How does climate change affect migration intention? Evidence from China

ABSTRACTPrevious studies have found that higher temperatures lead to an increase in population outflow. However, relying solely on migrant flow and stock data that document migration behavior may underestimate the comprehensive effects of climate change on migration, as many individuals are willing to migrate but cannot afford the costs, particularly for underprivileged groups. In this study, using data from the 2015–2018 China Migrants Dynamic Survey, we examine the effects of climate change on migration intention in China. A linear probability model is used to estimate the average effect of climate change and the temperature bin model is used to examine the potential nonlinearity. Our results suggest that higher temperature leads to higher migration intention. These effects are more eminent among individuals with lower education levels, females, and rural residents. These findings have important policy implications for climate change. Policymakers should consider the different impacts of climate change on various demographic groups and provide support to underprivileged groups to bolster their ability to adapt to climate change.KEYWORDS: Climate changemigration intentionsocioeconomic statusheterogeneity effectsJEL CLASSIFICATION: I10I18H75 AcknowledgementsWe thank members of Beihang Microeconometrics Workshop and seminar participants at the 14th International Symposium on Human Capital and Labor Markets for their valuable comments. We also acknowledge the National Natural Science Foundation of China (NSFC) (No. 72134006, 72001015, 72021001, 72004007), and the Fundamental Research Funds for the Central Universities (YWF-22-L-1231, YWF-22-WKQN-110), for financial support. Any errors are solely ours.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Available at: https://www.chinaldrk.org.cnAdditional informationFundingThe work was supported by the Fundamental Research Funds for the Central Universities [YWF-22-WKQN-110, YWF-22-L-1231]; National Natural Science Foundation of China [72134006, 72001015, 72021001, 72004007].