Review of Finance, 2016, 1–30
doi: 10.1093/rof/rfw055
Trust and Household Debt*
Danling Jiang1 and Sonya S. Lim2
1
State University of New York at Stony Brook and Southwest Jiaotong University and 2DePaul
University
Abstract
Using a large sample of US individuals, we show that individuals with higher levels
of trust have lower likelihoods of default in household debt and higher net worth.
The effect is driven by trust values inherited from cultural and family backgrounds
more than by trust beliefs about others. We demonstrate a causal impact of trust on
financial outcomes by extracting the component of trust correlated with early-life experiences. The effect of trust is more pronounced among females, those with lower
education, lower income, lower financial literacy, and higher debt-to-income ratio.
Further evidence suggests that enhancing individuals’ trust, to the right amount, can
improve household financial well-being.
JEL classification: D14, H31, G11
Keywords: Trust, Trustworthiness, Household debt, Culture, Early-life experiences
1. Introduction
While voluminous studies have demonstrated the effect of trust on aggregate economic and
financial outcomes, there has been limited research on how trust affects individuals or
* We would like to thank Burton Hollifield (the editor), an anonymous referee, Sumit Agarwal, James
Ang, Don Autore, Kelley Bergsma, Jonnathan Berk, Peter Bossaerts, Michael Brennan, Tim Burch,
Lauren Cohen, Werner DeBondt, Joey Engelberg, Mark Grinblatt, David Hirshleifer, David
Humphrey, George Korniotis, Alok Kumar, Jose Liberti, Adair Morse, Charles Schnitzlein, Stephan
Siegel, participants at the Utah Winter Finance Conference 2013, AEA 2014, and seminar participants at DePaul University, Florida State University, Fudan University, KAIST, Korea University,
Loyola University, McMaster University, Nanjing University, Peking University-HSBC Business
School, University of Central Florida, University of Hong Kong, University of Miami, and Yonsei
University for helpful discussions and valuable comments. We thank Hope Han, Michael Pacca,
and Yuting Wang for helpful research assistance. This research was conducted with restricted access to Bureau of Labor Statistics (BLS) data. The views expressed here do not necessarily reflect
the views of the BLS. Previous versions were circulated under the titles “Trust, Consumer Debt,
and Household Finance” and “Trust and Household Finance.” We are responsible for all remaining
errors and omissions.
C The Authors 2016. Published by Oxford University Press on behalf of the European Finance Association.
V
All rights reserved. For Permissions, please email: [email protected]
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D. Jiang and S. S. Lim
households.1 The limited research at the individual level has mostly focused on the effect of
trust on retirement savings and stock investment.2 In this article, we take the analysis of
trust to the household level and study, in particular, the effect of trust on household debt
management and overall financial well-being.
Using a large sample of US individuals, we find that trust has an average positive effect
on an array of measures of household debt management as well as on net worth. Such a
positive effect of trust is brought out by the values inherited from an individual’s family
and cultural backgrounds more than by her beliefs regarding the average trust level of people in her community. We also identify a causal impact of trust on financial outcomes
through extracting the component of trust correlated with an individual’s early life experiences. Further, we document a nonmonotonic effect of trust that suggests a moderately, rather than extremely, high level of trust pays off in household finance.
We consider trust a collection of beliefs and values associated with both trusting
others and being trustworthy. Intuitively, the extent to which an individual trusts others
and her own trustworthiness are highly correlated (Glaeser et al., 2000; Butler, Giuliano,
and Guiso, 2015). One the one hand, people tend to project their own trustworthiness to
form an assessment of the trustworthiness of other people. As a result, a trustworthy individual is likely to be more trusting. On the other hand, the collective level of trust of a
community is evolved as the outcome of the reinforcing effect of trusting others and being
trustworthy (Guiso, Sapienza, and Zingales, 2004), and therefore, reflects the average
trust and trustworthiness of the community.3 The culture of trust of a community, in
turn, influences the trust level of individuals residing in the community. Unlike the effect
of trusting others that has received a great deal of attention in the literature, we argue
that trustworthiness is an often overlooked aspect of individual trust, which has important implications for household debt management and financial well-being. Our tests are
deigned to explore these implications for household finance using data from a sample of
over 6,000 US households.
Our individual-level measure of trust is obtained from the 1979 National Longitudinal
Survey of Youth (NLSY79). In this dataset, a representative sample of American individuals
who were born between 1957 and 1964 were interviewed annually between 1979 and
1994, and biennially afterwards. In 2008, they were asked to rate how much they trust
others using the following question: “Generally speaking, how often can you trust other
people?” Respondents chose one of five answers (always, most of the time, about half the
time, once in a while, never), which we translate into a rating of 1–5, with 1 indicating the
lowest level (never) and 5 the highest level of trust (always). Our basic premise is that this
measure of trust captures not only the extent to which one trusts others but also the degree
of one’s own trustworthiness. This premise follows prior literature asserting that questions
that intend to solicit the general trust toward others also solicit the trustworthiness of the
respondents (e.g., Glaeser et al. (2000), Butler, Giuliano, and Guiso (2015). In our context,
1 See Knack and Keefer (1997), LaPorta et al. (1997), Guiso, Sapienza, and Zingales (2004) for studies
at the aggregate level and Section 2 for detailed discussions.
2 See Agnew et al. (2007), Guiso, Sapienza, and Zingales (2008), Georgarakos and Pasini (2011),
Balloch, Nicolae, and Philip (2015), and Changwony, Campbell, and Tabner (2015).
3 Consistent with this assertion, many studies have used trust as an important measure of social
capital (Guiso, Sapienza, and Zingales, 2004, 2008) and societal culture in general (e.g., Guiso,
Sapienza, and Zingales (2006); Ahern, Daminelli, and Fracassi (2015); Pevzner, Xie, and Xin (2015).
Trust and Household Debt
3
trusting others and trustworthiness are highly correlated and reflected in this trust measure,
which jointly influence household finance.
We link this measure of trust to a set of household debt management measures and net
worth, all obtained from the same dataset. Our main conjecture is that high trust individuals are less likely to mis-manage debt and accumulate a higher value of net worth.
Specifically, we expect trust to promote responsible borrowing, as a greater degree of trustworthiness discourages one from defaulting or taking excessive debt ex ante (e.g., Agarwal,
Chomsisengphet, and Liu (2011)). In addition, we expect that higher levels of trust are
associated with greater investment as a more trusting individual has greater confidence that
others will return what is promised (Guiso, Sapienza, and Zingales, 2008). Taken together,
since high-trust individuals are less likely to run into debt problems and more likely to
make profitable investment, we expect them to cumulate greater net worth.
Our empirical evidence provides strong support for these conjectures. Consistent with
the idea that high-trust individuals strive to avoid default and excessive debt, we find that
these high-trust individuals, defined as those with above-average trust, are 30% less likely
to miss a payment or be late in paying bills, 45% less likely to be in debt (having negative
net worth), 21% less likely to declare bankruptcy, and 47% less likely to go through foreclosure compared to low-trust individuals. Owning to a combination of better asset and
debt management, high-trust individuals have a 121% higher value of household net worth
as compared to the low-trust individuals.
The effect of trust on household finance remains statistically and economically significant after we control for a host of economic, demographic, psychological, and cognitive
factors, as well as state fixed effects that account for geographic variations in institutional
and social environments that may impact household finance.4 In particular, our controls include measures of several personal traits that are known to correlate with household finance, such as financial literacy (van Rooij, Lusardi, and Alessie, 2011), optimism (Puri
and Robinson, 2007), satisfaction (Guven, 2011), sociability (Georgarakos and Pasini,
2011; Changwony, Campbell, and Tabner, 2015), and cognitive ability (Korniotis and
Kumar, 2010; Agarwal and Mazumder, 2013) in addition to risk aversion, savings preference and time preference.
The marginal effect of trust is also significant. With the comprehensive set of controls,
our regression estimates indicate that a one standard deviation increase in trust on average
reduces the probability of being in debt by 6.8%, the probability of missing or being late
for a payment by 8.8%, the probability of filing bankruptcy by 14%, and the probability of
foreclosure by 41%, relative to the unconditional mean probability of each type of event.
Moreover, a one standard deviation increase in trust leads to a marginal increase of over
$35K in net worth with the host of controls, more than a 13% increase relative to the unconditional mean household net worth. Overall, our evidence suggests that high-trust individuals on average benefit significantly from better debt management and greater
investments.
Our next set of tests identifies the channels through which trust influences household
debt and net worth. We consider the belief- versus value-based channels of trust. The
belief-based channel predicts that differences in individual trust arise in part as a rational
4 In particular, in all debt-variable regressions we control for income and assets. In all regressions,
we control for a set of cognitive and psychological traits that may be correlated with the level of individual trust.
4
D. Jiang and S. S. Lim
response to the average trustworthiness of people in the same community. The value-based
channel, however, states that trust heterogeneity stems from differences in trust values instilled by parents and acquired through cultural transmissions (e.g., Dohmen et al. (2012))
that are invariant with environments. Apart from these two systematic channels, trust can
also be shaped by individual-specific life experiences. To identify these channels, we decompose the trust measure into belief- and value-based components, with the former predicted
by the average trust of people in the same community and the latter predicted by parents’
trust and trust embedded in an individual’s religion or ethnicity. The residual trust is likely
to capture the individual-specific component. We then replace the individual trust with the
three trust components in the regressions. The results show that the value-based component
has a more consistent influence across the board than the belief-based or the residual
component.
Next, we deal with the concern for reverse causality. As our trust measure and most of the
household finance variables are surveyed at the same time, our finding can be explained alternatively as greater financial success causing stronger trust, in contrast to our hypothesis that
stronger trust causes better household finance. To address this concern, we instrument trust
using early life experiences during an individual’s teenage years or early adulthood that may
have affected her trust. Such an example is found in the 1982 survey that asked whether the
respondent experienced discrimination on the basis of age when searching for a job. We
show that this perceived, age-based discrimination has a temporary negative effect on the individual’s early-life incomes, but has no effect on long-term life incomes. This is consistent
with the notion that the effect of age-based discrimination on well-beings of young adults
tend to be temporary (Garstka et al., 2004). Furthermore, after adding the lifelong income
history and family net worth at the time of the early-life experiences to the set of controls
including the current income and assets and various personal traits, we continue to find that
the instrumented trust using this age-based discrimination variable explains household debt
management. In other words, our evidence suggests that trust shaped by early-life experiences
exerts a significant influence on household financial well-being decades later. Thus, our main
findings are at least in part driven by a causal impact of trust on household finance.
Finally, we examine how the effect of trust on household finance varies in the crosssection and across different levels of trust. Similar to the effect of trust on stock investments
(Guiso, Sapienza, and Zingales, 2008), we show that the effect of trust is most pronounced
among individuals in a relatively weak position of managing household finance: those who
have below college degrees, lower incomes, lower financial literacy, and females. In the
context of debt management, these results suggest that trust helps individuals to overcome
the limited understanding of the negative consequences of default by imposing the internal
norm that one should keep her promises in financial contracts (e.g., Guiso, Sapienza, and
Zingales (2004)). When it comes to asset management, trust helps one to overcome the anxiety associated with the inability of fully understanding investment contracts.
Finally, we document a nonmonotonic effect of trust on household finance. Specifically,
trust has a U-shaped effect on debt-related measures and a hump-shaped effect on net
worth, with the effect peaking at the trust rating of 4 and then declining substantially for
the highest level of trust (rating of 5). No statistical difference exits between individuals
with the highest and the lowest level of trust. Our findings echoes the hump-shaped effect
of trust on household income documented by Butler, Giuliano, and Guiso (2015) for an intuitive reasoning: extremely low levels of trust lead to overly conservative priors on investments and weaker disciplines to comply with debt contracts, while extremely high levels of
Trust and Household Debt
5
trust, or being credulous, lead to a higher probability of being cheated in investment or
debt contracts. Thus, it is important to have the right amount of trust.
2. Motivation and Hypotheses
2.1 Trust and Decision Making: Individual Level Evidence
It is well-documented that trust has a positive effect on aggregate economic and political
phenomena. The most common measure of trust is based on the World Values Survey question, also used in the General Social Survey in the USA: “Generally speaking, would you
say that most people can be trusted or that you have to be very careful in dealing with people.” Respondents are given three choices: (i) most people can be trusted; (ii) one has to be
very careful with other people; (iii) I don’t know. Usually choice (i) is coded as one with the
other two choices coded as zero (e.g., Guiso, Sapienza, and Zingales (2008)).
By a rough estimate, the World Values Survey question on trust has been used by over
500 papers to study the economic effect of trust (Sapienza, Toldra-Simats, and Zingales,
2010). Most of these papers have focused on the effect of trust at the aggregate level, showing that a higher level of average trust in a nation or region is correlated with greater aggregate economic growth and investments (Knack and Keefer, 1997), greater judicial
efficiency, less corruption, better bureaucratic quality, higher tax compliance (LaPorta
et al., 1997), and better financial development (Guiso, Sapienza, and Zingales, 2004).
In contrast, only a few papers have studied the effect of trust on the economic or financial outcomes at the individual level. Karlan (2005) finds that borrowers in a Peruvian
microcredit program who answer positively to this trust question have a lower probability
of default, but do not have significantly higher levels of savings. Guiso, Sapienza, and
Zingales (2006) show that trust is positively associated with the probability of becoming an
entrepreneur. Using data from three 401(k) plans in a company, Agnew et al. (2007) show
that less trusting individuals are more likely to opt out of automatic enrollment plans.
Guiso, Sapienza, and Zingales (2008) find that trusting individuals in the Netherlands are
more likely to participate in stock markets and invest more conditional on participation.
Balloch, Nicolae, and Philip (2015) show a positive effect of trust on stock market participation using the US data. Butler, Giuliano, and Guiso (2015) uncover a hump-shaped effect
of trust on individuals’ income using the European Social Survey.5
Although these existing studies suggest that trust has a positive effect on the economic
and financial well-being of an individual, they do not offer a comprehensive picture of the
effect of trust on household financial well-being. Furthermore, with an exception of Karlan
(2005), none of these studies examine how trust affects debt management. Our goal is to
demonstrate the effect of trust on household debt as well as net worth using a large sample
of US households that are previously unexamined.
2.2
Hypothesis Development
Trust is likely to play an important role in economic activities where the transaction
takes place over a period of time and individuals need to rely on the future and/or
5 These studies all use survey responses to the question about how much one trusts others. On the
other hand, the study of El-Attar and Poschke (2011) imputes the trust level of Spanish household
based on personal and demographic information, and shows that individuals with lower levels of
imputed trust invest more in housing and less in financial assets.
6
D. Jiang and S. S. Lim
unobservable actions of others, such as investments decisions (e.g., Guiso, Sapienza,
and Zingales (2006)). Trusting individuals are more likely to invest because they
believe they are going to get a fair return in the investment contract (Guiso,
Sapienza, and Zingales, 2008). Prior evidence shows that trusting individuals are more
likely to participate in the stock market, invest more in risky assets, and less likely
to opt out of defaulted 401(k) plans (Agnew et al., 2007; Guiso, Sapienza, and
Zingales, 2008). Thus, we conjecture that these high-trust individuals will have higher
asset holdings, or net worth, as a result of taking advantage of valuable investment
opportunities.
We also conjecture that individuals with higher levels of trust manage household debt
better, as an individual’ trust toward other people and her own trustworthiness are highly
correlated. Glaeser et al. (2000) show that survey questions that solicit the degree of trust
toward others also capture the degree of one’s trustworthiness. Butler, Giuliano, and Guiso
(2015) posit that people tend to extrapolate their own trustworthiness when forming their
expectation of the trustworthiness of others. More generally, trust reflects not only the belief about the trustworthiness of the counterparty but also the internal values that drive
one’s trust toward others and trustworthy behavior. A trustworthy individual is more likely
to comply with her debt contract and strive to keep her promises by making payments on
time. Further, since a trustworthy individual is committed to pay back debt and interest, ex
ante, she would avoid excessive borrowing that leads a lower default probability. Taken together, we hypothesize that
H1: High-trust individuals better manage household debt and asset.
H1a: High-trust individuals are less likely to miss payments, be in debt, declare bankruptcy, and
go through foreclosure.
H1b: High-trust individuals have higher values of net worth (assets minus debt).
Trust reflects both beliefs and values. A trusting individual likely builds her trust beliefs toward others based on the trustworthiness of people she deals with, such as those in her
community. Trustworthiness can also be self-fulfilling in that it adapts in response to how
much the individual is trusted by surrounding people. Thus, communities with better law
enforcement and legal environments likely have higher prevailing level of trust.
Communities with strong social capital encourage members to keep their promises and attach social stigma to deviants by imprinting moral attitudes in the community (e.g., Guiso,
Sapienza, and Zingales (2004)). Such social norms are internalized by community members
and enhance the average trustworthiness and trusting in the community. Thus, the average
trust level of individuals in a community captures this trust belief resulting from external
factors.
Trust also has a component that varies across individuals due to internal values. We
refer to this component as trust values, which determine not only the extent to which one
trusts others but also the degree of one’s own trustworthiness. Such values can be transmitted from parents to children (Dohmen et al., 2012) and include a cultural component
that is often correlated with ethnic and religious backgrounds (Guiso, Sapienza, and
Zingales, 2004). LaPorta et al. (1997) find that trust is lower in countries with a higher
percentage of population belonging to hierarchical religions such as Catholic, Eastern
Orthodox, or Muslim. Their finding is consistent with the argument by Putnam (1993)
that hierarchical religions discourage the formation of trust since trust is likely to be
formed through horizontal networks of cooperation among people. We hypothesize that
Trust and Household Debt
7
both the belief and value components of trust play a role in determining the status of
household finance:
H2: Both trust beliefs and trust values have distinct impacts on the management of household
finance.
Further, the effect of trust may differ across groups. Guiso, Sapienza, and Zingales (2008)
find that trust has a stronger effect on stock market participation among less educated individuals. Since financial contracts are complex and difficult to comprehend for less-educated
individuals, trust can help them overcome the concern of being cheated, and thus encourages them to invest through financial vehicles. By the same token, trustworthiness can encourage an individual to comply with financial contracts even when she does not fully
comprehend the consequence of default and overindebtedness. In general, individuals on a
relatively weak position in managing household finance should benefit more from having a
higher level of trust. Therefore, we conjecture that:
H3: The effect of trust is more visible among individuals who are in a relatively weak position
in managing household finance.
Finally, the effect of trust may not be monotonic. Butler, Giuliano, and Guiso (2015) show
that the relation between the level of trust and an individual’s income is hump-shaped.
They argue that extremely trusting individuals underperform those with moderately high
levels of trust because they tend to form beliefs that are too optimistic, and as a result they
take too much social risk and get cheated more often. Similar arguments can be made regarding debt management. Individuals with extremely high levels of trust can credulously
take debt contracts with bad terms, which results in a higher likelihood of default. We state
our conjecture in the following hypothesis.
H4: Trust has a nonmonotonic effect on household financial outcomes; those with extremely
high levels of trust perform worse than those with moderate levels of trust.
In the sections that follow, we introduce our data source, describe the measures of household financial decisions and outcomes, and test each of the hypotheses.
3. Data
The primary data source of our analysis is the 1979 National Longitudinal Survey of Youth
(NLSY79). The NLSY79 is a nationally representative sample of 12,686 young men and
women who were 14 to 22 years of age when first surveyed in 1979. The respondents were
interviewed annually through 1994, after which they were interviewed every other year.
The NLSY79 is known for its exceptional retention rate among all longitudinal studies. For
example, in 2008, 82% of eligible respondents who were not known to be deceased participated in the survey. Such a high response rate mitigates the self-selection issue faced by
many other surveys.
Our key variable on trust is based on the following question in the 2008 survey:
“Generally speaking, how often can you trust other people?” (Always, Most of the time,
About half the time, Once in a while, Never). We assign 1 to “Never” and 5 to “Always,”
so that a higher number corresponds to a higher level of trust.
Panel A of Table I shows that the mean trust level is 2.95, with a standard deviation of
1.02. About 9% individuals have the lowest trust level of 1, and 2% have the highest level
8
D. Jiang and S. S. Lim
Table I. Sample statistics
This table reports the summary statistics for the variables used in our analyses. To avoid the influence of extreme values, Asset and NetWorth are winsorized at the 99th percentiles. Income
is top coded at the 2% in the survey. All variables are defined in the Appendix.
Variable
N
Mean
Panel A: trust variables
Trust
7,673
2.95
(Trust¼1)
7,673
0.09
(Trust¼2)
7,673
0.25
(Trust¼3)
7,673
0.29
(Trust¼4)
7,673
0.35
(Trust¼5)
7,673
0.02
Panel B: financial outcome variables
Indebt
7,539
0.12
MissPmt
7,633
0.22
Bankruptcy
7,645
0.16
Foreclosure
5,058
0.04
NetWorth ($K)
7,433 263.95
Panel C: other variables
Income ($K)
6,660
73.65
Income (1979–85) ($K)
7,543
19.59
Income (1986–92) ($K)
7,494
34.34
Income (1994–2000) ($K) 7,309
48.87
Income (2002–06) ($K)
7,278
64.62
Asset ($K)
7,673 878.85
Age
7,673
46.67
Male
7,673
0.49
Married
7,673
0.56
FamSize
7,673
2.84
NumChild
7,673
1.05
EduHigh
7,584
0.54
EduCollege
7,584
0.24
EduGradprof
7,584
0.07
EduOther
7,584
0.02
FinIndustry
6,298
0.04
AFQTmath
7,340
93.67
AFQTverbal
7,340
91.74
RiskAver
7,115
3.06
SavePref (%)
7,257
35.26
Impatience ($K)
6,914
1.37
FinLiteracy
7,673
2.85
PosAttitude
7,406
2.26
Satisfied
7,415
2.19
SelfConfidence
7,424
2.35
Social
7,446
2.87
DiscrimAge
7,488
0.31
Stdev
10th
25th
50th
75th
90th
3
0
0
0
0
0
4
0
1
1
1
0
4
0
1
1
1
0
1.02
0.29
0.43
0.45
0.48
0.14
2
0
0
0
0
0
2
0
0
0
0
0
0.32
0.41
0.37
0.20
548.16
0
0
0
0
0
0
0
0
0
6.20
0
0
0
0
86.00
0
0
0
0
282.01
1
1
1
0
640.00
26.00
10.44
15.57
20.47
26.65
30.00
45
0
0
2
0
0
0
0
0
0
78
75
2
0
0.10
2
2
2
2
2
0
56.00
17.06
25.89
37.38
51.72
280.30
47
0
1
3
1
1
0
0
0
0
89
96
4
10
0.25
3
2
2
2
2
0
97.00
26
38.58
58.64
82.68
931.69
48
1
1
4
2
1
0
0
0
0
108
110
4
70
1.00
4
3
3
3
3
1
147.20
36
53.74
84.25
123.8
2265.00
50
1
1
5
3
1
1
0
0
0
123
117
4
100
1.20
5
3
3
3
3
1
75.56
12.28
44.28
58.28
60.76
1,672.78
2.24
0.50
0.50
1.49
1.16
0.50
0.43
0.25
0.15
0.21
18.53
21.27
1.19
40.00
24.98
1.48
0.61
0.61
0.56
0.71
0.46
9.00
6.55
8.68
10.1
11.7
0.60
44
0
0
1
0
0
0
0
0
0
73
60
1
0
0
0
2
2
2
2
0
Trust and Household Debt
9
of 5, and with the other three (2, 3, 4) levels each accounting for 25%–35% of the sample.
Our measure of trust is similar to the one used in many previous studies based on the
World Values Survey and the General Social Survey, but it has an advantage to capture the
intensity of trust.6
We relate the trust measure to measures of household finance in 2008 or in 2010, depending on availability. We measure the quality of debt management by the following four
indicator variables (taking the value of zero or one): (i) Indebt (whether the respondent
indicated that she has a negative net worth in 2008); (ii) MissPmt (whether the respondent
indicated that she has completely missed a payment or been at least 2 months late in paying
any of the bills in the last 5 years in 2008); (iii) Bankruptcy (whether the respondent has
indicated that she has ever declared bankruptcy as of 2008); (iv) Foreclosure (whether the
respondent ever went through foreclosure between 2007 and 2010). The foreclosure information is from the 2010 survey.
We also examine the effect of trust on household net worth, which captures the net effect of household investments and borrowing. The values of the following asset items were
collected for the 2008 survey: CDs, bonds, business assets, vehicles, saving/checking/money
market accounts, mutual funds, employer-sponsored retirement accounts, saving bonds,
stock, IRA/Keogh/other tax-advantaged accounts, residential properties, collections, cashvalue insurance, items each worth $1,000 or more, and personal or mortgage loans made
to others. For liabilities, the values of credit card debt, car loans, business debt, student
loans, mortgages and back taxes, other debt on residential properties, debt to other businesses, and personal loans were collected also from the 2008 survey. Net worth is defined
as the difference between total assets and total debt.
NLSY79 has also gathered information on various demographic characteristics, income,
risk aversion, savings preference, time preference (impatience), cognitive ability, financial
literacy, positive attitudes, self-confidence, life satisfaction, and sociability. We provide the
definition of each variable used in our article in the Appendix and descriptive statistics in
Table I.
Some of the variables are collected in every round, while others are collected in certain
years. For example, the Armed Forces Qualifying Test (AFQT) scores, which we use as
measures of cognitive ability, are taken from the 1981 survey. The AFQT scores are based
on four areas (Arithmetic Reasoning, Mathematical Knowledge, Word Knowledge, and
Paragraph Comprehension) of the Armed Services Vocational Aptitude Battery (ASVAB),
and are widely used in the literature as measures of cognitive ability (e.g., Agarwal and
Mazumder (2013)).
Satisfaction (or happiness, e.g., Guven (2011)) is measured by response to the question
“I am satisfied with myself.” Self-confidence and optimism (Puri and Robinson, 2007) are
identified by responses to following two questions: “I am able to do things as well as most
people,” which measures confidence in own ability (SelfConfidence), and “I take a positive
attitude toward myself,” which is likely to capture optimism (PosAttitude). Sociability
(Georgarakos and Pasini, 2011; Changwony, Campbell, and Tabner, 2015) is measured by
response to the question “Think yourself as an adult, would you describe yourself as 1: extremely shy, 2: somewhat shy, 3: somewhat outgoing, 4: extremely outgoing.” As these
6 The wording of this question is similar to that in the widely used World Values Survey and the
General Social Survey (see Section 2.1).
10
D. Jiang and S. S. Lim
0.400
High Trust (3, 4, 5)
0.350
Low Trust (1, 2)
$325K
0.300
0.269
0.250
0.200
0.167
0.189
0.187
0.148
$147K
0.150
0.091
0.100
0.059
0.031
0.050
0.000
Indebt
Miss Payments
Bankruptcy
Foreclosure
Net Worth
Figure 1. Sorts of debt and asset management measures based on trust. This figure plots the probability of being indebt, missing a payment, filing bankruptcy, and going through foreclosure, and the
mean net worth in the high- and low-trust groups. Households of individuals with above-average trust
levels (3 or higher) are assigned to the high-trust group and those with the trust level of 1 or 2 are assigned to the low-trust group. The means of the dummy variables of being indebt, missing a payment,
filing bankruptcy, and going through foreclosure are reported as the probability. Data points are
shown at the top of each bar. The mean net worth is plotted in millions of dollars and labeled in thousands of dollars.
psychological factors are likely to correlate with trust, it is important to account for them
when evaluating the marginal effect of trust on household finance.
4. Empirical Results
4.1
Trust and Household Financial Outcomes
We first test Hypothesis 1, which predicts that high-trust individuals manage debt better
and have higher values of net worth. The quality of debt management is measured by four
indicator variables: Indebt, MissPmt, Bankruptcy, and Foreclosure. We log transform the
net worth variable to LogNetWorth (the logarithm of net worth), which is defined as
log(1 þ net worth) for a nonnegative net worth, and –log(1–net worth) for a negative net
worth. Hypothesis H1 predicts that trust should have a negative relation with all four debtrelated variables and a positive relation with net worth.
4.1.a Sorts based on trust
To gain a first glance at the effect of trust on personal finance, we sort individuals into a
high or a low trust group, where high trust refers to the above-mean level of trust (rating of
3 or above) and low trust refers to the below-mean level of trust (rating of 1 or 2). We report the means of the debt and asset variables of the two groups in Figure 1.
Figure 1 plots the mean values of the variables on debt management and net worth
across the high- and low-trust groups. The high-trust group has a probability of 9.1% of
being in debt, 18.9% of missing payments, 14.8% of declaring bankruptcy, and 3.1% of
going through foreclosure. These numbers are uniformly and significantly lower than those
of the low-trust group: 16.7%, 26.9%, 18.7%, and 5.9%. In other words, low-trust individuals are about 30%–90% more likely to run into a problem associated with debt as
compared to the high-trust group. For net worth, the high-trust group on average has a net
worth of $325K, in contrast to a much lower $147K for the low-trust groups, a more than
100% difference. All differences between the two groups are highly statistically significant.
Trust and Household Debt
11
Therefore, Figure 1 clearly shows that high-trust individuals have significantly better household financial outcomes than low-trust individuals, before we account for individual differences in demographics and economic status.
4.1.b Univariate and multivariate regressions
Next, we verify the relationship between trust and various financial outcome variables
using regressions with and without various controls. We first report the univariate regression results in Table II (Panel A). When the dependent variable is an indicator variable
(Indebt, MissPmt, Bankruptcy, and Foreclosure), we use probit regressions. When the dependent variable is LogNetWorth, we use OLS regressions. We adjust standard errors for
clustering at the county level since the NLSY79 survey uses county as the primary geographic cluster for selecting respondents. The results, reported in Panel A of Table II, confirm our earlier findings using simple sort and support Hypothesis H1: the debt-related
variables are all negatively related to trust, while net worth is positively related to trust. In
all regressions, the coefficient estimate on trust is statistically significant at the 1% level.
In Panel B of Table II, we include a set of control variables that are likely to affect household debt decisions, including (i) economic factors: current income and current assets7; (ii)
demographic factors: age, gender, marital status, family size, and number of children; (iii)
education/knowledge: a set of indicator variables for high-school diploma, college degree,
graduate or professional degrees, an indicator variable for working in finance-related industry, and financial literacy scores; (iv) preference variables: risk aversion, savings preference,
and impatience;8 (v) cognitive factors, measured by the math and verbal scores of the
AFQT tests; and (vi) psychological factors: positive attitude, satisfaction, self-confidence,
and sociability.
When it comes to explain net worth, we exclude the value of asset holdings as it is part
of net worth.9 Finally, in all regressions we control for state fixed effects to account for unobserved differences in financial constraints, law enforcement, legal environments, and social capital across states.10 In other words, our controls capture not only economic,
demographic, preference, and educational factors, but also psychological, cognitive, and
geographic factors. This is by far one of the most comprehensive sets of controls used in the
literature of household finance, owing to the rich longitudinal information in our dataset.
The large set of controls can at least partially alleviate, although may not completely rule
out, the concerns of omitted variables.
7
We find qualitatively similar results when we use additional controls for one’s economic status.
The results are reported in Supplementary Appendix.
8 The impatience variable, which is measured by the amount of money demanded by an individual
to delay the receipt of a $1,000 prize, can be correlated with the extent of self-control problem. In
untabulated tests, we used another measure of self-control, the Drug Use indicator variable that
is equal to one if the respondent reported in 1998 survey that he or she had used marijuana,
crack, or cocaine, as drug addiction is associated with the lack of self-control (Ersche et al.,
2012). We find that Drug Use is significantly associated with a higher likelihood of being in debt,
missing payment, and filing bankruptcy, and our trust measure remains in the predicted sign and
statistically significant in the presence of Impatience and Drug Use.
9 In untabulated tests, we find that high-trust individuals have higher levels of asset holdings, consistent with the idea that trusting individuals invest more.
10 The state fixed-effects also absorb the differences in trust across states. Not surprisingly, our results are stronger without controls for the state fixed effect.
12
D. Jiang and S. S. Lim
Table II. Trust and financial outcomes
This table reports the regression results of household financial outcomes on the level of individual trust with a set of control variables. Regressions (1)–(4) are probit regressions and the average marginal effect (probability) is reported as the coefficient. Regression (5) is an OLS
regression. The dependent variables are a dummy for having negative net worth (Indebt), a
dummy for missing a payment or being late in paying bills (MissPmt), a dummy for ever filing
for bankruptcy (Bankruptcy), a dummy for foreclosure (Foreclosure), and logarithm of net worth
(LogNetWorth). The key independent variable is the level of individual’s trust (Trust). All variables are defined in the Appendix. We report the z-statistics for probit regressions, (1)–(4), and
t-statistics for OLS regression (5), below the coefficients in parentheses based on standard
errors adjusted for clustering at the county level. The statistical significance at the 10%, 5%, and
1% levels are indicated by *, **, and ***.
Panel A: univariate regressions
Dependent variables
Trust
(Pseudo/Adj.) R2
Obs
(1)
Indebt
(2)
MissPmt
(3)
Bankruptcy
(4)
Foreclosure
(5)
LogNetWorth
–0.040***
(–11.71)
2.4%
7,456
–0.041***
(–8.50)
1.0%
7,548
–0.021***
(–4.92)
0.4%
7,560
–0.011***
(–4.01)
1.0%
4,814
0.711***
(22.52)
6.8%
7,352
(1)
Indebt
(2)
MissPmt
(3)
Bankruptcy
(4)
Foreclosure
(5)
LogNetWorth
–0.008**
(–2.10)
–0.028***
(–12.76)
–0.014***
(–2.76)
0.000
(0.09)
–0.003
(–0.40)
0.002
(0.16)
–0.007
(–1.22)
0.005
(0.70)
0.001
(0.09)
–0.019***
(–3.09)
–0.025***
(–7.29)
–0.025***
(–3.32)
–0.000
(–0.04)
–0.045***
(–3.79)
–0.029*
(–1.91)
–0.001
(–0.13)
0.024*
(1.93)
0.009
(0.39)
–0.022***
(–3.68)
–0.012***
(–3.45)
0.007
(0.91)
–0.000
(–0.06)
–0.048***
(–4.36)
0.011
(0.70)
–0.022**
(–2.43)
0.026**
(2.49)
0.020
(1.05)
–0.016***
(–3.96)
–0.007**
(–2.47)
–0.012**
(–2.23)
–0.001
(–0.48)
–0.003
(–0.39)
0.011
(1.01)
0.003
(0.38)
0.002
(0.22)
0.001
(0.07)
0.123***
(3.45)
Panel B: multivariate regressions
Dependent variables
Trust
LogAsset
LogIncome
Age
Male
Married
FamSize
NumChild
EduHigh
1.018***
(18.66)
0.025*
(1.84)
0.098
(1.60)
0.793***
(7.61)
–0.109**
(–2.32)
0.137**
(2.44)
0.328***
(2.80)
(continued)
Trust and Household Debt
13
Table II. Continued
Panel B: multivariate regressions
Dependent variables
EduCollege
EduGradprof
EduOther
FinIndustry
AFQTmath
AFQTverbal
RiskAver
SavePref
Impatience
FinLiteracy
PosAttitude
Satisfied
SelfConfidence
Social
State dummies
(Pseudo/Adj.) R2
Obs
(1)
Indebt
(2)
MissPmt
(3)
Bankruptcy
(4)
Foreclosure
(5)
LogNetWorth
0.021
(1.28)
0.030
(1.29)
0.031
(1.25)
–0.031
(–1.44)
–0.117***
(–3.25)
0.054*
(1.81)
–0.001
(–0.36)
–0.001***
(–4.78)
0.086
(0.58)
0.000
(0.13)
–0.002
(–0.22)
–0.004
(–0.56)
–0.003
(–0.44)
0.000
(0.05)
yes
20.6%
4,466
0.005
(0.19)
–0.045
(–1.36)
0.026
(0.63)
–0.033
(–1.25)
–0.107**
(–2.14)
0.143***
(2.89)
–0.014***
(–2.90)
–0.001***
(–4.27)
0.087
(0.42)
–0.002
(–0.49)
0.010
(0.91)
–0.019*
(–1.71)
0.021**
(2.03)
0.018**
(2.01)
yes
7.9%
4,529
–0.017
(–0.75)
–0.126***
(–3.55)
0.007
(0.17)
0.031
(1.20)
–0.158***
(–3.09)
0.061
(1.31)
0.004
(0.86)
–0.001***
(–3.85)
–0.349*
(–1.71)
–0.000
(–0.06)
0.022**
(2.23)
–0.018*
(–1.78)
0.018*
(1.75)
–0.007
(–0.85)
yes
7.0%
4,538
–0.020
(–1.17)
–0.075**
(–2.27)
–0.020
(–0.71)
0.029**
(2.13)
–0.000
(–0.01)
0.031
(1.04)
0.000
(0.02)
–0.000*
(–1.74)
–0.013
(–0.18)
0.001
(0.25)
0.010
(1.27)
0.010
(1.52)
–0.000
(–0.07)
0.011**
(2.17)
yes
12.5%
2,949
0.456***
(3.36)
0.363*
(1.91)
0.422**
(1.97)
0.081
(0.55)
0.654**
(2.53)
0.983***
(3.62)
0.111***
(3.97)
0.004***
(5.44)
–0.958
(–0.79)
0.063**
(2.51)
–0.024
(–0.35)
0.127**
(1.98)
–0.093
(–1.46)
0.033
(0.67)
yes
36.3%
4,486
After adding all of the above-mentioned control variables, we find that trust remains
statistically significant at the 1% level in all regressions. These regression estimates suggest
that a one-standard deviation change (1.02) in trust leads to a reduction of 6.8%
(¼ 0.008*1.02/0.12) in the likelihood of being in debt relative to the mean likelihood of
12%. Similarly, a one-standard deviation change in trust is associated with a reduction of
8.8% in the likelihood of missing a payment, 14% in the likelihood of filing bankruptcy,
and 41% in the likelihood of going through foreclosure. Since the foreclosure question was
asked in 2010, we have also tried to identify foreclosures that are likely to have occurred
after the measurement of trust in the 2008 survey. If the respondent reported owning a residential property in the 2008 survey and a foreclosure in the 2010 survey, the foreclosure is
14
D. Jiang and S. S. Lim
likely to have occurred since the 2008 survey. We find that the results are qualitatively the
same when we use foreclosures since 2008. For net worth, our regression estimate implies a
change of trust by one standard deviation leads to a marginal increase of nearly $35K in net
worth, or a 13% increase relative to the mean net worth, incremental to the effect of a host
of controls.
The regression estimates of control variables in Panel B are also generally consistent
with prior findings. For example, individuals with higher income or assets, higher cognitive abilities in math, and stronger savings preferences tend to exhibit better debt management (e.g., Lusardi and Mitchell (2007); Grinblatt, Keloharju, and Linnainmaa (2012)).
However, unlike prior studies, age is insignificant in our regressions. This can be
attributed to the fact that our respondents come from a cohort with similar ages (born between 1957 and 1964), therefore there is little dispersion in age for our sample of
individuals.11
Overall, our regression estimates in Table II provide a strong support for Hypothesis 1
that trust has strong and consistent effects on the quality and outcomes of household financial decisions.12
4.2
Channels of the Trust Effect
Next we test Hypothesis 2 by exploring the belief- versus value-based channels of trust to
affect household debt. The belief channel refers to trust formed in response to environment,
such as how much people trust an individual, or in general trust each other, within her community. Following Butler, Giuliano, and Guiso (2015), we use the average trust level of all
other respondents residing in the same county as a proxy of the trust of neighbors
(Trust_neighbor)13, and denote the trust component predicted by this measure as TrustBelief*.
The value-based channel refers to trust values that define the endowed trust of an individual. These values can be acquired through cultural transmission and instilled by parents.
Ethnicity and religion are commonly used as proxies for cultural background in the literature (e.g., Guiso, Sapienza, and Zingales (2006)). Thus, we capture the component of trust
values acquired through cultural transmission by predicting trust using the average trust of
all individuals with the same ethnicity and that of all individuals with the same religion in
which one is raised, obtained from the 1979 survey. Our use of the religion in which an individual is raised in as opposed to the current religion of the individual is likely to capture
the cultural background more precisely. We denote the component of trust predicted by the
average trust of one’s religion and ethnicity as Trust-Culture*. The second component of
the value-based channel is Trust-Family*, the predicted trust using mother’s trust level,
which is proxied by the average trust of all individuals residing in the birth state of an individual’s mother. Dohmen et al. (2012) show that trust of mothers has a stronger impact on
11 As a further test to account a possible nonlinear relationship between age and household finance
induced by the life-cycle effect, we replace age with a set of age dummies. We find none of the
age indicator variables is significant, while the coefficients on trust remain almost identical to
those in Table II (untabulated).
12 Our analyses employ a measure of generalized trust. We find that trust in financial markets and institutions has similar effects on household financial outcomes using Rand American Life Panel.
See Supplementary Appendix for details.
13 As discussed in the Introduction, at the aggregate level, trust and trustworthiness are consistent.
Trust and Household Debt
15
the trust of children than that of father.14 We denote the value-based trust component as
Trust-Value*, which is the sum of Trust-Culture* and Trust-Family*.
Specifically, we decompose trust using the following regression estimates:
Trust ¼ 2:603 þ 0:0926 Trust neighbor þ 0:5728 Trust religion þ 0:7940 Trust ethnic
ðt¼4:50Þ
ðt¼1:95Þ
ðt¼2:96Þ
ðt¼14:63Þ
þ0:4320 Trust momstate þ Trust Res
ðt¼6:44Þ
N ¼ 5;185; Adjusted R2 ¼ 9:13%;
(1)
where Trust_neighbor is the average trust of other individuals in the same county,
Trust_religion is the average trust of the individuals that were raised in the same religion,
Trust_ethnic is the average trust of the individuals of the same ethnicity,15 and
Trust_momstate is the average trust of the individuals in the birth state of one’s mother. We
calculate the predicted trust for each component using the coefficient multiplied by the variable value for each individual: Trust-Belief* ¼ 0.0926 Trust_neighbor, Trust-Culture* ¼
0.5728 Trust_religion þ 0.7940 Trust_ethinic, and Trust-Family* ¼ 0.4320 Trust_momstate, and Trust-Value* ¼ Trust-Culture* þ Trust-Family*. The residual trust
(Trust-Res) is trust minus the intercept and all the predicted components, and it is likely to
capture trust beliefs and values shaped by individual-specific life experiences and values.
Next, we replace trust in the baseline regressions in Panel B of Table II with the three trust
components (Trust-Belief*, Trust-Value*, and Trust-Res) and study their coefficients across
the five household finance variables. To facilitate the comparison of their economic impacts
across the components, we standardize each trust component using its mean and standard deviation. Our regression estimates are reported in Panel A of Table III. We find that TrustBelief* has a significant negative relation with MissPmt and a significant positive relation with
net worth. Its coefficient is marginally significant in the regression of Bankruptcy, and insignificant for Foreclosure and Indebt. In contrast, Trust-Value* has a significant coefficient in all
five regressions with the expected sign and greater magnitude, suggesting a more consistent
and important influence across different measures of debt management and on the net worth.
We further decompose Trust-Value* into the culture- and family-based components in
Panel B (Table III). We find that both Trust-Culture* and Trust-Family* yield significant
coefficients for MissPmt and LogNetWorth. Trust-Culture* has a significant impact on
Foreclosure but not on Indebt, and the reverse holds for Trust-Family*. Both are insignificant for Bankruptcy. Compared to the estimates on Trust-Belief*, the collective patterns of
the trust value variables based on family and cultural influences have somewhat stronger effects in terms of the magnitude of coefficients and statistical significance. In particular,
Indebt and Foreclosure are driven by the value-based, but not the belief-based, component
of trust. Finally, the coefficient on Trust-Res is significant for MissPmt, Bankruptcy, and
Foreclose, suggesting the individual-specific trust also has a considerable effect on some aspects of household debt management.
To sum up, the estimates in Table III show that trust values derived from culture or family heritage have significant impacts on all measures of household finance, while trust beliefs and trust shaped by individual specific factors have significant impacts on a majority
14 Adding the average trust of father’s birth state in the regression does not alter the results.
15 See the Appendix for the categories of religion and ethnicity.
16
D. Jiang and S. S. Lim
Table III. Channels of trust effects on financial outcomes
This table reports the regression estimates of household financial outcomes on different components of individual trust with a set of control variables: (1) trust predicted by neighbors’ average
trust (Trust-Belief*), (2) trust predicted by cultural and family values (Trust-Value*), which is the
sum of the following two components: the average trust of all individuals sharing one’s religion
and ethnicity (Trust-Culture*) and the trust predicted by the average trust of all individuals from
mother’s birth state (Trust-Family*); (3) residual trust not explained by the above three components (Trust-Res). Panel A uses Trust-Value* while Panel B uses the two components of TrustValue*: Trust-Culture* and Trust-Family*. All of these key independent variables are standardized
to have zero mean and unit standard deviation. Regressions (1)–(4) are probit regressions and the
average marginal effect (probability) is reported as the coefficient. Regression (5) is OLS. The dependent variables include a dummy for having negative net worth (Indebt), a dummy for missing
a payment or being late in paying bills (MissPmt), a dummy for ever filing for bankruptcy
(Bankruptcy), a dummy for foreclosure (Foreclosure), and logarithm of net worth (LogNetWorth).
The control variables are identical to those in Panel B of Table II. We report the z-statistics for probit regressions, (1)–(4), and t-statistics for OLS regression (5) below the coefficients in parentheses
based on standard errors adjusted for clustering at the county level. The statistical significance at
the 10%, 5%, and 1% levels are indicated by *, **, and ***.
Panel A: belief- versus value-based trust
Dependent variables
Trust-Belief*
Trust-Value*
Trust-Res
Controls
(Pseudo/Adj.) R2
Obs
(1)
Indebt
(2)
MissPmt
(3)
Bankruptcy
(4)
Foreclosure
(5)
LogNetWorth
–0.006
(–1.01)
–0.018***
(–2.79)
–0.002
(–0.53)
–0.023**
(–2.49)
–0.039***
(–3.78)
–0.021***
(–2.77)
–0.015*
(–1.79)
–0.018**
(–1.98)
–0.025***
(–3.54)
0.008
(1.43)
–0.037***
(–5.31)
–0.018***
(–3.27)
0.102**
(2.11)
0.361***
(6.85)
0.047
(1.11)
21.2%
3,030
Same as in Panel B of Table II
8.4%
8.4%
15.6%
3,072
3,087
1,756
40.0%
3,054
Panel B: decomposing the value-based trust into culture- and family-based components
Trust-Belief*
Trust-Culture*
Trust-Family*
Trust-Res
Controls
(Pseudo/Adj.) R2
Obs
–0.007
(–1.13)
–0.008
(–1.46)
–0.016**
(–2.27)
–0.002
(–0.52)
21.3%
3,030
–0.024**
(–2.57)
–0.022**
(–2.32)
–0.029***
(–2.98)
–0.021***
(–2.78)
–0.016*
(–1.82)
–0.011
(–1.24)
–0.012
(–1.35)
–0.025***
(–3.56)
0.009
(1.56)
–0.033***
(–5.03)
–0.004
(–0.58)
–0.018***
(–3.22)
Same as in Panel B of Table II
8.5%
8.4%
15.8%
3,072
3,087
1,756
0.104**
(2.14)
0.262***
(5.11)
0.162***
(2.95)
0.048
(1.14)
40.0%
3,054
Trust and Household Debt
17
of the measures. The results suggest a slightly stronger effect of trust values compared to
that of trust beliefs. In other words, while beliefs about the trust of others may induce better
debt management, trust values inherited from culture and family play a more important
role in shaping both household debt management and overall financial health.
4.3 The Causal Impact of Trust on Household Finance
Returning to our main dataset, we have demonstrated that trust has a significant relation
with an array of household debt management measures. We also show that our findings
survive when we include not only the current income and assets but also historical incomes
and early-life family net worth. These additional controls help to address the concern for reverse causality. As an alternative approach, in the next test we address this concern using
the instrumental variable approach in which we distill the component of trust that is correlated with an early-life experience.
4.3.a Predicting trust based on early-life experiences
We consider an instrumental variable for trust using an individual’s early-life experience
during her teenage years or early adulthood, decades before an individual has accumulated
meaningful amounts of debt or assets. One such early-life experience is obtained from the
1982 survey and denoted as DiscrimAge, which is equal to one if the respondent felt she
was discriminated on the basis of age when she was searching for a job. We hypothesize the
experience of being discriminated based on age is likely to reduce an individual’s trust, as
Alesina and LaFerrara (2002) show that negative life experiences and discrimination tend
to adversely affect one’s trust and such adverse effects tend to persist into adulthood.
The early-life age-based discrimination makes a good candidate for an instrument for a
few reasons. First, we find that such an early-life experience is negatively related only to
short-term, but not to long-term, incomes (see Panel B of Table IV). Thus, there is no evidence that discrimination on the basis of young age has a direct adverse effect on the longterm financial outcomes. Second, Garstka et al. (2004) show that such age-based discrimination experienced at young ages tends to have only a temporary effect on an individual’s life
satisfaction and personal self-esteem.16 This contrasts with discrimination based on group
memberships that are relatively more enduring and permanent (e.g., race, gender, old age).
Thus, discrimination based on young age is unlikely to channel through other psychological
traits to impact later-life financial well-being.17 Third, the likelihood of experiencing agebased discrimination is considerably lower when the young adults move to the high status,
middle-aged category (Garstka et al., 2004). There is further evidence in that age is negatively
related to the likelihood of reporting an age-based discrimination in both our sample (see
Panel B of Table IV) and the General Social Survey (GSS) for young and middle-age groups.18
16 The respondents were between ages 17 and 25 years when they answered the question about
age-based discrimination.
17 Further, our extensive set of controls including optimism, confidence, satisfaction, and incomes
(current and historical), minimizing any possible effect of age discrimination through other channels than trust.
18 We employ the GSS in years 2002, 2006, 2010, 2012, and 2014 in which a question about age-based
discrimination was asked. We find that young people who are teens and in early 20’s are most
likely to report age-based discrimination. However, the likelihood of self-reported age-based discrimination decreases with age and only resurges until one reaches 50 years or above.
18
D. Jiang and S. S. Lim
Table IV. Predicting trust based on early life experiences
Panel A reports the OLS regression results of the determinants of trust. The dependent variable
is the level of individual’s trust (Trust). The key independent variable is the dummy for perceived discrimination based on age (DiscrimAge, from the 1982 survey). The control variables
include those in Panel B of Table II, the respondent’s family net worth in 1985
(LogNetWorth1985) and measures of historical income levels. Panel B reports the results of the
OLS regressions of DiscrimAge on the control variables. All variables are de_ned in the
Appendix. The coe_cients of SavePref are multiplied by 1000 for readability. Below the coe_
cients reported are the t-statistics in parentheses that are based on standard errors adjusted for
clustering at the county level. The statistical signi_cance at the 10%, 5%, and 1% levels are indicated by *, **, and ***. F-statistic tests the hypothesis that the DiscrimAge has zero incremental
e_ect on trust. Below the F-statistics reported are the p-values in italics and parentheses.
Panel A: predicting trust
Dependent variable: trust
DiscrimAge
(1)
(2)
(3)
(4)
–0.098***
(–3.44)
–0.098***
(–3.41)
0.028***
(2.94)
–0.095***
(–3.18)
–0.096***
(–3.19)
0.032***
(3.09)
0.004
(0.42)
0.086***
(3.10)
0.036
(1.29)
–0.012
(–0.37)
–0.020
(–0.61)
–0.001
(–0.03)
LogAsset
LogNetWorth1985
0.008
(0.89)
0.085***
(3.04)
0.039
(1.41)
–0.001
(–0.03)
0.001
(0.03)
0.018
(0.70)
LogIncome (1979–85)
LogIncome (1986–92)
LogIncome (1994–2000)
LogIncome (2002–06)
LogIncome
Other controls
State dummies
Adj. R2
Obs
F-statistic
0.044**
(2.49)
Yes
16.4%
4,526
11.82
(0.0006)
0.014
(0.68)
Same as in Panel B of Table II
Yes
Yes
16.6%
16.9%
4,526
4,340
11.65
10.09
(0.0007)
(0.0015)
Yes
17.1%
4,340
10.18
(0.0015)
Panel B: relation of DiscrimAge with control variables
Dependent variable: DiscrimAge
(1)
LogAsset
LogNetWorth1985
–0.001
(–0.23)
(2)
(3)
(4)
(5)
0.010*
(1.90)
0.002
(0.40)
0.009*
(1.83)
0.003
(0.70)
(continued)
Trust and Household Debt
19
Table IV. Continued
Panel B: relation of DiscrimAge with control variables
Dependent variable: DiscrimAge
(1)
(2)
(3)
(4)
(5)
LogIncome (1979–85)
–0.040**
–0.040**
–0.045***
–0.045***
(–2.54)
(–2.55)
(–2.83)
(–2.82)
LogIncome (1986–92)
–0.016
–0.017
–0.020
–0.021
(–1.15)
(–1.18)
(–1.41)
(–1.43)
LogIncome (1994–2000)
–0.011
–0.012
–0.012
–0.012
(–0.75)
(–0.82)
(–0.77)
(–0.81)
LogIncome (2002–06)
–0.005
–0.007
–0.007
–0.008
(–0.31)
(–0.43)
(–0.44)
(–0.50)
LogIncome
–0.013
–0.001
–0.003
0.002
0.001
(–1.20)
(–0.08)
(–0.22)
(0.12)
(0.04)
Age
–0.043***
–0.043***
–0.043***
–0.044***
–0.044***
(–14.24)
(–14.12)
(–14.09)
(–13.94)
(–13.86)
Other controls included
Male, Married, FamSize, NumChild, Education dummies, FinIndustry,
AFQTverbal, AFQTmath, RiskAver, SavePref, Impatience, FinLiteracy,
PosAttitude, Satisfied, SelfConfidence, Social
State dummies
Yes
Yes
Yes
Yes
Yes
4.9%
5.4%
5.4%
5.4%
5.3%
Adj. R2
Obs
4,526
4,415
4,415
4,340
4,340
Taken together, there is no obvious violation of the exclusion criteria for the early-life
age-based discrimination as an instrument, especially in the presence of the controls for the
individual’s life-long income history and family financial status in 1985 in addition to the
current levels of income and assets and various psychological traits variables.
We start with examining whether DiscrimAge adversely impacts trust to establish the
relevance of the instrument. We regress trust on DiscrimAge together with the controls, and
report the estimates in Panel A of Table IV. Regressions (1) and (2) include DiscrimAge
with the baseline controls in Panel B of Table II and the remaining regressions additionally
control for the historical income levels and financial net worth in 1985. These regressions
are equivalent to the first-stage regressions in the instrumental variable regressions.19
We find that DiscrimAge carries a highly significant, negative coefficient throughout all
regressions in Panel A of Table IV. The coefficient ranges from –0.095 to –0.098, all significant at the 1% level. This is consistent with our conjecture that experiencing discrimination
early in life has a negative impact on an individual’s trust. The F-statistic of DiscrimAge
ranges from 10.09 to 11.82, all with p-values below 0.01, suggesting that DiscrimAge is unlikely to be a weak instrument (Staiger and Stock, 1997). To summarize, our regression estimates in Table IV provide strong support that trust is correlated with the early-life
experience of age-based discrimination with controls for past incomes and net worth,
19 The number of observations used in actual first-stage regressions may differ because we only
use observations with nonmissing values for the dependent variable in the second-stage regression. But the estimates are qualitatively similar.
20
D. Jiang and S. S. Lim
current income and assets, and a host of economic, preference, psychological, and cognitive
factors.
In Panel B of Table IV, we regress DiscrimAge on our control variables, including the
historical incomes levels and financial net worth in 1985. We find that DiscrimAge is not
significantly related to current income or assets. Furthermore, the coefficient on the
LogIncome (1979–85) is negative and significant, while those on the other historical income variables as well as the current income are statistically indifferent from zero. In other
words, supporting our earlier assertion, the age-based discrimination has only a temporary
association with incomes around the time of the age-based discrimination experience but
has no significant relation to long-term incomes or assets.
4.3.b Instrumental variable regressions
Having established that DiscrimAge is a relevant instrumental variable to trust, we proceed
to test whether the instrumented trust can explain variations in management of household
debt and net worth. We have demonstrated that experiencing discrimination on the basis of
age is not associated with long-term incomes and assets. Nevertheless, to ensure that this
temporary and any possible permanent income effects are accounted for, we also consider
these historical income levels and the 1985 family net worth as additional controls in the instrumental variable regressions.
We report the coefficient estimates of the instrumented trust (Trust*) using DiscrimAge
in Table V. In Panel A, only the baseline controls are employed. In Panel B, the four historical income levels and the 1985 family net worth are added as additional controls. We focus
on the economic and statistical significance of Trust*.
In Table V (Panel A), we find Trust* has the expected sign for all debt variables and net
worth. The coefficients are significant at the 5% level or better for all but Indebt, for which
the coefficient of Trust* is significant at the 10% level. When in Panel B we include additional controls for the 1985 family net worth and the historical income levels from 1979 to
2006, all coefficients but that for the net worth regression are significant at the 5% level or
better. Overall, these estimates show that the instrumented trust based on an early-life experience has significant power to explain the variation in household debt management. Its
influence on net worth decades later is somewhat sensitive to the controls of income history
and early life family financial status.
One possible concern is that we observe discrimination only for the individuals that
look for jobs, and the propensity of a job search at young ages may correlate with financial status that can also influence trust. In other words, perceived discrimination in job
search may simply reflect the individual’s financial status at that time. This is a valid concern. We control for family net worth in 1985 in Panel B to account for the family financial status. An alternative approach to address the concern, however, is to restrict the
sample to those who had a job in 1982, when they were asked about discrimination experience. Since this subsample consists of only those who did a job search, we remove a
possible relation between family financial status that may prompt a job search and the experience of discrimination on the basis of age. We report the estimates in Panels C and D.
Again, we continue to observe significant coefficients on Trust* for all debt variable regressions, and marginally significant or insignificant coefficient on Trust* for the net
worth regressions. The patterns remain quite similar to the full sample estimates. Thus,
our instrumented variable estimates are unlikely to be driven by omitted variables that can
drive a job search.
Trust and Household Debt
21
Table V. The causal effect of trust: instrumental variable approach
This table reports the regression results of household financial outcomes on the instrumented
trust (Trust*). We instrument trust with a dummy for perceived discrimination based on age
(DiscrimAge, from the 1982 survey) together with all controls and report the coefficients on the
instrumented trust (Trust*). Panels A and B report the results using the full sample and Panels
C and D report the results using a subsample restricted to the individuals who had a job in
1982. Panels A and C include the baseline controls identical to those in panel B of Table II, and
Panels B and D additionally include the controls for family net worth in 1985
(LogNetWorth1985) and measures of historical income levels: LogIncome (1979–85),
LogIncome (1986–92), LogIncome (1994–2000), LogIncome (2002–06). Regressions (1)–(4) are
probit regressions and the average marginal effect (probability) is reported as the coefficient.
Regression (5) is OLS. The dependent variables include a dummy for having negative net worth
(Indebt), a dummy for missing a payment or being late in paying bills (MissPmt), a dummy for
ever filing for bankruptcy (Bankruptcy), a dummy for foreclosure (Foreclosure), and logarithm
of net worth (LogNetWorth). We report the z-statistics for probit regressions, (1)–(4), and t-statistics for OLS regression (5) below the coefficients in parentheses based on standard errors adjusted for clustering at the county level. The statistical significance at the 10%, 5%, and 1%
levels are indicated by *, **, and ***.
Panel A: full sample with baseline controls
Dependent variables
(1)
Indebt
Trust*
Controls
Obs
–0.122*
(–1.91)
4,442
(2)
MissPmt
(3)
Bankruptcy
(4)
Foreclosure
(5)
LogNetWorth
–0.253***
(–7.12)
–0.193***
(–3.01)
–0.213***
(–7.55)
1.847**
(2.01)
Baseline Controls as in Panel B of Table II
4,505
4,515
2,933
4,462
Panel B: full sample with additional controls
Trust*
Controls
Obs
–0.136**
(–2.32)
–0.252***
(–6.55)
–0.184**
(–2.52)
–0.211***
(–6.88)
1.342
(1.56)
Baseline Controls, LogNetWorth1985, and Historical Income Levels
4,260
4,320
4,330
2,825
4,284
Panel C: restrict the sample to those who had a job in 1982 with baseline controls
Trust*
Controls
Obs
–0.144***
(–2.59)
4,135
–0.268***
(–9.89)
–0.207***
(–3.42)
–0.217***
(–8.10)
Baseline Controls as in Panel B of Table II
4,233
4,243
2,792
1.949*
(1.92)
4,194
Panel D: restrict the sample to those who had a job in 1982 with additional controls
Trust*
Controls
Obs
–0.171***
(–3.36)
–0.270***
(–9.35)
–0.208** *
(–3.17)
–0.216***
(–7.41)
1.466
(1.48)
Baseline Controls, LogNetWorth1985, and Historical Income Levels
3,892
4,060
4,070
2,687
4,028
22
D. Jiang and S. S. Lim
To summarize, the estimates in Table V show that trust shaped by an early-life experience encourages more responsible debt management and positively predicts net worth. The
life experience occurs nearly three decades before the household finance variables are measured. Thus, our findings suggest that trust has a causal effect on the quality and outcome of
household finance.
5. Additional Evidence for the Effect of Trust
5.1
Subsample Analyses
In this subsection we turn to Hypothesis 3, which predicts that trust should have a more
pronounced effect on those who are more susceptible to poor financial decisions. To identify those individuals, we use four sorting variables: education, income, gender, and financial literacy. Prior studies show that those with low income, education, or financial literacy
and female are more likely to make suboptimal investment and savings decisions (e.g.,
Lusardi and Mitchell (2011); Guiso, Sapienza, and Zingales (2008)). We want to ascertain
whether a similar pattern is present when it comes to the effect of trust on household debt.
We report the results on the subsample analyses in Table VI. In Panels A through D, the
sample is split into two groups based on education, income, gender, and financial literacy,
respectively. The results overall suggest that trust has a stronger effect on those who are in
a relatively weak position of making sound financial decisions. For example, among individuals with below college education, trust has a statistically significant effect on all debt
variables and net worth, while among individuals with college education or above, trust
has a significant negative impact only on foreclosure, a marginally significant effect on net
worth, and no effect on the remaining three variables.
Using the other three sorting variables, we observe statistically significant or marginally
significant effects of trust on nearly all dependent variables for low-income or lowfinancial-literacy individuals or females. In contrast, trust has a statistically significant or
marginally significant effect only for two or three dependent variables among high-income,
high-financial-literacy individuals and among males.20 Overall, we echo prior research by
showing that the effect of trust is most visible among more vulnerable groups in household
finance.
5.2
The Nonmonotonic Effect of Trust
Finally, we test Hypothesis 4, which posits that the effect of trust on household finance is
not uniformly positive. This is a prediction that further establishes the causal impacts of
trust, as the alternative explanation based on reverse causality cannot explain a humpshaped relationship between trust and net worth (e.g., Butler, Giuliano, and Guiso (2015)),
nor a U-shaped relationship between trust and measures of debt management. In this alternative explanation, better financial outcomes should always enhance trust, suggesting a
monotonic relationship between the two. In contrast, Hypothesis 4 predicts that individuals
with extremely high levels of trust perform worse than those with moderately high levels of
trust, as they may take others at their word and not perform due diligence before entering
into a contract. As the level of trust ranges from 1 to 5, with 1 indicating the lowest level
and 5 the highest level, we rerun all regressions in Table II with four trust dummies that
20 In Supplementary Appendix, we show that the effect of trust is more pervasive among the high
debt-to-ratio subsample.
Trust and Household Debt
23
Table VI. Trust and financial outcomes: subsample analyses
This table reports the coefficient of Trust in regressions of household financial outcomes with a
set of control variables for subsamples split based on education (Panel A), income (Panel B),
gender (Panel C), and financial literacy (Panel D). Regressions (1)–(4) are probit regressions and
the average marginal effect (probability) is reported as the coefficient. Regression (5) is OLS.
The dependent variables are a dummy for having negative net worth (Indebt), a dummy for
missing a payment or being late in paying bills (MissPmt), a dummy for ever filing for bankruptcy (Bankruptcy), a dummy for foreclosure (Foreclosure), and logarithm of net worth
(LogNetWorth). We report the coefficients of the key independent variable (Trust). The control
variables are identical to those in Panel B of Table II. In Panel A, individuals with other education are excluded. We report the z-statistics for probit regressions, (1)–(4), and t-statistics for
OLS regressions (5) below the coefficients of Trust in parentheses based on standard errors adjusted for clustering at the county level. The statistical significance at the 10%, 5%, and 1% levels are indicated by *, **, and ***.
Panel A: subsamples based on education
Low education
(Below college)
High education
(College or above)
(1)
Indebt
(2)
MissPmt
(3)
Bankruptcy
(4)
Foreclosure
(5)
LogNetWorth
–0.009*
(–1.72)
–0.007
(–1.09)
–0.023***
(–2.81)
–0.008
(–0.82)
–0.024***
(–2.99)
–0.014
(–1.40)
–0.017***
(–2.62)
–0.023***
(–3.30)
0.117***
(2.68)
0.098*
(1.66)
–0.023**
(–2.20)
–0.014*
(–1.72)
–0.026***
(–2.72)
–0.018**
(–2.32)
–0.027***
(–2.98)
–0.018***
(–3.31)
0.172***
(2.95)
0.042
(1.05)
–0.024**
(–2.43)
–0.015*
(–1.74)
–0.029***
(–3.10)
–0.019**
(–2.22)
–0.019***
(–3.06)
–0.021***
(–2.78)
0.175***
(3.18)
0.070
(1.44)
–0.033***
(–4.14)
–0.011
(–1.20)
–0.010
(–1.62)
–0.028***
(–4.30)
0.149***
(3.10)
0.078
(1.40)
Panel B: subsamples based on income
Low income
(median or below)
High income
(above median)
–0.013*
(–1.66)
–0.006
(–1.41)
Panel C: subsamples based on gender
Female
Male
–0.015**
(–2.51)
–0.002
(–0.30)
Panel D: subsamples based on financial literacy
Low FinLiteracy
(median or below)
High FinLiteracy
(above median)
–0.013**
(–2.20)
0.002
(0.46)
–0.019**
(–2.15)
–0.021**
(–2.19)
indicate the trust level of 2, 3, 4, and 5. Thus, the coefficient of each dummy captures the
incremental effect of that trust level relative the lowest trust level.
The results shown in Table VII provide strong support for our Hypothesis 4. When each
of the debt management measures is regressed on the four trust dummies with control variables, we find that the coefficients of various trust level variables are mostly in the expected
24
D. Jiang and S. S. Lim
Table VII. The nonmonotonic effect of trust on financial outcomes
This table reports the regression results of household financial outcomes on dummies for different levels of individual trust with a set of control variables. Regressions (1)–(4) are probit regressions and the average marginal effect (probability) is reported as the coefficient.
Regression (5) is OLS. The dependent variables include a dummy for having negative net worth
(Indebt), a dummy for missing a payment or being late in paying bills (Miss Pmt), a dummy for
ever filing for bankruptcy (Bankruptcy), a dummy for foreclosure (Foreclosure), and logarithm
of net worth (LogNetWorth). The key independent variable is the four higher levels of individual’s trust (Trust ¼ 2, 3, 4, 5), which is compared to the lowest level of trust (Trust ¼ 1). The
control variables are identical to those in Panel B of Table II. We report the z-statistics for probit
regressions, (1)–(4), and t-statistics for OLS regression (5) below the coefficients in parentheses
based on standard errors adjusted for clustering at the county level. The statistical significance
at the 10%, 5%, and 1% levels are indicated by *, **, and ***.
Dependent variables
(Trust¼2)
(Trust¼3)
(Trust¼4)
(Trust¼5)
Controls
(Pseudo/Adj.) R2
Obs
(1)
Indebt
(2)
MissPmt
(3)
Bankruptcy
(4)
Foreclosure
(5)
LogNetWorth
–0.025*
(–1.76)
–0.028**
(–2.02)
–0.035**
(–2.47)
–0.025
(–0.89)
–0.007
(–0.31)
–0.036
(–1.54)
–0.051**
(–2.17)
–0.057
(–1.20)
0.009
(0.40)
–0.025
(–1.10)
–0.049**
(–2.06)
–0.054
(–1.14)
–0.007
(–0.46)
–0.033**
(–2.22)
–0.046***
(–2.90)
–0.022
(–0.74)
0.270*
(1.69)
0.315**
(2.04)
0.492***
(3.18)
0.320
(1.01)
20.7%
4,466
7.9%
4,529
Same as in Panel B of Table II
7.1%
12.9%
4,538
2,949
36.3%
4,486
direction within each regression. However, the size of the coefficient on trust all increases
from the level of 2 to 3, peaks at 4, followed by a sharp decline at the level of 5. The statistical significance also follows this pattern. In fact, there is no significant relationship between the trust level of 5 and the dependent variable, suggesting having an extremely high
level of trust makes one no better off than having an extremely low level of trust. As for net
worth, we observe a gradual increase in the effect of trust up the level 4 followed by a sharp
decrease at the highest level of trust in regression (5). To summarize, our regression results
in Table VII support Hypothesis 4 and show that it is the right level of trust, not an extremely high level of trust, that leads to better household finances.
6. Conclusion
There are widespread concerns about American household debt management and the
health of household finance in general, from the debt-to-income ratio to personal bankruptcy and foreclosure. An individual’s financial decisions affect not only her household,
but also the overall economy. Household overindebtedness is believed by many to have
played a prominent role leading up to the 2008 financial crisis and economic downturn
Trust and Household Debt
25
(e.g., Cynamon and Fazzari (2008); Mian and Sufi (2011, 2010)). Thus, it is crucial to
understand the determinants of household financial decisions, particularly the management
of household debt. In this article, we show that trust is an important factor that underlies
major household debt management and overall financial well-being.
Using a representative sample of a generation of American individuals and a broad set
of measures of household financial debt management together with net worth, we show
that on average individuals with higher trust levels fare better in household debt management and finances. High-trust individuals have lower probabilities of being in debt, missing
payments, filing bankruptcy, or going through foreclosure, and higher net worth. Our estimates show that trust is one of the strongest and most consistent determinants of household
finance outcomes among a host of economic, psychological, cognitive, and preference variables we consider. We show that both trust beliefs and trust values play import roles in the
effect of trust on household finance, with a slightly stronger effect of trust values that are
acquired through cultural transmission and parental influences. Further, we address the
concern of reverse causality by extracting the component of trust correlated with one’s
early life experience.
It is usually difficult to assess the optimal level of borrowing for a given household.
However, at the minimum, our evidence suggests that trust helps households better prepare
for financial crisis by avoiding default and cumulating more net worth. This is particularly
valuable for individuals that are in a relatively weak position of managing household finance, for whom we find that trust has the most visible effect. The fact that personal earlylife experiences shape an individual’s trust level and affect household financial outcomes
offers a venue to improve household finance. Taken together, our results suggest that building good community environments and fostering the right amount of trust can benefit individual households as well as the aggregate economy.
Supplementary Material
Supplementary data are available at Review of Finance online.
References
Agarwal, S., Chomsisengphet, C. and Liu, C. (2011) Consumer bankruptcy and default: the role
of individual social capital, Journal of Economic Psychology 32, 632–650.
Agarwal, S. and Mazumder, B. (2013) Cognitive abilities and household financial decision making, American Economic Journal – Applied Economics 5, 193–207.
Agnew, J., Szykman, L., Utkus, S. P. and Young, J. A. (2007) Do financial literacy and mistrust affect 401(k) participation?, Center for Retirement Research Issue Brief 7–17, http://crr.bc.edu/cat
egory/briefs/.
Ahern, K., Daminelli, D. and Fracassi, C. (2015) Lost in translation? The effect of cultural values
on mergers around the world, Journal of Financial Economics 117, 165–189.
Alesina, A. and La Ferrara, E. (2002) Who trusts others?, Journal of Public Economics 85,
207–234.
Balloch, A., Nicolae, A. and Philip, D. (2015) Stock market literacy, trust, and participation,
Review of Finance 19, 1925–1963.
Butler, J., Giuliano, P., and Guiso, L. (2015) The right amount of trust, Journal of the European
Economic Association, forthcoming.
26
D. Jiang and S. S. Lim
Changwony, F. K., Campbell, K. and Tabner, I. T. (2015) Social engagement and stock market
participation, Review of Finance 19, 317–366.
Cynamon, B. Z. and Fazzari, S. M. (2008) Household debt in the consumer age: source of
growth—Risk of collapse, Capitalism and Society 3, Article 3.
Dohmen, T., Falk, A., Huffman, D. and Sunde, U. (2012) The intergenerational transmission of
risk and trust attitudes, Review of Economic Studies 79, 645–677.
El-Attar, M. and Poschke, M. (2011) Trust and the choice between housing and financial assets:
evidence from Spanish households, Review of Finance 15, 727–756.
Ersche, K. D., Jones, P. S., Williams, G. B., Turton, A. J., Robbins, T. W. and Bullmore, E. T.
(2012) Abnormal brain structure implicated in stimulant drug addiction, Science 335, 601–604.
Garstka, T. A., Schmitt, M. T., Branscombe, N. R. and Hummert, M. L. (2004) How young and
older adults differ in their responses to perceived age discrimination, Psychology and Aging 19,
326–335.
Georgarakos, D. and Pasini, G. (2011) Trust, sociability, and stock market participation, Review
of Finance 15, 693–725.
Glaeser, E. L., Laibson, D. I., Scheinkman, J. A. and Soutter, C. L. (2000) Measuring trust,
Quarterly Journal of Economics 115, 811–846.
Grinblatt, M., Keloharju, M. and Linnainmaa, J. (2012) IQ, trading behavior, and performance,
Journal of Financial Economics 104, 339–362.
Guiso, L., Sapienza, P. and Zingales, L. (2004) The role of social capital in financial development,
American Economic Review 94, 526–556.
Guiso, L., Sapienza, P. and Zingales, L. (2006) Does culture affect economic outcomes?, Journal
of Economic Perspectives 20, 23–48.
Guiso, L., Sapienza, P. and Zingales, L. (2008) Trusting the stock market, Journal of Finance 63,
2557–2600.
Guven, C. (2011) Are happier people better citizens?, Kyklos 64, 178–192.
Karlan, D. (2005) Using experimental economics to measure social capital and predict financial
decisions, American Economic Review 95, 1688–1699.
Knack, S. and Keefer, P. (1997) Does social capital have an economic payoff? A cross-country investigation, Quarterly Journal of Economics 112, 1251–1288.
Korniotis, G. M. and Kumar, A. (2010) Cognitive abilities and financial decisions, in: H. K. Baker
and J. Nofsinger (eds.), Behavioral Finance: Investors, Corporations, and Markets, Chapter 30,
John Wiley & Sons, Inc., Hoboken, NJ, pp. 559–576.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny, R. (1997) Trust in large organizations,
American Economic Review 87, 333–338.
Lusardi, A. and Mitchell, O. S. (2007) Baby boomer retirement security: the roles of planning,
financial literacy, and housing wealth, Journal of Monetary Economics 54, 205–224.
Lusardi, A. and Mitchell, O. S. (2011) Financial literacy and retirement planning in the United
States, Journal of Pension Economics and Finance 10, 509–525.
Mian, A. and Sufi, A. (2010) Household leverage and the recession of 2007 to 2009, IMF
Economic Review 58, 74–117.
Mian, A. and Sufi, A. (2011) Household debt and the weak U.S. economic recovery, Federal
Reserve Bank of San Francisco Economic Letter
Pevzner, M., Xie, F. and Xin, X. (2015) When firms talk, do investors listen? The role of trust in
stock market reactions to corporate earnings announcements, Journal of Financial Economics
117, 190–223.
Puri, M. and Robinson, D. (2007) Optimism and economic choice, Journal of Financial
Economics 86, 71–99.
Putnam, R. D. (1993) Making Democracy Work: Civic Traditions in Modern Italy, Princeton
University Press, Princeton, New Jersey.
Trust and Household Debt
27
Sapienza, P., Toldra-Simats, A. and Zingales, L. (2013) Understanding trust, The Economic
Journal 123, 1313–1332.
Staiger, D. and Stock, J. H. (1997) Instrumental variables regression with weak instruments,
Econometrica 65, 557–586.
van Rooij, M., Lusardi, A. and Alessie, R. (2011) Financial literacy and stock market participation, Journal of Financial Economics 101, 449–472.
Appendix: Variable Definition
All variables are from NLSY79 unless noted otherwise.
Variable name/description
AFQTmath
Sum of standard scores on ASVAB section 2 (arithmetic reasoning) and section 8 (mathematics knowledge), scaled by 100, from the 1981 survey.
AFQTverbal
Sum of standard scores on ASVAB section 3 (word knowledge) and section 4 (paragraph
comprehension), scaled by 100, from the 1981 survey.
Age
Age at the interview date, from the 2008 survey.
Bankruptcy
Equal to 1 if the respondent has ever declared bankruptcy, from the 2008 survey.
Debt-to-Income Ratio
The ratio of total debt over the annual household income in 2008.
DiscrimAge
Equal to 1 if the respondent indicates that discrimination on the basis of age caused problems in getting a good job, from the 1982 survey.
EduHigh
Equal to 1 if the respondent’s highest degree received is High school diploma or equivalent
(category 2), from the 2008 survey.
EduCollege
Equal to 1 if the respondent’s highest degree received is College (AA, BA, BS) (categories 3,
4, or 5), from the 2008 survey.
EduGradProf
Equal to 1 if the respondent’s highest degree received is Graduate/Professional (MA, MBA,
MS, MSW, PhD, MD, LLD, DDS) (categories 6, 7, or 8), from the 2008 survey.
EduOther
Equal to 1 if the respondent’s highest degree received is Others (category 9), from the 2008
survey.
Ethnicity
The origin/descent of the respondent (For multiple origins: one the respondent feels closest
to): (1) African American (Black), (2) Hispanic; (3) Others, from the 1979 survey.
FamSize
The number of family members, from the 2008 survey.
FinIndustry
Equal to 1 if the respondent’s primary employer is in finance or insurance industry, from the
2008 survey.
(continued)
28
D. Jiang and S. S. Lim
Continued
Variable name/description
FinLiteracy
Number of correct answers to the following five questions: 1. Do you think that the following statement is true or false? Buying a single company stock usually provides a safer return
than a stock mutual fund. 2. Suppose you had $100 in a savings account and the interest
rate was 2% per year. After 5 years, how much do you think you would have in the account
if you left the money to grow: more than $102, exactly $102, or less than $102? 3. Imagine
that the interest rate on your savings account was 1% per year and inflation was 2% per
year. After 1 year, would you be able to buy more than, exactly the same as, or less than
today with the money in this account? 4. If interest rates rise, what will typically happen to
bond prices? 5. Do you think that the following statement is true or false? A 15-year mortgage typically requires higher monthly payments than a 30-year mortgage, but the total
interest paid over the life of the loan will be less, from the 2012 survey.
Foreclosure
Equal to 1 if the respondent ever went through foreclosure on the house or other residential
properties since January 2007, from the 2010 survey.
Impatience
The smallest amount of additional money that the respondent would have to receive one
month from now to convince him/her to wait one month rather than claim a $1,000 prize
now. Scaled by 1,000, from the 2006 survey.
Indebt
Equal to 1 if the answer is “in debt” on the following question: “Suppose you were to sell
all of your major possessions, turn all of your investments and other assets into cash, and
pay all of your debts. Would you have something left over, break even, or be in debt?” from
the 2008 survey.
LogAsset
The log(1þasset), where asset is the sum of the values (in $1,000) of CDs, bonds, business
asset, vehicles, saving/checking/money market accounts, mutual funds, employer-sponsored
retirement accounts, saving bond, stock, IRA/Keogh/other tax advantaged accounts, residential properties, collections, cash-value insurance, items, and personal loans made to
others, from the 2008 survey.
LogIncome
Log(1þincome), where income is the total net family income (in $1,000) in the past calendar year, from the 2008 survey.
LogIncome (1979–85)
Log (1þ average income over the period 1979–85 (in $1,000)), from the 1979 to 1985
surveys.
LogIncome (1986–92)
Log (1þ average income over the period 1986–92 (in $1,000)), from the 1986 to 1992
surveys.
LogIncome (1994–2000)
Log (1þ average income over the period 1994–2000 (in $1,000)), from the 1994 to 2000
surveys.
LogIncome (2002–06)
Log (1þ average income over the period 2002–06 (in $1,000)), from the 2002 to 2006
surveys.
LogNetWorth
(continued)
Trust and Household Debt
29
Continued
Variable name/description
For positive net worth, LogNetWorth is defined as log(1þNet Worth), and for negative net
worth, LogNetWorth is defined as –log(1–Net Worth), where Net Worth is the 2008 family
net worth (in $K), created by summing all asset values and subtracting all debt, from the
2008 survey.
LogNetWorth1985
For positive net worth in 1985, LogNetWorth1985 is defined as log(1 þ Net Worth 1985),
and for negative net worth, LogNetWorth_1985 is defined as –log(1–Net Worth 1985),
where the Net Worth 1985 (in $K) is family net worth in 1985 created by summing all asset
values and subtracting all debt, from the 1985 survey.
Male
Equal to 1 if the respondent is male, from the 2008 survey.
Married
Equal to 1 if the respondent is married, from the 2008 survey.
MissPmt
Equal to 1 if the respondent has completely missed a payment or been at least 2 months late
in paying any of the bills in the last 5 years, from the 2008 survey.
NumChild
Number of children in the household, from the 2008 survey.
PosAttitude
I take positive attitude toward myself (1: strongly disagree, 2: disagree, 3: agree, 4: strongly
agree), from the 1980 survey.
Religion
Religion the respondent was raised:(1) Protestant, (2) Catholic, (3) Jewish, and (4) Other,
from the 1979 survey.
RiskAver
1: the respondent is willing to take a new job that will either double the income or cut it in
half with equal probabilities. 2: the respondent is willing to take a new job that will either
double the income or cut it by a third with equal probabilities, but is not willing to take the
job if it can cut the income in half. 3: the respondent is willing to take a new job that will
either double the income or cut it by 20% with equal probabilities, but is not willing to take
the job if it can cut the income by a third. 4: the respondent is not willing to take a new job
that will either double the income or cut it by 20% with equal probabilities, from the 2006
survey.
Satisfied
I am satisfied with myself (1: strongly disagree, 2: disagree, 3: agree, 4: strongly agree), from
the 1980 survey.
SavingPref
How much (in percentage) the respondent would be willing to save rather than spend if he/
she received a certain amount, from the 2006 survey.
SelfConfidence
I am able to do things as well as most other people (1: strongly disagree, 2: disagree, 3:
agree, 4: strongly agree), from the 1980 survey.
Social
Think about yourself as an adult, would you describe yourself as 1: extremely shy, 2: somewhat shy, 3: somewhat outgoing, 4: extremely outgoing, from the 1985 survey.
Trust
(continued)
30
D. Jiang and S. S. Lim
Continued
Variable name/description
Generally speaking, how often can you trust other people? (1: Never, 2: Once in a while, 3:
About half the time, 4: Most of the time, 5: Always), from the 2008 survey.
Trust-Belief*
Trust predicted by neighbors’ average trust (Trust_neighbor) based on the 2008 survey.
Trust-Culture*
Trust predicted by the average trust of all individuals sharing one’s religion (Trust_religion)
and ethnicity (Trust_ethnic), based on the 1979 and 2008 surveys.
Trust-Family*
Trust predicted by the average trust of all individuals from mother’s birth state
(Trust_momstate), based on the 1979 and 2008 survey.
Trust_ethnic
Average trust of the individuals of the same ethnicity, based on the 1979 and 2008 survey.
Trust_momstate
Trust predicted by the average trust of all individuals from mother’s birth state, based on
the 1979 and 2008 survey.
Trust_neighbor
Average trust of all other individuals (require at least five observations) residing in the same
county as the respondent, based on the 2008 survey.
Trust_religion
Average trust of the individuals that were raised in the same religion, based on the 1979 and
2008 survey.
Trust-Res
Residual trust not explained by culture, family values, or neighbor’s trust, based on the
2008 survey.