Luxembourg Income Study database. As an alternative source of country-level inequality, we exploit the Standardized World Income Inequality Database (SWIID). SWIID provides standardized Gini income inequality measures for market and net outcomes based on the same concept, and thus allows the comparison of income inequality before and after redistribution by taxation and transfers over time.

# **B3. Weather data**

We match our poverty and inequality data with the ERA5 satellite reanalysis data, which is taken from ECMWF. The ERA5 provides hourly estimates of several climate-related variables at a grid of approximately 0.25 longitude by 0.25 latitude degree resolution with data available since 1979 (Dell _et al._ , 2014). We use air temperature and precipitation, both measured as annual averages, and map the grid spacings in ERA5 to the country/region in our poverty datasets. We follow previous studies and aggregate the gridded data to the region level by computing area-weighted averages (i.e., averaging all grid cells that fall into a region) (e.g., Heyes and Saberian, 2022; Kalkuhl and Wenz, 2020). Figure B3 provides a distribution of average temperature in our sample. It shows that most regions in our sample belong to the temperature range of between 24<sup>◦</sup> C and 28<sup>◦</sup> C. Another dataset that we use in the paper is the global gridded CRU data which provides monthly estimates at 0.5<sup>◦</sup> resolution. The CRU data, however, is subject to absence of data in regions with less coverage of weather stations. Therefore, our main analysis exploits the ERA5 data which combines information from ground stations, satellites, weather balloons and other inputs with a climate model, and therefore is less prone to station weather bias (Auffhammer _et al._ , 2013).

To explore the potential impacts of future climate change on poverty and inequality, we use climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This project offers a variety of scenarios, each depicting a different trajectory for global development, energy usage, policy implementation, and climate action, thus allowing a comprehensive assessment of future climate change impacts under diverse conditions. The scenarios we consider include SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Each of these scenarios represent distinct pathways reflecting varying levels of greenhouse gas emissions, socio-economic factors, and environmental developments. For instance, SSP1-1.9 represents a scenario where strong mitigation and sustainable development lead to a temperature rise below 1.4°C by 2100, illustrating a situation where global warming is limited to well below 2°C. In contrast, SSP5-8.5 portrays a scenario of high socio-economic growth coupled with high greenhouse gas emissions, leading to a temperature rise of 5.0°C, representing a trajectory with minimal climate mitigation efforts. By examining these diverse scenarios, we aim to gain nuanced insights into how differing future conditions and policy decisions can impact poverty and inequality, thereby providing a broad perspective on the societal implications of climate change trajectories.

# **B4. Other data**

To examine the role of agriculture as the mechanism, we utilize annual production of four major crops (maize, wheat, soybean, rice) available from Iizumi and Sakai (2020). The dataset records global gridded data of annual crop yields, measured in tonnes/hectare, at 0.5<sup>◦</sup> resolution and covers the period 1982–2015. The dataset was created by combining agricultural census data, satellite remote sensing and information on crop calendar and crop harvested area. Although the data include only four main crops, thereby partly limiting our analysis, the tradeoff permits us to assemble consistent long panel data. Finally, in some specifications, we exploit data from different sources including type of regime from The Economist Intelligence, broadband internet coverage provided by Collins Bartholomew’s Mobile Coverage Explorer, and other country-level characteristics (i.e., population density, elevation, distance to the 65