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Saving behavior and cognitive abilities
Published online by Cambridge University Press: 14 March 2025
Abstract
Experiments on saving behavior reveal substantial heterogeneity of behavior and performance. We show that this heterogeneity is reliable and examine several potential sources of it, including cognitive ability and personality scales. The strongest predictors of both behavior and performance are two cognitive ability measures. We conclude that complete explanations of heterogeneity in dynamic decision making require attention to complexity and individual differences in cognitive constraints.
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- Copyright © 2011 Economic Science Association
Footnotes
This research was supported by grants from the National Science Foundation with award numbers SES-0350748 (Ballinger) and SES-0350565 (Wilcox), as well as by the University of Houston Research Council. We are grateful to Randall Engle and Richard Heitz of the Attention and Working Memory Lab at Georgia Tech for providing us with their “automatic operation span” software (and advice from D. Stephen Lindsay that led us to them). Bram Cadsby, Glenn Harrison, Ondrej Rydval, Tomomi Tanaka, Mark Thompson, two anonymous referees and the editor provided help or useful commentary, and Sharon O'Donnell and Rick Wilson provided valuable advice on software programming. None of these people are responsible for any errors that remain.
Electronic supplementary material The online version of this article (doi: 10.1007/s10683-010-9271-3) contains supplementary material, which is available to authorized users.
References
Ballinger, T. P., Palumbo, M. G., & Wilcox, N. T. (2003). Precautionary saving and social learning across generations: An experiment. Economic Journal, 113, 920–947.CrossRefGoogle Scholar
Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3, 439–449.CrossRefGoogle Scholar
Benjamin, D. J., Brown, S. A., & Shapiro, J. M. (2006). Who is “behavioral”? Cognitive ability and anomalous preferences. Harvard University working paper.CrossRefGoogle Scholar
Brown, A. L., Chua, Z. E., & Camerer, C. F. (2009). Learning and visceral temptation in dynamic saving experiments. Quarterly Journal of Economics, 124, 197–231.CrossRefGoogle Scholar
Browning, M., & Lusardi, A. (1996). Household saving: Micro theories and micro facts. Journal of Economic Literature, 34, 1797–1855.Google Scholar
Cacioppo, J. T., Petty, R. E., Feinstein, J. A., & Jarvis, W. B. G. (1996). Dispositional differences in cognitive motivation: the life and times of individuals varying in need for cognition. Psychological Bulletin, 119(2), 197–253.CrossRefGoogle Scholar
Camerer, C. F., & Hogarth, R. M. (1999). The effects of financial incentives in experiments: a review and capital-labor-production framework. Journal of Risk and Uncertainty, 19(1-3), 7–42.CrossRefGoogle Scholar
Camerer, C. F., Ho, T.-H., & Chong, K. (2004). A cognitive hierarchy model of behavior in games. Quarterly Journal of Economics, 119(3), 861–898.CrossRefGoogle Scholar
Campbell, J. Y., & Mankiw, N. G. (1990). Permanent income, current income and consumption. Journal of Business and Economic Statistics, 8, 265–279.CrossRefGoogle Scholar
Carbone, E. (2006). Understanding intertemporal choices. Applied Economics, 38, 889–898.CrossRefGoogle Scholar
Carbone, E., & Hey, J. D. (2004). The effect of unemployment on consumption: an experimental analysis. Economic Journal, 114, 660–683.CrossRefGoogle Scholar
Carroll, J. B. (1993). Human cognitive abilities: a survey of factor-analytic studies. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Conlisk, J. (1980). Costly optimization versus cheap imitators. Journal of Economic Behavior and Organization, 1, 275–293.CrossRefGoogle Scholar
Conlisk, J. (1996). Why bounded rationality? Journal of Economic Literature, 34, 669–700.Google Scholar
Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005). Working memory span tasks: a methodological review and user's guide. Psychonomic Bulletin and Review, 12, 769–786.CrossRefGoogle ScholarPubMed
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.CrossRefGoogle Scholar
Deaton, A. S. (1992). Understanding consumption. London: Oxford University Press.CrossRefGoogle Scholar
Dyer, D., & Kagel, J. H. (1996). Bidding in common value auctions: how the commercial construction industry corrects for the winner's curse. Management Science, 42, 1463–1475.CrossRefGoogle Scholar
Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11, 19–23.CrossRefGoogle Scholar
Engle, R. W., & Kane, M. J. (2004). Executive attention, working memory capacity, and a two-factor theory of cognitive control. In Ross, B. (Ed.), The psychology of learning and motivation (Vol. 44, pp. 145–199). Amsterdam: Elsevier.Google Scholar
Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A. (1999). Working memory, short-term memory, and general fluid intelligence: a latent variable approach. Journal of Experimental Psychology: General, 128, 309–331.CrossRefGoogle ScholarPubMed
Feldman-Barrett, L., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of the mind. Psychological Bulletin, 130, 553–573.CrossRefGoogle Scholar
Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19, 25–42.CrossRefGoogle Scholar
Gabaix, X., & Laibson, D. (2000). A boundedly rational decision algorithm. American Economic Review, 90, 433–438.CrossRefGoogle Scholar
Hall, R. E., & Mishkin, F. S. (1982). The sensitivity of consumption to transitory income: estimates from panel data on households. Econometrica, 50, 461–481.CrossRefGoogle Scholar
Harrison, G. (1989). Theory and misbehavior of first-price auctions. American Economic Review, 79, 749762.Google Scholar
Harrison, G., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055.CrossRefGoogle Scholar
Hey, J., & Dardanoni, V. (1988). Optimal consumption under uncertainty: an experimental investigation. Economic Journal, 98(390), 105–116 (supplement).CrossRefGoogle Scholar
Hogarth, R. (1975). Decision time as a function of task complexity. In Wendt, D. & Vlek, C. (Eds.), Utility, probability and human decision making (pp. 321–338). Dordrecht: Reidel.CrossRefGoogle Scholar
Hunt, E. (1999). Intelligence and human resources: past, present and future. In Ackerman, P. L., Kyllunon, P., & Roberts, R. (Eds.), Learning and individual differences: process, trait and content determinants (pp. 3–30). Washington: American Psychological Association.CrossRefGoogle Scholar
Kahneman, D., Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47, 263–291.CrossRefGoogle Scholar
Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychonomic Bulletin and Review, 9, 637–671.CrossRefGoogle ScholarPubMed
Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne, T. W., & Engle, R. W. (2004). The generality of working-memory capacity: a latent-variable approach to verbal and visuo-spatial memory span and reasoning. Journal of Experimental Psychology: General, 133(2), 189–217.CrossRefGoogle Scholar
Kellogg, C. E., & Morton, N. W. (1999). Beta III manual. San Antonio: The Psychological Corporation.Google Scholar
McDaniel, T. M., & Rutström, E. E. (2001). Decision making costs and problem solving performance. Experimental Economics, 4, 145–161.CrossRefGoogle Scholar
Millner, E., & Pratt, M. (1992). A test of risk inducement: is inducement of risk-neutrality neutral? Virginia Commonwealth University Department of Economics working paper.Google Scholar
Nagel, R. (1995). Unraveling in guessing games: an experimental study. American Economic Review, 85(5), 1313–1326.Google Scholar
Nisbett, R. E., & Ross, L. D. (1980). Human inference: strategies and shortcomings of social judgment. Prentice-Hall: Englewood Cliffs.Google Scholar
Payne, J., Bettman, J., & Johnson, E. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory and Cognition, 14, 534–552.Google Scholar
Porteus, S. D. (1965). Porteus maze tests: fifty years’ application. Palo Alto: Pacific Book Publishers.Google Scholar
Raven, J., Raven, J. C., & Court, J. H. (1998). Manual for Raven's progressive matrices and vocabulary scales. San Antonio: The Psychological Corporation.Google Scholar
Read, D., Loewenstein, G., & Rabin, M. (1999). Choice bracketing. Journal of Risk and Uncertainty, 19, 171–197.CrossRefGoogle Scholar
Russo, J. E. (1978). Comments on behavioral and economic approaches to studying market behavior. In Mitchell, A. A. (Ed.), The effect of information on consumer and market behavior (pp. 65–74). Chicago: American Marketing Association.Google Scholar
Rydval, O., & Ortmann, A. (2004). How financial incentives and cognitive abilities affect task performance in laboratory settings: an illustration. Economics Letters, 85(3), 315–320.CrossRefGoogle Scholar
Spearman, C. (1904). “General intelligence” objectively determined and measured. American Journal of Psychology, 15, 201–293.CrossRefGoogle Scholar
Selten, R., Sadrieh, A., & Abbink, K. (1999). Money does not induce risk neutral behavior, but binary lotteries do even worse. Theory and Decision, 46, 211–249.CrossRefGoogle Scholar
Shah, P., & Miyake, A. (1996). The separability of working memory resources for spatial thinking and language processing: an individual differences approach. Journal of Experimental Psychology: General, 125, 4–27.CrossRefGoogle ScholarPubMed
Shah, P., & Miyake, A. (1999). Models of working memory: an introduction. In Miyake, A. & Shah, P. (Eds.), Models of working memory: mechanisms of active maintenance and executive control (pp. 1–26). Cambridge: Cambridge University Press.Google Scholar
Smith, V. L. (1982). Microeconomic systems as an experimental science. American Economic Review, 72, 923–955.Google Scholar
Stahl, D., & Wilson, P. (1995). On player's models of other players: theory and experimental evidence. Games and Economic Behavior, 7, 218–254.CrossRefGoogle Scholar
Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: implications for the rationality debate? Behavioral and Brain Sciences, 23, 645–665.CrossRefGoogle ScholarPubMed
Thaler, R. H. (1994). Psychology and savings policies. American Economic Review, 84, 186–192.Google Scholar
Tuckman, B. W. (1991). The development and concurrent validity of the procrastination scale. Educational and Psychological Measurement, 51, 473–480.CrossRefGoogle Scholar
Turley-Ames, K. J., & Whitfield, M. M. (2003). Strategy training and working memory task performance. Journal of Memory and Language, 49, 446–468.CrossRefGoogle Scholar
Turner, M. L., & Engle, R. W. (1989). Is working memory capacity task-dependent? Journal of Memory and Language, 28, 127–154.CrossRefGoogle Scholar
Wilcox, N. (1993a). Lottery choice: incentives, complexity and decision time. Economic Journal, 103, 1397–1417.CrossRefGoogle Scholar
Wilcox, N. (1993b). On a lottery pricing anomaly: time tells the tale. Journal of Risk and Uncertainty, 7, 311–324.CrossRefGoogle Scholar
Whiteside, S. P., & Lynam, D. R. (2001). The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences, 30, 669–689.CrossRefGoogle Scholar
Winkler, R. L., & Murphy, A. M. (1973). Experiments in the laboratory and the real world. Organizational Behavior and Human Performance, 20, 252–270.CrossRefGoogle Scholar
Ballinger et al. supplementary material
Saving Behavior and Cognitive Abilities: Supplementary Materials
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