Did Partial Lockdown Policy Effectively Reduce the First Wave of COVID-19 in Indonesia? A Regression Discontinuity Approach

Abstract

One of the crucial issues in determining pandemic mitigation is deciding the optimal lockdown policy, whether a complete or partial lockdown could effectively reduce the spreading of COVID-19 without a vaccination policy, which could avoid global economic recession. The conventional pandemic model has limitations in predicting these linkages. The latest Causal Inference method that has been powerful in economic and health policy evaluation, such as Regressions Discontinuity Design (RDD), can contribute to this analysis. In contrast, RDD analysis in existing references is underused. This paper's objective is how the partial lockdown policy in Indonesia effects on spreading COVID-19 virus using RDD analysis. The results indicate that the partial lockdown policy reduced new cases of COVID-19 compared with having no partial lockdown policy. The results close to complete lockdown. This study uses panel data from official health, Waze, and other social variables. The pandemic model was developed from theoretical and empirical estimation. The spillover analysis was estimated using a Bayesian panel fixed-effect with Markov Chain Monte Carlo to relax the SUTVA problem. The DKI Jakarta has effectively reduced the spread of confirmed COVID-19 cases by about 10% to 22% from the baseline. The impact was less in West Java, at about 5%–18.5% from baseline. The government can control mobility during weekend periods and at the beginning of Ramadan. Policy enacted at the level of government (subdistrict level) most proximal to households had more influence on spillover effect reduction than those enacted at higher levels of government. The RDD analysis offers new implications to existing references for incorporating economic and pandemic variables for further research. Collaborating across the government level against the pandemic is paramount to controlling human mobility close to households.