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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
2
(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
5
(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.
6
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
7
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
8
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
9
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
11
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= + +
12
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
14
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
15
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
16
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
19
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.