The Credit Card Debt Puzzle and Noncognitive Ability

The Credit Card Debt Puzzle and Noncognitive Ability Abstract * We thank an anonymous referee, David Gross, Sol Polachek, colleagues at Binghamton and Purdue Universities, and seminar participants at University of Seoul, Korea University, Seoul National University, Ehwa Woman’s University, and KAIST (Korea Advanced Institute of Science and Technology) for helpful discussions and comments. The opinions expressed in this article are the authors’ own and do not reflect the views of Compass Lexecon.Many households concurrently hold low-yield liquid assets while incurring costly credit card debt. In our sample, more than 80% of households with credit card debt also have low-yield liquid assets. Using data from the Health and Retirement Study (N = 30,517), we examine the role of noncognitive skills as well as the economic, financial, and demographic factors that affect the likelihood of co-holding. We find that the “Big Five” personality traits have a statistically significant and economically important effect: households with a more agreeable, introvert, and less conscientious head of household are more likely to co-hold. We also examine the role of intra-household dynamics. 1. Introduction In the US, almost 40% of all households carry a credit card balance with an average interest rate of 13% (Bricker et al., 2012). Most households with credit card debt also hold considerable amounts of low-yield liquid assets such as checking and savings account balances that have a negligible return. Gross and Souleles (2002) report that among households with credit card debt, 95% have positive net wealth and almost 70% have positive home equity that can be used to get lower-cost home equity loans to pay down their credit card loans. Among the households in our sample that have a credit card balance not paid in full (22% of households), more than 84% simultaneously have a positive checking and/or savings account balance. This financial phenomenon is seemingly at odds with a no-arbitrage condition and has been referred to as a “puzzle” in the literature (e.g., Gross and Souleles, 2002; Bertaut, Haliassos, and Reiter, 2009; Telyukova, 2013). In this article, we study the role of noncognitive skills in explaining the credit card debt puzzle using data for 12,976 households from the Health and Retirement Study (HRS). Our focus on noncognitive skills is motivated in part by a growing literature in economics that examines the role of cognitive limitations (Simon, 1955) and other psychological factors in explaining empirical anomalies in consumption and savings (Rabin, 1998), accumulation of wealth (Ameriks, Caplin, and Leahy, 2003), portfolio choice (Barberis and Thaler, 2003), and labor market outcomes (Heckman, Stixrud, and Urzua, 2006). The HRS data contain detailed longitudinal information on financial, economic, health, and psychosocial measures, which allows us to investigate the role of noncognitive factors while controlling for a host of financial and demographic variables. We define the “puzzle group” as households with a positive credit card balance carried over to the next billing cycle (commonly referred to as “revolvers”) and $500 or more in low-yield liquid assets (checking, savings, and money market account balances). As in Telyukova (2013), our preferred specifications use $500 as the threshold for low-yield liquid assets, as such assets may be more convenient or necessary for certain types of expenses. There are two main components at the heart of our empirical identification strategy for estimating the effect of the noncognitive factors. First, we exploit the longitudinal nature of our data to overcome the inherent simultaneity between spending and saving decisions, as well as other financial decisions. This also allows us to address other nonfinancial factors such as a change in family composition or health shocks. Second, we exploit the constancy of the noncognitive measures we use during adulthood. Though this assumption is used by many, our data allow us to examine the validity of this assumption. The finding of a credit card debt puzzle by Gross and Souleles (2002) has led to several proposed explanations. Lehnert and Maki (2002) examine whether people strategically increase credit card debt prior to filing for bankruptcy. Zinman (2007) finds a high premium on holding liquid assets. Becker and Shabani (2010) calculate that some households would be better off redeeming their debt using their equity holdings. Fulford (2015) focuses on the role of uncertainty in future credit availability that may lead households to not pay down their debt. Telyukova (2013) also examines the demand for low-yield liquid assets by developing a structural model in which credit card borrowers need low-yield liquid assets for certain types of transactions for which credit cards cannot be used, such as rent or mortgage payments. Others have focused on the role of nonfinancial factors. Bertaut, Haliassos, and Reiter (2009) construct an accountant-shopper model where high credit card debt is used as a way to exert self- (or spousal-) control. The study by Gathergood and Weber (2014) is the only one, to the best of our knowledge, to empirically examine the role of a noncognitive skill (self-control) in explaining the puzzle. It studies the role of self-control and financial literacy, and concludes that the former, rather than the latter, affects the likelihood of co-holding low-yield liquid assets and credit card debt, using a cross-section of British households. This article makes several important contributions to the existing literature. First, we identify the factors that play a role in explaining the credit card debt puzzle by investigating a much wider range of noncognitive skills than self-control. Second, using the rich data set at our disposal our results complement the other types of explanations suggested in the literature. For example, we control for the need for liquidity (e.g., Telyukova, 2013), and self-control and financial literacy (e.g., Gathergood and Weber, 2014). Finally, our article is the first to examine the effect of noncognitive abilities among (intra) household members on households’ co-holding behavior. To capture a broad and comprehensive range of noncognitive skills, we employ the “Big Five” personality traits (McCrae and Costa, 1987, 1999). Personality traits are also referred to by some as character skills, soft skills, or noncognitive abilities (see discussion in Heckman and Kautz, 2012, p. 452). We follow the prevalent naming convention and also refer to the traits as noncognitive skills. The five traits are Openness (O), Conscientiousness (C), Extraversion (E), Agreeableness (A), and Neuroticism (N). For example, John, Naumann, and Soto (2008, p. 120) describe Conscientiousness as “socially prescribed impulse control … such as thinking before acting, delaying gratification, following rules, planning, organizing, and prioritizing tasks”; Agreeableness is conceptually defined as “prosocial communal orientation toward others … such as altruism, tender-mindedness, trust and modesty.”1 We further discuss the measurement of the Big Five in Section 3.1. The Big Five personality traits are by far the most commonly used in the field of psychology and have been widely studied over the last couple of decades. The Big Five personality traits provide several major advantages for our setting. First, they cover a very broad domain of noncognitive abilities. Second, they have been extensively studied in other settings, and have been shown to be a tractable set of measures for describing variation across people in types of personality. Third, the measures have been shown to be relatively rank-stable among adults, reducing the threat of endogeneity in our study. Although psychologists have been studying correlations between various personality traits and financial outcomes (e.g., income, debt, consumption, and saving) for several decades,2 in recent years there has been a growing interest among economists in incorporating personality traits such as self-control, perseverance, and grit. Borghans et al. (2008) and Almlund et al. (2011) provide an introduction to the recent developments in the intersection of psychology and economics. Personality traits, including the Big Five, have been shown to be an important complement to more traditionally economic measures of human capital in explaining education attainment, labor market outcomes, wealth, etc.3 We build on several previous papers in the economics literature that have either: (i) considered only a narrow facet of noncognitive skills and its effect on savings, borrowing, or the propensity to co-hold assets and debt (e.g., Ameriks, Caplin, and Leahy, 2003; Laibson, Repetto, and Tobacman, 2003; Gathergood and Weber, 2014, respectively); or (ii) used a neo-classical framework and do not consider noncognitive skills to explain the co-holding of assets and debt (e.g., Bertaut, Haliassos, and Reiter, 2009; Telyukova, 2013; Fulford 2015). We combine these previous studies and their proposed mechanisms, and hypothesize the channels through which personality traits operate. We hypothesize that four of the Big Five personality traits might play a role in the likelihood that a household is in the puzzle group: Conscientiousness, Extraversion, Agreeableness, and Openness might play a role for the dimension of spending and/or borrowing; and Conscientiousness, Extraversion, and Openness might play a role in the dimension of (not) using low-yield liquid assets to pay down debt. In Section 2, we consider three main channels through which co-holding may occur. First, certain personality traits may help (hinder) a decision maker when dealing with their finances. For example, those with higher levels of Conscientiousness might be more likely to notice that they have sufficient low-yield liquid assets to pay down their debt. The second channel we consider is precautionary saving for expected or unexpected liquidity demand. The third channel focuses on the role of personality traits in intra-household (or dual-self) dynamics. We find that even after controlling for differences in age and education levels among couples, the personality of both partners explains some of the observed co-holding patterns in the data. We first implement a reduced-form approach. We find that the effects of Conscientiousness, Extraversion, and Agreeableness tend to be the most persistent of the Big Five personality traits across our various specifications. In our preferred specification a one standard-deviation increase in Conscientiousness, Extraversion, and Agreeableness changes the likelihood of being in the puzzle group by −0.54, −1.09, and 1.62 percentage points, respectively. We find that this result holds after we additionally control for measures suggested in previous studies, such as liquidity demand, self-control, and financial sophistication. In Section 4.1 we take into account the simultaneity between spending and saving, and examine the role of personality in borrowing, and holding low-yield assets conditional on borrowing separately. Taken together, our findings suggest that regulatory policies, personal debt default options, debt counseling, and educational programs are all domains that can be made more cost effective by taking into account the role of noncognitive abilities. To illustrate the economic significance of our results, we estimate that in the US, among those 50 years and older, even a reduction of 1% in the number of households co-holding low-yield liquid assets and credit card debt would translate into an annual decrease of $327 million in interest payments while maintaining the same level of consumption.4 The rest of the article is organized as follows. We first describe our empirical framework and source of identification in Section 2. The HRS data and our construction of the personality measures are described in Section 3. The results are in Section 4. Section 5 concludes and discusses some potential areas for future research. 2. Empirical Framework The decision of how much to consume and save (or borrow) has long been studied, and often modeled using the neo-classical expected life-cycle utility maximization framework. There is also a large literature in economics and finance examining asset allocation across types of assets and across time. Given that the focus of our article is on the role of personality traits, and not on the calculation of inter-temporal substitution rates or elasticity measures, we implement our empirical strategy using a reduced-form examination of the decision of how much low-yield liquid assets and credit card debt to concurrently hold. Our approach has two main advantages. First, we require far fewer assumptions by not estimating a structural model. Second, our examination of the credit card debt puzzle avoids the need to address the inherent simultaneity in the decision of consumption and saving that consequently determine asset and debt accumulation. In Section 4.1, however, we do examine the underlying mechanisms of our findings by studying the relationship between asset holding and debt utilization. We focus on a definition of the puzzle group in which holding more than $500 in low-yield liquid assets, that is, checking, savings, and money market accounts, with positive revolving credit card debt is considered a puzzle. However, we have also examined alternative definitions that allow households to have different levels of low-yield liquid assets for liquidity purposes or different levels of credit card debt, and have found that our results are robust to using alternative definitions.5 We estimate the probability of being in the puzzle group using the linear-probability model (OLS). We have also used a logit model, and obtained very similar qualitative and quantitative results. Our base reduced-form specification can be written as:   Yit=β0+x′i(t−2)β+εit, (1) where x′i(t−2) is a vector of the time invariant and (2-year lagged) control variables and we assume that E(εit|xi(t−2))=0. Our empirical strategy exploits the panel nature of our data, thereby allowing us to address the potential simultaneity inherent in the financial and demographic measures we examine. For example, a health shock could affect the need for credit (due to large medical bills), uncertainty in future earnings, and one’s employment (requiring someone to retire earlier than planned). Our preferred specifications therefore use 2-year lags of financial measures. Financial measures such as income and wealth are, of course, crucial for one’s saving and borrowing decisions as they affect both the need for saving or borrowing and the returns or costs (as different borrowers would face different interest rates). Personality measures may cause two households with the same demographic and financial measures to have a different need for liquid assets and debt. For example, those with higher levels of Conscientiousness may be able to better interpret and more accurately perceive their financial situation. More extravert people may be able to better negotiate and leverage their financial situation when restructuring their debt with a lender, etc. To examine the role of personality, we augment the model in Equation (1) by adding the (5 × 1) vector pi of the Big Five personality traits. We include these measures additively, and allow them in some of the specifications to have an interactive effect with another characteristic zit:   Yit=β0+x′i(t−2)β+p′iγ+zitp′iδ+εit. (2)Equation (2) is useful in demonstrating how noncognitive ability, such as personality, might affect a household’s financial decision to co-hold low-yield liquid assets and credit card debt. Researchers have previously proposed various explanations (see Section 1) for the credit card puzzle. An advantage of our reduced-form model is that it allows us to succinctly control for those proposed explanatory factors. We use financial controls (such as income, various assets and debts), education levels, and demographic controls (such as age, and marital status) that have been suggested in the literature as important in determining the decision to save and borrow. In addition to the very detailed financial data at our disposal, we are also able to control for other demographic variables that are likely to affect household financials such as health status (both self-reported, and by controlling for medical expenditures) or changes in family composition (due to death, marriage, or divorce). The reduced-form specification examines the overall effect of a household’s characteristics. Therefore, the specification in Equation (2) does not separate out the decision to be a revolver, and the factors that affect the likelihood of being in the puzzle group (i.e., become a revolver and hold a low-yield liquid assets balance simultaneously). As such, our findings potentially encompass several channels or mechanisms at work. We further examine the decomposition of the effect of personality to understand the relative importance of the potential mechanisms at play. For example, the overall reduced-form effect of Conscientiousness might be zero. However, this might be because the underlying effects nullify each other. Conscientious individuals might be more likely to qualify for or have access to credit, but at the same time might be less likely to borrow and hold large amounts of cash at the same time, since they carefully examine their monthly statements, or consider the cost of debt. To examine the decomposition, we consider two necessary conditions for being in the puzzle group through which personality traits may operate. First, a necessary condition to be included in the puzzle group is to be a revolver. Second, conditional on being a revolver, one may or may not be in the puzzle group depending on whether one holds low-yield liquid assets that are not used to pay down debt. We therefore separately examine the propensity to be a revolver, and the propensity of revolvers to hold low-yield liquid assets and not pay one’s debt down.6 One could think of the two conditions as being related to two dimensions: consumption and financial management of the household’s accounts for a given level of consumption. For the dimension of incurrence of debt, several personality traits are likely to affect levels of spending and/or borrowing, and financial terms (such as interest rates and credit limits) that affect debt levels. Self-control (Laibson, Repetto, and Tobacman, 2003; Bertaut, Haliassos, and Reiter, 2009), impulse spending (Gathergood and Weber, 2014), and the propensity to plan (Ameriks, Caplin, and Leahy, 2003) have been shown to be related to incurrence of debt and wealth accumulation. These traits are all captured by Conscientiousness, and the effect on debt is likely to be negative. Agreeableness may lead to higher levels of spending and debt because agreeable people tend to spend more on others, and might be more susceptible to marketing campaigns.7 A large literature has documented lower incomes among those with higher levels of Agreeableness (e.g., Judge et al., 1999; Babcock and Laschever, 2003; Mueller and Plug, 2006). A similar trade-off (less financial gains in return for less conflict or an increased preference for others’ utility) is likely to play a role in this instance as well. Extravert people may acquire financial advice from their peers, or may be able to better negotiate and leverage their financial situation when restructuring their debt with a lender. Those with higher levels of Openness may be more likely to consume and spend more leading to higher levels of debt.8 The direction of the effect of Neuroticism is ambiguous. Higher levels of Neuroticism would lower the likelihood of borrowing due to the increased psychological cost of worrying about the future ability to repay. On the other hand, lower levels of Neuroticism have been found to be associated with more discretionary savings (e.g., Brandstätter, 2005, p. 70). For example, Wang, Lu, and Malhotra (2011) find a negative relationship between revolving credit use and measures related to low levels of Neuroticism. Donnelly, Iyer, and Howell (2012) show that Neuroticism is positively related to compulsive buying. For the dimension of co-holding low-yield liquid assets and credit card debt, we consider three main channels through which personality may operate: the management of household finances; liquidity demand; and intra-household dynamics. For the first channel, conscientious individuals are more likely to notice they have sufficient low-yield liquid assets to pay down their debt. Extraverts may be more likely to discuss their finances and solicit possible solutions from others on how to pay down their credit card debt.9 The effect of Neuroticism is a priori ambiguous. Those with higher levels of Neuroticism might be constantly worried about their finances or missing a payment, thereby having a heightened awareness of their ability to pay down credit card debt. On the other hand, people with low levels of Neuroticism may make financial decisions in a calm and deliberate manner.10 For the second channel of precautionary saving, conscientious individuals might be more likely to hold low-yield liquid assets even when they have debt, as they are more likely to plan ahead. The effect of Neuroticism is a priori ambiguous. More neurotic individuals might have higher demand for liquidity because they worry about their uncertain future. On the other hand, they may worry about being burdened with debt and prefer to pay down as much of it as they can. The third channel we consider is intra-household (or dual-self) dynamics. For example, as suggested by Bertaut, Haliassos, and Reiter (2009), an “accountant” may choose to maintain high levels of credit utilization to control the spending temptation of their “shopper” spouse. The personality traits we consider readily translate into those two types. For example, an “accountant” is likely to have a high level of Conscientiousness, whereas a “shopper” may have a low level of Conscientiousness and a high level of Agreeableness for the aforementioned reasons. Because a household might be a revolver for reasons correlated with the likelihood of being in the puzzle group, we must find an exclusion restriction that would predict being a revolver, but would not affect a household’s likelihood of being in the puzzle group. In Section 4.1, we employ an exclusion restriction strategy and examine whether personality has a differential effect on checking/savings balance among debt holders and those with no debt. Our identification strategy is akin to using the 2-year lag of revolving behavior to predict current revolving behavior. Finally, our specification also allows us to test whether personality might also interact with a spouse’s personality, or a proxy for the household’s power structure. Here our identification strategy uses single households’ co-holding decisions to examine decisions among couple households, and separate out the contribution of each family member to the overall household decision. 3. Data and the Big Five Personality Traits Our data are based on the HRS (2012).11 The HRS is a biennial longitudinal survey that collects detailed demographic, health, economic, and financial information from a nationally representative sample of the population over age 50.12 The HRS has three main advantages for our setting. First, the longitudinal nature of the data is crucial for our identification strategy as explained in the previous section. Second, the data have high-quality personality measures,13 as well as detailed financial information. Third, as explained in Section 1, personality measures are more likely to be stable among older adults thereby reducing the threat of validity to our results.14 The HRS contains both respondent-level and household-level data. Because most of the financial measures are collected at the household level, and financial decisions depend on and impact the entire household, our primary unit of analysis is at the household level. The households in the data consist of singles and couples (we also control for the presence of additional household members). For the households with couples, because our dependent variables of interest are financial, we focus on the demographics and personality of the person who has answered the survey questions related to household finances. In the HRS data, this person is identified as the “financial respondent” of the household.15 Although we model household behavior, we also use respondent-level information from financial respondents with the assumption that the coordination within a household is not a significant factor. However, as an extension, we relax this assumption in Section 4.2 and investigate the effect of spouse characteristics using the data on couple-households. Our main sample consists of 12,976 households between 2008 and 2012.16Table 1 reports the summary statistics for the full sample and for the subgroup of households that are revolvers. The average credit card balance among revolvers is $8,972. On average, 51% of the households are a single household. Table I. Summary statistics   Mean   Standard deviation   Variables  Main sample  Revolvers only  Main sample  Revolvers only  Observations  30,517  6,833      Couple household  49%  54%  50%  50%  Household-level variables           Revolver  22%  100%  42%  0%   Credit card debt  $2,009  $8,972  $7,638  $14,076  Puzzle group   Revolver and low-yield liquid assets >$500  15%  68%  36%  47%  Assets and debts   Checking and savings  $29,089  $11,142  $91,731  $42,253   No checking or savings  20%  16%  40%  36%   Financial assets  $128,270  $40,175  $474,272  $158,769   Debts including credit card debt  $4,430  $13,159  $31,472  $36,658   Value of business  $38,617  $18,843  $345,359  $189,679   IRA balance  $58,987  $29,660  $180,903  $100,089   Own home  75%  78%  43%  42%   Real estate  $221,746  $198,198  $517,890  $486,374   Mortgages and home equity loans  $34,019  $58,234  $85,027  $100,255  Income and medical expense   Income  $58,061  $58,352  $355,317  $75,613   Received food stamp  8%  8%  27%  28%   Out-of-pocket medical expense  $4,954  $4,786  $10,851  $8,443   Below the poverty line  13%  8%  33%  28%  Financial respondent-level variables  Personality traits (1–4)   Openness  2.92  2.97  0.54  0.52   Conscientiousness  3.35  3.36  0.46  0.45   Extraversion  3.18  3.19  0.53  0.52   Agreeableness  3.51  3.56  0.46  0.43   Neuroticism  2.02  2.06  0.59  0.59  Demographic and other variables   Age  70.41  66.25  10.66  9.09   White  79%  75%  41%  43%   Male  41%  39%  49%  49%   High school  54%  49%  50%  50%   Some post-secondary schooling  23%  29%  42%  45%   College (4 years) or more  22%  22%  42%  41%   Married  46%  50%  50%  50%   Separated/divorced  16%  19%  37%  39%   Widowed  30%  22%  46%  41%   Poor health (excellent (1)−poor (5))  2.90  2.91  1.09  1.06   Employed  32%  46%  47%  50%   Self-employed  8%  9%  26%  28%   Retired  60%  47%  49%  50%    Mean   Standard deviation   Variables  Main sample  Revolvers only  Main sample  Revolvers only  Observations  30,517  6,833      Couple household  49%  54%  50%  50%  Household-level variables           Revolver  22%  100%  42%  0%   Credit card debt  $2,009  $8,972  $7,638  $14,076  Puzzle group   Revolver and low-yield liquid assets >$500  15%  68%  36%  47%  Assets and debts   Checking and savings  $29,089  $11,142  $91,731  $42,253   No checking or savings  20%  16%  40%  36%   Financial assets  $128,270  $40,175  $474,272  $158,769   Debts including credit card debt  $4,430  $13,159  $31,472  $36,658   Value of business  $38,617  $18,843  $345,359  $189,679   IRA balance  $58,987  $29,660  $180,903  $100,089   Own home  75%  78%  43%  42%   Real estate  $221,746  $198,198  $517,890  $486,374   Mortgages and home equity loans  $34,019  $58,234  $85,027  $100,255  Income and medical expense   Income  $58,061  $58,352  $355,317  $75,613   Received food stamp  8%  8%  27%  28%   Out-of-pocket medical expense  $4,954  $4,786  $10,851  $8,443   Below the poverty line  13%  8%  33%  28%  Financial respondent-level variables  Personality traits (1–4)   Openness  2.92  2.97  0.54  0.52   Conscientiousness  3.35  3.36  0.46  0.45   Extraversion  3.18  3.19  0.53  0.52   Agreeableness  3.51  3.56  0.46  0.43   Neuroticism  2.02  2.06  0.59  0.59  Demographic and other variables   Age  70.41  66.25  10.66  9.09   White  79%  75%  41%  43%   Male  41%  39%  49%  49%   High school  54%  49%  50%  50%   Some post-secondary schooling  23%  29%  42%  45%   College (4 years) or more  22%  22%  42%  41%   Married  46%  50%  50%  50%   Separated/divorced  16%  19%  37%  39%   Widowed  30%  22%  46%  41%   Poor health (excellent (1)−poor (5))  2.90  2.91  1.09  1.06   Employed  32%  46%  47%  50%   Self-employed  8%  9%  26%  28%   Retired  60%  47%  49%  50%  Table I. Summary statistics   Mean   Standard deviation   Variables  Main sample  Revolvers only  Main sample  Revolvers only  Observations  30,517  6,833      Couple household  49%  54%  50%  50%  Household-level variables           Revolver  22%  100%  42%  0%   Credit card debt  $2,009  $8,972  $7,638  $14,076  Puzzle group   Revolver and low-yield liquid assets >$500  15%  68%  36%  47%  Assets and debts   Checking and savings  $29,089  $11,142  $91,731  $42,253   No checking or savings  20%  16%  40%  36%   Financial assets  $128,270  $40,175  $474,272  $158,769   Debts including credit card debt  $4,430  $13,159  $31,472  $36,658   Value of business  $38,617  $18,843  $345,359  $189,679   IRA balance  $58,987  $29,660  $180,903  $100,089   Own home  75%  78%  43%  42%   Real estate  $221,746  $198,198  $517,890  $486,374   Mortgages and home equity loans  $34,019  $58,234  $85,027  $100,255  Income and medical expense   Income  $58,061  $58,352  $355,317  $75,613   Received food stamp  8%  8%  27%  28%   Out-of-pocket medical expense  $4,954  $4,786  $10,851  $8,443   Below the poverty line  13%  8%  33%  28%  Financial respondent-level variables  Personality traits (1–4)   Openness  2.92  2.97  0.54  0.52   Conscientiousness  3.35  3.36  0.46  0.45   Extraversion  3.18  3.19  0.53  0.52   Agreeableness  3.51  3.56  0.46  0.43   Neuroticism  2.02  2.06  0.59  0.59  Demographic and other variables   Age  70.41  66.25  10.66  9.09   White  79%  75%  41%  43%   Male  41%  39%  49%  49%   High school  54%  49%  50%  50%   Some post-secondary schooling  23%  29%  42%  45%   College (4 years) or more  22%  22%  42%  41%   Married  46%  50%  50%  50%   Separated/divorced  16%  19%  37%  39%   Widowed  30%  22%  46%  41%   Poor health (excellent (1)−poor (5))  2.90  2.91  1.09  1.06   Employed  32%  46%  47%  50%   Self-employed  8%  9%  26%  28%   Retired  60%  47%  49%  50%    Mean   Standard deviation   Variables  Main sample  Revolvers only  Main sample  Revolvers only  Observations  30,517  6,833      Couple household  49%  54%  50%  50%  Household-level variables           Revolver  22%  100%  42%  0%   Credit card debt  $2,009  $8,972  $7,638  $14,076  Puzzle group   Revolver and low-yield liquid assets >$500  15%  68%  36%  47%  Assets and debts   Checking and savings  $29,089  $11,142  $91,731  $42,253   No checking or savings  20%  16%  40%  36%   Financial assets  $128,270  $40,175  $474,272  $158,769   Debts including credit card debt  $4,430  $13,159  $31,472  $36,658   Value of business  $38,617  $18,843  $345,359  $189,679   IRA balance  $58,987  $29,660  $180,903  $100,089   Own home  75%  78%  43%  42%   Real estate  $221,746  $198,198  $517,890  $486,374   Mortgages and home equity loans  $34,019  $58,234  $85,027  $100,255  Income and medical expense   Income  $58,061  $58,352  $355,317  $75,613   Received food stamp  8%  8%  27%  28%   Out-of-pocket medical expense  $4,954  $4,786  $10,851  $8,443   Below the poverty line  13%  8%  33%  28%  Financial respondent-level variables  Personality traits (1–4)   Openness  2.92  2.97  0.54  0.52   Conscientiousness  3.35  3.36  0.46  0.45   Extraversion  3.18  3.19  0.53  0.52   Agreeableness  3.51  3.56  0.46  0.43   Neuroticism  2.02  2.06  0.59  0.59  Demographic and other variables   Age  70.41  66.25  10.66  9.09   White  79%  75%  41%  43%   Male  41%  39%  49%  49%   High school  54%  49%  50%  50%   Some post-secondary schooling  23%  29%  42%  45%   College (4 years) or more  22%  22%  42%  41%   Married  46%  50%  50%  50%   Separated/divorced  16%  19%  37%  39%   Widowed  30%  22%  46%  41%   Poor health (excellent (1)−poor (5))  2.90  2.91  1.09  1.06   Employed  32%  46%  47%  50%   Self-employed  8%  9%  26%  28%   Retired  60%  47%  49%  50%  3.1 The Big Five Personality Traits The Big Five personality traits have been measured in the HRS biennially since 2006. They are measured as part of a questionnaire which is given to about half of the full sample in every wave.17 As a consequence, we have personality traits for almost all individuals in either 2006/2010 or 2008/2012. The HRS uses 26 personality survey items developed originally for the Midlife in the United States Survey.18 The 26 variables are self-administered adjectival measures. Participants are asked to “Please indicate how well each of the following DESCRIBES YOU” for 26 adjectives. Each adjective is coded from 1 (“not at all”) to 4 (“a lot”). The adjectives are then grouped and averaged to create a score for each of the five traits. For example, Conscientiousness is constructed from these five items (with “−” indicating an inverse coding): organized, responsible, hardworking, careless (−), and thorough.19 The stability of personality measures over time has been widely studied in the field of developmental psychology. For example, some studies have emphasized the hereditary and biological factors that shape traits (e.g., Bouchard Jr and Loehlin, 2001; Canli, 2006; DeYoung et al., 2010). In financial and economic settings, many scholars assume that personality traits are fixed among adults (e.g., Nyhus and Pons, 2005; Mueller and Plug, 2006; Heineck and Anger, 2010). In recent years, an emerging view is that personality traits are influenced by hereditary and biological factors, but can change over time and may be mutable by intervention especially during early childhood. However, after early adulthood, the mean level changes relatively less and the rank ordering of personality traits in a population becomes increasingly more consistent (stable) as one ages (Roberts and DelVecchio, 2000).20 For the purpose of our study, the crucial issue is whether the measurement of personality is endogenous with respect to financial decisions. For example, Roberts, Walton, and Viechtbauer (2006) find some mean-changes of personality traits over the life cycle. However, we control for age and only examine adults later in life, so life-cycle patterns are not a concern for our setting. Cobb-Clark and Schurer (2012) show that the mean level of the Big Five personality traits is stable over a 4-year period among working-age adults. Further, they show that intra-individual changes over time are not correlated with life events in an economically significant way. Cobb-Clark and Schurer (2013) also show that the changes of the mean level of locus of control, which is the personality trait of their focus, are mild, and consistent rather than idiosyncratic. They argue that for working-age adults the changes are economically insignificant.21 Taken together, the results suggest that our identification assumption regarding the exogeneity of personality measures is likely to hold. However, given the longitudinal data at our disposal, we are further able to test the stability and inverse-causality of the Big Five personality traits. We find that the personality traits are stable in our sample, and we do not find any evidence of an inverse-causal relationship with our dependent variables of interest after controlling for the relevant variables. In our study, we use the personality measures calculated by the average of the personality traits over all available years for each of the Big Five personality traits. We then standardize the personality traits measures (Z-score) by subtracting the average and dividing by the sample standard deviation calculated using the 2010 data. 4. Results We first examine the reduced-form estimates in Table 2 corresponding to Equation (2) without the interaction term using the pooled data from 2008 to 2012. In all columns, the dependent variable is the binary indicator of whether a household is in the puzzle group (i.e., not paying their credit card balance in full, and having low-yield liquid assets of $500 or more). Throughout, we report the linear-probability model results using the OLS method with cluster-robust standard errors. For all regressions in this article, we use clusters defined by the cross product of nine US geographical regions and three rural–urban groups based on county population sizes (more than million, 250,000–1,000,000, and 250,000 or less). This allows us to capture regional unobservable correlated shocks that may affect households in the same local economy.22 Table II. The effect of personality on being in the puzzle group (reduced form) Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for race, marital status, employment status, whether self-employed, whether in nursing home, household size (whether 2 or more), education (high-school, some college, and college or more dummies), and region and metro type fixed effects. Columns 3–5 also include assets (transportation, housing), whether underwater, and percent of household members employed. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. †Measures lagged by 2 years (previous wave) in columns 4–5. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  Openness    0.0076**  0.0053*  0.0051*  0.0050*    (0.0030)  (0.0027)  (0.0027)  (0.0027)  Conscientiousness    −0.0090***  −0.0050*  −0.0054*  −0.0054*    (0.0029)  (0.0029)  (0.0028)  (0.0029)  Extraversion    −0.0130***  −0.0112***  −0.0110***  −0.0109***    (0.0033)  (0.0028)  (0.0027)  (0.0027)  Agreeableness    0.0210***  0.0153***  0.0163***  0.0162***    (0.0031)  (0.0028)  (0.0027)  (0.0027)  Neuroticism    −0.0012  −0.0003  −0.0005  −0.0003    (0.0030)  (0.0026)  (0.0028)  (0.0028)  Age  0.0104***  0.0105***  0.0111***  0.0114***  0.0115***  (0.0027)  (0.0028)  (0.0022)  (0.0023)  (0.0023)  Age-squared (divided by 100)  −0.0100***  −0.0100***  −0.0094***  −0.0096***  −0.0097***  (0.0018)  (0.0018)  (0.0015)  (0.0016)  (0.0016)  Is male  −0.0247***  −0.0177**  −0.0160**  −0.0174**  −0.0169**  (0.0067)  (0.0074)  (0.0070)  (0.0069)  (0.0068)  In poor health  0.0034  0.0024  0.0042**  0.0040**  0.0042**  (0.0021)  (0.0020)  (0.0018)  (0.0018)  (0.0018)  Ln(financial assets excluding low-yield liquid assets)†      −0.0266***  −0.0239***  −0.0238***      (0.0023)  (0.0021)  (0.0020)  Ln(retirement assets)†      −0.0246***  −0.0238***  −0.0237***      (0.0026)  (0.0029)  (0.0029)  Is home owner†      −0.0287**  −0.0333**  −0.0330**      (0.0138)  (0.0157)  (0.0155)  Ln(income)†      0.0096***  0.0079***  0.0083***      (0.0019)  (0.0015)  (0.0015)  Ln(medical spending)†      0.0056***  0.0059***  0.0061***      (0.0009)  (0.0009)  (0.0009)  Below poverty line†      −0.0463***  −0.0345***  −0.0329***      (0.0061)  (0.0059)  (0.0062)  Ln(mortgage+HELOC)†      0.0118***  0.0107***  0.0107***      (0.0009)  (0.0008)  (0.0008)  2-year change in welfare and food stamps assistance          −0.0183**          (0.0079)  2-year change in household size          0.0257***          (0.0091)  R2  0.05  0.06  0.11  0.10  0.10  Number of households  12,976  12,976  12,976  12,976  12,976  Observations  30,517  30,517  30,517  30,517  30,517  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  Openness    0.0076**  0.0053*  0.0051*  0.0050*    (0.0030)  (0.0027)  (0.0027)  (0.0027)  Conscientiousness    −0.0090***  −0.0050*  −0.0054*  −0.0054*    (0.0029)  (0.0029)  (0.0028)  (0.0029)  Extraversion    −0.0130***  −0.0112***  −0.0110***  −0.0109***    (0.0033)  (0.0028)  (0.0027)  (0.0027)  Agreeableness    0.0210***  0.0153***  0.0163***  0.0162***    (0.0031)  (0.0028)  (0.0027)  (0.0027)  Neuroticism    −0.0012  −0.0003  −0.0005  −0.0003    (0.0030)  (0.0026)  (0.0028)  (0.0028)  Age  0.0104***  0.0105***  0.0111***  0.0114***  0.0115***  (0.0027)  (0.0028)  (0.0022)  (0.0023)  (0.0023)  Age-squared (divided by 100)  −0.0100***  −0.0100***  −0.0094***  −0.0096***  −0.0097***  (0.0018)  (0.0018)  (0.0015)  (0.0016)  (0.0016)  Is male  −0.0247***  −0.0177**  −0.0160**  −0.0174**  −0.0169**  (0.0067)  (0.0074)  (0.0070)  (0.0069)  (0.0068)  In poor health  0.0034  0.0024  0.0042**  0.0040**  0.0042**  (0.0021)  (0.0020)  (0.0018)  (0.0018)  (0.0018)  Ln(financial assets excluding low-yield liquid assets)†      −0.0266***  −0.0239***  −0.0238***      (0.0023)  (0.0021)  (0.0020)  Ln(retirement assets)†      −0.0246***  −0.0238***  −0.0237***      (0.0026)  (0.0029)  (0.0029)  Is home owner†      −0.0287**  −0.0333**  −0.0330**      (0.0138)  (0.0157)  (0.0155)  Ln(income)†      0.0096***  0.0079***  0.0083***      (0.0019)  (0.0015)  (0.0015)  Ln(medical spending)†      0.0056***  0.0059***  0.0061***      (0.0009)  (0.0009)  (0.0009)  Below poverty line†      −0.0463***  −0.0345***  −0.0329***      (0.0061)  (0.0059)  (0.0062)  Ln(mortgage+HELOC)†      0.0118***  0.0107***  0.0107***      (0.0009)  (0.0008)  (0.0008)  2-year change in welfare and food stamps assistance          −0.0183**          (0.0079)  2-year change in household size          0.0257***          (0.0091)  R2  0.05  0.06  0.11  0.10  0.10  Number of households  12,976  12,976  12,976  12,976  12,976  Observations  30,517  30,517  30,517  30,517  30,517  Table II. The effect of personality on being in the puzzle group (reduced form) Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for race, marital status, employment status, whether self-employed, whether in nursing home, household size (whether 2 or more), education (high-school, some college, and college or more dummies), and region and metro type fixed effects. Columns 3–5 also include assets (transportation, housing), whether underwater, and percent of household members employed. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. †Measures lagged by 2 years (previous wave) in columns 4–5. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  Openness    0.0076**  0.0053*  0.0051*  0.0050*    (0.0030)  (0.0027)  (0.0027)  (0.0027)  Conscientiousness    −0.0090***  −0.0050*  −0.0054*  −0.0054*    (0.0029)  (0.0029)  (0.0028)  (0.0029)  Extraversion    −0.0130***  −0.0112***  −0.0110***  −0.0109***    (0.0033)  (0.0028)  (0.0027)  (0.0027)  Agreeableness    0.0210***  0.0153***  0.0163***  0.0162***    (0.0031)  (0.0028)  (0.0027)  (0.0027)  Neuroticism    −0.0012  −0.0003  −0.0005  −0.0003    (0.0030)  (0.0026)  (0.0028)  (0.0028)  Age  0.0104***  0.0105***  0.0111***  0.0114***  0.0115***  (0.0027)  (0.0028)  (0.0022)  (0.0023)  (0.0023)  Age-squared (divided by 100)  −0.0100***  −0.0100***  −0.0094***  −0.0096***  −0.0097***  (0.0018)  (0.0018)  (0.0015)  (0.0016)  (0.0016)  Is male  −0.0247***  −0.0177**  −0.0160**  −0.0174**  −0.0169**  (0.0067)  (0.0074)  (0.0070)  (0.0069)  (0.0068)  In poor health  0.0034  0.0024  0.0042**  0.0040**  0.0042**  (0.0021)  (0.0020)  (0.0018)  (0.0018)  (0.0018)  Ln(financial assets excluding low-yield liquid assets)†      −0.0266***  −0.0239***  −0.0238***      (0.0023)  (0.0021)  (0.0020)  Ln(retirement assets)†      −0.0246***  −0.0238***  −0.0237***      (0.0026)  (0.0029)  (0.0029)  Is home owner†      −0.0287**  −0.0333**  −0.0330**      (0.0138)  (0.0157)  (0.0155)  Ln(income)†      0.0096***  0.0079***  0.0083***      (0.0019)  (0.0015)  (0.0015)  Ln(medical spending)†      0.0056***  0.0059***  0.0061***      (0.0009)  (0.0009)  (0.0009)  Below poverty line†      −0.0463***  −0.0345***  −0.0329***      (0.0061)  (0.0059)  (0.0062)  Ln(mortgage+HELOC)†      0.0118***  0.0107***  0.0107***      (0.0009)  (0.0008)  (0.0008)  2-year change in welfare and food stamps assistance          −0.0183**          (0.0079)  2-year change in household size          0.0257***          (0.0091)  R2  0.05  0.06  0.11  0.10  0.10  Number of households  12,976  12,976  12,976  12,976  12,976  Observations  30,517  30,517  30,517  30,517  30,517  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  Openness    0.0076**  0.0053*  0.0051*  0.0050*    (0.0030)  (0.0027)  (0.0027)  (0.0027)  Conscientiousness    −0.0090***  −0.0050*  −0.0054*  −0.0054*    (0.0029)  (0.0029)  (0.0028)  (0.0029)  Extraversion    −0.0130***  −0.0112***  −0.0110***  −0.0109***    (0.0033)  (0.0028)  (0.0027)  (0.0027)  Agreeableness    0.0210***  0.0153***  0.0163***  0.0162***    (0.0031)  (0.0028)  (0.0027)  (0.0027)  Neuroticism    −0.0012  −0.0003  −0.0005  −0.0003    (0.0030)  (0.0026)  (0.0028)  (0.0028)  Age  0.0104***  0.0105***  0.0111***  0.0114***  0.0115***  (0.0027)  (0.0028)  (0.0022)  (0.0023)  (0.0023)  Age-squared (divided by 100)  −0.0100***  −0.0100***  −0.0094***  −0.0096***  −0.0097***  (0.0018)  (0.0018)  (0.0015)  (0.0016)  (0.0016)  Is male  −0.0247***  −0.0177**  −0.0160**  −0.0174**  −0.0169**  (0.0067)  (0.0074)  (0.0070)  (0.0069)  (0.0068)  In poor health  0.0034  0.0024  0.0042**  0.0040**  0.0042**  (0.0021)  (0.0020)  (0.0018)  (0.0018)  (0.0018)  Ln(financial assets excluding low-yield liquid assets)†      −0.0266***  −0.0239***  −0.0238***      (0.0023)  (0.0021)  (0.0020)  Ln(retirement assets)†      −0.0246***  −0.0238***  −0.0237***      (0.0026)  (0.0029)  (0.0029)  Is home owner†      −0.0287**  −0.0333**  −0.0330**      (0.0138)  (0.0157)  (0.0155)  Ln(income)†      0.0096***  0.0079***  0.0083***      (0.0019)  (0.0015)  (0.0015)  Ln(medical spending)†      0.0056***  0.0059***  0.0061***      (0.0009)  (0.0009)  (0.0009)  Below poverty line†      −0.0463***  −0.0345***  −0.0329***      (0.0061)  (0.0059)  (0.0062)  Ln(mortgage+HELOC)†      0.0118***  0.0107***  0.0107***      (0.0009)  (0.0008)  (0.0008)  2-year change in welfare and food stamps assistance          −0.0183**          (0.0079)  2-year change in household size          0.0257***          (0.0091)  R2  0.05  0.06  0.11  0.10  0.10  Number of households  12,976  12,976  12,976  12,976  12,976  Observations  30,517  30,517  30,517  30,517  30,517  We control for household size with a dummy indicator for having more than one member (including dependents), as well as marital status. For reasons explained in the previous section, in the case of households with couples, we use the personal measures of the financial respondent (i.e., age, race, education, and personality traits). However, for couples, our analysis shows that the personal characteristics of the financial respondent are a sufficient control, as our results remain very similar if we additionally include some key spousal measures. In Section 4.2, we further examine the effect of within-couple income and personality differences. The first column in Table 2 includes basic demographic controls and employment status. The Big Five personality traits Z-scores (standardized to have a mean of zero and standard deviation equal to one) are added in column 2. Conscientiousness and Extraversion are shown to have a negative effect (statistically significant at the 1% level) whereas Openness and Agreeableness increase the likelihood of being in the puzzle group (statistically significant at the 5% and 1% level, respectively). Neuroticism is almost never statistically significant across the various specifications. We then include financial measures in column 3, such as financial assets (excluding checking/savings), housing debt, income, and medical spending. To alleviate concerns of simultaneity, we lag the financial measures by two years in column 4. We add controls for changes to a household’s size and food-stamp usage, in column 5.23 The coefficients of the personality traits remain qualitatively the same. The coefficients for Openness, Conscientiousness, Extraversion, and Agreeableness are similar in columns 2–5, and are statistically significant at the 10% level or better throughout. In summary, the effects of the personality measures remain similar across our different specifications, even after controlling for a wide range of financial measures. For example, in column 5 which is our preferred specification, a one standard-deviation increase in Conscientiousness and Extraversion, all else equal, decreases the propensity to be in the puzzle group by 0.54 and 1.09 percentage points, respectively.24 For Openness and Agreeableness, the probability increases by 0.50 and 1.62 percentage points, respectively. In regards to demographic variables, we find an inverse U-shape effect of education that is robust across our specifications. This could be due to the fact that those with the lowest levels of education have less access to credit. The effects of the financial measures have the expected signs. For example, households with higher financial assets such as stock, bonds, certificate of deposits, real estate, or IRA accounts are less likely to be in the puzzle group. We also include dummy indicator variables for zero assets and IRA balances to account for the non-linearity of these factors. Generally, we find that household income tends to have a positive effect on the likelihood of being in the puzzle group (significant at the 1% level in Table 2). Similarly, we find that households with an income below the poverty line are less likely to be in the puzzle group. These findings are consistent with low-income households having more difficulty in qualifying for credit cards. Our controls for employment status, health status, etc. in the reduced-form estimates capture the differential access to credit cards among households. We also find that households with high mortgage debt and negative home equity are more likely to be in the puzzle group. This is consistent with those having less access to cheaper forms of credit (such as home equity loans and mortgages) are more likely to have to resort to more expensive forms of credit, such as credit cards. Our measures of personality are multiple-period averages. As a further robustness test, we examine individual fixed effects specifications where we allow personality to vary yearly. We find that none of the yearly components of the personality measures are statistically significant, nor are they jointly significant (p-value of 0.81).25 This finding is consistent with the yearly within-individual variation in the personality measures being idiosyncratic. We have also examined Table 2’s specifications using lagged personality traits measured in the past survey waves instead of our preferred multiple-period averages. This further reduces the concern of endogeneity of the personality traits, as discussed in Section 3.1. We find that our results are robust to the use of the lagged Big Five personality measures from the most recent previous survey wave. The only exception is that in some specifications, Conscientiousness is no longer significant at the 10% level. To address the possible concern of reverse causality, that an individual’s personality measures are influenced by their unobservable propensity to be in the puzzle group, we have further examined both the stability of our personality traits, and the personality traits’ response to changes in financial measures, by exploiting the longitudinal nature of our data. Within individuals, the overall change in personality over time is quite small. The average change is between −0.07 and −0.03, and the sample medians are zero for all traits. In addition, we find that being in the puzzle group two years earlier does not have a statistically significant effect on the level of changes for each of the Big Five personality traits. In summary, controlling for a wide range of demographic, financial, health, and location measures, we find a persistent effect of personality traits on the likelihood of being in the puzzle group. 4.1 Decomposing the Effects on Credit Card Debt and Checking / Savings Balance The reduced-form results in the previous section examine measures that are functions of both checking/savings balance and credit card debt, and as such circumvent the need to address the inherent simultaneity in the decision of allocating assets. However, as explained in Section 2, an aggregate effect of personality on co-holding may mask two opposing effects, that is, one on checking/savings and the other, with an opposite sign, on credit card debt. Those effects may in turn cancel each other out. Therefore, in this section, we first examine whether the personality traits are affecting credit card debt, and then study checking/savings balances conditional on credit card debt. We first examine the likelihood that a household is a revolver, using the entire sample of households.26 We estimate the determinants of credit card debt revolving using a dichotomous variable for the existence of credit card debt, and the results are shown in Table 3. Throughout the table, Neuroticism is never statistically significant at conventional levels, whereas Openness, Conscientiousness, Extraversion, and Agreeableness are almost always statistically significant at the 5% level or lower. The effect of Conscientiousness and Extraversion on the likelihood of having credit card debt is negative, and the effect of Openness and Agreeableness is positive. Table III. The effect of personality on having revolving credit card debt Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for race, marital status, employment status, whether self-employed, whether in nursing home, household size (whether two or more), education (high-school, some college, and college or more dummies), and region and metro type fixed effects. Columns 3–6 also include assets (transportation, housing), whether underwater, and percent of household members employed. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). Column 6 only includes households in 2010–2012. *Significant at 10%. **Significant at 5%. ***Significant at 1%. †Measures lagged by two years (previous wave) in columns 4–6. Linear probability model; Dependent variable: Have a Revolving Credit Card Balance   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  Openness    0.0106**  0.0090**  0.0085**  0.0084**  −0.0015    (0.0040)  (0.0037)  (0.0036)  (0.0036)  (0.0033)  Conscientiousness    −0.0169***  −0.0091***  −0.0095***  −0.0095***  −0.0038    (0.0027)  (0.0027)  (0.0028)  (0.0028)  (0.0024)  Extraversion    −0.0140***  −0.0123***  −0.0121***  −0.0120***  −0.0036    (0.0042)  (0.0040)  (0.0041)  (0.0041)  (0.0035)  Agreeableness    0.0298***  0.0207***  0.0221***  0.0220***  0.0137***    (0.0041)  (0.0034)  (0.0035)  (0.0035)  (0.0025)  Neuroticism    0.0048  0.0049  0.005  0.005  0.002    (0.0044)  (0.0038)  (0.0040)  (0.0040)  (0.0025)  Age  0.0108***  0.0111***  0.0155***  0.0156***  0.0157***  0.0138***  (0.0032)  (0.0032)  (0.0029)  (0.0030)  (0.0029)  (0.0026)  Age-squared (divided by 100)  −0.0121***  −0.0123***  −0.0139***  −0.0140***  −0.0140***  −0.0108***  (0.0022)  (0.0022)  (0.0020)  (0.0021)  (0.0020)  (0.0018)  Is male  −0.0382***  −0.0274***  −0.0234***  −0.0256***  −0.0242***  −0.0174**  (0.0069)  (0.0078)  (0.0074)  (0.0076)  (0.0074)  (0.0064)  In poor health  0.0187***  0.0158***  0.0113***  0.0122***  0.0117***  0.0080**  (0.0031)  (0.0032)  (0.0028)  (0.0028)  (0.0028)  (0.0031)  Ln(financial assets excluding liquid assets)†      −0.0289***  −0.0272***  −0.0273***  −0.0128***      (0.0022)  (0.0021)  (0.0021)  (0.0014)  Ln(income)†      0.0056**  0.0032  0.0037  0.0001      (0.0026)  (0.0023)  (0.0023)  (0.0021)  Ln(medical spending)†      0.0079***  0.0085***  0.0092***  0.0046***      (0.0013)  (0.0012)  (0.0012)  (0.0011)  Below poverty line†      −0.0659***  −0.0542***  −0.0575***  −0.0286***      (0.0111)  (0.0119)  (0.0122)  (0.0098)  Ln(mortgage + HELOC)†      0.0134***  0.0125***  0.0125***  0.0053***      (0.0010)  (0.0009)  (0.0009)  (0.0006)  2-year change in welfare and food stamps assistance          0.0398***  0.0394***          (0.0119)  (0.0094)  2-year change in household size          0.0353***  0.0298**          (0.0098)  (0.0122)  Previously a revolver            0.4663***            (0.0101)  R2  0.06  0.07  0.13  0.12  0.12  0.32  Observations  30,517  30,517  30,517  30,517  30,517  20,602  Linear probability model; Dependent variable: Have a Revolving Credit Card Balance   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  Openness    0.0106**  0.0090**  0.0085**  0.0084**  −0.0015    (0.0040)  (0.0037)  (0.0036)  (0.0036)  (0.0033)  Conscientiousness    −0.0169***  −0.0091***  −0.0095***  −0.0095***  −0.0038    (0.0027)  (0.0027)  (0.0028)  (0.0028)  (0.0024)  Extraversion    −0.0140***  −0.0123***  −0.0121***  −0.0120***  −0.0036    (0.0042)  (0.0040)  (0.0041)  (0.0041)  (0.0035)  Agreeableness    0.0298***  0.0207***  0.0221***  0.0220***  0.0137***    (0.0041)  (0.0034)  (0.0035)  (0.0035)  (0.0025)  Neuroticism    0.0048  0.0049  0.005  0.005  0.002    (0.0044)  (0.0038)  (0.0040)  (0.0040)  (0.0025)  Age  0.0108***  0.0111***  0.0155***  0.0156***  0.0157***  0.0138***  (0.0032)  (0.0032)  (0.0029)  (0.0030)  (0.0029)  (0.0026)  Age-squared (divided by 100)  −0.0121***  −0.0123***  −0.0139***  −0.0140***  −0.0140***  −0.0108***  (0.0022)  (0.0022)  (0.0020)  (0.0021)  (0.0020)  (0.0018)  Is male  −0.0382***  −0.0274***  −0.0234***  −0.0256***  −0.0242***  −0.0174**  (0.0069)  (0.0078)  (0.0074)  (0.0076)  (0.0074)  (0.0064)  In poor health  0.0187***  0.0158***  0.0113***  0.0122***  0.0117***  0.0080**  (0.0031)  (0.0032)  (0.0028)  (0.0028)  (0.0028)  (0.0031)  Ln(financial assets excluding liquid assets)†      −0.0289***  −0.0272***  −0.0273***  −0.0128***      (0.0022)  (0.0021)  (0.0021)  (0.0014)  Ln(income)†      0.0056**  0.0032  0.0037  0.0001      (0.0026)  (0.0023)  (0.0023)  (0.0021)  Ln(medical spending)†      0.0079***  0.0085***  0.0092***  0.0046***      (0.0013)  (0.0012)  (0.0012)  (0.0011)  Below poverty line†      −0.0659***  −0.0542***  −0.0575***  −0.0286***      (0.0111)  (0.0119)  (0.0122)  (0.0098)  Ln(mortgage + HELOC)†      0.0134***  0.0125***  0.0125***  0.0053***      (0.0010)  (0.0009)  (0.0009)  (0.0006)  2-year change in welfare and food stamps assistance          0.0398***  0.0394***          (0.0119)  (0.0094)  2-year change in household size          0.0353***  0.0298**          (0.0098)  (0.0122)  Previously a revolver            0.4663***            (0.0101)  R2  0.06  0.07  0.13  0.12  0.12  0.32  Observations  30,517  30,517  30,517  30,517  30,517  20,602  Table III. The effect of personality on having revolving credit card debt Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for race, marital status, employment status, whether self-employed, whether in nursing home, household size (whether two or more), education (high-school, some college, and college or more dummies), and region and metro type fixed effects. Columns 3–6 also include assets (transportation, housing), whether underwater, and percent of household members employed. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). Column 6 only includes households in 2010–2012. *Significant at 10%. **Significant at 5%. ***Significant at 1%. †Measures lagged by two years (previous wave) in columns 4–6. Linear probability model; Dependent variable: Have a Revolving Credit Card Balance   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  Openness    0.0106**  0.0090**  0.0085**  0.0084**  −0.0015    (0.0040)  (0.0037)  (0.0036)  (0.0036)  (0.0033)  Conscientiousness    −0.0169***  −0.0091***  −0.0095***  −0.0095***  −0.0038    (0.0027)  (0.0027)  (0.0028)  (0.0028)  (0.0024)  Extraversion    −0.0140***  −0.0123***  −0.0121***  −0.0120***  −0.0036    (0.0042)  (0.0040)  (0.0041)  (0.0041)  (0.0035)  Agreeableness    0.0298***  0.0207***  0.0221***  0.0220***  0.0137***    (0.0041)  (0.0034)  (0.0035)  (0.0035)  (0.0025)  Neuroticism    0.0048  0.0049  0.005  0.005  0.002    (0.0044)  (0.0038)  (0.0040)  (0.0040)  (0.0025)  Age  0.0108***  0.0111***  0.0155***  0.0156***  0.0157***  0.0138***  (0.0032)  (0.0032)  (0.0029)  (0.0030)  (0.0029)  (0.0026)  Age-squared (divided by 100)  −0.0121***  −0.0123***  −0.0139***  −0.0140***  −0.0140***  −0.0108***  (0.0022)  (0.0022)  (0.0020)  (0.0021)  (0.0020)  (0.0018)  Is male  −0.0382***  −0.0274***  −0.0234***  −0.0256***  −0.0242***  −0.0174**  (0.0069)  (0.0078)  (0.0074)  (0.0076)  (0.0074)  (0.0064)  In poor health  0.0187***  0.0158***  0.0113***  0.0122***  0.0117***  0.0080**  (0.0031)  (0.0032)  (0.0028)  (0.0028)  (0.0028)  (0.0031)  Ln(financial assets excluding liquid assets)†      −0.0289***  −0.0272***  −0.0273***  −0.0128***      (0.0022)  (0.0021)  (0.0021)  (0.0014)  Ln(income)†      0.0056**  0.0032  0.0037  0.0001      (0.0026)  (0.0023)  (0.0023)  (0.0021)  Ln(medical spending)†      0.0079***  0.0085***  0.0092***  0.0046***      (0.0013)  (0.0012)  (0.0012)  (0.0011)  Below poverty line†      −0.0659***  −0.0542***  −0.0575***  −0.0286***      (0.0111)  (0.0119)  (0.0122)  (0.0098)  Ln(mortgage + HELOC)†      0.0134***  0.0125***  0.0125***  0.0053***      (0.0010)  (0.0009)  (0.0009)  (0.0006)  2-year change in welfare and food stamps assistance          0.0398***  0.0394***          (0.0119)  (0.0094)  2-year change in household size          0.0353***  0.0298**          (0.0098)  (0.0122)  Previously a revolver            0.4663***            (0.0101)  R2  0.06  0.07  0.13  0.12  0.12  0.32  Observations  30,517  30,517  30,517  30,517  30,517  20,602  Linear probability model; Dependent variable: Have a Revolving Credit Card Balance   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  Openness    0.0106**  0.0090**  0.0085**  0.0084**  −0.0015    (0.0040)  (0.0037)  (0.0036)  (0.0036)  (0.0033)  Conscientiousness    −0.0169***  −0.0091***  −0.0095***  −0.0095***  −0.0038    (0.0027)  (0.0027)  (0.0028)  (0.0028)  (0.0024)  Extraversion    −0.0140***  −0.0123***  −0.0121***  −0.0120***  −0.0036    (0.0042)  (0.0040)  (0.0041)  (0.0041)  (0.0035)  Agreeableness    0.0298***  0.0207***  0.0221***  0.0220***  0.0137***    (0.0041)  (0.0034)  (0.0035)  (0.0035)  (0.0025)  Neuroticism    0.0048  0.0049  0.005  0.005  0.002    (0.0044)  (0.0038)  (0.0040)  (0.0040)  (0.0025)  Age  0.0108***  0.0111***  0.0155***  0.0156***  0.0157***  0.0138***  (0.0032)  (0.0032)  (0.0029)  (0.0030)  (0.0029)  (0.0026)  Age-squared (divided by 100)  −0.0121***  −0.0123***  −0.0139***  −0.0140***  −0.0140***  −0.0108***  (0.0022)  (0.0022)  (0.0020)  (0.0021)  (0.0020)  (0.0018)  Is male  −0.0382***  −0.0274***  −0.0234***  −0.0256***  −0.0242***  −0.0174**  (0.0069)  (0.0078)  (0.0074)  (0.0076)  (0.0074)  (0.0064)  In poor health  0.0187***  0.0158***  0.0113***  0.0122***  0.0117***  0.0080**  (0.0031)  (0.0032)  (0.0028)  (0.0028)  (0.0028)  (0.0031)  Ln(financial assets excluding liquid assets)†      −0.0289***  −0.0272***  −0.0273***  −0.0128***      (0.0022)  (0.0021)  (0.0021)  (0.0014)  Ln(income)†      0.0056**  0.0032  0.0037  0.0001      (0.0026)  (0.0023)  (0.0023)  (0.0021)  Ln(medical spending)†      0.0079***  0.0085***  0.0092***  0.0046***      (0.0013)  (0.0012)  (0.0012)  (0.0011)  Below poverty line†      −0.0659***  −0.0542***  −0.0575***  −0.0286***      (0.0111)  (0.0119)  (0.0122)  (0.0098)  Ln(mortgage + HELOC)†      0.0134***  0.0125***  0.0125***  0.0053***      (0.0010)  (0.0009)  (0.0009)  (0.0006)  2-year change in welfare and food stamps assistance          0.0398***  0.0394***          (0.0119)  (0.0094)  2-year change in household size          0.0353***  0.0298**          (0.0098)  (0.0122)  Previously a revolver            0.4663***            (0.0101)  R2  0.06  0.07  0.13  0.12  0.12  0.32  Observations  30,517  30,517  30,517  30,517  30,517  20,602  Overall, the results in columns 1–5 show that personality traits predict the propensity to have credit card debt, which is a necessary condition for being in the puzzle group. We find that the magnitudes of the effects of personality traits are similar across our specifications. The results remain qualitatively similar even when we control for 2-year lag of being a revolver (column 6). The effect of Agreeableness remains significant at the 1% level. Those with a high level of Conscientiousness are perhaps more likely to avoid credit card borrowing because they are more likely to be self-controlled, plan ahead, and execute their plan. This is consistent with the overall findings on the importance of Conscientiousness in determining health, positive aging, and human capital (see Roberts et al., 2014 for a recent survey). On the other hand, Agreeableness has a strong positive effect on the likelihood of holding credit card debt. As discussed in Section 1, Agreeableness may lead to higher levels of spending. In addition, as discussed in Section 2, several studies have found that Agreeableness is negatively correlated with income. Similar preferences and mechanisms may be at work in this case. Those with higher levels of Agreeableness prefer less conflict (with employer in the case of wages, with self and friends in the case of incurring debt) over financial gains such as higher wages or less debt. Next, we examine the effect of personality on checking/savings balance, while controlling for credit card debt revolving status. Because revolving is endogenous to the decision of how much to save, it is crucial to address this potential bias. One common empirical strategy is to use an instrumental variables approach, or more generally, an exclusion restriction. We use 2-year lagged revolving status as our exclusion restriction. In our setting, we are interested in estimating the differential effect of the Big Five personality traits among those who are revolvers and those who are not. Given that there are 10 such effects (5 × 2), we do not directly estimate the model using two-stage least squares. Instead, we consider a reduced-form regression and include the instruments directly into the “second-stage” equation (Angrist and Pischke, 2008, p. 213). In other words, we estimate the effect of our instruments, instead of their endogenous counterparts, on the amount of checking/savings. For our exclusion to be valid, it must hold that 2-year lagged revolving behavior, conditional on our other measures, is not correlated with the unobservable propensity to have a checking/savings balance 2 years later.27 The results in column 1 of Table 4 demonstrate that the Big Five personality traits have a statistically significant effect on having a checking and savings balances of $500 or more both for those who are revolvers and those who are not. Both for revolvers and non-revolvers, Openness, Conscientiousness, Extraversion, and Neuroticism have a statistically significant effect on checking and savings balance. The results remain very similar when we add the lagged revolving status in column 2. In column 3, we add the interaction terms between the personality traits and the lagged revolving status. Our results suggest that the effect of personality on having low-yield liquid assets is similar between revolvers and non-revolvers. Table IV. The effect of personality on having a checking/savings account balance Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: Have a Checking/Savings Account Balance Over $500   Explanatory Variables  (1)  (2)  (3)  Openness  −0.0097***  −0.0098***  −0.0096**  (0.0035)  (0.0035)  (0.0046)  Conscientiousness  0.0228***  0.0228***  0.0213***  (0.0048)  (0.0048)  (0.0054)  Extraversion  −0.0216***  −0.0216***  −0.0207***  (0.0034)  (0.0034)  (0.0035)  Agreeableness  0.0051  0.0051  0.0072**  (0.0044)  (0.0043)  (0.0034)  Neuroticism  −0.0164***  −0.0165***  −0.0148***  (0.0032)  (0.0032)  (0.0031)  Previously a revolver (2 years earlier)    0.0025  0.0031    (0.0083)  (0.0082)  Lagged revolving status (revolved a credit card balance two years earlier)   interacted with personality traits   Was a revolver × Openness      −0.0009      (0.0105)   Was a revolver × Conscientiousness      0.006      (0.0108)   Was a revolver × Extraversion      −0.0038      (0.0078)   Was a revolver × Agreeableness      −0.0088      (0.0088)   Was a revolver × Neuroticism      −0.0065      (0.0065)  R2  0.35  0.35  0.35  Observations  20,602  20,602  20,602  Linear probability model; Dependent variable: Have a Checking/Savings Account Balance Over $500   Explanatory Variables  (1)  (2)  (3)  Openness  −0.0097***  −0.0098***  −0.0096**  (0.0035)  (0.0035)  (0.0046)  Conscientiousness  0.0228***  0.0228***  0.0213***  (0.0048)  (0.0048)  (0.0054)  Extraversion  −0.0216***  −0.0216***  −0.0207***  (0.0034)  (0.0034)  (0.0035)  Agreeableness  0.0051  0.0051  0.0072**  (0.0044)  (0.0043)  (0.0034)  Neuroticism  −0.0164***  −0.0165***  −0.0148***  (0.0032)  (0.0032)  (0.0031)  Previously a revolver (2 years earlier)    0.0025  0.0031    (0.0083)  (0.0082)  Lagged revolving status (revolved a credit card balance two years earlier)   interacted with personality traits   Was a revolver × Openness      −0.0009      (0.0105)   Was a revolver × Conscientiousness      0.006      (0.0108)   Was a revolver × Extraversion      −0.0038      (0.0078)   Was a revolver × Agreeableness      −0.0088      (0.0088)   Was a revolver × Neuroticism      −0.0065      (0.0065)  R2  0.35  0.35  0.35  Observations  20,602  20,602  20,602  Table IV. The effect of personality on having a checking/savings account balance Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: Have a Checking/Savings Account Balance Over $500   Explanatory Variables  (1)  (2)  (3)  Openness  −0.0097***  −0.0098***  −0.0096**  (0.0035)  (0.0035)  (0.0046)  Conscientiousness  0.0228***  0.0228***  0.0213***  (0.0048)  (0.0048)  (0.0054)  Extraversion  −0.0216***  −0.0216***  −0.0207***  (0.0034)  (0.0034)  (0.0035)  Agreeableness  0.0051  0.0051  0.0072**  (0.0044)  (0.0043)  (0.0034)  Neuroticism  −0.0164***  −0.0165***  −0.0148***  (0.0032)  (0.0032)  (0.0031)  Previously a revolver (2 years earlier)    0.0025  0.0031    (0.0083)  (0.0082)  Lagged revolving status (revolved a credit card balance two years earlier)   interacted with personality traits   Was a revolver × Openness      −0.0009      (0.0105)   Was a revolver × Conscientiousness      0.006      (0.0108)   Was a revolver × Extraversion      −0.0038      (0.0078)   Was a revolver × Agreeableness      −0.0088      (0.0088)   Was a revolver × Neuroticism      −0.0065      (0.0065)  R2  0.35  0.35  0.35  Observations  20,602  20,602  20,602  Linear probability model; Dependent variable: Have a Checking/Savings Account Balance Over $500   Explanatory Variables  (1)  (2)  (3)  Openness  −0.0097***  −0.0098***  −0.0096**  (0.0035)  (0.0035)  (0.0046)  Conscientiousness  0.0228***  0.0228***  0.0213***  (0.0048)  (0.0048)  (0.0054)  Extraversion  −0.0216***  −0.0216***  −0.0207***  (0.0034)  (0.0034)  (0.0035)  Agreeableness  0.0051  0.0051  0.0072**  (0.0044)  (0.0043)  (0.0034)  Neuroticism  −0.0164***  −0.0165***  −0.0148***  (0.0032)  (0.0032)  (0.0031)  Previously a revolver (2 years earlier)    0.0025  0.0031    (0.0083)  (0.0082)  Lagged revolving status (revolved a credit card balance two years earlier)   interacted with personality traits   Was a revolver × Openness      −0.0009      (0.0105)   Was a revolver × Conscientiousness      0.006      (0.0108)   Was a revolver × Extraversion      −0.0038      (0.0078)   Was a revolver × Agreeableness      −0.0088      (0.0088)   Was a revolver × Neuroticism      −0.0065      (0.0065)  R2  0.35  0.35  0.35  Observations  20,602  20,602  20,602  Taken together, the results in Tables 3–4 suggest that personality measures are important predictors both for credit card debt and for holding low-yield liquid assets. We find that Conscientiousness is important for both being a revolver and having a checking/savings balance (but with opposite signs), whereas the effect of Agreeableness operates mainly on the likelihood of being a revolver. 4.2 Intra-Household Dynamics The effect of household dynamics on economic and financial outcomes has been widely studied.28 These studies indicate that the financial behavior of a household could depend on the inner dynamics within the household. In the context of credit card debt, Bertaut, Haliassos, and Reiter (2009) derive a model where high credit card debt is used as a means to curb the spending of a spouse (or one’s self) by committing to have less available funds through a high level of existing debt. This is conceptually similar to the model considered in Fudenberg and Levine (2006) who propose a dual-self model for impulse control. In this section, we broaden our focus beyond that of the financial respondent’s personality. In our setting, the intra-household dynamics between the financial respondent and their spouse may play an important role as each side may not be able to fully monitor or control the spending and saving behavior of their partner. For example, each partner may have their own credit card that can be used for spending without the others’ pre-approval.29 Personality differences (similarities) may exacerbate (alleviate) this dynamic. We examine both a reduced-form specification, where household members are treated symmetrically (columns 3 and 4), and specifications with some additional structure (columns 5 and 6) where we proxy for power imbalance within a household using the income difference (financial respondent’s income − spouse’s income). Finally, we examine the role of the financial respondent’s gender (column 7). Column 1 of Table 5 demonstrates that the effects of the financial respondent personality traits remain largely the same when only couple households are examined. Next, to allow for a more parsimonious examination, we define a “puzzle personality index” by estimating the propensity to be in the puzzle group among single households.30 By examining single households, we circumvent the added complication of intra-household dynamics. Table V. The effect of intra-household dynamics on being in the puzzle group Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. Age and education (years) differences measured as the absolute value of (financial respondent’s−spouse’s). The PPI aggregates the effect of the Big Five personality traits based on single household estimates. Couple power difference based on income difference between couple members. The sample includes all couple households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  Puzzle personality (index)    0.0148***  0.0141***  0.0136***  0.0152***        (0.0039)  (0.0039)  (0.0040)  (0.0040)      Spouse’s puzzle personality (index)      0.0064*  0.0049  0.005  0.0056        (0.0034)  (0.0035)  (0.0035)  (0.0034)    Interaction of couple’s puzzle personalities indices        −0.0086***  −0.0080***  −0.0080***          (0.0026)  (0.0025)  (0.0026)    Couple’s age difference          −0.0001  −0.0001  −0.0002          (0.0006)  (0.0006)  (0.0006)  Couple’s years-of-schooling difference          −0.0004  −0.0005  −0.0007            (0.0017)  (0.0017)  (0.0017)  Couple’s power difference (income)          −0.0184*  −0.0196*            (0.0106)  (0.0105)    Openness ×  Couple’s power difference (income)          −0.0127  −0.0117            (0.0125)  (0.0122)    Conscientiousness ×  Couple’s power difference (income)          0.0082  0.0105            (0.0145)  (0.0152)    Extraversion ×  Couple’s power difference (income)          0.0071  0.0073            (0.0112)  (0.0122)    Agreeableness ×  Couple’s power difference (income)          −0.0177**  −0.0190**            (0.0072)  (0.0071)    Neuroticism ×  Couple’s power difference (income)          −0.0099  −0.0112            (0.0103)  (0.0095)    Male’s puzzle personality (index)              0.0025              (0.0052)  Male’s puzzle personality (index) ×  Financial respondent is male              0.0088              (0.0067)  Female’s puzzle personality (index)              0.0192**              (0.0072)  Female’s puzzle personality (index) ×  Financial respondent is male              −0.0096              (0.0079)  Openness  0.0061          0.006    (0.0048)          (0.0048)    Conscientiousness  −0.0071          −0.0085    (0.0048)          (0.0053)    Extraversion  −0.0126**          −0.0131**    (0.0051)          (0.0053)    Agreeableness  0.0186***          0.0195***    (0.0051)          (0.0051)    Neuroticism  0.0018          0.0025    (0.0043)          (0.0039)    R2  0.11  0.11  0.11  0.11  0.11  0.11  0.11  Observations  12,988  12,988  12,988  12,988  12,988  12,988  12,988  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  Puzzle personality (index)    0.0148***  0.0141***  0.0136***  0.0152***        (0.0039)  (0.0039)  (0.0040)  (0.0040)      Spouse’s puzzle personality (index)      0.0064*  0.0049  0.005  0.0056        (0.0034)  (0.0035)  (0.0035)  (0.0034)    Interaction of couple’s puzzle personalities indices        −0.0086***  −0.0080***  −0.0080***          (0.0026)  (0.0025)  (0.0026)    Couple’s age difference          −0.0001  −0.0001  −0.0002          (0.0006)  (0.0006)  (0.0006)  Couple’s years-of-schooling difference          −0.0004  −0.0005  −0.0007            (0.0017)  (0.0017)  (0.0017)  Couple’s power difference (income)          −0.0184*  −0.0196*            (0.0106)  (0.0105)    Openness ×  Couple’s power difference (income)          −0.0127  −0.0117            (0.0125)  (0.0122)    Conscientiousness ×  Couple’s power difference (income)          0.0082  0.0105            (0.0145)  (0.0152)    Extraversion ×  Couple’s power difference (income)          0.0071  0.0073            (0.0112)  (0.0122)    Agreeableness ×  Couple’s power difference (income)          −0.0177**  −0.0190**            (0.0072)  (0.0071)    Neuroticism ×  Couple’s power difference (income)          −0.0099  −0.0112            (0.0103)  (0.0095)    Male’s puzzle personality (index)              0.0025              (0.0052)  Male’s puzzle personality (index) ×  Financial respondent is male              0.0088              (0.0067)  Female’s puzzle personality (index)              0.0192**              (0.0072)  Female’s puzzle personality (index) ×  Financial respondent is male              −0.0096              (0.0079)  Openness  0.0061          0.006    (0.0048)          (0.0048)    Conscientiousness  −0.0071          −0.0085    (0.0048)          (0.0053)    Extraversion  −0.0126**          −0.0131**    (0.0051)          (0.0053)    Agreeableness  0.0186***          0.0195***    (0.0051)          (0.0051)    Neuroticism  0.0018          0.0025    (0.0043)          (0.0039)    R2  0.11  0.11  0.11  0.11  0.11  0.11  0.11  Observations  12,988  12,988  12,988  12,988  12,988  12,988  12,988  Table V. The effect of intra-household dynamics on being in the puzzle group Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. Age and education (years) differences measured as the absolute value of (financial respondent’s−spouse’s). The PPI aggregates the effect of the Big Five personality traits based on single household estimates. Couple power difference based on income difference between couple members. The sample includes all couple households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  Puzzle personality (index)    0.0148***  0.0141***  0.0136***  0.0152***        (0.0039)  (0.0039)  (0.0040)  (0.0040)      Spouse’s puzzle personality (index)      0.0064*  0.0049  0.005  0.0056        (0.0034)  (0.0035)  (0.0035)  (0.0034)    Interaction of couple’s puzzle personalities indices        −0.0086***  −0.0080***  −0.0080***          (0.0026)  (0.0025)  (0.0026)    Couple’s age difference          −0.0001  −0.0001  −0.0002          (0.0006)  (0.0006)  (0.0006)  Couple’s years-of-schooling difference          −0.0004  −0.0005  −0.0007            (0.0017)  (0.0017)  (0.0017)  Couple’s power difference (income)          −0.0184*  −0.0196*            (0.0106)  (0.0105)    Openness ×  Couple’s power difference (income)          −0.0127  −0.0117            (0.0125)  (0.0122)    Conscientiousness ×  Couple’s power difference (income)          0.0082  0.0105            (0.0145)  (0.0152)    Extraversion ×  Couple’s power difference (income)          0.0071  0.0073            (0.0112)  (0.0122)    Agreeableness ×  Couple’s power difference (income)          −0.0177**  −0.0190**            (0.0072)  (0.0071)    Neuroticism ×  Couple’s power difference (income)          −0.0099  −0.0112            (0.0103)  (0.0095)    Male’s puzzle personality (index)              0.0025              (0.0052)  Male’s puzzle personality (index) ×  Financial respondent is male              0.0088              (0.0067)  Female’s puzzle personality (index)              0.0192**              (0.0072)  Female’s puzzle personality (index) ×  Financial respondent is male              −0.0096              (0.0079)  Openness  0.0061          0.006    (0.0048)          (0.0048)    Conscientiousness  −0.0071          −0.0085    (0.0048)          (0.0053)    Extraversion  −0.0126**          −0.0131**    (0.0051)          (0.0053)    Agreeableness  0.0186***          0.0195***    (0.0051)          (0.0051)    Neuroticism  0.0018          0.0025    (0.0043)          (0.0039)    R2  0.11  0.11  0.11  0.11  0.11  0.11  0.11  Observations  12,988  12,988  12,988  12,988  12,988  12,988  12,988  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  Puzzle personality (index)    0.0148***  0.0141***  0.0136***  0.0152***        (0.0039)  (0.0039)  (0.0040)  (0.0040)      Spouse’s puzzle personality (index)      0.0064*  0.0049  0.005  0.0056        (0.0034)  (0.0035)  (0.0035)  (0.0034)    Interaction of couple’s puzzle personalities indices        −0.0086***  −0.0080***  −0.0080***          (0.0026)  (0.0025)  (0.0026)    Couple’s age difference          −0.0001  −0.0001  −0.0002          (0.0006)  (0.0006)  (0.0006)  Couple’s years-of-schooling difference          −0.0004  −0.0005  −0.0007            (0.0017)  (0.0017)  (0.0017)  Couple’s power difference (income)          −0.0184*  −0.0196*            (0.0106)  (0.0105)    Openness ×  Couple’s power difference (income)          −0.0127  −0.0117            (0.0125)  (0.0122)    Conscientiousness ×  Couple’s power difference (income)          0.0082  0.0105            (0.0145)  (0.0152)    Extraversion ×  Couple’s power difference (income)          0.0071  0.0073            (0.0112)  (0.0122)    Agreeableness ×  Couple’s power difference (income)          −0.0177**  −0.0190**            (0.0072)  (0.0071)    Neuroticism ×  Couple’s power difference (income)          −0.0099  −0.0112            (0.0103)  (0.0095)    Male’s puzzle personality (index)              0.0025              (0.0052)  Male’s puzzle personality (index) ×  Financial respondent is male              0.0088              (0.0067)  Female’s puzzle personality (index)              0.0192**              (0.0072)  Female’s puzzle personality (index) ×  Financial respondent is male              −0.0096              (0.0079)  Openness  0.0061          0.006    (0.0048)          (0.0048)    Conscientiousness  −0.0071          −0.0085    (0.0048)          (0.0053)    Extraversion  −0.0126**          −0.0131**    (0.0051)          (0.0053)    Agreeableness  0.0186***          0.0195***    (0.0051)          (0.0051)    Neuroticism  0.0018          0.0025    (0.0043)          (0.0039)    R2  0.11  0.11  0.11  0.11  0.11  0.11  0.11  Observations  12,988  12,988  12,988  12,988  12,988  12,988  12,988  We first estimate the specification in column 5 of Table 2 with only single households. Using the estimated coefficients for the personality traits from the single household estimates, we create a puzzle personality index (PPI) as the Z-score of 0.007 × Openness − 0.003 × Conscientiousness − 0.009 × Extraversion + 0.012 × Agreeableness − 0.0006 × Neuroticism. Column 2 of Table 5 includes the PPI only for the financial respondent. The coefficient is significant at the 1% level, suggesting the index performs well in capturing the likelihood of being in the puzzle group for the couple households. Next, we allow the personality of both members of the household to have an effect (column 3). The coefficient for the financial respondent’s PPI remains statistically significant and of the same magnitude. The spouse’s PPI is also statistically significant suggesting that both household members’ personality traits contribute to the likelihood of being in the puzzle group.31 In columns 4–6, we add an interaction effect between the couple’s PPIs. The interaction coefficient is negative and statistically significant at the 1% level in columns 4–6, implying an attenuating effect of a spouse’s PPI on the marginal effect of the PPI of the financial respondent. The specification in columns 5 and 6 of Table 5 examines the interaction effect of a measure of power imbalance between a couple and the financial respondent’s personality traits as well as intra-household differences (in absolute values) of age and schooling levels.32 A positive value of this measure (the financial respondent earns more than their spouse) could proxy for more power for the financial respondent within the relationship. In column 5 we use the PPI, and in column 6 we instead use the Big Five personality traits for the financial respondent and obtain similar results. In both cases, the likelihood of being in the puzzle group increases when the nonfinancial respondent spouse earns more than the financial respondent. Also, the interaction term between the financial respondent’s Agreeableness and the income difference is negative and statistically significant at the 5% level. The more disagreeable the financial respondent is, the more likely it is that the financial respondent would be able to impose his or her preference when facing a powerful spouse. Our finding is consistent with high levels of disagreeableness (i.e., less willing to accommodate or compromise) being a substitute for power. In the last specification in Table 5 (column 7), we examine the role of the financial respondent’s gender in addition to their spouse’s role. We control for the male’s PPI, the female’s PPI, the financial respondent’s gender, and interactions between the male and female’s PPI and the gender of the financial respondent. Our results suggest that the effect of personality is larger for women, and that the effect of personality is the same regardless of the financial respondent’s gender. In contrast to the effects of personality, age and schooling differences among couple members do not have a statistically significant effect on a household’s likelihood of being in the puzzle group, and the coefficients are small in magnitude. Overall, our analysis suggests that co-holding behavior is influenced by the dynamics within a household. This highlights that potential policy interventions and future research should consider both members of a household. 4.3 Alternative Explanations To examine whether our findings of the effect of personality measures using the Big Five traits are just proxies for other measures, we examine the effects of other alternative measures, such as financial sophistication or self-control. The first column in Table 6 adds a self-control/impulsiveness measure to our main specification in Table 2 as a proxy for self-control which Bertaut, Haliassos, and Reiter (2009) and Gathergood (2012) suggested as an important factor for the puzzle. We find that controlling for self-control/impulsiveness, three of the personality measures remain statistically significant at the 5% level or lower. Column 2 in Table 6 examines the effect of internal locus of control. Locus of control is the degree to which people believe in their own ability to control events that influence them.33 People with a high internal locus of control believe that outcomes are due to their own behavior, and people with a high external locus of control believe that events occur for reasons out of their control, or external factors. The coefficient for internal locus of control is statistically significant at the 5% level. However, four of the personality traits remain statistically significant at the 10% level. In column 3, we include cognitive functioning measures such as memory and mental status (which includes numeracy).34 We also include the number series score which was developed for measuring fluid intelligence. Though some of the cognitive measures have a statistically significant effect, by-and-large, the effect of personality (both in magnitude and statistical significance) remains the same or larger and our results are robust to various measures of cognitive ability. Table VI. Robustness checks with alternative explanations for the puzzle Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. In column 8, the excluded category is 7–14% interest rate. *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  Openness  0.0076**  0.0058*  0.0140***  −0.0067  0.0082**  0.0151  0.0118  0.0106  (0.0031)  (0.0028)  (0.0046)  (0.0096)  (0.0034)  (0.0229)  (0.0206)  (0.0264)  Conscientiousness  −0.0021  −0.0052*  −0.0088**  −0.0212**  −0.0032  0.011  −0.0065  −0.0082  (0.0033)  (0.0029)  (0.0040)  (0.0100)  (0.0038)  (0.0179)  (0.0146)  (0.0238)  Extraversion  −0.0122***  −0.0102***  −0.0124***  −0.0113  −0.0124***  −0.0417  −0.0498  −0.045  (0.0031)  (0.0028)  (0.0036)  (0.0099)  (0.0034)  (0.0366)  (0.0310)  (0.0473)  Agreeableness  0.0164***  0.0162***  0.0166***  0.0302***  0.0146***  0.0428**  0.0461**  0.0621**  (0.0032)  (0.0028)  (0.0042)  (0.0071)  (0.0042)  (0.0196)  (0.0186)  (0.0254)  Neuroticism  −0.0011  −0.0013  0.0028  −0.0056  −0.0036  0.0091  0.0018  −0.0053  (0.0026)  (0.0028)  (0.0034)  (0.0061)  (0.0034)  (0.0188)  (0.0168)  (0.0226)  Self-control/Impulsiveness  −0.0068**                (0.0031)                Internal locus of control    −0.0050**                (0.0022)              Total memory score (word recall)      0.0042***                (0.0011)            Total mental status score (numeracy)      0.0067***                (0.0019)            Total number series score (fluid intelligence)      0.0001                (0.0001)            Risk aversion (6 levels)        Included           Are any of the risk aversion levels  statistically significant?        No          In financial control (lagged)          −0.0028**                 (0.0011)        Financial literacy            0.0144                 (0.0461)      Credit card rates   0% interest rate                −0.2315***                 (0.0584)   1-6% interest rate                −0.1609                 (0.0998)   More than 15% interest rate                −0.1174***                 (0.0360)  Economic/financial understanding (7 levels)              Included     Are any of the of econ/financial understanding levels statistically significant?              Only the highest level    R2  0.11  0.10  0.09  0.10  0.10  0.18  0.18  0.24  Observations  24,820  30,412  11,330  3,366  14,102  647  784  484  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  Openness  0.0076**  0.0058*  0.0140***  −0.0067  0.0082**  0.0151  0.0118  0.0106  (0.0031)  (0.0028)  (0.0046)  (0.0096)  (0.0034)  (0.0229)  (0.0206)  (0.0264)  Conscientiousness  −0.0021  −0.0052*  −0.0088**  −0.0212**  −0.0032  0.011  −0.0065  −0.0082  (0.0033)  (0.0029)  (0.0040)  (0.0100)  (0.0038)  (0.0179)  (0.0146)  (0.0238)  Extraversion  −0.0122***  −0.0102***  −0.0124***  −0.0113  −0.0124***  −0.0417  −0.0498  −0.045  (0.0031)  (0.0028)  (0.0036)  (0.0099)  (0.0034)  (0.0366)  (0.0310)  (0.0473)  Agreeableness  0.0164***  0.0162***  0.0166***  0.0302***  0.0146***  0.0428**  0.0461**  0.0621**  (0.0032)  (0.0028)  (0.0042)  (0.0071)  (0.0042)  (0.0196)  (0.0186)  (0.0254)  Neuroticism  −0.0011  −0.0013  0.0028  −0.0056  −0.0036  0.0091  0.0018  −0.0053  (0.0026)  (0.0028)  (0.0034)  (0.0061)  (0.0034)  (0.0188)  (0.0168)  (0.0226)  Self-control/Impulsiveness  −0.0068**                (0.0031)                Internal locus of control    −0.0050**                (0.0022)              Total memory score (word recall)      0.0042***                (0.0011)            Total mental status score (numeracy)      0.0067***                (0.0019)            Total number series score (fluid intelligence)      0.0001                (0.0001)            Risk aversion (6 levels)        Included           Are any of the risk aversion levels  statistically significant?        No          In financial control (lagged)          −0.0028**                 (0.0011)        Financial literacy            0.0144                 (0.0461)      Credit card rates   0% interest rate                −0.2315***                 (0.0584)   1-6% interest rate                −0.1609                 (0.0998)   More than 15% interest rate                −0.1174***                 (0.0360)  Economic/financial understanding (7 levels)              Included     Are any of the of econ/financial understanding levels statistically significant?              Only the highest level    R2  0.11  0.10  0.09  0.10  0.10  0.18  0.18  0.24  Observations  24,820  30,412  11,330  3,366  14,102  647  784  484  Table VI. Robustness checks with alternative explanations for the puzzle Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. In column 8, the excluded category is 7–14% interest rate. *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  Openness  0.0076**  0.0058*  0.0140***  −0.0067  0.0082**  0.0151  0.0118  0.0106  (0.0031)  (0.0028)  (0.0046)  (0.0096)  (0.0034)  (0.0229)  (0.0206)  (0.0264)  Conscientiousness  −0.0021  −0.0052*  −0.0088**  −0.0212**  −0.0032  0.011  −0.0065  −0.0082  (0.0033)  (0.0029)  (0.0040)  (0.0100)  (0.0038)  (0.0179)  (0.0146)  (0.0238)  Extraversion  −0.0122***  −0.0102***  −0.0124***  −0.0113  −0.0124***  −0.0417  −0.0498  −0.045  (0.0031)  (0.0028)  (0.0036)  (0.0099)  (0.0034)  (0.0366)  (0.0310)  (0.0473)  Agreeableness  0.0164***  0.0162***  0.0166***  0.0302***  0.0146***  0.0428**  0.0461**  0.0621**  (0.0032)  (0.0028)  (0.0042)  (0.0071)  (0.0042)  (0.0196)  (0.0186)  (0.0254)  Neuroticism  −0.0011  −0.0013  0.0028  −0.0056  −0.0036  0.0091  0.0018  −0.0053  (0.0026)  (0.0028)  (0.0034)  (0.0061)  (0.0034)  (0.0188)  (0.0168)  (0.0226)  Self-control/Impulsiveness  −0.0068**                (0.0031)                Internal locus of control    −0.0050**                (0.0022)              Total memory score (word recall)      0.0042***                (0.0011)            Total mental status score (numeracy)      0.0067***                (0.0019)            Total number series score (fluid intelligence)      0.0001                (0.0001)            Risk aversion (6 levels)        Included           Are any of the risk aversion levels  statistically significant?        No          In financial control (lagged)          −0.0028**                 (0.0011)        Financial literacy            0.0144                 (0.0461)      Credit card rates   0% interest rate                −0.2315***                 (0.0584)   1-6% interest rate                −0.1609                 (0.0998)   More than 15% interest rate                −0.1174***                 (0.0360)  Economic/financial understanding (7 levels)              Included     Are any of the of econ/financial understanding levels statistically significant?              Only the highest level    R2  0.11  0.10  0.09  0.10  0.10  0.18  0.18  0.24  Observations  24,820  30,412  11,330  3,366  14,102  647  784  484  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  Openness  0.0076**  0.0058*  0.0140***  −0.0067  0.0082**  0.0151  0.0118  0.0106  (0.0031)  (0.0028)  (0.0046)  (0.0096)  (0.0034)  (0.0229)  (0.0206)  (0.0264)  Conscientiousness  −0.0021  −0.0052*  −0.0088**  −0.0212**  −0.0032  0.011  −0.0065  −0.0082  (0.0033)  (0.0029)  (0.0040)  (0.0100)  (0.0038)  (0.0179)  (0.0146)  (0.0238)  Extraversion  −0.0122***  −0.0102***  −0.0124***  −0.0113  −0.0124***  −0.0417  −0.0498  −0.045  (0.0031)  (0.0028)  (0.0036)  (0.0099)  (0.0034)  (0.0366)  (0.0310)  (0.0473)  Agreeableness  0.0164***  0.0162***  0.0166***  0.0302***  0.0146***  0.0428**  0.0461**  0.0621**  (0.0032)  (0.0028)  (0.0042)  (0.0071)  (0.0042)  (0.0196)  (0.0186)  (0.0254)  Neuroticism  −0.0011  −0.0013  0.0028  −0.0056  −0.0036  0.0091  0.0018  −0.0053  (0.0026)  (0.0028)  (0.0034)  (0.0061)  (0.0034)  (0.0188)  (0.0168)  (0.0226)  Self-control/Impulsiveness  −0.0068**                (0.0031)                Internal locus of control    −0.0050**                (0.0022)              Total memory score (word recall)      0.0042***                (0.0011)            Total mental status score (numeracy)      0.0067***                (0.0019)            Total number series score (fluid intelligence)      0.0001                (0.0001)            Risk aversion (6 levels)        Included           Are any of the risk aversion levels  statistically significant?        No          In financial control (lagged)          −0.0028**                 (0.0011)        Financial literacy            0.0144                 (0.0461)      Credit card rates   0% interest rate                −0.2315***                 (0.0584)   1-6% interest rate                −0.1609                 (0.0998)   More than 15% interest rate                −0.1174***                 (0.0360)  Economic/financial understanding (7 levels)              Included     Are any of the of econ/financial understanding levels statistically significant?              Only the highest level    R2  0.11  0.10  0.09  0.10  0.10  0.18  0.18  0.24  Observations  24,820  30,412  11,330  3,366  14,102  647  784  484  In column 4, we include a risk aversion measure, as those with higher risk aversion may opt to keep a larger buffer in the form of less credit card debt or a higher checking account balance. We use the 6-categories risk aversion measure from the HRS’s “income gamble” scenario questions. None of the risk categories are statistically significant at the 10% level, and overall the effect of the personality measures remains the same. In column 5, we use the self-perception of how much a household has control over their financial situation.35 We find that households who assess themselves as having more control over their finances are less likely to be in the puzzle group. To reduce the likelihood of reverse causality, we use the lagged self-perception of financial control (though financial problems may be persistent over longer horizons). We find that the effects of the Big Five traits are quite similar to those in our base specifications. Columns 6–7 of Table 6 include various measures of financial literacy (see Lusardi and Mitchell, 2014) for a summary of the important role financial literacy has on economic outcomes). We construct the financial literacy measure based on two questions (one on compound interest and one on the effects of inflation). Column 6 in Table 6 uses a dummy indicator for answering both questions correctly as the measure of financial literacy. Column 7 uses a self-assessed measure of the degree of understanding of economics and finance (7 levels). The results in columns 6–7 show that the effect of Agreeableness remains statistically significant and the coefficients of the Big Five personality traits generally have the same sign as those in our base specification in Table 2 even after controlling for financial literacy. This is consistent with the result of Gathergood and Weber (2014). Our final specification controls for credit card interest rates on the card used most often. Column 8 in Table 6 shows that interest rates have a statistically significant effect. Yet, the magnitudes of the Big Five personality trait effects remain largely the same as those in column 6–8. Openness, Conscientiousness, and Extraversion lose their significance at the 10% level, but Agreeableness remains statistically significant. While the sample size for this specification is rather small (only 1 in 20 of our households in 2010 were asked the question), the results suggest that our findings capture “true” interest-paying revolvers and are not solely driven by households that are taking advantage of 0% balance transfer offers. In summary, we find that the personality effects are robust to controlling for self-control, locus of control, cognitive abilities, risk aversion, financial literacy, and credit card interest rates. 5. Conclusion Using a rich longitudinal data set, we find that controlling for a host of demographic, financial, and economic factors, personality traits play a role in explaining the credit card puzzle. We find that Conscientiousness, Extraversion, and Agreeableness have statistically significant effects, and that the signs of the effects are consistent with findings in other domains. The results complement other types of explanations suggested in the literature for the credit card puzzle, as they hold after controlling for other suggested factors. Our findings that a broad set of noncognitive measures are important, suggest that researchers should be cautious of focusing on a single aspect or dimension of noncognitive ability when studying financial decision making. Our results also suggest that intra-household dynamics might play an important role in financial decisions, and highlight the importance of both coordination and power dynamics among household members. The effect of intra-household interactions on the economic and financial well-being of families remains an important area for future research. The findings in this article contribute to an emerging and growing set of economic and financial outcomes (e.g., earnings, education, etc.) in which noncognitive skills play an important role. It is, therefore, plausible that noncognitive skills would play a role in areas of finance that have yet to be examined. There are two types of polices to consider that could yield substantial returns: investments in noncognitive skills, and policies that target the noncognitive aspects of financial decision-making. Investments in noncognitive skills, such as planning, may have large benefits.36 However, personality is mostly/solely malleable at early childhood, so such investments require an early intervention and a long-term horizon. The second type of policies is related to the design of interventions or marketing campaigns to address debt. Both the government and non-profit organizations have programs to assist people with managing their finances.37 Participants in these programs could be asked to answer a short survey that would assess their personality type, and could then receive an intervention that would be tailored to their profile. Financial planners already ask their clients about their investment goals and risk tolerance. Similar assessments could be performed by debt counselors or by consumers visiting websites.38 In addition, banks and credit unions routinely have access to credit reports. They are, therefore, potentially positioned to combine information on the availability of low-yield liquid assets and revolving credit card balances and alert their clients to costly levels of co-holding. Co-holding credit card debt and low-yield liquid assets exerts a non-negligible toll on households. Extrapolating our results, in the US, even a 1-percentage-point decrease in the fraction of households in the puzzle group, would generate interest payment savings of over half-a-billion dollars per year, while maintaining the same level of consumption. Footnotes 1 Openness “describes the breadth, depth, originality, and complexity of an individual’s mental and experiential life [with a behavioral example] of tak[ing] time to learn something simply for the joy of learning.” Extraversion “implies an energetic approach toward the social and material world … such as sociability, activity, assertiveness, and positive emotionality.” Neuroticism “contrasts emotional stability and even-temperedness with negative emotionality, such as feeling anxious, nervous, sad, and tense.” Ibid.John and Srivastava (1999) provide an overview of the traits as well as a historical account of the last several decades. 2 See Livingstone and Lunt (1992), Nyhus and Webley (2001), Norvilitis et al. (2006), Rabinovich and Webley (2007), and Conti and Heckman (2014) for some examples. 3 Examples include earnings (Bowles, Gintis, and Osborne, 2001; Nyhus and Pons, 2005; and Mueller and Plug, 2006); household finances (Brown and Taylor, 2014); educational attainment (Lundberg, 2013); and academic achievements (Heckman, Stixrud, and Urzua, 2006; and Heckman and Kautz, 2012). 4 We calculate the annual interest cost of co-holding per household using the average credit card debt amount that could have been paid down after a month’s income is set aside and the average interest rate of 14%. When extrapolating to the US population, we apply the annual interest cost estimate to 1% of households in the US with a head over 50 (Table H2, U.S. Census Bureau, 2015). 5 In other definitions, we consider households to not be in the puzzle group if they have up to $500 in credit card debt as in Telyukova (2013); $1,200 or one-half of monthly income, whichever is larger, in checking or savings accounts (following Bertaut, Haliassos, and Reiter, 2009); and one month’s income in checking or savings accounts. We have also examined continuous measures that can be interpreted as the cost of being in the puzzle group such as min⁡{ln⁡(A),ln⁡(D)}, ln⁡(A)1{D>0}, and ln⁡(A)0.5ln⁡(D)0.5, where A and D are the amounts of low-yield liquid assets and credit card debt, respectively, in excess of certain thresholds such as 0 or $500. 6 This sequential framework is, of course, for exposition purposes only. An alternative would involve the decision to hold low-yield liquid assets, and conditional on holding those assets, the decision to incur debt instead of using one’s available assets. 7 Bernerth et al. (2012) note that “the trusting, submissive, and accommodating tendencies of agreeable individuals can put them in precarious positions as they sacrifice personal resources for others.” 8 For example, Brown and Taylor (2014) find that Openness is positively correlated with having credit card debt. Matz, Gladstone, and Stillwell (2016) find that higher levels of Openness are associated with higher levels of spending on entertainment, eating out, pubs, and tourism. 9 For example, in the context of coping and coping effectiveness under stress or constraints, McCrae and Costa (1986) find that “Extraversion is correlated with rational action, positive thinking, substitution, and restraint.” Related, Carver and Connor-Smith (2010) find that “Extraversion predicted more problem solving, use of social support, and cognitive restructuring.” See also Connor-Smith and Flachsbart (2007). 10 For example, Donnelly, Iyer, and Howell (2012) find that Neuroticism is negatively related to the management of personal finances. 11 Additional information can be found at http://hrsonline.isr.umich.edu/ 12 The co-holding of credit card debt and low-yield liquid assets is prevalent among all age groups. Telyukova (2013) reports that 27% of households with heads of age 25–64 years co-hold based on the 2001 Survey of Consumer Finances (SCF). Using 1995 SCF data, Gross and Souleles (2002) report in Table 6 that among bank card borrowers younger than 35 years, 95% hold positive liquid assets and 25% hold more than one month’s income in liquid assets. 13 Other commonly used datasets may have a wider range of age groups but either lack any personality measures (e.g., the SCF) or have less complete personality measures (e.g., The National Longitudinal Survey of Youth). 14 While our preferred specification (column 5 in Table 2) uses all households, the magnitudes of the personality effects are similar for households with a head 55 years or younger. However, we acknowledge that our sample cannot be used to estimate the effect on those under 50 years old. Our predictions for the entire population therefore require us to assume the effects are similar among younger households. 15 Smith, McArdle, and Willis (2010) find that males and those with more years of education are more likely to be the financial respondent of the household in the HRS survey. Our analysis controls for both of these factors. 16 The attrition rate in the HRS is relatively low. For example, from 2010 to 2012, the attrition rate is 4.9% due to death, and 3.7% due to nonresponse. We also examined potential attrition bias by testing whether membership in the puzzle group could predict attrition and find the effect to be small and not statistically significant. 17 Although the core HRS survey is a biennial survey and participants are interviewed every two years, some of the questionnaires, including those for personality, are only administrated every 4 years (alternating half of the sample every two years) to reduce the burden on survey participants (Juster and Suzman, 1995). 18 http://www.midus.wisc.edu/ 19 The other measures are Openness (7 items): creative, imaginative, intelligent, curious, broadminded, sophisticated, adventurous; Extraversion (5 items): outgoing, friendly, lively, active, talkative; Agreeableness (5 items): helpful, warm, caring, softhearted, sympathetic; and Neuroticism (4 items): moody, worrying, nervous, calm (−). 20 Borghans et al. (2008) and Roberts, Wood, and Caspi (2008) provide a review of this matter. 21 The average age in our sample is higher, but our results remain qualitatively and quantitatively the same if we just focus on the younger working-age segment of our sample. 22 However, our results both in terms of the magnitude of the standard errors and statistical significance are almost identical when we instead cluster at the household level. 23 Our results remain the same when we additionally examine shocks to health and employment status. 24 To illustrate the effect of the personality traits, one can translate a personality effect into the equivalent effect of a financial variable. An increase of $25,591 in financial assets (or 0.2269 in logs) would decrease the likelihood of being in the puzzle group by 0.54 percentage points (column 5). This is the same decrease in the likelihood of being in the puzzle group that would occur if Conscientiousness were to increase by one standard deviation (as the coefficient on Conscientiousness is − 0.0054). Hence, an increase of $25,591 in financial assets has the same effect on the likelihood of being in the puzzle group as an one-standard-deviation increase in Conscientiousness. 25 We also examined a random-effects model that would allow for the inclusion of fixed-over-time personality measures. The results for the personality traits are of the same sign and magnitude of our preferred specifications, and Extraversion and Agreeableness remain statistically significant at the 5% level. 26 There is a large body of literature on personal debt focusing on both economic and financial factors, and on psychological factors. For example, in the psychology literature, Livingstone and Lunt (1992), Wood (1998), Donnelly, Iyer, and Howell (2012), and Wilcox, Block, and Eisenstein (2011) find effects that by and large are consistent with our findings. 27 To investigate the validity of the exclusion, we examined the typical measures associated with a two-stage-least-squares framework using the specification in column 2 of Table 4. The first stage R-squared is 0.32. The excluded instrument’s F-statistic (2126.95) suggests that there is no “weak” instrument problem (Stock and Yogo, 2005). Furthermore, we could not reject the null that the instruments are valid using the Sargan–Hansen test of overidentification (with 4-year lag revolving status being the additional instrument). 28 For example, see Lundberg and Pollak (2007) for a recent review of some of the issues in the US context. 29 Schaner (2015) argues that large differences in discount factors among couples can lead to holding individual bank accounts that have lower interest earnings than joint accounts. 30 To improve statistical power, we summarize the five personality traits with a single index that has the most explanatory power. Otherwise, to examine both partners’ personality and an interaction term would require as many as 20 variables. 31 This result is consistent with that of Schaner (2015), where the difference in the preferences of household members leads to costly separate individual financial accounts rather than joint accounts. 32 For the power imbalance measure, we use log⁡(z+1+z2), where z is the income difference (financial respondent’s−spouse’s). Our measure encompasses both unearned income (see Lundberg and Pollak, 1996 for a discussion) and earned income. For example, Basu (2006) examines the effect of intra-couple power relationships, measured by income disparity, on household decision making, and Ashraf (2009) uses an experimental design in the Philippines to examine the role of spousal control in consumption and savings decisions. 33 Locus of control has been shown to be an important factor in economic decision making. For example, college attendance (Coleman and DeLeire, 2003); job search (Caliendo, Cobb-Clark, and Uhlendorff, 2015); and loan delinquency (Kuhnen and Melzer, 2017). 34 For memory, we use the total of the immediate and delayed (5 min) word recall scores. For mental status, we use the total of the serial 7’s test, counting backwards from 20 or 86, date recall, recalling object names, and naming the President/Vice President. 35 We use the question: “Using a 0 to 10 scale where 0 means ‘no control at all’ and 10 means ‘very much control,’ how would you rate the amount of control you have over your financial situation these days?” 36 Some schools, such as NY City’s KIPP charter school, teach skills, such as grit, as part of their curriculum. See Kautz et al. (2014) for a review of early intervention programs targeting noncognitive skills. 37 For example, the Consumer Financial Protection Bureau launched in 2015 a financial coaching initiative with 60 certified coaches all across the US (Consumer Financial Protection Bureau, 2015). 38 The National Foundation for Credit Counseling, the oldest and foremost nonprofit organization for financial counseling, now accepts credit counseling requests online. See https://www.nfcc.org/agency-locator/online-counseling/ References Almlund M., Angela Lee D., James H., Tim K. ( 2011) Personality psychology and economics, in: Hanushek Eric A, Machin Stephen J, Woessmann Ludger (eds.), Handbook of the Economics of Education , Vol . 4, chap. 1, Elsevier B.V., North-Holland, pp. 1– 181. Google Scholar CrossRef Search ADS   Ameriks J., Caplin A., Leahy J. ( 2003) Wealth accumulation and the propensity to plan, Quarterly Journal of Economics  118, 1007– 1047. Google Scholar CrossRef Search ADS   Angrist J. 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G., Eisenstein E. M. ( 2011) Leave home without it? The effects of credit card debt and available credit on spending, Journal of Marketing Research  48, S78– S90. Google Scholar CrossRef Search ADS   Wood M. ( 1998) Socio-economic status, delay of gratification, and impulse buying, Journal of Economic Psychology  19, 295– 320. Google Scholar CrossRef Search ADS   Zinman J. ( 2007) Household borrowing high and lending low under no-arbitrage. Working paper, Dartmouth College. © The Authors 2017. Published by Oxford University Press on behalf of the European Finance Association. All rights reserved. For permissions, please email: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Finance Oxford University Press

The Credit Card Debt Puzzle and Noncognitive Ability

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Oxford University Press
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© The Authors 2017. Published by Oxford University Press on behalf of the European Finance Association. All rights reserved. For permissions, please email: journals.permissions@oup.com
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Abstract

Abstract * We thank an anonymous referee, David Gross, Sol Polachek, colleagues at Binghamton and Purdue Universities, and seminar participants at University of Seoul, Korea University, Seoul National University, Ehwa Woman’s University, and KAIST (Korea Advanced Institute of Science and Technology) for helpful discussions and comments. The opinions expressed in this article are the authors’ own and do not reflect the views of Compass Lexecon.Many households concurrently hold low-yield liquid assets while incurring costly credit card debt. In our sample, more than 80% of households with credit card debt also have low-yield liquid assets. Using data from the Health and Retirement Study (N = 30,517), we examine the role of noncognitive skills as well as the economic, financial, and demographic factors that affect the likelihood of co-holding. We find that the “Big Five” personality traits have a statistically significant and economically important effect: households with a more agreeable, introvert, and less conscientious head of household are more likely to co-hold. We also examine the role of intra-household dynamics. 1. Introduction In the US, almost 40% of all households carry a credit card balance with an average interest rate of 13% (Bricker et al., 2012). Most households with credit card debt also hold considerable amounts of low-yield liquid assets such as checking and savings account balances that have a negligible return. Gross and Souleles (2002) report that among households with credit card debt, 95% have positive net wealth and almost 70% have positive home equity that can be used to get lower-cost home equity loans to pay down their credit card loans. Among the households in our sample that have a credit card balance not paid in full (22% of households), more than 84% simultaneously have a positive checking and/or savings account balance. This financial phenomenon is seemingly at odds with a no-arbitrage condition and has been referred to as a “puzzle” in the literature (e.g., Gross and Souleles, 2002; Bertaut, Haliassos, and Reiter, 2009; Telyukova, 2013). In this article, we study the role of noncognitive skills in explaining the credit card debt puzzle using data for 12,976 households from the Health and Retirement Study (HRS). Our focus on noncognitive skills is motivated in part by a growing literature in economics that examines the role of cognitive limitations (Simon, 1955) and other psychological factors in explaining empirical anomalies in consumption and savings (Rabin, 1998), accumulation of wealth (Ameriks, Caplin, and Leahy, 2003), portfolio choice (Barberis and Thaler, 2003), and labor market outcomes (Heckman, Stixrud, and Urzua, 2006). The HRS data contain detailed longitudinal information on financial, economic, health, and psychosocial measures, which allows us to investigate the role of noncognitive factors while controlling for a host of financial and demographic variables. We define the “puzzle group” as households with a positive credit card balance carried over to the next billing cycle (commonly referred to as “revolvers”) and $500 or more in low-yield liquid assets (checking, savings, and money market account balances). As in Telyukova (2013), our preferred specifications use $500 as the threshold for low-yield liquid assets, as such assets may be more convenient or necessary for certain types of expenses. There are two main components at the heart of our empirical identification strategy for estimating the effect of the noncognitive factors. First, we exploit the longitudinal nature of our data to overcome the inherent simultaneity between spending and saving decisions, as well as other financial decisions. This also allows us to address other nonfinancial factors such as a change in family composition or health shocks. Second, we exploit the constancy of the noncognitive measures we use during adulthood. Though this assumption is used by many, our data allow us to examine the validity of this assumption. The finding of a credit card debt puzzle by Gross and Souleles (2002) has led to several proposed explanations. Lehnert and Maki (2002) examine whether people strategically increase credit card debt prior to filing for bankruptcy. Zinman (2007) finds a high premium on holding liquid assets. Becker and Shabani (2010) calculate that some households would be better off redeeming their debt using their equity holdings. Fulford (2015) focuses on the role of uncertainty in future credit availability that may lead households to not pay down their debt. Telyukova (2013) also examines the demand for low-yield liquid assets by developing a structural model in which credit card borrowers need low-yield liquid assets for certain types of transactions for which credit cards cannot be used, such as rent or mortgage payments. Others have focused on the role of nonfinancial factors. Bertaut, Haliassos, and Reiter (2009) construct an accountant-shopper model where high credit card debt is used as a way to exert self- (or spousal-) control. The study by Gathergood and Weber (2014) is the only one, to the best of our knowledge, to empirically examine the role of a noncognitive skill (self-control) in explaining the puzzle. It studies the role of self-control and financial literacy, and concludes that the former, rather than the latter, affects the likelihood of co-holding low-yield liquid assets and credit card debt, using a cross-section of British households. This article makes several important contributions to the existing literature. First, we identify the factors that play a role in explaining the credit card debt puzzle by investigating a much wider range of noncognitive skills than self-control. Second, using the rich data set at our disposal our results complement the other types of explanations suggested in the literature. For example, we control for the need for liquidity (e.g., Telyukova, 2013), and self-control and financial literacy (e.g., Gathergood and Weber, 2014). Finally, our article is the first to examine the effect of noncognitive abilities among (intra) household members on households’ co-holding behavior. To capture a broad and comprehensive range of noncognitive skills, we employ the “Big Five” personality traits (McCrae and Costa, 1987, 1999). Personality traits are also referred to by some as character skills, soft skills, or noncognitive abilities (see discussion in Heckman and Kautz, 2012, p. 452). We follow the prevalent naming convention and also refer to the traits as noncognitive skills. The five traits are Openness (O), Conscientiousness (C), Extraversion (E), Agreeableness (A), and Neuroticism (N). For example, John, Naumann, and Soto (2008, p. 120) describe Conscientiousness as “socially prescribed impulse control … such as thinking before acting, delaying gratification, following rules, planning, organizing, and prioritizing tasks”; Agreeableness is conceptually defined as “prosocial communal orientation toward others … such as altruism, tender-mindedness, trust and modesty.”1 We further discuss the measurement of the Big Five in Section 3.1. The Big Five personality traits are by far the most commonly used in the field of psychology and have been widely studied over the last couple of decades. The Big Five personality traits provide several major advantages for our setting. First, they cover a very broad domain of noncognitive abilities. Second, they have been extensively studied in other settings, and have been shown to be a tractable set of measures for describing variation across people in types of personality. Third, the measures have been shown to be relatively rank-stable among adults, reducing the threat of endogeneity in our study. Although psychologists have been studying correlations between various personality traits and financial outcomes (e.g., income, debt, consumption, and saving) for several decades,2 in recent years there has been a growing interest among economists in incorporating personality traits such as self-control, perseverance, and grit. Borghans et al. (2008) and Almlund et al. (2011) provide an introduction to the recent developments in the intersection of psychology and economics. Personality traits, including the Big Five, have been shown to be an important complement to more traditionally economic measures of human capital in explaining education attainment, labor market outcomes, wealth, etc.3 We build on several previous papers in the economics literature that have either: (i) considered only a narrow facet of noncognitive skills and its effect on savings, borrowing, or the propensity to co-hold assets and debt (e.g., Ameriks, Caplin, and Leahy, 2003; Laibson, Repetto, and Tobacman, 2003; Gathergood and Weber, 2014, respectively); or (ii) used a neo-classical framework and do not consider noncognitive skills to explain the co-holding of assets and debt (e.g., Bertaut, Haliassos, and Reiter, 2009; Telyukova, 2013; Fulford 2015). We combine these previous studies and their proposed mechanisms, and hypothesize the channels through which personality traits operate. We hypothesize that four of the Big Five personality traits might play a role in the likelihood that a household is in the puzzle group: Conscientiousness, Extraversion, Agreeableness, and Openness might play a role for the dimension of spending and/or borrowing; and Conscientiousness, Extraversion, and Openness might play a role in the dimension of (not) using low-yield liquid assets to pay down debt. In Section 2, we consider three main channels through which co-holding may occur. First, certain personality traits may help (hinder) a decision maker when dealing with their finances. For example, those with higher levels of Conscientiousness might be more likely to notice that they have sufficient low-yield liquid assets to pay down their debt. The second channel we consider is precautionary saving for expected or unexpected liquidity demand. The third channel focuses on the role of personality traits in intra-household (or dual-self) dynamics. We find that even after controlling for differences in age and education levels among couples, the personality of both partners explains some of the observed co-holding patterns in the data. We first implement a reduced-form approach. We find that the effects of Conscientiousness, Extraversion, and Agreeableness tend to be the most persistent of the Big Five personality traits across our various specifications. In our preferred specification a one standard-deviation increase in Conscientiousness, Extraversion, and Agreeableness changes the likelihood of being in the puzzle group by −0.54, −1.09, and 1.62 percentage points, respectively. We find that this result holds after we additionally control for measures suggested in previous studies, such as liquidity demand, self-control, and financial sophistication. In Section 4.1 we take into account the simultaneity between spending and saving, and examine the role of personality in borrowing, and holding low-yield assets conditional on borrowing separately. Taken together, our findings suggest that regulatory policies, personal debt default options, debt counseling, and educational programs are all domains that can be made more cost effective by taking into account the role of noncognitive abilities. To illustrate the economic significance of our results, we estimate that in the US, among those 50 years and older, even a reduction of 1% in the number of households co-holding low-yield liquid assets and credit card debt would translate into an annual decrease of $327 million in interest payments while maintaining the same level of consumption.4 The rest of the article is organized as follows. We first describe our empirical framework and source of identification in Section 2. The HRS data and our construction of the personality measures are described in Section 3. The results are in Section 4. Section 5 concludes and discusses some potential areas for future research. 2. Empirical Framework The decision of how much to consume and save (or borrow) has long been studied, and often modeled using the neo-classical expected life-cycle utility maximization framework. There is also a large literature in economics and finance examining asset allocation across types of assets and across time. Given that the focus of our article is on the role of personality traits, and not on the calculation of inter-temporal substitution rates or elasticity measures, we implement our empirical strategy using a reduced-form examination of the decision of how much low-yield liquid assets and credit card debt to concurrently hold. Our approach has two main advantages. First, we require far fewer assumptions by not estimating a structural model. Second, our examination of the credit card debt puzzle avoids the need to address the inherent simultaneity in the decision of consumption and saving that consequently determine asset and debt accumulation. In Section 4.1, however, we do examine the underlying mechanisms of our findings by studying the relationship between asset holding and debt utilization. We focus on a definition of the puzzle group in which holding more than $500 in low-yield liquid assets, that is, checking, savings, and money market accounts, with positive revolving credit card debt is considered a puzzle. However, we have also examined alternative definitions that allow households to have different levels of low-yield liquid assets for liquidity purposes or different levels of credit card debt, and have found that our results are robust to using alternative definitions.5 We estimate the probability of being in the puzzle group using the linear-probability model (OLS). We have also used a logit model, and obtained very similar qualitative and quantitative results. Our base reduced-form specification can be written as:   Yit=β0+x′i(t−2)β+εit, (1) where x′i(t−2) is a vector of the time invariant and (2-year lagged) control variables and we assume that E(εit|xi(t−2))=0. Our empirical strategy exploits the panel nature of our data, thereby allowing us to address the potential simultaneity inherent in the financial and demographic measures we examine. For example, a health shock could affect the need for credit (due to large medical bills), uncertainty in future earnings, and one’s employment (requiring someone to retire earlier than planned). Our preferred specifications therefore use 2-year lags of financial measures. Financial measures such as income and wealth are, of course, crucial for one’s saving and borrowing decisions as they affect both the need for saving or borrowing and the returns or costs (as different borrowers would face different interest rates). Personality measures may cause two households with the same demographic and financial measures to have a different need for liquid assets and debt. For example, those with higher levels of Conscientiousness may be able to better interpret and more accurately perceive their financial situation. More extravert people may be able to better negotiate and leverage their financial situation when restructuring their debt with a lender, etc. To examine the role of personality, we augment the model in Equation (1) by adding the (5 × 1) vector pi of the Big Five personality traits. We include these measures additively, and allow them in some of the specifications to have an interactive effect with another characteristic zit:   Yit=β0+x′i(t−2)β+p′iγ+zitp′iδ+εit. (2)Equation (2) is useful in demonstrating how noncognitive ability, such as personality, might affect a household’s financial decision to co-hold low-yield liquid assets and credit card debt. Researchers have previously proposed various explanations (see Section 1) for the credit card puzzle. An advantage of our reduced-form model is that it allows us to succinctly control for those proposed explanatory factors. We use financial controls (such as income, various assets and debts), education levels, and demographic controls (such as age, and marital status) that have been suggested in the literature as important in determining the decision to save and borrow. In addition to the very detailed financial data at our disposal, we are also able to control for other demographic variables that are likely to affect household financials such as health status (both self-reported, and by controlling for medical expenditures) or changes in family composition (due to death, marriage, or divorce). The reduced-form specification examines the overall effect of a household’s characteristics. Therefore, the specification in Equation (2) does not separate out the decision to be a revolver, and the factors that affect the likelihood of being in the puzzle group (i.e., become a revolver and hold a low-yield liquid assets balance simultaneously). As such, our findings potentially encompass several channels or mechanisms at work. We further examine the decomposition of the effect of personality to understand the relative importance of the potential mechanisms at play. For example, the overall reduced-form effect of Conscientiousness might be zero. However, this might be because the underlying effects nullify each other. Conscientious individuals might be more likely to qualify for or have access to credit, but at the same time might be less likely to borrow and hold large amounts of cash at the same time, since they carefully examine their monthly statements, or consider the cost of debt. To examine the decomposition, we consider two necessary conditions for being in the puzzle group through which personality traits may operate. First, a necessary condition to be included in the puzzle group is to be a revolver. Second, conditional on being a revolver, one may or may not be in the puzzle group depending on whether one holds low-yield liquid assets that are not used to pay down debt. We therefore separately examine the propensity to be a revolver, and the propensity of revolvers to hold low-yield liquid assets and not pay one’s debt down.6 One could think of the two conditions as being related to two dimensions: consumption and financial management of the household’s accounts for a given level of consumption. For the dimension of incurrence of debt, several personality traits are likely to affect levels of spending and/or borrowing, and financial terms (such as interest rates and credit limits) that affect debt levels. Self-control (Laibson, Repetto, and Tobacman, 2003; Bertaut, Haliassos, and Reiter, 2009), impulse spending (Gathergood and Weber, 2014), and the propensity to plan (Ameriks, Caplin, and Leahy, 2003) have been shown to be related to incurrence of debt and wealth accumulation. These traits are all captured by Conscientiousness, and the effect on debt is likely to be negative. Agreeableness may lead to higher levels of spending and debt because agreeable people tend to spend more on others, and might be more susceptible to marketing campaigns.7 A large literature has documented lower incomes among those with higher levels of Agreeableness (e.g., Judge et al., 1999; Babcock and Laschever, 2003; Mueller and Plug, 2006). A similar trade-off (less financial gains in return for less conflict or an increased preference for others’ utility) is likely to play a role in this instance as well. Extravert people may acquire financial advice from their peers, or may be able to better negotiate and leverage their financial situation when restructuring their debt with a lender. Those with higher levels of Openness may be more likely to consume and spend more leading to higher levels of debt.8 The direction of the effect of Neuroticism is ambiguous. Higher levels of Neuroticism would lower the likelihood of borrowing due to the increased psychological cost of worrying about the future ability to repay. On the other hand, lower levels of Neuroticism have been found to be associated with more discretionary savings (e.g., Brandstätter, 2005, p. 70). For example, Wang, Lu, and Malhotra (2011) find a negative relationship between revolving credit use and measures related to low levels of Neuroticism. Donnelly, Iyer, and Howell (2012) show that Neuroticism is positively related to compulsive buying. For the dimension of co-holding low-yield liquid assets and credit card debt, we consider three main channels through which personality may operate: the management of household finances; liquidity demand; and intra-household dynamics. For the first channel, conscientious individuals are more likely to notice they have sufficient low-yield liquid assets to pay down their debt. Extraverts may be more likely to discuss their finances and solicit possible solutions from others on how to pay down their credit card debt.9 The effect of Neuroticism is a priori ambiguous. Those with higher levels of Neuroticism might be constantly worried about their finances or missing a payment, thereby having a heightened awareness of their ability to pay down credit card debt. On the other hand, people with low levels of Neuroticism may make financial decisions in a calm and deliberate manner.10 For the second channel of precautionary saving, conscientious individuals might be more likely to hold low-yield liquid assets even when they have debt, as they are more likely to plan ahead. The effect of Neuroticism is a priori ambiguous. More neurotic individuals might have higher demand for liquidity because they worry about their uncertain future. On the other hand, they may worry about being burdened with debt and prefer to pay down as much of it as they can. The third channel we consider is intra-household (or dual-self) dynamics. For example, as suggested by Bertaut, Haliassos, and Reiter (2009), an “accountant” may choose to maintain high levels of credit utilization to control the spending temptation of their “shopper” spouse. The personality traits we consider readily translate into those two types. For example, an “accountant” is likely to have a high level of Conscientiousness, whereas a “shopper” may have a low level of Conscientiousness and a high level of Agreeableness for the aforementioned reasons. Because a household might be a revolver for reasons correlated with the likelihood of being in the puzzle group, we must find an exclusion restriction that would predict being a revolver, but would not affect a household’s likelihood of being in the puzzle group. In Section 4.1, we employ an exclusion restriction strategy and examine whether personality has a differential effect on checking/savings balance among debt holders and those with no debt. Our identification strategy is akin to using the 2-year lag of revolving behavior to predict current revolving behavior. Finally, our specification also allows us to test whether personality might also interact with a spouse’s personality, or a proxy for the household’s power structure. Here our identification strategy uses single households’ co-holding decisions to examine decisions among couple households, and separate out the contribution of each family member to the overall household decision. 3. Data and the Big Five Personality Traits Our data are based on the HRS (2012).11 The HRS is a biennial longitudinal survey that collects detailed demographic, health, economic, and financial information from a nationally representative sample of the population over age 50.12 The HRS has three main advantages for our setting. First, the longitudinal nature of the data is crucial for our identification strategy as explained in the previous section. Second, the data have high-quality personality measures,13 as well as detailed financial information. Third, as explained in Section 1, personality measures are more likely to be stable among older adults thereby reducing the threat of validity to our results.14 The HRS contains both respondent-level and household-level data. Because most of the financial measures are collected at the household level, and financial decisions depend on and impact the entire household, our primary unit of analysis is at the household level. The households in the data consist of singles and couples (we also control for the presence of additional household members). For the households with couples, because our dependent variables of interest are financial, we focus on the demographics and personality of the person who has answered the survey questions related to household finances. In the HRS data, this person is identified as the “financial respondent” of the household.15 Although we model household behavior, we also use respondent-level information from financial respondents with the assumption that the coordination within a household is not a significant factor. However, as an extension, we relax this assumption in Section 4.2 and investigate the effect of spouse characteristics using the data on couple-households. Our main sample consists of 12,976 households between 2008 and 2012.16Table 1 reports the summary statistics for the full sample and for the subgroup of households that are revolvers. The average credit card balance among revolvers is $8,972. On average, 51% of the households are a single household. Table I. Summary statistics   Mean   Standard deviation   Variables  Main sample  Revolvers only  Main sample  Revolvers only  Observations  30,517  6,833      Couple household  49%  54%  50%  50%  Household-level variables           Revolver  22%  100%  42%  0%   Credit card debt  $2,009  $8,972  $7,638  $14,076  Puzzle group   Revolver and low-yield liquid assets >$500  15%  68%  36%  47%  Assets and debts   Checking and savings  $29,089  $11,142  $91,731  $42,253   No checking or savings  20%  16%  40%  36%   Financial assets  $128,270  $40,175  $474,272  $158,769   Debts including credit card debt  $4,430  $13,159  $31,472  $36,658   Value of business  $38,617  $18,843  $345,359  $189,679   IRA balance  $58,987  $29,660  $180,903  $100,089   Own home  75%  78%  43%  42%   Real estate  $221,746  $198,198  $517,890  $486,374   Mortgages and home equity loans  $34,019  $58,234  $85,027  $100,255  Income and medical expense   Income  $58,061  $58,352  $355,317  $75,613   Received food stamp  8%  8%  27%  28%   Out-of-pocket medical expense  $4,954  $4,786  $10,851  $8,443   Below the poverty line  13%  8%  33%  28%  Financial respondent-level variables  Personality traits (1–4)   Openness  2.92  2.97  0.54  0.52   Conscientiousness  3.35  3.36  0.46  0.45   Extraversion  3.18  3.19  0.53  0.52   Agreeableness  3.51  3.56  0.46  0.43   Neuroticism  2.02  2.06  0.59  0.59  Demographic and other variables   Age  70.41  66.25  10.66  9.09   White  79%  75%  41%  43%   Male  41%  39%  49%  49%   High school  54%  49%  50%  50%   Some post-secondary schooling  23%  29%  42%  45%   College (4 years) or more  22%  22%  42%  41%   Married  46%  50%  50%  50%   Separated/divorced  16%  19%  37%  39%   Widowed  30%  22%  46%  41%   Poor health (excellent (1)−poor (5))  2.90  2.91  1.09  1.06   Employed  32%  46%  47%  50%   Self-employed  8%  9%  26%  28%   Retired  60%  47%  49%  50%    Mean   Standard deviation   Variables  Main sample  Revolvers only  Main sample  Revolvers only  Observations  30,517  6,833      Couple household  49%  54%  50%  50%  Household-level variables           Revolver  22%  100%  42%  0%   Credit card debt  $2,009  $8,972  $7,638  $14,076  Puzzle group   Revolver and low-yield liquid assets >$500  15%  68%  36%  47%  Assets and debts   Checking and savings  $29,089  $11,142  $91,731  $42,253   No checking or savings  20%  16%  40%  36%   Financial assets  $128,270  $40,175  $474,272  $158,769   Debts including credit card debt  $4,430  $13,159  $31,472  $36,658   Value of business  $38,617  $18,843  $345,359  $189,679   IRA balance  $58,987  $29,660  $180,903  $100,089   Own home  75%  78%  43%  42%   Real estate  $221,746  $198,198  $517,890  $486,374   Mortgages and home equity loans  $34,019  $58,234  $85,027  $100,255  Income and medical expense   Income  $58,061  $58,352  $355,317  $75,613   Received food stamp  8%  8%  27%  28%   Out-of-pocket medical expense  $4,954  $4,786  $10,851  $8,443   Below the poverty line  13%  8%  33%  28%  Financial respondent-level variables  Personality traits (1–4)   Openness  2.92  2.97  0.54  0.52   Conscientiousness  3.35  3.36  0.46  0.45   Extraversion  3.18  3.19  0.53  0.52   Agreeableness  3.51  3.56  0.46  0.43   Neuroticism  2.02  2.06  0.59  0.59  Demographic and other variables   Age  70.41  66.25  10.66  9.09   White  79%  75%  41%  43%   Male  41%  39%  49%  49%   High school  54%  49%  50%  50%   Some post-secondary schooling  23%  29%  42%  45%   College (4 years) or more  22%  22%  42%  41%   Married  46%  50%  50%  50%   Separated/divorced  16%  19%  37%  39%   Widowed  30%  22%  46%  41%   Poor health (excellent (1)−poor (5))  2.90  2.91  1.09  1.06   Employed  32%  46%  47%  50%   Self-employed  8%  9%  26%  28%   Retired  60%  47%  49%  50%  Table I. Summary statistics   Mean   Standard deviation   Variables  Main sample  Revolvers only  Main sample  Revolvers only  Observations  30,517  6,833      Couple household  49%  54%  50%  50%  Household-level variables           Revolver  22%  100%  42%  0%   Credit card debt  $2,009  $8,972  $7,638  $14,076  Puzzle group   Revolver and low-yield liquid assets >$500  15%  68%  36%  47%  Assets and debts   Checking and savings  $29,089  $11,142  $91,731  $42,253   No checking or savings  20%  16%  40%  36%   Financial assets  $128,270  $40,175  $474,272  $158,769   Debts including credit card debt  $4,430  $13,159  $31,472  $36,658   Value of business  $38,617  $18,843  $345,359  $189,679   IRA balance  $58,987  $29,660  $180,903  $100,089   Own home  75%  78%  43%  42%   Real estate  $221,746  $198,198  $517,890  $486,374   Mortgages and home equity loans  $34,019  $58,234  $85,027  $100,255  Income and medical expense   Income  $58,061  $58,352  $355,317  $75,613   Received food stamp  8%  8%  27%  28%   Out-of-pocket medical expense  $4,954  $4,786  $10,851  $8,443   Below the poverty line  13%  8%  33%  28%  Financial respondent-level variables  Personality traits (1–4)   Openness  2.92  2.97  0.54  0.52   Conscientiousness  3.35  3.36  0.46  0.45   Extraversion  3.18  3.19  0.53  0.52   Agreeableness  3.51  3.56  0.46  0.43   Neuroticism  2.02  2.06  0.59  0.59  Demographic and other variables   Age  70.41  66.25  10.66  9.09   White  79%  75%  41%  43%   Male  41%  39%  49%  49%   High school  54%  49%  50%  50%   Some post-secondary schooling  23%  29%  42%  45%   College (4 years) or more  22%  22%  42%  41%   Married  46%  50%  50%  50%   Separated/divorced  16%  19%  37%  39%   Widowed  30%  22%  46%  41%   Poor health (excellent (1)−poor (5))  2.90  2.91  1.09  1.06   Employed  32%  46%  47%  50%   Self-employed  8%  9%  26%  28%   Retired  60%  47%  49%  50%    Mean   Standard deviation   Variables  Main sample  Revolvers only  Main sample  Revolvers only  Observations  30,517  6,833      Couple household  49%  54%  50%  50%  Household-level variables           Revolver  22%  100%  42%  0%   Credit card debt  $2,009  $8,972  $7,638  $14,076  Puzzle group   Revolver and low-yield liquid assets >$500  15%  68%  36%  47%  Assets and debts   Checking and savings  $29,089  $11,142  $91,731  $42,253   No checking or savings  20%  16%  40%  36%   Financial assets  $128,270  $40,175  $474,272  $158,769   Debts including credit card debt  $4,430  $13,159  $31,472  $36,658   Value of business  $38,617  $18,843  $345,359  $189,679   IRA balance  $58,987  $29,660  $180,903  $100,089   Own home  75%  78%  43%  42%   Real estate  $221,746  $198,198  $517,890  $486,374   Mortgages and home equity loans  $34,019  $58,234  $85,027  $100,255  Income and medical expense   Income  $58,061  $58,352  $355,317  $75,613   Received food stamp  8%  8%  27%  28%   Out-of-pocket medical expense  $4,954  $4,786  $10,851  $8,443   Below the poverty line  13%  8%  33%  28%  Financial respondent-level variables  Personality traits (1–4)   Openness  2.92  2.97  0.54  0.52   Conscientiousness  3.35  3.36  0.46  0.45   Extraversion  3.18  3.19  0.53  0.52   Agreeableness  3.51  3.56  0.46  0.43   Neuroticism  2.02  2.06  0.59  0.59  Demographic and other variables   Age  70.41  66.25  10.66  9.09   White  79%  75%  41%  43%   Male  41%  39%  49%  49%   High school  54%  49%  50%  50%   Some post-secondary schooling  23%  29%  42%  45%   College (4 years) or more  22%  22%  42%  41%   Married  46%  50%  50%  50%   Separated/divorced  16%  19%  37%  39%   Widowed  30%  22%  46%  41%   Poor health (excellent (1)−poor (5))  2.90  2.91  1.09  1.06   Employed  32%  46%  47%  50%   Self-employed  8%  9%  26%  28%   Retired  60%  47%  49%  50%  3.1 The Big Five Personality Traits The Big Five personality traits have been measured in the HRS biennially since 2006. They are measured as part of a questionnaire which is given to about half of the full sample in every wave.17 As a consequence, we have personality traits for almost all individuals in either 2006/2010 or 2008/2012. The HRS uses 26 personality survey items developed originally for the Midlife in the United States Survey.18 The 26 variables are self-administered adjectival measures. Participants are asked to “Please indicate how well each of the following DESCRIBES YOU” for 26 adjectives. Each adjective is coded from 1 (“not at all”) to 4 (“a lot”). The adjectives are then grouped and averaged to create a score for each of the five traits. For example, Conscientiousness is constructed from these five items (with “−” indicating an inverse coding): organized, responsible, hardworking, careless (−), and thorough.19 The stability of personality measures over time has been widely studied in the field of developmental psychology. For example, some studies have emphasized the hereditary and biological factors that shape traits (e.g., Bouchard Jr and Loehlin, 2001; Canli, 2006; DeYoung et al., 2010). In financial and economic settings, many scholars assume that personality traits are fixed among adults (e.g., Nyhus and Pons, 2005; Mueller and Plug, 2006; Heineck and Anger, 2010). In recent years, an emerging view is that personality traits are influenced by hereditary and biological factors, but can change over time and may be mutable by intervention especially during early childhood. However, after early adulthood, the mean level changes relatively less and the rank ordering of personality traits in a population becomes increasingly more consistent (stable) as one ages (Roberts and DelVecchio, 2000).20 For the purpose of our study, the crucial issue is whether the measurement of personality is endogenous with respect to financial decisions. For example, Roberts, Walton, and Viechtbauer (2006) find some mean-changes of personality traits over the life cycle. However, we control for age and only examine adults later in life, so life-cycle patterns are not a concern for our setting. Cobb-Clark and Schurer (2012) show that the mean level of the Big Five personality traits is stable over a 4-year period among working-age adults. Further, they show that intra-individual changes over time are not correlated with life events in an economically significant way. Cobb-Clark and Schurer (2013) also show that the changes of the mean level of locus of control, which is the personality trait of their focus, are mild, and consistent rather than idiosyncratic. They argue that for working-age adults the changes are economically insignificant.21 Taken together, the results suggest that our identification assumption regarding the exogeneity of personality measures is likely to hold. However, given the longitudinal data at our disposal, we are further able to test the stability and inverse-causality of the Big Five personality traits. We find that the personality traits are stable in our sample, and we do not find any evidence of an inverse-causal relationship with our dependent variables of interest after controlling for the relevant variables. In our study, we use the personality measures calculated by the average of the personality traits over all available years for each of the Big Five personality traits. We then standardize the personality traits measures (Z-score) by subtracting the average and dividing by the sample standard deviation calculated using the 2010 data. 4. Results We first examine the reduced-form estimates in Table 2 corresponding to Equation (2) without the interaction term using the pooled data from 2008 to 2012. In all columns, the dependent variable is the binary indicator of whether a household is in the puzzle group (i.e., not paying their credit card balance in full, and having low-yield liquid assets of $500 or more). Throughout, we report the linear-probability model results using the OLS method with cluster-robust standard errors. For all regressions in this article, we use clusters defined by the cross product of nine US geographical regions and three rural–urban groups based on county population sizes (more than million, 250,000–1,000,000, and 250,000 or less). This allows us to capture regional unobservable correlated shocks that may affect households in the same local economy.22 Table II. The effect of personality on being in the puzzle group (reduced form) Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for race, marital status, employment status, whether self-employed, whether in nursing home, household size (whether 2 or more), education (high-school, some college, and college or more dummies), and region and metro type fixed effects. Columns 3–5 also include assets (transportation, housing), whether underwater, and percent of household members employed. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. †Measures lagged by 2 years (previous wave) in columns 4–5. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  Openness    0.0076**  0.0053*  0.0051*  0.0050*    (0.0030)  (0.0027)  (0.0027)  (0.0027)  Conscientiousness    −0.0090***  −0.0050*  −0.0054*  −0.0054*    (0.0029)  (0.0029)  (0.0028)  (0.0029)  Extraversion    −0.0130***  −0.0112***  −0.0110***  −0.0109***    (0.0033)  (0.0028)  (0.0027)  (0.0027)  Agreeableness    0.0210***  0.0153***  0.0163***  0.0162***    (0.0031)  (0.0028)  (0.0027)  (0.0027)  Neuroticism    −0.0012  −0.0003  −0.0005  −0.0003    (0.0030)  (0.0026)  (0.0028)  (0.0028)  Age  0.0104***  0.0105***  0.0111***  0.0114***  0.0115***  (0.0027)  (0.0028)  (0.0022)  (0.0023)  (0.0023)  Age-squared (divided by 100)  −0.0100***  −0.0100***  −0.0094***  −0.0096***  −0.0097***  (0.0018)  (0.0018)  (0.0015)  (0.0016)  (0.0016)  Is male  −0.0247***  −0.0177**  −0.0160**  −0.0174**  −0.0169**  (0.0067)  (0.0074)  (0.0070)  (0.0069)  (0.0068)  In poor health  0.0034  0.0024  0.0042**  0.0040**  0.0042**  (0.0021)  (0.0020)  (0.0018)  (0.0018)  (0.0018)  Ln(financial assets excluding low-yield liquid assets)†      −0.0266***  −0.0239***  −0.0238***      (0.0023)  (0.0021)  (0.0020)  Ln(retirement assets)†      −0.0246***  −0.0238***  −0.0237***      (0.0026)  (0.0029)  (0.0029)  Is home owner†      −0.0287**  −0.0333**  −0.0330**      (0.0138)  (0.0157)  (0.0155)  Ln(income)†      0.0096***  0.0079***  0.0083***      (0.0019)  (0.0015)  (0.0015)  Ln(medical spending)†      0.0056***  0.0059***  0.0061***      (0.0009)  (0.0009)  (0.0009)  Below poverty line†      −0.0463***  −0.0345***  −0.0329***      (0.0061)  (0.0059)  (0.0062)  Ln(mortgage+HELOC)†      0.0118***  0.0107***  0.0107***      (0.0009)  (0.0008)  (0.0008)  2-year change in welfare and food stamps assistance          −0.0183**          (0.0079)  2-year change in household size          0.0257***          (0.0091)  R2  0.05  0.06  0.11  0.10  0.10  Number of households  12,976  12,976  12,976  12,976  12,976  Observations  30,517  30,517  30,517  30,517  30,517  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  Openness    0.0076**  0.0053*  0.0051*  0.0050*    (0.0030)  (0.0027)  (0.0027)  (0.0027)  Conscientiousness    −0.0090***  −0.0050*  −0.0054*  −0.0054*    (0.0029)  (0.0029)  (0.0028)  (0.0029)  Extraversion    −0.0130***  −0.0112***  −0.0110***  −0.0109***    (0.0033)  (0.0028)  (0.0027)  (0.0027)  Agreeableness    0.0210***  0.0153***  0.0163***  0.0162***    (0.0031)  (0.0028)  (0.0027)  (0.0027)  Neuroticism    −0.0012  −0.0003  −0.0005  −0.0003    (0.0030)  (0.0026)  (0.0028)  (0.0028)  Age  0.0104***  0.0105***  0.0111***  0.0114***  0.0115***  (0.0027)  (0.0028)  (0.0022)  (0.0023)  (0.0023)  Age-squared (divided by 100)  −0.0100***  −0.0100***  −0.0094***  −0.0096***  −0.0097***  (0.0018)  (0.0018)  (0.0015)  (0.0016)  (0.0016)  Is male  −0.0247***  −0.0177**  −0.0160**  −0.0174**  −0.0169**  (0.0067)  (0.0074)  (0.0070)  (0.0069)  (0.0068)  In poor health  0.0034  0.0024  0.0042**  0.0040**  0.0042**  (0.0021)  (0.0020)  (0.0018)  (0.0018)  (0.0018)  Ln(financial assets excluding low-yield liquid assets)†      −0.0266***  −0.0239***  −0.0238***      (0.0023)  (0.0021)  (0.0020)  Ln(retirement assets)†      −0.0246***  −0.0238***  −0.0237***      (0.0026)  (0.0029)  (0.0029)  Is home owner†      −0.0287**  −0.0333**  −0.0330**      (0.0138)  (0.0157)  (0.0155)  Ln(income)†      0.0096***  0.0079***  0.0083***      (0.0019)  (0.0015)  (0.0015)  Ln(medical spending)†      0.0056***  0.0059***  0.0061***      (0.0009)  (0.0009)  (0.0009)  Below poverty line†      −0.0463***  −0.0345***  −0.0329***      (0.0061)  (0.0059)  (0.0062)  Ln(mortgage+HELOC)†      0.0118***  0.0107***  0.0107***      (0.0009)  (0.0008)  (0.0008)  2-year change in welfare and food stamps assistance          −0.0183**          (0.0079)  2-year change in household size          0.0257***          (0.0091)  R2  0.05  0.06  0.11  0.10  0.10  Number of households  12,976  12,976  12,976  12,976  12,976  Observations  30,517  30,517  30,517  30,517  30,517  Table II. The effect of personality on being in the puzzle group (reduced form) Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for race, marital status, employment status, whether self-employed, whether in nursing home, household size (whether 2 or more), education (high-school, some college, and college or more dummies), and region and metro type fixed effects. Columns 3–5 also include assets (transportation, housing), whether underwater, and percent of household members employed. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. †Measures lagged by 2 years (previous wave) in columns 4–5. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  Openness    0.0076**  0.0053*  0.0051*  0.0050*    (0.0030)  (0.0027)  (0.0027)  (0.0027)  Conscientiousness    −0.0090***  −0.0050*  −0.0054*  −0.0054*    (0.0029)  (0.0029)  (0.0028)  (0.0029)  Extraversion    −0.0130***  −0.0112***  −0.0110***  −0.0109***    (0.0033)  (0.0028)  (0.0027)  (0.0027)  Agreeableness    0.0210***  0.0153***  0.0163***  0.0162***    (0.0031)  (0.0028)  (0.0027)  (0.0027)  Neuroticism    −0.0012  −0.0003  −0.0005  −0.0003    (0.0030)  (0.0026)  (0.0028)  (0.0028)  Age  0.0104***  0.0105***  0.0111***  0.0114***  0.0115***  (0.0027)  (0.0028)  (0.0022)  (0.0023)  (0.0023)  Age-squared (divided by 100)  −0.0100***  −0.0100***  −0.0094***  −0.0096***  −0.0097***  (0.0018)  (0.0018)  (0.0015)  (0.0016)  (0.0016)  Is male  −0.0247***  −0.0177**  −0.0160**  −0.0174**  −0.0169**  (0.0067)  (0.0074)  (0.0070)  (0.0069)  (0.0068)  In poor health  0.0034  0.0024  0.0042**  0.0040**  0.0042**  (0.0021)  (0.0020)  (0.0018)  (0.0018)  (0.0018)  Ln(financial assets excluding low-yield liquid assets)†      −0.0266***  −0.0239***  −0.0238***      (0.0023)  (0.0021)  (0.0020)  Ln(retirement assets)†      −0.0246***  −0.0238***  −0.0237***      (0.0026)  (0.0029)  (0.0029)  Is home owner†      −0.0287**  −0.0333**  −0.0330**      (0.0138)  (0.0157)  (0.0155)  Ln(income)†      0.0096***  0.0079***  0.0083***      (0.0019)  (0.0015)  (0.0015)  Ln(medical spending)†      0.0056***  0.0059***  0.0061***      (0.0009)  (0.0009)  (0.0009)  Below poverty line†      −0.0463***  −0.0345***  −0.0329***      (0.0061)  (0.0059)  (0.0062)  Ln(mortgage+HELOC)†      0.0118***  0.0107***  0.0107***      (0.0009)  (0.0008)  (0.0008)  2-year change in welfare and food stamps assistance          −0.0183**          (0.0079)  2-year change in household size          0.0257***          (0.0091)  R2  0.05  0.06  0.11  0.10  0.10  Number of households  12,976  12,976  12,976  12,976  12,976  Observations  30,517  30,517  30,517  30,517  30,517  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  Openness    0.0076**  0.0053*  0.0051*  0.0050*    (0.0030)  (0.0027)  (0.0027)  (0.0027)  Conscientiousness    −0.0090***  −0.0050*  −0.0054*  −0.0054*    (0.0029)  (0.0029)  (0.0028)  (0.0029)  Extraversion    −0.0130***  −0.0112***  −0.0110***  −0.0109***    (0.0033)  (0.0028)  (0.0027)  (0.0027)  Agreeableness    0.0210***  0.0153***  0.0163***  0.0162***    (0.0031)  (0.0028)  (0.0027)  (0.0027)  Neuroticism    −0.0012  −0.0003  −0.0005  −0.0003    (0.0030)  (0.0026)  (0.0028)  (0.0028)  Age  0.0104***  0.0105***  0.0111***  0.0114***  0.0115***  (0.0027)  (0.0028)  (0.0022)  (0.0023)  (0.0023)  Age-squared (divided by 100)  −0.0100***  −0.0100***  −0.0094***  −0.0096***  −0.0097***  (0.0018)  (0.0018)  (0.0015)  (0.0016)  (0.0016)  Is male  −0.0247***  −0.0177**  −0.0160**  −0.0174**  −0.0169**  (0.0067)  (0.0074)  (0.0070)  (0.0069)  (0.0068)  In poor health  0.0034  0.0024  0.0042**  0.0040**  0.0042**  (0.0021)  (0.0020)  (0.0018)  (0.0018)  (0.0018)  Ln(financial assets excluding low-yield liquid assets)†      −0.0266***  −0.0239***  −0.0238***      (0.0023)  (0.0021)  (0.0020)  Ln(retirement assets)†      −0.0246***  −0.0238***  −0.0237***      (0.0026)  (0.0029)  (0.0029)  Is home owner†      −0.0287**  −0.0333**  −0.0330**      (0.0138)  (0.0157)  (0.0155)  Ln(income)†      0.0096***  0.0079***  0.0083***      (0.0019)  (0.0015)  (0.0015)  Ln(medical spending)†      0.0056***  0.0059***  0.0061***      (0.0009)  (0.0009)  (0.0009)  Below poverty line†      −0.0463***  −0.0345***  −0.0329***      (0.0061)  (0.0059)  (0.0062)  Ln(mortgage+HELOC)†      0.0118***  0.0107***  0.0107***      (0.0009)  (0.0008)  (0.0008)  2-year change in welfare and food stamps assistance          −0.0183**          (0.0079)  2-year change in household size          0.0257***          (0.0091)  R2  0.05  0.06  0.11  0.10  0.10  Number of households  12,976  12,976  12,976  12,976  12,976  Observations  30,517  30,517  30,517  30,517  30,517  We control for household size with a dummy indicator for having more than one member (including dependents), as well as marital status. For reasons explained in the previous section, in the case of households with couples, we use the personal measures of the financial respondent (i.e., age, race, education, and personality traits). However, for couples, our analysis shows that the personal characteristics of the financial respondent are a sufficient control, as our results remain very similar if we additionally include some key spousal measures. In Section 4.2, we further examine the effect of within-couple income and personality differences. The first column in Table 2 includes basic demographic controls and employment status. The Big Five personality traits Z-scores (standardized to have a mean of zero and standard deviation equal to one) are added in column 2. Conscientiousness and Extraversion are shown to have a negative effect (statistically significant at the 1% level) whereas Openness and Agreeableness increase the likelihood of being in the puzzle group (statistically significant at the 5% and 1% level, respectively). Neuroticism is almost never statistically significant across the various specifications. We then include financial measures in column 3, such as financial assets (excluding checking/savings), housing debt, income, and medical spending. To alleviate concerns of simultaneity, we lag the financial measures by two years in column 4. We add controls for changes to a household’s size and food-stamp usage, in column 5.23 The coefficients of the personality traits remain qualitatively the same. The coefficients for Openness, Conscientiousness, Extraversion, and Agreeableness are similar in columns 2–5, and are statistically significant at the 10% level or better throughout. In summary, the effects of the personality measures remain similar across our different specifications, even after controlling for a wide range of financial measures. For example, in column 5 which is our preferred specification, a one standard-deviation increase in Conscientiousness and Extraversion, all else equal, decreases the propensity to be in the puzzle group by 0.54 and 1.09 percentage points, respectively.24 For Openness and Agreeableness, the probability increases by 0.50 and 1.62 percentage points, respectively. In regards to demographic variables, we find an inverse U-shape effect of education that is robust across our specifications. This could be due to the fact that those with the lowest levels of education have less access to credit. The effects of the financial measures have the expected signs. For example, households with higher financial assets such as stock, bonds, certificate of deposits, real estate, or IRA accounts are less likely to be in the puzzle group. We also include dummy indicator variables for zero assets and IRA balances to account for the non-linearity of these factors. Generally, we find that household income tends to have a positive effect on the likelihood of being in the puzzle group (significant at the 1% level in Table 2). Similarly, we find that households with an income below the poverty line are less likely to be in the puzzle group. These findings are consistent with low-income households having more difficulty in qualifying for credit cards. Our controls for employment status, health status, etc. in the reduced-form estimates capture the differential access to credit cards among households. We also find that households with high mortgage debt and negative home equity are more likely to be in the puzzle group. This is consistent with those having less access to cheaper forms of credit (such as home equity loans and mortgages) are more likely to have to resort to more expensive forms of credit, such as credit cards. Our measures of personality are multiple-period averages. As a further robustness test, we examine individual fixed effects specifications where we allow personality to vary yearly. We find that none of the yearly components of the personality measures are statistically significant, nor are they jointly significant (p-value of 0.81).25 This finding is consistent with the yearly within-individual variation in the personality measures being idiosyncratic. We have also examined Table 2’s specifications using lagged personality traits measured in the past survey waves instead of our preferred multiple-period averages. This further reduces the concern of endogeneity of the personality traits, as discussed in Section 3.1. We find that our results are robust to the use of the lagged Big Five personality measures from the most recent previous survey wave. The only exception is that in some specifications, Conscientiousness is no longer significant at the 10% level. To address the possible concern of reverse causality, that an individual’s personality measures are influenced by their unobservable propensity to be in the puzzle group, we have further examined both the stability of our personality traits, and the personality traits’ response to changes in financial measures, by exploiting the longitudinal nature of our data. Within individuals, the overall change in personality over time is quite small. The average change is between −0.07 and −0.03, and the sample medians are zero for all traits. In addition, we find that being in the puzzle group two years earlier does not have a statistically significant effect on the level of changes for each of the Big Five personality traits. In summary, controlling for a wide range of demographic, financial, health, and location measures, we find a persistent effect of personality traits on the likelihood of being in the puzzle group. 4.1 Decomposing the Effects on Credit Card Debt and Checking / Savings Balance The reduced-form results in the previous section examine measures that are functions of both checking/savings balance and credit card debt, and as such circumvent the need to address the inherent simultaneity in the decision of allocating assets. However, as explained in Section 2, an aggregate effect of personality on co-holding may mask two opposing effects, that is, one on checking/savings and the other, with an opposite sign, on credit card debt. Those effects may in turn cancel each other out. Therefore, in this section, we first examine whether the personality traits are affecting credit card debt, and then study checking/savings balances conditional on credit card debt. We first examine the likelihood that a household is a revolver, using the entire sample of households.26 We estimate the determinants of credit card debt revolving using a dichotomous variable for the existence of credit card debt, and the results are shown in Table 3. Throughout the table, Neuroticism is never statistically significant at conventional levels, whereas Openness, Conscientiousness, Extraversion, and Agreeableness are almost always statistically significant at the 5% level or lower. The effect of Conscientiousness and Extraversion on the likelihood of having credit card debt is negative, and the effect of Openness and Agreeableness is positive. Table III. The effect of personality on having revolving credit card debt Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for race, marital status, employment status, whether self-employed, whether in nursing home, household size (whether two or more), education (high-school, some college, and college or more dummies), and region and metro type fixed effects. Columns 3–6 also include assets (transportation, housing), whether underwater, and percent of household members employed. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). Column 6 only includes households in 2010–2012. *Significant at 10%. **Significant at 5%. ***Significant at 1%. †Measures lagged by two years (previous wave) in columns 4–6. Linear probability model; Dependent variable: Have a Revolving Credit Card Balance   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  Openness    0.0106**  0.0090**  0.0085**  0.0084**  −0.0015    (0.0040)  (0.0037)  (0.0036)  (0.0036)  (0.0033)  Conscientiousness    −0.0169***  −0.0091***  −0.0095***  −0.0095***  −0.0038    (0.0027)  (0.0027)  (0.0028)  (0.0028)  (0.0024)  Extraversion    −0.0140***  −0.0123***  −0.0121***  −0.0120***  −0.0036    (0.0042)  (0.0040)  (0.0041)  (0.0041)  (0.0035)  Agreeableness    0.0298***  0.0207***  0.0221***  0.0220***  0.0137***    (0.0041)  (0.0034)  (0.0035)  (0.0035)  (0.0025)  Neuroticism    0.0048  0.0049  0.005  0.005  0.002    (0.0044)  (0.0038)  (0.0040)  (0.0040)  (0.0025)  Age  0.0108***  0.0111***  0.0155***  0.0156***  0.0157***  0.0138***  (0.0032)  (0.0032)  (0.0029)  (0.0030)  (0.0029)  (0.0026)  Age-squared (divided by 100)  −0.0121***  −0.0123***  −0.0139***  −0.0140***  −0.0140***  −0.0108***  (0.0022)  (0.0022)  (0.0020)  (0.0021)  (0.0020)  (0.0018)  Is male  −0.0382***  −0.0274***  −0.0234***  −0.0256***  −0.0242***  −0.0174**  (0.0069)  (0.0078)  (0.0074)  (0.0076)  (0.0074)  (0.0064)  In poor health  0.0187***  0.0158***  0.0113***  0.0122***  0.0117***  0.0080**  (0.0031)  (0.0032)  (0.0028)  (0.0028)  (0.0028)  (0.0031)  Ln(financial assets excluding liquid assets)†      −0.0289***  −0.0272***  −0.0273***  −0.0128***      (0.0022)  (0.0021)  (0.0021)  (0.0014)  Ln(income)†      0.0056**  0.0032  0.0037  0.0001      (0.0026)  (0.0023)  (0.0023)  (0.0021)  Ln(medical spending)†      0.0079***  0.0085***  0.0092***  0.0046***      (0.0013)  (0.0012)  (0.0012)  (0.0011)  Below poverty line†      −0.0659***  −0.0542***  −0.0575***  −0.0286***      (0.0111)  (0.0119)  (0.0122)  (0.0098)  Ln(mortgage + HELOC)†      0.0134***  0.0125***  0.0125***  0.0053***      (0.0010)  (0.0009)  (0.0009)  (0.0006)  2-year change in welfare and food stamps assistance          0.0398***  0.0394***          (0.0119)  (0.0094)  2-year change in household size          0.0353***  0.0298**          (0.0098)  (0.0122)  Previously a revolver            0.4663***            (0.0101)  R2  0.06  0.07  0.13  0.12  0.12  0.32  Observations  30,517  30,517  30,517  30,517  30,517  20,602  Linear probability model; Dependent variable: Have a Revolving Credit Card Balance   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  Openness    0.0106**  0.0090**  0.0085**  0.0084**  −0.0015    (0.0040)  (0.0037)  (0.0036)  (0.0036)  (0.0033)  Conscientiousness    −0.0169***  −0.0091***  −0.0095***  −0.0095***  −0.0038    (0.0027)  (0.0027)  (0.0028)  (0.0028)  (0.0024)  Extraversion    −0.0140***  −0.0123***  −0.0121***  −0.0120***  −0.0036    (0.0042)  (0.0040)  (0.0041)  (0.0041)  (0.0035)  Agreeableness    0.0298***  0.0207***  0.0221***  0.0220***  0.0137***    (0.0041)  (0.0034)  (0.0035)  (0.0035)  (0.0025)  Neuroticism    0.0048  0.0049  0.005  0.005  0.002    (0.0044)  (0.0038)  (0.0040)  (0.0040)  (0.0025)  Age  0.0108***  0.0111***  0.0155***  0.0156***  0.0157***  0.0138***  (0.0032)  (0.0032)  (0.0029)  (0.0030)  (0.0029)  (0.0026)  Age-squared (divided by 100)  −0.0121***  −0.0123***  −0.0139***  −0.0140***  −0.0140***  −0.0108***  (0.0022)  (0.0022)  (0.0020)  (0.0021)  (0.0020)  (0.0018)  Is male  −0.0382***  −0.0274***  −0.0234***  −0.0256***  −0.0242***  −0.0174**  (0.0069)  (0.0078)  (0.0074)  (0.0076)  (0.0074)  (0.0064)  In poor health  0.0187***  0.0158***  0.0113***  0.0122***  0.0117***  0.0080**  (0.0031)  (0.0032)  (0.0028)  (0.0028)  (0.0028)  (0.0031)  Ln(financial assets excluding liquid assets)†      −0.0289***  −0.0272***  −0.0273***  −0.0128***      (0.0022)  (0.0021)  (0.0021)  (0.0014)  Ln(income)†      0.0056**  0.0032  0.0037  0.0001      (0.0026)  (0.0023)  (0.0023)  (0.0021)  Ln(medical spending)†      0.0079***  0.0085***  0.0092***  0.0046***      (0.0013)  (0.0012)  (0.0012)  (0.0011)  Below poverty line†      −0.0659***  −0.0542***  −0.0575***  −0.0286***      (0.0111)  (0.0119)  (0.0122)  (0.0098)  Ln(mortgage + HELOC)†      0.0134***  0.0125***  0.0125***  0.0053***      (0.0010)  (0.0009)  (0.0009)  (0.0006)  2-year change in welfare and food stamps assistance          0.0398***  0.0394***          (0.0119)  (0.0094)  2-year change in household size          0.0353***  0.0298**          (0.0098)  (0.0122)  Previously a revolver            0.4663***            (0.0101)  R2  0.06  0.07  0.13  0.12  0.12  0.32  Observations  30,517  30,517  30,517  30,517  30,517  20,602  Table III. The effect of personality on having revolving credit card debt Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for race, marital status, employment status, whether self-employed, whether in nursing home, household size (whether two or more), education (high-school, some college, and college or more dummies), and region and metro type fixed effects. Columns 3–6 also include assets (transportation, housing), whether underwater, and percent of household members employed. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). Column 6 only includes households in 2010–2012. *Significant at 10%. **Significant at 5%. ***Significant at 1%. †Measures lagged by two years (previous wave) in columns 4–6. Linear probability model; Dependent variable: Have a Revolving Credit Card Balance   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  Openness    0.0106**  0.0090**  0.0085**  0.0084**  −0.0015    (0.0040)  (0.0037)  (0.0036)  (0.0036)  (0.0033)  Conscientiousness    −0.0169***  −0.0091***  −0.0095***  −0.0095***  −0.0038    (0.0027)  (0.0027)  (0.0028)  (0.0028)  (0.0024)  Extraversion    −0.0140***  −0.0123***  −0.0121***  −0.0120***  −0.0036    (0.0042)  (0.0040)  (0.0041)  (0.0041)  (0.0035)  Agreeableness    0.0298***  0.0207***  0.0221***  0.0220***  0.0137***    (0.0041)  (0.0034)  (0.0035)  (0.0035)  (0.0025)  Neuroticism    0.0048  0.0049  0.005  0.005  0.002    (0.0044)  (0.0038)  (0.0040)  (0.0040)  (0.0025)  Age  0.0108***  0.0111***  0.0155***  0.0156***  0.0157***  0.0138***  (0.0032)  (0.0032)  (0.0029)  (0.0030)  (0.0029)  (0.0026)  Age-squared (divided by 100)  −0.0121***  −0.0123***  −0.0139***  −0.0140***  −0.0140***  −0.0108***  (0.0022)  (0.0022)  (0.0020)  (0.0021)  (0.0020)  (0.0018)  Is male  −0.0382***  −0.0274***  −0.0234***  −0.0256***  −0.0242***  −0.0174**  (0.0069)  (0.0078)  (0.0074)  (0.0076)  (0.0074)  (0.0064)  In poor health  0.0187***  0.0158***  0.0113***  0.0122***  0.0117***  0.0080**  (0.0031)  (0.0032)  (0.0028)  (0.0028)  (0.0028)  (0.0031)  Ln(financial assets excluding liquid assets)†      −0.0289***  −0.0272***  −0.0273***  −0.0128***      (0.0022)  (0.0021)  (0.0021)  (0.0014)  Ln(income)†      0.0056**  0.0032  0.0037  0.0001      (0.0026)  (0.0023)  (0.0023)  (0.0021)  Ln(medical spending)†      0.0079***  0.0085***  0.0092***  0.0046***      (0.0013)  (0.0012)  (0.0012)  (0.0011)  Below poverty line†      −0.0659***  −0.0542***  −0.0575***  −0.0286***      (0.0111)  (0.0119)  (0.0122)  (0.0098)  Ln(mortgage + HELOC)†      0.0134***  0.0125***  0.0125***  0.0053***      (0.0010)  (0.0009)  (0.0009)  (0.0006)  2-year change in welfare and food stamps assistance          0.0398***  0.0394***          (0.0119)  (0.0094)  2-year change in household size          0.0353***  0.0298**          (0.0098)  (0.0122)  Previously a revolver            0.4663***            (0.0101)  R2  0.06  0.07  0.13  0.12  0.12  0.32  Observations  30,517  30,517  30,517  30,517  30,517  20,602  Linear probability model; Dependent variable: Have a Revolving Credit Card Balance   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  Openness    0.0106**  0.0090**  0.0085**  0.0084**  −0.0015    (0.0040)  (0.0037)  (0.0036)  (0.0036)  (0.0033)  Conscientiousness    −0.0169***  −0.0091***  −0.0095***  −0.0095***  −0.0038    (0.0027)  (0.0027)  (0.0028)  (0.0028)  (0.0024)  Extraversion    −0.0140***  −0.0123***  −0.0121***  −0.0120***  −0.0036    (0.0042)  (0.0040)  (0.0041)  (0.0041)  (0.0035)  Agreeableness    0.0298***  0.0207***  0.0221***  0.0220***  0.0137***    (0.0041)  (0.0034)  (0.0035)  (0.0035)  (0.0025)  Neuroticism    0.0048  0.0049  0.005  0.005  0.002    (0.0044)  (0.0038)  (0.0040)  (0.0040)  (0.0025)  Age  0.0108***  0.0111***  0.0155***  0.0156***  0.0157***  0.0138***  (0.0032)  (0.0032)  (0.0029)  (0.0030)  (0.0029)  (0.0026)  Age-squared (divided by 100)  −0.0121***  −0.0123***  −0.0139***  −0.0140***  −0.0140***  −0.0108***  (0.0022)  (0.0022)  (0.0020)  (0.0021)  (0.0020)  (0.0018)  Is male  −0.0382***  −0.0274***  −0.0234***  −0.0256***  −0.0242***  −0.0174**  (0.0069)  (0.0078)  (0.0074)  (0.0076)  (0.0074)  (0.0064)  In poor health  0.0187***  0.0158***  0.0113***  0.0122***  0.0117***  0.0080**  (0.0031)  (0.0032)  (0.0028)  (0.0028)  (0.0028)  (0.0031)  Ln(financial assets excluding liquid assets)†      −0.0289***  −0.0272***  −0.0273***  −0.0128***      (0.0022)  (0.0021)  (0.0021)  (0.0014)  Ln(income)†      0.0056**  0.0032  0.0037  0.0001      (0.0026)  (0.0023)  (0.0023)  (0.0021)  Ln(medical spending)†      0.0079***  0.0085***  0.0092***  0.0046***      (0.0013)  (0.0012)  (0.0012)  (0.0011)  Below poverty line†      −0.0659***  −0.0542***  −0.0575***  −0.0286***      (0.0111)  (0.0119)  (0.0122)  (0.0098)  Ln(mortgage + HELOC)†      0.0134***  0.0125***  0.0125***  0.0053***      (0.0010)  (0.0009)  (0.0009)  (0.0006)  2-year change in welfare and food stamps assistance          0.0398***  0.0394***          (0.0119)  (0.0094)  2-year change in household size          0.0353***  0.0298**          (0.0098)  (0.0122)  Previously a revolver            0.4663***            (0.0101)  R2  0.06  0.07  0.13  0.12  0.12  0.32  Observations  30,517  30,517  30,517  30,517  30,517  20,602  Overall, the results in columns 1–5 show that personality traits predict the propensity to have credit card debt, which is a necessary condition for being in the puzzle group. We find that the magnitudes of the effects of personality traits are similar across our specifications. The results remain qualitatively similar even when we control for 2-year lag of being a revolver (column 6). The effect of Agreeableness remains significant at the 1% level. Those with a high level of Conscientiousness are perhaps more likely to avoid credit card borrowing because they are more likely to be self-controlled, plan ahead, and execute their plan. This is consistent with the overall findings on the importance of Conscientiousness in determining health, positive aging, and human capital (see Roberts et al., 2014 for a recent survey). On the other hand, Agreeableness has a strong positive effect on the likelihood of holding credit card debt. As discussed in Section 1, Agreeableness may lead to higher levels of spending. In addition, as discussed in Section 2, several studies have found that Agreeableness is negatively correlated with income. Similar preferences and mechanisms may be at work in this case. Those with higher levels of Agreeableness prefer less conflict (with employer in the case of wages, with self and friends in the case of incurring debt) over financial gains such as higher wages or less debt. Next, we examine the effect of personality on checking/savings balance, while controlling for credit card debt revolving status. Because revolving is endogenous to the decision of how much to save, it is crucial to address this potential bias. One common empirical strategy is to use an instrumental variables approach, or more generally, an exclusion restriction. We use 2-year lagged revolving status as our exclusion restriction. In our setting, we are interested in estimating the differential effect of the Big Five personality traits among those who are revolvers and those who are not. Given that there are 10 such effects (5 × 2), we do not directly estimate the model using two-stage least squares. Instead, we consider a reduced-form regression and include the instruments directly into the “second-stage” equation (Angrist and Pischke, 2008, p. 213). In other words, we estimate the effect of our instruments, instead of their endogenous counterparts, on the amount of checking/savings. For our exclusion to be valid, it must hold that 2-year lagged revolving behavior, conditional on our other measures, is not correlated with the unobservable propensity to have a checking/savings balance 2 years later.27 The results in column 1 of Table 4 demonstrate that the Big Five personality traits have a statistically significant effect on having a checking and savings balances of $500 or more both for those who are revolvers and those who are not. Both for revolvers and non-revolvers, Openness, Conscientiousness, Extraversion, and Neuroticism have a statistically significant effect on checking and savings balance. The results remain very similar when we add the lagged revolving status in column 2. In column 3, we add the interaction terms between the personality traits and the lagged revolving status. Our results suggest that the effect of personality on having low-yield liquid assets is similar between revolvers and non-revolvers. Table IV. The effect of personality on having a checking/savings account balance Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: Have a Checking/Savings Account Balance Over $500   Explanatory Variables  (1)  (2)  (3)  Openness  −0.0097***  −0.0098***  −0.0096**  (0.0035)  (0.0035)  (0.0046)  Conscientiousness  0.0228***  0.0228***  0.0213***  (0.0048)  (0.0048)  (0.0054)  Extraversion  −0.0216***  −0.0216***  −0.0207***  (0.0034)  (0.0034)  (0.0035)  Agreeableness  0.0051  0.0051  0.0072**  (0.0044)  (0.0043)  (0.0034)  Neuroticism  −0.0164***  −0.0165***  −0.0148***  (0.0032)  (0.0032)  (0.0031)  Previously a revolver (2 years earlier)    0.0025  0.0031    (0.0083)  (0.0082)  Lagged revolving status (revolved a credit card balance two years earlier)   interacted with personality traits   Was a revolver × Openness      −0.0009      (0.0105)   Was a revolver × Conscientiousness      0.006      (0.0108)   Was a revolver × Extraversion      −0.0038      (0.0078)   Was a revolver × Agreeableness      −0.0088      (0.0088)   Was a revolver × Neuroticism      −0.0065      (0.0065)  R2  0.35  0.35  0.35  Observations  20,602  20,602  20,602  Linear probability model; Dependent variable: Have a Checking/Savings Account Balance Over $500   Explanatory Variables  (1)  (2)  (3)  Openness  −0.0097***  −0.0098***  −0.0096**  (0.0035)  (0.0035)  (0.0046)  Conscientiousness  0.0228***  0.0228***  0.0213***  (0.0048)  (0.0048)  (0.0054)  Extraversion  −0.0216***  −0.0216***  −0.0207***  (0.0034)  (0.0034)  (0.0035)  Agreeableness  0.0051  0.0051  0.0072**  (0.0044)  (0.0043)  (0.0034)  Neuroticism  −0.0164***  −0.0165***  −0.0148***  (0.0032)  (0.0032)  (0.0031)  Previously a revolver (2 years earlier)    0.0025  0.0031    (0.0083)  (0.0082)  Lagged revolving status (revolved a credit card balance two years earlier)   interacted with personality traits   Was a revolver × Openness      −0.0009      (0.0105)   Was a revolver × Conscientiousness      0.006      (0.0108)   Was a revolver × Extraversion      −0.0038      (0.0078)   Was a revolver × Agreeableness      −0.0088      (0.0088)   Was a revolver × Neuroticism      −0.0065      (0.0065)  R2  0.35  0.35  0.35  Observations  20,602  20,602  20,602  Table IV. The effect of personality on having a checking/savings account balance Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. The sample includes households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: Have a Checking/Savings Account Balance Over $500   Explanatory Variables  (1)  (2)  (3)  Openness  −0.0097***  −0.0098***  −0.0096**  (0.0035)  (0.0035)  (0.0046)  Conscientiousness  0.0228***  0.0228***  0.0213***  (0.0048)  (0.0048)  (0.0054)  Extraversion  −0.0216***  −0.0216***  −0.0207***  (0.0034)  (0.0034)  (0.0035)  Agreeableness  0.0051  0.0051  0.0072**  (0.0044)  (0.0043)  (0.0034)  Neuroticism  −0.0164***  −0.0165***  −0.0148***  (0.0032)  (0.0032)  (0.0031)  Previously a revolver (2 years earlier)    0.0025  0.0031    (0.0083)  (0.0082)  Lagged revolving status (revolved a credit card balance two years earlier)   interacted with personality traits   Was a revolver × Openness      −0.0009      (0.0105)   Was a revolver × Conscientiousness      0.006      (0.0108)   Was a revolver × Extraversion      −0.0038      (0.0078)   Was a revolver × Agreeableness      −0.0088      (0.0088)   Was a revolver × Neuroticism      −0.0065      (0.0065)  R2  0.35  0.35  0.35  Observations  20,602  20,602  20,602  Linear probability model; Dependent variable: Have a Checking/Savings Account Balance Over $500   Explanatory Variables  (1)  (2)  (3)  Openness  −0.0097***  −0.0098***  −0.0096**  (0.0035)  (0.0035)  (0.0046)  Conscientiousness  0.0228***  0.0228***  0.0213***  (0.0048)  (0.0048)  (0.0054)  Extraversion  −0.0216***  −0.0216***  −0.0207***  (0.0034)  (0.0034)  (0.0035)  Agreeableness  0.0051  0.0051  0.0072**  (0.0044)  (0.0043)  (0.0034)  Neuroticism  −0.0164***  −0.0165***  −0.0148***  (0.0032)  (0.0032)  (0.0031)  Previously a revolver (2 years earlier)    0.0025  0.0031    (0.0083)  (0.0082)  Lagged revolving status (revolved a credit card balance two years earlier)   interacted with personality traits   Was a revolver × Openness      −0.0009      (0.0105)   Was a revolver × Conscientiousness      0.006      (0.0108)   Was a revolver × Extraversion      −0.0038      (0.0078)   Was a revolver × Agreeableness      −0.0088      (0.0088)   Was a revolver × Neuroticism      −0.0065      (0.0065)  R2  0.35  0.35  0.35  Observations  20,602  20,602  20,602  Taken together, the results in Tables 3–4 suggest that personality measures are important predictors both for credit card debt and for holding low-yield liquid assets. We find that Conscientiousness is important for both being a revolver and having a checking/savings balance (but with opposite signs), whereas the effect of Agreeableness operates mainly on the likelihood of being a revolver. 4.2 Intra-Household Dynamics The effect of household dynamics on economic and financial outcomes has been widely studied.28 These studies indicate that the financial behavior of a household could depend on the inner dynamics within the household. In the context of credit card debt, Bertaut, Haliassos, and Reiter (2009) derive a model where high credit card debt is used as a means to curb the spending of a spouse (or one’s self) by committing to have less available funds through a high level of existing debt. This is conceptually similar to the model considered in Fudenberg and Levine (2006) who propose a dual-self model for impulse control. In this section, we broaden our focus beyond that of the financial respondent’s personality. In our setting, the intra-household dynamics between the financial respondent and their spouse may play an important role as each side may not be able to fully monitor or control the spending and saving behavior of their partner. For example, each partner may have their own credit card that can be used for spending without the others’ pre-approval.29 Personality differences (similarities) may exacerbate (alleviate) this dynamic. We examine both a reduced-form specification, where household members are treated symmetrically (columns 3 and 4), and specifications with some additional structure (columns 5 and 6) where we proxy for power imbalance within a household using the income difference (financial respondent’s income − spouse’s income). Finally, we examine the role of the financial respondent’s gender (column 7). Column 1 of Table 5 demonstrates that the effects of the financial respondent personality traits remain largely the same when only couple households are examined. Next, to allow for a more parsimonious examination, we define a “puzzle personality index” by estimating the propensity to be in the puzzle group among single households.30 By examining single households, we circumvent the added complication of intra-household dynamics. Table V. The effect of intra-household dynamics on being in the puzzle group Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. Age and education (years) differences measured as the absolute value of (financial respondent’s−spouse’s). The PPI aggregates the effect of the Big Five personality traits based on single household estimates. Couple power difference based on income difference between couple members. The sample includes all couple households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  Puzzle personality (index)    0.0148***  0.0141***  0.0136***  0.0152***        (0.0039)  (0.0039)  (0.0040)  (0.0040)      Spouse’s puzzle personality (index)      0.0064*  0.0049  0.005  0.0056        (0.0034)  (0.0035)  (0.0035)  (0.0034)    Interaction of couple’s puzzle personalities indices        −0.0086***  −0.0080***  −0.0080***          (0.0026)  (0.0025)  (0.0026)    Couple’s age difference          −0.0001  −0.0001  −0.0002          (0.0006)  (0.0006)  (0.0006)  Couple’s years-of-schooling difference          −0.0004  −0.0005  −0.0007            (0.0017)  (0.0017)  (0.0017)  Couple’s power difference (income)          −0.0184*  −0.0196*            (0.0106)  (0.0105)    Openness ×  Couple’s power difference (income)          −0.0127  −0.0117            (0.0125)  (0.0122)    Conscientiousness ×  Couple’s power difference (income)          0.0082  0.0105            (0.0145)  (0.0152)    Extraversion ×  Couple’s power difference (income)          0.0071  0.0073            (0.0112)  (0.0122)    Agreeableness ×  Couple’s power difference (income)          −0.0177**  −0.0190**            (0.0072)  (0.0071)    Neuroticism ×  Couple’s power difference (income)          −0.0099  −0.0112            (0.0103)  (0.0095)    Male’s puzzle personality (index)              0.0025              (0.0052)  Male’s puzzle personality (index) ×  Financial respondent is male              0.0088              (0.0067)  Female’s puzzle personality (index)              0.0192**              (0.0072)  Female’s puzzle personality (index) ×  Financial respondent is male              −0.0096              (0.0079)  Openness  0.0061          0.006    (0.0048)          (0.0048)    Conscientiousness  −0.0071          −0.0085    (0.0048)          (0.0053)    Extraversion  −0.0126**          −0.0131**    (0.0051)          (0.0053)    Agreeableness  0.0186***          0.0195***    (0.0051)          (0.0051)    Neuroticism  0.0018          0.0025    (0.0043)          (0.0039)    R2  0.11  0.11  0.11  0.11  0.11  0.11  0.11  Observations  12,988  12,988  12,988  12,988  12,988  12,988  12,988  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  Puzzle personality (index)    0.0148***  0.0141***  0.0136***  0.0152***        (0.0039)  (0.0039)  (0.0040)  (0.0040)      Spouse’s puzzle personality (index)      0.0064*  0.0049  0.005  0.0056        (0.0034)  (0.0035)  (0.0035)  (0.0034)    Interaction of couple’s puzzle personalities indices        −0.0086***  −0.0080***  −0.0080***          (0.0026)  (0.0025)  (0.0026)    Couple’s age difference          −0.0001  −0.0001  −0.0002          (0.0006)  (0.0006)  (0.0006)  Couple’s years-of-schooling difference          −0.0004  −0.0005  −0.0007            (0.0017)  (0.0017)  (0.0017)  Couple’s power difference (income)          −0.0184*  −0.0196*            (0.0106)  (0.0105)    Openness ×  Couple’s power difference (income)          −0.0127  −0.0117            (0.0125)  (0.0122)    Conscientiousness ×  Couple’s power difference (income)          0.0082  0.0105            (0.0145)  (0.0152)    Extraversion ×  Couple’s power difference (income)          0.0071  0.0073            (0.0112)  (0.0122)    Agreeableness ×  Couple’s power difference (income)          −0.0177**  −0.0190**            (0.0072)  (0.0071)    Neuroticism ×  Couple’s power difference (income)          −0.0099  −0.0112            (0.0103)  (0.0095)    Male’s puzzle personality (index)              0.0025              (0.0052)  Male’s puzzle personality (index) ×  Financial respondent is male              0.0088              (0.0067)  Female’s puzzle personality (index)              0.0192**              (0.0072)  Female’s puzzle personality (index) ×  Financial respondent is male              −0.0096              (0.0079)  Openness  0.0061          0.006    (0.0048)          (0.0048)    Conscientiousness  −0.0071          −0.0085    (0.0048)          (0.0053)    Extraversion  −0.0126**          −0.0131**    (0.0051)          (0.0053)    Agreeableness  0.0186***          0.0195***    (0.0051)          (0.0051)    Neuroticism  0.0018          0.0025    (0.0043)          (0.0039)    R2  0.11  0.11  0.11  0.11  0.11  0.11  0.11  Observations  12,988  12,988  12,988  12,988  12,988  12,988  12,988  Table V. The effect of intra-household dynamics on being in the puzzle group Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. Age and education (years) differences measured as the absolute value of (financial respondent’s−spouse’s). The PPI aggregates the effect of the Big Five personality traits based on single household estimates. Couple power difference based on income difference between couple members. The sample includes all couple households in 2008–2012 (with lagged measures in 2006–2010). *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  Puzzle personality (index)    0.0148***  0.0141***  0.0136***  0.0152***        (0.0039)  (0.0039)  (0.0040)  (0.0040)      Spouse’s puzzle personality (index)      0.0064*  0.0049  0.005  0.0056        (0.0034)  (0.0035)  (0.0035)  (0.0034)    Interaction of couple’s puzzle personalities indices        −0.0086***  −0.0080***  −0.0080***          (0.0026)  (0.0025)  (0.0026)    Couple’s age difference          −0.0001  −0.0001  −0.0002          (0.0006)  (0.0006)  (0.0006)  Couple’s years-of-schooling difference          −0.0004  −0.0005  −0.0007            (0.0017)  (0.0017)  (0.0017)  Couple’s power difference (income)          −0.0184*  −0.0196*            (0.0106)  (0.0105)    Openness ×  Couple’s power difference (income)          −0.0127  −0.0117            (0.0125)  (0.0122)    Conscientiousness ×  Couple’s power difference (income)          0.0082  0.0105            (0.0145)  (0.0152)    Extraversion ×  Couple’s power difference (income)          0.0071  0.0073            (0.0112)  (0.0122)    Agreeableness ×  Couple’s power difference (income)          −0.0177**  −0.0190**            (0.0072)  (0.0071)    Neuroticism ×  Couple’s power difference (income)          −0.0099  −0.0112            (0.0103)  (0.0095)    Male’s puzzle personality (index)              0.0025              (0.0052)  Male’s puzzle personality (index) ×  Financial respondent is male              0.0088              (0.0067)  Female’s puzzle personality (index)              0.0192**              (0.0072)  Female’s puzzle personality (index) ×  Financial respondent is male              −0.0096              (0.0079)  Openness  0.0061          0.006    (0.0048)          (0.0048)    Conscientiousness  −0.0071          −0.0085    (0.0048)          (0.0053)    Extraversion  −0.0126**          −0.0131**    (0.0051)          (0.0053)    Agreeableness  0.0186***          0.0195***    (0.0051)          (0.0051)    Neuroticism  0.0018          0.0025    (0.0043)          (0.0039)    R2  0.11  0.11  0.11  0.11  0.11  0.11  0.11  Observations  12,988  12,988  12,988  12,988  12,988  12,988  12,988  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  Puzzle personality (index)    0.0148***  0.0141***  0.0136***  0.0152***        (0.0039)  (0.0039)  (0.0040)  (0.0040)      Spouse’s puzzle personality (index)      0.0064*  0.0049  0.005  0.0056        (0.0034)  (0.0035)  (0.0035)  (0.0034)    Interaction of couple’s puzzle personalities indices        −0.0086***  −0.0080***  −0.0080***          (0.0026)  (0.0025)  (0.0026)    Couple’s age difference          −0.0001  −0.0001  −0.0002          (0.0006)  (0.0006)  (0.0006)  Couple’s years-of-schooling difference          −0.0004  −0.0005  −0.0007            (0.0017)  (0.0017)  (0.0017)  Couple’s power difference (income)          −0.0184*  −0.0196*            (0.0106)  (0.0105)    Openness ×  Couple’s power difference (income)          −0.0127  −0.0117            (0.0125)  (0.0122)    Conscientiousness ×  Couple’s power difference (income)          0.0082  0.0105            (0.0145)  (0.0152)    Extraversion ×  Couple’s power difference (income)          0.0071  0.0073            (0.0112)  (0.0122)    Agreeableness ×  Couple’s power difference (income)          −0.0177**  −0.0190**            (0.0072)  (0.0071)    Neuroticism ×  Couple’s power difference (income)          −0.0099  −0.0112            (0.0103)  (0.0095)    Male’s puzzle personality (index)              0.0025              (0.0052)  Male’s puzzle personality (index) ×  Financial respondent is male              0.0088              (0.0067)  Female’s puzzle personality (index)              0.0192**              (0.0072)  Female’s puzzle personality (index) ×  Financial respondent is male              −0.0096              (0.0079)  Openness  0.0061          0.006    (0.0048)          (0.0048)    Conscientiousness  −0.0071          −0.0085    (0.0048)          (0.0053)    Extraversion  −0.0126**          −0.0131**    (0.0051)          (0.0053)    Agreeableness  0.0186***          0.0195***    (0.0051)          (0.0051)    Neuroticism  0.0018          0.0025    (0.0043)          (0.0039)    R2  0.11  0.11  0.11  0.11  0.11  0.11  0.11  Observations  12,988  12,988  12,988  12,988  12,988  12,988  12,988  We first estimate the specification in column 5 of Table 2 with only single households. Using the estimated coefficients for the personality traits from the single household estimates, we create a puzzle personality index (PPI) as the Z-score of 0.007 × Openness − 0.003 × Conscientiousness − 0.009 × Extraversion + 0.012 × Agreeableness − 0.0006 × Neuroticism. Column 2 of Table 5 includes the PPI only for the financial respondent. The coefficient is significant at the 1% level, suggesting the index performs well in capturing the likelihood of being in the puzzle group for the couple households. Next, we allow the personality of both members of the household to have an effect (column 3). The coefficient for the financial respondent’s PPI remains statistically significant and of the same magnitude. The spouse’s PPI is also statistically significant suggesting that both household members’ personality traits contribute to the likelihood of being in the puzzle group.31 In columns 4–6, we add an interaction effect between the couple’s PPIs. The interaction coefficient is negative and statistically significant at the 1% level in columns 4–6, implying an attenuating effect of a spouse’s PPI on the marginal effect of the PPI of the financial respondent. The specification in columns 5 and 6 of Table 5 examines the interaction effect of a measure of power imbalance between a couple and the financial respondent’s personality traits as well as intra-household differences (in absolute values) of age and schooling levels.32 A positive value of this measure (the financial respondent earns more than their spouse) could proxy for more power for the financial respondent within the relationship. In column 5 we use the PPI, and in column 6 we instead use the Big Five personality traits for the financial respondent and obtain similar results. In both cases, the likelihood of being in the puzzle group increases when the nonfinancial respondent spouse earns more than the financial respondent. Also, the interaction term between the financial respondent’s Agreeableness and the income difference is negative and statistically significant at the 5% level. The more disagreeable the financial respondent is, the more likely it is that the financial respondent would be able to impose his or her preference when facing a powerful spouse. Our finding is consistent with high levels of disagreeableness (i.e., less willing to accommodate or compromise) being a substitute for power. In the last specification in Table 5 (column 7), we examine the role of the financial respondent’s gender in addition to their spouse’s role. We control for the male’s PPI, the female’s PPI, the financial respondent’s gender, and interactions between the male and female’s PPI and the gender of the financial respondent. Our results suggest that the effect of personality is larger for women, and that the effect of personality is the same regardless of the financial respondent’s gender. In contrast to the effects of personality, age and schooling differences among couple members do not have a statistically significant effect on a household’s likelihood of being in the puzzle group, and the coefficients are small in magnitude. Overall, our analysis suggests that co-holding behavior is influenced by the dynamics within a household. This highlights that potential policy interventions and future research should consider both members of a household. 4.3 Alternative Explanations To examine whether our findings of the effect of personality measures using the Big Five traits are just proxies for other measures, we examine the effects of other alternative measures, such as financial sophistication or self-control. The first column in Table 6 adds a self-control/impulsiveness measure to our main specification in Table 2 as a proxy for self-control which Bertaut, Haliassos, and Reiter (2009) and Gathergood (2012) suggested as an important factor for the puzzle. We find that controlling for self-control/impulsiveness, three of the personality measures remain statistically significant at the 5% level or lower. Column 2 in Table 6 examines the effect of internal locus of control. Locus of control is the degree to which people believe in their own ability to control events that influence them.33 People with a high internal locus of control believe that outcomes are due to their own behavior, and people with a high external locus of control believe that events occur for reasons out of their control, or external factors. The coefficient for internal locus of control is statistically significant at the 5% level. However, four of the personality traits remain statistically significant at the 10% level. In column 3, we include cognitive functioning measures such as memory and mental status (which includes numeracy).34 We also include the number series score which was developed for measuring fluid intelligence. Though some of the cognitive measures have a statistically significant effect, by-and-large, the effect of personality (both in magnitude and statistical significance) remains the same or larger and our results are robust to various measures of cognitive ability. Table VI. Robustness checks with alternative explanations for the puzzle Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. In column 8, the excluded category is 7–14% interest rate. *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  Openness  0.0076**  0.0058*  0.0140***  −0.0067  0.0082**  0.0151  0.0118  0.0106  (0.0031)  (0.0028)  (0.0046)  (0.0096)  (0.0034)  (0.0229)  (0.0206)  (0.0264)  Conscientiousness  −0.0021  −0.0052*  −0.0088**  −0.0212**  −0.0032  0.011  −0.0065  −0.0082  (0.0033)  (0.0029)  (0.0040)  (0.0100)  (0.0038)  (0.0179)  (0.0146)  (0.0238)  Extraversion  −0.0122***  −0.0102***  −0.0124***  −0.0113  −0.0124***  −0.0417  −0.0498  −0.045  (0.0031)  (0.0028)  (0.0036)  (0.0099)  (0.0034)  (0.0366)  (0.0310)  (0.0473)  Agreeableness  0.0164***  0.0162***  0.0166***  0.0302***  0.0146***  0.0428**  0.0461**  0.0621**  (0.0032)  (0.0028)  (0.0042)  (0.0071)  (0.0042)  (0.0196)  (0.0186)  (0.0254)  Neuroticism  −0.0011  −0.0013  0.0028  −0.0056  −0.0036  0.0091  0.0018  −0.0053  (0.0026)  (0.0028)  (0.0034)  (0.0061)  (0.0034)  (0.0188)  (0.0168)  (0.0226)  Self-control/Impulsiveness  −0.0068**                (0.0031)                Internal locus of control    −0.0050**                (0.0022)              Total memory score (word recall)      0.0042***                (0.0011)            Total mental status score (numeracy)      0.0067***                (0.0019)            Total number series score (fluid intelligence)      0.0001                (0.0001)            Risk aversion (6 levels)        Included           Are any of the risk aversion levels  statistically significant?        No          In financial control (lagged)          −0.0028**                 (0.0011)        Financial literacy            0.0144                 (0.0461)      Credit card rates   0% interest rate                −0.2315***                 (0.0584)   1-6% interest rate                −0.1609                 (0.0998)   More than 15% interest rate                −0.1174***                 (0.0360)  Economic/financial understanding (7 levels)              Included     Are any of the of econ/financial understanding levels statistically significant?              Only the highest level    R2  0.11  0.10  0.09  0.10  0.10  0.18  0.18  0.24  Observations  24,820  30,412  11,330  3,366  14,102  647  784  484  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  Openness  0.0076**  0.0058*  0.0140***  −0.0067  0.0082**  0.0151  0.0118  0.0106  (0.0031)  (0.0028)  (0.0046)  (0.0096)  (0.0034)  (0.0229)  (0.0206)  (0.0264)  Conscientiousness  −0.0021  −0.0052*  −0.0088**  −0.0212**  −0.0032  0.011  −0.0065  −0.0082  (0.0033)  (0.0029)  (0.0040)  (0.0100)  (0.0038)  (0.0179)  (0.0146)  (0.0238)  Extraversion  −0.0122***  −0.0102***  −0.0124***  −0.0113  −0.0124***  −0.0417  −0.0498  −0.045  (0.0031)  (0.0028)  (0.0036)  (0.0099)  (0.0034)  (0.0366)  (0.0310)  (0.0473)  Agreeableness  0.0164***  0.0162***  0.0166***  0.0302***  0.0146***  0.0428**  0.0461**  0.0621**  (0.0032)  (0.0028)  (0.0042)  (0.0071)  (0.0042)  (0.0196)  (0.0186)  (0.0254)  Neuroticism  −0.0011  −0.0013  0.0028  −0.0056  −0.0036  0.0091  0.0018  −0.0053  (0.0026)  (0.0028)  (0.0034)  (0.0061)  (0.0034)  (0.0188)  (0.0168)  (0.0226)  Self-control/Impulsiveness  −0.0068**                (0.0031)                Internal locus of control    −0.0050**                (0.0022)              Total memory score (word recall)      0.0042***                (0.0011)            Total mental status score (numeracy)      0.0067***                (0.0019)            Total number series score (fluid intelligence)      0.0001                (0.0001)            Risk aversion (6 levels)        Included           Are any of the risk aversion levels  statistically significant?        No          In financial control (lagged)          −0.0028**                 (0.0011)        Financial literacy            0.0144                 (0.0461)      Credit card rates   0% interest rate                −0.2315***                 (0.0584)   1-6% interest rate                −0.1609                 (0.0998)   More than 15% interest rate                −0.1174***                 (0.0360)  Economic/financial understanding (7 levels)              Included     Are any of the of econ/financial understanding levels statistically significant?              Only the highest level    R2  0.11  0.10  0.09  0.10  0.10  0.18  0.18  0.24  Observations  24,820  30,412  11,330  3,366  14,102  647  784  484  Table VI. Robustness checks with alternative explanations for the puzzle Standard errors, in parentheses, are clustered at the region × metro type (highly-urban, medium-size, and rural). Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism are standardized to have a mean of zero and a variance of one. All specifications control for all variables in column 5 of Table 2, including region and metro type fixed effects. In column 8, the excluded category is 7–14% interest rate. *Significant at 10%. **Significant at 5%. ***Significant at 1%. Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  Openness  0.0076**  0.0058*  0.0140***  −0.0067  0.0082**  0.0151  0.0118  0.0106  (0.0031)  (0.0028)  (0.0046)  (0.0096)  (0.0034)  (0.0229)  (0.0206)  (0.0264)  Conscientiousness  −0.0021  −0.0052*  −0.0088**  −0.0212**  −0.0032  0.011  −0.0065  −0.0082  (0.0033)  (0.0029)  (0.0040)  (0.0100)  (0.0038)  (0.0179)  (0.0146)  (0.0238)  Extraversion  −0.0122***  −0.0102***  −0.0124***  −0.0113  −0.0124***  −0.0417  −0.0498  −0.045  (0.0031)  (0.0028)  (0.0036)  (0.0099)  (0.0034)  (0.0366)  (0.0310)  (0.0473)  Agreeableness  0.0164***  0.0162***  0.0166***  0.0302***  0.0146***  0.0428**  0.0461**  0.0621**  (0.0032)  (0.0028)  (0.0042)  (0.0071)  (0.0042)  (0.0196)  (0.0186)  (0.0254)  Neuroticism  −0.0011  −0.0013  0.0028  −0.0056  −0.0036  0.0091  0.0018  −0.0053  (0.0026)  (0.0028)  (0.0034)  (0.0061)  (0.0034)  (0.0188)  (0.0168)  (0.0226)  Self-control/Impulsiveness  −0.0068**                (0.0031)                Internal locus of control    −0.0050**                (0.0022)              Total memory score (word recall)      0.0042***                (0.0011)            Total mental status score (numeracy)      0.0067***                (0.0019)            Total number series score (fluid intelligence)      0.0001                (0.0001)            Risk aversion (6 levels)        Included           Are any of the risk aversion levels  statistically significant?        No          In financial control (lagged)          −0.0028**                 (0.0011)        Financial literacy            0.0144                 (0.0461)      Credit card rates   0% interest rate                −0.2315***                 (0.0584)   1-6% interest rate                −0.1609                 (0.0998)   More than 15% interest rate                −0.1174***                 (0.0360)  Economic/financial understanding (7 levels)              Included     Are any of the of econ/financial understanding levels statistically significant?              Only the highest level    R2  0.11  0.10  0.09  0.10  0.10  0.18  0.18  0.24  Observations  24,820  30,412  11,330  3,366  14,102  647  784  484  Linear probability model; Dependent variable: In the Puzzle Group   Explanatory Variables  (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  Openness  0.0076**  0.0058*  0.0140***  −0.0067  0.0082**  0.0151  0.0118  0.0106  (0.0031)  (0.0028)  (0.0046)  (0.0096)  (0.0034)  (0.0229)  (0.0206)  (0.0264)  Conscientiousness  −0.0021  −0.0052*  −0.0088**  −0.0212**  −0.0032  0.011  −0.0065  −0.0082  (0.0033)  (0.0029)  (0.0040)  (0.0100)  (0.0038)  (0.0179)  (0.0146)  (0.0238)  Extraversion  −0.0122***  −0.0102***  −0.0124***  −0.0113  −0.0124***  −0.0417  −0.0498  −0.045  (0.0031)  (0.0028)  (0.0036)  (0.0099)  (0.0034)  (0.0366)  (0.0310)  (0.0473)  Agreeableness  0.0164***  0.0162***  0.0166***  0.0302***  0.0146***  0.0428**  0.0461**  0.0621**  (0.0032)  (0.0028)  (0.0042)  (0.0071)  (0.0042)  (0.0196)  (0.0186)  (0.0254)  Neuroticism  −0.0011  −0.0013  0.0028  −0.0056  −0.0036  0.0091  0.0018  −0.0053  (0.0026)  (0.0028)  (0.0034)  (0.0061)  (0.0034)  (0.0188)  (0.0168)  (0.0226)  Self-control/Impulsiveness  −0.0068**                (0.0031)                Internal locus of control    −0.0050**                (0.0022)              Total memory score (word recall)      0.0042***                (0.0011)            Total mental status score (numeracy)      0.0067***                (0.0019)            Total number series score (fluid intelligence)      0.0001                (0.0001)            Risk aversion (6 levels)        Included           Are any of the risk aversion levels  statistically significant?        No          In financial control (lagged)          −0.0028**                 (0.0011)        Financial literacy            0.0144                 (0.0461)      Credit card rates   0% interest rate                −0.2315***                 (0.0584)   1-6% interest rate                −0.1609                 (0.0998)   More than 15% interest rate                −0.1174***                 (0.0360)  Economic/financial understanding (7 levels)              Included     Are any of the of econ/financial understanding levels statistically significant?              Only the highest level    R2  0.11  0.10  0.09  0.10  0.10  0.18  0.18  0.24  Observations  24,820  30,412  11,330  3,366  14,102  647  784  484  In column 4, we include a risk aversion measure, as those with higher risk aversion may opt to keep a larger buffer in the form of less credit card debt or a higher checking account balance. We use the 6-categories risk aversion measure from the HRS’s “income gamble” scenario questions. None of the risk categories are statistically significant at the 10% level, and overall the effect of the personality measures remains the same. In column 5, we use the self-perception of how much a household has control over their financial situation.35 We find that households who assess themselves as having more control over their finances are less likely to be in the puzzle group. To reduce the likelihood of reverse causality, we use the lagged self-perception of financial control (though financial problems may be persistent over longer horizons). We find that the effects of the Big Five traits are quite similar to those in our base specifications. Columns 6–7 of Table 6 include various measures of financial literacy (see Lusardi and Mitchell, 2014) for a summary of the important role financial literacy has on economic outcomes). We construct the financial literacy measure based on two questions (one on compound interest and one on the effects of inflation). Column 6 in Table 6 uses a dummy indicator for answering both questions correctly as the measure of financial literacy. Column 7 uses a self-assessed measure of the degree of understanding of economics and finance (7 levels). The results in columns 6–7 show that the effect of Agreeableness remains statistically significant and the coefficients of the Big Five personality traits generally have the same sign as those in our base specification in Table 2 even after controlling for financial literacy. This is consistent with the result of Gathergood and Weber (2014). Our final specification controls for credit card interest rates on the card used most often. Column 8 in Table 6 shows that interest rates have a statistically significant effect. Yet, the magnitudes of the Big Five personality trait effects remain largely the same as those in column 6–8. Openness, Conscientiousness, and Extraversion lose their significance at the 10% level, but Agreeableness remains statistically significant. While the sample size for this specification is rather small (only 1 in 20 of our households in 2010 were asked the question), the results suggest that our findings capture “true” interest-paying revolvers and are not solely driven by households that are taking advantage of 0% balance transfer offers. In summary, we find that the personality effects are robust to controlling for self-control, locus of control, cognitive abilities, risk aversion, financial literacy, and credit card interest rates. 5. Conclusion Using a rich longitudinal data set, we find that controlling for a host of demographic, financial, and economic factors, personality traits play a role in explaining the credit card puzzle. We find that Conscientiousness, Extraversion, and Agreeableness have statistically significant effects, and that the signs of the effects are consistent with findings in other domains. The results complement other types of explanations suggested in the literature for the credit card puzzle, as they hold after controlling for other suggested factors. Our findings that a broad set of noncognitive measures are important, suggest that researchers should be cautious of focusing on a single aspect or dimension of noncognitive ability when studying financial decision making. Our results also suggest that intra-household dynamics might play an important role in financial decisions, and highlight the importance of both coordination and power dynamics among household members. The effect of intra-household interactions on the economic and financial well-being of families remains an important area for future research. The findings in this article contribute to an emerging and growing set of economic and financial outcomes (e.g., earnings, education, etc.) in which noncognitive skills play an important role. It is, therefore, plausible that noncognitive skills would play a role in areas of finance that have yet to be examined. There are two types of polices to consider that could yield substantial returns: investments in noncognitive skills, and policies that target the noncognitive aspects of financial decision-making. Investments in noncognitive skills, such as planning, may have large benefits.36 However, personality is mostly/solely malleable at early childhood, so such investments require an early intervention and a long-term horizon. The second type of policies is related to the design of interventions or marketing campaigns to address debt. Both the government and non-profit organizations have programs to assist people with managing their finances.37 Participants in these programs could be asked to answer a short survey that would assess their personality type, and could then receive an intervention that would be tailored to their profile. Financial planners already ask their clients about their investment goals and risk tolerance. Similar assessments could be performed by debt counselors or by consumers visiting websites.38 In addition, banks and credit unions routinely have access to credit reports. They are, therefore, potentially positioned to combine information on the availability of low-yield liquid assets and revolving credit card balances and alert their clients to costly levels of co-holding. Co-holding credit card debt and low-yield liquid assets exerts a non-negligible toll on households. Extrapolating our results, in the US, even a 1-percentage-point decrease in the fraction of households in the puzzle group, would generate interest payment savings of over half-a-billion dollars per year, while maintaining the same level of consumption. Footnotes 1 Openness “describes the breadth, depth, originality, and complexity of an individual’s mental and experiential life [with a behavioral example] of tak[ing] time to learn something simply for the joy of learning.” Extraversion “implies an energetic approach toward the social and material world … such as sociability, activity, assertiveness, and positive emotionality.” Neuroticism “contrasts emotional stability and even-temperedness with negative emotionality, such as feeling anxious, nervous, sad, and tense.” Ibid.John and Srivastava (1999) provide an overview of the traits as well as a historical account of the last several decades. 2 See Livingstone and Lunt (1992), Nyhus and Webley (2001), Norvilitis et al. (2006), Rabinovich and Webley (2007), and Conti and Heckman (2014) for some examples. 3 Examples include earnings (Bowles, Gintis, and Osborne, 2001; Nyhus and Pons, 2005; and Mueller and Plug, 2006); household finances (Brown and Taylor, 2014); educational attainment (Lundberg, 2013); and academic achievements (Heckman, Stixrud, and Urzua, 2006; and Heckman and Kautz, 2012). 4 We calculate the annual interest cost of co-holding per household using the average credit card debt amount that could have been paid down after a month’s income is set aside and the average interest rate of 14%. When extrapolating to the US population, we apply the annual interest cost estimate to 1% of households in the US with a head over 50 (Table H2, U.S. Census Bureau, 2015). 5 In other definitions, we consider households to not be in the puzzle group if they have up to $500 in credit card debt as in Telyukova (2013); $1,200 or one-half of monthly income, whichever is larger, in checking or savings accounts (following Bertaut, Haliassos, and Reiter, 2009); and one month’s income in checking or savings accounts. We have also examined continuous measures that can be interpreted as the cost of being in the puzzle group such as min⁡{ln⁡(A),ln⁡(D)}, ln⁡(A)1{D>0}, and ln⁡(A)0.5ln⁡(D)0.5, where A and D are the amounts of low-yield liquid assets and credit card debt, respectively, in excess of certain thresholds such as 0 or $500. 6 This sequential framework is, of course, for exposition purposes only. An alternative would involve the decision to hold low-yield liquid assets, and conditional on holding those assets, the decision to incur debt instead of using one’s available assets. 7 Bernerth et al. (2012) note that “the trusting, submissive, and accommodating tendencies of agreeable individuals can put them in precarious positions as they sacrifice personal resources for others.” 8 For example, Brown and Taylor (2014) find that Openness is positively correlated with having credit card debt. Matz, Gladstone, and Stillwell (2016) find that higher levels of Openness are associated with higher levels of spending on entertainment, eating out, pubs, and tourism. 9 For example, in the context of coping and coping effectiveness under stress or constraints, McCrae and Costa (1986) find that “Extraversion is correlated with rational action, positive thinking, substitution, and restraint.” Related, Carver and Connor-Smith (2010) find that “Extraversion predicted more problem solving, use of social support, and cognitive restructuring.” See also Connor-Smith and Flachsbart (2007). 10 For example, Donnelly, Iyer, and Howell (2012) find that Neuroticism is negatively related to the management of personal finances. 11 Additional information can be found at http://hrsonline.isr.umich.edu/ 12 The co-holding of credit card debt and low-yield liquid assets is prevalent among all age groups. Telyukova (2013) reports that 27% of households with heads of age 25–64 years co-hold based on the 2001 Survey of Consumer Finances (SCF). Using 1995 SCF data, Gross and Souleles (2002) report in Table 6 that among bank card borrowers younger than 35 years, 95% hold positive liquid assets and 25% hold more than one month’s income in liquid assets. 13 Other commonly used datasets may have a wider range of age groups but either lack any personality measures (e.g., the SCF) or have less complete personality measures (e.g., The National Longitudinal Survey of Youth). 14 While our preferred specification (column 5 in Table 2) uses all households, the magnitudes of the personality effects are similar for households with a head 55 years or younger. However, we acknowledge that our sample cannot be used to estimate the effect on those under 50 years old. Our predictions for the entire population therefore require us to assume the effects are similar among younger households. 15 Smith, McArdle, and Willis (2010) find that males and those with more years of education are more likely to be the financial respondent of the household in the HRS survey. Our analysis controls for both of these factors. 16 The attrition rate in the HRS is relatively low. For example, from 2010 to 2012, the attrition rate is 4.9% due to death, and 3.7% due to nonresponse. We also examined potential attrition bias by testing whether membership in the puzzle group could predict attrition and find the effect to be small and not statistically significant. 17 Although the core HRS survey is a biennial survey and participants are interviewed every two years, some of the questionnaires, including those for personality, are only administrated every 4 years (alternating half of the sample every two years) to reduce the burden on survey participants (Juster and Suzman, 1995). 18 http://www.midus.wisc.edu/ 19 The other measures are Openness (7 items): creative, imaginative, intelligent, curious, broadminded, sophisticated, adventurous; Extraversion (5 items): outgoing, friendly, lively, active, talkative; Agreeableness (5 items): helpful, warm, caring, softhearted, sympathetic; and Neuroticism (4 items): moody, worrying, nervous, calm (−). 20 Borghans et al. (2008) and Roberts, Wood, and Caspi (2008) provide a review of this matter. 21 The average age in our sample is higher, but our results remain qualitatively and quantitatively the same if we just focus on the younger working-age segment of our sample. 22 However, our results both in terms of the magnitude of the standard errors and statistical significance are almost identical when we instead cluster at the household level. 23 Our results remain the same when we additionally examine shocks to health and employment status. 24 To illustrate the effect of the personality traits, one can translate a personality effect into the equivalent effect of a financial variable. An increase of $25,591 in financial assets (or 0.2269 in logs) would decrease the likelihood of being in the puzzle group by 0.54 percentage points (column 5). This is the same decrease in the likelihood of being in the puzzle group that would occur if Conscientiousness were to increase by one standard deviation (as the coefficient on Conscientiousness is − 0.0054). Hence, an increase of $25,591 in financial assets has the same effect on the likelihood of being in the puzzle group as an one-standard-deviation increase in Conscientiousness. 25 We also examined a random-effects model that would allow for the inclusion of fixed-over-time personality measures. The results for the personality traits are of the same sign and magnitude of our preferred specifications, and Extraversion and Agreeableness remain statistically significant at the 5% level. 26 There is a large body of literature on personal debt focusing on both economic and financial factors, and on psychological factors. For example, in the psychology literature, Livingstone and Lunt (1992), Wood (1998), Donnelly, Iyer, and Howell (2012), and Wilcox, Block, and Eisenstein (2011) find effects that by and large are consistent with our findings. 27 To investigate the validity of the exclusion, we examined the typical measures associated with a two-stage-least-squares framework using the specification in column 2 of Table 4. The first stage R-squared is 0.32. The excluded instrument’s F-statistic (2126.95) suggests that there is no “weak” instrument problem (Stock and Yogo, 2005). Furthermore, we could not reject the null that the instruments are valid using the Sargan–Hansen test of overidentification (with 4-year lag revolving status being the additional instrument). 28 For example, see Lundberg and Pollak (2007) for a recent review of some of the issues in the US context. 29 Schaner (2015) argues that large differences in discount factors among couples can lead to holding individual bank accounts that have lower interest earnings than joint accounts. 30 To improve statistical power, we summarize the five personality traits with a single index that has the most explanatory power. Otherwise, to examine both partners’ personality and an interaction term would require as many as 20 variables. 31 This result is consistent with that of Schaner (2015), where the difference in the preferences of household members leads to costly separate individual financial accounts rather than joint accounts. 32 For the power imbalance measure, we use log⁡(z+1+z2), where z is the income difference (financial respondent’s−spouse’s). Our measure encompasses both unearned income (see Lundberg and Pollak, 1996 for a discussion) and earned income. For example, Basu (2006) examines the effect of intra-couple power relationships, measured by income disparity, on household decision making, and Ashraf (2009) uses an experimental design in the Philippines to examine the role of spousal control in consumption and savings decisions. 33 Locus of control has been shown to be an important factor in economic decision making. For example, college attendance (Coleman and DeLeire, 2003); job search (Caliendo, Cobb-Clark, and Uhlendorff, 2015); and loan delinquency (Kuhnen and Melzer, 2017). 34 For memory, we use the total of the immediate and delayed (5 min) word recall scores. For mental status, we use the total of the serial 7’s test, counting backwards from 20 or 86, date recall, recalling object names, and naming the President/Vice President. 35 We use the question: “Using a 0 to 10 scale where 0 means ‘no control at all’ and 10 means ‘very much control,’ how would you rate the amount of control you have over your financial situation these days?” 36 Some schools, such as NY City’s KIPP charter school, teach skills, such as grit, as part of their curriculum. See Kautz et al. 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