_HOTTER PLANET, HOTTER FACTORIES_ _19_ in the non-agriculture sector is still developing, relatively recent studies document estimates that are comparable to those from this study. Using data from half a million manufacturing firms in China, Zou and Zhong (2022) find that a day with a temperature above 90<sup>0</sup> F (32<sup>0</sup> C) is associated with a TFP loss of 0.56 percent, relative to a day with a temperature between 50<sup>0</sup> F (10<sup>0</sup> C) and 60<sup>0</sup> F (15 _._ 6<sup>0</sup> C). In a study of Indian manufacturing firms, Somanathan et al. (2021) find that plant output falls by about 2 percent per 1<sup>0</sup> C increase in temperature. In a US study, Deryugina and Hsiang (2014) show that the productivity of individual days declines by 1.7 percent for each 1<sup>0</sup> C (1 _._ 8<sup>0</sup> F) increase in daily average temperature above 15<sup>0</sup> C (59<sup>0</sup> F). Using data from personnel records, Cai et al. (2018) find that productivity at temperatures below 60<sup>0</sup> _F_ (15 _._ 6<sup>0</sup> C) is about 11% less than that at 75<sup>0</sup> F and 79<sup>0</sup> F. The productivity losses associated with higher temperatures that we find in this study are relatively larger compared to these and other similar studies. The main reason is that our study identifies nonlinear impacts at the higher end of the temperature distribution, while impacts at the lower end could even be positive, as we have shown. Hence, the average effects that are often reported may hide these important uneven effects. In addition, the thresholds for the temperature categories that register negative and large impacts are much higher than the thresholds used in many of the earlier studies and are higher than the human body can tolerate. There are large variations across temperature zones and income groups and additional variations that yield values that are different from those of other studies that focus on individual countries. Hence, the estimates we show here are reasonable estimates of the impacts on productivity of changes in temperature deviations from the long-run average.

The relatively large coefficients must be interpreted in the context of a slow (taking several decades) increase in temperatures, in that a unit (1<sup>0</sup> _C_ ) increase in the long-term average is quite large. The average deviation for the pooled observations in our sample is 0 _._ 62<sup>0</sup> _C_ over four decades, between 1982 and 2022. The magnitude of these temperature anomalies varies by climate zones, and regions of the world, and the rate of increase has been faster in recent decades. According to US National Aeronautics and Space Administration’s National Center for Environmental Information, for instance, in each decade since 1981, the annual dry-bulb temperatures have increased by 0 _._ 27<sup>0</sup> _C_ in North America, 0 _._ 22<sup>0</sup> _C_ in South America, 0 _._ 46<sup>0</sup> _C_ in Europe, 0 _._ 28<sup>0</sup> _C_ in Africa, 0 _._ 37<sup>0</sup> _C_ in Asia, and 0 _._ 19<sup>0</sup> _C_ in Oceania. Although not directly comparable to dry-bulb temperatures, the average WBT deviations in our sample, which covers almost four decades, are closer to these continental averages.

# _B. Heterogeneity_

Although they are not based on linear relationships, the estimates in the previous section are averages, so they mask a great deal of heterogeneity. The effects of increased temperatures on firm productivity could vary depending on various ob-