Occupational Enclaves and the Wage Growth of Latino Immigrants

[Word count: 8,830]

Sergio Chavez Ted Mouw

Jacqueline Hagan

University of North Carolina, Chapel Hill

Occupational Enclaves and the Wage Growth of Latino Immigrants

ABSTRACT

Does the concentration of recent Latino immigrants into occupational enclaves—

occupations with large numbers of limited English speakers—restrict their wage growth? On the

one hand, it is possible that immigrants who are concentrated in jobs with co-ethnics may have

less need to learn English and/or less on-the-job exposure to it, which may isolate them socially

and linguistically and limit their subsequent economic mobility. On the other hand, enclave

employment can be seen as a “stepping stone” for upwardly mobile immigrants who can find

work while they improve their English. Using longitudinal data from the 1996, 2001, and 2004

panels of the Survey of Income and Program Participation (SIPP), we test for the effect of

occupational level English proficiency on wage growth. We supplement this data with in-depth

interviews and observations from immigrants employed in the construction industry. The results

indicate that although the proportion of limited English speakers in the respondent’s occupation

is associated with lower wages for Latino immigrants in the cross section, it is not associated

with lower levels of wage growth. These findings demonstrate that occupational enclaves do not

“trap” immigrant workers—at least on average—but instead can provide a path for immigrants to

familiarize themselves with the U.S. labor market.

1

Introduction

A central question in the public and academic debate on immigration focuses on the

economic assimilation of recent immigrants. While conventional models of assimilation treat the

low wages of recent immigrants as the first step on a ladder of upward mobility, proponents of

the “segmented assimilation” perspective argue that reduced opportunities for less educated

workers in a postindustrial economy combined with phenotype discrimination may result in the

downward assimilation of less educated, darker skinned immigrants (Bean, Leach, and Lowell

2004; Portes and Rumbaut 2006). As the largest group of post-1965 immigrants, with relatively

low education levels and the possibility of social and labor market discrimination, Latino

immigrants present an important test case for these contrasting perspectives on contemporary

immigration in the United States.

Empirical evidence on the economic mobility of Latino immigrants paints a mixed

picture. Using repeated cross-sections of Census data, researchers have documented that Latino

immigrants—with the exception of Cubans—earn lower wages, on average, than their native

counterparts throughout their working lives (Borjas 1982, 1985, 1995, Trejo 1997). Borjas and

Katz (2005) show that the largest group of Latinos, Mexicans, lags the farthest behind the native-

born in terms of wages and education and argue that this disadvantage is transmitted across

generations. Lubotsky (2007) uses longitudinal data on earnings and finds that Latino

immigrants have lower rates of wage growth than other immigrant groups, and that wage

convergence with native-born workers stalls after 10 years in the United States (Lubotsky 2007).

In contrast, Smith (2003, 2006) argues that there is considerable evidence of intergenerational

educational and earnings gains among Latino immigrants and that concerns about a lack of

assimilation are unwarranted. Similarly, Bean, Leach, and Lowell (2004) and Hall and Farkas

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(2008) argue that there is considerably more upward occupational and wage mobility among

recent immigrants than one might expect given the expectations of racial stratification and

segmented labor market theories. Overall, while there is widespread agreement that a substantial

portion of the wage gap between Latino immigrants and native-born workers is due to

differences in education and English language ability at the time of immigration (e.g., see

Catanzarite and Aguilera 2002), there is considerable disagreement as to the explanation of the

remaining wage gap, the degree to which it persists over time, and what this portends for the

future.

One explanation for the residual wage gap and the apparent lack of wage convergence for

Latino immigrants focuses on occupational segregation. Catanzarite (2004) argues that Latino

immigrants are crowded into “brown collar” occupations that have been typecast as immigrant

jobs where they receive low wages and have limited prospects for upward mobility. Catanzarite

and Aguilera (2002) find that working in jobsites with co-workers who are Latino is associated

with lower wages for Latino workers, concluding that “working with co-ethnics erodes pay by

the equivalent of 8.0 years of education for men or 7.4 for women”. Kmec (2003:54) argues

that “individuals with mostly white co-workers have an unmistakable advantage over those with

mostly black or Latino co-workers.” Chiswick and Miller (2002) argue that working alongside

co-ethnics who speak a minority language has a feedback effect that slows the rate of economic

assimilation for immigrant workers.

Without discounting the potential role that discrimination and job labeling might play in

reducing the wages of workers in “brown collar” occupations, we argue in this paper that the

existing literature is misleading because it relies on cross-sectional data and is not based on a

realistic model of immigrant assimilation, where low-skilled immigrants arrive with poor English

3

language skills and limited U.S. labor market experience. In contrast, we borrow from the

literature on ethnic enclaves to propose the concept of “occupational enclaves” for Latino

immigrants—occupations with substantial numbers of Spanish speakers where recent immigrants

can find employment while they accumulate job skills, U.S. experience, and English language

ability. Rather than hurting wages and opportunities, we argue that occupational enclaves can

actually promote the subsequent wage growth of immigrant workers. There are, however, two

key differences with the conventional definition of an “ethnic enclave.” First, there is no

presumption that the owner of the hiring firm is a co-ethnic, although in many cases this may be

true. Second, enclave occupations are “stepping stones” for subsequent mobility, and the

potentially benefit is only visible in longitudinal data that can trace the upward mobility of

successful immigrant workers.

In this paper, we use longitudinal data from the 1996, 2001, and 2004 panels of the

Survey of Income and Program Participation (SIPP) to test for the effect of working in linguistic

enclave occupations on wage growth for Latino immigrants. We merge our SIPP data with state

level data on English language ability and Spanish language use within 3-digit Census

occupations from the 2005 American Community Study. This allows us to define occupational

enclaves based on geographically specific information on the occupational distribution of recent

immigrants. We compare cross-sectional and longitudinal estimates of the effect of working in

enclave occupations on wages to test whether working in occupations with a substantial

proportion of Spanish speakers affects the economic mobility of Latino immigrants.

4

Literature Review: Occupational Segregation and Ethnic Enclaves

As discussed above, a number of recent papers have made a link between the relatively

high levels of occupational concentration among Latino workers, particularly recent immigrants,

and the Latino/white pay gap. Catanzarite (2000), for example, uses Public Use Micro Sample

(PUMS) data from Los Angeles for 1980 and 1990 to show that Latinos are highly segregated

from white workers, and that their earnings are lower than whites’ even after controlling for

education, potential labor market experience, English ability, and family composition. She uses

the term “brown-collar” occupations to refer to occupations with a high proportion of Latino

workers, and argues that the labeling of jobs as “immigrant” jobs simultaneously makes them

less desirable, lowers their status, and reduces wages. In a follow-up study, Catanzarite (2003)

uses 1990 PUMS data from 18 metropolitan areas to estimate a multilevel model of the effect of

immigrant density on wages, and finds that working in an occupation with recent Latino

immigrants reduces the wages of all workers, with more pronounced negative effects for blacks

and earlier Latino immigrants.

In a related study, Catanzarite (2002) uses 1980 and 1990 Census data from Los Angeles

to test the relationship between earnings and Latino concentration at the occupational level using

a cross-lagged regression model. She finds a negative relationship between occupational

earnings in 1980 and the change in the percent of male immigrant Latino workers in the

occupation between 1980 and 1990, but this effect disappears after she controls for the

occupation’s rate of employment growth and the education and experience of native born male

incumbents. This finding suggests that a decline in the earnings of “brown collar” occupations

might be an artifact of a general erosion of pay for less-educated workers of all race and ethnic

groups during the 1980s. In contrast to the results in Catanzarite (2002), Howell and Mueller

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(2000) use PUMS data from the New York metropolitan area to estimate models of the change in

wages on changes in the immigrant occupational share from 1980-1990, and find no effect of

changes in immigrant occupation density on changes in the wages of recent immigrants or Latino

workers.

In addition to effects at the occupational level, it is possible that the negative effect of

immigrant concentration on wages is more pronounced at the job or firm level. To test this,

Catanzarite and Aguilera (2002) use data from the 1992 Legalized Population Survey, which

includes a categorical variable asking respondents to identify the largest race/ethnic group among

their coworkers. They found that, on average, legalized Latino males tend to earn 13% less

when work at predominately Latino jobsites, even after controlling for occupational

characteristics and an extensive set of human capital variables. Kmec (2003) uses data on the

race and ethnic composition of jobs from the employer survey of the Multi-City Study of Urban

Inequality, and finds that working in jobs with predominately Latino or black co-workers reduces

the wages of all workers by 18 and 15 percent less per hour, respectively, compared to jobs in

which whites are the majority (Kmec 2003).

In perhaps the most comprehensive test of the correlation between Latino concentration

and wages at the firm level, Hellerstein and Neumark (2002) use a large data set of matched

employer-employee records constructed from the Decennial Census of 1990. They document

substantial firm level segregation of Latino workers by ethnicity and the degree of English

proficiency. Their regression results for log wages show that the share of co-workers who are

Latino reduces wages by 0.168 log points for Latino workers and .037 log points for white

workers.

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Although the occupational segregation literature reviewed above argues that working

with co-ethnics has a negative impact on the wages of Latino immigrants, the existing evidence

is primarily based upon analysis of cross-sectional data or aggregate-level analysis at the

occupational level. While it may be true, as this literature suggests, that the crowding of recent

immigrants into brown-collar occupations reduces wages in those occupations and represents a

structural constraint on race and ethnic equality, the overall portrayal of the economic

assimilation of Latino immigrants is too static. Most importantly, the lack of longitudinal data

doesn’t permit an accurate assessment of the degree to which some immigrant workers move out

of these highly segregated occupations over time. If substantial numbers of successful

immigrants do experience upward mobility, then any cross-sectional estimate of the effect of

working in a brown collar occupation on wages will be biased by the negative selectivity of the

remaining workers. In the next section, we turn to the ethnic enclave hypothesis to develop an

alternative hypothesis based upon a consideration of the challenges facing recent immigrants,

who often arrive in the United States with little English ability and no experience in the U.S.

labor market.

Ethnic Enclaves: Stepping Stone or Dead End?

The ethnic enclave debate of the 1980s helps to situate our reinterpretation of the role of

occupational concentration on immigrant wage growth. The central issue in the ethnic enclave

literature is whether the sorting of immigrants into segregated neighborhoods and workplaces

promotes or impedes their economic and social assimilation (see Sanders and Nee 1987; Wilson

and Portes 1980; Zhou and Logan 1989). In general, this literature begins by noting that when

immigrants initially arrive in the U.S., they are often relegated to the secondary employment

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sector, which is characterized by low-wages and job insecurity (Piore 1979). Portes and Bach

(1985) and Wilson and Portes (1980) argue that immigrants may benefit from avoiding the

secondary sector of the labor market and working instead in “ethnic enclaves” composed of

immigrant owned firms. Wilson and Portes (1980) claimed that Cubans employed in an enclave

economy earned wages and received a rate of return to human capital comparable to those

employed in the primary sector. In contrast, however, Sanders and Nee (1987) argue that these

results are misleading and that the main beneficiaries of enclave employment are the employers

who benefit by hiring cheap immigrant labor. Sanders and Nee claim that the enclave hypothesis

is counterintuitive in that it assumes that “despite the social isolation of the enclave, there is no

cost to segregation.” (Sanders and Nee 1987: 746).

Recent research on the ethnic enclave hypothesis has produced a pessimistic and an

optimistic view. On the optimistic side, Evans (2004) uses cross-sectional census data from

Australia and finds that the proportion of co-ethnics who are entrepreneurs is positively

correlated with the occupational status of immigrant workers, while an interaction term between

entrepreneurial density and the respondent’s English language ability indicated that the benefits

of working in enclaves decline as English language skills increase. Edin et al. (2003) uses initial

government placement of refugees in Sweden to attempt a quasi-experimental test of the enclave

hypothesis, arguing that the initial placement was independent of unobserved factors that

otherwise would have influenced the location decision. They find that the earnings of the less-

educated refugees were 13% higher when the size of the ethnic enclave size was increased by a

standard deviation. Damm (2006) uses a similar approach based upon a government relocation

program in Denmark and finds that a standard deviation increase in the size of the ethnic enclave

results in a 4 percentage point increase in employment and a 21 percentage point increase in

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earnings. She interprets these results by arguing that ethnic enclaves provide access to ethnic

networks that transmit information about their host country which they would otherwise be

excluded from. Finally, Hagan (1998) and Waldinger and Lichter (2003) provide cases studies

of the dynamics of ethnic enclaves and argue that in a mature ethnic economy, co-ethnic

networks of immigrant workers may come to dominate certain firm or occupational niches, from

recruiting and hiring networks to managing work schedules and supervising promotion, resulting

in a form of social closure. In these ethnic based economies English is not a requirement for

landing a job or acquiring new skills to augment wages.

The pessimistic side of the enclave debate focuses on the possibility that working

alongside co-ethnics impedes the social assimilation needed for subsequent wage growth. One

line of inquiry focuses on linguistic assimilation. Studies of the determinants of wages for

Latino and immigrant workers demonstrate that English proficiency is a critical component of

the wages of immigrant workers (Cobb-Clark and Kossoudji 2000; Dustmann and Fabbri 2003;

McManus 1985; McManus et al. 1983). Building on this, Chiswick and Miller (2002) use

PUMS data from the 1990 Census to test a version of the enclave hypothesis based on linguistic

concentration. Using a measure of the proportion of speakers of the immigrant’s language group

at the state level, they find that linguistic concentration is associated with both lower levels of

English proficiency and lower earnings for immigrant workers. In a similar article, Warman

(2007) uses synthetic cohorts constructed from Canadian Census data from 1981-2001 and finds

that living in an ethnic enclave is negatively associated with wage growth for immigrants.

Overall, the findings based on the quasi-experimental studies of Edin (2003) and Damm (2006)

discussed above would seem to be preferable to the results in Chiswick and Miller (2002) and

Warman (2007), as the latter studies are based on cross-sectional or synthetic cohort data and

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hence are vulnerable to problems based self-selection based on English ability, as discussed in

our theoretical model below. On the other hand, the relatively small size of the ethnic enclaves

in the European data that Edin (2003) and Damm (2006) use might point to a qualitatively

different process among more larger, more concentrated ethnic enclaves in the U.S or Canada.

Overall, the debate on the ethnic enclave hypothesis highlights the complexity of the

immigrant economic assimilation process. A crucial component of this hypothesis is the idea

that an ethnic enclave may provide an alternative to employment in the secondary sector, and, as

such, shelter recent immigrants from direct competition with native workers. Particularly with

respect to language, working alongside co-ethnics may provide an entrée into the U.S. labor

market for recent immigrants with poor English language skills. As Chiswick and Miller

(2002:10) note “working within a linguistic enclave is a mechanism for sheltering oneself from

or mitigating the adverse labor market consequences of limited destination language

proficiency.”

Three other studies provide important antecedents to our research. First, Chiswick and

Miller (2007) use Census PUMS data matched to occupational-level information on English

language requirements to test a variant of the enclave hypothesis, and they find that there is a

strong positive correlation between occupational English requirements and wages in the cross-

section. As a result, immigrants are assumed to be disadvantaged if they do not posses the

language of the workplace because they must rely on co-ethnics in the labor process. Second,

Hall and Farkas (2008) use data from the 1996 and 2001 panels of the Survey of Income and

Program Participation (SIPP) to estimate growth-curve models of wage growth among

immigrants and native workers. They find that while the initial wage level is considerably less

for immigrants compared to native workers, the estimates of wage growth are statistically

10

indistinguishable among natives and different immigrant groups. Finally, Lubotsky (2007)

matches earnings data from Social Security records to demographic information from the SIPP

and the Current Population Survey (CPS) to study wage growth among immigrant workers. In

contrast to Hall and Farkas (2008), Lubotsky’s longer panels, augmented with Social Security

data, suggest that earnings growth for Latino workers lags behind that of other immigrant groups.

In the following section, we combine the theoretical perspective of the ethnic enclave

literature with an empirical focus of the “brown collar” occupations literature to depict a simple

model of occupational sorting among immigrant workers. We argue that although immigrants

may initially “sort” into enclaves, this concentration is not necessarily bad during an initial

period of adjustment if they eventually are able to develop the skills needed to succeed in the

larger labor market. Then, in the analysis section, we propose to combine the empirical

approaches of Chiwsick and Miller (2007), Lubotsky (2007) and Hall and Farkas (2008) in order

to test a dynamic model of brown collar occupations and wage growth with multiple panels of

SIPP data.

A Theoretical Model of Occupational Enclaves

For recent immigrants, a lack of English fluency and limited knowledge about

opportunities represent major constraints in the labor market. Given these constraints,

“occupational enclaves”—occupations where there are a significant number of Spanish

speakers—provide employment for immigrant Latino workers with insufficient English ability,

where either the demand for English fluency is minimal or where the presence of large numbers

of co-ethnics eases the language difficulties. The key question is whether the sheltering effect of

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working with other Spanish speaking workers reduces wage growth by slowing the process of

linguistic and social assimilation.

Equations 1 and 2 present this sorting argument more formally. In Equation 1, we depict

log wages for immigrant i in occupation j at time t as a function of the level of occupational

English proficiency:

(1) 1 2 3ln occ-englishijt j i i i itw English Xβ β β α ε= + + + +

Where jocc english− is the proportion of workers with limited English in occupation j, iEnglish

is the worker’s English language proficiency, X is a set of other observed individual level control

variables, itε is an error term, and iα represents fixed unobserved factors that affect wages. Note

that we will refer to our key independent variable—the proportion of workers with limited

English—as “occupational English” for the sake of brevity, even though it refers to the

proportion of workers in the occupation who lack fluent English language skills.

In our theoretical model, we hypothesize that iα represents traits such as ambition and

skills that are not measured on typical surveys or adequately proxied by educational credentials,

but observed by employers and rewarded in the labor market. In Equation 1, we expect that

working in an occupation with a high proportion of workers with limited English is associated

with lower wages ( 1 0β < ) and that, everything else being equal, workers with better English

ability have higher wages 2( 0)β > .

In Equation 2, we present a simple model of occupational sorting based on English

language proficiency and unobserved productivity:

(2) 1 2occ-englishi i i itEnglishη η α ν= + +

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The benefit of an enclave occupation with lower English language requirements is that it

provides employment for immigrants who are not fluent in English, hence we would expect a

negative value for 1η . The coefficient 2η depicts the effect of unobserved factors that affect

wages such as “ambition” on sorting into enclave occupations. If occupations with lower

English requirements tend to be lower skilled occupations in general, or if more skilled (or

ambitious) immigrants learn English more rapidly, then we would expect a negative relationship

between occupational English and the unobserved individual-level skills that affect wages, i.e.,

2 0η < .

Referring back to Equation 1, we can develop an intuition about how skill-based

occupation sorting in Equation 2 will affect our coefficients in Equation 2. A negative

correlation between the unobserved factor iα and occupational English (as hypothesized in

Equation 2) will tend to result in a downward bias on the coefficient on occupational English in

Equation 1, as immigrants with less “ambition” or lower unobserved skills stay longer in

occupations with low English requirements. If this kind of negative sorting is taking place, then

regression estimates of 1β will overstate the negative effect of working in an enclave occupation.

If we are worried about the possibility that cross sectional data may overstate the effect of

occupational English on wages because of sorting, an alternative approach is to use longitudinal

data to model wage growth rather than wage levels. If occupational enclaves restrict economic

assimilation by delaying English language acquisition or other skills necessary for upward

mobility, then this should result in a negative effect of enclave occupations on subsequent wage

growth. This is depicted in Equation 3:

(3) 1 1 2

lnocc-englishijt

j i it

wZ

timeα φ φ ε

Δ= + + +

Δ

13

Where the dependent variable is the change in wages over time, 1iocc english− is the level of

occupational English in the first wave of data, and iZ represents a set of relevant control

variables. If working in a enclave occupation constrains wage growth, then we would expect

that 1 0φ < . In contrast, the “sorting” argument claims that although occupational enclaves are

associated with lower wages in the cross section—because of the sorting of workers with poor

English into those occupations—they do not affect the subsequent wage growth of immigrants

workers, hence 1 0φ = . In other words, the test is quite simple: do immigrants who work in

occupational enclaves have lower rates of subsequent wage growth than other immigrants?

Data

We draw on several complimentary data sources to understand the role of occupational

language use on the wage mobility of immigrant workers. First, we use data from the 2005 and

2006 American Community Survey (ACS) and the 1996, 2001, and 2004 panels of the Survey of

Income and Program Participation (SIPP). The 2005 and 2006 ACS are 1% samples of the U.S.

population and provide a broad overview of immigrant employment by detailed occupation. The

benefit of the ACS is that it provides a large number of cases, which allows us to maximize the

number of immigrant workers in enclave occupations. In contrast, the SIPP is a much smaller

data set of about 60,000 households per panel. The key advantage of the SIPP is that it is a

longitudinal study (each panel of data is followed for 3-4 years), which allows us to examine the

role of occupational enclaves on wage growth. All three of the SIPP panels provide us with

quarterly data on wages and occupation for all respondents who are currently working. The 2004

SIPP panel is the first SIPP panel to include data on language proficiency, so it is the best suited

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for our analysis as it allows us to disentangle the effect of individual language ability from

occupation-level effects.

We supplement the individual level data from the ACS and the SIPP with aggregate data

on the English ability of workers in each occupation at the state level. The variable

“occupational English” measures the proportion of workers in an occupation who report

speaking English not very well or not at all. We construct this variable using the 2005 and 2006

ACS by aggregating the individual data at the state level. The state-level variation by occupation

is important because it allows us to take regional variation in occupational composition into

account. There are geographic differences in the degree to which certain occupations function as

occupational enclaves; for example, the proportion of carpenters who speak Spanish may be

higher in Texas and California than in South Dakota. We would like to go to a more detailed

level of geography, as the 2005-2006 ACS would allow us to go the level of a Public Use Micro

Area (PUMA, about the size of a county), but the smallest level of geography in the SIPP is the

state. We add the variable “occupational English” to our individual ACS and SIPP data by

merging it at the state and occupational level.

Quantitative Results

[Table 1 about here]

American Community Survey

Table 1 shows the relationship between time since immigration and self-reported English

language ability. Among Latino immigrants who arrived in the last 5 years, about thirty-five

percent do not speak English and another thirty-four percent do not speak well. English

language proficiency increases steadily as time in the U.S. increases. By the time an immigrant

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has been in the U.S. for longer than 21 years, only about seven percent claim to speak English

“not very well”. The level of those who either speak only English or who speak it very well also

climbs from fifteen percent to fifty percent.

[Table 2 about here]

Table 2 depicts our measure of occupational enclaves, “occupational English”, which, as

discussed above, is the proportion of workers in the occupation, by state, who report either

speaking English not well or not at all. This variable is highly correlated with the proportion of

Spanish speakers and Latino immigrants in the occupation (above .9 for both) hence there is little

empirical difference between either of these variables as our measure of occupational enclaves.

Table 2 shows the level of occupational English by the respondent’s time since immigration.

While recent Latino immigrants work in occupations with, on average, 18.9% limited English

speakers, this number falls to 12.7% for immigrants who have been in the U.S. for more than 20

years. Although recent Latino immigrants may work with a large percentage of limited English

speakers, the cross-sectional data suggests that immigrants move out of occupational enclaves

over time as their experience in U.S. labor markets increases.

[Table 3 about here]

Table 3 lists the top paying occupations with at least 10% limited English speakers.

While Chiswick and Miller (2007) show that, in general, there is a strong negative correlation

between occupational English requirements and wages, it is important to note that there are a

number of occupations that pay relatively good wages despite having a high proportion of

limited English speakers. Inspection of Table 3 reveals that there are several construction related

occupations on this list as well as other occupations that require manual skills or involve difficult

or dangerous working conditions. The skills and/or danger involved in doing each of these jobs

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may keep wages up for those workers who are able to do the work. Table 3 also shows the

proportion of workers in each of these occupations who report speaking Spanish at home. Some

occupations on this list have very high proportions of Spanish speaking workers: 50.1% of

plasterers and 35.4% of cement masons report speaking Spanish. A worker in these occupations

would have a high probability of being in an environment where Spanish would be understood,

thereby reducing the necessity of English language fluency.

[Table 4 about here]

Table 4 presents OLS models of the effect of occupational English on log wages for

Latino immigrants in the ACS. In model 1, we estimate a bivariate regression between

occupational English and wages and find that a 10 percentage point increase in the proportion of

limited English speakers in the respondent’s occupation would decrease wages by about 9% (-

.921*.1). In Model 2 we add controls for time since immigration and self-reported English

ability, and find that the magnitude of the coefficient on occupational English drops by about

30% to -0.557.

In Model 3, we add a variable for construction related occupations and interact this with

the occupational English variable. The coefficient on the interaction term (.185) is positive,

indicating that the negative effect of occupational English is substantially smaller in construction

occupations, which makes sense given our earlier discussion of Table 3 where we found that a

number of construction related occupations pay relatively well despite the presence of other co-

ethnics who do not speak English. Thus, although the overall effect of occupational English on

wages in construction occupations is still negative (-0.460+0.185), the positive interaction term

indicates that the effect of working in an occupation with coworkers who are not fluent in

English is not as pronounced in construction occupations.

17

Finally, Model 4 adds interaction terms with years since immigration. The results of this

model are important in light of our earlier discussion about occupational sorting based on

English language ability. The excluded category for years in the U.S. is 0-5, so the coefficient

on occupational English in this model (-.281) is the estimated effect for this group. In contrast,

the negative interaction term between occupational English and time since immigration (-0.474)

indicates that the effect of working in an enclave economy is more pronounced for immigrants

with more experience in the U.S. For example, the estimated effect of occupational English in

Model 4 for immigrants who have been in the U.S. for more than 20 years is -0.755 (-0.281 plus

the interaction term, -0.474).

The results in Model 4 are consistent with a sorting model. We hypothesize that there are

two types of sorting going on. First, immigrants with lower levels of English proficiency sort

into occupations with lower English requirements. Jobs that don’t require English proficiency

tend to pay less, on average, because they either involve fewer skills or less complex tasks than

jobs that involve fluent interaction and communication in English or because of the crowding of

non-fluent immigrants into these jobs.

In addition to sorting based on language ability, we hypothesize that a second type of

sorting occurs over time. As discussed above with respect to Equations 1 and 2, immigrants with

higher levels of unobserved skills and motivation may start out in occupational enclaves, but

quickly move out as they gain an understanding of U.S. labor markets and the kinds of

opportunities available in different occupations. In contrast, immigrants with lower levels of

unobserved skills may continue to work in these occupations. As a result of the upward

occupational mobility of successful immigrants, the average level of unobserved skills and

ambition falls over time in enclave occupations. If we base our interpretation of these results on

18

an occupational sorting argument, then a comparison of the greater “effect” of occupational

English on long-term immigrants in Model 4 suggests that close to half of the observed

relationship between occupational English and wages observed in Model 2 may be spurious: i.e.,

the effect of occupational English for recent immigrants in Model 4 (-0.281) is substantially

smaller than the overall effect reported in Model 3 (-0.460). The results for the interaction terms

in Model 4 should make us cautious about interpreting the negative cross-sectional correlation

between wages and occupational English as the causal effect of working in an occupational

enclave. Nonetheless, the problem with the cross-sectional data from the ACS is it only tells us

what occupation immigrants are currently working in, not what occupations they worked in prior

to their current job.

SIPP data

In order to differentiate between enclave occupations as “stepping stones” or traps, we

turn to longitudinal data from the SIPP. As discussed above, we use data from three different

SIPP panels. The 2004 data is preferred because there is data on English language proficiency,

which is absent from the 1996 and 2001 panels. However, at the time of writing, only 4 of the 9

waves of data are available for analysis. The 1996 SIPP panel has 12 waves of data running from

1996-2000, and the 2001 SIPP panel has 9 waves of data from 2001-2004.

[Table 5 about here]

Table 5 provides a basic descriptive overview of wages and wage growth for Latino

workers in the 2004 SIPP data. The rows of the table correspond to quartiles of the proportion of

limited English speakers in the respondent’s occupation, as constructed from the 2005-2006 ACS

data described in the data section above. The second column shows the average proportion

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limited English, ranging from a low of .006 for the first quartile to a high of .350 for the fourth

quartile. The third column shows the average wage of the workers in each quartile. The results

here are consistent with what we learned with the ACS data in Tables 1-4. Workers in

occupations in the first category earned an average of $17.04, versus $9.91 in the bottom quartile

of occupational English proficiency.

While average wages indicate the strong negative relationship between occupational

English and earnings, it is not clear that the effect is causal. As discussed above, a negative

correlation between wages and occupational English may reflect sorting rather than a causal

effect of occupational characteristics on wages. Workers’ limited English proficiency may lead

some of them to sort temporarily into enclave occupations before transitioning into the

mainstream labor market.

The fourth column of Table 5 shows wage growth between 2004-2005 for Latino workers

based on the occupational English of their job in the first wave. If working in an enclave

occupation hurts wage growth, then we should see lower levels of wages for workers in the

fourth quartile, where the proportion of workers speaking poor English is 35%, compared to the

other categories. Table 5 shows that this is not the case; the level of wage growth is actually

higher among workers in the fourth quartile compared to the top two quartiles: workers in the

fourth quartile see their wages go up by .055 log points, compared to .022 for the first quartile

and .034 for the second quartile. This suggests that while working in an enclave occupation is

associated with lower wages in the cross section, it does not negatively affect wage growth.

To provide a more formal test of the effect of enclave occupations on wage growth, we

turn to growth curve models using SIPP data in Table 6. The growth curve model is estimated

using the command xtmixed in Stata by treating time as a random coefficient and including

20

interaction terms between time and selected covariates (see Rabe-Hesketh and Skrondal 2005 for

a more complete discussion of growth curve models). The benefit of the growth curve model is

that it takes advantage of the longitudinal SIPP data to model both wage levels and wage growth.

Hall and Farkas (2008) provide an example of using a growth curve model to study immigrant

earnings trajectories. A basic depiction of the growth curve model we estimate is as follows:

First, in Equation 4 we are modeling log wages of individual i at time t with a random

intercept, 0β , and slope, 1β where itε is a standard error term.

(4) 0 1ln (time )ijt it itw β β ε= + +

In Equation 5, we model the intercept as a function of sets of observed covariates X and Z, along

with a person specific random effect, iμ .

(5) 0 0 1 2it it iX Z uβ α α α= + + +

Finally, in Equation 6, we model the effect of time on wages with a constant, a subset of our

observed covariates, Z, and a person specific random effect:

(6) 1 0 1Zit iβ δ δ φ= + +

In all of the models in Table 6, the variable for occupational English measures the proportion of

limited English speakers in the respondent’s occupation in the first wave of data. As a result, we

test for the effect of working in an enclave occupation on subsequent wage growth. The results

for each model in Table 6 are presented in two panels. The “Levels” panel presents coefficients

for wage levels (the model for the intercept terms in Equation 5), while the “Slopes” panel

presents coefficient for the individual slope of wage growth over time (the model for wage

growth depicted in Equation 6). The slope coefficients measure the effect of time and the

interaction effects of selected independent variables with time

21

Model 1 presents results for the 2004 SIPP panel. The slope coefficient for occupational

English (.0362, s.e. .066) indicates the impact of this variable on wage growth. While

occupational English is negatively correlated with the level of wages, the effect on wage growth

is not statistically significant at the .05 level. This result is consistent with the sorting

explanation of occupational enclaves; immigrant workers work in enclave occupations because

they offer employment opportunities while they are learning English, and there is no negative

effect on wage growth over time.

Models 2-4 of Table 6 test this hypothesis with the combined 1996 and 2001 SIPP panels.

In Models 3 and 4 we find that the effect of occupational English on wage growth is positive but

not statistically significant at the .05 level. In Model 4, the effect of occupational English on

wage levels is smaller for recent immigrants (i.e., the interaction term between recent immigrant

and occupational English is .171), consistent with an interpretation of the results from the ACS

data in Table 4 based on sorting.

Overall, the results presented in Tables 4, 5, and 6 point to an important divergence in

results. An analysis of the “effect” of occupational enclaves based on cross sectional data with

the ACS in Table 4 suggests that working in occupations with a large number of poor-English

speakers reduces wages, even after controlling for a large number of individual level variables.

In contrast, the longitudinal data from the SIPP indicates that working in an occupational enclave

does not constrain wage growth in Tables 5 and 6.

Case Study Evidence of Latino Construction Workers

To complement our quantitative findings, we now turn to a case study discussion based

on a year of field work (2007-2008) in the construction and buildings trade in the Raleigh-

22

Durham-Chapel Hill area of North Carolina, an industry and location in which immigrant

workers are increasingly concentrated in several occupations where they work alongside co-

ethnics. Complete results from this study are reported elsewhere (Hagan and Lowe 2008), and

the goal here is to supplement our statistical results with detailed qualitative data. The study

interviewed roughly 50 immigrant workers, their supervisors (encargados), and their employers

at job sites, public places, and their homes. Most of the immigrant workers were undocumented

and recent newcomers, having migrated from Mexico or Guatemala to North Carolina since

2000. None spoke English well. Their supervisors were also Latino but had established work

and residential histories in the United States, and many were bilingual and legal residents of the

United States. The ethnicity of the employers/owners was more varied: most were white,

although a number were either Latino or Black. The field data examines the social mechanisms

through which newcomer immigrants experience social mobility in occupational enclaves as it

unfolds in the construction industry of the American South.

The construction and buildings trade sector of the U.S. economy is a major source of jobs

for Latino immigrants. In 2006, Latinos filled two out of every three new construction jobs, and

the vast majority of these jobs were filled by newcomers from Mexico and Central America

(Pew Latino Center 2007). In North Carolina, the construction industry is the largest employer of

Latinos immigrants; at least 40% of the state’s construction force is Latino, with estimates

reaching as high as 70% in urban areas. The state’s Latino construction workers are

concentrated in drywall, carpentry, roofing, masonry, concrete and paving, and landscaping. In

these areas newcomer immigrants are usually hired as ayudantes (helpers or construction

workers) where they earn between $8 and $14, depending upon demonstration of skills, legal

status, time on the job, and cooperation with coworkers. At the time of the study, entry level

23

workers were earning $8.00 dollars which was significantly higher than the state’s $6.55

minimum wage hour.

In the study sample, almost all the workers interviewed were recruited to their jobs

through social networks. Some workers migrated from Mexico or Guatemala to join family and

friends in North Carolina. Others left jobs in Texas and California to take advantage of the

booming construction industry and higher wages in the southeast. Most interviewed had for

some time enjoyed steady work in a firm or with a work team when they were interviewed.

Many agreed that although construction work was grueling and exhausting, it was the best job

for a newcomer who lacked English skills and formal training because of the high wages and

opportunities to learn new skills.

Interestingly, although most immigrant workers we interviewed argued that knowledge of

English was very important for economic advancement, most experienced considerable wage

growth despite not knowing English. These workers depended on co-ethnics for initial entry into

the construction sector and it was after they gained work-related skills that they began to see

changes in wage growth. Manuel’s narrative reflects the experiences and expectations of many

newcomer immigrants we interviewed in construction. Manuel arrived from Guatemala in 2006.

Back home, he worked as a bank teller. In North Carolina, he found an entry-level job as an

ayudante at a large commercial firm that builds schools and other public works throughout the

southeast. He was recruited by his cousin, Ricardo, one of several bilingual encargados at the

firm (first or second line supervisors). Manuel works in a team with six other Latinos (four

Mexicans and two Guatemalans) and under the supervision of Ricardo. Some in the team are

more experienced than others, so on-the-hand training is a constant feature of the work process.

None of Manuel’s co-workers speak more than few sentences in English.

24

When Manuel was first hired, his main job was cleaning up the job site, directing traffic,

and fetching things for other workers and encargados. As an informal apprentice to his cousin,

he learned basic carpentry, drywall installation, and masonry. When Manuel was hired in 2006,

he earned $8 an hour; two years later, in 2008, he earned $12 an hour. Because most workers do

not speak English, Ricardo’s boss relies on him to train his team and reward skills learned with

wage increases. Manuel depends on the support of his cousin to receive instructions on what

needs to get done at the jobsite. Manuel believes that he can earn up to $17 an hour without

English skills, knowledge of blueprints, and operation of heavy machinery. His goal is to follow

Ricardo’s footsteps and become a maestro (skilled craftsperson who has mastered multiple

skills) to surpass his cousin’s $28 an hour wage. Eventually, he hopes to start his own

construction business. To achieve these goals, Manuel plans to enroll in a local community

college that provides English classes and Spanish language classes in carpentry and blueprint

reading. He recognizes that learning English is the most important factor to move beyond entry

level jobs in the construction sector. In particular, English will allow him to communicate with

his white employer and market his own skills without having to depend on an intermediary.

Francisco, 35 years of age, is a recently arrived migrant from Guanajuato, Mexico. At

the time of the interview, he had only been residing in the U.S. for six months. While in Mexico,

he worked as an ayudante in construction before becoming a maestro. Since arriving in the U.S.,

he has worked as a day laborer and recently had landed a job with a Latino sub-contractor. He

recognized that by working for a sub-contractor, he was not remunerated for the construction

skills that he brought from Mexico. Still, he recognized that he needed “to do his time” and learn

how to navigate the labor market before he could earn higher wages. Francisco understands that

his inability to speak English prevents him from marketing his skills to mainstream construction

25

firms. As a result, he was forced to work for a Latino subcontractor that marketed his skills but

did not pay him the appropriate wage given his extensive training in Mexican construction. He

recognizes this as a temporary roadblock and uses his current job to earn wages until the ideal

job comes along. More importantly, he understands that working alongside co-ethnics allows

him to acquire U.S. based construction skills. Though he currently only earns $8.00 dollars, he

believes that the skills he brought from Mexico will eventually allow him to double his hourly

wages.

Working alongside co-ethnic bosses and employees has allowed Manuel, Francisco, and

other newcomer immigrants the opportunity to acquire on-the-job skills and learn about the

construction industry and the U.S. labor market more generally. In the short run, both men

sacrificed wages with the understanding that their current situation was only temporary. With

the skills, knowledge, and information that they acquire through working with co-ethnics on

construction sites, both workers hope to continue to climb the occupational ladder within the

construction and building trades. The basic pattern of wage mobility described in these

narratives is consistent with a sorting model of occupational enclaves: although the initial

occupations of Manuel and Francisco did not pay high wages and involved work among Spanish

speaking co-ethnics, they were, at least in these two cases, stepping stones to subsequent upward

mobility.

Discussion and Conclusion

This study uses cross-sectional data from the 2005 and 2006 American Community

Survey and longitudinal data from the 1996, 2001, and 2004 panels of the Survey of Income and

Program Participation to analyze the effect of working in occupations with large numbers of

limited English speakers on the wages and wage growth of Latino workers. In order to measure

26

English proficiency at the occupational level, we aggregated data from the ACS to the

occupational and state level and then merged this back on to our individual level ACS and SIPP

data. Our findings point to a crucial distinction between wage levels and wage growth: although

the proportion of workers in the respondent’s occupation with limited English was negatively

associated with wages in the cross-section, it had no effect on wage growth, based on our

analysis of longitudinal data from the SIPP.

These results have important implications for understanding the process of economic

assimilation for Latino immigrants. While recent studies on Latino occupational segregation

have argued that the crowding of immigrant Latinos workers into “brown collar” occupations

and segregated jobs reduces their wages (Catanzarite 2000; Catanzarite and Aguilera 2002;

Kmec 2003), our results indicate that this literature paints an overly pessimistic picture of the

effect of working with co-ethnics because it relies upon cross-sectional data and, as a result,

misses upwardly mobile workers who move on to other occupations over time. Instead, we offer

an alternative explanation based on the ethnic enclave hypothesis, which stresses the “sheltering”

effect of working in an ethnically based economy for immigrant workers (e.g., Wilson and Portes

1980, Evans 2004, Bailey and Waldinger 1991). We recast brown collar occupations as

“occupational enclaves”—occupations with substantial numbers of Spanish speakers and/or

workers with limited English ability—and argue that in many cases these occupations provide

immigrants with employment opportunities while they adjust to new labor market conditions,

learn English, and acquire U.S. based human capital. In contrast to the ethnic enclave

hypothesis, however, we maintain that the benefit of these occupational enclaves is temporary;

for upwardly mobile immigrants, they are stepping stones to better jobs, a “means to an end”

rather than an end themselves. For immigrant workers who don’t move on, however, the

27

opposite is true: the relatively low average pay of these occupations indicates that they are not

desirable jobs to end up with. In our theory section, we present a simple model of occupational

sorting for immigrant workers which illustrates how the upward mobility of successful workers

might bias estimates of the cross-sectional “effect” of occupational characteristics on wages,

leading to an overestimation of any negative occupation-level effects.

Two important qualifications of our paper should be noted. First, we are not claiming

that occupation-level discrimination and crowding do not have potentially important implications

at a structural level for race and ethnic inequality. Instead, our goal is to point out the

complexity of the process of economic mobility for immigrant Latinos, many of whom arrive in

the U.S. with limited English ability and only partial knowledge of the U.S. labor market. It is

quite possible that the two effects could coexist simultaneously: a structural effect could

constrain immigrant economic assimilation at the aggregate level even as a steady flow of

upwardly mobile immigrants make a successful transition from “brown collar” occupations to

the mainstream labor market.

The second qualification of our paper focuses on our empirical results in the growth-

curve models with the SIPP data in Table 6. The key finding of these models is that the level of

occupational English during wave 1 of the data, i.e., the proportion of individuals in the

respondent’s initial occupation who report limited English proficiency, has no effect on

subsequent wage growth, a result that is consistent with the descriptive evidence of average wage

changes by quartile of occupational English in Table 5. This suggests that working in an

occupational enclave does not reduce wage growth. At the same time, however, there is also no

evidence that the effect on wage growth is positive (the coefficient in Table 6 is positive, but not

statistically significant from 0 at the p=.05 level). What does this null result for occupational

28

English mean? We would argue that the key to interpreting the effect of occupational English in

the growth-curve models in Table 6 is to compare it to the levels coefficient for occupational

English or the cross-sectional results using the ACS data in Table 4. The cross-sectional

coefficient is large in size and negative, consistent with the brown-collar perspective, while the

coefficient for wage growth shows no effect. This divergence between the cross-sectional and

longitudinal results is consistent with our theoretical model, which allows for the possibility that

upwardly mobile workers will move on to other occupations over time: if enough workers move

up, then there will be an increasing divergence between the cross-sectional and longitudinal

coefficients.

The quantitative results presented here are also consistent with our qualitative results

based on ethnographic field work and interviews with immigrant Latino workers in the

construction industry. As discussed above, employment histories of these workers document

nontrivial levels of mobility since immigration, even for those workers who were still largely

working in Latino dominated jobsites. In addition, conversations with these workers revealed

their awareness of the steps necessary for further advancement (e.g., English language

proficiency and job related skills) as well as the expectation that their initial jobs in the U.S. were

temporary, based on their observation of the work trajectories of other immigrants.

In conclusion, we would like to stress the importance of dynamic models of immigrant

economic mobility that can capture both the complexity of trajectories that different workers

experience as well as larger structural factors, such as those identified by the brown-collar

occupation literature, which may be operating at the same time. The persistently large wage gap

between white and Latino workers, combined with evidence of lower overall levels of wage

growth among Latino immigrants (Lubotsky 2007), means that it would be naïve to expect the

29

upward mobility of successful Latino immigrants to eliminate ethnic stratification between

Latinos and other groups in the U.S. labor market. At the same time, however, we argue that

advising recent immigrants to avoid working with other co-ethnics—e.g., advice based upon a

literal interpretation of the brown-collar occupations literature—would be misleading,

particularly if the immigrants are constrained by a lack of fluency in English and limited U.S.

labor market skills. Instead, our results show that working in occupational enclaves may be

beneficial—in the sense that they provide employment to recent immigrants given these essential

constraints of language and experience—and that, on average, they do not appear to restrict

subsequent wage growth. Ideally, future research on this topic would attempt to identify

occupation-specific effects on wage growth, under the assumption that some immigrant

occupations may promote mobility while others may impede it, as well as extend these SIPP

panels by linking them to Social Security data (i.e. similar to the approach adopted in Lubotsky

2007) to see if the results we have documented here with relatively short SIPP panels hold up

over longer periods of time.

30

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34

TABLES

Table 1: English ability of Latino immigrants, by years in the U.S. Years in the U.S. English Ability 0-5 6-10 11-15 16-20 21+ Total Speaks only English 3.03 2.64 2.71 3.44 7.06 4.39 Speaks very well 11.99 19.13 27.29 31.48 43.32 29.39 Speaks well 16.2 24.23 27.27 28.06 24.75 24.01 Not well 33.84 35.29 29.41 26.37 18.33 26.87 Does not speak English 34.93 18.72 13.33 10.64 6.55 15.34 Total 100 100 100 100 100 100(N) 26,203 25,459 20,516 23,140 51,150 146,468 Source: 2005 & 2006 ACS

35

Table 2: Proportion of poor English speakers in respondent’s occupation. Latino immigrants by years in the U.S.

Years in the U.S. Proportion in occupation who

do not speak English well Number of

cases 0-5 0.189 26,203 6-10 0.173 25,459 11-15 0.166 20,516 16-20 0.159 23,140 21+ 0.127 51,150 Total 0.157 146,468 Source: 2005 & 2006 ACS

36

Table 3: Top paying occupations with at least 10% limited English speakers

Occupation Occ. code1

Average wage2

Proportion limited

English3 Importance of English4

Number of cases

Reinforcing iron and rebar workers 650 19.25 0.108 2.9 158 Brickmasons, blockmasons, and stonemasons 622 15.55 0.188 3.0 3602 Carpenters 623 14.12 0.104 2.9 27319 Molders, shapers, and casters 892 13.35 0.106 2.4 725 Cement masons, concrete finishers, and terrazzo workers 625 13.35 0.213 3.0 1519 Aircraft structure, surfaces, rigging, and systems assemblers. 771 13.35 0.113 3.1 177 Drywall installers, ceiling tile installers, and tapers 633 12.83 0.261 2.7 3282 Painting workers 881 12.83 0.111 2.3 3011 Insulation workers 640 12.64 0.126 3.4 728 Metalworkers and planning machine setter 822 12.50 0.108 2.5 8635 First-line supervisors 600 12.50 0.101 2.7 1216 Grinding, lapping, polishing, and buffing 800 12.42 0.105 2.6 1035 Plasterers and stucco masons 646 12.15 0.332 2.5 753 Carpet, floor, and tile installers 624 12.00 0.160 2.3 3777 Upholsterers 845 11.74 0.103 3.1 905 Food and tobacco roasting 783 11.68 0.120 2.0 191 Construction laborer 626 11.66 0.181 3.0 26716 Other Production workers, 896 11.66 0.105 2.8 22104 Chefs and head cooks 400 11.66 0.120 3.5 4619 Roofers 651 11.66 0.235 2.6 3479 Notes: 1 3-digit code, 2000 census occupations 2Source: Current Population Survey data, all workers in occupation. 3Proportion of workers in occupation who report speaking English poorly or not at all. 4Importance of English for occupation, 1=not important, 5=very important (source ONET occupation data) Source: 2005 & 2006 ACS

37

Table 4: The Effect of Occupational Language on Log Wages 2005 and 2006 American Community Survey, Latino immigrant workers.

(1) (2) (3) (4) Coefficient Lnwage Lnwage lnwage lnwage Years in US1: 6-10 0.0525*** 0.0822*** 0.0979*** (0.0048) (0.0046) (0.0067)

11-15 0.0976*** 0.152*** 0.173*** (0.0052) (0.0050) (0.0072) 16-20 0.154*** 0.214*** 0.236*** (0.0052) (0.0050) (0.0070) 21+ 0.288*** 0.349*** 0.423***

(0.0045) (0.0044) (0.0061) Occupation limited English2 -0.921*** -0.557*** -0.460*** -0.281*** (0.0092) (0.0097) (0.010) (0.019) English Ability: speaks very well 0.0116 0.00810 0.0114 (0.0077) (0.0074) (0.0073)

Speaks well -0.105*** -0.0634*** -0.0525*** (0.0079) (0.0075) (0.0075) Not well -0.211*** -0.135*** -0.125*** (0.0078) (0.0075) (0.0076) Does not speak English -0.274*** -0.172*** -0.169***

(0.0084) (0.0081) (0.0081) Female -0.195*** -0.195*** (0.0031) (0.0031) Construction occ. 0.0892*** 0.0979*** (0.0074) (0.0074) Interaction terms, Occ English by: construction

0.185*** 0.136***

(0.027) (0.027) Years in US1: 6-10 -0.0755*** (0.027)

11-15 -0.0993*** (0.029) 16-20 -0.102*** (0.028) 21+ -0.474***

(0.025) Dummy variables for education No No Yes Yes Constant 2.549*** 2.479*** 2.309*** 2.277*** (0.0022) (0.0080) (0.012) (0.012) Observations 146468 146468 146468 146468 R-squared 0.06 0.14 0.22 0.23

Notes: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 1Excluded category: 0-5 years 2Proportion in occupation who speak English not very well or not at all.

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Table 5: Wages and wage growth by quartile of % in occupation with poor English, 2004 SIPP Occupational English quartile

Proportion poor English Average wage

1-year Wage growth (N)

Top 25% 0.014 17.13 0.003 827 2 0.037 14.21 0.008 828 3 0.108 12.09 0.004 826 Bottom 25% 0.320 10.61 0.007 826 Total 0.122 13.45 0.006 3307

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Table 6: Growth curve models of log wages, Latino workers 1996, 2001, and 2004 SIPP 2004 SIPP 1996 &2001 SIPP .

Coefficient (1) (2) (3) (4) Slopes Time (years) 0.0193** 0.0310*** 0.0314*** 0.0317*** (0.0087) (0.0016) (0.0030) (0.0030) Occupational English3 0.0362 0.0129 0.00609 (0.066) (0.010) (0.011) Recent immigrant x Occupation English 0.0163 (0.022) Recent Immigrant -0.00204 -0.00168 (0.0034) (0.0035) Female -0.00262 -0.00205 (0.0033) (0.0033) Levels Immigrant 0.0404* -0.0270*** -0.0260*** -0.000324 (0.023) (0.0087) (0.0089) (0.0099) Recent immigrant2 -0.139*** (0.023) Occupation English -0.687*** -0.536*** -0.542*** -0.558*** (0.089) (0.029) (0.029) (0.032) Occupation English x Recent immigrant 0.171* (0.089) Education1: High School 0.0855*** 0.0854*** 0.0874*** (0.0097) (0.0097) (0.010)

Some college 0.196*** 0.196*** 0.197*** (0.011) (0.011) (0.011) College 0.442*** 0.442*** 0.451*** (0.015) (0.015) (0.015) Post-college 0.679*** 0.679*** 0.681***

(0.023) (0.023) (0.023) Female -0.168*** -0.173*** -0.172*** -0.180*** (0.018) (0.0084) (0.0086) (0.0088) Constant 2.091*** 2.482*** 2.482*** 2.485*** (0.044) (0.010) (0.011) (0.011) Observations 7797 57525 57525 54949 Number of groups 2430 8913 8913 8435 . . .

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 1 Excluded category: less than high school 2Immigrated in the last 5 years 3Proportion in occupation who speak English not very well or not at all.