Table A.8 in the Annex provides the results of the probit regression at the national level. These results include the regression parameters and corresponding standard errors, ChiSquare statistics and _p-values_ . The Table also provides estimates of marginal probabilities. At the national level, the likelihood ratio statistics shows that the model is significantly different from the null or intercept only model. All predictors have estimates significantly different from 0, as judged by the size of β<sup>ˆ</sup> relative to the asymptotic standard error, and further by the size of _p-value_ which gives the upper bound of Type I error probability.<sup>39</sup> The most significant predictors include household asset ownership, age dependency ratio, employment status of household head, and socioeconomic group of the household head. Note that the latter has one of the largest marginal effects, which increases even more when the ranking of socioeconomic groups is conditioned upon the poverty mapping to capture a possible interaction effect between socioeconomic group (SEG) and initial conditions of poverty. Though consistent with earlier findings, education level of household heads is not significant at the national level, however. This further corroborates the fact that education may not be a discriminating factor between poor and non poor in Latvia.<sup>40</sup> The marginal effect associated with the variable SEG on the event probability is about 12%. This suggests that a unit change in this predictor would result in increased event probability of being poor by approximately 12 percent (in absolute terms), holding other predictors constant at their mean values. When this variable is conditioned on the poverty ranking, assigning the largest score to the least poor SEG and the smallest score to the poorest group in a scale of 1-to-5, the marginal effect increases to about 24.7%, suggesting that a unit change in SEG ranking after controlling for welfare status would result in an increased event probability by over 24%. This increase is significant and suggests a possible strong interaction between poverty and sector of employment. It also suggests that the prospects for moving across income groups given that one is poor, are likely to result in high payoffs.

In the transitional context, where movement across sectors is already limited, the probability of such a lateral move may be even smaller for the most vulnerable and poverty-stricken individuals and households, as it may require additional investment in skills acquisition and training. Empirical evidence suggest that despite the significant decline in real wages, as a result of the dramatic fall in output and declining labor productivity, the movement across sectors, from say, contracting firms to growing ones with high prospects for employment creation has not followed (World Bank [2000b]). The stagnation in employment flows, especially in a context of falling real wages may reflect the lack of alternative opportunities to low wage employment.

With a marginal effects of about 28.4%, the burden of age dependency appears as one of the strongest correlates of welfare in Latvia. This marginal effect implies that a unit change in the burden of age dependency would result in increased event probability of being poor by approximately 28% (in absolute terms), _ceteris paribus_ . The burden of age dependency is correlated with household size and structure, which have been identified as strong correlates of poverty in most transition economies. For instance, investigating the determinants of poverty in Eastern Europe and Former Soviet Union, it was found that the headcount index,

> 39 Type I error probability here refers to non-poor individuals and households that are classified as poor.

> 40 Also, this may reflect the high level of public investment in economic and social infrastructure during the socialism era where education was mandatory for all, resulting in high enrollment and literacy rates. This is certainly one of the legacies of the pre-transition era, and a challenge will be to sustain high enrollment and literacy rates over time.

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