Do foreign-educated nurses displace native-educated nurses?

The US population is aging rapidly as the baby boomers reach retirement age. While the number of US citizens aged 65 and over is projected to almost double from 43.1 million in 2012 to 83.7 million by 2050, the working population is expected to decline (Ortman et al., 2014). With this aging population, the prevalence of chronic health problems is expected to increase, and these demographic trends will result in a high demand for nursing care. Therefore, labor-market strategies that can satisfy health-care demands in the long run are needed.

Hiring foreign-educated nurses is one of the strategies that have been actively used to balance demand and supply of health care (Aiken et al. 2007; Kline 2003; Lafer 2005; Tsitouras and Lopez 2009). The US Congress and the Executive Branch have used the immigration policy to increase the nursing workforce. Policies that vary from year to year lead to wide swings in the waiting time for immigrant nurses to obtain work visas. For example, compared to a total of 140,000 permanent employment-based immigrant visas (EB-3) available in 2005 for all skilled workers, the US government earmarked 50,000 special category permanent EB-3 visas for nurses in May 2005 (Arends-Kuenning 2006). In contrast, the Trump administration has been restricting immigration, including through decreases in the number of visas issued (National Foundation for American Policy 2019), historically high denial rates for H-1B visas (Anderson 2019), and slow-walking adjudication processes (Hobson 2019). In particular, the administration has mandated that all applicants for employment-based permanent residency must undergo an in-person interview (Pierce 2019).

The Trump administration argues that immigration has produced lower wages and higher unemployment for US citizens (Borjas 2016); moreover, the strategy of recruiting foreign workers is controversial in nursing markets. Several papers have raised questions about the effects of recruitment of foreign nurses on the quality of patient care in the United States, the quality of care in the sending countries, and labor market opportunities for native workers (Glaessel-Brown 1998; Trucios-Haynes 2002; Martineau et al., 2002; Brush et al., 2004; Lovell 2006; Aiken et al. 2004). The goals of this paper are to evaluate whether the inflow of foreign-educated nurses in the US nursing market displaces native-educated nurses and to answer whether the short-run supply of native-educated nurses is different from the long-run supply.

An emerging literature contributes to the debate about the relationship between recruiting foreign-educated registered nurses (RNs)

Throughout the paper, the term “nurses” refers to registered nurses.

To observe whether there is any particular movement in the supply of native-educated nurses caused by an increase in foreign-educated nurses, we use two data sets, the 1980–2000 US Censuses and the American Community Survey (ACS) multiyear aggregates, in addition to several empirical strategies. The sample includes employed RNs of ages 25–70 years. Because an RN requires greater skill and has experienced greater employment growth as well as lower unemployment than other major nursing occupations (Benson 2012), this paper considers only RNs. In addition, focusing on one specific occupation alleviates empirical problems. Previous studies examining the effects of immigration on a broader range of native workers did not make the adjustment for demand shifts that may confound the relationship between immigration and wages/employment (Kaestner and Kaushal 2012). Variables that affect the demand for nurses, such as the number of physicians or demographic composition in an area, are controlled for in this study. Therefore, restricting the sample to RNs improves the internal validity of the analysis.

For the empirical strategy, we use an approach based upon the linear model of Cortes and Pan (2014) with ordinary least squares (OLS) and instrumental variable (IV) methods. There are two main differences between our approach and the approach of Cortes and Pan. First, we focus on foreign-educated nurses, not foreign-born nurses. Second, our IV for the inflows of foreign-educated nurses per 1,000 population in a commuting zone (CZ) is the interaction term between the historical distribution by source country of skilled immigrants, excluding nurses across areas in the United States, and the national inflow of new foreign nurses educated abroad. This past settlement instrument helps identify exogenous labor supply shocks. We find no evidence of displacement effects in either the OLS or the IV approach. Instead, we find evidence of complementary effects in the IV approach – for every foreign-educated nurse that migrates to a CZ to work, between one and two native-educated nurses are additionally employed in the area. The difference between our results and those of Cortes and Pan (2014) is primarily driven by our definition of immigrant nurses as foreign educated instead of as foreign born. Because a single instrument may conflate the short- and long-run responses to newly arrived foreign-educated nurses, we implement a multiple instrumentation procedure introduced by Jaeger et al. (2018). Foreign-educated nurses’ inflows and their lagged inflows into a CZ are instrumented by the past settlement instrument and its lag. We fail to find any statistically significant displacement effects in either the short or the long run. The double IV estimates are smaller than their corresponding conventional IV estimates, and this is consistent across most of the different age and education groups. In terms of native wages, the estimated impact of the lagged foreign-educated nurses is positive and significant. Our results highlight the importance of longer-term adjustment processes and of the sensitivity of results to the choice of IV.

This study contributes to the literature about the effects of foreign-educated nurses on the US labor market in two ways. First, we approximate local US labor markets by a different geographic unit, compared to existing studies that conduct the analysis across metropolitan statistical areas (MSAs). MSAs cover areas of the United States with major urban population centers and are adjusted periodically to reflect the growth of cities. In contrast, CZs are a time-consistent measure of local labor markets and cover the entire United States. A CZ is excluded if we observe either zero native or zero foreign-educated RNs.

Table A1 of the Appendix shows the distribution of the numbers of native and foreign-educated nurses across commuting zones. Each commuting zone in the sample has at least one native nurse, whereas 217 commuting zones have no foreign-educated nurses sampled in 2015. When we exclude commuting zones that have less than either five US- or five foreign-educated nurses from 1990 to 2015, we have 133 commuting zones as the final areas. Likewise, 120 commuting zones are left if we only include commuting zones with more than both 10 native and 10 foreign-educated nurses.

A large body of literature exists on the overall performance of immigrants in the US economy; however, recently, economists have focused attention on the labor market for nurses. To provide general trends in the reliance of the US health-care system on foreign-born health professionals and to suggest how policymakers and health-care practitioners ought to deal with the issue of nursing migration, Arends-Kuenning (2006) examines the relative earnings of RNs, licensed practical nurses, and nursing aides over the time period 1990–2000. She shows the wage differentials by using simple mean comparisons and finds some evidence that foreign-born health professionals have higher annual and hourly wages than their native counterparts.

Schumacher (2011) uses econometric analysis and investigates whether immigrant nurses depress the wages of native nurses. By using the Current Population Survey, which contains information about the country of birth and citizenship status, he finds a lower wage for non-citizen nurses born outside the United States. However, his estimates of the returns to foreign education may be because some immigrant nurses may have obtained training in the States after they migrated. To address this issue, he runs additional regressions with data from the National Sample Survey of Registered Nurses (NSSRN), which identifies nurses who received their training outside the United States. The results indicate a significant wage disadvantage for newly arrived foreign-educated nurses. However, his research on the effects of immigration on the wages of native nurses did not address the nonrandom location decisions of foreign nurses. Kaestner and Kaushal (2012) apply an IV approach with the same NSSRN data set. They adapt the methods of Altonji and Card (1991), using the lagged numbers of the foreign-educated nurses to instrument for the current supply of nurses, concluding that there is no statistically significant relationship between wages and an increase in supply induced by foreign-educated nurses.

Cortes and Pan (2014) also attempt to alleviate the endogeneity issue by an alternative instrument in the nursing market. They exploit the US Censuses for 1980–2000 and the ACS 3-year aggregates for 2010 to estimate the displacement effects of an increase in the supply of foreign-born nurses per capita on the employment of native nurses. For the instrument, they interact the historical distribution of skilled migrants by country of origin across MSAs with the total number of foreign-born nurses from the country. The standard shift-share IV interacts the historical distribution with the inflow of newly arrived immigrant nurses, not the stock at one point in time.

The article of Cortes and Pan (2014) is ambiguous about the instrument that they used. On page 165, the description implies that they used a shift-share instrument. However, on page 168, the description given in the text and Eq. (2) indicate that the authors used the stock of foreign nurses from a specific country in year t, rather than the flow of nurses who entered the United States from time t – 1 to t.

Combining the national inflow of immigrants with their past geographic distribution is a well-known instrument to identify exogenous labor shocks in the literature on immigration. However, relying heavily on a single instrument may cause inconsistent results because of the compound effect generated by the short- and long-run responses to immigrant arrivals (Jaeger et al., 2018). General equilibrium adjustments and the high correlation between the country-of-origin composition and immigrant’s settlement patterns are key factors that result in this problem. They argue that multiple instruments ought to be used to avoid conflating a fall in wages when new immigrants enter and a positive movement toward equilibrium. Instrumenting two endogenous variables with the past settlement instrument and its lagged instrument, they address the endogeneity of current and past immigrant inflows to current and past labor demand shocks. The base period to construct the two instruments is 1970, when the country of origin of immigrants was less concentrated and showed more variation than in subsequent years. Furthermore, the authors use US Census data starting from 1980 to avoid a mechanical relationship between the lagged independent variable and the instrument because the instrument is a function of past values of the lagged independent variable. For example, the instrument is correlated with the lagged independent variable if it is determined by the historical distribution of the immigrant share in 1980. They find a negative effect and a positive effect of immigration on natives’ wage in the short and long runs, respectively. Their strategy can be used in the study of the US nursing labor market, and hence, this paper separates the initial response and the long-period effects of an increase in foreign-educated nurses on native nurses by adding a lagged past settlement instrument to the regression.

The 1980–2000 US Censuses and the ACS’s 3-year aggregates for both 2007 (2005–2007) and 2010 (2008–2010) and the 5-year aggregate for 2015 (2011–2015) are used as the data sources. All data were obtained through the Integrated Public Use Microdata Series from the United States (IPUMS-USA) (Ruggles et al. 2017). The decennial Census samples and the ACS sample for 2015 include 5% of the US population, and the ACS samples for 2007 and 2010 include 3% of the population. They contain information on personal and demographic characteristics, the country of birth, earnings, and employment. However, their major limitation is the absence of data on the country of education.

The National Sample Survey of Registered Nurses (NSSRN) includes information about whether the nurse was educated in the United States or another country, and previous literature used this data set. We cannot use it because it was discontinued in 2008. It is also a small data set relative to the Census and the ACS.

The samples that represent the population include all individuals not residing in group quarters, while the employment samples are restricted to employed workers aged 25–70 years who reported their occupation to be that of an RN. The population and employment samples are weighted by the Census personal weights. The sample only includes nurses who worked a positive number of hours and weeks in the previous year and received a strictly positive hourly wage. The hourly wage is calculated as the annual earnings divided by the product of usual hours worked per week and weeks worked last year. The wage samples are weighted by the product of the Census personal weight and the number of weeks worked in the previous year. The variable “weeks worked last year” for 2010 and 2015 is a categorical variable, namely, intervals of weeks worked (such as 14–26 weeks or 50–52 weeks). In contrast, the variable “weeks worked last year” for the period 1980–2007 is the precise number of weeks worked. Thus, each variable for 2010 and 2015 is converted to a continuous variable by using the 50th percentile of each interval in 2007. The annual earnings variable includes wages, salaries, commissions, cash bonuses, or tips from all jobs, before tax deductions. Wages are deflated so that they are in terms of the 1999 wages using the Bureau of Labor Statistics (BLS) Consumer Price Index for All Urban Consumers (CPI-U). Wages that were calculated to be less than the minimum wage are set to the minimum wage, and wages calculated to be more than the top-coded yearly wages are set to dollars

For example, hourly wages that are more than $184.55 that are censored at the top are set to $184.55 for 2015 (Economic Policy Institute 2018).

Previous studies, e.g., Kaestner and Kaushal (2012) and Cortes and Pan (2014), have often used MSAs as a proxy for US nursing labor markets . MSAs are defined by the US Office for Management and Budget for statistical purposes and adjusted periodically. However, geographic inconsistency is problematic for an analysis of the changes in employment composition over time. In addition, MSAs only cover areas of the States with major urban population centers. Hence, the main geographical unit of this paper is the CZ, which is a time-consistent measure of local labor markets developed by Tolbert and Sizer (1996). CZs are clusters of counties that are characterized by strong within-cluster and weak between-cluster commuting ties. To match the geographic information contained in the IPUMS data to the CZs, the crosswalk developed by Autor and Dorn (2013) is applied. We multiply the person weights with an adjustment factor that accounts for that fraction of a Census Public Use Micro Area (PUMA) that maps to a given CZ.

Table 1 compares the demographic and labor supply characteristics for US- and foreign-educated RNs. The foreign-educated RNs are more likely to be older and male than US-educated nurses. The average age of nurses and the proportion of nurses who are male have been increasing over time, suggesting that the RN workforce is aging and gender role attitudes have liberalized. The foreign-educated RNs earn higher hourly wages than their counterparts. One explanation for wage differentials in favor of internationally educated nurses is that the foreign educated have higher levels of education on average than the US-educated nurses. Each year, the foreign-educated RNs are more likely to have at least a Bachelor’s degree than domestic nurses. In 2015, only 45% of the US-educated RNs had a Bachelor’s degree, compared to 61% of the foreign-educated RNs. The foreign-educated RNs work more than the US-educated ones and are also less likely to work part time, which is defined as working ≤35 hours during a typical week. Thus, the foreign-educated nurses show greater attachment to the workforce, which would tend to result in higher hourly wages.

Table 2 presents the top 10 source countries for employed foreign-educated RNs from 1980 to 2015. These 10 countries account for close to 70% of the total number of foreign-educated nurses. The largest country of origin is the Philippines. The dominance of the Philippines as a source country can be explained by its history as a colony of the United States and its government policy that encourages overseas employment as a source of funds (Yang and Martinez 2006; Garchitorena, 2007; McDonald and Valenzuela 2012). India has been second, at 7% from 2000, followed by Canada. Canada was the second largest among the source countries, but the percentage of nurses trained in Canada is steadily falling. Table 2 illustrates that there is no large change in the percentage of total foreign-educated nurses accounted for by the top 10 source countries from 1980 to 2015 and that the country-of-origin composition for foreign-educated nurses might not vary much over time.

This paper has investigated the employment responses of native nurses in the United States to a change in the number of employed foreign-educated nurses. Using the 1980–2000 US Censuses and the ACS multiyear aggregates obtained from the IPUMS-USA (Ruggles et al. 2017), the paper quantifies the displacement effects.

To examine the causal effects of migration of foreign-educated nurses on native nurses, the analysis first closely follows a simple linear model that Cortes and Pan (2014) used. Because local markets are not closed and immigrants tend to settle in locations where previous waves have settled, the IV strategy and the OLS are implemented. The instrument for the number of full-time employed foreign-educated nurses is the interaction between the 1980 distribution of skilled immigrants by country across CZs and the national inflow of newly arrived foreign-educated nurses from the different countries. Cortes and Pan (2014) use the historical share of skilled immigrants and the stock of foreign-born nurses from the same country of origin in a given MSA to construct their instrument.

We fail to find displacement effect of an inflow of foreign-educated nurses on the number of native nurses by the OLS and IV methodologies with a single instrument. On the contrary, the IV estimates rather indicate that an increase in the number of native nurses might stem from an increase in foreign nurses educated abroad. The difference between our results for displacement and those of Cortes and Pan (2014) stems from the differences in definitions of immigrant nurses, with us using “foreign educated” and Cortes and Pan (2014) using “foreign born”. We explore the native groups that are most affected by foreign-educated nurses and observe that newly educated US nurses and the US-educated nurses with the highest education level benefit from complementarity. Turning to the possible factors that might drive employment effects of native nurses, we find little evidence that a change in native wages causes the positive effects.

The spatial distribution of immigrant inflows tends to be stable over time, and a single instrument is highly and serially correlated with responses to previous labor shocks. Thus, a multiple instrumentation procedure introduced by Jaeger et al. (2018) is implemented to separate the short-and long-term responses to recent foreign-educated nurses’ inflows. Under the preferred model specification with the double instruments, we find no significant effect of foreign-educated nurses on the employment of native nurses in both the short and long runs. We find marginally significant displacement effects on the youngest native nurses in the long run, whereas highly educated natives are positively affected by the foreign-educated nurses in the short run. We fail to find the negative impact of foreign-educated nurses on native wages but observe larger in magnitude, double IV estimates than the corresponding single IV estimates, which implies that researchers should control for dynamic adjustments to past local shocks. Taken together, the findings suggest that relying on foreign-educated nurses to fill gaps in the US health-care workforce does not harm the employment of native nurses, but rather the entry of foreign-educated nurses to the US nursing market causes an increase in native wages in the long run.