A parametric approach offers a number of advantages over empirical variance decompositions and reduced-form regressions. First, it allows a systematic assessment of the role of country-specific initial conditions for the poverty-reducing effects of growth and distributional change. While the literature has stressed how initial inequality shapes the growth elasticity of poverty, little if any attention has been paid to the roles of the initial level of development or the poverty line itself for the choice of poverty-reducing policies. For example, should the balance between pro-growth and pro-distributional policies be the same in Zambia and El Salvador, which have similar Gini coefficients, despite their wide disparity in terms of per capita income (US$361 and US$2,200 respectively in 2002)? The approach taken in this paper allows a rigorous answer to this kind of question.

Second, the parametric approach permits removing the straightjacket of a common poverty line across countries, which otherwise is virtually unavoidable in crosscountry empirical work. Most of the available studies use poverty statistics based on the international PPP US$1 a day poverty line. However, such definition is of little interest for many middle income developing countries (not to mention industrial economies). As an example, in Argentina the headcount poverty was in 2002 close to 60 percent when calculated on the basis of the nationally-defined poverty line, while internationallycomparable poverty indicators based on a dollar-a-day poverty line would place the poverty rate around 3 percent. One implication of these diverging assessments is that the findings from empirical work using cross-country poverty databases, based on a common poverty line, tend to give more weight to countries where the US$1 a day poverty measure makes sense -- i.e., low-income countries where our results below suggest that growth should be expected to dominate distributional change from the viewpoint of poverty reduction.

Of course, the advantages of the parametric approach matter only if the chosen parameterization fits the data well. We find that the null hypothesis of lognormality cannot be rejected when applied to the distribution of per capita income, regardless of whether income is measured in gross terms (i.e. before taxes and transfers) or net terms (after taxes and transfers). However, the same null hypothesis is unambiguously rejected when applied to per capita consumption data. We conjecture that this rejection may be due to consumption smoothing, under which the log of consumption may not be normally distributed even if the log of income is.

The paper derives some implications of this result for the relative roles of growth and inequality in poverty reduction under alternative initial conditions. We highlight four main points: (i) inequality hampers poverty reduction, both because of its negative impact on the growth elasticity of poverty (as stressed in the literature) but, in most scenarios, also because of its negative impact on the inequality elasticity of poverty; (ii) for a given poverty line, the impact of growth on poverty is stronger in richer than in poorer countries, and hence the latter will find it harder than the former to achieve fast poverty reduction; (iii) the share of the variance of poverty changes attributable to growth should be generally lower in richer and more unequal countries; and (iv) given the initial levels of development and inequality, the relative poverty-reduction effectiveness of growth and inequality changes depends on the poverty line -- the higher the poverty line, the bigger the role of growth and the smaller the role of distributional change.

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