Immigration of Nurses

Immigration of Nurses

DAVID KALIST, STEPHEN SPURR, and TATSUMA WADA*

*Econ

1

2

IND

Pub

This paper examines the effects of immigration on a specific occupation,

registered nurses (RNs). To learn whether immigrant nurses reduced the earnings

of RNs, we applied techniques developed by Goldin (1994) and Borjas, Freeman,

and Katz (1996), but found the effect of immigrant penetration either positive or

insignificant. We also found that the supply of immigrant RNs was far more

elastic than the supply coming from natives.

It is often argued that it will be hard to detect negative effects on wages and

employment of natives in local markets, because natives will avoid a market

which many immigrants have entered. This study finds no support for this

hypothesis in this market, based on data that measures the rate of entry of RNs

exactly. We find no adverse effect of immigration on native workers in this

occupation.

Introduction

RECENTLY, there has been considerable interest in the effects of immigrationon the earnings and employment of workers in the United States. Mostresearch has examined the impact of immigration on large aggregates of thelabor force, especially natives who are unskilled workers, or workers without acollege education.This article considers the effects of immigration on a specific skilled occupa-

tion, registered nurses (RNs). Use of a narrowly defined occupation tends tominimize problems of heterogeneity and measurement error entailed in analysisof a large aggregate class of workers, and there are very few studies of specificoccupations in the immigration literature.1 As of 2004 about 3.5 percent of regis-tered nurses practicing in the United States received their basic nursing educationoutside the United States and its territories.2 However, in some states, the pro-portion is much higher. The states with the largest numbers of foreign-educatedRNs were California (25.5 percent), Florida (9.6 percent), New York (9.3

The authors’ affiliations are, respectively, Shippensburg University – Economics; Department ofomics, Wayne State University; and Wayne State University – Economics. E-mail: [email protected].

Some exceptions are Sussman and Zakai (1998), Borjas (2005), and Schumacher (2008).

Preliminary Findings: 2004 NSSRN.

406

USTRIAL RELATIONS, Vol. 49, No. 3 (July 2010). � 2010 Regents of the University of Californialished by Wiley Periodicals, Inc., 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington

Road, Oxford, OX4 2DQ, UK.

Immigration of Nurses / 407

percent), Texas (6.7 percent), and New Jersey (6.1 percent).3 Increases in immi-gration have been sought by buyers of nursing services and generally opposedby sellers. The President of the American Nurses Association (ANA) stated that‘‘… if the health-care industry has access to a large number of foreign-educatednurses there is no incentive for them to address the … problems that exist withregard to the work environment and nursing wages.’’4 The ANA has supportedlegislation that would impose ‘‘prescreening’’ before foreign health-care profes-sionals obtain a work permit or visa, arguing that this precaution is ‘‘necessaryfor public safety.’’ A common theme in the ANA’s statements of policy is thatthe health care systems of less developed countries are damaged by emigrationof their nurses to the United States. The American Hospital Association, how-ever, favors an increase in immigration.5

In this article, we wanted to determine the extent to which immigrationmight have reduced the earnings of registered nurses in the United States.However, over the period of our data, 1980–2000, the estimated effect ofimmigration on nurses’ earnings has been negligible. We also find evidencesuggesting that in terms of interstate migration, the supply of immigrant nursesis elastic, but the supply of native nurses is not.

The Literature

There has been a great deal of research on the question of how immigrationaffects the earnings and employment of native workers. One approach, thatmight be called the ‘‘area approach,’’ has analyzed cross-sectional data todetermine how wages of natives in different local labor markets are affected bydifferences in the share of immigrants in the local labor supply. This methodhas been criticized on the grounds that (1) immigrants do not choose where togo randomly, and (2) the scope of the relevant market may not be local. Withrespect to point (1), immigrants may choose to enter growing or high-wageareas. With respect to point (2), if many immigrants enter a city, native workersmay leave, or choose not to locate there, or there may be movements of capitalthat make it difficult to capture the effect of immigration on wages.Some studies that have applied the area approach have used instrumental vari-

ables in an attempt to strip the endogeneity from the variable representing theextent of penetration of immigrants. Friedberg (2001) analyzed the effect on the

3 The countries estimated to have contributed the most nurses were the Philippines (50.2 percent),Canada (20.2 percent), the United Kingdom (8.4 percent), and Nigeria (2.3 percent).

4 Statement of Barbara A. Blakeney, President, American Nurses Association, May 17, 2006.5 See, e.g., ‘‘Immigration bill seen choking off nurse recruitment,’’ AHA News, June 11, 2007.

408 / KALIST, SPURR, AND WADA

earnings and employment of native workers in different occupations in Israel ofa massive migration from the former Soviet Union after 1989. The ‘‘areas’’ inquestion here were the different occupations in Israel, which differed in the lev-els of immigrant penetration. Her instrument was the previous occupation of theimmigrants, when they were in Russia. This variable was positively correlatedwith the occupation these individuals entered after arrival in Israel, but was un-correlated with the unobserved determinants of wage and employment growthexperienced by that occupation after their arrival. Friedberg found no adverseimpact of immigration on native employment outcomes.Another approach, that might be called the ‘‘factor proportions’’ approach,

posits a national labor market, in which wages and employment equilibraterapidly, whether through movements of capital, labor, or output. This approachusually uses time series data to relate changes in national wages or employmentof a particular skill group to changes in the share of immigrants in that group; ineffect, the skill groups are the different ‘‘areas.’’ A disadvantage of this approachis that the assumption that the labor market is nationwide in scope may not beappropriate for occupations with location-specific human capital, e.g., lawyers,or at least should be demonstrated rather than accepted a priori.6 Also, over timethere may be large changes in the relative supply of different skill groups.Finally, changes in relative wages of skill groups may result from changes indemand (Acemoglu 2002), and it may be difficult to disentangle changes insupply and demand from the effects of immigrant penetration.7

Studies that apply the area approach have generally found that the effects ofimmigration on earnings and employment of natives are very small or negligi-ble, e.g., Card (1990, 2001), Altonji and Card (1991), Butcher and Card(1991), LaLonde and Topel (1991), Pischke and Velling (1997), NationalResearch Council (1997), and Longhi, Nijkamp, and Poot (2005). There are,however, some notable exceptions. Goldin (1994) used data from the period1870–1923 to determine how much wages in different occupations andindustries were affected by the proportion of foreign-born workers in differentU.S. cities. She found that, in general, a 1 percent increase in the share of thepopulation that was foreign-born reduced wages between 1 and 1.5 percent.

6 The assumption of a national labor market does not square with research that finds strong persistenceof shocks to state employment, wages, and unemployment. See, e.g., Blanchard and Katz (1992). In somecases, the scope of the labor market may be international (Card 2005).

7 Some research, e.g., Sullivan (1989), suggests that employers of nurses have substantial monopsonypower. Other recent work (Hirsch and Schumacher 2005), casts doubt on this hypothesis, finding in a cross-sectional analysis that relative wages of RNs are uncorrelated with hospital system concentration, and thatRNs have greater inter-employer mobility than do women or men in general. Our empirical work does notdirectly involve the degree of competition for nursing services, although some of the empirical models arebased on the assumption of a competitive labor market. As noted above, we do find that, at least at the statelevel, the supply of immigrant nurses seems to be more elastic than that of U.S. natives.


Moreover, some studies that employ the ‘‘factor proportions’’ approach havefound significant negative effects of immigration on employment opportunitiesof natives. Borjas (2003) developed a model in which labor markets arenational in scope, and divided the national labor force into groups with fourdifferent levels of education, eight levels of experience, and five time periods.Thus, a particular skill group was defined by education, experience, and time.Using an array of fixed effects to try to capture changes in supply of anddemand for different skill groups, he estimated the effect of the foreign-bornshare of the skill group on a native worker’s annual earnings, weekly earnings,and fraction of time worked.8 He found significant negative effects of immi-gration. In addition, Borjas (2005) analyzed the impact of foreign studentswho obtained doctorates in the United States on the earnings of others obtain-ing doctorates in the same field at roughly the same time. He found that a 10percent increase in the supply of doctorates resulting from immigrationreduced the wage of competing workers by about 3–4 percent. It is noteworthythat this market is truly national in geographic scope.Ottaviano and Peri (2008) noted some problematic assumptions in the

approach in Borjas (2003) and Borjas and Katz (2007); those articles implicitlyassume capital is fixed, and posit a constant elasticity of substitution aggregateof four education groups: some high school, high school graduates, some col-lege, and college graduates, and further assume an identical elasticity of substi-tution between any two of these groups. However, it is clear, for example thatworkers with no high school diploma are more similar to those with a highschool diploma than to college graduates. Ottaviano and Peri employed a spec-ification relaxing the restrictions of Borjas (2003) and Borjas and Katz(2007).9 They also allowed for imperfect substitution between natives andimmigrants, even within the same education and experience levels.Ottaviano and Peri (2008) find, contrary to the assumptions in Borjas (2003)

and Borjas and Katz (2007), that the elasticity of substitution between workerswith and without a high school diploma is essentially infinite, but that the elas-

8 In one specification, building on the approach of Card and Lemiux (2001), he posited a three-levelconstant-elasticity-of-substitution technology in which workers with the same level of education, but differ-ent levels of experience, were aggregated to form the total supply of an education group, and then the edu-cation groups were aggregated to form the national labor force. This approach enabled him to estimateelasticities of substitution across experience groups with a given level of education, and across the aggre-gated education groups. See, however, the critique of this approach by Ottaviano and Peri (2008) discussedbelow.

9 In the setup of Ottaviano and Peri (2008), the supply of capital may increase in response to an increasein labor resulting from immigration, in accordance with the growth and real business cycle literatures, andthe four education groups are assigned to two larger groups—one high-education and one low-education,with two elasticities of substitution—one between workers without, and those with a high school diploma;and another between workers with some college and those with a college degree.


ticity between workers with some college and for those with a college degreeis much less, approximately 2. For immigration to the United States between1990 and 2006, they find an average positive long-run effect on native wagesof 0.6 percent. They also find that the negative effect of immigration on wagesis modest even in the short run, )0.7 percent for workers with no high schooldiploma in 2007. Borjas and Katz (2007) estimated an elasticity of )7.8percent, and the discrepancy evidently results from their restrictive assumptionsof a fixed capital stock and a small elasticity of substitution between workerswithout and with a high school diploma (of about 2.4).One possible reason for the persistent findings that immigration has no

negative effect is complementarity of native and foreign workers, evenwithin the same occupation. Sussman and Zakai (1998) examined the mar-ket for physicians in Israel in the early 1990s. They found that physicianswho had emigrated from Russia were generally relegated to positions asgeneralists at the lower end of the pay scale in Israeli hospitals. Thisenabled more native Israeli physicians to be promoted to higher-paying jobsin the health-care system; also, the additional staffing provided by theRussians freed native Israelis to spend more time on higher-paying privatepractice. Similarly, an Israeli government committee (Eckstein et al. 1996)found that Russian workers were often assigned to more routine tasks orsupport roles, allowing Israelis to work as supervisors or to make morecritical decisions on projects. Friedberg (2001) also found evidence suggest-ing that this kind of complementarity worked in favor of natives in bothhigh-skilled and low-skilled occupations in Israel.

The Geographic Scope of the Market. Whether the area approach, possiblysupplemented by methods correcting for endogeneity, or the factor proportionsapproach is better suited to our data depends partly on the geographic scope ofthe market. We will see some evidence that the market is not larger than thestate where the RN works. The state is of course the geographic area in whicha nurse is licensed to practice. Moreover, it turns out that (1) about half ofthose who enter the nursing profession in a given state in a given year arestate residents (unlike, say, the market for Ph.D.s in economics), and that (2)the supply of those entering from other states is not elastic, suggesting thatthose nurses are entering the state for family or other reasons (94 percent ofRNs are female). Groups 1 and 2 together account for 93.5 percent of thoseentering the nursing profession in a state.10 If the scope of the market for

10 The breakdown of all RNs entering into practice between 1984 and 2005 is: residents of the state,49.5 percent; those entering from other states (an estimated 94.4 percent of whom are U.S. citizens), 44.0percent; and nurses educated outside the United States, 6.5 percent.


nurses is the state, it is important not to define the market more broadly, totake advantage of the substantial variation across states in the proportion ofRNs who are immigrants. In contrast, there is some indication of a national (orinternational) market from RNs entering states by immigration, who seem tobe drawn to the states with highest wages.11 To cover all bets, we will beagnostic about the scope of the market; our estimation employs both the areaand factor proportions approaches, as modified to suit our data.

Displacement Effects of Immigration. Borjas, Freeman, and Katz (1996,1997), in a critique of the area approach, argued that it will be difficult todetect the effect of immigrants on the labor market opportunities of nativesin a given location, if (1) those immigrants subsequently go elsewhere, or(2) there is an offsetting effect of reverse migration by natives, i.e., if aninflux of immigrants into a city leads to an offsetting departure of nativeworkers, so that there is little or no net increase in labor supply.12 If thereare such responses by natives, the effect of immigration is more likely tobe measurable in a broader geographic area. Attempts to measure suchreverse migration have had mixed results. Some researchers find that inter-nal migrants avoid areas where immigrants have arrived: Filer (1992),White and Hunter (1993), Frey (1995, 1996), and Borjas, Freeman, andKatz (1997), whereas others find very small or no displacement effects:Butcher and Card (1991), White and Imai (1993), Wright, Ellis, and Reibel(1997), and Card and DiNardo (2000).

Research on Immigration of Nurses. Finally, there is one study of the effectof immigration on the earnings of nurses. Schumacher (2008) used data from theCurrent Population Survey to determine the effect of an influx of foreign-bornnurses on the wages of native nurses. He regressed the earnings of native nursesin a given area on the fraction of foreign-educated nurses in the area and othervariables, including the wages of a control group in the area, which was alterna-tively (1) college-educated females not employed in healthcare, and (2) otherworkers in the health-care industry. The idea is that if the fraction of foreign-edu-cated nurses was higher because of greater demand and economic growth in agiven area, that would increase the wages of the control group as well as nurses;thus, if with nurse immigration the wages of native nurses increased less thanthose of the control group, that would be evidence of a negative effect of

11 The estimated elasticities of supply of foreign-educated nurses are shown below in the last twocolumns of Table 6.

12 Another possibility is that capital will be attracted to an area that has experienced an increase in thesupply of labor, a complementary factor. The influx of capital will in turn increase the marginal productivityand wage of labor. This effect is generally assumed to be of second-order importance.


immigration on the wages of native nurses.13 It turned out that Schumacher’sspecification (1) suggested a small negative effect of nurse immigration, butspecification (2) found no significant effect.Our research seeks to determine both whether immigration of RNs into differ-

ent states and counties has reduced RN earnings, and whether there is an offset-ting migration response by U.S. nurses, through a decline in entry. A thresholdissue is exactly how we should define an ‘‘immigrant nurse.’’ Is this a nurse whoreceived her nursing training in another country, or should the term also includea nurse who emigrates to the United States at any age and then receives her nurs-ing training there? If we are primarily concerned with policy involving the sup-ply and demand of nurses, e.g., how many visas should be issued for nurses asskilled workers, we might prefer the first definition, because the alternative,broader definition applies potentially to all immigrants; any immigrant mightdecide after entry to become a nurse, although very few of them will do so. Wewill only know who decides to obtain training as a nurse in the United Statesex post the decision to allow them into the country, whereas with the narrowdefinition we know who the nurses are ex ante.

Cross-Sectional Regressions. Goldin (1994) examined a cross-section of cit-ies to learn how wages were affected by the percentage of immigrants in the city.She found that the share of immigrants had a strong positive coefficient, fromwhich she concluded, not that immigration increased wages, but that immigrantswent to cities with high wages. We did essentially the same thing with nurses,but with individual-level data that enabled us to control for variables that havebeen found to affect their earnings.

Estimating the Wage Effects of Immigration

Data. These data are from the National Sample Surveys of RegisteredNurses (NSSRN) for 1980 and 2000.14 These surveys have been conductedapproximately every 4 years since 1977 by the U.S. Department of Health and

13 There is an implicit assumption that the control group in the specified area is less affected by immi-gration than RNs.

14 As explained below, we have data for four other years: 1984, 1988, 1992, and 1996. We used all6 years of data we have in the regression described below in ‘‘Applying the Methods of Borjas (2003),’’ butonly 1980 and 2000 in Tables 1–5, which follow the area approach. We do so because maximizing theinterval of time (1) allows for greater changes in the area’s independent variables, such as per capita income,population, and especially the share of foreign-educated nurses, and thus should increase the precision ofour estimates; and (2) follows the structure of the models we are building on. The specification reported in‘‘Applying the Methods of Borjas (2003),’’ on the other hand, calls for the use of multiple skill groups andcohorts, so there we have used all six cross-sections.


Human Services,15 and are mailed to a stratified random sample of individualswith an RN license in the United States.16 The 2000 data set, for example con-sists of records of 35,579 individuals drawn from among an estimated2,714,671 registered nurses who held active licenses around the end of 1999in one or more of the fifty states and the District of Columbia. These surveysprovide information on the individual’s nursing education, the type of nursingposition and practice setting, demographic characteristics, and earnings. Withregard to demographic information, there are variables for five different racialclassifications, age, sex, marital status, and ages of children living at home, ifany. With regard to the native-foreign distinction, one variable indicateswhether the RN graduated from a U.S. or foreign program, and another indi-cates the state or country where the individual’s nursing training was provided.However, there is no data on the country of birth, nor on how long the indi-vidual has been in the United States. With regard to the type of nursingemployment, there are variables indicating whether the individual is anadvanced practice nurse (a nurse anesthetist, nurse midwife, clinical nurse spe-cialist, or nurse practitioner) and there are other breakdowns by practice setting(e.g., children’s hospital, nursing home, public health, etc.), position (e.g.,supervisor, instructor in nursing school, private duty nurse, etc.), and nature ofemployment (full time, part time, temporary, or unemployed).Table 1 provides means of the variables used in our estimation. Table 2

reports the results of regressions of the log earnings of registered nurses onthese variables, one of which is the ratio of immigrants (foreign-educatednurses) to all registered nurses in the geographic area specified. In theseregressions, we use the cross-sections from the 1980 and 2000 surveys, andthree different geographic areas, the region (of which there are nine in the Uni-ted States), state, and county.17 Standard errors are clustered at the region levelin the region regression, at the state level in the state regression, and at thecounty level in the county regression. Like Goldin,18 we find that the share ofimmigrants has a significant positive effect on earnings in every specification.This undoubtedly reflects the fact that, as noted by Friedberg and Hunt (1995),‘‘Immigrants, likely to be the most mobile of workers, will probably move to

15 The surveys are conducted by the Division of Nursing, Health Resources and Services Administrationof the HHS.

16 Although compliance is voluntary, the response rate is good. For example, it was 80.0 percent for the1980 survey, and 71.7 percent for the 2000 survey.

17 The geographic area is the location of the individual’s principal nursing position. The regressionsinclude only full-time workers, and exclude (1) nurses educated outside the United States, and (2) certifiedregistered nurse anesthetists, virtually none of whom are immigrants.

18 Altonji and Card (1991) also found a significant positive cross-sectional effect of immigration onwomen’s wages in 1980. Borjas, Freeman, and Katz (1996, 1997) found a positive effect for female nativeworkers in 1980 and 1990 and for male natives in 1990.

TABLE 1

MEANS OF VARIABLES

1980 (County) 2000 (County)

M SD M SD

Ln earnings 9.71 0.325 10.68 0.393Associate degree 0.227 0.419 0.471 0.499Baccalaureate degree 0.195 0.396 0.294 0.456Black 0.076 0.265 0.126 0.331Experience 12.5 9.85 16.0 11.6Experience squared 252.5 356 390.5 449Male 0.035 0.18 0.068 0.25Has one or more children at home 0.469 0.499 0.535 0.499Married 0.625 0.484 0.661 0.474Works in an msa 0.790 0.407 0.809 0.393Self-employed 0.014 0.116 0.010 0.101Administrative position 0.072 0.259 0.079 0.269Supervisory position 0.075 0.263 0.040 0.195Head nurse 0.105 0.306 0.064 0.245Nurse practitioner 0.015 0.122 0.033 0.177School nurse 0.043 0.204 0.037 0.188Hospital 0.648 0.478 0.581 0.493Nursing home 0.065 0.247 0.075 0.263Market share of foreign-educated nurses 0.0389 0.060 0.0377 0.055n 12,733 18,072

NOTE: These data are from the National Sample Surveys of Registered Nurses for 1980 and 2000. msa, metropolitanstatistical area.


those regions whose demand shocks have led to higher wages.’’ It is also note-worthy that the estimated correlation increases as the area in questionincreases, from county to state to region.Given the suggestion that the results in Table 2 are endogenous, we

decided to employ the instrumental variable approach used in studies, such asFriedberg (2001). In Table 3, we estimated the specification in Table 2 at thecounty level, using as an instrument the proportion of county residents bornoutside the United States, as reported in Census data for 1970. The proportionof county residents born outside the United States is highly correlated with thefraction of nurses who received their nursing education outside the UnitedStates, because the larger the fraction of immigrants from a given country, thelower are the costs of immigration for nurses from that country (with Censusdata for 2000, we ran a regression of the ratio of foreign-educated nurses toall nurses on the percentage of the foreign-born population in the county; thecorrelation between the two variables was 0.71). However, the fraction of for-eign-born residents should not directly affect or be affected by the wages ofnurses, given the other variables in Table 3.

TABLE

2

DETERMIN

ANTSOFLOGEARNIN

GSOFRN

S

1980

(Region)

2000

(Region)

1980

(State)

2000

(State)

1980

(Cou

nty)

2000

(Cou

nty)

Intercept

9.32

10.20

9.35

10.23

9.37

10.24

Associate

degree

0.0452

(0.013

)0.0194

(0.013

)0.0406

(0.011)

0.0154

(0.010

)0.0424

(0.008

2)0.01

77**

(0.009

2)Baccalaureate

degree

0.102(0.014

)0.0476

(0.013

)0.102(0.012

)0.0476

(0.011)

0.099(0.008

9)0.0445

(0.011)

Black

0.0437

**(0.014

)0.0321

*(0.016

)0.0400

(0.013

)0.0277

**(0.013

)0.025*

(0.013

)0.0141

(0.011)

Exp

erience

0.0163

(0.002

0)0.0244

(0.001

0)0.0162

(0.001

4)0.0244

(0.001

)0.016(0.001

3)0.0244

(0.001

)Exp

eriencesquared

)0.0003

(0.000

1))0.0004

(0.000

0))0.0003

(0.000

0))0.0004

(0.00)

)0.0003

(0.00)

)0.0004

(0.00)

Male

0.122(0.017

)0.0915

(0.011)

0.123(0.015

)0.0932

(0.010

)0.131(0.014

)0.0923

(0.009

)Has

oneor

more

children

athome

)0.022(0.004

5))0.0121

*(0.006

))0.0218

(0.006

))0.0127

**(0.005

))0.0188

(0.007

1))0.012*

(0.007

)

Married

)0.0287

(0.005

2))0.0229

(0.005

))0.0279

(0.005

))0.0218

(0.005

))0.0266

(0.006

2))0.0190

(0.006

4)Works

inan

msa

0.119(0.013

)0.118(0.018

)0.115(0.011)

0.112(0.013

)0.108(0.009

7)0.103(0.011)

Self-em

ployed

)0.173(0.043

)0.0287

(0.051

))0.174(0.053

)0.0286

(0.051

))0.174(0.051

)0.030(0.061

)Adm

inistrativepo

sition

0.275(0.014

)0.294(0.017

)0.274(0.013

)0.294(0.015

)0.272(0.013

)0.293(0.012

)Sup

ervisory

position

0.124(0.015

)0.083(0.011)

0.122(0.011)

0.082(0.019

)0.123(0.01)

0.084(0.018

)Headnu

rse

0.066(0.014

)0.158(0.009

)0.065(0.013

)0.158(0.010

)0.065(0.013

)0.156(0.008

1)Nurse

practitioner

0.178(0.017

)0.283(0.043

)0.180(0.023

)0.283(0.032

)0.175(0.020

)0.283(0.029

)Schoo

lnu

rse

)0.108(0.027

))0.208(0.016

))0.108(0.029

))0.208(0.028

))0.101(0.021

))0.204(0.020

)Hospital

0.106(0.018

)0.0870

(0.006

)0.107(0.014

)0.0883

(0.008

)0.106(0.008

3)0.086(0.007

6)Nursing

home

)0.121(0.019

))0.0183

(0.012

))0.119(0.018

))0.0164

(0.011)

)0.114(0.015

))0.016(0.011)

Marketshareof

foreign-educated

nurses

Inregion

1.341*

(0.658

)1.341*

(0.606

)In

state

0.964*

(0.536

)1.083*

*(0.425

)In

county

0.732(0.146

)0.908(0.150

)n

12733

18072

12733

18072

12733

18072

R2

0.183

0.198

0.183

0.200

0.187

0.206

NOTES:The

dependentvariable

isthelogearnings

ofregistered

nurses

inthegeographic

area

specified.The

regressionsincludeonly

full-tim

eyear-round

workers,andexclude(1)

nurses

educated

outsidetheUnitedStatesand(2)certified

registered

nurseanesthetists,virtuallynone

ofwhom

areim

migrants.

Standarderrors

arein

parentheses.

The

standard

errors

arerobust

(Huber–W

hite),andbecauseof

thevariable

forthemarketshareof

foreign-educated

nurses,areclusteredat

thecounty

levelin

thecounty

regression,at

thestate

levelin

thestateregression,andat

theregion

levelin

theregion

regression.The

regressionsareweightedby

thesamplingweightprovided

bytheNationalSam

pleSurveys

ofRegisteredNursessurvey.Allvariableswithestimated

coefficients

inbold-facetype

aresignificant

atthe1percentlevel.*,

**Statistical

significanceat

the10

and5percentlevel,

respectively.These

data

arefrom

theNSSRN

for1980

and2000.msa,metropolitanstatisticalarea.


TABLE 3

DETERMINANTS OF LOG EARNINGS OF RNS—INSTRUMENTAL VARIABLE REGRESSION

1980 (County) 2000 (County)

Intercept 9.36 10.23Associate degree 0.0378 (0.0088) 0.0050 (0.010)Baccalaureate degree 0.0944 (0.0094) 0.0306** (0.014)Black 0.0123 (0.015) )0.0514 (0.017)Experience 0.0160 (0.0014) 0.0240 (0.001)Experience squared )0.0003 (0.00) )0.0004 (0.000)Male 0.1344 (0.015) 0.0844 (0.010)Has one or more children at home )0.0166** (0.0071) )0.0101 (0.008)Married )0.0236 (0.0061) )0.0084 (0.007)Works in an msa 0.0969 (0.011) 0.0451 (0.017)Self-employed )0.176 (0.051) 0.0222 (0.062)Administrative position 0.272 (0.013) 0.2883 (0.013)Supervisory position 0.124 (0.010) 0.0851 (0.020)Head nurse 0.0640 (0.013) 0.1495 (0.0091)Nurse practitioner 0.169 (0.021) 0.2795 (0.030)School nurse )0.101 (0.021) )0.2048 (0.020)Hospital 0.106 (0.0084) 0.0853 (0.008)Nursing home )0.111 (0.015) 0.0040 (0.014)Market share of foreign-educatednurses in county

1.15 (0.264) 2.88 (0.51)

n 12733 18072R2 0.185 0.146

NOTES: The dependent variable is the log earnings of registered nurses in the county. The regressions include only full-timeyear-round workers, and exclude (1) nurses educated outside the United States and (2) certified registered nurseanesthetists, virtually none of whom are immigrants. Standard errors are in parentheses. The instrumental variable is theproportion of county residents born outside the United States, as reported in Census data for 1970. The standard errorsare robust (Huber–White), and the regressions are weighted by the sampling weight provided by the National SampleSurveys of Registered Nurses survey. All variables with estimated coefficients in bold-face type are significant at the 1percent level. ** Statistical significance at the 5 percent level. These data are from the NSSRN for 1980 and 2000. msa,metropolitan statistical area.


The results in Table 3 show that our attempt to control for endogeneity didnot change the sign of the variable for immigrant penetration; quite the con-trary, the effects on earnings of native nurses in 1980 and 2000 not onlyremain positive and highly significant, but are now substantially greater thanthe previous estimates.Goldin (1994), in her study of unskilled workers, tried another method of

dealing with the endogeneity of choice of location by immigrants. She notedthat some cities could have a greater demand for unskilled labor than others. Ifdifferences in demand across cities were relatively stable over time, thesimultaneity problem could be avoided by positing a fixed effect for each city,which would disappear on first differencing. This led her to estimate the effectof a change in the share of the immigrant population on the change in thewage of a particular occupational group in the city. Here, as noted above, shefound for the most part significant negative effects. However, there remained a


question about the possible effect of the ‘‘composition’’ problem, i.e., the issueof whether the results were driven by the fact that immigrants earned less thannative workers in the same occupation.19

Following Goldin, we used a similar specification for nurses in which thedependent variable was the change in the log salary of general duty, or staffnurses between 1980 and 2000. Our equation was:

Dðlog salaryÞ ¼aþ b1Dpopulationþ b2Dper capita income

þ b3ðDshare of foreign-educated nursesÞ þ l;

where l is a normally distributed error with zero mean and constant variance.We were able to avoid the composition problem by excluding observations onforeign-educated nurses. The other independent variables were the change inthe area’s population (the state or, alternatively, the county), the change in thearea’s per capita income, and the change in the percentage of nurses in thearea who were foreign educated. In the state regression, when the only inde-pendent variable used was the change in the percentage of foreign-educatednurses, we found that a 1 percent increase in the proportion of nurses in a statewho were foreign educated was associated with an increase of wages by 1.3percent (Table 4). When we added the change in state population, the foreignpenetration variable remained positive and significant at the 8 percent level,but when we also added the change in per capita income it became insignifi-cant. The foreign penetration variable was insignificant in all specifications atthe county level.

Regressions on Fixed Effects of Geographic Areas. Borjas, Freeman, andKatz (1996, 1997) avoided the composition problem by using earnings of onlynative workers on the left-hand side. They dealt with the simultaneity issue by(1) regressing log earnings of native workers on individual characteristics (ageand gender) and dummy variables representing fixed effects for geographicareas, in separate estimations of cross-sectional Census data for 1980 and1990. Then, (2) the difference in the fixed effects for each area was employedas the dependent variable in regressions on the change in the ratio of immi-grants to natives during that period. With this approach they obtained signifi-cant negative effects. A potential problem with this approach is that it allowsonly the change in the immigrant ratio an opportunity to explain the change inthe area’s earnings. Effects may be attributed to this variable, which are actu-ally attributable to other variables and that are correlated with it. For example,during a 10-year period, some areas will experience general economic decline,

19 As noted by Friedberg and Hunt (1995).

TABLE

4

EFFECTOFIM

MIG

RANTPENETRATIO

NONLOGEARNIN

GSOFRN

S,A

PPLY

INGA

PPROACH

OFG

OLDIN

(199

4)

Unitof

Observation

State

Cou

nty

Intercept

0.13

0.12

)0.0024

0.23

0.23

0.19

Chang

ein

thearea’s

population

from

1980

to20

00

0.0000

1(0.000

01)

0.0000

07(0.000

005)

5·10

)8(4

·10

)8)

1·10

)8(3

·10

)8)

Chang

ein

area’sper

capita

income

2·10

)5**

*(0.000

004)

1·10

)5**

*(2

·10

)6)

(%change

inarea’s

marketshareof

foreign-educated

nurses)

·10

0

0.0130

*(0.006

7)0.0124

*(0.006

9)0.0079

(0.006

6)0.0008

5(0.001

0)0.00

08(0.001

0)0.0007

7(0.000

97)

n51

5151

1012

1012

1002

R2

0.0600

0.0712

0.381

0.0008

0.00

140.018

NOTES:The

dependentvariable

isthechange

inthelogsalary

ofgeneralduty,or

staffregistered

nurses

inthespecified

area

(state

orcounty)between1980

and2000.Nurseseducated

outsidetheUnitedStatesareexcluded.Standarderrors

arein

parentheses.

The

standard

errors

arerobust

(Huber–W

hite).*,

***Statistical

significanceat

the10

and1percentlevel,

respectively.The

data

arefrom

theNationalSam

pleSurveys

ofRegisteredNursesfor1980

and2000.



low or negative population growth, declining income, and lower housingprices. It is possible that, ceteris paribus, high-earning native workers leavesuch areas, or choose not to enter them, and that immigrants tend to settlethere. This argues for including variables that can capture the changes in thearea’s general economic fortunes.We estimated essentially the same model with our data, but added variables

to control for changes in the area’s economy. Specifically, we ran cross-sectional regressions of log salary in 1980 and 2000 on the individual’scharacteristics and a dummy variable for the area, which was specified alterna-tively as the county, state, and region. Like Borjas et al., we (again) limitedour observations to natives, i.e., those who received their nursing education inthe United States. The individual-level characteristics employed by Borjaset al. were age, gender, and level of education; we used education, race, sex,experience, marital status, population density, the nurse’s self-employment sta-tus, her position, and employment setting. Our specification was

log yit ¼ ait þ bXit þ lit; ð1Þand

ai;2000 � ai;1980 ¼ cþ b1DZ1 þ b2DZ2 þ b3DZ3 þ Fi þ li; ð2Þ

where t = {1980, 2000}; ait is the fixed effect for the individual’s area, alterna-tively specified as the county, state, or region; Xit is a vector of education,race, and other individual characteristics described above and b is the corre-sponding vector of coefficients; DZ1 is the change in the area’s share of for-eign-educated nurses between 1980 and 2000; DZ2is the change in the area’spopulation; DZ3 is the change in the area’s real per capita income; Fi is a fixedeffect (for the state, in the county regression, and for the region, in the stateregression); and l is a normal error term with zero mean and constant vari-ance. Thus, in the second stage, we regressed the difference in the area’s fixedeffects on the change in the share of foreign-educated nurses, the percentagechange in the area’s population, and the percentage change in its per capitaincome. Here, we have supplemented the approach of Borjas et al.; the lasttwo variables are proxies for the change in the area’s economic fortunes, orchange in demand for nursing services. When the dependent variable was thechange in fixed effects of the county, we ran the regression with and withoutfixed effects for the states, and when the dependent variable was the change infixed effects of the state, we ran the regression with and without fixed effectsfor the regions. Results are shown in Table 5. The important point is that thevariable for immigrant penetration is generally not significant. In the formula-tion that allowed only this variable to be an independent variable (Table 5b),it had a significant positive effect on log earnings of RNs in one specification,

TABLE

5

EFFECTOFIM

MIG

RANTPENETRATIO

NONLOGEARNIN

GSOFRN

S:SECONDSTAGERESULTSBASED

ON

APPROACH

OFBORJA

S,FREEMAN,ANDK

ATZ(199

6,19

97)

Changein

fixed

effectsof

thecounty

(without

fixedeffects

forstates)

Chang

ein

fixed

effectsof

thecounty

(withfixedeffects

forstates)

Changein

fixed

effectsof

thestate

(without

fixedeffects

forregions)

Chang

ein

fixed

effectsof

thestate

(withfixedeffects

forregions)

Chang

ein

fixed

effectsof

theregion

Intercept

0.12

)0.12

)0.01

3)0.0043

)0.08

1%

change

inpo

pulation

)0.00

01(0.000

3)0.00

(0.000

3)0.00

03(0.000

2)0.00

(0.000

3)0.00

12(0.0011)

%change

inpercapita

income

0.00

16(0.000

5)0.0009

*(0.000

5)0.00

35(0.000

9)0.0033

**(0.001

4)0.00

64(0.001

4)%

change

inmarketshareof

foreign-educated

nurses

0.06

3(0.055

)0.034(0.061

)0.74

(0.47)

0.63

(0.39)

)0.47

(0.91)

n14

1114

1151

519

R2

.008

9.0595

.425

0.6947

.6115

Intercept

0.15

)0.092

0.14

0.12

0.14

%change

inmarketshareof

foreign-educated

nurses

0.08

1(0.059

)0.044(0.062

)1.14

(0.39)

0.80**

(0.32)

1.34

(1.64)

n14

2514

2451

519

R2

0.00

070.0570

0.10

680.5637

0.06

40

NOTES:The

dependentvariable

isthechange

infixedeffectsof

thearea

specified

between1980

and2000.The

regressionsincludeonly

full-tim

eyear-round

workers,andexclude(1)

nurses

educated

outsidetheUnitedStatesand(2)certified

registered

nurseanesthetists,virtuallynone

ofwhom

areim

migrants.

Standarderrors

arein

parentheses.

The

standard

errors

arerobust

(Huber–W

hite).

Variables

withestimated

coefficients

inbold-facetype

aresignificant

atthe1percentlevel.*,

**Statistical

significanceat

the10

and5percent

level,respectively.The

data

arefrom

theNationalSam

pleSurveys

ofRegisteredNursesfor1980

and2000.



which was based on the changes in fixed effects for the state. The only vari-able with consistent explanatory power is the change in the area’s per capitaincome, a proxy for the change in demand for nursing services.

Applying the Methods of Borjas (2003). Finally, we decided to apply theapproach of Borjas (2003), since that seemed one of the most promising exam-ples of recent research in terms of finding negative effects of immigration onvarious skill groups. In this approach (as adapted to our data), the dependentvariable is the mean log earnings of different skill groups, the independentvariable is the measure of immigrant penetration of the skill group (foreign-educated nurses divided by all nurses), and there are fixed effects for eachlevel of skill, experience, and time period, and for interactions of them. Here,the different ‘‘areas’’ are the skill groups, delineated by different levels of edu-cational credential, experience, and cohort.We posited five levels of skill (Borjas had used education level): RNs who

had graduated from hospital ‘‘diploma programs,’’ those with an associate’sdegree, those with a bachelor’s degree, nurse practitioners, and certified regis-tered nurse anesthetists. Our levels of experience were from 1 to 4, 5 to 9, 10to 15, and over 15 years.20 Finally, there were different times of the NSSRNfor 1980, 1984, 1988, 1992, 1996, and 2000. Like Borjas, we included inter-actions for (skill · time), (experience · time), and (skill · experience). Thisapproach yielded 120 observations (skill groups). We followed Borjas inweighting the regression (where the weight equals the number of observationsused to calculate the mean log earnings of the skill group) and in adjustingstandard errors for clustering by skill category. The estimated coefficient ofour measure of immigrant penetration was )0.0529, with a standard error of0.32.21 Here, for the first time, we obtained a negative sign, but were nowherenear statistical significance.

Elasticities of Supply of Immigrants and Natives

To analyze labor supply further, we exploited the portion of the data set weobtained from the National Council of State Boards of Nursing. The mission ofthe Council is to ‘‘lead in nursing regulation by assisting [state] member boards,

20 We chose from Table 2 the column for counties in 2000 (with fixed effects for states) that had an esti-mated coefficient of 0.0244 for experience and )0.0004 for experience squared. The experience componentof log (earnings) reaches a maximum of 0.3721 when experience equals 30.5 years. We then solved for thecutpoints that would allow an equal gain of 0.093025 in log (earnings) across four intervals. These arguedfor the use of experience categories 1–4, 5–9, 10–15, and over 15 years.

21 In the unweighted regression, the estimated coefficient was )0.302, with a standard error of 0.61.


… to promote safe and effective nursing practice ….’’ The Council collectsdata from individual state boards of nursing on both the ‘‘stock’’ and ‘‘flow’’ ofnurses in each state. With regard to the stock, it reports the total number ofnurses in each state with active licenses, both registered nurses and licensedpractical or vocational nurses. It also indicates the number of nurses in a statein these two categories who are graduates of foreign nursing programs.22

With regard to the flow, the Council also collects data on the number ofRNs who obtain a license each year in each state and whether they haveobtained their license by (1) taking the state’s nursing examination, (2) enter-ing from another state, thus qualifying by ‘‘endorsement’’ of that state, or (3)graduating from a foreign (outside the United States and its territories) nursingprogram.23 Virtually all RNs who qualify by examination are both residents ofthe state in which they take the examination and U.S. citizens. With respect togroup 2, a Probit indicates that immigrant nurses are 22 percent more likelythan natives to move from one state to another, ceteris paribus,24 but becausethe number of immigrants is relatively small on a national basis, they are esti-mated to be only 5.6 percent of group 2. These data cover the period from1984 through 2005.Our hypothesis is that the elasticity of supply will be smallest for those who

take the nursing exam within the state, greater for those coming by endorse-ment from another state, and greatest for those coming by immigration. Immi-grants are often recruited for positions within their state of destination, and aremore likely to be recruited by states with the greatest demand for nurses.Those coming from another state may also have been recruited (especiallythose who just graduated from nursing school), but since they are predomi-nantly natives their potential gain in earnings will normally be less than forimmigrants, relative to the cost of moving. Also, many of those coming fromother states move for reasons other than an increase in earnings, e.g., familyreasons. Native-born nurses tend to have certain ties to particular states, e.g.,the states where they grew up or went to college, where most of their relativeslive, or (since 94 percent of registered nurses are female)25 where their hus-band or significant other currently works, so we expect their response to thenursing wage to be more inelastic than it is for group 3 (the immigrants).

22 The Council also reports annually the number of advanced practice nurses (e.g., certified registerednurse anesthetists, clinical nurse specialists, nurse practitioners, etc.) in each state.

23 In some cases, data were missing, so we supplemented the Council’s data set by contacting individualstate boards by telephone, email, or regular mail.

24 A Probit indicates that at the sample mean values of experience (16.2 years) and age at graduationfrom nursing school (26.9 years), the probability that an immigrant nurse had ever moved from one state toanother was 32 percent, compared with 26 percent for a native nurse (results provided upon request).

25 The 2004 NSSRN indicates that 94.3 percent of RNs were female.


We estimated the equation

logNit ¼ aþ b1 log yi;t�1 þ b2 log yi;t�2 þ b3 log yi;t�3 þ � � � þ Fi þ lit;

where Nit is the number of those in state i obtaining a nursing license in thestate per capita in year t, alternatively by taking the nursing exam, by endorse-ment from another state, or by immigration. yi,t)1, for example, is the meanwages of RNs in the state in the previous year, and Fi is a fixed effect for thestate. We assume that the most recent information on wages considered bythose who obtain a license is from the year before they apply for it. It is ofcourse possible that they also consider wages of RNs in the state in earlieryears. Accordingly, we test for optimal lag length using the Bayes (BIC) andAkaike information criteria (AIC). Results are in Table 6. The optimal laglength varies depending on the information criterion we use and on the groupwhose supply function is being estimated. For example, the last two columnsof Table 6 estimate the elasticity of supply of immigrant nurses. The Akaikecriterion recommends the use of three lags, while the Bayes criterion requires

TABLE 6

ELASTICITIES OF SUPPLY OF IMMIGRANT (FOREIGN-EDUCATED) NURSES, NURSES TAKING EXAM

(I.E., WITHIN THE STATE), AND NURSES COMING FROM OTHER STATES

DependentVariable

Log [(number taking thestate’s nursing exam ⁄ statepopulation) · 100,000]

Log [(number enteringfrom other states ⁄ statepopulation) · 100,000]

Log [(number of enteringimmigrants ⁄ state

population) · 100,000]

Informationcriterion Akaike Bayes

Akaike andBayes Akaike Bayes

Intercept 2.92 2.71 2.06 )13.2 )12.2Log yt)1(meanreal salary ofRNs in thestate in theprevious year)

)0.440** (0.148) )0.636**(0.120)

0.298 (0.184) 2.12** (0.669) 2.07** (0.633)

Log yt)2 )0.285* (0.151) )0.136 (0.215) 0.992 (0.767) 0.986 (0.713)Log yt)3 )0.525** (0.183) 0.676 (0.815) 1.17* (0.664)Log yt)4 0.879 (0.678)n 600 600 561 511 551R2 0.469 0.466 0.739 0.578 0.575

NOTES: Standard errors, which are robust, are in parentheses. *, ** Statistical significance at the 10 and 5 percent level,respectively. All the regressions include fixed effects for the states, estimates of which are not reported. Wages aredeflated by the Consumer Price Index (CPI; all urban consumers). In other specifications (not shown), we used theregional CPI, and median wages instead of mean wages, deflating alternatively by the national and regional CPI; theresults were not qualitatively different. The data on the number of RNs in different categories who enter the state eachyear from 1984 through 2005 are from the National Council of State Boards of Nursing. There are a substantial numberof missing observations. The data on wages, covering years from 1983 through 2004, are from the Current PopulationSurvey’s Merged Outgoing Rotation Groups. For topcoded observations, estimation of the mean above the topcode wasdone by assuming the entire distribution of earnings was lognormal. For an explanation and an example, see Greene(2003).


only two. Wages are deflated alternatively by the national or a regionalConsumer Price Index.For those who take the exam (qualifying for a license from within the state),

historic wage data are not a significant determinant of supply except for asomewhat perverse negative effect of the wages of 3 years ago. There is noeffect of wages on those entering by endorsement from other states, but forthose entering as immigrants, there is a highly significant positive elasticity ofsupply of wages in the previous year. These results essentially confirm ourhypothesis, in terms of the order of importance of wages on the decisions ofthese groups to obtain a nursing license in the state. RN wages in a state thatare 5 percent higher than those of other states should increase the supply ofimmigrant nurses by over 10 percent, using the AIC results.

Offsetting Migration Responses

These data are also well suited to test for possible displacement effects, i.e.,whether the addition of another immigrant leads to the subtraction of anothernative worker, in whole or in part. We can examine whether an increase in therate of entry of immigrant RNs causes a decline in the entry rate of nativeRNs. Our study differs from others in the literature in that we have an exactmeasure of the flows of native and immigrant workers, at least with respect toentry into the market. Although we do not know the number of native nurseswho leave the market, we would note that findings about the rate of entry ofnative nurses have implications on the rate of their departure. This is sobecause when a native nurse leaves state A to enter state B, she counts as bothan entry for state B and a departure for state A. For example, a negative effectof immigration on the rate of nurses entering from other states would suggestthat immigration also increases departures from the state, since states with fewimmigrants would experience a high influx of nurses from other states, andthose nurses must be coming from states with higher rates of immigration.We want to allow for the possibility that the supply of nurses from one

source may be affected by the supply from another source, in either the currentor previous years. Accordingly, we again test for optimal lag length using theBayes and Akaike information criteria. There are fixed effects for the states ineach of these regressions.Results are in Table 7. We find in column 3 that the rate of entry of foreign-

educated nurses, and of nurses entering by endorsement from other states, bothhave a significant positive correlation with the number of those qualifying byexamination within the state. There is a significant positive correlation with thenumber of immigrants entering in the current year and from two lagged years.

TABLE 7

EFFECT OF ENTRY OF IMMIGRANT (FOREIGN-EDUCATED) NURSES ON ENTRY OF THOSE TAKING THE

EXAMINATION OR COMING FROM OTHER STATES

Dependent variable

Log [(number taking thestate’s nursing exam ⁄ statepopulation) · 100,000]

Log [(number entering thestate from other states ⁄ statepopulation) · 100,000]

Information criterion Akaike and Bayes Akaike and Bayes Akaike Bayes

Intercept 1.25 1.22 1.16 1.22Ln(It) = log [(numberof immigrants ⁄ statepopulation) · 100,000]

0.0315* (0.0137) 0.058* (0.016) 0.027 (0.022) 0.030 (0.017)

Ln(It)1) 0.038* (0.018) 0.041 (0.030) )0.017 (0.019)Ln(It)2) 0.019 (0.018) )0.035 (0.030)Ln(It)3) 0.058** (0.020) )0.0080 (0.034)Ln(OSt) = [(numberentering from otherstates ⁄ statepopulation) · 100,000]

0.13** (0.056)

Ln(OSt)1) 0.11** (0.028)Ln(OSt)2) 0.087 (0.056)Ln(OSt)3) )0.0074 (0.045)n 589 227 227 409R2 0.440 0.760 0.773 0.729

NOTES: Standard errors, which are robust, are in parentheses. *, ** Statistical significance at the 10 and 5 percent level,respectively. All the regressions include fixed effects for the states, estimates of which are not reported. The data,covering the number of RNs in three categories who enter the state each year from 1984 through 2005, are from theNational Council of State Boards of Nursing. There are a substantial number of missing observations.


On the other hand, from columns 4 and 5, the number of those entering byendorsement from other states is not affected by the entry of immigrant nurses.Thus, there is no evidence of a displacement effect of immigrant nurses.

Summary and Conclusion

Cross-sectional studies that seek to determine the effect of immigration onwages generally find a positive correlation between wages and the percentageof workers in the area who are immigrants; this is attributed to the fact thatimmigrants go to places with high wages. We find such a correlation in themarket for registered nurses. Researchers have used a variety of methods totry to circumvent this endogeneity problem. One method is to use instrumentalvariables in an attempt to isolate the exogenous effect of immigrants on wagesand employment of natives. We adopted this approach, using as an instrumentthe proportion of county residents born outside the United States, a variablewhich is highly correlated with the fraction of nurses who received their nurs-


ing education outside the United States, but should not directly affect thewages of nurses. This estimation suggested that immigration of nurses had astrong positive effect on the earnings of native nurses.Other studies have examined changes in wages within areas over time, in an

attempt to isolate the effect of the rate of entry of immigrant workers. To learnwhether immigrant nurses reduced the earnings of RNs, we applied the tech-niques developed by Goldin (1994), Borjas, Freeman, and Katz (1996, 1997),and Borjas (2003), but found that the estimated effect of immigrant penetrationwas either positive or insignificant.To test the hypothesis that immigrant nurses are more likely than natives to

choose locations with high wages, we estimated the elasticity of supply ofnew RNs in each state from different sources (in-state residents, those comingfrom other states, and immigrants), and found that higher RN wages were pos-itively correlated only with the supply of immigrant nurses. It appears thatonly the supply of immigrants is elastic.Finally, much of the debate of the effects of immigration turns on the

response of native workers to the arrival of immigrants. Borjas and othersposit that it may be hard to detect negative effects on wages and employmentof natives in local markets, because natives will leave or choose not to enter amarket which many immigrants have entered. This would suggest, ceterisparibus, a negative relationship between the rate of entry of immigrants andthe rate of entry of native workers. This study finds no support for this hypoth-esis in the market for registered nurses, based on data that measure the rate ofentry of workers into states exactly. We can find no adverse effect of immig-ration on native workers in this occupation.

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Documents

Immigration of Nurses