Who gets duped? The impact of education on fraud detection in an investment task

Abstract

Many financial scandals appear to depend on a lack of skepticism on the part of their victims. Sophisticated investors trusted Bernie Madoff, for example, despite early warning signs of implausible returns. Our study investigates how education explains fraud detection in financial decision-making. In a simple survey, economics and finance students are asked to make an investment recommendation from among four hypothetical funds, including one based on Madoff’s fund. We use Truth Default Theory to explain our results. We show that education increases the likelihood that students are suspicious of Madoff’s fund, and that for students whose suspicions are aroused, education makes them less likely to choose the Madoff fund.

Full text

Vol.:(0123456789)
Journal of Economics and Finance
https://doi.org/10.1007/s12197-024-09672-z
1 3
Who gets duped? The impact ofeducation onfraud
detection inaninvestment task
CalvinBlackwell1 · NormanMaynard1· JamesMalm2· MarkPyles2·
MarciaSnyder2· MarkWitte1
Accepted: 11 April 2024
© The Author(s) 2024
Abstract
Many financial scandals appear to depend on a lack of skepticism on the part of their
victims. Sophisticated investors trusted Bernie Madoff, for example, despite early
warning signs of implausible returns. Our study investigates how education explains
fraud detection in financial decision-making. In a simple survey, economics and
finance students are asked to make an investment recommendation from among four
hypothetical funds, including one based on Madoff’s fund. We use Truth Default
Theory to explain our results. We show that education increases the likelihood that
students are suspicious of Madoff’s fund, and that for students whose suspicions are
aroused, education makes them less likely to choose the Madoff fund.
Keywords Education· Fraud· Skepticism· Truth Default Theory
JEL Classification G41: Role and Effects of Psychological, Emotional, Social,
and Cognitive Factors on Decision Making in Financial Markets· I26: Returns to
Education
1 Introduction
In the United States, it has been estimated that as much as 11% of the population has
been a victim of financial fraud (Anderson 2013) and that fraud costs the US econ-
omy as much as $50 billion per year (Deevy etal. 2012). While fraud is fundamen-
tally a criminal problem, and therefore the result of individuals choosing to act in a
criminal matter, in some cases potential victims may be able to mitigate the costs
* Calvin Blackwell
blackwellc@cofc.edu
1 Department ofEconomics, College ofCharleston, 5 Liberty Street, Suite 400, Charleston,
SC29424, USA
2 Department ofFinance, College ofCharleston, 5 Liberty Street, Suite 400, Charleston,
SC29424, USA

Journal of Economics and Finance
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imposed on them by the criminal behavior. For example, although Bernie Madoff
made fraudulent claims about his investments, it was clear to many observers at
the time that the returns he claimed to be earning were so statistically unlikely that
they could not be true. Indeed, in the ensuing scandal, many people were blamed as
complicit because they did not appear to exhibit enough care in examining Madoff’s
reported returns (Henriques 2012).
Skeptical investors were able to avoid involvement in the Madoff scandal by
refusing to invest in his funds. After the fact, it appears that some potential investors
chose not to invest with him because they believed Madoff was dishonest (Henr-
iques 2012). If policy goals include not only preventing criminal financial fraud, but
encouraging potential investors to protect themselves from it, it is natural to ask why
some investors fall for scams like Madoff’s while others do not.
This research represents an entry into a relatively understudied area of financial
literacy – how literacy affects skepticism and therefore fraud avoidance. We bring
together literatures on financial literacy and deception in a new way to shed light on
the tendency to fall victim to financial fraud.
In a simple investment task, students are asked to make an investment recommen-
dation from among four hypothetical funds, including one based on Madoff’s fund.
We use Truth Default Theory (TDT) (Levine 2014) to explain our results. Briefly,
TDT argues that individuals generally assume other people are telling the truth,
unless some discrepancy suggests otherwise. If a discrepancy is detected, then the
individual may actively investigate the veracity of the other’s statements. We show
that education increases the likelihood that students are suspicious of Madoff’s fund,
and that for students whose suspicions are aroused, education makes them less likely
to choose the Madoff fund.
2 Background
The question of how education affects fraud detection in financial decision-mak-
ing is not only relevant for academic research, but also for policy implications and
practical applications. In this section, we provide a background on the link between
financial education, literacy and fraud, as well as our primary model for understand-
ing fraud detection, Truth Default Theory. We also discuss the gaps in this field and
how our research addresses those gaps.
2.1 Financial education andfinancial literacy
The literature on financial education and financial literacy suggests that they have
positive effects on financial decision making. Lusardi and Mitchell (2014) reviewed
studies that showed higher financial literacy was linked to better financial behavior.
However, some researchers (Willis 2011; Hastings etal. 2013) challenged this rela-
tionship. Kaiser and Menkhoff (2017) and Kaiser and Menkhoff (2020) conducted
meta-analyses of financial literacy studies and found evidence that financial educa-
tion improved financial literacy and behavior. Despite some conflicting results, the

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general consensus is that more education and literacy enhance financial decision
making. Therefore, it is reasonable to expect that greater financial understanding
would also reduce the vulnerability to financial fraud.
2.2 Susceptibility tofinancial fraud
There are few academic papers on financial fraud, but some non-peer reviewed
papers have been produced by organizations such as the Pension Research Coun-
cil (e.g., Kieffer and Mottola 2016), which explored how individual characteristics
influenced the likelihood of being defrauded. Knutson and Samanez-Larkin (2014)
examined how personality traits affected skepticism and found that investors with
lower impulse control were more prone to financial fraud, while cognitive abil-
ity and risk attitude did not matter. Anderson (2016) and Anderson (2013) found
that consumer literacy and numeracy were associated with the ability to identify
and avoid fraudulent advertising and schemes, respectively. Recently, some peer-
reviewed papers have emerged on this topic. Andreou and Philip (2018), Engels
etal. (2020), and Wei etal. (2021) all found that higher financial literacy helped
individuals detect fraud in their financial transactions.
2.3 Truth Default Theory
Fraud is a form of deception, a topic that has been investigated more extensively in
other social sciences. Levine (2014) has outlined what he calls Truth Default The-
ory (TDT), a comprehensive theory of deception and deception-detection. One key
assertion of TDT is that people presume honesty in communication. When two peo-
ple communicate, both people assume that the other person is telling the truth. While
Bernie Madoff and the data presented on financial fraud provide obvious exceptions
to this assumption, even a brief bit of introspection reveals its overall plausibility.
Human beings communicate with each other constantly, and it is beyond our cogni-
tive facilities to constantly monitor every bit of communication for deception. TDT
argues that it is evolutionarily adaptive for humans to assume honesty, and Levine
(2019) provides significant evidence in support of this argument.
If humans assume honesty, how is deception ever uncovered? TDT (illustrated in
Fig.1) argues some events push people into a state of suspicion, in which statements
are evaluated for their honesty. Potential trigger events include the speaker having a
motive for deception (like personal gain) or a lack of correspondence between com-
munication content and some knowledge of reality. Once in the suspicious state,
the person actively evaluates the veracity of the statements, taking account of the
internal logic of the statements, and the correspondence between the statements and
other known facts. If the statements fail to have internal logical consistency or con-
tradict other information, the person updates his/her beliefs that the communication
is honest. If this updating results in the level of perceived veracity falling below
some threshold, then the statements are evaluated as dishonest.
TDT also helps to provide context for research by Zhang etal. (2015), who
examined how cues could be used to help increase investors’ skepticism. In

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Zhang et al.’s decision task, survey respondents from Amazon MTurk were
asked to play the role of a financial advisor and recommend a mutual fund for
a hypothetical client. The survey participant had to choose between five differ-
ent fictionalized funds, one of which was based on Madoff’s fund. Zhang etal.
found that 68% of their respondents recommended the Madoff fund; however,
when respondents were asked about which fund was most suspicious, this cue
reduced the number choosing the Madoff fund to 51%. In terms of TDT, the cue
acted as a trigger that helped to move some participants into a state of suspicion.
TDT helps explain the results presented earlier regarding the role of financial
literacy. First, having more financial literacy could make people more likely to
become suspicious in a situation involving fraud, and to investigate the fraudu-
lent opportunity further. Second, having more financial literacy could also make
people more skilled at detecting fraud once they are suspicious. In this paper,
we present a decision task that is very similar to the one used by Zhang etal.
(2015), but with students of economics and finance as participants.
The works by Andreou and Philip (2018), Engels etal. (2020) and Wei etal.
(2021) represent all the published work in the fields of economics and finance on
individual, non-professional investors and fraud detection. All those papers rely
upon self-report surveys to investigate fraud detection. In particular, survey par-
ticipants were asked if they had detected fraud in the past, and then this behavior
was correlated with other collected variables like education, financial literacy,
gender, etc. Importantly, all this data was self-reported. From the surveys we are
able to learn if an individual thought that someone had tried to defraud them;
however, we do not know if what the individual detected was actual fraud, nor
do we know if other fraud attempts were successful. The current paper addresses
this issue by presenting survey respondents with a decision task (based on
Zhang etal. 2015) in which the nature of the fraud is known, allowing us to draw
stronger conclusions about fraud detection. Furthermore, we advance the field of
fraud detection by using TDT as our model – we are unaware of any papers in
economics or finance that use this framework to understand fraud detection.
Fig. 1 Truth default theory

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3 Methodology
3.1 Participants
The sample for this study consisted of undergraduate students from the Col-
lege of Charleston.1 Students were recruited from a variety of finance and
economics backgrounds, ranging from no prior coursework to extensive
coursework. Students were offered extra credit in their courses in return for
completing the survey. All students gave consent before starting the survey.
Anonymity was guaranteed; credit was offered only for participation, not
performance.
At the College of Charleston, the undergraduate economics and finance
classes from which our sample was selected are taught in sequence, starting with
Principles of Microeconomics, which introduces market dynamics and resource
allocation. The next class is Principles of Macroeconomics, which focuses on
aggregate economic analysis. The third class, Business Finance focuses on cor-
porate finance fundamentals, leading to Intermediate Business Finance where
students apply these principles using financial software. The sequence culmi-
nates in Applied Portfolio Management, where students actively manage diverse
asset portfolios, integrating knowledge from all previous courses into real-world
financial analysis and decision-making. Because enrollment in these sequenced
classes is mutually exclusive, there was no chance that a student might be asked
to take the survey twice.
A total of 479 students attempted the decision task, with 430 finishing the survey.
Of those students, 390 undergraduates filled out all fields required for our analy-
sis. The full dataset and survey instrument are available at https:// doi. org/ 10. 17632/
6d4y9 g5jt4.2 (Blackwell 2022).
3.2 Decision task
The basic structure of the survey was as follows:
1. Main decision task
a. Information on 4 investment options (plus the S&P 500 for reference)
b. Selection of the participants’ preferred investment option
c. Measures of suspiciousness and unethicality of investment options
2. Big 5 Personality Inventory
3. Socioeconomic information
4. Educational background information
1 Human subjects approval was granted by the College of Charleston’s Institutional Review Board, for
Protocol IRB-2018–03-14–083129.

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For the main decision task, survey participants were asked to make an invest-
ment recommendation from among four different hypothetical funds (whose returns
are shown in Fig.2 with S&P 500 for comparison).2 All the funds are based on
real investments, although the fund names and dates are disguised (i.e. the data do
not necessarily span between 2009 and 2014).One of the funds, “Fortitude Invest-
ments,” is based on Madoff’s fund. The main decision task was taken almost verba-
tim from the task presented by Zhang etal. (2015).
The participants were provided basic information about average returns and vola-
tility for each fund. The subjects were then offered the opportunity to review more
information about each fund. Choosing this option gave participants specific infor-
mation about the fund’s investment strategy and auditors, with the Madoff fund’s
auditor description listed as:
Fortitude uses SA & Associates, CPA for their auditing purposes. SA & Asso-
ciates was established 15 years ago. The chief auditor was formerly a VP at
Fortitude Investments.
This information provided the only additional opportunity to trigger the partici-
pants’ suspicion before submitting their recommendation. We collected data on the
number of funds for which each participant asked for additional information as well
as which funds they examined.
After submitting their recommendation, participants rated each fund based
on how suspicious or unethical they found the fund to be, completed the Big Five
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
120%
543210
Cumulave Returns of All Funds
Power Trade
Investments
Fortude Investments
Alpha Investments
Tobacco Trade
Investments
S&P500
Fig. 2 Fund performance
2 Note that our decision task required participants to make a recommendation for a client, not make an
investment choice for themselves. A recent meta-analysis by Polman and Wu (2020) indicates that gener-
ally, people are willing to take on more risk when making decisions for others. However, when making
decisions for clients, people are slightly more conservative than when making decisions for themselves.

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Journal of Economics and Finance
Personality Inventory (John etal. 1991, 2008), and submitted information on their
socio-economic and educational background. The post-decision data is discussed
further below.
3.3 Data description
Descriptions of all variables used in our analysis are given in Table 1, with sum-
mary statistics for the analysis sample provided in Table 2. Our primary outcome
measure is a binary variable indicating if the participant recommended the Madoff
Fund or an alternative. We assume that participants who did not pick the Madoff fund
did so because they detected potential fraud from that mutual fund. Participants pro-
vided demographic data by self-reporting their age, gender, and race. Because there
may be a difference in the effect of additional information depending on whether the
Madoff Fund is one of the funds being compared, we split this variable into those
who included the Madoff Fund in their examination and those who excluded it.
For educational information, participants indicated which sequenced econom-
ics and finance classes they have completed. We used this data to create cumulative
course indicators. This means the total effect of education for a participant who has
completed Business Finance will also include the effect of Principles of Micro- and
Macroeconomics, and coefficient estimates indicate the marginal effects of complet-
ing each course.
The Big Five Personality Inventory (BFI) measures participants on the five traits
of extraversion, conscientiousness, agreeableness, neuroticism, and openness. The
BFI consists of statements with which each individual could agree or disagree.
An example BFI statement is: “I am someone who is talkative” (John etal. 1991).
A person who agrees with this statement would rank highly for extraversion. The
American Psychological Association Dictionary definitions for the BFI traits are
included in Table1.
To measure skepticism, we followed Zhang etal. (2015) by asking students to
rank how suspicious they found each fund. To avoid triggering scrutiny due to ask-
ing the question, the question was left until after participants had submitted their
recommendation. To ensure that these scores are comparable across observations,
we created the variable SUS by dividing the score assigned to the Madoff Fund by
the average of all scores provided by each participant.
Finally, our dataset includes the time taken to complete the survey in sec-
onds. While almost all the 390 undergraduates who filled out all required fields
took between 3 and 30min to complete it, 20 participants completed the sur-
vey in less than 3min. These participants chose all five fund options in nearly
equal proportions, suggesting that all their answers may have been randomly
selected to complete the survey as quickly as possible. To avoid introducing
random noise into all variables used, we drop these 20 observations from our
primary analysis sample.
In the remaining 370 observations of our analysis sample, the median dura-
tion was 434s, or slightly over 7min. The mean duration was 1,997 s (more than

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Table 1 Variable definitions
on race: Participants self-identified into one of seven categories—African American, Asian, Caucasian,
Hispanic, Multi-Racial, Native American, and Other. Only one participant in the complete sample identi-
fied as Native American, so this category was combined with Other, leaving six categories to include as
dummies. African American was taken as the excluded dummy, such that dummies indicate differences
between the listed group and African American participants
on gender: Participants self-identified into one of three gender categories—Male, Female, and Other. No
participants identified as Other
Variable Definition
Madoff Indicator variable which equals 1 if participant chose Madoff Fund
SUS Participant response to question regarding how suspicious Madoff Fund is, ranging
from relative suspiciousness of the Madoff Fund; equal to a Likert value from 1 (least
suspicious) to 7 (most suspicious) assigned to the Madoff Fund, divided by the mean
score of the same type assigned to all funds
Duration Time spent (in seconds) by participant completing the survey
Age Age in years
Race & Gender Indicator variables which equal 1 if the participant self-identified into the listed cat-
egory
Female Female
Asian Asian
Caucasian Caucasian
Hispanic Hispanic
Multi- Multi-racial
Other Native-American or Other
BFI Score on Big 5 Inventory for the listed trait(Source: American Psychological Associa-
tion 2019)
Extra Extraversion; APA Definition: “characterized by an orientation of one’s interests and
energies toward the outer world of people and things rather than the inner world of
subjective experience”
Agree Agreeableness; APA Definition: “the tendency to act in a cooperative, unselfish man-
ner”
Con Conscientiousness; APA Definition: “the tendency to be organized, responsible, and
hardworking”
Neuro Neuroticism; APA Definition: “characterized by a chronic level of emotional instability
and proneness to psychological distress”
Open Openness; APA Definition: “the tendency to be open to new aesthetic, cultural, or intel-
lectual experiences”
Education Indicator variables which equal 1 if the participant has completed the listed course in
the sequence or higher (i.e. a participant with a 1 for “Macro” will also have a 1 for
“Micro”)
Micro Principles of Microeconomics; 1st in sequence
Macro Principles of Macroeconomics; 2nd in sequence, with Micro as prerequisite
Finance Business Finance; 3rd in sequence, with Macro as prerequisite
Intermed Intermediate Business Finance; 4th in sequence, with Finance as prerequisite
Portfolio Applied Portfolio Management; 5th in sequence, with Intermed as prerequisite
Information Count variables of the number of funds for which the participant sought more info
Excluding Counts if participant did not request info on the Madoff fund; ranges from 0 to 3
Including Counts if participant requested info on the Madoff fund; ranges from 0 to 3

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Journal of Economics and Finance
33min), reflecting a small number of extreme outliers. We include a dummy vari-
able for participants who took more than 30min to control for these outliers.3
4 Hypotheses
4.1 Truth Default Theory
According to TDT, deception and fraud are extraordinarily difficult to detect under most
circumstances. Only those who are triggered to scrutinize have a real chance to detect fraud,
and even then, only if enough inconsistencies can be identified. In the context of our invest-
ment task, this suggests a two-stage relationship between education, skepticism, and detect-
ing the fraud of the Madoff Fund. In the first stage, the level of skepticism is determined.
Table 2 Descriptive statistics
(n = 370) Variable Mean Std Dev Min Max
Madoff 0.47 0.50 0 1
SUS 1.00 0.44 0.25 2.8
Duration 1996.73 11376.82 186 170604
Demographics
Age 20.61 2.28 10 42
Female 0.55 0.50 0 1
Asian 0.02 0.13 0 1
Caucasian 0.84 0.37 0 1
Hispanic 0.03 0.18 0 1
Multi- 0.03 0.17 0 1
Other 0.04 0.18 0 1
BFI
Extra 3.52 0.74 1.38 5.00
Agree 3.83 0.59 2.00 5.00
Con 3.65 0.58 1.89 5.00
Neuro 2.80 0.68 1.00 4.75
Open 3.57 0.52 1.90 4.80
Education
Micro 0.91 0.29 0 1
Macro 0.66 0.47 0 1
Finance 0.36 0.48 0 1
Intermed 0.14 0.34 0 1
Portfolio 0.09 0.28 0 1
Information
Excluding 0.29 0.69 0 3
Including 1.92 1.62 0 4
3 We also ran our analysis dropping the 26 observations (about 7% of our sample) with a duration
greater than 30min. This loss of data reduced the statistical significance of some results without substan-
tially altering the coefficient estimates for our variables of interest.

Journal of Economics and Finance
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In the second stage, participants choose, conditional on their level of skepticism, to recom-
mend either the Madoff Fund or an alternative.
If the participant has not been triggered to scrutinize for possible deception, they will
take the information presented at face value in the second stage. In this “trusting” case, we
would expect additional information about the fund options to reinforce the benefits of the
high-return, low-risk Madoff Fund, increasing the likelihood of recommending it. Simi-
larly, more economics and finance coursework should enable the participant to recognize
the desirability of these fund traits, also increasing the likelihood of recommending the
Madoff Fund.
If the participant has been triggered to scrutinize, however, we would expect the
opposite effects. Since deception detection relies on recognizing inconsistencies
between the deception and other known information, additional information about
the fund options and additional background in economics and finance should pro-
vide more opportunities to detect the deception, which should reduce the likelihood
of recommending the Madoff Fund.
Mathematically, TDT suggests a model for fund choice such as the following:
where f(·) is increasing in both education and information, g(·) is decreasing in both,
and controls include demographic and personality factors which may also affect the
choice of fund.
4.2 Determinants ofskepticism
Since the functional form of the binary choice of fund is dependent on the level of skep-
ticism, the first stage involves identifying what determines suspicion. TDT does not
make sharp predictions about what determines initial suspicion, but as it seems likely
that individuals may differ in levels of innate skepticism, we control for personality
and demographics. While we would not expect education to have a significant effect
on innate skepticism directly, familiarity with economics and finance should make par-
ticipants more confident in requesting additional financial information. We hypothesize
that the effect of education on SUS should be mediated by information gathering.
5 Results
Table 3 shows the proportion of students choosing each of the five funds. Forty-
seven percent of participants in our analysis sample chose the Madoff fund. Interest-
ingly, more than 5% of students chose the S&P 500, even though the S&P data was
only provided for comparison purposes.
Figure 3 shows how the Madoff decision is related to educational background. Partici-
pants who had not completed any coursework in economics or finance were the most likely
to choose Madoff, and participants who had completed Business Finance were less likely to
choose Madoff than those with only one or two courses. There appears to be a u-shape in
(1)
P(Madof f
|
SUS)=
{
f(Education,Inf ormation,Controls)if SUS ≤
Threshold
g(Education,Inf ormation,Controls)if SUS >
Threshold

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Journal of Economics and Finance
education, however, as participants who have completed Intermediate Business Finance or
Applied Portfolio Management were more likely to choose Madoff than those who have only
completed Business Finance. This u-shape may be the result of the relatively small number
of students in the analysis sample who have taken the higher-level courses, making the rela-
tionship with education difficult to generalize from this simple comparison. If we divide the
subjects into those who have taken finance and those who have not, 43% of the students with
finance chose Madoff, while 49% of the students without finance chose Madoff.
While these preliminary results suggest that choosing the Madoff Fund depends
on education, personality, and demographics, a more formal analysis is required to
see the role of skepticism and test our TDT-related hypotheses.
5.1 Threshold model ofTDT
TDT suggests a structural break in the probability of recommending the Madoff
Fund, with the coefficients related to education and information changing between
Table 3 Fund choice Fund % choosing fund
(all complete sur-
veys)
% choosing fund
(analysis sample)
Tobacco Trade 5.81 5.41
Power Trade 24.65 25.68
Madoff 45.58 47.03
Alpha 17.21 16.76
S&P 500 6.74 5.14
Fig. 3 Percentage choosing Madoff by education level

Journal of Economics and Finance
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those who are trusting and those who become skeptical. However, there is no obvi-
ous a priori value of SUS that should be used to test such a break. Since TDT
claims that most individuals have a high threshold, a high level of suspicion may
be required to ensure sufficient ‘state’ skepticism. On the other hand, the context of
the survey and the type of students who select into economics and finance courses
may suggest a high unobservable innate skepticism, requiring only a modest level of
suspicion to trigger scrutiny.
The simplest econometric approach to searching for a breakpoint is a threshold
linear probability model. This extends the method of ordinary least squares (OLS)
regression, which chooses coefficients that minimize the sum of squared residuals
(SSR), to the choice of breakpoint. The data are sorted by relative suspicion, split
into high and low suspicion groups at all possible values of the threshold variable,
and linear probability regressions are run for each possible split. The model that pro-
vides the lowest SSR is then reported.4
The linear probability model results are provided in Table 4. The threshold for
SUS is 0.57, placing less than 15% of the analysis sample in the trusting group.
Extroverted and female participants are still less likely to choose Madoff, while con-
scientious participants are more likely to do so.
Gathering more information seems consistent with TDT. For the trusting
group, asking for more fund information without comparing it to the Madoff
Fund increases the likelihood of choosing Madoff. For the skeptical group, the
coefficient is similar in magnitude but opposite in sign, indicating that gath-
ering more information makes choosing Madoff less likely for those who are
scrutinizing.5
The effect of education is less obvious, with no statistically significant edu-
cation coefficients in our sample. This may be due to the small sample, as
expanding the sample to include all undergraduates with the required data sug-
gests that completing the Business Finance course has a positive and signifi-
cant (5% level) effect, increasing the likelihood of choosing Madoff. For the
skeptical group in the expanded sample, having completed the same course
reduces the likelihood of choosing Madoff, although this effect is only signifi-
cant at the 10% level.
To properly determine if there is a difference in the effect of education between
the trusting and skeptical groups, we must test for differences in corresponding coef-
ficients in our analysis sample. The χ2-statistic for difference in coefficients is 5.00,
which is statistically significant at the 5% level. Although we cannot confidently say
completing Business Finance makes choosing Madoff more likely for the trusting
4 The Stata 17 command used, threshold, selects based on the Bayesian Information Criterion, which is a
penalized version of SSR.
5 We also considered modeling the threshold effect of education without the inclusion of information
gathering variables. The impact of this change is negligible: none of the education variables that lack
statistical significance gain it by dropping information, and no statistically significant variables change
sign. Considering the risk of introducing omitted variable bias by dropping the information variables, we
retain these variables in our reported TDT models and our model of suspicion below.

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Journal of Economics and Finance
Table 4 Truth default theory: binary choice for Madoff fund
Linear probability model Logit model
Variable Expanded Analysis sample Pooled With break
I(Dur > 1800) 0.247*** (0.086) 0.246*** (0.085) 1.288*** (0.497) 1.129** (0.514)
I(Dur < 1800) -0.141 (0.105) — — —
Demographics
Age -0.012 (0.012) -0.010 (0.012) -0.001 (0.051) -0.041 (0.061)
Female -0.129** (0.052) -0.130** (0.054) -0.484* (0.265) -0.624** (0.289)
Asian -0.270* (0.141) -0.302** (0.140) -2.184** (0.952) -2.057** (0.940)
Caucasian -0.015 (0.108) -0.037 (0.110) -0.061 (0.496) -0.168 (0.524)
Hispanic -0.045 (0.179) -0.063 (0.181) -0.204 (0.912) -0.380 (0.933)
Multi- 0.149 (0.173) 0.128 (0.173) 0.440 (0.823) 0.380 (0.851)
Other 0.129 (0.163) 0.099 (0.177) 0.350 (0.800) 0.440 (0.929)
BFI
Extra -0.070** (0.032) -0.070** (0.033) -0.349** (0.164) -0.367** (0.179)
Agree -0.033 (0.046) -0.040 (0.046) -0.184 (0.218) -0.166 (0.244)
Con 0.103** (0.044) 0.105** (0.044) 0.428* (0.221) 0.555** (0.255)
Neuro 0.009 (0.039) 0.005 (0.039) -0.096 (0.195) 0.008 (0.210)
Open 0.031 (0.045) 0.029 (0.046) 0.163 (0.231) 0.126 (0.251)
Trusting (SUS ≤ 0.57, n = 53) (SUS ≤ 0.57, n = 51)
Constant 0.835* (0.441) 0.874** (0.445) -1.993 (2.533)
Education
Micro -0.022 (0.176) 0.023 (0.178) 1.718 (1.299)
Macro 0.021 (0.123) 0.021 (0.124) 0.670 (1.171)
Finance 0.192** (0.098) 0.159 (0.100) 14.573** (1.280)
Intermed -0.347 (0.241) -0.318 (0.237) -19.526** (2.762)
Portfolio -0.067 (0.276) -0.091 (0.273) 1.738 (1.813)

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Table 4 (continued)
Linear probability model Logit model
Variable Expanded Analysis sample Pooled With break
Information
Excluding 0.224*** (0.080) 0.185** (0.082) 14.963*** (1.292)
Including 0.067** (0.034) 0.046 (0.037) 1.410** (0.651)
Skeptical (SUS > 0.57, n = 337) (SUS > 0.57, n = 319)
Constant 0.820** (0.393) 0.837** (0.394) 1.049 (1.919) 3.697** (1.577)
Education
Micro -0.120 (0.102) -0.135 (0.102) -0.454 (0.450) -2.382* (1.390)
Macro 0.017 (0.071) 0.019 (0.072) 0.189 (0.303) -0.695 (1.221)
Finance -0.117* (0.066) -0.111 (0.070) -0.545* (0.315) -15.098*** (1.317)
Intermed 0.149 (0.154) 0.131 (0.154) 0.301 (0.615) 20.294*** (2.842)
Portfolio -0.056 (0.166) -0.057 (0.168) -0.209 (0.668) -2.073 (1.960)
Information
Excluding -0.186*** (0.033) -0.203*** (0.033) -0.856*** (0.242) -16.253*** (1.315)
Including -0.037** (0.018) -0.033* (0.019) -0.083 (0.083) -1.597** (0.659)
Observations 390 370 370 370
R2 & Pseudo R20.255 0.257 0.089 0.222
Dependent variable is Madoff, described in Table1 Variable Definitions. I(Dur > 1800) is a dummy variable for participants who took more than 30 min to complete the
survey. All other variable descriptions are in Table1 Variable Definitions. Heteroscedasticity-robust standard errors are in parentheses. * denotes statistical significance at
the 10% level, ** is 5% level, *** is 1% level

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Journal of Economics and Finance
group and less likely for the skeptical, there is a difference between the groups in the
direction suggested by TDT.
None of the other education variables show a significant difference in coefficients.
This suggests that the skills that differentiate the choices of trusting and skeptical
students are not acquired after a single introduction to economic and finance course-
work. This seems reasonable since only basic financial concepts are covered in the
two economics courses studied. Key models such as the CAPM are not covered in
detail until Business Finance.
5.2 Logit model ofTDT
One possible shortcoming of this analysis is the use of linear probability models,
which have a variety of known shortcomings. One popular alternative for binary
choices is the logit model, which we also present in Table4. We consider versions
with no break (pooled) and with the same break in coefficients suggested by the
threshold model.
The signs and general significance of gender, extraversion, and conscientiousness
match the threshold model, both in the pooled model and the model with the break.
In the pooled model, both completing Business Finance and gathering more infor-
mation reduce the likelihood of choosing Madoff, although the effect of the course is
only significant at the 10% level.
The break is highly statistically significant (χ2 = 1,370), providing strong evi-
dence that skepticism alters some or all the effects of education and information
gathering. We again see statistically significant differences (and signs that match
TDT) in the effect of the Business Finance course and gathering information
between the trusting group and the skeptical group.
The magnitudes of the coefficients on Business Finance are very close, and we can-
not reject the null that they sum to zero (χ2 = 2.2). If financial education increases the
likelihood of already-skeptical students avoiding a Ponzi scheme, it is difficult to identify
such an effect in our data. However, gathering information, whether excluding or includ-
ing information about the Madoff Fund, seems to not just cancel out but actively reduce
the likelihood of choosing Madoff (χ2 = 19.4 and 4.3, respectively).
Finally, the effect of the Intermediate Business Finance course in the break model
deserves some comment. In both groups, the coefficient seems to have a dampening or
corrective response to that of the previous course. In the case of the trusting group, we can
reject the null that the coefficients sum to zero (χ2 = 5.6) at the 5% level, suggesting that
students with more practice using workhorse finance models are less inclined to jump at a
fund that promises high returns for free. However, we are unable to reject the null that the
effect among skeptical students sums to zero (χ2 = 0.1).
5.3 Skepticism anddue diligence
Given the evidence that skepticism has an important effect on fund recommendation
in our decision task, we now consider our first-stage hypotheses. Results of an OLS

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regression of SUS on our demographic, personality, education, and information var-
iables are presented in Table5.
We see that none of the demographic variables are statistically significant, and
among the BFI only extraversion is significant (at the 10% level). Among the educa-
tion variables, only the Principles of Microeconomics course is significant, also at
the 10% level.
Table 5 Skepticism estimation: participant disposition toward Madoff fund
SUS indicates an OLS regression the relative suspiciousness measure defined in Table1 Variable Defi-
nitions as dependent variable. Information indicates the dependent variable is the number of funds for
which participants request more information. I(Dur > 1800) is a dummy variable for participants who
took more than 30 min to complete the survey. All other variable descriptions are in Table1. Heterosce-
dasticity-robust standard errors are in parentheses. * denotes statistical significance at the 10% level, **
is 5% level, *** is 1% level
† The constant term for the ordered logit regression has been suppressed
SUS Information
Variable Poisson Ordered logit
Constant 0.984** (0.441) 0.358 (0.528) †
I(Dur > 1800) -0.086 (0.078) -0.019 (0.115) -0.107 (0.316)
Demographics
Age -0.023 (0.017) -0.003 (0.015) 0.003 (0.056)
Female -0.081 (0.054) -0.153** (0.074) -0.484** (0.223)
Asian 0.161 (0.142) -0.112 (0.358) -0.339 (1.142)
Caucasian -0.012 (0.097) -0.034 (0.176) -0.095 (0.530)
Hispanic 0.011 (0.131) 0.056 (0.202) -0.028 (0.601)
Multi- 0.275 (0.204) -0.353 (0.281) -0.777 (0.683)
Other 0.092 (0.131) 0.190 (0.225) 0.575 (0.738)
BFI
Extra 0.064* (0.038) -0.070 (0.044) -0.230* (0.132)
Agree 0.000 (0.048) 0.090 (0.062) 0.290 (0.187)
Con 0.051 (0.040) 0.094 (0.063) 0.254 (0.195)
Neuro 0.039 (0.037) 0.049 (0.057) 0.163 (0.178)
Open -0.009 (0.044) -0.036 (0.062) -0.091 (0.200)
Education
Micro -0.145* (0.075) 0.105 (0.143) 0.271 (0.390)
Macro 0.051 (0.059) -0.030 (0.095) -0.058 (0.259)
Finance 0.048 (0.067) 0.121 (0.094) 0.319 (0.266)
Intermed 0.137 (0.159) 0.336*** (0.100) 1.389*** (0.430)
Portfolio -0.094 (0.162) -0.266** (0.113) -1.068** (0.506)
Information
Excluding 0.015 (0.030)
Including 0.055*** (0.017)
Observations 370 370 370
R2 & Pseudo R20.090 0.021 0.03

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Journal of Economics and Finance
The negative sign on this coefficient may be concerning since we would not like
to think an economics course makes students less adept at detecting fraud. However,
this may reflect any number of driving forces. Since students in this course have
only just been introduced to basic ideas like volatility, such students may simply
associate high risk with suspiciousness.
In contrast to the other groups of explanatory variables, asking for additional
information has a strong and clear effect on the perceived relative suspiciousness
of the Madoff Fund. When the Madoff Fund is included among the funds for which
more information is considered, each additional piece of information increases how
suspicious the Madoff Fund seems. This act of comparing the Madoff Fund’s infor-
mation to multiple alternatives could be viewed as financial due diligence and may
represent the participants actively scrutinizing returns that seem too good to be true.
These results broadly align with the interpretation of the SUS variable as captur-
ing ‘state’ rather than ‘trait’ skepticism. To examine our hypothesis that the effect
of education on ‘state’ skepticism should be mediated through information gather-
ing, we consider a regression of requests for additional information on education and
our controls. Since our dependent variable is count data, we use Poisson and ordered
logit regressions.
Of our controls, only gender is statistically significant in both models. Female
participants are less likely to ask for more information than male participants, which
is curious given that female participants are also less likely to choose Madoff.
The impact of education on information gathering is present but is more complex
than expected. We do find that students with a finance background are more likely
to request additional information, but the effect does not become statistically sig-
nificant until students have completed Intermediate Business Finance. Even more
unexpectedly, this effect seems to be reduced or canceled entirely for students who
have also taken the Applied Portfolio Management course (χ2 = 0.34 for the Poisson
regression and 0.57 for the ordered logit).
Likely causes for this bounce back are not immediately obvious. Perhaps these
students have such a high degree of confidence that they do not think the additional
information will be necessary. This finding is similar to what the professional skep-
ticism literature shows: less experienced auditors are more skeptical than more
experienced ones. However, this does not mean that more experience or knowl-
edge reduces skepticism. It could mean that more advanced students have learned
to select the most relevant information for their investment decisions and ignore the
less important ones. Or perhaps the bounce back is simply the result of normal vari-
ation among a small number of participants who fall into these two highest educa-
tion categories (18 for Intermediate and 32 for Portfolio). Whatever the explanation,
the results seem to align with TDT-based predictions, but only up to a point.
6 Discussion
One of the most common and important claims made by higher education is that
it teaches critical thinking skills. Skepticism regarding investment seems to be one
area where this claim should apply, especially in the context of economics and

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1 3
finance education. In this paper we have tried to evaluate this claim. We find limited
evidence that education impacts participants’ investment decisions. The results of
the threshold model suggest that students use information from their finance classes.
It is perhaps surprising that education did not have a larger impact. However,
none of the classes we examined explicitly teach fraud detection. Basic finance pre-
sents CAPM, which implies the risk-return profile of the Madoff fund is suspicious.
But in no finance classes are students taught to look for fabricated returns. Without
more explicit training, students may default to trusting what they see. However, the
small impacts of education on skepticism need to be set in proper context. Accord-
ing to TDT, it is difficult to detect any fraud because humans are strongly biased to
believe each other. That we observe some small effects in this context is encourag-
ing regarding financial fraud detection by educated investors.
Compared to the results generated by Zhang etal. (2015), our participants are less
likely to choose the Madoff fund overall. Zhang etal. reported that 68% of their respond-
ents chose the Madoff fund, compared to our rate of 46%. Their participants came from
Amazon MTurk, and although they did not report the average education level of their
respondents, it was most likely lower than our sample. This suggests education may have
a greater impact on skepticism in financial decision-making that our data does not capture.
In this paper we wanted to limit our analysis to investors who are not specifically
trained to detect fraud. However, there is a substantial literature in accounting on
fraud detection. Our paper is consistent with a basic finding in that literature – more
education and expertise lead to a higher likelihood of identifying fraud (e.g. Grenier
2011; Plumlee etal. 2012; Carpenter etal. 2011).
One of the limitations of this study is the lack of information on participants’ risk atti-
tudes. Some recent research indicates risk attitudes are correlated with some of the BFI per-
sonality traits (Frey etal. 2017; Andersson et al. 2020), while other research suggests that
education and risk attitudes both affect each other (see Outreville 2015, for a review). Given
these relationships, our regression estimates of the impact of education and personality may
be subject to omitted variable bias. Future research should investigate how risk attitudes
impact the Madoff decision.
In terms of policy, we interpret these results as supportive of the idea that more
education in economics and finance should reduce the likelihood that consumers are
victimized by financial fraud. However, given the human propensity to believe most
of what we are told, education can only play a limited role and must be part of a
broader set of strategies to deal with fraud.
Funding Open access funding provided by the Carolinas Consortium.
Declarations
Conflict of interest None of the authors have any conflicts of interest to declare.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
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Journal of Economics and Finance
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licenses/by/4.0/.
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