value of the Kakwani index corresponds to a progressive tax or transfer, while for regressive taxes or transfers the index takes on negative values. Deciles are defined based on disposable income plus taxes for direct taxes, disposable income minus transfer for direct transfers and pensions, and pure disposable income in other cases.

# **Box 1: Progressivity of taxes and transfers – main principles**

<mark>One of the ways of determining the effectiveness of different programs in reducing inequality is through the</mark> analysis of _progressivity_ of programs. In this analysis, the degree of progressivity is determined by the share of benefits (or taxes) going to different population groups (such as deciles) relative to their respective share of income. Thus, a program can be deemed to be progressive if the share of benefits (as a proportion of pre-fiscal income) decreases with income; a tax can be deemed to be progressive if the share of tax (or the tax rate) increases with income. Progressivity, thus defined, is known as _relative_ progressivity. Alternatively, a program can be deemed to be _absolutely_ progressive if the per capita amount decreases as income rises.

The CEQ analysis relies mainly on the concept of relative progressivity, which is also consistent with the principle that a transfer or tax is progressive (regressive) if applying that tax or transfer alone results in, conditional on no re-ranking, a less (more) unequal distribution than that of pre-fiscal income.

One of the (relative) progressivity indicators is the _Kakwani_ index, defined as the difference between the concentration coefficient of the tax and the Gini for market income in the case of taxes, and as the difference between the Gini for market income and the concentration coefficient of the transfer in the case of transfers (Kakwani, 1977). Thus, the Kakwani index will be positive (negative) if a tax is globally progressive (regressive), where global progressivity (regressivity) is in terms of the concentration curve associated with the particular tax lying everywhere below (above) the prefiscal income Lorenz curve. Similarly, in the case of transfers, a transfer is globally progressive ( _in relative terms)_ if its concentration curve lies everywhere between the prefiscal income Lorenz curve and the 45-degree line. If the concentration curve of a transfer lies everywhere above the 45degree line, the transfer is globally progressive _in absolute terms_ .

_Source: Lustig (2018)_

# **3.2. Data and consistency between administrative and survey information**

The analysis is based on the Household Budget Survey (HBS) for 2017 -- the latest year of microdata available at the time of analysis. This is the official survey conducted by the National Bureau of Statistics of the Republic of Moldova for purposes of poverty and inequality monitoring, among other desiderata. The HBS survey sample covers the whole of Moldova’s territory, with the exception of the Transdniestria region. The statistical population includes households / individuals who are citizens of Moldova, including persons absent for long periods of time (exceeding 1 year) if they maintain family ties to the households from which they originate. The HBS sample is drawn from the master sample based on the 2004 Population Census, comprised of 129 localities grouped in 150 primary sampling units. The sample is stratified based on geographic criterion (North, Center, South regions), town size and type of area (urban/rural). In 2017, the survey sample covers 5,149 households or 11,772 individuals and is representative at the national and regional levels.

One of the implications of the HBS sample being based on the 2004 Population Census, and not on the 2014 Population Census (the new sampling frame, taken from the 2014 Population Census, will start being used from the 2019 HBS round onward), is that there are some discrepancies between the weighted population distribution in the HBS, and the population distribution from the 2014 Census. In addition to the difference in the estimate of the population total between the two data sources, the distribution of the population across regions also differs between the HBS and the 2014 Population Census, with a somewhat higher share of the population in the HBS being from Chisinau, and relatively smaller shares from the other statistical regions for the same year (Figure 6). These differences stem primarily from the fact that Moldova’s demographics have been affected by significant migration flows, which complicates capturing population dynamics during the ten-year intercensal periods. It is difficult to speculate on the precise ways in which the CEQ analysis in particular may be affected by the sample discrepancies, and an update of the analysis based on HBS data from 2019, based on the new sample frame, would be instructive in this regard.

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