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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: LaurenRussell@dartmouth.edu. 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
−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 by State
−.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 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
Institution−Year Obs: 37749Institutions: 3040Mergers: 50
Time Fixed Effects: Year
−.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 10Years Before or After Merger
Coeff 95% CI
Institution−Year Obs: 37749Institutions: 3040Mergers: 50
Time Fixed Effects: Year by State
−.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 10Years Before or After Merger
Coeff 95% CI
Institution−Year Obs: 37749Institutions: 3040Mergers: 50
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 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
Time Fixed Effects: Year
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
−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
Time Fixed Effects: Year by State
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
−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
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 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
Institution−Year Obs: 38772Institutions: 3255Mergers: 89
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)
−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: 42101Institutions: 3284Mergers: 66
Time Fixed Effects: Year
−.4
−.3
−.2
−.1
0.1
.2Ln
(Num
ber
Deg
ree
Pro
gram
s)
−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: 42101Institutions: 3284Mergers: 66
Time Fixed Effects: Year by State
−.4
−.3
−.2
−.1
0.1
.2Ln
(Num
ber
Deg
ree
Pro
gram
s)
−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: 42101Institutions: 3284Mergers: 66
Time Fixed Effects: Year by State byPublic/Private Non−Profit
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]
Year FE Yes No NoYear x State FE No Yes NoYear x State x Public/Private FE No No Yes
Observations 41,525 41,525 41,525Institutions 3,299 3,299 3,299Mergers 63 63 63
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)
Year FE Yes No NoYear x State FE No Yes NoYear x State x Public/Private FE No No Yes
Observations 39,372 39,372 39,372Institutions 3,732 3,732 3,732Mergers 61 61 61
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
Institution−Year Obs: 50303Institutions: 4105Mergers: 98
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)
−5 −4 −3 −2 −1 0Years Before Closure
Coeff 95% CI
Institution−Year Obs: 54065Institutions: 4043Closures: 210
Closing InstitutionsLn Fall FTE Undergrad Enrollment
−.0
50
.05
.1Ln
Fal
l FT
E U
nder
grad
Enr
ollm
ent
−5 −4 −3 −2 −1 0Years Before Merger
Coeff 95% CI
Institution−Year Obs: 55848Institutions: 4169Mergers: 112
Merging InstitutionsLn Fall FTE Undergrad Enrollment
−.4
−.2
0.2
.4
Ln S
pend
ing
Per
Stu
dent
(HE
PI A
djus
ted)
−5 −4 −3 −2 −1 0Years Before Closure
Coeff 95% CI
Institution−Year Obs: 41595Institutions: 3900Closures: 146
Closing InstitutionsLn Spending Per Student
−.0
50
.05
.1.1
5
Ln S
pend
ing
Per
Stu
dent
(HE
PI A
djus
ted)
−5 −4 −3 −2 −1 0Years Before Merger
Coeff 95% CI
Institution−Year Obs: 42501Institutions: 3988Mergers: 73
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
Institution−Year Obs: 24648Institutions: 1730Mergers: 51
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
−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: 24648Institutions: 1730Mergers: 51
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
−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: 20409Institutions: 1627Mergers: 23
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
Institution−Year Obs: 54895Institutions: 4100Mergers: 40
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)
−5 −4 −3 −2 −1 0Years Before Merger
Coeff 95% CI
Institution−Year Obs: 54944Institutions: 4100Mergers: 40
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
−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
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
−5 −4 −3 −2 −1 0 1 2 3 4 5Years Before or After Merger
Same CZ 95% CIDifferent CZ 95% CI
Institution−Year Obs: 38869Institutions: 3269Mergers: 100
Geographic Proximity
−.2
−.1
0.1
.2.3
.4Ln
Tui
tion
and
Fee
s P
er C
redi
t
−5 −4 −3 −2 −1 0 1 2 3 4 5Years Before or After Merger
Above Median Overlap 95% CIBelow Median Overlap 95% CI
Institution−Year Obs: 38775Institutions: 3256Mergers: 90
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.
47
Appendix Tables
Table A.1: E�ects of a Competitor's Merger on Tuition and Fees
(1) (2) (3)
Public and Private - PooledMean (in logs) 5.606 5.606 5.606
Post-Merge -0.017 -0.003 -0.008(0.016) (0.016) (0.019)
Public and Private - Separate E�ectsPublic Mean (in logs) 4.864 4.864 4.864Private Non-Pro�t Mean (in logs) 6.363 6.363 6.363
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)
Test: βPublic = βPrivateNonProfit
F-Statistic 10.01 5.78 0.03P-value [0.002] [0.016] [0.858]
Year FE Yes No NoYear x State FE No Yes NoYear x State x Public/Private FE No No Yes
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.
48
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
Public and Private - PooledMean (in logs) 5.606 5.606 5.606
Post-Merge 0.079 0.063 0.048(0.086) (0.067) (0.059)
Public and Private - Separate E�ectsPublic Mean (in logs) 4.865 4.865 4.865Private Non-Pro�t Mean (in logs) 6.364 6.364 6.364
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)
Test: βPublic = βPrivateNonProfit
F-Statistic 5.10 3.47 1.11P-value [0.024] [0.063] [0.293]
Year FE Yes No NoYear x State FE No Yes NoYear x State x Public/Private FE No No Yes
Observations 40,867 40,867 40,867Institutions 3,249 3,249 3,249Mergers 13 13 13
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.
49
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.
50
Table A.4: E�ects on Tuition and Fees for Mergers of Institutions in the Same Institutional Category
(1) (2) (3)
Public and Private - PooledMean (in logs) 5.600 5.600 5.600
Post-Merge 0.151*** 0.103*** 0.053*(0.036) (0.030) (0.031)
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 0.235*** 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)
Test: βPublic = βPrivateNonProfit
F-Statistic 15.52 3.90 0.03P-value [0.000] [0.048] [0.862]
Year FE Yes No NoYear x State FE No Yes NoYear x State x Public/Private FE No No Yes
Observations 41,516 41,516 41,516Institutions 3,291 3,291 3,291Mergers 58 58 58
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.
51
Table A.5: E�ects on Log of Number of Degree Types Awarded
(1) (2) (3)
Public and Private - PooledMean (in logs) 3.095 3.095 3.095
Post-Merge 0.002 0.000 0.014(0.033) (0.033) (0.033)
Public and Private - Separate E�ectsPublic Mean (in logs) 3.586 3.586 3.586Private Non-Pro�t Mean (in logs) 2.577 2.577 2.577
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)
Test: βPublic = βPrivateNonProfit
F-Statistic 3.58 3.32 1.87P-value [0.059] [0.069] [0.172]
Year FE Yes No NoYear x State FE No Yes NoYear x State x Public/Private FE No No Yes
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.
52
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