Chris Elbers

Micro--Level Estimation of Poverty and Inequality

Recent theoretical advances have brought income and wealth distributions back into a prominent position in growth and development theories, and as determinants of specific socio-economic outcomes, such as health or levels of violence. Empirical investigation of the importance of these relationships, however, has been held back by the lack of sufficiently detailed high quality data on distributions. Household surveys that include reasonable measures of income or consumption can be used to calculate distributional measures, but at low levels of aggregation these samples are rarely representative or of sufficient size to yield statistically reliable estimates. At the same time, census (or other large sample) data of sufficient size to allow disaggregation either have no information about income or consumption, or measure these variables poorly. This note outlines a statistical procedure to combine these types of data to take advantage of the detail in household sample surveys and the comprehensive coverage of a census. It extends the literature on small area statistics (Ghosh and Rao (1994), Rao (1999)) by developing estimators of population parameters that are nonlinear functions of the underlying variable of interest (here unit level consumption), and by deriving them from the full unit level distribution of that variable. In examples using Ecuadorian data, our estimates have levels of precision comparable to those of commonly used survey based welfare estimates—but for populations as small as 15,000 households, a ‘town.’ This is an enormous improvement over survey based estimates, which are typically only consistent for areas encompassing hundreds of thousands, even millions, of households. Experience using the method in South Africa, Brazil, Panama, Madagascar, and Nicaragua suggest that Ecuador is not an unusual case (Alderman et al. (2002), and Elbers et al. (2002)).

True and Spurious Duration Dependence: The Identifiability of the Proportional Hazard Model

Lancaster and Nickell (1980) have argued that in the proportional hazard model the effects of time dependence (true duration dependence) and unobserved sample heterogeneity (spurious duration dependence) cannot be distinguished. We show that both effects can be distinguished if the model allows for observed explanatory variables in the hazard. We also discuss the application of our result to practical situations.

Intersectoral Transfer, Growth, and Inequality in Rural Ecuador

Abstract In this paper we study intersectoral transfer and its impact on the distribution of income in Ecuador. We find that income shares between farm and nonfarm activities are roughly equal, on average, although the rich in rural areas typically receive a greater share of income from nonfarm sources. Thus decomposing inequality by income source reveals that a rise in nonfarm incomes increases inequality. Drawing on a new method to estimate local-level distributional outcomes, growth of the high-productivity nonfarm sector is observed to have a strong and positive association with average consumption and inequality. Growth of the low-productivity nonfarm sector is associated with little change in either average income or income inequality. Irrespective of subsector, growth of the nonfarm sector is associated with a substantial fall in poverty.

Environmental Policy, Population Dynamics and Agglomeration

Abstract We present and discuss a simple model of international trade in agricultural and manufactured commodities. Production of the latter takes places under monopolistic competition, and causes pollution. We incorporate mobility of skilled labor, the input in the manufacturing sector. One of the main findings is that pollution and environmental policy tend to countervail clustering that would occur in their absence.

How Good a Map? Putting Small Area Estimation to the Test

The authors examine the performance of small area welfare estimation. The method combines census and survey data to produce spatially disaggregated poverty and inequality estimates. To test the method, they compare predicted welfare indicators for a set of target populations with their true values. They construct target populations using actual data from a census of households in a set of rural Mexican communities. They examine estimates along three criteria: accuracy of confidence intervals, bias, and correlation with true values. The authors find that while point estimates are very stable, the precision of the estimates varies with alternative simulation methods. While the original approach of numerical gradient estimation yields standard errors that seem appropriate, some computationally less-intensive simulation procedures yield confidence intervals that are slightly too narrow. The precision of estimates is shown to diminish markedly if unobserved location effects at the village level are not well captured in underlying consumption models. With well specified models there is only slight evidence of bias, but the authors show that bias increases if underlying models fail to capture latent location effects. Correlations between estimated and true welfare at the local level are highest for mean expenditure and poverty measures and lower for inequality measures.

Brazil within Brazil: Testing the Poverty Map Methodology in Minas Gerais

The small-area estimation technique developed for producing poverty maps has been applied in a large number of developing countries. Opportunities to formally test the validity of this approach remain rare due to lack of appropriately detailed data. This paper compares a set of predicted welfare estimates based on this methodology against their true values, in a setting where these true values are known. A recent study draws on Monte Carlo evidence to warn that the small-area estimation methodology could significantly over-state the precision of local-level estimates of poverty, if underlying assumptions of spatial homogeneity do not hold. Despite these concerns, the findings in this paper for the state of Minas Gerais, Brazil, indicate that the small-area estimation approach is able to produce estimates of welfare that line up quite closely to their true values. Although the setting considered here would seem, a priori, unlikely to meet the homogeneity conditions that have been argued to be essential for the method, confidence intervals for the poverty estimates also appear to be appropriate. However, this latter conclusion holds only after carefully controlling for community-level factors that are correlated with household level welfare.

Re-interpreting Sub-group Inequality Decompositions

The authors propose a modification to the conventional approach of decomposing income inequality by population sub-groups. Specifically, they propose a measure that evaluates observed between-group inequality against a benchmark of maximum between-group inequality that can be attained when the number and relative sizes of groups under examination are fixed. The authors argue that such a modification can provide a complementary perspective on the question of whether a particular population breakdown is salient to an assessment of inequality in a country. As their measure normalizes between-group inequality by the number and relative sizes of groups, it is also less subject to problems of comparability across different settings. The authors show that for a large set of countries their assessment of the importance of group differences typically increases substantially on the basis of this approach. The ranking of countries (or different population groups) can also differ from that obtained using traditional decomposition methods. Finally, they observe an interesting pattern of higher levels of overall inequality in countries where their measure finds higher between-group contributions.

Vulnerability in a Stochastic Dynamic Model

Most measures of vulnerability are a-theoretic and essentially static. In this paper we use a stochastic Ramsey model to find a households optimal welfare and we measure vulnerability as the shortfall from the welfare attained if the household consumed permanently at the poverty line. The results indicate that vulnerability is very sensitive to the time horizon considered. We find that the accuracy of existing regression-based vulnerability measures can be greatly improved by including asset measures in the regression.

Convergence, Shocks and Poverty

Using a unique panel data set for rural households in Zimbabwe we estimate amicroeconomic model of growth under uncertainty, a stochastic version of the Ramsey modelwith livestock as the single asset. We use the estimation results in simulation experiments(over a 20-year period) to quantify the importance of convergence, household fixed effectsand shocks. First, we find powerful convergence. In the absence of shocks and withouthousehold fixed effects there is rapid growth over the period (5.6% growth p.a. in percapita assets) even though there is no technical progress. The process of adjusting thecapital stock (livestock) to its steady state value is - as expected - strongly equalising:the coefficient of variation (across households) of livestock ownership falls from 78% to6%. Secondly, when we allow for household fixed effects - the case of conditionalconvergence - the aggregate growth rate is very similar but inequality remains highthroughout the period.Finally, we find that shocks have strong and persistent effects. In this model shocksaffect aggregate growth both ex ante and ex post. These effects are strong: shocks reduceaggregate growth over the period by a fifth and increase inequality substantially.

Insurance and Rural Welfare: What Can Panel Data Tell Us?

Assessing the scope for insurance in rural communities usually requires a structural model of household behaviour under risk. One of the few empirical applications of such models is the study by Rosenzweig and Wolpin (1993) who conclude that Indian farmers in the ICRISAT villages would not benefit from the introduction of formal weather insurance. In this article we investigate how models such as theirs can be estimated from panel data on production and assets. We show that if assets can take only a limited number of values the coefficients of the model cannot be estimated with reasonable precision. We also show that this can affect the conclusion that insurance would not be welfare improving.