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Overconfidence through Group Composition

The Effect of Group Composition on Overconfidence

Msc Behavioral Economics

Student: Arjan den Besten

Student Number: 415298

Supervisor: Chen Li

Co-Reader: Prof. Peter Wakker

Date of Completion: 17-6-2015

Erasmus School of Economics

1Overconfidence through Group Composition, Arjan den Besten (415298ab), Behavioural Economics 2014-2015, Erasmus School of Economics ©

Table of Contents

Abstract……………………………………………………………………………………….3

1. Introduction………………………………………………………………………………..5

2. Literature review…………………………………………………………………………..8

Overconfidence……………………………………………………………………...…8

Group Composition…………………………………………………………………...12

Willingness to bet……………………………………………………………………..13

Anchoring……………………………………………………………………………..13

3. Research question, conceptual model and

hypotheses………………………………….15

4.

Methodology……………………………………………………………………………….17

Participants and procedure design ……………………………………………………17

Dependent measures…………………………………………………………………..18

Individual differences measures………………………………………………………19

The questionnaire……………………………………………….….........................…19

5. Statistical Analysis………………………………………………………………………..21

6. Results……………………………………………………………………………………..23

7. Conclusion………………………………………………………………………………...30

Main findings…………………………………………………………………………31

Recommendations…………………………………………………………………….32

Future research………………………………………………………………………..33

Bibliography………………………………………………………………………………....35


Appendix……………………………………………………………………………………..38

Abstract

Moore and Healy (2008) present 3 distinct ways in which the research literature has defined

overconfidence: (a) overestimation of one’s actual performance, (b) over-placement of one’s

performance relative to others and (c) excessive precision in one’s belief (over-precision). This

thesis has attempted to find out what the influence of group composition is on the three types of

overconfidence through conducting a hypothetical pub quiz among students. In the treatment

group (n=30), subjects were told that they were competing with lower-skilled and less-educated

participants, whereas in the control group (n=30) their opponents were university students just

like the respondents. The results have shown that overestimation occurs more when a student is

hypothetical surrounded by lower-skilled and less-educated participants than themselves. On

the other hand, the student is under-placing herself/himself to other participants. Further, it is

shown that people, especially men, who are very confident about their own performance, are

willing to bet more money in competitions. A number of possible explanations for the results and

corresponding recommendations will be outlined in the conclusion of this study.


Chapter 1: Introduction

People are influenced by other people’s presence all the time. This could happen consciously,

but unconsciously as well. For example, the presence of other people around you has an

influence on the meal size you choose (De Castro & De Castro, 1989). The more people

around you when you choose your meal, the larger the meal size that you choose will be. One

other study reveals the social influence on people their driving speed in a car. Passengers,

parents, but also unknown other drivers have an influence on the behavior of the driver (Judy

& Alexia, 2010). For instance, the presence of passengers has a slowing influence on drivers,

because the driver feels responsible for the safety of people in the car. More research can still

be done on the influence of the presence of others on people´s behavior. The aim of this thesis

is to measure the influence of the presence of two different groups on people their

overconfidence. Overconfidence is an often occurring phenomenon in daily life, whose

relation to stock investment, gender, CEO decision making, corporate investment and

entrepreneurship (Barber et al., 2015; Bernardo & Welch, 1997; Malmendier & Tate, 2005)

has been studied extensively in the literature. However the definition of overconfidence

differs from study to study. Moore and Healy (2008) categorized the definition of

overconfidence in the literature into the following three types. This first type of

overconfidence is overestimation of one’s actual performance. Overestimation basically

means that people believe that their own abilities are greater than they indeed are. For

example, you could be overconfident in your physical strength when doing weight lifting

exercises in the gym, and end up not being able to finish your weight lifting sets. The second

type of overconfidence is over-placement of one’s performance relative to others. Over-

placement means that people believe that they are better than others. For instance, a student

has to do an exam. She estimates that her own grade will be an eight and the average grade of

the class will be a seven. Then she does the exam and she only scores a seven. The actual

average grade of the class is an eight. In this example the student is over-placing herself to

others in class. The third type of overconfidence is over-precision, which is often defined as

excessive precision in one’s beliefs. For instance, when people are ask to estimate a

confidence interval of the length of a river, they have the tendency to choose a confidence

interval that is too narrow. More specific, people estimate that the length of a river will fall

within the 2-4 kilometers interval. They are 90% sure that this interval is correct. But, the

actual length of the river is 5 kilometers. This means that people become over-precise in their


estimation of the confidence interval.

Healy and Moore (2008) show interesting findings about how the three types of

overconfidence can interact. On difficult tasks, people overestimate their actual performances

but also believe that they are worse than others (under-placement). On easy tasks, people

underestimate their actual performance, but also believe that they are better than others. There

is a theory that can explain these inconsistencies. After experiencing a task, people often have

imperfect information about their own performances but even worse information about the

performances of others. As a result, people’s post-task estimates of themselves are regressive,

and their estimates of others are even more regressive. Thus, when the actual performance is

extremely high, people will underestimate their own performances, underestimate others even

more so, and thus believe that they are better than others. When performance is low, people

will overestimate themselves, overestimate others even more so, and therefore believe that

they are worse than others. The last type of overconfidence is over-precision, which appears

to be more persistent than either of the other 2 types of overconfidence, but its presence

reduces the magnitude of both overestimation and over-placement.

The design of this thesis is adapted from the study of Moore and Healy

(2008). In this research subjects participate in a hypothetical pub quiz competition, where the

composition of participants in the quiz competition differ between the treatment and control

group. In the treatment group, the participant pool consists of low-skilled workers whose

education background is different from the subjects (university students), whereas in the

control group, the participants are mostly university students. Next to studying the possible

relationship between group composition and overconfidence, this thesis also studies the

possible relationship between relative estimation and willingness to bet on their own quiz

performance. It is already shown that when people have excessive confidence in their

knowledge and are ready to bet on it, they even accept bets with a negative expected value

(Lichtenstein & Fischhoff, 1977). For this reason, the aim is to find a positive correlation

between overconfidence and willingness to bet.

To summarize, the aim of this thesis is to find an answer on the following

research question: “What is the effect of group composition on the overconfidence of people

and is overconfidence positively correlated with people their willingness to bet on their own

performance?

The findings of this thesis can be used by organizations, managers and policy makers

when they start forming work teams within their organizations. When the findings show that


people become more overconfident in a specific group composition, organizations could take

this into account when they form new work teams in order to prevent overconfidence within

their organization. For instance, organizations should be aware of possible overconfidence

when a team is formed with only one high-skilled worker and many low-skilled workers.

When the high-skilled worker has to take an important and risky decision, he might become

overconfident when he is only surrounded with lower-skilled and less-educated workers

around him. This problem can be solved by forming teams that consists a proper mix of

people with different educational backgrounds. This thesis could also be interesting for

organizations where risky decisions have to be made by people where overconfidence may

easily occur. Traders, brokers, bankers and workers at financial institutions should consider to

take into account the findings of this research (Dittrich, Güth, & Maciejovsky, 2005). Even

primary schools could think about the formation of the groups of children in school. Putting

one very intelligent child in a group with three less intelligent children might give the

intelligent child too much confident. To prevent this, it is better to form groups where there is

a proper mix of different levels of intelligence of children. On the other hand, not in all cases

overconfidence has to play a negative role in organizations. For instance, managerial

overconfidence can increase the value of the firm. So even if organizations want to stimulate

overconfidence instead of preventing it, this thesis could be helpful (Gervais, Heaton, &

Odean, 2002).


Chapter 2: Literature reviewOverconfidence

Overconfidence can have some serious consequences. Researchers have offered

overconfidence as an explanation for entrepreneurial failures, stock market bubbles and even

wars (Moore & Healy, 2008). According to Johnson and Fowler (2011) humans exhibit many

psychological biases, but one of the most consistent, powerful, and widespread bias is

overconfidence. Overconfidence amounts to an “error” of judgment or decision-making,

because it leads to: (1) overestimating one’s capabilities, and/or underestimating an opponent;

(2) underestimating the difficulty of a task; (3) or underestimating possible risks. According

to (Minniti, 2005) overconfidence is a perceptual distortion in the sense that one’s own

abilities are systematically overestimated. Own ability means one’s own success potential.

This definition is in line with the definition of overestimating one’s capabilities of Johnson

and Fowler (2011).

Moore and Healy (2008) show that the research literature has defined overconfidence

in three distinct ways. The first definition of overconfidence is overestimation of one’s actual

ability, performance, level of control, or chance of success. People believe that their own

abilities are greater than they indeed are. For example, students robustly overestimate their

exam performance (Clayson, 2005). This definition is very similar to the definition of

(Johnson & Fowler, 2011; Minniti, 2005). The second definition of overconfidence is over-

placement of one’s performance relative to others, such as when a majority of a group of

people rate themselves better than the median. In other words, people believe that they are

better than others. The third definition of overconfidence is excessive precision in one’s

beliefs, which is also known as over-precision. In other words, individuals believe that their

estimates are more accurate than they actually are, they are overconfident about their

precision (Croson & Croson, 2008). For example, when people are asked to estimate a

confidence interval of the length of a river, they have the tendency to choose a confidence

interval which is too narrow. In other words, people are too sure that they give the right

answer and through this overconfidence they are often too precise with their estimations.

Over-precision appears to be more persistent than over-placement and over-precision (Moore

& Healy, 2008). Russo and Schoemaker (1992) show

interesting results about over-precision in group judgments compared to individual judgments.

They found that group judgments were significantly less over-precise than individual 8

Overconfidence through Group Composition, Arjan den Besten (415298ab), Behavioural Economics 2014-2015, Erasmus School of Economics ©

judgments. So when people discuss their confidence ranges with other people, their private

judgments and the group judgments become less over-precise. When people form their

confidence ranges without discussing it with other people, their private judgments becomes

more over-precise.

Overestimation and over-precision are about an individual’s own ability and beliefs,

while over-placement is about one’s performance relative to others. Even though, over-

placement and overestimation can appear together and they often do, they are not necessarily

correlated (Croson & Croson, 2008). The study of Moore & Healy (2008) demonstrates useful

findings about overestimation and over-placement that both types of overconfidence are not

necessarily correlated. Participants underestimated their performances on easy quizzes,

overestimated them on difficult quizzes, and were accurate on medium quizzes. Participants

over-placed their performances on easy quizzes, under-placed their performances on difficult

quizzes and demonstrated no over- or under-placement on medium quizzes. Noteworthy is the

fact that participants underestimated their performances on easy quizzes, but at the same time

they over-placed their performances on these same easy quizzes. This contradiction also

applies for the difficult quizzes, but then the other way around. Participants overestimated

their performances on difficult quizzes, but at the same time they under-placed their

performances on these same difficult quizzes. Healy and Moore (2008) relate the negative

relationship between overestimation and over-placement to a theory of confidence. Namely,

after experiencing a task, people often have imperfect information about their own

performances but even worse information about the performances of others. As a result,

people’s post-task estimates of themselves are regressive, and their estimates of others are

even more regressive. Thus, when performance (difficult task) is low, people will

overestimate themselves, overestimate others even more so, and therefore believe that they

are worse than others. The theory of confidence assumes that people hold a

subjective probability distribution (SPD) of their own score and a SPD of others’ scores over

the range of possible outcomes on any task. Overestimation occurs when a person’s SPD of

his or her own score has a mean that is greater than the person’s actual score on the task.

Over-placement occurs when a person’s SPD of his or her own score has a mean that is

greater than the mean of the SPD of others’ scores and this belief is not justified by

differences in actual scores between self and others. Strongly involved with

over-placement are self-perception and our perception of others. Wojciszke (2005)

demonstrates that our perception of others is different from our self-perception. With the


perception of others, the perceiver is more interested in morality than competence of a person.

The perceiver construes their behavior in moral terms and emotional responses are more

based on morality than competence considerations. On the other hand, for self-perception our

behavior is more construed in competence terms than in moral terms. In addition, our own

competence influences self-evaluations and emotional responses more than our own morality

does. In other words, when forming evaluations of others, the perceiver is more interested in

the moral behavior of a person, while our own behavior is more construed in competence

terms. It is reasonable to assume that self-perception is involved in overestimation, over-

placement and over-precision. And others-perception is only involved in over-placement.

The definitions of overconfidence defined in the paper of Moore and Healy (2008) are

best suited to use in this research paper, because they demonstrate that the research literature

has defined overconfidence mostly in three distinct ways: overestimation, over-placement and

over-precision. These three definitions are included and tested in this thesis. According to

Moore and Healy (2008) overestimation, over-placement and over-precision are conceptually

and empirically distinct. But still, all three types are defined in the literature as

overconfidence. Russo and Schoemaker (1992) demonstrate how overconfidence can be

managed. They examine the causes and remedies for overconfidence. The potential cognitive

causes of overconfidence are the availability bias, anchoring, the confirmation bias and

hindsight. First, the availability bias means that people have difficulties in imaging all the

ways that events can unfold. Second, anchoring is the tendency to anchor on one value or idea

and not adjust away from it sufficiently. This thesis will investigate the effect of anchoring on

overconfidence as well. The section ‘Anchoring and adjustment’ elaborates on this effect.

Third, the confirmation bias means that when we make predictions or forecasts, we often lean

toward one perspective and the natural tendency is to seek support for our initial view rather

than to look for disconfirming evidence. And lastly, hindsight makes us believe that the world

is more predictable than it really is.

Klayman and Soll (1999) give two different explanations why people are generally

overconfident. The first explanation comes from the biases of people when they process

information. The judge, the person who does the judgment about something in his own mind,

first searches memory for relevant information and arrives at a tentative answer. Then, with

this answer in mind, the judge searches for more evidence. The problem is that the judges

believe that their processes are unbiased and thus perceive more support for an initial guess

than is actually warranted. Motivational factors can intensify this bias. People like to think


that they are intelligent and knowledgeable, and they may have reasons for wanting a

particular answer to be true. This first explanation of Klayman and Soll (1999) is very similar

to the confirmation bias that is given as a potential cognitive cause of overconfidence by

Russo and Schoemaker (1992). The confirmation bias means that when we make predictions

or forecasts, we often lean toward one perspective and the natural tendency is to seek support

for our initial view rather than to look for disconfirming evidence.

A second explanation of being overconfident highlights the role of unbiased

judgmental error in producing overconfidence. People’s judgements about the quality of their

information include some unsystematic error. Given an imperfect correlation between

accuracy and confidence. Low accuracy is on average associated with high confidence. It also

leads to an effect of difficulty: overconfidence for hard questions (low accuracy) and under-

confidence for easy questions (high accuracy). This explanation is in line with the findings of

the study of Moore and Healy (2008) about overconfidence, where people indeed

underestimate their own performance on easy quizzes and overestimate their own

performance on hard quizzes. On the other hand, this paper also demonstrates that people

over-place themselves in easy quizzes and under-place themselves in hard quizzes.

Having discussed the possible causes of

overconfidence, Russo and Schoemaker (1992) also demonstrate possible cognitive remedies

to overconfidence. The first remedy is giving accelerated feedback to people. For example, by

giving good feedback to new employees who have to do a simple quiz with trivia questions.

Managers managed to reduce overconfidence quickly. This technique is relatively cheap for

the company. The second remedy to overconfidence is counter argumentation, which means

that you try to find reasons why your initial beliefs might be wrong. The third remedy is

drawing paths to potential trouble to prevent overconfidence in predicting success. The fourth

remedy states that explicit scenario analysis may be useful. And the last, but very important

remedy is awareness alone. Awareness alone may be all that is needed for reducing

overconfidence. Barber et al. (2015) show that men and

women have different levels of overconfidence. Both genders exhibit overconfidence. Men

are generally more overconfident than women. Gender differences in overconfidence are

highly task dependent. Men are inclined to feel more competent than women do in financial

matters. Men claim to have more ability than women, but this claim emerges most strongly on

masculine tasks. And specifically in trading, men trade more than women because they are

generally more overconfident. But at the same time, men perform worse on trading than


women. In the questionnaire of this research the demographic variable gender is included to

examine if the same conclusions can be drawn as previous literature showed.

Overconfidence is mostly seen as a negative thing, but it can also play a positive role

in organizations. For instance, managerial overconfidence can increase the value of the firm.

In addition, moderately overconfident managers make decision that are in the better interest of

shareholders than rational managers do (Gervais et al., 2002). Further overconfidence could

be seen as an advantage, because it serves to increase ambition, morale, resolve, persistence,

generating a self-fulfilling prophecy in which exaggerated confidence actually increases the

probability of success (Johnson & Fowler, 2011).

Group Composition

Group composition is the main treatment in this research paper. Group composition refers to

the nature and attributes of group members (Guzzo & Dickson, 1996). It could be influenced

by different attributes. For example, age, gender, ethnicity, education level or even physical

appearance. In this thesis group composition differs on the education level of the group

members. In the treatment group the group members are clearly less-educated than the group

members in the control group. In the control group, the group members have the same level of

education as the subject.

The effect of group composition has been studied in other studies. One relevant study

of Hooper and Hannafin (1991) demonstrates that high-ability students complete instructions

more efficiently in homogeneous groups than in heterogeneous groups. This is something to

keep in mind, because the subjects in the control group in this thesis are hypothetically in a

homogeneous group as well when they answer the pub quiz questions. Therefore these

subjects might be better in answering the quiz questions correctly. On the other hand, the

subjects are only hypothetically with other participants in a pub quiz whereas in the study of

Hooper and Hannafin (1991) real groups were composed.

There is a gap in the previous literature about the influence of group

composition on overconfidence. Overconfidence and groups in general has been studied in the

past. Bernardo and Welch (1997) show that a group with a few entrepreneurs and many

individuals that just “followed the herd” falls too easily in making an incorrect choice. The

entire group follows the wrong path, because of poor aggregation of information across

individuals. Groups with too many entrepreneurs has too many group members who rely on


their own information. These kind of groups make mistakes too often and suffers from high

attrition between the group members.

Camerer & Lovallo (2015) show a phenomenon

specifically related to competition, overconfidence and groups. It is called "reference group

neglect", which basically means the tendency to under-adjust changes in the reference group

one competes with. People focus on themselves and simply neglect consideration of others

(rather than miscalculating others’ performance) when making comparative judgments. For

example, it is raining outside and you decide to take the metro and you think this is the best

solution to the problem, because you don’t get wet. But actually this it is not the best solution

for you, because you haven’t thought about all the other people that take the metro as well.

You forget to think that everybody (the group) is thinking the same thing as you think. This is

called the “reference group neglect”. In the online-questionnaires the reference groups are

students in the control group and the lower-skilled workers in the treatment group. The

“reference group neglect” could have an influence when respondents have to fill in questions

about over-placement and willingness to bet. By these two variables the influence of the

“reference group neglect” should be considered, because you think about others in the

competition. In this thesis the influence

of group composition on overconfidence is measured. The variable group composition are the

other participants in a hypothetical pub quiz. There is a clear difference between the treatment

group and the control group on the education level of the other participants. In the treatment

group the other participants are lower-skilled and less-educated than the respondents. In the

control group the other participants are students just like the respondents are.

Willingness to bet

Willingness to bet on one’s own performance is an extra variable added to this research

design. This variable is included to see if there is a positive correlation between willingness to

bet and overconfidence. Willingness to bet is assessed as a measurement how much people

are willing to bet to participate in the quiz competition (Campbell, Goodie, & Foster, 2004).

In this research the willingness to bet question is described as following: “How much are you

willing to pay to participate in the competition?”.

Anchoring


Russo and Schoemaker (1992) demonstrate that anchoring is one of the potential cognitive

causes of overconfidence. Tversky and Kahneman (1974) suggest that a person uses

anchoring and adjustment in the process of making judgments under conditions of

uncertainty. Anchoring is a cognitive bias that describes the common human tendency to rely

too heavily on the first piece of information offered (the “anchor”) when making decisions.

During the online questionnaire of this thesis a different anchor/scenario is sketched for the

treatment group and the control group. My whole research goal is to find out if a different

scenario has an impact on people their estimations and overconfidence. The scenario that is

sketched can be seen as an anchor for the respondents, before they start estimating. Anchoring

and adjustment is a psychological heuristic that influences the way people intuitively assess

probabilities. The probabilities in this thesis are the estimations that people need to make.

Through a different scenario sketch (anchor) in the treatment group, the estimations might be

significantly different from the estimations in the control group.


Chapter 3: Research question, conceptual model and hypotheses

Research question

This thesis is conducted in order to gain more knowledge on the research field of

overconfidence and the influence of presence of others on people. In order to fulfill this goal

the following research question is formulated:

“What is the effect of group composition on the overconfidence of people and is

overconfidence positively correlated with people their willingness to bet on their own

performance?”

Conceptual model

By drawing on previous research I suggest that group composition could have an influence on

people their overconfidence. Further, I suggest that there will be a positive correlation

between overconfidence and people their willingness to bet.

Figure 1: Conceptual framework


Overconfidence

Willingness to bet

Group composition

Hypotheses

My hypotheses regarding overconfidence (overestimation, over-placement and over-

precision) and willingness to bet are partly based on findings in previous research in the

‘literature review’ and partly on my own reasoning.

The first hypothesis is about the influence of group composition on overestimation of people.

H1: The level of overestimation is different for different group composition.

The second hypothesis is about the influence of group composition on over-placement of

people.

H2: The level of over-placement is different for different group composition.

The third hypothesis is about the influence of group composition on over-precision of people.

H3: The level of over-precision is different for different group composition.

The fourth hypothesis is about the possible correlation between willingness to bet and

overconfidence (relative estimation).

H4: There is a positive correlation between willingness to bet and overconfidence (relative

estimation).


Chapter 4: Methodology

Participants and procedure design

This thesis is based on two online-surveys (one treatment group and one control group) and

one paper pencil-survey. In all the surveys the same 15 quiz questions are asked. The quiz

includes five topics: science, culture, history, sports, geography. The quiz exist out of 15

questions, where every topic serves 3 questions. For each topic there is one easy, one medium

and one hard question. This is to reach an average level of the quiz questions. It is widely

found in the research literature that overconfidence is more pronounced for harder sets of

questions and for harder questions within a set. For easy questions, judges may even be under-

confident. For medium questions judges are neither under-confident or overconfident. For this

reason there are asked as much easy as hard questions in the quiz, to reach an average level of

the quiz questions (Klayman & Soll, 1999; Moore & Healy, 2008).

The paper pencil-survey is conducted among 20 lower-skilled and less-educated

people. The survey is in Dutch and is conducted at the entrance of supermarkets, because this

is an easy place to get in contact with less-educated people. This survey is conducted in order

to gain an average score of both groups on the quiz. Appendix 3 gives a translated example in

English of how the Dutch paper-pencil questionnaire looked like. The quiz question that are

asked, are the exact same questions as the quiz questions asked in the online-surveys. The

average score of the 20 students is calculated after they filled in the online-surveys. These

results are necessary to measure over-placement among the subjects of the online-surveys.

For the online-questionnaires, college students have been

approached to fill in the online-questionnaire, using the web software ‘Qualtrics’

(www.qualtrics.com). Facebook groups have been used to distribute the online-questionnaires

among the college students. In the section ‘The Research Questionnaire’ the online-

questionnaire will be explained in more detail. The only difference between the questionnaire

of the treatment group (n=30) and the control group (n=30) was the scenario sketches before

the respondents filled in the quiz questions. Figure 2 and figure 3 below demonstrate the

different scenarios that were shown to the respondents.


http://www.qualtrics.com/

Figure 2: Scenario in the control group

Figure 3: Scenario in the treatment group

Dependent measures

As has been mentioned in the literature review, research by Moore and Healy (2008) shows

three clear measures of overconfidence. The key dependent measures are overestimation,

over-placement, and over-precision by comparing participants’ score estimations with actual

scores.

Overestimation. A participant’s actual score x i was subtracted from his or her reported

expected score E [ X i ]to measure overestimation (x i−E [ X i ]). The difference of overestimation

between the treatment group and the control group is tested to see if there is a significance

difference.

Over-placement. The measure of over-placement takes into account whether a participant

really is better than others. Therefore is used the following formula: Over-placement =

(E [ X i ]−E [ X j ] )– ( x i – x j ), where E [ X i ]is an individual’s belief about his or her own expected


Imagine: It’s a Friday evening. You joined a friend of yours from college on a quiz night in a student café at university. The majority of the people in the café are college students. You are going to answer 15 quizzes. You may also enter a competition. To participate in the competition you have to pay a participation fee. Only the first 20 participants in the café who are willing to pay the highest participation fee are enrolled in the competition. Once you enter the competition, you will win €50,- if you achieve a score that brings you in the top 3 best-scoring participants of the competition. How much are you willing to pay to participate in the competition?

Imagine: It’s a Friday evening. You joined a construction worker friend of yours on a quiz night in a café where he usually hangs out with his co-workers. The majority of the people in the café are low-skilled workers and are less-educated than you.

You are going to answer 15 quizzes. You may also enter a competition. To participate in the competition you have to pay a participation fee. Only the first 20 participants in the café who are willing to pay the highest participation fee are enrolled in the competition. Once you enter the competition, you will win €50,- if you achieve a score that brings you in the top 3 best-scoring participants of the competition. How much are you willing to pay to participate in the competition?

performance, E [ X j ]is that person’s beliefs about the expected performance of the average

participant on that quiz, andx i and x j refer to the actual scores of the individual and the

average participant on that quiz. The paper-pencil survey among lower-skilled and less-

educated people is used to generate the actual score of the average participant in the treatment

group. The over-placement measure begins with an individual’s belief that he or she is better

than others and corrects that for the degree to which he or she actually is better than others.

Over-precision. The measure of over-precision takes into account whether a participant is

over-precise with his estimation of the range of his own score. Question 2 in Figure 5 below

here shows how the question is designed to measure over-precision. When a participant fills

in that his range is between 8-12, he would be over-precise when his actual score is 13.

Individual differences measures

Figure 4 in the section ‘The research questionnaire’ below shows the demographics and

questions to measure individual differences among respondents. These questions are about

gender, age, number of siblings, nationality, happiness and political conservatism (Abdel-

Khalek, 2006; Ames & Kammrath, 2004; Djankov, Qian, Gerard, & Zhuravskaya, 2007;

Moore & Healy, 2008; Niederle & Vesterlund, 2007; Raskin & Terry, 1988).

The questionnaire

The questionnaire is conducted among the control group (n=30) and the treatment group

(n=30). The questionnaires for both groups are exactly the same, except from the scenario’s

that are shown before the respondents make the quiz. When the questionnaire starts,

respondents first have to fill in their demographics, shown in figure 4. After that, respondents

have to imagine a scenario as depicted in Figure 2 and 3 respectively for the treatment group

and the control group. After reading the scenario, the respondent has to fill in how much he or

she is willing to pay to participate in the competition. Then, the respondents are asked four

questions before they fill in the quiz questions, these questions are shown in figure 5 below.

These questions have been used in the study of Benoıt, Dubra, & Moore (2008). These

questions are asked to measure overconfidence and willingness to bet. It was only possible to

give answers between 0 to 15 correct answers. The first question of figure 5 is asked to

measure overestimation and over-placement. The second question is asked to measure over-

precision. The third question is asked to measure over-placement. The fourth question is


asked to measure the possible correlation between willingness to bet and overconfidence

(relative estimation). After filling in the quiz questions, the same questions, as shown in figure

5, are asked again. At the end the respondents will be thanked for filling in the questionnaire.

Figure 4: Demographics

Figure 5: Questions before and after the quiz


Before you proceed to the quizzes, please answer the questions below.

1. How do you think you will score on the quiz? Please estimate your score: I estimate that I answer ______ of the 15 questions correctly.

2. You probably aren’t completely sure of how you will score. Please write down a range of scores below, such that you are 90% sure that your actual score falls somewhere in the range. My range falls from___to___

3. How do think the average participant in the café will score on the quiz? I estimate that the average participant answers ______ of the 15 questions correctly.

4. How do think the third best performing participant in the café will score on the quiz? I estimate that the third best performing participant answers of the pub quiz ______ of the 15 questions correctly.

1. What is your gender?

2. What is your age?

3. What is your country of birth?

4. What is your highest education? Bachelor / Master / PhD

5. How many siblings do you have (without counting yourself)?

6. How liberal or conservative do you tend to be when it comes to social issues?

Answers: 1 2 3 4 5 6 7

(1= very liberal) (4=neutral) (7=very conservative)

7. How liberal or conservative do you tend to be when it comes to economic issues?

Answers: 1 2 3 4 5 6 7

(1= very liberal) (4=neutral) (7=very conservative)

8. Do you feel happy in general? Please give your happiness a grade between 0 and 10:

Happiness scale: 0 1 2 3 4 5 6 7 8 9 10

Chapter 5: Statistical analysis

In this section, the statistical methods with which the analyses have been carried out will be

outlined and explained, for each of the four hypotheses. Table 1 below shows the general

design of measure for overconfidence and willingness to bet.

Group 1: Treatment group Group 2: Control group

Score estimation before test Before group 1 Before group 2

Score estimation after test After group 1 After group 2

Table 1.1: Design of measure

For a general description of the research sample table 1.2 in Appendix 1 can be consulted.

Table 1.3 in Appendix 1 shows that the Shapiro-Wilk test is conducted to test whether the

data is parametric or not. The test demonstrates that the data is non-parametric, because the

data is not normally distributed.

Hypothesis 1: The level of overestimation is different for different group composition.

Overestimation (x i−E [ X i ]) is calculated by the difference between a participant’s actual score

x iand his or her reported expected score E [ X i ]. In order to test the first hypothesis, a

Wilcoxon rank-sum test (Mann-Whitney U test) and a Wilcoxon signed-rank test is

conducted. The Wilcoxon rank-sum test is conducted for the level of overestimation before

and after doing the quiz. The first hypothesis will not be rejected, when the p-values will be

significant, at a 10% significance level. The Wilcoxon rank-sum test is the correct procedure

to test this hypothesis, since two independent samples are compared, the data is non-

parametric and interval-scaled (McCrum-Gardner, 2008).

To investigate how the level of overestimation has evolved before and

after answering the quizzes, a Wilcoxon signed-rank test is conducted for the treatment group

and the control group, respectively. This is the correct test to use, since the data is non-

parametric, interval-scaled and it consists of repeated measurements on one sample, as the

questions to measure overconfidence are asked before and after doing the quiz.

Hypothesis 2: The level of over-placement is different for different group composition


Over-placement is calculated by using the following formula: (E [ X i ]−E [ X j ] )– ( x i – x j ), where

E [ X i ]is an individual’s belief about his or her own expected performance, E [ X j ]is that

person’s beliefs about the expected performance of the average participant on that quiz, andx i

and x j refer to the actual scores of the individual and the average participant on that quiz

(Moore & Healy, 2008). The paper-pencil survey among lower-skilled and less-educated

people gives the actual score of the average participant in the treatment group. The actual

score of the average participant in the treatment group is 4.5 correct answers and in the

control group 9.2 correct answers of the 15 questions. The other scores that are necessary to

calculate over-placement come from the online-questionnaires. Similarly a Wilcoxon rank-

sum test will be used to investigate the treatment effect, and a Wilcoxon signed-rank test will

be conducted to investigate the evolvement over time.

Hypothesis 3: The level of over-precision is different for different group composition.

Over-precision is measured by the proportion of respondents whose estimated 90%

confidence interval contained their actual score. An incorrect estimation is classified as 1 and

a correct estimation is classified as 0. The aim is to find out if group composition has a

significant influence on overestimation. In order to test the third hypothesis a Fisher’s exact

test is conducted, because it contains nominal data and there are small sample sizes. The

Fisher’s exact test allows a researcher to examine the significance of the association between

two kinds of classifications (Lowry, 2014). The Fisher’s exact test has been carried out for the

classifications control/treatment group and over-precision (incorrect estimation)/no over-

precision (correct estimation).

Hypothesis 4: There is a positive correlation between willingness to bet and overconfidence

(relative estimation).

In order to test the fourth hypothesis, a Spearman correlation test is conducted between the

variables willingness to bet and relative estimation (McCrum-Gardner, 2008). Relative

estimation is defined as the difference in estimating one’s own score and the score of the third

best performing participant. A small negative or even a positive difference is an indication of

people who are really sure of achieving a good or very good result at the quiz. It is interesting

to see if there is a correlation between people who are confident or very confident about

achieving a good result on the quiz and whether these same people show a higher willingness

to bet to participate in the quiz competition.


Chapter 6: Results

Interpretation of general results

Table 1.4 presents the summary statistics of subjects’ willingness to pay, expected pay-off,

expectations of own and other performances, and actual performances. The first significant

results is that respondents in the treatment group have a higher estimation of their own quiz

score than the respondents in the control group (Wilcoxon rank-sum test, p-values 0.025**

(before) & 0.037** (after)). The second significant result is that respondents in the treatment

group have a lower estimation of the score of the average participant in the quiz (Wilcoxon

rank-sum test, p-values 0.005** (before) & 0.044** (after)). This indicates that the

respondents in the treatment group are aware of the fact that the other participants in the

competition are less-educated and lower-skilled than they are. Thirdly, respondents in the

treatment group have a significant lower estimation of the score of the 3th best participant

after they participated in the quiz (Wilcoxon rank-sum test, p-value 0.007**). This also

indicates that the respondents in the treatment group are aware of the fact that the other

participants in the competition are less-educated and lower-skilled than they are. Even though,

these results are only indications of a treatment effect, the effect of the treatment is already

visible in table 1.4. Last interesting result to discuss, is the difference in expected payoff

between the treatment and control group. This difference is huge, because 83% of the

respondents in the treatment group scored higher than the score of the 3rd best performing

participant (7 questions correct) of the low-skilled worker group. This leads to a bigger

chance to win the €50,- in the competition. Only 17% of the respondents in the treatment

group lost their money.

Average results before and after the quiz Treatment group Control groupWillingness to bet before (in €) €10.2 €9.6Willingness to bet after (in €) €9.3 €7Expected pay-off before (in €) €39.8 €-0.6Expected pay-off after (in €) €39.9 €1.55Own score estimation before 10.8 9.5Own score estimation after 9.8 8.4Estimation score of the average participant before 7.7 8.9Estimation score of the average participant after 7.7 8.7


Estimation of the score of the 3th best participant before 11.3 12.1Estimation of the score of the 3th best participant after 10.6 12.0Actual score 9.7 9.2Actual score (opponents) 4.5 9.2

Table 1.4: Average results of questions of the online-questionnaire

Hypothesis 1: The level of overestimation is different for different group composition.

Explanation

The first priority of this research has been to investigate whether people’s levels of

overconfidence change when the other people that participate in the pub quiz are lower-skilled

and less-educated than themselves. Overconfidence is measured by overestimation, over-

placement and over-precision. The expectation is that subjects in the treatment group are more

likely to over-estimate their own abilities and performance than the subjects in the control

group. Question 1 of figure 5 in the ‘Methodology’ section is asked in the online

questionnaire to measure overestimation.

Results and discussion

In table 1.5 below, the p-values of the Wilcoxon rank-sum test are shown for the two time

points of measurement of overestimation. Although overestimation does not differ between

the treatment and control before the quiz, respondents do feel more confident about their own

score after the quiz in the treatment group than in the control group (p-value 0.09*). The

median in the treatment group is 0, while the median of the control group is -1. This means

that respondents feel more confident about their own performance in the treatment group, but

not necessarily overconfident. Interesting is that respondents in the control group were

slightly overestimating their own score before the quiz, but after the quiz they underestimated

their own score (Friedman’s test, p-value 0.003**). There is even a significant negative

change in overconfidence in the control group, while such a significant change did not occur

in the treatment group. On the other hand, in both groups overestimation has decreased over

the two time points of measurements looking at the means of overestimation in table 1.7 in

Appendix 1. The Wilcoxon signed-rank test shows that respondents are significantly less

overconfident in their own scores after the quiz in the control and treatment group, p-values

are 0.035** and 0.004**, respectively. The medians of the treatment group are not indicating

this result. By looking at the means (before: 1.03 & after 0.03) there is indeed less


overconfidence after the quiz in the treatment group.

In conclusion, respondents who are surrounded by lower-

skilled workers suffer more from overestimation, suggesting that group composition has an

impact on people’s self-perception. Also, the general level of overestimation dropped after

they answered the quiz questions. But only in the control group is this change significant.

Treatment group: median

Control group: median

P-values: Wilcoxon rank sum test

Before the quiz 0 1 0.6

After the quiz 0 -1 0.09*

P-values: Wilcoxon signed-rank test

0.035** 0.004**

P-values: Friedman’s test

0.221 0.003**

*P-value is significant at 10% significance level. Table 1.5**P-value is significant at 5% significance level.

Hypothesis 2: The level of over-placement is different for different group composition

Explanation

The second measure of overconfidence is over-placement. To measure over-placement,

question 3 of figure 5 of the ‘Methodology’ section is asked to the respondents before and

after the quiz. Over-placement has been studied in past studies. It appears that people under-

place themselves for difficult tasks and over-place themselves for easy tasks. For medium

tasks people neither under-place or over-place themselves (Moore & Healy, 2008). The quiz

of the online-questionnaire is a medium task for the respondents, because of every topic an

easy, medium and hard question is picked out. This would indicate that people will neither

under-place or over-place themselves in this research. But on the other hand, it is reasonable

to expect that the respondents in the treatment group respondents will sooner over-place

themselves than the respondents in the control group. Because the respondents in the

treatment are participating in hypothetical pub quiz with lower-skilled and less-educated other

participants.



In table 2.1, the p-values of the Wilcoxon rank-sum test are shown for the two time points of

measurement of over-placement. At both time points of measurement the treatment group

shows surprisingly high under-placement (p-value 0.00**). The control group show two times

low over-placement before and after the quiz (p-value of 0.00**). The averages of over-

placement given in table 1.7 in Appendix 1 show even low under-placement after the quiz in

the control group. The average score of over-placement in the treatment group is -2.1

(before) and -3.1 (after). The average score of over-placement in the control group is 0.5

(before) and -0.6 (after). In conclusion, there is a significant effect of group composition on

over-placement. However, the effect is in the opposite direction of what was expected.

Treatment group: median

Control group: median

P-values: Wilcoxon rank sum test

Before the quiz -2.5 0.60 0.00**

After the quiz -3.5 0.60 0.00**

P-values: Wilcoxon signed-rank test

0.038* 0.052*


An explanation for the under-placement of the respondents in the treatment group is

that there is a huge difference in the estimation of the expected performance of the average

participant and the actual score of the average participant on that quiz. The average expected

performance of the average participant in the pub quiz was 7.7 correct answers among the

respondents in the treatment group. The actual score of the average participant (from the

paper-pencil survey) on that quiz answered 4.5 answers correctly. Looking at the formula of

calculating over-placement explains why respondents in the treatment group were under-

placing themselves: (E [ X i ]−E [ X j ] )– ( x i – x j ), where E [ X i ]is an individual’s belief about his or

her own expected performance, E [ X j ]is that person’s beliefs about the expected performance

of the average participant on that quiz, andx i and x j refer to the actual scores of the individual

and the average participant on that quiz. In other words, thex j was lower than the respondents

expected x jwould be. Table 2.2 in Appendix 2 gives a clear overview of how the under-

placement is caused in the treatment group. The respondents in the treatment group

overestimate their own score, but they overestimate their lower-skilled opponents’ scores

even more. This is the main reason why the respondents in the treatment group under-place

themselves. The respondents in the control group start with over-placement and after the quiz


there is a still over-placement.

One explanation for the fact that respondents overestimate the score of the lower-

skilled and less-educated participants might be the ‘reference group neglect’. This is the

tendency to under-adjust to changes in the reference group one competes with, who are in this

case the lower-skilled and less-educated other participants (Camerer & Lovallo, 2015).

Respondents focus on themselves and simply neglect to think about the others when making

comparative judgments. The comparative judgment in this case is the estimation of the score

of the lower-skilled and less-educated other participants. Through only thinking about

themselves, the estimation of the score of the lower-skilled and less-educated other

participants is not really realistic.

Hypothesis 3: The level of over-precision is different for different group composition.

Explanation

The third measure of overconfidence is over-precision. To measure over-precision, question 2

of figure 5 of the ‘Methodology’ section is asked to the respondents before and after the quiz.

Over-precision occurs when a respondent gains a score which falls outside their estimated

range where the respondent was 90% sure of. The expectation about over-precision is that it

will be there, because literature has shown that it is more persistent than overestimation and

over-placement (Moore & Healy, 2008). It can be doubted if a different group composition

would have an influence on how over-precise or under-precise someone is. Nevertheless, the

expectation is that a different group composition has an influence on the level of over-

precision of people.


Table 2.3 gives an overview of how many times over-precision has occurred. For instance,

before the quiz 17% of the treatment group estimated their confidence interval incorrectly.

After the quiz, this was only 7%. In other words, after the quiz 7% of the respondents have

been too precise in their estimations. In the control group before the quiz 7% estimated their

confidence interval incorrectly. After the quiz, this was 10%. However, these results are

insignificant. Table 2.4 below demonstrates the p-values of the Fisher’s exact test for the two

time points of measurement of over-precision. The p-value of the Fisher’s exact test is the


probability of that there is no relationship between over-precision and group composition. For

both time points of measurement there is no significant relationship between group

composition and over-precision. Thus, the third hypothesis is rejected. This means that a

different group composition has no influence on the level of over-precision of people.

Treatment group (n=30): proportion of over-precision occurred

Control group (n=30): proportion of over-precision occurred

Before the

quiz

0.17 0.07

After the quiz 0.07 0.1

Table 2.3

P-values: Fisher’s exact testOver-precision before the quiz 0.424

Over-precision after the quiz 1.000


Hypothesis 4: There is a positive correlation between willingness to bet and relative

estimation.

Explanation

In order to test the fourth hypothesis, a Pearson correlation test is conducted between the

variables willingness to bet and the difference in estimating people their own score and the

score of the third best performing participant. To clarify, the estimation of the third best

performing participant is subtracted of the estimation of people their own score. A small

negative or even a positive difference is an indication of people who are really sure of

achieving a good result at the quiz. It is interesting to see if there is a correlation between

people who are sure about achieving good results on the quiz and a higher willingness to bet

to participate in the quiz. My expectation is that there will be a positive correlation between

willingness to bet and people their relative estimation. It is quite logical that when you are


very confident about winning the competition, you are willing to bet more money to

participate in the competition.


In tables 2.5 and 2.6 below the results of two Spearman correlation tests are shown. The first

Spearman correlation test is conducted between willingness to bet and relative estimation as it

is defined in the ‘Explanation’ section above. It appears that there is a weak positive

correlation between willingness to bet and relative estimation before the quiz. This result is

marginally significant with a p-value of 0.075*, as can be seen in table 2.5. This table 2.5

shows further that there is also a weak positive correlation between willingness to bet and

relative estimation after the quiz. This result is with a p-value of 0.018 significant at a 5%

significance level. After this result, an extra Spearman correlation test on gender is conducted

only on the results on the results after the quiz. Table 2.6 shows that there is a strong positive

correlation between willingness to bet and relative estimation among males. This result gives

a p-value of 0.002, which is significant at a 1% significance level. To conclude, the fourth

hypothesis is not rejected, because there is a positive significant correlation between

willingness to bet and relative estimation before and after the quiz. So, the more confident a

respondent is about winning the competition, the more money the respondent bets. And under

males there is even a strong positive correlation between willingness to bet and relative

estimation after the quiz. This means the more confident a male is about winning the

competition, the more money he bets. This is in line with previous research that shows that

males are more overconfident when it comes to financial decisions (Barber et al., 2015).

Correlation willingness to bet & overconfidence P-values

Before the quiz 0.232 0.075*

After the quiz 0.304 0.018**


Correlation willingness to bet & overconfidence P-values

Males 0.550 0.002***

Females 0.020 0.914


*P-value is significant at 10% significance level. Table 2.6**P-value is significant at 5% significance level.***P-value is significant 1% significance level.

Individual differences

Correlation between our measures of overconfidence and a variety of individual difference

measures are all insignificant. Tables 2.7 and 2.8 below present no significant correlations

between age, number of siblings, happiness, social conservatism, economic conservatism and

on the other side overestimation or over-placement.

Measures Correlation with

overestimation

P-value

Age -0.110 0.403Number of siblings 0.164 0.210Happiness -0.042 0.752Social conservatism 0.167 0.203Economic conservatism 0.112 0.393

**P-value is significant at 5% significance level. Table 2.7

Measures Correlation with over-placement P-value

Age -0.076 0.564Number of siblings -0.076 0.565Happiness 0.061 0.643Social conservatism 0.017 0.898Economic conservatism 0.183 0.161

**P-value is significant at 5% significance level. Table 2.8

Chapter 7: Conclusion

Overconfidence has been studied in the past in relation to quiz questions, stock investment,

gender, CEO’s of organizations, corporate investment and entrepreneurship (Barber et al.,

2015; Bernardo & Welch, 1997; Malmendier & Tate, 2005; Moore & Healy, 2008). The

design of this thesis is partly adapted of the study of Moore and Healy (2008), where

overconfidence is also measured by people that participate in a quiz. This study is different 30

Overconfidence through Group Composition, Arjan den Besten (415298ab), Behavioural Economics 2014-2015, Erasmus School of Economics ©

from the study of Moore and Healy (2008), because the aim of this study is to find out if there

is a significant effect of group composition on overconfidence. Group composition and

overconfidence has only been studied a few times in the past (Bernardo & Welch, 1997).

Further, there is an extra variable added to this research by asking people their willingness to

bet when they get the option to participate in a quiz competition and see if this correlates with

people their relative estimation of their quiz performance. Lichtenstein & Fischhoff (1977)

already studied excessive confidence in relation to willingness to bet. For this reason, relative

estimation is studied in this thesis instead of overconfidence.

Main findings

In this thesis a hypothetical pub quiz on an average level is conducted among students.

Previous research has shown that people are not over- or under-estimating their performance

on quizzes of a medium level. The same applies for over-placement, people are not over- or

under-placing their performance on medium quizzes. Over-precision seems to be more

persistent than the other two types of overconfidence. The three types of overconfidence are

conceptually and empirically distinct. Overall, the aim of this research has been to discover

whether group composition has an influence on either one of the three types of

overconfidence (hypotheses 1,2,3) and whether a positive correlation exists between

willingness to bet and relative estimation (hypothesis 4).

The main finding of this research is that group

composition has an influence on overconfidence. First of all, respondents who are surrounded

by lower-skilled workers suffer more from overestimation, suggesting that group composition

has an impact on people’s self-perception. Also, the general level of overestimation dropped

after they answered the quiz questions. But only in the control group is this change

significant. So, the first hypothesis should not be rejected. Second, students under-place

themselves more to others when they participate in a hypothetical pub quiz with lower-skilled

and less-educated other participants than when they participate in a hypothetical pub quiz

where the other participants are students. This is the opposite effect of what was expected.

One possible explanation of this result is that students were not able to make a realistic

estimation of the score of low-skilled and less-educated participants. The expected score of

the low-skilled and less-educated other participants was much higher than the actual score of


this group. ‘The reference group neglect’ is a possible explanation for this, which is the

tendency to under-adjust to changes in the reference group. The reference group in this case

are the lower-skilled other participants. Respondents could possibly focused too much on

themselves and simply neglect consideration of others (Camerer & Lovallo, 2015). Lastly,

there is no influence of group composition on over-precision. Thus, the third hypothesis

should be rejected. To conclude,

overestimation and over-placement are both influenced by group composition, but in opposite

directions. This finding is in line with the finding of Healy and Moore (2008), because in their

study they also found a negative relationship between over-estimation and over-placement.

However the reason of this negative relationship might not be the same. In this thesis the most

reasonable explanation of under-placement is described in the paragraph here above, where

Healy and Moore (2008) relate the negative relationship to a theory. Namely, after

experiencing a task, people often have imperfect information about their own performances

but even worse information about the performances of others. As a result, people’s post-task

estimates of themselves are regressive, and their estimates of others are even more regressive.

Thus, when performance (difficult task) is low, people will overestimate themselves,

overestimate others even more so, and therefore believe that they are worse than others. So,

for difficult tasks people under-place themselves to others, but overestimate their own

performance, just like the respondents did on the quiz in this thesis. In conclusion,

overconfidence is influenced by group composition, but not in one specific direction. Further,

the findings of this thesis confirm that overestimation, over-placement and over-precision are

conceptually and empirically distinct, because all three types reacted different on the

treatment group composition (Moore & Healy, 2008).

The second important finding of this thesis is that willingness to bet is

positively correlated with relative estimation. This means that when people their own score

estimation of the quiz becomes closer to their estimation of the 3th best performing

participant, their willingness to bet increases as well. This means, that when people become

more confident about their own quiz performance, they are willing to bet more money.

Further, it is tested if there is a difference in the results among males and females, because

previous research has shown that males are more overconfident when it comes to financial

decisions (Barber et al., 2015). And indeed, males have a strong positive correlation between

relative estimation and willingness to bet, where there is no correlation between relative


estimation and willingness to bet among females. So, when a male is feeling more confident

about his own skills and performance, he is strongly willing to bet more money.

Recommendations

First, this study has found that overestimation occurs more when a student is surrounded by

lower-skilled and less-educated people than themselves. But at the same time, the student is

under-placing herself/himself to others. These findings are interesting for organizations,

managers and policy makers. It is reassuring for these actors that an employee does not get

completely overconfidence when he or she is surrounded by lower-skilled and less-educated

colleagues. Moreover, not in all cases overconfidence has to play a negative role in

organizations (Gervais et al., 2002). But still, the findings could be reassuring, because in

many organization there are teams formed with only one high-skilled worker and many low-

skilled workers. For instance, a high-skilled manager of a supermarket is often working

together with only low-skilled shelf fillers. When the high-skilled manager has to take an

important and risky decision about marketing or purchasing, he might overestimate his own

abilities and skills when he is only surrounded with lower-skilled and less-educated workers

around him. On the other hand, the manager would not be ashamed to ask his colleagues for

help when this is needed, because he is not thinking that he is better than his colleagues. In

other words, the manager is not necessarily over-placing himself according to the findings of

this thesis. The second main finding of this study is that willingness to bet

is positively correlated with relative estimation. In other words, when people, especially men,

are confident about their own performance, they are willing to bet more money in

competitions. This is in line with previous research that has shown that males are more

overconfident when it comes to financial decisions (Barber et al., 2015). The study of Barber

et al. (2015) shows even that men trade more than women, through overconfidence, however

men generate lower returns on their investments than women. In organizations were important

financial decisions has to be made, for instance financial institutions, they should take these

findings into account (Dittrich et al., 2005). Therefore, it is reasonable to recommend that

organizations should consider to place more females on positions in the organizations were

financial decisions are made.


Limitations and Future Research

The first limitation of this thesis is that respondents participate in a quiz by filling in an

online-questionnaire. Respondents are friendly asked not to use the internet to search for quiz

answers, but still, it is impossible to control this. The second limitation is that there is a

scenario sketched in the online-questionnaire that respondents are hypothetically participating

in a pub quiz. The results of this study are based on respondents who only participated in a

pub quiz hypothetically. In other words, this study has a lack of external validity. The third

limitation of this thesis is that prior research has found that overconfidence is related to

individual differences, such as gender (Pulford & Colman, 2007). This thesis cannot explain

these prior results, because there were no significant correlations between any of the

individual differences that were measured and any of the three varieties of overconfidence.

The only result that correlated with gender, was the correlation between willingness to bet

and relative estimation under males. The fourth limitation of this study is that the sample size

of the treatment and control group exist out only 30 participants for each group. This makes

some results only marginally significant at a 10% significance level.

Future research could be done with this research design conducted in a field

experiment setting. The results will become more external validate. Real money could be

used, the participants would really be in a pub when they participate in the quiz and the other

participants would be there as well. In the ideal situation, the participants would even not be

aware of the fact that they participate in the experiment. With this natural field experiment

setting, it is interesting to see how people will behave and how this affect people their

overconfidence and willingness to bet. Further, the same research could be done in the future

with a larger sample size to find more significant results. Individual differences and over-

precision gave no significant results so far, possibly through the relative small sample size.

One other possible future research design is to combine this current research design with

measuring overconfidence for easy, medium, and hard quizzes. In order to do so, it becomes

possible to find out if the negative relationship between overestimation and over-placement

found in this thesis and in the study of Moore & Healy (2008) can be explained in the same

way. Because now, it is still not sure whether the negative relation between overestimation

and over-placement found in this thesis is caused through the unrealistic estimations of the

quiz score of low-skilled participants or through the theory of confidence mentioned in the

study of Moore & Healy (2008).


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Appendix

Appendix 1

Table 1.2

Summary statistics on samples of students


Treatment group (n=30): students participate in quiz with low skilled and less-educated other participants

Control group (n=30): students participate in quiz with other participants that are students as well

Age (years, mean) 24.2 22.1

Male (nr. of students) 20 9

Female (nr. of students) 10 21

Bachelor (nr. of students) 13 22

Master (nr. of students) 17 8

Phd (nr of students) 0 0

Average number of siblings 1.6 1.8

Happiness grade (0-10) 7.5 7.3

Table 1.3

Test of normality: Shapiro-Wilk test

Dependent variables P-values

overestimation before the quiz 0.014**

over-placement before the quiz 0.015**

over-precision before the quiz 0.005**

overestimation after the quiz 0.376

over-placement after the quiz 0.000**

Over-precision after the quiz 0.000**

**P-value is significant at 5% significance level.

Table 1.6

Descriptive statistics of the dependent variable overestimation before and after the quiz

Descriptive statistics overestimation Treatment group Control groupMean before 1.03 0.268Mean after 0.03 -0.83Median before 0 1Median after 0 -1Minimum & Maximum before -2 & 8 -4 & 3Minimum & Maximum after -3 & 4 -5 & 2Standard deviation before 2.62 1.76


Standard deviation after 1.38 1.76

Table 1.7

Descriptive statistics of the dependent variable over-placement before and after the quiz

Descriptive statistics over-placement Treatment group Control groupMean before -2.1 0.5Mean after -3.1 -0.6Median before -2.5 0.6Median after -3.5 0.6Minimum & Maximum before -6.5 & 3.5 -3.5 & 3.6Minimum & Maximum after -9.5 & 0.5 -4.4 & 4.6Standard deviation before 2.38 2.13Standard deviation after 2.56 2.26

Appendix 2

Table 2.2

Overview numbers to measure over-placement before and after the quiz:

(E [ X i ]−E [ X j ] )– ( x i – x j )

Treatment group before:

Control group: before

Treatment group: after

Control group: after

E [ X i ] (self-expectation) 10.8 9.5 9.8 8.4

E [ X j ] (others expectation) 7.7 8.7 7.7 8.7

x i (self-performance) 9.7 9.5 9.7 9.5

x j (others performance) 4.5 9.2 4.5 9.2

Over-placement -2.1 0.5 -3.1 -0.6

Appendix 3Paper-pencil Questionnaire

Gender:

Age:

Your highest education level:


Questions:

1. Who was the leader of Germany's Nazi party during World War 2?...............................

2. What period in European cultural history took place from the 14th to the 17th century, after

the Dark Ages?................................................................................................................

3. In what year did Nigeria gain its independence from Great Britain?.......................

4. John Lennon, Paul McCartney, George Harrison and Ringo Starr were the four members of

what famous classic rock band?……………………………………… ………………….

5. Who wrote and directed “Kill Bill” Volumes 1 and 2?…………………………………

6. Who played Mozart in the 1984 film "Amadeus"?……………………………………….

7. What continent lies directly south of Europe?………………………………………………

8. What is the name of the world’s largest coral reef, located off the coast of Australia?

…………………………………………………………………………………………………..

9. What is the capital city of Uganda?............................................................................

10. Mohammed Ali and Mike Tyson have each held the Heavyweight Championship Title in

what sport?……………………………………………

11. What country’s team won the 2010 World Cup?……………………………………….....

12. Who was the only unseeded man to win the Wimbledon singles title?…………………

13. The theory of evolution by means of natural selection is attributed to whom?.....................

14. What African predator is the fastest land animal?………………………………… …

15. The psychoactive ingredient in marijuana is THC. What does THC stand for?…………..

Answers:

1.Adolph Hitler

2.Renaissance

3.1960

4. The Beatles


5. Quentin Tarantino

6. Tom Hulce

7. Africa

8. Great Barrier Reef

9. Kampala

10. Boksing

11. Spain

12. Boris Becker

13. Charles Darwin

14. Cheetah

15. Tetrahydrocannabinol


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