Dataset on Bidirectional Relationships Between Government Budgetary Expenditure and Marriage and Fertility Rates in China (2010–2024)

This dataset supports the study titled “Bidirectional Relationships Between Government Budgetary Expenditure and Marriage and Fertility Rates in China.” It contains panel data used to examine the dynamic and bidirectional associations between fiscal expenditure and demographic outcomes in China.
The dataset covers the period from 2010 to 2024 and integrates data from multiple authoritative sources, including the Ministry of Finance of China, the National Bureau of Statistics of China, policy documents from the CPC Central Committee and the State Council, and the World Bank.
The dataset includes the following categories of variables:
Dependent variables: crude marriage rate and total fertility rate
Core explanatory variables: government budgetary expenditures, classified into direct and indirect categories (e.g., family planning affairs, healthcare, social security, education, housing, and science and technology)
Instrumental variables: public budget revenue, central government expenditure, historical marriage rate, and policy dummy variables (including single-child policy, partial two-child policy, full two-child policy, and three-child policy)
Control variables: aging rate, GDP per capita, female labor force participation rate, and crude divorce rate
To ensure robustness and comparability, missing values were removed, and stationarity tests were conducted. First-order differencing was applied to selected variables where necessary. Variable definitions, transformations, and naming conventions are provided in the accompanying documentation.
This dataset was used to perform Granger causality tests and instrumental variable regressions to explore both forward (fiscal expenditure → marriage and fertility rates) and reverse (marriage and fertility rates → fiscal expenditure) relationships.
The dataset can be reused for research on demographic economics, public finance, and policy evaluation, particularly in the context of low fertility and population aging.
All data have been anonymized and are provided for research and academic purposes only.