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Federal Reserve Bank of Chicago

Immigrant-Native Differences in
Financial Market Participation
Una Okonkwo Osili and Anna Paulson

REVISED December, 2006
WP 2004-18

Immigrant-Native Differences in Financial Market Participation

The goal of this paper is to investigate the prospects for the wealth assimilation of
immigrants by studying the financial market behavior of U.S. immigrants, compared to the
native-born. Compared to similar natives, immigrants are less likely to own a wide range of
financial assets, including savings and checking accounts.

Immigrant status also has a

significant impact on transitions out of account ownership. We find that lower rates of financial
market participation tend to persist even for immigrants who have lived in the U.S. for several
years. Our results suggest that a large share of the immigrant-native gap in financial market
participation is driven by group differences in education, income, and geographic location. For a
given immigrant, the likelihood of financial market participation decreases with higher levels of
ethnic concentration in the metropolitan area.
Keywords: Immigrants, financial markets, ethnic concentration, location

I.

Introduction

A central question facing researchers and policymakers is the extent to which immigrants
will adapt to economic, social, and political life in United States. One crucial facet of economic
and social well-being is wealth. While there is a rich literature that examines the sources of
immigrant-native differences in labor market, health, and educational outcomes, relatively little
is known about the determinants of wealth differences between immigrants and the native born.
In a growing number of studies, researchers have documented that immigrants have substantially
lower wealth levels and that differential patterns in asset holdings can explain a large share of the
immigrant-native wealth gap (Amuedo-Dorantes and Pozo, 2002; Hao, 2004, Cobb-Clark and
Hildebrand, 2006a, 2006b; Krivo and Kaufman, 2004). In particular, the median wealth levels of
natives are estimated to be about 2.3 times higher that of immigrants, and immigrants are less
likely to hold financial and real estate wealth compared to natives (Cobb-Clark and Hildbrand,
2006a).
In this paper, we investigate the factors that influence the decision of immigrants and
natives to hold wealth in a particular form—financial assets. By focusing on the extensive
margin, rather than the intensive margin, we hope to shed light on the reasons for the wide
disparities in immigrant- native wealth holdings. Based on recent data, financial wealth holdings
are the second largest component of household wealth in the U.S., after housing, and account for
42% of overall household assets in 2001 (Survey of Consumer Finances, 2001). The relative
liquidity of financial assets means that they can play an important role in allowing households to
self-insure against negative income shocks induced, for example, by job loss, illness, and marital
disruption. Beyond their role in helping households cope with income uncertainty, financial
assets also tend to be associated with high expected returns over time and can contribute
significantly to understanding long-run differences in wealth accumulation. Several empirical
studies have shown that the ownership and the value of financial assets is correlated with a wide
range of economic and social decisions, including investments in health (Roberts and House,
1996), well-being at retirement (Bender, 2004), home ownership (Haurin et al., 1997), business
formation (Holtz-Eakin et al., 1994), children’s educational outcomes (Duncan and BrooksGunn, 1997; Mayer, 1997), and political participation (Scanlon and Page-Adams, 2001).
We use panel data from the 1996 – 2000 Survey on Income and Program Participation
(SIPP) to analyze financial market decisions of immigrants relative to those of natives and to

3

estimate the impact of being an immigrant on the likelihood of transitions into and out of
financial market participation. In addition to documenting differences in immigrant versus native
financial market participation, we show how duration of stay in the United States impacts
immigrant behavior relative to the native-born. Based on unconditional means, immigrants are
much less likely to hold U.S. financial assets compared to their native-born counterparts. For
example, 40% of immigrants have a savings account, compared to 55% of the native-born.
There is a similar gap in the percentage of immigrants and native-born who hold an interestbearing checking account: 22% versus 36%. While 12% of immigrants own stock, ownership
rates are much higher among the native-born at 25%. We find that immigrants also report lower
rates of ownership of a wide range of additional financial assets, including individual retirement
accounts and mutual funds when compared to the native-born.
Controlling for education, income, and other individual and household characteristics, the
financial market behavior of immigrants remains significantly different from that of similar
native-born individuals. An important advantage of the SIPP panel is that we observe not just
financial asset holdings at one point in time, but also transitions into and out-of financial market
participation at frequent intervals (every four months) for both savings and interest-bearing
checking accounts. Taken together, we find that immigrants transition out of financial asset
ownership at higher rates than similar natives and that recent immigrants are significantly less
likely to enter into savings and checking account ownership compared to natives. We present
additional evidence that the explanation for differential behavior of immigrants relative to
natives has to do with the characteristics of the geographic area where a given immigrant resides.
For a given immigrant, the likelihood of financial market participation decreases with the
percentage of the population in a given metropolitan area from the same origin country as the
immigrant in question. One potential explanation for these results is that social interactions may
play an important role in determining whether immigrants participate in financial markets or that
immigrant networks provide informal substitutes for formal financial markets.

Like wage

growth (Borjas, 1998 and 2000), human capital accumulation, and language proficiency
(Chiswick and Miller, 1996), immigrant participation in formal financial markets appears to be
inhibited when there is a large network of immigrants from the same country of origin to interact
with.

4

The rest of the paper is organized as follows. Section II describes the conceptual
framework and empirical methods. We describe the SIPP data and summarize the data on the
financial market participation of immigrants relative to natives in Section III. In Section IV, we
present results. Section V presents conclusions.
II.

Understanding Immigrant-Native Differences in Financial Market Participation

A simple life-cycle model is a useful starting point for exploring immigrant-native
differences in financial market behavior (Modigliani and Brumberg, 1954). Individuals, whether
immigrant or native-born select a portfolio from a wide range of assets, comparing returns,
transaction costs, risk profiles, and liquidity in order to smooth consumption over time.1 This
approach suggests that differences in financial market participation between immigrants and the
native-born may be driven (at least in part) by differences in household income, education, age,
and family structure. The decision to hold a particular asset will also depend on information,
tastes and preferences, and the degree of risk aversion, which may differ across otherwise similar
immigrants and natives. We recognize that it may be important to account for additional sources
of immigrant-native differences, including race and ethnicity, legal status, English language
skills, years of U.S. experience, and patterns of residential settlement, which are likely to affect
financial decisions (Amuedo-Dorantes et al., 2005; Cobb-Clark and Hildenbrand, 2006a). In the
section below, we describe the empirical models used to estimate the gap between immigrant and
native-born financial market participation.
Empirical Specification
1.

Immigrant-Native Differences in Financial Market Participation

The basic specification investigates the likelihood that an individual holds a given financial asset
during a given period. The benefits and costs associated with the financial market participation
for individual i living in a destination community j at time t can be defined as Uijt, which is a
function of (Zijt), a vector of socio-economic and demographic variables including, education,
race, income, household size, and other control variables. In addition, for immigrants, Uijt may
be a function of immigrant status, (Ii), and duration of stay in the United States, Di. The net
benefits and costs associated with holding a given financial asset may also vary by community,

1

There are additional motives for savings and wealth accumulation, which include precautionary motives and the
desire to leave bequests.

5

Cj, with time vt, and be subject to an error term, εijt, that is particular to the individual. For each
time period, Uijt can be measured as:
Uijt= α + β1Zijt + β2Ii +β3(Di*Ii) + γj*Cj +vt+ εijt

(1)

We do not observe Uijt, but we observe whether the household has participated in a given
financial market. Thus, we observe:
Pijt

= 1 if Uijt > 0
= 0 otherwise
Equation (1) represents the fully specified model. We build up to this model and first

estimate a parsimonious specification, which includes individual characteristics and an indicator
variable for immigrant status.

We use a maximum likelihood logit model to estimate the

probability that an individual has participated in a given financial market in the survey period.
The parameter on the immigrant indicator, β2, will capture the effect of being an immigrant on
the likelihood of holding a given financial asset, after having controlled for time in the U.S. and
socio-economic and demographic characteristics.

The parameter on the cohort of arrival

variable, β3, measures how duration of stay in the U.S. affects the immigrant’s likelihood of
participating in a given financial market. The set of parameters, γj, measure community level
fixed-effects. We identify the community as the Metropolitan Statistical Area (MSA) where an
individual resides.

In our empirical estimates, we include MSA-level fixed-effects, which

capture the effect of community variables such as the density of formal financial institutions in
the MSA, employment conditions, and other economic attributes of the MSA. We also include
time controls in all estimates to capture any time variation in financial market participation over
the sample period. All reported standard errors are adjusted to allow for correlation across
observations for a given individual.
2.

Immigrant-Native Differences in Exit and Entry in Financial Markets
Financial market participation for both immigrants and natives, measured at a given point

in time will depend on both exit and entry rates into holding a given financial asset. In the
empirical analysis, we also focus on the impact of immigrant status on transitions into and out of
financial market participation as these estimates can provide insights into why immigrant
financial behavior differs from that of natives. Let Vijt represent the net benefits of entry (or
exit) into the use of a given financial service from time t – 1 to time t. The net benefits of entry
(or exit) are defined to be a function of individual and household characteristics at time t – 1,

6

immigrant status, year of arrival controls, as well as community controls.

Specifically, we

measure Vijt as:
Vijt = α + β1Zijt-1+ β2Ii +β3(Di*Ii) + γj*Cj + vt+ ηijt

(3)

We do not observe the net benefits of entry or exit; instead we know whether the household has
experienced a transition into (or out of) the use of a given financial service. Hence, we estimate
using logit maximum likelihood:
Eijt

= 1 if Vijt > 0

(4)

= 0 otherwise,
For estimates of entry, the dependent variable is equal to one if the individual reports owning an
account at time t and not owning an account at time t – 1. The dependent variable is equal to
zero if the individual reports no account ownership at time t and at time t – 1. For exit, the
dependent variable is equal to one if an individual reports ownership at t – 1 and no ownership at
time t and is equal to zero if the individual reports ownership at both t and t – 1.2 All of the
transition estimates include the explanatory variables described in the discussion of the baseline
results and standard errors are adjusted to allow for correlation across observations at the
individual level.
III.

Data and Characteristics of Immigrants and the Native-born
The empirical analysis uses longitudinal data from the 1996-2000 waves of the Survey of

Income and Program Participation (SIPP). The SIPP is a panel survey which provides detailed
information about adults residing within households, and is conducted by the U.S. Census
Bureau.

The SIPP collects data by interviewing individual respondents (about 65,000

individuals) once a quarter about their economic experiences, including ownership of savings
accounts, checking accounts, and stocks. The 1996 SIPP panel consists of twelve waves of
interview questions, where the interview questions depend on the wave. We include only
individuals who are 18 or older in our study. The analysis deals with individuals who reside in a
Metropolitan Statistical Area (MSA). This allows us to control for MSA-level variation in the
availability of financial services by including MSA controls in estimates of financial market

2

We should note that the entry estimates are restricted to those who report no ownership at time t – 1 and that the
exit estimates are restricted to those who do own an account at time t – 1.

7

behavior.3 The sample includes, about 28,633 native-born, and 4,450 immigrants. Because we
observe individuals multiple times, the total sample is made up of 356,769 quarterly
observations.
In addition to information on financial asset holdings, the SIPP data include information
on immigrant status, country of origin, and year of arrival in the U.S., coded into 5-year intervals
to protect respondent confidentiality. The SIPP data are well-suited for this study because they
include information on financial market behavior and on immigration. Other data sources
available from the Bureau of the Census, or from the monthly Current Population Survey,
contain a large number of immigrants and provide detailed information on immigration, but
include very limited information on participation in financial markets or transitions into and out
of ownership.4 The immigrant population in the 1996 SIPP closely mirrors 2000 Census data on
U.S. immigrants. Out of a total sample of 29,731 MSA residents, 14% are immigrants.5
Our analysis is conducted at the individual level to capture the extent to which
immigrants and the native-born differ in the distribution of financial asset holdings.6 The SIPP
data provides detailed information for all adults in the household are interviewed on
demographic characteristics, ownership of interest or dividend-earning financial accounts, and
income. These data are available for each of the 12 waves, at approximately 3-month intervals.
While the SIPP panel is relatively short, the large sample sizes available provide an opportunity
to observe within-sample changes in financial asset holdings for both immigrants and the nativeborn.7

3

By focusing on an urban sample, we can also eliminate an important source of heterogeneity between immigrants
and natives since about 75% of the SIPP immigrant sample lives in a MSA compared to about half of natives.
4
The SIPP data do not include any information on remittances or the use of informal financial institutions. This
makes it difficult to directly assess how participation in formal financial markets in the U.S. is impacted by
immigrant financial ties to origin countries and the use and availability of informal financial substitutes.
5
In the 2000 Census, 11.4% of the total population was born abroad. The higher percentage of immigrants that we
find in our sample is due to the fact that we restrict our attention to MSA residents, and immigrants are more likely
to live in metropolitan areas than in rural areas.
6
An advantage of our approach is that we do not have to impose social norms about the degree to which assets are
jointly held within the household as these may vary by country of origin. However, we find comparable results on
immigrant-native differences in financial market behavior when we restrict the sample to household heads. These
results are available upon request.
7
With any data that tracks individuals and/or households over time, the problem of individuals dropping out of the
sample during the course of data collection arises. Our analysis indicates that while immigrants drop out of the
sample at higher rates between Wave 1 and Wave 2, after that patterns of attrition are fairly similar for natives and
immigrants.

8

A.

Financial Market Participation
The empirical work focuses on two indicators of financial market participation:

ownership of savings and interest-bearing checking accounts.8

We emphasize savings and

checking account ownership because these represent entry-level financial assets with relatively
low barriers to participation. However, we also examine additional indicators of financial market
participation including individual retirement accounts (IRA) or Keogh and mutual fund
ownership, although mean ownership rates tend to be lower for these indicators of financial
market participation. Figure 1 summarizes this information. The SIPP data provide information
on whether a given survey respondent held a particular financial asset in the previous month at
four month intervals for the duration of the panel. To allow a comparison across the two of the
major components of asset holdings, we present immigrant-native gaps in homeownership (see
Borjas, 2002; Krivo and Kauffman, 2004).
Table 1 summarizes patterns of financial asset ownership for immigrants and the nativeborn. Compared to the native-born, immigrants are less likely to hold mainstream financial
assets. We find that ownership of savings accounts appears relatively widespread in the SIPP
data, with 53% of the pooled immigrant-native-born sample reporting ownership of a savings
account. However, only 40% of immigrants own a savings account compared to 55% of natives.
Ownership of an interest-bearing checking account is less common, with only 34% of the sample
reporting ownership. For checking accounts, the gap between immigrants and natives is even
larger, with immigrant ownership rates of 22% being only 60% that of the native-born at 36%.
While 18% of the pooled sample report stock ownership, only 9% of immigrants own stock
compared to the ownership rate of the native-born at 20%.
Table 1 also reports summary statistics on exit and entry from the SIPP for savings,
checking accounts, and stock. Transitions into and out of account ownership differ in important
ways by immigrant-native status. Over the course of the panel, immigrants are less likely to
participate in mainstream financial markets. For example, about 43% of immigrants report never
owning a savings account throughout all 12 waves, compared to 30% of the native-born. We
also note that for immigrants the percentage of immigrants who never owned a checking account
at any time during the panel is about 66%, compared to 51% for the native-born. Similarly, for
8

We focus our attention on interest-bearing checking accounts because information on ownership of non-interest
bearing checking accounts is available less frequently in the SIPP (approximately once a year). We obtain similar
results when we combine both interest and non-interest bearing checking accounts (see Figure 1).

9

immigrants, the percentage of immigrants who never owned stock at any time during the panel is
about 85%, compared to 71% for the native-born.
Immigrants report more volatility in their financial market participation status. We find
that immigrants are more likely than the native-born to report exits from financial asset holdings.
Specifically, exit rates out of savings and checking account ownership for immigrants are about
60% higher for immigrants than for the native-born. Immigrants also have lower rates of entry
into savings and checking account, and stock ownership compared to the native-born.
B.

Characteristics of Immigrants and the Native-born

Socioeconomic and Demographic Characteristics
Table 2 provides a detailed comparison of the characteristics of immigrants and the
native-born. Compared to the native-born, immigrants are younger, more likely to be married,
have more children, and more likely to be unemployed or economically inactive. Immigrants
also tend to be less educated than the native born. Nearly 36% of the immigrant sample has not
completed high school compared to only 15% of the native-born sample.

However, the

percentage of immigrants and the native-born who have an advanced degree is comparable at
about 7%. Monthly per capita household income is significantly lower for immigrants compared
to the native-born. For immigrants, average monthly per capita household income is $1,619,
compared to $2,195 for the native-born. We also note that immigrants are more likely to be nonwhite. About 75% of the immigrant sample is non-white compared to about 23% of the nativeborn sample. Nearly 30% of the immigrant sample was born in Central America, while about
15% of the immigrant sample is of European descent. A sizeable share of the immigrants in the
SIPP can be classified as recent immigrants, with almost 40% of the immigrants arriving in the
U.S. after 1990.
IV.

Results

A.

Baseline Findings for Participation in Financial Markets
We present the baseline findings in Tables 3. The key dependent variables in the analysis

are indicator variables that capture whether or not an individual owned a savings account
(column 1) or a checking account (column 2) during the survey reference period. All of our
estimates include MSA fixed-effects, as well as the following explanatory variables: age, age

10

squared, labor force status, per capita income, per capita income squared, marital status, the
number of children in the household, sex, race, and education.9 We present additional results for
stock, individual retirement accounts and other financial assets in Appendix Table.10
Individual and Household Variables
First, we discuss the results on individual and household characteristics – income,
education, race, and household structure in on financial market participation. These results are
presented in Table 3. In general, the effect of individual and household level variables on savings
account and checking account ownership are similar.
Income has a strong positive effect on financial market participation. If monthly per
capita household income were to increase by one standard deviation from its mean, by $2,764,
the likelihood of savings account ownership would increase by 12 percentage points, a 23%
increase relative to the observed percentage of the individuals in the sample who have a savings
account of 53%. Similarly, checking account ownership would increase by 12 percentage points,
and this represents a 30% increase relative to the observed likelihood of owning a checking
account of 35%. Being unemployed or out of the labor force has a strong negative impact on
savings account ownership, but a small positive impact on the probability of owning a checking
account. The different effect of age and labor market status on savings and checking account
ownership is most likely driven by greater ownership of interest-bearing checking accounts
among retirees.
Educational attainment plays a very important role in explaining patterns of financial
market participation. For example, compared to those with less than a high school diploma, high
school graduates are about 13 percentage points more likely to own a savings account and 17
percentage points more likely to have a checking account. Individuals who have completed
some college are 21 percentage points more likely to have a savings account and 26 percentage
points more likely to have a checking account compared to those who did not complete high
school. The predicted gap in account ownership between college graduates and those who did
not complete high school is even larger, 24 percentage points for savings accounts and 35
9

While household wealth may provide a more suitable measure of permanent income or the lifetime resources for a
given household, the SIPP wealth variable is only available in the topical modules (and is measured every 8
months).
10
We should note that we find similar results on stock, individual retirement account, and mutual fund ownership
(and these results are shown in Appendix I). We omit a detailed discussion of these results in the interest of space.

11

percentage points for checking accounts. The figures are similar when we compare individuals
with an advanced degree to individuals who did not complete high school.
Older individuals are more likely to own checking accounts. There are some nonlinearities with respect to the effect of age on savings account ownership.

While age is

negatively associated with savings account ownership, age squared has a positive and significant
impact on savings account ownership. Being married has a large positive impact on savings and
checking account ownership, increasing the probability of savings account ownership by more
than 20 percentage points and the likelihood of checking account ownership by 17 percentage
points. Interestingly, men are significantly less likely to report holding a savings and checking
accounts. We also note that, compared to whites, non-whites are 11 – 12 percentage points less
likely to have a savings or a checking account.

The number of children in the household

reduces the likelihood of having a savings or a checking account by about 2 percentage points for
each additional child.
Impact of Immigrant Status
We now turn to the key variable of interest. Immigrants are significantly less likely to
participate in financial markets, compared to the native-born. Specifically, immigrants are 7.4
percentage points less likely to own a savings account compared to a similar native-born
individual. Immigrants are also 6.1percentage points less likely to own a checking account
compared to a similar native-born individual. Across a range of additional financial assets—
notably, stock, mutual funds, and individual retirement accounts, we note that immigrants have a
lower likelihood of financial asset ownership, compared to the native born. From Appendix I,
immigrants are 5.6 and 4 percentage points less likely to own stock and individual retirement
accounts, respectively compared to a similar native-born individual.
In Table 4, we consider the role of time in the U.S. on the financial market participation
of immigrants relative to the native-born. Specifically, we estimate the additional effect of being
a recent immigrant on savings account ownership (column 1) and checking account ownership
(column 3). We define recent immigrants to be those who arrived in the U.S. in 1990 or more
recently. At most they would have lived in the U.S. for six years at the beginning of the SIPP
survey. Columns (2) and (4) include a full-set of year of arrival controls and allow us to consider

12

how the impact of being an immigrant on savings and checking account ownership, respectively,
varies more generally with time in the U.S.11
While immigrants as a group are 7.4 percentage points less likely to have a savings
account and 6 percentage points less likely to have a checking account compared to the nativeborn, recent immigrants are 18 percentage points less likely to have a savings account and 12
percentage points less likely to have a checking account (see Table 4, columns 1 and 3).12
Recent immigrants are particularly likely to differ in important ways from the native-born in
their familiarity and knowledge of U.S. financial markets. English language ability and legal
status are likely to be important concerns for recent immigrants compared to their more
established counterparts.

In addition, information costs may impose significant barriers to

immigrant participation in formal financial markets although these costs would tend to decrease
as immigrants gain U.S. experience. The estimates presented in columns 2 and 4 suggest that
this is indeed the case. While immigrants who arrived between 1990 and 1996 are 18 percentage
points less likely to have a savings account and 12 percentage points less likely to have a
checking account, immigrants who arrived between 1985 and 1989 are only 9 percentage points
less likely to have a savings account and only 8 percentage points less likely to have a checking
account, compared to the native-born.
With one exception, the cohort controls are not significantly different from zero for
immigrants who arrived before 1985, suggesting that partial financial market assimilation may
happen in the first ten to fifteen years after migration and then stops. Interestingly, we find that
immigrants who arrived between 1975 and 1979 are as likely as the native born to have a savings
account.13 Altogether, the estimates presented in Table 4 indicate that immigrant financial
market assimilation is partial at best. Taking into account U.S. experience and a rich set of
controls, immigrants are about 5 percentage points less likely to have a savings account or a
checking account compared to the native-born.
B.

Decomposing the Immigrant-Native Gap in Financial Market Participation

11

In addition to controls for being an immigrant and duration of stay in the U.S., the estimates presented in Table 4
also contain the same set of control variables that were included in the estimates presented in Table 3.
12
For stock and IRA ownership, recent arrivals are 12 percentage points and 17 percentage points less likely to own
these assets compared to similar native-born.
13
The 1975-1979 cohort may have been particularly impacted by the 1986 Immigration Reform and Control Act
which provided amnesty in the form of legal permanent residence for undocumented immigrants who could prove
that they had been living continuously in the U.S. prior to January 1, 1982. Agricultural workers who had worked in
the U.S. for at least 90 days in the year prior to May 1, 1986 were also eligible for amnesty.

13

Having documented that there is an important gap in immigrant-native financial market
participation, we turn now to quantifying the fraction of the gap that can be explained by
characteristics and by returns to characteristics (or “prices”). Specifically, we are interested in
quantifying the key characteristics that drive the portion of the gap that can be attributed to group
differences in characteristics, and quantifying the relative importance of group differences in
education, income, and metropolitan areas in explaining immigrant-native gaps in participation
in financial markets. Given the non-linearity of the logit equation, we use a variation of the
Blinder-Oaxacca decomposition (Blinder, 1973; Oaxaca, 1973), which is described in Fairlie
(2003).
Table 5 summarizes the nonlinear decomposition of the immigrant-native gap in financial
market participation based on Fairlie (2003). The decomposition results presented in Table 5
suggest that group differences in characteristics and the returns to characteristics between
immigrants and the native-born are equally important in explaining the gap in financial market
participation for the two groups.14 We also consider the role that specific characteristics play in
creating the observed differences between immigrants and the native-born. As one might expect,
education and income differences between immigrants and the native-born play a key role in
increasing the gap in financial market participation that is due to characteristics. According to
our decomposition results, individual, family, and MSA characteristics account for about 50 to
70% of the difference in financial market participation that can be attributed to characteristics.
Interestingly, differences in the metropolitan areas where immigrants and the native-born live
play an important role, accounting for about 17 percentage points of the gap that is due to
characteristics. This suggests that on average, the financial market participation of immigrants
would be higher if they lived in the same MSAs as the native-born.
14

For the logit equation, the decomposition of the native/immigrant gap is expressed below. F(.) is the cumulative
distribution function from the logistic distribution, Xm is a row vector of average values for the individual

characteristics and MSA effects, βˆ m is a vector of coefficient estimates for group m, and Ym , is the average
probability of owning an account for group m. We present the decomposition using immigrant coefficients in the
first term:

Y
.

N

(

)

(

)

NI
 N N F X iN βˆ I
F X iI βˆ I 
− Y = ∑
−∑

NN
NI
i =1

 i =1
.
NN
 N N F X iN βˆ N
F X iN βˆ I 
+ ∑
−∑

NN
NN
i =1
 i =1

I

(

)

(

)

14

C.

Unobserved Heterogeneity and Financial Market Participation

The decomposition results presented above are useful in quantifying the separate role that
group differences in measurable characteristics play in explaining differences in the financial
behavior of immigrants and the native-born. However, while observed characteristics such as
household income, race and ethnicity, education, age, and family structure play an important
role, it is also likely that unobservables including tastes and preferences and the degree of risk
aversion, may differ across otherwise similar immigrants and the native-born. While we cannot
observe all these characteristics directly, various empirical techniques help us examine the extent
to which differences in immigrant-native financial market behavior are driven by unobserved
heterogeneity.15
We take two approaches to dealing with unobserved heterogeneity. First we investigate
the impact of adding control variables to the estimates presented in Table 3 in an effort to better
account for omitted variables. We are concerned that to the extent that omitted variables are
correlated with being an immigrant, they will bias the coefficient on the immigrant indicator
variable in the baseline estimates of financial market participation. In the estimates presented in
Table 6 and discussed in sub-section [1] below, we explore the role of ethnicity, legal status,
language, and other potential sources of bias. In addition, we make use of the panel nature of the
SIPP data and estimate transitions into and out of financial market ownership. The estimates of
changes in financial market behavior from one period to the next, account for unobserved
heterogeneity by implicitly differencing out the effect of fixed individual characteristics. If
being an immigrant has a similar effect on owning a savings or a checking account as it does on
transitions in ownership, then we gain confidence that our baseline findings are not overly
influenced by unobserved heterogeneity. These estimates are presented in Tables 7A (Entry) and
7B (Exit) and discussed in sub-section [2] below.
1.

Unobserved Heterogeneity – Additional Control Variables

Before discussing the results which include additional controls, it is useful to reconsider
the estimates which include year of arrival controls in the light of unobserved heterogeneity
15

In particular, financial support of relatives in the country of origin and the use and availability of informal
substitutes for formal financial products and services are also likely to be important sources of unobserved
heterogeneity.

15

(Table 4). To some extent, potential biases in the effect of being an immigrant on financial
market participation due to unobserved heterogeneity are addressed in these estimates. By
including year of arrival controls, the influence of omitted variables (such as English language
ability and legal status which tend to vary over time and by year of arrival cohort) will tend to
show up in the coefficients on the year of arrival controls and will reduce the coefficient on
immigrant status. We find that including the year of arrival controls reduces the impact of being
an immigrant on financial market participation from negative 7 percent to negative 4 percent for
savings and from negative 6 percent to negative 4 percent for checking.
In Table 6, we investigate the effect of specific omitted variables on the financial market
participation of immigrants relative to the native-born. While we are interested in the direct
effect of the additional control variables, we are also interested in how much the coefficient on
immigrant status changes as a result of adding controls.
For comparison purposes, the baseline results from Table 3 are presented in column (1)
of Table 6. The first source of unobserved heterogeneity that we consider is racial differences
within the immigrant community.

This estimate addresses the possibility that non-white

immigrants differ significantly in their use of (or, potentially, access to) formal financial
institutions compared to that of white immigrants because of discrimination by financial
institutions or beliefs about discrimination by financial institutions. Recent empirical studies of
household financial behavior have documented significant differences in the use of financial
services by race, even after controlling for income and education (Altonji and Doraszelski, 2005;
Blau and Graham, 1990; Chiteji and Stafford, 1999). In column (2) we allow the effect of race
to differ for immigrants and the native-born. In the baseline estimates, the effect of being “nonwhite” is restricted to be the same for immigrants and the native-born. We find relatively small,
but significant differences in the financial market behavior of immigrants by race. We should
note that adding the interaction of immigrant status and race actually increases the negative
effect of being an immigrant on financial market participation.
In column (3), we consider the effect of legal status at the time of migration on financial
market participation. Immigrants who lack the legal right to live and work in the U.S. may face
additional barriers to opening a savings or checking account.

Many financial institutions,

particularly during the survey period, required a social security number and a U.S. Driver’s

16

License to open an account.16 While the SIPP data do not include information on whether an
immigrant is undocumented upon arrival or at the time of the survey, they do report whether an
immigrant was a legal permanent resident at the time of migration. Our results suggest that
permanent residence has a positive and significant impact for both savings and checking account
ownership. Immigrants who arrived in the U.S. as permanent residents are about 2 to 3
percentage points more likely to own savings and checking accounts, compared to other
immigrants. However, adding the legal status variable does not significantly reduce the negative
effect of being an immigrant on financial market participation.
The baseline estimates of financial market participation include controls for education
and assume that education has the same impact on financial market participation for immigrants
and the native-born. In column (4) of Table 6 we consider the possibility that the impact of
being an immigrant on financial market participation varies with education among the immigrant
population. If immigrants with exposure to higher education (beyond high school) also have
better employment opportunities, enhanced English skills, and access to different sources of
information about financial markets, their behavior may differ significantly from less-educated
immigrants. We allow for this possibility by adding an interaction term to the set of control
variables: immigrant multiplied by a variable that is equal to one if an individual has completed
more than a high school education.
We find that immigrants with more than a high school degree are 16 percentage points
more likely to have a savings account and 25 percentage points more likely to have a checking
account compared to immigrants who have at most completed a high school degree. Among the
native-born, those who have a high school degree or more are 13 percentage points more likely
to have a savings account and 15 percentage points more likely to have a checking account.
Education appears to have a bigger impact on immigrant financial market behavior than it does
on native financial market behavior. This suggests that education captures additional aspects of
the immigrant experience like access to job sources, English language ability, and information
about financial products and services.

For immigrants as a whole, however, adding the

interaction of immigrant with a high school education or greater makes the contrast between
immigrant and native financial market participation even starker: immigrants are 10 percentage
16

Although many U.S. financial institutions require a Social Security number in order to open an account, a
growing number of banks accept Individual Taxpayer Identification Numbers (ITINs) as an alternative and
recognize identification cards issued by consular offices of the immigrant’s country of origin.

17

points less likely to have a savings account and 11 percentage points less likely to have a
checking account in these estimates. It appears that failing to allow education to have a different
effect immigrants and the native-born in the baseline estimates led to a downward bias in the
estimated impact of being an immigrant on financial market participation.
In column (5), we repeat the estimation of the baseline specification on a sample that
excludes Mexican immigrants. Mexican immigrants make up approximately one-third of the
immigrant sample and have some distinguishing characteristics that are difficult to measure in
the SIPP data and that are also potential sources of bias. Specifically, Mexican immigrants are
more likely to be undocumented. They also have higher propensities for return migration
compared to other immigrants and this may have implications for savings behavior (see
Dustmann, 1997; Galor and Stark, 1990), for example). In addition, Mexican immigrants tend to
have lower English ability and education compared to other immigrants.

Eliminating this

immigrant group from the sample does not substantively alter the conclusions of the baseline
estimates. Excluding the Mexican sample, we find that immigrants are 5 percentage points less
likely than natives to have a savings account (compared to 7 percentage points in the baseline
case) and 5 percentage points less likely to have a checking account (compared to 6 percentage
points in the baseline estimates).
In column 6, we restrict the sample to native and immigrant Hispanics. Consistent with
several studies that have documented low rates of financial asset holdings among Hispanic
immigrants compared to the Hispanic native-born, we also find low rates of savings account
ownership among Hispanics compared to other ethnic groups (Cobb-Clark and Hildebrand,
2006c; Smith, 1995). In particular, we find that ownership rate of savings accounts for Hispanic
immigrants is low at 19% when compared to ownership rates of 38% for native-born Hispanics.
For checking accounts, Hispanic immigrants ownership rates are 10% compared to much higher
ownership rates of 28% among Hispanic native-born individuals. However, for Hispanics, taken
as a group, it is not clear how much of their lower participation rates can be explained by
immigrant status and how much can be explained by English language proficiency and other
barriers. While information on English language proficiency is not available in the SIPP data,
we can learn about the relative importance of language proficiency (compared to other factors)
by restricting our sample to Hispanics. When we restrict our sample to Hispanics, we still find
significant differences, of roughly the same magnitude as the baseline estimates, in financial

18

market participation between native-born Hispanics and Hispanic immigrants. Hispanic
immigrants are 6 percentage points less likely to have a savings account and a checking account
compared to native-born Hispanics. These estimates increase our confidence that the baseline
estimates of the gap in immigrant-native financial market behavior is not driven by omitted
variables like English language ability.
We have examined a number of potential sources of bias in the baseline results and found
that they are robust to adding additional controls for race, legal status, and education and also to
studying a sample which exclude Mexican immigrants and a sample made up solely of Hispanic
immigrants and natives. If anything, adding controls for race, legal status, and education widens
the gap in the predicted financial market participation of immigrants and natives. The estimates
of the gap in financial market behavior derived from the sample which excludes Mexicans and
from the sample of all Hispanics are similar in magnitude and substance to the baseline results.
Unobserved heterogeneity along the dimensions discussed above does not seem to account for
the gap in immigrant-native financial market participation.
2.

Unobserved Heterogeneity – Entry into and Exit out of Account Ownership

In Tables 7A and 7B, we estimate transitions into and out of account ownership. These
estimates are of interest for at least two reasons. First, they offer additional insights into why
immigrant financial behavior differs from that of natives. If differences in behavior are driven
by differences in the propensity to enter into account ownership, then one reason for immigrantnative differences may lie in differential access to information about financial services and
products that impacts the decision to open an account. If the gap is driven by differences in the
likelihood of closing an account, then lower financial market participation among immigrants
may be driven by increased vulnerability to economic shocks and the presence of informal
financial services. A second motivation for examining transitions into and out of account
ownership is that these estimates provide another means for controlling for unobserved
heterogeneity. Since the dependent variable in these estimates reflects changes in financial
market decisions, the impact of time-invariant individual characteristics (tastes and preferences,
in particular, risk aversion, unobserved ability, home country experiences, English language
proficiency, propensity for return migration or for private transfers to relatives living outside the
U.S., for example) have been implicitly differenced out.

19

For estimates of entry, the dependent variable is equal to one if the individual reports
owning an account at time t and not owning an account at time t – 1.17 The dependent variable is
equal to zero if the individual reports no account ownership at time t and at time t – 1. All of the
transition estimates include the explanatory variables described in the discussion of the baseline
results in Table 3. Our estimates are based on logit maximum likelihood specification and
standard errors are adjusted to allow for correlation across observations at the individual level.
Estimates of transitions into account ownership are found in panels A and B of Table 7
for savings and checking accounts, respectively. There are two estimates of entry into account
ownership. The estimates presented in column (1) of Table 7A include a control for being an
immigrant. In column (2) an additional control for being a recent immigrant is added. The
estimates also include all of the control variables described above in the discussion of Table 3.
From column (1) we see that immigrants are less likely to enter into savings and checking
account ownership, although the effect is not statistically significant. The likelihood of opening
a checking account is predicted to be lower for immigrants, but only significant at the 11% level
of significance. The estimates presented in column (2) suggest that the differences in the
likelihood of opening savings and checking accounts for immigrants and natives is driven by
recent immigrants, who are 1 percentage point less likely to open a savings account and 0.7
percentage points less likely to open a checking account.
In panels C and D of Table 7 we present estimates of the likelihood of transitions out of
account ownership (exits) for savings accounts (panel C) and checking accounts (panel D). The
dependent variables in panels C and D are equal to one if an individual, who had an account at
time t – 1, reports not having an account at time t. The dependent variable (exit) is equal to zero
if the individual reports having an account at both t – 1 and t. Individuals with no account at
both t-1 and t are excluded from the analysis.
From column (1) we see that immigrants are 1.4 percentage points more likely to exit
from both savings and checking account ownership.

This corresponds to a 27% higher

likelihood of closing a savings account or a checking account for immigrants compared to
natives, relative to the observed frequency of savings account closures of 5.2% and the observed
17

We should note that the entry estimates are restricted to those who report no ownership at time t – 1 and that the
exit estimates are restricted to those who do own an account at time t – 1.

20

frequency of checking account closures of 5.3%. In contrast to the estimates for opening
accounts, the difference in account closures for immigrants and natives is not driven by recent
immigrants. The estimates in column (2) show that being a recent immigrant has no significant
additional impact on the likelihood of closing an account compared. The coefficient on being an
immigrant remains basically unchanged in size and significance when the recent immigrant
control is included.
Concerns about bias due to unobserved heterogeneity in the baseline estimates are
mitigated by the fact that we see a similar effect of being an immigrant on estimates of
transitions into and out of account ownership. In addition, the transition estimates suggest that
the underlying causes of differences in financial market participation among immigrants and
natives are likely to differ for recent and established immigrants. Recently arrived immigrants
are less likely to open accounts than both natives and more established immigrants, which is
consistent with barriers to information and the likelihood of return migration limiting entry into
mainstream financial markets. Information barriers and the potential for return migration do not
seem to limit the entry of more established immigrants, however. In contrast, all immigrants,
regardless of how recently they arrived in the U.S., are more likely to close savings and checking
accounts compared to the native-born. It seems highly unlikely that this effect is driven by
information, since this group had enough information about U.S. financial services to open an
account in the first place. The fact that immigrants are more likely to close accounts suggests
that part of the explanation for differences in the financial behavior of immigrants and natives
may lie in their relative vulnerability to economic shocks. One possibility here is that adverse
economic circumstances force immigrants to liquidate accounts more frequently than they do
natives. This could be due to the fact that immigrants are over-represented in sectors of the
economy – agriculture and services, for example – that are particularly cyclical.

Another

potential explanation could be that immigrants rely on informal financial markets or are more
likely to be subject to adverse shocks compared to the native born because they provide
economic support to more people, including family members who live in their country of origin.
In contrast, the family members of the native-born are more likely to be covered by U.S. social
safety net programs.
D.

Location and Financial Market Participation

21

A final issue in our analysis is how location contributes to immigrant-native differences
in financial market behavior. There are several reasons for focusing on location (MSA of
residence) in seeking to better understand why immigrants make different financial decisions
compared to otherwise similar native-born individuals.

First, the decomposition exercise

presented in Table 5 found that if immigrants lived in the same metropolitan areas (MSAs) as the
native-born, the explained difference in immigrant-native financial market participation would
fall by about 17%. Second, many other researchers have found important effects of residential
settlement on immigrant behavior. For example, Borjas (1998, 2000) finds that immigrants who
live in ethnic enclaves have lower wage growth and greater income uncertainty. The geographic
clustering of immigrants has also been shown to lower educational attainment and language
proficiency (see Gang and Zimmerman, 2000; Chiswick and Miller, 2002). 18
We examine the possibility that an immigrant who lives in an urban community where
there is a high concentration of people who have emigrated from the same country may differ in
financial market behavior from an immigrant from the same country who lives in a community
with a lower concentration of people who emigrated from the same country. Focusing on a
measure of the network that immigrants are likely to interact with seems very reasonable given
our interest financial behavior as a number of recent studies have shown that social interactions
have important effects on financial decisions. 19 One testable hypothesis here is that low financial
market participation of immigrants may be reinforced when immigrants have a large network of
individuals to interact with who came from the same country of origin. Related to this, ethnic
networks may also provide information about and be sources of informal alternatives for formal
financial services.
To measure ethnic concentration, we supplement the SIPP data with information from the
1990 Integrated Public Use Microdata Sample as (IPUMS) 1% sample of the U.S. Census to
construct the fraction of a given MSA population that was born in a specific country. Summary
information about the Ethnic Concentration variable is found in Appendix II. For an immigrant
from a given country, k, (for example, Mexico), ethnic concentration is measured as the share of
18

Munshi (2003) shows that ethnic networks affect employment opportunities for Mexican immigrants even after
instruments for network size are used.
19
For example, Hong, Stein, and Kubik (2004) show that social interactions have important effects on stock market
participation. Similarly, Madrian and Shea (2000) and Duflo and Saez (2003) show that decisions to participate in
employer-sponsored retirement plans are influenced by the choices of co-workers.

22

immigrants from that country (for example, Mexico) that reside within a given MSA, j. More
formally, we define ethnic concentration for country k and MSA j as follows:
Ethnic Concentrationkj =

# of individuals born in country k residing in destination community j
Total # of individuals (including natives) residing in destination community j

Estimates which include the ethnic concentration variable are found in Table 8. Column
(1) adds this variable to the baseline estimates for savings (in panel A) and checking (in panel B).
In column (2) we also add a control for immigrants who have arrived in the U.S. since 1990. In
column (3), the interaction of the arrival variable and the ethnic concentration is also included in
the estimation. As always, the estimates in columns (1) – (3) also include MSA fixed-effects.
The results in Table 8 provide evidence that patterns of residential settlement may play an
important role in understanding immigrant participation in U.S. financial markets. We find that
the size of the ethnic network has a significant negative impact on financial market participation.
Immigrants who live in MSAs with higher ethnic concentrations are less likely to use
mainstream financial services. In order to quantify these effects, we consider the case of
Mexican immigrants living in the Chicago and Milwaukee MSAs. The Milwaukee MSA is
located only 90 miles north of Chicago. However, Mexican immigrants account for 4.22% of the
population in the Chicago MSA (which is the highest representation among immigrants in
Chicago) while Mexican immigrants have the second highest representation among immigrants
in Milwaukee and account for 0.51% of the population. Across all of the MSAs in the sample,
the average concentration of Mexicans is 2.61%, and this ranges from a low of 0.01% to a high
of 33.04%.
Looking at column (1) of Table 8, we see that immigrants in general are 6 percentage
points less likely than the native-born to have a savings account. For Mexican immigrants living
in Milwaukee, the community characteristics do not change this figure too much: they are an
additional 0.31 percentage points less likely to have savings account.

However, Mexican

immigrants living in Chicago are an additional 2.5 percentage points less likely to have a savings
account.

Holding other variables fixed, if a Mexican immigrant moved from Chicago to

Milwaukee, the likelihood that they would have a savings account would go up by 2.2
percentage points.20

20

For checking account ownership, the overall effect of being an immigrant is somewhat smaller. Immigrants
overall are predicted to be 3 percentage points less likely to have a checking account. However, the magnitude of

23

In column (2) of Table 8, we add an additional control variable for being a recent
immigrant. As we have seen before, recent immigrants are much less likely than similar natives
to own a savings account or a checking account. In these estimates, recent immigrants are
predicted to be 16 percentage points less likely to have a savings account and 10 percentage
points less likely to have a checking account. Adding the recent immigrant control variable does
not appreciably change the size or the significance of the ethnic concentration variable, however.
In column (3) of Table 8, we consider the possibility that the impact of living among a
substantial population of immigrants from the same origin country may differ for recent and
more established immigrants. We find evidence that this is in fact the case. The size of the
potential immigrant network appears to have an important effect on the financial market
participation of immigrants, particularly for recent immigrants who may be especially reliant on
other immigrants who share the same country of origin for information about U.S. financial
markets. This is consistent with the finding in Table 7 that recent immigrants are less likely to
open savings and checking accounts compared to the native-born, but that more established
immigrants behave similarly to natives when it comes to opening accounts.21
Compared to our baseline findings, the effect of being an immigrant is lower in the
estimates that include the ethnic concentration variable. For savings account ownership, the
effect of being an immigrant is 56 – 81 percent lower according to the estimates that include the
ethnic concentration variable compared to the analogous estimates which do not control for
ethnic concentration. For checking account ownership, the impact of being an immigrant is
estimated to be 39 – 49 percent lower when the ethnic concentration variable is included.
According to these estimates, somewhere between 20 and 60 percent of the effect of being an
immigrant may operate through residential settlement.
By including MSA fixed-effects in the estimates, we address the concern that residents of
a given community share a common economic environment, or have similar preferences. For
example, there may be a lower supply of financial services or limited employment prospects in
ethnic concentration variable is larger. Mexican immigrants living in Milwaukee are an additional 5.2 percentage
points more likely to have a checking account compared to Mexican immigrants living in Chicago.
21
Estimates of financial market transitions that include ethnic concentration (available from the authors) reinforce
the message that the entry behavior of recent immigrants, but not more established immigrants, is influenced by
patterns of residential settlement. Exit from savings account ownership is higher for immigrants but does not vary
with ethnic concentration. In contrast, exit rates from checking account ownership are higher for immigrants and go
up with ethnic concentration. We note that these estimates come closest to dealing with unobserved heterogeneity,
including decisions about where to live and country of origin effects, because they implicitly difference out fixed
characteristics.

24

one MSA compared to another. MSA fixed effects do not, however, capture all forms of
unobserved heterogeneity such as variation in the supply of financial services or employment
prospects by country of origin within a MSA.22 Moreover, estimates of the impact of residential
settlement on financial market participation may be biased because the decision about where to
live is unlikely to be random.23 For example, it is quite possible that immigrants who choose to
live in Milwaukee differ in some unmeasured way from those who choose to live in Chicago and
that the characteristics that impact the choice about where to live also impact financial market
behavior.
These estimates do not tell us the exact mechanism through which ethnic concentration
impacts financial market participation. It is certainly possible that there is a direct effect of
ethnic concentration on financial market participation. Specifically, immigrants residing in
ethnically concentrated locations are more likely to interact with and get information about
financial products and services from other immigrants from the country of origin and this
reinforces already low levels of formal financial market participation among this group.

It is

also possible that there is an indirect effect of ethnic concentration on financial market
participation.

For example, as noted above, other researchers have found that ethnic

concentration reduces immigrant language acquisition, raises income uncertainty, lowers wage
growth, and reduces human capital accumulation. While we are able to hold education and
income constant in our estimates, we do not have data on language proficiency or income
uncertainty, so the coefficient on the ethnic concentration variable will capture both direct and
indirect effects. If there is an indirect effect it would mainly operate through language or income
uncertainty, since the estimates control for education, income, and employment status.

22

If country of origin characteristics also influence the choice of destination community (as in Bauer et al, 2005)
then the unobserved determinants of immigrant location choice are likely to vary by country of origin within a given
MSA. However, we find that effect of ethnic concentration on financial market behavior is robust when we interact
country characteristics (such as the level of financial development in the country of origin) with ethnic concentration
in a given MSA. This specification allows us to include both MSA and country fixed effects.
23
Bauer, Epstein, and Gang (2005) find that as immigrants gain English language proficiency they choose
communities with smaller ethnic networks, and Bartel (1989) finds that skilled immigrants are less geographically
concentrated than their unskilled counterparts.

25

V.

Conclusions

This paper seeks to add to our existing knowledge on the prospects for immigrant wealth
assimilation, and immigrant assimilation more generally, by studying the financial market
behavior of U.S. immigrants and comparing it to the native-born. Compared to similar natives,
immigrants are less likely to participate in financial markets. We show that lower rates of
financial market participation tend to persist even for immigrants who have lived in the U.S. for
several years, compared to the native-born. In addition, immigrant status has a significant
impact on transitions into and out of account ownership. Specifically, immigrants are somewhat
less likely to open accounts and more likely to close accounts compared to similar native-born
individuals. Concerns that the results are driven by unobserved heterogeneity are reduced,
because the effect of being an immigrant is similar for financial market participation and for
changes in financial market participation.
Our results suggest that a large share of the immigrant-native gap in financial market
participation is driven by education, income, and geographic location.

We present some

evidence that the explanation for differential behavior of immigrants relative to natives has to do
with variations in patterns of residential settlement, specifically ethnic concentration within a
given MSA. Our results on entry into account ownership are consistent with social interaction
effects, in which immigrants, particularly recent arrivals, have fewer connections with
mainstream society and lack information about formal financial markets. However, the finding
that immigrants have higher exit rates from account ownership suggests that the informational
hypothesis cannot be the sole explanation for low rates of immigrant participation in mainstream
financial markets. Past research has shown that immigrant residing in ethnically concentrated
areas have low levels of English proficiency and higher income uncertainty. Thus an additional
channel through which ethnic concentration may affect financial participation is through greater
labor market insecurity and greater language barriers among immigrants residing in ethnic
enclaves. Finally, immigrants residing in ethnically concentrated areas may have access to
informal alternatives to formal financial services.
Our findings on ethnic concentration are intriguing in light of a growing number of
studies that have shown that social interactions play an important role in many economic
decisions, including financial market participation, welfare usage, and criminal behavior. An
important goal of future research in this area is to identify the precise mechanism through with

26

ethnic concentration affects immigrant behavior – controlling in particular for factors that may
influence financial market indirectly through location choice. Understanding the mechanism
through which ethnic concentration impacts immigrant behavior may have important policy
implications. For example, if ethnic concentration mainly affects financial market participation
through word-of-mouth learning about mainstream financial services, then financial literacy
programs may have large multiplier effects within immigrant populations. However, to the
extent that ethnic enclaves provide immigrants with informal alternatives to formal financial
markets, then additional research may be needed to understand the factors that increase the
attractiveness informal financial services for immigrants, compared to similar natives. Because
financial transactions rely on trust and confidence in institutions, the financial market behavior of
immigrants can provide key insights into the process of immigrant adaptation to U.S. social and
economic life.

27

REFERENCES
Altonji, Joseph A. and Ulrich Doraszelski. 2005. “The Role of Permanent Income and
Demographics in Black/White Differences in Wealth,” Journal of Human Resources 40(1):1-30
Amuedo-Dorantes, Catalina, Cynthia Bansak and Susan Pozo. 2005. “On the Remitting Patterns of
Immigrants: Evidence from Mexican Survey Data,” Economic Review, Federal Reserve bank of
Atlanta, 1st Quarter.
Amuedo-Dorantes, Catalina and Susan Pozo. 2002. “Precautionary Saving by Young Immigrants and
Young Natives,” Southern Economic Journal, 69(1):48–71.
Bartel, Ann P. 1989. “ Where Do the New Immigrants Live?”, Journal of Labor Economics, 7(4):371391.
Bauer, Thomas, Gil S. Epstein, and Ira N. Gang, 2005. “Enclaves, Language and the Location Choice of
Migrants,” Journal of Population Economics, 18(4): 649-662
Bender, Keith A., 2004. “The Well-Being of Retirees: Evidence Using Subjective Data
Boston College Center for Retirement Research Working Paper #2004-24
Blau, Francine D. and John Graham, 1990. “Black-White Differences in Wealth and Asset Composition,”
Quarterly Journal of Economics, 105(2):321-39.
Blinder, Alan S. 1973. “Wage Discrimination: Reduced Form and Structural Estimates,” Journal of
Human Resources, 8(4):436-455.
Borjas, George 2002. “Homeownership in the Immigrant Population,” Journal of Urban Economics
52(3): 448-476
Borjas, George J. 2000. “Ethnic Enclaves and Assimilation”, Swedish Economic Policy Review: 89-122
Borjas, George J., 1998. “To Ghetto or Not to Ghetto? Ethnicity and Residential Segregation,” Journal of
Urban Economics 44: 228-253.
Borjas, George J. 1995 “Ethnicity, Neighborhoods, and Human-Capital Externalities”, American
Economic Review, 85(3): 365-90.
Borjas, George J. 1987. “Self-Selection and the Earnings of Immigrants,” American Economic Review,
77(4):531-53.
Borjas, George J. 1985. “Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants,”
Journal of Labor Economics, 3(4): 463-489.
Carroll, Christopher D., Rhee, Byung-Kun and Changyong Rhee, 1999. “Does Cultural Origin Affect
Saving Behavior? Evidence from Immigrants”, Economic Development and Cultural Change,
48(1): 33-50.
Chiswick, Barry R. and Paul W. Miller 2002 “Immigrant Earnings: Language Skills, Linguistic
Concentrations and the Business Cycle”, Journal of Population Economics, 15(1): 31-57

28

Chiswick, Barry R and Paul W. Miller. 1996 “Ethnic Networks and Language Proficiency among
Immigrants in the United States,” Journal of Population Economics, 9(1): 19-35
Chiswick Barry R. 1978 “The Effect of Americanization on the Earnings of Foreign-born Men” Journal
of Political Economy, 86(5): 897-921.
Chiteji, Ngina S. and Frank. P. Stafford. 1999. “Portfolio Choices of Parents and Their Children as Young
Adults: Asset Accumulation by African-American Families,” American Economic Review, 89(2):
377-80
Cobb-Clark, Deborah and Vincent Hildenbrand, 2006a. “The Wealth and Asset Holdings of U.S. and
Foreign Born Households: Evidence from SIPP data,” Review of Income and Wealth 52(1)17-42
Cobb-Clark, Deborah and Vincent Hildenbrand, 2006b. “The Wealth of Mexican Americans,” Journal of
Human Resources forthcoming
Cobb-Clark, Deborah and Vincent Hildenbrand, 2006c. “Social Science Quarterly,” forthcoming
Cobb-Clark, Deborah and Sherrie Kossoudji 2002. “Coming Out of the Shadows: Learning about Legal
Status and Wages from the Legalized Population." Journal of Labor Economics, 20(3):598-628
Duflo, Esther and Emmanuel Saez 2003 “The Role of Information and Social Interactions in Retirement
Plan Decisions: Evidence from a Randomized Experiment”, Quarterly Journal of Economics,
118(3): 815-42
Duncan, Greg J. and Jeanne Brooks-Gunn, 1997 (eds). Consequences of Growing Up Poor, New York:
Russell Sage.
Dustmann, Christian, 1997. “Return Migration, Savings, and Uncertainty,” Journal of Development
Economics, 52: 295-316
Fairlie, Robert W. 2003. “An Extension of the Blinder-Oaxacca Decomposition Technique to Logit and
Probit Models,” Economic Growth Center Yale University, 17(1), Center Discussion Paper No. 873.
Fairlie, Robert W. 1999. “The Absence of the African-American Owned Business: An Analysis of the
Dynamics of Self-Employment,” Journal of Labor Economics, 17(1), 80-108.
Gang, Ira N. and Zimmermann, Klaus F. 2000. “Is Child like Parent? Educational Attainment and Ethnic
Origin”, Journal of Human Resources, 35(3): 550-69
Galor, Oded and Stark, Oded 1990. " Migrants' Savings, the Probability of Return Migration and
Migrants' Performance" International Economic Review, 31(2): 463-67.
Guiso, Luigi, Sapienza, Paola, and Zingales, Luigi. 2004. “The Role of Social Capital in Financial
Development,”. American Economic Review, 94 (3):526-556.
Hao, Lingxin 2004 “Wealth of Immigrant and Native-Born Americans.” International Migration Review
38(2): 518-546
Hao, Lingxin 2001 “Immigrants and Wealth Stratification in the U.S.” Joint Center for Poverty Research
Working Paper #216

29

Haurin, Donald R., Patric Hendershott, and Susan Wachter, 1997. "Borrowing Constraints and the.
Tenure Choice of Young Households" Journal of Housing Research 9(1):137-154.
Holtz-Eakin David, Joulfaian, David and Harvey S. Rosen, 1994. “Sticking it Out: Entrepreneurial
Survival and Liquidity Constraints,” The Journal of Political Economy, 102(1): 53-75
Hong, Harrison, Stein, Jeremy, and Jeffrey Kubik 2004. “Social Interaction and Stock-Market
Participation,” The Journal of Finance 59(1):137-163
Krivo, Laurie and Robert L. Kauffman, 2004. “Housing and Wealth Inequality: Racial-ethnic Differences
in Home Equity in the United States,” Demography 41(3):585-605.
Light, Ivan 1972. Ethnic Enterprise in America Berkeley: University of California Press.
Madrian, Brigitte and D. Shea, 2000. “Peer Effects and Savings Behavior in Employer-Sponsored
Savings Plans,” University of Chicago Working Paper
Mayer, Susan E. 1997. What Money Can't Buy: Family Income and Children's Life Chances Cambridge,
MA: Harvard University Press.
Modigliani, Franco and Richard Brumberg 1954. “Utility Analysis and the Consumption Function: An
Interpretation of Cross-Section Data,” In: edited by Kurihara, K.K: Post-Keynesian Economics.
New Brunswick, NJ: Rutgers University Press.
Munshi, Kaivan 2003. “Networks in the Modern Economy: Mexican Migrants in the U. S. Labor
Market”, Quarterly Journal of Economics, 118(2):549-99
Oaxaca, Ronald. 1973. "Male-Female Differentials in Urban Labor Markets," International Economic
Review, 14: 693-709.
Portes,

Alejandro 1987. “The Social Origins
of Miami,” Sociological Perspectives 30:340-372

of

the

Cuban

Enclave

Economy

Robert, Stephanie and James S. House, 1996. “SES Differentials in Health by Age and Alternative
Indicators of SES,” Journal of Aging and Health, 8(3): 359-388.
Sanders, Jimy M. and Victor Nee, 1996. “Immigrant Self-Employment: The Family as Social Capital and
the Value of Human Capital,” American Sociological Review, 61(2): 231-249.
Scanlon, Edward, and Deborah Page-Adams. 2001. “Effects of Asset Holding on Neighborhoods,
Families, and Children: A Review of Research.” Pp. 25 49 in Building Assets: A Report on the
Asset-Development and IDA Field, edited by Ray Boshara. Washington, D.C.: Corporation for
Enterprise Development.
Smith, James P. 1995. “Racial and Ethnic Differences in Wealth,” Journal of Human Resources, 30
(Supplement):S158-S183
U.S. Board of Governors of the Federal Reserve Board, 2001. Survey of Consumer Finances
U.S. Census Bureau, 2000. Foreign-Born Population in the United States: Population Characteristics

30

Table 1: Financial Market Participation and Transitions, 1996 – 2000 SIPP Panel
All Native-born Immigrants

Immigrant/Native

A: Savings Account Ownership
Own %

52.66%

54.72%

39.72%

0.73

Never Owned %
Ever Owned %
Entry %
Exit %
Always Owned %

31.91%
31.10%
5.82%
5.15%
36.99%

30.18%
30.77%
5.92%
4.86%
39.06%

42.80%
33.24%
5.32%
7.66%
23.96%

1.42
1.08
0.90
1.58
0.61

Observations

356,769

307,894

48,875

B: Checking Account Ownership
Own %

34.11%

36.08%

21.74%

0.60

Never Owned %
Ever Owned %
Entry %
Exit %
Always Owned %

53.28%
23.60%
3.05%
5.25%
23.11%

51.33%
23.93%
3.15%
4.98%
24.75%

65.60%
21.58%
2.53%
8.07%
12.82%

1.28
0.90
0.80
1.62
0.52

Observations
C: Stock Ownership
Own %

356,769

307,894

48,875

18.42%

20.00%

8.50%

0.42

Never Owned %
Ever Owned %
Entry %
Exit %
Always Owned %

73.08%
15.37%
1.73%
7.50%
11.55%

71.17%
16.20%
1.84%
7.30%
12.63%

85.12%
10.13%
1.10%
10.61%
4.75%

1.20
0.63
0.60
1.45
0.38

Observations
356,769
307,894
48,875
Note: The sample consists of all MSA residents greater than or equal to the age of 18.
"Own" means that the respondent had a saving account or checking account (interest bearing) during the
interview period. "Never Owned" means that the respondent had no saving account or checking account
(interest bearing) in all the interview periods."Ever Owned" means that the respondent had a saving account
or checking account (interest bearing) in some of the interview periods, but not all. "Always Owned"
means that the respondent had a saving account or checking account (interest bearing) in all the
interview periods.
The sum of the percentage of Never Owned, Ever Owned and Always Owned is equal to 1.
Entry is defined as the individual switches from non-ownership to ownership.
Exit is defined as the individual switches from ownership to non-ownership.

Table 2: Characteristics of the Native-born and Immigrants in the MSA Sample, 1996 – 2000 SIPP Panel
All
Natives
Immigrants
Age
45.98
46.18
44.70
(17.34)
(17.47)
(16.41)
Number of Children < 18
0.78
0.72
1.13
(1.14)
(1.09)
(1.36)
Monthly Per Capita Household Income
2116.31
2195.18
1619.47
(2764.29)
(2810.94)
(2391.05)
% Male
45.81%
45.70%
46.46%
% Married
58.45%
57.31%
65.65%
% unemployed or out of the labor force
33.95%
33.48%
36.94%
Race (%)
White
70.08%
77.15%
25.53%
Black
13.06%
14.20%
5.83%
Hispanic
11.98%
6.97%
43.52%
Asian
4.42%
1.16%
24.93%
Other
0.47%
0.51%
0.20%
Education (%)
Less than High School
17.86%
15.03%
35.73%
High School Graduate
29.67%
30.48%
24.59%
Some College
29.12%
30.58%
19.95%
College Graduate
15.40%
15.87%
12.42%
Advanced Degree
7.94%
8.04%
7.31%

Immigrant Characteristics
Years In U.S. (%)
Less Than 10 Years
10 < Duration < 14
15 < Duration < 30
More Than 30 Years
Immigrant Region of Origin (%)
Central America
Asia
European
Caribbean
South America
North America
Middle East
Other
Number of Observations
356,769
307,894
Note: The sample consists of all MSA residents greater than or equal to the age of 18.
Standard deviations are shown in parentheses ONLY for continuous variables.

37.74%
17.28%
16.96%
15.20%
32.51%
20.63%
15.11%
7.73%
4.53%
1.62%
1.14%
16.73%
48,875

Table 3: Logit Estimates of Financial Market Participation
Savings Account
(1)
Coef.
M.E.
Immigrant
-0.296 *** -0.074
(0.031)
Age
-0.004
-0.001
(0.003)
Age Squared
0.014 *** 0.004
(x100)
(0.003)
Unemployed/Out
-0.293 *** -0.073
of Labor Force
(0.025)
Per Capita HH
0.021 *** 0.005
Income (x100)
(0.001)
Per Capita HH
-0.006 ** -0.002
Income Squared (x106)
Married

Checking Account
(2)
Coef.
M.E.
-0.303 ***
-0.061
(0.037)
0.015 ***
0.003
(0.004)
0.009 **
0.002
(0.004)
0.080 ***
0.017
(0.028)
0.021 ***
0.004
(0.001)
-0.005 ***
-0.001

(0.0004)
(0.0003)
0.873 *** 0.215
0.837 ***
0.170
(0.022)
(0.025)
Male
-0.299 *** -0.074
-0.268 ***
-0.056
(0.021)
(0.023)
Non-White
-0.432 *** -0.108
-0.629 ***
-0.121
(0.026)
(0.032)
No of children < 18
-0.082 *** -0.020
-0.080 ***
-0.017
(0.009)
(0.011)
High School
0.543 *** 0.133
0.776 ***
0.172
(0.030)
(0.039)
Some College
0.861 *** 0.208
1.177 ***
0.264
(0.031)
(0.040)
College
1.037 *** 0.241
1.489 ***
0.348
(0.038)
(0.045)
Advanced Degree
0.940 *** 0.217
1.581 ***
0.373
(0.048)
(0.053)
No of Obs
356,769
356,769
Log-likelihood
-215936.94
-193291
Pseudo R-squared
0.125
0.156
Note: The sample consists of all MSA residents greater than or equal to the age of 18.
The dependent variable is equal to one if the respondent had a saving account or checking account (interest
bearing) during the interview period in question and is zero otherwise.
Logit model with fixed effects at MSAs level is used and standard errors are corrected for
clustering at the individual level.
All regressions include a constant term, age as linear and square terms, per capita income as linear and square
terms, marital status, male, non-white, labor force status, number of kids, schooling dummies and wave dummies.
The omitted education category is less than a high school education.
*** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.

Table 4: Logit Estimates of Financial Market Participation (with duration controls)
Savings Account
(1)
(2)
Coef.
M.E.
Coef.
M.E.
Immigrant
-0.212 *** -0.053
-0.186 ***
-0.047
(0.034)
(0.059)
Recent (after 1990)
-0.501 *** -0.124
(0.068)
1990-1996
1985-1989
1980-1984
1975-1979
1970-1974
1964-1969
1960-1964

-0.531 ***
(0.085)
-0.172 *
(0.090)
-0.062
(0.089)
0.192 **
(0.098)
-0.048
(0.110)
-0.008
(0.122)
-0.036
(0.148)

-0.131
-0.043
-0.016
0.048
-0.012
-0.002
-0.009

Checking Account
(3)
(4)
Coef.
M.E.
Coef.
M.E.
-0.250 *** -0.051
-0.217 *** -0.044 ***
(0.040)
(0.067)
-0.354 *** -0.069
(0.089)
-0.383 *** -0.074
(0.106)
-0.186 *
-0.038
(0.111)
-0.142
-0.029
(0.110)
0.188
0.041
(0.119)
0.089
0.019
(0.126)
-0.042
-0.009
(0.139)
-0.186
-0.038
(0.166)

(Omitted Category: Before 1960)
No of Obs
356,769
356,769
356,769
356,769
Log-likelihood
-215760.57
-215718
-193235.03
-193228.2
0.156
0.126
0.156
0.156
Pseudo R-squared
Note: The sample consists of all MSA residents greater than or equal to the age of 18.
The dependent variable is equal to one if the respondent had a saving account or checking account (interest bearing) during the
interview period in question and is zero otherwise.
Logit model with fixed effects at MSAs level is used and standard errors are corrected for clustering at the individual level.
All regressions include a constant term, age as linear and square terms, per capita income as linear and square terms, marital status,
male, non-white, labor force status, number of kids, schooling dummies and wave dummies. The omitted education category is less
than a high school education.
*** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.

Table 5: Decomposition of Immigrant-Native Differences in Financial Market Participation
Participation
Saving Acct
Checking Acct

F
U
L
L

S
A
M
P
L
E

Mean:
Gap:

Overall Difference:

N

I

From (X -X )
N

I

From (β -β )

Immigrant
0.397

Native
0.547
0.150

Immigrant
0.217

Native
0.361
0.143

(1)

(2)

(3)

(4)

0.076
50.70%

0.084
55.92%

0.083
57.76%

0.103
71.61%

0.074
49.30%

0.066
44.08%

0.061
42.24%

0.041
28.39%

Contribution to the gap from the
following variables:
0.003
0.004
0.005
0.008
1.69%
2.63%
3.74%
5.92%
Per Capita HH Income
0.021
0.019
0.021
0.019
14.32%
12.66%
14.96%
13.08%
Education
0.028
0.036
0.024
0.032
18.63%
23.81%
16.88%
22.41%
Male
0.0007
0.0008
-0.0003
0.0002
R S
0.46%
0.51%
-0.23%
0.11%
-0.009
-0.017
-0.001
-0.008
A A
Marital status
-6.23%
-11.26%
-0.68%
-5.55%
N M
Non-white
-0.003
0.022
0.007
0.037
D P
O L
-2.14%
15.00%
4.62%
25.72%
M E
No of children < 18
0.008
0.006
0.004
0.004
5.24%
4.12%
2.87%
2.64%
Unemploy
0.002
0.002
-0.0002
0.000
1.47%
1.52%
-0.16%
-0.33%
MSA Effects
0.026
0.010
0.023
0.011
17.26%
6.92%
15.76%
7.62%
All variables
0.076
0.084
0.083
0.103
50.70%
55.92%
57.76%
71.61%
Note: The full sample consists of All MSA residents greater than or equal to the age of 18. To keep the native
and immigrant samples comparable, some of the MSAs are dropped where MSA fixed effects
cannot be estimated separately for the immigrant sample due to a lack of observations.
The random sample includes 10,000 native and 10, 000 immigrants randomly drawn from the full sample with
replacement.
Column (1) and (3) use the coefficients from the immigrant sample, and Column (2) and (4) use the coefficients
from the native sample. See Appendix III for the detailed coefficients.
Logit models with the fixed effects at MSAs level are used and the standard errors are corrected for
clustering at the individual level.
The dependent variable is equal to one if the respondent had a saving account or checking account (interest
bearing) during the interview period in question, and is zero otherwise.
All regressions include a constant term, age as linear and square terms, per capita income as linear and square
terms, marital status, male, non-white, labor force status, number of kids, and schooling dummies.
The omitted education category is less than high school.
Age and Age Square

Table 6: Immigrant Heterogeneity and Financial Market Participation
(Marginal Effects Only)
Ownership
(2) Race

(3) Legal Status

(4) Greater
than high
school

-0.09 ***

-0.10 ***

(1)Baseline

(5) Exclude
Mexican
Immigrants

(6) Hispanics only

A: Savings Acct
Immigrant
-0.07 ***
Immi*Non-white
Non-white
Immi*Permanent Resident

-0.13 ***
0.16 ***
-0.14 ***

Greater Than High School
Immi * Greater Than High School

-0.06 ***

0.02 *

Greater Than High School
Immi * Greater Than High School
Number of obs
Log-likelihood
Pseudo R-squared
B: Checking Acct (Interest Bearing)
Immigrant
-0.06 ***
Immi*Non-white
Non-white
Immi*Permanent Resident

-0.05 ***

0.13 ***
0.03 **
356,769
-215531
0.13
-0.10 ***
0.17 ***
-0.15 ***

356,769
-215926
0.13
-0.08 ***

356,769
-217133
0.12
-0.11 ***

343,464
-208910
0.12
-0.05 ***

42,667
-22683
0.16
-0.06 ***

0.03 **
0.15 ***
0.10 ***

Number of obs
356,769
356,769
356,769
343,464
42,667
Log-likelihood
-192971
-193276
-195226
-208910
-22683
Pseudo R-squared
0.16
0.16
0.15
0.12
0.16
Note: The sample consists of all MSA residents greater than or equal to the age of 18. The young sample only
includes the MSA residents between the age of 18 and 25.
The dependent variable is equal to one if the respondent had a saving account or checking account (interest bearing)
during the interview period in question and is zero otherwise.
Logit model is used and standard errors are clustered at the individual level.
All regressions include a constant term, age as linear and square terms, per capita income as linear and square
terms, marital status, male, non-white, labor force status, number of kids, schooling dummies and wave dummies.
The omitted education category is less than high school graduate.
*** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.

Table 7: Logit Estimates of Financial Market Transitions
I.

ENTRY INTO ACCOUNT OWNERSHIP
(1)
Coef.
M.E.
A: Savings Acct
Immigrant
-0.06
-0.003
(0.04)
Recent (>1990)
No of Obs
Log-likelihood
Pseudo R-squared
B: Checking Acct
Immigrant

145,530
-30838.41
0.041
-0.10 **
(0.05)

-0.002

Recent (>1990)
No of Obs
Log-likelihood
Pseudo R-squared

204,202
-26430.93
0.051

EXITS OUT OF ACCOUNT OWNERSHIP
(1)
Coef.
M.E.
C: Savings Acct
Immigrant
0.281 *** 0.014
(0.041)
Recent (>1990)

(2)
Coef.
-0.01
(0.04)
-0.21 ***
(0.08)
145,530
-30833.90
0.036
-0.043
(0.049)
-0.309 ***
(0.108)
204,202
-26723.14
0.049

M.E.
-0.001
-0.009

-0.001
-0.007

II.

No of Obs
Log-likelihood
Pseudo R-squared
D: Checking Acct
Immigrant
Recent (>1990)

164,900
-32261.92
0.036
0.273 ***
(0.052)

0.013

(2)
Coef.
0.278 ***
(0.040)
0.019
(0.098)
164,900
-32261.89
0.032
0.275 ***
(0.055)
-0.016
(0.129)
107299
-21375.79
0.035

M.E.
0.013
0.001

0.014
-0.001

No of Obs
107299
Log-likelihood
-21375.79
Pseudo R-squared
0.035
Note: The sample is restricted to individuals over 18 living in MSAs
The dependent variable is equal to one if the individual switches from non-ownership to ownership (Entry) or from
ownership to non-ownership (Exit) for savings account or checking account (interest bearing), respectively.
A logit model is used and standard errors are corrected for clustering at the individual level.
All regressions include a constant term, age as linear and square terms, per capita income as linear and square
terms, marital status, male, non-white, labor force status, number of kids, schooling dummies and wave dummies.
The omitted education category is less than high school.
*** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.

Table 8: The Impact of Location on Financial Market Participation
(Marginal Effects Only)
Ownership
(1)
A: Savings Acct
Immigrant
Immi * Ethnic Concentration in MSA

-0.06 ***
-0.66 ***

Recent (after 1990)
Recent * Ethnic Concentration in MSA

Number of obs
Log-likelihood
Pseudo R-squared
B: Checking Acct (Interest Bearing)
Immigrant
Immi * Ethnic Concentration in MSA
Recent (after 1990)
Recent * Ethnic Concentration in MSA

353083
-213488
0.125
-0.04 ***
-1.38 ***

(2)

(3)

-0.03 ***
-0.72 ***

-0.04 ***
-0.67 ***

-0.13 ***

-0.12 ***
-0.53

353083
-213321
0.126

353083
-213317
0.126

-0.02 **
-1.42 ***

-0.02 **
-1.37 ***

-0.08 ***

-0.07 ***
-0.74

Number of obs
353,083
353,083
353,083
Log-likelihood
-191138
-191073
-191069
Pseudo R-squared
0.157
0.157
0.157
Note: The sample consists of all MSA residents greater than or equal to the age of 18.
The dependent variable is equal to one if the respondent had a saving account or checking account (interest bearing) during the
interview period in question and is zero otherwise.
Logit model is used and standard errors are corrected for clustering at the individual level.
All regressions include a constant term, age as linear and square terms, per capita income as linear and square terms, marital status, male,
non-white, labor force status, number of kids, schooling dummies and wave dummies. The omitted education category is less than high
school graduate. *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.

Appendix I: Logit Estimates of Financial Market Participation, 1996 – 2000 SIPP Panel
Additional Indicators of Financial Asset Participation
Participation
(1)
(2)
(3)

Immigrant
Age
Age Squared
(x100)
Unemployed/Out
of Labor Force
Per Capita HH
Income (x100)
Per Capita HH
Income Squared (x106)
Married
Male
Non-White
# of children < 18
High School
Some College
College
Advanced Degree
MSA Fixed Effects
No of Obs
Pseudo R-squared
1

Stock
Coef.
-0.611
(0.052)
0.060
(0.005)
-0.036
(0.005)
0.241
(0.036)
0.024
(0.001)
-0.005
(0.000)
0.758
(0.032)
-0.224
(0.029)
-0.770
(0.044)
-0.068
(0.014)
1.185
(0.065)
1.650
(0.066)
2.155
(0.068)
2.142
(0.074)
YES
356,769
0.180

(4)

Combined Checking1
IRA or Keogh
Mutual Funds
M.E.
Coef.
M.E.
Coef.
M.E.
Coef. M.E.
-0.056
-0.436
-0.039
-0.418
-0.031
-0.342
-0.052
(0.055)
(0.058)
(0.033)
0.006
0.215
0.022
0.057
0.005
0.015
0.002
(0.006)
(0.006)
(0.004)
-0.004
-0.177
-0.018
-0.038
0.000
0.009
0.001
(0.006)
(0.006)
(0.004)
0.027
0.131
0.013
0.404
0.036
-0.201
-0.029
(0.039)
(0.041)
(0.027)
0.003
0.017
0.002
0.023
0.002
0.025
0.035
(0.001)
(0.001)
(0.001)
0.001
-0.004
0.000
-0.005
0.000
-0.006
-0.001
0.079
-0.024
-0.070
-0.007
0.155
0.233
0.375
0.398

(0.000)
0.621
(0.034)
-0.210
(0.031)
-1.075
(0.051)
-0.153
(0.016)
0.978
(0.066)
1.440
(0.066)
2.041
(0.069)
2.166
(0.075)
YES
330,042
0.217

0.061
-0.021
-0.085
-0.015
0.116
0.185
0.334
0.388

(0.000)
0.819
(0.036)
-0.336
(0.033)
-0.762
(0.052)
-0.044
(0.015)
1.242
(0.081)
1.737
(0.080)
2.463
(0.082)
2.670
(0.087)
YES
330,042
0.189

0.065
-0.028
-0.053
-0.004
0.130
0.200
0.382
0.465

(0.001)
0.980
(0.024)
-0.372
(0.023)
-0.805
(0.003)
-0.114
(0.010)
0.886
(0.032)
1.386
(0.034)
1.769
(0.041)
1.824
(0.054)
YES
196,787
0.207

0.151
-0.052
-0.134
-0.016
0.109
0.163
0.173
0.158

The combined checking account indicator measures whether an individual held a checking account (interest
bearing or non-interest bearing) during the interview period in question and is zero otherwise.
Because non-interest bearing checking is measured less frequently, we have fewer observations for this indicator.
Note: The full sample consists of All MSA residents greater than or equal to the age of 18. To keep the native
and immigrant samples comparable, some of the MSAs are dropped where MSA fixed effects
cannot be estimated separately for the immigrant sample due to a lack of observations.
Logit models with the fixed effects at MSAs level are used and the standard errors are corrected for
clustering at the individual level.
All regressions include a constant term, age as linear and square terms, per capita income as linear and square
terms, marital status, male, non-white, labor force status, number of kids, and schooling dummies.
The omitted education category is less than high school.

Appendix II: Top-25 MSAs (Based on Population)
Metropolitan Statistical Area
% Foreign Born Imm Pop 90 % Foreign Born
Largest Ethnic Concentration
(1990 U.S. census)
(SIPP 1996
Country 1 Ethnic Conc1 Country 2
Sample)
New York-Northern New Jersey
Dominican
22.65%
3260551
18.84%
Italy
1.62%
-Long Island, NY
Republic
Los Angeles-Riverside
32.98%
2905552
39.03% Mexico
13.46%
El Salvador
-Orange County, CA
Chicago-Gary-Kenosha,
15.01%
753332
15.48% Mexico
4.22%
Poland
IL-IN-WI
San Francisco-Oakland
23.62%
1164254
29.97% Mexico
4.37%
Philippines
-San Jose, CA
Washington-Baltimore,
10.95%
500004
13.00% El Salvador
0.82%
Korea
DC-MD-VA-WV
Philadelphia-Wilmington
-Atlantic City,
6.32%
271774
6.46%
Italy
0.57%
Germany
PA-NJ-DE-MD
Detroit-Ann Arbor-Flint, MI
6.66%
242155
7.09% Canada
1.14%
Italy
Boston-Worcester-Lawrence,
12.77%
435377
13.22% Canada
1.34%
Italy
MA-NH-ME-CT
Dallas-Fort Worth, TX
9.41%
265538
14.72% Mexico
4.28%
Vietnam
Houston-Galveston-Brazoria, TX
14.83%
389256
18.46% Mexico
6.40%
El Salvador
Miami-Fort Lauderdale, FL
39.06%
958188
41.98%
Cuba
17.32%
Colombia
Seattle-Tacoma-Bremerton, WA
9.31%
203895
14.29% Canada
1.16%
Philippines
Atlanta, GA
4.80%
100422
9.15%
Korea
0.41%
Germany
San Diego, CA
19.51%
367263
27.30% Mexico
8.03%
Philippines
Anaheim-Santa Ana
27.55%
502450
\
Mexico
11.38%
Vietnam
-Garden Grove, CA
Minneapolis-St. Paul, MN
4.37%
78899
6.53%
Laos
0.62%
Canada
St. Louis, MO-IL
2.56%
45894
2.92% Germany
0.31%
Italy
Cleveland-Akron, OH
5.94%
105152
3.97% Yugoslavia
0.78%
Italy
Tampa-St. Petersburg
8.37%
137736
8.00% Canada
1.06%
Cuba
-Clearwater, FL
Pittsburgh-Beaver Valley, PA
2.95%
47556
3.48%
Italy
0.47%
Germany
Phoenix, AZ
8.64%
134719
11.61% Mexico
3.59%
Canada
Denver-Boulder-Greeley, CO
6.31%
93315
8.97% Mexico
1.61%
Germany
Cincinnati-Hamilton, OH-KY-IN
2.34%
29902
1.67% Germany
0.44%
India
Milwaukee-Racine, WI
4.40%
51816
8.79% Germany
0.70%
Mexico
Sacramento-Yolo, CA
12.00%
131261
20.95% Mexico
2.74%
Philippines
Note: The Census sample consists of all MSA residents greater than or equal to the age of 18 in Census 1990 1% Sample.
The SIPP sample consists of all MSA residents greater than or equal to the age of 18 in the wave 2 of 1996-2000 SIPP Panel.

Ethnic Conc2
1.57%
2.00%
1.35%
3.32%
0.70%
0.51%
0.50%
1.02%
0.59%
1.09%
2.13%
1.02%
0.34%
2.75%
2.68%
0.31%
0.16%
0.46%
1.01%
0.39%
0.76%
0.57%
0.17%
0.51%
1.00%

Appendix III: Logit Estimates of Financial Market Participation, 1996 – 2000 SIPP Panel
Native and Immigrant Samples
Participation
Saving Acct
Checking Acct
Native
Immigrant
Native
Immigrant
(1)
(2)
(3)
(4)
Age
-0.007 *
0.016
0.014 ***
0.050 ***
(0.004)
(0.010)
(0.004)
(0.013)
Age Squared
0.017 ***
-0.006
0.010 **
-0.031 **
(x100)
(0.004)
(0.010)
(0.004)
(0.013)
Unemployed/Out
-0.287 ***
-0.319 ***
0.075 **
0.117
of Labor Force
(0.027)
(0.069)
(0.030)
(0.081)
Per Capita HH
0.020 ***
0.022 ***
0.019 ***
0.031 ***
Income (x100)
(0.001)
(0.003)
(0.001)
(0.002)
Per Capita HH
-0.006 ***
-0.006 ***
-0.004 ***
-0.008 ***
Income Squared (x106)
Married

(0.0004)
(0.0018)
(0.0003)
(0.0009)
0.879 ***
0.792 ***
0.852 ***
0.646 ***
(0.024)
(0.062)
(0.027)
(0.079)
Male
-0.298 ***
-0.301 ***
-0.253 ***
-0.409 ***
(0.023)
(0.057)
(0.025)
(0.069)
Non-White
-0.574 ***
0.069
-0.784 ***
-0.166 **
(0.030)
(0.059)
(0.037)
(0.072)
# of children < 18
-0.072 ***
-0.113 ***
-0.0666 ***
-0.1186 ***
(0.011)
(0.023)
(0.012)
(0.029)
High School
0.522 ***
0.464 ***
0.710 ***
0.847 ***
(0.034)
(0.071)
(0.044)
(0.101)
Some College
0.843 ***
0.763 ***
1.115 ***
1.266 ***
(0.036)
(0.078)
(0.044)
(0.104)
College
1.046 ***
0.688 ***
1.436 ***
1.397 ***
(0.042)
(0.093)
(0.050)
(0.115)
Advanced Degree
0.910 ***
0.848 ***
1.500 ***
1.706 ***
(0.054)
(0.122)
(0.059)
(0.136)
MSA Fixed Effects
YES
YES
YES
YES
No of Obs
302,247
48,822
298,532
48,256
Pseudo R-squared
0.121
0.131
0.148
0.182
Note: The full sample consists of All MSA residents greater than or equal to the age of 18. To keep the native
and immigrant samples comparable, MSAs are dropped where MSA fixed effects
cannot be estimated separately for the immigrant sample due to a lack of observations.
Logit models with the fixed effects at MSAs level are used and the standard errors are corrected for
clustering at the individual level.
The dependent variable is equal to one if the respondent had a saving account or checking account (interest
bearing) during the interview period in question and is zero otherwise.
All regressions include a constant term, age as linear and square terms, per capita income as linear and square
terms, marital status, male, non-white, labor force status, number of kids, and schooling dummies.
The omitted education category is less than a high school education.
*** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.

90%

Figure 1: Financial Asset Ownership of Native born and Foreign born Individuals
Source: Survey of Income and Program Participation (SIPP) 1996-2000

80%
70%

Percent

60%
50%
40%

Native born
30%

Foreign born

20%
10%
0%
Savings
Account

Interest-bearing Interest-bearing
Checking
or Non-interestAccount
bearing
Checking
Account

Stock

Asset

Individual
Retirement or
Keogh Account

Mutual Funds

Homeownership

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