Immigrant–Native Pay Disparities Unveiled by Multinational Administrative Data Analysis
A groundbreaking multinational study leveraging linked employer–employee administrative records has illuminated persistent pay gaps between immigrants and native-born workers. With data spanning nine countries across Europe and North America—Canada, Denmark, France, Germany, the Netherlands, Norway, Spain, Sweden, and the USA—researchers harnessed detailed wage information combined with occupational and establishment identifiers to dissect the roots of immigrant–native earnings disparities. Their findings pinpoint restricted access to high-paying jobs, rather than human capital differences, as a primary driver of wage inequality.
The study’s distinct advantage lies in its use of linked administrative data, which connects individual workers with their specific employers and coworkers. This data architecture enables unprecedented granularity by comparing pay differences within identical job roles, industries, and establishments — a level of comparison impossible in many survey-based studies. By harmonizing datasets with varying definitions and measurement standards from different national statistical agencies, the authors overcome significant cross-country comparability challenges, ensuring robust and reliable estimates.
To operationalize their analysis, the researchers employed a series of rigorous ordinary least squares (OLS) regressions, progressively introducing fixed effects to isolate wage differentials attributable to sorting mechanisms at multiple labor market layers. Starting with base adjustments for sex, age, education, and geographic region, the models incrementally accounted for effects within industries, occupations, establishments, and finally, occupation–establishment units—unique job definitions combining specific roles at particular workplaces. This stratified model sequence revealed how much of the immigrant–native pay gap is explained by differential distribution across job types versus unequal pay within the same roles.
The dependent variable throughout this modeling framework was the natural logarithm of annual pre-tax earnings, capturing the essential earnings components including base wages, overtime, bonuses, and other compensations. Where data permitted, hourly wage analogues refined these earnings measures, underscoring compensation controlling for variations in hours worked. Immigrant background was meticulously coded based on country of birth and parental nativity where possible, distinguishing immigrants, children of immigrants, and natives. Additionally, immigrants were categorized by world region of origin, which exposed important subgroup disparities.
Crucially, the analysis restricted samples to workers aged 25 to 60 and excluded marginal employment below half the lowest earning decile, ensuring the focus remained on labor market participants with substantial engagement. For workers with multiple jobs recorded in the data year, only the highest-earning position was analyzed, aligning results with the most significant employment relationship per individual. These sample constraints further elevated the study’s precision.
As immigrant workers frequently face complex integration patterns, the study disaggregated results by duration of stay, defining recent, established, and childhood immigrants. This nuanced approach helped distinguish between adaptation effects and structural labor market barriers. Moreover, the linkage to establishment identifiers—distinct physical workplaces—allowed the team to parse pay discrepancies within single employers, a test for discriminatory or institutionalized wage-setting practices.
The cross-national meta-analytic approach synthesized country-specific regression outputs into cohesive conclusions. Using random-effects models allowed for heterogeneity in immigrant-native pay differences expected across diverse labor markets. This meta-regression aggregated estimates both at the all-immigrant level and by detailed world-region origins, accommodating variation in migrant compositions. The statistical framework accounted for sampling variance and unobserved country-level heterogeneity, providing weighted, generalizable effect size estimates.
Findings demonstrated that a large proportion of the immigrant pay gap could be attributed to a lack of access to well-paying occupations and establishments rather than within-job wage disparities. In other words, immigrants and their children are underrepresented in higher-paying jobs even after adjusting for education, age, and geographic factors. The magnitude of pay differences narrowed substantially when fixed effects for occupation and establishment were incorporated, highlighting the critical role of occupational and employer sorting.
Interestingly, children of immigrants often exhibited smaller pay gaps relative to natives, suggesting partial intergenerational assimilation in labor market outcomes. However, gaps persisted, especially for children descending from immigrants originating from regions historically experiencing higher discrimination or lower social capital, such as Sub-Saharan Africa and the Middle East/North Africa. These results underline the enduring challenges faced by immigrant communities and their descendants in accessing economic opportunity.
Robustness checks reinforced the conclusions, as sensitivity analyses varying wage definitions, occupational granularity, employer definitions (establishment vs. firm levels), and control variables consistently pointed to restricted job access as the core issue rather than measurement artifacts. The research team also examined integrated job cells containing both immigrant and native workers, ruling out compositional biases due to job segregation.
This comprehensive and meticulously executed investigation marks a significant advance in understanding labor market inequalities on a multinational scale. By emphasizing within-job pay comparisons and employing fine-grained administrative data, it challenges narratives attributing immigrant-native earnings gaps solely to human capital deficiencies. Instead, it exposes systemic labor market structures and barriers constraining immigrants’ economic mobility.
As global migration intensifies, these findings have urgent policy implications. They call for targeted interventions aimed not just at skill enhancement but at dismantling institutional and structural barriers that prevent immigrants from accessing high-paying roles and workplaces. Equitable recruitment practices, anti-discrimination enforcement, and integration policies sensitive to occupational sorting effects emerge as vital tools to close persistent wage gaps and promote inclusive growth.
The study also highlights the importance of harmonized, linked employer-employee data infrastructures for labor market research. Cross-national collaborative frameworks that enable comparable measurement and joint analyses open new horizons for tracing the socio-economic trajectories of migrant populations. Future research may build upon these insights by incorporating longitudinal dynamics, examining firm-level practices in greater detail, or exploring intersectional disadvantages shaping immigrant labor market experiences.
In sum, this extensive, multi-country investigation delivers a compelling narrative: immigrant-native pay gaps are less about individual qualifications and more about access to better jobs. Comprehensive labor market reforms and inclusive policies must address this structural inequality head-on to unlock the economic potential of immigrant workers and foster a fairer labor market for all.
Subject of Research:
Immigrant–native earnings differences and labor market integration using linked employer–employee administrative data.
Article Title:
Immigrant–native pay gap driven by lack of access to high-paying jobs.
Article References:
Hermansen, A.S., Penner, A., Boza, I. et al. Immigrant–native pay gap driven by lack of access to high-paying jobs. Nature (2025). https://doi.org/10.1038/s41586-025-09259-6
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