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Federal Reserve Bank of Chicago
What Can We Learn about Financial
Access from U.S. Immigrants?
Una Okonkwo Osili and Anna Paulson
WP 2006-25
What Can We Learn about Financial Access from U.S. Immigrants?1
Una Okonkwo Osili
Indiana University Purdue University Indianapolis
Anna Paulson
Federal Reserve Bank of Chicago
November 28, 2006
Abstract
We find that wealthier and more educated immigrants are more likely to make use of
basic banking services and other formal financial services. Holding these (and other)
factors constant, we find immigrants from countries with more effective institutions are
more likely to have a relationship with a bank and use formal financial markets more
extensively. Institutional quality appears to be an important factor in both determining
both the breadth and the depth of financial access. It can explain approximately 17
percent of the country-of-origin-level variation in bank account usage among immigrants
in the U.S., after other characteristics, including wealth, education and income, are
controlled for. Institutional quality is even more important for explaining more extensive
participation in financial markets, accounting for 27 percent of the analogous variation.
We examine various measures of institutional effectiveness and are careful to control for
unobserved individual characteristics, including specifications with country fixed-effects.
1
We thank the Russell Sage Foundation for their support of this research and Shirley Chiu for excellent
research assistance. Please address all correspondence to Anna Paulson, Federal Reserve Bank of Chicago,
230 S. LaSalle Street, Chicago, IL 60604-1413; phone: (312) 322 2169, email: anna.paulson@chi.frb.org.
The views presented in this paper are those of the authors and do not necessarily reflect those of the Federal
Reserve Bank of Chicago.
1. Introduction
There is a growing interest in understanding what determines the availability and the
usage of financial services in both developing and developed countries. Even among
developed countries, there is significant variation in the fraction of individuals who use
financial services. In the U.S., a significant subset of the population makes little use of
even basic financial services. Between 10 to 25% of individuals in the U.S. have neither
a savings nor a checking account.2 Participation in retirement savings and stock markets
is even lower. In Sweden, Germany and Canada the fraction of people without a bank
account is much lower than in the U.S, closer to 3 percent. Although the data is sparse, in
some developing countries the norm is to be without a bank account. Approximately
75% of households in Mexico lack and account as do 90% of Kenyans.3 Since financial
markets provide important tools for enhancing welfare – tools to transfer resources across
time and across states of the world, to make payments, to mitigate risk and to fund
investments -- low rates of participation may be associated with lower welfare.
This paper examines the determinants of financial market participation among
immigrants in the U.S., examining the role of both individual level determinants like
wealth and education as well as the effect of the country of origin institutional
environment. In contrast to our earlier work on stock market participation (Osili and
Paulson, 2005), this paper is concerned with two more fundamental aspects of financial
market participation: breadth and depth. Financial breadth is equal to one if an individual
has any relationship with a bank (a savings or a checking account). The second measure,
financial depth, captures how many of the functions of financial markets the individual
uses: safe savings products (saving accounts or certificates of deposit), payment services
(checking or money market accounts) or investment services (stock, IRAs or Keogh
accounts).4
One important reason for being concerned with both the breadth and the depth of
financial market participation is the extensive literature that shows that better developed
financial markets lead to improved conditions at the country level. This literature uses
cross-country data to show that financial development accelerates growth, decreases
poverty and reduces inequality. See for example, Rajan and Zingales (1998), Levine
(2005), Beck, Demirguc-Kunt and Levine (2004), and Clarke, Xu and Zou (2003). In
addition, other papers show that greater financial development allows firms and
individuals to realize growth opportunities and to take advantage of new technologies
(Beck, Demirguc-Kunt and Levine, forthcoming). Finally, financial development allows
2
The exact estimate depends on the dataset. The Survey of Consumer Finances delivers estimates closer to
10%, while the 1996 – 2000 Survey of Income and Program Participation data that we analyze produces
estimates closer to 25%.
3
Beck, Demirgüç-Kunt and Peria (2005)
4
We do not examine usage of insurance products explicitly. However, financial depth measure is likely to
capture the ability of an individual to use financial markets to smooth consumption. Entrepreneurial
activity would be an alternative way to measure financial depth. However, there are not enough selfemployed immigrants in the SIPP data to distinguish “entrepreneurs”, who may need to raise capital, from
individuals whose self-employment is an informal alternative to a wage paying job.
2
firms to operate on a larger scale and encourages asset allocation and eases entry of new
firms (Klapper, Laeven and Rajan (2004)).
Our work is also related to the a growing number of studies show that the ability of a
country’s institutions to protect private property and to provide incentives for investment
can explain a large fraction of the persistent disparity in financial development and
economic performance across countries. These studies include Knack and Keefer (1995),
La Porta et al. (1997, 1998, 2000), Levine (1998, 1999), Levine, Loayza and Beck
(2000), Rajan and Zingales (2003), Beck, Demirguc-Kunt and Levine (2003a, 2003b),
Açemoglu, Johnson and Robinson (2001 and 2002) and Açemoglu and Johnson (2005).5
In addition, Rodrik, Subramanian and Trebbi (2002) present evidence that, not only do
high quality institutions contribute to economic development – institutions are, in fact,
the key determinant of economic development.
We focus our attention on U.S. immigrants for several reasons. First, immigrants are a
large and growing segment of the U.S. population. Second, international migration
allows us to study the impact of placing an individual into a different formal institutional
environment while holding past experience with institutions fixed. In the process of
migrating from one country to another, individuals move from one formal institutional
environment to another but may maintain beliefs about institutions acquired in their
countries of origin. This allows us to explore the importance of the second component of
North’s (1990) definition of institutions: “formal constraints – rules that human beings
devise” and “informal constraints – such as conventions and rules of behavior.” By
studying the behavior of international migrants in the U.S., we isolate the impact of
informal institutional constraints, since all of the migrants face the same set of formal
rules in the U.S.6 Understanding the role of informal institutional constraints is a crucial
component of predicting the impact of formal institutional change.
In addition, by studying immigrants in the U.S. we retain some of the interesting diversity
of a cross-country study, but eliminate some confounding factors. To a first
approximation, studying immigrants in the U.S. controls for factors that impact the
supply of financial services across countries. For example, all of the immigrants whose
behavior we study face the same competitive environment; make decisions in the same
regulatory environment; are subject to the same legal structure, the same infrastructure
and so on.7 We focus our attention on how the experience of these supply factors in the
country of origin manifests itself in preferences and beliefs and by extension the usage of
financial services.
5
Besley (1995) and Johnson, McMillan and Woodruff (2002) use firm-level data to show that investment
increases when institutions do a better job of protecting property rights.
6
We relax this assumption in the empirical work by including Metropolitan Statistical Areas (MSAs).
Effectively, the empirical strategy assumes that formal institutional constraints are the same for immigrants
living in the same MSA.
7
Undocumented immigrants may not have access to the same legal protections as documented immigrants;
however, our results are robust to restricting the sample to citizen immigrants for whom this potential
discrepancy is not an issue.
3
Studying immigrants creates some new empirical challenges. Immigrants are not random
representatives of their birth countries. Instead they have, typically, chosen to come to
the U.S. The decision to emigrate may be influenced by country of origin qualities that
are correlated with unobservable individual characteristics. We take a number of steps to
ensure that our findings are robust to this type of unobserved heterogeneity.8
We find that wealthier and more educated immigrants are more likely to make use of
basic banking services and have higher measures of financial depth. Holding these (and
other) factors constant, we find immigrants from countries with more effective
institutions are more likely to have a relationship with a bank and also use formal
financial markets more extensively. These results are robust to alternative measures of
institutional effectiveness, adding additional country of origin controls and various
methods of addressing potential bias due unobserved individual characteristics, including
specifications with country fixed-effects. Country of origin institutions affect the
financial market participation of recent immigrants as well as those with up to 27 years of
U.S. experience. They also influence the behavior of immigrants who arrive in the U.S.
as children as well as those who migrate as adults. Institutional quality appears to shape
preferences and beliefs in a way that influences both the breadth and the depth of
financial access.
The rest of the paper is organized as follows: Section 2 describes the framework we use
to develop the hypotheses. Section 3 describes the data. In section 4 we discuss our
empirical findings. Section 5 concludes.
2. Conceptual Framework
In this section, we draw on Osili and Paulson (2005) to sketch out a simple reduced-form
framework to make the hypotheses that we test clear. Following North (1990), we define
institutions as “the rules of the game in a society,” including formal institutions that
protect and enforce the various property rights as well as informal institutions, which are
society’s underlying norms of conduct. Both formal and informal aspects of institutions
can affect the incentives and costs associated with financial institutions.
We illustrate the framework in terms of an individual’s decision about whether to hold a
given financial asset –demand deposits, stock, and other financial assets. For example,
consider an individual, i, from country J who is considering whether or not to open a
bank account. The individual’s demand for a given financial asset is represented by:
S i = f ( R, X i )
8
Our empirical strategy is similar to that of Fernandez and Fogli (2005) who show that country-of-ancestry
fertility and female labor force characteristics influence the fertility and work behavior of U.S.-born
children of immigrants. Carroll, Rhee and Rhee (1994) also use a conceptually similar approach in their
study of the cultural determinants of savings. Hendricks (2004) examines the behavior of immigrants in the
U.S. to explain variation in hours worked across countries. For hours worked in the U.S. he finds that
home-country characteristics are important for women but not for men. Borjas (1987) also looks at the
impact of country-of-origin characteristics on immigrant wage assimilation.
4
where Si is the amount that individual i invests in a given financial asset, R is the
expected return from the investment, and Xi is a vector of individual characteristics (risk
aversion, wealth, income, education, years in the U.S., age at migration, and other
characteristics) that affect the demand for the financial asset.
Institutional quality is modeled by assuming that the investor believes there is some
probability, πi that the bank or other financial institution will abscond with the investor’s
funds. This variable captures the investor’s beliefs about the likelihood of expropriation
by firm managers or by the government. It measures not only the possibility that the
bank managers or financial institution will abscond with funds but also the possibility
that the institutional framework is not sufficient to ensure that funds will be invested in
profit-maximizing projects and that investment proceeds will be appropriately reinvested
or returned to investors. We assume that all brokers are governed by the same
institutional framework and therefore they face a common cost of absconding. This
means that broker variation in the likelihood of absconding can be safely ignored.
Given her beliefs, the investor’s expected return on the investment will not be R, the
expected return on the financial asset, but πi x 0 + (1 – πi) x R. The probability that an
investor places on the likelihood that the bank manager absconds is a function of the
quality of the institutions in the country that investor was born in, J, which may in turn be
a function of the length of time the investor experienced those institutions, yJ, and the
length exposure that the investor has to U.S. institutions, yUS: πi = π(J, yJ, yUS).
For the typical immigrant who comes from a country where institutions are weaker than
in the U.S., π is decreasing in origin-country institutional quality, increasing in years
spent in the origin country, and decreasing in years spent in the U.S. Given this
framework, demand for a given financial asset will be increasing in home-country
institutional quality, and for a given level of institutional quality, π will be higher for
individuals who have recently arrived in the U.S. and who have arrived as adults.
A closely related channel through which origin country institutions exert an influence on
financial market behavior is by influencing the expected costs of participation in financial
markets.9 In standard theoretical models of financial behavior, the fixed costs of
participation may deter an individual from holding a given financial asset. Guiso at al
(2003) provide empirical evidence on the importance of participation costs across
countries. We can modify the framework described above to take this into account.10
Specifically, we assume that the investor forms beliefs about the fixed costs of
participation that vary with institutional quality. The investor believes that there is some
probability, αi, that the costs of investing are high, FH and some probability, (1 - αi), that
9
For example, several authors have studied the effect of market frictions mostly in the form of fixed entry
and/or transaction costs on stock market participation.
10
It is straightforward to extend the model to consider the effect of institutions as mediated through the
ability to transfer ownership or liquidate financial assets. With weak institutions, the expected costs of
transferring or liquidating financial assets are likely to be high.
5
the cost of investing will be zero.11 Given these beliefs, the investor’s expected net return
on the financial asset is equal to:
[πi x 0 + (1 – πi) x R] – [αi x Fi. + (1 - αi) x 0]
We assume that perceptions regarding the fixed costs of investment, αi, for a given
financial asset will vary with quality of the institutions in the country that investor was
born in, J, which may in turn be a function of the length of time the investor experienced
those institutions, yJ, and the length exposure that the investor has to U.S. institutions,
yUS: Fi = F(J, yJ, yUS).12
An additional channel through which origin country institutions may affect immigrant’s
financial decisions is through supply. Several empirical studies emphasize the role of
legal and regulatory institutions in the emergence and development of financial
markets.13 The key assumption here is that better institutional quality will lower the costs
that the financial institutions incur in providing services to individuals and households.
For example, better institutions would lower the cost of monitoring and screening for
banks, allowing the expansion of banking institutions. If better institutions lead to an
expansion in the supply of origin country financial institutions, then immigrants will have
had more opportunities for direct exposure to financial institutions prior to migration.
3. Data
Individual data
The challenge in using individual data is to find meaningful variation in institutional
quality within a single data set. We achieve this by looking at a large sample of
immigrants living in the U.S. Historically high rates of migration to the U.S. in the past
two decades mean that at least 10 percent of the U.S. population was born abroad. The
1996 – 2000 Survey on Income and Program Participation (SIPP) data that we use are
designed to be representative of the U.S. population and include approximately 46,000
individuals, of whom 11 percent are immigrants. These individuals face a common set of
formal institutional constraints in the U.S., but the immigrants vary in the institutional
constraints that they have experienced prior to coming to the U.S.
The indicators of financial access that we focus on are financial depth (owning a
checking or a savings account) and financial depth (the number of distinct functions of
financial markets that an individual makes use of: safe savings, payment services and
11
Assuming the fixed costs would be zero simplifies the analysis a bit. We would get the same substantive
results if we assumed that the investor believed there was some probability that fixed costs would be low.
12
The World Bank database on financial market access suggests that transaction costs associated with
financial market activity can be quite high. For example, minimum amounts required to open a checking
account in some African countries like Malawi and Uganda can be as high has over 30 to 50 percent of
GDP per capita.
13
For example, Beck, Demirgüç-Kunt and Peria (2006) document large differences in the supply of banks
and other financial institutions across countries. For example, in many countries in Sub Saharan Africa
there is less than one bank branch per 100,000 people, while some developed countries (Portugal, Spain)
have 50 or more bank branches per 100,000 people.
6
investment services). We find low rates of financial market participation among
immigrants compared to the native-born. Sixty-one percent of the immigrant sample has
a savings or a checking account, compared with 76 percent of the native-born (see Table
2A). The median measure of financial depth is one for immigrants and two for the
native-born. In addition, we see that 47 percent of immigrants have a checking account
compared with 64 percent of the native-born. Savings account ownership has a similar
pattern. Forty percent of the immigrant sample has a savings account, compared with 55
percent of the native-born.
Table 2C and Figure 0 provide further evidence of relatively low rates of financial market
participation among immigrants compared to the native-born. Based on a wide range of
financial indicators, the median immigrant is likely to use the formal financial sector for
either safe savings or for payment services, while the median native-born individual is
likely to use both of these functions of financial markets.
We restrict the sample to immigrants who are over 18 and live in a metropolitan
statistical area (MSA), for a total sample of 15,043 observations, with (approximately) 4
annual observations per person.14 Table 2A summarizes these data for immigrants and
the native-born. Compared to the native-born, immigrants are younger, more likely to be
married, non-white, have more children and more likely to be unemployed or
economically inactive. Immigrants also tend to be less educated than the native-born.
Slightly less than 36 percent of the immigrant sample has never completed high school
compared to only 15 percent of the native-born sample. However, the percentage of
immigrants and the native-born who have an advanced degree is roughly the same at 7
percent and 8 percent, respectively.
Monthly per capita household income is significantly lower for immigrants compared to
the native born. For immigrants, average monthly per capita household income is
$1,640, compared to $2,224 for the native-born. In addition to having lower incomes,
immigrant households have also accumulated less wealth compared to households headed
by individuals who were born in the U.S. The median immigrant household has wealth
of $29,001 compared to $71,123 for the native-born.
Additional immigrant characteristics are described in Table 2B. Nearly one-half of the
immigrants arrived in the U.S. within the 10 prior to the start of the survey. Just under
half of the immigrants were born in a North American country and about 15 percent were
born in Europe.15 Most of the immigrants arrived in the U.S. as adults, with almost 71
percent arriving at twenty-one years or older.
14
We restrict our attention to the four annual survey waves where wealth data are available. Other SIPP
data are collected quarterly.
15
Mexico accounts for just over one-quarter of the immigrants in the sample. We assign institutional
quality measures to individuals who were born in the Soviet Union, the former Yugoslavia or
Czechoslovakia in the following way: individuals who reported that they were born in Russia, Armenia,
Azerbijan, the Baltic States, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyztan, Latvia, Lithuania,
Moldova, Tajikistan, Turkmenistan, Ukraine, USSR, Uzebekistan are mapped to the institutional quality
measure for the Soviet Union; individuals who reported that they were born in Czechoslovakia, Slovakia,
Czech Republic are mapped to the institutional quality measure for Czechoslovakia and individuals who
7
Country data
The individual data are augmented with country-level data compiled from various
sources. These data include various measures of institutional quality, and other important
country characteristics. Table 1 defines each variable and describes its source.
The measure of institutional quality that we use is “protection from expropriation”.16
This variable evaluates the risk “outright confiscation and forced nationalization” of
property and comes from the International Country Risk Guide (ICRG) IRIS-3 Data.
Ratings range from 1 to 10 and lower ratings “are given to countries where expropriation
of private foreign investment is a likely event.” We average annual country observations
from 1982 to 1995 to form the “protection from expropriation” variable that is used in the
empirical work.
In addition to institutional quality, we also examine how financial market participation is
influenced by geography (as captured by the absolute value of the latitude of the
country’s capital divided by 90) and legal origins (in particular whether the country has a
common law tradition, or not). Other important country of origin variables include: gdp
per capita, religious composition, measures of infrastructure including internet usage, an
indicator for whether immigrants from the country in question are likely to speak English,
and characteristics of the financial sector in the country of origin.
Table 3A presents summary statistics for each of the country-level variables that we use.
U.S. values for each variable are reported in the column on the far right of the table.
Protection from expropriation, ranges from 1.83 (Iraq) to 10.00 (the Netherlands,
Switzerland, the U.S.). The average is 7.50. One attractive feature of the “protection
from expropriation” measure of institutional quality is that improvements in “protection
from expropriation” are correlated with future equity returns (Erb, Harvey and Viskanta
(1996)).17 Açemoglu, Johnson and Robinson (2001 and 2002) find that institutional
quality, as measured by protection from expropriation, causes long-run economic and
financial development. Correlations between the various country of origin variables are
presented in Table 3B.
4. Empirical Findings
This section reports on our empirical findings. We estimate financial breadth (Bisj) and
financial depth (Disj) using the following linear model:
Bisj or Disj = α + β1Xi + β2Zj + δs + εisj,
reported that they were born in Yugoslavia, Bosnia and Herzogovina, Croatia, Macedonia, Montenegro,
Slovenia, Serbia are assigned the institutional quality measure for Yugoslavia.
16
In previous work (see Osili and Paulson, 2005) we have investigated alternative measures of institutional
quality. The findings presented in this paper are also robust to alternative measures of institutional quality,
including: constraints on the executive and measures of domestic protection from expropriation. These
results are available from the authors.
17
Erb, Harvey and Viskanta (1996) find that changes in “rule of law” and other ICRG-IRIS-3 variables are
not correlated with future equity returns.
8
where Bisj is the decision to have a bank account (or the intensity of financial market
participation, Disj) for individual i who lives in metropolitan statistical area s and comes
from country j. Individual controls are incorporated in Xi and include age, age squared,
wealth quartiles, income, labor force status, education, sex, marital status, number of
children in household, and race. A full set of MSA controls are included in δs, and Zj
measures supply characteristics in country j. To the extent that individual characteristics,
like wealth and education, are influenced by supply characteristics in the country of
origin, the regression will tend to produce overly conservative estimates of the direct
effect of institutions, β2.
By including measures of individual characteristics, we are able to isolate the effect of
country of origin institutions.18 All of the reported standard errors are adjusted to allow
for correlation across observations for immigrants that come from the same country.
When the dependent variable is Bisj we also correct for the heteroscedasticity that is
implicit in a linear probability model.19
Baseline Findings and Institutions v. Geography and Legal Origins
The relationship between financial market participation and institutional quality is
explored in Table 4 for a variety of country of origin characteristics. Columns [1] – [4]
report the results for financial breadth and columns [5] – [8] report the results for
financial depth. Figures 1A and B summarize the relationship between “protection from
expropriation” and financial market participation implied by regressions [1] and [5].
The sample is restricted to immigrants who are at least 18 years of age, live in a MSA
and come from one of the 78 countries for which institutional quality data are available.
In addition to a measure of institutional quality, human capital or geography, the
explanatory variables include age, age squared, wealth quartiles, labor force status,
income, marital status, sex, race, education, number of children and controls for the MSA
where the immigrant lives.20
We find that institutional quality has a positive and significant effect on having a bank
account. According to these estimates, if an individual from a country with “average”
institutions, as captured by “protection from expropriation” had instead come from a
country that had institutions that were one standard deviation above the mean, the
likelihood that they had a savings or a checking account would increase by 4.7
18
This addresses an important concern with some earlier cross-country studies that focus on the impact of
institutions on financial development. For example, the identification strategy used by Açemoglu, Johnson
and Robinson (2001 and 2002) stresses the link between institutional development and settler mortality
during the colonial period, but leaves open the possibility that the human capital of colonial settlers played
a role in future economic development.
19
We use a linear probability model because it is computationally attractive given the large number of
fixed effects, is consistent under weak assumptions and because the coefficient estimates are easy to
interpret. In particular, the coefficients on interaction terms are straight-forward to interpret (see Ai and
Norton, 2003). Non-linear estimation methods, such as probit or logit, generate similar results.
20
See Appendix Table 2 for the impact of these independent variables.
9
percentage points, a 7.7 percent increase in the likelihood of having a bank account,
relative to the observed likelihood for immigrants of 61 percent. The same change is
associated with a 10 percent increase in financial depth. This is roughly equivalent to
considering what would happen if Argentina’s “protection from expropriation” had been
the same as Germany’s between 1982 and 1995.
In cross-country studies, geographic attributes of countries have been found to have a
significant effect on income per capita and economic growth, see for example Gallup,
Sachs and Mellinger (1998) or Sachs and Malaney (2002). Beck, Demirgüç-Kunt and
Levine (2003b) show that latitude helps to explain financial development. We use the
absolute value of the latitude of the capital city divided by 90 to capture this effect and
find that individuals who were born in countries that are further from the equator are
significantly more likely to have a bank account and to otherwise participate in the U.S.
financial markets (see columns [2] and [6]). A one standard deviation increase in the
distance from the equator is associated with a 3.7 percentage point increase in the
likelihood of having a bank account and a 8% increase in financial depth.
La Porta et al. (1998 and 2000) show that greater protection is offered to shareholders in
countries with a British legal tradition and that financial development is accelerated in
these countries. This possibility is explored in columns [3] and [7]. Our findings parallel
theirs: the probability of having a bank account among immigrants from countries with a
British legal tradition is 4.4 percentage points higher than that of immigrants from
countries with a different legal tradition. Financial depth is estimated to be 11 percent
higher for immigrants from countries with a British legal tradition.
Rodrik, Subramanian and Trebbi (2004) and Açemoglu, Johnson and Robinson (2001)
find that geography has an important effect through its impact on the quality of
institutions. They contend that countries that are further from the equator tend to develop
stronger institutions. Açemoglu, Johnson and Robinson (2001) argue that European
colonialists adopted different colonization policies depending on the host country
environment, leading to more effective institutional arrangements in some countries.
Although their work emphasizes the role of settler mortality rates in determining the
colonization policy, they also show that places where effective institutional arrangements
were established tend to be further from the equator. In regressions [4] and [8], we allow
financial market participation to be influenced by institutions, geography and legal origin.
Our findings parallel those of Rodrik, Subramanian and Trebbi (2004) and Açemoglu,
Johnson and Robinson (2001). Once institutional quality is controlled for, geography and
legal origin no longer have a significant impact on financial market participation.21
Institutional quality continues to have a strong, significant effect on both financial
breadth and depth.
21
We should note that other aspects of geography may be important. For example, Sachs (2003) finds that
malaria transmission, which is related to geography, impacts the level of income per capita after controlling
for institutional quality.
10
These baseline findings suggest that immigrants come to the U.S. with attitudes shaped
by the effectiveness of home-country institutions, and that the ability of home-country
institutions to protect investment and provide incentives for investment has a significant
effect on immigrant behavior in the U.S. over and above the impact of individual
characteristics including wealth, income and education. In addition, institutional quality
is trumps the importance of geography and legal origins when all three country of origin
characteristics are included in the regression. For ease of exposition, the rest of the paper
uses a single measure of institutional quality, “protection from expropriation”. We have
replicated the results using other measures of institutional quality with largely the same
conclusions.22
Omitted Country Variables
We turn our attention now to exploring the robustness of our findings. The first issue we
consider is that there may be other important country-of-origin characteristics that are
correlated with institutional quality that were left out of the baseline results. We explore
the impact of adding additional country characteristics in Tables 5A and 5B, for having a
bank account and for financial depth, respectively. In addition to the explanatory
variables reported on in Tables 5A and B, each of these estimates includes all of the same
control variables that were included in the baseline estimates.23
For purposes of comparison, the first column of Tables 5A repeats the results from Table
4 column [1], and the first column of Table 5B repeats the results from Table 4 column
[5]. In column [2] of Tables 5A and B, we report on estimates that include continent
controls in addition to protection from expropriation. The size of the coefficient on
protection from expropriation declines modestly, but otherwise results are unchanged.
This rules out the possibility that the results were driven by discrimination against
individuals based on their continent of origin, say Africa or Central or South America, for
example, and that countries in the same continent tend to share institutional qualities.24
We also examine the possibility that religious influences on institutional quality are
responsible for our earlier findings in column [3] of Tables 5A and B. Stulz and
Williamson (2003), for example, document a link between a country’s religious attributes
and investor rights, particularly creditor rights. This estimate adds controls for the
percentage of the country-of-origin population that is Catholic, Protestant and Muslim to
the baseline specification. Adding these variables lowers the point estimate of the impact
of institutional quality from 0.027 to 0.023 in the case of having a bank relationship and
from 0.071 to 0.057 in the case of financial depth.
22
In addition to the results reported here, we find similar results when we use alternative measures of
institutional quality including: “rule of law”, the “quality of the bureaucracy” (both from the ICRG-IRIS-3
data set), “domestic protection from expropriation” (see Osili and Paulson (2005) for details on how this
variable is created), and “constraints on the executive” (from the Polity IV database).
23
The issue of omitted country characteristics is also addressed in specifications which include country
fixed effects (see Table 6) and in placebo regressions (see Table 7).
24
Recall that the estimates include a control for being “non-white”, so the continent controls capture
differential treatment based on continent of origin, holding racial characteristics fixed.
11
Another potential explanation for our findings is that financial market adaptation may be
easier for immigrants from countries that are more similar to the U.S. This would mean
that the positive coefficient on protection from expropriation should be interpreted to
mean that individuals from countries with institutions like the U.S. are more likely to
participate in U.S. financial markets, rather than as an indication that better institutions to
protect private property encourage financial market participation. If this is the case, then
including other, potentially better, measures of the similarity between the country-oforigin economy and the U.S. should eliminate the significance of “protection from
expropriation”.
In order to capture this possibility, we include average per capita GDP from 1982 to 1995
in the country of origin in the estimate presented in column [4] of Tables 5A and B.
Countries with better institutions have higher GDP per capita (see Rodrik, Subramainan
and Trebbi (2004), for example), so including this variable may absorb some of the effect
of institutional quality. The likelihood of having a bank relationship and the number of
financial relationships are predicted to be significantly higher when GDP per capita is
higher.25 Adding this variable reduces the coefficient on “protection from expropriation”
from 0.027 to 0.020 for the probability of having a bank account and from 0.071 to 0.044
in the case of financial depth, however its effect remains highly significant.
Since countries with high institutional quality also tend to have been colonized by
Britain, it is possible that the positive coefficient on protection from expropriation is
capturing not institutional quality, but the ability of individuals who were born in some
former British colonies to speak English. The ability or inability to speak English may
play an important role in determining the ease of financial market participation.26
Ideally, we would include an individual measure of English speaking ability in the
estimates.27 However, the SIPP data do not include any measure of this characteristic, so
we try to capture it at the country level instead.
Column [5] in Tables 5A and B adds an indicator variable that is equal to one if the
country of origin has English as an official language of the country and if a majority of
immigrants from that country surveyed in the 1980 U.S. Census spoke English at home
(see Bleakley and Chin, 2004). Coming from a country where English is spoken does not
have a significant impact on the likelihood of owning a bank account or on the number of
financial relationships. Adding this variable to the estimates lowers the point estimate of
the coefficient on institutional quality somewhat for both dependent variables. However,
it remains positive and significant.
25
Many studies, including Hall and Jones (1999), Açemoglu, Johnson and Robinson (2001, 2002),
Açemoglu and Johnson (2005), Easterly and Levine (2003), and Rodrik, Subramainian and Trebbi (2004)
find that GDP per capita is higher in countries with better institutions.
26
Chiswick (1978) and Borjas (1987) show that immigrants from English speaking countries experience
more rapid wage assimilation.
27
We should note that our estimates do include household income and education, which are likely to
partially account for the ability to speak English. Black, Haviland, Sanders and Taylor (2006) find that
college-educated immigrants who speak English at home have wages that are the same as similar nativeborn individuals.
12
The availability of home-country financial markets may also influence the likelihood of
financial market participation among immigrants in the U.S. The supply of banks may be
lower in countries where entry is costly. In column [6] of Table 5A and B, we add “bank
freedom” to the regression.28 This variable measures the absence of government
interference in the banking sector and is likely to be higher in countries where entry is
less costly (see Table 1 for a detailed definition). The freedom of the country of origin
banking sector has a positive and significant effect on financial market participation.
However, home country institutions remain important. The coefficient on institutional
quality in the regression where having a bank account is the dependent variable is highly
significant and equal to 0.029. Note that the freedom of the banking sector is likely to be
influenced, perhaps quite strongly, by the quality of country-of-origin institutions. We
have replicated these results using a measure of banking concentration (the percentage of
banking assets held by the three largest banks) with similar results.29
In column [7] of Tables 5A and B, we add a proxy for infrastructure conditions in the
country of origin. The measure we use is the number of internet users per 1,000 people.
The specific measure does not seem to be important, and we get similar results when we
use telephone usage or the percentage of roads that are paved. In countries where
infrastructure is weak, individuals may have little direct experience with banks and other
financial institutions since they may be costly to get to and to communicate with. These
conditions appear to spill over to behavior in the U.S. Immigrants from countries with
more extensive infrastructure are more likely to participate in U.S. financial markets,
whether we use financial breadth or financial depth as the dependent variable. The
coefficient on institutional quality remains positive and strongly significant in these
regressions.
Overall the results presented in Tables 5A and 5B suggest that the finding that financial
market participation in the U.S. is influenced by the quality of institutions in the country
of origin is robust to including additional attributes of the country of origin.
Unobserved Heterogeneity
We turn our attention now to what is an important empirical issue for any study of
immigrant behavior and for ours in particular.
Immigrants are not random
representatives of their country of origin. They choose to migrate and that decision may
be influenced by characteristics that are not observable. If unobserved individual
characteristics are correlated with country-of-origin institutional quality, then we need to
be concerned that our findings capture the effect of unobserved individual characteristics,
rather than the effect of institutional quality.
28
Beck, Demirgüç-Kunt and Maksimovic (2004) find that firms’ access to finance is more restricted by
concentration in the banking industry in countries where bank freedom is lower.
29
We have also examined the effect of controlling for remittances to the home country. If immigrants are
not investing in U.S. financial markets perhaps they are investing at home through remittances. Including a
measure of remittances received in the home country does not alter the effect of institutional quality. We
find that migrants from countries that receive higher per capita remittances are more likely to participate in
U.S. financial markets. This is consistent with work by Aggarwal, Demirgüç-Kunt and Peria (2005) who
find that remittances promote financial development.
13
Borjas (1987) describes one avenue through which unobserved heterogeneity could bias
the results. In his model, the decision to migrate will be a function of, among other
things, unobserved migrant ability and the distribution of income in the country of origin
and the destination country. Because high ability migrants are only concerned with the
right tail of the income distribution, they will tend to migrate from more equal societies to
less equal ones. In contrast, low ability migrants will move from less equal societies to
more equal ones, to protect themselves against a draw from the low end of the wage
distribution. Assuming that unobserved ability affects financial behavior as well as labor
market outcomes, this type of selection could bias our results.
Since countries with low inequality also tend to have strong institutions, our finding that
financial market participation increases with country-of-origin institutional quality could
be driven by ability bias.30 For example, immigrants from Sweden, a country with low
inequality (relative to the U.S.) and high quality institutions, are likely to be of high
ability. In contrast, immigrants from Brazil, a country with high inequality and less
effective institutions will tend to have lower unobserved ability.
In addition to unobserved ability, there are other individual characteristics that we cannot
observe that may play a role in the decision to participate in financial markets and may
also be correlated with country-of-origin institutional quality. For example, the degree of
risk aversion may be correlated with the likelihood of migration from particular countries
and also influence the decision to own stock. Similarly, variation in the quality of
schooling across countries may impact the cost of obtaining information about U.S.
financial markets.31
In order to produce unbiased estimates of the effect of country-of-origin institutional
quality on financial market participation in the U.S., we need to eliminate the possibility
that omitted individual characteristics are correlated with country-of-origin institutional
quality. If we can do this, we can confidently interpret the coefficient estimate on
institutional quality, despite the fact that there may be important individual characteristics
that we do not observe.
To do this, we create a new measure of institutional quality that captures both
institutional quality and the potential size of an immigrant network. The new measure of
institutional quality is the interaction of “protection from expropriation” with “ethnic
concentration. Ethnic concentration is defined as the percentage of people in an MSA
who come from the same country as the immigrant in question:
ECsj =
# of immigrants from country j living in MSA s
total population in MSA s
30
Engermann and Sokoloff (2002) provide evidence that in societies with high initial inequality the
evolution of institutions favored a narrow elite.
31
In addition, parental participation in financial markets is likely to be correlated with country-of-origin
institutional quality and with the decision of the current generation to participate in financial markets
(Chiteji and Stafford (2000)).
14
Because this new institutional quality measure varies by country of origin, we can include
country-of-origin fixed effects in the estimation. By including country-of-origin fixed
effects, we eliminate correlation between unobserved individual attributes and country of
origin. Appendix Table 1 reports the median ethnic concentration for immigrants from
each country. We use data from the 1990 Census IPUMS to calculate this measure for
each country of origin and MSA.
In Table 6, we estimate:
Bisj or Fisj = α + β1Xi + β2ZjxECsj + β3ECsj + δs + δj + εisj,
where ZjxECsj is the interaction of institutional quality and ethnic concentration for an
individual from country j who lives in MSA s. We include a full set of country-of-origin
controls in δj. All of the other variables are defined above.
By including MSA fixed effects in all of the estimates, we rule out another potential
source of bias in the new institutional quality measure.32 Since location choice is nonrandom, immigrants who choose to live in an MSA with a large fraction of immigrants
from the same country of origin are likely to be systematically different along
unobservable dimensions from immigrants who choose to live in an MSA with very few
immigrants from the same country of origin. By including MSA fixed effects, we ensure
that the coefficient on protection from expropriation interacted with ethnic concentration
will not be biased by these unobservable characteristics.
In addition to dealing with a potential source of bias, this approach may also shed light on
why the quality of country-of-origin institutions matters for financial market
participation. A significant and positive coefficient on the new institutional quality
measure means that the impact of coming from a country with weak institutions is
reinforced when individuals from countries with weak institutions live near one another.
These estimates are reported in Table 6 The first two columns report the results for the
probability of having a bank account and the last two report the results for the number of
financial relationships. The estimates presented in columns [1] and [3] do not include
country fixed effects. Columns [2] and [4] add country fixed effects. These estimates are
summarized in Figures 2A and B. The coefficient on institutional quality interacted with
ethnic concentration remains positive and significant when country fixed effects are
included.
For the median immigrant who lives in a metropolitan area where 0.78 percent of the
population comes from the same country, the estimates imply that the likelihood of
having a bank account would increase by 1.1 percentage points and that an individual
would make use of 0.03 more of the functions of financial markets if institutional quality
were one standard deviation higher from 1982 to 1995. By comparison, the baseline
32
Note that ECsj varies by country-of-origin for a given MSA, so we can include both country and MSA
fixed effects in the regressions.
15
findings, which are not corrected for unobserved heterogeneity, imply that the same
increase in institutional quality is associated with a 4.7 percentage point increase in the
likelihood of having a bank account and a 0.12 increase in financial depth.
Enforcement of Informal Institutional Constraints
In addition to addressing an important econometric issue, the estimates which include
institutional quality interacted with ethnic concentration speak to an important
substantive one. North (1993) defines institutions as a trinity: the formal rules of the
game, informal institutional constraints and the enforcement of formal and informal
constraints. One role of neighborhoods with a large population of immigrants from a
single country is the enforcement of country-of-origin norms and customs (see for
example Kandori (1992)). When immigrants live in a place where country-of-origin
institutional constraints are more likely to be enforced, these constraints should matter
more.
We find evidence in favor of this view. Ethnic concentration is roughly twice that of the
median immigrant for Filipino immigrants and about one-half of the median for
Portuguese immigrants. A one standard deviation improvement in institutions in the
Philippines is predicted to increase the probability that Filipinos have a bank account by
2.6 percentage points and financial depth by 0.08. The same improvement in institutional
quality would increase bank account ownership by 0.52 percentage points and financial
depth by 0.02 for Portuguese immigrants.
The finding that the effect of institutional quality varies with size of the potential
immigrant network is consistent with work by Madrian and Shea (2000), Duflo and Saez
(2003) and Hong, Kubik and Stein (2004) who show that social interactions have
important effects on financial decisions. Immigrant networks have also been show to be
important in a number of other non-financial contexts, including employment
probabilities (Munshi, 2003), wage growth and human capital accumulation (Boras, 1995
and 2000) and language proficiency (Chiswick and Miller, 1996).33
The effect of institutional quality on other behavior
In Table 7 we present estimates of the effect of country-of-origin institutional quality on
the decision to own stock, to have a savings account, to have a checking account, to be
self-employed, to drive one’s own car to work, and to visit a doctor. These estimates
serve two purposes. First, they allow us to test the hypothesis that the importance of
institutional quality declines with the level of institutional support required to make a
particular investment decision reasonable. Second, these estimates address the possibility
that institutional quality is proxying for some other country-of-origin characteristic –
national attitudes regarding self-reliance or altruism, for example -- that explains all sorts
of behavior, not just behavior that should be governed by the institutions that determine
protection of private property and incentives for investment. In other words, the
33
In addition, Fernandez and Fogli (2005) show that the impact of country-of-ancestry norms on fertility
and women’s labor force participation is also amplified for the children of immigrants who reside in
neighborhoods with other people who share the same country of ancestry.
16
regressions in Table 7 tell us if institutional quality matters when it is supposed to and
does not matter when it should not.
The first panel of Table 7 uses the baseline specification and the second panel controls
for unobserved heterogeneity using the specification from Table 6, which interacts
protection from expropriation with ethnic concentration and includes country-of-origin
fixed effects.
Looking first at the decision to open a savings account (column [2]), we see that in the
baseline specification higher institutional quality is associated with a higher likelihood of
having a savings account. A one standard deviation increase in institutional quality is
associated with a 3.8 percentage point increase in the likelihood of having a savings
account, a 9.5 percent increase in the likelihood of having a savings account relative to
the observed percentage of immigrants with a savings account of 40.1 percent. The same
increase in institutional quality is predicted to increase stock market participation by 29
percent (column [1]). As we expect from the relative importance of institutional support
required by the two investments, institutional quality has a larger impact on the likelihood
of owning equity compared to the likelihood of having a savings account. In addition, we
cannot rule out the possibility that the savings account results are due to biases induced
by unobserved heterogeneity.
Owning a checking account and using checks are more institutionally intense compared
to having a savings account. Not only must an individual be convinced that the bank will
keep funds safe and available upon demand, but they must also be convinced that the
payment system as a whole and the system for getting checks from one place to another is
sufficiently secure to prevent fraud. At the same time, the threshold of institutional
quality that is required to support checks is lower than that required to support
investments in the stock market. The results bear out this ranking (see column [3]). In
contrast to the findings for savings accounts, the impact of institutional quality on having
a checking account is robust to controlling for unobserved heterogeneity, but
improvements in institutional quality are more important for stocks than for checks. A
one standard deviation increase in institutional quality is associated with an 11 percent
increase in stock market participation and a 4.7 percent increase in the likelihood of
having a checking account. Similarly, Guiso, Sapienza and Zingales (2004) find that
households in high social capital areas are more likely to use checks and invest more in
the stock market.
In column [4], we examine the impact of country of origin institutions on selfemployment. While higher quality institutions are associated with a higher likelihood of
self-employment, this finding is not replicated in the specifications that include country
of origin fixed effects. This result is somewhat surprising. Entrepreneurial activities that
require outside financing would seem to be very institutionally intense. However, it
appears that many of the self-employment activities undertaken by immigrants in the
SIPP do not rely on outside financing. Instead, they employ the entrepreneur and
possibly a family member or two and very few make use of debt financing. These types
17
of investments appear to be primarily intermediated through the family rather than
through formal financial institutions and the regression results are consistent with this.
We see a similar pattern with a selection of other investment activities that are
intermediated through the family: driving to work and visiting a doctor (columns [5] and
[6]). Investments in these activities are not significantly influenced by country-of-origin
institutional quality in the baseline specification. In addition, these results are not robust
to controlling for unobserved heterogeneity.
The fact that institutional quality has impacts financial decisions that require more
institutional support and does not appear to influence investments that are mediated
through the family raises our confidence that our findings are driven by individuals who
embody home-country institutions and not by some spurious correlation between
country-of-origin characteristics and immigrant behavior more generally.
The effect of institutional quality on different types of people
We turn now to analyzing how institutional quality affects different groups of
immigrants. These estimates help to identify the potential channels through which homecountry institutions come to influence behavior and also serve as further robustness
checks on our main results.
Education, occupation, citizenship
In Table 8, we examine how the impact of institutions varies with education and with
occupational characteristics. In the top panel the dependent variable is equal to one if an
individual has a savings or a checking account. The dependent variable in the bottom
panel is financial depth. In columns [2] and [3], we provide estimates for two education
groups: high education (those with a college degree or more schooling) and low
education (those who have not completed high school). Country of origin institutions are
important for both groups, with slightly different patterns depending on whether we are
looking at the probability of having a bank relationship or depth of financial access.
Country of origin institutions appear to be more important among immigrants with less
schooling. A one standard deviation increase in home-country institutional quality is
predicted to raise the likelihood of having a bank account by 7.7 percentage points for
immigrants in the low education group and by 3.8 percentage points for immigrants in the
high education group. A similar, but less pronounced, pattern is seen for financial depth.
This is consistent with Guiso, Sapienza and Zingales (2004 and 2006) who find that the
effect of social capital and culture is muted for those with greater education.
In columns [4] and [5], we compare the effect of institutional quality on high and low
skill workers, respectively.34 One reason for making this comparison is because foreign
34
“High Skill Workers” are workers whose 3-digit occupation code from SIPP is mapped into the
“Professional and Technical” or “Executive, Administrative, and Managerial” categories according to
Bureau of Labor Statistics (BLS) classifications. “Low Skill Workers” include workers whose 3-digit
occupation code from SIPP is similarly mapped into “Transportation and Material Moving Occupations”,
18
educational credentials are often not accepted by U.S. employers. This means that a
highly educated immigrant may be working in a low skill occupation. It may be the
occupational milieu rather than education itself that drives the differential impact of
institutions for high and low education immigrants. This is consistent with the patterns
that we see for the likelihood of having a bank account. Among high skill workers,
country of origin institutions do not have a significant impact on the probability of having
a bank account. The effect is large and significant for low skill workers. Financial depth
increases significantly with institutional quality for both skill groups, with a slightly
larger effect for low skill workers. Recall that all of the regressions include controls for
education.
In column [6], the sample is restricted to immigrants who are naturalized U.S. citizens.
Among U.S. citizen immigrants, the likelihood of having a bank account and financial
depth are significantly increasing in home-country institutional effectiveness. Restricting
the sample in this way has (at least) two effects. First, we make sure that the link
between financial market participation and home-country institutional quality is not
driven by the reluctance of undocumented immigrants to buy stock and the potential
correlation between being undocumented and coming from a country with weak
institutions. Second, immigrants must choose to become citizens and by doing so signal
their general orientation toward U.S. society and institutions. However, despite this
decision, their investment behavior continues to be influenced by country-of-origin
institutions, suggesting that informal institutional constraints cannot be shed at will.
Finally in column [7], we eliminate Mexican immigrants from the sample. Just over a
quarter of the immigrants were born in Mexico, and we want to make sure that the results
are not driven by the large number of immigrants who share the same institutional
background. Eliminating Mexican immigrants from the sample has no effect on the
results.
Persistence of Institutions
We now consider the persistence of the effects of home-country institutions. We address
this question in columns 2 - 6 of Table 9 which examines the effect of country-of-origin
institutional quality on financial market participation in the U.S. for subsets of
immigrants based on the number of years they have lived in the U.S. The top panel
reports results for the likelihood of having a bank account and the bottom panel reports
on regressions where the dependent variable is financial depth.
Columns 2 – 6 of Table 9 divide the immigrant sample into five sub-samples based on
how many years they have been living in the U.S. For each sub-sample, two estimates
are produced: one which includes controls for how old the immigrant was when she
arrived in the U.S. and one which does not. Controlling for age at arrival in the U.S.
produces slightly more conservative results, so we discuss those estimates in the text.
The effects of informal institutional constraints are very persistent. The effect of
“Handlers, Equipment Cleaners, Helpers and Laborers”, or “Service Occupations, Except Private
Household” categories according to the same BLS classifications.
19
protection from expropriation is positive and significant for every sub-sample, except for
the sub-sample of immigrants who have been in the U.S. for more than 28 years.
The persistent impact of country of origin institutions suggests that the “supply” channel
is not the major mechanism by which individuals behavior is influenced by institutions.
If lack of experience with institutions due to restricted supply conditions in the country of
origin was the primary mechanism through which institutions influence individual
behavior, it would be likely to decay with time spent in the U.S. The long lasting effect
of country-of-origin institutions is akin to the finding that individuals who lived through
the Great Depression have persistently higher savings rates (see Meredith and Schewe,
1994).
Learning about Institutions
We have presented evidence that informal institutional constraints are embodied in
individuals and that these constraints influence financial market decisions even in a new
formal institutional framework. However, these findings do not address the question of
how or when these constraints become embodied in individuals. For example, are they
inherited and present even in individuals who migrated at a very young age? Or are they
only observed individuals who migrate as mature adults, consistent with the view that
they are shaped by an individual’s experience in their country of origin? Answers to
these questions can help us to better understand the channels through which informal
institutional constraints impact behavior.
We take an initial step toward answering these questions via the estimates presented in
Table 10. This table examines the effect of country-of-origin institutional quality on the
likelihood of having a bank account (top panel) and financial depth (bottom panel) for
subsets of immigrants based on their age of arrival in the U.S.
Table 10 divides the immigrant sample into three sub-samples based on age at arrival in
the U.S.: those who arrived before age 16, those who arrived when they were between 16
and 20 years and those who arrived when they were 21 years or older. Two estimates are
produced: one which includes controls for the calendar year when the immigrant arrived
in the U.S. and one which does not. Controlling for year of arrival in the U.S. generally
produces slightly smaller coefficients on institutional quality, so we only discuss the
findings which include these controls.
Informal institutional constraints from the country of origin are present even in very
young migrants. The effect of protection from expropriation is positive and statistically
significant for all groups, including the youngest migrants. The point estimate for the
likelihood of having a bank relationship ranges from 0.032, for immigrants who arrived
in the U.S. before the age of 16, to 0.019 for immigrants who arrived after their 21st
birthday. For financial depth, the estimates are somewhat smaller for the youngest
migrants, 0.054, versus 0.061 for the other groups. Note that differences in the size of the
coefficient across age groups are not significant.
20
The effect of country-of-origin institutions is present even in those who migrated when
they were less than 16 and before many of them would have been likely to have had
much direct experience with their country-of-origin institutions outside of school. They
would have been unlikely, for example, to have owned stock or had a bank account or to
have had direct experience with their country of birth’s legal system. This suggests that
families and possibly the educational system, and not just direct experience, play an
important role in shaping an individual’s perception of the trustworthiness of institutions.
Intergenerational Transmission of Informal Institutional Constraints
In a final set of estimates, we take another approach to examine the robustness of the
findings. This approach also helps to illuminate the mechanism through which
institutions come to influence behavior. Expanding on the findings from Table 10, which
show that the behavior of immigrants who arrived in the U.S. as young children is
influenced by home-country institutions, we consider whether institutional attitudes are
inherited. To do this we take advantage of the fact that the SIPP data provide information
on region or country of ancestry for individuals born in the U.S. We can map some of
these responses to individual countries and then estimate the effect of institutional quality
on having a bank account and financial depth for natives as well as for immigrants.35 The
estimates are presented in Table 11. The top panel reports results for the probability of
having a checking or a savings account, and the bottom panel reports results for financial
depth.
For immigrants, we find a positive and significant effect of institutional quality on
financial market participation. For those who were born in the U.S., but trace their
ancestry to one of the same countries, institutional quality has no effect on financial
market participation. When the formal institutional environment is altered as profoundly
as it is when an individual migrates from one country to another, the influence of
informal institutional constraints for financial market behavior do not appear to be passed
along to future generations. The SIPP data do not include information on the generation
of ancestry, so we are not able to refine these estimates by comparing the children of
immigrants and the grand-children of immigrants, for example.
Assessing the importance of institutions
One way to assess the importance of institutions is to see how much of the variation in
financial market participation for immigrants from different countries can be explained
by variation in institutional quality across countries. To do this, we regress bank account
ownership and financial depth on a full set of the control variables (age, age squared,
wealth quartiles, income, labor force status, education, sex, marital status, number of
children in household, race, MSA controls) as well as controls for country of origin. The
coefficients on the country of origin controls represent the “country” components of
financial market participation holding the control variables listed above fixed.
35
We form samples of natives and of immigrants who map their ancestry to or were born in one of the
following thirteen countries: Canada, France, the Netherlands, England, Germany, Hungary, Ireland, Italy,
Poland, Russia, Cuba, Mexico and the Dominican Republic.
21
These country components are then regressed on protection from expropriation. The Rsquared in that regression represents the percentage of the country level variation in
financial market participation that is explained by institutional quality. For bank account
ownership, the R-squared is 17 percent and for financial depth it is 27 percent. This
exercise suggests that institutions play an important role in explaining cross-country
variation in financial market participation.
5. Conclusions
We find that immigrants from countries with institutions that more effectively protect
private property and provide incentives for investment are more likely to have a bank
account in the U.S. and participate more extensively in U.S. financial markets. The effect
of home-country institutions effects immigrants for at least the first 28 years that they live
in the U.S. and is present in even immigrants who arrive in the U.S. as young children.
The impact of institutions is amplified by living in a neighborhood with many other
immigrants from the same country of origin. These findings are robust to alternative
measures of institutional effectiveness and to various methods of controlling for
unobserved individual characteristics, including specifications with country fixed-effects.
The results hint at the channel through which the institutional environment comes to
influence subsequent behavior. The fact that the impact of institutions is very persistent
and is present even in the youngest migrants, suggests that the main channel is not lack of
experience with banks and other institutions because a weak institutional environment
restricted supply and therefore the opportunity to gain experience with banks and other
financial entities. Instead, the findings seem most consistent with the idea that the
institutional environment becomes part of the cultural capital that individuals bring with
them wherever they move to. These preferences and beliefs appear to be quite resistant
to updating. Our findings are consistent with recent survey evidence that finds that
households often choose not to have bank accounts because they dislike dealing with
banks. According to a recent analysis of Survey of Consumer Finances data, “often they
are imbued with a cultural distrust of banks, and they may be concerned with privacy.”
What do our findings tell us about the likely results of efforts to increase financial access
in developing countries? First we learn that institutional reform is a very important tool
in the effort to expand financial access. In addition, institutional reform is likely to
increase financial access both directly (through the expansion of banks) and indirectly
(through beliefs and perceptions about the trustworthiness of financial institutions). In
addition, institutions matter even after controlling for wealth, income, and education.
This suggests that limited usage of financial services is not simply a problem of poverty
(see Claessens, 2005) and while poverty reduction is likely to increase financial market
participation, institutional reform has an important role to play as well.
However, mistrust of banks is deeply rooted in informal institutional constraints and is
resistant to change. We can think about the immigrant experience in the U.S. as an
experiment in institutional reform. In some sense this experiment corresponds to a best
case scenario for institutional reform: the change in the institutional environment is
credible, it is multi-faceted, affecting fiscal, monetary and trade policy as well as the
22
judicial and political system; and the majority of the individuals whose behavior we study
have, in some sense, sought out institutional change and are motivated to succeed
economically. Even in this environment, informal institutional constraints influence the
behavior of international migrants in the U.S. for decades.
23
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28
Figure 0: # of Financial Relationships, Natives and Immigrants
Natives: Number of Relationships to the Financial Sector
c
C1>
u o
(j)N
a..
0
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0 ~~~~--~~--~
~ -~
- ~
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1
2
3
4
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Number of F inancial Relationships
6
7
Immigrants: Number of Relationships to the Financial Sector
0
(")
0
.....
0
'---
0
1
2
3
4
Number of financial relationships
•5
-
6
7
29
Figure 1A : Bank Relationships and Institutional Quality
source: Table 4 Column (1)
•
•
•
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eiEI Salvador • Dominican Republic
2
4
8
6
protection from expropriation
I• pbankrelationavg
•
10
- - - Fitted values
Figure 1B: Number of Financial Relationships
and Institutional Quality (source: Table 4 Column (5))
•
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30
Figure 2A: Bank Relationships and Institutional Quality
Including Country Fixed Effects: Table 6 Column (2)
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paccessbkavg
Figure 28: Number of Financial Relationships and Institutional Qua
Including Country Fixed Effects: Table 6 Column (4)
•
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31
Table 1: Definitions and Sources of Country-level Variables
Variable
Protection from Expropriation
of Private Investment
British Legal Origin
English Speaking
Latitude
Av. Per Capita GDP
Catholic, Protestant, Muslim
Banking Freedom
Bank Concentration
Internet Usage
Definition and Source
This variable evaluates the risk "outright confiscation and forced
nationalization" of property. Lower ratings "are given to countries
where expropriation of private foreign investment is a likely event."
Variable is the average over annual country observations 1982 – 1995.
Source: International Country Risk Guide (ICRG) IRIS-3 Data
This variable is equal to one if the legal regime of the country is British
and zero otherwise. Source: “The Quality of Government” LaPorta,
Lopez-de-Silanez, Schleifer, Vishny (1999).
http://www.som.yale.edu/faculty/fl69/datasets.asp
This variable is equal to one if English is one of the official languages of
the country and if at least 50% of the immigrants from the country who
were surveyed in the 1980 U.S. Census report that they do not speak a
language other than English at home.
Source: Bleakley, Hoyt and Aimee Chin. “Language Skills and
Earnings: Evidence from Childhood Immigrants”, Review of Economics
and Statistics, May 2004.
This variable is equal to the absolute value of the latitude of the
country’s capital divided by 90.
Source: “The Quality of Government” LaPorta, Lopez-de-Silanez,
Schleifer, Vishny (1999).
http://www.som.yale.edu/faculty/fl69/datasets.asp
Average real GDP per capita 1982 – 1995, 1995 dollars.
Source: World Bank World Development Indicators.
The percentage of people in the country (x 100) who are a particular
religion.
Source: CIA Factbook.
This variable indicates the absence of government interference in the
banking system and is averaged over the period 1995-99. This indicator
is based on 5 questions: Specifically, this indicator is based on five
questions: (1) Does the government own banks? (2) Can foreign banks
open branches and subsidiaries? (3) Does the government influence
credit allocation? (4) Are banks free to operate without government
regulations such as deposit insurance? (5) Are banks free to offer all
types of financial services like buying and selling real estate, securities,
and insurance policies?
Source: Thorston Beck, Asli Demirguc –Kunt, and Vojislav
Maksimovic, “Bank Competition and Access to Finance:
International Evidence”, Journal of Money, Credit, and Banking, Vol.
36, No. 3 (June 2004, Part 2) (citing to data indicator from the Heritage
Foundation, http://www.heritage.org/)
This variable indicates the share of the assets of the largest three banks
in total banking sector assets, averaged over 1995-1999. The variable is
calculated using commercial, savings, and cooperative banks as well as
nonbank credit institutions data from the Bankscope database.
Source: Thorston Beck, Asli Demirguc –Kunt, and Vojislav
Maksimovic, “Bank Competition and Access to Finance:
International Evidence”, Journal of Money, Credit, and Banking, Vol.
36, No. 3 (June 2004, Part 2)
Average number of Internet users per 1,000 people, 1982-1995.
“Internet users” is defined as people with access to the worldwide
network.
Source: World Bank World Development Indicators.
32
Table 2A: Characteristics of Immigrants and the Native Born in the SIPP Data
Characteristic
Individual Characteristics
Age
% Male
% Married
% non-white
% unemployed or out of the labor force
# of children < 18 in household
Average monthly per capita household income
Median monthly per capita household income
Average household wealth
25th percentile of household wealth
Median household wealth
75th percentile of household wealth
Educational Attainment (%)
Less than High School
High School Graduate
Some College
Bachelor Degree
Advanced Degree
Financial Market Participation
Financial Breadth (% with bank relationship)
Financial Depth (# of types of financial relationships) mean
Native Born
Immigrant
46.47
(17.52)
45.6%
57.4%
16.4%
33.8%
0.720
(1.090)
$2,224.44
(2,832.45)
$1,578
$185,754
(1,398,146)
$14,660
$71,123
$186,512
45.22
(16.51)
46.2%
65.6%
32.2%
36.7%
1.118
(1.347)
$1,639.53
(2,375.44)
$1,050
$122,685
(978,910)
$3,017
$29,001
$117,917
15.0%
30.4%
30.6%
15.9%
8.1%
35.8%
24.5%
20.1%
12.5%
7.1%
76.3%
1.71
(1.02)
2.00
20.0%
63.8%
54.8%
61.0%
1.22
(1.01)
1.00
8.6%
47.0%
40.1%
Financial Depth - median
% who own stock
% with a checking account (interest or non-interest)
% with a savings account
Other characteristics (%)
% who are self-employed
9.1%
8.7%
% who drive own car to work
81.7%
75.1%
% who visited doctor in past 12 months
78.8%
79.3%
% who purchased prescription drugs for children
51.8%
34.1%
Number of Individuals
31,046
5,020
Number of Observations
100,839
15,043
Notes: Unless otherwise noted, mean values are reported. Standard deviations are in parentheses. The unit
of observation is a person-wave. Sample is restricted to the four waves of the Survey on Income and
Program Participation with wealth information, to individuals 18 and over, to those who live in a
Metropolitan Statistical Area and to those who have non-missing data for “expropriation risk”. See text for
definitions of financial breadth and depth.
33
Table 2B: Immigrant Characteristics
Characteristic
Immigrant
Year of Arrival in the U.S. (%)
Before 1964
11.5%
1965 – 1969
8.2%
1970 – 1974
10.1%
1975 – 1979
12.8%
1980 – 1984
17.9%
1985 – 1989
18.4%
1990 – 1996
21.2%
Age at Migration (%)
five years or younger
3.7%
six to ten years
4.6%
eleven to fifteen years
6.8%
sixteen to twenty years
14.3%
over twenty years
70.6%
Continent of Origin (%)
North America
46.9%
Europe
15.4%
Asia
30.3%
Africa
0.9%
South America
6.3%
Australia and Oceania
0.2%
Notes: Unless otherwise noted, mean values are reported. Standard deviations are in parentheses. The unit
of observation is a person-wave. Sample is restricted to the four waves of the Survey on Income and
Program Participation with wealth information, to individuals 18 and over, to those who live in a
Metropolitan Statistical Area and to those who have non-missing data for “expropriation risk”.
34
Table 2C: Distribution of Financial Asset Ownership by Number of Financial
Relationships
Natives
% of Total
Savings
Checking
Stock
IRA
CD
Moneymarket
Keogh
0
22.4%
1
23.4%
37.3%
58.6%
2.4%
1.3%
1.1%
0.1%
0.0%
# of Financial Relationships
2
3
4
26.4%
15.1%
7.8%
84.2%
84.6%
83.8%
88.9%
92.6%
94.9%
10.9%
47.1%
65.6%
8.1%
36.5%
63.1%
5.7%
24.3%
40.9%
4.6%
19.7%
48.9%
0.2%
0.7%
2.9%
5
3.9%
91.8%
97.6%
84.6%
80.7%
59.4%
80.6%
5.2%
6
1.1%
98.7%
99.6%
98.6%
99.1%
92.9%
97.9%
13.0%
7
0.1%
100%
100%
100%
100%
100%
100%
100%
0
38.2%
1
28.7%
37.9%
58.3%
2.2%
1.2%
1.1%
1.0%
0.0%
# of Financial Relationships
2
3
4
19.3%
7.9%
3.7%
84.3%
84.8%
83.6%
89.1%
92.5%
94.8%
10.4%
46.3%
65.3%
7.9%
36.3%
63.2%
5.9%
24.7%
40.9%
4.8%
19.9%
49.2%
0.1%
0.1%
3.0%
5
1.5%
91.6%
97.7%
84.5%
81.0%
59.3%
80.8%
5.2%
6
0.7%
98.6%
99.7%
98.7%
98.9%
93.2%
98.1%
12.8%
7
0.02%
100%
100%
100%
100%
100%
100%
100%
Immigrants
% of Total
Savings
Checking
Stock
IRA
CD
Moneymarket
Keogh
35
Table 3A: Summary of Country Variables
Characteristic
Measures of Institutional Quality
Protection from Expropriation
British Legal Origin
Latitude
Other Country Characteristics
English Speaking
GDP Per Capita
Catholic
Protestant
Muslim
Internet Usage
Banking Freedom
Bank Concentration
N
Mean
Standard
Deviation
Min
Median
Max
U.S.
value
79
79
79
7.50
0.29
0.33
1.74
0.46
0.19
1.81
0.00
0.01
7.51
0.00
0.33
10.00
1.00
0.71
10.00
1.00
0.42
79
74
74
74
74
77
48
48
0.139
8,704
39.29
10.43
16.82
4.46
3.34
0.52
0.35
10,376
40.01
19.39
33.77
8.40
0.73
0.18
0.00
106
0.00
0.00
0.00
0.00
1.60
0.18
0.00
3,208
27.2
0.35
0.00
.43
3.30
0.51
1.00
42,873
97.00
87.00
100.00
41.77
5.00
0.97
1.00
24,831
24.00
52.00
1.00
33.96
4.00
0.18
36
Table 3B: Correlation between Country Variables
Protection
British Legal
Latitude
Internet Access
Banking
From Exp
Origin
Freedom
Protection from Expropriation
---British Legal Origin
0.119
---Latitude
0.572***
-0.200*
---Internet Usage
0.607***
0.114
0.530***
---Banking Freedom
0.334**
0.046
0.283**
0.312**
---Bank Concentration
-0.296**
0.006
-0.069
-0.173
-0.335**
Notes: *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.
Characteristic
Bank
Concentration
----
Table 4: The Effect of Institutional Quality on Financial Market Participation
Explanatory Variable
Protection from
Expropriation
Latitude
British Legal Origin
Probability of Having a Bank Relationship
[1]
[2]
[3]
[4]
0.027***
0.022***
(0.005)
(0.007)
0.192***
0.057
(0.070)
(0.085)
0.044*
0.017
(0.023)
(0.020)
Depth of Financial Market Participation
[5]
[6]
[7]
[8]
0.071***
0.056***
(0.012)
(0.016)
0.507***
0.156
(0.165)
(0.176)
0.137**
0.068
(0.059)
(0.049)
Adjusted R-Squared
0.2666
0.2644
0.2625
0.2668
0.3969
0.3932
0.3906
0.3976
Number of Observations
14,232
14,232
14,232
14,232
14,232
14,232
14,232
14,232
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, marital
status, sex, ethnicity, education, number of children and MSA controls. The number of observations differs depending on the number of countries for which a
particular measure of institutional quality is available. A linear model is used and standard errors are corrected for heteroskedasticity and clustering at the
country level. Standard errors are in parentheses. *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.
Table 5A: The Effect of Institution Quality on the Probability of Having a Bank Relationship, Additional Country Controls
Explanatory Variable
Protection from Expropriation
Av. Per Capita GDP†
[1]
0.027 ***
(0.005)
[2]
0.023 ***
(0.007)
[3]
0.023 ***
(0.005)
[4]
0.020 ***
(0.008)
2.490 **
(1.180)
English Speaking
[5]
0.019 **
(0.008)
2.470 **
(1.150)
0.024
(0.018)
Bank Freedom
[6]
0.029 **
(0.008)
-0.301
(1.090)
-0.020
(0.035)
0.037 **
(0.015)
Internet Usage
Religion Controls
No
No
Yes
No
No
No
Continent Controls
No
Yes
No
No
No
No
[7]
0.030
(0.008)
-3.290
(1.370)
-0.078
(0.035)
0.040
(0.014)
0.010
(0.002)
No
***
**
**
***
***
No
Adjusted R-Squared
0.2666
0.2697
0.2706
0.2687
0.2688
0.2877
0.2887
Number of Observations
14,232
14,232
10,799
10,799
13,250
13,336
13,336
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, marital
status, sex, ethnicity, education, number of children and MSA controls. The number of observations differs depending on the number of countries for which a
particular country characteristic is available. A linear model is used and standard errors are corrected for heteroskedasticity and clustering at the country level.
The reported coefficients and standard errors of explanatory variables marked by a † are the actual ones multiplied by 1,000,000. Standard errors are in
parentheses. *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.
Table 5B: The Effect of Institution Quality on Depth of Financial Market Participation, Additional Country Controls
Explanatory Variable
Protection from Expropriation
Av. Per Capita GDP†
[1]
0.071 ***
(0.012)
[2]
0.056 ***
(0.015)
[3]
0.057 ***
(0.013)
[4]
0.044 ***
(0.016)
8.730 ***
(2.770)
English Speaking
[5]
0.039 **
(0.017)
8.660 ***
(2.760)
0.080 *
(0.049)
Bank Freedom
[6]
0.055 ***
(0.014)
7.170 ***
(2.620)
-0.0001
(0.068)
0.056 **
(0.026)
Internet Usage
[7]
0.056 ***
(0.014)
2.30
(3.260)
-0.095
(0.091)
0.061 **
(0.026)
0.016
(0.007) **
No
Religion Controls
No
No
Yes
No
No
No
Continent Controls
No
Yes
No
No
No
No
No
0.3969
14,232
0.3997
14,232
0.3981
13,250
0.4043
13,336
0.4047
13,336
0.4179
10,799
0.4185
10,799
Adjusted R-Squared
Number of Observations
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, marital
status, sex, ethnicity, education, number of children and MSA controls. The number of observations differs depending on the number of countries for which a
particular country characteristic is available. A linear model is used and standard errors are corrected for heteroskedasticity and clustering at the country level.
The reported coefficients and standard errors of explanatory variables marked by a † are the actual ones multiplied by 1,000,000. Standard errors are in
parentheses. *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.
Table 6: Does Enforcement Matter?
Controlling for Unobserved Heterogeneity, Institution Quality and Ethnic Concentration
Explanatory Variable
Protection from Expropriation * Ethnic Concentration
Ethnic Concentration
Country Controls
Probability of Having a Bank Relationship
[1]
[2]
1.118***
0.827*
(0.300)
(0.487)
-8.638***
-5.734*
(2.225)
(3.581)
No
Yes
Depth of Financial Market Participation
[3]
[4]
2.704***
2.422**
(0.604)
(1.075)
-20.989***
-18.090**
(4.516)
(7.995)
No
Yes
Adjusted R-Squared
0.2626
0.2754
0.3914
0.4104
Number of Observations
13,867
13,867
13,867
13,867
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, marital
status, sex, ethnicity, education, number of children and MSA controls. A linear model is used and standard errors are corrected for heteroskedasticity and
clustering at the individual level. Standard errors are in parentheses. *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least
the 10% level.
Table 7: Do Institutions Matter Differently for Different Types of Behavior?
A. Baseline specification
Protection from Expropriation
Mean of Dependent Variable
Adjusted R-Squared
Number of Observations
Stock Ownership
[1]
0.016 ***
(0.005)
Savings Account
[2]
0.022 ***
(0.006)
Checking
Account
[3]
0.024 ***
(0.006)
Self-Employment
[4]
0.007 *
(0.004)
Drive own Car to
Work
[5]
-0.003
(0.006)
Visited a Doctor
in Past 12
Months
[6]
0.002
(0.003)
8.6%
40.1%
47.0%
8.7%
75.1%
79.3%
0.2315
14,232
0.1835
14,232
0.2386
14,232
0.1090
14,231
0.0573
7,546
0.0035
8,705
B. Controlling for Unobserved Heterogeneity
Protection from Expropriation x
Ethnic Concentration
0.696 *
-0.282
1.625 ***
0.037
0.892
-0.268
(0.369)
(0.598)
(0.496)
(0.340)
(0.658)
(0.492)
Ethnic Concentration
-5.142 *
2.533
-12.004 ***
-0.400
-6.702
1.839
(2.757)
(4.417)
(3.665)
(2.499)
(4.820)
(3.619)
Country Controls
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R-squared
0.2599
0.1973
0.2492
0.1230
0.0682
0.0017
Number of Observations
13,867
13,867
13,867
13,866
7,340
8,474
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, marital
status, sex, ethnicity, education, number of children and MSA controls. A linear model is used and standard errors are corrected for heteroskedasticity and
clustering at the country level. Standard errors are in parentheses. *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the
10% level. “Checking Account” is equal to one if the respondent has a checking account that either does or does not pay interest. “Savings Account” is equal to
one if the respondent has a savings account and zero otherwise. “Entrepreneur” is equal to one if the respondent is the owner or part-owner of a business, and
zero otherwise. “Drives own Car to Work” is asked of respondents who are employed or own a business and is equal to one if the respondent drives to work and
is equal to zero otherwise. “Visited a Doctor in the past 12 months” is equal to one if the responded visited a doctor in the 12 months before the survey question
and zero otherwise.
Table 8: Do Institutions Matter Differently for Different Types of Immigrants
High Educ.
Low Educ.
High Skill
Low Skill
Exclude
Baseline
Immig.
Immig.
Workers
Workers
Citizens
Mexico
Probability of Bank Relationship
[2]
[3]
[4]
[5]
[7]
[1]
[6]
Protection from Expropriation
0.027***
0.022***
0.044***
0.006
0.033***
0.021***
0.031***
(0.005)
(0.006)
(0.010)
(0.005)
(0.010)
(0.006)
(0.005)
Adjusted R-Squared
0.2666
0.1558
0.2453
0.1478
0.2482
0.1819
0.2281
Depth of Financial Market Participation
Protection from Expropriation
0.071***
0.074***
0.082***
0.059***
0.071***
0.066***
0.073***
(0.012)
(0.019)
(0.019)
(0.019)
(0.020)
(0.014)
(0.011)
Adjusted R-Squared
0.3969
0.3270
0.2875
0.3311
0.3060
0.3314
0.3688
Number of Observations
14,232
2,842
5,127
1,984
2,408
5,829
10,199
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, marital
status, sex, ethnicity, education, number of children and MSA controls. A linear model is used and standard errors are corrected for heteroskedasticity and
clustering at the country level. High education immigrants are those with a bachelor’s degree or more education. Low education immigrants are those with less
than a high school degree. Standard errors are in parentheses. *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10%
level. “High Skill Workers” are workers whose 3-digit occupation code from SIPP is mapped into “Professional and Technical” or “Executive, Administrative,
and Managerial” according to Bureau of Labor Statistics classifications. “Low Skill Workers” include workers whose 3-digit occupation code from SIPP is
similarly mapped into “Transportation and Material Moving Occupations”, “Handlers, Equipment Cleaners, Helpers and Laborers”, and “Service Occupations,
Except Private Household”.
Table 9: The Persistence of Institutions
The Effect Institution Quality on the Financial Market Participation by Years of
U.S. Experience
Probability of Bank Relationship
ALL
No Age at Arrival Controls
Protection from Expropriation
Adjusted R-Squared
Age at Arrival Controls
Protection from Expropriation
0.027***
(0.005)
0.2666
Years in the U.S.
13 – 17
18 – 27
1–7
8 – 12
0.022**
(0.011)
0.3653
0.029**
(0.011)
0.3168
0.023***
(0.008)
0.3033
0.027***
(0.009)
0.2653
28+
0.012
(0.011)
0.2369
0.027***
0.022**
0.027** 0.022*** 0.027***
0.010
(0.005)
(0.011)
(0.011)
(0.008)
(0.009)
(0.011)
Adjusted R-Squared
0.2673
0.3679
0.3191
0.3046
0.2656
0.2427
Depth of Financial Market Participation
Years in the U.S.
ALL
1–7
8 – 12
13 – 17
18 – 27
28+
No Age at Arrival Controls
Protection from Expropriation
0.071***
0.052** 0.078*** 0.081*** 0.083***
0.050*
(0.012)
(0.023)
(0.024)
(0.019)
(0.020)
(0.029)
Adjusted R-Squared
0.3969
0.4293
0.4593
0.4369
0.4284
0.3967
Age at Arrival Controls
Protection from Expropriation
0.069***
0.052** 0.075*** 0.081*** 0.083***
0.039
(0.012)
(0.022)
(0.023)
(0.020)
(0.020)
(0.028)
Adjusted R-Squared
0.3978
0.4312
0.4630
0.4364
0.4283
0.4012
Number of Observations
14,232
2,619
2,192
2,145
2,750
2,955
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared,
wealth quartiles, labor force status, income, marital status, sex, ethnicity, education, number of children and
MSA controls. A linear model is used and standard errors are corrected for heteroskedasticity and
clustering at the country level. Standard errors are in parentheses. *** indicates significance at at least the
1% level, ** at at least the 5% level, * at at least the 10% level.
Table 10: Learning about Institutions
The Effect Institution Quality on the Financial Market Participation by Age at
Migration
Probability of Bank Relationship
ALL
No Year of Arrival Controls
Protection from Expropriation
Adjusted R-Squared
Year of Arrival Controls
Protection from Expropriation
0.027***
(0.005)
0.2666
1 – 15
0.032***
(0.011)
0.3322
Age at Arrival in U.S.
16 – 20
0.026**
(0.013)
0.3319
21+
0.019***
(0.005)
0.2720
0.028***
0.029**
0.025*
0.019***
(0.005)
(0.013)
(0.013)
(0.005)
Adjusted R-Squared
0.2698
0.3350
0.3319
0.2769
Depth of Financial Market Participation
Age at Arrival in U.S.
ALL
1 – 15
16 – 20
21+
No Year of Arrival Controls
Protection from Expropriation
0.071***
0.061***
0.061**
0.060***
(0.012)
(0.020)
(0.029)
(0.013)
Adjusted R-Squared
0.43969
0.4727
0.4344
0.4051
Year of Arrival Controls
Protection from Expropriation
0.071***
0.054**
0.062**
0.061***
(0.013)
(0.022)
(0.028)
(0.014)
Adjusted R-Squared
0.4006
0.4742
0.4384
0.4126
Number of Observations
14,232
1,677
1,639
7,963
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared,
wealth quartiles, labor force status, income, marital status, sex, ethnicity, education, number of children,
and MSA controls. A linear model is used and standard errors are corrected for heteroskedasticity and
clustering at the country level. Standard errors are in parentheses. *** indicates significance at at least the
1% level, ** at at least the 5% level, * at at least the 10% level.
Table 11: Intergenerational Transmission of Institutional Lessons
The Effect of Institution Quality on Financial Market Participation
Selected Natives and Immigrants
Probability of Bank Relationship
Protection from Expropriation
Adjusted R-Squared
Depth of Financial Market Participation
Protection from Expropriation
Native
Immigrant
-0.0001
(0.012)
0.041***
(0.010)
0.2226
Native
0.039
(0.038)
0.2964
Immigrant
0.127***
(0.029)
Adjusted R-Squared
0.3666
0.4300
Number of Observations
44,181
7,040
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared,
wealth quartiles, labor force status, income, marital status, sex, ethnicity, education, number of children and
MSA controls. A linear model is used and standard errors are corrected for heteroskedasticity and
clustering at the country level. Standard errors are in parentheses. *** indicates significance at at least the
1% level, ** at at least the 5% level, * at at least the 10% level. The native-sample used in these estimates
includes U.S. born individuals who identified their ancestral country as: Canada, France, the Netherlands,
England, Germany, Hungary, Ireland, Italy, Poland, Russia, Cuba, Mexico, and the Dominican Republic.
The immigrant sample includes foreign-born individuals who were born in these same countries.
Appendix Table 1: Ethnic Concentration and Number of observations per country
Country
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
36
37
Argentina
Australia
Austria
Bahamas, The
Bangladesh
Belgium
Bolivia
Brazil
Canada
Chile
China
Colombia
Costa Rica
Cuba
Czechoslovakia36
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
England37
Ethiopia
Finland
France
Germany (East and West)
Ghana
Greece
Guatemala
Guyana
Haiti
Honduras
Hong Kong
Hungary
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kenya
Korea, South
Lebanon
Median Ethnic
Concentration (%)
0.0747%
0.0580%
0.0633%
----0.0314%
0.0479%
0.0965%
0.6848%
0.0717%
0.8739%
0.7335%
--17.3184%
0.1673%
0.0704%
1.5686%
0.6477%
0.1477%
1.0890%
0.5252%
0.0815%
0.0145%
0.1185%
0.4858%
--0.3521%
0.1697%
0.5743%
0.6296%
0.1611%
0.1917%
0.1195%
0.3301%
0.1241%
0.0987%
0.1540%
0.1394%
0.1350%
0.5840%
1.0060%
0.3189%
0.0270%
--0.5308%
0.0330%
Number of Observations
Includes individuals who reported that they were born in: Czechoslovakia, Slovakia, Czech Republic.
Includes individuals who reported that they were born in: England, United Kingdom, Scotland, Wales, Northern Ireland.
96
30
64
11
40
21
36
55
392
77
595
217
34
617
38
7
267
172
38
494
419
5
3
88
373
16
124
158
134
219
143
139
72
417
1
145
53
136
53
290
318
182
16
7
438
52
Appendix Table 1: Ethnic Concentration and Number of observations per country,
continued
Country
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
Malaysia
Mexico
Morocco
Myanmar
Netherlands
New Zealand
Nicaragua
Nigeria
Norway
Pakistan
Panama
Peru
Philippines
Poland
Portugal
Romania
Russia38
Singapore
South Africa
Spain
Sweden
Switzerland
Syria
Taiwan
Thailand
Trinidad & Tobago
Turkey
Uruguay
Venezuela
Vietnam
Yugoslavia39
All
Median Ethnic
Concentration (%)
0.0232%
8.5920%
----0.1492%
0.0027%
0.3377%
0.0562%
0.0386%
0.0764%
0.0652%
0.2147%
1.8140%
0.7874%
0.3592%
0.0873%
0.4919%
0.0130%
0.0297%
0.1249%
0.0388%
0.0464%
0.0511%
0.2270%
0.0547%
0.4149%
0.0555%
0.0644%
0.3400%
0.7256%
0.1243%
0.7829%
Number of Observations
19
4,163
6
23
46
3
81
39
19
84
26
108
916
249
88
52
365
3
24
66
29
16
42
216
79
131
15
7
46
663
117
15,043
38
Includes individuals who reported that they were born in: Russia, Armenia, Azerbijan, the Baltic States, Belarus, Estonia, Georgia,
Kazakhstan, Kyrgyztan, Latvia, Lithuania, Moldova, Tajikistan, Turkmenistan, Ukraine, USSR, Uzebekistan.
39
Includes individuals who reported that they were born in: Yugoslavia, Bosnia and Herzogovina, Croatia, Macedonia, Montenegro,
Slovenia, Serbia.
Appendix Table 2: The Effect of Control Variables on the Probability of Having a
Bank Relationship and Depth of Financial Market Participation
Explanatory Variable
Age†
Age Squared†
2nd Wealth Quartile
3rd Wealth Quartile
4th Wealth Quartile
Unemployed or Out of Labor Force
Per Capita Income††
Per Capita Income Squared††
Male
Married
Number of Children
Non-white
High School Graduate
Some College
Bachelor Degree
Advance Degree
Protection from Expropriation
Constant
MSA Controls
Probability of
Having a Bank
Relationship
[1]
0.704 ***
(0.153)
-0.004 ***
(0.002)
0.183 ***
(0.015)
0.173 ***
(0.017)
0.168 ***
(0.019)
-0.086 ***
(0.013)
37.900 ***
(8.540)
-0.001 ***
(0.000)
-0.050 ***
(0.007)
0.163 ***
(0.014)
-0.022 ***
(0.005)
0.019
(0.018)
0.126 ***
(0.015)
0.187 ***
(0.015)
0.200 ***
(0.018)
0.189 ***
(0.021)
0.027 ***
(0.005)
-0.115 ***
(0.056)
Yes
Depth of Financial
Market
Participation
[2]
1.818 ***
(0.394)
-0.015 ***
(0.004)
0.331 ***
(0.029)
0.444 ***
(0.044)
0.689 ***
(0.048)
-0.073 **
(0.032)
115.200 ***
(16.300)
-0.003 ***
(0.001)
-0.120 ***
(0.017)
0.302 ***
(0.029)
-0.046 ***
(0.014)
0.012
(0.041)
0.235 ***
(0.022)
0.404 ***
(0.033)
0.449 ***
(0.046)
0.595 ***
(0.049)
0.071 ***
(0.012)
-0.731 ***
(0.119)
Yes
Adjusted R-Squared
0.2666
0.3969
Number of Observations
14,232
14,232
Notes: A linear model is used and standard errors are corrected for heteroskedasticity and clustering at the
country level. Standard errors are in parentheses. The reported coefficients and standard errors of
explanatory variables marked by a † are the actual ones multiplied by 100, by a †† are multiplied by
1,000,000. The lowest wealth quartile is the omitted wealth category, and 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.
Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
A Proposal for Efficiently Resolving Out-of-the-Money Swap Positions
at Large Insolvent Banks
George G. Kaufman
WP-03-01
Depositor Liquidity and Loss-Sharing in Bank Failure Resolutions
George G. Kaufman
WP-03-02
Subordinated Debt and Prompt Corrective Regulatory Action
Douglas D. Evanoff and Larry D. Wall
WP-03-03
When is Inter-Transaction Time Informative?
Craig Furfine
WP-03-04
Tenure Choice with Location Selection: The Case of Hispanic Neighborhoods
in Chicago
Maude Toussaint-Comeau and Sherrie L.W. Rhine
WP-03-05
Distinguishing Limited Commitment from Moral Hazard in Models of
Growth with Inequality*
Anna L. Paulson and Robert Townsend
WP-03-06
Resolving Large Complex Financial Organizations
Robert R. Bliss
WP-03-07
The Case of the Missing Productivity Growth:
Or, Does information technology explain why productivity accelerated in the United States
but not the United Kingdom?
Susanto Basu, John G. Fernald, Nicholas Oulton and Sylaja Srinivasan
WP-03-08
Inside-Outside Money Competition
Ramon Marimon, Juan Pablo Nicolini and Pedro Teles
WP-03-09
The Importance of Check-Cashing Businesses to the Unbanked: Racial/Ethnic Differences
William H. Greene, Sherrie L.W. Rhine and Maude Toussaint-Comeau
WP-03-10
A Firm’s First Year
Jaap H. Abbring and Jeffrey R. Campbell
WP-03-11
Market Size Matters
Jeffrey R. Campbell and Hugo A. Hopenhayn
WP-03-12
The Cost of Business Cycles under Endogenous Growth
Gadi Barlevy
WP-03-13
The Past, Present, and Probable Future for Community Banks
Robert DeYoung, William C. Hunter and Gregory F. Udell
WP-03-14
1
Working Paper Series (continued)
Measuring Productivity Growth in Asia: Do Market Imperfections Matter?
John Fernald and Brent Neiman
WP-03-15
Revised Estimates of Intergenerational Income Mobility in the United States
Bhashkar Mazumder
WP-03-16
Product Market Evidence on the Employment Effects of the Minimum Wage
Daniel Aaronson and Eric French
WP-03-17
Estimating Models of On-the-Job Search using Record Statistics
Gadi Barlevy
WP-03-18
Banking Market Conditions and Deposit Interest Rates
Richard J. Rosen
WP-03-19
Creating a National State Rainy Day Fund: A Modest Proposal to Improve Future
State Fiscal Performance
Richard Mattoon
WP-03-20
Managerial Incentive and Financial Contagion
Sujit Chakravorti and Subir Lall
WP-03-21
Women and the Phillips Curve: Do Women’s and Men’s Labor Market Outcomes
Differentially Affect Real Wage Growth and Inflation?
Katharine Anderson, Lisa Barrow and Kristin F. Butcher
WP-03-22
Evaluating the Calvo Model of Sticky Prices
Martin Eichenbaum and Jonas D.M. Fisher
WP-03-23
The Growing Importance of Family and Community: An Analysis of Changes in the
Sibling Correlation in Earnings
Bhashkar Mazumder and David I. Levine
WP-03-24
Should We Teach Old Dogs New Tricks? The Impact of Community College Retraining
on Older Displaced Workers
Louis Jacobson, Robert J. LaLonde and Daniel Sullivan
WP-03-25
Trade Deflection and Trade Depression
Chad P. Brown and Meredith A. Crowley
WP-03-26
China and Emerging Asia: Comrades or Competitors?
Alan G. Ahearne, John G. Fernald, Prakash Loungani and John W. Schindler
WP-03-27
International Business Cycles Under Fixed and Flexible Exchange Rate Regimes
Michael A. Kouparitsas
WP-03-28
Firing Costs and Business Cycle Fluctuations
Marcelo Veracierto
WP-03-29
Spatial Organization of Firms
Yukako Ono
WP-03-30
Government Equity and Money: John Law’s System in 1720 France
François R. Velde
WP-03-31
2
Working Paper Series (continued)
Deregulation and the Relationship Between Bank CEO
Compensation and Risk-Taking
Elijah Brewer III, William Curt Hunter and William E. Jackson III
WP-03-32
Compatibility and Pricing with Indirect Network Effects: Evidence from ATMs
Christopher R. Knittel and Victor Stango
WP-03-33
Self-Employment as an Alternative to Unemployment
Ellen R. Rissman
WP-03-34
Where the Headquarters are – Evidence from Large Public Companies 1990-2000
Tyler Diacon and Thomas H. Klier
WP-03-35
Standing Facilities and Interbank Borrowing: Evidence from the Federal Reserve’s
New Discount Window
Craig Furfine
WP-04-01
Netting, Financial Contracts, and Banks: The Economic Implications
William J. Bergman, Robert R. Bliss, Christian A. Johnson and George G. Kaufman
WP-04-02
Real Effects of Bank Competition
Nicola Cetorelli
WP-04-03
Finance as a Barrier To Entry: Bank Competition and Industry Structure in
Local U.S. Markets?
Nicola Cetorelli and Philip E. Strahan
WP-04-04
The Dynamics of Work and Debt
Jeffrey R. Campbell and Zvi Hercowitz
WP-04-05
Fiscal Policy in the Aftermath of 9/11
Jonas Fisher and Martin Eichenbaum
WP-04-06
Merger Momentum and Investor Sentiment: The Stock Market Reaction
To Merger Announcements
Richard J. Rosen
WP-04-07
Earnings Inequality and the Business Cycle
Gadi Barlevy and Daniel Tsiddon
WP-04-08
Platform Competition in Two-Sided Markets: The Case of Payment Networks
Sujit Chakravorti and Roberto Roson
WP-04-09
Nominal Debt as a Burden on Monetary Policy
Javier Díaz-Giménez, Giorgia Giovannetti, Ramon Marimon, and Pedro Teles
WP-04-10
On the Timing of Innovation in Stochastic Schumpeterian Growth Models
Gadi Barlevy
WP-04-11
Policy Externalities: How US Antidumping Affects Japanese Exports to the EU
Chad P. Bown and Meredith A. Crowley
WP-04-12
Sibling Similarities, Differences and Economic Inequality
Bhashkar Mazumder
WP-04-13
3
Working Paper Series (continued)
Determinants of Business Cycle Comovement: A Robust Analysis
Marianne Baxter and Michael A. Kouparitsas
WP-04-14
The Occupational Assimilation of Hispanics in the U.S.: Evidence from Panel Data
Maude Toussaint-Comeau
WP-04-15
Reading, Writing, and Raisinets1: Are School Finances Contributing to Children’s Obesity?
Patricia M. Anderson and Kristin F. Butcher
WP-04-16
Learning by Observing: Information Spillovers in the Execution and Valuation
of Commercial Bank M&As
Gayle DeLong and Robert DeYoung
WP-04-17
Prospects for Immigrant-Native Wealth Assimilation:
Evidence from Financial Market Participation
Una Okonkwo Osili and Anna Paulson
WP-04-18
Individuals and Institutions: Evidence from International Migrants in the U.S.
Una Okonkwo Osili and Anna Paulson
WP-04-19
Are Technology Improvements Contractionary?
Susanto Basu, John Fernald and Miles Kimball
WP-04-20
The Minimum Wage, Restaurant Prices and Labor Market Structure
Daniel Aaronson, Eric French and James MacDonald
WP-04-21
Betcha can’t acquire just one: merger programs and compensation
Richard J. Rosen
WP-04-22
Not Working: Demographic Changes, Policy Changes,
and the Distribution of Weeks (Not) Worked
Lisa Barrow and Kristin F. Butcher
WP-04-23
The Role of Collateralized Household Debt in Macroeconomic Stabilization
Jeffrey R. Campbell and Zvi Hercowitz
WP-04-24
Advertising and Pricing at Multiple-Output Firms: Evidence from U.S. Thrift Institutions
Robert DeYoung and Evren Örs
WP-04-25
Monetary Policy with State Contingent Interest Rates
Bernardino Adão, Isabel Correia and Pedro Teles
WP-04-26
Comparing location decisions of domestic and foreign auto supplier plants
Thomas Klier, Paul Ma and Daniel P. McMillen
WP-04-27
China’s export growth and US trade policy
Chad P. Bown and Meredith A. Crowley
WP-04-28
Where do manufacturing firms locate their Headquarters?
J. Vernon Henderson and Yukako Ono
WP-04-29
Monetary Policy with Single Instrument Feedback Rules
Bernardino Adão, Isabel Correia and Pedro Teles
WP-04-30
4
Working Paper Series (continued)
Firm-Specific Capital, Nominal Rigidities and the Business Cycle
David Altig, Lawrence J. Christiano, Martin Eichenbaum and Jesper Linde
WP-05-01
Do Returns to Schooling Differ by Race and Ethnicity?
Lisa Barrow and Cecilia Elena Rouse
WP-05-02
Derivatives and Systemic Risk: Netting, Collateral, and Closeout
Robert R. Bliss and George G. Kaufman
WP-05-03
Risk Overhang and Loan Portfolio Decisions
Robert DeYoung, Anne Gron and Andrew Winton
WP-05-04
Characterizations in a random record model with a non-identically distributed initial record
Gadi Barlevy and H. N. Nagaraja
WP-05-05
Price discovery in a market under stress: the U.S. Treasury market in fall 1998
Craig H. Furfine and Eli M. Remolona
WP-05-06
Politics and Efficiency of Separating Capital and Ordinary Government Budgets
Marco Bassetto with Thomas J. Sargent
WP-05-07
Rigid Prices: Evidence from U.S. Scanner Data
Jeffrey R. Campbell and Benjamin Eden
WP-05-08
Entrepreneurship, Frictions, and Wealth
Marco Cagetti and Mariacristina De Nardi
WP-05-09
Wealth inequality: data and models
Marco Cagetti and Mariacristina De Nardi
WP-05-10
What Determines Bilateral Trade Flows?
Marianne Baxter and Michael A. Kouparitsas
WP-05-11
Intergenerational Economic Mobility in the U.S., 1940 to 2000
Daniel Aaronson and Bhashkar Mazumder
WP-05-12
Differential Mortality, Uncertain Medical Expenses, and the Saving of Elderly Singles
Mariacristina De Nardi, Eric French, and John Bailey Jones
WP-05-13
Fixed Term Employment Contracts in an Equilibrium Search Model
Fernando Alvarez and Marcelo Veracierto
WP-05-14
Causality, Causality, Causality: The View of Education Inputs and Outputs from Economics
Lisa Barrow and Cecilia Elena Rouse
WP-05-15
5
Working Paper Series (continued)
Competition in Large Markets
Jeffrey R. Campbell
WP-05-16
Why Do Firms Go Public? Evidence from the Banking Industry
Richard J. Rosen, Scott B. Smart and Chad J. Zutter
WP-05-17
Clustering of Auto Supplier Plants in the U.S.: GMM Spatial Logit for Large Samples
Thomas Klier and Daniel P. McMillen
WP-05-18
Why are Immigrants’ Incarceration Rates So Low?
Evidence on Selective Immigration, Deterrence, and Deportation
Kristin F. Butcher and Anne Morrison Piehl
WP-05-19
Constructing the Chicago Fed Income Based Economic Index – Consumer Price Index:
Inflation Experiences by Demographic Group: 1983-2005
Leslie McGranahan and Anna Paulson
WP-05-20
Universal Access, Cost Recovery, and Payment Services
Sujit Chakravorti, Jeffery W. Gunther, and Robert R. Moore
WP-05-21
Supplier Switching and Outsourcing
Yukako Ono and Victor Stango
WP-05-22
Do Enclaves Matter in Immigrants’ Self-Employment Decision?
Maude Toussaint-Comeau
WP-05-23
The Changing Pattern of Wage Growth for Low Skilled Workers
Eric French, Bhashkar Mazumder and Christopher Taber
WP-05-24
U.S. Corporate and Bank Insolvency Regimes: An Economic Comparison and Evaluation
Robert R. Bliss and George G. Kaufman
WP-06-01
Redistribution, Taxes, and the Median Voter
Marco Bassetto and Jess Benhabib
WP-06-02
Identification of Search Models with Initial Condition Problems
Gadi Barlevy and H. N. Nagaraja
WP-06-03
Tax Riots
Marco Bassetto and Christopher Phelan
WP-06-04
The Tradeoff between Mortgage Prepayments and Tax-Deferred Retirement Savings
Gene Amromin, Jennifer Huang,and Clemens Sialm
WP-06-05
Why are safeguards needed in a trade agreement?
Meredith A. Crowley
WP-06-06
6
Working Paper Series (continued)
Taxation, Entrepreneurship, and Wealth
Marco Cagetti and Mariacristina De Nardi
WP-06-07
A New Social Compact: How University Engagement Can Fuel Innovation
Laura Melle, Larry Isaak, and Richard Mattoon
WP-06-08
Mergers and Risk
Craig H. Furfine and Richard J. Rosen
WP-06-09
Two Flaws in Business Cycle Accounting
Lawrence J. Christiano and Joshua M. Davis
WP-06-10
Do Consumers Choose the Right Credit Contracts?
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles
WP-06-11
Chronicles of a Deflation Unforetold
François R. Velde
WP-06-12
Female Offenders Use of Social Welfare Programs Before and After Jail and Prison:
Does Prison Cause Welfare Dependency?
Kristin F. Butcher and Robert J. LaLonde
Eat or Be Eaten: A Theory of Mergers and Firm Size
Gary Gorton, Matthias Kahl, and Richard Rosen
Do Bonds Span Volatility Risk in the U.S. Treasury Market?
A Specification Test for Affine Term Structure Models
Torben G. Andersen and Luca Benzoni
WP-06-13
WP-06-14
WP-06-15
Transforming Payment Choices by Doubling Fees on the Illinois Tollway
Gene Amromin, Carrie Jankowski, and Richard D. Porter
WP-06-16
How Did the 2003 Dividend Tax Cut Affect Stock Prices?
Gene Amromin, Paul Harrison, and Steven Sharpe
WP-06-17
Will Writing and Bequest Motives: Early 20th Century Irish Evidence
Leslie McGranahan
WP-06-18
How Professional Forecasters View Shocks to GDP
Spencer D. Krane
WP-06-19
Evolving Agglomeration in the U.S. auto supplier industry
Thomas Klier and Daniel P. McMillen
WP-06-20
Mortality, Mass-Layoffs, and Career Outcomes: An Analysis using Administrative Data
Daniel Sullivan and Till von Wachter
WP-06-21
7
Working Paper Series (continued)
The Agreement on Subsidies and Countervailing Measures:
Tying One’s Hand through the WTO.
Meredith A. Crowley
WP-06-22
How Did Schooling Laws Improve Long-Term Health and Lower Mortality?
Bhashkar Mazumder
WP-06-23
Manufacturing Plants’ Use of Temporary Workers: An Analysis Using Census Micro Data
Yukako Ono and Daniel Sullivan
WP-06-24
What Can We Learn about Financial Access from U.S. Immigrants?
Una Okonkwo Osili and Anna Paulson
WP-06-25
8
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