BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF PRICES AND LIVING

CONDITIONS

Quality Adjusted Indices for Four Year Colleges

Amy Ellen Schwartz, New York University

Benjamin P. Scafidi, Georgia State University

Working Paper 337

March 2001

 

The views expressed are those of the author and do not necessarily reflect the policies of the U.S. Bureau of Labor

Statistics or the views of other staff members. This paper was part of the U.S. Bureau of Labor Statistics Conference on

Issues in Measuring Price Change and Consumption in Washington, DC, June 2000.
Quality Adjusted Price Indices for Four Year Colleges

Amy Ellen Schwartz

Wagner School of Public Service

New York University

Amy.schwartz@nyu.edu

Benjamin P. Scafidi

Andrew Young School of Policy Studies

Georgia State University

 bscafidi@gsu.edu

Report Prepared for the Bureau of Labor Statistics

This work is funded under a contract with the Bureau of Labor Statistics (BLS). Any findings or views

expressed in this paper are the authors' and do not necessarily represent the views of BLS. This work has

benefited tremendously from the insight and advice of Dennis Fixler.

November 2000
1

Introduction

Since the earlier 1980’s the “sticker price” or “list price” of a college education in the

United States has, according to estimates from the Consumer Price Index, risen significantly

faster than the overall rate of inflation. This has raised considerable concern among

policymakers, parents and students that college attendance was becoming less and less affordable

even as it was becoming more and more important for economic success in the job market.

Interestingly, for the CPI, the government collects data on the “sticker price” of college (tuition

and fees) without adjusting for scholarships given or other discounts. Further, no adjustments

are made for changes in the quality or characteristics of the services provided, such as attributes

of the faculty, the course offerings, or the facilities. Thus, the estimated price indices reflect

changes in quality and characteristics of college as well as changes in prices.1 In this paper, we

develop and explore the construction of a quality-adjusted price index for US colleges, based on

the estimation of a hedonic model of the price of college. Our focus is on estimating an index

that reflects the out-of-pocket costs paid by a consumer in order to attend college and not, for

example, an index of the cost of producing higher education services.

While many studies have considered the underlying causes of the growth in tuition, these

have typically focused on investigating changes in the costs of higher education, the sources of

revenues (especially government support and private contributions), or more generally on

estimating aspects of the supply or demand for college education. There has been, to our

 

1

 For additional information, see “How BLS Measures Price Change in the Consumer Price Index for College

Tuition and Fixed Fees” (1997).
2

knowledge, no previous work estimating quality-adjusted price indices and hedonic equations for

college education.2

To estimate hedonic price models, we use data from the Annual Survey of Colleges

(ASC) data from the College Board. These include data on financial aid and scholarships,

characteristics of the student body (including test scores, demographics, enrollment), and

attributes of the services provided by the college (including faculty characteristics, course

offerings, athletics, etc.) We supplement the ASC data with data on tuition and fees from the

Integrated Postsecondary Education Sata System (IPEDS) collected by the National Center for

Education Statistics, U.S. Department of Education.

 The purpose of this study is to identify quantifiable measures of quality of higher

education services; to develop a methodology for quality-adjusting prices for changes in the

attributes of college services; and to provide an empirical demonstration of the proposed method

of quality adjustment.

The next section presents the theoretical and conceptual background for the empirical

analysis including a simple economic model of college choice, and section III describes a

hedonic model for estimation. Section IV contains a description of our data, and section V the

estimates of the hedonic models. Conclusions are in section VI.

II. The Market for College

The market for college education is characterized by discrete goods in which each

contains a bundle of attributes valued by consumers, differentiated products, not-for-profit firms,

imperfect competition, and heterogeneous consumers, among other ‘familiar but curious’

 

2

 Articles by Charles T. Clotfelter and Gordon C. Winston and others in a Symposium on the Economics of Higher

Education in the Winter 1999 Journal of Economic Perspectives pp. 3-116 provide a nice overview and introduction
3

idiosyncrasies which present formidable challenges to modeling supply and demand and

understanding price determination.3 In this section, we review some of the most important

features of the market for college that are relevant and important to motivating and

understanding the hedonic analysis that forms the centerpiece of our method of estimating price

indices below.

What do colleges produce?

While fully modeling the production process of a college is clearly outside the scope of

this work, some discussion of the output of colleges is necessary. Colleges and universities are

best viewed as multi-product firms, producing a variety of services including:

- Education

- Food, accommodations, and amusements

- Minor league “professional” athletics

- Research

- Investment management.4

Alternatively, Verry and Davies (1976) focus on the educational mission of colleges, listing

outputs as follows:

“(i) Instructional or teaching outputs (the transmission of knowledge). This involves the

teaching of various kinds (general, vocational etc.), in different subjects and at different

levels, all generally leading to certification of some description.

(ii) Research outputs (the extension of knowledge).

(iii) General Social Services. This is something of a catch-all category for the less

tangible and often most controversial activities of the university. It is intended to include

the general socialization function (the instillation of desirable work habits, co-operative

behaviour, respect for laws and institutions, and, some would say, docility and

 

to the field.

3

 See “The Familiar but Curious Economics of Higher Education: Introduction to a Symposium” by Charles

Clotfelter (1999)

4

 This list is, in part, implicit in Ehrenberg (1999). Here, investment management refers to the management of the

university’s endowment and other financial resources. Minor league “professional” sports refers to high quality

amateur sports where the athletes receive compensation in the form of scholarships in exchange for participation on

an athletic team.
4

obedience), and the related function, primarily benefiting employers, of sorting, selecting

and screening individuals...” (page 10.)

Understanding the objectives of universities is far from straightforward. Most

universities are not-for-profit and might be viewed as acting to maximize some alternative

objective function such as ‘prestige,’ ‘human capital’ or ‘endowment’ rather than profits.

Universities compete with one another for the ‘best’ students – through the allocation of financial

aid, the setting of tuition and the provision of attributes – in the face of resource constraints.

Better students may enhance an institution’s prestige/human capital/endowment by increasing

the production of some of the outputs of college. Therefore, some of the customers of college—

students—are inputs to the production process as well. (See Rothschild and White (1993) for

more on this point.) For our purposes, the supply curve for the college attributes should be

upward sloping – providing better attributes costs more and universities face capacity constraints

that suggest increasing costs at some student body size.

What do students buy?

In paying for college, the consumer of undergraduate education buys only output (i). The

college student (or her parents) is not buying research outputs (ii) or general social services (iii)

directly with the price of tuition—the price of tuition does not entitle you to (ii) or (iii), which

may be available to some other community. For example, research may enhance the prestige

and/or learning experience at a particular college. These outputs are valued by various consumers

that include, but are not limited to, college students. For example, university research is

‘consumed’ by governmental agencies and industrial clients far more frequently than by college

students. The implication is that research, general social services and the ‘other’ outputs will be
5

important in the hedonic analysis only to they extent they enhance the undergraduate college

experience.

While universities produce many products, college admissions officers like to say that

students (consumers) choose which college to attend based on “Resources and Reputation.” That

is, student choose a college based upon their perception of the reputation the school has (which

may vary across regions for a particular school, for example) and the resources the school offers

– such as language offerings, the availability of dormitories, or sororities, or the ratio of students

to faculty. This suggests that in addition to including attributes that describe the resources

provided by a college, a hedonic price equation should capture or control for the reputation of the

college. ‘Reputation effects’ can be viewed as essentially the same as the ‘brand effects’

utilized in the hedonic analysis of automobiles or other goods. Our estimation strategy for the

hedonic model described below allows for these brand effects.

More recently, some have argued that an additional and important output of a college is

the increase in the student’s future earnings or their ‘market value,’ often referred to as the ‘value

added’ of the college. Thus, some would include a measure of ‘value added’ in the specification

of the hedonic model. While calculations about the impact of a school on a child’s future

earnings is clearly an important consideration for students and parents choosing schools, it is

unclear how those calculations are made given the available data. Put simply, we know of no

broadly available data on the salaries of recent (or other) graduates or any other direct measure of

the value-added to a student’s education, for individual American colleges for the sample period

of our analysis.5

 

5

 It seems that the relevant measures of value-added for a student considering enrolling in a particular college at time

t would be the value-added to students who graduated at t-1. Presumably, prospective students would have better

information about the value-added that individual schools provided to its recent graduates, than the value-added 4-5

years in the future. Recent research by Dale and Krueger (1999), however, has suggested that there are relatively
6

A model of consumer choice

This section describes a simple model of college choice, for a consumer who has already

decided to attend a four-year college. (The choice of whether to go to college at all or whether to

attend a two-year of four-year college is viewed as predetermined and therefore outside the

model.) That is, we model a consumer’s choice of a college, conditional on attendance at a fouryear college.6

 The purpose of this exercise is to motivate the empirical model and to make clear

the role that tuition and financial aid play in the college choice decision.

For our purposes, we view college as a discrete commodity with several attributes that

contribute directly to consumer utility. Consumers cannot buy attributes directly and the prices

of each individual attribute are not observed to the consumer. Instead, consumers can only buy

attributes in bundles and the price of the bundle of attributes of college, the out-of-pocket tuition

cost, is observed.

To be specific, assume that consumers value two attributes of college, X1j and X2j. X1j

and X2j are observed by each consumer.7 Cij is the consumption of all other goods if student i

attends college j, Yi is household income, Tj is the sticker price of tuition at college j, and Aij is

the aid offered to student i at college j.8 Let U(X1j,X2j,Cij) be the utility that student i derives

from attendance at college j. Student i chooses college j over college k if:

 

small differences in the earnings of students with similar characteristics due to the college that they attend. Thus,

there may be smaller differences across colleges in the ‘value-added’ to student earnings than some suspect.

6

 Whether the student or her parents is the primary decision-maker will likely affect a household’s willingness to pay

for various attributes of college. Since this paper seeks to estimate the market value of those attributes and because

the primary decision maker is unobserved, in the empirical work we assume that the average decision making power

among students and their parents is unchanged over the sample period.

7

 In the empirical specification of the hedonic model in section III, some of the X’s are observed and some are

unobserved to the analysts. Since we have panel data, we allow for the presence of unobserved time invariant

university attributes, “brand effects,” in the hedonic model through a first differences specification.

8

 Rothschild and White (1996) suggest two reasons why colleges offer financial aid (A). 1) Given high fixed costs

and low marginal costs, colleges can maximize net revenues through price discrimination. 2) Students can be

viewed as inputs in the production of education. For colleges that offer aid to maximize net revenues, Rothschild

and White believe that financial aid can be correctly viewed as a price discount. When financial aid is actually a

payment to individual students for the inputs each provides, they believe that financial aid must be considered
7

(1) U(X1j,X2j,Cij) > U(X1k,X2k,Cik), ∀ k ≠ j,

where

(2) Cij = Yi – (Tj – Aij) and Cik = Yi – (Tk – Aik).

Substituting (2) into (1):

(3) U(X1j,X2j, Yi – (Tj – Aij)) > U(X1k,X2k, Yi – (Tk – Aik)), ∀ k ≠ j,

Each consumer observes its net price of sending its student to college j, (Tj – Aij), but the prices

of individual attributes of college are unobserved. Each consumer chooses which college to

attend based on the bundle of attributes (the X’s) that each college provides and the net price that

she faces (T-A). Thus, from the consumer’s point of view it is the net price and not the sticker

or list price that matters for decision-making. Note that this implication is consistent with the

empirical findings of Manski and Wise (1983). Their analysis of the postsecondary activity

choices of 4000 students finds coefficients on tuition and financial aid that are almost equal and

of opposite sign.9 This suggests the net price should be used in the hedonic analysis and in the

formation of price indices for the CPI.

 

differently. In our model, financial aid that is a payment for inputs provided by individual students may also be

viewed as a price discount—from the consumer’s point of view—if student inputs have a market value of zero

outside of college. From her own point of view, an individual student’s input is a sunk cost. Inputs provided by

students can be viewed as their presence at the college providing excellence and/or diversity, each of which may

provide positive peer effects in the production of education. In deciding which college to attend, consumers thus

care about the excellence and diversity of their peers (and all other attributes of college) and their own net price of

college—tuition minus aid. In our hedonic models described below, we include measures of excellence and

diversity as attributes of college.

9

 Manski and Wise also include dormitory costs in their analysis and find similarly consistent results. “We might

expect that a one-dollar increase in aid would have the same effect on a student’s choice as a one-dollar decrease in

cost. That is, other things equal, if tuition at school A is one dollar more than at school B, but if scholarship aid is

one dollar more at school A than at school B, then a student should be indifferent between A and B. In fact, this is

essentially what we find at four-year colleges and universities. The negative value associated with high tuition is

almost identical to the negative value associated with dormitory cost, and each of these is approximately equal but

opposite in sign to the positive value associated with scholarship aid.” Page 19.
8

III. Hedonic Model

Estimating a quality-adjusted price index for college tuition proceeds with a hedonic

analysis of the price of college based, conceptually, on the work in Rosen (1974) and following,

essentially, the methodology outlined in Triplett (1990), Berndt (1991) and others.

Adopting a log-linear specification, the price of a year of school at the jth college at time

t is written as a function of the characteristics of a year of college:

(4) Pjt

 

=

 α + βZ Zj + βX Xjt + βS Sjt + ρt It + Cj + εjt; j=1,....,J; t=1,...,T.

where Zj is a vector of time invariant characteristics of college j i.e., location, etc; Xjt is a vector

of time varying characteristics of college j at time t, i.e., size of the undergraduate student body,

student/faculty ratio, computers per students, availability of dorms, course offerings, religious

affiliation, quality of the faculty; Sjt is a vector of time varying characteristics of the student body

attending college j in time t, It are a vector of year dummies variables that take a value of one in

year t for t=1,..., T; Cj is a dummy that takes on a value of one for college j (a college fixed,

“brand,” effect) and Pjt is the logarithm of the average price of a year at college j in time t.

Since few students actually pay the ‘list’ price for college because they receive at least

some financial aid, we define Pjt as the ‘net’ or discounted price:

(5) Pjt

 

=

 ln( Tjt - Ajt)

where Tjt is the tuition (plus fees) price for one year for one undergraduate student (full-time) at

college j in time t, (the ‘sticker price’ or list price) and Ajt is average financial aid for one year at

college j in time t. Financial aid was restricted to grants only—thus, student loans and work-
9

study income were not included.10 All public and institutional grants to students are included in

the College Board’s ASC data: Pell grants, other federal grants such as grants from the GI Bill,

state merit and need-based grants, grants to students from institutions themselves, etc. Any

private grants to students not reported to a university, such as an employer writing a check to an

employee in order to offset a tuition payment for the employee’s child, would not be included in

the ASCs grants data.

As usual, equation (4) can be estimated to yield estimates of the marginal impacts of the

characteristics of the colleges on their prices (βs). Each β is an estimate of the shadow price to

consumers of a particular attribute of college. The coefficients ρ can be used to form a price

index. Normalizing the level of the quality adjusted college price index to 100 in t, estimates of

the price index for the following years can be created by exponentiating the ρ’s. For example, the

quality adjusted price index for t + 1 can be found as 100*exp(ρt+1); the quality adjusted price

index for t + 2 can be found as 100*exp(ρt+2).

We estimate equation (4) in first differences, in order to purge the equation of the college

fixed (brand) effects, which also eliminates the time-invariant variables Zj from the equation.

This has the added advantage of reducing any bias due to the omission of unobserved timeinvariant characteristics. The disadvantage is that the coefficients are identified only by the

variation across years. We estimate (4) using weighted least squares, where each observation is

weighted by full-time equivalent enrollment in the first year of our sample to allow the estimates

 

10 The College Board’s ASC data include measures of average student loans and average income from work-study

jobs. We did not include these variables in the financial aid measure. Ideally, we would include measures of the

value of student loan terms and the value of work study jobs that are better than could be obtained in private capital

or labor markets. If one of these forms of financial aid has increased (decreased) over time in terms of its scope

and/or generosity, then our methodology will overstate (understate) increases in the net price of college.
10

to better reflect the actual distribution of spending in the market, as described in greater detail

below.11

While the econometric strategy is relatively straightforward, estimation presents a host of

conceptual and practical questions. In particular, what are the relevant characteristics that need to

be included in the analysis? Are public and private colleges in the same market? To some

extent, the answers depend upon the availability of data.

Note an important caveat about the price indices. The indices we estimate reflect only

the change in net price of college services to students/consumers – and not the full set of services

and outputs produced by colleges. Thus, since colleges are multi-product firms, we cannot use

the price indices estimated in this paper to deflate total university expenditures to get a measure

of university “output.” For example, the benefits of research—an important university output

valued by non-students—may be only partially capitalized in the net price of an undergraduate

education. Obtaining measures of the value of all the outputs of a university and the full cost of

providing those outputs is exceeding difficult (Winston 1999; Clotfelter, 1999) and is not the

subject of this paper.

IV. Data

We use five years of data from the College Board’s Annual Survey of Colleges (ASC) for

the academic years 1990-91 to 1994-95 and the National Center for Education Statistics’

Integrated Postsecondary Education Data System (IPEDS) to estimate hedonic models following

1). The explanatory variables of a hedonic model of college should include only attributes of

 

11 Note that fixing the weights used in the regression is analogous to fixing the quantity shares in the market basket

used to create price indices. This weighting strategy, while providing a better measure of actual spending in the

market relative to not weighting at all, leads to estimates of the upper bound of the actual changes in the price of
11

college that consumers value. This next subsection discusses the characteristics of college, and

the subsection that follows describes our data.

The Characteristics of College

In principle, there is a long list of characteristics that would be necessary to fully describe

the services (education; food, accommodations, and amusements; minor league professional

athletics) provided by a college in exchange for a year’s (discounted) tuition. These generally

fall into four categories – characteristics of the instructional program and student body (peers),

physical characteristics of the school and other non-academic amenities,

institutional/organizational characteristics of the school, and the value-added to each student’s

human capital. We use a relatively parsimonious specification due, in large part, to the fixed

effects specification which excludes all variables which are time-invariant either in principle or

in practice – that is, some variables are excluded that could vary across time but are essentially

unchanging in our sample and study period.

The Instructional Program. These include a broad range of characteristics and qualities

of the instructional program - Average class size, number of seminars, course offerings (i.e.,

languages, labs, etc.), required (distribution) courses, pedagogical techniques, faculty

characteristics (i.e., percent with Ph.D., percent with MA, number of Nobel prize winners?),

percent of undergraduate courses taught by graduate students, publications of the faculty.

The Student Body. The characteristics of the study body include a broad range of potential

variables describing the students’ academic ability (i.e., average SAT scores, high school grades)

 

college. An alternative approach would be to update the weights annually, which would understate actual changes

in the price of college. See Abraham, Greenlees, and Moulton (1998) for an explanation of this “substitution” issue.
12

and the students’ socioeconomic backgrounds (race/ethnicity, age, sex), as well as their interests

and abilities in non-academic areas.

School Characteristics – Physical Characteristics. These include physical characteristics

(such as dorms or gyms) and environmental characteristics (i.e., location, climate, proximity to

major city, or important amenities like beaches or ski slopes).

School Characteristics – Institutional Attributes. These should also include institutional

characteristics such as their religious affiliation, accreditation, the presence of other schools,

participation in an athletic conference, whether they are public or private colleges, and if public

whether they are state or local colleges.

The Sample

The College Board’s ASC data includes information on: (1) institutional aspects of

colleges, for example, source of control (i.e., private vs. public), Carnegie classification,

religious affiliation or accreditation; (2) environment (i.e, urban vs. rural ) (3) facilities such as

library holdings, availability of dorms (3) enrollment (part time, full time, etc.) (4) academic

offerings and policies (5) fields of study (6) placement and credit policies (7) freshman

admissions/profile (8) transfer study policies (9) Student life (sororities, intercollegiate sports,

etc) and (10) Financial Aid. Given the high rate of missing data on tuition and fees in the ASC

data, we obtain the information on tuition and fees from IPEDS. Using the tuition & fees data

from IPEDS increased our sample by approximately 100 colleges per year. Tables 1 and 2

provide data definitions and descriptive statistics for 1994-95 the variables that we include in our

model. Although we estimate the empirical models with data from the years 1990-91 to 1994-
13

95, for ease of exposition, we report summary statistics for only the most recent year (table 2)

and means for all years (table 3).

Our analysis sample includes only 4-year colleges that reported non-zero enrollment and

expenditures with at least 20% of the student body undergraduates, following Winston (1999).

In addition, they must have a Carnegie classification and must be located in the 50 U.S. states or

Washington DC.12 Like Winston, we exclude “Specialized Schools” as well; Appendix I

describes these and the other Carnegie classifications.13

There are missing values for particular data elements in some observations. In addition

observations may be missing for a particular school in a particular year for a variety of reasons.

Most straightforward is that a college may have failed to return the data in a given year.

Colleges have an incentive to provide data, since the College Board provides that data to high

school seniors shopping for schools, however, that incentive may be more important for some

schools than others.

 Alternatively, a school may ‘enter’ or ‘exit’ because they fail to meet the criteria for

inclusion in the sample – schools offering only a two-year degree in the early part of the study

period may have offered a four year degree in the end, thus ‘entering’ the sample. Or, schools

may have exited the 4-year market. This “entry” and “exit” is infrequent in terms of the number

of students served by these schools. Given the missing data and entry and exit, we estimate the

 

12 As described in the foreword of the 1994 Carnegie Foundation Report ,“the Carnegie Classification of Higher

Education groups American colleges and universities according to their missions.” All degree-granting colleges and

universities in the United States that are accredited by an agency recognized by the U.S. Secretary of Education are

classified in the following groups: Research Universities I, Research Universities II, Doctoral Universities I,

Doctoral Universities II, Master's (Comprehensive) Universities and Colleges I, Master's (Comprehensive)

Universities and Colleges II, Baccalaureate (Liberal Arts) Colleges I, Baccalaureate (Liberal Arts) Colleges II,

Associate of Arts Colleges, Specialized Institutions. The groups are distinguished by the emphasis placed upon

research, the degrees and courses of study offered and their admissions criteria, among others. See Appendix I for

more information on these classifications.

13 The Associates of Arts Colleges, which are also excluded from our analysis, typically do not graduate many (or

any) students with bachelor’s degrees, and the “Specialized School” are very small and very different in focus than

the institutions included in our analysis.
14

hedonic model with a “balanced panel” of 534 colleges. Our 534-college sample differs from

this “unbalanced panel” of observations in the following ways. Our sample of colleges has, on

average, lower tuition, percent minority, average age of entering freshmen, part-time faculty, and

pupil-teacher ratio. In addition, our balanced panel has colleges that are much more likely to be

members of the NCAA or offer fraternities and sororities. Interestingly, the balanced panel is

very similar to the unbalanced panel in terms of SAT scores and number of students.

Table 2 also reports 1994-95 means for the two groups of colleges in our sample:

- Graduate institutions. Colleges that offer Ph.D. and/or Masters’ degrees

(Carnegie classifications Research I and II, Doctoral Universities I and II,

Comprehensive Colleges I and II).

- Undergraduate institutions. Colleges that are purely undergraduate

institutions (Carnegie classifications Liberal Arts I and II).

We split the sample in this manner because, as table 2 demonstrates, these institutions differ

greatly. Undergraduate institutions have a much higher mean sticker price than graduate

institutions, $13,572 to $6,190. In addition, undergraduate institutions tend to be much smaller

in size, have much smaller class sizes, fewer minority students, entering freshman with higher

verbal SAT scores, and are less likely to be members of the NCAA or have fraternities and

sororities. 

Table 3 provides a time series of means for the variables used to estimate the hedonic

model. Although the sticker price of college, tuition plus fees, increased by 31.6% over the five

year period, the net price of college, tuition plus fees minus aid, increased by only 19% over

those five years.
15

V. Results

Table 4 contains baseline regression results. As shown in specification (1) in table 4, an

unweighted regression of the log of tuition and fees on the year dummies yields a monotonic

annual increase in the “sticker” price of college. Weighting the regression by the number of FTE

undergraduates in 1990-91, as done in specification (2) leads to a larger annual increase in the

sticker price of college. Although a regression of the log of the net price of college [tuition plus

fees minus aid—specification (3)] on the year dummies shows a monotonic annual increase, this

rise in the net price of college is lower than the rise in the sticker price over the time period. As

with the sticker price, weighting the regression of the log of the net price of college leads to a

larger estimate of the annual increase in the net price of college. Specification (5) includes a

dummy variable for purely undergraduate institutions, and the coefficient on this variable

suggests that undergraduate institutions experienced slower net price increases over this time

period, and the coefficient is statistically significant at the 1% level. Given this difference in the

estimated net price inflation, we separate graduate and undergraduate institutions in

specifications (6) and (7), and, again, the results show that net prices rose much slower in the

undergraduate institutions. Given the possibility of substitution bias and the differences in the

unweighted and weighted estimates, we believe that weighting the regressions is appropriate.

Table 5 contains the results of three weighted regressions of the log of net college price

(tuition plus fees minus aid) on year dummies and several attributes of college. We present

results for the full sample, graduate institutions, and undergraduate institutions, respectively. All

variables are first differences in order to purge the regression of time-invariant “brand” effects

and any omitted variable bias from unobserved and time-invariant attributes of college.

Estimates of the coefficient on each attribute are interpreted as their shadow market prices. Note
16

that the R2 statistics from both the regressions estimated in first differences (the fixed effects

estimator) are much lower than the R2 statistics from ordinary least squares levels regressions

(OLS) that do not purge the regressions of brand effects. 14 This is unsurprising since the fixed

effects specification reflects only the time series variation while the levels specification reflects

both time series and cross-sectional variation across colleges. An alternative method for

estimating the fixed effects model explicitly includes a series of college-specific dummy

variables and corresponding coefficients. The result is that the R2 for the full sample regressions

rises from .201 to .976, for the graduate institutions only rises from .208 to .972 and for

undergraduate institutions from .208 to .969. The large increase in the R2 reflects the substantial

explanatory power of the college brand effects. The parameter estimates under this fixed effects

specification are the same as those estimated in the first difference specification.

In the full sample results in table 5, most coefficients on the college attributes have the

expected signs. Colleges with more part-time faculty and older entering freshman have lower

net market prices, ceteris paribus.15 Colleges that offer Ph.D. degrees, higher SAT scores of

entering freshman, and a higher percentage of faculty with Ph.D. degrees have higher market net

prices.16 The brand effects were found to be jointly statistically significant. The estimates on the

year dummy variables, the price index coefficients, are monotonically increasing over the sample

 

14 As discussed previously, OLS in levels will yield biased estimates of the model parameters because of the

presence of college “brand” effects.

15 The model considers percent of students from racial and ethnic minorities, the average age of entering freshman,

and standardized test scores of freshman as attributes of college that are valued by consumers of college. In the

regressions reported in table 5, these three attributes are lagged. For example, the log of the net price charged for

the 1993-94 academic year is a function of the percent of minority students enrolled in the 1992-93 academic year.

Contemporaneous attributes of the student body (peers) may be correlated with the error term, and by using the

lagged measures of these attributes we escape this problem. If marginal consumers truly choose which college to

attend based on net prices and “resources and reputation,” then the prior year measures are the measures of college

quality on which they base their enrollment decisions.

16 Some colleges reported SAT scores only, some reported ACT scores only, and some reported neither. To adjust

for missing information on standardized test scores and the high correlation between SAT and ACT scores, we

included an SAT score variable (75th percentile verbal score of the previous year’s entering freshman) and two
17

period, but the estimated increase between 1992-93 and 1993-94 is very small. The sticker price

rise between those years was very low, 6%, while financial aid increased by an average of 20%.

We cannot point to one sweeping policy change regarding financial aid that caused the

substantial increase, but two small changes were contributors:

- A 12% rise in federal tuition aid to veterans (McPherson and Shapiro, 1998)

- The beginning of the state of Georgia’s HOPE scholarship program, which

offered free tuition and books at any Georgia public college to any Georgia

high school graduate with a “B” average or better, and a substantial grant to

eligible students attending a private college in the state (McPherson and

Shapiro, 1998).

Based on the financial aid information reported in McPherson and Shapiro, the balance of the

rise in grants between 92-93 and 93-94 is likely due to state merit aid and grants from institutions

themselves.17

Regression results for the graduate and undergraduate institutions suggest that quality

adjusted price increases were much higher in the graduate institutions. Interestingly, the small

increase in net prices between 1992-93 and 1993-94 seems to be largely due to the lack of a net

price increase for undergraduate institutions. These regressions also reveal some differences in

the coefficients on the hedonic characteristics between graduate and undergraduate institutions.

These need to be interpreted with caution – differences may derive from differences in the

supply or demand factors determining prices in graduate and undergraduate institutions - and

disentangling these is beyond the scope of this paper.

Estimates from the seven baseline specifications in table 4 and three hedonic

specifications in table 5 are used to construct price indexes in table 6. These price indexes are

 

dummy variables. The first dummy equals one if the school reports and SAT score and is zero otherwise. The

second equals one if SAT or ACT scores are required for admission.

17 Based on tables in McPherson and Shapiro, we are able to rule out several programs as causes of the rise in aid

between 92-93 and 93-94, Pell grants, state need-based aid, etc.
18

compared to the CPI-U and the CPI for college tuition and fees. The CPI-U rose 11.9% between

1991 and 1995, while the CPI for tuition and fees rose 37.3%. Using our weighted sample of

four-year colleges, we estimate that tuition and fees rose 36.8% over the sample period—very

close to the estimate in the CPI. Adjusting for financial aid, our weighted sample suggests that

the net price of college increased by only 28.6% over the sample period for the whole sample.

For graduate institutions the net price of college increased by 31% over the sample period, while

the increase was only 14.8% for undergraduate institutions.

Quality-adjusting the price indexes leads to further changes in the estimated price index

over this time period. The estimates from the quality-adjusted hedonic model for all colleges

suggest that the net price of tuition increased by 29.8% from 1991 to 1995. Thus, quality

adjusted price indexes show a slightly larger increase than the unadjusted price indexes. One

interpretation of this result is that, controlling for the college ‘brand’ effect, the quality of college

services provided are declining. Note that the small difference between the adjusted and

unadjusted price index is not driven by the change in any one of the attributes included in the

hedonic analysis. Rather it is explained by small changes in a number of the attributes. For

example, increases over the five years in pupil-teacher ratios and the percent of minority students

have small effects on the estimated price index.

In summary, adjusting for financial aid leads to a 29% decline in the estimated price

index, while quality adjusting the results leads to a 4.2% estimated price increase over the

sample period. Given both adjustments, our estimate of a price index of college over this time

period is 25% below the current price index in the CPI.
19

VI. Concluding Remarks

Our analysis indicates that estimating price indexes using hedonic methods is both

feasible and useful. Four particular recommendations emerge from this research. First, the price

of college should be measured as the ‘net’ rather than the ‘sticker’ price for computing a

consumer price index. Price indexes computed based upon tuition plus fees net of financial aid

indicate significantly lower price rises than the price indexes computed based only upon tuition

plus fees. Second, our results indicate that the ‘brand’ effect of individual colleges is important,

so that price indexes should be computed controlling for the college-fixed effect. Third, it is

important to include the attributes of colleges in constructing the price index in order to control

for changes in the quality of college. And fourth, colleges that have graduate schools

demonstrate different pricing than colleges that do not.

While the conceptual framework for implementing a quality-adjusted price index for

higher education is straightforward, practical implementation presents empirical challenges –

identifying data sources for characteristic variables with consistent definitions, and with

consistently available data. An important concern is to construct a representative sample for

which data is consistently available over time. Our analysis is based on secondary data provided

by the College Board and the Department of Education and contains a significant number of

missing values, which introduces the possibility of sample selection bias. Undoubtedly the

Bureau of Labor Statistics could assemble a more complete data set for computing price indexes

in the future.
20

References

Abraham, Katharine G., John S. Greenlees, and Brent R. Moulton (1998) “Working to Improve

the Consumer Price Index,” Journal of Economic Perspectives Winter, p. 27-36.

Berndt, Ernst R. (1991) “The Measurement of Quality Change: Constructing an Hedonic Price

Index for Computers Using Multiple Regression Methods” Chapter 4 in The Practice of

Econometrics Classic and Contemporary Addison-Wesley:New York pp. 102-130.

Berndt, Ernst. R. and Griliches, Zvi (1993) “Price Indexes for Microcomputers: An Exploratory

Study” in Price Measurements and Their Uses Foss, Manser and Young, ed. National Bureau of

Economic Research, p. 63-102

Bowen, Howard R. (1980) The Costs of Higher Education Jossey-Bass Publishers, San

Fransisco.

Bowen, William G. and Derek Bok (1998) The Shape of the River: Long Term Consequences of

Considering Race in University Admissions Princeton University Press, Princeton, NJ.

Carnegie Foundation for the Advancement of Teaching (1994) A Classification of Institutions of

Higher Education, 1994 Edition, available at:

http://www.carnegiefoundation.org/OurWork/Classification/CIHE94/classification1994.htm

Clotfelter, Charles T. (1996) Buying the Best: Cost Escalation in Elite Higher Education NBER,

Princeton University Press, Princeton, NJ

Clotfelter, Charles T. (1999) “The Familiar but Curious Economics of Higher Education”

Introduction to a Symposium” Journal of Economic Perspectives Winter p. 3-12

Clotfelter, Charles T. and Michael Rothschild (1993) editors Studies of Supply and Demand in

Higher Education National Bureau of Economic Research, University of Chicago Press, Chicago

Dale, Stacy Berg and Alan B. Krueger (1999) “Estimating the Payoff to Attending a More

Selective College: An Application of Selection on Observables and Unobservables” National

Bureau of Economic Research Working Paper No. W7322

Griliches, Zvi (1971) editor Price Indexes and Quality Change: Studies in New Methods of

Measurement, Harvard University Press, Cambridge, MA

Griliches, Zvi, editor (1992) Output Measurement in the Services Sector National Bureau of

Economic Research, University of Chicago Press, Chicago

Hauptman, Arthur M. with Jamie Merisotis (1990) The College Tuition Spiral The American

Council on Education and the College Board
21

Hoxby, Caroline M. (1997) “How the Changing Market Structure of U.S. Higher Education

Explains College Tuition,” NBER Working Paper 6323, December 1997.

Kane, Thomas (1999) The Price of Admission: Rethinking How Americans Pay for College

Brookings Institution Press, Washington, D.C.

Liegey, Paul R. (1993) “Adjusting Apparel Indexes in the Consumer Price Index for Quality

Differences” in Price Measurements and Their Uses Foss, Manser and Young, ed. National

Bureau of Economic Research p. 209-226.

Manski, Charles F. and David A. Wise (1983) College Choice in America Harvard University

Press, Cambridge, MA

McPherson, Michael S. and Morton Owen Shapiro (1998) The Student Aid Game: Meeting Need

and Rewarding Talent in American Higher Education Princeton University Press, Princeton, NJ.

Rosen, Sherwin (1974) “Hedonic Prices and Hedonic Markets: Product Differentiation in Pure

Competition,” Journal of Political Economy, April, pp. 34-55.

Rothschild, Michael and Lawrence White (1993) “The University in the Marketplace: Some

Insights and Some Puzzles” Studies of Supply and Demand in Higher Education NBER pp.11-

37.

Rothschild, Michael and Lawrence White (1996) “The Analytics of the Pricing of Higher

Education and Other Services in Which Customers are Inputs,” Journal of Political Economy,

June, pp. 573-586.

Triplett, Jack E. (1971) The Theory of Hedonic Quality Measurement and Its Use in Price

Indexes BLS Staff Paper 6, United States Government Printing Office, 2901-0634, Washington,

DC

Triplett, Jack (1990) “Hedonic Methods in Statistical Agency Environments” in Berndt, E.R. and

J.E. Triplett (eds.) Fifty Years of Economic Measurement: The Jubilee Conference on Research

in Income and Wealth, NBER Studies in Income and Wealth, Chicago: University of Chicago

Press.

U.S. Department of Labor, Bureau of Labor Statistics (1997) “How BLS Measures Price Change

in the Consumer Price Index for College Tuition and Fixed Fees” BLS Fact Sheet 96-2

Verry, Donald and Bleddyn Davies (1976) University Costs and Outputs Elsevier, New York

Winston, Gordon C. (1999) “Subsidies, Hierarchy and Peers: The Awkward Economics of

Higher Education” Journal of Economic Perspectives Winter p. 13-36.
22

Appendix I

The following definitions of the categories used in the Carnegie Classifications is taken from:

The Carnegie Foundation for the Advancement of Teaching (1994) A Classification of

Institutions of Higher Education, 1994 Edition,

http://www.carnegiefoundation.org/OurWork/Classification/CIHE94/classification1994.htm

Research Universities I: These institutions offer a full range of baccalaureate programs, are

committed to graduate education through the doctorate, and give high priority to research. They

award 50 or more doctoral degrees1 each year. In addition, they receive annually $40 million or

more in federal support.

Research Universities II: These institutions offer a full range of baccalaureate programs, are

committed to graduate education through the doctorate, and give high priority to research. They

award 50 or more doctoral degrees1 each year. In addition, they receive annually between $15.5

million and $40 million in federal support.

Doctoral Universities I: These institutions offer a full range of baccalaureate programs and are

committed to graduate education through the doctorate. They award at least 40 doctoral degrees

annually in five or more disciplines.

Doctoral Universities II: These institutions offer a full range of baccalaureate programs and are

committed to graduate education through the doctorate. They award annually at least ten doctoral

degrees-in three or more disciplines-or 20 or more doctoral degrees in one or more disciplines.

Master's (Comprehensive) Universities and Colleges I: These institutions offer a full range of

baccalaureate programs and are committed to graduate education through the master's degree.

They award 40 or more master's degrees annually in three or more disciplines.

Master's (Comprehensive) Universities and Colleges II: These institutions offer a full range of

baccalaureate programs and are committed to graduate education through the master's degree.

They award 20 or more master's degrees annually in one or more disciplines.

Baccalaureate (Liberal Arts) Colleges I: These institutions are primarily undergraduate

colleges with major emphasis on baccalaureate degree programs. They award 40 percent or more

of their baccalaureate degrees in liberal arts fields and are restrictive in admissions.

Baccalaureate Colleges II: These institutions are primarily undergraduate colleges with major

emphasis on baccalaureate degree programs. They award less than 40 percent of their

baccalaureate degrees in liberal arts fields or are less restrictive in admissions.

Associate of Arts Colleges: These institutions offer associate of arts certificate or degree

programs and, with few exceptions, offer no baccalaureate degrees.
23

Specialized Institutions: These institutions offer degrees ranging from the bachelor's to the

doctorate. At least 50 percent of the degrees awarded by these institutions are in a single

discipline.

Specialized institutions include:

Theological seminaries, Bible colleges and other institutions offering degrees in religion:

This category includes institutions at which the primary purpose is to offer religious

instruction or train members of the clergy.

Medical schools and medical centers: These institutions award most of their professional

degrees in medicine. In some instances, their programs include other health professional

schools, such as dentistry, pharmacy, or nursing.

Other separate health profession schools: Institutions in this category award most of their

degrees in such fields as chiropractic, nursing, pharmacy, or podiatry.

Schools of engineering and technology: The institutions in this category award at least a

bachelor's degree in programs limited almost exclusively to technical fields of study.

Schools of business and management: The schools in this category award most of their

bachelor's or graduate degrees in business or business-related programs.

Schools of art, music, and design: Institutions in this category award most of their

bachelor's or graduate degrees in art, music, design, architecture, or some combination of

such fields.

Schools of law: The schools included in this category award most of their degrees in law.

The list includes only institutions that are listed as separate campuses in the1994 Higher

Education Directory.

Teachers colleges: Institutions in this category award most of their bachelor's or graduate

degrees in education or education-related fields.

Other specialized institutions: Institutions in this category include graduate centers,

maritime academies, military institutes, and institutions that do not fit any other

classification category.
 Table 1

Variable Definitions

Variable* Definition

Tuition + Fees Undergraduate tuition plus fees

Aid Per Student Average grants per student

Tuition + Fees - Aid per student Undergraduate tuition plus fees minus average grants per student

Pupil-Teacher ratio (# FTE undergrads + grads) / (# FTE Faculty)

% Faculty with PhD % of faculty with PhD degree

% Part-time Faculty % of faculty who are part-time

LN Full-time Students Natural log of the number of full-time undergraduates

LN Part-time Students Natural log of the numer of part-time undergraduates

PhD granting institution =1, if institutions grants PhDs; 0 otherwise

NCAA Member =1, if institution is a member of the National Collegiate Athletic Association

Fraternities and/or Sororities =1, if institution has fraternity and/or sororities; 0 otherwise

Verbal SAT Score 75th Percentile* Verbal SAT score of the 75th percentile of entering freshman 

Reports SAT Score =1, if institution reports SAT scores

Requires SATs for Admission =1, if institution requires SATs for entering freshman

Average Age of Entering Freshman average age of entering freshman

% Minority Students % of undergraduates who are racial or ethnic minorities

Weight Number of FTE undergraduates in 1990-91

* All variables except tuition plus fees comes from the College Board. Tuition and Fees comes from the U.S. 

Department of Education's Integrated Postsecondary Education Database System (IPEDS)
Table 2

Summary Statistics for 1994-95

 Whole Sample Graduate Institutions Undergraduate Institutions

Mean Std. Dev. Range Mean Std. Dev. Range Mean Std. Dev. Range

Tuition + Fees 7,220 5,388 1612, 26,325 6,190 4,755 1,612, 21,727 13,572 4,679 2,175, 26,325

Aid Per Student 2,619 2,140 339, 10,307 2,180 1,808 463, 9,789 5,325 2,040 339, 10,307

Tuition+Fees-Aid per student 4,601 3,642 0, 20512 4,010 3,275 0, 17,355 8,247 3,679 0, 20,512

Pupil-Teacher ratio 20.366 4.847 6, 55.221 21.051 4.432 8.766, 55.221 16.147 5.174 6, 46.744

% Faculty with PhD 0.787 0.186 0.147, 1 0.788 0.187 .292, 1 0.779 0.182 .147, 1

% Part-time Faculty 0.274 0.162 0.017, .903 0.269 0.163 .017, .869 0.309 0.158 .025, .903

LN Full-time Students 8.596 0.998 4.127, 10.200 8.845 0.823 6.333, 10.200 7.063 0.484 4.127, 8.102

LN Part-time Students 6.452 2.009 0, 9.290 6.816 1.821 0, 9.290 4.209 1.626 0, 7.195

PhD granting institution 0.480 0.500 0, 1 0.553 0.498 0, 1 0.033 0.180 0, 1

NCAA Member 0.909 0.288 0, 1 0.944 0.230 0, 1 0.691 0.463 0, 1

Fraternities and/or Sororities 0.847 0.360 0, 1 0.896 0.306 0, 1 0.547 0.499 0, 1

Verbal SAT Score 75th Percentile* 606 46 420, 750 597 46 470, 750 628 54 470, 750

Reports SAT Score 0.736 0.441 0, 1 0.725 0.447 0, 1 0.804 0.398 0, 1

Requires SATs for Admission 0.987 0.112 0, 1 0.991 0.096 0, 1 0.967 0.180 0, 1

Average Age of Entering Freshman 18.578 1.036 17, 32 18.638 1.077 18, 32 18.207 0.620 17, 24

% Minority Students 0.176 0.166 0, 1 0.182 0.168 0, 1 0.137 0.151 .010, .999

N 534 297 237

Source: 1994-95 College Board Data; 1994-95 IPEDS data.

All variables weighted by FTE in 1990-91.

* Colleges who did not report an SAT score received a value of 0,

and are not included in the mean and standard deviation reported.
Table 3

Means by Year

Whole Sample

Variable 1991 1992 1993 1994 1995

Tuition + Fees 5,480 5,971 6,406 6,810 7,220

Aid Per Student 1,600 1,791 1,998 2,399 2,619

Tuition+Fees - Aid per student 3,880 4,179 4,407 4,412 4,601

Pupil-Teacher ratio 20.643 20.710 20.572 20.270 20.366

% Faculty with PhD 0.806 0.751 0.758 0.766 0.787

% Part-time Faculty 0.272 0.264 0.263 0.268 0.274

LN Full-time Students 8.617 8.620 8.611 8.600 8.596

LN Part-time Students 6.592 6.571 6.607 6.605 6.452

PhD granting institution 0.470 0.458 0.476 0.481 0.480

NCAA Member 0.870 0.882 0.896 0.901 0.909

Fraternities and/or Sororities 0.838 0.837 0.838 0.841 0.847

Verbal SAT Score 75th Percentile* 607 606 605 605 606

Reports SAT Score 0.667 0.693 0.698 0.705 0.736

Requires SATs for Admission 0.987 0.993 0.987 0.987 0.987

Average Age of Entering Freshman 18.628 18.581 18.588 18.530 18.578

% Minority Students 0.145 0.155 0.163 0.170 0.176

N 534

Graduate Institutions

Variable 1991 1992 1993 1994 1995

Tuition + Fees 4,649 5,097 5,478 5,831 6,190

Aid Per Student 1,329 1,482 1,661 2,003 2,180

Tuition+Fees - Aid per student 3,320 3,615 3,817 3,827 4,010

Pupil-Teacher ratio 21.399 21.470 21.270 20.946 21.051

% Faculty with PhD 0.818 0.751 0.759 0.766 0.788

% Part-time Faculty 0.268 0.258 0.257 0.262 0.269

LN Full-time Students 8.871 8.875 8.864 8.850 8.845

LN Part-time Students 6.979 6.950 6.997 6.999 6.816

PhD granting institution 0.543 0.527 0.548 0.554 0.553

NCAA Member 0.908 0.921 0.934 0.939 0.944

Fraternities and/or Sororities 0.884 0.886 0.886 0.891 0.896

Verbal SAT Score 75th Percentile* 599 598 597 598 597

Reports SAT Score 0.658 0.686 0.688 0.692 0.725

Requires SATs for Admission 0.987 0.995 0.988 0.988 0.991

Average Age of Entering Freshman 18.687 18.631 18.646 18.578 18.638

% Minority Students 0.150 0.159 0.168 0.176 0.182

N 297

Undergraduate Institutions

Variable 1991 1992 1993 1994 1995

Tuition + Fees 10,600 11,358 12,123 12,848 13,572

Aid Per Student 3,269 3,697 4,077 4,834 5,325

Tuition+Fees - Aid per student 7,331 7,661 8,046 8,014 8,247

Pupil-Teacher ratio 15.981 16.024 16.267 16.096 16.147

% Faculty with PhD 0.730 0.752 0.754 0.763 0.779

% Part-time Faculty 0.297 0.301 0.299 0.306 0.309

LN Full-time Students 7.049 7.044 7.050 7.059 7.063

LN Part-time Students 4.207 4.237 4.207 4.175 4.209

PhD granting institution 0.022 0.030 0.033 0.033 0.033

NCAA Member 0.634 0.645 0.660 0.668 0.691

Fraternities and/or Sororities 0.552 0.541 0.543 0.532 0.547

Verbal SAT Score 75th Percentile* 624 623 624 626 628

Reports SAT Score 0.726 0.735 0.763 0.784 0.804

Requires SATs for Admission 0.987 0.980 0.980 0.976 0.967

Average Age of Entering Freshman 18.264 18.277 18.230 18.232 18.207

% Minority Students 0.118 0.126 0.133 0.134 0.137

N 237

Source: 1991-95 College Board Data; 1991-95 IPEDS.

All variables weighted by FTE in 1990-91.

* Mean for colleges who did report an SAT score.
Table 4

Results - Price Equations

 Weighted** Weighted**

(1) LN(Tuition+Fees) (2) LN(Tuition+Fees) (3) LN_Net Price* (4) LN_Net Price*

Estimate Std. Error Estimate Std. Error Estimate Std. Error Estimate Std. Error

p92 0.084 0.042 0.103 0.045 0.082 0.047 0.116 0.050

p93 0.155 0.042 0.181 0.045 0.147 0.047 0.186 0.050

p94 0.216 0.042 0.246 0.045 0.155 0.047 0.200 0.050

p95 0.277 0.042 0.313 0.045 0.190 0.047 0.252 0.050

N 2,670 2,670 2,670 2,670

R**2 0.020 0.022 0.007 0.011

Weighted Weighted Weighted

class dummy graduate undergrad

(5) LN_Net Price (6) LN_Net Price (7) LN_Net Price

Estimate Std. Error Estimate Std. Error Estimate Std. Error

p92 0.121 0.007 0.127 0.010 0.050 0.008

p93 0.195 0.011 0.199 0.014 0.105 0.011

p94 0.214 0.013 0.215 0.017 0.109 0.013

p95 0.270 0.016 0.270 0.020 0.138 0.015

undergrad -0.033 0.010

N 2,136 1,188 948

R**2 0.164 0.171 0.103

* Net Price equals tuition + fees - aid per student

** Weighted by the number of FTE undergraduates in 1990-91.
Table 5

Results - Hedonic Equation*

Full Sample Graduate Institutions Undergraduate Inst.

Estimate Std. Error Estimate Std. Error Estimate Std. Error

Undergraduate institution dummy -0.033 0.010

p92 0.114 0.008 0.117 0.011 0.052 0.008

p93 0.186 0.011 0.186 0.016 0.105 0.011

p94 0.205 0.015 0.200 0.020 0.103 0.013

p95 0.261 0.017 0.255 0.023 0.132 0.016

Pupil-Teacher ratio 0.005 0.002 0.006 0.003 -0.001 0.001

% Faculty with PhD 0.002 0.010 -0.001 0.014 -0.009 0.044

% Part-time Faculty -0.066 0.059 -0.082 0.085 0.032 0.053

LN Full-time Students 0.363 0.054 0.329 0.080 0.441 0.042

LN Part-time Students 0.011 0.002 0.013 0.003 0.001 0.003

PhD granting institution 0.024 0.032 0.026 0.042

NCAA Member 0.037 0.033 0.056 0.050 -0.008 0.025

Fraternities and/or Sororities -0.054 0.0483 -0.033 0.076 -0.080 0.034

Verbal SAT Score 75th Percentile 0.001 0.000 0.001 0.000 0.000 0.000

Reports SAT Score -0.132 0.063 -0.183 0.101 -0.073 0.041

Requires SATs for Admission -0.450 0.142 -0.585 0.207 0.091 0.121

Average Age of Entering Freshman -0.020 0.006 -0.022 0.008 0.000 0.010

% Minority Students 0.318 0.240 0.426 0.350 -0.102 0.200

N 2,136 1,188 948

R**2 0.201 0.208 0.208

* Dependent variable equals LN(Net_price). Weighted by the number of 

FTE undergraduates in 1990-91. 

Each regression estimated in first differences.
Table 6

Price Indices

(1) (2) (3) (4) (5) (6) (7)

Weighted Weighted Weighted

CPI: College Weighted Weighted class dummy graduate undergrad

Year CPI Tuition and Fees LN(Tuition+Fees) LN(Tuition+Fees) LN_Net Price LN_Net Price LN_Net Price LN_Net Price LN_Net Price

1991 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

1992 103.0 110.7 108.8 110.9 108.6 112.3 112.9 113.5 105.2

1993 106.1 121.1 116.8 119.8 115.8 120.4 121.5 122.0 111.0

1994 108.8 129.6 124.1 127.9 116.7 122.2 123.9 124.0 111.5

1995 111.9 137.3 132.0 136.8 120.9 128.6 131.0 131.0 114.8

CPI: College Adjusted Adjusted Adjusted

Year CPI Tuition and Fees full sample Graduate inst. Undergraduate inst.

1991 100.0 100.0 100.0 100.0 100.0

1992 103.0 110.7 112.1 112.5 105.3

1993 106.1 121.1 120.4 120.4 111.0

1994 108.8 129.6 122.7 122.2 110.9

1995 111.9 137.3 129.8 129.0 114.2