# 3. Empirical analysis

## 3.1 Specification and main findings

To obtain a more robust estimate of the GEP and explore differences across regions and time periods, we estimate the following basic specification (Ravallion and Chen, 1997): ∆L i i + ∆∈i Where the rate of poverty reduction is regressed on the rate of growth (either in household consumption LLPP = αα+ ββ∆L LLμμ per capita, GDP per capita, or HFCE per capita). ∆L i denotes the change in log poverty rates between time _t_ and _t-1_ in country _i_ (expressed in annual terms) while ∆L i is the change in real GDP per capita LLPP (or real per capita survey means) in the same country during the same period. We estimate this basic LLμμ specification with and without country fixed effects. While this basic specification forms the core of the analysis, we will sequentially add several other variables to control for initial differences (in poverty rates, in inequality, etc.) and assess whether elasticities differ significantly across regions or time periods.

Results of the basic specification are reported in Columns (1) and (2) of Table 3 for elasticity with respect to survey means, Columns (3) and (4) for elasticity with respect to GDP per capita, and Columns (5) and (6) when growth is measured by HFCE per capita. Overall, using household consumption as the measure of growth, the GEP amounts to -1.8 and a slightly higher -2.0 when country effects are controlled for. In other words, on average within countries, a 1 percent increase in household per capita consumption (or income) as measured from surveys has been associated with a 2 percent decrease in the poverty rate. The GEP with respect to GDP per capita is higher, with a 1 percent increase in GDP per capita being associated with a 2.8 percent decrease in the poverty rate, on average within countries (Column (4) of Table 3). If economic growth is measured by HFCE per capita, the GEP is only slightly lower compared to using GDP per capita and ranges from -2.2 to -2.5 depending on whether or not country effects are controlled for (Columns 5 and 6 of Table 3).

The first salient observation from these results is just how robust the estimated elasticities are to different samples. Based on a sample of 120 spells, Ravallion (2004) writes “ _The elasticity of the “$/day” poverty rate to growth in the survey mean is around –2,_ [...]”, which is remarkably close to the GEP with respect to the survey mean we find in our sample of 575 spells.<sup>10</sup> The second salient observation is that in our sample, the GEP with respect to per capita GDP is larger than the GEP with respect to survey means, which is contrary to earlier research on smaller samples. Based on 126 spells, Adams (2004) finds a GEP of -2.3 with respect to GDP per capita, which is statistically indistinguishable from zero (compared to a significant -2.8 with respect to survey means). Ravallion (2001) also finds a lower elasticity when growth is measured by GDP per capita. We however find elasticities based on GDP per capita to be consistently larger than those based on survey means.<sup>11</sup> Next, we add a number of variables that influence the relative pace of poverty reduction (our left-hand side variable). As expected, a higher poverty rate at the beginning of a spell is associated with a significantly slower relative reduction in poverty, as large relative reductions in poverty are more difficult to achieve when the base poverty rate is high (this relates to the earlier point that poor countries usually

> 10 Ram (2011) finds a lower GEP but this is based on a higher poverty line. The GEP decreases when higher poverty lines are used (Ravallion and Chen, 1997).

> 11 This result is independent of the sample used. Replicating the analysis on the full sample of 1,604 spells also results in a higher GEP with respect to GEP per capita than with respect to survey means.

6