Research and Principles Underlying The Rule of 10: A Benchmark for Making College Affordable

The literature is clear: complex financial systems without clear guideposts are likely to be ineffective at helping individuals reach their self-identified financial goals. Our system of postsecondary financing is a perfect example of this mix of complexity, ambiguity, and unpredictability. The Rule of 10: A Benchmark for Making College Affordable, outlines an approach to combat this challenge by completely retooling the way in which the nation’s postsecondary education system orients itself, financially. This brief outlines the principles of this Benchmark to the existing research.

The overarching principle of the Affordability Benchmark is that we need an easily understandable metric to gauge the ability of students and their families to pay for postsecondary education—colloquially called “college affordability”. Without such a metric, it is difficult to determine what policy prescriptions will lead us to actually ‘make college more affordable’. For more information on the rationale and development of the Rule of 10 please see: https://www.luminafoundation.org/affordability-a-benchmark-for-making-college-affordable.


Overarching Principle: Easily Understandable and Predictable Metric

  1. Theodos, B., Stacy, C.P., and Simms, N. (2016). An Evaluation of the Impacts of Two “Rules of Thumb” for Credit Card Revolvers. Washington, DC: Urban Institute. Retrieved from http://www.urban.org/sites/default/files/publication/83986/2000846-An-Evaluation-of-the-Impacts-of-Two-Rules-of-Thumb-for-Credit-Card-Revolvers-1.pdf
    • Theodos et al. (2016) conducted a factorial randomized control trial to examine the impact of rules of thumb-based financial education on consumer financial behavior. Targeting individuals who carry debt on their credit card from month to month, the study tested the efficacy of two rules: 1) participants should use cash instead of credit for purchases under $20; and 2) paying with a credit card can add approximately 20 percent to the total cost of the purchase. Researchers found that the ‘cash under 20 rule’ helped reduce participants’ revolving debt by an average of $104, but the ‘20 percent added’ rule had no detectable effect on credit card outcomes. Since the effect was different for the two rules, researchers argue that spending behavior was affected by the rules themselves, as opposed to simply being driven by a reminder effect.
    • Results also reveal that participants under 40 who received the ‘cash under $20 rule’ had higher net savings, lower credit card balances, and fewer purchases than those in the control group. While rules of thumb are not completely accurate in every situation, they can serve as helpful reference points that guide individuals to make timely and clear decisions in otherwise complicated situations.
    • Connections to Benchmark:

    • This study helps justify the Benchmark’s Rule of 10 framework. The study demonstrates that rules of thumb can serve as an effective method for improving financial health and behavior, especially for people under the age of 40.
    • This study also helps demonstrate that the wording/framing of a rules of thumb intervention matters, and it can have an influence on behavior independent from the sole effect of being a reminder.
  2. Drexler, A., Fischer, G., and Schoar, A. (2014). “Keeping it simple: Financial Literacy and rules of thumb.American Economic Association, 6(2), 1-31.
    • This study conducts a randomized control trial with a bank in the Dominican Republic to compare the effects of two financial literacy training programs on microentrepreneurs’ (small business owners) financial practices. The first approach used standard accounting training; the second used a simplified, rule-of-thumb training that teaches basic financial heuristics. Participants receiving rule of thumb training were significantly more likely to show improvements in financial practices. The effect was largest for those with lower skills and poor initial financial practices.
    • Connections to Benchmark:

    • This study supports the Benchmark’s use of easily understandable metrics for affordability and savings. The Rule of 10 serves as a rule of thumb for understanding affordability, which relates to the positive benefits of a rule of thumb approach discussed in the study.
  3. Madrian, B. C. (2012). Matching contributions and savings outcomes: A behavioral economics perspective. NBER Working Papers 18220. Cambridge, MA: National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w18220.pdf
    • Madrian (2012) offers a literature review on the impact of providing a match on savings plan outcomes. The report first outlines the economic theory behind savings schemes, then offers evidence from a range of studies, and finally explores the comparative impact of non-financial approaches to encouraging saving (e.g. automatic enrollment, planning aids, and reminders). In theory, adding a match contribution should increase individuals’ participation in a savings scheme, since having a match makes consuming income more expensive than saving it (substitution effect). However, collective research suggests that increasing a match rate on savings leads to only small increases in participation and contributions. Rather, the more important element is the match threshold, which serves as a focal point for individuals to determine how much they should save.
    • A match threshold may also provide an endorsement effect in that it could be seen as an implicit recommendation for appropriate saving amount. Madrian (2012, p. 14) explains that “providing a match of 25 percent on contributions up to 10 percent of pay will induce individuals to save more than a match of 50 percent up to 5 percent of pay at a similar (or lower) cost to the organization providing the match.”
    • Further, data from one study indicates that contribution rates spike at multiples of 5 (i.e., employees meeting 5%, 10%, and 15% thresholds), which suggests supports the notion that individuals adopt heuristics to simplify complicated decisions (p. 11).
    • Connections to Benchmark:

    • This literature review supports the Benchmark’s emphasis on having an easily understandable and predictable metric of affordability. The article presents findings from a variety of studies to show that having a match threshold helps increase savings participation since it serves as a reference point for people to work towards. This conclusion supports Lumina’s Rule of 10 framework, since having a 10% annual savings target is analogous to the match threshold component of matching-contribution savings schemes.
    • The literature review also supports the Benchmark’s Rule of 10 framework for its use of a simple and consistent number (10) for measuring how much to save per year, how many years to save, and how many hours a student should work a week. College affordability and finance can be complicated, so giving people a simple “rule of thumb” way of thinking helps make the college process seem more manageable.
  4. Dynarski, S.M. & Clayton, J.E. S. (2006a). The cost of complexity in federal student aid: Lessons from optimal tax theory and behavioral economics. Cambridge, MA: Harvard University. Retrieved from http://bit.ly/2goDEHU
    • Dynarksi and Clayon (2006) use 2003-04 NPSAS data to explore the relationship between information captured in the FAFSA and the distribution of federal aid received by full-time, dependent undergraduate students. The study assesses whether the compliance costs of a complex financial aid system are outweighed by the benefit of having more precise measurements of ability to pay. Findings indicate that reducing the aid formula to parents’ income and family structure—a drop from 70 questions on the FAFSA to six—explains 77 percent of the variation in Pell Grants for the study’s sample. When also including family assets and student income, the formula accounts for 90 percent of the variation in Pell Grants. The researchers suggest that accepting complexity for the sake of progressivity is misguided; rather, it is a worthwhile tradeoff to sacrifice precision for simplicity.
    • To see a follow-up study extending the analysis to independent students, refer to the following Dynarski, S.M. & Clayton, J.E.S. (2006b). The feasibility of streamlining aid for college using the tax system. National Tax Association, 99, 250-262. Retrieved from http://bit.ly/2gltMAd
    • Connections to Benchmark:

    • This article justifies the Benchmark’s easily understandable metric component, because it shows that having a simple understanding of ability to pay could cut down on compliance costs (a major affordability issue) and still be progressive and identify the students most in need of aid.
    • This article also justifies the Benchmark’s simple sliding scale of ability to pay. It shows that having a complicated FAFSA isn’t that much more precise than a simple calculation that only accounts for a family’s income and size.
  5. Knoll, M.A.Z. (2010). “The Role of Behavioral Economics and Behavioral Decision Making in Americans’ Retirement Savings Decisions.Social Security Bulletin, 70(4). Retrieved from https://www.ssa.gov/policy/docs/ssb/v70n4/v70n4p1.html
    • Knoll (2010) reviews the behavioral economics and judgement and decision-making literature to identify various factors that affect individuals’ saving behavior. Concepts are sorted into four categories, each with examples: 1) informational issues (ambiguity aversion and anecdotal evidence), 2) heuristics and biases (rules of thumb, status quo bias, and default effects), 3) intertemporal choice (self-control, procrastination, hyperbolic discounting, and emotions), and 4) the decision context (reference dependence, choice bracketing, framing effects, and choice architecture).
    • The report suggests that employers should offer employees short-term saving “points”/goals to help incentivize employees to adequately prepare for the future. Showing how modest amounts accumulate over time emphasizes long-term benefits rather than short-term costs. The report also suggests encouraging employees to mentally subtract the amount of money that would otherwise be automatically deducted for savings. Such a mental exercise helps create a new reference point/status quo for individuals to refer to when evaluating a change as a loss or gain.
    • Connections to Benchmark:

    • This article supports the Benchmark’s easily understandable metric of affordability component. The article finds that employees are incentivized to prepare for the future when given clearly articulated, short-term goals, which is exactly what the Benchmark provides when it says students or families should save 10 percent of their discretionary income per year. Setting short-term targets (years) for saving seems more management than if one were to work towards a seemingly impossible dollar amount needed by the time of the event (e.g. college or retirement).
    • Altering an individual’s reference point alters the way that individual thinks about savings. Specifically, a reference point is thought of as someone’s baseline, so if the baseline is changed, then that person will think of changes to the baseline in either a more positive or more negative light (depending on nature of the change).
    • This article also supports the Benchmark’s time horizon component. The article explains that emphasizing long-term benefits through modest savings over time makes saving more appealing.
  6. Bettinger, E.P., Long, B.T. Oreopoulos, P., & Sanbonmatsu, L. (2012). The role of application assistance and information in college decisions: Results from the H&R Block FAFSA experiment. The Quarterly Journal of Economics 127 (3): 1205–42.
    • Bettinger et al. (2012) collaborated with H&R Block to examine the impact of providing students with information about financial aid eligibility and offering streamlined personal assistance to complete the FAFSA. The study found that low- to moderate-income families receiving aid eligibility information and help completing the FAFSA had more positive outcomes (i.e. increased college financial aid applications, improved timeliness of aid application submission, increased receipt of need-based grant aid, and increased likelihood of college attendance and persistence) than peer families who only received aid eligibility information. The families receiving the combined information and assistance treatment had an 8-percentage point increase in the child remaining continuously enrolled in college for at least two years following high school.
    • Connections to Benchmark:

    • The study’s findings suggest that a streamlined and straightforward process is appealing to low- and middle-income families. Having a clear process and perception of financial aid helps families visualize the possibility of change. The Benchmark is an example of a tool that can be easily understood by families and help them look beyond the sticker price of college.
  7. Liebman, J. & Zeckhauser, R. (2004). “Schmeduling.” Harvard University, unpublished manuscript. Retrieved from https://www.hks.harvard.edu/jeffreyliebman/schmeduling.pdf
    • Liebman and Zeckhauser (2004) analyze the causes and implications of people’s misperceptions of payment schedules—a behavior they term “schmeduling”. The researchers argue that schmeduling is more likely to occur when pricing schedules are complex; the connection between consumption and payoffs is remote; and other features of the economic environment make it difficult to learn from personal experience. Tax and transfer programs—like student loans—are particularly prone to people making inaccurate calculations of marginal prices.
    • Connections to Benchmark:

    • This article offers data that evidences the relationship between complexity and misperceptions. If people are more likely to misunderstand/misperceive payment schedules when such schedules are complex, then one would assume that simpler schedules are easier to understand. The Benchmark’s clear, simple, and predictable definition of affordability improves the accuracy of individuals’ perceptions about the price and cost of college.
  8. Camerer, C., & Weber, M. (1992). Recent Developments in Modeling Preferences: Uncertainty and Ambiguity. Journal of Risk and Uncertainty, 5, pp. 325-370.
    • Camerer and Weber (1992) explore empirical work demonstrating how people weigh decisions based on their beliefs and/or knowledge about the likelihood of events, termed subjective expected utility (SEU). People are more likely to opt-in in situations that they feel that they know the probable outcome.
    • Connections to Benchmark:

    • This article supports the Benchmark’s predictability component. The article details the subjective expected utility theory, which supports the notion that people are risk averse. Since people are risk averse, having a benchmark that lays out constant measures to adhere to over time (i.e. Rule of 10) will enable people to opt-in to postsecondary education.

Component No. 1: Time Horizon: How to Encourage Those Who Have the Capacity to Save to Do So

  1. Milkman, K.L., Rogers, T., & Bazerman, M.H. (2010). I’ll have ice cream soon and the vegetables later: A study of online grocery purchases and order lead time. Market Letters, 21, pp. 17-35. Retrieved from https://scholar.harvard.edu/files/todd_rogers/files/ill_have.pdf
    • Milkman et al. (2010) look at the differences between quality of choices when made for the immediate future versus the more distant future. Using online grocer data, the researchers explore how the delay between an order’s completion and its delivery relates to a given consumer’s 1) overall spending, 2) purchases of should groceries (e.g. healthy foods), and 3) purchases of want groceries (e.g. unhealthy foods). Consistent with intertemporal choice theory, data indicate that between 2 and 5 days in advance of delivery, for each additional day in advance of delivery, consumers spend less; they order a higher percentage of should goods; and they order a lower percentage of want goods (p. 27). Construal level theory (CLT) suggests that people tend to focus more on the abstract when making decisions about the distant future (p. 29).
    • Connections to Benchmark:

    • This study supports the Benchmark’s time horizon component, that advises people to make short-term (annual) payments as a part of a long-term college savings plan. The study finds that people make better choices (like saving more) when they enter a transaction where the benefit is not seen until a later point in time, compared the choices they make when they enter a transaction with a more immediate payoff. While the study focused on people making more healthy choices, the same benefits may apply in other decision contexts, like college savings.
  2. Ariely, D. and Wertenbroch, K. (2002). “Procrastination, Deadlines, and Performance: Self-Control by Precommitment.American Psychological Science, 13(3): 219-24. Retrieved from http://people.duke.edu/~dandan/webfiles/PapersPI/Procrastination%20Deadlines%20and%20Performance.pdf
    • Ariely and Wertenbroch (2002) examine the extent to which attempts to self-impose restrictions on oneself improves performance, with a focus on procrastination. The researchers conducted two studies at MIT. The first study required students to write three short papers where students set their own deadlines, under the conditions that the deadlines had to be announced in advance and were binding. This study found that students in the no-choice section (each paper due at the end of each third of the course) had higher grades than those that set their own deadlines. The second study had the same structure as the first, except it involved students who were being paid to proof-read other students work. Similar to the results of the first study, the second study found that proofreaders who had a self-imposed deadline spotted less errors than proofreaders with assigned, evenly-spaced-deadlines. The researchers find that while self-imposed deadlines can help address procrastination, constraints are more effective when they are externally imposed.
    • Connections to Benchmark:

    • This article supports the Benchmark’s time horizon component. The article supports the notion that an individual will save more if assigned evenly spaced deadlines, compared to the smaller savings effect of imposing deadlines on oneself. People will procrastinate less if they are assigned deadlines in a manageable fashion.
  3. Rogers, T., & Bazerman, M. H. (2008). Future lock-in: Future implementation increases selection of 'should' choices. Organizational Behavior and Human Decision Processes, 106(1), 1–20. Retrieved from https://scholar.harvard.edu/files/todd_rogers/files/future_lock-in._future_implementation_increases_selection.pdf
    • Rogers and Bazerman (2008) conducted several studies to demonstrate that individuals have an increased willingness to select should policies (e.g. increased charitable spending, increased taxes on gas, increased exercise, increased savings, etc.) when such policies will be implemented in the distant future rather than the near future—a strategy termed “future lock-in.” Researchers also find that the future lock-in effect can be induced when altering the temporal emphasis of a choice, by emphasizing a choice as “you choose now” versus “the choice will be implemented later” (p. 2).
    • Connections to Benchmark:

    • This article supports the Benchmark’s time horizon component. This study further substantiates the claims made in the Milkman et al. (2010) study. It’s relevant to the Benchmark in that it suggests that people are more inclined to put away current income and save for college if it is emphasized that they are making a conscious choice now to invest in an event (college) that will be implemented in the distant future.
  4. Elliott, W. and Beverly, S. (2010). The role of savings and wealth in reducing “wilt” between expectations and college attendance. (CSD Working Paper 10-01). St. Louis, MO: Washington University, Center for Social Development. Retrieved from https://csd.wustl.edu/publications/documents/rb10-04.pdf
    • Elliott and Beverly (2010) use individual-level longitudinal data to estimate the relationship between savings and wealth on the percentage of ‘wilt’—the incidence of a young person who expects to graduate from a four-year college but does not end up attending college by the ages of 19 to 22. The study looks at whether participants were saving in 2002 and if they attended college by 2005. The study finds that youth with parents who had money set aside for them and youth with accounts and school savings of their own were more likely to expect to graduate than those without an account (p. 2). Findings suggest that not having a savings account is an important predictor of wilt. Among all subgroups examined, youth with no account experienced the highest level of wilt (55 percent). Multivariate analyses reveal that youth who had a savings account are seven times more likely to attend college than similar youth without an account (p. 2). The study suggests that having at least three years of savings accumulation (assumed by the existence of a savings account and having a designated portion of savings in the account for schools) strongly predicts college attendance for high school students that expect to graduate from college.
    • Connections to Benchmark:

    • This study’s findings support the Benchmark’s time horizon component. The Benchmark encourages earlier savings, and this study’s findings suggest that having a savings account raises the likelihood of college attendance for high school students who expect to graduate. Note that the study is limited in that it doesn’t show how youth savings predict college attendance for high school students who do not expect to graduate from college.

Component No. 2: Sliding Scale of Ability to Pay/Income Exclusion: How to Determine Who Has the Capacity to Save for College?

  1. Stone, M.E. (2006). What is housing affordability? The case for the residual income approach. Housing Policy Debate, 17(1), 151-184. Retrieved from http://www-tandfonline-com.proxy-um.researchport.umd.edu/doi/pdf/10.1080/10511482.2006.9521564?needAccess=true
    • This article seeks to build a compelling case for the residual income concept of housing affordability as an alternative to the use of the ratio approach (the 30 percent rule-of-thumb). It provides an overview of debates about housing affordability indicators and standards, describes the range of diverse and incompatible definitions of housing affordability (i.e., relative, subjective, family budget, ratio, and residual), and illustrates the sound logic and possible implementation of a residual income approach to affordability. The residual income concept considers a household to have an affordability problem “if it cannot meet its non-housing needs at some basic level of adequacy after paying for housing” (p. 163).
    • The approach utilizes a sliding scale of housing affordability with the maximum affordable amount and fraction of income varying with household size, type, and income (p. 164). This approach assumes that non-housing expenses of small households are typically less than those of large households, and “the former can reasonably devote a higher percentage of income to housing than large households with the same income” (p. 163). It also assumes that low-income and higher-income households of the same size and type require the same amount of money to meet their non-housing needs, but higher-income households can afford to spend a higher portion of income to housing than similar low-income households (p. 163-164). A residual income standard would need to account for taxes in order to avoid misidentification of households with affordability problems.
    • Connections to Benchmark:

    • The residual income approach to housing affordability mirrors some of the elements proposed by the Affordability Benchmark. Like the residual income concept, the Benchmark calls for a sliding scale of savings based on discretionary income and family size—an element with logic that has broad validity.
    • The residual income approach bases housing affordability off of a household having enough money left-over to fulfill non-housing needs; similarly, the Benchmark bases college affordability off of the approximate amount of income left-off after the deducting the costs of personal necessities.
      • Benchmark considers any income above 200% poverty line to be an approximate amount of “discretionary income”
      • The article’s other definitions of housing affordability—relative, subjective, family budget, and ratio—should be reviewed if interested in their logical flaws as compared to the residual income approach (see p. 158-163).
  2. Dynan, K.E., Skinner, J., and Zeldes, S.P. (2004). “Do the Rich Save More?” Journal of Political Economy, 112(2): 397-444. Retrieved from https://www.dartmouth.edu/~jskinner/documents/DynanKEDotheRich.pdf
    • Dynan et al. (2004) examine the link between lifetime income and saving rates to determine whether higher-income households save at higher rates than lower-income households. The researchers find that estimated median saving rates range from 1 percent for families in the lowest income quintile to 24 percent for families in the top income quintile (p. 417). Results strongly suggest that higher-income households save more and have a greater marginal propensity to save than lower-income households. When examining explanatory models for the saving differentials, the study suggests that there is some “support for models emphasizing uncertainty with respect to income and health expenses, bequest motives, and asset-based means testing or behavioral factors causing minimal saving rates among low-income households” (p. 398).
    • Connections to Benchmark

    • This article supports the Benchmark’s income exclusion of families with incomes that fall below 200% of the official poverty line. Results suggest that a number of factors impede upon low-income households’ abilities to save, and that higher-income families can save larger proportions of their incomes than lower-income families. Saving 10 percent of discretionary income over 10 years is likely not a feasible benchmark for low-income families to meet.
  3. Meyers, M.K. and Lee, J.M. (2003). Working but Poor: How Are Families Faring? Children and Youth Services Review, 25(3), 177-201.
    • This article uses cross-section data of families with children in New York City to investigate the well-being (i.e. accumulation of assets, living conditions, or receipt of government assistance) of working-poor families (defined as those with poverty income levels and earned income for at least part of the year), compared to non-poor and non-working poor families. The study found that working-poor families only fared somewhat better than poor families in terms of reporting having any financial assets. Financial insecurity has a negative effect on saving among both poor and working-poor households.
    • Connections to Benchmark

    • This article supports the Benchmark’s income exclusion component, since it uses survey data to demonstrate the challenges of asset accumulation for families in poverty.

Component No. 3: Work/Student Contribution

  1. Pike, G.R., Kuh, G.D., & Massa-McKinley, R.C. (2009). “First-Year Students’ Employment, Engagement, and Academic Achievement: Untangling the Relationship between Work and Grades.” NASPA Journal, 45(4), pp. 560-582.
    • Pike et al. (2009) use data from the 2004 National Survey of Student Engagement to explore the relationships between first-year college students’ employment, engagement, and academic achievement. The study finds a statistically significant, negative relationship between students’ grades and students’ working more than 20 hours per week. The study also looks at indirect relationships between work and grades and finds that working 20 hours or less on campus is positively related to grades.
    • Connections to Benchmark:

    • This study relates to the Benchmark’s student contribution component. The Benchmark measure assumes that students can and should reasonably work an average of 10 hours per week while in school, and that they should contribute those earnings toward the cost off education. This study supports the work component in that it sets a ceiling for the number of hours (20) that a student should work without negatively impacting grades.
  2. Tessema, M.T., Ready, K.J., and Astani, M. (2014). “Does Part-Time Job Affect College Students’ Satisfaction and Academic Performance (GPA)? The Case of a Mid-Sized Public University.” International Journal of Business Administration, 5(2). Retrieved from www.sciedupress.com/journal/index.php/ijba/article/viewFile/4388/2517
    • Using a large sample of students (n=5,223) from a mid-sized public university, Tessema et al. (2014) examine the effect of the number of working hours on college students’ satisfaction and GPA. The study first grouped students into ‘working’ and ‘non-working’, and found that those who did not work had higher satisfaction and GPA levels. To assess the difference in number of hours worked, the researchers grouped students into 5 categories: those who worked 0 hours, 1-10 hours, 11-15 hours, 16-20 hours, 21-30 hours, and 31 hours or more. This new grouping led researchers to find that students working 10 or fewer hours positively impacted student satisfaction and GPA. As students worked more hours, satisfaction and GPA declined.
    • Connections to Benchmark:

    • This study shows that working between 1 and 10 hours positively affects GPA and student satisfaction, which supports the Benchmark’s 10-hour student work component.
  3. Nuñez, A. M., & Sansone, V. A. (2016). Earning and learning: Exploring the meaning of work in the experiences of first-generation Latino college students. The Review of Higher Education, 40(1), 91-116. Retrieved from muse.jhu.edu.proxy-um.researchport.umd.edu/article/629910/pdf
    • Nuñez and Sansone (2016) conduct a qualitative study examining how work influences the college experiences of a small group of first-generation Latino students at a highly selective four-year public university. Each student participated in a semi-structured interview addressing students’ familial backgrounds and experiences in relation to college-going; preparation for and choice of college; coursework and major; extracurricular activities; and working status. Using a “pattern matching technique” to interpret interview findings, researchers found that students
      1. come to college with strong familial support and ideology towards work as a vehicle for upward occupational status;
      2. feel they develop new skills and a sense of community on campus through work experiences; and
      3. find work to be intrinsically satisfying. The study demonstrates that working while in college can have both financial and intrinsic benefits that previous literature suggests may increase student GPA and the likelihood of graduation.

      Connections to Benchmark:

    • This study supports the Benchmark’s work requirement, since it suggests that students find working in college increases their economic and social opportunities and can be intrinsically rewarding across several dimensions.