China Labor Income-Temperature Panel Dataset (2012-2016)
Description
This panel dataset integrates three core sources: the China Labor-force Dynamic Survey (CLDS, 2012/2014/2016), the U.S. NCEI’s Global Summary of the Day (GSOD) meteorological data, and the China City Statistical Yearbook. Meteorological data were spatially interpolated via inverse distance weighting (IDW) into 0.1°×0.1° grids, with missing values imputed using five nearest stations. City-level annual average temperature (primary variable, plus its squared term for non-linearity testing) and annual precipitation were derived. The dependent variable is the natural logarithm of individual annual total income. Controls include individual attributes (age, education years) and urban socioeconomic indicators (fiscal expenditure-to-GDP ratio, economic agglomeration, secondary industry share, GDP growth rate). Standard data cleaning (coding unification, outlier removal, price deflation) was applied. The final sample includes 29,629 valid observations, with a mean log income of 9.773, average annual temperature of 16.425°C, and mean age of 44.117 years. This dataset merges micro individual heterogeneity with macro climatic and economic data, supporting analyses of temperature’s causal effects on labor income and related mechanisms.