Full text of Working Papers (Federal Reserve Bank of Chicago) : The Occupational Assimilation of Hispanics in the U.S. : Evidence From Panel Data, Working Paper 2004-15

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

Federal Reserve Bank of Chicago

The Occupational Assimilation of
Hispanics in the U.S.: Evidence from
Panel Data
Maude Toussaint-Comeau

WP 2004-15

The Occupational Assimilation of Hispanics in the U.S.: Evidence from Panel Data
Maude Toussaint-Comeau
Federal Reserve Bank of Chicago*
230 South LaSalle
Chicago, Illinois 60604
(312) 322-8443
maude.toussaint@chi.frb.org

JEL Codes: J15, J24, J61, j62
Keywords: Occupational segregation, Occupational Status, Immigrant Assimilation,
Hispanic Immigrants

*The views expressed are the author’s and do not necessarily reflect the views of the
Federal Reserve Bank of Chicago or the Board of Governors of the Federal Reserve System

Occupational Assimilation of Hispanics, page 2

The Occupational Assimilation of Hispanics in the U.S.: Evidence from Panel Data
Maude Toussaint-Comeau
Abstract
This study investigates whether Hispanic immigrants assimilate in occupational status
with natives and the factors that determine occupational status. A theoretical framework is
proposed that models occupational status and convergence of Hispanics relative to U.S.-born
non-Hispanics as a function of human capital and demographic exogenous variables, U.S.
experience (assimilation effects) and periods of migration (cohort effects). In addition, the
model also controls for aggregate economic conditions and location effects. The empirical
testing is based on a random effects model estimation procedure to accommodate the
longitudinal PSID panel data used in the analysis.
The results suggest that length of time resided in the U.S. narrows the occupational gap
between Hispanic immigrants and non-Hispanic Whites and U.S.-born Hispanic counterparts.
The level of individuals’ human capital affects the rate of occupational mobility and determines
whether convergence occurs in occupational status. Mexican immigrants with low human capital
start in occupations with relatively low status and they do not experience much occupational
mobility. Their occupational status does not converge with that of non-Hispanic or U.S.-born
Hispanic counterparts. However, Mexican immigrants with high human capital experience
occupational mobility, and catch up with non-Hispanic Whites after 15 years and with U.S.-born
Hispanics after 10 years of working in the U.S.
I.

Introduction
Hispanic immigrants constitute a sizable and growing segment of the U.S. labor force, yet

research suggests they are among the most economically disadvantaged workers in the nation.
Hispanic immigrants’ wages have been declining since the 1980s relative to those of natives
(Reimers, 1997; Rivera-Batiz, 1994; Chiswick, 1986). Borjas (1995) noted that the wage decline
experienced by the Hispanic immigrant population contributed to the decline in the wages for all
immigrants observed during that period. Orrenius and Zavodny (2003) also noted that, following
the 1965 Amendment to the Immigration and Nationality Act, the inflow of less-skilled
immigrants, including Hispanic immigrants, has lead to the decline in real wages and rising

Occupational Assimilation of Hispanics, page 3

unemployment among less-skilled natives in the 1970s and 1980s.1 Therefore, the skills of
Hispanics and their potential implications remain a subject of intense debate (e.g., Borjas, 1985;
Duleep and Regets, 1992; LaLonde and Topel, 1991; Chiswick, 1978).
Most previous researches have considered earnings to make inferences about the skills
composition of Hispanics and very few have looked at occupations. Occupation is however as
important a measure of the skill sets of individuals that convey the socioeconomic status of
workers. The occupational attainment of immigrants is an inherent part of their socioeconomic
adjustment. Initially immigrants may have an occupational disadvantage because they lack
knowledge on how to find employment, or their pre-migration skills may not be fully
transferable. But with a longer stay in the U.S. and more investment in U.S.-specific skills, their
socioeconomic status is expected to improve and resemble more closely that of natives’, a
process known as assimilation.
The objective of this research is to determine the factors that affect differences in
occupational attainment of Hispanics and to test the hypothesis that occupational assimilation
occurs overtime for Hispanic immigrants. The results of a random effects model suggest that,
consistent with the assimilation hypothesis, the length of time resided in the U.S. narrows the
occupational gap between Hispanic immigrants and non-Hispanic Whites and U.S.-born
Hispanic counterparts. However, the level of individuals’ human capital affects the rate of
occupational mobility and determines whether convergence occurs in the groups’ socioeconomic
occupational status. The occupational status of Mexican immigrants with low human capital does
not converge with that of non-Hispanic counterparts. However, those with high human capital

1

The 1965 Act established a system under which visas were allocated mostly to applicants with relatives residing in
the United States. Prior to the 1965 amendment, visas were granted on a quota system based on national origin.
Immigrants from Europe, however, had no restriction on the number of visas they obtained.

Occupational Assimilation of Hispanics, page 4

experience a sharp rise in occupational status throughout their stay in the U.S., and eventually
catch up with non-Hispanic Whites and U.S.-born co-ethnics.
The next section provides an overview of the literature. Section III describes the data. In
Section IV, a theoretical framework is proposed. The results are presented in Section V. Section
VI concludes with the implications of the findings.

II.

An Overview of the literature
Researchers have proposed several theories to explain why individuals or groups of

individuals, such as immigrants, select and change jobs. These theories can provide some
insights as to the reasons why we may find that Hispanics are at an occupational disadvantage
and the extent to which they may or may not assimilate in their occupational profile. These
theories include the human capital accumulation and assimilation, cohort quality, labor market
segmentation, and economic restructuring.
A recurring theme in research about the Hispanic population is that they tend to be in
occupations with lower economic status than U.S.-born individuals because they have less
human capital (lower educational attainment, less U.S. labor market experience, and greater
English language deficiency (Stolzenberg, 1982)). Research shows that individuals’ human
capital characteristics, such as education and work experience are both expected to promote
occupational mobility in the labor market. These characteristics provide a positive signal to
employers as to the ability of workers to assume greater responsibilities within an organization.
They allow the individual to have greater access to information about job opportunities. The
ability to speak English is also an important human capital characteristic that affects access to
certain occupations. Kossoudji (1998) noted that occupations tend to be heterogeneous in their
use of language and, consequently, the contribution of language to productivity varies by

Occupational Assimilation of Hispanics, page 5

occupation. For example, in occupations that have traditionally been held by immigrants,
employers are less likely to screen out those with a lack of English knowledge. This may partly
explain why Hispanic immigrants tend to cluster in relatively few occupations —those who are
not fluent in English are likely to be concentrated in occupations that require relatively lower
human capital/skills. By the same token these occupations tend to provide lower incomes and
have more limited opportunities for advancement.
Whether or not one is an immigrant affects the extent to which one experiences
occupational mobility. Initially, immigrants tend to have an occupational disadvantage because
they lack knowledge on how to find employment, or their pre-migration skills may not be fully
transferable to the U.S. labor market. As they gain experience in the U.S., and invest in U.S.specific human capital skills, their socioeconomic status tends to improve. Eventually, their
occupational profile may resemble that of the natives —a process referred to as occupational
assimilation. For example, suppose that at the time of entry the typical Hispanic immigrant does
not have the complement of skills that are valued in the U.S. labor market—these skills include
education, the required license or certification, English language proficiency, an understanding
of how the U.S. labor market operates, and U.S.-specific labor market experience. As such, the
immigrant must take a lower level or a lower status occupation while he/she builds U.S. labor
market specific human capital. As the immigrant assimilates into the country and reaches
‘information parity’ with natives, he/she tends to move into occupations of higher status or
occupations more similar to those of natives.
The extent to which an immigrant experiences occupational assimilation depends on the
“skill set”, ability, and incentives of the immigrant to gain U.S.-specific skills. For example, an
educated immigrant from Mexico is expected to quickly gain U.S. specific skills (e.g., it is easier
to learn English if one is already educated), and as such is expected to move up the occupational

Occupational Assimilation of Hispanics, page 6

ladder. On the other hand, an immigrant with less education (e.g., a Mexican farmer) is likely to
experience a more limited upward occupational mobility because the accumulation of U.S.
specific skills may be relative harder (e.g., it is more difficult to learn English when one has very
little pre-migration education, and one has little incentive to do so if one arrived in the U.S. at an
older age).
Chiswick (1977) develops a model of occupational mobility to predict the likely
occupational profile of immigrants over time in the host country. The underlying assumption is
that skills are not perfectly transferable across countries. After migration, immigrants make
investments to complement their pre-migration skills that increase transferability in the
destination country. The investments encompass the acquisition of labor market information,
destination language proficiency, occupational licenses, or other task-specific skills.
Subsequently, immigrants tend to experience an occupational trajectory that follows a U-shaped
pattern. The downward-sloping portion of the pattern is due to the fact that, initially, they
experience a decline in their occupational status relative to their pre-migration occupational
status (e.g. a doctor from Mexico without a medical license). The steeper the initial decline, the
steeper will be the subsequent increase for a highly-skilled immigrant from a country of origin
that is dissimilar (e.g. in language) to the host country. Immigrants from countries that are
similar (e.g., in quality of education, closeness of language spoken, and parity in economic
development level) are likely to have a shallow U-shaped occupational status curve, since their
pre-migration skills are more transferable. The occupational profile of immigrants with very low
skills will also be depicted by a shallow U-shaped curve, since they are likely to find it very
costly to acquire high skills —an example being, unskilled farm laborers from Mexico who are
likely to remain unskilled workers in the United States. Their occupational profile will be
depicted by a shallow U-shaped curve. Refugees (this would be the case for Cubans) tend to be

Occupational Assimilation of Hispanics, page 7

individuals with skills (e.g. generals, judges). They are expected to have a fairly steep
improvement in skill acquisition after a steep initial decline in their occupation status.2
Borjas (1999) proposes a human capital production function to model the post-migration
rate of skill acquisition, which can be related to the occupation or skill profile of immigrants in
the host country. Put in his terms, immigrants have K numbers of efficiency units acquired in the
source country. He also assumes that human capital is not perfectly transferable across countries.
The less-than-perfect transferability implies that the immigrant, initially (during the investment
period in a two-period model), devotes a fraction of his efficiency units to the production of
additional human capital. Highly-skilled immigrants are more able to acquire additional human
capital skills because there is greater "complementarity" between pre-migration and postmigration human capital. On the other hand, because the costs of human capital investment are
mainly forgone earnings, having high initial skills makes it more expensive to acquire additional
skills. This “substitutability” effect suggests that high-skilled individuals may choose not to
augment their human capital skills. Consequently it is theoretically ambiguous the extent to
which highly skilled immigrants may improve their occupational profile.
It is worth noting that both Chiswick and Borjas assume that the assimilation process to
be the convergence between immigrants and the U.S.-born natives. However, Lalonde and Topel
(1991) argue that because immigrants and natives are so different, assimilation should be
construed to occur simply when “the skill of an immigrant cohort rises with time spent in the
United States.” Therefore the immigrant group himself becomes the base or comparison group,
as opposed to natives

2

Using a longitudinal survey of recent immigrants in Australia, Chiswick et al. (2002) using data that provide
information on pre-migration occupations, found support for these hypotheses.

Occupational Assimilation of Hispanics, page 8

The Cohort Quality model (Borjas, 1995) identifies that there are differences between
immigrants and native populations. This model also suggests that there are differences between
groups, or cohorts, of immigrants. It is possible that immigrants who entered the U.S. at a
particular time period, 1950 for example, had significantly higher skills or a more acceptable or
transferable skill set than other immigrant cohorts. If this is the case, then the path of
occupations and wages for this group will be different than the path of occupations and wages for
other cohorts. According to Borjas (1995), the low wages experienced by Hispanics in the 1980s
can be explained by the lowering in the “quality” of successive immigrant cohorts.3
The extent to which immigrants assimilate can depend on the host country’s immigration
policy vis-à-vis the types of skills that are required for the immigrants as conditions for entry.
For example, Richmond and Kalbach (1980) note that in a context where the host country’s
immigration law requires that new immigrants be endowed with specific skills as pre-conditions
for admission into the country, immigrants would have an occupational distribution that remains
dissimilar to that of the natives. They suggest that in Canada, this may explain why immigrants
are relatively over-represented in professional, semi-professional and manufacturing occupations
and under-represented in primary and transportation occupations. Green (1999) finds that,
immigrants who are assessed based on their skills tend to be more occupationally mobile, even
long after their arrival. By contrast, immigrants who are assessed on their skills upon entry are
less occupationally mobile.

3

There are basic problems with equating decline in wages to be a decline in skills of new and more recent
immigrants. It is well known that structural changes in the U.S. labor market have resulted in increasing returns to
human capital. Wages have increased for individuals with high human capital and declined for those with less
human capital or education (Murphy and Welch, 1997). Since Hispanic immigrants tend to have relatively lower
education, this suggests that their earnings would have decreased even if their skills remained unchanged from those
of previous cohorts (Borjas, 1995).

Occupational Assimilation of Hispanics, page 9

Another perspective offered by previous researchers is the importance of ethnic
concentration in potentially shaping the occupational distribution of immigrants. Since
immigrants tend to concentrate in specific locations (Bartel, 1989), the labor market conditions
of the location are likely to influence occupational outcomes. The location may also provide
greater opportunity for ethnic networks, including informational advantages, and a large enough
ethnic markets that would reduce economic disadvantages, including occupational disadvantages
(Portes and Rumbaut, 1996).
A group of theories predict that assimilation may in fact not take place. The labor
segmentation theory contends the labor market is divided in a primary sector, made up of wellpaying jobs with opportunities for mobility (Sanders and Nee, 1987). The secondary sector is
comprised of low-paying job with limited advancement opportunities. Hispanics tend to be
concentrated in the secondary sectors (were minorities, women and immigrants tend to be
overrepresented) and there may in fact not assimilate in occupational status.
The economic restructuring theoretical view considers both the individuals’
characteristics and economic structural changes. The economic restructuring elements, over the
decades, have been characterized by a general decline in stable, well-paying manufacturing jobs
and an increase in low-paying, service-oriented jobs. Macro economic changes, increases in
global competition have affected the nature of employment. Structural shifts are reinforced by
changes in the labor supply created by extensive immigration (Morales and Ong, 1990). Since
the late 1970s there has been a growing polarization of job opportunities as evident from the
changes in the distribution of industries and occupations (Harrison and Bluestone, 1988). The
economy is based largely on services and a decline in manufacturing employment. For example,
in Los Angeles, traditional durable goods industries (such as steel, auto and rubber) were
replaced by jobs in both very high and very low technology industries. In Los Angeles, this

Occupational Assimilation of Hispanics, page 10

consists of aerospace, communications equipment and electronics on the one hand, that has a
labor intensive component. The new structure of the employment base is characterized by a large
number of low-wage jobs and a small number of high wage jobs. In the changing economic
climate, college education is primordial for upward occupational mobility. Since Hispanics tend
to have lower educational attainment, immigrant status, language barriers, they have been
incorporated in occupational niches that can accommodate individuals with lower human capital.
Hispanics have been absorbed in the growing low-level, service oriented labor market, with very
low prospect for occupational mobility. According to this view, the low-skilled Hispanics are not
likely to assimilate in occupational status.

III.

Data and Summary Statistics
This analysis is based on the Panel Study of Income Dynamics (PSID) for the years 1990

to 1993. It exploits the fact that the PSID oversampled the Hispanic population to investigate
their occupational experience in the labor market over this period. The use of the PSID provides
some advantages over previous studies that have made use of other longitudinal data. For
example, the Legalized Population Surveys (LPS1 and LPS2) were used by Powers et al. (1998)
to study the occupational status of undocumented immigrants. However, information on
demographic and socioeconomic characteristics such as education and income lack in this data.4
The Public Use Micro Statistics (PUMS) provides a very large sample and have been used before
to study the earnings assimilation of Hispanics. The cross-sectional nature of the PUMS required
that “artificial age cohorts” be created in making inferences about longitudinal behavior (Borjas,
1995). Therefore, in principle the PUMS could be used in this analysis as well. The use of the
4

The Survey of Income and Program Participation (SIPP) is a potentially viable longitudinal data to use as well. It
was not used in this paper because in the public version of the data, the information on the years of migration was
aggregated in terms of period intervals for confidentiality.

Occupational Assimilation of Hispanics, page 11

PSID is new and as such, will allow us to confirm some common patterns and trends learned
from the PUMS regarding the economic adaptation of immigrants.
The PSID data provides detailed information about the family’s individual members.5
However, the information on occupation is provided only for the primary adults heading the
family and, in addition, the wife where applicable. Although the question on occupation was
included in the questionnaire for 1990 to 1995 and the special sampling of Hispanics was
conducted over that entire period, as of now, unfortunately, information on occupation can only
be obtained up to the year of 1993.
The descriptive statistics of selected variables from the PSID data are reported in Table 1,
Panels A to D, each panel corresponding to a survey sample year. As shown in Panel A,
education is lower for Hispanics compared to non-Hispanic Whites. The average number of
years of schooling for Hispanic immigrants is 8.3. By contrast, non-Hispanic Whites have an
average of 12.7 years of schooling. It can be noted that the number of years of schooling
completed by Cubans is 11.9, which is somewhat comparable with non-Hispanic Whites. The
Mexicans, Puerto Ricans and other Hispanics are relatively younger than non-Hispanic Whites.
Hispanics tend to be geographically concentrated. For example, close to 86 percent of
Cubans in the sample reside in the South, particularly in the state of Florida. Over half of the
Puerto Ricans are in the Northeast region, mostly in the state of New York. Close to half of the
Mexicans are located in the Pacific West, which includes the state of California. Close to another
half are in the West region, with a strong concentration in the state of Texas. As proposed in
previous research, geographic distribution may play a role in shaping the resulting occupational
distribution of Hispanics.

5

For a description of the data and a review of studies that have used the PSID, see Brown et al. (1996).

Occupational Assimilation of Hispanics, page 12

There are stark differences in the occupational distributions of different groups. For
example, for the 1990 sample, over 21 percent of non-Hispanic Whites are in professional and
technical occupations, and 16 percent are in managerial and administrative occupations. By
contrast, 6 percent of Hispanic immigrants are in professional and technical occupations and 5
percent are in managerial and administrative positions. A relative concentration of Hispanic
immigrants (mostly Mexicans) is in farm-related occupations. Other occupations with a
relatively high concentration of Hispanic immigrants include operative, service, craftsmen and
kindred services. A consistent pattern can be seen for the subsequent years reported in Table 1,
Panels B to D.
The data on occupation in the PSID are coded according to the 1970 Census
Occupational Classification System (OCS), which identifies 428 specific occupations. In order to
assess the quantitative meaning of the categorical occupations, this study makes use of an index
of socioeconomic status score, NAM-POWERS, developed by Nam and Powers (1983). In this
analysis, the three-digit codes of the OCS were matched with the PSID occupation entries for
each respondent, for each of the years from 1990 to 1993, and were assigned the corresponding
NAM-POWERS scores. The NAM-POWERS score is an ordinal scale derived from the
education requirements and wages of the job. Ranging from 0 to 99, the scores represent the
socioeconomic standing of a particular occupation in the universe of detailed occupations of all
individuals in the labor force.6 The NP score is also based on a regression analysis of education
and income as a mean to capture the relative importance that society places on the occupation.7

6

Similar measures have been developed for Australia (see Jones, 1989), and the United Kingdom (see Goldthorpe
and Hope, 1974).

7

See Nam (2000) for a comparison of the NP scores with other socioeconomic scores.

Occupational Assimilation of Hispanics, page 13

Table 2 reports the mean value and standard deviations of the NAM-POWERS scores for
12 major occupational categories in the PSID data for the year 1990. Professional and
managerial occupations considered “high-skill occupations,” consistently, have the highest
socioeconomic scores at 80 points or more. “Medium-skill occupations” are considered to be
sales, clerical, craftsmen, transport equipment, and operative occupations. These occupations
have the second highest sets of occupational status scores, ranging from an average of 36 to 62.
“Low-skill occupations” include farmers and farm managers, service workers and laborers. They
range from an average of 23 to 32. Finally, the “very low-skill occupations” are comprised of
farm laborers, foremen, and private household workers. Average scores for these occupations go
from 4 to 7.
The results from Table 2 show that apart from the farm occupations, the socioeconomic
scores for Hispanic men are lower in all the occupation categories. The one exception is Cuban
male professionals and managers, who on average have a higher socioeconomic status. Table 2
also shows that women across all racial/ethnic groups have a lower occupational status than men.
The exception here is private household workers, for whom socioeconomic status is among the
lowest. In general, non-Hispanic White women have a higher average socioeconomic status than
Hispanic female counterparts. An exception is in managerial occupations, where Hispanic
women have a slight advantage over non-Hispanic White women.
Table 3 reports the average wages earned by different groups in the 12 major occupation
categories. With the exception of farm-related occupations, where wages are already very low,
the wages earned by Hispanics in each category are lower. The gap in earnings is largest among
individuals in “high-skill occupations.” For example, Hispanic male managers and administrators
earn on average $27,000, compared to non-Hispanic counterparts, earning close to $60,000.
Similarly, Hispanic professionals and technicians earn $33,000, compared to $44,000 earned by

Occupational Assimilation of Hispanics, page 14

non-Hispanic White counterparts. These results suggest a number of possibilities. There may be
divergences in the kinds of tasks that Hispanics and non-Hispanic perform on the job, and/or the
market may value occupation differently by group.
Focusing on gender differences in Table 3, women earn less than men do and nonHispanic women earn more than Hispanic women. Again, the exception is in the managerial
occupations where Hispanic women appear to have a slight earnings advantage over nonHispanic Whites.8
Table 4 reports the average occupational scores for Hispanic and non-Hispanic White
men and women for the years 1990 to 1993. The results show that Hispanics have lower average
occupational scores than non-Hispanic Whites. Non-Hispanic White men have a score of 63.9
but Hispanic men have a score of 47.1. To a large extent, immigrant status contributes to
Hispanics’ occupational disadvantage. The U.S.-born Hispanic men have an average score of
52.5, whereas the immigrant counterparts have a score of 41. The same pattern is consistent for
women and persists for the next 3 years over the period.
Table 5 reports the average occupational status scores for Hispanic immigrants by entry
cohort. Tracking individuals who reported an occupation for each of the years in the survey
period, a number of facts can be noted: first, the average socioeconomic status of Hispanics has
declined across successive cohorts. For example, for 1990, the ‘<=5 years’ group have an
average score of 29.7. Those in the ‘6-10 years’ cohort group have an average score of 37.9.
Those in the ‘>20 years’ group have an average score of 47.6.
Table 6 reports the average occupational status scores for various Hispanic ethnic groups
and by entry cohort for the year 1990. Again for each of the groups, the socioeconomic status

8

A potential explanation may be a higher return to bilingualism in managerial occupations for Hispanic women.

Occupational Assimilation of Hispanics, page 15

declines across successive cohorts.9 Mexicans have relatively lower socioeconomic occupational
status irrespective of the entry cohorts.
Even within the short 4-year timeframe, there is some evidence of occupational mobility
among Hispanic immigrants over time. Table 7 reports the changes in occupation experienced by
individuals between 1990 and 1993. Thirty-one percent of Hispanics experienced upward
occupational mobility compared to 26 percent of non-Hispanic Whites. A higher proportion
among the most recent Hispanic cohorts experienced upward occupational mobility. For example
34 percent of the cohorts who arrived 5 years or less prior to the survey experienced an upward
mobility, whereas 22 percent of the cohorts who arrived 20 years prior to the survey or earlier
experienced a similar upward movement.
We note that the proportion of individuals who change occupations upward or downward
is very high in the PSID, close to 50 percent, which is somewhat unlikely. This situation is
probably due to coding errors in the PSID data on occupation. Indeed to correct the errors, the
PSID released Retrospective Occupational Industry Supplemental Data Files that recoded
occupations (and industries) for the period of 1968-1980 (Survey Research Center, webpage).
Data after 1980 that still rely on the originally coded data is still subject to substantial error and it
would be imperative to control for coding error after 1980 in an analysis of workers’
occupational change based on the PSID data for an analysis of change in occupation.
We found that although the changes in occupation are high, the occupational status scores
which is the focus of this analysis did not change by much from one occupation to the next for
the same individual. As can be seen from Table 4, the range in average Hispanic occupational
9

Contrary to the pattern, recent Puerto Rican cohorts have higher scores than earlier cohorts. Ramos
(1992) finds that Puerto Rican immigrants who return to Puerto Rico, a U.S. possession, are relatively more skilled
and those who are relatively unskilled tend to reside longer in the mainland U.S. Following Ramos’ study, our
finding may be an artifact of the group’s return migration pattern. The small sample size of the most recent cohort
suggests that any interpretation be given with caution.

Occupational Assimilation of Hispanics, page 16

status scores varies from 47.1 to 48.9 over the period, suggesting more moderate occupational
differences than is suggested by the number of individuals who report changes in occupation.
The rate of change in the occupation scores was also higher for Hispanics. On average
non-Hispanics experienced an 18.4 percent increase in occupation scores. By contrast the most
recent (less than 5 years) had a 38.9 percent increase in their average occupation scores. The 11to-20 year cohorts experienced an increase of an average of 45 percent. The higher change in
occupation score has to do with the lower status of the occupation of immigrants.
In summary, the descriptive statistics suggest a number of potential results. The
socioeconomic occupational status of Hispanic immigrants is relatively lower than nonHispanics. Differences exist in the socioeconomic characteristics and occupational distribution
across various Hispanic ethnic groups. In particular, Mexicans and Puerto Ricans tend to have
the greatest gap relative to non-Hispanic Whites. Consistent with assimilation theory, even in
this univariate environment, the results point to a pattern whereby Hispanics who have been in
the country for a longer period of time experience a higher rate of occupational mobility. This
trend may follow from the fact that occupational mobility is a natural part of the adjustment
process of Hispanics in the country, an “assimilation effect.” Or, following Borjas’ proposition,
the relatively higher socioeconomic status achieved by Hispanics who have been in the country
for a longer period of time may also reflect differences in human capital skills across successive
entry cohorts, a “cohort effect.” To determine if these hypotheses are true, assimilation versus
cohort effects, a multivariate analytical framework is proposed.
IV.

Theoretical Framework
Pulling the 1990, 1991, 1992, and 1993 PSID panel data, the socioeconomic occupational

status of Hispanic immigrants over the sample period is considered in the following random
effect framework:

Occupational Assimilation of Hispanics, page 17
Ω-1

Sit = Xit βit + φitAit + γMit + δCit + ∑ λtPit + νi + εit

(1)

t=1

Where,
Sit

is the composite index of the socioeconomic occupational status score of the ith
immigrant person in the tth year of the sample period (t = 1 to Ω). One of the four periods
is omitted (hence Ω -1) in order for the model to be identified.

X

is a 1xk vector of human capital and demographic exogenous variables.

β

is -kx1 vector of parameters.

A

is the age of the individual.

φ

is the rate at which the individual experiences occupational mobility over the life cycle.
The life-cycle effect for the immigrants also encompasses the effect of the length of stay
in the host country.

Mi

is the years-since-migration variable, which is a proxy for the assimilation effect.

γ

conveys the socioeconomic value of a year spent in the U.S. labor market.

C

represents the different entry cohorts.

δ

stands for the differences in socioeconomic attainment across immigrant cohorts.

λ

gives the period effect.

P

is a vector of dummy variables indicating the years of the survey. They can be seen as
indicator variables for labor market and economic conditions in the given years.

νi , εit are random components of the model. νi is the individual random effects or the random
disturbance component characterizing the ith observation and is constant through time
over the sample period. It can be viewed as a collection of factors not in the regression
that are specific to the individual. The disturbances in different periods for a given

Occupational Assimilation of Hispanics, page 18

individual, εit, are correlated because of their common component νi. In sum, the error
terms, consistent with the random effect model, (Greene, 2003) are assumed as follows:
E[νi ] = 0, Var[νi ] = σu2, Cov[εit, νi ] = 0
Var[εit + νi ] = σν 2 = σε2 + σν 2
Corr[εit + νi ] = ρ
The natives’ socioeconomic occupational status, counterpart to equation (1) is given as
follows:
Ω-1

Snt = Xnt βnt + φntAnt + ∑ λtPnt + νn + ε nt

(2)

T= 1

The coefficients, the variables and the error terms are as previously defined, except that here they
apply to the nth U.S.-born person and immigration-related variables are not relevant.
Considering equation (1) and (2), the convergence between the rate of occupational status
between immigrant and native can be specified as follows:10
γ* =

∂logSi
– ∂log Sn
= (φi + γ) - φn
∂t | immigrant
∂t | U.S. born

(3)

Equation (3) shows that assimilation occurs when there is convergence with native (γ* =
0). The ambiguity that can arise without a clear determination of the comparison group is
obvious in this equation. If φi < φn, (which is very likely since the Hispanic immigrants are
relatively younger), it is possible to obtain a positive γ suggesting that assimilation occurs in the
sense of Lalonde and Topel (1991), but yet still observe γ* <0, which would indicate that
immigrants do not assimilate with respect to natives.

10

This definition of assimilation follows from Borjas’ (1999) earnings convergence model. Although the
comparison group in the equation is denoted as U.S. born, since the PSID data does not provide information on the

Occupational Assimilation of Hispanics, page 19

V.

Empirical Results

A random effects linear regression procedure is used to estimate the coefficients of the model.
The random effects linear regression technique is used because of the panel nature of the data,
where the variables in consideration have time-variant elements (e.g., age, years-since-migration
variables) as well as time-invariant characteristics (e.g., gender, race variables), (see Greene,
2003). The population sample is restricted to individuals aged less than 65 years to avoid
complications associated with individuals facing retirement decisions at the traditional retirement
age. To obtain a balanced panel, only individuals who reported an occupation for each year over
the period are included. The definitions of the variables are also provided in Table 8. Table 9
presents the set of regressions for the full sample. The dependent variable is the natural log of the
NAM-POWERS scores, denoted as OCCUPATION.
Column (1) in Table 9 reports the full specification of the model. The inclusion of
education in Column (1) means that we are comparing the various racial/ethnic groups who have
comparable levels of education with non-Hispanic Whites, the omitted category. Column (2)
reports the results obtained from running the same equation as in Column (1), but with omission
of the education variable. Not controlling for education means that we are comparing the various
groups, irrespective of their level of human capital, with non-Hispanic Whites, the omitted
category. Because the impact of ethnic differences on occupational status is severe, we also did
the same analysis for each Hispanic immigrant group, for non-Hispanic Whites, and for U.S.born Hispanics Conducting the analysis for each group separately allows us to isolate the ethnic
differences from the effects of other determinants of occupational achievement. In Table 10,
each of the regressions reported is conditioned on a distinct racial/ethnic group. If the

immigrant status of non-Hispanics, the analysis will be conducted comparing Hispanics with non-Hispanics, some
of whom may be immigrants.

Occupational Assimilation of Hispanics, page 20

assimilation process includes the acquisition of education, having education as an explanatory
variable in the equation may hide the fact that assimilation takes place (Borjas, 1999). In order
to test for this possibility, the specifications in Column (7), (9), and (11) of Table 10 omit the
education indicator variable.

Period and Regional Effects
We assume that the health of the economy plays a role in determining the occupational
positioning of workers. In our case, the 1990-1993 period covered by the data was a period of
recession (1990-1991) and then a slow recovery up to 1993, (followed by expansion up to 2001)
which could have important effects on occupational achievement. The effect is ambiguous. For
example, during a period of recovery, there may be relatively more opportunities for job
advancement, which would show up in higher occupational status scores. At the same time, there
may be increased employment opportunities at the bottom of occupational scale during an
expansion that might mask upward occupational mobility. A trough may coincide with fewer
opportunities for advancement. Workers may lose their jobs and have to resort to a lower ranked
occupation, which would lead to downward occupational mobility. (However, if workers stay
unemployed, we would not be able to capture this potential downward movement). It is also
possible to get upward occupational mobility during a recession if more of the people who lose
their jobs are in lower ranked occupations, as opposed to those who lose jobs in higher ranked
occupations. We use each year of the survey as an indicator variable to control for the impact of
aggregate economic condition in each year. We also control for regional differences with
indicator variables representing the North, the Midwest, etc. Regional economic conditions may
be important for Hispanic workers since they tend to be geographically concentrated.

Occupational Assimilation of Hispanics, page 21

The results show that period effects are significant explanatory factors for occupational
attainment of workers overall (Table 9). The period effects were statistically significant for nonHispanic Whites, U.S.-born Hispanics and Mexican immigrants (the groups with the largest
number of workers). For the other groups, the period effect on occupation was not statistically
significant (potentially due to their relatively smaller number in the labor market). We note that
the year 1991 was negative for occupations of U.S.-born Hispanics, consistent with research that
found they were particularly affected by the 1990-1991 recession. We find that regional factors
also impacted the occupation status of Mexican immigrants, but not other Hispanic groups.
Mexicans fared less well in terms of occupation status in the West, the Pacific West and the
Midwest compared to the South during 1990-1993.11

The Impact of Education and Language
Education is an important component of human capital that increases productivity; as
such, it affects the level of occupational achievement experienced by individuals in the labor
market.
Our analysis shows that in general, each additional year of schooling improves the
occupational achievement score by 8 percent. Conditioned on distinct ethnic groups (Table 10),
the results show that education also contributes significantly to enhancing occupational
achievement for each racial/ethnic group, although its contribution differs across groups. The
impact of education on occupational achievement is higher for non-Hispanic Whites (8.1
percent) and U.S.-born Hispanics (7.1 percent). The return to education in terms of occupational
achievement is also higher for Puerto Ricans (7.6 percent), who are technically from a U.S.
11

A full discussion of the impact of recession on Latino workers is beyond the scope of this research. See Suro and
Lowell (2002) for an excellent discussion of how Hispanics fared in the recession in 1990-1991 and the subsequent
period in different regions of the U.S.

Occupational Assimilation of Hispanics, page 22

territory. By contrast, the impact of education for Mexican immigrants is 6.2 percent and for
Cubans, it is 3.8 percent. All of these effects are statistically significant12
The relatively low contribution of education to occupational achievement for Mexican
and Cuban immigrants compared with non-Hispanic Whites and U.S.-born Hispanics may be due
to several factors. It is possible that disparity in the labor market affects the return to education
—there may have lower returns to education due to discrimination, as has been documented
elsewhere. Or, alternatively, other unobservable differences may be the cause, such as
differences in the level of transferability of education (education obtained abroad may not
translate well to the U.S. labor market).
The ability to speak English is also an important attribute that affects access to certain
occupations. In general, Hispanic immigrants who do not speak English have occupation scores
that are 9 percent lower than individuals who speak English or those whose native language is
English (Table 9, column 1).13 Interestingly, the impact of not speaking English is not a
significant factor in explaining differences in occupational status among Mexican and Cuban
immigrants when the analysis is done separately by group (Table 10). A potential explanation
may be ethnic segregation in the labor market. It has been noted that Cubans, for example, tend
to live in ethnic enclaves where the local labor market is such that it works as a viable alternative
for ethnic employment and renders lack of English skills more innocuous (Portes and Rumbaut,
1996). But even in the overall labor market, occupational segregation can serve to mitigate the
impact of not knowing English well. In occupations where Hispanics have traditionally been
concentrated, research has found that employers tend to be less likely to screen against those

12

The relatively lower coefficient for Cubans is also likely due to the lack of variability of education among Cubans
as most of them have fairly high educational attainment. In other words, education is not a strong predictor of
differences in occupation among the Cubans.

Occupational Assimilation of Hispanics, page 23

with a lack of English, circumventing this barrier by hiring managers who speak both English
and Spanish (Kossoudji, 1998).

The Impact of Demographic Characteristics
The results show that males in general have occupation scores that are 12.9 percent or
13.9 percent higher than females (Table 9, column 1 and 2). A much larger difference in
occupation exists among Mexican and Cuban immigrant men and women (Table 10). Holding all
else equal, Mexican men are in occupations that have scores that are 38.5 percent higher than
Mexican females, and Cuban males have scores that are 27.4 percent higher than Cuban females.
Part of the explanation for the gender gap may be occupational segregation that keeps differences
in access to information. Low-skilled Hispanic females who work disproportionately in livein/household cleaning occupations may have less access to outside contacts and networks to
learn about other employment leads (Kossoudji, 1998).
The age of individuals also affects their occupational attainment. AGE is specified in
cubic terms to allow the variable to vary with time, an approach which reveals a better fit for the
model. The results show it first contributes to increasing the occupational score, and thereafter
decreases it. This is consistent with previous research that finds that occupational mobility
declines with worker age (Kambourov and Manovskii (2004). This life-cycle effect on
occupational achievement is particularly significant for non-Hispanic Whites and Cubans, the
two groups who are older on average than Hispanics.

13

For interpretation purpose, the coefficients of the dummy variables reported in the text are exponentiated
(Kennedy, 1981; Halvosen and Palmquist, 1980).

Occupational Assimilation of Hispanics, page 24

The Impact of Length of Time Residing in the U.S. (Assimilation)
The longer one lives in this country the greater the opportunity to acquire and process
information that would be useful in promoting occupational achievement. We find that each
additional year of living in the U.S. contributes significantly to closing the gap in the
occupational attainment of Hispanics relative to non-Hispanic Whites, at a rate of 0.7 percent per
year (Table 9). (Later in this section, we will discuss estimates of how long it would take for the
gap to close). The coefficient for the assimilation effects becomes statistically significant for
Mexicans [Column (7)] when education is omitted, consistent with Borjas’ prediction, indicating
that part of the assimilation process of Mexicans in the United States may include acquisition of
education. Every additional year of living in the U.S. increases the socioeconomic status score
for Mexican immigrants by 10 percent [the YSM coefficient/ (2 x YSM_SQUARE coefficient)].
For the remaining groups, the omission of education in the equation does not alter the other
variables significantly. For Cubans, significant factors that contribute to upward occupational
mobility are labor market experience and life-cycle or age effects. For Puerto Ricans, a lack of
English language knowledge significantly impedes occupational status in the mainland U.S.

The Impact of Different Immigration Cohorts
Independent of the impact of assimilation (length of time in the U.S.), there may be
systematic cohort effects. Immigrants who arrived at different periods in the U.S. may have
different propensities to assimilate. The results show that U.S.-born Hispanics have occupational
scores that are 6.5 percentage points lower than non-Hispanic Whites with comparable U.S. labor
market experience and education (Table 9). Occupational attainment for Hispanic immigrants
declines with successive cohorts. These can be attributed, to some extent, to differences in
education. For example, not controlling for education (Table 9, column 2), we find that the most

Occupational Assimilation of Hispanics, page 25

recent cohort (who arrived after 1985) has occupation scores that are 91 percent lower than those
of non-Hispanic Whites. When we control for education (column 1), this group has occupational
scores that are 57.5 percent lower. Similarly, the oldest cohort, the 1960’s, has occupational
scores that are 79.1 percent lower than those of non-Hispanic Whites (column 2). But when we
control for education, the difference is insignificant (column 1). Except for the earliest cohort,
the differences in occupational scores between Hispanic immigrants by cohort and non-Hispanic
Whites remain even when controlling for education and labor market experience.

Occupational-Age Profile of Hispanics and non-Hispanic Whites
It is customary to use the estimated coefficients from the regression model to obtain fitted
values for each individual —the predicted occupational scores for each individual in each of the
years represented in the survey. We graph the predicted scores of occupational status against age
to obtain the predicted occupational-age profile of U.S.-born and immigrant Hispanics and nonHispanic Whites, respectively.14 This gives a clearer idea of how occupational status evolves
with age. Figure 1 below depicts the results.
The results show a substantially different occupational profile of non-Hispanic Whites
and Hispanics. Non-Hispanic Whites begins their work history in occupations with a predicted
score of above 50 (the top curve in Figure 1). They experience a steady increase in occupational
status until their early 40s, after which, their score declines. U.S.-born Hispanics start below a
score of 50 and experience an increase in occupational status until their late 40s (the second
curve in Figure 1). By contrast, Hispanic immigrants start below a score of 35. They also
experience an increase in occupational status until their 40s (the bottom curve in Figure 1). The
14

The fitted values are obtained from regression analyses run for each non-Hispanic White, Hispanic immigrant and
U.S.-born, separately. Other characteristics affecting occupational status beside age are held constant.

Occupational Assimilation of Hispanics, page 26

downward sloping section of the occupational status profile for each of the groups is consistent
with standard human capital and occupational-matching theories and is corroborated by evidence
from Miller (1984), McCall (1990) and Kambourov and Manovskii (2004). Human capital is
accumulated with occupational experience and, as such, the opportunity cost of switching
occupations rises with occupational tenure. Hence as average occupational experience in a crosssection of workers rises with age, occupational mobility declines with age. In addition, life-cycle
factors reduce mobility of occupation with age because the pay-off from investing in new skills
in a new occupation declines with age.

Occupational Assimilation of Hispanic Immigrants (the case of Mexican Immigrants)
Do Hispanic immigrants assimilate in occupational status the longer they live in the
United States? We graph the predicted occupational status scores against years-since-migration
to simulate the potential occupational trajectory of Mexican immigrants, the largest Hispanic
immigrant group in the U.S., by education level.15
Consider a Mexican immigrant who enters the United States at the age of 20. From one
year arrival to 30 years later, what course will his/her occupation trajectory take over this period?
What does his/her occupational profile look like if he/she has a college education, or if he/she
has a high school education or less? How long does it take him/her to reach occupational parity
with U.S.-born ethnic counterparts and non-Hispanic White counterparts with the same level of
education? We define occupational parity to be achieved when the predicted occupational status

15

As the larger group of Hispanic immigrants, the results for Mexicans are similar with that of all Hispanics as a
group. The fitted values or the predicted occupational scores are obtained from regression analyses for Mexican
immigrants by education. Other characteristics affecting occupational status beside age are held constant. The
analysis is not done separately for Puerto Rican and Cuban immigrants by education due to concerns about the
reliability of obtaining predictions from too small a sample size.

Occupational Assimilation of Hispanics, page 27

of Hispanics is equal to the median predicted value of occupational status for non-Hispanic
Whites or their U.S.-born Hispanic counterparts.
Figure 2 traces the occupational trajectories for less-educated Mexicans (defined as
having a high school degree or less education), and for educated Mexicans (defined as having at
least some college education). Mexican immigrants with less education start at a low level of just
above 20 and the maximum predicted is below 40. Their occupational score never reaches that of
their U.S.-born Hispanic counterparts with the same education (who have a median predicted
score of 48). There is also no convergence in terms of occupational status with less-educated
non-Hispanic Whites (who have a median predicted score of 51).
For more educated Mexican immigrants, there is a steady upward trend in occupational
status with years of U.S. experience. They achieve a predicted score of above 40 after 5 year in
the U.S. and their predicted score is above 60 after 10 year in the U.S. Their predicted score
converges with the median predicted score for U.S.-born Hispanics with at least a college degree
(with a median predicted score of 60) after 10 years of U.S. experience. Their score converges
with the median predicted score for non-Hispanic Whites with at least some college education
(with a median predicted score of 68) after approximately 15 years of U.S. experience.

IV D. Summary and Implications
This research analyses the determinants of the occupational status of Hispanics. Overall,
the occupational status of Hispanics is lower relative to that of non-Hispanics. However,
differences exist in status of occupations by Hispanic ethnicity (country of origin). Compared to
non-Hispanics, Mexicans and Puerto Ricans tend to have the greatest gaps in occupational status.
By contrast, Cubans’ occupational status is comparable with non-Hispanic Whites. The
heterogeneity of the Hispanic population suggests that any initiatives designed to address

Occupational Assimilation of Hispanics, page 28

occupational and socioeconomic deficiencies should take into consideration the particular needs
of the population served.
The results show that human capital characteristics, formal education and labor market
experience have a positive impact on occupational status. The appropriate policy response will
entail that we have a clear understanding of the effectiveness of the type of education, whether it
is education obtained as a child, formal training, or job-matching training programs that work
best to enhance occupational prospects. To the extent that formal education acquired as a child is
what matters the most, this would support the role for policy initiatives that enhance access to
formal education. To the extent that job-matching training programs are also effective, the
expansion of workforce opportunities through job training programs for low-skilled workers
would also be an appropriate response to help improve the socioeconomic position of the group
overall. However, without reliable measures of the effectiveness of specific training programs,
which is beyond the scope of this research, the role of job training programs in closing gaps in
occupations remain uncertain.16
This research highlighted the concentration of Hispanic workers and noted that the
occupational niches held by less-educated immigrant Hispanics tend to provide them with
relatively low wages, implying that a substantial part of this population is working poor. This
might suggest that Hispanic workers could benefit from policies aimed at altering the course of
the employment experience of the working poor in general. It has been proposed that such
policies may include and are not limited to, minimum wage increases, reforms that mandate a

16

As a result of uncertainty about the ability of American schools and firms to educate and train workers, especially
non-college bound youth, a “new consensus” has emerged that proposes to implement an apprenticeship system
patterned after programs in Germany, creating a nation-wide system of vocational credentialing and increasing the
availability of government training programs. Heckman et al (1993) examine the assumptions underlying the current
proposals and find no empirical or theoretical justification for many of the proposed programs. While some of these
programs aim toward desirable ends, they claim that there are other more efficient, less costly means to attain these
objectives.

Occupational Assimilation of Hispanics, page 29

minimum standard of health care benefits, and affordable insurance. However, the net effect on
overall employment of such policies must be carefully considered.
The effects of human capital vary by Hispanic group. Education contributes less to
improving the occupational achievement of Mexican, Cuban, and Puerto Rican immigrants
compared to non-Hispanic Whites and U.S.-born Hispanics. This may reflect the fact that
education received abroad may not transfer well to the U.S. labor market, or that the market may
value education differently by group. Language ability does not seem to be relevant for
understanding differences in occupational status among Mexicans and among Cubans.
Occupational segregation and a dual labor market, whereby labor market transactions are
conducted in Spanish, may shield individuals from these groups from the potential disadvantage
of not speaking English. However, language ability is very important for understanding the
overall occupational status of Hispanics. A lack of English ability is detrimental to their
achievement in occupational status. Therefore, initiatives that help bridge language barriers in
the workplace are important.
The length of time that Hispanic immigrants have been in the country contributes toward
narrowing the occupational status gap with non-Hispanic Whites. This supports the proposition
that as Hispanics gain U.S. experience their occupational status does improve. Independent of
assimilation effects, we also found evidence of cohort effects consistent with Borjas (1995). We
find evidence that more recent cohorts have lower human capital over and above that measured
by education. We show that more recent cohorts of Hispanic immigrants have a greater gap in
occupational status compared to non-Hispanic Whites. Although part of the gap can be explained
by the fact that earlier cohorts have more education, even when we control for education and
labor market experience, there remains a substantial disparity in occupational status between
different Hispanic immigrant cohorts and non-Hispanic Whites.

Occupational Assimilation of Hispanics, page 30

Based on the coefficients of the model of occupational status, we obtained predicted
occupational scores for each individual over the period, which we use to simulate the
occupational-age profile of U.S.-born and immigrant Hispanics and non-Hispanic Whites. The
occupational-age profile of U.S.-born Hispanics, and even more so, that of Hispanic immigrants,
are lower than the occupational-age profile of non-Hispanic Whites throughout their lifecycles.
This research simulated the occupational trajectory of Mexican immigrants by education
level and noted how many years of U.S. experience it would take for their predicted scores to
equal the median predicted score for non-Hispanic Whites and their U.S.-born Hispanic
counterparts with the same level of education. That point is referred to as the point of
convergence or assimilation. Mexican immigrants begin their time in the U.S. in very low status
jobs. The changes we observe with time in the U.S. are consistent with human capital
accumulation and assimilation theory, suggesting that there is a natural adjustment process for
Mexican immigrants.
Education not only affects the level of occupational status, but also impacts the pace of
occupational mobility and the potential for convergence. Educated Mexicans experience faster
rates of occupational status improvement over time compared to less-educated Hispanics. We
find that there is convergence in occupational status between educated Hispanics and U.S.-born
Hispanic counterparts and non-Hispanic Whites with the same level of education. On the other
hand, the results suggest that less-educated Mexican immigrants will never reach the
occupational status of U.S.-born Hispanics or non-Hispanic Whites. This group is likely to start
and remain in occupations that are distinct from those of U.S.-born Hispanics and non-Hispanic
Whites. This suggests that recent proposals to provide legal admission status to Mexican
immigrants that would be sponsored by an employer, provided that there is demonstrated need
for such workers in the face of the jobs not being filled by willing U.S. legal residents would not

Occupational Assimilation of Hispanics, page 31

harm non-Hispanic Whites, particularly from an occupational status perspective. The fact that
Hispanic immigrants tend to be occupationally segregated into a distinct set of occupations likely
mutes the effect of increased immigration on the wages of natives.

References
Bartel, A, P. 1989, “Where do the New U.S. Immigrants Live?” Journal of Labor Economics,
Vol. 7, Issue 4, October, 571-591.
Borjas, G., 1999, “The Economic Analysis of Immigration,” in Handbook of Labor Economics,
Vol. 3, Edited by O. Ashenfelter and D. Card, Elsevier Science, 1697-1760.
Borjas, G., 1995, “Assimilation and Changes in Cohort Quality Revisited: What Happened to
Immigrant Earnings in the 1980s?” Journal of Labor Economics, Vol. 13, Issue 2, 201245.
Borjas, G., 1985, “Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants,”
Journal of Labor Economics, Vol. 3, 463-489.
Brown, C; G. J. Duncan and F. P. Stafford, 1996, “Data Watch: The Panel Study of Income
Dynamics,” Journal of Economics Perspectives, Vol.10, No. 2, 155-168.
Chiswick B. R; Y.L. Lee and P.W. Miller, 2002, “Longitudinal Analysis of Immigrant
Occupational Mobility: A Test of the Immigrant Assimilation Hypothesis,” Mimeo,
University of Illinois at Chicago.
Chiswick, B. R., 1986, “Is the New Immigration Less Skilled than the Old?” Journal of Labor
Economics, Vol. 4, 168-192.
Chiswick, B. R., 1978, “The Effect of Americanization on the Earnings of Foreign-Born Men,”
Journal of Political Economy, Vol. 85, No. 5, 897-921.
Chiswick, B. R., 1977, “A Longitudinal Analysis of the Occupational Mobility of Immigrants,”
in Barbara D. Dennis, ed. Proceedings of the Thirtieth Annual Winter Meeting,
Industrial Relations Research Association, Madison, IRRA, 20-27.
Duleep, H. O and M. C. Regets, 1992, “The Elusive Concept of Immigrant Quality,” Washington
DC: Urban Institute.
Goldthorpe, J. H and K. Hope, 1974, The Social Grading of Occupations: A New Approach and
Scale, Clarendon Press, Oxford.

Occupational Assimilation of Hispanics, page 32

Green, D. A., 1999, “Immigrant Occupational Attainment: Assimilation and Mobility over
Time,” Journal of Labor Economics, Vol. 17, Issue 1, 49-79.
Greene, W. H., 2003, Econometric Analysis, Fifth Edition, Prentice Hall, New Jersey.
Halvosen, R. and R. Palmquist, 1981, “The Interpretation of Dummy Variables,” American
Economic Review, Vol. 70, No. 3, June, 474-475.
Harrison, B. and B. Bluestone, 1988, The Great U-Turn, Corporate Restructuring and the
Polarizing of America, Basic Books, New York.
Heckman, J. J., R.L Roselius and J. A. Smith, 1993, “U.S. Education and Training Policy: A Reevaluation of the Underlying Assumptions Behind the “New Consensus,” Harris School
of Public Policy, Working Paper Series, Supplement # 09, December 1993.
Jones, F., 1989, “Occupational Prestige in Australia: a New Scale," Australian and New Zealand
Journal of Sociology, Vol. 25, No. 2, 187-199.
Kambourov, G. and I. Manovskii, 2004, “Rising Occupational and Industry Mobility in the
United States: 1968-1993,” Departments of Economics, University of Toronto and
University of Pennsylvania, working paper, January 5.
Kennedy, 1981, “Estimation with Correctly Interpreted Dummy Variables in Semi Logarithmic
Equations,” American Economic Review, Vol. 71, No. 4, September, 801.
Kossoudji, Sherrie A., 1998, “English Language Ability and the Labor Market Opportunities of
Hispanic and East Asian Immigrant Men,” Journal of Labor Economics, Vol. 6, Issue 2,
205-228.
Lalonde, R. J. and R. H. Topel, 1991, “Immigrants in the American Labor Market: Quality,
Assimilation, and Distributional Effects,” The American Economic Review, Vol. 81, Issue
2, 297-302.
McCall, B. P., 1990, “Occupational Matching: A test of Sorts,” Journal of Political Economy,
Vol. 98, No. 1, 45-69.
Miller, R. A., 1984, “Job Matching and Occupational Choice,” Journal of Political Economy,
Vol. 92, No. 6, 1086-1120.
Morales, R. and P. M. Ong, 1990, “Immigrant Women in Los Angeles,” ISSR Working Papers in
the Social Sciences, Vol. 5, No. 11, University of California, Los Angeles.
Murphy, K. M., and F. Welch, 1992, “The Structure of Wages,” Quarterly Journal of
Economics, Vol. 107, February, 215-326.

Occupational Assimilation of Hispanics, page 33

Nam C. B, 2000, “Comparison of Three Occupational Scales,” Florida State University Center of
Population, Working paper.
Nam C. B. and M. G. Powers, 1983, The Socioeconomic Approach to Status Measurement.
Houston TX: Cap and Gown Press.
Orrenius P.M and M Zavodny, 2003 “Does Immigration Affect Wages? A Look at OccupationLevel Evidence,” Research Department Working Paper 0302, Federal Reserve Bank of
Dallas.
Portes, A and R. G. Rumbaut, 1996, Immigrant America: A Portrait, 2nd ed. Berkeley, University
of California Press.
Powers, M. G., S. William and S. Jing, 1998, “Gender Differences in the Occupational Status of
Undocumented Immigrants in the United States: Experience Before and After
Legalization,” International Migration Review, Vol. 32, No. 4, 1015-1046.
Ramos, F., 1992, “Out-Migration and Return Migration of Puerto Ricans,” in Immigration and
the Work Force: Economic Consequences for the United States and Source Areas, edited
by George J. Borjas and Richard B. Freeman, University of Chicago Press, 49-66.
Reimers, C. W., 1997, “The Progress of Mexican and White Non-Hispanic Immigrants in
California and Texas, 1980-1990,” The Quarterly Review of Economics and Finance,
Vol. 37 Special Issue, 315-434.
Richmond, a. H., and W. E. Kalbach, 1980, Factors in the Adjustment of Immigrants and Their
Descendants, Ottawa: Statistics Canada.
Rivera-Batiz, F. L, 1994, Falling Back: The Declining Socioeconomic Status of the Mexican
Immigrant Population in the United States, 1980-1990. Institute for Urban and Minority
Education Research Report 94-4, Teachers College, Columbia University, April.
Sanders, J. M. and V. Nee, 1987, “Limits of Ethnic Solidarity in the Enclave Economy,”
American Sociological Review, Vol. 52, 745-767.
Stolzenberg, R. M., 1982, “Occupational Differences Between Hispanics and Non-Hispanics,” A
Rand Note, N-1889-NCEP, prepared for The National Commission for Employment
Policy, Rand, Santa Monica, CA, July.
Suro R. and B. L. Lowell, 2002, “New Lows from New Highs: Latino Economic Losses in the
Current Recession,” The Pew Hispanic Center, January 24.
Survey Research Center, Institute for Social Research, University of Michigan, 1999, “a Panel
Study of Income Dynamics: 1968-1980 Retrospective Occupation-Industry Files
Documentation (Release 1),
http://psidonline.isr.umich.edu/data/Documentation/cbks/occindint.html.

Occupational Assimilation of Hispanics, page 34
Table 1, Panel A: Descriptive Statistics, 1990 PSID

All Sample
Average Age
Average Years Since Migration
Average Age at Immigration
Average Educational Attainment
Educational Attainment
<= 5th Grade
6th to 8th Grade
9 to 11 Grade
High School Degree
College w/o Degree
College Degree
Advance Degree
Total
Interview in Spanish
Percent
Total
Employment
Employed
Not in Labor Force
Retired/Disabled
Unemployed
Total
Region of Residence
Midwest
Northeast
Pacific West
South
West
Total
Occupation
Clerical
Craftsman
Farm Laborers and Foreman
Farmers and Farm Managers
Laborers, Except Farm
Managers and Administrators
Operatives, Except Transport
Private Household Workers
Professional, Technical
Sales Workers
Service Workers
Transport Equip. Operatives
Total

Non-Hisp US Born
White
Hisp

Hisp
Immig

Mexican

Puerto
Rican

Cuban

Other
Hisp

35
--12.4

36
--12.7

22
--11.8

42
17
23
8.3

25
16
23
10.2

25
21
23
10.4

35
19
34
11.9

27
19
36
12.6

4.5
8.8
19.1
36.7
17.6
8.1
5.1
18262

0.9
4.6
14.4
39.4
19.6
12.8
8.3
8306

4.8
9.9
26.9
34.5
17.0
4.2
2.6
3065

20.5
26.0
11.9
24.4
10.9
3.7
2.7
2065

12.7
18.2
22.0
29.1
13.1
2.9
2.0
2876

12.1
14.9
27.8
28.4
12.1
3.0
1.8
879

9.0
17.1
13.5
32.9
16.5
6.6
4.5
1129

4.6
11.9
18.4
35.9
21.5
5.4
2.3
739

14.1
5425

0.0
0

41.0
3611

79.3
1814

47.7
3286

54.9
1076

64.2
1167

30.2
485

59.4
20.7
12.9
7.0
18792

65.3
17.3
13.4
4.0
8409

52.8
29.3
9.8
8.2
3196

55.3
21.5
14.8
8.4
2258

56.9
25.8
8.2
9.0
3068

41.6
32.6
16.0
9.8
948

52.1
24.0
17.8
6.0
1191

61.8
21.7
10.6
5.9
764

19.8
14.5
15.8
31.7
18.3
38475

29.1
19.8
11.8
24.5
14.8
16255

8.5
14.2
31.3
14.7
31.3
8838

7.0
14.7
26.6
37.4
14.2
2302

10.3
1.5
45.9
2.2
40.1
6885

9.7
65.4
6.3
15.8
2.8
1978

2.0
6.6
2.2
85.9
3.3
1839

4.5
12.1
22.3
26.8
34.3
1611

15.3
13.1
1.5
0.8
4.8
11.6
12.2
1.3
14.9
4.9
15.1
4.5
13487

15.5
13.3
0.4
1.3
3.2
16.4
7.7
0.8
20.8
6.6
10.1
3.8
6712

16.0
12.9
2.4
0.2
6.0
9.3
13.5
1.0
11.2
4.5
18.0
5.0
2085

9.0
16.8
6.6
0.4
6.8
5.3
23.6
1.2
5.8
3.1
17.6
3.8
1442

11.2
13.4
6.6
0.4
7.5
6.2
20.6
1.1
7.7
3.1
17.9
4.2
2161

17.0
12.2
0.4
0.0
4.1
7.5
16.1
0.6
9.4
3.4
24.2
4.9
466

13.4
19.1
0.0
0.1
5.7
10.5
14.2
1.3
11.3
5.3
14.8
4.2
716

13.7
16.3
0.5
0.2
7.0
9.5
14.2
1.5
9.7
5.2
16.5
5.8
600

Note: Results are weighted to reflect sample stratification

Occupational Assimilation of Hispanics, page 35
Table 1, Panel B: Descriptive Statistics, 1991 PSID
All
Sample
Average Age
Average Years Since Migration
Average Age at Migration
Educational Attainment
<= 5th Grade
6th to 8th Grade
9th to 11th Grade
High School Degree
College w/o Degree
College Degree
Advance Degree
Total
Interview in Spanish
Percent
Total
Employment
Employed
Not in Labor Force
Retired/Disabled
Unemployed
Total
Region of Residence
Midwest
Northeast
Pacific West
South
West
Total
Occupation
Clerical and Kindred
Craftsman
Farm Laborers and Foreman
Farmers and Farm Managers
Laborers, Except Farm
Managers and Administrators
Operatives, Except Transport
Private Household Workers
Professional, Technical
Sales Workers
Service Workers
Transport Equip. Operatives
Total

Non-Hisp US Born
White
Hisp

Hisp
Immig

Mexican

Puerto
Rican

Cuban

Other
Hisp

36
---

37
---

23
---

42
18
24

26
17
23

26
22
26

36
20
33

28
19
22

4.3
8.5
19.0
37.0
17.9
8.2
5.1
18019

0.9
4.4
13.8
39.7
20.0
12.8
8.4
8295

5.1
9.4
27.2
33.9
17.5
4.5
2.4
3020

18.9
25.1
11.1
28.5
10.3
3.5
2.5
2013

12.5
17.3
23.1
28.8
13.1
3.1
2.0
2837

12.2
13.8
28.4
28.6
12.5
3.3
1.2
818

7.8
18.6
12.4
33.5
17.2
6.2
4.2
1046

4.0
10.7
18.9
35.4
22.5
6.2
2.4
721

13.2
4836

0.0
4

39.4
3177

77.5
1653

45.6
2917

53.9
928

64.7
1068

28.5
429

58.8
20.4
13.2
7.5
18509

64.7
16.6
13.8
4.5
8409

52.2
30.5
8.7
8.6
3152

52.9
22.2
16.0
8.8
2087

54.7
27.9
8.3
9.0
3034

39.2
33.6
15.8
11.4
878

53.0
22.4
17.7
7.0
1091

61.7
21.6
8.9
7.9
751

20.2
14.3
15.4
31.8
18.3
36432

29.7
19.7
11.5
24.4
14.6
15683

8.1
13.7
31.2
14.7
32.3
8001

6.6
14.0
26.9
37.8
14.7
2120

9.6
1.6
45.4
2.3
41.0
6335

8.6
65.7
6.8
16.1
2.8
1704

2.1
5.8
2.1
86.9
3.0
1604

5.4
12.3
21.8
26.6
33.8
1508

15.6
13.7
1.5
0.8
4.4
11.7
11.5
1.3
15.0
4.7
15.2
4.7
12873

15.9
14.1
0.4
1.4
2.8
16.5
7.0
0.7
21.0
6.1
10.4
3.6
6475

18.2
14.8
2.6
0.3
5.3
9.7
11.8
1.4
11.6
3.8
15.9
4.8
1955

9.4
16.2
6.2
0.3
5.6
6.2
23.6
1.4
6.0
3.3
17.0
5.0
1325

13.5
15.4
6.3
0.3
6.3
6.9
18.3
1.6
8.1
2.7
16.3
4.5
2032

15.7
13.9
0.5
0.0
5.1
7.2
16.2
0.7
9.2
3.0
21.9
6.7
433

14.1
17.6
0.2
0.3
3.1
12.2
15.9
0.9
11.3
5.6
13.4
5.4
647

19.3
15.0
1.6
0.2
6.1
9.4
13.4
2.1
9.6
4.9
14.6
4.0
575

Note: Results are weighted to reflect sample stratification

Occupational Assimilation of Hispanics, page 36
Table1, Panel C: Descriptive Statistics, 1992 PSID

Average Age
Average Years Since Migration
Average Age at Migration
Educational Attainment
<= 5th Grade
6th to 8th Grade
9th to 11th Grade
High School Degree
College w/o degree
College Degree
Advance Deg
Total

All Sample
35
---

Non-Hisp US Born
White
Hisp
37
23
-----

Hisp
Immigr
44
19
25

Mexican
25
18
23

Puerto
Rican
26
25
22

Cuban
36
21
34

Other
Hisp
27
20
22

4.3
8.2
18.7
37.1
18.2
8.4
5.1
18839

0.9
4.1
13.8
39.3
20.3
13.1
8.5
8412

5.5
8.9
26.5
34.5
17.9
4.6
2.1
3572

19.3
26.0
11.6
26.0
10.0
4.4
2.6
2021

12.1
16.2
22.6
30.4
13.5
3.3
1.8
3261

11.9
13.4
28.5
29.6
12.0
3.5
1.2
956

7.3
16.7
12.8
34.2
17.6
7.4
4.0
1136

4.2
10.0
18.7
34.9
23.0
6.7
2.4
790

Prefer Spanish
Percent
Total

13.55
4982

0
0

6.08
1713

9.5
3280

44.66
3018

47.84
910

63.27
1082

27.9
430

Employment
Employed
Not in Labor Force
Retired/Disabled
Unemployed
Total

57.6
20.6
13.7
8.2
19429

63.6
17.2
14.3
5.0
8524

51.4
30.1
8.7
9.8
3747

53.3
21.0
17.2
8.5
2186

55.0
27.4
8.0
9.6
3480

39.7
31.5
16.5
12.2
1034

50.6
22.4
19.7
7.2
1173

61.2
21.7
8.1
8.9
823

Region of Residence
Midwest
Northeast
Pacific West
South
West
Total

20.0
14.2
16.0
31.3
18.5
36598

29.8
19.5
11.7
24.5
14.5
15478

8.5
14.1
31.4
14.0
32.0
8486

7.1
14.9
27.5
36.3
14.2
2214

10.2
1.7
45.5
1.9
40.7
6674

9.7
65.3
7.4
14.9
2.7
1859

2.4
7.5
1.8
85.9
2.4
1704

4.9
12.3
22.1
25.6
35.0
1539

Occupation
Clerical
Craftsman
Farm Laborers and Foreman
Farmers and Farm Managers
Laborers, Except Farm
Managers and Administrators
Operatives, Except Transport
Private Household Workers
Professional, Technical
Sales Workers
Service Workers
Transport Equipment Operatives
Total

15.6
13.2
1.4
0.7
4.6
12.0
11.0
1.3
15.5
4.9
15.4
4.5
12779

15.3
13.7
0.4
1.2
3.2
16.6
6.8
0.7
21.5
6.2
10.7
3.6
6345

18.2
13.0
2.2
0.2
5.6
10.1
12.8
0.9
11.9
5.3
15.6
4.3
2098

10.2
15.7
5.4
0.4
5.9
6.3
23.0
1.3
6.6
3.0
17.3
4.7
1347

13.8
13.6
5.4
0.4
7.1
7.2
19.2
1.2
7.8
3.7
16.2
4.4
2166

17.0
12.8
0.4
0.0
4.0
9.5
16.1
0.2
9.9
3.1
20.8
6.2
453

14.7
16.9
0.2
0.2
2.9
11.6
14.2
1.2
14.8
5.3
14.2
3.8
655

19.1
11.9
0.7
0.0
5.3
9.9
15.2
1.9
10.9
5.6
15.8
3.7
587

Note: Results are weighted to reflect sample stratification

Occupational Assimilation of Hispanics, page 37

Table 1, Panel D: Descriptive Statistics, 1993 PSID

All Sample
Average Age
Average Years Since Migration
Average Age at Migration
Educational Attainment
<= 5th Grade
6th to 8th Grade
9th to 11th Grade
High School Degree
College w/o Degree
College Degree
Advance Degree
Total
Prefer Spanish
Percent
Total
Employment
Employed
Not in Labor Force
Retired/Disabled
Unemployed
Total
Region of Residence
Midwest
Northeast
Pacific West
South
West
Total
Occupation
Clerical
Craftsman
Farm Laborers and Foreman
Farmers and Farm Managers
Laborers, Except Farm
Managers and Administrators
Operatives, Except Transport
Private Household Workers
Professional, Technical
Sales Workers
Service Workers
Transport Equip. Operatives
Total

Non-Hisp US Born
White
Hisp

Hisp
Immig

Mexican

Puerto
Rican

Cuban

Other
Hisp

34
---

36
---

23
---

45
20
24

26
19
23

27
26
22

35
22
34

28
21
22

7.7
3.7
12.7
36.1
20.2
11.9
7.8
9454

0.7
3.9
13.6
38.8
21.8
12.8
8.4
8789

5.4
8.4
23.7
36.8
18.7
4.7
2.3
3521

18.6
26.0
10.9
2.6
10.2
4.4
27.4
1744

10.9
15.4
20.8
32.5
14.9
3.7
1.8
3118

12.3
12.2
27.3
31.8
12.2
3.1
1.2
912

6.6
16.1
10.1
37.1
18.4
7.4
4.3
1009

4.1
9.5
17.1
35.2
24.2
7.0
3.0
739

4.4
1557

0.0
2

14.0
1068

26.0
499

16.3
977

17.7
292

22.4
326

8.0
111

59.5
19.1
13.9
7.5
19658

65.8
15.6
14.0
4.6
8934

54.7
27.0
9.7
8.6
3659

52.6
21.3
19.0
7.1
1885

56.8
25.5
9.6
8.1
3302

40.9
31.5
17.6
10.0
969

53.4
20.5
19.7
6.5
1038

62.9
20.9
9.1
7.1
780

20.8
13.8
15.3
31.6
18.6
35894

30.3
18.8
11.7
24.3
15.0
15816

8.9
13.7
31.2
13.5
32.8
7560

7.5
14.4
27.1
35.7
15.4
1898

10.4
1.5
44.5
1.8
41.9
5948

10.5
64.2
7.1
15.8
2.4
1613

2.7
7.6
2.1
85.2
2.4
1455

6.1
13.4
23.4
23.6
33.5
1382

16.1
12.4
1.0
0.7
4.9
12.1
10.6
1.3
16.0
5.1
15.3
4.4
12532

15.8
12.7
0.3
1.3
3.6
16.3
7.1
0.7
21.4
6.2
11.1
3.3
6387

19.3
12.4
1.7
0.2
5.4
10.7
10.5
1.6
12.7
5.1
15.5
4.9
1914

10.7
14.4
4.0
0.4
7.1
6.4
22.2
1.7
7.0
3.0
18.5
4.8
1136

14.5
13.0
3.9
0.4
7.1
7.8
16.9
1.9
9.1
3.8
16.1
5.4
1925

21.7
10.7
0.0
0.0
5.1
7.3
14.6
0.2
8.3
3.6
22.4
6.1
411

16.9
15.1
0.2
0.0
3.7
12.7
11.0
0.9
15.7
5.2
15.7
2.9
543

18.0
14.5
0.2
0.0
5.7
11.0
11.5
2.2
10.6
6.1
14.9
5.5
511

Note: Results are weighted to reflect sample stratification

Occupational Assimilation of Hispanics, page 38
Table 2: Average Occupational Status Scores by Category, 1990 PSID
Non-Hisp US Born
All Sample White
Hisp
High –Skill Occupations
Professional, Technical, and Kindred Workers
Men
87.9
87.9
90.4
Women
78.3
78.4
77.9
Both gender
82.9
83.0
85.9
Managers and Administrators, Except Farm
Men
80.9
81.0
80.0
Women
79.6
79.5
82.5
Both gender
80.4
80.5
81.4
Medium-Skill Occupations
Sales Workers
Men
71.1
71.6
66.9
Women
53.8
53.9
49.7
Both gender
62.2
62.7
59.2
Clerical and Kindred Workers
Men
57.9
59.2
61.4
Women
53.2
54.1
50.0
Both gender
54.0
54.9
52.6
Craftsman and Kindred Workers
Men
54.5
55.1
54.9
Women
53.5
53.7
51.5
Both gender
54.3
54.8
53.8
Transport Equipment Operatives
Men
42.0
42.1
43.4
Women
41.2
41.5
40.5
Both gender
41.8
42.0
42.3
Operatives, Except Transport
Men
42.6
43.5
37.7
Women
30.4
31.2
29.6
Both gender
35.9
37.2
33.2
Low-Skill Occupations
Farmers and Farm Managers
Men
31.9
31.7
31.0
Women
33.1
33.1
--Both gender
32.0
31.9
31.0
Service Workers, Except Private Household
Men
36.2
39.3
35.5
Women
24.9
25.9
24.4
Both gender
27.9
29.3
27.8
Laborers, Except Farm
Men
23.2
23.5
24.5
Women
22.5
21.6
25.1
Both gender
23.1
23.1
24.6
Very Low–Skill Occupations
Farm Laborers and Farm Foreman
Men
6.7
4.7
13.4
Women
6.2
4.0
7.2
Both gender
6.6
4.6
11.1
Private Household Workers
Men
2.5
2.5
--Women
4.5
5.6
3.3
Both gender
4.4
5.4
3.3

Hisp
Immig

Mexican

Puerto
Rican

Cuban

Other
Hisp

81.2
75.3
79.0

85.9
74.5
81.2

75.3
73.3
74.7

93.8
78.3
90.1

92.2
86.0
90.6

80.0
83.3
81.7

78.0
81.4
80.0

79.2
82.9
82.3

82.9
78.9
80.4

81.6
86.2
83.7

58.8
44.2
52.1

67.7
55.3
61.0

61.6
45.7
56.1

74.0
52.7
71.1

60.9
41.1
53.4

51.3
48.7
49.5

56.4
48.5
49.9

57.5
50.9
53.5

53.1
58.2
56.9

64.4
50.4
54.9

50.9
49.6
50.6

51.8
52.8
52.1

54.6
44.2
53.3

53.7
53.1
53.5

54.7
51.5
53.1

40.8
40.8
40.8

42.9
40.2
41.9

40.4
48.0
40.7

42.0
45.4
43.5

43.4
41.5
42.5

36.1
23.5
29.3

37.3
26.6
31.2

42.0
29.9
33.1

39.2
24.5
30.4

35.7
31.7
33.9

48.3
31.0
46.5

42.9
31.0
42.0

----

31.0
-31.0

31.0
--31.0

20.6
19.0
19.7

24.2
21.1
22.3

26.3
26.7
26.6

33.0
27.1
29.4

50.3
22.2
31.4

20.4
18.8
20.2

22.5
22.7
22.6

25.0
23.9
24.7

25.3
29.6
26.5

29.3
21.5
27.8

7.3
7.3
7.3

9.4
7.2
8.6

4.0
-4.0

----

4.0

--2.9
2.9

--3.2
3.2

--6.9
6.9

--2.4
2.4

--3.5
3.5

4.0

Occupational Assimilation of Hispanics, page 39
Table 3: Average Wages by Occupation, 1990 PSID
Total
Clerical
Men
23142
Women
14755
Both gender
16333
Craftsman
Men
22842
Women
15440
Both gender
22218
Farm Laborers and Farm Foreman
Men
10880
Women
5391
Both gender
8898
Farmers and Farm Managers
Men
11093
Women
5577
Both gender
10699
Laborers, Except Farm
Men
13903
Women
7339
Both gender
13118
Managers and Administrators
Men
49346
Women
21862
Both gender
38531
Operatives
Men
19553
Women
9682
Both gender
14523
Private Household Workers
Men
12480
Women
5710
Both gender
5822.77
Professional, Technical
Men
39849
Women
21960
Both gender
31118
Sales
Men
27110
Women
10370
Both gender
18594
Service
Men
15997
Women
8358.80
Both gender
10965
Transport Equipment Operatives
Men
20941
Women
12499
Both gender
20027

Hispanic

Non-Hispanic

US Born
Hispanic

Hispanic
Immigrant

Non-Hisp
White

22,674.2
14,263.2
16,050.6

23,882.3
15,300.9
16,667.5

24,554.8
14,560.0
16,339.2

20,793.7
13,772.8
15,623.2

23,645.9
15,263.7
16,735.7

20,952.3
13,611.9
20,301.0

26,297.7
19,400.0
25,773.8

25,207.9
11,187.4
23,473.4

18,964.9
15,690.0
18,730.9

28,298.8
21,700.0
27,960.4

11,419.8
5,563.4
9,208.7

6,762.5
2,200.0
5,850.0

11,498.2
3,806.4
8,331.0

11,404.5
5,973.4
9,393.0

6,650.0
6,650.0

18,492.0
5,577.0
15,263.2

8,873.2
--8,873.2

-------

18,492.0
5,577.0
15,263.2

8,873.2
--8,873.2

13,727.5
8,477.9
13,183.1

14,419.6
5,345.0
12,938.1

14,452.2
6,650.0
13,645.1

13,181.4
9,848.7
12,835.2

15,410.8
5,251.4
12,566.2

27,232.1
21,067.7
24,126.4

59,612.9
22,645.2
47,647.5

30,070.3
19,979.3
34,343.0

24,479.8
22,972.5
23,845.2

60,761.1
22,293.5
49,140.7

17,417.3
8,585.8
12,888.8

25,696.9
12,999.3
19,348.1

18,776.8
9,461.0
14,359.8

16,860.7
8,295.8
12,347.1

25,269.7
11,775.3
18,859.8

5,087.1
5,087.1

12,480.0
6,354.2
6,558.4

--5,109.4
5,109.4

--5,074.2
5,074.2

12,480.0
10,176.0
10,560.0

33,030.6
19,030.9
26,348.9

44,205.7
23,706.1
34,063.8

35,623.4
19,646.8
28,178.0

29,176.5
18,209.7
23,768.2

45,038.9
23,805.1
35,329.4

22,712.5
11,035.8
17,263.4

31,018.1
9,929.9
19,613.3

25,138.4
12,338.6
18,738.5

19,747.4
8,831.0
15,169.6

30,541.3
10,141.5
20,341.4

15,394.0
7,560.7
10,836.1

17,513.8
9,208.4
11,145.3

15,677.4
7,833.4
10,821.6

15,216.2
7,337.4
10,846.7

17,392.0
8,241.0
10,864.3

20,062.9
15,650.0
19,652.4

22,046.8
9,697.4
20,481.4

19,429.8
20,675.0
19,545.6

20,696.0
10,625.0
19,759.2

21,789.1
11,713.7
20,314.6

Note: Results are weighted to reflect sample stratification.

Occupational Assimilation of Hispanics, page 40
Table 4: Average Occupational Status Scores, 1990-1993 PSID
Non-Hisp White
Men
Women
Hispanic
Men
Women
U.S. Born Hispanic
Men
women
Hispanic Immigrant
Men
women

1990

1991

1992

1993

63.9
55.6

63.7
56.1

64.0
55.9

63.3
56.0

47.1
41.0

48.3
41.6

48.0
43.2

48.9
43.7

52.5
44.5

53.4
45.5

52.1
46.9

53.7
47.0

41.0
34.8

42.5
33.9

42.8
35.8

42.8
36.1

Table 5: Average Occupational Status Scores by Hispanic Entry Cohort, 1990-1993 PSID
Years since
migration
<= 5 years
6-10 years
11-20 years
> 20 yrs

N

1990

1991

1992

1993

80
150
218
260

29.7
37.9
38.6
47.6

29.5
38.4
39.3
47.9

26.9
39.9
42.3
48.6

29.4
36.5
39.2
47.7

Occupational Assimilation of Hispanics, page 41

Table 6: Average Occupational Status Scores by Hispanic Ethnicity and Entry Cohort, 1990 PSID
Mexicans
Less than 5 yrs
Men
women
Both genders
# of obs
6 to 10 years
Men
women
Both genders
# of obs
11 to 20 years
Men
women
Both genders
# of obs
over 20 years
Men
women
Both genders
# of obs
All
Men
women
Both genders
total # of obs

Puerto Ricans

Cubans

Other Hispanics

20.5
18.4
19.7
100

61.1
61.0
61.0
12

62.3
42.9
52.4
35

12.3
27.8
22.9
10

28.9
22.3
26.3
36

47.4
51.9
49.8
20

43.1
36.2
40.0
136

41.0
35.2
37.3
49

31.7
26.0
29.4
271

46.6
31.0
39.6
41

57.9
48.7
53.2
92

41.5
35.6
39.4
45

38.3
29.7
35.1
164

44.4
42.3
43.5
107

54.3
57.2
50.7
216

57.6
42.9
52.3
58

42.6
39.7
41.2
571

47.9
41.2
44
180

68.2
54.1
62.2
479

65.6
51.1
58.7
162

Table 7: Occupational Mobility 1990-1993

Total
Obs.
Non-Hisp White
Hispanic
Hisp Immigrant
<=5 years
6-10 years
11-20 years
>20 years

5167
2167
812
94
168
255
289

% people
upward
26.3
31.3
27.2
34.0
27.4
30.2
22.1

% people
downward
27.1
30.0
28.4
34.0
32.7
28.6
23.5

Average %
change
18.4
32.8
30.9
38.9
24.6
45
25.2

median %
upward
36.9
65.3
71.4
63.2
79.2
92.9
61.47

median %
downward
-27.3
-36
-39.2
-41.1
-32.8
-40.8
-36.3

Occupational Assimilation of Hispanics, page 42
Table 8: Definition of Variables
OCCUPATION
YEAR90
YEAR91
YEAR92
MALE
EDUCATION
EXPERIENCE
AGE
YSM
MEXICAN
PUERTORICAN
CUBAN
OTHER HISPANIC
SPANISH
U.S-BORN HISP
HISP IMMIGRANT
COHORT60
COHORT60-69
COHORT70-79
COHORT80-85
COHORT85-90
PACIFIC WEST
WEST
NORTHEAST
MIDWEST
SOUTH

BLACK
ASIAN
OTHER RACES
WHITE

Nam-Powers socioeconomic occupational score, continuous.
= 1 if year is 1990, 0 otherwise
= 1 if year is 1991, 0 otherwise
= 1 if year is 1992, 0 otherwise
= 1 if male, 0 otherwise
Number of years of schooling, continuous
Number of years of working full time since 18 years of age, continuous. A
quadratic specification is also included.
Age of respondent, continuous. Quadratic and cubic specifications are also
included.
Number of years since a respondent first came to leave permanently in the
country. A quadratic specification is also included.
= 1 if Mexican, 0 otherwise
= 1 if Puerto Rican, 0 otherwise
= 1 if Cuban, 0 otherwise
= 1 if all other Hispanic origin, 0 otherwise
= 1 if respondent preferred to have survey interviewed done in Spanish, 0
otherwise.
= 1 if respondent is of Hispanic origin and was born in the United States, 0
otherwise.
= 1 if respondent of Hispanic origin, reported a year of migration to the U.S., 0
if Hispanic but did not report a years of migration and or reported having been
born in the U.S.
= 1 if years of migration for a Hispanic is before 1960, 0 otherwise
= 1 if years of migration for a Hispanic is 1960 to 1969, 0 otherwise
= 1 if years of migration for a Hispanic is 1970 and 1979, 0 otherwise
= 1 if years of migration for a Hispanic is 1980 to 1985, 0 otherwise
= 1 if years of migration for a Hispanic is 1986 to 1990
= 1 if region of employment is the pacific west, which includes the following
states: Alaska California Hawaii Oregon Washington
= 1 if region of employment is in the west, which includes the following states:
Arizona, Arkansas, Colorado, Idaho, Louisiana, Montana, Nevada, New
Mexico, Oklahoma, Texas, Utah ,Wyoming
= 1 if region of employment is in the northeast, which includes the following
states: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New
York, Pennsylvania, Rhode Island ,Vermont
= 1 if region of employment is in the Midwest, which includes the following
states: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri,
Nebraska, North Dakota, Ohio, South Dakota, Wisconsin
= 1 if region of employment is in the south, which includes the following
states: Alabama, Delaware, DC, Florida, Georgia, Kentucky, Maryland,
Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West
Virginia
= 1 if race is black
= 1 if race is Asian
= 1 if race is American Indian and others
= 1 if race is white

Occupational Assimilation of Hispanics, page 37

Table 9: Random Effects GLS Estimates, 1990 to 1993 PSID data, Full Sample
Dependent Variable = Log of OCCUPATION (standard errors in parentheses)
Constant
MALE
EDUCATION
EXPERIENCE
EXPERIENCE2
AGE
AGE2
AGE 3
BLACK
ASIAN
OTHER RACES
U.S. BORN HISPN
SPANISH
YEAR90
YEAR91
YEAR92
PACIFIC WEST
WEST
NORTHEAST
MIDWEST
HISPN_IMMIGRANT*YSM
COHORT60
COHORT60-69
COHORT70-79
COHORT80-85
COHORT86-90
R-sq. within
R-sq. between
R-sq. overall
No. of obs.
No. of groups

(1)
2.058***
(.122)
.141***
(.006)
.085 ***
(.002)
.022***
(.001)
-.0004***
(.00004)
.048***
(.010)
-.001***
(.0003)
.086***
(.022)
-.274***
(.019)
-.274
(.019)
.0031
(.019)
-.063***
(.017)
-.089***
(.020)
.008**
(.008)
.0167***
(.008)
.023***
(.008)
.018
(.019)
-.0403**
(.018)
.009
(.019)
-.016
(.017)
.007*
(.004)
-.178
(.174)
-.215*
(.120)
-.294 ***
(.083)
-.238***
(.062)
-.454***
(.059)
0.0832
0.4564
0.2932
28733
2899

(2)
2.791***
(.115)
.144***
(.007)
--.022***
(.001)
-.0004***
(.00004)
.076***
(.009)
-.002***
(.0002)
-.121***
(.0002)
-.316***
(.022)
.059
(.049)
-.007
(.019)
-.105***
(.018)
-.132***
(.021)
.004
(.008)
.013*
(.008)
.022***
(.008)
.015
(.021)
-.057***
(.019)
.008 ***
(.020)
-.032
(.018)
.012
(.004)
-.583***
(.179)
-.588***
(.124)
-.635***
(.087)
-.535***
(.065)
-.646***
(.063)
0.0373
0.2257
0.1400
29569
2927

(3)
2.061***
(.122)
.141***
(.006)
.085***
(.002)
.022***
(.001)
-.0004***
(.00004)
.048***
(.010)
-.001***
(.0003)
.086***
(.022)
-.275***
(.019)
-.014
(.047)
.003
(.019)
-.063***
(.017)
-.095***
(.020)
.007
(.008)
.016**
(.008)
.023***
(.008)
.018
(.019)
-.040**
(.018)
.0091
(.019)
-.016
(.017)
--.084*
(.052)
-.033
(.034)
-.174**
(.034)
-.165***
(.040)
-.414***
(.054)
0.0832
0.4558
0.2931
28733
2899

Occupational Assimilation of Hispanics, page 44

Table 10: Random Effects GLS, 1990 to 1993 PSID data, Selected Ethnic/Racial Groups
Dependent Variable = Log of OCCUPATION (Standard errors in parentheses)
(4)
Non-Hisp
White

(5)
U.S. Born
Hisp

(6)
Mexican
Immig

(7)
Mexican
Immig

(8)
Cuban
Immig

2.320***
(.130)
.130***
(.007)
.081***
(.002)
.020***
(.001)
-.0004***
(.00004)
.026***
(.011)
-.001**
(.0002)
.036**
(.024)
---

3.059***
(.332)
.093***
(.020)
.071***
(.005)
.023***
(.004)
-.0005***
(.0001)
-.024
(.028)
.001
(.001)
-.067
(.067)
---

2.610*
(1.417)
.347***
(.050)
---

YSM

.018***
(.008)
.024***
(.008)
.027
(.008)
-.016
(.020)
-.034*
(.019)
-.009
(.019)
-.005
(.018)
---

-.006**
(.020)
.002
(.020)
.021
(.019)
-.145**
(.054)
-.167**
(.052)
-.039
(.074)
-.126*
(.072)
---

YSM2

---

---

.0840
.4265
.2562
21263
2134

.0403
.2804
.2041
3757
640

1.73
(1.50)
.326***
(.507)
.062***
(.008)
.334***
(.010)
-.001***
(.0002)
.049
(.117)
-.001
(.003)
.121
(.240)
-.062
(.050)
.130*
(.065)
.085
(.058)
.076
(.054)
-.452**
(.181)
-.594**
(.192)
-.435
(.336)
-.450**
(.220)
.022
(.020)
.0001
(.0003)
-.572
(.362)
-.203
(.266)
-.084
(.181)
.025
(.128)
.0739
.3468
.2700
1204
267

.5057
(1.695)
.220***
(.043)
.038***
(.009)
.032***
(.009)
-.0005***
(.0002)
.197*
(.119)
-.005**
(.003)
.425***
(.201)
.005
(.042)
.050
(.060)
.048
(.052)
.045
(.047)
.308
(.598)
-.276
(.224)
.171
(.184)
-.008
(.350)
-.001
(.026)
.000
(.000)
.231
(.426)
.207
(.386)
.172
(.386)
-.046
(.215)
.0796
.3497
.2772
768
164

Constant
MALE
EDUCATION
EXPERIENCE
EXPERIENCE2
AGE
AGE2
AGE 3
SPANISH_LANG
YEAR90
YEAR91
YEAR92
PACIFIC WEST
WEST
NORTHEAST
MIDWEST

COHORT60
COHORT60-69
COHORT70-79
COHORT80-85
Rs-sq within
R-sq between
R-sq overall
No. of obs
No. of groups

.035***
(.010)
-.001
(.0002)
.031
(.111)
-.001
(.003)
.105
(.226)
-.076
(.049)
.135**
(.063)
.092*
(.056)
.087*
(.053)
-.408**
(.176)
-.512***
(.189)
-.380
(.216)
-.370*
(.216)
.040**
(.020)
.00002
(.0003)
-.743**
(.362)
-.394
(.261)
-.242
(.178)
-.084
(.126)
.0723
.1764
.1601
1284
281

(9)

Cuban
Immig
2.610**
(1.417)
.242***
(.047)
--.034***
(.009)
-.001***
(.0001)
.237***
(.117)
-.006***
(.003)
.495***
(.199)
-.001
(.042)
.063
(.060)
.063
(.052)
.056
(.047)
.319
(.628)
-.262
(.201)
.222
(.191)
.265
(.293)
.006
(.026)
-.00004
(.0004)
.068
(.436)
.014
(.396)
.036
(.316)
-.203
(.221)
.0830
.2203
.1830
779
165

(10)
P_Rican
Immig

2.68
(3.14)
.136
(.102)
.076***
(.018)
.020
(.021)
-.0001
(.0005)
-.123
(.238)
-.0002
(.006)
.024
(.460)
-.100
(.067)
.040
(.098)
.065
(.085)
.085
(.076)
-.010
(.478)
-.085
(.502)
.206
(.121)
.279
(.215)
-.052
(.044)
.0007
(.0006)
1.434**
(.773)
1.273***
(.638)
1.036***
(.468)
1.237***
(.302)
.2006
.2925
.2758
261
65

(11)
P_Rican
Immig

6.60***
(3.02)
.081
(.098)
--.009
(.018)
.0002
(.0004)
-.240
(.228)
.006
(.005)
-.497
(.439)
-.144***
(.067)
.0003
(.095)
.038
(.084)
.088
(.075)
-.005
(.488)
.384
(.501)
.113
(.121)
-.139
(.180)
-.055
(.043)
.0008
(.0006)
1.375*
(.735)
1.211**
(.615)
1.014**
(.459)
1.142***
(.303)
.1295
.1777
.1712
297
72

Occupational Assimilation of Hispanics, page 45
Figure 1

Predicted NP Occupational Score
35 40
45
50
55
60

Figure 17: Occupational-Age Profile

20

30

40
ER30645
Age

non-Hispanic White
Hispanic Immigrant

50

60

U.S.-born Hispanic

source: Authors' calculations based on 1990-1993 PSID, NP ref ers to Nam-Powers Scores

Figure 2: Occupational Assimilation of Mexican Immigrants

Predicted NP Occupational Score

80

60

40

20

0
0

5

10

15

Years in U.S
college-educated

high school or less educated

source: Authors' calculations based on 1990-1993 PSID, NP refers to Nam-Powers Scores

20

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Does Bank Concentration Lead to Concentration in Industrial Sectors?
Nicola Cetorelli

WP-01-01

On the Fiscal Implications of Twin Crises
Craig Burnside, Martin Eichenbaum and Sergio Rebelo

WP-01-02

Sub-Debt Yield Spreads as Bank Risk Measures
Douglas D. Evanoff and Larry D. Wall

WP-01-03

Productivity Growth in the 1990s: Technology, Utilization, or Adjustment?
Susanto Basu, John G. Fernald and Matthew D. Shapiro

WP-01-04

Do Regulators Search for the Quiet Life? The Relationship Between Regulators and
The Regulated in Banking
Richard J. Rosen
Learning-by-Doing, Scale Efficiencies, and Financial Performance at Internet-Only Banks
Robert DeYoung
The Role of Real Wages, Productivity, and Fiscal Policy in Germany’s
Great Depression 1928-37
Jonas D. M. Fisher and Andreas Hornstein

WP-01-05

WP-01-06

WP-01-07

Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy
Lawrence J. Christiano, Martin Eichenbaum and Charles L. Evans

WP-01-08

Outsourcing Business Service and the Scope of Local Markets
Yukako Ono

WP-01-09

The Effect of Market Size Structure on Competition: The Case of Small Business Lending
Allen N. Berger, Richard J. Rosen and Gregory F. Udell

WP-01-10

Deregulation, the Internet, and the Competitive Viability of Large Banks
and Community Banks
Robert DeYoung and William C. Hunter

WP-01-11

Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards
Christopher R. Knittel and Victor Stango

WP-01-12

Gaps and Triangles
Bernardino Adão, Isabel Correia and Pedro Teles

WP-01-13

A Real Explanation for Heterogeneous Investment Dynamics
Jonas D.M. Fisher

WP-01-14

Recovering Risk Aversion from Options
Robert R. Bliss and Nikolaos Panigirtzoglou

WP-01-15

Economic Determinants of the Nominal Treasury Yield Curve
Charles L. Evans and David Marshall

WP-01-16

1

Working Paper Series (continued)
Price Level Uniformity in a Random Matching Model with Perfectly Patient Traders
Edward J. Green and Ruilin Zhou

WP-01-17

Earnings Mobility in the US: A New Look at Intergenerational Inequality
Bhashkar Mazumder
The Effects of Health Insurance and Self-Insurance on Retirement Behavior
Eric French and John Bailey Jones

WP-01-18

The Effect of Part-Time Work on Wages: Evidence from the Social Security Rules
Daniel Aaronson and Eric French

WP-01-20

Antidumping Policy Under Imperfect Competition
Meredith A. Crowley

WP-01-21

WP-01-19

Is the United States an Optimum Currency Area?
An Empirical Analysis of Regional Business Cycles
Michael A. Kouparitsas

WP-01-22

A Note on the Estimation of Linear Regression Models with Heteroskedastic
Measurement Errors
Daniel G. Sullivan

WP-01-23

The Mis-Measurement of Permanent Earnings: New Evidence from Social
Security Earnings Data
Bhashkar Mazumder

WP-01-24

Pricing IPOs of Mutual Thrift Conversions: The Joint Effect of Regulation
and Market Discipline
Elijah Brewer III, Douglas D. Evanoff and Jacky So

WP-01-25

Opportunity Cost and Prudentiality: An Analysis of Collateral Decisions in
Bilateral and Multilateral Settings
Herbert L. Baer, Virginia G. France and James T. Moser

WP-01-26

Outsourcing Business Services and the Role of Central Administrative Offices
Yukako Ono

WP-02-01

Strategic Responses to Regulatory Threat in the Credit Card Market*
Victor Stango

WP-02-02

The Optimal Mix of Taxes on Money, Consumption and Income
Fiorella De Fiore and Pedro Teles

WP-02-03

Expectation Traps and Monetary Policy
Stefania Albanesi, V. V. Chari and Lawrence J. Christiano

WP-02-04

Monetary Policy in a Financial Crisis
Lawrence J. Christiano, Christopher Gust and Jorge Roldos

WP-02-05

Regulatory Incentives and Consolidation: The Case of Commercial Bank Mergers
and the Community Reinvestment Act
Raphael Bostic, Hamid Mehran, Anna Paulson and Marc Saidenberg

WP-02-06

2

Working Paper Series (continued)
Technological Progress and the Geographic Expansion of the Banking Industry
Allen N. Berger and Robert DeYoung

WP-02-07

Choosing the Right Parents: Changes in the Intergenerational Transmission
of Inequality  Between 1980 and the Early 1990s
David I. Levine and Bhashkar Mazumder

WP-02-08

The Immediacy Implications of Exchange Organization
James T. Moser

WP-02-09

Maternal Employment and Overweight Children
Patricia M. Anderson, Kristin F. Butcher and Phillip B. Levine

WP-02-10

The Costs and Benefits of Moral Suasion: Evidence from the Rescue of
Long-Term Capital Management
Craig Furfine

WP-02-11

On the Cyclical Behavior of Employment, Unemployment and Labor Force Participation
Marcelo Veracierto

WP-02-12

Do Safeguard Tariffs and Antidumping Duties Open or Close Technology Gaps?
Meredith A. Crowley

WP-02-13

Technology Shocks Matter
Jonas D. M. Fisher

WP-02-14

Money as a Mechanism in a Bewley Economy
Edward J. Green and Ruilin Zhou

WP-02-15

Optimal Fiscal and Monetary Policy: Equivalence Results
Isabel Correia, Juan Pablo Nicolini and Pedro Teles

WP-02-16

Real Exchange Rate Fluctuations and the Dynamics of Retail Trade Industries
on the U.S.-Canada Border
Jeffrey R. Campbell and Beverly Lapham

WP-02-17

Bank Procyclicality, Credit Crunches, and Asymmetric Monetary Policy Effects:
A Unifying Model
Robert R. Bliss and George G. Kaufman

WP-02-18

Location of Headquarter Growth During the 90s
Thomas H. Klier

WP-02-19

The Value of Banking Relationships During a Financial Crisis:
Evidence from Failures of Japanese Banks
Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman

WP-02-20

On the Distribution and Dynamics of Health Costs
Eric French and John Bailey Jones

WP-02-21

The Effects of Progressive Taxation on Labor Supply when Hours and Wages are
Jointly Determined
Daniel Aaronson and Eric French

WP-02-22

3

Working Paper Series (continued)
Inter-industry Contagion and the Competitive Effects of Financial Distress Announcements:
Evidence from Commercial Banks and Life Insurance Companies
Elijah Brewer III and William E. Jackson III

WP-02-23

State-Contingent Bank Regulation With Unobserved Action and
Unobserved Characteristics
David A. Marshall and Edward Simpson Prescott

WP-02-24

Local Market Consolidation and Bank Productive Efficiency
Douglas D. Evanoff and Evren Örs

WP-02-25

Life-Cycle Dynamics in Industrial Sectors. The Role of Banking Market Structure
Nicola Cetorelli

WP-02-26

Private School Location and Neighborhood Characteristics
Lisa Barrow

WP-02-27

Teachers and Student Achievement in the Chicago Public High Schools
Daniel Aaronson, Lisa Barrow and William Sander

WP-02-28

The Crime of 1873: Back to the Scene
François R. Velde

WP-02-29

Trade Structure, Industrial Structure, and International Business Cycles
Marianne Baxter and Michael A. Kouparitsas

WP-02-30

Estimating the Returns to Community College Schooling for Displaced Workers
Louis Jacobson, Robert LaLonde and Daniel G. Sullivan

WP-02-31

A Proposal for Efficiently Resolving Out-of-the-Money Swap Positions
at Large Insolvent Banks
George G. Kaufman

WP-03-01

Depositor Liquidity and Loss-Sharing in Bank Failure Resolutions
George G. Kaufman

WP-03-02

Subordinated Debt and Prompt Corrective Regulatory Action
Douglas D. Evanoff and Larry D. Wall

WP-03-03

When is Inter-Transaction Time Informative?
Craig Furfine

WP-03-04

Tenure Choice with Location Selection: The Case of Hispanic Neighborhoods
in Chicago
Maude Toussaint-Comeau and Sherrie L.W. Rhine

WP-03-05

Distinguishing Limited Commitment from Moral Hazard in Models of
Growth with Inequality*
Anna L. Paulson and Robert Townsend

WP-03-06

Resolving Large Complex Financial Organizations
Robert R. Bliss

WP-03-07

4

Working Paper Series (continued)
The Case of the Missing Productivity Growth:
Or, Does information technology explain why productivity accelerated in the United States
but not the United Kingdom?
Susanto Basu, John G. Fernald, Nicholas Oulton and Sylaja Srinivasan

WP-03-08

Inside-Outside Money Competition
Ramon Marimon, Juan Pablo Nicolini and Pedro Teles

WP-03-09

The Importance of Check-Cashing Businesses to the Unbanked: Racial/Ethnic Differences
William H. Greene, Sherrie L.W. Rhine and Maude Toussaint-Comeau

WP-03-10

A Structural Empirical Model of Firm Growth, Learning, and Survival
Jaap H. Abbring and Jeffrey R. Campbell

WP-03-11

Market Size Matters
Jeffrey R. Campbell and Hugo A. Hopenhayn

WP-03-12

The Cost of Business Cycles under Endogenous Growth
Gadi Barlevy

WP-03-13

The Past, Present, and Probable Future for Community Banks
Robert DeYoung, William C. Hunter and Gregory F. Udell

WP-03-14

Measuring Productivity Growth in Asia: Do Market Imperfections Matter?
John Fernald and Brent Neiman

WP-03-15

Revised Estimates of Intergenerational Income Mobility in the United States
Bhashkar Mazumder

WP-03-16

Product Market Evidence on the Employment Effects of the Minimum Wage
Daniel Aaronson and Eric French

WP-03-17

Estimating Models of On-the-Job Search using Record Statistics
Gadi Barlevy

WP-03-18

Banking Market Conditions and Deposit Interest Rates
Richard J. Rosen

WP-03-19

Creating a National State Rainy Day Fund: A Modest Proposal to Improve Future
State Fiscal Performance
Richard Mattoon

WP-03-20

Managerial Incentive and Financial Contagion
Sujit Chakravorti, Anna Llyina and Subir Lall

WP-03-21

Women and the Phillips Curve: Do Women’s and Men’s Labor Market Outcomes
Differentially Affect Real Wage Growth and Inflation?
Katharine Anderson, Lisa Barrow and Kristin F. Butcher

WP-03-22

Evaluating the Calvo Model of Sticky Prices
Martin Eichenbaum and Jonas D.M. Fisher

WP-03-23

5

Working Paper Series (continued)
The Growing Importance of Family and Community: An Analysis of Changes in the
Sibling Correlation in Earnings
Bhashkar Mazumder and David I. Levine

WP-03-24

Should We Teach Old Dogs New Tricks? The Impact of Community College Retraining
on Older Displaced Workers
Louis Jacobson, Robert J. LaLonde and Daniel Sullivan

WP-03-25

Trade Deflection and Trade Depression
Chad P. Brown and Meredith A. Crowley

WP-03-26

China and Emerging Asia: Comrades or Competitors?
Alan G. Ahearne, John G. Fernald, Prakash Loungani and John W. Schindler

WP-03-27

International Business Cycles Under Fixed and Flexible Exchange Rate Regimes
Michael A. Kouparitsas

WP-03-28

Firing Costs and Business Cycle Fluctuations
Marcelo Veracierto

WP-03-29

Spatial Organization of Firms
Yukako Ono

WP-03-30

Government Equity and Money: John Law’s System in 1720 France
François R. Velde

WP-03-31

Deregulation and the Relationship Between Bank CEO
Compensation and Risk-Taking
Elijah Brewer III, William Curt Hunter and William E. Jackson III

WP-03-32

Compatibility and Pricing with Indirect Network Effects: Evidence from ATMs
Christopher R. Knittel and Victor Stango

WP-03-33

Self-Employment as an Alternative to Unemployment
Ellen R. Rissman

WP-03-34

Where the Headquarters are – Evidence from Large Public Companies 1990-2000
Tyler Diacon and Thomas H. Klier

WP-03-35

Standing Facilities and Interbank Borrowing: Evidence from the Federal Reserve’s
New Discount Window
Craig Furfine

WP-04-01

Netting, Financial Contracts, and Banks: The Economic Implications
William J. Bergman, Robert R. Bliss, Christian A. Johnson and George G. Kaufman

WP-04-02

Real Effects of Bank Competition
Nicola Cetorelli

WP-04-03

Finance as a Barrier To Entry: Bank Competition and Industry Structure in
Local U.S. Markets?
Nicola Cetorelli and Philip E. Strahan

WP-04-04

6

Working Paper Series (continued)
The Dynamics of Work and Debt
Jeffrey R. Campbell and Zvi Hercowitz

WP-04-05

Fiscal Policy in the Aftermath of 9/11
Jonas Fisher and Martin Eichenbaum

WP-04-06

Merger Momentum and Investor Sentiment: The Stock Market Reaction
To Merger Announcements
Richard J. Rosen

WP-04-07

Earnings Inequality and the Business Cycle
Gadi Barlevy and Daniel Tsiddon

WP-04-08

Platform Competition in Two-Sided Markets: The Case of Payment Networks
Sujit Chakravorti and Roberto Roson

WP-04-09

Nominal Debt as a Burden on Monetary Policy
Javier Díaz-Giménez, Giorgia Giovannetti, Ramon Marimon, and Pedro Teles

WP-04-10

On the Timing of Innovation in Stochastic Schumpeterian Growth Models
Gadi Barlevy

WP-04-11

Policy Externalities: How US Antidumping Affects Japanese Exports to the EU
Chad P. Bown and Meredith A. Crowley

WP-04-12

Sibling Similarities, Differences and Economic Inequality
Bhashkar Mazumder

WP-04-13

Determinants of Business Cycle Comovement: A Robust Analysis
Marianne Baxter and Michael A. Kouparitsas

WP-04-14

The Occupational Assimilation of Hispanics in the U.S.: Evidence from Panel Data
Maude Toussaint-Comeau

WP-04-15

7