Experimental Economics, Entrepreneurs and
The Entry Decision
by
Julie Ann Elston, Glenn W. Harrison and E. Elisabet Rutström †
May 2006
Abstract. Do entrepreneurs exhibit more over-confidence in their own skills, leading to
excess market entry? Building on previous laboratory experiments we propose
experimental tasks to identify and test the hypothesized characteristic of overconfidence and its alleged impact on the entry decision. Taking these tasks into the field
we identify and find striking differences between two types of entrepreneurs, which we
call full-time and part-time entrepreneurs. Part-time entrepreneurs appear extremely
reluctant to enter markets where profitability is based on their perception of their
relative skill ability. On the other hand full-time entrepreneurs and non-entrepreneurs
do not exhibit any systematic over-confidence in their relative skill abilities. Our results
support the notion that entrepreneurs are rational and do not exhibit excess entry due
to over-confidence as many have claimed.
†
Elston: Max Planck Institute of Economics (Jena, Germany) and Oregon State University (Bend,
Oregon, USA). Harrison and Rutström: University of Central Florida (Orlando, Florida, USA). Email: [email protected], [email protected] and
[email protected]. Rutström thanks the U.S. National Science Foundation for research
support under grants NSF/IIS 9817518 and NSF/POWRE 9973669. We are grateful to Mark
Schneider and Ryan Brosette for research assistance. All data, statistical code and experimental
instructions are available at the public ExLab Digital Library at http://exlab.bus.ucf.edu.
1. Introduction
What determines the decision of entrepreneurs to enter new markets? One answer provided
by economic theory is that an excess level of profitability induces entry into an industry. This is why
the entry of new firms is important: new firms provide an equilibrating function in the market,
restoring price and profit to competitive levels. One natural question that has evolved from this
theory is whether entrepreneurs exhibit more over-confidence than others, and therefore enter
industries more quickly than others.1 If so, then there would be “excess entry” and the potential for
an inefficient market outcome if exit is slow or costly.2
We make use of experimental economics to examine the impact of over-confidence on the
entry decision for a group of high-technology entrepreneurs and a control group of nonentrepreneurs. Experimental methods have become standard in economics, as a way of generating
data that allows the investigator to control many of the features of the environment that can only be
proxied with naturally occurring data. A central feature of experiments in economics is that the
design provides incentives for the subjects to make different decisions: there are always real
consequences to taking one action or another.3 Our approach marries the rigorous control of an
experimental laboratory environment with the relevance of having real entrepreneurs as subjects. We
view the experimental data we obtained as methodologically complementary to naturally occurring
data and previous surveys, recognizing that each has its strengths and limitations.
There are several reasons to apply experimental methods to study entrepreneurial decision
making. First, a well designed experiment can potentially elicit and measure the dynamic decision
1
De Meza and Southey [1996] present a formal argument to rationalize the oft-repeated claim that
entrepreneurs have poor access to capital because there is a tendency for those who are excessively optimistic to
dominate new entrants. They conclude that the tendency to unrealistic optimism on the part of entrepreneurs leads to
excess entry and maximum use of self financing by a self selected group of risk-lovers. Hence banks should be
applauded, so the argument goes, for “stemming” the rush for capital that would otherwise just be wasted by irrational
entrepreneurs. In related studies, Bénabou and Tirole [2002], Dosi and Lovallo [1997] and Hoelzl and Rustichini [2005]
all point to the role of self-confidence in influencing the entrepreneurial decision making process.
2
For example, Parker [2006] provides evidence that entrepreneurs in Britain do not rapidly update their beliefs
about unobserved productivity in the light of new information.
3
Our experiments are “artefactual field experiments” in the terminology of Harrison and List [2004]: we take
laboratory experiments out into the field, to study a field population of interest. Our experiments also differ from the
artefactual field experiments of Harrison, Lau and Williams [2002], who attempt to characterize a national population.
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making of the entrepreneur. This can be done by controlling for or randomizing influences in the
study environment, which cannot be effectively implemented with a static pencil and paper
questionnaire. Thus we study how actual decisions are affected by the real-time flow of information.
Second, the ability to accurately measure the impact of economic incentives on entrepreneurial
decision making and performance is enhanced by the ability to use real cash incentives to induce
incentive compatible behavior from subjects. Third, since it has historically been difficult to observe
those who did not decide to become entrepreneurs, field experiments are ideal for accessing
individuals who did and did not decide to become entrepreneurs, allowing potential differences to be
directly observed and empirically tested.4
We define over-confidence as a judgmental error where the individual estimates their own
skill or ability to be better than the average. Our tasks are structured to separate this decision from
the entry decision which is based on the entrepreneur’s forecast of the competition -that is the entry
decision of others. The empirical results of this study reveal that full-time entrepreneurs are no
more likely to enter a market in which performance depends on perceived skill in relation to others.
But we find that “wanna be entrepreneurs,” those that hold on to salaried employment while starting
up a business venture, are significantly less likely to choose to enter such a market. These findings
are consistent with Levesque and Schade [2005; p.314], who report survey evidence that the
“...tendency to work too much in the wage job instead of entirely focusing on the [entrepreneurial]
venture is found to be most pronounced for risk-averse individuals.”
4
Schade [2005; p.417] offers a survey of experimental studies in entrepreneurship and lists 14 studies, only 2 of
which actually use real entrepreneurs. Of the remaining 12 studies, students are the most common type of experimental
subject, which raises questions about the value of using students to generalize to real entrepreneurs raised by Robinson,
Hueffner and Hunt [1991]. Moreover, all of the studies referred to use hypothetical surveys, and none of the tasks was
incentivized in the sense that subjects earned more or less money depending on different choices. The use of real,
controlled incentives has been a hallmark of experimental economics since Smith [1982] defined the “salience” and
“dominance” precepts of an experimental micro-economy. The studies reviewed by Schade [2005] are better described as
using what some have called the “questionnaire-experimental method,” where different hypothetical survey questions are
exogenously posed to subjects with some experimental design. Amiel and Cowell [1999] illustrate the method, and clearly
note (p.24) that their “... approach involves presenting individuals with questionnaires in a way that uses many of the
features of experimental methodology.” The use of hypothetical questionnaires also has a long tradition in psychology
and environmental valuation: see Harrison [2006a][2006b], Harrison and Rutström [2006] and Hertwig and Ortmann
[2001] for extensive surveys of experimental evidence on the biases introduced by eliciting hypothetical responses.
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Empirical evidence has been accumulating supporting the notion that over-confidence
influences the entrepreneurial decision. For example, Cooper et al. [1988], Busenitz and Barney
[1997], and Camerer and Lovallo [1999] conclude that the perception of both the risk associated
with entrepreneurial activity as well as the entrepreneur’s own capabilities can result in what has been
termed as over-confidence. Forbes [2005] reviews the literature, and presents evidence that many
individual and contextual factors influence over-confidence. In particular, he shows that foundermanagers are more overconfident than comparable new-venture managers that played no role in the
entry decision. Koellinger, Minniti and Schade [2005] use survey responses from the Global
Entrepreneurship Monitor data base to show that perceptions of risk and own ability have a
systematic influence on the decision for individuals to start a new business. Those individuals who
suffer from over-confidence tend to have a greater propensity to enter into entrepreneurship. Their
analysis spans many components of confidence. They ask questions about (a) whether the
entrepreneur believes that they have sufficient skills, knowledge and ability to start a new business,
(b) their perception of good business opportunities, (c) their optimism about their household
financial security in the near future, (d) and their fear of failure. These surveys cover 18 countries,
and focus on individuals in the process of starting a business venture.
In section 1 we review the literature on the importance of over-confidence in the
entrepreneurial entry decision, and suggest hypotheses to test with our experimental design. In
section 2 we describe the experimental tasks we developed to study the entry decision, in section 3
we document the field experiments conducted, in section 4 we present the empirical results, and in
section 5 we draw conclusions and make suggestions on directions for future research.
2. Experimental Tasks
We build on experimental tasks which are well established in the literature, and have been
applied to study the behavior of the traditional subject pool of college students. Specifically, we start
with a market entry task developed by Camerer and Lovallo [1999] (CL), and then adapt it for our
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field application.
A. Previous Laboratory Experiments
CL consider a class of entry games in which the subjects have to decide whether to enter a
market or not. Their payoffs, if they decide to enter, depend on how many others enter the market
and their relative skill. They motivate this experimental design by a discussion of hypotheses
advanced by March and Shapira [1987] to explain why most new businesses fail within a few years:
the “hubris hypothesis,” to borrow the expression coined by Roll [1986].
CL view these games as well-suited to study the behavior of potential entrepreneurs. They
justify their use of graduate and undergraduate students from business schools as follows: “Business
students, especially MBA’s, are an appropriate sample because many go on to start businesses or
participate in corporate entry decisions (e.g., entrepreneurship is the fifth most popular major among
Wharton MBAs).” (p. 310). We extend this line of research into field settings with actual
entrepreneurs.
The key insight in their design is to allow relative profitability in the market to be determined
by a skill task involving a series of quiz questions.5 Thus subjects that had a higher skill ranking
would earn more money if they entered, and subjects that had a lower skill ranking would earn less
money and might even lose an initial stake provided by the experimenter. Subjects that chose not to
enter would be allowed to keep the initial stake. No subject knew their score on the skill test prior to
entry, so entry was determined in part by the subject’s belief about their skill level in relation to the
others that might enter.6 They found “excess entry” with student subjects when profitability was
5
treatment.
They review a more extensive literature studying this basic entry game in various settings without the skill
6
Entry is also affected by risk attitudes, since entry is risky and non-entry generates a non-stochastic payoff (the
initial stake). CL used a within-subjects control treatment, in which rankings were determined by a random device instead of
by their skill ranking, as a control on risk attitudes. By comparing differences between the random and skill treatments
for the same subject, they could discern the pure effect of the skill treatment. A similar design is developed by Hoelzl and
Rustichini [2005] to identify over-confidence in skill abilities: subjects vote on being rewarded either by a 50/50 lottery,
or being rewarded with the same monetary prize by scoring in the top 50% of a skill test. If a subject votes for the latter
option, they exhibit over-confidence in their skill ability.
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determined by perceived skill, and attribute this to over-confidence.
The left panel of Figure 1 displays the essential results, in terms of average realized profit per
entrant in the two main treatments.7 The initial stake for each subject was $10, so the horizontal line
at $10 shows the certain opportunity cost of choosing to enter. Excess entry occurs whenever a
subject’s risk-adjusted return from entering is lower than $10. If we assume for the moment that
subjects were risk-neutral or risk averse, any average profit less than $10 is inefficient. And we see
that apart from the first few rounds in the random rank treatment in which expected skill was
irrelevant, average profits were below $10, signaling excess entry. The differential between the two
treatments (and graphs) further shows that over-confidence in skill generates even more entry,
particularly for early rounds. These results are pooled over different capacity levels in their
experiments, but the qualitative result of excess entry is generally the same across those levels.8
Figures 2 and 3 display results broken down by market capacity levels.
If entrepreneurs exhibit more over-confidence than others and enter industries more quickly
than others, then the implied outcome is that there will be relatively more subsequent failures and
decisions to exit the market. However, this outcome is not one that is directly measured in this
design. Such outcomes could be observed if we could run this market for a number of periods, and
observe if some early entrants decide later to stay out of the market.
Of course, before one can say that excess entry has occurred we must elicit information to
test for alternative explanations for entry. Some subjects might think of themselves as invincible in
skill quizzes of this kind, resulting in what might be called a “Ken effect” after the über-champion of
the popular U.S. TV show Jeopardy! who won in 74 straight episodes and amassed winnings of over
$2,520,700. Of course, others might have the opposite “Barbie effect,” in which they think of
7
We only examine the random sample selection treatment (experiments 1-4), and the first 12 periods. The skill
sample selection treatment raises additional issues of control, and behavior in periods 13-24 must be evaluated
conditional on the history that preceded it. The balanced in-sample design of CL allows these controlled comparisons to
be made at the level of individual responses, but not at the level of aggregate market responses. Furthermore, behavior in
these initial periods amply illustrates their main qualitative conclusion.
8
The literature generally shows excess entry for games with low levels of capacity and under-entry for games
with higher levels of capacity, as reviewed by Camerer [2003; §7.3].
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themselves as dumb blondes with no chance of being the most skilled of the entrants. By directly
ascertaining how well they see themselves ranking in skill tasks of this kind we can proxy for
subjective beliefs of perceived skill at answering quiz questions.
An additional treatment employed by CL was the use of deliberate sample recruitment
procedures in which subjects were told that their earnings would depend in part on how good they
were at sports trivia. Subjects were reminded of this when they turned up, and told that all others in
the experiment that day had been similarly recruited. The results are shown on the right panel of
Figure 1. The clear effect from this treatment was to make the average profits from the game in
which skill was irrelevant even lower than when subjects were randomly recruited. Of course, this
treatment might also have encouraged differences in sample composition which could account for
the difference: if there is a gender bias in perceived skill at answering sports trivia, such that more
males were recruited when this was announced beforehand, then this could just be a gender effect
rather than an effect from a failure to account for the sample being skewed towards those that
perceive themselves to be skillful. Despite these concerns, this treatment is pregnant with
implications for the study of entrepreneurship: a natural question to ask, when starting a new
business, is whether the people that enter at the same time are just as smart at this niche as I believe
that I am.
Moore and Cain [2005] argue that one should think in more general terms of biases in
comparative judgment that are contextual, rather than simply think in terms of over-confidence or
under-confidence as generic traits of individuals. The reason is that there is (disputed) evidence from
a range of tasks in psychology that the same person can be overconfident on simple tasks and underconfident on difficult tasks.9 Of course, what is simple for one person may be difficult for another
9
The disputes are well summarized by Juslin, Winman and Olsson [2000]. Over-confidence in the psychology
literature refers to a specific concept and construct. Imagine that one has elicited answers to a two-alternative multiplechoice question, and that the proportion of correct answers is some fraction C. Then imagine that one has elicited a
measure of the subjective probability that the answer a given subject has provided is correct, and that the average of
these elicited probabilities is B. Then over-confidence is said to occur when B>C. To an experimental economist, one
immediate methodological concern is that neither of the responses is typically generated with incentives for the subject to
be accurate or thoughtful. Our experimental design does provide such incentives.
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person, and vice versa. But assume that this difference in perceived difficulty can be made operational.
They argue that it could rationalize why one might have excess entry in some industries where
profitability is viewed as a relatively simple goal (“you just have to work hard”) and yet also have
insufficient entry where profitability is viewed as a relatively difficult goal (“many people have tried
to make money here before, and all have failed”). Moore and Cain [2005] essentially replicate the
design of CL, who generally used simple trivia questions.10 They then varied the difficulty of the
questions, identifying one set as relatively difficult and providing some sharply contrasting examples.
They observe slightly higher rates of entry in the simple skill treatment compared to the difficult skill
treatment.
B. Our Experiments
In our design we operationalize the difficulty of the skill questions by using general
knowledge questions from a large test-bank. In the “difficult” treatment the questions have to be
answered in an open-ended way, with no prompts to assist in recognizing the right answer. In the
“simple” treatment the same questions are multiple-choice, so the subject sees the correct answer
along with several incorrect answers. There is considerable evidence to support the intuition that the
multiple-choice questions are easier (Bridgeman [1992], Kennedy and Walstad [1997] and Snow
[1993]). We also randomized the order and sub-set of questions from the larger test-bank.11
Market entry games are implemented in our experiments using the following instructions,
which were given to each subject:
10
In two sessions CL (p.308) used quizzes consisting of 10 “logic puzzles,” and in the remaining six sessions
they used quizzes consisting of 10 “trivia questions about sports or current events.”
11
The original source of our questions was the commercial test bank http://www.funtrivia.com/.
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Instructions to Participants
In this task you will have the opportunity to earn more money, by deciding if you
want to enter a competitive market. We will determine your earnings from this task at
5.15pm today, here at our booth. You can come at any time from 5.15 pm until 6 pm
and collect the remainder of your earnings. If you prefer, we can also mail them to you.
You will be given a stake of $10 at the outset of this task. This task involves a
decision as to whether to enter or not enter a market. 4 other people will be invited to
enter or not enter this same market, but you will not know in advance how many of them
have accepted. We are going to match people into markets randomly at the end of the
day. The capacity of each market is 1 - only one person can make a profit on each
market. If you decide to enter, your success will be under your control and will depend
on your skill in answering some questions. We will rank all entrants according to how
well they answer these questions. The highest ranked entrant will receive $35. Nobody
else in the same market will earn anything.
In order to enter you have to give up the $10 stake. This is the fee for entering
the market. If you do not enter the market, you keep your $10 stake.
The ranking system. The way that entrants will be ranked in this market is on the
basis of a 7-item quiz of general knowledge. The questions will cover topics such as
movie trivia, world history, geography, science, pets & animals, and “the world around
us.” Those with higher scores will be ranked higher. If there is a tie for the top rank we
will flip a coin to choose one person. You will take the quiz after you make your decision
to enter the market.
The questions will either be open-ended or multiple-choice. The open-ended
questions will require you to write down the correct answer. The multiple-choice
questions will require that you pick the correct answer from three alternatives. Many
people believe that multiple-choice questions are much easier than open-ended
questions.
You will be given multiple-choice questions, and you will be competing
against people who also have multiple-choice questions.
To summarize: If you decide not to enter, you will keep the $10 stake. You will
earn nothing beyond that from this task. Therefore, to guarantee that you will not lose,
simply do not enter the market. If you decide to enter, you will receive $35 instead of the
$10 stake if you are the highest-ranked out of those who enter. You will receive nothing
from this task if you are ranked lower. At the most 5 people can enter each market.
Of course, whatever you decide here, and whatever the earnings in this task, you
will also be paid $10 for completing the task, plus the earnings from the first task as
soon as you are done. You will have to return between 5.15 pm and 6 pm to claim the
earnings from this task, or they will be mailed to you.
DO YOU CHOOSE TO ENTER THE MARKET AND COMPETE OR DO YOU CHOOSE TO STAY OUT?
G
Enter the market and give up the $10 stake
G
Stay out of the market and keep the $10 stake
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The text that tells the subject that they have been given multiple-choice questions
was replaced with the obvious variant for those subjects given the open-ended
questions. There were no other changes in the instructions between these two
treatments. In order to better identify the contribution of skill over-confidence to excess
entry, we followed CL by asking some additional questions to gauge other possible
reasons for entry:
We have three final questions for you to answer.
1. Here is one more opportunity to earn money. Please answer this question:
Across all of today’s markets that have multiple-choice questions, how many people do
you think will enter on average? You will receive $10 if you estimate the number
exactly. You will receive $8 if you are off by 1, $6 if you are off by 2, and so on. Please
round off to an integer value.
PICK ONE: 0
1
2
3
4
5
2. We would like to get your estimate of how confident you are about your ability to
answer these skill questions. We are not paying you for your answer to this question,
but would appreciate you thinking about it carefully. Specifically:
If we compared your quiz answers with those of 100 other people picked at random
from this conference, what rank do you think you would have? A rank of 1 means that
you answered the questions better than anyone else, a rank of 50 that you think you
answer better than half but not as well as the other half, and a rank of 100 means that
you think everyone else does better than you.
ANSWER: ____________
3. Finally,
How did your hear about our booth?
01
02
03
Walk-by
Heard from somebody that participated
Other ______________________________________________
The first question elicits beliefs about the expected number of entrants, with a simple reward
for accuracy. It was important not to tie the answer to this question to the entry decision in the
specific game that the subject was playing, to avoid obvious endogeneity issues. The second question
assesses the subject’s perceptions of their relative ability at answering skill questions, where relative
ability is deliberately measured in comparison to the people that they would be competing with. The
third question tries to identify if the subject could have known anything substantive about the tasks
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from prior contacts. We also kept track of the order in which subjects answered these questions, to
further identify subjects that might have self-selected into this task in order to control for post-entry
feedback.
3. Field Experiments
A. Field Data
In 2005 we conducted field experiments at both of the two biennial Small Business
Innovation Research (SBIR) National Conferences. Appendix A lists the announcement used to
inform entrepreneurs of the scope of the conference. These national conferences attract actual
entrepreneurs from around the United States who attend to learn how to apply for SBIR funding for
their entrepreneurial firm. The SBIR program is highly competitive and distributes about $2 billion
dollars annually to small firms providing products and services to US government agencies. Since its
enactment in 1982, as part of the Small Business Innovation Development Act, SBIR has helped
thousands of small businesses to compete for federal research and development awards, and as such,
is relatively representative of small high-technology firms in the US. Our sample of entrepreneurs
included firms in business consulting (36%), engineering (21%), medical research & development
(10%), information technology and software development (30%), and agriculture (3%).
B. Procedures
Our field experiment was conducted from a booth set up in the exhibitors area of the
national conference with a banner titled “Research Study: Case for Participation.” The booth was
run by Elston, Rutström and two graduate students, all wearing matching university polo shirts and
slacks in school colors. The picture below illustrates the environment. All tasks involved real
economic consequences to the individual to provide motivation for subjects.
We started each cohort by telling people that this was a study on economic decision making
and if they were interested in participating it would take about 15 minutes for which they would
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receive $10 or possibly more. If they were interested we told them that we needed them to first read
and sign a release form for the study. We then proceeded to explain the general information on the
study provided on the cover page of the participant packets, while they read through the sheet
themselves. A complete set of instructions for one treatment is in Appendix B. Each packet was
housed in a folder so that participants could work privately given the tight spacing around the booth
and to minimize subject interaction.
The subject’s first task was to complete a survey of firm and individual characteristics before
advancing to the decisions tasks, which were handled on a one to one basis. Subjects completed two
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decisions tasks, only one of which we focus on here.12 The order of these two decision tasks was
randomized, to control for possible order effects.
Since the market entry task required that we group subjects, we “closed” each market at the
end of the day (5:45pm), when we knew that all conference activities were over. Subjects were told
that they could come back then to collect their earnings from that task, or we would mail them out.
To ensure credibility we completed the other decision task and paid every subject in cash for their
participation fee and earnings in that task. The use of delayed closings is standard practice in field
experiments of this kind that involve groupings in auctions or markets (e.g., List and Lucking-Reilly
[2000; p. 965]). To facilitate the efficient conduct of the paperwork in the market entry game, we
had a research assistant grading each quiz after it was completed and preparing the associated
paperwork and payment records.
4. Empirical Results
A. Data Description
Data were collected from 182 individuals. Responses to the questionnaires allowed us to
stratify the sample into Full-Time Entrepreneurs, Part-Time Entrepreneurs, and Salaried NonEntrepreneurs. We classify 42 (23%) as being Full-Time Entrepreneurs, in the sense that they report
this as their sole business focus and state that they have no additional source of salaried income. We
classify 38 (21%) as being Part-Time Entrepreneurs, since they report an entrepreneurial venture and
state that they additionally have full-time salaried jobs. Thus we have information on 80 individuals
that report some entrepreneurial experience. In each case we required that subjects provide detailed
information on the nature of their entrepreneurial firm so that we could classify them as an
entrepreneur or otherwise. We classify 92 (51%) as being Salaried Non-entrepreneurs, and 10 (5%)
as being none of these categories. We therefore consider the first two groups to be our treatment
12
Elston, Harrison and Rutström [2005] describe and evaluate the other task, as well as other field experiments
we have conducted with entrepreneurs.
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groups of entrepreneurs, and the others to be our controls. Unless otherwise stated, the expression
“entrepreneurs” refers to Full-Time and Part-Time Entrepreneurs as defined above, and the
comparison group would be all others.
The characteristics of the two groups differ slightly. The entrepreneurs in our sample are
roughly the same age (44 versus 42), the same fraction are female (34% versus 37%), and are just as
likely to have some higher education (49% versus 51%). But they are more likely to be Asian (13%
versus 5%) and have a higher income (48% versus 42%). In short, in terms of major demographic
characteristics this sample of entrepreneurs is relatively similar to the sample of non-entrepreneurs.
We do have two noteworthy differences between the two types of entrepreneurs: the full-time
entrepreneurs have more females (44%) and blacks (14%) compared to the part-time entrepreneurs
(23% and 3%, respectively), and these differences in means are significant at the 6% level. The mix
of industries represents by the two groups is virtually identical, so there is no difference in the
samples in terms of industry of the entrepreneurial activity.13
B. Analysis
Figure 4 shows the results for the question eliciting beliefs about the number of potential
entrants. We break the responses down according to the entrepreneurship categories used in our
analysis. It is apparent that FT Entrepreneurs expected more entrants on average than the control
group, although the latter has two modes. The striking result from Figure 3 is that PT Entrepreneurs
as a group had such diffuse priors on the number of potential entrants. Roughly as many expected
only 1 entrant as expected 5 entrants. Thus we see the most striking difference within the class of
entrepreneurs.
Figure 5 shows the results for the question eliciting beliefs about the skill that the subject
perceived themselves as having. Since this is a critical variable for our analysis, consider again the
13
We test this hypothesis by means of a Fisher Exact Test of the null that the assignment of entrepreneurs by
FT or PT status is not associated with their industry. The p-value on this null is 0.36, so we cannot reject the hypothesis
of no difference.
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question that elicited these responses:
If we compared your quiz answers with those of 100 other people picked
at random from this conference, what rank do you think you would have?
A rank of 1 means that you answered the questions better than anyone
else, a rank of 50 that you think you answer better than half but not as well
as the other half, and a rank of 100 means that you think everyone else
does better than you.
Thus larger numerical responses indicated subjects that thought themselves less skillful in relation to
the other subjects in the sample, and which they might expect to compete with in the market entry
game. To display this response in the most transparent manner, we simply take it’s negative: hence in
Figure 5 the subjects that are more to the right are those that have greater confidence in their relative
skill. The results in Figure 5 are quite striking as they suggest: PT Entrepreneurs think very highly of
themselves compared to all others.
Putting the results from Figures 4 and 5 together, we can form some hypotheses about the
forces leading to entry across the entrepreneurship categories. FT Entrepreneurs are less likely to
enter since they expect more potential competitors and have no particularly crisp belief that their
skills are better than others in this task. On the other hand, PT Entrepreneurs might be expected to
enter more readily, since they have diffuse priors about the number of potential entrants, but
perceive themselves as being more skilled than most.
Figures 6, 7 and 8 contrast these elicited beliefs in skill level with actual outcomes. The actual
outcomes are, of course, given by the score that the subject received out of 7 possible correct
answers. To compare to the measure of confidence, we normalized this score by multiplying by
14.29 = 100÷7, and then taking the negative. Thus a normalized score of -100 indicated someone
that did as poorly as possible (0 out 7 correct), and a normalized score of 0 indicated someone that
did as well as possible (7 out of 7 correct). We should add that our quiz questions were not a
pushover! The average score in the difficult frame was only 1.59 correct, and in the easy frame it was
still only 3.99 correct. The results in Figures 6-8 are again striking: the PT Entrepreneurs were
significantly over-confident in their skill ability, whereas the FT Entrepreneurs and Control group
were quite accurate as a whole and exhibited no general tendency to over-confidence.
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Figure 9 brings us to the actual entry decisions, and compares them by market to the
predictions. We observe significant entry into these markets. Over 73% of the markets had complete
entry, with 5 out of 5 potential entrants choosing to enter. Around 18% of the markets had 4
entrants, and a handful of markets (around 4% each) had 2 or 3 entrants. The average number of
firms to enter was 4.6. The distributions in Figure 9 are pooled across entrepreneurial categories, but
it is an easy matter to gauge errors in belief for each category by comparison with Figure 3. Clearly
FT Entrepreneurs do the best job of predicting entry, and PT Entrepreneurs the worst.
To put these results in perspective, Table 1 spells out the arithmetic underlying this entry
decision. In the final column we see that a risk-neutral subject, who only cared about average
earnings, would be marginally inclined to enter if there were 3 entrants, since the average earnings
would be $11 and the deterministic cost of entry is $10. So an average entry level of 4.6 is consistent
with some slight risk-loving behavior, some over-confidence in ability to be the one entrant to earn
the $35 jackpot, or simple mis-perception of the task (which can, of course, explain any outcome).
Since the $10 from not entering is risk-free, and the $11 average earnings at an entry level of three
has a considerable range, either $35 or $0, one would need to assume a considerable amount of risk
loving to justify entry to that level.
In Table 2 we evaluate the determinants of entry using a simple logit statistical model. Entry
is the binary dependent variable. Our main focus is on the propensity of FT or PT entrepreneurs to
enter compared to the control group. If we ignore all procedural differences in the various tasks, we
calculate that FT Entrepreneurs are no more likely than the control group to enter, but that PT
entrepreneurs are significantly less likely to enter. We calculate the estimated marginal effect of
binary variables for each category.14 Specifically, FT entrepreneurs are 5.9% less likely to enter than
the control, but the standard error on that estimate is 10.3 percentage points, so the p-value on the
null hypothesis of no effect has a significance level of 0.569. Hence we cannot infer that there is any
14
These marginal effects are calculated at the means of all variables. For dummy variables they show the effect
of a change from 0 to 1. Virtually identical effects are obtained if one calculates the average marginal effects over all
observations, using procedures developed by Bartus [2005].
-15-
statistically significant difference between the control group and FT entrepreneurs. But PT
entrepreneurs are estimated to be 30.8% less likely to enter, and the p-value on the null hypothesis is
only 0.010 in this case. The 95% confidence interval on this estimated effect is between 54.3% and
7.4%.
Are these conclusions robust to the various treatments and controls we have available?
Essentially, yes. We obtain the same results if we add controls for whether the subject was in a
multiple-choice quiz or not, whether they had heard about the task from a previous participant,
whether they participated on the second day, whether the entry task was first or second in order, and
an array of standard demographic characteristics. The marginal effect for FT (PT) Entrepreneurs is
then estimated to be -0.02% (-41.8%), with a p-value of 0.80 (0.002). We do observe a striking effect
from the subject being Black, associated with a reduction in the probability of entry of roughly 40
percentage points.
Extending the robustness check to the inclusion of variables measuring individual
confidence in skill levels, over-confidence in skill levels, and beliefs about likely entry, we find the
same results.15 The marginal effects for FT (PT) Entrepreneurs are now 0.03% (-32.9%), with a pvalue of 0.43 (0.005). Those that predicted more entrants were more likely to enter, which is
counter-intuitive unless subjects were risk-loving. Confidence in skill ability had no significant effect
on entry, of course after all other factors are controlled for. But subjects that were over-confident in
their skill levels were significant less inclined to enter, consistent with the finding that PT
Entrepreneurs were particularly over-confident. This effect is consistent with the reduction in the
marginal effect of being a PT Entrepreneur, from to -41.8% to -32.9% when these variables are
controlled for.
Thus we conclude that there is no effect of being a FT Entrepreneur on market entry relative to
15
The over-confidence measure does contain an element of endogeneity, since one of the entrants being
counted in the actual entry for the market is of course the subject. Since the dependent variable is that entry decision,
errors would tend to be correlated with the over-confidence measure. However, the entrant is typically only 1 of 5 (in
73% of cases) or 1 of 4 (in 18% of cases), so this effect cannot be quantitatively large.
-16-
those with no entrepreneurial experience. But there is a dramatic effect from being a PT Entrepreneur: these
individuals are significantly less likely to enter. The difference between FT and PT Entrepreneurs is
particularly telling because we have already controlled for differences in their individual
characteristics and market beliefs which might independently explain the lower propensity of PT
Entrepreneurs to enter.
5. Conclusions
We examine the hypothesis, based on rich anecdote, that over-confidence in their own
abilities leads entrepreneurs to excessively enter markets. We design and implement field
experimental tasks to directly elicit choices of entrepreneurs among market entry alternatives. We
control for beliefs about likely market entry, and their actual skill in determining the likelihood of
success post-entry. We find robust evidence that full-time entrepreneurs are no more likely to enter a
market in which performance depends on perceived skill in relation to others. But “wanna be
entrepreneurs,” who simultaneously hold salaried employment, are significantly less likely to choose
to enter such a market.
Our approach offers a methodological bridge between early decades of entrepreneurial
research into the individual traits of successful entrepreneurs, and the later decades of research into
cognitive traits of successful entrepreneurs. The earlier literature found few characteristics of
entrepreneurs that seemed to predict behavior with any reliability, leading Hatten [1997; p.40] to
conclude that “The conclusions of 30 years of research indicate that there are no personality
characteristics that predict who will be a successful entrepreneur. [...] Successful small business
owners and entrepreneurs come in every shape, size, color, and from all backgrounds.” The latter
literature found many cognitive characteristics that seemed to characterize entrepreneurs, as
summarized by Baron [1998]. The bridge we offer is to use the methods of experimental economics,
in which incentivized tasks are presented to subjects that bring the characteristics and context of the
field to bear on responding to those tasks. Our experimental design illustrates how one can take
-17-
well-posed laboratory instruments into the field, to the subjects that interest us.16
Our results on entry and over-confidence provide an obvious motivation for future
experiments to study exit, given that we find that there is no lack of entry into our experimental
markets. The key issue, then, is whether entrepreneurs behave differently when losses start piling up
in these markets, and they have to decide whether to exit or keep producing. Our experiments did
not extend beyond the one-shot entry stage, so we cannot address that issue within this design,
although it raises fascinating issues such as the potential effects of differing access to credit, aversion
to losses, and the horizon over which entrants frame their choices.
16
Harrison and List [2004] discuss the complementarity of laboratory and field experiments in greater detail.
-18-
Figure 1: Returns to Entry
Average Profits of Entrants
Data from rounds 1-12 of experiments of Camerer-Lovallo, AER 1999
Pooled over all market capacity levels
Random Sample Selection
Skill Sample Selection
15
15
10
10
Skill Irrelevant
5
5
0
Skill Irrelevant
0
Skill Relevant
Skill Relevant
-5
-5
1
2
3
4
5
6
7
Ro und
8
9
10 11 12
-19-
1
2
3
4
5
6 7
Round
8
9
10 11 12
Figure 2: Industry Profit By Market Capacity if Skill is Irrelevant
Data from Random Selection Experiments of Camerer-Lovallo, AER 1999
Capacity = 2
Capacity = 4
Capacity = 6
Capacity = 8
40
20
0
-20
-40
40
20
0
-20
-40
1
2
3
4
5
6
7
8
9 10 11 12
1
2
3
4
5
6
7
8
9 10 11 12
Round
Figure 3: Industry Profit By Market Capacity if Skill Is Relevant
Data from Random Selection Experiments of Camerer-Lovallo, AER 1999
Capacity = 2
Capacity = 4
Capacity = 6
Capacity = 8
40
20
0
-20
-40
40
20
0
-20
-40
1
2
3
4
5
6
7
8
9 10 11 12
Round
-20-
1
2
3
4
5
6
7
8
9 10 11 12
Figure 4: Elicited Beliefs About Entry
Kernel density estimates, by Entrepreneurship category
Density
FT Entrepreneurs
PT Entrepreneurs
Control
1
2
3
Predicted Number of Entrants
4
5
Figure 5: Elicited Confidence in Skill
Negative of the elicited percentile that the subject said that they were in
Kernel density estimates, by Entrepreneurship category
Density
FT Entrepreneurs
PT Entrepreneurs
Control
-100
-75
-50
Confidence in Quiz Aptitude
-21-
-25
0
Figure 6: Over-Confidence of Part-Time Entrepreneurs
Density
Actual
Predicted
-100
-75
-50
Quiz Aptitude
Figure 7: Over-Confidence of Full-Time Entrepreneurs
0
Figure 8: Over-Confidence of Control Group
Actual
Predicted
Actual
Predicted
Density
Density
-100
-25
-75
-50
Quiz Aptitude
-25
0
-22-
-100
-75
-50
Quiz Aptitude
-25
0
Figure 9: Errors in Beliefs About Entry
Density
Actual
Predicted
1
2
3
Predicted Number of Entrants
4
5
Table 1: Profitability of Market Entry
Number of
Entrants
Total
Industry
Profit ($)
Total
Non-entrant
Profit ($)
Total
Earnings ($)
Average
Earnings
Per Entrant ($)
Average
Earnings
Per Subject ($)
0
1
2
3
4
5
0
35
35
35
35
35
50
40
30
20
10
0
50
75
65
55
45
35
75
32.5
18.33
11.25
7
10
15
13
11
9
7
-23-
Table 2: Hypothesis Tests for Market Entry Decision
Dependent variable is Entry, where entry is denoted by 1.
Marginal effects from logit model, with market entry as the dependent variable. N=118.
Variable
FT Entrepreneur
PT Entrepreneur
FT Entrepreneur
PT Entrepreneur
Quiz_MC
Heard
Day2
OrderTask
Age
Female
Black
Asian
HigherEd
Married
HighInc
FT Entrepreneur
PT Entrepreneur
Quiz_MC
Heard
Day2
OrderTask
Age
Female
Black
Asian
HigherEd
Married
HighInc
Predict
Confident
OverConf
Estimate
-0.059
-0.308
Std. Error
p-value
A. No Controls
0.103
0.569
0.119
0.010
95% Confidence Interval
-0.261
-0.543
Mean
0.143
-0.074
0.212
0.186
0.153
-0.150
0.152
0.079
0.160
0.239
0.006
0.070
0.080
0.181
0.141
0.227
0.123
0.212
0.186
0.500
0.441
0.475
0.500
43.740
0.364
0.051
0.076
0.500
0.754
0.407
C. Including Procedural Controls, Demographics, and Measures of Confidence
0.034
0.043
0.426
-0.050
0.117
-0.329
0.118
0.005
-0.560
-0.098
-0.076
0.099
0.444
-0.270
0.118
-0.040
0.046
0.378
-0.130
0.050
0.011
0.038
0.772
-0.064
0.086
0.097
0.050
0.055
-0.002
0.196
-0.001
0.002
0.560
-0.005
0.002
0.000
0.034
0.985
-0.072
0.073
-0.456
0.231
0.049
-0.909
-0.003
0.042
0.038
0.269
-0.033
0.117
-0.005
0.040
0.905
-0.084
0.074
0.037
0.062
0.548
-0.084
0.159
0.059
0.040
0.138
-0.019
0.136
0.075
0.026
0.003
0.025
0.126
-0.001
0.002
0.559
-0.005
0.003
-0.003
0.002
0.083
-0.007
0.000
0.212
0.186
0.500
0.441
0.475
0.500
43.740
0.364
0.051
0.076
0.500
0.754
0.407
3.550
-33.020
-6.813
B. Including Procedural Controls and Demographic Characteristics
-0.023
0.090
0.789
-0.199
-0.418
0.138
0.002
-0.689
0.255
0.646
0.694
-0.101
-0.044
0.063
0.481
-0.168
0.034
0.064
0.593
-0.091
0.104
0.069
0.131
-0.031
0.000
0.003
0.868
-0.005
-0.057
0.065
0.379
-0.185
-0.398
0.243
0.102
-0.875
0.013
0.086
0.879
-0.155
0.016
0.063
0.794
-0.108
0.050
0.090
0.578
-0.127
0.000
0.063
0.993
-0.124
Definitions: FT Entrepreneur is a dummy variable for subjects classified as being Full-Time Entrepreneurs; PT Entrepreneur
is a dummy variable for subjects classified as being Part-Time Entrepreneurs; Quiz_MC is a dummy variable denoting the
use of the multiple choice quiz questions; Heard is a dummy variable denoting someone that had heard about this task
from another person at the conference; Day2 is a dummy variable for subjects responding on the second day of the
conference; OrderTask is a dummy variable measuring subjects who were given the market entry task before the risk
attitude task; HigherEd indicates subjects that have completed a higher college degree (e.g., MBA, JD or PhD); Married
indicates subjects that are currently married; HighInc indicates subjects whose household income in 2003 exceeded
$100,000; Predict is the number of entrants the subject predicted; Confident is a measure of how confident the subject was
of their quiz aptitude (it is the negative of the elicited percentile that they believed they were in); OverConf is Confidence
minus the normalized score actually obtained by the subject, as explained in the text; other variables defined obviously.
-24-
References
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Harrison, Glenn W., and Rutström, E. Elisabet, “Experimental Evidence on the Existence of
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-27-
Appendix A
2005 Spring National SBIR/STTR Conference: Omaha, NE
Date: Monday, March 07, 2005 to Thursday, March 10, 2005
Web Site: www.sbirworld.com/omaha
Conference Address: Hilton Omaha, 1001 Cass Street, Omaha, NE
Description: The National Science Foundation, in association with the Small Business
Administration and all 11 SBIR agencies, is sponsoring this 2005 National SBIR/STTR
Conference.
Annually, SBIR and STTR programs provide over $2 billion to small businesses through
federal programs to help entrepreneurs take their ideas from conception to reality. This conference
will give you the tools you need to obtain part of the $2+ billion available to small business
innovators.
•
•
•
•
•
•
•
•
•
•
•
Topics will include:
Proposal Preparation -The Basics
Business Basics
Partnerships & Resources
Accounting for SBIR/STTR
Marketing: How to Sell Yourself and Your Idea
Focus on Regional Projections & Trends
Focus on Homeland Security
Focus on Software Development
Focus on BioMed
Focus on Maximizing University- Business Relationships and the SBIR/STTR Program
Corporations -Why They’re Interested and How to Access Them
Each participant will also have multiple opportunities to meet and network with SBIR and
STTR Program Managers, and fellow attendees, including SBIR/STTR award winners, speakers, and
experts from businesses and the government willing to work with you to move your business ahead.
Who Should Attend: Attendees include sales and marketing professionals, small business owners,
entrepreneurs, university researchers, scientists seeking commercialization strategies, venture
capitalists, and all small businesses seeking to secure federal funding.
Email: [email protected]
-28-
Appendix B
Instructions for Experimental Sessions (Treatment REM)
WELCOME TO THE RESEARCH STUDY
DESCRIPTION
This is a study of economic decision making. We think you will find it interesting, you will
be paid $10 for your participation and you could earn additional money. How much you earn will
depend partly on chance and partly on the choice you make in decision problems which you will be
presented with. The instructions are simple and you will benefit from following them carefully.
The problems are not designed to test you. What we want to know is what choices you
would make in them. The only right answer is what you really would choose. That is why the
problems give you the chance of earning real money. You will be paid in cash today. Part will be
paid right after you have finished the task and the rest you can collect from 5.15 pm to 6 pm tonight,
or else we can mail it to you.
The task will proceed in three short parts.
The first part consists of a few questions about your firm and you. This information is for
research use only. The published results of our research will not identify any firm or individual, or
the choice he or she made in any way. Nor will we give this identifying information to anyone else.
The second and third part are short decision problems in which chance may play a part.
Each decision-problem requires you to make a choice. This is described in more detail when you
have completed the first part. Both of these parts may result in additional earnings over and above
the $10 participation fee.
We expect the entire task to take 20 - 30 minutes.
ID: _______
Part 1: Some Questions About You and Your Firm
In this survey most of the questions asked are descriptive. We will not be grading your
answers and your responses are completely confidential. We will not be recording your name or the
name of your firm on this sheet. Please think carefully about each question and give your best
answers.
Some Questions About You
1.
What is your AGE? ____________ years
2.
What is your sex? (Circle one number.)
01
3.
Male
02
Female
Which of the following categories best describes you? (Circle one number.)
01
02
03
04
05
White
African-American
African
Asian-American
Asian
06
07
08
09
-29-
Hispanic-American
Hispanic
Mixed Race
Other
4.
What is your current employment status, and that of your spouse or domestic partner?
(Circle all that apply)
You
01
02
03
04
05
06
5.
Self-employed part-time
Self-employed full-time
Part-time employment in another firm (or government)
Full-time employment in another firm (or government)
Actively seeking employment
Unemployed
Less than high school
GED or High School Equivalency
High school
04
05
06
Vocational or trade school
Bachelor’s degree at college
Higher degree at college
03
04
Separated or divorced?
Widowed?
Are you currently...
01
02
7.
01
02
03
04
05
06
What is the highest level of education you have completed? (Circle one number)
01
02
03
6.
Your spouse or
domestic partner
Single and never married?
Married?
How many people live in your household? Include yourself, your spouse and any
dependents. Do not include your parents or roommates unless you claim them as
dependents.
___________________
8.
Please circle the category below that describes the total amount of INCOME earned in 2003
by the people in your household (as “household” is defined in the previous question).
[Consider all forms of income, including salaries, tips, interest and dividend payments,
scholarship support, student loans, parental support, social security, alimony, and child
support, and others.]
01
02
03
04
05
$25,000 or under
$25,001 - $45,000
$45,001 - $65,000
$65,001 - $85,000
$85,001 - $100,000
06
07
08
09
10
$100,001 - $125,000
$125,001 - $150,000
$150,001 - $175,000
$175,001 - $200,000
over $200,000
IF YOU SELECTED “SELF-EMPLOYED” IN QUESTION 4 THEN PLEASE COMPLETE
THE QUESTIONS BELOW. OTHERWISE, YOU MAY PROCEED TO PART 2.
Questions About Your Firm
1.
How old is your firm, in years? ____________
2.
What type of product or service do you provide? _____________________________
3.
Have you ever experienced a shortage of capital in running your firm? (Circle one number)
01
04
Never
Often
02
05
Rarely
Always
-30-
03
Occasionally
4.
Do you have a shortage of capital now?
5.
How did you primarily finance your firm’s start up? (Circle all that apply)
01
02
03
04
6.
Inheritance
Gift
Credit cards
Earnings from another job
05
06
07
Yes
02
No
SBIR grant
Private loan from a bank or person
Other
Have you ever applied for or received an SBIR grant?
Applied:
Received:
8.
01
01
01
Yes
Yes
02
02
No
No
How do you finance your firm now? Enter rough percentages for each:
01
02
03
04
05
06
07
Government loans or grants
Private loans from banks or people
Credit cards
Earnings from another job
Cash from operations
Equity capital
Other
__________
__________
__________
__________
__________
__________
__________
9.
What would you estimate to be the annual revenue of your firm? _________________
10.
What would you estimate to be the value of the assets of your firm? _________________
11.
What is the state and ZIP code of the main location of your firm? ____________________
Part 2 Decision Task: see Elston, Harrison and Rutström [2005]
ID: REM _________
Part 3: Decision Task
In this task you will have the opportunity to earn more money, by deciding if you want to
enter a competitive market. We will determine your earnings from this task at 5.15pm today, here at
our booth. You can come at any time from 5.15 pm until 6 pm and collect the remainder of your
earnings. If you prefer, we can also mail them to you.
You will be given a stake of $10 at the outset of this task. This task involves a decision as to
whether to enter or not enter a market. 4 other people will be invited to enter or not enter this same
market, but you will not know in advance how many of them have accepted. We are going to match
people into markets randomly at the end of the day. The capacity of each market is 1 - only one
person can make a profit on each market. If you decide to enter, your success will be under your
control and will depend on your skill in answering some questions. We will rank all entrants
according to how well they answer these questions. The highest ranked entrant will receive $35.
Nobody else in the same market will earn anything.
In order to enter you have to give up the $10 stake. This is the fee for entering the market. If
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you do not enter the market, you keep your $10 stake.
The ranking system. The way that entrants will be ranked in this market is on the basis of a
7-item quiz of general knowledge. The questions will cover topics such as movie trivia, world
history, geography, science, pets & animals, and “the world around us.” Those with higher scores
will be ranked higher. If there is a tie for the top rank we will flip a coin to choose one person. You
will take the quiz after you make your decision to enter the market.
The questions will either be open-ended or multiple-choice. The open-ended questions will
require you to write down the correct answer. The multiple-choice questions will require that you
pick the correct answer from three alternatives. Many people believe that multiple-choice questions
are much easier than open-ended questions.
You will be given multiple-choice questions, and you will be competing against
people who also have multiple-choice questions.
To summarize: If you decide not to enter, you will keep the $10 stake. You will earn nothing
beyond that from this task. Therefore, to guarantee that you will not lose, simply do not enter the
market. If you decide to enter, you will receive $35 instead of the $10 stake if you are the highestranked out of those who enter. You will receive nothing from this task if you are ranked lower. At
the most 5 people can enter each market.
PLEASE TURN OVER
Of course, whatever you decide here, and whatever the earnings in this task, you will also be
paid $10 for completing the task, plus the earnings from the first task as soon as you are done. You
will have to return between 5.15 pm and 6 pm to claim the earnings from this task, or they will be
mailed to you.
DO YOU CHOOSE TO ENTER THE MARKET AND COMPETE OR DO YOU CHOOSE TO STAY OUT?
G
Enter the market and give up the $10 stake
G
Stay out of the market and keep the $10 stake
We have three final questions for you to answer.
1. Here is one more opportunity to earn money. Please answer this question:
Across all of today’s markets that have multiple-choice questions, how many people do you
think will enter on average? You will receive $10 if you estimate the number exactly. You
will receive $8 if you are off by 1, $6 if you are off by 2, and so on. Please round off to an
integer value.
PICK ONE: 0
1
2
3
4
5
2. We would like to get your estimate of how confident you are about your ability to answer these
skill questions. We are not paying you for your answer to this question, but would appreciate you
thinking about it carefully. Specifically:
If we compared your quiz answers with those of 100 other people picked at random from
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this conference, what rank do you think you would have? A rank of 1 means that you
answered the questions better than anyone else, a rank of 50 that you think you answer
better than half but not as well as the other half, and a rank of 100 means that you think
everyone else does better than you.
ANSWER: ____________
3. Finally,
How did your hear about our booth?
01
02
03
Walk-by
Heard from somebody that participated
Other ______________________________________________
Participation Fee
$10.00
$______________
Total payment in this part:
Print Name:
_________________________________________
SSN:
_________________________________________
Your SSN is required by the University of Central Florida Accounting in order for us to get
reimbursed from our research funds for these payments.
Signature that you have received the TOTAL earnings indicated above in cash:
Sign here:
_________________________________________
You may also receive additional earnings from Task 2 plus you will get your participation fee when
you return this evening.
Last Name:_____________________
Subject ID:________
Second Payment Record
No Entry Stake
$______________
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Task 2 (market entry decision)
Your score in the quiz:
Your rank in the group:
Earnings for Task 2
$______________
Task 3 (prediction)
Your prediction of entrants:
Actual entrants on average:
Earnings for Task 3
$______________
TOTAL Earnings from these two tasks:
$______________
Print Name:
_________________________________________
SSN:
_________________________________________
Your SSN is required by the University of Central Florida Accounting in order for us to get
reimbursed from our research funds for these payments.
Signature that you have received the TOTAL earnings indicated above in cash, in addition to the
earnings received earlier for participating and from Task 1:
Sign here:
_________________________________________
Market:___________________
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