and finds that highly exposed industries such as construction, mining, or transportation experience impacts that are roughly twice as large as those that occur primarily indoors.<sup>11</sup> On average, these studies suggest a per‐degree‐C point impact magnitude of around minus 1% to 3%, although there is some evidence that the impacts are smaller in developed economies such as the United States and firms or regions with higher levels of air conditioning.<sup>12</sup> While there is as yet limited research regarding the heterogeneity in temperature driven impacts across different levels of occupational exposure to heat stress (e.g. indoor/outdoor), intuition and anecdotal evidence (for instance, from agriculture, or in the realm of time use decisions<sup>13</sup> ) suggests that there may be important differences in impact magnitudes by occupation.

Interestingly, those studies that are able to use daily temperature data find significant impacts from extreme heat days, much like the impact of killing degree days in the agricultural literature (Schlenker and Roberts, 2006; Butler and Huybers, 2013; Burke and Emerick, 2014). This may suggest that the heat‐related welfare impacts from climate change primarily from the incidence of extreme heat day events, rather than from the mean‐shift of average annual temperatures per se. This is relevant in the context of distributional implications, specifically in projecting the incidence of effective extreme heat stress due to climate change across different regions (e.g. already hot places vs temperate or cold places), as discussed in more detail below.

To summarize the literature to date: Building on the longstanding cross‐sectional fact that hotter countries tend to be poorer (Sachs et al, 2001; Horowitz, 2001), and that hotter municipalities within some countries tend to be poorer (Acemoglu and Dell, 2012), recent quasi‐experimental research suggests that temperature stress exerts a causal impact on economic outcomes. While it is as yet unclear how much of the impact can be attributed to institutional correlates (Acemoglu, Johnson, and Robinson, 2001), or other geographically correlated factors such as agricultural productivity (Gallup et al, 2001), the emerging picture is one of non‐trivial welfare impacts, arising from a combination of health effects, consumption disamenities, and production impacts.

But some important puzzles remain. It is still unclear whether the primary impacts (along all three channels) are from heat, or whether both heat and cold matter. For instance, is there an

> 11 Park suggests furthermore that the magnitude of these extreme heat day impacts vary by level of average heat exposure, which may suggest scope for adaptive investments that could offset some of these impacts in the medium to long run. 12 As noted above, these point estimates may be biased predictors of the labor productivity impacts of future climate change, due to the possibility of long‐run adaptation. Adaptations may be as simple as reductions in labor effort or hours (especially during particular times of the day) or investment in air conditioning equipment. Of course, such seemingly simple adaptations may be prohibitively costly or effectively unavailable in many developing country contexts. An air conditioner is of no use if electric infrastructure fails at precisely the times of day when its cooling services are most in need.

> 13 Graff Zivin and Neidell (2013, 2014)

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