Content uploaded by Piotr Białowolski
Author content
All content in this area was uploaded by Piotr Białowolski on Jun 10, 2021
Content may be subject to copyright.
ARTICLE
Good credit, bad credit: The differential role
of the sources of debt in life satisfaction
Piotr Bialowolski
1,2
| Dorota Weziak-Bialowolska
2
1
Department of Management, WSB
University, Dąbrowa G
ornicza, Poland
2
Harvard T. H. Chan School of Public
Health, Sustainability and Health
Initiative (SHINE), Department of
Environmental Health, Boston,
Massachusetts, USA
Correspondence
Piotr Bialowolski, Sustainability and
Health Initiative (SHINE), Department of
Environmental Health, Harvard T. H.
Chan School of Public Health,
665 Huntington Avenue, G28, Boston,
MA 02115, USA.
Email: pbialowolski@hsph.harvard.edu
Funding information
Ministry of Science and Higher
Education, Poland, Grant/Award
Number: 018/RID/2018/19
Abstract
This study evaluated the short-term links between dif-
ferent forms of household debt—credit card debt, stu-
dent debt, debt from relatives, mortgage debt, car debt,
and debt arrears—and life satisfaction. To this end, a
longitudinal dataset for the US population from the
Panel Study of Income Dynamics (PSID) was used and
the propensity score difference-in-differences approach
was applied. Credit card debt and student loans nega-
tively impacted life satisfaction in the short term (up to
2 years). Mortgages and external financing for a car,
however, were found to increase life satisfaction. The
effects associated with the initial uptake and final
repayment of a loan turned out to not be
symmetrical—the end of any type of loan contract was
not related to life satisfaction. In the case of involun-
tary debt (i.e., mortgage arrears), a significant negative
impact on life satisfaction was noted when problems
emerged, while a positive effect was found when the
debts were paid off.
1|INTRODUCTION
Despite the assertion that a debt which can be repaid is not problematic (Fitch et al. 2007), regu-
lar debt has already been shown to contribute to decreased life satisfaction of household
Received: 10 March 2020 Revised: 10 February 2021 Accepted: 17 May 2021
DOI: 10.1111/joca.12388
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits
use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or
adaptations are made.
© 2021 The Authors. Journal of Consumer Affairs published by Wiley Periodicals LLC on behalf of American Council on Consumer
Interests.
J Consum Aff. 2021;1–28. wileyonlinelibrary.com/journal/joca 1
members (Białowolski et al. 2019). In the third quarter of 2020, the indebtedness of American
households stood at $14.35 trillion (Federal Reserve Bank of New York 2020) being almost
equal to their annual disposable income. Accounting for almost 69% of the total, mortgages
were the largest component of household indebtedness. The other significant contributors to
total household debt were student loans (11% of the total), car loans (9%), and credit card loans
(6%). Even though the nonmortgage debt of American households was much lower than mort-
gage debt ($3.72 trillion vs. $10.45 trillion), nonmortgage debt was much more prone to delin-
quencies. Delinquencies of 90+days were below 1% of the total value of mortgage debt.
However, for auto loans they accounted for approximately 5% of debt and for student loans
6.5% of debt; for credit card debt, almost one in 10 dollars was not repaid on time.
Increasing levels of debt and problems with debt repayment are a major reason for personal
bankruptcy (Zhu 2011). In the event of debt default and when faced with adverse financial con-
ditions, stress is likely to surface, leading to negative psychological outcomes (Bridges and Dis-
ney 2010; Selenko and Batinic 2011; Sweet et al. 2013), decreased subjective well-being
(Bialowolski et al. 2021a; Nettleton and Burrows 1998), and increased risk of depression and
anxiety (Bialowolski et al. 2021b; Burgard et al. 2012). The reasons for negative well-being out-
comes following default and credit uptake stem from individual responses to financial choices.
Credit decisions have been shown to be often misaligned with the predictions of the life-cycle
model (Bertaut et al. 2009; Shefrin and Thaler 1988) and the sole credit acquisition process was
proven to be complex and multistage (Kamleitner and Kirchler 2007) with numerous emotional
and possibly irrational decisions involved, which can translate differentially into well-being
even in the short term.
The effects related to hedonic adaptation might also play a role in the assessment of the credit–
well-being link. Two effects are expected to be in place. One is related to the purchase of a good or
service that the loan was intended for, and it is positive. After an initial boost in happiness
resulting from the purchase, however, the positive effects are expected to wane. It is expected in
the case of nondurables because they quickly disappear from the utility function, but even in the
case of durables, repeated sensory and cognitive stimuli are expected to create habituation and
thus the good's contribution to utility is likely to decline over time (Emmerling and Qari 2017;
Frederick and Loewenstein 1999). The second effect is related to the negative appraisal of credit.
Credit naturally becomes a burden even shortly after its acquisition because it requires repayment.
However, similarly to the effect related to the purchase of the good/service, the negative burden of
debt is expected to decline as the processes of hedonic adaptation starts.
Alternatively, deficiencies in consumer financial literacy might be a culprit for the negative
appraisal of credit. Many studies have already questioned whether individuals are well-
equipped to comprehend the consequences that credit decisions have on their well-being
(Białowolski et al. 2020; Loewenstein 2000). Different types of debts are linked to different inter-
est rates, posing a challenge to understanding the actual burden of credit. Households face diffi-
culties in understanding the role of interest rates, maturity, and installments (Kamleitner et
al. 2012), eventually ending up with suboptimal and sometimes harmful bundles of financial
products (Wonder et al. 2008). This lack of initial understanding of the principal features of loan
products might negatively translate into well-being, even very shortly after credit acquisition,
when the first installments become due.
Considering the limited understanding of debt among individuals and bearing in mind that
even nonproblematic debt can be a concern for well-being, our main goal was to examine the
short-term link between the acquisition of five different types of debt (credit card debt, student
debt/loans, mortgages, car loans, and family loans) and the subsequent change in life
2BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
satisfaction. The interpretation of the results accounts for the principles of the hedonic adapta-
tion model (Emmerling and Qari 2017) and takes into account the twofold effect of credit
uptake (a purchase-related positive one and a repayment-related negative one). We also con-
ducted a secondary set of analyses that investigated whether becoming free of debt favorably
influences well-being. Consequently, we examine whether the acquisition of a loan product ren-
ders qualitatively different outcomes than the final repayment of debts. Finally, knowing that
debt arrears are a serious concern, our supplementary goal was to evaluate the impact of mort-
gage arrears on well-being.
The study offers the following novel findings. First, by adding a range of available credit
choices and examining their potential links with the utility of the good/service purchased, the
study extends Kamleitner and Kirchler's (2007) theoretical model of the financial decision to
take out a loan. Second, it examines the differential role of different loan products and falling
into mortgage arrears in life satisfaction. Specifically, this study examines the influence on life
satisfaction of both acquiring and finally repaying (at the end of a set loan period) credit card
debt, student debt, mortgages, car loans, and family loans, as well as the impact of mortgage
arrears. By including numerous loan types in one study and scrutinizing the effects of credit
uptake and repayment, not only do the results allow for a comprehensive examination of
credit's impact on well-being, but they also expose a pattern of associations that might not have
been discernible if merely a single type of debt were examined.
Additionally, the study used longitudinal data, which allows the differential role of different
types of debt in life satisfaction to be more rigorously established and reduces the risk of resid-
ual confounding, which is often a concern in cross-sectional studies.
2|THEORETICAL BACKGROUND AND HYPOTHESES
2.1 |Credit, utility, and life satisfaction
Credit plays a fundamental role in the basic life-cycle theory. It is necessary to smooth con-
sumption in the periods of lower incomes and thus to maximize household lifetime utility
(Ando and Modigliani 1963; Modigliani and Brumberg 1954). The concept of utility used by
economists, however, is often vague and under-defined, since it serves mostly theoretical con-
siderations (Abdellaoui et al. 2013). A practical solution to associate utility with happiness or
life satisfaction has been already proposed and broadly accepted (Frey and Stutzer 2002; Saba-
tini and Sarracino 2017).
According to the life-cycle theory, two identical households should make the same choices
regarding consumption and credit. However, knowing that some households are credit-
constrained (Jappelli and Pagano 1989), if one household is observed to acquire credit (between
t=1andt=2), that household should experience an overall gain in utility in comparison to its
credit-constrained counterpart. Following the revealed preferences assumption, if credit uptake
were not beneficial, the household would refrain from taking on any debt and would remain more
satisfied. The situation when uptake is beneficial can be shown by the following inequality
1
:
uc
1
ðÞþβuc
2
ðÞþβ2uc
3
ðÞþ…þβN1uc
N
ðÞ<
<uc
1
ðÞþβuc
2þserv2
ðÞþβ2uc
3d3þserv3
ðÞþ…þβN1uc
N
ðÞ,ð1Þ
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 3
where βis an intertemporal discount factor, c
1
,c
2
,…,c
N
represent consumption in periods, 1, 2,
…,N—excluding the value of services provided by the good purchased on credit, d
3
,d
4
,…,d
K
(K<N) represent debt repayments of a loan obtained in t=2 (which begin at t=3), and serv
2
,
serv
3
,…, serv
L
(L<N) represent services provided by a good/service that was financed with the
loan. However, it is not always the case that following the initial credit uptake, instantaneous
utility increases in each of the successive periods, especially as values of Kand Lcan vary sub-
stantially between one purchase financed with debt and another. Specifically, even one period
after the debt was acquired in order to finance a specific expenditure, the following inequality
might not hold:
uc
3d3þserv3
ðÞ>uc
3
ðÞ:ð2Þ
This inequality is true only under the assumption that services provided by the good/service
purchased in period t=2 with a credit instrument contribute more to utility in t=3 than the
financial burden from repaying the debt decreases utility. However, one can also easily picture
a situation in which the difference uc
2þserv2
ðÞuc
2
ðÞ, representing the benefits of the good/
service purchased on credit in t=2, is large enough that the burden of debt in all successive
periods exceeds the utility provided by the good/service purchased. Thus, a valid question
would be for which types of debt is the overall gain in utility sufficiently long-lasting that in the
period following credit uptake inequality 2 holds.
Credit uptake is not a simple, straightforward decision. This decision is not only associated
with a number of available options from which the choice can be made, but also with a complex
psychological process that includes decisions about needs and financial choices, among other
things. The process leading to the financial decision of credit uptake was thoroughly described
by Kamleitner and Kirchler (2007). It comprises the following elements linked in a recursive
Decision process before credit uptake
Financing Purchase with own
funds
Needs, utility, satisfaction
Credit uptake, acquisition of a loan
corresponding to the intended purchase (credit
card, student, car, family loan, mortgage)
Regular behavior during
the payback period –
timely payments
Irregular repayment –
arrears (involuntary debt)
No purchase
FIGURE 1 A model of credit uptake and the link between a credit decision and utility and life satisfaction
4BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
way: (1) needs, utility, and satisfaction, (2) the decision process before the credit uptake,
(3) financing choice, (4) credit uptake, and (5) the behavior during the payback period.
We extended the model of Kamleitner and Kirchler (2007) by adding the scope of available
credit choices and their potential links with utility (Figure 1). We assume that once a credit
decision is made, a loan is taken and its intended use is usually closely linked to the purpose of
the initial purchase. This, in turn, may impact both the repayment behavior and ultimate utility
and satisfaction.
The links with utility are assumed to vary depending on the stage in the credit life-cycle.
It can be presupposed that the association between credit and utility at the moment of credit
uptake is positive, as the benefits of the good/service purchased with the loan are already
present but the repayment has not started yet. The association with the utility during the
repayment period depends on the combination of the smoothness of the repayment process
and the benefits provided by the good/service purchased on credit. In particular, if the repay-
mentprocessissmoothbutthereisaninsufficientstreamofbenefitsprovidedbythegood/
service purchased with the loan, it is likely that the well-being of the credit holder will suffer
from lower utility in later periods. However, if the stream of services in the future is suffi-
cient to exceed the burden of repayments, the impact on well-being (utility) can be positive.
Finally, when irregularities in servicing the debt emerge, the debtholder faces an involuntary
increase in the value of debt, which might negatively affect their well-being. Knowing all
this, in the subsequent analysis we try to capture the most instantaneous impact of a credit
decision (including its consequences) on life satisfaction—measured within 2 years after
credit acquisition.
2
2.2 |Credit card debts
There is ambiguity in the empirical assessment of the effect of credit card debt on well-being.
This stems from the public perception of creditcardsasbeingmerelyamethodofpayment,
thus facilitating the purchase of goods (Dholakia 2012), while in fact credit cards allow con-
sumers to spend more than their available resources and, as such, their use often leads to
persistent debts. The positive perception of credit cards as a method of payment is related to
the belief that they facilitate access to contemporary consumer culture, contribute to the
prestige of the cardholder, enable participation in loyalty and rebate programs, and even
stimulate savings thanks to the built-in delayed payment mechanism (Wickramasinghe and
Gurugamage 2009). The unfavorable perception of credit cards comes mostly from the conse-
quences of their overuse, leading to over-indebtedness or even bankruptcy (Godwin 1999;
Porter 2008) and consequently posing a significant threat to well-being. Although the credit
card purchasing function is clear for most users, its credit function remains hidden because
little is known whether a person utilizing the instrument actually has available resources or
intends to take on debt. Credit card debtors often demonstrate different purchasing habits
than other consumers. They spend much more online and tend to avoid cash payments (Lin
et al. 2019), but they are also likely to already be vested in credit markets with other loans
and having generally a more positive attitude toward credit (Kim and DeVaney 2001; Lin
et al. 2019).
The features of credit cards are likely to lead to the trap of overspending, particularly among
overconfident individuals who are more susceptible to risk-taking behaviors (Camerer and
Lovallo 1999; Kahneman and Tversky 1979; Xia et al. 2014). Additionally, credit card customers
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 5
more often consider themselves at risk of default at the moment of uptake than other debtors
(Sullivan et al. 1989), which might be indicative of a higher probability of strategic default
behaviors in the group (Crook and Banasik 2012). This mix of overconfidence and strategic
default translates into the myopic behavior of credit card debtors, which includes their hyper-
bolic discounting of utility (see, e.g., Ainslie 1991; Laibson 1997). This, in turn, generates retro-
spective negative appraisals of possibly short-term-driven decisions (e.g., impulsive buying
facilitated by credit card ownership). Subsequently, credit card debt reduces the customer's abil-
ity to increase their consumption in the future (Ekici and Dunn 2010) and translates into higher
levels of stress.
The market for credit cards is also often not competitive (Berlin and Mester 2004; Calem
et al. 2006), contributing further to the negative effects of credit card use on well-being. Con-
sumers are often offered high interest rates (prices), which they are inclined to accept. Espe-
cially for individuals with a lower socioeconomic status, once they acquire credit card debt,
they lose flexibility in their financial decision-making (Calem and Mester 1995). A lack of suffi-
cient resources to repay their debts in full often hinders their ability to switch credit card pro-
viders. This subsequently leads to a perception of being trapped, which is a clear justification
for why, following the acquisition of a credit card, consumers experience a decline in well-
being.
Based on the theoretical considerations and empirical findings presented above, we hypoth-
esize that credit uptake has a negative short-term effect on well-being (H1). As the purchases
financed with a credit card are mostly of a short-term nature, we presuppose that after even a
short time repayment constitutes a burden higher than the utility provided by the purchased
good/service (inequality 2 will not hold).
2.3 |Student loans
An increase in returns to higher level education have led to higher demand for education
and have translated into higher college tuition fees (Lochner and Monge-Naranjo 2016),
while at the same time increasing the financial risk associated with pursuing higher educa-
tion (Houle and Warner 2017). There is no doubt that higher tuition fees generate additional
demand for debt and translate into a significant financial burden for students and graduates.
Student loans lead to a reduced ability to accumulate wealth (Rothstein and Rouse 2011),
limit the likelihood of homeownership (Cooper and Christina Wang 2014), and bias occupa-
tional choices toward jobs that are more financially rewarding but not those best suited to
the individual (Rothstein and Rouse 2011). Even relatively small student loans have
been shown to contribute to psychological stress (Elliott and Lewis 2015), a lower likelihood
of flourishing and understanding one's purpose in life, less physical and social well-being
(Cho et al. 2015), and poorer psychological functioning—especially in early adulthood
(Walsemann et al. 2015).
It has been shown that after graduation the burden of student loans becomes less trouble-
some than the benefits of having a degree (Henager and Wilmarth 2018). However, since the
ability to repay debts is contingent on academic success and the outcome of one's higher educa-
tion is not guaranteed (Stinebrickner and Stinebrickner 2008), stress associated with mounting
debts and liquidity constraints can negatively impact well-being, especially in the short term.
Finally, theoretical and empirical evidence has shown that family resources shape college deci-
sions (Belley and Lochner 2007). Among family resources the role of parents is especially
6BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
important, since they not only can or cannot provide financial support to their offspring, but they
also influence general attitudes toward debt (Cho et al. 2015). Moreover, according to the human
capital theory (Becker 1993), parents are likely to invest their time and resources in their children;
thus, as posited in the status attainment model (Sewell and Hauser 1980), parents' socioeconomic
status, including educational attainment, is likely to be passed on to children.
Our hypothesis regarding student loans is that their uptake is associated with lower life sat-
isfaction in the short term, because the benefits from education purchased on credit (student
loans) are likely to emerge only in the long-term (H2).
2.4 |Loans from family
The largest source of informal lending is family and friends (Lee and Persson 2016). Access to
loans from these sources has been shown to be a significant economic factor in mitigating
shocks related to unexpected expenses (Loibl et al. 2017), especially for low-income families
(Loke 2016) and those who are self-employed (Davutyan and Öztürkkal 2016). Family loans,
however, depend on the breadth of social networks and the wealth of social network members
(Fan et al. 2017). Since informal and family lending is based on trust (Karaivanov and
Kessler 2013), the majority of such loans are offered without interest (Turvey et al. 2010).
Although the effect of this specific indebtedness on health and psychological well-being has
not yet been examined, the effects on social connectedness and the quality of relationships have
been theoretically considered (Lee and Persson 2016). Defaulting on such a loan should be per-
ceived as costly because it implies breaking a social contract. Defaulting individuals are exposed
to social sanctions, which should be considered a nonpecuniary cost of defaulting (Besley and
Coate 1995; Karlan et al. 2009).
Thus, two contradictory effects can be at play when considering the impact of loans from
family and friends on an individual's well-being. On the one hand, they allow for some flexibil-
ity in repayment because there is no formal contract, thus leading to less psychological stress.
On the other hand, they impose additional social sanctions, which are much more burdensome
in terms of well-being than a formal contract with a lending institution.
In the case of family loans, we expect a negative short-term evaluation of debt. As these
loans are associated with a high social cost and potential social pressure, their costs are
expected to exceed their benefits (H3).
2.5 |Car loans
In both the American and European populations, car loans are the most widespread form of
nonmortgage borrowing (Agarwal et al. 2008; The Eurosystem Household Finance and Con-
sumption Survey 2013; Xiao 2015). Car loans are commonplace among already indebted indi-
viduals (Fry 2014) and affect different populations differently (Charles et al. 2008). Demand for
car loans can be driven by need-related factors, such as limited access to public transport
(Park 1993) or family situation (e.g., multichildren families) (Choo and Mokhtarian 2004). How-
ever, it can also be driven by want-related factors, such as a desire to have a vehicle with a more
powerful engine and better performance (Mienert 2002). It was also shown that personality,
including factors like nervousness, might influence car purchase and thus translate into credit
uptake (Choo and Mokhtarian 2004). Nevertheless, car loans have been shown to be relatively
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 7
safe because of the low probability of delinquency associated with their uptake (Xiao and
Yao 2014).
Despite the ubiquity of car loans, their role in well-being has not yet been studied. Some
assumptions on this topic have been made in the literature, suggesting that because of their
short-term character, car loans are unlikely to affect consumer well-being in the long run
(Xiao 2015), or that because they are a form of closed-end credit (similar to mortgages) they
pose a different set of risks than open-end credit (Kozup and Hogarth 2008). Similarly to mort-
gages, car loans are also associated with the purchase of a good (i.e., a car) that yields substantial
utility over many periods and, as presented in Equation (2), the value of services provided in the
periodsfollowingthepurchasemightbehighenough to exceed the costs related to servicing a car
loan. Despite these arguments, no definitive conclusions have been drawn about the influence of
car loans on well-being. Finally, taking out a car loan leads to car acquisition and ownership, which
in turn may result in increased life satisfaction—an effect similar to acquiring a mortgage.
In the case of car loans, we hypothesize that the negative perception of loan acquisition is
likely to be significantly reduced by the benefits resulting from car ownership (i.e., benefits
associated with the ownership of a durable good), leading to the positive effects associated with
car ownership balancing out with the negative effect of indebtedness (H4).
2.6 |Mortgages
In all developed economies, mortgages constitute the largest part of household debt in terms of
value (Federal Reserve Bank of New York 2020). However, at the individual and local level,
there are various determinants of mortgage indebtedness. Locally, mortgage debt is driven by
the average prices of real estate and the average wages in the neighborhood; individually, condi-
tions like the number of household members—particularly children—in the household can
stimulate mortgage uptake (Moore and Stockhammer 2018).
Mortgage ownership is usually treated as a special case when analyzing the connection
between indebtedness and well-being. Principally, its impact on well-being is considered favor-
able even some time after mortgage uptake (Brown et al. 2005; Plagnol 2011). This stems from
the fact that mortgages are strictly connected to home acquisition and ownership, which may
lead to increased life satisfaction, security, self-esteem, prestige, and strengthened community
bonds (see Ren et al. 2018, for review). Consequently, as in the case of car loans, we assume that
the benefits of house/apartment ownership are sufficiently high to balance out the negative
appraisal of mortgage repayments (H5).
There is substantial empirical evidence that mortgage arrears (sometimes leading to fore-
closures) play a detrimental role in well-being (Nettleton and Burrows 1998), lead to poorer
mental health (Cannuscio et al. 2012)—for example, causing depression or anxiety (Burgard
et al. 2012)—and decreased physical health (Cannuscio et al. 2012). Repayment difficulties
are often associated with over-optimism during mortgage uptake (Dawson and Henley 2012).
This over-optimism might transpire in cases of a high loan-to-value ratio, as optimistic indi-
viduals might think that their property will only gain value. A high loan-to-value ratio might
instead trigger strategic default, and has been already shown to be linked with mortgage
repayment difficulties (Elul et al. 2010; Gerlach-Kristen and Lyons 2018). Consequently, we
hypothesize that delays in mortgage installment payments contribute to decreased subse-
quent well-being (H6).
8BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
2.7 |Repayment of debt
We hypothesize that the effects of paying back a loan yield positive outcomes in terms of life
satisfaction (H7). We also hypothesize that becoming free of mortgage arrears is favorable in
terms of life satisfaction (H8).
3|METHODS
3.1 |Data
Four recent waves (2011, 2013, 2015, and 2017) of the Panel Study of Income Dynamics (PSID)
were used in this analysis and the situation of 23,453 US households during this period was
assessed. The PSID is a biennial study that collects data on US household income, wealth, and
expenditures. It also gathers information about the employment, health, and well-being of the
heads
3
of these households (Panel Study of Income Dynamics 2019).
The sample used in the analysis on the effects of credit acquisition and mortgage arrears on
well-being was drawn from participants who were granted a new loan in the period of analysis
(or fell into arrears in their mortgage payments) and, for comparative purposes, those who did
not have and did not obtain a new loan (or did not fall into mortgage arrears) within the same
period. The analysis was conducted at the household level because information about credit
ownership and delayed mortgage repayments was available only from the household question-
naire (i.e., it was considered a household characteristic). Data on well-being and other individ-
ual control variables were collected only from a single respondent in the household (either the
head of household or spouse) and also retrieved from the main questionnaire.
Two subsequent waves linked with the head of household identifier were necessary for the
first step of data merging. Specifically, three datasets linking data from 2011 and 2013, 2013 and
2015, and 2015 and 2017 were constructed and subsequently appended to obtain a pooled
dataset (i.e., a pooled cross-sectional time series (Lebo and Weber 2015)) providing a sufficiently
large pool of longitudinal data to ensure valid inference about the influence that taking out
(and repaying) a loan (taking into account different loan types) and facing mortgage arrears
have on well-being.
3.2 |Measures
3.2.1 | Exposure assessment
To provide comprehensive insights into the impact of loan uptake and arrears on life satisfac-
tion, multiple credit instruments (including credit card debts; student, family, and car loans;
and mortgages) were examined. Table 1 presents the main sociodemographic characteristics of
households with different types of debt at the baseline of the analysis.
Credit card debt—In the PSID, the respondents reported whether they had any credit card
debt. The wording of the question was, “Aside from the debts that we have already talked
about, (like any mortgage on your main home (or/like) vehicle loans,) do you (or anyone in
your family living there) currently have any credit card or store card debt? Do not count new
debt that will be paid off this month.”
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 9
TABLE 1 Sociodemographic characteristics of individuals participating in the PSID by type of debt uptake
Sample characteristics
Credit
card
Student
loans Mortgages
Car loans/
lease
Loans from
family
Mortgage
arrears Total
Division (percentage of respondents in
each division)
New England 2.5 2.6 3.6 3.0 3.6 2.7 2.7
Middle
Atlantic
9.6 9.3 7.9 9.3 7.9 10.1 9.9
East North
Central
16.8 17.9 14.4 16.2 14.4 15.3 16.6
West North
Central
6.9 6.3 6.8 7.9 6.8 5.3 7.9
South Atlantic 25.3 26.5 25.5 26.0 25.5 27.5 25.4
East South
Central
8.3 9.9 9.3 8.2 9.3 15.3 8.7
West South
Central
10.7 12.1 12.1 11.8 12.1 7.4 10.5
Mountain 5.7 4.4 7.2 5.5 7.2 5.3 5.2
Pacific 14.1 11.2 13.2 12.2 13.2 11.1 13.0
Age (percentage of respondents in each
age group)
<25 7.3 12.7 6.4 6.6 6.4 1.6 6.6
25–34 27.9 34.2 39.8 28.4 39.8 16.8 25.3
35–44 23.2 21.4 22.9 22.5 22.9 30.0 19.4
45–54 16.9 20.2 12.9 19.0 12.9 29.0 18.1
55–64 15.6 8.4 10.5 15.6 10.5 15.8 17.4
65+9.1 3.1 7.5 7.8 7.5 6.8 13.2
Number of household members 2.59 2.82 2.62 2.69 2.63 2.91 2.51
Gender (% male) 69.0 66.0 78.4 74.0 70.2 67.4 68.8
Income 53,714 48,955 63,562 62,351 53,369 46,990 54,660
Savings (% yes) 71.0 59.9 77.2 73.7 69.5 63.7 67.2
10 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
TABLE 1 (Continued)
Sample characteristics
Credit
card
Student
loans Mortgages
Car loans/
lease
Loans from
family
Mortgage
arrears Total
Labor market status Employed 75.7 76.0 79.4 78.5 79.4 77.4 68.3
Unemployed 6.1 9.0 6.7 6.2 6.7 6.8 8.5
Inactive 18.1 15.0 13.9 15.4 13.9 15.8 23.2
Alcohol consumption (times per week) 2.0 2.0 2.3 2.2 2.6 1.9 2.1
Smoking (% yes) 19.7 22.4 18.5 18.2 28.2 23.2 20.3
Body mass index 30.1 30.0 29.1 29.7 29.5 31.2 29.9
Chronic illness (% yes) 12.4 9.6 10.1 10.9 19.7 13.7 12.4
N1990 1137 1102 3608 188 190 27,018
Abbreviation: PSID, Panel Study of Income Dynamics.
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 11
Student loans—Student loans were measured using responses to the following question:
“Do you (or anyone in your family living there) currently have any other debts such as student
loans, medical or legal bills, or loans from relatives?”Since this was a multiple response ques-
tion, responses indicating student loan ownership were used.
Loans from relatives—In order to address whether a person is carrying debt owed to family
members, the following question was asked: “Do you (or anyone in your family living there)
currently have any other debts such as student loans, medical or legal bills, or loans from rela-
tives?”This was a multiple response question, so responses indicating ownership of loans from
relatives were used to construct this variable.
Car loan or car lease—The following question capturing the source of financing for vehicles
was used to determine the presence of any car loans: “For your (first/second/third vehicle/
[DESCRIPTION OF CAR]), did you buy it, lease it, receive it as a gift, or what?”Only house-
holds that reported a loan/lease on at least one of their vehicles were considered indebted for
the purpose of purchasing a car.
Mortgage—Mortgage debt on the main residence was captured by the question, “Do you
have a mortgage or loan on this property (main residence)?”
Mortgage arrears—In the PSID, the respondents were asked the following question: “Some
people have had difficulties in making their mortgage or loan payments. Are you (or anyone in
your family living there) currently behind on your mortgage payments?”Since this was a multi-
ple response question, in order to distinguish between the number of possible mortgages only
responses related to the first mortgage were used.
3.2.2 | Outcome variable
A single outcome was considered for the study—life satisfaction. Life satisfaction refers to how
people evaluate their lives and is the main component and indicator of subjective well-being
(Batz-Barbarich et al. 2018; Diener 2000). It is also argued to reflect eudemonic sentiment
(National Research Council 2013).
Life satisfaction was assessed using a single question: “Please think about your life as a
whole. How satisfied are you with it? Are you…”The response categories were positioned on a -
5-point Likert scale with the following responses: 5—completely satisfied, 4—very satisfied, 3—
somewhat satisfied, 2—not very satisfied, 1—not at all satisfied.
3.2.3 | Control variables
A rich set of controls, which have already been proven significant for life satisfaction and bor-
rowing behaviors, was used. All of the controls included in the analysis were measured before
treatment, that is, before taking out a particular type of loan or being faced with mortgage
arrears in the case of credit uptake, and before paying back the loan in the case of becoming
free of credit or mortgage arrears. Such an approach was adopted to avoid endogeneity issues
(Gebel and Voßemer 2014).
Specifically, in both approaches the set of control variables included sociodemographic vari-
ables (gender, age, number of people in the household, and place of residence—which was asso-
ciated with division
4
), health status as measured by the presence of a chronic illness, body mass
index (BMI), smoking, and alcohol consumption. Chronic illness, BMI, smoking, and alcohol
12 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
consumption all increase the chance of a health shock, which is likely to impact life satisfaction
as well as to change the consumption patterns by increasing coping strategies, especially
borrowing (Babiarz et al. 2013; Kim et al. 2012). The analysis also included markers of economic
situation, such as per capita household incomes (after log transformation), possession of
savings, and labor market status, as they have been shown to moderate impact of debt on well-
being (Xiao et al. 2021). Finally, each analysis controlled for the baseline wave in each cross-
section.
Beyond this standard set of control variables used to balance the sample of households that
were granted a given debt (or experienced arrears), we used variables that were found in previ-
ous studies to be instrumental in shaping demand for specific types of debts. In the case of
credit cards, as suggested by Lin et al. (2019), the number of other loan types in the household
portfolio and the presence of a mortgage were used in the analysis. We also controlled for mort-
gage arrears, as arrears were identified as a factor which positively relates to a positive outstand-
ing balance on credit cards (Kim and DeVaney 2001).
In the case of student loans, the standard set of covariates was expanded with the educa-
tional status of the head of household's parents (Cho et al. 2015). An additional analysis on car
loans was conducted, controlling for commuting time (after log transformation), nervousness
(Choo and Mokhtarian 2004), and household size and composition (the number of children and
the age of the youngest child; Peters et al. 2015). Informal and family loans were additionally
controlled for self-employment status, geographical mobility of the head of household, help
received from relatives, and the wealth of the parents who are the most important part of the
social network (Davutyan and Öztürkkal 2016; Fan et al. 2017; Turvey and Kong 2010). Mort-
gage debt was additionally controlled for variables related to housing needs, which were proxied
by the number of children, the age of the youngest child, and the presence of people from out-
side the household in the current place of residence, as well as for the affordability of housing,
which was approximated by the average price of an apartment per bedroom in the state of resi-
dence (Moore and Stockhammer 2018). Finally, for mortgage arrears, the initial set of controls
was broadened by including variables related to the duration of unemployment of the head of
household in the year preceding the survey (May and Tudela 2005) and the loan-to-value ratio
for the outstanding mortgage (Elul et al. 2010).
3.2.4 | Treatment and control groups
The treatment for credit uptake was defined as an initial exposure to credit (i.e., credit uptake)
or the first exposure to mortgage arrears. This implies that exposure occurred when the value of
debt in a given credit category or of mortgage arrears went from zero to a positive value. Conse-
quently, six different treatment groups were defined, corresponding to six groups of respondents
subject to treatment, that is, for whom the value of debt in a given credit category or of mort-
gage arrears went from zero to a positive value between the previous wave (Wave 1) and the
subsequent wave (Wave 2) of the PSID. Additionally, six control groups were defined. They
comprised individuals (heads of household) who had no loans (or mortgage arrears) in a given
credit category over the 2-year period of the study. Consequently, 1,910 individuals who experi-
enced a change from zero to a positive debt on their credit card were identified, 1,137 who were
granted a student loan, 188 who became indebted to their family or friends, as well as 3,608
who purchased a car financed by a car loan or a lease. Additionally, there were 1,102 individ-
uals who took out mortgages and 190 who fell behind with their mortgage debt. Their
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 13
representative control groups were significantly larger in all cases, amounting to 8,074 people
in the case of car loans or leases and 17,135 in the case of family debt (the least and the most
numerous groups).
We mirrored the above conditions to define the treatment and control groups for paying
back a loan. Specifically, treatment was defined as a total credit repayment or mortgage arrears
repayment, and it occurred when the value of debt in a given credit category or of mortgage
arrears went from a positive value down to zero. Again, six different treatment groups were
defined for whom the value of debt in a given credit category or regarding mortgage arrears
went from a positive value to zero between Waves 1 and 2. Additionally, six control groups were
defined. They comprised individuals (heads of household) for whom mortgage arrears/loans in
a given credit category dissolved over the 2-year period of study. Consequently, 2,414 persons
experienced a change from a positive debt on their credit card to zero between Waves 1 and 2,
1,370 entirely repaid their student loans, 279 repaid their family loan and 2,969 paid back their
entire car loan or lease. There were also 1,027 individuals who paid off their mortgages and
307 who paid off their overdue mortgage installments. Their representative control groups were
significantly larger in all cases, amounting to 8,074 people in the case of car loans or leases and
17,135 in the case of family debt (the least and the most numerous groups).
3.3 |Statistical methods
Longitudinal data from the PSID enabled the utilization of the combined strengths of two
approaches designed for the evaluation of causal inference: (1) propensity score matching
(PSM) and (2) the difference-in-differences (DID) method (Caliendo and Kopeinig 2008;
Heckman et al. 1997). The former is designed to account for selection bias in the data. By condi-
tioning on a number of control variables, it accounts for the possibility of different links
between the exposure and the outcome among households with different sets of characteristics.
The latter—DID—allows researchers to establish the effect of treatment (exposure) by compar-
ing the evolution of the outcome in the treatment and control groups. The strategy was
previously described by Austin (2011b, 2011c).
The basic idea behind PSM–DID is to recreate the conditions of the experimental study
design. This is achieved by identifying pairs (or groups) of households that are closely matched
according to their baseline characteristics (Caliendo and Kopeinig 2008). The approach (PSM–
DID) has certain advantages over traditional multivariate regression. First, it is resilient to the
situation where households in the treatment and control groups are very different in terms of
their baseline characteristics (Benedetto et al. 2018). Households in the treatment group without
a good corresponding match in the control group are excluded from the analysis. Second, multi-
variate linear regression relies on the assumption of a linear relationship between the baseline
characteristics and the outcome; in PSM, this assumption is not necessary. Third, the PSM–DID
approach provides results that can be interpreted in a counterfactual way, that is, it estimates
the magnitude of effect that describes the change in outcome observed among those treated
(conditional on being in the treatment group). Despite its strengths, PSM–DID has also some
limitations. As any other nonexperimental method, it cannot account for variables that have
not been measured or observed. Additionally, if there is a large group of households without a
close match in terms of propensity score (i.e., the propensity score is very different between the
treatment and control groups), a significant proportion of observations will be excluded from
the analysis, limiting the sample size (Andrade 2017).
14 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
The psmatch2 function (Leuven and Sianesi 2003) in Stata 15 was used in this study to
implement the approach. The algorithm employed in the study utilized caliper matching, with
the calipers ranging between 0.002 and 0.037. Such caliper values were a consequence of the
recommendation provided by Austin (2011a) and based on extensive simulations which showed
that calipers with a width of 0.2 of the standard deviation of the logit of the propensity score
should be used.
For each of the five examined credit types and for mortgage arrears, separate propensity
scores were obtained from a probit regression model using with the set of control variables
detailed in Table 1 and a dedicated set of additional, credit-specific control variables. In the final
prediction equation for the propensity scores, all control variables—regardless of their
significance—were retained, following the suggestions of Rubin and Thomas (1996). Conse-
quently, six probit regression models were estimated in order to establish the probability of
being included in a treatment group associated with the acquisition of a particular type of credit
or mortgage arrears. Six probit regression models were also estimated for the inverse case—total
repayment of a loan or repayment of mortgage arrears.
Based on propensity score similarity and using caliper matching, individuals from the con-
trol and treatment groups were linked. This strategy facilitated obtaining a balance between the
treatment and control groups with respect to the observed characteristics. Covariate balancing
between the treatment and control groups was verified using a likelihood ratio (LR) test, which
allows an evaluation of the similarity between the two groups before and after matching.
Changes in life satisfaction in the groups of those who acquired (or repaid) specific types of
loans (treated) and their matched counterparts without debt over the 2-year period (control)
were subsequently used to obtain the average treatment effect on the treated (ATT). The ATT
in our study corresponded to either the impact on life satisfaction of the acquisition of a specific
type of debt or facing mortgage arrears or the effect on life satisfaction of the repayment of a
specific type of debt or mortgage arrears. To reliably assess the significance of the ATT, boo-
tstrapping was used, with 500 draws to estimate 95% bootstrap confidence intervals.
Following the recommendation of Austin (2011b), a sensitivity analysis was conducted. We
tested outcomes of the procedure using the caliper algorithm with modified calipers: calipers
ranging between 50 and 150% of the recommended value were used. The results (available upon
request) were robust to these modifications (the significance levels were close to those obtained
in the baseline specifications).
4|RESULTS
4.1 |Covariate balancing
Despite substantial differences according to the control variables before matching, participants
from the treatment and control groups did not differ considerably after matching in any of the
six analyses performed for debt acquisition or in any of the six analyses related to total debt
repayment (Figure 2). Acceptable matching was confirmed by the LR test. Although the LR
chi-squared statistic showed high values for unmatched samples according to credit uptake,
yielding p=0.000 in the case of each of the five credit types and mortgage arrears, for matched
samples the LR statistic was sufficiently small to yield p> 0.05. Exactly the same conclusion
was formulated for the analysis on paying back loans, for which the LR statistic for matched
samples was also not statistically significant.
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 15
This implied that although a substantial dissimilarity between unmatched samples was
recorded for credit uptake and for total loan repayment, the PSM allowed all differences
between the treatment and control groups to be accounted for. This further meant that the mat-
ched treatment and control groups could be reliably compared (through the DID estimator)
FIGURE 2 Average standardized bias (in %) for particular variables and the bias reduction achieved by the
matching algorithm for credit uptake (1) Dots represent original bias and crosses represent bias after propensity
score matching. (2) Variables used at baseline in all models: savings—possession of savings; income(ln)—natural
logarithm of household income per capita in USD; NE div—New England Division; MA div—Middle Atlantic
Division; ENC div—East North Central Division; WNC div—West North Central division; SA div—South
Atlantic Division; ESC div—East South Central Division; WSC div—West South Central Division; Mountain
div—Mountain Division (divisions according to the US administrative classification; reference category: Pacific
Division); age <24—up to 24 years old, age 25-34—25 to 34 years of age; age 35-44—35 to 44 years of age; age
55-64—55-64 years of age; age 65+—65 years old or more (age groups; reference group: age 44–54); hh size—
number of people in the household; sex—gender; unemployed—head of household currently unemployed;
inactive—head of household currently inactive on the job market; chronic—head of household suffering from a
chronic illness; alcohol freq—frequency of alcohol consumption; BMI—body mass index; smoke—smoker;
2013—dummy variable for 2013 baseline; 2015—dummy variable for 2015 baseline; other debt—presence of
other debts (beyond credit cards but excluding mortgages); mortgage—mortgage debt; debt behind—household
is in arrears on mortgage; father edu—education level of head of household's father; mother edu—education
level of head of household's mother; children(no.)—number of children in the household; years to adult—
number of years until the youngest child reaches maturity; add. People—number of people living in the same
house that are not members of the household; avgpr(ln)—natural logarithm of apartment prices in the
respondent's state; commute(ln)—logarithm of time required to commute; nervous—head of household declared
being nervous in the past 30 days; self-employed—binary variable representing self-employment; move state—
head of household lives in a different state than where they grew up; move region—lives in a different region
than where they grew up; help from rel—head of household receives help from relatives; poor parents—head of
household's parents are poor; unempl(mo.)—number of months the head of household was unemployed in the
previous year; ltv—loan-to-value ratio for the mortgage
16 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
with respect to change in life satisfaction to evaluate the impact of credit types and mortgage
arrears on life satisfaction. In analyzing the five types of loans and mortgage arrears, no more
than one observation per model was outside the common support range and therefore excluded
from analysis, which implies the exclusion of less than 0.01% of the total number of
observations.
With respect to the models evaluating the impact on life satisfaction of taking on credit card
debt, a mortgage, or a car loan, the most significant differences between the treatment and con-
trol groups were observed in savings, income, and labor market status. In the case of student
loans, the principal sources of bias were age, labor market status, number of household mem-
bers, education level of parents, and income level. A slightly different situation was observed in
the case of family loans, for which the source of bias was also related to age, but lifestyle factors
and health issues additionally accounted for the discrepancies between the treatment and con-
trol groups. The most important differentiating factor, however, was related to the support pro-
vided by relatives. The frequency of alcohol consumption, smoking, and chronic illness also
differed between the treatment and control groups in the study of family loans. Finally, the dif-
ferences between those who have issues with mortgage repayments and their counterparts
without such issues pertained to household composition (number of persons), age, and the
loan-to-value ratio. Similar patterns were observed for paying back loans (detailed results are
available upon request).
4.2 |Impact evaluation
We found that there were significant effects on subsequent life satisfaction from the acquisition
of four types of loans: credit card, student loan, car loan/car lease, and mortgage (Table 2). Tak-
ing out a student loan and getting into credit card debt were found to negatively affect life satis-
faction, while acquiring mortgages and car loans had a favorable effect on life satisfaction.
Additionally, a significant negative effect from mortgage arrears was also found, but not from
debt from family members.
Our results also clearly indicate that, depending on the type of debt, different impacts of credit
uptake on life satisfaction can be expected. Acquiring credit card debt led to a decrease of 0.063
points on the 5-point scale of overall life satisfaction in comparison with individuals with the same
characteristics as defined by the control variables who did not acquire credit card debt. Student debt
carried a burden similar to that of credit card debt. On average, a decline of 0.068 points (on a
5-point scale) in overall life satisfaction was experienced over the 2-year period after the acquisition
of a student loan, in comparison to those who did not receive a student loan. Following a mortgage
uptake, people were likely to report an improvement in their life satisfaction of 0.095 points (on a
5-point scale) compared to their counterparts with the same sociodemographic characteristics and
in similar health, labor, and financial situations but no mortgage. Similarly, a positive effect of a
smaller magnitude was noted for car loans. Acquiring a car loan or a lease for a vehicle was associ-
ated with a subsequent improvement in life satisfaction of 0.037 points.
Regarding total debt repayment, not a single credit exposure was found to be related to life
satisfaction at the end of the loan contract. Repaying regular debts, irrespective of their initial
impact on life satisfaction, proved to yield no significant change in life satisfaction.
People who experienced mortgage arrears were found to report a decrease in life satisfaction
by 0.21 points (on a 5-point scale) following the onset of difficulties with mortgage repayment.
This was the largest observed impact in terms of life satisfaction, which indicates the substantial
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 17
adverse consequences of involuntary debt. However, eliminating mortgage arrears from the
household finances was found to yield a significant improvement in life satisfaction—0.123
points on the life satisfaction scale.
5|DISCUSSION
With the constantly growing indebtedness of US households, there is an increasing interest in
studying its effects on human well-being. Special concerns have been formulated with respect
to constantly increasing levels of student loans and car loans, as well as transitions into serious
delinquency for credit card accounts (Federal Reserve Bank of New York 2019). By examining
the short-term impacts of indebtedness with the use of data from a large survey on the income
of US adults, this study adds to the literature providing evidence that the role of debt in life sat-
isfaction can depend on the source/type of debt and, consequently, on the kind of product or
service purchased. Falling into mortgage arrears and taking on certain types of debt (i.e., credit
card debt and student loans) may unfavorably impact life satisfaction (which confirms research
Hypotheses H1 and H2, respectively). This study also shows that the acquisition of a car loan or
a mortgage have a positive effect on life satisfaction (which provides support for research
Hypotheses H4 and H5), while the effect of a family loan is inconclusive (which does not sup-
port research Hypothesis H3). This provides some evidence in favor of the diverse effects of
credit uptake on life satisfaction, even in the short-term. In particular, our findings show that
the effects of credit taken with an intention to purchase a durable good may be positive in the
short-term despite the burden of the loan repayment. However, the effect of other types of
credit is rather negative.
This study also clearly shows that repayment of a debt (regardless of its type) does not
change the level of life satisfaction, which does not confirm Hypothesis H7. This, in turn,
TABLE 2 Estimates of the ATT and 95% bootstrap confidence intervals
ATT SE Z p-value
95% bootstrap
confidence interval
Credit card debt Uptake 0.063 0.021 2.94 0.003 (0.104; 0.021)
Final repayment 0.021 0.020 1.08 0.282 (0.018; 0.060)
Student loans Uptake 0.068 0.029 2.34 0.019 (0.124; 0.011)
Final repayment 0.015 0.029 0.53 0.597 (0.072; 0.041)
Car loan/Car lease Uptake 0.037 0.018 2.02 0.043 (0.001; 0.073)
Final repayment 0.013 0.018 0.73 0.466 (0.050; 0.023)
Loans from family Uptake 0.044 0.066 0.66 0.508 (0.174; 0.086)
Final repayment 0.092 0.055 1.69 0.091 (0.015; 0.199)
Mortgage Uptake 0.095 0.029 3.31 0.001 (0.039; 0.151)
Final repayment 0.013 0.028 0.47 0.636 (0.041; 0.067)
Mortgage arrears Occurrence 0.210 0.075 2.79 0.005 (0.358; 0.063)
Repayment 0.119 0.060 1.98 0.047 (0.001; 0.237)
Note: Life satisfaction is measured on a 5-point Likert scale (5—completely satisfied, 4—very satisfied, 3—somewhat satisfied,
2—not very satisfied, 1—not at all satisfied).
Abbreviation: ATT, average treatment effect on the treated.
18 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
implies that the effects of credit uptake and credit repayment are not symmetrical. The effect
can be linked to the hedonic adaptation process, according to which the effects of both positive
and negative stimuli on general well-being tend to dissolve over time (Emmerling and
Qari 2017; Frederick and Loewenstein 1999).
The evidence from this study indicates that the repayment of mortgage arrears positively
contributes to life satisfaction, which confirms Hypothesis H8, regarding arrears. This contribu-
tion, however, is lower than the reduction in life satisfaction due to the emergence of mortgage
arrears, which provides further evidence that the effects of an emerging and disappearing finan-
cial burden are not symmetrical. Regarding estimated effects, they were found to be the largest
for mortgage arrears—a decrease of 0.210 in life satisfaction on a 5-point scale (which confirms
H6) in the case of emerging arrears and an increase of 0.123 in life satisfaction in the case of
becoming free of arrears (confirming H8).
This study corroborated the findings of Parker et al. (2012), indicating that borrowing can
be beneficial to well-being in some cases, and detrimental in others. As far as credit types are
concerned, our study showed that the largest significant negative effect was observed for indi-
viduals exposed to credit card debt and student loans. Although earlier studies have shown that
credit cards may be perceived as life facilitators and can be used as a security blanket to cover
unexpected expenditures (Bernthal et al. 2005), our study suggests that drawing on their debt
function contributes negatively to subsequent life satisfaction. Our findings are congruent with
the arguments that credit card debt may lead to a metaphorical debtor's prison, where life
choices following credit decisions become significantly limited because of mounting debt
(Bernthal et al. 2005) and limited access to debt refinancing options (Kerr and Dunn 2008). Our
findings on the negative impact of credit cards on life satisfaction are also in agreement with
the reasoning of Bridges and Disney (2004), who claimed that if they are not liquidity-con-
strained, people manage the problem of arrears by revolving their debts. If credit cards are used
for this purpose, credit card debt is likely to exert psychological pressure.
Our findings suggest that despite their increasing importance to consumers and especially
increasing penetration rates among low-income households (Barba and Pivetti 2009; Di Giulio and
Milani 2013), credit cards should be treated with caution. Previous research has shown that finan-
cially disadvantaged individuals and households are much more vulnerable in relation to credit
card providers. Consequently, they tend to accept credit card offers more easily, and their demand
for credit card debt is much less dependent on interest rate changes (De Lucinda and Vieira 2014;
Qi and Yang 2003), leading them to be more susceptible to swings in well-being.
Our findings regarding the negative effect of taking out a student loan on life satisfaction
corroborate previous research. Specifically, it has been shown that the cumulative amount of
student loans borrowed over the course of schooling, together with lengthening repayment
periods and higher levels of debt, associated with an increased probability of delinquency
(Brown et al. 2014), affect psychological functioning (Walsemann et al. 2015), contributing to
increased anxiety, problems with sleep, and a lack of social connectedness (Cooke et al. 2004).
Our study adds that even the sole action of taking on a student loan may negatively affect psy-
chological well-being by significantly decreasing life satisfaction, while repayment of such a
debt does not contribute to increased life satisfaction.
Since the volume of student loans in the United States has been constantly growing (Federal
Reserve Bank of New York 2019) and student loans remain a particularly difficult type of debt
to discharge—as current regulations prohibit those obligations from being discharged even in
the case of bankruptcy (Elliott and Lewis 2015)—our findings match the intuition of the signifi-
cant negative impact of student loans on well-being. Nevertheless, our findings disagree with
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 19
those of Zhang and Kemp (2009), who did not find associations between more student debt and
personal happiness. They are also slightly at odds with the recommendations of Akers and
Chingos (2014), who argue that “broad-based policies aimed at all student borrowers, either
past or current, are likely to be unnecessary and wasteful given the lack of evidence of wide-
spread financial hardship.”
Our findings on the favorable impact of taking out a mortgage or car loan on life satisfaction point
to the possibly positive role of these types of credit. The long-term nature of mortgages and the pres-
ence of collateral, which often exceeds the value of debt, apparently mitigate the negative impact of
having such a debt in the face of the positive impact of home ownership. As posited by Thaler and
Shefrin (1981), short-term debt is likely driven by a lack of self-control and impatience, which often
translates into decisions that lead to negative well-being outcomes. Our study shows that these forces
do not necessarily influence long-term commitments, such as the purchase of a principal property or
a car. It can even be argued that mortgages, as they limit access to further short-term debts, improve
utility and satisfaction by reducing the risks of excessive short-term debts.
Our findings on the positive effect of taking out a mortgage are congruent with the reason-
ing that since mortgages are usually granted to the most affluent households with high incomes
and good credit histories (Białowolski 2017), the probability of their negative impact on well-
being is reduced. Additionally, since previous research has shown that mortgage debt leads to
much lower distress among households in countries where a significant percentage of house-
holds carry mortgage debt (Georgarakos et al. 2010), our findings concerning the United
States—a market with significant penetration rates, seem to corroborate this reasoning. Even in
light of this, substantial shocks can keep well-being levels untouched, since—due to their long-
term nature—they do not usually lead to significant fluctuations in repayment obligations
(Białowolski and Węziak-Białowolska 2017). The situation in the United States is also unique,
as mostly fixed-rate mortgages are marketed and sold. As argued by Campbell and Cocco (2015),
households with fixed-rate mortgages are much less exposed to adverse income shocks. As the
economic environment was very favorable for homeowners over the period of investigation,
and most of them experienced a positive evolution of their financial and economic situation,
those households which had a fixed-rate mortgage should have particularly benefitted, which
further substantiates our findings.
In the case of car loans, the favorable effect of credit on life satisfaction implies that the ben-
efits of car ownership compensate for the burden of credit repayment despite the substantially
faster depreciation of car values compared to home values.
Our results indicating that difficulties with mortgage repayments lead to decreased life satis-
faction corroborate the findings of Burgard et al. (2012) and Nettleton and Burrows (1998), who
showed that unsupportable debt may increase the incidence of anxiety attacks and depression.
Reduced life satisfaction levels following the onset of mortgage arrears might also be triggered
by negative events following delays, such as foreclosures, which are especially detrimental to
well-being (Nettleton and Burrows 1998). However, it is also worth noting that repayment of
mortgage arrears can favorably contribute to life satisfaction, while no such effect was found for
any of the credit types examined in this study.
Our findings indicating no effect from family loans on life satisfaction provide novel insights
into the field, since the effects of acquiring and maintaining this type of credit on health and
psychological well-being had not yet been examined. This lack of effect may result from the fact
that the expected negative effect of this type of credit uptake is offset by the favorable conditions
granted, such as not having to pay interest.
20 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
Despite certain strengths, this study has some limitations. First, our study used observa-
tional and self-reported data. However, the longitudinal nature of the PSID provides some reas-
surance that the findings are not entirely subject to reporting bias. Second, only one well-being
outcome was used in the analysis. It would be beneficial to use a wider set of indicators for the
assessment of credit uptake on subjective well-being in future studies. Third, the analysis did
not account for the value of the loan taken, since the focus was on the effect of credit uptake.
Replication of the results by controlling for the level of indebtedness should be considered in
future analyses. Finally, only heads of household were included in the analysis. However, these
were the only respondents for whom data on well-being was collected in the PSID. Neverthe-
less, future studies should examine the impact on other family members.
These limitations are balanced by the strengths of our study. First, this study not only
examined the role that the uptake of a specific type of credit may play in well-being, but it
also contrasts it with the effect of credit repayment. Second, by examining five different
types of credit in addition to mortgage arrears, this study provides a comprehensive exami-
nation of the impacts on life satisfaction of the decision to take on credit and to repay credit
or arrears. Third, the longitudinal design and imitation of the conditions of a randomized
control trial allowed us to establish a temporal relationship and to control for unmeasured
confounding.
5.1 |Policy recommendations
5.1.1 | Guide consumers into making less myopic credit choices
Our results suggest that people are often myopic and if they use credit for short-term expenses,
they are likely to experience a decline in well-being. We showed that credit card debt, which
often results from a myopic decision, should be targeted by policy with the objective of limiting
the use of such debts, as they have a negative impact on life satisfaction. Policy actions could
aim at drawing the attention of potential debtors to such adverse short-term effects.
5.1.2 | Devise a system that considers the costs and benefits of student debt
Our analysis clearly shows that student loans are potentially detrimental to well-being, especially in
the short-term, and thus should be closely monitored. Student debt in the US has more than dou-
bled over the past decade (Federal Reserve Bank of New York 2020), which has additionally
increased the already high risks associated with pursuing a college degree (Houle and Warner 2017).
American universities have increased tuition fees, which not only leads to a decline in graduates'
ability to accumulate wealth, as Rothstein and Rouse (2011) suggested, but also creates the percep-
tion of overwhelming debt that can be hard to repay. Linking the repayment of student loans to
individual benefits related to higher education could ease the burden for those who are anxious
about their employment prospects. Additionally, as graduates with debt are more likely to search
for more financially rewarding jobs instead of jobs linked to their competences (Rothstein and
Rouse 2011), linking repayment with the levels of individual benefits achieved thanks to higher
education could reduce the levels of stress associated with repayment and lead to more rewarding
careers.
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 21
5.1.3 | Cautiously reduce barriers in access to mortgages and car loans
Both mortgages and car loans turned out to be positively linked with life satisfaction in the
short-term. As mentioned, the impact can be attributed to the ownership of a good
(a house/apartment or a car) that exceeds the costs of the debt. However, recent data show
that access to these types of credit instruments is becoming ever more limited to only those
with the highest credit scores (Federal Reserve Bank of New York 2020). A lack of access for
the general population might imply that the potential benefits from acquiring these prod-
ucts are not available to those most in need of credit. Nevertheless, our analysis identified a sub-
stantial impact of delays in mortgage repayment on well-being. This indicates that especially
involuntary debt bears considerable emotional cost; therefore, access to debt—even in its potentially
beneficial forms—should not be unconditional. As soon as households lose their ability to service
debt regularly, they become exposed to the highly stressful debt collection practices of financial
institutions. The introduction of policy provisions that allow more flexible repayment schemes
might improve well-being. Despite the valid case of financial institutions reclaiming their funds in
the case of nonrepayment, stiff rules and a lack of flexibility push households into arrears, delin-
quency, and even foreclosure after relatively short periods of nonrepayment. Bearing in mind that
the current market interest rates are extremely low—close to zero, in fact—extending the repay-
ment of debts would cost a financial institution very little and would be associated with relatively
low additional risk.
5.1.4 | Promote financial literacy
The promotion and provision of financial literacy education should be considered an important
tool for increasing well-being at the onset of a credit decision. Financial literacy has been
already proven to facilitate a better selection of credit products (Fornero et al. 2011) and
decrease the cost of debt (Bialowolski et al. 2020; Gathergood and Weber 2017). Thus, it can
translate into higher satisfaction with credit and consequently with life in general.
ACKNOWLEDGMENTS
The research was supported by the project funded under the program of the Ministry of Science
and Higher Education, Poland titled “Regional Initiative of Excellence”in 2019–2022, project
number 018/RID/2018/19.
ORCID
Piotr Bialowolski https://orcid.org/0000-0003-4102-0107
Dorota Weziak-Bialowolska https://orcid.org/0000-0003-2711-2283
ENDNOTES
1
Under the life-cycle theory, household that decides to take a credit makes a rational, utility-maximizing decision,
which means that this decision was voluntary and, if it was not utility improving, it would not have been made.
2
The 2-year period ensures relatively little confounding of the hedonic adaptation process in the case of durables
(based on the formula provided by Emmerling and Qari (2017, p. 36) and our own calculations, only about 14%
of the effect of a car purchase on life satisfaction is expected to wane over a 2-year period due to the process of
hedonic adaptation).
22 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
3
The study defines the husband in a married couple to be the head of household as long as he is not physically
or mentally incapacitated. In single-person households, the head of household can be also a single female
(McGonagle et al. 2008).
4
Each of the nine divisions in the United States contains between three and nine states: New England Division,
Middle Atlantic Division, East North Central Division, West North Central Division, South Atlantic
Division, East South Central Division, West South Central Division, Mountain Division, and Pacific Division.
REFERENCES
Abdellaoui, M., Bleichrodt, H., L'Haridon, O. & Paraschiv, C. (2013) Is there one unifying concept of utility? An
experimental comparison of utility under risk and utility over time. Management Science, 59(9), 2153–2169.
https://doi.org/10.1287/mnsc.1120.1690
Agarwal, S., Ambrose, B.W. & Chomsisengphet, S. (2008) Determinants of automobile loan default and prepay-
ment. Economic Perspectives, 32(3), 1689–1699. https://doi.org/10.1017/CBO9781107415324.004
Ainslie, G. (1991) Derivation of ‘rational’economic behavior from hyperbolic discount curves. The American
Economic Review, 81(2), 332–340 http://www.jstor.org/stable/2006881
Akers, B. & Chingos, M.M. (2014) Is a student loan crisis on the horizon? Washington, DC: The Brookings
Institution.
Ando, A. & Modigliani, F. (1963) The “life cycle”hypothesis of saving: aggregate implications and tests. The
American Economic Review, 53(1), 55–84.
Andrade, C. (2017) Propensity score matching in nonrandomized studies: a concept simply explained using anti-
depressant treatment during pregnancy as an example. Journal of Clinical Psychiatry, 78(2), e162–e165.
https://doi.org/10.4088/JCP.17f11446
Austin, P.C. (2011a) Optimal caliper widths for propensity-score matching when estimating differences in means
and differences in proportions in observational studies. Pharmaceutical Statistics, 10(2), 150–161. https://doi.
org/10.1002/pst.433
Austin, P.C. (2011b) A tutorial and case study in propensity score analysis: an application to estimating the effect
of in-hospital smoking cessation counseling on mortality. Multivariate Behavioral Research, 46(1), 119–151.
Austin, P.C. (2011c) An introduction to propensity score Methods for reducing the effects of confounding in
observational studies. Multivariate Behavioral Research, 46(3), 399–424.
Babiarz, P., Widdows, R. & Yilmazer, T. (2013) Borrowing to cope with adverse health events: liquidity con-
straints, insurance coverage, and unsecured debt. Health Economics, 22, 1177–1198. https://doi.org/10.1002/
hec.2877
Barba, A. & Pivetti, M. (2009) Rising household debt: its causes and macroeconomic implications—a long-period
analysis. Cambridge Journal of Economics, 33(June), 113–137. https://doi.org/10.1093/cje/ben030
Batz-Barbarich, C., Tay, L., Kuykendall, L. & Cheung, H.K. (2018) A meta-analysis of gender differences in sub-
jective well-being: estimating effect sizes and associations with gender inequality. Psychological Science,29
(9), 1491–1503. https://doi.org/10.1177/0956797618774796
Becker, G.S. (1993) Human capital: a theoretical and empirical analysis, with special reference to education, 3rd
edition. Chicago, IL: University of Chicago Press.
Belley, P. & Lochner, L. (2007) The changing role of family income and ability in determining educational
achievement. Journal of Human Capital, 1(1), 37–89. https://doi.org/10.1086/524674
Benedetto, U., Head, S.J., Angelini, G.D. & Blackstone, E.H. (2018) Statistical primer: propensity score matching
and its alternatives. European Journal of Cardio-Thoracic Surgery, 53(6), 1112–1117. https://doi.org/10.1093/
ejcts/ezy167
Berlin, M. & Mester, L.J. (2004) Credit card rates and consumer search. Review of Financial Economics, 13(1–2),
179–198. https://doi.org/10.1016/j.rfe.2003.06.001
Bernthal, M.J., Crockett, D. & Rose, R.L. (2005) Credit cards as lifestyle facilitators. Journal of Consumer
Research, 32(1), 130–145. https://doi.org/10.1086/429605
Bertaut, C.C., Haliassos, M. & Reiter, M. (2009) Credit card debt puzzles and debt revolvers for self control.
Review of Finance, 13(January), 657–692. https://doi.org/10.1093/rof/rfn033
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 23
Besley, T. & Coate, S. (1995) Group lending, repayment incentives and social collateral. Journal of Development
Economics, 46(1), 1–18. https://doi.org/10.1016/0304-3878(94)00045-E
Białowolski, P. (2017) Forecasting household debt with latent transition modelling. Applied Economics Letters,24
(15), 1088–1092. https://doi.org/10.1080/13504851.2016.1257099
Białowolski, P. & Węziak-Białowolska, D. (2017) What does a Swiss franc mortgage cost? The tale of Polish trust
for foreign currency denominated mortgages: implications for well-being and health. Social Indicators
Research, 133, 285–301. https://doi.org/10.1007/s11205-016-1363-9
Białowolski, P., Węziak-Białowolska, D. & VanderWeele, T.J. (2019) The impact of savings and credit on health
and health behaviours: an outcome wide longitudinal approach. International Journal of Public Health,64
(4), 573–584. https://doi.org/10.1007/s00038-019-01214-3
Białowolski, P., Cwynar, A., Cwynar, W. & Węziak-Białowolska, D. (2020) Consumer debt attitudes: the role of
gender, debt knowledge and skills. International Journal of Consumer Studies, 44(3), 191–205. https://doi.
org/10.1111/ijcs.12558
Bialowolski, P., Cwynar, A., Xiao, J.J. & Weziak-Bialowolska, D. (2020) Consumer financial literacy and the effi-
ciency of mortgage-related decisions: new evidence from the panel study of income dynamics. International
Journal of Consumer Studies, (December), 1–45. https://doi.org/10.1111/ijcs.12646
Bialowolski, P., Weziak-Bialowolska, D. & McNeely, E. (2021a) The role of financial fragility and financial con-
trol for well-being. Social Indicators Research, 155(3), 1137–1157. https://doi.org/10.1007/s11205-021-02627-5
Bialowolski, P., Weziak-Bialowolska, D., Lee, M.T., Chen, Y., VanderWeele, T.J. & McNeely, E. (2021b) The role
of financial conditions for physical and mental health. Evidence from a longitudinal survey and insurance
claims data. Social Science and Medicine. https://doi.org/10.1016/j.socscimed.2021.114041.
Bridges, S. & Disney, R. (2004) Use of credit and arrears on debt among low-income families in the United King-
dom. Fiscal Studies, 25(1), 1–25. https://doi.org/10.1111/j.1475-5890.2004.tb00094.x
Bridges, S. & Disney, R. (2010) Debt and depression. Journal of Health Economics, 29(3), 388–403. https://doi.
org/10.1016/j.jhealeco.2010.02.003
Brown, S., Taylor, K. & Price, S.W. (2005) Debt and distress: evaluating the psychological cost of credit. Journal
of Economic Psychology, 26(5), 642–663. https://doi.org/10.1016/j.joep.2005.01.002
Brown, M., Haughwout, A., Lee, D., Scally, J. & Klaauw, W.V. (2014) Measuring student debt and its performance.
New York, NY: Federal Reserve Bank of New York.
Burgard, S.A., Seefeldt, K.S. & Zelner, S. (2012) Housing instability and health: findings from the Michigan reces-
sion and recovery study. Social Science and Medicine, 75(12), 2215–2224. https://doi.org/10.1016/j.socscimed.
2012.08.020
Calem, P.S. & Mester, L.J. (1995) Consumer behavior and the stickiness of credit-card interest rates. The Ameri-
can Economic Review, 85(5), 1327–1336 http://www.jstor.org/stable/2950992
Calem, P.S., Gordy, M.B. & Mester, L.J. (2006) Switching costs and adverse selection in the market for credit
cards: new evidence. Journal of Banking & Finance, 30(6), 1653–1685. https://doi.org/10.1016/j.jbankfin.
2005.09.012
Caliendo, M. & Kopeinig, S. (2008) Some practical guidance for the implementation of propensity score
matching. Journal of Economic Surveys, 22(1), 31–72. https://doi.org/10.1111/j.1467-6419.2007.00527.x
Camerer, C. & Lovallo, D. (1999) Overconfidence and excess entry: an experimental approach. American Eco-
nomic Review, 89(1), 306–318. https://doi.org/10.1257/aer.89.1.306
Campbell, J.Y. & Cocco, J.F. (2015) Household risk management and optimal mortgage choice. The Quarterly
Journal of Economics, 118(4), 1449–1494.
Cannuscio, C.C., Alley, D.E., Pag
an, J.A., Soldo, B., Krasny, S., Shardell, M. et al. (2012) Housing strain, mort-
gage foreclosure, and health. Nursing Outlook, 60(3), 134–142. https://doi.org/10.1016/j.outlook.2011.08.004
Charles, K.K., Hurst, E. & Stephens, M. (2008) Rates for vehicle loans: race and loan source. American Economic
Review, 98(2), 315–320. https://doi.org/10.1257/aer.98.2.315
Cho, S.H., Xu, Y. & Elizabeth Kiss, D. (2015) Understanding student loan decisions: a literature review. Family
and Consumer Sciences Research Journal, 43(3), 229–243. https://doi.org/10.1111/fcsr.12099
Choo, S. & Mokhtarian, P.L. (2004) What type of vehicle do people drive? The role of attitude and lifestyle in
influencing vehicle type choice. Transportation Research Part A: Policy and Practice, 38(3), 201–222. https://
doi.org/10.1016/j.tra.2003.10.005
24 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
Cooke, R., Barkham, M., Audin, K., Bradley, M. & Davy, J. (2004) Student debt and its relation to student mental
health. Journal of Further and Higher Education, 28(1), 53–66. https://doi.org/10.1080/0309877032000161814
Cooper, D. & Christina Wang, J. (2014). Student loan debt and economic outcomes, Current Policy Perspectives,
(Vol. 14). Boston, MA: Federal Reserve Bank of Boston.
Crook, J. & Banasik, J. (2012) Forecasting and explaining aggregate consumer credit delinquency behaviour.
International Journal of Forecasting, 28(1), 145–160. https://doi.org/10.1016/j.ijforecast.2010.12.002
Davutyan, N. & Öztürkkal, B. (2016) Determinants of saving-borrowing decisions and financial inclusion in a
high middle income country: the Turkish case. Emerging Markets Finance and Trade, 52(11), 2512–2529.
https://doi.org/10.1080/1540496X.2016.1187596
Dawson, C. & Henley, A. (2012) Something will turn up? Financial over-optimism and mortgage arrears. Eco-
nomics Letters, 117(1), 49–52. https://doi.org/10.1016/j.econlet.2012.04.063
de Lucinda, C.R. & Vieira, R.L. (2014) Interest rates and informational issues in the credit market: experimental
evidence from Brazil. World Development, 59(March 2012), 47–58. https://doi.org/10.1016/j.worlddev.2014.
01.027
Dholakia, R.R. (2012) Buy now, pay later: financing the future. In: Technology and consumption. Understanding
consumer choices and behaviors. New York, NY: Springer New York, pp. 143–171. International Series on
Consumer Science. https://doi.org/10.1007/978-1-4614-2158-0
di Giulio, D. & Milani, C. (2013) Plastic money diffusion and usage: an empirical analysis on Italian households.
Economic Notes, 42(1), 47–74. https://doi.org/10.1111/ecno.12002
Diener, E. (2000) Subjective well-being. The science of happiness and a proposal for a national index. American
Psychologist, 55(1), 34–43. https://doi.org/10.1037//0003-066X.55.1.34
Ekici, T. & Dunn, L. (2010) Credit card debt and consumption: evidence from household-level data. Applied Eco-
nomics, 42(4), 455–462. https://doi.org/10.1080/00036840801964526
Elliott, W. & Lewis, M. (2015) Student debt effects on financial well-being: research and policy implications.
Journal of Economic Surveys, 29(4), 614–636. https://doi.org/10.1111/joes.12124
Elul, R., Souleles, N.S., Chomsisengphet, S., Glennon, D. & Hunt, R. (2010) Mortgage market and the financial
crisis. What ‘triggers’mortgage default? American Economic Review: Papers & Proceedings, 100(May), 490–
494. https://doi.org/10.1257/aer.100.2.490
Emmerling, J. & Qari, S. (2017) Car ownership and hedonic adaptation. Journal of Economic Psychology, 61, 29–
38. https://doi.org/10.1016/j.joep.2017.02.014
Fan, Y., Wu, J. & Yang, Z. (2017) Informal borrowing and home purchase: evidence from urban China. Regional
Science and Urban Economics, 67(May), 108–118. https://doi.org/10.1016/j.regsciurbeco.2017.09.003
Federal Reserve Bank of New York. (2019) Quarterly report on household debt and credit. New York, NY: Federal
Reserve Bank of New York.
Federal Reserve Bank of New York. (2020) Quarterly report on household debt and credit. 2020: Q1. New York,
NY: Federal Reserve Bank of New York.
Fitch, C., Chaplin, R., Trend, C. & Collard, S. (2007) Debt and mental health: the role of psychiatrists. Advances
in Psychiatric Treatment, 13(3), 194–202. https://doi.org/10.1192/apt.bp.106.002527
Fornero, E., Monticone, C., & Trucchi, S. (2011) The effect of financial literacy on mortgage choices. 121/11.
Torino. https://doi.org/10.2139/ssrn.1950040.
Frederick, S. & Loewenstein, G. (1999) Hedonic adaptation. In: Kahneman, D., Diener, E. & Schwarz, N. (Eds.)
Well-being: the foundations of hedonic psychology. New York, NY: Russel Sage Foundation, pp. 302–329.
Frey, B.S. & Stutzer, A. (2002) What can economists learn from happiness research? Journal of Economic Litera-
ture, 40(2), 402–435. https://doi.org/10.1257/jel.40.2.402
Fry, R. (2014) Young adults, student debt and economic well-being. Washington, D.C.: PEW Research Center's
Social and Demographic Trends project. http://www.pewsocialtrends.org/2014/05/14/young-adults-student-
debt-and-economic-well-being/
Gathergood, J. & Weber, J. (2017) Financial literacy, present bias and alternative mortgage products. Journal of
Banking and Finance, 78, 58–83. https://doi.org/10.1016/j.jbankfin.2017.01.022
Gebel, M. & Voßemer, J. (2014) The impact of employment transitions on health in Germany. A difference-in-
differences propensity score matching approach. Social Science and Medicine, 108, 128–136. https://doi.org/
10.1016/j.socscimed.2014.02.039
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 25
Georgarakos, D., Lojschova, A. & Ward-Warmedinger, M. (2010) Mortgage, indebtedness and household finan-
cial distress. Working Paper Series, 1156, 1–59. https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1156.pdf
Gerlach-Kristen, P. & Lyons, S. (2018) Determinants of mortgage arrears in Europe: evidence from household
microdata. International Journal of Housing Policy, 18(4), 545–567. https://doi.org/10.1080/19491247.2017.
1357398
Godwin, D.D. (1999) Predictors of Households' debt repayment difficulties. Journal of Financial Counseling and
Planning, 10(1), 67–78.
Heckman, J.J., Ichimura, H. & Todd, P.E. (1997) Matching as an econometric evaluation estimator: evidence
from evaluating a job training programme. The Reivew of Economic Studies, 64(4), 605–654.
Henager, R. & Wilmarth, M.J. (2018) The relationship between student loan debt and financial wellness. Family
and Consumer Sciences Research Journal, 46(4), 381–395. https://doi.org/10.1111/fcsr.12263
Houle, J.N. & Warner, C. (2017) Into the red and back to the nest? Student debt, college completion, and
returning to the parental home among young adults. Sociology of Education, 90(1), 89–108. https://doi.org/
10.1177/0038040716685873
Jappelli, T. & Pagano, M. (1989) Consumption and capital market imperfections: an international comparison.
The American Economic Review, 79(5), 1088–1105.
Kahneman, D. & Tversky, A. (1979) Prospect theory: an analysis of decision under risk. Econometrica, 47(2),
263–291.
Kamleitner, B. & Kirchler, E. (2007) Consumer credit use: a process model and literature review. Revue
Européenne de Psychologie Appliquée/European Review of Applied Psychology, 57(4), 267–283. https://doi.org/
10.1016/j.erap.2006.09.003
Kamleitner, B., Hoelzl, E. & Kirchler, E. (2012) Credit use: psychological perspectives on a multifaceted phenom-
enon. International Journal of Psychology: Journal International de Psychologie, 47(1), 1–27. https://doi.org/
10.1080/00207594.2011.628674
Karaivanov, A., & Kessler, A. (2013) A friend in need is a friend indeed: theory and evidence on the (dis)advan-
tages of informal loans. 13–03.
Karlan, D., Mobius, M., Rosenblat, T. & Szeidl, A. (2009) Trust and social collateral. The Quarterly Journal of Eco-
nomics, 124(3), 1307–1361. https://doi.org/10.1162/qjec.2009.124.3.1307
Kerr, S. & Dunn, L. (2008) Consumer search behavior in the changing credit card market. Journal of Business &
Economic Statistics, 26(3), 345–353. https://doi.org/10.1198/073500107000000133
Kim, H. & DeVaney, S.A. (2001) The determinants of outstanding balances among credit card revolvers. Journal
of Financial Counseling and Planning, 12(1), 67–79.
Kim, H., Yoon, W. & Zurlo, K.A. (2012) Health shocks, out-of-pocket medical expenses and consumer debt
among middle-aged and older Americans. Journal of Consumer Affairs, 46(3), 357–380. https://doi.org/10.
1111/j.1745-6606.2012.01236.x
Kozup, J. & Hogarth, J.M. (2008) Financial literacy, public policy, and consumers' self-protection - more ques-
tions, fewer answers. Journal of Consumer Affairs, 42(2), 127–136. https://doi.org/10.1111/j.1745-6606.2008.
00101.x
Laibson, D. (1997) Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 112(2),
443–477.
Lebo, M.J. & Weber, C. (2015) An effective approach to the repeated cross-sectional design. American Journal of
Political Science, 59(1), 242–258. https://doi.org/10.1111/ajps.12095
Lee, S. & Persson, P. (2016) Financing from family and friends. Review of Financial Studies, 29, 2341–2386.
https://doi.org/10.1093/rfs/hhw031
Leuven, E., & Sianesi, B. (2003) PSMATCH2: Stata module to perform full Mahalanobis and propensity score
matching, common support graphing, and covariate imbalance testing.
Lin, L., Revindo, M.D., Gan, C. & Cohen, D.A. (2019) Determinants of credit card spending and debt of Chinese
consumers. International Journal of Bank Marketing, 37(2), 545–564. https://doi.org/10.1108/IJBM-01-2018-
0010
Lochner, L. & Monge-Naranjo, A. (2016) Student loans and repayment. Theory, evidence, and policy. In: Hand-
book of the economics of education, Vol. 5, 1st edition. Amsterdam, The Netherlands: Elsevier B.V. https://
doi.org/10.1016/B978-0-444-63459-7.00008-7
26 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
Loewenstein, G. (2000) Emotions in economic theory and economic behavior. American Economic Review, 90(2),
426–432.
Loibl, C., Snyder, A. & Mountain, T. (2017) Connecting saving and food security: evidence from an asset-building
program for families in poverty. Journal of Consumer Affairs, 51(3), 659–681. https://doi.org/10.1111/joca.
12137
Loke, Y.J. (2016) Living beyond one's means: evidence from Malaysia. International Journal of Social Economics,
43(1), 2–18. https://doi.org/10.1108/IJSE-11-2013-0248
May, O., & Tudela, M. (2005) When is mortgage indebtedness a financial burden to British households? A
dynamic probit approach. Vol. 44. The Bank of England Working Papers.
McGonagle, K.A., Schoeni, R.F., Sastry, N. & Freedman, V.A. (2008) The panel study of income dynamics: over-
view, recent innovations, and potential for life course research. Longitudinal Life Course Studies, 3(2),
612–615. https://doi.org/10.1038/jid.2014.371
Mienert, M. (2002) Fahr und fahrzeugbezogene werthaltungen (values related to driving and vehicles). In:
Scholl, W. & Sydow, H. (Eds.) Mobilitaet im jugend und erwachsenenalter (mobility in adolescence and adult-
hood). Muenster: Waxmann, pp. 292–328.
Modigliani, F. & Brumberg, R. (1954) Utility analysis and the consumption function: an interpretation of the
cross-section data. In: Kurihara, K. (Ed.) Post-Keynesian economics. New Brunswick, N.J.: Rutgers University
Press.
Moore, G.L. & Stockhammer, E. (2018) The drivers of household indebtedness reconsidered: an empirical evalua-
tion of competing arguments on the macroeconomic determinants of household indebtedness in OECD
countries. Journal of Post Keynesian Economics, 41(4), 547–577. https://doi.org/10.1080/01603477.2018.
1486207
National Research Council. (2013) Subjective well-being: measuring happiness, suffering, and other dimensions of
experience. Washington, DC: The National Academies Press.
Nettleton, S. & Burrows, R. (1998) Mortgage debt, insecure home ownership and health: an exploratory analysis.
Sociology of Health and Illness, 20(5), 731–753. https://doi.org/10.1111/1467-9566.00127
Panel Study of Income Dynamics (2019). Ann Arbor, MI.
Park, S. (1993) The determinants of consumer installment credit. Review - Federal Reserve Bank of St. Louis,75
(6), 23–38. https://doi.org/10.20955/r.75.23-38
Parker, A.M., de Bruin, W.B., Yoong, J. & Willis, R. (2012) Inappropriate confidence and retirement planning:
four studies with a national sample. Journal of Behavioral Decision Making, 25(4), 382–389. https://doi.org/
10.1002/bdm.745
Peters, A., de Haan, P. & Scholz, R.W. (2015) Understanding car-buying behavior: psychological determinants of
energy efficiency and practical implications. International Journal of Sustainable Transportation, 9(1), 59–72.
https://doi.org/10.1080/15568318.2012.732672
Plagnol, A.C. (2011) Financial satisfaction over the life course: the influence of assets and liabilities. Journal of
Economic Psychology, 32(1), 45–64. https://doi.org/10.1016/j.joep.2010.10.006
Porter, K. (2008) The debt dilemma (review of charging ahead: the growth and regulation of payment card mar-
kets by Ronald J. Mann). Michigan Law Review, 106(6), 1167–1191.
Qi, M. & Yang, S. (2003) Forecasting consumer credit card adoption: what can we learn about the utility func-
tion? International Journal of Forecasting, 19(1), 71–85. https://doi.org/10.1016/S0169-2070(01)00118-2
Ren, H., Folmer, H. & Van der Vlist, A.J. (2018) The impact of home ownership on life satisfaction in urban
China: a propensity score matching analysis. Journal of Happiness Studies, 19(2), 397–422. https://doi.org/
10.1007/s10902-016-9826-x
Rothstein, J. & Rouse, C.E. (2011) Constrained after college: student loans and early-career occupational choices.
Journal of Public Economics, 95(1–2), 149–163. https://doi.org/10.1016/j.jpubeco.2010.09.015
Rubin, D.B. & Thomas, N. (1996) Matching using estimated propensity scores: Relating theory to practice. Bio-
metrics, 52(1), 249–264. https://doi.org/10.2307/2533160
Sabatini, F. & Sarracino, F. (2017) Online networks and subjective well-being. Kyklos, 70(3), 456–480. https://doi.
org/10.1111/kykl.12145
Selenko, E. & Batinic, B. (2011) Beyond debt. A moderator analysis of the relationship between perceived finan-
cial strain and mental health. Social Science and Medicine, 73(12), 1725–1732. https://doi.org/10.1016/j.
socscimed.2011.09.022
BIALOWOLSKI AND WEZIAK-BIALOWOLSKA 27
Sewell, W.H. & Hauser, R.M. (1980) The Wisconsin longitudinal study of social and psychological factors in aspi-
rations and achievements. In: Kerckhoff, A.C. (Ed.) Research in sociology and education. Greenwich, CN: JAI
Press, pp. 59–99.
Shefrin, H.M. & Thaler, R.H. (1988) The behavioral life-cycle hypothesis. Economic Inquiry, 28(October),
609–643.
Stinebrickner, R. & Stinebrickner, T. (2008) The effect of credit constraints on the college drop-out decision: a
direct approach using a new panel study. American Economic Review, 98(5), 2163–2184. https://doi.org/10.
1257/aer.98.5.2163
Sullivan, T.A., Warren, E. & Westbrook, J.L. (1989) As we forgive our debtors: bankruptcy and consumer credit in
America. New York, NY: Oxford University Press.
Sweet, E., Nandi, A., Adam, E.K. & McDade, T.W. (2013) The high price of debt: household financial debt and
its impact on mental and physical health. Social Science and Medicine, 91, 94–100. https://doi.org/10.1016/j.
socscimed.2013.05.009
Thaler, R.H. & Shefrin, H.M. (1981) An economic theory of self-control. Journal of Political Economy, 89(2),
392–406. https://doi.org/10.1086/260971
The Eurosystem Household Finance and Consumption Survey. (2013) Results from the first wave. Frankfurt:
European Central Bank,
Turvey, C.G. & Kong, R. (2010) Informal lending amongst friends and relatives: can microcredit compete in rural
China? China Economic Review, 21(4), 544–556. https://doi.org/10.1016/j.chieco.2010.05.001
Turvey, C.G., Kong, R. & Huo, X. (2010) Borrowing amongst friends: the economics of informal credit in rural
China. China Agricultural Economic Review, 2(2), 133–147. https://doi.org/10.1108/17561371011044261
Walsemann, K.M., Gee, G.C. & Gentile, D. (2015) Sick of our loans: student borrowing and the mental health of
young adults in the United States. Social Science and Medicine, 124, 85–93. https://doi.org/10.1016/j.
socscimed.2014.11.027
Wickramasinghe, V. & Gurugamage, A. (2009) Consumer credit card ownership and usage practices: empirical
evidence from Sri Lanka. International Journal of Consumer Studies, 33(4), 436–447. https://doi.org/10.1111/
j.1470-6431.2009.00779.x
Wonder, N., Wilhelm, W. & Fewings, D. (2008) The financial rationality of consumer loan choices: revealed pref-
erences concerning interest rates, down payments, contract length, and rebates. Journal of Consumer Affairs,
42(2), 243–270. https://doi.org/10.1111/j.1745-6606.2008.00107.x
Xia, T., Wang, Z. & Li, K. (2014) Financial literacy overconfidence and stock market participation. Social Indica-
tors Research, 119, 1233–1245. https://doi.org/10.1007/s11205-013-0555-9
Xiao, J.J. (Ed.). (2015) Consumer economic wellbeing. New York, NY: Springer. https://doi.org/10.1007/978-1-
4939-2821-7_7
Xiao, J.J. & Yao, R. (2014) Consumer debt delinquency by family lifecycle categories. International Journal of
Bank Marketing, 32(1), 43–59. https://doi.org/10.1108/IJBM-02-2013-0007
Xiao, J.J., Yan, C., Bialowolski, P. & Porto, N. (2021) Consumer debt holding, income and happiness: evidence
from China. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-08-2020-0422
Zhang, J. & Kemp, S. (2009) The relationships between student debt and motivation, happiness, and academic
achievement. New Zealand Journal of Psychology, 38(2), 24–29.
Zhu, N. (2011) Household consumption and personal bankruptcy. The Journal of Legal Studies, 40(1), 1–37.
https://doi.org/10.1086/649033
How to cite this article: Bialowolski, P., & Weziak-Bialowolska, D. (2021). Good credit,
bad credit: The differential role of the sources of debt in life satisfaction. Journal of
Consumer Affairs,1–28. https://doi.org/10.1111/joca.12388
28 BIALOWOLSKI AND WEZIAK-BIALOWOLSKA
