Linked Employer-Employee (LEE) datasets provide a unique perspective on labor markets by combining detailed information about both workers and their employers. These datasets typically include key identifiers such as an employer ID, which links employees to specific firms or institutions, and a worker ID that allows researchers to track individuals over time. They also contain time markers for each observation period and measures of work income, such as monthly earnings or hourly wages. In some cases, an establishment-level identifier is available, distinguishing between different locations of the same company.
Beyond these structural elements, LEE datasets generally include basic demographic details like age, gender, and country of birth. Many countries also provide occupational classifications, often following the International Standard Classification of Occupations (ISCO) or a national equivalent. Despite their fundamental nature, these core data points have enabled groundbreaking research into labor market dynamics.
One of the most significant applications of LEE data is in studying wage disparities. By linking workers to their employers, researchers can isolate factors influencing wage differences, such as firm-specific pay policies, workforce composition, and occupational segregation. Studies have examined how these elements contribute to overall wage inequality and how they evolve over time. Researchers also use these datasets to analyze wage gaps across gender, race, and skill levels, distinguishing whether disparities arise within firms or across different companies. Such insights are particularly valuable for cross-country comparisons.
The ability to track employees over multiple years has expanded research on career trajectories and job mobility. For instance, in countries where administrative data records detailed employment histories—including precise start and end dates—researchers can study unemployment duration and assess the impact of policy changes on job search behavior. Similarly, tracking workers across firms has provided insights into professional networks and the role of social connections in job placement and wage growth. These datasets also enable the study of knowledge spillovers, where skills and expertise transfer between firms through employee mobility.
Long-term LEE datasets further allow for the study of career outcomes, including the long-term effects of job loss or employment in lower-quality firms. They also help analyze the financial returns of entrepreneurship compared to traditional employment. As these datasets continue to evolve, they offer increasingly rich opportunities for understanding labor market trends and informing policy decisions.