Cross-Border Mergers and Acquisitions: The importance of Local

Credit and Source Country Finance

June 2015

Ivan T. Kandilov Aslı Leblebicioğlu Neviana Petkova

North Carolina State University University of Texas at Dallas U.S. Department of the Treasury

Abstract:

We study host and source country financial market conditions and the interplay between the two in determining

the incidence and intensity of cross-border mergers and acquisitions (M&As) into the U.S. We find that states

with improved credit conditions following interstate banking deregulation attract a greater number and higher

value cross-border M&A deals. We also document a positive impact of source country financial markets depth on

the incidence of cross-border M&As and uncover a substitution effect between local and source country

financing. The effects are smaller for publicly traded firms and larger for firms that are more dependent on

external finance.

Keywords: Cross-border Mergers and Acquisitions; Banking Deregulation; External Finance Dependence

J.E.L. Classifications: F23, F36, G21, G28, G34

Ivan T. Kandilov: North Carolina State University, Department of Agricultural and Resource Economics, Box 8109,

Raleigh, NC 27695 (E-mail: ivan_kandilov@ncsu.edu). Aslı Leblebicioğlu: University of Texas at Dallas, Department of

Economics, 800 West Campbell Road, Richardson, TX 75080 (E-mail: asli.leblebicioglu@utdallas.edu). Neviana Petkova:

U.S. Department of the Treasury, 1500 Pennsylvania Ave NW, Washington, DC 20220 (E-mail:

neviana.petkova@treasury.gov). The opinions expressed in this paper are those of the authors and do not necessarily reflect

the views of the U.S. Department of Treasury.

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1. Introduction

Our paper provides evidence on the importance of host and source country financial market conditions for cross-

border mergers and acquisitions (M&As). While traditional models of cross-border investment focus on real

economic factors to explain the direction and volume of investment flows, our study contributes to a growing

literature exploring the importance of financial markets in cross-border investment. In a seminal study, Froot and

Stein (1991) presented a parsimonious model with imperfect capital markets, in which real exchange rate

movements could generate changes in cross-border investment flows. Subsequent research explored the

importance of capital markets further by examining the role of source country financial markets for cross-border

investment (di Giovanni (2005), Klein et al. (2002)). Our study is among the first to explicitly examine the role of

host country credit markets and to scrutinize the interplay between source and host country financial market

conditions in determining the incidence and intensity of cross-border M&As.

We focus on deals with U.S. targets and foreign acquirers, using data on cross-border M&As from Thomson

Reuter’s SDC Platinum dataset. We exploit the staggered timing of interstate banking and intrastate branching

deregulation in the U.S. throughout the 1980s and 1990s as a source of exogenous variation in state credit

conditions (Black and Strahan (2002)). We measure the depth of source country capital markets by the ratio of

market capitalization to GDP and the ratio of credit provided to the private sector to GDP. We hypothesize that

cross-border M&As are more prevalent in states that adopt a banking deregulation. It has been well documented

that state banking deregulations increased competition in the banking industry, lowered the cost of borrowing and

improved access to credit (see e.g., Amore et al. (2013), Kerr and Nanda (2009)). It is also known that foreign

affiliates of multinationals use host country finance, e.g. Marin and Schnitzer (2011) show that Eastern European

affiliates of German and Austrian firms source 30 to 40 percent of their external financing needs from host

country sources. Desai et al. (2004) and Huizinga et al. (2008) point out that multinationals tend to borrow more

in high tax jurisdictions, such as the U.S., because of international tax planning. Regardless of whether bank

finance is explicitly used to pay for cross-border M&A deals, improved credit market conditions are likely to

positively affect the ongoing operations of enterprises and improve the economic climate in the state, encouraging

more cross-border M&As into deregulated states. We also conjecture that there would be more cross-border

M&A activity originating from source countries with greater financial markets depth. Source country financial

development has been previously shown to positively affect cross-border M&A activity, e.g., Erel et al. (2013), di

Giovanni (2005), Klein et al. (2002). We are agnostic regarding the impact of the interaction between source

country and host country financial development, since there exist plausible arguments for why the two should act

as complements or substitutes. Improvements in local credit markets may be a substitute for source country

financing, as foreign acquirers may have a choice between raising external funds from their source country or the

host state in the U.S. On the other hand, source and host country financing may be complements, as the ability to

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raise funds at home could serve as a signal of creditworthiness, allowing the acquirer to raise funds in the host

country also.

Our analysis shows that states that adopt the interstate banking deregulation attract more and higher value

cross-border M&A deals, underscoring the importance of host state credit conditions in cross-border investment

flows. In particular, we find that the number of cross-border M&A transactions increases between 37 and 57

percent following the adoption of the interstate banking deregulation, and the average transaction value increases

between 40 and 46 percent. As expected, we also find that source country financial markets depth boosts the

number of cross-border M&A deals. Quantitatively, we find that a ten percentage point increase in the source

country credit to GDP ratio raises the number of deals between 14 and 25 percent. Also, a ten percentage point

increase in source country stock market value to GDP leads to a 5 percent increase in the number of cross-border

M&As. Moreover, we find that while the host state interstate banking deregulation acts as a substitute for source

country credit market development, when it comes to the frequency and size of cross-border M&A deals, it

complements the impact of source country stock market depth.

We next turn to exploring the mechanisms for these effects by considering the relative importance of source

and host country capital market conditions for public versus private firms engaged in cross-border M&A

transactions. We posit that publicly traded firms, which have access to public debt and equity markets, in addition

to more universally available sources of finance such as bank finance, are less impacted by state banking

deregulation relative to private firms. Furthermore, we conjecture that publicly traded acquirers are more likely to

engage in cross-border M&A deals when market returns in the source country are high (see, e.g. Erel et al.

(2012)). Our results confirm that both the size and number of deals involving publicly traded acquirers or targets

are less affected by the interstate banking deregulation. Consistent with our priors, we find that high market

returns in the source country increase the number of deals involving publicly traded acquirers or targets.

To further probe the possible mechanisms for these effects, we exploit variation in external finance

dependence of the acquirer and target’s industry of operation. We hypothesize that firms in sectors that are more

dependent on external finance are more likely to be affected by source and host country credit conditions in their

cross-border M&A activity. We use Rajan and Zingales’ (1998) measure of external finance dependence in

manufacturing industries as defined in Cetorelli and Strahan (1998). As anticipated, we find that both the number

and size of M&A deals involving external finance dependent acquirers or targets are more positively affected by

the interstate banking deregulation. Greater availability of credit in the source country also increases the number

of cross-border M&A deals that involve acquirer or target firms that are more dependent on external finance. We

additionally provide suggestive evidence that credit availability in the host state and the source country matter

more for cash deals, where cash is the predominant method of payment for the cross-border M&A.

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Lastly, we examine how host and source country capital market conditions affect the overall volume of cross-

border M&A deals. We find that it is particularly sensitive to host state credit conditions, with the interstate

banking deregulation having a positive and statistically significant effect on deal volume. We conjecture that the

interstate banking deregulation is more critical to cross-border M&As relative to intrastate bank branching

deregulation, because multi-state banks are better able to serve the needs of foreign investors and better equipped

to evaluate multinational projects. The role of source country financial depth in this context is also important –

source country credit to GDP ratio has a positive and statistically significant effect on cross-border M&A deal

volume in some specifications and source country stock market value to GDP ratio has a positive and statistically

significant effect in other specifications. Importantly, our analysis of deal volume indicates that there is a

substitution effect between the interstate banking deregulation, which improves local, host country credit

conditions, and source country credit to GDP ratio, i.e., countries with improving credit markets invest less in

states that adopt the interstate banking deregulation.

While most of the attention in the literature has been on studying patterns in domestic M&As, our study

contributes to a growing body of work exploring the determinants of cross-border M&As. In an influential study,

Rossi and Volpin (2004) addressed the role of differences in laws and regulations across countries in cross-border

M&As and found evidence that acquirers from countries with better investor protection regimes target firms in

countries with poorer investor protection regimes, providing a market-based mechanism for improving the degree

of investor protection worldwide. Chari et al. (2010) present evidence that the gains from cross-border M&As are

particularly large when developed country acquirers gain majority control in emerging market targets, with the

effect being greater for targets from countries with weaker contracting environments and in industries with higher

asset intangibility. A recent study by Karolyi and Taboada (2014) provides further evidence that acquirers

engaged in cross-border M&A deals in the banking industry tend to be from countries with better regulatory

regimes. Huizinga and Voget (2009) emphasize the role of international tax planning in determining the direction

and volume of cross-border M&As, with multinational enterprises choosing to expand in low tax jurisdictions.

Ahern et al. (2012) report that cultural values are another key determinant of the volume and direction of cross-

border M&As. In a cross-country setting, Erel et al. (2012) consider many of the previously examined factors

jointly and document that geography, bilateral trade and regulatory regime quality are good predictors of cross-

border investment flows, as are valuation factors, including stock market returns, market to book values and

exchange rates.

Our paper also contributes to the broader literature on foreign investment that explores the role of financial

market conditions in cross-border flows. Klein et al. (2002) demonstrate that impaired access to bank credit at

home hinders Japanese firms’ foreign investment activity in the U.S. Poelhekke (2015) uses data on outbound

foreign direct investment (FDI) from the Netherlands to provide evidence that Dutch global banks make investing

4

abroad easier for their home country customers. Di Giovanni (2005) shows that both the relative size of stock

markets and credit markets are important predictors of cross-border investment activity. In contemporaneous

work to ours, Bilir et al. (2014) use data on foreign direct investment undertaken by U.S. multinational abroad to

show the importance of host country financial development. Kandilov et al. (2014) use data on inbound FDI

undertaken by foreign multinationals in the U.S. manufacturing sector to assess the impact of host country

banking deregulation on the size and incidence of these transactions. The present study focuses on foreign

multinationals’ cross-border M&A activity into U.S. states.1 It extends the literature by focusing on two important

aspects of cross-border M&As that have not received attention previously – local, host country finance and its

interaction with source country credit conditions. One advantage of our research design is that we employ

variation in local credit market conditions across U.S. states thereby implicitly controlling for a number of

potentially confounding host country factors that affect cross-border M&As and are common to all states.

Finally, our work is also related to an extensive literature in finance on the impact of banking deregulations

across U.S. states and their effects on real domestic, as opposed to cross-border, economic activity. While many

studies focus on intrastate branching deregulation alone (Jayaratne & Strahan (1996), Black & Strahan (2002),

Berger et al. (2012)), or interstate banking deregulation alone (Amore et al. (2013), and Michalski and Ors

(2012)), we explore the effect of both interstate banking and intrastate branching deregulation, similar to Black &

Strahan (2002), Demyanyk et al. (2007), Kerr & Nanda (2009, 2010), and Levine et al. (2014).

The rest of the paper is organized as follows: Section 2 introduces the data, Section 3 presents the

econometric strategy, Section 4 discusses the results and Section 5 concludes.

2. Data

2.1 SDC Platinum

We use data on completed M&A deals from the SDC Platinum database to assess the impact of the two banking

deregulations and source country financial development on the incidence and the intensity of cross-border

M&As.2 Our sample encompasses foreign acquirers investing in targets located across the 48 contiguous states,

excluding Delaware and South Dakota because of the preponderance of credit card banks in these states (Black &

Strahan (2002), Berger et al. (2012)). We exclude deals in which the target or the acquirer is a government agency

or in the financial industry. Furthermore, we omit transactions for which the acquirer’s country of incorporation

1 Cross border M&A is an important subset of FDI, where total FDI can also include greenfield investment, extensions of

capital and financial restructuring (see OECD (2009)).

2 The SDC Platinum database is widely used to study cross-border M&As, see e.g. Aguiar & Gopinath (2005) and Erel et al.

(2012).

5

and residence is not identified, or for which variables related to the acquirer country’s financial development, such

as stock market capitalization or credit provided to the private sector, are not available. We focus on M&A deals

that were completed between 1983 and 1994. We choose 1983 as the starting point of our analysis as there is an

extremely small number of cross-border mergers and acquisitions that pass our data filters prior to 1983. We end

our sample in 1994, which is the year the Riegle-Neal Interstate Banking and Branching Efficiency Act – the

federal regulation that ended state restrictions on bank expansions across local and interstate markets— was

passed. The various filters result in a sample of 3052 deals from 21 source countries (see Table 1, Panel B for the

list of countries). 3

The information on cross-border M&A deals obtained from SDC Platinum includes the transaction value

of the deal, identity of the foreign acquirer, acquirer country of origin, location of the target (state), main four

digit Standard Industrial Classification industries in which the acquirer and the target operate in, and the year of

deal completion. We also observe the public status of the acquirer and the target firms. Except for the transaction

value, data on all other variables are always recorded. Out of 3052 cross-border M&A deals in our sample, 1803

deals have recorded transaction values. We find little difference in the distribution of transaction covariates (such

as location, year of completion, source country, acquirer industry and target industry) across the two groups of

M&A deals – those with and those without recorded transaction values. The pseudo-R2 for a logistic regression

with a dependent variable indicating if the observation has a reported transaction value and a set of independent

variables that includes dummies for all transaction covariates (state, year of completion, source country, acquirer

industry and target industry) is hardly 0.10, indicating that there is likely little selection on these observables.

To assess the effect of increased access to local credit on the incidence and intensity of cross-border

M&A deals, and compare it to the role of source country financing opportunities, we first construct a state-source-

country-level panel dataset counting the number of foreign M&A deals for each state, originating from each of the

21 source countries, and for every year between 1983 and 1994. Subsequently, we examine the effects of local

and source country finance on average deal size by focusing on transaction values.4

3 The number of deals from these 21 countries constitutes 95.6% of the total number of (non-government and non-finance

sector) deals, where the acquirer country is reported.

4 Another potential concern about the timing of the banking deregulation is that they may be correlated with the anti-takeover

laws (laws that constrained hostile takeovers, see e.g. Bertrand and Mullainathan (2003), and Atanassov (2013).) that were

passed in the U.S. during our sample period. The correlation between the anti-takeover laws and the banking deregulation

indicators in our sample ranges between 0.27 and 0.42. In unreported estimations, we found that the effect of the banking

deregulation on cross-border M&A activity remains unchanged when we include the anti-takeover laws in our empirical

models.

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2.2 U.S. State Banking Deregulation

We use the staggered adoption of banking deregulation by U.S. states as the source of variation in host market

credit conditions. Until the 1970s, banks in the U.S. were restricted by state statutes in their ability to expand

across state borders and to branch within a state. The 1956 Douglas Amendment of the Bank Holding Company

Act prohibited bank holding companies from acquiring banks in other states unless state regulations permitted

such transactions, effectively banning interstate bank M&As. Starting in the late 1970s, states began allowing

bank holding companies headquartered in other states, with which they had entered into reciprocal agreements, to

acquire local banks (see Table 2). The Garn-St. Germain Act of 1982 further amended the Bank Holding

Company Act to allow any bank holding company, regardless of its state, to acquire failed banks (Jayaratne &

Strahan (1996)). However, it was not until the Riegle-Neal Interstate Banking and Branching Efficiency Act of

1994 that interstate banking was deregulated nationwide, unless individual states opted out, superseding between-

state agreements and effectively putting out-of-state banks on an equal footing with local banks (Kerr & Nanda

(2009)).5

Similarly, until the 1970s only a handful of states allowed unrestricted within state bank branching. The

majority of states either explicitly prohibited or severely limited branching activity (Jayaratne & Strahan (1996)),

although banks could effectively branch by adopting a multi-bank holding company organizational form (Kerr &

Nanda (2009)). Throughout the 1970s and 80s state bank branching deregulation allowed banks to establish

multiple branches within a state through M&As and de novo branching. Branching through M&As allowed multi-

bank holding companies to transform subsidiaries into branches, as well as to acquire branches. Most states

permitted de novo branching (the set-up of brand new branches) at a later stage. Since branching through M&As

deregulation marks the leading edge of state branching deregulation reform (Cetorelli & Strahan (2006),

Demyanyk et al. (2007)), we use those dates to mark a state’s adoption of intrastate branching deregulation.

A potential concern is that the timing of banking deregulation is somehow driven by cross-border

investment into the deregulating state, rather than the other way around. We address this concern in two ways: by

considering the political economy of deregulation and by checking for pre-trends in cross-border M&A activity.

Kroszner and Strahan (1999) argue that the timing of banking deregulation is related to the relative strength of

private interest groups standing to gain from deregulation, e.g., large banks as well as small firms, which are

dependent on bank finance. In addition to this private interest argument, Freeman (2002) and Berger et al. (2012)

point out that the timing of banking deregulation is correlated with a state’s past economic performance, while

Huang (2008) suggests that the timing of deregulation could also be correlated with anticipated changes in future

economic activity. It is unlikely that the timing of banking deregulation is directly linked to cross-border M&A

5 Only Texas and Montana passed legislation to opt out of the interstate banking provisions of the Riegle-Neal Act before

they were to go into effect in 1997 (Kroszner & Strahan (1999)).

7

activity. In unreported regression results we find that there is no economically or statistically significant

relationship between initial levels of M&A activity and the timing of the adoption of banking deregulations across

states.

2.3 Source Country Financial Depth

In order to identify how financing opportunities in the source country (or lack thereof) interact with the

improvements in local credit markets stemming from banking deregulation, we focus on two measures of

financial deepening that are widely used in the literature on financial development (see e.g., King and Levine

(1993) or Wurgler (2000)). First, to capture the depth of credit markets, we use total credit provided to the private

non-financial sector by all domestic lending institutions (source: Bank of International Settlements) as a

percentage of GDP.6 Second, we use total stock market value (source: Datastream) as a percentage of GDP as a

proxy for the depth of public capital markets in the source country.

2.4 Additional Control Variables

In addition to the variables measuring the depth of the financial system, we control for other source-country

determinants of cross-border M&As as established in previous studies (see e.g., Erel et al. (2012)). These

variables include real GDP per capita and its growth rate, stock market return index, real exchange rate, bilateral

trade with the U.S., and the statutory corporate tax rate. To this list, we add state-level factors that can affect

which states acquirers choose to invest in. These variables are gross state product, state unemployment rate,

average wage rate, statutory state corporate tax rate and number of foreign trade zones. Definitions for all

variables included in our specifications and their data sources can be found in the Data Appendix. Summary

statistics for all variables included in our analysis are presented in Panels A and B of Table 1. We describe some

of the patterns we observe in cross-border M&As originating from different source countries across states in the

Results section. The next section provides details of our econometric strategy and describes the different source-

country-level and state-level time-varying covariates that may affect either the number of cross-border M&A

deals or their transaction values.

6 Previous studies that have incorporated financial development measures in their analysis, such as Bekeart, et al. (2007), use

data on private credit to GDP ratio from the World Bank’s World Development Indicators (WDI). Because this series is

missing for Hong Kong in the 1980s, we use the private credit data from the Bank of International Settlements instead. The

correlation between the two series during our sample period is 0.85.

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3. Econometric Strategy

In this section, we lay out our econometric strategy for estimating the impact of facilitating access to financing,

both in the U.S. (the host country), and the acquirer’s country of origin (the source country), on the incidence and

the volume of cross-border M&A activity. We divide our empirical analysis into three parts. First, we assess the

effect of local and source-country finance, and the interaction of the two, on the number of cross-border M&A

transactions across U.S. states. We then evaluate whether easier access to finance (at home and abroad) affects

the average value of these M&A transactions. Finally, we estimate the impact of better access to finance on total

cross-border M&A flows across the U.S.

In our empirical analysis, we employ two measures of improved access to credit in host states – the

adoption of the intrastate branching and interstate banking deregulation by a given state. As we already

discussed, the former lifted restrictions on bank expansions within a state, while the latter allowed banks to

expand their business across state lines, thus paving the way to larger regional and ultimately national banking

institutions (Jayaratne & Strahan (1996), Kerr & Nanda (2009)). These changes brought about greater competition

and higher efficiency throughout the U.S. banking system thereby facilitating access to cheaper local credit across

states (Kerr & Nanda (2009)). We use the cross-state variation in the timing of adoption of the two banking

deregulations as measures of improved access to local finance.

To proxy for access to finance in the investor’s country of origin (source country), we consider two

measures previously used in the literature – the ratio of total credit issued to the private non-financial sector to

GDP and the stock market value as a fraction of GDP (di Giovanni (2005), Bekaert (2007)). The first measure

captures the depth of the banking system in the acquirer’s source country and is widely used as a proxy for access

to credit. The second measure accounts for the depth of capital markets in the source country and is often used as

a proxy for access to public finance. Improvements in both measures suggest an increase in the level of financial

development of the source country, but they affect the firm’s ability to raise external funds in different ways, and

therefore can affect how acquirer firms respond to banking deregulation in the host country differently.7

In addition to access to finance, a number of other variables can affect the likelihood and the volume of

M&A activity across states. Some factors vary across states, such as local taxes and cost of operation (e.g.,

average wage), while other factors vary across acquirers’ countries of origin – such as per capita income. We

include a comprehensive list of control variables in our econometric specifications in order to account for all

possible determinants of cross-border M&A flows.

7 There is a large literature that studies how countries’ financial markets structure affect firms’ financing choices, and

analyzes the merits of bank-based versus market-based systems for economic growth. See Levine (2002) for a summary of

the theoretical arguments and empirical evidence on this topic.

9

We begin by specifying an empirical model to estimate the impact of easier access to finance on the

number of cross-border M&As in a given state, in a given year. To this end, we compute the number of new

M&A deals in each state and year, using the sample of 48 contiguous states excluding Delaware and South

Dakota. We employ a negative binomial specification commonly used for count data to model the number of new

cross-border M&As from a given country, in a given state and year. Our choice is driven by the flexibility of the

negative binomial model over the Poisson model, which imposes the mean-variance assumption that is

particularly restrictive (see Cameron and Trivedi (1998)). Formally, if is the number of new cross-border

M&A deals originating from source country j with the target firm located in state s during year t, the negative

binomial distribution is given by

The parameter is the mean of the negative binomial distribution and (0) is a shape parameter that quantifies

the amount of over-dispersion. The mean and the variance are and respectively. The

negative binomial regression model relates , which equals to , to the explanatory variables as follows

𝜆 = exp(𝛼1𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑎𝑛𝑘𝑠𝑡−1 + 𝛼2𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑟𝑎𝑛𝑐ℎ𝑠𝑡−1 + 𝛽1𝐶𝑟𝑒𝑑𝑖𝑡/𝐺𝐷𝑃𝑗𝑡 + 𝛽2𝑆𝑡𝑜𝑐𝑘𝑀𝑘𝑡/𝐺𝐷𝑃𝑗𝑡

+𝛾1𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑎𝑛𝑘𝑠𝑡−1 ∗ 𝐶𝑟𝑒𝑑𝑖𝑡/𝐺𝐷𝑃𝑗𝑡 + 𝛾2𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑎𝑛𝑘𝑠𝑡−1 ∗ 𝑆𝑡𝑜𝑐𝑘𝑀𝑘𝑡/𝐺𝐷𝑃𝑗𝑡

+𝛾3𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑟𝑎𝑛𝑐ℎ𝑠𝑡−1 ∗ 𝐶𝑟𝑒𝑑𝑖𝑡/𝐺𝐷𝑃𝑗𝑡 + 𝛾4𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑟𝑎𝑛𝑐ℎ𝑠𝑡−1 ∗ 𝑆𝑡𝑜𝑐𝑘𝑀𝑘𝑡/𝐺𝐷𝑃𝑗𝑡

+𝑿𝑠𝑡𝜌 + 𝒁𝑗𝑡𝜃 + 𝜔𝑠 + 𝜓𝑗 + 𝜏𝑡 +𝜔𝑠 ∗ 𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑 + 𝜓𝑗 ∗ 𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑)

The two indicator variables InterstateBankst and InterstateBranchst in the above equation equal to unity starting

in the year in which each respective state allowed interstate banking and statewide bank branching, respectively,

and zero otherwise. We include one period lags of these two indicator variables as proxies for local, state-level

credit conditions. In general, we expect the estimates of both and to be positive as credit conditions are

thought to have improved as a result of banking deregulation (Kerr & Nanda (2009)).

As discussed above, we incorporate two different proxies for access to finance in the acquirer’s source

country – total domestic credit to GDP ratio ( ) and stock market value as a fraction of GDP

(StockMkt/GDP). We expect both of these proxies to have a positive effect on the number of cross-border M&A

deals, i.e. 0 and 0, as both measures capture the availability of financing opportunities in the source

country. Because foreign acquirers may have a choice between raising external funds from their source country

or the host state, improvements in local credit markets may be a substitute for source country financing.

jstN

)( jstNE

2

)( jstNVar

)( jstNE

1 2

jtCredit/GDP

1 2

,...2,1,0,11)(!

)()()1(

jst

y

jst

jst

jstjst nn

nnNP

st

10

Alternatively, availability of source and host country financing may be complements, as the ability to raise funds

in one market can act a signal of creditworthiness. To check for the type of interplay between local and source

country financing, we also include interaction terms between the banking deregulation indicators and the source

country financial depth variables. A positive effect of the interaction implies that the two sources of finance

reinforce one another, i.e. their impacts on the number of new M&A deals are complementary. If the estimated

coefficient on the interaction term is negative, the two sources of finance are instead substitutable.

Our econometric model also includes a host of time-varying, state-specific ( ) as well as time-varying

source-country-specific control variables ( ) that are likely to affect cross-border M&As and may be correlated

with financial conditions both in the U.S. and in the acquirer’s source country. The state-specific controls

collected in the vector include four proxies for state economic conditions: (1) the natural logarithm of the

gross state product for state s in year t, (2) the current and lagged values of the growth rate of gross state product,

which may be correlated with the timing of the adoption of banking deregulation (Freeman (2002), Huang

(2008), Berger et al. (2012)), (3) the unemployment rate in state s in year t, as well as three proxies for the local

cost of doing business – (4) the number of foreign trade zones (FTZs) in state s in year t, (5) the natural logarithm

of the average wage, and (6) the state statutory corporate tax rate. The source-country control variables in the

vector are: (1) the natural logarithm of real gross domestic product per capita for country j in year t, (2) the

growth rate of gross domestic product per capita for country j in year t, (3) the extent of trade links between the

source country and the U.S. as measured by the maximum of imports and exports between country j and the U.S.

in year t (see Erel et al. (2012)), (4) the real exchange rate measured as country j foreign currency per USD in

year t, (5) the real stock market return in source country j in year t, and (6) the statutory corporate tax rate in

source country j in year t.

In addition to the control variables listed above, we include a full set of state-specific fixed effects, ,

and source-country-specific fixed effects, , in order to control for unobservable, time-invariant, state-specific

and source-country-specific characteristics that affect the number of new cross-border M&A deals in a given state

and may be correlated with the financial environment in the host state and the source country. Additionally, we

include year fixed effects, , to capture economy-wide shocks that affect all states. Finally, to allow for cross-

state and cross-country differences in trends in M&A deals, we also include state-specific and source-country-

specific time trends, and . It is important to account for such differences in

stX

tZ j

stX

tZ j

s

j

t

ts TimeTrend* tj TimeTrend*

11

trends since productivity growth may differ across states and countries, and these differences can affect the cross-

border M&A decisions of foreign investors.8

We estimate two versions of the negative binomial model using maximum likelihood estimation. In the

first one we focus on the observed number of deals from source country j with target companies located in state s

during year t. That is, we estimate the model using an unbalanced panel that contains non-zero counts for source-

country-state-year cells. In the second version, we estimate the negative binomial model using a source-country-

state-year (weakly) balanced panel that records observations as zero if there are no transactions coming from a

particular source country j, into state s during year t.9 We show that the results are very similar in both cases. We

adjust the standard errors for heteroskedasticity and cluster by state in all empirical specifications. Also, we

weight all of the empirical specifications by the average state employment in foreign multinationals over the

period 1977-1994 (see, for example, Kerr & Nanda (2009)).10

Note that these weights are time-invariant and

hence are not affected by the two banking deregulations over time. The weights are used in order to produce

population estimates of the treatment effects of banking deregulation.11

In the second part of our empirical analysis, we estimate the impact of easier access to finance on the

average value of cross-border M&A transactions. To do so, we specify the following econometric equation

(2)log𝑉𝑖𝑗𝑠𝑡𝑙𝑘 = 𝛼1𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑎𝑛𝑘𝑠𝑡−1 + 𝛼2𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑟𝑎𝑛𝑐ℎ𝑠𝑡−1 + 𝛽1𝐶𝑟𝑒𝑑𝑖𝑡/𝐺𝐷𝑃𝑗𝑡 + 𝛽2𝑆𝑡𝑜𝑐𝑘𝑀𝑘𝑡/𝐺𝐷𝑃𝑗𝑡

+𝛾1𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑎𝑛𝑘𝑠𝑡−1 ∗ 𝐶𝑟𝑒𝑑𝑖𝑡/𝐺𝐷𝑃𝑗𝑡 + 𝛾2𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑎𝑛𝑘𝑠𝑡−1 ∗ 𝑆𝑡𝑜𝑐𝑘𝑀𝑘𝑡/𝐺𝐷𝑃𝑗𝑡

+𝛾3𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑟𝑎𝑛𝑐ℎ𝑠𝑡−1 ∗ 𝐶𝑟𝑒𝑑𝑖𝑡/𝐺𝐷𝑃𝑗𝑡 + 𝛾4𝐼𝑛𝑡𝑒𝑟𝑠𝑡𝑎𝑡𝑒𝐵𝑟𝑎𝑛𝑐ℎ𝑠𝑡−1 ∗ 𝑆𝑡𝑜𝑐𝑘𝑀𝑘𝑡/𝐺𝐷𝑃𝑗𝑡

+𝑿𝑠𝑡𝜌 + 𝒁𝑗𝑡𝜃 + 𝜔𝑠 + 𝜓𝑗 + 𝜏𝑡 + 𝜂𝑙 + 𝜋𝑘 + 𝜔𝑠 ∗ 𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑 + 𝜓𝑗 ∗ 𝑇𝑖𝑚𝑒𝑇𝑟𝑒𝑛𝑑 + 𝜀𝑖𝑗𝑠𝑡𝑙𝑘

where is the natural logarithm of the value (expressed in 2010 USD) of cross-border M&A deal i, from

source country j, in state s, in year t. The two-digit SIC industry of operation of the acquirer and the target are

indexed by l and k, respectively. The vectors of explanatory variables and contain the state-specific and

source-country-specific, time-varying controls described above. In addition to all of the state, source country, and

year fixed effects, in this specification, we also include industry of the acquirer and the target fixed effects. The

industry fixed effects control for potential time-invariant unobservable industry-specific characteristics that affect

the average cross-border M&A transaction value. We estimate the model with OLS, and as in the previous model

8 Also, differences in productivity growth across states may be correlated with the adoption of the intrastate branching and

interstate banking deregulation (Freeman (2002), Berger et al. (2012)). 9 A fully balanced panel data would give us 11,592 observations (46 states x 21 source countries x 12 years). However, we

estimate the model using a weakly balanced panel with 10,534 observations due to missing information on some of the

source country variables (e.g., stock market capitalization) at the beginning of our sample. 10

Data on total state employment in foreign multinationals are available from the Bureau of Economic Analysis (BEA). 11

We obtain economically and statistically similar results in unweighted regressions.

ijstlkVlog

stXtZ j

12

for M&A counts, we compute robust standard errors that are clustered by state. Also, as before, we weight by the

average state employment in foreign multinationals over the period 1977-1994.

We conclude our empirical analysis by providing evidence on the effects of access to local and source

country finance on the total volume of cross-border M&As, by estimating an aggregated version of equation (2).

To that end, we sum up the deal values coming from source country j to state s during year t. As discussed in the

Data Section, close to half of the deals do not have reported transaction values. For these deals, we recode the

missing value as the average transaction value of deals coming from country j to state s, in order to construct an

estimate of the total volume. As in the specification for the deal counts, we present results using the unbalanced

source country, state, year panel for total volume as well as the (weakly) balanced panel. We estimate the former

with OLS using the log of total volume (in 2010 USD) as the dependent variable, and the latter with a Tobit

specification using total volume as the dependent variable, due to the observations with zero values.

4. Results

In this section, we present the results from our econometric model for the number of cross-border M&A deals, the

average deal value, and the total investment volume from a given source country into a given U.S. state. For the

aggregate state level specification with the total number of cross-border M&A deals and the deal-level analysis of

the transaction values, we consider various dimensions of financing opportunities and constraints and how they

reinforce or mitigate the impact of host and source country financial market conditions. These dimensions include

the public status of the acquirer and the target, the external finance dependence of the industries in which the

acquirer and the target operate, and the method of payment used in the M&A transaction. Before we discuss the

results, we describe some patterns in the cross-border M&A flows in our sample.

4.1 Patterns in Cross-border M&As into the U.S.

Figure 1 plots the total number of cross-border M&As by foreign acquirers for the 46 states in our sample. The

figure shows the marked increase in the number of cross-border deals in the 1980s, and it also captures the decline

in the early 1990s documented by Erel et al. (2012). When we consider the balanced panel with the combination

of 21 acquirer countries and 46 target states, that is, when we record the total count as zero when a source country

does not have any deals in a given state for a given year, we get an average of 0.29 M&A deals per source-

country-state annually. When we take the average of existing deals in the unbalanced panel, we get an annual

average of 2.16 transactions per state from a given source country, and a maximum of 46 deals per source country

and state—Japanese acquirers investing in California in 1990.

13

As Panel B of Table 1 reports, the largest number of acquiring companies come from the United Kingdom

with a total of 978 deals taking place in 43 states over the 1983-1994 period. The majority of source countries

invest in more than a quarter of the 46 states at least once throughout the sample period. While most of the

acquirer companies are based in developed economies, our sample also includes deals by acquirers in three

emerging markets— Malaysia, Mexico and South Korea. Panel C of Table 1 reports the total and the average (per

year) number of cross-border M&A deals that took place in each state, ranked by the total number of deals

received by the states. Over the 1983-1994 sample, California received the highest number of deals—664,

averaging 55.33 deals per year, followed by New York (307 deals) and Texas (212 deals). On average, states

received 5.53 deals per year. Finally, Panel A of Table 1 also shows that the average transaction value over the

sample period is $232.7 million (in 2010 USD), but there is considerable variation – the smallest transaction is

only $44,100 while the largest is close to $14 billion.

4.2 Impact on the Number of Cross-border M&A Deals

We begin our analysis by examining the role of state banking deregulation and source country financial depth in

determining the number of cross-border M&A deals into U.S. states. Column (1) of Table 3 presents a baseline

model regression of the number of cross-border M&A transactions from country j into state s during year t on the

banking deregulation indicators, the set of time-varying state control variables, as well as country, state, and year

fixed effects. The coefficient on the interstate banking indicator is both statistically and economically significant,

with an estimated magnitude of 0.449 (with a standard error of 0.079), implying that the number of cross-border

M&A transactions increased by 57 percent following the adoption of the interstate banking deregulation,

translating into 1.22 additional transactions per source country going into each state that deregulated interstate

banking.12

Unlike the coefficient on the interstate banking indicator, the coefficient on intrastate branching is

positive but insignificant. The lack of a significant effect from the intrastate bank branching deregulation is

aligned with the findings of Kerr and Nanda (2009), who document that while interstate banking brought about

significant growth in entrepreneurship as well as business closures across states, intrastate branching had little

effect. Their results could be due to intrastate branching having a smaller impact on competition in the banking

sector, or to multi-state banks having the technology to serve new start-ups better than single-state banks. The

latter argument also applies to multinational companies investing abroad. Furthermore, national banks may have a

comparative advantage relative to single-state banks in evaluating cross-border M&A deals.

Column (2) of Table 3 augments the model in column (1) with the source country credit to GDP ratio and

market value to GDP ratio, both proxying for source country financial development, and a set of relevant time-

12

Because the indicator variable only changes discontinuously, the effect of the interstate banking deregulation is calculated

as (e0.449-1) = 0.567. For estimated coefficients that are small in magnitude, this procedure makes little difference.

14

varying source country covariates. The magnitude and significance of the coefficients on interstate banking and

intrastate branching deregulation remain largely unchanged from column (1), confirming the importance of host

state financing in cross-border M&A deals. The two proxies for depth of source country financial markets are

both positive and economically and statistically significant, suggesting that access to source country financing is

also important for explaining cross-border M&A activity. Column (3) of Table 3 further adds state and country

linear time trends to the specification in column (2). The positive and significant coefficient of 0.312 on interstate

banking deregulation implies a 37 percent increase in the number of cross-border M&A deals from a given source

country to a state that deregulated interstate banking. The effect of source country financing remains large and

statistically significant: for a 10 percentage point increase in the source country credit to GDP or market value to

GDP, the number of cross-border M&A deals increases by 25 percent or 5 percent, respectively.

Turning to the rest of the covariates in the specification with state and source country trends in column

(3), we find a positive and significant coefficient on the source country GDP per capita growth rate, suggesting

that faster growth within a country translates into more cross-border M&A deals. In terms of the state covariates,

both contemporaneous and lagged gross state product growth have negative coefficients, with only the latter being

significant, suggesting that states with faster growth receive less cross-border M&A deals. Also, we find that high

state unemployment discourages M&A deals. Trade promotion polices such as free trade zones appear to have a

positive impact on the number of cross-border M&A deals as evidenced by the positive and statistically

significant coefficient on the number of free trade zones within a state.

The interplay between host state financing and source country financing is explored in column (4) with

interaction terms between the two banking deregulation indicators and the two measures of source country

financial development. The coefficient on interstate banking deregulation remains positive and statistically

significant while the coefficient on intrastate branching deregulation turns negative and becomes statistically

significant. The coefficient on source country credit to GDP ratio also remains positive and statistically

significant; however, the coefficient on source country market value to GDP turns negative and loses significance.

The coefficient on the interaction between interstate banking deregulation and source country credit to GDP ratio

is negative and statistically significant, suggesting that there is a substitution effect between host state interstate

banking deregulation and the depth of the source country credit market. At the same time, the coefficient on the

interaction between intrastate branching deregulation and source country market value to GDP is positive and

statistically significant indicating that the effect of intrastate branching deregulation is greater for M&As from

source countries with larger stock markets.

As a robustness check, columns (5) and (6) repeat the analysis of columns (3) and (4), but estimating the

negative binomial model on the balanced panel, where country-state-year cells with no cross-border mergers and

acquisitions transactions are denoted with zero. The main results on the effect of interstate banking deregulation,

15

source country credit to GDP ratio and their interaction remain the same. In this specification, the interaction

between interstate banking and source country market value to GDP become positive and significant, pointing

towards complementarities between local credit and the depth of the source country’s capital markets.

Additionally, the coefficient on intrastate branching deregulation and the interaction between intrastate branching

deregulation and market value to GDP become smaller in absolute value and lose significance. As an additional

robustness check, we estimate empirical specifications where we consider the number of cross-border M&A

transactions from country j into state s during year t normalized by the total number of foreign and domestic

M&A deals as the dependent variable (see, for example, Rossi and Volpin (2004) and Erel et al. (2012)). The

results, which we present in the Appendix Table A1, point to the same mechanisms shown in Table 3. We find a

positive and significant effect of banking deregulation on the fraction of cross-border deals in total M&A activity

in a given state. Moreover, the results show that the increase in the fraction of cross-border deals is lessened with

the depth of the source country credit markets and amplified with the depth of their stock markets.

Next, we hypothesize that publicly traded acquirer and publicly traded target firms are less dependent on

bank finance relative to privately held acquirer and target firms, since publicly traded firms have access to public

markets for their financing needs. The testable implication is that the effect of banking deregulation should be

more muted when public acquirers or targets are involved. Furthermore, publicly traded acquirer firms should be

more likely to engage in M&A activity when there is a run-up in stock prices in the source country as argued by

Erel et al. (2012). To test these hypotheses, we introduce publicly traded status dummies and their interactions

with banking deregulation as well as interactions with variables proxying for source country financial

development and market returns. In particular, we categorize all cross-border M&A deals into four groups based

on whether the acquirer is a publicly traded firm and whether the target is a publicly traded firm. With this

categorization, we change our empirical analysis from a state-year-source country level to a state-year-source

country -acquirer status - target status level (e.g. mergers initiated by publicly traded Japanese firms with private

U.S. companies from California in 1987). This change quadruples the number of observations we previously had.

Using these data, we estimate an expanded econometric model similar to that in equation (1), which additionally

includes publicly traded status dummies along with their interactions.

Column (1) of Table 4 presents the negative binomial estimates for the number of cross-border mergers

and acquisitions, and includes interaction terms between banking deregulation indicators, source country financial

development and market return variables and the public acquirer dummy, regardless of the target status. The

coefficients on both banking deregulation indicators are positive and statistically significant. The coefficients on

the two financial market depth variables– credit to GDP and market value to GDP— are also positive and

significant; however, the coefficient on the source country market return is negative but not statistically

significant. Turning to the interaction effects, as expected, the coefficient on both interaction terms between the

16

acquirer public dummy and interstate banking deregulation dummies are negative and significant, albeit only

marginally so for the interstate banking interaction. The public acquirer dummy with source country credit to

GDP interaction is positive and significant, suggesting that publicly traded acquirers from countries with better

developed credit markets are more likely to invest in the U.S. The interaction between the public acquirer dummy

and source country market value to GDP is also positive, but not statistically significant. Our hypothesis that high

market returns spur publicly traded acquirers to invest in the U.S. is confirmed by the positive and significant

coefficient on the interaction between the public acquirer dummy and source country market return.

Column (2) of Table 4 considers the publicly traded target dummy. The coefficient on the interstate

banking deregulation indicator is positive and significant, while the coefficient on the intrastate branching

deregulation dummy is positive, but not statistically significant. The coefficients on the two measures of source

country financial markets depth are both positive and significant, but the coefficient on the market return variable

is positive and not significant. Our hypothesis that cross-border M&As involving publicly traded target firms

should be less affected by banking deregulation is supported by the negative and significant coefficients on the

interactions between the banking deregulation indicators and the publicly traded target dummy. The interaction

between source country credit to GDP and the publicly traded dummy is negative and significant, suggesting that

better access to source country credit is less important for cross-border M&A deals involving publicly traded

targets. Interestingly, a high source country market return dampens the number of deals involving publicly traded

targets as suggested by the negative and significant coefficient on the interaction term between the market return

and the publicly traded dummy.

Results from our preferred specification, which includes both public acquirer and target dummies and

interactions are presented in column (3) of Table 4. Banking deregulation leads to an increase in the number of

cross-border M&A deals as evidenced by the large positive and significant coefficients on the banking

deregulation dummies. At the same time, source country financial development boosts the number of cross-border

M&A deals, reflected in the positive and significant coefficient on the proxies for financial market depth. Source

country market returns do not appear to have a statistically significant effect on the number of cross-border M&A

deals. Examining the interaction between publicly traded status and state banking deregulation reveals that,

consistent with our hypothesis, banking deregulation and the accompanying improvement in access to local

finance is less important for the number of deals involving publicly traded acquirers or targets. Turning to the

interactions between publicly traded status and source country financial development reveals that a greater depth

of source country credit markets boosts the number of deals involving publicly traded acquirers while at the same

time depressing the number of deals involving publicly traded targets. Similarly, a higher source country market

return buoys the number of cross-border M&A deals initiated by public acquirers and depresses the number of

deals targeting public firms.

17

Next, we explore the interaction between industry external finance dependence and host state and source

country access to finance by categorizing acquirers and targets in manufacturing cross-border M&A deals into

two groups—those in industries that are more external finance dependent versus industries that are less external

finance dependent— based on a measure of external finance dependence as defined in Cetorelli and Strahan

(1998). Because their external finance dependence measure is defined for manufacturing industries only, we focus

our attention on deals where both the acquirer and the target are in the manufacturing sector. We construct

separate external finance dependence dummies for acquirers and targets that take on values of one when the

acquirer or target respectively belong to a more external finance dependent industry. We hypothesize that

improved access to local finance and greater depth of source country markets have a greater impact on the number

of deals involving acquirers or targets more dependent on external finance.

The first three columns of Table 5 focus on specifications that include interaction terms between the

banking deregulation indicators, source country financial development variables and the acquirer external finance

dependence dummy. Column (1) presents a specification without any interaction terms, showing the overall effect

of banking deregulation on the number of cross-border M&A deals, where the acquirers are in the manufacturing

sector. The results show an overall positive and significant coefficient on interstate banking deregulation, a

negative and significant coefficient for intrastate branching and positive and significant coefficients for source

country credit to GDP and source country market value to GDP (only marginally so for credit to GDP). Column

(2) introduces interactions between the acquirer external finance dependence dummy and state banking

deregulation. As anticipated, the interaction coefficients are positive and significant, confirming that the number

of cross-border M&A deals involving acquirers from manufacturing industries that are more dependent on

external finance are more positively affected by the two banking deregulations relative to deals initiated by

acquirers in industries less dependent on external finance. Column (3) includes an additional set of interactions

between acquirer external finance dependence and source country financial development. The coefficients on

banking deregulation and source country financial development are similar to the estimates of column (1),

however the coefficient on source country credit to GDP loses its marginal statistical significance. Interestingly,

the interactions between acquirer external finance dependence and the two banking deregulations both switch sign

and lose statistical significance. The interaction between source country credit to GDP and acquirer external

finance dependence is positive and significant, suggesting that source country financial markets depth boosts the

number of cross-border M&A deals originated by acquirers in external finance dependent industries.

Columns (4) through (6) of Table 5 present regression specifications using the target external finance

dependence dummy. Results from the specification without any interaction effects, focusing on the overall effect

on the number of deals with targets in the manufacturing sectors are reported in column (4). While the coefficient

on interstate banking is positive and significant, the coefficient on intrastate branching is negative but not

18

significant. The coefficient on source country credit to GDP has the same sign and magnitude as in column (1),

but loses statistical significance. The source country market value to GDP coefficient is positive and significant

and of similar magnitude to the one reported in column (1). Introducing the interactions between target external

finance dependence and banking deregulation in column (5) reveals that, consistent with our hypothesis, state

banking deregulation boosts the number of cross-border M&A deals involving targets in industries that are more

reliant on external finance. The specification in column (6) introduces additional interactions between target

external finance dependence and source country financial development. As in the case of column (3), the

additional interaction terms drive down the magnitude and significance of the interaction between target external

finance dependence and state banking deregulation. A greater source country credit to GDP ratio favors the

number of cross-border M&A deals targeting firms in industries more reliant on external finance. The coefficient

on the interaction between source country market value to GDP and target external finance dependence is

positive, but not statistically significant. Our results suggest that source country depth of credit markets stimulates

cross-border investment for both acquirers and targets in industries that are more dependent on external finance.

As an additional mechanism, we probe how the choice of method of payment interacts with host state and

source country financing opportunities. We conjecture that cash deals are more likely to be sensitive to credit

market conditions, both in the host state and the source country. Unfortunately, out of the 3052 deals we have in

our sample, only 880 include information on the method of payment. We classify these deals into two categories:

cash deals and non-cash deals. We specify a deal as a cash-deal if more than 50 percent of the transaction value

was paid in cash (source: SDC Platinum), and classify the other transactions as non-cash deals. This

categorization yields 617 cash deals, and 263 non-cash deals, which can be aggregated to a subsample of 405

state-country-year cells for the former and 207 for the latter.13

Given the limited coverage of the sample and to

save space, we present the results in the Appendix. Column (1) of Table A2 presents the results for the number of

cash deals from country j into state s during year t, and column (2) presents the results for the non-cash deals. As

expected, we find a larger effect of banking deregulation on the number of cash-deals, with a coefficient of 0.588

compared to the estimate of 0.428 for the full sample in column (2) of Table 4. The effect of banking deregulation

on the number of non-cash deals is much smaller at 0.258. Neither the source country credit to GDP ratio nor the

market value to GDP ratio is significant for the cash-deal subsample, whereas the latter is positive and significant

for the non-cash deal sample.

13

Out of the 263 non-cash deals, 74 deals can be considered stock deals, where more than 50 percent of the transaction value

was paid in stocks.

19

4.3 Impact on the Average M&A Deal Value

In this subsection, we turn to the deal-level analysis and look at the impact of banking deregulation on average

deal value, as well as the source country financial market conditions, and consider how these effects change with

the public status and external finance dependence of the target and the acquirer. Column (1) of Table 6 presents

the results from a basic specification for the natural logarithm of the real deal value (in 2010 USD) that includes

the deregulation indicators, state covariates (not reported in the table to conserve space), public status dummy

variables for the acquirer and the target along with a full set of state, source country, year, acquirer and target

industry fixed effects. Using this specification, we obtain a positive and significant coefficient of 0.375 (with a

standard error of 0.179) on the interstate banking deregulation indicator, suggesting that the average M&A deal

value increased by 45.5 percent following the adoption of interstate banking deregulation. This finding suggests

that by lowering the cost of capital and increasing the availability of credit, interstate banking deregulation

allowed acquirer firms to undertake larger deals. Similarly, the coefficient on the public acquirer dummy is

positive and significant (0.707 with a standard error of 0.206), implying that public firms on average undertake

M&A deals that are more than twice as big (102.8 percent higher) as deals initiated by private firms, which tend

to be smaller and more credit-constrained. Even though the coefficient on the target public status dummy is

positive, it is not statistically significant. As in the case of the number of deals in the baseline specification in

Table 3, the coefficient on the intrastate branching indicator is not statistically significant.

The second column of Table 6 expands the specification with source-country covariates, including the

source country financial development variables. The coefficient on interstate banking remains very similar to the

baseline specification without source country covariates in column (1). While the coefficient on stock market

value to GDP is positive (albeit not significant), the coefficient on source country credit to GDP ratio is negative

and statistically significant. The estimated coefficient of -0.018 implies that a 10 percentage points increase in

total credit as a fraction of GDP in the source country is associated with an 18 percent decline in average deal

value. This finding suggests that by lowering the cost of financing, the increased availability of credit in the

source country allows smaller M&A deals to take place, which in turn lowers the average value of M&A deals.

In the third column of Table 6, we augment the specification in column (2) with interaction terms between

the acquirer and target public dummy variables and the banking deregulation indicators. The coefficient on

interstate banking captures the effect of deregulation on deals for which both the target and the acquirer are

private. The coefficient of 0.509 implies that the average M&A deal value involving private targets and acquirers

increased by 66.4 percent following the adoption of the interstate banking deregulation. This effect is 21

percentage points higher than the overall effect shown in the previous columns. The negative coefficient on the

interaction terms suggest that the deals involving public acquirers and/or public targets were less affected by the

20

banking deregulation as public firms have access to public markets for financing and are relatively less dependent

on bank financing.

In columns (4) and (5), we further interact the banking deregulation indicators with the source country

financial development variables, and add country and state trends in the latter. While the coefficient on interstate

banking deregulation in column (4) is negative and not significant, its interaction with the source country credit to

GDP ratio is positive and significant. The two coefficients taken together imply that the value increasing effects

of banking deregulation accrue to acquirers located in countries with a credit to GDP ratio higher than 42.79

percent, which encompasses all of the countries in our sample except for Mexico. When we include country

specific trends in column (5), both coefficients markedly decline in size and lose significance. Additionally,

columns (4) and (5) show that source country stock market capitalization to GDP has a positive and significant

effect on M&A deal value. The coefficient in column (5) implies that a 10 percentage point increase in source

country stock market capitalization to GDP increases the size of cross-border M&A deals by 19 percent.

Moreover, its interactions with the interstate banking and intrastate branching indicators are negative, even though

the former is very small and not significant, and the latter is statistically significant. The negative interaction term

shows the increase in deal values following an increase in stock market capitalization in the source country is

smaller in states that adopt branching deregulation.

Next, we consider how the interstate banking and branching deregulations impact the average value of

cross-border M&A deals taking place in sectors more reliant on external finance versus sectors that are less reliant

on external finance. As in the specifications for the number of transactions, we focus our attention on deals where

both the acquirer and the target are in the manufacturing sector. This reduces the number of deals in the sample

from 1803 to 886. To formally test if the effects of banking deregulation and source country financial

development on deal values change with the need for external finance, we include interaction terms between the

continuous external finance dependence measure of Cetorelli and Strahan (1998) and the deregulation indicators,

as well as with source country financial development measures in equation (2).

Columns (1) and (2) of Table 7 present the results when we interact the banking deregulation indicators

with the external finance dependence measure associated with the acquirer and target industry, respectively. In

both specifications, the main effect of interstate banking deregulation is positive but not statistically significant.

However, the interaction terms with the acquirer and the target industries’ external finance dependence measures

are positive and significant at the 5 percent and 1 percent levels. When we include both interaction terms

simultaneously in column (3), only the interaction between the interstate banking deregulation and the external

finance dependence measure for the target industry remains significant, showing that the effects of improved

access to local finance are more pronounced for foreign firms investing in more external finance dependent

industries. Specifically, the coefficient on the interaction term (4.352) together with the main effect (0.149)

21

implies that banking deregulation increased the deal values involving targets in the most external finance

dependent industry (chemical and allied products with a dependence measure of 0.28) by 137 percent, whereas it

lead to an increase of 19 percent in target industries with an external finance dependence measure equal to the

median value of 0.01 (e.g., industrial machinery and equipment and transportation equipment). This result

highlights the importance of improved access to local finance, especially in more external finance dependent

industries.

In column (4) of Table 7, we add interaction terms between the banking deregulation indicators and the

source country financial development variables, as well as the public status dummies for the acquirer and the

target. Even when we control for the differential effects of banking deregulation across public versus private

acquirers and targets, and across different levels of source country financial development, in terms of market

capitalization and the depth of credit markets, the interaction term between interstate banking deregulation and

target industry external finance dependence remain significant.

Finally, we provide suggestive evidence that banking deregulation increased the average transaction

values for predominantly cash deals. Columns (3) and (4) of Table A2 estimate the baseline specification in

column (2) of Table 6 for the cash and non-cash deal subsamples. The effect of banking deregulation on the

average transaction value of cash deals is estimated to be twice as large as its effect on the average value of non-

cash deals, and the latter effect is not statistically significant. This result suggests that the improved access to local

credit is more pertinent to deals involving cash payments. Interestingly, we also find that improvements in the

source country credit conditions significantly lower the average transaction value for cash deals, whereas

improvements in the stock markets significantly lower the average value for non-cash deals.

4.4 Impact on the Total M&A Volume

We conclude our analysis by presenting the effects of banking deregulation and its interaction with source country

financial development on total cross-border M&A volume. Column (1) of Table 8 displays the results from a

basic specification that includes the banking deregulation indicators, state-specific covariates (not reported to

conserve space), as well as source country, state and year fixed effects. Column (2) adds source country-specific

covariates to the specification in column (1). In both cases, we obtain a positive and highly significant coefficient

on the interstate banking deregulation indicator. The estimate in column (2) suggests that the total volume of

cross-border M&As increased by 66 percent in states that deregulated interstate banking. As in the case of the

deal count and average value analysis, the coefficient on the intrastate branching deregulation indicator is not

significant.

22

Unlike the statistically significant effect of interstate banking deregulation, which represents an

improvement in local (host state) credit conditions, the coefficients on the source country financial development

variables are not significant. Once we control for state and source country time trends in column (3), the

coefficient on the source country market value to GDP ratio becomes significant, implying that source countries

with deeper equity markets invest more in the U.S. The coefficient on source country credit to GDP ratio remains

insignificant when we include the time trends. The lack of significance is not surprising, given that the effects of

source country credit market deepening on the number of transactions and on average deal value move in opposite

directions. While improvements in source country credit markets increase the number of deals acquirers undertake

(see the results for deal counts in Table 3), they also lower the average deal value (see the average value results in

Table 6). As a result, the regression estimates yield a positive but insignificant effect of the source country credit

to GDP ratio on total cross-border M&A volume.

In column (4) of Table 8, we consider the interaction effects of banking deregulation with source country

financial development on total M&A volume. We obtain estimates for the interaction effects that are very similar

to the findings for the number of transactions. The negative and significant coefficient on the interaction between

the interstate banking deregulation indicator and the source country credit to GDP ratio points to a substitution

effect between local and source country credit. Although the main effect of source country stock market value to

GDP on the total volume of cross-border M&A deals is positive and significant, its interaction with interstate

banking is negative, very small and not significant. Neither the main effect of intrastate branching nor its

interaction with source country financial development variables are significant.

Finally, we check the robustness of the results for total M&A volume to balancing the panel with zeros

for the source-country-state-year combinations that do not have any transactions. Since we are balancing the

panel with zeros, we use the total real value of cross-border M&A deals as the dependent variable, as opposed to

its logarithm. The results in column (5) show a positive but insignificant effect for both interstate banking and

intrastate branching deregulation on total real value. However, when we include interaction terms between

banking deregulation and source country financial development in column (6), we obtain positive and significant

coefficients on the interstate banking deregulation indicator and the source country credit to GDP ratio. Moreover,

we obtain a negative and significant interaction between the interstate banking deregulation indicator and the

source country credit to GDP ratio, and a positive and significant interaction between the interstate banking

deregulation indicator and the source country market value to GDP ratio. These estimates for total cross-border

M&A volume underscore our finding that there is a substitution effect between the host state and source country

credit conditions, whereas there is a complementary effect between the size of the source country stock market

and host state banking deregulation.

23

5. Conclusion

In this paper, we provide novel evidence of the impact of host and source country finance, as well as the interplay

between the two, on the incidence and magnitude of cross-border M&As. Using data on foreign acquisitions of

U.S. firms throughout the 1980s and the early 1990s, we show that both the frequency and the size of M&A deals

in a given U.S. state increase following the adoption of the interstate banking deregulation, which heightened

banking competition and subsequently lowered the cost of borrowing and improved access to credit. As expected,

we also document that deeper financial markets in the investor’s country of origin are associated with a larger

number and magnitude of cross-border M&As. Moreover, we provide evidence that improvements in local credit

conditions related to the adoption of the interstate banking deregulation by the host U.S. state act as a substitute to

enhancements in source country credit conditions in their impact on both the frequency and size of cross-border

M&A deals. On the other hand, host state credit conditions complement improvements in the source country stock

market in increasing both the number and the value of M&A deals.

Our estimates further suggest that publicly traded foreign acquirers, which have access to public debt and

equity markets in addition to bank finance, experience a smaller impact from interstate banking deregulation

relative to private firms. As expected, we also find that improved access to host or source country finance is more

important for M&As between foreign acquirers and local targets operating in industries with greater external

financial dependence. Finally, we show that interstate banking deregulation has a positive effect on the overall

volume of cross-border M&A deals and that there is a substitution between host country finance and source

country finance at that level, with investors from countries with deeper financial markets, as measured by a higher

credit to GDP ratio, for whom host country credit is not as important, being relatively less attracted to states that

adopt the interstate banking deregulation.

In an increasingly global economy where policy-makers are eager to attract foreign investors, cross-border

M&A deals have become more prevalent and important for local economic activity. Our work extends the small

but growing empirical literature that analyzes the determinants of cross-border M&As (e.g. Erel et al. (2012),

Rossi and Volpin (2004)). We provide the first set of estimates of the impact of local, host-country, finance on

the incidence and magnitude of cross-border M&As. We also document the relative importance of host country

versus source country finance and the interplay between these two alternative sources of finance. The evidence

strongly suggests that they both matter, underscoring the importance of local credit conditions for international

investors.

24

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26

Figure 1. Total number of cross-border mergers and acquisitions. This figure plots the total number of cross-border

M&A deals that took place across the 46 contiguous states (excluding Delaware and South Dakota) from 21 source countries

between1983-1994 in our sample. The transactions counted exclude deals in which the target or the acquirer is a government

agency or in the financial industry.

0

100

200

300

400

Tota

l n

um

ber

of

cro

ss-b

ord

er

de

als

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994

27

Table 1. Summary Statistics. This table presents the summary statistics for the data in our analysis. The cross-border M&A transactions data are from the

SDC Platinum database. We use data for the foreign acquisitions of target firms located across the 48 contiguous states, excluding Delaware and South

Dakota, between 1983 and 1994. The total number of cross-border M&A deals is 3052, and the number of deals with non-missing transaction value is

1803.

Panel A: Main Characteristics

Variable Mean St.Dev. Min Median Max

No. of cross-border M&A deals in the balanced panel with zeros for state-country-year cells 0.290 1.317 0 0 46

No. of cross-border M&A deals in the unbalanced panel 2.157 2.982 1 1 46

Average transaction value (2010 USD, millions) 232.7 826.4 0.0441 33.44 13,935

Interstate banking 0.798 0.402 0 1 1

Intrastate branching 0.807 0.395 0 1 1

Source country credit to GDP ratio (percent) 125.80 36.40 16.40 120.30 217.80

Source country market value to GDP ratio (percent) 39.90 29.80 0.64 29.20 167.50

GDP per capita (2010 USD) 27,156 6,187 2,969 26,737 50,377

Real exchange rate/100 0.683 2.723 0.00524 0.0148 22.16

Max (Import, Export) 0.0923 0.0843 0.00284 0.0603 0.264

Market return/100 23.36 28.33 0.708 9.551 114.3

Corporate tax (percent) 38.47 9.140 9.800 38 56

Gross State Product (2010 USD, millions) 344,547 299,459 15,950 237,508 1,178,285

State unemployment rate 6.336 1.709 2.277 6.223 14.72

State wage rate 18.52 2.016 13.88 18.40 22.86

Number of foreign trade zones 5.076 4.829 0 3 27

State corporate tax (percent) 6.790 3.005 0 7.750 12.25

28

Panel B: Additional Characteristics

Source country Total number of M&A deals Total number of states invested in

United Kingdom 978 43

Japan 558 36

Canada 557 43

France 166 29

Germany 152 31

Netherlands 107 28

Australia 105 32

Sweden 86 28

Switzerland 73 19

Italy 58 19

Singapore 36 12

Belgium 29 18

Hong Kong 27 13

Norway 25 14

Finland 22 17

South Korea 19 5

Mexico 17 9

Denmark 17 10

Austria 7 7

Spain 7 6

Malaysia 6 5

29

Panel C: Additional Characteristics

State

Total number of cross-

border M&A deals

Average number of

cross-border M&A

deals per year

State

Total number of

cross-border

M&A deals

Average number of

cross-border M&A

deals per year

California 664 55.33

Nevada 27 2.25

New York 307 25.58

Oregon 26 2.17

Texas 212 17.67

Utah 20 1.67

New Jersey 182 15.17

South Carolina 20 1.67

Massachusetts 151 12.58

Oklahoma 19 1.58

Pennsylvania 135 11.25

Kentucky 19 1.58

Illinois 131 10.92

Alabama 18 1.50

Ohio 125 10.42

Kansas 18 1.50

Florida 120 10.00

New Hampshire 16 1.33

Michigan 90 7.50

Iowa 14 1.17

Connecticut 80 6.67

Louisiana 14 1.17

Colorado 60 5.00

Rhode Island 12 1.00

Minnesota 59 4.92

New Mexico 11 0.92

Missouri 58 4.83

Mississippi 10 0.83

Georgia 51 4.25

Arkansas 9 0.75

Washington 51 4.25

Idaho 8 0.67

Virginia 50 4.17

Vermont 8 0.67

North Carolina 50 4.17

West Virginia 8 0.67

Maryland 40 3.33

Maine 7 0.58

Tennessee 40 3.33

Nebraska 6 0.50

Arizona 37 3.08

Wyoming 4 0.33

Indiana 34 2.83

Montana 3 0.25

Wisconsin 27 2.25

North Dakota 1 0.08

30

Table 2. Banking Deregulation Dates. State Statewide Branching through

M&A Permitted

Interstate Banking

Permitted State Statewide Branching

through M&A Permitted

Interstate Banking

Permitted

Alabama 1981 1987

Nebraska 1985 1990

Arizona Before 1970 1986

Nevada Before 1970 1985

Arkansas 1994 1989

New Hampshire 1987 1987

California Before 1970 1987

New Jersey 1977 1986

Colorado 1991 1988

New Mexico 1991 1989

Connecticut 1980 1983

New York 1976 1982

Delaware Before 1970 1988

North Carolina Before 1970 1985

Florida 1988 1985

North Dakota 1987 1991

Georgia 1983 1985

Ohio 1979 1985

Idaho Before 1970 1985

Oklahoma 1988 1987

Illinois 1988 1986

Oregon 1985 1986

Indiana 1989 1986

Pennsylvania 1982 1986

Iowa 1997 1991

Rhode Island Before 1970 1984

Kansas 1987 1992

South Carolina Before 1970 1986

Kentucky 1990 1984

South Dakota Before 1970 1988

Louisiana 1988 1987

Tennessee 1985 1985

Maine 1975 1978

Texas 1988 1987

Maryland Before 1970 1985

Utah 1981 1984

Massachusetts 1984 1983

Vermont 1970 1988

Michigan 1987 1986

Virginia 1978 1985

Minnesota 1993 1986

Washington 1985 1987

Mississippi 1986 1988

West Virginia 1987 1988

Missouri 1990 1986

Wisconsin 1990 1987

Montana 1990 1993

Wyoming 1988 1987

Source: Amel (1993), Kroszner and Strahan (1999), and Demyanyk et al. (2007).

31

Table 3. Panel analysis of the effect of state banking deregulation and source country financial development on the

number of cross-border mergers and acquisitions. The dependent variable Njst denotes the number of cross-border mergers and

acquisitions deals from country j, in state s, during year t. Columns (1) through (4) present negative binomial estimates from the unbalanced

panel, while columns (5) and (6) report the negative binomial estimates obtained from the balanced panel, where country-state-years with no

cross-border mergers and acquisitions transactions are denoted with 0. Country, state and year fixed effects are included in all specifications.

Columns (3) – (6) also include state and country linear trends. All standard errors are corrected for clustering at the state level and are reported

in parentheses. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.

Dependent variable: No. of cross-border M&A deals (1) (2) (3) (4) (5) (6)

Interstate banking 0.449*** 0.428*** 0.312*** 1.719*** 0.355*** 1.664***

(0.079) (0.079) (0.086) (0.480) (0.084) (0.446)

Int. banking x Source country credit to GDP ratio

-0.013***

-0.014***

(0.004)

(0.005)

Int. banking x Source country market value to GDP ratio

0.003

0.008***

(0.002)

(0.003)

Intrastate branching 0.035 0.042 -0.041 -0.661** 0.220 -0.338

(0.111) (0.122) (0.169) (0.319) (0.209) (0.405)

Int. branch. x Source country credit to GDP ratio

0.001

0.005

(0.002)

(0.004)

Int. branch. x Source country market value to GDP ratio

0.009***

-0.002

(0.003)

(0.003)

Source country credit to GDP ratio

0.014*** 0.025*** 0.037*** 0.023*** 0.032***

(0.003) (0.007) (0.011) (0.006) (0.008)

Source country market value to GDP ratio

0.006*** 0.005*** -0.002 0.004* 0.001

(0.002) (0.001) (0.004) (0.002) (0.003)

Log GDP per capita

1.551*** 1.373 1.270 -0.891 -1.145

(0.528) (1.273) (1.198) (0.813) (0.915)

GDP per capita growth rate

0.074*** 0.084*** 0.095*** 0.146*** 0.154***

(0.015) (0.021) (0.028) (0.021) (0.021)

Max (Import, Export)

-3.086*** -1.021 -0.904 0.945 0.780

(0.726) (2.950) (2.203) (1.936) (1.844)

Real exchange rate/100

0.003 -0.030 -0.003 -0.049 -0.020

(0.033) (0.045) (0.041) (0.063) (0.072)

Market return/100

-0.007*** 0.001 0.004 0.002 0.003

(0.003) (0.008) (0.010) (0.007) (0.006)

Corporate tax

-0.005 -0.009 -0.002 0.013 0.020

(0.007) (0.009) (0.006) (0.015) (0.014)

Log GSP 2.154*** 2.242*** 1.137 1.188 0.626 0.738

(0.432) (0.377) (0.810) (0.897) (1.168) (1.272)

GSP growth rate -0.012 -0.012 -0.019 -0.017 -0.024* -0.023*

(0.015) (0.016) (0.012) (0.012) (0.014) (0.013)

GSP growth rate lag -0.037*** -0.037*** -0.041*** -0.044*** -0.060*** -0.062***

(0.013) (0.013) (0.015) (0.014) (0.010) (0.010)

State unemployment rate -0.068* -0.062 -0.141*** -0.118*** -0.095 -0.092

(0.039) (0.038) (0.043) (0.042) (0.075) (0.072)

State log wages 0.628 0.564 1.092 0.805 3.168 3.064

(1.410) (1.443) (2.158) (2.223) (2.689) (2.665)

Foreign trade zones 0.047*** 0.048*** 0.050** 0.073*** 0.080*** 0.086**

(0.018) (0.017) (0.025) (0.019) (0.029) (0.034)

State corporate tax 0.002 -0.002 -0.006 0.001 -0.040 -0.031

(0.030) (0.033) (0.034) (0.038) (0.045) (0.044)

State trends no no yes yes yes yes

Source country trends no no yes yes yes yes

No. Obs. 1,415 1,415 1,415 1,415 10,534 10,534

Log-likelihood -312,601 -307,925 -305,000 -302,016 -466,283 -464,356

32

Table 4. Panel analysis of the effect of publicly traded firms interacted with state banking deregulation

and source country financial development on the number of cross-border mergers and acquisitions. We

categorize the deals into four groups based on whether the acquirer or the target is a publicly traded firm. We construct a dummy variable

that equals 1 if the acquirer is a public firm (zero otherwise), and a similar dummy variable for public targets. Column (1) presents the

negative binomial estimates for the number of cross-border mergers and acquisitions, and it includes interaction terms between the banking deregulation indicators, source country financial development variables and the public acquirer dummy. Column (2) includes interaction

terms between the banking deregulation indicators, source country financial development variables and the public target dummy. Column

(3) includes interaction terms with both the public acquirer and public target dummy variables. All specifications include country, state and year fixed effects and state and country linear trends. State covariates are suppressed. Standard errors are corrected for clustering at the

state level and are reported in parentheses. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.

(1) (2) (3)

Interstate banking 0.475*** 0.342*** 0.513***

(0.132) (0.088) (0.119)

Interstate banking x Public acquirer dummy -0.221*

-0.223**

(0.122)

(0.112)

Interstate banking x Public target dummy

-0.154** -0.124*

(0.067) (0.064)

Intrastate branching 0.255*** 0.116 0.409***

(0.093) (0.163) (0.129)

Intrastate branching x Public acquirer dummy -0.421***

-0.414***

(0.139)

(0.138)

Intrastate branching x Public target dummy

-0.586*** -0.607***

(0.129) (0.130)

Source country credit to GDP ratio 0.015** 0.022*** 0.018***

(0.006) (0.005) (0.006)

Source country credit to GDP ratio x Public acquirer dummy 0.008***

0.008***

(0.001)

(0.001)

Source country credit to GDP ratio x Public target dummy

-0.005** -0.005**

(0.002) (0.002)

Source country market value to GDP ratio 0.004*** 0.004* 0.003*

(0.002) (0.002) (0.002)

Source country market value to GDP ratio x Public acquirer dummy 0.001

0.002

(0.002)

(0.002)

Source country market value to GDP ratio x Public target dummy

0.004 0.004

(0.003) (0.003)

Market return/100 -0.006 0.005 -0.003

(0.008) (0.007) (0.009)

Market return/100 x Public acquirer dummy 0.010***

0.010***

(0.002)

(0.002)

Market return/100 x Public target dummy

-0.010*** -0.010***

(0.002) (0.002)

Log GDP per capita 0.771 1.102 1.172

(1.432) (1.459) (1.440)

GDP per capita growth rate 0.069*** 0.070*** 0.079***

(0.017) (0.017) (0.018)

Max (Import, Export) -0.671 -0.907 -1.342

(2.976) (2.911) (2.912)

Real exchange rate/100 -0.026 -0.041 -0.038

(0.048) (0.051) (0.047)

Corporate tax -0.005 -0.010 -0.009

(0.008) (0.009) (0.009)

State covariates yes yes yes

No. obs. 5,660 5,660 5,660

Log-likelihood -726,603 -691,191 -654,014

33

Table 5. Panel analysis of the effect of external finance dependence interacted with state banking deregulation and source

country financial development on the number of cross-border mergers and acquisitions. We categorize the manufacturing deals into two

groups—acquirers that are in more and less external finance dependent industries— based on Cetorelli and Strahan’s (1998) measure, and construct an external finance dummy that takes on a value of one when the acquirer belongs to a more external finance dependent industry. Using a similar categorization for the targets,

we construct another external finance dummy that takes on a value of one when the target belongs to a more external finance dependent industry. Columns (1)-(3)

present the specifications that include interaction terms between the banking deregulation indicators, source country financial development variables and the acquirer external finance dependence dummy. Columns (4)-(6) present the specifications that include interaction terms with the target external finance dependence dummy. All

specifications include country, state and year fixed effects and state and country linear trends. All source country and state covariates are suppressed to conserve space.

Standard errors are corrected for clustering at the state level and are reported in parentheses. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level. (1) (2) (3) (4) (5) (6)

Interstate banking 0.415*** 0.251 0.473*** 0.429*** 0.313** 0.516***

(0.111) (0.157) (0.121) (0.101) (0.138) (0.141)

Int. banking x Acquirer ext. finance dependence

0.269** -0.072

(0.121) (0.152)

Int. banking x Target ext. finance dependence

0.178* -0.131

(0.093) (0.131)

Intrastate branching -0.249** -0.559*** -0.243* -0.125 -0.422*** -0.163

(0.104) (0.133) (0.143) (0.090) (0.099) (0.104)

Int. branching x Acquirer ext. finance dependence

0.507*** -0.010

(0.116) (0.139)

Int. branching x Target ext. finance dependence

0.498*** 0.050

(0.080) (0.088)

Source country credit to GDP ratio 0.016* 0.015 0.011 0.016 0.016 0.014

(0.010) (0.009) (0.010) (0.011) (0.011) (0.012)

Source country credit to GDP x Acquirer ext. finance dependence

0.006***

(0.002)

Source country credit to GDP x Target ext. finance dependence

0.005***

(0.001)

Source country market value to GDP ratio 0.007*** 0.007*** 0.008*** 0.009*** 0.009*** 0.008***

(0.001) (0.001) (0.002) (0.001) (0.001) (0.001)

Source country market value to GDP x Acquirer ext. finance dep.

-0.001

(0.002)

Source country market value to GDP x Target ext. finance dep.

0.002

(0.002)

Source country covariates yes yes yes yes yes yes

State covariates yes yes yes yes yes yes

No. obs. 1,047 2,120 2,120 1,063 2,126 2,126

Log-likelihood -221,674 -355,642 -352,672 -220,446 -350,033 -347,456

34

Table 6. Deal-level analysis of the effect of state banking deregulation and source country financial development on cross-

border mergers and acquisitions values. The dependent variable Vijst denotes the real USD value of cross-border M&A deal i, from country j, in state s,

in year t. Country, state, year, acquirer and target industry fixed effects are included in all specifications. Column (5) additionally includes state and country linear trends. State covariates are suppressed to conserve space. Models are estimated using OLS. All standard errors are corrected for clustering at the state level and are

reported in parentheses. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level.

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

Interstate banking 0.375** 0.334* 0.509** -0.599 0.027

(0.179) (0.198) (0.224) (0.366) (0.455)

Int. banking x Public acquirer dummy

-0.202* -0.181 -0.218*

(0.114) (0.109) (0.120)

Int. banking x Public target dummy

-0.084 -0.198 -0.366**

(0.165) (0.159) (0.160)

Int. banking x Source country credit to GDP ratio

0.014*** 0.003

(0.004) (0.005)

Int. banking x Source country market value to GDP ratio

-0.010 -0.002

(0.010) (0.009)

Intrastate branching -0.203 -0.194 -0.814 -0.608 -0.229

(0.270) (0.279) (0.606) (0.613) (0.545)

Int. branching x Public acquirer dummy

0.818 0.895 0.866

(0.637) (0.546) (0.598)

Int. branching x Public target dummy

-0.103 0.009 0.154

(0.275) (0.278) (0.302)

Int. branching x Source country credit to GDP ratio

0.003 0.001

(0.003) (0.003)

Int. branching x Source country market value to GDP ratio

0.015*** 0.015***

(0.004) (0.005)

Public acquirer dummy 0.707*** 0.704*** 0.115 0.022 0.121

(0.206) (0.201) (0.675) (0.612) (0.635)

Public target dummy 0.055 0.049 0.216 0.185 0.190

(0.081) (0.086) (0.217) (0.211) (0.200)

Source country credit to GDP ratio

-0.018** 0.019*** 0.039*** -0.025**

(0.007) (0.006) (0.005) (0.011)

Source country market value to GDP ratio

0.005 0.005 0.027*** 0.019**

(0.005) (0.005) (0.007) (0.008)

Log GDP per capita

2.269** 2.329** 2.368** 1.595

(0.901) (0.880) (1.025) (2.895)

GDP per capita growth rate

-0.047 -0.049 -0.070 -0.058

(0.062) (0.061) (0.055) (0.058)

Real exchange rate/100

-0.114** -0.124** -0.156** -0.169

(0.053) (0.055) (0.059) (0.110)

Max (Import, Export)

-8.468* -8.228* -6.535 12.324**

(4.977) (4.822) (5.142) (5.716)

Market return/100

0.006 0.006 0.014** 0.039**

(0.006) (0.006) (0.005) (0.018)

Corporate tax

-0.018 -0.017 -0.024* -0.006

(0.011) (0.012) (0.013) (0.009)

State covariates yes yes yes yes yes

No. obs. 1,803 1,803 1,803 1,803 1,803

R-squared 0.314 0.319 0.320 0.326 0.351

35

Table 7. Deal-level analysis of the effect of external finance dependence interacted with state banking

deregulation and source country financial development on cross-border mergers and acquisitions values. The dependent variable Vijst denotes the real 2010 USD value of cross-border mergers and acquisitions deal i, from country j, in state s,

during year t. The sample is limited to acquirers and targets in manufacturing industries. A continuous external finance dependence

variable external finance dependence as constructed by Cetorelli and Strahan (1998) is defined separately for acquirer and target firms. Country, state, year, acquirer industry and target industry fixed effects are included in all specifications. All specifications include source

country and state covariates. Standard errors are corrected for clustering at the state level and are reported in parentheses. *** denotes

significance at the 1% level, ** at the 5% level, and * at the 10% level.

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

Interstate banking 0.233 0.131 0.149 -1.291**

(0.170) (0.188) (0.188) (0.615)

Int. banking x Acquirer ext. finance dependence 1.009**

-0.913 -0.960

(0.488)

(0.600) (0.772)

Int. banking x Target ext. finance dependence

3.787*** 4.352*** 4.510***

(0.702) (0.607) (0.656)

Int. banking x Public acquirer dummy

-0.137

(0.349)

Int. banking x Public target dummy

-0.392

(0.333)

Int. banking x Source country credit to GDP ratio

0.016***

(0.005)

Int. banking x Source country market value to GDP ratio

-0.006

(0.006)

Intrastate branching 0.059 0.055 0.108 -0.788

(0.406) (0.354) (0.372) (0.703)

Int. branching x Acquirer ext. finance dependence -0.000

-1.835* -1.305

(0.866)

(1.070) (1.354)

Int. branching x Target ext. finance dependence

-0.291 1.152 0.771

(1.095) (1.700) (1.960)

Int. branching x Public acquirer dummy

0.512

(0.391)

Int. branching x Public target dummy

0.843***

(0.297)

Int. branching x Source country credit to GDP ratio

0.007

(0.007)

Int. branching x Source country market value to GDP ratio

-0.013

(0.009)

Public acquirer dummy 0.934*** 0.971*** 0.981*** 0.629

(0.179) (0.173) (0.168) (0.467)

Public target dummy 0.015 0.007 0.001 -0.426

(0.080) (0.077) (0.074) (0.353)

Source country covariates yes yes yes yes

State covariates yes yes yes yes

No. obs. 886 886 886 886

R-squared 0.359 0.369 0.370 0.379

36

Table 8. Panel analysis of the effect of state banking deregulation and source country financial development on the total value of

cross-border mergers and acquisitions. The dependent variable Vjst is the total real 2010 USD value of cross-border mergers and acquisitions deals from

country j into state s , during year t. In columns (1) through (4) the dependent variable is in natural logarithm form. In columns (5) and (6), the panel is balanced so that

country-state-years with no cross-border mergers and acquisitions deals have zero total value, and the dependent variable in these columns is the real value in levels.

Country, state and year fixed effects are included in all specifications. Columns (3) - (6) also include state and country linear trends. Source country and state covariates are suppressed. All standard errors are corrected for clustering at the state level and are reported in parentheses. *** denotes significance at the 1% level, ** at the 5%

level, and * at the 10% level.

(1) (2) (3) (4) (5) (6)

Dependent variable log real value log real value log real value log real value real value real value

Interstate banking 0.534*** 0.508*** 0.404 3.020** 0.141 1.440**

(0.159) (0.164) (0.258) (1.394) (0.148) (0.681)

Int. banking x Source country credit to GDP ratio

-0.022**

-0.016**

(0.011)

(0.007)

Int. banking x Source country market value to GDP ratio

-0.002

0.017**

(0.007)

(0.008)

Intrastate branching 0.200 0.189 0.109 -1.026 0.325 0.182

(0.182) (0.182) (0.315) (0.762) (0.203) (0.707)

Int. branching x Source country credit to GDP ratio

0.011

0.003

(0.007)

(0.007)

Int. branching x Source country market value to GDP ratio

-0.007

-0.005

(0.004)

(0.003)

Source country credit to GDP ratio

0.004 0.002 0.014 0.005 0.020**

(0.004) (0.011) (0.015) (0.008) (0.010)

Source country market value to GDP ratio

0.004 0.009** 0.024*** 0.009 -0.000

(0.004) (0.004) (0.007) (0.007) (0.006)

Source country covariates yes yes yes yes yes yes

State covariates yes yes yes yes yes yes

State trends no no yes yes yes yes

Country trends no no yes yes yes yes

No. obs. 1,267 1,267 1,267 1,267 10,386 10,386

R-squared/Log-likelihood 0.367 0.379 0.413 0.423 -413,229 -412,175

37

Data Appendix: Description of Variables

This table describes the sources and the construction of the variables used in our analysis.

Variable Description

No. of cross-border M&A deals The total number of cross-border M&A deals (Xjst) in which the acquirer is from country j, the target is located in state s,

and the transaction is completed in year t. Source: SDC Platinum Database.

Average transaction value

(2010 USD, millions)

The real USD value of cross-border mergers and acquisitions (Vijst) of deal i, from country j, in state s, in year t. The

transaction value is obtained from the SDC Platinum Database, and deflated using the 2010 constant USD consumer price

index obtained from International Financial Statistics (IFS).

Public acquirer (target) Acquirer (target) with the "Public" status. Source: SDC Platinum Database.

Cash deals A deal is categorized as a cash-deal if more than 50% of the deal value was paid in cash. If less than 50% of the deal value

was paid in cash, it is classified as a non-cash deal. Source: SDC Platinum Database.

Interstate banking Interstate banking deregulation indicator that takes on a value 1 starting from the year following the adoption of the interstate

banking deregulation, and 0 before then. See Table 1 for the dates. Sources: Amel (1993), Kroszner and Strahan (1999), and

Demyanyk et al. (2007).

Intrastate branching Intrastate branching deregulation indicator that takes on a value 1 starting from the year following the adoption of the

intrastate branching deregulation, and 0 before then. See Table 1 for tyhe dates. Sources: Amel (1993), Kroszner and

Strahan (1999), and Demyanyk et al. (2007).

Source country credit to GDP ratio Total credit given to private non-financial sectors by all domestic lending institutions as a percentage of GDP. The source of

the credit data is Bank of International Settlements. The quarterly series is averaged to obtain the annual values, and then

divided by source country's GDP (source: IFS)

Source country market value

to GDP ratio

Total stock market value as a percentage of GDP. The source of the stock market value index is Datastream.

GDP per capita (2010 USD) Real GDP per capita in constant 2010 US Dollars. Source: World Development Indicators (WDI).

Real exchange rate/100 Real exchange rate defined as the foreign currency per US Dollar nominal exchange rate adjusted by the 2010 constant USD

consumer price indexes. The source for the nominal exchange rates and the price indexes is IFS.

Max (Import, Export) The maximum of imports and exports between the US and the source country. We calculate the import (export) series as the

value of imports (exports) as percentage of total imports (exports) from (to) the source country j to the US. Source: The

Center for International Data, UC Davis.

38

Market return/100 The real stock market return of the source country. We use the total-value weighted return indexes in local currency (source:

Datastream) and deflate them by the 2010 consumer price index of the corresponding country (source: IFS) to obtain the real

stock returns.

Corporate tax (percent) In percentages. Source: World Tax Database, Office of Tax Policy Research, University of Michigan

Gross State Product

(2010 USD, millions)

Gross state product deflated by the 2010 constant USD consumer price index. Source: U.S. Bureau of Economic Analysis

State unemployment rate In percentages. Source: U.S. Bureau of Labor Statistics

State wages Average nominal state wages deflated by the 2010 constant USD consumer price index. Source: Current Population Survey,

U.S. Census Bureau, a

Number of foreign trade zones Source: U.S. Foreign-Trade Zones Board, International Trade Administration, U.S. Department of Commerce

State corporate tax rate In percentages. Source: World Tax Database, Office of Tax Policy Research, University of Michigan

39

APPENDIX TABLES

Table A1. Panel analysis of the effect of state banking deregulation and source country financial development on the number

of cross-border mergers and acquisitions. The dependent variable Xjst denotes the number of cross-border mergers and acquisitions deals from

country j, in state s, during year t scaled by the total number of (domestic and cross-border) deals in state s, year t . Columns (1) through (4) present

estimates from a tobit model from the unbalanced panel, while columns (5) and (6) report tobit estimates obtained from the balanced panel, where

country-state-years with no cross-border mergers and acquisitions transactions are denoted with 0. Country, state and year fixed effects are included

in all specifications. Columns (3) – (6) also include state and country linear trends. Source country and state covariates are suppressed. All standard

errors are corrected for clustering at the state level and they are reported in parentheses. *** denotes significance at the 1% level, ** at the 5% level,

and * at the 10% level.

(1) (2) (3) (4) (5) (6)

Interstate banking 0.010*** 0.010*** 0.009*** 0.041*** 0.005* 0.020**

(0.003) (0.003) (0.003) (0.007) (0.002) (0.009)

Int. banking x Source country credit to GDP ratio

-0.036***

-0.022***

(0.006)

(0.008)

Int. banking x Source country market value to GDP ratio

0.029***

0.032***

(0.007)

(0.009)

Intrastate branching -0.003 -0.003 -0.003 -0.012* 0.002 0.001

(0.002) (0.002) (0.003) (0.007) (0.004) (0.008)

Int. branching x Source country credit to GDP ratio

0.000

0.000

(0.005)

(0.007)

Int. branching x Source country market value to GDP ratio

0.023**

0.002

(0.009)

(0.007)

Source country credit to GDP ratio

0.016*** 0.038*** 0.078*** 0.032** 0.056***

(0.005) (0.010) (0.014) (0.014) (0.015)

Source country market value to GDP ratio

0.016*** 0.019*** -0.021** 0.021*** -0.009

(0.005) (0.007) (0.009) (0.008) (0.011)

Source country covariates yes yes yes yes yes yes

State covariates yes yes yes yes yes yes

State trends no no yes yes yes yes

Source country trends no no yes yes yes yes

No. Obs. 1,415 1,415 1,415 1,415 10,534 10,534

Log-likelihood 492,536 495,977 507,670 512,057 245,971 246,863

40

Table A2. Method of payment subsample analysis. Columns (1) and (2) present the negative binomial specifications for the number of cross-border mergers

and acquisitions deals from country j, in state s, during year t for cash and non-cash deals. A transaction is classified as a cash-deal if more than 50% of the deal value

is paid in cash. Columns (3) and (4) present the average deal value specifications for the cash and non-cash transactions. Country, state and year fixed effects are

included in all specifications. Columns (3) and (4) also include acquirer and target industry fixed effects. Source country and state covariates are suppressed. All

standard errors are corrected for clustering at the state level and they are reported in parentheses. *** denotes significance at the 1 percent level, ** at the 5 percent

level, and * at the 10 percent level.

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

Cash transactions Non-cash transactions

Cash transactions Non-cash transactions

Dependent variable:

Number of cross-border

M&A's

Number of cross-border

M&A's

log real value of

the deal

log real value of the

deal

Interstate banking 0.588*** 0.258*

0.554* 0.274

(0.102) (0.156)

(0.326) (1.169)

Intrastate branching -0.345 0.121

-0.661 0.415

(0.233) (0.118)

(0.493) (1.023)

Source country credit to GDP ratio -0.001 0.008

-0.064** 0.007

(0.008) (0.006)

(0.030) (0.052)

Source country market value to GDP

ratio 0.002 0.007***

0.013 -0.062**

(0.002) (0.001)

(0.009) (0.026)

Public acquirer dummy

0.828** 1.683**

(0.375) (0.746)

Public target dummy

0.099 0.078

(0.178) (0.563)

Source country covariates yes yes yes yes

State covariates yes yes yes yes

No. obs. 405 207 617 263

Log-likelihood/R-squared -78,212 -37,912 0.500 0.757