전이확률과 마코프체인을 활용한 코로나19 전후 한국 소득계층의 구조적 이동성 연구

This study examines the impact of COVID-19 on income mobility in South Korea using Markov chain analysis. While traditional research often relies on static metrics like the Gini coefficient in income distribution research, this study focuses on dynamic income mobility, comparing pre-pandemic (2017-2019) and pandemic (2020-2022) periods. We employ three metrics to assess income mobility: the Prais-Bibby index for simple mobility, steady-state income distribution for long-term structural changes, and absorption time for low-income households' upward mobility. Our methodology involves multiple imputation for missing data, bootstrap resampling, and Markov chain analysis. Utilizing data from the Survey of Household Finance and Living Conditions, the analysis reveals a decline in income mobility during the pandemic, a decrease in income convergence, and a longer duration for low-income households to move up the income ladder. These findings suggest a need for targeted policies to support upward mobility in the post-pandemic period. Our approach demonstrates the importance of dynamic analysis in understanding income distribution changes, moving beyond static inequality measures to capture the complexities of income mobility during economic shocks like the COVID-19 pandemic.