DISCUSSION PAPER SERIESIZA DP No. 2974

Modeling Immigrants’ Language Skills
Barry R. Chiswick
Paul W. Miller

August 2007

Forschungsinstitut
zur Zukunft der Arbeit
Institute for the Studyof LaborModeling Immigrants’ Language Skills
Barry R. Chiswick
University of Illinois at Chicago
and IZAPaul W. Miller
University of Western Australia
and IZA

Discussion Paper No. 2974
August 2007

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IZA Discussion Paper No. 2974
August 2007

ABSTRACT
Modeling Immigrants’ Language Skills*
One in nine people between the ages of 18 and 64 in the US, and every second foreign-born
person in this age bracket, speaks Spanish at home. And whereas around 80 percent of adult
immigrants in the US from non-English speaking countries other than Mexico are proficient inEnglish, only about 50 percent of adult immigrants from Mexico are proficient. The use of alanguage other than English at home, and proficiency in English, are both analyzed in thispaper using economic models and data from the 2000 US Census. The results demonstrate
the importance of immigrants’ educational attainment, their age at migration and years spentin the US to their language skills. The immigrants’ mother tongue is also shown to affect theirEnglish proficiency; immigrants with a mother tongue more distant from English being lesslikely to be proficient. Finally, immigrants living in ethnic enclaves have lesser proficiency inEnglish than immigrants who live in predominately English-speaking areas of the US. The
results for females are generally very similar to those for males, the findings from an ordered
probit approach to estimation are similar to the findings from a binary probit model, and theconclusions drawn from the analyses mirror those in studies based on the 1980 and 1990 US
Censuses. Thus, the model of language skills presented appears to be remarkably robust
across time and between the genders.

JEL Classification:
Keywords:

F22, J15, J24, J40

immigrants, language, enclaves, human capital

Corresponding author:
Barry R. Chiswick
Department of Economics
University of Illinois at Chicago
601 South Morgan Street
Chicago, IL 60607-7121
USA
E-mail: brchis@uic.edu*

This paper is for presentation at the “Conference on Immigration: Trends, Consequences andProspects for the United States”, September 9-10, University of Illinois at Chicago. We thank Derby
Voon for research assistance. Chiswick acknowledges research support from the Institute ofGovernment and Public Affairs, University of Illinois. Miller acknowledges financial assistance from theAustralian Research Council.

MODELING IMMIGRANTS’ LANGUAGE SKILLS
I. INTRODUCTION
Immigrants typically fare quite poorly in the labor market in the US and in otherimmigrant receiving countries in the immediate post-arrival period. Using data from the1990 US Census, Miller and Neo (2003) show that the earnings in 1989 of adult malerecent arrivals in the US were up to 28 percent below the earnings of comparable nativeborn workers. This disadvantage arises because of the less-than-perfect internationaltransferability of immigrants’ human capital, their lack of knowledge of the institutionsof the US labor market, and perhaps through discrimination. The earnings disadvantage
is temporary for most groups: Immigrants tend to rapidly catch up with the native born,and much of the empirical research into immigrant labor market outcomes has focused on
the factors that enhance this adjustment process (Chiswick (1978)(1979)). Attention has
been directed at post-arrival investment in human capital in general, and destinationspecific language capital in particular. Among immigrants in the US labor market,
English language skills attract considerable wage premiums: Chiswick and Miller (1995)
show that the earnings gain in 1989 associated with proficiency in English among adultmale immigrants was approximately 17 percent, or the equivalent of around three yearsof schooling. This earnings increase is similar to that associated with dominant language
proficiency in Canada, and is more than twice the increase in earnings associated withEnglish language proficiency among immigrants in Australia. Destination language skills
have been found to be important in non-English speaking destinations, such as Germany
and Israel (Chiswick and Miller (1998)). Moreover, part of immigrants’ economic
progress captured by duration of residence variables may be linked to improvements intheir English language skills (see, for example, McManus, Gould and Welch (1987)).

Presumably largely reflecting these rewards, the English language skills of the foreignborn in the US improve rapidly with duration of residence. Among adult male workers
who had lived in the US for fewer than four years, the 1990 US Census reveals that as
few as 13 percent spoke English only. A further 44 percent spoke a language other thanEnglish at home, but spoke English very well. However, fully 43 percent of this groupspoke a language other than English at home and reported that they spoke English “not2

well” or “not at all” (Chiswick and Miller (1996)). Among those who had resided in the
US for 21-30 years, however, monolingual English speakers were more prevalent (31percent), and those with either limited or no English skills (i.e. they spoke English “notwell” or “not at all”) were far less prevalent (14 percent).
This paper examines the processes associated with the acquisition of English language
skills among the foreign born in the US. It has a special focus on the role that ethnic
networks and linguistic distance play in the acquisition of dominant language skills, andemploys alternative measures of ethnic networks to that pioneered in Chiswick and Miller(1998)(2002)(2005a). Extensions of the analysis of language practice to consider originlanguage retention are also presented. The paper also gives attention to females as wellas to male immigrants, and consolidates a series of modifications to the language model
made in various papers. Being based on data from the 2000 US Census, it permits an
update of the evidence using the 1990 Census reported in Chiswick and Miller(1998)(2002)(2005a).

The structure of the paper is as follows. Section II outlines briefly the model of dominantlanguage acquisition, introduced in Chiswick (1991) and Chiswick and Miller (1992),
and subsequently developed in Chiswick and Miller (1995)(1998)(2005a). Section III
outlines the data set to be used, the US 2000 Census of Population, and presents estimatesof models of dominant language proficiency for males. Estimates from Binary Probit andOrdered Probit models are considered. Similar sets of estimates for females are presented
in Section IV. Section V contains the analyses of origin-language retention among
immigrants. These are also presented for both males and females. Concluding comments
are provided in Section VI.

II. MODEL OF DOMINANT LANGUAGE PROFICIENCY
Immigrant decision making in relation to the learning of dominant language skills can beanalyzed using a human capital framework. Destination language proficiency among
immigrants is a form of human capital that is productive in consumption and/or labormarket activities, it is costly in terms of time and out-of-pocket expenditures to acquire,3

and it is embodied in the person. Thus, the optimal investment in destination language
proficiency for immigrants is determined in the same manner as for other human capital
investments: as the level of investment that will equate the marginal rate of return on theinvestment to the marginal interest cost of the funds they invest. This decision-making
process is outlined in Figure 1 (which is based on Becker and Chiswick (1966)) for
immigrants from a non-English speaking country.

The demand for language skills (D) is given by the marginal rate of return on the investmentin these skills. The position of this curve depends on the costs of, and benefits from, theinvestment in language skills. The demand (or marginal return schedule) will be higher the
lower are the costs and the greater are the benefits from investment in language capital. The
costs of the investment include the direct costs of language classes, as well as the indirect
costs of foregone earnings. The benefits from destination language acquisition include
higher wages, lower chances of being unemployed, greater efficiency in consumption, andgreater participation in social activities and political processes.
Figure 1
Supply and Demand for Funds for Investment by Immigrants From
non-English speaking Countries in English Language Capital

Marginal rate of
return (D),Marginal
Interest Cost ofFunds (S)S

r0

D
Dollars Invested
in English
Language Capital

I0

The demand curve is downward sloping for several reasons. First, individuals will invest
first in those dimensions of language skills that have the highest payoff (marginal returns)
4

in labor market and non-labor market activities. These are followed by less productive
investments. Second, as previous investments raise the opportunity cost of time, themarginal rate of return declines even if the dollar value of the benefit is unchanged.
Third, since investments take place over time, the greater the investments already made,other things the same, the fewer the time periods remaining in the future and hence thelower the return on additional investments. And, finally, as with most activities, whether
investment, production or consumption, beyond some point diminishing returns set in thegreater the intensity of the activity. The marginal product of an hour of language learning
per day will, at some point, start declining the greater the number of hours in language
study.

The supply of funds for investment in language skills (S) is given by the marginal interest
cost of funds. This curve will be upward sloping because immigrants will use lower-costsources of finance (own savings, family and friends) before they access more expensive
sources. For these reasons, this curve will also be lower for those with greater access toresources, including greater wealth, for financing the investment in language capital.

The intersection of the demand and supply curves gives an optimal level of English
language proficiency (I0) for the immigrant.
The estimating equation to be developed below can be thought of as a reduced form
equation incorporating both the supply and demand conditions for funds for investment inlanguage capital. The actual dollar amounts invested cannot be estimated, but othervariables being the same, the immigrants’ level of English language proficiency can serve
as a proxy for the dollars invested. The explanatory variables considered below (e.g., age
at migration, educational attainment, years of residence in the US) may, for example,
shift the demand curve outward or inward, resulting in higher or lower proficiency inEnglish.1
1

For example, due to greater efficiency in language acquisition, an immigrant who arrived in the US at 10
years of age would be expected to develop greater proficiency in English from any dollar investment than
one who arrived in the US at 50 years of age. Thus, age at migration would be a shift variable in the
demand for funds equation and affect investment levels and proficiency.

5

For an immigrant from an English-speaking country, however, the marginal rate of returnon investments in English language training are so low that no or minimal investments
would be made. This is illustrated in Figure 2, where there are no investments in postmigration English language training. Given the trivial magnitude of the investments bythis group, models of English language acquisition have generally been applied only to
non-English-speaking background immigrants.

Figure 2
No Investment in English Language Capital

S

Marginal rate of
return (D),Marginal
Interest Cost ofFunds (S)D
Dollars Invested
in English
Language CapitalThe demand and supply curves will shift for a variety of reasons. The demand curve will
shift to the right if an immigrant is more efficient in the production of language capital(D1 compared to D0 in Figure 3). This might be associated with a higher educational
attainment, or being of a younger age at the time of migration. The supply curve for an
immigrant with greater wealth will be to the right of that for an immigrant with relatively
little wealth (S1 compared to S0 in Figure 3).

Figure 3
Shifts in Supply and Demand for Funds for Investment by Immigrants From
non-English speaking Countries in English Language Capital

6

Marginal rate of
return (D),Marginal
Interest Cost ofFunds (S)

S0

S1

r0

D1
D0
Dollars Invested
in Destination
Language Capital

I0

Variations in English proficiency among the immigrant population will therefore belinked to factors that shift the demand and supply curves in Figure 3. Chiswick andMiller (1992)(1995)(1998)(2005) categorise these as exposure, efficiency, and economic
incentive factors. Hence, destination language proficiency (LANG) can be expressed as:

LANG = f(Exposure, Efficiency, Economic Incentives)(1)

A. Language Measures
Three measures of language practice are used in the statistical analyses. The first is abinary measure (LANG2), which is set equal to one for individuals who speak only
English at home, or if a language other than English is spoken in the home, the individual
speaks English either “very well” or “well.” The variable is set to zero where a language
other than English is spoken in the home and the respondent speaks English either “notwell” or “not at all.” The second measure (LANG5) is also a measure of proficiency, and
it is a polychotomous (five categories) variable, defined to include all proficiency
categories contained in the relevant Census variables: (i) speaks English only at home;speaks a Language other than English at home and speaks English (ii) very well; (iii)well; (iv) not well; (v) not at all. The third language variable (MT) is a binary variable
that records whether the individual speaks a language other than English at home (MT =71) versus speaking only English at home (MT = 0). Where a language other than English
is spoken at home it is assumed, for ease of discussion, to be the individual’s mothertongue. This is a measure of origin language retention.

B. Exposure Factors
Exposure to the destination language can occur before or after immigration. The degreeof pre-immigration exposure depends, in large part, on the extent to which English isused in the origin country. This could be due to a British/US colonial past, or a major US
military presence, though with advances in telecommunications and the world-wide
spread of American and British media and movies, and more recently the internet, it
would seem that pre-immigration exposure could become reasonably widespread. A
dichotomous variable for whether the origin was a colony of the United States or theUnited Kingdom (COLONY) is used to capture some of these influences..

Post-immigration experience will depend on two main factors. First, there is the timeunits of exposure to English. This is the extensive margin, and can be measured by the
number of years since the immigrant came to the US to stay. A quadratic specification(YSM and YSMSQ) is used to allow the effect of an extra year in the United States to belarger in the early years than in subsequent years.2

The variable assumes that theimmigrant has lived continuously in the US from the time of arrival. Some immigrants,
however, spend time outside the US after the initial migration. The potential effects ofthis sojourner migration, or to- and fro-migration, on English language skills can beassessed for those immigrants who arrived before 1995 by a dichotomous variable(ABROAD5) which is unity where the immigrant lived abroad five years ago, and is zerootherwise.

2

While improvements in English skills can be expected with practice, most investment in language skills
should occur just after migration, since investments in language skills tend to be more profitable if theperiod over which the benefits will be received is longer, since the opportunity cost of investment inlanguage training is lower in the early period when wages are lower, and since the returns are greater if
investments with high rates of return are made sooner rather than later. The complementarity in the labormarket of language skills and schooling and post-migration labor market experience also encourages earlier
investments. Consequently, a quadratic specification for duration of residence should be used.

8

Second, there is the intensive margin, which is the intensity of exposure per unit of time.
The intensity of exposure per unit of time depends on the immigrant’s neighborhood andfamily experiences.

Ethnic enclaves can be expected to play a major role in theimmigrant’s language skills. An immigrant who lacks English language skills can avoid
having to learn English by living in an area in which many others use his or her origin
language. Similarly, working in a linguistic enclave can limit the benefits from acquiring
English skills. These effects can be measured by the proportion of the population of thearea, regardless of nativity, that speaks the immigrant’s origin language (CONC). The
top 25 non-English languages spoken at home are utilized in the construction of theCONC variables. These cover about 91 percent of the sample of immigrants from nonEnglish speaking countries used in the analyses reported below.3 Language rather thanbirthplace or ancestry is used as the defining element of an enclave on the grounds that itbetter measures the cultural linguistic concept developed in Chiswick and Miller (2005b).
For example, the use of birthplace in the construction of this measure would encounter
difficulties with bilingual (e.g., Belgium) and multilingual countries (e.g., India), andareas over which there is a common language used in many countries (e.g., Spanish in
Mexico, Central America and much of South America).

Of special interest in the work reported below is the most appropriate definition of “area”
when attempting to capture these ethnic enclave effects in a model of destination
language proficiency. Three alternatives are employed, and these are distinguished by
the level of geographic identifiers used in their construction. The first is based on theState (50 States and the District of Columbia) of residence (CONC-State, which was usedin earlier Chiswick and Miller research). The second is based on the Super Public Use
Microdata Areas (Super-PUMAs) used in the 1 percent Public Use Microdata Sample(PUMS) of the 2000 US Census (CONC-Super).4 All 532 separate areas are utilised forthe construction of this variable. The third variable is constructed using the information

3

See Appendix A for a list of these languages and their shares of the immigrant population.

4

A Super-PUMA is a geographic entity that comprises at least 400,000 people.

9

on Metropolitan Statistical Areas (CONC-MSA).5 The 106 separate regions are used informing this measure using the 1 percent sample. In this instance, immigrants living
outside metropolitan areas are assigned the value of the concentration measureconstructed for the non-metropolitan components of their state of residence. The CONCvariable is set equal to zero in all three definitions for those reporting a language that is
not among the top 25 languages on the grounds that the density or concentration of
speakers of these languages is too low to matter.

The CONC variables constructed may not capture the intended influences outside
metropolitan areas. This is for several reasons, depending on the particular measureemployed. First, where the CONC variable is computed at the state level, it would be
expected that it would over-estimate the minority language concentration in nonmetropolitan areas. Second, where the CONC variable is computed from the moredisaggregated data on MSA-PMSAs, there is insufficient detail on the Census files topermit identification of non-metropolitan areas in each of the states. Accordingly, asingle non-metropolitan dichotomous variable (NON-MET) is included in the estimation
equation. A variable for the southern states (SOUTH) is also included to capture regional
influences. An alternative specification (described below) of regional variables is used
when the regression analysis is limited to those born in Mexico.

Language practice within the family will also influence the individual’s dominantlanguage proficiency.

Chiswick, Lee and Miller (2005a)(2005b) show that, due tosimilarities in the observed and unobserved characteristics of family members, there arelinks between their dominant language fluency. These factors include assortative mating,genetic and home investment linkages between parents and children, language learning in
the family, migration as a family unit and systematic reporting errors within a household,as well as the similarity in the processes governing dominant language proficiency forfamily members. The correlations in language proficiency are stronger for spouses than5

A metropolitan area is one of a large population nucleus, together with adjacent communities that have ahigh degree of economic and social integration with that nucleus. Where a metropolitan area has 1 million
people or more, two or more primary metropolitan statistical areas (PMSAs) may be defined within it.
Information on the Metropolitan Statistical Areas (MSA) and Primary Metropolitan Statistical Areas
contained in the variable MSA-PMSA1 is used in the construction of the CONC-MSA variable.

10

for parent-offspring combinations. The similarities in the underlying factors that giverise to these outcomes would be expected to be more apparent for those married prior toimmigration (where marriage is more likely to be to a spouse from the same country ofbirth with the same linguistic background) than for those married after immigration.
Where marriage takes place after immigration, it is more likely to be to a person who is
not proficient in the immigrant’s mother tongue. This may encourage the use of thedominant language.

Accordingly, a marital status variable (MARRIED is unity if

married, spouse present) is also employed in the model. It is not possible in the 2000
Census data to distinguish between pre- and post-migration marriages.6

The presence of children in the household could have a range of effects on immigrants’dominant language proficiency. Four channels have been identified in the literature. The
first concerns children as teachers, based on the greater ability of children to learn new
languages, and the intense exposure to the destination language in schools (Long (1990),
Newport (1990), Service and Clark (1993)).7 The rapid learning of the dominant language
among children enables them to assist the development of the dominant language skills of
their parents.

The second is children as translators. As children learn the dominant language, they
move into a position where they can serve as translators for their parents. This possibility
lessens the need for parents to acquire dominant language skills, at least from theperspective of consumption and home production. Children are unlikely to be able toserve this function in the workplace, other than possibility having a role in the context ofthe self-employed. This effect is likely to be stronger for mothers than for fathers.

Third, children can affect labor supply, particularly among females. To the extent thatinvestments in language skills are made in anticipation of labor market activity, and to
6

Year of first marriage was last asked in the 1980 Census.

7

However, Fathman’s (1975) review of the evidence indicated that “younger children are not necessarily
better second language learners than adults in all respects”, with her research showing that “after puberty
the ability to learn rules, to make generalizations or to memorize patterns may be more fully developed”.

11the extent that the workplace provides an environment conducive to the further
development of dominant language skills, reduced labor supply can dampen dominantlanguage proficiency. This effect is also likely to be stronger for mothers than for fathers.

Fourth, where parents seek to transmit the culture of their country of origin, they may
encourage the learning of their origin language among their children. Origin-language
use within the home may therefore compete with dominant language use, with thepotential to limit the development of dominant language skills among all family
members.

Thus, while the learning of dominant language skills from their children will have apositive influence on the dominant language skills of their parents, the remaining threefactors will tend to dampen the incentives for parents to acquire dominant language skills.
As such, the sign of the overall effect of children on parental language skills isambiguous. The effects of children on parental language skills would be expected todiffer between the mother and the father, being less positive or more negative for themother than for the father.

C. Efficiency Factors
There are four important measurable efficiency factors that can influence thedevelopment of dominant language skills among immigrants: age at migration,
educational attainment, refugee status, and linguistic distance.

The young appear, for biological reasons, to have a greater capacity to learn a new
language than do older individuals (Long (1990), Newport (1990), Service and Clark(1993)).8 Age at migration (AGE) would therefore be expected to have a negative impact
on dominant language skills. The effect of age at migration is measured by the partial
effect of age (AGE) when years since migration are held constant. The age variable isentered in quadratic form, Age and Age Squared, in the analysis reported in the text.

8

Although, see Fathman (1975) for an alternative perspective.

12Appendix B contrasts this measure with age at migration entered as a set of dichotomous
variables to test for discontinuities in the effects of age at migration.

Similarly, educational attainment (EDUC) is expected to be closely related to dominantlanguage outcomes.

The better educated may have technically superior language

production functions. This could arise through the better educated having greater innate
learning ability or unmeasured variables that enhance both forms of human capital. Or it
could be that having greater knowledge of one’s own language enhances the ability tolearn other languages. It is also likely that the destination language, particularly where itis an international language such as English, may have been learned as part of thecurriculum in either secondary school or tertiary studies abroad.9

The difficulty in learning a second language depends in part on the person’s mothertongue. The argument here can be put as follows: it should be more difficult for a
Chinese speaker to learn French than it is for a Spanish speaker to learn French because
the differences between the languages are that much greater in the former case than in thelatter case. In other words, the “linguistic distance” between Chinese and French isgreater than the distance between Spanish and French. The greater the linguistic distance
between the destination and origin language, the lower would be the efficiency of an
immigrant for learning the destination language.

This concept of linguistic distance has been developed by Chiswick and Miller (1998,
2005b). Their measure is based on the ability of Americans to learn a variety oflanguages in fixed periods of time. The lower the scores on a standardized proficiency
test, the greater the assumed distance between these languages and English.

Theequivalences outlined in Table 1 of Chiswick and Miller (2005b) are used in the data
analyses reported below. In the case of those who report that they speak only English athome, the mean of the linguistic distance scores of immigrants in the US from theperson’s country of origin was used. Fully 97 percent of the sample have valid data for

9This last factor could not explain the greater level of Hebrew language proficiency of immigrants with ahigher level of secular schooling in Israel (Chiswick and Repetto (2001)).

13

this measure. The remaining individuals are assigned the sample mean (their exclusionfrom the analyses yields similar results).

Linguistic distance may also be related to the degree of self-selection in immigration.
Individuals with a mother tongue more distant from English, perceiving greater difficulty
learning English, may only migrate if they have relatively high levels of unobservables
that are related to the ability to learn English, and with immigrant adjustment in general.

Admission criteria may be relevant for understanding immigrant adjustment (seeChiswick, Lee and Miller (2005c)). Unfortunately, the US Census does not provide
information on the visa used at entry, or the current visa status, other than whether theimmigrant has become a naturalized citizen.

Yet, research suggests that refugees

experience a different adjustment than family or economic immigrants. Refugee status
may impact on dominant language skills because refugees tend to be less favorably
selected for a successful adjustment in the destination than are economic migrants. The
less-intense selection arises because of the greater importance of factors in the migration
decision other than the expectation of economic success. Refugees often have less time toprepare for the move. The refugee variable (REFUGEE) is based on country of birth,
period of immigration, and age at migration. The latter criterion permits refugee status to
influence dominant language outcomes only where the person entered the US as anadult.10 A variable for US citizenship is not included since a degree of proficiency inEnglish is generally required to become a naturalized citizen.

D. Economic Factors
Economic incentives for dominant language proficiency are central to the model outlined
above. However, finding empirical counterparts to this set of factors is difficult. Only
variables that broadly correspond to the underlying influences can be considered. Hence,from the theoretical perspective, it is desirable to include the expected increments inearnings for each individual in the empirical applications. While this is not possible, it is10

As an example, adult immigrants from Cuba would be classified as refugees for this analysis if they
entered the US after Castro came to power in 1959, but not earlier Cuban immigrants, or those who came to
US as children.

14

known that there are strong links between educational attainment and the economic
returns from becoming proficient in the dominant language, and this suggests that theindividual’s level of education (EDUC) may serve as a proxy for the expected economic
returns for the investment in dominant language skills.

The incentive for an immigrant to acquire English proficiency will be greater the longer
the expected duration in the US, as this will be associated with greater returns from any
given investment. It is expected that the degree of return migration and the degree offavorable self-selection in immigration will vary with the distance of the origin country
from the US. Greater geographic distance implies a higher cost of migration and of return
migration.

This should deter the less able and be associated with better dominantlanguage skills among those who do immigrate. It also implies a lower propensity for
return migration which should also be associated with greater proficiency in English.
This distance effect is captured through a variable for the number of thousands of miles(MILES) from the major city in the origin country to New York, Miami or Los Angeles,
whichever is the shorter. A quadratic specification is used.

When the analysis is limited to immigrants from Mexico, the geographic distancevariable is computed with reference to the capital of their state of residence, and threecites in Mexico, namely Mexico City, Tijuana and Ciudaf Juarez. Two alternative
continuous measures were considered, namely the distance between Mexico City and thecapital of the immigrant’s current state of residence, and the minimum of the direct line
distance between the capital of their current state of residence and either Tijuana or
Ciudaf Juarez. The latter measure is used in the statistical analyses reported below as ityielded slightly stronger results. Moreover, in the analysis limited to immigrants from
Mexico, an alternative measure of the regional variable for the US is employed. Further
comment on these is provided below.

Hence, the empirical counterpart to equation (1) that is the basis for the analysis that
follows is:

15

LANG = f(Educational Attainment, Age at Migration, Age at Migration Squared, YSM,YSMSQ, ABROAD5, MARRIED, Children, NON-MET, SOUTH, MILES,MILESQ, Linguistic Distance, CONC, COLONY, REFUGEE)(2)
Following Chiswick and Miller (2001), the estimating equation includes five variables
based partly on country of birth, namely the proportion of individuals living in the same
region as the immigrant that speak his home language (CONC), whether the person is a
refugee (REFUGEE), whether the origin is a former British or American colony(COLONY), linguistic distance, and miles of the country of origin from the US.11 Unlike
dichotomous variables for country of birth, these variables have behavioral
interpretations, and they provide for greater understanding of the factors affecting
language practice among immigrants. Chiswick and Miller (2001) show that the
behavioral variables based on birthplace provide almost as much explanatory power as
the birthplace dummy variables. Accordingly, birthplace fixed effects are not included in
the model.

The data for the estimations presented below are from the 2000 Census of Population,
Public Use Microdata Sample, and are for the 1 percent sample of the foreign-born adult(25-64 year old) men and women from non-English speaking countries.12 This age
bracket is the group of immigrants for whom the issues surrounding language choice aremost acute. Separate analyses are conducted for men and women, and the extent towhich this is necessary is examined. The analyses are performed overall and separately
for immigrants from Mexico and all other countries. Mexico is the largest single source
country, providing over one-third of the men and women in the sample. Moreover,
Mexican migrants have much lower levels of skills (among adult males they have 8 yearsof schooling compared to 13 years for other immigrants) and a much greater proportion
11

The emigration rate variable employed by Chiswick and Miller (2001) is not used here, as theinformation is dated and more recent comparable data do not appear to be available. These data were also
affected by the presence of illegal immigrants in 1980 and their receiving amnesty by 1990.

12

Immigrants from the main English-speaking countries (UK, Ireland, Canada, Australia, New Zealand andthe Caribbean) are excluded as, for the reasons given in Section II, the language issues do not exist to any
great extent for this group.

16

of illegal aliens than migrants from other countries, and they may be of special interestfor these reasons. Moreover, Mexico and Canada are the only countries sharing a landborder with the United States. The variables are defined in detail, and the means andstandard deviations are reported, in Appendix A.

III. ESTIMATES: MALE IMMIGRANTS
Table 1 reports the basic regression analyses for foreign born men. The first column ofthis table lists estimates of a probit model examining variations in the summary measureof English proficiency given by the binary variable LANG2 described above. Three
figures are reported in each cell for the binary probit models. The first is the estimatedcoefficient for the probit index; the second is the associated ‘t’ statistic; and the third themarginal effect of the variable on the probability of being proficient in English. As there
are multiple marginal effects with the ordered probit model (one for each of the language
categories), only the estimated coefficient for the probit index and the ‘t’ statistic arepresented.

These estimates largely accord with the results reported in previous studies using the1990 Census (see, for example, Chiswick and Miller (2005a)). English proficiency
increases with years of schooling, with the partial effect of an additional year of
education being an improvement of 3.2 percentage points in the predicted probability of
being proficient in English.13 In comparison, English proficiency decreases at anincreasing rate with age at migration. That is, the older an immigrant is at the time ofentry into the US the less likely he is to become proficient in English, and this effect getsstronger with age. This is a reflection of the phenomenon established in the linguistics
literature of language skills being more difficult to acquire for older than for younger
individuals. Separate analyses (not presented here) indicate that the pattern of age effects
established in Table 1 carries across to an alternative specification of the estimating
The partial effects have been computed using the formula φ ( X β ) β k for continuous variables (where φis the standard normal density function), and as differences in predictions for groups distinguished within
the dichotomous variables. For example, the partial effect for marital status (MS) is the difference between
the predicted rate of proficiency for those who are married and that for those who are not married, wherethese predictions are sample averages.
1317equation based on a number of dichotomous variables for age at migration. These
analyses suggest that age at migration does not matter among immigrants from countries
other than Mexico up to around age 15. Increases in age at migration are associated withlower rates of English proficiency beyond this threshold. For immigrants from Mexico,
however, increases in age at migration are associated with lower rates of English
proficiency across the full range of ages at migration represented in the data. That is, forthis birthplace group there does not appear to be any critical age for the learning ofEnglish as a second language in the US.

Table 1
Probit Estimates of Language Models, Adult Foreign Born Men by Origin, 2000
Total Sample
Variables
Constant
Education

Age at Migration

Age at Migration Squared/100

Years Since Migration (YSM)YSM Squared/100

Abroad 5 years ago

MarriedWith own children under 6
years only
With own children 6 to 17
years only

Immigrants
from AllCountries
except Mexico

Immigrants
fromMexicoProbit-0.584(6.31)

Ordered Probit
0.882(13.43)

Probit0.293(2.21)

Probit-0.920(6.07)

0.107(88.89)[0.032]-0.008(1.84)[-0.010]-0.025(4.83)[-0.010]0.073(43.83)[0.020]-0.052(12.09)[0.020]-0.358(10.80)[-0.121]0.143(11.61)[0.044]-0.055(3.09)[-0.017]-0.066(4.60)[-0.020]0.084(95.84)0.122(68.18)[0.024]-0.044(7.16)[-0.010]0.009(1.30)[-0.010]0.080(34.92)[0.017]-0.063(10.75)[0.017]-0.415(8.65)[-0.102]
0.120(6.81)[0.024]-0.024(0.91)[-0.005]-0.062(3.15)[-0.013]0.082(47.48)[0.033]-0.001(0.09)[-0.009]-0.028(3.36)[-0.009]
0.071(28.00)[0.022]-0.054(8.46)[0.022]-0.293(6.43)[-0.116]0.172(9.90)[0.069]-0.037(1.45)[-0.015]-0.027(1.27)[-0.011]-0.008(2.62)-0.021(5.94)
0.053(48.54)-0.017(6.46)-0.242(9.80)
0.074(8.46)-0.082(6.57)-0.076(7.62)

18

With own children under 6
years and 6 to 17 years
Non Metropolitan

South

Miles (‘000) From Origin

Miles (‘000) From Origin
Squared
Linguistic Distance

Minority Language
Concentration CONCSTATEColonyRefugeeµ1-0.093(5.55)[-0.029]
0.053(1.09)[0.016]0.072(6.04)[0.021]
0.258(19.11)[0.029]-0.022(14.85)[0.029]-1.305(25.03)[-0.395]-0.014(18.75)[-0.004]0.800(30.21)[0.189]-0.236(9.31)[-0.077](a)-0.112(9.27)
0.024(0.67)
0.068(8.15)
0.243(26.82)-0.027(27.62)-0.943(26.61)-0.016(29.45)0.591(42.69)-0.072(3.85)-0.022(0.85)[-0.005]
0.301(2.63)[0.050]0.103(5.89)[0.020]0.227(13.73)[0.016]-0.021(12.62)[0.016]-1.396(25.88)[-0.278]-0.013(9.42)[-0.003]
0.797(28.81)[0.123]-0.236(8.87)[-0.053](a)-0.074(3.30)[-0.030]
0.090(1.68)[0.036]0.002(0.14)[0.001]-0.153(1.25)[-0.030]0.038(0.74)[-0.030](a)-0.010(6.24)[-0.004]
(a)

(a)

1.082
(a)
(224.10)
(a)
1.982
(a)
(a)
µ2
(410.08)
(a)
3.369
(a)
(a)
µ3
(475.70)
Chi-Squared
30226.7637258.38
16965.45
6217.43
Prediction Success Rate
77.95
46.17
84.10
67.99
Sample Size
85865
85865
54001
31864
Source: US Census of Population, 2000, Public Use Microdata Sample, 1 Percent Sample.
Note: Figures in parentheses are ‘t’ statistics, and the figures in square brackets for the binary probit models
are partial effects, with effects for variables entered into the model in quadratic form being evaluated at themean and listed for both terms of the quadratic; (a) Variable not relevant.

The immigrant adjustment process summarized in the years since migration variable is astrong influence on English proficiency. According to the estimates, English proficiency

19

improves at a decreasing rate with duration of residence in the US.14 This improvement isregistered across all levels of residency in the US recorded in the sample. In the first few
years after arrival, the improvement in English proficiency is about 2.5 percentage points
per year. At 10 years of residence, English proficiency improves by close to 2 percentage
points per year. Even at 20 years there is improvement in the rate of English proficiency
of around 1.8 percentage points per year of residence in the US. The relationship between
English language proficiency and years in the US is portrayed in Figure 4. The profiles inthis figure have been calibrated so that each one passes through the mean rate of English
proficiency for the particular group when that group’s duration of residence is equal to
the group-specific mean.

Analyses performed for the 1980 Census and the 1990 Census (Chiswick and Miller
1992, 1998) show a similar pattern of a steeper rise in proficiency rates in the early years
after immigration, with the rate of increase diminishing with duration. Selected partial
effects of years since migration on English proficiency for male immigrants aged 25-64
years from non-English speaking countries and for those from Mexico are listed in Table
2. Across each year of data, the effect of years since migration is stronger for immigrants
from Mexico. The effects of years since migration also get stronger across cohorts. That
the increase in proficiency with duration repeats itself in repeated censuses suggests that
it is reflecting a longitudinal phenomenon, rather than merely a decline in the linguistic
proficiency of more recent cohorts or selective emigration of the least proficient
immigrants in each arrival cohort.

14

As Age = (Age at Migration + YSM), the model is LANG = β 0 + β1 ( Age at Mig. + YSM ) + β 2YSM + ... ,and so ∂LANG= β1 + β 2 , or the effect of one year in the US plus the effect of an extra year of age.
∂YSM
As the effects associated with years in the US are far stronger than those associated with age, the same
pattern as established here carries over to the interpretation based on both β1 and β 2 , though as β1 < 0 theyears since migration effects are smaller. The discussion here has a focus on β 2 .

20

Figure 4
Predicted English Proficiency by Duration of Residence in the US, Adult Foreign
Born Men by Origin, 2000 US Census
English Proficiency (%)

100
80
60
40
20
0
1

4

7

10

13

16

19

22

25

28

Duration of Residence (Year)
Total Sample

Non-Mexican

Mexican

Source: Authors’ calculations based on Table 1.

Table 2
Partial Effect of Years since Migration on English Proficiency, Adult Foreign Born
Men by Origin, 1980 1990 and 2000
1980

1990

2000

NonNonNonEnglish
EnglishEnglishYears Since
Speaking
Speaking
Speaking
Migration
Countries
MexicoCountries
MexicoCountries
Mexico
1.62
2.15
1.95
2.50
2.41
2.61
5
1.34
1.80
1.70
2.20
2.23
2.39
10
1.06
1.45
1.45
1.90
2.05
2.18
15
0.78
1.10
1.20
1.60
1.86
1.96
20
0.50
0.75
0.95
1.30
1.68
1.74
25
Source: 1980: Chiswick and Miller (1992); 1990: Chiswick and Miller (1998); 2000; This paper, Table 1.

Just as the length of time an immigrant has spent in the US has a pronounced positive
impact on English proficiency, spending time abroad after immigration diminishes
English proficiency. Hence immigrants who came to the US to stay more than five years
ago but who lived abroad in 1995 have a rate of English proficiency around 12
percentage points less than immigrants who were living in the US five years ago. This
impact is the equivalent of the improvement that comes about through the first four yearsof residence in the US (see Figure 4). The intermittent nature of the stay among21

sojourners, and perhaps the expectation of a relatively short future stay in the US among
them, should be viewed as a major negative influence on immigrants’ English language
skills.

Immigrants who are married have a rate of English proficiency that is 4 percentage points
above that of their non-married counterparts. As noted above, while it is desirable whenmodeling dominant language proficiency to be able to distinguish between marriage prior
to migration (expected negative influence on dominant language skills) and marriage
after migration (expected positive influence on dominant language skills where themarriage is to a dominant language speaker), this cannot be done with the 2000 Census
data. The positive coefficient of the marriage variable on the rate of dominant language
proficiency shows that the positive factors dominate.

Children are associated with a slight reduction in the English language skills of theirfathers. Where the family only has a child (or children) under 6 years of age, or where it
only has a child (or children) 6-17 years of age, the rate of English proficiency is reduced
by 1.7 to 2.0 percentage points compared to having no children. This is the same
magnitude of impact that would be associated with a reduction in educational attainmentof around two-thirds of one year. In each of these cases there may be one or morechildren in the family, though only children cannot be distinguished from multiple childcases. However, where the family has children under 6 years and between 6 and 17 years(that is, there are at least two children present) the English proficiency of the adult males
is reduced by 2.9 percentage points, an effect that is equivalent to a reduction of about
one year of education.

Residence in the Southern states is also shown to be associated with greater rates ofEnglish proficiency. The small positive influence of living in the South is similar to theeffect recorded in Chiswick and Miller (2005a). As is apparent from the results presented
in Table 1, columns (iii) and (iv), this finding only holds for immigrants from source
countries other than Mexico.

22

Five further variables with behavioral interpretations that are constructed using birthplaceinformation are included in the model – the miles of the origin country from the US,
linguistic distance, minority language concentration and the colony and refugee variables.
While the two language-related variables are constructed using information on thelanguage other than English that is spoken in the immigrant’s home, birthplace is also
used to assign values for English-only speakers. The results show that English language
skills improve with miles from the origin country, up to around 6,000 miles. The changes
in English skills with miles from the origin country beyond this point to the maximum
observed in the data (Indonesia, 8,985 miles), are relatively minor. Figure 5 portrays thisrelationship. To assist reading the figure a number of source countries are identified on
the graph. As with Figure 4, the diagram has been calibrated so that the predicted
relationship passes through the mean rate of English proficiency when the miles from theorigin is at the sample mean (3,672 miles).
Figure 5
Predicted English Proficiency by Miles of Origin Country from the US, Adult
Foreign Born Men by Origin, 2000 US Census

80

Czechoslovakia

Ghana

China

Oman

Vietnam20

Bolivia40

Ecuador60

GuatemalaEnglish Proficiency (%)

100

4000

5000

6000

7000

8000

0
0

1000

2000

3000

9000

Miles from Origin

Total Sample

Non-Mexican

Source: Authors’ calculations based on Table 1.

As argued when providing the conceptual background for the model of dominantlanguage skills, the further the country of origin from the US the more intense the selfselection in migration and the less likely is return migration – both of which should be23

associated with higher rates of English language proficiency. This is exactly the
relationship depicted in Figure 5.

The measure of linguistic distance is included in the model as the reciprocal of thelanguage scores presented in Chiswick and Miller (2005b). The language scores that form
the basis for this measure range in value from 1.0 (Korean) to 3.0 (Swedish, Norwegian –see Table 1 of Chiswick and Miller (2005b)). As the reciprocal of the score is taken toform the linguistic distance variable, the variable in the model will range from 1 to 0.3& ,with a value of 1 indicating a home language quite far from English and 0.3& indicating avalue close to English.15

The linguistic distance has a marked influence on English language proficiency. There isa potential difference of 0.66 in the linguistic distance measure, and this is associatedwith a difference of -0.861 in the probit index – an impact that is the equivalent of 8.1
years of education. In other words, the linguistic distance measure has a major impact on
English proficiency. The predictions in Table 3 highlight this further.

Table 3
Predicted English Fluency by Linguistic Score, Adult Foreign Born Men by Origin.
Predicted English Proficiency (%)
Linguistic
Score
1.0
1.5
2.0
2.5
3.0

Linguistic
Distance

Illustrative
Languages

1.00
0.67
0.50
0.40
0.33

Korean, Japanese
Vietnamese, Arabic
Polish, Indonesia
Portuguese, Italian
Norwegian, Swedish

Total
Sample
44.01
61.20
69.22
73.65
76.41

Excluding
Immigrants from
Mexico
57.65
74.49
81.36
84.87
86.95

Source: Authors’ calculations based on Table 1.
Note: All other variables evaluated at their means.
15

The reciprocal of the original score was employed in the early research by Chiswick and Miller, as ameans of assigning a score for English only speakers had not been developed and a zero value was used. In
the current research, where the mean of the valid scores for the individual’s birthplace is assigned where
the individual speaks only English at home, there is less need to use the reciprocal functional form. While itis used here for consistency with past research, it is noted that entering the linguistic scores from Chiswick
and Miller (2005b) in the probit index in linear form yields results similar to those listed in Table 1 andTable 6.

24

The minority language concentration variable in this first set of results is formed using
State-level data (CONC-State). The results indicate that living in an area with a highrepresentation of others who speak the same home language has a negative impact on an
immigrant’s English language proficiency. The minority language concentration variable
ranges in value from 0 to 30 (percent), and so the variable, with a coefficient of -0.014, isassociated with a change of up to 0.42 in the probit index. This is the equivalent of the
impact on the probit index of four years of education. While this effect is far less thanthat associated with the linguistic distance measure, it is quite a pronounced effect,
particularly considering that it is a neighborhood characteristic rather than an individual
characteristic.

While the estimated impact of the minority language concentration variable isconsiderable, it appears to be less pronounced than that reported in the research based on
the 1990 Census by Chiswick and Miller (2005a). Chiswick and Miller (2005a) estimate
only a model that included birthplace fixed effects. Hence, to facilitate comparisons, asimilar specification was estimated using the 2000 Census data.

The estimatedcoefficient in this instance was -0.006. Comparison of this estimate with that presented
in Chisiwck and Miller (2005a) needs to be sensitive to the changes between the 1990
and 2000 Censuses in both the rate of proficiency and the mean of the measure ofminority language concentration. Hence, an elasticity is computed. The elasticity ofEnglish language proficiency with respect to the minority language concentration was 4.3
using the 1990 US Census (Chiswick and Miller (2005a)) and 3.0 using the 2000 US
Census. Estimation of the model in Table 1 stratified by age and period of residence
suggested the effect of a minority language concentration was greater (by about 15
percent) among those aged 35-64 years than among 25-34 year olds, and also among
those resident in the US for 10 or more years (by about 45 percent) than for those whohave resided in the US for less than 10 years. This suggests there has been a dilution of
the negative impact of minority language concentration on English language proficiency
among the more recent immigrants.

25

The final sets of variables in Table 1 record the influence on English language skills of
being from a former British or US colony, or being a refugee. Coming from a former
US/British colony is associated with higher rates of English proficiency, with the partial
effect being a substantial 19 percentage points.16 Being classified as a refugee isassociated with poorer English skills, the partial effect being -8 percentage points. This is
the equivalent of over two years of schooling.

Separate models were estimated in Table 1 for adult male immigrants from Mexico(column (iv)) and for all immigrants other than those from Mexico (column (iii)). For
these disaggregated analyses, the estimating equations are modified as follows. First, for
immigrants from Mexico the COLONY, REFUGEE and Linguistic Distance variables
are not relevant.17 Second, as noted above, the model for immigrants uses geographic
distance variables defined with reference to the State of residence and Mexico City,
Tijuana and Ciudaf Juarez. An alternative that was considered involved the use of
dichotomous variables for US states that are near Mexico. Four groups are considered:
California (benchmark); Texas; Arizona, New Mexico, Nevada; Rest of the US. Neither
the geographic distance nor the State dichotomous variables have much explanatory
power when the sample is confined to just immigrants from Mexico. The estimatespresented are based on a geographic distance variable defined as the minimum of the
distance between the capital of the immigrant’s current state of residence and either
Tijuana or Ciudaf Juarez, whichever is shorter.

While the distance variables are

insignificant, they are included for consistency with the other model specifications.

The estimates show that the effects discussed above are reasonably robust with respect tothis disaggregation of the data between Mexico and all other countries. There are two
exceptions though. First, the effects of age at migration on English proficiency follows
different patterns, though in each instance the partial effect of age is negative across all
16

Recall that immigrants from Canada, Australia, New Zealand and the English-speaking islands in the
Caribbean have been deleted from the data set.

17

While some immigrants from Mexico report speaking indigenous languages at home, there are nomeasures for the linguistic distance of Native American languages from English. Among the adult male
immigrants from Mexico, 94.3 percent report speaking Spanish, 5.4 percent report only English and 0.3
percent all other languages.

26relevant age groups. For immigrants other than those from Mexico, the linear age term isnegative and the squared term positive. Both are statistically significant. The partial effecton English proficiency is around -1.04 percentage points at 40 years of age. For
immigrants from Mexico, however, only the square of age is statistically significant. The
partial effect of age on English proficiency for this group at 40 years of age is -0.92
percentage points, only marginally less than for the non-Mexican immigrants. With theestimated pattern of effects, however, it is apparent that migrating at an older age has arelatively larger negative effect on the English proficiency of immigrants from Mexico
compared to the effect for other immigrants. This pattern is illustrated in Figure 6.

Figure 6
Partial Effect of Age at Migration on English Proficiency Among Adult Foreign
Born Men, by Origin, 2000 US Census
0.00

Partial Effect (%)

-0.20
-0.40
-0.60
-0.80
-1.00
-1.20
-1.40
-1.60
20

25

30

35

40

45

50

55

60

65

Age at Migration
Non-Mexican

Mexican

Source: Authors’ calculations based on Table 1.

The second difference between the results for immigrants from Mexico and all otherimmigrants is in the influence of children on the English skills of their fathers. While theeffects of children on their father’s English proficiency are always negative, these effects
are much weaker in the disaggregated analysis, with the effect of the variable for livingwith children under 6 and 6-17 years being significant only for immigrants from Mexico.
Among other immigrants the negative effect of children is significant only for the groupof fathers with children only in the 6 to 17 age category. Note, however, that, taken as a27

set the children variables are negative and statistically significant in both the Mexicanand other analyses in Table 1.

Table 1, column (ii) lists results from the estimation of an ordered probit model with thefull range of data on immigrants’ language skills recorded in the LANG5 measurediscussed above. The main feature of these results is that the estimated effects on theunderlying ordered probit index are very much the same as for the index function in thebinary probit model.

The effects of variables in the ordered probit model are difficult to assess, as the sign of acoefficient indicates unambiguously only the direction of the effect on the highest andlowest English proficiency groups. Accordingly, to illustrate the variation in membershipof the five language categories distinguished in the LANG5 variable, predictedprobabilities of being in a language category can be computed for the regressors includedin the estimating equation. Sets of prediction are presented in Table 4 for education andduration of residence. These predictions have been computed using the mean values of all
variables in the estimating equation other than for the specific variable that is the focus of
the panels in the table.

The data in panel A of Table 4 show that increases in the level of education areassociated with marked shifts away from the “not at all” and “not well” categories to the“very well” and “only English” categories. For education levels above the mean, there isalso a shift away from the “well” category with more schooling. Comparison of thepredicted distributions at 8 and 20 years of schooling shows that the change in
proficiency, as measured by the binary scale adopted above, from 64 to 91 percent is also
associated with quite pronounced shifts within the two groups “Not Proficient” and“Proficient”.

28

Table 4
Predicted Distribution across Language Proficiency Categories by Level of
Education and Duration of Residence, Adult Foreign Born Men, 2000.
A.
Level of
Education
8
10
12
14
16
18
20
B.
Duration of
Residence
1
5
10
15
20
25
30

Speaks Language Other than English at Home
Not at All
7.51
5.41
3.80
2.60
1.74
1.14
0.72

Not Well
28.54
24.57
20.62
16.87
13.44
10.44
7.91

Well
34.61
34.66
33.80
32.12
29.72
26.79
23.52

Very Well
26.67
31.47
36.24
40.73
44.69
47.87
50.06

15.81
11.34
7.23
4.45
2.65
1.54
0.87

37.35
33.61
28.07
22.34
17.03
12.50
8.88

30.48
33.09
34.66
34.29
32.21
28.89
24.88

15.46
20.42
27.24
34.17
40.54
45.74
49.30

Speaksonly
English

Total

2.68
3.90
5.54
7.68
10.40
13.76
17.79

100.00100.00100.00100.00100.00100.00100.00

0.90
1.54
2.81
4.76
7.57
11.34
16.07

100.00100.00100.00100.00100.00100.00100.00Source: Authors’ calculations based on Table 1.
Note: Rows may not sum to 100.00 due to rounding. All other variables are evaluated at their means.

The data in panel A of Table 4 show that increases in the level of education areassociated with marked shifts away from the “not at all” and “not well” categories to the“very well” and “only English” categories. For education levels above the mean, there isalso a shift away from the “well” category with more schooling. Comparison of thepredicted distributions at 8 and 20 years of schooling shows that the change in
proficiency, as measured by the binary scale adopted above, from 64 to 91 percent is also
associated with quite pronounced shifts within the two groups “Not Proficient” and“Proficient”.

The predictions in Table 4, Panel B, for duration of residence, show a similar trend
towards improvement in English skills with length of time in the US. Comparison of the29two extreme duration of residence levels represented in Table 4 indicate that the
improvements in English skills with duration of residence are more pronounced thanthose associated with educational attainment.

In each case illustrated in Table 4, and in general, it is seen that the marked
improvements in English proficiency associated with particular characteristics, such as
educational attainment, duration of residence in the US and a younger age at migration,
do not result in major changes in the proportion of immigrants becoming monolingualEnglish speakers in the home. Rather, there is an extremely strong tendency for themother tongue to be retained, so that most of the improvement in English proficiency is
associated with increased representation in the category “Speaks a language other thanEnglish at home and speaks English Very Well”. This phenomenon of mother tongue
retention is analyzed in Section V.
Finally, each of the thresholds ( µ ’s) in the ordered probit model is highly significant,indicating that the language categories coded in the Census are distinct, and as suchanalysis using an ordered polychotomous probit model may offer advantages over a study
using the binary probit model. The similarity of the findings with the two approaches, andKominski’s (1989) caution that there is an absence of a clear differentiation between eachof the four levels of English-speaking ability used in the US Census, suggests that thebinary and polychotomous approaches to modeling English language proficiency offer
similar findings.

Among the many strong influences on immigrants’ English proficiency documented
above, one that is of particular interest is associated with the minority language
concentration variable. This variable captures an influence on English language skills of
the “neighborhood” in which the immigrant lives. It was argued that living among otherswith whom the immigrant has a non-English language in common raises the cost oflearning English and lowers the benefit from becoming proficient in English.

30

In the analysis reported in Table 1, the minority language concentration variable wasconstructed using state-level data. Two alternative geographic units are available for use
in the construction of this variable: the Super Public Use Microdata Areas (CONC-Super)
and the Metropolitan Statistical Areas (CONC-MSA). As discussed above, over 500
separate areas are identified with the Super-PUMA data, and over 100 with the MSA
data, representing, respectively, a 10-fold increase and a doubling of the number of
separate geographic entities compared to the State-level data. This more refined
information may be important to use where the influences on English language
proficiency associated with the neighborhood are more local than captured by the Statelevel aggregates.

Table 5 presents the coefficients for the alternative minority language concentration
variables for the series of models presented in Table 1. There are two broad features of
the results summarized in this Table. First, the impact of a minority language
concentration on English language proficiency tends to be weaker when estimated withmore disaggregated data than when state-level data are used, though the differences in theestimates do not affect in any way the overall interpretation that can be attached to thisconcentration or enclave influence. Second, the effects of a minority language
concentration obtained with the more disaggregated data, reflecting the greater variability
in the explanatory variable, appear to be estimated more precisely in the analyses
disaggregated by birthplace region. With ‘t’ values of 6 or more with the state-level data,
however, obtaining even greater precision in estimation is not a major consideration.
While it is not obvious which level of aggregation among the three is the “best” from ananalytical point of view, and while it is not obvious which is the “best” from ameasurement perspective (measurement errors), they all point to the negative associationof ethnic/linguistic enclaves and immigrants’ English language proficiency.18

18

See Chiswick and Miller (2005c) for an “ethnic goods” model that hypothesizes this negative
relationship.

31

Table 5
Estimates of Minority Language Concentration Variables, Language Models for
Adult Foreign Born Men by Origin, 2000

Minority Language
Concentration Variable:(Number of Areas)
State Data (51)

MSA (106)

Super PUMA (532)

Total Sample
Probit-0.0138(18.75)[-0.0042]-0.0079(18.57)[-0.0024]-0.0060(17.88)[-0.0018]

Ordered Probit-0.0163(29.45)-0.0116(36.04)-0.0071(27.79)Immigrants from All
Countries except
MexicoImmigrants
fromMexicoProbit-0.0135(9.42)[-0.0027]-0.0086(12.78)[-0.0017]-0.0084(14.43)[-0.0017]

Probit-0.0096(6.24)[-0.0038]-0.0079(10.57)[ -0.0032]-0.0041(9.16)[-0.0016]

For notes to Table, see Table 1.

IV. ESTIMATES: FEMALE IMMIGRANTS

Table 6 presents estimates of the models of English language proficiency for females.19
In cases where the estimated effects of a variable for females and males differ
significantly, these are denoted by an asterisk against the estimated impact for females.
These tests of statistical significance are based on equations estimated on a pooled
sample of males and females with a full set of interaction terms for females. Tests wereconducted of whether the data for males and females could be pooled and the effects on
English proficiency represented by a single set of parameters common to both males andfemales. In each case the hypothesis of a common set of parameters was rejected.

In general, the direction of impact given by the estimated coefficients for female
immigrants are the same as those established for males. For most variables themagnitudes of the estimated effects for females are of the same order as the estimatedeffects for males. Where significant differences arise, the differences (with the exception

19

Females are generally shown to have an initial rate of learning advantage over males in first language
acquisition. The limited evidence available suggests that this may carry over to second language
acquisition. See, for example, Larsen-Freeman and Long (1991, pp. 204-205).

32

of the miles from the origin country variable) are small, though interesting. Five maindifferences arise.

Table 6
Probit Estimates of Language Models, Adult Foreign Born Women by Origin, 2000
Total Sample
Variables
Constant
Education

Age at Migration

Age at Migration
Squared/100
Years Since Migration(YSM)
YSM Squared/100

Abroad 5 years ago

MarriedWith own children under 6
years only
With own children 6 to 17
years only
With own children under 6
years and 6 to 17 years
Non Metropolitan

South

Miles (‘000) From Origin

Probit-0.694(7.24)
0.114*(89.64)[0.037]-0.006(1.30)[-0.011]-0.029(5.53)[-0.011]0.073(43.19)[0.022]-0.041*(9.77)[0.022]-0.238*(6.25)[-0.083]-0.057*(4.88)[-0.018]-0.099*(5.28)[-0.033]-0.116*(8.19)[-0.038]-0.209*(12.27)[-0.071]0.004(0.08)[0.001]0.123*(10.13)[0.039]0.315*

Ordered Probit
0.893(13.26)0.084(91.97)-0.011(3.35)-0.017(4.74)
0.054(49.44)-0.020(7.95)-0.154*(5.50)-0.057*(7.00)-0.101(7.72)-0.099*(10.09)-0.175*(14.32)-0.016(0.40)
0.103(12.24)0.209*

33

Immigrants
from AllCountries
except Mexico
Probit-0.096(0.75)
0.126*(72.51)[0.030]-0.032(5.52)[-0.011]-0.006(0.90)[-0.011]0.086*(39.48)[0.020]-0.067(12.24)[0.020]-0.333(6.75)[-0.092]-0.048*(3.10)[-0.011]-0.080(3.32)[-0.020]-0.110*(6.23)[-0.027]-0.166*(6.86)[-0.042]0.552(4.78)[0.097]0.119(7.36)[0.027]0.297*

Immigrants
from Mexico
Probit-0.298(1.74)
0.086(43.41)[0.034]-0.009(1.16)[-0.009]-0.016(1.81)[-0.009]0.060*(21.60)[0.021]-0.019*(2.87)[0.021]-0.095*(1.59)[-0.037]-0.054*(2.92)[-0.021]-0.068(2.15)[-0.027]-0.055(2.27)[-0.022]-0.137*(5.26)[-0.054]-0.092*(1.36)[-0.036]
0.095*(4.72)[0.038]-0.696*µ1(23.53)[0.023]-0.031*(22.01)[0.023]-1.204(26.54)[-0.391]-0.019*(23.57)[-0.006]
0.830(34.74)[0.215]-0.224(9.02)[-0.077]
(a)

µ2

(a)

µ3

(a)

Miles (‘000) From Origin
Squared
Linguistic Distance

Minority Language
Concentration
ColonyRefugee(22.69)-0.025(25.93)-0.762*(24.22)-0.022*(36.63)0.596(43.73)-0.101(5.48)
1.041(217.58)
1.848(381.65)3.184(457.46)
37940.77
46.19
83832

(18.45)[0.014]-0.032*(19.65)[0.014]-1.282(27.49)[-0.304]-0.017*(12.01)[-0.004]
0.841(33.86)[0.157]-0.203(7.85)[-0.052](a)(4.97)[-0.123]
0.192*(3.16)[-0.123](a)-0.021*(11.49)[-0.008]
(a)

(a)

(a)

(a)

(a)

(a)

(a)

Chi-Squared
33990.68
21084.55
Prediction Success Rate
78.99
83.05
Sample Size
83832
58000
Source: 2000 US Census.
For notes to Table, see Table 1; * = estimate significantly different from that for males.

5826.60
71.27
25832

In general, the direction of impact given by the estimated coefficients for female
immigrants are the same as those established for males. For most variables themagnitudes of the estimated effects for females are of the same order as the estimatedeffects for males. Where significant differences arise, the differences (with the exceptionof the miles from the origin country variable) are small, though interesting. Five maindifferences arise.

First, educational attainment is shown to have a slightly stronger (more positive) effect on
the English skills of female immigrants than it has on the English skills of male
immigrants. The differences in the partial effects, however, are all less than one
percentage points. Educational attainment is included in the language model on the
ground that it will capture efficiency factors. An implication of the current finding,therefore, is that these efficiency factors are more closely related to educational

34

attainment in the case of females than in the case of males. It may also capture labor
supply effects as educational attainment is a far more important determinant of labor
supply for women than for men.

The second variable where reasonably consistent differences emerge from the analyses
for female and male immigrants is the “Lived Abroad 5 years Ago” variable. The
sojourner behavior captured through this variable has a less negative impact on the
English skills of female immigrants than it has on the English skills of their malecounterparts. This difference may be a reflection of tied mobility, in which case thesojourner behavior would be more reflective of male intentions concerning length of stayin the US than it would be of female intentions.

Consistent with the discussion in Section II, children have a much more negative effecton the English skills of female immigrants than they have on the English skills of male
immigrants. The partial effects for the samples pooled across birthplace regions arealmost twice as large (in absolute value) for female immigrants as they are for male
immigrants. Indeed, the negative effect of children on female English language
proficiency is highly significant for all the children variables, even when separate
analyses are done for Mexican and other immigrants.

The minority language concentration variable is also associated with a more negative
impact on English language proficiency for female immigrants than it has for male
immigrants. This finding could be associated with the lesser involvement of female
immigrants in market work, an activity that is likely to offset some of the negative effects
associated with living in a language enclave.

The final variable where a difference between males and females is apparent is for thegeographic distance of the origin country from the US. The main difference here arises in
the case of immigrants from Mexico. In the case of male immigrants from Mexico, thedistance between the capital city of the immigrants’ current state of residence and either
Tijuana or Ciudal Juarez (whichever was the shorter) was not a significant determinant of35

their English proficiency. Among female immigrants from Mexico, however, thegeographic distance variable is statistically significant. Surprisingly, over the relevant
range of distances represented in the data, the estimated coefficients indicate that
immigrants living further away from the border with Mexico are less likely to be
proficient in English than those who live close to the border. To put this another way,
female immigrants living close to the border, for example in Texas, are more likely to be
proficient in English than their counterparts who, say, live in Illinois.

When the geographic distance variable was replaced by a number of dichotomous
variables for residence of a state that borders Mexico, similar findings were generated:
the states that border Mexico are associated with much higher rates of English
proficiency for female immigrants from Mexico than are states in the rest of the US.
When similar variables for state of residence are entered into the estimating equation formale immigrants from Mexico, they are typically statistically insignificant, or where
significant, are associated with very small impacts.

The reasons for this finding are unclear, and the most likely possibilities cannot be tested
with the census data used in the current analysis. For example, it is possible that
immigrants who live close to the border with Mexico originate from different regions of
Mexico, possibly in border regions, compared to Mexican immigrants who live elsewherein the US, who may originate from a broader catchment area. To the extent that they
originate from border regions in Mexico, those resident in the states in the US near
Mexico may have had greater exposure to English through more frequent trips to the US
and US media prior to migration.20

V. ANALYSES OF MOTHER TONGUE RETENTION

The discussion of the results from the estimation of the ordered probit model showed thatas years of residence in the US increase, immigrants’ English language skills improveconsiderably. This improvement largely comes about through shifts to the better English

20

The Census does not include information on the areas in Mexico in which the immigrants lived prior to
migrating to the US.

36ability categories (“well”, “very well”) from the poorer English ability categories (“Not atall”, “Not well”) among immigrants who speak a language other than English at home.
While there is an important increase in the percentage of immigrants speaking only
English at home, mother tongue retention is the dominant feature of the analysis of the
five-category language proficiency variable (LANG5) above.

The aim of this section is to sharpen the focus on this issue of mother tongue retention by
estimating language models where the dependent variable is a dichotomous variable(MT), set equal to one where the immigrant speaks a language other than English athome, and set equal to zero where the immigrant speaks only English at home. Around
90 percent of each of the samples of immigrants studied speak a language other thanEnglish at home. Even with such high rates of mother tongue retention, interesting
patterns emerge from the analysis. Tables 7 and 8 list results from the model for males
and females, respectively. The specification of the estimating equation that was used forthe study of English skills in the previous section is employed in this analysis.
Table 7
Probit Estimates of Model of Mother Tongue Retention,
Adult Foreign Born Men by Origin, 2000

Variables
Constant
Education

Age at Migration

Age at Migration
Squared/100
Years Since Migration(YSM)
YSM Squared/100

Total Sample

Immigrants from All
Countries except Mexico

Immigrants from
MexicoProbit1.331(11.78)Probit1.057(7.51)

Probit1.150(5.15)-0.005(3.49)[-0.001]-0.007(1.30)[0.002]
0.026(4.21)[0.002]-0.006(3.64)[-0.004]-0.056-0.015(7.13)[-0.002]-0.007(1.15)[0.001]
0.034(4.49)[0.001]-0.015(6.97)[-0.006]-0.0520.014(5.42)[0.002]0.014(1.37)[0.000]-0.018(1.49)[0.000]0.009(2.55)[0.000]-0.02937

Abroad 5 years ago

MarriedWith own children under 6
years only
With own children 6 to 17
years only
With own children under 6
years and 6 to 17 years
Non Metropolitan

South

Miles (‘000) From Origin

Miles (‘000) From Origin
Squared
Linguistic Distance

Minority Language
Concentration CONCSTATEColonyRefugee

Chi-Squared
Prediction Success Rate
Sample Size
For notes to Table, see Table 1.

(14.95)[-0.004]
0.035(0.81)[0.005]0.141(9.37)[0.021]
0.133(5.92)[0.018]0.091(5.23)[0.013]0.149(6.75)[0.020]-0.081(1.38)[-0.012]-0.041(2.86)[-0.006]-0.226(14.57)[0.009]0.035(21.06)[0.009]0.372(6.11)[0.053]0.020(19.26)[0.003]-0.677(31.40)[-0.133]-0.071(1.77)[-0.011]6138.56
90.77
85865

(11.76)[-0.006]
0.093(1.67)[0.014]0.147(7.89)[0.024]
0.184(6.51)[0.027]0.102(4.83)[0.016]0.160(5.46)[0.024]-0.376(4.25)[-0.076]-0.038(2.13)[-0.006]-0.095(5.20)[0.021]
0.025(13.71)[0.021]0.359(5.77)[0.057]0.034(17.57)[0.005]-0.675(30.63)[-0.136]-0.091(2.18)[-0.015]5755.66
88.65
54001

(3.52)[0.000]-0.081(1.21)[-0.009]
0.096(3.56)[0.010]0.035(0.90)[0.004]0.027(0.83)[0.003]0.092(2.61)[0.009]0.033(0.39)[0.003]0.046(1.68)[0.005]-0.138(0.77)[0.004]0.086(1.08)[0.004](a)

0.000(0.13)[0.001](a)(a)

104.8694.71
31864

Many of the broad patterns from the study of mother tongue retention for males (Table 7)
are the complement of those reported from the analysis of English proficiency. There are,however, a number of cases where the impact of variables on mother tongue retention

38

diverge from that which might have been expected given the results from the study ofEnglish proficiency.

In the analyses pooled across birthplace regions, mother tongue retention is less likely
among the better educated than it is among the less-well educated. However, in theseparate analyses conducted for the broad birthplace regions, it is found that the impact ofeducational attainment differs markedly for immigrants from Mexico and for immigrants
from countries other than Mexico. For male immigrants from countries other than
Mexico, mother tongue retention is less likely among the better educated. In comparison,
for male immigrants from Mexico, mother tongue retention is more likely among thebetter educated.

This difference could arise for a number of reasons. It could be associated with a greater
likelihood of return migration for the better educated from Mexico compared to the lesswell educated, relative to the pattern for immigrants from countries other than Mexico. A
second possibility is that there is less inter-birthplace marriage among the better educatedfrom Mexico than for the less-well educated. Finally, the finding could be a reflection of
the different segments of the distribution of educational attainments that are relevant ineach sample. Hence, the mean educational attainment of immigrants from countries otherthan Mexico is 13.3 years, and that of immigrants from Mexico is only 8.3 years. If this is
the case, however, the educational attainment – mother tongue relationship for the fullsample would have to be characterized by very intense non-linearities. Inclusion of an“education squared” term in the estimating equation for immigrants from countries otherthan Mexico did not reveal any evidence for a positive relationship between mothertongue retention and educational attainment among the less-well educated. However, in
the analyses for Mexico, the mother tongue retention – educational attainment
relationship was positive among the less-well educated (schooling levels up to 12.7 years
for males, and up to 13.6 years for females) and negative among the better educated. Thisevidence offers support for the third proposition advanced above.

39

The impact of age at migration on mother tongue retention also differs between male
immigrants from Mexico and those from other countries. For immigrants from countries
other than Mexico, the older the age at migration, the more likely the immigrant is toretain his mother tongue. Age at migration, however, is not a significant determinant ofmother tongue retention among immigrants from Mexico. Immigrants from Mexico are
more likely to have lived abroad (presumably in Mexico) 5 years ago than are otherimmigrants. They presumably are characterized by much more to-and-fro migration
between their country of origin and the US than are other immigrants. This could be
associated with incentives to retain the origin country language that dominate the age at
migration influences.

Similar to the patterns associated with education and age at migration, the duration ofresidence effects on mother tongue differ between immigrants from Mexico andimmigrants from other countries. For immigrants from countries other than Mexico, theprobability of retaining the mother tongue declines with duration of residence. The partial
effect of duration of residence on the probability of speaking a language other thanEnglish at home is 0.4 percentage points when evaluated at 15 years of residence. Among
immigrants from Mexico, however, there is virtually no change with duration ofresidence in the probability of speaking a language other than English at home, as
illustrated in Figure 7.

Figure 7
Predicted Probability of Speaking a Language other than English at Home, Adult
Foreign Born Men by Origin, 2000 US Census

40Mother Tongue Retention
(%)

100

90

80

70

60
1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

Period of Residence
Non-Mexican

Mexican

Source: Authors’ calculations based on Table 7.

The family structure variables have strong impacts on the probability of mother tongue
retention. Mother tongue retention is greater among those who are married and who havechildren. However, the partial effects of these significant variables are double inmagnitudes for Mexican immigrants compared to immigrants from other countries. Thisdifference could arise from the importance of Spanish in the US (see Appendix Table
A.1).21 In this situation, where these general neighborhood influences appear so strong,
the more immediate neighborhood effect associated with the family might be expected tohave less influence.

In Table 7, column (i), for the pooled sample, the probability of mother tongue retention
declines with the distance of the country of origin from the US up to around 3,500 miles,
and increases thereafter. This result is reasonably consistent with the finding for theordered probit model (Table 1), where the probability of speaking only English at homerises with geographic distance over the first 4,700 miles, and declines thereafter.

21

In the US in 2000, fully 11 percent of individuals aged 18-64, and 60 percent of the foreign born of thesame age group, speak Spanish at home. The next most frequently reported language is French, spoken athome by 0.6 percent of the adult population and by 3.4 percent of the foreign born.

41

The results for the linguistic distance variable indicates that immigrants whose mothertongue is closer to English (and hence who should find it easier to learn English) are most
likely to shift from speaking their mother tongue to English. This finding is in the
expected direction. Note that the sizes of the estimated partial effects are much smaller
than the results in the study of English proficiency (Table 1). Given the motivation in thedesign of the linguistic distance variable, as a measure of how easy it is for foreign
language speakers to learn English, the relative magnitudes of these estimated effects
make sense.

The likelihood of immigrants speaking a language other than English at home rises withthe extent to which the immigrant lives among others who speak the same (non-English)
home language as the immigrant. However, this effect does not carry across toimmigrants from Mexico.22 This minority language concentration variable is intended tocapture the ease with which conversation in a language other than English is possible.
With the dominant position of Spanish in the US (see Appendix Table A.1) and theproximity to Mexico, and hence to Mexican media, this may not be as important aconsideration as it is in the study of the incidence of mother tongue retention among nonMexican immigrants.

Finally, it is seen that immigrants from a former colony of the US or UK, and those whoare likely to have been refugees, are less likely to speak a language other than English athome. The latter may reflect a lower prospect of return migration.

Similar conclusions can be drawn from the study of mother tongue retention among adultfemale immigrants. These results, presented in Table 8, have four main features.

Table 8Probit Estimates of Model of Mother Tongue Retention,
Adult Foreign Born Women by Origin, 2000

22

The mean value of the minority language concentration (STATE) variable is 20.6 for immigrants from
Mexico compared to a value of only 4.9 for other immigrants.

42

Variables
Constant
Education

Age at Migration

Age at Migration
Squared/100
Years Since Migration(YSM)
YSM Squared/100

Abroad 5 years ago

MarriedWith own children under 6
years only
With own children 6 to 17
years only
With own children under 6
years and 6 to 17 years
Non Metropolitan

South

Miles (‘000) From Origin

Miles (‘000) From Origin
Squared
Linguistic Distance

Total Sample

Immigrants from All
Countries except Mexico

Immigrants from
MexicoProbit0.928(8.25)

Probit0.772(5.74)

Probit0.152(0.64)

0.008(4.91)[0.001]0.000(0.09)[0.002]-0.003(1.56)[-0.001]
0.005(0.74)[0.002]0.023(8.15)[0.003]0.002(0.21)[-0.000]

0.016(2.61)[0.002]-0.009(5.25)[-0.004]-0.039*(10.63)[-0.004]
0.006(0.13)[0.001]0.103*(7.61)[0.016]
0.108(4.79)[0.016]0.109(6.65)[0.016]0.136(6.36)[0.019]-0.035(0.57)[-0.006]-0.077*(5.50)[-0.012]-0.128*(8.18)[0.015]0.027*(16.25)[0.015]0.024*

0.017(2.37)[0.002]-0.016(8.06)[-0.005]-0.037*(8.67)[-0.005]
0.065(1.17)[0.010]0.147(9.20)[0.025]0.115(4.36)[0.018]0.124(6.48)[0.020]0.159(5.77)[0.024]-0.340(4.13)[-0.069]-0.076(4.58)[-0.013]-0.056(3.06)[0.023]0.021(11.62)[0.023]-0.002*-0.006(0.52)[-0.000]
0.009(2.51)[0.000]-0.023(2.71)[0.000]-0.154(1.87)[-0.020]-0.320*(1.18)[-0.004]
0.044(0.96)[0.005]0.023(0.66)[0.003]0.046(1.22)[0.005]0.267(2.58)[0.026]-0.085*(2.93)[-0.010]1.172*(6.25)[0.056]-0.358*(4.20)[0.056](a)43

Minority Language
Concentration CONCSTATEColonyRefugee

Chi-Squared
Prediction Success Rate
Sample Size

(0.46)[0.004]0.026*(24.45)[0.004]-0.688(32.40)[-0.142]-0.009(0.24)[-0.001]
5192.54
89.97
83832

(0.03)[-0.000]
0.035(18.46)[0.006]-0.696(32.17)[-0.146]-0.080(2.05)[-0.014]5064.45
88.40
58000

0.029*(11.99)[0.003](a)(a)

255.8393.74
25832

First, the model estimated for females are, as a set, statistically different from theestimated for males.23

Second, the way educational attainment impacts on mother tongue retention differs formales and females. In particular, while mother tongue retention decreases witheducational attainment among male immigrants from countries other than Mexico,
educational attainment is not a significant determinant of mother tongue retention amongfemale immigrants from countries other than Mexico. Like their male counterparts, thebetter educated female immigrants from Mexico are more likely to speak a language
other than English at home than is the case for less-well educated female immigrants.
However, also similar to the case for males, when the education variable is entered in themodel in quadratic form, the results indicate that mother tongue retention initially
increases with educational attainment, but after around 13.6 years of schooling, additional
years of education are associated with decreases in the likelihood of a language other thanEnglish being spoken at home.

Third, neither the marital status variable nor the variable for the presence of children is asignificant determinant of mother tongue retention among female immigrants from
Mexico.
The χ 2 test of whether the female shift variable and female interaction terms on all slope variables were
jointly significantly different from zero was highly significant in each sample.

2344

Fourth, among female immigrants from Mexico, the geographic distance variables are
significant, whereas they were insignificant among male immigrants from Mexico. The
estimated coefficients indicate that, over the range of distances represented in the sample
for immigrants from Mexico, the likelihood of speaking a language other than English athome increases at a decreasing rate the further from Mexico the female immigrants live.
While this is an unexpected finding, it sits comfortably alongside the finding reported
earlier to the effect that the probability of being proficient in English decreases with
distance from the border between US and Mexico.

Finally, the linguistic distance variable is not statistically significant in any of the models
estimated for females. While this variable was statistically significant in the models formales, the estimated impacts were far less than those established in the model for English
proficiency. The linguistic distance variable therefore appears to be more relevant tomodels capturing shifts to English than it is to models of origin language retention.

It is apparent from this discussion of the incidence of immigrants speaking a language
other than English at home that the influences of both the personal characteristics and the
behavioral variables included in the model vary between immigrants from Mexico andthose from other countries. Given the extent to which Spanish is used in the US – as
noted above, one in nine people between the ages of 18 and 64, and every second foreignborn person in this age bracket, speaks Spanish at home – differences of this nature might
be expected. However, the high degree of mother tongue retention among adult
immigrants (of 90 percent or more) suggests that the appropriate research questions might
be focused on the language use of the children of immigrants. Chiswick, Lee and Miller(2005a)(2005b) show how the family setting can be incorporated into the study ofEnglish speaking skills. Applying the same framework to the study of mother tongue
retention is an appropriate direction for future research.

45

VI. SUMMARY AND CONCLUSION

One in nine people between the ages of 18 and 64 in the US, and every second foreignborn person in this age bracket, speaks Spanish at home. And whereas around 80 percent
of adult immigrants in the US from non-English speaking countries other than Mexico
are proficient in English, only about 50 percent of adult immigrants from Mexico are
proficient.

A theoretical model, based in human capital theory, is presented in this paper to accountfor these empirical facts. Variations in English proficiency are shown to be linked tofactors that shift the supply and demand for funds for investment in English language
capital. The model has three key sets of factors: exposure, efficiency and economic
incentives.

The exposure factors are separated into exposure to English before immigration (e.g.,
living in a former US or British colony), and exposure after immigration. Exposure after
immigration has both extensive and intensive margins. The extensive margin can bemeasured by the number of years since the immigrant arrived in the US, taking into
account sojourner effects. The intensive margin is the intensity of exposure per unit oftime in the US, and this will depend on the immigrant’s neighborhood and family
experiences. Special attention is given in the study to the most appropriate measure ofneighborhood or enclave in the context of models of English proficiency.

Four measurable efficiency factors that can influence the development of language skills
among immigrants are age at migration, educational attainment, refugee status, andlinguistic distance. The conceptual basis for the measure of linguistic distance is the easewith which immigrants of particular linguistic backgrounds (mother tongues) can learn
English. It is argued that it should be more difficult for a Chinese speaker to learn
English than it is for a Spanish speaker to learn English because the differences between
the languages are much greater in the former case than in the latter case. A measure ofthis “linguistic distance” is provided by Chiswick and Miller (2005b).

46

The economic factors included in the conceptual framework are the wage, consumption
and social gains associated with English proficiency, including expected duration in thedestination. Several proxies for these are considered, including educational attainment
and the geographical distance between the country of origin and the US.

The empirical analyses are conducted using the 2000 US Census 1 percent Public Use
Microdata file. The research is limited to adults (aged 25-64 years), though in contrast to
most of the literature which focuses on males, the language practices of both male andfemale immigrants are considered. Binary probit analysis of a dichotomous proficiency
index, and ordered probit analyses of a five-category language skills variable, arepresented. As well, a binary measure of mother tongue retention is analysed.

The analyses show that immigration at a younger age, increases in the level of education
and a longer duration in the US are associated with a higher probability of being
proficient in English. English language proficiency tends to be lower the greater the age
at migration. It is shown that among immigrants from countries other than Mexico, age
15 is a threshold in terms of the learning of English as a second language in the US. A
similar threshold apparently does not exist among immigrants from Mexico, and this was
attributed to the widespread use of Spanish as a home language.

English proficiency improves at a decreasing rate with duration of residence. Analyses
performed for the 1980 and 1990 Censuses show a similar pattern, although it is notedthat the effects of years since migration get slightly stronger across cohorts. That thepattern repeats itself suggests that it is reflecting a longitudinal phenomenon rather thanmerely a decline in the linguistic proficiency of more recent cohorts, or selective
emigration of the least proficient immigrants in each arrival cohort. That the relationshipgets stronger in the more recent data may be linked to the slightly lower measured
proficiency in English of recent arrival cohorts: there is a well established pattern in theimmigrant adjustment literature of faster rates of adjustment among those with the fewest
skills at arrival.

47

Spending time abroad after immigration diminishes English proficiency.

Thus, theintermittent nature of the stay in the US among those who came to the US to stay morethan five years ago, but who lived abroad in 1995, and perhaps the expectation of a
relatively short future stay in the US among them, appears to have a major negative
influence on immigrants’ English skills.

A greater geographic distance between the country of origin and the US was associatedwith greater proficiency in English, and a greater linguistic distance between theimmigrant’s mother tongue and English was associated with lesser proficiency inEnglish. Both patterns are consistent with the theoretical model, and with results reported
in previous research. The estimated effect is also very large: the estimated differences inEnglish proficiency of the immigrants with mother tongues at the extremes of themeasure of linguistic distance (e.g., Korean, Japanese versus Norwegian, Swedish) is theequivalent of over eight years of schooling.

The proportion of individuals living in the same region as the immigrant that speak his or
her mother tongue was also shown to have a major influence on the immigrant’s English
skills. Three measures of minority language concentration were used. The first wasconstructed using state-level data. As in previous research, 51 separate areas are used in
the construction of this variable.

The second was constructed using Metropolitan

Statistical Areas (106 separate areas), and the third was based on the Super-PUMA (532separate areas). The impact of a minority language concentration on English language
proficiency tends to be weaker when estimated with more disaggregated data than whenstate-level data are used. It was not possible to assess which level among the three is thebest from an analytical point of view, or even from a measurement perspective.
However, the results with each of the three alternative measures point to a negative
association of ethnic/linguistic enclaves and immigrants’ English language proficiency.

The directions of impact of variables in the models of English skills for females wereremarkably similar to those for males. The estimated coefficients for females as a set
were, however, statistically different from those for males. Among the variables giving48

rise to this statistically significant result are the minority language concentration variable
and the variables for children. There is a more negative impact on English proficiency ofliving in a minority language concentration for female immigrants than for male
immigrants. It was suggested that this difference could be associated with the lesser
involvement of female immigrants in market work.

Children have a much more negative effect on the English skills of female immigrants
than they have on the English skills of male immigrants.

It was argued that thisdifference may arise because of the negative effect of children on mother’s labor supply
and children serving as translators for their mothers in consumption activities.

The analysis of mother tongue retention showed that many of the variables that affected
immigrants’ proficiency in English were significant determinants of their likelihood ofretaining their mother tongue in the US. In many cases variables that are associated withgreater proficiency (e.g., years since migration, fewer children) are associated with alower probability of speaking a language other than English at home. However, in somecases variables have impacts of the same sign on both English proficiency and mothertongue retention (e.g., years of education among male immigrants from Mexico) or weresignificant in one set of analyses and insignificant in the other (e.g., the linguistic
distance variable for female immigrants). These differences indicate that additional
information can be gained from the separate study of mother tongue retention.

The results from study of mother tongue retention, however, showed much morevariability across birthplace groups and between males and females than was the casewith the study of English proficiency. The differences between immigrants from Mexicoand other countries are presumably associated with the dominant role of Spanish in the
US. The differences between males and females could be associated with females having
a more influential role than males in the transmission of the language and culture of theorigin country within the family. Study of mother tongue retention using the family as
the unit of observation may therefore be a priority in research. In this context, the very
high rates of mother tongue retention among adult immigrants (of 90 percent or more)49

suggests that the appropriate focus in this research would be on the home language use of
the children of immigrants. This will provide information on the determinants of whether
the use of languages other than English at home is more than a one-generational
phenomenon.

There are two main lessons from this research. First, English proficiency varies acrossimmigrants in predictable ways. Immigrants with higher educational attainment, who
migrate at a younger age, who live in predominately English-speaking areas of the US,
and who have a mother tongue close to English or who had been exposed to English prior
to immigration are more likely to be proficient in English than other immigrants. Thesefindings could be used in immigrant selection (e.g., in a points system such as that usedin Australia). The similarity of the estimated impacts across the separate groups analyzed
above (males, females, immigrants from Mexico, immigrants from other countries)
suggest that the application of the findings in this way would be neutral across thesebroad groups. The results also have implications for settlement policy, suggesting a
focus on areas of ethnic language concentrations. Moreover, comparisons between men
and women and with the 1980 and 1990 Census suggest that the results are robust acrossthe two genders and across time.

Second, mother tongue retention also varies across immigrants in predictable ways. The
overall rate of mother tongue retention among adult immigrants in the US is very high, ataround 90 percent. Perhaps the more interesting issue that arises from this is thetransmission of the mother tongue from adult immigrants to their children, and the labormarket implications of bilingualism among the children of immigrants. These issues are
the topics for future research.

50

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Becker, Gary S. and Chiswick, Barry R. (1966). “Education and the Distribution of
Earnings”, American Economic Review, Vol. 56, No. 2, pp.358-369.
Chiswick, B., (1978) ‘The Effects of Americanization on the Earnings of Foreign Born
Men’, Journal of Political Economy, Vol. 86, pp.897-921.
Chiswick, B., (1979) ‘The Economic Progress of Immigrants: Some Apparently
Universal Patterns’, in William Fellner, ed. Contemporary Economic Problems
1979, American Enterprise Institute, Washington, pp.357-399.
Chiswick, Barry R. (1991). “Speaking, Reading and Earnings Among Low-Skilled
Immigrants,” Journal of Labor Economics, Vol. 9, No. 2, pp.149-70.
Chiswick, Barry R., Lee, Yew Liang and Miller, Paul W., (2005a). “Family Matters: The
Role of the Family in Immigrants’ Destination Language Acquisition”, Journal of
Population Economics, Vol. 18, No. 1, 2005, pp.1-17.
Chiswick, Barry R., Lee, Yew Liang and Miller, Paul W., (2005b). “Parents and Children
Talk: English Language Proficiency Within Immigrant Families”, Review of
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Chiswick, Barry R., Lee, Yew Liang and Miller, Paul W., (2005c) “Immigrants’ Language
Skills and Visa Category”, International Migration Review, (forthcoming).
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Language and Ethnicity: Canada and the United States, American EnterpriseInstitute, Washington D.C..
Chiswick, Barry R. and Miller, Paul W., (1995). “The Endogeneity Between Language andEarnings: An International Analysis”, Journal of Labor Economics, Vol. 13, No. 2,
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Chiswick, Barry R. and Miller, Paul W., (1998). “English Language Fluency Among
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Chiswick, Barry R. and Miller, Paul W., (2002). “Immigrant Earnings: Language Skills,
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Adjustment?”, City and Community, Vol. 4, No. 1, pp.5-35.
Chiswick, Barry R. and Miller, Paul W., (2005b) “Linguistic Distance: A Quantitative
Measure of the Distance Between English and Other Languages”, Journal ofMultilingual and Multicultural Development, Vol. 26, No. 1, pp.1-11.
Chiswick, Barry R. and Repetto, Gaston, (2001). “Immigrant Adjustment in Israel: Literacy
and Fluency in Hebrew and Earnings”, in Slobodan Djajic (ed.) International
Migration: Trends, Policy and Economic Impact, New York: Routledge, pp. 204228.
Fathman, Ann., (1975). “The Relationship Between Age and Second Language
Productive Ability”, Language and Learning, Vol. 25, No. 2, pp.245-253.
Fitzpatrick, Gary L. and Modlin, Marilyn J. (1986). Direct-line Distances: United States
Edition, Metuchen, NJ: The Scarecrow Press Inc.
Hart-Gonzalez, Lucinda and Lindermann, Stephanie, (1993). “Expected Achievement in
Speaking Proficiency, 1993” School of Language Studies, Foreign Service
Institute, U.S. Department of State, Mimeographed.
Kominski, R., (1989). “How Good is “How Well”? An Examination of the Census
English-Speaking Ability Question”, American Statistical Association 1989
Proceedings of the Social Statistics Section, pp.333-338.
Larsen-Freeman, Diane and Long, Michael H. (1991). An Introduction to Second
Language Acquisition Research, Longman, New York.
Long, Michael H. (1990). “Maturational Constraints on Language Development”, Studies inSecond Language Acquisition, Vol. 12, No. 3, pp.251-285.
McManus, Walter, Gould, William and Welch, Finis, (1983). ‘Earnings of Hispanic Men:
The Role of English Language Proficiency’, Journal of Labor Economics, Vol. 1,
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Miller, Paul W. and Neo, Leanne, (2003). “Labour Market Flexibility and Immigrant
Adjustment”, Economic Record, Vol. 79, No. 246, pp.336-356.
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52

Service, Elisabet and Clark, Fergus I. M., (1993). “Differences Between Young and Older
Adults in Learning a Foreign Language,” Journal of Memory and Language, Vol.
32, pp.608-623.

53

APPENDIX A
DEFINITIONS OF VARIABLES

The variables used in the statistical analyses are defined below. Mnemonic names are also
listed where relevant.
Data Source: 2000 Census of Population, Public Use Microdata Sample, 1 percent
sample of the foreign born, except where noted otherwise.
Definition of Population: Foreign-born men and women aged twenty-five to sixty-four,born in countries other than the main English-speaking countries (UK, Ireland, Canada,
Australia, New Zealand and the English-speaking Caribbean), territories of the UnitedStates, at sea or born abroad of American parents. Only residents of the 50 States and theDistrict of Columbia are considered.
Dependent Variables:
English Language Fluency (LANG2 and LANG5): LANG2 is set equal to one forindividuals who speak only English at home, or if a language other than English is
spoken in the home, who speak English either “very well” or “well.” The variable is set
to zero where a language other than English is spoken in the home and the respondent
speaks English either “not well” or “not at all.” LANG5 is a polychotomous dependent
variable (five categories), defined to include all proficiency categories: (i) speaks English
only at home; speaks a Language other than English at home and speaks English (ii) very
well; (iii) well; (iv) not well; (v) not at all.
Mother Tongue (MT) This is a binary variable, set equal to one for individuals who
speak a language other than English at home (assumed to be their mother tongue). It is set
equal to zero for individuals who speak only English at home.
Explanatory Variables:
Minority Language Concentration (CONC): Each respondent is assigned a measureequal to the percentage of the population aged eighteen to sixty-four in the region inwhich he/she lives, who reports the same non-English language as the respondent. In theconstruction of this variable, only the twenty-five largest non-American Indian language
groups nationwide and the top five Indian language groups are considered. Details areprovided in Table A.1.

For the 18-64 year old group used in the compilation of Table A.1, these constitute 92
percent of all responses where a language other than English is used at home.
Representation in the other language groups is so small numerically that the proportions
are approximately zero, and this value is assigned. Those who reported speaking only
English are assigned the mean value of the CONC measure for other-language speakersof their birthplace group.

54

Three separate regional classifications and hence measures of CONC are considered.
CONC-State is based on the state (50 States and the District of Columbia) of residence—a measure that corresponds to that used by Chiswick and Miller (2005a). The second isbased on the Super-PUMA data available on the 1 percent file (CONC-Super). 532
separate regions are distinguished in the empirical analysis. The third measure is anintermediate case, and is constructed using the information on Metropolitan Statisticalareas (CONC-MSA).24 106 separate regions are used in forming this measure.
Location: The three location variables record residence of a non-metropolitan area
(NON-MET) or of the Southern States (SOUTH). The states included in the latter are:Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky,
Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina,
Tennessee, Texas, Virginia, West Virginia. In the analyses for Mexico, four
dichotomous variables for US states that are near Mexico are used: California(benchmark); Texas; Arizona, New Mexico, Nevada; Rest of the US.
Colony (COLONY): Countries that are current or former colonies of English-speaking
countries are coded one. All other countries are coded zero. Dependencies of the UK,
US, Australia, New Zealand and South Africa are coded as colonies under this definition.
Years Since Migration (YSM). This is computed from the year the foreign born personcame to the United States to stay.
Lived Abroad Five Years Ago (ABROAD5): This is set equal to one if the individual
had resided in the US for more than 5 years and lived abroad in 1995, otherwise it is set
equal to zero. Note that ABROAD5 is zero for immigrants in the US for five or fewer
years.
Marital Status (MARRIED): This is a binary variable that distinguishes individuals who
are married, spouse present (equal to 1) from all other marital states.
Years of Education (EDUC): This variable records the total years of full-time equivalenteducation. It has been constructed from the Census data on educational attainment by
assigning the following values to the Census categories: completed less than fifth grade(2 years); completed fifth or sixth grade (5.5); completed seventh or eighth grade (7.5);
completed ninth grade (9); completed tenth grade (10); completed 11th grade (11);completed 12th grade or high school (12); attended college for less than one year (12.5);attended college for more than one year or completed college (14); Bachelor's degree(16); Master's degree (17.5); Professional degree (18.5); Doctorate (20).

24

A metropolitan area is one of a large population nucleus, together with adjacent communities that have ahigh degree of economic and social integration with that nucleus. Where a metropolitan area has 1 million
people or more, two or more primary metropolitan statistical areas (PMSAs) may be defined within it.
Information on the metropolitan statistical areas (MSA) and primary metropolitan statistical areas
contained in the variable MSA-PMSA1 is used in the construction of the CONC-MSA variable.

55

Refugee (REFUGEE): This variable is constructed to identify the major sources of postWWII refugees to the U.S. It is defined only for immigrants who migrated at age 25 andolder. Individuals who migrated from Cambodia, Laos or Vietnam in 1975 or later, Iranin 1980 or later, Cuba in 1960 or later, the Former Soviet Republic in 1950 or later, from
China between 1950 and 1990, Somalia or the former Yugoslavia between 1990 and2000 are assigned a value of one for this variable. All other immigrants are assigned avalue of zero.
Linguistic Distance (DISTANCE): This is a measure of the difficulty of learning aforeign language for English-speaking Americans. It is based on a set of language scores(LS) measuring achievements in speaking proficiency in foreign languages by Englishspeaking Americans at the U.S. Department of State, School of Language Studies,
reported by Hart-Gonzalez and Lindermann (1993). It is described in detail in Chiswick
and Miller (2005b).

In the construction of this variable, foreign-born persons who speak only English at homeand hence do not report speaking a non-English language are assigned the mean value of thelinguistic score measure for individuals reporting a foreign language from their birthplacegroup.
The variable in the regression equations is linguistic distance, which is one divided by the
linguistic score, DISTANCE = 1/LS.
Direct-Line Distances (MILES): The miles between the major city in the immigrant’s
country of origin and the nearest large port of entry in the United States (New York,Miami, Los Angeles) are constructed from data in Fitzpatrick and Modlin’s (1986) Direct
Line Distances, United States Edition.

Means and standard deviations for these variables are reported in Table A.2 (for men)
and Table A.3 (for women).

56

Table A.1
Frequency of Language Use Among Adults in the United States, 2000(a)Language% of All Languages
Frequency

SpanishFrenchChinese, Min
Tagalog/Filipino
German , Austrian, Swiss
Vietnamese
KoreanItalianRussianArabicPortuguese
PolishJapanese, Ainu
French Creole, HaitianCreoleHindi
Persian, Dari, Farsi, Pushto
Greek
Urdu
Cantonese, Toishan
Gujarathi
Kru, Ibo, Yoruba, Akan,
Ashanti, Ewe, Fanti, Ga,
Igbo, Nigerian, Twi
Hebrew
Mandarin
Mon-Khmer, Cambodian,
Khmer
Armenian
Navajo*Dakota, Assiniboine, Lakota,
Oglala, Sioux*
Cherokee*
Yupik*
Apache*Other languages specified% of All Languages other thanEnglishFrequency
Cumulative
Frequency
59.74359.7433.364
63.1073.316
66.4232.962
69.385

11.228
0.632
0.623
0.557

Cumulative
Frequency
11.22811.86012.48313.040

0.505
0.433
0.383
0.329
0.274
0.258
0.252
0.226
0.185

13.54513.97814.361
14.69014.96415.22215.47415.70015.885

2.689
2.302
2.038
1.751
1.459
1.371
1.343
1.202
0.983

72.07474.37676.41478.16579.62480.99582.33883.54084.523

0.175
0.150
0.136
0.135
0.109
0.106
0.099

16.06016.21016.34616.481
16.59016.69616.795

0.932
0.800
0.725
0.719
0.580
0.562
0.529

85.45586.25586.98087.69988.27988.84189.370

0.086
0.083
0.077

16.881
16.96417.041

0.457
0.442
0.408

89.82790.26990.677

0.069
0.069
0.073

17.11017.17917.252

0.365
0.365
0.386

91.04291.40791.793

0.008
0.008
0.006
0.006

17.26017.26817.27417.280

0.042
0.041
0.033
0.032

91.83591.87691.90991.941

1.514
18.7948.056
99.997
Other languages not
specifically listed
0.001
18.7950.004
100.000
Not reported
0.000
18.7950.002
100.000
English Only
81.206100.000
Note: (a) All persons, regardless of nativity, aged 18 to 64 as reported in the 2000 Census; Refers tolanguage spoken in the home if English is not the only language; * = American Indian Language
Source: 2000 US Census of Population, Public use Microdata Sample, 1 Percent Sample.

57

Table A.2
Means and Standard Deviations of Variables in Language Models, Adult Foreign
Born Men by Origin, 2000
Total Sample

Immigrants from All
Countries except
MexicoImmigrants from
Mexico0.695(0.46)
11.525(4.96)
40.053(10.30)1710.296(882.51)
16.135(11.04)382.307(472.27)0.023(0.15)
0.622(0.48)
0.129(0.33)
0.259(0.44)
0.152(0.36)
0.010(0.10)
0.282(0.45)
3.672(2.64)
20.443(23.11)0.508(0.13)
10.447(11.10)0.800(0.40)
13.323(4.20)
41.506(10.47)
1832.273(911.21)16.413(11.51)401.889(503.57)
0.020(0.14)
0.644(0.48)
0.119(0.32)
0.251(0.43)
0.107(0.31)
0.005(0.07)
0.273(0.45)
4.906(2.56)
30.614(23.24)0.543(0.15)
4.798(7.92)

0.507(0.50)
8.279(4.55)
37.429(9.44)
1490.107(781.40)
15.634(10.13)346.956(407.38)
0.028(0.16)
0.583(0.49)
0.147(0.35)
0.274(0.45)
0.233(0.42)
0.020(0.14)
0.299(0.46)
0.701(0.39)
0.644(0.81)(a)12.880(16.13)15.746(19.64)7.369(14.38)8.799(16.13)22.829(14.24)28.287(19.19)0.150(0.36)
0.053(0.22)

0.233(0.42)
0.082(0.27)(a)Variables
Proficient in English(LANG2)
Education
Age at Migration
Age at Migration SquaredYears Since Migration(YSM)
YSM Squared
Abroad 5 years ago
MarriedWith own children under 6
years only
With own children 6 to 17
years only
With own children under 6
years and 6 to 17 years
Non Metropolitan
South
Miles (‘000) From Origin
Miles (‘000) From Origin
Squared
Linguistic Distance
Minority Language
Concentration CONCSTATE
Minority Language
Concentration CONC-MSA
Minority Language
Concentration CONCSUPERColonyRefugee58

20.645(8.40)(a)Sample Size
85865
54001
Source: US Census of Population, 2000, Public Use Microdata Sample, 1 Percent Sample.
Note: Figures in parentheses are standard deviations; (a) Variable not relevant.

31864

Table A.3
Means and Standard Deviations of Variables in Language Models, Adult Foreign
Born Women by Origin, 2000
Total Sample

Immigrants from All
Countries except
MexicoImmigrants from
Mexico0.666(0.47)
11.483(4.73)
41.074(10.62)1799.821(922.16)
16.573(11.46)405.906(501.44)0.018(0.13)
0.670(0.47)
0.120(0.33)
0.296(0.46)
0.155(0.36)
0.009(0.09)
0.277(0.45)
3.883(2.65)
22.086(23.29)0.520(0.15)
9.860(11.04)0.762(0.43)
12.815(4.12)
42.157(10.69)1891.493(938.83)
16.762(11.76)419.201(519.07)
0.017(0.13)
0.672(0.47)
0.116(0.32)
0.279(0.45)
0.107(0.31)
0.006(0.07)
0.276(0.45)
4.919(2.53)
30.575(23.04)0.552(0.16)
4.803(7.91)

0.440(0.50)
8.344(4.60)
38.523(9.99)
1583.786(843.12)
16.127(10.70)374.577(455.67)0.020(0.14)
0.667(0.47)
0.130(0.34)
0.338(0.47)
0.269(0.44)
0.017(0.13)
0.280(0.45)
0.650(0.35)
0.546(0.73)(a)12.670(16.76)15.5267.433(14.48)8.763

25.012(15.21)31.466

Variables
Proficient in English(LANG2)
Education
Age at Migration
Age at Migration SquaredYears Since Migration(YSM)
YSM Squared
Abroad 5 years ago
MarriedWith own children under 6
years only
With own children 6 to 17
years only
With own children under 6
years and 6 to 17 years
Non Metropolitan
South
Miles (‘000) From Origin
Miles (‘000) From Origin
Squared
Linguistic Distance
Minority Language
Concentration CONCSTATE
Minority Language
Concentration CONC-MSA
Minority Language

59

21.778(7.70)

Concentration CONCSUPERColony(20.28)(16.10)

0.160
0.228
(0.37)(0.42)
Refugee
0.056
0.080(0.23)(0.27)
Sample Size
83832
58000
Source: US Census of Population, 2000, Public Use Microdata Sample, 1 Percent Sample.
Note: Figures in parentheses are standard deviations; (a) Variable not relevant.

60(20.18)(a)(a)
25832