specific factors that may affect welfare but also determine the economic opportunities available in the area and affect welfare. The description of all these variables, as well as the test of covariates balancing between treatment and control groups can be found in Appendices 1 through 3. Those balancing tests indicate significant differences in education, assets endowment, and resource constraints between participants and non-participants in non-farm wage employment and non-farm self-employment. We also notice significant differences between the poor and the non-poor in terms of household covariates (Appendix 4).

Second, we investigate whether participation in RNFE influences household consumption expenditure enough to affect poverty status. For the poverty analysis, the class of poverty metrics proposed by Foster, Greer and Thorbecke (FGT, 1984) is used. Poverty incidence is based on the HHPCE, relative to the 85,852 MKW (real 2013 prices) local poverty line per person per year in Malawi. As opposed to the $1.90/day international poverty line, this local poverty line indicates the cost of maintaining a reference welfare level (here defined as satisfying necessary energy and nutritional requirements to have a healthy and active life) to a given person, at a given time in the specific context of Malawi (Ravallion, 1998).<sup>13</sup> Household poverty incidence takes value 1 if household consumption falls below the poverty line, and 0 otherwise. The summary statistics presented in table 1 indicate that the proportion of rural households with consumption expenditure below the poverty line is 37 percent over the 2010-13 pooled sample, confirming that poverty is relatively widespread in rural Malawi.<sup>14</sup> The main specification used to explore the effect of the RNFE on poverty is a CRE Probit model, estimated using the Generalized Estimating Equation (GEE) approach.<sup>15</sup> Poverty gap (defined as the consumption shortfall relative to the poverty line, as a fraction of the poverty line) takes a value 0 for all non-poor households, creating a censoring at 0 and a continuous set of values between 0 and 1. The squared poverty gap (sensitive to extreme poverty) takes continuous values between 0 and 1 with a pile up at zero. On average, the poor in rural Malawi appear to be about 30 percent short of the poverty line, and the squared poverty gap is 0.13. We use the CRE fractional Probit approach to estimate responses of both poverty variables to RNFE participation. As demonstrated by Gallani et al. (2015), Fractional Response Model (FRM) is preferable since it overcomes the limitations of other approaches for the statistical analysis of dependent variables that are bounded in nature and present a significant number of observations at one of the boundary points. For all these three poverty measures, we also estimate the linear FE model to serve as benchmark for comparison as it requires fewer distributional assumptions and generally gives reasonable approximation of APEs (Wooldridge, 2010). The same set of covariates used in the estimation of the HHPCE response model are used for the poverty analyses.

Third, we explore effects of RNFE on subjective measures of food security, captured by self-reported measures of food insecurity and food consumption adequacy (see Table 1). The food

> 13 More details about this can be found in the <u>LSMS documentation for Malawi.</u>

14 This refers to the average proportion of poor rural households in the pooled sample. Over the period 2010 to 2013, the proportion of poor rural _households_ dropped from 40% to 33%. Overall, the proportion of poor rural _people_ , the rural poverty headcount, fell from 44% in 2010 to 41% in 2013.

> 15 The GEE approach consistently identifies average partial effects (APE); it is robust to violation of the conditional independence assumption as opposed to the full maximum likelihood approach; it is more efficient than pooled Probit approach but clustered robust standard errors have to be used. (See Wooldridge (2002) for a full discussion.)

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