In-State Merit Aid and College Choice: New Jersey s STARS Program as a Tuition Subsidy

Transcription

1 In-State Merit Aid and College Choice: New Jersey s STARS Program as a Tuition Subsidy Namir Shah 1 Department of Economics Stanford University, Stanford, CA namir@stanford.edu Under the direction of Prof. Caroline Hoxby May 8, 2014 Abstract In this paper, I test how the implementation of the NJ STARS program has changed patterns of college enrollment choice among eligible students. Applying difference-in-difference logic to a college choice model, I analyze the variation in college enrollment choices of students who were eligible for NJ STARS scholarship aid and students who were similarly qualified but not eligible. In addition, I measure several indicators of college quality and other characteristics of the school(s) in which the students enrolled for post-estimation analysis. My conditional logit choice model shows that students eligible for NJ STARS are 1.7% more likely to enroll in an in-state institution and 61% more likely to enroll in an instate, public, two-year college. Based on the enrollment shifts, I also find that eligible students, on average, enroll in an institution with a 0.79% lower graduation rate and 0.71% less in instructional spending, indicating adverse effects of the program. Keywords: College choice, college quality, in-kind subsidy, academic spending, merit aid, NJ STARS, Peltzman hypothesis 1 I would like to extend my warmest thanks, first and foremost, to Professor Caroline Hoxby, who has developed the foundation for my understanding of the economics of education and has supported my research on this topic for over one year. Similarly, I would like to thank Professor Michael Boskin, who has guided me through the Economics major, and Marcelo Clerici-Arias, who has continuously offered his assistance with the Honors program. Finally, I would be remiss in not mentioning the other mentors and advisors who have contributed to my interest in education policy, even if they were not directly involved with this thesis: Ana McCullough and Bill Abrams.

2 Namir Shah 1 1 Introduction State and federal governments allocate billions of dollars in grants and subsidized loans annually to students through both merit and need-based financial aid. These programs are intended to increase college enrollment, generate more optimal human capital investments, reduce the costs faced by families, and retain students in their state of residence. Many of these justifications are rooted in the belief that society underinvests in education because part of the return is nonprivate or, alternatively, that individuals underinvest in education because they are liquidity constrained. In either case, greater investments in education may pay for themselves over time. The state might even be more than repaid for its expenditures through higher future tax payments (Long, Does the Format 5). Regardless of the intended purpose, economic theory tells us that scholarships should be portable in the sense that they are tied to the student. However, public college tuition subsidies in most states are contingent upon enrollment in one of a particular group of colleges, usually the state s public colleges and universities. In this way, tuition subsidies are tied to the college rather than the student. Because states design their financial aid schemes differently, including the degree of portability, eligibility requirements, and type of aid, there is significant variability regarding the effects of each state s aid program(s). In particular, conditioning financial aid on a student's attending a specific school can create incentives for the student to change her education investment in unintended ways. That is, while public college tuition subsidies are launched to expand educational

3 Namir Shah 2 opportunities to those who may otherwise be unable to afford college, they could have the opposite effect of inducing students to attend schools with more meager resources. If the financial aid induces students to enroll in colleges of lesser quality, often measured by instructional spending, the program could undesirably decrease aggregate investment in education. This paper tests these hypotheses through the lens of the New Jersey Student Tuition Assistance Reward Scholarship (NJ STARS) program, a meritbased scholarship that allowed New Jersey students in the top two deciles of their graduating high school class to receive free tuition and fees at public, in-state twoyear colleges. Though larger programs like Georgia s HOPE Scholarship have received considerable attention from education economists, there has been no significant study of the NJ STARS program or others with similar subsidy structures. Toward this end, I present an empirical evaluation of the effects of the NJ STARS program. The central question is whether the program negatively or positively affects students' investments in education. My empirical strategy exploits (i) the discontinuity in eligibility on the basis of class rank and (ii) the timing of the introduction NJ STARS program. Using these sources of variation simultaneously in a differences-in-differences method, I can credibly estimate the program s causal effects on college enrollment. Figure 2 shows the percentage of New Jersey students above and below the eligibility cut-off who enroll at different types of colleges. The data shows different enrollment trends between eligible and non-eligible students from the inception of

4 Namir Shah 3 the scholarship program. These patterns provide a preliminary indication that the scholarship is altering college enrollment of eligible and/or non-eligible students. The percentage of second-decile students enrolling at public two-year colleges rose from 2.0% to 3.8%, an 87% increase, while the equivalent statistic for second quintile students was only 23%. In-state enrollment rates also showed disparate patterns between eligible and non-eligible students, though not quite as drastic. This simple data analysis provides support for the more detailed econometric analysis of the program. However, the enrollment decision is inherently a choice between many alternatives, not a binary decision to enroll or not enroll or a binary decision to enroll at a public or private school. Therefore, to understand the effects of NJ STARS, I must fully model all of the factors that affect students college choices including the scholarship program as one of those factors. This type of analysis requires an econometric choice model such as conditional logit or multinomial probit. I use the former because there are so many college choices and so many students that the latter model is computationally infeasible. My empirical strategy, then, is to estimate a college choice model using data on students who are and are not eligible for NJ STARS (the first difference) and from cohorts before and after NJ STARS was enacted (the second difference). That is, I estimate an appropriate choice model but derive identifying variation through a difference-in-differences logic. Using this strategy, I can answer questions such as: Do students switch to instate colleges or two-year colleges when eligible for in-kind subsidies valid only at

5 Namir Shah 4 such institutions? Are high-achieving students willing to sacrifice relatively large amounts of college quality for relatively small financial incentives? Does an exclusively in-state, two-year college-based subsidy increase or decrease the aggregate investment in education? I rely upon a combination of administrative data from the National Student Clearinghouse, the College Board, and the National Center for Education Statistics Integrated Postsecondary Education Data System. My data set includes information on over 57,186 students and 1,400 colleges and universities. This data set contains student performance data, student demographic data, actual college enrollment, and college characteristics, including expected costs and admissions data. I find that the NJ STARS has increased enrollment into STARS-eligible instate, two-year colleges by 61% among eligible students. Similarly, overall two-year enrollment rates increased by 41%, in-state enrollment rates increased by 1.7%, and the percentage of students enrolling in colleges unranked by the Barron s Selectivity Index increased by 26%. Based on the enrollment changes, my analysis finds that students eligible for NJ STARS enroll in colleges with 0.71% less educational inputs, 0.79% lower graduation rates, and 0.85% higher admissions rates. This indicates that students are shifting to less selective and less resourced institutions. This paper is organized into the following sections. Section 2 provides a brief overview of the NJ STARS program. Section 3 consists of an overview of the Peltzman model of in-kind subsidies, which is the foundation of my college choice analysis. In Section 4, I review the relevant literature: papers that study the impact

6 Namir Shah 5 of financial aid on college enrollment and choice, with an emphasis on tuition subsidy distortions and the choices of high-achieving students. Section 5 describes my empirical methodology, particularly the difference-in-difference and conditional logistic econometric models. Section 6 discusses the sources of my data set, the strengths and limitations of the available data, and descriptive statistics of the students and colleges. In Section 7, I explain the results of the econometric model and show the effects of the NJ STARS program on college choice patterns. Finally, Section 8 contains a broader discussion of the impact of NJ STARS and similar programs. 2 Background New Jersey s public college system includes eleven four-year colleges and nineteen two-year colleges. The two-year colleges are associated with individual counties offer tuition discounts for county residents. Thus, students most commonly attend the community college associated with their respective county. Rutgers University New Brunswick, the largest of the three campuses, is commonly considered the flagship university of New Jersey and enrolls more students than any other postsecondary school in New Jersey. In addition to the public institutions, there are also fourteen not-for-profit four-year colleges and two for-profit institutions that could be considered four-year colleges. 2 2 If one considers the campuses of Rutgers University and Farleigh Dickinson University as separate colleges, there would be thirteen public four-year colleges and fifteen private, not-forprofit four-year colleges.

7 Namir Shah 6 The NJ STARS program was passed by the New Jersey legislature and signed into law by Governor McGreevey in 2004 to be implemented immediately for the graduating high school class of It was designed to allow students who graduated in the top 20% of their high school class to attend any of New Jersey s 19 two-year community colleges free of charge. The original grant could be applied to tuition, books, and required fees for a maximum of five semesters. In 2006, NJ STARS II was passed to provide an additional partial scholarship to NJ STARS students to continue their education in a New Jersey four-year college. This second scholarship provided $7,000 toward tuition and fees to all NJ STARS students who completed their associate degree at a two-year institution with at least a 3.0 GPA. The state s public four-year colleges were originally the only eligible participants in the NJ STARS II program, though several private colleges created reciprocal programs to match the NJ STARS grant. Since the inception of the programs, only students with household incomes below $250,000 have been eligible. In the first year of the program, 789 students used the scholarships to attend two-year public colleges at a cost to state of about $1.7 million (Washington Times). Participation in the program peaked in the academic year at over 5,700 students. Subsequently, the eligibility requirements and monetary values were revised owing to budget constraints. Specifically, in 2009, eligibility for NJ STARS was narrowed to the top 15% of graduating high school classes. At the same time, NJ STARS II increased its GPA requirement to 3.25, with a two-tier structure: the scholarship was maintained at $7,000 for students who completed their associate

8 Namir Shah 7 degree with a GPA at least 3.5 and decreased to $6,000 for students with a GPA greater than 3.25 but less than 3.5. For students in both NJ STARS and NJ STARS II, the scholarship was limited to only tuition and students were made responsible for books and student fees. Starting in 2012, the state further cut the NJ STARS II subsidy amount to $2,500 per year but began to allow student to use their scholarship to transfer to fourteen of the state private four-year colleges, including one for-profit institution: Berkeley College. The only private not-for-profit four-year college that is ineligible is Princeton University because it does not participate in New Jersey s Tuition Aid Grant (TAG) Program. The state projects that participation in NJ STARS will fall to 3,000 in the school year, with fouryear college recipients dropping from 1,844 in the school year to 1,200 in This reflects a five-year decline in state funding for the program, from $18 million in the academic year to $8.5 million proposed for The program s stated primary goals are to increase college attendance among high-achieving students, retain top New Jersey students who may have otherwise attended out-of-state colleges and perhaps been more likely to make careers out-ofstate, and provide a path to bachelors degrees that the state has touted as more successful than direct enrollment in a four-year institution. In analyzing the efficacy of the NJ STARS program, an important question is whether the program actually affects students' decision to enroll or not or affects where they enroll. It could simply subsidize their education without affecting any decision that is, be a

9 Namir Shah 8 mere income transfer. If NJ STARS does affect students' decisions, it could have important effects on their educational outcomes and lifetime incomes. New Jersey s merit-based aid program is particularly interesting due to the unique design of NJ STARS. It gives high-achieving students an incentive to attend two-year colleges which are more often the destination of students who are only marginally college-ready and therefore unable to gain admission at selective fouryear colleges. Tables 1 and 2 shows costs and educational inputs of New Jersey s two-year and four-year colleges for the academic year. 3 Figures 3(a) and 3(b) provide a graphical representation of the cost-input consideration. Colleges that spend exactly what they charge students would be located on the 45-degree line. Public colleges and universities tend to fall above the line due to public tuition subsidies, while private colleges can be found closer to the line or sometimes below it. However, some of the country s most elite private universities are often also located above the line because they subsidize students through endowment spending. Princeton University is a prime example. It is immediately obvious that educational inputs, specifically instructional spending, are significantly lower at two-year colleges than at four-year colleges. Simultaneously, graduation rates at four-year colleges are dramatically higher than the two-year colleges. These are cause for interest because it presents the possibility that students may have worse educational outcomes if they are persuaded by the scholarship to attend a two-year college instead of a four-year college. However, NJ 3 All monetary values in this paper are in nominal dollars or multiples thereof.

10 Namir Shah 9 STARS participants may be much more likely to graduate than would the average student of the two-year colleges. 3 Related Literature 3.1 Financial Aid & College Choice Peltzman (1973) develops a model to test whether subsidies-in-kind increases total consumption of higher education. His theoretical framework provides the foundation for understanding the impact of subsidies on student enrollment choices and the tradeoff between cost and quality. Peltzman s empirical model attempts to measure the extent to which government expenditures through subsidies-in-kind decrease private higher education expenditures. Given the strength of the theoretical model, Peltzman s empirical methodology lacks the same analytical precision. His analysis uses a relatively simple econometric model and lacks a true identification strategy on which to base it. Ganderton (1992) built upon Peltzman s research by using data on individual student characteristics, including ability and wealth, and college characteristics (e.g. quality) to further explain the effect of public in-kind subsidies on choices made within higher education. Ganderton attempts to answer three questions in particular: (1) what college quality would a student with given characteristics choose in the public sector? (2) what quality college would be chosen in the private sector? and (3) what quality college would be chosen if forced to choose a private college due to the closure of a public college? Based on applications to four-year colleges, Ganderton attempts estimates the likelihood of choosing a private vs.

11 Namir Shah 10 public college and the quality of the most preferred college in each of the private and public sectors. Specifically, the model estimates the impact of student demographics, socio-economic status, and academic performance on the SAT score of the student s first choice college to estimate the desired quality. His identification strategy takes advantage of cross-state variation in tuition subsidies. However, Ganderton does not make use of a choice model in his econometric analysis, which provides significant limitations in understanding the impact of his covariates on the college choice decision. Long (2004) further examines subsidy schemes of several different states, including Massachusetts, California, Illinois, and Nebraska, and simulates how decisions would change if the aid were awarded in different ways. Long uses extensive match-specific data between individuals and nearly 2,800 colleges in a conditional logistic choice model. The conditional logit model used here controls for student body characteristics, college expenditures, and distance to measure the impact of cost on likelihood of attendance. Her identification comes from the fact that states have different tendencies to subsidize their public colleges. The model finds that when offered large in-kind subsidies, students choose public colleges even when there is a substantial gap between the resources offered by public and private college options. In addition, the subsidies introduce incentives for students to choose public four-year colleges over two-year colleges. If these in-kind subsidies are instead offered as a transferable credit, Long estimates that up to 29 percent more students would prefer to attend a private four-year college. As a result, she

12 Namir Shah 11 concludes that these non-transferable subsidies lead students to choose colleges of lower quality. From her interstate comparisons, Long finds, for example, that when faced with California s generous subsidies and diverse system of public colleges as opposed to Massachusetts less generous and limited array of public options, individuals paid far less but received a similar amount in resources. Long s findings validate Peltzman s hypothesis in states with average or above average amounts of state aid but limited availability of public options with high levels of resources. Avery and Hoxby (2003) track a group of high-achieving students through the college admissions process, collecting information on college applications, high school academic performance, parental preferences, enrollment, and college costs. Avery and Hoxby use a conditional logit model in this paper to estimate college choice. This conditional logit considers the colleges to which each student was admitted to be the student s college choice set. Within-student variation comes from the actual selection of one college from the choice set. The estimation relates the binary choice outcome for each option to the college-specific attributes and match characteristics. Avery and Hoxby find that students are more likely to attend more selective colleges that offer larger grants, loans, and/or work -study opportunities, with less differentiation between the three types of aid. They calculate that onethird of students lose lifetime present value based on colleges instructional resources because they respond unwisely to financial aid offers. The relevant finding from this paper is that students are differently sensitive to financial aid depending on the structure of the aid. Estimates are identified from variation across

13 Namir Shah 12 states in their public colleges expenditure levels, state-provided subsidies, and aid packages. Cohodes and Goodman (2013) find compelling evidence to support Peltzman s hypothesis using Massachusetts high school students who were awarded merit scholarships through the Adams Scholarship (top 25% of high school graduates in each school district). Their identification strategy exploits a regression discontinuity in the eligibility requirement to estimate the impact of the subsidy on college quality in the enrollment decisions of students just above and below the threshold. Students on either side of the eligibility cutoff are theoretically very similar other than their exact percentile rank, so they make strong control and treatment groups. Cohodes and Goodman provide evidence of reduced consumption of higher education driven by an exogenous shock of the in-kind subsidy and show that the reduced costs come at the potential sacrifice of degree completion. They note that the Adams Scholars were granted tuition waivers at in-state public colleges of lower quality than the average alternative available to them and show that students are willing to sacrifice college quality for relatively small amounts of money. Furthermore, the choice of a lower quality college reduces the probability of graduating on time by 40%, indicating that the subsidy has effects on not only on enrollment, but potentially also on achievement. Their results confirm the hypothesis that merit aid is effective at keeping students in state but that marginal students are a small fraction of total aid recipients.

14 Namir Shah Financial Aid & College Attendance There is a fairly robust economic literature regarding the impact of financial aid on college attendance and persistence. In general, economists agree that increased financial aid increases the probability of a student enrolling in college, though the distributive impact and accompanying effects are less clear. Dynarski (2003) analyzes the impact of the elimination of the Social Security Student Benefit Program on college attendance and completed schooling. The program, which provided monthly payments to the 18- to 22-year-old children of deceased, disabled, or retired beneficiaries while the children were enrolled in college, was eliminated in Using a difference-in-differences methodology, the study estimates that increasing aid by $1,000 increases the probability of attending college by about 3.6 percentage points and finds that aid eligibility increases completed school. Dynarski s (2000) study of Georgia s Helping Outstanding Students Educationally (HOPE) Scholarship program estimates the impact of grants on the college attendance of middle- and upper-income students. It shows that the program, which covers a significant portion of tuition for public colleges in Georgia or an equivalent amount for private colleges, had a substantial impact on college attendance, increasing attendance rates by 7 to 8 percentage points. The effects were concentrated among whites, with little to no effect on the schooling of Blacks, widening the racial and income gaps in college attendance in Georgia. This article

15 Namir Shah 14 suggests that a well-designed program may successfully increase college attendance, though the associated side effects may be undesirable. Dynarski (2004) analyzes changes in merit aid programs, particularly in Georgia and Arkansas, and their effect on schooling decisions. She reviews evidence regarding whether colleges increase tuition in response to increased aid, whether colleges decrease other types of aid offered, and whether merit aid linked to performance leads to grade inflation, among other topics. This article provides a summary of state merit aid program changes since the early 1990s and describes the historical and economic context associated with the important revisions. Using more recent data than in Dynarski (2000), this paper reaches a similar conclusion to find that the Georgia HOPE scholarship increased the college attendance rate by 8.6 percentage points relative to the rates of other Southern, nonmerit states. In addition, Dynarski (2004) finds that the HOPE Scholarship increases the likelihood of attending a four-year public institution by 4.5 percentage points, increases the likelihood of attending four-year private institutions by 2.2 to 2.8 percentage points, and decreases the probability of attending a two-year public institution by 1.7 to 5.5 percentage points. 3.3 Contribution This paper contributes to the existing literature by providing an analysis of one of the more obvious redirections of academic talent to less-resourced colleges. The exogenous subsidy shock at public two-year colleges provided by the NJ STARS program allows us to determine the causal effect of tuition subsidies with a natural

16 Namir Shah 15 experiment. New Jersey is a particularly interesting case study of tuition subsidies because of the unique structure that funnels high-achieving students to two-year colleges. In addition, the above-average college preparedness and demographic diversity of New Jersey high school students and the relatively small range of instate public and private college options provide compelling circumstances for determining how conditional tuition subsidies alter college choices. 4 Theoretical Framework When economists model students' college choices, they typically assume that students weigh the benefits of each college (and the non-college option) against the costs of each college. The benefits include the college's effect on future income and the utility gained from the experience itself. Since a student's utility from college may be affected by its geography, her peers, and her match with its curriculum, these factors potentially affect her choice. The costs of a college include opportunity costs (lost wages, lost time), tuition, and fees. Any econometric college choice model should attempt to include, as explanatory variables, a full array of measures of or proxies for these costs and benefits. Although some goods and services bundled into "college" are consumption (housing, food, and so on), economists typically treat the educational services provided by colleges as an investment in human capital. Therefore, classic microeconomic theory largely predicts that individuals choose the college (or noncollege option) with the highest return on their investment (tuition, fees, effort, foregone earnings, and so on). In such a case, institution-specific scholarships in the

17 Namir Shah 16 form of in-kind tuition subsidies always lead students to invest weakly less in education than they otherwise would if given the same amount in the form of fully portable scholarship. It is even possible that in-kind tuition subsidies will lead some students to invest less in education than they would if they were given no aid at all. The former result is fairly obvious because it merely depends on the idea that students are unable to top up (at an efficient cost) the education offered by the colleges with in-kind subsidies. For instance, a student cannot efficiently reduce the student-faculty ratio at her chosen college by hiring faculty on the side to teach her or her. The latter result, in-kind subsidies potentially causing students to less than they would with no aid, can occur because if the only colleges that qualify for subsidies offer a fairly low amount of educational inputs. As a rule, the lower and narrower are the range of colleges with in-kind subsidies, the more likely are students' investments likely to be distorted downwards. To see this, consider Peltzman's (1973) model of the effects of government subsidies-in-kind on college choice. Peltzman s model finds that some students will be induced to pick lower quality colleges if subsidies are college-specific compared to the choices that would prevail without any subsidy. Using a human capital investment model to describe these subsidies, students would like to choose a college with the greatest amount of educational inputs for the lowest cost, subject to a set of preferences described by iso-input/tuition curves. Assuming educational inputs can be measured monetarily, academic spending by a college becomes a reasonable, though imperfect, parameter. The full range of college choices are now

18 Namir Shah 17 depicted as occupying points on the 45-degree line of the input-cost space in Figure 1(a). Student preferences, similar to indifference curves, display the tradeoff that students are willing to make between cost and quality. In a perfect market, the consumer may be seen as choosing from an infinite number of higher education institutions, each offering a different amount of educational inputs at $1 per unit, allowing the consumer to choose any point on the 45-degree line of Figure 1(a). In the market for higher education, though, a consumer does not directly choose a given dollars amount of higher education. Instead, the student selects a college, based on her aggregate preferences, that she expects to deliver some amount of education. Using Figure 1(a) to illustrate the market for higher education, consider an unsubsidized student who selects college A. Given a relatively small general subsidy, a subsidy with no requirements on where it is used, she would prefer to enroll in college B with greater quality than college A. With general subsidies, the student is unambiguously better off than without the subsidy and will choose to invest in at least as much quality as before. However, most students are not offered general subsidies but instead a subsidy-in-kind by their state governments, which operate a university or several universities providing some maximum amount or quality of education. More often than not, these subsidies are restricted to use at one of these public, in-state colleges. The student can either accept the subsidy at the colleges(s) allowed by the state or choose from the entire market without the subsidy. In such states, the student faces a more limited range of choice than with an equivalent subsidy not restricted to a particular subset of colleges, placing a

19 Namir Shah 18 limit on the amount of quality that the student may receive if she decides to use the subsidy. Figure 1(b) describes this situation with institution-specific subsidies. Because the student may only use the subsidy at a specific subset of colleges, the relative treatment of college choices has changed for students. It is this change that opens the possibility that students could change to a college of lower quality. For example, the student can consider colleges C and D. Their subsidies are exactly the same size, but a college-specific subsidy valid only at college C and not D would induce the student to choose college C, a choice with much lower quality than without any subsidy at all. As a result, utility-maximizing behavior for some individuals will induce acceptance of a subsidy-in-kind even though the quality of their unsubsidized choice would exceed that amount attained with the subsidy. If these individuals are numerous enough, the subsidy-in-kind could reduce total investment in higher education among eligible students. Because the market for higher education diverges from the stylized version modeled above, several revisions are required to understand the effects of the NJ STARS program. First, students do not face an infinite number of choices; unlike Figure 1(a), there are a finite number of colleges, leading to discontinuities in the 45-degree line. Second, as seen in Figure 3(b), even unsubsidized colleges do not necessary provide $1 worth of education at a cost of $1. This is partly because the measures of expenditure on students are far from perfect. Third, merit programs that focus on high-achieving student necessarily have different effects than a population-wide subsidy. Subsidies, like the NJ STARS program, that target the top

20 Namir Shah 19 performers are more likely to include students whose original unsubsidized choices would have fairly high levels of quality. Moreover, the NJ STARS program included only a small group of colleges with relatively low educational inputs. This, combined with the high-achieving nature of the eligible students, makes it more likely that NJ STARS induced students to enroll in colleges with lower educational inputs. This could translate into a lower educational attainment and decreased lifetime earnings, In short, theory suggests that NJ STARS is a program fairly likely to change college choices and not necessarily in a way that raises educational investments. 5 Empirical Methodology I am interested in discovering the impact of eligibility for the NJ STARS program on students college choice beyond the factors that normally influence college choice. As such, conditional logit is best suited for this estimation problem because it estimates the probability of choosing each alternative based on the empirical factors built into the model. These factors are divided into three categories: (i) characteristics of the student, (ii) characteristics of the college, and (iii) characteristics that describe the match between the student and the college choice. Student characteristics include GPA, class rank, and standardized test scores. Because the student characteristics do not vary based on the alternatives being considered, they have no influence on the conditional logit choice model. College characteristics include costs, educational inputs, student population attributes, and selectivity. Finally, match characteristics include distance from the

21 Namir Shah 20 student s home and the difference between the student s test scores and the median accepted student s test scores. In this case, NJ STARS eligibility falls into all three categories. The student must be academically eligible for the scholarship and the college must be a participating institution in the program, which creates a match dummy variable for joint eligibility. Conditional logit groups together every student-choice pair so that the total number of observations is equal to the product of the number of students and number of alternatives. Because I do not know the exact colleges to which the student applied and was admitted, I include as options all colleges to which students in my data set have enrolled, at times combined together into specific groups of similar colleges. This partially endogenous choice set formation helps to eliminate irrelevant alternatives, though not as precisely as if the data set included all colleges to which he student applied. For each student-choice pair, there exists a binary outcome dummy variable that indicates the actual choice of the student; only one of these outcome variables is equal to 1 for each student. Each observation is linked to the alternative-specific characteristics and the characteristics that depend both upon the student and the alternative. Conditional logit is specifically appropriate because it is able to examine choices in the presence of match-specific variables, unlike a multinomial logit. Conditional logit relies on the binary outcome variable to maximize the similarity between estimated likelihoods and actual enrollment outcomes. Formally, the equation I estimate is:

22 Namir Shah 21 h = = to maximize ln = ln h = and where collegei is the college choice of student i, each j represents a college alternative, the vector xij includes the college characteristics and match-specific ij variables, and is the vector of estimated effects (Avery and Hoxby 2003, p. 14). The college-specific variables that I include in xij are: total academic spending 4, enrollment size, graduation rate, median admitted student s Math + Verbal SAT score, total costs 5, and the following dummies: in-state, for-profit, two-year, open enrollment, and eligibility for NJ STARS. Several other variables, such as admissions rate, were excluded for fear of imposing too much multicollinearity among the covariates. It is the case that several indicators of academic achievement and selectivity are strongly multicollinear; including the full set of such variables would make interpretation of the regression results quite difficult, if not somewhat meaningless. The match-specific variables include distance, distance-squared, the difference between the student s SAT score and the college s median SAT score, a joint eligibility treatment dummy, and a joint eligibility treatment dummy for only students for whom the program was in place. I additionally include dummy ij 4 Total academic spending includes instructional spending, academic support, institutional support, and student services. 5 Total costs include tuition and fees, on-campus room and board, books, and other required costs reported by the institution.

23 Namir Shah 22 variables to indicate whether the following are missing and/or unreported in my data set: median SAT score, graduation rate, academic spending, total cost, and the difference between the college s median and the student s SAT scores. The variation that drives my estimates arises from within-student differences in the college alternatives rather than differences between students. Because college characteristics are exogenous to the student, she must accept the alternatives before her and make a decision based on the alternative-specific and match-specific variables described above. Student characteristics can influence the college choice decision by affecting the manner in which she responds to college or other match characteristics. In the case of significant disparities in these considerations, it can be appropriate to estimate the choice model separately based on the student characteristics. Characteristics that vary by student but not by alternative are thus not included independently in the model. However, characteristics that vary by college but not by student are modeled because they can be direct factors in the college choice. I display results of the conditional logit estimation using odds ratios. The odds ratio indicates the ratio of post- odds of a choice to the pre- odds of a choice given a ceteris paribus change in the variable in question. Formally, the odds ratio is. Positive odds ratios denote an increase in likelihood of a choice as the variable in question increases, while negative odds ratios signify the opposite. Using the estimated odds ratios, I am able to manipulate the regression results to yield estimated probabilities of enrollment for each choice before and after NJ STARS.

24 Namir Shah 23 These predictions represent counterfactual probabilities based on all academically qualified students being treated as eligible for NJ STARS and none of the academically qualified students being treated as eligible. This involves turning the match variable for joint eligibility on and off to mimic pre- and post-nj STARS scenarios. From these pre- and post-stars probabilities, I am able to measure the effects of the program on a specific set of college characteristic and educational outcome variables, such as in-state enrollment, two-year college enrollment, graduation rates, and academic spending. 6 Data 6.1 Data Construction and Sources To address the questions posed at the beginning of this paper, studentspecific and college-specific administrative data are most helpful. For student data, the above model requires measures of high school academic performance, particularly class rank percentiles and standardized testing scores, the institution in which the student enrolls after graduating from high school, the type of degree program, and information on college transfers. To create an effective choice model, I additionally require college-specific characteristics, including location, enrollment, and costs. In order to measure college quality, it is also important to consider perstudent academic spending and measures of peer aptitude such as the median student's standardized test scores. In this paper, I draw on three primary data sets. Student performance and demographic data comes from the College Board, and enrollment information from

25 Namir Shah 24 the National Student Clearinghouse (NSC) data is liked to the College Board data set using unique identifiers. College variables, including academic characteristics, selectivity information, and spending data are obtained from the National Center for Education Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS). Here, I describe these three data sets and the construction of the full data set that I use in my final analysis. The College Board data set provides the foundation of New Jersey student information, including descriptions of students college enrollment preferences, including location, size, and type, and a list of the colleges to which each student sent an SAT score report. In addition, the College Board provides demographic information, academic performance metrics, and standardized test scores. Students in the top quintile are considered the treatment students, while students in the second quintile are the control. Class rank is self-reported by students when they take the SAT so it is not a completely accurate measure of the class rank that a student has at the time she graduates. Nevertheless, it is a strong predictor of eligibility for the NJ STARS scholarship. To improve the accuracy of the prediction, I removed all students who reported themselves to be in the two quintiles but who also reported themselves to have a GPA of less than a B. The NSC data set contains information about students college enrollment, location, and educational history. The data includes every college in which the student has enrolled, as well as some degree information depending on the college, particularly the students degree type and major. From this baseline, three

26 Namir Shah 25 categories were calculated: (i) the college in which the student first enrolled after high school graduation, (ii) the college in which the student was enrolled for the longest period of time after high school graduation, and (iii) the college in which the student was most recently enrolled. For the purposes of this paper, I am interested in the initial college choice decision and must designate a single institution to consider the student s actual choice for the regression. However, the first college is not always the best fit for the analysis. As a result, I created a basic algorithm to assign a single college as each student s choice. First, if the college that the student first attended and the college that the student most recently attended are the same, that college is assigned. By extension, if all three college categories were identical, the same rule applies. Approximately 81% of the students satisfied this first criterion. Next, if the student s first period of college enrollment after high school lasted for at least three months, that college was used. Finally, if the student s first period of college enrollment was less than three months, I assigned the college in which they were enrolled for the longest period of time. In the rare case that NCS records could not be located for the three aforementioned categories, the student was dropped from the analysis. For these students, it is possible that the student did not enroll in college after graduating high school or the student enrolled in a college not covered by the NCS. The NCS data was then merged with the College Board data using the students unique random identification number. From the NSC data set, I extract a list of the colleges in which the students have enrolled. Using the IPEDS database, I compile a range of detailed college

27 Namir Shah 26 variables. Because the program was passed and first implemented in 2004, I use IPEDS data from 2004 to gather college characteristics corresponding with the inception of the program. For most colleges, this data would have contained information for the academic year, which is the most recent information that the graduating class of 2004 would have had when making their college choice decision. The IPEDS data set includes detailed data on college characteristics, enrollment, admissions criteria and selectivity, graduation rates, institutional expenditures, published student costs, and financial aid. College characteristics, particularly institutional sector (e.g. four-year, not-for-profit) and location were primarily used to construct the college categories used in the choice model. Graduation rates and institutional spending are the primary observable college quality characteristics, while median test scores and the admissions rate are helpful as indicators of peer quality and the likelihood of admission conditional on applying. The characteristics that I expect, based on the previous literature, to be most important in college choice are total enrollment, instructional spending, total academic spending, the admissions rate, the median Math + Verbal SAT score, the graduation rate, total costs, and average net cost. Because these variables tend to be multicollinear, it can be difficult to interpret the coefficient on one of them without considering the coefficients on the others. Using the available IPEDS data, I construct variables for each college s admissions rate and median SAT scores, computed as the mean of the 75 th and 25 th percentiles of SAT scores of the schools. For colleges that reported only ACT score percentiles and no SAT score percentiles,

28 Namir Shah 27 I use the College Board and ACT s published concordance tables to calculate the equivalent SAT scores. For a small group of colleges, expenditure information could not be found in the IPEDS database. In these cases, I consulted the Delta Cost Project s publicly available data set for academic spending metrics. Beyond these three data sets, I also utilized the Barron s Admissions Competitiveness Index. The Barron s Admissions Competitiveness Index classifies institutions of higher education into seven categories based on selectivity: Most Competitive (1), Highly Competitive (2), Very Competitive (3), Competitive (4), Less Competitive (5), Noncompetitive (6), and Special (7). Furthermore, there are three plus subcategories: 2+, 3+, and 4+, which contain colleges at the upper echelon of their respective score. Hundreds of colleges, particularly two-year colleges, are not included; I classify these as unranked by Barron's. To construct the list of 170 college categories for the conditional logit model, I began with individual categories for each New Jersey college with at least 15 observations in the combined College Board-National Student Clearinghouse data set. Next, I added the 50 out-of-state colleges with the greatest number of enrollment observations. For the remaining colleges, I sort them into groups based on Barron s Admissions Competitiveness Index scores, location, and sector. Categories with too few observations were merged into similar categories. For example, given low enrollment in distant colleges, I combined all colleges in the Far West, Plains, Rocky Mountain, and Southwest regions by Barron s score. In some cases, categories were compressed even further. For each category, I then calculated

29 Namir Shah 28 category-specific variables for use in the choice model regression by using an enrollment-weighted-average of the values of the colleges within that category. Thus, colleges that students chose more often within a category were given more weight in the calculation of the category variables; colleges that did not report those variables were given no weight. These variables included total academic spending, admissions rate, median Math + Verbal SAT score, graduation rate, total costs, and average net cost. To estimate a conditional logit model, it is necessary to structure the data set in such a way that there is an observation for every possible student-category choice pair. The data set includes not only the characteristics of the choices but also match-specific ( match ) variables that depend on both the student and the college category choice. For instance, I calculate the difference between the student s SAT score and the median score of students in the choice category. I also compute the distance in miles between the student and the average location of a college within each category. Crucially, I include a dummy variable that indicates whether the student-college match was eligible for the NJ STARS program. Because the NJ STARS program changed so substantially in 2009, I focus on New Jersey high school graduating classes from 2000 to Another reason for focusing on these classes is that the families of students in the graduating classes of 2009 onwards were potentially seriously affected by macroeconomic and financial market conditions. These could have had independent effects on students' college choices that would confound an investigation of the effects of NJ STARS.

30 Namir Shah Weakness of the Data Set The data sets described above do not include a precise measure of students financial circumstances, particularly household income. Because only students with household incomes below $250,000 are eligible for NJ STARS grants, the treatment group may also include students who are not financially eligible. From 2004 to 2008, the percentage of New Jersey households exceeding this income eligibility stayed fairly consistent at about 3.8% to 3.9% (State of New Jersey). Since only a small fraction of New Jersey families fall outside this eligibility range, I estimate that the estimation s precision is only slightly diminished by the inability to determine financial ineligibility for the scholarship. However, due to the structure of the program, it is conceivable that the program would have varying degrees of impact on students based on their family s financial circumstances. Students from lower-income households may be more heavily swayed by free tuition to a two-year college, while a more affluent student may not react at all to the relatively small financial incentive. 6.3 Summary and Descriptive Statistics My completed data set includes 57,186 New Jersey students from the graduating classes of 2000 to Approximately 47.6 percent of the students graduated before the inception of NJ STARS in 2004, so the program was in place for the later 52.4 percent of students. 45,455 of these students self-identified their class rank as the top quintile, while 11,731 listed their rank as the second quintile. The former subset comprises the treatment group, while the latter make up the

31 Namir Shah 30 control group. 49 percent of the students reported at least an A average. Across the full set of students, the mean SAT Critical Reading and Math scores are 613 and 629, respectively, with combined scores in the range of 1010 to Using the NSC data set, I find that that colleges in which these students most frequently enroll first are Rutgers New Brunswick, The College of New Jersey, Rowan University, Montclair State University, and New York University, in that order. From 2000 to 2008, the percentage of students in the top two quintiles enrolling in public colleges has remained fairly consistent, while the percentage enrolling in New Jersey colleges slightly increased over the same period. Enrollment in two-year colleges, however, grew significantly more among top quintile students than second quintile students, an increase of 87% and 23%, respectively. Table 2 contains a summary of New Jersey s two-year and four-year colleges with selected academic and selectivity data for the academic year. Enrollment in public, two-year colleges increased by 16.5 percent from fall 2004 to fall 2009, while enrollment in public, four-year colleges increased by only 12.2 percent over the same period. Among New Jersey s public, four-year colleges, The College of New Jersey is the only college with a Barron s Admissions Competitiveness Index score of 1; its 50 th percentile SAT score of 1265 is also the highest of New Jersey s public colleges. Rutgers University s $10,226 in instructional spending and $15,376 in total academic spending position it as the best-resourced public college in New Jersey based on educational inputs. Union

32 Namir Shah 31 County College s $4,206 in instructional spending, the highest of New Jersey s public, two-year colleges, falls slightly short of Montclair State University s $4,466, the lowest of the public, four-year colleges. Overall, the summary statistics indicate that top quintile students have disproportionately shifted their enrollment from four-year colleges to two-year colleges with less academic resources. Such a shift impacts the educational outcomes of the students through potentially lower-quality education, diminished persistence to graduation, and decreased future income. However, this simple analysis does not fully account for the numerous factors that may influence students college enrollment decisions. Thus, my empirical strategy going forward employs an appropriate econometric multinomial choice model in addition to variation in eligibility caused by the program's rules and introduction. 7 Results 7.1 Conditional Logit Enrollment Rates My basic conditional logit results on the determinants of college choice are summarized in Table 6. Reported values are odds ratios for all students, control and treatment, regardless of year. The overall patterns of signs, magnitude, and significance make sense in relation to the college choice model. The results are broadly close to my expectations and likely the expectations of most economists estimating a college choice model. Students are 1.2% more likely to enroll in a college with a one percentage point higher graduation rate, 3.6% more likely to enroll in a college with a ten point increase in median Math + Verbal SAT score,

33 Namir Shah 32 and 26.5% less likely to enroll in a college 100 miles further from their homes. Next, for every ten points that the student s SAT score exceeds the college s median SAT score, the probability of enrollment increases by 1%. When looking at the inputs-cost consideration, I find that students are 3.8% less likely to enroll in a college with a $1,000 increase in total costs but also 0.8% less likely to enroll in a college with a $1,000 increase in total academic spending. While the latter may seem counterintuitive, it is helpful to remember that total costs and academic spending are highly multicollinear, leading to difficulty in interpreting either covariate independently. Finally, we see that students who are academically equivalent to STARS-eligible students but graduated before the program s inception NJ STARS are 62.4% less likely to enroll in one of the eligible public, two-year colleges in New Jersey, while joint eligibility between the student and the college choice increases probability of enrollment by 62.2%. This is intuitive because students who have no financial incentive to attend one of the public, twoyear colleges would likely otherwise attend a much more selective and betterresourced institution. The initial conditional logit model shows fairly good results in terms of precision and model fitness. Due to the large number of students and studentcollege observations (over 9.7 million), it is logical that the coefficients and odds ratios are measured with great precision. The R-squared of indicates that 8.55 % of variation in the sample is modeled by the existing covariates. In a college choice model designed to analyze the effects of the scholarship program, this degree

34 Namir Shah 33 of model fitness is quite adequate. Many variables that would increase the R- squared are of little importance to my analysis, including availability of desired major and legacy status. At the same time, other variables that could may would enhance the fit of the model could overly complicate interpretation of regression results by adding further multicollinearity. For example, instructional spending, student-faculty ratio, admissions rate, and other selectivity measures are correlated with variables already included in my analysis. Furthermore, selectivity tends to be correlated with college resources generally, even without including multiple measurements of each. The lack of convergence in the model, specifically for two variables, is an area of caution but not of a point of great concern. 7.2 Counterfactual Enrollment Effects Beyond the conditional logit odds ratios, I manipulate the data set to calculate counterfactual summary statistics. That is, I estimate enrollment for students that are academically eligible for NJ STARS (i.e. in the top quintile) with and without NJ STARS. Predicted enrollment rates for each college category are reported in Table 7. Overall, the model predicts that enrollment in New Jersey s public, two-year colleges increases at the sacrifice of enrollment in nearly every other college category. As summarized in Table 8, we see that the NJ STARS program has induced academically eligible students to enroll in two-year colleges at a rate 41% higher and four-year colleges at a rate 1% lower than the hypothetical scenario in which the same students were not eligible for the program. Similarly, students enroll in New Jersey colleges and universities 1.7% more frequently. These

35 Namir Shah 34 are equivalently thought of as the differences in enrollment rates between the scenarios in which the STARS program does and does not exist. 6 To assess the post-enrollment effects of the program on students, I also calculate the mean admissions rate, graduation rate, and total academic spending of students college choices with and without NJ STARS. Under the STARS incentive scheme, students in the top quintile enroll in colleges with, on average, a 0.85% higher admissions rate, a 0.79% lower graduation rate, and 0.71% less in total academic spending. These correspond with less selective and less resourced colleges when compared with enrollment in the absence of the program. 8 Conclusion Overall, I would describe the students in my sample as altering their college choice behavior in an expected manner. The conditional logit choice model indicates that eligible students reacted quite significantly to NJ STARS by shifting their enrollment patterns from four-year colleges to the in-state, public, two-year colleges that qualify for the program. This result is less surprising given the unusual structure of the program, which requires students to first attend one of New Jersey s community colleges to receive the scholarship. However, my analysis also points strongly toward adverse, unintended consequences. As a result of directing high-achieving students to two-year colleges, I find a nontrivial decrease in the quality of colleges that eligible students attend in the 6 Importantly, this analysis is conditional on the students in question enrolling in a college with or without the NJ STARS program. The model is unable to consider whether students would not have attended college at all in the absence of the program.

36 Namir Shah 35 presence of the NJ STARS incentives. Total academic spending is an accurate indicator of the academic resources that a college provides to its students. Though a $151 per-student decrease in total academic spending may seem small, that amount aggregated over the entire eligible population results in a significant quantity of resources foregone. When the state of New Jersey is paying over $4,000 per NJ STARS scholar in an attempt to increase their educational attainment, the decreased investment by students who would otherwise have attended a better resourced institution calls into question the net benefit of the program. Beyond educational inputs during college, a widespread shift of students from four-year college to two-year colleges has important consequences on actual educational attainment. While STARS-eligible students are high-achieving, twoyear colleges have much lower graduation rates than their four-year rivals and often lack the institutional resources to necessary to support persistence to graduation. In a program designed to encourage students to transfer to four-year colleges at the completion of a two-year degree, it is a reasonable concern whether students are actually graduating with a two-year degree or dropping out of college before doing so. Furthermore, after receiving an associate s degree, the opportunity cost of attending a four-year college is even greater, so graduates of NJ STARS may choose not to continue their education in a four-year college at all. This is not only a potential barrier to educational attainment, but could impact students future earnings, as well.

37 Namir Shah 36 Conditioning scholarship on in-state and public college enrollment, particularly two-year colleges with a limited range of college quality, greatly distorts college choice in a way that scholarships transferable to private, out-ofstate, and/or four-year colleges could avoid. Students already planning to attend college would be able to optimize their quantity of education investment while keeping the subsidy, while those who would not be able to attend college could still use the scholarship at the institution of their choice. With between 87,000 and 90,000 students in each graduating high school class in New Jersey, about 18,000 students per year would have been eligible for NJ STARS using the top 20% class rank eligibility. Applying the average decrease in total academic spending among eligible students of $151 to this eligible population yields an aggregate loss of roughly $2.7 million in academic spending. Compared to the $1.7 million spent by the state for the inaugural class s 789 students and even the $18 million spent at the program s peak in the academic year, the adverse effects seem fairly large. There is a certain cost-benefit analysis that should be considered when implementing an in-kind subsidy like New Jersey s STARS program. At some level, the actual cost of the scholarships combined, the opportunity cost of reduced investment by students already attending college, and potentially worse educational outcomes could exceed the benefits of the students who are able to afford a college education. This consideration becomes even more important when the eligible options for use of the scholarship are as limited as they are in New Jersey. The

38 Namir Shah 37 unintended consequences of the program may be undermining the objectives it was created to achieve. At the very least, the NJ STARS program merits further study in order to evaluate the net benefits as the state continues to alter its eligibility requirements and scholarship structure.

39 Namir Shah 38 9 References Avery, C. and Hoxby, C Do and Should Financial Aid Packages Affect Students College Choices? in College Choices, ed. C. Hoxby. Chicago: University of Chicago Press. Cohodes, S. and Goodman, J Merit Aid, College Quality and College Completion: Massachusetts Adams Scholarship as an In-Kind Subsidy, Harvard University (Cambridge, MA). Working Paper. Dynarski, S Hope for Whom? Financial Aid for the Middle Class and Its Impact on College Attendance, National Tax Journal. Dynarski, S The New Merit Aid. in College Choices, ed. C. Hoxby. Chicago: University of Chicago Press. "Fewer NJSTARS Enrolling in 4-year NJ Colleges." Washington Times. Associated Press, 19 Apr Web. 20 Apr Ganderton, P The effect of subsidies in kind on the choice of a college, Journal of Public Economics, 48(3): pp Long, B.T Does the Format of a Financial Aid Program Matter? The Effect of State In-Kind Tuition Subsidies, The Review of Economics and Statistics: pp Peltzman, S The Effect of Government Subsidies-in-Kind on Private Expenditures: The Case of Higher Education, Journal of Political Economy, 81: pp

40 Namir Shah 39 State of New Jersey Department of the Treasury, Office of Revenue and Economic Analysis. Statistics of Income: 2004 Income Tax Returns United States Department of Education, National Center for Education Statistics, Integrated Postsecondary Education Data System, Higher Education Finance Data File. Electronic data, 2001.

41 Namir Shah Figures and Tables Figure 1: Peltzman Model with College Quality (a) Inputs-cost model with money subsidy Educational Inputs 45-degree line with subsidy 45-degree line without subsidy B A Tuition and Fees

42 Namir Shah 41 (b) Inputs-cost model with in-kind subsidy Educational Inputs 45-degree line D D A C C Tuition and Fees

43 Namir Shah 42 Figure 2: Enrollment Types as a Percentage of Student Quintile (a): Two-year college enrollment for top and second quintile over time 7.00% 6.00% 5.00% 4.00% 3.00% 2.00% 1.00% Percent Enrolling in Two-Year Colleges 0.00% Top Quintile Second Quintile (b): In-state enrollment for top and second quintile over time 50.00% 45.00% 40.00% 35.00% 30.00% 25.00% Percent Enrolling in In-State Colleges 20.00% Top Quintile Second Quintile Source: The College Board and National Student Clearinghouse data

44 Namir Shah 43 (c): Public enrollment for top and second quintile over time 70.00% Percent Enrolling in Public Colleges 60.00% 50.00% 40.00% 30.00% 20.00% Top Quintile Second Quintile Source: The College Board and National Student Clearinghouse data

45 Namir Shah 44 Figure 3: Inputs-Cost for New Jersey Colleges (a) Average net cost vs. educational inputs New Jersey Colleges Educational Input vs. Avg. Net Cost Princeton Total Academic Spendin ($) Rutgers New Brunswick Drew University Average Net Cost ($) Private, 4-year Public, 4-year Public, 2-year Source: Delta Cost Project and National Center for Education Statistics (NCES) data

46 Namir Shah 45 (b) Tuition and fees vs. educational inputs New Jersey Colleges Educational Input vs. Cost Princeton Total Academic Spending ($) Rutgers New Brunswick Tuition and Fees ($) Private, 4-year Public, 4-year Public, 2-year Source: Delta Cost Project and National Center for Education Statistics (NCES) data

47 Namir Shah 46 (c) Average net cost vs. selectivity (median SAT) New Jersey Colleges Educational Input vs. Selectivity Princeton Total Academic Spendin ($) Drew University Rutgers New Brunswick Selectivity (50th Percentile Math + Critical Reading SAT) Private, 4-year Public, 4-year Public, 2-year Source: Delta Cost Project and National Center for Education Statistics (NCES) data

48 Namir Shah 47 Figure 4: Enrollment-Weighted Mean Graduation Rates of Four-Year and Two-Year Public, New Jersey Colleges Over Time 70.00% Average Graduation Rates of NJ Public Colleges 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Two-year Four-year Source: National Center for Education Statistics (NCES) data

49 Namir Shah 48 Figures 5: Total Enrollment in New Jersey Colleges Over Time 180, , , , , , , ,000 Total Enrollment in NJ Public Colleges 140,000 Fall 2004 Fall 2005 Fall 2006 Fall 2007 Fall 2008 Fall 2009 Two-year Four-year Source: National Center for Education Statistics (NCES) data

50 Namir Shah 49 Figures 6: Enrollment-Weighted Tuition Rates at Two-Year and Four-Year, Public New Jersey Colleges Over Time $10,000 Average Tuition of NJ Public Colleges $8,000 $6,000 $4,000 $2,000 $ Two-year Four-year Source: National Center for Education Statistics (NCES) data

51 Namir Shah 50 Figure 7: Changes in Enrollment due to NJ STARS 2.00% Effect of NJ STARS on Enrollment Statistics Percent Change 1.00% 0.00% -1.00% -2.00% Four-Year College Total Academic Spending Admissions Rate In-State College Graduation rate Counterfactual statistics above indicate the effect on STARS-eligible students only.