The Effects of Policy Mixes on Urban Population Mobility: Evidence from Chinese Cities During the COVID-19 Pandemic

Effective crisis governance requires nuanced policy design capable of addressing complex and interrelated challenges. One emerging approach is the use of policy mixes to enhance policy effectiveness. However, existing research lacks robust methods for the quantitative assessment of such mixes. This study develops a theoretical framework that integrates government and policy networks to measure policy mixes quantitatively. Drawing on a dataset of 7,460 pandemic-related policy documents from Chinese cities, we examine the impact of policy mixes on urban population mobility during the COVID-19 pandemic. The empirical findings reveal a temporal dynamic in policy effectiveness and heterogeneity across policy types and urban contexts. First, a U shape pattern effects was detected that policy mixes significantly reduced urban population mobility during the infection control phase but facilitated increased mobility during the social recovery phase. Second, regulatory policies had a greater influence on mobility patterns compared to other policy tools. Third, the positive effects of policy mixes were more pronounced in cities with higher levels of institutional development and administrative authority. This study advances the theoretical understanding of policy mix effectiveness and offers practical insights for enhancing local governments’ crisis governance capabilities through the strategic design of policy combinations.