Discussion Papers In Economics
And Business

Are facets of homo economicus associated withhigher earnings and happiness?
Shoko Yamane
Hiroyasu Yoneda
Yoshiro Tsutsui
Discussion Paper 14-33

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANAre facets of homo economicus associated withhigher earnings and happiness?
Shoko Yamane
Hiroyasu Yoneda
Yoshiro Tsutsui
Discussion Paper 14-33

October 2014

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANAre facets of homo economicus associated withhigher earnings and happiness?
Shoko Yamane†
Hiroyasu Yoneda‡
Yoshiro Tsutsui§

Abstract
This paper investigates the individual outcomes of irrational thinking, including
paranormality and non-scientific thinking. These modes of thinking are identified byfactor analysis from a 2008 survey. Income and happiness are used as measures of
performance. Empirical results reveal that both paranormality and non-scientificthinking lower income. While non-scientific thinking lowers happiness, paranormality
raises it. Extending the model, we find that higher ability results in higher income andhappiness. Self-control only raises happiness. These results suggest that many elementsof homo economicus, except paranormality and selfishness, raise economic performanceand happiness.
PsycINFO Classification code; 2920; 3100; 3650
JEL classification number; D03
Keywords; irrational belief; happiness; paranormality; factor analysis; ability†

Faculty of Economics, Kinki University, 3-4-1 Kowakae, Higashi-Osaka, Osaka, 577-8502,
JAPAN, Phone: +816-6721-2332, e-mail: syamane@kindai.ac.jp‡
Graduate School and Faculty of Pharmaceutical Sciences, Kyoto University, 46-29 YoshidaShimo-Adachi-cho, Sakyo-ku, Kyoto 606-8501, JAPAN, phone & Fax: +81-75-753-9273,
e-mail: yoneda@pharm.kyoto-u.ac.jp§
Faculty of Economics, Konan University, 8-9-1 Okamoto, Higashinada-ku, Kobe, 658-8501
JAPAN, Phone & Fax: +81-78-435-2920, e-mail: tsutsui@center.konan-u.ac.jp

1. Introduction
This paper aims to discover whether logical thinking is necessary for better humanperformance (income and happiness), using Japanese data.
Rationality is the essential assumption of traditional economics, meaning thatagents think logically, not emotionally, about how to act in order to achieve their goals
given their constraints.1 In addition to rationality, the following elements are commonly
ascribed to “Homo economicus”: unlimited cognitive resources, pure self-interest, andperfect self-control (Cartwright, 2014). However, this is not the only possible
description of economic agents; Homo economicus is often simply adopted formodeling convenience. Therefore, it is interesting to ask: “Does Homo economicus
exhibit performance that is superior to real-world human beings?” In this paper, we
focus on the effect of irrationality on performance, although we also investigate theimpact of the other characteristics of Homo economicus.2
The consequences of irrationality have been studied in the field of behavioral
finance. DeLong et al. (1990) analyzed the efficiency of a financial market that consistsof a mix of rational and irrational agents, and showed that this market can be inefficient1

For more discussion of rationality, see Wilkinson (2008).
Konow and Earley (2008), based on a dictator game experiment, found that more generous
people report greater happiness.

2

1

if irrational agents comprise a substantial fraction of market participants.3 The result
suggests that whether or not irrationality leads to lower profitability depends on the total
number of irrational agents in the market. If irrational agents dominate, behaving
irrationally in concordance with many other irrational agents can be profitable.
Shumway and Wu (2006) empirically analyzed the Shanghai stock exchange andfound that traders who show the disposition effect earn less profit. Barber and Odean(2001) showed that men transact too often because of their overconfidence, leading to
low profitability. These empirical results are consistent with the hypothesis that
irrationality reduces profitability.
To be more rational, it is necessary to think logically, which is the unique tool bywhich human beings identify the appropriate actions to achieve their goals. We measureindividuals’ degree of logical thinking by assessing the degree to which they believe inscience and the degree to which they believe in paranormal phenomena. Although these
may seem to be two sides of the same coin, they actually have different characteristics,
as will be demonstrated in section 2.4 While paranormality has not often been analyzed
in the economics literature, there have been many studies on this topic in the field of

3

The authors assume that rational agents are risk averse, which restricts them from making
unlimited arbitrage.
4
Lindeman and Aarnio (2007) offer some support for this; they report that superstition is well
predicted by ontological confusion, but not by analytical thinking.
2

psychology. However, the relationship between paranormal beliefs and individual
success has, to our knowledge, not yet been studied. Since people who believe inparanormal phenomena do not understand the world correctly, they tend to miss chances
for achieving success.
The majority of Japanese people do not believe in any religion: according to ourlarge scale-survey, 56% answered “None” to the question “Please indicate if you areaffiliated with any of the following religions.” Also, 58.5% answered “It doesn’t holdtrue at all” to the statement “I am deeply religious;” those who answered “It isparticularly true” comprised only 3.4%. Nonetheless, belief in paranormal phenomena ispopular, especially for the younger generation, as in many countries (Williams et al.
2007, Rice 2003, Peltzer 2003). Thus, religious beliefs, at least, may not be the only
source for the spread of paranormal beliefs, especially among younger individuals;
superstitions spread by mass media may be an important origin.5
We assess an individual’s performance with two measures: 1) income, and 2)
happiness. Success in the world is often evaluated by income, assets, and social status;
attainment of these goals is largely determined by objective conditions including
personal traits and environment, although chance also plays a large role. On the other5

Fortune-telling based on blood types or horoscopes is broadcasted daily on many Japanese TV
channels.
3hand, human beings also pursue subjective happiness, which is strongly affected by
one’s mental condition. Indeed, Wills (2009) reports that higher satisfaction withspirituality and religiosity brings about significantly higher well-being (see also Cohen
2002). Although happiness depends on income to some degree, the two may sometimes
diverge. Thus, we investigate how paranormality and non-scientific thinking affect bothincome and happiness.
The remainder of this paper is organized as follows. In section 2, we explain thedata and methods used. In section 3, we present the basic results and check robustness.
In section 4, we extend the basic model to examine how characteristics of Homoeconomicus other than rational thinking affect performance. Section 5 concludes.

2.

Data and methods

In this section, we explain our dataset and report the results of an analysis regarding theorigins of belief in paranormality and non-scientific thinking.

2.1 The questionnaire survey
All data used in this paper were obtained from a survey conducted by the COE (Centerof Excellence) project of Osaka University in February 2008. The questionnaire consists4

of 92 questions, some of which include sub-questions. Although the survey had been
conducted since 2003, questions to elicit paranormal beliefs and non-scientific thinking
were only asked in 2008. Three thousand and forty eight (3,048) people aged 20-75
were selected from all over Japan by double stratified random sampling, so that we
obtained a representative sample of the Japanese populace. Selected respondents werevisited at their homes and handed the questionnaire. Several days later, completed
questionnaires were picked up. Two thousand seven hundred and thirty one (2,731)
questionnaires were returned (response rate of 89.6%). At the same time, 3000
individuals were randomly chosen from all over Japan, and the same questionnaire wassent by postal mail to these. One thousand two hundred and eighty seven (1,287) of
these were returned (response rate of 42.9%). In this paper, these two samples arepooled and used for the analysis, so that the total number of observation is 4,018. The
respondents were rewarded with a voucher valued of 1500 yen (about US$14).6

2.2 Questions associated with irrationality
We designed questions that elicit an individual’s degree of belief in paranormality(henceforth “paranormality”) and non-scientific thinking; we collectively refer to these6$1=¥106.58 on February 1, 2008.
5

two measures as “irrationality.” Paranormality is assessed with eight questions andnon-scientific thinking with three; these questions are listed in Table 1. Each response ison a five-point scale.
The standard questions used to measure paranormality, the Paranormal Belief
Scale (PBS), were developed by Tobacyk and Milford (1983) and are widely used
(Hergovich and Arendasy 2005, Aarnio and Lindeman 2005, Dagnall et al. 2007,
Peltzer 2003). These authors propose a 25-item questionnaire based on the results fromfactor analysis of a 61-item pool. Factor analysis revealed seven independent
dimensions comprising belief in the paranormal. Out of our eight questions onparanormality, four are related to factor 1 in Tobacyk and Milford, one belongs to factor
2, and one to factor 7. Thus, our questions are somewhat similar to those in Tobacyk
and Milford. However, Wiseman and Watt (2004) criticize the PBS, pointing out “thisscale refers solely to negative superstitions (e.g., breaking a mirror will cause bad luck)and omits items referring to positive superstitions (e.g., carrying a lucky charm willbring good luck).” Another measure, the Belief in Paranormal Scale (BIP), has been
proposed, which assesses both paranormal beliefs and experiences (Thalbourne andDelin 1993, Rattert and Bursik 2001). Williams et al. (2006) and Rice (2003) each
propose their own measures. In sum, although the PBS is the most common measure6

used to assess paranormal beliefs, there exist a variety of alternatives.
From Table 1, we can see that belief in paranormality and non-scientific thinking
are very common. Except for the question regarding whether “human beings evolved
from other living things,” the modal response is in the middle of the 5-point scale.7
Though the number of responses indicating non-belief in science is a minority, thedistribution of the answer is almost symmetric for the questions on the existence of gods,
heaven, ghosts, and the afterlife. Many answered “true” for questions such as “God orgods exist” “Life after death exists,” and “God knows about all the wrong things we’ve
done.”

2.3 Factor analysis
We asked eleven questions concerning irrationality. Needless to say, irrationality is not
the sole determinant of the answers to these questions. Therefore we performed a factor
analysis on the set of eleven questions.8
The results of that factor analysis are presented in Table 2. It is clear that the eightquestions associated with paranormalilty have a large loading on the first factor, while

7

Although in the original questionnaire, 1=totally agree and 5=totally disagree, we reversed thenumber for some questions so that larger numbers indicate greater paranormality and more
non-scientific thinking.
8
Specifically, we use principal factor analysis (PFA) with promax rotation.
7the second factor strongly relates to the three questions associated with non-scientificthinking. We therefore name the first factor PARANORMAL and the second
NONSCIENTIFIC.

2.4 Where do belief in paranormality and non-scientific thinking come from?
In this subsection we examine how paranormality and non-scientific thinking emerge.
We particularly focus on the effect of the respondents’ childhood environments.
We define the average years of schooling of a respondent’s parents as
EDUCTION_P.9 We hypothesize that higher parental education directly and indirectly
leads to children thinking more rationally. Another variable is standard of living at age
15 (LIVING_15), which ranges from 0 (poorest) to 10 (wealthiest). Although thedirection of the effect on rationality is not intuitively obvious, we hypothesize thatgrowing up with hardship will discipline children and keep them from subscribing toirrationality, other thing being equal. Another important variable is intelligence, which,
we hypothesize, increases rationality. We proxy for intelligence with a respondent’s
self-reported grades (averaged across all academic subjects) at the age of 15

9

When respondents have only one parent, the variable is defined as the schooling years of thatparent.
8

(ABILITY).10
Gender may play an important role, because boys and girls are often educated tobehave differently and to seek different life goals. We thus include a dummy variable
DMAN, which takes on a value of unity for males and zero for females. Age may
represent generation, which could reflect the degree to which the media reported onparanormal phenomena when that generation was young; alternatively, belief inparanormality might depend on age itself. Thus, we add age decade dummies (e.g.
D_AGE20 is a dummy representing whether a respondent is in his or her 20s) to theregression. The dummy for 70s is deleted as the benchmark.
Estimation results by ordinary least squares (OLS) are presented in Table 3.
Factors influencing paranormality appear on the left. Females tend to have stronger
belief in paranormality than males, which is consistent with previous studies (Rice 2003,
Wolfradt 1997, Wiseman and Watt 2004, Williams et al. 2007).11 Paranormality tends
to be strongest when respondents are in their 40s. Those who had higher grades at age
15 tend to exhibit less paranormality, as do those whose parents are more educated.
Interestingly, a higher childhood standard of living is associated with more

10

Musch and Ehrenberg (2002) used grades in junior high school as a proxy for cognitive
ability and examined the correlation with paranormality.
11
However, Peltzer (2003) finds no significant gender differences among secondary anduniversity students in South Africa.
9paranormality.
Results regarding non-scientific thinking are shown in the right-hand columns.
The results are similar to those for paranormality. The only differences are that parents’
education and childhood standard of living are here only significant at the 10% level.
Another difference is that non-scientific thinking decreases monotonically with age.
This last result presents an interesting contrast with the result for paranormality.
Respondents in their 40s were teenagers around 1978, when paranormal phenomena spoon-bending by Uri Geller and Kokkuri-san (a kind of table-turning), etc. - were very
popular in the media. Therefore, we might be seeing a generational effect.

2.5 Income and happiness as measures of life performance
We use two measures of an agent’s life performance: income and happiness. The former
is a purely economic measure of performance, while the latter is psychological andself-evaluative. 2007 pretax income (on a 12-point scale) is included as a question in thesurvey. Following Barsky et al. (1997) and Kimball et al. (2008), we fit a lognormal
distribution to the income histogram and estimate the income for each class; thelogarithm of this estimation result is called INCOME.
HAPPINESS is defined by the answer to the following question: Overall, how

10happy would you say you are currently? Using a scale from 0 - 10 where “10” is“very happy” and “0” is “very unhappy”, how would you rate you current level ofhappiness?

2.6 Statistical analysis
We regress the performance variables, INCOME and HAPPINESS, on the variables
representing irrationality, PARANORMAL and NONSCIENTIFIC, and or control
variables. Following Hashimoto and Raisian (1985), Mincer and Higuchi (1988), andClark and Ogawa (1992), we assume that the wage rate is determined by (potential)
working experience (WEXP) and its squared (WEXPSQ), and employment tenure(TENURE) and its squared (TENURE2). We also add male dummy DMAN, because
males tend to earn higher wages than females.
Annual income is determined by the wage rate as well as by the number of hours
worked in a year. Thus, for the income equation, we add working hours per week
(WHOUR) and working days in a year (WDAY), which are asked in our survey.12 Thus,
the income equation is:

12

Because many respondents didn’t tell us their number of working days and working hours,
the sample size for the income regression is about the half of the total number of observations.
11

a

a
a

(1)
Here INCOME is the logarithm of annual income. Working experience (WEXP) is
defined as age minus the age of the last school graduation. Employment tenure(TENURE) is the working years at the current employer. i indexes individuals andisa disturbance term..
We calculate the wage rate (WAGERATE) as the logarithm of (annual income /working hour per week * 52), and we then estimate the wage equation:13
a

a

a
(1)’,

where WAGERATE is in logs.
Since it is well known that happiness depends on gender and age (Frey andStutzer 2002), we add these variables to the regression equation for HAPPINESS:
b
b

b

_

b

b

b

(2)

13

We deleted 5 samples whose wage rate exceeds ¥15000, since a wage of this level seems
quite rare in Japan.
12

It is also known that subjective happiness depends on income in cross sectional
analyses (Frey and Stutzer 2002). Thus, we estimate a variant of equation (2) that
includes income:
b
b

b

_

b

b

b

b
(3)

We must be careful regarding the interpretation of the estimation results forequation (3): b1 and b2 represent the direct effects of irrationality, but even if b1 and b2
are found to be statistically insignificant, these variables may indirectly affect
HAPPINESS through INCOME. Equations (2) and (3) are estimated by ordered probit,
since HAPPINESS is an ordered variable.

3. Estimation results
3.1 Effect of irrationality on income
The estimation results for equation (1) and its variants are presented in Table 4. On theleft are shown the results when INCOME (log of annual income) is taken as thedependent variable. The coefficients on both PARANORMAL and NONSCIENTIFIC are
negative; the former is significant at the 1% level and the latter at the 5%, implying thatparanormal and non-scientific thinking lower economic performance. The coefficient of

13

EXPERIENCE is significantly positive, and its square is significantly negative,
implying that longer experience contributes to a higher wage, but at a diminishing rate.
Similar results are obtained for job tenure. The coefficient on the male dummy issignificantly positive, implying that males enjoy higher wages than females with thesame EXPERIENCE and TENURE. While the coefficient on working hours is
significantly positive at the 1% level, working days are negative but insignificant.14
The second columns present our estimates for WAGERATE. The coefficient on
PARANORMAL is negative and significant at the 1% level, and the coefficient of
NONOSCIENTIFIC is also negative and significant at the 10% level. The othervariables are similar to those in the first columns. The maleness dummy, DMAN, issignificantly positive, as expected.
In sum, we have a robust result that both belief in paranormality andnon-scientific thinking lower income as well as wages.

3.2 Effect of irrationality on happiness
Estimation results for equation (2) (the HAPPINESS regression) are presented in panel
A of Table 5. The left-hand columns show the results when only the two key
14

This might be due to the fact that workers at medium and small firms tend to work on
Saturdays and national holidays more often than workers at large firms, and to have a lower
wage rate.
14

explanatory variables are included. Interestingly enough, the coefficient on
PARANORMAL is positive here, indicating that those who believe in paranormal
phenomena are happier, while the coefficient on NONSCIENTIFIC is significantly
negative, implying that non-scientific thinking lowers happiness. This result highlights
the substantial functional difference between belief in paranormality and non-scientificthinking.
In the right columns, results for the full specification of equation (2) are shown.
Once again, the coefficients of the two key variables, PARANORMAL andNONSCIENTIFIC, are significantly positive and negative, respectively. Age andsquared age are not significant at all. Another statistically significant finding is thatfemales are happier than males.
In panel B of Table 5, we present the results of equation (3), where INCOME is
added to equation (2). While the coefficient on INCOME is highly significant as
expected, the coefficients on PANRANORMAL and NONSCIENTIFIC are almost
unchanged from panel A. The coefficient on DMAN is larger and age and squared age
are now significant, indicating that the happiness function is U-shaped in age, which isconsistent with many previous studies (Clark 2007). 15 We should note that the15

The results using age dummies indicate that happiness is lowest during people’s 50s.
15

coefficients of PARANORMAL and NONSCIENTIFIC in panel B represent only theirdirect effect on happiness; they have also an indirect effect through income.

3.3

Robustness

check:

Alternative

measuresfor

PANRANORMALandNONSCIENTIFICIn this subsection, we check the robustness of the results presented in the previous
subsections. In particular, we check whether the results remain unchanged whenalternative measures for paranormality and non-scientific thinking are used.
Previous studies have examined the relationship between religious and paranormal
beliefs (Williams et al. 2006, Rice 2003, Smith and Simmonds 2006). Education is also
believed to relate to paranormal beliefs (Aarnio and Lindeman 2005, Peltzer 2003).
Thus, it may be reasonable to include attitudes towards religion (RELIGION) andeducation level (EDUCATION) in our set of raw outcome variables, from whichparanormality and non-scientific thinking are extracted by factor analysis. Specifically,
RELIGION is defined as a survey respondent’s agreement with the statement “I amdeeply religious,” and EDUCATION is defined by years of schooling.
We conduct a factor analysis of 13 outcome variables, including RELIGION and
EDUCATION (results not shown). The factor loading of RELIGION on factor 1 is 0.216,

16

implying that it contributes to factor 1 as expected, but only weakly. On the other hand,
EDUCATION has a factor loading of -0.132 on factor 2, which is an only small
contribution. The other variables show similar factor loadings on both factors 1 and 2.
Based on these results, we construct new variables PARANORMAL2 andNONSCIENTIFIC2.
We

estimateequations(1)through(3)

with

PARANORMAL2

andNONSCIENTIFIC2 on the left-hand side. All of the results in Tables 4 and 5 arequalitatively confirmed (results not shown); NONSCIENTIFIC2 is now more significant,and its coefficient is now larger than in the previous estimations. Thus, the conclusions
in the previous subsections are robust to the inclusion of religion and education in thefactor analysis that we use to define paranormality and nonscientific thinking.

4.

Performance of the Homo economicus: An extension

Homo economicus is characterized by rationality, perfect intellectual ability, perfectselfishness, and perfect self-control. Although we have focused on the effects ofrationality, it is interesting to ask how the other attributes of Homo economicus affectindividual performance. In this section, we define proxies for the other aforementioned
characteristics of Homo economicus, using other answers to our survey questions, and17

we investigate the effects of each characteristic on our performance measures.

4.1 Definitions of variables
In this subsection, we define the additional variables used in the regression analysis.
Intellectual ability (ABILITY)
For intellectual ability, we use subjects’ self-reported school grades at age 15.
Respondents were asked to choose from a 5-point scale, from 1 (“in the lower rank”) to5 (“in the higher rank”).
Selfishness (SELFISH)
To measure selfishness, we used the answer to the following survey question: Does thefollowing statement hold true for you? “I don’t sit in a priority seat on public
transportation because I want to offer it to others.” Again, the response is on a
5-point scale, from 1 (“This is particularly true for me”) to 5 (“This is not true for me atall”). SELFISH is defined by this answer.
Self-control (SELFCONTROL)
For the self-control variable, we asked whether the following six statements held true
for the respondents, on a 5-point scale from 1 (does not hold true at all) to 5
(particularly true).
18a) Even if I make plans, I end up procrastinating.
b) I always keep my promises.
c) When I have something I want, I can’t bear not to buy it.
d) I always plan carefully before making an action.
e) No matter how angry I get, I don’t shout at others.
f) When I am faced with a problem, I usually act before I think.
SELFCONTROL is defined by the sum of these answers; where appropriate, the signs ofanswers are reversed so as to assign higher values to greater self-control.16
Our regression equations are just equations (1) through (3), adding ABILITY,SEFLISH, and SELFCONTROL as regressors. Larger coefficients on these variables
means that subjects are more akin to Homo economicus.

4.2 Estimation results for the extended model
Estimation results for the extended model are presented in Table 6. In the left-handcolumns, the results for the extended equation (1) are shown. The coefficients onparanormality and non-scientific thinking are negative, but only paranormality issignificant at the 10% level. This may be due to the inclusion of ABILITY. As was
16

We also define two other variables, SC_PLAN, which is defined as the sum of answers to thequestions a) through d), and SC_FEEL, which is defined as the sum of e) and f). However,estimation results using these are not different from those using SELFCONTROL.
19

shown in Table 3, both PARANORMAL and NONSCIENTIFIC are related to a lack ofability. When ABILITY is deleted from the specification, PARANORMAL andNONSCIENTIFIC are negative and significant at the 1% and the 5%, respectively(results are not shown). Gender has almost the same impact on income as in the original
equation (1). The coefficient on ABILITY is positive and significant, while thecoefficients on SELFISH and SELFCONTROL are not significant.
In the middle of the table, results for the extended equation (2) are shown.
Paranormality affect happiness positively, and non-scientific thinking negatively, as in
Table 5. Thus, the impacts of these variables on happiness are robust. The results forgender and age also do not change much. As for the effects of the other newly-added
explanatory variables, coefficients of ABILITY and SELFCONTROL are significantly
positive. SELFISH is negative but insignificant. The negative sign of the point estimate
is consistent with previous studies on altruism (Konow and Earley 2008, Phelps 2001).
When income is added (extended equation (3)), the estimation results are almost
unchanged (right-hand columns).

5. Conclusions
This paper investigated the individual-level outcomes of irrationality. Although20rationality usually brings about better outcomes, if irrationality predominates in society,this may not be the case (DeLong et al. 1990). Thus, it is interesting to examine theoutcome of irrationality empirically. We used two main concepts of irrationality,
paranormal beliefs and non-scientific thinking, which were abstracted from elevenquestions by factor analysis. Although both of these beliefs represent irrationality in thatthey contradict facts, factor analysis revealed that they are distinct phenomena.
This paper adopts two measures of individual performance: income and happiness.
While income measures economic performance, happiness may be a good measure ofself-evaluated performance, including psychological aspects.
Empirical results reveal that both paranormality and non-scientific thinking lower
income. Interestingly enough, non-scientific thinking and paranormality affecthappiness in opposite directions: the former lowers happiness while the latter raises it.
As belief in religion is known to raise happiness (Cohen 2002), belief in paranormality
might act similarly to religiosity.
We extended our analysis to investigate the effects of various other characteristics
of Homo economicus. Higher ability resulted both in higher income and happiness.
Higher self-control contributes to higher happiness. Selfishness did not have asignificant impact. These results suggest that though many elements of homo21economicus raise economic performance and happiness, paranormality and selfishness
act differently. While paranormality lowers economic performance, it raises happiness;selfishness does not affect either of them significantly, though our point estimate for its
effect is negative. As it is known that altruistic and/or religious people tend to be happy,
these are not surprising results.
The present paper has many limitations. First, endogeneity of the regressors may
be a problem. The regressors are basic traits of human beings, which are probably
inherited or determined in childhood, so that they should essentially be exogenous to theincome and happiness of adults. However, we cannot totally deny the possibility thatthese basic traits are influenced by standard of living in adulthood. Since it seems like aformidable task to find appropriate instrumental variables for the questions asked in our
survey, addressing the possible endogeneity problem remains as future work.
Second, Homo economicus may have basic traits that the present paper has
overlooked. For example, Homo economicus is a maximizer rather than a satisficer, andmany studies report that the former tend to be unhappier than the latter (Schwartz et al.
2002, Oishi et el. 2014). Another example is that hyperbolic discounting produces time
inconsistency and less efficient behavior (Laibson 1997), whereas Homo economicus
may be characterized by exponential discounting. In addition, human beings in the real22

world cannot be homogenous: for example, naïve and sophisticated persons havesystematically different behavior (O’Donoghue and Rabin 1999). Thus, analysis ofvarious types of human beings is called for. Given these arguments, the present paper
takes only a small step toward the study of Homo economicus as a benchmark forhuman effectiveness.

23

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Table 1. Questions on irrationality and descriptive statistics of the answers
Category

paranormality

non-scientificthinking

Question
Spirits and Ghosts exist.
Heaven exists.
God or Gods exist.
Life after death exists.
God knows about all the wrong
things we've done.
It is possible to move an object byusing psychokinesis.
I believe in fortune telling.
A person's blood type indicates
their character.
Human beings evolved from otherliving things.
You should place a greater valueon thinking with your head thanwith your heart.
What is written in science text
books is true.

Name of variables

GHOST
HEAVENGOD
AFTERWORLDWRONGDOINGPSYCHOKINESISFORTUNETELLINGBLOODTYPE
EVOLUTION

1
15.21
10.40
9.58
14.71

2
15.33
11.10
10.55
12.96

Share (%)
3
40.58
48.86
40.41
43.18

4
20.60
22.08
28.53
20.62

5
8.27
7.55
10.93
8.53

10.71

12.72

39.70

26.31

27.90

25.57

38.03

16.39

19.74

12.74

mean

Standard
deviation

2.914
3.053
3.207
2.953

1.136
1.023
1.081
1.124

10.56

3.133

1.106

7.04

1.45

2.286

0.995

44.48

17.52

1.87

2.687

1.003

20.03

42.72

23.33

1.17

2.802

0.975

18.33

36.64

34.96

6.22

3.84

2.406

0.981

5.12

33.52

56.01

4.72

0.62

2.622

0.685

5.41

38.65

49.66

5.13

1.15

2.580

0.724

HEADTHANHEARTSCIENCETEXT

Note: Each question asks: “Do you agree with the following idea?” Larger numbers indicate greater paranormality and more
non-scientific thinking.

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Table 2. Rotated factor loadings and uniqueness
Variable

AFTERWORLDGOD
HEAVENGHOST
WRONGDOINGFORTUNETELLINGPSYCHOKINESISBLOODTYPEHEADTHANHEARTEVOLUTIONSCIENCETEXT

Factor1
0.856
0.809
0.802
0.704
0.702
0.644
0.530
0.427
-0.001-0.066-0.087

Factor2 Uniqueness
0.015
0.265
-0.0540.349-0.038
0.360
0.041
0.498
-0.090
0.509
0.058
0.577
0.149
0.685
0.020
0.816
0.502
0.748
0.362
0.868
0.338
0.883

Note: Principal factor analysis with promax rotation was applied.

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Table 3. Causes of paranormality and non-scientific thinking
PARANORMAL
Coef.

NONSCIENTIFIC

Std. Err

Coef.

Std. Err

Constant

0.214 [0.108]**0.204 [0.074]***DMAN-0.436 [0.030]***-0.112 [0.021]***

D_AGE200.401 [0.087]***0.380 [0.060]***

D_AGE300.440 [0.073]***0.255 [0.050]***

D_AGE400.521 [0.068]***0.256 [0.047]***

D_AGE50

0.306 [0.067]***0.208 [0.046]***

D_AGE60

0.075 [0.067]

0.143 [0.046]***EDUCATION_P-0.024 [0.009]***-0.011 [0.006]*

LIVING_15

0.051 [0.009]***0.004 [0.006]*

ABILITY-0.087 [0.015]***-0.080 [0.010]***Adjusted R

2

0.116

0.044

Number of Observations

3588

3588

Note: *, ** and *** indicate that the coefficient is significant at the 10%, 5% and 1%
level, respectively.

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Table 4. Estimation results for equation (1)INCOME
Coef.

WAGERATE

Std. Err

Coef.

Std. Err

PARANORMAL-0.046 [0.016]***-0.047 [0.017]***NONSCIENTIFIC-0.054 [0.023]**-0.045 [0.025]*

EXPERIENCE

0.029 [0.006]***0.024 [0.007]***

EXPERIENCE2-0.001 [0.000]***-0.0004 [0.000]***TENURE

0.034 [0.004]***0.029 [0.005]***TENURE2-0.0004 [0.000]***-0.0003 [0.000]***D_MAN

0.617 [0.034]***0.404 [0.034]***WHOUR

0.008 [0.001]***

WDAY
Constant-0.0004 [0.000]
4.508 [0.112]***

6.443 [0.100]***

Obs

1775

1801

R-squared

0.38

0.21

Note: Dependent Variable is income. Estimation method is ordinary least squares.
*, ** and *** indicate that the coefficient is significant at the 10%, 5% and 1% level,
respectively.

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Table 5. Estimation results for happiness equations
Panel A. Results for equation (2)Coef.

Panel B.

Std. Err

Coef.

Std. Err

PARANORMAL

0.073 [0.017]***0.044 [0.018]**NONSCIENTIFIC-0.146 [0.026]***-0.161 [0.026]***

D_MAN-0.185 [0.034]***AGE-0.0004 [0.009]

AGE2-0.00002 [0.000]

Obs

3895

3895

Pseudo R-Squared

0.003

0.005

Results for equation (3)Coef.

Std. Err

Coef.

Std. Err

PARANORMAL

0.065 [0.020]***0.043 [0.021]**NONSCIENTIFIC-0.136 [0.031]***-0.144 [0.031]***

D_MAN-0.341 [0.049]***AGE-0.029 [0.011]***AGE20.0003 [0.000]**

INCOME

0.115 [0.022]***0.231 [0.027]***

Obs

2794

2794

Pseudo R-Squared

0.005

0.01

Note: Dependent variable is HAPPINESS. Estimation method is ordered probit. *, **and *** indicate that the coefficient is significant at the 10%, 5% and 1% level,
respectively.

33

Table 6.

Estimation results for the extended model

ln_INCOME
Coef.

HAPPINESS

Std. Err

Coef.

Std. Err

Coef.

Std. Err

PARANORMAL-0.027 [0.016]*

0.06 [0.019]***0.056 [0.022]**NONSCIENTIFIC-0.02 [0.023]-0.083 [0.027]***-0.082 [0.032]**

SELFISHNESS-0.004 [0.014]-0.017 [0.016]-0.031 [0.019]

ABILITY

0.131 [0.014]***0.179 [0.016]***

0.124 [0.019]***SELFCONTROL

0.002 [0.005]

0.043 [0.006]***

0.043 [0.007]***

D_MAN

0.628 [0.034]***-0.177 [0.035]***-0.294 [0.050]***EXPERIENCE

0.03 [0.006]***EXPERIENCE2-0.001 [0.000]***TENURE

0.032 [0.004]***TENURE2-0.0004 [0.000]***WHOUR

0.008 [0.001]***AGE-0.001 [0.010]-0.022 [0.011]**AGE2-0.0001 [0.000]

0.0002 [0.000]*

WDAY
Constant-0.0002 [0.000]
3.962 [0.158]***

INCOME

0.192 [0.028]***

Obs

1735

3752

2719

R-squared

0.41

0.02

0.02

Note: We show Adjusted R-squared for the income regression and pseudo R-squared for the happiness regression. *, ** and *** indicatethat the coefficient is significant at the 10%, 5% and 1% level, respectively.
34