In-State Merit Aid and College Choice: New Jersey’s

STARS Program as a Tuition Subsidy

Namir Shah1

Department of Economics Stanford University, Stanford, CA 94305

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 in-state, 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.

KeywordsKeywordsKeywordsKeywords: 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.

Namir Shah 1

1111 IntroductionIntroductionIntroductionIntroduction

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 non-

private 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

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 merit-

based 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 two-

year 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

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 in-

state colleges or two-year colleges when eligible for in-kind subsidies valid only at

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 in-

state, 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

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.

2222 BackgroundBackgroundBackgroundBackground

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-for-

profit four-year colleges.

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 2004. 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 2008-2009 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

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 2014-2015 school year, with four-

year college recipients dropping from 1,844 in the 2013-2014 school year to 1,200 in

2014-2015. This reflects a five-year decline in state funding for the program, from

$18 million in the 2008-2009 academic year to $8.5 million proposed for 2014-2015.

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-of-

state, 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

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 four-

year colleges. Tables 1 and 2 shows costs and educational inputs of New Jersey’s

two-year and four-year colleges for the 2003-2004 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.

Namir Shah 9

STARS participants may be much more likely to graduate than would the average

student of the two-year colleges.

3333 Related Literature Related Literature Related Literature 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.

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

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 one-

third 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

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.

Namir Shah 13

3.2 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 1982. 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

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

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 in-

state public and private college options provide compelling circumstances for

determining how conditional tuition subsidies alter college choices.

4444 Theoretical Theoretical Theoretical Theoretical FrameworkFrameworkFrameworkFramework

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 non-

college 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

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

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

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

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.

5555 Empirical MethodologyEmpirical MethodologyEmpirical MethodologyEmpirical 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

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:

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 xxxxijijijij includes the college characteristics and match-specific

variables, and � is the vector of estimated effects (Avery and Hoxby 2003, p. 14).

The college-specific variables that I include in xxxxijijijij are: total academic spending4,

enrollment size, graduation rate, median admitted student’s Math + Verbal SAT

score, total costs5, 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

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.

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.

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.

6666 DataDataDataData

6.1 Data Construction and Sources

To address the questions posed at the beginning of this paper, student-

specific 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 per-

student 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

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

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

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 2003-2004 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 75th and 25th percentiles of SAT scores of the schools.

For colleges that reported only ACT score percentiles and no SAT score percentiles,

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

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 2008. 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.

Namir Shah 29

6.2 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 2008. 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

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 1600.

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 2003-2004 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 50th 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

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.

7777 ResultsResultsResultsResults

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,

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, two-

year colleges would likely otherwise attend a much more selective and better-

resourced 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 student-

college observations (over 9.7 million), it is logical that the coefficients and odds

ratios are measured with great precision. The R-squared of 0.0855 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

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

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.

8888 ConclusionConclusionConclusionConclusion

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.

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, two-

year 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.

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-of-

state, 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 2008-2009 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

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.

Namir Shah 38

9999 ReferencesReferencesReferencesReferences

Avery, C. and Hoxby, C. 2003. “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. 2013. “Merit Aid, College Quality and College

Completion: Massachusetts’ Adams Scholarship as an In-Kind Subsidy,”

Harvard University (Cambridge, MA). Working Paper.

Dynarski, S. 2000. “Hope for Whom? Financial Aid for the Middle Class and Its

Impact on College Attendance,” National Tax Journal.

Dynarski, S. 2004. “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. 2014. Web. 20 Apr. 2014.

Ganderton, P. 1992. “The effect of subsidies in kind on the choice of a college,”

Journal of Public Economics, 48(3): pp. 269-292.

Long, B.T. 2004. “Does the Format of a Financial Aid Program Matter? The Effect of

State In-Kind Tuition Subsidies,” The Review of Economics and Statistics:

pp. 767-782.

Peltzman, S. 1973. “The Effect of Government Subsidies-in-Kind on Private

Expenditures: The Case of Higher Education,” Journal of Political Economy,

81: pp. 1-27.

Namir Shah 39

State of New Jersey Department of the Treasury, Office of Revenue and Economic

Analysis. “Statistics of Income: 2004 Income Tax Returns.” 2006.

United States Department of Education, National Center for Education Statistics,

Integrated Postsecondary Education Data System, Higher Education Finance

Data File. Electronic data, 2001.

Namir Shah 40

10101010 Figures and TablesFigures and TablesFigures and TablesFigures and Tables

Figure 1: Peltzman Model with College Quality (a) Inputs-cost model with money subsidy

Educational Inputs

Tuition and Fees

45-degree line without subsidy

B

A

45-degree line with subsidy

Namir Shah 41

(b) Inputs-cost model with in-kind subsidy

Educational Inputs

Tuition and Fees

45-degree line

D

D’

C

C’

A

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

(b): In-state enrollment for top and second quintile over time

Source: The College Board and National Student Clearinghouse data

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

2000 2001 2002 2003 2004 2005 2006 2007 2008

Percent Enrolling in Two-Year Colleges

Top Quintile Second Quintile

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

2000 2001 2002 2003 2004 2005 2006 2007 2008

Percent Enrolling in In-State Colleges

Top Quintile Second Quintile

Namir Shah 43

(c): Public enrollment for top and second quintile over time

Source: The College Board and National Student Clearinghouse data

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

2000 2001 2002 2003 2004 2005 2006 2007 2008

Percent Enrolling in Public Colleges

Top Quintile Second Quintile

Namir Shah 44

Figure 3: Inputs-Cost for New Jersey Colleges (a) Average net cost vs. educational inputs

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Drew University

Princeton

Rutgers New

Brunswick

0

10000

20000

30000

40000

50000

60000

0 5000 10000 15000 20000 25000 30000 35000

Total Academic Spendin ($)

Average Net Cost ($)

New Jersey Colleges Educational Input vs. Avg. Net Cost

Private, 4-year Public, 4-year Public, 2-year

Namir Shah 45

(b) Tuition and fees vs. educational inputs

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Princeton

Rutgers New

Brunswick

0

10000

20000

30000

40000

50000

60000

0 5000 10000 15000 20000 25000 30000

Total AcademicSpending ($)

Tuition and Fees ($)

New Jersey Colleges Educational Input vs. Cost

Private, 4-year Public, 4-year Public, 2-year

Namir Shah 46

(c) Average net cost vs. selectivity (median SAT)

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Drew University

Princeton

Rutgers New

Brunswick

0

10000

20000

30000

40000

50000

60000

0 200 400 600 800 1000 1200 1400 1600

Total Academic Spendin ($)

Selectivity (50th Percentile Math + Critical Reading SAT)

New Jersey Colleges Educational Input vs. Selectivity

Private, 4-year Public, 4-year Public, 2-year

Namir Shah 47

Figure 4: Enrollment-Weighted Mean Graduation Rates of Four-Year and Two-Year Public, New Jersey Colleges Over Time

Source: National Center for Education Statistics (NCES) data

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

2004 2005 2006 2007 2008 2009

Average Graduation Rates of NJ Public Colleges

Two-year Four-year

Namir Shah 48

Figures 5: Total Enrollment in New Jersey Colleges Over Time

Source: National Center for Education Statistics (NCES) data

140,000

145,000

150,000

155,000

160,000

165,000

170,000

175,000

180,000

Fall 2004 Fall 2005 Fall 2006 Fall 2007 Fall 2008 Fall 2009

Total Enrollment in NJ Public Colleges

Two-year Four-year

Namir Shah 49

Figures 6: Enrollment-Weighted Tuition Rates at Two-Year and Four-Year, Public New Jersey Colleges Over Time

Source: National Center for Education Statistics (NCES) data

$0

$2,000

$4,000

$6,000

$8,000

$10,000

2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09

Average Tuition of NJ Public Colleges

Two-year Four-year

Namir Shah 50

Figure 7: Changes in Enrollment due to NJ STARS

Counterfactual statistics above indicate the effect on STARS-eligible students only.

-2.00%

-1.00%

0.00%

1.00%

2.00%

Percent Change

Effect of NJ STARS on Enrollment Statistics

Four-Year College In-State College

Total Academic Spending Graduation rate

Admissions Rate

Namir Shah 52

Table 1: Cost Breakdown of New Jersey Colleges

(a) Public, two-year colleges

Institution Name STARS Tuition & Fees

Total Costs

Costs with STARS

Average Net Cost

Atlantic Cape Community College Yes $2,440 $12,185 $9,745 $10,881.25

Bergen Community College Yes $2,273 $8,445 $6,172 $7,001.58

Brookdale Community College Yes $2,432 $16,184 $13,752 $14,914.19

Burlington County College Yes $2,067 $15,621 $13,554 $14,776.72

Camden County College Yes $2,310 $12,970 $10,660 $11,349.17

County College of Morris Yes $2,465 $13,331 $10,866 $12,624.53

Cumberland County College Yes $2,640 $15,485 $12,845 $13,462.04

Essex County College Yes $2,478 $10,182 $7,704 $7,520.61

Gloucester County College Yes $2,736 $15,211 $12,475 $13,992.52

Hudson County Community College Yes $3,033 $15,153 $12,120 $13,850.23

Mercer County Community College Yes $1,992 $11,192 $9,200 $10,218.88

Middlesex County College Yes $2,513 $16,561 $14,048 $15,189.79

Ocean County College Yes $2,524 $16,678 $14,154 $15,793.08

Passaic County Community College Yes $2,483 $13,062 $10,579 $11,192.00

Raritan Valley Community College Yes $2,470 $9,860 $7,390 $9,053.16

Salem Community College Yes $2,975 $7,965 $4,990 $5,842.87

Sussex County Community College Yes $2,514 $11,536 $9,022 $10,309.27

Union County College Yes $2,243 $20,243 $18,000 $19,098.30

Warren County Community College Yes $2,064 $14,286 $12,222 $13,203.21

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Namir Shah 53

(b) Public, four-year colleges

Institution Name STARS

II Tuition & Fees

Total Costs

Costs with STARS II

Average Net Cost

Kean University Yes $6,723 $16,460 $9,460 $14,186.94

Montclair State University Yes $6,460 $19,625 $12,625 $16,789.56

New Jersey City University Yes $6,051 $18,280 $11,280 $14,551.86

New Jersey Institute of Technology Yes $8,500 $20,522 $13,522 $18,562.00

Ramapo College of New Jersey Yes $7,412 $19,240 $12,240 $15,510.46

Rowan University Yes $7,258 $19,027 $12,027 $17,720.05

Rutgers University-Camden Yes $7,756 $18,769 $11,769 $14,442.06

Rutgers University-New Brunswick Yes $7,927 $19,439 $12,439 $15,335.36

Rutgers University-Newark Yes $7,592 $19,297 $12,297 $14,711.38

The College of New Jersey Yes $8,206 $19,815 $12,815 $15,519.26

The Richard Stockton College of New Jersey Yes $6,224 $17,429 $10,429 $15,224.36

Thomas Edison State College Yes - - - -

William Paterson University of New Jersey Yes $7,120 $18,672 $11,672 $15,772.81

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Namir Shah 54

(c) Private, four-year colleges

Institution Name STARS

II Tuition & Fees

Total Costs

Costs with STARS II

Average Net Cost

Bloomfield College In 2012 $13,100 $23,139 $23,139 $14,726.54

Caldwell College In 2012 $17,060 $27,660 $27,660 $17,381.19

Centenary College In 2012 $17,986 $28,240 $28,240 $22,705.72

College of Saint Elizabeth In 2012 $16,450 $29,605 $29,605 $15,706.71

Drew University In 2012 $27,906 $41,092 $41,092 $29,156.95

Fairleigh Dickinson University-Florham In 2012 $21,400 $36,240 $36,240 $24,753.29

Fairleigh Dickinson University-Metropolitan In 2012 $19,854 $35,036 $35,036 $23,597.09

Felician College In 2012 $14,500 $29,285 $29,285 $25,178.64

Georgian Court University In 2012 $16,272 $29,124 $29,124 $21,475.04

Monmouth University In 2012 $18,768 $30,845 $30,845 $23,723.63

Princeton University No $28,540 $41,910 $41,910 $30,304.04

Rider University In 2012 $21,050 $33,900 $33,900 $24,016.57

Saint Peter's College In 2012 $18,592 $30,080 $30,080 $16,123.76

Seton Hall University In 2012 $21,855 $35,496 $35,496 $24,261.27

Stevens Institute of Technology In 2012 $26,000 $39,700 $39,700 $24,621.85

Berkeley College-Woodland Park In 2012 $15,135 $28,600 $28,600 $21,800.14

DeVry University No $10,100 $24,790 $24,790 $22,105.50

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Namir Shah 55

Table 2: Academic Statistics of New Jersey Colleges

(a) Public, two-year colleges

Institution Name Instructional Spending

Total Academic Spending per FTE

Graduation Rate Enrollment Admissions

Atlantic Cape Community College $2,771 $6,397 15% 6,515 Open

Bergen Community College $3,226 $5,994 10% 14,325 Open

Brookdale Community College $3,416 $6,809 19% 13,083 Open

Burlington County College $1,972 $4,832 11% 7,514 Open

Camden County College $2,184 $4,547 9% 15,116 Open

County College of Morris $3,799 $6,676 23% 8,422 Open

Cumberland County College $3,610 $7,307 21% 3,174 Open

Essex County College $2,550 $4,889 5% 11,268 Open

Gloucester County College $3,345 $6,562 15% 5,636 Open

Hudson County Community College $2,203 $4,825 6% 6,492 Open

Mercer County Community College $4,099 $7,502 15% 9,033 Open

Middlesex County College $3,497 $5,946 11% 12,984 Open

Ocean County College $2,818 $6,314 21% 8,335 Open

Passaic County Community College $3,602 $6,675 9% 6,989 Open

Raritan Valley Community College $2,962 $5,891 13% 6,451 Open

Salem Community College $4,184 $9,114 23% 1,163 Open

Sussex County Community College $2,850 $6,385 17% 3,153 Open

Union County College $4,206 $7,117 6% 11,058 Open

Warren County Community College $3,438 $7,829 12% 1,332 Open

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Namir Shah 56

(b) Public, four-year colleges

Institution Name Instructional Spending

Total Academic Spending per FTE

Graduation Rate Enrollment

Kean University $6,239 $9,956 38% 12,897

Montclair State University $4,466 $8,314 50% 15,637

New Jersey City University $6,528 $12,528 N/A 8,799

New Jersey Institute of Technology $6,703 $13,438 44% 8,249

Ramapo College of New Jersey $4,901 $10,142 56% 5,617

Rowan University $6,994 $12,309 59% 9,688

Rutgers University-Camden $10,226 $15,376 44% 5,563

Rutgers University-New Brunswick $10,226 $15,376 66% 34,696

Rutgers University-Newark $10,226 $15,376 48% 10,293

The College of New Jersey $6,357 $11,565 79% 6,812

The Richard Stockton College of New Jersey $5,017 $8,871 61% 7,002

Thomas Edison State College - $8,723 - 11,000

William Paterson University of New Jersey $5,303 $10,333 41% 11,409

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Namir Shah 57

(b) Public, four-year colleges (continued)

Institution Name Admissions

Rate Barron’s

Selectivity Score Median M+VR SAT Score

Kean University 56.6% 4 940

Montclair State University 49.8% 4 1030

New Jersey City University 51.2% 4 915

New Jersey Institute of Technology 57.8% 3 1155

Ramapo College of New Jersey 40.4% 2 1120

Rowan University 51.7% 3 1077.5

Rutgers University-Camden 56.0% 3 1100

Rutgers University-New Brunswick 61.2% 2 1195

Rutgers University-Newark 50.7% 3 1085

The College of New Jersey 48.0% 1 1265

The Richard Stockton College of New Jersey 46.2% 3+ 1115

Thomas Edison State College Open 7 N/A

William Paterson University of New Jersey 66.2% 4+ 985

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Namir Shah 58

(c) Private, four-year colleges

Institution Name Instructional Spending

Total Academic Spending per FTE

Graduation Rate Enrollment

Bloomfield College $5,551 $13,043 23% 2,166

Caldwell College $5,032 $13,749 55% 2,172

Centenary College $6,802 $16,304 32% 2,339

College of Saint Elizabeth $7,164 $15,857 60% 1,976

Drew University $10,640 $24,742 72% 2,675

Fairleigh Dickinson University-Florham $5,622 $13,915 47% 3,684

Fairleigh Dickinson University-Metropolitan $5,622 $13,915 38% 7,634

Felician College $6,225 $15,948 23% 1,699

Georgian Court University $4,837 $13,405 63% 3,065

Monmouth University $6,402 $14,684 51% 6,329

Princeton University $29,407 $55,361 92% 6,708

Rider University $8,399 $17,199 59% 5,502

Saint Peter's College $5,263 $13,687 44% 3,152

Seton Hall University $7,821 $19,404 55% 9,824

Stevens Institute of Technology $10,230 $19,875 72% 4,638

Berkeley College-Woodland Park $3,105 $9,771 50% 2,313

DeVry University $2,773 $8,786 41% 2,007

Kean University $6,239 $9,956 38% 12,897

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Namir Shah 59

(c) Private, four-year colleges (continued)

Institution Name Admissions

Rate Barron’s

Selectivity Score Median M+VR SAT Score

Bloomfield College 53.2% 4 830

Caldwell College 79.3% 5 960

Centenary College 76.0% 5 915

College of Saint Elizabeth 76.9% 5 910

Drew University 69.9% 3 1215

Fairleigh Dickinson University-Florham 71.9% 4 1045

Fairleigh Dickinson University-Metropolitan 61.7% 4+ 1010

Felician College 76.3% 4 910

Georgian Court University 81.6% 5 925

Monmouth University 65.8% 4 1050

Princeton University 12.6% 1 1480

Rider University 78.4% 4 1045

Saint Peter's College 63.2% 5 955

Seton Hall University 86.7% 3 1090

Stevens Institute of Technology 51.3% 2 1290

Berkeley College-Woodland Park 75.4% 5 N/A

DeVry University 59.3% 5 N/A

Source: Delta Cost Project and National Center for Education Statistics (NCES) data

Namir Shah 60

Table 3: Student Summary Statistics

(a) Sex

Sex Frequency Percent Female 29,863 52.22 Male 27,321 47.78

Total 57,184 100

(b) Race/Ethnicity

Ethnicity Frequency Percent Asian/Asian American 6,713 11.74 Black/African-American 2,024 3.54 Latin American 1,872 3.27 Mexican American 145 0.25 Native American/Alaska Native 135 0.24 Puerto Rican 779 1.36 White 43,151 75.46 Other 1,891 3.31 Did not answer 476 0.83

Total 57,186 100

(c) Self-Reported GPA

Self-Reported Predicted GPA Frequency Percent Cumulative Percent A+ 8,310 14.53 14.53 A 19,646 34.35 48.89 A- 15,180 26.54 75.43 B+ 9,672 16.91 92.34 B 4,378 7.66 100

Total 57,186 100

Namir Shah 61

(d) Self-Reported Class Rank

Self-Reported Class Rank Frequency Percent Cumulative Percent Top Decile 26,736 46.75 46.75 Second Decile 18,719 32.73 79.49 Second Quintile 11,731 20.51 100

Total 57,186 100

(e) Graduating High School Class

Year Frequency Percent Cumulative Percent 2000 7,467 13.06 13.06 2001 7,258 12.69 25.75 2002 6,493 11.35 37.1 2003 5,977 10.45 47.56 2004 5,754 10.06 57.62 2005 5,846 10.22 67.84 2006 6,194 10.83 78.67 2007 6,524 11.41 90.08 2008 5,673 9.92 100

Total 57,186 100

(f) SAT Score

Variable Mean Std. Dev. Min Max SAT Verbal (most recent) 612.6022 70.70623 510 800 SAT Math (most recent) 629.3206 74.98205 500 800

Student's SAT M+V 1241.923 127.2929 1010 1600

Source: The College Board data

Namir Shah 62

Table 4: Colleges Most Enrolled

Rank College First Enrolled College Used for Analysis

1 Rutgers University-New Brunswick Rutgers University-New Brunswick

2 The College of New Jersey The College of New Jersey

3 Rowan University Rowan University

4 Montclair State University New York University

5 New York University Montclair State University

6 University of Delaware University of Delaware

7 Rutgers University-Newark Drexel University

8 Drexel University Pennsylvania State University-Main Campus

9 Pennsylvania State University-Main Campus Rutgers University-Newark

10 University of Pennsylvania University of Pennsylvania

11 The Richard Stockton College of New Jersey The Richard Stockton College of New Jersey

12 Boston University Boston University

13 Villanova University Villanova University

14 Seton Hall University Stevens Institute of Technology

15 Stevens Institute of Technology Seton Hall University

16 William Paterson University of New Jersey Columbia University in the City of New York

17 Columbia University in the City of New York Ramapo College of New Jersey

18 Ramapo College of New Jersey William Paterson University of New Jersey

19 New Jersey Institute of Technology University of Maryland-College Park

20 University of Maryland-College Park New Jersey Institute of Technology

21 George Washington University Rutgers University-Camden

22 Rutgers University-Camden George Washington University

23 Cornell University Cornell University

24 Temple University Temple University

25 Lehigh University Lehigh University

Namir Shah 63

Table 5: Choice Categories

Category Category Description

1 Rutgers University-New Brunswick

2 The College of New Jersey

3 Rowan University

4 Montclair State University

5 Rutgers University-Newark

6 The Richard Stockton College of New Jersey

7 Seton Hall University

8 William Paterson University of New Jersey

9 Stevens Institute of Technology

10 Ramapo College of New Jersey

11 New Jersey Institute of Technology

12 Rutgers University-Camden

13 Kean University

14 Rider University

15 Monmouth University

16 Brookdale Community College

17 Princeton University

18 Fairleigh Dickinson University-Metropolitan Campus

19 Fairleigh Dickinson University-College at Florham

20 Ocean County College

21 Drew University

22 Burlington County College

23 Camden County College

24 New Jersey City University

25 Georgian Court University

26 Middlesex County College

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Category Category Description

27 Gloucester County College

28 Saint Peter's College

29 County College of Morris

30 Atlantic Cape Community College

31 Raritan Valley Community College

32 Sussex County Community College

33 Bergen Community College

34 Centenary College

35 Thomas Edison State College

36 Caldwell College

37 Mercer County Community College

38 Cumberland County College

39 Union County College

40 Felician College

41 College of Saint Elizabeth

42 Bloomfield College

43 Essex County College & Hudson County Community College

44 Passaic County Community College

45 Salem Community College

46 Warren County Community College

47 New York University

48 Drexel University

49 University of Delaware

50 Pennsylvania State University-Main Campus

51 University of Pennsylvania

52 Boston University

53 Villanova University

54 Columbia University in the City of New York

Namir Shah 65

Category Category Description

55 University of Maryland-College Park

56 George Washington University

57 Cornell University

58 Temple University

59 Lehigh University

60 Syracuse University

61 Harvard University

62 Georgetown University

63 Fordham University

64 Johns Hopkins University

65 Boston College

66 American University

67 Northeastern University

68 Saint Joseph's University

69 University of Michigan-Ann Arbor

70 Loyola University Maryland

71 James Madison University

72 Virginia Polytechnic Institute and State University

73 University of Phoenix

74 Yeshiva University

75 University of Connecticut

76 Carnegie Mellon University

77 University of Virginia-Main Campus

78 University of Pittsburgh-Pittsburgh Campus

79 University of Miami

80 Marist College

81 Bucknell University

82 University of the Sciences

Namir Shah 66

Category Category Description

83 Pace University-New York

84 Northwestern University

85 Tufts University

86 Duke University

87 La Salle University

88 Yale University

89 Towson University

90 Muhlenberg College

91 University of Scranton

92 Quinnipiac University

93 Fairfield University

94 Ithaca College

95 Rensselaer Polytechnic Institute

96 University of Notre Dame

97 In-state For profit four-year

98 In-state For-profit two-year and less

99 Out-of-state, for profit, four-year

100 Out-of-state, Mid-East, two-year

101 Out-of-state, other regions, two-year

102 Out-of-state, Mid-East, most competitive (Barron's 1), four-year

103 Out-of-state, New England, most competitive (Barron's 1), four-year

104 Out-of-state, Great Lakes, most competitive (Barron's 1), four-year

105 Out-of-state, Southeast, most competitive (Barron's 1), four-year

106 Out-of-state, other regions, most competitive (Barron's 1), four-year

107 Out-of-state, Mid-East or New England, highly competitive (Barron's 2), public, four-year

108 Out-of-state, Great Lakes, highly competitive (Barron's 2), public, four-year

109 Out-of-state, Southeast, highly competitive (Barron's 2), public, four-year

110 Out-of-state, other regions, highly competitive (Barron's 2), public, four-year

Namir Shah 67

Category Category Description

111 Out-of-state, Mid-East, highly competitive (Barron's 2), private, four-year

112 Out-of-state, New England, highly competitive (Barron's 2), private, four-year

113 Out-of-state, Great Lakes, highly competitive (Barron's 2), private, four-year

114 Out-of-state, Southeast, highly competitive (Barron's 2), private, four-year

115 Out-of-state, other regions, highly competitive (Barron's 2), private, four-year

116 Out-of-state, Mid-East, highly competitive plus (Barron's 2+), four-year

117 Out-of-state, New England, highly competitive plus (Barron's 2+), four-year

118 Out-of-state, Great Lakes, highly competitive plus (Barron's 2+), four-year

119 Out-of-state, Southeast, highly competitive plus (Barron's 2+), four-year

120 Out-of-state, other regions, highly competitive plus (Barron's 2+), four-year

121 Out-of-state, Mid-East, very competitive (Barron's 3), public, four-year

122 Out-of-state, New England, very competitive (Barron's 3), public, four-year

123 Out-of-state, Great Lakes, very competitive (Barron's 3), public, four-year

124 Out-of-state, Southeast, very competitive (Barron's 3), public, four-year

125 Out-of-state, other regions, very competitive (Barron's 3), public, four-year

126 Out-of-state, Mid-East, very competitive (Barron's 3), private, four-year

127 Out-of-state, New England, very competitive (Barron's 3), private, four-year

128 Out-of-state, Great Lakes, very competitive (Barron's 3), private, four-year

129 Out-of-state, Southeast, very competitive (Barron's 3), private, four-year

130 Out-of-state, other regions, very competitive (Barron's 3), private, four-year

131 Out-of-state, Mid-East, very competitive plus (Barron's 3+), four-year

132 Out-of-state, New England, very competitive plus (Barron's 3+), four-year

133 Out-of-state, Great Lakes, very competitive plus (Barron's 3+), four-year

134 Out-of-state, Southeast, very competitive plus (Barron's 3+), four-year

135 Out-of-state, other regions, very competitive plus (Barron's 3+), four-year

136 Out-of-state, Mid-East, competitive (Barron's 4), public, four-year

137 Out-of-state, New England, competitive (Barron's 4), public, four-year

138 Out-of-state, Great Lakes, competitive (Barron's 4), public, four-year

Namir Shah 68

Category Category Description

139 Out-of-state, Southeast, competitive (Barron's 4), public, four-year

140 Out-of-state, other regions, competitive (Barron's 4), public, four-year

141 Out-of-state, Mid-East, competitive (Barron's 4), private, four-year

142 Out-of-state, New England, competitive (Barron's 4), private, four-year

143 Out-of-state, Great Lakes, competitive (Barron's 4), private, four-year

144 Out-of-state, Southeast, competitive (Barron's 4), private, four-year

145 Out-of-state, other regions, competitive (Barron's 4), private, four-year

146 Out-of-state, Mid-East, competitive plus (Barron's 4+), four-year

147 Out-of-state, New England, competitive plus (Barron's 4+), four-year

148 Out-of-state, Great Lakes, competitive plus (Barron's 4+), four-year

149 Out-of-state, Southeast, competitive plus (Barron's 4+), four-year

150 Out-of-state, other regions, competitive plus (Barron's 4+), four-year

151 Out-of-state, Mid-East, less competitive (Barron's 5), public, four-year

152 Out-of-state, New England, less competitive (Barron's 5), public, four-year

153 Out-of-state, Great Lakes, less competitive (Barron's 5), four-year

154 Out-of-state, Southeast, less competitive (Barron's 5), public, four-year

155 Out-of-state, other regions, less competitive (Barron's 5), public, four-year

156 Out-of-state, Mid-East, less competitive (Barron's 5), private, four-year

157 Out-of-state, New England, less competitive (Barron's 5), private, four-year

158 Out-of-state, other regions, less competitive (Barron's 5), private, four-year

159 Out-of-state, Mid-East or New England, noncompetitive (Barron's 6), public, four-year

160 Out-of-state, Great Lakes, noncompetitive (Barron's 6), four-year

161 Out-of-state, Southeast, noncompetitive (Barron's 6), four-year

162 Out-of-state, other regions, noncompetitive (Barron's 6), four-year

163 Out-of-state, Mid-East or New England, noncompetitive (Barron's 6), private, four-year

164 Out-of-state, Mid-East, other (Barron's 7), four-year

165 Out-of-state, other regions, other (Barron's 7), four-year

166 Out-of-state, Mid-East, Barron's unranked, four-year

Namir Shah 69

Category Category Description

167 Out-of-state, New England, Barron's unranked, four-year

168 Out-of-state, Great Lakes, Barron's unranked, four-year

169 Out-of-state, Southeast, Barron's unranked, four-year

170 Out-of-state, other regions, Barron's unranked, four-year

Categories 102-170 include both public and private not-for-profit institutions unless otherwise noted.

Namir Shah 70

Table 6: Conditional Logistic Regression Results

Variable (1)

Student and Choice Eligible for STARS7 0.376***

(0.027)

Student and Choice Eligible While STARS in Place8 1.622***

(0.110)

Median SAT score (M+CR) 1.036***

(0.002)

Unreported SAT Score Dummy 0.604

(did not converge)

Graduation Rate 1.012***

(0.0005)

Unreported Graduation Rate Dummy 439.063***

(98.164)

Total Academic Spending (Inputs) 0.992***

(0.0005)

Unreported Academic Spending Dummy 0.655***

(0.018)

Total Cost 0.962***

(0.001)

Unreported Total Cost Dummy 0.001***

(0.0002)

Enrollment (1000s) 1.041***

(0.0004)

In-state Dummy 1.912***

(0.024)

STARS-Eligible College Dummy 0.042***

(0.004)

For-profit Dummy 5.073***

(0.344)

Two-year Dummy 0.821**

(0.063)

Open Enrollment Dummy 1.115

(did not converge)

Distance 0.735***

(0.003)

7 This is analogous to the “treatment” dummy in a difference-in-difference model.

8 This is analogous to the “treatment/after” dummy in a difference-in-difference model.

Namir Shah 71

Variable (1)

Distance-squared 1.001***

(0.000001)

Difference between Student's SAT and Choice's Median 1.01***

(0.002)

Missing SAT Score Difference Dummy 1.179

(0.390)

Product of Student's SAT score and Choice's Admissions Rate

0.9993**

(0.0003)

N (student-choice pairs) 9721450

Students 57185

Pseudo R2 0.0855

Prob > chi2 0.0000

Odds ratios reported. * p < 0.10, ** p < 0.05, *** p < 0.01 Notes on units:

SAT scores are Math + Verbal in 10s of points. Graduation Rate in whole numbers. Monetary terms in $1000s dollars. Enrollment in 1000s of students. Distance in 100s of miles.

Namir Shah 72

Table 7: Category Enrollment from Conditional Logit Model Predictions

Category Percent Enrolling

With STARS Percent Enrolling Without STARS

1 6.901% 6.971%

2 3.055% 3.086%

3 1.614% 1.631%

4 1.784% 1.802%

5 1.549% 1.565%

6 1.779% 1.797%

7 0.846% 0.854%

8 1.201% 1.214%

9 1.189% 1.201%

10 1.573% 1.589%

11 1.565% 1.581%

12 1.212% 1.224%

13 1.235% 1.248%

14 0.712% 0.719%

15 0.778% 0.786%

16 0.173% 0.108%

17 1.930% 1.949%

18 0.546% 0.552%

19 0.547% 0.553%

20 0.141% 0.088%

21 0.829% 0.837%

22 0.128% 0.080%

23 0.183% 0.114%

24 0.808% 0.817%

Category Percent Enrolling

With STARS Percent Enrolling Without STARS

25 0.292% 0.295%

26 0.159% 0.099%

27 0.119% 0.074%

28 0.519% 0.525%

29 0.167% 0.104%

30 0.137% 0.085%

31 0.158% 0.099%

32 0.130% 0.081%

33 0.219% 0.136%

34 0.402% 0.406%

35 0.016% 0.016%

36 0.626% 0.633%

37 0.169% 0.105%

38 0.110% 0.068%

39 0.119% 0.074%

40 0.340% 0.344%

41 0.532% 0.538%

42 0.378% 0.382%

43 0.157% 0.098%

44 0.134% 0.084%

45 0.134% 0.084%

46 0.101% 0.063%

47 2.438% 2.463%

48 0.678% 0.685%

49 1.480% 1.495%

50 1.656% 1.672%

51 1.525% 1.541%

Namir Shah 73

Category Percent Enrolling

With STARS Percent Enrolling Without STARS

52 0.891% 0.901%

53 0.797% 0.805%

54 1.596% 1.612%

55 1.975% 1.996%

56 0.734% 0.742%

57 1.020% 1.031%

58 1.342% 1.355%

59 0.701% 0.708%

60 0.629% 0.635%

61 0.971% 0.981%

62 0.731% 0.738%

63 0.574% 0.580%

64 0.795% 0.803%

65 0.688% 0.695%

66 0.502% 0.507%

67 0.278% 0.280%

68 0.495% 0.500%

69 1.005% 1.016%

70 0.476% 0.481%

71 0.770% 0.778%

72 0.874% 0.883%

73 0.342% 0.345%

74 0.328% 0.331%

75 0.754% 0.762%

76 0.431% 0.436%

77 1.373% 1.387%

78 0.756% 0.764%

Category Percent Enrolling

With STARS Percent Enrolling Without STARS

79 0.120% 0.121%

80 0.626% 0.632%

81 0.539% 0.544%

82 0.263% 0.265%

83 0.534% 0.539%

84 0.244% 0.246%

85 0.593% 0.600%

86 0.435% 0.440%

87 0.461% 0.466%

88 0.643% 0.650%

89 0.676% 0.682%

90 0.566% 0.572%

91 0.454% 0.458%

92 0.421% 0.425%

93 0.468% 0.473%

94 0.429% 0.433%

95 0.520% 0.525%

96 0.261% 0.263%

97 0.051% 0.052%

98 0.094% 0.095%

99 0.393% 0.397%

100 0.251% 0.254%

101 0.050% 0.051%

102 0.533% 0.538%

103 0.506% 0.511%

104 0.202% 0.204%

105 0.324% 0.327%

Namir Shah 74

Category Percent Enrolling

With STARS Percent Enrolling Without STARS

106 0.276% 0.278%

107 1.377% 1.391%

108 1.215% 1.227%

109 0.668% 0.675%

110 0.349% 0.353%

111 0.500% 0.505%

112 0.349% 0.353%

113 0.138% 0.139%

114 0.197% 0.199%

115 0.109% 0.110%

116 0.629% 0.636%

117 0.359% 0.363%

118 0.484% 0.489%

119 0.322% 0.325%

120 1.379% 1.393%

121 0.709% 0.716%

122 0.413% 0.417%

123 0.487% 0.492%

124 0.457% 0.462%

125 0.322% 0.325%

126 0.501% 0.506%

127 0.236% 0.238%

128 0.165% 0.167%

129 0.105% 0.106%

130 0.215% 0.217%

131 0.494% 0.499%

132 0.328% 0.331%

Category Percent Enrolling

With STARS Percent Enrolling Without STARS

133 0.300% 0.303%

134 0.415% 0.419%

135 0.072% 0.073%

136 0.579% 0.585%

137 0.353% 0.356%

138 0.198% 0.200%

139 0.217% 0.220%

140 0.273% 0.276%

141 0.378% 0.382%

142 0.248% 0.251%

143 0.094% 0.095%

144 0.111% 0.112%

145 0.055% 0.056%

146 0.814% 0.822%

147 0.820% 0.829%

148 0.159% 0.161%

149 0.242% 0.245%

150 0.142% 0.144%

151 0.345% 0.349%

152 0.201% 0.203%

153 0.129% 0.130%

154 0.126% 0.127%

155 0.073% 0.073%

156 0.229% 0.231%

157 0.184% 0.186%

158 0.074% 0.074%

159 0.408% 0.412%

Namir Shah 75

Category Percent Enrolling

With STARS Percent Enrolling Without STARS

160 0.119% 0.120%

161 0.079% 0.080%

162 0.122% 0.123%

163 0.508% 0.513%

164 0.364% 0.367%

165 0.173% 0.175%

166 0.455% 0.460%

167 0.150% 0.152%

168 0.093% 0.094%

169 0.192% 0.194%

170 0.066% 0.066%

Total 100.000% 100.000%

Namir Shah 76

Table 8: Summary Statistics of Effect of NJ STARS on Enrollment and Outcomes

Enrollment Statistics Without NJ STARS With NJ STARS Change Percent Change

Two-Year College 2.39% 3.38% 0.99 41.34%

Four-Year College 97.61% 96.62% -0.99 -1.01%

In-State College 36.76% 37.40% 0.64 1.74%

Public, NJ Two-Year College 1.64% 2.64% 1.00 60.55%

Total Academic Spending $21,350.39 $21,199.23 -$151.16 -0.71%

Graduation Rate 64.74% 64.23% -0.51 -0.79%

Admissions Rate 54.48% 54.94% 0.46 0.85%

Counterfactual statistics above indicate the effect on STARS-eligible students only.