FINSER_31-2_Complete_Issue Assessing the relative need and demand for financial education programs: A case study of graduate students Brent J. Davisa,*, David P. Richardsona, Jason S. Seligmanb aTIAA Institute, 8500 Andrew Carnegie Boulevard, Charlotte, NC 28262, USA bInvestment Company Institute, 1401 H Street NW, Washington, DC 20005, USA Abstract We measure the relative need and demand for financial education programs among graduate stu- dents at a large university system. We find that self-assessed and measured financial literacy is sig- nificantly related to interest and demand for financial education. Individuals who self-report a high level of financial literacy but have low measured financial literacy are significantly less likely to be interested in financial education, while the opposite is true for financially literate individuals self- reporting a low level of financial literacy. Our study adds to research showing the importance of both believed and actual financial literacy measures and has implications for financial education pro- grams. © 2023 Academy of Financial Services. All rights reserved. JEL classifications: D14; G53 Keywords: Financial education; Financial literacy; Overconfidence 1. Introduction Over the last several decades, there has been a substantial increase in the investment, health, and longevity risk burdens borne by individuals. This shift has increased focus on the importance of household financial literacy in managing these risks and enhancing financial well-being. As noted by Lusardi and Mitchell (2014), a lack of financial literacy can have long-term impacts on households’ financial well-being. For example, not properly managing *Corresponding author. Tel.: +1-704-988-6271. E-mail address: Brent.Davis@tiaa.org (B. J. Davis) 1057-0810/23/$ – see front matter © 2023 Academy of Financial Services. All rights reserved. Financial Services Review 31 (2023) 133–150 debt or planning for retirement can have substantial impacts on financial well-being over the lifecycle. While recent research has documented the link between low financial literacy and finan- cial fragility, many adults continue to have poor understanding of basic personal finance concepts.1 However, effectively engaging individuals in improving their financial literacy has proven challenging. In this paper, we conduct a case study on the need and demand for financial education programming by graduate students in a large university system. Leveraging the differences between self-assessed and actual financial literacy levels, we find that students most in need of additional financial education are least likely to take advantage of programming. Graduate students are good candidates to receive financial education programming. They are among the most receptive consumers of education, command commensurately higher lifetime earnings postgraduation, and may be more likely to have complex financial planning needs over the lifecycle. Many also have prior workforce experience, which may expose them to employer-sponsored benefits and retirement savings plans. However, this prior workforce participation may make graduate students more anxious about their financial se- curity due to low earnings and savings and likely increased debt, during graduate studies. Providing financial education programming during this time can help students gain confi- dence in their long-term financial well-being. Our interest in studying financial education programming is whether it is economically rational for individuals to invest in financial education, and relatedly, can financial education programs improve outcomes. Regarding the latter, evidence increasingly suggests that finan- cial education affects outcomes across a variety of contexts.2 With respect to financial edu- cation investment, Lusardi, Michaud, and Mitchell (2017) discuss that investment in financial literacy should be a function of the associated expected benefits. They find that low-education low-lifetime income groups might rationally choose not to invest in obtaining financial literacy skills because of their larger relative reliance on public social welfare pro- grams. Because our target sample is of high education and higher-than-median expected life- time income, they should rationally choose to invest in financial literacy education. We measure self-assessed and tested financial literacy for graduate students in a large public university system. Similar to past studies, we find measurable weakness in the popu- lations’ understanding of basic personal finance concepts, despite most students expressing concern regarding their own personal finances. Regarding financial education engagement, we document two main findings. The first is that interest in financial education relates signif- icantly to an individual’s self-assessed level of financial knowledge relative to their actual measured financial literacy level. Individuals who estimate they have a high level of finan- cial literacy but perform poorly on a financial literacy quiz are significantly less likely to be interested in financial education, while the opposite is true for financially literate students who self-report a low level of financial acumen. We also find that while receptivity to finan- cial education programming increases significantly with financial literacy levels, overall engagement in financial education is low, despite nearly half of survey respondents signaling interest in programming. The students most likely to indicate interest in financial education are the ones with higher financial literacy and lower overconfidence in their financial liter- acy. Our work adds to the small body of existing research, namely Allgood and Walstad 134 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 (2016) and Anderson et al. (2017), that perceived financial literacy and actual financial liter- acy are dually important. Our results indicate that employers using financial education pro- gramming need to adopt innovative engagement strategies to improve the financial literacy and well-being of those employees who need it most. 2. Research design and methodology Our educational protocol was developed using an early career workplace education offering from a large financial services organization.3 We targeted the financial education offering to the entire pool of graduate students with the following research protocol. At the beginning of the academic year, and ahead of inviting the graduate student population to take our initial sur- vey, we developed a series of prompts. These consisted of 5 ! 7 inch postcards announcing the project with lighthearted financial literacy questions on a front side and the correct answer along with a description of what to expect next on the back.4 We developed three distinct cards, with the goal that students might compare the particular card they received to others and discuss them. Regarding signals of validity, integrity, and quality of the effort, the cards prominently featured the collaborating university and were placed in orientation packets for incoming students by the university. The university also placed cards at each department’s reception desk. A research assistant (RA) was assigned to attend graduate student council meetings and other graduate student group meetings of various types across the university sys- tem. In these student meetings, the RA was granted five-minute slots to discuss the project and the potential benefits of participation and left cards at each meeting.5 Two weeks after the cards were distributed initial invitations for the online survey were sent to all graduate students (17,819) in a large public university system. These invitations were designed to resonate with the information on the cards we had just distributed. The sur- vey was designed to record information on students’ individual and educational characteristics, financial aspirations, personal financial concerns, self-assessed financial acumen, and a finan- cial literacy quiz.6 Once the survey was completed, we provided quiz scores to students and offered them the correct answers to missed questions. We then asked whether a respondent was interested in taking part in a financial education seminar or webinar in the near future. Our survey was open for one month between mid-September and mid-October, ahead of midterm examinations. Over this four-week period the student received an initial invitation and up to four reminders targeted to students who had not taken the survey nor opted out of email engagement. Initial and reminder response survey engagement rates are shown in Fig. 1. Our email prompts to engage the survey were successful with approximately 60% of the survey sample engaged the survey following a reminder as seen with the spikes in the response rate, supporting Dechausay et al.’s (2015) result that reminders can improve engagement. From the initial invite population, 2,487 students (14%) engaged the survey. To set up the second stage of our study we invited a matched, random subsample of our surveyed students to a financial education seminar or webinar, whichever they preferred. We randomly selected 1,632 students to match to invited and noninvited groups. Of the 1,632 students eli- gible for invitation, roughly two-thirds (1,101) were invited to participate.7 Our invited B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 135 student group was designed to be a balanced representation of participants across several dimensions: gender, degree of concern regarding financial matters, score on the financial lit- eracy quiz, and whether or not the student was in a quantitative field.8 Invitations were also balanced across those who did or did not initially indicate interest in the program. This allowed students to change their mind if they later decided they wanted to attend. The invita- tion email contained a link for those wishing to sign up and clicking the link brought the stu- dent to a standard web-based submission form. In the invitation to participate, we offered two mid-day and two early evening times for either a seminar or webinar at each campus in the university system. We also offered lunch or dinner to seminar participants; something our pilot run in the previous year had revealed as being important. 3. Results In this section we present results from our survey and financial education engagement protocol. We begin by examining the demographic, educational field, and financial literacy characteristics of students who engaged the survey. Section 3.2 examines how financial liter- acy (both self-assessed and actual) relate to interest in attending a financial education semi- nar. In Section 3.3, we use regression analyses to examine correlates of interest in financial education interest and engagement. 3.1. Survey sample characteristics Table 1 presents characteristics of survey respondents. The average student who partici- pated in the survey is nearly 30 years old and has almost 10 graduate course credits. Women (57%) were more likely to participate compared to men, approximately a quarter are Fig. 1. Survey response rates. 136 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 international students, 57% of the graduate students attend the flagship campus, and nearly one in four graduate students work as research or teaching assistants. Half of the students in the sample are pursuing a Master’s degree, over a quarter a doctorate, and other degree types represent less than 10% of the survey respondents. About two-thirds of survey participants have prior work experience. We categorize a student’s program or major into four mutually exclusive groups: Liberal Arts (e.g., liberal arts, humanities, language, music, and social sciences), STEM (e.g., sci- ence, engineering, medicine, mathematics, and technology), Professional (e.g., public health, public administration, education, nursing, law, and other (pre)professional programs), and Business (e.g., economics, finance, business, and accounting). Enrollment in a professional program represents the plurality of students surveyed (37%) followed by enrollment in STEM (27%), Liberal Arts (20%), and Business (14%) programs. Table 2 displays financial education engagement numbers. Of the nearly 18,000 students we sent a survey, about 14% (2,487) responded. Of the representative 1,101 students to whom we sent invitations for a financial education session, 16% (176) accepted. Among the accepted group, 36% (64) attended a session with 44 opting for an in-person seminar and 20 for an online webinar. Acceptance rates for students who initially indicated interest (25%) were significantly higher than those who did not signal interest (8%). Program attendance rates for those initially indicating interest were also higher (39% vs. 29%) but the difference is not significant. We next tabulate personal financial characteristics and engagement across program type and survey variables. Table 3 Panel A displays measurements of financial literacy, personal financial concerns, and education engagement for all surveyed students by program type. Financial Literacy Quiz Score is the average number of questions students answered cor- rectly on our financial quiz (out of 12).9 Financial IQ is the average of students self-assessed rating of how high their financial knowledge or IQ is, ranging from 1 (very low) to (7 very high). Relative FinIQ is an individual’s relative financial knowledge calculated as the Table 1 Characteristics of targeted graduate student population Student survey sample summary statistics Mean/ proportion Standard deviation Obs Proportion Obs Student characteristics Degree type Age 29.5 7.5 2,487 Masters 0.50 2,487 Credits taken 9.8 5.1 2,487 Certificate 0.02 2,487 Married 0.37 2,306 Law 0.07 2,487 Female 0.57 2,487 Doctorate 0.27 2,487 International student 0.26 2,487 Medical 0.08 2,487 Research/teaching asst 0.24 2,487 Post-doc 0.01 2,487 Flagship campus 0.57 2,487 Other/non-degree 0.05 2,487 Prior work experience 0.68 2,288 Education program type Liberal arts 0.20 2,487 STEM 0.27 2,487 Professional 0.38 2,487 Business and economics 0.14 2,487 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 137 relative difference between performance on the financial literacy quiz and one’s self- assessed financial IQ level and normalized on a scale of 0 to 1. Values below 0.5 represent overconfidence and values above 0.5 represent underconfidence. For example, a value of 0 indicates complete overconfidence in the self-assessment compared to their actual measured financial literacy knowledge and corresponds to a student answering 0 out of 12 financial lit- eracy questions correctly and indicating a very high level (7) of Financial IQ on the self- assessment, whereas a value of 1 indicates complete underconfidence. A value of 0.5 indi- cates neither over- nor underconfidence. Our personal finance concern metric is measured on an intensity scale from 1 (no con- cern) to 5 (great concern) across five areas (career goals, current finances, future finances, owning a home, and retirement), with a possible range from a low of 5 to a high of 25, which we normalized from 0 to 1. For engagement metrics, we list the percentage of students who responded to the survey, indicated that they were interested in financial education, accepting an invitation for an educational session, and attended a seminar or webinar (conditional on accepting an invitation). Table 3 shows the average student got 65% of the financial literacy questions correct, with business students scoring significantly higher than any of the other program groups.10 The mean self-reported financial IQ was 4.7 out of 7, again with business students indicating a significantly higher level of self-reported financial knowledge compared to nonbusiness Table 3 Financial literacy, financial concern, and financial education engagement Characteristic Overall Program type Liberal arts STEM Business Professional Financial literacy and concern Financial literacy quiz score (0–12) 7.81 7.57 7.66 8.71 7.71 Financial IQ, self-assessed (1–7) 4.67 4.58 4.42 5.28 4.73 Relative FinIQ (0–1) 0.52 0.52 0.53 0.51 0.51 Personal finance concern (0–1) 0.72 0.73 0.70 0.71 0.73 Education engagement Responded to survey 14% 14% 14% 14% 14% Interested in financial education 48 47 48 46 50 Accept invite j invited 16 17 15 13 17 Attend financial education conditional on accepting invite 36 51 27 42 33 Note. Means or percents reported. Table 2 Financial education engagement results Engagement Number Number invited Proportion Survey respondents 2,487 17,819 0.14 Eligible for invite 1,101 1,632 0.67 Interested in Fin Ed j eligible 511 1,101 0.46 Accepted j invited 176 1,101 0.16 Attended j accepted invite 64 176 0.36 Conditional on indicating interest Accepted invite 383 511 0.25 Attended j accepting invite 50 128 0.39 138 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 students (5.28 vs. 4.60 for nonbusiness students). The average student was slightly under- confident in their financial knowledge. There are limited differences in the relative measure by program type; however, STEM students display significantly greater underconfidence compared with the rest of the surveyed population. We find no significant differences by program for students who responded to the survey or for those who indicated interested in attending a financial education seminar or for acceptance rates. For seminar attendance, however, we find a significantly greater proportion of Liberal Arts students attended a semi- nar compared to students in other programs. Table 4 shows asset and debt characteristics of the surveyed students. We hypothesize that greater participation in financial services and the incidence of debt would be positively correlated to signaling interest in financial education. And having life insurance or an invest- ment account may signal greater interest in financial planning over the life cycle. We include the incidence of students with a checking account, savings account, investment account, or a life insurance policy. Nine of 10 students surveyed have a checking account, with three in four having a savings account. The third row list the proportion of students with an invest- ment account, which we define as having a brokerage account, an IRA, or an employer- sponsored retirement savings plan. This is owned by a minority of students (38%), but varies significantly by program type, ranging from 22% for STEM students to 50% for business students. We posit debt should be positively correlated with financial education interest. Graduate students with student loan debt need to manage loan repayments in conjunction with other consumption, savings, and investment goals and in context of their postgraduate career out- look. Over half of the sample has some form of student loan debt, and this varies signifi- cantly by a student’s major field. Professional students were significantly more likely to have any debt, both debt from graduate and undergraduate studies and from credit cards. Table 4 Other student characteristics, assets and debt Characteristic Overall Program type Liberal arts STEM Business Professional Banking, assets, insurance Checking account 90% 93% 88% 88% 90% Savings account 74 78 71 72 75 Investment account 38 38 22 50 44 Life insurance 34 29 23 38 42 Student debt Only undergraduate 9 12 9 5 8 Only graduate 19 16 22 26 17 Both undergrad and grad 28 30 22 15 36 Any student debt 56 57 53 46 61 Other debt Credit card 31 34 23 26 37 Auto 20 16 15 23 24 Mortgage 20 20 11 24 25 Home ownership Home owner 22 21 12 27 28 Plan to purchase home 46 39 55 50 43 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 139 Credit card debt may demonstrate greater need for financial education since it relates to household balance sheets and not to human capital acquisition or indicates the reliance on high-cost credit card debt to finance education expenses. Our final category is home ownership. About one in five already own a home and roughly 50% of students are planning to purchase one in the next 10 years, which varies significantly by a student’s major field. We later control for this in our regression analysis because pur- chasing a home involves a substantial amount of financial planning, and we expect those planning to purchase a home to have greater interest in financial education. 3.2. Interest in financial education In this section, we explore the relationship between self-assessed and measured financial literacy and graduate student initial interest in attending a financial education seminar. Nearly half (48%) of the students who participated in the survey indicated they were inter- ested in financial education. In Table 5, we find differences for indicated interest across nearly all individual characteristics. While individuals who do not interact with financial institutions may gain marginally greater benefit from financial education, we find that those without checking and savings accounts, or life insurance are significantly less interested in financial education. However, on the liability side of household balance sheets, students with student loan debt or credit card debt were more likely to be interested in the educational offerings. Table 5 Student characteristics by financial education interest Survey characteristics Interested Not interested Sig Financial literacy and concern Financial literacy quiz score 8.31 7.19 *** Financial IQ (self-assessed) 4.59 4.82 *** Relative FinIQ 0.55 0.48 *** Personal finance concern 0.75 0.69 *** Banking, assets, insurance Checking account 98% 81% *** Savings account 83 65 *** Investment account 42 34 *** Life insurance 37 31 *** Student debt Only undergraduate 11% 7% *** Only graduate 21 18 * Both undergrad and grad 32 24 *** Any student debt 64 48 *** Other debt Credit card 37% 26% *** Auto loan 23 17 *** Mortgage 20 20 Home ownership Own home 21% 23% Plan to own home 55 39 *** Note. Means or percents shown. * and *** indicates differences are significant at the 10% and 1% levels, respectively. 140 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 Graduate students signaling interest in financial education have significantly higher finan- cial literacy, with interested students averaging about 1.2 more correct answers compared to non-interested students. Further, non-interested students are significantly more overconfident in their financial knowledge. This is also shown by the distribution of Relative FinIQ shown in Fig. 2, which displays kernel density estimates of Relative FinIQ by indicated interest in financial education. The two distributions are significantly different (p < .01), with non- interested students having a greater estimated density in the overconfidence range (values below 0.5). This result is economically meaningful as the mean difference of 0.07 in the rel- ative measure between interested and non-interested students equates to 1.68 fewer ques- tions answered correctly (out of 12) on our financial quiz for the relatively more overconfident student given a fixed self-assessed score. Alternatively, a 0.07 difference results in a 0.84 greater self-assessed score, given a fixed quiz score, for the relatively more overconfident student. Overconfident students with low measured financial literacy would arguably benefit the most from financial education. Unfortunately, Table 6 and Fig. 3 indicates that these stu- dents were least interested in improving their financial knowledge. In Table 6, we examine the percentage of students interested in financial education by their self-assessed financial IQ rating and how well they did on the financial literacy quiz, grouping the latter into four categories. The data suggest there is a strong negative correlation between overconfidence and interest in financial education.11 This finding is highlighted by Fig. 3, which displays a wireframe surface plot on the data points shown in Table 6. Financial literacy and self- assessed financial IQ are shown on the x and y axes, and the percentage of students who indicated they are interested in financial education is shown on the z (vertical) axis. Here the relationship is clear: students who have high financial literacy but self-assess a low level of financial IQ are the most interested in financial education (often over 70%). Comparatively, students with low financial literacy but self-assess a high level of financial IQ are less inter- ested (generally less than 30% of the time). Fig. 2. Kernel density estimates of Relative FinIQ by indicated interest in financial education. B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 141 To understand this relationship over all possible values, Fig. 4 displays predicted proba- bilities of financial education interest. The predicted probabilities are generated using a sim- ple logit model estimating the likelihood that students indicated interest in financial education regressed on a student’s FinIQ and Financial Literacy Quiz Score, treating the two exogenous variables as categorical variables.12 The full profile of predicted probabilities is shown in Appendix Table A.1. Fig. 4 replicates and smooths the relationship shown in Fig. 3. Generally, the predicted probability that a student signals interest increases signifi- cantly in one’s measured financial literacy but significantly decreases as a student’s self- assessment increases. Students with a combination of high self-assessed financial IQ and low actual financial literacy are predicted to be the least likely to signal interest in financial education. For example, a student answering all questions correctly but self-assesses the lowest level of financial knowledge is predicted to signal interest with a probability above 60%, which decreases to 45% for a student self-assessing the highest level of financial knowledge. By contrast, a student answering no questions correctly on the financial literacy Table 6 Financial education interest by measured and self-assessed financial literacy Percent interested in financial education Self-assessed Percent of financial quiz questions correct Financial IQ 0–25% 26–50% 51–75% 76–100% Mean N 1 (very low) 50% 80% 50% 0% 55% 20 2 29% 65% 72% 82% 63% 80 3 26% 61% 68% 70% 62% 236 4 20% 49% 61% 58% 56% 646 5 22% 30% 64% 59% 57% 481 6 6% 30% 57% 55% 51% 484 7 (very high) 22% 0% 50% 45% 42% 153 Mean 20% 55% 62% 39% 48% N 221 257 974 1,035 2,100 Fig. 3. Surface plot of financial education interest by measured and self-assessed financial literacy. 142 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 quiz but is most confident in their financial knowledge, is predicted to signal interest with a likelihood of under 6%, increasing to only 11% for a student self-assessing the lowest level of financial knowledge. Program sponsors need to be innovative when thinking about how to engage the population of students who would most benefit from financial education. 3.3. Regression analysis of financial education interest and engagement We begin our regression analysis with first considering who takes the survey. Table 7 uses ordinary Probit regression estimating whether a student engages in the survey or not using student and program characteristics, displaying marginal effects on the coefficients and standard errors in parenthesis. Because those who do not take the survey do not offer us data on their financial literacy, we only leverage the university system’s administrative data. Women and student workers are significantly more likely to engage the survey. While there is no significant relationship for graduate students at the flagship campus, student workers at the flagship campus are significantly less likely to engage the survey, indicated by the inter- action term in Model 2. These students may be more time constrained with their studies and work duties than their counterparts. We find no significant effect for international students. Students who have taken more graduate credits are significantly less likely to take the sur- vey, but the marginal effect is small and has significant attenuation. There were no signifi- cant differences in a student’s major subject area. When examining degree type, law students were significantly less likely and doctorate students only marginally less likely to engage in the survey, highlighting the possibility of time constraints for students in terminal degree programs. Table 8 estimates the likelihood that a student indicates interest in financial education using ordinary Probit specifications showing marginal effects and standard errors in paren- thesis. We display five specifications: Model 1 uses administrative data plus financial liter- acy characteristics, and Models 2 and 3 add asset and debt characteristics. Model 4 adds Fig. 4. Predicted probabilities of education interest by measured and self-assessed financial literacy. B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 143 T ab le 7 R eg re ss io n es ti m at es o f su rv ey en g ag em en t (1 ) (2 ) (3 ) (4 ) M ar . co ef f. S ta n d ar d er ro r M ar . co ef f. S ta n da rd er ro r M ar . co ef f. S ta n d ar d er ro r M ar . co ef f. S ta n d ar d er ro r S tu de n t ch ar ac te ri st ic s W o m an 0 .0 3 0* * * (0 .0 0 5 ) 0 .0 3 0 * * * (0 .0 0 5 ) 0 .0 3 0 * * * (0 .0 0 5) 0 .0 3 0 * * * (0 .0 0 5 ) A g e " 0 .0 0 0 (0 .0 0 0 ) 0 .0 0 0 (0 .0 0 0 ) " 0 .0 0 0 (0 .0 0 0) 0 .0 0 0 (0 .0 0 0 ) In tl st u d en t 0 .0 0 9 (0 .0 0 6 ) 0 .0 0 7 (0 .0 0 6 ) 0 .0 0 8 (0 .0 0 6) 0 .0 0 6 (0 .0 0 6 ) M ai n ca m p u s 0 .0 0 2 (0 .0 0 6 ) 0 .0 0 9 (0 .0 0 6 ) 0 .0 0 7 (0 .0 0 6) 0 .0 0 8 (0 .0 0 6 ) S tu d en t te ac h in g /r es ea rc h as st 0 .0 3 7* * * (0 .0 0 7 ) 0 .0 7 5 * * * (0 .0 1 4 ) 0 .0 7 7 * * * (0 .0 1 4) 0 .0 7 3 * * * (0 .0 1 4 ) E d u c cr ed it s " 0 .0 0 6* * * (0 .0 0 1 ) " 0 .0 0 6 * * * (0 .0 0 1 ) " 0 .0 0 6 * * * (0 .0 0 1) " 0 .0 0 5 * * * (0 .0 0 2 ) ln (e d u c cr ed it s) 0 .0 6 1* * * (0 .0 1 1 ) 0 .0 6 1 * * * (0 .0 1 1 ) 0 .0 6 0 * * * (0 .0 1 2) 0 .0 5 6 * * * (0 .0 1 2 ) M ai n C am p u s * S tu d en t A ss t " 0 .0 5 0 * * * (0 .0 1 6 ) " 0 .0 4 9 * * * (0 .0 1 6) " 0 .0 4 6 * * * (0 .0 1 6 ) P ro g ra m ty p e (b as el in e ¼ L ib er al A rt s) S T E M " 0 .0 0 2 (0 .0 0 9) P ro fe ss io n al 0 .0 0 3 (0 .0 0 8) B u si n es s 0 .0 0 7 (0 .0 1 0) P ro g ra m ty p e (b as el in e ¼ M as te rs ) D o ct o ra te " 0 .0 1 2 * (0 .0 0 7 ) L aw " 0 .0 3 3 * * * (0 .0 0 9 ) O th er /n o n -d eg re e " 0 .0 0 0 (0 .0 1 3 ) C er ti fi ca te " 0 .0 2 7 (0 .0 1 8 ) P o st -d o c " 0 .0 1 7 (0 .0 3 2 ) M ed ic al " 0 .0 1 6 (0 .0 1 1 ) O b se rv at io n s 1 7 ,7 5 0 1 7 ,7 5 0 1 7 ,7 5 0 1 7 ,7 50 N o te . P ro b it sp ec ifi ca ti o n s w it h m ar g in al co ef fi ci en ts re p o rt s an d st an d ar d er ro rs in p ar en th es is . * , * * , * * * re p re se nt s si g n ifi ca n ce at th e 1 0 % , 5 % , an d 1 % le v el s, re sp ec ti v el y . 144 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 T ab le 8 R eg re ss io n es ti m at es o f se m in ar in te re st (1 ) (2 ) (3 ) (4 ) (5 ) S tu de n t an d p ro gr am ch ar ac te ri st ic s W o m an 0 .0 2 3 (0 .0 2 2) 0 .0 2 1 (0 .0 2 3 ) 0 .0 2 1 (0 .0 2 3) 0 .0 1 9 (0 .0 2 2 ) 0 .0 0 1 (0 .0 2 2) A g e " 0 .0 0 4 (0 .0 0 2) * * * " 0 .0 0 4 (0 .0 0 2 )* * " 0 .0 0 3 (0 .0 0 2) * * " 0 .0 0 1 (0 .0 0 2 ) " 0 .0 0 1 (0 .0 0 2) In t’ l st u d en t 0 .0 3 5 (0 .0 2 7) 0 .0 2 9 (0 .0 2 8 ) 0 .0 6 5 (0 .0 3 1) * * 0 .0 5 9 (0 .0 3 1 )* 0 .0 4 2 (0 .0 3 1) M ai n ca m p u s 0 .0 0 8 (0 .0 2 4) 0 .0 0 3 (0 .0 2 4 ) 0 .0 0 9 (0 .0 2 4) 0 .0 0 5 (0 .0 2 4 ) 0 .0 0 4 (0 .0 2 5) R es ea rc h /t ea ch in g as st " 0 .0 0 1 (0 .0 2 6) " 0 .0 0 8 (0 .0 2 6 ) " 0 .0 1 0 (0 .0 2 7) " 0 .0 1 5 (0 .0 2 7 ) " 0 .0 1 7 (0 .0 2 7) C re d it s " 0 .0 0 5 (0 .0 0 2) * * " 0 .0 0 6 (0 .0 0 3 )* * " 0 .0 0 6 (0 .0 0 3) * * " 0 .0 0 6 (0 .0 0 3 )* * " 0 .0 0 6 (0 .0 0 3) * * S T E M 0 .0 4 4 (0 .0 3 3) 0 .0 4 3 (0 .0 3 3 ) 0 .0 4 7 (0 .0 3 3) 0 .0 3 8 (0 .0 3 3 ) 0 .0 3 5 (0 .0 3 4) B u si n es s 0 .0 1 4 (0 .0 3 8) 0 .0 2 1 (0 .0 3 9 ) 0 .0 2 3 (0 .0 3 9) 0 .0 1 8 (0 .0 3 9 ) 0 .0 3 6 (0 .0 3 9) P ro fe ss io n al 0 .0 5 3 (0 .0 2 9) * 0 .0 5 6 (0 .0 2 9 )* 0 .0 5 5 (0 .0 2 9) * 0 .0 5 1 (0 .0 2 9 )* 0 .0 5 4 (0 .0 2 9) * F in an ci al li te ra cy ch ar ac te ri st ic s F in an ci al li te ra cy 0 .0 4 3 (0 .0 0 4) * * * 0 .0 4 3 (0 .0 0 4 )* * * 0 .0 4 4 (0 .0 0 4) * * * 0 .0 4 4 (0 .0 0 4 )* * * S el f- as se ss ed fi n an ci al IQ " 0 .0 5 2 (0 .0 0 8) * * * " 0 .0 5 1 (0 .0 0 9 )* * * " 0 .0 5 0 (0 .0 0 9) * * * " 0 .0 5 0 (0 .0 0 9 )* * * R el at iv e F in IQ 0 .8 5 3 (0 .0 7 2) * * * P er so n al fi na n ce co n ce rn 0 .4 5 8 (0 .0 5 5) * * * 0 .4 6 1 (0 .0 5 5 )* * * 0 .4 5 2 (0 .0 5 5) * * * 0 .4 3 1 (0 .0 5 6 )* * * 0 .4 2 6 (0 .0 5 6) * * * B an k in g, as se ts , in su ra n ce In v es tm en t o r re ti re m en t " 0 .0 1 9 (0 .0 2 6 ) " 0 .0 1 5 (0 .0 2 6) " 0 .0 0 5 (0 .0 2 7 ) 0 .0 1 0 (0 .0 2 6) S av in g s ac co u n t 0 .0 2 5 (0 .0 2 9 ) 0 .0 2 8 (0 .0 2 9) 0 .0 2 8 (0 .0 2 8 ) 0 .0 3 6 (0 .0 2 8) L if e in su ra n ce " 0 .0 2 7 (0 .0 2 5 ) " 0 .0 3 1 (0 .0 2 5) " 0 .0 1 9 (0 .0 2 5 ) " 0 .0 1 8 (0 .0 2 5) D eb t S tu d en t d eb t (u n d er g ra d o n ly ) 0 .1 0 8 (0 .0 3 8) * * * 0 .1 0 1 (0 .0 3 8 )* * * 0 .0 9 9 (0 .0 3 8) * * * S tu d en t d eb t (g ra d o n ly ) 0 .0 2 7 (0 .0 3 0) 0 .0 1 9 (0 .0 3 0 ) 0 .0 1 6 (0 .0 3 0) B o th u n d er g ra d an d g ra d d eb t 0 .0 6 0 (0 .0 2 9) * * 0 .0 5 1 (0 .0 2 9 )* 0 .0 4 3 (0 .0 2 9) C re d it C ar d " 0 .0 5 1 (0 .0 6 3) " 0 .0 5 9 (0 .0 6 3 ) " 0 .0 5 0 (0 .0 6 3) A u to 0 .0 3 2 (0 .0 2 7) 0 .0 4 2 (0 .0 2 7 ) 0 .0 4 1 (0 .0 2 7) H o m e o w n er sh ip O w n h o m e " 0 .0 6 8 (0 .0 3 7 )* " 0 .0 5 7 (0 .0 3 7) P la n to p u rc h as e h o m e 0 .0 5 3 (0 .0 2 7 )* * 0 .0 6 1 (0 .0 2 7) * * O b se rv at io n s 2 ,0 9 8 2 ,0 9 8 2 ,0 9 8 2 ,0 9 8 2 ,0 9 8 N o te . P ro b it w it h m ar g in al ef fe ct s sh o w n . S ta n d ar d er ro rs sh o w n in p ar en th es is . * , * * , * * * in d ic at es si g n ifi ca n ce at th e 1 0 % , 5 % , o r 1 % le v el . B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 145 home ownership characteristics, and Model 5 uses the relative FinIQ measure on the full specification instead of quiz score and self-assessed financial IQ. Beginning with Model 1, younger students are significantly more likely to signal interest, but this is not robust in later specifications. Professional students are significantly more likely to signal interest; however, this is only significant at the 10% level when controlling for debt characteristics. We find no significant correlation for business students.13 In all specifications, financial literacy quiz scores and self-assessed Financial IQ have large significant effects on the likelihood to signal interest. These effects pull in the opposite directions, as discussed in Section 3.2., with predicted interest increasing in quiz score but decreasing in the self-assessed measure. An additional question answered correctly on the quiz score increases the estimated likelihood to indicate interest by 4%, while a one unit increase in the self-assessed measure decreases the likelihood by 5%. The personal finance degree of concern composite measure is significantly and positively related to indicating in- terest, following our hypothesis. In Model 5, we use Relative FinIQ as a regressor, instead of quiz score and the self-assess measure; and find estimated interest significantly increases (decreases) as under- confidence (overconfidence) increases. Adding individual asset and banking in Model 2 has no significant impact. Model 3 includes student loan debt as a categorical variable (with no student loan debt as the base- line). Undergraduate student loan debt has a significant impact on the likelihood to be inter- ested in financial education, compared to those without student loan debt. Although we do not find a (robust) significant effect for either graduate debt only or both debt from under- graduate and graduate school, the coefficient is positive—in the hypothesized direction. Debt management is likely to become a larger concern for graduate students in the accumu- lation phase of their lifecycle, especially since this group delays employment income, sav- ings, and loan repayment before (re)entering the labor force, albeit at an expected relatively higher salary. Credit card debt was not a significant correlate. This may be of concern because some students may be using high-cost debt to finance part of their education and this group would benefit from financial education. When including home ownership charac- teristics in Model 4 we find those planning to purchase a home in the next ten years are sig- nificantly more likely to signal interest, following our hypothesis. 4. Discussion This paper examined financial education interest among graduate students in a large pub- lic university system. We find a strong positive and significant correlation between under- confidence (overconfidence) in self-measured financial knowledge and (lack of) interest in financial education programming. This finding suggests the need for innovative engagement strategies to identify and provide programming to individuals who would benefit the most from improving their financial literacy. The results speak to several components regarding the timing and delivery of financial education. The first is whether the timing is optimal for graduate students to engage in improving their financial literacy. Because many graduate students are close to (re)entering 146 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 the workforce, they may be focused on the near-term issues of graduating, finding a job, or moving. This can make them subject to present bias through the belief that they have scant time to devote additional resources to improving their long-term financial well-being. Innovative engagement strategies, such a providing lunch or tchotchkes, may nudge active participation in financial education. However, we find these nudges did not address to our main finding that those confident in their financial knowledge but have low financial literacy are significantly less interested in financial education. How should these individuals be engaged in financial education? Mandatory financial edu- cation could be one response, and many states have begun to institute mandatory financial lit- eracy programs in high school. Stoddard and Urban (2020), Urban, Schmeiser, Collins, and Brown (2018), and Collins (2013) find some benefits to mandatory education. However, there needs to be further research in this area as there remain many open questions, including whether the education benefits persist in later life, how such programs are implemented, what is included in the content, and when in the lifecycle are they delivered, among others. With most programs continuing to rely on voluntary education efforts, often offered by employer benefit programs, designers and implementers of financial education will need to consider how to attract individuals overconfident in their knowledge of personal finances. Notes 1 For research on financial literacy see Yakoboski, Lusardi, and Hasler (2019), Clark, Lusardi, and Mitchell (2017), Lusardi and Mitchell (2014), Lusardi, Mitchell, and Curto (2014), Lusardi and Mitchell (2011), among others. For a broader discussion on financial literacy, financial education, and economic outcomes see Hastings, Madrian, and Skimmyhorn (2013). 2 Bernheim and Garrett (2003), Lusardi (2004), Maki (2004), and Bayer, Bernheim, and Scholz (2009) have studied employer-sponsored financial literacy programs and retirement preparedness. Other studies have found other positive benefits to financial education programs (i.e., Clark et al., 2006; Skimmyhorn et al., 2016; and Seligman and Bose 2012). 3 In the previous year, we piloted a similar survey and education to a small group of students. While the test group gave good feedback and generally positive reviews, we adjusted both our materials and engagement strategy to improve participation. 4 This postcard campaign is consistent with findings on the positive value of prompts from Dechausay, Anzelone, and Reardon (2015). 5 The RA had previously served as President of the Graduate Student Council and thus was a familiar and respected source of information across the body of groups we engaged. 6 Lusardi, Mitchell, and Curto (2014), Schmeiser and Seligman (2013), and Knoll and Houts (2012) have published work evaluating questions on measuring financial liter- acy using three independent methodologies. We take our financial literacy questions from this work, and consultation with financial counselors, and use a set of 12 ques- tions from these studies. B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 147 7 A control group that was roughly one-third of eligible participants was not invited so as to be able to carry forward with other research questions. 8 We defined a student as being in a quantitative field if the student’s program is in economics, business, engineering, statistics, mathematics, physics, chemistry, or computer science. 9 The financial literacy quiz covered questions on interest, inflation, and bond prices. 10 We control for degree type in our regression analysis. 11 Our relative measure cannot examine whether there are differences in student inter- est across the entire cross product of the financial literacy score and self-assessed fi- nancial IQ measure. 12 The overall model is significant (p < .01 with a x2 test). Moreover, the predicted probabilities for each combination of FinIQ and Quiz score (91 combinations) are all significant at the 5% level. 13 In separate regressions (not shown) we controlled for degree type and prior work ex- perience; there were no significant differences. Appendix Table A1 Predicted probabilities of indicating interest in financial education by financial literacy and self- assessed financial IQ Financial literacy quiz score Self-assessed financial IQ Very low 1 2 3 4 5 6 Very high 7 0 (0%) 11.1% 14.3% 13.7% 10.3% 10.5% 7.9% 5.9% 1 30.6 37.1 35.9 28.9 29.3 23.3 18.2 2 33.6 40.3 39.1 31.8 32.2 25.8 20.3 3 35.8 42.6 41.4 33.9 34.4 27.7 21.9 4 52.3 59.4 58.1 50.2 50.7 42.9 35.6 5 61.1 67.7 66.6 59.2 59.7 51.9 44.3 6 (50%) 54.1 61.2 60.0 52.1 52.6 44.8 37.3 7 65.9 72.1 71.1 64.1 64.5 57.1 49.4 8 63.0 69.4 68.3 61.1 61.5 53.9 46.2 9 64.5 70.8 69.8 62.7 63.1 55.6 47.9 10 62.0 68.6 67.5 60.1 60.6 52.9 45.2 11 62.2 68.7 67.6 60.2 60.7 53.0 45.4 12 (100%) 62.3 68.8 67.7 60.3 60.8 53.2 45.5 Note. Predicted probabilities from Logit model as described in Section 3.2, all are significant at the 5% level. 148 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 Acknowledgments We thank Annamaria Lusardi and Carrie Houts for their comments on our financial literacy survey. We thank James Winbush, Dan Rives, Keatrick Johnson, and others at Indiana University for their engagement and contributions. We have benefited from the capable research assistance of Benjamin Bissette, Katherine Jones, and LaNita Rahjina. We acknowledge the financial support of Indiana University in this work. Any errors are our own. The views are the authors’ and do not necessarily represent the views of the TIAA, the TIAA Institute, or the Investment Company Institute. References Allgood, S., & Walstad, W. (2016). The effects of perceive and actual financial literacy on financial behaviors. Economic Inquiry, 54, 675–697. Available at: https://doi.org/10.1111/ecin.12255 Anderson, A., Baker, F., & Robinson, D. (2017). Precautionary savings, retirement planning and misperceptions of financial literacy. Journal of Financial Economics, 126, 383–398. Available at: https://doi.org/10.1016/j. jfineco.2017.07.008 Bayer, P., Bernheim, D., & Scholz, J. (2009). The effects of financial education in the workplace: Evidence from a sur- vey of employers. Economic Inquiry, 47, 605–624. Available at: https://doi.org/10.1111/j.1465-7295.2008.00156.x Bernheim, D., & Garrett, D. M. (2003). The effects of financial education in the workplace: Evidence from a survey of households. Journal of Public Economics, 87, 1487–1519. Available at: https://doi.org/10.1016/S0047-2727 (01)00184-0 Clark, R., Lusardi, A., & Mitchell, O. (2017). Employee financial literacy and retirement plan behavior: A case study. Economic Inquiry, 55, 248–259. Available at: https://doi.org/10.1111/ecin.12389 Clark, R. L., d’Ambrosio, M. B., McDermed, A. A., & Sawant, K. (2006). Retirement plans and saving decisions: The role of information and education. Journal of Pension Economics and Finance, 5, 45–67. Available at: https://doi.org/10.1017/S1474747205002271 Collins, M. (2013). The impacts of mandatory financial education: Evidence from a randomized field study. Journal of Economic Behavior & Organization, 95, 146–158. Available at: https://doi.org/10.1016/j.jebo.2012.08.011 Dechausay, N., Anzelone, C., & Reardon, L. (2015). The power of prompts: Using behavioral insights to encour- age people to participate. OPRE Report 2015-75. Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. Hastings, J., Madrian, B., & Skimmyhorn, W. (2013). Financial literacy, financial education, and economic outcomes. Annual Review of Economics, 5, 347–373. Available at: https://doi.org/10.1146/annurev-economics-082312-125807 Knoll, M., & Houts, C. (2012). The financial knowledge scale: An application of item response theory to the assessment of financial literacy. Journal of Consumer Affairs, 46, 381–410. Available at: https://doi.org/ 10.1111/j.1745-6606.2012.01241.x Lusardi, A. (2004). Saving and the effectiveness of financial education. In: O. Mitchell & S. Utkus (Eds.), Pension design and structure: New lessons from behavioral finance (pp. 157–184). Oxford: Oxford University Press. Lusardi, A., & Mitchell, O. (2011). The outlook for financial literacy. In: O. Mitchell & A. Lusardi (Eds.), Financial literacy: Implications for retirement security and the financial marketplace (pp. 1–16). Oxford: Oxford University Press. Lusardi, A., & Mitchell, O. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52, 5–44. Available at: https://doi.org/10.1257/jel.52.1.5 Lusardi, A., Mitchell, O., & Curto, V. (2014). Financial literacy and financial sophistication in the older population. Journal of Pension Economics & Finance, 13, 347–366. Available at: https://doi.org/10.1017/S1474747214000031 Lusardi, A., Michaud, P.-C., & Mitchell, O. (2017). Optimal financial knowledge and wealth inequality. The Journal of Political Economy, 125, 431–477. Available at: https://doi.org/10.1086/690950 Maki, D. (2004). Financial education and private pensions. In W. Gale, J. Shoven, & M. Warshawsky (Eds.), Private pensions and public policies (pp. 126–139). Washington, DC: Brookings Institution Press. Schmeiser, M., & Seligman, J. (2013). Using the right yardstick: Assessing financial measures by way of finan- cial well-being. Journal of Consumer Affairs, 47, 243–262. Available at: https://doi.org/10.1111/joca.12010 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150 149 Seligman, J., & Bose, R. (2012). Learning by doing: Active employer sponsored retirement savings plan partici- pation and household wealth accumulation. The Quarterly Review of Economics and Finance, 52, 162–172. Available at: https://doi.org/10.1016/j.qref.2012.02.002 Skimmyhorn, W., Davies, E., Mun, D., & Mitchell, B. (2016). Assessing financial education methods: Principles vs. rules-of-thumb approach. The Journal of Economic Education, 47, 193–210. Available at: https://doi.org/ 10.1080/00220485.2016.1179145 Stoddard, C., & Urban, C. (2020). The effects of state mandated financial education on college financing behav- iors. Journal of Money, Credit and Banking, 52, 747–776. Available at: https://doi.org/10.1111/jmcb.12624 Urban, C., Schmeiser, M., Collins, J., & Brown, A. (2018). The effects of high school personal financial educa- tion policies on financial behaviors. Economics of Education Review, 78, 101786. Yakoboski, P., Lusardi, A., & Hasler, A. (2019). Financial literacy in the United States and its link to financial wellness. The 2019 TIAA institute-GFLEC personal finance index. Charlotte, NC: TIAA Institute. 150 B. J. Davis et al. / Financial Services Review 31 (2023) 133–150