growth is based on GDP per capita. Using the median elasticity across spells results in more reasonable values that are in line with previous estimates in the literature (a median GEP of -1.8 with respect to the survey mean and -2.1 with respect to per capita GDP). The outliers will however need to be dealt with in light of the forthcoming regression analysis.

Data intricacies and data comparability issues that give rise to implausible elasticity estimates have in previous research been dealt with by making defensible though largely arbitrary decisions. One approach has been to simply drop spells that are deemed incomparable. This approach has typically been adopted for the transition countries in Europe and Central Asia (ECA), where the exceptional increases in poverty during the post-Soviet Union economic meltdown result in implausibly large elasticities in absolute terms (see Ravallion, 2001). This is also borne out by our dataset, in which the average GEP across spells in ECA amounts to an implausibly large -42 (with respect to survey means) and a positive elasticity with respect to GDP growth (indicating that growth in GDP per capita has been associated with increases in poverty). Another approach has been to drop spells with positive elasticities from the analysis to avoid artificially reducing the average elasticity (Christiaensen and Hill, 2019). More recent research has also attempted to weaken the influence of outliers by trimming the top and bottom 5 percent of elasticity estimates. Having considered different options, we have decided to drop spells for which both the initial-year and end-year poverty rates (as measured by the US$2.15 2017 PPP a day line) are below 2 percent. We believe this is justified by the fact that (i) we are mostly interested in the relationship between growth and poverty reduction in countries that still have some extreme poverty to begin with and (ii) small absolute changes in poverty where baseline poverty rates are low tend to be large in relative terms, mechanically pushing up the absolute values of the estimated elasticities.<sup>9</sup> The curated sample consists of 575 comparable spells between 1981 and 2021, based on 715 nationally representative household surveys for 89 countries representing over 92 percent of the population in the “developing world”. The regional distribution of spells in the curated sample is shown in Panel B of Table 1. Compared to the full sample of 1,604 spells, the largest changes are observed in the ECA and SSA regions. As a result of their low poverty rates, the weighted share of ECA in the sample drops from 35 to 20 percent, while the share of SSA increases from 24 to 36 percent. There are no substantial changes in the weighted distribution of spells by time period (Table 2). About 20 percent of spells started before 2000, 40 percent started in the first decade of the 2000s and another 40 percent started after 2010. The distribution of spells by type of welfare aggregate is more skewed towards consumption in the curated sample, with 70 percent of spells using consumption as a measure of welfare and 30 percent using income. In this sample, the average descriptive GEP with respect to survey means and GDP per capita amount to - 2.1 and -0.9, respectively, while the passthrough between growth in GDP per capita and growth in survey means is approximately unity (indicating that a 1 percent change in per capita GDP has on average been associated with a 1 percent change in household consumption as measured from surveys).

> 9 For instance, a reduction in poverty from 1.7 percent to 1.1 percent, though small in absolute terms, comes down to a relative decrease of over 35 percent, which will result in a large elasticity (absolute value). Richer countries therefore tend to have larger elasticity estimates than poor countries. Klasen and Misselhorn (2007) propose the growth semi-elasticity of poverty as a preferred measured. The semi-elasticity measures the _percentage point_ change in poverty for a percent change in mean consumption/income or GDP per capita. While the semi-elasticity is less sensitive to base effects, it tends to be lower for richer countries, as a, say, 5 percentage point reduction in poverty tends to be easier for a country with a baseline poverty rate of 70 percent than for a country with a baseline poverty rate of 8 percent.

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