Noncognitive Abilities and Financial Delinquency: …public.kenan-flagler.unc.edu/faculty/kuhnenc/RESEARCH/...loans. In proportion to sample averages, these differences represent declines

THE JOURNAL OF FINANCE • VOL. LXXIII, NO. 6 • DECEMBER 2018

Noncognitive Abilities and FinancialDelinquency: The Role of Self-Efficacy

in Avoiding Financial Distress

CAMELIA M. KUHNEN and BRIAN T. MELZER∗

ABSTRACT

We investigate a novel determinant of financial distress, namely, individuals’ self-efficacy, or belief that their actions can influence the future. Individuals with highself-efficacy are more likely to take precautions that mitigate adverse financial shocks.They are subsequently less likely to default on their debt and bill payments, especiallyafter experiencing negative shocks such as job loss or illness. Thus, noncognitive abil-ities are an important determinant of financial fragility and subjective expectationsare an important factor in household financial decisions.

FINANCIAL DECISIONS REQUIRE INDIVIDUALS to consider intertemporal trade-offs inan uncertain environment. For example, a prospective saver evaluates whetherthe expected benefits of precautionary savings outweigh the cost of reducingconsumption today. A borrower struggling to repay a loan compares the poten-tial benefits of avoiding default to the costs of reducing spending or getting asecond job today to avoid default. Or a middle-aged couple evaluates whetherinsuring their long-term care is worthwhile at the expense of the insurancepremium. In each of these choices, the agent considers a trade-off involving anaction that is costly today and has an uncertain effect on the distribution offuture outcomes. Therefore, individuals’ subjective perception of this trade-offis likely to influence their decisions.

Building on this idea, we examine the effect of subjective beliefs on finan-cial behavior. People vary in their self-efficacy, or belief that their actions oreffort can influence the future. A growing economics literature on noncognitiveskills shows that self-efficacy is important for educational attainment and la-bor market success (e.g., Heckman, Sixrud, and Urzua (2006) and Lindqvist

∗Camelia Kuhnen is at the Kenan-Flagler Business School at the University of North Carolinaat Chapel Hill and NBER. Brian Melzer is at the Tuck School of Business at Dartmouth College andthe Federal Reserve Bank of Chicago. We thank Wei Xiong (the Editor); two anonymous referees,as well as Gadi Barlevy; Tom DeLeire; Benjamin Keys; Lindsey Leininger; seminar participants atUC Berkeley, the University of Virginia, NC State University, University of Colorado – Boulder, andparticipants at the AEA 2014 meeting, the 2014 FDIC Consumer Research Symposium, and the2015 University of Alberta Frontiers in Finance conference for helpful comments and discussion. Allremaining errors are ours. This paper represents the views of the authors and does not necessarilyreflect the opinions of the Federal Reserve Bank of Chicago or the Federal Reserve System. We haveread the disclosure policy of the Journal of Finance and have no conflicts of interest to disclose.

DOI: 10.1111/jofi.12724

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and Vestman (2011)). In this paper, we investigate whether self-efficacy alsomatters for financial choices, such as defaulting on debt, setting aside emer-gency savings, and insuring against risks.

Why might self-efficacy matter for financial decisions? Self-efficacy influencesan individual’s perception of the benefits from acting. Consider a borrower atrisk of default. His efforts to avoid default are immediately costly and may beineffective. If he has low self-efficacy, he will perceive his sacrifices to have littleeffect on his financial future and defaulting will appear optimal. By contrast,an individual with high self-efficacy will believe his actions can reduce hischance of default, and thus may choose to sacrifice consumption or work longerhours today. A simple way to conceptualize the role of self-efficacy is withinan effort choice problem, where providing effort is costly but increases thechance of avoiding a poor outcome in the future. People with lower self-efficacyforesee less marginal benefit from exerting effort or making sacrifices today,so they choose to spend less effort, which in turn increases the likelihood of abad outcome. Within this framework, we would expect individuals with lowerself-efficacy to have higher rates of financial distress, to spend less effort toprepare for potential adverse shocks, and upon encountering such shocks, tobecome delinquent at higher rates.

Drawing on these insights, we use data from the National Longitudinal Sur-vey of Youth (NLSY) to test whether self-efficacy affects financial choices andoutcomes. Our main analysis uses the NLSY Child and Youth panel, whichfollows survey participants from early childhood through adulthood. The sur-vey tracks individuals’ cognitive and noncognitive abilities, including theirself-efficacy, from an early age. Once participants move into adulthood, theyalso report their labor and financial market experiences. The survey’s finan-cial variables include measures of borrowing, delinquency on loans and bills,bankruptcy and asset repossession, precautionary saving, and health insur-ance take-up. The sample includes roughly 6,000 younger adults (ages 21 to41) between 2010 and 2014. We extend the analysis by using the NLSY 1979panel (the parents of those in the Child and Youth panel) to study additional fi-nancial choices—credit applications and denials, retirement preparations, andpurchases of long-term care insurance—among older adults (ages 47 to 56).

We document a strong negative correlation between self-efficacy and finan-cial distress. Individuals with high self-efficacy, measured earlier in life, aresubsequently less likely to default on outstanding loans or fall behind on billpayments than their peers with low self-efficacy. In turn, they are also lesslikely to experience foreclosure, asset repossession, or personal bankruptcy. In-dividuals with more self-efficacy also display greater use of traditional creditproducts such as credit cards, automobile loans, and mortgages, and are lesslikely to be rejected for credit and to turn to high-cost payday loans. The mag-nitudes of these effects are meaningful in comparison to sample averages. Aone-standard-deviation increase in self-efficacy corresponds to a 1.9 percent-age point decrease in the probability of loan default, a 0.5 percentage pointdecrease in the probability of foreclosure, repossession, or bankruptcy, and a0.7 percentage point decrease in the probability of borrowing through payday

Noncognitive Abilities and Financial Delinquency 2839

loans. In proportion to sample averages, these differences represent declines of11% to 14% in the rates of default and payday borrowing.

We use the detailed data of the NLSY to explore why self-efficacy dis-plays a negative correlation with financial distress. We first rule out poten-tial differences—in ability, preferences, education, earnings, net worth, andparental support—that may confound the effects of self-efficacy. We find thatself-efficacy remains negatively correlated with financial distress after condi-tioning on measures of cognitive ability, risk tolerance, and time preference.As documented in prior studies, both education and earnings rise with self-efficacy. Nevertheless, self-efficacy remains strongly negatively correlated withdistress after controlling for educational attainment and income. We also ex-amine sibling groups, for whom the NLSY collects data on each individual,to test whether parental support accounts for differences in self-efficacy anddelinquency. In models with sibling group fixed effects, which limit the iden-tifying variation in self-efficacy to differences across siblings, we continue tofind a strong negative correlation between self-efficacy and financial distress.This finding implies that shared family support does not confound self-efficacyin our main analysis. We also show that all of our findings for self-efficacy arerobust to controlling for further differences in noncognitive ability, as measuredby the “Big-Five” personality traits. Finally, we show that lower self-efficacyindividuals are not defaulting more because they are more indebted. As notedearlier, these individuals are in fact less likely to borrow through traditionalcredit market products.

Consistent with our theoretical motivation, we find that individuals withhigh self-efficacy are more likely to take precautionary actions to avoid finan-cial distress. They are more likely to set aside emergency savings, purchaseinsurance, and plan for retirement. Roughly one-third of the respondents inthe NLSY Child and Youth panel maintain emergency savings sufficient tocover three months of expenses. Our estimates indicate that a one-standard-deviation increase in self-efficacy raises the likelihood of emergency savings by2 percentage points, or 6%, relative to the sample average. This relationshippersists over the life-cycle, as the older adults in the NLSY 1979 panel alsoset aside emergency savings at higher rates when they have high self-efficacy.Individuals with high self-efficacy are also more likely to obtain insurance cov-erage and plan for retirement. Among younger adults, the purchase of healthinsurance increases with self-efficacy, even after controlling for income and theavailability of employer-sponsored coverage. Among older adults, purchases oflong-term care insurance and preparation for retirement (e.g., visiting with afinancial planner) increase with self-efficacy, even after controlling for incomeand net worth.

Individuals with high self-efficacy are not only more financially prepared, butalso more resilient when facing income and health shocks. Individuals with lowself-efficacy who experience a job loss or health problem default on their debtand bill payments at very high rates—more than 50% higher than the rateof those who remain employed and healthy. By contrast, individuals with thehighest self-efficacy experience little to no increase in default after an income


or health shock. These findings are consistent with the notion that self-efficacy,through its impact on financial preparedness, promotes financial stability. Theprecautionary actions that self-efficacy engenders appear to matter the mostfor individuals from poorer backgrounds. When we link young adults to theirparents, we find that the negative correlation between self-efficacy and defaultis strongest for individuals who grew up in the least wealthy families. Specif-ically, the marginal effect of self-efficacy on default triples when moving fromthe top tertile to the bottom tertile of parental net worth.

An important caveat to our analysis is that the relationships we documentbetween self-efficacy and financial choices may not be causal in nature. Al-though we control for observable differences and account for parental supportin a siblings fixed effect analysis, omitted variables bias is certainly possible.For example, some limitations of the NLSY Child and Youth survey are thatit fails to measure respondents’ financial wealth and expectations for incomegrowth, and its measure of time preferences is quite coarse.

Our results have implications for the understanding of household financialfragility. According to a recent Federal Reserve Board survey (Board of Gov-ernors of the Federal Reserve System (2016)), nearly half of U.S. adults areill-prepared for a financial disruption and would struggle to cover emergencyexpenses should they arise.1 The precarious financial position of Americanhouseholds was particularly evident in the recent financial crisis, when onein 12 households defaulted on outstanding loans and delinquent debt becamean issue of macroeconomic significance. It is therefore important to under-stand why some households become financially delinquent, but many others donot.

Economic models typically characterize default as a strategic decision inwhich an agent trades off the benefits of expunging remaining debt paymentsagainst the costs of credit market exclusion, forgone collateral, and socialstigma (see, for example, Chatterjee et al. (2007)). Our insight is that thedimensions of individual heterogeneity in these models—varying time prefer-ences and costs of credit market exclusion—exclude an important factor thatinfluences default. In particular, individuals’ subjective beliefs affect how theyperceive the costs and benefits of default. In addition to this implication fortheoretical models, our study contributes to the empirical literature on de-fault by identifying self-efficacy as a meaningful predictor alongside otherfactors such as income and spending shocks (Elul et al. (2010), Gross andNotowidigdo (2011), Hsu, Melzer, and Matsa (2018), Keys (2018)), strategicmotivations (Deng, Quigley, and Van Order (2000), Foote, Gerardi, and Willen(2008), Campbell and Cocco (2015)), the structure of bankruptcy law (Fay,Hurst, and White (2002)), and cultural norms (Guiso, Sapienza, and Zingales(2013)).

Our finding that noncognitive abilities are important for financial decisionsand outcomes complements the recent literature in economics and psychology

1 Forty-six percent of U.S. adults report that they could not cover an emergency expense costing$400 or would cover it by selling something or borrowing money.


that documents the significant effect of noncognitive skills on educational at-tainment (Coleman and DeLeire (2003), Heckman, Sixrud, and Urzua (2006),Cadena and Keys (2013, 2015)), wages (Lindqvist and Vestman (2011)), andhealth outcomes (Taylor and Seeman (2006), Conell-Price and Jamison (2015)).In recent work, Parise and Peijnenburg (2017) show that emotional stabilityand conscientiousness, individual characteristics that are likely related to theability to execute planned actions, are negatively related to financial delin-quency, which complements our findings regarding the role of expectations oneffort provision and the avoidance of financial distress.

Our results also complement studies of cognitive ability and financial out-comes. This literature shows that greater cognitive skills predict better out-comes in terms of mortgage delinquency, credit card usage, loan choices,and savings and investment behavior (Stango and Zinman (2009), Grinblatt,Keloharju, and Linnainmaa (2011), Agarwal and Mazumder (2013), Gerardi,Goette, and Meier (2013)). Financial literacy skills help individuals choosebetter investment portfolios and plan better for retirement (Choi, Laibson, andMadrian (2010), Lusardi and Mitchell (2014)). Education has positive effects onfinancial market participation, credit market access, and credit management(Campbell (2006), Cole, Paulson, and Shastry (2012), Ru and Schoar (2016)).We contribute to this line of work by showing that noncognitive skills also helpprevent financial distress.

Finally, our paper adds to an emerging literature that relates household fi-nancial decisions to subjective expectations. Individuals who are moderatelyoptimistic about their life work harder, invest more in equities, and have moresavings (Puri and Robinson (2007)). People who believe their future is not un-der their control are more likely to lack interest in learning about investmentoptions, to have low savings, and to be indebted over long horizons (Shapiroand Wu (2011), Caputo (2012), Cole, Paulson, and Shastry (2012)). Also, subjec-tive expectations about macroeconomic conditions, which are driven by broadeconomic developments (Greenwood and Shleifer (2014), Kuchler and Zafar(2016), Malmendier and Nagel (2016)) and by socioeconomic status (Kuhnenand Miu (2017)), have been shown to predict investment decisions (Kezdi andWillis (2011), Das, Kuhnen, and Nagel (2017)).

The remainder of this paper is organized as follows. In Section I, we discussself-efficacy and how it relates to financial outcomes. In Section II, we describethe data. Section III presents our results. In Section IV, we consider alternativehypotheses and present the results of robustness tests. Finally, in Section V weconclude.

I. Self-Efficacy and Financial Decision Making

Psychologists define self-efficacy as a person’s belief that he can control, orexert influence over, the important events in his life. Gecas (1989) argues thatself-efficacy is molded early in life through parent-child interactions and hasa high level of stability over time. Nevertheless, significant events, such asjob loss, that cause economic strain can change a person’s self-efficacy to some


degree. High self-efficacy has been shown to predict better physical and mentalhealth (Taylor and Seeman (2006)), better academic achievements (Kalil andKhalid (2010)), higher job satisfaction and job performance (Judge and Bono(2001)), and a less negative impact of economic hardship or being in a low-income group on psychological well-being and physical health (Lachman andWeaver (1998), Pudrovska et al. (2005)).

Self-efficacy is distinct from other psychological measures of personality, suchas the Big-Five traits of agreeableness, openness, conscientiousness, extraver-sion, and neuroticism. This distinction is evident in Stajkovic et al. (2018),who find that self-efficacy and the Big-Five personality traits have correlationsranging from 0.07 to 0.25.

A. Measuring Self-Efficacy

The measure of self-efficacy available to us in the NLSY Child and Youthpanel is the Pearlin Mastery score.2 The Mastery score, which is compiled fromresponses to a battery of seven statements, is designed to measure generalizedmastery, or self-efficacy, rather than domain-specific mastery. For each state-ment, the respondent ranks the strength of his agreement on a scale of one(“strongly agree”) to four (“strongly disagree”). The seven Pearlin statementsare: (1) “No way I can solve some of the problems I have,” (2) “Sometimes I feelthat I am being pushed around in life,” (3) “I have little control over the thingsthat happen to me,” (4) “I can do just about anything I really set my mind to,”(5) “I often feel helpless in dealing with the problems of life,” (6) “What happensto me in the future mostly depends on me,” and (7) “There is little I can do tochange many of the important things in my life.”

After reversing the scoring for items (4) and (6) so that higher scores corre-spond to greater mastery or self-efficacy, the seven scores are summed to givea total raw score ranging between 7 and 28. In our analysis, we assign eachindividual to a percentile in the distribution of these raw scores, and use thispercentile as our self-efficacy measure, to help with interpreting the magni-tude of the estimated effects of self-efficacy on variables related to financialdistress. Using the raw Pearlin score, rather than the percentile measure, inour regressions leads to similar results in terms of economic importance andstatistical significance.

B. How Can Self-Efficacy Affect the Likelihood of Financial Distress?

We provide a simple framework for understanding the effect of self-efficacyon an individual’s ability to avoid financial distress. Consider an effort choiceproblem in which a person has to spend effort to avoid distress. In this situation,increasing effort e has some cost c(e) but it increases the probability that the

2 Psychologists have measured self-efficacy through three related concepts: locus of control (Rot-ter (1966)), sense of mastery (Pearlin et al. (1981)), and self-efficacy (Bandura (1986)). High self-efficacy is equivalent to high mastery and an internal locus of control.


person will obtain a high (H) rather than a low (L) outcome later. The actualprobability of the high outcome H is equal to e/2, and the probability of the lowoutcome L is 1 − e/2. However, people believe that the probability of outcome His e ∗ s, where s ∈ [0, 1] measures their perceived self-efficacy. We assume thatthe true level of efficacy is s = 0.5. It follows that if s = 0.5, the agent correctlyassesses the probability of obtaining the high outcome as one-half of the efforte he provides. If s < 0.5 or s > 0.5, the agent’s view of his self-efficacy is toopessimistic or optimistic, respectively.

The agent chooses effort e to maximize the perceived benefit minus the cost ofeffort, that is, he maximizes the expression −c(e) + β[esH + (1 − es)L], whereβ captures the person’s patience. Assuming that the cost function is c(e) = γ e2

2 ,with γ > 0, the first-order condition implies that the effort level selected ise∗ = βs(H−L)

γ. Hence, the probability that the low outcome L is realized is equal

to 1 for individuals with the lowest self-efficacy (i.e., s = 0) and 1 − β(H−L)2γ

forindividuals with the highest self-efficacy (i.e., s = 1). Consequently, a simpleexpression relates differences in self-efficacy to differences in the probability offinancial distress: Pr{L if s = 0} − Pr{L if s = 1}= β(H−L)

2γ.

This simple and stylized model provides the intuition for our main empiricalpredictions. In particular, we expect lower self-efficacy to be associated withgreater risk of financial distress and less effort spent on preparations to avoiddistress. We note that individuals with too much self-efficacy (s > 0.5) will errby exerting too much effort to avoid default. Hence, we do not draw normativeconclusions from our subsequent empirical analysis. Though we expect higherself-efficacy to be associated with lower default risk, we do not assume thathigher self-efficacy necessarily corresponds to higher welfare.

II. Data and Summary Statistics

A. Data

Our data source is the NLSY. The initial survey, the NLSY79, began in 1979with a cohort of nearly 13,000 men and women between the ages of 14 and21. As female sample members had children, the NLSY formed the Child andYoung Adult cohort (NLSY CYA) to follow each of these children throughoutchildhood and into adulthood. Our analysis focuses primarily on the NLSYCYA, which contains richer information on self-efficacy and financial distressthan the NLSY79. We use the NLSY79 for supplementary analysis of creditdenials and retirement preparedness among older adults.

The NLSY CYA provides biannual data that begin in 1986 and continuethrough 2014. The survey has two components, a child questionnaire adminis-tered to those of age 14 or younger and a young adult questionnaire adminis-tered to those of age 15 and older. The child survey focuses on the family andschooling environments; the child’s health; as well as the child’s cognitive, emo-tional, and social development. The young adult survey continues to focus onschooling, psychological development, and social development, but also tracks


respondents’ marital, employment, and financial histories as they move intoadulthood. Despite the label “young adult,” the latter questionnaire is usedfor sample members throughout adulthood, which means that by 2014 manysample members are well into their 30s.

The NLSY CYA sample includes 11,512 individuals as of the 2014 interview.In our analysis of financial distress and self-efficacy, we focus on adults thatare 21 years old or older, at which point they are more likely to be financiallyindependent. This portion of the sample includes 5,933 individuals for whichwe have complete information. The data on financial outcomes begin withthe 2010 interview, when many personal finance questions were added to theNSLY CYA questionnaire, and continue with the 2012 and 2014 interviews.The analytic sample includes responses from each of these three interviewsand thus includes multiple observations for individuals who complete multipleinterviews.

We use the NLSY79 sample to analyze measures excluded from the NLSYCYA, such as credit applications and denials, and preparations for retirement.We use the most recently released biannual interview, which was conducted in2012. As of this interview, the respondents were between the ages of 47 and 56.There are 5,322 adults in the sample.

B. Summary Statistics

We report summary statistics for the NLSY CYA sample in Table I. The panelincludes 11,782 person-year observations, referring to 5,933 unique individu-als, or roughly two interviews per individual across the years 2010, 2012, and2014. Our independent variable of interest is derived from the Pearlin Mas-tery scores obtained in interviews between 1986 and 2008, which precede themeasures of financial delinquency. For each individual, we average the rawPearlin Mastery scores obtained before 2010. The average score across these5,933 individuals is 22.06, with a standard deviation of 2.45. Scores are aslow as 13 points and as high as 28 points, the maximum possible given theformulation of the Pearlin questionnaire. Figure 1 shows the distribution ofPearlin scores in the sample. Given the unusual range (i.e., between 7 and 28points) of the Pearlin measure, to simplify the interpretation of regression coef-ficients in later parts of the analysis, we rescale the Pearlin score by calculatingeach person’s percentile rank within this distribution. The resulting Pearlinpercentile measure, which we label Self-Efficacy, varies between zero andone.

To quantify time preferences, we measure participants’ patience based oninterviews conducted in childhood. In each interview, the patience score rangesfrom 1 to 3, depending on the mother’s assessment of whether the child is“impulsive, or acts without thinking.” Within the rating scale, 1, 2, and 3 cor-respond to “often true,” “sometimes true,” and “never true,” so a higher scoreindicates greater patience. We compute the average score across all childhoodinterviews. A similar measure of patience is used by DellaVigna and Paserman


Table ISummary Statistics

This table reports summary statistics for the National Longitudinal Survey Youth 1979 Childand Young Adult sample, which includes all individuals aged 21 or older who were surveyed inyears 2010, 2012, and 2014. The sample includes 11,782 person-year observations for 5,933 uniqueindividuals.

Variable Mean St. Dev. N

Self-Efficacyi (Pearlin percentilescore)

0.51 0.28 11,782

Patiencei 2.44 0.44 11,782Risk aversioni (normalized) −0.02 0.98 11,782Cognitive abilityi (normalized) −0.08 0.98 11,782Ageit(years) 27.07 4.19 11,782Incomeit ($, in thousands) 33.16 35.18 11,782Assetsit ($, in thousands) 40.20 100.93 11,782Debtit ($, in thousands) 26.50 66.08 11,782Net worthit ($, in thousands) 13.71 64.91 11,782

Variable Proportion N

Gender 47% men, 53% women 11,782Race 21.77% Hispanic 11,782

34.65% Black 11,78243.57% non-Black, non-Hispanic 11,782

Education Less than bachelor’s degree: 80.82% 11,782Bachelor’s degree or higher: 19.18% 11,782

Has debtit (i.e., loans outstandingrecently)

Yes: 68.56% 11,782

Credit cardit Yes: 42.85% 11,782Vehicle loanit Yes: 33.26% 11,782Mortgageit Yes: 15.68% 11,782Late debtit for those with loans

outstandingYes: 11.42% 11,782

. . . for those with loans outstandingrecently

Yes: 14.10% 8,078

Late billsit Yes: 12.44% 11,782Accounts in Collectionit Yes: 18.39% 11,782. . . for those with loans outstanding

recentlyYes: 19.21% 8,078

Foreclosure, repossession, orbankruptcyit

Yes: 3.18% 11,782

. . . for those with loans outstandingrecently

Yes: 3.53% 8,078

Payday loanit Yes: 5.98% 11,782Emergency fundsit (only collected in

2012 and 2014)Yes: 35.12% 8,206

No health insuranceit Yes: 33.84% 11,782


00.

10.

20.

150.

05Fr

eque

ncy

12 14 16 18 20 22 24 26 28Pearlin score

Histogram of self-efficacy scores5,933 unique individuals

Figure 1. Histogram of self-efficacy scores. The histogram above shows the frequency ofaverage Pearlin Mastery scores (which can range from 7 to 28 points) across the 5,933 individualsin the NLSY Child and Youth panel used in our main empirical analysis. For each person, wecalculate the average of the Pearlin Mastery scores obtained in survey waves prior to 2010.

(2005), who document that more impatient individuals exert less effort tosearch for jobs and have longer unemployment spells.3

To quantify risk preferences, we measure participants’ propensity to avoidrisky behaviors based on interviews conducted in early adulthood. Participantsconvey their attitude toward risk by responding to three statements: “I enjoytaking risks,” “I enjoy new and exciting experiences, even if they are a littlefrightening or unusual,” and “Life with no danger in it would be too dull forme.” For each statement, the participant provides a numerical rank between 1(“strongly disagree”) and 4 (“strongly agree”). We flip the scale so that higherscores correspond to greater risk aversion and, for each statement, computethe average score across all pre-2010 interviews. Finally, we extract the firstprincipal component from the three responses to form a single measure of riskaversion.

To measure cognitive ability, we use scores on the Peabody IndividualAchievement Test (PIAT), which tests math ability, reading recognition, andreading comprehension, and the Peabody Picture Vocabulary Test. These fourtests of cognitive ability are taken throughout childhood. In each test, the abil-ity scores are measured in percentiles and normalized by age group relative to

3 Our measure relies on the parent’s assessment of their child’s patience whereas DellaVignaand Paserman (2005) use the NLSY interviewer’s assessment of the respondent’s patience duringthe interview.


scores in a national sample in 1968. For each ability, we compute the averagescore across all childhood interviews. We then extract the first principal com-ponent of the four average scores as our measure of the respondent’s cognitiveability.

The average respondent is 27 years old and lives in a household with averageincome of $33,160, real assets (vehicles and homes) of $40,200, and debt of$26,500. To capture net worth, we take the difference between real assets anddebt. This measure excludes financial assets, which are not measured in theNLSY CYA.

Approximately 47% of the sample are men. Racially, the composition of thesample is tilted toward minorities, with both Blacks (35% of the sample) andHispanics (22% of the sample) being intentionally oversampled in the originalNLSY data set. Turning to educational attainment, the respondents reporthaving a bachelor’s or higher degree in 19% of (person-year) observations, andless than a bachelor’s degree in 81% of observations. The rate of college degreecompletion is lower than U.S. averages because some sample members are stillin their 20s, accumulating education.

Among household financial variables, the data on household debt are quiterich. Each respondent reports his outstanding balances on credit card, vehicle,and mortgage loans, as well as the estimated value of the underlying collateralfor the latter two categories. More than two-thirds of the sample has a creditcard or a debt outstanding. The incidence of having a credit card, a vehicleloan, or a mortgage is 43%, 33%, and 16%, respectively.

As of 2010, the survey added various questions about credit delinquency,asset loss, difficulties accessing credit, and financial preparedness. These mea-sures comprise the key dependent variables in our study. The variable Latedebtit captures whether person i in year t has been late by 60 days or moreon required debt payments (credit card, vehicle loan, mortgage, or other debt).Likewise, Late billsit captures whether the person has been late by 60 days ormore on medical, utilities, or other bills over the past year. The frequencies oflate debt payments and late bill payments are 11.42% and 12.44%, respectively.Conditional on having debt outstanding in the current or prior interview, thefrequency of late debt payments is 14.10%. The incidence of having accountssent to collection agencies is 18.39%, and the incidence of home foreclosure,asset repossession, or personal bankruptcy is 3.18%. In 5.98% of observations,individuals report having borrowed from a payday lender over the past year. In35.12% of observations, respondents report having emergency funds saved up,and in 33.84% of observations, respondents report having no health insurance.

C. Temporal Stability of Cognitive and Noncognitive Abilities

It is important to consider whether self-efficacy and the other traits thatwe examine are stable over time. Both measurement error and fundamentalchanges to these traits can affect their power to predict future financial delin-quency. The structure of the NLSY CYA, in which traits are measured multipletimes for a given participant, allows us to measure pairwise serial correlations,


typically at four-year intervals. The four-year correlation for self-efficacy is0.41, similar to that of patience (0.37) and risk aversion (0.42) and smallerthan that of cognitive ability (close to 0.6 for each of the four components).4

These correlations indicate relative stability in each of these traits over time.Nevertheless, there is some time-variation, perhaps due to measurement error.

To capture the stable component of self-efficacy and these other character-istics over time, we use the average values obtained in surveys prior to 2010,when the financial delinquency and preparedness outcomes are first assessed.For the self-efficacy measure, the average value is based on up to six measure-ments of Pearlin score per individual; the typical individual’s score is based onthree measurements. By using an average value, we reduce the attenuationbias caused by measurement error.5 By excluding values from 2010 and later,we avoid the omitted variable bias that might arise from a significant life event(e.g., job loss) that may reduce an individual’s self-efficacy and, contemporane-ously or in the near future, also cause financial distress.

III. Results

A. Self-Efficacy and Delinquency on Loan and Bill Payments

We estimate the relationship between financial distress and self-efficacy us-ing the model

Distressit = α + βSelfEfficacyi + γ ′Xit + εit, (1)

where Distress is a binary indicator of financial distress (e.g., missing a loan orbill payment) for individual i in year t and SelfEfficacy is the percentile rank ofindividual i’s average Pearlin Mastery score prior to 2010. The model includescontrol variables, contained in the vector X. We expand the set of control vari-ables across specifications. We first control for demographic characteristics—fixed effects for age, race, and gender—as well as cognitive ability, risk aversion,and patience. We then add controls for educational attainment (an indicatorfor bachelor’s degree completion), household income during year t, and house-hold net worth as of year t − 2 (the time of the prior NLSY interview). For theanalysis of missed loan payments, we exclude from the sample individuals whowere not at risk of default, that is, individuals who did not have a credit cardor a loan (mortgage, vehicle, or payday loan) in the current or prior survey. Weestimate the model using ordinary least squares, with observations clusteredby individual in calculating standard errors.6

The coefficient estimate ̂β measures the relationship between self-efficacyand financial distress, conditional on the control variables in X. It is important

4 These figures are in line with prior findings, such as a one-year correlation in time preferencesof 0.5 (Meier and Sprenger (2015)) and a two-year correlation in self-efficacy of 0.78 (Gecas (1989)).

5 Consistent with such an attenuation bias, we find smaller (in magnitude) effects on financialoutcomes of a single-year self-efficacy score as opposed to an average self-efficacy score.

6 Using a Probit model rather than the linear probability model has little impact on the estimatedeffects of self-efficacy.


Table IICorrelations between Self-Efficacy and Other Individual

CharacteristicsThis table reports correlations between self-efficacy and other individual characteristics. Income,education, and net worth are averages across all observations for an individual during the surveyyears over which they are included in the analysis. Significance at the 5% level or better is indicatedby ∗.

Self-Efficacy

CognitiveAbility

RiskAversion Patience

CollegeDegree Income

NetWorth

Self-efficacy 1Cognitive ability 0.26∗ 1Risk aversion −0.05∗ −0.22∗ 1Patience 0.13∗ 0.14∗ 0.08∗ 1College degree 0.19∗ 0.39∗ −0.02∗ 0.14∗ 1Income 0.21∗ 0.24∗ −0.04∗ 0.06∗ 0.29∗ 1Net worth 0.04∗ 0.03∗ 0.01 0.00 0.04∗ 0.16∗ 1

to emphasize that our findings are not subject to reverse causality concerns. Ifself-efficacy and distress were measured contemporaneously, one might worrythat respondents would express having less control over their lives becausethey have recently defaulted on debt or bill payments. For this reason, we takeadvantage of the longitudinal structure of the NLSY and use the self-efficacymeasure obtained years before the financial outcomes are realized. Neverthe-less, we do not necessarily isolate exogenous variation in self-efficacy. Thecorrelation matrix presented in Table II shows that self-efficacy is positivelycorrelated with cognitive ability, patience, educational attainment, income, andnet worth. Although we control for these observable differences and accountfor parental support in a siblings fixed effect analysis, omitted variables bias iscertainly possible. For example, we do not observe financial wealth and expecta-tions for income growth and uncertainty that may also help explain delinquencydecisions. We thus interpret the model estimates cautiously, as evidence thatis suggestive of a link between self-efficacy and financial distress.

Before examining the regression estimates, in Figure 2 we show the relation-ship between self-efficacy and financial delinquency in a simple plot. We plotthe incidence of late bill payments among all individuals in the sample and theincidence of late debt payments among individuals with current or recent debtoutstanding. As can be seen in the figure, delinquency decreases significantlyfrom the lowest quintile of self-efficacy to the highest. For people with the low-est self-efficacy, corresponding to raw Pearlin scores below 20, the incidenceof delinquency is 18.32% as measured by being late making debt payments,and 16.57% as measured by being late making bill payments. For people in thehighest quintile of self-efficacy, that is, those with raw Pearlin scores greaterthan 24, the incidence of delinquency is 10.22% with respect to paying debt,and 8.84% with respect to paying bills. The differences in delinquency ratesbetween these categories of individuals are both economically and statisticallysignificant (p < 0.01).


89

1011

1213

1415

1617

1819

20Fi

nanc

ial d

elin

quen

cy ra

te (%

)

13−20 20.2−21.3 21.4−22.6 22.7−24 24.2−28Average Pearlin score prior to 2010

Late debtLate bills

Financial delinquency rates and self-efficacy

Figure 2. Financial delinquency rates and self-efficacy. We plot the average delinquencyrates, as well as standard error bars, for individuals in each quintile of self-efficacy. We report therange of self-efficacy scores for each quintile. Financial delinquency is measured as the fraction ofrespondents with late debt (red dashed line) or late bill (black solid line) payments. These fractionsare measured in the full sample for late bill payments and in the subsample of individuals withdebt outstanding in the current or prior interview for late debt payments. (Color figure can beviewed at wileyonlinelibrary.com)

We further examine the relationship between self-efficacy and delinquencyby estimating equation (1). The regression results are displayed in Table III.The results show a strong negative correlation between a person’s self-efficacyearlier in life and his subsequent likelihood of missing debt and bill payments.The estimate in the first specification shows that, without control variables,the probability of missed debt payments declines by 9.59 percentage points(p < 0.01) when moving from the bottom of the self-efficacy distribution to thetop (from 0th to the 100th percentile). After conditioning on demographic- andpreference-related control variables, the estimated effect is −8.66 percentagepoints (p < 0.01). Finally, with additional controls for education, income, andnet worth, the self-efficacy coefficient is −6.82 percentage points (p < 0.01).For a one-standard-deviation increase in self-efficacy (0.28), this coefficient es-timate implies a 1.91 percentage point decline in loan default. This decline ismeaningful, as it amounts to 14% of the average loan default rate in the sam-ple (14.1%). As shown in the remaining columns of Table III, we also observe anegative and significant relationship between self-efficacy and delinquency onbills. In the specification without controls, the coefficient on self-efficacy indi-cates a 9.63 percentage point decline (p < 0.01) in the delinquency rate for a 100percentile change in the Pearlin score. Control variables reduce the magnitudeof this estimate, to −7.31 (p < 0.01) with demographic and preference con-trols and to −5.24 (p < 0.01) with education, income, and net worth controlsas well. This last coefficient estimate implies that a one-standard-deviation


Table IIISelf-Efficacy and Financial Delinquency

This table reports the effects of self-efficacy on financial delinquency. The dependent variables areindicators for being late on debt payments (columns (1) to (3)) or bill payments (columns (4) to (6)).For the analysis of late debt payments, the regression sample is restricted to individuals with debtoutstanding in the current or prior interview. The table presents coefficient estimates from linearprobability models, with standard errors clustered by individual. Self-Efficacyi varies betweenzero and one and refers to person i’s percentile rank in the distribution of average Pearlin scores.Income and net worth are measured in thousands of dollars. t-statistics are shown in parentheses.∗∗∗, ∗∗, and ∗ indicate significance at the 1%, 5%, and 10% level, respectively.

DependentVariable

Late Debtit(1)

Late Debtit(2)

Late Debtit(3)

Late Billsit(4)

Late Billsit(5)

Late Billsit(6)

Self-Efficacyi −9.59 −8.66 −6.82 −9.63 −7.31 −5.24(−6.23)∗∗∗ (−5.52)∗∗∗ (−4.38)∗∗∗ (−8.26)∗∗∗ (−6.11)∗∗∗ (−4.43)∗∗∗

Cognitive abilityi −0.98 0.41 −1.82 −0.32(−1.97)∗∗ (0.75) (−4.73)∗∗∗ (−0.79)

Risk aversioni −0.98 −0.85 −1.28 −1.19(−1.99)∗∗ (−1.74)∗ (−3.43)∗∗∗ (−3.23)∗∗∗

Patiencei −3.99 −3.03 −4.23 −3.15(−3.80)∗∗∗ (−2.88)∗∗∗ (−5.46)∗∗∗ (−4.08)∗∗∗

College degreei,t −5.30 −5.32(−5.34)∗∗∗ (−7.25)∗∗∗

Incomeit −0.08 −0.11(−7.71)∗∗∗ (−12.86)∗∗∗

Net worthi,t−1 −0.01 −0.01(−1.75)∗ (−1.63)

Age FEs No Yes Yes No Yes YesGender FEs No Yes Yes No Yes YesRace FEs No Yes Yes No Yes YesR2 0.01 0.03 0.04 0.01 0.03 0.05Observations 8,078 8,078 8,078 11,782 11,782 11,782

increase in self-efficacy reduces delinquency by 1.47 percentage points, or 12%relative to the average bill delinquency rate in the sample (12.4%).

Among the covariates, we find that educational attainment, risk aversion,patience, income, and net worth are negatively correlated with delinquency.Cognitive ability is also negatively correlated with delinquency, but its ex-planatory power diminishes after controlling for educational attainment, in-come, and net worth. Finally, demographic factors are also important, witholder people, women, and non-White respondents having higher rates of finan-cial delinquency.

The effect of self-efficacy on loan default is meaningfully large in compar-ison to the effects of these covariates. For example, in the second model aone-standard-deviation increase in cognitive ability corresponds to a 1 per-centage point decline in loan default, which is roughly half the size of a one-standard-deviation increase in self-efficacy. Income and education, on the otherhand, predict larger changes in default than does self-efficacy. A one-standard-deviation increase in income corresponds to a 2.81 percentage point decline


in loan default, and attainment of a college degree corresponds to a 5.30 per-centage point decline in load default, both of which exceed the correspondingestimate for self-efficacy of 1.91 percentage points. Finally, if we include indi-cators for negative shocks such as job loss and health problems as we do in theanalysis in Table X, we find that those shocks predict increases in loan defaultof 6.97 and 4.15 percentage points, respectively, for individuals with averageself-efficacy. The overall picture, therefore, is that self-efficacy has meaning-ful effects on delinquency, albeit somewhat smaller effects than education andeconomic shocks.7

We note that the R2’s in these models remain very low (below 0.05), asis typical in individual-level models of loan default. Though some variables,such as self-efficacy, reliably predict default, the portion of individual defaultbehavior that they explain is small compared to unmeasured factors.

B. Self-Efficacy and Consequences of Financial Delinquency

In Tables IV and V, we examine the relationship between self-efficacy andthe likelihood of facing negative consequences caused by financial delinquency.In Table IV, we focus on indicators of asset loss, whereas in Table V we focuson indications of credit denial.

The results in Table IV show that individuals with higher self-efficacy areboth less likely to have accounts in collection and less likely to experience fore-closure, asset repossession, or bankruptcy. We conduct these analyses in boththe full sample and the subsample of individuals with loans outstanding inthe current or prior interview. While individuals without prior loans can facebankruptcy or collections on delinquent bills, ceteris paribus they are less atrisk of such consequences than individuals who also have outstanding debts.Accordingly, we would expect the effects of self-efficacy to be stronger amongthe subsample with outstanding debts. We find that the direction of the esti-mated effects of self-efficacy on asset-related consequences of delinquency isthe same in either sample, but, as expected, the effects are more pronouncedin the subsample with preexisting loans. In this subsample, we find that amove from the bottom of the self-efficacy distribution to the top corresponds toa 5.48 percentage point decline in the probability of having an account in col-lection (p < 0.01) and a 1.77 percentage point decline in the probability of hav-ing a property foreclosed, having assets repossessed, or declaring bankruptcy(p < 0.05). For a one-standard-deviation change in self-efficacy, these estimatesimply proportional declines of 8% and 14%, respectively, relative to the averageincidence of account collections and foreclosure, repossession, or bankruptcy inthe sample.

Another potential consequence of loan default is being excluded from thecredit market in the future or, alternatively, receiving credit at expensive terms.The results in Table V show that self-efficacy is positively related to credit

7 We investigate which of the seven items in the Pearlin Mastery questionnaire are most pre-dictive of financial distress and find these to be items 1, 2, 3, 5, and 7.


Table IVSelf-Efficacy and Asset-Related Consequences of Delinquency

This table reports the effects of self-efficacy on asset-related consequences of delinquency. Thedependent variables are indicators for having accounts in collection or having recently experiencedasset foreclosure, repossession, or bankruptcy. In columns (3) and (4), the regression sample is re-stricted to individuals with debt outstanding in the current or prior interview. The table presentscoefficient estimates from linear probability models, with standard errors clustered by individual.Self-Efficacyi varies between zero and one and refers to person i’s percentile rank in the distri-bution of average Pearlin scores. Income and net worth are measured in thousands of dollars.t-statistics are shown in parentheses. ∗∗∗, ∗∗, and ∗ indicate significance at the 1%, 5%, and 10%level, respectively.

Dependent Variable

Accounts inCollectionit

(1)

Foreclosure,Repossession, or

Bankruptcyit(2)


(3)


Bankruptcyit(4)

Self-Efficacyi −2.16 −1.52 −5.48 −1.77(−1.43) (−2.35)∗∗ (−3.01)∗∗∗ (−2.11)∗∗

Cognitive abilityi 3.01 −0.00 1.77 −0.68(5.87)∗∗∗ (−0.02) (2.80)∗∗∗ (−2.29)∗∗

Risk aversioni −0.63 −0.15 −0.54 −0.23(−1.35) (−0.74) (−0.97) (−0.91)

Patiencei −5.64 −0.11 −5.78 −0.33(−5.53)∗∗∗ (−0.27) (−4.63)∗∗∗ (−0.61)

College degreei,t −11.59 −1.99 −11.18 −1.73(−11.38)∗∗∗ (−4.64)∗∗∗ (−10.06)∗∗∗ (−3.47)∗∗∗

Incomeit −0.07 −0.01 −0.10 −0.02(−6.61)∗∗∗ (−1.27) (−8.48)∗∗∗ (−3.30)∗∗∗

Net worthi,t−1 −0.01 −0.00 −0.01 −0.00(−2.01)∗∗ (−0.15) (−1.93)∗ (−0.10)

Age FEs Yes Yes Yes YesGender FEs Yes Yes Yes YesRace FEs Yes Yes Yes YesR2 0.04 0.01 0.05 0.02Observations 11,782 11,782 8,078 8,078

access and borrowing via traditional, lower-cost loans, and negatively relatedto borrowing through high-cost payday loans.

The first two models use measures from the NLSY79 on whether the respon-dent applied for credit and whether any of his credit applications were rejected.(These questions are not asked in the Child and Young Adult survey.) The firstregression, which includes the full set of controls, shows that self-efficacy doesnot predict whether people apply for credit.8 The composition of people applying

8 One might argue that applying for credit—for example, arranging a line of credit on a precau-tionary basis—is the type of costly action that individuals with high self-efficacy would undertakeas preparation for the future. From that perspective, the null finding for credit applications iscontrary to the predictions of our conceptual framework. However, many credit applications aresubmitted for current financing rather than on a precautionary basis. Furthermore, credit linesmight be an ineffective form of insurance against future shocks, as borrowing limits can be (and


Table VSelf-Efficacy and Credit-Related Consequences of Delinquency

This table reports the effects of self-efficacy on credit-related consequences of delinquency. Thedependent variables are indicators for applying for credit; having a credit application rejected;having a credit card, vehicle loan, or mortgage; or having borrowed through a payday loan. Thedata are from the NLSY79 original cohort in the first two columns, and from the NLSY CYA inthe last two columns. The table reports coefficient estimates from linear probability models, withstandard errors clustered by individual. Self-Efficacyi refers to person i’s percentile rank in thedistribution of average Pearlin scores. t-statistics are shown in parentheses. ∗∗∗, ∗∗, and ∗ indicatesignificance at the 1%, 5%, and 10% level, respectively.

Dependent Variable

Applied forCreditit

(1)

Applied andWas Rejectedit

(2)

Credit Card, VehicleLoan, or Mortgageit

(3)Payday Loanit

(4)

Self-Efficacyi 3.81 −8.18 6.71 −2.37(1.60) (−2.43)∗∗ (3.79)∗∗∗ (−2.67)∗∗∗

Cognitive abilityi 0.36 0.04 4.99 0.58(11.20)∗∗∗ (0.98) (8.44)∗∗∗ (1.86)∗

Risk aversioni −0.33 −1.04 1.22 −0.12(−1.46) (−2.67)∗∗∗ (2.29)∗∗ (−0.41)

Patiencei 0.89 −2.44 3.90 −1.53(0.44) (−0.74) (3.43)∗∗∗ (−2.47)∗∗

College degreeit −3.77 −2.40 11.27 −3.57(−2.00)∗∗ (−1.10) (9.57)∗∗∗ (−6.19)∗∗∗

Incomeit 0.08 −0.03 0.48 −0.01(7.17)∗∗∗ (−3.14)∗∗∗ (28.37)∗∗∗ (−1.18)

Net worthi,t−1 −0.00 −0.04 0.00 −0.01(−1.24) (−10.27)∗∗∗ (0.72) (−2.38)∗∗

Age FEs Yes Yes Yes YesGender FEs Yes Yes Yes YesRace FEs Yes Yes Yes YesR2 0.10 0.13 0.24 0.02Observations 5,322 2,246 11,782 11,782

for credit therefore appears unaffected by self-efficacy. Among people who applyfor credit, however, the rejection rate declines significantly with self-efficacy:an increase in the self-efficacy percentile from 0% to 100% corresponds to a8.18 percentage points decrease in the probability of rejection (p < 0.05). Fora one-standard-deviation change in self-efficacy, credit rejections decline by2.32 percentage points, or 8% of the average rejection rate (27.7%). These find-ings suggest that low-efficacy individuals experience a decline in credit access,perhaps as a result of past delinquency.

The same conclusion can be inferred, albeit indirectly, within the Child andYoung Adult sample. For that sample we do not observe credit applicationsand denials, but we do observe credit usage. The dependent variable in thethird model of Table V is an indicator for whether the respondent has either a

often are) reduced or eliminated at the lender’s discretion (see Fulford (2015) and Druedahl andJorgensen (2018)).


credit card, a car loan, or a mortgage loan (“traditional credit”). The dependentvariable in the fourth model is an indicator for whether the person has taken outa payday loan in the past 12 months. We find that increases in self-efficacy areassociated with increased use of traditional credit and decreased use of paydaycredit. An increase in the self-efficacy percentile from 0% to 100% corresponds toa 6.71 percentage point (p < 0.01) increase in the likelihood of using traditionalcredit and a 2.37 percentage point (p < 0.01) decrease in the likelihood of usingpayday credit. Compared to sample averages, a one-standard-deviation changein self-efficacy implies a 3.2% increase in the use of traditional credit and an11.1% decrease in the use of payday credit. In summary, our analysis of creditdenials and credit usage suggests that individuals with low self-efficacy aremore likely to be denied credit and to rely on high-cost credit such as paydayloans.

C. Self-Efficacy, Financial Distress, and Parental Support

An important factor that is outside of our stylized theoretical frameworkbut could be related to both self-efficacy and financial distress is parentalinvestment, or intrafamily insurance. Specifically, it is possible that peoplewith high levels of self-efficacy grow up in families where parents invest morein their offspring in terms of character formation, and, also, offer more financialsupport should their children encounter adversity in life.

In Tables VI and VII, we examine whether differential parental investmentcan explain the relationship we document between self-efficacy and financialdelinquency and its consequences. In the regression models in Table VI, we takeadvantage of the fact that many of the individuals in our sample have siblings,and thus we can include mother fixed effects as additional determinants offinancial delinquency and its consequences. An implicit assumption necessaryfor this analysis is that an individual’s self-efficacy is not perfectly correlatedwith that of his mother. This is indeed what we find in the data. For example,looking at self-efficacy scores assessed in the 2008 survey wave, we find that thecorrelation of the mother’s score with that of the child is 0.11. The correlationof the mother’s score with the child’s average score (i.e., average score acrossall survey waves) is 0.16.

Table VI presents estimation results for the regressions with mother fixedeffects. For each of the outcomes—delinquency on loan and bill payments, ac-counts in collection, asset loss, and access to credit—the effects of self-efficacyare statistically significant and similar in sign and magnitude to those docu-mented in the prior tables. These results suggest that intrafamily insurancecannot fully account for the effects of self-efficacy on financial delinquency andits consequences in terms of asset loss and credit access.9

9 As an additional check on the role of external support, we include an indicator for whetheranyone outside of the household contributes to the household’s mortgage or rent payment. Control-ling for this source of external support does not change the size or magnitude of the coefficients onself-efficacy across the various outcomes of interest, which adds further evidence to the view thatexternal support does not explain our findings regarding self-efficacy.


Table VISelf-Efficacy and Financial Delinquency: Parental Investment

as a MechanismThis table reports the effects of self-efficacy on financial delinquency, controlling for parentalinvestment. The table reports coefficient estimates from linear probability models, with standarderrors clustered by individual. The models include a fixed effect for each sibling group (“MotherFEs”). Self-Efficacyi refers to person i’s percentile rank in the distribution of average Pearlin scores.The controls include the respondents’ cognitive ability, risk aversion, patience, education, income,and net worth. t-statistics are shown in parentheses. ∗∗∗, ∗∗, and ∗ indicate significance at the 1%,5%, and 10% level, respectively.

DependentVariable

LateDebtit

(1)

LateBillsit

(2)


(3)


Bankruptcyit(4)

Credit Card,Vehicle Loan, or

Mortgageit(5)

PaydayLoanit

(6)

Self-Efficacyi −5.04 −3.46 −7.99 −3.84 8.15 −2.39(−1.96)∗∗ (−1.85)∗ (−2.82)∗∗∗ (−2.70)∗∗∗ (3.33)∗∗∗ (−1.96)∗∗

Controls Yes Yes Yes Yes Yes YesMother FEs Yes Yes Yes Yes Yes YesAge FEs Yes Yes Yes Yes Yes YesGender FEs Yes Yes Yes Yes Yes YesRace FEs Yes Yes Yes Yes Yes YesR2 0.14 0.11 0.21 0.09 0.37 0.12Observations 8,078 11,782 8,078 8,078 11,782 11,782

Moreover, when we split the data based on the net worth of the respondents’mothers in Table VII, we find that the largest effect of self-efficacy on thelikelihood of financial delinquency, as measured by the indicator Late debtit, isobtained among individuals whose mothers are in the lowest tertile in termsof net worth. For these individuals, the difference in the probability of delin-quency between those with the highest and those with the lowest self-efficacyscores is 11.10 percentage points (p < 0.01), which is more than 50% largerthan the coefficient of 6.82 percentage points from the main specification (Ta-ble III, column (3)) and three times as large as the self-efficacy coefficient forindividuals with mothers in the wealthiest tercile of the sample (Table VII, col-umn (4)). We conclude that parental investments do not confound self-efficacy,but instead interact with self-efficacy, as the beneficial effects of self-efficacyare largest for those who grew up with the most economic disadvantage.

D. Self-Efficacy and Financial Preparedness

Our stylized model suggests that one mechanism explaining the connectionbetween self-efficacy and distress is based on effort provision. Individuals withhigh self-efficacy believe that their effort has a greater impact on their future,and thus are more willing to take costly actions today to avoid distress in thefuture. The results presented above are consistent with this idea, but do notclarify the mechanisms through which self-efficacy reduces fragility. In the


Table VIISelf-Efficacy and Financial Delinquency: Heterogeneous Effects

Based on Parental Net WorthThis table reports the effects of self-efficacy on financial delinquency, across different parental en-vironments. The dependent variable is an indicator for being late on debt payments. The regressionsample is restricted to individuals with debt outstanding in the current or prior interview. Thetable presents coefficient estimates from linear probability models, with standard errors clusteredby individual. Self-Efficacyi refers to person i’s percentile rank in the distribution of average Pearlinscores. The controls include the respondents’ cognitive ability, risk aversion, patience, education,income, and net worth. Income and net worth (of the respondent and their mother) are measured inthousands of dollars. Respondents are assigned to a tertile of mother net worth before conditioningon whether they have recently had an outstanding loan. t-statistics are shown in parentheses. ∗∗∗,∗∗, and ∗ indicate significance at the 1%, 5%, and 10% level, respectively.

Late Debtit

AllObservations

Lowest Tertile ofMother’s Net

Worth

Middle Tertile ofMother’s Net

Worth

Highest Tertile ofMother’s Net

WorthDependent Variable (1) (2) (3) (4)

Self-Efficacyi −6.84 −11.10 −7.25 −3.61(−4.40)∗∗∗ (−3.21)∗∗∗ (−2.60)∗∗∗ (−1.67)∗

Mother’s net worthi −0.01(−4.02)∗∗∗

Controls Yes Yes Yes YesAge FEs Yes Yes Yes YesGender FEs Yes Yes Yes YesRace FEs Yes Yes Yes YesR2 0.04 0.03 0.02 0.04Observations 8,078 1,996 2,791 3,291

analysis below, we investigate whether individuals with high self-efficacy takeactions that improve their financial security and insulate them from negativeeconomic shocks.

To capture financial actions that help one prepare for and potentially miti-gate adverse events, we use several variables related to precautionary saving,insurance purchases, and retirement preparation available in the NLSY.

In Table VIII, we use two indicators of financial preparedness observed in theNLSY CYA: having “emergency” savings of at least three months of expensesand purchasing health insurance. We find that increasing the self-efficacy per-centile from 0% to 100% corresponds to a 7.08 percentage point (p < 0.01) in-crease in the probability that an individual has emergency savings and a 6.60percentage point (p < 0.01) decrease in the probability of going without healthinsurance. The effect of self-efficacy on health insurance continues to be largeand significant (−5.13 percentage points, p < 0.01) even after we control forwhether health insurance is offered to the individual through his job. This con-trol accounts for possible effects of self-efficacy on a person’s employment andchoice of job, and thus allows us to isolate the effect of self-efficacy on the deci-sion to purchase health insurance, rather than on the availability of insurance.


Table VIIISelf-Efficacy and Financial Preparedness Early in Adulthood

This table shows the effects of self-efficacy on financial preparedness early in adulthood. Thedependent variables are indicators for having set aside emergency savings or lacking health in-surance. The table presents coefficient estimates from linear probability models, with standarderrors clustered by individual. Self-Efficacyi refers to person i’s percentile rank in the distributionof average Pearlin scores. t-statistics are shown in parentheses. ∗∗∗ and ∗∗ indicate significance atthe 1% and 5% level, respectively.

Emergency Fundsit No Health Insuranceit No Health InsuranceitDependent Variable (1) (2) (3)

Self-Efficacyi 7.08 −6.60 −5.13(3.43)∗∗∗ (−3.54)∗∗∗ (−2.78)∗∗∗

Cognitive abilityi −1.62 0.38 0.69(−2.31)∗∗ (0.60) (1.10)

Risk aversioni 0.20 −2.12 −1.95(0.33) (−3.77)∗∗∗ (−3.50)∗∗∗

Patiencei 7.18 −5.77 −5.21(5.53)∗∗∗ (−4.77)∗∗∗ (−4.35)∗∗∗

College degreeit 13.88 −10.31 −9.36(8.63)∗∗∗ (−8.20)∗∗∗ (−7.56)∗∗∗

Incomeit 0.31 −0.26 −0.20(17.88)∗∗∗ (−19.20)∗∗∗ (−15.13)∗∗∗

Net worthi,t−1 0.04 0.00 0.00(2.47)∗∗ (0.29) (0.03)

Health insurance −13.26offered by employerit (−13.44)∗∗∗

Age FEs Yes Yes YesGender FEs Yes Yes YesRace FEs Yes Yes YesR2 0.10 0.08 0.10Observations 8,206 11,782 11,782

In Table IX, we use three indicators of financial preparedness available inthe NLSY79 sample of older adults: setting aside emergency funds, makingfinancial preparations for retirement, and purchasing long-term care insur-ance. We find that an increase in self-efficacy from the 0th to 100th percentileimplies increases of 8.12 percentage points (p < 0.01) in the probability of hav-ing emergency funds, 10.55 percentage points (p < 0.01) in the probability ofretirement planning activities, and 3.85 percentage points (p < 0.05) in theprobability of having long-term care insurance.

Thus, among both younger and older adults, we find that self-efficacy ispositively related to actions that are costly today but mitigate adverse financialoutcomes in the future.

E. Self-Efficacy and Fragility after Economic Shocks

The finding that self-efficacy is positively correlated with financial prepared-ness suggests that, as implied by our theoretical setting, people with higher


Table IXSelf-Efficacy and Financial Preparedness Late in Adulthood

This table shows the effects of self-efficacy on financial preparedness late in adulthood. The de-pendent variables are indicators for having set aside emergency savings, having taken actions toprepare for retirement, or having purchased long-term care insurance. The data come from theNLSY79 original sample. The table presents coefficient estimates from linear probability models,with standard errors clustered by individual. Self-Efficacyi refers to person i’s percentile rank inthe distribution of average Pearlin scores. Income and net worth are measured in thousands ofdollars. t-statistics are shown in parentheses. ∗∗∗, ∗∗, and ∗ indicate significance at the 1%, 5%, and10% level, respectively.

Emergency Fundsit Retirement Planningit Long-Term Care InsuranceitDependent Variable (1) (2) (3)

Self-Efficacyi 8.12 10.55 3.85(3.67)∗∗∗ (4.50)∗∗∗ (2.45)∗∗

Cognitive abilityi 0.06 0.31 −0.01(2.18)∗∗ (10.06)∗∗∗ (−0.46)

Risk aversioni −0.31 −1.00 0.16(−1.44) (−4.26)∗∗∗ (1.01)

Patiencei 1.78 −0.02 1.63(0.98) (−0.01) (1.28)

College degreeit 5.98 7.65 1.47(3.37)∗∗∗ (4.35)∗∗∗ (1.14)

Incomeit 0.07 0.06 0.04(7.80)∗∗∗ (6.06)∗∗∗ (5.19)∗∗∗

Net worthi,t−1 0.06 0.04 0.00(18.24)∗∗∗ (11.39)∗∗∗ (1.87)∗

Age FEs Yes Yes YesGender FEs Yes Yes YesRace FEs Yes Yes YesR2 0.21 0.18 0.03Observations 5,322 5,322 5,322

self-efficacy should be more resilient in response to negative financial shocks.The evidence in Table X supports this implication. We consider two proxies fornegative economic shocks that are observable in the NLSY CYA: job loss andhealth shocks that require medical care.

In the first and second regression models in the table, we show that self-efficacy is unrelated to the incidence of such shocks over the prior year. How-ever, both shocks increase the likelihood of financial delinquency, and theirimpacts are much larger among individuals with low self-efficacy.

In columns (3) and (4), we regress the indicator for late bill payments onan indicator for the shock and its interaction with self-efficacy. For individualswith the lowest self-efficacy (0th percentile), the frequency of late bill pay-ments rises by 8.53 percentage points (p < 0.01) and 10.62 percentage points(p < 0.01), respectively, with a prior year health shock or job loss. The largeand statistically significant negative coefficients on the Shock × SelfEfficacyinteraction terms show that individuals with high self-efficacy are more re-silient. Indeed, the sum of the coefficients on the shock and the interaction termare quite close to zero (and statistically insignificant) for both models, which


Table XSelf-Efficacy and Dealing with Financial Shocks

This table reports the effects of self-efficacy, negative financial shocks, and their interactions, onfinancial delinquency. The dependent variables are indicators for the incidence of health problems,job loss, late debt payments, or late bill payments. The table presents coefficient estimates fromlinear probability models, with standard errors clustered by individual. Self-Efficacyi refers toperson i’s percentile rank in the distribution of average Pearlin scores. The controls include therespondents’ cognitive ability, risk aversion, patience, education, income, and net worth. t-statisticsare shown in parentheses. ∗∗∗ and ∗∗ indicate significance at the 1% and 5% level, respectively.

DependentVariable

Health Shockit(1)

Lost Jobit(2)

Late Billsit(3)

Late Billsit(4)

Late Debtit(5)

Late Debtit(6)

Self-Efficacyi 0.01 −0.00 −3.15 −5.77 −6.68 −6.77(0.72) (−0.06) (−2.41)∗∗ (−4.83)∗∗∗ (−3.76)∗∗∗ (−4.30)∗∗∗

Health shocki,t 8.53 4.65(5.14)∗∗∗ (2.23)∗∗

Health shocki,t −7.68 −0.97X Self-Efficacyi (−2.99)∗∗∗ (−0.30)Lost jobi,t 10.62 13.68

(3.22)∗∗∗ (3.43)∗∗∗Lost jobi,t −12.60 −13.16X Self-Efficacyi (−2.36)∗∗ (−2.05)∗∗Controls Yes Yes Yes Yes Yes YesAge FEs Yes Yes Yes Yes Yes YesGender FEs Yes Yes Yes Yes Yes YesRace FEs Yes Yes Yes Yes Yes YesR2 0.04 0.01 0.05 0.04 0.04 0.04Observations 11,782 11,782 11,782 11,782 8,078 8,078

implies that delinquency does not increase with shocks among individuals withthe highest self-efficacy (100th percentile).

In columns (5) and (6), we repeat the above analysis for late debt payments.We again find that a health shock or a job loss are associated with higher delin-quency, as indicated by 4.65 percentage point (p < 0.05) and 13.68 percentagepoint (p < 0.01) increases in late debt payments, respectively. The coefficienton the interaction between self-efficacy and the health shock indicator, whilenegative, is not statistically significant. For job loss, on the other hand, we finda large and statistically significant negative coefficient on the interaction term.The estimate (−13.16, p < 0.05) suggests that job loss has a negligible effecton late debt payments for individuals with the highest self-efficacy.

Overall, these results show that self-efficacy, through the actions that itinduces, can significantly mitigate the impact of negative shocks on financialdelinquency.

IV. Alternative Hypotheses and Robustness Checks

A. Distinguishing Self-Efficacy from Optimism, Time Preferences, and OtherNoncognitive Abilities

Self-efficacy may contain aspects of general optimism, as studied in Puriand Robinson (2007), but there are important differences between the two


concepts. General optimism refers to a shift upward in the distribution ofoutcomes people believe are possible, which does not specifically entail greaterindividual agency and control over the future, as self-efficacy does. Optimismand self-efficacy therefore have different implications for the level of effortexerted to achieve good outcomes. On the one hand, individuals with highlevels of optimism may not work very hard, since they expect the good outcometo happen anyway, and this implies that excessive optimism is detrimental,as documented by Puri and Robinson (2007). On the other hand, individualswith high self-efficacy will expend high levels of effort because of the perceivedhigh return to effort. In our data, we find no detrimental effect of having thehighest levels of self-efficacy. On the contrary, as can be seen, for example, inFigure 2, individuals with the highest self-efficacy are those with the lowestrates of financial distress, and as shown in the analysis in Tables VIII andIX, they are also most likely to take steps that help them prepare to weatherpotential negative financial shocks.

That being said, as we discuss in the context of our theoretical framework, itis possible that high levels of self-efficacy can decrease people’s overall utility.In particular, excessive levels of self-efficacy may spur individuals to exerttoo much effort in terms of financial preparedness, with negative effects onother dimensions of their lives, for example, by working multiple jobs and notdedicating time to family matters.

It is also important to distinguish the effects of self-efficacy from time pref-erences, since both self-efficacy and time-discounting can affect an individual’sperception of the intertemporal trade-offs implicit in loan repayment. Meierand Sprenger (2012) show that standard experimental measures of time pref-erences can predict creditworthiness. Their finding that more patient individ-uals have higher credit scores is consistent with our finding that more patientindividuals are less likely to default on their debt. Our measure of time prefer-ences, however, is quite coarse—we rely on the parent’s report of whether theirchild is impulsive or acts without thinking—so it is possible that unmeasureddifferences in time preferences are confounded with self-efficacy.

To examine this possibility, we expand our analysis to include an additionalmeasure of time preferences. The NLSY CYA does not have a full elicitationof time preferences as in Meier and Sprenger (2012), but it does have thecomponents necessary to employ a revealed-preference-based measure of timepreferences, as in DellaVigna and Paserman (2005). Specifically, we use thesurvey responses prior to 2010 to construct indicators for being restless or im-patient during any NLSY interview, having ever smoked, having ever been aheavy drinker, and having had sexual intercourse without contraceptives whileunmarried. Following DellaVigna and Paserman (2005), we use the first prin-cipal component of these four measures as a proxy for latent time preferences.As shown in Table XI, this additional control for time preferences predicts vari-ation in financial distress and preparedness as one would expect: delinquencydecreases and preparedness increases with patience. The additional control,however, has little effect on our main findings: self-efficacy remains stronglynegatively correlated with financial delinquency and positively correlated with


Tab

leX

IR

obu

stn

ess

Ch

eck

s:A

dd

itio

nal

Con

trol

sfo

rIn

div

idu

alT

rait

san

dP

refe

ren

ces

Th

ista

ble

repo

rts

robu

stn

ess

chec

ksas

sess

ing

wh

eth

erse

lf-e

ffica

cyen

com

pass

esot

her

indi

vidu

alch

arac

teri

stic

sth

atm

ayre

late

tofi

nan

cial

outc

omes

.T

he

tabl

epr

esen

tsco

effi

cien

tes

tim

ates

from

lin

ear

prob

abil

ity

mod

els,

wit

hst

anda

rder

rors

clu

ster

edby

indi

vidu

al.T

he

con

trol

vari

able

sin

thes

em

odel

sin

clu

deea

chpa

rtic

ipan

t’sco

gnit

ive

abil

ity,

risk

aver

sion

,pat

ien

ce(o

ur

orig

inal

mea

sure

),a

coll

ege

degr

eein

dica

tor,

tim

e-va

ryin

gin

com

ean

dn

etw

orth

,an

d,in

the

spec

ifica

tion

inth

ela

stco

lum

n,a

nin

dica

tor

for

wh

eth

erth

epa

rtic

ipan

tis

offe

red

hea

lth

insu

ran

ceby

his

empl

oyer

.t-s

tati

stic

sar

esh

own

inpa

ren

thes

es.∗

∗∗,∗

∗ ,an

d∗

indi

cate

sign

ifica

nce

atth

e1%

,5%

,an

d10

%le

vel,

resp

ecti

vely

.

Lat

eD

ebt it

Lat

eB

ills

it

Acc

oun

tsin

Col

lect

ion

it

For

eclo

sure

,R

epos

sess

ion

,or

Ban

kru

ptcy

it

Cre

dit

Car

d,

Veh

icle

Loa

n,o

rM

ortg

age i

t

Pay

day

Loa

nit

Em

erge

ncy

Fu

nd

s it

No

Hea

lth

Insu

ran

ceit

Dep

ende

nt

Var

iabl

e(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)

Sel

f-E

ffica

cyi

−6.3

9−3

.81

−4.1

3−1

.94

4.54

−2.3

75.

05−4

.51

(−3.

80)∗

∗∗(−

2.98

)∗∗∗

(−2.

15)∗

∗(−

2.10

)∗∗

(2.4

2)∗∗

(−2.

43)∗

∗(2

.30)

∗∗(−

2.31

)∗∗

Agr

eeab

len

ess i

−0.4

9−0

.65

−0.5

1−0

.13

0.61

0.05

0.50

−0.6

2(−

1.17

)(−

2.07

)∗∗

(−1.

03)

(−0.

61)

(1.4

0)(0

.20)

(0.9

9)(−

1.34

)O

pen

nes

s i0.

09−0

.01

0.75

−0.0

3−0

.93

0.64

−1.2

51.

08(0

.20)

(−0.

03)

(1.5

0)(−

0.14

)(−

2.13

)∗∗

(2.7

5)∗∗

∗(−

2.44

)∗∗

(2.3

4)∗∗

Con

scie

nti

ousn

ess i

−0.0

3−0

.47

0.10

0.26

0.96

−0.1

61.

430.

32(−

0.07

)(−

1.56

)(0

.22)

(1.3

3)(2

.24)

∗∗(−

0.71

)(2

.95)

∗∗∗

(0.7

1)E

xtra

vers

ion

i0.

330.

010.

010.

020.

170.

291.

09−0

.41

(0.9

9)(0

.06)

(0.0

2)(0

.13)

(0.4

5)(1

.43)

(2.4

6)∗∗

(−1.

06)

Neu

roti

cism

i0.

460.

891.

260.

04−0

.85

0.14

−0.3

10.

70(1

.19)

(3.0

2)∗∗

∗(2

.78)

∗∗∗

(0.1

8)(−

2.13

)∗∗

(0.6

7)(−

0.67

)(1

.65)

∗P

atie

nce

ias

inD

ella

Vig

na

−2.3

2−2

.56

−4.4

8−1

.21

4.87

−1.0

53.

28−5

.23

&P

aser

man

(200

5)(−

4.83

)∗∗∗

(−6.

62)∗

∗∗(−

7.77

)∗∗∗

(−4.

62)∗

∗∗(8

.89)

∗∗∗

(−3.

51)∗

∗∗(4

.89)

∗∗∗

(−9.

05)∗

∗∗C

ontr

ols

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Age

FE

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sG

ende

rF

Es

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Rac

eF

Es

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

R2

0.04

0.05

0.06

0.02

0.25

0.02

0.10

0.11

Obs

erva

tion

s7,

905

11,5

167,

905

7,90

511

,516

11,5

168,

031

11,5

16


financial preparedness. Hence, patience and self-efficacy are not overlappingindividual characteristics. This result is in line with prior work in the psychol-ogy literature that finds no significant correlation between self-efficacy andtime preferences (Charlton, Gossett, and Charlton (2011)).

Finally, it is important to distinguish the effects of self-efficacy from thoseof other personality traits, since noncognitive skills might be interrelated andmutually reinforcing. In Table XI, we include controls for each of the Big-Fivepersonality traits—agreeableness, openness, conscientiousness, extraversion,and neuroticism—as measured through the personality inventory in Gosling,Rentfrow, and Swann (2003). As shown in the table, the Big-Five traits do notshow strong relationships with financial delinquency, nor do they crowd out theeffect of self-efficacy. Only for emergency savings do the Big-Five traits displaybroadly statistically significant effects, and even in that case the coefficient onself-efficacy remains positive and strongly significant.

B. Robustness to the Construction of the Sample and the Self-Efficacy Measure

By design, the original NLSY79 survey oversampled minorities. Therefore,our main sample of NLSY79 children also differs from a nationally representa-tive cross-section of U.S. young adults. In Panel A of Table XII, we reestimateour main empirical models with sample weights that adjust for nonrandomsample attrition and the oversampling of minorities. Using these alternativesurvey weights does not meaningfully change the relation of self-efficacy tofinancial distress and preparedness. Only for foreclosure, bankruptcy, and re-possession does the self-efficacy coefficient decline slightly in magnitude andstatistical significance.

Another potential concern regarding our sampling strategy relates to our useof multiple observations per person in a panel regression analysis. Our mainindependent variable of interest—self-efficacy—is time-invariant, but our mea-sures of financial distress and preparedness vary over time, as do other covari-ates such as educational attainment, income, and net worth. These covariatesare related to self-efficacy, so controlling for them as precisely as possible is im-portant in establishing that self-efficacy has an independent association withfinancial distress and preparedness. To do so, we use panel regression analysisand cluster standard errors by individual to account for serial correlation in anindividual’s financial distress and, by construction, in his self-efficacy score. Tofurther ensure that our results are robust in a purely cross-sectional analysis,we conduct two further analyses. First, we collapse the person-year observa-tions to the person level, coding the indicators for distress and preparedness asthe maximum of the three year-over-year measures. For example, the indicatorfor late debt payments takes the value of one if the individual was late mak-ing debt payments in any of the three surveys. For the time-varying controlvariables, such as age and income, we use their average value over the threesurveys. The person-level regression results in Panel B of Table XII confirmprecisely the same patterns as our main analysis. Second, we repeat our main


Tab

leX

IIR

obu

stn

ess

Ch

eck

s:A

lter

nat

ive

Dat

aS

amp

lin

gS

trat

egie

san

dM

easu

res

ofS

elf-

Effi

cacy

Th

ista

ble

repo

rts

robu

stn

ess

chec

ksre

gard

ing

alte

rnat

ive

data

sam

plin

gst

rate

gies

and

mea

sure

sof

self

-effi

cacy

.T

he

tabl

epr

esen

tsco

effi

cien

tes

tim

ates

from

lin

ear

prob

abil

ity

mod

els,

wit

hst

anda

rder

rors

clu

ster

edby

indi

vidu

al.I

nP

anel

A,w

eu

sesu

rvey

wei

ghts

for

alle

stim

atio

ns.

InP

anel

B,w

eco

llap

seth

eda

taat

the

part

icip

ant

leve

l.In

Pan

elC

,we

esti

mat

eth

em

odel

sin

year

lysu

bsam

ples

.In

Pan

elD

,we

use

resi

dual

self

-effi

cacy

net

ofag

eef

fect

s,an

din

Pan

elE

we

mea

sure

self

-effi

cacy

asth

era

wP

earl

inM

aste

rysc

ore

(on

the

7-to

28-p

oin

tsc

ale)

,rat

her

than

asa

perc

enti

lera

nk

(fro

m0

to1)

.Con

trol

vari

able

sin

thes

em

odel

sin

clu

deea

chpa

rtic

ipan

t’sco

gnit

ive

abil

ity,

risk

aver

sion

,pat

ien

ce,a

coll

ege

degr

eein

dica

tor,

inco

me

and

net

wor

th(a

vera

ged

over

surv

eyw

aves

inP

anel

B),

age,

gen

der,

and

race

fixe

d-ef

fect

s,an

d,in

the

spec

ifica

tion

inth

ela

stco

lum

n,a

nin

dica

tor

for

wh

eth

erth

epa

rtic

ipan

tis

offe

red

hea

lth

insu

ran

ceby

thei

rem

ploy

er.t

-sta

tist

ics

are

show

nin

pare

nth

eses

.∗∗∗

,∗∗ ,

and

∗in

dica

tesi

gnifi

can

ceat

the

1%,5

%,a

nd

10%

leve

l,re

spec

tive

ly.

Lat

eD

ebt it

Lat

eB

ills

it

Acc

oun

tsin

Col

lect

ion

it

For

eclo

sure

,R

epos

sess

ion

,or

Ban

kru

ptcy

it

Cre

dit

Car

d,

Veh

icle

Loa

n,o

rM

ortg

age i

t

Pay

day

Loa

nit

Em

erge

ncy

Fu

nd

s it

No

Hea

lth

Insu

ran

ceit

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elA

:Est

imat

ion

sU

sin

gS

urv

eyW

eigh

ts

Sel

f-E

ffica

cyi

−6.1

2−5

.50

−6.0

0−1

.26

7.03

−2.5

57.

30−6

.28

(−3.

72)∗

∗∗(−

4.27

)∗∗∗

(−2.

96)∗

∗∗(−

1.27

)(3

.45)

∗∗∗

(−2.

92)∗

∗∗(2

.98)

∗∗∗

(−2.

98)∗

∗∗

Pan

elB

:Est

imat

ion

sU

sin

gD

ata

Col

laps

edat

the

Per

son

Lev

el

Sel

f-E

ffica

cyi

−9.0

7−6

.93

−6.0

9−2

.92

7.91

−3.1

07.

80−6

.12

(−3.

96)∗

∗∗(−

3.73

)∗∗∗

(−2.

48)∗

∗(−

2.21

)∗∗

(3.9

2)∗∗

∗(−

2.23

)∗∗

(3.3

7)∗∗

∗(−

2.66

)∗∗∗

(Con

tin

ued

)


Tab

leX

II—

Con

tin

ued

Lat

eD

ebt it

Lat

eB

ills

it

Acc

oun

tsin

Col

lect

ion

it

For

eclo

sure

Rep

osse

ssio

n,o

rB

ankr

upt

cyit

Cre

dit

Car

d,

Veh

icle

Loa

n,o

rM

ortg

age i

t

Pay

day

Loa

nit

Em

erge

ncy

Fu

nd

s it

No

Hea

lth

Insu

ran

ceit

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elC

:Est

imat

ion

son

Eac

hS

urv

eyIn

terv

iew

Sep

arat

ely

2010

inte

rvie

wS

elf-

Effi

cacy

i−6

.10

−5.2

3−3

.02

−2.5

46.

14−0

.52

–−5

.69

(−2.

04)∗

∗(−

2.43

)∗∗

(−0.

94)

(−1.

50)

(2.1

9)∗∗

(−0.

36)

–(−

1.91

)∗20

12in

terv

iew

Sel

f-E

ffica

cyi

−9.8

0−4

.83

−4.8

3−1

.32

5.02

−3.8

05.

98−5

.47

(−4.

00)∗

∗∗(−

2.52

)∗∗

(−1.

73)∗

(−1.

04)

(1.9

2)∗

(−2.

69)∗

∗∗(2

.20)

∗∗(−

2.01

)∗∗

2014

inte

rvie

wS

elf-

Effi

cacy

i−5

.67

−6.5

2−8

.69

−1.9

28.

79−2

.58

7.97

−7.1

9(−

2.59

)∗∗∗

(−3.

80)∗

∗∗(−

3.49

)∗∗∗

(−1.

73)∗

(3.5

8)∗∗

∗(−

2.03

)∗∗

(3.0

4)∗∗

∗(−

2.97

)∗∗∗

Pan

elD

:Age

-Adj

ust

edS

elf-

Effi

cacy

(Mea

sure

das

Per

cen

tile

Ran

k,fr

omZ

ero

toO

ne)

Sel

f-E

ffica

cyi

−6.8

6−5

.47

−5.6

8−1

.86

6.31

−2.3

07.

04−5

.35

(−4.

50)∗

∗∗(−

4.70

)∗∗∗

(−3.

18)∗

∗∗(−

2.26

)∗∗

(3.6

3)∗∗

∗(−

2.63

)∗∗∗

(3.4

6)∗∗

∗(−

2.94

)∗∗∗

Pan

elE

:Raw

Sel

f-E

ffica

cy(M

easu

red

asR

awP

earl

inM

aste

ryS

core

,on

the

7to

28P

oin

tsS

cale

)

Sel

f-E

ffica

cyi

−0.8

7−0

.62

−0.6

8−0

.21

0.78

−0.2

90.

84−0

.57

(−4.

80)∗

∗∗(−

4.48

)∗∗∗

(−3.

19)∗

∗∗(−

2.10

)∗∗

(3.7

0)∗∗

∗(−

2.76

)∗∗∗

(3.5

1)∗∗

∗(−

2.62

)∗∗∗


regressions in yearly subsamples in Panel C of the table. The results reportedconfirm that our main effects are generally quite robust across years.

Last, we consider adjustments to the measurement of self-efficacy. Specif-ically, in Panel D of Table XII we compute an age-adjusted measure of self-efficacy. We regress the raw Pearlin scores on a quadratic in the respondent’sage at the time of the survey and compute a residual mastery score net of thecomponent explained by age. We then average each individual’s age-adjustedPearlin score and assess the percentile rank in the overall distribution of age-adjusted scores. The correlation between this age-adjusted percentile rank andthe unadjusted version is 0.99. Unsurprisingly, therefore, the results in PanelD of the table show that using age-adjusted self-efficacy in our main regressionmodels does not change the significance or magnitude of any of our results.Furthermore, in Panel E of the same table we use the individual’s raw PearlinMastery score (again averaged over all of the survey waves prior to 2010), whichis on the 7- to 28-point scale. As in our main analysis, we find robust evidencethat self-efficacy is negatively related to financial delinquency and its con-sequences and positively related to financial preparedness. Furthermore, themagnitudes of the implied effects are very similar to those in the main anal-ysis. For example, a one-standard-deviation increase in the average Pearlinscore (2.41) is associated with a 2.10 percentage point reduction in late debtpayments, which is very close to the corresponding estimate of 1.91 percentagepoint for the Pearline percentile rank in the main analysis.

V. Conclusion

Recent work in economics and psychology emphasizes the importance ofnoncognitive abilities in educational and job market success. Here, we findthat noncognitive skills also play an important role in determining financialsuccess. We find that self-efficacy predicts substantial differences in both finan-cial preparedness and financial distress. Individuals who believe more stronglythat they can influence the future though their actions are less likely, yearslater, to be delinquent on loans, and to suffer consequences such as havingassets repossessed or property foreclosed, or to lose access to traditional credit.Individuals with higher self-efficacy also prepare more to avoid financial dis-tress by setting aside emergency savings, purchasing insurance, and planningfor retirement. Consistent with being more financially prepared, individualswith higher self-efficacy are less likely to become delinquent upon facing anegative shock such as job loss, an accident, or an illness.

Identifying the role of noncognitive abilities such as self-efficacy on householdfinancial outcomes is useful because, unlike other characteristics that may bepredetermined, noncognitive skills can be improved via interventions at vari-ous stages in life. For example, Heckman, Pinto, and Savelyev (2013) show thatan intervention in early childhood, the Perry Preschool program, improved par-ticipants’ schooling and labor market outcomes mainly through an increase innoncognitive skills. It is therefore possible that by boosting people’s belief intheir own capacity to influence the future, they will in fact take actions that


help them achieve better financial outcomes. Such interventions can improvewelfare to the extent that they instill more accurate beliefs about self-efficacy.However, we should caution that individuals who overestimate their efficacymight be harmed by having their overoptimistic views further distorted. Webelieve that understanding self-efficacy in the financial domain and studying fi-nancial counseling interventions targeted at improving self-efficacy are fruitfulavenues for future work in household finance.

Initial submission: April 20, 2017; Accepted: April 13, 2018Editors: Stefan Nagel, Philip Bond, Amit Seru, and Wei Xiong

REFERENCES

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