Rural Income Generation and Diversification - A Case Study ... - Doria

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Since a lack of money was given as the main reason for not acquiring fertiliser, the income
obtained by converting some assets into money eased access to fertilisers. The respondents
indicated that the income from business activities and wage employment was mainly used
for purchasing food, and as such was insufficient to pay for the inputs. Those with business
income, however, purchased fertilisers more often than the wage earners, thus suggesting
that households with business had more flexibility in their resource allocation.
The farm and total income of households using oxen for ploughing was significantly (at the
0.01 level) higher than among those using hand hoes, whereas the non-farm-income level
was only slightly higher among the latter group. Having oxen facilitated the cultivation of
larger areas and also ensured its timeliness, which in turn generated higher income. No data
were collected on income obtained from hiring out oxen for ploughing or transport purposes.
One could assume, however, that this would have contributed positively to the total income
of the livestock owners. The proportions of business and livestock income were negatively,
although not significantly, correlated, suggesting that they were alternative rather than complementary
sources.
Cotton growing reduced the proportion of non-farm income significantly: the households
involved earned 25 per cent of their income from non-farm sources, while among the noncotton
households the share was 33 per cent. This suggests that non-farm activities and income
were needed to some extent to compensate for missing crop income.
There was some evidence that some of the crop income was invested in business activities,
such as in purchasing commodities in bulk to be sold later, which levelled out the income
flow.
6.1.6 Income diversification, distribution and inequality
The Simpson diversification index, which is bounded between zero and one and increases
along the degree of diversification, was calculated for the income fractiles. The differences
between the fractiles were small, the index being 0.38 in the lowest category, 0.42 in the
middle, and 0.43 in the high-income group. The calculated indices thus suggested that the
higher incomes originated from slightly more diversified sources than the lower incomes.
A decomposed Gini coefficient 39 was calculated in order to analyse the impact of farm/nonfarm
income on income distribution using the total-income, farm-income, and non-farm- income
values. The Gini coefficient varies from a value of zero, representing perfect equality,
to a value of one, representing maximum inequality: therefore, the higher the value, the more
unequal is the distribution.
39 An alternative method for studying the decomposition of income inequality – the Theil index- was used by
Davis et al. (2007).