Assessing the Impact of the "Double Reduction" Policy on the After-school Tutoring Industry Using Causal Forests

Abstract

The "Double Reduction" policy, implemented in China in 2021, represents a significant regulatory intervention aimed at alleviating academic burdens on students and curbing the expansion of the after-school tutoring industry. This study employs causal forest methodology to rigorously evaluate the policy’s causal effects on key industry outcomes, including market size, employment, and service pricing. Using panel data from major tutoring enterprises and regional educational statistics, the analysis identifies heterogeneous treatment effects across geographic and socioeconomic contexts. Results indicate a substantial average reduction in industry revenue and a decline in private tutoring service offerings. However, the policy’s impact varies significantly, with pronounced negative effects in urban and high-income areas, while rural and economically disadvantaged regions exhibit relative resilience. These findings underscore the importance of accounting for regional disparities when designing and implementing educational reforms. The study contributes to the literature by providing empirical evidence on the efficacy of regulatory policies in reshaping educational markets and highlights the potential of machine learning techniques like causal forests for policy evaluation.