livelihoods amongst adult women almost the same at 7.3 percent in treatment areas when compared to control areas.

This is a significant result from a policy point of view for two reasons. First, the increase in WPR is for the entire village and not just for program beneficiaries. Second, results seem to suggest that, in treatment areas, the program might have been able to arrest the decline in female labor force participation. To illustrate, in 2011, for every 1,000 women in a village, 490 were working before the program. After the intervention, in 2017, the number of working women in the control areas went down to 404 keeping with the overall trend but in the treatment villages, the decline was much less to 459. So, in that village, due to the intervention, 55 more women have joined or been retained in the labor force – amounting to a 13.6% increase in WPR.

**Table 5: Changes in WPR for full village - 2011 to 2016-17**<sup>**18**</sup>

|**Work Participation Rate**|**Control**<br>**Villages**|**Treatment**<br>**Villages**|**Difference between control**<br>**and treatment villages**|
|---|---|---|---|

> 15 High-value crops are all crops except cereals and millets.

> 16According to Census (2011), the Work Participation Rate is defined as the ratio of the total number of workers in the workforce (who have worked in the reference period of the last 12 months) to the total population. Due to the limitations in the survey tool, we cannot report on the often discussed labor force participation rate (LFPR).

> 17 In fact, the rate was slightly lower in treatment areas.

> 18 This table is only for illustrative purpose to explain the results and provide the policy context. Due to the cross-sectional nature of the data we cannot estimate the standard error of the difference in WPR between Census 2011 and household survey 201617

16