08832323.2014.909769
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
A Classroom Labor Market Game Illustrating
the Existence, and Implications of, Statistical
Discrimination
Kevin E. Henrickson
To cite this article: Kevin E. Henrickson (2014) A Classroom Labor Market Game Illustrating
the Existence, and Implications of, Statistical Discrimination, Journal of Education for Business,
89:7, 352-360, DOI: 10.1080/08832323.2014.909769
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Date: 11 January 2016, At: 20:46
JOURNAL OF EDUCATION FOR BUSINESS, 89: 352–360, 2014
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2014.909769
A Classroom Labor Market Game Illustrating the
Existence, and Implications of, Statistical
Discrimination
Kevin E. Henrickson
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Gonzaga University, Spokane, Washington, USA
Many undergraduate students report a lack of concern about facing labor market discrimination
throughout their careers. However, there is ample evidence that discrimination based on
race, gender, and age still persists within the labor market. The author outlines a classroom
experiment demonstrating the existence of discrimination, even when the participants consider
themselves above discriminating against others. The results can be used to facilitate
discussions surrounding various labor market outcomes and the prevalence of discrimination
within labor markets. As such, this experiment is appropriate for many business courses,
including courses in economics, management, and ethics.
Keywords: classroom games, economic education, labor market discrimination, statistical
discrimination
Traditional models of discrimination such as Becker (1971)
focus on discrimination as a taste for which employers are
willing to pay.1 This preference is typically assumed to
come from employers, their customers or their employees
and be based on personal prejudices toward one particular
group based on race, gender, sexual orientation, etc. Yet,
evidence from Eisner and Harvey (2009), Sipe, Johnson,
and Fisher (2009a), and Kelan and Jones (2010) all points
to younger generations not being very concerned about discrimination in the workplace, even in the face of ample evidence that they are likely to encounter discrimination
throughout their careers, such as Neumark, Bank, and Van
Nort (1996), Bertrand and Mullainathan (2004), Carlsson
and Rooth (2007), Sipe et al. (2009b), and Kaas and Manger (2012), among others. To help explain this seeming paradox, this article presents an easily administered classroom
game/experiment, designed for undergraduate business students, that leads to statistical discrimination, as described
and developed by Arrow (1972a, 1972b), Phelps (1972),
Aigner and Cain (1977), Lundberg and Startz (1983), Lang
(1986), Coate and Loury (1993), and Oettinger (1996).2
In a large survey of undergraduate business students,
Sipe et al. (2009a) found evidence that the current
Correspondence should be addressed to Kevin E. Henrickson, Gonzaga
University, Department of Economics, 502 E. Boone Ave., Spokane, WA
99258, USA. E-mail: henrickson@jepson.gonzaga.edu
generation of undergraduate students generally underestimate the potential impact of discrimination on their future
career prospects. Similarly, Kelan and Jones (2010) found
that MBA students tend to view gender as a nonissue, while
Eisner and Harvey (2009) found that members of Generation Y are neutral as to whether discrimination represents a
problem for women today, while also being optimistic that
deliberate discrimination is on the decline. One potential
explanation for this seeming disregard of the potential
effects of discrimination on their careers is posited by Hira
(2007), who argued that this generation has been raised in a
more diverse atmosphere, having come to expect and anticipate equality. Others point toward a more gender-neutral
workplace where discrimination is not discussed, stemming
from gender fatigue within organizations (Kelan, 2009).
Regardless of the cause, teaching about discrimination, its
causes and its consequences is an important part of business
education, yet potentially a topic that needs more motivation in light of the previous evidence that students underestimate its importance.
Statistical discrimination, sometimes referred to as experience based discrimination, is originally attributable to
Phelps (1972) and Arrow (1973). Previous models of discrimination, following Becker (1971), focused on discrimination based on personal biases, while statistical
discrimination focuses on market inefficiencies caused by
incomplete information, and the role that group averages
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STATISTICAL DISCRIMINATION IN A CLASSROOM GAME
and/or stereotypes can play in conveying information.3 As
such, statistical discrimination developed as a way to model
the incorporation of group characteristics, including race
and gender, into decision making behavior. In this sense,
statistical discrimination, while still a form of discrimination, is not necessarily based on prejudices, hatred or spite,
but rather is a tool used by uninformed parties to make decisions by attributing group stereotypes to applicants based
on their observable characteristics.4
For example, in the automobile insurance market, the
insurer does not know how good of a driver any individual
is, so they use gender and age to help determine what rate
to charge. In this respect, all individuals who fit into a certain category are attributed with the insurer’s belief regarding the average driving ability of that group, implying that
good drivers in a bad group are charged higher rates than
optimal as they are considered poor drivers based on their
grouping, while bad drivers with good characteristics would
be given lower rates than optimal as they are credited with
being better drivers based on their characteristics. Notice
that as the insurer learns more about these drivers over time,
these rates should converge on optimal rates as the impact
of the driver’s characteristics are less important than their
individual driving history. Indeed, within the literature there
are several studies examining firm learning and the impacts
of this learning on statistical discrimination (e.g., Altonji &
Pierret, 2001; Farber & Gibbons, 1996; Hendricks,
DeBrock, & Koenker, 2003; Pinkston, 2005; Lange, 2007).
One of the aforementioned potential explanations for the
results found by Eisner and Harvey (2009), Sipe et al.
(2009a), and Kelan and Jones (2010) is that the students
surveyed believe that traditional sources of discrimination
based on prejudices are becoming less and less prevalent
over time (Hira, 2007). However, statistical discrimination,
while not necessarily based on prejudiced views, is still discrimination, and is highly unlikely to disappear over time,
as this practice can actually improve firm efficiency if the
firm lacks complete information and their stereotypes conform to actual group differences. As Arrow (1998) illustrated, this makes statistical discrimination particularly
problematic as it will not be driven out of the market by
competitive forces, implying that these same students who
do not believe discrimination will adversely impact their
careers, may face statistical discrimination which will
either help or hurt their careers depending on how firms
view individuals with their characteristics.5 For example,
Holzer, Raphael, and Stoll (2006) found that if employers
do not conduct background checks on their prospective
employees, they use race as a proxy for criminal histories.
In particular, they found that employers statistically discriminate against African American men resulting in significant disparities between Black and White men in terms of
their employment and earnings. African American men
with no criminal histories therefore face an increased likelihood of being attributed with a higher probability of being
353
a criminal, as they are assessed the employer’s view of the
group, while White men with criminal histories are helped
by this statistical discrimination as they are assigned a
lower probability of being a criminal based on the group
for which they belong.6
There are many models and empirical estimates of the
prevalence and impact of discrimination within the labor
market, as there are several field and classroom experiments demonstrating statistical discrimination.7 Many of
these studies use field experiments outside of the classroom
to test for statistical discrimination in a variety of settings
including customer relations (e.g., List, 2004) and attitudes
toward applicants based on the perceived ethnicity of their
name (e.g., Bertrand & Mullainathan, 2004; Ewens, Tomlin, & Wang, 2014). These studies typically seek to differentiate between taste-based discrimination stemming from
prejudiced views, and statistical discrimination, something
that Arrow (1998) argued is very difficult, finding strong
support for statistical discrimination playing a key role in
market outcomes. Still other studies use an experimental
approach to derive results from the interaction of risk preferences and discrimination (Dickinson & Oaxaca, 2009) or
the speed of employer learning when the employers have
little prior experience with different groups from which to
base their statistical discrimination (Feltovich & Papageorgiou, 2004). The common link between all of these studies
is their focus on testing for statistical discrimination in
experimental settings, and then identifying the factors that
influence this type of discrimination. However, two previous studies have focused on using experimental designs as
an instructional tool, rather than as a research tool. Closer
to the aims of this study, Anderson and Haupert (1999) outlined an in-class experiment in which workers belong to
one of two groups and are interviewed for jobs, while Fryer,
Goeree, and Holt (2005) used a similar approach, but developed the experiment electronically where students are randomly paired via a web application.
With this study, I contribute to this literature by providing an adapted version of the experimental approach developed by Anderson and Haupert (1999), and refined by
Fryer et al. (2005). Specifically, I expand on the experimental design created by Anderson and Haupert (1999) to
incorporate fewer changes round by round, simplifying the
experimental setup, but also incorporating a third category
of worker.8 In addition, the experiment described here is
geared toward the undergraduate business student, with a
great deal of postexperiment discussion aimed at students
with experience, or interests in, a variety of business disciplines. The experiment is designed to be run in one class
period of 50 min or longer, with class discussion following
the experiment. In addition, the setup is flexible, based on
the number of students present on the day in which it is run.
By the end of the experiment, students will have a much
greater understanding of statistical discrimination, and how
this type of discrimination can occur even if there are no
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354
K. E. HENRICKSON
prejudices against any particular group, helping to connect
the results of Eisner and Harvey (2009), Sipe et al. (2009a),
and Kelan and Jones (2010) with the myriad of evidence that
discrimination persists within the labor market. This experiment has been used in a variety of undergraduate classes and
can be modified to account for a wide variety of topics,
including, but not limited to, business attitudes toward risk,
wage determination, government programs, wage inequality,
and unemployment rates across racial groups.
The remainder of this article is divided into three sections. The next section details the setup and operation of the
statistical discrimination experiment, with an example of the
class handout and record sheet provided in the Appendix.
The following section offers discussion items following the
experiment and points out nuances of the experiment that
are purposefully incorporated to stimulate different types of
discussions depending on the participating group. Finally,
the final section offers concluding comments and remarks.
STATISTICAL DISCRIMINATION
EXPERIMENT SETUP
This experiment is designed to be run in a 50 min or longer
class, and can be easily scaled up or down depending on the
number of participants present the day it is run, although
the setup described here is for use in a class of 40 students.9
The intended audience for this experiment is undergraduate
business students who have prior experience with principles
level economics; however, this experiment has been run
with many different groups of participants, and there are no
issues with participants who have little to no previous
business/economics experience, other than requiring modification of the postexperiment discussion described subsequently. On the day of the experiment, all participants
receive a copy of the experiment design and record sheet
located in the Appendix. The students are asked to read this
over individually, before the instructor verbally covers this
information.10
Once students have finished reading the experiment
directions from the Appendix, they are divided into two
groups: workers and firms. For a class of 40 students, 30 of
them would be assigned the role of workers, while 10
would be assigned the role of firms. Index cards are then
distributed to each of the 30 workers, with the worker’s
TABLE 1
Distribution of Worker Productivity by Color Grouping
Purple
Yellow
Green
Quality levels 1,1,2,2,2,2,2,2,3,3 1,2,3,4,5,6,7,8,9,10 5,5,5,5,5,6,6,6,6,6
M
SD
2
0.67
5.5
3.03
5.5
0.53
color type on one side of the card, and their productivity,
which varies by experiment period/round on the other.
Table 1 shows the distribution of worker productivity/quality by color type, including the mean and standard deviation
of worker quality for each group. Given that the objective is
for firms to learn how to incorporate worker color into their
decision making rather than learning that a particular
worker is high or low productivity, the output levels shown
in Table 1 cycle between the 10 workers of each color
grouping over the 10 rounds such that each worker
“represents” each productivity level for their color group
by the end of the experiment.11
In addition to being able to inform potential employers
of their color type, prospective employees also have the
option of choosing whether to invest in education, a signal
that they can share with potential employers. If they choose
to invest in education, there is a $0.25 charge for that round
to the worker that will be subtracted from their negotiated
wage to determine their earnings for the period. Education
does not carry over from one round to the next, and the cost
of education does not vary by color group. However, investing in education does double the worker’s productivity
level, so firms know that if the worker has chosen to invest
in education their native productivity level will be
doubled.12
The students assigned to the role of employers do not
have any information on the distribution of workers prior to
the experiment, and do not have any prior information on
the color groups to know how to incorporate this information into their decision making. This setup is the key to
demonstrating statistical, or experience based, discrimination as the employers will learn over the 10 rounds if
worker color matters, and if so, how they should use this
information. At the beginning of each round, employers are
charged with hiring two workers from the pool of 30 potential employees. To accomplish this, firms are able to
“interview” workers, a task that consists of finding out their
color grouping and whether they chose to invest in education.13 The firms then negotiate a wage with the prospective
employees, which, for simplicity, is limited to $0.25 increments from $0.25 to $1.00. Once the wage is determined,
the worker reveals the productivity for the round and
records the earnings, which are equal to the negotiated
wage minus any education costs that they incurred for the
round. The firm then combines the productivity of their two
hired workers and reports that to the instructor, who tracks
the total productivity hired by each firm. Firm payoffs are
then competitively determined based on the relative productivity of firms, with the firm employing the most productive workers receiving $2.50 for the round, and the firm
(s) employing the least productive workers receiving $0.50
for the round as shown in Table 2, with all other firms earning an amount between these two levels.14 At the end of
each round, both the firms and workers record their outcomes on the record sheet shown in the Appendix, with
STATISTICAL DISCRIMINATION IN A CLASSROOM GAME
TABLE 2
Firm Payoffs Based on Total Productivity Rank
Productivity rank
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1
2
3
4
5 and below
Revenue
$2.50
$2.00
$1.50
$1.00
$0.50
firm profits for the round calculated as their revenue minus
their negotiated wage costs.
As discussed in more detail in the following section,
each component of this experiment is designed to generate
classroom discussion around various elements of discrimination. In particular, with workers being of three different
color groups, and firms having no prior expectations/biases
in regard to the productivity of these groups, the issue of
how and why color is used by firms, along with its implications for workers, and firm outcomes is well motivated by
the experiment. This experiment has been run in a variety
of settings and has generated consistent results and lively
discussions whether the students participate for fun, or are
actually monetarily compensated based on their earnings
over the 10 periods.
AREAS FOR EXAMINATION
FOLLOWING THE EXPERIMENT
Many undergraduate students think of discrimination as a
thing of the past that will not impact their lives, yet through
this experiment they demonstrate discriminatory behavior
themselves. While this is not discrimination based on prejudices that they hold, given that they have no prior experience with green, yellow, and purple workers, it still is
discrimination, and leads to many potential topics of discussion, several of which are outlined subsequently. Many
of these topics are best discussed immediately following
the experiment, while others may be better suited to the following class period so that students can reflect on their
experiences and/or the instructor can summarize the data
generated.
Unemployment, Discouraged Workers,
and Government Assistance
The experimental setup described previously necessitates
that some workers are unemployed each round, since there
are more workers available than there are available positions at firms. A natural way to start the postexperiment discussion is to ask students to calculate the unemployment
rate in each period. Often this requires either teaching students how to calculate an unemployment rate, or reminding
355
them how to do this, by taking the number of unemployed
workers and dividing it by the total number of individuals
who were seeking jobs. With the setup described previously, this would imply a typical unemployment rate of
33.33%; however, it is not uncommon to have this rate
move higher toward the end of the experiment because
firms get more demanding in their wage negotiations once
they learn how to use all of the available information in
their hiring decisions.
With knowledge of the class-wide unemployment rate, a
follow-up question is whether this unemployment rate is
representative of each group’s unemployment rate. Without
calculating this rate, the students immediately, having gone
through this experience, know that some groups have much
higher/lower unemployment rates in later rounds. Participants are then asked to calculate the unemployment rate for
each of the color groupings based on the data collected in
round 1 and round 10. In round 1, the unemployment rate
tends to be relatively equal across color groups, but by
round 10 there are wide discrepancies in the unemployment
rate across groups. Specifically, the purple color group
tends to have much higher unemployment rates than either
the green or yellow groups. This then prompts a discussion
regarding why employers would hire more yellow and
green workers if they do not have any prior prejudices
against purple workers. The notion of statistical- and experienced-based discrimination can be introduced at this
point. Specifically, the instructor can ask yellow workers
how they felt in the period that they were assigned productivity levels one or two versus when they were assigned
productivity levels 9 or 10. The typical responses to this
line of questioning are consistent with statistical discrimination, as these workers were treated as an average yellow
worker, which hurt them when they were a high productivity yellow worker, but helped them when they were a low
productivity yellow worker.
Depending on the focus of the class in which the experiment is being conducted, this is also a good point to interject discussion of how insurance companies use gender and
age to determine car insurance rates. Another area of examination following the calculation of unemployment rates is
the ethical implications of statistical discrimination. While
students seem quite comfortable describing discrimination
in its most general form as unethical, they tend to be more
introspective and cautious about making such claims after
this experiment, as the 10 students assigned the role of firms
were not being prejudiced or hateful in their discrimination.
An easy extension to this experiment that can be incorporated into the latter rounds is to offer government assistance to unemployed workers in the form of compensation,
say $0.25, for any workers who do not get hired in a given
round. This adaptation usually causes the average wages
received to increase as workers have stronger negotiating
positions. More interestingly, it often leads to discouraged
workers who simply don’t bother trying to find
356
K. E. HENRICKSON
employment. Specifically, purple workers tend to not interview with firms, because they have low hopes of being
employed, and know that they will receive some compensation whether employed. This can then lead to discussion
surrounding biases in the calculation of the unemployment
rate and the incentive effects of government assistance.
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Wage Determination/Attitudes Toward Risk
Another area for discussion following the experiment is the
distribution of wages across color groupings. Using the
results of the experiment, the instructor can calculate the
average wage by color group for any period, but it is typically most illustrative to calculate the average wages in
period 1 and period 10. In period 1, average wages are relatively uniform across color groupings as the firms do not
have enough experience with the different groupings to
know how to incorporate the workers’ color into their decision making. However, by period 10, all of the firms are
using this additional information to help inform their hiring
decisions. This change will result in differences in wages
across color groupings, which can be used to generate
actual measures of wage inequality such as a Gini coefficient, or, alternatively, can be used qualitatively to discuss
how statistical discrimination can lead to wage disparity
across groups.
Once shown the actual distribution of worker productivities across color groups, most students understand why
purple workers are paid less than yellow and green workers.
However, it is often less obvious that, because there are
three color groupings, there may exist discrimination
between the yellow and green workers. These two color
groupings have the same average productivity, as shown in
Table 1, but rarely end up with the same average wage as
employers, facing competition from other employers, end
up preferring one of these color groupings over the other.
However, unlike the case of purple workers, the direction
of this discrimination is largely influenced by the risk aversion of the participants assigned to the role of the 10 firms.
Some of these individuals learn that if they want to come in
first place for the round, they need to hire yellow workers
as the highest ability yellow workers are of higher quality
than the highest ability green workers. Other firms end up
preferring green workers in order to minimize the risk of
hiring extremely low productivity workers, since the lowest
productivity green worker is much more productive than
the lowest productivity yellow worker. This is a useful way
to incorporate attitudes toward risk, a common topic in
accounting, finance, and economics courses.
Firm Profits
Once students are comfortable understanding that firms are
using color type to statistically discriminate, a natural question is why they do this if they do not dislike, or have
prejudices against, any of the color groupings. This experiment lends itself well to this discussion as the class can
examine profits in early rounds with no statistical discrimination and compare this with profits in later rounds with
discrimination. The results show that those who incorporate
color type into their decision making process do much better than those who are slower to figure out how to use this
information. This can then be expanded into discussion of a
variety of real-world situations in which statistical discrimination may exist, including why it could be possible to see
young women face this type of discrimination if firms
assume that they may have interrupted careers due to family
considerations.
This is also another area that the topic of business ethics
can come into play. For example, health insurance is typically more costly for those who fall into categories determined to have a higher likelihood of illness. Health
insurance companies would simply argue that insurance
requires some to pay more than what they use to compensate for those who receive more in benefits than the premiums they paid. However, it is possible to ask if firms
ethically should be able to use statistical discrimination to
subsidize sicker patients, keeping in mind the need for the
insurance companies to earn a profit.
Educational Attainment
A final avenue of examination following this experiment is
examining the educational choices made across groups. In
all cases, choosing to invest in education doubles worker
productivity, making it a productive investment from society’s perspective given that the cost of this education is relatively low. However, purple workers typically learn that it
is not worthwhile to invest in education since they anticipate facing statistical discrimination. However, this also
makes them less likely to be hired because they both belong
to a group being statistically discriminated against, and also
have chosen not to invest in education, which would have
doubled their productivity. This dynamic can be used to discuss how discrimination, in any form, can lead to such
cycles of self-fulfilling prophecies whereby certain groups
believe they will be discriminated against, and therefore do
not invest in themselves, making them less likely to be
employed in high paying jobs because they lack this training, or as Arrow (1998) argued, when discrimination causes
segregation of groups, in this case based on educational
attainment, there will be little effort by firms to update their
stereotypes.
This result tends to generate many comments and questions from students as they are currently investing in education, which they believe will improve their productivity,
and enhance their job prospects. As such, they tend to have
strong prior assumptions regarding the value of education.
In addition, as Eisner and Harvey (2009), Sipe et al.
(2009a), and Kelan and Jones (2010) found, these students
STATISTICAL DISCRIMINATION IN A CLASSROOM GAME
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also tend not to believe that they will face discrimination in
their career, marking it worthwhile to ask if this experience
has caused them to believe that discrimination may be
more problematic than they had anticipated going into the
experiment (it could be possible to even do a pre- and postexperiment survey), and if so, whether this calls into question their level of educational investment. Typically, one
experiment will not sway anyone from believing that they
will never be discriminated against to believing that they
should not be investing in education; however, experiencing non–hate-based discrimination by their classmates often
gives them enough perspective to understand why some
may choose not to invest in education as a logical response
to the risk of facing discrimination in their careers.
CONCLUSION
In this article I present a modified version of the experiment
developed by Anderson and Haupert (1999) in which students are either assigned the role of a worker or a firm hiring workers. The setup is geared toward undergraduate
business students, and is intended to generate employer
learning overtime, which may eventually lead to statistical
discrimination. The version of the experiment presented
here differs from that of Anderson and Haupert by incorporating three categories of workers, education, and also having other more nuanced differences which are geared
toward participants in business classes. In addition, I spent
a great deal of time explaining how the instructor can use
this experiment in a wide variety of educational settings,
tailoring the postexperiment discussion to the particular
focus of the course. As such, I believe that this experiment
offers an elegant way of teaching statistical discrimination
and its potential labor market consequences to a wide variety of undergraduate business disciplines.
The need to have students experience discrimination
through an experiment such as this is particularly important
in light of work by Eisner and Harvey (2009), Sipe et al.
(2009a), and Kelan and Jones (2010), who all find evidence
suggesting that current college students do not believe that
discrimination will impact their work lives. Yet there is
ample evidence that discrimination still persists in many
labor markets and will negatively impact different categories of workers during their careers. One explanation for
this discrepancy in student expectations and labor market
outcomes is the prevalence of statistical discrimination in
the labor market. Under statistical discrimination, employers assign categories of workers with their perceptions of
the average characteristics of this group based on their
experience, making this type of discrimination potentially
efficient, and yet not necessarily based on hate or prejudices. If indeed an employer statistically discriminates without having any underlying prejudices, one would expect
this type of discrimination to disappear over time if
357
employers gain new information about the potential workers they are evaluating. However, as shown by Pager and
Karafin (2009), this also requires that employers update
their beliefs as they gain more information, something that
often doesn’t happen, with employers instead viewing deviations from their preconceptions as the exception, not the
rule. Getting students to experience this type of discrimination can help illustrate how they can still face discrimination in their careers without it requiring spiteful, prejudiced
employers.
NOTES
1. For a detailed overview of the development of the
discrimination literature, including empirical findings of discrimination, see Arrow (1998) or Altonji
and Blank (1999).
2. Pager and Karafin (2009) show that employers often
subtype exceptions to their preconceived stereotypes, which may also help to illustrate why students don’t view discrimination as a problem, since
they view cases of discrimination as being the
exception to the norm.
3. Note that this does not preclude prejudiced views,
but also does not require them to explain labor market discrimination.
4. Note that if considering discrimination to be treating
individuals differently, discrimination occurs everywhere daily. For example, two workers who start
working for a firm at the same time may be promoted at different rates because of differences in
the quality of their work. This type of discrimination, based on the merits of their work is not something that one would typically worry about as an
ethical issue. Instead it is typically discrimination
based on arbitrary characteristics of the worker
(e.g., race, gender, religion) that would be deemed
impermissible forms of discrimination. As such,
this experiment is designed to demonstrate the
potential for discrimination in the absence of
prejudiced employers, but does not delve into the
many nuances of permissible versus impermissible
discrimination as this is not the focus of this
experiment.
5. It could be argued that over time unprejudiced
employers will learn whether their stereotypes are
correct, driving out even statistical discrimination;
however, as shown by the aforementioned employer
learning literature and Pager and Karafin (2009),
this learning may not occur, and instead the employers may view deviations from their preconceived
stereotypes as exceptions rather than changing their
hiring rules.
6. Note that such generalizations can sometimes lead
to legislation which could help ease this impact for
358
K. E. HENRICKSON
7.
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8.
9.
10.
11.
12.
individuals are do not fit the stereotype of their
group, such as antiprofiling laws or affirmative
action initiatives.
Riach and Rich (2002) provided a critical review of
the use of field experiments/studies in testing for
discrimination.
The experience with the electronic experiment
described by Fryer et al. (2005) is that it is most
appropriate for small classes, or in instances where
the instructor would prefer that the experiment be
conducted outside of class. When run with classes
of more than 20 individuals, the electronic medium
has proven to be difficult if students get even
slightly off task, or if the class is not held in a computer lab setting, making the instructor more reliant
on student laptops and internet access. In addition,
the benefits of having the experiment conducted
electronically, such as ease of matching students
and the ease of tracking the data generated come at
the cost of being able to illustrate fewer labor market outcomes through the experiment.
To scale the experiment for different class sizes, it
would be necessary to add/eliminate worker types
evenly from each of the three groups described later,
focusing on adding/subtracting workers from the
middle of the distribution to minimize the impact of
changing the number of participants on the experimental outcomes. The number of students representing firms can also be altered, but to appropriately
demonstrate many of the concepts, there should
always be excess workers such that some are unemployed in each period of the experiment.
Note that for shorter classes, it may be useful to distribute the instructions the day prior to the actual
experiment so that students can read it over before
class, saving class time for the actual experiment.
So a worker in the yellow group in Table 1 may
have a productivity level of one in round 1, 5 in
round 2, eight in round 3, and three in round 4, and
so on, such that by the end of the 10 rounds he or
she has had one round with each of the worker productivity levels between 1 and 10. For the other
groups, the distribution of worker productivity
implies that individuals will not necessarily have a
different productivity level each round, but will
have each of that color group’s productivity values
by the end of the 10-period experiment.
With only two color groupings, this doubling of
worker productivity through education could be
thought of as biasing or even driving the statistical
discrimination result. However, with three color
groups whereby two of the groups have the same
average productivity, this assumption that education
doubles productivity should not influence the statistical discrimination of purple workers, but may
generate a second use of statistical discrimination
when choosing between yellow and green workers
as further explained below. Also, the experiment
has also been run without education, with very similar results to those described here.
13. Note that in Anderson and Haupert (1999), interviewing workers is costly, causing firms to weigh
the potential benefits of interviewing another
worker with the costs of interviewing that worker.
The setup described here does not incorporate costly
interviewing as the focus is more on having students
see and experience statistical discrimination, something better accomplished if firms learn how to
incorporate worker categories faster.
14. Note that this approach to determining firm earnings
each round also differs from the aforementioned
experiments, and is used because it forces firms to
directly compete with one another and more quickly
figure out how to incorporate worker characteristics
into their decision making.
REFERENCES
Aigner, D. J., & Cain, G. G. (1977). Statistical theories of discrimination in
labor markets. Industrial & Labor Relations Review, 30, 175–187.
Altonji, J. G., & Blank, R. M. (1999). Race and gender in the labor market.
In O. Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol.
3, pp. 3143–3259). New York, NY: Elsevier Science.
Altonji, J. G., & Pierret, C. R. (2001). Employer learning and statistical
discrimination. The Quarterly Journal of Economics, 116, 313–350.
Anderson, D. M., & Haupert, M. J. (1999). Employment and statistical discrimination: A hands-on experiment. The Journal of Economics, 25, 85–
102.
Arrow, K. J. (1972a). Models of job discrimination. In A. H. Pascal (Ed.),
Racial discrimination in economic life (pp. 83–102). Lexington, MA:
D.C. Health.
Arrow, K. J. (1972b). Some mathematical models of race discrimination in
the labor market. In A. H. Pascal (Ed.), Racial discrimination in economic life (pp. 187–204). Lexington, MA: D.C. Health.
Arrow, K. J. (1973). The theory of discrimination. In O. Ashenfelter &
A. Rees (Eds.), Discrimination in labor markets (pp. 3–33). Princeton,
NJ: Princeton University Press.
Arrow, K. J. (1998). What has economics to say about racial discrimination? The Journal of Economic Perspectives, 12, 91–100.
Becker, G. S. (1971). The economics of discrimination. Chicago, IL: University of Chicago Press.
Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more
employable than Lakisha and Jamal? A field experiment on labor market
discrimination. The American Economic Review, 94, 991–1013.
Carlsson, M., & Rooth, D.-O. (2007). Evidence of ethnic discrimination in
the Swedish labor market using experimental data. Labour Economics,
14, 716–729.
Coate, S., & Loury, G. C. (1993). Will affirmative-action policies eliminate
negative stereotypes? The American Economic Review, 83, 1220–1240.
Dickinson, D. L., & Oaxaca, R. L. (2009). Statistical discrimination in
labor markets: An experimental analysis. Southern Economic Journal,
76, 16–31.
Eisner, S. P., & Harvey, M. E. O. (2009). C-change? Generation Y and the
glass ceiling. S.A.M. Advanced Management Journal, 71, 13–28.
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Ewens, M., Tomlin, B., & Wang, L. C. (2014). Statistical discrimination or
prejudice? A large sample field experiment. Review of Economics and
Statistics, 96, 119–134.
Farber, H. S., & Gibbons, R. (1996). Learning and wage dynamics. The
Quarterly Journal of Economics, 111, 1007–1047.
Feltovich, N., & Papageorgiou, C. (2004). An experimental study of statistical discrimination by employers. Southern Economic Journal, 837–849.
Fryer, R. G., Goeree, J. K., & Holt, C. A. (2005). Experience-based discrimination: Classroom games. The Journal of Economic Education, 36,
160–170.
Hendricks, W., DeBrock, L., & Koenker, R. (2003). Uncertainty, hiring
and subsequent performance: The NFL draft. Journal of Labor Economics, 21, 857–886.
Hira, N. A. (2007, May 15). Attracting the twentysomething worker.
Fortune. Retrieved from http://archive.fortune.com/magazines/fortune/
fortune_archive/2007/05/28/100033934/index.htm
Holzer, H. J., Raphael, S., & Stoll, M. A. (2006). Perceived criminality,
criminal background checks, and the racial hiring practices of employers. Journal of Law and Economics, 49, 451–480.
Kaas, L., & Manger, C. (2012). Ethnic discrimination in Germany’s labour
market: A field experiment. German Economic Review, 13, 1–20.
Kelan, E. K. (2009). Gender fatigue: The ideological dilemma of gender
neutrality and discrimination in organizations. Canadian Journal of
Administrative Sciences, 26, 197–210.
Kelan, E. K., & Jones, R. D. (2010). Gender and the MBA. Academy of
Management Learning & Education, 9, 26–43.
Lang, K. (1986). A language theory of discrimination. The Quarterly Journal of Economics, 101, 363–382.
Lange, F. (2007). The speed of employer learning. Journal of Labor Economics, 25, 1–35.
359
List, J. A. (2004). The nature and extent of discrimination in the marketplace: Evidence from the field. The Quarterly Journal of Economics,
119, 49–89.
Lundberg, S. J., & Startz, R. (1983). Private discrimination and social
intervention in competitive labor markets. The American Economic
Review, 73, 340–347.
Neumark, D., Bank, R. J., & Van Nort, K. D. (1996). Sex discrimination in
restaurant hiring: An audit study. The Quarterly Journal of Economics,
111, 915–941.
Oettinger, G. S. (1996). Statistical discrimination and the early career evolution of the black-white wage gap. Journal of Labor Economics, 14,
52–78.
Pager, D., & Karafin, D. (2009). Bayesian bigot? Statistical discrimination, stereotypes, and employer decision making. The Annals
of the American Academy of Political and Social Science, 621,
70–93.
Phelps, E. S. (1972). The statistical theory of racism and sexism. The
American Economic Review, 62, 659–661.
Pinkston, J. C. (2005). A test of screening discrimination with employer
learning. Industrial & Labor Relations Review, 59, 267.
Riach, P. A., & Rich, J. (2002). Field experiments of discrimination in
the market place. The Economic Journal, 112(483), F480–F518.
Sipe, S. R., Johnson, C. D., & Fisher, D. K. (2009a). University
students’ perceptions of gender discrimination in the workplace:
Reality versus fiction. Journal of Education for Business, 84, 339–
349.
Sipe, S. R., Johnson, C. D., & Fisher, D. K. (2009b). University
students’ perceptions of sexual harassment in the workplace: A
view through rose-colored lenses. Equal Opportunities International,
28, 336–350.
APPENDIX—Employment Experiment Instructions
Setup:
In this experiment some of you will be assigned the role of a “worker,” while others will be assigned the role of a “firm.”
The experiment will consist of 10 rounds of play in which each employer/firm will attempt to hire 2 workers (note that there are too
few firms for all of the workers, so some workers will be unemployed each round, and will subsequently receive a wage of $0.00 for
the round).
Workers belong to one of three groups: Yellow, Green or Purple. Each group has the same number of workers, and worker productivity may, or may not, vary by group (however, individual worker productivity will change from round to round, so even if you hire the
same worker, they will not necessarily have the same productivity).
In each round, the firm will “interview” workers until they find the two that they want to hire. The interview process consists of the
worker telling the employer their color group and whether they have chosen to get educated or not. In addition, the worker and firm
have to agree on a wage (for simplicity, the available wages to be paid are $0.25, $0.50, $0.75 or $1.00).
Education costs $0.25 for each round (and does not vary with worker productivity), and doubles worker productivity.
The worker’s payoff each round will be their negotiated wage minus any education costs.
The firm’s revenue each round will be based on competitive forces and found according to the total productivity of the two workers
hired compared to that of other firms, and given by the following table:
If assigned the role of a worker . . .
Round
1
2
3
4
5
6
7
8
9
10
Total payoff
Worker
color
Hired
(yes/no)
Negotiated
wage
Productivity
If assigned the role of an employer . . .
Payoff
(Wage – educ.
costs)
Negotiated
wage costs
Worker
colors
Total
output
Payoff
(Rev. – wages)
360
K. E. HENRICKSON
Productivity rank
1
2
3
4
5 and below
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If multiple firms have the same productivity in the same round, they will split the earnings associated with their ranks.
The firm’s total payoff is then their revenue given in the table above minus their wage costs.
Revenue
$2.50
$2.00
$1.50
$1.00
$0.50
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
A Classroom Labor Market Game Illustrating
the Existence, and Implications of, Statistical
Discrimination
Kevin E. Henrickson
To cite this article: Kevin E. Henrickson (2014) A Classroom Labor Market Game Illustrating
the Existence, and Implications of, Statistical Discrimination, Journal of Education for Business,
89:7, 352-360, DOI: 10.1080/08832323.2014.909769
To link to this article: http://dx.doi.org/10.1080/08832323.2014.909769
Published online: 29 Sep 2014.
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Date: 11 January 2016, At: 20:46
JOURNAL OF EDUCATION FOR BUSINESS, 89: 352–360, 2014
Copyright Ó Taylor & Francis Group, LLC
ISSN: 0883-2323 print / 1940-3356 online
DOI: 10.1080/08832323.2014.909769
A Classroom Labor Market Game Illustrating the
Existence, and Implications of, Statistical
Discrimination
Kevin E. Henrickson
Downloaded by [Universitas Maritim Raja Ali Haji] at 20:46 11 January 2016
Gonzaga University, Spokane, Washington, USA
Many undergraduate students report a lack of concern about facing labor market discrimination
throughout their careers. However, there is ample evidence that discrimination based on
race, gender, and age still persists within the labor market. The author outlines a classroom
experiment demonstrating the existence of discrimination, even when the participants consider
themselves above discriminating against others. The results can be used to facilitate
discussions surrounding various labor market outcomes and the prevalence of discrimination
within labor markets. As such, this experiment is appropriate for many business courses,
including courses in economics, management, and ethics.
Keywords: classroom games, economic education, labor market discrimination, statistical
discrimination
Traditional models of discrimination such as Becker (1971)
focus on discrimination as a taste for which employers are
willing to pay.1 This preference is typically assumed to
come from employers, their customers or their employees
and be based on personal prejudices toward one particular
group based on race, gender, sexual orientation, etc. Yet,
evidence from Eisner and Harvey (2009), Sipe, Johnson,
and Fisher (2009a), and Kelan and Jones (2010) all points
to younger generations not being very concerned about discrimination in the workplace, even in the face of ample evidence that they are likely to encounter discrimination
throughout their careers, such as Neumark, Bank, and Van
Nort (1996), Bertrand and Mullainathan (2004), Carlsson
and Rooth (2007), Sipe et al. (2009b), and Kaas and Manger (2012), among others. To help explain this seeming paradox, this article presents an easily administered classroom
game/experiment, designed for undergraduate business students, that leads to statistical discrimination, as described
and developed by Arrow (1972a, 1972b), Phelps (1972),
Aigner and Cain (1977), Lundberg and Startz (1983), Lang
(1986), Coate and Loury (1993), and Oettinger (1996).2
In a large survey of undergraduate business students,
Sipe et al. (2009a) found evidence that the current
Correspondence should be addressed to Kevin E. Henrickson, Gonzaga
University, Department of Economics, 502 E. Boone Ave., Spokane, WA
99258, USA. E-mail: henrickson@jepson.gonzaga.edu
generation of undergraduate students generally underestimate the potential impact of discrimination on their future
career prospects. Similarly, Kelan and Jones (2010) found
that MBA students tend to view gender as a nonissue, while
Eisner and Harvey (2009) found that members of Generation Y are neutral as to whether discrimination represents a
problem for women today, while also being optimistic that
deliberate discrimination is on the decline. One potential
explanation for this seeming disregard of the potential
effects of discrimination on their careers is posited by Hira
(2007), who argued that this generation has been raised in a
more diverse atmosphere, having come to expect and anticipate equality. Others point toward a more gender-neutral
workplace where discrimination is not discussed, stemming
from gender fatigue within organizations (Kelan, 2009).
Regardless of the cause, teaching about discrimination, its
causes and its consequences is an important part of business
education, yet potentially a topic that needs more motivation in light of the previous evidence that students underestimate its importance.
Statistical discrimination, sometimes referred to as experience based discrimination, is originally attributable to
Phelps (1972) and Arrow (1973). Previous models of discrimination, following Becker (1971), focused on discrimination based on personal biases, while statistical
discrimination focuses on market inefficiencies caused by
incomplete information, and the role that group averages
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STATISTICAL DISCRIMINATION IN A CLASSROOM GAME
and/or stereotypes can play in conveying information.3 As
such, statistical discrimination developed as a way to model
the incorporation of group characteristics, including race
and gender, into decision making behavior. In this sense,
statistical discrimination, while still a form of discrimination, is not necessarily based on prejudices, hatred or spite,
but rather is a tool used by uninformed parties to make decisions by attributing group stereotypes to applicants based
on their observable characteristics.4
For example, in the automobile insurance market, the
insurer does not know how good of a driver any individual
is, so they use gender and age to help determine what rate
to charge. In this respect, all individuals who fit into a certain category are attributed with the insurer’s belief regarding the average driving ability of that group, implying that
good drivers in a bad group are charged higher rates than
optimal as they are considered poor drivers based on their
grouping, while bad drivers with good characteristics would
be given lower rates than optimal as they are credited with
being better drivers based on their characteristics. Notice
that as the insurer learns more about these drivers over time,
these rates should converge on optimal rates as the impact
of the driver’s characteristics are less important than their
individual driving history. Indeed, within the literature there
are several studies examining firm learning and the impacts
of this learning on statistical discrimination (e.g., Altonji &
Pierret, 2001; Farber & Gibbons, 1996; Hendricks,
DeBrock, & Koenker, 2003; Pinkston, 2005; Lange, 2007).
One of the aforementioned potential explanations for the
results found by Eisner and Harvey (2009), Sipe et al.
(2009a), and Kelan and Jones (2010) is that the students
surveyed believe that traditional sources of discrimination
based on prejudices are becoming less and less prevalent
over time (Hira, 2007). However, statistical discrimination,
while not necessarily based on prejudiced views, is still discrimination, and is highly unlikely to disappear over time,
as this practice can actually improve firm efficiency if the
firm lacks complete information and their stereotypes conform to actual group differences. As Arrow (1998) illustrated, this makes statistical discrimination particularly
problematic as it will not be driven out of the market by
competitive forces, implying that these same students who
do not believe discrimination will adversely impact their
careers, may face statistical discrimination which will
either help or hurt their careers depending on how firms
view individuals with their characteristics.5 For example,
Holzer, Raphael, and Stoll (2006) found that if employers
do not conduct background checks on their prospective
employees, they use race as a proxy for criminal histories.
In particular, they found that employers statistically discriminate against African American men resulting in significant disparities between Black and White men in terms of
their employment and earnings. African American men
with no criminal histories therefore face an increased likelihood of being attributed with a higher probability of being
353
a criminal, as they are assessed the employer’s view of the
group, while White men with criminal histories are helped
by this statistical discrimination as they are assigned a
lower probability of being a criminal based on the group
for which they belong.6
There are many models and empirical estimates of the
prevalence and impact of discrimination within the labor
market, as there are several field and classroom experiments demonstrating statistical discrimination.7 Many of
these studies use field experiments outside of the classroom
to test for statistical discrimination in a variety of settings
including customer relations (e.g., List, 2004) and attitudes
toward applicants based on the perceived ethnicity of their
name (e.g., Bertrand & Mullainathan, 2004; Ewens, Tomlin, & Wang, 2014). These studies typically seek to differentiate between taste-based discrimination stemming from
prejudiced views, and statistical discrimination, something
that Arrow (1998) argued is very difficult, finding strong
support for statistical discrimination playing a key role in
market outcomes. Still other studies use an experimental
approach to derive results from the interaction of risk preferences and discrimination (Dickinson & Oaxaca, 2009) or
the speed of employer learning when the employers have
little prior experience with different groups from which to
base their statistical discrimination (Feltovich & Papageorgiou, 2004). The common link between all of these studies
is their focus on testing for statistical discrimination in
experimental settings, and then identifying the factors that
influence this type of discrimination. However, two previous studies have focused on using experimental designs as
an instructional tool, rather than as a research tool. Closer
to the aims of this study, Anderson and Haupert (1999) outlined an in-class experiment in which workers belong to
one of two groups and are interviewed for jobs, while Fryer,
Goeree, and Holt (2005) used a similar approach, but developed the experiment electronically where students are randomly paired via a web application.
With this study, I contribute to this literature by providing an adapted version of the experimental approach developed by Anderson and Haupert (1999), and refined by
Fryer et al. (2005). Specifically, I expand on the experimental design created by Anderson and Haupert (1999) to
incorporate fewer changes round by round, simplifying the
experimental setup, but also incorporating a third category
of worker.8 In addition, the experiment described here is
geared toward the undergraduate business student, with a
great deal of postexperiment discussion aimed at students
with experience, or interests in, a variety of business disciplines. The experiment is designed to be run in one class
period of 50 min or longer, with class discussion following
the experiment. In addition, the setup is flexible, based on
the number of students present on the day in which it is run.
By the end of the experiment, students will have a much
greater understanding of statistical discrimination, and how
this type of discrimination can occur even if there are no
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354
K. E. HENRICKSON
prejudices against any particular group, helping to connect
the results of Eisner and Harvey (2009), Sipe et al. (2009a),
and Kelan and Jones (2010) with the myriad of evidence that
discrimination persists within the labor market. This experiment has been used in a variety of undergraduate classes and
can be modified to account for a wide variety of topics,
including, but not limited to, business attitudes toward risk,
wage determination, government programs, wage inequality,
and unemployment rates across racial groups.
The remainder of this article is divided into three sections. The next section details the setup and operation of the
statistical discrimination experiment, with an example of the
class handout and record sheet provided in the Appendix.
The following section offers discussion items following the
experiment and points out nuances of the experiment that
are purposefully incorporated to stimulate different types of
discussions depending on the participating group. Finally,
the final section offers concluding comments and remarks.
STATISTICAL DISCRIMINATION
EXPERIMENT SETUP
This experiment is designed to be run in a 50 min or longer
class, and can be easily scaled up or down depending on the
number of participants present the day it is run, although
the setup described here is for use in a class of 40 students.9
The intended audience for this experiment is undergraduate
business students who have prior experience with principles
level economics; however, this experiment has been run
with many different groups of participants, and there are no
issues with participants who have little to no previous
business/economics experience, other than requiring modification of the postexperiment discussion described subsequently. On the day of the experiment, all participants
receive a copy of the experiment design and record sheet
located in the Appendix. The students are asked to read this
over individually, before the instructor verbally covers this
information.10
Once students have finished reading the experiment
directions from the Appendix, they are divided into two
groups: workers and firms. For a class of 40 students, 30 of
them would be assigned the role of workers, while 10
would be assigned the role of firms. Index cards are then
distributed to each of the 30 workers, with the worker’s
TABLE 1
Distribution of Worker Productivity by Color Grouping
Purple
Yellow
Green
Quality levels 1,1,2,2,2,2,2,2,3,3 1,2,3,4,5,6,7,8,9,10 5,5,5,5,5,6,6,6,6,6
M
SD
2
0.67
5.5
3.03
5.5
0.53
color type on one side of the card, and their productivity,
which varies by experiment period/round on the other.
Table 1 shows the distribution of worker productivity/quality by color type, including the mean and standard deviation
of worker quality for each group. Given that the objective is
for firms to learn how to incorporate worker color into their
decision making rather than learning that a particular
worker is high or low productivity, the output levels shown
in Table 1 cycle between the 10 workers of each color
grouping over the 10 rounds such that each worker
“represents” each productivity level for their color group
by the end of the experiment.11
In addition to being able to inform potential employers
of their color type, prospective employees also have the
option of choosing whether to invest in education, a signal
that they can share with potential employers. If they choose
to invest in education, there is a $0.25 charge for that round
to the worker that will be subtracted from their negotiated
wage to determine their earnings for the period. Education
does not carry over from one round to the next, and the cost
of education does not vary by color group. However, investing in education does double the worker’s productivity
level, so firms know that if the worker has chosen to invest
in education their native productivity level will be
doubled.12
The students assigned to the role of employers do not
have any information on the distribution of workers prior to
the experiment, and do not have any prior information on
the color groups to know how to incorporate this information into their decision making. This setup is the key to
demonstrating statistical, or experience based, discrimination as the employers will learn over the 10 rounds if
worker color matters, and if so, how they should use this
information. At the beginning of each round, employers are
charged with hiring two workers from the pool of 30 potential employees. To accomplish this, firms are able to
“interview” workers, a task that consists of finding out their
color grouping and whether they chose to invest in education.13 The firms then negotiate a wage with the prospective
employees, which, for simplicity, is limited to $0.25 increments from $0.25 to $1.00. Once the wage is determined,
the worker reveals the productivity for the round and
records the earnings, which are equal to the negotiated
wage minus any education costs that they incurred for the
round. The firm then combines the productivity of their two
hired workers and reports that to the instructor, who tracks
the total productivity hired by each firm. Firm payoffs are
then competitively determined based on the relative productivity of firms, with the firm employing the most productive workers receiving $2.50 for the round, and the firm
(s) employing the least productive workers receiving $0.50
for the round as shown in Table 2, with all other firms earning an amount between these two levels.14 At the end of
each round, both the firms and workers record their outcomes on the record sheet shown in the Appendix, with
STATISTICAL DISCRIMINATION IN A CLASSROOM GAME
TABLE 2
Firm Payoffs Based on Total Productivity Rank
Productivity rank
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1
2
3
4
5 and below
Revenue
$2.50
$2.00
$1.50
$1.00
$0.50
firm profits for the round calculated as their revenue minus
their negotiated wage costs.
As discussed in more detail in the following section,
each component of this experiment is designed to generate
classroom discussion around various elements of discrimination. In particular, with workers being of three different
color groups, and firms having no prior expectations/biases
in regard to the productivity of these groups, the issue of
how and why color is used by firms, along with its implications for workers, and firm outcomes is well motivated by
the experiment. This experiment has been run in a variety
of settings and has generated consistent results and lively
discussions whether the students participate for fun, or are
actually monetarily compensated based on their earnings
over the 10 periods.
AREAS FOR EXAMINATION
FOLLOWING THE EXPERIMENT
Many undergraduate students think of discrimination as a
thing of the past that will not impact their lives, yet through
this experiment they demonstrate discriminatory behavior
themselves. While this is not discrimination based on prejudices that they hold, given that they have no prior experience with green, yellow, and purple workers, it still is
discrimination, and leads to many potential topics of discussion, several of which are outlined subsequently. Many
of these topics are best discussed immediately following
the experiment, while others may be better suited to the following class period so that students can reflect on their
experiences and/or the instructor can summarize the data
generated.
Unemployment, Discouraged Workers,
and Government Assistance
The experimental setup described previously necessitates
that some workers are unemployed each round, since there
are more workers available than there are available positions at firms. A natural way to start the postexperiment discussion is to ask students to calculate the unemployment
rate in each period. Often this requires either teaching students how to calculate an unemployment rate, or reminding
355
them how to do this, by taking the number of unemployed
workers and dividing it by the total number of individuals
who were seeking jobs. With the setup described previously, this would imply a typical unemployment rate of
33.33%; however, it is not uncommon to have this rate
move higher toward the end of the experiment because
firms get more demanding in their wage negotiations once
they learn how to use all of the available information in
their hiring decisions.
With knowledge of the class-wide unemployment rate, a
follow-up question is whether this unemployment rate is
representative of each group’s unemployment rate. Without
calculating this rate, the students immediately, having gone
through this experience, know that some groups have much
higher/lower unemployment rates in later rounds. Participants are then asked to calculate the unemployment rate for
each of the color groupings based on the data collected in
round 1 and round 10. In round 1, the unemployment rate
tends to be relatively equal across color groups, but by
round 10 there are wide discrepancies in the unemployment
rate across groups. Specifically, the purple color group
tends to have much higher unemployment rates than either
the green or yellow groups. This then prompts a discussion
regarding why employers would hire more yellow and
green workers if they do not have any prior prejudices
against purple workers. The notion of statistical- and experienced-based discrimination can be introduced at this
point. Specifically, the instructor can ask yellow workers
how they felt in the period that they were assigned productivity levels one or two versus when they were assigned
productivity levels 9 or 10. The typical responses to this
line of questioning are consistent with statistical discrimination, as these workers were treated as an average yellow
worker, which hurt them when they were a high productivity yellow worker, but helped them when they were a low
productivity yellow worker.
Depending on the focus of the class in which the experiment is being conducted, this is also a good point to interject discussion of how insurance companies use gender and
age to determine car insurance rates. Another area of examination following the calculation of unemployment rates is
the ethical implications of statistical discrimination. While
students seem quite comfortable describing discrimination
in its most general form as unethical, they tend to be more
introspective and cautious about making such claims after
this experiment, as the 10 students assigned the role of firms
were not being prejudiced or hateful in their discrimination.
An easy extension to this experiment that can be incorporated into the latter rounds is to offer government assistance to unemployed workers in the form of compensation,
say $0.25, for any workers who do not get hired in a given
round. This adaptation usually causes the average wages
received to increase as workers have stronger negotiating
positions. More interestingly, it often leads to discouraged
workers who simply don’t bother trying to find
356
K. E. HENRICKSON
employment. Specifically, purple workers tend to not interview with firms, because they have low hopes of being
employed, and know that they will receive some compensation whether employed. This can then lead to discussion
surrounding biases in the calculation of the unemployment
rate and the incentive effects of government assistance.
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Wage Determination/Attitudes Toward Risk
Another area for discussion following the experiment is the
distribution of wages across color groupings. Using the
results of the experiment, the instructor can calculate the
average wage by color group for any period, but it is typically most illustrative to calculate the average wages in
period 1 and period 10. In period 1, average wages are relatively uniform across color groupings as the firms do not
have enough experience with the different groupings to
know how to incorporate the workers’ color into their decision making. However, by period 10, all of the firms are
using this additional information to help inform their hiring
decisions. This change will result in differences in wages
across color groupings, which can be used to generate
actual measures of wage inequality such as a Gini coefficient, or, alternatively, can be used qualitatively to discuss
how statistical discrimination can lead to wage disparity
across groups.
Once shown the actual distribution of worker productivities across color groups, most students understand why
purple workers are paid less than yellow and green workers.
However, it is often less obvious that, because there are
three color groupings, there may exist discrimination
between the yellow and green workers. These two color
groupings have the same average productivity, as shown in
Table 1, but rarely end up with the same average wage as
employers, facing competition from other employers, end
up preferring one of these color groupings over the other.
However, unlike the case of purple workers, the direction
of this discrimination is largely influenced by the risk aversion of the participants assigned to the role of the 10 firms.
Some of these individuals learn that if they want to come in
first place for the round, they need to hire yellow workers
as the highest ability yellow workers are of higher quality
than the highest ability green workers. Other firms end up
preferring green workers in order to minimize the risk of
hiring extremely low productivity workers, since the lowest
productivity green worker is much more productive than
the lowest productivity yellow worker. This is a useful way
to incorporate attitudes toward risk, a common topic in
accounting, finance, and economics courses.
Firm Profits
Once students are comfortable understanding that firms are
using color type to statistically discriminate, a natural question is why they do this if they do not dislike, or have
prejudices against, any of the color groupings. This experiment lends itself well to this discussion as the class can
examine profits in early rounds with no statistical discrimination and compare this with profits in later rounds with
discrimination. The results show that those who incorporate
color type into their decision making process do much better than those who are slower to figure out how to use this
information. This can then be expanded into discussion of a
variety of real-world situations in which statistical discrimination may exist, including why it could be possible to see
young women face this type of discrimination if firms
assume that they may have interrupted careers due to family
considerations.
This is also another area that the topic of business ethics
can come into play. For example, health insurance is typically more costly for those who fall into categories determined to have a higher likelihood of illness. Health
insurance companies would simply argue that insurance
requires some to pay more than what they use to compensate for those who receive more in benefits than the premiums they paid. However, it is possible to ask if firms
ethically should be able to use statistical discrimination to
subsidize sicker patients, keeping in mind the need for the
insurance companies to earn a profit.
Educational Attainment
A final avenue of examination following this experiment is
examining the educational choices made across groups. In
all cases, choosing to invest in education doubles worker
productivity, making it a productive investment from society’s perspective given that the cost of this education is relatively low. However, purple workers typically learn that it
is not worthwhile to invest in education since they anticipate facing statistical discrimination. However, this also
makes them less likely to be hired because they both belong
to a group being statistically discriminated against, and also
have chosen not to invest in education, which would have
doubled their productivity. This dynamic can be used to discuss how discrimination, in any form, can lead to such
cycles of self-fulfilling prophecies whereby certain groups
believe they will be discriminated against, and therefore do
not invest in themselves, making them less likely to be
employed in high paying jobs because they lack this training, or as Arrow (1998) argued, when discrimination causes
segregation of groups, in this case based on educational
attainment, there will be little effort by firms to update their
stereotypes.
This result tends to generate many comments and questions from students as they are currently investing in education, which they believe will improve their productivity,
and enhance their job prospects. As such, they tend to have
strong prior assumptions regarding the value of education.
In addition, as Eisner and Harvey (2009), Sipe et al.
(2009a), and Kelan and Jones (2010) found, these students
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also tend not to believe that they will face discrimination in
their career, marking it worthwhile to ask if this experience
has caused them to believe that discrimination may be
more problematic than they had anticipated going into the
experiment (it could be possible to even do a pre- and postexperiment survey), and if so, whether this calls into question their level of educational investment. Typically, one
experiment will not sway anyone from believing that they
will never be discriminated against to believing that they
should not be investing in education; however, experiencing non–hate-based discrimination by their classmates often
gives them enough perspective to understand why some
may choose not to invest in education as a logical response
to the risk of facing discrimination in their careers.
CONCLUSION
In this article I present a modified version of the experiment
developed by Anderson and Haupert (1999) in which students are either assigned the role of a worker or a firm hiring workers. The setup is geared toward undergraduate
business students, and is intended to generate employer
learning overtime, which may eventually lead to statistical
discrimination. The version of the experiment presented
here differs from that of Anderson and Haupert by incorporating three categories of workers, education, and also having other more nuanced differences which are geared
toward participants in business classes. In addition, I spent
a great deal of time explaining how the instructor can use
this experiment in a wide variety of educational settings,
tailoring the postexperiment discussion to the particular
focus of the course. As such, I believe that this experiment
offers an elegant way of teaching statistical discrimination
and its potential labor market consequences to a wide variety of undergraduate business disciplines.
The need to have students experience discrimination
through an experiment such as this is particularly important
in light of work by Eisner and Harvey (2009), Sipe et al.
(2009a), and Kelan and Jones (2010), who all find evidence
suggesting that current college students do not believe that
discrimination will impact their work lives. Yet there is
ample evidence that discrimination still persists in many
labor markets and will negatively impact different categories of workers during their careers. One explanation for
this discrepancy in student expectations and labor market
outcomes is the prevalence of statistical discrimination in
the labor market. Under statistical discrimination, employers assign categories of workers with their perceptions of
the average characteristics of this group based on their
experience, making this type of discrimination potentially
efficient, and yet not necessarily based on hate or prejudices. If indeed an employer statistically discriminates without having any underlying prejudices, one would expect
this type of discrimination to disappear over time if
357
employers gain new information about the potential workers they are evaluating. However, as shown by Pager and
Karafin (2009), this also requires that employers update
their beliefs as they gain more information, something that
often doesn’t happen, with employers instead viewing deviations from their preconceptions as the exception, not the
rule. Getting students to experience this type of discrimination can help illustrate how they can still face discrimination in their careers without it requiring spiteful, prejudiced
employers.
NOTES
1. For a detailed overview of the development of the
discrimination literature, including empirical findings of discrimination, see Arrow (1998) or Altonji
and Blank (1999).
2. Pager and Karafin (2009) show that employers often
subtype exceptions to their preconceived stereotypes, which may also help to illustrate why students don’t view discrimination as a problem, since
they view cases of discrimination as being the
exception to the norm.
3. Note that this does not preclude prejudiced views,
but also does not require them to explain labor market discrimination.
4. Note that if considering discrimination to be treating
individuals differently, discrimination occurs everywhere daily. For example, two workers who start
working for a firm at the same time may be promoted at different rates because of differences in
the quality of their work. This type of discrimination, based on the merits of their work is not something that one would typically worry about as an
ethical issue. Instead it is typically discrimination
based on arbitrary characteristics of the worker
(e.g., race, gender, religion) that would be deemed
impermissible forms of discrimination. As such,
this experiment is designed to demonstrate the
potential for discrimination in the absence of
prejudiced employers, but does not delve into the
many nuances of permissible versus impermissible
discrimination as this is not the focus of this
experiment.
5. It could be argued that over time unprejudiced
employers will learn whether their stereotypes are
correct, driving out even statistical discrimination;
however, as shown by the aforementioned employer
learning literature and Pager and Karafin (2009),
this learning may not occur, and instead the employers may view deviations from their preconceived
stereotypes as exceptions rather than changing their
hiring rules.
6. Note that such generalizations can sometimes lead
to legislation which could help ease this impact for
358
K. E. HENRICKSON
7.
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8.
9.
10.
11.
12.
individuals are do not fit the stereotype of their
group, such as antiprofiling laws or affirmative
action initiatives.
Riach and Rich (2002) provided a critical review of
the use of field experiments/studies in testing for
discrimination.
The experience with the electronic experiment
described by Fryer et al. (2005) is that it is most
appropriate for small classes, or in instances where
the instructor would prefer that the experiment be
conducted outside of class. When run with classes
of more than 20 individuals, the electronic medium
has proven to be difficult if students get even
slightly off task, or if the class is not held in a computer lab setting, making the instructor more reliant
on student laptops and internet access. In addition,
the benefits of having the experiment conducted
electronically, such as ease of matching students
and the ease of tracking the data generated come at
the cost of being able to illustrate fewer labor market outcomes through the experiment.
To scale the experiment for different class sizes, it
would be necessary to add/eliminate worker types
evenly from each of the three groups described later,
focusing on adding/subtracting workers from the
middle of the distribution to minimize the impact of
changing the number of participants on the experimental outcomes. The number of students representing firms can also be altered, but to appropriately
demonstrate many of the concepts, there should
always be excess workers such that some are unemployed in each period of the experiment.
Note that for shorter classes, it may be useful to distribute the instructions the day prior to the actual
experiment so that students can read it over before
class, saving class time for the actual experiment.
So a worker in the yellow group in Table 1 may
have a productivity level of one in round 1, 5 in
round 2, eight in round 3, and three in round 4, and
so on, such that by the end of the 10 rounds he or
she has had one round with each of the worker productivity levels between 1 and 10. For the other
groups, the distribution of worker productivity
implies that individuals will not necessarily have a
different productivity level each round, but will
have each of that color group’s productivity values
by the end of the 10-period experiment.
With only two color groupings, this doubling of
worker productivity through education could be
thought of as biasing or even driving the statistical
discrimination result. However, with three color
groups whereby two of the groups have the same
average productivity, this assumption that education
doubles productivity should not influence the statistical discrimination of purple workers, but may
generate a second use of statistical discrimination
when choosing between yellow and green workers
as further explained below. Also, the experiment
has also been run without education, with very similar results to those described here.
13. Note that in Anderson and Haupert (1999), interviewing workers is costly, causing firms to weigh
the potential benefits of interviewing another
worker with the costs of interviewing that worker.
The setup described here does not incorporate costly
interviewing as the focus is more on having students
see and experience statistical discrimination, something better accomplished if firms learn how to
incorporate worker categories faster.
14. Note that this approach to determining firm earnings
each round also differs from the aforementioned
experiments, and is used because it forces firms to
directly compete with one another and more quickly
figure out how to incorporate worker characteristics
into their decision making.
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APPENDIX—Employment Experiment Instructions
Setup:
In this experiment some of you will be assigned the role of a “worker,” while others will be assigned the role of a “firm.”
The experiment will consist of 10 rounds of play in which each employer/firm will attempt to hire 2 workers (note that there are too
few firms for all of the workers, so some workers will be unemployed each round, and will subsequently receive a wage of $0.00 for
the round).
Workers belong to one of three groups: Yellow, Green or Purple. Each group has the same number of workers, and worker productivity may, or may not, vary by group (however, individual worker productivity will change from round to round, so even if you hire the
same worker, they will not necessarily have the same productivity).
In each round, the firm will “interview” workers until they find the two that they want to hire. The interview process consists of the
worker telling the employer their color group and whether they have chosen to get educated or not. In addition, the worker and firm
have to agree on a wage (for simplicity, the available wages to be paid are $0.25, $0.50, $0.75 or $1.00).
Education costs $0.25 for each round (and does not vary with worker productivity), and doubles worker productivity.
The worker’s payoff each round will be their negotiated wage minus any education costs.
The firm’s revenue each round will be based on competitive forces and found according to the total productivity of the two workers
hired compared to that of other firms, and given by the following table:
If assigned the role of a worker . . .
Round
1
2
3
4
5
6
7
8
9
10
Total payoff
Worker
color
Hired
(yes/no)
Negotiated
wage
Productivity
If assigned the role of an employer . . .
Payoff
(Wage – educ.
costs)
Negotiated
wage costs
Worker
colors
Total
output
Payoff
(Rev. – wages)
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K. E. HENRICKSON
Productivity rank
1
2
3
4
5 and below
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If multiple firms have the same productivity in the same round, they will split the earnings associated with their ranks.
The firm’s total payoff is then their revenue given in the table above minus their wage costs.
Revenue
$2.50
$2.00
$1.50
$1.00
$0.50