Beyond our two treatment variables describing participation in non-farm wage employment and non-farm self-employment, the first main outcome variable considered is household per capita consumption expenditure (HHPCE) as a direct measure of household welfare. It includes annual aggregate expenditures on food, non-food, durable goods and housing, deflated by spatial and temporal prices indices to adjust for cost of living differences, and adjusted for household size. We prefer this consumption-based measure as opposed to its main competitor (income-based), because consumption is considered less prone to seasonal variations in living standards, especially in rural areas of developing countries. Deaton and Zaidi (2002) mention that consumption aggregates, over a relatively short period of time such as annual, offer a practical advantage over income aggregates by informing more about longer-term living standards. As indicated in Table 1, average HHPCE over both survey years is 57,750MKW.<sup>9</sup> The conditional expectation model of HHPCE is a linear model that we estimate using the FE approach. We also estimate the treatment effects on Log HHPCE using the FE, which gives a sense of percentage effects of our treatment variable, and allows interpretation in relative terms. The conditional quantile model is also a linear model in which the quantiles of the conditional distribution of HHPCE are expressed as linear functions of the independent variables (see equation 14). This model includes the same explanatory variables as in the conditional expectation model, and is estimated using generalized least absolute deviation (LAD) approach. As suggested by equations 12 and 14, the independent variables in the estimating equations include primarily our two treatment variables, in addition to which we include the following set of control variables guided by on the basis of theory and the available literature:

- household characteristics (such as education, age of head, household composition);

- household ownership of productive capital (captured by normalized agricultural assets, normalized landholdings,<sup>10</sup> as well as normalized total livestock unit<sup>11</sup> );

- distance to infrastructures (road and markets to capture transaction costs faced by households);

- community level characteristics that may affect productive activities (prices of fertilizer in the EA, price of maize in the EA,<sup>12</sup> rainfall, temperature, etc.).

We also include a time dummy variable in the equation to account for time specific shocks that could affect welfare directly, but also affect RNFE participation. In the CRE model, the time averages of all the explanatory variables are included to capture time invariant unobserved heterogeneity. In order to reduce remaining unobserved characteristics that might affect our estimates of the treatment effects, we include a set of 26 dummy variables to capture district

> 9 This is equivalent to about 346.5 USD given the prevailing exchange rate (0.006USD/MKW) during the survey period. See <u>http://www.xe.com.</u>

10 An alternative specification includes also a dummy variable for landless households. But adding this variable does not change the results at all because the proportion of landless represents only 7 percent of the sample. So our reported results exclude this variable purposely. 11 Households’ wealth and productive capital indices such as wealth index and agricultural assets index are generated using factor analysis as in FILMER, D. & PRITCHETT, L. H. 2001. Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of india*. _Demography,_ 38 **,** 115-132. The index was then normalized such that norm_index = ((index - min(index)) / (max(index)-min(index))).

12 Fertilizer is a key modern input in agriculture production, and maize is the most popular food crop grown and consumed in Malawi.

11