Hispanic Immigrant Poverty: Does Ethnic Origin Matter

Hispanic Immigrant Poverty: Does Ethnic OriginMatter?

Dennis H. Sullivan Æ Andrea L. Ziegert

Received: 4 June 2006 / Accepted: 22 August 2007 / Published online: 30 May 2008

� Springer Science+Business Media B.V. 2008

Abstract Hispanic immigrant poverty is nearly double that of other immigrants.

Furthermore, poverty rates among Hispanic families differ substantially by eth-

nicity. This paper analyzes poverty rates for Hispanic and non-Hispanic immigrants,

and also for individual Hispanic ethnic groups, to determine the relative importance

of different covariates of poverty. The general conclusion is that low levels of

education and fluency in English contribute to high Hispanic poverty rates and are

also contributing factors to differences in poverty among Hispanic ethnic groups. In

particular, the high poverty rate of Mexican immigrant households is associated

with the low educational attainments of household heads, along with a relatively

large number of children, relatively low English fluency and a relatively short tenure

in the U.S. Immigrants from Guatemala and El Salvador have substantially lower

poverty rates than Mexican immigrants despite a similar constellation of observable

traits. Immigrants from South America have low poverty rates, largely due to strong

family work effort and high educational attainments. The relatively low family work

effort and high incidence of single parent families among Puerto Ricans overpowers

the beneficial effects of higher rates of citizenship and English fluency.

Keywords Ethnicity � Immigration � Poverty

Background

One can scarcely underestimate the importance of the Hispanic immigrant

population when discussing poverty in the United States: of the 36.95 million poor

D. H. Sullivan (&)

Department of Economics, Miami University, Oxford, OH 45056, USA

e-mail: [email protected]

A. L. Ziegert

Department of Economics, Denison University, Granville, OH, USA

123

Popul Res Policy Rev (2008) 27:667–687

DOI 10.1007/s11113-008-9096-3

persons in 2005, 5.87 million were foreign-born, and 3.79 million were foreign-

born Hispanics.1 Our tabulations from the Census 2000 microdata show that there

are large ethnic differences among the poverty rates of immigrants. For example, we

find that the poverty rate among Hispanic immigrant households was 23.4%,

compared to only 12.7% for non-Hispanic immigrant households. Hispanic ethnic

groups also differ from each other, with a poverty rate of almost 25% among the

Mexican majority, but only 15% among Hispanic immigrants with South American

ethnicities. Despite the numbers, there has been little research on the details of

Hispanic immigrants’ poverty, particularly on the ethnic pattern.

This paper investigates observable factors that are generally associated with

poverty—family demographics, work effort, race, single parenthood, educational

attainment, English fluency, citizenship, time since immigration and regional

location—to determine the relative importance of these covariates of poverty for

Hispanic immigrants in the United States in 1999. The results may have

implications for immigration policy, and they clearly have implications for policies

designed to mitigate the poverty of Hispanic immigrants after they immigrate.

In addition to conducting the analysis for the Hispanic immigrant population

generally, we take advantage of the sheer size of the Census Public Use Microdata

Sample (over 250,000 immigrant Hispanic households) to sub-divide the Hispanic

immigrant population into six specific ethnicities (Mexican/Chicano, Puerto Rican,2

Cuban, Guatemalan, Salvadoran, and Dominican) and three residual categories

(other Central American, South American, and other Hispanic). These ethnic groups

differ in several ways observable ways, including: family size; family work effort;

educational attainment, race, gender and English fluency of the head; citizenship;

time in the U.S.; and region of residence. We employ an adaptation of the Blinder–

Oaxaca decomposition to determine which covariates are primarily responsible for

the differentials in poverty rates.

One can interpret our results as an exercise in determining both: (1) whether a

parsimonious set of observables can account for poverty differentials among

immigrant Hispanic ethnic groups; and (2) to the extent that the answer to the first

question is affirmative, which observables matter most. For example, the high

poverty rate of Mexican immigrants is primarily associated with the youth, low

educational attainment and poor English fluency of that population.

Previous Research

In spite of the increasing prevalence of Hispanics in the U.S. poverty population,

Hispanic poverty has not been extensively examined with microdata. The most

1 The exact meaning of ‘‘Hispanic’’ and other ethnic terms in discussed under Data below. The statistic

here may be found at http://ferret.bls.census.gov/macro/032006/pov/new29_000.htm.2 Persons born in Puerto Rico are U.S. citizens, and therefore not ‘‘immigrants’’ in the typical sense of the

term. The Census separately tabulates individuals who have ‘‘immigrated’’ from U.S. territories, however,

and the Puerto Rican immigrants provide a valuable benchmark because they have the advantage of

citizenship.

668 D. H. Sullivan, A. L. Ziegert

123

http://ferret.bls.census.gov/macro/032006/pov/new29_000.htm

important exceptions are Iceland’s recent work (2003a, b) and the paper by Van

Hook et al. (2004).

Since both family income and family structure are part of the poverty calculation,

it is useful to distinguish their effects. Iceland (2003a, b) conducts a decomposition

of changes in poverty rates over time into the effects of income growth, family

structure changes, and inequality changes (calculated as a residual) for alternative

definitions of poverty. His analysis (2003b, p. 513) shows that during the 1969–

1990 period, the beneficial effects of income growth on Hispanic poverty were

almost totally offset by the adverse effects of increasing inequality, with a small

adverse effect of changes in family structure, while the reduced poverty of

Hispanics in the 1990s was almost entirely due to income growth, with a minor

added effect due to reduced inequality. Immigration is barely mentioned (2003a, pp.

85–87), however, and does not have any role in the analysis.

In terms of data design, the principal precursor to our work is the paper by Van

Hook et al. (2004), which uses the Census Public Use 1% Microdata Samples,

including Census 2000, to examine poverty among the children of immigrants. They

use a detailed list of covariates to capture both income potential and family structure

traits, including: race/ethnicity; parental education and employment status; living

arrangements and parental marital status; family size and the ages of parents and

children; and parents’ time in the United States. Regarding Hispanic immigrants in

particular, their principal conclusion is that high poverty among the children of

Hispanic immigrants can be substantially accounted for by these covariates,

especially in the most recent Census. However, their research does not investigate

differentials among Hispanic ethnic groups, the focus of our research here.

Another relevant strand of recent literature concerns the geographical pattern of

poverty. Jargowsky (1997) was a pioneer in studying the geographical pattern of

Hispanic poverty. A recent example is the conclusion of Partridge and Rickman

(2006, p. 177) that when a high share of a metropolitan area’s population is made up

of recent immigrants, that area tends to have smaller declines in metropolitan

poverty during the 1990s. This literature does not rely on data about individuals,

however, nor does it focus on ethnic origin. The only recent study that differentiates

Hispanic ethnic groups is Mogull (2005). However, that study only uses the

published tabulations for aggregate poverty of each ethnic group, and concerns itself

almost entirely with trends in aggregate poverty. It does not address the specific

issue of immigrant poverty, and because it does not use microdata, it cannot address

the basic question of which covariates might account for the poverty differentials it

describes.

Since earnings are the principal source of income for almost all immigrant

households, we expect factors associated with low earnings to be associated with

poverty, and we look to that literature to identify the key covariates for our model.

Papers by Trejo (1997, 2003) and Chiswick and Miller (2002, 2003) are the most

important background for our work.3

3 Unemployment is also relevant. De Jong and Madamba (2001, p. 25) show that after including controls

for human capital and fluency, Hispanic workers are significantly more likely to be unemployed and (for

males only) among the working poor.

Hispanic Immigrant Poverty 669

123

Trejo’s (1997) study of Mexican American wages during the 1980s emphasizes

the importance of age, literacy and educational attainment using a decomposition

design similar to ours, and concludes that ‘‘Mexican Americans earn low wages

primarily because they possess less human capital than other workers, not because

they receive smaller labor market rewards for their skills’’ (Trejo 1997, p. 1235).

Further analysis by the same author (Trejo 2003) concludes that ‘‘the sizable

earnings advantage U.S.-born Mexican Americans enjoy over Mexican immigrants

arises not just from intergenerational improvements in years of schooling and

English proficiency, but also from increased returns to human capital’’ (2003, p. 3).

Chiswick and Miller (2002, 2003) focus on the earnings of foreign-born men,

using 1990 Census microdata.4 They show (2002, pp. 37–38) that after the

introduction of numerous controls, there is still an earnings penalty (relative to men

with Western European birthplaces) of over 20 log points for immigrant men of

Mexican origin, among the highest of any of the seventeen immigrant groups

distinguished. The penalties for Cuban immigrants and Spanish-speaking immi-

grants from Latin America are somewhat smaller (about 15 log points), but still

much larger than those for immigrants from southern or eastern Europe. Among the

statistically significant controls are: education, potential work experience, years

since immigration, marital status, and citizenship status. English language skills are

also emphasized: the point estimate of the earnings bonus for English fluency

approximates the earnings effect of 3–4 years of additional education.

Taken together, the research on earnings suggests that controlling for age,

education, race, marital status, geographic location, English fluency, and length of

time in the US is important to understand Hispanic employment and earnings, while

the research that focuses directly on poverty suggests the need to add controls for

family demographics, time in the United States, and geographical location. This

paper extends the research base by determining the degree to which these covariates

are important determinants of poverty for Hispanics of different ethnic origins.

Data

As a data source we employ the 5% Public Use Microdata Sample (PUMS) from

Census 2000, as provided by the Integrated Public Use Microdata Series at the

Minnesota Population Center (see Ruggles et al. 2004). The value of using the 5%

sample rather than the 1% sample used by Van Hook et al. (2004) is that it permits

us to sub-divide the ethnic origin of Hispanics in greater detail, with group sample

sizes of over 1,000 poor households per group.

It is useful to begin with a description of what the Census means by the

‘‘Hispanic’’ population. In Census 2000, all ‘‘Hispanics’’ are distinguished by

4 Butcher and DiNardo (2002) also use Census microdata (for 1960–1990), but in a somewhat different

way. They create counterfactual wage distributions controlled for age, schooling, race/ethnicity, marital

status, geography, industry, region of origin and time in the U.S. While their paper is focused on different

issues, the details of their analysis verify the ‘‘tremendous importance of race and ethnicity’’ (2002, p. 10)

in determining the wage distribution of recent immigrants.


123

having self-identified as ‘‘Spanish/Hispanic/Latino’’ (the question no longer

contains the words ‘‘origin or descent’’ that appeared in the two previous Census

forms).5 Mexicans/Mexican Americans/Chicanos, Puerto Ricans, and Cubans were

given separate boxes with which to identify themselves. Other Hispanics were

permitted to write in an ethnic identification. According to Guzman, 58.5% of

Hispanics described themselves as Mexican, 9.6% as Puerto Rican and 3.5% as

Cuban (Guzman 2001, p. 2). The most common write-in origins were (in descending

order of numbers) Dominicans, Salvadorans, and Guatemalans. For the purposes of

the results we report below, we distinguish all of the ethnicities listed above, and

group the remainder into three residual categories: other Central Americans, South

Americans, and other Hispanics (some of whom are European, but a majority of

whom actually wrote in ‘‘Hispanic’’ or ‘‘Latino’’).

Since we want to study poverty, we must employ a standard of poverty. In a

nutshell, the Census poverty variable reports a family’s income as a percentage of

the ‘‘official’’ poverty threshold for that family, and attributes that poverty number

(top-coded at 500% of the poverty line) to every family member.6 For this reason,

we have elected to study the covariates of the poverty of household heads. In this

sense we are examining poverty among primary families and unrelated individual

household heads, as opposed to poverty among persons. The principal individuals

who are excluded are those in ‘‘group quarters’’ like dormitories or nursing homes,

and household members who are not members of the household head’s family. The

‘‘official’’ poverty line calculation has been subjected to a great deal of criticism

(Citro and Michael 1995; see also Iceland 2003b, who derives several results about

an alternative poverty measure), and much of this criticism is widely accepted in the

research community. We have selected the ‘‘official’’ poverty line calculation for

convenience, not because we endorse it. Our supposition is that a better measure

would be sufficiently correlated with the official measure that the relative

importance of covariates would not be greatly altered, though the levels of poverty

might be.

Because poverty lines are determined by household demographics, the first

covariates in our analysis are the number of adults and the number of own children

in the family of the household head. A larger family needs more resources, and is

more likely to be poor at any given level of income. The number of adults also

affects the family’s earnings potential. Children may also establish eligibility for

benefits that are not available to adult-only families, immigrant adults, or both.

5 In thinking about what ‘‘Hispanic/Latino’’ means to those who fill out the form, it is useful to examine

the results for the ‘‘Race’’ question on the Census 2000 form, in which it was possible for a person to list

‘‘some other race.’’ In fact, 41% of persons who gave their ethnicity as Hispanic or Latino also wrote in

Hispanic or Latino as their sole answer to the question about race, and another 5% listed Hispanic or

Latino as one of two racial identifiers (Grieco and Cassidy 2001, p. 10). In other words, the distinction

between ‘‘race’’ and ‘‘Hispanic ethnicity’’ that drives the standard Census tabulations is either

misunderstood or rejected by about half of the Hispanic population.6 The approach used for poverty calculation in Census 2000 is explained at http://www.ipums.umn.edu/

usa/pincome/povertya.html. Throughout the paper, we use the term ‘‘family’’ to cover both primary

families (a household head and his or her relatives) and household heads who are unrelated individuals.


123

http://www.ipums.umn.edu/usa/pincome/povertya.html

http://www.ipums.umn.edu/usa/pincome/povertya.html

The next key covariate is the total number of hours that members of the head’s

family unit (including the head) are ‘‘usually’’ employed. Because of restrictions on

access to government benefits by immigrants, legal as well as illegal, some groups

have work incentives that are very different from others, a factor that will be

captured by a control for family work effort.

Most of the remaining covariates are traits of the household head.7 We include

age and its square and years of education and its square as the principal controls for

the human capital of the household head, similar to the earnings studies discussed

above.8 Following some of the earnings studies, we include a variable distinguishing

a household head who is self-identified as ‘‘white’’ or white in combination with

some other racial identifier, possibly serving as a measure of acculturation.9

Because of the well-known correlation of poverty with the presence of a single

female household head, we include a variable for that status. We also include

controls for citizenship status,10 English fluency,11 and the number of years an

immigrant head has been in the U.S. and its square,12 since all have been identified

as important in previous studies of immigrant earnings.

We have included controls for Census regions (Northeast, Midwest, South-

Atlantic, South-Central and West), which may affect measured poverty in several

ways. First of all, concentration into a region may affect earnings, either positively

(easier adaptation, less discrimination) or negatively (crowding in specific

occupations, reduced incentive to assimilate). Second, the poverty calculation uses

a nation-wide set of income standards, so that those who receive higher incomes

because they live in areas with a high cost of living are less likely to have incomes

below the Federal poverty line. We also control for metropolitan status, expecting

that poverty rates for immigrants may be higher in rural areas.

7 The characteristics of the household head and his or her family members are ordinarily correlated:

young parents generally have young children, poorly educated husbands generally have poorly educated

wives, etc. If one were to include the characteristics of spouses, for example, it would be necessary to

conduct separate analyses for single and married heads, and it would therefore no longer be possible to

estimate the independent effect of marital status.8 We have also experimented with dummy variables for educational attainment, but found the results less

interpretable than the ones reported below. Educational attainment is actually reported as nine grouped

grade levels. We have attributed years equal to the mid-point of the attainments for all groups except the

college educated, who were assigned a value of 16 years.9 As noted in footnote 6, Census 2000 allowed a person to report a multiracial identity, leading to the new

nomenclature ‘‘white alone or in combination.’’ To further complicate the issue, Darity et al. (2002) claim

that Hispanics often self-report race in ways that contrast with their phenotypes.10 The Census asks about citizenship, but not, of course, whether non-citizens are illegally present. This

distinction is important because ethnicities differ greatly based on this unobservable. At one end of the

spectrum, Mexicans can take advantage of a long border to immigrate illegally when prevented from

doing so legally, while at the other end of the spectrum, Puerto Ricans have a citizen’s right to immigrate

freely.11 Chiswick and Miller (2002) find that the key distinction for immigrants’ earnings is between those

who speak English ‘‘not well/not at all’’ and those who speak it ‘‘well/very well,’’ and they call the latter

‘‘fluent in English.’’ We employ the identical distinction.12 Redstone and Massey (2004) find that the Census responses are not completely reliable, but that

improved data do not generate statistically significant differences in wage equations.


123

Differences in Means

The upper panel of Table 1 shows the values of the means for the sample

population, tabulated by Hispanic ethnicity.13 In 1999, the published U.S. poverty

rate was 11.9%, and we have calculated the poverty rate for native-born non-

Hispanic households in the Census microsample as 10.8%. By those benchmarks,

poverty among non-Hispanic immigrant households is not especially high at 12.7%.

Their principal liability is that about 1 out of 8 non-Hispanic immigrant household

heads lacks English fluency. Given the attention in the press to Hispanic

immigration, it is perhaps surprising that there are actually more non-Hispanic

immigrant households than Hispanic immigrant households, though the fact that the

Hispanic immigrant households are larger means that the total number of persons is

about the same.

The poverty rate among Hispanic immigrant households is 23.4%, almost twice

that of non-Hispanic immigrants. Compared to non-Hispanic immigrant households,

Hispanic immigrant households are distinguished by having both more adults and

more children, and slightly greater family work effort. The Hispanic heads have

much lower educational attainments and English fluency and are relatively unlikely

to be citizens. Hispanic immigrant households are much more likely to be located in

regions that are relatively near the border with Mexico, with over 56% living in the

West and South-Central regions, compared to 40% of non-Hispanic immigrants.

Table 2 sub-divides the Hispanic immigrant population by ethnicity. Since

slightly more than half of all Hispanic immigrants are from Mexico, their high

poverty rate of 24.6% is especially notable. When compared with other Hispanic

ethnic groups, Mexicans have somewhat larger households. The heads are generally

younger, less educated, and less likely to have the advantages of English fluency and

citizenship, though they also have greater family work effort and are less likely to be

single women. They are located primarily in regions near the border with Mexico,

and they are substantially less likely to live in a metropolitan area. It will require

further investigation to indicate which of these factors are most important.

Puerto Rican immigrant households, the second most common group, are very

different from the others. Persons born in Puerto Ricans are separately tabulated by

the Census, but are, of course, U.S. citizens (the handful who do not report it either

answered erroneously or were actually born elsewhere). It proves to be very

illuminating to have a citizen ‘‘immigrant’’ group as a benchmark. Turning to the

data in Table 2, immigrant household heads from Puerto Rico are older and more

fluent than most other Hispanic immigrant household heads. This constellation of

traits would seem likely to lead to a relatively low poverty rate, yet Puerto Rican

immigrant households have even higher poverty than Mexican immigrant house-

holds. Factors that may explain this higher poverty are that Puerto Ricans have very

low family work effort, and the heads are much more likely to be single women. A

majority lives in the Northeast region.

13 We report sample results rather than weighted population estimates. We have experimented

extensively with weighted results and have never found them to differ from sample results in any

meaningful way.


123

Cuban immigrant households have lower poverty rates than any of the other

Hispanic immigrant ethnic groups except those from South America. Like the

Puerto Rican immigrant population, much of the Cuban immigrant population has

Table 1 Means of variables by Hispanic ethnicity, census 2000

All immigrants means Non-Hispanic means Hispanic means

All immigrant household heads

Poverty rates 0.172 0.127 0.234

Number of adults in family 1.94 1.80 2.13

Number of children in family 1.19 0.93 1.55

Usual hours of family work 32.44 31.93 33.14

Head’s age 46.28 48.80 42.84

Head white 0.525 0.529 0.520

Head’s years of education 11.72 13.23 9.65

Single female head 0.227 0.240 0.210

Citizenship status of head 0.559 0.630 0.462

Head does not speak English fluently 0.240 0.122 0.402

Head’s years in USA 22.40 24.04 20.14

Northeast 0.255 0.303 0.188

Midwest 0.104 0.127 0.072

South-Atlantic 0.172 0.169 0.177

South-Central 0.108 0.072 0.156

West 0.362 0.330 0.406

Metropolitan 0.939 0.944 0.932

N (households) 623,326 360,200 263,126

Poor immigrant household heads

Number of adults in family 1.72 1.50 1.88


Usual hours of family work 17.91 13.97 20.83

Head’s age 44.01 46.69 42.02

Head white 0.475 0.452 0.492


Single female head 0.374 0.400 0.355

Citizenship status of head 0.424 0.479 0.383

Head does not speak English fluently 0.413 0.253 0.532

Head’s years in USA 18.17 18.33 18.05

Northeast 0.259 0.314 0.218

Midwest 0.083 0.121 0.055

South-Atlantic 0.150 0.163 0.141

South-Central 0.138 0.074 0.185

West 0.370 0.328 0.401

Metropolitan 0.926 0.936 0.919

N (households) 107,397 45,676 61,721


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mil

y2

.16

1.1

80

.53

1.6

61

.82

1.5

51

.09

1.6

51

.84

Usu

alh

ou

rso

ffa

mil

yw

ork

25

.35

9.0

29

.71

24

.19

23

.81

21

.29

18

.58

14

.95

20

.04

Hea

d’s

age

38

.98

48

.29

59

.27

36

.89

38

.44

40

.55

43

.50

42

.78

42

.04

Hea

dw

hit

e0

.483

0.4

73

0.8

60

0.4

48

0.3

93

0.4

60

0.6

56

0.2

51

0.4

45

Hea

d’s

yea

rso

fed

uca

tion

7.5

59.1

59.5

67.8

47.9

49.9

111.7

59.6

58.6

4

Sin

gle

fem

ale

hea

d0

.251

0.6

00

0.4

46

0.3

10

0.3

73

0.4

32

0.3

62

0.5

85

0.3

89

Cit

izen

ship

stat

us

of

hea

d0

.241

0.9

88

0.5

72

0.1

64

0.2

30

0.2

86

0.3

63

0.3

96

0.3

67

Hea

dd

oes

no

tsp

eak

En

gli

sh

flu

entl

y

0.5

83

0.3

53

0.6

14

0.5

71

0.5

27

0.4

39

0.4

18

0.5

56

0.4

96

Hea

d’s

yea

rsin

US

A1

6.4

32

6.4

62

2.6

61

3.2

01

4.7

21

5.0

31

4.5

81

6.9

21

7.7

5

No

rthea

st0

.025

0.7

71

0.1

17

0.1

70

0.1

35

0.2

38

0.4

45

0.8

95

0.2

17

Mid

wes

t0

.067

0.0

62

0.0

15

0.0

54

0.0

25

0.0

34

0.0

40

0.0

09

0.0

39

So

uth

-Atl

anti

c0

.059

0.1

29

0.7

85

0.1

68

0.1

32

0.3

28

0.3

37

0.0

83

0.1

38

So

uth

-Cen

tral

0.2

81

0.0

13

0.0

24

0.0

76

0.1

43

0.1

29

0.0

42

0.0

07

0.1

65

Wes

t0

.568

0.0

25

0.0

58

0.5

32

0.5

65

0.2

71

0.0

14

0.0

06

0.4

40

Met

rop

oli

tan

0.8

78

0.9

85

0.9

88

0.9

44

0.9

72

0.9

68

0.9

82

0.9

95

0.9

29

N(h

ou

seho

lds)

34

,06

68

,21

73

,471

1,0

25

1,7

77

1,6

08

2,7

28

2,8

56

5,9

73


123

been in the U.S. a long time, and the household heads are much older on average

than those of any other Hispanic immigrant group. They are relatively highly

educated and likely to be citizens, but have relatively low family work effort and a

relatively low rate of English fluency, despite their relatively long length of

residence in the U.S. A majority lives in the South-Atlantic region.

Guatemalan and Salvadoran household heads generally resemble Mexican

household heads, so it is somewhat surprising that they have a lower household

poverty rate. About half of them live in the West region. Other Central Americans

have considerably greater educational attainment and English fluency than

Guatemalans or Salvadorans, though the benefits are partly offset by a greater

incidence of single female heads. South American immigrant households have even

lower poverty than Cuban immigrants. They somewhat resemble the Cubans

(relatively small households, with highly educated heads), though they are younger,

less fluent in English, and have been in the U.S. a shorter time. Dominicans have the

highest poverty rate of any ethnic group studied, and they also have notable

economic liabilities that one would expect to be associated with poverty, including

low educational attainment and English fluency and an especially high incidence of

single female heads.

The lower panel of Table 2 shows the means for the poverty population only. It

contains no surprises at all. For every ethnic group, poor households are more likely

to have low work effort, a female head, fewer adults, more children, and younger

and less-educated heads. Poor households have spent less time in the U.S., are less

fluent in English, and are less likely to be citizens. What Table 2 cannot tell us is the

relative importance of these various factors, though that would seem to be very

important for discerning any policy implications.

We proceed in three steps. First, as a basic means to evaluate the relative

importance of different covariates of poverty, we conduct probit analyses of poverty

for the entire immigrant population and for its non-Hispanic and Hispanic

components. Second we apply the same model to the Hispanic ethnic groups to

see whether there are substantial differences in the effect of covariates on different

groups. And third, we decompose ethnic group differences into those associated

with different mean values of the covariates (different average traits) and those

associated with different probit coefficients (different effects of traits on poverty).

Probit Results

If an analysis of poverty is to inform policymakers about the relative importance of

various covariates, that analysis ought to indicate the ceteris paribus effects of the

covariates, suggesting the use of a regression analysis. Because poverty is a

dichotomous variable we have employed a probit model to calculate the effects.

Table 3 shows probit results for the total sample, sub-divided by Hispanic ethnicity.

The coefficients have been restated as marginal probabilities at the mean for

continuous variables, and the marginal probability effect of changing from zero to

one in the case of dummy variables. In samples this size, almost any coefficient is

statistically significant; we have marked the exceptions.


123

Adding an adult reduces the probability of poverty by 0.03–0.04, and adding a

child increases the probability of poverty by 0.01–0.03; the effect of an additional

child is substantially larger for Hispanic immigrants. The results show that an

additional 10 hours of family work effort reduces the probability of poverty by more

than 0.04 among non-Hispanic immigrants and more than 0.07 among Hispanic

immigrants. We would expect work effort to be strongly associated with poverty,

and it is.

The quadratic specifications for age and education are employed to permit a

curvilinear effect. We find that poverty falls with the age of the head at a decreasing

rate, and falls monotonically with the head’s years of education at an increasing rate

that is larger for Hispanics than non-Hispanics.

White households are somewhat more likely to be poor for non-Hispanics, but

racial identification does not seem to affect Hispanic poverty. The marginal effect of

being a female-headed household is dramatic, however, particularly for Hispanics.

Table 1 showed that Hispanics hardly differ from non-Hispanics in the propensity to

have a single woman as household head. However, this factor has a much larger

impact on poverty for Hispanics than for non-Hispanics. To use a comparative

metric, being a single female head has about the same poverty impact as losing

5 years of education for a non-Hispanic head, but about 8 years of education for an

Hispanic head.

Table 3 Marginal probabilities from probit analysis of poverty

Dependent variable: whether

poor (or not)

All immigrants All non-Hispanic

immigrants

All Hispanic

immigrants

Number of adults in family -0.034 -0.037 -0.040


Usual hours of family work 9 10 -0.059 -0.044 -0.077

Head’s age 9 10 -0.050 -0.039 -0.063

Head’s age squared/1,000 0.016 0.015 0.025

Head white -0.009 -0.015 0.001*


Years of education squared/100 -0.076 -0.056 -0.099

Single female heada 0.075 0.034 0.128

Citizenship status of heada -0.023 -0.010 -0.036

Head does not speak English fluentlya 0.044 0.037 0.043

Head’s years in USA -0.003 -0.003 -0.004

Years in USA squared/100 0.017 0.018 0.033

Northeast 0.003 0.005 0.019

Midwest -0.016 0.002* -0.044

South-Atlantic -0.001 0.006 -0.013

South-Central 0.033* 0.017 0.041

Metropolitan -0.032 -0.030 -0.045

Pseudo R-squared 0.254 0.276 0.213

a Dummy variable; * coefficient is not significant at 0.01


123

The citizenship of the head has a relatively small independent impact on poverty

for Hispanic immigrants and non-Hispanic immigrants alike. Households with

heads who lack English fluency, on the other hand, are substantially more likely to

be poor: The effect of being fluent in English is equivalent to approximately 4 years

of additional education for the typical immigrant. Heads who have immigrated

within the last 8 years have a higher risk of poverty.

Taking the West as the baseline region, the marginal effect of living in the

Northeast or South-Central regions is increased poverty for Hispanics, while the

marginal effect of living in the Midwest or South-Atlantic regions is reduced

poverty for Hispanics. The regional effects are of limited substantive importance for

non-Hispanic immigrants. A metropolitan location tends to reduce poverty for

Hispanic and non-Hispanic immigrants alike.

Turning to the results for detailed Hispanic ethnicities, we will summarize some

selected results in Table 4. Adding a child somewhat increases the probability of

poverty, particularly in Mexican and Central American households. Since Table 2

showed that those are the ethnicities with the largest households, family size

differences are a potentially important source of intergroup poverty differentials.

The effect of 10 additional hours of family work does not vary greatly across ethnic

groups, though it is useful to note that it is highest for Puerto Ricans and

Dominicans.

Poverty falls monotonically with the head’s years of education at an increasing

rate in each ethnic group. The rate of decline (indicated by the coefficient on the

squared term) is particularly high for Puerto Ricans. Households with heads who

lack English fluency are more likely to be poor in every ethnic group, though the

effect is relatively small in the Cuban and Dominican populations.

Race generally does not usually have a substantial impact, Cubans and Puerto

Ricans being the notable exceptions. On the other hand, a female single head has the

least impact on immigrant poverty among Cubans, and the most among Mexicans

and Puerto Ricans. The effect of the citizenship of the head varies from insignificant

(essentially inapplicable to Puerto Ricans) to substantial for Mexicans and

Guatemalans.

For most groups, the geographical variables are neither independently statisti-

cally significant nor potentially substantively significant. Correlating the results in

Table 4 with those in Table 2, there is some tendency for groups to have higher

poverty rates where they are most concentrated, consistent with the contention of

Chiswick and Miller (2003) that living in areas in which one’s own group is highly

concentrated tends to be correlated with reduced rates of economic success. Since

Mexicans are less likely to have a metropolitan location than other ethnic groups, it

is useful to note that Mexican immigrants who live outside the metropolitan area are

considerably more likely to be poor.

Decomposition Results

The previous two sections of this paper have displayed the differences between the

poverty rates and covariates of the various ethnic groups, then assessed the effect of


123

Tab

le4

Mar

gin

alp

rob

abil

itie

sfr

om

pro

bit

anal

ysi

so

fp

ov

erty

Dep

enden

tvar

iable

:w

het

her

poor

(or

not)

His

pan

icim

mig

rants

—et

hnic

sub-g

roups

Mex

ican

Pu

erto

Ric

an

Cu

ban

Gu

atem

alan

Sal

vad

ora

nO

ther

Cen

tral

Am

eric

an

So

uth

Am

eric

an

Do

min

ican

Oth

er

His

pan

ic

Nu

mb

ero

fad

ult

sin

fam

ily

-0

.03

4-

0.0

59

-0

.070

-0

.051

-0

.039

-0

.055

-0

.032

-0

.07

7-

0.0

36

Nu

mb

ero

fch

ild

ren

infa

mil

y0

.04

50

.013

0.0

03

*0

.043

0.0

39

0.0

38

0.0

15

0.0

28

0.0

39

Usu

alH

ou

rso

ffa

mil

yw

ork

91

0-

0.0

72

-0

.106

-0

.058

-0

.073

-0

.071

-0

.071

-0

.054

-0

.09

6-

0.0

75

Hea

d’s

age

91

0-

0.0

73

-0

.079

-0

.011

*-

0.0

68

-0

.064

-0

.053

-0

.028

-0

.08

4-

0.0

74

Hea

d’s

age

squ

ared

/1,0

00

0.0

36

0.0

08

*-

0.0

06

*0

.037

*0

.036

*0

.025

*0

.006

*0

.03

1*

0.0

34

Hea

dw

hit

e0

.01

1-

0.0

28

-0

.043

0.0

15

*0

.012

*-

0.0

09

*-

0.0

03

*-

0.0

05

*-

0.0

05

*

Hea

d’s

yea

rso

fed

uca

tio

n0

.00

30

.008

0.0

05

0.0

08

*0

.001

*0

.002

*0

.006

0.0

11

0.0

04

Yea

rsof

educa

tion

squar

ed/1

00

-0

.08

3-

0.1

47

-0

.068

-0

.102

-0

.053

-0

.063

-0

.059

-0

.11

3-

0.0

79

Sin

gle

fem

ale

hea

da

0.1

40

0.1

30

0.0

43

0.1

00

0.1

14

0.0

81

0.0

82

0.1

47

0.1

19

Cit

izen

ship

stat

us

of

hea

da

-0

.05

20

.016

*-

0.0

41

-0

.064

-0

.045

-0

.047

-0

.016

-0

.04

3-

0.0

28

Hea

dd

oes

no

tsp

eak

En

gli

shfl

uen

tly

a0

.04

40

.025

0.0

26

0.0

49

0.0

38

0.0

38

0.0

45

0.0

27

0.0

48

Hea

d’s

yea

rsin

US

A-

0.0

05

-0

.004

-0

.004

-0

.003

*-

0.0

04

-0

.004

-0

.006

-0

.00

3*

-0

.00

3

Yea

rsin

US

Asq

uar

ed/1

00

0.0

40

0.0

33

0.0

17

*0

.042

*0

.034

*0

.043

*0

.065

0.0

17

*0

.01

2*

No

rth

east

0.0

06

*0

.086

0.0

07

*-

0.0

23

*-

0.0

45

-0

.004

*0

.001

*0

.15

4-

0.0

27

Mid

wes

t-

0.0

62

0.0

47

-0

.004

*-

0.0

26

*-

0.0

32

*-

0.0

30

*0

.001

*0

.16

9*

-0

.04

2

South

-Atl

anti

c-

0.0

28

0.0

34

*0

.039

-0

.013

*-

0.0

68

-0

.003

*0

.009

*0

.16

0-

0.0

26

So

uth

-Cen

tral

0.0

49

0.0

28

*0

.049

0.0

02

*-

0.0

13

*0

.051

0.0

10

*0

.19

40

.02

4

Met

rop

oli

tan

-0

.05

3-

0.0

08

*-

0.0

26

*-

0.0

39

*-

0.0

38

*-

0.0

50

*-

0.0

22

*-

0.0

25

*-

0.0

41

Pse

ud

oR

-sq

uar

ed0

.17

70

.314

0.2

92

0.2

16

0.2

13

0.2

55

0.2

55

0.2

56

0.2

22

aD

um

my

var

iab

le;

*co

effi

cien

tis

no

tsi

gn

ifica

nt

at0

.01


123

the covariates on the poverty rates. The final step is to determine the extent to which

the differences in covariates can account for the differences in poverty rates, using

the probit coefficients derived in Section ‘‘Probit Results’’. We employ the ‘‘All

Immigrants’’ coefficients as a baseline for a decomposition of the differences across

ethnic groups.14

Thus, the final exercise we perform is to decompose the difference between the

poverty of each ethnic groups of immigrants and poverty among immigrants

generally, using the technique developed by Even and Macpherson (1990) to adapt

the Blinder–Oaxaca technique to a probit specification. To be clear, the analysis

variable, which we will call ‘‘the poverty rate differential,’’ is the difference

between the individual immigrant ethnic group poverty rate and the overall

immigrant poverty rate of 0.172. The decomposition exercise is this: for each

variable, we show how much of the poverty differential can be ‘‘explained’’ by the

difference in the means of that variable, evaluated with the probit coefficients for

‘‘all immigrants.’’ The portion ‘‘explained’’ by a variable can be negative, and

sometimes it is. For example, the poverty rate of Hispanic immigrants is higher than

the poverty rate for all immigrants, but the fact that the Hispanic immigrants have

more adults in the household would, in and of itself, cause the poverty rate among

Hispanic immigrants to be lower (by 0.007). It is also possible for the differences in

means to collectively ‘‘explain’’ more than 100% of the poverty differential if, for

example, the mean traits of the group predict a differential that is larger than the

actual one; we will show that this happens with Guatemalans and Salvadorans.

Table 5 shows the results of the decomposition, measured in poverty rate units.

As in previous sections, we begin by examining the results for the Hispanic

immigrant population generally. The data in Table 1 showed that the poverty rate of

Hispanic immigrant households is higher than that of all immigrant households (the

benchmark) by 0.062 (=0.234–0.172). Turning to the first column of Table 5, we see

that the larger number of adults and the larger number of children in Hispanic

households have essentially offsetting effects, but the relative youth of the Hispanic

population is of some importance. Fully half of the differential (0.031/0.062) can be

attributed to the combined effects of education and English fluency. Hispanic

immigrant heads are only slightly less likely to be single females than in the

benchmark group, so despite the importance of this factor in determining immigrant

poverty, it does not substantially affect the poverty differential. While Hispanic

immigrants are substantially less likely to be citizens, this factor is not very

important; Hispanics’ relatively short tenure in the U.S. is somewhat more so. All

variables combined ‘‘explain’’ 79% (=0.049/0.062) of the differential. The

‘‘unexplained’’ remainder is due to differences in the coefficients.

The other results in Table 5, for particular ethnic groups, can be considered

through either of two lenses: the rows (which groups of covariates are most

consistently important in accounting for poverty differentials?) and the columns (for

14 The coefficients are the actual probit estimates, not the marginal probabilities shown in Table 3. We

combine the effects of the linear and squared terms of the quadratic specifications when reporting the

decompositions.


123

Ta

ble

5D

ecom

posi

tion

of

His

pan

icim

mig

rant

pover

tyra

tedif

fere

nce

susi

ng

‘‘al

lim

mig

rant’’

pro

bit

coef

fici

ents

His

pan

ic

po

ver

ty

rate

Mex

ican

po

ver

ty

rate

Pu

erto

Ric

an

po

ver

ty

rate

Cub

an

po

ver

ty

rate

Guat

emal

an

po

ver

tyra

te

Sal

vad

ora

n

po

ver

tyra

te

Oth

erC

entr

al

Am

eric

an

po

ver

tyra

te

So

uth

Am

eric

an

po

ver

tyra

te

Do

min

ican

po

ver

ty

rate

Oth

er

His

pan

ic

po

ver

tyra

te

Su

b-g

rou

pp

ov

erty

rate

min

us

all

imm

igra

nt

po

ver

tyra

te=

0.0

62

0.0

74

0.1

14

0.0

07

0.0

39

0.0

27

0.0

20

-0

.022

0.1

25

0.0

52

Du

eto

dif

fere

nce

sin

Nu

mb

ero

fad

ult

sin

fam

ily

-0

.007

-0

.013

0.0

10

0.0

03

-0

.006

-0

.00

8-

0.0

02

-0

.001

0.0

02

-0

.00

5

Nu

mb

ero

fch

ild

ren

infa

mil

y0

.009

0.0

16

-0

.003

-0

.00

90

.006

0.0

09

0.0

03

-0

.002

0.0

11

0.0

09

Usu

alh

ou

rso

ffa

mil

yw

ork

-0

.004

-0

.017

0.0

62

0.0

24

-0

.023

0.0

22

-0

.014

-0

.026

0.0

28

-0

.00

2

Hea

d’s

age

0.0

12

0.0

22

-0

.010

-0

.02

50

.028

0.0

25

0.0

13

0.0

08

0.0

11

0.0

10

Hea

dw

hit

e0

.000

0.0

00

0.0

00

-0

.00

30

.001

0.0

01

0.0

00

-0

.002

0.0

03

0.0

00

Hea

d’s

yea

rso

fed

uca

tion

0.0

24

0.0

36

0.0

19

0.0

03

0.0

28

0.0

27

0.0

06

-0

.012

0.0

20

0.0

19

Sin

gle

fem

ale

hea

d-

0.0

01

-0

.006

0.0

13

0.0

02

-0

.003

-0

.00

10

.002

-0

.001

0.0

12

0.0

01

Cit

izen

ship

stat

us

of

hea

d0

.002

0.0

05

-0

.013

-0

.00

30

.007

0.0

05

0.0

03

0.0

02

0.0

02

0.0

02

Hea

dd

oes

no

tsp

eak

En

gli

sh

flu

entl

y

0.0

07

0.0

10

0.0

00

0.0

06

0.0

08

0.0

06

0.0

02

0.0

01

0.0

11

0.0

05

Hea

d’s

yea

rsin

US

A0

.005

0.0

08

-0

.018

-0

.01

00

.017

0.0

14

0.0

09

0.0

12

0.0

10

0.0

05

Reg

ion

0.0

02

0.0

00

-0

.001

-0

.00

2-

0.0

01

0.0

01

0.0

01

-0

.001

0.0

00

0.0

02

Met

rop

oli

tan

Sta

tus

0.0

00

0.0

01

-0

.001

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which ethnic groups is the model most ‘‘explanatory?’’). We begin by considering

the rows.

The most consistently important variables are family work effort and the head’s

years of education. One of those factors is the single most important (in absolute

value) for every ethnic group. Fluency is also somewhat important for Mexicans,

Central Americans and Dominicans. Given the close association of poverty with

single female headship, it is perhaps surprising that the considerable ethnic

differentials in that trait are not more important in explaining poverty differentials.

Except for Puerto Ricans and Dominicans, who have the highest incidence of single

female heads, this factor is simply swamped by the effect of differences in other

factors. Self-identified race and regional residence differ substantially across ethnic

groups, but never contribute much to explaining poverty differentials. The effects of

immigration traits (citizenship and years in the U.S.) are sometimes moderately

important, but very inconsistent across ethnic groups.

As to the columns, the model accounts for the differentially high poverty of

Mexicans very well. Since Mexicans make up more than half of the Hispanic

immigrant households, this is an interesting result. Though the ‘‘unexplained’’

portion of such decompositions is sometimes interpreted as ‘‘the effects of

discrimination,’’ it would not be proper to infer from our result that Mexicans suffer

little ‘‘discrimination.’’ For example, the benchmark group is ‘‘all immigrants,’’ who

may face some discrimination in general. It is, accurate, however, to infer that the

high poverty rate of Mexican immigrant households (relative to all immigrant

households) is associated with the youth and low educational attainments of

Mexican immigrant household heads, with the relatively large number of children in

Mexican immigrant households, with relatively low English fluency and with a

relatively short tenure in the U.S—all partly offset by a relatively high level of

family work effort.

The decomposition model does not perform as well in explaining the differential

poverty of some of the other Hispanic ethnic groups, and generates some puzzles

worthy of further investigation. For example, the decomposition only explains half

of the especially high poverty of immigrants from Puerto Rico. Table 2 showed that

their exceptional trait is a high proportion of single female household heads and low

family work effort; those factors and relatively low educational attainment account

for about 80% of the poverty differential, but some of the remaining variables (age,

citizenship) should actually work to mitigate poverty among Puerto Ricans. The

high poverty of Puerto Ricans is not so much a result of their observable traits as of

the fact that, according to Table 4, those adverse traits are much more important in

generating poverty among Puerto Rican immigrants than among immigrants

generally. In the light of the extent of poverty among Puerto Ricans, this topic

deserves further study.

Cuban poverty is similar to that of the benchmark group of all immigrant

households. We see that there are two important, but offsetting factors at work:

Cuban poverty is increased by low family work effort, offset by having household

heads that are, on average, substantially older than most.

As we pointed out in conjunction with Table 2, Guatemalan and Salvadoran

immigrants resemble Mexican immigrants in several ways, but their poverty rate is


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substantially lower. Table 5 reiterates the point. Looking at the Salvadorans, every

covariate except the number of adults and the incidence of female headship has

values associated with a high poverty rate. In fact, however, the actual poverty rate

is 0.199, while the model predicts a poverty rate of 0.273 (=0.199+0.074; this is the

interpretation of the negative ‘‘coefficient difference’’). The same phenomenon

holds for Guatemalans, though somewhat less dramatically. The evidence from

Table 4 is that the liabilities of low educational attainment and low rates of

citizenship have greater impact on the Mexicans, perhaps because the Mexicans are

less likely to have immigrated legally. The reason why Guatemalan and Salvadoran

immigrants have poverty lower than Mexicans despite similar traits is another topic

that merits further investigation. For other Central Americans the model accounts

for the poverty differential almost exactly.

The unusually low poverty rates of South American immigrants can be attributed

to unusually high family work effort and to high educational attainments of the

household heads. The model performs very well in accounting for the unusually

high poverty of Dominicans. Every trait of the Dominican population (except

metropolitan status) is associated with high poverty; low family work effort and low

educational attainment are the two most important factors.

Summing up, covariate differences effectively account for the unusually high

poverty of Mexicans and Dominicans and the unusually low poverty of South

Americans. They only account for about half of the unusually high poverty of

Puerto Ricans, and cannot account for the fact that Guatemalans and Salvadorans

have poverty rates so much lower than Mexicans in spite of a similar constellation

of traits.

Discussion

Although Trejo (1997) actually studies earnings, not poverty, he advances the claim

that ‘‘any war on Mexican-American poverty will need to attack the disturbingly

low levels of human capital possessed by Mexican-American workers’’ (Trejo 1997,

p. 1265). Our analysis leads to exactly the same result: the single most important

reason why Mexican immigrants have such high poverty rates is that they have, on

average, very low educational attainments. In fact, it is commonplace to associate

Hispanic immigrant poverty with low educational attainment. Almost all of the

Hispanic immigrant groups we identify have lower educational attainments than

non-Hispanic immigrants, and it is by far the most important factor contributing to

their high poverty rates.

The Chiswick and Miller (2002) analysis finds that English fluency is also

important for immigrants. The results presented in this paper suggest that fluency is

indeed important in explaining poverty among immigrants generally, though not

especially important in explaining the poverty differences among ethnic groups.

Improving fluency could decrease immigrant poverty substantially for Hispanics

and non-Hispanics alike.

Another crucial factor in accounting for poverty is family hours of work. It has a

very large impact on every ethnic group, and is the most important factor associated


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with the high poverty of immigrants from Puerto Rico. That it is especially

important among Puerto Ricans, who are (almost) all citizens, and Cubans, who are

disproportionately citizens, suggests that there is a potential path of causation by

which eligibility for benefits is associated with low family work effort. However,

the same problem is present for Dominicans, who are not disproportionately

citizens.

That households headed by a single female are much more likely to be poor is

well-known. The tabulations in Table 2 show that this key factor differs

considerably across Hispanic ethnic groups. Since having a single female head

increases an Hispanic immigrant household’s probability of poverty by more than

12 percentage points, it should play a substantial role in the analysis. It seems to be

especially important in accounting for the poverty of Puerto Ricans (citizens, and

eligible for welfare benefits) and also Dominicans (majority non-citizens, and not

eligible for welfare).

We find that self-reported race has essentially no role in explaining poverty

differences among Hispanic immigrant ethnic groups, while citizenship per se has a

modest direct effect on the poverty of immigrants. Being a recent arrival in the U.S.

(less than 5 years) is associated with increases in the probability of poverty. By the

time an immigrant head has been in the U.S. for 10 years, this factor no longer has

an independent effect.

The principal result that falls out from the analysis of regional effects is that there

may be an adverse poverty impact from being located in the same region as the rest

of one’s ethnic group, whether it be Mexicans in Arizona or Cubans in Florida or

Puerto Ricans in New York. It is unclear how much of this is just ‘‘selection on

unobservables’’ (for example, huddling together of illegal immigrants and others

most at risk of poverty), but it is consistent with the Chiswick and Miller (2003)

hypothesis that ethnic concentration has an adverse impact on conventional

measures of economic success.

The common practice in poverty research has been to investigate Hispanic

poverty without regard to immigrants’ ethnicity. This is the first study of Hispanic

immigrant poverty to distinguish among the ethnic origins of Hispanic immigrants.

The approach is fruitful because it turns out that the different Hispanic immigrant

ethnic groups vary considerably in their typical traits. For example, the Mexican

majority has several traits that help to explain their high poverty rate: in particular,

the household heads have, on average, low educational attainments, English fluency

and tenure in the U.S. These factors account for the differentially high poverty of

the Mexican majority very well.

There are also some puzzles worthy of further investigation, particularly

regarding immigrants from Guatemala, El Salvador, and Puerto Rico. Immigrants

from Guatemala and El Salvador are rather similar to those from Mexico in their

observable traits, but their poverty rates are substantially lower, and it would be

useful to know more about why. Immigrants from Puerto Rico have low family

work effort, a high proportion of single female household heads and fairly low

educational attainment, but those factors do not fully account for the group’s very

high poverty rate, another topic worthy of further research.


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Policy Implications

The policy implications of our results depend on whether the focus is immigration

policy or policy to address the poverty of immigrants and their families. It is correct,

for example, that immigrants from Mexico are an important component of the U.S.

poverty population, and that low educational attainment is their principal liability.

While it is obvious from our results that an immigration policy that focused on

admitting immigrants with higher educational qualifications and English fluency

would improve U.S. poverty statistics, it is not at all obvious that measured poverty

among immigrants is an especially important metric for judging the economic

consequences of immigration policy.

Turning to policies that might affect the poverty of immigrants, the ability of

policy to alter the educational attainment of adult immigrants is presumably limited.

It is more realistic for immigrant adults to achieve reasonable fluency in English,

and our results affirm the emphasis on language skills by Chiswick and Miller

(2002).

As the paper by Borjas (2001) and the comment on it by Fix (2001) indicate,

there are major disagreements about transfer payment policy for legal immigrants.

The 1996 welfare reform essentially said that noncitizen immigrants who arrived in

the U.S. after the time of enactment were ineligible for means-tested Federal

benefits,15 though Borjas claims that the intent of the legislation has been partially

undermined by Congressional backsliding and the willingness of many states to

fund benefits for those ineligible for Federal support. Our results show that

citizenship, which conveys eligibility, is associated with meaningful reductions in

poverty, especially among immigrants from Mexico and Central America. While

Borjas and Fix disagree about the effect of immigrant eligibility restrictions, they

both agree that the strong financial incentive for low-skill immigrants to naturalize

is an unfortunate by-product of the current policy. A related issue that neither Borjas

nor Fix takes up is the eligibility of immigrant parents, particularly single parents,

for programs that support their work effort. While eligibility could create ‘‘policy

magnets’’ that draw low-skill immigrants seeking the benefits, it would also enhance

the welfare of the immigrants’ children, many of whom are citizens. Our results

show that among immigrants, especially Hispanic immigrants, single female heads

have high poverty rates that could presumably be mitigated by better work

opportunities and child care alternatives. On the other hand, eligibility for benefits

conferred by citizenship has not led to low poverty among Puerto Ricans—in fact,

quite the contrary.

References

Borjas, G. J. (2001). Welfare reform and immigration. In R. B. Blank & R. Haskins (Eds.), The new worldof welfare (pp. 369–385). Washington, DC: Brookings Institution Press.

Butcher, K. F., & DiNardo, J. (2002). The immigrant and native-born wage distributions: Evidence from

United States censuses. Industrial and Labor Relations Review, 56(1), 97–121.

15 It also restricted the eligibility of some who had arrived earlier.


123

Chiswick, B. R., & Miller, P. W. (2002). Immigrant earnings: Language skills, linguistic concentrations

and the business cycle. Journal of Population Economics, 15(1), 31–57.

Chiswick, B. R., & Miller, P. W. (2003). Do enclaves matter in immigrant adjustment? Working paper,

Institute of Government and Public Affairs, University of Illinois.

Citro, C. F., & Michael, R. T. (Eds.). (1995). Measuring poverty: A new approach. Washington, DC:

National Academy of Sciences.

Darity, W., Jr., Hamilton, D., & Dietrich, J. (2002). Passing on blackness: Latinos, race, and earnings in

the USA. Applied Economic Letters, 9, 847–853.

De Jong, G. F., & Madamba, A. B. (2001). A double disadvantage? Minority group, immigrant status, and

underemployment in the United States. Social Science Quarterly, 82, 117–130.

Even, W. E., & Macpherson, D. A. (1990). Plant size and the decline of unionism. Economics Letters, 32,

393–398.

Fix, M. (2001). Comment. In R. B. Blank & R. Haskins (Eds.), The new world of welfare (pp. 385–390).

Washington, DC: Brookings Institution Press.

Grieco, E. M., & Cassidy, R. C. (2001). Overview of race and Hispanic origin. Washington, DC: U.S.

Census Bureau Census 2000 Brief.

Guzman, B. (2001). The Hispanic population. Washington, DC: U.S. Census Bureau Census 2000 Brief.

Iceland, J. (2003a). Poverty in America: A handbook. Berkeley, CA: University of California Press.

Iceland, J. (2003b). Why poverty remains high: The role of income growth, economic inequality, and

changes in family structure. Demography, 40, 499–519.

Jargowsky, P. A. (1997). Poverty and place: Ghettos, barrios, and the American city. New York: Russell

Sage Foundation.

Mogull, R. G. (2005). Hispanic-American poverty. Journal of Applied Business Research, 21, 91–101.

Partridge, M. D., & Rickman, D. S. (2006). The geography of American poverty. Kalamazoo, MI: W.E.

Upjohn Institute for Employment Research.

Redstone, I., & Massey, D. S. (2004). Coming to stay: An analysis of the U.S. Census question on

immigrants’ year of arrival. Demography, 41, 721–738.

Ruggles, S., Sobek, M., Alexander, T., Fitch, C. A., Goeken, R., Hall, P. K., et al. (2004). Integratedpublic use microdata series: Version 3.0 [Machine-readable database]. Minneapolis, MN:

Minnesota Population Center.

Trejo, S. J. (1997). Why do Mexican Americans earn low wages? Journal of Political Economy, 105,

1235–1268.

Trejo, S. J. (2003). Intergenerational progress of Mexican-origin workers in the U.S. labor market. TheJournal of Human Resources, 38, 467–489.

Van Hook, J., Brown, S. L., & Kwenda, N. M. (2004). A decomposition of trends in poverty among

children of immigrants. Demography, 41, 649–670.


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