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Dopamine receptor genes predict risk preferences, time preferences, and related economic choices

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Abstract

Outside of economics, researchers have recently identified genetic predictors of “sensation-seeking” that have been linked to risky and impulsive behaviors. We examine the implications of these genetic polymorphisms for economic behavior. Our analysis indicates that the 7-repeat allele of the DRD4 gene that regulates dopamine uptake in the brain predicts risk-taking and time preferences in economic experiments that allow for ambiguity, losses and discounting. These genetic polymorphisms can also be used to directly predict financial choice patterns that are consistent with previous findings in the behavioral genetics literature.

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Notes

  1. Which has some history within economics (e.g., Taubman 1976).

  2. The instructions for the experiment appear in the methods Appendix.

  3. The experiment and survey were computerized and coded in zTree (Fischbacher 2007).

  4. Following Holt and Laury (2002), we can use the constant relative risk aversion utility function, U(x) = (x 1 − r)/(1 − r) to evaluate the specific risk attitudes at which people should be indifferent between any two neighboring lotteries. Picking $33∣$33 indicates extreme risk aversion, r > 1.77. Picking $25∣$47 indicates 0.82 ≤ r ≤ 1.77, $18∣$62 indicates 0.48 ≤ r ≤ 0.82, $11∣$77 indicates 0.28 ≤ r ≤ 0.48, $4∣$91 indicates 0 ≤ r ≤ 0.28, and picking $0∣$92 indicates r < 0 or risk seeking behavior.

  5. The exchange rate for the experiment was one lab dollar equaled ten cents.

  6. To prevent any confounding effects of trusting that one would be paid in the future, we implemented the shortest possible front end delay in the first block (Coller and Williams 1999) and to equalize the transactions costs across the two blocks, we paid everyone using the postal service. Middlebury is small enough that local letters posted on one day are delivered on the next.

  7. All our procedures were approved by the appropriate institutional review boards.

  8. Listed p-values are the result of two-tailed t-tests.

  9. We continue to work with the number of patient choices for two reasons. First, because our participants were so consistent, there is little to be gained from maximum likelihood methods. Second, like others we interpret our data conservatively in that, while we think of our procedures as eliciting good proxies of time preferences, it is not clear that we capture inherent discount rates, per se. This second point also explains why we number our lotteries instead of using the implied coefficients of relative risk aversion in our analysis.

  10. The details of the genotype amplification procedures are in the methods Appendix.

  11. Given the results of Burks et al. (2009) who show that higher IQ is correlated with being closer to risk neutral and more patient, one might worry that our regressions have omitted important controls like cognitive ability or perhaps “optimism” which might affect one’s subjective assessments of the odds of the gambles. However, there is no evidence that DRD4 is correlated with intelligence (Kebir et al. 2009) and optimism is much more likely to correlate with serotonin than dopamine (Fox et al. 2009).

  12. To follow up on an excellent comment made by our referee, we also stacked the 20 time preference choices for each participant and ran another set of regressions with individual random effects. One of the major advantages of doing this was that we were able to get a precise estimate of how much less willing 7-repeats are to wait when the front-end delay expands to a month. We find that anti-hyperbolic 7-repeats are 5% less likely to wait (p = 0.01) and quasi-hyperbolic 4-repeats are 8% more likely to wait (p < 0.01).

  13. However, not all the survey questions yielded interesting results: the size, in dollars, of one’s ideal financial reserve, for example, was not predicted by genotype.

  14. The exact wording of each question is presented in the methods Appendix.

  15. A promising recent direction is offered by Caplin and Dean (2008).

  16. The notion of one’s “just noticeable difference” has a long tradition in psychology and physiology. Imagine two bags filled with the same number of marbles. If you were asked to hold both, your JND is the number of marbles that one could secretly add to just one of the bags before you noticed a difference.

  17. This is also a common starting point for many theories of ambiguous choice (e.g., Gilboa and Schmeidler 1989).

  18. For example, one might consider a model in which utility is simply less concave for 7-repeats. While this would make some of the same predictions concerning risk, without auxiliary assumptions, it makes the opposite predictions concerning discounting. Most importantly, however, we feel that our formalization is consistent with the dopamine muting and the just noticeable differences literatures.

References

  • Alcaro, A., Huber, R., & Panksepp, J. (2007). Behavioral functions of the mesolimbic dopaminergic system: And affective neuroethological perspective. Brain Research Review, 56(2), 283–321.

    Article  Google Scholar 

  • Aleskerov, F., Bouyssou, D., & Monjardet, B. (2007). Utility maximization, choice and preference. Springer: Berlin.

    Google Scholar 

  • Anderson, S., Harrison, G., Lau, M., & Rutstrom, E. (2008). Lost in state space: Are preferences stable? International Economic Review, 49(3), 1091–1112.

    Article  Google Scholar 

  • Angeletos, G. M., Laibson, D., Repetto, A., Tobacman, J., & Weinberg, S. (2001). The hyperbolic consumption model: Calibration, simulation and empirical evaluation. Journal of Economic Perspectives, 13(3), 47–68.

    Article  Google Scholar 

  • Asghari, V., Sanyal, S., Buchwaldt, S., Paterson, A., Jovanovic, V., et al. (1995). Modulation of intracellular cyclic AMP levels by different human dopamine D4 receptor variants. Journal of Neurochemistry, 65(3), 1157–1165.

    Article  Google Scholar 

  • Barsky, R., Juster, F. T., Kimball, M., & Shapiro, M. (1997). Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study. Quarterly Journal of Economics, 112, 537–579.

    Article  Google Scholar 

  • Binswanger, H. (1980). Attitudes toward risk: Experimental measurement in rural india. American Journal of Agricultural Economics, 62, 395–407.

    Article  Google Scholar 

  • Binswanger, H. (1981). Attitudes towards risk: Theoretical implications of an experiment in rural india. Economic Journal, 91, 867–890.

    Article  Google Scholar 

  • Bouchard, T., & Propping, P. (1993). Twins as a tool of behavioral genetics: Report of the Dahlme workshop on “What are the mechanisms mediating the genetic and environmental determinants of behavior”? John Wiley: Chichester.

    Google Scholar 

  • Burks, S., Carpenter, J., Goette, L., & Rustichini, A. (2009). Cognitive skills affect economic preferences, strategic behavior, and job attachment. Proceedings of the National Academy of Sciences, 106(19), 7745–7750.

    Article  Google Scholar 

  • Caplin, A., & Dean, M. (2008). Dopamine, reward prediction error, and economics. Quarterly Journal of Economics, 123(2), 663–701.

    Article  Google Scholar 

  • Cardenas, J. C., & Carpenter, J. (2009). Risk attitudes and well-being in Latin America. Working paper, Department of Economics, Middlebury College.

  • Cesarini, D., Dawes, C., Fowler, J., Johannesson, M., & Lichtenstein, P. (2008). Heritability of cooperative behavior in the trust game. Proceedings of the National Academy of Science, 105(10), 3721–3726.

    Article  Google Scholar 

  • Cesarini, D., Dawes, C., Johannesson, M., Lichtenstein, P., & Wallace, B. (2009). Genetic variation in preferences of giving and risk-taking. Quarterly Journal of Economics, 124(2), 809–842.

    Article  Google Scholar 

  • Chen, C., Burton, M., Greenberge, E., & Dmitrieva, J. (1999). Population migration and the variation of dopamine D4 receptor (DRD4) allele frequencies around the globe. Evolution and Human Behavior, 20, 309–324.

    Article  Google Scholar 

  • Cloninger, C. R., Svrakic, D., & Przybeck, T. (1993). A psychobiological model of temperament and character. Archives of General Psychiatry, 50, 975–989.

    Google Scholar 

  • Coller, M., & Williams, M. B. (1999). Eliciting individual discount rates. Experimental Economics, 2(2), 107–127.

    Google Scholar 

  • Comings, D. E., Gade-Andavolu, R., Gonzalez, N., Wu, S., Muhleman, D., et al. (2001). The additive effect of neurotransmitter genes in pathological gambling. Clinical Genetics, 60, 107–116.

    Article  Google Scholar 

  • Dickhaut, J., McCabe, K., Nagode, J., Rustichini, A., Smith, K., et al. (2003). The impact of the certainty context on the process of choice. Proceedings of the National Academy of Science, 100(6), 3536–3541.

    Article  Google Scholar 

  • Ding, W., Lehrer, S., Rosenquist, J. N., & Audrain-McGovern, J. (2009). The impact of poor health on academic performance: New evidence using genetic markers. Journal of Health Economics, 28(3), 578–597.

    Article  Google Scholar 

  • Donkers, B., Melenberg, B., & Van Soest, A. (2001). Estimating risk attitudes using lotteries: A large sample approach. Journal of Risk and Uncertainty, 22(2), 165–195

    Article  Google Scholar 

  • Dreber, A., Apicella, C., Eisenberg, D., Garcia, J., Zamore, R., et al. (2009). The 7R polymorphism in the dopamine receptor D4 gene (DRD4) is associated with financial risk taking in men. Evolution & Human Behavior, 30(2), 85–92.

    Article  Google Scholar 

  • Eckel, C., & Grossman, P. (2002). Sex differences and statistical sterotyping in attitudes towards financial risks. Evolution and Human Behavior, 23(4), 281–295.

    Article  Google Scholar 

  • Eckel, C., & Grossman, P. (2008). Forecasting risk attitudes: An experimental study of actual and forecast risk attitudes of women and men. Journal of Economic Behavior & Organization, 68(1), 1–17.

    Article  Google Scholar 

  • Eisen, S., Lin, N., Lyons, M., Scherrer, J., Griffith, K., et al. (1998). Familial influence on gambling behavior: An analysis of 3359 twin pairs. Addiction, 93(9), 1375–1384.

    Article  Google Scholar 

  • Eisenberg, D., MacKillop, J., Modi, M., Beauchemin, J., Dang, D., Lisman, S., et al. (2007a). Examining impulsivity as an endophenotype using a behavioral approach: A drd2 taql a and drd4 48-bp vntr association study. Behavioral and Brain Functions, 3(2), 1–14.

    Google Scholar 

  • Eisenberg, D., Campbell, B., MacKillop, J., Modi, M., Dang, D., et al. (2007b). Polymorphisms in the dopamine D4 and D2 receptor genes and reproductive and sexual behaviors. Evolutionary Psychology, 5(4), 696–715.

    Google Scholar 

  • Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. Quarterly Journal of Economics, 75(4), 643–669.

    Article  Google Scholar 

  • Feigelson, H. Spencer, R., Carmen, R., Andrea S., Jacobs, E., Calle, E., et al. (2001). Determinants of DNA yield and quality from buccal cell samples collected with mouthwash. Cancer Epidemiology Biomarkers & Prevention, 10(9), 1005–1008.

    Google Scholar 

  • Fischbacher, U. (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171–178.

    Article  Google Scholar 

  • Fletcher, J., & Lehrer, S. (2009). Using genetic lotteries within families to examine the causal impact of poor health on academic achievement. NBER Working Paper No. 15148.

  • Fox, E., Ridgewell, A., & Ashwin, C. (2009). Looking on the bright side: Biased attention and the human serotonin transporter gene. Proceedings of the Royal Society B, 276(1663), 1747–1751.

    Article  Google Scholar 

  • Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40(2), 351–401.

    Article  Google Scholar 

  • Garbarino, E., Slonim, R., & Sydnor, J. (2011). Digit ratios (2d:4d) as predictors of risky decision making for both sexes. Journal of Risk and Uncertainty, 42(1), 1–26.

    Article  Google Scholar 

  • Gilboa, I., & Schmeidler, D. (1989). Maxmin expected utility with non-unique prior. Journal of Mathematical Economics, 18, 141–153.

    Article  Google Scholar 

  • Harrison, G., & Rutström, E. (2008). Risk aversion in the laboratory. In J. C. Cox, & G. Harrison (Eds.), Research in experimental economics (Vol. 12, pp. 41–196). Bingley: Emerald Group Publishing Limited.

    Google Scholar 

  • Harrison, G., Lau, M., & Williams, M. (2002). Estimating individual discount rates in Denmark: A field experiment. American Economic Review, 92(5), 1606–1617.

    Article  Google Scholar 

  • Holt, C., & Laury, S. (2002). Risk aversion and incentive effects. American Economic Review, 92(5), 1644–1655.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of choice under risk. Econometrica, 47, 263–291.

    Article  Google Scholar 

  • Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press: Cambridge.

    Google Scholar 

  • Kebir, O., Grizenko, N., Sengupta, S., & Joober, R. (2009). Verbal but not performance IQ is highly corelated to externalizing behavior in boys with adhd carrying both DRD4 and DAT1 risk genotypes. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 33, 939–944.

    Article  Google Scholar 

  • Kobayashi, S., & Schultz, W. (2008). Influence of reward delays on responses of dopamine neurons. Journal of Neuroscience, 28(31), 7837–7846.

    Article  Google Scholar 

  • Krupka, E., & Stephens, M. (2009). The stability of elicited discount rates over time. Working Paper, Department of Economics, University of Michigan.

  • Kuhnen, C., & Chiao, J. (2009). Genetic determinants of financial risk taking. PLoS ONE, 4(2), e4362.

    Article  Google Scholar 

  • Leland, J. (1994). Generalized similarity judgments: An alternative explanation for choice anomalies. Journal of Risk and Uncertainty, 9(2), 151–172.

    Article  Google Scholar 

  • Leland, J. (2002). Similarity judgement and anomalies in intertemporal choice. Economic Inquiry, 40(4), 574–581.

    Article  Google Scholar 

  • Lewald, J., Schirm, S., & Schwarz, M. (2004). Sound lateralization in Parkinson’s disease. Cognitive Brain Research, 21(3), 335–341.

    Article  Google Scholar 

  • McClure, S., Laibson, D., Loewenstein, G., & Cohen, J. (2004). Separate neural systems value immediate and delayed monetary rewards. Science , 306, 503–507.

    Article  Google Scholar 

  • Ng, Y. K. (1975). Bentham or bergson? Finite sensibility, utility functions and social welfare functions. Review of Economic Studies, 42(4), 545–569.

    Article  Google Scholar 

  • Noble, E., Ozkaragoz, T., Ritchie, T., Zhang, X., Belin, T., et al. (1998). D2 and D4 dopamine receptor polymorphisms and personality. American Journal of Medical Genetics, 81(3), 257–267.

    Article  Google Scholar 

  • Perez de Castro, I., Ibanez, A., Torres, P., Saiz-Ruiz, J., & Fernandez-Piqueras, J. (1997). Genetic association study between pathological gambling and a functional DNA polymorphism at the D4 receptor gene. Pharmacogentics, 7(5), 345–348.

    Google Scholar 

  • Plomin, R., DeFries, J., McClearn, G., & McGuffin, P. (2009). Behavioral genetics. Worth: New York.

    Google Scholar 

  • Rubinstein, A. (1988). Similarity and decision-making under risk (is there a utility theory resolution to the Allais paradox?). Journal of Economic Theory, 46(1), 145–153.

    Article  Google Scholar 

  • Sahm, C. (2007). How much does risk tolerance change? Federal Reserve Board, Finance and Economics Discussion Series 2007-66, Washington D.C.

  • Sayman, S., & Öncüler, A. (2009). An investigation of time inconsistency. Management Science, 55(3), 470–482.

    Article  Google Scholar 

  • Schultz, W. (1999). The reward signal of midbrain dopamine neurons. News in Physiological Sciences, 14(6), 249–255.

    Google Scholar 

  • Stigler, G., & Becker, G. (1977). De gustibus non est disputandum. American Economic Review, 67(2), 76–90.

    Google Scholar 

  • Stoel, R., De Geus, E., & Boomsma, D. (2006). Genetic analysis of sensation seeking with an extended twin design. Behavioral Genetics, 36(2), 229–237.

    Article  Google Scholar 

  • Swanson, J., Kinsbourne, M., Nigg, J., Lanphear, B., Stefanatos, G., et al. (2007). Etiologic subtypes of attention-deficit/hyperactivity disorder: Brain imaging, molecular genetic and environmental factors and the dopamine hypothesis. Neuropsychologial Review, 17, 39–59.

    Article  Google Scholar 

  • Taubman, P. (1976). The determinants of earnings: Genetics, family, and other environments: A study of white male twins. American Economic Review, 66, 858–870.

    Google Scholar 

  • Wallace, B., Cesarini, D., Lichtenstein, P., & Johannesson, M. (2007). Heritability of ultimatum game responder behavior. Proceedings of the National Academy of Science, 104(40), 15631–15634.

    Article  Google Scholar 

  • Zhong, S., Chew, S. H., Set, E., Zhang Junsen, X., Hong, S., Pak Ebstein, R., & Israel, S. (2009). The heritability of attitude toward economic risk. Twin Research and Human Genetics, 12(1), 103–107.

    Article  Google Scholar 

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Acknowledgements

We thank Middlebury College and Binghamton University for funding parts of this research. We also thank the editor, an anonymous referee, Lorenz Götte, Douglas Krupka, Erin Krupka, Caitlin Myers and Corinna Noelke for constructive comments on earlier drafts of this paper. Valuable laboratory assistance was provided by Rita Spathis. Carpenter and Garcia were involved in all aspects of this project; Lum participated in formulating the research question, editing the manuscript, and by overseeing the genetics laboratory.

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Authors and Affiliations

  1. Department of Economics, Middlebury College & IZA, 601 Warner Hall, Middlebury, VT, 05753, USA

    Jeffrey P. Carpenter

  2. Laboratory of Evolutionary Anthropology and Health, Departments of Anthropology and Biological Sciences, Binghamton University, Binghamton, NY, 13902, USA

    Justin R. Garcia & J. Koji Lum

Authors
  1. Jeffrey P. Carpenter
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  2. Justin R. Garcia
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Corresponding author

Correspondence to Jeffrey P. Carpenter.

Appendix A Genotyping, experiment instructions and survey questions

Appendix A Genotyping, experiment instructions and survey questions

1.1 A.1 The gene amplification procedures

The human DRD4 gene on chromosome 11 contains a 48bp variable number tandem repeat (VNTR) polymorphism in exon 3. Problems associated with consistent genotyping of the DRD4 VNTR region suggested the need for multiple PCR and electrophoresis runs for each sample to control for allelic dropout. Thus, the PCR reaction was modified to reflect the high GC content (see below) and all samples that were initially scored as homozygotes were reanalyzed with different starting template concentrations to unambiguously confirm genotypes. The PCR reaction consisted of 1x Q-Solution (Qiagen), 1x Buffer (Qiagen), 1 μM Primer 1 (5’ GCGACTACGTGGTCTACTCG 3’), 1 μM Primer 2 (5’ AGGACCCTCATGGCCTTG 3’), 200 μM dATP, 200 μM dTTP, 200 μM dCTP, 100 μM dITP, 100 μM dGTP, 0.3 units HotStar Taq (Qiagen), and 1 μl of DNA template, in a total volume of 10 μl. The PCR profile began with 15 min at 95°C for enzyme activation and denaturing of template DNA followed by 40 cycles consisting of 1 min denaturation at 94°C, 1 min annealing at 55°C, 1.5 min extension at 72°C, and finished with a 10 min extension at 72°C. Amplicons were electrophoresed through 1.4–2.0% agarose gels containing ethidium bromide and genotypes were determined by comparison with a 100 bp ladder.

1.2 A.2 The experimental instructions

1.2.1 A.2.1 Overview

Today’s experiment will consist of three components. Your total payoff for the experiment will be your $5 show-up fee plus the sum of the payoffs you earn in the different components (note that the exchange rate between experimental dollars and real dollars is $E10 = $1). We expect that you will earn approximately $25, on average, and that the experiment will not last much more than an hour. Your participation is strictly confidential and once the data has been compiled any personal identifiers will be removed. Detailed instructions for each part of the experiment will appear on the computer screen at the beginning of the component.

1.2.2 A.2.2 Risk, Ambiguity and Loss

To motivate this part of the experiment, think of a bag containing ten balls. Like in billiards, each ball has a number written on it. These numbers represent dollar payoffs. To determine your payoff, you will pick among six bags that differ in the numbers written on the balls and then reach in and draw one ball from the chosen bag. In this sense, each bag represents a possible lottery for you.

You will make three decisions in which you choose one from a set of six bags/lotteries. Each of the lotteries will be represented by a circle and two numbers. The two numbers represent the two possible dollar payoffs for each bag/lottery. When you have made all three decisions, the computer will act out the lotteries that you have chosen and any proceeds from the lotteries will be added to your final payout.

Click the button below to start the first decision.

Decision one

In the first set of six bags one bag has the same number written down on both sides of the dividing line which means that if you pick this lottery all the balls have the same number written on them and you will get this amount of money for sure. The other five bags/lotteries have five high value balls and five low value balls. Remember that each ball has an equal chance of getting picked so your chances of getting the high amount or the low amount are exactly the same. Pick a bag/lottery by clicking on the appropriate button to the right.

Decision two

In the second set of six lotteries each circle has a shaded area which represents the fact that you do not know for sure the value of six of the ten balls in each bag. You know for sure that there are two high value balls and two low value balls but the other six balls can be either high or low value and you won’t know for sure the final distribution until the end of the session.

However, as in the first decision, the first bag has the same number written on both sides of the dividing line which means that if you pick this lottery all ten balls have the same number written on them and you will get this amount of money for sure.

Remember that each ball has an equal chance of being picked so your chances of getting the high amount or the low amount will depend on the final mixture of balls. Pick the bag/lottery from which you would like to have a ball drawn by clicking on the appropriate button to the right.

Decision Three

At the beginning of this decision your final payoff was increased by $50 but instead of lotteries in which your final payoff can only increase, you now must choose among six lotteries, some of which include potential losses.

In this case, the first bag has the same negative number written on both sides of the dividing line which means that if you pick this lottery all the balls have the same number written on them and you will lose this amount of money for sure. The other five bags/lotteries have five high value balls and five low value balls.

Remember that each ball has an equal chance of getting picked so your chances of getting the high amount or the low amount are exactly the same. Pick a bag/lottery by clicking on the appropriate button to the right.

1.2.3 A.2.3 Time

In this part of the experiment you will choose between two amounts of money. In each case, you will choose between a smaller amount of money and a larger amount of money. The choice seems obvious but it is not because if you choose the larger amount of money you will have to wait longer to receive it. In other words, your choice will be between a smaller amount of money which you will receive relatively soon and a larger amount of money that you will have to wait longer to receive.

The amounts of money and the amount of time you have to wait for the larger payoff change from one choice to the next so please consider each situation carefully. At the end of the experiment one of the choices that you have made will be picked randomly and you will actually be paid based on what you have chosen. This means that you will be sent (via mail) the chosen amount of money at the time you specified in your decision.

Click the button below to start making choices.

1.3 A.3 The survey

While we determine how much money we owe you, please complete a short questionnaire to be used in our analysis of the experimental data. The questionnaire should take less than 10 min to complete. All responses will be kept confidential and will not be stored with any personally identifiable information. By completing this survey, you consent to having this anonymous information used solely for purposes of academic research.

  1. (1)

    How old are you?

  2. (2)

    What is your sex?

  3. (3)

    From which group were you recruited?

    Student, Staff, Faculty, Not directly affiliated with the College

  4. (4)

    Which of these racial/ethnic groups describes you best?

    White/Caucasian, African-American, Asian-American/Asian, Latino/Hispanic, Other/Mixed

  5. (5)

    How much schooling have you had?

    less than High School, High School degree, some College, College degree, Graduate degree

  6. (6)

    What is your annual household income?

    less than $25,000, $25,001–$50,000, $50,001–$75,000, $75,001–$100,000, $100,001–$125,000, $125,001–$150,000, more than $150,000

  7. (7)

    Consider your use of credit cards, do you:

    pay the minimum each month, pay as much as I can each month, pay he total amount due each month.

  8. (8)

    Considering all the money you typically have in your checking and savings accounts, how much is in the savings account (as a percentage, between 0 and 100, of the total)?

  9. (9)

    Do you have overdraft protection on your checking account?

  10. (10)

    Do you use automatic deductions to pay your bills?

  11. (11)

    What do you think your credit rating (i.e., FICO score) is like?

  12. (12)

    If you need to, imagine that you own a car. All cars need to carry “liability” insurance to cover hitting someone but they do not need to carry “collision” insurance to cover damage to one’s own car. Do you (or would you) buy the collision insurance too?

  13. (13)

    When you go to the ATM to get cash do you tend to get about the amount of money that you think you will need for your immediate needs or do you take out more so that you always have some cash in your wallet?

  14. (14)

    When you are going on a trip, do you buy travel insurance which allows you to cancel a flight and re-book without a fee?

  15. (15)

    About how much do you think that you need to have in savings for emergencies and other unexpected things that may come up (round to the nearest dollar)?

  16. (16)

    When you make routine purchases which card are you more likely to use your credit or your debit card?

    Please wait for the experimenter to call you to pay you for your participation. Your final payoff (in dollars and including any amounts you might actually receive in the future) from the experiment is:

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Carpenter, J.P., Garcia, J.R. & Lum, J.K. Dopamine receptor genes predict risk preferences, time preferences, and related economic choices. J Risk Uncertain 42, 233–261 (2011). https://doi.org/10.1007/s11166-011-9115-3

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