 42. Some Contributions of Economics to the Study of Personality 865
that differences in preferences across coun -
tries are related to differences in per capita 
income, entrepreneurial activities, and the 
frequency of armed conflicts. Choi et al. 
(2014) showed that a one standard deviation 
increase in choice inconsistency is associated 
with an almost 20% increase in household 
wealth. Stango and Zinman (2019) found a 
similar impact of an increase in the number 
of exhibited behavioral biases on objective 
and subjective measures of financial well-
being.
There is comparatively little evidence of 
the predictive power of early measures of 
preferences on later life outcomes. Golsteyn, 
Grönqvist, and Lindahl (2014) showed that 
survey-  based time preferences elicited in 
Swedish data at age 13 predict outcomes 
decades later, mirroring the findings from 
Mischel’s marshmallow study. In fact, the 
impact seems to grow over time. Individuals 
who were more patient at age 13 had high -
er grades through high school and college, 
higher incomes, a lower probability of being 
unemployed, lower obesity, and a lower like -
lihood of dying before the age of 50. The effects are heterogenous, and the associa -
tion between early measures of patience and 
outcomes seems to be mediated by increased 
investments in human capital. Cadena and 
Keys (2015) confirmed the link between 
patience and human capital accumulation 
using U.S. data. They found that impatience 
is related to time-  inconsistent behavior such 
as dropping out of college after having al -
ready attended it for 3 years and conjecture 
that present bias (or short-run impatience) is 
the main culprit. Not only do impatient in -
dividuals accumulate less human capital and 
thus earn less, they also tend to express more 
regret later in life.
Accounting for randomness in observed 
decisions seems crucial and offers a promis -
ing avenue for future research. A large body 
of previous literature treated estimates of 
preferences and skills as if they were error-
free. This is a strong assumption. In a study 
of twins, Beauchamp et al. (2017) showed 
that accounting for measurement error in -
creases the predictive power of estimates of 
risk preferences. Lower risk aversion is re -
lated to more risky financial decisions and a 
0.10
0.05
0
SES IQ C E/PE N A0.15Correlation0.200.250.30
FIGURE 42.5.  Correlations of mortality with noncognitive skills, IQ, and socioeconomic Status (SES). 
Source: B. Roberts et al. (2007). Note: The figure represents results from a meta-  analysis of 34 studies. 
Average effects (in the correlation metric) of low SES, low IQ, low conscientiousness (C), low extra -
version/positive emotion (E/PE), neuroticism (N), and low agreeableness (A) on mortality. Error bars 
represent standard errors. The lengths of the studies represented vary from 1 year to 71 years.