Jens Hainmueller

Citizenship is widely summarized as producing a “naturalization premium” for immigrants, but naturalization is selected: immigrants who naturalize differ in language, residence, employment, and motivation. We provide a quantitative meta-analysis of citizenship effects on immigrant earnings, synthesizing 60 preferred estimates from 31 studies. How a study separates the effect of citizenship from selection into citizenship—its identification strategy—explains more variation in estimates than destination country, citizenship regime, or any other study feature we code. Averaging published estimates with a multilevel random-effects model yields a pooled premium of 9.9% (95% CI 5.1 to 14.9), and 11.0% (95% CI 5.7 to 16.6) when pooling within study-country cells. But this average conceals sharp differences across research designs. The randomized encouragement study relying on a lottery to overcome selection implies a near-zero effect of 1.9% (95% CI −1.3 to 5.3); the regression-discontinuity study yields 11.3% (95% CI 5.2 to 17.8); estimates from policy reforms and within-unit panels are positive on average but too imprecise to distinguish from either zero or the pooled average; and observational instrumental-variable estimates average 53.0% (95% CI 27.6 to 83.5). Excluding observational instrumental-variable estimates reduces the pooled premium to 6.0% (95% CI 3.8 to 8.2). Reanalyzing the randomized trial data shows the same pattern: Experimental estimates are near zero, while an observational difference-in-differences comparison based on self-selected citizenship uptake implies a 9.6% gain (95% CI 4.0 to 15.5) four years post lottery, a bias standard pretrend tests do not detect. The published citizenship premium is positive, but its size and causal interpretation depend on research design.

We study the effects of H-1B immigration on U.S. industries that employ H-1B workers and their trading partners. Using a novel cross-industry design and the 1999–2003 expansion of the H-1B visa cap for identification, we find that H-1B exposure raised incomes for natives and pre-existing immigrants, with gains concentrated in non-STEM occupations. Income gains propagate forward through supply chains to downstream industries but not backward to upstream industries, consistent with a productivity shock rather than a labor supply shock. We find no direct effect on patenting, suggesting that productivity gains arise from better task execution rather than patentable invention.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Governments face persistent challenges in integrating refugees into the local labor market, and many past interventions have shown limited impact. This study examines the Job-Turbo program, a large-scale initiative launched by the German government in 2023 to accelerate employment among refugees—primarily individuals from Ukraine and eight other major countries of origin. Using monthly administrative panel data from Germany’s network of public employment service offices and a difference-in-differences design, we find that the program significantly increased both caseworker–refugee contact and job placements over a 23-mo follow-up period. Among Ukrainian refugees, the exit-to-job rate nearly doubled. Effects were broad-based—spanning demographic subgroups, unemployment durations, skill levels, regions, and local labor-market conditions—and were concentrated in regular, unsubsidized employment. The program also raised both the rate and share of placements followed by sustained employment, consistent with improved placement quality. Other refugee groups saw meaningful gains as well, but increases in job placements were concentrated among males and in low-skilled jobs, with only limited effects for females. We detect no negative spillovers on contact rates or exit-to-job rates for unemployed German or other immigrant job seekers, finding no evidence of resource reallocation or displacement. The results offer insights for governments responding to displacement crises. They indicate that intensified job-search assistance—embedded within the early stage of integration and implemented at scale through public employment infrastructure—can meaningfully improve refugees’ labor-market outcomes, even amid significant arrivals.

Sudden displacement crises require rapid expansion of refugee accommodation, yet little is known about how different types of initial housing affect integration outcomes. Exploiting linked administrative registers covering the full population of Ukrainian refugees arriving in Denmark in Spring 2022 and a representative survey, we classify each refugee’s initial accommodation from address and co-residence records and track outcomes for 18 months. The majority of arrivals during the peak were absorbed in pop-up shelters (36%) and private hosting (43%). These scalable alternatives to the inelastic supply of conventional refugee housing proved durable, with mean stays of about seven months. Moreover, by leveraging quasi-random assignment generated by within-municipality capacity and time constraints, we estimate effects of accommodation type while conditioning on locality, arrival timing, and sociodemographics. Relative to conventional public housing, private hosting led to higher early employment, higher earnings, persistently lower public-transfer receipt, and improved psychological well-being. Pop-up housing performed at least as well on labor-market outcomes and showed modest gains in social integration. By holding locality constant, we show that how refugees are housed within municipalities has an independent, first-order effect on integration—distinct from the well-studied importance of where they are placed. These findings highlight the potential for civic-led accommodation to complement public systems during displacement shocks and shape long-term refugee trajectories.

Governments continue to face challenges integrating refugees into the local labor market, and many past interventions have shown limited impact. This study examines the Job-Turbo program, a large-scale initiative launched by the German government in 2023 to accelerate employment among refugees—primarily individuals from Ukraine and eight other major countries of origin. Using monthly administrative panel data from Germany’s network of public employment service offices and a difference-in-differences design, we find that the program significantly increased both caseworker–refugee contact and job placements over a 23-month follow-up period. Among Ukrainian refugees, the exit-to-job rate nearly doubled. Effects were broad-based—spanning demographic subgroups, unemployment durations, skill levels, regions, and local labor-market conditions—and concentrated in regular, unsubsidized employment. The program also raised both the rate and share of sustained job placements, consistent with improved match quality. Other refugee groups saw meaningful gains as well, but increases in job placements were concentrated among males and in low-skilled jobs, with only limited effects for females. We detect no negative spillovers for German or other immigrant job seekers, finding no signs of either resource reallocation or displacement. The results offer insights for governments responding to displacement crises. They indicate that intensified job-search assistance---embedded within the early stage of integration and implemented at scale through public employment infrastructure---can meaningfully improve refugees' labor-market outcomes, even amid significant arrivals.

Background: Linking mothers to their newborns in health records is crucial for understanding the impact of policies, programs, and medical treatments on intergenerational health outcomes. While previous studies have used shared identifiers for linkage, such data are often unavailable in Medicaid records due to privacy concerns. Existing algorithms are not sufficiently flexible to accommodate Medicaid data from all states and from both Medicaid Analytic Extract (MAX) and Transformed Analytical Files (TAF) data systems. Methods: We present a scalable framework and linking algorithm that connects deliveries and infants without relying on names, addresses, or linkage to vital records. First, we confirm our ability to identify newborn beneficiaries and deliveries resulting in live birth across states and over time by comparing our findings to the total number of Medicaid births recorded in the National Vital Statistics System (NVSS). Second, we confirm that our algorithm accommodates variations in Medicaid records over time and across states for MAX and TAF data, supporting matches at different levels of stringency. Finally, we assess the extent to which our algorithm is effective across demographic groups. Results: Using data from all 50 states over 9 years, our algorithm linked 11.68 million mother-infant dyads, covering 68% of Medicaid-enrolled infants, over 30% of all U.S. infants. Our linked cohort is approximately representative of the broader population of Medicaid beneficiaries on key observable characteristics including race and ethnicity, age, gender, and region. However, linked beneficiaries are more likely to be white and from the Midwest or Northeast relative to those we are unable to link. Conclusions: Despite substantial variation in the nature of Medicaid data across states and over time, it is possible to identify family units in all states between 2011 and 2019 without linking claims to vital records. This algorithm will facilitate research on social determinants of health and the intergenerational effects of medical interventions and public policy.

Amid the Ukrainian displacement crisis, private hosting of refugees in Europe has surged, yet its impact on integration remains understudied. This research examines the short- to medium-term effects of private hosting on Ukrainian refugee integration in Germany. Using data from one of the largest non-profit platforms that matches private hosts with refugees, we compare the multidimensional integration outcomes of refugees who were matched with private hosts to those of observably similar refugees who applied for private hosting but were not matched (n = 1,700). Our findings show significant improvements in the social, psychological and navigational integration of privately hosted refugees, with no discernible effects on linguistic, economic or political integration. This study provides causal evidence on the effectiveness of private hosting in enhancing refugee integration, underscoring its potential to complement traditional public asylum reception and housing systems, and to harness civil society engagement for refugee integration during humanitarian crises.

Medicaid births represent nearly 40% of all births nationwide and in states such as Oregon and California, births covered by Emergency Medicaid (EM) are 20-30% of all Medicaid births. EM provides restricted/limited benefits coverage for life-threatening conditions, including labor and delivery care for individuals who are otherwise ineligible for Medicaid because of immigration status. Despite EM-covered populations experiencing social and economic disadvantage, prior research on outcomes has included select states, such as Oregon, South and North Carolina, and Texas. This study uses national data to compare health outcomes for infants born to individuals receiving EM and non-EM coverage.

Simonsohn (2024a) and Simonsohn (2024b) critique Hainmueller, Mummolo and Xu (2019, HMX), arguing that failing to model nonlinear relationships between the treatment and moderator leads to biased marginal effect estimates and uncontrolled Type-I error rates. While these critiques highlight the issue of under-modeling nonlinearity in applied research, they are fundamentally flawed in several key ways. First, the causal estimand for interaction effects and the necessary identifying assumptions are not clearly defined in these critiques. Once properly stated, the critiques no longer hold. Second, the kernel estimator HMX proposes recovers the true causal effects in the scenarios presented in these recent critiques, which compared effects to the wrong benchmark, producing misleading conclusions. Third, while Generalized Additive Models (GAM) can be a useful exploratory tool (as acknowledged in HMX), they are not designed to estimate marginal effects, and better alternatives exist, particularly in the presence of additional covariates. Our response aims to clarify these misconceptions and provide updated recommendations for researchers studying interaction effects through the estimation of conditional marginal effects.

Given the global displacement crisis, the integration of refugees has emerged as a critical policy issue for many host countries. A key challenge involves supporting refugees in learning the language of their host country. While several European nations have instituted publicly funded language training for asylum seekers and refugees soon after their arrival, evidence on the efficacy of these early language programs in promoting economic integration remains limited. This study examines the impact of a pioneering, large-scale ad hoc program introduced by German policymakers, which provided basic language training to over 230,000 refugees arriving in 2015-16. Utilizing register data on the population of asylum seekers and exploiting a cutoff date in program eligibility, we assess the program's effectiveness using a regression discontinuity design. Our findings reveal no discernible effect on refugee employment over the subsequent two years. To explore whether language programs are generally ineffective during refugee crises, we contrast these results with the impacts of a more comprehensive, preexisting, yet smaller-scale program. Using a variety of difference-in-differences estimators, we find that this program considerably increased refugee employment. These contrasting findings offer important insights for policymakers on designing effective language training programs for refugees.