Successful Strategies in Cross-Border Mergers & Acquisitions ...
Cross-Border Mergers and Acquisitions: The importance of ......border mergers and acquisitions...
Transcript of Cross-Border Mergers and Acquisitions: The importance of ......border mergers and acquisitions...
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: [email protected]). Aslı Leblebicioğlu: University of Texas at Dallas, Department of
Economics, 800 West Campbell Road, Richardson, TX 75080 (E-mail: [email protected]). Neviana Petkova:
U.S. Department of the Treasury, 1500 Pennsylvania Ave NW, Washington, DC 20220 (E-mail:
[email protected]). 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
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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).
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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)).
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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.
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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