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schooling and is assumed to capture the skills the individual may bring to a given job in the off-farm
labor market. Previous studies have also shown that migration (which is included in this study’s off-farm
labor supply) is influenced inversely by age; older people are less likely to migrate since they have less
time to pay back the investment (Lanzona, 1998). In the conceptual model, education and age are
included in zo, one of the factors that is assumed to help determine the off-farm labor supply. To test
whether the program’s effect on off-farm labor is influenced by the households’ access to human capital,
we again divide the sample into quartiles based on an initial level of education and on age cohorts.
The results show that levels of human capital, in terms of both age and of education, impact
how the program affects off-farm labor (Table 8). The estimates imply that adult family members who
are younger are more likely to shift to the off-farm labor market after the onset of Grain for Green than
are older ones. For example, for adults in the youngest quartile, the program increased the probability of
off-farm labor participation by 37 percent; for the oldest quartile, Grain for Green decreased off-farm
employment by 13 percent (columns 3 and 4). This result is convincing considering that the types of
off-farm jobs that are first available to rural farmers are physically demanding (jobs such as construction
work) and naturally favor young adults.
Perhaps more importantly, the results show that Grain for Green did not have a positive effect
on off-farm employment for adults who had only limited education prior to the program (columns 1 and
2). If the individual was in the lowest quartile for education, participation in the program did not change
the likelihood of that person gaining an off-farm job, and the likelihood of finding off-farm employment
increases as educational attainment increases. This result suggests that the program may not be able to