in the boundary over time or survey representativeness is different, efforts are needed to maintain a long panel of data to have comparable statistics spatially and over time. Such efforts could be (1) regroup areas to a new area that matches the previous definition of areas, or (2) a higher level of geographical disaggregation over time. In this version of the panel data, on average a country has data for 14 geographical areas over the period of 3 years.

We also employ poverty data from different sources available at the country level and subnational level. The first is taken from the World Development Indicator (WDI) which provides different measures including the poverty headcount ratio, poverty gap, and number of poor at both international and national poverty lines. Our measures of interest are poverty headcount ratio at US$1.90 a day. It is calculated by the percentage of the population living on less than $1.90 a day at 2011 international prices. For richer analysis, we also use other poverty lines including the poverty headcount ratio at $3.20 and $5.50 a day.

As an alternative source of subnational poverty, we exploit the annual GRP data provided by Kalkuhl and Wenz (2020), which is available from 1981 to 2016 for more than 1,500 regions in 77 countries worldwide. The dataset, however, includes only a few countries in Africa. We calculate the incidence of poverty by assuming the poverty line of $1.90, $3.20, and $5.50 for all countries in our sample.<sup>14</sup> We also exploit annual gridded datasets for GDP per capita (PPP) from Kummu _et al._ (2020) which covers 26-year period from 1990 to 2015 for 82 countries. In this dataset, each grid cell is recorded at 5 arc-min resolution. We then apply a similar exercise as in the dataset of Kalkuhl and Wenz (2020) and measure the incidence of poverty at different thresholds. We present the list of countries in our datasets in Table B2.

# **B2. Inequality data**

The GSAP dataset is unique in terms of country-time coverage and in the breadth of the measures of inequality covered. As the main outcomes, we use the most widely accepted measures of income distribution including the Gini index and Theil index. These indices are computed on the income available to households after government taxes and transfers, excluding indirect and value-added taxes, public services, and indirect government transfers. The Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus, a Gini index of zero represents perfect equality, while an index of 100 implies perfect inequality. Similarly, the Theil index measures an entropic ‘distance’ the population is away from the ‘ideal’ egalitarian state of everyone having the same income. The Theil index ranges between zero and infinity, with zero representing an equal distribution and higher values representing a higher level of inequality. As alternative measures of income inequality, we use the distribution of income share held by each decile and calculate different percentile ratios, namely the 90/10 ratio, the 80/20 ratio, and the Palma ratio (90/40 ratio). All income measures are converted to real terms using 2011 Purchasing Power Parity (PPP) dollars for comparison across survey years.

We also employ Gini data from different sources available at the country level. The first is taken from the World Bank Poverty and Inequality Platform (PIP). The data are based on primary household surveys obtained from government statistical agencies and World Bank country departments. In the case of high-income economies, they are mostly derived from the

> 14 To illustrate, we fix the poverty line for all regions in our sample and identify a region as poor if its gross income (per day) is below the poverty line.

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