Merger Synergies along the Supply Chain

November 2010

Abstract

This paper makes three contributions to the literature examining the e¤ects of mergers on�rms�product market rivals, customers, and suppliers. We provide the �rst analysis of the e¤ect ofhorizontal merger synergies along the supply chain, by utilizing a novel, hand-collected dataset of in-siders�projections of synergies. We �nd that synergies are an important determinant of the marketreactions by rivals, customers, and suppliers of merging �rms to horizontal merger announcements.Second, we revisit the empirical literature examining the market power (collusion) motive for hor-izontal mergers by demonstrating theoretically that omitting a measure of synergies, as in paststudies, may lead to biased inferences regarding the e¤ects of market power along the supply chain.Our tests of the market power hypothesis, which account for merger synergies, demonstrate thatmarket power is generally an important determinant of announcement returns to rivals, customers,and suppliers of merging �rms. This result stands in sharp contrast to past studies that generallyreport inconclusive evidence regarding the e¤ects of market power along the supply chain. Third,we contribute to the empirical merger literature by providing a �rst (exploratory) analysis of thee¤ects of vertical mergers in general and synergies in vertical mergers in particular along the supplychain. Despite employing a small sample of vertical mergers with available synergy projections, we�nd that forecasted synergies are signi�cantly related to returns of rivals, customers, and suppliersof bidders in vertical mergers.

Keywords: horizontal mergers, synergy, market power, vertical mergers

JEL Classi�cation Numbers: G34, L13.

1 Introduction

Mergers and acquisitions in�uence not only the merging �rms, but also �rms competing with

them in product markets (�rivals�) and companies along merging �rms� supply chain (�customers�

and �suppliers�). Two widely accepted e¤ects of horizontal mergers on merging �rms�product market

rivals, customers, and suppliers relate to the interaction among �rms in product markets. According

to the market power (collusion) hypothesis, a decrease in the number of �rms competing in an industry

following a horizontal merger reduces the industry�s competitiveness and raises industry participants�

rents (e.g., Stigler (1964)). In addition, increased collusion raises prices for industry participants�

output, hurting �rms in downstream industries (�customers�) and reduces the amount of required

inputs, hurting �rms in upstream industries (�suppliers�).

According to the synergy hypothesis (e.g., Maksimovic and Phillips (2001)), merger synergies, such

as economies of scale and elimination of overlapping facilities, lead to a competitive advantage of the

joint entity over its product market rivals, reducing the values of the latter. In addition, synergies

lead to increased combined output of the merged �rm and lower equilibrium prices for its products,

bene�ting �rms in the downstream and, potentially, in upstream industries.1

The empirical literature examining the e¤ects of horizontal mergers on �rms along merging �rms�

supply chain and on their product market competitors is rather limited and concentrates on tests of

the market power hypothesis. Three main �ndings emerge from existing literature. First, values of

merging �rms�rivals tend to increase around horizontal merger announcements (e.g., Eckbo (1983,

1985), Stillman (1983), Song and Walkling (2000), Fee and Thomas (2004), and Shahrur (2005)).2

Second, the returns to customers and suppliers of merging �rms are generally statistically insigni�cant

(e.g., Shahrur (2005) and Fee and Thomas (2005)). Third, in most samples and speci�cations, measures

of market power, such as industry concentration and change in it caused by the merger, do not have

signi�cant e¤ects on product market rivals�, customers�and suppliers�returns (e.g., Shahrur (2005)

and Fee and Thomas (2005)). The synergy hypothesis is generally ignored in the existing empirical

1Another potential e¤ect of a merger on merging �rms�suppliers, which is present regardless of the interaction among

�rms in output markets, is the �buyer power�hypothesis (e.g., Stole and Zwiebel (1996)), according to which an increase

in industry concentration can lead to increased bargaining power vis-a-vis suppliers. An additional, non-product-market-

driven e¤ect on rivals�of merging �rms is the �acquisition probability�hypothesis (e.g., Eckbo (1983), Stillman (1983),

and Song and Walkling (2000)), according to which a merger announcement increases the probability of acquisition of

other �rms in the target �rm�s industry.2On the other hand, Banerjee and Eckard (1998) report negative announcement returns for rivals of �rms involved in

�mega-mergers�at the turn of twentieth century and Singal (1996) �nds that some rivals of airlines involved in horizontal

mergers bene�t from them, while other rivals are hurt by these merger.

1

studies, likely because data on merger synergies has been unavailable.

In this paper we �ll this gap and test the merger synergy hypothesis by using a novel hand-

collected dataset of estimated merger synergies, �rst developed and employed in Bernile and Bauguess

(2010). This estimate is based on merging �rms�insiders�projections of expected merger gains due to

production e¢ ciencies and cost savings. These synergy projections are shown to be strongly related

to the actual merger gains realized post-merger, accounting for the potential self-selection due to the

projections�voluntary character. Therefore, insiders�projections of synergies are argued to be a valid

measure of expected merger synergies.

The �rst contribution of our paper is showing that expected synergies have substantial e¤ects on

merging �rms�rivals, customers and suppliers, while controlling for the increased market power of �rms

operating in merging �rms�industries. Speci�cally, returns to product market rivals of merging �rms

around deal announcements are signi�cantly negatively related to projected synergies. In addition,

synergies tend to be positively associated with returns to �rms operating in upstream and downstream

industries. An interesting �nding is that the e¤ect of synergies on customers and suppliers is not

uniform: the e¤ects of horizontal merger synergies are concentrated among relatively small customers

and relatively large suppliers.

Our second contribution is a reassessment of the evidence on the market power hypothesis. One

possible reason for the inconclusive evidence on the e¤ect of market power on returns of �rms along

merging �rms� supply chain is that increased market power and larger synergies have the opposite

e¤ects on �rms that have product market associations with the merging �rms. Increased collusion

following a horizontal merger a¤ects merging �rms�product market rivals positively by raising pro�ts

in the merging �rms� industry. In addition, increased collusion hurts �rms in upstream and down-

stream industries by raising equilibrium prices and reducing production quantities in the merging

�rms�industry, leading to reduced demand for production inputs. On the other hand, ceteris paribus,

merger synergies lower equilibrium prices and pro�ts in the merging �rms�industry and raise output

levels and possibly inputs demanded, hurting merging �rms�rivals and bene�ting their suppliers and

customers.

Using a simple model of oligopolistic competition we show that depending on the level of operat-

ing e¢ ciencies and cost savings, a horizontal merger can be either bene�cial or harmful for product

market rivals, suppliers and customers. While for mergers resulting in relatively high synergies, the

synergy e¤ect outweighs the market power e¤ect, in other mergers, characterized by lower syner-

gies, the market power e¤ect dominates the synergy e¤ect. This suggests that results obtained from

empirical speci�cations that abstract from the existence of merger synergies may be uninformative.

2

Speci�cally, the signs of stock returns of competitors, customers, and suppliers following horizontal

merger announcements, as well as the coe¢ cients on measures of market power in return regressions

depend crucially on the level of synergies. Therefore, it is hard to make inferences about the market

power e¤ects of mergers without conditioning on the level of expected synergies.

Incorporating expected synergies into tests leads to interesting conclusions regarding the market

power e¤ect of mergers, which are quite di¤erent from the existing studies. Large changes in industry

concentration following a merger in a relatively concentrated industry are associated with signi�cantly

positive returns to product market rivals and signi�cantly negative returns to customers and relatively

large suppliers of merging �rms�industries. This evidence seem to provide a much more robust support

for the market power hypothesis than the evidence in existing empirical studies. It also demonstrates

that controlling for merger synergies while examining the market power e¤ect of mergers is crucial.

Existing literature on market power in M&As concentrates on horizontal mergers. We extend this

literature and examine the e¤ects of projected synergies and market power on customers, suppliers,

and rivals of �rms involved in vertical mergers.3 Thus, our third contribution to the merger literature

is an analysis of the e¤ects of synergies in vertical mergers on �rms having product market relations

with merging �rms. Merger synergies tend to be negatively associated with changes in values of rivals

of bidders, this e¤ect being more pronounced for smaller rivals. The e¤ects of vertical mergers on

bidders� customers and suppliers depends on whether a merger is �up-vertical� or �down-vertical�

(i.e. a bidder �rm operates in a downstream or an upstream industry). Suppliers and customers of

bidders in up-vertical mergers tend to bene�t from merger synergies, while customers and suppliers

of bidders in down-vertical mergers tend do be hurt by these mergers. To the best of our knowledge,

this is the �rst paper to examine empirically the e¤ects of vertical mergers along the supply chain.

However, these results should be viewed as exploratory because of the small sample size and the lack of

a theoretical model of synergies and market power in vertical mergers that would guide the empirical

analysis.

The paper proceeds as follows. In the next section we present a stylized model of oligopolistic

competition that allows us to form hypotheses regarding the relations between synergies, industry

concentration, and the e¤ects of a horizontal merger on merging �rms�product market rivals, cus-

tomers, and suppliers. In Section 3 we present the data, discuss the methods of determining the type

of relatedness among �rms, used in construction of our samples of merging �rms�rivals, customers,

and suppliers, and discuss our measure of expected operating synergies that is based on merging �rms�

3The only empirical study of the e¤ects of vertical mergers that we are aware of, Fan and Goyal (2006), examines the

e¤ects of such mergers on merging �rms, but not on their product market competitors, customers, and suppliers.

3

insiders� projections. We examine the determinants of the e¤ects of horizontal mergers on related

�rm values, with the goal of separating the market power e¤ect from the synergy e¤ect, in Section 4.

Section 5 presents a preliminary analysis of the e¤ects of merger synergies on customers, suppliers,

and rivals of �rms merging vertically. We summarize our �ndings and conclude in Section 6. Proofs

are found in the Appendix.

2 Amodel of mergers with market power and production e¢ ciencies

In this section we provide a simple illustration of the e¤ects of mergers that

a) increase the market power of merging �rms and their product market rivals, and

b) increase the operating e¢ ciency of the merging �rms,

on �rms operating in merging �rms� industry (rivals), and in upstream and downstream industries

(suppliers and customers respectively).

Assume that an industry consists of n identical �rms, which compete a-la Cournot in homogenous

products. The industry demand is given by

p(Q) = a� bQ = a� bnXi=1

qi; (1)

where p(Q) is the equilibrium output price, Q is the combined output of n �rms, each of which produces

qi. Firms�production functions are of the Cobb-Douglas speci�cation with two inputs (�xed input K

and variable input I):

qi = K�i I

12i : (2)

This production function exhibits decreasing (constant, increasing) returns to scale when � < 12

(� = 12 , � >

12) and, therefore, � is a convenient measure of production e¢ ciencies obtained by joining

capital.

The cost of employing one unit of variable input I by �rm i is c

K�i

. The cost of unit of variable

input is assumed to be a decreasing function of �rm�s capital (�xed input) in order to enable modelling

the cost savings (e.g., elimination of duplicate functions) that are cited as one of the most important

reasons for merging (e.g., Kaplan (2000)). Firm i�s variable cost of producing qi units of output is,

therefore,

C(qi) =q2i c

K2�+�i

: (3)

In what follows, to simplify the algebra by assuming that each �rm�s level of �xed input Ki equals

one.4

4This assumption is innocuous and all of the results hold for Ki 6= 1.

4

Maximizing each �rm�s pro�t, �i, given by

�i = pqi � C(qi); (4)

with respect to the �rm�s output quantity results in �rms� optimal outputs as a function of their

rivals�outputs. Solving the resulting system of n equations, results in the following equilibrium �rm

pro�ts, ��no_merger, total industry output, Q�no_merger, and total required quantity of variable input,

nXi=1

I�no_merger. (Note that because of symmetry, the equilibrium output levels and pro�ts are identical

across �rms.)

��no_merger =a2(b+ c)

(b(n+ 1) + 2c)2; (5)

Q�no_merger = nq�no_merger =an

b(n+ 1) + 2c; (6)

nXi=1

I�no_merger =na2

(b(n+ 1) + 2c)2: (7)

Assume now that two of the n �rms merge and combine capital. The merged �rm�s cost of

producing qi units of output is:

C(qi) =q2i c

(2Ki)2�+�=

q2i c

22�+�=

q2i c

2����; (8)

where �� = 22��1 (i.e. �� > 1 (�� < 1) in case of increasing (decreasing) returns to scale) and

�� = 2�. The total cost in (8) is clearly decreasing in �� as long as the production function exhibits

increasing returns to scale (� > 12) and is decreasing in �� if the per-unit �overhead�cost is decreasing

in the level of �xed input (� > 0).

Solving for the merged �rm�s and n� 2 stand-alone �rm�s (rivals�) optimal output levels results in

the following equilibrium merged �rm�s pro�t, ��merged, rivals�pro�ts, ��st:_alone, total industry output,

Q�merger, and total input demanded,n�1Xi=1

I�merger:

��merged =a2����(b+ 2c)

2(b���� +12c)

((b(bn+ 4c)���� + c(b(n� 1) + 2c2))2; (9)

��st:_alone =a2(b+ c)(b���� + c)

2

((b(bn+ 4c)���� + c(b(n� 1) + 2c2))2; (10)

Q�merger =a((b(n� 1) + c)���� + c(n� 2))(b(bn+ 4c)���� + c(b(n� 1) + 2c2)

; (11)

n�1Xi=1

I�merger =12a2(�2�(b+ 2c)

2 + (n� 2)(b���� + c)2)((b(bn+ 4c)���� + c(b(n� 1) + 2c2))2

: (12)

A comparison of merging �rms�rivals�values after the merger, ��st:_alone in (10), and their values

prior to the merger, ��no_merger in (5), leads to the following results.

5

Proposition 1 1) Following a merger, the return of merging �rms�rivals, ��st:alone��no_merger

� 1, is positive

(negative) if and only if operating synergies (��) and cost savings (��) are su¢ ciently low (high).

2) Rivals�return is decreasing in �� and in �� ;

3) Rivals�return is increasing (decreasing) in n (decreasing (increasing) in industry concentration) if

and only if �� and �� are su¢ ciently high (low).

Horizontal merger has two e¤ects on rival �rms. The �rst is the market power e¤ect, which is

the focus of existing empirical studies of the e¤ects of mergers along the supply chain (e.g., Shahrur

(2005) and Fee and Thomas (2005)). Abstracting from possible production e¢ ciencies, a merger

increases industry concentration, leading to higher equilibrium output prices and pro�ts of merging

�rms� competitors. The second e¤ect, which is the main focus of this paper, is that of �merger

synergies�. The synergies can be due to either increasing returns to scale (operating e¢ ciencies,

�� > 0) or reduced overhead costs (�� > 0). Abstracting from the market power e¤ect, both types

of synergies increase the combined output of the merging �rms, hurting their product market rivals.

Thus, rivals�returns are decreasing in both types of synergies. For high enough levels of �� and/or ��

(i.e. for increasing enough returns to scale and/or strong enough e¤ect of the level of �xed input on

the overhead costs of employing variable input), the e¤ect of synergies outweighs the market power

e¤ect and the returns of rival �rms are negative. For lower levels of synergies, their e¤ect is dominated

by that of market power, leading to positive rivals�returns.

Both e¤ects of a merger on rivals� returns are stronger the more concentrated the industry in

which the merger occurs (the lower the number of �rms operating in it). Thus, the e¤ect of industry

concentration on rivals� returns depends on whether the e¤ect of synergies outweighs the e¤ect of

market power. Increasing market power (increasing 1n) magni�es both e¤ects and the net e¤ect of a

merger on rivals (whether positive or negative).

In the next proposition we examine the e¤ects of a merger on total industry output, Q�:

Proposition 2 1) Following a merger, the relative change in industry output, Q�mergerQ�no_merger

�1 is positive

(negative) if and only if operating synergies (��) and cost savings (��) are su¢ ciently high (low).;

2) Relative change in industry output is increasing in �� and in ��;

3) Relative change in industry output is increasing (decreasing) in n (decreasing (increasing) in in-

dustry concentration) if and only if �� and �� are su¢ ciently low (high).

Both types of synergies lead to higher equilibrium production quantities by the merging �rms

relative to combined pre-merger level, ceteris paribus. For high (low) enough �� and/or �� , the synergy

e¤ect dominates (is dominated by) the market power e¤ect, and merging �rms� output increases

6

(decreases) following a merger. Industry rivals react optimally by reducing (increasing) their output,

however this e¤ect is second-order, leading to an overall increase (reduction) in total industry output.

Industry concentration magni�es the net e¤ect, leading to a positive (negative) relation between market

power and post-merger change in output quantity for high (low) levels of synergies.

A change in equilibrium industry output, Q�, has a direct e¤ect on �rms operating in downstream

industries (customers). Customers bene�t (are hurt) if, for a given demand function (i.e. intercept and

slope of demand in (1)) for the industry output, the equilibrium industry output increases (decreases)

because industry output is used as an input in the customer industry. Next, we examine the e¤ects of

a merger on the total quantity of variable input demanded by the industry.

Proposition 3 1) Following a merger, the relative change in the equilibrium quantity of variable

input,I�merger

I�no_merger� 1, is positive for su¢ ciently low �� and for su¢ ciently high �� and is negative

otherwise;

2) Relative change in the equilibrium quantity of variable input is a) decreasing in ��; and b) increasing

in �� as long as �� is su¢ ciently low;

3) Relative change in the equilibrium quantity of variable input is decreasing in n (increasing in industry

concentration) for su¢ ciently low �� and su¢ ciently high �� and is increasing in n (decreasing in

industry concentration) otherwise.

The e¤ects of a horizontal merger on the industry demand for variable input are somewhat more

subtle than the e¤ects on the equilibrium industry output and rivals� pro�ts. The reason is that

abstracting from market power considerations, merger synergies have two e¤ects on the quantity of

variable input demanded by the industry �rms. Operating e¢ ciencies (i.e. �� > 12) lead to higher

equilibrium output of merged �rms. However, a lower quantity of variable input is required to produce

any given level of output. It turns out that the second e¤ect dominates the �rst one for any industry

structure, and the quantity of input demanded by the merging �rms decreases in operating e¢ ciencies

even as the output quantity increases in them. Even if the resulting output of the merging �rm declines

post-merger relative to combined pre-merger output, the resulting optimal increase in rivals�output

levels (and of input demanded by rivals) is not su¢ cient to reverse the relation between �� and the

equilibrium overall quantity of variable input demanded.

Reduction of overhead costs ( �� > 0) does not a¤ect the quantity of variable input required

to produce a given output. Higher �� leads to higher output (and the quantity of variable input

demanded) by the merging �rms, resulting in lower optimal output (and input) levels of the merging

�rms�competitors. For relatively low production e¢ ciencies (��), the increase in the inputs required by

the merging �rms dominates the decrease in the inputs demanded by their rivals, leading to a positive

7

relation between �� and the overall industry demand for variable input. However, for su¢ ciently high

��, the merging �rms�production function is su¢ ciently e¢ cient and its demand for variable input

is low, leading to the e¤ect of lower rivals�demand for input to dominate, In this case, the relation

between cost savings and overall industry demand for variable input is negative.

To summarize, the relative change in the quantity of input demanded following a merger is decreas-

ing in the measure of operating e¢ ciencies and is increasing in the measure of overhead cost savings.

As in the case of rivals� pro�ts and industry output, higher marker power magni�es the e¤ects of

�� and �� , leading to a positive or negative relation between the relative change in demanded input

following a merger and market power depending on the relative strength of the e¤ects of �� and ��.

Note that the relations in Proposition 3 are conditional on the price of the variable input being

constant. In equilibrium that accounts for the optimal response of upstream �rms to the merger,

which is outside the scope of our stylized model, decreased demand for variable input (which is the

output of �rms operating in an upstream industry) would reduce their price and increase the total

output of the upstream industry. However, this e¤ect is second-order relative to those described in

Proposition 3, and, therefore, would mitigate the strength of the relations in Proposition 3, without

reversing their signs.

The following table summarizes the results in Propositions 1-3.

Rivals Customers Suppliers

E¤ect of a merger�for high �� and ��

+ for low �� and ��

+ for high �� and ��

�for low �� and ��

�for high �� and low ��

+ for low �� and high ��

E¤ect of concentration ( 1n )�for high �� and ��

+ for low �� and ��

+ for high �� and ��

�for low �� and ��

�for high �� and low ��

+ for low �� and high ��

E¤ect of e¢ ciency (��) � + �

E¤ect of cost savings (��) � ++ for low ��

�for high ��

The model presented here illustrates the basic e¤ects of synergies and market power on �rms�

operating in merging companies�industry, as well as in upstream and downstream industries. However,

the model is based on a number of restrictive assumptions. First, assuming symmetric �rms does not

allow making predictions regarding the di¤erential e¤ects of synergies and market power on relatively

small and large rivals. In addition, the symmetry assumption links industry concentration and change

in it following a merger, preventing an analysis of comparative statics of the relation between synergy

and collusion e¤ects and changes in market power for di¤erent levels of industry concentration. Second,

8

the model is silent on the di¤erential e¤ect of a horizontal merger on customers and suppliers possessing

di¤erent degrees of market power in their respective industries. Third, the assumption of a Cobb-

Douglas speci�cation, resulting in linearly increasing marginal cost of production, does not allow

making predictions regarding how the e¤ects of synergies and market power depend on merging �rms�

production functions. We address some of these questions in the empirical analysis.

3 Data, industry relatedness measures, and synergy estimates

3.1 Industry relatedness measures

The main goal of this paper is to examine the e¤ects of mergers on �rms operating along merging

�rms�supply chain. Therefore, obtaining a sample of �rms linked to merging �rms along their supply

chain is crucial. To that end, we use benchmark input-output (IO) tables, constructed by the Bureau

of Economic Analysis of the U.S. Department of Commerce. Speci�cally, we employ the annual �use�

tables that report for any pair of industries the dollar value of the �rst industry�s output that is

used as an input of the second industry. Determining customer-supplier relations among industries

on the basis of IO tables is quite standard in empirical studies of e¤ects of corporate events on �rms�

customers and suppliers (e.g., Shahrur (2005), Fan and Goyal (2006), and Ahern (2010) for the case

of various types of M&As, Fan and Lang (2000) for the case of corporate diversi�cation, and Chang

and Tsai (2009) for the case of strategic alliances).

We de�ne industries according to four-digit North American Industrial Classi�cation System

(NAICS) classi�cation.5 There are 61 NAICS industries in our sample. For each pair of industries, i

and j, we compute the following ratios, which we call customer and supplier shares:

Cust_coeffj;i = Supp_sharei;j =Salesi;jSalesi

; (13)

Suppl_coeffj;i = Cust_sharei;j =Salesi;jSalesj

; (14)

where Salesi;j is the dollar amount of output of industry i sold to industry j, and Salesi (Salesj)

is the overall dollar sales of industry i (j). Supp_sharei;j (Cust_coeffj;i) measures how dependent

(important) supplier (customer) industry i (j) is on (to) customer (supplier) industry j (i). Similarly,

Cust_sharei;j (Suppl_coeffj;i) measures how dependent (important) customer (supplier) industry j

(i) is on (to) supplier (customer) industry i (j).

In the spirit of Ahern (2010), for each industry one can thus identify four associated industries: the

�most important�customer and supplier industries and the �most dependent�customer and supplier

5See Fan and Goyal (2006) for details on de�ning industries according to NAICS classi�ation.

9

industries. Following Shahrur (2005), we de�ne industry i (j) as a �dependent supplier�(�important

customer�) of industry j (i) if:

Cust_coeffj;i = Supp_sharei;j > 5%, and

Supp_sharei;j > Supp_sharei;k8k 6= j

(Cust_coeffj;i > Cust_coeffk;i8k 6= j):

Similarly, we de�ne industry j (i) as a �dependent customer�(�important supplier�) of industry i (j)

if:

Supp_coeffj;i = Cust_sharei;j > 5%, and

Cust_sharei;j > Cust_sharei;k8k 6= j

(Supp_coeffj;i > Supp_coeffk;i8k 6= j):

Our empirical results generally hold for various alternative cuto¤s of Cust_coeffi;j and Supp_coeffi;j .

In the discussion of the empirical analysis, we concentrate on �rms operating in important customer

and supplier industries. The empirical results and inference, however, are similar when we examine

dependent customer and supplier industries. We de�ne all single-division �rms operating in industry

i�s important customer (supplier) industry as industry i�s customers (suppliers).6

A merger is classi�ed as horizontal if the two merging �rms operate in the same four-digit NAICS

industry. A merger between �rms operating in di¤erent NAICS industries i and j is classi�ed as

vertical if both Cust_coeffi;j and Supp_coeffi;j (or Cust_sharej;i and Supp_sharej;i) exceed �ve

per cent.7 In all other cases, a merger is classi�ed as conglomerate (other).

3.2 Merger sample and merger synergies

Our sample of mergers and acquisitions is obtained from Thomson Financial�s Security Data Com-

pany mergers and acquisitions database and consists of deals announced between January 1995 and

December 2005, involving non-�nancial (i.e. SIC<6000 or SIC>6999) bidders and targets listed on

NYSE, NASDAQ, or AMEX. We restrict attention to mergers in which a bidder holds less that �fty

6Our methodology, based on IO tables, identi�es sets of potential customers and suppliers. See Fee and Thomas

(2005) for an analysis of actual vertical relations among �rms, obtained from Compustat Segment data, which report

�rms�most important customers and suppliers.7This cuto¤ is stricter than that in Fan and Goyal (2006), who classify a merger as vertical as long as either

Cust_coeffi;j or Supp_coeffi;j exceeds a certain threshold. Our results are robust to such a speci�cation.

10

per cent of the target�s common stock on the o¤er date, seeks to acquire 100% of the target equity,

and the deal has come to a resolution (i.e. it was completed or withdrawn). These selection criteria

result in 2,008 M&A deals in our sample period that have required stock returns data on CRSP and

accounting data on Compustat.

Numerous studies highlight the importance of synergies in merger decisions (see Kaplan (2000)

for an extensive review of the synergy motive for mergers). Many existing studies attempt to infer

the existence of merger synergies by examining short-term returns of bidders and targets upon merger

announcements and post-merger long-term stock returns and operating performance. However, it may

be impossible to draw inferences regarding the existence and magnitude of merger synergies from such

tests (e.g., Bradley, Desai and Kim (1988), Kaplan (2000), Maksimovic and Phillips (2001), Houston,

James and Ryngaert (2001), and Jovanovic and Rousseau (2002)). Thus, in this study we employ a

relatively new measure of merger synergies, �rst employed in Houston, James and Ryngaert (2001) in

a small sample of bank mergers and extended by Bernile and Bauguess (2010) to a large M&A sample,

which is based on projections of synergistic gains from proposed mergers, made by merging �rms�

insiders. Using a sample of close to a thousand mergers with available synergy projections, Bernile and

Bauguess (2010) show that there is a signi�cantly positive relation between these projected synergies

and merging �rms�post-merger stock market and operating performance, while controlling for self-

selection due to the voluntary character of projections. In this paper, we use synergy projections in

horizontal and vertical mergers in order to examine the e¤ects of M&As on merging �rms�product

market rivals, customers, and suppliers.

For each M&A deal we look for insiders�projections from news stories and press releases published

between an o¤er announcement and its resolution. The vast majority of synergy forecasts focus on

expected cost savings due to production e¢ ciencies (e.g., combination of production capabilities)

and reduction in �overhead� costs (e.g., elimination of duplicate functions and redundant R&D).

Estimating the value of projected synergies is not straightforward. We follow Bernile and Bauguess

(2010), as well as Kaplan and Ruback (1995), Gilson, Hotchkiss and Ruback (2000), and Houston,

James and Ryngaert (2001), and use the discounted cash �ow method to estimate the present value of

after-tax synergies. However, the level of detail provided in projections varies widely and ranges from

providing an estimate of annual synergies for multiple years following a merger through an estimate of

synergies for the �rst post-merger year to a general estimate of annual synergies with no indication of

their timing. Therefore, we follow the algorithm in Bernile and Bauguess�(2010) in estimating each

merger�s synergies.8

8See Bernile and Bauguess (2010) for a detailed description of search criteria, as well as a extensive discussion of types

11

Table 1 presents the frequency of mergers in each of the years in our sample period, the number

of horizontal and vertical deals, as well as the proportion of mergers with available insiders�synergy

projections.

Insert Table 1 here

Close to sixty percent of the two thousand mergers in our sample are horizontal (i.e. mergers

between two �rms in the same four-digit NAICS industry), while only six percent of deals are vertical.

This is due to the relatively restrictive requirement that both the supplier and customer coe¢ cients

in (13) and (14) exceed �ve per cent. Similar to Bernile and Bauguess (2010), about a quarter of all

mergers are accompanied by insiders�projections. The proportion of vertical mergers with available

projections equals one third.

Importantly, despite the relatively low share of mergers with available insiders�projections, many

of the relatively large merger feature such projections: mergers with projections account for over

seventy per cent of the market value of all deals. Thus, while we lose a large fraction of the sample

due to unavailability of projections, �important�mergers involving relatively large �rms that can have

signi�cant consequences along the supply chain generally make it to the sample.9 Panel A of Table 2

reports summary statistics of projected merger synergies for mergers in which projections are available.

Insert Table 2 here

The mean (median) projected synergies amount to approximately 8% (6%) of the combined value of

bidder and target in horizontal and vertical mergers. There is a substantial variation in estimated

projected synergies, as follows from the spread between the �rst and third synergy quartiles.

As discussed in the introduction, there is an important non-synergy-related motive for mergers

�market power. A successful horizontal merger increases industry concentration, leading to higher

oligopolistic rents for merging �rms (as well as for their product market rivals). A vertical merger allows

coordination of pricing strategies between upstream and downstream �rms, leading to internalization

of some pricing externalities. One of the goals of this paper is to extend the existing literature

examining the market power motive for merging (e.g., Eckbo (1983), Fee and Thomas (2005), and

Shahrur (2005)) by incorporating production synergies into the analysis. Panel B of Table 2 reports

summary statistics of the structure of industries in which merging �rms operate, as well as merging

companies�competitive positions within their industries.

of synergy projections and synergy value estimation.9Estimating a �projection prediction�model and using predicted values from this model for mergers that do not have

available projections leads to similar qualitative results, albeit less signi�cant in some speci�cations.

12

The mean (median) number of �rms in an industry experiencing a horizontal merger is 105. These

�rms are typically quite uneven in size, as suggested by the quite high mean (median) Her�ndahl index

of 0.137 (0.091). Merging �rms are typically large relative to their rivals, having a mean (median)

combined market share of 12% (4%), approximately two thirds of which is attributable to bidders. In

the case of vertical mergers, the typical number of �rms in an industry is signi�cantly smaller and the

Her�ndahl index is somewhat larger in both the upstream and downstream industries than those in

the case of horizontal mergers. In addition, �rms merging vertically typically have somewhat larger

market shares in their industries than bidders and targets in horizontal mergers.10

3.3 Deal characteristics, projections, and performance

The decision to provide synergy forecasts is endogenous. Therefore, it may be important to correct for

the potential self-selection when examining the e¤ects of projected synergies on �rms along merging

�rms�supply chain. We begin by examining the determinants of insiders�decision whether to provide

synergy estimates. In particular, we estimate a probit regression, in which the dependent variable is

an indicator equalling one if insiders�projections are available for a given merger and equalling zero

otherwise, and the set of independent variables includes merger characteristics that are hypothesized

to in�uence the decision to announce projected synergies. These characteristics include the pre-merger

propensity of bidder and target to make voluntary disclosures (guidance indicators), the number of

analysts following the merging �rms, the number of institutional investors in them, deal characteris-

tics, various bidder�s and target�s accounting characteristics, industry characteristics, and measures

of industry concentration.11 To account for merger clustering, the probit regression is estimated with

year and industry �xed e¤ects.

The results of estimating synergy projection propensity regression are reported in Table 3.

Insert Table 3 here

The general tendency of a bidder to voluntarily disclose information is signi�cantly related to avail-

ability of merger synergy projections. Projections are signi�cantly less likely in cash o¤ers, in deals

involving large bidders, and in low growth industries, presumably because there is less uncertainty

about merger currency in such deals, resulting in lower incentives to provide synergy projections.

However, projections are signi�cantly more likely in the case of larger targets. Insiders�propensity to

provide synergy forecasts is higher for bidders with more tangible assets and fewer growth opportu-

nities, as follows from the signi�cantly positive coe¢ cient on industry-adjusted PP&E-to-assets ratio

10A possible reason for this is the stronger opposition of the Antitrust authorities to large horizontal mergers.11See Bernile and Bauguess (2010) for detailed description of variables and their construction.

13

and the signi�cantly negative coe¢ cient on the industry-adjusted market-to-book ratio. Synergy pro-

jections are more prevalent in more concentrated industries, perhaps due to the increased incentive of

merging �rms to provide arguments to the Antitrust authorities that a merger is not anti-competitive.

An important condition for using insiders�synergy projections as a measure of operating synergies

and cost savings is that synergy projections are related to �rms�post-merger performance. Bernile

and Bauguess (2010) �nd that insiders� forecasts indeed predict post-merger stock and operating

performance. In Table 4 we con�rm this result in our sample of mergers. Speci�cally, we estimate

regressions of various performance measures on projected synergies, scaled by the combined market

value of bidder and target, and on the inverse Mills ratio from the probit regression reported in Table

3, which can be interpreted as the surprise component of the decision to provide synergy projections

and is used to correct for possible selection bias.

Insert Table 4 here

The most important �nding is that synergy projections are strongly and signi�cantly related to post-

merger performance. A one standard deviation increase in scaled synergy estimate results in a 4.5%

increase in the combined cumulative abnormal return during the forty days surrounding merger an-

nouncement. Measures of the change in post-merger operating performance, such as the return on

assets, asset turnover, and pro�t margin, are also signi�cantly related to insiders�synergy forecasts.

Speci�cally, a one standard deviation increase in synergy projections is associated with a 1.8 per-

centage point increase in return on assets and 3.5 percentage points increase in post-merger pro�t

margin.

Having established the link between insiders�synergy projections and actual post-merger perfor-

mance, in the next section we examine the e¤ects of synergies, as proxied by insiders�forecasts, and

reactions to horizontal merger announcements by merging �rms�competitors, customers and suppliers.

4 The e¤ects of synergies and market power along the supply chain:

the case of horizontal mergers

We begin the analysis by reporting summary statistics of returns to rivals, customers, and suppliers

of �rms merging horizontally. For each merger we compute equally-weighted and value-weighted

abnormal mean returns of all single-segment �rms belonging to the merging �rms�four-digit NAICS

industry (product market rivals) for a three-day window [-1,1] around deal announcement. A �rm�s

daily abnormal return is computed relative to the market-model-based expected return. Similarly, we

compute equally-weighted and value-weighted cumulative mean abnormal returns of all �rms belonging

14

to industries classi�ed as an important customer or important supplier. We present summary statistics

of these mean announcement returns in Table 5.

Insert Table 5 here

Horizontal mergers tend to bene�t relatively small rivals of the merging �rms, as follows from a

signi�cantly positive mean return of the equally-weighted portfolio of rivals and a much lower and

insigni�cant return of the value-weighted rivals� portfolio. Abnormal returns of �rms operating in

important customer industries are negative, but generally insigni�cant. (The returns of relatively

large customers tend to be larger in magnitude, as follows from the di¤erence between the equally-

weighted and value-weighted returns.) The mean returns of merging �rms�suppliers is close to zero

and are insigni�cant regardless of the weighting scheme.

These results are generally consistent with the evidence in Shahrur (2005) and Fee and Thomas

(2005), who report positive (and signi�cant in some speci�cations) returns to product market rivals

and largely insigni�cant returns to customers and suppliers of merging �rms.12 However, as discussed

above, because of the countervailing e¤ects of synergies and market power on values of merging �rms�

competitors, customers, and suppliers, it is hard to make inferences from mean returns of �rms along

merging �rms� supply chain. In other words, it is hard to separate the e¤ects of synergies from

those of market power in a univariate analysis. Thus, we proceed to multivariate tests that examine

the relations between rivals�, customers�, and suppliers�returns on one side, and measures of merger

synergies and market power on the other side.

Speci�cally, in Table 6 we present results of regressing equally-weighted and value-weighted returns

of rivals on a measure of merger synergies, a measure of industry concentration, as well as control

variables that are hypothesized to be related to rivals� returns. A measure of synergies is based

on insiders� synergy projections, discussed in Section 3. For each merger, we normalize estimated

synergies by the combined pseudo-market value of all single-segment �rms operating in the merging

�rms�industry that are not part of the merger.13

We measure the change in market power following a merger by the product of industry Her�ndahl

index, computed at the end of the year prior to the year of the deal, and the change in Her�ndahl index

12The mean returns to customers and suppliers are generally insigni�cantly positive in Shahrur (2005), while they are

insigni�cantly negative in Fee and Thomas (2005). Similar to our study, as well as Shahrur (2005) and Fee and Thomas

(2005), Eckbo (1983), who focuses on rivals of merging �rms, also reports generally positive and signi�cant returns to

rivals around merger announcements.13Pseudo-market value is computed as the sum of the market value of equity and book value of debt at the end of the

year preceding the merger year.

15

implied by the merger.14 A large merger, resulting in signi�cant change in industry concentration, is

likely to have larger market power e¤ect. In addition, the market power e¤ect is expected to be more

substantial in industries in which market power considerations are more important to begin with, i.e.

industries with relatively high Her�ndahl indexes pre-merger. In addition to the interaction variable,

we also include pre-merger industry Her�ndahl index and implied change in it following a merger.

The set of control variables in regressions of rivals�returns include merging �rms�combined pre-

merger market capitalization as well as rivals�combined capitalization, with the idea that deals in-

volving �rms that are large relative to their rivals are likely to have more pronounced e¤ects on the

rivals. In addition, we include the inverse Mills ratio from the regression of propensity to provide

merger forecasts, reported in Table 3, which can be interpreted as the unexpected component of the

availability of insiders�forecasts.

The results of regressions of rivals� returns, which are estimated using industry and year �xed

e¤ects, are presented in Table 6.

Insert Table 6 here

The �rst important result in Table 6 is that projected synergies are negatively related to both equally-

weighted and value-weighted returns of merging �rms�product market competitors. This result is

highly statistically signi�cant. Perhaps more importantly, it is economically large. A one standard

deviation increase in scaled synergies (that equals 0.094) is associated with a reduction of rivals�

returns ranging between -0.8% and -1%, depending on the model. The economic signi�cance of this

relation is large compared with the mean rival returns and their standard deviation, reported in Table

5. This result is the �rst indication that horizontal merger synergies are important for �rms operating

in merging �rms�industries.

The second interesting result is that the product of an industry�s Her�ndahl index and post-merger

change in it, used as a measure of the market power e¤ect, is signi�cantly positive. The economic

signi�cance of this relation is also quite substantial. A one standard deviation increase in industry

concentration is associated with an increase of 1.6% in mean rivals�value-weighted returns. This result

is consistent with typical merger synergy not being high enough to reverse the positive market power

e¤ect of a horizontal merger. Notably, the signi�cantly positive relation between the market power

measure and rivals�returns is very di¤erent from the insigni�cantly negative coe¢ cients on measures

of market power in rivals�returns regressions reported in Shahrur (2005).15 The di¤erences between

14A similar measure of market power is used in Shahrur (2005).15Fee and Thomas (2005) report that mean returns for rivals in industries with relatively high Her�ndahl indexes are

larger than those in the whole sample of mergers. However, they do not report the signi�cance of this result and do not

16

our results and those in previous studies suggest that accounting for estimated synergies is important

in an analysis of the market power e¤ect of horizontal mergers. Put di¤erently, a possible reason

for the inconclusive evidence of empirical merits of the market power hypothesis is that the e¤ect of

market power discussed in the existing literature is really the net e¤ect of market power and merger

synergies.

Returns to rivals are negatively related to combined rivals�size, consistent with a merger of a given

size having a smaller e¤ect on larger rivals. The coe¢ cients on the inverse Mills ratio are signi�cantly

positive, suggesting that unexpected propensity of insiders to provide synergy projections are associ-

ated with higher rivals�returns. One possible reason for this �nding is that insiders�propensity to

provide synergy projections is related to their desire to portray deals as e¢ ciency enhancing when

they are more likely to be found to have potentially harmful anticompetitive e¤ects. Thus, a possible

interpretation of the inverse Mills ratio is that it is an alternative measure of market power. Under this

interpretation, the positive coe¢ cient of the inverse Mills ratio is consistent with the market power hy-

pothesis. Alternatively, it is possible that synergies projections become more likely when industry-wide

opportunities for e¢ ciency enhancing restructuring activities are available. Under this interpretation,

the positive coe¢ cient of the inverse Mills ratio is consistent with the market anticipation hypothesis,

as rivals stock prices impound the bene�ts of future e¢ ciency improvements. Although both intepre-

tations are consistent with the evidence from the rival return model, they have opposite implications

for the customer and supplier return models, to which we turn next.

We start by examining the e¤ects of horizontal merger synergies on �rms operating in important

customer industries of merging �rms. The dependent variable in these regressions takes the form of

equally-weighted and value-weighted single-segment customer returns, while the independent variables

are the same as in rivals�return regressions reported in Table 6.

Insert Table 7 here

The model in Section 2 and simple intuition suggest that, ceteris paribus, merger synergies increase

the total optimal output of the merging �rms�industry for any given output price. The data reveal

that the e¤ect of synergies on merging �rms�industry�s customers depends on whether rivals�returns

are equally-weighted or value-weighted. The di¤erences between these two speci�cations suggest that

synergies seem to be positively related to returns of smaller customers but not to returns of larger

customers. This result may be due to di¤erences in production functions and resulting output strategies

of small and large customers (e.g., relative proportions of �xed costs out of total costs and relative

equilibrium pro�t margins). However, our current model is unable to explain this empirical result.

control for synergies and for other potential determinants of rivals�returnsin their univariate analysis.

17

Consistent with the market power hypothesis, customers�value-weighted returns are negatively

related with the implied change in industry concentration, and the relation is stronger the higher pre-

merger concentration. This result is inconsistent with Shahrur (2005) and Fee and Thomas (2005),

who report a positive relation between market power measures and returns to merging �rms�industry�s

customers. This discrepancy is another piece of evidence demonstrating the importance of accounting

for merger synergies when testing the market power motive behind horizontal mergers.

In Table 8 we examine the relation between returns of �rms operating in important supplier

industries of merging �rms on one side, and projected merger synergies and market power on the

other side. The speci�cation of supplier return regressions is the same as those of customer and rival

regressions, discussed above.

Insert Table 8 here

The e¤ect of projected synergies on suppliers depends crucially on whether suppliers� returns are

equally-weighted or value-weighted. While large suppliers seem to bene�t from merger synergies,

this is not the case for small suppliers �the relation between equally-weighted supplier returns and

projected synergies is insigni�cantly negative. Part of the reason may be that the relative e¤ects of

cost savings and operating e¢ ciencies depend on production functions and levels of market power of

suppliers, since the model presented in Section 2 results in comparative statics of suppliers�returns

with respect to cost savings that are very di¤erent from those with respect to operating e¢ ciencies.

However, a more detailed model is required to address this question theoretically.

Market power is negatively related to suppliers�returns around horizontal merger announcements.

However, the negative relation is only signi�cant in the case of value-weighted returns, suggesting

that larger suppliers are hurt more by the increased market power in the downstream industry. The

signi�cantly negative coe¢ cient on the measure of market power stands in contrast with the existing

empirical evidence �the results of both Shahrur (2005) and Fee and Thomas (2005) imply insigni�cant

relations between market power and returns to suppliers of merging �rms�industry.

Overall, the results discussed in this section have two potentially important implications. First, for

the majority of empirical speci�cations, synergies matter for �rms along merging companies�supply

chain. Speci�cally, horizontal merger synergies hurt product market rivals, and, in some speci�cations,

bene�t suppliers and customers. Second, unlike past studies, we document that market power exhibits

a signi�cant relation to rivals�, customers�, and suppliers�returns around announcements of horizontal

mergers. However, the results highlight a need of a more detailed theoretical model examining the

e¤ects of synergies and market power on merging �rms�rivals, customers and suppliers, having di¤erent

production functions and market shares, and of further empirical tests of the determinants of the

18

market power and synergy e¤ects of horizontal mergers.

5 The e¤ects of synergies and market power along the supply chain:

the case of vertical mergers

In this section we present an analysis of the e¤ects of synergies projected in vertical mergers on

acquirers�product market rivals, customers, and suppliers. This analysis is exploratory in nature for

two reasons. First, our sample of vertical mergers with available synergy projections is small. Second,

we do not yet have a theoretical model that would guide us in developing empirical test speci�cations.

However, the e¤ects of vertical mergers in general, and merger synergies in particular, along the supply

chain have not been analyzed in existing empirical merger literature. Therefore, any evidence of this

sort would be novel and could serve as a basis of more extensive investigations of various e¤ects of

vertical mergers on product market rivals, customers, and suppliers of the merging �rms.

117 mergers in our initial sample of two thousand mergers are classi�ed as vertical based on

criteria outlined in Section 3. 39 of these mergers feature insiders�synergy projections. Since bidders

are generally larger than targets, we choose to examine the e¤ects of vertical mergers along bidders�

supply chain. In addition, we split the vertical merger sample into �up-vertical mergers��those in

which a bidder acquires a upstream industry, and �down-vertical mergers��those in which a bidder

acquires a downstream industry.

Table 9 presents summary statistics of bidder rivals�, customers�, and suppliers�returns.

Insert Table 9 here

Mean returns of �rms along bidders� supply chain in up-vertical mergers are generally small and

insigni�cant, as are the returns to competitors and customers of bidders in down-vertical mergers.

Mean returns to suppliers in up-vertical mergers are negative and marginally signi�cant.

In the next two tables we examine the e¤ects of merger synergies on rivals, customers, and suppliers

of bidders in vertical mergers in a multivariate framework. Speci�cally, we present results of regressions

of rivals�, customers�, and suppliers�portfolio returns on projected synergies, as well as control variables

similar to those used in Tables 6-8. Table 10 presents results for the sample of up-vertical mergers,

while Table 11 reports results for the sample of down-vertical mergers.

Insert Tables 10 and 11 here

Rivals of merging �rms�bidders, especially the relatively small ones, are generally hurt by vertical

merger synergies, as follows from the signi�cantly negative coe¢ cients on projected synergies in re-

19

gressions of equally-weighted portfolio returns in Panel A of Tables 10 and 11. While we do not have

a formal model illustrating the e¤ects of synergies in vertical mergers, this result is consistent with

simple intuition, according to which synergies are likely to increase production in both the upstream

and downstream segments of a newly joint �rm, hurting rivals of the merging �rms, regardless of

whether a merger is up-vertical or down-vertical.

Within the sample of up-vertical mergers, synergies are positively associated with returns to port-

folios of bidders� industries� customers and suppliers, especially for larger customers and suppliers.

This result is, again, consistent with synergies leading to increased production by the segments of the

merged entity relative to pre-merger levels, bene�ting merging �rms�customers and suppliers. The

results are di¤erent within the sample of down-vertical mergers. Synergies in such mergers are signi�-

cantly negatively related to returns to bidders�customers and suppliers around merger announcements.

A possible reason is that synergies that take the form of operating e¢ ciencies may lead to a lower

demand for upstream segment�s output (downstream segment�s input) even if there is an increase in

optimal downstream segment�s output. This could lead to lower demand by (upstream) bidder for

inputs, leading to negative returns to �rms operating in its supplier industry, and lower upstream

bidder�s output, hurting �rms in its customer industry.

We emphasize again that the results reported in Tables 9-11 should be taken with a grain of salt,

mainly because of the small size of our sample of vertical mergers with available synergy projections.

Importantly, the main goal of the analysis in this section is to initiate a more detailed investigation of

the e¤ects of vertical mergers along the supply chain. Such an investigation, as well as a model of the

synergy and market power e¤ects of vertical mergers along the supply chain, are a subject of ongoing

research.

6 Conclusions and future research

This paper makes three contributions to the literature examining the e¤ects of mergers on �rms�

product market rivals, customers, and suppliers. First, we employ a novel, hand-collected dataset of

projected merger synergies and �nd that horizontal merger synergies are negatively related to returns

to rivals upon merger announcements and are generally positively associated with returns to �rms

operating in upstream and downstream industries. These �ndings are consistent with a stylized model

of mergers in the presence of market power and synergies, which take the form or operating e¢ ciencies

and/or cost savings. Our paper is the �rst to provide an analysis of horizontal merger synergies on

�rms along the supply chain. The conclusion, that merger synergies are an important determinant of

the market reaction by rivals, customers, and suppliers to merger announcements, is consistent with

20

the view that synergies are an important reason for mergers and is inconsistent with the (somewhat

cynical) view of horizontal mergers being driven solely by market power considerations. This conclusion

supports recent evidence in Bernile and Bauguess (2010), who �nd that projected merger synergies

tend to be positively associated with post-merger performance of the merging �rms.

Second, we revisit the empirical literature examining the market power (collusion) motive for

horizontal mergers. Our model shows that the e¤ects of market power on rivals, customers, and

suppliers of merging �rms can be positive or negative, depending on the level (and type) of synergies.

Thus, examining the empirical relation between measures of market power and returns to �rms along

the supply chain without controlling for a measure of synergies, as in past studies, may lead to biased

inferences. An omitted measure of merger synergies may be responsible for the generally inconclusive

evidence regarding the e¤ects of market power reported in past studies. Our tests of the market

power hypothesis, which account for merger synergies, demonstrate that market power is generally an

important determinant of returns to rivals, customers, and suppliers of merging �rms upon merger

announcements, consistent with the underlying theoretical arguments.

Third, we contribute to the empirical merger literature by providing a �rst (exploratory) analysis

of the e¤ects of merger synergies in vertical mergers along the supply chain. Despite employing a small

sample of vertical mergers with available synergy projections, we �nd that forecasted synergies exhibit

a signi�cant relation with returns of rivals, customers, and suppliers of bidders in vertical mergers.

The sign of the e¤ect of synergies on customers and suppliers depends crucially on whether a bidder

is in an upstream or downstream industry.

There are numerous interesting avenues for future research that we are currently undertaking. On

the theory side, extending the model to account for heterogeneity in sizes and production functions of

�rms in the merging �rms�industry, as well as in upstream and downstream industries, can potentially

explain some of the empirical �ndings regarding the e¤ects of synergies and market power that the

current stylized model is silent about. In addition, while there is a large theoretical literature that

examines various reasons for merging vertically, there is no model examining the synergy and market

power motives for vertical mergers at the same time. Such a model could serve as a guidance in further

empirical tests of the e¤ects of vertical mergers along the supply chain.

On the empirical side, the tests in this paper are a tip of an iceberg. First, examining in more

detail the e¤ects of the structure of both the merging �rms�industries and upstream and downstream

industries on rivals, customers, and suppliers having di¤erent characteristics (i.e. di¤erent production

functions and market power in their industries) is likely to improve our understanding of both the

synergy and market power motives for mergers. Second, synergies and market power considerations

21

are likely to interact in their e¤ects on rivals, customers, and suppliers of merging �rms. Empirically

examining this interaction could be interesting. Third, expanding the sample of vertical mergers

and performing a more detailed analysis of the contemporaneous e¤ects of synergies and market

power on rivals, customers, and suppliers of �rms merging vertically would increase the power of the

tests. Fourth, it would be useful to improve the measure of vertical relations among �rms, which is

currently based on the U.S. Census Bureau input-output tables and results in samples of potential

(as opposed to actual) customers and suppliers. Two potential improvements of the sample selection

criteria is incorporating a measure of geographic proximity among �rms in de�nitions of horizontal

and vertical relations, developed in Bernile and Bauguess (2010), and/or augmenting the de�nition of

�rms�similarity and relatedness by a text-based analysis of �rms�product descriptions, developed by

Hoberg and Phillips (2009, 2010a, 2010b).

22

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Fan, J., Lang. L., 2006, On the Patterns and Wealth E¤ects of Vertical Mergers, Journal of Business79, 877-902.

Fee, E., Thomas S., Sources of Gains in Horizontal Mergers: Evidence from Customer, Supplier, andRival Firms, Journal of Financial Economics 74, 423-460.

Gilson S., Hotchkiss, E., Ruback, R., Valuation of Bankrupt Firms, Review of Financial Studies 13,43-74.

Hoberg, G., Phillips, G., 2009, New Dynamic Product Based Industry Classi�cations and EndogenousProduct Di¤erentiation, University of Maryland working paper.

Hoberg, G., Phillips, G., 2010a, Real and Financial Industry Booms and Busts, Journal of Finance,forthcoming.

Hoberg, G., Phillips, G., 2010b, Product Market Synergies in Mergers and Acquisitions: A TextBased Analysis, University of Maryland working paper.

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Jovanovic, B., Rousseau, P., 2002, The Q-Theory of Mergers, American Economic Review 92, 198-204.

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23

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24

Appendix �Proofs

Proof of Proposition 1

Using (5) and (10), the return to a rival �rm following a merger, ret_rival, is

ret_rival =��st:alone��no_merger

� 1 = �b((2c� b)���� � 2c)(b(2bn+ b+ 6c)���� + c(2bn+ 4c))((b(bn+ 4c)���� + c(b(n� 1) + 2c2))2

: (15)

The second term in the numerator, as well as the denominator, are positive. Thus, the sign of��st:alone��no_merger

� 1 is opposite to the sign of (2c � b)���� � 2c, i.e. it is positive if ���� < 2c2c�b and it is

negative if ���� > 2c2c�b .

Di¤erentiating ret_rival in (15) with respect to �� results in

@ret_rival@��

= �2bc��(b+ 2c)(b(n+ 1) + 2c)2(b���� + c)

((b(bn+ 4c)���� + c(b(n� 1) + 2c2))3< 0: (16)

The result of di¤erentiating ret_rival with respect to �� except for �� is substituted with �� in (16).

Di¤erentiating ret_rival with respect to n results in

@ret_rival@n

=2b2((2c� b)���� � 2c)(b(n+ 1) + 2c)(b���� + c)2

((b(bn+ 4c)���� + c(b(n� 1) + 2c2))3: (17)

(17) is positive if ���� > 2c2c�b and it is negative if ���� <

2c2c�b .�

Proof of Proposition 2

Using (6) and (11), the relative change in total industry output, rel_�Q, is

rel_�Q =Q�mergerQ�no_merger

� 1 = � ((2c� b)���� � 2c)(b+ 2c)((b(bn+ 4c)���� + c(b(n� 1) + 2c2))n

: (18)

Di¤erentiating rel_�Q in (18) with respect to �� results in

@ret_rival@��

=(b+ 2c)2c��(b(n+ 1) + 2c)

((b(bn+ 4c)���� + c(b(n� 1) + 2c2))2n> 0: (19)

Di¤erentiating rel_�Q with respect to n results in

@ret_rival@n

=(b+ 2c)((2c� b)���� � 2c)(2b����(bn+ 2c) + c(b(2cn� c) + 2c)

((b(bn+ 4c)���� + c(b(n� 1) + 2c2))2n2: (20)

The rest of the proof is identical to the proof of Proposition 1.�

25

Appendix A �Examples of news stories and press releases containing insiders�forecasts

of merger-related gains

Following are extracts from some news stories and press releases that include managerial forecasts

of merger-related gains. The relevant sections are in italics.

Example 1: Bell Atlantic, GTE Merger -4: Cost Synergies Within 3 Yrs. 28 July

1998, Dow Jones News Service: Bell Atlantic and GTE said based on anticipated revenue and

cost synergies, the transaction is expected to add to earnings per share [. . . ] The companies said

in a joint press release Tuesday that they see the transaction producing cost synergies totaling $2

billion within three years of the deal�s completion. The merged company is also expected to generate

an additional $2 billion in revenue synergies.

Example 2: Newell Faces a Big Challenge in Rubbermaid Takeover � It Hopes

�Newellization�Can Revitalize Household-Products Maker. 3 November 1998, The Wall

Street Journal: Newell Co., renowned for squeezing costs out of acquired companies, faces a tough

test in its proposed $5 billion acquisition of Rubbermaid Inc. [. . . ] Newell Chief Executive John Mc-

Donough is promising that the merger plan, announced late last month, will deliver $300 million to

$350 million in synergies by 2000, 25% of that from selling Rubbermaid products to Newell customers.

[. . . ] Newell hopes Rubbermaid will accelerate the combined company�s growth. Besides the usual

cost savings, Mr. McDonough predicts the merger will produce $70 million to $90 million in new sales

by 2000 as Newell introduces its customers to Rubbermaid. [. . . ]

Example 3: Food Lion Will Buy Hannaford For About $3.3 Billion, Plus Debt. 19

August 1999, Dow Jones Business News: Food Lion Inc. Wednesday con�rmed it will acquire

supermarket operator Hannaford Bros. Co. for about $3.3 billion in cash and stock, a deal that

would create the sixth-largest food retailer in the U.S. [. . . ] The combined Food Lion and Hannaford

will have nearly $14 billion in pro-forma annual revenue. The combined company is expected to result

in synergies estimated at about $40 million in the �rst year and about $75 million annually by the

third year. Operations that may be a¤ected include distribution, information systems, training and

marketing. [. . . ]

Example 4: Hilton to buy Promus Hotel for $4 billion. 7 September 1999, Reuters

News: Hilton Hotels Corp. on Tuesday said it would buy Promus Hotel Corp. for $4 billion in

cash, stock and debt, creating a giant with 1,700 hotels and operations in almost every segment of the

26

industry. [. . . ] The combined company will have pro forma 2000 EBITDA of $1.3 billion, and result

in annual cost savings and operating e¢ ciencies of about $55 million in the �rst year and $90 million

thereafter. [. . . ]

Example 5: Kroger to merge with Fred Meyer. 19 October 1998, Reuters News:

Kroger Co said Monday it would merge with Fred Meyer in a deal that creates the nation�s largest

grocer. The 1-for-1 stock swap is valued at about $13 billion including debt. A full text of the

company�s press release follows. [. . . ] Kroger plans to generate annual cost savings of approximately

$225 million within three years, including approximately $75 million in the �rst year. Kroger plans to

generate these savings through combined procurement of goods and services, reduced corporate overhead,

in-market synergies, and consolidation of support services. [. . . ]

Example 6: Unicom, Peco Con�rm Plans To Merge, Create Utility-Industry Giant.

23 September 1999, Dow Jones Business News: Chicago-based Unicom Corp. and Philadelphia-

based Peco Energy Co. Thursday formally announced plams to merge in a deal they valued at more

than $8 billion. [. . . ] The merger will boost earnings in the �rst year after the deal closes, excluding

one-time merger-related charges, the companies said. They expect annual cost savings of $100 million

in the �rst year, growing to $180 million by the third year. The majority of the savings will come from

eliminating redundant corporate and administrative positions and programs.

Example 7: Amax Inc., Cyprus Minerals merger to create $5 billion company. 25

May 1993, Reuters News: Amax Inc. and Cyprus Minerals Co., two of the nation�s biggest

metals producers, said Tuesday they plan a corporate marriage that will create a new company �

Cyprus-Amax � with assets of $5 billion. [. . . ] The combined coal operations will produce more

than 70 million tons of coal a year and strong oil and gas and lithium businesses, Ward said. "The

combination of the companies will present signi�cant opportunities to reduce operating and corporate

and divisional overhead costs, with anticipated annual cost savings of at least $100 million," he said.

But the cost-cutting, he said, may result in job cuts. Cyprus has already completed a restructuring

that has trimmed 650 jobs in the past year. [. . . ]

Example 8: Viatel/Synergies -2: Sees $500M Savings Over 5 Yrs. 27 August 1999,

Dow Jones News Service: Viatel Inc. (VYTL) expects its planned acquisition of Destia Commu-

nications Inc. (DEST) to generate cost savings of $500 million over �ve years, company o¢ cials said

in a press conference Friday. [. . . ].

27

Appendix B.1 �Adjustment to raw forecasts

Sometimes insiders provide time tables specifying the magnitude of the annual synergy gains

expected to be realized during intermediate years before the bene�ts of the merger fully materialize.

In the majority of cases, however, managers� projections are not as detailed. In some cases, the

forecast is limited to the �rst post-completion year. In other cases, the forecast refers to annual

synergies expected between two to four years post-completion, but provides no guidance on all or

some of the intermediate years. In other cases, the forecast quanti�es annual gains with no indication

about the timing. Finally, in other cases, insiders disclose the cumulative amount of synergies expected

to be realized over 3, 5, or 10 years after the deal�s completion.

In general, when annual forecasts are provided, we always assume that the last projected year is

the steady-state level of synergies. When no detail about the timing is provided (i.e. single annual

forecast with no timing or 5- or 10-year cumulative forecasts), we assume the steady-state level is

reached in year 4 after completion. Whenever necessary (i.e. cumulative forecasts or annual forecast

with no timing or annual forecast with missing intermediate years), similar to HJR (2001), we assume

that expected synergies grow at a rate of 100% until the steady-state is reached.

Therefore, if managers forecast annual gains of x dollars by year t, where t > 1 (or provide no

timing, in which case we assume t = 4), we assume the merged entity realizes x=2 in year t � 1,

x=4 in year t � 2, and so forth until year 1 after completion of the merger is reached. If managers

forecast annual gains of x dollars by year t and y dollars by year t + i, where i > 1, we interpolate

the expected synergy gains for the intermediate years, assuming the gains increase linearly over the

missing forecast years. When a cumulative forecast is provided, we adopt the following convention.

For a 3-year cumulative forecast equal to x, we assume the �rst year forecast is equal x=7, the second

2x=7, and the third 4x=7. For a 5-year cumulative forecast equal to x, we assume the �rst year forecast

is equal x=23, the second 2x=23, the third 4x=23, and 8x=23 in each remaining year. For a 10-year

cumulative forecast equal to x, we assume the �rst year forecast is equal x=63, the second 2x=63, the

third 4x=63, and 8x=63 in each remaining year.

Appendix B.2 �Valuation of merger-related synergies forecasts

To estimate the present value of the merger-related incremental cash �ows projected by man-

agement, we follow Kaplan and Ruback (1995), Gilson et al. (2000), HJR (2001), and Bernile and

28

Bauguess (2010). In particular, using the adjusted projections described in Appendix B.1, we compute

the present value of the after-tax synergistic cash �ows assuming they are realized in perpetuity as:

PV (Synergies) =5Pt=i

(1�0:36)TSt(1+r)t + (1�0:36)TSi+5

r(1+r)i+5

where i = 1 + Number of Months to Completion12 ; (1 � 0:36)TSt is the after-tax synergy forecast for the tth

year after completion, assuming a �at 36% tax rate; r is the discount rate; and Number of Months

to Completion is, for completed deals, the actual number of months to completion or, for withdrawn

deals, the average number of months to completion of completed deals in the same target industry

based on Fama-French (1997) 49-industry classi�cation. In the 5-year Model, the present value of

synergies is restricted to the �rst element on the right-hand side of the equation above.

Following Kaplan and Ruback (1995), the synergies�discount rate, ru, is the weighted-average of

the merging �rms�cost of unlevered equity, using as weights the �rms�pseudo-market value of assets

�i.e. market value of common equity, E, plus book value of long-term debt, D, and liquidation value

of preferred equity, P , measured 60 trading days prior to when the target company is �put in play�. To

calculate each merging �rm cost of unlevered equity: 1) we estimate the �rm�s equity beta, �E , using

at least 60 and at most 250 daily �rm stock returns and the CRSP value-weighted portfolio returns

ending 60 days prior to when the target is �put in play�; 2) we assume the �rms�debt and preferred

equity betas, �D and �P , are equal to 0.25; 3) assuming a �at 36% tax rate, we calculate the unlevered

equity beta as �U = [(�EE + �PP + 0:64�DD) = (E + P + 0:64D)]; 4) we apply the CAPM equation

to the unlevered equity beta, �U , assuming a 7:5% risk-premium and setting the risk-free rate equal

to the 10-year Treasury bond yield at the time of the announcement.

29

Table 1�Frequency of mergers and propensity to provide merger-related operating synergiesforecasts. This table reports the sample frequency of merger o¤ers and the associated propensity to providemerger-related operating synergies forecasts by year. The sample of mergers is from SDC and includes dealsbetween non-�nancial, publicly traded companies announced between January 1995 and December 2005, forwhich the acquirer holds less than 50% of the target equity prior to the o¤er and aims to own 100% of the targetequity. Num. [Horiz., Vert.] Deals is the number of [horizontal, vertical] deals announced. Horizontal dealsinvolve two �rms with the same four-digit primary NAICS code; Vertical deals involve two �rms that do notshare the same four-digit primary NAICS code and whose industries have a signi�cant vertical relation based onthe Bureau of Economic Analysis�annual input-output use tables between 1997 and 2005. A signi�cant verticalrelation implies that the downstream industry purchases at least 5% of the upstream industry output and theupstream industry provides at least 5% of the value of inputs used in the downstream industry. Fract. w/ Syn.Fcst. (EW) is the equal-weighted fraction of o¤ers accompanied by management projections of merger-relatedsynergies. Fract. w/ Syn. Fcst. (VW) is the fraction of o¤ers accompanied by management projections ofmerger-related synergies weighted by the book value of the target �rm assets at the �scal year end prior to theo¤er announcement.

Ann.Year

Num.Deals

Fract.w/ Syn.

Fcst. (EW)

Fract.w/ Syn.

Fcst.(VW)

Num.Horiz.Deals

Fract.Horiz. w/

Syn.Fcst. (EW)

Fract.Horiz. w/

Syn.Fcst. (VW)

Num.Vert.Deals

Fract.Vert. w/

Syn.Fcst. (EW)

Fract.Vert. w/

Syn.Fcst. (VW)

1995 162 0.191 0.648 88 0.227 0.759 12 0.083 0.1481996 179 0.196 0.630 104 0.231 0.653 14 0.214 0.5301997 229 0.197 0.717 132 0.212 0.764 16 0.250 0.7971998 274 0.259 0.758 161 0.267 0.776 17 0.235 0.8191999 289 0.246 0.660 179 0.307 0.699 18 0.444 0.4632000 238 0.214 0.553 139 0.245 0.712 12 0.333 0.9312001 195 0.241 0.605 112 0.250 0.592 10 0.600 0.9422002 109 0.229 0.512 63 0.317 0.844 2 0.500 0.8042003 113 0.292 0.552 69 0.232 0.501 5 0.600 0.9822004 107 0.458 0.877 63 0.429 0.849 4 0.750 0.9812005 113 0.363 0.852 63 0.381 0.874 7 0.286 0.400

Total 2,008 0.249 0.683 1,173 0.272 0.737 117 0.333 0.711

30

Table 2 �Merger-related synergies and market structure. This table reports summary statistics forthe value of the projected synergies and merging �rms�market power and industry structure. The sample ofmergers and the Horizontal/Vertical classi�cations are as de�ned in Table 1.

Panel A �Present value of projected synergies scaled by combined bidder�s and target�s assets.This panel reports summary statistics for the value of projected synergies as a percentage of merging �rms�combined pseudo-market value of asset as of the �scal year end prior to the merger announcement. The pseudo-market value of asset is the sum of the merging �rm market capitalization (i.e., number of common sharesoutstanding multiplied by stock price) at the �scal year end plus the book value of liabilities. The present valueof net-of-tax projected synergistic cash �ows is calculated following Kaplan and Ruback (1995), Gilson, Hotchkissand Ruback (2000), and Houston, James, and Ryngaert (2001). The valuation model assumes synergies arerealized in perpetuity.

All Horizontal Vertical Other

N 499 319 39 141

Mean 8.61% 8.87% 8.45% 8.07%

Q1 2.55% 2.83% 2.17% 1.97%

Median 5.68% 6.14% 5.60% 4.50%

Q3 10.52% 10.87% 10.55% 9.53%

St. Dev. 11.07% 11.33% 9.78% 10.86%

Panel B �Market power and industry structure by deal typeThis panel reports summary statistics for the merging �rms�market power and industry structure as of the �scalyear end prior to the merger announcement. Market Share is equal to �rm sales divided by the total sales of all�rms reporting the same primary four-digit NAICS code. Ind. Num. Firms is the number of �rms reportingthe same primary four-digit NAICS code. Ind. HHI is the industry Her�ndahl Index measured as the sum ofsquared market shares of all �rms reporting the same primary four-digit NAICS code.

Acquirer Target

Market ShareInd.

Num. FirmsInd.HHI Market Share

Ind.Num. Firms

Ind.HHI

Horizontal DealsMean 0.079 105.2 0.137 0.044 105.2 0.137

Median 0.027 61 0.091 0.012 61 0.091

St. Dev. 0.151 101.3 0.145 0.095 101.3 0.145

Vertical DealsMean 0.133 60.5 0.160 0.065 83.0 0.147

Median 0.050 41 0.122 0.028 45 0.083

St. Dev. 0.182 58.5 0.164 0.094 88.7 0.123z­stat:

Vert=Horiz 1.81* ­4.1*** 0.83 1.30 ­1.45 0.48

31

Table 3 �Relation between the propensity to provide synergy forecasts and �rm and deal char-acteristics This table reports probit regression estimates of the relation between the propensity to providemerger-related synergies forecasts and �rm, merger, and industry characteristics. All explanatory variables arede�ned in the Appendix. Marg. E¤ect is the e¤ect at the mean of a one standard deviation change of thecorresponding continuous variable or of a change from zero to one of the corresponding indicator variable. *,**, *** indicate signi�cance of the coe¢ cient estimate at the 10%, 5%, and 1% probability level, respectively.Pseudo-R2 is the Veal-Zimmerman R-square.

Coeff.Estim.

Marg.Effect t­stat

Intercept ­2.496 ­3.92***Bidder Guidance Indicator 0.348 7.1% 3.89***Target Guidance Indicator 0.133 2.7% 1.49

Num. Bidder Analysts ­0.006 ­0.1% ­0.67Num. Target Analysts 0.010 0.2% 0.7

Num. Bidder Inst. Investors ­0.001 0.0% ­3.3***Num. Target Inst. Investors 0.002 0.0% 2.03**

Toehold Indicator ­0.230 ­4.7% ­1.03Pct. Offer Cash ­0.005 ­0.1% ­4.54***

Bidder MV Assets ­0.097 ­2.0% ­2.16**Target MV Assets 0.433 8.9% 8.15***Ind. Adj. PPE/AT 0.885 18.1% 2.4**

Bidder Ind. Adj. EBITDA/AT ­0.102 ­2.1% ­0.3Bidder Ind. Adj. SALES/AT 0.133 2.7% 1.55

Target Ind. Adj. EBITDA/AT ­0.212 ­4.3% ­0.91Target Ind. Adj. SALES/AT 0.033 0.7% 0.44

Deregulation Intensity 0.001 0.0% 0.01Bidder Ind. Adj. M/B Asset ­0.149 ­3.1% ­4.74***

Similar MB Asset 0.014 0.3% 0.17Related Deal ­0.170 ­3.5% ­1.36

Horizontal Deal ­0.015 ­0.3% ­0.15Low Growth Industries 0.386 7.9% 2.95***High Growth Industries ­0.118 ­2.4% ­1.14

Herfindhal Index 0.802 16.4% 2.52**Change in Herfindhal ­0.186 ­3.8% ­0.29

Year Fixed Effects YesIndustry Fixed Effects Yes

Number of Observations 1,979Pseudo­R2 53.0%

32

Table 4 �Relation between the projected synergies and merging �rms�performance The tablereports second-stage OLS estimates of the relation between merger performance and the scaled value of synergies,based on Heckman (1979) two-step estimator. In the Stock Market Reaction models, the dependent variable isthe combined (toehold-adjusted) cumulative abnormal return over the relevant window, while Scaled Synergiesis the present value of after-tax synergies scaled by the combined pre-merger toehold-adjusted market value ofequity. Ann CAR (-20,20) is sum of market model residuals for the combined (toehold-adjusted) value-weightedportfolio of bidder and target �rm common equity from 20 trading days prior to 20 trading days after the mergerannouncement date. In the Post-merger Change in Operating Performance models, the dependent variable isthe change in the corresponding industry-adjusted �nancial ratio measured between the �rst and third �scalyear ends after the merger is completed, while Scaled Synergies is the average annual synergies forecast for the�rst three years scaled by the value of assets or sales reported as of the �rst �scal year end after the merger iscompleted. Return on Asset refers to the change in the ratio of earnings before interest, taxes, depreciation andamortization to book asset. Asset Turnover refers to the change in the ratio of sales to book asset. Pro�t Marginrefers to the change in the ratio of earnings before interest, taxes, depreciation and amortization to sales. In allmodels, Inv. Mills Ratio is the inverse Mills ratio computed using the residuals of the probit model reportedin Table 3. *, **, *** indicate signi�cance of the coe¢ cient estimate at the 10%, 5%, and 1% probability level,respectively.

InterceptScaled

SynergiesInv.

Mills ratio N Adj­R2

StockMarket Reaction

Ann CAR (­20,20) ­0.0163 0.3553 0.0274 480 5.6%1.19 6.39*** 1.99**

Ann­Compl CAR (­20,20) ­0.0128 0.8329 ­0.0133 408 8.6%0.47 7.72*** 0.50

Post­merger Changein Operating Performance

Returnon Asset

0.0269 0.9996 ­0.0211 298 6.7%4.35*** 5.27*** 3.28***

AssetTurnover

0.2128 2.0439 ­0.0922 298 1.8%6.00*** 1.87* 2.50**

ProfitMargin

­0.0159 1.0514 0.0133 298 7.6%1.34 5.88*** 1.10

33

Table 5 �Supply chain �rms abnormal returns around horizontal merger announcements Thistable reports summary statistics for the three-day cumulative abnormal return experienced by portfolios of �rmsalong the merged entity�s supply chain. The sample of �rms along the merged entity�s supply chain is restrictedto relevant single-segment �rms - i.e. �rms having a four-digit segment NAICS code accounting for at least 90%of the total sales reported. Competitors are non-rival single segment �rms whose primary four-digit NAICScode is equal to the merging �rms�. Important Customers are non-rival single segment �rms in the industry thatpurchases the largest fraction of the merging �rms�industry output, if the fraction is at least 5%. ImportantSuppliers are non-rival single segment �rms in the upstream industry that provides the largest fraction of inputto the merging �rms�industry, if the fraction is at least 5%. Equally-(Value-)weighted Portfolios 3-day Ann.CAR (%) is sum of market model residuals for the equal- (value-) weighted portfolio of relevant supply chain�rm equities over the three-day window around a deal announcement. *, **, *** indicate the signi�cance level(10%, 5%, and 1%, respectively) for rejection of the null hypothesis of mean CAR=0.

Equally­weighted Portfolios 3­day Ann. CAR (%) Value­weighted Portfolios 3­day Ann. CAR (%)

N Mean 25% 50% 75% StdDev t Mean 25% 50% 75% Std

Dev t

Competitors 316 0.21 ­0.84 ­0.02 1.16 1.90 1.98** 0.09 ­1.15 0.05 1.21 2.86 0.53

ImportantCustomers 278 ­0.13 ­1.19 ­0.22 0.69 1.66 ­1.3 ­0.23 ­1.28 ­0.21 0.95 2.26 ­1.7*

ImportantSuppliers 304 ­0.01 ­0.83 0.00 0.80 1.34 ­0.13 0.03 ­0.81 0.03 1.01 1.95 0.27

34

Table 6 �Regressions of rivals�returns around horizontal deal announcements The table reportssecond stage WLS regression estimates of the relation between rivals� three-day CAR, the scaled value ofprojected merger synergies, the pre-merger industry concentration and the expected e¤ect of the merger onindustry concentration, based on Heckman (1979) two-step estimator. The weights are equal to the square rootof the number of �rms in the relevant portfolio. Scaled Synergies is the present value of after-tax synergies scaledby the total pre-merger market capitalization of the rival �rms�equities. Merging Firms Log Capitalization isthe natural logarithm of the combined market value of the merging �rms�equity as of 60 trading days priorto the merger announcement. Portfolio Firms Log Capitalization is the natural logarithm of the total marketvalue of the portfolio �rms�equity as of 60 trading days prior to the merger announcement. Merging FirmsIndustry HHI is the Her�ndahl-Hirschman Index measured as the sum of squared market shares of all �rms inthe merged entity�s industry. Change Merging Firms Industry HHI is equal to twice the target market sharetime the acquirer market share. In all models, Inv. Mills Ratio is the inverse mills ratio computed using theresiduals of the probit model reported in Table 3. *, **, *** indicate signi�cance of the coe¢ cient estimate atthe 10%, 5%, and 1% probability level, respectively.

EW Portfolio VW PortfolioCoeff.T­stat

Coeff.T­stat

Coeff.T­stat

Coeff.T­stat

Intercept 0.1554 0.1561 0.1061 0.10767.42*** 7.46*** 3.43*** 3.49***

ScaledSynergies

­0.1097 ­0.1112 ­0.0831 ­0.0861­8.45*** ­8.57*** ­4.32*** ­4.48***

Log Merging FirmsCapitalization

0.0003 0.0004 0.0004 0.00050.75 0.92 0.56 0.78

Log Portfolio FirmsCapitalization

­0.0079 ­0.0080 ­0.0056 ­0.0058­7.22*** ­7.3*** ­3.46*** ­3.56***

Merging FirmsIndustry HHI

­0.0195 ­0.0206 0.1090 0.1068­0.89 ­0.94 3.34*** 3.29***

Change MergingFirms Industry HHI

0.0245 ­0.0430 0.0477 ­0.08800.82 ­0.99 1.08 ­1.36

InteractionInd HHI­Change HHI

0.3861 0.77632.12** 2.88***

InverseMills ratio

0.0031 0.0032 0.0057 0.00592.15** 2.23** 2.68*** 2.8***

Year Fixed Effect Yes Yes Yes YesIndustry Fixed Effect Yes Yes Yes Yes

N 313 313 313 313Adj­R2

32.7% 33.0% 33.7% 34.2%

35

Table 7 - Regressions of customers�returns around horizontal deal announcements The table re-ports second stage WLS regression estimates of the relation between customers�three-day CAR, the scaled valueof projected merger synergies, the pre-merger industry concentration and the expected e¤ect of the merger onindustry concentration, based on Heckman (1979) two-step estimator. The weights are equal to the square rootof the number of �rms in the relevant portfolio. Scaled Synergies is the present value of after-tax synergies scaledby the total pre-merger market capitalization of the customer �rms�equities. Merging Firms Log Capitalizationis the natural logarithm of the combined market value of the merging �rms�equity as of 60 trading days priorto the merger announcement. Portfolio Firms Log Capitalization is the natural logarithm of the total marketvalue of the portfolio �rms�equity as of 60 trading days prior to the merger announcement. Merging FirmsIndustry HHI is the Her�ndahl-Hirschman Index measured as the sum of squared market shares of all �rms inthe merged entity�s industry. Change Merging Firms Industry HHI is equal to twice the target market sharetime the acquirer market share. Customer Industry HHI is the Her�ndahl-Hirschman Index measured as thesum of squared market shares of all �rms in the relevant downstream industry. In all models, Inv. Mills Ratio isthe inverse mills ratio computed using the residuals of the probit model reported in Table 3. *, **, *** indicatesigni�cance of the coe¢ cient estimate at the 10%, 5%, and 1% probability level, respectively.

EW Portfolio VW PortfolioCoeff.T­stat

Coeff.T­stat

Coeff.T­stat

Coeff.T­stat

Intercept ­0.0099 ­0.0146 0.0316 0.0251­0.41 ­0.6 0.9 0.72

ScaledSynergies

0.0038 0.0038 ­0.0003 ­0.00033.04*** 3.06*** ­0.15 ­0.14

Merging FirmsLog Capitalization

0.0006 0.0007 ­0.0015 ­0.00141.22 1.45 ­2.21** ­2.01**

Portfolio FirmsLog Capitalization

­0.0008 ­0.0007 ­0.0005 ­0.0003­0.79 ­0.69 ­0.33 ­0.23

Merging FirmsIndustry HHI

0.0009 0.0124 0.0104 0.02600.2 2.01** 1.48 2.65***

Change MergingFirms Industry HHI

­0.0260 0.0401 ­0.0761 0.0137­1.4 1.18 ­2.45** 0.3

InteractionInd HHI­Change HHI

­0.3764 ­0.5119­2.76*** ­2.6***

CustomerIndustry HHI

­0.0121 ­0.0123 ­0.0089 ­0.0092­0.66 ­0.67 ­0.34 ­0.35

InverseMills ratio

0.0000 ­0.0003 ­0.0078 ­0.0082­0.01 ­0.22 ­4.05*** ­4.25***

Year Fixed Effect Yes Yes Yes YesIndustry Fixed Effect Yes Yes Yes Yes

N 276 276 276 276Adj­R2 10.8% 11.7% 17.0% 17.7%

36

Table 8 - Regressions of suppliers�returns around horizontal deal announcements The table reportssecond stage WLS regression estimates of the relation between suppliers�three-day CAR, the scaled value ofprojected merger synergies, the pre-merger industry concentration and the expected e¤ect of the merger onindustry concentration, based on Heckman (1979) two-step estimator. The weights are equal to the square rootof the number of �rms in the relevant portfolio. Scaled Synergies is the present value of after-tax synergies scaledby the total pre-merger market capitalization of the supplier �rms�equities. Merging Firms Log Capitalizationis the natural logarithm of the combined market value of the merging �rms�equity as of 60 trading days priorto the merger announcement. Portfolio Firms Log Capitalization is the natural logarithm of the total marketvalue of the portfolio �rms�equity as of 60 trading days prior to the merger announcement. Merging FirmsIndustry HHI is the Her�ndahl-Hirschman Index measured as the sum of squared market shares of all �rms inthe merged entity�s industry. Change Merging Firms Industry HHI is equal to twice the target market sharetime the acquirer market share. Supplier Industry HHI is the Her�ndahl-Hirschman Index measured as the sumof squared market shares of all �rms in the relevant upstream industry. In all models, Inv. Mills Ratio is theinverse mills ratio computed using the residuals of the probit model reported in Table 3. *, **, *** indicatesigni�cance of the coe¢ cient estimate at the 10%, 5%, and 1% probability level, respectively.

EW Portfolio VW PortfolioCoeff.T­stat

Coeff.T­stat

Coeff.T­stat

Coeff.T­stat

Intercept 0.0543 0.0537 0.0627 0.06032.26** 2.23** 1.68* 1.62

ScaledSynergies

­0.0029 ­0.0029 0.0059 0.0058­1.37 ­1.37 1.91* 1.9*

Merging FirmsLog Capitalization

­0.0005 ­0.0006 ­0.0002 ­0.0002­2.03** ­2.05** ­0.47 ­0.53

Portfolio FirmsLog Capitalization

­0.0026 ­0.0026 ­0.0037 ­0.0036­1.9* ­1.88* ­1.71* ­1.67*

Merging FirmsIndustry HHI

­0.0021 0.0001 0.0053 0.0138­0.65 0.00 0.96 2.05**

Change MergingFirms Industry HHI

­0.0448 ­0.0302 ­0.1188 ­0.0589­2.62*** ­1.47 ­4.42*** ­1.53

InteractionInd HHI­Change HHI

­0.0592 ­0.2432­0.83 ­2.17**

SupplierIndustry HHI

0.0047 0.0044 0.0199 0.01880.45 0.42 0.95 0.89

InverseMills ratio

­0.0033 ­0.0034 ­0.0039 ­0.0041­3.75*** ­3.8*** ­2.83*** ­2.98***

Year Fixed Effect Yes Yes Yes YesIndustry Fixed Effect Yes Yes Yes Yes

N 302 302 302 302Adj­R2

16.2% 16.2% 10.6% 11.0%

37

Table 9 �Supply chain �rms abnormal returns around vertical merger announcements This tablereports summary statistics for the three-day cumulative abnormal return experienced by portfolios of �rms alongthe acquiring �rm�s supply chain around vertical merger announcements. The sample of �rms along the mergedentity�s supply chain is restricted to relevant single-segment �rms - i.e. �rms having a four-digit segment NAICScode accounting for at least 90% of the total sales reported. Up-Vertical deals are those in which a downstream�rm intends to takeover a �rm in a supplier industry. Down-Vertical deals are those in which a upstream�rm intends to takeover a �rm in a customer industry. Vertical deals are restricted to cases where the relationbetween up- and dwon-stream merging �rms is mutually important: the upstream �rm industry supplies at least5% of the input used in the downstream industry and the downstream �rm industry purchases at least 5% ofthe output of the downstream industry. Competitors are single segment �rms whose primary four-digit NAICScode is equal to the acquiring �rm�. Important Customers are non-rival single segment �rms in the industrythat purchases the largest fraction of the acquiring �rm�s industry output, if the fraction is at least 5% andother than the target �rm industry in �down-vertical�mergers. Important Suppliers are non-rival single segment�rms in the upstream industry that provides the largest fraction of input to the acquiring �rm�s industry, if thefraction is at least 5% and other than the target �rm industry in �up-vertical�mergers. Equally-(Value-)weightedPortfolios 3-day Ann. CAR (%) is sum of market model residuals for the equal- (value-) weighted portfolio ofrelevant supply chain �rm equities over the three-day window around a deal announcement. *, **, *** indicatethe signi�cance level (10%, 5%, and 1%, respectively) for rejection of the null hypothesis of mean CAR=0.

Equally­weighted Portfolios (%) Value­weighted Portfolios (%)

N Mean 25 50 75 StdDev t Mean 25 50 75 Std

Dev t

Up­Vertical (Acquirer merging with a Supplier)Competitors 20 0.07 ­0.74 0.15 0.91 1.98 0.2 ­0.31 ­2.21 0.05 0.41 2.66 ­0.5

ImportantCustomers 20 ­0.05 ­1.63 0.23 0.82 2.00 ­0.1 0.03 ­1.64 ­0.63 0.95 2.65 ­0.1

ImportantSuppliers 20 0.15 ­1.20 0.18 1.10 2.66 0.2 ­0.15 ­2.26 ­0.53 0.99 2.76 ­0.2

Down­Vertical (Acquirer merging with a Customer)Competitors 19 ­0.01 ­0.56 ­0.08 0.15 1.64 ­0.0 0.51 ­0.81 0.35 1.66 3.32 ­0.7

ImportantCustomers 19 1.32 ­0.62 0.11 1.61 3.65 1.5 0.22 ­1.20 ­0.23 1.82 2.78 ­0.3

ImportantSuppliers 18 ­0.93 ­2.23 ­0.03 0.82 2.07 ­1.9* ­0.50 ­1.59 0.02 0.61 1.67 ­1.4

38

Table 10 �Regressions of returns of rivals, customers, and suppliers of bidders in up-verticalmergers The table reports second stage WLS regression estimates of the relation between the acquiring �rmsupply chain �rms�three-day CAR around up-vertical merger announcements and the scaled value of projectedmerger synergies, based on Heckman (1979) two-step estimator. The weights are equal to the square root ofthe number of �rms in the relevant portfolio. Scaled Synergies is the present value of after-tax synergies scaledby the total pre-merger market capitalization of the supplier �rms�equities. Merging Firms Log Capitalizationis the natural logarithm of the combined market value of the merging �rms�equity as of 60 trading days priorto the merger announcement. Portfolio Firms Log Capitalization is the natural logarithm of the total marketvalue of the portfolio �rms�equity as of 60 trading days prior to the merger announcement. In all models, Inv.Mills Ratio is the inverse mills ratio computed using the residuals of the probit model reported in Table 3. *,**, *** indicate signi�cance of the coe¢ cient estimate at the 10%, 5%, and 1% probability level, respectively.

Panel A �Rivals.

EW VWCoeff.Estim. t­stat

Coeff.Estim. t­stat

Intercept ­0.0638 ­1.23 0.0947 1.32Log(Merging Firms Capitalization) ­0.0012 ­0.71 ­0.0006 ­0.24Log(Portfolio Firms Capitalization) 0.0036 1.25 ­0.0042 ­1.07

Scaled PV(Synergies) ­0.0939 ­2.61** ­0.0016 ­0.03Inv. Mills ratio 0.0083 1.04 ­0.0269 ­2.43**

Number of Observations 20 20Adj­R2 13.4% 8.9%

Panel B �Customers.

EW VWCoeff.Estim. t­stat

Coeff.Estim. t­stat

Intercept ­0.0293 ­0.97 0.0229 0.51Log(Merging Firms Capitalization) ­0.0025 ­2.14** ­0.0045 ­3.08***Log(Portfolio Firms Capitalization) 0.0023 1.16 ­0.0004 ­0.18

Scaled PV(Synergies) 0.0951 3.36*** 0.1609 4.61***Inv. Mills ratio 0.0059 0.89 0.0143 1.75*

Number of Observations 20 20Adj­R2 19.7% 27.5%

39

Panel C �Suppliers.

EW VWCoeff.Estim. t­stat

Coeff.Estim. t­stat

Intercept ­0.0277 ­0.67 ­0.2465 ­5.83***Log(Merging Firms Capitalization) ­0.0028 ­1.76* ­0.0047 ­2.56**Log(Portfolio Firms Capitalization) 0.0032 1.27 0.0148 5.75***

Scaled PV(Synergies) ­0.0496 ­0.57 0.3526 3.97***Inv. Mills ratio ­0.0078 ­1.16 0.0109 1.67

Number of Observations 20 20Adj­R2 10.3% 33.2%

40

Table 11 �Regressions of returns of rivals, customers, and suppliers of bidders in down-verticalmergers The table reports second stage WLS regression estimates of the relation between the acquiring�rm supply chain �rms� three-day CAR around down-vertical merger announcements and the scaled valueof projected merger synergies, based on Heckman (1979) two-step estimator. The weights are equal to thesquare root of the number of �rms in the relevant portfolio. Scaled Synergies is the present value of after-taxsynergies scaled by the total pre-merger market capitalization of the supplier �rms�equities. Merging FirmsLog Capitalization is the natural logarithm of the combined market value of the merging �rms�equity as of 60trading days prior to the merger announcement. Portfolio Firms Log Capitalization is the natural logarithm ofthe total market value of the portfolio �rms�equity as of 60 trading days prior to the merger announcement. Inall models, Inv. Mills Ratio is the inverse mills ratio computed using the residuals of the probit model reportedin Table 3. *, **, *** indicate signi�cance of the coe¢ cient estimate at the 10%, 5%, and 1% probability level,respectively.

Panel A �Rivals.

EW VWCoeff.Estim. t­stat

Coeff.Estim. t­stat

Intercept 0.0559 1.44 0.1888 2.19**Log(Merging Firms Capitalization) 0.0020 1.66 ­0.0004 ­0.16Log(Portfolio Firms Capitalization) ­0.0030 ­1.52 ­0.0083 ­1.89*

Scaled PV(Synergies) ­0.3005 ­4.37*** ­0.2030 ­1.34Inv. Mills ratio ­0.0171 ­2.85** ­0.0441 ­3.33***

Number of Observations 19 19Adj­R2 24.4% 14.8%

Panel B �Customers.

EW VWCoeff.Estim. t­stat

Coeff.Estim. t­stat

Intercept 0.1682 4.02*** 0.0304 0.97Log(Merging Firms Capitalization) 0.0012 0.5 0.0068 3.81***Log(Portfolio Firms Capitalization) ­0.0079 ­3.86*** ­0.0032 ­2.08*

Scaled PV(Synergies) ­0.0229 ­3.7*** ­0.0196 ­4.21***Inv. Mills ratio ­0.0377 ­3.8*** ­0.0352 ­4.71***

Number of Observations 19 19Adj­R2 36.4% 46.8%

41

Panel C �Suppliers.

EW VWCoeff.Estim. t­stat

Coeff.Estim. t­stat

Intercept ­0.0312 ­0.68 0.1549 3.63***Log(Merging Firms Capitalization) 0.0018 1.53 ­0.0046 ­4.1***Log(Portfolio Firms Capitalization) 0.0010 0.39 ­0.0076 ­3.17***

Scaled PV(Synergies) ­0.0562 ­2.87** 0.0510 2.79**Inv. Mills ratio ­0.0025 ­0.41 ­0.0045 ­0.99

Number of Observations 18 18Adj­R2 17.2% 41.3%

42