the Caribbean. Taken together, these regions account for 78 percent of observations and if not corrected, results will be dominated by these two regions. To correct the bias towards countries with many surveys, we construct weights equal to the inverse number of spells in each country.<sup>6</sup> By doing this, we made sure each country gets an equal weight of one in the analysis regardless of the number of spells available. This changes the regional distribution of spells by giving more weight to countries with fewer observations. The share of the Europe and Central Asia (ECA) and Latin America and the Caribbean (LAC) regions drops from 78 percent to 50 percent once weights are used, while the share of spells in Sub-Saharan Africa (SSA) increases from 5 to 24 percent. In terms of distribution through time, the lion’s share of surveys started after 2010 (almost half of all weighted observations), followed by the period from 2000 to 2010 (37 percent) and the period before 2000 (14 percent). This is in line with the substantial investments in household survey data collection in low-income countries, and SSA in particular, since 2010.

For each spell, we calculate mean household consumption (or income) per capita (from surveys) as well as the US$2.15 a day poverty rate at both the start and the end of the spell. Survey means are expressed in 2017 Purchasing Power Parity (PPP) terms for reasons of comparability. Each of the country-spell years is merged with national accounts data (GDP and household final consumption expenditures, expressed in constant dollars and per capita terms) from the World Development Indicators (WDI).<sup>7</sup> Next, we calculate the “descriptive elasticity”: The annualized change in poverty during a spell divided by the annualized change in survey mean consumption/income per capita during the spell (alternatively: GDP per capita). This results in an estimate of the GEP: the annualized change in the poverty rate for each percent change in per capita mean or GDP.

For the purposes of this paper, we are mainly interested in the elasticity of poverty to economic growth, as measured by growth in GDP per capita. It is this growth rate of GDP that is routinely reported by countries and international organizations, and which is often a target in economic development plans and policies. And it is also by this yardstick that the economic performance of countries is evaluated. We thus assess to what extent headline economic growth translates into improved household welfare, though we also report the elasticity with respect to survey means.<sup>8</sup> As one might argue that the concept of GDP includes many components that do not directly affect the lives of the poor, we also look at the elasticity between household final consumption expenditure as measured by national accounts (henceforth HFCE) and poverty.

Annex Table 1 presents the descriptive elasticities based on the full dataset of 1,604 spells. The summary statistics suggest that some data cleaning will be required to handle extreme values: The descriptive elasticities of poverty with respect to survey means range from -26,348 (indicating that a 1 percent increase in survey means is associated with a 26,348 percent reduction in poverty) to 2,593 (a 1 percent increase in survey means is associated with a 2,593 percent increase in poverty), while the elasticities with respect to GDP per capita range between -8,114 and 9,393. The outliers have a large influence on the average elasticity in our dataset of consecutive spells: The average GEP across all spells amounts to an implausibly large (in absolute value) -17.6 when growth is measured by survey means and -3.8 when

> 6 If country X has five “spells” each of them will get weight of 1/5, while country Y with one spell will get the weight of one.

> 7 Whenever a survey spans two years, we use weighted GDP across both years as our measure of GDP per capita.

> 8 Given that poverty rates and household consumption are both measured from the same surveys, there is a clear and approximate identity-like relationship between both variables (see for instance Bergstrom, 2022). This is not the case for GDP, which is measured through national accounts.

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