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Cognitive skills affect economic preferences, strategic behavior, and job attachment

  1. Stephen V. Burksa,1,
  2. Jeffrey P. Carpenterb,
  3. Lorenz Goettec and
  4. Aldo Rustichinid,e
  1. aDivision of Social Sciences, University of Minnesota, 600 East 4th Street, Morris, MN 56267-2134;
  2. bDepartment of Economics, Middlebury College, Middlebury, VT 05753;
  3. cDepartment of Economics, University of Geneva, 40 Boulevard du Pont d'Arve, 1211 Geneva, Switzerland;
  4. dDepartment of Economics, University of Minnesota, 1925 Fourth Street South, 4-101 Hanson Hall, Minneapolis, MN 55455-0462; and
  5. eFaculty of Economics, University of Cambridge, Cambridge CB2 3EB, United Kingdom

  1. Edited by Avinash K. Dixit, Princeton University, Princeton, NJ, and approved March 17, 2009 (received for review December 7, 2008)

Abstract

Economic analysis has so far said little about how an individual's cognitive skills (CS) are related to the individual's economic preferences in different choice domains, such as risk taking or saving, and how preferences in different domains are related to each other. Using a sample of 1,000 trainee truckers we report three findings. First, there is a strong and significant relationship between an individual's CS and preferences. Individuals with better CS are more patient, in both short- and long-run. Better CS are also associated with a greater willingness to take calculated risks. Second, CS predict social awareness and choices in a sequential Prisoner's Dilemma game. Subjects with better CS more accurately forecast others' behavior and differentiate their behavior as a second mover more strongly depending on the first-mover's choice. Third, CS, and in particular, the ability to plan, strongly predict perseverance on the job in a setting with a substantial financial penalty for early exit. Consistent with CS being a common factor in all of these preferences and behaviors, we find a strong pattern of correlation among them. These results, taken together with the theoretical explanation we offer for the relationships we find, suggest that higher CS systematically affect preferences and choices in ways that favor economic success.

Footnotes

  • 1To whom correspondence should be addressed. E-mail: svburks{at}morris.umn.edu

  • Author contributions: S.V.B., J.P.C., L.G., and A.R. designed research; S.V.B. performed research; S.V.B., J.P.C., L.G., and A.R. analyzed data; and S.V.B., J.P.C., L.G., and A.R. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • 2

    ↵2 There is a single common factor in our three measures (SI Appendix). Its correlation with all of the outcomes we consider is shown in the bottom row of Table 3.

  • 3

    ↵3 Cash payments contingent upon choices were offered in all experiments; see Methods and SI Appendix.

  • This article contains supporting information online at www.pnas.org/cgi/content/full/0812360106/DCSupplemental.

  • 4

    ↵4 The gray area shows the standard error of both estimates (which overlap); the fact that it tracks the estimated value is evidence that the results displayed are statistically reliable.

  • 5

    ↵5 All regression-adjusted results shown in the figures or described in the text include the following demographic control variables: schooling, age, race, gender, and household income. We also include the 11 personality factors of the Multidimensional Personality Questionnaire (see Methods and SI Appendix).

  • 6

    ↵6 A possible assumption: subjects with higher CS more effectively learn the efficient equilibrium in a repeated interaction or better acquire reciprocity heuristics and norms. This suggests higher transfers as first mover, and as second mover in response to a positive transfer.

  • 7

    ↵7 The observed peak of mean IQ in Fig. 3D is statistically significant at P < 0.001 (see SI Appendix).

  • 8

    ↵8 27.8%, P < 0.0001.

  • 9

    ↵9 Thus sending $5 paid off among our subjects, returning on average $7.46 ($3.73 doubled), for an expected gain of almost 50%.

  • 10

    ↵10 Thus, those with better CS behave as they expected others to, when responding as a second mover to receiving $0.

  • 11

    ↵11 We use the coefficient of risk aversion estimated from the lotteries Win $10/$2 and Win $5/$1.

  • 12

    ↵12 We use each subject's estimate of the amounts others will return on average; see Methods.

  • 13

    ↵13 The coefficients are shown as risk ratios, which are multiplied times the baseline hazard, and so increase exit risk if above 1, decrease it if below, and show no effect if equal to 1.

  • 14

    ↵14 In Table 15 in the SI Appendix, the smaller N leads to statistical insignificance for IQ in both exit subtype models when Hit 15 is added, but Hit 15 is always significant.

  • 15

    ↵15 The driver must deliver a load to a point perhaps thousands of miles away by a target day and time, taking into account loading time, distances, speed limits, weather and traffic conditions, and especially, the government regulations governing allowable hours of service for drivers.

  • 16

    ↵16 If the random outcome has expected value x, then for a risk-averse individual there is some fixed amount y <x that is subjectively equivalent to facing the gamble on the random outcome. The difference between x and y is the risk premium.

  • 17

    ↵17 Such a process could be cultural, genetic, or both, but the genetic version is the most controversial.

  • 18

    ↵18 We ran a factor analysis on these measures; see Footnote 2.

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