Golden or Graying? Cognitive Ability and Knowledge Predict Real-World Financial Outcomes

Ye Li

University of California, Riverside

Eric J. Johnson, Zeynep Enkavi, Jie Gao, Lisa Zaval, and Elke U. Weber Columbia University

ABSTRACT Average age in the developed world is rising rapidly, and age-related deterioration in cognitive ability raises fears that older adults facing major financial decisions may be unable to make them. We explore the possibility that older adults’ accumulated expertise and knowledge from past decisions can compensate for their slowing brains to maintain decision-making ability. Using a unique dataset combining measures of cognitive ability, general and financial knowledge, and credit report data, we find that domain-specific expertise provides an alternative pathway for making sound financial decisions in a range of financial tasks and real-world financial outcomes. These results have important implications for public policy and for the design of effective interventions that can aid decision making for people of all ages.

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COGNITIVE ABILITY AND KNOWLEDGE PREDICT REAL-WORLD FINANCIAL OUTCOMES Word Count: 2694

Over the next decades, changes in population demographics will profoundly affect

financial decision making across the developed world. Average age is rising rapidly. For

example, one in five Americans is expected to be over 65 years old by 2030 (1). This

important shift will drive two trends: The first, described by the economic life-cycle

model (2), is that people accumulate wealth up to retirement. They then face decisions

about how to consume that wealth, while guarding against the possibility of running out

of money. Figure 1 shows this accumulation, with bars representing total wealth, as well

as equities—financial holdings that require active investment. Americans over 65

collectively manage 43% of all household wealth and 47% of all stocks and mutual funds.

In addition, retirement savings increasingly occur via defined contribution retirement

plans (e.g., 401(k)s) that require complex financial choices later in life.

The second age-related trend represents one of the most sizeable and robust in all of

psychology: The brain slows with age. Fluid intelligence (Gf)—i.e., speed and capacity

for generating, transforming, and manipulating information—falls by nearly two standard

deviations from age 20 to 70 (3, 4). This change is equivalent to decreasing from the 75th

to the 25th percentile in the adult population. The grey lines in Figure 1 illustrate these

declines for working memory, processing speed, and reasoning. Given mounting

evidence that cognitive ability is a key determinant of decision-making ability (5-9), age-

related deterioration of Gf raises the specter that older adults facing major financial

decisions may be less able to make them.

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Figure 1. Average U.S. household wealth, stock and mutual fund holdings in 2011 (Survey of Income and Program Participation1) and four cognitive abilities, by age.

One factor potentially tempers this depressing projection: The decline in Gf with age

is accompanied by an increase in crystallized intelligence (Gc) (10, 11)—i.e., knowledge,

experience, and expertise acquired over a lifetime (3, 12-14). The gold line in Figure 1

illustrates the accumulation of Gc with age into the 60s. Gc may therefore represent a kind

of intellectual capital that provides an alternative pathway to making decisions. For

example, imagine the bewilderment of a newly-arrived immigrant grocery shopping for

the first time in their new country. Now contrast that with an experienced shopper who

1 http://www.census.gov/people/wealth/data/dtables.html

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knows what brands are better, what prices are cheap, and where their favorite products

are located. This accumulated knowledge and expertise greatly reduces the need for

information processing and active search (15, 16).

More generally, is it possible that this accumulation of Gc from past decisions

compensates for declining Gf to maintain or perhaps even improve decision-making

ability with age?

We examine this question using a new dataset that uniquely combines extensive

measurements of cognitive ability and economic phenotypes (i.e., risk, loss, and time

preferences) with field observations of economic performance from a major credit-

reporting bureau. The richness of this dataset allows us to test whether Gf and Gc relate to

real-world financial performance and how age differences in these cognitive abilities

relate to differences in financial performance. Examining these underlying changes in

cognitive ability is necessary to understand age-related differences in decision-making

ability and thereby design effective interventions to assist decision-makers of all ages

facing important financial and health decisions (17).

The Study

Our data about cognitive ability and economic phenotypes comes from a four-part

web-based study in which 478 U.S. residents between 18 and 86 completed a battery of

cognitive, decision-making, and demographic measures. (See Supplementary Materials

for details on all measures). To the best of our knowledge, this study is the first to

combine credit report data with multiple standard measures of Gf, Gc, and economic

phenotype assessments. Gf measures included Raven’s Progressive Matrices, Letter Sets,

and a task combining a Numeracy test and the Cognitive Reflection Test. Crystallized

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abilities are usually thought of measuring general knowledge but we collected data on

two different levels of crystallized abilities: The first, which we term general indicators

of Gc, were measured using widely-used knowledge tests: Shipley Vocabulary, Antonym

Vocabulary, and the WAIS-III Information task. To better understand the role of

crystalized abilities we also assessed domain-specific Gc: financial literacy (18) and

specific knowledge about health insurance.

We obtained credit report data from a major credit reporting bureau. Credit scores are

a standard U.S. metric of creditworthiness widely used by potential lenders, landlords,

and employers. Maintaining a high credit score reflects a sustained ability to make good

financial decisions over one’s lifetime (19) and brings substantial benefits such as lower

interest rates and insurance premiums, increased likelihood of obtaining loans, and even

better chances of getting a job or apartment.2

Our main analyses consist of seven models of credit score as a function of age and

other demographic variables, along with cognitive ability, financial experience, economic

phenotypes, and personality variables. Our goal is to assess the effects of Gf and Gc as we

control for other variables that could affect financial decision-making.

We conducted all analyses using structural equation modeling (SEM) but, for ease of

exposition, present all results as linear regressions on factor scores from the SEM

analysis. Factor scores represent the composites of the multiple variables representing the

underlying constructs in the SEM, for example Gf and Gc, and provide more reliable

measures by accounting for measurement error. The SEM (see the Supplementary

Materials) and linear regressions showed the same pattern of results. Figure 2 shows the

2 We use the newer FICO 08 credit score, since it is the most predictive indicator of consumer credit risk, but results using the older FICO 04 score and VantageScore (another credit scoring model developed by the three major credit bureaus) were nearly identical.

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positive relationships of credit score with age (r = 0.268), Gf (r = 0.177), and Gc (r =

0.315).

Figure 2. Credit scores as a function of age, Gf, and Gc. The orange and black lines on the left show 90% confidence intervals of relationships between age, Gf, and Gc. The other coefficients represent…

Table 1 shows the results of all seven models. Model 1, which regresses credit score

on demographic variables, shows that credit scores increase by an average of 13 points

per decade of life, comparable to the effect of an additional year of education or a

doubling of income.

Model 2 adds Gf and Gc to Model 1. Importantly, it verifies the positive relationship

between credit scores and Gf, it finds that domain-general Gc does not predict financial

decision-making ability. This result is perhaps unsurprising, considering the general

knowledge and vocabulary tasks used to measure Gc.

We therefore substituted a domain-specific measure of financial Gc in Model 3 and all

subsequent analyses: Financial literacy (Gc-FL) measures ability to understand financial

Age$ FICO$

Domain$Crystallized$Intelligence$

(Gc)$

Fluid$Intelligence$

(Gf)$

Without'cogni+ve'variables'

With'cogni+ve'variables'

.200***$

.157**$

.254***$

.121*$

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information and decisions (18, 20). People with greater Gc-FL have been found to be more

likely to accumulate and manage wealth effectively (21), invest in the stock market (22),

and choose mutual funds with lower fees (23).

Model 3 shows that credit scores positively relate to Gf and Gc-FL. These results are

consistent with compensating competencies hypothesis—that higher levels of Gc provide

an alternative route to good decision-making when Gf is less available in older adults (17).

One standard deviation increase in Gf (roughly equivalent to 15 points of IQ) corresponds

to a credit score increase of 22 points, whereas one standard deviation of Gc-FL

corresponds to an increase of 47 points. In addition, the reduction in the size of the age

coefficient from Model 1 to Model 3 is consistent with Gf and Gc-FL underlying the effect

of chronological age on credit scores.

Table 1. Results of linear regressions on FICO Credit Scores. Standard errors in parentheses. *** p < 0.001; ** p < 0.01; * p < 0.05 ; † p < 0.1

(1) (2) (3) (4) (5) (6) (7) Constant 693.032 695.583 696.996 696.996 693.051 691.044 694.753 (6.009)*** (5.950)*** (5.792)*** (5.796)*** (5.910)*** (6.799)*** (5.711)*** Demographic Variables Age 1.293 1.498 1.017 1.205 0.986 0.996 0.763 (0.297)*** (0.356)*** (0.335)** (0.377)** (0.343)** (0364)** (0.343)* Gender – 0.213 – 6.093 – 15.542 – 15.768 – 8.204 – 10.787 – 11.601 (10.291) (10.392) (10.133) (10.163) (10.621) (11.307) (9.879) Years of Education 11.536 7.441 5.845 5.871 7.834 6.167 7.043 (2.443)*** (2.618)** (2.505)* (2.509)* (2.617)** (2.669)* (2.490)** Log Income3 18.839 15.788 13.877 16.370 17.222 14.594 14.520 (5.368)*** (5.292)** (5.194)** (5.714)** (5.443)** (5.878)* (5.119)** Financial Experience – 13.799

(13.136) Intelligence Variables Gf 32.725 21.553 21.734 15.713 15.816 20.269 (10.587)** (9.503)* (9.524)* (10.303) (10.635) (9.448)* Gc 9.492 (8.680) Gc-FL 46.943 50.432 33.629 45.739 45.233 (10.516)*** (11.125)*** (10.942)•• (11.689)*** (10.461)*** Economic phenotypes Discount Factor 26.480 35.844 (10.691)* (8.938)*** Present Bias 0.734 (6.967) Loss aversion 0.482

3 See supplementary materials for different measures of income, wealth and net worth.

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(6.778) Distortion of probability – 9.569

(5.950) Curvature of value function

– 6.221 (6.731)

DOSPERT (Financial Risk Taking subscale)

– 2.636 (0.834)**

Psychological variables Intellect – 1.566 (0.738)* Emotional Stability 0.660 (0.625) Extraversion – 2.183 (0.938)* Agreeableness – 1.517 (1.244) Conscientiousness 0.334 (1.165) Number of observations 415 415 415 414 387 315 415 𝑅! 0.161 0.202 0.237 0.238 0.259 0.327 0.275 𝑅! - adjusted 0.153 0.190 0.225 0.225 0.238 0.309 0.255

The fact that Gc-FL is positively related to credit scores may be due to the fact that

people with more financial history should also know more about financial products

because they have more experience using them. Model 4 therefore controls for financial

experience as self-reported on 20 different types of financial instruments (e.g., checking

accounts, credit cards, mortgages, mutual funds, payday loans, etc.). Even controlling for

experience, the effect of Gc-FL remains equally strong. This surprising result suggests that

good financial decisions require people to understand financial products, not just

experience using them. We return to this issue in the discussion.

We next consider the role of economic phenotypes, i.e., preferences for time, loss,

and risk that influence a wide real-world decisions with important financial and health

consequences. Recent research has found that people with greater cognitive ability are

more patient and more willing to take risks (17, 24). At the same time, older adults

become more risk averse with age (24) but less loss averse (25), and the relationship

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between time preference and age is U-shaped: middle-aged adults are more patient than

both younger and older adults (26).

We measured individual differences in risk, loss, and time preferences using two

adaptive measurement tasks (27), each administered twice. Model 5 adds model estimates

for risk aversion, loss aversion, and time preference as controls to Model 3. Consistent

with recent findings (28), credit scores were higher for people with more patient time

preferences. Importantly, the effect of Gf is no longer significant after controlling for time

preference, consistent with a positive relationship between Gf and patient time

preferences (17, 24). That is, part of the reason that people with higher Gf have higher

credit scores is that they more appropriately weight future outcomes.

Finally, we also control for other important psychological factors known to influence

a wide range of behaviors. Model 6 controls for the DOSPERT risk-taking scale (29) and

Model 7 controls for Big Five personality measures (30). Again, we find similar results

for the effects Gf and Gc-FL even though these other psychological variables had

significant coefficients as well.

Performance in other financial decisions

To compliment the observation of the consequences of real-world financial decisions,

we ran two additional experiments using the same set of participants, assessing

performance on two other important financial decisions: The first is people’s ability to

optimally pay off credit card debt. In the credit card repayment task, participants chose

how to repay debts on two credit card accounts. Although participants should always pay

off as much of the higher APR credit card as possible, the tempting but naïve choice is to

pay off the lower APR credit card in full. The second decision is selecting the best

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healthcare plan. Participants read a specific health profile (e.g., see the doctor 11 times

and get $250 of prescription medication) and chose the optimal plan from either four or

eight different options varying on premium, deductible, and copay. For both tasks, greater

Gf and domain-specific Gc led to improved performance, even after controlling for

demographics, economic phenotypes, personality variables. (See supplementary materials

for detailed results.)

DISCUSSION

Given the realities of cognitive aging, the combination of increased wealth and

decreased fluid intelligence might imply disaster for older adults’ financial decision-

making. Instead, we show that their improved crystallized intelligence, particularly

domain-specific knowledge and expertise, seems to provide an alternative pathway for

making sound financial decisions. Results held across a wide range of decisions: attaining

higher credit scores, efficiently repaying credit card debts, and choosing the best health

care plan. These complementary pathways cannot be explained by age differences in

demographics, financial experience, economic phenotypes, or personality traits.

To better appreciate the magnitude of the effects of cognitive ability on credit scores,

we translate effect sizes to their impact on the cost of credit. Lower credit scores

correspond to more risky borrowers and thus higher interest rate loans. As an example,

consider a representative participant in our sample, a 44-year old woman with a college

degree earning $50,000 per year and of average Gf and Gc-FL. Her predicted credit score

would be 693, which means she would pay about 4.48% APR on a 30-year, $300,000

mortgage, or $1,517 a month.4 If she had one standard deviation more Gf, we would

4 According to http://www.myfico.com/myfico/creditcentral/loanrates.aspx

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expect her credit score to be 21 points higher, and if she had one standard deviation more

Gc-FL, we would expect her credit score to be 47 points higher. Over the life of her

mortgage, these higher cognitive abilities would be associated with $25,277 less in total

interest payments.

These results suggest focusing on the effects of age on decision making is misleading.

Instead, we need to focus on the decrease in Gf and the compensation provided by Gc.

This is a more useful and nuanced analysis since the effect of age for any decision will

depend on the relative importance of each type of intelligence. For tasks in which

decision-makers have a chance to develop expertise over past experiences, decision-

making can actually improve with age. While such increases in Gc may compensate for

declines in Gf for many decisions, we still expect performance declines with age for tasks

that more heavily require Gf. Indeed, although both Gf and Gc contributed to selecting

optimal healthcare plans, older participants on average performed worse on this

information-dense task (see supplementary materials). In addition, compensation for

deficits in Gf can only happen in situations where Gc could have accumulated. Many

important decisions, such as what age to start claiming one’s pension and Social Security

benefits, happen only once and have little opportunity for building expertise.

Conclusion The oldest baby-boomers have reached their late sixties, representing the front-end of

an unprecedented, though highly foreseeable increase in the country’s senior population.

The ability of older adults to make financial decisions should be an important for anyone

who presents financial information, be they policy-makers or financial services firms as

they contemplate the potential effects of this demographic shift. Our results suggest that

financial decisions and opportunities do not need to be summarily taken out of older

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hands because of decreases in fluid intelligence. Instead, the finding that cognitive

collapse may be offset by older adults’ decision-making experience and knowledge has

important implications for the design of effective decision environments to improve

performance for different age groups.

To broadly foster better decision-making, policy-makers and task-designers should

focus on ways to (1) minimize the role of declining fluid intelligence (e.g., by alleviating

processing loads using external memory aids) and (2) maximize the impact of crystallized

intelligence among older adults (e.g., by providing decision environments analogous to

environments in which they have more experience). But younger adults’ lack of financial

knowledge and experience also deserve attention to prevent pitfalls that can haunt credit

histories for decades to come.

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Acknowledgements: This research was supported by NIA grant 1R01AG044941 to Eric Johnson and Elke Weber and National Endowment for Financial Education grant 5236 to Ye Li and Eric Johnson. All authors declare they have no conflict of interests with respect to their authorship or publication of this article.

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SUPPLEMENTARY MATERIALS

Participants

We recruited participants from the Columbia University Center for Decision Sciences'

Virtual Lab Panel (n = 469) and from a private survey sampling company (n = 150). Participants

completed four waves of a web-based survey consisting of cognitive, decision-making, and

demographic measures. All participants were U.S. residents and indicated English as their native

language. Participants were emailed invitations to complete the study between January and April

2013 (waves 1-3) and June and July 2013 (wave 4). The two to three month delay between the

completion of Wave 3 and 4 allowed us to test reliability for several decision measures.

Participants received an invitation to each subsequent wave only if they completed the previous

wave and were reminded to complete each wave every 2 to 3 days up to three times. Participants

received $28 to $32 (with an average of $30 varying based on a measure not discussed here) for

completing the first three waves and $12 for completing the fourth wave via PayPal.

In total, 843 participants completed the first wave, 815 (3.32% attrition) completed the

second, 777 (4.66 % attrition) completed the third. After the third wave 158 were systematically

excluded from further participation for not providing legitimate answers in various tasks in the

third wave. Therefore, 619 participants with legitimate answers were invited to the fourth wave

and 478 completed it (22.78% dropout). None of the demographic, cognitive, decision-making or

personality measures predicted whether participants dropped out, suggesting no selective

attrition. Participants were aged between 18 and 86 and grouped into four age groups: young

from ages 18 to 30 (M = 24.46, Median = 24, SD = 3.36), middle-younger from 31 to 45 (M =

38.32, Median = 38, SD = 4.47), middle-older from 46 to 60 (M = 54.01, Median = 55, SD =

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3.88), and old from 61 to 86 (M = 67.47, Median = 67, SD = 4.91).

Table S1 shows demographic information by age group. Older participants were

somewhat more educated than younger participants, with a higher percentage attaining post-

graduate degrees (from old to young, 42.6% vs. 15.9% vs. 14.4% vs. 5.5.0%, χ2(3) = 71.4, p <

.01) and more years of education on average (from old to young, 15.7 vs. 14.5 vs. 15.2 vs. 14.7,,

F(1, 612) = 9.02, p < .01). However, they have similar levels of household income (medians,

Medold = $58.6K and Medyoung = $61.1K, t = .58, ns), somewhat higher than the U.S. median of

$49,445 in 2010 (U.S. Census). Household income was positively correlated with years of

education (r = .37, p < .01).

Table S1. Demographic information for the four age groups subjects who completed all four waves and had matched credit score data. Young Middle-

Younger Middle-Older Old

N 81 92 120 124 Age 24.6 38.0 54.5 67.8

Female 66.7% 59.1% 68.2% 59.8% Married 20.9% 60.4% 57.5% 50.4%

Have 1 or more Children 12.3% 76.9% 74.2% 78.9% Income (median) $40.00 $60.00 $60.00 $50.00 Income (mean) 47.16 65.22 63.75 61.62

Education(at least some college) 90.1% 88.0% 85.0% 91.1% Race (Caucasian) 71.6% 75.8% 88.3% 93.5%

Measures

To the best of our knowledge, this study is the first to combine credit report data with

multiple standard measures of both fluid and crystallized intelligence, as well as measures of

financial-domain specific crystallized intelligence, and multiple measures of important economic

decision-making preferences such as risk and time preference. Collecting multiple measures for

each of these factors allows us to explicitly model measurement error using structural equation

modeling rather than making the unjustified assumption that measures are perfectly reliable.

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All participants completed three measures each of fluid and crystallized intelligence. Among

our measures of fluid intelligence, the most widely used is Raven’s Progressive Matrices, a non-

verbal test of inductive and analytic reasoning. Our version asked participants to determine

which option correctly filled in the missing cell for each of 18 3×3 matrices (32). We also

included two other standard measures of inductive and reasoning ability: Letter Sets (32) asked

participants which of five letter sets (e.g., NOPQ, DEFL, ABCD, HIJK, and UVWX) did not fit

the rule that the other four fit (e.g., DEFL). Finally, we included a multiple-choice test that

combines the Cognitive Reflection Test (CRT; 33, 34), which consists of three math questions

that yield quick but incorrect first responses, and Numeracy (35), which tests understanding of

probability and mathematical concepts.

We included three standard measures of generalized crystallized intelligence. We included a

10-item version of the Shipley Vocabulary multiple-choice synonym vocabulary test, adapted

from CREATE’s Common Core Battery of Measures (36, 37). Similarly, Antonym Vocabulary

(38) measured vocabulary using 10 multiple-choice antonym selection items. Finally, WAIS-III

Information (39), also adapted from CREATE (36, 37), asked participants 28 open-ended

general-knowledge questions about events, objects, places, and people.

In addition, we measured specific crystallized intelligence relevant to financial decision-

making. Financial literacy is the ability to understand financial information and decisions (20).

People with greater financial literacy are more likely to accumulate and manage wealth

effectively (21), plan for retirement (20, 40, 41), choose mutual funds with lower fees (23), and

invest in the stock market at all (22). It has been positively related to education but the

relationship between Gc-FL and age has found mixed results (17, 42-44). Financial literacy was

measured using a 13-item questionnaire (18) designed to assess knowledge of fundamental

18

economic concepts. We also measured financial experience as self-reported on 20 different types

of financial instruments (e.g., checking accounts, credit cards, mortgages, mutual funds, payday

loans, etc.).

For example, loss aversion, or the degree to which valuations of losses outweigh the

valuation of gains, has been shown to lead investors to hold onto losing stocks too long (45) and

homeowners to overvalue their homes (46). Risk preference, the degree to which people prefer

safer to riskier options, has been shown to be related to debt holdings (47) and frequency of stock

trading (48). Time preference is the degree to which people discount future gains and losses (34).

More patient people have been found to have higher credit scores (28) and have less credit card

debt (49).

Credit Report Data

We obtained credit report data for 67.4% of the full sample of participants who

completed the first three waves (55.5% young; 50.8% middle-younger; 79.5% middle-older; and

87.9% older) from a major credit reporting bureau for April 2013 (the latest available at the time

of request), 2011, and 2009 time periods. We were not able to obtain a perfect match rate due to

many participants lacking a traceable credit history, particular young and middle-younger

participants. The higher probability of matching for older participants is not surprising,

considering that they have longer credit histories than the younger participants and are more

likely to have a permanent residence. Of the non-matched data points, 65 of 202 (32.2%),

respectively, were younger participants. Older participants (p < 0.001) and women (p < 0.001)

were more likely to have credit scores.

The credit report data consists of 52 different variables, including the FICO 5.0, FICO

2009, and VantageScore 3.0 credit scores. Correlations between these three definitions are all

19

greater than .9 at all three time points. The non-credit score variables consist of account counts,

ages, balances, past due balances, credit limits, and delinquencies for different types of accounts.

Table 2.1 lists the account types categorized by “tradeline.” Delinquencies are further broken

down into number, percentage, and delinquent balances of accounts that are 30, 60, 90, or 120

days past due, are in bankruptcy, or in default (have a major derogatory event such as charge off,

in collections, etc.). These 52 variables are the most important subset of the 336 variables tracked

by the credit reporting bureau, as determined by previous analyses.

Table 2. Equifax Account Types by “Tradeline” Type of Tradeline

Installment (Traditional amortizing debt

and leases)

Revolving (Credit accounts where consumers are able to carry a balance from month to

month)

Other (Must pay off entire balance every month)

Accounts and Loans in Category

-1st Mortgage -CES (Closed-end Second) -Auto Loan -Student Loan -Unsecured Personal Loan -Consumer Finance (high-interest loans given to people with poor credit histories)

-HELOC (home equity line of credit) -Credit Card (A.K.A. bank card) -Retail Account (e.g., jewelry store line of credit) -Department Store Credit Card -Unsecured Personal Line of Credit (including consumer finance accounts)

-Charge Card (e.g., AMEX)

Structural equation models

We followed procedures standard to the cognitive aging literature (e.g., Del Missier et al., 2011;

Lindenberger et al., 1993; Salthouse, Atkinson, & Berish, 2003; Li, Y. et al., 2013) for analyzing

the relationships between age, cognitive capability, and other abilities. We first characterize the

cognitive measurement model by testing for convergent and discriminant validity, and showing

measurement invariance between younger and older groups. We do the same for the decision-

making variables. Finally, we combine these models with age in a structural equation model to

test the CCH. We ran all analyses in Mplus (Muthén & Muthén, 2010) using both standard and

20

bootstrapped estimation procedures using 10,000 bootstrapped samples (Preacher & Hayes, 2008;

Shrout & Bolger, 2002). We report the results for standard analyses but, for all tests, the

significance levels for bootstrapped analyses (corresponding to the widest bias-corrected

confidence interval not including zero) were equally or even more significant.

We used standard indices to evaluate model fit. Root mean square error of approximation

(RMSEA) is a measure of the difference between predicted and observed covariances, with

values under .08 considered adequate (Browne & Cudeck, 1993; Steiger, 1990). The Bentler

comparative fit index (CFI) indicates the relative improvement of the hypothesized model over

the null or independent model (in which all variables are unrelated). Values of CFI above .90 are

considered adequate (Hu & Bentler, 1999). Both indices are penalized for model complexity and,

therefore, favor models that can more parsimoniously explain the observed covariance patterns.

We also report difference in chi-squares for model comparisons but do not interpret overall chi-

square due to our large sample size (Kline, 2010).

We standardized all variables, and coded all variables so that higher scores corresponded to

better performance. Table X and X’ shows the mean and variance for each cognitive measure for

participants in different age groups, as well as the pairwise correlations between the measures

across all age groups. Different measures for each cognitive factor were significantly correlated

with one another (rs=.48 to .56 for fluid intelligence, and .52 to .74 for crystallized intelligence;

all ps < .0001).

Table X. Means and Standard Deviations for all Measures as a Function of Age Group

Mean Young

Mean MediumYoung

Mean MediumOld

Mean Old

SD Young

SD MediumYoung

SD MediumOld

SD Old

Credit Score 677.26 664.35 700.61 737.81 89.61 117.2 109.83 95.84 Raven 8.31 7.5 6.3 5.6 3.58 4.43 2.95 2.79 Numeracy 4.56 4.58 3.75 4.19 2.03 2.53 1.89 2.17

21

Letter Set 10.42 10.35 10.09 9.93 3.05 3.4 2.33 2.49 Synonym 6.3 6.66 6.93 8.09 2.89 2.93 2.6 2.31 Antonym 6.54 6.63 7.05 8.05 2.65 2.75 2.57 2.15 WAIS 19.13 18.09 18.99 21.26 5.03 5.71 4.72 4.14 Financial Literacy -0.27 -0.09 0.06 0.28 0.58 0.62 0.55 0.5 Beta 0.44 0.32 0.55 0.6 0.7 0.82 0.65 0.58 Discount Factor 0.65 0.53 0.64 0.72 0.65 0.63 0.61 0.56 Alpha -0.08 -0.12 -0.01 -0.04 0.25 0.31 0.25 0.26 Sigma 0.43 0.49 0.21 0.31 0.45 0.57 0.44 0.45 Lambda -0.03 0.12 -0.15 -0.08 0.55 0.64 0.56 0.56 Dospert 0.09 0.36 -0.19 -0.32 0.62 0.9 0.57 0.61 Intellect 46.33 44.67 43.91 45.17 5.89 8.41 7.36 7.64 Emotion Stability 39.31 37.32 34.42 33.28 9.02 11.55 9.4 9.43 Extraversion 43.12 41.89 40.98 40.25 6.94 7.9 6.16 7.33 Agreeableness 42.05 41.14 40.6 40.22 6.1 7.03 4.77 6.09 Conscientious 40.88 40.71 41.01 40.91 5.55 6.95 5.3 5.63 Experience -0.47 -0.01 0.17 0.36 0.46 0.48 0.5 0.4

Table X’. Correlations between Measures across All Age Groups

Credit Score Raven Numeracy Letter Set Synonym Antonym WAIS Financial Literacy

Credit Score Raven 0.11*

Numeracy 0.22*** 0.56*** Letter Set 0.16** 0.52*** 0.48***

Synonym 0.26*** 0.34*** 0.41*** 0.37*** Antonym 0.28*** 0.33*** 0.42*** 0.39*** 0.74***

WAIS 0.31*** 0.25*** 0.38*** 0.36*** 0.53*** 0.52*** 0.52*** Financial Literacy 0.42*** 0.29*** 0.47*** 0.32*** 0.44*** 0.43*** 0.40*** Beta 0.23*** 0.18*** 0.27*** 0.24*** 0.20*** 0.20*** 0.22*** 0.25***

Discount Factor 0.25*** 0.09* 0.22*** 0.15*** 0.21*** 0.17*** 0.21*** 0.22*** Alpha 0 -0.22*** -0.18*** -0.05 -0.08* -0.07 0.02 -0.07 Sigma 0.02 0.36*** 0.33*** 0.18*** 0.20*** 0.19*** 0.06 0.13** Lambda -0.09 0.03 0.01 -0.11** -0.10* -0.12** -0.18*** -0.04 Dospert -0.25*** 0.27*** 0.16*** -0.02 -0.01 -0.05 -0.13*** -0.05 Intellect -0.10* -0.04 0.05 -0.06 0.07 0.11** 0.06 -0.04 Emotion Stability -0.13** 0.09* 0.01 0.01 0 -0.02 -0.07 -0.15*** Extraversion -0.24*** -0.01 -0.08 -0.16*** -0.10* -0.11** -0.16*** -0.17*** Agreeableness -0.16*** 0 -0.09* -0.16*** -0.05 -0.10* -0.11** -0.18*** Conscientious -0.05 0.04 0.01 -0.10* 0.04 0.02 -0.04 -0.03 Experience 0.29*** -0.02 0.12** 0.01 0.29*** 0.29*** 0.19*** 0.47***

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Cognitive Measurement Model

To determine the validity of the cognitive measurement model, we conducted a confirmatory

factor analysis (CFA) on the cognitive measures. As seen in the factor loadings in Figure 2, the

two-factor model consisting of fluid intelligence (Gf) and crystallized intelligence (Gc) factors

showed convergent validity, with significant loadings for all cognitive measures on their

hypothesized factors. The model showed reasonable fit to the data (CFI = .98, RMSEA = 0.06,

SRMR = .031). Fluid intelligence was positively correlated with crystallized intelligence (r = .62,

p<.0001). When Gc and Gf were forced to be uncorrelated, the model fit is lower (CFI = .88,

RMSEA = 0.18, SRMR = .20).

23

Measurement Invariance for Cognitive Model

We first examine whether the cognitive measures assess the same underlying factors in

the same ways in each age group, by testing for measurement invariance (Kline, 2010;

Vandenberg & Lance, 2000). We also consider the change of CFI (ΔCFI) as additional criteria to

judge the model comparison results (Chen, Sousa, West, 2005, p.482), in that “a difference of

larger than .01 in the CFI would indicate a meaningful change in model fit for testing

measurement invariance”. We tested for factor invariance using multiple-group CFA, which

separately fits the measurement model simultaneously to the data from the younger and older

groups (younger defined as age<=45, and older as age>45). Table 4 shows the fit indices for

successively more restrictive models. Model M1 specifies the same measurement model for both

age groups with all parameters freely estimated within each group. M1 fit the data well (CFI

= .989, RMSEA = .054), suggesting that the measurement model satisfies configural invariance

(Kline, 2010).

Table 4 Multiple-Group Analysis for Younger and Older Cognitive Factor Invariance

Table 4. Invariance Test for Cognitive Measurement Model

24

Model d.f. chi-2 RMSE

A CFI ΔCFI Δχ2/ Δdf p

M1: unconstrained 16 30.66 0.054 0.98

9 -- -- -- M2: M1+equal loadings across age groups 20 41.75 0.059

0.984 0.005 (ns) 11.09/4 0.022

M2': M1+equal loadings across age groups except raven 19 35.32 0.053

0.988 0.001 (ns) 4.66/3

0.198(ns)

M3: M2'+equal intercepts 23 66.27 0.078 0.96

7 0.021 30.95/4 <0.001 M3': M2'+equal intercepts except raven 22 39.55 0.051

0.987 0.001 (ns) 4.23/3 0.37(ns)

M4: M3'+equal factor variances and covariances 25 51.21 0.058 0.98 0.007 (ns) 11.66/3 0.009

With the exception of Raven’s Progressive Matrices, our proposed factor structure

demonstrated strong metric invariance. Model M2, which restricts the factor loadings to be equal

across age groups for each cognitive measures, did not fit the data as well as M1 (χ2 (4) = 11.09,

p < .05). This discrepancy appeared to be due to a difference in the loading of Raven’s

Progressive Matrices. An alternative model M2’, with equal factor loadings except for Raven’s,

did not fit the data significantly differently from model M1 (χ2 (3) = 4.66, ns).

Similarly, model M3, in which factor intercepts were restricted to be equal across age

groups, fit worse than M2’, (χ2 (4) =30.95, p < .001), whereas alternative model M3’, with equal

factor intercepts except for on Raven’s, fit about the same as M2’ (χ2 (3) = 4.23, ns). These

results suggest that the measurement properties of Raven’s Progressive Matrices were different

between younger and older groups. However, relaxing this restriction did not change the results

of subsequent analyses, so we continue under the assumption of partial strong metric invariance

for the cognitive measurement model.

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Finally, model M4, in which factor variances and covariances were restricted to be equal

across age groups, fit about as well as model M3’ (χ2(3) = 11.66, p<0.01), however, ΔCFI

between these two models are 0.007 (<0.01), thus, suggesting that the factor variances and

covariances were equivalent across age groups.

Cognitive Measurement Model II (Gf and Gc-specific)

To determine the validity of the cognitive measurement model, we conducted a

confirmatory factor analysis (CFA) on the cognitive measures. As seen in the factor loadings in

Figure X, the two-factor model consisting of fluid intelligence (Gf) and crystallized intelligence

in financial domain (Gc-spec, i.e., financial literacy test) factors showed convergent validity,

with significant loadings for all cognitive measures on their hypothesized factors. The model

showed reasonable fit to the data (CFI = .975, RMSEA = 0.035). Fluid intelligence was

positively correlated with financial literacy factor (r = 0.59, p<.0001).

Measurement Invariance for Cognitive Model II

We tested for factor invariance using multiple-group CFA, which separately fits the

measurement model simultaneously to the data from the younger and older groups (younger

defined as age<=45, and older as age>45). The results are shown in the following table. However,

unconstrained model (M1) and equal Financial Literacy intercept and loadings model (M2) did

not converge, thus model fit indices were not produced.

d.f. chi-2 RMSEA CFI

M1: unconstrained no converge M2: M1+equal Finlit intercept and loading no converge M3: M2+equal GF loadings 179 314.66 0.049 0.938

M4: fully constrained 182 369.02 0.058 0.915

26

measurement model only 83 146.69 0.035 0.975

Multiple Pathway Analysis

In the final analysis step, we used the approach outlined in Figure 1 (illustration of model)

to test whether age differences in the cognitive capabilities can help explain the observed age

differences in decision performance. Importantly, we were able to simultaneously estimate and

test all direct and indirect effects within a single SEM framework. Doing so allows us to estimate

indirect effects of age even if the total effect of age on a given factor is not significant (Zhao et

al., 2010). That is, age differences in cognitive capabilities may partially explain decision

performance in young and old participants.

Recall that Figure 1 represents the CCH as a path model. In the language of this model,

the CCH predicts that any age differences in credit score are partially due to opposing indirect

effects of age via fluid and crystallized intelligence in financial domain (financial literacy),

where older participants’ lower levels of fluid intelligence may be offset by their higher levels of

financial literacy In other words, older participants’ higher levels of financial literacy may

provide an alternate pathway to good financial decision making, which may make up for the

decrement in the fluid intelligence pathway.

Table Y shows the standardized coefficients of the relevant paths in the final SEM

models, with different variables controlled. This multiple pathway models fit the data reasonably

well (CFI=0.88~0.96, SRMR =.031~0.11 except for the worst Model 3). Recall that the total

effect of age (c in Figure 1) for a given decision trait is equivalent to the path from age to the

financial decision-making factor in a model without fluid and financial literacy. The significance

of an indirect effect of age tests whether that cognitive capability contributes to the effect of age

27

on the decision-making factor. The direct effect of age (c’) is the path from age to the decision-

making factor after all indirect effects have been accounted for in the model.

In Table Y, CCH would be confirmed by fluid and crystallized intelligence in financial

domain (financial literacy) having negative and positive indirect effects on decision making,

respectively. This pattern, and in particular the similar magnitudes and opposite directions of the

indirect effects, is evident for the financial decision-making factor. Looking, for example, at

model 3, both fluid and financial literacy positively contributed to credit score, but have

opposing indirect effects—due to opposing changes with age—that perfectly offset each other

(when demographic variables are controlled for). That is, older participants’ higher levels of

crystallized intelligence in financial domain provided another pathway to better financial

decisions, preventing them from making the worse choices that their lower levels of fluid

intelligence would otherwise predict.

Table Y. SEM Model Results (1) (2) (3) (4) (5) (6) (7) Loadings: Mediators on Age

Gf on Age -0.202*** -0.202*** -0.202*** -0.203*** -0.203*** -0.203*** Gc on Age

0.278***

Gc-FL on Age

0.416*** 0.416*** 0.416*** 0.416*** 0.416*** Loadings: Credit Score on Demographic variables

Age 0.222*** 0.255*** 0.165** 0.183** 0.16** 0.122* 0.124* Gender 0.002† -0.009 -0.064 -0.061 -0.046 -0.005 -0.043 Education 0.237*** 0.184*** 0.161** 0.159** 0.164** 0.167*** 0.188*** Log Income 0.141** 0.123* 0.102* 0.12* 0.104* 0.114* 0.107* Panel -0.091† -0.04 -0.029 -0.033 -0.045 -0.04 -0.043 Experience

-0.066

Intelligence variables (Mediators)

Gf

0.224** 0.17** 0.167** 0.123* 0.139* 0.158* Gc

0.108

Gc-FL

0.312*** 0.325*** 0.281*** 0.249*** 0.284*** Economic preference

Discount factor

0.014 0.194** Beta

0.239*

Alpha

-0.128 Sigma

-0.107

Lambda

0.038 Personality

Dospert

-0.31***

28

Intellect

-0.115* Emotional Stability

0.038

Extraversion

-0.128* Aggreableness

-0.126†

Conscientiousness

0.093 Total, Direct and Indirect Effects of Age Total Effect

0.239** 0.26*** 0.284*** 0.252*** 0.197*** 0.21***

Direct Effect

0.255*** 0.165** 0.183** 0.16** 0.122* 0.124* Total Indirect

-0.015 0.095* 0.101* 0.091* 0.075* 0.086*

Indirect effect through Gf

-0.045* -0.034* -0.034* -0.025† -0.029* -0.032* Indirect effect through Gc

0.03

Indirect effect through Gc-FL

0.13*** 0.135*** 0.119*** 0.104** 0.118***

CFI 0.956 0.908 0.891 0.84 0.886 0.879 0.892 SRMR 0.031 0.12 0.1 0.167 0.098 0.109 0.099 *** p < 0.001; ** p < 0.01; * p < 0.05 ;†  p < 0.1

REAL-WORLD DECISION MAKING TASKS We used two real-world decision making tasks to check the robustness of the

Complementary Capabilities model: the credit card repayment and healthcare plan selection

tasks. Parallel sets of analyses were conducted as in Table 1 in the main text for these two tasks.

In the credit card repayment task, participants are asked to allocate a given budget ($100

or $1000) to two credit card debts: One with lower APR and lower balance and another with

higher APR and higher balance in the $100 budget condition; one with higher APR lower

balance and another with lower APR higher balance in the $1000 budget condition. All

participants saw both options but the order was randomized. We have coded the correct answer

as allocating as much as possible to the high APR balance to either cover the debt either fully or

as much as possible. The answers to the conditions were significantly correlated (r = 0.29, p <

0.001) and 50% of all correct responses were correct for both conditions. We combined the

responses to the two conditions by coding the correct answer to either condition as correct and

used it as the dependent measure for the regressions in Table 3. The results remain the same

when responses to either condition are used individually.

Mirroring the results for credit scores, we found that greater Gf and domain-specific Gc-FL

but and not general Gc led to greater likelihood of paying off the highest APR credit card. These

29

results again held after controlling for demographics, economic phenotypes, and personality

variables. Also parallel to before controlling for Financial Experience (Model 4) had only a

marginal effect and people who were more risk-taking according to the DOSPERT scale (Model

6) performed worse. Interestingly, people who are more loss averse also perform better in this

task (Model 5).

Table 3. Results of logistic regressions on credit card payment

Results of logistic regressions using credit card repayment task as dependent variable

(1) (2) (3) (4) (5) (6) (7) Intercept 1.004*** 1.211*** 1.278*** 1.273*** 1.216*** 1.143*** 1.228*** (0.139) (0.153) (0.156) (0.156) (0.165) (0.160) (0.157) Demographic variables Age 0.028*** 0.032*** 0.027*** 0.034*** 0.024** 0.021* 0.028*** (0.007) (0.008) (0.008) (0.009) (0.009) (0.009) (0.008) Gender 0.155 -0.142 -0.284 -0.249 0.067 0.142 -0.175 (0.224) (0.244) (0.245) (0.247) (0.271) (0.274) (0.253) Education -0.025 -0.125* -0.133* -0.120* -0.093 -0.102† -0.093 (0.055) (0.060) (0.059) (0.060) (0.065) (0.061) (0.061) Income -0.017 -0.107 -0.113 0.017 -0.099 0.038 -0.090 (0.113) (0.120) (0.122) (0.139) (0.135) (0.130) (0.122) Financial Experience -0.582† (0.298) Intelligence variables Gf 0.815*** 0.676** 0.618** 0.505* 0.612** 0.647** (0.220) (0.217) (0.221) (0.245) (0.221) (0.225) Gc 0.286 (0.181) Financial Literacy 0.744** 0.892*** 0.589* 0.692** 0.606* (0.246) (0.260) (0.264) (0.256) (0.252) Economic phenotypes Discount factor 0.397 0.301 (0.244) (0.189) Present bias 0.108 (0.150) Loss Aversion 0.536** (0.169) Distortion of probability -0.066 (0.147) Curvature of value function 0.101 (0.170) DOSPERT Financial Risk Taking -0.072*** (0.017) Psychological variables Intellect -0.011 (0.018) Emotional Stability 0.013 (0.015) Extraversion -0.015

30

(0.022) Agreeableness -0.035 (0.028) Conscientiousness -0.019 (0.029)

Observations 468 468 468 467 439 468 468 Akaike Inf. Crit. 527.161 499.727 492.840 490.395 447.815 472.722 492.587

Note: *** p < 0.001; ** p < 0.01; * p < 0.05 ; † p < 0.1

Similar complementary capabilities patterns were found in the health care choice task. In

this task participants were instructed to choose the optimal healthcare plan from 4 or 8 options

given a scenario including how many doctor’s visit to expect that year. All participants had to

pass a test to make sure that they understood the concepts involved in calculating the expected

annual cost (e.g. copay, premium, deductible). We ran hierarchical models using both conditions

for all participants (two measures for everyone) and specifying random intercepts to control for

individual differences.

Tables 4 and 5 show the parallel regressions for this task. Note that we do not have an

equivalent of a financial experience measure for these decisions, hence the table includes six

instead of seven regressions. In table 4 the dependent measure is whether the correct option was

chosen and in table 5, the additional annual cost due to not choosing the optimal healthcare plan.

Both Gf and domain-specific Gc (from a survey of healthcare knowledge including questions both

on prescriptions and doctor visits as well as insurance terms) have an effect for more correct

responses and smaller losses. In choosing the optimal health care plan in addition to Gf and

domain-specific Gc (health care knowledge survey), general Gc also shows significant influence;

it does not help, however, when calculating the loss; which is a task that depends more on Gf as it

has the largest effect and an expected negative age effect, where younger participants perform

better.

Table 4. Results of logistic regressions on optimal choice of health plan

31

Results of logistic regressions on optimal choice of health plan

(1) (2) (3) (4) (5) (6)

Intercept 1.474*** 1.803*** 1.669*** 1.371*** 1.545*** 1.310*** (0.234) (0.256) (0.252) (0.212) (0.228) (0.200) Demographic variables Age 0.016 0.017 0.004 0.012 0.011 0.017† (0.011) (0.013) (0.013) (0.011) (0.011) (0.010) Gender 0.322 -0.414 -0.249 -0.121 -0.483 -0.222 (0.371) (0.410) (0.406) (0.343) (0.371) (0.321) Education 0.117 -0.132 -0.079 -0.005 0.021 0.006 (0.091) (0.100) (0.099) (0.083) (0.085) (0.077) Income 0.008 -0.255 -0.203 0.031 -0.136 -0.079 (0.197) (0.210) (0.211) (0.178) (0.188) (0.165) Intelligence measures Gf 1.752*** 1.122** 1.280*** 1.336*** 1.280*** (0.381) (0.389) (0.325) (0.321) (0.281) Gc 0.910** 0.855** (0.314) (0.314) Healthcare score 0.772*** 0.549** 0.814*** 0.665*** (0.215) (0.190) (0.199) (0.179) Economic phenotype Discount factor 0.354 0.672* (0.322) (0.271) Present bias 0.276 (0.204) Loss Aversion -0.122 (0.225) Distortion of probability 0.194 (0.190) Curvature of value function 0.084 (0.226) DOSPERT Health and Safety score 0.030 (0.195) Psychological measures Intellect -0.006 (0.023) Emotional Stability 0.010 (0.019) Extraversion 0.051† (0.028) Agreeableness -0.073* (0.036) Conscientiousness -0.057 (0.038)

Observations 876 876 876 822 876 876 Akaike Inf. Crit. 1,018.011 960.646 950.125 899.625 950.197 957.494 Bayesian Inf. Crit. 1,046.663 998.849 993.103 960.878 997.951 1,019.574

Note: *** p < 0.001; ** p < 0.01; * p < 0.05 ; † p < 0.1

Table 5. Results of linear regressions on total monetary loss by not choosing the optimal health plan

32

Results of regressions using loss from suboptimal healthcare plan choices

(1) (2) (3) (4) (5) (6)

Intercept 288.636*** 257.754*** 265.034*** 268.496*** 266.838*** 267.542*** (31.911) (31.432) (31.153) (31.425) (31.310) (30.678) Demographic variables Age -4.541** -5.049** -3.892* -3.738* -4.423** -4.918** (1.535) (1.681) (1.699) (1.618) (1.571) (1.575) Gender 26.566 105.061* 95.006† 80.511 90.055† 93.389† (49.959) (51.302) (50.811) (51.583) (52.124) (49.577) Education 6.504 31.382* 27.406* 21.552† 23.235* 14.098 (12.160) (12.568) (12.483) (12.504) (11.827) (12.054) Income -11.158 14.772 -0.228 -2.570 -3.975 1.171 (26.461) (25.711) (25.821) (26.429) (25.541) (25.320) Intelligence measures Gf -182.170*** -132.503** -133.109** -150.676*** -168.768*** (46.127) (48.012) (47.176) (43.868) (42.310) Gc -82.096* -61.676 (40.232) (40.252) Healthcare score -90.546*** -76.430** -81.168** -71.331* (27.372) (29.328) (28.069) (27.917) Economic phenotype Discount factor -97.163* -119.265** (48.581) (37.859) Present bias -42.556 (31.258) Loss Aversion -5.016 (33.897) Distortion of probability -12.025 (28.611) Curvature of value function -10.352 (33.923) DOSPERT Health and Safety score 13.138 (27.224) Psychological variables Intellect -1.287 (3.538) Emotional Stability 1.627 (2.977) Extraversion -5.228 (4.449) Agreeableness 14.626* (5.709) Conscientiousness 8.829 (5.898)

Observations 876 876 876 822 876 876 Akaike Inf. Crit. 13,611.190 13,558.630 13,541.340 12,652.830 13,527.050 13,516.300 Bayesian Inf. Crit. 13,644.610 13,601.610 13,589.100 12,718.790 13,579.580 13,583.160

Note: *** p < 0.001; ** p < 0.01; * p < 0.05 ; † p < 0.1

Results for credit score components

33

Aside from FICO scores we also obtained other decomposed measures on participants’

financial records. An exploratory factor analysis revealed three factors on a subset on these

variables that made intuitive sense as well. The factors and the variables they included are

depicted in Table 6 below.

Table 6: Variables for other credit factors along with fit measures and factor loading for each variable Bad Credit Good Credit Mortgage Credit

(SRMR = 0.016, CFI = 0.994) (SRMR = 0.032, CFI = 0.964) (SRMR < 0.001, CFI > 0.999)

• Number of all accounts 60 days past due or worse

0.720 • Balance in all current accounts

0.969 • Number of first mortgages

0.944

• Number of accounts that were ever 60 days past due

0.900 • Credit limit for all accounts

0.840 • Current balance on first mortgages

1.000

• Number of installment accounts that were ever 60 days past due

0.701 • Current balance in non-mortgage accounts

0.873 • Original balance on all first mortgages

1.000

• Past Due amount on all accounts

0.783 • Number of all accounts that were not 30 days past due in the last 6 months

0.798 • Current balance on all installment accounts

0.695

We regressed factor scores from these new variables on the same set of predictors used

for FICO. The role of the complementary competencies were not as clear in this case but the role

of pertinent knowledge on decisions was. Specifically, people who scored higher in our financial

literacy measure (domain specific Gc) had higher good and mortgage scores and lower bad credit

scores but general intelligence measures did not have an effect.

Table 7

Results of linear regressions using three credit factors as dependent variable

Dependent variable: Bad credit Mortgage credit Good credit

(1) (2) (3) Intercept -0.007 -0.059 0.017

34

(0.041) (0.053) (0.044) Age -0.004 0.013*** 0.003 (0.002) (0.003) (0.003) Gender 0.032 0.063 -0.103 (0.071) (0.092) (0.076) Education -0.015 0.023 0.056** (0.018) (0.023) (0.019) Income -0.042 0.255*** 0.147*** (0.036) (0.047) (0.039) Gf -0.185** 0.024 0.051 (0.071) (0.092) (0.076) Gc 0.044 0.019 0.095 (0.057) (0.075) (0.062)

Observations 434 436 417 R2 0.033 0.157 0.135 Adjusted R2 0.019 0.145 0.122

Note: *** p < 0.001; ** p < 0.01; * p < 0.05 ; † p < 0.1

Table 8

Results of linear regressions using three credit factors as dependent variable

Dependent variable: Bad credit Mortgage credit Good credit

(1) (2) (3) Intercept -0.010 -0.050 0.027

(0.040) (0.052) (0.044) Age 0.001 0.009** 0.002 (0.002) (0.003) (0.003) Gender 0.060 0.003 -0.155* (0.070) (0.091) (0.077) Education 0.001 0.010 0.056** (0.017) (0.022) (0.019) Income -0.031 0.243*** 0.141*** (0.036) (0.046) (0.039) Gf -0.071 -0.082 0.044 (0.066) (0.085) (0.071) Financial Literacy -0.220** 0.310*** 0.180* (0.073) (0.093) (0.079)

Observations 434 436 417 R2 0.052 0.178 0.140 Adjusted R2 0.038 0.166 0.128

Note: *** p < 0.001; ** p < 0.01; * p < 0.05 ; † p < 0.1

Controlling for self-report effects on the income measure

35

Because our income variable was self-reported by our participants and it did not capture

other potential assets that factors in to one’s total net worth (wealth) we tried multiple different

measures to correct for potential self-report effects.

First we used 2012 Census data on zip code level. The zip code level census income data,

however, still did not address the potential discrepancy between income and net worth. To

address this issue we used two other measures: ESRI’s zip code level net worth data and

Zillow.com’s zip code level house value index. Though we had mostly one data point for all the

zip codes in our sample all three measures correlated significantly with our self-reported income

measure (albeit low – coefficients ranged from 0.13 to 0.26).

When we replaced the income variable in our two main regressions demonstrating

complementary competencies (models 2 and 3) with these alternative measures there were no

significance changes for any of the other variables. The main difference was an improvement in

the model fit: Models including census income data consistently had better models fits, followed

by the Zillow House Value Index, the self-reported income measure and ESRI’s net worth data.