Impact Of Agricultural Market Reforms On Smallholder Farmers In ...

off) and earning more money from non-farm work (25 percent). The other less frequently cited
reasons were health of family member was better, better crop prices, and better weather. As
Tables 5.66 and 5.67 demonstrate, feeling better off is positively associated with farm size and
expenditure levels. This may indicate that the reforms have benefited larger and wealthier
farmers.
Regression analysis of perceived changes in well-being. We use regression analysis to
explore the factors associated with the perceived change in household well-being. Because the
dependent variable is a qualitative variable representing “better”, “same”, and “worse”, an
ordered probit analysis is used 33 . A positive coefficient means that higher values of the
independent variable are associated with a more positive assessment of changes in household
well-being. The results, shown in Table 5.68, are interpreted as follows.
Households with higher per capita expenditures are more likely to report that they feel
better off since 1995. This may be a limited indication that the reforms have benefited
better off farmers than poorer farmers, or it could be that richer farmers are usually more
optimistic about reforms in general than poorer farmers.
Higher marketed surplus also increased the likelihood of feeling better off since 1995.
Again this may confirm the evidence that more commercially oriented farmers have
benefited from market liberalization, while subsistence farmers either feel the same or
worse off since the reforms.
Households with larger family size are more likely to feel better off, but it is not clear
why that would be so.
Literate household heads are less likely to feel better off than their non-literate
counterparts. This may be because they are a bit more open and critical in general and the
returns to literacy in rural areas have not changed much since the reforms.
33 An ordered probit regression analysis is used when the dependent variable takes three or more values and
represents a qualitative variable with a natural ordering. Unlike linear regression, the dependent variable is
a discrete variable, such as a multiple-choice response, rather than a continuous variable such as income.
Unlike a standard probit, the dependent variable takes more than two values. And unlike a multinominal
logit, the dependent variable has a natural order.
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