A Double/Debiased Machine Learning-Based Method for the Analysis of Panel Data
摘要
This study evaluates the impact of air pollution on economic activity in 309 regions across England between 2010 and 2021. Using concentration of particulate matter ( \(PM_{2.5}\) ) as a measure of air pollution and the gross value added (GVA) as a measure of economic activity, we apply Two-Stage Least Squares (2SLS) and double/debiased machine learning models with thermal inversion as an instrumental variable. Our findings reveal a significant negative causal relationship: A 1 \(\upmu \textrm{g}/\textrm{m}^3\) increase in \(PM_{2.5}\) concentrations leads to a 4.3% reduction in GVA per capita and a 1.6% reduction in GVA per job. Additionally, we find that air pollution impacts urban productivity more than rural areas, and it effects on high-skilled employment. These results emphasize the need for stricter air pollution policies. These findings have significant implications for the development of air pollution policies and regulations.