A worker's first job often leaves them overqualified or underqualified, and that mismatch can depress their wages for years. This research tackles a gap in understanding why skills mismatches happen in the first place. While economists know mismatches are common and costly, they do not agree on the root causes—whether workers misjudge their own abilities, misunderstand job demands, or face psychological barriers that steer them into ill-fitting roles. The project also asks how large-scale shocks, such as the Covid-19 pandemic or rapid automation, reshape the distribution of available jobs and worsen mismatches for certain groups. If successful, the work could inform early-career interventions—such as better career guidance or redesigned job-matching platforms—that help people avoid mismatches before wage penalties set in. At the macro level, understanding how technological change and economic shocks shift job-offer distributions could help policymakers design retraining programmes that target the workers most at risk of being left behind. The research is applied, not fundamental science, and aims directly at improving labour market outcomes.
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Skills mismatch - defined as the discrepancy between worker's abilities and job skill requirements - it is ubiquitous and associated with long lasting wage penalties. What are the sources of skills mismatch and how to mitigate its adverse consequences on workers? This proposal seeks to advance our understanding of these two questions concerning skills mismatch in two complementary strands. The first strand focuses on skills mismatch at the onset of workers' careers. The research will identify and compare the relative importance of various sources of mismatching at the micro level (uncertainties about abilities, job characteristics and psychological constraints), so that early interventions on individuals may help reduce the unfavorable consequences of mismatches. The analysis will apply the combined approaches of randomized controlled trials (RCT), reduced form, and structural modeling techniques to (i) jointly analyze the roles and processes of beliefs about abilities, pecuniary and non-pecuniary aspects of jobs on occupational choices, and (ii) identify the relative importance of the sources and mechanisms affecting mismatching at the early stages of careers. The second strand concentrates on analyzing aggregate factors at the macro level - such as changes in the distribution of job offers - which could affect worker-job allocations at any career stages. The recent global shock from the Covid-19 pandemic and the growing technological transformation call for a deeper assessment of the issues as they are likely to cause drastic changes in job offer distributions (characteristics and matching quality) and result in unequal impacts across skill and experience distributions. Using the RCT, online job postings, and matched employer-employee data, we will study (i) the roles of beliefs, and changes in job characteristic distribution due to the pandemic on mismatching, and (ii) the drivers of the unequal impacts of changes in firms' technologies on work-job allocations.
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