# 2 Data

Our primary data source is household survey data on disposable income or consumption available in PIP. We use data from 1,989 surveys covering 168 countries for the period 1991 and 2020. We exclude data before the 1990s as the quality was generally worse then, particularly for low- and middle-income countries. The data are standardized as far as possible but differences exist with regards to the method of data collection, and whether the welfare aggregate is based on income or consumption. We use information on per capita household welfare expressed in 2017 USD PPPs. We use PIP’s public percentile database (version 20230919), utilizing 99 percentiles on the distribution from each income or consumption vector. Concretely, we use the values of income or consumption such that the cumulative density function takes the following values {0.01, 0.02, ... ,0.99} . That is, we retain the 99 poverty lines that result in poverty rates of FF(yy) {1%, 2%, ... ,99%} . The final dataset consists of 196,903 quantile-country-year observations on pairs of daily per-capita welfare and the associated quantile in the distribution.

We combine this survey data with various possible predictors of welfare at the country level. We use data from the World Development Indicators (WDI) of the World Bank, which is one of the largest databases of country-year development indicators spanning a wide range of topics. The WDI contains information on around 1,400 indicators covering topics such as health, agriculture, education, climate change, infrastructure and more. We also use all data from the World Economic Outlook of the International Monetary Fund, which contains dozens of variables on macroeconomic indicators, and all data from the UN’s World Population Prospects, which contains dozens of variables on population, health, and demographics. We compliment GDP data from the sources above with estimates from the Madison Database (Bolt and Van Zanden 2024). We also use country and region classifications by the World Bank and data on political rights, civil liberties, and freedom status from the Freedom House.

In addition, we use remote sensing data available from the Google Earth Engine. We use data on nighttime lights, precipitation, temperature, impervious surface, cropland, the normalized difference water/snow/vegetation index, and the enhanced vegetation index. While spatial coverage of remote sensing variables is not a concern as they cover the earth’s entire surface, temporal coverage is at times limited. Nighttime light data, for example, dates back to 1992. To fit into this exercise, the remote sensing data need to be aggregated to the country-year level. We first aggregate them to annual data by calculating the mean, max, min, and standard deviation of each location (e.g., a pixel) over a year. Afterwards, we aggregate them spatially by taking the mean, max, min, and standard deviation of the annual data for a country. This gives 16 features for each type of variable. Nighttime lights are also converted to a per capita level by dividing the sum of lights by the population size. We weigh each grid equally, and hence the indices reflect, for example, the mean temperature in the territory of a country, not the mean temperature experienced by a person in a country. Given that many of the variables impact welfare through agriculture, it is not clear that population weighting (which would give a dominance to urban areas) would make them more related to welfare distributions. Yet we also add population-grid weighted estimates of temperature and rainfall from Gortan et al. (2024).

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