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Kenichi Ueda is a Ph.D. candidate at the University of Chicago and contributed to this paper while he was an intern in the Expenditure Policy Division of the Fiscal Affairs Department (FAD). The authors would like to thank Klaus Deininger, Mimi Klustein-Meyer, John Okidi, and Ritva Reinikka of the World Bank for responding to many questions and requests for data. Sanjeev Gupta, Edgardo Ruggiero, and Mwanza Nkusu provided valuable comments. Getachew Mekonnen, an intern in the Expenditure Policy Division of FAD, provided substantial research assistance.
See Appendix 2 for a detailed description of the household expenditure data that were used in this study. Most data in Uganda is reported on a fiscal year basis (July-June).
See, e.g., Sarel (1997). Although several studies have considered the relationship between macroeconomic variables and growth, and the link between growth and income distribution, few studies have considered the direct link between macroeconomic factors and income inequality.
It should be noted that for 1989/90, the Gini coefficient is based on an expenditure survey, whereas for the more recent years, the data is based on income surveys. Typically, capital assets or savings are accumulated to smooth consumption or expenditures. Thus, there is likely to be more variability in measured income than in expenditures, which may be reinforced by underreporting. It is also more likely that the distribution of capital assets would be more skewed as higher-income groups have greater access to financial and other markets. As a result, expenditure surveys will tend to underestimate the accumulation and distribution of capital assets, resulting in lower Gini coefficients. In addition, because expenditure is a function of disposable income, then assuming a progressive tax system, inequality will tend to be higher when gross incomes are used as the measure of inequality. Thus the significant rise in inequality in the data between 1989/90 and 1992/93 is partly due to the shift from expenditure to income surveys. Irrespective of the methodology used to measure inequality, there is a clear trend of rising inequality between 1989 and 1995.
The poverty line is drawn at two-thirds of mean per capita expenditure using adult equivalence scales. Mean per capita expenditure from the household survey of 1992/93 is used to obtain poverty lines for the monitoring surveys in 1993/94 and 1994/95, but these were adjusted for price changes between these periods.
Yet another approach could be used to indicate the possible widening of the gap between urban and rural incomes. According to the 1991 census in Uganda, approximately 89 percent of the population lived in rural areas. If the Gini coefficients in rural and urban areas are weighted by the relative population sizes, and a weighted average of these two values are taken, then the difference between urban and rural incomes is evidenced by the smaller value of this average compared to the national Gini coefficient. Between 1989 and 1995, the difference between the weighted average of rural and urban Gini coefficients and the national coefficient widened from 14.8 percentage points to 23.7 percentage points.
For a discussion of the desirable properties of these measures and their derivation, see Cowell (1995).
Townsend (1995) found that people in rural villages in Thailand tend to smooth their consumption more than those in urban areas. The evidence of a well-functioning informal financial system is also documented for Indian villages (Townsend, 1994). Similar tests conducted by Mace (1991) and Cochrane (1991) show that this is not the case for the United States. See Udry (1994), for the study on Nigeria.
Townsend (1994) shows that the test for risk-sharing among households is the test for the Pareto optimality of the allocation of consumption. In the decentralized economy, the welfare theorems suggest that the Pareto optimal allocation coincides with the allocation in the competitive equilibrium with complete security markets. Because Uganda lacks formal complete security markets, the test actually examines how the informal risk-sharing system works.
After solving the problem associated with the social weights, the remaining problem is the assumption of the constant absolute risk-aversion among households (see the detailed discussion in Appendices 1 and 2). The parameter can be allowed to vary by exploiting the information from a large number of communities.
Note that the sensitivity of consumption to own income in lower deciles may be a result of measurement errors of the population of crops (see the discussion in Appendix 2). These problems can be seen in the regression results in subgroups of occupations. The consumption levels of the lower-middle and the lower classes of farmers depend significantly on their own income. This result is not quite believable because even the richest farmers participate in the mutual insurance system in their communities. This biased estimation probably comes from measurement errors of crop production. It may explain why the lower-income classes in rural areas are less likely to be insured than those in urban areas.
Includes recurrent spending only. The limited data coverage is partly due to difficulties in separating donor-financed capital spending by function.
In both primary and secondary schools, government funding for education has been supplemented by contributions from parents through parent–teacher associations, and through tuition payments. Private sector contribution has been significant though has declined over time. Since 1997, the government has been implementing a universal primary education program (UPE), whereby the government will bear all the tuition costs and some of the material costs for up to four children per family.
Data refers to current expenditures only.
A 1995/96 survey has recently been completed but because of the need to complete the processing of the data, it cannot as yet be used for econometric work.
Some communities in large towns contain 30 observations.