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

We have standardized the codes to the HS2002 version and defined product categories based on the first six digits of the HS2002 version. For the purposes of this study, only export data is kept and the data are aggregated at the firm-year level. Therefore, we can obtain detailed firm export behavior data at the firm-city-year level, including the number of export product cate- gories, the number of export destinations, and the number of export relationships for each firm. The China Customs database reports the cities where exporting firms are located, and we have also obtained city-level meteorological data. Therefore, we match the two databases using city codes. Table 1 presents descriptive statistics for the variables used in this study. Firms show con- siderable diversity in the number of product categories exported. Some firms export only one product, while others export up to 4285 product categories at the 6-digit HS2002 level. The cli- mate data also show considerable variation between China’s cities. For instance, some cities have average daily temperatures below -7˚C for more than 149 days a year, while others have temperatures consistently above -7˚C. Similarly, in terms of high temperatures, some cities have 163 days a year when temperatures do not fall below 28˚C, while others have average daily temperatures below 28˚C. These variations in both export strategies and climatic condi- tions across different firm locations provide a robust empirical basis for our research. Empirical results Baseline regression

We examine the relationship between temperature and firms’ export product diversity using Eq (11). Table 2 shows the baseline regression results. In column (1), without controlling for additional variables and fixed effects, a U-shaped relationship between temperature and export diversity is observed, but with a modest R2 of 0.11, indicating the presence of an important uncontrolled effect. Introducing firm and province-year fixed effects in column (2) reveals pre- dominantly negative and significant coefficients for temperature bins. The R2 increases to 0.737, highlighting the need to control for the fixed effects in Eq (11). In column (3), when other meteorological variables that are highly correlated with temperature are included in the model, all temperature bins have negative and statistically significant coefficients, showing an Table 1. Summary statistics. Variables Obs Mean SD Min Max Diversity 3,149,535 14.43 49.97 1 4285 <-7˚C 3,126,982 4.872 16.61 0 149.4 -7~-2˚C 3,126,982 8.739 14.89 0 93.82 -2~3˚C 3,126,982 22.41 20.40 0 99.37 3~8˚C 3,126,982 36.86 22.17 0 98.67 13~18˚C 3,126,982 43.44 15.26 0 113.33 13~18˚C 3,126,982 52.76 12.41 0 154.2 18~23˚C 3,126,982 70.25 13.05 0 181.7 23~28˚C 3,126,982 81.91 27.29 0 224 �28˚C 3,126,982 44.04 27.33 0 163 Prec 3,126,982 3.452 1.504 0.0529 8.594 Wind 3,126,982 2.427 0.548 0.546 5.692 Sun 3,126,982 5.268 0.924 2.017 9.694 Wet 3,126,982 4.261 0.0972 3.322 4.515 Atmo 3,126,982 100.4 2.553 59.24 101.7

https://doi.org/10.1371/journal.pone.0308391.t001 PLOS ONE Climate effects on the diversification strategy of export firms PLOS ONE | https://doi.org/10.1371/journal.pone.0308391 August 16, 2024 7 / 28