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Gender differences in the stability of risk attitudes

Abstract

We investigate gender differences in the stability of risk preferences over time and across stake sizes of lottery choices. Subjects participate in a 12-week, online experiment with high- and low-stakes lottery choices. Our study incorporates self-reported measures of the states of mind of the participants – specifically sources and levels of stress and happiness, as well as financial well-being – to explain the weekly lottery choices of males and females. At the aggregate level, we do not observe a significant gender difference in average risk attitudes either within or across stakes. For both genders, risk tolerance for low-stakes lotteries increases over time; for high-stakes lotteries, only females become more risk tolerant. At the individual level, both genders exhibit a relatively high level of choice stability. For both males and females there is some evidence that risk tolerance increases across time for low-stake lotteries. We observe a gender difference in the response to stress and happiness. Males become more risk tolerant at both stake sizes when experiencing family-sourced stress. For females, we find greater risk aversion for high-stakes lotteries when experiencing university-sourced stress and relationship-sourced happiness and greater risk tolerance when feeling more financially stable. Males exhibit a decrease in the instability (variability) of lottery choices as time passes, suggesting learning or increasing comfort with riskier choices with experience. Finally, regardless of gender, roughly equal numbers of subjects exhibit risk tolerance that is decreasing, constant, or increasing with stakes, and the risk tolerance of our subjects across stakes are relatively stable.

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Notes

  1. The Eckel and Grossman task is similar to that developed by Binswanger (1980, 1981) for use in rural India.

  2. Higher financial risk aversion among females may explain lesser wealth possession by females as compared to males.

  3. Participants do not see the right five columns reporting Expected Payoffs, Risk, and CRRA range.

  4. Participants do not see the right three columns reporting Expected Payoffs and CRRA range.

  5. A number of studies have compared the Holt and Laury and Eckel Grossman tasks, as well as other elicitation methods (Dave et al. 2010; Charness et al. 2013; Crosetto and Filippin 2016).

  6. Inconsistencies are much more common in studies in developing countries (Charness and Viceisza 2016; Jacobson and Petrie 2009).

  7. Csermely and Rabas (2016) note, enforcing a single switching point means non-detection of people with inconsistent preferences. Andersen et al. (2006) offers a more detailed discussion of the issues with the multiple price list elicitation format.

  8. The staff and students at Monash University use Moodle mostly for course unit details. We created our own Moodle webpage for this experiment. Moodle enabled us to restrict the times during which participants could access the surveys and decision tasks.

  9. We could have paid participants for every lottery decision made but this would introduce different problems. First, there would be income effects as payoffs are earned from week to week. This could result in increased or decreased risk taking. Second, participants could diversify their risk taking, taking greater risks in some weeks and less risk in others or within a week taking more risk in, say, Holt/Laury and Eckel/Grossman and less risk in 10Eckel/Grossman. We believe the design chosen was likely to introduce the fewest confounds.

  10. Responses were re-scaled from the original scale of 1 to 100.

  11. Appendix B, Table 2 (in the Electronic Supplementary Material), reports summary statistics by gender for time-invariant participant characteristics. Other than the likelihood of attending a single-sex (primary or secondary) school (p < 0.01) and likelihood of residing with parents (p = 0.01), the differences between male and female participants in terms of time-invariant characteristics are insignificant.

  12. Alternative measures might include lottery in which the last Option A choice was made or the lottery in which the first Option B was made, neither of which better deals with inconsistent choices.

  13. In week 1, 30.6% and 27.3% of females and males, respectively, made multiple switches and 19.4% and 15.2% of females and males, respectively, made dominated alternative choices. Regressing the two indicators of inconsistent choices on Week, we do not find that errors of these sorts either significantly increase or decrease over the 12-week period, either for the full sample or for males and females separately (results available upon request).

  14. Spearman’s rho by week are reported in Appendix B, Table 3 (in the Electronic Supplementary Material). With few exceptions, the risk elicitation measures are significantly correlated (p < 0.05 in all but a few weeks).

  15. Holt and Laury (2002) find that males are slightly more risk tolerant at their low stakes, but that the differences disappear at the higher stakes.

  16. For completeness, Appendix B, Table 4 (in the Electronic Supplementary Material) reports the comparison between standardized EG and standardized HL.

  17. Given the small number of weeks, this is only an approximate measure of stability of risk preferences across time.

  18. Booth et al. (2014) find that females who attended single-sex schools were more risk tolerant than those who attended mixed-sex schools.

  19. We also ran linear probability panel data models with similar results (see Appendix B, Table 6, in the Electronic Supplementary Material)).

  20. Results for regressions 6–17 are reported in Appendix Table 7 (in the Electronic Supplementary Material). Complete results are available upon request.

  21. Panel regression results were also estimated (see Appendix B, Table 8 in the Electronic Supplementary Material).

References

  • Andersen, S., Harrison, G. W., Lau, M. I., & Rutström, E. E. (2006). Elicitation using multiple price list formats. Experimental Economics, 9, 383–405.

    Google Scholar 

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

    Google Scholar 

  • Anderson, L. R., & Mellor, J. M. (2009). Are risk preferences stable? Comparing an experimental measure with a validated survey-based measure. Journal of Risk and Uncertainty, 39(2), 137–160.

    Google Scholar 

  • Arch, E. (1993). Risk-taking: A motivational basis for sex differences. Psychological Reports, 73, 6–11.

    Google Scholar 

  • Barseghyan, L., Prince, J., & Teitelbaum, J. C. (2011). Are risk preferences stable across contexts? Evidence from insurance data. American Economic Review, 101, 591–631.

    Google Scholar 

  • Baucells, M., & Villasís, A. (2010). Stability of risk preferences and the reflection effect of prospect theory. Theory and Decision, 68, 193–211.

    Google Scholar 

  • Beine, M., Charness, G., Dupuy, A., & Joxhe, M. (2020). Shaking things up: On the stability of risk and time preferences. 13084. IZA Discussion Paper.

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

    Google Scholar 

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

    Google Scholar 

  • Booth, A., Cardona-Sosa, L., & Nolen, P. (2014). Gender differences in risk aversion: Do single-sex environments affect their development? Journal of Economic Behavior and Organization, 99, 126–154.

    Google Scholar 

  • Brañas-Garza, P., Galizzi, M. M., & Nieboer, J. (2018). Experimental and self-reported measures of risk taking and digit ratio (2d: 4d): Evidence from a large, systematic study. International Economic Review, 59, 1131–1157.

    Google Scholar 

  • Brody, L. R. (1993). On understanding gender differences in the expression of emotion. In S. L. Ablon, D. Brown, E. J. Khantzian, & J. E. Mack (Eds.), Human feelings: Explorations in affect development and meaning (pp. 87–121). Analytic Press.

    Google Scholar 

  • Brody, L. R., Hay, D., & Vandewater, E. (1990). Gender, gender role identity and children’s reported feelings toward the same and opposite sex. Sex Roles, 21, 363–387.

    Google Scholar 

  • Brown Kruse, J., & Thompson, M. A. (2001). A comparison of salient rewards in experiments: Money and class points. Economics Letters, 74, 113–117.

    Google Scholar 

  • Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125, 367–383.

    Google Scholar 

  • Callen, M., Isaqzadeh, M., Long, J. D., & Sprenger, C. (2014). Violence and risk preference: Experimental evidence from Afghanistan. American Economic Review, 104, 123–148.

    Google Scholar 

  • Cameron, L., & Shah, M. (2015). Risk-taking behavior in the wake of natural disasters. Journal of Human Resources, 50, 484–515.

    Google Scholar 

  • Cavatorta, E., & Groom, B. (2020). Does deterrence change preferences? Evidence from a natural experiment. European Economic Review, 127, 103456.

  • Charness, G., Eckel, C., Gneezy, U., & Kajackaite, A. (2018). Complexity in risk elicitation may affect the conclusions: A demonstration using gender differences. Journal of Risk and Uncertainty, 56(1), 1–17.

    Google Scholar 

  • Charness, G., & Gneezy, U. (2012). Strong evidence for gender differences in risk taking. Journal of Economic Behavior and Organization, 83, 50–58.

    Google Scholar 

  • Charness, G., Gneezy, U., & Imas, A. (2013). Experimental methods: Eliciting risk preferences. Journal of Economic Behavior & Organization, 87, 43–51.

    Google Scholar 

  • Charness, G., & Viceisza, A. (2016). Three risk-elicitation methods in the field: Evidence from rural Senegal. Review of Behavioral Economics, 3, 145–171.

    Google Scholar 

  • Crosetto, P., & Filippin, A. (2016). A theoretical and experimental appraisal of four risk elicitation methods. Experimental Economics, 19, 613–641.

    Google Scholar 

  • Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of Economic Literature, 47, 1–27.

    Google Scholar 

  • Csermely, T., & Rabas, A. (2016). How to reveal people’s preferences: Comparing time consistency and predictive power of multiple price list risk elicitation methods. Journal of Risk and Uncertainty, 53(2–3), 107–136.

    Google Scholar 

  • Dai, Z., Galeotti, F., & Villeval, M. C. (2018). Cheating in the lab predicts fraud in the field: An experiment in public transportation. Management Science, 64, 1081–1100.

    Google Scholar 

  • Daly, M., & Wilson, M. (1988). Homicide. ldine De Gruyter.

  • Daruvala, D. (2007). Gender, risk and stereotypes. Journal of Risk and Uncertainty, 35(3), 265–283.

    Google Scholar 

  • Dasgupta, U., Gangadharan, L., Maitra, P., & Mani, S. (2017). Searching for preference stability in a state dependent world. Journal of Economic Psychology, 62, 17–32.

    Google Scholar 

  • Dave, C., Eckel, C. C., Johnson, C. A., & Rojas, C. (2010). Eliciting risk preferences: When is simple better? Journal of Risk and Uncertainty, 41(3), 219–243.

    Google Scholar 

  • Deaux, K., & Farris, E. (1977). Attributing causes for one’s own performance: The effects of sex, norms, and outcomes. Journal of Research in Personality, 11, 59–72.

    Google Scholar 

  • Eckel, C. C., El-Gamal, M. A., & Wilson, R. K. (2009). Risk loving after the storm: A Bayesian-Network study of Hurricane Katrina evacuees. Journal of Economic Behavior & Organization, 69, 110–124.

    Google Scholar 

  • Eckel, C. C., & Grossman, P. J. (2002). Sex differences and statistical stereotyping in attitudes toward financial risk. Evolution and Human Behavior, 23, 281–295.

    Google Scholar 

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

    Google Scholar 

  • Eckel, C. C., & Grossman, P. J. (2008b). Men, women and risk aversion: Experimental evidence. In C. R. Plott & V. L. Smith (Eds.), Handbook of experimental economics results (pp. 1061–1073). North Holland/Elsevier Press.

    Google Scholar 

  • Exley, C. L. (2016). Excusing selfishness in charitable giving: The role of risk. The Review of Economic Studies, 83, 587–628.

    Google Scholar 

  • Fehr-Duda, H., De Gennaro, M., & Schubert, R. (2006). Gender, financial risk, and probability weights. Theory and Decision, 60, 283–313.

    Google Scholar 

  • Fetchenhauer, D., & Rohde, P. A. (2002). Evolutionary personality psychology and victimology sex differences in risk attitudes and short-term orientation and their relation to sex differences in victimizations. Evolution and Human Behavior, 23, 233–244.

    Google Scholar 

  • Filippin, A., & Crosetto, P. (2016). A reconsideration of gender differences in risk attitudes. Management Science, 62, 3138–3160.

    Google Scholar 

  • Fujita, F., Diener, E., & Sandvik, E. (1991). Gender differences in negative affect and well-being: The case for emotional intensity. Journal of Personality and Social Psychology, 61, 427–434.

    Google Scholar 

  • Fyhri, A., & Backer-Grøndahl, A. (2012). Personality and risk perception in transport. Accident Analysis and Prevention, 49, 470–475.

    Google Scholar 

  • Gangadharan, L., Grossman, P.J., & Xue, N. (2021). Identifying self-image concerns from motivated beliefs: Does it matter how and whom you ask? Working paper.

  • 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.

    Google Scholar 

  • Geary, D. C. (1998). Male, female: The evolution of human sex differences. American Psychological Association.

    Google Scholar 

  • Greiner, B. (2015). Subject pool recruitment procedures: Organizing experiments with ORSEE. Journal of the Economic Science Association, 1, 114–125.

    Google Scholar 

  • Grossman, P. J. (2013). Holding fast: The persistence and dominance of gender stereotypes. Economic Inquiry, 51, 747–763.

    Google Scholar 

  • Grossman, P. J., & Eckel, C. C. (2015). Loving the long shot: Risk taking with skewed lotteries. Journal of Risk and Uncertainty, 51(3), 195–217.

    Google Scholar 

  • Grossman, P. J., & Lugovskyy, O. (2011). An experimental test of the persistence of gender-based stereotypes. Economic Inquiry, 49, 598–611.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Holt, C. A., & Laury, S. K. (2005). Risk aversion and incentive effects: New data without order effects. American Economic Review, 95, 902–904.

    Google Scholar 

  • Jacobson, S., & Petrie, R. (2009). Learning from mistakes: What do inconsistent choices over risk tell us? Journal of Risk and Uncertainty, 38(2), 143–158.

    Google Scholar 

  • Jianakoplos, N. A., & Bernasek, A. (1998). Are women more risk averse? Economic Inquiry, 36, 620–630.

    Google Scholar 

  • Johnson, J. E., & Powell, P. L. (1994). Decision making, risk and gender: Are managers different? British Journal of Management, 5, 123–138.

    Google Scholar 

  • Kruse, J. B., & Thompson, M. A. (2003). Valuing low probability risk: Survey and experimental evidence. Journal of Economic Behavior and Organization, 50, 495–505.

    Google Scholar 

  • Lerner, J. S., Gonzalez, R. M., Small, D. A., & Fischhoff, B. (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological Science, 14, 144–150.

    Google Scholar 

  • Loewenstein, G. F., Hsee, C. K., Weber, E. U., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127, 267–286.

    Google Scholar 

  • Low, B. S. (2000). Why sex matters. Princeton University Press.

    Google Scholar 

  • Lundeberg, M. A., Fox, P. W., & LeCount, J. (1994). Highly confident, but wrong: Gender differences and similarities in confidence judgments. Journal of Educational Psychology, 86, 114.

    Google Scholar 

  • Niederle, M., & Vesterlund, L. (2007). Do women shy away from competition? Do men compete too much? The Quarterly Journal of Economics, 122, 1067–1101.

    Google Scholar 

  • Powell, M., & Ansic, D. (1997). Gender differences in risk behaviour in financial decision-making: An experimental analysis. Journal of Economic Psychology, 18, 605–628.

    Google Scholar 

  • Rubin, P., & Paul, C. W. (1979). An evolutionary model of taste for risk. Economic Inquiry, 17, 585–596.

    Google Scholar 

  • Sahm, C. (2012). How much does risk tolerance change? Quarterly Journal of Finance, 2, 1–38.

    Google Scholar 

  • Schubert, R., Brown, M., Gysler, M., & Brachinger, H. W. (1999). Financial decision-making: Are women really more risk averse? American Economic Review, 89, 381–385.

    Google Scholar 

  • Severiens, S., & ten Dam, G. (1994). Gender differences in learning styles: A narrative review and quantitative meta-analysis. Higher Education, 27, 487–501.

    Google Scholar 

  • Severiens, S., & ten Dam, G. (1998). A multilevel meta-analysis of gender differences in learning orientations. British Journal of Educational Psychology, 68, 595–608.

    Google Scholar 

  • Siegrist, M., Cvetkovich, G., & Gutscher, H. (2002). Risk preference predictions and gender stereotypes. Organizational Behavior and Human Decision Processes, 87, 91–102.

    Google Scholar 

  • Sjöberg, L., & Wåhlberg, A. A. (2002). Risk perception and New Age beliefs. Risk Analysis: An International Journal, 22, 751–764.

    Google Scholar 

  • Stapley, J. C., & Haviland, J. M. (1989). Beyond depression: Gender differences in normal adolescents’ emotional experiences. Sex Roles, 20, 295–308.

    Google Scholar 

  • Straznicka, K. (2012). Temporal stability of risk preference measures. GATE Working Paper No. 1236.

  • Swanson, J. M., Dibble, S. L., & Trocki, K. (1995). A description of the gender differences in risk behaviors in young adults with genital herpes. Public Health Nursing, 12, 99–108.

    Google Scholar 

  • Tanaka, T., Camerer, C. F., & Nguyen, Q. (2010). Risk and time preferences: Linking experimental and household survey data from Vietnam. American Economic Review, 100, 557–571.

    Google Scholar 

  • Voors, M. J., Nillesen, E. E., Verwimp, P., Bulte, E. H., Lensink, R., & Van Soest, D. P. (2012). Violent conflict and behavior: A field experiment in Burundi. American Economic Review, 102, 941–964.

    Google Scholar 

  • Weller, J. A., & Thulin, E. W. (2012). Do honest people take fewer risks? Personality correlates of risk-taking to achieve gains and avoid losses in HEXACO space. Personality and Individual Differences, 53, 923–926.

    Google Scholar 

  • Wölbert, E.M., & Riedl, A. (2013). Measuring time and risk preferences: Reliability, stability, domain specificity. GSBE Research Memorandum, #041.

  • Yu, C. W., Zhang, Y. J., & Zuo, S. X. (2021). Multiple switching and data quality in the multiple price list. The Review of Economics and Statistics, 103, 136–150.

    Google Scholar 

  • Zeisberger, S., Vrecko, D., & Langer, T. (2012). Measuring the time stability of prospect theory preferences. Theory and Decision, 72, 359–386.

    Google Scholar 

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Acknowledgements

We gratefully acknowledge the financial support of the Department of Economics, Monash University. We thank the editor and anonymous reviewer for their helpful comments.

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Correspondence to Philip J. Grossman.

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Bandyopadhyay, A., Begum, L. & Grossman, P.J. Gender differences in the stability of risk attitudes. J Risk Uncertain 63, 169–201 (2021). https://doi.org/10.1007/s11166-021-09361-w

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  • DOI: https://doi.org/10.1007/s11166-021-09361-w

Keywords

  • Risk
  • Lotteries
  • Stability
  • Gender
  • High and low stakes

JEL classification

  • C91
  • D03
  • D81