Our preferred source of growth data is annualized growth in real GDP per capita from national accounts, as reported in the World Development Indicators (WDI). When such data are not available for the whole period, we complement it with annualized growth in real GDP per capita from the WEO. In two economies, data are also missing from WEO (Syria, and the West Bank and Gaza). In these cases, we rely on GDP data from the Economist Intelligence Unit.

Beyond 2018, we use three different growth scenarios: (1) that each country grows according to its annualized growth rate from national accounts for the last 10 years for which we have data (2007-2017); (2) that each country grows according to its annualized growth rate from national accounts for the last 20 years (1997-2017); and (3) that each country grows according to its annualized projected growth rate from 2018-2023, which currently is the last year for which WEO has growth projections. The simulations relying on the 20-year historic growth rates (1997-2017) may be optimistic, as the rapid growth experienced in the early 2000s is showing signs of slowing down. For example, Rodrik (2014) suggests that the rapid growth experienced by emerging economies in recent decades is unlikely to persist indefinitely and that convergence will slow down in coming decades.

3.3 The relationship between GDP/capita growth and welfare growth from surveys A challenge with using growth rates of GDP/capita is that prior evidence has shown that only a fraction of growth observed in national accounts is passed through to the growth observed in household surveys (Ravallion, 2003; Deaton, 2005; Pinkonvskiy & Sala-i-Martin, 2016). Estimating this fraction across our entire sample is fairly straightforward. One would simply regress the annualized growth in the survey means on the annualized growth in real GDP/capita, under the constraint that the intercept is zero, s s G , and use as the fraction of growth in GDP/capita that is passed-through to the welfare observed in surveys. Using a sample of 1,351 spells for which we can calculate changes in ssyy GG/c cciic gg = ββ∗gg + εε ββ the survey mean suggests that . Each spell relies on two adjacent surveys from the same country with welfare measured in the same way (either income or consumption). ββ= 0.83 Yet there is no reason to believe that is constant across different contexts. It may differ by geographical region, by income level, by whether income or consumption is used as the input into poverty ββ measurement, over time, etc. Although interactions for these additional covariates can easily be accommodated in the equation, it is not clear which variables should be used to define the interactions and using all possible interactions will likely overfit the data. Applying a selected number of interactions is common practice in adjusting between household survey growth and national accounts growth rates (see for example Birdsall et al., 2014; Chen and Ravallion, 2010; and Chandy et al., 2013), but it is not entirely clear on what basis the interactions were selected.

To circumvent this issue, we apply a machine learning algorithm, Model-based Recursive Partitioning, to determine when there is reason to believe that the passthrough rate varies in different contexts (Zeileis et al. 2008). This algorithm can take as input all potential variables that might matter for the passthrough rate. In our case, as input variables we use geographical region (we use two versions, the official World Bank geographical regions, and the regions from PovcalNet, where most high-income countries form a separate region), a dummy for whether consumption or income is used, mean consumption, median consumption, the Gini index, population, GDP/capita, and the year of the survey.

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