Climate effects on the diversification strategy of export firms: Evidence from China

Therefore, we alternatively control for the impact of city development trends by adding a city- year interaction term as a control variable in model (11). The result is shown in column (2) of Table 3, where we find that temperature still has an inverted U-shaped relationship with export product diversification. These results suggest that the omitted variable problem does not affect the basic conclusions of this study. Subsequently, we address selection bias, encompassing both sample selection bias and self- selection bias. Our sample comprises all exporting firms registered in Chinese customs data, thereby minimizing sample selection bias. There are two problems with self-selection bias. Firstly, there is bias from unobserved firms that did not enter or exit the export market. Since data on the product categories of firms selling domestically are not available, we cannot inves- tigate this issue empirically. However, logically, firms that do not enter or exit the export mar- ket are typically those that are eliminated due to lower competitiveness and risk resistance. Therefore, extreme temperatures are likely to have a greater negative impact on the diversifica- tion strategies of these firms, potentially underestimating the overall impact of temperature. Therefore, this bias doesn’t affect the conclusion of an inverted U-shaped relationship between temperature and export product diversification. Secondly, self-selection bias arises from firms’ location choices. Temperature-sensitive firms may prefer regions with comfortable tempera-

tures when choosing a location. To address this, we examine the effect indirectly. Firms are likely to consider temperature conditions in different regions when selecting locations, sug- gesting a spillover effect of temperature across regions. To assess this, we use the inverse of city distances as weights to calculate the weighted average of temperature variables for other cities, constructing spatial lag terms for each temperature bin. The results in column (3) of Table 3 Table 3. Endogeneity issue. (1) (2) (3) Variables Diversity Diversity Diversity <-7˚C -0.1453*** -0.1181*** -0.1061* (0.047) (0.044) (0.056) -7~2˚C -0.1001*** -0.0891*** -0.1003** (0.035) (0.033) (0.047) -2~3˚C -0.0704*** -0.0641** -0.0766** (0.027) (0.026) (0.034) 3~8˚C -0.0319 -0.0317 -0.0407 (0.021) (0.020) (0.029) 13~18˚C 0.0015 -0.0087 0.0064 (0.019) (0.019) (0.026) 18~23˚C -0.1020** -0.1057*** -0.0914** (0.040) (0.040) (0.046) 23~28˚C -0.0934*** -0.0962*** -0.0835** (0.033) (0.034) (0.041) �28˚C -0.0920*** -0.1057*** -0.0876** (0.033) (0.035) (0.039) CV Yes Yes Yes FE Yes Yes Yes Observations 2,922,853 3,015,824 3,015,824 R2 0.741 0.737 0.737 Note: CV includes all unreported control variables as well as the constant term. FE represents fixed effects, including both firm-specific and province-year fixed effects. Unless explicitly stated, the following tables follow this note. https://doi.org/10.1371/journal.pone.0308391.t003 PLOS ONE Climate effects on the diversification strategy of export firms

PLOS ONE | https://doi.org/10.1371/journal.pone.0308391 August 16, 2024 10 / 28