The rapidly evolving landscape of the labor market, supercharged by advancements in AI, presents a complex challenge for policymakers striving to understand and mitigate its impact. A recent IZA discussion paper by Michael Johannes Böhm, Ben Etheridge, and Aitor Irastorza-Fadrique introduces an innovative equilibrium model designed to shed light on how workers and wages will adapt to these technological shifts. This model moves beyond simplistic categorizations of “automatable” jobs, instead offering a nuanced perspective by integrating expert data on automation’s reach, a sophisticated model of worker mobility, and historical evidence of past labor market adjustments.
One of the study’s core findings is the significant heterogeneity in workers’ ability to switch jobs. Occupations like those held by doctors and teachers are deemed “inelastic,” meaning substantial wage fluctuations do not translate into significant employment shifts. Conversely, roles in IT and administrative support demonstrate greater adaptability, with employment more responsive to wage changes.
Furthermore, the research reveals that occupational transitions are far from uniform in their ease. While some shifts, such as from lab technician to nursing, are common, others like moving from manufacturing to coding are infrequent and often entail considerable costs. This nuanced understanding of transition costs is crucial for effective policy design.
A particularly salient finding is the tendency for automation to impact similar jobs concurrently. This correlation in shocks limits workers’ fallback options, often preventing individuals in shrinking sectors from easily transitioning into growing, substitutable occupations.
Looking ahead, the model provides forward-looking projections for the labor market. It predicts an increase in employment for IT and construction-related occupations, alongside wage increases in the health and education sectors. Conversely, manufacturing jobs and even some high-skilled professions, including accountants and auditors, are expected to face declining wages. The analysis suggests that IT and tech roles will expand by attracting workers from a wide array of business and technical backgrounds, while manufacturing workers face limited attractive alternative occupations.
These findings carry profound implications for policy. Effective strategies must extend beyond merely identifying at-risk jobs to consider the realistic pathways for workers to transition elsewhere and the capacity of other occupations to absorb new labor. This necessitates targeted retraining initiatives for occupations with limited alternatives, along with comprehensive transition support such as job-matching and counseling along realistic career paths. Additionally, policymakers may need to explore wage subsidies or other support mechanisms for workers in low-mobility roles.
Ultimately, this research provides a powerful framework for policymakers to anticipate structural changes, predict the interplay between wages and employment, identify viable job transition routes, and craft more intelligent and targeted interventions to address potential inequalities in the labor market.