the two ends of the income distribution.<sup>5</sup> At the micro level, Jalan and Ravallion (2002), using household panel data from China, find a significant role of aggregate (at the local level) physical and human capital endowments for household consumption growth, which they argue is consistent with the existence of geographic poverty traps.

Strictly speaking, the evidence uncovered by these studies could at most be viewed as consistent with, rather than proof of, the existence of poverty traps. An alternative empirical strategy is to investigate specific sources of non convexities and multiple equilibria. One such approach is the calibration of models consistent with the poverty trap hypothesis. Once a model has been calibrated, its empirical relevance can be assessed. For example, Graham and Temple (2004) calibrate a two sector variable-returns-to-scale model. The model can account for some 40 to 50 percent of the observed variation in per capita income, which lends some support to the poverty trap notion. In turn, Kraay and Raddatz (2005) calibrate simple aggregate models capable of generating poverty traps through low savings and/or low technology at low levels of development. Their results cast doubt on the relevance of these mechanisms for the existence of poverty traps. At the micro level, McKenzie and Woodruff (2004) search for non-convexities in the production function generated by large fixed investment costs. Using Mexican microenterprise data, they find little evidence in favor of this particular poverty trap mechanism.

This paper takes a different approach to testing for the deterrent effects of poverty on growth. Its starting point is the observation that if poverty hampers growth, then _ceteris paribus_ countries with higher initial poverty should grow less rapidly than comparable countries with lower poverty. This hypothesis is a weaker version of the predictions derived from the analytical models mentioned above, in that to support it we do not need to find evidence of multiple equilibria, but just empirical proof that poverty tends to hold back growth.

The paper is also related to two other strands of empirical literature. One has explored the growth-poverty link focusing on the poverty-reducing effect of growth and the factors that shape it (Bourguignon 2004, Ravallion 2004, and Kraay 2005). This is exactly the reverse of the question pursued in this paper. The other strand of literature has been concerned with the growth impact of inequality, with less than unanimous conclusions.<sup>6</sup> It is important to stress that the core hypothesis of that literature is different from ours in that our concern is not so much the relative distribution of income, but rather the interaction between relative distribution and average income (i.e., the size distribution of _absolute_ income) – which underlies measured poverty.

> 5 Bloom, Canning and Sevilla (2003) also test for the existence of convergence clubs controlling for a number of exogenous geographic variables (such as distance from the equator, rainfall, temperature, etc). Their findings also support the hypothesis that the distribution of income is bimodal.

> 6 For example, Alesina and Rodrik (1994) and Perotti (1996) found a negative relationship between inequality and growth on the basis of cross section data, but subsequently Li and Zou (1998) and Forbes (2000) obtained the opposite result using aggregate panel data. In turn, Barro (2000) found that inequality may affect growth in different directions depending on the country's level of income, while Banerjee and Duflo (2003) concluded that the response of growth to inequality changes has an inverted U- shape.

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