Ensuring that workers are employed in jobs that make the best use of their skills is a key driver of productivity, wage growth, and firms’ performance. When people are well matched to their roles, they are more productive, firms operate more efficiently, and economies grow faster. Yet measuring how well workers and jobs fit together has always been challenging. Traditional indicators typically compare a worker’s education to the “required” education of an occupation, or rely on predefined lists of skills. These measures are often too rigid, too broad, and difficult to compare across countries with different labor market institutions.
This section introduces a new, data-driven way of measuring job match quality: the Job Allocation Quality (JAQ) indicator. Rather than relying on external definitions of what a job requires, JAQ uses detailed administrative data that link workers to their employers. With machine-learning techniques, the method identifies how highly productive firms allocate workers across occupations. These firms are assumed to be relatively effective at placing workers where they contribute most. Their observed job assignment patterns are used to infer an “efficient” benchmark.
Each worker’s actual job is compared to the job that the model predicts would suit them best, given their characteristics and career history. If the two coincide, the worker is considered well matched. Mismatch is therefore defined not as a gap from a theoretical standard, but as a distance between actual occupation and what happens in successful firms. JAQ can be measured at both the individual and firm levels—capturing how closely a worker’s job aligns with their predicted best match, and the overall quality of job allocation within a company.
The analysis applies this harmonized methodology to rich employer–employee data from four European countries: Sweden, Portugal, Italy, and the Netherlands. Despite differences in institutions and labor market structures, the results reveal several consistent patterns.
First, job match quality improves sharply at the beginning of workers’ careers and then stabilizes. During the first years of employment, workers move into roles that better suit their abilities, and firms learn more about their employees’ strengths. After this early phase, improvements become much smaller. This suggests that most gains in allocative efficiency occur early in working life.
This is true measuring experience with both age and tenure.
Second, better job matches are strongly associated with higher wages. Across countries, workers whose jobs more closely align with their predicted best assignment earn systematically more, even after accounting for occupation, firm characteristics, and individual traits. This confirms that JAQ captures an economically meaningful dimension of labor market performance.
Third, firms with higher job allocation quality are more productive. Companies that allocate workers more effectively generate higher output per employee. However, the link between match quality and profitability is weaker, suggesting that productivity gains from better matching are at least partly shared with workers through higher wages.
Fourth, firm characteristics matter. Match quality rises quickly with firm size—especially up to around 20 to 30 employees—and tends to be higher in older firms. This points to the importance of managerial capacity and organizational experience in assigning workers efficiently. Small and young firms may face structural disadvantages in this respect.
The report also shows that match quality varies across sectors, with more complex industries exhibiting greater mismatch. By contrast, demographic characteristics such as gender or immigrant status are only weakly related to match quality, suggesting that mismatches are driven more by career dynamics and firm practices than by observable worker traits.
Overall, the findings demonstrate that job mismatch is economically significant, closely linked to wages and productivity, and dynamic rather than fixed. By combining administrative data with machine-learning tools, JAQ provides a scalable and internationally comparable way to monitor how efficiently labor markets allocate talent—offering valuable insights for policymakers seeking to improve productivity and labor market performance.
You may find additional information regarding the data and the methodology used in this report.