Full text of Working Papers (Federal Reserve Bank of Chicago) : Individuals and Institutions : Evidence From International Migrants in the U.S., Working Paper 2004-19

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

Federal Reserve Bank of Chicago

Individuals and Institutions:
Evidence from International
Migrants in the U.S.
Una Okonkwo Osili and Anna Paulson

REVISED September, 2005
WP 2004-19

Individuals and Institutions: Evidence from International Migrants in the
U.S.1
Una Okonkwo Osili
Indiana University at Purdue University, Indianapolis
Anna Paulson
Federal Reserve Bank of Chicago

Abstract
We investigate the impact of institutional quality on individuals using data on the
financial decisions of immigrants in the U.S. While all of the individuals whose decisions
we analyze face the same formal institutional framework in the U.S., they bring with
them their impressions from and experiences with institutions in their home countries.
We find that immigrants from countries with institutions that more effectively protect
private property and provide incentives for investment are more likely to participate in
U.S. financial markets. The effect of home country institutions is persistent and absorbed
early in life. In addition, the impact of institutions is amplified for immigrants who live
in places where informal institutional constraints are likely to be reinforced, those who
live in neighborhoods 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.

1

We are grateful for comments from seminar participants at USC, UC Davis, UC Berkeley, Federal
Reserve Bank of Chicago, Northwestern, Stanford, University of Texas, Austin and Cornell. In addition,
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
Although there is widespread agreement that institutions shape economic outcomes and
are important determinants of financial market development, we know relatively little
about what lies inside the institutional “black box”. What are the channels through which
institutions provide incentives for investment and influence behavior? Douglass North
defines institutions as “formal constraints -- rules that human beings devise” and
“informal constraints – such as conventions and rules of behavior”.2 Understanding the
role of informal institutional constraints is a crucial component of predicting the impact
of formal institutional change and of making appropriate policy recommendations. It is
relatively straight-forward to change formal institutions by altering the written rules that
govern society, but changing the informal institutional constraints that manifest
themselves in culture and norms of behavior is much more challenging.3
This paper focuses on measuring the importance of informal institutional constraints for
financial development. Our approach takes advantage of the fact that in any given year,
vast numbers of individuals confront new institutional surroundings and, in some
fortuitous cases, detailed data are collected on the financial decisions they make in their
new institutional environments. More than 175 million people live outside their country
of birth and about twenty percent of these international migrants live in the U.S. (World
Migration, 2005). Together with their skill and talents, international migrants bring
attitudes and experiences acquired in their country of origin to the destination country.
North argues that individuals embody the informal institutional constraints reflected in
their customs, traditions and codes of conduct.4
In the process of migrating from one country to another, individuals move from one
formal institutional environment to another but may maintain the informal institutional
constraints of their country of origin. 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. 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.
By analyzing how financial decisions in the U.S. are influenced by the quality of home
country institutions, we also gain insights into how the institutional framework becomes
embedded in individuals and how susceptible it is to change. For example, we can
compare the importance of home country institutions for recent migrants relative to
migrants who have been in the U.S. for many years. This comparison provides some
insight into the potential pace of economic progress and financial development following
institutional reform.
2

“Institutions, Institutional Change and Economic Performance” (1990), page 4.
For example, Murell (1996), citing North (1990), describes policies in the countries that made up the
former Soviet Union as a “mélange of the old and the new, a pattern typical of times of great institutional
change, when revolutions in formal rules move far ahead of modifications in informal arrangements and
behavior.”
4
“Economic Institutions Through Time”, Nobel Lecture (1993).
3

The bulk of the evidence to date on the importance of institutions for financial
development comes from cross-country studies which reveal the total impact of
institutions: formal and informal. A growing number of studies show that the ability of a
country’s institutions to protect private property and provide incentives for investment is
a key explanation for the persistent disparity in financial market development. 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) and Acemoglu, Johnson and Robinson (2001
and 2002). Using micro-economic data, Besley (1995) and Johnson, Macmillan and
Woodruff (2002) show that effective property rights encourage investment. In addition to
demonstrating the importance of informal institutional constraints, our paper provides
independent evidence that institutions which effectively protect private property are a key
determinant of financial market development.
Glaeser et al. (2004) raise some important issues about how to appropriately measure
institutional quality. One advantage of our focus on informal institutional constraints,
rather than on formal ones, is that it relegates these issues to the sidelines, at least in the
current context. From the perspective of an individual, it does not matter whether
embodied informal constraints are the results of outcomes (a dictator who protects private
property, for example) or the result of formal constraints (a constitution that prevents a
government from seizing private property). The experience and observation of the
protection of private investment will lead an individual to embody this constraint. That
being said, we examine many different measures of institutional effectiveness, including
protection from expropriation and constraints on the executive and find that our
conclusions are not driven by the way institutional quality is measured.
Regardless of the institutional quality measure, we find that higher country of origin
institutional quality is associated with statistically and economically meaningful increases
in stock market participation. For example, if Argentina’s institutions increased in
quality by one standard deviation – that is if its “protection from expropriation” was as
good as in Germany, then stock market participation among Argentine immigrants in the
U.S. would increase by 2.8 percentage points, a 29% increase. A similar increase in
“constraints on the executive” would increase stock market participation by 1.1
percentage points, a 12% increase.
Our approach 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.5 One important difference between their
work and ours is that we study the behavior of immigrants, not their children. Because
we study the behavior of individuals who have chosen to migrate to the U.S., we have to
5

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.

take seriously the concern that the migration decision is influenced by the home country
institutional environment in a way that is manifested in unobservable individual
characteristics.
We take a number of steps to ensure that our findings are robust to potential biases due to
the correlation of country of origin institutional quality and unobserved individual
characteristics. First, we show that our findings are robust to controlling for immigrant
selection that is related to country of origin inequality as described in Borjas (1987).
Second, and more definitively, we eliminate the possibility that unobserved individual
attributes are correlated with country of origin institutional quality by including country
fixed effects in a specification where institutional quality is interacted with “ethnic
concentration”, a country and city specific measure of the size of an immigrant network.
These results indicate that coming from a country with German rather than Argentinean
institutions would increase stock market participation by 0.9 percentage points for the
median immigrant, a 10 percent increase.
The finding that institutional quality matters more for immigrants who cluster together in
neighborhoods with other immigrants from the same country addresses a substantive as
well as an econometric issue. The third component of the institutional environment,
according to North, is the “enforcement characteristics” of formal and informal
institutional constraints.6 We find that informal institutional constraints matter more
when they are likely to be enforced and reinforced through immigrant networks.
We find that institutional quality has the largest impact on investment decisions which
require the most institutional support. Institutional quality matters more for stock market
participation than for owning a savings or a checking account, for example. It also
matters more for stock market participation than for investments in health or human
capital that are mediated largely through the family rather than through formal
institutions. The fact that institutional quality matters when it should and does not matter
when it should not makes it unlikely that the findings are due to unobserved country of
origin characteristics.
In an effort to better understand the mechanism through which country of origin
institutions work, we investigate how the impact of home country institutions varies with
length of time in the U.S., with age at migration and with education. Home country
institutional effects are very long-lasting, affecting all immigrants, except those who have
lived in the U.S. for more than 28 years. The decisions of all immigrants who arrive in
the U.S. at age 16 or later are influenced by institutions. Only immigrants who arrive in
the U.S. as young children are not influenced by country of origin institutions. The fact
that immigrants who leave their birth countries when they are 16 to 20 years old, and not
yet adults, suggests that individuals absorb some lessons about institutions in the family
and at school, rather then through direct experience.
We find that the impact of institutions increases with education. This contrasts with the
findings of Guiso, Sapienza and Zingales (2004 and 2005), who find that the effects of
6

“Economic Institutions Through Time”, Nobel Lecture (1993).

social capital and culture, are smaller for those with greater education.7 This hints at a
potentially important distinction between how some aspects of culture and informal
institutional constraints influence behavior. Educated individuals in a country may have
more direct experience with their country’s institutions compared to the less-educated.
They clearly have more direct experience with educational institutions. Individuals with
more schooling may be more likely to participate in other home country institutions as
well. These educational “elites” may also belong to a relatively small minority whose
property is protected by institutions that are more generally ineffectual.8
We explore the distinction between culture and informal institutional constraints in more
detail by examining the impact of country of ancestry institutions on individuals who
were born in the U.S. For those who were born in the U.S., institutional quality has no
effect on stock market participation, suggesting that the norms and rules of behavior that
make up informal institutional constraints are distinct from other components of culture
that persist across generations.
The next section describes the framework we use to derive the predicted relationship
between institutional quality and financial market decisions. In section 3, we describe the
country and individual level data that we analyze. Section 4 outlines the empirical
strategy, discusses our findings and their robustness. Section 5 presents conclusions.
2. Framework
It is helpful to sketch out a simple reduced form framework in order to make the
hypotheses that we test clear. While we illustrate the framework in terms of an
individual’s decision about how much stock to purchase, this framework could easily
apply to other financial decisions as well. Consider an individual, i, from country J who
is considering how much stock to purchase. The individual’s demand for stock is
represented by:
S i = f ( ER, X i )
where Si is the amount that individual i invests in stock, ER is the expected return from
the investment, and Xi is a vector of individual characteristics (risk aversion, income,
education, years in the U.S., age at migration, and so on) that affect the demand for stock.
Institutional quality is modeled by assuming that the investor believes there is some
probability, πi that the stock broker 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 stock broker will
abscond with funds, but also the possibility that the institutional framework is not
7

In contrast, Glaeser et al (2001) find that individuals who invest in human capital also tend to have higher
levels of social capital.
8
See, for example, the discussion in Bates (1981) of government policies in Nigeria and in Ghana that
benefited elites at the expense of other less politically influential groups. Similarly, the politicization of
economic activity under Suharto in Indonesia clearly benefited Suharto and those with connections to him
(see Temple in Rodrik (2003)).

sufficient to ensure that funds will be invested in profit maximizing projects, that
investment proceeds will be appropriately reinvested or returned to investors. We
assume that 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 stock, but πi x 0 + (1 – πi) x R. The probability that an investor
places on the likelihood that the stock broker 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.9 Given this
framework, demand for stock 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.
We can also use this framework to think about how the effect of institutional quality will
vary depending on the type of investment. The level of confidence in institutions
required to make an investment vehicle reasonable depends on how institutionally
intensive it is. Investing in stock, for example, requires a great deal of confidence in
many institutions. The investor must be convinced that the stock broker will not abscond
with her investment and that the institutional and legal framework is sufficient to ensure
that funds will be invested in profitable profits and that the proceeds of these projects will
be returned to investors and not be expropriated by management, either in the form of
non-productive investment or through outright theft. Investing in savings, opening a
savings account, on the other hand, requires relatively less confidence in institutions and
confidence primarily in a single institution, a bank. An investor must be convinced that
the bank will keep her funds safe, accurately pay any interest due and return accumulated
funds upon demand.
For a given home country institutional background, an individual is likely to put more
weight on the possibility that an investment in stock will be stolen compared to the
possibility that money invested in a savings account will disappear. This means that πStock
is greater than πSavings and that the effect of home country institutions should be greater
for stock than for savings and other “safer” investments.
Other investments, say in children’s health or education, which are primarily mediated
through the family, require even less confidence in U.S. institutions. For investments that
require no institutional support – that are entirely mediated through the family –
institutions should not matter, πFamily should be close to zero.
9

Note that regardless of how we measure institutional effectiveness, institutional quality in the U.S. is
equal to ten, the maximum possible value.

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
individuals 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% 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.
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.10 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 our
economically inactive. Immigrants also tend to be less educated than the native born.
Slightly less than 36% of the immigrant sample has never completed high school
compared to only 15% of the native-born sample. However, the percentage of
immigrants and the native-born who have an advanced degree is roughly the same at 7%
and 8%, 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.
Stock market participation is the logical individual-level counterpart to country-level
stock market capitalization, which is the measure of financial market development used
in many cross-country level studies of institutions and financial market development.
Eight and a half percent of the immigrant sample owns stock, compared with 20% of the
native-born. Forty-seven percent of immigrants have a checking account compared with
64% of the native-born. Savings account ownership has a similar pattern. Forty percent
of the immigrant sample has a savings account, compared with 55% of 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% were born
10

We restrict our attention to the four annual survey waves where wealth data are available. Other SIPP
variables are collected quarterly.

in Europe. Most of the immigrants arrived in the U.S. as adults, with almost 71%
arriving at twenty-one years or older.
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.
Measures of Institutional Quality
What are “good” institutions? Adam Smith powerfully captures some of the most
important components of effective institutions in The Wealth of Nations:
Commerce and manufactures can seldom flourish long in any state which does
not enjoy a regular administration of justice, in which people do not feel
themselves secure in the possession of their property, in which the faith of
contracts is not supported by law, and in which the authority of the state is not
supposed to be regularly employed in enforcing the payments of debts from those
who are able to pay. Commerce and manufactures, in short, can seldom flourish
in any state in which there is not a certain degree of confidence in the justice of
government.
Transforming this image of effective institutions into a number that can be used in
empirical analysis is a challenge. The literature emphasizes a number of different ways
to operationalize what is meant by institutional effectiveness, including: “protection from
expropriation”, “constraints on the executive”, “rule of law” and legal origin. The
various institutional measures have conceptual strengths and weaknesses as well as
strengths and weaknesses related to the number of countries that they cover and the time
period that they are available for.
Our approach is to look at a number of different measures of institutional quality in an
effort to ensure that the results do not depend on the specific way that institutional
effectiveness is transformed into a number. In this section we describe the various
measures of institutional quality (and its determinants) that we use. The institutional
quality measures are summarized in Table 3A and their correlations are reported in Table
3B. A discussion of the advantages of particular measures and how they have been used
in the literature is included with the presentation of the results in the next section.
We use two measures of institutional quality from the International Country Risk Guide
(ICRG) IRIS-3 data. These measures are “protection from expropriation of private
investment” and “rule of law”. “Protection from expropriation” 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.” The
“rule of law” variable “reflects the degree to which the citizens of a country are willing
to accept the established institutions to make and implement laws and adjudicate
disputes.” Higher scores indicate: “sound political institutions, a strong court system,
and provisions for an orderly succession of power.” Lower scores indicate: “a tradition
of depending on physical force or illegal means to settle claims.” These institutional

quality measures are formed from averages of annual observations from 1982 to 1995
and are rescaled, if necessary so that the maximum possible value is ten.
A third measure of institutional quality focuses is drawn from the Polity IV Database and
focuses specifically on limitations to executive power. “Constraints on the executive”
measures the extent of institutionalized constraints on the power of the chief executive.
This institutional quality measure is also formed from averages of annual observations
from 1982 to 1995 and has been rescaled so that the maximum possible value is ten.
In addition to “rule of law,” we also use each country’s legal tradition as a measure of
institutional quality. “British legal origin” is equal to one it the country has a British
legal tradition and is zero otherwise.
In addition to these four measures, which have been used extensively in the literature, we
create a new measure, “domestic protection from expropriation of private investment”.
This measure is specifically designed to capture investment conditions from the
perspective of domestic, rather than foreign, entrepreneurs. This variable is created from
country level surveys of local entrepreneurs. These surveys were completed in August
1996 – February 1997 as part of a World Bank project and are discussed in the 1997
World Development Report. We use responses to questions about the impact of theft and
crime on the cost of business, the ability of state authorities to protect person and
property and the impact of the predictability of the judiciary on business operations to
create an index for each country. Respondents were asked to rank current conditions and
conditions 10 years ago. The current and historical responses to each of the three
questions were averaged and then an overall average was formed. One drawback of this
measure is that it is only available for 31 countries.11
In addition direct measures of institutional quality, we look at two potential determinants
of institutional quality: geography and human capital. Geographic endowments are
proxied by the absolute value of the latitude of the country’s capital, and human capital is
measured by the average years of schooling among adults in the country in 1960.
The institutional quality measures are available for 79 countries (including the U.S.), with
three exceptions. “Constraints on the executive” is available for 76 countries, “Domestic
Protection from Expropriation” is available for 31 countries, and average schooling in
1960 is available for 61 countries. The various measures and their relationship to the
literature are discussed in more detail in the following section.
Table 3A presents some 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.
Institutional quality ranges, as measured by protection from expropriation, ranges from
1.83 (Iraq) to 10.00 (the Netherlands, Switzerland, the U.S.). The average is 7.50.
“Constraints on the executive” has similar properties. It ranges from 1.43 (Cuba, Iraq,
Saudi Arabia, Syria) to 10.00 (including the Netherlands, Switzerland, the U.S.), and its
11

Because we do not look separately at the transition countries, we are able to use data from just 31of the
67 country surveys that were completed.

mean is 6.96. There are some important distinctions between these the measures of
institutional quality, however. For example, Mexico and China both have above average
protection from expropriation but below average executive constraints.
That being said, the various measures of institutional quality tend to be highly correlated
with one another (see Table 3B). For example, the correlation coefficient between
“protection from expropriation” and “constraints on the executive” is 0.62. One
exception to this pattern is British legal origin. This variable is not significantly
correlated with “protection from expropriation” or “constraints on the executive”.
However, it is negatively correlated with latitude. “Domestic protection from
expropriation” is positively correlated with its international counterpart, suggesting that
in most cases a country’s institutional climate is similar for domestic and foreign
investors. “Domestic protection from expropriation” is not significantly correlated with
“constraints on the executive” and “British legal origin”. However, it is positively related
to both latitude and average years of schooling in 1960.
Other country variables
In addition to measures of institutional quality, important country-level explanatory
variables include: whether people from the country can speak English (from Bleakley
and Chin, 2004); income, as measured by the average real per capita GDP from 1982 to
1995; financial development, measured as the average stock market capitalization from
1982 to 1995; inequality, measured by the average of all high quality Gini coefficient
observations from 1985 to 1995 from Deininger and Squire (1996); and religion. These
variables are also described in Table 1 and summarized in Table 3A.
4. Empirical Findings
This section reports on our empirical findings. We estimate the decision to participate in
the stock market using the following linear probability model:
Sisj = α + β1Xi + β2Zj + δs + εisj,
Where Sisj is the decision to own stock 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 institutional quality in country j. All of the reported
standard errors have been corrected to account for the heteroscedasticity that is implicit in
the linear probability model and are also adjusted to allow for correlation across
observations for immigrants who come from the same country.12
Baseline Findings and Alternative Measures of Institutional Quality
12

We use a linear probability model because it works well with 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.

The relationship between stock market participation and institutional quality is explored
in Table 4 for a variety of measures of institutional effectiveness. In addition we also
examine the relationship between stock market participation and some potential
determinants of institutional quality: country of origin geographical attributes and
country-level human capital. 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.13
We find that institutional quality has a positive and significant effect on stock market
participation. 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 owned stock would increase by 2.8 percentage points, a 32% increase
in the likelihood of stock market participation, relative to the observed participation rate
for immigrants of 8.6%. 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. The results are the same if we use “rule of law” to measure institutional
quality.14
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)). Changes in “rule of law” and other
ICRG-IRIS-3 data are not correlated with future equity returns.
Glaeser et al. (2004) argue that “constraints on the executive” is a more appropriate way
to capture North’s description of institutions as constraints. An important issue that they
raise is that measures like “protection for expropriation” measure outcomes rather than
constraints. From the point of view of an individual, rather than a country, this
distinction is less relevant. The informal institutional constraints that an individual
comes to embody could be the result of formal institutional constraints or of outcomes.
Our findings support this view, since we find largely the same results regardless of
whether we use “constraints on the executive” or other measures of institutional
effectiveness. For example, a one standard deviation increase in constraints on the
executive is associated with a 1.1 percentage point increase in stock market participation.
Another concern with the “protection from expropriation” and the “rule of law” measures
is that they are specifically designed to capture conditions from the perspective of private,
foreign investors. To address this concern, we use “domestic protection from
expropriation”, which is based surveys of domestic investors, to measure institutional

13

See Appendix Table 2 for the impact of these independent variables.
The results are also the same if we use the ICRG measure “quality of the bureaucracy”. This variable is
highly correlated with “protection from expropriation”.

14

quality. When we use this measure of institutional quality, we continue to find a strong
positive relationship between institutional quality and stock market participation.
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. Our findings parallel theirs: stock market participation among
immigrants from countries with a British legal tradition is 3.3 percentage points higher
than that of immigrants from countries with a different legal tradition.15
Rodrik, Subramanian and Trebbi (2004), Bloom and Sachs (1998), and Acemoglu,
Johnson and Robinson (2001) find that geography has an important effect on the quality
of institutions. Countries that are further from the equator tend to develop stronger
institutions. Acemoglu, 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 addition, Beck, Demirgüç-Kunt and Levine
(2002) 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 participate in the U.S. stock market.
Some authors argue and provide evidence that the ability of a country to acquire good
institutions is determined by the availability of human capital (see Barro (1999) and
Glaeser et al. (2004), for example). We examine this possibility in the final column of
Table 4 where we examine the impact of average years of schooling in 1960 in the
country of origin on stock market participation in the U.S.16 Immigrants from countries
with greater human capital in 1960 are more likely to own stock in the U.S. A one
standard deviation increase (two and a half years) in average years of schooling in 1960,
is associated with a 2.6 percentage point increase in stock market participation in the U.S.
These baseline findings suggest that immigrants come to the U.S. with attitudes shaped
by the effectiveness of home country institutions, regardless of how they are measured,
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. For
ease of exposition, the rest of the paper uses a single measure of institutional quality,
“protection from expropriation”.
Additional Country Controls
15

Some studies find that the degree of ethnic tensions in a country is an important predictor of institutional
quality, since the greater ethnic diversity may lead to the adoption of policies that favor expropriation of
resources, rather than the emergence of open and competitive systems (Easterly and Levine, 2002). We
find no significant relationship between country of origin ethnic concentration and stock market
participation among immigrants in the U.S.
16
The smaller sample size for this estimate is due to the lack of data on schooling in 1960 for Mexico.

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. For
example, perhaps it is not institutional quality that matters, but income in the country of
origin. We may have found a significantly positive effect of institutional quality on
financial market participation because institutional quality is positively correlated with
country income and country income was not included in the baseline estimates. We
explore the possibility that our findings are the result of omitted country characteristics in
Table 5. In addition to the explanatory variables reported on in Table 5, each of these
estimates includes all of the same control variables that were included in the baseline
estimates.
For purposes of comparison, the first column of Table 5 repeats the results from Table 4
column [1]. In column [2], we report on estimates that include continent controls in
addition to protection from expropriation. One possible explanation for our findings is
that there is 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.17 This leaves open the possibility that the protection
from expropriation risk variable is measuring the effect of discrimination rather than
institutional quality. In order to explore the feasibility of this potential explanation, we
add a set of continent controls to the estimates. Adding continent controls leaves the
coefficient on protection from expropriation positive and significant.
We also examine the possibility that religious influences on institutional quality are
responsible for our earlier findings in column [3] of Table 5. 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 0.016 to 0.011.
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 of
origin 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
Table 5. 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. GDP per capita has a positive but insignificant
17

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.

effect on stock market participation.18 Adding this variable reduces the coefficient on
“protection from expropriation” from 0.016 to 0.013.
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 if participating in the stock market
participation.19 Ideally, we would include an individual measure of English speaking
ability in the estimates. However, the SIPP data does not include any measure of this
characteristic, so we try to capture it at the country level instead. Column [5] in Table 5
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 has a positive, and statistically
insignificant effect on the likelihood of owning stock. Adding this variable to the
estimates lowers the point estimate of the coefficient on institutional quality somewhat
(from 0.013 to 0.011). However, it remains positive and significant.
The availability and sophistication of home country financial markets may also influence
the likelihood of stock market participation among immigrants in the U.S. 20 Immigrants
from countries where the stock market is relatively small, for example, may simply be
unfamiliar with this type of investment. Ideally, we would control for past usage of the
stock market at the individual level. However, those data are unavailable. In column [6]
of Table 5, we add average stock market capitalization in the country of origin from 1982
to 1995 and its square to the regression. Home country stock market capitalization has a
positive but insignificant effect on stock market participation. The coefficient on
institutional quality in this regression is highly significant and equal to 0.017. Note that
stock market capitalization in the country of origin is also determined to some extent by
the quality of country of origin institutions.21
Overall the results presented in Table 5 suggest that the finding that the decision to own
stock 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.
18

Many studies, including Hall and Jones (1999), Acemoglu, Johnson and Robinson (2001, 2002), Easterly
and Levine (2003), and Rodrik, Subramainian and Trebbi (2004) find that GDP per capita is higher in
countries with better institutions.
19
Chiswick (1978) and Borjas (1987) show that immigrants from English speaking countries experience
more rapid wage assimilation.
20
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. stock markets.
21
Using “contract intensive money” as a measure of financial market development leads to the same
findings. Contract intensive money is equal to the non-cash fraction of the money supply and is associated
with higher rates of investment and growth. See Clague, Keefer, Knack and Olson (1999).

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. We take a number of steps to ensure that
this is not the case.
Self-selection and home country inequality
According to Borjas (1987), 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 they are only concerned with the right tail of the
income distribution, high ability migrants 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. Since countries with low inequality also tend to have strong institutions, we
have to be concerned that our finding that financial market participation increases with
country of origin institutional quality is driven by ability bias.22 For example, high ability
individuals from countries with low inequality and high quality institutions are more
likely to migrate and low ability individuals from countries with high inequality and less
effective institutions tend to migrate. We address this by adding a measure of country of
origin inequality, the Gini coefficient, to the baseline estimates.
These results are found in Table 6. Because Gini coefficient data are only available for a
subset of countries, these estimates use a smaller sample. The first column in Table 6
reports the baseline findings for the smaller sample. The second column adds the country
of origin Gini coefficient. Greater inequality in the country of origin is associated with
lower financial market participation in the U.S. We find some evidence that selective
immigration of the sort described above may bias the baseline estimates of the coefficient
on institutional quality. Adding the Gini coefficient to the estimation reduces the
coefficient on “protection from expropriation” from 0.024 to 0.013. The impact of
having German rather than Argentinean institutions is estimated to be a 2.3 percentage
point increase in stock market participation, rather than a 4.2 percentage point increase.
Ethnic concentration and country of origin fixed effects
In addition to unobserved ability, there are other individual characteristics that we cannot
observe that may have an important 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 influences the decision to own stock. Similarly, variation in
educational quality across countries may impact the cost of obtaining information about

22

Engerman and Sokoloff (2000) provide evidence that in societies with high initial inequality the
evolution of institutions favored a narrow elite.

U.S. financial markets. Parental participation in financial markets is likely to be
correlated with country of origin institutional quality and with the decision to own stock
of the current generation (Chiteji and Stafford (2000)).
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:

EC sj =

# of immigrants from country j living in MSA s
total population in MSA s

See Appendix Table 1 for 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 7, we estimate:
Sisj = 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.
Because this new institutional quality measure varies by country of origin for a given
MSA, we can include both country and MSA fixed effects in the estimation. By
including country of origin fixed effects, we eliminate correlation between unobserved
individual attributes and country of origin.
By including MSA fixed effects in all of the estimates, we rule out another potential
source of bias in the new institutional quality measure. 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 be unbiased.

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 7. The first column reports estimates of stock
market participation using “protection from expropriation” multiplied by “ethnic
concentration” to measure institutional quality. This estimate also includes the direct
effect of ethnic concentration. The estimates presented in column [1] do not include
country fixed effects. Column [2] adds country fixed effects. Columns [1] and [2] use
the entire sample of 77 countries. Columns [3] and [4] repeat this exercise for the 29
countries in the sample with more than 100 observations per country. The number of
observations for each country is relevant here because we estimate country of origin fixed
effects.
The coefficient on institutional quality interacted with ethnic concentration remains
positive and significant when country fixed effects are included. The point estimate is
lowest in column [4] when country fixed effects are included and when the sample is
restricted to the 29 countries with more than 100 observations, so we will discuss those
findings. For the median immigrant who lives in a city where 0.78 percent of the
population comes from the same country, the estimates imply that the likelihood of
owning stock would increase by 0.9 percentage points if institutional quality had been
one standard deviation higher from 1982 to 1995. By comparison, the baseline findings,
which are not corrected for unobserved heterogeneity, imply that the same increase in
institutional quality is associated with a 2.8 percentage point increase in stock market
participation.
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 Filipino stock market participation by 2.1 percentage
points. The same improvement in institutional quality would increase stock market
participation by 0.43 percentage points 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). 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.
The effect of institutional quality on other behavior
We continue our exploration of the robustness of the link between stock market
participation and institutional quality by considering the effect of institutional quality on
other behavior. In Table 8 we present estimates of the effect of country of origin
institutional quality on the decision to have a checking account, a savings account, to
invest in children’s health via prescription drugs, 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 character, 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 regressions in Table 8 tell us if institutions matter when they are
supposed to and do not matter when they should not.

The first panel of Table 8 uses the baseline specification and the second panel controls
for unobserved heterogeneity using the specification from Table 7, which interacts
protection from expropriation with ethnic concentration and includes country of origin
fixed effects. For ease of comparison column [1] repeats the regressions from Table 4,
column [1] and Table 7, column [2].
Looking first at the decision to open a savings account, 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%. The same increase in
institutional quality is predicted to increase stock market participation by 29 percent. In
addition, we cannot rule out the possibility that the savings account results are due to
biases induced by unobserved heterogeneity. 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.

Owning a checking account and using checks is 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. 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 owning checks.
Similarly, Guiso, Sapienza and Zingales (2004) find that households in high social capital
areas are more likely to own checks and invest more in the stock market.
We see a similar pattern with a range of investment activities that are intermediated
through the family: investing in children’s health through prescription drugs, driving to
work and visiting a doctor. While investments in children’s health is positively and
significantly correlated with country of origin institutional quality in the baseline
specification, the magnitude of the effect of an increase in institutional quality is much
lower than that for owning stock. In addition, none of these results are robust to
controlling for unobserved heterogeneity. The fact that institutional quality influences
the decision to own stock but does not influence other investment decisions, which
require less institutional support, 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 examining how institutional quality affects the likelihood that different
groups of immigrants own stock. In addition to providing further robustness checks on
our main results, these estimates help to identify the potential channels through which
home country institutions come to influence behavior.

Education, occupation, citizenship
In Table 9, we examine how the impact of institutions varies with education and with
occupational characteristics. 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). The findings are quite
striking. Immigrants who have more education are more influenced by home country
institutions than their counterparts who have completed less schooling. A one standard
deviation increase in home country institutional quality is predicted to raise the stock
market participation of highly educated immigrants by 5.4 percentage points and by 1.0
percentage points for immigrants in the low education group.23

23

The effect is even larger if we restrict attention to immigrants with advanced degrees.

In contrast, Guiso, Sapienza and Zingales (2004 and 2005) find that the effect of social
capital and culture is muted for those with greater education. This hints at a potentially
important distinction between how culture and institutions influence behavior. Educated
individuals in a country are likely to participate more and to have more direct experience
with their country’s institutions compared to the less-educated. At a minimum, they have
had more direct exposure to educational institutions in their country of origin.24 In
addition, these educational “elites” may belong to a relatively small minority whose
property is protected by institutions that are more generally less effective. The effects of
culture, on the other hand, appear to be more concentrated among less educated
individuals, suggesting that cultural lessons are transmitted, at least partially, through
different mechanisms than lessons about the institutional environment.
In columns [4] and [5], we compare the effect of institutional quality on high and low
skill workers, respectively.25 One reason for making this comparison is because foreign
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 does not appear to be the case.
The impact of home country institutions is higher for immigrants with high skill jobs
compared to those with low skill jobs. 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 owning stock is 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 stock market participation
and home country institutional quality is not driven by the reluctance of undocumented
immigrants to buy stock and the 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 reflects an
orientation toward 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 this large number of immigrants who share the same institutional
background. Eliminating Mexican immigrants from the sample has no effect on the
results.
24

Recall that the vast majority of immigrants arrive in the U.S. when they are twenty-one years of age or
older, after the bulk of their formal education has been completed.
25
“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”,
“Handlers, Equipment Cleaners, Helpers and Laborers”, or “Service Occupations, Except Private
Household” categories according to the same BLS classifications.

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 10 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.

Columns 2 – 6 of Table 10 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
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.
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? We take an
initial step toward answering these questions via the estimates presented in Table 11.
This table examines the effect of country of origin institutional quality of financial
market participation in the U.S. for subsets of immigrants based on their age of arrival in
the U.S.

Table 11 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 except the youngest migrants.
The point estimate is somewhat smaller, 0.014 for immigrants who arrived before their
21st birthday versus 0.019 for those who arrived after age twenty-one, but this difference
is not statistically significant.
The effect of country of origin institutions is present even in those who migrated when
they were 16 to 20 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, rather than 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
finding that immigrants who come from countries with weak institutions are less likely to
participate in U.S. financial markets. Expanding on the findings from Table 11, which
show that the behavior of immigrants who arrived in the U.S. as teenagers 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
stock market participation for natives as well as for immigrants.26 The estimates are
presented in Table 12.

For immigrants, we find a positive and significant effect of institutional quality on stock
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, informal institutional constraints do not
appear to be passed along to future generations. This contrasts with findings in other
areas which suggests that some aspects of culture do seem to persist across generations
and across the same transformation in the formal institutional environment: norms about
fertility and female labor force participation (Fernandez and Fogli, 2005), for example.
5. Conclusions

This paper adds to the growing body of theoretical and empirical work that identifies the
ability of a country’s institutions to protect private property and provide incentives for
investment as a key explanation for the persistent disparity in financial market
development across countries. We investigate the impact of institutional quality on
financial market development using data on the financial decisions of immigrants in the
U.S. While all of the individuals whose decisions we analyze face the same formal
institutional framework in the U.S., they bring with them their impressions and
experiences with institutions in their home countries.
We find that immigrants from countries with institutions that more effectively protect
private property and provide incentives for investment are more likely to participate 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 all but the youngest
migrants. The impact of institutions is amplified by living in a neighborhood with many
26

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.

other immigrants from the same country of origin and is greater for immigrants who have
more education. 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 approach that we use allows us to take a glimpse inside the institutional “black box”
and draw several conclusions. First, North is right -- individuals “embody” informal
institutional constraints. Second, the process by which institutional lessons become
embedded in individuals occurs early in life, probably in the family and in school, and is
enhanced by additional formal schooling. Third, the effect of informal institutional
constraints is different from other aspects of culture. It does not appear to decay with
education, nor is it transmitted across generations when the formal institutional
environment is altered.
What do these findings tell us about the likely results of formal institutional reform?
Examining the behavior of immigrants in the U.S. corresponds to a best case scenario for
institutional reform: the change in the institutional environment is credible, it is multifaceted, affecting fiscal, monetary and trade policy as well as the judicial and political
system; and 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. Translating these findings into a more complex real
world environment, where institutional reforms are likely to be less comprehensive,
potentially lack credibility and permanence, and sometimes do not have the full support
of those they affect, suggests that frequently the impact of institutional reforms will
unfold over generations. On a more optimistic note, the legacy of weak institutions does
not have to persist across generations: when the formal institutional environment is
fundamentally altered, the next generation will be governed by updated informal
institutional constraints.

References

Acemoglu, Daron, Simon Johnson and James A. Robinson, 2001. “The Colonial Origins
of Comparative Development: An Empirical Investigation,” American Economic
Review, 91, 1369-1401.
Acemoglu, Daron, Simon Johnson and James A. Robinson, 2002. “Reversal of Fortune:
Geography and Institutions in the Making of the Modern World Income
Distribution,” Quarterly Journal of Economics, 117(4), 1231-94.
Ai, Chunrong and Edward C. Norton. “Interaction Terms in Logit and Probit Models,”
Economic Letters, 80,123-129.
Barro, Robert J. 1999. “Human Capital and Growth in Cross-country Regressions,”
Swedish Economic Policy Review, 6(2), 237-277.
Bates, Robert H., 1981. Market and States in Tropical Africa: The Political Basis of
Agricultural Policies University of California Press: Berkeley, CA
Beck, Thorsten, Asli Demirgüç-Kunt and Ross Levine, 2003a, “Law and Finance: Why
Does Legal Origin Matter?,” Journal of Comparative Economics, 31,653-675.
Beck, Thorsten, Asli Demirgüç-Kunt and Ross Levine, 2003b, “Law, Endowments, and
Finance,” Journal of Financial Economics, 70: 181-187.
Beck, Thorsten, Asli Demirgüç-Kunt and Ross Levine, 2000. “A New Database on
Financial Development and Structure,” World Bank Economic Review, 14, 597605.
Beck, Thorsten, Ross Levine and Norman Loayza, 2000. “Finance and the Sources of
Growth,” Journal of Financial Economics, 58, 261-300.
Besley, Timothy, October 1995. “Property Rights and Investment Incentives: Theory and
Evidence from Ghana,” Journal of Political Economy, 103(5), 903-937.
Bitler, Marrianne P., Alicia M. Robb, and John D. Wolken, 2001. “Financial Services
Used by Small Businesses: Evidence from the 1998 Survey of Small Business
Finance,” Federal Reserve Bulletin, Board of Governors of the Federal Reserve
System, April, 183-205.
Bleakley, Hoyt and Aimee Chin, 2004. “Language Skills and Earnings: Evidence from
Childhood Immigrants,” The Review of Economics and Statistics, 86(2), 481-496.
Bloom, David E. and Jeffrey D. Sachs, 1998. “Geography, Demography, and Economic
Growth in Africa,” Brookings Papers on Economic Activity, 2, 207-295.

Borjas, George J., 1985. “Assimilation, Changes in Cohort Quality, and the Earnings of
Immigrants,” Journal of Labor Economics, 3(4), 463-489.
Borjas, George J., 1987. “Self-Selection and the Earnings of Immigrants,” American
Economic Review, 77(4), 531-553.
Borjas, George J., 1995. “Ethnicity, Neighborhoods and Human Capital Externalities,”
American Economic Review, 85, 365-390.
Borjas, George J., 2000. “Ethnic Enclaves and Assimilation,” Swedish Economic Policy
Review, 7: 91-122.
Brunetti, Aymo, Gregory Kisunko and Beatrice Weder 1997. “Institutional Obstacles to
Doing Business: Region-by-Region Results from a Worldwide Survey of the
Private Sector,” World Bank Policy Research Working Paper No. 1759,
Carroll, Christopher D, Rhee, Byung-Kun and Chanyong Rhee, 1994. “Are There
Cultural Effects on Saving? Some Cross-Sectional Evidence,” Quarterly Journal
of Economics, 109 (3), 685-700
Chiteji, Ninja and Frank Stafford. 2000. “Asset Ownership Across Generations.” PSC
Research Report No. 00-454. University of Michigan
Chiswick Barry R., 1978. “The Effect of Americanization on the Earnings of Foreignborn Men,” Journal of Political Economy, 86(5), 897-921.
Chiswick, Barry and Miller, 1996. “Ethnic Networks and Language Proficiency Among
Immigrants,” Journal of Population Economics, 9(1), 19-35.
Clague, Christopher; Philip Keefer, Stephen Knack, and Mancur Olson 1999. “Contractintensive Money.” Journal of Economic Growth, 4, 185-209.
Deininger, Klaus and Lyn Squire, 1996. “A New Data Set Measuring Income Inequality,”
World Bank Economic Review, 10(3), 565-91.
Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer,
2001. “The Regulation of Entry,” Quarterly Journal of Economics, 117, 1-38.
Duflo, Esther and Emmanuel Saez, 2002. “Participation and Investment Decisions in a
Retirement Plan: The Influence of Colleagues’ Choices,” Journal of Public
Economics, 85(1), 121-48.
Erb, Claude B., Campbell R. Harvey and Tadas E. Viskanta, 1996, "Expected Returns
and Volatility in 135 Countries" Journal of Portfolio Management, 46-58.

Easterly, William and Ross Levine, 2003. “Tropics, Germs, and Crops: How
Endowments Influence Economic Development,” Journal of Monetary
Economics, 50(1), 3-39.
Easterly, William and Ross Levine, November 1997. “Africa’s Growth Tragedy: Policies
and Ethnic Divisions,” Quarterly Journal of Economics, 112(4), 1203-50.
Engerman, Stanley and Kenneth Sokoloff, 2000, “History Lessons: Institutions, Factor
Endowments, and Paths of Development in the New World,” Journal of
Economic Perspectives 14, 217--232.
Fernandez, Raquel and Allesandra Fogli, 2005. “Culture: An Empirical
Investigation of Beliefs, Work and Fertility.” NBER Working Paper
11268.
Glaeser, Edward L., Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer.
2004 “Do Institutions Cause Growth?” Journal of Economic Growth 9(3),271303
Glaeser, Edward L. and Andrei Shleifer, November 2002. “Legal Origins,” Quarterly
Journal of Economics, 117(4), 1193-1229.
Glaeser, Edward L., David Laibson and Bruce Saderdote, 2001. “The
Economic Approach to Social Capital,” NBER Working Paper 7728.
Guiso, Luigi, Paola Sapienza and Luizi Zingales, June 2004. “The Role of Social Capital
in Financial Development,” American Economic Review, 94 (3), 526-556
Guiso, Luigi, Paola Sapienza and Luizi Zingales, 2005. “Does Culture Affect Economic
Outcomes?” Manuscript, Northwestern University.
Hall, Robert E. and Charles I. Jones, February 1999. “Why Do Some Countries Produce
so Much More Output per Worker than Others?” Quarterly Journal of Economics,
114(1), 83-116.
Hendricks, Lutz, 2004. “Why do Hours Worked Differ Across Countries? Evidence from
U.S. Immigrants,” Manuscript, Iowa State University.
Hong, Harrison, Jeffrey D. Kubik and Jeremy C. Stein, 2004. “Social Interaction and
Stock-Market Participation,” Journal of Finance, 59(1), 137-163
Johnson, Simon, John McMillan and Christopher Woodruff, 2002. “Property Rights and
Finance,” American Economic Review, 92(5), 1335-56.
Kandori, Michihiro. 1992. “Social Norms and Community Enforcement,” Review of
Economic Studies, 59, 63-80.

Knack, Stephen and Philip Keefer,1995. “Institutions and Economic Performance: CrossCountry Tests Using Alternative Measures,” Economics and Politics, 7(3), 207227.
La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer and Robert W. Vishny,
2002. “Investor Protection and Corporate Valuation,” Journal of Finance, 57,
1147-70.
La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer and Robert W. Vishny,
2000. “Investor Protection and Corporate Governance,” Journal of Financial
Economics, 58, 3-27.
La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer and Robert W. Vishny,
1998. “Law and Finance,” Journal of Political Economy, 106(6), 1113-55.
LaPorta, Rafael, Florencio Lopez-de-Silanes,Andrei Shleifer, and Robert W.
Vishny, 1997. “Legal Determinants of External Finance” Journal of Finance 52
(3), 1131-1150.
La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer and Robert W. Vishny,
February 1994. “The Quality of Government,” Journal of Law, Economics and
Organization, 15(1), 222-279.
Levine, Ross, Norman Loayza and Beck Thorston, 2000. “Financial Intermediation and
Growth: Causality and Causes,” Journal of Monetary Economics, 46, 31-77.
Levine, Ross, 1999. “Law, Finance, and Economic Growth,” Journal of Financial
Intermediation, 8, 36-67.
Levine, Ross, 1998. “The Legal Environment, Banks, and Long-run Economic Growth,”
Journal of Money, Credit, and Banking, 30, 688-726.
Madrian, Brigitte and D. Shea, 2000, “Peer Effects and Savings Behavior in EmployerSponsored Savings Plans,” University of Chicago Working Paper.
Munshi, Kaivan, 2003. “Networks in the Modern Economy: Mexican Migrants in the US
Labor Market.” Quarterly Journal of Economics, 118(2), 549-597.
Murrell, Peter, 1996. “How Far Has the Transition Progressed?” Journal of Economic
Perspectives 10(2), 25-44
Paulson, Anna and Audrey Singer, 2003. “Savings and Settlement: Evidence from
Mexican Migrants in the U.S.” Manuscript.

North, Douglass C., 1988. “Institutions, Economic Growth and Freedom: An Historical
Introduction,” Freedom, Democracy and Economic Welfare: Proceedings of an
International Symposium, Vancouver: Fraser Institute, 3-25.
North, Douglass C., 1990. Institutions, Institutional Change, and Economic Performance
Cambridge University Press, New York.
North, Douglass C., 1993. “Economic Institutions Through Time”, Nobel Lecture.
Rajan, Raghuram G. and Luigi Zingales, 2003. “The Great Reversals: The Politics of
Financial Development in the Twentieth Century,” Journal of Financial
Economics, 69(1), 5-50.
Rodrik, Dani, editor, 2003. In Search of Prosperity: Analytic Narratives on Economic
Growth. Princeton University Press, Princeton, NJ.
Rodrik, D., A. Subramanian and F. Trebbi. 2004 “Institutions Rule: The Primacy of
Institutions over Geography and Integration in Economic Development”, Journal
of Economic Growth, 9(2), 136-165 J
Smith, Adam. 1776. An Inquiry into the Nature and Causes of the Wealth of Nations, 5th
ed. (edited by Edwin Cannan, 1930), vols. I and II, Metheun and Co, London
Stulz, René M. and Rohan Williamson, 2003. “Culture, Openess and Finance,” Journal
of Financial Economics, 70(3), 313-349.
Temple, Jonathan 2003. “Growing Into Trouble: Indonesia after 1966,” in Rodrik, Dani,
editor, In Search of Prosperity: Analytic Narratives on Economic Growth.
Princeton University Press, Princeton, NJ.
World Bank, 1997 World Development Report. World Bank, Washington D.C.
World Migration 2005: Costs and Benefits of International Migration. International
Migration Organization.

Table 1: Definitions and Sources of Country Level Variables
Variable
Protection from Expropriation
of Private Investment

Constraints on the Executive

Domestic Protection from
Expropriation of Private
Investment

Rule of Law

British Legal Origin

Definition and Source
This variables 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 measures the extent of institutionalized constraints on the
power of the chief executive. The variable takes on seven different
values: (1) unlimited authority (there are no regular limitations on the
executive’s actions, as distinct from irregular limitations such as the
threat or actuality of coups and assassinations); (2) intermediate
category; (3) slight to moderate limitation (there are some real but
limited restraints on the executive); (4) intermediate category; (5)
substantial limitations on executive authority (the executive has more
effective authority than any accountability group but is subject to
substantial constraints by them); (6) intermediate category; (7) executive
parity or subordination (accountability groups have effective authority
equal or greater than the executive in most areas of activity). Variable is
the average over annual country observations 1982 – 1995. We have
normalized the variable so it ranges from 1 to 10.
Source: Polity IV Database: http://www.cidcm.umd.edu/inscr/polity/
This variable is drawn from country level surveys of local entrepreneurs.
Responses to questions about the impact of theft and crime on the cost
of business, the ability of state authorities to protect person and property
and the impact of the predictability of the judiciary on business
operations were used to create an index for each country. Respondents
were asked to rank current conditions (the surveys were conducted
August 1996 – February 1997) and conditions 10 years ago. The current
and historical responses to each of the three questions were averaged
and then an overall average was formed.
Source: Author’s calculations from the World Bank World Development
Report 1997, Private Sector Survey:
http://www.worldbank.org/wbi/governance/wdr97data.html
This variable "reflects the degree to which the citizens of a country are
willing to accept the established institutions to make and implement
laws and adjudicate disputes." Higher scores indicate: "sound political
institutions, a strong court system, and provisions for an orderly
succession of power." Lower scores indicate: "a tradition of depending
on physical force or illegal means to settle claims." Upon changes in
government new leaders "may be less likely to accept the obligations of
the previous regime." Variable is the average over annual country
observations 1982 – 1995. We have normalized the variable so it ranges
from 1 to 10.
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

Table 1: Definitions and Sources of Country Level Variables, continued
Variable
English Speaking

Latitude

Gini Coefficent

Av. Per Capita GDP
Stock Market Capitalization
Years of Schooling in 1960

Catholic, Protestant, Muslim

Definition and Source
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 of Gini-coefficients across one country over all
“high-quality” observations 1985-95.
Source: Deininger and Squire (1996)
http://www.worldbank.org/research/growth/dddeisqu.htm
Average real GDP per capita 1982 – 1995, 1995 dollars.
Source: World Bank World Development Indicators.
Average per capita market capitalization of listed countries, 1982 –
1995, 1995 dollars. Source: World Bank Development Indicators.
Years of schooling of the total population over 25 in 1960.
Source: Barro, Robert J. and Jong-Wha Lee, International Data on
Educational Attainment: Updates and Implications:
http://www.cid.harvard.edu/ciddata/ciddata.html
The percentage of people in the country (x 100) who are a particular
religion.
Source: CIA Factbook.

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

Native-Born
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

Immigrant
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

Educational Attainment (%)
Less than High School
15.0%
35.8%
High School Graduate
30.4%
24.5%
Some College
30.6%
20.1%
Bachelor Degree
15.9%
12.5%
Advanced Degree
8.1%
7.1%
Financial Market Participation (%)
% who own stock
20.0%
8.6%
% with a checking account (interest or non-interest)
63.8%
47.0%
% with a savings account
54.8%
40.1%
Other characteristics (%)
% 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”.

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”.

Table 3A: Summary of Country Variables
Characteristic

N

Mean

Standard
Deviation

Min

Median

Max

U.S.
value

Measures of Institutional Quality
Protection from Expropriation
79
7.50
1.74
1.81
7.51
10.00
10.00
Constraints on the Executive
76
6.96
2.87
1.43
7.14
10.00
10.00
Domestic Protection from Exp.
31
5.31
1.16
3.63
5.12
7.78
5.27
Rule of Law
79
6.16
2.51
1.94
5.98
10.00
10.00
British Legal Origin
79
0.29
0.46
0.00
0.00
1.00
1.00
Latitude
79
0.33
0.19
0.01
0.33
0.71
0.42
Av. Years of Schooling, 1960
61
4.27
2.54
0.21
4.06
10.07
8.66
Other Country Characteristics
English Speaking
79
0.139
0.35
0.00
0.00
1.00
1.00
Gini Coefficent
52
38.80
10.48
22.27
36.24
59.71
41.19
GDP Per Capita
74
8,704
10,376
106
3,208
42,873
24,831
Stock Market Cap. Per Capita
65
4,875
8,300
3.38
993
36,406
18,750
Catholic
74
39.29
40.01
0.00
27.2
97.00
24.00
Protestant
74
10.43
19.39
0.00
0.35
87.00
52.00
Muslim
74
16.82
33.77
0.00
0.00
100.00
1.00
Note: Protection from expropriation, constraints on the executive, quality of the bureaucracy, and rule of
law have been rescaled so that the maximum possible value is 10.

Table 3B: Correlation between Institutional Quality Measures
Characteristic

Protection
From Exp

Constraints on
Exec

Domestic
Protection
from Exp.

Rule of Law

British Legal
Origin

Protection from Expropriation
---Constraints on the Executive
0.619***
--Domestic Protection from Exp.
0.557***
0.250
---Rule of Law
0.869***
0.501***
0.552***
---British Legal Origin
0.119
0.060
0.021
0.036
---Latitude
0.572***
0.386***
0.348*
0.584***
-0.200*
Av. Years of Schooling, 1960
0.715***
0.575***
0.201
0.701***
0.023
Notes: *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.

Latitude

--0.661***

Average Years
of Schooling,
1960

---

Table 4: The Effect of Institutional Quality on Immigrant Stock Market Participation
Explanatory Variable
Protection from Expropriation
Constraints on the Executive
Domestic Protection from Expropriation
Rule of Law
British Legal Origin
Latitude
Average Years of Schooling, 1960

[1]
0.016***
(0.005)

[2]

[3]

[4]

[5]

[6]

[7]

0.004*
(0.002)
0.033***
(0.009)
0.012***
(0.003)
0.033**
(0.017)
0.114**
(0.047)
0.006**
(0.003)

Adjusted R-Squared
0.2315
0.2306
0.2876
0.2314
0.2275
0.2289
0.2296
Number of Observations
14,232
14,052
7,814
14,232
14,232
14,232
7,856
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 probability 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 5: The Effect of Institution Quality on Immigrant Stock Market Participation, Additional Country Controls
Explanatory Variable
Protection from Expropriation

Stock Market Capitalization
Squared†
Religion Controls

No

No

Yes

No

No

[6]
0.017 **
(0.008)
0.766
(1.380)
0.009
(0.023)
3.700
(5.070)
-0.0002
(0.0001)
No

Continent Controls

No

Yes

No

No

No

No

Av. Per Capita GDP†

[1]
0.016 ***
(0.005)

[2]
0.014 ***
(0.005)

[3]
0.011 **
(0.005)

[4]
0.013 **
(0.005)
1.090
(0.959)

[5]
0.011 *
(0.005)
1.060
(0.994)
0.031 **
(0.010)

English Speaking
Stock Market Capitalization†

Adjusted R-Squared
0.2315
0.2324
0.2341
0.2365
0.2374
0.2375
Number of Observations
14,232
14,232
11,509
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 probability 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: Controlling for Unobserved Heterogeneity
The Effect of Institution Quality on Immigrant Stock Market Participation
Controlling for Home Country Inequality
Explanatory Variable
Protection from
Expropriation

[1]
0.024
(0.006)

[2]

***

0.013
(0.006)
-0.003
(0.001)

Gini Coefficient

**
***

Adjusted R-Squared
0.2408
0.2435
Number of Observations
10,206
10,206
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 probability 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.

37

Table 7: Does Enforcement Matter?
Controlling for Unobserved Heterogeneity
The Effect of Institution Quality and Ethnic Concentration on Immigrant Stock Market Participation

Explanatory Variable
Protection from Expropriation * Ethnic Concentration
Ethnic Concentration
Country Controls

77 countries
[1]
0.913***
(0.199)
-6.877***
(1.501)

77 countries
[2]
0.696*
(0.369)
-5.142*
(2.757)

29 countries w/ at
least 100 obs.
[3]
0.928***
(0.200)
-7.004***
(1.509)

29 countries w/ at
least 100 obs.
[4]
0.680*
(0.371)
-5.030*
(2.766)

No

Yes

No

Yes

Adjusted R-Squared
0.2356
0.2599
0.2328
0.2538
Number of Observations
13,867
13,867
13,675
13,675
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 probability 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.

38

Table 8: 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)

Prescription
Drugs for
Children
[4]
0.023 ***
(0.008)

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%

34.1%

75.1%

79.3%

0.2315
14,232

0.1835
14,232

0.2386
14,232

0.0786
3,221

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 ***
1.409
0.892
-0.268
(0.369)
(0.598)
(0.496)
(1.059)
(0.658)
(0.492)
Ethnic Concentration
-5.142 *
2.533
-12.004 ***
-10.529
-6.702
1.839
(2.757)
(4.417)
(3.665)
(7.889)
(4.820)
(3.619)
Country Controls
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R-squared
0.2599
0.1973
0.2492
0.0972
0.0682
0.0017
Number of Observations
13,867
13,867
13,867
3,143
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 probability 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. “Prescription Drugs for Children” is asked of respondents who have a child
aged 0-14 and is equal to one if the respondent purchased prescription drugs for the child, 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.

39

Table 9: 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
[2]
[3]
[4]
[5]
[7]
[1]
[6]
Protection from Expropriation
0.016***
0.031***
0.006
0.039***
0.011**
0.025***
0.016***
(0.005)
(0.012)
(0.004)
(0.010)
(0.005)
(0.008)
(0.005)
Adjusted R-Squared
0.2315
0.2801
0.0890
0.2910
0.1221
0.2427
0.2297
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 probability 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”.

40

Table 10: The Persistence of Institutions
The Effect Institution Quality on Immigrant Stock Market Participation,
by Years of U.S. Experience
Explanatory Variables
ALL
No Age at Arrival Controls
Protection from Expropriation
Adjusted R-Squared
Age at Arrival Controls
Protection from Expropriation

0.016***
(0.005)
0.2315

1–7
0.014**
(0.006)
0.2138

8 – 12

Years in the U.S.
13 – 17
18 – 27

0.013*
(0.008)
0.3324

0.028***
(0.006)
0.3629

0.023***
(0.008)
0.2660

28+
0.008
(0.010)
0.2844

0.016***
0.014**
0.013 0.028*** 0.022***
0.003
(0.005)
(0.006)
(0.008)
(0.006)
(0.008)
(0.010)
Adjusted R-Squared
0.2331
0.2135
0.3334
0.3631
0.2663
0.2909
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 probability 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.

41

Table 11: Learning about Institutions
The Effect Institution Quality on Immigrant Stock Market Participation,
by Age at Migration
Explanatory Variables
ALL
No Year of Arrival Controls
Protection from Expropriation
Adjusted R-Squared
Year of Arrival Controls
Protection from Expropriation

1 – 15

0.016***
(0.005)
0.2315

0.007
(0.006)
0.2585

Age at Arrival in U.S.
16 – 20
0.013*
(0.007)
0.2715

21+
0.018***
(0.006)
0.2387

0.016***
0.007
0.014*
0.019***
(0.005)
(0.006)
(0.007)
(0.006)
Adjusted R-Squared
0.2335
0.2609
0.2721
0.2427
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 probability 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.

42

Table 12: Intergenerational Transmission of Institutional Lessons
The Effect of Institution Quality on Stock Market Participation
Selected Natives and Immigrants
Explanatory Variable
Protection from Expropriation

Native

Immigrant
-0.002
(0.019)

0.021***
(0.008)

Adjusted R-Squared
0.2090
0.2523
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 probability 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.

43

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
27
28

Argentina
Australia
Austria
Bahamas, The
Bangladesh
Belgium
Bolivia
Brazil
Canada
Chile
China
Colombia
Costa Rica
Cuba
Czechoslovakia27
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
England28
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.

44

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

47
48

Malaysia
Mexico

0.0232%
8.5920%

45

19
4,163

Appendix Table 1: Ethnic Concentration and Number of observations per country,
continued
Country
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

Median Ethnic
Concentration (%)

Morocco
Myanmar
Netherlands
New Zealand
Nicaragua
Nigeria
Norway
Pakistan
Panama
Peru
Philippines
Poland
Portugal
Romania
Russia29
Singapore
South Africa
Spain
Sweden
Switzerland
Syria
Taiwan
Thailand
Trinidad & Tobago
Turkey
Uruguay
Venezuela
Vietnam
Yugoslavia30
All

----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
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

29
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.
30
Includes individuals who reported that they were born in: Yugoslavia, Bosnia and Herzogovina, Croatia, Macedonia, Montenegro,
Slovenia, Serbia.

46

Appendix Table 2: The Effect of Control Variables on Stock Market Participation
Explanatory Variable

[1]

Age†

0.215
(0.157)
-0.002
(0.002)
0.010
(0.005)
0.040
(0.012)
0.179
(0.020)
0.027
(0.008)
30.700
(4.590)
-0.001
(0.000)
-0.021
(0.005)
0.024
(0.010)
-0.004
(0.002)
0.003
(0.012)
-0.0001
(0.007)
0.043
(0.011)
0.050
(0.018)
0.161
(0.024)
0.016
(0.005)
-0.201
(0.052)
Yes

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

*
***
***
***
***
***
***
**
*

***
***
***
***
***

Adjusted R-Squared
0.2315
Number of Observations
14,232
Notes: Dependent variable is equal to one if the respondent owned stock during the interview period in
question and is zero otherwise. A linear probability 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.

47

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

WP-01-01

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

WP-01-02

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

WP-01-03

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

WP-01-04

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

WP-01-05

WP-01-06

WP-01-07

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

WP-01-08

Outsourcing Business Service and the Scope of Local Markets
Yukako Ono

WP-01-09

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

WP-01-10

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

WP-01-11

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

WP-01-12

Gaps and Triangles
Bernardino Adão, Isabel Correia and Pedro Teles

WP-01-13

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

WP-01-14

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

WP-01-15

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

WP-01-16

1

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

WP-01-17

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

WP-01-18

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

WP-01-20

Antidumping Policy Under Imperfect Competition
Meredith A. Crowley

WP-01-21

WP-01-19

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

WP-01-22

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

WP-01-23

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

WP-01-24

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

WP-01-25

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

WP-01-26

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

WP-02-01

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

WP-02-02

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

WP-02-03

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

WP-02-04

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

WP-02-05

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

WP-02-06

2

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

WP-02-07

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

WP-02-08

The Immediacy Implications of Exchange Organization
James T. Moser

WP-02-09

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

WP-02-10

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

WP-02-11

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

WP-02-12

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

WP-02-13

Technology Shocks Matter
Jonas D. M. Fisher

WP-02-14

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

WP-02-15

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

WP-02-16

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

WP-02-17

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

WP-02-18

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

WP-02-19

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

WP-02-20

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

WP-02-21

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

WP-02-22

3

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

WP-02-23

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

WP-02-24

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

WP-02-25

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

WP-02-26

Private School Location and Neighborhood Characteristics
Lisa Barrow

WP-02-27

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

WP-02-28

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

WP-02-29

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

WP-02-30

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

WP-02-31

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

WP-03-01

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

WP-03-02

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

WP-03-03

When is Inter-Transaction Time Informative?
Craig Furfine

WP-03-04

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

WP-03-05

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

WP-03-06

Resolving Large Complex Financial Organizations
Robert R. Bliss

WP-03-07

4

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

WP-03-08

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

WP-03-09

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

WP-03-10

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

WP-03-11

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

WP-03-12

The Cost of Business Cycles under Endogenous Growth
Gadi Barlevy

WP-03-13

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

WP-03-14

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

WP-03-15

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

WP-03-16

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

WP-03-17

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

WP-03-18

Banking Market Conditions and Deposit Interest Rates
Richard J. Rosen

WP-03-19

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

WP-03-20

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

WP-03-21

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

WP-03-22

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

WP-03-23

5

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

WP-03-24

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

WP-03-25

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

WP-03-26

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

WP-03-27

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

WP-03-28

Firing Costs and Business Cycle Fluctuations
Marcelo Veracierto

WP-03-29

Spatial Organization of Firms
Yukako Ono

WP-03-30

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

WP-03-31

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

WP-03-32

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

WP-03-33

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

WP-03-34

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

WP-03-35

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

WP-04-01

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

WP-04-02

Real Effects of Bank Competition
Nicola Cetorelli

WP-04-03

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

WP-04-04

6

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

WP-04-05

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

WP-04-06

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

WP-04-07

Earnings Inequality and the Business Cycle
Gadi Barlevy and Daniel Tsiddon

WP-04-08

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

WP-04-09

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

WP-04-10

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

WP-04-11

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

WP-04-12

Sibling Similarities, Differences and Economic Inequality
Bhashkar Mazumder

WP-04-13

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

WP-04-14

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

WP-04-15

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

7