Network Learning Effects in
Cross-border Mergers and Acquisitions
Jackie M.L. Chan∗

Chih-Sheng Hsieh†

November 15, 2018
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Abstract
In this paper, we study the impact of learning in foreign markets on firms’ entry decision
into new destinations. We propose local knowledge spillovers within spatial networks inthird countries (i.e., prior destinations) as a channel through which firms learn about theirdestination of interest. In the context of cross-border mergers and acquisitions (M&A), acquirers obtain knowledge on new destinations from their third-country targets through theneighborhood learning effect. We present a heterogeneous-firm model of cross-border M&A
with learning. Firms sort as acquirers, non-participants of M&A, and targets. Expected
profits and the probability of entry into a destination increase with acquirers’ accumulated
experience abroad and the strength of signals observed from their third-country targets.
Using data on global cross-border M&A activity from 1995 to 2016, we find strong empiricalsupport for the model at both the macro and micro levels. In particular, at the micro level,
we examine over 2,800 non-US acquirers that invested in the US and subsequently in nonUS foreign destinations. Controlling for accumulated experience in other third countries, wefind that the cross-border M&A activity (purchases and sales) of the US targets’ neighbors
has a positive impact on the destination choice of the non-US acquirers. Moreover, learning
effects are complementary across firms’ sources of information.
JEL classification codes: F14, F21, F23, D85
Keywords: Cross-border mergers and acquisitions, spatial networks, knowledge spillovers,
third countries, neighborhood learning.

∗

We thank Yishu Wang for his research assistance, as well as Treb Allen, Eik Swee, Ben Li, and participants ofseminars at Nanyang Technological University, Singapore Management University, National Singapore University
Lee Kuan Yew School of Public Policy, University of Melbourne, and Deakin University for their comments.
Support from Chinese University of Hong Kong Research Committee Direct Grant for Research is gratefully
acknowledged. Correspondence: Jackie M.L. Chan, Department of Economics, 9/F, Esther Lee Building, Chinese
University of Hong Kong, Shatin, N.T., Hong Kong, jmlchan@cuhk.edu.hk.
†
Chih-Sheng Hsieh, Department of Economics, 9/F, Esther Lee Building, Chinese University of Hong Kong,
Shatin, N.T., Hong Kong, cshsieh@cuhk.edu.hk.

1

1

Introduction

With the integration of world markets, firms are more connected now than ever before. Empirical evidence suggests that firms do not operate in isolation, and their decisions to export orinvest abroad are influenced by the networks they belong to. In both the origin country of thefirm and destination of interest, information frictions may be alleviated by knowledge spillovers
from personal and business connections. For example, the cross-border transactions of domestic
firms generate positive externalities within their networks for entry into international markets.
Such networks may arise with the interaction of firms through spatial proximity (e.g., Fernandes and Tang, 2014; Kamal and Sundaram, 2016), buyer-supplier relationships (e.g., Bai et al.,2017), and the intra-firm movement of employees (e.g., Mion and Opromolla, 2015; Muendler
and Rauch, 2018). In the destination market, social networks formed through labor migration
have been shown to impact both trade and foreign direct investment (FDI) (e.g., Rauch andTrindade, 2002; Javorcik et al., 2011; Burchardi et al., 2017).
Besides the origin and destination countries, information about new markets may be obtained through other countries that have been previously served (e.g., Schmeiser, 2012; Albornoz
et al., 2012; Chaney, 2014). Cross-border transactions connect firms to other participants ininternational markets, and in particular, multinationals that invest abroad using cross-bordermergers and acquisitions (M&A) are exposed to the networks of their target companies in prior
destinations. Therefore, these prior destinations, which we will refer to as “third countries,”
provide an additional channel for information on the new destination of interest to be gained. In
this paper, the role of knowledge spillovers for cross-border M&A is examined in a multilateralsetting. We propose and test the hypothesis that acquirer firms learn about new destination
markets through their targets’ networks in third countries. We focus on the spatial networks
formed between targets and their neighboring firms, and show theoretically and empiricallytheir impact on the destination choice of acquirers.
Our theoretical framework introduces third-country learning effects into the cross-border
M&A model of Nocke and Yeaple (2007). In each period, firms of heterogeneous productivity
may participate in the cross-border M&A market as acquirers or targets. The former pays afixed cost of entry and an acquisition price to purchase the brand and production capabilitiesof the latter.1 Hence, firms sort as acquirers, non-participants, and targets by productivity.
Importantly, the sorting pattern is also affected by firms’ expected profits in foreign markets,
which depend on the acquisition match quality. Firms hold prior beliefs about the match quality,with the mean increasing in their accumulated experience in third countries. The value of thisexperience is weighted by the (economic or geographic) closeness of each third country to thedestination of interest. Using the micro-level signals received from the spatial networks of their1
As in Nocke and Yeaple (2007), the productivity of acquirers is assumed to be mobile. While Nocke andYeaple (2007) also introduce a non-mobile capability to examine the choice of entry modes between exporting,greenfield investment, and M&A, we focus on learning effects in cross-border M&A and abstract from the various
entry modes of FDI. For other theories on the determination of FDI entry mode, see, for example, Helpman et al.
(2004), Chen and Moore (2010), and Norbäck and Persson (2007). Alternative models of cross-border M&A withfirm heterogeneity are studied in, for instance, Head and Ries (2008) and Blonigen et al. (2014).

2

third-country targets, acquirers learn about the destination and update their prior beliefs. In
contrast to accumulated experience, the impact of these networks, which consists of neighboring
acquirers to and targets of the destination of interest, varies depending on the location of theacquirer’s target within the third country. The model predicts that an acquirer’s probabilityof entry into a new destination is increasing in the bidirectional M&A activity (purchases andsales) of its target’s neighbors to the destination, and in the firm’s own accumulated experiencein third countries. Furthermore, learning effects are complementary across the firm’s sources of
information, as each effect becomes more pronounced with greater knowledge on the destination
obtained via the other channels.
We provide strong empirical support for the model’s predictions at both the macro andmicro levels. The analysis uses the universe of cross-border M&A data for 189 origin and 209
destination countries from Thomson-Reuters SDC Platinum. We study the extensive marginof M&A activity from 1995 to 2016, but utilize the full history of firms’ investments abroad
available starting from 1981. At the macro level, the number of bilateral acquisitions from an
origin country to a destination is positively associated with the origin’s aggregate experience inforeign markets, as measured by the lagged weighted sum of acquisitions across third countries.
Weights reflect the closeness of each third country to the destination, and we use the inverse ofgeographic distance and the volume of cross-border M&A activity as proxies. The latter, which
includes both acquisitions and sales, provides a measure of the economic interaction between thetwo countries. These results hold controlling for other determinants of cross-border investment,such as market size and bilateral distance.
At the micro level, we investigate the role of learning in third countries on the destination choice of acquirers. The third-country neighborhood learning effect and the impact of
accumulated experience are examined both individually and jointly. Given the comprehensive
information available on firm locations in the US, we study the neighborhood learning effectassociated with the spatial networks in this third country. However, the full global M&A data isused to measure firms’ aggregate experience overseas. Hence, our sample consists of over 2,800
non-US acquirers from 54 countries that invested at least once in the US, and subsequently,
purchased one or more non-US targets. We geocode the locations of US firms, and use a radiusof 100km to define the neighborhoods of acquirers’ US targets. The regression results show apositive impact of the (lagged) cross-border M&A activity of the US targets’ neighboring firms
on the destination choice of non-US acquirers. This effect is observed in both directions forneighboring acquisitions and target sales, and suggests that knowledge spillovers from thirdcountry targets are transferred to acquirers in the origin. Thus, there is knowledge diffusion
both within the country and across countries through the ownership structure of multinationals. Using a comprehensive set of controls, including destination-year, origin-year, and firm-year
fixed effects, we demonstrate that these results are not driven by other demand- and supply-side
shocks to cross-border investment. In addition, our regressions account for the potential endogenous choice of US target location by controlling for the cross-border M&A activity of the sameneighborhood in the year before the acquirer’s entry into the US. The results are also robust3to an instrumental variable strategy, where the interaction between the origin and destination’s
historical migrant population in the US at the county level is used as the instrument.
The third-country neighborhood learning effect is found to be stronger for neighboring firms
in the same state, same sector, and downstream (as opposed to upstream) sectors. Furthermore, we construct the cross-border M&A network as a two-mode (bipartite) network, and use
eigenvector centrality to capture both the number and quality of links between US neighboringfirms and destinations (Faust, 1997; Borgatti and Everett, 1997). For example, acquirers thatare more central in the global M&A network invest not only in a larger number of destinations,but the destinations themselves also have more links to firms. Thus, we construct a measure offirms’ information on international markets based on their observed investment patterns. We
find that the learning effect is increasing in the network centrality of neighboring firms, and canlargely be explained by the presence of experienced and well-connected acquirers and targets
within the US target’s spatial network.
We also show that accumulated experience, measured by the firm-level weighted sum ofacquisitions to third countries, is an important determinant of acquirers’ destination choice.
While the source of knowledge spillovers in these non-US third countries cannot be identified,prior entry into countries that are closer and more integrated with the destination of interestis associated with a higher probability of acquisition. Importantly, the neighborhood learning
effect remains even after controlling for this additional learning channel. Consistent with themodel’s predictions, the neighborhood learning effects of both directions are complementary,and more pronounced when firms have greater experience overseas and more target locations in
the US. Therefore, the value of third-country knowledge spillovers to multinationals increases
with both entry into more third countries, as well as more locations within a third country.

1.1

Literature review

Our findings contribute to a literature that seeks to understand how firms learn about foreign
markets in cross-border transactions. In particular, recent research has emphasized the role of
networks for knowledge diffusion in international trade. For instance, empirical evidence fromboth firm- and transaction-level data suggests that exporters’ choice of destination market,
volume of sales, and even identities of their trade partners are affected by the information gained
through their geographic neighbors (e.g., Koenig et al., 2010; Fernandes and Tang, 2014; Kamal
and Sundaram, 2016; Cassey et al., 2016). However, to the best of our knowledge, this is the first
paper to study the knowledge spillovers of micro-level spatial networks in the context of FDI.2
Moreover, whereas the impact of exporters or importers are generally examined in isolation due
to data constraints, we analyze the spillovers from both directions (i.e., purchases by acquirers
and sales of targets) in the same empirical framework using our global cross-border M&A
2
Importers also receive and benefit from the information of their geographic neighbors (e.g., Bisztray et al.,2017; Hu and Tan, 2017). Meanwhile, Cai et al. (2015) show that target firms located in urban areas are more
likely to receive a takeover bid and be purchased compared to firms in rural areas. Our findings suggest thatstronger knowledge spillovers among urban firms that are more clustered may be a potential explanation for theirresults.

4

transaction-level data, and compare the two channels both qualitatively and quantitatively.
Besides spatial networks, knowledge on markets abroad can be gained through other externalsources. A related strand of literature in finance studies social ties between companies and M&A
outcomes. For instance, board members of different companies may be connected throughtheir past education or employment (Cai and Sevilir, 2012; Ishii and Xuan, 2014; Renneboog
and Zhao, 2014). Personal connections created from the firm-to-firm movement of employees
and managers also facilitate the transfer of destination-specific knowledge between companies(e.g., Mion and Opromolla, 2015; Mion et al., 2017; Muendler and Rauch, 2018). Similarly,
information frictions of trade and FDI are alleviated by the networks formed through crossborder ethnic ties and migration (e.g., Rauch and Trindade, 2002; Parsons and Vézina, 2018;
Javorcik et al., 2011; Burchardi et al., 2017; Chan and Zheng, 2018).
Learning also occurs with sales and operations in international markets. In particular, thecorrelation of profits across destinations implies a pattern of sequential entry into foreign markets based on firms’ history of cross-border transactions (e.g., Albornoz et al., 2012).3 Schmeiser(2012) explains this export pattern by modeling learning as a decline in entry costs, and numerically demonstrates firms’ initial entry into a subset of profitable destinations, followed bya gradual transition towards more distant markets.4 Furthermore, the economic integrationof foreign countries may also facilitate firms’ entry into subsequent destinations. For example,
Chaney (2014) proposes a model where the network of contacts established in foreign markets
reduces search frictions and allows firms to search remotely for trade partners in new destinations.5 These networks may be formed by trade relationships between exporters and importers,as He and Lugovskyy (2018) document recently using a subset of matched Colombian exporters
and Chilean firms.
This paper makes theoretical and empirical contributions to the literature with a detailed
analysis of learning effects in third countries. First, we demonstrate the role of firms’ history
in the cross-border M&A market for their choice of destination.6 Second, we provide a microfoundation for the transmission of information from third-country networks to firms in theorigin through the direct ownership link between targets and acquirers. Using comprehensive
transaction-level data on cross-border M&A, we analyze these two effects both separately andjointly to understand how firms aggregate information from different sources of knowledge.
In addition, we find substantial heterogeneity within the networks with respect to geography,
industrial linkages, and the centrality of firms.
Our results have important implications for the sequential entry of acquirers into foreign
3
Nguyen (2012) presents a similar model where firms forecast profits in other destinations upon entering aforeign market. Separately, learning may result in dynamic outcomes such as improvements in productivity or
adaption to foreign market demand shocks (e.g., De Loecker, 2007; Berman et al., 2018).
4
For additional empirical evidence of sequential exporting, see for example, Freund and Pierola (2010), Berthou
and Ehrhart (2017), and Sheard (2014).
5
A related literature examines the role of trade intermediaries in overcoming trade frictions. These intermediaries are not only restricted to the origin or destination countries, but may be located in foreign countries (e.g.,
Antràs and Costinot, 2010; Chan, 2018).
6
Blonigen et al. (2007) study the impact of spatial networks on FDI at the aggregate level. Using a modified
gravity model, they show that US outbound FDI is influenced by spatial lags as well as surrounding-market
potential.

5markets. Traditional gravity forces predict a hierarchy or pecking order of destinations for firms,
in which, for example, countries that are closer to the home market are more attractive (e.g.,
Chan and Manova, 2015). However, Eaton et al. (2011) find significant deviations from sucha hierarchy for French exporters. The order in which firms enter foreign markets may also be
predicted by “extended gravity” forces, as exporters are more inclined to sell in destinations thatare similar to their previous export markets (Morales et al., 2017; Defever et al., 2015). Likewise,
Egger et al. (2014) show similar patterns for the establishment of foreign affiliates. Consistent
with this prior literature, we find that acquirers are indeed more likely to invest in countries
that are similar to their previous destinations. However, we also show how multinationals may
deviate from this hierarchy of destinations predicted by extended gravity forces. In particular,
deviations will be observed when acquirers receive strong positive signals about other markets
from their third-country targets.
The rest of paper is organized as follows. In Section 2, we present the theoretical model andderive the aggregate and firm-level implications. Section 3 describes the data, while Section 4
discusses the empirical framework. Section 5 presents the macro- and micro-level empiricalresults. Lastly, Section 6 concludes.

2

Theoretical model

We build a theoretical model of cross-border M&A to study the impact of learning on firms’
foreign acquisitions. In each period, firms in the origin country decide whether to become an
acquirer in the cross-border M&A market, and if so, which destination countries to enter. We
refer to a prior destination of the acquirer as a third country. Knowledge is accumulated in thisthird country, enabling the multinational firm to learn about its new destination of interest.

2.1

DemandThe world consists of N countries, indexed by m for the origin, n for the destination of interest,
and r for a third country (i.e., prior destination). Each country n has a mass Ln of identicalagents sharing aggregate income Yn . Preferences are defined by:
Z
Un =



−µn (ω)e1

σqn (ω)

σ−1
σ

σ
 σ−1

dω

.

ω∈Ωn

Because consumers are identical, we may denote qn (ω) as the aggregate consumption of variety
ω in the set of varieties Ωn in country n, with σ > 1 as the elasticity of substitution acrossvarieties. The term e−µn (ω) serves to shift the variety-specific demand of a firm, and accordingly,
its profits. Solving the consumer’s maximization problem, the aggregate demand for variety ωis:qn (ω) = eµn (ω) pn (ω)−σ Yn Pnσ−1 ,
R
 1
1−σ
1−σ
is the ideal price index.
where Pn = ω∈Ωn pn (ω)
dω

6

(1)

Each country n has G cities, indexed by g. Each city is populated by consumers Lng ∈ (0, Ln )P
as determined by the demand for labor from firms, with G
g=1 Lng = Ln . With competitivemarkets, wages c are equalized across cities within a country, i.e., cng = cn , ∀c. Migration acrosscities within a country is assumed to be costless, while migration across countries is prohibited.7

2.2

Firms and productiongOn the supply side, each city g in country m has a mass Kmof risk neutral firms, indexed byi (and later by j for i’s neighbors). Firms produce only in the city where they are located, but
sales are made to the entire domestic market, and goods are distributed across cities withina country at zero cost. We restrict our attention to cross-border M&A, and assume no tradeacross international borders, nor domestic M&A. Prior to production, firms decide whether or
not to participate in the cross-border M&A market. As a participant, the firm must choose
to be either an acquirer (i.e., buyer) or a target (i.e., seller). We first describe the production
process, then move backwards to discuss the cross-border M&A market.
Each firm produces a unique variety, and is endowed with productivity ϕ > 0 drawn from the
distribution F (ϕ). An unacquired firm produces for the domestic market given its productivity,
maximizing its profits subject to Eq. (1). As in the standard Melitz (2003) model, for firm i in
σ cmcountry m, the optimal price is a constant markup over marginal costs, pim (ϕ) = σ−1
ϕ . For

simplicity, we assume that local sales do not require fixed costs, and are made with µm (ϕ) = 0,
∀ϕ.8 Thus, domestic profits are:πim (ϕ) = Sm ϕσ−1 ,where Sm ≡ σ1σσ−1 cm

1−σ

(2)

σ−1 .
Ym Pm

Next, consider an acquirer with productivity ϕ in country m that has bought a target firmwith productivity ϕ0 in country n for the price Vn (ϕ0 ). Assume that the acquirer replaces thebrand of the target with its own upon purchase, and that this relationship endures for one
period.9 This implies that in each period, firms have no incentive to purchase more than one
target in a destination.10 As in Nocke and Yeaple (2007), the acquirer’s productivity is “mobile”
and transferred to the target during the cross-border M&A transaction. Thus, in destinationn, the acquired target only uses the highest productivity available between itself (ϕ0 ) and theacquirer from country m (ϕ), with ϕ > ϕ0 assumed. In other words, all acquired target firms
7
The majority of our theoretical results follows even without introducing multiple cities within a country. In
our estimation sample with the US as the third country, 49% of foreign acquirers have purchased targets in morethan one location in the US over the sample period. The average number of locations is 2.80, and the averageamongst firms that have more than one location is 4.69. Because the share of acquirers with multiple locations
is non-trivial, we introduce multiple locations in the general setup of the model.
8
The main results of the model would not change with fixed costs for domestic production. Assuming that
fixed costs of domestic production are lower than those for cross-border M&A, the least productive firms would
not be able to cover the costs to produce for the local market, and would instead choose to sell itself in the
cross-border M&A market. This is almost identical to the implications of the model below.
9
Firms are assumed to make myopic decisions in this model, and are only concerned with expected profits
in the upcoming period. Thus, we abstract from analyzing the dynamic stream of profits and its impact on theacquisition price.
10
Roughly 11% of firm-destination pairs in the entire sample, as well as the estimation sample, involve multiple
transactions in a year.

7

produce with the productivity of their parent company. Profits of firm i from purchasing atarget with productivity ϕ0 in destination n are:πimn (ϕ, ϕ0 ) = eµimn (ϕ) Sn ϕσ−1 − Fmn − Vn (ϕ0 ).

(3)

In addition to the acquisition price, the firm incurs the fixed cost of investing abroad, Fmn .
Moreover, in the context of M&A, eµimn (ϕ) may be thought of as the match quality between
the acquirer and target, which improves synergy and profits. Information about the destination
increases the match quality. In particular, this knowledge is obtained through the entry of third
countries.

2.3

Cross-border M&A and sorting

Cross-border M&A transactions are made before any production occurs. Firms decide whether
or not to be a participant of the perfectively competitive cross-border M&A market, and if
so, whether to be an acquirer or target. Because the target produces with the acquirer’s productivity ϕ instead of its own productivity ϕ0 , any target is equally valuable to the acquirer.
Therefore, the acquisition price in destination n for all ϕ0 may be denoted as Vn (0). Firms thatparticipate as targets in the cross-border M&A market are pooled within their country, andthe acquirer is randomly matched to one of these (ex-post) identical targets. In particular, theacquirer cannot choose the city in which the target is located.
Although a firm will not make more than one acquisition within a country in any given
period, it may invest in multiple foreign destinations. In particular, we defineµimn (ϕ) = αεimn + (1 − α)Dimn (ϕ), where Dimn (ϕ) =NX\ imr (ϕ).
Wrn 1[acquirer]

(4)

r=1

This is a weighted average of an idiosyncractic component, εimn , and a term capturing the firm’s
accumulated experience and knowledge in third countries, Dimn (ϕ).11 The former is a randomvariable normally distributed with mean 0 and variance ν (i.e., εimn ∼ N (0, ν)), revealed to
the firm only after its investment decision is made. We use hat notation to denote the past,\ imr (ϕ) ∈ {0, 1} indicates whether the firm made an acquisition in third country
and 1[acquirer]r previously. For each country that contributes to the total experience of the firm, we use
Wrn to weight the closeness of third country r to destination n. Empirically, we construct theweights based on geographic distance and measures of economic interaction such as the volumeof cross-border M&A activity. Dimn may be interpreted as a “macro-level” signal that thefirm receives about destination n based on its accumulated experience and knowledge in third
countries. Therefore, given the normal distribution of εimn , expected profits are:α2 νE[πimn (ϕ, ϕ0 )] = e 2 +(1−α)Dimn (ϕ) Sn ϕσ−1 − Fmn − Vn (0).
11

(5)

The weights may be chosen arbitrarily and do not affect the theoretical result below, i.e., Proposition 1. Thisparticular form of weights is chosen to draw a parallel with the weighted average of signals in Eq. (10) below.

8

We can now characterize and sort firms as acquirers, targets, and non-participants of thecross-border M&A market. For the least productive firms, they find the acquisition price Vm (0)to be larger than domestic profits πim , and become targets for sale. While firms with intermediate levels of productivity prefer domestic profits to the acquisition price, they are unable toovercome the costs of acquisition in n, and will choose to be non-participants of the cross-border
M&A market in n. Lastly, the most productive firms expect positive profits in market n, andwill choose to buy a target in n as an acquirer. They also invest in more destinations, further
amplifying their profits. This implies that there are two cutoff productivities in determining thesorting pattern. The first cutoff determines the firm’s decision to become a target. It depends
on the market characteristics of the firm’s origin country m, but is independent of destinationn. We denote this cutoff asϕm ≡Vm (0)
Sm

 1

σ−1

.

(6)

The second cutoff pins down the set of firms that invest in a given destination n. Specifically,
the expected profits of an acquisition in n are positive when productivity is greater thanϕmn ≡Fmn + Vn (0)

! 1

α2 ν

σ−1

.

(7)

Sn e 2

Note that because shocks εimn are only revealed ex-post, the choice to make a purchase in n isdetermined only by the initial draw of productivity ϕ, and not by the (past) realizations of theshocks themselves.
The sorting pattern is displayed in Figure 1. The dotted line shows domestic profits πim ,while the dashed line plots profits for an acquisition in destination n if they are not affected bypast experience (i.e., µimn (ϕ) = 0). The solid line instead includes the influence of accumulated
experience on expected profits through µimn . Firms with productivity less than ϕm find it more
profitable to be acquired for the price of Vm (0) than to earn profits domestically, and choose to
become targets. To the right of ϕm , only more productive firms with strong experience abroad
are able to overcome the cost of foreign acquisition, i.e., ϕ > ϕmn , and make a purchase in n.
Thus, we have three mutually exclusive sets of firms with respect to destination n:
Set of target firms = {ϕ : ϕ < ϕm }Set of non-participants in n = {ϕ : ϕm ≤ ϕ < ϕmn }Set of acquirer firms in n = {ϕ : ϕmn ≤ ϕ}.
Note that three sets of firms may also be defined across all destinations, where the productivityof non-participants is less than ϕmn for all n, and acquirers have productivity above ϕmn forany n.
Denote Amn as the aggregate number of acquisitions from origin m to destination n. For
now, acquirers in different cities within an origin country are identical if they have the same

9

π
ϕσ−1
m

Targetsϕσ−1
mn

Nonparticipantsin nE[πimn (ϕ, 0)]

Acquirers in nπim
Sn ϕσ−1 − Fmn − Vn (0)Vm (0)
ϕσ−1
Fmn + Vn (0)Figure 1: This figure shows domestic profits in m and profits in foreign market n through M&A.
Countries m and n are drawn with the same market size. Firms sort as targets, non-participantsin n, and acquirers in n.
productivity draw. Thus, we can simply sum across all cities within the country and obtain:
Amn =

G
X

g
Km
[1 − F (ϕmn )] .

(8)

g=1

Now, suppose that Dimn (ϕ) increases for an individual firm. From Figure 1, we can see thatthe set of acquirer firms must be non-decreasing in the past experience of all firms that are nottargets. In particular, if Dimn (ϕ) increases for all firms with productivity above ϕm , ϕmn must
decrease, and some non-participants will switch to become acquirers for destination n. Thus,
we have Proposition 1:
Proposition 1. The aggregate number of acquisitions from country m to n, Amn , is nondecreasing in the number of third countries r previously entered and their closeness to n.

2.4

Neighborhood learning effects

To examine the learning mechanism in greater detail at the micro level, we now provide aheterogeneous source of variation for the random signal εimn . Firms continue to face uncertainty about µimn (ϕ), but their beliefs are partially based on their experience in third countries.
We follow the literature in applying the DeGroot (2004) linear updating model (e.g., Fernandes and Tang, 2014; Timoshenko, 2015; Berman et al., 2018). Thus, assume that firms holda prior belief that µimn (ϕ) is normally distributed with mean Dimn (ϕ) and variance v1 (i.e.,µimn (ϕ) ∼ N (Dimn (ϕ), v1 )). That is, the macro-level signal observed from accumulated experience overseas establishes the mean of the prior distribution. Firms update their prior belief
from the micro-level signals observed in third countries. Because we focus on the entry choice10Destination n
Origin m (e.g., UK)

t?

Japan

t?
Acquirer i
China
t1

Neighbors jt−1

Target of i
Third country r(e.g., US)

Figure 2: Schematic diagram of entry into a third country and destination of interest.
to new destinations empirically, we further assume that firms do not update their beliefs abouta market from the realizations of µimn (ϕ).12
Each of the acquirer’s targets in the third countries obtain knowledge spillovers from aneighborhood learning effect. As part of the spatial network of cross-border M&A, the target inthird country r is exposed to localized knowledge spillovers from either acquirers to or targetsof destination n. While the acquirer-target ownership link only lasts for one period, we assume
that the acquirer maintains a connection to the target, and this knowledge diffusion, of both
directions, is transferred to the acquirer.13 Therefore, the neighborhood learning effect is specificto the location of the acquirer’s target in the third country. In contrast, the externalities of themacro-level signal are common across foreign acquirers that invest in the same third country,
regardless of their target’s location. The scenario is depicted in Figure 2, where a shaded region
indicates a firm in one city. Acquirer i in origin m (e.g., the United Kingdom) has purchased atarget in third country r (e.g., the US) at a certain time t1 . At the current time t, it chooses
amongst the set of destinations n, such as Japan and China. Suppose at time t−1, the neighborsof i’s target purchase and/or sell to some firm in China. Then, acquirer i learns about China
through its target in r, and this has a positive impact on its choice of destination at time t.
Thus, for each city g in third country r that acquirer i invested in previously, the acquirer
receives two signals from the cross-border M&A activity observed through their target, one
from the neighboring acquirers and the other from the neighboring targets. Due to the random
assignment of foreign entrants to a destination’s cities, acquirers in a third country with thesame productivity are no longer identical if their targets are located in different cities. Cities will
12This assumption is generally supported by our empirical study. In the full data sample, 83.5% of firmdestination pairs are new entries into the destination by firms, while the remaining 16.5% are re-entries.
13
Because we do not observe the sale of ownership stake in the US target by the non-US acquirer, the same
implicit assumption is made for the empirical analysis.

11generally differ by the cross-border activity of their firms, and the composition of neighbors forthe acquirers’ targets will also vary. Therefore, the productivity of acquirers from origin m andthe micro-level signals obtained from the neighborhood learning effects in r are uncorrelated,
and we cannot use ϕrn to denote the cutoff productivity above which all firms in r will beacquirers to n. The signals obtained from neighboring acquirers and targets are, respectively:
dgimrn =
g
δimrn
=

G
X
h=1
G
X

Krh

"Z
∞

#
\ jrn (ϕ)dF (ϕ) ,wijr 1[acquirer](9a)\ jrn (ϕ)dF (ϕ) .
wijr 1[target]

(9b)

ϕr

Krh

Z ϕ

r

0

h=1

\ jrn (ϕ) is equal to 1 if firm j in country r is a target of an
The indicator variable 1[target]
acquisition from n, and zero otherwise. A neighboring firm may operate in the same city as i’starget, or in another city h. Moreover, in the equations above, each neighboring firm j in city
h is weighted by wijr , an element of the updating matrix wr . The importance of j to i’s target
may be a function of, for instance, the geographic distance between city h of firm j and city gof i’s target.
Both signals are assumed to be drawn from the normal distribution with mean µ0imn (ϕ)
and variance v2 . Define GA
imr as the number of cities in r that had a target purchased by theacquirer. In the empirical analysis, we are restricted to the study of US as the third country.
Therefore, for simplicity, we do not concern ourselves with how micro-level signals from different
third countries are aggregated. However, by adding up the signals within the US, we define the
PGA
PGA
imr g
imr g
dimrn and δimrn = g=1
δimrn . Then, by DeGroot
total signals received as dimrn = g=1(2004), the posterior distribution of µimn (ϕ) is normally distributed with mean:µ0imn (ϕ) =where a =

2GA
imr
v2



A

2Gimr
1
v1 + v2

a 
GA
imr

−1


dimrn + δimrn + (1 − a)Dimn (ϕ),, and the variance is v 0 =



A

2Gimr
1
v1 + v2

−1(10). Thus, the meanof the posterior distribution is essentially a weighted average of the micro-level signals fromthe neighborhood learning effect, and the macro-level signal of accumulated experience in third
countries. Expected profits are:0v0

E[πimn (ϕ, ϕ0 )] = eµimn (ϕ)+ 2 Sn ϕσ−1 − Fmn − Vn (0).

(11)

The timing is as follows. At the start of each period, firms choose whether or not toparticipate in the cross-border M&A market, either as an acquirer or target, based on µ0imn
observed from last period’s cross-border M&A activity. The acquisition price Vn (0) at each
destination must adjust such that the cross-border M&A market clears in all countries. Next,
all acquirers in a destination are (randomly) matched to a target, and production takes place.
At the end of each period, firms that invested in a new destination r update the mean of theirprior beliefs Dimn (ϕ) for each destination n. Meanwhile, all firms that invested abroad observe12the micro-level signals to update their beliefs about n for the next period.
Firms are assumed to make myopically optimal entry decisions into destinations that maximize profits in the current period, without considering their effect on future profits in either
the same or different destinations (Golub and Sadler, 2016). As before, in partial equilibrium,
an increase in expected profits can only serve to raise the probability that the firm will invest
abroad. Thus, both micro-level signals have a positive impact on the firm’s destination choice:
Proposition 2. The probability of acquisition in destination n is increasing in the strength of
micro-level
dimrn , and targets, δimrn , of the third country
 signals from neighboring acquirers,∂E(πimn )∂E(πimn )targets∂dimrn > 0, and ∂δimrn > 0 .
In addition, the information obtained from neighboring acquirers and targets have complementary effects on the entry decision:
Proposition 3. The impact of the micro-level signals dimrn and δimrn on the probability of ac-
quisition in destination n is stronger when the other micro-level signal is large∂ 2 E(πimn )∂dimrn ∂δimrn > 0

Next, we examine the role of GA
imr , i.e., the number of cities in third country r, for theneighborhood learning effect. Because this variable is not destination specific, it would have asimilar qualitative effect across all destination countries. Nevertheless, the number of locations
influences the weight placed on the micro- versus macro-level signals in the updating process,and therefore, the marginal effect of the micro-level signals themselves.
Proposition 4. If 1 + a(dimrn + δimrn − Dimn ) > 0, the impact of the micro-level signals dimrnand δimrn on the probability of acquisition in destination
 n is stronger with more locations in
the third country2∂ 2 E(πimn )imn )> 0, and ∂δ∂ E(π∂G>0A∂dimrn ∂GA
imrn
imr
imr

.

The cross-partial derivative of expected profits with respect to dimrn (or δimrn ) and GA
imr
2

imn )
(i.e., ∂d∂ E(π∂G) is proportional to 1 + a(dimrn + δimrn − Dimn ). The weight placed on the
A
imrn

imr

micro-level signals, a, is increasing in the number of cities, so the marginal effect of the microlevel signals increases as well. There is also a secondary effect on the level of expected profits. If
the firm updates positively with dimrn + δimrn > Dimn , then a larger weight on the micro-levelsignals is associated with greater expected profits. On the other hand, if dimrn + δimrn < Dimn ,then a smaller weight on the micro-level signals is preferred. In this case, a sufficiently small
weight a mitigates the second effect and can still guarantee that the cross-partial derivative ispositive.
Experience in third countries that are closer or more similar to the destination of interest
increases the probability of entry. Furthermore, the model predicts that the neighborhood
learning effects are complementary to accumulated experience, in which information from one
source is reinforced by the other:
Proposition 5. The probability of acquisition in destination n is increasing in accumulated
experience in third countries r weighted by their closeness to n, Dimn , especially when the micro-

level signals, dimrn and δimrn , are large∂ 2 E(πimn )∂ 2 E(πimn )∂E(πimn )∂Dimn > 0, ∂Dimn ∂dimrn > 0 and ∂Dimn ∂δimrn > 0

13

.

.

It should be noted that we have chosen to model learning as generating greater revenue, as
opposed to a reduction in (fixed) costs. The latter approach is taken by, for instance, Schmeiser(2012). While this alternative modeling assumption would deliver the same predictions withregards to the levels of either the micro- or macro-level signals, it would also give the opposite
prediction for the interaction effects (i.e., Proposition 3 and the latter part of Proposition 5).
That is, all three signals would act as substitutes instead of complements. As Section 5 below
demonstrates, we are motivated by the empirical evidence to model the effect of learning onrevenues instead of costs.

3

Data

Our empirical analysis uses the universe of cross-border M&A data from 1981 to 2016 provided
by Thomson Reuters Security Data Company (SDC) Platinum.14 In order to have sufficient
cross-border M&A activity to study the neighborhood learning effects and the role of firms’
accumulated experience, the estimation sample covers the period from 1995 to 2016. However,
we utilize the full data beginning in 1981 to track the full history of companies’ investments
overseas. Moreover, due to incomplete information on deal valuations, we focus on the extensive
margin of cross-border M&A, both in terms of the countries entered and the number of purchases
made.

3.1

Macro-level descriptive statistics

Our sample includes 189 origin and 209 destination countries. From 1995 to 2016, an averageof 8663 purchases are made annually around the world, with some minor fluctuations acrossyears.15 Appendix Table A.1 lists the top origin and destination countries across the sample
period. Not surprisingly, developed countries dominate as the origin countries, with the US accounting for one-fifth of the aggregate cross-border acquisitions. However, developing countries
like China and Malaysia rank as the 13th and 17th most common origin countries, respectively.
Moreover, the distribution is heavily skewed, as 5 (15) origin countries contribute to almost
50% (75%) of all international M&A transactions. Targets are slightly more dispersed acrossdestination countries, with 9 (24) destinations accounting for roughly 50% (75%) of sales. Investment into developed countries is generally more common, but large developing countries
also serve as popular destinations. In particular, the BRICS countries (Brazil, Russia, India,
China, and South Africa) are ranked 15th, 13th, 8th, 4th, and 29th, respectively.
Table 1 provides additional descriptive statistics. Because these statistics are generally
skewed by the presence of large countries, we present both the mean and median, along with
14

This database has previously used for the analysis of global cross-border investment activity in, for example,
Rossi and Volpin (2004), di Giovanni (2005), Martynova and Renneboog (2008), Erel et al. (2012), and Chanand Zheng (2018).
15
We drop all deals in which the name of the acquirer or target is “Investors”, “Investor Group”, “Shareholders”,“Creditors”, “US Dept of the Treasury”, “Bondholders”, “IFC”, “Employee Stock Ownership Plan”, or it contains
the word “Undisclosed”. We also exclude transactions in which either the origin or destination country is“Unknown” or “Multi-National”.

14

Table 1: Descriptive Statistics at the Aggregate Level

1. Number of acquisitions across all years and destinations
2. Number of acquisitions across destinations per year
3. Number of acquisitions per destination per year
4. Number of destinations across all years
5. Number of destinations per year

Panel A: Origin countries
Mean
MedianStd dev.
996
27
3569
80.2
8
216
5.81
2
18.3
31.0
12
38.6
13.8
6
17.8

1. Number of sales transactions across all years and origins
2. Number of sales transactions across origins per year
3. Number of origins across all years
4. Number of origins per year

Panel B: Destination countries
Mean
MedianStd dev.
901
85
2526
54.9
7
136
28.1
22
22.9
9.46
5
10.4

Notes: Authors’ calculations using cross-border M&A data between 1995 and 2016 from Thomson Reuters SDC Platinum.

the standard deviation. For example, Panel A indicates that, on average, an origin country
acquires close to 1000 companies abroad during this 22 year period. However, there is large
variation across the sample, and the median number is much smaller at 27. For each origindestination pair, the average number of annual transactions is 5.81.16 At the extensive margin
in terms of the number of destinations, origin countries enter around 14 foreign markets every
year, which means that only a very small share of countries have bilateral investment in theform of M&A. Although this share has been increasingly slowly over time, over 90% of country
pairs today do not have acquisitions in either direction. Helpman et al. (2008) document asimilar, but less drastic, pattern for international trade, and the analogous graph to Figure I in
Helpman et al. (2008) for cross-border M&A is presented in Figure 3.

3.2

Micro-level descriptive statistics

At the micro level, we analyze the impact of neighborhood learning effects in the third country
for acquirers in the origin country and their choice of destination market. In order to define
spatial networks, data on firms’ locations is required. In SDC Platinum, city-level address
information is by far the most available for firms in the US, making it the most suitable candidate
as a third country to study the neighborhood learning effect. Data limitations unfortunately
preclude us from examining micro-level networks or alternative sources of information in othercountries. Therefore, we study non-US acquirers that make cross-border acquisitions in both the
US and non-US destinations, and the spatial interactions of their US targets with neighboring
participants in the cross-border M&A market.
Out of all global M&A transactions, the US is the origin (destination) country in 21% (13%)
of the deals, and the remaining 66% are unaffiliated with the US.17 Descriptive statistics for
16This is a bilateral statistic, so the number of acquisitions per destination per year for an origin country isexactly the same as the number of sales per origin per year for a destination country. Also, the statistics inPanel A rows 2 to 5 and Panel B rows 2 to 4 are computed excluding zeros. The inclusion of zeros lowers boththe average and median while increasing the standard deviation, but magnitudes are similar to those shown in
Table 1.
17
Similar statistics for the period from 1990 to 2007 are provided by Erel et al. (2012). We exclude the territory15

Figure 3: Distribution of country pairs based on direction of cross-border M&A. The sample
contains 189 origin and 209 destination countries.
the micro-level analysis are presented in Table 2. We identify 2,832 non-US acquirers from 54
origin countries that make an acquisition at least once in the US, and subsequently, purchaseat least one non-US target in a foreign country.18 The two most common origin countries are
the United Kingdom and Canada, which account for about 18% and 12% of the multinationalfirms, respectively. Other large origin countries include Germany, Japan, and France.19 Thesefirms account for a total of 9,387 cross-border M&A deals to 160 non-US destinations, and on
average, they enter 1.96 (unique) destinations before buying a target in the US for the firsttime, and 2.85 non-US destinations after.
While SDC Platinum provides the vast majority of information on US firms’ locations, in aneffort to maximize coverage, we supplement it with data from Bloomberg L.P. (2018a,b). This
online database maintains a repository of company profiles that are created and managed by
S&P Global Market Intelligence. We deem the city information of SDC Platinum less reliable
when street addresses are missing. For this set of firms and those that have the city missing
entirely, we extract city and state information from Bloomberg L.P. (2018a,b) where available,
and use it as a replacement.20
of Guam, Northern Mariana Islands, Puerto Rico, US Virgin Islands, as well as the Marshall Islands, which has
been governed by the US in the past.
18
To identify a firm, we use both the acquirer name (“an”) and CUSIP code (“acu”). Because of the many-tomany correspondence between the two variables (e.g., the firm may change its name), we search across all firms
and link those that share the same name or same CUSIP code.
19
The remaining countries are: Algeria, Argentina, Australia, Austria, Bahamas, Bahrain, Belgium, Belize,
Brazil, Bulgaria, China, Colombia, Cyprus, Denmark, Egypt, Finland, Greece, Hong Kong, Iceland, India,
Indonesia, Ireland, Israel, Italy, Kazakhstan, Kuwait, Luxembourg, Malaysia, Mexico, Netherlands, New Zealand,
Norway, Philippines, Poland, Portugal, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea,
Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Arab Emirates, and Venezuela.
20
We take multiple steps in this process: 1) We first use the website’s search function to directly look up firmsthat have missing street addresses (“astr” or “tstr”). City and state information can be extracted with relative
ease because they are displayed on different lines. However, some companies that exist in the database may
not be found due to minor spelling mistakes or problems with Bloomberg’s company name registry. 2) For the16Table 2: Descriptive Statistics at the Micro Level
Panel A: Non-US acquirers
Median Std dev. Total number
14
97.7
2832
1
1.49
2
2.91
1
3.49

1. Number of firms per origin country
2. Number of non-US destinations before entering US
3. Number of non-US destinations after entering US
4. Number of US locations per firm per year

Mean
52.4
1.96
2.85
2.80

1. Number of firms per city
2. Number of acquisitions per city across years
3. Number of acquisitions per city per year

Panel B: US neighboring acquirers
Mean Median Std dev. Total number
6.31
1
38.8
14,5854.51
2
5.41
36,2793.48
1
12.7

1. Number of firms per city
2. Number of sales transactions per city across years
3. Number of sales transactions per city per year

Mean
5.22
2.97
1.90

Panel C: US neighboring targets
Median Std dev. Total number
1
26.1
18,4261
3.85
19,9371
3.61

Notes: Authors’ calculations using cross-border M&A data for the US between 1995 and 2016 from
Thomson Reuters SDC Platinum. Calculations for row 3 of Panels B and C use non-zero values only.

Abbreviations, alternate names, or spelling mistakes prevent us from simply matching thecity information provided to a list of US cities with geographic coordinates (i.e., latitude andlongitude). For example, we found that “South San Francisco” is in fact associated with “SanFrancisco South”, “S San Fransisco”, “So San Francisco”, and 6 other misspelled versions.
Thus, additional steps must also be taken here. We obtain two lists of cities and counties in
the US from Geonames (2018), which contains coordinate information, as well as Grammakov
et al. (2014), which does not. The combined dataset gives 64,209 locations in the US. A bigram
matching algorithm is used to check for any overlapping locations (e.g., Miami and MiamiDade County) or other errors in the data (e.g., Washington DC coded as Washington the state,or Delaware-incorporated companies coded with Delaware as the state). Finally, for all cities
matched that still have missing coordinates, we either find the closest city from Geonames(2018), or search for the coordinates online. After this cleaning procedure, we obtain a listof 4,706 US locations (i.e., towns, cities, or counties) in which cross-border M&A activity isobserved. The bilateral distance between locations is computed with ArcGIS. We rely on thedriving distance as the main measure (94% of pairs), and the direct (great circle) distance where
the quality of the road map is poor (6%).
In summary, we are able to identify the city and associated geographic coordinates in 36,279(19,937) cross-border transactions involving a US acquirer (target). This corresponds to 96%
remaining set of companies, we perform probabilistic record linkage using a bigram matching algorithm (i.e., fuzzy
matching) with the entire list of all US company names downloaded from Bloomberg. Specifically, we use Stata’s
reclink2 command with a minimum field-similarity score of 0.95 to find the best match. The matches (11,198
observations) are then manually inspected. Addresses of confirmed matches are then retrieved from Bloomberg.
3) With the addresses from steps 1 and 2, we first compare the state information, which is always given by SDC
Platinum. We discard Bloomberg addresses for which the state does not match with SDC Platinum. 4) If thecity is missing in SDC Platinum, we replace it with the city from Bloomberg. Otherwise (for 483 observations),
we manually inspect and compare the city information provided, searching online for the correct city associatedwith the firm.

17(83%) of the total number of US acquisitions (sales), where 1.2% (4.0%) of the observed locations were originally missing and recovered, and an additional 4.0% (10%) were corrected.
Figures 4(a) and (b) show the geographic distribution of acquirers and targets, respectively. The
size of the dots indicates the mass of firms, and the most popular source and host cities are New
York and Houston. Targets are slightly more dispersed than acquirers. Appendix Table A.2
lists the top 20 cities with the most acquisitions made and targets sold throughout the sample
period. Not surprisingly, both lists contain large cities and overlap heavily. As indicated inTable 2, each city has, on average, 6.31 acquirers (5.22 targets), though the standard deviation
across locations is large. These companies account for roughly 3.48 acquisitions and 1.90 sales
transactions every year in each city.
In Figure 4(c), we plot the locations of the targets purchased by the non-US acquirers,
a subset of Figure 4(b). The distribution of this subset of targets is roughly similar to theentire sample. This suggests that our regression estimates of the neighboring learning effect are
somewhat representative of the knowledge spillovers from US multinationals or foreign affiliates
in the US.

4

Empirical framework

4.1

Macro-level regressions

We test the macro- and micro-level predictions of the model empirically using our global M&A
data. At the aggregate level, we estimate the following regression equation:
Amnt = B0 + B1

X

Wrn,t−1 Amr,t−1 + B2 Xmnt + B3

r6=m,nX

Amrt + B4 Amn,t−1(12)r∈N (n)+ cm + cn + ct + mnt ,where the dependent variable Amnt is the number of acquisitions from origin m to destination nPat year t, and the regressor of interest is r6=m,n Wrn,t−1 Amr,t−1 , the weighted sum of acquisitions from m to third countries r in the previous year. We use measures of both geographic andeconomic closeness between r and n for the weights Wrn,t−1 . These include the inverse of distance (1/Distancern ), the volume of (lagged) cross-border M&A activity (Arn,t−1 or Anr,t−1 ),as well as combinations of the two variables.
In Eq. (12), we control for other determinants of cross-border M&A activity using a set ofvariables Xmnt from a standard gravity model. These include the origin and destination’s (log)real GDP, (log) distance, and indicator variables for sharing a border, legal origins, language,
colonial ties, and a free trade agreement (FTA). Data are retrieved from the World BankWorld Development Indicators, CEPII, La Porta et al. (1999), and de Sousa (2012). We also
account for contemporaneous demand-side forces of n’s region N (n) with the total number ofPacquisitions to the neighboring countries of n, r∈N (n) Amrt . A neighboring country of n (which

18

(a)

(b)

(c)

Figure 4: This figure shows, from 1995 to 2016, the locations of (a) US acquirers; (b) all US
targets; (c) US targets of 2,832 non-US acquirers in estimation sample. Hawaii and Alaska are
excluded from the figure but included in the estimation sample.

19

excludes n itself) is either contiguous, separated by less than 500km, or the closest country.21
In addition, given the persistence of cross-border investment, the lagged dependent variable
Amn,t−1 is included as a regressor. Lastly, we control for unobserved heterogeneity using origin,destination, and year fixed effects.
In estimating Eq. (12), we use logarithms of the regressorsPr Wrn,t−1 Amr,t−1 ,Pr∈N (n) Amrt ,and Amn,t−1 . To retain observations with zero counts of M&A in these explanatory variables,
the constant of 1 is added before taking logarithms. Moreover, as shown in Figure 3, the share ofcountry pairs with positive cross-border M&A investment is small. The large incidence of zeros
generates over dispersion in the dependent variable, as the standard deviation of M&A counts
is roughly 10 times larger than the mean. Hence, we estimate a negative binomial regression(e.g., Davies et al., 2015). Standard errors are clustered by origin-destination pair.

4.2

Micro-level regressions

At the micro level, we examine a set of non-US acquirers and their choice of non-US destinations
for cross-border M&A. We are interested in studying the impact of their US targets’ neighborson this choice, and how this effect varies with their accumulated experience abroad. Thus, weestimate the following linear probability model:
P r(1[acquirer]imnt = 1) = b0 + b1 dimn,t−1 + b2 Dimn,t−1 + b3 Locationsimr,t−1 + b4 Xmnt (13)X
\ jrn,t0 + ci + cn + ct + eimnt .
+ b5 A−m,nt + b6
1[acquirer]j∈N (i’s target)

The dependent variable is an indicator variable equal to 1 if firm i from origin m makes at least
one acquisition in destination n at time t. We follow Fernandes and Tang (2014) and Kamal andSundaram (2016) to examine firms’ destination choice conditional on entering foreign markets
after investing in the US.22 The set of destinations n for firm i include new destinations withat least one US acquisition or sale in the previous year The regressor of interest for the thirdcountry neighborhood learning effect is dimn,t−1 , i.e., the number of neighboring firms nearacquirer i’s US target that made an acquisition in destination n last year. While the durationof learning may vary, the neighboring cross-border M&A activity of the previous year is arguably
21

For example, the neighbors of the United Kingdom are Ireland, Belgium, France, Germany, Luxembourg,and the Netherlands. Ireland is contiguous to the United Kingdom, while bilateral distances to the remaining
countries are 324, 343, 495, 494, and 360km respectively. A country such as Japan does not share a border withany other nation, and the distances between Japan and all other countries are greater than 500km. Although
Korea is 1,157km away, it is the closest country to Japan, and therefore, assigned as its neighbor.
22
In order for there to be sample selection bias with regards to entry either into or out of the US, unobservables
that influence these firm-specific entry margins must be correlated with the destination-specific M&A activity of
US neighboring firms. For example, consider China as the origin country, and Korea as the destination of interest.
A Chinese acquirer may not enter the US because it is already close to Korea. If it were to enter the US, it may
choose to enter locations that have a lot of cross-border M&A activity with Korea. However, the determinants ofentry into Korea are controlled by Xmnt , the set of gravity equation variables, and are therefore not unobserved.
As we discuss below, Xmnt and A−m,nt (i.e., the number of acquisitions to n from all other countries) accounts
for both a market-specific as well as global hierarchy of destinations. Thus, it is highly unlikely for unobservables
that affect firm entry from an origin country to a non-US destination to be unrelated to the market or geographic
characteristics of either country.

20

the most relevant and therefore also the most valuable for the acquirer. In addition, we consider
the network influence of neighboring targets with δimn,t−1 as defined by Eq. (9b). The impact of
accumulated experience is captured by Dimn,t−1 , the firm-level weighted sum of acquisitions tothird countries up to time t−1 (excluding the US). As discussed in Section 5.2.4 below, the same
weights from the macro-level regression Eq. (12), Wrn,t−1 , are employed. The number of US
target locations that the acquirer has, Locationsimr,t−1 , corresponds to GA
imr in the theoreticalmodel.
Our estimation examines the impact of networks in US, a third country, on the acquisition
decisions of firms in origin m to destination n. Arguably, any endogeneity problems of thisregression related to demand and/or supply shocks are less severe compared to a study of thedomestic neighborhood learning effect in country m, in which an acquirer and firms within
its spatial network would face similar shocks. Common supply-side shocks that affect both theacquirer and the neighbors of its US target are unlikely as they are situated in different countries.
On the destination demand side, we rule out plausible sources of omitted variable bias byincluding the same set of regressors Xmnt as the macro-level regression to account for traditional
gravity forces, and the number of contemporaneous acquisitions by all non-m non-US countries
to destination n, A−m,nt . By controlling for these time-variant destination characteristics, welimit the bias to demand shocks that must have a similar impact simultaneously on the origin
country and the US, without having the same effect on other countries. The occurrence of such
pairwise demand shocks also seems improbable. In addition, the inclusion of variables such as
market size in Xmnt and A−m,nt allows for the possibility of a global hierarchy of destinations,in which the ranking of destination market potential would be identical for firms from different
origin countries (i.e., non-US and US).
In the model, firms are assumed to behave myopically, and enter third countries without
considering the future impact of learning about other countries. Because Canada and Mexico areboth geographically and economically close to the US (e.g., through NAFTA), the regressions
may suffer from sample selection bias if foreign firms enter the US with motivations of either
expanding operations into these two neighboring countries or learning about them. However, inthe data, only 3.6% and 1.6% of all cross-border M&A deals from outside the US are directed
to Canada and Mexico, respectively, as ensuing destinations after the US. Thus, the empiricalanalysis treats firms’ decision to enter the US as largely exogenous.
We must, however, take into account the endogeneity problem associated with the acquirer
firm’s choice of target location within the US. That is, the non-US acquirer may purchase a US
target in a particular location for the purpose of learning about their destination of interest. We
take several steps to alleviate this potential endogeneity concern. First, given such motivations
by the acquirer, one might expect its next acquisition to occur soon after its entry into the US.
The data indicates that firms, on average, wait 4.47 years (standard deviation 4.71) after theentering the US before investing in a new non-US destination. This is not an insignificant periodof time, and is also slightly higher than the global average of 2.58 years. Second, we address thisendogeneity problem explicitly in our regression analysis by treating it as an omitted variable21

bias. If firms are driven to learn about foreign destinations by entering a US city, or are attracted
by the presence of industry clusters, we should observe substantial cross-border M&A activity
to the destination of interest in both directions from the firms of that city before the non-USP\ jrn,t0
acquirer enters the US. Therefore, we include as regressors (log) j∈N (i’s target) 1[acquirer]P\
as well as (log)j∈N (i’s target) 1[target]jrn,t0 , i.e., the number of neighboring acquirers andtargets, respectively, to destination n at time t0 , the year before acquirer i’s entry in the US.
We also employ an instrumental variable (IV) strategy to demonstrate the robustness of ourmain findings, which we discuss below in Section 5.2.1.
Lastly, to further mitigate endogeneity concerns, Eq. (13) includes firm fixed effects ci tocontrol for unobserved firm-level heterogeneity (e.g., productivity), destination fixed effects
cn for the time-invariant characteristics of destination countries, and year fixed effects ct tocapture any global shocks to cross-border investment. Because firms operate in a single sector
as observed in our data, firm fixed effects absorb sector (and origin country) fixed effects. In
robustness checks, we also employ origin-destination pair fixed effects, as well as destinationyear, origin-year, and firm-year fixed effects. Due to the inclusion of firm fixed effects and theincidental parameters problem, we estimate Eq. (13) with ordinary least squares (OLS), andcluster standard errors at the firm (i.e., acquirer) level.

5

Empirical results

5.1

Macro-level regression results

We provide preliminary evidence of learning effects in third countries at the aggregate level inTable 3. Across the columns, we consider various weights Wrn,t−1 to measure the geographic
and economic closeness of third country r to destination n. First, in column 1, we simply
sum up the (lagged) number of acquisitions from origin m to all third countries r, assigningan equal weight of 1 to these prior destinations. The estimate indicates that a 1% increase in
the number of acquisitions to third countries raises the number of acquisitions globally fromm by about 0.2%. The results from the remaining columns of Table 3 lend strong support
to Proposition 1. In columns 2 and 3, we use the inverse of distance (1/Distancern ) and thenumber of acquisitions from r to n (Arn,t−1 ) as weights, respectively. Holding the number ofacquisitions to third countries fixed, a 1% decrease in total distance from third countries r to n
increases the number of acquisitions from m to n by around 0.3%. Likewise, a 1% increase in
the number of lagged acquisitions from r to n raises Amnt by roughly 0.2%.
Next, in column 4, we combine the two weights as Arn,t−1 /Distancern to reflect both proximity and cross-border economic interaction. Column 5 instead utilizes the number of sales
from r to n, Anr,t−1 (equivalently, the number of acquisitions from n to r), and column 6 again
combines it with distance. Smaller marginal effects are observed in columns 5 and 6 compared
to columns 3 and 4. However, the results are robust for all measures of closeness, and suggeststhat experience in third countries, proxied by the weighted sum of previous acquisitions, has apositive impact on aggregate M&A activity into the destination of interest.

22

Table 3: Aggregate Experience and Number of Acquisitions
Weight Wrn,t−1 :
(log)

P

r Wrn,t−1 Amr,t−1(log) GDPmt(log) GDPnt(log) Distancemn
Land bordermn
Legalmn
Languagemn
Colonial tiesmn
FTAmnt(log) Amn,t−1
(log)

P

r∈N (n) Amrt

Origin FE
Destination FE
Year FE
N

Equal
(1)
0.218***(7.79)
0.926***(10.53)0.193***(3.74)-0.603***(-26.13)
0.093(1.34)
0.325***(10.22)0.434***(9.06)
0.375***(5.71)
0.213***(6.40)
0.599***(28.84)0.172***(12.87)
YYY
249,5171
Distancern(2)0.291***(14.12)0.753***(8.88)
0.193***(3.84)-0.509***(-21.35)0.127(1.90)
0.320***(10.24)0.418***(8.90)
0.356***(5.53)
0.185***(5.60)
0.590***(29.15)0.129***(9.96)YYY
249,517

Arn,t−1(3)0.192***(14.92)0.946***(11.83)0.040(0.79)-0.580***(-25.22)0.108(1.61)
0.312***(9.90)
0.419***(8.67)
0.391***(5.92)
0.214***(6.37)
0.560***(25.86)0.150***(11.45)YYY
249,517

Arn,t−1
Distancern(4)0.151***(16.66)
0.972***(12.46)0.041(0.81)-0.551***(-23.65)
0.117(1.78)
0.309***(9.94)
0.414***(8.75)
0.380***(5.83)
0.191***(5.72)
0.570***(27.94)0.138***(10.77)
YYY
249,517

Anr,t−1(5)0.128***(13.13)
1.166***(14.17)-0.015(-0.28)-0.583***(-25.60)
0.093(1.40)
0.299***(9.59)
0.428***(8.94)
0.382***(5.79)
0.220***(6.64)
0.563***(26.73)0.157***(12.10)YYY
249,517

Anr,t−1
Distancern(6)0.100***(15.23)
1.164***(14.45)-0.005(-0.10)-0.562***(-24.56)
0.104(1.58)
0.302***(9.78)
0.420***(8.93)
0.372***(5.74)
0.203***(6.17)
0.573***(28.73)0.147***(11.41)
YYY
249,517

Notes: In all columns, regressions are estimated using the negative binomial model, and the dependent
variable is the number of acquisitions from m to n at time t, Amnt . T -statistics are in parentheses,with standard errors clustered by origin-destination pair. ***, **, and * indicate statistical significanceat the 1%, 5%, and 10% level.

Consistent with literature (e.g., Blonigen and Piger, 2014), Table 3 also demonstrates that
greater international M&A activity is observed between larger origin and destination countries
that are geographically less distant with common legal origins, language, colonial ties, and a free
trade agreement. Cross-border M&A is highly persistent and autocorrelation is strong, as a 1%
increase in lagged acquisitions increases current acquisitions by around 0.6%. The number ofacquisitions to a destination is also highly correlated with purchases to its neighboring region.

5.2

Micro-level regression results

5.2.1

Neighborhood learning effectWe now proceed to evaluate the theoretical implications of the model empirically at the micro level, beginning with the third-country neighborhood learning effect. For acquirer i fromorigin m, micro-level signals are observed from neighboring acquirers and targets of its US
P\target, denoted as dimn,t−1 = (log)j∈N (i’s target) 1[acquirer]jrn,t−1 and δimn,t−1 = (log)P\j∈N (i’s target) 1[target]jrn,t−1 , respectively. Again, the constant of 1 is added before taking
logarithms of any explanatory variables involving counts of M&A deals. Our baseline measure23

of a target’s neighborhood, N (i’s target), is a 100km radius around its location. As pointsof reference, the distance from San Francisco to San Jose is 69km, Philadelphia to New Yorkis 166km, and Houston to Austin is 233km. Table 4 provides estimates of the third-country
neighborhood learning effect for both directions, with acquisitions in Panel A and sales in Panel
B. The results are strongly supportive of Proposition 2, as the coefficients on the regressors
of interest, dimn,t−1 and δimn,t−1 , are positive and statistically significant across all columns(at the 1% level). Thus, the empirical evidence demonstrates the impact of the third-country
spatial networks on firms’ destination choice, and implies substantial knowledge spillovers thatare transferred to the acquirer in the origin country.
Throughout, we ensure our results are not driven by other determinants of cross-border M&A
activity by including the same set of variables for the gravity equation, Xmnt , as well as the number of acquisitions from non-origin (and non-US) countries to the destination A−mnt .23 In colP
umn 2, we account for the potential endogeneity of target location choice using (log) j∈N (i’s target)P\ jrn,t0 and (log) j∈N (i’s target) 1[target]\ jrn,t0 . These are the purchases and sales of
1[acquirer]
the target’s neighborhood in the year before the acquirer’s entry into the US. While these past
purchases and sales at t0 are both strongly correlated with their t − 1 values at 0.773 and 0.766,
respectively, the impact of the latter on firms’ contemporaneous destination choice remains
statistically significant. The high correlation between past and present M&A activity may be
explained by domestic knowledge spillovers across the US acquirers and targets themselves.
This is documented for exporters and importers in, for instance, Fernandes and Tang (2014),
Kamal and Sundaram (2016), and Bisztray et al. (2017). While we do not focus on the local
diffusion of information between domestic firms, the propagation of these externalities over time
would be consistent with our results.
In column 3, we employ bilateral country-pair fixed effects to control for unobserved heterogeneity between origin and destination countries. To account for time-variant demand andsupply shocks, columns 4 and 5 include the combinations of destination-year with origin-year
and firm-year fixed effects, respectively. Because we observe destination choices for many firms
across multiple years after their entry into the US, the regression does not suffer from perfectcollinearity even with the inclusion of firm-year fixed effects. The main findings are also robust
to these alternative specifications.
In column 6, we further address concerns of endogeneity using an IV strategy. We needan instrument that can predict not only where in the US acquirer i from origin m will invest,
but also, the M&A activity of that US location to the destination of interest. Furthermore, theprediction of the US location choice must be independent of the destination of interest to satisfy
the exclusion restriction. Our IV strategy relies on county-level historical migrant population
data in the US. Prior literature has found the size of social networks formed by migration fromthe origin to destination country to have a positive impact on bilateral cross-border trade or
FDI (e.g., Burchardi et al., 2017; Cohen et al., 2017). In particular, the historical migrant pop23

Because the impact of the gravity equation variables on the probability of firm entry and the aggregate
number of acquisitions from the macro-level regressions are qualitatively similar, we omit their coefficients fromthe regression tables. Full results for all tables are available upon request.

24

Table 4: Neighborhood Learning Effects in the US

dimn,t−1 = (log)P\j∈N (i’s target) 1[acquirer]jrn,t−1(log) Locationsimr,t−1(log) A−mnt
(log)

P

\

(log)

P

\

(1)
0.0063***(12.23)-0.0017***(-2.78)0.0025***(8.27)j∈N (i’s target) 1[acquirer]jrn,t0j∈N (i’s target) 1[target]jrn,t0

Panel A: Neighboring acquirers
(2)
(3)
(4)
(5)
0.0042***0.0041***0.0028***0.0030***(7.56)(7.32)(4.76)(4.99)-0.0024*** -0.0024*** -0.0020***(-3.82)(-3.73)(-3.10)0.0025***0.0035***(8.58)(11.96)0.0024***0.0027***0.0028***0.0029***(4.16)(4.50)(4.65)(4.71)
0.0033***0.0035***0.0037***0.0037***(3.49)(3.75)(3.87)(3.83)(6)
0.0142***(3.74)-0.0035***(-4.62)0.0019***(5.18)-0.0016(-0.98)0.0004(0.30)

Controls:

GDP, Distance, Land border, Legal, Language, Colonial ties, FTA

Firm FE
Destination FE
Year FE
Origin-Destination FE
Destination-Year FE
Origin-Year FE
Firm-Year FE
Underidentification test (p-value)
N
R2

Y
Y
Y

δimn,t−1 = (log)P(log) Locationsimr,t−1(log) A−mnt
\

(log)

P

\

Y

Y
Y
Y

Y
Y
Y
Y

\

P

Y

Y
Y

j∈N (i’s target) 1[target]jrn,t−1

(log)

Y
Y
Y

496,076
0.044

496,0760.044(1)0.0067***(8.27)-0.0009(-1.52)0.0028***(9.52)(2)
0.0032***(3.69)-0.0020***(-3.24)0.0028***(9.32)
0.0036***(6.37)
0.0032***(3.30)j∈N (i’s target) 1[acquirer]jrn,t0j∈N (i’s target) 1[target]jrn,t0

496,076
0.061

496,0760.050

496,076
0.053

Panel B: Neighboring targets
(3)
(4)
(5)
0.0033***0.0034***0.0033***(3.73)(3.56)(3.51)-0.0020*** -0.0018***(-3.18)(-2.77)0.0038***(12.66)0.0038***0.0033***0.0035***(6.52)(5.79)(5.93)
0.0034***0.0032***0.0033***(3.48)(3.26)(3.27)< 0.01
496,076(6)0.0408***(3.71)-0.0031***(-4.37)0.0025***(8.26)-0.0027(-1.39)-0.0121***(-2.62)Controls:

GDP, Distance, Land border, Legal, Language, Colonial ties, FTA

Firm FE
Destination FE
Year FE
Origin-Destination FE
Destination-Year FE
Origin-Year FE
Firm-Year FE
Underidentification test (p-value)
N
R2

Y
Y
Y

Y
Y
Y

Y

Y

Y
Y
Y

Y
Y
Y
Y

Y
Y

496,076
0.044

496,076
0.044

496,076
0.061

496,0760.050

496,076
0.053< 0.01
496,076

Notes: The dependent variable is an indicator variable for acquisition in n by firm i at time t. Columns 1 to 5 are estimated with
OLS, and column 6 reports the second stage IV estimates. T -statistics are in parentheses, with standard errors clustered at the firmlevel. The p-values associated with the Kleibergen-Paap rank Lagrange Multiplier test statistic for the underidentification test arereported. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level.

25

ulation has been used as an instrument for identification. Data on migrant populations in the
US is obtained from IPUMS (Ruggles et al., 2018) for the year 1940, in which the residence ofindividuals is reported at the county level, and matched to the observed locations (i.e., towns,
cities, counties) of our sample using the information provided by Geonames (2018). Acquirer
i from origin m is expected to enter locations with larger migrant populations from its homecountry. We denote (log) P opgmr as the population of migrants from country m to the US in the
county g of i’s target (averaged across counties if i has multiple US target locations). Note that
this variable is orthogonal to any characteristics of the destination market. By the same logic,
the population of destination country n’s migrants in the US within the county of i’s target,(log) P opgnr , may be used to predict cross-border M&A activity between US locations and thedestination of interest. Hence, our instrument is the interaction variable (log) P opgmr × (log)P opgnr . The first-stage estimates presented in Appendix Table A.3 demonstrate that our instrument is highly correlated with both micro-level signals, i.e., the number of neighboring acquirers
to or targets of the destination of interest. Importantly, the second-stage regression results inTable 4 column 6 are consistent with our previous findings, and suggest strong neighborhood
learning effects.
Next, we compare the magnitudes of the neighborhood learning effects for both directions.
In the theoretical model, we remained agnostic about the strength of each learning channel, andassumed that both micro-level signals are drawn from the same (prior) distribution. Empirically,
the effects are comparable in magnitude, as neither the coefficients of Table 4 Panel A nor B are
uniformly larger than the other. From the baseline specification in column 2, a one-standarddeviation increase in the (lagged) number of neighboring acquirers (targets) to the destination,
or 5.67 (3.00) firms, raises the probability of acquisition by around 1.6 (0.7) percentage points.
Given that the unconditional probability of entry into a destination is roughly 1.7 percentage
points, this implies that the neighborhood learning effects are also economically significant.24
In Table 5, we provide empirical evidence supportive of Propositions 3 and 4. First, incolumn 1, we jointly study the micro-level signals received from neighboring acquirers and targets. Compared to the baseline specification, we find that the individual impact of each channel
falls, but both sources of knowledge spillovers remain significant for the acquirer’s destination
choice. Next, we interact the two neighborhood learning effects in column 2. Consistent with
Proposition 3, the learning effects are complementary, as the coefficient of the interaction term
is positive and statistically significant. Thus, when the acquirer receives stronger signals about
a destination from its third-country neighboring acquirers, the marginal impact of neighboringtargets on its destination choice is also larger, and vice versa. In columns 3 and 4, we test
Proposition 4 by interacting each of the micro-level signals with the number of target locationsin the US. Although the individual effects of the micro-level signals lose significance, the interaction terms are positive and statistically significant. The results imply that the information
24

The beta coefficient on dimn,t−1 (δimn,t−1 ) from a regression with standardized values is 0.0033 (0.0018).
PP\ jrn,t−1 (\The standard deviation of (log) j∈N (i’s target) 1[acquirer]j∈N (i’s target) 1[target]jrn,t−1 ) is 0.79 (0.56),
0.79
which is equivalent to e
− 1 = 1.20 (0.75) neighboring firms. Thus, for a one-standard-deviation increase in
the number of neighbors, the marginal effect is 5.67 ÷ 1.20 × 0.0033 = 0.016 (3.00 ÷ 0.75 × 0.0018 = 0.007).

26

Table 5: Interaction Effects and Intensity with Number of Locations
dimn,t−1δimn,t−1(1)0.0039***(6.86)
0.0021**(2.42)

dimn,t−1 × δimn,t−1(2)0.0030***(4.81)-0.0006(-0.55)0.0017***(2.66)

dimn,t−1 × (log) Locationsimr,t−1(3)0.0005(0.70)-0.0010(-0.86)0.0042***(9.23)

δimn,t−1 × (log) Locationsimr,t−1
Controls
Firm, Destination, and Year FE
N
R2

(4)

Y
Y
496,076
0.044

YY
496,076
0.044

YY
496,076
0.044

0.0041***(5.68)
YY
496,076
0.044

Notes: The dependent variable is an indicator variable for acquisition in n byfirm i at time t. All regressions are estimated with OLS, and include controls for GDP, Distance, Land border, Legal, Language, Colonial ties, FTA, (log)P\
Locationsimr,t−1 , (log) A−mnt , (log)j∈N (i’s target) 1[acquirer]jrn,t0 , and (log)P\j∈N (i’s target) 1[target]jrn,t0 . T -statistics are in parentheses, with standard errors
clustered at the firm level. ***, **, and * indicate statistical significance at the 1%,5%, and 10% level.

obtained through spillovers at different locations may be used as validation for the knowledge
gained elsewhere. In other words, the greater the number of information sources, the stronger
the impact of each individual source. This reflects an advantage of having wider exposure todifferent spatial networks of the target companies in the third country.
5.2.2

Heterogeneity of networks

In the theoretical model, weights wijr capture the importance of neighboring firms to the foreignacquirer’s US target. Empirically, we exploit the heterogeneity of firms in the target’s spatial
network to gain further insights on the neighborhood learning effect. In particular, we vary thedefinition of neighbors with respect to geographical aspects and industrial composition. First,
in Table 6 column 1, we decompose neighboring firms within the 100km radius by state borders.
Hence, the M&A activity of firms is split between those that reside in the same state, and thosethat operate in a different state. We restrict the sample to non-US acquirer firms with targets
in locations within 100km of a state border. Non-acquirers with targets only in the states of
Alaska and Hawaii are also excluded based on this criterion. The coefficients of dimn,t−1 andδimn,t−1 are statistically significant only for neighboring firms within, as opposed to outside,state borders, and the magnitudes of the learning effects are also much larger for the formergroup. This suggests a strong border effect in impeding the diffusion of knowledge, and thatthe interactions between firms are likely more common within state boundaries.
In Table 6 columns 2 and 3, we study the role of industrial linkages between the nonUS acquirers and firms in the spatial networks of their US targets. We begin with a simple
decomposition of neighboring firms that operate in the same and different sectors as the acquirer

27

Table 6: Heterogeneity of Networks

dimn,t−1 (same state)
dimn,t−1 (different state)
dimn,t−1 (same sector)
dimn,t−1 (different sector)
dimn,t−1 (upstream sector)
dimn,t−1 (downstream sector)
Controls
Firm, Destination, and Year FE
N
R2

δimn,t−1 (same state)δimn,t−1 (different state)δimn,t−1 (same sector)δimn,t−1 (different sector)δimn,t−1 (upstream sector)δimn,t−1 (downstream sector)
Controls
Firm, Destination, and Year FE
N
R2

Panel A: Neighboring acquirers
(1)
(2)
(3)
0.0050***(7.14)
0.0003(0.35)
0.0067***(7.51)
0.0020***(3.34)
0.0002(0.15)
0.0033**(2.00)YY
Y
Y
YY
326,719
496,076
496,076
0.044
0.044
0.044
Panel B: Neighboring targets
(1)
(2)
(3)
0.0056***(5.09)-0.0013(-0.77)0.0084***(6.07)
0.0005(0.50)-0.0057**(-2.34)0.0082***(3.24)YY
Y
Y
YY
326,719
496,076
496,076
0.044
0.044
0.044

Notes: The dependent variable is an indicator variable for acquisition in n by firm i at time t. All regressions are estimatedwith OLS, and include controls for GDP, Distance, Land border, Legal, Language, Colonial ties, FTA, (log) Locationsimr,t−1 ,P\
and (log)(log) A−mnt , (log)j∈N (i’s target) 1[acquirer]jrn,t0 ,P\
1[target].
T-statistics
areinparentheses,
with
jrn,t0j∈N (i’s target)
standard errors clustered at the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level.

in column 2. The SDC Platinum database classifies all firms into one of 84 industries at
the SIC 2-digit level (as well as the narrower 4-digit level). However, for our purposes, thisnarrow definition of sectors is not useful. Besides, for example, the finance or business services
industries, the prevalence of neighboring firms that share the same industry at the 2-digit levelis very small. By aggregating industries into one of 9 SIC 1-digit sectors, around 27% (28%)
of US neighboring acquirers (targets) are classified in the same sector as the non-US acquirer.
We find that neighboring firms operating in the same sector account for much of the impact
from the target’s spatial networks. In Panel A, the within-sector marginal effect is several times
28

larger, and in Panel B, the impact of neighboring targets in different sectors disappears entirely.
As with state boundaries, this evidence suggests that there are varying degrees of knowledge
spillovers from the target’s spatial network to the foreign acquirer. Information provided byfirms with similar characteristics, such as sector classification, are more relevant and have greater
impact.
The group of neighboring firms operating in different sectors is large, and the estimates ofcolumn 2 mask the potential heterogeneity that exists within this subset of the spatial network.
Hence, we further decompose the neighbors of column 2 into two groups, depending on theirposition in the supply chain relative to foreign entrant i. From the Bureau of Economic Analysis(BEA), we obtain the US 1996 annual input-output (I-O) accounts published in the Survey ofCurrent Business, January 2000, which provides the use of commodities by sectors at the same
SIC 1-digit level.25 The data table is replicated in Appendix Figure A.1. Because column 2of Table 6 already investigated the role of firms in the same sector, we classify input-output
linkages only for sectors that are different. Thus, for a particular sector, we define the top four
sectors supplying intermediate goods as upstream. Likewise, the top four buyers or users of intermediates are downstream sectors. Table 6 column 3 reports large and statistically significant
effects for neighboring firms making foreign purchases or sales in downstream sectors, and not in
upstream sectors. The results suggest an asymmetry with respect to upstream and downstream
linkages in determining the acquirer’s destination choice. In particular, information about sales
in the destination market is more valuable than knowledge on suppliers of intermediate goods
for production.
5.2.3

Network centrality of neighboring firms

In this section, we take another direction to examine the heterogeneity of the neighborhood
learning effect by incorporating the variation of neighboring firms’ past cross-border M&A experience to compute weights wijr for dimn,t−1 and δimn,t−1 in Eq. (9). Up to now, neighboring
acquirers or targets receive a weight of either 1 or 0. However, this does not reflect the degreeof importance for each firm within the spatial network, and the potentially unequal diffusion ofknowledge to acquirer i. Intuitively, more experienced neighbors may be able to share information that is more useful than neighbors that have had less success overseas, and their signals
should be weighted more heavily. To measure neighboring firms’ experience and therefore theirimportance, we take a network perspective to examine the central position of each firm in the
global M&A network. Specifically, we construct this global M&A network as a two-mode (bipartite) network (Faust, 1997; Borgatti and Everett, 1997). Firms and countries stand for two
separate camps of the network, and links are formed between firms and countries with crossborder acquisitions. There are no links within a mode of this two-mode network, i.e., amongst
firms or amongst countries.
Figure 5 provides a simple illustration of a two-mode M&A network of four acquirer firms
and three countries. According to Panel (a), firm A invests in country 1; firm B invests in
25

See https://www.bea.gov/industry/historical-industry-accounts-data.

29

Firm j in r

Country n

A
1
B
2
C
3
D
(a)

Firm / Country
A
B
C
D
Column sum

1
1
1
0
1
3

2
0
1
1
0
2

3
0
0
1
0
1

Row sum
1
2
2
1

D
0
0
0
0
1
0
0

1
1
1
0
1
0
0
0

2
0
1
1
0
0
0
0

(b)

A
B
C
D
1
2
3

A
0
0
0
0
1
0
0

B
0
0
0
0
1
1
0

C
0
0
0
0
0
1
1

3
0
0
1
0
0
0
0

(c)

Figure 5: This figure depicts a two-mode M&A network of firms and countries as: (a) an
undirected graph; (b) an incidence matrix; and (c) a bi-adjacency matrix.
countries 1 and 2; firm C invests in countries 1 and 3; and firm D invests in country 1. We havedrawn undirected links in this figure, but in application, we use directed links and constructweights separately for the US neighboring acquirers and targets. Following the conventional
network (graph) analysis, this two-mode network can be presented as an incidence matrix in
Panel (b), and based on this incidence matrix, we can obtain the number of connections of eachfirm from the row sum ([1, 2, 2, 1]’) and the number of connections of each country from thecolumn sum ([3, 2, 1]). These numbers of connections represent the degree centrality of eachfirm and country, respectively.
Furthermore, we can extend this incidence matrix to a bi-adjacency matrix in Panel (c) of
Figure 5. Using this square bi-adjacency matrix, we can compute the eigenvector centrality foreach firm and each country following the spectral analysis of Borgatti and Everett (1997).26 In
our application, eigenvector centrality is preferred to degree centrality because it captures theimportance of each firm proportional to the importance of each country that it invests in. In
26
Based on the square bi-adjacency matrix, the eigenvector centrality is computed as the eigenvector withrespect to the largest eigenvalue.

30other words, eigenvector centrality goes beyond merely counting the number of connections byconsidering the structure of the whole network when evaluating the quality of each link. For
example, in the M&A network of Figure 5, country 1 is more important than country 2, which
in turn is more important than country 3. This is because three firms enter country 1, twofirms enter country 2, and only one firm enters country 3. A higher eigenvector centrality is
associated with investing in country 1 than the two other countries. Consequently, althoughboth firms B and C invest in two countries, the eigenvector centrality of firm B (0.5) is higher
than C (0.29) because B invests in country 1 while firm C does not. Firm B will also have ahigher eigenvector centrality than firms A and D (0.29) because it enters more destinations.
We construct the two-mode incidence matrix using the global M&A investment transaction
records available beginning in 1981. Each transaction establishes a link between a firm and acountry. To capture the experience of firms, we let this link persist to the end of the sample
period. As mentioned, we distinguish the links built by purchases and sales. On the buying(selling) side, the M&A network is built between an acquirer (target) firm and a target (source)
country. Using the corresponding bi-adjacency matrices, we then compute the eigenvector centrality for each US acquirer (target) firm j in each year, and use them as weights for the measureP\ jrn,t−1of the neighborhood learning effect dimn,t−1 = (log) j∈N (i’s target) wijr,t−1 1[acquirer]P\ jrn,t−1 ).
(δimnt = (log) j∈N (i’s target) wijr,t−1 1[target]
Table 7 column 1 presents estimates of our baseline specification using these new measuresof dimn,t−1 and δimn,t−1 , where neighboring firms are weighted by their eigenvector centrality
in the global M&A network. Because the cross-border M&A network is sparse, the computed
eigenvector centrality values for a number of relatively inactive firms can be very small, which
inherently affects the values of dimn,t−1 and δimn,t−1 . To avoid this scaling problem, we performthe regressions with standardized values of dimn,t−1 and δimn,t−1 , and report beta (i.e., standardized) coefficients in Table 7. We leave the regression results with non-standardized values
in Appendix Table A.4 for comparison. Note that the statistical significance of the estimateswith non-standardized values are identical to Table 7. Column 1 shows robust evidence of theneighborhood learning effect after accounting for the heterogeneous influence of neighboringfirms by their overseas investment experience. The effects of dimn,t−1 (δimn,t−1 ) in Panel A (B)are again shown to be statistically significant.27
As before, this heterogeneity can be examined further by separating firms in the spatial
network into different groups. Therefore, we rank firms annually by their eigenvector centrality,
and use the annual median to split the sample between more and less central firms. Comparing
the coefficient magnitudes and statistical significance in column 2 to column 1, we find thatthe neighborhood learning effect can largely be accounted for by the presence of central firms
within the spatial network. Firms that are more central have more experience, especially inimportant destinations, and knowledge spillovers from this group have the greatest impact. In
contrast, firms that are less central have few investments abroad, and not much is gained by
27

Similar qualitative results are obtained when firms are weighted by their degree centrality. However, because
degree centrality only measures the number of links and not their quality, there is much less variation in theseweights and they are less informative. These results are available upon request.

31

Table 7: Centrality of Neighbors and Size of Spatial Network
Spatial network radiusdimn,t−1 (wijr,t−1 = eigenvector centrality)

0-100km(1)0.0024***(4.68)

dimn,t−1 (more central)

0-100km(2)

dimn,t−1 (same sector)
dimn,t−1 (different sector)
Controls
Firm, Destination, and Year FE
N
R2

Y
Y
496,076
0.044

YY
496,076
0.044

Spatial network radius

0-100km(1)0.0012**(2.52)

0-100km(2)

δimn,t−1 (more central)

0.0020***(4.28)
0.0011**(2.21)
YY
496,0760.044

0.0008*(1.80)
0.00003(0.08)
YY
496,0760.044

0.0011***(2.97)-0.0005(-1.33)
YY
496,076
0.044

Panel B: Neighboring targets
0-100km100-200km 200-300km
(3)
(4)
(5)

0.0007*(1.70)
0.0003(0.60)
YY
496,0760.044
300-400km(6)0.0012**(2.52)-0.0001(-0.45)δimn,t−1 (less central)δimn,t−1 (same sector)δimn,t−1 (different sector)
Controls
Firm, Destination, and Year FE
N
R2

300-400km(6)0.0022***(3.91)
0.0006(1.10)

dimn,t−1 (less central)δimn,t−1 (wijr,t−1 = eigenvector centrality)

Panel A: Neighboring acquirers
0-100km100-200km 200-300km
(3)
(4)
(5)

Y
Y
496,076
0.044

YY
496,0760.044

0.0008**(2.03)
0.0008*(1.76)
YY
496,0760.044

0.0007*(1.75)-0.0005(-1.30)YY
496,0760.044

0.0008**(2.05)
0.0003(0.69)
YY
496,0760.044

0.0005(1.23)
0.0004(0.93)
YY
496,076
0.044

Notes: The dependent variable is an indicator variable for acquisition in n by firm i at time t. All regressions are estimatedwith OLS, and include controls for GDP, Distance, Land border, Legal, Language, Colonial ties, FTA, (log) Locationsimr,t−1 ,PP\ jrn,t0 , (log)\(log) A−mnt , (log) j∈N (i’s target) 1[acquirer]j∈N (i’s target) 1[target]jrn,t0 , firm, destination, and year fixed
effects. Standardized coefficients are shown. T -statistics are in parentheses, with standard errors clustered at the firm level.
***, **, and * indicate statistical significance at the 1%, 5%, and 10% level.

interacting with them. Additional information from SDC Platinum also reveals that network
centrality is correlated with other firm characteristics. While data for sales, total assets, andemployment is provided only for around half of the acquirers (and an even smaller percentage oftargets), the average sales, total assets, and number of employees are all roughly 5 times larger
for firms above the median compared to below. Thus, not only are the more central companies
are larger, they presumably have more expansive domestic networks as well.
In the remaining columns of Table 7, we explore the sensitivity of our results with respect
to the spatial proximity of neighbors, and continue to weight neighbors by their eigenvector
centrality measures to capture their experience. We vary the distance of the neighborhood byincrements of 100km up to 400km, and show that the influence of firms at farther distances
is explained by the connections of companies within sectors. The positive impact of firms in32the same sector is observed until 400km for neighboring acquirers in Panel A, and the effectdisappears for neighboring targets only at 400km. According to Appendix Table A.5, which
reports the largest distance between locations within state borders for all 50 US states, this isapproximately half of the average width of a state (582km). Meanwhile, in unreported results,
we find without differentiating the sector of firms, spatial networks do not have any influence
beyond 100km. Thus, along with the results from column 2, this suggests that the geographic
diffusion of knowledge is limited by spatial networks, but not the networks created throughconnections in industry.
5.2.4

Accumulated experience

Finally, we explore the role of firms’ accumulated experience at the micro level by introducing
PP\
measures of accumulated experience Dimn,t−1 = (log) t−1
s=0
r Wrn,t−1 1[acquirer]imrs into theregression equation. In the macro-level regression, we began by applying equal weights to
Wrn,t−1 . However, because the dependent variable for the micro-level estimating equation is
simply an indicator variable for destination choice, the lack of variation across destinations withequal weights would not be meaningful. Therefore, in Table 8, we utilize the same weights forthe closeness of (non-US) third country r to destination n from Table 3 columns 2 to 6. These
are the inverse of the distance between r and n, the aggregate number of acquisitions from r
to n last year Arn,t−1 , aggregate sales Anr,t−1 , as well as combinations that summarize the two
measures of closeness.
To test Proposition 5, we begin by examining the impact of firms’ global experience solely in
Table 8 Panel A. As in the macro regressions, the micro-level weighted sum of acquisitions also
has an impact on the extensive margin of the firm. The coefficients on Dimn,t−1 are all positiveand statistically significant (at the 1% level). Although we are unable to identify the source oflearning in these non-US third countries, the evidence of Table 8 shows that previous experiencein other non-US third countries that are geographically closer and more economically integrated
with the destination of interest has an strong impact on the current destination choice. For
example, in column 1, holding the number of acquisitions in third countries fixed, a 10% decreasein distance between all third countries r and destination n increases the probability of entry byaround 0.05 percentage points (0.1 × 0.0047). If, for simplicity, we take firms’ entry decisions
to be independent, then the orders of magnitude for the estimates of Panel A are roughly
consistent with the macro-level regression results from Section 5.1. Comparing columns 2 and3 against columns 4 and 5, we also find a larger impact of experience in third countries withmore purchases in the destination as opposed to sales.
Next, in Panels B and and C, we jointly study the learning effects from accumulated experience and the local knowledge spillovers in the third country of the US. Neighboring firms
are weighted equally, as in Table 4, to facilitate comparisons with the baseline results. Even
after controlling for acquirers’ history of investments abroad, the impact of the third-country
neighborhood learning effect remains.
Lastly, in Table 9, we interact firms’ micro-level signals in the US with their accumulated33

Table 8: Impact of Accumulated Investment Experience1Distancern

Weight Wrn,t−1 :Controls
Firm, Destination, and Year FE
N
R2

(1)
0.0047***(13.33)
YY
496,0760.044

Weight Wrn,t−1 :1Distancern

Dimn,t−1 = (log)Pt−1 P
s=0\
r Wrn,t−1 1[acquirer]imrs

Controls
Firm, Destination, and Year FE
N
R2

(1)
0.0041***(7.43)
0.0046***(13.22)
YY
496,0760.044

Weight Wrn,t−1 :1Distancern

dimn,t−1 = (log)Pj∈N (i’s target) 1[acquirer]jrn,t−1\

Dimn,t−1δimn,t−1 = (log)P\j∈N (i’s target) 1[target]jrn,t−1

Dimn,t−1
Controls
Firm, Destination, and Year FE
N
R2

(1)
0.0031***(3.58)
0.0046***(13.27)
YY
496,076
0.044

Panel A: Accumulated experience
Arn,t−1
Arn,t−1
Anr,t−1
Distancern
(2)
(3)
(4)
0.0063*** 0.0055*** 0.0031***(22.51)(17.32)(20.18)YYY
YY
Y
496,076
496,076
496,076
0.045
0.045
0.045
Panel B: With neighboring acquirers
Arn,t−1
Arn,t−1
Anr,t−1
Distancern
(2)
(3)
(4)
0.0033*** 0.0038*** 0.0036***(5.90)(6.82)(6.52)
0.0061*** 0.0054*** 0.0030***(21.83)(16.99)(19.60)YYY
YYY
496,076
496,076
496,076
0.045
0.045
0.045
Panel C: With neighboring targets
Arn,t−1
Arn,t−1
Anr,t−1
Distancern
(2)
(3)
(4)
0.0026***0.0018**0.0028***(2.93)(2.08)(3.19)
0.0062*** 0.0054*** 0.0030***(22.28)(16.89)(20.00)YYY
YYY
496,076
496,076
496,076
0.045
0.045
0.045

Anr,t−1
Distancern(5)0.0031***(18.12)
YY
496,076
0.045
Anr,t−1
Distancern(5)0.0038***(6.91)
0.0030***(17.80)YY
496,076
0.045
Anr,t−1
Distancern(5)0.0022**(2.52)
0.0030***(17.75)
YY
496,076
0.045

Notes: The dependent variable is an indicator variable for acquisition in n by firm i at time t. All regressions are
estimated with OLS, and include controlsPfor GDP, Distance, Land border, Legal, Language,
Colonial ties, FTA,P
\ jrn,t0 , and (log)\(log) Locationsimr,t−1 , (log) A−mnt , (log) j∈N (i’s target) 1[acquirer]j∈N (i’s target) 1[target]jrn,t0 .
T -statistics are in parentheses, with standard errors clustered at the firm level. ***, **, and * indicate statisticalsignificance at the 1%, 5%, and 10% level.

experience in all third countries. In both Panels A and B and across all columns, the interaction term is positive and statistically significant. This is consistent with the latter part of
Proposition 5, which predicts the neighborhood learning effect to be stronger with more transactions into third countries in the past. Comparing two foreign firms with the same numberof acquisitions in third countries, a one-standard-deviation increase in the (lagged) number ofneighboring acquirers (targets) would raise the probability of entry by 0.09 (0.04) percentage
points more for the firm that invests in third countries that are all 10% closer in distance to thedestination. Together with the results of Table 5, the evidence indicates that, regardless of theknowledge source, the information obtained through the various channels have complementary
effects on the acquirer firm’s choice of destination market.

34

Table 9: Intensity of Neighborhood Learning Effect with Accumulated Experience
Weight Wrn,t−1 :dimn,t−1
Dimn,t−1
dimn,t−1 × Dimn,t−1
Controls
Firm, Destination, and Year FE
N
R2
Weight Wrn,t−1 :δimn,t−1
Dimn,t−1δimn,t−1 × Dimn,t−1
Controls
Firm, Destination, and Year FE
N
R2

Panel A: Interaction with neighboring acquirers
Arn,t−1
Anr,t−1
Arn,t−1
Anr,t−1
Distancern
Distancern
(1)
(2)
(3)
(4)
(5)
0.0225***-0.0013*0.0016*** 0.0142*** 0.0118***(10.52)(-1.90)(2.63)(9.59)(7.16)
0.0031*** 0.0038*** 0.0039*** 0.0018*** 0.0021***(8.76)(11.27)(10.15)(10.22)(10.55)0.0025*** 0.0025*** 0.0015*** 0.0015*** 0.0011***(9.20)(8.14)(4.80)(8.36)(5.56)
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
496,076
496,076
496,076
496,076
496,076
0.045
0.046
0.045
0.046
0.045
1
Distancern

Panel B: Interaction with neighboring targets
Arn,t−1
Anr,t−1
Arn,t−1
Anr,t−1
Distancern
Distancern
(1)
(2)
(3)
(4)
(5)
0.0157***-0.00130.00040.0105*** 0.0063***(5.02)(-1.20)(0.38)(5.05)(2.97)
0.0040*** 0.0053*** 0.0050*** 0.0025*** 0.0027***(11.47)(16.89)(13.49)(15.37)(14.05)0.0018*** 0.0018***0.00070.0012***0.0006**(4.38)(4.13)(1.54)(4.59)(2.37)
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
496,076
496,076
496,076
496,076
496,076
0.044
0.045
0.045
0.045
0.045
1
Distancern

Notes: The dependent variable is an indicator variable for acquisition in n by firm i attime t. All regressions are estimated with OLS, and include controls for GDP, Distance,
Land border, Legal, Language, Colonial ties, FTA, (log) Locationsimr,t−1 , (log) A−mnt , (log)PP\\
T -statistics are inj∈N (i’s target) 1[target]jrn,t0 .
j∈N (i’s target) 1[acquirer]jrn,t0 , and (log)parentheses, with standard errors clustered at the firm level. ***, **, and * indicate statisticalsignificance at the 1%, 5%, and 10% level.

6

Conclusion

In this paper, we propose local knowledge spillovers within spatial networks in prior destinations
as a channel through which firms learn about their destination of interest. Thus, acquirers
obtain knowledge on new destinations through their targets in third countries. We present aheterogeneous-firm model of cross-border M&A that provides a micro-foundation for learning inthird countries, either through multinationals’ accumulated experience, or through the networksof their third-country targets.
Using data on global cross-border M&A activity from 1995 to 2016, we find strong empiricalsupport for the model at both the aggregate and micro levels. The number of acquisitions to a
destination is increasing in the number of prior acquisitions to third countries that are closer tothat market. At the micro level, we test our hypotheses by examining foreign acquirers in theUS, and the spatial networks of their targets. We find that the number of acquisitions, as well as
sales, by the neighboring firms of the US target has a positive impact on the destination choice ofthe non-US acquirer. The third-country neighborhood learning effects are found to be stronger
for firms in the same state, same sector, and in downstream (as opposed to upstream) sectors.
35

In addition, using eigenvector centrality, we show that the learning effect increases with thecentrality of neighbors. Furthermore, prior experience in third countries that are geographicallycloser to or more integrated with the destination of interest also raises the chance of investingin that destination. These learning effects are complementary across the various information
sources of the firm.
Economic markets are generally more efficient in the US compared to other countries, which
implies greater knowledge spillovers and less information frictions. A comparison of the learning effects between the US and other countries may improve our understanding of the role ofinformation frictions and measures to mitigate them. We leave this for future research.

36

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40

A

Appendix

Figure A.1: Input output matrix for the US in 1996. For each row, upstream sectors areindicated with a solid border. For each column, downstream sectors are shaded.

Table A.1: Top 20 Origin and Destination Countries
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

Origin country
United States
United Kingdom
CanadaGermany
FranceHong Kong
Netherlands
Japan
Singapore
Australia
SwedenSwitzerland
China
Spain
Italy
Belgium
Malaysia
Denmark
CyprusNorwayAcquisitions made
38,756
18,503
13,498
10,162
9292
7859
7044
5911
5647
5531
5455
5149
3729
2994
2883
2727
2467
2357
2335
2320

Percentage
20.6
9.83
7.17
5.40
4.94
4.18
3.74
3.14
3.00
2.94
2.90
2.74
1.98
1.59
1.53
1.45
1.31
1.25
1.24
1.23

Destination country
United States
United Kingdom
Germany
China
CanadaFranceAustralia
India
Italy
Spain
Netherlands
Hong Kong
RussiaSwedenBrazilSwitzerland
Singapore
NorwayBelgium
PolandTargets sold
24,49915,074
11,348
9826
9020
7628
7097
4872
4586
4546
4520
4073
3979
3955
3563
3110
2793
2630
2551
2496

Percentage
13.0
8.01
6.03
5.22
4.79
4.05
3.77
2.59
2.44
2.42
2.40
2.16
2.11
2.10
1.89
1.65
1.48
1.40
1.36
1.33

Notes: Authors’ calculations using cross-border M&A data between 1995 and 2016 from Thomson Reuters
SDC Platinum.

41

Table A.2: Top 20 Cities for Acquisitions and Sales Transactions
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

Acquirer city
New York
Houston
ChicagoSan Francisco
Los Angeles
BostonDallasAtlantaLas Vegas
San Diego
Denver
San Jose
SeattleMiami
Salt Lake City
Washington
Stamford
Minneapolis
IrvineAustinAcquirer state
New York
Texas
Illinois
California
California
Massachusetts
Texas
Georgia
NevadaCalifornia
Colorado
California
Washington
FloridaUtah
D.C.
Connecticut
Minnesota
California
Texas

Acquisitions made
1615
469
288
286
231
215
215
207
159
147
139
130
126
122
114
113
106
103
100
93

Target city
New York
HoustonSan Francisco
ChicagoLos Angeles
San Diego
DallasAtlanta
BostonSan Jose
SeattleMiami
DenverSanta Clara
IrvineAustinWashington
Minneapolis
Las Vegas
Sunnyvale

Target state
New York
Texas
California
Illinois
California
California
Texas
Georgia
Massachusetts
California
Washington
FloridaColorado
California
California
Texas
D.C.
Minnesota
NevadaCalifornia

Targets sold
1090
520
376
330
308
257
216
201
193
190
186
169
138
133
132
130
119
119
110
109

Notes: Authors’ calculations using cross-border M&A data for the US between 1995 and 2016 from Thomson Reuters SDC Platinum.

Table A.3: Instrumental Variables Regression, First Stage
Neighboring

Acquirers

Targets

Dependent variabledimn,t−1(1)0.0032***(36.14)0.0940***(12.45)
0.0613***(35.86)0.3777***(88.94)0.2736***(73.54)δimn,t−1(2)0.0011***(22.78)0.0222***(7.28)
0.0068***(8.08)
0.1575***(66.69)0.4027***(77.15)(log) Popgmr × (log) Popgnr(log) Locationsimr,t−1(log) A−mnt
(log)

P

\

(log)

P

\

j∈N (i’s target) 1[acquirer]jrn,t0j∈N (i’s target) 1[target]jrn,t0

Controls:

GDP, Distance, Land border
Legal, Language, Colonial ties, FTA

Firm, Destination, and Year FE
NY
496,076Y
496,076

Notes: Column 1 (2) shows estimates for the first-stage regression of Table 4 Panel
A (B) column 6. T -statistics are in parentheses, with standard errors clustered at
the firm level. ***, **, and * indicate statistical significance at the 1%, 5%, and
10% level.

42

Table A.4: Centrality of Neighbors and Size of Spatial Network, Unstandardized Coefficients
Spatial network radiusdimn,t−1 (wijr,t−1 = eigenvector centrality)

0-100km(1)0.0458***(4.68)

dimn,t−1 (more central)

0-100km(2)

300-400km(6)0.0412***(3.91)
0.3625(1.10)

dimn,t−1 (less central)dimn,t−1 (same sector)
dimn,t−1 (different sector)
Controls
Firm, Destination, and Year FE
N
R2

Y
Y
496,076
0.044

YY
496,076
0.044

Spatial network radius

0-100km(1)0-100km(2)

δimn,t−1 (wijr,t−1 = eigenvector centrality)

0.6640**(2.52)

δimn,t−1 (more central)

0.0815***(4.28)
0.0261**(2.21)
YY
496,076
0.044

0.0623*(1.80)
0.0016(0.08)
YY
496,076
0.044

0.0983***(2.97)-0.0264(-1.33)
YY
496,076
0.044

Panel B: Neighboring targets
0-100km100-200km 200-300km
(3)
(4)
(5)
(1.76)(-1.30)(0.69)

0.0488*(1.70)
0.0099(0.60)
YY
496,0760.044
300-400km(6)(0.93)

0.6642**(2.52)-103.2002(-0.45)δimn,t−1 (less central)δimn,t−1 (same sector)δimn,t−1 (different sector)
Controls
Firm, Destination, and Year FE
N
R2

Panel A: Neighboring acquirers
0-100km100-200km 200-300km
(3)
(4)
(5)

Y
Y
496,076
0.044

YY
496,076
0.044

0.9225**(2.03)
0.5664*(1.76)
YY
496,076
0.044

1.0777*(1.75)-0.5352(-1.30)YY
496,076
0.044

0.0044***(5.09)
0.0031**(2.31)
YY
496,076
0.044

0.8694(1.23)
0.3711(0.93)
YY
496,076
0.044

Notes: The dependent variable is an indicator variable for acquisition in n by firm i at time t. All regressions are estimatedwith OLS, and include controls for GDP, Distance, Land border, Legal, Language, Colonial ties, FTA, (log) Locationsimr,t−1 ,PP\ jrn,t0 , (log)\(log) A−mnt , (log) j∈N (i’s target) 1[acquirer]j∈N (i’s target) 1[target]jrn,t0 , firm, destination, and year fixed
effects. T -statistics are in parentheses, with standard errors clustered at the firm level. ***, **, and * indicate statisticalsignificance at the 1%, 5%, and 10% level.

43

Table A.5: Largest Distance between Locations within States
Rank StateDistance (km) Rank State
1
Alaska2044
26
Kentucky
2
Texas
1208
27
Missouri3
California
1188
28
Alabama4
Florida
873
29
Utah
5
Montana848
30
Illinois
6
Idaho
787
31
Wisconsin
32
Mississippi
7
Nevada745
33
Washington
8
Michigan740
9Tennessee
716
34
Hawaii10
North Carolina
657
35
Iowa
36
Pennsylvania
11
Kansas653
12
Oklahoma
645
37
Maine
38
Indiana13
Wyoming
644
39
Ohio
14
New Mexico
637
40
Arkansas
15
Nebraska636
16South Dakota
632
41West Virginia
17
New York
616
42South Carolina
18
Virginia615
43
Maryland19
Arizona
610
44
Massachusetts
45
Vermont
20
Oregon606
21
Minnesota
597
46
New Jersey
47
New Hampshire
22
Colorado
593
23
Georgia586
48
Connecticut
24
Louisiana
585
49
Delaware50
Rhode Island
25
North Dakota
566
Average (standard deviation)
Average (standard deviation) excluding Alaska and Hawaii

Distance (km)
561
558
549
548
531
528
514
512
506
498
492
490
472
445
435
400
389
336
304
236
211
197
183
130
73
582 (304)
554 (224)

Notes: Author’s calculations for largest driving distance between 4,706 locations withcross-border M&A activity.

44