Fifth, we replicate our main analysis to different subsamples to investigate the sensitivity of our finding, as shown in Panels A and B of Table A5 (Appendix A). First, we show that our findings remain consistent when using a balanced sample in Column (2). Second, there are countries in our samples that contain only a small number of regions. We show in Column (2) that our results remain consistent when excluding these countries. The same finding is found when we exclude large countries that may drive our results such as United States, India, and Brazil (Columns 3 and 4). We also employ subsamples of countries without extremely cold weather (Column 5) and extremely hot weather (Column 6) using the 10 percent threshold. In Column (7), we cluster standard errors at the country level, while in Column (8) we use Conley standard errors that allow for spatial correlation in the error term. In overall, we find the estimated coefficients and significance levels are largely unchanged compared to our main finding.

Sixth, we exploit poverty and inequality data from alternative sources to check the robustness of our results. We exploit the annual (subnational/grid level) GDP data coming from Kalkuhl and Wenz (2020) and Kummu _et al._ (2018) to construct poverty measures. An advantage of these datasets is that we are able to use a longer period-average (10-year) in the long differences model compared to our analysis using GSAP data. Using both panel and long differences models, Tables A6 and A7 (Appendix A) show that our findings are not sensitive to the alternative datasets, and the results are consistent across different specifications. We then conduct a similar exercise using country-level data from the World Development Indicators (WDI) and the Standardized World Income Inequality Database (SWIID). The results presented in Table A8 confirm our expectation.

Finally, we conduct a placebo test of our study design. It is motivated by the fact that if estimating our chosen specification, but replacing the true value of the regressor of interest with an alternative we know should be irrelevant, we should expect to see no evidence of the effects on poverty. We do this exercise by using a within-sample randomization. First, the ‘true’ temperature of a region is replaced by temperature from another, randomly chosen in our sample without replacement. Second, the specification from Column (1) of Table 1 and Table 2 was estimated using the resulting placebo temperature series and the resulting coefficient and _t_ -statistic on the temperature variable collected. This process is repeated with 1,000 randomizations and we present in Figure A3 (Appendix A) the coefficients and _t_ -statistics harvested. Panel A shows that none of the placebo runs generate values anywhere close to those derived under true assignment, denoted by the dashed vertical lines. In Panel B, we find that only 5 percent of these estimates are larger in magnitude than the actual coefficient. It thus provides further support to our main estimates of the effects of temperature on poverty and inequality.

# **_C2. Heterogeneity analysis_**

Consistent with the idea that warmer temperature leads to higher poverty rate and inequality, we also expect the impacts to be heterogenous across regions. We expect that countries bearing the largest effects of global warming tend to be poorer (i.e., low-income countries) or located in poor regions. This is explained by the fact that poor countries are less prepared for the effects of climate change. They are also more likely to suffer more damages, have proportionately higher material losses, and face greater obstacles during the phases of response, recovery, and reconstruction. To explore this, we split our sample into six regions and plot the coefficient estimates of temperature in Figure 3 (Panels A and B) using the temperature bin approach. As expected, hot temperatures are found to increase poverty and income inequality in most regions relative to temperature in the reference group, particularly poorer regions such as Sub-Saharan Africa, Middle East and North Africa, and South Asia. Furthermore, we also observe the negative effects of cold temperature among countries in East Asia and Pacific and Europe and 78