are more family orientated than West-European **s** o they tend to look after tfieir children and aged parents better than in Anglo-Saxon societies. However, because of male dominated society, women tend to perform more domestic work than in Western societies which may contribute to inequality in welfare within households. This aspect of welfare is not the focus of the present paper.

Taxes, fees and fines paid by individuals are not subtracted from personal income. This may lead to an over-estimation of economic welfare. Since data on the amount of taxes, fees and fines paid by individuals are avaiable, an attempt is made to quantify their effect on the total individual welfare in Section 6.

To compare welfare and poverty across different time periods, one needs to adjust the distributions given in current pnces for pnce changes over time. We used the official price indices for Ukraine as presented in Table 1. The only official price index available until 1990 is the retail price index. This index has severe limitations, the main among them being that it is constrcted on the basis of official list prices in the stae retail outlets. It ignores the prices charged in colectve farm markets and other secondary markets. A new price indexcalled consumer price index (CPI) was introduced in January 1991. This is a superior index based on a wide range of prices actually charged.

As can be seen from Table 1, prices in Ukaine have been increasing at a phenomenal rate since January 1992. It seems the country is on the brink of hyperinflation. The government has lost control over the budget. The currency has been depreciating at an accelerating rate. This clearly has severe implications for the standard of living. If wages lag behind price inflation, the standard of living for a large majority of people will certainly decline.

Measurement of economic welfare is problematic under the unstable situation of hypernflation. Relative prices will be changing constantly rsulting in severe disotions.

Since in the Family Budget Surveys, households are not interviewed at the same time, so if there is a high rate of inflation, the distribution of nominal household income which these surveys provide will differ from that of real household income. Needless to say, it is the real income of households that is relevant for the purpose of analyzing the inequality of income. Kakwani (1987) has demonstrated that the inequality of nominal income from these surveys tends to overestimate the inequality of real household income (income adjusted for inflation occurrng dung the survey period).

The FBS data are available in grouped form giving only the percentage of people in each group. This may be supplemented by information on overal mean income. The mean income of each income range were not available. Esdmates of inequality from such data will clearly be biased because of loss of infonnation due to grouping. Clearly, if the number of groups is small, the bias in estimates can be quite large. To estimate inequality from such data, one needs to employ some interpolation device. A commonly used procedure is to fit a density function to the entire income range and then compute inequality mcasures from the parameters of the fitted function. The difficulty with this approach is that there exists no single function which fits the entre income range.

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