Big Data and Rural Consumption Dynamics

This study examined the impact of big-data technology on addressing China's challenges of population aging and fragmented rural pension systems. By integrating multi-source data—demographic, financial, and health records—the research explored how data-driven strategies improve pension policies, streamline enrollment, and boost rural consumption. Using econometric models like difference-in-differences (DiD) and machine learning algorithms, the study analyzed data from four provinces with varied economic profiles. Findings showed significant increases in pension enrollment and consumption, especially in underdeveloped regions, with a 16.3% rise in enrollment and a 16.8% boost in consumption in the west. Big data helped shift spending from survival needs to investments in health care and education. The research emphasizes the need for tailored interventions, like digital dashboards and targeted subsidies, to ensure equitable outcomes and highlights the importance of expanding digital infrastructure in underserved areas to support sustainable, inclusive growth.