Job Automation and Worker Reallocation
JOB MARKET PAPER
How does job automation affect reallocation decisions of displaced workers? I show that stagnant occupational mobility rates reported since 1990s are the result of a composition effect: positive trend for occupations with high risk of automation is offset by the decline in mobility among low risk occupations. Displaced workers with high exposure to automation have on average 10 percentage points higher probability of changing their broad occupational category, a pattern that has increased significantly over the past two decades. The mobility rates within high exposure occupations are monotone, pointing that low earners switch their occupations more frequently. Furthermore, the direction of mobility is downward: individuals at risk of automation switch into occupations with lower average wages. To evaluate the role of job automation in the evolution of occupational mobility, this paper proposes a search and matching model with technological acceleration and human capital accumulation. The reallocation decision of unemployed individuals depends on their human capital level and skill transferability between two occupations. The results show that the response of the economy to automation shock follows closely patterns observed in the data between 1996 and 2012. Job automation accounts for 79 percent of the increase in mobility gap. This in turn leads to output losses due to skill transferability mechanism and the fact that human capital is not fully transferable across occupations.
Keywords: automation, robots, occupational mobility, employment, job search
JEL Codes: J23, J24, J61, J62, J64
Between 30% and 60% of all jobs are obtained through referrals. What renders informal contacts attractive to employers? Does firm's social network simply speed up the hiring process or it additionally facilitates selection of high-skilled individuals? Using matched employer-employee data from Veneto, an industrial region in northern Italy, this paper studies the role of co-worker links in firm’s hiring decisions and its consequences for the firm productivity and output. Novel empirical findings show that hires from firm's own network increase significantly its productivity. I find that 10% surge in linked hires increases productivity by approximately 1%. The effect lasts up to three years following the hire. The evidence points that the co-worker links increase firm productivity mainly through industry-specific skills, which suggests that employers may use informal contacts to poach high-skilled workers. I also show that informal ties play a larger role for job-to-job transitions within industrial clusters. Hence, social networks might play a role in the transmission of job-specific skills and knowledge diffusion.