college students' knowledge of personal finance

8/14/2019 College Students' Knowledge of Personal Finance

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WORKING PAPER --- PLEASE DO NOT QUOTE WITHOUT PERMISSION

A Survey of College Students Knowledge of Personal Finance

Katherine M. SauerAssistant Professor of EconomicsUniversity of Southern Indiana

8600 University Blvd.Evansville, IN 47712Phone: (812) 465-7034Fax: (812) [email protected]

Gregory P. ValentineProfessor, Business and Economic Education

University of Southern Indiana8600 University Blvd.Evansville, IN 47712Phone: (812) 465-1610Fax: (812) 465-1044

[email protected]


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ABSTRACT

This paper provides evidence of a lack of financial literacy among college students in

southern Indiana. A 44 question survey covering five areas of personal finance (credit, vehicle

insurance, banking, life insurance, and housing rental) was given to 234 students at a mid-sized

comprehensive university. Results indicate that the average score was 53.5% correct, which is in

line with some national studies but lower than the findings from the most recent national

assessment of college students personal financial literacy. Students scored the lowest on life

insurance questions and the highest on housing rental questions; traditionally aged college

students scored lower than non-traditional students. Students who are employed scored slightly

better than those who did not work and students who paid their own vehicle insurance scored

better than those who did not. Regression analysis revealed that several demographic

characteristics influenced the students test scores. This study suggests that there are

opportunities for colleges and universities to implement a personal finance course requirement at

the undergraduate level.

ACKNOWLEDGEMENT

We are grateful to participants of the 2007 NCEE/NAEE/GATE annual conference in

Denver, Colorado on October 3, for helpful comments.

INTRODUCTION

Alan Greenspan (2003), Chairman of the Board of Governors of the United States

Federal Reserve, stated that in order to achieve an effective and efficient functioning of financial

markets, there is a need for more financial education at all levels of society. Recent evidence


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highlights that the need is growing. In January 2008, the U.S. Federal Reserve reported that by

the end of 2007 consumers had amassed $937.5 billion worth of revolving credit at an average

interest rate of 14.35%. According to Halperin and Smith (2007), researchers for the Center for

Responsible Lending, overdraft fees on bank accounts total $17.5 billion. The mortgage

delinquency rate is the highest in two decades, and the number of homes in foreclosure is the

highest ever (Mortgage Bankers Association, 2007). The American Bankruptcy Institute

reported that in 2007 over 800 thousand consumers declared bankruptcy. CNNMoney (2006)

reported that 43% of households are in danger of running short of retirement funds.

Given the state of American consumers finances, it would seem that Greenspan was

correct. Where then are consumers getting their financial education? The Hartford Financial

Services Group (2007) recently found that the majority of college students learn the most about

personal finance from their parents. In addition, less than half of the students say that their

parents make a consistent and conscientious effort to teach them about personal finance. The

study further stated that students and parents both agree that college students are not well

prepared to deal with the financial challenges that lie ahead.

Anecdotal evidence aside, the level of personal financial literacy of young adults has

been examined several times by various organizations. The Consumer Federation of America

and the American Express Company assessed financial literacy among high school students in

1991 and college students in 1993. They administered a 52 question survey to 428 teenagers at

shopping malls in eight metropolitan areas around the country. The students answered 42% of

the questions correctly. The survey of college students was given to 1,000 full-time college

juniors and seniors at 75 institutions with only 51% of the questions answered correctly. The

Jump$tart Coalition nationally surveys high school seniors on personal finance every two years.


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The 2008 study revealed that the students answered 48.3% of the questions correctly. The survey

was given to approximately 6,800 students in 40 states. Also in 2008, Jump$tart Coalition

released its first ever survey of college students. The survey was given to 1,030 students

nationwide and on average 62% of the questions were answered correctly.

Personal finance literacy has received some attention in the academic literature; however,

the focus has been mainly on high school students as opposed to college students. Notable

examples of high school studies include Tennyson and Ngyen (2001) and Valentine and Khayum

(2005). Tennyson and Ngyen compared financial literacy levels among high school students

from states with and without mandates for financial literacy curriculum. They administered the

Jump$tart Coalition survey to 1,643 high school students in 31 states. Roughly half of the

students in the sample had been exposed to personal finance education. They did not find a

significant difference in test scores. Students from states that mandate a personal finance course

scored on average 56.9% correct and students from states without a mandate scored 56.5%.

Valentine and Khayum used the Consumer Federation of America/American Express survey to

study financial literacy among urban and rural high school students. They surveyed 312 high

school students in southwestern Indiana. They found no significant difference between the scores

of urban and rural students. On average, urban students scored 50% and rural students scored

51%.

There are several related strands of literature on college students and personal finance.

Financial attitudes and financial behaviors have received attention. Examples include Roberts

and Jones (2001) work on debt attitudes and Xiao, Noring, and Andersons (1995) study of

students and credit cards. The impact of financial knowledge has been explored by Borden, Lee,

Serido, and Collins (2008); Peng, Bartholomae, Fox, and Cravener (2007); and Fox,


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Bartholomae, and Lee (2005) among others. Several studies (e.g. Chen and Volpe (1998) and

Markovich and DeVaney (1997)) examine links between financial attitudes and knowledge and

explore the effects of student characteristics like gender, ethnicity, and employment.

At the college level, financial literacy has been examined by two studies, Danes and Hira

(1987) and Chen and Volpe (1998). Danes and Hira questioned 323 students at Iowa State

University to determine their level of knowledge of money management, credit cards, and

insurance. They found that students had a low level of personal financial knowledge. Chen and

Volpe conducted a national study. They surveyed 924 students from 14 college campuses. On

their assessment, the average score was 51%.

In light of the rather sparse academic literature on college students personal financial

literacy, the authors sought to contribute the following. First, we benchmarked the level of

personal finance literacy among college students in southern Indiana. A survey of personal

financial literacy was administered to college students taking an elective personal finance course.

Next, we analyzed the scores from each section of the survey (credit, vehicle insurance, banking,

life insurance, housing rental) to see where students financial knowledge deficiencies

specifically lie. Finally, we examined demographic factors to determine if personal financial

knowledge differed with student characteristics.

METHODOLOGY

A survey of personal financial knowledge was given to college students in southern

Indiana. From individual students scores, the mean percentage correct score was computed to

benchmark the level of financial literacy. T-tests were used to analyze any differences in mean

scores for various groups of students (e.g. males vs. females). Regression analysis was used to


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determine whether student characteristics affected their scores. Mean scores, t-tests, and

regression analysis was also performed for each of five sections of the survey.

The survey consisted of the financial literacy questions developed by the Consumer

Federation of America. This instrument was chosen because Chen and Volpes 1998 instrument

had solely been used in a pilot study while the selected instrument came out of collaborations

from Educational Testing Services (ETS), American Express, the Consumer Federation of

America, and The Psychological Corporation. The survey instrument covered five areas of

personal finance: credit, banking, vehicle insurance, life insurance, and housing rental. In

addition to personal finance questions, the survey included demographic questions and questions

about students exposure to personal finance issues (e.g. credit card usage).

College students enrolled in sections of an elective personal finance class at a mid-sized

comprehensive public four-year university were included in the survey. Students were given the

option of participating or not participating in the survey without any adverse consequences if

they chose not to participate; all students participated in the survey. The instrument was

administered on the first day of class to those students who were enrolled during the fall

semesters of 2006 and 2007 and during the spring semester of 2007. This class is an elective in

the Associate Degree in Business program as well as a general elective for all baccalaureate

degree programs. The sample was comprised of 234 respondents. Table 1 presents the sample

demographics and Table 2 presents the data on experience with personal finance matters.

Respondents were predominantly Caucasian, business majors of traditional college age, not

married, and without children. A majority of students worked at least one hour per week, had at

least one credit card, had some type of bank account, and had a vehicle.


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The survey contained 44 multiple choice questions related to personal finance. Fourteen

questions on credit focused on personal finance terms, where to obtain credit, best indicators of

the cost of credit, determining interest, and making payments. The topics covered in the six

checking and saving account questions dealt with computing the monthly cost of a checking

account, avoiding checking account fees, analyzing the most important factors when shopping

for an account, and calculating the various types of interest compounding. The six items on

automobile insurance dealt with coverage, costs, and complaint resolution. The twelve questions

that dealt with life insurance concerned types of insurance, costs, and problem resolution. The

six items about housing rental concerned tenants rights and obligations, landlord rights and

obligations, and how to negotiate a lease. A sample question from each section can be found in

Table 3.

After administering the survey, the mean percent of correct scores for each question, each

section, and the entire survey were computed. Standard deviations were also noted. The

students scores were then grouped by demographic characteristics and t-tests with unequal

variances were performed for determining significant difference in scores. Characteristics used

for comparison were age, gender, hours worked per week, credit card usage, party responsible

for paying vehicle insurance, and college major. These groups were chosen on the basis of

previous scholarly works (Chen and Volpe, (1998); Tennyson and Nguyen, (2001); and

Valentine and Khayum, (2005)).

To determine the effect students characteristics and experiences may have had on their

survey scores, regression analysis was performed. The students scores were regressed on

dummy variables for student characteristics and experiences. The regression equation used was


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Scorei = i +1AgeDummy + 2GenderDummy + 3MaritalStatusDummy + 4ChildrenDummy +5JobDummy +

6EthnicityDummy + 7CreditCardDummy +8VehicleDummy +9VehicleInsPayDummy + 10BankingDummy +

11HousingDummy+12MajorDummy + errori

where the dummy variables were assigned for each category of student characteristic in Table 1

and Table 2. Separate regressions were run with the overall score and score from each section as

the dependent variable. The Shapiro-Francia test confirmed normal data. Because of the wide

variety in the variables, the Breusch-Pagan test revealed that heteroskedasticity was a problem.

Thus, robust standard errors were used. Multicollinearity was not found to be an issue (no VIF

values were greater than 10).

FINDINGS

The overall mean percent correct score was 53.5%. Mean scores were found to be

statistically significantly different for students classified by age, working status, and party

responsible for paying vehicle insurance. When breaking the scores down by content section

(e.g. banking questions), students scored the highest on the housing rental section and the lowest

on the life insurance section. The results from the t-tests for differences in mean scores were

varied when classifying students into groups. When regressing student scores on student

demographic data, several characteristics were found to have significant impact.

Table 4 reports the summary statistics for the survey. The average score was 23.5 correct

out of a possible 44 questions, or 53.5% with standard deviation of 0.092. The mean percents

correct for the credit, vehicle, and banking sections of the survey were similar with scores of

52.5%, 51.1%, and 53.6% respectively. Students scored the lowest on the life insurance

questions (47.9%) and highest on the housing rental section (69.2%).

The high and low section scores are perhaps unsurprising. One would not expect many

college students to have purchased or dealt with life insurance. As for the higher score on the


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housing rental questions nearly half of the students in the sample lived in an apartment. Another

30 students are home owners, and it is plausible that they had lived in rental housing before

purchasing their home. Sixty-nine students lived in campus housing; one might consider this

experience with quasi-rental housing to increase student knowledge on rental issues. In addition,

these students likely have friends who live in apartments or are looking to rent an apartment at

some point themselves.

The scores on the other three sections might be lower than one would expect. With all

the types of credit related situations that students encounter (e.g. credit cards, student loans,

mobile phone account approval); one would speculate that students would do better on this

section. The low score means that students are fundamentally lacking knowledge on many credit

related matters. Turning to the banking section, the vast majority of the students had a checking

and savings account. Students did well on the questions relating to these accounts but were

lacking awareness of bank accounts beyond these two types of accounts. Perhaps most surprising

was the low score on the vehicle insurance question. Every student with a vehicle also had

insurance. However, vehicle insurance is required in the state of Indiana. One might think it

possible that the students purchase insurance without taking time to understand their coverage.

To determine if there was a significant difference in the scores of various groups of

students, the mean scores were further analyzed. The students were broken down by age, gender,

employment, credit card usage, the party responsible for paying for vehicle insurance, and by

college major. Students are grouped by gender to check for any differences among the sexes. In

the age category, students were categorized by those who were of traditional college age and

those who were not. One would expect that older students might have more familiarity with

personal finance issues and would thus score higher. Another measure of exposure to personal


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finance might come through employment. Students earning their own money might have more

experience managing their money as well. Credit card use is another natural way to group

students by exposure to personal finance issues, as is whether or not the student pays for their

own vehicle insurance. Finally, since this survey was given in a College of Business course,

students were grouped into business majors and non-business majors. Through their coursework,

business students might be expected to have a higher level of knowledge of personal finance

issues.

Table 5 shows the results of t-tests for data with unequal variances. For gender, major,

and number of credit cards, the mean scores were not significantly different. However, as

expected, traditionally aged college students scored lower (52.1%) than non-traditional students

(58.7%). Also, students who were not currently employed scored lower (51.5%) than students

who were working (54.53%). Those who did not pay the cost of their vehicle insurance scored

lower (51.6%) than those who did (56.0%).

Tables 6 through 10 report the results of t-tests for differences among students, broken

down for each section of the survey instrument. Table 6 shows the credit section results. Similar

to the overall mean score results, students age, working status, and payment for vehicle

insurance produced significant differences in mean scores. Again, traditional college students

scored lower than non-traditional students (51% vs. 58.3%). Students without jobs scored lower

than students with jobs (50% vs. 53.8%). Students who did not pay for their vehicle insurance

scored lower than those who did (51.1% vs. 54.4%). For gender, major, and number of credit

cards, the mean scores were not significantly different.

Table 7 reports the vehicle insurance section. Again, students who personally paid for the

cost of their vehicle insurance scored higher than those who did not (55.2% vs. 48.8%) and non-


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not have a vehicle, currently resides in a university residence hall, did not have home/rental

insurance, and indicated business as a major area of study. For the regression using the overall

score as the dependent variable, several student characteristics were significant.

From the age dummy variables, being aged 30-39 exerted positive influence on the

students score. Perhaps this is not unexpected since as people get older they may have had

personal experience with more financial instruments. However, this would not explain why being

aged 40 and older did not affect scores. By closer inspection, our sample contains only five

students from this age group. It is very possible that those five students are not representative of

the financial experience of their 40 and older peers in general.

Some of the race and marital status variables influenced the test scores; being African-

American or Hispanic negatively affects scores. This result is very similar to the findings of

Murphy (2005). As for marital status and children, being single or divorced positively

influenced scores. Having one or two children raised the overall score, but having three children

did not affect the score. However, only three students in our sample had three children.

Several other variables were found to be significant. Working 1 to 5 hours per week

positively impacts scores; curiously, working more than 5 hours did not have an effect on scores.

For the credit card variables, having exactly two credit cards negatively affected scores. There

does not seem to be an immediate reason for such a phenomenon. Having a checking account did

not exert significant influence while having a savings account resulted in a negative impact on

scores. Since most students had a checking account, perhaps there was not enough variation in

the data for that variable to be significant. For the housing variables, living in a fraternity or

sorority house had positive impact on scores. The other living arrangements did not significantly

affect the scores. Some of the student major choices had impact on the scores. Health


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professions, engineering, and liberal arts majors exerted positive influence on overall scores.

The reference case was also significant and positively impacted overall scores. This means that a

married, Caucasian, male, business student, age 18-22, with no children, who was not working,

who had no credit cards, no vehicle, who resided in a university residence hall, without

home/rental insurance translated into a higher overall score.

The results for regressions using section scores as the dependent variables were varied.

For the age variables, there was some significance on the scores in the banking section. Being a

student in the mid to late twenties produced a negative impact on scores while being aged 30-39

positively impacted the score. In the credit, vehicle insurance, life insurance, and housing

sections, age was not a significant variable. Traditionally aged college students were included in

the reference case.

As for gender, being female had a significant impact on both the vehicle insurance

section score and the banking section score. For vehicle insurance, the impact was positive and

for banking the impact was negative. Males were included in the reference case.

The ethnicity variables were significant in several of the regressions. The African-

American coefficient was negative and significant in the credit section, banking section, and

housing section. Being African-American resulted in lower scores on these sections. The

coefficient on the Hispanic variable was only significant in the life insurance section. It produced

a negative impact on the life insurance section score. The coefficient on the other ethnic

variable was not significant in any of the specifications. Caucasian was included in the reference

case.


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In terms of marital status, being divorced was significant in each of the regressions. The

coefficient was positive in all regressions. For unmarried students, there was a positive impact

on the banking, life insurance, and housing scores.

Turning to the number of students children, coefficient significance varied by section.

For students with one child, the credit, vehicle insurance, and housing scores were positively

impacted but the life insurance score was lower. For students with two children, the coefficient

was positive and significant solely for the life insurance regression. For students with three

children, credit and banking scores were negatively impacted while housing scores were

positively impacted. Students with no children were included in the reference case.

There was no clear trend for the variables for hours worked. Working 1 to 5 hours per

week positively impacted credit and vehicle insurance section scores. Working 6 to 10 hours

positively impacted credit section scores. The coefficient on working 11 to 20 hours was positive

for the banking section. Working 21 to 30 hours and 31 to 40 hours had positive influence on the

vehicle insurance scores. Working over 40 hours a week was not significant. The reference case

was working zero hours.

The coefficients on credit cards also varied in significance. Students with one or two

credit cards had lower scores in the housing section. Having three credit cards resulted in a

positive impact on the banking section and housing section scores. The coefficients for four or

five credit cards were not significant in any of the regressions. However, having six credit cards

produced positive impacts on the credit, banking, and life insurance scores and negative impacts

on the housing section scores. The reference case was having no credit cards.

For students with vehicles, the coefficients were not significant in any of the regressions.

96.6% of the students in the sample had a vehicle. As for the party responsible for paying for


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vehicle insurance, students whose parents paid scored lower on the life insurance section.

Students whose spouse paid scored higher on the vehicle insurance section. In the sample, only 3

students had spouses who paid the vehicle insurance. Students whose relative paid the vehicle

insurance scored lower on the vehicle insurance section, life insurance section, and housing

section. Students paying for their own insurance were the reference case.

For the banking instruments, the coefficients for students with checking accounts were

positive for the banking and housing regressions but negative for the life insurance regression.

The coefficient on savings accounts was negative for the credit, vehicle insurance, life insurance,

and housing regressions. It was not significant for the banking regression. The reference case

was a student without a savings or checking account.

Few of the housing coefficients were significant. Having home/rental insurance

negatively impacted the vehicle insurance score. Living with parents had positively affected

scores in the banking and housing regressions. The same was true for students living alone in an

apartment. None of the coefficients for shared apartments or living in a dormitory were

significant. For students living in a fraternity or sorority house, the coefficient was positive for

the housing regression. Students living in a home that they owned were the reference case.

Finally, the students choice of major was examined. The education majors had higher

scores in the life insurance regression and lower scores in the housing regression. The health

professions major coefficients were positive in the credit and vehicle insurance regressions. The

scores on the credit, life insurance, and housing sections were higher for engineering majors. The

engineering coefficient was negative in the banking regression. For liberal arts majors, the

coefficient was positive for the life insurance regression. The life insurance and housing scores

were positively impacted by undecided majors. Business majors were the reference case.


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CONCLUSIONS and RECOMMENDATIONS

This paper provides evidence of a lack of financial literacy amount college students in

southern Indiana. The average score on a personal finance literacy survey was 53.5%. This is in

line with some national studies but lower than the findings from the most recent national

assessment of college students personal finance literacy. This paper further analyzed students

financial literacy by examining students scores for questions in specific areas of personal

finance: credit, vehicle insurance, banking, life insurance, and housing. Scores on the credit,

vehicle insurance and banking sections were all in the low 50% range. Students did slightly

worse on the life insurance section (48%) and much better on the housing section (69%).

Regression analysis was used to see if students demographic characteristics or familiarity with

financial instruments impacted their scores on the financial literacy survey. Results were varied.

While students in southern Indiana demonstrated financial literacy consistent with the

knowledge of students nationwide, the lack of personal finance knowledge is far from laudable.

This study suggests that there are opportunities to design and add a personal finance requirement

to the university curriculum.

REFERENCES

American Bankruptcy Institute. (2007). Annual business and non-business filings by year

(1980-2007). Retrieved January 11, 2008, from

http://www.abiworld.org/AM/AMTemplate.cfm?Section=Home&TEMPLATE=/CM/Co

ntentDisplay.cfm&CONTENTID=51718

Borden, L., Lee, S. , Serido, J. , & Collins, D. (2008). Changing college students financialknowledge, attitudes, and behavior through seminar participation.Journal of Family and

Economic Issues, 29(1), 23-40.

Chen, H. & Volpe, R. (1998). An analysis of personal financial literacy among collegestudents. Financial Services Review, 7(2), 107-129.


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CNNMoney.com (2006, June 6). Study: 43% wont have enough in retirement. RetrievedNovember 1, 2007, fromhttp://money.cnn.com/2006/06/06/retirement/risk_index/index.htm

Consumer Federation of America. (1991). High school student consumer knowledge: A

nationwide test. Washington, DC.

Consumer Federation of America. (1993). College student consumer knowledge: A nationwidetest. Washington, DC.

Danes, S., & Hira, T. (1987). Money management knowledge of college students. The Journal ofStudent Financial Aid, 17(1), 416.

Fox, J., Bartholomae, S., & Lee, J. (2005). Building the case for financial education. The Journalof Consumer Affairs, 39(1), 195214.

Greenspan, A. (2003). The importance of financial and economic literacy. Social Education.67(2), 70-72.

Halperin, E. & Smith, P. (2007). Out of balance: Consumers pay $17.5 billion per year. Centerfor Responsible Lending. Retrieved November 1, 2007, fromhttp://www.responsiblelending.org/issues/overdraft/reports/page.jsp?itemID=33341925

Hartford Financial Services Group, The. (2007, April 21).New survey by the Hartford revealsfinancial literacy communication gap among college students and parents. RetrievedJanuary 11, 2008, from http://ir.thehartford.com/releasedetail.cfm?releaseid=237682

Jump$tart Coalition for Personal Financial Literacy. (2008). 2008 Survey of personal financialliteracy among high school students. Retrieved May 30, 2008, fromhttp://www.jumpstart.org/fileindex.cfm

Jump$tart Coalition for Personal Financial Literacy. (2008). 2008 Survey of personal financialliteracy among college students. Retrieved May 30, 2008, fromhttp://www.jumpstart.org/fileindex.cfm

Markovich, C., & DeVaney, S. (1997). College seniors personal finance knowledge andpractices.Journal of Family and Consumer Sciences, 89(2), 6165.

Murphy, A.J. (2005). Money, money, money: An exploratory study on the financial literacy of

black college students. College Student Journal, 39(3), 478488.

Mortgage Bankers Association. (2007, December 6). Delinquencies and foreclosures increase inlatest MBA national delinquency survey. Retrieved January 11, 2008, fromhttp://mortgagebankers.org/NewsandMedia/PressCenter/58798.htm

Peng, T., Bartholomae, S., Fox, J., & Cravener, G. (2007). The impact of personal finance


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education delivered in high school and college courses. Journal of Family and EconomicIssues, 28(2), 265284.

Roberts, J., & Jones, E. (2001). Money attitudes, credit card use, and compulsive buying amongAmerican college students. The Journal of Consumer Affairs, 35(2), 213240.

Tennyson, S. & Nguyen, C. (2001). State curriculum mandates and student knowledge ofpersonal finance. Journal of Consumer Affairs, 35(2), 241-262.

U.S. Federal Reserve. (2008, January 10). Consumer credit. Federal Reserve Statistical Release.Retrieved January 11, 2008, from http://federalreserve.gov/releases/g19/20080110/

Valentine, G. & Khayum, M. (2005). Financial literacy skills of students in urban and rural highschools. Delta Pi Epsilon Journal, 67(1), 1-10.

Xiao, J., Noring, F., & Anderson, J. (1995). College students attitudes towards credit cards.

Journal of Consumer Studies and Home Economics, 19(3), 155174.


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Table 1

Student Characteristics: Demographics

(N= 234)

n percentage

Age18 - 22 186 79.5%

23 - 29 34 14.5%

30 - 39 9 3.8%

40 and up 5 2.1%

Gender

male 104 44.4%

female 130 55.6%

Ethnicity

Caucasian 219 93.6%

African Am. 9 3.8%

Hispanic 4 1.7%

Other 2 0.9%

Marital Status

not married 168 71.8%

married 35 15.0%

divorced 31 13.2%

Number of Children

none 216 92.3%

one 4 1.7%

two 11 4.7%

three 3 1.3%

Hours Worked

0 hours 82 35.0%

1-5 hours 7 3.0%

6-10 hours 25 10.7%

11-20 hours 42 17.9%

21-30 hours 42 17.9%

31- 40 hours 24 10.3%

over 40 hours 12 5.1%

Major

business 154 65.8%

education 11 4.7%

health professions 14 6.0%

engineering 7 3.0%

liberal arts 14 6.0%

undecided 34 14.5%


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Table 2

Student Characteristics: Financial Experience

(N= 234)

n percentage

Number of Credit CardsNone 92 39.3%

One 70 29.9%

Two 45 19.2%

Three 10 4.3%

Four 6 2.6%

Five 5 2.1%

Six 6 2.6%

Vehicle

Yes 226 96.6%

no 8 3.4%

Vehicle Insurance

insured 226 96.6%

uninsured 0 0.0%

no vehicle 8 3.4%

Who Pays for Vehicle Insurance

student 100 42.7%

parent 116 49.6%

spouse 3 1.3%

relative 7 3.0%

no vehicle 8 3.4%

Checking Account

Yes 221 94.4%

No 13 5.6%

Savings Account

Yes 174 74.4%

No 60 25.6%

Housing Situation

home owner 30 12.8%

with parents 49 20.9%

single apartment 43 18.4%

shared apartment 38 16.2%

Greek system house 5 2.1%

dormitory 69 29.5%

Home or Rental Insurance

No 183 78.2%

Yes 51 21.8%


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Table 3

Sample Questions

Survey Section Question

Credit The most important factors that lenders use when deciding to approve a loan area) marital status and number of children.

b) education and occupation.

c) age and gender.

d) bill-paying record and income.

Vehicle Insurance If a car is stolen, what type of insurance coverage pays for its replacement?

a) uninsured motorist

b) liability

c) comprehensive

d) collision

Banking Which deposit account usually pays the most interest?

a) certificate of deposit

b) passbook savings account

c) NOW account

d) money market account

Life Insurance A policy loan is not available on which type of life insurance?

a) term life

b) whole lifec) universal life

d) variable life

Housing Rental If a tenant if forced to break a 12-month lease after 9 months because of a family

emergency, the tenant is legally obligated to pay the landlord

a) no additional rent

b) 1 month additional rent

c) 2 months additional rent

d) 3 months additional rent


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Table 4

Summary Statistics

total creditvehicle

insurancebank

accountslife

insurancehousingrental

mean score 23.5 7.3 3.1 3.2 5.7 4.1

mean percent correct 53.5% 52.5% 51.1% 53.6% 47.9% 69.2%

median score 24 7 3 3 6 5median percentcorrect 54.5% 50.0% 50.0% 50.0% 50.0% 83.3%

standard deviation 0.092 0.108 0.216 0.201 0.157 0.221

maximum score 35 11 6 6 10 6

minimum score 14 3 0 1 1 1possible score 44 14 6 6 12 6


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Table 5

Analysis of Overall Mean Scores

n average score difference t statistic p valueAge -0.066** -3.831 0.000

18-22 186 52.11%

23 and older 48 58.71%

Gender -0.005 0.938 0.350

male 104 53.21%

female 130 53.67%

Working -0.030** -2.505 0.013

not working 82 51.50%

working 152 54.53%

Credit Cards -0.010 -0.763 0.446

none 92 52.89%one or more 142 53.84%

Vehicle Insurance 0.043** -3.493 0.001

other pays 126 51.75%

pay own 100 56.03%

Major -0.016 -1.118 0.266

business 154 52.94%

other 80 54.49%

** significant at 5%


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24

Table 6

Analysis of Mean Scores, Credit Section


18-22 186 50.96%

23 and older 48 58.33%

Gender 0.000 0.000 1.000

male 104 52.47%

female 130 52.47%

Working -0.038** -2.547 0.012


working 152 53.80%

Credit Cards -0.023 -1.581 0.115

none 92 51.09%one or more 142 53.37%

Vehicle Insurance -0.034** -2.327 0.021


pay own 100 54.43%

Major -0.015 -0.940 0.349

business 154 51.94%

other 80 53.48%



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25

Table 7

Analysis of Mean Scores, Vehicle Insurance Section


18-22 186 48.92%

23 and older 48 59.38%

Gender -0.103** -3.802 0.000

male 104 45.35%

female 130 55.64%

Working -0.054 -1.970 0.050


working 152 52.96%

Credit Cards -0.027 -0.951 0.343

none 92 49.46%one or more 142 52.11%



pay own 100 55.17%

Major 0.073** 2.352 0.020

business 154 53.57%

other 80 46.25%



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Table 8

Analysis of Mean Scores, Banking Section

Naveragescore difference t statistic p value

Age 0.033 0.862 0.392

18-22 186 54.30%

23 and older 48 51.04%

Gender 0.119** 4.811 0.000

male 104 60.26%

female 130 48.33%

Working -0.028 -1.042 0.299


working 152 54.61%

Credit Cards -0.048 -1.856 0.065

none 92 50.72%

one or more 142 55.52%

Vehicle Insurance 0.071** 2.595 0.010


pay own 100 49.83%

Major 0.001 0.048 0.962

business 154 53.68%

other 80 53.54%** significant at 5%


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Table 9

Analysis of Mean Scores, Life Insurance Section

naveragescore difference t statistic p value

Age -0.094** -3.474 0.001

18-22 186 45.97%

23 and older 48 55.38%

Gender -0.007 -0.330 0.742

male 104 47.52%

female 130 48.21%

Working -0.021 -0.939 0.349


working 152 48.63%

Credit Cards 0.021 1.018 0.310

none 92 49.18%




pay own 100 54.00%

Major -0.090** -4.390 0.000

business 154 44.81%



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Table 10

Analysis of Mean Scores, Housing Rental Section

naveragescore difference t statistic p value

Age -0.052 -1.508 0.136

18-22 186 68.10%23 andolder 48 73.26%

Gender -0.036 -1.243 0.215

male 104 67.15%

female 130 70.77%

Working -0.010 -0.356 0.722


working 152 69.52%

Credit Cards 0.016 0.525 0.600

none 92 70.11%


Vehicle Insurance -0.028 -0.972 0.332


pay own 100 70.83%

Major 0.028 0.938 0.350

business 154 70.13%



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Table 11

Regression Results: Demographic Independent Variables

Independent Dependent Variable

Variables Overall Score Credit Vehicle Ins. Banking Life ins. Housing

age 23 - 29 0.0073 0.0281 0.0378 -0.1419** 0.0170 0.0580

(0.46) (1.08) (1.13) (-4.33) (0.48) (1.70)

age 30 - 39 0.0776** 0.0193 0.0408 0.1644** 0.0814 0.1563

(3.31) (0.50) (0.76) (2.54) (1.79) (1.79)

age 40 and up -0.0108 0.0157 -0.0527 0.0735 -0.0397 -0.0569

(-0.28) (0.51) (-0.93) (1.01) (-0.84) (-0.33)

female -0.0115 -0.0084 0.0675** -0.0831** 0.0005 -0.0502

(-1.05) (-0.55) (2.16) (-2.76) (0.03) (-1.46)

African American -0.0975** -0.0895** 0.0118 -0.1624** -0.0689 -0.2178**

(-2.92) (-2.82) (0.15) (-2.26) (-1.80) (3.00)

Hispanic -0.1421** -0.0342 -0.1368 -0.0284 -0.4419** 0.0869

(-3.93) (-0.61) (-1.73) (-0.24) (-7.67) (0.87)

other -0.0051 -0.0933 -0.0683 0.1099 0.0452 0.0487(-0.12) (-1.01) (-0.33) (1.41) (0.40) (0.36)

not married 0.1073** 0.0329 0.0792 0.1101** 0.1553** 0.2100**

(5.49) (1.27) (1.72) (2.51) (4.41) (3.79)

divorced 0.1589** 0.0727** 0.2305** 0.1433** 0.1883** 0.2449**

(6.46) (2.16) (4.27) (2.42) (4.08) (3.59)

one child 0.1186** 0.0848** 0.4414** 0.1398 -0.1023** 0.2950**

(5.93) (2.63) (7.49) (1.86) (-2.05) (3.44)

two children 0.0754** 0.0551 0.0236 0.0626 0.1974** -0.0563

(2.35) (1.1) (0.34) (0.91) (3.23) (-0.43)

three children -0.0883 -0.1779** 0.1789 -0.6583** -0.0897 0.4263**

(-1.91) (-3.24) (1.59) (-4.78) (-1.25) (3.00)

education 0.0183 0.0323 -0.0190 -0.0486 0.1282** -0.1299**

(0.60) (0.59) (-0.29) (-0.60) (3.01) (-2.58)

health professions 0.0791** 0.1167** 0.1051** -0.0127 0.0617 0.0918

(4.09) (4.32) (2.68) (-0.30) (1.92) (1.57)

engineering 0.0911** 0.1731** -0.2925** 0.1065 0.1216** 0.2069**

(4.04) (5.56) (-4.39) (1.53) (2.76) (2.79)

liberal arts 0.0746** 0.0344 0.0887 0.0514 0.1426** 0.0415

(3.18) (1.29) (1.13) (0.97) (2.98) (0.63)

undecided 0.0280 -0.0349 0.0365 -0.0209 0.0837** 0.1043**

(1.75) (-1.62) (0.83) (-0.56) (2.78) (2.30)

Note: Robust standard errors were used. t -statistics in parenthesis.

** significant at 0.05 or better


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Table 12

Regression Results: Financial Experience Independent Variables

Independent Dependent Variable

Variables Overall Score Credit Vehicle Ins. Banking Life ins. Housing

one credit card -0.0178 0.0007 0.0367 -0.0299 -0.0291 -0.0806**

(-1.35) (0.04) (1.06) (-0.85) (-1.02) (-2.05)

two cards -0.0509** -0.0034 -0.0612 -0.0699 -0.0349 -0.1649**

(-3.19) (-0.15) (-1.77) (-1.66) (-1.38) (-3.44)

three cards 0.0178 -0.0150 -0.0538 0.2042** -0.0715 0.1581**

(0.76) (-0.37) (-0.91) (4.40) (-1.37) (3.15)

four cards 0.0069 0.0149 -0.1431 0.0953 -0.0118 0.0871

(0.23) (0.31) (-1.15) (1.74) (-0.17) (0.85)

five cards 0.0087 -0.0026 0.1211 0.0961 -0.0750 0.0032

(0.34) (-0.06) (1.41) (0.97) (-1.59) (0.05)

six cards 0.0026 0.1259** -0.1858 0.2998** 0.1287** -0.6463**

(0.07) (2.70) (-1.96) (2.93) (2.01) (-5.99)

has a vehicle 0.0039 0.0288 -0.0616 0.0294 -0.0874 0.1683

(0.13) (0.60) (-0.76) (0.34) (-1.63) (1.93)parent pays vehicleins -0.0196 0.0053 -0.0176 0.0336 -0.0498** -0.0727

(-1.56) (0.29) (-0.55) (0.92) (-2.13) (-1.74)spouse pays vehicleins -0.0075 0.0171 0.2417** -0.0282 -0.0462 -0.2161

(-0.25) (0.38) (3.35) (-0.33) (-0.79) (-2.28)relative pays vehicleins -0.1014 0.0175 -0.2472** 0.0156 -0.1163** -0.3207**

(-1.98) (0.45) (-2.24) (0.24) (-2.35) (-3.81)

has checking account -0.120 -0.0352 0.0663 0.1552** -0.1674** 0.1072**

(-0.65) (-1.19) (0.92) (3.53) (-2.83) (2.35)

has savings account -0.0681** -0.0409** -0.1865** -0.0045 -0.0576** -0.1013**

(-5.84) (-2.55) (-6.40) (-0.05) (-2.61) (-3.54)

has home/rental ins -0.0223 0.0079 -0.1064** -0.0069 -0.0201 -0.0288

(-1.57 (0.36) (-2.31) (-0.19) (-0.62) (-0.84)

live with parents 0.0171 -0.0501 0.0155 0.1369** -0.0432 0.1778**

(0.77) (-1.67) (0.25) (2.59) (-0.97) (2.43)

single apartment (0.0235 -0.0295 0.0097 0.1192** -0.0280 0.1685**

(0.95) (-0.95) (0.15) (2.17) (-0.65) (2.18)

shared apartment 0.0313 -0.0122 0.0239 0.0894 0.0167 0.1162

(1.40) (-0.42) (0.42) (1.67) (0.36) (1.66)

Greek System house 0.0956** 0.0659 0.1359 -0.0428 0.1178 0.2211**

(2.99) (1.00) (1.70) (-0.39) (1.77) (2.39)

dormitory 0.0227 0.0456 0.0320 -0.0221 -0.0205 0.09165

(0.98) (1.26) (0.49) (-0.40) (-0.50) (1.23)

constant 0.4757** 0.5004** 0.4671** 0.2385** 0.6503** 0.3147**

(12.09) (7.96) (3.52) (2.44) (7.82) (2.93)

n 234 234 234 234 234 234

Rsquared 0.6543 0.5123 0.5341 0.5008 0.6028 0.4953

Note: Robust standard errors were used. t -statistics in parenthesis.

** significant at 0.05 or better

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