A Predictive Model for Analyzing the Economic and Social Impact of Technological Innovation on Job Markets

Abstract

This research paper presents a model that predicts the effects of technological innovation on the labor market. Changes in employment patterns and industries are occurring rapidly due to advances in technology, such as artificial intelligence (AI), automation, and renewable energy technologies. This model incorporates econometrics and machine learning, which are considered very accurate for predicting job displacements and creations across industries, taking into account the effects of technology on industries, employees, and other relevant variables, such as differences in industry demographics and the spread of technology. It was determined that industries such as manufacturing and agriculture have jobs to be displaced at 25% and 30%, respectively. On the other hand, the growth of information technology and renewable energy is expected to create more jobs, with respective increases of 15% and 10%. The results also indicated a rise in the demand for technical skills and a reduction in manual/lower-skill jobs by 20% each. Socially and economically, an increased income disparity is anticipated owing to wage hikes and job insecurity among high-skill workers. At the same time, low-skill workers experience job deprivation and lower wages. From the conclusions of the paper, to alleviate the adverse effects of automation, education, retraining, and UBI will be essential. There is a need to conduct further research to examine the regional variation and implications of automation in international labor markets.