Off the Boat, Now Off toWork: Refugees in the Labour ... · the Labour Market in Portland, Maine ... This study of the labour market in Portland from 1996 to 2000 ﬁnds that amongst

Off the Boat, NowOff toWork: Refugees inthe LabourMarket in Portland,Maine

VA ISHAL I MAMGAIN

Department of Economics, University of SouthernMaine, Portland,ME

i n c o l l a b o r a t i o n w i t h

K AREN COLL INS

(formerly of) Catholic CharitiesMaine Refugee and Immigration Services, Portland,ME

In Maine, refugees with good English skills at arrival earn the highest wagesirrespective of educational attainment in their home country. This study of thelabour market in Portland from 1996 to 2000 finds that amongst refugee men, thebest predictor for higher wages is English proficiency, but for women education is

an important factor. In the local economy, refugee women are over-represented asentrepreneurs. Amongst men who are not self-employed, age at arrival and workexperience in the source country have no significant impact on wages. For both

men and women, belonging to an established ethnic group yields beneficial effectsin securing higher paid first jobs, but our qualitative research shows that it issocial connections with the white community that translate into longer run

success. Finally, for both men and women, it appears that the tightening labourmarket contributed to higher wages in the period studied.

Introduction

For the past 22 years, refugees from war torn regions have been resettled inPortland, Maine under the aegis of the Catholic Charities Maine Refugee andImmigration Services (CCMRIS hereafter). Every year about 250 individualsare resettled and this number is supplemented by secondary migrants.Secondary migrants are refugees who enter the US elsewhere but choose tomigrate within the US, either to be closer to their ethnic community or becausethey are attracted to the quality of life available. Estimates vary, ranging from100–150 per year (Rugaba 2002) to 200–250 per year (Ward, Director,CCMRIS 2003). It is agreed, however that the past couple of years has seen adramatic increase in secondary migration.The total increase in Portland (about 10,000 over the past 22 years) includes

refugees from Afghanistan, Bosnia, Burma (Myanmar), Burundi, Congo andSomalia (23 countries in all). This population has had a dramatic cultural and

Journal of Refugee Studies Vol. 16, No. 2 2003

&Oxford University Press 2003

economic impact on Portland (population: 65,000, 96.9 per cent white, Census2000). In a state that is the 38th poorest in the nation, this influx has alsotranslated into a larger than usual number of people vying for entry level jobs.This problem is particularly acute in the case of Lewiston, Maine

(population 36,690, 16,000 French-Canadian ancestry, 1,500 non-white,Census 2000). In the past two years, about 1,100 Somalis have migratedthere. (This number is an estimate by City officials, based on schoolenrolments and use of social services by Somali refugees.) This rapid rate ofmigration has proved a catalyst for race relations and refugee issues in thestate. The first Somalis to move to Lewiston were those who had beenresettled in Portland and could not find adequate housing. Lewiston, adeclining former mill city, had much lower rents; the median rent was $408compared to $598 in Portland (Census 2000). Since then, Somalis from otherparts of the United States have moved to Lewiston. This migration has beenviewed with concern by some residents of Lewiston who fear that theresources of their city are being diverted to the new residents. Theirapprehension has been exacerbated by misinformation. Rumours circulatingamongst the native population range from the prosaic, ‘Somalis moving toLewiston are given cars upon their arrival,’ to the exotic, ‘Somali youth areallowed to wash their feet in the water fountains at Lewiston High School’(Minkowski 2002).Matters came to a head when the mayor of Lewiston wrote a letter to the

Somali elders asking them to discourage relocation into the city. ‘The Somalicommunity must exercise discipline and reduce the stress on our limitedfinances and our generosity’ (Raymond 2002). This letter prompted concernamongst the Somali community who feared attacks by the native population.In their response to the mayor’s letter, the elders stressed that as Americansthey are free to move anywhere in the United States. Secondly, they stated,their presence has attracted hundreds of thousands of dollars in state andfederal funds to boost existing social services for all residents of Lewiston. Thedemand for housing by Somalis has rejuvenated a downtown that had manyabandoned housing units, raising the market value of real estate. Finally, theSomali elders said, more than 50 per cent of ‘able bodied men and women’ arecurrently employed, and three Somali businesses have opened in Lewiston inthe past year (text of letter from Somali elders, Portland Press Herald,8 October 2002).This issue has generated national attention and has been politicized further

by a white supremacist group, the Illinois-based World Church of the Creator,which held a rally in Lewiston on 11 January 2003. Although this rally wasattended by a mere 32 people, it is not clear how much local support whitesupremacists currently have or will garner. The rally and a pro-diversity rally(with 4,000 people in attendance), highlight the need to address the underlyingfears and insecurities of both the native and refugee populations. This paperaims to make a contribution here. Although the focus of research is Portland, ithas important ramifications for policies to facilitate refugee resettlement in

114 Vaishali Mamgain

Maine. The data for the study were collected in 2000; interviews wereconducted in 2001, 2002 and 2003.The paper presents a picture of how refugees reconstruct their work lives.

Occupational integration is key to overcoming poverty. The study asks whatvariables predict success for refugees in the labour market; how refugees enterthe labour market and how local businesses and institutions respond; if thehuman capital model applies, and whether there are positive returns toeducation; what policies can make it easier for refugees to find meaningfulwork? This is the central curiosity of this work.While previous studies (Duleep et al. 2001) have used Census data to study

human capital investment by refugees, the present work draws on locallygenerated data about refugees settled in the Greater Portland area to examinein detail the first forays by refugees in a local labour market. The studyincorporates both qualitative research and an econometrically estimatedhuman capital model.The interaction between refugees as employees and local businesses as

employers is presented in the qualitative aspect of this paper. For both parties,this interaction might involve roles never assumed before. Some refugees arefrom primarily rural, agricultural societies or from socialist economies wherethey were protected from the vagaries of the labour market, whereas localbusinesses, used to a largely homogenous, white population are not alwayscognizant of the cultural differences that refugees bring to the workplace. Bothrefugees and employers were asked how they negotiate these roles; refugeeswere asked how they learn about employment opportunities.Quantitatively, the study aimed to identify factors that yield higher wages.

Are there positive returns to education and skill, especially if these wereacquired in a foreign country? What are the effects of English proficiency,region of origin, length of stay in the US, and the business cycle?After a review of the US refugee resettlement programme, the data utilized in

this study are introduced and some demographic information is presented. Theliterature is reviewed and the model to be studied is contextualized throughinterviews with refugees, caseworkers, ESL teachers and local businesses. Thiscontext is used to frame the questions asked. The quantitative model to beestimated is then presented and some trends in the data are discussed. Theresults of the study are followed by the conclusion and questions that meritfurther research.

Details of the United States Refugee Programme

In the United States, refugee resettlement is a Federal Government programmeand the quota of refugees accepted in a given fiscal year is determined by thePresident. The numbers are allocated by refugee region of origin. For example,in 2002, the President set an intake goal of 70,000. Of this, 40 per cent were tobe refugees from Africa, 28 per cent from the former Soviet Union, 14 per centfrom the Near East or South Asia, 8 per cent from East Asia, 5 per cent from

Refugees in the Labour Market in Maine 115

Eastern Europe, and 5 per cent from Latin America and the Caribbean. Anadditional ‘20,000 unallocated reserve’ was established to be allocated ‘asneeded to regional ceilings where shortfalls develop’. In any given year, due toprocedural difficulties or the political climate, the actual number of applicantsapproved and admitted may differ from the quota set by the President. In thedecade from 1991 to 2001, the highest number admitted was 131,291 in thefiscal year 1992, and the lowest was 68,426 in 2001.After receiving a referral from the United Nations High Commissioner for

Refugees (UNHCR), or through a mandated non-governmental organization, arefugee applicant is interviewed by the Immigration and Naturalization Service(INS) to determine eligibility for refugee resettlement under US law. If approved,a refugee applicant and her immediate family are assigned to a resettlementprogramme. Resettlement programmes are operated by community based non-profit groups that are affiliated with one of ten agencies (nine voluntary and onestate agency) which have been contracted by the US Department of State toreceive and place refugees. Amongst these groups, the most prominent areLutheran Immigration and Refugee Services (LIRS), United States Conferenceof Catholic Bishops/Office of Migration and Refugee Services (USCCB/MRS),International Rescue Committee (IRC), Hebrew Immigrant Aid Society (HIAS),World Relief (WR), and Church World Services (CWS).Transportation to the US is arranged by the International Organization for

Migration and refugees are expected to repay all transportation costs overtime, beginning 18 months after arrival. Private resettlement agencies and theiraffiliates are permitted to restrict admission to their programmes based onfactors such as religion, ethnicity, and national origin. A local affiliate maychoose, for example, to resettle only Kurds from Iraq, or only Russian Jews, orMuslims from Kosovo. (Our subject agency, CCMRIS, however, does notreject refugees on the basis of religion, ethnicity, national origin etc.) Wheneverpossible, agencies seek to reunite refugees with existing family and friends inthe United States.Resettlement agency services include the provision of initial housing and

household needs, food, clothing, cultural and community orientation, referralto schools, social services and health agencies. Federal funding covers only asmall portion of resettlement agencies’ overheads and expenses to providereception and placement services for the first thirty days after a refugee’sarrival. Agencies are expected to utilize volunteers, donated material goods andcash donations to fulfil the programme’s expectations.Individual states are granted federal funding to continue the resettlement

process after the first 30 days. Our subject agency, CCMRIS, is contracted bythe state of Maine to provide continued resettlement services. But this is fairlyunusual as most states absorb the resettlement services into the social servicesagencies. Refugees arrive in the United States with full employment privilegesand resettlement agencies are expected to refer refugee clients for employmentas early as possible. It is the goal of the State Department that refugees findemployment within six months of their arrival.


A person is considered a refugee for the first twelve months following herarrival in the US. A refugee may change her status to legal permanent residentafter completing one year, and five years after acquiring permanent residency, arefugee may apply for citizenship.A distinction is made between refugees and asylees. The term asylee refers to

any foreign individual who arrives in the US legally or illegally, and whoapplies for, and is granted political asylum status by either the INS or animmigration judge. Asylum seekers are not permitted to work in the UnitedStates until they receive an employment authorization from the FederalGovernment. Current regulations require asylum seekers to wait for a period of150 days from the date their application for asylum is received, before they canapply for employment authorization. If asylum is granted, the person becomesan asylee and is free to live and work in the US with virtually the same status asa refugee.

Data

The data used in this study have been collected by CCMRIS for two differentpurposes. The first dataset (Arrivals) compiles information on all individualsresettled in Maine by the agency. This dataset includes information about afamily’s origin, the relationship to the primary applicant, dates of birth, race,ethnicity, primary and secondary languages spoken. It is also CCMRIS’mandate to help provide employment for this population. The second dataset(Employment) is a record of the jobs secured and wages and benefits offered byemployers. This dataset reflects the presence of secondary migrants as they tooare entitled to help by the agency’s placement programme. However, sincesecondary migrants entered the US elsewhere, there is no information for themin the Arrivals data.This work was originally conceived to study the period from 1993 to the

present. However, as work began, it became obvious that while the data forrecent years (1996–2000) were complete, the years 1993–1995 represented someproblems. Central to these was the fact that in the early years, caseworkers,who no doubt had more pressing concerns than preparing perfect data forsocial scientists, had, in the Arrivals data conflated entire families into oneobservation. Consequently, demographic information, about, say, a family offour, was reduced to one observation. Despite this setback, the data availabledoes allow us to present some information based on country of origin.In the years studied, refugees from 23 nations were relocated to Portland.

The countries of origin reflect the interaction of civil wars and US foreignpolicy (Stickney 2001). The perceived vagaries of international policiestowards refugees add yet another layer to this. In a bid to ensure safe passageout of a refugee camp, people may give authorities a misleading nationalidentity, under the impression that providing refuge to people of their ownnationality is not a high priority with international agencies. Thus, our datacould present as Rwandan, a person from Burundi who thought that


portraying herself as Rwandan would increase her probability of a safepassage. Although it is not a common occurrence, a few cases of this havebeen noted (Yankauskas 2001).The voluntary agencies (volag) associated with resettlement in the Portland

area are USCCB (United States Conference of Catholic Bishops), CWS(Church World Services), DFMS (now known as EMM: Episcopal MigrationMinistries), HIAS (Hebrew Immigrant Aid Society), LIRS (LutheranImmigration and Refugee Services) and WRRS (World Refugee ReliefServices). Of these, the bulk of resettlement has been facilitated by CCMRIS.


Table 1a

Refugee Resettlement in Maine by Voluntary Agencies (Volag) October 1982–December

2000*

Volag CCMRIS CWS DFMS HIAS LIRS WRRS OtherVolags

Total

Placements 4016 56 76 80 53 80 11 4372

Source: Rugaba, P., State of Maine, Dept. of Human Services, Refugee Resettlement Programme*These numbers reflect new arrivals resettled originally in Maine and do not include asylees orsecondary migrants.

Table 1b

Refugee Placements by Country of Origin October 1982–December 2000*

Country Placements

Afghanistan 370

Bosnia 335Cambodia 754Ethiopia 143

Croatia 124Iran 157Poland 388Romania 79

Somalia 315Sudan 291Ukraine 79

Soviet Union 190Vietnam 568Other 579

Total 4372

Source: Rugaba, P., State of Maine, Dept. of Human Services, Refugee Resettlement Programme*These numbers reflect new arrivals resettled originally in Maine and do not include asylees orsecondary migrants.

Secondary Migration

‘The Office of Refugee Resettlement defines the term secondary migrant as arefugee, asylee, Amerasian, or entrant who has resided in the US for 36 monthsor less as of the reporting data and has migrated into the State and currentlyresides there’ (Rugaba 2003). Until recently, reported data indicated thatsecondary migration of refugees was not a very significant factor in the UnitedStates (Annual Reports to Congress, Office of Refugee Resettlement [ORR]).According to Mr Rugaba (2002), the state refugee coordinator, prior to 2000there was an inflow of between 100 and 150 secondary migrants per year toMaine. This number rose to 492 in 2000 and 608 in 2001. These statistics reflectthe number of secondary migrants reported to the ORR but Rugaba adds thatthere ‘is no question that these figures are much lower than the actual numbersof people who entered the US as refugees and currently reside in Maine’(Rugaba 2003). This is confirmed by Matt Ward, director of CCMRIS (2003)and Cheryl Hamilton of Portland City Social Services (2003). According toWard (2003), Rugaba’s statistics are based on the use of social services bysecondary migrants, and these estimates may be subject to error. For instance,if a family with five children enrols only two of their children in the schoolsystem, the two children are counted whereas the others are not. Hamilton(2003) stressed that prior to three years ago, no agency in the state wasmeasuring in any substantive way the inflow of secondary migration. Sheestimates that for the past four years secondary migrants to Maine numberedabout a thousand per year—a figure that is four times the number of primaryrefugees resettled. Based on these estimates, Portland City Social Services hasjust been awarded a grant from the ORR to help provide for the unanticipatedarrivals in the past few years.Rugaba also points out that the term secondary migrant needs clarification.

In Maine there is a tendency to refer to anyone has moved to Maine after havingbeen resettled elsewhere as a secondary migrant regardless to [sic] how long theymay have been in the US. Some ‘secondary migrants’ have been in the US for

fifteen to twenty years and a number of them are US citizens, yet they are referredto as secondary migrants. Very often, other immigrants, asylum seekers andundocumented aliens are assimilated into this category (Rugaba 2003).

Notwithstanding the dramatic character of the Somali secondary migration,the data available does not allow us to identify the ethnicity, race, or country oforigin of other secondary migrants (Rugaba 2002).

Demographics

In our dataset, the majority are two-parent families. In 354 households, both ahusband and wife are present, whereas 256 households are headed by a singlemale or female. Of the latter 155 are male and 101 are female headedhouseholds. This somewhat surprising result is explained by the fact that 132 ofthese male headed households are single males living alone. Of the female


headed households, 55 are households which include at least one more memberbesides the female head of the household. For both types of households, thenorm is towards smaller families, although there are some families with twelvemembers. This information is particularly significant for policy makers. Detailsof family size are presented in Table 2.

Literature Review

There exists a substantial body of literature discussing the challengesconfronted by refugees both before flight and after arrival in the destinationcountry (Stein 1981). This review starts by highlighting differences betweenimmigrants and refugees with reference to labour market participation. Theeconomic integration of refugees has been studied using the human capitalmodel, and the role of prior education, transferable skills, retraining andproficiency in English (or French in Canada) are key to this analysis. However,the importance of these factors may be modulated by an individual’sparticipation in an ethnic enclave and the literature that discusses thisphenomenon is briefly reviewed.At the outset, it is important to distinguish between refugees and

immigrants. This distinction may have important ramifications regardingassimilation into the labour market. It is argued that in contrast to immigrantswho have a choice in the matter, refugees are forced to migrate. Cohon (1981)cites Kunz who describes refugees as being ‘pushed out of’ and immigrantsbeing ‘pulled away from’ their homeland—referring to different motivationsand choices. Cohon however notes that this distinction may be too rigid asvoluntary migration may not ‘be as voluntary as implied’ (1981: 256).Adhering to the more strict distinction between refugees and immigrants,

Chiswick (1979) says that whereas economic advancement is a primary motivein immigration, it plays only a secondary role in refugee movements. Since thedecision to migrate is viewed as being motivated predominantly by economicneeds it is assumed that as rational decision makers, immigrants may well haveinvested in country-transferable skills to maximize the return on theireducation. It is further postulated that refugees do not relocate voluntarily,


Table 2

Family Size 1996–2000

Family size 1 2 3 4 5 6 7 8 9 10 11 12

FrequencySingle 178 26 19 15 7 6 2 1 0 0 1 1

Male headed 132 11 4 4 1 1 2 0 0 0 0 0Female headed 46 15 15 11 6 5 0 1 0 0 1 1Couple headed 0 60 88 106 48 20 12 4 8 2 4 2

Source: CCMRIS dataset

therefore may not have the same levels of education (Chiswick 1979). ‘Refugeesare subject to different forms of self-selection than other migrants, and this mayaffect human capital characteristics of those migrating’ (Duleep et al. 2001:ch.14, p. 2).To test this, Duleep et al. (2001) have compared refugees from Indochina

(Vietnam, Laos, Cambodia) with immigrants from Thailand and the big fourdeveloping countries (India, Korea, China and Philippines). Analysing the 1 percent and 5 per cent Public Use Microdata samples from the 1980 and the 1990censuses, they find that ‘compared with working-age immigrant men fromother developing Asian countries, the refugee groups have a much higherpercentage with only a grade school education and a much lower percentagewith post secondary degrees’ (ch.14, p. 3) Their results show that in 1979, themedian earnings of the Indo-Chinese refugee cohort that entered the USbetween 1975 and 1980 was $10,144, a figure that was 38.7 per cent of nativeborn earnings. By comparison, the non-Southeast Asian groups (Chinese,Indian, Korean, and Filipino), who entered the US in the same period, earned$14,300 (54.6 per cent of native born earnings).Stein (1979) has shown that refugees experience severe downward occupa-

tional mobility in the destination country. Finnan (1981) identifies the socialand cognitive aspects associated with this downward occupational assimila-tion. The social aspect emphasizes the importance of support within the refugeecommunity and the cognitive process examines how, ‘as refugees begin toidentify with an occupation, they shape their self-images to complement thechosen occupational role’ (Finnan 1981: 292). Amongst Bosnian refugees in theWest, the psychological adaptation needed to ‘engage the labour market’ isgreater in the case of the more highly educated. For most of these individuals,‘the adjustment to lower social and economic status proved to be toopsychologically difficult to overcome’ (Bulic and Dongieux 1999: 105). In astudy of refugee claimants who entered Canada between 1994 and 1997,Renaud and Gingras (1998) show that although 78 per cent were employedprior to their arrival in Canada, yet on average it took them 32 months tosecure a job after arrival. Krahn et al. (2000) in a study of highly educatedrefugees who had been in professional or managerial positions prior to theirarrival to Canada, depict a similar pattern of downward occupational mobility.One of the reasons for the downward occupational mobility may be a lack of

transferable educational skills. This may be compounded by an inability towork due to physical or mental traumas suffered (Potocky 1996). For instance,in a ten year study of Southeast Asian refugees, Beiser and Hou (2001) see thatdepression is an important predictor of employability. Finally, Gold (1991) hasshown that discrimination may be yet another factor undermining a refugee’ssuccessful adaptation to the labour market.Once in the destination country, do immigrants and refugees recalibrate their

skills to suit the local labour market? According to the human capital modelpostulated in economics which takes as its basis, rational, self-interestmaximizing individuals, there exist incentives to retrain. Since initial earnings


of refugees are lower, their opportunity costs of going to school are lower.Besides, acquiring destination country skills may be complementary to sourcecountry skills and thus provide a disproportionately large return on theirinvestment. Birman and Trickett’s (2001) study of Soviet Jewish emigres showsthat retraining is a crucial part of economic adjustment and 62 per cent of thosewho retrained said that they found it helpful in finding work. English languageclasses were also considered extremely helpful and 91 per cent of refugeesattended these classes. (It must be remembered, however, that their subjectpopulation is a very well educated cohort, of whom 62 per cent possess collegedegrees.)In their study, Renaud and Gingras (1998) find that 75 per cent of the

respondents were enrolled either in either a full-time or part-time trainingprogramme. The course enrollment was 91.7 per cent in the 18–25 age-groupand 72.8 per cent in the 26–40 age-group.Duleep et al. (2001) also show high rates of school attendance amongst the

Indochinese population. For example 26.1 per cent of Vietnamese men fromthe 1975–1980 cohort were in school in 1980 but by 1990, this dropped to 10.5per cent. The only exception was the Hmong population, 24.5 per cent ofwhom were in school in 1980 and whose school participation stayed at 20.5 percent in 1990. As a comparison 19.1 per cent of immigrant men from China,India, Korea and Philippines were in school in 1980 and this number droppedto 6.3 per cent in 1990. (The continued school attendance may reflect thedifferences in eligibility for public assistance between refugee and non-refugeegroups.) In support of the human capital investment model, Duleep et al.(2001) find high earnings growth for the 1975–1980 cohort. Wages forIndochinese refugees increased from 32.3 per cent of the native-born wages in1979, to 77.9 per cent in 1989.

English Proficiency

An important factor that affects assimilation into the economic mainstream isproficiency in the language of the destination country (Chiswick 1991; Tainer1988). In a study of Hispanic men in the United States, McManus et al. (1983)show that English proficiency is an important determinant of earnings. Birmanand Trickett (2001) also find that proficiency in English significantly influencesjob status. An important distinction is made by Portes and Bach (1980) whoshow that it is English proficiency upon arrival, and not English learnedsubsequently in the United States, that is a more significant determinant oflabour market participation.

Ethnic Networks

The literature examined so far highlights the importance of individualhuman capital as a determinant of an individual’s earnings. However,economic behaviour may be driven not just to maximize individual profit but


may be ‘embedded’ in social relations (Granovetter 1985). These socialrelations and interpersonal relationships may contribute to a pool of socialcapital that supplements an individual’s human capital. This social capital,when generated in the context of an ethnic enclave, may lead to greatersocio-economic mobility than may be possible for an individual with similarhuman capital characteristics outside an ethnic enclave (Portes and Bach1985).Can ethnic enclaves supplement human capital and present an alternative

career trajectory for immigrants and refugees? Zhou (1992), in the context ofChinatown in New York City, has shown that the transaction costs associatedwith finding a good job are minimized in an ethnic enclave. Immigrants canalso use family and clan resources to tap into sources of credit and learn aboutbusiness opportunities. Immigrants who start their life in the new country asemployees, may, given access to credit and family support, venture intoentrepreneurship. Importantly, there is a symbiotic relationship between ethnicentrepreneurs and enclave workers. The latter may be willing to work for lowerwages than they could get in the larger economy, but in return, theentrepreneurs serve as mentors to train workers and help their transition toself-employment (Portes and Bach 1985).Using census data, Zhou finds that for male enclave workers, college

education affects earnings positively. The rate of return on an education insidethe enclave, however, may not be consistently greater than the return earnedoutside the enclave. Her findings contradict the idea that within Chinatown,one does not need much education to move ahead. Interviews with enclaveworkers stress the attractiveness of self-employment in the enclave. Educa-tional achievement and English proficiency are prized, the latter more so in theenclave than outside it.Wilson and Portes’ (1980) study of the Cuban enclave in Miami also shows

that within the enclave, there is a higher return on past human-capitalinvestment, than in the open secondary labour market. But McManus (1990)demonstrates that the returns to English proficiency are lower in ethnicenclaves, but not uniformly lower. They depend on the skill distribution withinthe ethnic enclave.However, to view the choice to live and work in an ethnic enclave as a purely

economic one may be too simplistic. Logan et al. (2002) find that, in somecases, living in an ethnic neighbourhood may be unrelated to economicnecessity. Instead, it may reflect a preference to stay with one’s community,even when spatial assimilation with the white majority is otherwise possible.An ethnic network may prove to be an ongoing source of social andoccupational networking even after refugees have been in the US for asignificant number of years. For instance, Birman and Trickett (2001) find that27 per cent of Soviet Jewish refugees surveyed in the Baltimore andMontgomery county area said they found their first job through Russianfriends. What is quite significant in this regard is that even to acquire their sixthjob, 22 per cent said they relied on friends.


The Model in Context

This section aims to accomplish two related goals. Similar, though notidentical, questions are asked of refugees, caseworkers, English as a secondlanguage (ESL) teachers, human resource managers and the data. Thissymbiotic view presents a picture more complete and at the same time richer inpossibilities—both from a researcher’s and a policy practitioner’s point ofview. To address the qualitative issues, interviews were conducted withrefugees, the job placement director at CCMRIS, ESL teachers, the director ofESL programmes, and Portland school officials. The human resource managerat Barber Foods, the largest employer of refugees in the Greater Portland area,was also interviewed.

Expectations

How do refugees adapt to the American workplace? Expectations are the firstthing both employers and potential employees bring to the table. Is there anyconsonance between what employees expect and employers offer; how aredifferences negotiated by the two groups?

Network Effect or Ethnic Enclave?

Does having a larger community translate into greater possibilities fornetworking and thus better jobs?

Retraining

Do refugees with poorly transferable skills go to school to retrain? Differencesbetween refugees and immigrants may manifest here. Whereas immigrants,who make a conscious choice to migrate, may acquire skills (in their nativecountries) which help smooth their transition to the US labour market,refugees are victims of largely unforeseen events—circumstances that preventrational planning with regard to the acquisition of transferable skills.Moreover, refugees start their lives in debt to the Federal Government. ‘ASomali family of ten who leaves Nairobi, Kenya and arrives in Boston isalready $20,000 in debt to the government’ (Ward 2000). Unfortunately noquantitative data exists about what percentage of refugees retrain. However,interviews with refugees, caseworkers, the director of ESL programmes atPortland Adult Education, ESL teachers and human resource managersprovided some insights into how refugees recalibrate their skills.

Quantitative Model

This model seeks to determine factors that influence wages for refugees. It isassumed that the human capital model applies, but with certain caveats. Forthe native and immigrant population the link between education, experience


and earnings has been well documented (see Borjas 1992; Chiswick 1979). It isnot altogether clear that a similar relationship would hold for refugees.Experience acquired in another country is not expected to be as important indetermining wages as experience garnered in the local economy (Schaafsmaand Sweetman 2001). Finally, a dramatic influence on wages is expected for thefollowing factors: fluency in English, the length of time an individual has beenin the US, and whether the economy was in a recession or an expansion.We estimate a log linear model, and estimate the effects of these variables on

the natural log of wages. The first specificationmay be written in general form as:

ln(wages) ¼ f(education, experience/skills, hiredate, length of time in the US)

lnðWijÞ ¼ b0Xij þ Uij ð1Þ

whereWij is the wage rate per hour for individual i of sex j, ln(Wij) is the naturallog of wages, b0 represents the coefficients to be estimated and Uij is a randomdisturbance.In the first specification, the variables included in Xij are education, previous

employment, length of time in the US, and the date of hire.

Model 1

EDUCATION: This is introduced as a dummy variable in the model, todistinguish between the returns to a college, trade school, or a high schooleducation. The variable HIGHSCHOOL assumes the value one if the person hasa high school diploma or some college experience, zero otherwise; COLLEGEassumes the value one if the person has a college or post graduate degree, and iszero otherwise. Similarly, TECHSCHOOL assumes the value one if the personwent to a technical or trade school, zero otherwise. Included in the data are alsoindividuals with either primary or secondary education: the dummy SECOND-ARY assumes a value one for individuals with a secondary education and zerootherwise; secondary education is a broad category which includes eighththrough twelfth grades. The control group (PRIMARY) describes a broad rangeof educational exposure, from one to five years of schooling.WORK EXPERIENCE: Since the individuals in the study have little job

experience in the United States, information about their previous employmentis incorporated. To introduce WORK EXPERIENCE in this model, differentjobs are defined and rationalized. The rationalization is dictated by themultiplicity of job titles and job descriptions offered as information to thecaseworkers. In many cases, the same job might have a different title indifferent countries and this has been harmonized. However, in a number ofcases, there are not enough individuals in any given profession to incorporatethat profession alone as a category in a dummy variable analysis. Conse-quently, certain categories have been consolidated. For instance, there is onlyone economist in the dataset; so as a category, he is included with doctors,


engineers and professors. The codes are presented in Appendix II. This variableis introduced as a set of dummy variables too.LENGTH OF TIME IN THE US: The length of time an individual has been

in the United States may significantly influence wages in both direct andindirect ways. Birman and Trickett (2001) in their study of Soviet Jewishrefugees find that length of time improves refugees’ status and their satisfactionwith their job. It is possible that a longer time period may allow for a longeracculturation period during which an individual may find higher paying workwhich may be more suited to her skills. She may also use her time in the UnitedStates to educate herself which might allow her to get a better job. Theeducation and English proficiency variables considered in this model are thoseestimated upon arrival to the United States. Hence the variable, LENGTH OFTIME IN THE US, may capture the effects of education and English skillsacquired since coming to the US.HIREDATE: Finally, as a measure of the macroeconomic outlook, the

variable HIREDATE is included. An individual may reap the benefits of atight labour market in the form of higher wages. All else constant, anindividual is expected to get a higher return on her education and experience ina business expansion. The date of hire is used as a proxy to capture this effect.In the context of this data (1996–2000), it is an approximation of the state ofthe economy which went from strength to strength.

Model 2

A second specification of the model includes PROFICIENCY IN ENGLISH.This is a measure of the English skills upon arrival in the US, as ascertained bya caseworker. This variable is also introduced as a set of dummies:POORENGL which takes the value one if the person has poor English skills,zero otherwise; LITTENGL takes the value one if the person has a few Englishskills, zero otherwise. Similarly, SOMENGL is defined as assuming the valueone if the person has some English skills, zero otherwise; FAIRENGL is one ifthe person has fair skills, zero otherwise; GOODENGL is one if the person hasgood English skills, and zero otherwise; lastly if the person has excellentEnglish skills, the dummy EXCENGL assumes the value one, zero otherwise.NOENGL is the control group which includes people who have no Englishlanguage skills at all. The reason for using these categories, which might seemarbitrary, is that caseworkers at CCMRIS do so. Conflating some categories(say POORENGL and LITTENGL into a single category) could have theperverse effect of imposing the authors’ bias, and thus compounding the errorsinherent in the measurement of such a qualitative variable.

Model 3

A third specification includes the effects of REGION OF ORIGIN. Casemanagers have mentioned that employers might discriminate in favour of


employees from particular countries or regions. For instance, employersperceive that refugees from ex-Soviet bloc countries have a higher level ofeducation. There might also exist a tendency to hire (white) Europeans sinceMaine is a predominantly white state. In the sample studied, refugees are fromAfrica, Asia and Eastern Europe. To see whether nationality influences wagessignificantly, a dummy variable AFRICA assumes a value one if the person isfrom Africa, zero otherwise; the dummy variable EUROPE takes the value oneif the person is from Europe, and is zero otherwise. The control group in thiscase is ASIA. While there are important differences amongst people from acontinent, the dataset does not have enough individuals from particularcountries or ethnic groups to allow each nationality or ethnic group to bemodelled as a separate dummy variable.In equation (1) Uij reflected a stochastic disturbance which was said to be

randomly distributed. However, the sample studied is not randomly selectedbut is subject to self selectivity. Only those individuals are observed who chooseto participate in the labour market and thus have nonzero wages. This can leadto a selection bias and to correct this, Heckman’s (1979) two step procedure isused. A binary probit model is first estimated, where the dependent variableis one if the person has a nonzero wage and is included in the wage sample, andis zero if the person is not included in the sample. (See Appendix I for details.)On the basis of this estimation, the inverse of the Mills ratio is computed—INVMILLSij for individual i of sex j. The inclusion of the inverse of the Millsratio in the regression model corrects for the sample selectivity bias and thecoefficients thus obtained are consistent estimates. The model to be estimated ispresented as equation (2)

ln Wij ¼ b0Xij þ �ij þ INVMILLSij þ Uij ð2Þ

Before presenting some trends, certain reservations about the data must beexpressed. As regards the education data, it is not clear that one can compare ahigh school education in Bosnia with one in the Congo. This difficulty mirrors,of course, the problems inherent in measuring the quality of one school versusanother, even within a metropolitan area of the United States. It is alsopossible that not all refugees in this study are ‘genuine’ refugees, in that theywere involuntarily displaced. If some refugees were economically motivated tocome to the United States, then as argued in the literature, they may have skillsmore suited to the US labour market than other, genuine refugees. This mayintroduce another bias in the data.

Wages, English Proficiency, Race and Gender: Some Interactions

It is instructive to look at the trends in education, English proficiency andwages. Some refugees have obtained more than one job through the placementoffice of CCMRIS. Since length of stay in the United States may influencewages, the information has been separated based on whether it was a person’s


first, second, third or fourth job. It is also possible that in the interim anindividual may have secured a job by herself, so what appears as her second jobmay actually be her third, but there is no way of controlling for thisinformation. Overall, it appears that refugee wages replicated the general trendin the Greater Portland area. Wages increased from 1996 through 2000 and the


Table 3

First Employment (1996–2000) Broken Down along Gender Lines

Year 1996 1997 1998 1999 2000

First HiresAverage wage/hour: Men $5.86 $6.18 $6.75 $7.70 $8.23Number of men employed 26 40 52 90 53

Number of men employed full time 13 25 42 74 49Average wage/hour: Women $7.00 $5.59 $6.79 $7.00 $8.02Number of women employed 3 23 37 68 27Number of women employed full time 3 7 23 50 21

Source: CCMRIS data

Table 4

Subsequent Employment (1996–2000) Broken Down along Gender Lines

Year 1996 1997 1998 1999 2000

Second Hires

Average wage/hour: Men $6.12 $5.81 $7.07 $7.77 $8.27Number of men employed 13 14 22 50 12Number of men employed fulltime 7 9 19 48 12

Average wage/hour: Women n.a. $5.71 $6.52 $7.67 $9.05Number of women employed 6 11 29 7Number of women employed fulltime 4 6 20 6

Third Hires

Wage/hour: Men $7.00 $6.17 $6.74 $7.94 $10.50Number of men 3 5 5 14 1Number of men employed fulltime 1 3 4 12 1

Wage/hour: Women n.a. $5.50 $6.25 $8.67 $7.25Number of women employed n.a. 1 2 6 1Number of women employed full time 1 2 3 0

Fourth HiresWage/hour: Men $6.05 $7.50 $8.69 n.a.Number of men 3 2 8 n.a.Number of men employed fulltime 3 2 7

Wage/hour: Women n.a. n.a. n.a. $8.09 n.a.Number of women employed 2 n.a.Number of women employed full time 2

Source: CCMRIS data

wage gap between men and women narrowed in this period. Relative to men,women are over-represented in part-time work (Tables 3 and 4).Table 5 presents English proficiency levels of the refugees in the study. A

much higher percentage (65 per cent) of women in the sample have no Englishskills compared to men (41 per cent). This is not a surprising result, since inmost countries men have greater opportunities to learn second or thirdlanguages (one of which might be English). This may be an outcome of theirpaid work outside the household. In some societies, it may reflect the highersocial status of men, which allowed them greater access to education. Whilealmost 30 per cent of men have fair to excellent English skills, only 15 per centof the women fall in this category. The source of this information iscaseworkers’ observations upon refugees’ arrival.Table 6 presents the level of educational attainment for those individuals

who were 18 or older upon arrival in the United States. Once again, a greaterpercentage of men (19 per cent) have a college education or better when


Table 5

English Speaking Skills as Assessed at First Contact

No

English

Poor

English

Little

English

Some

English

Fair Good Excellent Total

Male 128 14 25 53 12 78 3 313as percentage

of total

40.89% 4.47% 7.99% 16.93% 3.83% 24.92% 0.96%

Female 153 9 14 25 8 27 0 236as percentageof total

64.83% 3.81% 5.93% 10.59% 3.39% 11.44% 0.00%

281 13 39 78 20 105 3 549

Source: CCMRIS data

Table 6

Educational Attainment by Continent at Time of Arrival in the United States

Men(Africa)

Women(Africa)

Men(Europe)

Women(Europe)

College 25 5 22 16

Technical Training 6 3 25 16High School 19 10 97 65Secondary 21 18 63 58

Primary 21 16 25 25Total (for whom educational data exists) 92 52 171 142Total in wage sample 159 127 204 178

Source: CCMRIS data

compared to the women (13 per cent). However, this percentage is dramaticallydifferent when one separates refugees by continent of origin. Amongst refugeesfrom Europe, 12 per cent have a college degree. Of this percentage, 60 per centare men and about 40 per cent are women. Amongst Africans, however, of the30 refugees with college degrees, only five are women. Overall, 20 per cent ofAfrican men have college degrees, but this figure is an overestimate of the truenumber since we do not have educational information for all refugees. In thesample, although there are wage data on 286 African individuals, educationalinformation exists only for 144 of these individuals. By contrast, of the 382Europeans in the wage data, there is educational information for 313. In theeconometric model estimated, observations with missing educational data havebeen excluded.The differences in the educational systems of these two regions, particularly

with regard to technical training, are noteworthy. About 15 per cent ofEuropean men and 12 per cent of European women have technical training ofsome sort—ranging from photography and welding to computer repairs.Amongst Africans, less than six per cent have any technical training and thisnumber is an overestimate, given the large proportion of Africans for whomthere is no educational information at all.

Some Results: Qualitative and Quantitative

A Question of Expectations

To prepare the narrative for this work, Marc Larrivee, the job placementdirector at CCMRIS, was asked what expectations his clients had for their firstjob. ‘Most folks,’ he said, ‘are very realistic about their job prospects.’ If theyhave poor English skills, they are especially cognizant of their limitedopportunities. Larrivee confirmed what a preliminary look at the datasuggested: English language skills are a very important determinant forsuccess in the US labour market. Although he stressed how realistic most of hisclients are, Larrivee did point out that several assumed, perhaps fromAmerican movies, that opportunities were unlimited and that employmentwould be for a lifetime. ‘I don’t think they realize how brutal this labourmarket can be’ (Larrivee 2001). Recently a local textile mill closed and leftseveral of his clients with their last checks non-cashable. They were stunned,incredulous that this could happen to them in the United States.When this question was posed to refugees, their answers reflected the overall

uncertainty they felt upon arriving in the US. Some agreed with Larrivee’sresponse. They knew that their lack of proficiency in English would be astumbling block. And yet, most say they were not prepared for howchallenging it would be to find satisfying work. Although most said that it isnot difficult to find work, most jobs are assembly line production jobs, often inchicken, meat or seafood factories. The physical pain involved in standingin refrigerator like conditions, hour after hour, was mentioned often in


interviews. Some said they knew their educational attainment (acquired in theirhome country) would not be worth very much in the US, but they had hoped tosecure a job in their field of expertise. A couple of young men who arrivedwithout families, and were placed in high school when they first arrived, said theyhad expected that they would go to school and be able to study. But they foundthat to live, they needed to work after school hours—typically in restaurantswashing dishes. These jobs finished after midnight and left them tired andunprepared for school the following day. Many refugees reported working attwo or more jobs to save money to help their extended family back home.Cultural disconnectedness between the employees and employer can also

lead to miscommunication which further exaggerates this sense of living inparallel realms. For instance, some local employers interviewing refugees aspotential employees asked them what they thought their greatest weaknesseswere—a question which the refugees declined to answer as they thought it wasdesigned to trick them. Another employer asked potential employees todescribe themselves. The responses reflected values in the ‘old country’.Amongst Africans and Asians, the most common responses were ‘I am a goodson’, and ‘I am a good mother’. Amongst Bosnians and Russians ‘I am highlyeducated’ was the usual response. Finnan (1981) has discussed the developmentof new occupational identities by Vietnamese refugees as a response todownward occupational mobility. In Portland, some refugee responses indicatea situation where they continue to feel dislocated and prefer to identify withtheir past situation.Another aspect of cultural disconnectedness relates to punctuality and

paperwork. Refugees from Eastern Europe and the former Soviet Union aremore accustomed to the routine of work in an industrialized economy.Refugees from Africa, on the other hand, report that in their first jobs theywere sometimes unpunctual, and when family emergencies arose, they missedwork without informing their supervisors. Cultural and religious differenceswere particularly prominent in the case of Somali refugees, some of whomreported friction with their employers regarding prayers. Supervisors objectedto Somalis praying during times that were not designated breaks, while theSomali employees felt that their religion was not being respected. Theydescribed instances where they were praying and the supervisor would standnext to them talking at them. In some cases, Somalis lost their jobs because inaccordance with Islam, they refused to pick up beer cans and bottles whencleaning. However, one elder did notice a change in this environment. He citesthe growing number of employers who contact community elders to ask for atimetable of prayers to be able to schedule these times for their employees.

Network Effects or Ethnic Enclaves?

A simple answer to this question would suggest that it is easier to get a job ifone has a large community in the area. For instance, Peter Bickford (HumanResource Manager, Barber Foods), in an interview with the authors, states that


Barber Foods strongly encourages people to get relatives to work with them.Bickford states that this arrangement works for the company because peopleonly get friends and relatives they can be proud of, thus eliminating theuncertainty surrounding new hires. Grieco (1987) finds that employers mayconsciously recruit within families to intensify kin linkages. These linkagesallow employers to exercise greater social control over their workforce. In thecase of refugees in Portland, this ethnic effect is reinforced by the expense andpaucity of interpreters. The existence of a large Vietnamese community atBarber Foods, for instance, means that a new arrival does not struggle to makeherself understood and Barber Foods is spared the expense of an interpreter.Both Larrivee (2001) and Chiswick (1991) emphasize that this initial comfort atbeing understood might lead to some hesitation to move on to a higher payingjob. ‘A language-minority enclave may, however, limit training opportunitiesand job mobility, whether it is geographic, occupational or employer mobility,and thereby limit earnings opportunities’ (Chiswick 1991: 150).Refugees often report finding their first job through family and tribal

networks. As they become more familiar with the environment, some refugeesreport looking to friends (who may be from a different clan or region) for helpand a few said that the best jobs they have found have been through classifiedadvertisements in the newspapers. These results are partially in keeping withthe ‘strength of weak ties’ argument made by Granovetter (1973). Theparticular case presented here is not one of an ethnic enclave but one of anethnic network in which although strong ties may help one acquire a job, betteropportunities may present themselves through weaker ties.Notwithstanding this tendency toward ethnic segregation in jobs, does

belonging to a large community constitute any real economic progress for anindividual? Not necessarily, according to Jann Yankauskas, director of StartSmart, a business development programme of Coastal Enterprise Inc., anorganization that provides technical assistance and funds to refugee entrepre-neurs. In her experience, when an individual believes that she has some supportin the larger community, not merely in her own ethnic group, she is more likelyto make choices that may translate into economic gains. For instance, theSudanese population in Portland, most of whom are Christians, have strongsupport networks through their (mostly white) churches. They are regarded ashaving much more of a presence and a voice than the Vietnamese, whosenumbers are greater but who do not have many social connections with whites.

Retraining

Is there a significant proportion of refugees who recalibrate their skills to suitthe local labour market? At present it seems there is no dataset that could beused to address this issue. However, if one considers the importance of Englishlanguage skills, enrolment in an ESL class might be a first step towardsretraining. ESL classes (for adults) are offered at several schools, includingwork sites such as Barber Foods and Jordan Meats. Most refugees enrol in


these classes very soon after arrival, but tend to leave when they secureemployment. Only the most motivated stay to finish the course and perhapstake the next level. Some people do come back to take a class when theyperceive that their lack of English is an impediment at work. (Interviews withValerie de Vuyst, Linda Evans, Paul Manoff, Nancy Martz, and AndreaPacillo, ESL teachers 2001.)When asked this question, refugees stated that they were very keen to

improve their English speaking ability. Almost everyone had enrolled in anESL class upon arrival, with varying degrees of success. Bosnian refugees whohad received instruction in English grammar in secondary schools in Bosnia,felt that their language skills improved dramatically. But they acknowledgedthat this was related to their prior knowledge of English. They had not had achance to use the language before arrival in the US; once they were immersedin an English speaking environment, their fluency in English improved.Progress in English proficiency was, in general, an area where refugees

seemed frustrated. Some felt that they were not making satisfactory progress.One refugee mentioned that he felt that he was stuck in his ability to learncorrect syntax. He felt that he needed a teacher who was bilingual—conversantin English, and in the refugee’s language. This would allow him to explain hisdifficulties, and continue learning. Recently, the Center for WorkplaceLearning at the University of Southern Maine has taken steps towards hiringbilingual teachers.Unfortunately, refugees with the least proficiency in English were the least

likely to attend ESL classes. Financial reasons force most refugees to gainemployment as quickly as possible. Those who lack proficiency in English getthe most demanding, production line jobs—work they can do without Englishlanguage skills. The physically challenging work leaves most too exhausted toattend ESL classes, no matter what time they may be offered.The authors’ discussion with Peter Bickford gives further information on the

retraining issue. Barber Foods offers a $2,000 tuition credit to their employeestowards any courses they take at local universities. Since 44 per cent of BarberFoods’ employees are refugees, the question was whether they were over-represented in the population that took advantage of this offer. His recordsshow a balanced participation in this benefit.

Some Quantitative Results: Educational Attainment, English Proficiency andWages

The results of the two step estimation procedure are presented in Table 7a formen and Table 8a for women. To allow for comparison, three models areestimated: the first includes as determinants of hourly wage, the level ofeducational attainment, previous experience/skills, length of time in the US,and the date of hire. Model 2 incorporates English proficiency and Model 3tests to see whether being African, Asian or European significantly affects one’swage.


Results for Men

Education Effects

Relative to a primary school education, having a college degree does increaseone’s wage but neither a high school nor technical training yields significantlydifferent returns than a primary education. Having a secondary schooleducation, on the other hand, is negatively significant, a surprising result whichdoes not accord with our prior assumption that education positively affectswages. This is especially because the categories SECONDARY and PRI-MARY are defined very broadly, so that it is not clear that a fifth gradeeducation is significantly different from a seventh grade one.

English Proficiency

When English proficiency is included in the model (Model 2), the premium on acollege education decreases and education (all categories) is no longer significantin explaining difference in wages. Relative to someone with no English skills,having any English proficiency at all significantly increases one’s wage. This isnot a surprising result. Caseworkers and employers emphasize the importance ofEnglish language skills (Larrivee 2001; Bickford 2001). How does Englishproficiency interact with higher education? ESL teachers (de Vuyst 2001;McFarland 2001) say that someone with a college degree might possess goodstudy skills that improve their speed of acquiring proficiency in English.However, they emphasize that higher education is clearly not the onlyprecondition. Anecdotes abound in the local media about physicians andeconomists who are unable to recalibrate their skills and work at entry-level jobs.

Hiredate

The date an individual was hired is very significant in explaining wages. This istrue for all three models. As discussed earlier, this is a reflection of thetightening labour market from 1996–2000.

Previous Employment

Dummy variables were used to see whether, after controlling for education,one set of skills transferred better relative to another. No category of previousemployment is significant in explaining wages. The results of that model are notpresented here but details are available from the authors upon request.

Continent

Compared to an Asian with the same level of education and Englishproficiency, Europeans and Africans receive significantly lower wages. (Thisis true at the 10 per cent level of significance.) Since Asians (Vietnamese,Cambodians, Afghans) have been in the community for a longer period of


Refu

gees

intheLabourMarket

inMaine

135

Table 7a

Results for Men

Independent VariableParameterEstimate

Model 1

t-statisticH0: b¼0 Prob4t

ParameterEstimate

Model 2


ParameterEstimate

Model 3


Intercept �6.9972 �10.85 0.000 �7.4800 �10.58 0.000 �7.7060 �10.80 0.000

COLLEGE 0.0799 2.82* 0.005 0.0531 1.20 0.232 0.0408 0.91 0.363HIGHSCHOOL �0.0286 �1.24 0.217 �0.0592 �1.46 0.148 �0.0759 �1.83** 0.070TECHSCHOOL �0.1454 �0.48 0.635 0.0186 0.45 0.654 0.0167 0.39 0.698

SECONDARY �0.0597 �2.39* 0.017 �0.0479 �1.14 0.257 �0.0622 �1.46 0.147POORENGLISH 0.1717 3.38* 0.001 0.1847 3.54* 0.001LITTLENGLISH 0.0949 2.92* 0.004 0.1164 3.42* 0.001SOMENGLISH 0.1033 3.65* 0.000 0.1160 3.43* 0.001

FAIRENGLISH 0.0696 1.61 0.111 0.0787 1.81** 0.073GOODENGL 0.0920 3.42* 0.001 0.1064 3.30* 0.001EXCENGL 0.5228 7.21* 0.000 0.5475 7.22** 0.000

EUROPE �0.0907 �1.94** 0.054AFRICA �0.0869 �1.80** 0.075HIREDATE 0.0002 14.16* 0.000 13.70* 0.000 0.0003 13.86* 0.000

INVMILLS �0.0400 �2.19 0.029 �1.55 0.123 �0.0283 �1.15 0.251

Adjusted R-Sq 0.45 0.71 0.71No. of observations 321 144 144

* significant at the 5% level of significance.** significant at the 10% level of significance.

time, their access to jobs through ethnic connections may be greater. A caveatis in order; we have examined data which refers to the first few times a person ishired by a local business. This does not necessarily translate into a higherpaying job over time. It might well be that a Cambodian may get a higherpaying job than a Rwandan with a similar level of education and Englishproficiency because at a local business there exists a pool of English speakingCambodians who can translate for the person concerned. However, Larrivee(2001) emphasizes that this ethnic network can sometimes be a deterrent tofuture professional progress as some employees may never acquire proficiencyin English, or they may choose a lower paying job because they do not want toleave the comfort of working with their ethnic group.

Length of Time in the United States

This variable may be a proxy for the effects of retraining and the acquisition ofbetter language skills. However, it is insignificant in explaining variation inwages in the model.


Table 7b

The Importance of Repeated Interactions in the Local Labour Market

(Results for Men)

Model 2 Model 3IndependentVariable

ParameterEstimate


ParameterEstimate


Intercept �7.9950 �9.16 0.000 �8.224 �9.44 0.000COLLEGE 0.1039 1.60 0.114 0.0878 1.37 0.174HIGHSCHOOL �0.0617 �1.04 0.300 �0.0869 �1.49 0.141TECHSCHOOL 0.0112 0.17 0.866 0.0183 0.27 0.787

SECONDARY �0.0523 �0.81 0.418 �0.0988 �1.52 0.133POORENGLISH 0.2687 3.40* 0.001 0.3036 3.86* 0.000LITTLENGLISH 0.1127 2.87* 0.005 0.1617 3.78* 0.000

SOMENGLISH 0.1269 3.64* 0.000 0.1627 3.49* 0.001FAIRENGLISH 0.0811 1.54 0.127 0.1007 1.95** 0.055GOODENGL 0.1057 3.09* 0.003 0.1283 3.09* 0.003

EXCENGL 0.4886 6.50* 0.000 0.5249 6.40* 0.000EUROPE �0.1441 �2.57* 0.012AFRICA �0.1198 �2.02* 0.046

HIREDATE 0.0003 11.58* 0.000 0.002 11.87* 0.000INVMILLS �0.0698 �2.17 0.033 �0.0450 �1.35 0.181NO. OF PRIOR JOBS 0.0323 2.87* 0.005 0.0269 2.35* 0.021

Adjusted R-Sq 0.75 0.77

No of observations 92 92

* significant at the 5% level of significance.** significant at the 10% level of significance.

Number of Prior Jobs Held in the Local Labour Market

Another way to examine this variable is to look at whether one’s wage isinfluenced by the NUMBER OF PRIOR JOBS held in the Portland labourmarket. To do this, a dataset was generated which included only those menwho had held multiple jobs. For that population, Models 1, 2 and 3 were re-estimated. The results are presented in Table 7b. The number of prior jobs inthe local labour market indeed proves to be quite significant in predictingwages. There may be a couple of explanations for this. First, most employers inthe area offer refugees a temporary, short term (one to three months) contract.Upon successful completion, they are offered a higher wage and sometimesbetter benefits. Second, repeated interaction in the labour market may allowrefugees to find work that is better suited to their skills and is better paying.Both these factors would contribute to higher wages in successive jobs. Thusalthough length of time spent in the US is not significant per se (especially ifone was unemployed for a major part of that time), repeated forays into thelabour market are significant in explaining variation in wages. This is animportant result.Model 1 has a poorer fit than Models 2 and 3 and only these two models are

presented here. The results of Model 1 are available from the author uponrequest.

Results for Women

For women, Model 1 is the best specification. A college education and the dateof hire are both significant in the model. When the variable English proficiencyis introduced, the number of women for whom there is data drops to 33, andthe model has no explanatory power. The continent of origin is not significantin explaining differences in wages.As in the case of men, we tested to see whether the length of stay in the US or

the number of prior jobs held in the local labour market have any impact onwages. This model was estimated using a subset of the data and includes onlythose women for whom there are multiple observations. The results of theestimation are presented in Table 8b.A more textured picture about women in the job market emerges in

interviews with Marc Larrivee (placement director, CCMRIS) and JannYankauskas. Single women, especially secondary migrants, who have passableEnglish skills do come in to CCMRIS to get jobs. Sometimes these women aresecond or third wives and do not have a family to raise. While most of them getjobs cleaning, recently several have been placed in cabinetry assembly positionsat a local computer firm.Importantly, women constitute 50 per cent of those seeking to be

entrepreneurs (Yankauskas 2001). Between October 1997 and September2000, 46 per cent of the refugees served by Start Smart were women. Thisnumber rose to 51 per cent between October 2000 and September 2002, and in


the fiscal year 2002, 65 per cent were female (data from Start Smart 2003). Thisis in contrast to the nationwide female microenterprise rates of 33 per cent.Coastal Enterprise Inc.’s Ellen Golden (2003) says this high percentage ofwomen may be explained by the fact that in recent years, Start Smart hastargeted the childcare industry. This has attracted a higher percentage ofwomen, who want to be trained and become childcare providers. When StartSmart first started, Yankauskas (2001) recounts, most communities sent mento the business classes. Slowly, women have stepped forward, both becausethey have received assent from their community elders and because Start Smartis perceived as a safe place to be.Businesses started by women range from selling crafts, sewing, baking,

owning restaurants, and providing day care facilities. In interviews, refugeeentrepreneurs expressed differing motives and approaches to starting andrunning a business. Some women entrepreneurs see their business as a place ofcontact between Americans and their community. They also see their role asfacilitators in the community so that if secondary migrants come to Portland,they make their way to the establishment and can be directed to appropriateresources. Other entrepreneurs say that they started their business as a way ofsupporting themselves and their co-owners.


Table 8a

Results for Women

IndependentVariable

ParameterEstimate

Model 1


ParameterEstimate

Model 2


Intercept �5.7170 �3.68 0.000 �4.4660 �1.43 0.166

COLLEGE 0.1367 2.50* 0.014 0.0286 0.30 0.766HIGHSCHOOL �0.0030 �0.06 0.949 �0.0461 �0.53 0.603TECHSCHOOL 0.0833 1.18 0.239 0.1223 1.07 0.296SECONDARY 0.0248 0.55 0.587 0.0176 0.21 0.836

POORENGLISH �0.0274 �0.17 0.868LITTLENGLISHSOMENGLISH 0.0768 0.98 0.337

FAIRENGLISH 0.0811 0.72 0.478GOODENGL �0.0007 �0.01 0.994EXCENGL

EUROPEAFRICAHIREDATE 0.0002 4.96* 0.000 0.0002 2.14* 0.043

INVMILLS �0.0434 �0.34 0.737 �0.1854 �0.88 0.390

Adjusted R-Sq 0.26 0.0000No of observations 105 33

* significant at the 5% level of significance.

As indicated by Else and Clay-Thompson (1998) in their assessment of therefugee microenterprise programme, refugees seem to have greater access tocapital than has been commonly assumed. Entrepreneurs state that they areable to raise interest-free loans through friends and family. Some refugees whoused the services of Start Smart were very cognizant of the quality of technicalsupport and help offered. They preferred borrowing from Start Smart, despitethe fact that they had to pay interest on these loans.The good news about women’s participation is tempered by how slow

progress can be. Yankauskas, for instance, mentioned a group of women whoformed a sewing circle, more as a social group than as an economic entity, butin the context of which, they started exchanging recipes, crafts and were readyto consider starting a business. That summer they were reunited with theirhusbands and retreated to their traditional gender roles. In another instance,Linda Evans, ESL teacher at the University of Southern Maine described anESL class that comprised women who despite having been in the country forfifteen years spoke little English and were completely isolated from theworkforce. Revisiting this issue, Evans (2003) is happy to report that some ofthe women in this cohort have since entered the labour force successfully.


Table 8b

The Importance of Repeated Interactions in the Local Labour Market

(Results for Women)

Independent

Variable

Parameter

Estimate

Model 1t-statistic

H0: b¼0 Prob4t

Parameter

Estimate

Model 2t-statistic

H0: b¼0 Prob4t

Intercept �5.7170 �3.68 0.000 �4.4660 �1.43 0.166COLLEGE 0.1367 2.50* 0.014 0.0286 0.30 0.766HIGHSCHOOL �0.0030 �0.06 0.949 �0.0461 0.53 0.603

TECHSCHOOL 0.0833 1.18 0.239 0.1223 1.07 0.296SECONDARY 0.0248 0.55 0.587 0.0176 0.21 0.836POORENGLISH �0.0274 �0.17 0.868

LITTLENGLISHSOMENGLISH 0.0768 0.98 0.337FAIRENGLISH 0.0811 0.72 0.478

GOODENGL �0.0007 �0.01 0.994EXCENGLEUROPEAFRICA

HIREDATE 0.0002 4.96* 0.000 0.0002 2.14 0.043INVMILLS �0.0434 �0.34 0.737 �0.1854 �0.88 0.390NO OF PRIOR

JOBS

0.0127 2.49* 0.017

Adjusted R-Sq 0.60 0.0000No of observations 45 33

* significant at the 5% level of significance.

Conclusion

The research objective was to present a qualitative and quantitative picture ofrefugees’ transition to the local labour market. It first examined whether thehuman capital model helps to predict wages in the Greater Portland area.Results vary for men and women. For men, it appears that education is not therelevant factor, English proficiency is. The length of time in the United Statesdoes not have an impact on wages, but the number of prior jobs held in thelocal labour market is an important predictor in securing higher wages. Aftercontrolling for education and experience, Asians earn higher wages than doAfricans and Europeans. Since Asians have been in the area for the longestduration, and given the importance of ethnic networks in hiring practices, thisis not a surprising result. It must be emphasized, however, that these resultspertain only to the first forays into the labour market. In the longer run, ethnicnetworks may prevent refugees from acquiring English language skills, andhamper professional growth. Ultimately, success depends not on how manypeople one knows. Rather, what matters is how many people of the dominantracial group one knows socially.For women, education is the most important determinant in explaining

wages. There are not enough women in the dataset whose English proficiencylevels are recorded to determine whether it is education per se or the interactionwith English proficiency that influences wages. In entrepreneurial ventures,women own 50 per cent of all refugee owned businesses.Very little data exists regarding retraining but interviews with ESL teachers

in the area reveal that economic hardship keeps many away from classes. Thepopular perception of refugees is dictated by the articles in local newspapers,which have highlighted individual struggles and successes. The present workshows that refugees as employees have been a valuable resource for the localeconomy. In our data, 70 per cent of men were employed and of these, 90 percent acquired jobs within the first three months. Amongst women, 43 per centwere employed, 80 per cent of them within the first six months. In the boomingeconomy of the late 1990s, refugees helped ease the labour shortages in theeconomy. It will be the subject of further study to examine the impact ofthe slowdown in the economy in Maine. As jobs become more scarce, will thenative population displace refugee groups at entry level jobs?In preliminary work that addresses this question, it appears that following

September 11, the effects of recession are difficult to separate from the generalcautiousness regarding hiring of foreign workers. Immediately following theWorld Trade Center tragedy, the number of refugees entering the United Statescame to a virtual standstill as officials overseas were brought home to guard USborders. New security measures are still being put in place that affect refugeetravel. According to Larrivee (2002), job searches in the winter of 2001–2002were longer and job choices were limited but this effect may be the result oflocal manufacturing industries traditionally not hiring from December throughFebruary. Faced with a glut of workers, employers take the most qualified with


training and domestic job experience; most refugees are at a disadvantage whenit comes to US labour market skills. Overall, according to Larrivee (personalcommunication, 2002), ‘While we have felt the recession, it has not translatedinto our clients facing long term unemployment.’

Acknowledgements

We would like to thank Matt Ward, Director, Catholic Charities MaineRefugee and Immigrant Services for access to data. Thanks also to PeterBickford, Valerie De Vuyst, Linda Evans, Ellen Golden, Val Hart, BarryKenney, Marc Larrivee, Paul Manoff, Steve Mcfarland, Nancy Martz, AndreaPacillo, Grace Valenzuela, Rob Wood, and Jann Yankauskas. We would alsolike to thank the anonymous referees and the editor whose commentsbroadened the scope and inquiry of this research.Please address all inquiries to [email protected].

BEISER, M. andHOU, F. (2001) ‘Language Acquisition, Unemployment and Depressive Disorderamong Southeast Asian Refugees: A 10-Year Study’, Social Science and Medicine 53(10): 1321–1334.

BICKFORD, P. (2001) Human Resources Manager, Barber Foods, 54 St. Johns St., interview withauthors, Portland, Maine.

BIRMAN, D. and TRICKETT, E. J. (2001) Psychosocial and Work-Related Adaptation of SovietJewish Refugees in Maryland, report for the Maryland Office of New Americans, Baltimore,Maryland.

BORJAS, G. (1992) ‘Ethnic Capital and Intergenerational Mobility’, Quarterly Journal ofEconomics 107(1): 123–150.

BULIC, K. and DONGIEUX, H. (1999) ‘Meaningful and Satisfactory Work as a Pathway toIntegration: Bosnian Refugees in the West’, Revija za sociologiju 30(1–2): 99–115.

CHISWICK, B. (1979) ‘The Economic Progress of Immigrants: Some Apparently UniversalPatterns’, in Fellner, W. (ed.) Contemporary Economic Problems, Washington, DC, AmericanEnterprise Institute.

—— (1991) ‘Speaking, Reading, and Earnings among Low-skilled Immigrants’, Journal of LabourEconomics 9(2): 149–170.

COHON, Jr. J. D. (1981) ‘Psychological Adaptation and Dysfunction among Refugees’,International Migration Review 15(1): 255–275.

DE VUYST, V. (2001) ESL teacher. Interview with authors, Portland, Maine.DULEEP, H. O., REGETS, M. C. and SANDERS, S. (2001) ‘A New Look at Human CapitalInvestment: A Study of Asian Immigrants and Their Family Ties’, The Upjohn Institute forEmployment Research.

ELSE, J. F. and CLAY-THOMPSON, C. (1998) Refugee Microenterprise Development:Achievements and Lessons Learned, Institute for Social and Economic Development, Iowa.

EVANS, L. (2001) ESL teacher. Interview with authors, Gorham, Maine.—— (2003) Interview with authors, Gorham, Maine.FINNAN, C. R. (1981) ‘Occupational Assimilation of Refugees’, International Migration Review15(1): 292–309.

GOLD, S. J. (1991) Refugee Communities: A Comparative Field Study, Newbury Park, California:Sage Publications.

GOLDEN, E. F. (2003) Coastal Enterprise Inc. Wiscasset, Maine, email and telephonecommunication with authors.

GRANOVETTER, M. S. (1973) ‘The Strength of Weak Ties’, American Journal of Sociology, 78(6):1360–1380.

—— (1985) ‘Economic Action and Social Structure: The Problem of Embeddedness’, AmericanJournal of Sociology, 91(3): 481–510.


GRIECO, M. (1987) Keeping It in the Family: Social Networks and Employment Chance, London:Tavistock.

HAMILTON, C. (2003) Portland City Social Services, Interview with authors.HECKMAN, J. (1979) ‘Sample Selection Bias as Specification Error’, Econometrica 47(1): 153–161.KRAHN, H., DERWING, T., MULDER, M. and WILKINSON, L. (2000) ‘Educated andUnderemployed: Refugee Integration into the Canadian Labour Market’, International Journalof Migration and Integration 1(1): 59–84.

LARRIVEE, M. (2001) Job Placement Director, Catholic Charities, Maine, telephone and emailcommunication with authors, Portland, Maine.

—— (2002) Job Placement Director, Catholic Charities, Maine, e-mail communication withauthors, Portland, Maine.

LOGAN, J. R., ALBA, R. D. and ZHANG, W. (2002) ‘Immigrant Enclaves and EthnicCommunities in New York and Los Angeles’, American Sociological Review 67(2): 299–322.

MANOFF, P. (2001) ESL teacher. Interview with authors, Portland, Maine.MARTZ, N. (2001) ESL teacher and coordinator ESL programmes, Interview with authors,Gorham, Maine.

McFARLAND, S. (2001) ESL teacher, administrator of Workplace Education programmes.Interviews with authors, Portland, Maine.

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McMANUS, W., GOULD, W. and WELCH, F. (1983) ‘Earnings of Hispanic Men: The Role ofEnglish Language Proficiency’, Journal of Labour Economics 1(2): 101–130.

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PORTES, A. and BACH, R. L. (1980) ‘Immigrant Earnings: Cuban and Mexican Immigrants inthe United States’, International Migration Review 14(3): 315–341.

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Appendix I

Probit Model for the Probability of Being in the Wage Sample Modeland Results of First Stage Estimation

As noted earlier, the data on labour force participation by refugees are atruncated sample. In this case, there are data on refugee arrivals andemployment. The only people who enter the analysis are those who participatein the labour market. This truncated sample may lead to biased estimates. Tocorrect for this, a binary probit analysis of the probability of being in the wagesample is carried out (see Heckman 1979). In the probit model estimated, thedependent variable is one, if the person is an employee in the wage sample andzero otherwise. The model is estimated with separate equations for men andwomen, by using the maximum likelihood procedure.In the probit model, the independent variables are factors that influence or

determine the probability that a refugee is an employee. FAMSIZE (the size ofthe family) is introduced as a variable; in the case of women, the size of theirfamily may be a deterrent to participation in the labour market, whereas formen it might have the opposite effect. Another important determinant in thedecision to participate in the labour market may be whether the person is theprimary applicant for refugee status. Typically, the person who is most likely togain asylum is listed as the primary applicant. The data show that the primaryapplicants for refugee status are the men in the family. Women are generallyprimary applicants either when they are heads of households or when they haverelatives in the local area, with whom they seek reunification. So theprobability of being in the workforce increases when women are the primaryapplicants in the family. PA is an indicator variable which takes the value oneif the person is a primary applicant and zero otherwise.The probit model estimated, calculates the probability of being an employee

for an individual i of sex m (m ¼ 1 for females, 2 for males) and uses asexplanatory variables PA and FAMSIZE.

Pm ¼Z�0Xim

�1

1/ð2�Þ1=2 expð�t2/2Þdt

�0Xim ¼ �0m þ �1m FAMSIZEim þ �2mPA2m

where the �jm coefficients are estimated by the model.


The maximum likelihood estimation results are shown in Table A1.

Table A1

Probit Analysis for the Probability of Being in the Wage Sample

Dependent variable ¼ 1 if person is in wage sample, ¼ 0 otherwiseA. MenVariable Maximum Likelihood

Estimates

z-statistic Prob>|z|

INTERCEPT 0.8414 5.18 0.000FAMSIZE �0.0169 �0.64 0.523

PA �0.3827 �2.82 0.005LR �2(2) ¼ 8.34, Prob > �2 ¼ 0.0154�2*(Log Likelihood Ratio) = 656.9

OBSERVATIONS: In wage sample: 419, Not in wage sample: 166.

B. WomenVariable Maximum Likelihood

Estimates

z-statistic Prob>|z|

INTERCEPT 0.1812 1.07 0.287FAMSIZE �0.0799 �2.51 0.012

PA �0.2534 �1.66 0.096LR �2(2) ¼ 7.28, Prob >�2 ¼ 0.0263�2*(Log Likelihood Ratio) ¼ 437.42

Number of observations: In wage sample 151, Not in wage sample 196.

For women the size of the family is a deterrent to work, and whether or notone is the primary applicant for refugee status is not significant in explaininglabour market participation. For men, results for the Probit Analysis do notcoincide with prior expectations. According to the results, one is less likely towork if one is a primary applicant. Caseworkers at CCMRIS explain that thisresult is not perverse: men with large families and without a spouse may stay athome. In such a family, typically the older children are in the workforce. Forexample, the oldest daughter might play the primary caregiver role whereas theoldest son might be the principal breadwinner for the family.Does the decision to enter the labour market depend on whether one is an

ASIAN, AFRICAN, or EUROPEAN? In a probit model, dummy variablesfor CONTINENT prove to be very significant. The control variable isAFRICAN. The following model implies that compared to Africans,Europeans are much more likely to be in our sample. The dummy variablefor Asians is not significant at the ten per cent level of significance. It was notedby caseworkers and the placement director that Asians tend to use CatholicCharities employment service less—perhaps because there is a large Asian(particularly Vietnamese and Cambodian) community present in the GreaterPortland area.


Table A2

Probit Analysis for the Probability of Being in the Wage Sample: The Continental Effect

Dependent binary variable: WagecodeDependent variable ¼1 if person is in wage sample, ¼ 0 otherwise

Independent Variable Maximum LikelihoodEstimates

z statistic Prob > |z|

INTERCEPT �0.0675 �0.7 0.486EUROPEAN 0.7732 5.66 0.000

ASIAN �0.3902 �1.60 0.109Number of observations: 405LR �2(2) ¼ 44.71, Prob > �2¼ 0.000

Appendix II

Dummy Variables defined for Skill Codes (presented in parentheses)

The category of the farmers/agriculturists was used as a control group.

P2 ¼ 1 if the person was a cleaner/housekeeper in his country of origin (Cleaners).0 otherwise

P3 ¼ 1 if the person worked in building trades (Construction)

0 otherwiseP4 ¼ 1 if the person worked in a factory (Factory)

0 otherwiseP5 ¼ 1 if the person worked for his/her government

0 otherwise (Government)P6 ¼ 1 if the person was a lawyer, doctor, engineer etc. (Professional 1)

0 otherwise

P7 ¼ 1 if the person was a teacher, nurse, business administrator (Professional 2)0 otherwise

P8 ¼ 1 if the person was a waiter, waitress, nurse assistant, cashier etc. (Professional 3)

0 otherwiseP9 ¼ 1 if the person was a shopkeeper, store clerk (Sales)

0 otherwise

P10 ¼ 1 if the person was a carpenter, boiler maker, mechanic, etc. (Trade)0 otherwise

Appendix III

Notes on Skill Codes

The following shows how different categories of work self-reported byindividuals were rationalized. This rationalization involves an assumption


that job types are comparable across countries, which may not be the case;however, there is no other way available to compare work experience.Farmers and agricultural workers were put under the heading Agri.Railway workers, civil servants, police officers, economists and other

government workers—Govt.Housewives, housekeepers, janitors, etc.—Clean.Lawyers, doctors, engineers, scientists and professors—Prof1.Teachers, nurses, nutritionists, computer programmers and business

administrators—Prof2.Shopkeepers, vendors and store clerks—Sales.Students—Student.Waiters, waitresses, babysitters, nurse assistants, bookkeepers, cashiers, tellers

and office workers—Prof3.Virtually all occupations that one would normally consider trades are under

the Trade heading (carpenters, boiler makers, mechanics, food slaughterers,butchers, plumbers, electricians, tailors, seamstresses etc.); along with theseoccupations are included dental technicians, oil workers, chemical andmining technicians, and draftsmen.

Production and assembly, textile worker jobs—Factory.Labourers—Const.

MS received July 2002; revised MS received February 2003


Off the Boat, Now Off toWork: Refugees in the Labour ... · the Labour Market in Portland, Maine ... This study of the labour market in Portland from 1996 to 2000 ﬁnds that amongst

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