According to the Pew Research, the immigrant share of the US population reached 15.8% in 2025, marking the highest share ever recorded.[1] Alongside the rise of immigration has come copious debates. Should we open up immigration, or should we restrict immigration? Answering this question requires us to understand the causal impacts of immigration. In particular, the impacts of immigration have drawn significant interest from healthcare economists. According to the Kaiser Family Foundation, immigrants made up 32% of home care workers, 21% of nursing facility workers, and 24% of residential care workers.[2]

It is not a simple task to estimate the impacts of immigration because of endogeneity. For example, Silicon Valley attracts foreign workers for its renowned tech industries. At the same time, the Silicon Valley region has one of the highest incomes per capita in the nation. To disentangle the causal relationship between immigration and productivity requires extra caution, as there is, commonly called, confounding factor bias. Without addressing this issue, researchers may find a strong but spurious correlation between immigration and local productivity.

Theoretically, immigration can affect the wages of locals in either direction. On the one hand, migrants can substitute domestic workers, putting downward pressure on their wages (Borjas, 2003). On the other hand, migrant workers can complement the skills of non-immigrant workers and increase their productivity (Caiumi and Peri, 2024; Ottaviano and Peri, 2012).

Economists have long been interested in the causal effects of immigration on local labor market outcomes. A common technique to estimate such a relationship relies on “natural experiments,” in which naturally occurring events function as good as random control trials.

A classic example is the Mariel Boatlift. In 1980, the Castro regime in Cuba declared that whoever wanted to leave for the US was free to do so. As a result, around 125,000 immigrants arrived in Miami, which was equivalent to 7% of the local workforce (Card, 1990). Using this event as a natural experiment, Card (1990) found limited impacts on local wages and unemployment rates. These results were even more striking given that the majority of the immigrants were relatively low-skilled workers. Debates surrounding the Mariel Boatlift continued in recent years, with research finding conflicting evidence (Borjas, 2017; Clemens and Hunt, 2019; Peri and Yasenov, 2019).

Some more recent evidence comes from Hurricane Maria in 2017, which caused many Puerto Ricans to relocate to Orlando. Peri et al. (2024) find a small wage decline in occupations that are most exposed to immigration and a positive impact on less exposed professions.

Another significant stream of literature relies on the shift-share instrument approach (Jaeger et al., 2018). Like other instrumental variables, the validity of shift-share instrument requires that it affects the outcomes only through the endogenous variable. The instrument commonly takes two parts: a local exposure by country of origin and a national immigration shock. To put it simply, this instrument predicts the shocks of immigration inflows using historical settlement patterns of immigrants.

Now, imagine that immigration is shaped by two forces: push and pull. Why is migration from developing countries to developed countries far more common than migration in the opposite direction? The answer lies in these push and pull factors. Individuals may leave their countries because of, say, low wages, poor environments, political instability, etc. These adverse conditions “push” people out of their home countries. At the same time, developed countries provide better higher wages, better living conditions, and more stable institutions, which “pull” migrants in.

It is widely recognized that new immigrants tend to settle in areas where past immigrants of similar backgrounds moved to, creating “immigrant enclaves.” For example, Chinatowns across the United States experienced large inflows of Chinese immigrants and became important entry points for many Chinese migrants. Shift-share instruments exploit this fact, using past settlement patterns of immigrants as an exposure to current immigrant shocks. If immigration from China suddenly increases, the areas with the most pre-existing Chinese community are the most likely to receive more new arrivals. This literature continues to evolve rapidly, with recent advances incorporating broader international migration beyond traditional European immigrant groups (Terry et al., 2026).

Researchers have adopted these techniques to assess the impacts of immigration on the health outcomes of the existing non-immigrant population. As previously mentioned, elderly care heavily relies on migrant workers. Therefore, changes in immigration are likely to have a direct impact on the supply of healthcare workers in these areas. Indeed, a stream of literature finds that immigration significantly improves the quality of nursing homes, citing an increased supply of staffing (Furtado and Ortega, 2026; Grabowski et al., 2026). Furthermore, Braga et al. (2024) find that reducing immigration restrictions increases local physician supplies without displacing US-trained physicians. This translates to lower early mortality and COVID death rates. Other studies demonstrate that immigration improves health outcomes of non-immigrants by reducing the burden of night shifts on non-immigrant workers (Bond et al., 2023).

Overall, the effects of immigration remain actively debated. What is certain is that the debate is unlikely to end anytime soon.

References

Bond, T. N., Giuntella, O., and Lonsky, J. (2023). Immigration and work schedules: Theory and evidence. European Economic Review, 152:104358.

Borjas, G. J. (2003). The labor demand curve is downward sloping: Reexamining the impact of immigration on the labor market. The quarterly journal of economics, 118(4):1335– 1374.

Borjas, G. J. (2017). The wage impact of the Marielitos: A reappraisal. ILR Review,

70(5):1077–1110.

Braga, B., Khanna, G., and Turner, S. (2024). Migration policy and the supply of foreign physicians: evidence from the Conrad 30 waiver program. Journal of Economic Behavior & Organization, 226:106682.

Caiumi, A. and Peri, G. (2024). Immigration’s effect on US wages and employment redux. Technical report, National Bureau of Economic Research.

Card, D. (1990). The impact of the Mariel boatlift on the Miami labor market. ILR Review, 43(2):245–257.

Clemens, M. A. and Hunt, J. (2019). The labor market effects of refugee waves: reconciling conflicting results. ILR Review, 72(4):818–857.

Furtado, D. and Ortega, F. (2026). Does immigration improve quality of care in nursing homes? Journal of Human Resources, 61(1):1–40.

Grabowski, D. C., Gruber, J., and McGarry, B. E. (2026). Immigration, the long-term care workforce, and elder outcomes in the U.S. American Journal of Health Economics, 0(ja). forthcoming.

Jaeger, D. A., Ruist, J., and Stuhler, J. (2018). Shift-share instruments and the impact of immigration. NBER Working Papers 24285, National Bureau of Economic Research,

Inc.

Ottaviano, G. I. and Peri, G. (2012). Rethinking the effect of immigration on wages. Journal of the European Economic Association, 10(1):152–197.

Peri, G., Rury, D., and Wiltshire, J. C. (2024). The economic impact of migrants from Hurricane Maria. Journal of Human Resources, 59(6):1795–1829.

Peri, G. and Yasenov, V. (2019). The labor market effects of a refugee wave: Synthetic control method meets the Mariel boatlift. Journal of Human Resources, 54(2):267–309.

Terry, S. J., Chaney, T., Burchardi, K. B., Tarquinio, L., and Hassan, T. A. (2026). Immigration, innovation, and growth. American Economic Review, 116(3):828–61.

[1] https://www.pewresearch.org/short-reads/2025/08/21/key-findings-about-us-immigrants/

[2] https://www.kff.org/medicaid/what-role-do-immigrants-play-in-the-direct-long-term-care