Discussion Paper No. 844

HOMO ECONOMICUS VS. HUMAN BEING:OUTCOMES OF IRRATIONALITY

Shoko Yamane
Hiroyasu Yoneda
Yoshiro Tsutsui

May 2012

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Homo Economicus vs. Human Being：
Outcomes of Irrationality
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 non-scientific thinking lowers income,whereas paranormality does not affect it. While non-scientific thinking lowers
happiness, paranormality raises it. Extending the model, we find that higher ability andself-control result in higher income and happiness. Selfishness raises income, butdiminishes happiness. These results suggest that Homo economicus generally achieves
higher individual performance, except that belief in paranormality raises happiness.

JEL classification number; D03
Keywords; paranormality, non-scientific thinking, irrationality, happiness, factor
analysis, Homo economicusThe authors are grateful to comments by Noah Smith and to financial support by 21st Century
Center of Excellence Program of Osaka University.
†
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‡
Institute of Social and Economic Research, Osaka University; 6-1 Mihogaoka, Ibaraki, Osaka
567-0047; phone +81-6-6879-8552; fax +81-6-6879-8584;
e-mail: hiroyasu.yoneda@gmail.com§
Graduate School of Economics, Osaka University, 1-7 Machikaneyama, Toyonaka, 560-0043
JAPAN, Phone: +81-6-6850-5223, Fax: +81-6850-5274,
e-mail: tsutsui@econ.osaka-u.ac.jp

1. Introduction
This paper aims to discover how irrationality affects the performance (income andhappiness) of human beings.
Rationality is the essential assumption of traditional economics, meaning thatagents act in a self-interested fashion given their constraints. In addition to rationality
the following elements are commonly ascribed to “Homo economicus”: unlimited
cognitive resources, pure self-interest, and perfect self-control. However, this is not the
only possible description of economic agents; Homo economicus is often simply
adopted for modeling convenience. Therefore, it is interesting to ask: “Does Homoeconomicus exhibit performance that is superior to real-world human beings?” In thispaper, we focus on the effect of irrationality on performance, although we also
investigate the impact of the other characteristics of Homo economicus.1
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 inefficientif irrational agents comprise a substantial fraction of the market participants.2 The result

1

Konow and Earley (2008), based on their dictator game experiment, found that more generous
people report greater happiness.
2
The authors assume that rational agents are risk averse, which restricts them from making anunlimited arbitrage.
1

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.
We consider the core of rationality to consist of logical thinking, which is theunique tool by which human beings identify the appropriate actions to achieve theirgoals. We measure individuals’ degree of logical thinking by assessing the degree towhich they believe in science 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. 3 Whileparanormality has not often been analyzed in the economics literature, there have been
many studies on this topic in the field of psychology. However, the relationship between
paranormal beliefs and individual success has, to our knowledge, not yet been studied.

3

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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 2007,
Rice 2003, Peltzer 2003). Thus, belief in paranormal phenomena in Japan, especiallyamong younger individuals, may not stem from religious beliefs, but from superstitions
spread by mass media.4
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 physical laws, although chance also
plays a large role. On the other hand, human beings also pursue subjective happiness,
which is strongly affected by one’s mental condition. Indeed, Wills (2009) reported thathigher satisfaction with spirituality and religiosity brings about significantly higher
well-being (see also Cohen 2002). Although happiness depends on income to somedegree, the two may sometimes diverge. Thus, we investigate how paranormality and4Fortunetelling based on blood types or horoscopes is broadcasted daily on many Japanese TV
channels.
3non-scientific thinking affect both income 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 a preliminary analysis
regarding the origins 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. Three thousand and forty
eight (3,048) people aged 20-75 were selected from all over Japan by double stratified
random sampling. Selected respondents were visited at their homes and handed thequestionnaire. Several days later, completed questionnaires were picked up. Twothousand 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 over4

Japan, and the same questionnaire was sent 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 are pooled and used for the analysis, so that the total
number of observation is 4,018.

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 these
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 factor5

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 measureused 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 predominant. Except for the question regarding “Human beings evolved from otherliving things,” the modal response is in the middle of the 5-point scale. Though thenumber of responses indicating non-belief in science is a minority, the distribution 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 or gods exist”“Life after death exists,” and “God knows about all the wrong things we’ve done.”

2.3 Factor analysis

6

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.5
The results of that factor analysis are presented in Table 2. It is clear that the first
factor has a large loading on the eight questions associated with paranormalilty, while
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.6 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 that
5

Specifically, we use principal factor analysis (PFA) with promax rotation.
When respondents have only one parent, the variable is defined as the schooling years of thatparent.

6

7

growing 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(ABILITY).7
Gender may play an important role, because boys and girls are often educated tobehave differently and to seek different life goals. We 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 on paranormal
phenomena when that generation was young; alternatively, belief in paranormality
might depend on age itself. Thus, we add age decade dummies (e.g. D_AGE20 is adummy representing whether a respondent is in his or her 20s) to the regression. 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,

7

Musch and Ehrenberg (2002) used grades in junior high school as a proxy for cognitive ability
and examined the correlation with paranormality.
8

Wolfradt 1997, Wiseman and Watt 2004, Williams et al. 2007).8 Paranormality tends tobe 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 moreparanormality.
Results regarding non-scientific thinking are shown in the right-hand columns.
The results are similar to those for paranormality. The only differences are thatchildhood standard of living is not significant, and that non-scientific thinking decreases
monotonically with age. This last result presents an interesting contrast with the resultfor paranormality. Respondents in their 40s were teenagers around 1978, whenparanormal phenomena (spoon-bending by Uri Geller and Kokkuri-san, a kind oftable-turning, etc.) were very popular in the media. Therefore, we might be seeing agenerational 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 and

8

However, Peltzer (2003) finds no significant gender differences among secondary anduniversity students in South Africa.
9

self-evaluative. 2007 pretax income (on a 12-point scale) is included as a question in thesurvey. Following Barsky et al. (1997), we fit a lognormal distribution to the income
histogram and estimate the income for each class; this estimation result is called
INCOME.
HAPPINESS is defined by the answer to the following question: Overall, how
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 on other control
variables. Because males tend to earn higher incomes than females, we include thedummy variable DMAN. In Japan, wage rates are often tied to age; therefore, we add thevariables of age (AGE) and its square (AGE_SQ).9
Thus, the regression equation for INCOME is:
a

a

a

9

a

We also estimated a specification using age dummies instead of age and its square. However,
the results are almost identical.
10

a

a

_

(1),

where i indexes individuals andis a disturbance term. Equation (1) is estimated by

OLS. However, the inclusion of the male dummy variable may cause a problem. Since
females tend to be more paranormal, the dummy may be collinear with PARANORMAL.
Therefore we also estimate a variant of equation (1) on male-only and female-onlysubsamples.
Since it is well known that happiness depends on gender and age (Frey and Stutzer
2002), we add these variables to the regression equation for HAPPINESS:
b
b

b

b

_

b

b

(2)

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 affect11

HAPPINESS through INCOME. Equations (2) and (3) are estimated by ordered probit,
since HAPPINESS is an ordered variable. In addition, in order to compare themagnitude of direct and indirect effects, we conduct a path estimation.

3. Estimation results
3.1 Effect of irrationality on income
The estimation results for equation (1) are presented in Table 4. On the left are shown
the results when income is regressed over only the key variables, PARANORMAL andNONSCIENTIFIC. Both coefficients are significantly negative, implying thatparanormal and non-scientific thinking lowers economic performance.
In the right columns, estimates for equation (1) are presented. The coefficient on
non-scientific thinking is negative and significant. The maleness dummy, DMAN, issignificantly positive, as expected. Age and squared age are also significant, and are
positive and negative respectively, implying that income peaks at age 48. The
coefficient on PARANORMAL is insignificant.
In order to check if belief in paranormality affects income independent of gender,
we estimate equation (1) for single-gender subsamples. The results for males are similar
to

the

total

sample;whereasNONSCIENTIFIC

12

issignificantly

negative,

PARANORMAL is negative but insignificant (p-value = 0.266, results not shown). On
the other hand, for the female-only subsample, most of the coefficients are notsignificant, suggesting that the specification is not adequate to explain female income. It
is not easy to explain how female earnings are determined: for example, marital status
and income of husband (if married) may strongly affect how much a woman earns. The
result that PARANORMAL is not significant for either gender suggests that income may
not depend on belief in paranormality.
In sum, controlling for gender, we have a robust result that non-scientific thinking
lowers income, while there is no clear evidence that belief in paranormality has any
effect on income.

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
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

13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.
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 becomes larger and age andsquared age become significant, indicating that the happiness function is U-shaped inage, consistent with many previous studies (Clark 2007).10 We should note that thecoefficients of PARANORMAL and NONSCIENTIFIC in panel B represent their direct
effect on happiness; they have also an indirect effect through income.
In order to compare the magnitude of direct and indirect effects, we conducted apath estimation. The results are shown in Figure 1. Here, we assume that paranormality
and non-scientific thinking are affected by gender and age, that income is affected by all

10

The results using age dummies indicate that happiness is lowest during people’s 50s.
14

four of these variables, and that happiness depends on these four and income. The figure
reveals that income is influenced by non-scientific thinking, but not by paranormality,
and that happiness is influenced by both (with opposite signs). This confirms the results
shown in Tables 4 and 5. Looking at standardized coefficients, the indirect influence of
non-scientific thinking on happiness through income appears to be much smaller thanthe direct influence.

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 if the results remain unchanged when alternative
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,

15

RELIGION is defined as a survey respondent’s agreement with the statement “I amdeeply religious,” and EDUCATION is defined by years of schooling.
We conducted 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, implying that it contributes to factor 1 as expected, but weakly. On the other
hand, EDUCATION has a factor loading of -0.132 on factor 2, which is a 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 becomes moresignificant, and its coefficients become larger, than in the previous estimations. Thus,
the conclusions in the previous subsections are robust to the inclusion of religion andeducation in the factor analysis by which we define paranormality and nonscientificthinking.

4.

Performance of the Homo economicus: An extension

16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, andwe 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 at17

all”). 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 (not hold true at all) to 5 (particularlytrue).
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.11
Our regression equations are just equations (1) through (3), adding ABILITY,SEFLISH, and SELFCONTROL as regressors. Higher values of these variables meansthat 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. Paranormality and11

We also defined two other variables, SC_PLAN, which is defined by the sum of answers to
the questions a) through d), and SC_FEEL, which is defined by the sum of e) and f). However,estimation results using these are not different from those using SELFCONTROL.
18

non-scientific thinking are negative but insignificant, though this may be due tocollinearity. Gender and age give almost the same impact on income as in the original
equation (1). ABILITY and SELFCONTROL are positive and significant, while
SELFISH is not significant; this indicates that those who are generally akin to Homoeconomicus tend to achieve higher incomes.
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. The effects of the other newly-added
explanatory variables are almost the same as in the income regression. The only
difference is that SELFISH is now negative and insignificant rather than positive andinsignificant. 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. Althoughrationality usually brings about higher outcome, if irrationality predominates in society,19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 represent irrationality in that they
contradict facts, factor analysis revealed that they are distinct phenomena.
This paper adopts two measures of individual performance: income and happiness.
Although intuition suggests that income is a good measure of economic performance,
this does not hold true for females in Japan. On the other hand, happiness is a goodmeasure of self-evaluated performance, including psychological aspects.
Empirical results reveal that non-scientific thinking lowers income, while
paranormality does not. Interestingly enough, non-scientific thinking and paranormality
affect happiness in opposite directions: the former lowers happiness while the latter
raises it. As belief in religion is known to raise happiness (Cohen 2002), belief inparanormality might act similarly to religiosity.
We extended our analysis to investigate the effects of various other characteristics
of Homo economicus. Higher ability and self-control resulted in higher income andhappiness. Selfishness did not have a significant impact. These results, in general,
support the idea that Homo economicus achieves higher performance than other20

subspecies of humanity.
The present paper has many limitations. First, income does not work well as ameasure of economic performance for women in Japan. Since we obtained similar
results for the male-only and total samples, the results for income seem credible. Still,
finding a good measure for economic performance for females is a goal for the future.
Second, 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 thatthey should essentially be exogenous to the income and happiness of adults. However,
we cannot totally deny the possibility that these basic traits are influenced by standardof living in adulthood. Since it seems formidable task to find appropriate instrumental
variables from the questions of our survey, addressing the possible endogeneity problem
remains as a future work.
Third, Homo economicus may have basic traits that the present paper overlooked.
For example, hyperbolic discounting produces time inconsistency and less efficient
behavior (Laibson 1997), whereas Homo economicus may be characterized byexponential discounting. In addition, human beings in the real world cannot behomogenous: for example, naïve and sophisticated persons have systematically different
behavior (O’Donoghue and Rabin 1999). Thus, analysis of various types of human21beings is called for. Given these arguments, the present paper only takes a small step
toward the study of Homo economicus as a benchmark for human effectiveness.

22

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

Question

Spirits and Ghosts exist.
Heaven exists.
God or Gods exist.
Life after death exists.
God knows about all the wrong
paranormality things we've done.
It is possible to move an object
by using psychokinesis.
I believe in fortune telling.
A person's blood type indicates
their character.
Human beings evolved fromother living things.
You should place a greater
non-scientificvalue on thinking with yourthinking
head than with 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

2.914
3.053
3.207
2.953

Standard
deviation
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

mean

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

Factor1

Factor2

Uniqueness

AFTERWORLD

0.856

0.015

0.265

GOD

0.809

-0.054

0.349

HEAVEN

0.802-0.038

0.360

GHOST

0.704

0.041

0.498

WRONGDOING

0.702-0.0900.509

FORTUNETELLING

0.644

0.058

0.577

PSYCHOKINESIS

0.530

0.149

0.685

BLOODTYPE

0.427

0.020

0.816

HEADTHANHEART-0.001

0.502

0.748

EVOLUTION-0.066

0.362

0.868

SCIENCETEXT-0.087

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

p valueCoef.

p valueConstant

0.214 [0.047]**0.204 [0.006]***DMAN-0.436 [0.000]***-0.112 [0.000]***

D_AGE200.401 [0.000]***0.380 [0.000]***

D_AGE300.440 [0.000]***0.255 [0.000]***

D_AGE400.521 [0.000]***0.256 [0.000]***

D_AGE50

0.306 [0.000]***0.208 [0.000]***

D_AGE60

0.075 [0.257]

0.143 [0.002]***EDUCATION_P-0.024 [0.005]***-0.011 [0.063]*

LIVING_15

0.051 [0.000]***0.004 [0.489]

ABILITY-0.087 [0.000]***-0.080 [0.000]***Adjusted R

2

0.116

0.044

Number of Observations

3588

3588

28

Table 4.

Estimation results for equation (1)Coef.
Constant
PARANORMALNONSCIENTIFIC
DMAN
AGE
AGE_SQp valueCoef.

p value

312.783 [0.000]*** -560.175 [0.000]***-40.511 [0.000]***-6.878 [0.192]-38.322 [0.000]*** -21.744 [0.004]***327.185 [0.000]***31.794 [0.000]***-0.328 [0.000]***Adjusted R2
Number ofobservations

0.022

0.299

3235

3235

Note: Dependent Variable is income. Estimation method is ordinary least squares.

AGE_SQ is squared age.

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Table 5. Estimation results for happiness equations
Panel A. Results for equation (2)Coef.
PARANORMALNONSCIENTIFIC
DMAN
AGE
AGE_SQ
Pseudo R2
Number ofobservations
Panel B.

p valueCoef.

p value

0.073 [0.000]***-0.146 [0.000]***0.044-0.161-0.185-0.0004-0.00002

0.003[0.014]**[0.000]***[0.000]***[0.969][0.836]
0.005

3895

3895

Results for equation (3)Coef.
PARANORMALNONSCIENTIFIC
INCOMEDMAN
AGE
AGE_SQ
Pseudo R2
Number ofobservations

p valueCoef.

p value

0.076 [0.000]***-0.152 [0.000]***0.0003 [0.000]***0.042-0.1680.001-0.368-0.0220.00020.005[0.038]**[0.000]***[0.000]***[0.000]***[0.038]**[0.064]*
0.011

3211

3211

Note: Dependent variable is HAPPINESS. Estimation method is ordered probit.

30

Table 6.

Estimation results for the extended model
INCOME

Constant
PARANORMALNONSCIENTIFIC
DMAN
AGE
AGE__SQSELFISHABILITY
SELFCONTROLINCOME
R2
Number ofobservations

HAPPINESS

Coef.

p valueCoef.

p valueCoef.

p value-819.365-1.357-8.593330.31932.895-0.3435.733
44.9354.115[0.000]***[0.797][0.268][0.000]***[0.000]***[0.000]***[0.202][0.000]***[0.014]**0.060-0.083-0.177-0.0010.000-0.0170.179
0.043[0.001]***[0.002]***[0.000]***[0.957][0.655][0.293][0.000]***[0.000]***0.056-0.099-0.327-0.0180.000-0.025
0.142
0.040
0.001

[0.007]***[0.001]***[0.000]***[0.096]*[0.211][0.156][0.000]***[0.000]***[0.000]***

0.324

0.018

0.020

3144

3752

3122

Note: We show Adjusted R-squared for the income regression, and pseudo R-squared for the happiness regression.

31

-0.168
DMAN
0.515-0.2270.037-0.098PARANORMALINCOMEe1 = 0.95

0.173

HAPPINESS-0.034-0.176NONSCIENTIFIC

e3 = 0.85-0.102-0.115 e2 = 0.99
AGE

e4 = 0.98-0.031Figure 1. Path diagram
Note: Values in the figure are standardized coefficients. e1 to e4 are random terms.

32