Risk Tolerance and its Effect on Majoring in Art

A Thesis Submitted in Partial Fulfillment of theRequirements of the Renée Crown University Honors Program at

Syracuse University

Austin Church

Candidate for Bachelor of Science in Economicsand Renée Crown University Honors

Spring 2020

Honors Thesis in Economics

Thesis Advisor: _______________________ Dr. Perry Singleton, Associate Professor

Thesis Reader: _______________________ Dr. William Osborne III, Honors Adjunct Professor

Honors Director: _______________________ Dr. Danielle Smith, Director

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© (Austin Church 2020)

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Abstract

This paper looks into risk attitudes of college students and how those attitudes affect the likelihood of majoring in art in college. The theory of human capital investment provides the baseline for the analysis. The financial prospects of art graduates make a degree in the arts risky relative to other majors1. Data from the National Longitudinal Survey of Youth 1997 was used to show that more risk tolerant individuals are more likely to major in art. The willingness to bend rules was used as a proxy for an individual's attitude towards risk; bending rules is a risky endeavor, and attitudes towards that risky activity serve to represent overall attitudes towards risk. To come to this conclusion, a logistic regression model was used to determine the effect of increased willingness to bend rules on the likelihood of majoring in art. Ultimately, the difference in the probability of majoring in art between a risk averse student and a risk tolerant student was shown to be roughly 3.5%. Risk-taking attitudes are also shown to have an effect on the likelihood of individuals majoring in the humanities. The effect of risk-taking attitudes on the likelihood of majoring in art also has implications for the structure of art classes and in art outreach efforts.

1 This comes from The Economic Value of College Majors by Carnevale et al.

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Executive Summary

This paper looks into the effect of students' risk-taking attitudes on the likelihood of

studying art in college. There is a large body of literature on the economic decision to attend

college or not; there has also been some research into what causes students to select their topic of

study. The research reported on in this paper was conducted by first finding sufficient data on

students; the National Longitudinal Survey of Youth 1997 provided this data. The survey

interviewed a population of young people once a year, and their current college major and risk

attitudes were among the topics asked about. The independent variable used was the students'

likelihood to bend rules. This variable is not exactly equivalent to risk-taking attitudes; the

likelihood to bend rules serves as a proxy for risk-taking attitudes because bending rules is a

risky endeavor and attitudes towards this instance of risk is telling of one's overall attitude

towards risk. The dependent variable was a binary variable that took the value of one if the

student majored in art, and it took the value of zero if the student majored in something else.

With the variables in place, the effect of risk-taking attitudes on studying art was measured

through a logistic regression. Regression analysis is the use of a function to estimate causal

relationships. After the regression was run, the coefficient on the independent variable was

generated; with the coefficients in place, the resulting function can be seen as an estimated line

of best fit that relates the independent to the dependent variable. A logistic regression is a form

of regression that is non-linear, which provides a more accurate estimate if the effect in question

is non-linear. The dependent variable is binary, either a student is majoring in art or they are not,

so the equation gives the probability that a student studies art; changes in the independent

variable change the probability of a student studying art. This effect is what the regression

captured.

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This regression analysis supported the overall argument this paper is presenting. The

argument is that art majors are some of the lowest earning majors on average (Carnevale et al.

12-18); this low level of earnings provides risk to the students who study art as their investment

in their education is more likely to not pay off. For any given cost of college, majoring in art has

a relatively higher likelihood of not recuperating that cost compared to higher earning majors.

Students who are more willing to take on risk would be more likely to take on this risk and study

art in college, holding interest and other relevant factors constant. The regression supports this.

Other results that were uncovered through the research process were also listed. Among

those results was the causal relationship between risk-taking attitudes and studying the

humanities, and the inconclusive causal relationships between risk-taking attitudes and majoring

in the social sciences, STEM, or professional fields. The standard errors here were too large to

either confirm or deny the causal relationship.

This result furthers economic understanding of what factors influence the subjects that

are studied in college. Knowing this could help schools target students who are more likely to

take to art and help art departments recruit students who would be more likely to excel in those

departments. This result could also help existing art departments structure classes to maximize

their effectiveness (especially at the elementary and high school levels).

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Table of Contents

Abstract……………………………………….……………….………….. iiiExecutive Summary………………………….……………….………….. iv

Chapter 1: Introduction ……………………………………………… 1

Chapter 2: Background ……………………………………………… 2Human Capital Investment …………………………………... 3Literature Review ……………………………………………... 5

Chapter 3: Empirical Strategy ……………………………………… 6

Chapter 4: Data ……………………………………………… 8

Chapter 5: Results ……………………………………………… 9Robustness ……………………………………………... 10Other Results ……………………………………………... 11

Chapter 6: Conclusion ……………………………………………… 12

Works Cited.……………………………………………………………… 14Appendices………………………………………………………………… 15

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1

Chapter 1: Introduction

One's educational goals are influenced in many ways. Personal interest, future employment

prospects, and familial background are just some of the relevant factors. Knowing what factors

influence these education decisions is important, since further education is expensive and has a

dramatic effect on lifetime earnings. The specific factor that was investigated in this paper is risk

aversion; this paper focused on art as a specific major of interest. The question this paper aims to

answer is as follows: what is the effect of risk-taking attitudes on the likelihood of studying art in

college? The theory of human capital investment was spelled out as was how major choice fits

into this broader framework. An overview of existing literature on the subject was also

conducted to provide a more complete grounding for the results.

A logit discrete choice model was used to uncover the effect of risk-taking attitudes on the

likelihood of majoring in art. Attitudes towards bending rules were used as a proxy for overall

attitudes towards risk. All data was collected from the National Longitudinal Survey of Youth 97

which was conducted by the US Bureau of Labor Statistics. The independent variable was

crafted from students’ responses to a survey question about their attitudes towards bending rules.

The individuals surveyed self-reported their majors (if in college) and the major variables were

created from that data.

The effect uncovered is that more risk loving students are more likely to study art in

college, and more risk averse students are less likely to study art in college. This effect was also

found to stand up to robustness checks which further support the veracity of the relationship. It

was also shown that risk-taking attitudes do have an effect on the likelihood of majoring in the

humanities. The effect of risk-taking attitudes on majoring in the social sciences, STEM fields,

and professionally oriented majors was shown to be inconclusive.

2

This research also provides ideas for further research that would build upon this result. One

idea for future research in this area is whether risk-taking attitudes affect the percentage of

college grads who followed their art major with a career in art. Another idea is to see if risk

attitudes affect the likelihood of pursuing an art career without formal education instead of

pursuing a formal education in art.

Chapter 2: Background

The choice of whether to obtain higher education or not is a worthy topic of research in its

own right; however, there is another choice for the people who do decide to pursue higher

education: what to study while attending. Before getting into the theory behind this important

choice, an overview of the United States higher education system will be useful in clarifying

terms and will provide a sturdy ground to grow the theory out of.

Students who attend college first obtain an undergraduate degree, the two broad categories

of which are associate and bachelor’s degrees. Associate degrees are typically two-year

programs, and bachelor’s degrees are usually four-year programs. A bachelor’s degree is

required for most post-graduate degrees, which are masters and doctorate degrees. The focus of

this paper is undergraduate studies, so postgraduate degrees will not be discussed further.

An important distinction that needs to be fleshed out is the difference between a college

and a university. These terms are commonly used interchangeably, but there is a distinction in

the United States. While both are institutions that provide undergraduate degrees, universities

often contain multiple colleges and they also provide more extensive postgraduate programs. In

America, the distinction is not drastic, but there are differences (Wellman). The distinction is not

vital to the results, so "college" will be used to refer to both colleges and universities in this

paper unless specified otherwise.

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While at college, students are required to select a major; a major is a field that the student

specializes in. Some schools may call their specialization requirements different things,

concentration is a popular alternative name. Harvard University is an example of an institution

that uses concentrations instead of majors; however, the overall demand for a specialization is

the same. Different majors have different requirements that must be satisfied in addition to the

overall school requirements. These requirements are typically a certain number of courses in the

field, with some courses being mandatory. Majors can be broadly categorized into a few

categories: humanities, social sciences, professional, creative, and STEM, which is an acronym

for Science, Technology, Engineering, and Mathematics. Humanities majors are sometimes

called the liberal arts; a few examples of majors that fall into this category are philosophy,

history, English, and languages/linguistics. Social Sciences are majors that deal with social

interactions/the study of society; a few examples of such majors are economics, political science,

and sociology. Professional majors are majors that are designed to prepare the student for a

specific career; a few examples of majors in this category are business and nursing. Creative

majors are majors that focus on creative expression; a few examples of this type of major are the

arts and architecture. STEM majors are majors that fall under the four fields in the acronym.

There can be some overlap between these major groups, but these categories are helpful for a

rough picture. This paper will be focusing on art majors (which falls under the creative category)

and what factors influence students to choose those majors in college.

Human Capital Investment

Before diving into the college major decision, an overview of the economic theory

regarding attending college will be helpful. The predominant theory regarding the choice of

going to college for further education treats this decision as a financial investment. A person

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continues their education if they believe the investment is a good one. A good investment in this

instance is when the benefit of the additional education outweighs the monetary and opportunity

costs of that education. The opportunity costs of education are the lost wages that a person

forgoes when they enter an education program. Further education enhances an individual's

position in the labor market; this superior position allows the worker to command higher wages.

An important consequence of this is that the education must carry value; education that brings

lower wage premiums is less likely to make the expected utility of the investment positive. Other

important factors regarding the investment decision are the cost of borrowing money (interest

rate), the opportunity cost of entering the labor market with their current level of education, and

expected length of career. The intrinsic value of education to an individual also plays a role, but

these effects will not be delved into in this paper (Borjas 229-276). This model is making the

reasonable assumption that students are considering the costs and benefits of college when they

make the college and major choice decisions.

Not all of those factors have the same influence on the decision of which major to select

once in college. One factor that is important to this decision is the value of the major. Certain

majors have wildly variant average starting salaries. The majors that are associated with lower

incomes still have students enrolled in them. So, while this factor is important, it cannot be the

sole explanation. Personal interest is likely a large influence, but that is very hard to measure; it

will not be the focus of this paper. Since expected return plays a role in the overall college

decision, choosing a major to maximize this return is important. The majors that are lower

earning on average are riskier than other, more lucrative, majors. This measure of risk is the

focus of this paper. When looking at art majors, willingness to accept risk is what will be shown

to have an impact on the likelihood of selecting this major.

5

Literature Review

A prior study by De Paola and Gioia looked into risk aversion and major choice and found

that risk aversion does have an effect on what majors are selected. This paper looked at general

fields and found that risk averse people are more likely to study any other field instead of the

social sciences. This research, which sampled students at a midsized Italian University, shows

there is a link between risk aversion and college major choice (de Paola and Gioia 1-19); this

link will be built upon in this paper.

In a study by Lisa Dickson, both race and gender were shown to affect college major

choice. She found a gap of 16 percentage points in the probability of white women studying

engineering and computer science compared to white males; white males are 16 percent more

likely to major in engineering or computer science than white women, even after adding control

variables. This study also found that women are more likely to major in the humanities and other

majors relative to the social sciences (Dickson 1-17). Her paper shows that race and gender have

an effect on the probability of studying different majors.

Research by Wiswall and Zafar looked into the determinants of college major choice. They

found that the expected earnings of each major affect the likelihood of selecting that major.

Interestingly, they also found that the individuals' beliefs about their future earning in each major

also has an effect. The individuals' beliefs about the likelihood of graduation in a given major is

also a causal factor. Personal taste is also identified as a causal factor (Wiswall and Zafar 791-

824). Their research helps provide a richer understanding of all of the factors that influence

college major choice. It also provides empirical evidence that students do take expected earnings

into effect when selecting a major. This lends support to the idea that students have some level of

understanding of the risk involved in each major. Risk tolerance has also been shown to have an

6

effect on college major choice. Belzil and Leonardi found that risk aversion is a deterrent to

pursuing higher education. This means that more risk averse students are less likely to enter

college (Belzil and Leonardi 35-70). This paper looks into whether risk aversion has a further

effect on the likelihood of studying art in college.

In a Georgetown study on the economic value of college majors, art was shown to have

one of the lowest average starting salaries. The median salary of college graduates in art in the

21-24 year old age range was $28,000. This figure rose to $49,000 in the 25-49 year age range.

Both of these figures rank near the bottom of all college majors. Additionally, the 25th percentile

of art earnings in the later group is around $30,000; this is slightly below the median salary of

high school graduates. The data show how majoring in art is a financially risky proposition. Art

majors also have some of the lowest rates of graduate degree attainment, so additional wage

premiums from those degrees are also less risk reducing than for most majors (Carnevale et al.

12-18).

Chapter 3: Emprical Strategy

A proxy will be used in order to uncover the effect of risk tolerance on the choice to major

in art in college. This proxy is the subjects' attitude towards bending rules. This proxy is

effective because bending the rules without outright disregarding them is a risky endeavor; while

bending the rules is not a direct disobedience to the rules, it does show a willingness to walk the

line on what is acceptable, and that behavior is risky. The more likely the student is to tolerate

that risk, the more likely they are willing to tolerate other risks. The variable is a self-reporting of

the subjects' own attitudes. The question that generated the variable is "Even if I knew how to get

around the rules without breaking them, I would not do it." The survey respondents then selected

their answer on a scale from 1-7, with 1 being disagree strongly and 7 being agree strongly. An

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answer of disagree strongly means that they would act in a way to get around the rules without

breaking them if the opportunity arose. So, a person who answers 1 is most likely to bend rules,

and a person who answered 7 is least likely to bend rules. This variable is the independent

variable and will be denoted as X.

The variable of interest is FineArt, a binary variable that takes a value of one if the student

majored in the arts, and it takes the value zero if the student did not major in the arts. This

variable was generated through the following process. The raw data had the students report their

current major during multiple periods of each year from 1997 to 2017. A new variable was

created that started out as all missing observations, and then each term of each year was replaced

with the most recent major declared in the surveys. This was done in order to have one data point

that represented the final major that each student reported. The major that is associated with each

college going individual in the sample is the last major the individual reported to the interviewer.

With the majors variable in place, the dummy variable for arts majors was left to be created. The

way the majors were entered into the dataset changed in 2010, so there was a cross reference

between the majors before the switch and after the switch; this cross reference was done by

having a separate major variable for the two periods. The fine arts major was coded differently in

the two time periods (before 2010 and after 2010). The fine arts variable was crafted by looking

at the major variable and coding a value of one if the student had fine arts selected before and

nothing after, or nothing in the before period and fine arts in the after period, or fine arts selected

in both periods, or a non-fine arts major in the first period and fine arts in the later period.

The regression equation will look like the following:

Pr ( y=1|X )=1 /¿)

8

This type of regression is a logit non-linear probability model. The coefficient measures

how much a unit change in the independent variable changes the z-score of the standard logistic

distribution function. The z-score here is equal to α +βX . For example, β= 0.5 means a unit

change in X increases the z-value by 0.5. It will be shown later that this model stands up to

robustness checks of two other probability models.

Chapter 4: Data

The data used in this paper come from the National Longitudinal Survey of Youth 1997.

This survey was ran by the US Bureau of Labor Statistics and it looks at students born between

1980 and 1984; there were yearly interviews with the subjects of the survey starting in 1997 and

ending in 2017/2018. The survey started with 8,984 children in 1997, and there were 6,734

responses in the latest round of interviews. There were two subsamples that comprised the

overall sample. The main subsample was a cross-sectional sample comprised of 6,748 children

who were meant to serve as a representative sample of the United States population during round

one of the surveys. The second subsample was a smaller sample of 2,236 children that

oversampled black and Hispanic or Latino people that were born in the same period as the first

subsample. The data covered a wide range of topics, from parental and environmental

information to attitudes, health, and crime information.

The interviews for the survey were conducted through a computer-assisted personal

interview instrument that was administered by an interviewer with a laptop. The interviews were

conducted in person if possible; in person interviews were preferred. An important result of this

is that the answers to all the survey questions are self-reported; this self-reporting allows the

people surveyed to decide their attitudes towards certain questions directly.

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In total, there were 4,955 students in the sample that had data relating to college major

choice. The gender split in the data was pretty even, with 3,714 students being male (50.34%)

and 3,664 students being female (49.66%).2

Chapter 5: Results

With the variables prepared and the regression model specified, a preliminary investigation

into the effect of a positive attitude towards bending rules on majoring in art was performed. A

two-way line graph was generated between the density of fine arts majors and the willingness to

bend rules. This graph (Figure 1) shows a distinct trend: as the willingness to bend rules

decreases, so does the likelihood of majoring in art. This preliminary result provided the impetus

for running the regression proper. After running the logit regression, the results were significant

at the 95% confidence level. The results of this regression and two other robustness regressions

are contained in Table 1. The coefficient of interest (β) is -0.1372 and the constant term (α ) is -

2.617. So, the likelihood of majoring in arts starts with a z-value of -2.7542 and

Pr ( y=1|X=1 )=1/¿)

is the model evaluated at X=1 with the estimated coefficients. This gives a probability of such a

student majoring in art of 0.0598 or 5.98%. As an individual's likelihood of bending rules

decreases, the likelihood of that person majoring in art also decreases. The likelihood bottoms

out at 2.71% for students with the highest disdain for bending rules. The overall change in the

likelihood of studying art predicted by the logit model is 3.27%.

These results show that the difference in the probability of studying art between a student

who has a disdain for bending rules and a student who has an affinity towards bending rules is

approximately 3.3%. Since the attitude towards bending rules is being viewed as a proxy for an

2 All data and information about NLSY97 were obtained from their website. Initial data download was November 26, 2019

10

attitude towards risk, the gap should be similar between a risk loving and a risk averse person.

The higher likelihood of risk loving students majoring in art makes sense because majoring in art

is financially risky relative to other majors.

These results signify a link between risk-taking attitudes and the likelihood of majoring in

art in college. There is an important caveat to this data that bears explanation. Many artists never

study art in college; they start producing and selling their art without formal college education.

These artists are not included in this paper. While it is likely that these results would hold for

those artists also, further research is needed to confirm this point.

Robustness

The presence of this effect is also supported by the linear probability model (LPM). The

linear probability model predicts the change in probability that the binary dependent variable

takes the positive value through a linear means; the predicted probability is the y value once the

linear function is evaluated at a given value of the independent variable. The LPM has a

coefficient of -0.0053 which means that a unit change in the bend rules variable means a 0.53%

change in the likelihood of studying art in college. The t-value of the coefficient is -3.40 which is

well above the -1.96 threshold for the 95% confidence level; this means that this effect is

statistically significant. The model predicts a 3.71% total change. This is similar to the logit

model's prediction, and this supports the veracity of the models.

This effect is also supported by the probit model. This model differs from the logit model

in that it uses the standard normal distribution function. The expression α +βXgives the z-score

associated with the independent variable. When X=1, β= -0.0614 and α = -1.498. The initial z-

score is -1.559. This z-score means that the probability of such a student studying art is 6.06%.

When X=7, the z-score is -1.928. This z-score corresponds to the probability of such a student

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studying art of 2.68%. The total change in the probability of studying art is estimated to be

3.38%. This total change is very similar to the total change for the other two regression models

and provides further proof of the robustness of the results.

Other Results

At first, the dependent variable of interest was a group of creative majors. The two majors

that were selected in this group were arts and architecture. The group was restricted to only arts

majors since the financial risk associated with majoring in architecture is significantly lower than

majoring in art; therefore, architecture majors do not fit in the same group as art majors in the

model. Before the restriction, gender was also used as an independent variable; gender was

statistically significant. Men were more likely to major in one of these creative majors than

females. Once architecture was removed and art was looked at alone, gender's effect was no

longer statistically significant. Letting the gender variable be denoted as G, this regression

equation was the following:

Pr ( y=1|X )=1 /¿)

The standard error for gender's effect on majoring in art (Table 2) was very large, so

gender still may have a role in determining the likelihood of majoring in art, but further analysis

is needed to confirm this one way or the other.

Another result worth mentioning is the effect of risk-taking attitudes on majoring in the

humanities. This was done by taking a group of majors that fall under the umbrella of humanities

and running the same analysis as was performed for art. The group of humanities majors was

comprised of the following majors: foreign languages, education, English, history, and

philosophy/theology. The result was statistically significant at the 95% confidence level (Table

3); the overall shift in the likelihood of majoring in the humanities due to risk-taking attitudes

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was roughly 4.25%. The effect of risk-taking attitudes on studying the humanities has a different

direction than the effect on majoring in art; students who are more risk tolerant are less likely to

major in the humanities.

The following major groups were also explored: social sciences, STEM, and professional.

The social sciences group contained the following majors: area/ethnic studies, psychology, and

majors coded as ‘social sciences’ within the data. The STEM majors were the following:

biology, engineering, mathematics, and physical sciences. Lastly, the professional group was

comprised of the following majors: agriculture, business, communications, computer science,

and health related majors. There was no statistically significant effect of risk-taking attitudes on

selecting a major in any one of these categories. In all three cases, the estimated coefficients

were small with a large standard error (Tables 4-6). These results do not rule out the possibility

that the effect is present, but the size of the standard errors do not allow determination either

way.

Chapter 6: Conclusion

After looking into the National Longitudinal Survey of Youth 97, a link between the

willingness to bend rules and the likelihood of majoring in art in college was uncovered. The

willingness to bend rules can be seen as a proxy for risk-taking attitudes. This use of a proxy

means that this link can be seen as a relationship between risk-taking attitudes and the likelihood

of majoring in art in college. More risk loving individuals are more likely to major in art than

risk averse individuals, holding everything else constant.

This finding does not analyze everything about the selection of major choice and even the

choice to major in art; further research on this topic would be worthwhile. One potential further

topic of research would be if familial income has an effect on how sensitive students are to the

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risk that is associated with majoring in art; it is possible that higher familial incomes may

weaken the causal power of risk-taking attitudes on the likelihood of majoring in art. Another

potential further topic of research is whether risk-taking attitudes affect whether art majors

pursue a career in art. Many students' careers post-graduation are not directly related to their

major, so seeing the proportion of art graduates that pursue an art career and whether risk

tolerance has an effect on the decision to start a career in art would be valuable. A third potential

research topic involves the proportion of artists who pursue art in college versus the people who

forgo formal education to begin creating and selling art directly. Art is not a field that has college

as a prerequisite, so seeing whether risk tolerance affects the decision to pursue art without a

formal education or attain formal education would also be fruitful. Another topic that could be

looked into is if the different entrance requirements to certain majors influence the likelihood of

those majors being chosen. Some majors have higher entrance requirements than art does. Seeing

if these entrance requirements play a causal role in students’ major choice would be valuable.

This research also has the potential to manifest itself in future policy decisions. Knowing

what factors go into making major choice decisions would help high schools give better

education and advice in preparation of that choice. Additionally, this information could help

educators better structure art courses to be more informative and engaging for students,

especially the ones who are more likely to study art in college. With this in place, schools could

have a better idea of what a good structure would be. This could lower costs of running such

classes. Finally, this information can help college art departments in their outreach programs;

knowing more about prior art majors can allow them to target potential art majors more

efficiently and more cost effectively.

14

Works Cited

Belzil, Christian, and Marco Leonardi. “Risk Aversion and Schooling Decisions.” Annals of

Economics and Statistics, 2013, pp. 35–70. JSTOR.

Borjas, George J. “Human Capital.” Labor Economics, by George J. Borjas, McGraw-Hill

Education, 2016, pp. 229–276.

Carnevale, Anthony P, et al. “The Economic Value of College Majors.” Georgetown University

Center on Education and the Workforce in the McCourt School of Public Policy, 2015,

pp. 12–18., cew.georgetown.edu/cew-reports/valueofcollegemajors.

De Paola, Maria, and Francesca Gioia. “Risk Aversion and Major Choice: Evidence From Italian

Students.” pp. 1–19. Research Gate, 2011,

www.researchgate.net/profile/Maria_De_Paola2/publication/254399124_RISK_AVERSI

ON_AND_MAJOR_CHOICE_EVIDENCE_FROM_ITALIAN_STUDENTS/links/

02e7e53ce5ad6e80f2000000/RISK-AVERSION-AND-MAJOR-CHOICE-EVIDENCE-

FROM-ITALIAN-STUDENTS.pdf.

Dickson, Lisa. “Race and Gender Differences in College Major Choice.” The Annals of the

American Academy of Political and Social Science, 2010, pp. 1–17.

Wellman, Mitchell. “What's the Difference between a 'College' and a 'University'?” USA Today,

2017, www.usatoday.com/story/college/2017/03/01/whats-the-difference- between-a-

college-and-a-university/37428407/.

Wiswall, Matthew, and Basit Zafar. “Determinants of College Major Choice: Identification

Using an Information Experiment.” The Review of Economic Studies, 2015, pp. 791–824.

JSTOR.

15

Appendices

16

17