Measuring Regional Disparities in Small Countries - Boris A. Portnov; Daniel Felsenstein (European Regional Science Association (ERSA), 2005)
Though individual studies of regional disparity may deal with separate development measures - population growth, wages, welfare, regional productivity, etc. - the use of an integrated indicator is often essential, particularly if a comparative (cross-country) analysis is required. In order to measure the extent of disparities, various indices of inequality are commonly used. The goal of present study was to determine whether commonly used inequality measures (Gini, coefficient of variation, etc.) produce meaningful estimates when applied to small countries, thus making it possible to compare the results of analysis obtained for such countries with those obtained elsewhere. As we argue, a small country may differ from a country of larger size in three fundamental features. First, it is likely to have a relatively small number of regional divisions. Second, its regional divisions are likely to vary considerably in their population sizes. Lastly, regions of a small country may rapidly change their rank-order positions in the country-wide hierarchy, by changing their attributes (e.g., population and incomes). In contrast, in a large country such rank-order changes may be both less pronounced and slower-acting. In order to formalize these distinctions, we designed simple empirical tests, in which income and population distributions, presumably characteristic for small countries, were compared with a "reference” distribution, assumed to represent more accurately a country of a larger size. In the latter (reference) distribution, the population was distributed evenly across regional divisions and assumed to be static. In the first test, we checked whether the overall number of regions matters. In the second, we tested whether different inequality indices respond to differences in the regional distribution of population, viz., evenly spread population in the reference distribution vs. unevenly spread population in the test distribution. Finally, in the third test, we verified whether different inequality indices were sensitive to the sequence in which regions are introduced into the calculation. Somewhat surprisingly, none of the indices we tested appeared to pass all the tests, meaning that they may produce (at least in theory) misleading estimates if used for small countries. However, two population weighted indices – Williamson and Gini - appeared to exhibit only minor flaws and may thus be considered as more or less reliable regional inequality measures. Although further studies on the performance of different inequality indices may be needed to verify the generality of our observations, the present analysis clearly cautions against indiscriminate use of inequality indices for regional analysis and comparison.
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