coefficient and its magnitude suggest that the empirical distribution is more tailed off to the right. The rise in income inequality, and particularly at the higher end of the distribution is more important in urban areas, where changes in the coefficient of variation, skewness and entropy measures are more significant. This is especially reflected in the magnitude of the Theil index ( _E_ )1( ) and the second entropy measures ( _E_ (2) ).

The urban-rural contrast on the income inequality scale is even more pronounced and systematic from the distribution of income. While the income distribution-based Gini increases by over 5 percentage points in urban areas, a fall of about 4 percentage points is recorded in rural areas (see Table A.1 in Annex). In fact, at about 31 in 2000, the per capita income-based Gini in rural areas is almost equal to the national estimate recorded in the first period, suggesting tapering off of income inequality. Hence, the rapid increase at the national level reflects more the changing scope of income inequality in urban areas.

The increase in income inequality is also consistent across all income groups and deciles, as reflected in the smoothness of the two Lorenz curves (see Figure A.1 in Annex). This figure is based on the distribution of monthly household per capita expenditures in 1997 (the inner curve) and 2000 (the outer curve). The orthogonal distance between the 45 degree line and the outer curve is uniformly higher than the same distance up to the inner curve, confirming the over time increase in income inequality. However, the Gini and associated Lorenz curves capture variance in the middle part of the distribution, which may not fully account for changes at the higher and lower end of the distribution where the bulk of the poor are concentrated. To account for income variance across the entire spectrum of the distribution, we also estimate a number of income inequality measures from the generalized class of entropy _E_ (α) .<sup>10</sup> These entropy measures specifically capture income variances at the higher and lower ends of the distribution and should complement the Gini coefficient.

The parameter α can be assigned any real value: specifying a high value yields an index that is more sensitive to distributional changes at the higher end of the distribution, while a lower or negative value yields indices attaching larger weights to changes at the lower end of the distribution. Given our focus on the tails of the distribution, two entropy measures are of particular interest: the mean log deviation, which is more sensitive to changes in the lower end of the distribution _E_ (0) , and the half of the square of the coefficient of variation _E_ (2) , which is more sensitive to changes at the higher end of the distribution. Between these two measures, there is the second entropy measure ( _E_ )1( ), which is also known as the Theil

> 10 The generalized entropy family of income inequality measures _E_ (α) is defined by the following representation:

measure will be particularly relevant in assessing the changes at the higher end of the distribution. For further details, see Osberg [1991], Canagarajah, Mazumdar and Ye [1998].

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