is implemented by the Kleibergen-Paap LM statistic, distributed as chi-square under the null hypothesis that the equation is under-identified, and the instruments do not influence the endogenous variable, that is, program participation. Results in Table 4 suggest that we can easily reject the null hypothesis of irrelevant instruments. Another test is that of weak instrument which is implemented by the Cragg-Donald Wald statistic, distributed as Wald F-statistic. We compare the F-statistic with Stock-Yogo’s critical value defined for an IV bias that is 5 percent of the OLS bias. Therefore, we can reject the null hypothesis that the bias of IV estimate due to a weak instrument is greater than 5 percent of the corresponding bias in the OLS estimate. An F value of 11 or higher is considered sufficient to reject the null hypothesis (Khandker et al. 2014). Table 4 shows an F value of 84.85, implying that the instruments pass the weak identification test. Finally, Table 4 also reports the results of the endogeneity test by using the Wu-Hausman statistic, distributed as F and the null of exogeneity of program participation can be easily rejected.

# **4 Results**

## **4.1 Key findings**

The underlying hypothesis of these livelihoods programs, including NRLP, is that participation in community groups (such as self-help, producer groups etc.) when combined with trainings, savings and loans would lead to increased mobility, more access to economic resources and stronger social networks for women, eventually empowering them, and resulting in higher labor force participation. We find that a number of these objectives are being realized and are summarized below.

### **4.1.1 Labor Outcomes**

There is already a significant body of literature to demonstrate that participation in such programs has led to higher levels of empowerment, more favorable intra-household decision-making for women and higher female mobility (Majumdar and Rao 2017, Sanyal, Rao, and Majumdar 2015), but no previous study has measured female labor force participation or worker participation ratio.<sup>14</sup> The presence of a comprehensive member level livelihoods profile in this survey gives us an opportunity to fill this critical gap.

We begin by looking at three aspects of labor outcomes. First, we analyze if households and female members have diversified their livelihoods. Then we inspect the nature of this diversification and lastly, we examine if there are any meaningful changes in higher level outcomes such as labor force rate or worker participation rate.

We start by testing the assumption that program participation is leading to household members, especially women, taking-up more livelihood activities. There is overwhelming anecdotal evidence from project monitoring to support this assumption and our data are consistent with it. The data suggest a large and significant increase in the number of livelihoods being pursued by households (see Table A 7 for the full results). In the overall sample, the number of livelihoods has gone up almost 20.4 percent among the treatment households (with average livelihoods of 3.8 activities per household in the treatment areas). Most of this increase is due to the increase in the number of livelihoods of female members

> 14 One of the primary reasons for the lack of this analysis is also because no previous study has recorded a full comprehensive livelihoods profile. For example, the Bihar evaluation asked only primary and secondary livelihoods activities but not for all the members.

15