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Cognitive Abilities and Household Financial Decision Making

Cognitive Abilities and Household Financial Decision Making. Sumit Agarwal Federal Reserve Bank of Chicago Bhashkar Mazumder Federal Reserve Bank of Chicago. Background. Emerging literature documenting suboptimal financial decision-making by households

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Cognitive Abilities and Household Financial Decision Making

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  1. Cognitive Abilities and Household Financial Decision Making SumitAgarwal Federal Reserve Bank of Chicago BhashkarMazumder Federal Reserve Bank of Chicago

  2. Background • Emerging literature documenting suboptimal financial decision-making by households • Payday loans: Agarwal et al. (2009a), Bertrand and Morse (2009) • Credit Cards: Agarwal et al. (2006) • Financial Markets: Korniotis and Kumar (2009a, 2009b) • Costs to individuals and society may be very large • Financial crisis had its roots in housing and mortgage defaults, and its plausible that poor financial decision making played a role. Crisis has focused attention on the societal costs of poor consumer decisions. • A wide range of potential effects on individual welfare: • Human Capital Investment in Children • Retirement • Consumer Debt, Bankruptcy, Foreclosure

  3. Background • Financial decision-making requires ability to process and retain information, computational ability • Growing literature linking cognitive abilities to various aspects of financial behavior. (e.g. Frederick, 2005; Benjamin et al, 2006; Stango and Zinman, 2009; Grinblatt et al, 2009, McArdle et al. 2009, Gerardi et al, 2010) • Literature focuses on: • 1) aspects of information access or processing • 2) various forms of subjective biases

  4. Related literature • “Household finance” (Campbell 2006) • “Shrouded Attributes” (Gabaix and Laibson 2006) • Personal finance: Bernatzi and Thaler (2002,7) • Korniotis and Kumar (2006): older adults manage their stock portfolio less well. Also, Zinman (2006) • Lusardi and Mitchell (2006,7): decline in knowledge of basic financial concepts • Differences in rationality / IQ across people: Frederick (2005), Benjamin, Brown and Shapiro (2006), Massoud, Saunders and Sholnick (2006) • Credit cards: Agarwal, Chomsisengphet, Liu and Souleles (2007, 2009) • Einav, Jenkins, and Levin (2009, 2010) • Mortgages: Agarwal, Driscoll, Laibson (2007) • Consumer Credit: Agarwal, Driscoll, Gabaix, and Laibson (2009, 2010)

  5. Our Contributions • We directly link measures of cognitive ability to real-world examples of suboptimal behavior. • We use two very tightly defined examples of suboptimal behavior: • Balance Transfer Mistakes on Credit Cards • Home Price Estimation Mistakes on Home Equity Loans • Use ASVAB test scores from US Military • Extensively used and well validated

  6. Preview of Results • We find that a 1 s.d. increase in cognitive ability is associated with: • A 24 pp. increase in implementing the optimal strategy after making a credit card balance transfer. • An 11 pp. reduction in mistakes on home equity applications • Virtually all of the effect operates through math ability and almost none through verbal ability. • Few other demographic or financial controls matter. • We show a similar pattern of results when using a measure of “patience” in NLSY. • We find math abilities matter for financial outcomes and non-math abilities matter for non-financial outcomes using HRS

  7. Balance Transfer Mistakes • Customers are offered an opportunity to transfer existing balances from a previous credit card (card A) to a new card (card B), with a low teaser rate.

  8. We apply your payments to balances with lower APRs first

  9. Balance Transfer Mistakes • The Catch: It is optimal to make new purchases only on card A (and NOT on card B), if: • You have transferred the entire balance from Card A • You only make “convenience” transactions going forward(i.e. you pay off the full amount of new purchases within the grace period) • Under these conditions there are no interest charges with purchases made on card A but there are interest charges on purchases made with card B

  10. Balance Transfer Mistakes • A third of customers recognize this immediately, about a third learn this within 6 months. • We call this a “Eureka moment” (Agarwal et al 2009) • Construct a dependent variable, eureka = 0 if you never learn the optimal behavior, eureka = 1 if you learn within 6 months. • We also run models on: • learning this immediately • the time it takes to reach the Eureka moment.

  11. Home Price Estimation • On home equity loans and lines of credit applications you are asked to estimate the value of your home.

  12. What is your current property value?

  13. Home Price Estimation • This is used to calculate an estimated loan to value ratio (LTV). e.g. LTV =.80 for a loan of $80K on a home value estimated at $100K. • The bank independently estimates the value of your home. You are penalized for “mis-estimating” your home value if it puts you in a different LTV bin.Example 1: your estimate is $120K, the bank estimate is $100K. The bank LTV is 0.80 but your estimate is 0.67 => The bank goes off the schedule and charges you an even higher APR than the 0.80 Example 2: your estimate is $80,000, the bank estimate is $100K. The bank LTV is 0.80 but your estimate is 1 => The bank charges you based on your estimated LTV of 1.

  14. Home Price Estimation • We construct an outcome called “Rate Changing Mistake” or RCM if you are penalized for mis-estimation. • Approximately 13 percent have an RCM.

  15. Data We combine data from three large samples: • All 1993 Active Duty Military Personnel • Demographics and Armed Services and Vocational Aptitude Battery (ASVAB) scores • Start with 1.4 million individuals • Credit Card data from a large retail financial institution • 14,000 given balance transfer offers • Home Equity Loan/Lines data from a large retail financial institution • 1.4 million contracts

  16. Data Merge Military Data to Each Financial Dataset to form 2 subsamples: • Credit Card Sample • Individuals with non-missing and valid scores and who fit “eureka” requirements (e.g. make only convenience purchases during a 6 month window) • N=540 • Home Equity Borrower Sample • Individuals with non-missing and valid scores and non missing borrower data • N = 1393

  17. ASVAB Scores • Used by military as screening tool and to assess trainability and assign occupations • Widely used and extensively validatede.g. National Academy of Sciences study (Wigdor and Green, 1991) • Predicts many economic outcomes e.g. schooling, college enrollment, wages, risky/illegal behavior, financial market participation, discount rates • Contains composite score “AFQT”, and 10 subtests(numerical oper., word knowledge, arith. reasoning, math knowledge, electronics info., mechanical comp., general science, paragraph comp., coding speed and automotive and shop) • We convert into standard deviation units

  18. Credit Card Sample, Table A1, Panel A

  19. Credit Card Sample, Table A1, Panel B

  20. Home Equity Sample, Table A2, Panel A

  21. Home Equity Sample, Table A2, Panel B

  22. Credit Card Results Summary • Large and consistent effect of composite AFQT score, 1 s.d. increase associated with a 24% increase in eureka moments • Only math components matter! Verbal has zero effect suggesting limited scope for omitted variable bias. • Of financial controls, only “behavior” score matters. This is expected since it captures usage and payment behavior • Of demographic controls, black is insignificant. Those married (during military service) less likely to optimize behavior. Effect not significant when we enter subtests separately. • Two thirds of effects is due to immediate adoption (0.18). Monotonic decline in AFQT scores by months to adoption.

  23. Home Equity Results Summary • A one s.d. increase in AFQT associated with a 10% decrease in rate changing mistakes (RCM). About 70% effect size. • Math components are dominant . Here “word knowledge” matters but much less than math.. • RCMs are more common in loans than lines of credit. Higher APRs and debt/income ratios also associated with RCMs. • Blacks are less likely to have RCMS conditional on AFQT, women slightly more likely.

  24. Robustness Checks • Effects are unchanged when we include the other 6 subtests of the ASVAB (Table 3) • Coding speed, a test of numerical pattern recognition affects balance transfer mistakes but not RCM. • Numerical operations matter for RCM but not “Eureka” • Results are unaffected if sample is limited to whites or use state or zipcode fixed effects

  25. Is Cognitive Ability Related to Patience? • One possible mechanism is through greater patience • Warner and Pleater (AER, 2001) found higher AFQT associated with lower discount ratesThey exploit a real-world experiment where US military personnel were offered either a lump-sum payment at separation or an annuity • We explore using NLSY survey question • NLSY79 is a nationally representative sample tracking 14-21 year olds in 1979. Was used to norm the AFQT. Can use family fixed effects. • In 2006 respondents asked how much they’d need to be compensated in a month in order to forego a $1000 prize today.“Suppose you have won a prize of $1000, which you can claim immediately. However, you can choose to wait one month to claim the prize. If you do wait, you will receive more than $1000. What is the smallest amount of money in addition to the $1000 you would have to receive one month from now to convince you to wait rather than claim the prize now?

  26. NLSY Patience Results Summary • Results imply that a 1 s.d. increase in AFQT is associated with a 4 to 5 percent decline in the one month discount rate • Effect of education is much smaller – 4 years of schooling reduces discount rate by only 0.8 percent. • Effect mostly due to arithmetic reasoning. • Results are robust to controls for earnings, family background, and family fixed effects. • Reassuring that a similar pattern of results to our credit card/home equity results are obtained with a nationally representative sample

  27. What About Non-Financial Outcomes? • What if math scores simply capture unobservable characteristics associated with sub-optimal behavior even outside of financial decision making? • We use data from the Health and Retirement Survey (HRS) to estimate the effects of mathematical and non-mathematical aspects of cognitive ability on other forms of suboptimal behavior. • Data on wealth, income, and many other demographics on 50+ year olds • Cognitive ability measures include word recall, mental status, number series, retrieval fluency and numeracy. • Other measures of suboptimal behavior include difficulties with: reading a map; buying groceries; taking medication; making a phone call; preparing a meal and managing money.

  28. What About Non-Financial Outcomes? • We follow McArdle et al (2009) who use these cognitive measures to estimate effects on wealth, financial wealth and stock market participation • We first replicate their results and then compare the results to those using non-financial outcomes

  29. Summary of Results from HRS • Each additional math question (out of 3) answered correctly is associated with an increase in total wealth of $100,000 and financial wealth of $66,000. • For outcomes that require the least amount of mathematical aptitude, numeracy is never statistically significant and in some cases the point estimates are 0 or negative. • We do find that numeracy has a huge effect on “managing money”. • Numeracy also has a significant effect on reading a map, which arguably requires some quantitative skills.

  30. Concluding Thoughts • We make an important empirical contribution by directly linking cognitive ability to very tightly defined examples of suboptimal financial behavior. • Our effect sizes are very large but one may question their larger policy significance • We deliberately chose outcomes that are narrowly defined to make stronger causal claims • We think our results are just the tip of the iceberg. Effects are likely on a range of outcomes like defaults, foreclosures and bankruptcy • Gerardi et al (2010) link cognitive ability to foreclosures. Our HRS results find large effects on wealth

  31. Concluding Thoughts • Results are highly relevant for efforts to improve financial literacy • ASVAB scores are not measuring just “innate ability” • Growing evidence that military test scores are be influenced by other factors --education, experience, early life health, school quality.(Neal and Johnson 1996; Hansen, Heckman and Mullen, 2004; Cascio and Lewis 2006; Chay, Guryan and Mazumder, 2009; Aaronson and Mazumder, 2010)

  32. (1,2) Home Equity Loans and Home Equity Credit Lines • Proprietary data from large financial institutions • 75,000 contracts for home equity loans and lines of credit, from March-December 2002 • We observe: • Contract terms: APR and loan amount • Borrower demographic information: age, employment status, years on the job, home tenure, home state location • Borrower financial information: income, debt-to-income ratio • Borrower risk characteristics: FICO (credit) score, loan-to-value (LTV) ratio

  33. Home Equity Regressions • We regress APRs for home equity loans and credit lines on: • Risk controls: FICO score and Loan to Value (LTV) • Financial controls: Income and debt-to-income ratio • Demographic controls: state dummies, home tenure, employment status • Age spline: piecewise linear function of borrower age with knots at age 30, 40, 50, 60 and 70. • Next slide plots fitted values on age splines

  34. What is the Channel for the Age Effect? • Banks offer different APRs when the loan-to-value (LTV) ratio is: • less than 80 percent • between 80 and 90 percent • over 90 percent • Borrowers estimate their LTV by estimating their house value • Banks form their own LTV estimates • “Rate-Changing Mistake”: when borrower and bank LTVs straddle two of these categories • for example, borrower LTV<80, bank LTV >90.

  35. Rate Changing Mistakes generate two sources of disadvantage for the customer: • If I underestimate my LTV (Loan-to-Value ratio), the bank can penalize me by deviating from its normal offer sheet. • If I overestimate my LTV (i.e., underestimate the value of my house), the bank will penalize me by not correcting my mistake and allowing me to borrow at too high a rate.

  36. A Rate-Changing Mistake costs 125 to 150 basis points. • Next slides plot: • Probability of making a Rate-Changing Mistake by age • APRs for borrowers who do NOT make a Rate-Changing Mistake

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