Unveiling the causal effects of China’s minimum living standard guarantee on household transportation expenditures: A causal forest analysis

Abstract

We investigate the causal effects of China’s Minimum Living Standard Guarantee (MLSG, also called Dibao) subsidy on household transportation expenditures. Utilizing data from the Chinese Social Survey, we employ a combination of the propensity score matching (PSM), causal forest (CF) and marginal treatment effect model (MTE) methodologies to rigorously estimate the subsidy’s effects. The PSM allows us to mitigate selection bias by matching MLSG recipients with comparable non-recipients. While the causal forest captures the heterogeneity of treatment effects across various household profiles. The result of MTE indicate that observable and essential heterogeneity are present to influence the effect of their subsidies, which present the consistent with PSM and CF. The causal mediation analysis indicates the mediating mechanism that MLSG impacts on household transportation expenditures, while also revealing significant variations among different regions. The study not only refines our understanding of the MLSG’s effects on household spending but also offers novel insights into applicational advancements by incorporating machine-learning techniques for policy evaluation. These results have important implications for policy formulation and refinement, particularly in the urban-rural differences.