Artificial Intelligence and Unemployment in Indonesia with a Panel Data Approach

This study examines the influence of artificial intelligence on unemployment in Indonesia. Comprehensive empirical studies are limited in the literature. Therefore, this study uses a dataset, which covers 34 provinces from 2013 to 2022. Researching the use of computers to explain AI and the unemployment rate. In an empirical approach, we control the dynamic effects of unemployment by using panel data to determine the effect of AI on unemployment. As a variable control, we use economic growth. We use unemployment as a dependent variable and AI as an independent variable. We assume that current unemployment is affected by unemployment in previous periods. All data in this study uses secondary data obtained from the website of the Central Statistics Agency for several years from 2013 to 2022. This study uses panel data which is a combination of cross section data and time series data using the eViews 13 analysis tool. To facilitate cross section data in this study, a sequence number is used to mention the name of the province being analyzed. To facilitate the use of provincial data in Indonesia, the author provides a sequence number code for each province The results of this study are that the unemployment rate of the previous year and economic growth have an effect on the current unemployment rate, while AI has not yet had an effect on the unemployment rate.