The impact of climate change on economic output across industries in Chile

identification from weather shocks) are problematic. Therefore we present both results as alternative models and then comment on their findings. The quarterly model results (Table 4) only shows a statistically negative impact of tempera- ture on the Agriculture-Silviculture and Fishing sectors. The results by month (Table 5) show a statistically significant negative impact for the temperature of the January month in Agricul- ture-Silviculture, Fishing, EGA (Electricity, Gas, and Water) and Construction sectors. There- fore our analysis shows that the Agriculture-Silviculture and Fishing sectors in Chile were negatively impacted in a direct way by higher Summer temperatures over the last 35 years, with results being robust for both the monthly and quarterly models. The impact is estimated in terms of reduced-form coefficients, since we cannot verify which channels (such as input- output networks in Chile or value chains effects at the global level) are driving the reduced- form coefficient estimates. Robustness checks The main results are unweighted regressions, with all region-industry pairs with a weight of 1 observation, independently of their economic value. As a robustness check, we repeated the same models with constant weights for each industry and different clustering options (clusters just by year or clusters by region-year). The results were qualitatively similar, although the Table 4. Coefficients for the impact of temperature and precipitation (quarterly averages) on regional industry GDP: OLS with fixed-effects by time and region, sep-

arate regressions by industry. Agriculture Fishing Mining Manufact. EGA Constr. Commerce Transp. Finan. serv. Home Pers. serv. Pub. adm. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Coefficients for Temperature Jan-Mar -0.019�� -0.108� 0.052 0.015 -0.024 -0.023 0.005 0.007 0.006� -0.002 0.001 0.001 (0.009) (0.060) (0.032) (0.013) (0.018) (0.020) (0.008) (0.005) (0.003) (0.003) (0.002) (0.002) Apr-Jun 0.012 0.007 -0.078�� 0.009 0.018� 0.019 -0.010 -0.003 -0.003 0.001 -0.003 0.000 (0.009) (0.034) (0.032) (0.009) (0.010) (0.015) (0.009) (0.006) (0.004) (0.001) (0.002) (0.002) Jul-Sep -0.012 0.034 0.077� -0.008 0.018 0.011 0.005 0.000 0.005 -0.003 0.003 -0.002 (0.011) (0.039) (0.043) (0.013) (0.018) (0.016) (0.004) (0.007) (0.004) (0.003) (0.003) (0.002) Oct-Dec 0.028� -0.003 -0.066 0.010 -0.013 0.000 0.010� -0.001 -0.001 -0.002 0.002 -0.003 (0.015) (0.043) (0.041) (0.016) (0.024) (0.027) (0.006) (0.008) (0.005) (0.002) (0.002) (0.002) Coefficients for Precipitation Jan-Mar 0.005 -0.001 0.017 0.010�� 0.007 -0.003 -0.001 -0.002 -0.000 -0.000 0.001� -0.001 (0.004) (0.012) (0.018) (0.005) (0.008) (0.010) (0.002) (0.002) (0.001) (0.000) (0.001) (0.001) Apr-Jun 0.001 -0.011 -0.002 -0.002 0.009� -0.005 0.000 -0.001 -0.000 0.000 0.000 0.000 (0.002) (0.008) (0.006) (0.002) (0.005) (0.004) (0.001) (0.001) (0.001) (0.000) (0.000) (0.000)

Jul-Sep 0.002 0.020�� -0.005 0.004 0.005 0.006 0.001 -0.001 0.001 0.001 0.001 -0.001 (0.002) (0.010) (0.008) (0.004) (0.004) (0.005) (0.002) (0.002) (0.001) (0.001) (0.001) (0.000) Oct-Dec 0.000 -0.002 -0.011 -0.007�� 0.007� 0.009 -0.000 -0.006�� -0.001 -0.000 0.000 -0.000 (0.004) (0.008) (0.010) (0.003) (0.004) (0.007) (0.002) (0.003) (0.001) (0.001) (0.001) (0.001) N 465 436 395 465 460 465 465 465 465 465 465 465 R2 0.112 0.055 0.058 0.028 0.041 0.025 0.025 0.049 0.047 0.095 0.026 0.025 Notes: Observations not weighted for GDP in regressions. Lagged industry GDP growth included in each regression. Robust standard errors clustered by year and region in parentheses. �� p<0.01, �� p<0.05, � p<0.1 https://doi.org/10.1371/journal.pone.0266811.t004 PLOS ONE The impact of climate change on economic output across industries in Chile PLOS ONE | https://doi.org/10.1371/journal.pone.0266811 April 28, 2022 10 / 21