a low of 4,600MKW to a high of about 6,500 MKW for the 75<sup>th</sup> and 80<sup>th</sup> percentiles, before dropping again to a non-significant size of 2,000MKW at the 90<sup>th</sup> percentile. This indicates that, for non-farm wage employment, the middle segments of the population benefit the most; while, for non-farm self-employment, the upper segments benefit the most from participating. However, in both cases the poorest appear to benefit the least from participating, confirming the findings in (Bezu et al., 2012). This heterogeneity of effects also illustrates the fact that resource constraints faced by the poorest likely consign them to low return activities with less potential to increase their income considerably, potentially putting them into a poverty trap. Meanwhile the middle and upper segments earn significantly higher returns from participating in non-farm activities because they have access to the most lucrative employment opportunities. These results are largely consistent with our descriptive analysis, and also support the evidence in the existing literature on the existence of entry barriers that confine marginalized groups to low returns and non-lucrative employment categories (Abdulai and CroleRees, 2001, Woldenhanna and Oskam, 2001, Barrett et al., 2001a, Smith et al., 2001, Lanjouw et al., 2001, Reardon et al., 2000).

# **_Impacts on poverty and food security_**

We extend beyond HHPCE to analyze several other welfare outcomes. Our results indicate that both non-farm wage employment and non-farm self-employment have a negative and significant effect on all three FGT poverty metrics. Both the fractional Probit model and the linear FE model show that, on average, a household’s engagement in non-farm wage employment reduces the likelihood of its consumption expenditure falling below the poverty line by 7 percentage points. Similarly, self-employment reduces poverty incidence by a statistically significant margin of about 8.5 percentage points. This is an important margin relative to the pooled sample rural household poverty incidence of about 37 percent (see summary statistics in Table 1). For the depth and severity of poverty measured by poverty gap and squared poverty gap respectively, our CRE fractional Probit, and linear FE models produce consistent results indicating significant and negative effects of both non-farm wage employment and nonfarm selfemployment. This implies that though the poorest seem to benefit less from engaging in non-farm activities, the size of the effect is still enough to reduce significantly the depth and severity of poverty confirming that the rural non-farm sector can well serve as a pathway out of poverty for poor rural households in Malawi.

The positive effect of non-farm activities persists in the analysis of household subjective perceptions of food security. In particular, estimates from the CRE Probit model and linear FE model consistently indicate that participation in non-farm wage employment reduces the likelihood that a household feels food insecure by about 5 to 6 percentage points. Similarly, the ordered Probit results show that households engaged in non-farm wage employment perceive their food consumption level to be more adequate for the needs of the household, compared to nonparticipating households. For non-farm self-employment, the estimated effect does not appear statistically significant.

# **_RNFE and agricultural investment_**

Several studies in the RNFE literature have explored agricultural investment as a pathway through which non-farm employment increases welfare, and concluded a positive effect. For example, Oseni and Winters (2009) concluded a positive effect of both non-farm wage 19