Liberty in Law? Intellectual Property Rights and Global

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Liberty in Law? Intellectual Property Rights and Global Alliance Networks

Sarath Balachandran* [email protected]

Exequiel Hernandez* [email protected]

The Wharton School University of Pennsylvania

*Authors listed alphabetically and contributed equally

Running head: Intellectual Property Rights and Global Alliance Networks

Keywords: alliances, networks, institutions, intellectual property rights, global

Draft: 19 May 2016 Comments are welcome

Acknowledgments: We received constructive feedback from Matthew Bidwell, Adam Cobb, Julien Clement, Todd Gormley, Mauro Guillen, Witold Henisz, Lori Rosenkopf, Keyvan Vakili, and Todd Zenger; from participants in seminars at the University of Michigan ICOS and at Wharton; and from participants in the Global Strategy and Emerging Markets Conference and in the Wharton Technology and Innovation Conference. The Mack Institute for Innovation Management provided generous financial support.

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Liberty in Law? Intellectual Property Rights and Global Alliance Networks

Abstract: We explore how intellectual property rights (IPR), a type of formal institution, affect firms’ access to global alliance networks and their positioning within those networks. We employed a difference-in-difference design to assess the impact of IPR reforms across thirteen countries. We found that institutional improvements enabled firms from reforming countries to establish more international alliances, particularly if they operated in IP intensive industries, and to increase the geographic diversity of their partners. Access to global alliances became more ‘democratic’ by allowing firms that were of low status pre-reform to attract significantly more foreign partners post-reform. Stronger IPR laws also led the structure of firms’ networks to become significantly more closed (denser). The findings raise several implications for research on institutions, networks, and alliances.

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Global alliance networks have become sources of value creation for firms as knowledge

and resources are increasingly distributed across countries (Badaracco, 1991; Phelps, Heidl, and

Wadhwa, 2012; Rosenkopf and Almeida, 2003). How firms are positioned in these networks

affects important outcomes such as capability development, innovation, and financial

performance (Goerzen and Beamish, 2005; Gulati, 2007; Vasudeva, Zaheer, and Hernandez,

2013). Understanding what influences access to global alliance networks is thus increasingly

relevant. One important factor in this regard is the quality of the institutional environment of

firms’ home countries (North, 1990; Scott, 2001; Zucker, 1986). In particular, scholars and

managers have pointed to weak intellectual property rights (IPR) protection as one of the most

serious barriers to cross-border economic exchange (Economist Intelligence Unit, 2004; Oxley,

1999; Schotter and Teagarden, 2014). Firms in places with poor IPR protection struggle to attract

foreign partners, who are wary of losing their valuable knowledge-based resources (Teece,

1986). And with limited access to foreign partners, it becomes challenging for the majority of

firms from weak IPR regimes to obtain the benefits of global networks.

Despite the significance of this problem, research on the relationship between formal

institutions such as IPR and alliance network outcomes is lacking. Prior work has made an

important contribution by linking IPR to alliance governance choices (e.g. Oxley, 1999), but

other valuable outcomes remain unexplored. We study how institutional reforms that

significantly improve IPR lead to a series of three changes that impact firms’ access to global

alliance networks. First, we examine whether strengthening IPR laws increases the number of

foreign alliance partners for firms from the reforming countries. This would be consistent with

IPR reducing the risk of expropriation in the eyes of potential collaborators. This effect is of

great practical importance and we believe this is the first study to document its existence.

Second, we assess how the increase in foreign partnerships made possible by IPR reform is

distributed among firms. We explore whether access to global alliances becomes more

‘democratic’ or whether a few select firms accrue most of the benefits brought about by IPR

reforms. Pre-reform, the few foreign alliances that occur tend to accrue to a select group of

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prominent, well connected firms—as is common in emerging markets (Palepu, Khanna, and

Vargas, 2005; Siegel, 2007). This is manifested in a ‘rich get richer’ network characterized by a

few high status firms that accumulate most foreign partnership opportunities and many

peripheral firms with few alliance opportunities (Ahuja, 2000; Podolny, 1994). We argue that

institutional improvements in IPR laws increase the willingness of foreign firms to partner with a

broader set of firms from the reforming country. This should be manifested in a democratizing of

access to foreign alliances, where new ties accrue less than before to high status firms and are

thus more evenly distributed.

Third, we study how IPR reforms affect the diversity of firm’s global networks. Research

suggests that diverse networks expose firms to novel knowledge and resources (Burt, 2005;

Rosenkopf and Almeida, 2003), and that firms can access diversity through the composition or

the structure of their networks (Aral and Van Alstyne, 2011). To get at the former, we posit that

when IPR laws improve, the reduced appropriability hazards allow firms to access partners from

a greater variety of countries than before the reforms. This cross-national heterogeneity can help

firms become exposed to different national innovation systems (Nelson, 1993), which increases

the variety of resources and ideas to which they have access and is germane to the types of

knowledge-related activities impacted by IPR. In terms of structure, we expect that firms’ global

networks become more open (less dense) in response to IPR reforms. Open networks have been

shown to expose firms to distinct, non-overlapping resources (Aral and Van Alstyne, 2011; Burt,

2005). But the constraints on tie formation imposed under conditions of weak IPR lead networks

to be tightly clustered among few partners, who must rely on informal means of trust when

allying with each other (Zucker, 1986). Institutional improvements enable more impersonal

exchange (North, 1990), broadening the set of partners with whom firms are willing to ally. This,

coupled with increased access to collaborators from multiple countries, likely leads to networks

that are significantly more open than before IPR reforms.

We empirically explored these issues by examining the networks of firms from thirteen

countries that passed substantial patent protection reforms (Branstetter, Fisman, and Foley,

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2006). The sample covers 11,072 firms during 1988-2004 and their alliance networks composed

of partners from all over the world. An attractive feature of the setting is that we can exploit the

plausibly exogenous timing of the passage of IPR laws across countries using a multiple event

difference-in-difference design. Further, we can clearly show that the expected effects coincided

with the timing of the reforms and exhibited no pre-reform trends. We found that IPR

improvements significantly increased the number of international alliances formed by firms from

the reforming countries, particularly in IP-intensive industries. These new ties accrued

significantly less to firms of high status (network centrality) after the IPR reforms, in line with a

‘democratization’ of alliancing opportunities. We further found that the cross-national diversity

of firms’ alliance partners, a compositional attribute, increased significantly after IPR

improvements. Finally, and contrary to expectations, firms’ alliance networks became

structurally more closed (rather than open) post-reform.

The results of our study have several relevant implications. We show that IPR laws affect

access to alliance partners and, in turn, impact the composition and structure of networks.

Inasmuch as access to global networks is value-enhancing, understanding how institutions

impact the origins and evolution of such networks matters (Powell et al., 2005). The results

suggest that institutional improvements not only increase the number of foreign alliances on

average, but that opportunities to access to those ties becomes more ‘democratically’ distributed

among firms from the reforming country. Further, institutional changes appear to enable access

to a more diverse cross-national base of external resources for firms and to motivate firms to

develop denser network structures to exploit the opportunities made possible by the reforms.

Because institutions are a plausibly exogenous source of network change, this study addresses

calls to overcome endogeneity concerns in explaining network outcomes (Ahuja, Soda, and

Zaheer, 2012). Finally, the phenomenon of the study is of great managerial and policy

importance because it provides relevant facts about the efficacy of efforts to strengthen IPR laws

across countries.

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BACKGROUND

Intellectual Property Rights and Global Alliances

Cooperative exchange between firms can be beneficial for value creation but is unlikely

to happen in the first place if participants have concerns about value appropriation (Oxley, 1999;

Poppo and Zenger, 2002). While interfirm cooperation can take many forms, here we are

interested in that between firms engaged in cross-border alliances. These international

collaborations have increased dramatically over the last few decades as globalization has

enhanced opportunities to access knowledge and other types of resources across borders

(Rosenkopf and Almeida, 2003; Zaheer and Hernandez, 2011). Much writing has been dedicated

to the value creation aspects of such alliances (Lavie, 2006 provides a good review). But as firms

from various national jurisdictions come in contact with one another, issues of value

appropriation due to uneven quality in intellectual property rights (IPR) protection have become

one of the most troubling concerns reported by managers (Economist Intelligence Unit, 2004;

Teece, 1986). This has become a particularly salient issue in emerging markets, which are often

characterized by weaker IPR laws compared to more developed markets.

The issue of operating in markets with weak IPR has been addressed in prior literature.

For example, Zhao (2006) asked how multinational firms overcome the appropriability concerns

of conducting R&D in countries with incomplete IPR. She proposed an institutional arbitrage

framework by which the complexity and complementarity of internal linkages between

technologies within the firm serve as a barrier to undesired knowledge spillovers. Alcacer and

Zhao (2012) further show how such strong internal linkages allow firms to protect their IP from

imitation in industry clusters where knowledge spillovers are likely. Other research focuses on

different actions or strategies firms can follow to safeguard their knowledge in weak IPR settings

(Agarwal, Ganco, and Ziedonis, 2009; Schotter and Teagarden, 2014). Rather than focus on what

to do about weak IPR, some work has focused on how improvements in IPR laws affect the

knowledge-related activities of firms. For example, Branstetter, Fisman, and Foley (2006)

demonstrated that foreign firms were more willing to transfer technological activities to their

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subsidiaries in the reforming countries. And others have focused on whether reforms increased

the innovative output of firms from the reforming countries (Lerner, 2009; Qian, 2007)

Despite interest in a variety of outcomes, research in this area has the commonality of

focusing on how IPR affects activities that firms conduct internally. Yet, as just mentioned, a

significant portion of firms’ knowledge-related activities are done externally in collaboration

with other firms. Research linking IPR to global alliance outcomes has been fairly sparse,

focused on the issue of governance choice. Oxley’s (1999) influential study provided evidence

showing that firms from the U.S. adjusted the alliance governance mode to the intellectual

property institutions of their partners’ countries. Specifically, as the quality of IPR decreased in a

partners’ country, firms were significantly more likely to organize the alliance through an equity

JV than a non-equity governance mode. Subsequent studies, also in the transaction cost tradition,

have focused on a wider set of foreign entry modes (including alliances) and also found that IPR

impact the chosen organizational form (e.g. Hagedoorn et al., 2005).

While the governance mode is a crucial aspect of the global alliance activities of firms,

there is a dearth of research examining whether and how access to the global alliance network is

affected by IPR institutions. If concerns of value appropriation hamper firms’ ability to

participate in global partnerships to begin with, it will be impossible for firms to accrue the

benefits of cross-border alliances discussed by prior research. Indeed, the choice of alliance

governance mode is moot if firms avoid alliances in the first place. Hence we focus our inquiry

on the relationship between IPR protection and access to the network—whether firms can access

foreign partners in the first place, how this access is distributed across the firms from the

reforming country, as well as the types of partners and positions firms can access before vs. after

the reform. These outcomes are important from a networks perspective because prior work has

shown that the benefits firms accrue from participating in global alliances depend not only on

establishing ties but also on the position firms occupy in the network (the structure) and on the

types of partners firms attract (the composition) (see Gulati, 1998 for a good review).

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Institutional Reform and Global Alliance Networks

Intellectual property rights are a type of formal institution, usually defined as societal

arrangements that define the ‘rules of the game’ by which actors interact or exchange with one

another (North, 1990; Scott, 2001). Institutions function by establishing expectations, norms, and

constraints that reduce uncertainty in activities involving collaboration. Fundamentally,

institutions are about facilitating cooperation between economic actors, so that those actors can

engage in complex activities that they would otherwise lack incentives to undertake (Zucker,

1986). Hence it is not hard to see the link between formal institutions and interfirm networks—

both are concerned with collaborative activities among firms.

Access to Global Alliances. We start with the baseline expectation that IPR reforms

increase access to foreign alliance partners. While scholars and policy makers expect that the

passage or strengthening of IPR laws “works” by reducing appropriability concerns, the effect of

strengthening these laws on firms’ ability to attract foreign alliance partners has not been

empirically verified. Firms from countries with weak IPR regimes are likely to be disadvantaged

in attracting foreign partners, who will be wary of losing their IP without legal recourse to

adjudicate disputes. At the same time, participation in the global alliance network can be

particularly beneficial to firms from weak IPR regimes regardless of their technological

capabilities. If these firms lag technologically, foreign partnerships promise to provide needed

know-how to upgrade capabilities. And if firms from weak IPR countries have strong

technological capabilities, foreign partnerships are valuable in providing access to new markets

or complementary technologies. Hence firms from weak IPR countries have both strong

incentives and major constraints to participate in international alliance networks. The

strengthening of formal protections should reduce the perceived threat of expropriation in the

eyes of foreign firms considering alliances with partners from the reforming countries. Further,

any enhancement in the rate of alliance formation should be particularly pronounced for firms

that operate in industries that are IP intensive because they are involved in the types of

knowledge-related activities most directly affected by the reforms.

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Hypothesis 1a: Firms establish more international alliances after their countries strengthen IPR laws. Hypothesis 1b: Firms operating in IP-intensive industries experience a greater increase in international alliances than firms in non-IP-intensive industries after the strengthening of IPR laws.

Distribution of Access to Global Alliances. Knowing that institutional improvements

increase the volume of global alliance activity does not tell us how that increase is distributed

among the firms from the reforming country, which is important to understand who benefits

from the change in regime. To understand what happens to the distribution of global alliances

post-reform, we first consider how such ties are distributed among firms before the reforms. The

baseline hypothesis (H1a) implies that, in the pre-reform regime, firms tend to be involved in

few foreign alliances due to appropriability concerns. The few firms able to attract foreign

partners tend to be those that are well connected or ‘visible’ to foreign firms—that is, those of

high status in the alliance network. Status is defined as a hierarchical position in a pecking order,

and networks are a good forum in which to observe it because high status actors sort into central

positions and low status actors into peripheral positions (Sauder, Lynn, and Podolny, 2012).

Status has economic value because it is as a signal of firm quality and reliability as an

exchange partner. Organizations experience a loss of status when they engage in behavior

perceived to be inappropriate (Phillips, Turco, and Zuckerman, 2013; Rhee and Haunschild,

2006), so high status actors do not want to compromise their standing through the reputational

fallout from behaving badly (Jensen and Roy, 2008). Hence partnering with a high status actor

can lower the risk of expropriation since the cost of behaving opportunistically goes up as status

in the network increases. Status signals are of particular value when uncertainty makes it difficult

to ascertain the true reliability of a potential partner, so that status becomes a particularly

valuable means of attracting partners when uncertainty is high (Podolny, 1994, 2001).1

1 We are not directly concerned with the extent to which status or prominence in a network is a signal of real underlying quality. As long as network status makes firms more visible to potential foreign partners, that is sufficient for our arguments.

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We add to these arguments the consideration that changes in the formal institutional

environment modify how status affects the distribution of foreign alliancing opportunities. In the

absence of strong formal institutions, status becomes a crucial enabler of tie formation. In our

context, foreign firms should be more willing to form alliances with the most prominent firms in

countries with weak IPR. Consistent with this, research shows that the most well connected

firms, such as those with strong political ties or those in business groups, disproportionately

attract foreign partners in emerging economies (Palepu et al., 2005; Siegel, 2007). But when IPR

laws are significantly strengthened, a more formalized means of reducing appropriability

concerns exists for foreign firms establishing ties with firms from the reforming country. Formal

laws decrease uncertainty about how to deal with the potential loss of knowledge resources. This

should lead foreign firms to be more willing to ally with firms from the reforming country that

are less prominent in the network. Consistent with this argument, historical accounts have shown

how economies evolve from status-based arrangements to more arms’-length exchange as

institutional quality improves (North, 1990; Zucker, 1986).

This has implications for how access to foreign alliance opportunities is distributed

among firms. Because status can play an important assurance role pre-reform, firms of high

prominence in the network disproportionately accrue the few available chances to ally with

foreign firms. Post-reform, however, there should be a ‘democratization’ of participation in the

global alliance network because formal institutions play a role that is functionally equivalent to

status but available to all players in the local economy. Thus: Hypothesis 2: The effect of status in the global alliance network on the number of international alliances formed by a firm declines after the strengthening of IPR in the country of the focal firm.

Access to Geographically Diverse Partners. We consider how the composition of

firms’ networks changes following IPR reforms because the types of partners to which firms

have access affects the benefits they obtain from global networks (e.g. Vasudeva et al., 2013).

The strengthening of formal institutions should allow firms to not only attract more foreign

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partners, but those from a greater variety of countries than before the reform. There is a

difference between a firm from, say, Argentina, that engaged in alliances with Spanish firms

before IPR was strengthened which then increases the frequency and intensity of its alliances

with Spanish partners post reform and the same firm having access to new partners from Japan,

the U.S., and Brazil after the reform. In both cases we would observe an increase in the rate of

international tie formation (per H1a), but only in the latter would we observe that the global

diversity of ties also increases. If firms only formed more ties with firms from countries to which

they already had ties, then institutional changes would simply reinforce previous linkages among

countries that already exchanged with one another because of specific bilateral factors (such as

prior colonial ties or historical immigration patterns).

In contrast, if firms diversify the countries from which they find alliance partners, the

legal change is creating opportunities for firms to gain new resources through access to novel

parts of the global network. Exposure to partners from distinct national jurisdictions is a source

of resource and knowledge diversity. The persistent differences across countries along many

dimensions—institutional, cultural, or economic—create pockets of idiosyncratic knowledge,

technologies, practices, and other resources. Of relevance to the domain of IP-related activities is

research demonstrating that countries have unique national innovation systems (Nelson, 1993),

and that firms with networks that span various systems derive innovation and other types of

performance benefits (Lavie and Miller, 2008; Vasudeva et al., 2013). If the passage of laws that

strengthen IPR works as intended, the laws should lower expropriation concerns for foreign

firms from all other countries rather than just from the ones with whom firms in the reforming

country had pre-existing ties. Hence, Hypothesis 3: Firms establish alliances to partners from a more diverse set of countries after their countries strengthen IPR laws.

Access to Open Network Positions. Access to diverse resources can also come from the

structure of firms’ networks, which in turn may be affected by IPR reforms. We consider

whether the network evolves towards a more open structure. Firms with open networks tend to

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have alliance partners that are not connected to each other, resulting in webs characterized by

low density. Open networks are usually associated with access to novel resources and ideas

because the connections to non-overlapping partners expose firms to non-redundant resources or

knowledge (Burt, 2005; McEvily and Zaheer, 1999).

In the pre-reform regime, there is comparatively little alliance activity occurring on

average (per H1a). The appropriability risks involved likely create incentives for firms to ally

with few firms that they know or trust (Das and Teng, 1998; Gulati, 1995). Institutional scholars

have suggested that economic exchange based on relational forms of trust is a hallmark of

environments with weak institutions (e.g. Zucker, 1986). And opportunities to reach out broadly

across markets to explore and gain novel sources of resources or knowledge in activities related

to IP are also comparatively few. This would result in fairly closed (and small) networks for

firms from weak IPR countries. When the IPR regime improves significantly, many of these

factors are likely to be ameliorated. Stronger legal protections facilitate more impersonal forms

of exchange (Zucker, 1986). Firms are consequently less restricted in the set of potential alliance

partners they are able to ally with. This not only increases the rate of tie formation, but also

increases access to new sectors of the global alliance network by enabling partnerships with

firms from a greater variety of countries (per H3) or with new kinds of partners in general. This

should be associated with developing non-redundant ties, driven by the incentive to pursue novel

ideas, technologies, and resources (Vasudeva et al. 2013). These mechanisms would lead to

networks becoming significantly more open post IPR reform. Hypothesis 4: The networks of firms become structurally more open (less dense) after their countries strengthen IPR laws.

DATA AND METHODS

Our sampling frame consists of firms from thirteen countries that passed substantial laws

to strengthen patent protection during the 1990’s. We drew information about these IPR changes

and their timing from Branstetter, Fisman, and Foley (2006). They define an improvement in IPR

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as a governmental intervention that leads to expansions of eligible inventions, patent scope, and

patent length, as well as improvements in patent enforcement and administration. Other countries

engaged in IPR reforms during this period too, but the changes in the law and its enforcement

were not strong or credible enough to be considered meaningful. We refer the reader to the

appendix of Branstetter et al. (2006) for a detailed explanation of the reforms in each country.

We take advantage of differences in the timing of IPR changes across markets to assess the pre-

vs. post-change in outcomes of interest (i.e. a difference-in-difference design).

We drew information on interfirm alliances from SDC Platinum, the most comprehensive

source of global alliances across multiple industries (Schilling, 2009).2 Alliance data of a

reasonable quality in SDC Platinum is only available starting in 1988. We were thus forced to

drop firms from some of the original sixteen countries identified by Branstetter et al. (2006)

because the reforms in these countries occurred prior to 1988. These include Japan (1983), South

Korea (1987), Spain (1986) and Taiwan (1986). In addition to the remaining twelve countries in

their original study, our sample also included India (1999), which was identified by Branstetter et

al. (2006) as passing a significant IPR reform but not included in their analysis as 1999 was the

last year in their sample. The thirteen countries in our sample are listed in Table 1 along with the

years in which the legal changes took place. While the IPR systems of these countries remain

imperfect, our study relies on the significance of the change they experienced rather than on

them reaching an ideal level. There is disagreement as to the effectiveness of IPR changes in

China and Argentina, and concerns about idiosyncrasies in any one country could arise, but our

results remain robust if we exclude any one country from the analysis. Table 2 contains

information on the various processes leading up to the reforms in these countries based on our

own research. We will discuss later how we deal with possible empirical concerns arising from

these reform processes.

INSERT TABLES 1 AND 2 HERE

2 The only other source with fairly global coverage, the MERIT-CATI database, became defunct in 2013.

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Our sample includes 11,072 firms from the thirteen IPR-changing countries that

participated in alliances between 1988 and 2004. Though our focal firms are only from the

countries in which we observe the legal changes,3 their alliances could be with firms from

anywhere in the world and thus our network measures capture global ties. Put differently, the

network matrix used to generate the variables of interest includes all alliances in SDC Platinum

during the study period (not just those involving firms from the reforming countries). We

consider alliance ties to be active for a period of three years, consistent with similar standard

assumptions in alliance research (e.g. Gulati, 1995; Schilling and Phelps, 2007). For example, a

firm’s network in 1991 consists of all ties formed between 1989-1991, inclusive. Research shows

that any type of alliance can provide a path for knowledge diffusion, and that the actual breadth

of an alliance’s activity is often much greater than what is formally reported as the purpose of the

collaboration (Alcacer and Oxley, 2014; Powell, Koput, and Smith-Doerr, 1996). Hence we

include alliances of all types in our main sample. However, our results are robust to dropping

alliances that ostensibly have no knowledge component, as discussed later.

Dependent Variables

To test H1a/b and H2, we capture the number of new international ties as the natural log

of one plus the number of new alliances that the focal firm establishes in a given year with

partners that are based in a country different than its own, as classified by SDC’s alliance

participant location. We add one to the original count for logging purposes because many firms

do not form ties every year. To test H3, we capture the international dispersion of a firm’s

partners, partner international diversity, as 1 - ∑i si2 where si is the fraction of the firm’s partners

that are from country i.

Because we want to ensure that the results for H4 are not driven by one particular way of

measuring network closure, we use a variety of measures expecting to find similar results across

them. We follow Burt (1992) by calculating network constraint for the focal firm’s ego-network

3 Using only firms from countries that experienced a reform does not lead to biased estimates, as we explain in more detail below.

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as ∑j (pij + ∑q piqpqj)2 where i refers to the focal firm, j and q are indices that cycle through each

of the other firms in the network and pij is the proportion of i’s network that is composed of ties

to j. Higher values of constraint indicate networks with greater closure. As an alternative

measure we count the number of ties between a firm’s partners (common third parties). Since

this measure is likely to be influenced by the number of partners the firm has, we use a measure

of normalized bridging ties, which is the natural log of one plus the ratio between the count of

common third parties among the focal firm’s partners to the total number of partners the firm has

(i.e. its degree). We verified the robustness of our results to the use of the non-normalized

version of this variable. We also use a measure of network density, captured as the ratio of the

number of ties between the firm’s partners to the maximum possible number of ties that could

exist between them (i.e. count of bridging tiesi / !2 ), where n is the number of partners firm i has

in the year in question) (Wasserman and Faust, 1994).

Independent Variables

Our main independent variable of interest is post reform, coded as 1 for the year of the

reform and all years thereafter and 0 for the year before the reform and all years preceding it. To

assess the timing of changes in the network, we created a series of indicators capturing time

since the year of the reform. If reform(t) is 1 for the year of the reform and 0 for all other years,

reform(t-1), reform(t-2), and reform(t-3) capture 1, 2, and 3 years before the reform,

respectively, and reform(t+1), reform(t+2), and reform(t+3) capture 1, 2, and 3 years after the

reform, respectively. To avoid going indefinitely into the past or the future, the variables

reform(t-4) and reform(t+4) capture four or more years before or after of the reform, respectively

(see Branstetter et al., 2006 for an identical approach). We discuss the importance of these year-

by-year indicators for the research design in the next section.

Drawing on prior work, we capture status using two different measures of the firm’s

network centrality to test H3: degree is a simple count of the number of partners the firm

possesses in a given year, eigenvector centrality (Bonacich, 1987) is a measure that weights each

of the firm’s ties by the centrality of the actor to which it is associated and is the measure most

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frequently associated with status (Sauder et al., 2012). We use two measures to ensure that our

results are not sensitive to one operationalization of status.

The variable patent intensive indicates whether the industry to which the firm belongs

(based on the firm’s primary 4 digit SIC code) is classified as being patent intensive. We use two

different classifications to ensure robustness. The primary classification we use was generated by

the US Patent Office (USPTO, 2012) based on a multi-year analysis of patenting behavior across

all industries in the economy. The other classification was created by the European Patent Office

in a similar study (EPO and OHIM, 2013), and results in substantially similar findings.

Control Variables

Countries that pass IPR reform may simultaneously be undergoing other changes that

could influence alliance activity. Many of these countries were undergoing broader economic

liberalization and opening up their markets to external influences (see Table 2 and Henisz,

Zelner, and Guillen (2005)). We identify significant liberalizing regulatory changes in the

countries of interest from Wacziarg and Welch (2008) and include a liberalization dummy

variable to indicate the year in which such a change occurred in each country. Also along the

liberalization vein, we control for the extent to which countries become more open to capital

flows based on Quinn and Toyoda (2008). This variable (capital flows openness) is on a 0-100

scale with 100 representing an economy that is fully open to capital flows. In addition, the

passage of IPR reform in a proximate country may create a contagion effect on the alliance or

policy activity of actors in the focal country. To account for this, we include reformed neighbors,

which is a running count of the number of other countries in the same continent as the focal

country that have passed patent reforms based on our data.

We control for the number of new domestic ties that a firm generates in a particular year.

While our interest is on the formation of international alliances, the investment made in domestic

alliances can enable or constrain firms in establishing foreign ties. We also include a yearly

measure of political constraints (polcon) in the focal country, which reflects the number of veto

players present in the country’s governing structure (Henisz, 2000). This could influence the

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timing and nature of the laws passed as well as the degree to which firms are able to influence

the government to accelerate or delay laws according to business interests. We also control for

the FDI inflows into the country and total exports of goods and services out of the country, as

well as the country’s hi-tech exports, measured as the dollar value (in billions) of the country’s

exports in products with high R&D intensity as defined by the World Bank.

Quality of infrastructure could be an important facilitator of international alliances that

may vary concurrently with the passage of IPR laws. We thus included the number of cellphone

connections per 100 people as well as the Internet connectivity of the country measured as the

number of Internet users per 100 people. We also control for the total number of commercial

flight takeoffs during the year from the country’s airports, which may proxy for the accessibility

of the focal country to international markets. The relative wealth of the local population may also

be a consideration for foreign firms interested in local markets, therefore we control for the

country’s GNI per capita. Finally, we add the proportion of the country’s population living in

urban areas (urban population fraction) as a proxy for ease of access to labor, which may

influence the interest of foreign firms in forming alliances with local firms.

We include firm fixed effects to all our models to account for time invariant

heterogeneity between firms. Since the timing of IPR changes across markets is a central part of

the identification strategy (as explained next), the need for many time-varying firm-level control

variables is not as strong as in a cross-sectional or fixed-effects design. This is because firm-

specific factors that may impact tie formation or network structure are unlikely to be correlated

with the timing of laws across countries.4

Research Design and Estimation

We consider each IPR law change to be a ‘treatment’ event and use a multiple event

difference-in-difference design in which firms from countries in which the law change has not 4 One possible concern may be that the timing of laws is driven by the state of development of firms’ capabilities within a particular country, such that firms from a country push for IPR reform when they improve their capabilities sufficiently to be ready for international collaborations. This would make IPR reform endogenous to some unobserved readiness to partner. We did not find any evidence of this mechanism driving reforms. Instead, as Table 2 shows, the pressure to reform was mainly from forces outside the countries (e.g. U.S. sanctions, joining the WTO).

16

occurred act as a control group for the firms in countries that have experienced a law change. As

is typical for this research design, our primary independent variable is post reform, which we

defined previously. The coefficient of this variable gives us the difference-in-difference estimate.

In addition, some of the hypotheses are tested using a triple difference design by interacting post

reform with a further indicator. For instance, for H1b we interact post reform with patent

intensive and for H2 we interact post reform with indicators of status. The coefficient of these

interactions reflects the additional ‘difference’ in how various types of firms react after IPR

changes, allowing us to further probe the mechanisms leading to the observed effects.

This empirical design allows us to make reasonably strong inferences. We are examining

the prevs. post-treatment effect (the first difference) between the treated and control firms (the

second difference). For the estimates to be biased the data would need to violate the parallel

trends assumption: in the absence of treatment, the treated and control groups would have to

display systematically different variation in outcomes (i.e. they would need to be trending

differently) at the time of treatment (Angrist and Pischke, 2008). In other words, following the

reform year the firms from the treated countries would have to a display a systematically

different change in their alliance behavior compared to the firms from the untreated countries for

reasons unrelated to the IPR reform. Furthermore, since our identification relies on multiple

events rather than just one, this systematic difference would have to be consistent and in the

same direction across many events to bias the results.

The empirical identification assumes that the timing of the various law changes is

exogenous to alliance activity across the different markets. While we cannot perfectly verify

such exogeneity, we have tried to be as thorough as possible. As Table 2 shows, the process

leading to the reform within any given country is driven by distinct factors, some measurable and

some not. The heterogeneity in processes and initial conditions across countries makes it

plausibly unlikely that the timing across countries systematically coincides with factors

endogenous to the changes in the alliance network. To validate the parallel trends assumption,

we break down the analysis by year using dummy variables to capture a series of years before

17

and after the passage of the law (as explained above). This allows us to examine if the observed

changes in alliance activity and the strengthening of formal IPR protections coincide in time. It

also allows us to look for the existence of a pre-trend, i.e. whether the alliance activity of firms

from treated countries was already trending in the hypothesized direction before the reform

compared to firms from the untreated countries. If the cause of our expected changes is the

passage of the law instead of alternative explanations summarized in Table 2, we should expect a

distinct shift in alliance activity coinciding with the timing of the passage of the law. We use the

year-by-year indicators to assess such timing. The omitted year is the year before the passage of

the law so that all the coefficients for the yearly indicators show the effect (or the “difference”)

relative to that baseline pre-reform year (see Branstetter et al., 2006 for an identical approach).

Our estimates are conservative because the sample consists only of firms from countries

that experience the ‘treatment’ at some point. Similar studies relying on multiple events often

include firms from countries that never experience the treatment as additional ‘controls’, usually

because the number of treated observations is too low to generate sufficient statistical power.

The inclusion of non-treated observations introduces the possibility that the treated and never-

treated groups suffer from unobserved attributes that may violate the parallel trends or other

identification assumptions. By including only firms from treated countries, the comparison sets

are more homogenous and concerns of systematic distinctions between treated and control

groups are reduced. We estimated coefficients using ordinary least squares regressions with firm

fixed effects, which eliminate time invariant unobserved heterogeneity between firms. We also

include year fixed effects to control for macroeconomic effects that may cause fluctuations in

global alliance activity.

RESULTS

Table 3 shows the summary statistics and correlations for our variables of interest. We do

not show the coefficients of the control variables in most tables of results to preserve space, but

all of them include controls (tables A1-A3 in the appendix, submitted as a separate document,

show results with all the controls). Table 4 shows the results for models of new international

18

alliance formation. The positive coefficient of post reform in model 1 is supportive of H1a (p =

0.001), which suggested that the number of international ties would rise with the strengthening of

formal IPR protection in its home country. The partial elasticity, holding other variables at their

mean, reveals that IPR reforms on average increased new international ties by 45%. In model 2

we break down the effects by year using a set of dummy variables that indicate the timing of the

change relative to the year before the passage of the law. These coefficients are plotted in Figure

1 (with vertical lines indicating the 95% confidence interval). There is a significant rise in the

number of international alliances corresponding with year of the law (reform (t)). This spike is

sustained at a significant level for one more year (reform (t+1)), following which the change

becomes less significant statistically.

INSERT TABLE 3, TABLE 4, FIGURE 1, AND FIGURE 2 HERE

Next, we examine the difference in the strength of this effect between firms from patent

intensive and non-patent intensive industries. These results are shown in models 3 and 4. H1b

suggested that firms from patent intensive industries would display a more pronounced increase

in the number of international ties than those from non-patent intensive industries. The positive

coefficient of the triple difference term in model 3 lends support to this hypothesis (p = 0.034).

The results indicate that firms from patent intensive industries experience an increase in

international tie formation approximately 35% greater than that experienced by firms from non-

patent-intensive industries. In model 4 we break down this triple difference by year and observe

that the statistical distinction between these groups coincides precisely with the passage of the

law (patent intensity x reform (t)). Further, the distinction between them is sustained for at least

three years following the passage of the law, as depicted in Figure 2. These results suggest that

the mechanisms driving the changes are specific to IP-related considerations and not to other

events coinciding with the treatment.

Table 5 displays how the effect of status on the number of international ties changes

following the passage of IPR laws. To do this we interact post reform with two different

measures of centrality. We do not use centrality for years after the law was passed because

19

centrality itself is affected by the passage of the law, making it subject to the “bad control”

problem that creates biased estimates (Angrist and Pischke, 2008). To avoid this, we interact post

reform with centrality from the year prior to the passage of the law. Since this particular value is

time invariant the main effect of centrality is accounted for through the firm fixed effect.

However, we can still estimate the interaction effect of centrality with the post-reform indicator.

As we observe from the negative coefficients in models 5 and 6, hypothesis 2 receives strong

support (p ~ 0.000 in both cases). The substantive significance of these coefficients is somewhat

tricky to interpret given the centrality values we use are invariant. Broadly, the results in model 5

suggest than a one standard deviation increase in eigenvector centrality depresses the boost to

new international ties by 0.02, which is quite substantial given that mean value across the entire

sample is about 0.05. The year by year breakdown is shown in models 7 and 8. It demonstrates

that centrality plays an important role before the passage of the laws but a relatively insignificant

role subsequent to law changes. Figure 3 charts the coefficients from model 8 (eigenvector

centrality), clearly displaying this decline coinciding with the strengthening of IP protection. The

results are robust to the use of centrality measures from two or three years before the passage of

the law as indicators of pre-reform status instead of centrality from the year before.

INSERT TABLE 5 AND FIGURE 3 HERE

Hypothesis 3 suggested that the passage of the law would facilitate access to a greater

diversity of cross-national partners for firms in the focal country. Estimates from models testing

this are shown in table 6. Now the dependent variable is partner international diversity. The

positive coefficient of post reform in model 9 is in line with our expectation (p = 0.019). The

magnitude of the effect is substantial: partner international diversity increases by about 44%

after the reforms. Model 10 demonstrates that this increase coincides with the timing of the

reforms, as plotted in Figure 4.


Table 7 shows the results for models using various measures of closure as the dependent

variable. The results run contrary to the expectations outlined in hypothesis 4. The positive

20

coefficients of the post reform variable in models 11, 13, and 15 indicate that in fact firms tend to

adopt more closed networks following the strengthening of IPR laws (p = 0.001 in all three

cases). This corresponds to a 47% increase in network constraint and a 69% increase in ego-

network density, holding all other variables constant at their means. Models 12, 14, and 16 show

that the increase in network closure corresponded exactly with the passage of the laws, and

Figure 5 displays these coefficients for network density (model 16), showing that the increase in

closure was sustained over time.


Robustness Tests and Additional Considerations

We considered a variety of alternative specifications and subsamples to rule out various

sources of bias (the appendix, submitted separately, shows many of these results in detail). We

ran a number of tests to ensure that our findings were not sensitive to measurement choices. In

our primary results we logged the count of international ties as well as the measure of bridging

ties to improve the linear fit of the models. To ensure that these transformations did not influence

the results, we ran models using non-logged variables and found the results to be materially

unaltered (see table A4, model A1). We assessed the robustness of H3 to a different indicator of

partner international diversity by using a logged count of the number of countries in which firms

have partners rather than using the Herfindahl-based measure used in models 9 and 10. The

results are robust to the alternative measure. We also re-ran the models in table 7 using a non-

normalized count of bridging ties as the dependent variable and the results were materially

unaltered. We used the European Patent Office classification of patent intensive industries in

place of the USPTO classification and found the results in models 3 and 4 to exhibit similar

statistical and economic significance. For the models in table 5 that use centrality measures, we

replicated the results using the centrality from 2 and 3 years before the passage of the law rather

21

than the year before and the results were robust (see table A5). We also included county-year

fixed effects rather than year fixed effects and the results were materially unchanged.5

We also carried out various tests to ensure that our findings were not driven by a particular

subset of the observations in the sample. We replicated all our analyses after separately dropping

firms from the two largest countries in our sample, China and India, and the results were robust

both economically and statistically (table A4, model A2). Indeed, our results were not materially

changed by dropping the firms from any individual country in the sample. As previously

mentioned, our primary sample contains all types of alliances as captured by SDC (akin to

Schilling and Phelps, 2007). However, we also re-ran the analysis after dropping all the alliances

that with classification that ostensibly have no knowledge component (e.g. ‘retail and

wholesale’, ‘financial services’, ‘property development’, ‘insurance services’, etc.). These make

up about 30% of the alliances in our sample. Dropping these did not materially alter the main

results (table A4, model A4). This helps affirm that the results are not being driven by alliance

activity unrelated to knowledge considerations.

As table 2 shows, pressure from the United States government appears to have played an

important role in driving several of the IPR reforms. If U.S. firms were instrumental in bringing

about this pressure, in part to further their interests though alliances with firms from the

reforming markets, the increased alliance formation could be endogenous to the timing of the

legal changes. To rule out this potential source of bias, we recalculated new international ties to

include ties to firms from every foreign country except the United States. We found that the

results remained materially unaltered, statistically or economically (table A4, model A3).

Despite the robustness of the results, our approach has limitations. These regulatory

changes do not occur in isolation: they are often accompanied by other events that could make

the climate in the country more favorable for foreign firms or increase exposure to global

collaboration and competition in general. Despite our efforts to account for a myriad of issues

5 Country-year fixed effects are only feasible in models with triple differences as in all the other models they are collinear with the post reform variable.

22

unrelated to IPR (see Table 2), the observed effects could still be driven by unobserved factors

we have not considered. It is, however, worth making one clarification about the interpretation of

the effects. The fact that we observe no pre-trends and that significant changes systematically

occur in the year in which laws are passed alleviates the concern of reverse causality, i.e. the

possibility that increased alliance activity could somehow be driving the passage of the laws. Yet

the observed effects could still be consistent with firms ‘preparing the groundwork’ for

international ties in anticipation of IPR changes and then signing alliance contracts as soon as the

laws are passed. If that were the case, it underscores the importance of actual reform as the

source of the effect because it shows that firms do not establish ties until formal institutions are

actually in place—even if they anticipate their existence.

DISCUSSION

This study was motivated by the expectation that weak IPR create significant barriers for

firms seeking to attract foreign alliance partners and thus gain access to the resources available

from global networks. We sought to explore whether those barriers were alleviated after

meaningful improvements in IPR laws and found that the rate of foreign alliance formation

increased significantly for firms from countries that improved their IPR institutions. The

magnitude of the effect was quite large on average (a roughly 45% increase in the number of

ties), and even larger and sustained for a longer period of time for firms in IP-intensive

industries. While prior research has shown that improvements in IPR increase the amount of

internal knowledge-related activities for firms operating in the reforming countries, this is the

first study to document the effects of IPR reforms on the external, foreign alliance activities of

firms. This issue is of significant relevance to scholars, managers, and policy makers (e.g. Deere,

2008; Maskus, 2000).

The results also reveal that improvements in the institutional environment lead to a

‘democratization’ in access to foreign alliance opportunities, in the sense that being of high

status in the alliance network becomes less important to attract foreign partners after IPR

reforms. Inasmuch as foreign partnerships can help firms from emerging markets upgrade their

23

technological capabilities or access new markets (e.g. Siegel, 2007), this should be beneficial for

individual firms and for the economy as a whole. This result has intriguing theoretical

implications for research on status in social networks. Consistent with established theory, we

found that high status firms benefit from tie formation opportunities when uncertainty is

heightened due to weak IPR, and that the benefits of status diminish once IPR reforms decrease

uncertainty (e.g. Podolny, 1994). What is novel from our study, however, is the observation that

the institutional environment plays a significant role in determining how status affects network

dynamics. This gets at the debate surrounding “Matthew” vs. “Mark” effects (Bothner, Podolny,

and Smith, 2011). Matthew effects occur when the ‘rich get richer’, whereby prominent actors

disproportionately accrue networking opportunities and resources—leading to highly

differentiated, core-periphery economic structures. Mark effects refer to the opposite scenario,

where low status actors receive additional resources and networks become more egalitarian. Our

results suggest that improvements in formal institutions create a transition from “Matthew”

dynamics to “Mark” dynamics for the global alliance networks of firms.

The findings also reveal that institutional reforms impact the composition and structure of

firms’ alliance networks. This matters because the benefits firms get from such networks are

strongly affected by the types of partners they can attract and by the positions they occupy

(Gulati, 1998). One benefit of the network reconfiguration made possible by IPR changes is that

firms are able to increase their access to diverse sources of knowledge and resources through

partners from a greater variety of countries. Inasmuch as markets exhibit unique national

innovation systems, each producing distinct types of knowledge and resources, this

compositional change in the network should be associated with an increased opportunity to

innovate for firms from countries with improved institutions (Vasudeva et al., 2013). Of course,

managing a network of partners spanning multiple institutional environments is also more

complex. Firms may find increased frictions in having to source, transfer, and recombine

knowledge and resources from disparate geographies (Jensen and Szulanski, 2004). This aspect

of managing more complex networks post reform is worth exploring in future work.

24

We expected that, post reform, firms would be able to access a more diverse base of

resources through more open networks, which would expose firms to the benefits of structural

holes (Burt, 2005). We actually found the opposite—firms’ networks became significantly and

permanently denser, or more closed, after the strengthening of formal IPR institutions. Here we

offer two ex-post explanations for this result.

One explanation has to do with the nature of knowledge-related activities made possible

by stronger IPR. As mentioned earlier, institutions create incentives for economic actors to

collaborate on highly complex activities (North, 1990). Before the reform, such activities were

not possible or at least occurred much less frequently. The pre-reform network is thus likely to

be rather small in terms of the number of participants and also sparse in the number of ties

because incentives to collaborate are low. After institutional reform, the complexity and

frequency of cross-border activities is facilitated. The nature of these activities is likely to be

significantly more complex because, backed by stronger institutions, firms are willing to commit

more resources to external partnerships and engage in activities of greater scope (Oxley and

Sampson, 2004). Further, the reduced appropriability concerns may motivate firms to conduct

relatively more activities through contractual alliances than through internal ownership

(Williamson, 1991) and to disaggregate such external activities among many partners. Hence the

incidence of knowledge-related activities in which multiple partners come together to develop

new products or technologies for mutual gain increases post reform. This would be manifested in

much denser, or closed, networks among firms from the reforming countries.

A second possibility has to do with the governance role of networks. We have focused on

the resource function of networks, whereby they act as conduits of resources, and our arguments

have emphasized how institutional reforms enable access to the resources flowing through the

global alliance network. But networks can also play a governance role based on social control

(Granovetter, 1985). When network participants must protect a prized resource, closure is

valuable for at least two reasons. First, a dense web of ties allows firms to more easily obtain

information about the actions of others, in essence lowering the costs of monitoring. Second,

25

closed networks facilitate the development of norms of behavior and increase the odds that those

norms will be informally enforced (Coleman, 1988). Recent work has empirically validated this

proposition by showing that, when a firm’s sensitive knowledge is exposed to unwanted leakage,

the firm tends to increase network closure as a way to ‘fence in’ its knowledge (Hernandez,

Sanders, and Tuschke, 2015). We had implicitly assumed (H4) that the governance role of

networks is unnecessary once IPR laws improve because institutions functionally eliminate the

need for socially-based forms of knowledge protection. But formal institutions may not be fully

effective on their own (Helmke and Levitsky, 2004). For example, North states that: “[Formal

regulation] is never ideal, never perfect, and the parties to the exchange still devote immense

resources to attempting to clientize exchange relationships… Indeed, effective third-party

enforcement is best realized by creating a set of rules that then make a variety of informal

constraints effective” (1990:35).

If formal institutions are imperfect and contracts are incomplete, then network closure

may play a complementary role to IPR laws (cf. Poppo and Zenger, 2002). Closure can help

address many of the day-to-day interactions between partners that are not explicitly covered by

the legal framework. Indeed, trying to rely only on lawyers and courts would be inefficient when

minor disputes or unexpected situations arise. Reliance on a dense network to monitor situations

and appeal to a socially accepted set of values and norms is likely to be more effective and “fill

in” the gaps in formal legal frameworks (Ellickson, 1991; Uzzi, 1996). Interestingly, as the

results of this study imply, the demand for such informal network protections may not exist until

formal institutions enable the types of complex exchange that require network-based governance.

One of the empirical concerns we sought to address was whether the timing of the

reforms was truly exogenous to the pattern of changes in the alliance network. The prevs. post-

reform trends (Figures 1-5) suggest no concerning pre-trends. But they also reveal interesting

patterns in the post-reform changes in the network: some effects endure over time and others

disappear after a few years. For instance, the spike in the number of new international alliances

drops to pre-reform levels after 2-3 years (Figures 1-2). However, the breadth of countries from

26

which the firm can access partners is sustained for a longer period of time (Figure 4). And the

structural effects (decline in status as a predictor of tie formation, increase in network closure)

persist until the end of our sampling period (Figures 3, 5). These results in combination suggest

that institutional reform effectively causes a re-working of the firms’ alliance portfolios. There is

most likely a certain baseline rate at which firms tend to form new relationships. While this rate

is exceeded in the years immediately following the passage of the IPR changes, firms cannot

keep this up indefinitely due to resource and value-creation limits. However, the new ties made

possible by the reform lend a permanently different complexion to the alliance network in terms

of partner nationality and structure.

Specific results aside, a broad theoretical contribution of this study comes from

demonstrating the institutional origins of networks. Research on this topic is quite limited

(Owen-Smith and Powell, 2008; Powell et al., 2005; Vasudeva et al., 2013). While the ‘micro’

institutional drivers of interfirm exchange are theoretically more developed (Williamson, 1991),

there have been few empirical studies attempting to establish a relationship between the ‘macro’

institutions that govern society and firms’ propensities to collaborate. This hole in the literature

is surprising because research has long proven that institutions are fundamentally about exchange

and collaboration. They shape the preferences, incentives, and values that influence not only

whether actors will exchange but also how they will structure those exchanges. This study is an

important step towards understanding how institutional influences shape the way firms associate

via networks and how those networks evolve.

In addition to being theoretically important as a source of network change, macro

institutions are also plausibly exogenous to the micro interactions by which networks are

established. In the short run, firms are ‘institution takers’ because the forces that govern

institutional change are beyond their control (North, 1990). Many studies of network evolution

focus on the attributes of actors or certain rules of attachment that are inherently endogenous to

the dynamics they try to explain (see Ahuja et al., 2012 for a critique). The difference-in-

differences research design of this study (while not without flaws) coupled with a focus on

27

macro institutions, allows us to make a methodological contribution by offering a test of network

evolution that mitigates concerns of endogeneity common in network studies. We believe that

the theoretical issues and methodological advantages of linking macro institutions to network

effects merits further studies on the subject.

We discussed some of the methodological limitations of our study earlier, and here we

consider others. All the countries in our sample are emerging economies. This casts doubt over

whether the results may be generalizable to the impact of institutional changes in more

developed markets. For instance, the continuing need for closure as a complement to formal

institutions may be peculiar to these markets because they have not reached a level in which laws

are strong enough to address all or most appropriability concerns. While informal constraints are

also prevalent in developed markets, the types of informal institutions that coexist with formal

ones may differ across emerging and developed markets. Nevertheless, the findings are relevant

because most of the current debate about IPR reform concerns emerging markets. Our sample

also consists only of firms that have entered at least one alliance over the course of the study

period. While this is not a particularly stringent restriction in many industries, some industries do

not engage in much alliance activity and may respond in other ways to IPR strengthening. These

non-network responses are beyond the interest of the study to begin with, and some have been

documented in prior research (e.g. Branstetter et al., 2006; Lerner, 2009).

We believe this study is valuable because it establishes a link between formal institutions

(IPR) and access to global networks. While the fact that weak institutions create barriers to

economic activity is generally accepted, how improvements in formal institutions impact the

external, collaborative activities of firms is not straightforward. The results reveal that

institutional improvements enable access to foreign partnerships, that they do so in a way that

democratizes tie formation opportunities, and that they impact the composition and structure of

firms’ alliance networks. Taking these considerations into account is relevant for scholars,

managers, and policy makers with an interest in institutions, networks, and cross-border

collaboration.

28

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Figure1:EffectsofIPRreformsonnumberofnewinternationalalliances(Model2)

Graph shows the point estimates and 95% confidence intervals for the coefficients associated with the corresponding variable in model 2 of table 4. ‘t-4’ indicates all periods 4 or more years before the reform,‘t+4’ indicates all periods 4 or more years after the reform. All the other variables indicate the specific year in relation to the year of reform. ‘t-1’ is the omitted year. DV is log of 1 + the number of international alliances formed by the firm in a given year

Figure2:DifferenceintheeffectofIPRreformsoninternationaltieformationforpatentintensivevs.

nonpatentintensiveindustries(Model4–tripledifferences)

Graph shows the point estimates and 95% confidence intervals for the coefficients associated with the corresponding interaction variable in model 4 of table 4. ‘t-4’ indicates all periods 4 or more years before the reform,‘t+4’ indicates all periods 4 or more years after the reform. All the other variables indicate the specific year in relation to the year of reform. ‘t-1’ is the omitted year. DV is log of 1 + the number of international alliances formed by the firm in a given year

32

Figure3:EffectofIPRreformsontherelationshipbetweenstatus(centrality)andnewinternationalalliances(Model8)

Graph shows the point estimates and 95% confidence intervals for the coefficients associated with the corresponding interaction variable in model 8 of table 5. ‘t-4’ indicates all periods 4 or more years before the reform,‘t+4’ indicates all periods 4 or more years after the reform. All the other variables indicate the specific year in relation to the year of reform. ‘t-1’ is the omitted year.

Figure4:EffectofIPRreformsonthegeographicdiversityofalliancepartners(Model10)

Graph shows the point estimates and 95% confidence intervals for the coefficients associated with the corresponding variable in model 10 of table 6. ‘t-4’ indicates all periods 4 or more years before the reform,‘t+4’ indicates all periods 4 or more years after the reform. All the other variables indicate the specific year in relation to the year of reform. ‘t-1’ is the omitted year.

33

Figure5:EffectofIPRreformsonnetworkstructure(Model18)

Graph shows the point estimates and 95% confidence intervals for the coefficients associated with the corresponding variable in model 16 of table 7. ‘t-4’ indicates all periods 4 or more years before the reform,‘t+4’ indicates all periods 4 or more years after the reform. All the other variables indicate the specific year in relation to the year of reform. ‘t-1’ is the omitted year

Table1:Countriesinsampleandyearofreform

Country YearofreformArgentina 1996Brazil 1997Chile 1991China 1993Colombia 1994India 1999Indonesia 1991Mexico 1991Philippines 1997Portugal 1992Thailand 1992Turkey 1995Venezuela 1994

34

Table2:ProcessLeadingtoIPRReformsinDifferentCountriesPathtoReform IssueDescription EmpiricalImplications

PressurefromtheU.S.

TheU.S.TradeRepresentative(USTR)placedsomecountriesonits“prioritywatchlist”andthreatenedtoimposetradesanctionsiftheydidnotreformIPlaws(e.g.China,Indonesia,Mexico).TheU.S.alsomadeIPreformapreconditionfornegotiatingbilateraltradeagreements(ChileandMexicoinlate1980’s).Typically,countriesgavetheU.S.acrediblesignalthattheywouldmodifytheirlawsandweredroppedfromthewatchlist.Actualreformsbecameeffective1-3yearsaftertheinitialintenttoreform.Sources:Platikanova-Gross2006,Yu2001,Kusumadara2000,Goldstein&Strauss2009,Hufbauer&Schott1992,Baca1994,Shadlen2009

1)IfU.S.pressureinsteadofactualIPRreformdrivestheeffects,weshouldobserveapre-reformtrendbywhichnetworkchangesoccurinthewindowbetweenU.S.pressureandactualIPRreform.2)ChangesintheglobalnetworkparticipationcoulddrivenonlybyallianceswithU.S.firms,notfromtiestofirmsfromotherforeigncountries.SOLUTION:1)Weobserveyear-by-yeartrendsleadinguptotheactualIPRreformandfindnopre-trends.2)WedropU.S.firmsaspotentialinternationalpartnersandfindthattheresultsremainconsistent.

CompliancewithTRIPStoentertheWTO

WhenitbecameclearthatcompliancewiththeTrade-RelatedAspectsofIntellectualPropertyRights(TRIPS)wouldberequiredtojointheWTOintheearly1990’s,somecountriesdemonstratedwillingnesstoreformIPRrelativelyquicklybutthelegalchangesweren’talwaysimmediate.TurkeysignedtheWTOagreementin1994andreformedIPin1995.ThePhilippineswasopentoIPreformbutthecircuitouslegislativeprocessdelayedthelawuntil1997.Sources:Goldstein&Strauss2009;Ozkan2010

IftheintenttocomplywithTRIPSinsteadofactualIPRreformdrivestheeffects,weshouldobserveapre-reformtrendbywhichnetworkchangesoccurinthewindowbetweenexpressedintentandthepassageofthelaw.SOLUTION:Weobserveyear-by-yeartrendsleadinguptotheactualIPRreformandfindnopre-trends.

Broadneo-liberalizationduringthe1980’sand1990’s

The1980’sand1990’swereaperiodofsignificanteconomicliberalizationacrosstheworld.ManydevelopingcountriesadoptedseveralofthereformssuggestedbytheWashingtonConsensus.Reformsfocusedonfiscalpolicy,taxreform,tradeliberalization,andderegulation.Whilepropertyrightswereconsideredpartofthereformpackage,IPRtendedtobepartofalater,“secondwave”ofliberalizationthatcame5-10yearsafterthefirstsetofneoliberalreforms.Sources:Fukuyama1992,Williamson2000,Helferetal.2009

Broadneo-liberalizationcouldbeconfoundingtheeffectsofIP-specificreformsinourstudy.Theeffectsweobservecouldbedrivenbyamoregeneralopeningofthesecountriestoforeignexchange,whichisunrelatedtoIPorknowledgemechanisms.SOLUTION:Wecontrolfortradeneo-liberalization(Wacziarg&Welch2003)andopennesstocapitalflows(Quinn&Toyoda2008)aswellasahostofcountry-levelindicatorssuchasFDIinflows,exports,infrastructurechanges,etc.

Compliancewithregionaltreaties

Somecountriesreformedinunisonaspartofaregionalagreement.Forexample,ColombiaandVenezuelareformedIPRaspartoftheAndeanCommunitywhilePortugalreformed

IftheexpectationofreforminsteadofactualIPRreformdrivestheeffects,weshouldobserveapre-reformtrendbywhichnetworkchangesoccurinthewindowbetween

35

tocomplywiththeexpectationsoftheEuropeanSingleMarket.Inbothcasestheregionalagreementtoreformprecededactualreformbyatleast2-3years.Sources:Maskus2000;Helferetal.2009;Branstetteretal.2006

pressureorchangeinregimeandthepassageofthelaw.SOLUTION:Weobserveyear-by-yeartrendsleadinguptotheactualIPRreformandfindnopre-trends.

Internalgovernmentchanges

WhileexternalpressurefromtheWTOortheU.S.“watchlist”wascommonamongmanycountries,notallrespondedasquicklyorwillingly.SomecountriesresistedinternationaldemandsforafewyearsuntilanewgovernmentwithfavorableviewsonIPRreformwaselected.Forexample,IndiawasunwillingtoreformIPuntilanewpartywaselectedin1998(reformwasin1999).ArgentinaandBrazilhadsimilarlycontentiousinternalprocesses.Sources:Correa2000,Branstetteretal.2006,Ryan2010,Ramanna2012

1)Ifexternalpressureorthechangeinthegovernment’swillingnesstoreforminsteadofactualIPRreformdrivestheeffects,weshouldobserveapre-reformtrendbywhichnetworkchangesoccurinthewindowbetweenpressureorchangeinregimeandthepassageofthelaw.2)InternalprocessesidiosyncratictocertaincountriescoulddrivetheresultsSOLUTION:1)Weobserveyear-by-yeartrendsleadinguptotheactualIPRreformandfindnopre-trends.2)Wedropeachofthesecountriesfromtheanalysisandfindtheresultstobeconsistent.

Regionalspillovers

Giventheriseofregionaltreatiesandbroaderliberalizationduringtheperiodofstudy,therecouldberegionalcontagioninthetimingofIPRreforms.Source:Peck&Tickell2002

Ifgeographicspilloversaffectthetimingofreform,thetimingofneighboringcountries’reformsmaycreateanticipatedchangesinthenetwork.SOLUTION:WecontrolfortheyearinwhichneighboringcountriesintheregionadoptedIPRreform.Wealsoobserveyear-by-yeartrendsleadinguptotheactualIPRreformandfindnopre-trends.

REFERENCES:Baca,R.(1994).CompulsoryPatentLicensinginMexicointhe1990.JournalofLawandTechnology.Branstetter,L.G.,Fisman,R.,&Foley,C.F.(2006).DoStrongerIntellectualPropertyRightsIncreaseInternationalTechnologyTransfer?EmpiricalEvidencefromU.S.Firm-LevelPanelData.TheQuarterlyJournalofEconomics,121(1),321–349.Correa,C.M.(2000).ReformingtheIntellectualPropertyRightsSysteminLatinAmerica.WorldEconomy,23(6),851–872.Fukuyama,F.(2006).TheEndofHistoryandtheLastMan.SimonandSchuster.Goldstein,P.,&Straus,J.(2009).IntellectualPropertyinAsia:Law,Economics,HistoryandPolitics.SpringerScience&BusinessMedia.Helfer,L.R.,Alter,K.J.,&Guerzovich,M.F.(2009).IslandsofEffectiveInternationalAdjudication:ConstructinganIntellectualPropertyRuleofLawintheAndeanCommunity.TheAmericanJournalofInternationalLaw,103(1),1–47.Hufbauer,G.C.,&Schott,J.J.(1992).NorthAmericanFreeTrade:IssuesandRecommendations.PetersonInstitute.Kusumadara,A.(2000).AnalysisofthefailureoftheimplementationofintellectualpropertylawsinIndonesia.Maskus,K.(2000).IntellectualPropertyRightsintheGlobalEconomy.Washington,D.C.:PetersonInstitute.Özkan,A.F.(2010).TheRegulationandPatentProtectionofPharmaceuticalsinTurkey.TurkiyeKlinikleriJournalofMedicalEthics-LawandHistory,18(1),15–25.Peck,J.,&Tickell,A.(2002).NeoliberalizingSpace.Antipode,34(3),380–404.Platikanova-Gross,K.S.(2006).EssaysonSomeEconomicImplicationsofIntellectualPropertyRights.Ramanna,A.(2012).InterestgroupsandpatentreforminIndia(WorkingPaper).Ryan,M.P.(2010).PatentIncentives,TechnologyMarkets,andPublic–PrivateBio-MedicalInnovationNetworksinBrazil.WorldDevelopment,38(8),1082–1093.Shadlen,K.C.(2009).IntellectualpropertyfordevelopmentinMexico.FrederickS.PardeeCenterfortheStudyoftheLonger-RangeFuture,BostonUniversity.Williamson,J.(2000).WhatShouldtheWorldBankThinkabouttheWashingtonConsensus?TheWorldBankResearchObserver,15(2),251–264.

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Table3:SummaryStatsandCorrelations

SlNo Variable Mean SD Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 211 Newinternationaltiesb 0.05 0.20 0.00 3.40 1.0002 PartnerInt'nlDiversity 0.03 0.12 0.00 0.93 0.444 1.0003 NetworkConstraint 0.16 0.35 0.00 1.13 0.392 0.307 1.0004 Normalizedbridgingtiesb 0.02 0.13 0.00 4.22 0.192 0.597 0.246 1.0005 Networkdensity 0.05 0.21 0.00 1.00 0.300 0.658 0.424 0.652 1.0006 Degree 0.34 1.13 0.00 68.00 0.144 0.537 0.311 0.557 0.366 1.0007 EigenvectorCentrality 0.01 0.08 0.00 3.83 0.083 0.242 0.099 0.213 0.138 0.422 1.0008 PatentIntensive 0.20 0.40 0.00 1.00 0.021 0.004 0.049 -0.008 0.003 0.006 0.040 1.0009 Newdomesticties 0.04 0.29 0.00 12.00 0.310 0.292 0.210 0.200 0.314 0.090 0.038 -0.005 1.00010 Liberalizationdummy 0.35 0.48 0.00 1.00 0.018 0.019 0.030 0.033 0.022 0.043 -0.002 -0.138 0.018 1.00011 CapFlowsopenness 50.21 14.26 25.00 100.00 0.033 0.026 0.064 0.036 0.026 0.051 0.009 -0.093 0.036 0.577 1.00012 ReformedNeighbors 5.13 1.97 0.00 7.00 -0.008 0.032 0.037 0.018 0.022 0.053 0.029 0.071 0.019 -0.417 -0.164 1.00013 PolCon 0.32 0.34 0.00 0.89 0.009 0.010 0.022 0.010 -0.011 0.031 -0.008 -0.080 0.017 0.465 0.319 -0.173 1.00014 FDIInflows 0.19 0.19 -0.05 0.62 0.001 0.013 0.056 0.014 0.030 0.034 0.027 0.082 0.003 -0.494 -0.232 0.386 -0.670 1.00015 Hi-TechExports 0.35 0.64 0.00 2.73 -0.035 -0.026 -0.016 -0.021 -0.027 -0.009 0.014 0.050 -0.011 -0.313 -0.029 0.407 -0.434 0.754 1.00016 CellphoneConnections 6.19 10.89 0.00 100.89 -0.055 -0.057 -0.038 -0.036 -0.052 -0.017 -0.001 -0.033 -0.028 0.232 0.278 0.067 0.024 0.231 0.454 1.00017 Exports 22.26 12.27 6.60 70.70 -0.004 0.027 0.026 0.045 0.037 0.068 0.016 -0.070 0.023 0.525 0.210 0.201 0.070 -0.075 0.140 0.373 1.00018 GNIpercapita 1.53 1.87 0.33 16.12 0.004 -0.023 0.032 0.005 -0.009 0.021 -0.012 -0.094 -0.000 0.602 0.569 -0.560 0.367 -0.148 -0.079 0.475 0.123 1.00019 Internetconnectivity 2.37 4.30 0.00 34.50 -0.049 -0.049 -0.031 -0.032 -0.049 -0.009 -0.002 -0.049 -0.027 0.331 0.351 -0.007 0.162 0.055 0.257 0.881 0.318 0.490 1.00020 Flighttakeoffs 3.50 2.88 0.37 12.10 -0.026 -0.017 0.012 -0.015 -0.012 0.003 0.019 0.073 -0.011 -0.415 -0.147 0.381 -0.515 0.886 0.890 0.337 -0.106 -0.107 0.150 1.00021 UrbanPop.Fraction 40.66 17.86 25.55 89.90 -0.006 -0.031 0.014 -0.010 -0.022 0.005 -0.010 -0.104 -0.014 0.623 0.459 -0.565 0.330 -0.119 -0.056 0.329 0.017 0.729 0.449 -0.008 1.000

N=166080 b:LoggedVariable

37

Table4:EffectofIPRReformsonInternationalAllianceFormationDV:Newinternationaltiesb Model1 Model2 Model3 Model4 Coeff. pvalue Coeff. pvalue Coeff. pvalue Coeff. pvalue

PostReform 0.0190 0.001 0.0180 0.001

(0.0044) (0.0043)

PatentIntensityxPostReform 0.0064 0.034

(0.0027)

Reform(t-4) -0.0044 0.509 -0.0050 0.469

(0.0065) (0.0066)

Reform(t-3) -0.0044 0.588 -0.0056 0.475

(0.0079) (0.0076)

Reform(t-2) -0.0056 0.273 -0.0058 0.265

(0.0048) (0.0049)

Reform(t) 0.0116 0.019 0.0093 0.072

(0.0043) (0.0047)

Reform(t+1) 0.0213 0.006 0.0169 0.018

(0.0064) (0.0062)

Reform(t+2) 0.0055 0.498 0.0020 0.816

(0.0079) (0.0084)

Reform(t+3) 0.0110 0.201 0.0093 0.262

(0.0081) (0.0079)

Reform(t+4) 0.0025 0.668 0.0029 0.607

(0.0058) (0.0056)

PatentIntensityxReform(t-4) 0.0032 0.491

(0.0045)


(0.0090)


(0.0048)

PatentIntensityxReform(t) 0.0143 0.015

(0.0050)

PatentIntensityxReform(t+1) 0.0261 0.000

(0.0048)


(0.0051)


(0.0076)


(0.0039)

Controls Y Y Y Y

FirmFixedEffects Y Y Y Y

YearFixedEffects Y Y Y Y

NumberofObservations 166080 166080 166080 166080

Numberoffirms 11072 11072 11072 11072

b:LoggedVariable;Standarderrorsreportedinparenthesesareheteroskedasticityrobustandclusteredbycountry;The

dependentvariableisthenaturallogof1+thenumberofinternationalalliancesformedbythefirminagivenyear

38

Table5:Effectofstatus(centrality)onnewinternationaltieformation(prevs.postIPRreform)DV:Newinternationaltiesb Model5 Model6 Model7 Model8 Coeff. pval. Coeff. pval. Coeff. pval. Coeff. pval.

PostReform 0.0382 0.000 0.0231 0.000

(0.0056) (0.0044)

Degreec

xPostReform -0.0429 0.000

(0.0047)

Eigenvectorc

xPostReform -0.2966 0.000

(0.0454)

Reform(t-4) -0.0169 0.005 -0.0082 0.229

(0.0049) (0.0065)

Reform(t-3) -0.0286 0.002 -0.0097 0.261

(0.0073) (0.0082)

Reform(t-2) -0.0297 0.001 -0.0094 0.114

(0.0066) (0.0055)

Reform(t) 0.0150 0.020 0.0125 0.019

(0.0056) (0.0046)

Reform(t+1) 0.0292 0.001 0.0231 0.004

(0.0069) (0.0064)

Reform(t+2) 0.0162 0.025 0.0070 0.358

(0.0063) (0.0073)

Reform(t+3) 0.0221 0.015 0.0130 0.126

(0.0078) (0.0079)

Reform(t+4) 0.0156 0.031 0.0048 0.410

(0.0064) (0.0056)

Centrality1xReform(t-4) 0.0263 0.012 0.2138 0.000

(0.0089) (0.0324)


(0.0071) (0.0793)


(0.0126) (0.1319)

Centrality1xReform(t) 0.0055 0.579 0.0063 0.933

(0.0096) (0.0741)

Centrality1xReform(t+1) -0.0001 0.985 -0.0500 0.439

(0.0061) (0.0624)


(0.0050) (0.0420)


(0.0045) (0.0502)


(0.0054) (0.0602)

Controls Y Y Y Y

FirmFixedEffects Y Y Y Y

YearFixedEffects Y Y Y Y

NumberofObservations 166080 166080 166080 166080

Numberoffirms 11072 11072 11072 11072

b:LoggedVariable;c:Variablesaremeasuredintheyearpriortothereform;1:'Centrality'forModel6isdegreeandforModel

8isEigenvector,bothofwhicharemeasuredintheyearpriortothepassageofthelaw.Standarderrorsreportedin

parenthesesareheteroskedasticityrobustandclusteredbycountry.Thedependentvariableisthenaturallogof1+the

numberofinternationalalliancesformedbythefirminagivenyear

39

Table6:EffectofIPRReformsonNetworkComposition(PartnerInternationalDiversity)DV:PartnerInternationalDiversityc Model9 Model10 Coeff. pvalue Coeff. pvalue

PostReform 0.0109 0.019

(0.0041)

Reform(t-4) -0.0075 0.091

(0.0041)

Reform(t-3) -0.0042 0.257

(0.0035)

Reform(t-2) -0.0036 0.196

(0.0026)

Reform(t) 0.0041 0.246

(0.0034)

Reform(t+1) 0.0114 0.020

(0.0043)

Reform(t+2) 0.0157 0.008

(0.0049)

Reform(t+3) 0.0165 0.048

(0.0075)

Reform(t+4) 0.0142 0.168

(0.0097)

Newdomestictiesb 0.0897 0.000 0.0896 0.000

(0.0119) (0.0120)

Liberalizationdummy 0.0034 0.527 0.0017 0.741

(0.0053) (0.0051)

CapFlowsopenness 0.0000 0.737 0.0000 0.774

(0.0001) (0.0001)

ReformedNeighbors -0.0040 0.078 -0.0039 0.112

(0.0021) (0.0023)

PolCon 0.0186 0.274 0.0172 0.217

(0.0162) (0.0132)

FDIInflows -0.0075 0.825 -0.0112 0.763

(0.0330) (0.0363)

Hi-TechExports 0.0048 0.520 0.0085 0.378

(0.0072) (0.0093)

CellphoneConnections -0.0000 0.936 0.0000 0.909

(0.0004) (0.0003)

Exports -0.0002 0.698 -0.0001 0.706

(0.0004) (0.0004)

GNIpercapita -0.0024 0.243 -0.0021 0.253

(0.0019) (0.0017)

Internetconnectivity 0.0007 0.259 0.0007 0.262

(0.0006) (0.0006)

Flighttakeoffs 0.0023 0.137 0.0012 0.572

(0.0014) (0.0021)

UrbanPop.Fraction -0.0019 0.145 -0.0014 0.326

(0.0012) (0.0014)

Constant 0.0745 0.155 0.0631 0.256

(0.0491) (0.0529)

FirmFixedEffects Y Y

YearFixedEffects Y Y

NumberofObservations 166080 166080

Numberoffirms 11072 11072

b:LoggedVariable.PartnerinternationaldiversityisaHerfindahl-basedmeasureofthespread

ofafirm'spartnersacrosscountries;Standarderrorsreportedinparenthesesare

heteroskedasticityrobustandclusteredbycountry

40

Table7:EffectofIPRReformsonNetworkStructure(Opennessvs.Closure) DV:NetworkConstraint DV:NormalizedBridgingTiesb

c DV:NetworkDensity

Model11 Model12 Model13 Model14 Model15 Model16 Coeff. pval Coeff. pval Coeff. pval Coeff. pval Coeff. pval Coeff. pvalPostReform 0.0673 0.001 0.0199 0.001 0.0284 0.001

(0.0145) (0.0048) (0.0065)

Reform(t-4) -0.0542 0.006 -0.0084 0.200 -0.0162 0.107

(0.0162) (0.0062) (0.0093)

Reform(t-3) -0.0161 0.343 -0.0089 0.096 -0.0147 0.063

(0.0164) (0.0049) (0.0072)

Reform(t-2) -0.0019 0.877 -0.0082 0.006 -0.0107 0.015

(0.0117) (0.0025) (0.0038)

Reform(t) 0.0293 0.017 0.0095 0.082 0.0123 0.054

(0.0106) (0.0050) (0.0057)

Reform(t+1) 0.0690 0.000 0.0191 0.006 0.0287 0.001

(0.0104) (0.0058) (0.0063)

Reform(t+2) 0.1009 0.000 0.0295 0.001 0.0413 0.000

(0.0135) (0.0070) (0.0083)

Reform(t+3) 0.0947 0.000 0.0334 0.012 0.0403 0.004

(0.0194) (0.0112) (0.0113)

Reform(t+4) 0.0716 0.004 0.0292 0.055 0.0395 0.030

(0.0203) (0.0138) (0.0161)

Controls Y Y Y Y Y Y

FirmFixedEffects Y Y Y Y Y Y

YearFixedEffects Y Y Y Y Y Y

NumberofObservations 166080 166080 166080 166080 166080 166080

Numberoffirms 11072 11072 11072 11072 11072 11072Normalizedbridgingtiesarecalculatedaslog(1+countofbridgingties/degree);Standarderrorsreportedinparenthesesareheteroskedasticityrobustandclusteredbycountry

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Liberty in Law? Intellectual Property Rights and Global