Vertical Integration and Foreclosure: Evidence from
Production Network Data∗
Johannes Boehm†

Jan Sonntag

Sciences Po and CEPR
johannes.boehm@sciencespo.fr

Sciences Po
jan.sonntag@sciencespo.fr

September 9, 2021

Abstract
This paper studies the prevalence of potential anticompetitive effects of vertical mergers using
a novel dataset on U.S. and international buyer-seller relationships, and across a large range ofindustries. We find that relationships are more likely to break when suppliers vertically integratewith one of the buyers’ competitors than when they vertically integrate with an unrelated firm.
This relationship holds for both domestic and cross-border mergers, and for domestic and international relationships. It also holds when instrumenting mergers using exogenous downward pressure
on the supplier’s stock prices, suggesting that reverse causality is unlikely to explain the result.
In contrast, the relationship vanishes when using rumored or announced but not completed integration events. Firms experience a substantial drop in sales when one of their suppliers integrates
with one of their competitors. This sales drop is mitigated if the firm has alternative suppliersin place. These findings are consistent with anticompetitive effects of vertical mergers, such asvertical foreclosure, rising input costs for rivals, or self-foreclosure.
Keywords: Mergers and acquisitions, Market foreclosure, Vertical integration, Production networks
JEL: L14, L42∗

We thank Emeric Henry, Francine Lafontaine, Adrien Matray, Ezra Oberfield, Steven Salop, Florian Szücs,
and seminar participants at Princeton, Sciences Po, EARIE 2018, RES 2018, SED 2018, and the 2019 Northwestern Searle Antitrust Conference, as well as three anonymous referees for helpful comments. Boehm thanks
the International Economics Section at Princeton for their hospitality. We thank the Banque de France/Sciences
Po partnership for financial support. All errors remain our own.
†
Corresponding author: Département d’Economie, Sciences Po, 28 Rue des Saints-Pères, 75007 Paris.
johannes.boehm@sciencespo.fr

1

Introduction

Vertical integration of two firms has the potential to increase their economic efficiency by
exploiting synergies in the design, production, and distribution of their goods and services.
At the same time, firms may pursue integration as a strategy not only to create competitive
advantage, but also to engage in anti-competitive behavior. One such case arises when one of
the integrating firms controls access to a bottleneck input, such as access to vital infrastructureor technology. The integrated firm might use its access to the bottleneck to extend or preserve
its market power in the upstream markets by refusing to provide rival firms in downstream
markets with access to the bottleneck. These firms are said to be foreclosed.1 While a large
theoretical literature investigates the motives for vertical foreclosure2 , empirical evidence offirms using foreclosure as a business strategy is restricted to a few very particular cases3 , notleast because vertical relationships are rarely observed. This not only restricts our ability totest the theory, but also limits our understanding of the prevalence of foreclosure and related
anticompetitive practices in reality. Even less is known about potential strategies to mitigate
the effects of being foreclosed.
The empirical prevalence of vertical foreclosure is, of course, at least partly determined bycompetition law and its enforcement. Most of its forms are regarded as violating competition
laws in a large range of jurisdictions. In the United States the Sherman and Clayton Antitrust
Acts set out limitations to merger activity, and starting with Terminal Railroad Associationv. U.S. (1912) U.S. courts have established a doctrine on foreclosure. Competition authorities
typically issue guidelines on their assessment of vertical mergers to avoid unforeseen restrictions
on mergers. At the same time – or perhaps as a consequence – enforcement of these verticalmerger laws is relatively rare.4 With recent work arguing that concentration and market power
among US firms increased over the course of the last decades5 , and the finger being pointed
at regulatory authorities6 , one is led to ask: is enforcement lax, or is actual foreclosure justvery rare? What are the factors determining the prevalence of vertical foreclosure and related
anticompetitive practices, and how severe are the consequences? How can firms threatened byforeclosure mitigate its impact?
This paper examines the occurrence of vertical foreclosure across a range of industries andcountries. We exploit a novel panel dataset on vertical relationships — the network structure of
production — between large firms, both in the U.S. and abroad. These data allow us to study
whether buyer-seller relationships break following vertical mergers and acquisitions. We show
1

By foreclosure, we mean a buyer excluding a competitor from the market by means of acquiring its supplier.
This could be by refusing to supply or by (directly or indirectly) raising the rival buyer’s costs.
2
See Rey and Tirole (2007) for an overview. The classic references are Hart and Tirole (1990) and Ordover
et al. (1990).
3
Recent examples include Asker (2016) for the Chicago beer market and Crawford et al. (2018) for the US
cable TV industry. Lafontaine and Slade (2007) and Slade (2019) survey this literature.
4
Salop and Culley (2015) find only 46 vertical enforcement actions in the US over the period 1994–2013.
5
De Loecker et al. (2020) estimate a rise in average US markups using Compustat and US Census data;
Gutiérrez and Philippon (2017) document rising Herfindahl concentration indices in US industries, and Barkai(2016) documents a rise in the profit share of US non-financial corporations.
6
See Gutierrez and Philippon (2018) and The Economist (2018)1

that the breaking of a buyer-seller relationship in response to the supplier vertically integrating
downstream is more likely when the downstream merging firm is a competitor of the buyer —but not when the downstream merging firm is not a competitor of the buyer. Consistent withtheories of vertical foreclosure, the former break probability is even higher when there is little
competition in the upstream industry. The increased probability of links breaking cannot be
explained by common industry-level (or industry-pair-level) shocks to merger activity or the
break probability. We find this increased break probability in response to both domestic andcross-border mergers. Similarly, domestic and cross-border relationships are equally likely to
break in response to such vertical mergers.
The correlation we find does not immediately imply that vertical market foreclosure istaking place in the population of firms and relationships that we study. Causality could run
in the opposite direction: vertical integration could be the response to relationships breaking,or to the threat thereof. Alternatively, both integration and links breaking could be caused byunobserved shocks. Finally, the links breaking might not be the the consequence of foreclosure,but might be the consequence of the integrating parties being able to produce the final goodat such a low cost that the buyer decides to exit the market (and hence stops purchasing the
input).
A series of additional regressions indicates that these alternative explanations are unlikely
to account for the findings. To see whether our results stem from reverse causality, we follow
Edmans et al. (2012) to construct an instrumental variable for vertical mergers and acquisitions. The variable captures events where investor capital outflows of mutual funds put large
downward pressure on firms’ stock prices, thereby making the firm more likely to be acquired.
The correlation between vertical integration and links breaking prevails for vertical acquisitions
that follow situations where such fund outflow events put downward pressure on the bottlenecksupplier’s stock price. If the investor capital outflows are unrelated to the performance of the
supplier, these cases are integration events that are unlikely to happen for supply assurance
reasons (as, for example, in Bolton and Whinston (1993)). The relationship also prevails whenusing these events to construct instrumental variables. Furthermore, we find similar results
when conditioning on situations where the suppliers are “healthy” in the sense that they haveseen sales increases prior to integrating.
Moreover, we study events where firms are rumored to vertically integrate or announce anintegration, but end up not integrating. To the extent that these rumored integration eventsmight be caused by the same unobserved shocks as actual integration events, they make for agood comparison group. For relationships where suppliers are rumored to vertically integrate,
we do not find a higher hazard rate of links breaking than for the average relationship. We also
do not find the large difference in hazard rates between rumored integration with a competitorof the buyer versus firms unrelated to the buyer.
To investigate whether strong synergies (i.e. improved efficiency) among merging firms
force the downstream competitor out of the market and therefore break the link itself, westudy the sales response of firms whose competitor is vertically integrating but who did not2have a prior relationship with the integrating supplier. We find no statistically significant dropin sales for these firms, suggesting that strong synergies are unlikely to explain the breaking
of vertical relationships in our main result. This is consistent with the results of Blonigen and
Pierce (2016), who find no significant increases in physical productivity among US plants that
undergo a merger or acquisition, but an increase in market power as measured by markups.
Finally, we study the performance of firms in the wake of their supplier’s integration. Firms
which have a supplier that vertically integrates with one of its competitors experience a temporary decrease in sales. The sales drop is larger for firms that do not have another supplierfrom the same industry as the one that is integrating. Diversification of the supplier base ishence a possible way to mitigate the impact of being foreclosed.
We interpret our results as supporting the view that vertical mergers have, on average inthe population of firms and relationships that we study, anticompetitive effects. These anticompetitive effects can take the form of integrated firms excluding competitors from accessing
an upstream input. They could also take the form of integrated firms “raising rivals’ cost”,
potentially leading to the severance of the relationship, or self-foreclosure on the side of thecompetitor, such as to prevent the competitor from accessing strategically valuable information.
While the relationships we study are not representative of the overall population of buyer-seller
relationships in the United States, or among industrialized countries (the set of firms reporting
relationships in our data consists mostly of firms that are either listed on exchanges or issue
traded securities, and those firms are also more likely to report relationships with important
suppliers and customers), the relationships in our sample will be more likely to be in the spotlight of antitrust authorities, it is likely that anticompetitive effects from vertical integration
are also prevalent outside of the sample that we study.7
Our paper relates to three different literatures. The first is the literature that studies
the determinants and effects of mergers and acquisitions, both domestic (Malmendier et al.,2018, Maksimovic et al., 2013, Rhodes-Kropf and Viswanathan, 2004, Gugler et al., 2003,
Shenoy, 2012, Blonigen and Pierce, 2016, Cunningham et al., 2021, Harford et al., 2019) andinternational (Blonigen, 1997, Nocke and Yeaple, 2007, Ekholm et al., 2007, Breinlich, 2008,
Guadalupe et al., 2012, Stiebale, 2016). In contrast to most of this literature, we study the
impact not on integrating firms themselves, but on the vertically related ones.8 We also showthat foreclosure considerations — as determined by the structure of the production network —predict vertical mergers.
The second is the empirical literature on detecting vertical market foreclosure. Waterman
and Weiss (1996), Chipty (2001), and Crawford et al. (2018) (in the cable TV industry),
Hastings and Gilbert (2005) (in the gasoline retailing industry), and Lee (2013) (in the video
game industry) find evidence for vertical foreclosure; Hortaçsu and Syverson (2007) (in cement
and ready-mixed concrete markets) and Asker (2016) (in the beer industry) find no vertical
7

Wollmann (2019, 2020) finds that exemptions on premerger notifications severely limit antitrust enforcement.
8
Recent exceptions are Gugler and Szücs (2016) and Stiebale and Szücs (2019), who study the impact ofmergers on horizontally related firms.

3foreclosure in their respective industries.9 In contrast to this literature, we study a range ofindustries, which not only broadens the scope of statements that we can make, but also allows forcomparisons across industries by their degree of competitiveness. We draw from data on verticaland competitor relationships, which ties our hands on the definition of markets and vertical
integration. The drawback is that our data prevents us from studying prices or markups, andtherefore consumer welfare. Instead, we look at the supplier network of potentially foreclosed
firms, and how the relationship between integration and links breaking varies with market
structure in the upstream market.
Finally, our paper also relates to the growing literature on the importance of firm’s positionin the production network for its performance and exposure to shocks (Barrot and Sauvagnat,
2016, Giroud and Mueller, 2017, Bernard et al., 2017, Carvalho et al., 2016, Boehm et al., 2019,
Tintelnot et al., 2018, Kikkawa et al., 2018, Miyauchi, 2018). Atalay et al. (2019) study thetransaction cost imposed by trading across the firm border using buyer-seller transaction data.
Alfaro et al. (2016) study the relationship between prices and vertical integration across manyindustries. Related to our work, Bernard and Dhingra (2015) find increased integration andforeclosure following the 2012 Free Trade Agreement between Colombia and the United States.
Our paper shows how the network matters through the strategic incentives of horizontally
related firms, and for how the production network itself is shaped by those incentives. We also
introduce a new dataset on buyer-seller connections in the U.S. and abroad and document its
properties.
The next section describes the data; Sections 3 and 4 present the econometric evidence.

2

Data

We combine three different datasets for our empirical analysis: a dataset describing supply
chain and competitor networks, a dataset of mergers and acquisitions, and data on firm sales
and employment. The first dataset is FactSet Revere, a panel of almost 900,000 vertical andhorizontal relationships of large US and foreign firms. It describes the supplier, customer,
and competitor relationships as well as partnerships of a set of large (mostly publicly listed
or security-issuing) firms from the US and abroad (we call these companies the “covered”
companies). Each relationship is coded with a relationship type, the identity of the firms, anda start and end date. The data vendor collects this information annually through the covered
companies’ public filings, investor presentations, websites and corporate actions, and throughpress releases and news reports. Since the relationship data is the main content of the dataset,its coverage is much broader than supplier data in Compustat or Bloomberg. While the data
9

A substantial empirical literature has emerged that study vertical relationships and foreclosure in thehealthcare industry. Vertically integrated primary care providers tend to steer patients to in-network specialist
doctors (Chernew et al., 2018, Brot-Goldberg and de Vaan, 2018). Ho and Lee (2017) and Cuesta et al. (2018)
estimate quantitative models of bargaining in vertical relationships in the healthcare industry. In the empiricalmodel of Ho and Lee (2019), firms have an incentive to exclude some suppliers in order to threaten their trading
partners to replace them with the excluded ones and thereby keep prices low. The network of vertical relations
emerges as an equilibrium object.

4

coverage is specifically geared towards large firms, many small and non-listed firms neverthelessshow up in relationships with large firms, hence our overall network is much larger than theset of listed firms. Coverage varies by country; data for covered North American companies
is available from 2003 to present; Revere starts to cover publicly listed and security-issuing
companies from industrialized and major emerging economies (including Europe and China)
from around 2007.10 To the extent of our knowledge, our paper is the first one in the economics
literature to use this dataset, so we show summary statistics in more detail than we otherwise
would.
FactSet Revere contains thirteen different types of relationships (see Appendix A.1 formore details). We aggregate these relationship types into two networks: a directed network ofbuyer-supplier relationships (from supplier and customer relationships, as well as distribution,
production, marketing, and licensing relationships) and an undirected network of competitors.
Moreover, we annualize the relationship data: A relation of any kind is counted as active in agiven calendar year if there is at least one day between start date and end date of the relationthat falls into that calendar year. The result is a panel of relations that is identified by sourcecompany, target company and year.11
Table I—: Descriptive statistics for the firm network
Full Sample

Sample of buyers

Mean

SD

Max

Mean

SD

Max

# Customers# Suppliers# Competitors
Share of domestic customers
Share of domestic suppliers
Share of domestic competitors
Obs. per firm (years)
Log Sales
Log Employment

2.13
2.13
2.16
0.49
0.50
0.46
3.97
12.00
6.27

8.28
10.20
7.73
0.45
0.45
0.45
4.30
2.81
2.56

533
980
381
1
1
1
14
20
15

3.37
3.85
3.48
0.48
0.50
0.46
6.24
12.70
6.90

10.72
13.48
10.13
0.42
0.45
0.43
4.28
2.62
2.46

533
980
381
1
1
1
14
20
15

Firms

180,192

80,287

Note: Summary statistics for the number of links in the firm network (2003-2016). The left columns summarize
the full set of firms in the database, the right columns only those firms that have at least one supplier in the
database. We count relations as domestic when both firms are headquartered in the same country. “Observations
per firms” summarizes the coverage length of firms. Sales and employment data come from Compustat, Orbis
and FactSet Fundamentals. Note that coverage for sales (employment) is lower: 74,511 (73,613) firms in thefull sample and 40,576 (40,389) among buyers.

Table I summarizes the resulting links in the network of firms, which is much more dense
than suggested by data exclusively relying on SEC filings (as reported, for instance, by Barrot
and Sauvagnat (2016)). Among the more than 180,000 firms in our dataset, 80,000 have at least
10

See Appendix A for details on coverage by country and year.
Firms sometimes undergo organizational changes where a firm identifier ceases to exist and one or more
new ones may be created (e.g. in cases of mergers and splits). In such cases, FactSet records the successor
identifiers, and we say that a buyer-seller relationship breaks only if there is no buyer-seller relationship with
one of the successor firms.
115

one supplier link recorded. On average, our buyers have 3.85 suppliers, but many firms havesubstantially more. The average numbers of customers and competitors is just slightly lower,
allowing to construct a dense network. The average length of buyer-supplier relationships inour data is 4.46 years; the unconditional probability of buyer-supplier links breaking in any
given year is 22%. Only 6.3% of links that break over the observation period are reformed at
a later point in time, and almost never more than once. For buyer-supplier links the share oflinks that are reestablished later on is higher, at 12.9%.12

Density
0

0

.1

.2

Density
.2

.4

.3

.4

.6

Figure 1: Distribution of the number of suppliers and customers

0

10

20

30+

0

Number of Suppliers

10

20

30+

Number of Customers

Note: The sample consists of firms that have at least one supplier.

Figure 1 shows the distribution of the number of suppliers and customers among firms. The
distributions are very skewed, with most firms having few suppliers and customers, and somehaving many. Whenever we use the number of links in our regressions below, we will hence
use the log of one plus the number of links instead of the raw count in order to avoid our
results being driven by outliers. The fact that the number of relationships is heavily skewed iswell-known from the literatures on firm heterogeneity and superstar firms.13 Table II confirmsthat the firms with most connections account for a disproportionately large fraction of sales.
Table II—: Total sales by percentile of the # suppliers distribution
Fraction of Sales, %All
Top 25%
Top 10%Top 5%
Top 1%

100.0
78.1
58.6
46.8
25.8

Note: The table shows the average fraction of sales (over years) accounted for by firms in the top percentilesof the distribution of the number of suppliers (firms with at least one supplier only).

Finally, one word of caution about these data. While the coverage of relationships is better
12

In appendix B.5 we show that our main results are robust to not counting relationships as breaking if they
are subsequently reestablished.
13
The literature is vast; see, in particular, the recent empirical work by Bernard et al. (2017). Most similar tous, Carballo et al. (2018) document the skewness of the customer distribution and sales for international buyersof Latin American firms. In theoretical work, Oberfield (2018) explains how superstar firms emerge in a setting
where firms search for suppliers.

6

than in other large panels that span many industries and countries, it is probably still incomplete: relationships with small firms, and relationships that account for a small fraction of sales
or costs are presumably less likely to be recorded. Our data show about 500 listed suppliers forWalmart in 2016, and Walmart is — together with Apple, Samsung, and the large auto manufacturers — one of the firms with the highest number of recorded suppliers. In reality though,Walmart probably has tens of thousands of suppliers, suggesting that many relationships are
missing. The relationships recorded in our data are probably the larger or more important
ones.14The second dataset we use is the set of mergers and acquisitions in Bureau Van Dijk’s
Zephyr database. Zephyr records deals and rumors about deals for mergers and acquisitions inwhich at least a 2% stake in the target company changes owners and the deal’s value exceeds
GBP 1M (Bollaert and Delanghe (2015)). For each merger or acquisition, Zephyr reports thenature of the transaction, the identity of the target company, the acquiring company and theseller, as well as the date of announcement, the date when the transaction was finished, andthe stake of the acquirer in the target before and after the acquisition. Zephyr also contains
a large number of rumored deals that never materialized, which we will use as a comparisongroup in some of our regressions.
Analogously to the relationship data, we annualize the Zephyr data and construct a panel ofmergers and acquisitions between acquiring and acquired company. We focus on transactions
where one company fully acquires another or the entities merged. We infer the vertical or
horizontal nature of an integration by combining the M&A data with the input-output network:a vertical integration is a merger or acquisition between two firms that have an ongoing buyerseller relationship in the year of integration.
Table III reports the number of mergers and acquisitions between firms for which supply
chain information is available. The majority of mergers and acquisitions in our sample isbetween firms that are not vertically related. 6.7% of full acquisitions in our sample are verticalin the sense that they are between firms that have an active buyer-supplier relationship. The
share is almost the same for partial acquisitions, which we do not use in our analysis butreport here for completeness. The non-vertical mergers and acquisitions can be either purely
horizontal or between unrelated firms that neither compete directly nor supply each other with
inputs. For the sake of brevity, we will refer to both mergers and acquisitions as “mergers” forthe remainder of the paper.
There is a small but non-negligible number of cases with risk of vertical foreclosure. Table
IV summarizes key statistics about the buyer-supplier relations in our sample. While theunconditional probability that a relation ends in a given year is only 22.4%, this probability ismore than 50% in cases where the supplier integrates vertically with a competitor of the buyer.
In our data, this happens in 105 out of the 6865 cases in which a supplier vertically integrates
14

Alternatively, one could use administrative VAT transaction records, as are available for countries like
Belgium (Bernard et al., 2017) and Chile (Huneeus, 2018). However, in that case our study would be limited
to relatively small samples and few vertical merger cases (as well as additional constraints imposed by theconfidential nature of these data).

7

Table III—: Types of mergers and acquisitions
Non-vertical

Partial acquisitions
Full acquisitions & mergers
Total

Vertical

Total

Count

%

Count

%

Count

%

745
2,799
3,544

93.2
93.3
93.3

54
201
255

6.8
6.7
6.7

799
3,000
3,799

100.0
100.0
100.0

Note: Number of partial and full mergers and acquisitions by presence of a vertical relation between the merging
parties (2003-2016). Partial acquisitions exclude minority stakes. For a breakdown including horizontal mergers
see Appendix A.

with another buyer.
Table IV—: Buyer-supplier links: hazard rates of links breaking and risk of foreclosure
Value
P(link breaks)
Avg. relatation duration
Number of cases where supplier vertically integrates
Number of cases where supplier integrates w. competitor... and buyer-supplier link breaks

0.225
4.45
6865
206
105

Note: The first row reports the unconditional probability that a buyer-supplier relationship ends in a given
year. The second row reports the average length of these relations. The third row counts the number of cases
in which a supplier vertically integrates. The fourth row restricts this number to cases where the vertical
integration involves a competitor of the buyer. The fifth row counts the instances in which the buyer-supplierlink breaks following vertical integration of the supplier with a competitor of the buyer. In all but one of the
206 foreclosure risk cases, the downstream firm is acquiring the upstream supplier.

Figure 2 shows the industry-wise and year-wise distribution of cases where the relationshipbreaks following vertical integration of the supplier with a competitor of the buyer. Thesesituations are not confined to a narrow set of industries, but occur broadly across the economy.
A particularly large number of such cases falls into computer and electronics manufacturing,in which there are many large firms that are frequently undertaking mergers and acquisitions.
In the short panel that is available to us, there is no clear trend over time in the number ofthese potential foreclosure cases. Whereas recent research has documented a rise market power
since the early eighties (De Loecker et al., 2020), this does not translate into an increase in thenumber of potential foreclosure cases over time in our sample.
We complement the relationship and M&A data by sales and employment figures and industry codes from Compustat, Bureau Van Dijk’s Orbis database and FactSet Fundamentals(2003–2014). Since these data have been widely used in the literature, we will not describe
them here.15 The last rows of Table I show summary statistics for sales and employment.
15

See Kalemli-Ozcan et al. (2015) for detailed information on Orbis. We use a current and past vintage ofOrbis to have a better coverage.

8

Figure 2: Potential foreclosure cases by sector and year
Utilities

2003

Petr., Plastic & other Chemical.

2004

Computer & Electronics Man.

2005

Other Manufacturing
2006

Wholesale Trade
Retail trae and Transportation

2007

Information

2008

Fin. Services, Insurance & Real2009

Prof., Sci. & Techn. Services

2010

Management
2012

Admin. & Support Services
Other Services

2013

N.A.

2015
0

10

20
30
Frequency count

40

0

5

10
Frequency count15

Note: A potential foreclosure case is a situation where a buyer-seller relationship breaks following integrationof the supplier with a competitor of the buyer. The left panel describes the sector of the buyer (“N.A.” denotes
missing sector information, and we exclude these firms from all regressions with industry fixed effects). About
three quarters of potentially foreclosed firms are US firms.

3

Extensive-margin Foreclosure

3.1

Empirical Strategy

Our empirical strategy is to study whether vertical relationships are more likely to break after
the supplier integrated with a competitor of a buyer, than when it integrated with an unrelated
firm. Consider a vertical relationship between seller s and buyer b. If b is a competitor of thefirm b0 that s is integrating with, then the integrating parties may have an incentive to foreclose
b. If, on the other hand, b and b0 are in different markets, then b would not be threatened byforeclosure (see Figure 3). Our strategy is therefore to compare the probability of the (b, s)
relationship breaking between these two scenarios.
We define markets through the competitor relationships that we observe in FactSet Revere.
FactSet constructs these competitor relationships based on firm’s product portfolios and selfdisclosed competitor relationships from SEC filings. We prefer this definition over industry
code-based definitions for two reasons. Firstly, even 6-digit NAICS categories are often broadand encompass many different product markets (e.g. NAICS 334310: “Audio and Video Equipment Manufacturing”). Secondly, many of the firms in our sample are large firms that operate indifferent product markets, which are not always reflected in the SIC or NAICS codes. For thosefirms, competitor relationships are usually not transitive. As a result, the FactSet competitor
relationships are very different from co-memberships in industry cells: among competitor pairs
according to FactSet, only 43.5% are among firms that share a 4-digit NAICS code. Conversely,
among all pairs of firms that share a 4-digit NAICS code, only 0.03% coincide with a FactSetcompetitor link.16
16

The corporate finance literature is well aware that industry co-membership is a poor way to measurecompetitor relationships. Rauh and Sufi (2011) use competitor definitions from CapitalIQ and argue that thismethod captures competitor relationships much more accurately than using industry codes. Hoberg and Phillips

9Figure 3: Empirical strategy: compare situations where buyers b and b0 are in same vs. different
markets

n
tio
ra

at
n
io

competitors
b

g
te
In

s

gr
te
In

s

b0

b

(a) Buyers b and b0 in same market: foreclosure
potentialb0(b) Buyers b, b0 in different markets: no foreclosure
potential

Note: This figure illustrates the main empirical strategy. We compare two situations in which a seller s
integrates with a buyer b0 : one in which b and b0 compete in the same product markets (a) and one in which
they do not (b).

We estimate the following linear probability model17 on the set of all triples (b, s, t) wheres is listed as one of b’s suppliers (or b is listed as one of s’s customers) for at least one day inyear t:1{LinkBreaks}bst = α1{s vertically integrates}st+ β 1{s integrates vertically w. competitor of b}bst+ ηbs + ηbt + ηi(b)i(s)t + εbst

(1)

where 1{LinkBreaks}bst is a dummy variable that is one if and only if the vertical relationshipbetween b and s is active during year t, but not during year t + 1 (and also not between entities
that are successors to b or s in case of a split or change in organizational form). The right-handside variables are a dummy for whether s vertically integrates during year t, and a dummy
for whether s vertically integrates with a competitor of b during year t. We include (i) fixed
effects for buyer × year, ηbt , to control for time-varying characteristics of the buyer that could
make all its supplier relationships more likely to break during a given year (such as exit), (ii)buyer × supplier fixed effects, ηbs , thereby identifying the coefficients of interest, α and β, fromwithin-relationship variation in the hazard rate of the relationships breaking, and in the firms’
characteristics, and (iii) industry-pair × year fixed effects, ηi(b)i(s)t , which takes out industryspecific (or industry-pair-specific) shocks that may lead to a higher break probability (whereindustries are defined at the 3-digit NAICS level). We exclude relations from the regression
where the buyer and supplier themselves are the vertically integrating parties.
Table V shows the result from estimating equation 1 using ordinary least squares. The firstcolumn shows that when suppliers are vertically integrating, the probability of a given vertical
relationship breaking is higher by about 2.7 percentage points (though this is not statistically(2010) develop measures of product market competition from text analysis of firm filings.
17
We use a linear model as a benchmark specification because it allows us to include high-dimensional fixed
effects. We estimate hazard models in Appendix B.3, and find similar results.

10

Table V—: Correlation of buyer-supplier link breaking with vertical integration of supplier
Dependent variable: 1{LinkBreaks}bst
Supplier v. integrates

(1)

(2)

(3)

(4)

0.027
(0.019)0.017(0.019)0.009(0.020)0.024(0.018)0.176∗∗(0.059)0.176∗∗(0.062)0.148∗∗(0.052)

Supplier v. integrates w. competitor
Controls
Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

0.578
6407530.578
6407090.619
4728320.671
472832

Note: Controls: number of upstream customers and competitors, age of the link, dummy indicating other linksof the supplier breaking. Robust standard errors clustered at the supplier-year level. The number of reported
observations is the number of non-singleton observations. The drop in the number of observations in columns(3) and (4) is explained by firms with missing industry codes. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

significant). Given that the unconditional probability of a relationship breaking in our data
is about 22%, this would constitute an increase of about 12%. Column (2) shows that thelikelihood of the vertical relationship breaking is indeed much higher (18 percentage points
difference, or a 80% higher probability) when the buyer is a competitor to the downstream
merging firm. This difference remains large and statistically significant when including industry
pair × year fixed effects to control for sector- or sector-pair-specific shocks (column 3), and whencontrolling for a range of supplier and relationship characteristics (column 4).
It is worth pointing out that the results above are unlikely to be driven by the possibility
that a relationship may not be observed by FactSet following a merger, because the firm entity
has ceased to exist, or because it may not be tracked anymore: if that was the case, we should
be seeing a substantially increased hazard also following vertical mergers with firms that are
not competitors of the downstream merging firm.
Figure 4 shows graphically how break probabilities differ across these two types of vertical
integration events. The horizontal axis shows the time after a vertical integration event of the
supplier; the vertical axis shows the probability of the relationship having broken (i.e. one
minus the probability of the relationship being active). By definition of the sample, in theyear of integration of the supplier the buyer-seller relationship must be active. We see that
relationships where the supplier integrates with a competitor of the buyer (solid blue line) aremuch less likely to survive the post-integration years, in particular the year following integration,
than relationships where the supplier integrates with a non-competitor of the buyer (dashed
red line). The dotted green line shows relationship survival rates for simulated placebo events
that are generated to occur with 0.6% probability in any given year where a relationship is
active. This corresponds to the average probability that a supplier in a given relation verticallyintegrates. The regression that generates these marginal effects include relationship, buyer11

year, and industry-pair year fixed effects; the corresponding plot of a regression without fixed
effects looks very similar.

Probability of the relationship having broken
0
.2
.4
.6
.8

Figure 4: Probability of relationships having broken after supplier’s vertical integration

0

1

2
3
4
5
Years since vertical integration of supplierVI with competitor of buyer
Placebo

6

7

VI with non-competitor of buyer

Note: The figure shows coefficients on dummies capturing the years since a supplier’s vertical integration, in aregression of the probability of a buyer-seller relationship being inactive on time-since-integration dummies, aswell as relationship, buyer × year, and industry-pair × year fixed effects. Standard errors are clustered at the
supplier-year level. The solid blue line denotes relationships where the supplier integrates with a competitor of
the buyer; the dashed red line denotes relationships where the supplier integrates with a non-competitor of thebuyer; the dotted green line represents relationships where a placebo integration event has been drawn to occur.
That placebo event is randomly drawn to occur with 0.6% probability in any given year where a relationship is
active (and independently across relationship-years).

Next, we study variation across industries in the relationship between vertical integrationand links breaking. Most theories of vertical foreclosure, in particular the raising rivals’ costtheories and extending monopoly power theories of vertical foreclosure predict that market
power in the bottleneck market increases the incentives to foreclose. We want to empiricallyassess this prediction. In order to do so, we study whether the correlation between integration
with a competitor and relationships breaking is lower when the supplier has less market power.
We measure the supplier’s market power by the number of his competitors.18 More specifically,
we run the regression
18
Alternatively, one could measure the supplier’s market power with market shares. Our sales coverage amongsuppliers and in upstream markets generally, however, is very limited, so we prefer measuring supplier market
power through the number of competitors.

12

1{LinkBreaks}bst = α1{s vertically integrates}st+ β 1{s integrates vertically w. competitor of b}bst+ γ 1{s integrates vertically w. competitor of b}bst × Cst+ δCst+ ηbs + µbt + εbst(2)

where Cst is a variable capturing the number of competitors of the supplier s at time t. Just
like the number of buyers and suppliers is heavily skewed, so is the number of competitors,
therefore we use the log of one plus the number of competitors for Cst .
Table VI shows the results. We find that the correlation between buyer-supplier-links breaking and vertical integration of a supplier with a competitor is lower when the supplier has morecompetitors (columns (1) and (2)). This result is in line with theories of foreclosure: theexistence of more alternative suppliers to the buyer reduces the incentives of the acquirer toforeclose competitors. In columns (3) and (4) we also include interactions with the number ofcompetitors of the buyer. The point estimates of the coefficients on these interaction terms
are slightly positive (though not statistically significant). While not entirely conclusive, it ispossible that more competition in the downstream market increases the probability of links
breaking after integration with a competitor.
Tables V and VI show a correlation that by itself is not evidence for vertical foreclosure. We
see that relationships are relatively much more likely to break when the supplier is undergoinga vertical merger with a competitor of the buyer, than when it is merging with a firm that is
not a competitor of the buyer. The fact that this correlation is stronger when the supplier has
few competitors lends support to the view that vertical foreclosure, or another anticompetitiveeffect from vertical integration, could be occurring in the population of firms that we study. Yet,the regressions are not necessarily evidence for a causal link between mergers and the breakingof relationships, simply because mergers do not happen randomly. In particular, there are threemain confounding explanations:Firstly, it could be that the integration between the supplier and the competitor is a consequence of the relationship between buyer and supplier breaking; for instance because thesupplier’s acquirer might be concerned that the supplier would otherwise exit.19 In that caseour regression would suffer from reverse causality: integration with a competitor of the buyer
would be relatively more likely because the competitor could be purchasing exactly those goods
that the supplier is discontinuing.
Secondly, it could be that both the breaking of the relationship and the vertical integration
are the result of an unobserved shock hitting one of the firms. Such a shock would need to makethe supplier more likely to integrate with competitors of its buyers than with a non-competitor
19

Bolton and Whinston (1993) study firms’ incentives to vertically integrate for supply assurance reasons.
In this situation, “exit” does not have to be a complete exit of the supplier, but could be just an exit from aparticular market.

13

Table VI—: Interaction with the number of upstream competitors
Dependent variable: 1{LinkBreaks}bst
(1)

(2)

(3)

(4)

Supplier v. integrates w. competitor

0.557∗∗(0.171)0.449∗∗(0.159)0.323(0.340)0.241(0.309)Supp. v. int. w. comp. × # upstream comp.

-0.125∗(0.057)-0.106∗(0.053)-0.125(0.093)-0.106(0.085)

Supplier v. integrates

0.012(0.008)0.029∗∗(0.007)0.012(0.019)0.029(0.021)# upstream competitors-0.017∗∗(0.002)-0.023∗∗(0.002)-0.017∗∗(0.003)-0.023∗∗(0.003)0.063(0.046)0.056(0.040)Supp. v. int. w. competitor × # downstream competitors
Controls

Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

0.619
4728320.667
4728320.619
4728320.667
472832

Note: Controls: number of upstream customers, age of the link, dummy indicating other links of the supplier
breaking. “Upstream competitors” is the number of competitors of the supplier; “downstream competitors” isthe number of competitors of the buyer. Table reports robust standard errors, clustered at the supplier-yearlevel. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

in order to explain the different magnitude of the coefficient estimates in Table V. We discussthese alternative explanations in turn.
Thirdly, if synergies between the vertically integrating firms are very strong, the resulting
cost savings in the production of the downstream good could drive their competitors in thedownstream market out of the product market, and lead them to stop buying from the upstream
integrating firm.
We proceed to discuss the first two alternative explanations, and turn to the third one after
showing the impact of separations on sales in Section 4.

3.2

Reverse causality: vertical integration for supply assurance?

Our relationship between links breaking and vertical integration may be driven by suppliers’
motivation to exit certain product markets and cut ties with some of their customers, which
in turn may cause them to be acquired by one of their customers. We therefore apply an
instrumental variable strategy that exploits shocks that are outside of the control of the firm
and that make integration more likely. Our instrument builds on Edmans et al. (2012), whoshow that when large mututal funds experience an outflow of capital, they are forced to sell off
assets, which puts downward pressure on the share prices of firms in their portfolio. In turn,these firms become more likely to be acquired.

14

We follow Edmans et al. (2012) and Dessaint et al. (2019) and construct a variable capturing
the hypothetical (not actual) share sales of large U.S. mutual funds in response to an outflow ofinvestor capital. We first calculate the net inflow of capital to the fund based on its total net
asset holdings and returns reported in the CRSP mututal funds database. For funds j that see
a net outflow of more than five percent of its total net assets in a given quarter q, we calculate
the hypothetical sales of a stock i if holdings of all assets were reduced proportionally to theoutflow.20 More precisely, these total hypothetical sales of a stock i by large mutual funds are$
M F HSi,q=

X(Flowj,q · Sharesji,q−1 · Pricei,q−1 )j: Flowj,q <−0.05

where Flowj,q is the net inflow of fund j’s investor capital, as a fraction of total beginningof-period net assets, and (Sharesji,q−1 · Pricei,q−1 ) is the dollar value of the fund’s holdings ofstock i at the end of the previous quarter. We sum this variable over the four quarters in theyear and normalize the sum by the total trading volume in that year to obtain a normalizedmeasure M F HSj,t .
The normalized MFHS variable is meant to capture the downward pressure on prices thatis exerted by the capital outflows of mutual funds. Figure 5a shows the average response of
cumulative stock returns following a large mututal fund outflow event (defined as normalized
MFHS below the tenth percentile). Stock prices drop significantly as the shock hits and thenrecover to the pre-shock level. Figure 5b shows the response of the probability to be involvedin the completion of a vertical merger or acquisition before and after such an event. In the year
after the outflow event, the probability of integration is significantly higher. The one year lag
between outflow event and completion of the acquisition may reflect the time to negotiate theacquisition and the antitrust authority’s clearance.
This variable is useful to us because it effects an increase in the probability that a firm isbeing acquired, yet it is unlikely that the shock has a direct immediate impact on the operationsof the firm or its buyers (Dessaint et al. (2019) call them “nonfundamental”).21 While lower
stock prices may be associated with worsened access to financing and lower medium-term
investment, it seems reasonable to assume that this shock will not lead firms to sever their ties
with customers (or vice versa) other than through changes in the ownership status. Hence,these shocks make for a possible instrumental variable.
We first employ this variable in the form of an interaction. We estimate the linear model1{LinkBreaks}bst = α1{s vertically integrates}st × MFHSEventst+ β 1{s integrates vertically w. competitor of b}bst × MFHSEventst+ ηbs + µbt + εbst
where MFHSEventst is a dummy that is one if the supplier experiences an outflow event (nor20

Data on mututal fund stock holdings come from the Thomson Spectrum CDA database, and stock prices
from Thomson Worldscope. See Appendix A for data sources and definitions.
21
In Appendix B.9 we show that firms do not have lower sales during these events.

15

Vertical merger probability (de-meaned)
0
.005
.01
.015

.05

-.005

Cumulative stock returns
0
-.05
-.1
-4

-3

-2

-1

0
1
2
3
4
5
6
Quarters after MFHS outflow event

7

8

9

-2

(a) Cumulative stock returns

-1

0

1

2
3
4
5
6
Years after MF outflow event

7

8

9

(b) Vertical merger probability

Figure 5: Response to a mutual fund outflow event
Note: The figures show the average response of cumulative stock returns (vertical axis, left panel), and theaverage response of the probability to engage in a vertical merger or acquisition (vertical axis, right panel)
following a mutual fund capital outflow (defined as normalized MFHS being below the tenth percentile) at
quarter 0. The left panel is a regression of cumulative stock returns on time-since-event dummies; the rightpanel is a regression of vertical integration dummies on time-since event dummies. Both regressions containfirm and industry-time fixed effects; standard errors are clustered at the firm level.

malized MFHS variable below the tenth percentile) in periods t − 2, t − 1, or t. The coefficients
α and β capture the average difference in the probability of links breaking following supplier’s
vertical integration events that happens after outflow events, compared to the baseline as explained by fixed effects. Table VII show that these differences are similar to those of the
population of all supplier vertical integration events. Hence the vertical integration events that
follow MFHS outflows (i.e. events that are less likely to be affected by reverse causality) have
similar correlations with links breaking, suggesting that reverse causality is unlikely to explain
the increased probability of links breaking in the baseline specifications.
We now turn to an instrumental variable strategy. We have two challenges. First, the MFHS
outflows may shift the likelihood of vertical integration of the supplier, but we may also need an
instrument for the second part of the expression, namely whether the suppliers’ integration iswith a competitor of the buyer. For this we use the share of the supplier’s customers in periodt − 1 that are competitors of the buyer. A higher share of competitors among the supplier’s
customers will generally increase the likelihood that the acquiring customer is a competitor. 22
The second challenge is that the MFHS events are quite rare, potentially leading to a weak
instrument problem. To alleviate this problem we construct generated instruments from theexogenous variables through a generating regression (see Wooldridge (2010) Ch. 6.1, and inparticular Xu (2021) for an application to the weak instrument problem). Importantly, theestimates will be consistent even if the functional form of the generating regression is mis22

Like any statistic that describes market structure, this statistic may be determined by a host of characteristics, such as entry costs, technology, and demand-side characteristics. For our application it would be sufficientthat its determinants are independent of the particular shock that causes the link to break (other than throughthe suppliers’ vertical integration). Even though we cannot directly test this, it may be reassuring to know thatthe share itself does not predict links breaking conditional on the fixed effects.

16

Table VII—: Relationships breaking following Vertical Integration after MFHS events
Dependent variable: 1{LinkBreaks}bst
Supplier v. integrates (after MF outflow)

(1)

(2)

(3)

0.003
(0.021)-0.005(0.021)

0.009(0.020)0.276∗∗∗(0.080)0.223∗∗(0.084)

Supplier v. integrates w. competitor (after MF outflow)
Controls

Yes

Method

OLS

OLS

OLS

Relation FE
Buyer × Year FE
Industry Pair × Year FE

Yes
Yes

Yes
Yes

Yes
Yes
Yes

0.578
6407530.578
6407090.671
472832

R2
Observations
Note:

Controls: number of upstream customers, age of the link, dummy indicating other links of the supplier breaking.
Table reports robust standard errors, clustered at the supplier-year level. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

specified. Our baseline generating regression is a Poisson model,23
E (1{s vertically integrates}st |Z) = g(Z, λ)(3):= exp(λ1 MFHSEventst + λ2 MFHSEventst × ShareCustomersBCompetitorsbst−1 + λbs + λbt )where Z denotes the exogenous variables and λ the vector of parameters. We then use thefitted values of this regression, g(Z, λ̂), as well as g(Z, λ̂) × ShareCustomersBCompetitorsbst−1
as instruments to estimate the coefficients in (4) using 2SLS:

1{LinkBreaks}bst = α1{s vertically integrates}st(4)+ β 1{s integrates vertically w. competitor of b}bst + ηbs + µbt + εbst
If E(ε|Z) = 0, this produces consistent estimates of α and β.
Table VIII shows the results. While the standard errors are considerably larger than in thebaseline linear regressions, the difference in the point estimates for the two coefficients remains
large and possibly even increases. Estimates are similar for alternative ways of specifying thegenerating regression (3) (see Appendix B.16).
As an alternative to the IV strategy, we show results where we restrict attention to a
subsample of firms that are “healthy”, and are therefore less likely than the average firm to
cut substantial parts of their product mix. Table IX shows results of estimating equation (1)on the sample of firms that have positive sales growth between years t − 2 and t − 1 (columns(1) to (3)), or sales growth above the median of three percent (columns (4) to (6)). The point
23

The Poisson model has the advantage that the estimates are asymptotically unbiased as the number ofrelationships and time periods grows large. Appendix B.16 shows robustness to other specifications.

17

Table VIII—: Relationships breaking following Vertical Integration: Generated IV (Poisson)
Dependent variable: 1{LinkBreaks}bst
Supplier v. integrates

(1)

(2)

(3)

0.031
(0.020)0.016(0.020)0.022(0.019)0.319(0.201)0.297(0.190)

Supplier v. integrates w. competitor
Controls

Yes

MethodIVIVIV

Relation FE
Buyer × Year FE

Yes
Yes

Yes
Yes

Yes
Yes

R2
First stage F-stat
Observations

0.000
2460.292
5063730.000
17.5045063350.131
17.495506335

Note:
Controls: number of upstream customers, age of the link, dummy indicating other links of the supplier breaking.
Table reports robust standard errors, clustered at the supplier-year level. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

estimates of the coefficient on the integration with a competitor variable are larger than in ourbaseline specifications (even though the smaller sample makes the estimate less precise). Firms
that are growing are much less likely to exit product markets (Goldberg et al., 2010). For thefirms in this subsample, the causality is hence much less likely to run from the breaking of therelationship to vertical integration.
Table IX—: Regressions on relationships with “healthy” suppliers
Dependent variable: 1{LinkBreaks}bst
Sample: ∆ log Salesst−1 > 0

Sample: ∆ log Salesst−1 > median

(1)

(2)

(3)

(4)

(5)

(6)

Supplier v. integrates

0.034(0.026)0.023(0.028)0.034(0.025)0.049(0.031)0.024(0.035)0.037(0.030)

Supplier v. integrates w. competitor

0.375∗∗(0.117)0.312∗(0.129)0.212+(0.122)0.372∗∗(0.144)0.359∗(0.155)0.236(0.146)

Controls
Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

0.607
252057

0.675
191741

0.709
191741

0.616
1978110.686
1481930.720148193

Note: Columns (1) to (3) restrict the sample to buyer-supplier pairs where ∆ log Salesst−1 is above zero,columns (4) to (6) where it is above the median. Controls: number of upstream customers and competitors, ageof the link, dummy indicating other links of the supplier breaking. Number of observations exclude singletonobservations. Robust standard errors, clustered at the supplier-year level, in parentheses. + p < 0.10, ∗ p < 0.05,∗∗
p < 0.01.

18

3.3

Unobserved shocks: omitted variables

3.3.1

Comparison with rumors of mergers and acquisitions

Our next exercise speaks to the possibility that both vertical integration and the discontinuationof buyer-supplier relationships are the response to unobserved shocks. As discussed above, suchshocks must be directed to make integration with a competitor of the buyer more likely in order
to explain the correlation in the baseline tables. One could think of a market-level change intechnology which increases the need for customization of the supplied input, while also driving
some firms out of the market, causing links to break. The adopters of the new technology
and their suppliers choose to vertically integrate to reduce the inefficiency associated with the
hold-up problem (Klein et al., 1978).
We try to find a group of firms that is most comparable in terms of the shocks that they may
have been facing, but for an exogenous reason do not manage to vertically integrate. The closest
we can get to such a comparison group is by considering rumors of mergers.24 Zephyr collects
rumors from “unconfirmed reports”, which “may be in the press, in a company press release,or elsewhere” (Bureau Van Dijk, 2017). Our approach is hence similar to the comparison ofa placebo with the actual treatment in the sense that our rumor does not actually result invertical integration (but potentially with the difference that even an attempted merger may leadto buyers switching suppliers). Rumors are dated at the time when they are first mentioned.
While buyers in rumored and actual treatments are quite comparable, the suppliers in rumoredmergers are somewhat larger than the suppliers that actually integrate (see Table XVIII in the
appendix). Note that we can control for these differences in our regressions and also do notfind differential effects for larger or smaller suppliers.
We first study the benchmark specification, equation (1), with actual vertical integration
events replaced by the rumors. This specification compares the average probability of links
breaking outside of such events with the average break probability under a rumored vertical
integration, and one with a competitor of the buyer. Table X reports the results of these regressions. Links break slightly less often during rumored vertical integration with non-competitorsof the buyer, and slightly more often (though not statistically significantly so) during rumoredvertical integration with competitors. The point estimate of the coefficient on the “rumoredvertical integration with competitor” dummy is certainly much lower that the corresponding
point estimate in the benchmark regression with actual mergers (though note that the comparison is not straightforward: the dummy here is one at the rumor date, whereas it is one in
Table V on the completion date). Table XXI in Appendix B.2 shows results with both rumors
and actual integration events in the same regression.
To investigate more closely the timing aspect and to have the tightest possible comparison
between actual and rumored mergers, we compare the break probability before and after actual
mergers with buyers’ competitors to the break probability before and after rumored mergers
with buyers’ competitors. In both cases we use the date of the announcement. More precisely,
24

Another possible comparison group would be mergers that have been announced but never completed.
However, such events are very rare in our data.

19

Table X—: Links are not more likely to break following rumors of M&A
Dependent variable: 1{LinkBreaks}bst
(1)

(2)

Supplier v. integrates (rumor w/o unfinished)-0.020(0.020)-0.022(0.017)

Supplier v. integrates w. competitor (rumor w/o unfinished)

0.004(0.040)-0.003(0.035)Controls

Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes
Yes
Yes

Yes
Yes
Yes

0.586
5967460.639
596747

Note: Controls: number of upstream customers and competitors, age of the link, dummy indicating other linksof the supplier breaking. Number of observations exclude singleton observations. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗p < 0.001.

we run a regression of a binary variable that is one if the relationship is not active anymore
on a set of dummies for the number of years since announcement, separately for actual andrumored mergers (and separately by whether the merger is with a competitor of the buyer),
and including relationship, buyer × year, and sector-pair × year fixed effects.
Figure 6 shows the results. Following the announcement, break probabilities are substantially higher for actual than for rumored vertical mergers with competitors. Not only are
relationships where there is a rumor about the supplier integrating with a competitor not more
likely to break in the first period, but these relationships seem to be fairly long-lasting. To theextent that rumors are a good comparison group to actual merger events, vertical integrationand links breaking are unlikely to be driven by the same underlying unobserved shocks.

3.4

International relationships and cross-border mergers

We now turn to studying the international dimension in our regressions. Buyers with foreign
suppliers may be at higher risk of foreclosure if competition authorities do not take foreign
markets into account in their merger evaluation. Similarly, cross-border mergers, which accountfor about 20% of full vertical mergers (Table XI), may receive a different degree of scrutiny thanpurely domestic mergers. We therefore look at whether the extensive margin of (cross-border
or domestic) relationships correlates differently with integration.
Table XII shows the results. The first two columns are the same as in Table V with thedifference that we add country-pair × year fixed effects. Columns (3) and (4) include interactions with a dummy that is one if b and s are registered in different countries. Whereas thecoefficient on the interaction with any kind of vertical integration by a supplier is negative andweakly statistically significant, we do not find evidence suggesting that international relations
are more likely to become targets of foreclosure. Columns (5) and (6) include interactions with20

Probability of the relationship having broken
.6
0
.4
.8
.2

Figure 6: Probability of relationships breaking: actual vs rumored integration with competitor

0

1

2
3
4
5
Years since rumor/announcement

Actual VI with competitor

6

7

Rumored VI with competitor

Note: The figure shows coefficients on dummies capturing the years since a supplier’s rumored (dashed redline) or actual (solid blue line) vertical integration, in a regression of the probability of a buyer-seller relationshipbeing inactive on time-since-integration dummies (separately for rumored mergers with competitors, with noncompetitors, and actual mergers with competitors, and with non-competitors) as well as relationship, buyer ×
year, and industry-pair × year fixed effects. Here, time zero is the time of the rumor or the announcement of
the merger. We exclude rumors that are realized within three years.

Table XI—: Domestic and cross-border mergers and acquisitions
Non-vertical

Domestic
Cross-border
Total

Vertical

Total

Count

%

Count

%

Count

%

2,038
761
2,799

92.7
95.0
93.3

161
40
201

7.3
5.0
6.7

2,199
801
3,000

100.0
100.0
100.0

Note: Number of full mergers and acquisitions by presence of a vertical relation between the merging parties(2003-2016). M&As are counted as domestic if both merging parties are headquartered in the same country,
otherwise they are considered cross-border M&A.

a dummy that is one if the buyer that s is integrating with is located in a different country.
Their coefficients, too, are small and insignificant. International mergers seem to be no different
than domestic mergers when it comes to their likelihood of foreclosing the competition.

4

Impact on Downstream Firms

4.1

Impact on Sales

The results from the previous section show that buyer-seller relationships are more likely to
break when the downstream merging firm is a competitor of the buyer. The obvious nextquestion is: does it matter? If the input market is frictionless and perfectly competitive, the21Table XII—: International Relationships, International M&A’s
Dependent variable: 1{LinkBreaks}bst
(1)

(2)

(3)

(4)

(5)

(6)

Supplier v. integrates

0.007(0.020)0.022(0.019)0.017(0.021)

0.031(0.019)0.003(0.021)0.019(0.019)Supplier v. integrates w. competitor

0.185∗∗(0.063)0.157∗∗(0.052)0.161∗(0.065)0.148∗∗(0.055)0.180∗∗(0.066)

0.155∗∗(0.055)

S integrates × Intl. Rel.

-0.044+(0.023)-0.042+(0.023)

S integrates w. comp. × Intl. Rel.

0.101
(0.112)0.039(0.106)

S integrates × Intl. M&A.

0.072
(0.088)0.050(0.084)

S integrates w. comp. × Intl. M&A.

0.008
(0.231)-0.001(0.198)Controls

Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE
Country Pair × Year FE
R2
Observations

Yes

Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

0.636
4647050.683
4647050.636
4647050.683
4647050.636
4647050.683
464705

Note: Controls: number of upstream customers and competitors, age of the link, dummy indicating other linksof the supplier breaking. Robust standard errors clustered at the supplier-year level. The number of reported
observations is the number of non-singleton observations. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

cost to losing a supplier is zero (of course, in such a situation there is no foreclosure motive atall). If, on the other hand, the use of outside suppliers is associated with a higher variable cost,then the loss of the supplier will push the buyer along the demand curve to a point where thefirm operates at a lower scale.
We now study the response of firm sales to events where (1) a supplier of the firm vertically integrates; (2) a supplier of the firm vertically integrates with a competitor of the firm.
Specifically, we estimate the equationlog Salesbt = α1{A supplier vertically integrates}bt+ β 1{A supplier integrates vertically w. competitor of b}bt
+ ηb + µi(b)t + εbt(5)where ηb is a buyer fixed effect, and µi(b)t is an industry × year fixed effect. While our sales
variable is constructed from accounting data and is probably measured with error, this should
not bias our estimates as long as the measurement error is classical.
The first two columns of Table XIII show the results. In a year where a supplier of thefirm is integrating with a non-competitor, the firm’s sales are slightly higher; if the integration22

happens with a competitor, the sales are slightly lower than average. But this small coefficient ismasking a lot of heterogeneity. Columns (3) and (4) interact the dummy for vertical integration
with a competitor with a variable capturing the number of other suppliers from the same 3digit sector as the supplier that the firm is being cut off from (at the time of the integration).
This means that the coefficient on the “integration with competitor” variable now captures theaverage sales response for a firm that does not already have any “alternative” suppliers alreadyin place in the sector where its supplier vertically integrates.
Columns (3) and (4) of Table XIII show that the point estimates of this coefficient are large
and negative: firms that are cut off from a supplier that they do not already have an existingalternative to are suffering a large drop in sales. On the other hand, the presence of alternative
suppliers mitigates the sales impact. Note that the sales loss may capture both a movement
along the demand curve due to higher variable costs, as well as a potential loss of market share
due to the competitor experiencing cost reductions after the vertical integration. At the sametime, we see the sales drop only when a supplier vertically integrates with a competitor – sounless the cost reductions are particularly taking place in vertical integration episodes with thebuyer’s competitors, it is unlikely that this channel plays a major role in driving the buyer’s
sales response. There is also a small positive effect on sales when a supplier vertically integrates
with a non-competitor, which may point to efficiency gains from the integration. Columns (5)and (6) apply an IV strategy analogous to the one used in Table VIII. We first constructdummies whether the firm has a supplier that faces an MFHS outflow event up to two periods
before time t, and likewise take the maximum (across suppliers) of the interaction of MFHS
event dummies with the suppliers’ share of customers that are competitors of the firm. We
then regress the endogenous variables on these two variables and fixed effects using a Poisson
regression, and use the fitted values as generated instruments in a standard 2SLS regression.
The resulting coefficient estimates are similar to the OLS estimates, although somewhat lessprecise. In all specifications the model fit is very good – but that is due to the fixed effects
absorbing most of the variation in sales. In Appendix B.1 we show results with employmenton the left-hand side. We do not find a significant drop in firm employment when a supplierintegrates with a competitor.
Figure 7 shows an event study graph around the time of vertical integration of a supplierwith a non-competitor (dashed red line) and with a competitor, for firms that have no existingalternative suppliers (solid blue line). We see that in cases where the supplier is verticallyintegrating with a competitor, firms’ sales are substantially lower if they do not have existingalternative suppliers.
Finally, we look at the sales impact of suppliers’ vertical integration events in international
mergers. Table XIV shows results when we restrict attention to cross-border mergers. The
point estimates of the “integration with competitor” dummy are very similar to those in Table
XIII.

23

Table XIII—: Impact on buyer’s sales
Dependent variable: Log sales
(1)

(2)

(3)

(4)

(5)

(6)

Supplier v.integrates

0.042∗∗(0.010)0.018+(0.010)0.042∗∗(0.010)0.019+(0.010)0.036(0.025)-0.003(0.024)

Supplier v. integrates w. competitor-0.037(0.031)-0.052+(0.030)-0.137∗(0.061)-0.144∗(0.058)-0.115(0.105)-0.115(0.102)0.043∗(0.017)0.040∗(0.017)0.054+(0.031)

0.045(0.030)× log(1 + # alt. suppliers)
Buyer FE
Industry × Year FE
Controls

Yes
Yes

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Method

OLS

OLS

OLS

OLS

IV

IV

ObservationsFirst stage F-stat
R2

77202

77202

77202

77202

0.98

0.98

0.98

0.98

77202
641
0.00

77202
629
0.02

Note: Controls: number of customers, competitors and suppliers. In columns (5) and (6), the right-handside variables are all instrumented by the fitted values of a Poisson regression of endogenous variables (vertical
integration; vertical integration with competitor) on the maximum (over suppliers) of the MFHS outflow event
dummies and interactions with the share of suppliers’ customers that are competitors of the firm during periodt − 1. First stage F-statistics are Kleibergen-Paap. Robust standard errors, clustered at the firm level, are inparentheses. + p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01.

-.4

Log sales (de-meaned)
-.2
0

.2

Figure 7: Timing of the correlation of buyers’ log sales with vertical integration of a supplier

-2

-1

0
Years since merger completion

Sup. v. integrates w. competitor (no alt. suppliers)

1

2Sup. v. integrates

Note:
The figure presents the results of estimating equation 5 with two leads and lags for both1{A supplier integrates vertically w. competitor of b}bt and 1{A supplier integrates vertically}bt . Confidence
intervals are calculated using robust standard errors clustered at the firm level.

4.2

Can synergies account for breaking supplier links?

One potential alternative explanation of our finding that vertical relations are more likely to
end when the supplier vertically integrates with the buyer’s competitor is that there are verystrong synergies from the merger. If synergies give the integrated downstream firm a large cost24

Table XIV—: Sales impact: International Mergers
Dependent variable: Log sales
(1)

(2)

(3)

(4)

Supplier v. integrates (intl. M&A)

0.040∗∗(0.012)0.022∗(0.011)

0.041∗∗(0.012)0.023∗(0.011)

Supplier v. integrates w. comp. (intl. M&A)

-0.034(0.032)-0.050(0.031)-0.097+(0.057)-0.108+(0.057)0.027+(0.016)0.025(0.016)× log(1 + # alt. suppliers)
Buyer FE
Industry × Year FE
Controls

Yes
Yes

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Observations
R2

77,202
0.98

77,202
0.98

77,202
0.98

77,2020.98

Note: Controls: number of upstream customers and competitors, age of the link, dummy indicating otherlinks of the supplier breaking. Robust standard errors clustered at the firm level. The number of reported
observations is the number of non-singleton observations. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

advantage, the unintegrated downstream competitor may be forced to exit the product market,
which may lead it to cut its ties to the upstream firm.
If this explanation was driving our results, however, we would expect that vertical integration would adversely affect the market shares of all downstream firms in the industry, including
competitors that did not have a supplier relationship with the integrating upstream unit. Table
XV shows results from a regression of log firm sales on a dummy that is one if the firm has acompetitor in that year that vertically integrates (and firm and industry × year fixed effects, aswell as the set of controls from above). We find no statistically significant correlation between
a competitor vertically integrating and a change in firm sales. This stands in contrast to thesituation that we looked at above, where a competitor is vertically integrating with the buyer’s
supplier, and where we observed a drop in firm sales.
These results are in line with the findings of Blonigen and Pierce (2016), who study theeffect of mergers and acquisitions on physical productivity and markups of U.S. manufacturing
establishment. They use a similar dataset of public and private mergers and acquisitions,and find no effect of physical productivity of integrating plants, but a significant increase inmarkups. While their data allows for a much more direct investigation of the productivity
effects of mergers and acquisition than our indirect results on competitor’s sales, the results
support the view that much of the impact of M&A is to reduce competition, and little toincrease economic efficiency.

25

Table XV—: Impact of vertical integration on competitors’ sales
Dependent variable: Log sales

A competitor v.integrates

(1)

(2)

0.011
(0.039)-0.024(0.038)max(t, t − 1)

(3)

(5)

(6)

0.021
(0.035)0.013(0.049)0.054(0.036)

Yes
Yes

Controls(7)-0.039(0.060)max(t, t − 1, t − 2)
Buyer FE
Industry × Year FE

(4)

0.031
(0.045)

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes

Yes

Yes

Yes

Yes

Yes

Method

OLS

OLS

OLS

OLS

IV

IV

IV

Observations
R2

120,689
0.94

120,689
0.95

120,689
0.95

120,689
0.95

120,6890.01

120,6890.01

120,6890.01

Note: The variable in the second (third) row is a dummy that is one if a competitor has undergone a vertical
integration in the current or last year (current or last two years). Columns (5) to (7) use an instrumentation
strategy analogously to the buyer sales regressions where the instruments are the fitted values of a Poisson
regression of the vertical integration dummies on dummies for the MFHS events over the corresponding periods(and fixed effects). Controls: number of customers, competitors and suppliers. Robust standard errors, clusteredat the firm level, are in parentheses. The number of observations is larger here than in Table XIII because wehave more firms with sales data that have competitor relationships than firms with supplier relationships. +p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01.

4.3

Discussion

Interpretation
Our results suggest that existing antitrust measures have not managed to fully prevent anticompetitive effects from vertical mergers in the sample that we study. Since we find similar
results on domestic and international mergers, it does not seem to be the case that international
mergers are exposed to more scrutiny from competition authorities. Overall, we get the impression that vertical merger enforcement is lax throughout, possibly because of the intellectual
history of the question (Salop, 2017).
Our econometric exercises show the average effect of realized mergers and acquisitions in
our sample. These mergers are selected in the sense that they have not been blocked byantitrust authorities. This could be because efficiency gains were thought to be large, because
concerns for anticompetitive effects were small, because sufficient remedies could be imposed on
integrating firms, or because mergers were “below the radar” of competition authorities. This
sample is hence not necessarily representative for the population of proposed mergers, and the
expected average treatment effects may therefore differ.
At the same time, it is likely that there are many other cases of anticompetitive effects
of vertical mergers than the ones we highlight. Our events are those where a buyer-supplier
relationship fully breaks. A situation that is perhaps more prevalent is where the buyer is facing

26

higher prices offered by the bottleneck supplier. Evaluating such situations would require dataon prices.
Even if anticompetitive effects are taking place among the cases we studied, the overall
welfare consequences are not necessarily negative, in particular because consumer prices mightfall due to increases in productivity or changes in competition. Frictions in firm-to-firm markets
are likely to impose additional transaction costs, which will be reflected in the prices paid by
final consumers. A full structural analysis of the welfare cost of vertical foreclosure across a
broad range of industries is beyond the scope of this paper, but we view our reduced-form
evidence as a first step in this direction.
Mechanisms
Our empirical results point to anticompetitive effects from vertical mergers that lead to thebreaking of vertical relationships. There are several mechanisms that could give rise to such
effects, and we find patterns in the data that are consistent with their predictions.
One obvious explanation is that the integrated firm forecloses the competing downstream
firm. Such behaviour for foreclosure may be motivated from the supplier’s incentive to preserve
its market power (Hart and Tirole, 1990, O’Brien and Shaffer, 1992, McAfee and Schwartz,
1994). If an unintegrated monopolist supplier cannot commit to sell only to one downstream
firm, downstream firms will foresee the supplier’s temptation to also sell to the competitors andreject contracts that would implement the monopolist’s outcome. The supplier hence verticallyintegrates to solve the commitment problem and restore monopoly profits. Higher upstream(downstream) competition makes the commitment problem less (more) severe, which is broadly
in line with our results by upstream/downstream market competition shown in Table VI.
A second mechanism where anticompetitive effects from vertical integration arise is the“raising rivals’ cost” mechanism of Ordover et al. (1990). By cutting off the competitors’
access to the bottleneck input, the integrated downstream firm can raise the market powerof unintegrated alternative suppliers and thereby harm the competitors. Allain et al. (2016)
describe a related mechanism where vertical integration leads to increased hold-up problems(and therefore increased cost) for competitors. Results in Appendix B.11 show that buyer sales
decrease after vertical integration of the supplier with a competitor, even when the vertical
relationship with the supplier does not break.
Another possibility is that firms self-foreclosure to prevent information leakage when asupplier integrates with a competitor. Allain et al. (2011)25 formalize this mechanism in amodel where firms, in order to innovate, must share information with their suppliers, whichcannot be fully protected by intellectual property rights. This information, if shared with avertically integrated competitor, increases the risk of any innovation becoming available to thecompetitor. As a result, the effective cost of sourcing from an integrated supplier is higher, anddownstream firms may find themselves wanting to break a pre-existing supplier relationship.
Anticompetitive effects may exist if that cost increase is associated with higher effective cost25

See also related work by Hughes and Kao (2001) and Chen (2001).

27

of production (e.g. because unintegrated alternative suppliers have higher market power); thiswill then lead to lower innovation by the downstream firm.
Examples from newspaper coverage and SEC filings associated with integration events in ourdata corroborate the existence of such self-foreclosure considerations, where customers of theintegrating supplier want to break the relationships because continuing the relationship would
be associated with a strategic disadvantage on the output market. As an example, consider
the acquisition of hard drive disk platter producer Komag by its customer Western Digital(WF) in 2007. Komag had also been supplying WD’s rivals Seagate, Maxtor, and Hitachi,
and these relationships ceased after integration. In a conference call with market analysts,
a senior executive from WD said about Komag’s future relationships with their existing customers: “[...] we are prepared to provide all customers with the committed volumes outlined intheir existing volume purchase arrangement. However, customers will determine their [input]
requirement. Therefore, there could be a significant reduction in volume from those customers[...].” (Securities and Exchange Commission, 2007).

5

Conclusion

This paper presents results that suggest that anticompetitive effects from vertical mergers are
occurring among large firms, across a range of sectors in the economy, and both for domestic
and international mergers. These anticompetitive effects operate at least to some extent on theextensive margin of buyer-supplier connections: vertical relationships are much more likely to
break when the supplier is integrating with a competitor of the buyer, than when the supplieris integrating with an unrelated party. We find that this higher hazard rate for links breaking
remains statistically significant when considering integration events that occur following exogenous downward pressure on the suppliers’ stock price. Rumored integration that never takes
place is not associated with higher hazard rate. We find that on average firms whose suppliervertically integrated with one of their competitors experience a temporary drop in sales. This
sales drop is lower for firms that have relationships with other suppliers from the same industry
in place.

28

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Appendix
A

Data Sources and Definitions

We combine three components to construct the database used in this paper:• A production and competitor network between large firms from FactSet Revere• A comprehensive M&A database, Bureau van Dijk’s Zephyr, with information on deals
and rumors about deals• Company financials and industry classifications from Bureau van Dijk’s Orbis, Compustat
and FactSet Fundamentals
This appendix describes each of the data sources as well as the key variables we derive from
them.

A.1

FactSet Revere supply chain data

Content and data sources
FactSet is a commercial data provider that mainly sells to companies in the financial services
sector. Its supply chain data (called “Revere”) provides information on the nature and durationof vertical and horizontal relationships between firms. FactSet collects this information on
relations from primary public sources such as SEC filings, investor presentations, corporate
actions, company websites and press releases. For each firm, FactSet conducts an annual
review to update the database. In addition, press releases and corporate actions are monitored
daily for US firms.
Each relation between two companies is dated with a start date at which the relation was
first recorded by FactSet and with an end date at which it was noticed that the relation no longer
existed. In addition, each relation is categorized into buyer links, supplier links, competitor
links or partnerships. These broad categories are detailed into 13 subcategories (see Table
XVI). We use these categories to define two types of networks:• Buyer-supplier network: a directed graph on which an edge is created when the target
company is a supplier of the source company, i.e. at least one of the following is true:– the source company discloses the target company as a supplier of products or services– the target company discloses the source company as a customer of products or services– the target company provides paid manufacturing, distribution or marketing services
to the source company– the target company licenses products, patents, technology or IP to the source company
34

Table XVI—: Number of relationships in raw FactSet Revere data
Frequency

Percentage

Supplier
Competitor
CustomerPartner: Distribution
Partner: Equity investment
Partner: Production
Partner: Investor
Partner: Joint-VenturePartner: Licensing
Partner: Marketing
Partner: Other
Partner: Research Collaboration
Partner: Technology

114,136
197,423
290,893
24,72553,602
12,737
48,24429,84537,083
16,296876
46,273
26,189

12.71
21.98
32.38
2.75
5.97
1.42
5.37
3.32
4.13
1.81
0.10
5.15
2.92

Total

898,322

Note: Frequency table of the raw number of relations in the relationship dataset from which we construct thefirm network. In line with the description in the documentation of the data, we count companies providing paid
distribution, production, marketing and licenses as suppliers.

• Competitor network: an undirected graph on which an edge is created if at least one of
the two company discloses the other one as a competitor
We do not include the partnership links provided by fact set for our analyses (Joint ventures,
Equity stakes, research collaborations and integrated product offerings).
Finally, many relations are also provided with a few keywords explaining the links, though
not in a fully systematic fashion. Companies can have multiple links, for instance in order todocument that a supplier is also in competition with a given customer.
Coverage
The data contain observations on 180,192 firms, some of which are “covered” companies (in thesense that the data provider actively searches for information on these firms); the others show up
as suppliers, buyers, or competitors of covered companies. FactSet determines coverage mainly
based on membership of firms in major stock indexes. The provider aims to cover all companies
listed in a set of global indexes, such as the FTSE Global All Cap, Russel Global, Stoxx Global
and a range of global MSCI indexes. In addition, all US-based publicly traded firms are covered,as well as companies that are part of multiple local and regional stock market indexes, i.e. large
non-US multinationals. FactSet achieves high but not complete coverge of the indexes. For
example, 90.3% of the firms in MSCI ACWI All Cap have relationship information, 95.4% of
the S&P 500 and 94.5% of the Russell 3000. While these coverage rules favor large listed firms,
there are many smaller and non-listed firms in our sample because they have relationships withlarge firms.
Coverage varies by country. Figure 8 reports the number of firms in the database by thecountry of their headquarters. Consistent with the fact that FactSet originally only covered US
35

Figure 8: Number of firms by countryUS45,109CN

15,152

JP

9,204

GB

9,141

DE

6,011

CA

5,059

IN

4,910

AU

4,416

KR

4,267

FR

3,580

Other

59,417N.A.

13,9260

20,00040,00060,000

Number of firms

Note: The figure reports the number of firms in the FactSet database by country of headquarter.

firms, about a quarter of the firms is based in the US. Due to efforts to expand the database internationally starting in 2007, and because of foreign firms trading with US firms, international
coverage goes well beyond large multinationals.
Figure 9: Number of firms by industry

Agriculture
Mining, Oil & Gas Extraction
Utilities
Construction
Petr., Plastic & other Chemical.
Computer & Electronics Man.
Other Manufacturing
Wholesale Trade
Retail trae and Transportation
Information
Fin. Services, Insurance & RealProf., Sci. & Techn. Services
Management
Admin. & Support Services
Other Services

790
4,168
2,818
4,429
7,425
9,489
19,373
10,8658,749
5,698
16,054
12,115
6,911
4,420
7,967

0

5,000

10,000
Number of firms

15,00020,000Note: The figure reports the number of firms in the FactSet database by primary industry classification.

While the database is not representative even of the universe of US firms, it does containa wide range of industries. Figure 9 reports the number of firms in the sample by a high-level
aggregation of NAICS industry codes. The manufacturing sector contains the largest shareof firms in our data, followed by financial services and insurances, and then by professional
services.
36

When a company entity in the data ceases to exist, FactSet documents the reasons for it,
along with a successor company where it exists. This fact allows us, in particular, to identify
the successor company in the case of a complete merger or acquisition so that links are notmechanically breaking at acquisition.

150000Figure 10: Number of firms over time

100000117,694# Firms

99,51076,79450,00053,05556,20447,851
33,25918,145

19,18020,015

20,79020,52521,803

2003

2004

2005

2006

2007

2008

2009

0

18,662

2010

2011

2012

2013

2014

2015

2016

Note: The figure presents the number of firms in the FactSet database by year.

The data start in 2003 and have been gradually expanded over time (Figure 10). Non-US
firms were included from 2007 onwards.
Key variables
We annualize the relationship data in order to facilitate the matching with the company financials. A relation of any kind is counted as active in a given year if there is at least one day
between start date and end date of the relation that falls into that year. The result is a panelof relations that is identified by source company, target company and year.
Buyer-supplier link breaks: The variable is one if and only if (i) the relation was active inthe previous year but is no longer active and (ii), in case buyer and/or supplier were involved ina merger or acquisition, there is no active link between the successor company or acquiror andthe buyer or supplier. The second condition rules out purely mechanically breaks in the supply
chain that could result from mergers and acquisitions. This variable is the main left-hand side
variable in the regressions in Section 3.
We set this variable to missing in a few cases to avoid other possible mechanical breaks.
If a buyer has dropped out of coverage and, in case of a merger or acquisition, the successor
company or acquiror is not covered by FactSet in the current year, then its relations are notcounted as breaking. This is to rule out that we erroneously count a link as broken purely
because a firm is no longer covered. We also count the variable as missing when the buyer andthe supplier in the given relation are integrating.

37

A.2

Zephyr M&A data

We use Bureau van Dijk’s Zephyr database for information on mergers and acquisitions. Zephyrrecords deals and rumors about deals for mergers and acquisitions in which at least a 2% stakein the target company changes owners and the value of the deal exceeds GBP 1M. For an
overview of Zehphyr’s content, coverage, and how it compares to other M&A databases, see
Bollaert and Delanghe (2015). For the sake of brevity, we refer to any merger or acquisition
simply as merger in the following.
Matching and merging with other data sources
Zephyr reports the exact dates of rumors, announcements and (expected) completions or withdrawals of mergers. Analogously to the FactSet data, we convert these data to a panel ofmerger events, where each observation is identified by the target firm, the acquiring firm andthe calendar year of the completion date for completed mergers or the year of the rumor formergers that were rumored but never completed.
We match firms in the FactSet and Zephyr databases using security identifiers such as
CUSIP or ISIN, as well as ticker names wherever possible. For the remaining firms we use astring matching tool provided by Bureau van Dijk that takes into account company names and,
where available, addresses.
Table XVII—: Types of mergers and acquisitions
VerticalPartial acquisitions
Full acquisitions & mergers
Total

Horizontal

Both

Unrelated

Total

Count

%

Count

%

Count

%

Count

%

Count

%

46
132
178

5.8
4.4
4.7

51
568
619

6.4
18.9
16.3

8
69
77

1.0
2.3
2.0

694
2,231
2,925

86.9
74.4
77.0

799
3,000
3,799

100.0
100.0
100.0

Note: Number of partial and full mergers and acquisitions by presence of a vertical and horizontal relation
between the merging parties (2003-2016). Partial acquisitions exclude minority stakes.

Table XVII breaks down the mergers and acquisitions between firms in the matched sample
by the type of their relation in the FactSet Revere data. In addition to vertical mergers, the data
allow us to identify horizontal mergers (through competitor relationships) and mergers that areboth horizontal and vertical in nature. In our analyses, however, we focus on integrations thathave a vertical dimension to them.
Table XVIII reports summary statistics about buyer-supplier relations where the supplier
was vertically integrating or rumored to be vertically integrating with a competitor of the buyer.
While the buyers in both groups are quite comparable, it seems that rumors involve suppliers
that are on average somewhat larger than those suppliers which actually undergo integration.
Note that we control for these differences in our regressions and also do not find a differential
effect for larger or smaller suppliers.

38

Table XVIII—: Treated buyer-supplier relations and placebo counterparts
Vertical M&A with Comp.
Actual M&A

Rumored M&A

Difference

New relationships
Ending relationships
Buyer’s suppliers
Supplier’s buyers
Buyer’s competitors
Supplier’s competitors
Age of relationship
Sales (log m$): Buyer
Sales (log m$): Supplier
Sales (log m$): Competitor
Log Employment: Buyer
Log Employment: SupplierLog Employment: Competitor

0.10
0.52
69.40
27.67
66.74
19.49
3.49
8.26
6.92
9.33
9.23
6.96
10.33

0.14
0.16
78.18
48.44
68.13
36.32
3.97
8.96
8.78
9.78
9.84
9.47
9.87

0.04
-0.36∗∗∗
8.78
20.76∗∗∗
1.39
16.82∗∗∗
0.48
0.70∗∗
1.87∗∗
0.45∗∗
0.61∗
2.51∗∗-0.46∗

N

214

222

436

Note: Summary statistics for the buyer-supplier-years for which suppliers are involved in a vertical M&Atransaction with a competitor of the buyer.

Key variables
With the firm network and the merger information in place, we construct our main explanatory
variables. For ease of exposition, for a given buyer-supplier-year observation, we refer to thebuyer as firm b, to the supplier as firm s and to a firm that merges with the supplier as firm c.
Supplier vertically integrates: We construct a dummy variable that is equal to one at thebuyer-supplier-year level if firm s is involved in a merger with firm c which is also a customerof s. We restrict attention here to full mergers and acquisitions in the sense that the stake ofthe acquirer after the acquisition is 100% but was either zero or unknown before. Firm s canbe either the acquirer or the target in the M&A with firm c. Note that we only count mergers
as vertical if there was an active buyer-supplier relationship between s and c in the year of
integration.
Supplier vertically integrates with buyer’s competitor: This dummy variable is equal to one
at the buyer-supplier-year level if firm s and c are merging, s is an active supplier of c in thatyear and b and c have an active competitor relationship in that year.
For the placebo analyses we construct the same variables again using rumored mergers
instead of actual mergers. These rumors come from “unconfirmed reports”, which “may bein the press, in a company press release, or elsewhere” (Bureau Van Dijk, 2017). They may
indeed come from announcements by one of the involved firm as long as the other firms have
not yet confirmed the announcements. In the Zephyr database, this corresponds to deals forwhich the variable deal status is “Rumour”. The timing of these events differs slightly: insteadof the completion date (which is unavailable), we use the rumor date. In general however, there
is little time elapsing between a rumor and the completion of a deal: 145 days on average andabout 92% of rumors which turn out to be true are realized within a year. For our placebo39

analyses, we exclude all rumors that materialize within three years.

A.3

Company financials and industry classifications

To achieve best possible coverage of company financials and industry classifications for the firms
in our supply chain network, we combine data from Orbis, Compustat (through WRDS) and
FactSet Fundamentals. The combination of the various data sources is necessary in particular
because of varying coverage over time. While we have supply chain and merger information
available from 2003 to present, Orbis data is only available to us from 2007 onwards. In contrast,
Compustat and FactSet Fundamentals are available for earlier years as well.
Matching and merging with other data sources
As with the Zephyr database, we first match all firms for which securities identifiers are available. As Zephyr and Orbis share the same identifier, matching these data sources is straightforward. For the remaining firms and data sources we use the company names for string matching.
For firms where financials are available from multiple data sources, we only retain theinformation from the data source that provides the longest coverage of the sales variable of thatfirm. Hence, all of a given firm’s financial information always come from the same data source inorder to ensure consistency over time and across items. Wherever ties occur, preference is given
first to FactSet Fundamentals, then to Orbis. Note that the variables from several datasets arealmost perfectly correlated for the observations where we do have overlaps in coverage.
Key variables
Sales: The sales data are contained in the variables “ff sales” in FactSet Fundamentals, “sales”
in Orbis and “sale” in Compustat. Orbis reports all financials directly in USD, the sales
data from the other data sources are converted to USD where necessary using exchange rate
information included in those datasets. A few firms in the data exhibit unusual sales trajectories
that seem to suggest reporting or data entry issues. In order to rule out that our results are
driven by such observations, we exclude firms whose sales growth falls into the first or 99th
percentile in one or more years.
Employment: The number of employees is contained in the variables “ff emp” in FactSet
Fundamentals, and “emp” in Orbis and Compustat. We use these variables without further
processing.
NAICS codes: From Orbis and Compustat we can also retrieve NAICS industry codes(“naics primary” and “naics secondary” in Orbis, “naics” in Compustat). When several NAICS
codes are available, we restrict attention to the primary one for clustering or aggregation.
A.3.1

Mutual fund capital outflow instrument

To construct the MFHS instrument, we follow Appendix C of Dessaint et al. (2019) (which inturn is based on the construction in Edmans et al. (2012)). We construct quarterly capital net
40

outflows of US mutual funds using the CRSP mutual funds data, and the hypothethical stock
sales following large outflows using the funds’ portfolio data in CDA Spectrum/Thomson. We
match funds using the crosswalk provided by WRDS.
The CRSP mutual funds data reports monthly return and total net assets by asset classk. We first compute the return of fund j in month m of year t as the weighted average returnacross asset classes:
P(T N Ak,j,m,t × Returnk,j,m,t )P
Returnj,m,t = k
k T N Ak,j,m,t
We compound these returns to arrive at quarterly returns. The net inflow into fund j in quarterq of year t, as a fraction of its beginning-of-quarter net assets, is thenF lowj,q,t =T N Aj,q,t − T N Aj,q−1,t × (1 + Returnj,q,t ).
T N Aj,q−1,tFrom CDA Spectrum/Thomson we have share holdings Sharesj,i,q,t of each fund j in firmi at the end of quarter q of year t, as well as share prices. We calculate the hypothetical salesof j’s assets in i for funds j that experience large outflows (F lowsj,q,t < −0.05):
X$
M F HSi,q,t=(Flowj,q · Sharesji,q−1 · Pricei,q−1 ) .

j:Flowsj,q,t <−0.05

Finally, we sum this up over quarters and normalize by trading volume
M F HSi,q,t =4$X
M F HSi,q,t
q=1

A.4

V olumei,q,t.

Summary statistics for vertically integrating firms

Table XIX shows summary statistics for firms that vertically integrate with one of their buyers.
The left column contains firms that integrate with non-competitors of one of their buyers, theright column contains firms that integrate with a firm that is not in a competitor relationshipwith any other buyer.

B

Further results

B.1

Impact of foreclosure on employment

Table XX shows the impact of a supplier integrating with a competitor on firm employment.
The OLS results are similar to sales, though somewhat smaller (about half of the percentagewise effect on sales) and not statistically significant. IV estimates are not significantly different
from zero either. Overall, there does not seem to be an impact of foreclosure on employment.

41

Table XIX—: Summary statistics for vertically integrating firms
No foreclosure potential

Foreclosure potential

Mean

SD

Mean

SD

# Buyers# Suppliers# Competitors
Log sales
Log employment

45.03
45.34
45.45
15.50
9.37

69.12
66.11
65.84
2.04
2.23

18.51
15.38
17.57
13.31
6.79

15.29
16.81
12.50
2.29
2.23

Observations

150

53

Note: The table presents summary statistics on suppliers that vertically integrate with a non-competitor of
the buyer (first column) or a competitor of the buyer (second column). Coverage of sales and employment isvery poor (<10%) for suppliers in vertical integrations with foreclosure potential.

Table XX—: Impact on buyer’s employment
Dependent variable: Log employment
(1)

(2)

(3)

(4)

(5)

(6)

Supplier v.integrates

0.029∗∗(0.010)0.010(0.010)0.029∗∗(0.010)0.010(0.009)-0.010(0.025)-0.042+(0.025)

Supplier v. integrates w. competitor-0.015(0.053)-0.029(0.052)-0.078(0.101)-0.087(0.099)-0.104(0.126)-0.107(0.123)0.027(0.024)0.025(0.024)0.040(0.035)0.033(0.034)× log(1 + # alt. suppliers)
Buyer FE
Industry × Year FE
Controls

Yes
Yes

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Method

OLS

OLS

OLS

OLS

IV

IV

Observations
70983
70983
70983
70983
70983
70983
R2
0.98
0.98
0.98
0.98
-0.00
0.01
Note: Controls: number of customers, competitors and suppliers. Robust standard errors, clustered at thefirm level, are in parentheses. In columns (5) and (6), the right-hand side variables are all instrumented by
the fitted values of a Poisson regression of endogenous variables (vertical integration; vertical integration withcompetitor) on the maximum (over suppliers) of the MFHS outflow event dummies and interactions with theshare of suppliers’ customers that are competitors of the firm during period t − 1. + p < 0.1, ∗ p < 0.05, ∗∗p < 0.01.

B.2

Direct comparison of the rumored vs actual mergers

Table XXI shows regressions with both rumored and actual integration events included. This
allows for a direct comparison of the two types of events in the same specification. While themain coefficient of interest – the impact of an actual vertical merger of the supplier with thebuyer’s competitor – remains large and significant, the rumored counterparts of these events
have slightly negative and statistically not significant coefficients.

42

Table XXI—: Comparison of rumored and actual vertical mergers with competitor of the buyer
Dependent variable: 1{LinkBreaks}bst
(1)

(2)

(3)

Supplier v. integrates

0.018(0.020)0.010(0.020)0.025(0.019)Sup. v. integrates, rumor about competitor-0.031(0.043)-0.025(0.074)-0.042(0.067)

Supplier v. integrates w. competitor

0.206∗∗(0.071)0.201∗(0.095)0.189∗(0.083)Controls

Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

0.578
6407090.619
4728320.671
472832

Note: Controls: number of upstream customers and competitors, age of the link, dummy indicating other linksof the supplier breaking. Robust standard errors clustered at the supplier-year level. The number of reported
observations is the number of non-singleton observations. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

B.3

Hazard models

An alternative way to model the impact of a supplier’s vertical integration on the probability ofa buyer-supplier link breaking is in terms of a hazard model. In this framework, we can compare
the survival times of links where the supplier integrated with a competitor of the buyer to thesurvival times of links where there was a vertical integration with a non-competitor. When alink does not break during the observation period, the survival time is treated as censored.
Table XXII presents the result of a Cox proportional hazard model estimated on all buyersupplier links in which the supplier vertically integrated with one of its customers:hbs (t) = h0bs (t) exp β 1{s integrates vertically w. competitor of b}bs + ηb + ηt + ηi(b) + ηi(s)where hbs (t) is the hazard of a buyer-supplier link breaking and ηb , ηt , ηi(b) and ηi(s) are indicator variables for buyers, integration years, the buyer’s industry and the supplier’s industry
respectively. We use a partial likelihood framework that does not require to specify the baseline
hazard h0bs (t).26 The survival time is measured from the beginning of the year of integration
to the end of the year in which the relationship broke. Columns (1) to (3) retain all integration
events, even when a given buyer-supplier link undergoes multiple vertical mergers of the supplier. Columns (4) to (6) only keep the first of these events for each buyer-supplier link. We
calculate standard errors clustered among observations related to the same vertical integration
event.
The results confirm those presented in Section 3. Even after adding controls and including
buyer, year and industry dummies, a vertical relationship is expected to end 28% sooner when26

The results are similar when specifying an exponential or Weibull distribution.

43

the supplier vertically integrates with one of the buyer’s competitors compared to when vertical
integration occurs with a non-competitor of the buyer. The mean survival time of relations
is 4.3 years. While the point estimates vary slightly, the qualitative result that links threatened by vertical foreclosure are substantially more short-lived is robust to choosing alternative
specifications of the model and the underlying sample.
Table XXII—: Impact of supplier’s integration with buyer’s competitor on hazard rate of buyersupplier link breaking
Hazard ratio of buyer-supplier link breaking(1)
Supplier v. integrates w. competitor

(2)
∗∗

1.432
(0.115)

(4)
∗

1.231
(0.110)1.256(0.115)

Yes

Yes

Yes

Yes

All

All

Yes
Yes
Yes
All

Controls
Year FE
Buyer Industry FE
Supplier Industry FE
Events

(3)
∗

(5)
∗∗

(6)

1.197
(0.109)1.205∗(0.113)

Yes

Yes

Yes

Yes

First

First

Yes
Yes
Yes
First

1.399
(0.126)∗

R2
Observations
7031
7031
6308
5532
5532
4935
Note: Controls: number of upstream customers and competitors, age of the link. Robust standard errors
clustered at the supplier-year level. + p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01.

B.4

Within-merger identification

Our baseline strategy is to compare the probability of relationships breaking across integration
events, i.e. integrations with competitors versus non-competitors. An alternative strategy is toinstead look within suppliers’ integration events (call the supplier S), across different buyers
B, and compare the probability of links breaking by whether B is a competitor of the acquirer.
This is done using supplier-year fixed effects. The advantage is that the regression absorbs
unobserved heterogeneity at the supplier-year level; the disadvantage is that it is incompatible with our IV strategy, since the exogenous variation that we exploit there is exactly at the
supplier-year level.
Table XXIII shows regressions with supplier-year fixed effects. Column 1 shows the same
benchmark specification as column 3 of Table V but with supplier-year fixed effects. The estimated coefficient, which is close to zero, hides a lot of heterogeneity. Some (very few) vertical
integration events result in the breaking of many of the supplier’s sales relationships (whichsuggests a large change in the scope of the firm’s activities following integration), others—
the majority—result in few links breaking (see Figure 11 for the distribution of customer links
breaking following vertical integration). When we condition of vertical integration events where
many (more precisely, more than 40% in column 2) of the suppliers’ sales relationships break,44

we find a large negative coefficient. On the other hand, when we only look at vertical integration events where few relationships break (less than 40%, 20%, 10% in columns 3, 4, and 5),
the coefficient is large and positive, like in the benchmark regressions.
We think of this result in the following way. Some vertical integration events are associatedwith large changes in product scope. In these situations, the integrated firm may sell off somedivisions, and stop serving certain buyers. Buyers that are, however, in the “core” of the suppliers’ operations and are therefore less likely to stop being served are also more likely to becompetitors to the acquirer. This explains the large negative coefficient in column 2 of TableXXIII. On the other hand, vertical integration events where few relationships break are thosewhere firm scope remains unchanged, and in those cases buyers that are vs are not competitorsof the acquirer are more comparable.
Table XXIII—: Vertical integration and relationship break probability – Supplier-Year Fixed Effects
Dependent variable: 1{LinkBreaks}bst
(1)

(2)

(3)

(4)

(5)

-0.002(0.056)-0.149+(0.082)0.086(0.062)0.156∗∗(0.059)0.178∗(0.072)

Supplier × Year FE
Relation FE
Buyer × Year FE
Industry Pair × Year FE

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

VI break threshold

all

> 40%

< 40%

< 20%

< 10%

0.779
4025260.780
4025260.779
4025260.780
4025260.780
402526

Supplier v. integrates w. competitor

2

R
Observations
Note:

Specification varies by which vertical integration events are being considered in the independent variable. Column 1 uses all vertical integration events (as in baseline regressions). Column 2 uses only vertical integration
events where at least 40% of the supplier’s relationships break; column 3 uses only vertical integration events
where less than 40% of the supplier’s relationships break. Columns 4 and 4 lower this threshold to 20 and 10
percent, respectively. The Table reports robust standard errors, clustered at the supplier-year level. + p < 0.10,∗
p < 0.05, ∗∗ p < 0.01.

B.5

Alternative relationship definitions

As an additional robustness check, we repeat the baseline regressions with an alternative definition of the buyer-supplier network. In particular one might be worried that relationships break
only temporarily and are ultimately reestablished so that the effects we attribute to vertical
foreclosure are only transitory. To address this concern, we can remove gaps from buyer buyersupplier relations. We count a relation as active in a given year if it has been reported active
in previous and in future years, even when it is currently not reported to be active. Here, alink is not counted as breaking if it reforms at a later date.

45

0

2

Density
4

6

8

Figure 11: Fraction of supplier’s customer relationships breaking following vertical integration

0

.2
.4
.6
.8
Fraction of suppliers' relationship breaking following vertical integration

Note: The graph shows a histogram of the fraction of the suppliers’ customer relationships that break following
vertical integration.

Figure XXIV shows that the baseline results are robust to using this alternative definition.
The coefficients on the variable representing a merger of a supplier with the buyer’s competitor
remains statistically significant throughout and is even slightly larger than in the baseline
results. This means that the correlations are not driven by brief pauses in a relationship which
ultimately resumes.

B.6

Firm entry in the upstream segment?

Do vertical mergers with foreclosure potential deter entry of firms in the upstream segment? To
investigate this question we count the number of suppliers that enter an industry as measured
by three-digit NAICS cells in a given year and relate it to whether or not there has been
a potential foreclosure event in that industry. This exercise is more tentative in nature forthree main reasons: First, NAICS codes are a relatively crude measure of the upstream market
that does not account for product or geographical differentiation. Second, the industry codes
reported in our data are time invariant, meaning that we cannot capture existing firms moving
into new product markets. Finally, as we document above, our sample consists mainly of large
firms and we may therefore not be able to detect changes in the entry patterns of small firms.
Notwithstanding these caveats, we estimate the following regression:
LogEntryit = β 1{P otential f oreclosure}it + ηi + ηt + εit
where LogEntryit is defined as the log of one plus the number of suppliers in industry i thathave at least one customer in year t but did not have one the previous year.
Table XXV reports the results of this regression. We measure potential foreclosure in two
different ways. The first approach is a dummy indicating whether a supplier in the industry had
46

Table XXIV—: Extensive margin regressions with stable buyer-supplier relations
Dependent variable: 1{LinkBreaks}bst
Supplier v. integrates

(1)

(2)

(3)

(4)

0.012
(0.008)0.008(0.007)0.006(0.007)0.011(0.007)0.186∗∗(0.046)0.204∗∗(0.050)0.186∗∗(0.045)

Supplier v. integrates w. competitor
Controls
Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

0.434
33512070.434
33511880.451
25210970.492
2521097

Note: Controls: number of upstream customers and competitors, age of the link, dummy indicating other linksof the supplier breaking. Robust standard errors clustered at the supplier-year level. The number of reported
observations is the number of non-singleton observations. The drop in the number of observations in columns(3) and (4) is explained by firms with missing industry codes. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

a merger with foreclosure potential, i.e. the supplier merged with a buyer whose competitor it
also supplied (columns 1-2). The second approach is a dummy indicating that a merger with
foreclosure potential coincided with a break of the buyer-supplier link with the downstream
competitor (columns 3-4). While the estimates are quite noisy and not statistically significant,the point-estimates are negative throughout. Perhaps foreclosure events have a negative impact
on firm entry in the upstream industry. Because of the caveats mentioned above and the factthat estimates are not very precise, we hesitate to draw conclusions.
Table XXV—: Firm entry and vertical integrations with foreclosure potential
Dep. var.: log(1 + #entering suppliers)it(1)
V. integration w. foreclosure potential(2)-0.032(0.111)× buyer-supplier link breaks

(3)

(4)

-0.079(0.092)-0.045(0.130)Controls-0.110(0.107)
Yes

Yes

Year FE
Industry FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

R2
Observations

0.891
1236

0.891
1236

0.926
1236

0.926
1236

Note: Controls: number of buyer-supplier relations and number of suppliers in a given industry-year.
+p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

47

B.7

Relationship severance prior to integration?

We revisit the main regression presented in section 3 in order to investigate whether buyersupplier relations are more likely to end already in the year before the supplier verticallyintegrates. If there was reverse causality for supply assurance reasons for instance, we might
expect to find a positive correlation in the year before integration actually takes place. We
repeat the baseline specification but replace the right-hand side variables by dummies that are
one if a time-t competitor of b vertically integrates with s at time t + 1.
Table XXVI presents the results of this exercise. We find no evidence that relationships are
already more likely to break in the year preceding vertical integration. Quite to the contrary,these relationships are substantially less likely to break. This correlation is not mechanical andin particular persists when restricting the sample to relations with suppliers that keep at least
one customer in year t + 1 when the integration takes place.
Table XXVI—: Hazard of links breaking in year before vertical integration
Dependent variable: 1{LinkBreaks}bst
Supplier v. integrates in t+1

(1)

(2)

(3)

(4)

-0.031(0.020)-0.022(0.020)-0.008(0.019)0.008(0.019)-0.186∗∗(0.041)-0.174∗∗(0.043)-0.151∗∗(0.043)

Supplier v. integrates w. competitor in t+1
Controls

Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes
Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

0.576
638681

0.576
6386810.616
4707880.670
470788

Note: Controls: number of upstream customers and competitors, age of the link, dummy indicating other linksof the supplier breaking. Robust standard errors clustered at the supplier-year level. The number of reported
observations is the number of non-singleton observations. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

B.8

Robustness for interactions with number of competitors

Table XXVII shows robustness checks of the specification in Table VI with bins for the numberof the supplier’s and the buyer’s competitors.
Table XXVIII shows regressions only on the subsample of active buyer-seller relationships
where the number of competitors of the supplier is positive (all columns), and where the numberof competitors of the downstream firm is positive (columns 3 and 4). In this table, we also use
log(#competitors) instead of log(1 + #competitors).

48

B.9

Direct impact of mutual fund outflow events on firms?

In Table XXIX we show that sales performances of firms are no different during mutual fundoutflow events, supporting the view that outflow events do not have a direct short-term impact
on the operations of firms. We regress firm sales on dummies describing MFHS events as in
the IV specification (1 iff an outflow event in time t, t − 1, or t − 2). We find no statisticallysignificant drop in firm sales following such events.

B.10

Is foreclosure a merger motive?

The correlations presented in the body of the paper are consistent with theories of verticalmarket foreclosure. That said, even if the timing of a vertical integration of the supplieris exogenous, the party with whom the supplier integrates may not be unrelated to firm or
market structure: an acquirer that senses a foreclosure opportunity may be willing to pay a
premium, and is therefore more likely than alternative bidders to be the winning bidder.
To study whether vertical foreclosure is a merger motive, we run the regression1{b integrates with s}bst = α1{b has a competitor that is supplied by s}st
+ ηst + εbst

(6)

on the sample of active buyer-supplier relationships (b, s) at time t when the supplier s isundergoing a vertical integration with one of its customers. Competitor status here refers tobefore the merger, i.e. at time t − 1. The coefficient α tells us whether buyers that have acompetitor that is also a customer of the supplier are more likely to be the one that is integratingwith the supplier — conditional on the supplier vertically integrating. These buyers potentiallyhave a motive to foreclose their competitors.
Table XXX shows the results. The point estimate of α is positive and statistically significant.
Given that the unconditional probability of being the integrating party in this sample is about
three percent, having a foreclosure motive is associated with a roughly 55% higher probabilityof being the firm that integrates with the supplier. In column (2) we control for the buyer’s (logof one plus the) number of suppliers and competitors (again at time t−1), which proxies for size
and alleviates the concern that buyers with a competitor among the seller’s customers are justthose that are larger. In column (3) we include dummies for the buyer’s industry times year,
to control for industry-time-specific shocks. Neither of these controls affect the estimate of αmuch. Hence, firms that have a foreclosure motive (in the sense that s is also supplying theircompetitor) are more likely to be the integrating party at a time when s vertically integrates.
Columns (4) and (5) show that the correlation is weaker the more competitors the supplier has
at time t − 1.

49

B.11

Extensive vs. intensive margin in the anticompetitive effects

In Table XXXI we regress log sales on dummies for vertical integration of the supplier (andwhether it’s with a competitor) like in the baseline sales impact regression, but also including aninteraction with a dummy that is one if the relationship breaks following integration (the outcome in equation (1)). Hence, we look at whether there is a sales impact differential dependingon whether links break or not. Since the breaking of the relationship is clearly an endogenous
outcome for which we do not have an additional IV, we omit the IV specifications here. Allinterpretations are of course subject to the same caveats as the OLS specifications in the paper.
The results are not entirely conclusive because the estimate for the link break dummy interaction coefficient is somewhat imprecisely estimated. Taking the point estimate at facevalue would imply that the sales loss is (ceteris paribus) about 5 to 6 percentage points larger
when the relationship is cut (but note that the estimates are not significantly different fromzero even at the 10% level). The estimates for the other coefficients are similar to those in thebenchmark specifications of Table XIII, suggesting that there may be action on both the extensive margin (cutting of relationships) and intensive margin (higher costs while the relationshipremains active).

B.12

Do firms form new links?

In Table XXXII we regress the change in the firm’s number of suppliers over one- to five-year
horizons (and winsorized so that firms with very large numbers of suppliers do not on their own
drive results) on a dummy that is one if one of the firm’s relationship breaks following vertical
integration of the seller with a competitor of the firm (what we call a “potential foreclosure
event”). We find that the change in the number of suppliers is lower by about one comparedto the average change in the number of suppliers. Notably, that’s very similar across the fivedifferent horizons. It seems that the potential foreclosure events result in lower numbers of
suppliers even in the medium run.
The regressions in Table XXXIII are similar but do not condition on the events leading toa breaking of the buyer-supplier relationship (i.e. the independent variable is a dummy that is
one iff the firm has a supplier that integrates with a competitor of the firm). We find coefficientsthat are a little bit lower but still close to one (and not significantly different from one).

B.13

Partial Acquisitions

In Table XXXIV we show the baseline extensive margin regressions but where the “vertical
integration” dummy only includes partial acquisitions where the acquirer becomes a majority shareholder (pre-acquisition ownership share is in the interval [0%, 50%)) and the postacquisition ownership share is in the interval [50%, 100%)). The results are a bit less precisely
estimated, but qualitiatively similar to the benchmark specifications.

50

B.14

Alternative Suppliers and Break Probability

Here we investigate if links are more likely to break following vertical integration of the supplierwith a competitor if the potential target of foreclosure has fewer alternative suppliers. We
define alternative suppliers as those that are in competition with the integrating supplier andalso supplying the buyer that is the potential target of foreclosure. Table XXXV reports
regressions in which we interacted the number of alternative suppliers with the integration
event. The interactions to be negative and significant: when buyers have alternative suppliers,
their relationships with suppliers that vertically integrate with competitors are less likely to
break.

B.15

MFHS outflow events and vertical mergers

Table XXXVI shows the relationship between MFHS outflow events and vertical integration inregression form.

B.16

Robustness checks for the IV regressions

In the following we present some alternative specifications and robustness checks to the IV
regressions from the main text. First, Table XXXVII shows the results when using a simpler
generating regression, namely
E (1{s vertically integrates}st |Z) = exp(λ1 MFHSEventst + αbs + αbt ).
Table XXXVIII shows specifications where we use a different functional form (Probit or Logit)
for the generating regression (3). The point estimates are very similar to those from Table VIII.

51

Table XXVII—: Interaction with the number of upstream competitors – Robustness
Dependent variable: 1{LinkBreaks}bst
(1)

(2)

(3)

(4)

Supplier v. integrates

0.011(0.008)0.028∗∗(0.007)0.011(0.008)0.028∗∗(0.007)

Supplier v. integrates w. competitor

0.248+(0.146)0.178(0.136)

0.327∗(0.155)0.261+(0.144)× 6-10 Competitors

0.075(0.175)0.028(0.164)0.030(0.178)-0.013(0.166)× 11-20 Competitors-0.032(0.155)-0.015(0.145)-0.063(0.156)-0.045(0.146)× 21 - 30 Competitors-0.122(0.173)-0.118(0.161)-0.195(0.176)-0.183(0.164)× 31 - 40 Competitors-0.129(0.160)-0.054(0.150)-0.176(0.164)-0.091(0.153)× 41 - 50 Competitors-0.506(0.410)-0.453(0.383)-0.551(0.421)-0.482(0.393)× 50+ Competitors-0.268(0.381)-0.489(0.356)-0.288(0.400)-0.482(0.374)× 6-10 Downstream Competitors-0.287+(0.159)-0.273+(0.148)× 11-20 Downstream Competitors-0.059(0.087)-0.068(0.081)× 21 - 30 Downstream Competitors-0.034(0.111)-0.053(0.104)× 31 - 40 Downstream Competitors

0.125(0.129)0.080(0.120)× 41 - 50 Downstream Competitors-0.059(0.142)-0.089(0.133)× 50+ Downstream Competitors

0.000
(.)

0.000
(.)

Controls
Competitor Bin Dummies

Yes

Yes
Yes

Yes

Yes
Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

0.619
4728320.668
4728320.619
4728320.668
472832

R2
Observations
Note:

Controls: number of upstream customers, age of the link, dummy indicating other links of the supplier breaking.
“Upstream competitors” is the number of competitors of the supplier; “downstream competitors” is the numberof competitors of the buyer. Table reports robust standard errors, clustered at the supplier-year level. +p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

52

Table XXVIII—: Interaction with the number of upstream competitors: intensive margin of # competitors
Dependent variable: 1{LinkBreaks}bst
(1)

(2)

(3)

(4)

Supplier v. integrates w. competitor

0.691∗∗(0.183)0.602∗∗(0.174)0.279(0.272)0.230(0.258)Supp. v. int. w. comp. × # upstream comp.

-0.137∗(0.063)-0.130∗(0.060)-0.125+(0.067)-0.119+(0.066)

Supplier v. integrates

0.017∗(0.008)0.032∗∗(0.008)0.016(0.020)0.032(0.021)# upstream competitors-0.013∗∗(0.002)-0.026∗∗(0.002)-0.014∗∗(0.005)-0.027∗∗(0.004)0.103∗(0.046)

0.093∗(0.041)

Supp. v. int. w. competitor × # downstream competitors
Controls

Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE

Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

SampleSee note

See note

See note

See note

R2
Observations

0.607
349251

0.646
3492510.590
3160780.631
316078

Note:
Controls: number of upstream customers, age of the link, dummy indicating other links of the supplier breaking.
“Upstream competitors” is the log number of competitors of the supplier; “downstream competitors” is the log
number of competitors of the buyer. Sample consists of all active buyer-supplier relationships where the numberof competitors of the supplier is positive (all columns), and where the number of competitors of the downstream
firm is positive (columns 3 and 4). Table reports robust standard errors, clustered at the supplier-year level. +p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

Table XXIX—: Impact of MFHS outflow events on firm sales
Dependent variable: Log sales
(1)

(2)

(3)

(4)

-0.004(0.008)-0.006(0.008)0.001(0.008)-0.000(0.008)Firm trends
Industry FE
Industry × Year FE
Controls

Yes
Yes

Yes
Yes

Yes

Yes

Yes

Yes
Yes

Observations
R2

80,491
0.99

77,2020.99

77,202
0.99

MFHS event in t, t − 1, or t − 2

Yes80,4910.99

Note:
The independent variable is a dummy that is one if and only if the firm experienced an outflow event in timet, t − 1, or t − 2. Controls: number of customers, competitors and suppliers. Robust standard errors, clusteredat the firm level, are in parentheses. + p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01.

53

Table XXX—: Buyers with competitors that are also supplied by S are more likely to integrate withS
Dependent variable: 1{B and S integrate}bst(1)
B has competitor supplied by S

(2)
∗∗

0.014
(0.004)∗∗∗0.035(0.005)

(3)
∗∗∗

0.038
(0.006)× # upstream competitors
Controls

(4)

(5)

∗∗∗

0.080
(0.019)0.117∗∗∗(0.025)-0.008∗(0.004)-0.015∗∗(0.005)

Yes

Yes

Yes

Yes

Supplier × Year FE
Buyer Industry × Year FE

Yes

Yes

Yes
Yes

Yes

Yes
Yes

R2
Observations

0.101
6812

0.117
6201

0.184
5414

0.118
5181

0.179
4567

Note: Sample consists of all active buyer-seller relationships at a time where the supplier vertically integrates
with a buyer. Competitor status in independent variable and number of upstream competitors refer to year t−1.
Controls: number of buyer’s competitors and suppliers. Reported number of observations is net of singletonobservations. The drop in the number of observations in column (3) is explained by firms with missing industry
codes. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

Table XXXI—: Sales impact, extensive vs intensive margins
Dependent variable: Log sales
(1)

(2)

(3)

(4)

Supplier v.integrates

0.042∗∗(0.010)0.018+(0.010)0.042∗∗(0.010)0.018+(0.010)

Supplier v. integrates w. competitor-0.009(0.044)-0.026(0.043)-0.107(0.071)-0.117+(0.068)-0.057(0.053)-0.051(0.052)-0.061(0.051)-0.054(0.051)

0.044∗∗(0.017)0.041∗(0.016)× Link breaks
× log(1 + # alt. suppliers)
Buyer FE
Industry × Year FE
Controls

Yes
Yes

Yes
Yes
Yes

Yes
Yes

Yes
Yes
Yes

Method

OLS

OLS

OLS

OLS

Observations
R2

77202
0.98

77202
0.98

77202
0.98

77202
0.98

Note:
Regression specifications are as in Table XIII, but with an additional interaction with a dummy that is one if
and only if the relationship breaks following vertical integration of the supplier with the competitor. Robust
standard errors, clustered at the firm level, are in parentheses. + p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01.

54

Table XXXII—: Change in the number of suppliers: potential foreclosure cases
Dependent variable: ∆t,t+h #Suppliers
(1)

(2)

(3)

(4)

(5)

-1.396∗∗(0.300)-0.939∗(0.420)-1.557∗∗(0.302)-0.960∗∗(0.358)-0.990∗∗(0.331)

Buyer FE
Industry × Year FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Horizon (years)

1

2

3

4

5

Method

OLS

OLS

OLS

OLS

OLS

Observations
R2

89,6330.22

66,1820.31

48,7240.38

36,9690.46

28,651
0.53

Potential Foreclosure CaseNote:
The dependent variable is the change in the number of suppliers between time t and t + h, winsorized at the 5th
and 95th percentile of the distribution of annual changes (-4,4). The independent variable (potential foreclosurecase) is a dummy that is one if and only if the firm has a supplier that vertically integrates with a competitor
and the relationship with that supplier breaks between t and t + 1. Robust standard errors, clustered at thefirm level, are in parentheses. + p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01.

Table XXXIII—: Change in the number of suppliers (OLS)
Dependent variable: ∆t,t+h #Suppliers
(1)

(2)

(3)

(4)

(5)

-0.951∗∗(0.224)-0.919∗∗(0.262)-1.226∗∗(0.233)-0.925∗∗(0.278)-1.077∗∗(0.316)

Buyer FE
Industry × Year FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Horizon (years)

1

2

3

4

5

Method

OLS

OLS

OLS

OLS

OLS

Observations
R2

89,6330.22

66,1820.31

48,7240.38

36,9690.46

28,651
0.53

Supplier v. integrates w. competitor

Note:
The dependent variable is the change in the number of suppliers between time t and t + h, winsorized at the 5th
and 95th percentile of the distribution of annual changes (-4,4). The independent variable is a dummy that is
one if and only if the firm has a supplier that vertically integrates with a competitor. Robust standard errors,
clustered at the firm level, are in parentheses. + p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01.

55

Table XXXIV—: Impact of vertical integration on competitors’ sales – majority stake partial acquisitions only
Dependent variable: 1{LinkBreaks}bst
Supp. partially acquired / acquired by a buyer (majority stake)

(1)

(2)

(3)

(4)

-0.008(0.050)-0.013(0.052)-0.026(0.047)-0.031(0.052)0.091(0.076)

0.159+(0.088)0.155∗(0.077)

Supp. partially acquired / acquired by competing buyer (majority stake)Controls

Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes
Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes

0.580
6224810.580
6224780.621
4574960.673
457496

Note:
Specification as in the benchmark specification of Table V, except that vertical integration events are only
acquisitions where the pre-acquisition ownership share is a minority share (in the interval [0%, 50%)) and thepost-acquisition ownership share is in the interval [50%, 100%).
+
p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01.

Table XXXV—: Interaction with the number of upstream competitors and alternative suppliers
Dep. variable: 1{LinkBreaks}bst
(1)

(2)

Supplier v. integrates

0.012(0.008)0.028∗∗(0.007)

Supplier v. integrates w. competitor

0.338∗∗(0.061)0.267∗∗(0.057)

Supp. v. int. w. comp. × # alternative supp.

-0.126∗∗(0.043)-0.115∗∗(0.040)# alternative suppliers-0.032∗∗(0.002)-0.035∗∗(0.002)Controls

Yes

Relation FE
Buyer × Year FE
Industry Pair × Year FE
R2
Observations

Yes
Yes
Yes

Yes
Yes
Yes

0.619
4728320.668
472832

Note: Controls: number of upstream customers, age of the link, dummy indicating other links of the supplier
breaking. “Upstream competitors” is the number of competitors of the supplier; “alternative suppliers” is thenumber of competitors of the supplier that also supply the buyer. We use the log of one plus the number of
upstream competitors / alternative suppliers to account for the skewness of the distribution. Table reports
robust standard errors, clustered at the supplier-year level. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

56

Table XXXVI—: Vertical mergers following MFHS outflow events
Dependent variable: Firm vertically integrates with buyer(1)
Outflow event

(2)

(3)

(4)

-0.000(0.001)0.013∗∗(0.003)

t−1
t−2

0.003
(0.002)t−3

0.002(0.003)0.005∗∗(0.001)max(t, t − 1, t − 2)
Firm FE
Industry × Year FE
Observations
R2

(5)

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

154,0500.26

154,0500.26

154,0500.26

154,0500.26

154,050
0.26

Note:This table shows regressions where the dependent variable is a dummy indicating whether a firm verticallyintegrates with a buyer and the independent variables are indicators for mutual fund capital outflow (defined
as normalized MFHS being below the tenth percentile) at time t, t − 1, t − 2 and. t − 3. The last indicator
is equal to one if there was an outflow event in t, t − 1 or t − 2. All regression include firm and industry-year
fixed effects. Robust standard errors, clustered at the firm level, are in parentheses. + p < 0.10, ∗ p < 0.05, ∗∗p < 0.01.

Table XXXVII—: Relationships breaking following Vertical Integration: Generated IV (Poisson withsimple generating regression)
Dependent variable: 1{LinkBreaks}bst
Supplier v. integrates

(1)

(2)

(3)

0.026
(0.022)0.016(0.020)0.022(0.019)0.322(0.205)0.296(0.194)

Supplier v. integrates w. competitor
Controls

Yes

MethodIVIVIV

Relation FE
Buyer × Year FE

Yes
Yes

Yes
Yes

Yes
Yes

R2
First stage F-stat
Observations

0.000
2020.277
6407530.000
17.5965063350.131
17.587506335

Note:
Controls: number of upstream customers, age of the link, dummy indicating other links of the supplier breaking.
Table reports robust standard errors, clustered at the supplier-year level. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

57

Table XXXVIII—: Relationships breaking following Vertical Integration: Alternative Generating Regressions

1{LinkBreaksbst }Supplier v. integrates
Supplier v. integrates w. competitor

(1)

(2)

(3)

(4)

0.018
(0.020)0.309(0.206)0.025(0.019)0.286(0.195)0.019(0.020)0.308(0.204)0.026(0.019)0.284(0.194)Controls

Yes

Yes

Buyer × Year FE
Relation FE

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Estimator

IV

IV

IV

IV

ProbitProbit

Logit

Logit

0.583
16.494507,453

0.638
16.487507,453

0.583
16.573
507,453

0.638
16.566
507,453

Generating Regression
2

R
First-stage F statisticN
Note:

Controls: number of upstream customers, age of the link, dummy indicating other links of the supplier breaking.
Table reports robust standard errors, clustered at the supplier-year level. + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.

58