 42. Some Contributions of Economics to the Study of Personality 883
46. Sutter, Zoller, and Glätzle-Rützler (2019) 
provides a comprehensive summary of this litera -
ture.
47. Other research also suggests that preferences 
and personality are measured with a significant 
amount of error. For example, Beauchamp, Cesa -
rini, and Johannesson (2017) found that adjusting 
for measurement error increases both the predic -
tive power of risk preference in terms of observed 
outcomes and leads to higher measured correlations 
with noncognitive skills. Castillo, Jordan, and Pet -
rie (2018) found that only risk aversion estimates 
corrected for decision error predict life outcomes.
48. Risk and time preferences are also related to 
cognitive ability; however, the relationship is likely 
weaker. Higher cognitive ability is associated with 
more patient behavior (see, e.g., Dohmen, Falk, 
Huffman, & Sunde, 2018; Jagelka, 2018). The ef -
fect of cognitive skills on risk aversion is more con -
troversial and there have been studies that found a 
positive link, a negative link, or no link (see Dohm -
en et al., 2018, for a recent summary of the litera -
ture). Andersson et al. (2018) suggests that estimat -
ed relationships may have been biased by features 
of the experimental design, while Jagelka (2018) 
shows that explicitly modeling for noise in observed 
choices and survey responses flips the estimated 
link between cognitive ability and risk preference 
from negative to positive.
49. Note that “bias” is in general measured 
against a maintained model of rational decision 
making, which, in fact, may not be widely accepted.
50. A participant (usually a child) was given a 
marshmallow. The experimenter left the room and 
told the participant that he or she would receive 
a second marshmallow if he or she resisted con -
suming the marshmallow until the experimenter 
returns. The length of time that the participant 
waits is a measure of short-term discounting. The 
children who could wait had much better lifetime 
outcomes (for a recent discussion of this study, see 
Mischel et al., 2010).
51. See the special issue of the Journal of Re -
search in Personality (2009) entitled “Personality 
and Assessment at Age 40,” for a recent discussion.
52. R2 of .6–.8, where R2 is a measure of vari -
ance explained.
53. The authors use 13 indicators to construct a 
measure of self-  control, but only three for each of 
the Big Five personality traits, and only one general 
question for measuring each of risk and time prefer -
ence.
54. For a recent study, see Friedman and Martin 
(2011).
55. The timing of the measurements of noncog -
nitive skill relative to the outcomes varies by study.
56. See Friedman and Martin (2011), Martin, 
Friedman, and Schwartz (2007), Kern and Fried -
man (2008), Mroczek and Spiro (2007), Boyle et 
al. (2005), Schulz, Bookwala, Knapp, Scheier, and Williamson (1999), and Kubzansky, Sparrow, Vo -
konas, and Kawachi (2001).
57. Judge, Erez, Bono, and Thoresen (2002, 
p. 693) found that locus of control is highly related 
to emotional stability, which is the inverse of neu -
roticism; indeed, they say that “measures purport -
ing to assess self-  esteem, locus of control, neuroti -
cism, and generalized self-  efficacy may be markers 
of the same higher order concept.”
58. Castillo, Ferraro, Jordan, and Petrie (2011) 
showed that higher patience is also associated with 
fewer disciplinary referrals at school.
59. Selecting measures and verifying them is part 
of the mysterious and inherently subjective process 
of “construct validity” in psychology. For a discus -
sion, see Borghans et al. (2008).
60. See the Secretary’s Commission on Achiev -
ing Necessary Skills (1992).
61. See Barton (2006).
62. See the Washington Workforce Training 
Board (2008).
63. See Westwood (2004).
64. See Hillage, Regan, Dickson, and McLough -
lin (2002).
65. See Stasz (2001).
66. Antecol and Cobb-Clark (2013) corroborate 
these findings using U.S. data.
67. A reservation wage  is the minimum wage for 
which a person is willing to accept a job.
68. Similar results are found for females.
69. See the NLSY79, the National Longitudinal 
Survey of Youth 1997 (NLSY97), and the National 
Educational Longitudinal Survey (NELS). For dis -
cussion of these datasets, see Heckman, Humphries, 
and Veramendi (2018).
70. Most of the patterns found for women paral -
lel those found for men. However, there are some 
important differences. While female GED recipi -
ents share similar cognitive and personality skills 
as male GED recipients, their outcomes differ. 
After accounting for differences in cognitive ability, 
female GED recipients do not earn higher hourly 
wages than other dropouts, but unlike men, they 
have higher annual earnings, because they are more 
likely to participate in the labor force. The increased 
labor supply response is largely due to female GED 
recipients who attain some postsecondary educa -
tion or who have dropped out of high school due 
to pregnancy. See our book, Heckman, Humphries, 
and Kautz (2014), for a full discussion of the evi -
dence on the performance of GED recipients.
71. This framework draws on Almlund et al. 
(2011). 
72. Formally, this occurs when ∂ψ
≠∂∂2
0Ta
aT aPR.  
73. Todd and Wolpin (2006) find that differ -
ences in parental investment (home inputs) explain 
almost a third of the Black–White and Hispanic– 