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Using Economics to Inform the Public Agenda on the Allocation of State Funding for Higher Education

Robert K. Toutkoushian Educational Leadership and Policy Studies Education 4220 201 N. Rose Ave. Indiana University Bloomington, IN 47405 M. Najeeb Shafiq Assistant Professor Educational Leadership and Policy Studies Education 4232 201 N. Rose Ave. Indiana University Bloomington, IN 47405

DRAFT: October 19, 2007

------------------------------------------------------------------------------------------------------For presentation at the annual meetings of the Association for the Study of Higher Education, Louisville, KY, November 2007.

Using Economics to Inform the Public Agenda on the Allocation of State Funding for Higher Education

Introduction Higher education policy makers have focused considerable attention on how to identify strategies to improve access to higher education for all students, as well as traditionally underrepresented students and students with less ability to pay for college. Data from the National Center for Education Statistics (2006) suggest that while the overall college participation rate for all students has increased from 49 percent in 1980 to over 68 percent by 2005, the gains for underrepresented students have been less convincing. Financial trends in higher education could certainly be playing a part in the accessibility of college. Although state appropriations have increased in levels over time, they have failed to keep pace with education costs (National Center for Education Statistics, 2006; Toutkoushian, 2001). As a result, the sticker prices charged by both public and private institutions have consistently outpaced the rate of inflation for the past thirty years (College Board, 2006). The College Board’s annual report further revealed that students and their families on average are now paying an even greater share of the total education costs than in previous years due to a shift in financial support away from grants and towards loans. States have traditionally borne a large part of the responsibility for providing access to higher education. All states have established land grant and other state-supported (“public”) institutions to help ensure that state residents had postsecondary options that were more affordable than in the private sector. In addition, most states have financial aid programs such as Georgia’s HOPE Scholarship program and Indiana’s Twenty-First Century Scholars program for 1

allocating additional financial assistance directly to students. States vary substantially, however, in their levels of higher education support and how the support is distributed between appropriations and financial aid. In addition, states differ in their strategies for distributing financial aid on the basis of need or merit. To date, policy makers have not been able to identify the optimal strategy for supporting higher education, nor determine how to compare different approaches that could be used in higher education. In this paper, we use economic concepts to critique the ways in which states can provide financial support to higher education. We begin by reviewing the justification often used by economists for why states provide support for higher education; namely, that positive externalities are created for society when students acquire a postsecondary education. We then introduce a simple model to illustrate how economists might compare and contrast alternative state policies for supporting higher education. Through this framework, we will make the argument that it would be in the best interest of states to provide financial support in the form of need-based financial aid to students and not in the form of direct appropriations to institutions. We conclude with a discussion of some of the complicating factors that would need to be considered in attempting to reallocate state funding away from appropriations and towards needbased financial aid.

The Economic Justification for State Support of Higher Education In order to evaluate the alternative approaches that states can use to support higher education, it is essential to start by asking the question: why do states support higher education in the first place? What is it about this particular service that would lead to the government subsidization of it? Public sector economists have long been interested in explaining the 2

behavior of governments and their interaction in specific markets. According to Browning and Browning (1994), as cited by Paulsen (2001, p.95), “The economics of the public sector can be defined as ‘the study of how government policy, especially tax and expenditure policy, affects the economy and thereby the welfare of its citizens.’” Much of the work on this topic has focused on the efficiency and equity of governmental funding (McMahon, 1991; Browning and Browning, 1994; Stiglitz, 2000). Public sector economists generally attribute government support for higher education as a means of rectifying inefficiencies, inequities, or failures in the higher education market. The notion is that by giving financial support for higher education, governments can help make the market more equitable with regard to educational opportunities for all citizens, and lead to a more efficient provision of higher education services. Many economists assert that the government needs to become involved in the provision of goods/services when the competitive market fails to produce socially-optimum outcomes. The first instance where market failures are said to occur is when a good/service is a public good. A public good is a good/service where (a) the consumption of the good/service by one person does not preclude another from consuming the same good/service (non-rival), and (b) the supplier of the good/service cannot easily prevent someone who does not pay for the good/service from consuming it (non-excludable). Commonly used examples of public goods would include air and national defense. Higher education does not fall into this category, however, because students can be excluded from receiving the service, and in non-open admission institutions the acceptance of one student can lead to the denial of admission for another student. The other instance in which many economists believe that the competitive market leads to suboptimal production of a good/service is when the consumption of the good or service 3

results in either positive or negative externalities. A positive externality occurs when individuals other than those who consume the good/service also benefit from it, and vice-versa for a negative externality. For example, it is believed that when students receive an education, not only do they directly benefit from their education, but others in society also benefit. Accordingly, one theoretical argument in favor of government support for higher education is that the consumption of higher education leads to benefits for others in the state (Schultz, 1963; Weisbrod, 1968; Paulsen and Peseau, 1989; Wolfe, 1995; Bowen, 1977; Acemoglu and Angrist, 2001; Paulsen, 2001b; Moretti, 2004; Johnson, 2006). When students receive a higher education, not only do they reap benefits by improving their human capital/earnings potential and realizing nonpecuniary gains, but others in the state potentially benefit in many ways. The consumption of higher education by some may lead to pecuniary benefits for others from higher tax collections, stronger economic growth and employment prospects, and non-pecuniary benefits such as reduced crime and increased civic behavior. Economists have shown that when the consumption of a good leads to positive externalities, the good/service would be underproduced from society’s point of view if production was left solely to the competitive market. The underproduction would occur because individual consumers would not include the external benefits into their calculations of how much they would be willing and able to pay for the good or service. This is shown graphically in Figure 1. The first line (MPp) represents the (private) marginal benefits that individuals would receive from investments in higher education. The second line (MPp+s) shows the private plus social marginal benefits received, and the third line (MC) depicts the marginal cost of producing the good/service. If the provision of higher education was left to the competitive market alone, only Np students would enroll and the total benefits to society would be found by adding the 4

marginal benefits for the Np students. Another subset of students Ns would not enroll in higher education because their marginal cost of doing so outweighs their private marginal benefits. However, society would benefit from their enrollment because the total marginal benefit to society (private plus social) exceeds the marginal cost. As a result, a subsidy that offset some of the additional educational cost for these students might entice them to enroll in college and thus allow society to reap the additional benefits. --------------------------- Insert Figure 1 Here ------------------------A second argument that has been used by economists to explain why states support higher education is that the funding is due to the response of legislators to the demands of voters in their districts. This notion, referred to as the median voter model of public choice theory, has been used to identify factors that cause legislative demand for higher education to change (Borcherding and Deacon, 1972; Clotfelter, 1976; Bös, 1980; Coughlin and Erekson, 1986; Creedy and Francois, 1990; Hoenack and Pierro, 1990; Strathman, 1994; Toutkoushian and Hollis, 1998). In the median voter model, individual legislators are the decisionmakers because they vote on the levels of state funding to appropriate to competing interests. The goal of legislators is to act in ways that will maximize their perceived chances of being re-elected (Coughlin and Erekson, 1996). According to this model, the preferences of the middle, or median, voter would dominate the preferences of other voters when majority rule is used to decide issues. As a result, legislators would vote in accordance with the demands of the median voter in their region. McLendon, Heller, and Young (2005) and McLendon and Hearn (2007) have also examined other aspects of how politics affects the allocation of state financial assistance for higher education.

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Studies that rely on the median voter model specify the legislative demand for higher education to be a function of factors that would affect the median voter’s preferences for public higher education support. These factors may include the price of public higher education services, the size of the population who might benefit from public higher education, the ability of citizens to provide financial support for higher education, and the size of competing interest groups such as corrections, K-12 education, and Medicaid. Using this framework, Toutkoushian and Hollis (1998) examined the legislative demand for higher education over the period 1982 to 1996. Many of the results from this study supported the notions of the median voter model. The authors found, for example, that state support for higher education was positively related to the ability of citizens to pay for education, and the size of the population who would use public higher education services. Of the special interest groups considered in the study, only K-12 education was shown to detract funding away from public higher education, suggesting that K-12 and higher education were viewed as substitute services by legislators (however, see Delaney and Doyle, 2007).

Forms of State Support for Higher Education States have several choices available to them with regard to how to support the postsecondary aspirations of students. The most frequently-used option is to provide funding directly to a select set of institutions in the form of appropriations. The hope of state policy makers is that in return the appropriations will be used by publicly-supported institutions to offset some portion of the price charged to resident students. Figure 2 depicts how appropriations might reduce the price charged to resident students. In the absence of appropriations, all resident students would be charged price P0. It may be useful to think of P0 as 6

the average cost of higher education, and also the price that would be charged to nonresident students. If the institution distributed state funding, defined by area (A, B, P0, P1), evenly across all resident students, then the price for resident students could be reduced to P1. It is assumed here that all of the appropriations are used to reduce prices; however, because the appropriations are usually given in the form of a block grant, there is no requirement that public institutions must allocate every state dollar to reduce resident tuition rates by the same amount. As a result, it is best to think about the state funding as the portion of appropriations used by the institution to reduce prices for all resident students. ----------------------- Insert Figure 2 Here -----------------------A second alternative for states to financially support higher education is to distribute funding directly to students in the form of financial aid. The aid may be awarded on the basis of a student’s ability to pay for college (“need-based aid”), academic achievement (“merit-based aid”) or some combination of the two. Figure 3 shows how states might target financial aid towards a subset of students, in effect differentiating the net prices paid by state residents. In this example, the financial support is allocated over fewer students N1, enabling the institution to lower the price to P2 for these students and charge the full price P0 to the remaining (N0 – N1) students: ----------------------- Insert Figure 3 Here -------------------------State financial aid may be in the form of grants/scholarships that do not have to be repaid, or loans that would have to be repaid by the student. In this analysis, we focus exclusively on the choice that states make between appropriations and grants/scholarships because in each case the financial support does not have to be repaid by the student.

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As noted in the Introduction, states vary considerably in terms of their overall levels of financial support for higher education and their chosen mix between appropriations and financial aid. Table 1 provides a breakdown of financial support for higher education by appropriations and financial aid by state for 2005-06. The financial aid figures include both need-based and non need-based grant aid for undergraduate students. Overall, states allocated over $74 billion in financial assistance for higher education in 2005-06, with approximately 90 percent of the total being given in the form of appropriations to select institutions. ------------------------ Insert Table 1 Here --------------------To compare states according to their relative levels of financial support for higher education, we divided the grant and appropriation totals by the estimated population in the state ages 18-24 in 2005. This age group represents the category that would be most likely to benefit from state financial aid and appropriations. The results for each state are shown in Table 2. Based on this approach, on average states allocated $2,305 per person ages 18-24 in the form of direct appropriations, and $231 per person ages 18-24 in financial aid, for the year 2005-06. The last two columns indicate whether a state had above average or below average values for appropriations or financial aid per person. ------------------------- Insert Table 2 Here ------------------------Some states such as North Carolina, New Mexico, and New Jersey were high aid and high appropriation states because their per-pupil amounts for each exceed the national averages. At the other extreme, New Hampshire, Massachusetts and Oregon are states with both low per-pupil appropriations and financial aid. However, there were also a number of states that have chosen a high aid/low appropriation strategy (e.g., Vermont) or a low aid/high appropriation strategy (e.g., Wyoming). The distribution of states by level of appropriations and financial aid per pupil are 8

shown graphically in Figure 4, where the chart is divided into four quadrants at $300/person for state grant aid and $2000/person for state appropriations. Based on these break points, it can be seen that relatively few states pursued a strategy of high aid and low appropriations, whereas many states could be categorized as high appropriations and low grant aid: --------------------------- Insert Figure 4 Here ----------------------These two approaches to providing state financial support for higher education differ in several important respects. First, appropriations and grant aid can be expected to affect different numbers of students. State appropriations provide financial support to all resident students who attend an in-state public institution, and thus can benefit many students. State financial aid, on the other hand, only benefits those students who meet the eligibility criteria established by the state for financial support. The eligibility criteria for state grant aid may include demonstrated financial need, academic ability, membership in a traditionally-underrepresented student group, and perhaps willingness to enroll at an in-state institution. It is usually the case that more students benefits from appropriations than from state grant aid. A second important difference between appropriations and particularly need-based financial aid is that the two support mechanisms often benefit different groups of students. By definition, need-based financial aid will provide assistance to only those students who come from lower-income families or whatever group of students is specifically targeted by the state. In contrast, appropriations to public institutions tend to benefit a wider range of students, many of whom come from middle- and upper-income families and would not qualify for much needbased financial aid. On average, students from lower- and middle/upper-income families differ in their likelihoods of attending college in the absence of financial support for higher education. In addition, their families differ on average in terms of their political engagement and clout with 9

legislators. Both of these factors can have an impact on the relative effectiveness of appropriations versus financial aid for enticing more students to go to college.

Policy Analysis of State Funding Options To examine the alternative state policies for providing financial assistance to higher education, a starting place is to specify the conceptual arguments as to why financial assistance is thought to affect student behavior. A student’s decision to enroll in college is often described as being dependent on the student’s perceived utility of going to college versus not going to college. The utility for the i-th student (subscript i) from going to college (subscript c) will be positively related to the net financial gain from going to college. The net financial gain is the discounted stream of future incomes after completing college less the foregone earnings while in college (Yic), minus the direct costs of a college education (Tc). Similarly, it is assumed that each student can estimate the future income stream and hence the utility from not going to college (subscript n), and would base his or her decision about whether to go to college on a comparison of the two utilities. The student would opt to go to college provided that the net utility of going to college exceeds the net utility of not going to college, and vice-versa. This decision rule can be written as follows: (1)

1 0 otherwise

States can attempt to influence the college-going decisions of students by providing them with financial support (Sic). The support could be in the form of state appropriations, which would reduce the sticker price for resident students, or in the form of grant aid, which would allow the student to cover some portion of the sticker price. To simplify the comparison of 10

policy options, we treat both state appropriations and state grants as subsidies rather than price reductions. Introducing state financial assistance into the model changes the student’s college choice rule to be: 1

(2)

0 otherwise

Equation (2) simplifies to equation (1) when the student receives no financial subsidy (Sic=0). The positive externalities received by the state are assumed to be proportional to the number of students who go to college. To calculate the number of students going to college under each option, the decision rules can be summed for all individuals or expressed in terms of the probability of going to college: (3)

∑

Pr

1

(4)

∑

Pr

1

We assume here that positive externalities of α accrue to the state from each student who enrolls in college. Accordingly, the total positive externalities for the state in both scenarios are found as follows: (5)

∑

1i

(6)

∑

2i

The net gain in positive externalities from state financial support (∆X) is thus: (7)

∑

∆

2i –

E1i)

The state is assumed to have a fixed amount of financial support for higher education, S, that must be distributed among students in the form of either appropriations per student or financial aid. (8)

∑

ic

11

The objective function for the state then becomes how to maximize (7) subject to the constraint in (8). Using this framework, it can be seen that the only time in which state financial assistance would lead to positive externalities is when E2i = 1 and E1i = 0, meaning that the subsidy was high enough to change at least one student’s decision about going to college. For all students where E1i = 1, financial assistance from the state would not lead to gains in positive externalities because the state would still reap these benefits without having to make a financial investment. In this sense, the optimal method for distributing financial support would be one that leads to the greatest increase in the number of students deciding to go to college. The model gives rise to several interesting predictions concerning state financial support for higher education. The first prediction is that increases in the level of total state support for higher education would create gains in positive externalities for the state, provided that the change in S is sufficient to cause at least one student to go to college who previously would not have gone to college. To see this, we substitute equations (3) and (4) into (7) and rewrite in terms of probabilities: (9)

∆

Pr

- Pr

Differentiating equation (9) with respect to S shows that the state would gain positive externalities provided that the change in state support increases the probability of a student going to college: (10)

∆

P

The result shown in equation (10), however, does not imply that state support for higher education should increase indefinitely. There will be some point at which the additional subsidy 12

needed to entice a student to go to college (∆S) exceeds the positive externality gain received by the state (α). Accordingly, total state financial support should rise up to the point where ∆S = α. This is shown graphically in Figure 5. ------------------------------ Insert Figure 5 Here -------------------------------The second prediction from our model is that the state would receive more positive externalities by targeting assistance to students through grant aid than through broad-based financial support for higher education, as in the case of state appropriations. Figure 6 illustrates graphically why this must be true. The line (A,B) shows the distribution of students by utility from going to college in the absence of subsidies. Without any subsidy, N1 of the N students would be willing and able to go to college. Suppose that state financial support was distributed equally to all students. The uniform subsidy leads to a vertical shift in the utility received by all N students, leading to the new line (C,D). The amount of financial assistance may be high enough to entice (N2-N1) additional students to go to college who would not have gone to college without the aid. The appropriations are then evenly distributed among the N2 students who go to college (i.e., each student receives a benefit of S/N2), and the college going rate would increase from N1/N to N2/N. However, the policy is inefficient because some subsidies were provided to students who would have gone to college without the aid (E1i = 1). Although the N2 students would all personally benefit from the subsidy due to having more net income, the state does not receive any additional positive externalities by giving these students a subsidy. Alternatively, the state could distribute the financial assistance S uniformly for only those (N-N1) students who would not have gone to college in the absence of state support. As long as (N3-N1) < N2, the same total state dollars would be distributed over fewer students, and hence the 13

per-student subsidy would be larger, when states use grants as opposed to appropriations. The new utility distribution for students after the subsidy would be represented by the line segments (A,G,E,F). As a result, an additional (N3-N2) students would enroll in college due to using grant aid in place of appropriations, and the college-going rate (N3/N) would increase. -------------------------- Insert Figure 6 Here ---------------------------The same result can be shown mathematically by breaking the students into two groups: the N1 students who would have gone to college in the absence of financial assistance (group A), and the remaining (N-N1) students who would not have gone to college without financial assistance (group B). Equation (7) can be simplified as follows: (11) ∆

Pr =

1 Pr

Pr 1

1 Pr

1

Pr

1

Pr

1

B

B

because Pr(E2=1) = Pr(E1=1) =1 for all students in group A. As long as the additional subsidy given to students in group B leads at least one student to go to college who would not have done so with the smaller state appropriation, the positive externality gains from allocating funding in the form of grant aid would exceed the gains from state appropriations. One limitation of providing a uniform subsidy to a subset of students is that some students would still receive more in subsidy than would be necessary to cause them to choose to enroll in college. Alternatively, the state could vary the per-student support so that each of the (N-N1) students received just enough state aid to entice them to go to college. Such a policy would cause the utility line for students to become (A,G,H). The total cost of this option is represented by the area (G,B,H), and it would result in a college going rate of 100%. As noted before, however, there may be a point at which the additional subsidy needed to entice a student

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to enroll in college exceeds the positive externality. In this instance, the socially optimal level of enrollments is some value less than 100%. The third implication of the model is that states would be better off if they awarded financial aid on the basis of need rather than merit. If it is true that students who would be eligible for merit-based aid are more likely than students who are normally eligible for needbased aid to go to college, then state need-based financial aid would be the policy option most likely to lead to gains in positive externalities. This is not to say, however, that colleges and universities institutions should not award merit-based aid to students. In fact, an argument equally compelling to the one here could be made that institutions should only award merit-based aid and not need-based aid to students. This conclusion follows from the assumption that institutions award financial aid in order to maximize their prestige or reputation. If the prestige or reputation of an institution is a function of the average perceived quality of students attending the institution, then the institution’s objective is to award financial aid to students in such a way that it will lead to gains in the average quality of students. Therefore, institutions only benefit when the financial aid that they give to students results in an increase in the average quality of students; in other words, the aid entices an above-average student to enroll at the institution. Another way to illustrate the effects of grant aid versus appropriations on students is to simulate the effects of changes in support from state appropriations to need-based financial aid. In Table 3, we begin with data on appropriations and sticker prices charged by three public institutions in Indiana – Indiana University, Purdue University, and Ball State University – for the year 2006-07. In 2006-07, for example, Indiana University received state appropriations in the amount of $191.855 million, and charged tuition rates of $7,460 for resident students and $20,472 for nonresident students. Dividing total appropriations by the number of resident 15

students reveals that each student received a subsidy of $6,557 from the state, which accounted for roughly half of the nonresident/resident tuition differential. The remaining tuition difference ($6,455) was then paid for by the institution from other revenue sources. In Option 1, we examine the impacts of replacing state appropriations with an equal amount of need-based financial aid. The grant is set at a uniform amount to cover a student’s full cost of education minus the institutional funded gap. For Indiana University, the amount of financial aid given to each student who qualifies would be $20,472 - $6,455 = $14,017. Dividing the total appropriation level for each institution by the financial aid per student yields the number of students who would receive the full financial assistance. If all state appropriations were converted to state need-based financial aid, then Indiana University, Purdue University, and Ball State University would be able to cover the full cost of education for 47 percent, 52 percent, and 54 percent of their resident students, respectively. The resident tuition rate for all students would be set equal to the nonresident tuition rate minus the institutional funded gap. Accordingly, those resident students who did not receive state need-based financial aid would face notably higher in-state tuition rates. In Option 2, we replaced only half of the state appropriations for each institution with state need-based financial aid. In this scenario, the state would be able to cover the full cost of education for roughly one-quarter of the students at each institution, and the increase in resident tuition rates would be notable but more modest than under Option 1. ---------------------------- Insert Table 3 Here -------------------------

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Discussion State funding for higher education is a complex issue that poses a number of policy challenges for analysts. What level of support is needed for higher education? How should support be allocated to students? What barriers might states face in implementing policies, and how can they be overcome? In this paper, we have used economic concepts to show that it would be more efficient for states to provide financial support in the form of need-based grants to students rather than appropriations to select institutions. This result is at odds with the fact that the vast majority of state funding is in the form of direct appropriations to institutions rather than need-based grant aid. There are several reasons why states continue to provide most of their support in the form of direct appropriations. Lack of Political Support. The first is that it would likely be difficult to acquire enough political support to reallocate appropriations to need-based financial aid. According to the median voter model, legislators act in accordance with the wishes of the median voter in his/her district. By construction, state appropriations tend to benefit more individuals than do state need-based grants. In addition, lower-income families who would be helped by need-based aid are less likely to vote than are middle- and upper-income families who benefit from state appropriations. Taken together, there are more voters who would stand to lose if appropriations were eliminated than there are voters who would stand to gain if need-based grants were increased. Therefore, even if an argument could be made that it is in the state’s best interest to reallocate funding towards need-based grants, the politics behind funding issues may prevent this policy from being successfully adopted. Reductions in Institutional Aid. A second reason why states often favor appropriations over grant aid is that there is concern that increases in state financial aid would be partially or 17

totally offset by decreases in institutional financial aid. The analysis shown earlier in this paper ignores institutional financial aid, and yet colleges and universities provide substantial amounts of need- and merit-based aid to students. If institutions responded to increases in state grant dollars by reducing their own grants and scholarships, then there would be a smaller gain in the student’s ability to pay for college and hence the policy may not lead to an increase in the college-going rate of students. However, because the proposed state need-based grants discussed in this paper would fully cover the cost of attendance for recipients, they are likely to be larger than the amounts typically awarded by institutions and thus would still have a positive impact on the probability of a student going to college. Furthermore, institutions would now be able to increase merit aid because they would not have to give need-based aid to students. Concerns of Brain Drain. Third, states may be concerned that allocating more assistance in the form of grants may lead more students to leave the state following graduation. This is important from the perspective of policy makers because states will only receive the positive externalities when students choose to stay in-state following their education. If a student receives his or her education in State X and then moves to State Y after graduation, then the external benefits from the student’s education accrue to residents of State Y. Given this assumption, states would be less likely to support higher education in any form if there is a low probability that students receiving the support will reside in the state following their graduation. Appropriations to in-state institutions requires the student to use the subsidy at an in-state institution, and some policy makers believe that students who attend college in-state are more likely than others to stay in-state after they graduate. State grants, on the other hand, often can be used at out-of-state institutions. More research is needed, however, to determine if the state in which a student attends college affects where he/she will reside after graduation, holding 18

constant the student’s initial preference for staying in state. States could address this concern by restricting the state grants to be used only at in-state institutions, or even at in-state public institutions. Effect of Sticker Shock on Students. Fourth, it might be argued that the sharp increases in resident tuition rates that would accompany a reduction in appropriations would lead some students to decide that they cannot afford to go to college. It is possible that students may be affected by the higher sticker prices at public institutions and not fully understand how state grants would actually reduce the net prices that many students would pay at public institutions. The problem could be addressed through a more aggressive information campaign to convince low-income students and their families that the change will make college more affordable for them. One group that would be hurt, however, are middle- or upper-income students who are not eligible for the state grant and may have been at the margin for attending college with the state appropriations. It may be possible for institutions to direct more merit aid towards this group of students to help offset the net price increases that they would face. Difficulty in Targeting Aid. Fifth, states may argue that it is impossible to target needbased grants to only those students who would not have attended college without the grants. It is true that some students with very low levels of income may still opt to attend college without any financial assistance if their utility from going to college or their expected future income stream from going to college is sufficiently high. Similarly, some students who could definitely afford to go to college without financial assistance may not want to go to college due to their low preferences for college and/or low expected future income stream. It is also impossible for policy makers to determine how large of a financial incentive would be needed to entice each individual student to go to college because preferences are unobservable and a student’s ability 19

to pay for college will be affected by many factors, including family income, wealth, number and ages of children, and health. The best that states could do is to award grants on the basis of a student’s estimated ability to pay using established guidelines such as through the Free Application for Federal Student Aid (FAFSA), or provide larger uniform subsidies to groups of students that are known to have some reduced ability to pay for college. Concerns from Public Institutions. Finally, many public institutions are likely to argue against the policy change from appropriations to need-based grants. Such a change could reduce the number of students who would want to attend public institutions even though in-state tuition rates would still compare favorably with tuition rates at most private institutions. Similarly, because students may use state grants to attend private, religiously-affiliated institutions, public institutions could argue that the state would then be indirectly providing financial support to religious organizations, and thus blur the line separating church and state. If the need-based grants were restricted to be used at in-state public institutions, then much of the concern over revenues and the separation of church and state could be alleviated. In fact, public institutions may benefit financially from the movement away from appropriations if they are able to increase their enrollments due to the larger numbers of students who would demand a postsecondary education. Public institutions might also be concerned that the change in policy would decrease the stability of their revenue stream because they would be more dependent on tuition revenue than before. To help address this issue, states might phase in the change towards need-based grants so that public institutions would have ample opportunity to see how the change will affect their finances. Finally, public institutions may argue that they require appropriations to support particular programmatic needs of the state, in areas such as medicine and education. The above 20

model, however, does not preclude states from earmarking some portion of funding to support the costs of operating specific academic programs while distributing the remaining aid directly to students. Overall, the analysis presented here shows that states can improve the efficiency and effectiveness of their financial support programs by providing most support in the form of needbased financial aid to students. Political and economic concerns notwithstanding, such a policy shift is clearly a more efficient allocation of scarce state resources. Equally important, this shift has the potential to improve access to higher education for those students who have traditionally been underrepresented in postsecondary education. If states are truly committed to raising the educational attainment levels of their citizens, then a movement towards more need-based grants and less direct appropriations to public institutions would be desirable.

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References Acemoglu, D., and Angrist, J. “How Large are Human Capital Externalities? Evidence from Compulsory Schooling Laws.” In B. Bernanke and K. Rogoff (Eds.), NBER Macroeconomics Annual 2000 (pp. 9-59). Cambridge, MA: MIT Press, 2001. Borcherding, T., and Deacon, R. “A Theory of Competition Among Pressure Groups for Political Influence.” American Economic Review, 1972, 63, 280-296. Bös, D. “The Democratic Decision on Fees Versus Taxes.” Kyklos, 1980, 33, 76-99. Bowen H. Investment in Learning: The Individual and Social Value of American Higher Education. San Francisco, CA: Jossey-Bass, 1977. Browning, E., and Browning, J. Public Finance and the Price System. Englewood Cliffs: Prentice-Hall, 1994. Clotfelter, C. “Public Spending for Higher Education: An Empirical Test of Two Hypotheses.” Public Finance, 1976, 31, 177-195. College Board. Trends in College Pricing 2006. Washington, DC: College Board, 2006. Coughlin, C., and Erekson, O. “Determinants of State Aid and Voluntary Support of Higher Education.” Economics of Education Review, 1986, 5, 179-190. Creedy, J., and Francois, P. “Financing Higher Education and Majority Voting.” Journal of Public Economics, 1990, 43, 181-200. Delaney, J., and Doyle, W. (2007). “The Role of Higher Education in State Budgets.” In K. Shaw and D. Heller (Eds.), State Postsecondary Education Research: New Methods to Inform Policy and Practice. Sterling, VA: Stylus, 2007. Grapevine. Retrieved August 1, 2006, from http://www.coe.ilstu.edu/grapevine/. Hartman, R. “Equity Implications of State Tuition Policy and Student Loans.” Journal of Political Economy, 1972, 80, S142-S171. Heller, D. “State Support of Higher Education: Past, Present, and Future.” In D. Priest and E. St. John (Eds.), Privatization and Public Universities (pp. 11-37). Bloomington, IN: Indiana University Press, 2006. Hoenack, S., and Pierro, D. “An Econometric Model of a Public University’s Income and Enrollments.” The Journal of Economic Behavior and Organization, 1990, 14, 403-423. Johnson, G. “Subsidies for Higher Education.” Journal of Labor Economics, 1984, 2, 303-318. 22

Johnson, W. “Are Public Subsidies to Higher Education Regressive?” Education Finance and Policy, 2006, 1(3), 288-315. McLendon, M., and Hearn, J. “Incorporating Political Indicators into Comparative-State Study of Higher Education.” In K. Shaw and D. Heller (Eds.), State Postsecondary Education Research: New Methods to Inform Policy and Practice. Sterling, VA: Stylus, 2007. McLendon, M., Heller, D., and Young, S. “State Postsecondary Policy Innovation: Politics, Competition, and the Interstate Migration of Policy Ideas.” Journal of Higher Education, 2005, 76(4), 363-400. Moore, G. “Equity Effects of Higher Education Finance and Tuition Grants in New York State.” Journal of Human Resources, 1978, 13, 482-501. Moretti, E. “Estimating the Social Return to Education: Evidence from Longitudinal and Repeated Cross-Section Data.” Journal of Econometrics, 2004, 121, 175-212. National Association of State Student Grant and Aid Programs. 37th Annual Survey Report on State-Sponsored Student Financial Aid, 2005-2006 Academic Year. Washington, DC: National Association of State Student Grant and Aid Programs, 2006. National Center for Education Statistics. Digest of Education Statistics, 2005. Washington, DC: U.S. Department of Education, 2006. Paulsen, M. “The Economics of the Public Sector: The Nature and Role of Public Policy in the Finance of Higher Education.” In M. Paulsen and J. Smart (Eds.), The Finance of Higher Education: Theory, Research, Policy, and Practice (pp. 95-132). New York: Agathon Press, 2001. Paulsen, M., and Peseau, B. “Ten Essential Economic Concepts Every Administrator Should Know.” Journal for Higher Education Management, 1989, 5(1), 9-17. Schultz, T. The Economic Value of Education. New York: Columbia University Press, 1963. Stiglitz, J. Economics of the Public Sector, 3rd edition. New York: W. W. Norton, 2000. Toutkoushian, R. “Trends in Revenues and Expenditures for Public and Private Higher Education.” In M. Paulsen and J. Smart (Eds.), The Finance of Higher Education: Theory, Research, Policy, and Practice (pp. 11-38). New York: Agathon Press, 2001. Toutkoushian, R., and Hollis, P. “Using Panel Data to Examine Legislative Demand for Higher Education.” Education Economics, 1998, 6, 141-157.

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Weisbrod, B. “External Effects of Investment in Education.” In M. Blaug (Ed.), Economics of Education I. Baltimore, MD: Penguin Books, 1968. Wolfe, B. “External Benefits of Education.” In M. Carnoy (Ed.), International Encyclopedia of Economics of Education (2nd ed.). Tarrytown, NY: Elsevier, 1995.

24

Figure 1: Depiction of Positive Externalities Associated with Higher Education

$

MC

MBp+s MBp

Np

Np+s

25

Enrollments (N)

Figure 2. Effect of State Appropriations on Higher Education Pricing

Price (P)

P0

P1

Supply

A State Subsidy Appropriation

B

Tuition Tuition Revenue Revenue 0

Demand Enrollments (N)

N0

26

Figure 3. Effect of State Financial Aid on Higher Education Pricing

Price (P)

Supply

A

P0 State Financial Aid P1

Demand

Tuition Revenue 0

N1

Enrollments (N)

N0

27

Table 1: State Financial Assistance for Higher Education, 2005-06

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio

State Undergraduate Grant Aid, 2005-06 $7,626,000 $502,000 $2,798,000 $28,364,000 $757,809,000 $60,738,000 $39,366,000 $10,240,000 $412,018,000 $465,414,000 $410,000 $5,424,000 $380,348,000 $182,282,000 $53,814,000 $15,168,000 $172,866,000 $116,432,000 $13,387,000 $76,362,000 $80,093,000 $197,675,000 $131,010,000 $22,285,000 $42,068,000 $3,760,000 $9,918,000 $39,671,000 $3,753,000 $256,048,000 $61,779,000 $895,129,000 $192,018,000 $1,863,000 $221,411,000

State Appropriations for Total State Support, Higher Education, 2005-06 2005-06 $1,407,875,000 $252,124,000 $994,751,000 $736,924,000 $10,146,382,000 $597,454,000 $832,019,000 $216,168,000 $3,297,571,000 $2,088,286,000 $461,171,000 $350,672,000 $2,641,164,000 $1,430,424,000 $779,847,000 $754,550,000 $1,207,616,000 $1,242,769,000 $248,223,000 $1,268,850,000 $966,366,000 $2,012,271,000 $1,365,500,000 $795,882,000 $855,340,000 $172,767,000 $548,353,000 $559,616,000 $117,172,000 $2,029,443,000 $705,804,000 $4,390,661,000 $2,962,113,000 $215,031,000 $2,111,733,000 28

$1,415,501,000 $252,626,000 $997,549,000 $765,288,000 $10,904,191,000 $658,192,000 $871,385,000 $226,408,000 $3,709,589,000 $2,553,700,000 $461,581,000 $356,096,000 $3,021,512,000 $1,612,706,000 $833,661,000 $769,718,000 $1,380,482,000 $1,359,201,000 $261,610,000 $1,345,212,000 $1,046,459,000 $2,209,946,000 $1,496,510,000 $818,167,000 $897,408,000 $176,527,000 $558,271,000 $599,287,000 $120,925,000 $2,285,491,000 $767,583,000 $5,285,790,000 $3,154,131,000 $216,894,000 $2,333,144,000

Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Total

$58,215,000 $29,429,000 $403,956,000 $12,883,000 $255,744,000 $3,367,000 $173,907,000 $366,872,000 $7,409,000 $17,561,000 $132,720,000 $173,835,000 $70,980,000 $93,583,000 $163,000 $6,760,473,000

$840,072,000 $623,983,000 $2,047,114,000 $178,931,000 $790,144,000 $166,602,000 $1,164,332,000 $5,242,541,000 $677,668,000 $82,043,000 $1,594,605,000 $1,536,329,000 $346,670,000 $1,131,515,000 $235,415,000 $67,420,856,000

$898,287,000 $653,412,000 $2,451,070,000 $191,814,000 $1,045,888,000 $169,969,000 $1,338,239,000 $5,609,413,000 $685,077,000 $99,604,000 $1,727,325,000 $1,710,164,000 $417,650,000 $1,225,098,000 $235,578,000 $74,181,329,000

Notes: Data obtained from the 37th Annual Survey Report on State-Sponsored Student Financial Aid (NASSGAP, 2006). Financial aid totals include all need-based and non need-based aid given to undergraduate students. State appropriations were obtained from Grapevine and represent direct state appropriations for operating expenses.

29

Table 2: State Financial Assistance Per Pupil, 2005-06 State

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio

Grant Support Per Population Age 18-24 $17 $7 $5 $102 $211 $131 $125 $123 $259 $506 $3 $34 $304 $293 $173 $51 $424 $234 $106 $144 $135 $200 $246 $71 $71 $38 $52 $181 $30 $338 $295 $492 $231 $25 $198

Appropriation Per Population Age 18-24 $3,112 $3,267 $1,697 $2,637 $2,830 $1,289 $2,635 $2,600 $2,074 $2,270 $3,602 $2,199 $2,110 $2,301 $2,513 $2,538 $2,962 $2,497 $1,971 $2,385 $1,634 $2,033 $2,560 $2,523 $1,451 $1,731 $2,877 $2,555 $951 $2,681 $3,374 $2,414 $3,571 $2,875 $1,886 30

Total State Support Per Population Age 18-24 $3,129 $3,273 $1,702 $2,739 $3,041 $1,420 $2,760 $2,724 $2,333 $2,776 $3,605 $2,233 $2,414 $2,594 $2,686 $2,589 $3,386 $2,731 $2,078 $2,528 $1,770 $2,233 $2,806 $2,593 $1,522 $1,768 $2,929 $2,736 $981 $3,020 $3,669 $2,906 $3,802 $2,900 $2,084

Grant Per Pupil Category

Appropriation Per Pupil Category

Low Low Low Low Low Low Low Low High High Low Low High High Low Low High High Low Low Low Low High Low Low Low Low Low Low High High High High Low Low

High High Low High High Low High High Low Low High Low Low Low High High High High Low High Low Low High High Low Low High High Low High High High High High Low

Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Total

$152 $83 $342 $120 $600 $39 $302 $152 $23 $276 $176 $269 $422 $164 $3 $231

$2,198 $1,769 $1,734 $1,662 $1,855 $1,935 $2,021 $2,169 $2,122 $1,289 $2,110 $2,381 $2,062 $1,978 $4,132 $2,305

$2,350 $1,852 $2,077 $1,782 $2,455 $1,974 $2,323 $2,321 $2,145 $1,565 $2,286 $2,651 $2,484 $2,142 $4,135 $2,536

Low Low High Low High Low High Low Low High Low High High Low Low

Low Low Low Low Low Low Low Low Low Low Low High Low Low High


31

Figure 4: Scattergram of States by Appropriations and Financial Aid Per Pupil Ages 18-24

$4,500

High App, Low Aid

State Appropriations Per Pupil Ages 18‐24

$4,000

High Aid, High App

$3,500 $3,000 $2,500 $2,000 $1,500 $1,000

Low App, High Aid

Low Aid, Low App

$500 $0 $0

$100

$200

$300

$400

$500

$600

$700

State Grants Per Pupil Ages 18‐24


32

Figure 5: Optimal Level of State Financial Support for Higher Education

$ MC = Sic

MB = α

Enrollments

*

N

33

Figure 6: Effects of State Financial Subsidies on College Enrollment Decision

Utility C E

A

H G

F D B Number of Students N1

N2

34

N3

N

Table 3: Simulation of Effects of State Financial Aid Program on Selected Institutions

Appropriations = # Resident Students = Appropriations/Student = Nonresident Tuition = Resident Tuition = Institutional Funded Gap =

Indiana U

Purdue U

Ball State

$191,855,000 29,258 $6,557 $20,472 $7,460 $6,455

$241,259,000 31,038 $7,773 $21,266 $7,096 $6,397

$122,943,000 15,243 $8,066 $17,186 $6,810 $2,310

Option 1: All State Appropriations Converted to Financial Aid # Receiving Full Aid = % Receiving Full Aid = Appropriations/Student = Nonresident Tuition = Resident Tuition = Institutional Funded Gap =

Purdue U 16,226 52% $0 $21,266 $14,869 $6,397

Indiana U 13,687 47% $0 $20,472 $14,017 $6,455

Ball State 8,265 54% $0 $17,186 $14,876 $2,310

Option 2: Half State Appropriations Converted to Financial Aid # Receiving Full Aid = % Receiving Full Aid = Appropriations/Student = Nonresident Tuition = Resident Tuition = Institutional Funded Gap =

Indiana U 6,843 23% $3,279 $20,472 $10,739 $6,455

Purdue U 8,113 26% $3,887 $21,266 $10,983 $6,397

35

Ball State 4,132 27% $4,033 $17,186 $10,843 $2,310