given that income-based measures are among the key requirements for accession (Zile and Steinbuka [2001], Republic of Latvia [2003]). Notwithstanding this rapid increase in income and improvement in living conditions, poverty remains exceptionally high in a number of regions, particularly Latgale and Vitzeme where numerous pockets of poverty continue to exist. Achieving broad-based income growth and distribution will be essential to reducing the regional and socioeconomic income gaps and lifting welfare to EU standards. This, however, would require a prior identification of some of the key correlates of poverty, income inequality and growth. It would also require a better understanding of the dynamics of some of these correlates, as well as their relation to poverty and growth over time.

Some of the proximate causes and correlates of poverty in transition economies include output drop, income inequality, and a number of institutional and socioeconomic factors, particularly labor market outcomes such as unemployment rates, low wage rates for unskilled workers, and high dependency ratios at household levels (World Bank [2000b]). Unemployment and wages are likely to be particularly relevant in the Latvian context where over 80% of households derive their income from either wages and salary or pensions.<sup>32</sup> In fact, empirical evidence suggests that the presence in a household of one unemployed member could reduce the household welfare by over 25%; with this percentage decline in welfare increasing to over 40% if the household head is unemployed (World Bank [2000a]).

Similarly, assessing the correlates of poverty in the late 1990s, we found a significant association between poverty and a number of variables, including unemployment, dependency ratio, and household ownership of real and financial assets. The significance of the association is reflected in the magnitude of the correlation coefficient between poverty, measured by monthly household per capita expenditure, and unemployment ratio. The sign of the correlation is positive, as illustrated by the smallness of the _p-value_ and the magnitude of the correlation coefficient, which at about 75% is relatively high. The association is further illustrated by Figure 8 below, which plots unemployment ratio on the _y-axis_ against average monthly per capita expenditures over rank-ordered expenditure quantile incremental range of equal size, each accounting for about one-twentieth of total population. Small values of monthly per capita expenditure (the lowest expenditure percentile or high poverty rates) are associated with high unemployment ratios. It is worth pointing out the curvilinear shape and the convexity of the curve, whose second derivative is positive. The curvilinear shape suggests a non linear relationship, implying that there may be other congruent factors affecting the dynamics of poverty, particularly at the higher end of the distribution of income.

# **Figure 8: Distribution of Unemployment Ratios Across Monthly Per Capita Income Percentile (in LVL)**

> 32 To the extent that pensions may be viewed as proportional to workers’ salaries and after tax wage income in the form of a capital-reserve system for redistribution over the lifetime of an individual during retirement periods, unemployment, salary and wage income may be the key labor market correlates of poverty in Latvia.

22