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Banking crises and Investor Confidence: An Empirical Investigation*

Una Okonkwo Osili
Indiana University-Purdue University at Indianapolis
Anna Paulson
Federal Reserve Bank of Chicago
December 30, 2009
Abstract
In addition to their direct effects, banking crises may decrease investor confidence; lead some investors to
withdraw funds from the formal financial sector, and thereby exacerbate the impact of crises. We
quantify the effects of financial crises on investor confidence by studying the investment behavior of
immigrants in the U.S. who vary in their exposure to systemic banking crises prior to arriving in the U.S.
We find that individuals who have experienced a systemic banking crisis in their countries of origin are
11 percentage points less likely to use banks in the U.S. compared to otherwise similar individuals from
the same country that have not lived through a crisis. This finding is robust to including country-decade
of migration fixed effects and other methods to address potential unobserved heterogeneity. Consistent
with the view that personal experience plays an important role in decision-making, we also find that the
effects of living through a crisis are larger for individuals who are adults at the time of the crisis and for
people who experience crises in countries without deposit insurance.
Key words: systemic banking crises, investor confidence, deposit insurance, reinforcement learning,
immigrants
JEL codes: G01, G11, O16, D83

*

This paper has benefitted from the comments of seminar participants at Boston University, the Federal Reserve Banks of Boston
and Chicago, the University of Chicago, the IMF, IUPUI, Wellesley College and Yale University. We thank Dan Aaronson, Joe
Altonji, Sandeep Baliga, Kristin Butcher, Jeff Campbell, Eliana La Ferrara, Ulrike Malmendier, Mark Rosenzweig and Paola
Sapienza for helpful comments. We are grateful to Nathan Marwell for exceptional research assistance. The views presented
here are those of the authors and not necessarily those of the Federal Reserve Bank of Chicago, the Federal Reserve System or
the U.S. Census Bureau. This research was carried out at a U.S. Census Bureau Research Data Center facility. The results and
conclusions of the research are those of the authors and do not indicate concurrence by the Census Bureau. The results have
been screened to avoid revealing confidential data. Correspondence to: Anna Paulson, Federal Reserve Bank of Chicago, 230 S.
LaSalle Street, Chicago, IL 60604 (anna.paulson@chi.frb.org).

1. Introduction
Confidence in the stability of the banking sector is crucial element for well-functioning financial
markets.

The fallout from the 2008 subprime crisis has led to decreased confidence in financial

institutions. Indeed, 2008 saw old-fashioned bank runs with depositors lined up outside the doors of
institutions like Indy Mac in the United States and Northern Rock in the United Kingdom hoping to
withdraw their savings from those failing institutions. Speaking on the government’s response to the U.S.
banking crisis in late February and early March of 1933, President Franklin Roosevelt stressed “there is
an element in the readjustment of our financial system more important than currency, more important than
gold, and that is the confidence of the people.” His words remain relevant today.
Increased confidence in the banking sector can promote recovery and increase the perceived
credibility of post-crisis reforms. However, “crises … leave citizens wary of entrusting their savings to
the official banking sector. This diversion of savings is likely one of the great and unmeasured costs of
banking crises” (Gerard Caprio, World Bank, 2005). Measuring the impact of a crisis on investor
confidence is complicated by the fact the difficulty of disentangling whether financial decisions change
due to decreased confidence or because of decreased wealth and income as a result of the crisis.
Despite the importance of investor confidence in determining the costs of a crisis and paths to
recovery, it remains largely unstudied.1 We make progress in estimating the impact of crises on investor
confidence, by examining the financial decisions of otherwise similar individuals who differ exogenously
in their exposure to financial crises. Information on the investment behavior of immigrants in the U.S.,
together with measures of their exposure to systemic banking crises prior to migration, provides this
opportunity. If episodes of financial instability have long-lasting effects on investor confidence, then
individuals who have experienced a crisis may make different financial choices than otherwise similar
individuals who have not lived through a crisis. In particular, reduced investor confidence may manifest

1

Researchers have investigated the consequences of banking crises for firms. Dell’Ariccia, Detragiache, and Rajan (2008) find
that growth in externally dependent sectors tends to be lower during banking crises. Kroszner, Laeven, and Klingebiel (2007)
find that firms that are more dependent on external finance perform relatively worse during banking crises in countries with welldeveloped financial systems.

2

itself in lower usage of U.S. financial institutions among individuals who have experienced a systemic
banking crisis prior to arriving in the U.S.
Focusing on the investment behavior of individuals who have migrated to the U.S. offers distinct
advantages for understanding investor confidence. First, by studying investment decisions in a common
institutional, economic and financial environment, we minimize the potential impact of confounding
cross-country differences, including the success and credibility of post-crisis reforms.

Examining

investment decisions in the U.S. also helps to isolate factors that influence the demand for financial
products rather than the supply, since the supply of financial services in the U.S. is likely to be
independent of banking crises in other countries. In addition, because individuals from the same country
vary in their exposure to crises, we can include country of origin fixed-effects in our empirical
specifications. By doing this we hold constant country-level variation in economic, financial, institutional
and cultural factors.2 Variability in the severity and the origins of financial crises allows us to explore
how investor confidence is shaped by these features of crises as well. In addition, the availability of
detailed individual level data allows us to control for factors like income and wealth that are likely to
influence financial choices and also be directly impacted by exposure to a systemic banking crisis.3 The
availability of these data also permits an examination of how the impact of a crisis varies with individual
characteristics, like education and years in the U.S. Finally, individuals who move to the U.S. are
naturally aware that there are differences in the safety and soundness of U.S. financial institutions
compared to those in their countries of origin. As a result, our estimates of how investor confidence is
influenced by exposure to systemic banking crises may be conservative.
Our findings indicate that experiencing a systemic banking crisis has important long-term effects
on behavior. Individuals who have lived through a crisis are significantly less likely to participate in U.S.
financial markets compared to otherwise similar individuals from the same country.

In particular,

2

A number of studies demonstrate that country of origin characteristics impact a wide variety of immigrant and immigrant offspring behavior., including savings, stock market participation, banking and fertility. See Caroll, Rhee and Rhee (1994 and
1999), Osili and Paulson (2008a and 2008b) and Fernandez and Fogli (2009), for example.
3
McKenzie (2004) documents substantial and widespread declines in real incomes in the wake of the 2002 Argentine financial
crisis, for example.

3

individuals who have experienced a systemic banking crisis are 11 percentage points less likely to have a
checking account in the U.S. According to our estimates it is only after about two decades in the U.S. that
experiencing a crisis ceases to impact behavior.

While we cannot separately identify the impact of

additional time to learn about U.S. financial markets and passage of time since the crisis, this provides
some insights into how long it may take investor confidence to recover following a crisis episode.
Consistent with the hypothesis that direct experience of a crisis is important, we also find that the effect of
living through a crisis is larger for individuals who experience a crisis as adults and for individuals who
experience a crisis in a country without deposit insurance.
We take a number of steps to ensure that these findings are robust. The empirical issue that is the
largest concern is unobserved heterogeneity. Immigrants choose to migrate, so they are not random
representatives of their countries of origin. This could bias estimates of investor confidence if unobserved
factors that influence financial decisions are correlated with both exposure to banking crises and with the
decision to migrate following a crisis. We reduce the potential role for unobserved heterogeneity by
choosing checking account ownership as the main outcome variable. Because the decision to use checks
is essentially a decision about what sort of payment technology to use, it is unlikely to be influenced by
unobservables (such as risk aversion or time preference) that could be correlated with exposure to
banking crises. In addition to alleviating concerns about unobervables, we focus on checking accounts
because they are the most common means by which individuals entrust funds to banks, so they provide an
appropriate way to benchmark the effects of decreased investor confidence following a banking crisis.
Nearly 90 percent of households have a checking account according to recent data from the Survey of
Consumer Finances, much higher than the 48 percent who have savings accounts. 4 We also expect
estimates of checking account ownership to produce conservative estimates if investor confidence has a
larger impact on riskier investments.
4

Alternatives to checks would include some combination of check-cashers, money orders and cash. Although the costs of
checking accounts are quite low, they may be significant for some lower income immigrants. We control for this possibility by
including income and wealth in the estimation. Perceived legal barriers to opening a checking account may also deter some
individuals from opening an account. We address this concern by making sure that the results are robust to dropping individuals
who come from countries that are thought to generate the most undocumented immigrants. In addition, we confirm that the effect
of crisis exposure is the same for permanent residents and naturalized citizens compared to other immigrants.

4

Bias induced by unobserved heterogeneity at the country level is eliminated by including country
of origin fixed effects in all of the estimates.5 We also ensure that the results are robust to the possibility
of time-varying unobserved heterogeneity by including country-decade fixed effects in some
specifications.6 Additional tests also point to the strength of our results. For example, the findings are the
same when we drop countries, notably Mexico, where it would relatively easy for migration patterns to
change in response to financial market instability.7
Although we rule out many forms of potential unobserved heterogeneity by including country
fixed effects and country-decade fixed effects, as well as the other explanatory variables, it remains
possible that more recent migrants, even within a decade, are somehow different than earlier migrants.
This is a concern because more recent immigrants are more likely to have experienced a banking crisis
prior to coming to the U.S. One possibility is that more recent migrants have larger networks in the U.S.,
and these networks provide informal financial services that serve as alternatives to checking accounts. If
these networks were not as readily available to earlier migrants, earlier migrants may make greater use of
checking accounts as a result.8 To investigate this possibility, we constructed a placebo treatment that
randomly assigns individuals into early and late migrant groups. The placebo treatment variable has no
systematic relationship with checking account ownership, giving us further confidence in the strength of
our findings.
Additional estimates show that individuals who experience a crisis in a country that had deposit
insurance in place prior to the crisis are nearly as likely to participate in U.S. financial markets as their
counterparts from the same country who migrated before the crisis. There is an important policy debate
about the costs and benefits of deposit insurance, focusing particularly on the concern that deposit
insurance could destabilize the financial sector by increasing moral hazard, particularly in countries with
5

See Borjas (1987).
See Borjas and Friedberg (2009) for a recent discussion of this issue.
7
Borjas and Katz (2007) show that there are important differences in Mexican immigrants to the U.S. compared to immigrants
from other countries.
8
Another possibility is that more recent immigrants are more likely to send remittances (and more likely to have experienced a
banking crisis) and that they prefer non-bank remittance sending arrangements. However, our tabulations of data from the New
Immigrant Survey suggest that immigrants who send remittances are more likely to have bank accounts relative to those who do
not send remittances.
6

5

weak institutions or less developed financial systems. See for example, Demirgüç-Kunt and Detragiache
(2002), Demirgüç-Kunt and Huizinga (2004), Demirgüç-Kunt and Kane (2002), Laeven (2002) and
Hovakimian et al. (2003). Our findings suggest that these concerns should be weighed carefully against
deposit insurance’s potential to maintain investor confidence in the wake of financial turmoil. The deposit
insurance results also point to the importance of direct experience of losses being important for future
changes in behavior, consistent with the literature on reinforcement learning.
This study is related to a growing body of research that investigates how experience with
particular institutions or economic conditions impacts future attitudes and behavior. Important examples
of work in this area include: Fernandez, Fogli and Olivetti (2004) who show that men who grew up with
mothers who worked are more likely to have spouses who also work, potentially because their preferences
were influence by growing up in a home with a working mothers; Guiso, Sapienza and Zingales (2004)
who use data from Italy to document that the level of social capital that an individual is exposed to in their
region of birth has persistent effects on their financial behavior and that these effects persist even when
they migrate within Italy; Graham and Narasimhan (2005) who find that corporate managers that have
lived through the Great Depression in the U.S. choose a more conservative capital structure with less
leverage even after economic conditions improve; Fernandez and Fogli (2006) who find that fertility is
influenced by experience (the number of siblings that a woman has) as well as by culture; Alesina and
Fuchs-Schündeln (2007) who find that exposure to Communism influences East German attitudes toward
redistribution and state intervention after German reunification; Kaustia and Knüpfer (2008) who show
that IPO returns experienced by individual investors influence their future investment in IPOs;
Malmendier and Nagel (2009) who document that an individual’s early experience of stock and bond
returns impacts subsequent investment behavior; and Giuliano and Spilimbergo (2009) who find that
individuals who grew up during periods of macroeconomic volatility are more likely to support
government redistribution and to believe that luck has more to do with success than effort.
Because of our interest in how experiencing a banking crisis impacts future behavior, work in
behavioral economics and psychology that examines the role that personal experience plays in decision-

6

making is also very relevant. In models of reinforcement learning, information gained from personal
experience has a greater effect on behavior relative to other sources of information (see Cross, 1973,
Arthur, 1991, Ellison and Fudenberg, 1993 and Roth and Erev 1995, for example). Mookherjee and
Sopher (1994 and 1997), Erev and Roth (1998) and Charness and Levin (2005) provide experimental
evidence in favor of reinforcement learning.

In Camerer and Ho (1999) reinforcement learning is

combined with belief learning to create an “experience-weighted attraction” model of learning.

In

experimental evaluations of this model, actual payoffs are weighted about twice as heavily as foregone
payoffs.

Choi et al. (2009) provide evidence that individuals over-extrapolate from their personal

experience when making savings decisions.
The next section describes the framework we use to derive the predicted relationship between
banking crises and financial decisions. In section 3, we describe the country and individual level data that
we analyze. Section 4 outlines the empirical strategy, discusses our main findings and presents our
findings on how the impact the impact of living through a crisis varies with individual, country and
financial crisis characteristics. This section also explores the robustness of the findings. Section 5
presents conclusions.

2. Framework
Theoretical studies of bank fragility often emphasize investor confidence as a potential
contributor to bank runs. In particular, Diamond and Dybvig’s (1983) canonical model shows that a selffulfilling loss of confidence in the banking system may lead depositors to try to withdraw their funds from
banks, causing widespread failure of the banking system. An important insight from their model is that
systemic banking crises will be more likely in places where investor confidence is low. The literature on
bank fragility does not provide much guidance into the origins of investor confidence, however.

7

The mechanisms through which experiencing a banking crisis might influence future
interactions with financial institutions include preferences, beliefs and generalized trust. 9 The available
data do not permit us to cleanly distinguish between these three mechanisms, but we will discuss some
suggestive evidence in favor of the beliefs channel. To motivate the empirical work and make the
hypotheses that we test clear, we sketch out a simple reduced form framework to describe how an
individual’s demand for bank services would be affected by exposure to a banking crisis. The framework
emphasizes the beliefs channel as a matter of convenience.
Consider an individual, i, from country j who is considering whether to open a bank account.
The individual’s demand for bank services is represented by:

S ij

f ( R, X ij )

where Sij is the amount that individual i invests in the bank account, R is the expected return from the
investment, and Xij is a vector of individual characteristics (wealth, income, education, years in the U.S.,
age, for example) and country characteristics that affect the demand for bank services.
The effect of banking crises is modeled by assuming that the investor believes there is some
probability, πijc, of a banking crisis that will impact returns to bank services. The subscript c indicates
whether person i from country j experienced a systemic banking crisis or not. Given her beliefs, the
investor’s expected return on the investment will not be R, the expected return on the bank account, but
πijc x 0 + (1 – πijc) x R. This assumes that the return in the event of a crisis is zero. Assuming that returns
are negative, or positive, but lower than if there were not a crisis, does not change the analysis.
We imagine that individuals live for three periods (time 0, 1 and 2). At time 0 individuals are
endowed with a prior about the likelihood of a banking crisis. This prior may differ with individual and
country characteristics. At time 1, the country that individuals are living in either experiences a crisis or
not, and beliefs about the likelihood of a crisis are updated, taking this new information into account.
Updated beliefs about the likelihood of a crisis are represented by πijc. At time 2, we observe individuals

9

Alesina and La Ferrara (2002) find that individuals who have recently suffered a trauma or a financial loss are less trusting.

8

living in the U.S., along with their decisions about how much (or whether) to invest in a bank account.
We assume that individuals live in their country of birth at the beginning of time 0. They may move to
the U.S. at either the end of time 0 or at the end of time 1. That is, some individuals will arrive in the
U.S. without having lived through a financial crisis, even though they come from a country that
experiences a financial crisis at time 1. The goal of the analysis is to determine how exposure to banking
crises influences investment decisions in the U.S., controlling for individual and country characteristics.
Among similar individuals from the same country, we expect πijc to be higher and, consequently,
demand for bank services in the U.S. to be lower, for individuals who have lived through a crisis. An
individual’s estimate of the likelihood of a banking crisis, πijc, is also expected to be higher for individuals
who come from countries with particularly unstable financial systems and may be decreasing with years
spent in the U.S. To put the emphasis on the effect of living through a systemic bank crisis, we include
country of origin fixed effects in all of the empirical estimates. The fixed effects address time-invariant
country level differences in πijc. This would include variation that is due to differences in the level of
economic and financial development as well as the quality of governance in the country of origin. The
country of origin fixed effects also control for the possibility that the level of investor confidence in a
country could itself impact the frequency of banking crises due to the self-fulfilling dynamics that lead to
bank runs in the Diamond and Dybvig (1983) framework. We also explore whether the effect of πijc varies
with the age at which an individual experienced a crisis, with how long an individual has lived in the U.S.
and with characteristics of the country of origin economic and financial environment.

3. Data
Individual Data
The individual data that we use come from the 1996 Survey on Income and Program Participation
(SIPP), which is a nationally representative survey of U.S. households conducted by the U.S Census
Bureau. We restrict our attention to the first annual survey wave where financial market participation and

9

wealth data are available.10 The sample we analyze is restricted to individuals who are over eighteen and
who migrated to the U.S. after 1975 for a total of 3,609 individuals representing 80 countries. High
quality data on banking crises are available for the post-1975 period.
Table 2A summarizes these data for immigrants and the native-born. Although the empirical
analysis includes only immigrants, it is useful to understand the characteristics of this population relative
to individuals who were born in the U.S. Compared to the native-born, immigrants are younger, more
likely to be married, non-white and have more children. Immigrants also tend to be less educated than the
native-born. Thirty-seven percent of the immigrant sample has not completed high school compared to
only 16.7 percent of the native-born sample. However, the percentage of immigrants and the native-born
who have an advanced degree is about seven percent for both groups.
Monthly per capita household income is significantly lower for immigrants compared to the
native born. For immigrants, average monthly per capita household income is $1,666, compared to
$2,423 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 $12,160, compared to $67,615 for the native-born.
The main dependent variable in our analysis is the use of checks in the U.S. Checking account
ownership is relatively widespread compared with the usage of other financial assets: 42 percent of the
immigrants have a checking account compared with 64 percent of the native-born.11 Thirty-six percent of
immigrants have a savings account, compared with 53 percent of the native-born (see Table 2A). Six
percent of the immigrant sample owns stock outside of a retirement account, compared with 18 percent of
the native-born. More than three times as many (18 percent) native-born households have an IRA or
Keogh account compared to immigrant households. About 41 percent of immigrants own their own
homes compared to 72 percent of the native-born.

10

Other SIPP data are collected quarterly for four years. Because of the short length of the panel and concerns about sample
attrition, particularly among immigrants, we have not attempted to analyze changes in financial behavior.
11
The Survey of Consumer Finance figures are higher because they define checking accounts more broadly and look at
ownership at the household level, rather than at the level of an individual.

10

Additional immigrant characteristics are described in Table 2B. Just over one-third of the
immigrants arrived in the U.S. between 1990 and 1996, 17 percent arrived between 1975 and 1979 and
the remainder arrived during the 1980s. Just over half of the immigrants were born in a North American
country (including one-third in Mexico) and about 7 percent were born in Europe.

Most of the

immigrants arrived in the U.S. as adults, with nearly 90 percent arriving at age twenty years or older.

Banking Crisis Measures
We use data provided in Honahan and Laeven (2005) to identify and date episodes of systemic
banking sector crises. The data cover the period 1975 to 2002 and include 98 countries and 60 systemic
crisis episodes. Because the individual data come from interviews conducted in the U.S. in 1996, we
focus on crises that occurred between 1975 and 1995. Appendix Table 1 summarizes the crisis periods
that we examine for each country. We adopt Honhan and Laeven’s definition of a systemic banking
crisis. Episodes of banking sector distress are considered systemic if any of the following occur: nonperforming assets reach at least 10% of total assets at the peak of the crisis; the cost of rescue operations
is at least 2% of GDP; emergency measures (bank holidays, deposit freezes, blanket guarantees to
depositors or other bank creditors) are taken; large-scale nationalizations take place. A little less than
one-half of the countries in the sample experienced a systemic banking crisis between 1975 and 1995.
We create two measures of exposure to systemic banking crises. The first uses information on
the country of origin of individual migrants together with data on when they arrived in the U.S. to create a
bivariate measure of whether an individual was exposed to a banking crisis or not. The variable, Zij, for
individual i from country j, is equal to one if the individual lived in their birth country during the crisis
period and is equal to zero if they were living in the U.S. at the time of the crisis or if they come from a
country that did not experience a systemic banking crisis between 1975 and 1995.12 As an example,
consider immigrants from El Salvador, which had one banking crisis in 1989. Salvadorans who arrived in
the U.S. between 1975 and 1988 will have Zij equal to zero. Those who arrived in 1989 or later (and who
12

We use information on date of arrival from internal SIPP files accessed through the Chicago Census Center to create the crisis
exposure variable.

11

are born before 1989) will have Zij equal to one. Thirty-nine percent of the sample has Zij equal to one.
For individuals who have experienced multiple banking crises, we use information from the first crisis.13
The second method of quantifying exposure to a systemic banking crisis measures how old an
individual was at the time they experienced a crisis. This variable, Žijt, is equal to individual i’s age at the
beginning of the first crisis they were exposed to and is equal to zero if they never lived through a
systemic banking crisis. Returning to our Salvadoran example, Salvadorans who arrive in the U.S. after
1989 will have Žijt equal to their age in 1989. An individual who was born in 1979 is assigned Žijt equal to
ten, for example. Among individuals who have experienced a crisis, the average age at crisis is 19.4 with
a standard deviation of 12.2 years.
This measure has empirical as well as substantive advantages. From an empirical perspective,
age at crisis creates additional variation among migrants from a given country. This additional variation
allows us to include fixed effects which remove bias due to time varying heterogeneity among immigrants
from a particular country. In particular, we include country of origin interacted with decade of arrival
fixed effects. From a substantive perspective, the age at crisis measure provides a glimpse into the role of
reinforcement learning through direct experience as people who were older at the time of a banking crisis
are more likely to have had bank accounts and are therefore more likely to have lost money during the
crisis.

Other Country-Level Data
In addition to information on banking crises, we also examine the role of a number of other
features of the financial and economic environment in the country of origin. The country-level variables
and their sources are described in detail in Table 1. Tables 3A and B provide summary information about
these variables and their correlation with one another. Country-level measures of financial development
include private credit as a share of GDP (Beck, Demirgüç-Kunt and Levine (2000) and bank branches per

13

Only a small minority of people in the sample have experienced two or more crises, and their use of checks is very similar to
those who have experienced one crisis.

12

100,000 people (Beck, Demirgüç-Kunt and Peria, 2007). We also include measures of the level of
economic development, the quality of institutions (Kauffman, Kraay and Zoido-Lobaton, 1999) and how
trusting a country’s citizens are, as measured by summaries of responses to World and European Values
Survey questions.14
In an effort to explore how the nature of the crisis impacts investor confidence, we examine
several variables that describe the financial crisis. These variables include whether a systemic bank crisis
was accompanied by a GDP crisis. We define a GDP crisis as an episode of at least three consecutive
years of negative GDP growth.

Investors may also respond differently to banking crises that are

accompanied by currency or sovereign debt crises. We use information on the dates of currency and
sovereign debt crises collected by Laeven and Valencia (2008) to explore this possibility.
Exposure to a GDP, currency or debt crisis is measured much the same way as exposure to a
banking crisis, with individuals categorized as having experienced a crisis of a particular type if they
arrived in the U.S. after their country of origin experienced such a crisis. Individuals are defined to have
experienced overlapping crises, a twin crisis of a banking crisis and a currency crisis, for example, if they
lived through a systemic banking crisis and a currency crisis that began while the banking crisis was ongoing. Concurrent bank and GDP crises and bank and sovereign debt crises are defined analogously.
Deposit insurance is designed to protect account holders from losses in the event of a crisis.
Exploring differences in the financial choices of individuals who have experienced a systemic banking
crisis depending on whether they were protected by deposit insurance helps to shed light on whether
direct experience of a loss in bank assets is an important channel through which investor confidence can
be shaken. We examine the role of having enacted deposit insurance prior to, or after, a bank crisis
(combining information on the timing of the crisis from Honohan and Laeven, 2005, with information on
deposit insurance from Demirgüç-Kunt, Kane and Laeven, 2008). Among the countries that experienced
14

In particular, we calculate the fraction of respondents who answer the question “Generally speaking, would you say that most
people can be trusted, or that you can't be too careful in dealing with people?” with “Most people can be trusted”. We do this for
countries where European or World Value Surveys were completed between 1981 and 2004. Trust is defined to be one for
countries whose average response is in the upper third of the distribution and zero otherwise.

13

a systemic banking crisis between 1975 and 1995, and where there were individuals that migrated both
before and after the crisis, 30% had deposit insurance in place prior to the crisis and the remaining 70%
enacted it after the crisis. The severity of a financial crisis may also influence investor confidence. One
measure of the severity of a crisis is whether deposit insurance was enacted in the aftermath of the crisis.
We also use the information on the duration of banking crises crises from Honohan and Laeven (2005) to
see if responses vary with the length of the crisis.

4. Empirical Findings

In this section, we report our empirical findings. We present our main results and then address
potential empirical issues. Next, we discuss how the impact of banking crises varies with individual
characteristics, with country of origin characteristics, with characteristics of the crisis and with the
financial decision in question. This section ends with a discussion of what the findings imply about the
mechanism by which investor confidence is altered following exposure to a systemic banking crisis.
Main results
We estimate an individual’s decision to have a checking account using the following linear
probability model:
Sisj = α + β1Xi + β2Zij + δj + δs + εisj,
where Sisj is the decision of individual i who lives in county s and comes from country j to use a checking
account. 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 the household, and race.
The sample is restricted to immigrants who are at least 18 years of age and come from one of the 80
countries (excluding the U.S.) which are represented in the SIPP data. The variable Zij measures crisis
exposure and is equal to one if the individual experienced a bank crisis in their country of origin and zero
otherwise.

14

All of the specifications include country of origin fixed effects, δj. There are two important
reasons for including country of origin fixed effects. First, there are many time-invariant country of
origin characteristics that might influence the demand for various financial products. These include the
level of financial and economic development in the country of origin as well as the quality of institutions
that protect private property and provide incentives for investment (see Osili and Paulson, 2008a and
2008b). Many of these variables are likely to be correlated with the experience of banking crises. Table
3B shows the correlation between the banking crisis variables and other country of origin characteristics.
By including country of origin fixed effects, we ensure that the effect of banking crises is measured
holding these (and other) country level variables fixed.
The second reason for including country of origin fixed effects is to control for unobserved
individual heterogeneity. 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. By
including country of origin fixed effects, we eliminate the possibility that the estimated coefficient of
interest will be biased due to correlation between unobserved individual attributes and country of origin.
In addition to country of origin fixed effects all of the estimates also include a full set of county
of residence fixed effects, δs. The county fixed effects capture geographic variation in the supply of
banking services. In addition, the county fixed effects rule out bias due to unobserved characteristics that
influence an individual’s location choice that are also correlated with having lived through a banking
crisis. The reported standard errors have been corrected to account for the heteroscedasticity that is
implicit in the linear probability model and are also clustered to allow for correlation across observations
for individuals who come from the same country and migrated during the same period.15
The relationship between checking account ownership and exposure to systemic banking crises is
explored in Table 4. Recall that we focus on checking account usage because it is a conservative proxy

15

Non-linear estimation methods, such as probit or logit, generate similar results. We use a linear probability model because it is
computationally attractive given the large number of fixed effects, is consistent under weak assumptions and because the
coefficient estimates are easy to interpret. In particular, the coefficients on interaction terms are straightforward to interpret (see
Ai and Norton, 2003).

15

for investor confidence and because it is likely to be unrelated to unobservable individual attributes,
including risk aversion and time preference. Looking first at the estimates of owning a checking account
(column [1]) without wealth and income controls, we find that individuals who have experienced a
banking crisis are 13 percentage points less likely to have a U.S. bank account. When we include wealth
and income controls in column [2], individuals who have experienced a banking crisis are 11 percentage
points less likely to own a checking account compared to otherwise similar individuals. This is 26 percent
lower than the observed percentage of individuals who have a checking account of 41 percent.16 The
effects of the other control variables included in the regressions are reported in Appendix Table 2. These
findings suggest that investor confidence is significantly altered by experiencing a systemic banking crisis
and that the effects of this experience persist even after migration to the U.S.
Robustness checks
In order to explore the robustness of the findings, we take advantage of the fact that whether or
not a given individual will have had direct experience with a banking crisis depends on the country of
origin, when that individual migrated to the U.S., and also on the age of the individual at the time of the
crisis. Individuals who are adults at the time of a banking crisis are more likely to have directly
experienced the effects of the crisis compared to younger individuals. They are more likely to have had
bank accounts and other financial assets whose values were impacted by the crisis, for example.
Because “age at crisis” varies by country, by year of migration and by age, we can also include
controls for the decade of migration interacted with country origin in specifications which use age at
crisis. Specifically, we estimate:
Sisjdt = α + β1Xi + β2Žijt + md + δj + δs + δj x md+ εisjdt,
where Sisjdt represents the decision of individual i who lives in county s, comes from country j, migrated in
decade d and who was born in year t to have a checking account. Age at crisis is represented by Žijt, md
captures controls for the decade of migration and δj x md are country and decade of migration fixed
effects.
16

The effects of the other control variables are reported in Appendix Table 2.

16

Individuals from the same country who migrated to the U.S. during a particular time period may
share common characteristics such as unobserved ability, risk tolerance, or face similar labor market
conditions in the U.S.17 These “cohort” effects may affect the decision to own a bank account and be
correlated with having experienced a banking crisis. By including decade of migration controls in the
regression, we eliminate correlation between the age at crisis variable and unobserved immigrant
characteristics that vary with the timing of migration. As in the rest of the analysis, we include country of
origin and county of residence fixed effects in all of the specifications.
Columns [3] – [6] of Table 4 report on the relationship between checking account ownership and
age at crisis for various specifications. In Column [3], the indicator variable that captures whether an
individual has experienced a crisis is simply replaced with “age at crisis.” In column [4] we add decade
of migration fixed effects, and in column [5] we add decade of migration controls interacted with country
of origin fixed effects. When we add these controls, we are effectively comparing the effect of a crisis on
similar individuals from the same country of origin who all arrived in the U.S. in the same decade. 18 This
addresses concerns that the findings are driven by time varying unobserved ability or motivation, as
emphasized in the literature on migration. According to this estimate, the effect of living through a crisis
is larger for those who were adults than for those who were children at the time of the crisis, as one might
expect. An individual who was 30 years old at the start of the crisis would be 6 percentage points less
likely to have a checking account compared to someone from the same country who had not been exposed
to the crisis. Someone who was 45 at the time of the crisis would be 9 percentage points less likely to
have a checking account.19

17

An extensive literature discusses how unobserved individual characteristics (such as ability) may vary with the timing of
migration for a given country (see Borjas, 1994 and Borjas and Friedberg, 2009 for reviews). This literature emphasizes the
impact that unobserved factors have on labor market outcomes. Because our estimates include labor force status and income,
unobserved factors that work through those channels are accounted for.
18
The decade of migration controls also capture the effect of additional time in the U.S. To maintain consistency across the
estimates in Table 4 and to avoid over-controlling for time in the U.S., years in the U.S. is not included as an explanatory
variable. When years in the U.S. is included in the specification, the estimated effect of living through a crisis remains negative
and significant and the point estimate falls in absolute value by about 30%.
19
We have also explored non-linearities in the effect of age at crisis. We find that the impact of living through a crisis is smallest
for individuals who were less than fifteen at the time of the crisis (checking account ownership is 7 percentage points lower
compared to otherwise similar individuals) and largest for individuals who were 26 to 35 years old at the time of the crisis (19

17

Migrating to the U.S. in response to a financial crisis is more plausible for people from some
countries than from others. In particular, it may be relatively easy for people from Mexico to adapt their
migration and return migration plans in response to a crisis because of Mexico’s geographic proximity to
the U.S. To make sure that the findings are not driven by systematic differences among individuals from
Mexico based on the timing of migration or return migration, we exclude individuals from Mexico in
column [6]. The results are unchanged. We have also experimented with dropping additional individuals
from the Caribbean and Latin America with similar results. In addition, we have analyzed Department of
Homeland Security data on immigration flows into the U.S. by year and by country for major
immigration source countries to examine whether the immigration flows to the U.S. respond to crisis
conditions in the country of origin.20 We find no systematic relationship between migration flows from a
particular country in a given year and crisis conditions in that country.
More generally, the estimation strategy compares people from the same country who migrated
earlier (no crisis exposure) to similar people who migrated more recently (crisis exposure) either in
general or within a decade. Although we rule out many forms of potential unobserved heterogeneity by
including country fixed effects and country-decade fixed effects, as well as the other explanatory
variables, it remains possible that more recent migrants, even within a decade, are somehow different than
earlier migrants. One alternative explanation for our findings might operate through immigrant networks.
Perhaps more recent migrants have larger networks in the U.S. and these networks are large enough to
supply informal substitutes to formal financial products. If these networks were smaller when earlier
migrants arrived, these individuals may make greater use of checking accounts because fewer substitutes
were available.
To investigate the likelihood of alternative explanations that rely on differences between early
and more recent migrants, even within the narrow window of a decade, we construct a placebo treatment
that randomly divides individuals into early and recent migrant groups. The placebo treatment procedure
percentage points less likely to have a checking account). These differences are not statistically significant, however. These
estimates are available from the authors.
20
These estimates are available from the authors.

18

randomly assigns a year to each of the countries in our sample that experienced a banking crisis.
“Placebo banking crisis” and “placebo age at crisis” variables are created using the randomly assigned
year rather than the actual year of the banking crisis, and the regressions in Table 4 are recreated using the
placebo treatments. This procedure was repeated for 500 randomly assigned years. The results of this
exercise indicate that there is no systematic tendency for more recent arrivals to be less likely to have a
checking account. The average coefficient on the “placebo banking crisis” variable is -0.011, with a
standard deviation of 0.04 using the specification from Table 4, column [2]. The average coefficient on
the “placebo age at crisis” variable is -0.001 with a standard deviation of 0.009 using the specification
with country interacted with decade of arrival fixed effects (Table 4, column [5]).

This gives us

additional confidence that the results presented in Table 4 are driven by exposure to systemic banking
crises and not by unobserved differences related to the timing of migration.
The Effect of Banking Crises on Different Types of People
We turn now to analyzing how banking crises impact different groups of individuals. In Table 5,
we examine how the impact of a banking crisis varies with education, citizenship and time in the U.S.
These estimates help to identify the potential channels through which crises come to influence behavior
and also serve as further robustness checks on our main results.
We first examine how the impact of experience with a banking crisis varies with education.
Columns [2] and [3] present these results. In Table 5, we include the interaction of having experienced a
banking crisis with low education (in column [2]) and with high education (column [3]). Low education
is equal to one if the individual in question has not completed high school and zero otherwise. High
education is equal to one if the individual has a college degree or more schooling and zero otherwise. We
find that living through a crisis has a much larger impact on individuals with less than a high school
degree and that the effect of living through a crisis largely disappears for individuals with a college
degree or more. It is interesting to note that education appears to play a role in mitigating the impact of
experiencing a bank crisis. This mirrors the findings in Guiso, Sapienza and Zingales (2004) who show
that the effect of social capital on financial behavior is muted for those with greater education.

19

Columns [4] – [6] examine how the effect of living through a crisis changes with additional
exposure to the U.S. In column [4], we look at how the effect of living through a crisis varies with years
in the U.S. Each additional year in the U.S. lowers the effect of living through a crisis on checking
account ownership by 0.80 percentage points. After being in the U.S. for 21 years, the effect of living
through a crisis disappears. Note that each additional year in the U.S. has two effects. First, it represents
an additional year to adapt to the U.S. and learn about U.S. financial institutions, and second, it represents
an additional year of time since the crisis. The estimated coefficient in column [4] combines these two
effects.
In column [5], the interaction between the crisis variable and having lived in the U.S. for three
years or less is added. Among recent immigrants, the effect of having experienced a crisis is much larger.
For recent immigrants who have experienced a banking crisis, checking account usage is 18 percentage
points lower compared to immigrants who have not experienced a financial crisis. For their counterparts
who have also experienced a banking crisis but who have lived in the U.S. for more than three years,
checking account usage is predicted to be 9 percentage points lower. Finally, in column [6], we restrict
the sample to permanent residents and U.S. citizens. This helps to address the concern that the findings
could be driven by undocumented immigrants who avoid banks and may be more likely to have been
exposed to a banking crisis.

Living through a crisis has a essentially the same impact on the checking

account ownership of permanent residents and naturalized citizens as it does for the entire sample.
The Effect of Other Country Characteristics
In this section, we discuss how the effect of banking crises is influenced by other country
characteristics, including the level of economic and financial development, corporate governance as well
as a measure of how trusting the residents of the country are on average. Table 6 presents these results.
In each regression we investigate the extent to which the effect of a bank crisis varies with other country
of origin characteristics by including the interaction of the “experienced a banking crisis” variable with
other country characteristics. Recall that all of these regressions control for direct of effect of country of

20

origin characteristics on checking account ownership through the inclusion of country of origin fixed
effects.
We first explore how the impact of living through a crisis varies with the level of economic
development by including the interaction of experiencing a bank crisis with average real per capita GDP
from 1975 to 1995 in the estimate presented in column [2] of Table 6. We find that the interaction of
experience with a banking crisis and average real GDP per capita is positive and statistically significant.
According to these results, the effect of living through a banking crisis is smaller, but still negative and
significant, for individuals who come from places where the overall level of development is higher. While
the impact of living through a financial crisis does vary with the level of overall economic development,
as measured by the long-run average GDP per capita, there is no evidence that it varies systematically
with the level of financial development, as measured by private credit as a share of GDP (column [3]) or
more bank branches per 100,000 people (column [4]).
Coming from a country that has good governance appears to mitigate the effect of living through
a crisis substantially, however (see column [5]). A one standard deviation increase in governance, as
measured by the KKZ index, is associated with a 10 percentage point increase in the likelihood of having
a checking account after living through a crisis. The net effect is that individuals who have experienced a
bank crisis are 2 percentage points less likely to have a checking account (-12.3 + 10 = -2.3 percentage
points). These findings suggest that economic development and good governance may play an important
role in maintaining and/or restoring investor crisis during and following a systemic banking crisis. For
example, investor confidence may be restored even in the face of a systemic banking crisis if credible
government action is taken to resolve the crisis and this credible government intervention is associated
with high standards of institutional effectiveness (see Demirgüç-Kunt, Detragiache, and Gupta, 2006).
The impact of living through a banking crisis is also smaller for individuals from countries where
“trust” is higher (see column [6]). For individuals who come from a place where individuals are very
trusting, where average trust is in the upper third of the distribution of trust across countries, the impact
of living through a crisis is completely offset and their checking account usage is estimated to be the

21

same, or possibly even a bit higher, than their counterparts from the same country who did not live
through a banking crisis.
Of course, economic and financial development, good governance and trust are likely be
correlated. When we include all of the country variables in one regression, experiencing a bank crisis
remains significant and negative. The only other variable that is significantly different from zero is
private credit, which has a positive effect on the likelihood of having a checking account. A one standard
deviation increase in private credit is associated with a 40 percent decrease in the impact of experiencing
a banking crisis.
Does the Severity of the Banking Crisis Matter?
Banking crises vary in their severity. Some are prolonged and others are resolved quickly. In
addition, the severity of a financial crisis may vary depending on whether it is accompanied by other
shocks. For example, Kaminsky and Reinhart (1999) examine the relationships between banking crises
and currency crises and discuss how currency crises can exacerbate banking crises making “twin crises”
particularly severe. In Table 7, we investigate how various characteristics of a bank crisis impact
subsequent investor behavior.
We begin by examining the effect of experiencing a GDP crisis at the same time as a banking
crisis in column [2] of Table 7. A country is defined to have had a GDP crisis if it experienced a period
of three consecutive years of negative per capita GDP growth during the time period 1975-1995.
Individuals who live through a GDP crisis at the same time they experience a systemic bank crisis are
about 4 percentage points less likely to have a checking account compared to otherwise similar
individuals who did not live through a GDP crisis at the time of the banking crisis. While experiencing a
banking crisis accompanied by a GDP crisis exacerbates the impact of the banking crisis on investor
confidence, the coefficient on the banking crisis variable remains negative and significant. In column [3]
and [4], we examine the impact of experiencing a currency crisis or a sovereign debt crisis at the same

22

time as a banking crisis.21 Controlling for coincident currency crises or sovereign debt crises has little
impact on the estimated coefficient for experiencing a banking crisis, and there is no statistically
significant difference in the behavior of individuals who also experienced a currency or a debt crisis at the
same time as a banking crisis. In column [5], we examine how the length of the systemic banking crisis
influences investor behavior. The length of the financial crisis does not have a significant impact on the
likelihood of having a checking account in the U.S.22
In Table 8, we investigate how deposit insurance affects subsequent investor behavior.
Individuals who experienced a banking crisis in a country that has explicit deposit insurance in place
prior to the crisis are nearly as likely to have a bank account in the U.S. as individuals who never
experienced a banking crisis prior to moving to the U.S. (column [2]). The estimates suggest that having
deposit insurance prior to the crisis undoes the negative effect of living through a crisis on investor
confidence. In contrast, individuals who live through a banking crisis in a country that enacts deposit
insurance after the crisis experience no mitigating effects. The enactment of deposit insurance in the
wake of a crisis may be a measure of the severity of the crisis and reflect policymakers’ conclusions that
investor confidence has been sufficiently shaken by the crisis that it is desirable to enact deposit insurance
to restore confidence in an effort to bring savings back into the formal financial sector.
Do Bank Crises Matter for Other Behavior?
Finally, we consider the effect of a experiencing a banking crisis on other investment behavior.
In Table 9, we present estimates of experiencing a bank crisis on the decision to have any bank account, a
savings account, to own stock, to own an IRA or Keogh account, to own a home and to be self-employed.
These estimates help us to understand the channel through which banking crises impacts behavior. One
possibility is that exposure to bank crises affects an individual attribute – generalized trust, risk aversion,
21

For comparison purposes, we have also examined the impact of living through a GDP, currency or sovereign debt crisis that is
not accompanied by a systemic banking crisis. We find that individuals who have lived through a GDP crisis prior to coming to
the U.S. are 8 percentage points less likely to have a checking account compared to otherwise similar individuals. However,
there is no significant difference in checking account usage between individuals who have experienced a debt crisis or a currency
crisis and those who have not.
22
We have also examined the impact of other measures of the severity of banking crises including the fiscal costs of recovery
measures and the share of non-performing loans. These factors do not play a significant role in explaining differences in
checking account ownership among individuals from the same country who have and have not experienced a crisis.

23

or time preference, for example -- that is important for many financial decisions not just those involving
banks. Alternatively, exposure to a banking crisis may primarily impact an individual’s perception of the
expected returns to having a bank account, through an increase in the perceived likelihood of a future
banking crisis, for example.
We find that experiencing a banking crisis has a significant impact on investment decisions that
are mediated through banks: having any bank account, a savings account or purchasing a home (most
people borrow from banks to purchase a home). Compared to otherwise similar individuals from the
same country, people who lived through a banking crisis in their country of origin are 8.2 percentage
points less likely to have a savings account and 7.5 percentage points less likely to own a home.
Interestingly, exposure to systemic banking crises does not appear to have a significant impact on stock
market participation, IRA/Keogh ownership or self-employment. Although investor confidence in banks
appears to be shaken by banking crises, this experience does not seem to translate to investment decisions
that are not mediated through banks.
These findings are consistent with the view that living through a banking crises impacts investor
behavior by changing their beliefs about the future stability of banks. Survey evidence from Bulgaria
bolsters this view. In their analysis of 2008 survey data from Bulgaria, Mudd and Valev (2009) find that
people who lost money in during the 1996 Bulgarian banking crisis believe that a future episode of bank
instability is significantly more likely, compared to similar people who did not lose money in 1996.
Interpreting the Findings: The Role of Direct Experience
The literature on reinforcement learning emphasizes the role played by direct experience. While
the data do not allow us to examine this hypothesis directly, the results do suggest that it is important that
losses were experienced during the banking crisis for it to have an impact on financial behavior in the
U.S. For example, we find no effect of experiencing a banking crisis for individuals from countries that
had deposit insurance in place prior to the crisis. The finding that the impact of living through a crisis
increases with age at the time of the crisis is also consistent with the view that direct experience is
important.

24

As further checks on the importance of direct experience, we have examined whether banking
crises in the country of origin have an impact on people from that country who are living in the U.S. at the
time of the crisis. We have also examined whether banking crises affect the likelihood that individuals
from countries that border a crisis country have checking accounts in the U.S.

These individuals may

have learned about the crisis from friends and relatives, or through the media, for example. We find no
impact of a crisis in the country of origin on individuals already living in the U.S. or on individuals who
were living in countries that share a border with the crisis country and subsequently migrate to the U.S.
These findings suggest that direct experience of the banking crisis, in the sense of living in the country
when it happens, changes future behavior.

5. Conclusions
Our findings indicate that systemic banking crises have important effects on investor confidence.
Individuals who have experienced a banking crisis in their countries of origin are significantly less likely
to have bank accounts in the U.S. This finding is robust to including important individual controls like
wealth, education, income, and age, as well as country of origin fixed effects and decade of migration
controls. The results cannot be explained by time-varying individual-level heterogeneity that is correlated
with exposure to a banking crisis. In addition, because we focus on checking account ownership and
study individuals who have chosen to migrate to the U.S., the estimates that we present are likely to be
conservative. One can take the perspective, for example, that differences between the financial and
regulatory environment in the country of origin and the U.S. represent very credible institutional reforms.
Overall, the findings suggest that reduced investor confidence following a crisis is an important
component of the cost of a systemic banking crisis and can make recovery more challenging. Once
investor confidence is shaken, it appears quite difficult to restore. On a more optimistic note, having
deposit insurance in place prior to the onset of a banking crisis appears to be an effective way to protect
investor confidence. This is an important finding, given policy debates on deposit insurance and moral
hazard problems. We find that the impact of living through a crisis is larger for people from countries

25

with less developed economies and with weaker institutions. In addition, we find that the effect of
banking crises does not impact stock market participation. This suggests that, although investors are
unable to ignore their past bad experiences with banks in interacting with U.S. banks, these experiences
do not spill over to non-bank investments.
The results in this paper shed light on the significance of reinforcement learning for investment
decisions. We find that direct experience of a systemic banking crisis in a country without deposit
insurance has long-lasting consequences for investment decisions.

First-hand experience of a banking

crisis has a greater effect on behavior compared to hearing about a crisis in a neighboring country through
the media, or learning about a crisis from friends and relatives who are living through it, for example.
Individuals who experienced a banking crisis as adults are more likely to be impacted compared to those
who experienced a crisis as children. Given the role of direct experience with banking crises in shaping
investor confidence, deposit insurance seems to be an important mechanism to prevent the destructive
dynamics that can arise if a banking crisis decreases investor confidence and thereby increases the
likelihood of subsequent banking crises.

26

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Mudd, Shannon B. Neven T.Valev, 2009. “Once Bitten, Twice Shy: Experiences of Banking Crisis and
Expectations of Future Crises,” William Davidson Institute Working Paper No. 969.
Osili, Una Okonkwo and Anna Paulson. 2008a. “Institutions and Financial Development: Evidence from
International Migrants in the U.S.” Review of Economics and Statistics, 90(3): 498-517.

29

Osili, Una Okonkwo and Anna Paulson. 2008b. “What can we learn about Financial Access from U.S.
Immigrants? The role of Country of Origin Institutions and Immigrant Beliefs.” World Bank
Economic Review, Vol. 22, No. 3, pp. 431-455.
Roth, Alvin E., and Ido Erev 1995. “Learning in Extensive-Form Games: Experimental Data and Simple
Dynamic Models in the Intermediate Term,” Games and Economic Behavior, 8(1): 164-212.

30

Table 1: Definitions and Sources of Country and Crisis Variables
Variable
Experienced Banking
Crisis

Age at Banking Crisis

Experienced a Banking
Crisis and a GDP Crisis

Experienced a Banking and
a Currency Crisis

Experienced a Banking and
a Debt Crisis

Length of Bank Crisis
Average GDP
Private Credit

Bank Branches per 100,000
people
KKZ Index

Deposit Insurance at Time
of Crisis

Trust in upper 1/3rd

Definition and Source
An indicator variable equal to one if an individual has experienced a systemic financial crisis prior
to coming to the U.S and zero otherwise. Financial crises defined to be systemic if nonperforming assets reached at least 10% of total assets at the peak of the crisis, if the cost of rescue
operations was at least 2% of GDP, if emergency measures (bank holidays, deposit freezes,
blanket guarantees to depositors or other bank creditors) were taken, or if large-scale
nationalizations took place.
Source: authors’ calculations using 1996 SIPP data and information from Honohan and Laeven
(2005).
Equal to an individual’s age at the beginning of the first systemic banking crisis they experienced
prior to coming to the U.S. Equal to zero for individuals who did not experience a crisis.
Source: authors’ calculations using 1996 SIPP data and information from Honohan and Laeven
(2005).
An indicator variable equal to one if an individual experienced a banking crisis and a GDP crisis
simultaneously and zero otherwise.
A “GDP crisis” is defined as three consecutive years of negative real per capita GDP growth
before migrating to the U.S. The first year of each GDP crisis episode is the third year of negative
growth. For example, Turkey experienced negative The first year of each GDP crisis episode is
the third year of negative growth. For example, Turkey experienced negative per capita GDP
growth in 1978, 1979 and 1980, so it is defined to have a GDP crises beginning in 1980.
Source: Author’s calculations using World Bank World Development Indicators.
An indicator variable equal to one if an individual experienced a banking crisis and a currency
crisis simultaneously and zero otherwise.
A “currency crisis” is defined as a nominal depreciation of the currency of at least 30 percent that
is also at least a 10 percent increase in the rate of depreciation compared to the year before.
Source: authors’ calculations using 1996 SIPP data and data on currency crises from Laeven and
Valencia (2008).
An indicator variable equal to one if an individual experienced a banking crisis and a sovereign
debt crisis simultaneously.
A “sovereign debt crisis” is defined a period in which a country either defaults or is forced to
restructure its sovereign debt.
Source: authors’ calculations using 1996 SIPP data and information on sovereign debt crises from
Laeven and Valencia (2008).
Length of bank crisis in years.
Source: Honohan and Laeven (2005).
Average real GDP per capita 1975 - 1995 (2000 dollars). Source: Authors’ calculations using
World Bank World Development Indicator data.
A broad measure of financial intermediary development. It is calculated as the value of credits by
financial intermediaries to the private sector divided by GDP. Source: Beck, Demirgüç-Kunt, and
Levine (2000).
Number of bank branches per 100,000 people.
Source: Beck, Demirguc-Kunt and Peria (2007).
A composite of six governance indicators from 1998: voice and accountability, political stability,
government effectiveness, regulatory quality, rule of law, and corruption. Higher values
correspond to better governance.
Source: Kaufman, Kray and Zoido-Lobaton, (1999).
An indicator variable equal to one if a country had formal regulation requiring deposit insurance
through central bank law, banking law, or the country’s constitution before the time of the
country’s first crisis, and zero otherwise.
Source: Demirgüç-Kunt, Kane and Laeven (2008).
An indicator variable equal to one for countries whose average response to a question about trust
in the European or World Values Survey is in the upper third of the trust distribution and zero
otherwise. For each country, the fraction of respondents who answer the question “Generally
speaking, would you say that most people can be trusted, or that you can't be too careful in dealing
with people?” with “Most people can be trusted” is calculated. This calculation is performed for
countries where European or World Value Surveys were completed between 1981 and 2004.
Source: Authors calculations using data from European and World Value Survey data.

31

Table 2A: Characteristics of Immigrants and the Native Born in the SIPP Data
Characteristic
Individual Characteristics
Age
% Male
% Married
% non-white
% unemployed or out of the labor force
# of children < 18 in household
Average monthly per capita household income
Median monthly per capita household income
Average household wealth
25th percentile of household wealth
Median household wealth
75th percentile of household wealth
Educational Attainment (%)
Less than High School
High School Graduate
Some College
Bachelor Degree
Advanced Degree
Financial Market Participation (%)
% with banking relationship
% with a checking account (interest or non-interest)
% with a savings account
% own stock

Native Born

Immigrant

45.73
(17.24)
46.00
58.51
19.73
33.04
0.731
(1.113)
2423.05
(3081.70)
1707.82
171644.6
(692172.9)
13552.81
67614.86
180613.3

37.71
(13.50)
46.69
67.04
81.12
33.57
1.413
(1.444)
1666.06
(2594.10)
1085.11
74798.61
(204566.3)
1233.08
12160.4
63182.57

16.71
32.09
29.92
14.31
6.97

37.22
23.46
18.99
13.06
7.27

75.36
64.11
53.50
18.04

54.63
42.02
36.04
5.71

Other characteristics (%)
% own home
72.27
40.98
% IRA/Keogh
18.41
5.47
% Self-Employed
9.76
7.80
Number of Observations
48105
3729
Notes: Unless otherwise noted, mean values are reported. Standard deviations are in parentheses. Sample is
restricted to the one wave of the 1996 Survey on Income and Program Participation with wealth information and to
individuals 18 and over.

32

Table 2B: Immigrant Characteristics
Characteristic
Year of Arrival in the U.S. (%)
1975 – 1979
1980 – 1984
1985 – 1989
1990 – 1996
Age at Migration (%)
five years or younger
six to ten years
Eleven to fifteen years
sixteen to twenty years
over twenty years
Continent of Origin (%)
North America
Europe
Asia
Africa
South America
Australia and Oceania

Immigrant
17.48
22.82
25.40
34.30
2.46
0.97
2.24
4.57
89.75
52.78
6.92
32.45
1.18
6.17
0.51

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 one wave of the 1996 Survey on Income and Program
Participation with wealth information and to individuals 18 and over.

33

Table 3A: Summary of Country and Crisis Variables
Characteristic
Banking Crisis
GDP Crisis
Currency Crisis
Debt Crisis
Average GDP
Private Credit
KKZ Index
Deposit Insurance
Branches/100,000 People
Trust
Length of Crisis (yrs)

N
80
65
80
80
66
53
55
75
61
52
37

Mean
0.463
0.569
0.625
0.363
8241
0.557
0.497
0.173
16.56
0.288
4.73

Standard
Deviation
0.502
0.499
0. 487
0.484
10077
0.389
0.713
0.381
17.59
0.150
2.58

Min
0
0
0
0
106
0.046
-1
0
0.41
0.028
1

Median
0
1
1
0
3036
0.508
0.33
0
9.59
0.274
4

Max
1
1
1
1
42873
1.69
1.72
1
95.87
0.653
11

U.S.
Value
0
0
0
0
24831
0.460
1.29
1
30.86
0.350
N/A

34

Table 3B: Correlation between Country Characteristics
Banking
GDP
Currency
Debt
Length of Av. GDP
Priv.
Crisis
Crisis
Crisis
Crisis
Crisis
Credit
Banking Crisis
1.000
GDP Crisis
0.040
1.000
Currency Crisis
0.408*** 0.228*
1.000
Debt Crisis
0.396*** 0.393*** 0.423*** 1.000
Length of Crisis
N/A
-0.270
-0.018
-0.329**
1.000
Average GDP
-0.271**
-0.252**
-0.607*** -0.510*** 0.219
1.000
Private Credit
-0.321**
-0.463*** -0.515*** -0.426*** 0.075
0.665*** 1.000
KKZ Index
-0.390*** -0.332**
-0.586*** -0.543*** 0.019
0.814*** 0.653***
Deposit Insurance
0.112
-0.147
-0.111
-0.135
0.056
0.332*** 0.010
Branches/100,000
-0.274**
-0.270**
-0.329*** -0.448*** 0.222
0.584*** 0.415***
Trust
-0.223
-0.143
-0.398*** -0.366*** 0.105
0.486*** 0.351**
Notes: *** indicates significance at at least the 1% level, ** at at least the 5% level, * at at least the 10% level.
Characteristic

KKZ
Index

1.000
0.139
0.613***
0.527***

Deposit
Insurance

Branches/
100,000

1.000
0.193
0.111

1.000
0.188

Trust

1.000

35

Table 4: Experiencing a Banking Crisis and Checking Account Ownership
Explanatory Variable

Experienced Banking Crisis
Age at Crisis

[1]
No Wealth or
Income
Controls

-0.134***
(0.030)

[2]
With Wealth
and Income
Controls
(Baseline)

[3]
Age at Crisis

[4]
Age at Crisis
Decade of
Migration
Controls

[5]
Age at Crisis
Decade of
Migration *
Country
Controls

[6]
Age at Crisis
Decade of
Migration *
Country
Controls
No Mexico

-0.110***
(0.028)
-0.003***
(0.001)

-0.003***
(0.001)
Yes

-0.002***
-0.002**
(0.001)
(0.001)
Decade of Migration Fixed Effects
Yes
Yes
Decade of Migration*Country Effects
Yes
Yes
Country Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R-Squared
0.2898
0.3105
0.3099
0.3104
0.3214
0.2892
Number of Observations
3609
3609
3609
3609
3609
2465
Number of Countries
80
80
80
80
80
79
Notes: In addition to those reported on here, regressions [2] – [6] include controls for age, age squared, wealth quartiles, labor force status, income, income
squared, marital status, sex, ethnicity, education, number of children, and county controls. Regression [1] does not include income and wealth but does include
the other explanatory variables. A linear probability model is used and standard errors are corrected for heteroskedasticity and clustering at the country-cohort
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.

36

Table 5: Experiencing a Banking Crisis and Individual Attributes
Dependent Variable: Checking Account Ownership
Explanatory Variable

[1]
Baseline

Experienced Banking Crisis

-0.110***
(0.028)

Crisis*Low Education
Crisis*High Education
Crisis*Years in the U.S.

[2]
Low Education

-0.037
(0.032)
-0.143***
(0.018)

[3]
High Education

-0.131***
(0.029)

[4]
Years in the US

-0.172***
(0.027)

[5]
Arrived in US in
Last 3 years

-0.093***
(0.026)

[6]
Permanent
Residents and
U.S. Citizens
ONLY
-0.101***
(0.026)

0.130***
(0.044)
0.008***
(0.002)

Crisis*Arrived in U.S. in last 3 years

-0.089***
(0.017)
Country Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R-Squared
0.3105
0.2925
0.2925
0.3120
0.3121
0.2979
Number of Observations
3609
3609
3609
3609
3609
2360
Number of Countries
80
80
80
80
80
77
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, income
squared marital status, sex, ethnicity, education, number of children, and county controls. A linear probability model is used and standard errors are corrected for
heteroskedasticity and clustering at the country-cohort 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.

37

Table 6: Experiencing a Banking Crisis and Country Characteristics
Dependent Variable: Checking Account Ownership
Explanatory Variable

Experienced Banking Crisis
Crisis*Average GDP per capita†
Crisis*Private Credit
Crisis*Bank Branches/100,000
Crisis*KKZ Index of Institutional Quality

[1]
Baseline
-0.110***
(0.028)

[2]
GDP
-0.129***
(0.032)
0.518**
(0.228)

[3]
Private Credit
-0.147***
(0.045)

[4]
Branches/
100,000 People
-0.120**
(0.056)

[5]
KKZ Index
-0.123***
(0.030)

[6]
Trust
-0.110***
(0.030)

0.126
(0.105)
0.002
(0.006)
0.139**
(0.067)

Crisis*Trust in Upper 3rd of Distribution

0.144*
(0.077)
Country Controls
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R-Squared
0.3105
0.3196
0.3273
0.3259
0.3275
0.3122
Number of Observations
3609
3244
2855
2947
3008
3531
Number of Countries
80
66
53
61
55
78
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, income
squared marital status, sex, ethnicity, education, number of children, and county controls. The number of observations differs depending on the number of
countries for which a particular measure is available. A linear probability model is used and standard errors are corrected for heteroskedasticity and clustering at
the country-cohort 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. †Coefficient and standard error are the actual one multiplied by 100,000.

38

Table 7: Characteristics of the Crisis
Dependent Variable: Checking Account Ownership
Explanatory Variable

[1]
Baseline

Experienced Banking Crisis

-0.110***
(0.028)

Experienced GDP Crisis with Bank Crisis
Experienced Currency Crisis with Bank Crisis
Experienced Debt Crisis with Bank Crisis

[2]
GDP Crisis at Crisis
-0.091***
(0.025)
-0.040*
(0.021)

[3]
Currency Crisis at
Crisis
-0.104***
(0.032)

[4]
Debt Crisis at Crisis
-0.082**
(0.032)

[5]
Length of Crisis
-0.098**
(0.048)

-0.009
(0.028)
-0.041
(0.032)

Bank Crisis*Length of Bank Crisis

-0.001
(0.006)
Country Controls
Yes
Yes
Yes
Yes
Yes
Adjusted R-Squared
0.3105
0.3124
0.3103
0.3105
0.3085
Number of Observations
3609
3579
3609
3609
2495
Number of Countries
80
80
80
80
37
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, income
squared marital status, sex, ethnicity, education, number of children, and county controls. The number of observations differs depending on the number of
countries for which a particular measure is available. A linear probability model is used and standard errors are corrected for heteroskedasticity and clustering at
the country-cohort 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.

39

Table 8: Experiencing a Banking Crisis and Deposit Insurance
Dependent Variable: Checking Account Ownership
Explanatory Variable

Experienced Banking Crisis
Crisis*Deposit Insurance before crisis

[1]
Baseline
-0.110***
(0.028)

[2]
Deposit Insurance
Before Crisis
-0.136***
(0.031)
0.124***
(0.036)

[3]
Deposit Insurance
After Crisis
-0.021
(0.026)

Crisis*Deposit Insurance after crisis

-0.114***
(0.036)
Country Controls
Yes
Yes
Yes
Adjusted R-Squared
0.3105
0.3157
0.3155
Number of Observations
3609
3485
3485
Number of Countries
80
75
75
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, income
squared marital status, sex, ethnicity, education, number of children, and county controls. The number of observations differs depending on the number of
countries for which a particular measure is available. A linear probability model is used and standard errors are corrected for heteroskedasticity and clustering at
the country-cohort 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.

40

Table 9: Experiencing a Banking Crisis and Other Investment Decisions

Explanatory Variable

[1]
[2]
[3]
[4]
[5]
[6]
[7]
Baseline:
Any Bank
Savings
Stock
IRA/Keogh
Homeowner
SelfChecking
Account
Account
Employed
Account
Experienced Banking Crisis
-0.110***
-0.145***
-0.082***
0.0040
-0.009
-0.075***
-0.002
(0.028)
(0.040)
(0.026)
(0.006)
(0.018)
(0.019)
(0.022)
Country Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Adjusted R-Squared
3609
3609
3609
3609
3609
3609
0.0973
Number of Observations
0.3105
0.3202
0.2249
0.2390
0.1966
0.5057
3609
Number of Countries
80
80
80
80
80
80
80
Notes: In addition to those reported on here, all of these regressions include controls for age, age squared, wealth quartiles, labor force status, income, income
squared marital status, sex, ethnicity, education, number of children, and county controls. A linear probability model is used and standard errors are corrected for
heteroskedasticity and clustering at the country-cohort 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

Appendix Table 1: Systemic Banking Crises, GDP, Currency and Debt Crises,
Crises beginning 1975 – 1995
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

Afghanistan
Argentina
Australia
Bahamas
Bangladesh
Barbados
Belgium
Belize
Bolivia
Brazil
Burma
Cambodia
Canada
Chile
China
Colombia
Costa Rica
Cuba
Czechoslovakia
Dominica
Dominican
Republic
Ecuador
Egypt
El Salvador
Ethiopia
Fiji
Finland
France
Germany
Ghana
Greece
Guatemala
Guyana
Haiti
Holland
Honduras
Hong Kong
Hungary
India
Ireland
Iran
Iraq
Israel
Italy
Jamaica
Japan

Year(s) of Banking
Crisis
None
1980–82, 1989-90,
1995
None
None
1987–96
None
None
None
1986–88, 1994-1995
1990, 1994-99
None
None
None
1976, 1981-83
None
1982–87
1994–96
None
None
None
None

Year(s) of GDP
Crisis
No Data
1990

Initial Year(s) of
Currency Crisis
No Data
1975

Initial Year(s) of
Debt Crisis
No Data
1982

None
1992
None
1983, 1992
None
1983-85
1980-86
1983, 1992
No Data
No Data
1992
None
None
None
1982
No Data
No Data
No Data
None

None
No Data
1976
None
None
None
1981
1976
1975
1992
None
1982
None
1985
1981
No Data
No Data
None
1985

None
No Data
None
None
None
None
1980
1983
None
None
None
1983
None
None
1981
No Data
No Data
None
1982

1980-1983
1980-1983
1989
None
None
1991–94
None
None
1982–89
None
None
None
None
None
None
None
1991–95
None
None
None
None
1977–83
None
None
1992–2001

None
None
1981-82
1990-92
No Data
1992-93
None
None
1981-83
1982-83
1983-86
1979, 1984,1990
1983-90, 1994-95
No Data
1982-83
None
1992-93
None
None
1979-81, 1986-88
No Data
None
None
1975-80
None

1982
1979
1986
1993
None
1993
None
None
1978
1983
1986
1987
1992
None
1990
None
None
None
None
1985
No Data
1975
1981
1978
None

1982
1984
None
None
None
None
None
None
None
None
None
1982
None
None
1981
None
None
None
None
1992
No Data
None
None
1978
None

47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80

Jordan
None
1989-91
South Korea
None
None
Laos
None
No Data
Lebanon
1988–90
No Data
Malaysia
None
None
Mexico
1981–91, 1994-2000
1988
Morocco
1980-1983
None
New Zealand
None
1989-91
Nicaragua
1987-1989
1986-93
Nigeria
1991–95
1983-84, 1995
Norway
1990–93
None
Pakistan
None
None
Panama
1988–89
1976-77, 1989
Peru
1983–90
1978, 1990
Philippines
1983–87
1985, 1993
Poland
1992–95
No Data
Portugal
None
None
Romania
1990–96
1990-92
Singapore
None
None
South Africa
None
1987, 1992-93
Spain
1977–85
None
Sweden
1991–94
1993
Switzerland
None
1993
Syria
None
1984
Taiwan
None
No Data
Thailand
1983–87
None
Trinidad &
None
1985-89
Tobago
Turkey
1982–85
1980
UK
None
None
Uruguay
1981–84
1984
USSR
None
None
Venezuela
1994–95
1980-85
Vietnam
None
No Data
Yugoslavia
None
No Data
Notes: See Table 1 for definitions of each type of crisis

1989
None
1978
1984
None
1977
1981
None
1979
1983
None
1972
None
1976
1983
None
1983
None
None
1984
1983
1993
None
1988
None
None
1986

1989
None
None
None
None
1982
1983
None
1980
1983
None
None
1983
1978
1983
1981
None
1982
None
1985
None
None
None
None
None
None
1989

1978
None
1983
No Data
1984
1981
No Data

1978
None
1983
No Data
1982
1985
No Data

.

43

Appendix Table 2: The Effect of Control Variables on Having a Checking Account
Explanatory Variable
Age†

1.003***
(0.313)
Age Squared†
-0.013***
(0.003)
2nd Wealth Quartile
0.148***
(0.022)
3rd Wealth Quartile
0.158***
(0.038)
4th Wealth Quartile
0.142***
(0.028)
Unemployed or Out of Labor Force
-0.075***
(0.022)
Per Capita Income††
16.8**
(7.78)
Per Capita Income Squared††
-0.001***
(0.0002)
Male
-0.043***
(0.013)
Married
0.165***
(0.019)
Number of Children
-0.02***
(0.007)
Non-white
-0.052
(0.045)
High School Graduate
0.136***
(0.024)
Some College
0.185***
(0.025)
Bachelor Degree
0.251***
(0.033)
Advance Degree
0.313***
(0.041)
Experienced Banking Crisis
-0.110***
(0.028)
Constant
0.441***
(0.111)
County Fixed Effects
Yes
Adjusted R-Squared
0.3105
Number of Observations
3609
Number of Countries
80
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-cohort 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.

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