Intellectual property rights and cross-border mergers and acquisitions

Accepted Manuscript Intellectual property rights and cross-border mergers and acquisitions

Azizjon Alimov, Micah S. Officer PII: DOI: Reference:

S0929-1199(17)30261-4 doi: 10.1016/j.jcorpfin.2017.05.015 CORFIN 1209

To appear in:

Journal of Corporate Finance

Received date: Revised date: Accepted date:

27 April 2017 7 May 2017 25 May 2017

Please cite this article as: Azizjon Alimov, Micah S. Officer , Intellectual property rights and cross-border mergers and acquisitions, Journal of Corporate Finance (2017), doi: 10.1016/j.jcorpfin.2017.05.015

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ACCEPTED MANUSCRIPT

Azizjon Alimov

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City University of Hong Kong

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Intellectual property rights and cross-border mergers and acquisitions*

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[email protected]

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Micah S. Officer

Loyola Marymount University

Abstract

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[email protected]

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We investigate the role of intellectual property rights protection in cross-border merger and acquisition (M&A) activity. We document a significant increase in inbound cross-border M&As after a country implements reforms that strengthen local intellectual property rights. Importantly, we find that intellectual property rights have an impact on merger activity only in industries that are more intellectual capital-intensive, and when the target country has weaker intellectual

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We thank Kangzhen Xie (FMA discussant), Yaxuan Qi, Yue Ma, and the seminar participants at City University of Hong Kong, Saint Mary’s University, Simon Frazer University, and 2016 Financial Management Association Meeting (Las Vegas) for helpful comments. All errors are ours.

ACCEPTED MANUSCRIPT property rights protection than the acquirer country. We also find that synergy gains in crossborder M&As are positively related to reforms of intellectual property rights. These results are consistent with the notion that acquirers are concerned about the local protection of intellectual capital when considering foreign acquisitions, and care the most when the target firm is in an industry that uses intellectual property intensely and in a country that has lesser-developed

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protections that their own country does.

Keywords: intellectual property rights, cross-border mergers and acquisitions

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JEL classification: G34, O3

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ACCEPTED MANUSCRIPT 1.

INTRODUCTION The volume of cross‐border merges and acquisitions (M&A) has surged over the last

thirty years, becoming almost 50% of aggregate global deal volume by 2007 and accounting for a significant majority of foreign direct investment (Erel, Liao, and Weisbach 2012; UNCTAD 2008). Given the prevalence of cross-border deals and their profound impact on reallocation of

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economic activity across borders, it is important to understand factors that influence a firm’s

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decision to expand its boundaries internationally and the locations to which it decides to expand via an M&A. In particular, there is a long-standing and important question of the nature of

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country characteristics and institutions that might explain multinational firms’ decision to enter a

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foreign market via an M&A. For example, the existing evidence shows that the intensity of cross-border M&A activity and their value effects are related to differences in countries’

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institutional quality and corporate governance, currency and stock market valuation, and cultural and linguistic traits.

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To date, however, very little is known about the effect of intellectual property rights

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(IPR) on cross-border M&A deals or their value effects. This lack of evidence is surprising because international M&As increasingly involve intellectual property (IP): acquirers of foreign

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targets are typically interested in transferring (exporting) their own IP, such as proprietary technologies, trademarks, trade secrets, patentable process or products etc., to a target firm after

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an acquisition, with the intent of improving the target’s production process and therefore profitability (e.g., Markusen 1995). Acquirers may also be interested in buying foreign targets to gain ownership of, or access to, their IP (importing IP). This issue is also topical because the economic consequences of stronger IPR remain front-and-center in the policy debate around the world. For example, economists and policy makers are increasingly concerned that strict IPR, such as patent protection, in both developed and developing countries discourage subsequent innovations and thus stand in the way of

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ACCEPTED MANUSCRIPT economic growth (see, for example, Dosi and Stiglitz (2014), Wall Street Journal 2011, Economist 2015). This paper aims to fill this gap in the academic literature by investigating how changes in country-level patent rights affect the volume, direction, and value creation of crossborder acquisitions. Why would potential acquirers be concerned about IPR reforms in a foreign country?

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The main premise of this paper is that the value to an acquiring firm of IP in a foreign country is

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critically dependent on how defensible the IP is: the benefit of “owning” some IP in foreign jurisdictions is negligible if competitors can easily (and without consequence) copy, steal, or

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imitate it. 1 Therefore, a first-order consideration for acquirers that are interested in either

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transferring their own IP to a foreign target firm or gaining access to targets’ IP is the local protection that foreign laws provide for the IP in question.2, 3 For both these reasons (importing

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and exporting IP), we hypothesize that potential acquirers of foreign targets respond positively to reforms of the target country’s IPR, such as patent laws.

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Our hypothesis is rooted in the OLI (Ownership, Location and Internalization) framework

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proposed by Dunning (1993). The OLI framework predicts that one key motivation for firms to acquire foreign assets is to profitably deploy their proprietary assets that they own (such as IP)

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and therefore enhance their competitive advantage. In addition, the foreign country must offer a location advantage that makes it possible to transfer acquirers’ proprietary assets for production

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purposes (rather than simply exporting domestically produced products to that foreign market). We argue that one such important location advantage is the strength of local IPR protection.

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A survey conducted by the U.S. Chamber of Commerce (in cooperation with ASIS International and PricewaterhouseCoopers) finds that surveyed companies experienced IP losses in foreign markets valued at between $53 and $59 billion. (http://usa.marsh.com/NewsInsights/FeaturedContent/The2011IntellectualPropertySurveyReport.aspx) 2 Even if the ultimate owner of some IP is a foreign firm, the fundamental basis for protection of that IP is local to the registered owner. See: http://www.state.gov/e/eb/tpp/ipe/what/index.htm. 3 There are complex bilateral protocols for resolving multinational IP disputes. However, enforcement of court decisions in IP cases almost always depends on local laws and regulations, even if a foreign parent firm can protect their IP in their own jurisdiction.

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ACCEPTED MANUSCRIPT Following the established literature on IPR, this paper focuses on a key part of countries’ IPR regulations: patent laws. To empirically identify the impact of country-level patent laws on cross-border merger activity we employ an index of patent protections developed in Ginarte and Park (1997) (and updated in Park, 2008). This index is widely used in the literature because of its detailed construction and extensive coverage. Importantly for this study, there is a substantial

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time-series variation in the patent index corresponding to discrete changes in (i.e., reforms of)

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country-level patent protection. The use of these patent reforms helps us to address endogeneity

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and omitted variable problems to the extent the reforms are adopted at the country level and are not endogenously influenced by any individual acquiring or target firm in the sample.

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We create a sample of over 67,375 cross-border M&As involving firms from 50 countries in the period 1985- 2012 to test whether legal reforms of IPR protection affect the flow and value

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effects of cross-border M&A deals. Our empirical methodology is similar to a generalized difference-in-difference strategy, with regressions including fixed effects for country (and

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country-pair, where appropriate), year, and industry (in country-industry specifications). A key

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advantage of this empirical strategy is that it not only captures variation in shocks to IPR within the same country, but also helps to address the omitted variable concern by allowing for multiple

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shocks to different countries (and country-industry and country-pair combinations) at different times. The regressions also control for target and acquirer countries’ legal institutions, economic,

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and financial characteristics, which are shown in the existing literature to be first-order determinants of cross-border M&A patterns (Erel et al., 2012). We find that countries that implement reforms strengthening local IPR protection experience more inbound cross-border M&A activity targeting local firms. The effect is economically sizable. Going from the 25th to the 75th percentile of the distribution of the patent index increases the average annual number of inbound cross-border deals by about 7%. Given the sample average annual number of cross-border deals is 52, this translates into an increase of

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ACCEPTED MANUSCRIPT close to 3.7 deals per year. This effect is significantly larger for less economically developed countries, which tend to have weaker IPR protection. The positive relation between IPR and inbound M&A flows is robust to a number of different empirical specifications and sample restrictions. Importantly, the main results are unchanged when we restrict our attention to only substantial patent law reforms and use classical

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difference-in-difference strategy (via indicator variables). Nevertheless, there are still lingering

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econometric concerns that changes in patent laws and cross-border M&A may be jointly

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influenced by some unobservable omitted variables such as economic shocks or concurrent policy reforms. To address the omitted variables problem we employ an additional difference-in-

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differences strategy in the spirit of Rajan and Zingales (1998). This strategy attempts to identify the causal connection between changes in IPR and M&A flows by examining the differential

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impact of IPR across industries with systematically different reliance on IP for production. Intuitively, IP protection should matter more in industries where intellectual assets and

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proprietary technologies are more important. Consequently, when we conduct our analysis at the

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industry level we observe that IPR have an impact on merger activity only in industries that are more intellectual capital-intensive, such as those characterized by high intangibles, high R&D, or

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high number of awarded patents relative to assets. Next we conduct an analysis of bilateral cross-border M&A flows (i.e., between country

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pairs) and find that target-country IPR protection is only an important determinant of crossborder M&A activity when the target country has weaker IPR protection than the acquirer country. This is consistent with the notion that acquirers care most about protection of their property rights when making investments in countries with lesser-developed protections than their own. To complete our analysis, we investigate value effects in cross-border M&A. We test whether IP reforms in a target country induce value creation (i.e., greater synergies) in

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ACCEPTED MANUSCRIPT international M&As by looking into the combined announcement abnormal returns experienced by target and acquirer firms’ shareholders. We find that the combined announcement return is positively associated with increases in the patent index of the target country. This finding suggests that the benefits from cross-border acquisitions are related to the strength of the host countries’ regulations that protect proprietary assets of foreign buyers.

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The results of this paper contribute to a nascent but rapidly growing literature

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documenting the determinants of cross-border M&A activity. Erel et al., (2012) find that several

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target- and acquirer-country characteristics such as disclosure quality, geographic proximity, trade relationships, and relative stock market and exchange rate returns affect M&A activity

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between two countries. Rossi and Volpin (2004) and Ferreira et al., (2010) show that crossborder M&A activity is related to cross-country differences in investor protection and foreign

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institutional ownership. More recently, Ahern et al., (2015), Alimov (2015) and Fresard et al., (2017) show that cross-border M&A activity is also related to cultural distance between acquirer

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and target countries, country labor regulations and industry specialization of acquiring and target

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firms.

This paper also contributes to the literature concerning the effect of IPR on investment

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activity. While scholars and policy makers have long recognized (and debated) the importance of IPR in promoting cross-border business activity and technology transfers, there is no specific

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research (of which we are aware) of the effect on M&A activity. Ginarte and Park (1997) and Allred and Park (2007) find some evidence that IPR protection affects firms’ research and development (R&D) activities. Javorcik (2004) shows that the composition of FDI in Eastern Europe in the 1990s is related to differences across countries in IPR regimes. Branstetter et al., (2006) and Branstetter et al., (2011) demonstrate that IPR reforms around the world provide the impetus for U.S. multinational firms to transfer technology and expand production in foreign countries, and Bilir (2014) shows that this effect is stronger in industries with long product

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ACCEPTED MANUSCRIPT lifecycles (where imitation is a bigger risk than obsolescence) and product complexity. Overall, this literature suggests that IPR, and reforms thereto, have a significant impact on the transfer of technology between divisions of multinational firms and resulting economic growth in the reforming country. This paper contributes to this line of research by investigating whether and how the volume, direction, and value effects of cross-border M&A are related to the strength of

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country-level IPR.

DATA AND SUMMARY STATISTICS

2.1

The sample, the patent index, and summary statistics initial

sample

of

mergers

and

acquisitions

is

obtained

from

the

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Our

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Thomson Financial’s Global M&A Database (formerly known as SDC) and includes all deals

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(domestic and cross-border) announced between January 1, 1985 and December 31, 2012 that are completed by the end of 20134. We limit our sample to deals involving acquiring and target

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firms from any of the 50 largest countries in terms of M&A activity. From SDC we collect

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information on various deal characteristics such as announcement date, transaction value (in US$), deal consideration structure (method of payment), country of domicile, and SIC industry

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codes for both acquirer and target. Other acquirer and target company variables are from CRSP and Compustat for U.S. firms, and Compustat Global Securities for non-U.S. firms.

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Our sample satisfies the following requirements. Similar to existing cross-border M&A studies (e.g. Erel et al., 2012), we exclude LBOs, spinoffs, recapitalizations, self-tender offers, exchange offers, repurchases, partial equity stakes, acquisitions of remaining interest, privatizations, as well as deals in which the target or the acquirer is a government agency. Since the main focus of this study is aggregate merger activity, rather than individual deals and their wealth effects, we consider public, private, and subsidiary acquirers and targets, and include deals with both disclosed and undisclosed transaction values. After imposing these screens and 4

We end our sample in 2012 because the patent index is not available after 2010.

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ACCEPTED MANUSCRIPT eliminating duplicates, the final sample includes 284,032 completed M&A deals with a total disclosed transaction value of $18.3 trillion. 67,375 or 23.6% of our sample transactions are cross-border deals, where the target and acquirer are from different countries, with a total disclosed value of $5.6 trillion. Almost 60% of transactions have missing deal values. Importantly, the incidence of

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transactions with a missing deal value does not appear to be random as it strongly and negatively

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correlates with such key country-level characteristics, such as common law origin, the level of

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investor protection and the patent rights index. We therefore use the aggregate number of crossborder deals as a measure for M&A activity and not the deal value to reduce sample selection

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bias.

Panel A in Table 1 details the countries in our sample, with information about the number

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of inbound and outbound cross-border M&A deals per country throughout our sample window and the size of the economies averaged over the sample period. Firms from the U.S., the UK,

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Canada, Germany, and France are the most active most acquirers in our sample, and also the

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most popular destinations for inbound cross-border deals. The imbalance between inbound (1,541) and outbound (431) M&A deals in China is one of the more notable features of the

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sample, but other smaller, largely lesser-developed, countries (Brazil, Bulgaria, Hungary, Indonesia, Peru, and Turkey, for example) share that characteristic. IPR protection

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2.2

As in the established IPR literature (Allred and Park, 2007; Branstetter et al., 2006), we employ an index of patent rights protection developed by Ginarte and Park (1997) and updated in Park (2008), as a proxy for the overall strength of country-level IPR protection. This index is the most popular proxy used in the economics literature for international IPR because of its detailed construction and extensive coverage. As noted by Maskus (2000), among others, the patent rights index provides a consistent and objective measure of the existence and strength of IPRs across

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ACCEPTED MANUSCRIPT countries and over time5. The patent index proxies for the strength of enforcement of patents under the national laws of the country in question. Ginarte and Park (1997) and Park (2008) construct the index based on the statutory patent laws and their enforcement for over 120 countries from 1960 to 2010.6 The value of the index is based on a score of one or zero each for the following five components of patent-laws: extent of

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patent coverage, membership in international agreements, provisions for loss of protection,

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enforcement mechanisms, and duration of protection. The extent of coverage refers to the type of

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inventions that can be protected; membership in international agreements indicates the adoption of certain IP laws into international treaties (such as the global trade-related aspects of

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international property rights agreements); provisions for loss of protection refers to the less than exclusive use of protection; enforcement refers to country-level mechanisms that aid in enforcing

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patent rights; and duration refers to the length of patent protection. Each component is based on important characteristics determining its effective strength. The index of patent rights

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theoretically ranges from zero to five, with higher values indicating a stronger level of

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protection.

The patent indices are available in five-year intervals, and we match the year of each deal

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to the closest available index year for the country of the acquirer and target (e.g., a deal in 2008 would be matched with index observations from the 2010 year). This obviously invokes the

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assumption that patent laws change discretely at five-year intervals (rather than continuously over the windows), but we are unable to do better than this without higher-frequency patent law data. If this matching process induces errors in our independent variables (patent law strength and reform), such error will make it harder for us to find significant results. In other words, given that patent laws are generally strengthening over our sample window in most countries, any error

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While IPR is also important for other forms of IP, such as copyright, trademarks and trade secrecy, patents are arguably the most important form of protection for technological innovations. 6 We thank Walter Park for graciously providing the most recent version of the index and its components.

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ACCEPTED MANUSCRIPT induced by low-frequency patent-law data likely biases against us finding a statistically significant association between patent law changes and cross-border M&A volume. Table 1, Panel A contains the patent index for the countries in our sample at two points in time near the beginning and end of our sample (1985 or earliest available year and 2010). The country with the weakest IPR protection by these measures in 2010 is also the economically

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poorest country in our sample, as measured by gross domestic product (GDP) per capita:

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Pakistan. The country with the highest patent index in our sample is the United States (4.88 in

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2010; it was also highest in 1985). In general, the index mirrors economic wealth, although there are some exceptions. India, for example, is one of the poorest countries in our sample (in terms

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of per-capita GDP) but has patent strength above the first quartile in 2010 (it was weaker in 1985). The sample average of the index across all countries and years is 3. Importantly, all

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countries in our sample strengthened their patent laws between 1985 and 2010: the average increase in the patent rights index over this period is 2. A typical example is Brazil, which

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strengthened its index from 1.27 in 1985 to 3.42 in 2010.

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[Table 1 about here]

Figure 1 documents the evolution of the patent index between 1985 and 2010 (i.e., the

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beginning and end of the sample period) for six countries representative of our sample. The stepfunction characteristic of the lines in the figure is an artifact of our low-frequency patent-law

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data, as we observe the patent index once every five years for each country. As demonstrated in Figure 1, the dominant theme of the evolution of patent laws in these countries (and in the majority of countries in our sample) over the sample period is that patent laws have strengthened in all countries throughout this period. The one exception in the figure is Australia, which started with a fairly high patent index at the beginning of our sample, substantially reformed patent protections in the early 1990s, but then stayed fairly static for the remainder of our sample period.

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ACCEPTED MANUSCRIPT [Figure 1 about here] We also identify the instances of major discrete reforms during our sample period to show that our baseline results are robust to alternative measures of IPR. To construct the sample of patent reforms we started by identifying countries with large increases in the patent index, such as those with at least one full point increase. We then verified that these episodes indeed

on

the

World

Intellectual

Property

Organization’s

website

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treaties

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correspond to discrete patent law reforms by reading the history of each country’s IP laws and

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(http://www.wipo.int/directory/en/). We also verified that all those patent reforms appeared in the list of reforming countries used by Qian (2004), Branstetter et al., (2006) and Maskus (2000).

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Our search identify 38 discrete reforms that took place during our sample period. We list these reforms in Panel B of Table 1.

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The number of cross-border deals involving U.S. firms as either the acquirer (outbound) or target (inbound) vastly exceeds the number of deals involving firms from any of the 49 other

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countries we study. Notably, firms from U.K. and Germany are also actively involved in the

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cross-border market for corporate control. However, as we will document later in the paper, our results are robust to excluding those countries from our analysis.

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Panel C in Table 1 presents summary statistics for the cross-border M&A deals in our sample, all of which involve acquiring and target firms from one of the 50 countries in Panel A.

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The first thing to notice about this sample of deals is that very few of the targets (about 5%) are publicly traded firms: this makes data about the targets in our sample difficult to obtain. On the contrary, more than half (about 60%) of the acquirers in our sample are publicly traded. The transaction value is available for about 40% of the sample deals. The acquirers for which we can measure accounting data (i.e., those that are publicly traded) are considerably larger in terms of book value of assets on average (US$97.5 billion) than are the targets for which we can measure accounting data (US$32.3 billion on average).

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ACCEPTED MANUSCRIPT Also notable is the fact that very few of the cross-border deals in our sample (0.12%) are hostile deals, challenged by target management (i.e., almost all are friendly transactions). About 16% are majority-cash financed (i.e., > 50% of the method of payment is cash) and only about 3.4% are majority-stock financed transactions, with the remainder involving a mix of cash, acquirer equity, or other (not disclosed) securities.7

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Panel D in Table 1 presents summary statistics for the dependent and the key variable of

2.3

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interest used in the regressions models. Control variables

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We include in our regressions a large set of country-level economic and legal

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characteristics that previous research finds to be associated with aggregate cross-border M&A flows. Because all regression models in this paper include country fixed effects, which fully

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absorb permanent or slowly changing country factors (such as legal origin, culture, size and resource endowment factors), we include only those variables that exhibit time-series variation.

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Underlying economic conditions, trade relations, and the development of equity and debt

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markets are highly correlated with cross-country differences in the overall investment climate and the quality of legal institutions (e.g., La Porta, et al., 1998). To control for country-level

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economic development and growth, we include the log of yearly GDP per capita and the annual growth in real GDP. We measure the development of a country’s equity and credit markets using

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the market capitalization of all listed firms and total private credit, both scaled by GDP. To control for the level of openness of the economy we control for foreign trade activity, measured as the sum of exports and imports scaled by GDP. For our country-pair models, the intensity of economic ties between each country-pair in a given year is measured using bilateral trade flows. To control for the effect of valuations on M&A activity (e.g. Erel et al., 2012) we include annual stock market returns and currency exchange rates (relative to the U.S. dollar for 7

The percentage of cash and stock financed transactions in our sample is relatively small because the majority of our sample is comprised of deals with undisclosed transaction values and deals involving private acquirers.

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ACCEPTED MANUSCRIPT non-U.S. firms and relative to UK pound for U.S. firms) in our regressions. To control for the overall legal and business environment we include an index for the rule of law and an index for the level of government corruption. Lower values for the rule of law indicate that the overall legal system in a country functions poorly, while higher value of the corruption index indicates that the government is more corrupt.

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We obtain these (and other) country-level variables from MSCI, the World Bank’s World

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Development Indicators (WDI) and Governance Database, the IMF’s International Financial

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Statistics (IFS), the Pen World Tables, the CIA World Factbook, the OECD, and Kaufmann et al., (2010). In addition, we use data from Bris et al., (2010) and Lel and Miller (2014) to measure

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changes in a country’s takeover and competition laws, which reflect openness to foreign investment and thus affect merger activity.8 Because some of the control variables are available

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only after 1990, the regressions that include the full set of control variables therefore examine the period 1991-2012.

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Some specifications (focused on country-pairs) also control for geographic proximity and

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cultural similarities between the acquiring and target firms’ countries. To measure the geographic proximity between countries, we use the geographical distance between each

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country's capital city and an indicator for two countries sharing a common border from the Centre d'Etude Prospective et d'Information Internationale (CEPII).

Following Stulz and

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Williamson (2003), Guiso et al., (2009) and Ahern et al., (2015), we consider linguistic similarity, religious ties and the differences in the level of trust as cultural factors relevant for cross-border business activity. All variables used in this study and their source of those data are described in the Appendix Table A1.

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For example, since January 2002 the Corporate Takeover Act governs all acquisitions of publicly traded companies in Germany.

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ACCEPTED MANUSCRIPT 3.

EMPIRICAL RESULTS

3.1

Analysis of inbound cross-border M&A flows We use a fixed-effects panel regression to examine the relation between the strength of

country-level patent regulations and the intensity of inbound cross-border M&A flows at the individual country-year level. Our baseline regression is:

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Cross-border M&Ak,t =α+β1Patent Indexk,t-1+γControls k,t-1+ ωk + μt + εk,t

(1)

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where k indexes target countries; t indexes years; ωk and μt are country and year fixed effects. The dependent variable is the natural logarithm of (one plus) the total number of cross-border

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deals that involve a foreign acquirer targeting a domestic firm in country k in year t.9 The key

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independent variable is Patent index, which captures the strength of a country’s patent protection and enforcement laws. The target country fixed effects remove any permanent country-level

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characteristics that might be correlated with cross-border M&A activity and thus ensure that estimates for the effect of IPR on cross-border M&A flows (β1 in Eq. (1)) is identified from

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within-country variation in patent laws over time, and not from simple cross-country

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correlations. The year fixed effects account for transitory global economy-wide factors, such as financial crises or technological improvements. Other independent variables include various

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country-level characteristics discussed above. All independent variables are lagged by one year

country.

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to ensure that they are exogenous to cross-border M&A volume. We cluster standard errors by

Table 2 reports the results of five different specifications of Eq. (1). The differences across models are the control variables and interactive variables included in the regressions: all models include year and country fixed effects, controlling for the influence of country-level or year-specific omitted variables that may influence cross-border M&A volume into a particular country. Column (1) analyzes inbound international M&A flows with Patent index as the only

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Alternative definitions of the dependent variable are discussed later in the paper. See Tables 3 and 5.

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ACCEPTED MANUSCRIPT independent variable. The estimates show that reforms that result in increases in IP protection in a country (the time-series country-level mean of the patent index will be captured in the country fixed effects) are positively and significantly associated with the number of cross-border M&A deals targeting firms in a country: the coefficient on Patent index is 0.318 and statistically significant at better than the 1% level (a robust standard error of 0.072).

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[Table 2 about here]

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Models (2) and (3) examine the effect of IPR on cross-border merger activity by

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gradually including all remaining controls for economic and financial development, trade activity, currency and stock market movements, the quality of legal institutions, and relevant

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changes in takeover laws and regulations. The number of observations changes depending on the availability of control variables. In all these models the Patent index variable enters with a

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positive and significant coefficient indicating that countries that strengthen legal protection of IPR experience greater flows of inbound cross-border acquisitions by foreign acquirers.

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To estimate the significance of the results, we calculate predicted changes in the number

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of incoming deals that would result if a typical sample country at the 25th percentile of the Patent Index distribution (2.958 from Panel D of Table 1) were to improve its IP protection by 46% to

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the level of a country at the 75th percentile (4.333). Based on the coefficient estimate of 0.152 from Model (3), this inter-quartile increase in a country’s IPR protection would raise the annual

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number of inbound cross-border deals by about 7%, an economically large effect. Given that the average annual number of cross-border deals for all countries in the sample is about 52, this translates into an increase of close to 3.67 deals per year. Therefore, the relation between withincountry changes in IPR and cross-border merger activity is not only statistically significant but also economically important. We next show that our estimates of the effect of patent reforms on inbound cross-border M&A flows are stronger when the country is less economically developed and, thus, is more

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ACCEPTED MANUSCRIPT likely to have weaker IPR protection in place. In Model (4) we see that the effect of IPR protection on inbound cross-border M&A flows is sensitive to the wealth of the country. Specifically, the interaction between the Patent index and GDP per capita has a negative and significant coefficient. This suggests that acquirers are particularly sensitive to IPR protection issues when investing in less economically developed countries, which tend to have weaker IPR

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to begin with. Similarly, Model (5) shows that foreign acquirers are particularly sensitive to IPR

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protection when investing in countries with greater corruption; such countries are generally also

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poorer countries with weaker legal institutions. These findings illustrate the importance of examining the impact of IPR protection on countries conditional on different levels of economic

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development.

In terms of the control variables, the annual exchange rate is consistently negatively

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related to inbound M&A flows: this suggests that acquisition activity in a country is dampened when the currency gets more expensive, consistent with Erel et al. (2012). Furthermore, national

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economic development and wealth (GDP per capita) is consistently positively associated with

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inbound cross-border M&A deal volume. The flow of inbound M&A activity from foreign acquirers also appears to be consistently positively related to the number of M&A deals by

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domestic acquirers, indicative of an open and flourishing M&A market for domestic targets (regardless of the identity of the acquirer). In this sense, the Patent index is not proxying for the

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some legal aspect of a country that is correlated with viability of the local M&A market: even holding domestic M&A activity constant, we continue to observe a significantly positive impact of IPR protection on cross-border M&A flows. The Rule of law variable has a consistently significantly positive coefficient in the regressions in Table 2, indicative of greater inbound cross-border M&A activity into countries with stronger legal institutions and rule of law.

16

ACCEPTED MANUSCRIPT 3.2

Robustness tests In Table 3, we estimate several alternative specifications to establish the robustness of our

core empirical results. The three destination countries responsible for the most inbound crossborder M&A deals in our sample are Germany, the United Kingdom, and the United States (Table 1). To ensure that our results are not driven by that small subset of the 50 countries in our

PT

overall sample, in Model (1) of Table 3 we drop observations from those countries; this leaves us

RI

with fewer observations, but the specification of the regression is otherwise similar to that in

SC

Model (4) in Table 2. The coefficient on the Patent index variable in this regression excluding observations from those dominant countries is similar to that observed in Table 2: positive and

NU

significant.

MA

[Table 3 about here] In Model (2) of Table 3 we use an alternative measure for target country-level crossborder M&A intensity, which has been used by Rossi and Volpin (2004) and Erel et al. (2012):

PT E

D

the cross-border merger ratio. The cross-border merger ratio scales the total number of crossborder deals by all deals (domestic and cross-border) in a given target country and year. Because the cross-border ratio is truncated between 0 and 1, we estimate a Tobit model. The results show

CE

that the Patent index in the target country relates positively and statistically significantly to the

AC

cross-border merger ratio.

In Model (3) of Table 3 we conduct a Poisson regression, which is frequently used to model count data. In this model, the dependent variable is simply the number of inbound crossborder M&A deals targeting a country in a year (as opposed to the log transformation of that variable Table 2 and in the remainder of Table 3). Again, the coefficient on the Patent index variable is positive and significant, consistent with the other specifications, suggesting that countries with stronger protection of IPR have greater flows of inbound cross-border acquisitions.

17

ACCEPTED MANUSCRIPT Instead of using the continuous Patent index as the key explanatory variable, in Model (4) of Table 3 we employ an indicator variable (Major patent reforms) which is equal to one in our panel data if year t for country k is after the year in which country k enacts a reform to their patent laws that substantially strengthens IPR and zero otherwise. For China, Indonesia and Turkey, which enacted more than one patent reform, we code indicator to be equal to two (or

PT

three) after the latest patent reform episode and one (or two) between the patent reform

RI

episodes.(Section 2.2 describes the definition of discrete reform).10

SC

As can be seen in the table, countries that implement reforms that significantly strengthen their IPR protection experience significantly greater flows of inbound cross-border M&A deals

NU

in the years following. Notably, this regression (Model (4)) contains year and country fixed

MA

effects, which implies that this result is more likely to be associated with the strengthening of IPR protection itself as opposed to some unobservable time-series or country-specific effect.

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Model 5 of Table 3 reports the coefficients using the components of the Patent index,

PT E

instead of the index itself, as the key independent variables. As noted above, the Patent index is comprised of five components of patent-law strength: extent of patent coverage, membership in

CE

international agreements, rights concerning loss of protection, enforcement mechanisms, and duration of protection. Each of these components is a continuous variable ranging from zero to

AC

one, with higher values indicating greater IPR protection. The separate analysis of the index components thus helps us understand the sources of the IPR effects on international merger flows.

As can be seen in Table 3, the components of the index most robustly associated with cross-border M&A activity are the duration of patent protection and the country’s membership in international agreements concerning patent enforcement. The latter result is particularly intuitive:

10

We obtain similar results when we use the patent reform data from Branstetter, et al. (2006).

18

ACCEPTED MANUSCRIPT acquirers are likely to be especially concerned that violations of newly acquired IP in some foreign country are potentially enforceable in their home jurisdiction. 3.3

Using industry characteristics to address omitted variable concerns Although the regressions in the previous section include an extensive set of control

PT

variables as well as country, year and country-year fixed effects, there still may be lingering concerns about omitted variables potentially driving changes in both IPR and M&A flows, such

RI

as unobserved economic shocks or concurrent policy reforms. To alleviate such concerns and

SC

establish a causal impact of IP protection on M&A activity more convincingly, we use the influential methodology pioneered by Rajan and Zingales (1998) and exploit cross-industry

NU

differences in exposure to IP regulation across countries that did and did not change their IPR.

MA

The hypothesis is that if foreign patent laws indeed matter for acquirers considering acquisitions of foreign targets then the strength of such laws should have relatively stronger impact on cross-border M&A flows in industries where IP is naturally used more extensively in

PT E

D

production, i.e. industries with inherently higher intellectual asset intensity. By examining the interaction with industry-level intellectual asset intensity we potentially establish a causal impact of IPR on international cross-border merger flows, because this strategy (controlling for

CE

unobserved industry- and country-factors with fixed effects) uncovers the differential impact of

AC

IPR protection across industries with varying usage of IP in the production process. In this test we employ three proxies for industry-level intellectual asset intensity: intangible intensity, R&D intensity, and patenting intensity. In general, intangibles, R&D, and patents are assets that have no physical existence but represent rights to produce tangible assets or cash flows, and are the types of assets that most need IPR protection (since one cannot generally physically guard them). Following Rajan and Zingales (1998), we use U.S. data to compute intrinsic technological intellectual intensity characteristics for each industry. The idea here is that the large and well-developed financial markets in the U.S., and the well-protected

19

ACCEPTED MANUSCRIPT IPR (with the highest patent index of 4.88), should allow a typical U.S. firm in a given industry to achieve the desired (or optimal) intellectual asset intensity for that industry. It is important to note that the use of the U.S. data to construct proxies for intrinsic industry characteristics in foreign countries is noted by Rajan and Zingales (1998) to be a valid empirical strategy because most industries in foreign countries are structurally and technologically similar to those in the

PT

U.S.

RI

Table A2 in the Appendix reports the mean levels of intangible-, R&D-, and patenting-

SC

intensities for U.S. firms (using data from Compustat) in selected two-digit SIC code industries over the period 1980-2010. The mean intangibles to total assets (intangible-intensity), R&D to

NU

total assets (R&D-intensity), and number of patents to assets (relative patenting-intensity) ratios for U.S. firms during our sample are 8.9%, 1.6%, and 1.73%, respectively. The variation of these

MA

variables across industries is large. For example, intangible intensity ranges from as low as 1.26% for the metal mining industry to as high as 22% for the printing and publishing industry.

D

Similarly, R&D intensity ranges from as low as 0% in the retail (both general merchandise and

PT E

apparel stores) to as high as 16.9% in the chemicals and allied products industry. This variation concurs with conventional notions of what constitutes relatively knowledge-intensive industries

CE

(i.e., the chemical industry involves more R&D than does the retail industry). While all three measures are positively correlated, the magnitude of the correlation coefficients (between 9.7%

AC

and 43.5%) suggest that these measures still reflect somewhat distinct industry characteristics. Table 4 reports results of the country-industry-year level regressions of inbound crossborder M&A flows on the strength of host country’s IPR and control variables. The first three rows in Table 4 report the coefficients on the interactions of the Patent index with the three proxies for industry-level IP intensity discussed above. In these regressions, the unit of observation is industry-country-year, and the dependent variable is the log of (one plus) the number of inbound cross-border mergers per industry-country-year. As can be seen in the table,

20

ACCEPTED MANUSCRIPT the coefficients on the interaction terms are significantly positive in all three models, suggesting that the strength of national IPR protection matters more for cross-border M&A flows as the intellectual asset intensity of an industry increases. This speaks to the channel effect discussed above: concerns about endogeneity and causality are reduced because the empirics strongly support the theoretical notion that the potency of patent laws should have more dramatic impact

PT

on cross-border M&A flows in industries where IP is inherently a more intense part of the

SC

[Table 4 about here]

RI

production process.

The three models also contain the Patent index variable (fourth row). The coefficient on

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this variable are either negative or statistically equal to zero in all three regressions. This suggests that in industries with zero or low R&D or patenting intensity, such as forestry or

MA

fishing and hunting, there is no positive relation between the strength of IPR protection and cross-border M&A flows. This is also an intuitive result, since acquirers considering the

D

acquisition of a foreign target that does not use IP in its production process should not be

PT E

concerned about the strength of local IPR protection. The coefficients on the control variables in Table 4 are qualitatively similar to those in Table 2: again, cross-border M&A flows appear

CE

sensitive to national wealth (GDP per capita), the veracity of the local M&A market (acquisitions by domestic acquirers),the extent of local corruption and rule of law, and the exchange rate

AC

movements.

To compute the economic significance of the coefficient estimate, we follow Rajan and Zingales and calculate the interquartile difference in cross-border M&A activity in the high and low intellectual asset intensity industries across countries with high and low IPR protection. For example, the estimated coefficient for the interaction of the Patent index with Intangible intensity is 1.218, which suggests that, all else equal, if Hungary, the country at about the 25th percentile of the distribution of the Patent Index, were to improve its IPR protection to the level of Belgium,

21

ACCEPTED MANUSCRIPT the country at about the 75th percentile, Hungary would experience an increase the number of inbound cross-border M&As in the measuring and analyzing instruments industry (highly intangible intensive, 75th percentile) by about 7% relative to the number of deals in its leather and leather products industry (low intangible intensity, 25th percentile). These sectoral differences in cross-border M&A activity due to stronger patent laws

PT

suggest a quantitatively important role for IPR protection. Our results further increase confidence

RI

that we have identified a causal connection between a country’s patent laws and cross-border

especially sensitive to changes in IPR protection. IPR and bilateral M&A activity

NU

3.4

SC

M&A activity, because we find significant effects in precisely those industries that should be

We now extend our analysis of the impact of IPR on international M&A activity from a

MA

country-level analysis to a multi-country setting in which we ask whether IPR reforms in a given country affect the relative attractiveness of that country vis-à-vis other (non-reforming) countries

D

to potential foreign acquirers. The advantage of analyzing bilateral deal activity is that it allows

PT E

us to isolate an independent effect of IPR reforms on the timing and magnitude of cross-border M&As while controlling for the effects of a comprehensive set of factors in both the target and

CE

the acquirer country that could drive the cross-border merger activity. This analysis also allows us to examine differences in the country origin of acquirers that

AC

are enticed by IPR reforms in target countries. Specifically, based on arguments in the existing literature (e.g. Helpman, 1993; Branstetter et al., 2006), we expect that multinationals that originate from economically developed countries (that typically have stronger IPR) are more likely to place a higher value on the impact of improved IPR protection on acquisition opportunities into reforming developing markets than are acquirers from other countries. To examine this hypothesis, we explore whether the effect of changes in IPR on cross-border M&A flows varies by the level of economic development in acquiring and target countries. We identify

22

ACCEPTED MANUSCRIPT economically developed and developing (“emerging”) countries using the International Monetary Fund’s classification of “advanced” and “emerging” economies in 2000 (the midpoint of the sample period). Since we have 50 countries and 27 years in our sample, we have a matrix of 66,150 country-pair-year observations. However, this matrix has a large number of zeros in it, as there is

PT

no cross-border M&A activity between most country-pairs in a given year (less than 12,000

RI

country-pair-years have a cross-border M&A activity). Therefore, as in Erel et al., (2012), we only keep those country-pairs that had at least three deals during our sample period. This reduces

SC

the total number of country-pair-year observations to about 27,000.11

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We model bilateral acquisition flows using a specification akin to a gravity model widely used in the trade literature (e.g. Anderson and van Wincoop, 2003). The standard gravity model

MA

typically relates the amount of trade between two countries to geographic distance and other barriers to trade, such as economic development, tariffs, exchange rates, cultural differences, and

D

linguistic similarities. Specifically, we build on the models recently used by Erel et al., (2012)

PT E

and Karolyi and Taboada (2015) and estimate the following baseline specification:

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Bilateral Cross-Border M&Atgt,acq,t=α + β1PatentIndextgt,t-1 + β2PatentIndexacq,t-1 + γControlstgt-acq,t-1 + ωtgt,acq + μt + εtgt,acq,t

(2)

AC

where tgt indexes target countries; acq indexes acquirer countries; t indexes years; ωtgt,acq and μt are matched country-pair and year fixed effects. The dependent variable is the log of (one plus) the number of cross-border deals in year t where the target is from country tgt and the acquirer is from country acq (tgt≠acq). We include the acquirer and target countries’ Patent index as two separate variables to distinguish between the importance of IPR reforms in developed and

11

Following Erel et al., (2012), a country pair without a merger in a given year is assigned a value of zero.

23

ACCEPTED MANUSCRIPT emerging economies as “push” (acquirer-country specific) versus “pull” (target-country specific) factors driving cross-border merger activity. Similar to Erel et al., (2012) and Karolyi and Taboada (2015), most of the target and acquirer-level country control variables are included as differences (between acquirer and target countries) in economic and financial development, stock market and currency returns, legal

PT

institutions, and merger laws. We also control for economic ties, geographical proximity, and

RI

cultural similarities: specifically, the level of bilateral trade, geographic distance between

SC

capitals, differences in the level of trust, and three binary variables that indicate that a given country-pair shares the same official language or predominant religion, or has a common border.

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[Table 5 about here]

Table 5 presents the results of different specifications derived from Eq. (2). Models (1) –

MA

(4) report estimates for four subsamples based on whether the dependent variable counts deals involving acquirer and target firms from developed or emerging countries. Model (1) only counts

D

acquisitions where the acquirer is from a developed market and the target is from an emerging

PT E

market, Model (2) is for developed acquiring developed, Model (3) emerging acquiring emerging, and Model (4) emerging acquiring developed.

CE

We observe important differences by the country origin of acquiring and target firms. Specifically, we observe a positive and significant relation between the Patent index of the target

AC

country (Patent indextgt) and bilateral merger flows only in the subsample of deals where the acquirers are from developed markets and the targets from emerging markets (Model (1)). In contrast, the coefficient on the Patent index of the target countries is not significant in any of the other three subsamples. The findings in Model (1) are consistent with the notion that multinationals care most about protection of their IP rights when making acquisitions in reforming (or developing) countries, which generally have lesser-developed IP protections than their own.

24

ACCEPTED MANUSCRIPT The coefficient on the Patent index of the acquiring country (Patent indexacq) is not significant in any of the models in Table 5. The finding that only the Patent index of target countries and not of acquiring counties is significantly positive in these models explaining bilateral cross-border M&As (at least in Model (1)) provides support for the “pull” view of bilateral investment between countries: IPR reforms in the recipient countries are important

PT

factors in driving merger inflows to emerging markets from developed countries12.

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The estimates of the coefficients on the control variables are in line with existing findings

SC

in cross-border M&A research (e.g., Erel et al., 2012; Ahern et al., 2015). For instance, bilateral M&A activity is higher when two countries have more mutual trade, have a common border or

NU

are geographically close, or have similar linguistic and cultural traits. In a given year, acquirers, on average, are more likely to come from countries with higher GDP per capita, appreciating

MA

currencies and more developed stock markets.

In Models (5) – (8), we estimate several alternative specifications to demonstrate the

D

robustness of the main result. To save space, we present only the results for the subsample of

PT E

deals involving acquirers from developed economies and targets from emerging markets (i.e., as in Model (1)). The results for the other three subsamples are similar to those in Models (2) – (4)

CE

of the table (and are available upon request). A potential concern with the previous results (estimated after controlling for a

AC

comprehensive set of economic factors, and bilateral social and trade links) is that instead of being affected by the time-variation in IPR, the intensity of bilateral mergers between a particular country-pair might still be influenced by some unobservable but persistent factors specific to the country-pair combination. To alleviate any lingering concerns about unobserved heterogeneity driving merger activity between the acquirer and target countries, we saturate the

12

To be consistent with the other control variables, in an unreported model we did measure IPR protection as the difference in the Patent index between the target and acquirer country pair and find that this difference enters the regression equation with a positive and statistically significant sign, indicating the importance of reforms strengthening patent protection in target countries.

25

ACCEPTED MANUSCRIPT empirical model by including matched acquirer and target country-pair fixed effects. The results, reported in Model (5) of the table, show that the coefficient on the Patent index of target countries continues to be positive and highly significant. Note that in this model, as expected, the variables measuring time-invariant differences between a given country pair, such as their linguistic, religious, and cultural similarity and geographic distance, are now fully absorbed by

PT

the matched acquirer and target country in -pair fixed effects (and therefore excluded from the

RI

regression).

SC

We next show that the results are robust to alternative indicators of IPR protection in the target and acquiring countries. In Model (6), we measure IPR protection with the indicators for

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large changes in the Patent index (which proxies for major patent law reforms) that we used in Model (4) of Table 3. We find that the coefficient on Major patent reforms indicator for target

MA

(but not acquirer) countries is positive and significant at conventional levels. We next use an econometric approach recently proposed in the international trade literature to estimate the

D

intensity of bilateral cross-border trade flows. Specifically, in Model (7) we use the Poisson

PT E

pseudo-maximum likelihood (PPML) estimator developed by Santos and Tenreyro (2006) to address the prevalence of zeroes in the bilateral trade (and merger) flows. The results show that

CE

our results are very robust to this alternative econometric specification. Finally, in Model (8) we estimate the likelihood of a cross-border deal between the particular target-acquirer country pair

AC

in a given year in a probit framework. The coefficient on the patent reforms of target countries is again positive and highly significant. 3.5

IPR and merger gains The results so far suggest that IPR reforms at the country level affect the volume and

direction of cross-border mergers. Arguably, cross-border M&A deals tend to occur when the acquirer and target expect to create positive net present value (or synergies) from their union. This implies that strengthening of IPR protection in a given country should be positively

26

ACCEPTED MANUSCRIPT associated not only with greater propensity of foreign bidders agreeing to acquire domestic firms but also with greater value created by such mergers, as “protected” local technology is more valuable to foreign acquirers. Furthermore, M&A deals agreed to after reforms in a target country’s IPR protection are likely to result in greater synergies for the merging firms as the stronger legal protection of IP enhances the transfer of technologies from the foreign acquirer to

PT

the domestic target, potentially improving the domestic firm’s production processes.

RI

To test this conjecture, we examine how changes in the strength of IPR protection in a

SC

target country influence the overall value created (or destroyed) in cross-border M&A transactions. Following the M&A literature (e.g. Ahern et al., 2015; Bradley et al., 1988) we

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measure merger gains using the combined (value-weighted) target and acquirer announcement cumulative abnormal stock returns (CARs). We calculate CARs around the announcement date

MA

for both targets and acquirers using a market model, where the Morgan Stanley Capital International (MSCI) world stock market index is used to proxy for the market return. MSCI is from Thomson-

D

Reuters database. Our tests use abnormal returns cumulated over the five days (-2, +2)

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surrounding the announcement date, but the results are similar if we use abnormal returns cumulated over a three-day window (-1, +1) or a ten-day window (-5, +5). Since information on

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combined returns is available only for public companies, these tests focus on a much smaller sample (425 deals) of public firms acquiring public targets in cross-border transactions and that

AC

have accounting information on Compustat Global.13 Table 1, Panel B reports descriptive statistics for the combined CARs. The average (median) five-day combined CAR is 3.4% (2.3%). Of the 425 cross-border deals for which we can measure both acquirer and target returns, 329 are deals where the acquirer is from a developed-market country. These developed-market acquirers in our sample made 51 acquisitions in emerging markets and 278 acquisitions of targets in other developed markets. 13

Our sample size is comparable to Ahern et al., (2015) and other studies that used combined returns in cross-border M&A studies.

27

ACCEPTED MANUSCRIPT Table 6 presents coefficient estimates of the effect of changes in IPR in a target country (as measured by the natural log of the Patent index) on the combined returns in cross-border deals separately for two different subsamples of transactions. [Table 6 about here] For the subsample of transactions involving acquirers from developed markets and

PT

targets from emerging markets we find that, on average, increases in the Patent index of the

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target country are positively associated with the combined announcement returns of a cross-

SC

border merger. The coefficient on the Patent index of a target country is positive and statistically significant (1.63, with a p-value below 0.05). The estimated economic effects are large. Using

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the point estimate from Model (1) we estimate that increasing the Patent index from the 25th percentile by 7.7% to the 75th percentile of its distribution (Panel C of Table 1) leads to a 1.1

MA

percentage point increase in combined returns (which have an unconditional mean of 3.43%). Our interpretation of this result is that strengthening an emerging market country’s IPR

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protection results in greater synergies for cross-border acquirers from developed markets. This

PT E

occurs potentially because local technologies in the emerging market are able to create more value for the acquirer because they enjoy greater IP protection and are less likely to be stolen or

CE

mimicked. Another possibility is that the stronger legal protection of IP heightens the transfer of technologies from the foreign acquirer to the domestic target, potentially improving the domestic

AC

firm’s production processes.

The country-level and deal-specific control variables enter the CARs regressions in a manner that is consistent with previous studies of cross-border acquisitions. For example, we find that synergies are greater in mergers where a developed-market acquirer and emergingmarket target share the same language or religion, and when a developed-market acquirer buys a geographically close developed-market target (Model (2)).

28

ACCEPTED MANUSCRIPT 4.

CONCLUSIONS This paper presents new evidence that country-level reforms of intellectual property

rights of are an important determinant of international M&A flows. We find that over the period 1985-2012, countries that increase IPR protection (by strengthening patent laws) attract more foreign acquirers, especially those from countries with relatively strong IPR (i.e., from developed

PT

markets) and in industries that intensively use intellectual capital (where foreign acquirers would

RI

be particularly concerned about the protection of their intellectual property). These results are

SC

robust to alternative specifications, and increases in IPR in a target country are positively related to the economic value (synergy) created by these mergers.

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Our results are relevant for the ongoing debate over the extent to which countries should protect intellectual property. For example, the regulation of IP is one of the most contentious

MA

issues in the currently-debated free trade agreements (Wall Street Journal October 2, 2015). Our results suggest that stronger IP protections attract foreign acquirers, which can represent an

D

important channel for technology transfer to developing countries and can contribute

AC

CE

PT E

meaningfully to their economic development.

29

ACCEPTED MANUSCRIPT REFERENCES Ahern, K.R., Daminelli, D., and Fracassi, C., 2015. Lost in translation? The effect of cultural values on mergers around the world. Journal of Financial Economics, 117(1): 165-189. Alimov, A. 2015. Labor market regulations and cross-border mergers and acquisitions. Journal of International Business Studies, 46: 984–1009.

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ACCEPTED MANUSCRIPT Qian, Y.. 2007. Do National Patent Laws Stimulate Domestic Innovation in a Global Patenting Environment? A Cross-Country Analysis of Pharmaceutical Patent Protection, 1978–2002. Review of Economics and Statistics 89 (3): 436–53. Rajan, R. and Zingales, L., 1998. Financial dependence and growth. American Economic Review, 88(3): 559-586

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Rossi, S. and Volpin, P., 2004. Cross-country determinants of mergers and acquisitions. Journal of Financial Economics, 74(2): 277-304.

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Wall Street Journal, July 14, 2008, “Patent Gridlock Suppresses Innovation”. Wall Street Journal, October 2, 2015, “Rift Over Drug Protections Complicates Trans-Pacific Partnership

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CE

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Trade Talks”.

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ACCEPTED MANUSCRIPT

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CE

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MA

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Figure 1. The evolution of IPR protection in selected countries between 1985 and 2010.

33

ACCEPTED MANUSCRIPT Table 1. Descriptive statistics.

Panel A: Country-level patent index and M&A activity: sample period 1985 to 2012. This panel presents the beginning, end and sample average value of the patent index for all countries in our sample. The panel also contains the total number of inbound and outbound cross-border M&A deals

PT

for that country, and each country’s GDP per capita averaged over the sample period (measured in US$).

Patent

Sample

Total # of

Total # of

index:

index:

Average

inbound

outbound

GDP per

sample

sample

Patent

cross-border

cross-border

capita

start

end

Index

M&A deals

M&A deals

(US$m)

2.72

671

80

$6,676.60

3.9

2434

1737

$26,833.23

4.08

672

845

$29,534.92

4.51

1312

1215

$27,937.50

SC

Patent

NU

RI

All variables are defined in Appendix Table A1.

1.54

3.56

Australia

2.49

4.33

Austria

3.01

4.33

Belgium

3.23

4.67

Brazil

1.27

3.42

2.42

1262

221

$4,805.91

Bulgaria

1.74

3.88

3.23

123

5

$3,137.30

Canada

2.91

4.54

4.1

4344

5515

$27,544.90

2.01

4.67

3.69

384

109

$5,840.85

1.33

4.21

2.74

1541

431

$1,493.99

0.96

3.42

2.49

240

42

$2,857.41

Czech Republic

2.96

4.33

3.71

559

71

$10,512.91

Denmark

3.18

4.67

4.37

1144

1180

$36,467.51

Egypt

1.41

2.89

2.05

63

18

$1,289.14

Finland

2.98

4.67

4.18

935

1098

$29,814.43

France

3.63

4.67

4.38

4132

4122

$27,059.11

Colombia

D

PT E

AC

Chile China

MA

Argentina

CE

COUNTRY

Average

34

ACCEPTED MANUSCRIPT 3.81

4.67

4.43

5537

4258

$28,324.00

Greece

2.33

4.47

3.72

96

150

$15,380.37

2.5

3.81

3.31

867

937

$22,240.01

Hungary

2.12

4.33

3.71

316

60

$8,098.23

India

1.03

3.76

2.22

736

816

$598.75

0.2

2.77

1.71

308

53

$1,224.00

Ireland

2.03

4.67

3.78

730

Israel

2.78

3.96

3.42

350

Italy

3.36

4.67

4.36

1871

Japan

3.42

4.67

4.32

Malaysia

1.59

3.68

2.84

Mexico

0.79

3.75

2.57

Netherlands

3.77

4.67

New Zealand

2.37

3.67

Norway

2.98

4.42

Pakistan

1.05

2.23

Peru

0.59

Philippines Poland

Hong Kong

$29,114.14

398

$18,120.88

1222

$24,059.98

471

1796

$32,276.26

418

521

$4,623.28

850

215

$5,658.16

4.44

2341

2945

$30,797.59

3.27

725

318

$18,638.01

3.81

1002

1009

$47,082.44

1.65

23

5

$604.64

3.42

2.26

230

27

$2,386.11

2.16

3.88

3.15

151

60

$1,147.38

1.21

4

3.23

546

105

$6,624.63

1.67

4.33

3.26

370

172

$13,051.07

1.33

4

3.25

233

19

$3,652.83

1.21

3.67

2.94

455

200

$5,135.23

Singapore

1.71

4.21

3.4

659

948

$24,311.43

Slovakia

1.21

4.33

2.87

132

20

$8,948.44

South Africa

2.9

3.88

3.44

549

296

$4,201.65

South Korea

2.28

4.33

3.86

343

322

$12,366.61

Spain

2.44

4.33

3.83

1862

1016

$18,421.68

Romania Russia

SC

MA D

PT E

CE AC

Portugal

RI

1065

NU

Indonesia

PT

Germany

35

ACCEPTED MANUSCRIPT 3.28

4.54

4.26

1892

2600

$34,157.43

Switzerland

3.46

4.21

4.08

1495

2015

$46,319.10

Taiwan

1.26

3.74

2.79

203

199

$20,715.48

Thailand

1.21

3.22

2.16

223

98

$2,560.82

1.2

4.01

2.88

298

48

$4,712.77

United Kingdom

3.76

4.54

4.42

7995

8970

$26,809.13

United States

4.35

4.88

4.81

13125

Venezuela

0.92

3.15

2.31

109

AC

CE

PT E

D

MA

NU 36

17757

$33,876.64

21

$5,170.76

RI

SC

Turkey

PT

Sweden

ACCEPTED MANUSCRIPT Panel B: List of major patent reform episodes Country

reform year

country

reform year

1996

Japan

1995

Australia

1991

Mexico

1991

Brazil

1997

New Zealand

1993

Bulgaria

1993

Peru

1996

Canada

1993

Philippines

Chile

1991

Poland

2000

Egypt

2002

Finland

1995

Hong Kong

1997

Hungary

1995

India

1999

RI

Romania

1993

Russian Fed

1992

Singapore

1994

Slovakia

1993

South Korea

1995

Spain

1992

Taiwan

1993

Thailand

1992

1992

Turkey

2000

Venezuela

AC

Israel

1993, 1997

CE

Indonesia Ireland

SC

Czech republic

1993 1992

MA

1994

PT E

Colombia

1997

Portugal

NU

1993, 2001, 2008

D

China

PT

Argentina

37

1995, 1999 1994

ACCEPTED MANUSCRIPT Panel C: Deal-level summary statistics. This panel presents the statistics describing the merger and acquisition (M&A) sample used in the regressions. Firm and deal-level variables are obtained from Compustat, CRSP, Compustat Global and SDC (where available). Variable

N

Mean

Median

67375

Transaction value ($ Mil)

27736

210.31

18.32

Acquirer book assets ($ Mil)

24038

97494

1652.83

Acquirer market value of equity ($ Mil)

21224

50443

Acquirer market-to-book assets

21223

Acquirer operating income/assets

24014

RI

PT

Total number of cross-border deals

SC

1892.92

1.25

0.08

0.09

NU

1.8

1462

32270

382.87

Target market value of equity ($ Mil)

1276

28613

336.59

1275

2.75

1.02

1456

0.02

0.06

22200

1.32%

0.43%

1667

21.30%

15.44%

Target market-to-book assets

D

Target operating income/assets

CE

PT E

Acquirer CAR (-2;+2) Target CAR (-2;+2)

Variable

MA

Target book assets ($ Mil)

Percentage of sample 41.16

Acquirer is a public firm

59.86

AC

Deal with disclosed transaction value

Target is a public firm

5.21

Majority cash financed

16.12

Majority stock financed

3.42

Diversifying deal Hostile deal

44.06 0.12

38

ACCEPTED MANUSCRIPT Panel D: Summary statistics for the variables used in the regressions. This table presents summary statistics (means, standard deviations, percentiles, and the number of observations) for the dependent variable (number of deals) and the key variables of interest used in the regressions models we estimate.

Mean

Number of deals

1283

52.476

Patent index

1278

3.498

25th

Std. Dev

75th

PT

N

95.199

7

52

1.047

2.958

4.333

4.3

0

2

NU

SC

3):

RI

Target country level analysis (Table 2 and

30436

1.78

Patent index

30436

3.96

0.739

3.675

4.542

R&D intensity

26992

0.024

0.04

0.002

0.016

Patent intensity

0.018

0.021

0.003

0.021

26992

0.094

0.049

0.058

0.124

21715

2.23

5.13

0

2

21678

1.3

0.33

1.23

1.51

21695

1.35

0.31

1.32

1.51

MA

Number of deals

D

Target country and industry level analysis (Table 4):

PT E

26992

Intangible intensity

Number of deals

CE

Bilateral (country-pair) level analysis (Table 5):

AC

Patent index of acquirer Patent index of target

Merger announcement effects analysis (Table 6): Combined CAR

425

3.43%

9.60%

-1.40%

7.80%

Patent index of target

425

4.509

0.35

4.333

4.667

39

ACCEPTED MANUSCRIPT Table 2. IPR and cross-border mergers: target country-level analysis. The table reports the coefficients from regressions of cross-border merger activity on the patent index and control variables for a sample of cross-border M&A deals targeting firms in 50 countries in the period 1985-2012. The dependent variable in all models is the natural log of (one plus) the total number of crossborder mergers in the target country in year t. All independent variables are lagged by one year, and are

PT

defined in Appendix Table A1. Robust standard errors, clustered at the country level, are in brackets. *,

Patent index

(1)

(2)

(3)

0.318***

0.228***

0.152**

1.098***

0.138**

[0.072]

[0.065]

[0.060]

[0.158]

[0.056]

0.472*** 0.568***

0.923***

0.635***

[0.161]

[0.163]

[0.167]

-0.204

-0.285

-0.043

[0.655]

[0.603]

[0.625]

-1.523**

-1.261*

-1.763**

[0.700]

[0.721]

[0.724]

-0.04

-0.037

-0.046

[0.059]

[0.060]

[0.059]

0.064

0.081

0.047

[0.182]

[0.181]

[0.173]

0.219***

0.191***

0.207***

[0.041]

[0.034]

[0.035]

-0.009

-0.003

0.022

[0.040]

[0.039]

[0.038]

-0.017

0.023

0.056

[0.081]

[0.079]

[0.075]

0.388*

0.537***

0.464**

NU

Log GDP per capita

[0.131]

MA

GDP growth

PT E

D

Exchange rate

AC

Trade/GDP

CE

Market return

(4)

RI

VARIABLES

SC

**, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

# of domestic M&As

Stock market development

Credit market development

Rule of law

40

(5)

ACCEPTED MANUSCRIPT [0.187]

[0.195]

-0.089**

-0.086**

0.114

[0.044]

[0.042]

[0.081]

-0.063

-0.078

-0.041

[0.074]

[0.073]

[0.059]

0.011

-0.011

0.037

Takeover reform dummy

Anti-trust reform dummy

[0.072]

[0.072]

[0.061]

-0.125***

RI

Patent index*GDP per capita

PT

Corruption

[0.215]

SC

[0.024] -0.052*** [0.018]

-3.632***

-2.663**

-5.195***

-3.169**

[0.228]

[1.095]

[1.275]

[1.312]

[1.324]

Country FE

Yes

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

Yes

1,273

967

967

967

0.738

0.746

0.604

0.625

0.617

MA

-0.034

D

Constant

NU

Patent index*Corruption

1,278

AC

CE

Adjusted R-squared

PT E

Observations

41

ACCEPTED MANUSCRIPT Table 3. IPR and cross-border mergers: robustness tests. The table reports the coefficients from regressions of cross-border merger activity on the patent index and control variables for a sample of cross-border M&A deals targeting firms in 50 countries in the period 1985-2012. The dependent variable is the log of (one plus) the total number of cross-border mergers in the target country in year t, except in model (2) where the

PT

dependent variable is the cross-border ratio (the total number of cross-border mergers in the target country in year t scaled by the total number of mergers in the target country in year t) and in

RI

model (3) where it is the total number. All independent variables are lagged by one year, and are

SC

defined in Appendix Table A1. Robust standard errors, clustered at the country level, are in

NU

brackets. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

(1)

(2)

(3)

(4)

MA

Tobit

Drop

model:

U.S.,

D

U.K.,

Separate Major

regressions

patent

of Patent

reforms

index

Poisson

Cross-

Variables

(5)

regression border

PT E

Germany

CE

Patent index

ratio

components

0.162**

0.097***

0.171***

[0.061]

[0.021]

[0.063]

Duration

0.231***

AC

Major patent reforms indicator

[0.062] 0.872*** [0.245]

Enforcement

0.094 [0.134]

Rights

0.133 [0.333]

42

ACCEPTED MANUSCRIPT Membership

0.455** [0.226]

Coverage

0.293* [0.168]

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Observations

901

967

967

Yes

Yes

Yes

Yes

967

967

AC

CE

PT E

D

MA

NU

SC

RI

Country FE

PT

Other control variables as in Model (3) of Table 2

43

ACCEPTED MANUSCRIPT Table 4. IPR and cross-border mergers: target country and industry-level analysis. The table reports the coefficients from regressions of cross-border merger activity at the industry level on the patent index and control variables. The dependent variable is the log of (one plus) the total number of cross-border mergers in the 2-digit SIC industry of the target country in year t. Robust standard errors, clustered at the country level, are in

VARIABLES

[1] 1.218***

[3]

RI

Patent index*(Intangible intensity)

[2]

PT

brackets. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

SC

[0.225] Patent index*(R&D intensity)

2.780***

NU

[0.526]

MA

Patent index*(Patenting intensity)

D

Patent index

PT E

Log GDP per capita

AC

Exchange rate

CE

GDP growth

Market return

(Export+import)/GDP

Domestic M&A

3.728*** [0.758]

-0.0816**

-0.0564**

-0.04

[0.0340]

[0.0272]

[0.0273]

0.162**

0.163**

0.167**

[0.0641]

[0.0608]

[0.0653]

-0.0852

-0.0717

-0.0845

[0.223]

[0.218]

[0.220]

-0.319*

-0.361*

-0.334*

[0.186]

[0.183]

[0.187]

0.00941

0.00928

0.00971

[0.0185]

[0.0185]

[0.0182]

0.053

0.0443

0.0512

[0.0692]

[0.0727]

[0.0716]

0.121***

0.116***

0.124***

[0.0395]

[0.0386]

[0.0396]

44

Rule of law

Corruption

Takeover reform

-0.00358

-0.00562

[0.0155]

[0.0158]

[0.0152]

0.0216

0.0261

0.0225

[0.0316]

[0.0325]

[0.0318]

0.100**

0.114**

0.108**

[0.0470]

[0.0467]

[0.0472]

-0.0372*

-0.0343*

[0.0185]

[0.0187]

-0.0297

-0.0295

-0.0287

[0.0254]

[0.0257]

0.0227

0.0235

[0.0287]

[0.0290]

[0.0289]

-0.803

-0.677

-0.804

[0.527]

[0.499]

[0.534]

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

23,723

23,723

23,723

0.472

0.481

0.474

0.0207

MA

Constant

D

Country FE

Year FE Observations

AC

CE

Adjusted R-squared

PT E

Industry FE

[0.0187]

[0.0253]

NU

Anti-trust reform

-0.0365*

RI

Private credit to GDP

-0.00611

SC

Market capitalization to GDP

PT

ACCEPTED MANUSCRIPT

45

ACCEPTED MANUSCRIPT Table 5. IPR and bilateral M&A activity The table reports the coefficients from regressions of bilateral cross-border merger activity on the patent index of acquirer and target countries and control variables. In Models (1) – (8) the dependent variable is the log of (one plus) the total number of cross-border deals between target firms

T P

from country tgt and acquirer firms from country acq in year t. In Model (8) the dependent variable is an indicator variable that equals one if

I R

country tgt and country acq had at least one cross-border M&A deal in year t. Xtgt- acq is notation for the differences between the target country (tgt)

C S U

and the acquirer country (acq), measured in year t-1. Economically developed and emerging countries are classified based on the IMF classification in 2000. Robust standard errors, clustered at the country-pair level, are in brackets. *, **, *** indicate significance at the 10%, 5%, and 1% levels,

N A

respectively. (1)

(2)

Developed

Emerging

Patent indexacq

D E

Developed

T P E

AcquirerVARIABLES

Patent indextgt

(3)

A

C C

Acquirer-

Developed

M

Emerging

Acquirer-

Emerging

(4)

(5)

(6)

Target-

Patent

acquirer

Law

pair FE

reforms

Emerging AcquirerDeveloped

target

target

target

target

0.061**

-0.073

0.024

0.035

0.065**

[0.026]

[0.053]

[0.039]

[0.097]

[0.027]

-0.002

-0.011

0.031

0.051

0.008

[0.068]

[0.032]

[0.028]

[0.042]

[0.068]

(7)

(8)

Poisson

Probit

pseudo-

regression of

maximum

likelihood of

likelihood

CB deal

ACCEPTED MANUSCRIPT Major patent reforms indicatortgt

Major patent reforms indicatoracq

0.095***

0.113**

0.159**

[0.029]

[0.057]

[0.077]

0.007

0.334

0.051

[0.227]

[0.224]

0.040***

0.086***

0.079***

[0.010]

[0.019]

[0.022]

T P

[0.058] Bilateral trade

Δ GDP per capitatgt-acq

Δ GDP growthtgt-acq

ΔStock market return tgt-acq

Δ Corruption tgt-acq

0.017

0.021

[0.010]

[0.020]

[0.016]

[0.016]

[0.009]

0.304***

0.022

-0.1

-0.292***

0.327***

0.283***

0.466***

0.839***

[0.080]

[0.084]

[0.095]

[0.090]

[0.085]

[0.080]

[0.156]

[0.198]

-0.112

-0.594***

-0.153

-0.276

-0.103

-0.13

-0.186

-0.14

[0.178]

[0.208]

[0.256]

[0.252]

[0.181]

[0.178]

[0.451]

[0.589]

-0.021

-0.027

-0.003

-0.003

-0.016

-0.02

-0.071**

-0.101**

[0.013]

[0.020]

[0.017]

[0.018]

[0.013]

[0.013]

[0.036]

[0.047]

-0.192

-0.034

0.027

0.172

-0.193

-0.16

-0.225

-0.816**

[0.130]

[0.165]

[0.137]

[0.146]

[0.138]

[0.131]

[0.257]

[0.318]

-0.018

0.167***

-0.027

0.082*

-0.008

-0.01

0.149

0.128

[0.048]

[0.051]

[0.049]

[0.043]

[0.050]

[0.047]

[0.098]

[0.122]

0.011

-0.028*

-0.044

-0.089**

0.017

0.016

-0.132***

-0.03

T P E

Exchange rate tgt per acq

Δ Rule of law tgt-acq

0.066***

D E

A

C C

0.006

I R

0.040***

C S U

N A

M

47

ACCEPTED MANUSCRIPT [0.024]

[0.043]

[0.058]

0.273**

0.227**

-0.107

0.329*

[0.143]

[0.121]

[0.102]

[0.106]

[0.186]

-0.297***

-0.018

-0.145***

-0.306***

-0.574***

-0.660***

[0.026]

[0.037]

[0.050]

[0.038]

[0.026]

[0.044]

[0.054]

0.299***

0.252***

0.234

0.363***

0.299***

0.563***

0.509***

[0.082]

[0.063]

[0.153]

[0.083]

[0.082]

[0.094]

[0.136]

0.044

0.052

0.097*

0.127**

0.044

0.260***

0.023

[0.044]

[0.035]

[0.058]

[0.055]

[0.044]

[0.082]

[0.089]

0.019

-0.033*

-0.001

-0.056**

0.02

0.007

-0.041

-0.002

[0.019]

[0.019]

[0.029]

[0.023]

[0.019]

[0.018]

[0.043]

[0.055]

-0.035

0.045*

-0.158*

-0.008

-0.047

-0.04

0.008

-0.072

[0.032]

[0.025]

[0.080]

[0.041]

[0.033]

[0.032]

[0.072]

[0.089]

0.154

-0.827***

-0.924

-0.063

0.153

0.599*

-0.166

[0.215]

[0.224]

[0.578]

[0.180]

[0.215]

[0.310]

[0.380]

Constant

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Bordering countries

Geographic Distance

Same language indicator

Same religion indicator

ΔStock market developmenttgt-acq

[0.015]

[0.035]

[0.041]

0.227**

0.399***

0.285**

[0.102]

[0.083]

-0.306***

D E

T P E

ΔCredit market developmenttgt-acq

ΔTrust tgt-acq

[0.025]

A

C C

[0.026]

T P

I R

C S U

N A

M

48

ACCEPTED MANUSCRIPT Target and acquirer FE

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

No

No

No

No

Yes

No

No

No

Observations

5,836

8,954

1,849

3,123

5,836

5,836

5,836

5,836

Adjusted R-squared

0.489

0.712

0.324

0.408

0.603

0.49

Country-pair matched FE

I R

T P

C S U

N A

D E

M

T P E

C C

A

49

0.54

ACCEPTED MANUSCRIPT Table 6. IPR and combined abnormal announcement returns. The table reports the coefficients from regressions of combined acquirer and target 5-day (-2, +2) cumulative abnormal announcement returns on the natural log of the target country’s patent index and control variables. Robust clustered standard errors are in

(1)

Emerging target

Developed target

1.632**

SC

-0.187

[0.780]

[0.235]

-0.527*

-0.051

[0.260]

[0.054]

-0.726

0.015

[0.434]

[0.033]

-2.812

0.026

[4.594]

[0.320]

0.035

0.001

[0.070]

[0.007]

-0.25

-0.042*

[0.488]

[0.025]

-1.022

0.042*

[0.606]

[0.023]

-0.068

0.017*

[0.174]

[0.010]

0.308**

0.021

[0.142]

[0.016]

0.450**

-0.02

Patent index tgt

MA

NU

Bilateral trade (tgt-acq)

GDP per capita (tgt-acq)

PT E

D

GDP growth (tgt-acq)

CE

Exchange rate return (tgt-acq)

AC

Market return (tgt-acq)

Bordering countries

log(Geographic Distance)

Same language

Same religion

(2)

Developed Acquirer-

RI

Developed Acquirer-

PT

brackets. *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

ACCEPTED MANUSCRIPT

Market capitalization to GDP(tgt-acq)

Private credit to GDP (tgt-acq)

[0.013]

-1.738**

-0.039

[0.637]

[0.114]

0.299

0.006

[0.235]

[0.015]

-0.012

0

[0.145] -0.332**

RI

Rule of law (tgt-acq)

PT

Trust(tgt-acq)

[0.204]

0.003

SC

[0.021]

[0.012]

-0.076

0.033**

[0.103]

[0.015]

0.03

0.005

[0.160]

[0.009]

0.126

0.012

[0.129]

[0.016]

0.003

0.006

[0.150]

[0.008]

Target market-to-book

-0.002

0.002

AC

[0.123]

[0.020]

[0.016]

[0.004]

-0.007

-0.016***

[0.043]

[0.004]

-0.055

0

[0.036]

[0.003]

-7.133***

0.845

[2.205]

[1.096]

MA

Majority cash deal (1/0)

D

Transaction value

CE

PT E

Diversifying deal (1/0)

Target market value

-0.059

0.011

[0.100]

NU

Corruption (tgt-acq)

Acquirer market value

Acquirer market-to-book

Constant

51

ACCEPTED MANUSCRIPT Country FE

Yes

Yes

Year FE

Yes

Yes

51

278

0.409

0.086

Observations

AC

CE

PT E

D

MA

NU

SC

RI

PT

Adjusted R-squared

52

ACCEPTED MANUSCRIPT APPENDIX Table A1. Variable descriptions and data sources

Variable

Description (Data source)

Patent Index

Index obtained by summing the following five components: extent of

PT

coverage, membership in international treaties, duration of protection, absence of restrictions on rights, and statutory enforcement provisions. The

SC

RI

index ranges from zero (weakest) to five (strongest IPR).

Patent reforms

the sample of large patent reforms includes countries with large increases in

NU

the patent index, such as those with least one full point increase. We verified that these episodes indeed correspond to discrete patent law

World

MA

reforms by reading the history of each country’s IP laws and treaties on the Intellectual

Property

Organization’s

website

D

(http://www.wipo.int/directory/en/). We also verified that all those patent

PT E

reforms appeared in the list of reforming countries used by Qian (2004), Branstetter et al. (2006) and Maskus (2000).

AC

CE

Cross-border M&As

Total number of cross-border majority ownership deals involving a foreign acquiring firm that target a country’s firms in a given year (SDC).

Cross-border ratio of target

The total number of cross-border deals divided by all (domestic and

country

international) deals in a given target country and year.

Domestic M&As

Total number of majority ownership deals involving an acquiring and target firms in the same country in a given year (SDC).

53

ACCEPTED MANUSCRIPT Bilateral cross-border M&As

Total number of majority cross-border acquisitions in which the target is from country tgt and the acquirer is from country acq (tgt ≠ acq) (SDC).

GDP per capita

Log of real gross domestic product per capita in U.S. dollars (World Bank

PT

World Development Indicators (WDI)).

Real growth rate of gross domestic product in U.S. dollars (WDI).

Economically developed

Classified according to the 2000 IMF list of economically developed

countries

(advanced) countries (Australia, Austria, Belgium, Canada, Denmark,

SC

RI

GDP growth

NU

Finland, France, Germany, Greece, Ireland-rep., Italy, Israel, Japan, Netherlands, New Zealand. Norway, Portugal, Singapore, South Korea,

MA

Spain, Sweden, Switzerland, United Kingdom, United States) (Exports + imports of goods and services) scaled by GDP (WDI).

Stock market return

Local stock market index return in (IMF International Financial Statistics

PT E

D

Trade / GDP

AC

Real Exchange rate

CE

(IMF and IFS through WDI).

Exchange rate in US.$ divided by PPP (World Penn Tables).

Stock market development

Total stock market capitalization divided by GDP (WDI).

Credit market development

Total amount of private loans divided by GDP (WDI).

Developed countries

Countries classified as high-income in 1995 by the World Bank. Includes all OECD countries (World Bank).

54

ACCEPTED MANUSCRIPT Same language

Binary variable indicating that target and acquirer countries share the same official language (CIA World Factbook).

Same religion

Binary variable indicating that target and acquirer countries share the same

Bordering countries

PT

predominant religion (CIA World Factbook).

Binary variable indicating that target and acquirer countries share a

SC

RI

common geographic border (CEPII).

Geographic Distance

Log of geographic distance between capitals, calculated using the great

MA

city (CEPII).

NU

circle formula and latitudes and longitudes of the capital or most populous

Trust

Based on the country-level average responses to the question: Generally

D

speaking, would you say that most people can be trusted or that you cannot

PT E

be too careful in dealing with people? The response is coded one if the participant responds that most people can be trusted and zero otherwise

AC

CE

(i.e., higher score indicates that the country has more trust) (World Value

Bilateral trade

Rule of law

Survey and Europe Value Survey).

Value of imports by target country i from acquirer country k as a percentage of total imports by target country i (OECD).

Index that captures perceptions of the extent to which agents have confidence in, and abide by, the rules of society. In particular, the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence (Kaufmann, Kraay, and Mastruzzi

55

ACCEPTED MANUSCRIPT (2010) / World Bank Governance Database).

Corruption

A country governance indicator capturing perceptions of the extent to which public power is exercised for private gain (Kaufmann, Kraay, and

PT

Mastruzzi (2010) / World Bank Governance Database).

Takeover reform

Binary variable indicating that a country has passed takeover law between

SC

RI

1985 and 2009 (Lel and Miller, 2014).

Anti-trust reform

Binary variable indicating that a country has passed anti-trust law between

Combined five-day CAR

The

capitalization

[CCAR (-2;+2)]

announcement) weighted average of the five-day CARs of acquirer and

MA

market

NU

1985 and 2009 (Bris, et al., 2010).

(measured

three

months

before

deal

Acquirer or Target book

Total Assets (Compustat for U.S. and Canadian firms or Compustat Global for non-U.S. firms).

CE

assets

PT E

D

target (CRSP; MSCI).

AC

Acquirer or Target operating

Operating income before interest, taxes and depreciation divided by total

income/assets

assets (Compustat or Compustat Global).

Acquirer market-to-book

Market value of equity + book value of debt divided by the book value of

assets

assets (Compustat or Compustat Global).

Majority cash deals

Binary variable indicating that the deal is financed mainly with cash as identified by SDC (SDC Items “Percent Cash >50” or “Consideration

56

ACCEPTED MANUSCRIPT structure =CASHO”).

Majority stock deals

Binary variable indicating that the deal is financed mainly with shares as identified by SDC (SDC Items “Percent Stock >50” or “Consideration

PT

structure = SHARES”).

Industry-level median of intangible intensity, R&D intensity, and patenting

intellectual assets

intensity across all U.S. firms on Compustat in the period 1980-2010 in

intensity

each two-digit SIC industry. Intangible intensity is measured by the ratio of

SC

RI

Industry “natural”

intangible assets to total assets. R&D intensity is measured by the ratio of

NU

R&D expenditures to total assets. Patenting intensity is measured by the ratio of total patents granted to a firm (from the NBER patent database) to

MA

its asserts. Similar to Rajan and Zingales (1998), we first sum the firm’s intangibles, R&D and numbers of patents over sample period and divide by

D

the sum of firm’s total assets or sales. We then estimate the industry mean

AC

CE

PT E

over the sample period.

57

ACCEPTED MANUSCRIPT Table A2. Industry measures of intangible intensity, R&D intensity, and patenting intensity

The table reports the annual intangible, R&D, and patenting intensities for selected U.S. two-digit SIC industries with more than 10 firm-year observations from COMPUSTAT averaged over the period 1980 to 2010. Intangible intensity is measured by the ratio of intangible assets to total assets. R&D intensity is measured by the ratio of R&D expenditures to total assets. Patenting intensity is measured by the ratio of

Intangible

R&D

Patent

Intensity

Intensity

Intensity

0.0047

0.0005

0.1418

0.0062

0.0027

0.0890

0.0008

0.0044

0.0670

0.0061

0.0064

0.2199

0.0058

0.0081

0.0978

0.1690

0.0437

Sector Name

RI

SIC Code

PT

total patents granted to a firm (from the NBER patent database) to its assets.

METAL MINING

20

FOOD & KINDRED PRODUCTS

23

APPAREL

26

PAPER & ALLIED PRODUCTS MFRS

27

PRINTING PUBLISHING

28

CHEMICALS & ALLIED PRODUCTS

33

PRIMARY METAL INDUSTRIES

0.0577

0.0087

0.0044

34

FABRICATED METAL PRODUCTS

0.0804

0.0111

0.0171

35

INDUSTRIAL & COMMERCIAL MACHINERY

0.0752

0.0866

0.0339

36

ELECTRONIC & OTHER ELECTRICAL EQUIP

0.0807

0.0887

0.0429

37

TRANSPORTATION EQUIPMENT

0.0958

0.0297

0.0154

38

MEASURING & ANALYZING INSTRUMENTS

0.1011

0.1045

0.0612

50

WHOLESALE TRADE-DURABLE GOODS

0.0927

0.0049

0.0181

WHOLESALE TRADE-NONDURABLE

0.0999

0.0061

0.0131

NU

MA

D

PT E

CE

AC

51

0.0126

SC

10

GOODS 56

APPAREL & ACCESSORY STORES

0.0468

0.0000

0.0006

73

BUSINESS SERVICES

0.1396

0.0917

0.0186

80

HEALTH SERVICES

0.1970

0.0148

0.0726

82

EDUCATIONAL SERVICES

0.1895

0.0061

0.0015

87

ENGINEERING AND BUSINESS SERVICES

0.1359

0.0528

0.0693

0.0897

0.0162

0.0173

Mean

58

ACCEPTED MANUSCRIPT 0.0481

0.0018

0.0020

75th percentile

0.1050

0.0105

0.0189

AC

CE

PT E

D

MA

NU

SC

RI

PT

25th percentile

59

ACCEPTED MANUSCRIPT

Highlights

We study how IPR affect cross-border M&A>find significant increase in inbound M&A after IPR reforms> mainly in IP-intensive industries>synergy gains are higher after

AC

CE

PT E

D

MA

NU

SC

RI

PT

IPR reforms

60