Can government digitalization promote the urban–rural equalization of basic public services? Evidence from double machine learning

Information technology, as an important tool for realizing the modernization of national governance system and capacity, has brought new opportunities for improving the government's capacity to deliver basic public services (BPS). Taking the information benefiting people pilot policy as an exogenous shock, we use the double machine learning model to examine the impact of government digitization on urban – rural BPS equalization based on panel data of prefectural cities in China from 2006 to 2021, which overcomes the limitations of the linear setting of traditional causal inference models and maintains estimation accuracy under high-dimensional control variables. The findings suggest that government digitization can significantly contribute to urban – rural BPS equalization, and this conclusion holds after a series of robustness tests. Mechanism test reveals that government digitization promotes urban – rural BPS equalization by improving the efficiency of BPS supply and the efficiency of fiscal transfer payment. Moreover, for cities with low fiscal pressure, high fiscal transparency and low economic growth pressure, government digitization will have a stronger driving effect on urban – rural BPS equalization. This study provides valuable theoretical insights and policy ideas for promoting BPS equalization through digital governance.