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Market Power E�ects of College and University Mergers

Lauren Russell∗

September 15, 2017

Abstract

Colleges and universities across the US have merged to cut spending, citing economies of scale

and scope. However, these mergers may facilitate market power, increasing costs for students.

Using a national panel of public and private non-pro�t institutions, I characterize mergers between

2000 and 2015 and �nd that the average merger increases tuition and fees by 7% relative to non-

merging institutions in the same state and sector. I investigate three alternative explanations for

price increases, higher educational costs, higher quality, and new degree o�erings, but �nd that

the evidence is most consistent with the exercise of market power in higher education.

∗The Nelson A. Rockefeller Center for Public Policy and the Social Sciences, Dartmouth College, 6082 Rockefeller Hall,Hanover, NH, 03755. Phone: 603-646-1291. Email: [email protected]. I thank Heidi Williams, David Autor,Joshua Angrist, Sara Fisher Ellison, Glenn Ellison, Nikhil Agarwal, and Nancy Rose as well as the MIT Labor Lunch participantsand Dartmouth College Seminar participants for their feedback. This material is based upon work supported by the NationalScience Foundation Graduate Research Fellowship Program (under Grant No. 1122374). Any opinion, �ndings, and conclusionsor recommendations expressed in this material are those of the author and do not necessarily re�ect the views of the NationalScience Foundation.

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1 Introduction

Mergers are an important feature of higher education markets. From 2000-2015, 421 public

and private non-pro�t institutions were involved in a merger, representing more than 7% of

institutions operating during that time period (Figure 1). Governing boards cite economies

of scale and scope as primary motivations for undertaking mergers, but critics question cost

savings and point out that mergers rarely result in closed campuses (Wieder 2012). Mergers

may reduce competition which may allow institutions to raise tuition prices, reduce quality,

or reduce degree variety. Because potential cost synergies and anticompetitive e�ects work

in opposite directions, whether mergers will improve student welfare is an empirical question.

In contrast to externally initiated higher eduction mergers in Australia, Norway, Belgium,

and Finland, mergers in the United States are typically voluntary, meaning that the merg-

ing institutions themselves initiate the merger (Skodvin 1999). However, managed mergers,

initiated by state or regional authorities in public systems, are becoming more common

(Warren 2008; Ohman 2011; Diamond 2013; Hamilton 2013; Marcus 2013; Mytelka 2015).

The strategic nature of voluntary or managed mergers whereby institutions or entire public

systems may try to improve their position in the eduation hierarchy or increase their mar-

ket power leaves scope for anticompetitive e�ects. However, there is considerable debate

surrounding whether nonpro�t organizations, such as public and private non-pro�t colleges,

take advantage of market power (Lynk 1995; Simpson and Shin 1998; Prüfer 2011).

One view of nonpro�t organizations is that they function like consumer cooperatives

(Lynk 1995). This view applied to colleges and universities would say that institutions

redistribute any residual �pro�ts� back to attending students through increased institutional

aid (decreased tuition and fees) or by increasing quality. In this case, there is no strong

incentive for the organizations to exercise market power. On the other hand, there are

a wide range of objectives that would induce institutions to exercise market power. For

example, a public college facing lower levels of state appropriations during the recession

may use market power to raise tuition prices. Presidents at both public and private non-

pro�t institutions may want to increase prices to maximize institutional prestige or increase

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revenues. They may price discriminate by charging wealthy students higher prices to �nance

lower net prices for students from less advantaged backgrounds. In all of these cases, the

institution has incentives to exploit its market power.

The few existing studies on tuition setting and market power have generally found that US

higher education markets are not close to perfectly competitive. Deming, Lovenheim, and

Patterson (2016) show that the massive introduction of online degree programs following a

regulatory change in 2006 led to larger declines in enrollments in very concentrated markets.

This �nding is consistent with market power among public and private institutions. However,

the tuition estimates are more puzzling since they indicate increased competition leads to

higher tuition prices. Focusing solely on the for-pro�t sector, Cellini and Goldin (2014)

document that institutions have su�cient market power to increase tuition to capture federal

subsidies. By investigating the e�ects of mergers on tuition prices, this paper provides new

evidence on whether US public and private non-pro�t institutions exercise market power.

This paper also contributes to the literature investigating anticompetitive e�ects of merg-

ers of nonpro�t organizations. Several studies have concluded that mergers of non-pro�t

hospitals raise prices, with estimates ranging between +3% and 10% (Vita and Sacher 2001;

Dafny, Duggan, and Ramanarayanan 2012; Gowrisankaran, Nevo, and Town 2015). By con-

trast, Thompson (2011) �nds that while some insurers face higher prices (a 26% increase),

others face lower prices (a 23% decrease) after a 1998 acquisition of New Hanover Regional

Medical Center.

This study is the �rst of my knowledge to investigate price e�ects of mergers among

US non-pro�t colleges and universities. Unlike much prior work on hospital mergers that

has primarily analyzed individual case studies, this paper employs panel data with prices

and merger information covering all Title IV participating public and private non-pro�t

institutions from 2000 to 2015. The data allow me to estimate merger e�ects for all known

merger events in this population using data on prices and enrollments. The large number of

merger events (114) makes it possible to correlate price increases with characteristics of the

merging partners. I am able to assess, for example, whether mergers involving institutions

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that o�er similar degrees in close geographic proximity lead to larger price e�ects than

mergers involving dissimilar institutions.

By exploiting variation in merger timing, I estimate models with merged institution �xed

e�ects to control for unobservable di�erences between merging and non-merging institutions.

I �nd that prior to merging, merging institutions follow the same pricing trends as other

institutions in their state. However, immediately after the merger, the average institution

diverges from this trend and raises tuition and fees. I estimate that the average merger

involving public or private non-pro�t institutions raises tuition and fees by 7%. E�ects are

nearly identical for mergers of public or non-pro�t institutions when di�erences-in-di�erences

speci�cations include su�ciently �exible time e�ects.

I investigate heterogeneity in the price e�ects by measures of geographic proximity and

product similarity. First, I identify whether each merger involves institutions operating in the

same commuting zone. Then, I construct a measure of how close institutions are in product

space using data on the subject areas of degrees awarded in the pre-merger years. I �nd

that price e�ects are generally larger when the merging institutions have many overlapping

programs and when merging institutions are geographically close. The estimates from a

model that interacts both proximity measures indicates that a merger of institutions in the

same commuting zone that o�er degrees in all the same �elds would raise prices by 15%.

Institutions may increase institutional aid to compensate for higher sticker prices post-

merger, leaving the actual price for many �nancially needy students unchanged. I use data

on average institutional grant aid to students receiving federal �nancial aid to assess whether

price increases are borne only by the wealthiest students. The results are too imprecise to

pin down exactly how net prices change for students receiving �nancial aid. At the very

least, however, the sticker price results indicate that prices paid increase for the sizeable

fraction of students who receive no institutional aid (69% of student at merging institutions

in my sample).

To interpret the price e�ects, I investigate whether costs, quality, or degree o�erings

change after the merger. It is possible that mergers increase educational costs. If costs

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increase, higher tuition and fees could re�ect increased costs rather than market power,

though increased costs are the opposite of what we would expect given that most mergers

are undertaken with the stated goal of capturing economies of scale. There is no statistically

detectable change in spending per full-time equivalent (FTE) student after a merger, and

the point estimate suggests that total spending per FTE student falls by about 5%. Thus,

the result suggests that higher costs cannot account for the price e�ects.

My results also suggest that increases in quality cannot explain the price e�ects. There is

no statistically signi�cant e�ect on one-year student retention rates, controlling for student

test scores and the share of the cohort receiving a any federal grant aid. The point estimates

are small in magnitude, and some are negative. The 95% con�dence interval in my preferred

speci�cation rules out increases in one-year retention rates larger than 1.5 percentage points

for full-time students. Furthermore, I �nd little evidence that price e�ects simply re�ect

changes in perceived or actual quality driven by combining two-year and four-year institu-

tions. If I estimate merger price e�ects limiting the sample to the 88 mergers where all the

merging partners operate in the same institutional category, I still �nd positive price e�ects,

though the estimates are more imprecise.

Finally, I investigate whether institutions change the number of unique degrees they o�er

post-merger. If institutions increase degree variety, this could raise consumer welfare even

in the presence of price increases. I �nd that six years prior to merging, merging partners

lag behind other institutions in their state and sector in the rate at which they are adding

new degrees. However, they ramp up their degree o�erings in the few years immediately

preceding the merger until they are on par with other institutions. After the merger, there

is no statistically signi�cant change relative to the few years before the merger, suggesting

that mergers do not cause institutions to increase degree o�erings.

The rest of the paper is proceeds as follows: Section 2 describes stated motivations for

mergers, the data and sample restrictions; Section 3 presents the price e�ect results; Section

4 investigates whether the size of the price e�ects are predicted by geographic proximity and

degree similarity of the merging partners; Section 5 interprets the price e�ect results through

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an investigation of costs, quality, and degree o�erings. Section 6 concludes.

2 Mergers in US Higher Education

2.1 What Drives the Decision to Merge?

There are a wide range of stated motivations for mergers, but the most commonly cited are

the desire to capture cost savings by combining administration and eliminating duplicate

programs, to increase transferability of credits, to expand program o�erings, and to have a

greater presence in particular geographic locations.1 For example, a series of consolidations

in 2003 within the Community College System of Alabama had the expressed goal of reducing

overhead costs and increasing transferability of credits (Evelyn 2003). Similarly, the Board

of Regents of the University System of Georgia cited �fostering operational e�ciencies� as

the key justi�cation for their recent series of consolidations (Azziz 2013). A merger between

New York University and Polytechnic University allowed NYU to begin o�ering engineering

degrees and increase its presence in Brooklyn (NYU 2014). Avoiding immediate �nancial

crisis is mentioned occasionally. For instance, a merger between Johnson University and

Florida Christian College involved a $7.5 million bridge loan to help Florida Christian College

stay a�oat (DeSantis 2013). However, public discussion surrounding consummated mergers

generally suggests that most of these institutions are not at risk of closing in the absence of a

merger. In section 2.4, I present quantitative evidence that downward trends in enrollments

and net revenues are likely not driving the decision to merge. This is likely not the case for

closure decisions.

2.2 Data and Sample Construction

I obtain institution-level expenditures, tuition and fees, and student outcomes data from the

National Center for Education Statistics Integrated Postsecondary Education Data System

(IPEDS). My data form a national panel of all Title IV eligible US colleges and universities

1This motivations were frequently mentioned in new articles, accreditation documents, and new releases from college websitescorresponding to more than 25 merger events I reviewed.

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from 2000-2015. Title IV eligible institutions are institutions at which qualifying students

may utilize federal �nancial aid, including direct loans, Perkins loans, Pell Grants, and federal

work-study. Non-Title IV eligible institutions are not required to report data to IPEDS, so

my results may not be generalizable to these types of institutions.

My merger information also comes from IPEDS. Beginning in 2001, the IPEDS institu-

tional characteristics survey began collecting merger information. If an institution reports

combining with another in a given academic year, it usually does not report this to IPEDS

until the next academic year survey. For example, four mergers within the University Sys-

tem of Georgia took e�ect in January 2013 (the 2012-2013 academic year). However, it was

not until the 2013-2014 data collection period that the mergers were reported to IPEDS.

Therefore, I identify the school year of each merger by assigning the merger to the year prior

to when it was reported to IPEDS.2

I implement two primary restrictions for the analysis sample. First, I drop private for-

pro�t institutions (45% of institutions operating in the sample period; 13% of total FTE

enrollment) since they do not report detailed expenditures, and many do not report stu-

dent outcomes.3 Second, I drop institutions in US territories (2% of institutions) because

commuting zones are not drawn for these areas, and I will later examine heterogeneity by

whether merging partners operate in the same commuting zone.

2.3 Number of Mergers by Institutional Type

Figure 1 shows the number of institutions involved in mergers between 2000 and 2015 broken

out by sector (public or private non-pro�t) and type (less than two year, two year, or four-

year). The height of each yellow bar is the total number of institutions in each category

2I drop any institution that reports merging but then fails to a list an institution identi�er for the new merged institution orlists an invalid institution identi�er. For 24 institutions involved in multiple sequential mergers over the 2000-2015 time period,I use the year of the �rst merger as the merger year. I drop 15 merger events that involve institutions in di�erent states becauseI want to �exibly allow for state speci�c time e�ects in all regression speci�cations. I also drop any institutions involved in amerger that spans di�erent sectors (i.e. a merger of a public and a private non-pro�t institution). This restriction drops 12merger events that involve public and private non-pro�t institutions, 2 that involve public and private for-pro�t institutions,and 8 merger events that involve private non-pro�t and private for-pro�t institutions.

3Though analyzing the impacts of mergers of for-pro�t institutions is beyond the scope of this paper, mergers are alsocommon in this sector. Between 2000 and 2015, 214 for-pro�t institutions were involved in a merger (3% of for-pro�t institutionsoperating during the 2000-2015 period).

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while the height of each blue bar is the number of institutions involved in a merger. Mergers

are most common in the public 2-year sector (12% of institutions). Across all categories, 7%

of institutions operating in the 2000-2015 period are involved in a merger.

2.4 Characteristics of Merging Institutions

Table 1 displays pre-merger summary statistics for the sample of acquired, acquiring, and

never merged public and private non-pro�t institutions that were operating in 2002, the �rst

year for which I have data on retention rates, enrollments, tuitions and fees, and institutional

spending.4 I exclude institutions that do not report undergraduate per credit tuition and

fees in order to focus in on the institutions for which I will estimate price e�ects.5 Column

1 reports the mean for acquired institutions; column 2 reports the mean for acquiring insti-

tutions. Column 3 reports the mean for all institutions that will merge between 2003 and

2015 (acquired and acquiring), and column 4 reports the mean for institutions that were

never involved in a merger. The p-value in column 5 is the p-value from a test that the mean

of each variable is the same for the group of will merge institutions and the group of never

merging institutions.

Columns 1 and 2 show how merging partners compare. Across both public and private

non-pro�t sectors, acquired institutions have smaller enrollments and have lower tuition and

fees. In the public sector, institutions that are acquired have similar retention rates but

spend more per student. In the private non-pro�t sector, acquired institutions spend less

per student but also have lower retention rates.

Columns 3 and 4 compare institutions that will merge to those who will not. Public

institutions that will merge have lower enrollments, lower tuition and fees, and slightly lower

retention rates. However, spending per student is not statistically di�erent. This is consis-

tent with the fact that many merging institutions in the public sector are small, two-year4I identify acquired institutions as those which report combining with another institution and then report a new institution

ID number corresponding to an already operating institution. I de�ne acquiring institutions as those listed as the new institutionby at least one acquired institution, as long as this acquiring institution existed prior to the merger.

5Among institutions that report enrolling undergraduates, 20% of private non-pro�t institutions and 14% of public institu-tions are missing undergraduate tuition price data in 2002.

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colleges which could potentially bene�t from economies of scale created by mergers. By

contrast, merging institutions in the private non-pro�t sector do not have statistically di�er-

ent enrollments, tuition and fees, or spending per student, but they do have slightly higher

retention rates. Because some of the di�erences in observable characteristics are statistically

signi�cant between merging and non-merging institutions, I will estimate speci�cations with

merged institution �xed e�ects.

These summary statistics cannot reveal much about whether merging institutions face

particularly challenging �nancial conditions in years immediately prior to a merger. To

provide additional context for the empirical analysis that follows, Appendix Figure A.1

presents event studies that show trends in net tuition revenue, enrollments, and spending for

institutions that will merge or close.6 There are no obvious trends in net tuition revenue or

undergraduate enrollment among the set of institutions that will merge prior to the merger.

By contrast, institutions that will close have declining enrollments and net tuition revenue,

though it is unclear whether this is because some students switch to other institutions in

anticipation of closure. Spending per student appears to be increasing faster in the years prior

to a merger for institutions that will merge, relative to other institutions in the same state

and sector. However, if an undergraduate enrollment-weighted average is used, as is done

in the empirical analysis that follows, rather than weighting each institution equally (which

overstates the contribution of smaller institutions), this trend disappears. This evidence is

largely consistent with the most common stated reason for undertaking a merger: to capture

economies of scale from combining smaller institutions.

3 Price E�ects

To assess whether mergers impact the prices colleges and universities charge, I adopt an

event study approach. Following Gordon and Knight (2008), the unit of observation in my

analysis is a post-merge institution with variables aggregated up to this level in pre-merger

years. Speci�cally, I combine separate institutions that will eventually merge by summing6Between 2000 and 2015, 11% of institutions operating at some point during this period close.

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their expenditures and taking enrollment-weighted averages of tuition and fees and retention

rates. Because tuition and retention rates are reported for undergraduate students, I use

full-time equivalent measures of undergraduate enrollment for the weighting as reported by

institutions in the IPEDS fall enrollment survey.7 I choose these pre-merger aggregations so

that there will be no change in the aggregated outcome from the pre-period to the outcome

reported by the merged institution in the post-period if the merger has no e�ect (i.e. all the

campuses operate as before and have no changes in enrollments, tuition and fees, spending,

or retention rates). Note that I cannot track outcomes at the level of initially separate

institutions because after the merger, data are only reported at the merged institution level.

I estimate di�erences-in-di�erences speci�cations of the following form:

yisct = βM it + γsct + αi + εisct (1)

where yisct is an outcome for institution i in state s with sector c (public or private non-pro�t)

in year t. Mit is an indicator that takes value 1 in every year after the merger, and αi are

institution �xed e�ects. Year by state by sector �xed e�ects (γsct) allow for separate state-

speci�c trends for public and private non-pro�t institutions. In addition to this preferred

speci�cation, I also estimate simpler models with year �xed e�ects or state by year �xed

e�ects rather than state by year by public/private non-pro�t �xed e�ects. I cluster standard

errors at the merged institution level.

Obtaining unbiased estimates of β requires that merging institutions follow similar trends

to non-merging institutions in the same state and sector. This parallel trends assumption

would be violated if the exact year a merger is consummated is correlated with other factors

driving changes in outcomes of interest. However, it is unlikely that mergers can be used

to immediately respond to changes in the �nancial environment for a given college. Even

if two institutions desire to merge, it would be di�cult for them to predict exactly when

the proposed merger would take e�ect. Mergers typically involve long periods of negotiation

7Public institutions have two tuition rates, one for in-state students and another for out-of-state students. To construct asingle tuition and fees price measure for public institutions, I take the weighted average of these two rates where the weightsare freshman enrollment shares for in-state and out-of-state students.

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and require approval from several internal and external bodies. For instance, the merger

between New York University and Polytechnic University took six years from �rst merger

steps to consummation. The institutions formally a�liated in June 2008, but it took four

years for the Board of Trustees at both institutions to vote to approve a merger. In June

2013, the New York State Board of Regents �nally approved the merger, and the accrediting

organization approved the merger in July 2013 so that the merger could be consummated

in January 2014 (NYU 2014). Other mergers, such as those initiated by the University

System of Georgia, have had a much faster timeline. Fewer than 17 months passed between

when Chancellor Hank Huckaby announced his intentions to investigate the potential for

consolidations and when the �rst set of four mergers took e�ect (Hawks 2015).

To provide evidence on whether the parallel trends assumption holds, I estimate event

study models where I include leads and lags ranging from 5 years before up to 10 years after

the merger (Ijit):

yisct = Σ10j=−5βjI

jit + γsct + αi + εisct (2)

A key advantage of using an event study approach for assessing merger e�ects is that precise

market de�nitions are not required (Vita and Sacher 2001). Looking at pre and post merger

prices is su�cient to test whether a merger creates market power provided the model controls

for other factors changing concurrently with the merger that impact prices. By including

year by state by sector �xed e�ects, identi�cation comes from di�erences in prices relative

to other institutions in the same state and sector that are una�ected by the merger. It is

plausible that colleges in the same state are subject to similar demand and cost conditions,

which helps control for other factors in�uencing prices and quality that may be changing at

the time of the merger.

3.1 Baseline Estimates

Figure 2 shows the event study plots for undergraduate tuition and fees per credit using three

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di�erent sets of time �xed e�ects (equation 2). The top plot shows an event study using

merged institution �xed e�ects and year �xed e�ects. The middle plot shows an event study

using merged institution �xed e�ects and year by state �xed e�ects. The bottom plot shows

an event study using merged institution �xed e�ects and state by year by public/private

�xed e�ects.

The plots are striking. The tuition and fees charged by the merged institutions follow a

parallel trend to other institutions in the state prior to the merger. After the merger, prices

immediately increase by a statistically signi�cant amount, and the increase persists to ten

years after the merger. If institutions were raising prices to overcome �nancial insolvencies,

and this was unrelated to market power, we might have expected the increase to start in

years prior to the merger since tuition rates can be adjusted more quickly than mergers can

be consummated. However, there is no pre-trend in the plots with year by state �xed e�ects

or separate year by state e�ects for public and private non-pro�t institutions. The fact that

increases begin in the merger year suggests that these tuition hikes are directly related to

the change in market structure.

Estimating pre/post speci�cations (equation 1) provides a magnitude for the price e�ect.

The �rst column of Table 2 Panel A shows results from models with year and institution �xed

e�ects. Column 2 incorporates state by year �xed e�ects. Column 3 allows these state by

year �xed e�ects to di�er for public and private non-pro�t institutions and is thus the most

�exible speci�cation. As we would expect given the event study plots, adding more �exible

time e�ects decreases the magnitude of the price impact from 17% to 12% to 7%. Even

with the most �exible time e�ects (year by state by public/private), there is a statistically

signi�cant, positive impact on prices of 7%.

Even if institutions increase average tuition and fees, it is not obvious that all students will

pay more for their education. If institutional �nancial aid is also increased, net prices may

remain unchanged or even fall for some students. In this way, mergers may enable institutions

to increase price discrimination. In the 2014-2015 academic year, 47% of students at public

four-year degree-granting institutions and 78% at four-year private non-pro�t of received

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some form of institutional grant aid. Institutional grants are scarcer at two-year institutions

where 12% of students at public institutions and 15% of students at private non-pro�t

institutions receive institutional grant aid (U.S. Dept. of Ed. 2016). Institutional grants

directly o�set the sticker prices charged by the institution and lower the e�ective price paid,

allowing institutions to charge di�erent prices to students based on characteristics observed

at the time of �nancial aid application.

I begin by investigating how average institutional grant aid for �rst-time, full-time stu-

dents changes with the merger. The event studies in Figure 3 show that average institutional

grant aid increases after the merger. The estimate from the preferred pre-post speci�cation

(column 3 of Table 3) indicates that the average institutional grant increases by 9%. How-

ever, this e�ect is estimated less precisely than the e�ects on tuition and fees and is not

statistically signi�cant at conventional levels. As shown in rows 8-9 of column 3, the in-

crease in institutional grant aid is primarily driven by private non-pro�t institutions rather

than public institutions.

The average institutional grant per student is less than tuition and fees, so even if the

percentage increase in grant aid is larger than the percent increase in sticker price (i.e. 9%

vs. 7%), it is not clear what e�ect this has on the net price paid by students receiving

aid. To answer this question, I construct an average institutional revenue measure for each

college/university by taking the average tuition and fees for a full-time, �rst-time student and

then subtracting the average amount of institutional grant aid. This is average net revenue

for the institution rather than net price paid by the student because federal and state grant

aid can further o�set the sticker price for the student. Unfortunately, the estimation is too

imprecise to say how this average net revenue is a�ected. Regardless of how institutional

grand aid changes in response to mergers, the previous sticker price results indicate that

mergers raise prices for the sizeable fraction of students who receive no institutional grant

aid (69% of students at merging institutions in my sample).

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3.2 Robustness

A potential concern with adopting a di�erences-in-di�erence approach is that non-merging

institutions in the same state may change their prices in response to the merger of a com-

petitor. This poses a threat to identifying the price e�ect because in this case, the control

group is not a valid counterfactual for how prices would have involved in the absence of a

merger. Moreover, whether the di�erences-in-di�erences estimate would overstate or under-

state the true price e�ect is not clear because competitors' prices could either be strategic

complements or strategic subsitutes for the merging institutions'. It is even possible that

higher education markets may exhibit strategic heterogeneity where some institutions exhibit

strategic substitutes and others exhibit strategic complements (Monaco and Sabarwal 2016).

To address this concern, I investigate whether institutions in the control group change their

prices in response to a competitor's merger.

This analysis requires identifying competitiors, which is not straightforward for colleges

and universities. As a starting point, I use lists of peer institutions identi�ed by each merging

institution to identify potential competitors. About half of institutions submit a custom list

of comparison institutions to IPEDS which contains peer institutions that are similar in

Carnegie classi�cation, enrollment, and institutional level to the submitting institution. In

IPEDS Data Feedback reports, these institutions are directly contrasted with the reporting

institution for tuition and fees, average �nancial aid awards, and graduation rates. For my

competitor de�nition, I use institutions appearing on the custom comparison list that also

operate in the same state as the reporting institution as competitors. This is motivated

by the fact that custom comparison groups submitted by public institutions often include

similar institutions in other states. For example, the 2014 custom comparison group of the

University of North Carolina at Charlotte includes the University of Wisconsin-Milwaukee

(NCES 2014a). While these are similar institutions in terms of enrollments and sector, it is

unlikely they are directly competing for students.

For each merger event, I assign every comparison institution the merger year of the com-

petitor's merger. If more than one competitor merges, I take the year of the �rst competitor's

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merger. Finally, I estimate equation (2) to generate event studies showing trends in tuition

and fees before and after the competitor's merger. Institutions in the same state that do not

have a competitor who merged serve as the counterfactual, and all institutions that actu-

ally merged are omitted from the sample. The bottom plot of Figure 4 shows my preferred

speci�cation with year by state by sector �xed e�ects. I �nd little evidence that competitors

are adjusting prices in responses to competitors' mergers. The event study indicator coe�-

cients are small in magnitude and precisely estimated. The point estimate for the competitor

merger e�ect is small in magnitude (-0.004) and precisely estimated (standard error 0.017).

I also estimate speci�cations that allow price responses to depend on whether a competitor

is a public or private non-pro�t institution. The results, shown in Appendix Table A.1,

indicate no heterogeneity between private non-pro�t competitors and public competitors in

the most �exible speci�cation (column 3). These results suggest that other institutions in

the same state, some of which are direct competitors, can function as a valid control group

for merging institutions.

I also assess whether merger price e�ects di�er between public and private non-pro�t insti-

tutions. Anecdotally, it is more common for private non-pro�t mergers to involve �nancially

stressed institutions which may be more likely to raise tuition prices post-merger (Millet

1976; Skodvin 1999). By contrast, some public institution mergers are nonvoluntary consoli-

dations initiated by governing boards. These mergers may be forced upon �nancially solvent

institutions with high enrollments in an e�ort to more e�ciently provide public education.

In these cases, the goal may be to expand program o�erings or increase cost-e�ectiveness

rather than to create the opportunity to raise tuition prices. To test whether merger im-

pacts di�er between public and private non-pro�t institutions, I estimate e�ects separately

by institution type.

Appendix Figure A.2 displays separate event study plots for public and private non-pro�t

institutions. The top plot shows e�ects on in-state tuition and fees at public institutions,

the middle plot shows e�ects on out-of-state tuition and fees at public institutions, and

the bottom plot shows e�ects on tuition and fees at private non-pro�t institutions. While

15

slightly less precise than the pooled plots, these plots show that the price e�ects are not

driven solely by one group of institutions. Moreover, for public non-pro�t institutions, both

in-state and out-of-state tuition and fees increase. The results in Panel B of Table 2 test more

formally whether it is appropriate to pool public and private non-pro�t institutions. For the

speci�cations reported in this panel, I replace the single post-merge indicator in equation

(1) with separate post-merge indicators for public and private non-pro�t institutions. In

the �rst two columns, I �nd that e�ects do seem to di�er by public and private non-pro�t

status. However, this is due to the inability of coarse time e�ects to adequately control for

counterfactual pricing trends. My preferred speci�cation in column 3 shows that once the

model allows for separate time e�ects by state and public/private status, I no longer reject

the null hypothesis that the two price e�ects are the same. Based on this evidence, I decide

to pool public and private non-pro�t institutions to increase statistical power in the analysis

that follows.

I also investigate whether increases in tuition and fees are driven by enrollment compo-

sitional changes. Because my measure of undergraduate tuition and fees per credit is an

enrollment weighted average, it is possible that price e�ects re�ect declines in enrollment at

merging partners with lower tuition and fees and increases in enrollment at merging partners

with higher tuition and fees. I cannot directly investigate how enrollments change at the

level of the pre-merging institutions because after mergers, data are reported only for the

merged entity. However, I can look at trends in enrollments at the level of individual insti-

tutions in the years immediately prior to the mergers to check for a pre-trend. Appendix

Figure A.3 presents event studies for enrollments in the six years prior to a merger event

for institutions that are the higher tuition and fees merging partner(s) vs. lower tuition and

fees merging partner(s). I classify an institution as a higher tuition and fees merging partner

if it reports higher tuition and fees than all its merging partners in all years prior to the

merger. I classify an institution as a lower tuition and fees merging partner if it reports

lower tuition and fees than all its merging partners in all years prior to the merger. Merging

institutions that charge sometimes higher and sometimes lower tuition and fees relative to

16

other merging partners in the pre-merger years as well as merging institutions that charge

the same tuition and fees as all other merging partners are dropped. Institutions that never

merge help identify the state by year by public/private �xed e�ects e�ects. The event studies

reveal no enrollment pre-trends for either the high or low tuition and fees partners that could

be driving the increases in the enrollment weighted average tuition and fees.

As an additional check, I also investigate whether price e�ects are signi�cantly di�erent

for the subset of mergers involving partners charging the same undergraduate tuition and

fees per credit in every pre-merger year. If these institutions continue to charge the same

tuition and fees across institutional locations after the merger, changes in enrollments would

not move the average. Appendix Table A.2 displays the same tuition and fees analysis Table

2 shows except I keep only the merger events where the merging partners had the same

tuition and fees in all pre-merger years. The standard errors are large, so e�ects are not

statistically signi�cant, but the point estimates have similar magnitudes as those from the

sample of all merger events. The point estimate from my preferred speci�cation (column

3) indicates that mergers of institutions who charge the same tuition and fees prior to the

merger raises tuition and fees by 5%, and the 95% con�dence interval does not exclude the

7% e�ect I estimated from the sample of all mergers. This also suggests that the price e�ects

are not driven exclusively by enrollment composition changes.

Finally, I benchmark the price e�ects by constructing an event study where the dependent

variable in the pre-merge years is the highest undergraduate tuition and fees per credit

charged by any of the merging partners, and the dependent variable in the post-merge years

is the undergraduate tuition and fees per credit charged at the merged institution. This event

study sheds light on whether merger price e�ects could re�ect institutions with lower tuition

and fees pre-merger raising their prices to match the tuition and fees at the most expensive

merging partner. Appendix Figure A.4 provides indirect evidence that this phenemenon

could underly the price e�ects.There is no statistically signi�cant increase in tuition and

fees relative to the tuition and fees charged by the most expensive merging partner in the

pre-merge period, and the pre/post point estimate is very close zero. However, the analysis

17

is not precise enough to de�nitively rule out price increases by all merging partners. The

95% con�dence interval includes increases in tuition and fees as large as 6% relative to the

highest tuition and fees charged by a merging partner in the pre-period.

4 Importance of Geographic Proximity and Degree Similarity for

Price E�ects

The previous analysis estimated average merger e�ects. However, not all mergers have the

potential to reduce competition to the same extent. Mergers that combine institutions oper-

ating in similar markets should result in larger price increases than those in di�erent markets.

In order to investigate this hypothesis, I construct measures of potential substitutability

between merging institutions and test whether price e�ects are largest from mergers that

combine institutions engaging in the most intense competition in the pre-merger period.

4.1 Geographic Proximity

The �rst dimension of potential substitutability is based on geographic proximity. Most

students attend colleges and universities close to their permanent address which is usually

the location of their parents' home. The median distance between a student's permanent

residence and her college is 8 miles for public two-year colleges, 18 miles for public four-year

colleges, and 46 miles for private four-year non-pro�t colleges (Hillman and Weichman 2016).

Given these patterns, we would expect mergers that involve institutions operating in close

geographic proximity to lead to larger price increases.

There is no consensus regarding the most appropriate geographic market de�nitions for

higher education, so I start by considering commuting zones within the US.8 I choose this

de�nition since students can conceivably commute between institutions in this market area

without changing their permanent residence. Thus, institutions within a commuting zone

(CZ) are likely competing to attract many of the same students. I divide mergers into

two groups: those involving institutions in the same commuting zone and those involving8A recent working paper by Deming, Lovenheim, and Patterson (2016) uses metropolitan statistical areas (MSAs) or counties

if a county is not in an MSA as a measure of higher education markets. This approach is very similar to mine though I preferto use commuting zones since commuting zones are drawn without minimum population thresholds (USDA 2016).

18

institutions in di�erent commuting zones. There are 603 commuting zones with operating

colleges or universities, and the median institution has 39 other institutions operating in

its commuting zone in a given year. Of the 114 merger events between 2001 and 2015, 73

combine institutions in the same commuting zone.9

4.2 Degree Similarity

My second proximity measure is based on degree o�erings. Using the �rst two digits of CIP

(Classication of Instructional Program) codes for degrees awarded in pre-period, I identify the

number of degree �elds o�ered by at least two of the merging partners.10 Examples of these

60 unique �eld codes include computer science, engineering, education, history, construction

trades, and visual and performing arts. I calculate the share of degree �elds that overlap

by dividing the number of overlapping �elds by the count of unique �elds o�ered across the

merging institutions. I take the mean of this share during pre-merger years as a measure of

how close the institutions are in product space.

Figure 6 plots the distribution of this measure where each bin has width 0.01. All but

four public mergers involve institutions that o�er degrees in some of the same �elds. This

is perhaps unsurprising because an often-stated goal of public system consolidations is to

avoid the duplication of academic programs. All but one private non-pro�t merger involves

institutions granting degrees in some of the same �elds. As shown in the �gure, the share of

overlapping �elds is distributed relatively uniformly other than the spike at one.

9This is an easier comparison to make than looking at exactly how much Her�ndahl-Hirschman Index (HHI) changes withinmarkets. First, it is impossible to construct accurate HHI measures for commuting zones a�ected by mergers of institutionsoperating across commuting zones. After multiple institutions merge, there is only a single IPEDS record. This mergedinstitution reports a zip code usually corresponding to its main administrative o�ce, but it may continue to operate campusesin the zip codes that are no longer recorded. Calculated HHI will overstate HHI in the commuting zone where the reportingentity resides and understate HHI in the other commuting zones. Even setting aside this problem, constructing HHI measuresrequires making a decision about what types of institutions should be counted. Should HHI be constructed using all public,private non-pro�t, and public for-pro�t institutions? Should HHI distinguish between two-year and four-year degree seekingmarket shares? Rather than taking a stand on these issues, I simply look for heterogeneity between mergers that do and donot involve institutions in the same commuting zone.

10I have also checked robustness to using the entire CIP code which denotes narrower degree �elds (i.e. Bachelor's inAerospace Engineering) rather than the coarser �eld degree codes (e.g. engineering). The same substantive conclusions emerge.

19

4.3 Geographic Proximity, Product Similarity, and the Price E�ects of Mergers

I de�ne a merger as involving institutions which are close substitutes if the merging partners

operate in the same commuting zone and have above median overlap in degree o�erings

(more than 75% of degree �elds in common). Figure 5 shows an event study where merger

price e�ects are estimated separately for mergers involving close substitutes and not close

substitutes. Consistent with price e�ects being driven by market power, estimated price

e�ects are larger in magnitude when mergers involve close substitutes. However, the 95%

con�dence intervals do overlap. 11

To investigate the importance of the two proximity measures in predicting price e�ects, I

also estimate the following double-interaction model:

yisct = β1Mit + β2(Mit × Si) + β3(Mit ×Oi) + β4(Mit × Si ×Oi) + γsct + αi + εisct (3)

The panel speci�cation includes year by state by institutional sector �xed e�ects (γsct) and

merged institution �xed e�ects (αi). Si is an indicator that takes value 1 if the merging

combined institutions operating in the same commuting zone. Oi ranges from 0 to 1 and

indicates the share of degree �elds overlapping with institution i's merging partners. Mit is

again an indicator that takes value 1 in post-merger years for institution i. The coe�cient

β1 estimates a merger e�ect for institutions with no degree overlap operating in di�erent

commuting zones. If geographic proximity in�uences price e�ects, we should �nd that β2 > 0.

If product similarity matters for price e�ects, we should �nd that β3 > 0. If product similarity

matters more when merging partners are geographically close, we should �nd that β4 > 0.

The top section of Table 4 presents the coe�cients and standard errors from the double

interaction model. For convenience, the bottom section adds the appropriate coe�cients to

show estimated e�ects for four types of mergers that take the extreme values of geographic

proximity and degree overlap. The p-values, given in brackets, are from F tests of the

null hypothesis that the price e�ect is zero. The results are noisy. However, the signs of11Appendix Figure A.5 shows event studies for each dimension of potential substitutability separately.

20

the interaction terms in the top panel indicate that price e�ects tend to be larger when

institutions merge that have a large amount of degree overlap. The overlap e�ect tends to

be even larger if the institutions are in the same commuting zone.

The bottom section of the table shows that if the merger combines institutions operating

in di�erent commuting zones with no degree overlap or combines institutions operating in the

same commuting zone with no degree overlap, the model predicts no statistically signi�cant

impact of the merger on tuition and fees. However, once again, the point estimates are

imprecise. By contrast, the model predicts large and statistically signi�cant price impacts

when there is full overlap in degree o�erings, with slightly larger e�ects when the institutions

with full overlap operate in the same commuting zone. The e�ect for the case of full overlap

in the same commuting zone is statistically signi�cant across all three columns at the 1%

signi�cance level. The point estimate from column 3 predicts that a merger of institutions

in the same commuting zone that o�er degrees in all the same �elds would raise prices by

15%.

5 Interpreting the E�ects of Mergers on Students

Though market power is an obvious explanation for higher prices post-merger, there are

several alternative explanations. First, if mergers increase costs, for example, due to reor-

ganization di�culties, higher prices may re�ect increased costs rather than market power.

Second, if mergers cause institutions to increase the actual or perceived quality of the edu-

cation they provide, higher prices may re�ect increased quality. Third, mergers may cause

institutions to increase product variety, and increased variety could explain higher prices if

it is more expensive to o�er new degrees. I assess each of these alternative explanations in

turn. First, I test whether spending per full-time equivalent (FTE) student changes as a

result of a merger. I also present results for sub-categories of institutional spending most

likely to be a�ected by combining institutions. Next, I assess whether there are detectable

changes in quality due to mergers. I use student retention rates as a summary measure of

21

quality in the �rst part of this quality analysis. In the second part of the quality analysis, I

check whether price e�ects are driven solely by combining institutions with di�ering levels of

prestige (i.e. a two-year and four-year institution). Finally, to shed light on whether mergers

impact product variety, I test whether institutions change the number of unique degree types

they award in pre and post-merger years.

5.1 Costs

E�ciencies are the primary stated motivation for undertaking mergers. If there are economies

of scale, and institutions do not reinvest cost savings to improve quality, institutions should

experience decreases in spending per student after a merger. However, if the merger actually

leads to increased costs due to costs of reorganization, these increased costs could explain

higher prices post-merger. I estimate equation (1) where where yisct is the log of instruc-

tional, administrative, student services, or total spending per full-time equivalent student at

institution i operating in state s and sector c at time t.12 To investigate how the denominator

of spending per FTE student changes, I also estimate impacts on FTE enrollment.

The �rst two columns of Table 5 indicate that enrollments decline at merging institutions

by about 5%. This is unsurprising given that tuition and fees increase at merging institutions

and implies a sticker price elasticity of demand of close to -1. The results for spending

per FTE student are inconclusive. There is no clear pattern in the point estimates in the

categories most likely to be a�ected by a merger. Point estimates for instruction are negative

while point estimates for academic support and student services are positive. Moreover, the

95% con�dence intervals are extremely wide. The last two columns show results for overall

spending. The point estimate in column 10 says that mergers decrease overall spending

per student by 5%. While this is an economically meaningful magnitude (5% savings o� a

mean of e10.035 = $22, 811 is a substantial savings of about $1,140 per student per year), the

estimate is not statistically signi�cant. The 95% con�dence interval ranges from -20% to

12Public and private non-pro�t institutions �ll out separate �nance forms when reporting spending data. For public institu-tions, I use spending information reported on GASB (F1) forms, and for private non-pro�t institutions, I use spending informa-tion reported on FASB (F2) forms. I use the Delta Database mapping �le (https://nces.ed.gov/ipeds/deltacostproject/)to map variables from these individual forms to standardized spending variables by category.

22

+9%. Though the results are noisy, the point estimates suggest that increased costs cannot

account for higher tuition and fees post-merger.

5.2 Quality

Whether mergers a�ect quality is important for the interpretation of the price e�ects of

mergers. If mergers increase college quality, higher prices may re�ect that students are

willing to pay for a better product. It is possible that the increases in quality would be large

enough to raise consumer welfare even if prices are higher. To investigate whether changes

in institutional quality can explain changes in prices, I investigate whether mergers change

the retention rate of �rst-time, full-time undergraduate students. I choose student retention

rates as the quality measure for several reasons. Unlike graduation rates, which may depend

on a student's experiences over the course of several years, the �rst year retention rate is only

impacted by experiences in the �rst year. Moreover, each cohort either matriculates before

or after the merger; there are no partially treated cohorts, which makes it easier to estimate

impacts of mergers on retention rates as opposed to graduation rates. Furthermore, non-

academic improvements in quality along other dimensions appealing to students and parents

will likely be re�ected in retention rates. For example, if the institution improves dorms

and facilities, even if these changes do not impact the academic success of students, making

parents and students more satis�ed will likely increase retention rates. Most importantly,

retention rates are a measure of whether the institution is moving students along towards

earning a degree.

Table 6 shows there is no statistically signi�cant impact of mergers on retention rates for

full-time students. The 95% con�dence interval corresponding to the estimate in column 3,

my preferred speci�cation with state by year �xed e�ects, rules out any e�ect larger than a

1.5 percentage point increase. The mean retention rate is 68.7%, so I am able to rule out

more than a 2% increase in retention, which is a fairly small e�ect.

I also assess whether price e�ects could re�ect improvements in perceived quality driven

by combining institutions o�ering di�erent degree levels. If a two-year institution combines

23

with a four-year institution, the combined institution may be able to charge more to students

in its two-year programs if the combined institution bene�ts from the prestige associated with

the four-year institution. There several merger events where institutions of di�erent types

combine (Appendix Table A.3), but these mergers are less common than those combining

institutions of the same type. If I estimate equation (1), limiting the sample to the 88

mergers where all the merging partners operate in the same institutional category, I still �nd

positive price e�ects. Appendix Table A.4 shows that the estimates are less precise than on

the sample of all mergers, which is unsurprising given that I have fewer merger events. The

point estimate indicates that these types of mergers still raise tuition and fees by 5%, and

this estimate is signi�cant at the 10% level. The 95% con�dence interval ranges from -1%

to +11%, which does not exclude the +7% e�ect I estimate on the full merger sample.

5.3 Degree O�erings

Finally, I investigate whether mergers impact the number of degree types institutions o�er.

Assessing whether degree o�erings change is important because increased product variety

has the potential to raise consumer welfare (Berry and Waldfogel 2001). Using IPEDS data

on reported completions by 6-digit CIP code and award level, I tabulate the number of

unique degree programs at each institution. Examples of unique degrees in my data include

a Bachelor's Degree in Aerospace Engineering and an Associate's Degree in Carpentry. Then,

I estimate equation (2) with the natural log of the number of unique degree programs as the

dependent variable.

Figure 7 shows a pre-trend for merging institutions in the six years preceding the merger.

Merging institutions lag behind non-merging institutions in their state and sector four to

six years prior to the merger but increase o�erings to catch up by three years prior to the

merger. The merger itself has no statistically signi�cant impact on degree o�erings.13

Taken as a whole, the results of the cost, quality, and degree o�ering analysis suggest

13Appendix Table A.5 presents the pre/post speci�cation results. The point estimate from the pooled speci�cation withyear by state by public/private non-pro�t �xed e�ects indicates that degree o�erings increase by 1%, and the 95% con�denceinterval rules out increases larger than 8%.

24

that none of these alternative explanations can adequately account for increases in prices

post-merger. There is no statistically signi�cant change in educational spending per student

as a result of mergers, and the point estimates indicate spending actually falls. Thus, higher

costs do not obviously justify higher tuition and fees. Furthermore, quality as measured by

student retention rates reported by a few of the merging institutions is unchanged by mergers.

If I estimate price e�ects from mergers of similar institutions for which substantial changes

in perceived quality/prestige are unlikely, I still detect increases in tuition and fees. Lastly,

product variety, as measured by the number of unique degrees o�ered, is not signi�cantly

a�ected by mergers.

6 Conclusion

This paper provides evidence that mergers generate market power for colleges and univer-

sities. I compare tuition and fees prices before and after mergers in a national panel of US

public and private non-pro�t universities using event study regressions. I �nd that mergers

increase tuition and fees for undergraduate students by 7% on average. Because average

price e�ects may obscure signi�cant heterogeneity based on the extent of local competition,

I estimate a double interaction model which allows price e�ects to depend on whether merg-

ing institutions operate in the same commuting zone and whether they grant degrees in the

same �elds. The estimates, while imprecise, show that price e�ects tend to be largest for

mergers involving institutions that o�er degrees in the same �elds and operate in the same

commuting zones. This is consistent with mergers reducing local competition.

To interpret the price e�ects, I also investigate whether mergers a�ect educational costs,

quality, and degree o�erings. Educational costs are estimated imprecisely, but the point

estimate suggests that total educational costs per FTE student decrease by 5% after the

merger. Thus, obvious increases in costs cannot account for higher prices. Furthermore,

retention rates do not signi�cantly change after mergers, and I detect price increases from

mergers that combine institutions operating in the same institutional category with similar

25

levels of prestige. Finally, the number of unique degrees o�ered does not change after mergers,

so increases in product variety do not o�set price e�ects for students. The explanation most

consistent with the evidence is that mergers allow colleges and universities to exercise market

power.

26

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29

Figure 1: Number of Institutions Involved in Mergers 2000-2015

40

523

172

1378

57

798

5

418

29

578

118

23010

500

1,00

01,

500

2,00

02,

500

Public Private Non−Profit

<2 yr 2 yr 4 yr <2 yr 2 yr 4 yr

Merged Never Merged

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: The total height of each stacked bar is the number of institutions operating in each institutional type andcategory between 2000 and 2015. The height of each blue bar is the number of institutions that acquired anotherinstitution or were acquired between 2000 and 2015.

30

Figure 2: E�ects of Mergers on Tuition and Fees

−.1

−.0

50

.05

.1.1

5.2

.25

.3Ln

Tui

tion

and

Fee

s P

er C

redi

t

−5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10Years Before or After Merger

Coeff 95% CI

Institution−Year Obs: 41576Institutions: 3323Mergers: 68

Time Fixed Effects: Year

−.1

−.0

50

.05

.1.1

5.2

.25

.3Ln

Tui

tion

and

Fee

s P

er C

redi

t


Coeff 95% CI


Time Fixed Effects: Year by State

−.1

−.0

50

.05

.1.1

5.2

.25

.3Ln

Tui

tion

and

Fee

s P

er C

redi

t


Coeff 95% CI


Time Fixed Effects: Year by State byPublic/Private Non−Profit

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: These event studies plot the coe�cients and 95% con�dence intervals for lead and lag indicators in a log tuitionregression that includes merged institution �xed e�ects and the indicated time e�ects. The dependent variable is thenatural log of CPI-adjusted average undergraduate tuition and fees per credit for full-time students. The omittedcategory is one year before the �scal year in which the institution was merged. Standard errors are clustered at themerged institution level. The number of mergers reported is the number of mergers for which there are pre-mergerand post-merger data.

31

Figure 3: E�ects on Institutional Grant Aid

−.4

−.2

0.2

.4

Ln(A

vg A

mou

nt In

stitu

tiona

l Gra

nt A

id A

war

ded)

CP

I Adj

uste

d−5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10

Years Before or After Merger

Coeff 95% CI



−.4

−.2

0.2

.4

Ln(A

vg A

mou

nt In

stitu

tiona

l Gra

nt A

id A

war

ded)

CP

I Adj

uste

d


Coeff 95% CI



−.4

−.2

0.2

.4

Ln(A

vg A

mou

nt In

stitu

tiona

l Gra

nt A

id A

war

ded)

CP

I Adj

uste

d


Coeff 95% CI



Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: These event studies plot the coe�cients and 95% con�dence intervals for lead and lag indicators in a logaverage institutional grant aid regression that includes institution �xed e�ects and the indicated time e�ects. Theomitted category is one year before the �scal year in which an institution merged. Standard errors are clustered atthe merged institution level. The number of mergers reported is the number of mergers for which there are pre-mergerand post-merger data.

32

Figure 4: Pricing by Competitors of Merging Institutions

Pre/Post Spec: −0.019 (0.013)

−.1

−.0

50

.05

.1.1

5.2

.25

.3Ln

Tui

tion

and

Fee

s P

er C

redi

t

−5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10Years Before or After Competitor’s Merger

Coeff 95% CI

Institution−Year Obs: 38948Institutions: 3205Had Competitor(s) Who Merged: 328


Pre/Post Spec: −0.002 (0.015)

−.1

−.0

50

.05

.1.1

5.2

.25

.3Ln

Tui

tion

and

Fee

s P

er C

redi

t


Coeff 95% CI



Pre/Post Spec: −0.004 (0.017)

−.1

−.0

50

.05

.1.1

5.2

.25

.3Ln

Tui

tion

and

Fee

s P

er C

redi

t


Coeff 95% CI



Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: These event studies plot the coe�cients and 95% con�dence intervals for lead and lag indicators in a logtuition regression that includes institution �xed e�ects and the indicated time e�ects. The omitted category is oneyear before the �scal year in which an institution's competitor was merged where a competitor is de�ned as anyinstitution listed in the merging institution's custom comparison group that operates in the same state. If more thanone competitor merges over the period, the year of the �rst competitor's merger is used. Standard errors are clusteredat the merged institution level.

33

Figure 5: Price E�ect Heterogeneity by Geographic and Product Space Proximity

−.2

−.1

0.1

.2.3

Ln T

uitio

n an

d F

ees

Per

Cre

dit

−5 −4 −3 −2 −1 0 1 2 3 4 5Years Before or After Merger

Close Substitutes 95% CINot Close Substitutes 95% CI


Price Effect Heterogeneity

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFiles and Institutional Completions FilesNotes: This event study plots the coe�cients and 95% con�dence intervals for lead and lag indicators in a log tuitionand fees per credit regression that includes merged institution �xed e�ects and year by state by public/private non-pro�t �xed e�ects. The omitted category is one year before the �scal year in which the institution was merged.Standard errors are clustered at the merged institution level. A merger is considered a merger of close substitutes ifthe merging institutions operated in the same commuting zone and had overlap in degree o�erings that was abovethe median prior to the merger. E�ects more than �ve years after the merger are too imprecise to be meaningful, sothe �gure displays e�ects only up to 5 years post-merger.

34

Figure 6: Degree Overlap Measure

05

1015

Num

ber

of M

erge

rs

0 .2 .4 .6 .8 1Share of Overlapping Fields

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CompletionsFilesNotes: A �eld is de�ned by the �rst 2 digits of each degree's CIP Code. Overlapping �eld shares are calculated foreach pre-merger year, and then I take the mean over all the pre-merger years. Bin width is 0.01.

35

Figure 7: E�ects of Mergers on Degree O�erings

−.4

−.3

−.2

−.1

0.1

.2Ln

(Num

ber

Deg

ree

Pro

gram

s)


Coeff 95% CI



−.4

−.3

−.2

−.1

0.1

.2Ln

(Num

ber

Deg

ree

Pro

gram

s)


Coeff 95% CI



−.4

−.3

−.2

−.1

0.1

.2Ln

(Num

ber

Deg

ree

Pro

gram

s)


Coeff 95% CI



Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFiles and Completions FilesNotes: These event studies plot the coe�cients and 95% con�dence intervals for lead and lag indicators in a regressionthat includes institution �xed e�ects and state by year �xed e�ects. The omitted category is one year before themerger. The dependent variable is the natural log of the number of degree programs (as identi�ed by 6-digit CIPcode). Standard errors are clustered at the merged institution level. The number of mergers reported is the numberof mergers for which there are pre-merger and post-merger data.

36

Table 1: Characteristics of Merging and Non-Merging Institutions

(1) (2) (3) (4) (5)

Will Be Will All Will Never Sig. Di�.Acquired Acquire Merge Merging P-value

Public Institutions:Fall FTE Enrollment 1,009 5,745 2,730 5,323 [<0.001]In-State Per Credit Tuition and Fees $43 $71 $53 $93 [<0.001]Out-of-State Per Credit Tuition and Fees $109 $200 $142 $241 [<0.001]Total Spending Per FTE Student $23,719 $18,214 $21,705 $18,990 [0.953]Retention Rate (Full-Time Students) 57.5 57.2 57.8 60.7 [0.090]Institutions 87 47 129 1,585

Private Non-Pro�t Institutions:Fall FTE Enrollment 567 3,811 2,397 1,899 [0.649]Per Credit Tuition and Fees $405 $437 $423 $444 [0.506]Total Spending Per FTE Student $24,044 $35,087 $31,291 $33,374 [0.421]Retention Rate (Full-Time Students) 69.5 80.0 76.1 72.4 [0.015]Institutions 17 22 39 1,291

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFiles, Fall Enrollment Files, and Finance FilesNotes: The sample includes undergraduate public or private non-pro�t institutions operating in 2002, excluding thosethat fail to report undergraduate tuition and fees or do not enroll any undergraduates in fall 2002. Each cell displaysa mean corresponding to the indicated row variable for the institution category given in italics and the acquired,acquiring, or never merged group as indicated by the column. Tuition and fees and spending amounts are CPIadjusted to 2016 dollars.

37

Table 2: E�ects on Tuition and Fees

(1) (2) (3)

Public and Private - PooledMean (in logs) 5.596 5.596 5.596

Post-Merge 0.171*** 0.122*** 0.075**(0.033) (0.029) (0.030)

Public and Private - Separate E�ectsPublic Mean (in logs) 4.858 4.858 4.858Private Non-Pro�t Mean (in logs) 6.365 6.365 6.365

Post-Merge x Public 0.249*** 0.159*** 0.078*(0.038) (0.036) (0.042)

Post-Merge x Private Non-Pro�t 0.028 0.061 0.070*(0.045) (0.042) (0.039)

Test: βPublic = βPrivateNonProfit

F-Statistic 14.39 3.19 0.02P-value [0.000] [0.074] [0.889]

Year FE Yes No NoYear x State FE No Yes NoYear x State x Public/Private FE No No Yes

Observations 41,932 41,932 41,932Institutions 3,324 3,324 3,324Mergers 69 69 69

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: Each column in each panel is a separate regression. For private non-pro�t institutions, the tuition variableis the average tuition and fees per credit reported by the institution. For public institutions, the tuition measureis a fall enrollment weighted average of in-state and out-of-state tuition per credit where the weights are based onfall freshman enrollments of in-state and out-of-state students. All tuition amounts are Consumer Price Index (CPI)adjusted to 2016 dollars. All speci�cations also include merged institution �xed e�ects. Standard errors are clusteredat the merged institution level. The number of mergers reported is the number of mergers for which there arepre-merger and post-merger data.* p<0.10, **p<0.05, *** p<0.01.

38

Table3:E�ects

onAverageInstitutionalGrantAid

andNet

TuitionRevenue

(1)

(2)

(3)

(4)

(5)

(6)

Ln(AverageInstitutionalGrantAid)

Ln(N

etTuitionandFeesRevenue)

PublicandPrivate

-Pooled

Mean(inlogs)

8.263

8.263

8.263

8.545

8.545

8.545

Post-M

erge

0.134**

0.111*

0.089

0.060

0.011

-0.042

(0.060)

(0.060)

(0.055)

(0.073)

(0.068)

(0.067)

PublicandPrivate

-Separate

E�ects

PublicMean(inlogs)

7.555

7.555

7.555

7.741

7.741

7.741

Private

Non-Pro�tMean(inlogs)

8.922

8.922

8.922

9.294

9.294

9.294

Post-M

ergexPublic

0.091

0.025

0.044

0.214**

0.123

-0.019

(0.075)

(0.075)

(0.069)

(0.095)

(0.094)

(0.101)

Post-M

ergexPrivate

Non-Pro�t

0.199**

0.232***

0.149**

-0.171**

-0.147*

-0.074

(0.091)

(0.084)

(0.085)

(0.083)

(0.080)

(0.079)

Test:

βPublic=

βPriv

ateN

onProfit

F-Statistic

0.84

3.38

0.93

9.42

4.80

0.19

P-value

[0.359]

[0.066]

[0.336]

[0.002]

[0.029]

[0.665]

YearFE

Yes

No

No

Yes

No

No

YearxState

FE

No

Yes

No

No

Yes

No

YearxState

xPublic/Private

FE

No

No

Yes

No

No

Yes

Observations

35,639

35,639

35,639

35,639

35,639

35,639

Institutions

3,206

3,206

3,206

3,206

3,206

3,206

Mergers

55

55

55

55

55

55

Source:

NationalCenterforEducationStatisticsIntegratedPostsecondary

Data

System

InstitutionalCharacteristics

Files

Notes:

Each

columnin

each

panelisaseparate

regression.Allamounts

are

Consumer

Price

Index

(CPI)

adjusted

to2016dollars.Allspeci�cationsalsoinclude

merged

institution�xed

e�ects.Standard

errors

are

clustered

atthemerged

institutionlevel.Thenumber

ofmergersreported

isthenumber

ofmergersforwhich

thereare

pre-m

erger

andpost-m

erger

data.

*p<0.10,**p<0.05,***p<0.01.

39

Table 4: Price E�ects by Measures of Product Similarity and Geographic Proximity

(1) (2) (3)Post-Merge -0.020 -0.013 0.003

(0.188) (0.168) (0.159)Post-Merge X Same CZ -0.063 -0.043 -0.055

(0.197) (0.175) (0.167)Post-Merge X Overlap 0.331 0.240 0.143

(0.261) (0.223) (0.216)Post-Merge X Overlap X Same CZ 0.049 0.039 0.058

(0.280) (0.234) (0.221)

No Overlap, Di�erent Commuting Zone -0.020 -0.013 0.003[0.917] [0.936] [0.987]

No Overlap, Same Commuting Zone -0.082 -0.056 -0.053[0.166] [0.274] [0.333]

Full Overlap, Di�erent Commuting Zone 0.312*** 0.227*** 0.146*[0.002] [0.007] [0.080]

Full Overlap, Same Commuting Zone 0.299*** 0.223*** 0.148***[0.000] [0.000] [0.003]



Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFiles and Completions FilesNotes: Each column is a separate regression. All tuition price are CPI adjusted to 2016 dollars. All speci�cationsinclude merged institution �xed e�ects. Standard errors are clustered at the merged institution level. I assigncommuting zones based on each institution's zip code. A �eld is de�ned by the �rst 2 digits of each degree's CIPCode. Overlapping �eld shares are calculated for each pre-merger year, and then I take the mean over all the pre-merger years to assign each merger a �eld overlap share. The number of mergers reported is the number of mergersfor which there are pre-merger and post-merger data.* p<0.10, **p<0.05, *** p<0.01.

40

Table5:E�ects

onSpendingPer

FTEStudent

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

ln(FTE)

ln(Instruction)

ln(A

cad.Support)

ln(StudentServices)

ln(Total)

Mean(inlogs)

7.334

7.334

8.949

8.949

7.330

7.330

7.543

7.543

10.035

10.035

Post-M

erge

-0.052

-0.051

-0.014

-0.030

0.092

0.094

0.019

0.015

-0.032

-0.053

(0.038)

(0.039)

(0.075)

(0.075)

(0.097)

(0.097)

(0.106)

(0.109)

(0.074)

(0.075)

YearxState

FE

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

YearxState

xPublic/Private

FE

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

Observations

39,454

39,454

39,454

39,454

39,454

39,454

39,454

39,454

39,454

39,454

Institutions

3,756

3,756

3,756

3,756

3,756

3,756

3,756

3,756

3,756

3,756

Mergers

57

57

57

57

57

57

57

57

5757

Source:

NationalCenterforEducationStatisticsIntegratedPostsecondary

Data

System

Finance

Files

andFallEnrollmentFiles

Notes:

Each

columnisaseparate

regression.Thesample

includes

allpublicorprivate

non-pro�tinstitutionsthathavenon-m

issingandnon-zeroamounts

of

spendingoninstruction,administration/academ

icsupport,studentservices,andcurrentyearspending.Allspendingamounts

are

Higher

EducationPrice

Index

(HEPI)

adjusted.Allspeci�cationsalsoincludemerged

institution�xed

e�ects.Standard

errors

are

clustered

atthemerged

institutionlevel.Thenumber

of

mergersreported

isthenumber

ofmergersforwhichthereare

pre-m

erger

andpost-m

erger

data.

*p<0.10,**p<0.05,***p<0.01.

41

Table 6: E�ects on Retention Rates

(1) (2) (3)

Mean 68.7 68.7 68.7

Post-Merge 0.263 -0.180 -0.366(0.948) (0.942) (0.956)



Source: National Center for Education Statistics Integrated Postsecondary Data System Fall Enrollment Files, Stu-dent Financial Aid and Net Price Files, and Admissions and Test Scores FilesNotes: Each column is a separate regression. All speci�cations also include merged institution �xed e�ects. Standarderrors are clustered at the merged institution level. The number of mergers reported is the number of mergers forwhich there are pre-merger and post-merger data.* p<0.10, **p<0.05, *** p<0.01.

42

Appendix Figures

Figure A.1: Trends in Net Tuition Revenue, Enrollments, and Spending at Closing and Merging Institutions−

1−

.50

.5

Ln N

et T

uitio

n R

even

ue(C

PI A

djus

ted)

−5 −4 −3 −2 −1 0Years Before Closure

Coeff 95% CI

Institution−Year Obs: 49019Institutions: 3993Closures: 177

Closing InstitutionsLn Net Tuition Revenue

−.1

−.0

50

.05

.1

Ln N

et T

uitio

n R

even

ue(C

PI A

djus

ted)

−5 −4 −3 −2 −1 0Years Before Merger

Coeff 95% CI


Merging InstitutionsLn Net Tuition Revenue

−.6

−.4

−.2

0.2

Ln F

all F

TE

Und

ergr

ad E

nrol

lmen

t(H

EP

I Adj

uste

d)


Coeff 95% CI


Closing InstitutionsLn Fall FTE Undergrad Enrollment

−.0

50

.05

.1Ln

Fal

l FT

E U

nder

grad

Enr

ollm

ent


Coeff 95% CI


Merging InstitutionsLn Fall FTE Undergrad Enrollment

−.4

−.2

0.2

.4

Ln S

pend

ing

Per

Stu

dent

(HE

PI A

djus

ted)


Coeff 95% CI


Closing InstitutionsLn Spending Per Student

−.0

50

.05

.1.1

5

Ln S

pend

ing

Per

Stu

dent

(HE

PI A

djus

ted)


Coeff 95% CI


Merging InstitutionsLn Spending Per Student

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: These event studies plot the coe�cients and 95% con�dence intervals for lead and lag indicators in log nettuition revenue or log spending regressions that include institution �xed e�ects and year by state by public/private�xed e�ects. The dependent variable in the top plot is the natural log of CPI adjusted net tuition revenue, thedependent variable in the middle plot is the natural log of HEPI adjusted total spending, and the dependent variablein the bottom plot is the natural log of HEPI adjusted total spending per full time equivalent student. The omittedcategory is six years before the �scal year in which the institution was merged or closed. Standard errors are clusteredat the institution level. The sample includes institutions that were acquired between 2001 and 2015 and institutionsthat were never involved in a merger. 43

Figure A.2: E�ects of Mergers on Tuition and Fees Separately by Institution Sector

−.1

0.1

.2.3

Ln(I

n S

tate

Tui

tion

Per

Cre

dit)

CP

I Adj

uste

d−5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10

Years Before or After Merger

Coeff 95% CI


Sample: Public Only

−.1

0.1

.2.3

Ln(O

ut o

f Sta

te T

uitio

n P

er C

redi

t)C

PI A

djus

ted


Coeff 95% CI


Sample: Public Only

−.1

0.1

.2.3

Ln(I

n D

istr

ict T

uitio

n P

er C

redi

t)C

PI A

djus

ted


Coeff 95% CI


Sample: Private Non−Profit Only

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: These event studies plot the coe�cients and 95% con�dence intervals for lead and lag indicators in a logspending regression that includes merged institution �xed e�ects and year by state �xed e�ects. The dependentvariable is the natural log of CPI-adjusted average undergraduate tuition and fees per credit for full-time students.The omitted category is one year before the �scal year in which the institution was merged. Standard errors areclustered at the merged institution level. The number of mergers reported is the number of mergers for which thereare pre-merger and post-merger data.

44

Figure A.3: Pre-Merger Trends in Enrollments at High vs. Low Tuition Merging Partners

−.3

−.2

−.1

0.1

.2

Ln F

all F

TE

Und

ergr

ad E

nrol

lmen

t(H

EP

I Adj

uste

d)−5 −4 −3 −2 −1 0

Years Before Merger

Coeff 95% CI


Higher Tuition & Fees Merging Partners OnlyLn Fall FTE Undergrad Enrollment

−.3

−.2

−.1

0.1

.2

Ln F

all F

TE

Und

ergr

ad E

nrol

lmen

t(H

EP

I Adj

uste

d)


Coeff 95% CI


Lower Tuition & Fees Merging Partners OnlyLn Fall FTE Undergrad Enrollment

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: These event studies plot the coe�cients and 95% con�dence intervals for lead and lag indicators in a logenrollment regression that includes merged institution �xed e�ects and year by state by public/private �xed e�ects.The dependent variable is the natural log of full-time equivalent fall undergraduate enrollment. The omitted categoryis one year before the �scal year in which the institution was merged. Standard errors are clustered at the mergedinstitution level. I classify an institution as a higher tuition and fees merging partner if it reports higher tuitionand fees than all its merging partners in all years prior to the merger. I classify an institution as a lower tuitionand fees merging partner if it reports lower tuition and fees than all its merging partners in all years prior to themerger. Merging institutions that charge sometimes higher and sometimes lower tuition and fees relative to othermerging partners in the pre-merger years as well as merging partners that always charge the same tuition and feesare dropped. Institutions that never merge help identify the state by year by public/private �xed e�ects e�ects. Thenumber of mergers reported is the number of mergers for which there are pre-merger and post-merger data.

45

Figure A.4: Price E�ects Relative to Maximum Tuition and Fees Charged by Any of the Merging Partnersin the Pre-Merge Period

Pre/Post Spec: 0.004 (0.031)

Highest Tuit & FeesAcross Merging Inst Tuit & Fees at Merged Inst

−.1

−.0

50

.05

.1.1

5.2

.25

.3Ln

Tui

tion

and

Fee

s P

er C

redi

t


Coeff 95% CI


Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: This event study plots the coe�cients and 95% con�dence intervals for lead and lag indicators in a log tuitionregression that includes merged institution �xed e�ects and year by state by public/private �xed e�ects. The omittedcategory is one year before the �scal year in which the institution was merged. Standard errors are clustered at themerged institution level. The dependent variable in pre-merge years is the natural log of the maximum of the CPI-adjusted undergraduate tuition and fees per cerdit for full-time students at any of the merging partner institutions.The dependent variable in post-merge years is the natural log of the CPI-adjusted undergraduate tuition and feesper cerdit for full-time students at the merged institution. The number of mergers reported is the number of mergersfor which there are pre-merger and post-merger data.

46

Figure A.5: Price E�ect Heterogeneity by Geographic and Product Space Proximity

−.2

−.1

0.1

.2.3

.4Ln

Tui

tion

and

Fee

s P

er C

redi

t


Same CZ 95% CIDifferent CZ 95% CI


Geographic Proximity

−.2

−.1

0.1

.2.3

.4Ln

Tui

tion

and

Fee

s P

er C

redi

t


Above Median Overlap 95% CIBelow Median Overlap 95% CI


Product Space Proximity

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFiles and Institutional Completions FilesNotes: These event studies plot the coe�cients and 95% con�dence intervals for lead and lag indicators in a log tuitionand fees per credit regression that includes merged institution �xed e�ects and year by state by public/private non-pro�t �xed e�ects. The omitted category is one year before the �scal year in which the institution was merged.Standard errors are clustered at the merged institution level. The number of mergers reported is the number ofmergers for which there are pre-merger and post-merger data. E�ects more than �ve years after the merger are quiteimprecise, so the �gure displays e�ects only up to 5 years post-merger.

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Appendix Tables

Table A.1: E�ects of a Competitor's Merger on Tuition and Fees

(1) (2) (3)


Post-Merge -0.017 -0.003 -0.008(0.016) (0.016) (0.019)


Post-Merge x Public 0.026 0.031 -0.011(0.021) (0.020) (0.027)

Post-Merge x Private Non-Pro�t -0.068** -0.040 -0.005(0.022) (0.023) (0.026)




Observations 40,502 40,502 40,502Institutions 3,207 3,207 3,207Had Competitor(s) Who Merged 328 328 328

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFilesNotes: Each column in each panel is a separate regression. For private non-pro�t institutions, the tuition variableis the average tuition and fees per credit reported by the institution. For public institutions, the tuition measureis a fall enrollment weighted average of in-state and out-of-state tuition per credit where the weights are based onfall freshman enrollments of in-state and out-of-state students. All tuition amounts are Consumer Price Index (CPI)adjusted to 2016 dollars. All speci�cations also include merged institution �xed e�ects. Standard errors are clusteredat the merged institution level.* p<0.10, **p<0.05, *** p<0.01.

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Table A.2: E�ects on Tuition and Fees for Institutions Charging the Same Prices Pre-Merger

(1) (2) (3)

Always Same Tuition and Fees Pre-Merger


Post-Merge 0.079 0.063 0.048(0.086) (0.067) (0.059)


Post-Merge x Public 0.218* 0.159* 0.101(0.122) (0.094) (0.092)

Post-Merge x Private Non-Pro�t -0.087 -0.051 -0.014(0.058) (0.063) (0.059)






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Table A.3: Number of Merger Events by Institution Type Composition of Merging Partners (2001-2015)

Merging Partners Public Private Non-Pro�tLess than 2-Year with Less than 2-Year 2 1Less than 2-Year with 2-Year 10 2Less than 2-Year with 4-Year 3 02-Year with 2-Year 37 42-Year with 4-Year 5 54-Year with 4-Year 12 324-Year with 2-Year and Less than 2-Year 0 1

Total Events 69 45

Notes: The �rst six rows display merger counts when the merger involves two institutions. The last row displaysmerger counts for mergers of three institutions.

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Table A.4: E�ects on Tuition and Fees for Mergers of Institutions in the Same Institutional Category

(1) (2) (3)


Post-Merge 0.151*** 0.103*** 0.053*(0.036) (0.030) (0.031)


Post-Merge x Public 0.235*** 0.145*** 0.057(0.040) (0.037) (0.043)

Post-Merge x Private Non-Pro�t 0.001 0.035 0.046(0.044) (0.042) (0.039)






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Table A.5: E�ects on Log of Number of Degree Types Awarded

(1) (2) (3)


Post-Merge 0.002 0.000 0.014(0.033) (0.033) (0.033)


Post-Merge x Public -0.043 -0.044 -0.021(0.036) (0.037) (0.038)

Post-Merge x Private Non-Pro�t 0.094 0.085 0.077(0.063) (0.060) (0.061)




Observations 56,918 56,918 56,918Number Institutions 4,397 4,397 4,397Mergers 91 91 91

Source: National Center for Education Statistics Integrated Postsecondary Data System Institutional CharacteristicsFiles and Completions FilesNotes: Each column in each panel is a separate regression. The dependent variable is the natural log of the numberof the unique degree types granted. All speci�cations also include merged institution �xed e�ects. Standard errorsare clustered at the merged institution level. The number of mergers reported is the number of mergers for whichthere are pre-merger and post-merger data.* p<0.10, **p<0.05, *** p<0.01.

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market power e ects of college and university mergers€¦ · market power e ects of college and...

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