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.
2
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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WORKS CITED 1. G. K. Vincent, V. A. Velkoff, in Current Population Reports U. S. C. Bureau, Ed.
(Washington, DC, 2006). 2. F. Modigliani, Life cycle, individual thrift, and the wealth of nations. The
American Economic Review, 297 (1986). 3. T. A. Salthouse, Major Issues in Cognitive Aging. (Oxford University Press, New
York, 2010). 4. K. W. Schaie, The Seattle Longitudinal Studies of Adult Intelligence. Current
Directions in Psychological Science 2, 171 (1993). 5. S. Agarwal, J. C. Driscoll, X. Gabaix, D. Laibson, The age of reason: financial
decisions over the life cycle and implications for regulation. Brookings Papers on Economic Activity 39, 51 (2009).
6. S. Agarwal, B. Mazumder, Cognitive Abilities and Household Financial Decision Making. American Economic Journal: Applied Economics 5, 193 (2013).
7. S. V. Burks, J. P. Carpenter, L. Goette, A. Rustichini, Cognitive skills affect economic preferences, strategic behavior, and job attachment. Proceedings of the National Academy of Sciences, (April 24, 2009, 2009).
8. A. Mani, S. Mullainathan, E. Shafir, J. Zhao, Poverty Impedes Cognitive Function. Science 341, 976 (August 30, 2013).
9. J. J. McArdle, J. P. Smith, R. Willis, in Explorations in the Economics of Aging, D. A. Wise, Ed. (University of Chicago Press, Chicago, 2011), pp. 209-233.
10. R. B. Cattell, Intelligence: Its structure, growth, and action. (Elsevier Science Pub. Co., New York, 1987).
11. J. B. Carroll, Human cognitive abilities: A survey of factor-analytic studies. (Cambridge University Press, New York, 1993).
12. T. A. Salthouse, What and when of cognitive aging. Current Directions in Psychological Science 13, 140 (2004).
13. J. L. Horn, R. B. Cattell, Age differences in fluid and crystallized intelligence. Acta Psychologica 26, 107 (1967).
14. S.-C. Li et al., Transformations in the Couplings Among Intellectual Abilities and Constituent Cognitive Processes Across the Life Span. Psychological Science 15, 155 (March 1, 2004, 2004).
15. E. J. Johnson, J. E. Russo, Product familiarity and learning new information. Journal of Consumer Research 11, 542 (1984).
16. J. W. Alba, J. W. Hutchinson, Dimensions of consumer expertise. Journal of Consumer Research 13, 411 (1987).
17. Y. Li, M. Baldassi, E. J. Johnson, E. U. Weber, Complementary cognitive capabilities, economic decision making, and aging. Psychology and Aging 28, 595 (2013).
18. D. Fernandes, J. G. Lynch, R. G. Netemeyer, Financial Literacy, Financial Education and Downstream Financial Behaviors. Management Science Forthcoming, (2013).
19. L. J. Mester, What’s the point of credit scoring? Business review 3, 3 (1997).
13
20. A. Lusardi, O. S. Mitchell, Baby Boomer retirement security: The roles of planning, financial literacy, and housing wealth. Journal of Monetary Economics 54, 205 (2007).
21. M. A. Hilgert, J. M. Hogarth, S. G. Beverly, Household financial management: The connection between knowledge and behavior. Federal Reserve Bulletin, 309 (2003).
22. M. Van Rooij, A. Lusardi, R. Alessie, Financial literacy and stock market participation. Journal of Financial Economics, (2011).
23. J. Hastings, L. Tejeda-Ashton, Financial Literacy, Information and Demand Elasticity: Survey and Experimental Evidence from Mexico. NBER Working Paper No. 14538. (2008).
24. T. Dohmen, A. Falk, D. Huffman, U. Sunde, Are Risk Aversion and Impatience Related to Cognitive Ability? American Economic Review 100, 1238 (2010).
25. J. Strough, C. M. Mehta, J. P. McFall, K. L. Schuller, Are older adults less subject to the sunk-cost fallacy than younger adults? Psychological Science 19, 650 (2008).
26. D. Read, N. L. Read, Time discounting over the lifespan. Organizational Behavior and Human Decision Processes 94, 22 (2004).
27. O. Toubia, E. Johnson, T. Evgeniou, P. Delquié, Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters. Management Science 59, 613 (2013).
28. S. Meier, C. D. Sprenger, Time discounting predicts creditworthiness. Psychological Science 23, 56 (2012).
29. E. U. Weber, A.-R. Blais, N. E. Betz, A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making 15, 263 (2002).
30. J. M. Digman, Personality structure: Emergence of the five-factor model. Annual review of psychology 41, 417 (1990).
31. A. Lusardi, O. S. Mitchell, The Economic Importance of Financial Literacy: Theory and Evidence. Journal of Economic Literature 52, 5 (2014).
32. T. A. Salthouse, J. E. Pink, E. M. Tucker-Drop, Contextual analysis of fluid intelligence. Intelligence 36, 464 (2008).
33. S. Frederick, Cognitive reflection and decision making. Journal of Economic Perspectives 19, 25 (2005).
34. S. Frederick, G. Loewenstein, T. O'Donoghue, Time Discounting and Time Preference: A Critical Review. Journal of Economic Literature 40, 351 (2002).
35. I. M. Lipkus, G. Samsa, B. K. Rimer, General Performance on a Numeracy Scale among Highly Educated Samples. Medical Decision Making 21, 37 (2001).
36. S. J. Czaja et al., “CREATE common core battery of measures: Technical report No. CREATE-2006-01” (Center for Research and Education on Aging and Technology Enhancement (CREATE), Atlanta, GA, 2006).
37. S. J. Czaja et al., Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and Aging 21, 333 (2006).
38. T. A. Salthouse, Speed mediation of adult age differences in cognition Developmental Psychology 29, 722 (1993).
14
39. D. Wechsler, Wechsler Adult Intelligence Scale III (3rd ed.). (The Psychological Corporation, San Antonio, TX, 1997).
40. A. Lusardi, O. S. Mitchell. (Wharton School, University of Pennsylvania., 2006). 41. A. Lusardi, O. S. Mitchell, How Ordinary People Make Complex Economics
Decisions: Financial Literacy and Retirement Readiness. NBER Working Paper 15350, (2009).
42. A. Delavande, S. Rohwedder, R. Willis. (Lisbon, Portugal, 2008), pp. 1-50. 43. A. Lusardi, P. Tufano. (2009). 44. A. Lusardi, O. S. Mitchell, Financial literacy around the world: An overview.
Journal of Pension Economics and Finance 10, 497 (2011). 45. T. Odean, Are investors reluctant to realize their losses? The Journal of Finance
53, 1775 (1998). 46. D. Genesove, C. Mayer, Loss Aversion and Seller Behavior: Evidence from the
Housing Market. Quarterly Journal of Economics 116, 1233 (2001). 47. M. Daly, L. Delaney, S. McManus. (Geary Institute, University College Dublin,
Working Papers: 201049, 2010), pp. 24 pages. 48. Ł. Markiewicz, E. U. Weber, DOSPERT's Gambling Risk-Taking Propensity
Scale Predicts Excessive Stock Trading. Journal of Behavioral Finance 14, 65 (2013).
49. S. Meier, C. D. Sprenger, Present-Biased Preferences and Credit Card Borrowing. American Economic Journal: Applied Economics 2, 193 (2010).
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***
22
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.
25
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.