collect exhaustive and reliable information on household income and expenditures, we use the latter as the measure of welfare. In addition to the empirical argument, there are strong theoretical reasons supporting the use of expenditure over income, even in the presence of more comprehensive surveys with extensive information on income and expenditure (Deaton and Muellbauer [1980]).<sup>8</sup>

**Table 1: Per Capita Household Expenditure-Based Summary Statistics and Income Inequality Measures (in LVL)**

||**Natio**|**nal Level**|**Urb**|**an Area**|**Rur**|**al Area**|
|---|---|---|---|---|---|---|
|**Point Estimates**|**1997**|**2000**|**1997**|**2000**|**1997**|**2000**|
|Mean|58.11|74.06|60.62|81.98|51.73|54.68|
|Median|47.74|57.98|49.31|63.7|43.56|44.42|
|Coefficient of variation (CV)|925.62|1821.18|960.86|1941.14|800.56|1106.15|
|Kurtosis|65.01|1188.37|60.20|925.53|62.61|27.41|
|Skewness|5.69|26.89|5.55|24.65|5.33|3.65|
|Gini Coefficient|33.8|37.3|34.25|36.58|32.09|35.38|
|E(0)|19.67|24.12|20.16|22.98|17.51|21.43|
|E(1)|21.23|28.25|21.85|28.11|18.39|21.81|
|E(2)|31.21|64.77|32.27|67.65|25.72|28.58|

A trend analysis of the distribution of income in the late 1990s shows a persistence of income inequality. Higher levels of income inequalities are recorded at the national levels, but also in urban and rural areas during the recovery phase of the transitional period. Following the slow increase post-1995, which had a declining slope, a more rapid increase in income inequality was recorded in the late 1990s. At the national level, the per capita expenditure-based Gini coefficient, a standard measure of income inequality, increased by about 10%, from 33.87 to 37.35% between 1997 and 2000 (see Table 1).<sup>9</sup> The increase is largely driven by rapidly rising wages and salaries, which are more subject to short-term changes, at the higher end of the distribution. This rate of growth remains relatively low, however, compared to the early phase of the transitional period where income inequality was growing at about 40%.

The rise in income inequality in the late 1990s is also illustrated by the dramatic increase in the coefficient of variation, and increased length of the tails relative to the distribution of household per capita expenditure and income here especially captured by the skewness parameter, which measures the symmetry of the distribution. The coefficient of variation increased more than twofold between 1997 and 2000. This rapid increase reflects the high variability at the higher end of the distribution, to the extent that the shift in the measures of central tendency is of a small magnitude (see Table 1). The positive sign of the skewness

> 8 For instance, consumption provides a better idea on access to a bundle of goods because it can be smoothed by savings, or alternatively, consumption may be viewed as a best proxy for welfare because utility is normally regarded as the benefit from the consumption of goods.

individual _i_ for _i_ taking values between _1_ and _n_ , with n the number of individuals in a given distribution. μ( _y_ ) is the arithmetic mean of the distribution. High income inequality is associated with a large Gini coefficient.

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