Do College Applicants Respond to Changes in Sticker Prices Even When They Don't Matter?

Students from low- and moderate-income families are substantially underrepresented at leading public universities (Hoxby and Avery 2013). For instance, only 27 percent of students at public flagship institutions come from the bottom 60 percent of the parental income distribution.¹ A student at a public flagship is nine times more likely to come from the top 20 percent of the parental income distribution than the bottom 20 percent. Notably, the underrepresentation of those children at leading public institutions has been increasing over time. Three fourths of public flagships enroll a lower percentage of these students today than they did in the late 1990s.² These patterns are troubling given that these institutions have the potential to be engines of upward mobility (Chetty et al. 2020).

A lack of understanding of college pricing and the financial aid system may contribute to this pattern. Prior work, which has focused almost exclusively on low-income, high-achieving students, documents that it is at the college application stage—rather than at the point of admissions or matriculation—where the behavior of low-income students differs most from their higher income peers (Hoxby and Avery 2013; Hoxby and Turner 2015). Perceived costs are one of the most important factors influencing where students apply. Sixty-seven percent of families report factoring in the price of college when finalizing college application lists, and a majority of high school seniors report ruling out colleges based on sticker prices alone, without considering their likely financial aid awards (The College Board and Arts & Sciences Group 2012; Sallie Mae 2016). In another survey, 44 percent of students destined for public colleges and universities reported rejecting colleges at the application stage based on published sticker prices alone (Longmire and Company 2013).

“Sticker shock” occurs when students are discouraged from applying to schools based on the sticker price, ignoring the potential availability of financial aid. Although sticker prices for in-state students at leading public institutions are considerably lower than those at highly selective private colleges, they are still generally higher than alternative public options, like community colleges or regional, less-selective public four-year colleges. Inadequate knowledge of financial aid and the true cost of attending a top public institution may contribute to the observed enrollment patterns of low- and moderate-income students.

The 2008–09 financial crisis provides us with an opportunity to examine the existence and extent of sticker shock. Many states dramatically increased tuition and fees at public postsecondary institutions at that time due to dramatic declines in state appropriations (Long 2015). Because these tuition increases were heavily covered by the news media, it is likely that they were well known by students of college-going age or their parents, and some students may not have applied to these public institutions as a result.³

At some public institutions, though, financial aid policies were in place that would have protected aid-eligible students from experiencing increases in costs of attendance. A handful of leading public universities (including the University of Michigan, the University of North Carolina at Chapel Hill, the University of Virginia, and several institutions in the University of California system) have a policy of “meeting full demonstrated financial need,” at least for state residents. That is, they provide enough financial aid through grants, work study, and loans to fill the gap between the sticker price and the Expected Family Contribution (EFC), which is the amount a student and her family can ”afford" to pay as determined by the financial aid system. For aid-eligible students at an institution that meets full financial need, an increase in the sticker cost of attendance (COA) would not increase their EFCs, which are determined only by their finances.⁴ Moreover, because loan and work-study expectations in financial aid packages did not increase following the price increases, net prices are unchanged for aid-eligible students at public institutions that meet full need.

This institutional framework sets up a quasi-experiment that we use to examine the impact of the sticker price on college-going behavior. In this paper, we focus on students' decisions to apply to leading public universities, taking advantage of changes in tuition sticker prices and the fact that low- and moderate-income students in states with public institutions that meet full need were not subject to those price increases. We highlight the impact on applications to public flagships, but we extend our analysis to a broader group of public institutions that includes others designated R1 (very high research activity), and “elite” subsets of these two categories that typically admit students with higher test scores. We instrument sticker prices using the “state budget shocks” approach used by Deming and Walters (2017) to further focus our analysis on the impact of recession-related budget cuts. We also control for the local labor market conditions, which could directly impact students’ resources and college-going.

When tuition prices increased after the financial crisis hit, colleges became more expensive for financial aid recipients at public institutions that do not meet full need, but not at those that do meet full need. If we observe a reduction in applications among students likely to receive financial aid in public institutions that meet full need in the years immediately following the financial crisis, this would support the notion that sticker shock exists.

Our results indicate that sticker shock is indeed an issue that affects application decisions of potential college students who would be eligible for financial aid. Overall, students are sensitive to changes in sticker prices in the application stage of the process. A 10 percent increase in sticker prices at leading public institutions generates a 1 to 2 percentage point reduction in applications, as proxied by sending SAT scores to that institution.⁵ Importantly, we find little difference in that impact between students likely to be eligible for financial aid at schools that meet full need compared to those that do not. Because aid-eligible students should not respond to changes in sticker prices at meet-full-need schools, we interpret these results as evidence of sticker shock. These findings are consistent regardless of the definitions we use to define “leading public institutions.”

We also investigate a number of possible threats to internal validity. First, our results are robust to focusing just on those states where the SAT is the dominant exam relative to the ACT. This is important because we proxy applications by students sending SAT scores to an institution. Second, we obtain similar results when we omit California from our sample, which is important given its size and the dramatic protests that occurred there following large price increases at its public institutions. Third, we attempt to rule out the possibility that students are reacting to potentially higher loan amounts that could have accompanied higher sticker prices. To that end, we limit states with meet-full-need policies to Virginia and North Carolina, where the flagship institutions do not include loans in financial aid packages for low-income students during the time period of our study. We obtain similar but less-precise results. Finally, we find that minor modifications to the way we measure aid eligibility does not alter our results.

Another result of our analysis is that these price increases at public flagship institutions do not affect overall enrollment, but they do affect who enrolls in them. We observe a decline in the average SAT score of enrolled students and a commensurate increase in enrollments among high achievers at private not-for-profit colleges. Because applications greatly exceed enrollment slots, all seats can still be filled even if applications fall. It is possible that these institutions maintain their enrollment by lowering their admissions standards, enrolling fewer high-achieving students. Alternatively, yields of high-achieving students could be declining relative to lower-achieving students in response to sticker price increases.

Some prior work has investigated whether making net prices more salient affects college-going. The net price represents the sum of all resources that a family needs to pay, including cash, loans, and work-study funds, to cover the cost of attendance. It is equivalently calculated as the COA minus the direct grant aid offered to the student from any source (federal, state, institutional, or other).

The evidence on whether students respond to net prices is mixed, depending on whether the information provided is aggregate in nature or individualized. Providing students with the average price of attendance after factoring in financial aid does not have a large impact on college-going intentions (Bleemer and Zafar 2018). Similarly, making average net price information more easily available through the College Scorecard has had no discernible effect on college application as proxied by SAT score sending or online college search behavior (Huntington-Klein 2016; Hurwitz and Smith 2018).

Levine (2014) argues that what students want are estimates that are specific to them, not on average. This is consistent with Oreopoulos and Dunn (2013), who find that providing students with estimates of their own cost of college through the use of a net price calculator can help change those perceptions. It is also consistent with the findings of Bettinger et al. (2012), which show that providing individualized cost information along with assistance completing financial aid forms had a large impact on college attendance. Similarly, Hoxby and Turner (2013) find that providing semi-customized information on college net costs along with other information regarding academic “fit” causes high-achieving, low-income students to apply and be admitted to more colleges.⁶ Dynarski et al. (2021) find that a marketing campaign touting free tuition for those with incomes under $60,000, which was not a change from previous pricing policy, had a large impact on applications and enrollments. Although unrelated to costs, Mulhern (2021) finds that providing individualized information regarding institutions where students are likely to be accepted changed their application lists.

Other studies that examine the impact of tuition changes at public institutions on enrollment include Hemelt and Marcotte (2011, 2016) and Deming and Walters (2017). Hemelt and Marcotte (2016) use data on individual students in twelfth grade in 1992 and 2004; the other analyses rely on institution-level Integrated Postsecondary Data System (IPEDS) data. Deming and Walters (2017) directly tackle the potential endogeneity problem in determining levels of tuition and estimate their models using state budget shares as an instrument, which we describe in more detail below. They use data from 1990 through 2013 in their analysis. Neither of these papers, though, focuses on sticker shock.

Because we have student-level data with a rich set of covariates, we can distinguish students by their likely eligibility for financial aid and institutions by meet-full-need status, which allows us to study sticker price shock, something these prior papers were unable to do. We are also able to estimate application and enrollment elasticities for high-achieving versus low-achieving students, which generates important insights, as described in section 5.

Finally, we focus on a segment of the market— leading public institutions—where application rather than aggregate enrollment is the outcome of interest. At these institutions, applications routinely exceed the number of slots available for freshman students. Mechanically, we would expect no effect of a tuition increase on aggregate enrollment because schools can adjust admissions standards to maintain a desired enrollment level. However, tuition increases can cause important changes in the composition of enrolled students, fueling continued underrepresentation of students from low- and middle-income backgrounds.

The interaction between a meet-full-need financial aid policy and price increases is critical to our empirical strategy; we describe that here. A critical component of a financial aid system is a determination of what the family can afford to pay. Ignoring the obvious difficulty in setting that amount, this is the purpose of completing the FAFSA. The EFC is the calculated ability to pay, which is constructed based on a family's financial attributes as reported on the FAFSA.

At meet-full-need institutions, students with an EFC below COA receive enough financial aid (through grants, loans, or work-study) so that the net price they pay combined with their financial aid equals the COA.⁷ This is not true at institutions that do not meet full need. Students who are eligible for financial aid at those institutions are required to pay more than their EFC.⁸

When COA rises, families with sufficient financial resources to afford the COA at the original and new levels pay full price either way. They face the entire price increase. The impact of the price increase on financial aid recipients depends on the school's meet-full-need status. At a school that meets full need, financial aid adjusts to fill in the new, larger gap between COA and the student's EFC. Assuming loan and work-study expectations do not change, an increase in the COA has no bearing on their net price. If the school does not meet full need, then the student will be expected to pay more as tuition increases.

In theory, colleges could continue to meet full need and increase their revenue by increasing their loan and work-study expectations in their financial aid packages. In practice, most colleges and universities set their loan expectations at the maximum allowed by federal Stafford Loans (currently $3,500 subsidized or $5,500 total a year including an additional $2,000 of unsubsidized loans for freshman students). Loan burdens increased at many institutions in 2007–08, in response to a federal increase in the maximum Stafford Loan. However, year fixed effects in our regression specifications capture this change. Beyond that, we are unable to identify any school-specific changes in loan expectations.⁹ We have no definitive information regarding changes in work-study policies during this period, but our informal conversations with financial aid directors indicate that no systematic changes like that took place at that time.

One other complication in our discussion of the effect of COA increases on net prices concerns the group of students who are just on the cusp of financial aid eligibility before the COA increase at an institution that meets full need. That COA increase will qualify those students for aid, protecting them from some portion of the higher cost. In practice, the part of the income distribution where students are on that financial aid margin is thin enough that the number of students affected in this way is small. We do not address that issue directly in our main analysis as we cannot observe aid eligibility perfectly but do conduct robustness checks with different definitions of aid eligibility.

Overall, this institutional detail sets up the natural experiment we exploit in our empirical analysis. At institutions that do not meet full need, an increase in the sticker price affects all students. At institutions that meet full need, an increase in the sticker price only affects those students who can afford to pay that higher price. Therefore, the spike in sticker prices following the financial crisis should not have affected low- and moderate-income students at meet-full-need schools. It would have affected higher income students at those schools and all students at schools that do not meet full need.

To demonstrate the variability in financial aid systems across leading public institutions, we restrict our attention to public flagships and use each school's net price calculator to estimate financial aid awards for a dependent student with household income of $50,000 and no assets.¹⁰ According to the FAFSA4caster (now called the Federal Student Aid Estimator), an online tool that estimates eligibility for federal financial aid, this family is estimated to have an EFC of $2,600 (with rounding) in 2019.¹¹ Loans and work-study would be expected in addition to the EFC. First-year undergraduate students can borrow up to $5,500 through the federal student loan system (including subsidized and unsubsidized loans), and it is not uncommon for schools to offer students up to $3,500 in work-study funds. Accordingly, an institution that charged this student $11,600 after grant aid would be meeting her full need. If a school charged this student more than $11,600 after grant aid, the school would not have met full need. The gap is labeled “unmet need.”

For the sample period we use in our empirical analysis, we categorize the flagship institutions in California (Berkeley), Michigan (UM), North Carolina (UNC), and Virginia (UVA) as meeting full need, at least for residents of each state. Illinois and Wisconsin adopted their policies after our sample period ended (and only do so now for students with lower incomes), so for our analysis they are coded as not meeting full need. Delaware and Washington implemented their policies partway through our 2006–13 sample period, requiring us to drop them from our analysis altogether. The other public R1 institutions beyond flagships that meet full need throughout our sample period are all in the state of California (Davis, Irvine, Los Angeles, Riverside, San Diego, Santa Barbara, and Santa Cruz; Washington State University adopted such a policy at the same time as the University of Washington).

Also, we are forced to drop public institutions in Pennsylvania and Wyoming from our analysis. In Pennsylvania, SAT score sends cannot be reliably matched to specific public institutions in that state. The University of Wyoming does not have an official policy to meet full need. However, calculations from its net price calculator suggest that low-income students have no unmet need. How an increase in COA translates to a student's net price is unclear in such a system, so we have chosen to drop it from our analysis. This leaves us with forty-six states used in our primary analysis of flagship institutions (the District of Columbia has no flagship institution), four of which meet full need and forty-two that do not. There are eighty-eight institutions among our broader sample that includes other public R1 institutions, eleven of which meet full need (note that all of the additional institutions in this category are in California).¹⁴

Changes in cost of attendance, which are largely driven by changes in tuition and fees (and, typically, smaller changes in room and board), have very different impacts on actual net prices depending on whether the school meets full financial need or not. If a meet-full-need institution raises its tuition and fees by $1,000, the price paid by the student with household income of $50,000 will not change. Her net price is determined solely by the self-help expectation and EFC, and her grant aid would increase by exactly $1,000 to offset the price increase. The same student, however, at a school that does not meet full need would see her net price increase by the full amount of the tuition increase.

This is the intuition that underlies our empirical strategy. Aid-eligible students in states where the leading public institutions meet full financial need should not respond to sticker price increases because their actual price paid does not change with the increase. Aid-ineligible students and aid-eligible students in states where the leading public institutions do not meet full financial need may respond by decreasing application and enrollments.

Despite the importance of the full cost of attendance in determining the actual cost to students after factoring in financial aid as illustrated above, in the remainder of this analysis we focus on tuition (and fees) to capture “sticker price.” The difference between the two measures reflects room and board (most incoming students at leading public institutions live on campus), along with an estimate of the cost of books, travel, and other miscellaneous expenses. We focus on tuition because changes in tuition prices reflect what the media typically reports (cf. Duke 2009; Asimov 2009; Gordon and Khan 2009). We explore the sensitivity of our results to this decision subsequently, but that is what students are likely to know and to respond to.

Another advantage of using tuition rather than cost of attendance is that the former is the price that is often set by the state government or the state board of higher education.¹⁵ Expenses beyond tuition and fees generally are not set at the state level and are largely designed to cover costs. Our instrumental variable (IV) strategy is based on the relationship between state economic conditions, budget issues, and public universities’ need for increased revenues. Tuition is the component of college costs that captures this.

Low- and moderate-income students living in states where the public flagship meets full financial need were insulated from the increases in tuition and fees. Any increase in tuition would be met with additional grant aid for financial aid recipients, wiping out any revenue gain from those students. Any increase in revenue at those institutions would have been realized from students not receiving financial aid. As a result, larger tuition increases are required at meet-full-need flagships to generate the same revenue as that raised from flagships that do not meet full need. Indeed, that is what we see in the data. In 2010–11, institutions that meet full need increased their tuition at almost twice the rate of schools that do not (11.5 percent versus 5.9 percent).¹⁸

This finding sets up an interesting potential paradox. Schools with meet-full-need financial aid policies do so to benefit aid-eligible students. Yet when financial circumstances require them to increase tuition, they must increase tuition by a larger amount since those increases are only paid by a subset of students. But if students respond to the sticker price and not their true cost, the aid-eligible students (and others) may respond by reducing their likelihood of applying. Whether there is empirical support for this response is the focus of the remainder of our analysis.

To investigate the application response to prices, we use data on SAT score sends from the College Board.¹⁹ These data cover all students who graduated from high school in 2006 through 2013 who took the SAT and sent at least one score report to a college or university. Following prior work by Pallais (2015) and Hurwitz and Smith (2018), we use the sending of a test score to proxy for applying to a particular college. Smith (2018) shows that SAT score sends are a particularly good proxy for applications when they are to colleges with lower tuition, higher graduation rates, and are relatively near a student's home. Public flagships and other R1-level public institutions fit all of these criteria. Nevertheless, we later investigate whether our focus on SAT score sends is problematic. Note that we are not able to reestimate any of our results using application counts from IPEDS because IPEDS reports only aggregate application numbers that include out-of-state students. Our analysis is concerned only with state residents. Besides, testing the sticker shock hypothesis requires further disaggregating by aid eligibility, which is not available in IPEDS data.

We also have information on each student's SAT scores, demographics (race, gender, and parental education) and zip code. Our primary strategy is to use each student's zip code to determine whether the student is “likely aid-eligible.” We assume that the student is aid-eligible if the median family income in her zip code is $75,000 or less based on our analysis of data from the 2013 five-year sample of the American Community Survey. Eighty-four percent of families in these zip codes have incomes less than $100,000 and 95 percent have incomes less than $150,000. At a public institution with a cost of attendance around $30,000, the income cutoff for aid eligibility for families with typical asset values is in the vicinity of $125,000. Perhaps 90 percent of families we define as “likely aid-eligible” are actually eligible for financial aid. We report below sensitivity analyses designed to test alternative proxies for financial aid eligibility, including self-reported income.²⁰

Although we have data on the universe of SAT takers who sent at least one score, we do not have data corresponding to ACT score send reports. The SAT is the dominant test on the coasts, but not the Midwest, as shown in online appendix figure A.1. It is unlikely that changes in student preferences for sending an SAT versus ACT score would be impacted by the tuition of public institutions in their state, so we do not believe that our use of SAT score sending alone is an issue for our empirical strategy. Nevertheless, in our regressions, we control for the percent of high school graduates who take the SAT and send at least one score.²¹ We also include year fixed effects to control for the secular decline in SAT score sending relative to ACT score sending over our study period.²²

Among the 26.7 million graduating high school seniors in the 2006 to 2013 cohorts, 11.6 million (43 percent) had taken the SAT and 8.5 million (74 percent of SAT takers) sent scores to at least one college. Around 2.6 and 7.3 million SAT takers had sent scores to state flagships and the broader group of flagships and R1 institutions, respectively, during this period. Among elite institutions, those where the average combined SAT score of enrolled students in the 2013–14 school year is 1200 or over, the analogous statistics on score sending are 1.9 million and 3.6 million, respectively.

We examine enrollment behavior among applicants using merged College Board and National Student Clearinghouse (NSC) data. The NSC data cover about 98 percent of all undergraduate enrollment in the United States. It records up to four postsecondary institutions in which a student enrolls. For our analysis, we consider a student enrolled in a flagship if either of her first two postsecondary institutions within 180 days of high school graduation is a flagship. Using this definition, 9 percent of total SAT senders (or 23 percent of flagship SAT senders) in our analysis enrolled in a flagship.

Information on sticker prices comes from IPEDS. As we described earlier, we use in-state tuition and fees as our primary sticker price measure and CPI-adjust all prices to 2013 dollars using the CPI-U index in our analysis. Our state budget shock instrument is constructed using data on total state appropriations per student per year and the share of each flagship institution's total revenue that comes from state appropriations in a base year. The institution-specific data comes from IPEDS, the state appropriations data comes from the State Higher Education Executive Officers Association (2018), and the number of high school students in the state comes from the Western Interstate Commission for Higher Education (2016).

The upper plot of figure 3 shows that between 2006 and 2013, the percent of students sending an SAT score to the public flagship declined in all states regardless of the severity of the recession. The decline is more than twice as large, though, in states where the state unemployment rate increased by more than 5 percentage points relative to other states (a 12 percentage-point drop rather than a 5 percentage-point drop). These data are consistent with a significant score-sending response to the larger increase in tuition and fees in states where the recession was more severe. Of course, these descriptive patterns are also consistent with the direct impact of the recession on family income and the ability to afford a college education regardless of public flagship pricing decisions. We address this complication subsequently in our full econometric analysis.

The lower plot shows that declines in scores sent to flagships were also larger for states that meet full financial need. In combination, these data provide preliminary evidence that students respond to changes in tuition by reducing the likelihood of “applying” (i.e., sending SAT scores) to public flagship institutions. We pursue this further in a more fully specified econometric model below.

We have repeated this analysis for enrollment rates, describing patterns in enrollments by meet-full-need status and by the magnitude of the recession like we did in figure 3. No obvious patterns exist in these data (see online appendix figure A.2). We provide a more complete discussion of enrollment outcomes when we present the results from our full econometric analysis.

Although we use a Deming and Walters–style instrument, we note an important distinction between our work and theirs. We focus only on the 2006 through 2013 period.²⁶ The impact of the financial crisis on school budgets, which began in the 2009–10 academic year, was by far the largest factor influencing state budgets during this period. State variation in the severity of the recession provides an important source of exogenous variation on its own, reducing the potential influence of endogenous pricing decisions. The sensitivity of our results to using IV instead of OLS may be limited because of this.

Our focus on leading public institutions also alleviates one other concern that the Deming and Walters research raises regarding our analysis. These authors find that state budget shocks also affect spending at higher education institutions, not just tuition. This would pose a problem in our analysis because we may be conflating sticker shock, which should only affect some groups of students, with “spending shock,” which would affect all students. Bound et al. (2019) also examine this issue and find support for a relationship between business cycle conditions and spending at higher education institutions, in general. They also find, though, that research-intensive universities, like those we consider, do not adjust their spending in response to state budget shocks. Our sensitivity analysis supports this conclusion. The second column of online appendix table A.2 shows that our budget shock instrument is not correlated with total spending per student in year t − 1 for the sets of leading public institutions we examine.

Adding the appropriate coefficients provides the overall effect of a tuition increase for four different groups of students: “aid-ineligible” students in not-MFN states (β₁), “aid-eligible” students in not-MFN states (β₁ + β₂), “aid-ineligible” students in MFN states (β₁ + β₃), and “aid-eligible” students in MFN states (β₁ + β₂ + β₃ + β₄). These estimates can be converted into elasticities by dividing by the mean rate of score sending within each group.

If “aid-eligible” students applying to institutions that meet full need are aware of net prices and respond accordingly, we should find that their elasticity is 0. In other words, whatever impact higher sticker prices have on score sending for the other groups of students (i.e., β₁ + β₂ + β₃) should be completely counteracted by the fact that “aid-eligible” students in MFN states are the only group that face no net price increase when sticker tuition rises (i.e., β₁ + β₂ + β₃ = −β₄). If this is not true and if the elasticity for this group is negative and significant, it would provide evidence of sticker shock. In fact, if β₄ = 0, then students in this group would fully incorporate the sticker price change the same way that other groups do despite the fact it does not apply to them.²⁷ If β₄ > 0 but β₁ + β₂ + β₃ + β₄ < 0, aid-eligible students in MFN states still exhibit sticker price shock, but they are relatively less responsive to a price increase than other groups.

Our initial results focus on an analysis of public flagship institutions. Table 1 reports the estimated coefficients obtained from the models described in equations 1 through 3. Table 2 uses the coefficients from table 1 to estimate the absolute impact of a tuition increase on the probability of sending SAT scores to students’ own states’ public flagship institution. It also converts these estimates to elasticities.

The first row of table 1 corresponds to the estimates using scores sent from all students. The OLS estimate indicates that a 10 percent increase in tuition and fees at the public flagship decreases the probability that an SAT taker from that state sends a score to the public flagship by 1.8 percentage points (a 5.4 percent decrease relative to the mean score-sending rate of 33.7 percent). This corresponds to a price elasticity of −0.54, reported in table 2, because a 10 percent increases in price decreases score sends by 5.4 percent. The IV estimate is similar: A 10 percent increase in tuition and fees leads to a 1.2 percentage point reduction in score sending. This corresponds to a price elasticity of score sends of −0.35.²⁸ These results suggest that the demand curve for college applications, as measured by SAT score sends, is downward sloping, but inelastic.

The remainder of table 1 reports the estimated coefficients from the triple difference specification represented by equation 3. Table 2 reports the absolute impacts and associated elasticities by group obtained by adding the appropriate coefficients from table 1. For example, to obtain the absolute impact of an increase in price for aid-ineligible students in MFN states, we add the coefficients on ln(tuition) and ln(tuition * meet-full-need): −0.180 + (−0.047) = −0.227. The elasticity is −0.780 because a 2.27 percentage-point drop in score sending is a 7.80 percent decrease relative to the mean score send rate of 0.291 for aid-eligible students in non-MFN states.

All interacted coefficients are small and statistically insignificant, as shown in table 1, including the triple interaction. In OLS, the estimated impact is similar across all groups. The IV estimates are mainly consistent with OLS but less precise in this specification.²⁹ The estimated impact for aid-ineligible students in states that do not meet full need is smaller in an absolute sense, but it is not estimated precisely enough to statistically distinguish it from the other groups. In both OLS and IV, we cannot reject the null hypothesis that absolute impacts are equal. Elasticities are somewhat bigger (although not significantly different) for students in MFN states, which is attributable to similar point estimates, but lower mean rates of score sending.

Importantly, the estimated absolute impact and elasticity for “aid-eligible” students in MFN states is negative and statistically significant (the p-value on the absolute impact is very small in OLS and equal to 0.033 in IV). The OLS point estimate indicates that a 10 percent increase in tuition and fees decreases score sends by 1.79 percentage points for this group. The 95 percent confidence interval for the elasticity is [−0.32, −1.30]. As discussed earlier, the actual net price paid by these students would not change with an increase in tuition, so the fact that “aid-eligible” students in MFN states are less likely to send scores to public flagships when tuition increases is consistent with sticker shock.

Examining only flagship institutions may be too restrictive or too inclusive. On the one hand, many highly regarded public universities are not flagships (UCLA, Texas A&M, Virginia Tech) and some of them meet full need. We may yield additional power by including these other institutions. It is relevant to note, though, that all seven of the additional R1 institutions that meet full need are in California, potentially placing too much weight on the idiosyncrasies of one state. On the other hand, one might be concerned that our sample of institutions is too inclusive, combining schools whose level of selectivity differs significantly. All of the public flagships and some of the other R1 institutions that meet full need are highly selective. Perhaps the set of other schools that do not meet full need are not an adequate control group for these institutions.

To examine whether these selection issues affect our findings, we consider four categories of institutions. First, we augment our sample of flagship institutions with other R1-level public institutions. Second, we create an indicator of “elite” public institutions, distinguishing those with an average combined SAT score of 1200 or over.³⁰ The interaction of those two groups creates our four subsamples—all flagships, all flagships plus other R1 public institutions, elite flagships, and elite flagships plus other elite R1 public institutions.

The results of this analysis are reported in table 3. They indicate that the specific sample chosen does not meaningfully alter the results. The overall responsiveness of score sending to tuition increases is similar across the four categories of schools. In each case, a 10 percent increase in tuition reduces scores sent by 1 to 2 percentage points; IV estimates are slightly smaller than OLS estimates. Differences in impacts on score-sending behavior among students that differ by aid-eligibility and the meet-full-need status of the school are largely unsystematic (recognizing the greater imprecision in the IV estimates).

Our interpretation of these results is that our main findings described above regarding public flagships are generally robust to modifications in the types of schools included in the analysis. For the remainder of the analysis, we return to our focus on public flagship institutions.

We also consider whether any heterogeneity exists in the results across population subgroups that may be differentially affected by sticker shock in the application decisions to public flagship institutions. Of course, differences in behavioral responsiveness to price changes across groups is a possibility, but a simpler explanation would be the differences in underlying propensity to apply to one of these schools in the first place. If a student is less interested in applying to a school in the first place, that student is less likely to respond to a price increase by withholding their application.

In our analysis, we separate students into population subgroups with different underlying propensities to apply to a public flagship. We measure these propensities by the average SAT score send rate for the 2006 through 2008 cohorts who graduated from high school before the tuition price spikes brought about by the financial crisis. We distinguish two groups that are more likely to apply to a public flagship—students whose combined math and verbal SAT scores are above the state-specific flagship “median” among enrolled students³¹ and students who have a parent who graduated from college. In our data, 45.6 percent and 41.0 percent of these students from the 2006 through 2008 cohorts who took the SAT sent their scores to a public flagship, respectively. We also distinguish students by race/ethnicity, separating them into categories of white, underrepresented minorities (black or Hispanic), or Asian. Among these three groups, our baseline SAT score send rate to public flagships is highest for Asian students (51.6 percent) and lowest for underrepresented minorities (32.2 percent).

The results of our analysis are reported in table 4. We report absolute impacts on the probability of sending SAT scores to a public flagship in response to tuition increases, using OLS and IV as before. Across all these groups, we see results similar to our earlier findings that the responsiveness of score sending to tuition increases is generally comparable regardless of aid-eligibility and the MFN status of the public flagship in the student's state of residence. Sticker shock appears to be a problem for all population subgroups.

Yet the absolute impact of the score send response is different across groups. Our analysis yields point estimates that are largest for students with high SAT scores and for Asian students. These groups have the highest baseline propensity to apply to a public flagship.

Another potential issue in interpreting our results is that we restrict our sample to those students who sent any SAT scores, and those students may reflect a selected sample. It is possible that students are less likely to send any SAT scores if sticker prices are high. One might anticipate, though, that altering test-taking among the types of students who would attend leading public institutions is considerably less elastic. We have no direct evidence on this point. Other studies that focus on state ACT (Goodman 2016; and Hyman 2017), SAT mandates (Hurwitz et al. 2015), or making it easier to take the SAT (Bulman 2015) provide mixed evidence regarding the responsiveness of high-scoring students to these interventions. The two studies focusing on the SAT, though, find that SAT-taking among high-achieving students is not affected, supporting our point.

Another specification check examines the sensitivity of our results to alternative measures of aid-eligibility status. Our median family income in the student's zip code measure is imperfect in that some higher income families still reside in zip codes with relatively low median incomes, and vice versa. We estimated several alternative measures of likely aid-eligibility to test the sensitivity of our results. Instead of zip codes with median family income below $75,000, we tried an analogous measure with median family income less than $50,000. We also experimented with student-reported family income and parental education to distinguish those who are likely to be eligible for financial aid. The results of these analyses are reported in online appendix table A.3. They all yield similar estimates of the impact of tuition increases on scores sent to public flagship institutions.³²

We also consider the ACT as a substitute for the SAT as an additional specification check. An “application” in our data is sending an SAT score to a school, but in some parts of the country, that is unlikely just because the ACT is much more common. A student in one of those areas who takes and sends SAT scores may be selected in some nonrandom way that could have an impact on our results. To test this hypothesis, we restricted our sample to students residing in states where SAT score-sending rates are the highest—a third or more of a high school graduation cohort take the SAT. This occurs in about half the states. The results of this analysis are also reported in online appendix table A.3; the responsiveness of SAT score sends to changes in tuition are no different in this sample of the students.

We also report in online appendix table A.3 an additional specification check that examines the influence that California plays in driving the results in our analysis of scores sent to flagship institutions. As the most populous state and considering the protests that resulted following the announced tuition increases, perhaps the aggregate price sensitivity is driven primarily by UC Berkeley. We explore this possibility by simply dropping Berkeley from the sample. Again, we find our results to be strongly robust to this sample restriction.³³

The final section of online appendix table A.3 reports results for dropping both California and Michigan from the sample; this leaves only North Carolina and Virginia as MFN states. These two states did not include loans in their financial aid packages for low-income students during the sample period, so there is no scope for students to be reacting to increased loans amounts that could have theoretically accompanied sticker price increases. The results from excluding California and Michigan are less precise than in the specification with all four MFN states, but the results are qualitatively similar, suggesting that sticker price shock is indeed the most plausible explanation for our results.

The final set of specification checks we report addresses how score sending responds to changes in cost of attendance rather than tuition and fees. The cost of attendance represents tuition and fees plus other components of costs (room and board, transportation and personal expenses, and books and supplies). If, as we argue in the section “Trends in College Pricing,” students respond to changes in tuition because that is what is publicized, then incorporating these other elements of cost introduces a random component to our key explanatory variable. In terms of our IV strategy, these additional elements of cost (which are typically set by the campuses, not the state) are also less affected by state budget conditions, weakening our identification strategy.

The results of our analysis using COA rather than tuition and fees, shown in online appendix table A.4, support these propositions. OLS estimates for all students are similar between the COA and tuition and fees specifications, but the results by MFN status and aid eligibility are considerably less precisely estimated. In the IV models, the state budget shock instrument is a weaker predictor of COA compared to tuition and fees; the F-statistic from the first stage fell from 20.8 to 4.87. Consequently, our results for the impact of changes in COA on score sending are less precise than the results for the impact of changes in tuition and fees in the aggregate and in models broken down by MFN status and aid eligibility. Overall, we believe this analysis supports our preference to focus on tuition and fees in this analysis.

If students respond to changes in college pricing with full information regarding their actual financial impact, then a tuition increase has the potential to increase socioeconomic diversity at MFN colleges. At these schools, low- and moderate-income students are protected from those tuition increases and should not reduce their likelihood of applying. Those at other schools and higher income students will all face the higher cost and could reduce their likelihood of applying. What we just saw, however, is that aid-eligible students responded to an increase in the sticker price the same way as others even though it did not affect them. Applications of all students declined in response to tuition increases, regardless of their aid eligibility or their state flagship's MFN status.

This raises the question of where students eventually enrolled. If all students, including aid-eligible students in states whose public flagship meets full need, are less likely to apply to those flagships, what impact does that have on enrollments at those institutions and other institutions with which they compete? We can use similar methods to those described earlier to address this question. We simply replace the dependent variable, using enrollment rather than application. For this exercise, we use the NSC data for the same students included in our application analysis, focusing on enrollment at public flagship institutions.

Before reporting the results of this analysis, we note that the likely estimated impact on enrollments, if any, will be smaller than that on applications since only a relatively small percentage of applicants to public flagships are accepted and enroll. Even if some students did not apply because of the price increase, they may not have been accepted or may enrolled elsewhere anyway, reducing the potential impact on enrollment. The smaller estimated impact also creates a power issue, particularly in our IV specifications. With smaller anticipated effects and the larger standard errors associated with them (based on the properties of the binomial distribution), it becomes much more difficult to generate results precise enough to reject the null hypothesis of no effect.

Indeed, our attempts to estimate IV models of the same form as earlier generated mainly insignificant coefficients. We report only OLS estimates in the remainder of this discussion as a result. Despite this limitation, it is important to keep in mind that OLS estimates were similar, if perhaps slightly larger, than IV estimates in our analysis of applications. Endogeneity in tuition setting does not seem like a major problem during our sample window, but it may have a minor influence on our results.

The first issue we address is whether enrollments change at all at public flagships. As the most competitive public institution in the state, these schools receive considerably more applications than there are students who are accepted. A reduction in applications does not necessarily generate a reduction in enrollment. They can always adjust their admissions standards to maintain it.

In fact, this is what we see. The first row of table 5 reports the estimated impact on overall enrollment at public flagships in response to changes in tuition. We find no statistically significant changes in flagship enrollment in the aggregate and by MFN status or aid eligibility. This result is consistent with Barrow and Davis (2012), Long (2015), and Charles, Hurst, and Notowidigdo (2018), who show strong evidence of cyclicality in college enrollments at two-year public institutions and for part-time enrollments at four-year public institutions, but the impact on full-time enrollments at four-year public institutions is clearly smaller or perhaps non-existent. Hemelt and Marcotte (2011) and Deming and Walters (2017) also find small or zero estimated aggregate enrollment elasticities in their samples of four-year colleges and universities.

To determine the extent to which this result is attributable to changes in selectivity among public flagships in our sample, we examine the impact of changes in sticker prices on the combined math and verbal SAT scores of enrolled students who sent their SAT scores to the flagship. The second row of table 5 reports the results of estimating models that are identical in specification except that the dependent variable is now the combined SAT score of enrolled SAT score-sending students at public flagships. The results indicate that a tuition increase reduces those scores. A 10 percent increase in tuition leads to a 2.2-point drop in combined SAT scores in the aggregate. The differences in effects by MFN and aid-eligibility status are small and not statistically significant.

We extend this analysis by focusing solely on “high-achieving” students, whose SAT scores are above the “median” (midpoint of the 25th and 75th percentile) of enrolled students at each institution. For these students, in OLS specifications we see a statistically significant reduction in enrollment; overall, a 10 percent increase in tuition leads to a 0.7 percentage point decline in enrollments among this group.

This raises the question of where high-achieving students who otherwise would have enrolled in their home state's public flagship end up enrolling? One possibility is that they could have chosen to reduce their college cost by living closer to home and attending a non-flagship four-year state university.³⁴ Identifying the impact of flagship tuition on public, non-flagship enrollment, however, is complicated because flagship and non-flagship tuition are both set within the context of the same state's budgetary environment and are often determined through a similar process. Indeed, the correlation in tuition between flagships and non-flagships across states and years in our data is 0.91. Non-flagships do not necessarily have the same excess supply of applicants to maintain enrollment if a price increase reduces demand. If increases in flagship tuition are matched by tuition increases in non-flagship institutions, then we would expect to see a reduction in enrollments at those institutions, not an increase associated with substitution. In other words, the results of this analysis would have a negative bias.

By restricting our sample to high-achieving students, though, this should be less of a problem. In our data, high achievers are 2.5 times more likely to attend a public flagship than a public non-flagship institution (17.0 percent versus 6.8 percent). By restricting our sample to just high achievers, we reduce the bias associated with the correlated prices and can better identify substitution. The results of this analysis are reported in the fourth row of table 5. We find no statistically significant change in the enrollment of high achievers at public non-flagships in response to a tuition increase.

The remainder of the table considers the impact on other enrollment options among high-achieving students. We consider the following alternatives: out-of-state flagship institutions, four-year private not-for-profit institutions, and the subset of those institutions that have “meet full need” financial aid policies. The final category is likely to have the lowest cost of attendance for lower income students, but they are not that common and typically highly selective. We note that the huge jump in tuition at public institutions during the financial crisis was not matched by tuition increases at private institutions (Ma et al. 2019), suggesting that identifying the cross-price effect is easier here than in the case of public non-flagships. Own-price effects are adequately controlled for with year fixed effects since private nonprofit institutions tend to have national (or at least regional) markets. Competition among those institutions also restricts local differences in price variation over time. Similarly, the out-of-state flagship market is a national one, so year fixed effects will capture the average effect of price changes at those institutions.

The results indicate that high achievers are more likely to attend a private not-for-profit four-year institution when public flagship prices rise. A 10 percent increase in tuition at public flagships increased enrollment of these students at these private colleges by 0.8 percentage points. This roughly matches the decline in enrollment of high achievers at public flagships. We also see modest evidence of increases in enrollments at out-of-state flagships and MFN private institutions, but those effects are spotty and considerably smaller. These results are consistent with those found by Hemelt and Marcotte (2016).

Our student-level enrollment results may also help us better understand the small estimated aggregate own-price enrollment elasticities of Hemelt and Marcotte (2011) and Deming and Walters (2017). Hemelt and Marcotte (2011) estimate an average tuition and fee elasticity of total headcount of −0.1. Deming and Walters (2017) cannot reject the null of a zero price elasticity, but their confidence intervals contain the Hemelt and Marcotte (2011) average elasticity. Our results suggest that focusing on aggregate enrollment elasticities may obscure important distributional effects at selective institutions. Individual students—especially high-achieving students from low- and middle-income backgrounds—have enrollments much more responsive to prices than aggregate elasticities imply.

Price discrimination in the form of high sticker prices with generous financial aid for lower income students is a common pricing strategy at selective universities across the United States. In theory, this pricing strategy can maximize tuition revenue while maintaining access for lower income students because generous financial aid awards keep prices affordable. However, if students are unaware that they will qualify for financial aid and are discouraged from applying based on high sticker prices, this pricing strategy may not have its intended effect of increasing access to higher education. Instead, the high sticker price–high aid strategy may discourage low- and middle- income students from applying, defeating its purpose.

In this paper, we examine the existence of sticker shock using exogenous variation in sticker prices at leading public higher education institutions during and after the 2008 financial crisis. Using data on SAT score sends corresponding to students in the high school classes of 2006 to 2013, we investigate whether low- and moderate-income students responded to price increases by reducing their application rates at institutions that meet full financial need. Notably, in those institutions, aid-eligible students would have been fully insulated from sticker price increases. Yet we find that they apply less often, similar to higher income students and to those considering institutions that do not meet full need. These results suggest the existence of important informational frictions.

Interestingly, had low- and moderate-income students not exhibited sticker price shock, tuition price increases during the 2008 financial crisis would have led to significantly greater socioeconomic diversity at these public institutions that meet full need. As higher income students experienced actual price increases and reduced applications to them, unaffected low- and moderate-income students would not have adjusted their application behavior. Consequently, they would have constituted a larger share of the applicant pool and proportion of admitted students. However, our results indicate this did not occur.

Our results have important implications for public policy. One common proposal to address the complexity of the financial aid system is to simplify the FAFSA, which is used to determine a student's eligibility for Pell Grants and other forms of financial aid (cf. Dynarski and Scott-Clayton 2007).³⁵ Though simplifying the FAFSA might increase participation in the financial aid system and college enrollment more generally, it may not go far enough to eliminate sticker price shock. To get students to apply in the first place, they need the ability to better forecast what their college costs will be at an early stage of the admissions process to overcome sticker shock.

Our results document the direct existence of sticker shock; students eligible for financial aid respond to the sticker price, not the actual price. The work by Dynarski et al. (2021), among others, documents that providing better pricing information changes their behavior. These analyses are complementary. In the end, both highlight the need to better communicate pricing information and available financial aid to overcome student misinformation regarding what college will really cost them so that they can make informed educational decisions.

We are grateful to seminar participants at the National Bureau of Economic Research Fall Education Program Meeting, Dartmouth College, Harvard University Graduate School of Education, Lafayette College, Swarthmore College, Wellesley College, and the DC Economics of Education seminar for their comments and suggestions. This paper uses proprietary data from the College Board, which has reviewed this manuscript and granted permission to release it. The views and opinions expressed in this paper are those of the authors alone.