The Effect of Immigrant Communities on Foreign-Born Student Achievement

The Effect of Immigrant Communitieson Foreign-Born Student Achievement 1

Dylan CongerGeorge Washington University

Amy E. SchwartzNew York University

Leanna StiefelNew York University

This paper explores the effect of the human capital characteristics ofco-ethnic immigrant communities on foreign-born students’ mathachievement. We use data on New York City public school foreign-born students from 39 countries merged with census data on thecharacteristics of the immigrant household heads in the city from eachnation of origin and estimate regressions of student achievement onco-ethnic immigrant community characteristics, controlling for stu-dent and school attributes. We find that the income and size of theco-ethnic immigrant community has no effect on immigrant studentachievement, while the percent of college graduates may have a smallpositive effect. In addition, children in highly English proficientimmigrant communities test slightly lower than children from lessproficient communities. The results suggest that there may be someprotective factors associated with immigrant community members’education levels and use of native languages.

INTRODUCTION

The most recent wave of immigration to the United States (U.S.)hasbrought a large population of children who are limited in their English

1We are grateful to the Spencer Foundation and the National Poverty Center at theUniversity of Michigan for supporting our work. We also thank anonymous reviewers aswell as Susan Brown, Lori Diane Hill, Justin McCrary, Heather Rose, and participants at

the National Poverty Center Workshop and the annual meetings of the American Educa-tion Finance Association, the American Educational Research Association, and the Popula-tion Association of America for extremely helpful comments on earlier drafts. Any

opinions expressed are those of the authors.

� 2011 by the Center for Migration Studies of New York. All rights reserved.DOI: 10.1111/j.1747-7379.2011.00862.x

IMR Volume 45 Number 3 (Fall 2011):675–701 675

proficiency and from low-income families (Hernandez and Charney,1998; Van Hook, Brown, and Ndigume Kwenda, 2004). This increaseand demographic shift in the immigrant population has also triggered anew line of research aimed at understanding whether and why theseyoung newcomers succeed or fail in U.S. schools. Much of this researchfinds that, similar to native-born students, immigrant student achievementis driven by their own attributes (e.g., gender, poverty), the resources pro-vided by their parents (e.g., human capital, marital status), and the qualityof their schools. Several immigration scholars suggest that the way thatimmigrants from different nations are treated in the U.S. may also playa role in their children’s adaptation. As a key example, Portes andRumbaut’s (1996, 2001) segmented assimilation theory argues that immi-grant children’s adaptation will depend on how the children and theirparents are treated by U.S. government policies as well as how they arereceived by the institutions, individuals, and co-ethnic immigrants in theirlocal communities. Many ethnographies have documented the stronginfluence of concentrated and highly-resourced immigrant communitieson their children’s adjustment in the U.S. (e.g., Gibson, 1988; Caplan,Whitmore, and Choy, 1989; Suarez-Orozco, 1989; Zhou and Bankston,1998; Waters, 1999; Portes and Rumbaut, 2001; Kasinitz, Mollenkopf,and Waters, 2004).

Yet there have been few attempts to quantify these contextual effectson U.S. immigrant children’s adaptation and this gap in the literature islargely driven by data constraints. In principle, the ideal study wouldinclude large numbers of children from multiple immigrant communitiesalong with an extensive set of individual, family, school, and residentialcontrol variables as well as measures of the larger contexts in which thechildren are received. This would allow researchers to directly estimate therole of contextual influences that are unique to the immigrant group andthat are distinct from the composition of the children and their families,schools, and neighborhoods.

Though no such analysis exists to our knowledge, the goal of ourpaper is to begin to fill that gap. Specifically, we take advantage ofcommunity-level data to examine the effect of post-migration immigrantcommunity characteristics on student performance, holding constant thecharacteristics of the students, their families, and their schools. To do this,we use administrative records on the census of foreign-born students inNew York City’s public primary schools merged with U.S. Census dataon the characteristics of immigrant groups in the New York metropolitan

676 International Migration Review

area. Using this rich dataset on over 50,000 students from 39 countries,our models examine the effect of four immigrant community characteris-tics (the income, college education, English proficiency, and number ofadult immigrants from the students’ country who reside in New YorkCity) on foreign-born students’ math achievement. Importantly, ourregression analyses control for a large set of school and student character-istics, including students’ language ability, poverty status, and test scoresin the previous year. By conditioning on prior year test scores, we alsocondition on some set of unobserved characteristics of the children andtheir families that determine prior year test scores, such as parental educa-tion and quality of prior schooling. Holding these variables constantallows us to more confidently attribute relationships between immigrantcommunity characteristics and student performance as contextual effectsrather than effects of student, family, and school characteristics. If immi-grant community contexts matter, we expect that their effects will holdafter we adjust for the composition of students, their families, and theconditions of the schools that they attend. Note that we estimate ourmodels with and without school fixed effects to account for the possibilitythat immigrant community members influence the children’s performancethrough their assistance in selecting schools.

In addition to making research contributions, the findings of thisstudy are relevant to local policymakers and educators facing expansionsin their immigrant populations. Many districts, and even individualschools, experience large fluctuations in the national origins of their immi-grant populations from year to year (Ellen, O’Regan, and Conger, 2009).They need to know how immigrants from different parts of the world fareand what might explain their relative successes or failures. District andcity policymakers also need to understand the role of immigrant commu-nities in nurturing immigrant children’s performance. Cities faced withlarge ethnic enclaves that do not speak English well, for instance, requireinformation on the consequences of such cultural retention; the achieve-ment of the children from these communities is one such consequence.

THEORETICAL AND EMPIRICAL BACKGROUND

Literature on Immigrant Student Achievement

Previous literature on U.S. students has established that, similar to native-born students, the characteristics of immigrant students and the quality of

Effect of Immigrant Communities on Student Achievement 677

their schools and neighborhoods matter to their success. Immigrantswhose parents are high-income, married, and well-educated, and whothemselves are white, Asian, and fully proficient in English outperformthose who have do not have these characteristics (e.g., Kao and Tienda,1995; Hirschman, 2001; Glick and White, 2003; Perreira, Mullan Harris,and Lee, 2006; Schwartz and Stiefel, 2006; Pong and Hao, 2007; Stiefel,Schwartz, and Conger, 2009). Some immigrant groups also have strongcultural values regarding academic achievement, work, family, and author-ity figures that help them succeed even in the face of low levels of humancapital (e.g., Kao, 2004; Pong, Hao, and Gardner, 2005; Perreira, MullanHarris, and Lee, 2006). The quality of the U.S. schools that immigrantstudents attend and the neighborhoods that they live in have been shownto affect their performance. Immigrants who attend schools with highly-qualified teachers and high-achieving peers fare better than those in less-endowed educational settings (e.g., Portes and MacLeod, 1996; Perreira,Mullan Harris, and Lee, 2006; Schwartz and Stiefel, 2006; Pong andHao, 2007; Hao and Pong, 2008). And to the extent that neighborhoodconditions can be distinguished from school conditions, some studies havefound that the human capital of the nearby community members has amodest influence on immigrant student achievement (Perreira, MullanHarris, and Lee, 2006; Pong and Hao, 2007). Thus, the variation inachievement that is typically-observed between immigrant students fromdifferent countries may be entirely explained by the composition of thestudents or the characteristics of their schools and neighborhoods in theU.S.

Yet the previous quantitative studies of immigrant children gener-ally find that origin country differences in academic achievement andattainment remain even after controlling for differences in the attributesof the students, the human capital of their parents, the characteristics ofthe schools they attend, as well as the population density and socioeco-nomic characteristics of their neighborhoods (e.g., Portes and MacLeod,1996; Kao, 1999; Hirschman, 2001; Portes and Rumbaut, 2001; Glickand White, 2003; Chiswick and DebBurman, 2004; Perreira, MullanHarris, and Lee, 2006; Schwartz and Stiefel, 2006; Tillman, Guo, andMullan Harris, 2006; Pong and Hao, 2007). For instance, the Childrenof Immigrants Longitudinal Study (CILS) of immigrant youth from over70 countries in San Diego and Florida finds that the children of Viet-namese immigrants far outperform the children of Cambodian immi-grants on math exams, holding constant well-established inputs to


achievement, such as age, sex, parental socioeconomic status, familystructure, and bilingualism (Portes and Rumbaut, 2001). Another studythat uses these same data and controls for the characteristics of theschools children attend finds similar results: certain ethnic groups outper-form others irrespective of the conditions of the schools they attend orthe socioeconomic status of their parents (e.g., Portes and MacLeod,1996). Using administrative data on New York City elementary andmiddle school students, Schwartz and Stiefel (2006) examine the mathand reading test scores of immigrants by 12 origin region groups, reveal-ing wide regional variation, adjusted for individual student characteristics.Immigrant youth from Africa, for instance, score higher than immigrantyouth from the Caribbean in models that control for school fixed effectsand several student characteristics, such as poverty status, and prior yeartest score (which, as explained above, serve as a proxy for someunobserved set of family and student attributes). Each study relies on adifferent sample (some national, some local), a different age group, a dif-ferent set of origin countries, and controls for a different set ofattributes, but adjusted origin country achievement differences are com-monly-observed.

The Influence of Immigrant Communities

One explanation for these remaining differences between students fromdifferent nations may be found in the contexts that receive the students.Portes and Rumbaut’s (1996, 2001) segmented assimilation theory (as awidely-cited example) elaborates, suggesting segmented trajectories forimmigrant children that will be shaped by their individual features, theirparents’ resources, and the context of their reception in the U.S. Key con-textual determinants of immigrant children’s success include the degree ofsupport they receive from the U.S. government and how they are receivedby the individuals and institutions in their local communities. The eco-nomic and social supports of children’s larger co-ethnic immigrant com-munities, in particular, are expected to have substantial effects onchildren’s patterns of acculturation. Though some scholars take issue withthe specific processes and expectations outlined by the segmented assimila-tion theory, they typically agree that immigrant student progress cannotbe reduced solely to their individual features, the human capital of theirparents, or the schools and neighborhoods that receive them (e.g., Albaand Nee, 2003; Kasinitz, Mollenkopf, Waters, and Holdaway 2008).


As outlined by Portes and Rumbaut (1996, 2001), the human capitalof the broader community of immigrants can determine the opportunitiesavailable to immigrant children. For instance, a more educated co-ethnicimmigrant community may provide more opportunities for tutoring andcounseling to children about how to navigate and perform well in U.S.schools. More educated immigrant communities may also be able to betteridentify high quality schools for their children. Immigrant communitieswith higher average incomes might create better economic opportunitiesand resources available to parents, which in turn improves children’s learn-ing environments. Borjas (1992, 1995), for example, shows that the ‘‘eth-nic capital’’ available to children of immigrants (proxied by the averageearnings of co-country immigrants in the parents’ generation) can havesignificant effects on children’s later earnings. We, therefore, expect thatchildren from immigrant communities with high incomes and high ratesof college education will perform better in school than immigrant childrenfrom communities with fewer such resources.

We might also expect that immigrant children are better off whentheir co-ethnic immigrant community members are highly English profi-cient, as this proficiency might reflect a familiarity with U.S. institutions,norms, and culture that aids children in school. It is also possible thatcommunities that retain the customs, values, and languages of their nativecultures may actually be better able to buffer some immigrant childrenfrom the negative influences of racial discrimination and low aspirationsamong segments of the native-born population (e.g., Portes and Rumbaut,2001). In fact, several studies find that immigrant students who are bilin-gual or whose families predominantly speak a language other than Englishat home (an indication that the children retain some aspects of theirheritage) perform better in school than those who predominantly speakEnglish (e.g., Feliciano, 2001; Schwartz and Stiefel, 2006). Put differently,immigrant communities that are highly English proficient might be moreeasily mainstreamed to American norms and, consequently, less able toprotect their children from downward assimilation. The English profi-ciency of the immigrant community, therefore, could have either positiveor negative effects on children’s schooling outcomes.

Finally, the size of the co-ethnic immigrant community in the local-ity may also play a role in the children’s transition. On the one hand,immigrant communities that form a large share of a city’s immigrantpopulation (such as the Dominicans in New York City) may have exten-sive social and political networks that help children move ahead in school.


For example, such communities may be able to garner more publicresources and services than smaller immigrant communities. As explainedin Portes and Rumbaut (2001) and others, larger numbers of immigrantsof a specific group can also lead to more densely-knit communities, whichsupport parents’ control over and aspirations for their children. That is,even when an immigrant group has low human capital, a large and con-nected immigrant community can provide a supportive social networkthat helps to prevent family disruption and maintain cultural norms, suchas respect for elders. Examples of such networks are often found amongthe Vietnamese and other Asian immigrant communities (e.g., Caplan,Whitmore, and Choy, 1989; Zhou and Bankston, 1998).

It has also been hypothesized, however, that extensive ethnic net-works afford opportunities to children outside of the mainstream labormarket, which may lower their incentive to excel in school (Portes andRumbaut, 2001). For instance, the Cubans in Miami, the largest and old-est immigrant group in the city, have found success in small businessesthat may be an avenue for children who do not fare well in school.Perez’s (2001) analysis of the Cuban youth in the CILS data suggests that:‘‘the enclave, in other words, may function not as a golden springboardfor the second generation but as a basic safety net.’’ (p. 122). Similarresults show up in the labor market literature where there is evidence thatethnic enclaves provide better jobs for adults who are not English profi-cient, thus lowering the returns to English (McManus, 1990). The priorliterature, therefore, does not provide a straightforward hypothesis of howthe size of the immigrant community in the locality might influenceimmigrant children’s performance in school.

Prior Study and Empirical Considerations

Our quantitative approach to this complex question relies on parsimoni-ous models and forces us to focus on limited measures of immigrant com-munities. Nevertheless, we know of no prior research that has attemptedto estimate the effect of immigrant community characteristics on studentperformance using large samples of U.S. immigrant students from multi-ple source countries and with local measures of immigrant communities.Our study is, however, informed by one very relevant quantitative studyof the role played by source country conditions and co-ethnic immigrantcharacteristics on immigrant students’ achievement in Western Europe.Levels, Dronkers, and Kraaykamp (2008) use the 2003 wave of the


Project for International Student Assessment (PISA) dataset to examinehow immigrant students’ math test scores in 13 Western Europeannations are explained by the characteristics of their origin countries, theirdestination countries, and their immigrant communities, conditional onthe students’ individual attributes. The study focuses on the effect of twoimmigrant community characteristics: the immigrant groups’ share of thedestination country population and the education level of the parentsfrom each immigrant group relative to the destination country average.The study finds that both of these characteristics have positive effects onmath achievement, which the authors interpret as contextual effects. TheLevels, Dronkers, and Kraaykamp (2008) study has paved the way forexplorations of this kind and we seek to build upon this seminal paper inseveral ways.

Similar to Levels, Dronkers, and Kraaykamp (2008), the goal of ourmodels is to explore the effect of a conceptually-relevant set of immigrantcommunity characteristics on immigrant student achievement, controllingfor the effects of the students’ own characteristics and the schools theyattend. In sensitivity analyses (described further below), we also hold con-stant several characteristics of the students’ origin countries in an effort tobetter isolate the effect of immigrant communities as distinct from unob-served characteristics of the students that are driven by differential selec-tion from countries with certain characteristics.

Though our data do not include students from multiple destinationcountries (as in Levels et al.) and prevent us from examining destinationeffects, our data are advantageous to this task in a number of other ways.First, our study focuses on immigrants in the U.S., the largest settlementcountry in the world, and the group from which most of the prior theo-ries have been based. Second, by focusing on one school district and city,we can compare children who attend schools that are governed by a com-mon set of administrative procedures and rules and who are subject to thesame local economic, social, demographic, and political conditions. Withone large metropolitan area, we can also better isolate the influence of stu-dents’ local immigrant communities than an analysis that might use anational sample of immigrant youth because the limited numbers ofstudents from origin countries in national datasets require users to poolstudents from different parts of the country (e.g., Mexicans in Los Angelescannot be reliably distinguished from Mexicans in New York City).Third, the data identify each student’s school, which allows us to comparestudents attending similar schools. Fourth, the New York City case study


allows for comparisons of immigrants from a wide array of countries,unlike many other areas that are dominated by only a few large countries.We include newcomer groups that have not received much attention inthe recent literature, such as South Americans, West Africans, Europeans,and Asian-Caribbeans. Fifth, unlike most research that groups togethernative-born and foreign-born children of immigrants, we focus on for-eign-born children, a group that may be uniquely affected by their statusas immigrants (e.g., lack of eligibility for public benefits, unfamiliaritywith the host culture), their language needs, and their surrounding immi-grant communities. Sixth, we control for many individual attributes,including some that were not held constant in Levels, Dronkers, andKraaykamp (2008), such as students’ race ⁄ ethnicity (Asian, black,Hispanic, or white) and prior year test scores, which better allows us tointerpret effects as contextual versus compositional. Finally, with thecensus of New York City public elementary and middle school students,we can make definitive statements about origin country variation in testscores without concern for sampling error.

The limitations of our data also deserve discussion. By relying onquantitative indicators and public-use census data, we may be unable tocapture the complexity in the immigrant community. Qualitative methodswould naturally better describe the mechanisms through which adults inan immigrant community interact with the children. In addition, it ispossible that our reliance on metro-wide immigrant characteristics over-looks the connections that occur in the child’s more immediate neighbor-hood (a limitation that might bias our estimates towards zero). Our focuson New York City may also limit the generalizability of our results toother established immigrant gateways with a long history of immigration.We provide a more detailed discussion of the data in the next section.

DATA AND METHOD

Data Sources and Variables

We assembled a unique multi-level dataset of students nested in schoolsand origin countries. The primary data consist of administrative recordsprovided by the New York City Department of Education on 4th through8th grade foreign-born students in school year 2000–01. For each student,the data contain demographic (race ⁄ ethnicity, country of birth, gender,age, and the year the student entered the New York City school system)


and educational (grade level, participation in special education programsfor mild to moderate disabilities, and test scores) information.

The data also identify the language most frequently spoken at homeand students’ scores on a test of English language ability (the LanguageAssessment Battery, or LAB). Students in Kindergarten through the 12thgrade who are frequently exposed to a language other than English (asdetermined by a home language survey) are required to take the LABwhen they enter school and each year until their scores indicate Englishproficiency.2 The percentile rank on the test indicates how well the stu-dent scored compared to all other students who were tested. Studentswho score at or below the 40th percentile are eligible for English as a Sec-ond Language (ESL) and bilingual programs. Students who score at orbelow the 40th percentile on the LAB are also identified as English Lan-guage Learners (ELL). In addition, information is available on the povertystatus of the family through the students’ eligibility for subsidized meals.3

This pupil file also contains a school-level identifier for each student.We use students’ scores on the statewide math exams as our depen-

dent variable. (The results for reading achievement are very similar tothose for math achievement and are available upon request.) Students inthe 5th, 6th, and 7th grades took the California Achievement Test (CAT)in mathematics while students in the 4th and 8th grades took the State

2There is a two-step process for identifying a student to take the LAB. First, students areidentified to take the LAB if their home language is not English or their native language isnot English. Thus, some of the students who have a language other than English fre-

quently spoken at home are actually native-English speakers and not required to take theLAB (for instance, some second generation immigrant children). The second step in theprocess is an interview with the child to determine whether the student speaks a language

other than English and how well s ⁄ he seems to speak English. Students who speak no lan-guages other than English are not required to take the LAB. Students who speak anotherlanguage and whose English is minimal are required to take the LAB. Therefore, the stu-dents whose home language is not English and who did not take the LAB are either only

proficient in English or sufficiently proficient that they were not referred to take the LABat the informal interview.3Prior year test score data are missing for 19.2 percent of the sample; subsidized meals

data are missing for 5.9 percent of the sample. We impute the missing values using multi-ple imputation by chained equations creating five multiply-imputed datasets. All tablesreport statistics based on the means resulting from the five imputed datasets. The results

are not sensitive to this method of imputation; in an earlier draft, we used a differentimputation method (substituting missing values with zeros and including missing dataindicators) and the results were qualitatively similar (results can be obtained from

authors).


Mathematics tests. We standardize all scores by subtracting the mean ofthe grade and dividing through by the standard deviation of the grade.

To gather data on our primary independent variables of interest, wethen supplemented these data with information on the characteristics ofimmigrant adults in New York City from each of the origin countriesrepresented by our students. Using the New York City subset of the 5percent public use microdata sample of the 2000 U.S. Census, we firstsub-selected all immigrants ages 18 and over, then aggregated by origincountry in order to measure the median characteristics of immigrantsfrom each country (e.g., we computed the median household income ofimmigrants from the Dominican Republic and so on). We selected fourtheoretically-relevant variables for our analysis: median household income,the percent college graduates, the percent who speak English ‘‘well’’ or‘‘very well’’, and the number of adults per 1,000 of the New York Cityadult population. We then merged these immigrant community indicatorsto the student-level data by country of origin.

To be clear, our measures do not capture the characteristics of theimmigrants in the student’s residential neighborhood within New YorkCity, such as the census tract or block. Many New York City immi-grants are clustered in ethnic enclaves, the Dominicans in WashingtonHeights and the Jamaicans in Central Brooklyn, as examples. Instead, weuse the characteristics of co-ethnic immigrants in all of New York Cityto represent the larger ethnic capital available to children in the citythrough churches and other ethnic organizations. Many ethnographicaccounts of immigrants in New York City document that immigrantsrely on their co-ethnic compatriots in the larger metropolitan area, notjust those who live in their immediate neighborhoods (e.g., Waters,1999; Min, 2001; Zhou, 2001; Kasinitz, Mollenkopf, Waters, andHoldaway, 2004).

Finally, in order to perform a sensitivity analysis (described morefully in the results section on page 24–25), we further add data on thecharacteristics of children’s origin countries. We selected the followingfour origin country variables: (1) From the United Nations StatisticalDivision, we include the per capita gross national income (GNI) in 2000,converted to U.S. dollars; (2) From the Human Development Report, wecompute a countries’ relative income inequality by dividing each coun-tries’ Gini index of income inequality by the U.S. Gini index; (3) Fromthe Freedom in the World Comparative and Historical Data collected bythe Freedom House (http://www.freedomhouse.org), we use include the


political suppression score of each country, which ranges from 1 to 7,where 1 represents the highest degree of freedom and 7 the lowest; and(4) From http://www.chemical-ecology.net/java/capitals.htm, we includethe distance from the capital city of each country to New York City inthousands of kilometers.

Sample

In order to provide sufficient power to the multi-level models, we includein the sample only those students from countries with a substantial num-ber of immigrant students, at least 100 who took a math exam. Such arequirement excludes only a small share of immigrant students; approxi-mately 90 percent all immigrant students in the public primary schoolsare included in the 39 source countries (N = 50,728).

Table 1 provides the co-ethnic immigrant community characteris-tics measured in 2000 for each of the 39 country of origin groups thatare included in our models of achievement. As shown, the characteris-tics of immigrant adults in New York City vary widely by origin. Forinstance, immigrants in New York City from Canada earned a medianhousehold income of approximately $65,000 and 63 percent of themhad a college degree. Nearly all of these immigrant adults from Canada(97%) spoke English well to very well and they comprised roughly 3out of every 1,000 adults in the city. In contrast, immigrants from theDominican Republic (the largest immigrant group in the city) had a farlower rate of human capital: immigrants from the Dominican Republicearned an median household income of approximately $33,000; 11 per-cent of them held a college degree; and less than half of them spokeEnglish well to very well. See the Appendix Table for the means andstandard deviations on all of these characteristics as well as the studentcharacteristics that are held constant in the analysis to be describednext.

Analytic Strategy

Our estimating equation regresses test scores on immigrant communitycharacteristics, along with a set of student covariates and school fixedeffects. We use a multi-level model, which treats the within and betweencountry variation as random and models the two sources of variation withfirst (student) and second (country) level variables as follows:


TABLE 1CHARACTERISTICS OF ADULT IMMIGRANTS IN NEW YORK CITY BY COUNTRY OF ORIGIN, 2000

Origin countryMedian

household income% Collegegraduates

% SpeakEnglish well

Number oforigin adults per1,000 all NYC

AmericasBarbados $50,700 21.6 92.2 4.3Brazil $44,000 36.9 85.1 2.2Canada $65,060 63.1 97.3 3.0Colombia $41,900 19.0 56.1 12.6Dominican Rep. $32,800 11.3 46.9 54.5Ecuador $43,800 11.9 50.0 16.9El Salvador $39,400 8.2 53.8 3.7Grenada $45,000 22.0 88.5 2.8Guatemala $34,070 10.3 57.2 2.7Guyana $53,500 19.2 88.6 19.5Haiti $43,000 22.6 80.8 14.8Honduras $35,200 8.3 55.5 4.7Jamaica $47,700 22.3 93.5 26.5Mexico $40,200 5.2 39.1 17.8Peru $43,000 23.6 63.1 4.3Panama $38,690 19.7 90.8 3.7Saint Lucia $43,900 13.2 98.1 0.9Saint Vincent & Gren. $46,500 19.6 96.6 2.2Trinidad & Tobago $46,100 18.5 89.3 13.7Venezuela $39,000 35.3 71.0 1.2

AfricaGhana $46,000 30.3 94.6 2.2Nigeria $50,000 59.5 97.7 2.2

AsiaBangladesh $37,700 39.5 79.0 5.6China $36,800 22.0 37.5 31.8Hong Kong $58,000 43.8 75.8 5.0India $55,700 52.0 86.3 10.4Japan $44,200 65.7 80.2 3.0South Korea $42,000 46.6 58.1 1.7Pakistan $39,000 31.9 78.9 5.3Philippines $80,900 68.2 94.0 7.5Vietnam $50,600 31.8 58.8 2.2

EuropeGermany $54,000 45.3 96.8 5.0Israel $50,700 40.1 89.7 3.2Poland $40,000 25.0 71.4 10.2Romania $40,000 35.4 82.6 3.2Russia $35,000 52.7 67.0 12.4Ukraine $33,400 53.9 54.5 10.4United Kingdom $55,000 32.5 100.0 0.4Former Yugoslavia $46,000 16.7 73.3 2.5

Source: New York City Extract of the U.S. Census 2000 PUMS 5% sample.Note: Adult immigrants in New York City refers to all foreign-born persons aged 18 and over who reside in New

York City.


T ijk ¼ b0 þ b1C j þ b2S i þ b3Z k þ tj þ eijk ð1Þ

where Tijk is the standardized math score for student i from country jin school k. Cj is a vector of characteristics among New York City adultimmigrants from country j (the natural log of median householdincome, the percentage who are college graduates, the percentage whospeak English well or very well, and the number of adult immigrantsper 1,000 of the New York City population). Controls for student attri-butes (Si) include gender, age, race ⁄ ethnicity, eligibility for free lunch(referred to from here forward as ‘‘poor’’), eligibility for reduced-pricelunch (‘‘near-poor’’), participation in the part-time special educationprogram, whether a language other than English is the primary languagespoken a home, and three variables that capture the student’s Englishlanguage skills and eligibility for English language instruction: whetherthe student took the LAB, her score on the LAB (in percentiles), andwhether she scored at or below the 40th percentile, indicating ELL sta-tus and eligibility for English as a Second Language or bilingual educa-tion services. Si also includes the number of years that the student hasbeen enrolled in the New York City school system and the student’sgrade-level. Finally, Si includes the student’s prior year test score. Con-trolling for prior year test scores ameliorates potential bias on b1 due tounobserved variables that are correlated with both prior year score andimmigrant community conditions, such as attitudes towards school andwork ethic. We estimate models both with and without prior year testscores given that the prior year scores may provide insufficient variationto identify effects. Zk are school fixed effects, which hold constant theimpacts of differences in schools attended and other unobserved charac-teristics of students that drive school choice. Note that the use of schoolfixed effects may also reduce the bias due to unobserved neighborhoodcharacteristics to the extent that elementary and middle school childrenattend schools in their neighborhoods.

Estimation of the equation produces two random error compo-nents, which allow for a decomposition of the variance in foreign-bornstudent performance into the amount that can be attributed toacross-country variation versus that which can be attributed to studentvariation within countries. tj is the random error associated with thecountry-specific intercept such that the variation in test scores acrosscountries can be denoted s00 = Var(tj). eijk is the random error


associated with the ith student in the jth country such that the variationin test scores across students within countries can be denoted asr2 = Var (eijk). The proportion of variance in test scores that can beattributed to across-country sources is, then, measured as s00=ðs00 þ r2Þand the proportion that can be attributed to student variance withincountries is measured as r2=ðs00 þ r2Þ.

RESULTS

Unadjusted Test Scores and Immigrant Community Characteristics

Figure I displays the average performance of the 50,728 students from the39 source countries on the math exam. The scores have been standardizedto a mean of zero and a standard deviation of one within each grade.Since the New York City native-born average is nearly zero, these meansmeasure the gap between the average performance from students in eachsource country group and the average native-born student. The variation

-0.8

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Figure I. Mean Math Achievement Scores of Foreign-born Students by Source Coun-

try, New York City, 2000–01


in performance is wide, ranging from Haitians who score over 0.5standard deviations below the average native-born to those from HongKong who score over one standard deviation above. Students from Europeand Asia tend to score above average, while students from Latin Americaand the Caribbean countries perform at or below average.

As explained above, the estimated error components of the multi-level model allow for a decomposition of the variation in studentachievement into that which can be attributed to variation acrossstudents within countries versus across countries. In unconditional mod-els, where no independent variables are included, the majority of thevariation in foreign-born test scores is driven by differences withinsource countries rather than between them: 80.3 percent of the variationin math scores is due to within-country factors (and, correspondinglydifferences between students from the same co-ethnic immigrant com-munities). We aim to understand whether and to what extent immigrantcommunity characteristics drive the remaining 20 percent of immigranttest score variation.

We begin with a look at unadjusted relationships. Table 2 providesthe results from bivariate regressions of math achievement on each of thefour immigrant community variables that we have selected as theoreti-cally-relevant to immigrant achievement. That is, the results do not comefrom a model with all of the immigrant community variables on theright-hand side, just each one individually. We find that immigrants fromcommunities that have higher shares of college graduates perform betteron the exams than students from less-educated communities. The percentof co-ethnic community members who speak English well and the meanincome of the community members as well as the number of adults per1,000 in New York City have no relationship to math scores.

TABLE 2BIVARIATE REGRESSIONS OF MATH TEST SCORES

Ethnic communitiesLog household income 0.418 (0.416)Percent college graduates 0.017** (0.004)Percent speak English well )0.002 (0.004)Number of adults per 1,000 people )0.007 (0.007)

ObservationsNumber of students 50,728Number of countries 39

Notes: The estimated coefficients come from bivariate regressions of the standardized math test scores on eachcharacteristic separately. Standard errors in parentheses.

**significant at 1%.


Regression-Adjusted Relationships

The results shown in Table 2 are suggestive but any of the estimatedcoefficients could be upwardly or downwardly biased by the omission ofimportant variables. In Table 3, we reexamine the relationship betweenimmigrant community characteristics and student test scores, holdingconstant a large set of other characteristics of the students, their families,and the schools they attend.

We begin with Model 1, where student and family attributes areheld constant. The estimated coefficients on the immigrant communityvariables are substantially altered with the addition of these controls.

TABLE 3REGRESSIONS OF MATH TEST SCORES

(1) (2) (3)

b SE b SE b SE

Ethnic communitiesLog household income 0.250 0.257 0.165 0.171 0.166 0.163Percent college graduates 0.009** 0.002 0.006** 0.002 0.005** 0.002Percent speak English well )0.012** 0.003 )0.008** 0.002 )0.008** 0.002Number of adults per 1,000 people )0.003 0.004 )0.002 0.003 )0.002 0.002

StudentsPoor )0.414** 0.014 )0.202** 0.013 )0.170** 0.014Near-poor )0.194** 0.020 )0.091** 0.017 )0.072** 0.017Asian and other )0.044 0.037 )0.032 0.033 )0.002 0.033Black )0.307** 0.038 )0.142** 0.032 )0.109** 0.034Hispanic )0.213** 0.041 )0.080* 0.035 )0.060� 0.035Female )0.039** 0.008 )0.004 0.007 )0.009 0.007Age )0.095** 0.006 )0.022** 0.005 )0.028** 0.005Other than English at home 0.050* 0.021 0.021 0.018 0.038* 0.019Took LAB )1.435 ** 0.052 )0.938** 0.048 )0.959** 0.048LAB percentile 0.019** 0.001 0.015** 0.001 0.016** 0.001ELL 0.411** 0.045 0.309** 0.042 0.312** 0.042PTSE )0.571** 0.020 )0.296** 0.018 )0.309** 0.018Years in school system 0.011** 0.002 )0.009** 0.002 )0.013** 0.0024th grade )0.348** 0.027 )0.130** 0.024 )0.240** 0.0285th grade )0.293** 0.021 )0.122** 0.020 )0.226** 0.0256th grade )0.239** 0.017 )0.113** 0.015 )0.194** 0.0207th grade )0.125** 0.013 )0.058** 0.012 )0.089** 0.012Prior year test score 0.490** 0.005 0.468** 0.005School fixed effects No No YesNumber of students 50,728 50,728 50,728

Notes: Restricted F-tests indicate that all sets of variables (co-ethnic communities, students, and school fixed effects)in Model 3 are jointly significant at p < 0.05. Model 3 includes 897 school fixed effects. The % of within-country variation explained by Model 3 is 42.7%; the percent of across-country variation explained in Model3 is 93.8%. ELL, English Language Learners; LAB, language assessment battery; PTSE, part-time specialeducation.

�significant at 10%, *significant at 5%, **significant at 1%.


The effect of college graduates remains positive and statistically significant,though the magnitude is very small; a 10 percentage-point increase in thepercent college graduates would raise immigrant students’ math scores byonly 0.09 of a standard deviation, all else constant. The effect of theEnglish proficiency of the immigrant community is again negative, butnow statistically significant (though small in magnitude), suggesting thatimmigrant children from these communities may be slightly harmed bythe English proficiency of their immigrant communities. Finally, theincome and size of the immigrant community continues to have no rela-tionship with test scores.

The coefficients on the student control variables are in the expecteddirection and consistent with prior research. For instance, the coefficientson the four language variables (English not at home, took LAB, LAB per-centile, and ELL) are consistent with the results found in Schwartz andStiefel (2006). First, immigrant students whose primary home language isnot English perform better than those whose home language is English.Second, students who are referred to take the LAB due to insufficientEnglish ability perform worse on the exam than those who do not takethe LAB. Third, among those who take the LAB, a higher percentileassociates with higher performance. Finally, among students with equalEnglish language ability, those who are ELL score higher. Taken together,these findings indicate that students who have a language other than Eng-lish at home and who are English proficient themselves have the highestmath achievement (perhaps reflecting the benefits of bilingualism). Inaddition, students who are flagged as eligible for English language instruc-tion (the ELL group), many of whom receive bilingual or ESL services,perform better than other limited English-speaking students who are noteligible for such services (perhaps reflecting the positive effects of theseservices). Poverty, black, Hispanic, female, age, and part-time special edu-cation participation generally correlate negatively with math achievement.

Model 2 adds the students’ prior year test scores in an effort to con-trol for unobserved attributes of the immigrant children that might drivecurrent year test scores and correlate with immigrant characteristics. InModel 2, the coefficients on the ethnic community variables are somewhatsmaller than in Model 1, but otherwise the results (direction and statisti-cal significance) are similar. Model 3 then adds the school fixed effects,which only slightly weakens the effect of co-ethnic community members’education levels, suggesting that immigrant community members affectstudents in ways other than through the schools that they attend.


With the inclusion of all the variables shown in Table 3, we explainapproximately half (42.7%) of the within-country variation between stu-dents but almost all (93.8%) of the across-country variation (not shownin table). Notably, there remain several important differences betweenstudents from the same country that we have not accounted for in ourmodels, the implications of which are discussed in the conclusion.

Sensitivity and Heterogeneity Analyses

The results in Table 3 suggest that the co-ethnic community characteris-tics have negligible effects on immigrant student achievement; amongthose coefficients that are statistically significant, we find a small positiveeffect of the education and a small negative effect of the English profi-ciency of the surrounding co-ethnic community. We tested for the robust-ness of the estimates presented in Table 3 using different samples and analternate specification. The results are reported in Table 4. To determinewhether the findings are sensitive to unique origin country observations,we re-estimated the regressions omitting the three origin countries at thetop and the three origin countries at the bottom of the test score distribu-tion (Specification 2 of Table 4). To further investigate the negative coef-ficient on the English proficiency variable, we re-estimated the regressions

TABLE 4ROBUSTNESS CHECKS, REGRESSIONS OF MATH TEST SCORES

Originalmodel (1)

Omit extremetest scoregroups (2)

Omit extreme %who speakEnglish (3)

Include countryof origin

charact. (4)

Ethnic communitiesLog household income 0.166 (0.163) 0.023 (0.162) 0.222 (0.149) 0.041 (0.152)Percent college graduates 0.005** (0.002) 0.004* (0.002) 0.004* (0.001) 0.001 (0.002)Percent speak English well )0.008** (0.002) )0.006** (0.002) )0.006** (0.002) )0.004* (0.002)Number of adults

per 1,000 people)0.002 (0.002) )0.002 (0.002) )0.006 (0.003) )0.001 (0.002)

Log GNI 0.034� (0.019)Relative income

inequality0.028 (0.119)

Political suppression 0.026 (0.016)Geographic distance 0.025** (0.009)Number of students 50,728 46,319 32,209 50,728Number of countries 39 33 33 39

Notes: Standard errors in parentheses. In (2), children from the following countries were omitted: Hong Kong,Korea, Ukraine, Grenada, Haiti, Honduras. In spec (3), children from the following countries were omitted:Canada, Nigeria, St. Vincent & Grenadines, China, Dominican Republic, Mexico. All regressions includethe covariates listed in Model 3 of Table 3.

�significant at 10%, *significant at 5%, **significant at 1%.


omitting the three origin countries at the top and bottom of the distribu-tion of the percent of immigrants who speak English language well (Spec-ification 3 of Table 4).

Finally, to control for possible influences on children’s performancethat are either directly driven by a student’s origin country or indirectly asa proxy for unobserved traits, we include measures of the political and eco-nomic circumstances of the student’s origin country as well as the distancebetween the origin country and New York City (Specification 4 ofTable 4). To elaborate, there is a large body of research on the relationshipbetween origin country characteristics and immigrant outcomes. Much ofthis work focuses on the fact that the composition of immigrants from cer-tain countries differs on average in ways that are relevant to their educationand labor market performance in the U.S. We, therefore, hold constantthe following four variables with theoretical grounding in the prior litera-ture: natural log of GNI; income inequality relative to the U.S.; level ofpolitical suppression; and distance from New York City to the capital city.To explain this choice of variables, we provide a brief literature review:

Natural Log of GNI. In the literature on adult immigrants, those fromnations with a low GNI tend to have worse labor market performance inthe U.S. than those from more industrialized nations because they havelimited exposure to formal schooling, U.S. institutions, and the Englishlanguage (e.g., Borjas, 1987; Bratsberg and Ragan, 2002).

Income Inequality. The Roy model of human capital development postu-lates that immigrants to the U.S. from highly unequal economic distribu-tions relative to the U.S. (e.g., Mexico) are likely to be more negativelyselected than those from relatively equal income distributions relative tothe U.S. (e.g., Sweden). The reason is that highly-skilled workers in Swe-den may have more to gain by migrating than less skilled workers, whilethe opposite is true of Mexicans (e.g., Borjas, 1987). (For counter evi-dence, see Chiquar and Hanson (2005); Feliciano (2005); and Jensen,Gale, and Charpentier (2008), all of whom use data on Mexican migrantsand non-migrants, and find that Mexican migrants are positively selectedon human capital characteristics, such as education and skill.)

Political Suppression. Immigrants from more politically unstable countriesmay be less positively selected than immigrants from more politicallyunstable countries because they are migrating for non-economic reasons.


Political refugees have essentially been forced from their home lands andare less likely to harbor the motivation, skill, and drive that is characteristicof economic migrants.

Distance. The distance that immigrants travel to the U.S. has been foundto proxy for their ‘‘quality’’ – those who travel farther are considered tobe more able on traits that are typically not observed in part because thecosts to immigration are more substantial (e.g., Jasso and Rosenzweig,1990).

The relationships observed in the original model are slightly differ-ent in the alternative specification and restricted samples. The effect ofpercent college graduates holds at about 0.004 to 0.005 in most modelsexcept that it becomes smaller and statistically insignificant with controlsfor origin country characteristics, particularly the selection captured bygeographic distance (Specification 4 in Table 4). The negative effect ofpercent who speak English well ranges between 0.004 and 0.008 in allmodels, again suggesting slight negative effects of having more Englishproficient members of the surrounding co-ethnic community.

We also tested for heterogeneity in the effect of co-ethnic commu-nity English proficiency in the following three separate specifications: (1)adding squared co-ethnic English proficiency; (2) adding an interactionterm between co-ethnic English proficiency and English as the primaryhome language; and adding an interaction between co-ethnic English pro-ficiency and the child’s ELL status. The results are shown in Table 5. Theresults from the first two specifications yield similar results to the originalspecification; that is, the additional variables are statistically insignificantat conventional levels. The parameter estimate on the interaction of

TABLE 5HETEROGENEITY IN EFFECTS OF CO-ETHNIC ENGLISH PROFICIENCY

(1) (2) (3) (4)

Percent of ethnic communitythat speak English well

)0.008**(0.002)

)0.023�

(0.014))0.008**(0.002)

)0.007**(0.002)

Squared 0.000(0.000)

Interacted with English at home 0.000(0.000)

Interacted with student ELL )0.009**(0.001)

Notes: Specification (1) provides the results from Model 3 of Table 3. Standard errors in parentheses. All regressionsinclude the covariates listed in Model 3 of Table 3. ELL, English Language Learners.

�significant at 10%, **significant at 1%.


percent in co-ethnic community who speak English well and the ELLstatus of the student is, however, negative and statistically significant,implying an added negative effect of being both ELL and coming from aco-ethnic community that speaks English relatively well. We interpretthese results in the following section.

CONCLUSIONS

Prior research on immigrant students tells us that they often performbetter than native-born students but that their performance is heteroge-neous by country of origin. In our analysis of immigrant students from39 countries in New York City public schools, we find variation thatranges from over 0.5 standard deviations below native-born (the aver-age) to over 1 standard deviation above native-born on math exams,with similar variation on reading exams (not shown in the paper toconserve space). We go further to examine the specific contribution ofthe characteristics of their co-ethnic communities to this wide variationin their achievement.

Our simple variance decomposition reveals that the majority of thevariation in foreign-born achievement is across students within countriesrather than between them. An estimated 80 percent of the variation inforeign-born math achievement is due to differences between studentsfrom the same country. Thus, while we observe some groups of immi-grants to perform better than others, much of this variation is due to thedifferences in the students and the same characteristics that drive achieve-ment differences among native-born students, such as poverty, race,parental education, and English proficiency.

We do, however, find some evidence that the human capital charac-teristics of the broader co-ethnic immigrant community translate to thenext generation. The income and relative size of the co-ethnic communityappears to have no effect on students’ test scores. Note that the null effectof immigrant community size differs from the only prior test that weknow of by Levels, Dronkers, and Kraaykamp (2008) who use data onimmigrants from Western European nations where relative size is calcu-lated as the immigrants share of the country and the association withachievement is found to be positive. However, children from immigrantcommunities with higher shares of college graduates perform slightly bet-ter in school than children from other immigrant groups (consistent withLevels et al.), suggesting some positive spillovers of highly-educated adult


immigrants in the community. When we control for the distance thatimmigrants travel from their origin countries, we find that the effect ofcollege graduates weakens; thus, some of this relationship may be due toother unobserved traits of highly-educated immigrants that are not due totheir education per se.

In addition, children in highly English proficient immigrant com-munities score slightly lower on the exams than other children. The nega-tive relationship between the larger communities’ English proficiency andstudent test scores is conditional on the student’s own ELL status andwhether English is spoken in the home (both of which correlate withachievement in ways that are consistent with prior literature). The effectis also robust to several sensitivity checks and appears to be slightly largerfor students who are not yet minimally-English proficient. The findingssuggest that the existence and retention of native languages and perhapsother cultural traditions may be beneficial to immigrant children in largeurban districts, such as New York City. This benefit may ring particularlytrue for students with relatively limited English familiarity. Our finding isconsistent with some of the theoretical work on immigrant assimilation(e.g., Portes and Rumbaut, 2001) and suggests that school systems andcities may benefit from helping immigrants retain aspects of their culturesand languages.

We offer two important reminders. First, the magnitude of theeffects of co-ethnic community members’ education and English profi-ciency levels are very small. At its largest, a one percentage-point increasein the share of community members who speak English well associateswith a 0.012 standard deviation decrease in test scores. This is a modestchange and certainly not large enough to motivate public policy. Thesesmall effects, combined with the very high share of achievement variationwithin origin country groups, also suggest that co-ethnic communitymembers may play less of a role than the current theoretical literaturesuggests.

Second, and related, our models control for observed inputs to edu-cation and relevant factors that we cannot observe directly but that areproxied by prior year scores. However, it is still possible that the smalleffects of community characteristics that we observe here are biased byomitted variables, such as motivation and underlying ability that influencechildren’s achievement from one year to the next. The negative influencesof the English proficiency of the students’ co-ethnic communities, forinstance, may be due to the selection of such groups on these unobserved


traits. For example, people from English-speaking countries may be lesspositively selected than people from non-English-speaking countriesbecause there are fewer costs and barriers if they do not have to learn anew language. Whether these co-ethnic community contexts are trulyexogenous to test scores or determined by underlying motivation, thesenegative estimates of English exposure challenge some of the preconceivedviews of immigrants from certain parts of the world and call for furtherresearch. For instance, an effort to quantify the attitudes, motivations,and values of foreign-born from many different countries and communi-ties would help us better understand the drivers of origin countryvariation in their educational, labor market, political outcomes in theU.S.

APPENDIX

DESCRIPTIVE STATISTICS, NEW YORK CITY 4TH–8TH GRADE FOREIGN-BORN STUDENTS, 2000–01

Mean SD

Ethnic communitiesMedian household income $41,561 $8,508Percent college graduates 24.69 16.03Percent speak English well 66.26 20.39Number of origin adults per 1,000 all NYC adults 23.17 18.41

StudentsPoor 0.81 0.40Near-poor 0.08 0.28Asian and other 0.25 0.43Black 0.22 0.41Hispanic 0.40 0.49Female 0.50 0.50Age 11.80 1.55Other than English at home 0.72 0.45Took LAB 0.24 0.43LAB percentile 3.91 12.18ELL 0.21 0.41Part-time special education 0.04 0.20Prior year test score 0.07 1.07Years in NYC school system 4.06 2.384th grade 0.16 0.375th grade 0.18 0.386th grade 0.20 0.407th grade 0.23 0.42Number of students 50,728

Notes: Data on students are obtained from the New York City Department of Education. Data on ethnic commu-nities are obtained from the New York City extract of the 2000 Census. The students in the data hail from39 countries and the means and standard deviations on the ethnic community characteristics capture the dis-tribution in the characteristics of immigrant household heads from these 39 countries; Free and reduced-price lunch data (poor ⁄ near-poor) were multiply imputed for 5.9% of cases. Prior year test score data wasmultiply imputed for 19.2% of cases.


REFERENCES

Alba, R., and V. Nee2003 Remaking the American Mainstream: Assimilation and Contemporary Immigration.

Cambridge, MA and London, England: Harvard University Press.

Borjas, G. J.1987 ‘‘Self-Selection and the Earnings of Immigrants.’’ American Economic Review

77:531–553.

———1992 ‘‘Ethnic Capital and Intergenerational Mobility.’’ Quarterly Journal of Economics

107:123–150.

———1995 ‘‘Ethnicity, Neighborhoods, and Human-Capital Externalities.’’ The American Eco-

nomic Review 85:365–390.

Bratsberg, B., and J. F. Ragan Jr2002 ‘‘The Impact of Host-Country Schooling on Earnings – A Study of Male Immi-

grants in the United States.’’ Journal of Human Resources 37:63–105.

Caplan, N., J. K. Whitmore, and M. H. Choy1989 The Boat People and Achievement in America. Ann Arbor, MI: The University of

Michigan Press.

Chiquar, D., and G. H. Hanson2005 ‘‘International Migration, Self-Selection, and the Distribution of Wages: Evidence

from Mexico and the United States.’’ Journal of Political Economy 113:239–281.

Chiswick, B. R., and N. DebBurman2004 ‘‘Educational Attainment: Analysis by Immigrant Generation.’’ Economics of Educa-

tion Review 23:361–379.

Ellen, I. G., K. O’Regan, and D. Conger2009 ‘‘Immigration and Urban Schools: The Dynamics of Demographic Change in the

Nation’s Largest School District.’’ Education and Urban Society 41:295–316.

Feliciano, C.2001 ‘‘The Benefits of Biculturalism: Exposure to Immigrant Culture and Dropping out

of School Among Asian and Latino Youths.’’ Social Science Quarterly 82:865–879.

———2005 ‘‘Educational Selectivity in U.S. Immigration: How do Immigrants Compare to

Those Left Behind?’’ Demography 42:131–152.

Gibson, M. A.1988 Accommodation Without Assimilation: Sikh Immigrants in an American High School.

Ithaca, NY: Cornell University Press.

Glick, J. E., and M. J. White2003 ‘‘The Academic Trajectories of Immigrant Youths: Analysis Within and Across

Cohorts.’’ Demography 40:759–783.

Hao, L., and S.-L. Pong2008 ‘‘The Role of School in the Upward Mobility of Disadvantaged Immigrants’ Chil-

dren.’’ The Annals of the American Academy of Political and Social Science 620:62–89.


Hernandez, D. J., and E. Charney1998 From Generation to Generation: The Health and Well-Being of Children in Immigrant

Families. Washington, DC: National Academy Press.

Hirschman, C.2001 ‘‘The Educational Enrollment of Immigrant Youth: A Test of the Segmented-

Assimilation Hypothesis.’’ Demography 39:317–336.

Jasso, G., and M. R. Rosenzweig1990 The New Chosen People: Immigrants in the United States. New York: Russell Sage

Foundation.

Jensen, E. R., S. M. Gale, and P. E. Charpentier2008. ‘‘Are Mexican Migrants to the US Adversely Selected on Ability?’’ Presented at the

Association for Public Policy Analysis and Management Conference.

Kao, G.1999. ‘‘Psychological Well-Being and Educational Achievement Among Immigrant

Youth.’’ In Children of Immigrants: Health, Adjustment, and Public Assistance. Ed.D. J. Hernandez. Washington, DC: National Academy Press. Pp. 410–477.

———2004 ‘‘Parental Influence on the Educational Outcomes of Immigrant Youth.’’ Interna-

tional Migration Review 37:427–449.

———, and M. Tienda1995 ‘‘Optimism and Achievement: The Educational Performance of Immigrant Youth.’’

Social Science Quarterly 76:1–19.

Kasinitz, P., J. H. Mollenkopf, M. C. Waters, and J. Holdaway2004 Becoming New Yorkers: Ethnographies of the New Second Generation. New York, NY:

Russell Sage Foundation.

———, ———, ———, and ———2008 Inheriting the City: The Children of Immigrants Come of Age. Cambridge, MA: Har-

vard University Press.

Levels, M., J. Dronkers, and G. Kraaykamp2008 ‘‘Immigrant Children’s Educational Achievment in Western Countries: Origin,

Destination, and Community Effects on Mathematical Performance.’’ AmericanSociological Review 73:835–853.

McManus, W. S.1990 ‘‘Labor Market Effects of Language Enclaves: Hispanic Men in the United States.’’

The Journal of Human Resources 25:228–252.

Min, P. G.2001. ‘‘Koreans: An ‘‘Institutionally Complete Community.’’ In New Immigrants in New

York. Ed. N. Foner. New York, NY: Columbia University Press. Pp. 173–200.

Perez, L.2001. ‘‘Growing up in Cuban Miami: Immigration, the Enclave, and New Generations.’’

In Ethnicities: Children of Immigrants in America. Ed. R. Rumbaut, and A. Portes.Los Angeles, CA: University of California Press and Russell Sage Foundation.Pp. 90–126.

Perreira, K. M., K. Mullan Harris, and D. Lee2006 ‘‘Making it in America: High School Completion by Immigrant and Native

Youth.’’ Demography 43:511–536.


Pong, S.-L., and L. Hao2007 ‘‘Neighborhood and School Factors in the School Performance of Immigrants’ Chil-

dren.’’ The International Migration Review 41:206–341.

———, ———, and E. Gardner2005 ‘‘The Roles of Parenting Styles and Social Capital in the School Performance of

Immigrant Asian and Hispanic Adolescents.’’ Social Science Quarterly 86:928–950.

Portes, A., and D. MacLeod1996 ‘‘Educational Progress of Children of Immigrants: The Roles of Class, Ethnicity,

and School Context.’’ Sociology of Education 69:255–275.

———, and R. G. Rumbaut1996 Immigrant America: A Portrait. Los Angeles, CA: University of California Press.

———, and ———2001 Legacies: The Story of the Immigrant Second Generation. Los Angeles, CA: University

of California Press and Russell Sage Foundation.

Schwartz, A. E., and L. Stiefel2006 ‘‘Is There a Nativity Gap? Achievement of New York City Elementary and Middle

School Immigrant Students.’’ Education Finance and Policy 1:17–49.

Stiefel, L., A. E. Schwartz, and D. Conger2009 ‘‘Age of Entry and the High School Performance of Immigrant Youth.’’ Journal of

Urban Economics 67:303–314.

Suarez-Orozco, M. M.1989. Central American Refugees and U.S. High Schools: A Psychosocial Study of Motivation

and Achievement. Stanford, CA: Stanford University Press.

Tillman, K. H., G. Guo, and K. Mullan Harris2006 ‘‘Grade Retention Among Immigrant Children.’’ Social Science Research 35:129–

156.

Van Hook, J., S. L. Brown, and M. Ndigume Kwenda2004 ‘‘A Decomposition of Trends in Poverty Among Children of Immigrants.’’ Demog-

raphy 41:649–670.

Waters, M. C.1999 Black Identities: West Indian Immigrant Dreams and American Realities. New York,

NY: Russell Sage Foundation and Harvard University Press.

Zhou, M.2001. ‘‘Chinese: Divergent Destinies in Immigrant New York.’’ In New Immigrants in

New York. Ed. N. Foner. New York, NY: Columbia University Press. Pp. 141–172.

———, and C. L. Bankston III1998 Growing Up American: How Vietnamese Children Adapt to Life in the United States.

New York, NY: Russell Sage Foundation.


Documents

The Effect of Immigrant Communities on Foreign-Born Student Achievement