Joint Ventures And Governance

Joint Ventures Around the Globe from 1990-2000: Forms, Types, Industries, Countries and Ownership Patterns

Sviatoslav A. Moskalev*(a) Adelphi University R. Bruce Swensen* Adelphi University

Abstract: Joint ventures (JVs) and alliances are important forms of inter-organizational cooperation because they allow firms to achieve complex mutual tasks, otherwise impossible using simple arm’slength contracts, but without actually acquiring one another. In light of recent trends in globalization, this feature of JVs and alliances is vital to multi-national corporations (MNCs). These firms have complex operations, making simple arm’s-length contracts insufficient. MNCs are also very large, so that mergers and acquisitions and takeovers are extremely expensive. In this paper, we describe global trends in JVs and alliances for the period 1990 to 2000, utilizing the Thomson Financial SDC Platinum database. We survey existing theoretical and empirical literature on JVs and alliances, and provide a detailed description of the world of JVs and alliances as depicted by this database. We report a number of interesting facts regarding the forms and types of JVs and alliances, their industry and geographic distribution, and patterns of ownership.

(*)

Assistant and Associate Professors of Finance, respectively, at Adelphi University. Corresponding author: [email protected]; Phone: (516)-877-4417; Fax: (516)-877-4607. Mailing address: School of Business, Adelphi University, 1 South Avenue, Garden City, NY 11530. This paper is based on my dissertation work done at the University of Georgia. We are grateful to Kenneth Gaver, William Lastrapes, Jeffrey Netter, Annette Poulsen, Anand Srinivasan and the anonymous referee for valuable comments. Milind Shah provided excellent research assistance. All errors are ours alone. (a)

Introduction Companies cooperate with one another because they believe that the cooperation can be beneficial to them. Generally speaking, inter-organizational cooperation can take three forms: (a) simple contracts1 ; (b) mergers and acquisitions (M&As) and takeovers; (c) joint ventures (JVs) and alliances. The forms of cooperation have different consequences for the autonomy of the parties post closing. Consequently, the parties choose their desired form of cooperation based, at least in part, on the amount of autonomy they are willing to surrender. The most basic form of inter-organizational cooperation is the simple contract. Examples include customer-supplier contracts, service contracts, and distribution contracts, as well as many others. While simple contracts can be incomple te (e.g., Hart 1995; Aghion and Tirole (1997); Hart and Moore (1999)), their dominant feature, relative to other forms of inter-organizational cooperation, is that they typically do not require transfer of assets and control so that the parties to the contract remain autonomous. M&As and takeovers are the most sophisticated forms of inter-organizational cooperation. By definition, they entail transfer of assets and control, result ing in significant change in the autonomy of at least one of the parties. The finance literature generally proposes synergy (e.g., Berkovitch and Narayanan (1993)), agency (e.g., Amihud and Lev (1981); Jensen (1986); Shleifer and Vishny (1989)), and hubris (e.g., Roll (1986)) as primary motives for M&As and takeovers. JVs and alliances are intermediate forms of inter-organizational cooperation. JVs are separate business entities established by the partners in order to achieve a mutual task. The partners agree on the allocation of ownership of the JV, as well as its cash flows, based on negotiated terms that generally reflect the amount of assets and capital each contributed. Alliances are similar to JVs, but do not involve creation of a separate business entity. They do involve contribution of assets and capital, but the mutual task is achieved inside the partners’ respective firms, not inside a stand-alone JV company. Because JVs and alliances involve contribution of assets and capital, they are more complex forms of inter-organizational cooperation than simple arm’s-length contracts. Since partners maintain their autonomy in JVs and alliances, this form of inter-organizational cooperation is distinct from M&As and takeovers. One of the advantages of JVs and alliances is that they allow participants to preserve their organizational autonomy while realizing the benefits of coordinated activities (e.g., Kogut (1991)). In light of the recent trend in globalization (see 1

They are sometimes referred to as commercial contracts.

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e.g., World Bank (2002)), it is important to study JVs and alliances because they allow multinational corporations (MNCs) to perform complex mutual tasks, otherwise impossible with simple arm’s-lengths contracts, without acquiring one another.

This feature is especially

important to MNCs because their sophisticated operations often make simple arm’s-length contracts insufficient, but their substantial assets make M&As and takeovers very expensive. This paper describes global trends in JVs and alliances for the period 1990 to 2000, utilizing the Thomson Financial SDC Platinum Alliances/Joint Ventures database. We survey theoretical and empirical literature on JVs and alliances, and provide a detailed description of the world of JVs and alliances as depicted by the SDC database. Our results highlight a number of noteworthy observations related to the forms and types of JVs and alliances, their industry and geographic distribution, and patterns of ownership. First, we establish that JVs and alliances are flexible inter-organizational cooperative mechanisms that allow multiple domestic and foreign partners to form business entities which operate in single or multiple countries. While some deals include as many as twenty partners, others have operations in as many as eighteen countries. For international cooperation, firms use JVs more frequently than alliances; for domestic cooperation, alliances are more common. Data on the dollar size of JVs and alliances is rarely disclosed, making qualitative judgments difficult. Based on the relatively small number of transactions (about 7% of the dataset) that disclose dollar size, it appears that JVs are somewhat smaller than alliances.

The paper also confirms a

previously observed contraction in the frequency of JVs and alliances around the globe after 1995, which is commonly attributed to liberalization of foreign investment regimes in various host countries (e.g., UNCTAD (2000); Desai, Foley and Hines (2004)). Second, the data reveal strong industry clustering of JVs and alliances. More than half of the transactions in the dataset occurred in only ten industries, many of which are technologically intensive. Interestingly, in almost all industries in the “top-ten” list, the percentage of deals performed via strategic alliances is high, suggesting that alliances stand at the core of global cooperative activity. Additionally, we find that, when partners come from different industries they are more likely to establish an alliance than an independent JV firm, possibly because it is less expensive to dissolve an alliance if the project fails. Third, we study the types of JVs and alliances, which are classified by the SDC dataset as: licensing, technology, exploration, manufacturing, marketing, R&D, and supply and equipment manufacturing/value-added reseller. Since JVs and alliances often belong to mult iple categories, we observe the correlations among these types.

Additionally, we identify the

industries in which the various types of JVs and alliances occur most frequently. For example,

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we show that exploration-agreement JVs have the highest industry clustering, with over 95% in the mining and the oil and gas industries. The frequency of transactions with cross-border participants in technology-agreement JVs is higher than in R&D-agreement JVs, indicating that foreign partners are more likely to receive, than to jointly develop, new technology. Fourth, we find substantial country clustering in the geographic distribution of JVs and alliances. Of 179 countries around the globe, only seven countries accounted for 63.4% of global JVs and alliances. In addition, we provide evidence on the geographic distribution of the various types of JVs and alliances. Lastly, we study ownership patterns in JVs and alliances. We present evidence on the amount of partners’ equity stakes in JVs and alliances, and the frequency of equal ownership. Our results corroborate existing evidence (e.g., Hauswald and Hege (2004)) that partners in JVs and alliances have a preference for equal ownership , and we extend the literature by showing that this phenomenon holds internationally and for multiple -partner JVs and alliances. The paper is organized as follows.

First, we review the theoretical and empirical

literature on JVs and alliances. Second, we discuss the data. Third, we present our results. Lastly, we conclude and propose avenues for future research.

Review of the Theoretical Literature on JVs The transactions costs theory (TCT) and the property rights theory (PRT) establish the theoretical framework for understanding JVs. The TCT, developed by Klein, Crawford and Alchian (1978), and others, emphasizes that, as assets become more specific, potential gains from opportunistic behavior by trading partners increase. Further, contracting costs typically increase more than the costs of vertical integration so that vertical integration is more likely than contracting. Oxley (1997), in her study of the relationship between appropriability hazards and governance, finds strong support for the TCT. She first develops a market-hierarchy continuum of alliances, and then demonstrates empirically that, as appropriability hazards increase, more hierarchical alliances are chosen. The property rights theory (PRT) developed by Grossman and Hart (1986), Hart and Moore (1990), and others, is based on two types of contractual rights: specific rights and residual rights. A contract can identify the specific rights that one party will have over another party’s assets. Alternatively, it may be optimal for one party to purchase residual rights of control, that is, all rights except those specified in the contract. In the PRT framework, joint ownership is suboptimal because of the lack of incentives resulting from the sharing of residual control rights. Whinston (2002) provides a careful summary of the differences between the two theories and

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shows that the PRT’s predictions differ in important ways from those of the TCT, and that the existing empirical evidence that is supportive of the TCT sheds little light on the empirical relevance of the PRT. Cai (2003) extends the TCT to situations where parties can choose both the type and level of investment. He shows that the PRT results are obtained when specific and general investments are complementary but, when specific and general investments are substitutes, joint ownership provides incentives to make specific investments, and can therefore be an optimal structure. Moral hazard issues that arise in cooperative efforts are common in JVs. Holmstrom (1982) showed that a capitalistic firm has an advantage over a partnership in resolving the freerider problem in teams because ownership and labor are partly separated. Legros and Matthews (1993) build on Holmstrom’s work and show that, for some types of partnerships, a sharing rule exists that can elicit an efficient set of actions. These include partnerships in which partners’ actions are perfect complements (Leontief partnerships) or where one partner is not able to affect output. They show that efficiency can be approximated to any desired degree and that free-riding causes inefficiency only to the extent that either the partners’ liability is limited or partners’ wealth is bounded. Vislie (1994) provides similar results, concluding that, when inputs are strict complements (Leontief technology) free-riding can be avoided. In the theoretical literature, optimal ownership allocation has been modeled extensively on JVs. The results indicate that, as a consequence of various partner characteristics, optimal ownership allocation should be asymmetric . Darrough and Stoughton (1989) analyze the impact of private information on profit-sharing arrangements negotiated by partners in JVs. Chemla, Habib and Ljungqvist (2004), and Bhattacharyya and Lafontaine (1989) study the effect of incentive requirements on ownership allocation in JVs. Belleflamme and Bloch (2000) identify the parent’s resource costs as the primary determinant of the allocation of ownership in JVs. Hauswald and Hege (2004) find that, regardless of parents’ attributes, the higher the potential for unilateral value extraction, the more parents prefer equal shareholdings (50-50 equity allocation). Bai, Tao and Wu (2004) , in their study of two hundred Chinese JVs with foreign partners, demonstrate both less clustering of JVs at 50-50 ownership than do Hauswald and Hege (2004), as well as increased joint control as the severity of the expropriation problem increases. They also document that the foreign partner’s control rights and revenue share increase with the number of tasks assigned to the foreign partner and the technological sophistication of the industry, but they decrease with the need for marketing.

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Review of the Empirical Literature on Domestic JVs Empirical literature on domestic JVs is limited. One strand of this literature analyzes the reaction of stock price to announcements of formation of alliances and JVs. The other addresses issues related to allocation of control in strategic alliances between pharmaceutical and biotechnology research companies. A number of event studies document positive and significant announcement returns related to formation of domestic strategic alliances and JVs. In their investigation of 136 domestic JVs between 1972 and 1979, McConnell and Nantell (1985) find significant wealth gains from JVs. Since returns to stockholders are comparable to those resulting from mergers, the authors conclude that their results are supportive of the hypothesis that synergy is the source of gain from corporate combinations. Chan, Kensinger, Keown and Martin (1997) report similar results in their study of 345 strategic non-equit y alliances over the period 1983-1992. Johnson and Houston (2000) study returns generated by 119 domestic JVs, distinguishing between horizontal and vertical transactions. They find that horizontal JVs produce synergistic wealth gains that are shared by the parties to the JV, while vertical JVs create significantly positive mean excess return only for suppliers. The authors conclude that vertical JVs are used when the potential for hold-up problems is substantial and as a financing alternative for suppliers. Allen and Phillips (2000) examine the relationship between long-term block ownership by corporations and changes in target firms’ stock prices, investment policies and profitability. They find the largest significant increases in targets’ performance when corporate block ownership is accompanied by alliances, JVs, and other product market relationships between purchasing and target firms. Mohanram and Nanda (1998) report significant positive abnormal returns around announcement date for a sample of 253 domestic JVs announced between 1986 and 1993.

Further, they find a tendency for JV announcements to be made when parent

performance, measured by stock price and accounting data, is deteriorating. Event studies of domestic JVs have received some attention in the real estate literature. Ravichandran and Sa-Aadu (1988) find a statistically significant positive two-day announcement period return for a sample of real-estate JVs over the period 1972-1983. Further, they identify three characteristics of real estate markets that apparently contribute to the significant abnormal returns: information asymmetry, technical expertise in real property management, and the signaling effect of anchor tenants for commercial properties. Elayan (1993) reports results supportive of both the synergy effect and the hypothesis that informational asymmetries provide opportunities for real estate firms to earn greater returns from joint ventures. He finds a positive, statistically significant announcement effect for all real

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estate JVs in his sample, and a statistically significant difference in excess returns between real estate and non-real estate firms. The former result is consistent with the synergy hypothesis and the latter is consistent with the informational asymmetries hypothesis. In contrast, a study by Corgel and Rogers (1987) does not provide evidence of synergies in real estate JVs. Their analysis of twenty-four JVs over the period 1979-1985 indicates no significant announcement effect for real estate development JVs. The second strand of empirical literature on domestic JVs investigates the allocation of control in strategic alliance agreements, often between pharmaceutical and biotechnology research companies. Lerner and Merges (1998) examine the determinants of control rights in biotechnology alliances between R&D firms and larger firms with substantial financial resources and find that increased control rights are allocated to R&D firms as their financial resources increase.

Lerner, Shane and Tsai (2003) study two hundred alliance agreements between

biotechnology firms during the period 1980-1995. They find that, when external equity financing is readily available, alliance agreements tend to assign greater control rights to the R&D firm; such alliances are more successful. In a related work, Robinson (2005) develops a model that predicts firms will prefer alliances over internal projects when the risk of the alliance activity is greater than the risk of the firm’s primary activities. In support of his model, he finds that alliances tend to occur in risky, high-tech industries, and when industries have different risk characteristics. Elfenbein and Lerner (2003) analyze alliances involving Internet portals between 1995 and 1999 to determine the extent to which contract theory explains the division of ownership and the allocation of control rights. They find that, consistent with incomplete-contract theories, ownership was assigned to the party whose effort was most critical to project success, and the allocation of control rights was sensitive to the parties’ relative bargaining power. Review of the Empirical Literature on International JVs Empirical studies of international JVs (IJVs) find that multinational firms establish IJVs in a manner consistent with the predictions of the TCT. Beamish and Banks (1987) demonstrate empirically that, under certain circumstances, JVs in developing countries can be preferable to wholly-owned subsidiaries as a mode for foreign investment. Asiedu and Esfahani (2001) use a dataset of subsidiaries of U.S. multinationals to show that foreign equity share increases with the contribution of multinationals’ assets to the surplus created by FDI projects, and decreases with the contribution of local assets. Gatignon and Anderson (1988) support the TCT as well and find that parents seek a higher level of ownership in affiliates that make greater use of proprietary

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assets. Hennart (1991) reports that the results of his empirical study of the factors that determine the degree of ownership by Japanese manufacturing firms in their American subsidiaries is consistent with the TCT and with the determinants of ownership choices made by U.S firms; that is, Japanese manufacturing investors form JVs in order to combine intermedia te inputs that have high transaction costs. Ramachandran (1993) studies the transfer of technology from developed countries to firms in India and shows that 100%-owned subsidiaries of foreign multinationals receive more resources than do Indian-owned firms or subsidiaries partially-owned by foreign multinationals. The issue of resource complementarity has been empirically studied in the context of IJVs.

Balakrishan and Koza (1993) view the joint venture as an alternative to merger or

acquisition when information asymmetry about the target firm’s assets results in high transaction costs for M&As. They theorize that the joint venture is an efficient mechanism for pooling complementary assets when the parents come from dissimilar businesses.

Their theory is

supported by an event study of 64 JV announcements, which demonstrates that, in general, joint ventures create value, with abnormal returns that are significantly larger when the parents operate in businesses with technological and managerial differences. Furthermore, abnormal returns to acquirers and targets involved in M&As were greater when acquirer and target firms are in similar businesses. Blomstrom and Zejan (1991), using data from Swedish multinationals, find that firms with limited foreign production experience and diversified product lines are most likely to choose minority ventures. Gomes-Casseres (1989) utilizes data from subsidiaries of 180 U.S. multinationals and demonstrates that, in selecting ownership structure, multinationals choose JVs with host-country firms rather than wholly-owned subsidiaries if the local firm’s assets complement those of the multinational. An additional issue that has received attention in the IJV literature is the impact of government policy on foreign ownership of domestic firms. Gomes-Casseres (1990) studies the conditions under which firms facing significant government restrictions on foreign ownership choose to completely forego foreign investment. His results suggest that firms’ decisions whether to forego investing depend on the characteristics of both the acquiring firm and the proposed subsidiary, such as firm size and the intra-system sales of the subsidiary. Franko (1989) analyzed the increased frequency of minority and 50-50 joint ventures among U.S industrial multinationals operating in developing countries during the 1970s. He concluded that the observed trend in manufacturing and mining was a consequence of government policies designed to restrict control by foreign investors and promote shared ownership with local firms. Contractor (1990) used Commerce Department data on U.S. firms with foreign affiliates to study the effect of the

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subsequent liberalization of these restrictions during the 1980s. These policy changes led to small but consistent decreases in 50-50 and minority affiliates. Henisz (2000) and Gatignon and Anderson (1988) show that, in host countries with substantial political risk, multinationals can reduce their risk exposure by partnering with local firms. However, as political risk increases, so does the risk that the local firm will exploit the political system at the expense of the multinational firm. Desai, Foley and Hines (2004) study the determinants of partial ownership of foreign affiliates by U.S. multinationals and the decreased use of joint ventures since the 1980s. They show that 100% ownership is most frequent when firms coordinate international production and benefit from technology transfers and worldwide tax planning.

Liberalized ownership

restrictions and joint venture tax penalties imposed in 1986 led U.S. multinationals to increase use of 100% ownership and to increase intrafirm trade and transfer of technology. Finally, we note that a number of event studies have demonstrated that markets view IJVs as value enhancing. Numerous papers (e.g., Lummer and McConnell (1990) , Lee and Wyatt (1990) , Chen, Hu, and Shieh (1991) , Etebari (1993) , Crutchley, Guo, and Hansen (1991), Janakiraman, Lambda and McKeon (1999), Prather and Min (1998), Gleason, Lee and Mathur (2002), Irwanto, Vetter and Wingender (1999), He, Myer and Webb (1997)) document positive and significant abnormal returns associated with IJV announcements. Data The data in this study are from the Thomson Financial SDC Platinum Alliances/Joint Ventures database, which attempts to collect all worldwide JV transactions from SEC filings and its international counterparts, trade publications, wires and other news sources. SDC covers the period 1985 to the present, and is updated daily. This study is conducted for the period 1990 through 2000. The SDC database reports 79,212 transactions announced between 1990 and 2000, of which 61,431 transactions were completed. We impose a number of sample selection restrictions on these data. First, we delete deals that do not have both a “completed/signed” status and a completion date between 1990 and 2000. Second, we delete deals with “NUMP” fie ld (alliance number of participants) set to 1. We consider such deals to be errors since a partnership requires at least two partic ipants. Third, we delete deals with “PSTAKE“ field (Alliance JV Total Ownership by Participant) set to more than 100 percent for any of the first four participants. We also consider such deals to be errors, since a participant can not have more than 100 percent ownership. We are left with 60,446 transactions.

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Results Summary Statistics for Joint Ventures Table 1 shows basic summary statistics. The frequency of JV deals is summarized in Panel A of Table 1. In total, there were 60,446 JV transactions around the globe during the 19902000 period, of which 58.72% had cross-border participants and 37.48% had domestic participants only. SDC defines JVs with cross-border participants as deals where participants’ ultimate parents are not from the same nation. Domestic JVs are deals with all participants from the same nation. This distinction is significant because domestic JVs represent internal business activity in a given country, while JVs with cross-border participants represent international business cooperation.

Additionally, 5,839 multi-regional JVs (9.66% of the total) occurred

during this period; these are defined by SDC as deals with activities in more than one nation. They are created primarily to serve multiple countries, so it is not surprising that 96.35% (5,626 of 5,839) of multi-regional JVs have cross-border participants2 . While the SDC database accurately identifies the occurrence of JV deals, its description of their dollar size is poor. Panel A of Table 1 shows that only 7.42% of all JVs (4,484 of 60,446) disclosed their estimated capitalization3 and another 7.42% disclosed their estimated cost4 . This low rate of reporting is a consequence of the fact that companies typically do not view JVs as major corporate restructurings, and thus are not obligated to report their size. Among those that disclose the dollar amount, some have motivations that can create selection bias. Thus, results derived from SDC’s capitalization and cost data should be viewed with caution. Additionally, most firms that disclose information on the dollar size of JVs report either estimated capitalization or estimated cost, but not both. According to SDC, only 402 JV deals (0.67%) disclosed both items. We believe that companies are reluctant to report both items in order to avoid disclosing the portion of the JV that is financed by debt. By disclosing either the equity portion (i.e., estimated capitalization) or total assets (i.e., estimated cost), companies avoid disclos ing the amount of debt used to finance the deal. Panel B of Table 1 describes the number of participants in JVs. The vast majority of JV deals (87.02%) are comprised of two partners, and a fairly sizable number are established by three partners (9.07%). While two- and three-partner JVs are the most frequent, a small number of transactions have as many as twenty participants. We confirm these results by analyzing 2

In order to avoid additional complexity, we do not to distinguish between JVs with cross-border participants that are located in the country of one of the participants versus those located in a third country (i.e., an American and a Mexican partner establishing a JV in Mexico versus Canada). 3 "Estimated Capitalization" is defined by SDC as the JV’s assets minus debt. 4 "Estimated Cost" is the overall amount participants expect to spend on the JV.

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separately JVs with cross-border participants, multi-regional JVs and domestic JVs. The results are similar, with a slightly greater tendency towards two-partner JVs among domestic JVs. Panel C of Table 1, which reports the number of countries in which JVs operate, indicates that 85.0% of JVs intend to operate in only one country, and 8.2% in two countries, while the remaining deals cover as many as eighteen countries. Compared with the full sample , JVs with cross-border participants have a somewhat greater tendency to serve two countries (12.7% versus 8.2%), primarily because they are comprised of international participants and have a broader geographic scope. Multi-regional JVs operate primarily in two countries (90.68%), with some transactions serving three countries (5.69%). Virtually all domestic JVs serve only one (i.e., domestic) country (99.93%), with a small number of unique cases serving more than one country. Panel D of Table 1 describes the forms utilized in cooperative activities: strategic alliances versus independent JV firms.

SDC defines a strategic alliance as “a cooperative

business activity, formed by two or more separate organizations for strategic purposes, which does NOT create an independent business entity, but allocates ownership, operational responsibilities, and financial risks and rewards to each member, while preserving each member's separate identity/autonomy.” An independent JV firm is: “a cooperative business activity, formed by two or more separate organizations for strategic purposes, which creates an INDEPENDENT business entity, and allocates ownership, operational responsibilities, and financial risks and rewards to each member, while preserving each member's separate identity/autonomy. The new entity can either be newly formed or a combination of pre-existing units and/or divisions of the members.” The distinction between these forms is significant because of the differential consequences for the real economy. A strategic alliance benefits the real economy by allow ing partners to cooperate in flexible and inexpensive ways. Since no new stand-alone business entity is created in a strategic alliance, transaction costs are lower, allow ing more funds to be committed to investment projects, as compared to similar initiatives executed by independent JV companies. Although strategic alliances are inexpensive to establish, they are less likely to grow and to accumulate new assets than are independent JV firms. The growth of strategic alliances is impeded by the fact that they are embedded inside the assets of the parents’ companies. Hence, the real economy benefits from independent JVs because they grow and accumulate new assets more quickly than do similar strategic alliances. Panel D of Table 1 shows that 56.5% of JVs are set up as strategic alliances and 43.5% as independent JV firms. For JVs with cross-border participants, the relationship is reversed: most are formed as independent JV firms (53.6%) and a minority as strategic alliances (46.4%). We

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believe this latter result is a consequence of the greater difficulty inherent in coordinating strategic alliances with partners from different countries. This argument is supported by the data for domestic JVs: 69.2% are formed as strategic alliances and only 30.8% as independent JV firms. Strategic alliances are common among multi-regional JVs (64.8%), primarily because it is more expensive to achieve multi-regional access by establishing independent JV firms in different countries. Panel E of Table 1 shows the dollar size of JVs. The estimated capitalization of the median JV in the dataset is 8 million US dollars and the median estimated cost is 30 million US dollars. These medians are substantially less than the respective means (79.07 and 264.56 million US dollars), implying that some JV transactions are very large 5 . On the other hand, the mean and median estimated capitalization and cost of JVs with cross-border participants, multi-regional JVs and domestic JVs are similar6 . Strategic alliances are substantially larger than independent JV firms. The mean and median capitalization for strategic alliances are 138.6 and 20 million US dollars, respectively, but only 76.4 and 7.6 million US dollars, respectively, for independent JV companies. Similar differentials are observed for estimated cost (308.4 and 25.1 million US dollars versus 253.3 and 30 million US dollars, respectively). The finding that strategic alliances are larger than independent JV firms supports the argument that firms commit more funds to investment projects in strategic alliances than in independent JV firms because the former entities have lower transaction costs. Table 2 reports the frequency and size of JVs annually, from 1990 through 2000. The primary finding, also apparent in Graph 1, is that frequency of JVs contracted after 1995. The number of JVs increased steadily from 1990 through 1995, but then fell sharply in 1996. The reduced propensity of MNCs to use JVs can be attributed to a number of factors, including the liberalization of foreign investment regimes in various host countries (e.g., World Investment Report (2001); Desai, Foley and Hines (2003)).

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We manually verified that, for all transactions reporting estimated capitalization and cost, the CAPEST and COST fields (estimated capitalization and cost, respectively) are equal to the data in the synopsis of the deal (SYN field). We found that SDC often confused thousands with millions (i.e., inserted size figures in thousands rather than in millions). 6 The mean estimated cost of multi-regional JVs is substantially larger than that of the full sample (452.44 versus 264.56 million of US dollars). Since this result is based on only 292 observations, it may be biased by a few observations with very large values.

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Distribution of JVs by Industry In this section, we discuss the industry distribution of JV deals around the globe. Tables 3 through 5 present evidence regarding the frequency, size and form of JVs by industry in which they were established. Table 3 ranks all industries, defined by SDC original industry classification, according to the number of JV deals between 1990 and 2000. We find more than half of all JV deals clustered in only ten industries, with the largest number of JV deals in business services7 (13.6% of all JV deals), followed by the prepackaged software industry (8.2%).

The remaining “top-ten”

industries are: wholesale trade of durable and non-durable goods (6.7% and 2.9%, respectively), drugs (5.3%), electronic and electrical equipment (4.6%), investment and commodity firms (3.9%)8 , chemicals and allied products (3.4%), telecommunications (3.0%), and communications equipment (2.9%). With the exception of the wholesale trade of durable and non-durable goods, business services, and investment firms, the “top-ten” industries are technologically intensive. These are typically riskier industries, so that JVs often become the preferred form of organizational cooperation because they allow the partners greater ability to reduce risk. Compared with arm’slength contracting, for example, the risk is lower in JVs because partners share the costs, technologies and know-how, reduc ing uncertainties related to development and employment of the risky project. On the other hand, compared with M&As, risk is lower in JVs because they do 7

SDC defines the business services industry very broadly. The SIC codes used by SDC are : 7322Adjustment and collection services; 7323-Credit reporting services; 7331-Direct mail advertising services; 7334-Photocopying and duplicating services; 7335-Commercial photography; 7336-Commercial art and graphic design; 7338-Secretarial and court reporting services; 7342-Disinfecting and pest control devices; 7349-Building cleaning and maintenance services; 7352-Medical equipment rental and leasing; 7353Heavy construction equipment rental and leasing; 7359-Equipment rental and leasing; 7361-Employment agencies; 7363-Help supply services; 7369-Personnel supply services; 7371-Computer programming services; 7373-Computer integrated systems design; 7374-Data processing services; 7375-Information retrieval services; 7376-Computer facilities management services; 7377-Computer rental and leasing; 7378-Computer maintenance and repair; 7379-Computer related services; 7381-Detective, guard, and armored car services; 7382-Security systems services; 7383-News syndicates; 7384-Photofinishing laboratories; 7389-Business services; 8711-Engineering services; 8712-Architectural services; 8713Surveying services; 8721-Accounting, auditing, and bookkeeping services; 8731- Commercial physical and biological research; 8732-Commercial nonphysical research; 8733-Noncommercial research organizations; 8734-Testing laboratories; 8741-Management services; 8742-Management consulting services; 8743Public relations services; 8744-Facilities support management services; 8748-Business consulting services. 8

The SIC codes used by SDC for the investment and commodity firms industry are: 6722-Management investment offices, open-end; 6732-Educational, religious, and charitable trusts; 6733-Trusts, exc. educational, religious, & charitable; 6792-Oil royalty traders; 6794-Patent owners and lessors; 6799Investors; 6211-Security brokers, dealers, and flotation companies; 6221-Commodity contracts brokers and dealers; 6231-Security and commodity exchanges; 6282-Investment advice; 6289-Security and commodity services; 6726-Investment offices; 619B-Special purpose finance company; 6798-Real estate investment trusts.

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not require permanent transfer of assets and control (i.e., the parents’ respective companies stay intact), and, furthermore, if the project proves unsuccessful, the parents can quickly and inexpensively dissolve the JV. The results are confirmed for JVs with cross-border participants, multi-regional JVs and domestic JVs. The frequency rankings within each of the three sub-groups are similar to that of the full sample . The only substantial difference is found in the top two industries among domestic JVs (business services and prepackaged software), which exhibit a somewhat greater concentration of deals compared to the full sample . Together, these two industries accounted for 30.5% of all domestic JV deals, which is more than in the full sample (21.8%), in the sub-group of JVs with cross-border participants (15.8%), and among multi-regional JVs (15.7%). It is possible that this concentration is related to country clustering. We revisit this issue later in the paper in our discussion of the distribution of JVs by country in which they are established. Table 4 reports mean and median capitalization and cost of JVs by industry. In the full sample, the industries with the highest mean estimated capitalization are aerospace and aircraft (466.6 million US dollars), oil and gas (359.5 million US dollars), and telecommunications (202.3 million US dollars). The same result holds for mean estimated cost, with the exception that the telecommunications industry (399.2 million US dollars) is replaced by the paper and allied products industry (729.9 million US dollars). Within the “top-ten,” telecommunications has the highest mean estimated capitalization (202.3 million US dollars), followed by the communications equipment (124.3 million US dollars) and the chemicals and allied products industries (76.2 million US dollars). Similar results hold for estimated cost, with the business services industry reporting the second highest mean (237.5 million US dollars). Although these statistics should be viewed with caution, since they are based on a small subset of the database, it appears that the largest JV deals are in the aerospace and aircraft, oil and gas, telecommunications, paper and allied products, and chemicals and allied products industries. Since these industries are very capital intensive, firms in these industries often use JVs as a form of cooperation because JVs do not require acquisition of each other’s assets (i.e., to perform M&A), which would otherwise be very expensive given the substantia l size of these firms. Having ranked by mean and median estimated capitalization and cost for JVs with crossborder participants, multi-regional JVs and domestic JVs, we are able to identify other industries with large JV transactions. First, the motion picture and production industry has the third highest mean estimated capitalization (218.4 million of US dollars) among JVs with cross-border participants. Second, the rubber and miscellaneous plastic products industry has the highest mean estimated capitalization (486.0 million US dollars) for multi-regional JVs. Third, the credit

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institutions industry has the largest mean estimated cost (907.3 million US dollars) and the electric, gas and water distribution industry has the third largest mean estimated cost (599.5 million US dollars) for domestic JVs. Although these results are somewhat interesting, we do not have sufficient information regarding the dollar size of JVs to draw meaningful conclusions from our observations. Table 5 presents evidence on the relationship between the two forms of JVs (strategic alliances versus independent JV firms) and the industries in which they occur. The business services, prepackaged software, and wholesale trade of durable goods industries accounted for the most strategic alliances, with 17.8%, 13.0% and 9.0% of all deals, respectively.

Among

independent JV firms, the industries with the highest concentration of deals were business services, chemical and allied products, and transportation equipment (8.1%, 5.5% and 4.7%, respectively). Noteworthy differences in the industry distribution of strategic alliances and independent JV firms are revealed in Table 5. For example, the prepackaged software industry accounted for 13% of all strategic alliances but only 2.0% of all independent JV firms. Similarly , the drug industry experienced 7.9% of all strategic alliances but only 1.8% of all independent JV firms. These substantial differences suggest that strategic alliances and independent JV firms serve inherently different purposes in certain industries.

It is important that we improve our

understanding of the factors that influence companies in certain industries to prefer strategic alliances over independent JV firms, and thus more research is needed in this area. We also investigate the forms of JVs that are the most frequent in each industry9 . We find that strategic alliances are most frequent in the prepackaged software industry, where 89.5% of all transactions were executed as strategic alliances. Interestingly, for almost all industries in the “top-ten” list (with the exception of chemicals and allied products) the percentage of deals performed via strategic alliances is high (54.6% and above). This result , along with our earlier finding that the industries in the “top-ten” list account for the majority of global JV activity, suggests that strategic alliances stand at the core of this activity, while independent JV firms represent more specialized cases. Next, we investigate the form and industry of JVs having the highest percentage of crossborder participants. JVs with cross-border participants are economically important because they facilitate transfer and sharing of important tangible and intangible assets among partners from

9

Between strategic alliances and independent JVs, the percents (%) within an industry sum to 1 (i.e., the sum of columns (e) and (i) is equal to 1).

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different countries. Examples of these assets are technologies, management practices, accounting procedures and governance standards. Overall, 50.4% of strategic alliances have cross-border participants, with the highest percentage of strategic alliances with cross-border participants in the air transportation and shipping industry.

Of 271 transactions in that industry, 229 (84.5%) had cross-border

participants. The two industries with the highest concentration of JV transactions (i.e., business services and prepackaged software) were among the industries with the lowest ratios of strategic alliances with cross-border participants. Of 6,077 and 4,435 strategic alliances in the business services and prepackaged software industries, respectively, only 2,443 (40.2%) and 1,552 (35%) transactions had cross-border participants. The frequency of transactions with cross-border participants in independent JV firms is higher than it is for strategic alliances. For the average industry, 71.1% of independent JV firms were established by cross-border participants, as compared with only 50.4% of strategic alliances. Of 42 independent JV deals in the tobacco products industry, 41 (97.6%) transactions had crossborder participants, the highest ratio for this sub-group. Similarly for strategic alliances, the business services and prepackaged software industries account for some of the lowest frequencies of deals with cross-border participants (40.2% and 35.0%, respectively). An important observation derived from Table 5 is the correlation of 0.694 between the percentage of deals with cross-border participants in strategic alliances and in independent JV firms. This moderately high correlation is indicative of a tendency for industries to have a substantial percentage of deals with cross-border participants in both strategic alliances and independent JV firms. On the other hand, there are also industries in which strategic alliances and independent JV firms have different participation of cross-border partners. For example, why is it that 82.9% of independent JV firms in the textile and apparel products industry have crossborder participants and only 37.3% of strategic alliances in this industry have cross-border participants? Similar large differences are observed in the retail trade of general merchandise and apparel industry, the leather and leather products industry, and even in the communications equipment industry, which is on the “top-ten” list. Answers to such questions require further investigation of the complex motivations that lead domestic and foreign partners to choose strategic alliances versus independent JV firms. Lastly, we study the industry distribution of heterogeneous JVs, which we define as transactions where the participants come from different industries (i.e., at least one participant in the JV is from an industry different from the other partners). These JVs are important because they represent intra-industry cooperation, and facilitate creation of new technologies, products

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and services. While the distribution of heterogeneous JVs by industry is similar to that of the full sample 10 , the findings regarding the within-industry frequency of heterogeneous JVs are quite interesting. Our results indicate that, for the average industry, 80.1% of JVs are heterogeneous, pointing to substantial intra-industry cooperation in JVs. For most industries in the “top-ten” list, the percentage of heterogeneous JVs is high, with the greatest frequency (98.0%) in the wholesale trade of durable goods industry. While similar results are observed for many industries outside the “top-ten” list, a few industries have relatively low ratios of heterogeneous JVs. The three industries with the lowest frequency are mining, legal services, and air transportation and shipping, with heterogeneous JVs accounting for 40.4%, 45.2% and 47.2%, respectively, of all JVs. Surprisingly, for the drug industry, which is on the “top-ten” list, only 64.3% of JVs are heterogeneous, which is less than the percentage for many other industries on this list. We do not have a good explanation as to why the frequency of heterogeneous JVs is low in some industries. Clearly, because the operations of the mining and legal services industries are unique, firms in these industries exhibit little cooperation with firms from other industries. However, it is not clear why only 64.3% of drug industry JVs are heterogeneous, especially in light of the fact that that this is a broad, consumer-oriented, research-intensive industry. We also attempt to understand the frequency of the forms of JVs (strategic alliances versus independent JV firms) among heterogeneous transactions.

After sub-dividing

heterogeneous JVs into the two forms 11 , we find a 59.5% correlation between the frequency of heterogeneous JVs and the usage of strategic alliances. This result implies that, when partners from different industries cooperate, they are more likely to do so via strategic alliances. It is possible that, when partners are unfamiliar with each other’s operations, the probability of project failure is increased. Subsequently, they might prefer strategic alliances to independent JV firms because the cost of dissolution is much less for the former than for the latter.

Distribution of JVs by Type The SDC database classifies JVs into eight types of agreements: licensing, technology, exploration, manufacturing, marketing, R&D, supply, and equipment manufacturing/ value-added reseller. In this section of the paper, we describe the industry distribution of JVs by these types. 10

The correlation coefficient between the distribution of heterogeneous JVs and all JVs (full sample) by industry is 99% (i.e., the correlation between columns (l) and (b) is equal to 0.990). 11 The sum of the percentages (within heterogeneous JVs) of strategic alliances and independent JV firms equals the overall frequency of heterogeneous JVs (i.e., the sum of columns (n) and (o) is equal to column (m)).

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According to the SDC, licensing-agreement JVs arise “when one partner grants an exclusive, simple or cross licensing agreement to another partner.” Technology-agreement JVs are created “when an existing or new technology is transferred from one partner to another.” Exploration-agreement JVs arise “in order to explore natural resources, such as oil, gas or minerals.” Manufacturing, marketing, and R&D-agreement JVs are deals “which are based on some kind of manufacturing, marketing or R&D agreement among the partners.”

Supply-

agreement JVs are deals in which “one or more participants supply materials to other participants who then use the materials to create finished products.” Lastly, equipment-manufacturing/valueadded reseller-agreement JVs are deals where “the original manufacturer supplies a product to create and add value to a final product, usually computer equipment or software.” Panel A of Table 6 reports the frequency of the different types of JVs in the dataset. Marketing-agreement JVs are most frequent (28.4% of all deals), followed by manufacturing(22.8%), technology- (18.6%), R&D- (16.7%) and licensing-agreement JVs (15.5%). The three most infrequent types are exploration, supply and equipment manufacturing agreements, with only 3.1%, 2.8% and 1.5% of all deals, respectively. We note that these frequencies are inflated (i.e., sum to more than 100%) because any given JV can belong to more than one type. In order to better understand which JVs can be of multiple types, we report the correlation matrix for the different types of JVs (Panel B of Table 6) and make several observations. First, the data indicate that, when firms collaborate in the areas of technology, R&D, manufacturing or marketing, they often use licenses (48.7% of licensing-agreement JVs are also technology agreements, 33.3% are marketing agreements, 21.2% are R&D agreements, and 19.6% are manufacturing agreements). Second, technology-agreement JVs are often used in conjunction with R&D agreements, marketing agreements and manufacturing agreements (42.1% of all technology-agreement JVs are also classified as R&D agreements, 35% as marketing agreements, and 24.1% as manufacturing agreements), indicating that, when firms share technology they often collaborate on development and marketing as well. Third, manufacturing and marketing activities in JVs are often combined (31.2% of all manufacturing-agreement JVs are also classified as marketing-agreement JVs); this is analogous to the supply and equipment manufacturing activities which also have a high correlation (28.5% of all supply-agreement JVs are also classified as equipment manufacturing/value-added reseller-agreement JVs). Lastly, due to their distinctive nature, exploration-agreement JVs rarely involve other activities, which can be seen from the low correlations with other types of JVs. The last panel of Table 6 (Panel C) reports the dollar size of estimated capitalization and cost for various types of JVs. The results indicate that exploration-agreement JVs have the

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highest mean for both estimated capitalization and cost (220 and 709.3 million US dollars, respectively), primarily because of a number of very large exploration deals established by multinational energy companies and host governments.

Marketing-agreement JVs have the

lowest mean estimated capitalization (24.7 million US dollars), and are among those with the lowest mean estimated cost (94.1 million of US dollars), probably because they require the least commitment of real assets. The size of manufacturing-agreement JVs, whose mean estimated capitalization and cost are 57.7 and 165.5 million US dollars, respectively , is probably the most meaningful in the dataset because these are based on the largest number of observations. Lastly, technology-agreement JVs and R&D-agreement JVs are similar in size.

This is likely a

consequence of the fact that they are somewhat highly correlated since they represent similar fundamental activit ies. Next, we provide evidence on the relationship between the type of JV and the industry in which they occur. Table 7 shows the distribution of JVs by type and industry, and documents the existence of industry clustering. In addition, we report the amount of cross-border activity as related to type-industry dynamics. Of the eight types of JVs, exploration agreements exhibit the highest degree of industry clustering, as one would expect. Two industries, oil and gas together with mining, account for 95.1% of all exploration-agreement JVs. This high degree of industry clustering is not surprising, given the unique nature of natural resource exploration. Of 804 exploration-agreement JVs in the oil and gas industry and 969 in the mining industry, 73.3% and 57.2% of transactions, respectively, had cross-border participants. The higher than average (56.7%) ratio of transactions with cross-border participants in the oil and gas industry can possibly be attributed to its extended chain of activities. The fact that operations are functionally separated (extraction, refining, distribution) allows for extensive participation by cross-border partners. Technology-agreement JVs and R&D-agreement JVs display similar industry distributions. The correlation coefficient here is 0.965, which is not surprising given that they deal with two aspects of technology, either transferring or developing technology. The results in Table 7 confirm this argument and indicate that technologically intensive industries account for the bulk of technology- and R&D-agreement JVs. The prepackaged software industry experienced the most transactions (18.5% and 18.0% of all deals in each group, respectively), followed by the drug (15.3% and 17.5%), business services (10.3% and 16.6%) and the electronic and electrical equipment (9.2% and 8.6%) industries. Two industries outside the “top-ten” list that have relatively high percentages of technology and R&D agreements are the computer and office equipment and the measuring, medical and photo equipment industries (with

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5.0% and 4.0% of all technology agreements and 5.5% and 4.6% of all technology and R&D agreements, respectively). Interestingly, for the average industry, the frequency of transactions with cross-border participants within technology-agreement JVs is higher than within R&D-agreement JVs (63.5% versus 49.5%). This comparison indicates that foreign partners are more likely to receive new technology (in technology-agreement JVs, technology is transferred from one partner to another) than to develop it (in R&D-agreement JVs, partners jointly develop technology). The relatively low correlation coefficient (0.352) provides additional support for this argument. Manufacturing-agreement JVs exhibit a somewhat lower degree of industry clustering than do other types of JVs. The industries in the “top-ten” list account for only 40.4% of all manufacturing-agreement JVs, which is much lower than in other sub-groups 12 .

The two

industries in the “top-ten” list that account for the largest percentage of manufacturingagreements JVs are the electronic and electrical equipment and the chemicals and allied products industries, with 11.3% and 11.4% of all manufacturing-agreement JVs, respectively. Due to their broad appeal, manufacturing-agreement JVs cluster in a number of industries outside the “topten” which do not experience many other types of JVs. These industries are: transportation equipment (9.7%), machinery (6.2%), metal and metal products (6.8%), food and kindred products (6.4%). Additionally, manufacturing-agreement JVs have the highest frequency of transactions with cross-border participants. One of the likely explanations is that, in order to reduce costs, MNCs often outsource production to host countries with less expensive labor; consequently, 70.7% of manufacturing-agreement JVs have cross-border participants. Licensing- and marketing-agreement JVs have similar industry distributions.

The

correlation between these two types of agreements is 0.622, indicating a tendency towards similar industry clustering. The industries with the highest concentration of licensing agreements are prepackaged software, drugs, and investments and commodity firms (with 16.6%, 15.2% and 12.5% of all licensing agreements, respectively). Marketing agreements cluster in wholesale trade of durable goods (19.7% of all marketing agreements), business services (10.7%), prepackaged software (9.0%) and wholesale trade of non durable goods (8.3%). Outside the “top-ten” list, both types of JVs cluster in the computer and office equipment industry (3.4% and 3.4% of all licensing and marketing agreements, respectively), and the measuring, medical and photo equipment industry (3.7% and 2.9%, respectively). Additionally, the frequency of JVs with 12

The cumulative percentages of transactions for industries in the “top-ten” list in other sub-groups, which we do not report in Table 7, are: licensing-agreement JVs (75.1%), technology-agreement JVs (77.8%), marketing-agreement JVs (68.5%), R&D-agreement JVs (75.3%), supply-agreement JVs (62.8%) and equipment manufacturing-agreement JVs (74.6%).

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cross-border participants within licensing-agreement JVs is similar to that within marketingagreement JVs (53.5% versus 60.9%). Lastly, the industry distribution of supply-agreement JVs closely resembles that of equipment manufacturing-agreement JVs; the correlation coefficient is 0.912. Both types of agreements are heavily concentrated in the wholesale trade of durable goods industry (18.1% and 30.1% of all supply and equipment manufacturing agreements, respectively) and the prepackaged software industry (10.3% and 18.3%, respectively). Outside the “top-ten” industries, computer and office equipment accounted for 7.8% of all supply agreements and 11.2% of all equipment manufacturing agreements. Our results document that equipment manufacturing-agreement JVs are heavily concentrated in the wholesale trade of durable goods industry (30.1% of all deals). According to the SDC, these JVs are “transactions in which the original manufacturer supplies a product to create and add value to a final product, and they should be common in the computer equipment and software industries.” While our results confirm this observation, we also document the fact that equipment manufacturing-agreement JVs are frequent in the trade of durable goods industry. This finding may have significance for researchers studying the intricate relationships among participants in supplier-manufacturer-customer chains. Distribution of JVs by Country In this section, we describe the geographic distribution of JVs. Table 8 presents evidence on the distribution of JVs by form and country of operations. Table 9 describes the distribution by type and country of operations. Table 8 documents the existence of substantial country clustering in the distribution of JVs. Of 179 different countries around the globe, the following seven accounted for 63.4% of all JV transactions: the United States, in which 38.0% of all JVs were established13 , China (6.9%), Japan (6.6%), the United Kingdom (4.1%), Canada (3.0%), Australia (2.8%) and Germany (2.2%). Separate analysis of JVs with cross-border participants and domestic JVs14 , indicates that JVs with cross-border participants have substantially lower country clustering than do domestic JVs. The “top-seven” countries account for only 51.8% of all JVs with cross-border participants, compared to 85.9% of all domestic JVs. The United States and China are the primary source of this difference. The former experienced only 20.9% of JVs with cross-border participants, but

13

The results are based on the first nation of the JV’s operations (i.e., 38.0% of all JVs listed the United States as the first country of operations). 14 We do not sort multi-regional JVs by country because by definition they operate in multiple locations.

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67.1% of domestic JVs. The latter experienced 10.5% of JVs with cross-border participants and only 1.8% of domestic JVs. These noteworthy differences can be attributed to differences in the economic development of the two nations. The United States plays a dominant role in the global distribution of domestic JVs because of its active domestic cooperative market. The enormous size of its capital markets combined with stable politic al and legal environments make it relatively easy for domestic firms to cooperate. Consequently, 67.1% of domestic JVs occurred in the United States. On the other hand, relative to other countries, the United States generates a much smaller number of JVs with cross-border participants. While it still has a leading position (20.9% of all JVs with cross-border participants were established in the United States), other countries, such as China and Japan, have narrowed the gap, generating 10.5% and 7.8% of all JVs with cross-border participants, respectively. This result might be informative to researchers analyzing the behavior of MNCs who seek to better understand the factors affecting economic globalization. Additional results in Table 8 indicate that strategic alliances have much stronger country clustering than do independent JV firms. The “top-seven” countries generated 72.9% of strategic alliances and 51.1% of independent JV firms. The United States alone accounted for more than half of all strategic alliances (52.8%), followed by Japan (7.8%) and Canada (3.5%). The United States accounted for only 18.7% of independent JV firms, followed by China (13.7%) and Japan (5.1%). Some countries outside the “top-seven” accounted for a relatively large number of independent JV firms, including India (3.6%) and Malaysia (3.3%). The difference between the United States and China in terms of the frequency of strategic alliances and independent JVs can again be attributed to differences in economic development. China is very much foreign-production oriented15 , has substantial state involvement, and imposes significant restrictions on foreign investment. As a consequence, foreign MNCs are forced to use independent JV firms as a primary mode of entry into China. It seems reasonable to assume that the Chinese government forces MNCs to use independent JV firms for entering China in order to enhance development of Chinese infrastructure. The United States accounted for more than half of all strategic alliances for the same reason that it accounted for more than two-thirds of all domestic JVs: these strategic alliances are driven by the very active cooperative market in the United States. Our results, which are not reported in Table 8, indicate that, of 18,028 strategic alliances in the United States, 12,503 (69.35%) were comprised solely of domestic partners. 15

Our results, which are not reported in Table 8, indicate that, of 3,606 independent JV firms established in China, 3,249 transactions (90.15%) had cross-border participants.

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Table 9 reports the distribution of various types of JVs by country of operations. Analysis of this distribution can improve our understanding of host countries’ specializations regarding these types of activities. Two primary conclusions are apparent from an examination of Table 9. First, the United States is the leader in almost all types of JVs16 , which is not surprising given the size of the U.S. economy. Second, JVs with cross-border participants are less frequent in the United States than in other countries. This result is pr imarily driven by the high level of domestic cooperative activity in the United States. Consequently, the frequency of JVs with cross-border participants is lower in the United States. Our observations of each type of JV reveal that the United States generated 62.3% of all licensing agreements, followed by Japan (6.2%), Canada (3.3%) and the United Kingdom (2.7%). Only 32.2% of licensing-agreement JVs established in the United States had cross-border participants. In Japan, Canada and the United Kingdom, licensing-agreement JVs had much higher ratios of cross-border participants (93.3%, 71.1% and 76.3%, respectively). Technology- and R&D-agreement JVs exhibit similar country clustering. The United States generated the majority of these transactions (57.9% and 58.1%, respectively) , followed by Japan (8.3% and 8.5%, respectively), China (3.9% and 1.9%, respectively) and the United Kingdom (2.7% and 2.7%, respectively). China accounted for a somewhat greater number of technology-agreement JVs (3.9%) than R&D-agreement JVs (1.9%), probably because the former are more production oriented than the latter. The only category for which the United States is not the leading country is explorationagreement JVs. Australia generated the largest share (14.8% of all transactions), followed by the United States (14.4%), Canada (13.2%) and the Russian Federation (4.1%). The participation of cross-border partners in these agreements is somewhat low (38.8%, 41.0% and 42.5% for the top three countries, respectively), which points to the existence of substantial domestic cooperative activity. At the same time, the frequency of cross-border participants in exploration-agreement JVs in the Russian Federation was quite high (89.6%). This is probably a consequence of the fact that Russian domestic firms were not very active between 1990 and 2000, so that most exploration activity was generated through foreign cooperation. Interestingly and unfortunately, none of the natural resource-rich countries of Africa and Latin America are high on this list, likely a consequence of foreign investment restrictions and political instability. Manufacturing-agreement JVs are concentrated in the United States (24.3% of all transactions), China (17.5%), Japan (6.1%) and India (3.9%). Given that the United States and Japan are economically developed countries, it is not surprising to find them at the top of this list. 16

Except for exploration-agreement JVs.

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On the other hand, the substantial number of these agreements in China and India adds to the growing evidence on outsourcing of production from expens ive- to cheap-labor countries. The degree of foreign participation in manufacturing-agreement JVs is quite high. In the United States, 44.0% of manufacturing-agreement JVs had cross-border participants, the highest ratio for any type of JV in the U.S. In China and India , almost all manufacturing-agreement JVs had cross-border participants (92.4% and 90.7% of the transactions , respectively), which is solely a consequence of the outsourcing phenomenon. In Japan, 71.3% of manufacturing-agreement JVs had cross-border participants, indicating that, while foreigners play a substantial role in Japanese manufacturing-agreement JVs, some domestic firms there continue to cooperate with each other for production reasons. Further investigation of this issue reveals that 50.4% 17 of 240 Japanese manufacturingagreement JVs that did not have cross-border participants (i.e., 28.7% of 835 manufacturingagreement JVs) were established in technologically advanced industries. That is, 53 (22.1% of 240) transactions were established in the chemicals and allied products industry, 45 (18.8%) in the electronic and electrical equipment industry and 23 (9.6%) in the transportation equipment industry. We see that domestic Japanese manufacturing-agreement JVs cluster in high-tech industries, possibly as a consequence of these firms’ unwillingness to share technology with foreigners, which is partially an artifact of the Keiretsu system.

This result might have

importance for researchers who study industrial organization of the Japanese economy. Marketing-, supply- and equipment manufacturing-agreement JVs cluster in the United States, Japan and China, but otherwise, the frequency of transactions is fairly uniform. An important observation is the rarity of equipment manufacturing-agreement JVs established in China; these JVs are classified by the SDC as deals where an original manufacturer supplies a product to create and add value to a final product, usually computer equipment or software. Given that China generated 3.4% of worldwide supply agreements, one might expect a similar frequency of equipment manufacturing agreements there. However, according to the SDC, only three such transactions were signed in China, which raises the question as to whether this represents a recording error or a fundamental feature of the Chinese economy.

Ownership Patterns in JVs The last section of the paper addresses patterns of ownership in JVs. Table 10 shows the mean and median percentage partners’ equity stake in JVs, as well as the frequency of equal ownership. 17

The results are reported for two-, three-, four- and five-participant JVs, and

We do not report these results in Table 9.

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confirmed for JVs with cross-border participants, multi-regional JVs and domestic JVs. Examination of these data reveals that partners of lower count have lower equity stake in the JV; that is, on average, the second partner has less equity ownership than the first, the third partner has less ownership than the second and so on. The SDC does not assign any meaning to the order in which partners in the JV are listed18 , so these results are apparently related to the fact that the media list the partners in order of relative importance within the JV. This observation might be important to researchers who seek to understand the “acquirer-target” dynamics in JVs. The most important finding from Table 10 is the strong preference for equal ownership among JV partners. The empirical evidence shows that, in 71.0% of two-partner JVs, the owners have equal stakes (i.e., both have 50% ownership). Given that two-partner JVs represent 87.02% of global JV activity (see Panel B of Table 1), it is clear that that equal ownership is the dominant form of control. For three-, four- and five-partner JVs, the frequency of transactions with equal stakes is also relatively high (36.1%, 40.9% and 37.8% of all transactions, respectively), but significantly less than for two-partner JVs. There may be two reasons for these results. First, as the number of partners increases beyond two, it becomes increasingly difficult for partners to agree on equal stakes. Second, in deals with numerous partners, it becomes increasingly unlikely that each partner contributes equal assets to the JV. Consequently, it is reasonable to find that, as the number of participant in the JV increases, the probability that they have equal stakes decreases. We confirm these findings by looking separately at JVs with cross-border participants, multi-regional JVs and domestic JVs. Similar results have been previously reported by Hauswald and Hege (2004), who used the SDC data to analyze two-partner American JVs. They conclude that equal ownership is common in JVs because it prevents dominant partners from expropriating small participants. Our results extend the ir findings by show ing that an analogous phenomenon holds internationally and for multiple -partner JVs.

Summary and Future Research This paper describes a global trend in JVs and alliances for the period 1990 to 2000, utilizing the Thomson Financial SDC Platinum database. We survey the existing theoretical and empirical literature on JVs and alliances, and provide a detailed description of the world of JVs and alliances as depicted by the SDC database. Our results reveal that JVs and alliances are flexible inter-organizational cooperative mechanisms, come in a variety of types, have strong 18

It is very likely that the SDC inserts the data on partners in the way it appears in the news announcements.

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industry and country clustering, and the partners in these deals have a clear preference for equal ownership. While the theoretical research continues to develop a better understanding of the structure of various organizational forms, more work is needed in addressing the dynamic relationship among these forms. In particular, under what conditions do firms choose one form of cooperation and then extend it to a more complex form, or sometimes extend to a simple form. For example, when do JVs and alliances lead to M&As and takeovers, and vise versa? When do firms start with arm’s-length contracts, then continue to JVs and alliances, and then to M&As and takeovers? These questions are very important and, if answered, could improve on our understanding of the factors that affect the evolution of firms’ organizational structure.

26

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30

Table 1 Summary Statistics for Joint Ventures Panel A - Frequency of Joint Ventures (JVs) N 60446

All JVs with disclosed Estimated Capitalization with disclosed Estimated Cost with disclosed both Estimated Capitalization and Cost

4484 4487 402

JVs w/ Cross-Border Participants Multi-regional JVs JVs w/ Cross-Border Participants & Multi-regional Domestic JVs

Panel B - Number of Participants in JVs All JVs participants: N % 52597 87.02% 2 5484 9.07% 3 1406 2.33% 4 529 0.88% 5 426 0.70% >5 (max=20) Total 60442 100.00%

35495 5839 5626 22658

(%) 100% 7.42% 7.42% 0.67% 58.72% 9.66% 9.31% 37.48%

JVs w/ Cross-Border N % 29626 83.47% 4071 11.47% 1069 3.01% 417 1.17% 312 0.88% 35495 100.00%

Multi-regional JVs N % 5152 88.23% 469 8.03% 123 2.11% 56 0.96% 39 0.67% 5839 100.00%

Domestic JVs N % 20793 91.77% 1325 5.85% 319 1.41% 108 0.48% 113 0.50% 22658 100.00%

Panel C - Number of Countries where JVs will Operate All JVs JVs w/ Cross-Border countries: N % N % 85.0% 28578 80.5% 51400 1 8.2% 4508 12.7% 4930 2 0.6% 319 0.9% 356 3 0.2% 92 0.3% 103 4 115 0.2% 100 0.3% >4 (max=18) 3538 5.9% 1898 5.3% Missing Country Total 60442 100.00% 35495 100.00%

Multi-regional JVs N % __-_ 5295 90.68% 332 5.69% 99 1.70% 113 1.94% 0 0.00% 5839 100.00%

Domestic JVs N % 22642 99.93% 11 0.05% 2 0.01% 3 0.01% 0 0.00% 0 0.00% 22658 100.00%

Multi-regional JVs N % 3784 64.8% 2055 35.2% 5839 100.00%

Domestic JVs N % 15687 69.2% 6971 30.8% 22658 100.00%

Panel D - Form of JVs Form Strategic Alliance Independent JV firm Total

All JVs N % 34161 56.5% 26271 43.5% 60442 100.00%

Panel E - Dollar Size of JVs (data are in US$ Mil) All Joint Ventures Estimated Capitalization of JV Estimated Cost of JV JVs w/ Cross-Border Participants Estimated Capitalization of JV Estimated Cost of JV Multi-regional JVs Estimated Capitalization of JV Estimated Cost of JV Domestic JVs Estimated Capitalization of JV Estimated Cost of JV Strategic Alliance Estimated Capitalization of JV Estimated Cost of JV Independent JV firm Estimated Capitalization of JV Estimated Cost of JV

JVs w/ Cross-Border N % 16460 46.4% 19035 53.6% 35495 100.00%

Mean

Median

N

79.07 264.56

8 30

4484 4487

81.85 278.70

8.6 30

3560 3327

88.10 452.44

9.75 57.5

388 292

69.74 233.63

5.6 28.45

882 1096

138.6 308.4

20 25.1

196 922

76.4 253.3

7.6 30

4287 3563

31

Table 2 Frequency and Size of Joint Ventures by Year All JVs Est. Capitalization Mean Median N obs 228.1 20.0 210 91.1 8.8 529 75.8 9.0 338 90.1 10.0 711 41.8 6.0 792 40.9 6.6 636 70.0 7.7 252 106.5 10.0 385 96.4 13.2 229 86.6 5.0 160 61.9 3.2 242 79.1 8.0 4484

Estimated Cost Mean Median N obs 269.1 41.3 67 181.7 34.5 236 222.8 33.6 225 295.4 27.7 443 236.2 22.0 1005 233.6 30.0 1101 246.1 30.0 397 304.5 50.0 363 556.9 49.2 244 306.4 51.3 224 207.5 30.7 182 264.6 30.0 4487

Multi-Regional JVs Est. Capitalization N Mean Median N obs 121 198.6 30.0 6 260 161.1 9.1 20 1244 47.7 4.5 99 869 73.8 6.5 87 843 47.2 5.2 26 662 62.3 4.3 23 279 52.0 23.7 12 503 153.1 16.5 64 492 108.0 26.8 46 325 17.5 17.5 2 241 180.1 3.3 3 5839 88.1 9.8 388

Estimated Cost Mean Median N obs 340.4 269.3 4 382.0 200.0 15 264.4 100.0 48 813.0 37.0 35 333.3 50.0 51 432.3 63.5 48 126.1 23.5 21 698.3 50.5 38 572.1 283.5 24 566.0 47.5 4 235.3 107.6 4 452.4 57.5 292

N 3034 5193 5208 6139 7527 8044 4296 5540 4910 5043 5512 60446

Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Total

Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Total

JVs w/ Cross-Border Participants Est. Capitalization N Mean Median N obs 2012 263.8 17.3 163 3367 87.1 8.4 442 2945 82.3 8.9 278 3816 75.5 10.0 582 4758 45.3 7.1 667 5070 42.8 6.5 529 2608 79.2 7.8 214 3076 117.9 10.0 309 2635 111.5 20.0 157 2486 76.7 6.5 108 2722 100.1 4.7 111 8.6 35495 81.9 3560

Estimated Cost Mean Median N obs 296.1 42.0 53 220.1 48.5 177 215.9 52.8 167 336.4 30.0 346 224.6 22.0 774 232.7 30.0 854 253.1 30.0 287 370.9 57.8 255 690.2 54.6 176 270.0 50.0 119 225.0 46.8 119 278.7 30.0 3327

Domestic JVs N 411 821 2132 2186 2700 2901 1629 2378 2215 2530 2755 22658

Est. Capitalization Mean Median N obs 106.3 28.3 44 117.3 15.2 81 50.0 10.0 54 167.9 10.0 118 23.6 3.0 125 32.4 6.8 105 18.0 7.7 38 57.3 4.1 68 65.8 7.0 69 109.2 3.3 51 29.2 2.0 129 69.7 5.6 882

Estimated Cost Mean Median N obs 275.3 45.2 8 111.4 47.6 31 246.8 11.3 56 151.8 10.1 92 281.7 25.5 225 239.4 32.0 241 231.4 33.0 107 148.6 30.9 100 212.0 25.2 68 347.7 52.5 105 174.5 21.0 63 233.6 28.5 1096

Graph 1 Yearly Frequency of JV Deals

9000

All JVs 8000

6000

JVs w/ CrossBorder Participants

5000 4000 3000

Multi-Regional JVs

2000 1000

Domestic JVs

32

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

0 1990

Number of Deals

7000

Table 3 Frequency of Joint Ventures by Industry All JVs

JVs w/ Cross-Border Participants N % Cum %

Multi-Regional JVs

Domestic JVs

N

%

Cum %

N

%

Cum %

N

%

Cum %

Business Services

8205

13.6%

13.6%

3740

10.5%

10.5%

564

9.7%

9.7%

4308

19.0%

19.0%

Prepackaged Software

4953

8.2%

21.8%

1852

5.2%

15.8%

353

6.0%

15.7%

2608

11.5%

30.5%

Wholesale Trade-Durable Goods

4026

6.7%

28.4%

2259

6.4%

22.1%

485

8.3%

24.0%

1566

6.9%

37.4%

Drugs

3201

5.3%

33.7%

1824

5.1%

27.3%

491

8.4%

32.4%

1155

5.1%

42.5%

Electronic and Electrical Equipment

2785

4.6%

38.3%

1859

5.2%

32.5%

399

6.8%

39.3%

805

3.6%

46.1%

Investment & Commodity Firms/Dealers/Exch.

2375

3.9%

42.3%

1362

3.8%

36.3%

156

2.7%

41.9%

989

4.4%

50.5%

Chemicals and Allied Products

2050

3.4%

45.7%

1571

4.4%

40.8%

251

4.3%

46.2%

437

1.9%

52.4%

Telecommunications

1811

3.0%

48.6%

1129

3.2%

43.9%

185

3.2%

49.4%

632

2.8%

55.2%

Communications Equipment

1783

2.9%

51.6%

1059

3.0%

46.9%

233

4.0%

53.4%

590

2.6%

57.8%

Wholesale Trade-Nondurable Goods

1748

2.9%

54.5%

1142

3.2%

50.1%

228

3.9%

57.3%

568

2.5%

60.3%

Oil and Gas; Petroleum Refining

1586

2.6%

1142

3.2%

126

2.2%

427

1.9%

Transportation Equipment

1579

2.6%

1287

3.6%

174

3.0%

275

1.2%

Computer and Office Equipment

1484

2.5%

788

2.2%

168

2.9%

519

2.3%

Measuring, Medical, Photo Equipment; Clocks

1340

2.2%

728

2.1%

177

3.0%

525

2.3%

Mining

1318

2.2%

774

2.2%

43

0.7%

540

2.4%

Machinery

1258

2.1%

962

2.7%

148

2.5%

267

1.2%

Real Estate; Mortgage Bankers and Brokers

1224

2.0%

656

1.8%

39

0.7%

559

2.5%

Metal and Metal Products

1181

2.0%

923

2.6%

117

2.0%

234

1.0%

Construction Firms

1126

1.9%

785

2.2%

83

1.4%

330

1.5%

Food and Kindred Products

1071

1.8%

844

2.4%

85

1.5%

218

1.0%

Transportation and Shipping (except air)

1062

1.8%

753

2.1%

164

2.8%

286

1.3%

Electric, Gas, and Water Distribution

978

1.6%

624

1.8%

71

1.2%

354

1.6%

Radio and Television Broadcasting Stations

798

1.3%

434

1.2%

66

1.1%

349

1.5%

Insurance

765

1.3%

417

1.2%

45

0.8%

343

1.5%

Motion Picture Production and Distribution

575

1.0%

300

0.8%

48

0.8%

259

1.1%

Air Transportation and Shipping

523

0.9%

437

1.2%

150

2.6%

83

0.4%

Textile and Apparel Products

469

0.8%

308

0.9%

44

0.8%

153

0.7%

Stone, Clay, Glass, and Concrete Products

462

0.8%

368

1.0%

37

0.6%

84

0.4%

Miscellaneous Retail Trade

441

0.7%

237

0.7%

24

0.4%

195

0.9%

Printing, Publishing, and Allied Services

441

0.7%

240

0.7%

26

0.4%

190

0.8%

Credit Institutions

423

0.7%

227

0.6%

35

0.6%

191

0.8%

Rubber and Miscellaneous Plastic Products

417

0.7%

321

0.9%

40

0.7%

86

0.4%

Commercial Banks, Bank Holding Companies

397

0.7%

282

0.8%

40

0.7%

112

0.5%

Health Services

393

0.7%

105

0.3%

17

0.3%

286

1.3%

Hotels and Casinos

381

0.6%

258

0.7%

23

0.4%

117

0.5%

Amusement and Recreation Services

341

0.6%

174

0.5%

18

0.3%

161

0.7%

Aerospace and Aircraft

332

0.5%

257

0.7%

75

1.3%

67

0.3%

Miscellaneous Manufacturing

321

0.5%

164

0.5%

19

0.3%

145

0.6%

Paper and Allied Products

275

0.5%

196

0.6%

16

0.3%

77

0.3%

Advertising Services

248

0.4%

135

0.4%

21

0.4%

107

0.5%

Public Administration

236

0.4%

112

0.3%

50

0.9%

121

0.5%

Agriculture, Forestry, and Fishing

234

0.4%

151

0.4%

20

0.3%

75

0.3%

Retail Trade-Eating and Drinking Places

225

0.4%

145

0.4%

20

0.3%

73

0.3%

Sanitary Services

224

0.4%

118

0.3%

21

0.4%

100

0.4%

Soaps, Cosmetics and Personal-Care Products

193

0.3%

148

0.4%

19

0.3%

41

0.2%

Wood Products, Furniture, and Fixtures

182

0.3%

132

0.4%

18

0.3%

46

0.2%

Retail Trade-Home Furnishings

154

0.3%

91

0.3%

13

0.2%

58

0.3%

Retail Trade-General Merchandise & Apparel

149

0.2%

94

0.3%

13

0.2%

51

0.2%

Educational Services

128

0.2%

53

0.1%

5

0.1%

74

0.3%

Repair Services

123

0.2%

90

0.3%

12

0.2%

31

0.1%

Retail Trade-Food Stores

118

0.2%

92

0.3%

16

0.3%

26

0.1%

Other Financial

102

0.2%

51

0.1%

6

0.1%

50

0.2%

Leather and Leather Products

74

0.1%

46

0.1%

6

0.1%

25

0.1%

Miscellaneous Services

72

0.1%

35

0.1%

2

0.0%

35

0.2%

Tobacco Products

55

0.1%

51

0.1%

15

0.3%

4

0.0%

Holding Companies, Except Banks

35

0.1%

25

0.1%

6

0.1%

9

0.0% 0.1%

Industry of JV

Social Services

34

0.1%

9

0.0%

5

0.1%

23

Legal Services

31

0.1%

21

0.1%

5

0.1%

10

0.0%

Personal Services

20

0.0%

9

0.0%

1

0.0%

11

0.0% 0.0%

8

0.0%

4

0.0%

1

0.0%

4

No Industry Classification & Non-classifiable

Savings and Loans, Mutual Savings Banks

1903

3.1%

1095

3.1%

101

1.7%

594

2.6%

Total

60446

100%

35495

100%

5839

100%

22658

100%

33

Table 4 Size of Joint Ventures by Industry All JVs (Data are in US$ Mil)

Estimated Capitalization

JVs w/ Cross-Border Participants Estimated Capitalization Estimated Cost

Estimated Cost

Multi-Regional JVs Estimated Capitalization Estimated Cost

Domestic JVs Estimated Capitalization Estimated Cost

Industry of JV

Mean Median N obs

Mean Median N obs

Mean Median N obs

Mean Median N obs

Mean Median N obs

Mean Median N obs

Mean Median N obs

Mean Median N obs

Business Services

41.9

2.4

275

237.5

24.4

232

41.0

2.1

176

268.1

26.1

135

98.0

4.0

13

1354.1

47.8

17

43.6

2.8

99

197.6

18.0

95

Prepackaged Software

14.6

2.0

69

61.2

7.0

68

16.3

1.8

47

27.8

5.3

30

10.2

1.0

7

34.4

34.4

2

10.5

2.2

21

102.7

11.0

32

Wholesale Trade-Durable Goods

13.1

1.5

188

192.5

11.0

80

12.4

1.5

153

183.9

8.0

48

10.5

0.8

18

64.0

27.8

4

17.2

1.8

33

232.0

24.7

28

Drugs

47.9

10.0

85

28.3

15.8

165

51.9

10.0

63

29.3

17.1

112

307.6

8.4

10

44.6

30.0

25

9.4

5.8

20

27.7

20.0

49

Electronic and Electrical Equipment

59.4

9.0

279

211.7

25.0

237

68.6

9.0

231

174.0

26.9

194

81.5

13.8

22

247.8

63.5

14

15.6

8.2

46

410.6

16.8

40

Investment & Commodity Firms/Dealers/Exch. 54.8

10.1

141

114.4

42.5

69

48.7

10.1

112

106.4

31.3

42

57.4

22.9

16

294.0

200.0

6

78.2

12.9

29

126.7

45.7

27

Chemicals and Allied Products

76.2

7.9

272

140.1

44.5

310

53.3

8.1

230

143.8

46.4

263

96.1

14.0

31

182.0

118.7

22

216.8

6.2

39

117.6

33.0

43

Telecommunications

202.3

12.5

103

399.2

61.7

140

212.1

12.5

85

418.5

60.0

107

70.8

15.0

13

666.5

508.1

12

166.7

3.5

15

346.1

100.0

32

Communications Equipment

124.3

10.0

111

235.1

20.0

111

102.9

10.0

98

258.2

20.1

90

177.3

18.8

10

256.9

86.4

17

285.1

6.3

13

178.4

45.5

15

Wholesale Trade-Nondurable Goods

62.8

2.5

106

108.4

15.0

67

82.2

2.0

76

124.8

20.2

50

7.6

1.2

9

13.6

13.4

6

13.9

3.0

29

62.7

3.8

16

Oil and Gas; Petroleum Refining

359.5

40.0

126

1075.7

117.0

249

395.6

42.0

100

1148.3

225.0

200

318.8

28.5

9

1895.8

450.0

24

227.9

40.0

25

810.0

44.0

47

Transportation Equipment

80.5

12.0

278

191.1

27.3

218

84.5

12.5

246

205.8

29.2

200

99.1

10.4

33

199.0

67.5

12

61.1

12.1

26

27.4

17.4

18

Computer and Office Equipment

23.5

5.0

68

178.9

15.0

50

17.5

4.8

47

150.7

19.0

31

8.7

3.9

10

38.8

38.8

2

35.6

6.5

19

321.2

10.0

13

Measuring, Medical, Photo Equipment; Clocks

27.8

4.9

58

127.9

5.6

63

30.7

4.8

49

137.4

6.0

43

72.7

10.0

7

18.5

18.5

4

13.1

8.0

8

124.9

3.0

17

Mining

79.4

8.8

104

112.8

8.0

200

103.1

11.1

70

154.1

15.0

120

2.8

2.7

3

305.7

45.0

8

31.6

6.7

33

50.8

3.0

80

Machinery

52.0

6.0

177

56.8

20.0

112

53.0

6.0

148

61.3

20.0

92

113.8

5.3

10

92.4

65.0

10

46.9

6.8

29

36.2

15.6

20

Real Estate; Mortgage Bankers and Brokers

94.2

19.7

164

256.8

63.8

330

93.0

20.4

122

183.8

60.0

203

211.8

31.6

5

86.5

99.1

4

97.7

7.6

42

378.4

76.2

125

Metal and Metal Products

56.0

10.0

231

214.5

30.0

186

56.8

10.8

200

192.9

30.0

154

84.1

6.0

25

410.8

170.2

8

50.3

5.0

27

338.3

35.0

29

Construction Firms

167.4

15.0

118

525.3

87.0

253

179.1

15.0

97

647.2

99.0

181

288.3

38.0

11

463.4

84.3

18

113.1

12.5

21

225.1

78.0

69

Food and Kindred Products

48.5

10.7

163

49.7

16.8

177

52.5

11.9

145

44.5

17.7

154

10.1

6.0

7

12.1

13.5

5

17.3

10.4

17

87.8

10.2

22

Transportation and Shipping (except air)

35.8

3.8

110

190.2

19.5

91

32.5

3.0

93

220.5

19.5

71

16.6

5.1

18

108.5

18.0

7

54.0

14.5

17

82.9

17.5

20

Electric, Gas, and Water Distribution

165.5

36.3

101

610.8

180.0

222

207.9

46.0

66

614.5

200.0

167

90.1

30.0

6

818.0

458.0

13

85.7

29.0

35

599.5

138.0

55

Radio and Television Broadcasting Stations

155.9

30.0

49

204.3

51.7

58

185.7

21.1

28

258.2

122.5

30

75.0

75.0

2

376.3

251.8

4

117.1

30.0

20

146.5

30.0

28

Insurance

105.1

11.4

90

34.5

16.0

15

102.6

10.9

70

25.6

16.0

13

22.2

25.0

3

11.4

11.4

2

114.0

19.4

20

92.2

92.2

2

Motion Picture Production and Distribution

178.6

23.1

26

53.8

30.0

33

218.4

23.1

18

72.4

60.0

20

176.7

60.0

3

86.0

86.0

2

89.3

26.8

8

25.2

9.3

13

Air Transportation and Shipping

62.0

11.0

37

240.9

70.0

25

69.3

11.0

28

258.4

76.0

23

100.0

100.0

1

613.8

475.0

4

39.1

9.4

9

39.7

39.7

2

Textile and Apparel Products

15.5

4.7

96

44.1

15.6

52

16.9

4.6

83

34.4

15.0

42

56.1

5.6

12

27.5

27.5

2

5.5

3.8

10

85.1

35.0

10

Stone, Clay, Glass, and Concrete Products

39.3

11.4

103

95.8

41.3

88

43.1

12.3

88

98.9

43.5

79

66.7

33.3

7

102.3

100.0

7

17.0

9.8

15

83.9

100.0

7

Miscellaneous Retail Trade

25.6

2.2

36

285.7

110.0

17

28.2

1.8

23

361.4

70.0

12

1.4

0.3

3

111.5

111.5

1

22.8

4.2

12

102.0

123.8

4

Printing, Publishing, and Allied Services

76.0

9.6

16

82.5

30.0

13

92.4

12.0

13

115.4

68.0

9

293.5

293.5

2

.

.

.

4.9

4.7

3

8.5

9.0

4

Credit Institutions

27.0

5.5

45

523.7

100.0

9

23.6

5.0

34

331.9

98.6

6

5.8

5.7

6

.

.

.

37.7

16.2

11

907.3

100.0

3

Rubber and Miscellaneous Plastic Products

55.4

5.5

67

64.3

20.4

59

63.5

6.0

57

67.0

21.1

50

486.0

486.0

2

85.4

70.2

4

9.2

3.0

10

41.1

17.4

8

Commercial Banks, Bank Holding Companies

37.0

16.2

83

25.8

10.0

11

29.5

15.0

73

25.8

10.0

11

37.3

40.0

6

100.0

100.0

1

91.2

23.2

10

.

.

.

Health Services

15.4

6.5

20

61.1

23.4

20

21.4

22.5

9

31.4

26.1

11

50.0

50.0

1

0.4

0.4

1

10.6

3.3

11

97.5

20.0

9

Hotels and Casinos

70.2

40.0

43

106.8

43.9

80

55.9

20.0

33

106.7

37.5

60

60.2

46.7

4

100.0

100.0

1

122.3

54.3

9

97.2

70.0

19

Amusement and Recreation Services

64.1

12.1

26

165.0

50.0

35

86.5

12.1

18

108.9

40.0

21

.

.

.

8.0

8.0

1

13.8

12.0

8

241.4

73.5

13

Aerospace and Aircraft

466.6

45.0

22

698.2

200.0

33

509.3

45.0

20

739.2

148.0

24

3.2

1.0

3

1068.4

412.0

3

77.4

77.4

1

589.1

380.0

9

Miscellaneous Manufacturing

44.4

2.8

12

4.1

1.4

10

48.4

3.5

11

4.5

1.4

9

.

.

.

8.0

8.0

1

0.7

0.7

1

0.5

0.5

1

Paper and Allied Products

151.9

10.0

52

729.9

48.5

34

186.1

11.0

42

956.3

45.0

23

2.6

2.6

2

.

.

.

8.6

1.2

9

256.7

77.7

11

Advertising Services

13.4

0.6

22

53.5

5.0

8

16.7

0.4

17

41.2

32.0

4

23.1

4.5

4

.

.

.

2.2

0.9

5

65.9

3.8

4

Public Administration

22.6

3.0

5

121.1

60.4

6

36.0

15.0

3

156.4

149.1

4

90.0

90.0

1

327.0

327.0

1

2.5

2.5

2

50.4

50.4

2

Agriculture, Forestry, and Fishing

31.7

4.3

33

33.5

10.3

32

34.7

5.0

23

42.6

12.4

23

26.9

3.3

4

.

.

.

24.8

0.7

10

10.1

3.3

9

Retail Trade-Eating and Drinking Places

144.3

2.0

21

518.3

8.6

12

166.0

2.0

18

609.1

7.6

10

3.3

3.3

2

0.0

0.0

1

14.0

1.9

3

64.8

64.8

2

Sanitary Services

13.3

7.9

8

40.7

11.0

22

0.2

0.2

2

57.9

12.5

14

.

.

.

2.0

2.0

2

17.7

16.3

6

12.1

6.0

7

34

Soaps, Cosmetics and Personal-Care Products

9.8

7.4

29

24.6

15.1

28

10.0

7.4

23

23.1

15.6

24

13.5

13.5

2

52.8

52.8

2

9.0

6.7

6

9.3

12.0

3

Wood Products, Furniture, and Fixtures

26.6

3.0

37

43.6

25.0

21

31.3

3.0

31

51.5

25.0

15

63.3

2.0

5

.

.

.

3.2

3.5

5

23.8

15.8

6

Retail Trade-Home Furnishings

12.1

2.9

5

15.4

15.4

1

18.9

6.0

3

15.4

15.4

1

6.0

6.0

1

.

.

.

1.8

1.8

2

.

.

.

Retail Trade-General Merchandise & Apparel

41.4

5.5

16

55.3

26.3

12

44.1

6.0

15

32.5

18.7

8

5.0

5.0

1

259.0

259.0

1

1.2

1.2

1

48.2

31.5

3

Educational Services

2.0

0.7

5

31.6

3.6

5

0.4

0.4

1

2.2

2.0

3

.

.

.

2.0

2.0

1

2.5

0.8

4

75.6

75.6

2

Repair Services

3.3

1.2

18

116.2

1.7

7

2.6

1.1

14

135.3

2.7

6

6.2

6.2

1

1.0

1.0

1

5.5

5.6

4

1.7

1.7

1

Retail Trade-Food Stores

7.9

3.4

14

85.4

19.0

11

7.9

3.4

14

98.6

19.0

9

21.4

21.4

2

500.0

500.0

1

.

.

.

25.9

25.9

2

Other Financial

9.8

9.6

4

.

.

.

9.8

9.6

4

.

.

.

11.3

11.3

2

.

.

.

.

.

.

.

.

.

Leather and Leather Products

34.4

8.0

8

36.0

10.0

8

34.4

8.0

8

36.0

10.0

8

0.3

0.3

1

.

.

.

.

.

.

.

.

.

Miscellaneous Services

12.3

4.4

7

1.5

1.5

1

11.9

4.2

6

1.5

1.5

1

.

.

.

.

.

.

15.2

15.2

1

.

.

.

Tobacco Products

37.4

35.0

7

89.6

52.5

12

37.4

35.0

7

89.6

52.5

12

48.7

47.5

4

200.0

200.0

1

.

.

.

.

.

.

Holding Companies, Except Banks

48.1

4.0

7

9.0

1.5

3

67.2

76.0

5

13.3

13.3

2

100.0

100.0

1

.

.

.

0.1

0.1

2

0.4

0.4

1

Social Services

4.5

4.5

1

10.0

10.0

1

.

.

.

.

.

.

.

.

.

.

.

.

4.5

4.5

1

10.0

10.0

1

Legal Services

0.3

0.3

1

.

.

.

0.3

0.3

1

.

.

.

0.3

0.3

1

.

.

.

.

.

.

.

.

.

Personal Services

3.4

0.8

3

12.0

12.0

2

4.8

4.8

2

.

.

.

.

.

.

.

.

.

0.8

0.8

1

12.0

12.0

2

Savings and Loans, Mutual Savings Banks

70.0

70.0

2

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

70.0

70.0

2

.

.

.

No Industry Classification & Non-classifiable

158.0

7.4

81

251.4

30.0

73

114.0

6

61

284.2

31

53

144.3

10.8

6

133.3

39

7

340.0

10

17

175.2

32.3

16

35

Table 5 Frequency of Joint Ventures by Form All JVs (Full sample)

N

Industry of JV

column Business Services

%

Strategic Alliances

N

% of % within all industry

Heterogeneous JVs

Independent JV Firms

% JVs w/ crossborder partic.

N

% of % within industry all

% JVs w/ crossborder partic.

N

% of all

(k)

(l)

% within industry

Strategic Alliances (% within industry)

Independent JV firms (% within industry)

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(m)

(n)

(o)

8205

13.6%

6077

17.8%

74.1%

40.2%

2128

8.1%

25.9%

60.9%

7211 14.7%

87.9%

64.9%

23.0%

Prepackaged Software

4953

8.2%

4435

13.0%

89.5%

35.0%

516

2.0%

10.4%

57.4%

4312

8.8%

87.1%

77.5%

9.5%

Wholesale Trade-Durable Goods

4026

6.7%

3082

9.0%

76.6%

51.3%

944

3.6%

23.4%

71.9%

3945

8.1%

98.0%

75.5%

22.5%

Drugs

3201

5.3%

2714

7.9%

84.8%

54.1%

486

1.8%

15.2%

73.5%

2057

4.2%

64.3%

54.3%

10.0%

Electronic and Electrical Equipment

2785

4.6%

1643

4.8%

59.0%

57.0%

1142

4.3%

41.0%

80.7%

2186

4.5%

78.5%

46.5%

32.0%

Investment & Commodity Firms/Dealers/Exch.

2375

3.9%

1592

4.7%

67.0%

51.5%

783

3.0%

33.0%

69.2%

2040

4.2%

85.9%

62.5%

23.4%

Chemicals and Allied Products

2050

3.4%

615

1.8%

30.0%

64.9%

1435

5.5%

70.0%

81.7%

1539

3.1%

75.1%

23.4%

51.7%

Telecommunications

1811

3.0%

989

2.9%

54.6%

51.5%

822

3.1%

45.4%

75.4%

1374

2.8%

75.9%

41.4%

34.5%

Communications Equipment

1783

2.9%

1180

3.5%

66.2%

47.0%

601

2.3%

33.7%

83.9%

1637

3.3%

91.8%

61.2%

30.5%

Wholesale Trade-Nondurable Goods

1748

2.9%

1123

3.3%

64.2%

59.7%

624

2.4%

35.7%

75.6%

1704

3.5%

97.5%

63.4%

34.0%

Oil and Gas; Petroleum Refining

1586

2.6%

406

1.2%

25.6%

61.8%

1179

4.5%

74.3%

75.6%

874

1.8%

55.1%

11.9%

43.2%

Transportation Equipment

1579

2.6%

336

1.0%

21.3%

69.9%

1243

4.7%

78.7%

84.6%

1121

2.3%

71.0%

13.3%

57.7%

Computer and Office Equipment

1484

2.5%

1218

3.6%

82.1%

49.0%

266

1.0%

17.9%

71.8%

1297

2.6%

87.4%

71.4%

16.0%

Measuring, Medical, Photo Equipment; Clocks

1340

2.2%

946

2.8%

70.6%

46.2%

394

1.5%

29.4%

73.9%

1096

2.2%

81.8%

56.8%

25.0%

Mining

1318

2.2%

389

1.1%

29.5%

48.6%

928

3.5%

70.4%

62.9%

532

1.1%

40.4%

9.6%

30.6%

Machinery

1258

2.1%

465

1.4%

37.0%

66.5%

792

3.0%

63.0%

82.4%

1066

2.2%

84.7%

31.9%

52.8%

Real Estate; Mortgage Bankers and Brokers

1224

2.0%

272

0.8%

22.2%

37.1%

952

3.6%

77.8%

58.3%

1017

2.1%

83.1%

18.8%

64.3%

Metal and Metal Products

1181

2.0%

252

0.7%

21.3%

64.7%

929

3.5%

78.7%

81.8%

929

1.9%

78.7%

17.7%

61.0%

Construction Firms

1126

1.9%

290

0.8%

25.8%

63.1%

836

3.2%

74.2%

72.0%

961

2.0%

85.3%

22.5%

62.9%

Food and Kindred Products

1071

1.8%

233

0.7%

21.8%

60.1%

838

3.2%

78.2%

84.0%

621

1.3%

58.0%

9.5%

48.5%

Transportation and Shipping (except air)

1062

1.8%

257

0.8%

24.2%

58.0%

805

3.1%

75.8%

75.0%

648

1.3%

61.0%

14.3%

46.7%

Electric, Gas, and Water Distribution

978

1.6%

221

0.6%

22.6%

57.5%

757

2.9%

77.4%

65.7%

768

1.6%

78.5%

16.3%

62.3%

Radio and Television Broadcasting Stations

798

1.3%

331

1.0%

41.5%

47.1%

467

1.8%

58.5%

59.5%

630

1.3%

78.9%

32.5%

46.5%

Insurance

765

1.3%

296

0.9%

38.7%

35.8%

468

1.8%

61.2%

66.2%

483

1.0%

63.1%

23.3%

39.9%

Motion Picture Production and Distribution

575

1.0%

262

0.8%

45.6%

45.8%

313

1.2%

54.4%

57.5%

443

0.9%

77.0%

36.0%

41.0%

Air Transportation and Shipping

523

0.9%

271

0.8%

51.8%

84.5%

252

1.0%

48.2%

82.5%

247

0.5%

47.2%

13.6%

33.7%

Textile and Apparel Products

469

0.8%

177

0.5%

37.7%

37.3%

292

1.1%

62.3%

82.9%

348

0.7%

74.2%

27.1%

47.1%

Stone, Clay, Glass, and Concrete Products

462

0.8%

79

0.2%

17.1%

53.2%

383

1.5%

82.9%

85.1%

363

0.7%

78.6%

14.5%

64.1%

Miscellaneous Retail Trade

441

0.7%

194

0.6%

44.0%

45.4%

247

0.9%

56.0%

60.3%

427

0.9%

96.8%

42.0%

54.9%

Printing, Publishing, and Allied Services

441

0.7%

215

0.6%

48.8%

43.3%

225

0.9%

51.0%

64.9%

266

0.5%

60.3%

32.2%

27.9%

Credit Institutions

423

0.7%

227

0.7%

53.7%

41.9%

195

0.7%

46.1%

67.2%

404

0.8%

95.5%

51.3%

44.0%

Rubber and Miscellaneous Plastic Products

417

0.7%

114

0.3%

27.3%

63.2%

302

1.1%

72.4%

82.1%

368

0.8%

88.2%

25.2%

62.8%

Commercial Banks, Bank Holding Companies

397

0.7%

136

0.4%

34.3%

55.9%

261

1.0%

65.7%

78.9%

235

0.5%

59.2%

20.4%

38.8%

Health Services

393

0.7%

177

0.5%

45.0%

23.7%

216

0.8%

55.0%

29.2%

256

0.5%

65.1%

30.8%

34.4%

Hotels and Casinos

381

0.6%

70

0.2%

18.4%

58.6%

311

1.2%

81.6%

69.8%

319

0.7%

83.7%

14.4%

69.3%

Amusement and Recreation Services

341

0.6%

114

0.3%

33.4%

45.6%

227

0.9%

66.6%

53.7%

295

0.6%

86.5%

27.3%

59.2%

Aerospace and Aircraft

332

0.5%

121

0.4%

36.4%

66.9%

211

0.8%

63.6%

83.4%

244

0.5%

73.5%

24.4%

49.1%

Miscellaneous Manufacturing

321

0.5%

213

0.6%

66.4%

39.4%

108

0.4%

33.6%

74.1%

294

0.6%

91.6%

60.1%

31.5%

Paper and Allied Products

275

0.5%

49

0.1%

17.8%

49.0%

226

0.9%

82.2%

76.1%

211

0.4%

76.7%

12.7%

64.0%

Advertising Services

248

0.4%

93

0.3%

37.5%

37.6%

155

0.6%

62.5%

64.5%

172

0.4%

69.4%

32.3%

37.1%

Public Administration

236

0.4%

180

0.5%

76.3%

40.0%

56

0.2%

23.7%

71.4%

234

0.5%

99.2%

76.3%

22.9%

Agriculture, Forestry, and Fishing

234

0.4%

66

0.2%

28.2%

56.1%

168

0.6%

71.8%

67.9%

215

0.4%

91.9%

28.2%

63.7%

Retail Trade-Eating and Drinking Places

225

0.4%

75

0.2%

33.3%

52.0%

150

0.6%

66.7%

70.7%

178

0.4%

79.1%

26.7%

52.4%

Sanitary Services

224

0.4%

65

0.2%

29.0%

50.8%

159

0.6%

71.0%

53.5%

205

0.4%

91.5%

27.2%

64.3%

Soaps, Cosmetics and Personal-Care Products

193

0.3%

59

0.2%

30.6%

52.5%

134

0.5%

69.4%

87.3%

158

0.3%

81.9%

25.4%

56.5%

Wood Products, Furniture, and Fixtures

182

0.3%

27

0.1%

14.8%

63.0%

155

0.6%

85.2%

74.2%

143

0.3%

78.6%

11.0%

67.6%

Retail Trade-Home Furnishings

154

0.3%

96

0.3%

62.3%

55.2%

58

0.2%

37.7%

65.5%

149

0.3%

96.8%

62.3%

34.4%

Retail Trade-General Merchandise & Apparel

149

0.2%

54

0.2%

36.2%

37.0%

95

0.4%

63.8%

77.9%

129

0.3%

86.6%

33.6%

53.0%

Educational Services

128

0.2%

78

0.2%

60.9%

32.1%

50

0.2%

39.1%

56.0%

110

0.2%

85.9%

52.3%

33.6%

Repair Services

123

0.2%

39

0.1%

31.7%

48.7%

84

0.3%

68.3%

84.5%

114

0.2%

92.7%

28.5%

64.2%

Retail Trade-Food Stores

118

0.2%

31

0.1%

26.3%

61.3%

87

0.3%

73.7%

83.9%

104

0.2%

88.1%

22.9%

65.3%

Other Financial

102

0.2%

70

0.2%

68.6%

42.9%

32

0.1%

31.4%

65.6%

97

0.2%

95.1%

65.7%

29.4%

Leather and Leather Products

74

0.1%

34

0.1%

45.9%

41.2%

40

0.2%

54.1%

80.0%

60

0.1%

81.1%

36.5%

44.6%

Miscellaneous Services

72

0.1%

36

0.1%

50.0%

38.9%

36

0.1%

50.0%

58.3%

69

0.1%

95.8%

47.2%

48.6%

Tobacco Products

55

0.1%

13

0.0%

23.6%

76.9%

42

0.2%

76.4%

97.6%

32

0.1%

58.2%

16.4%

41.8%

Holding Companies, Except Banks

35

0.1%

5

0.0%

14.3%

60.0%

30

0.1%

85.7%

73.3%

35

0.1%

100.0%

14.3%

85.7%

Social Services

34

0.1%

20

0.1%

58.8%

20.0%

14

0.1%

41.2%

35.7%

29

0.1%

85.3%

50.0%

35.3%

36

Legal Services

31

0.1%

16

0.0%

51.6%

50.0%

15

0.1%

48.4%

86.7%

14

0.0%

45.2%

16.1%

29.0%

Personal Services

20

0.0%

9

0.0%

45.0%

33.3%

11

0.0%

55.0%

54.5%

19

0.0%

95.0%

45.0%

50.0%

Savings and Loans, Mutual Savings Banks

8

0.0%

5

0.0%

62.5%

40.0%

3

0.0%

37.5%

66.7%

7

0.0%

87.5%

62.5%

25.0%

No Industry Classification & Non-classifiable

1903

3.1%

1309

3.8%

68.8%

52.0%

593

2.3%

31.2%

69.8%

1903

3.9%

100.0%

68.8%

31.1%

Total

60446

100% 34161 100%

Average

26271 100% 50.4%

48944 100% 71.1% (f)-(j)

0.694

Correlation

37

80.1% (b)-(l)

0.990

(m)-(n)

0.595

Table 6 Summary Statistics for Frequency and Size of Joint Ventures by Type Panel A - Frequency of JVs by Type N 60446 9352 11241 1865 13780 17183 10104 1721 887

All JVs Licensing Agreement JVs Technology Agreement JVs Exploration Agreement JVs Manufacturing Agreement JVs Marketing Agreement JVs R&D Agreement JVs Supply Agreement JVs Equipment Manufacturing/Value Added Reseller Agreement JVs

Panel B - Correlation Across Types Licensing Licensing 9352 Technology 4551 (48.7%) Exploration 18 (0.2%) Manufacturing 1830 (19.6%) Marketing 3118 (33.3%) R&D 1987 (21.2%) Supply 255 (2.7%) Equipment 146 (1.6%)

Technology Exploration Manufacturing

Marketing

11241 24 (0.2%) 1865 2706 (24.1%) 47 (2.5%) 13780 3932 (35.0%) 41 (2.2%) 4300 (31.2%) 17183 4732 (42.1%) 41 (2.2) 1614 (11.7%) 3533 (20.6%) 406 (3.6%) 8 (0.4%) 389 (2.8%) 768 (4.5%) 329 (2.9%) 4 (0.2%) 146 (1.1%) 564 (3.3%)

R&D

(%) 100% 15.5% 18.6% 3.1% 22.8% 28.4% 16.7% 2.8% 1.5%

Supply

10104 329 (3.3%) 1721 140 (1.4%) 491 (28.5%)

Equipment

887

Panel C - Size of JVs by Type Mean All JVs Estimated Capitalization of JV 79.1 Estimated Cost of JV 264.6 Licensing Agreement JVs Estimated Capitalization of JV 59.9 Estimated Cost of JV 87.2 Technology Agreement JVs Estimated Capitalization of JV 52.1 Estimated Cost of JV 205.0 Exploration Agreement JVs Estimated Capitalization of JV 220.0 Estimated Cost of JV 709.3 Manufacturing Agreement JVs Estimated Capitalization of JV 57.7 Estimated Cost of JV 165.5 Marketing Agreement JVs Estimated Capitalization of JV 24.7 Estimated Cost of JV 94.1 R&D Agreement JVs Estimated Capitalization of JV 71.7 Estimated Cost of JV 205.7 Supply Agreement JVs Estimated Capitalization of JV 53.7 Estimated Cost of JV 398.1 Equipment Manufacturing/Value Added Reseller Agreement JVs Estimated Capitalization of JV 187.8 Estimated Cost of JV 81.1

38

Median

N obs

8 30

4484 4487

5 10

60 205

7.6 20

400 573

11.0 14.0

133 277

8.3 25.0

2057 1781

4.6 18.0

980 682

7.3 16.4

230 407

10.3 28.9

66 126

17.5 5.0

16 15

Table 7 Frequency of Joint Ventures by Industry and Type Licensing Agreement

N

Industry column

% JVs w/ % of crossall border participants

Technology Agreement

N

% of all (c )

% JVs w/ crossborder participants

Exploration Agreement

N

% JVs w/ % of cross-border all participants

(d)

Manufacturing

N

Marketing Agreement

% JVs w/ % of cross-border all participants

N

% JVs w/ % of cross-border all participants

N

(j)

% JVs w/ % of crossall border participants (k)

Supply Agreement

N

% JVs w/ cross% of border all participants

(l)

Equipment

N

% of all

% JVs w/ cross-border participants

(a)

(b)

(e)

(f)

(g)

(h)

(m)

(n)

(o)

(p)

Business Services

662

7.1%

40.0%

1162 10.3%

46.7%

6

0.3%

66.7%

256

1.9%

55.1%

1839 10.7%

46.2%

1678 16.6%

47.1%

79

4.6%

58.2%

80

9.0%

40.0%

Prepackaged Software

1555 16.6%

35.0%

2082 18.5%

40.6%

2

0.1%

0.0%

264

1.9%

42.8%

1548

9.0%

36.6%

1818 18.0%

35.0%

178 10.3%

30.9%

162 18.3%

25.9%

Wholesale Trade-Durable Goods

475

5.1%

51.8%

618

5.5%

59.5%

3

0.2%

66.7%

228

1.7%

62.3%

3383 19.7%

55.6%

255

2.5%

41.2%

312 18.1%

50.0%

267 30.1%

47.9%

Drugs

1423 15.2%

52.1%

1717 15.3%

61.2%

.

.

.

863

6.3%

67.8%

1312

7.6%

65.2%

1765 17.5%

51.6%

110

6.4%

57.3%

13

1.5%

46.2%

Electronic and Electrical Equipment

627

6.7%

56.8%

1037

9.2%

69.8%

.

.

.

1559 11.3%

74.2%

757

4.4%

69.0%

868

8.6%

56.2%

127

7.4%

66.1%

39

4.4%

66.7%

Investment & Commodity Firms/Dealers

1172 12.5%

51.3%

216

1.9%

59.3%

4

0.2%

75.0%

122

0.9%

46.7%

204

1.2%

54.9%

72

0.7%

43.1%

4

0.2%

25.0%

18

2.0%

38.9%

Chemicals and Allied Products

254

2.7%

67.7%

518

4.6%

73.9%

8

0.4%

37.5%

1572 11.4%

79.9%

475

2.8%

74.3%

280

2.8%

55.0%

56

3.3%

73.2%

3

0.3%

100.0%

Telecommunications

129

1.4%

49.6%

431

3.8%

68.7%

.

.

.

70

0.5%

71.4%

275

1.6%

44.7%

151

1.5%

55.6%

44

2.6%

59.1%

15

1.7%

33.3%

Communications Equipment

303

3.2%

42.9%

703

6.3%

62.0%

.

.

.

499

3.6%

77.6%

549

3.2%

57.4%

614

6.1%

48.2%

111

6.4%

56.8%

63

7.1%

49.2%

Wholesale Trade-Nondurable Goods

424

4.5%

54.5%

260

2.3%

68.1%

5

0.3%

40.0%

138

1.0%

65.2%

1423

8.3%

66.1%

104

1.0%

60.6%

59

3.4%

47.5%

2

0.2%

100.0%

Oil and Gas; Petroleum Refining

31

0.3%

77.4%

48

0.4%

66.7%

804 43.1%

73.3%

238

1.7%

82.8%

102

0.6%

72.5%

53

0.5%

49.1%

30

1.7%

66.7%

2

0.2%

100.0%

Transportation Equipment

64

0.7%

75.0%

119

1.1%

78.2%

.

.

.

1343

9.7%

84.1%

309

1.8%

82.5%

158

1.6%

60.8%

40

2.3%

85.0%

8

0.9%

75.0%

Computer and Office Equipment

321

3.4%

46.1%

559

5.0%

60.1%

.

.

.

409

3.0%

68.0%

580

3.4%

54.8%

552

5.5%

44.0%

135

7.8%

56.3%

99 11.2%

57.6%

Measuring, Medical, Photo Equipment; Clocks 343

3.7%

42.3%

453

4.0%

57.0%

1

0.1%

0.0%

540

3.9%

61.9%

499

2.9%

60.3%

469

4.6%

43.5%

64

3.7%

50.0%

22

2.5%

81.8%

9

0.1%

66.7%

10

0.1%

90.0%

57.2%

39

0.3%

82.1%

25

0.1%

84.0%

17

0.2%

64.7%

2

0.1%

100.0%

3

0.3%

66.7%

128

1.4%

72.7%

218

1.9%

77.1%

9

0.5%

44.4%

860

6.2%

84.1%

331

1.9%

78.9%

225

2.2%

52.9%

53

3.1%

75.5%

13

1.5%

61.5%

Real Estate; Mortgage Bankers and Brokers

3

0.0%

.

6

0.1%

66.7%

1

0.1%

100.0%

4

0.0%

100.0%

34

0.2%

55.9%

3

0.0%

66.7%

1

0.1%

100.0%

3

0.3%

66.7%

Metal and Metal Products

80

0.9%

63.8%

146

1.3%

78.1%

8

0.4%

75.0%

942

6.8%

80.7%

195

1.1%

74.9%

108

1.1%

55.6%

25

1.5%

92.0%

8

0.9%

87.5%

Construction Firms

10

0.1%

60.0%

21

0.2%

71.4%

6

0.3%

83.3%

75

0.5%

82.7%

29

0.2%

72.4%

14

0.1%

42.9%

15

0.9%

66.7%

3

0.3%

66.7%

Food and Kindred Products

88

0.9%

58.0%

31

0.3%

61.3%

1

0.1%

100.0%

883

6.4%

81.0%

373

2.2%

76.1%

43

0.4%

39.5%

13

0.8%

69.2%

4

0.5%

75.0%

Transportation and Shipping (except air)

9

0.1%

55.6%

9

0.1%

77.8%

3

0.2%

33.3%

12

0.1%

83.3%

71

0.4%

62.0%

5

0.0%

60.0%

12

0.7%

75.0%

4

0.5%

75.0%

Electric, Gas, and Water Distribution

13

0.1%

53.8%

57

0.5%

70.2%

15

0.8%

80.0%

29

0.2%

65.5%

81

0.5%

58.0%

33

0.3%

57.6%

34

2.0%

55.9%

1

0.1%

100.0%

Radio and Television Broadcasting Stations

47

0.5%

53.2%

80

0.7%

47.5%

.

.

.

6

0.0%

33.3%

90

0.5%

43.3%

39

0.4%

33.3%

5

0.3%

80.0%

1

0.1%

100.0%

Insurance

9

0.1%

55.6%

7

0.1%

28.6%

.

.

.

1

0.0%

100.0%

123

0.7%

40.7%

8

0.1%

50.0%

2

0.1%

0.0%

2

0.2%

0.0%

Motion Picture Production and Distribution

67

0.7%

52.2%

17

0.2%

35.3%

.

.

.

23

0.2%

52.2%

152

0.9%

54.6%

23

0.2%

52.2%

2

0.1%

0.0%

.

.

.

Air Transportation and Shipping

2

0.0%

100.0%

10

0.1%

90.0%

.

.

.

7

0.1%

85.7%

94

0.5%

85.1%

6

0.1%

100.0%

3

0.2%

100.0%

1

0.1%

100.0%

Textile and Apparel Products

114

1.2%

28.9%

20

0.2%

50.0%

.

.

.

393

2.9%

70.5%

161

0.9%

60.2%

23

0.2%

47.8%

5

0.3%

40.0%

6

0.7%

66.7%

Stone, Clay, Glass, and Concrete Products

27

0.3%

33.3%

33

0.3%

69.7%

3

0.2%

100.0%

400

2.9%

82.5%

90

0.5%

71.1%

23

0.2%

39.1%

11

0.6%

90.9%

1

0.1%

100.0%

Miscellaneous Retail Trade

28

0.3%

42.9%

11

0.1%

54.5%

.

.

.

17

0.1%

58.8%

224

1.3%

62.5%

3

0.0%

66.7%

9

0.5%

77.8%

1

0.1%

0.0%

Printing, Publishing, and Allied Services

46

0.5%

39.1%

13

0.1%

30.8%

.

.

.

46

0.3%

60.9%

78

0.5%

55.1%

15

0.1%

53.3%

1

0.1%

0.0%

2

0.2%

0.0%

Credit Institutions

14

0.1%

64.3%

15

0.1%

40.0%

.

.

.

2

0.0%

50.0%

67

0.4%

43.3%

7

0.1%

42.9%

1

0.1%

100.0%

1

0.1%

0.0%

Rubber and Miscellaneous Plastic Products

44

0.5%

65.9%

67

0.6%

80.6%

.

.

.

342

2.5%

78.7%

98

0.6%

73.5%

39

0.4%

64.1%

8

0.5%

75.0%

2

0.2%

100.0%

Mining Machinery

969 52.0%

(i)

R&D Agreement

Commercial Banks, Bank Holding Companies

7

0.1%

28.6%

10

0.1%

20.0%

.

.

.

.

.

.

19

0.1%

68.4%

5

0.0%

40.0%

.

.

.

.

.

.

Health Services

18

0.2%

44.4%

22

0.2%

54.5%

1

0.1%

0.0%

13

0.1%

46.2%

31

0.2%

25.8%

21

0.2%

23.8%

8

0.5%

50.0%

3

0.3%

100.0%

Hotels and Casinos

13

0.1%

76.9%

1

0.0%

100.0%

.

.

.

1

0.0%

.

14

0.1%

42.9%

5

0.0%

60.0%

.

.

.

.

.

.

Amusement and Recreation Services

29

0.3%

44.8%

15

0.1%

53.3%

.

.

.

9

0.1%

33.3%

37

0.2%

54.1%

9

0.1%

33.3%

1

0.1%

100.0%

.

.

.

Aerospace and Aircraft

13

0.1%

69.2%

60

0.5%

80.0%

1

0.1%

100.0%

213

1.5%

81.7%

48

0.3%

81.3%

87

0.9%

72.4%

11

0.6%

63.6%

3

0.3%

100.0%

Miscellaneous Manufacturing

140

1.5%

33.6%

24

0.2%

41.7%

.

.

.

218

1.6%

55.5%

124

0.7%

44.4%

40

0.4%

40.0%

7

0.4%

85.7%

2

0.2%

50.0%

Paper and Allied Products

20

0.2%

40.0%

14

0.1%

57.1%

.

.

.

224

1.6%

73.2%

52

0.3%

63.5%

9

0.1%

77.8%

3

0.2%

33.3%

.

.

.

Advertising Services

3

0.0%

33.3%

3

0.0%

66.7%

.

.

.

2

0.0%

50.0%

118

0.7%

56.8%

6

0.1%

50.0%

.

.

.

2

0.2%

100.0%

Public Administration

125

1.3%

39.2%

6

0.1%

66.7%

1

0.1%

0.0%

17

0.1%

47.1%

53

0.3%

49.1%

9

0.1%

55.6%

2

0.1%

50.0%

1

0.1%

0.0%

39

Agriculture, Forestry, and Fishing

18

0.2%

55.6%

18

0.2%

55.6%

.

.

.

62

0.4%

80.6%

43

0.3%

69.8%

38

0.4%

47.4%

7

0.4%

42.9%

.

.

.

Retail Trade-Eating and Drinking Places

24

0.3%

75.0%

1

0.0%

100.0%

.

.

.

8

0.1%

62.5%

44

0.3%

52.3%

.

.

.

5

0.3%

80.0%

.

.

.

Sanitary Services

21

0.2%

42.9%

30

0.3%

50.0%

.

.

.

15

0.1%

80.0%

29

0.2%

48.3%

18

0.2%

66.7%

8

0.5%

50.0%

.

.

.

Soaps, Cosmetics and Personal-Care Products

33

0.4%

45.5%

19

0.2%

68.4%

.

.

.

146

1.1%

86.3%

83

0.5%

72.3%

20

0.2%

70.0%

4

0.2%

50.0%

1

0.1%

0.0%

Wood Products, Furniture, and Fixtures

8

0.1%

25.0%

7

0.1%

85.7%

1

0.1%

100.0%

156

1.1%

72.4%

40

0.2%

67.5%

5

0.0%

60.0%

4

0.2%

75.0%

1

0.1%

0.0%

Retail Trade-Home Furnishings

11

0.1%

45.5%

5

0.0%

40.0%

.

.

.

8

0.1%

50.0%

76

0.4%

61.8%

3

0.0%

0.0%

7

0.4%

71.4%

3

0.3%

33.3%

Retail Trade-General Merchandise & Apparel

28

0.3%

35.7%

3

0.0%

66.7%

.

.

.

12

0.1%

50.0%

104

0.6%

66.3%

2

0.0%

0.0%

.

.

.

.

.

.

Educational Services

2

0.0%

50.0%

6

0.1%

.

.

.

.

.

.

.

4

0.0%

25.0%

6

0.1%

50.0%

1

0.1%

0.0%

.

.

.

Repair Services

3

0.0%

100.0%

1

0.0%

100.0%

.

.

.

10

0.1%

70.0%

18

0.1%

66.7%

7

0.1%

42.9%

1

0.1%

100.0%

1

0.1%

100.0%

Retail Trade-Food Stores

7

0.1%

71.4%

1

0.0%

100.0%

.

.

.

7

0.1%

100.0%

69

0.4%

84.1%

3

0.0%

33.3%

2

0.1%

50.0%

.

.

.

Other Financial

6

0.1%

16.7%

10

0.1%

40.0%

.

.

.

1

0.0%

.

11

0.1%

54.5%

3

0.0%

100.0%

.

.

.

1

0.1%

0.0%

Leather and Leather Products

24

0.3%

33.3%

7

0.1%

42.9%

.

.

.

67

0.5%

65.7%

29

0.2%

65.5%

2

0.0%

0.0%

1

0.1%

100.0%

1

0.1%

0.0%

Miscellaneous Services

.

.

.

.

.

.

2

0.1%

0.0%

1

0.0%

100.0%

19

0.1%

31.6%

5

0.0%

20.0%

.

.

.

.

.

.

Tobacco Products

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

49

0.4%

95.9%

13

0.1%

92.3%

1

0.0%

100.0%

1

0.1%

100.0%

.

.

.

Holding Companies, Except Banks

.

.

.

2

0.0%

50.0%

.

.

.

1

0.0%

100.0%

2

0.0%

50.0%

1

0.0%

0.0%

.

.

.

.

.

.

Social Services

.

.

.

1

0.0%

.

.

.

.

.

.

.

7

0.0%

.

.

.

.

.

.

.

.

.

.

Legal Services

.

.

.

.

.

.

.

.

.

.

.

.

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Personal Services

3

0.0%

66.7%

2

0.0%

50.0%

.

.

.

2

0.0%

.

7

0.0%

42.9%

.

.

.

.

.

.

.

.

.

Savings and Loans, Mutual Savings Banks

1

0.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

No Industry Classification & Non-classifiable 273

2.9%

61.2%

278

2.5%

77.3%

11

0.6%

72.7%

356

2.6%

75.8%

516

3.0%

63.6%

328

3.2%

47.9%

97

5.6%

54.6%

19

2.1%

57.9%

Total

100%

17183

100%

887

100%

Average

9352

11241 100%

53.5%

1865 100%

63.5%

13780 100%

56.7%

70.7%

10104 100% (a)-(i)

0.622

Correlation

40

1721 100%

60.9%

49.5% (c)-(k)

0.965

0.352

62.7%

57.4%

(d)-(l)

(m)-(o)

0.912

Table 8 Frequency of Joint Ventures by Country All JVs 1st Nation of the JV's operations

N

%

JVs w/ Cross-Border Participants Cum % N % Cum %

Domestic JVs N

%

Cum %

Strategic Alliances N

% of all Cum %

Independent JV Firms N

% of all Cum %

United States of America

22941 38.0% 38.0%

7402

20.9% 20.9% 15196 67.1% 67.1%

18028

52.8%

52.8%

4913

China

4141

6.9%

44.8%

3710

10.5% 31.3%

407

1.8%

18.7%

18.7%

68.9%

535

1.6%

54.3%

3606

13.7%

Japan

4006

6.6%

51.4%

2756

7.8%

39.1%

1204

32.4%

5.3%

74.2%

2674

7.8%

62.2%

1332

5.1%

United Kingdom

2459

4.1%

55.5%

1564

4.4%

43.5%

37.5%

856

3.8%

78.0%

1172

3.4%

65.6%

1287

4.9%

Canada

1820

3.0%

58.5%

1077

3.0%

42.4%

46.5%

728

3.2%

81.2%

1194

3.5%

69.1%

626

2.4%

Australia

1678

2.8%

61.3%

996

44.8%

2.8%

49.3%

664

2.9%

84.1%

790

2.3%

71.4%

888

3.4%

Germany

1305

2.2%

63.4%

48.2%

887

2.5%

51.8%

405

1.8%

85.9%

527

1.5%

72.9%

778

3.0%

India

1245

2.1%

51.1%

1095

3.1%

147

0.6%

300

0.9%

945

3.6%

Malaysia

1023

1.7%

677

1.9%

342

1.5%

157

0.5%

866

3.3%

France

856

1.4%

658

1.9%

179

0.8%

355

1.0%

501

1.9%

Thailand

731

1.2%

620

1.7%

108

0.5%

91

0.3%

640

2.4%

Singapore

695

1.1%

506

1.4%

181

0.8%

148

0.4%

547

2.1%

Russian Federation

689

1.1%

629

1.8%

56

0.2%

137

0.4%

552

2.1%

Indonesia

669

1.1%

586

1.7%

79

0.3%

101

0.3%

568

2.2%

South Korea

617

1.0%

539

1.5%

75

0.3%

284

0.8%

333

1.3%

Vietnam

551

0.9%

497

1.4%

51

0.2%

51

0.1%

500

1.9%

Italy

528

0.9%

394

1.1%

129

0.6%

231

0.7%

297

1.1%

Hong Kong

475

0.8%

384

1.1%

84

0.4%

141

0.4%

334

1.3%

Mexico

470

0.8%

397

1.1%

63

0.3%

159

0.5%

311

1.2%

Philippines

466

0.8%

379

1.1%

85

0.4%

90

0.3%

376

1.4%

Netherlands

400

0.7%

291

0.8%

100

0.4%

163

0.5%

237

0.9%

Taiwan

379

0.6%

323

0.9%

54

0.2%

154

0.5%

225

0.9%

Brazil

365

0.6%

308

0.9%

55

0.2%

106

0.3%

259

1.0%

Spain

344

0.6%

256

0.7%

81

0.4%

130

0.4%

214

0.8%

Poland

302

0.5%

279

0.8%

21

0.1%

58

0.2%

244

0.9%

Hungary

282

0.5%

254

0.7%

27

0.1%

32

0.1%

250

1.0%

South Africa

276

0.5%

211

0.6%

65

0.3%

80

0.2%

196

0.7%

Soviet Union

256

0.4%

250

0.7%

6

0.0%

8

0.0%

248

0.9%

Czechoslovakia

253

0.4%

243

0.7%

9

0.0%

38

0.1%

215

0.8%

Sweden

239

0.4%

180

0.5%

53

0.2%

114

0.3%

125

0.5%

Switzerland

223

0.4%

152

0.4%

68

0.3%

106

0.3%

117

0.4%

Belgium

176

0.3%

133

0.4%

40

0.2%

57

0.2%

119

0.5%

Argentina

168

0.3%

140

0.4%

26

0.1%

56

0.2%

112

0.4%

New Zealand

168

0.3%

121

0.3%

46

0.2%

51

0.1%

117

0.4%

Israel

157

0.3%

130

0.4%

25

0.1%

74

0.2%

83

0.3%

Saudi Arabia

154

0.3%

142

0.4%

12

0.1%

34

0.1%

120

0.5%

Republic of Ireland

137

0.2%

113

0.3%

21

0.1%

51

0.1%

86

0.3%

Chile

136

0.2%

114

0.3%

21

0.1%

40

0.1%

96

0.4%

Turkey

129

0.2%

115

0.3%

14

0.1%

21

0.1%

108

0.4%

Finland

121

0.2%

85

0.2%

35

0.2%

48

0.1%

73

0.3%

Norway

119

0.2%

89

0.3%

29

0.1%

68

0.2%

51

0.2%

Venezuela

114

0.2%

98

0.3%

16

0.1%

21

0.1%

93

0.4%

Pakistan

91

0.2%

79

0.2%

10

0.0%

12

0.0%

79

0.3%

Portugal

89

0.1%

70

0.2%

19

0.1%

30

0.1%

59

0.2%

Romania

87

0.1%

82

0.2%

4

0.0%

13

0.0%

74

0.3%

Austria

85

0.1%

67

0.2%

17

0.1%

29

0.1%

56

0.2%

Kazakhstan

84

0.1%

79

0.2%

4

0.0%

7

0.0%

77

0.3%

Uzbekistan

83

0.1%

73

0.2%

7

0.0%

7

0.0%

76

0.3%

Ukraine

78

0.1%

72

0.2%

5

0.0%

10

0.0%

68

0.3%

Denmark

74

0.1%

54

0.2%

19

0.1%

32

0.1%

42

0.2%

Egypt

60

0.1%

55

0.2%

5

0.0%

8

0.0%

52

0.2%

United Arab Emirates

60

0.1%

54

0.2%

6

0.0%

16

0.0%

44

0.2%

Myanmar (Burma)

58

0.1%

55

0.2%

3

0.0%

5

0.0%

53

0.2%

Peru

56

0.1%

45

0.1%

10

0.0%

17

0.0%

39

0.1%

Colombia

54

0.1%

45

0.1%

9

0.0%

11

0.0%

43

0.2%

Greece

54

0.1%

39

0.1%

13

0.1%

11

0.0%

43

0.2%

Bulgaria

52

0.1%

48

0.1%

2

0.0%

10

0.0%

42

0.2%

Cuba

51

0.1%

46

0.1%

5

0.0%

4

0.0%

47

0.2%

Czech Republic

51

0.1%

44

0.1%

7

0.0%

14

0.0%

37

0.1%

Sri Lanka

49

0.1%

45

0.1%

4

0.0%

6

0.0%

43

0.2%

Iran

41

0.1%

38

0.1%

3

0.0%

11

0.0%

30

0.1%

41

Cambodia

40

0.1%

37

0.1%

3

0.0%

3

0.0%

37

0.1%

Azerbaijan

37

0.1%

34

0.1%

1

0.0%

10

0.0%

27

0.1%

Slovak Republic

35

0.1%

32

0.1%

3

0.0%

1

0.0%

34

0.1%

Algeria

34

0.1%

31

0.1%

3

0.0%

8

0.0%

26

0.1%

Estonia

32

0.1%

30

0.1%

2

0.0%

2

0.0%

30

0.1%

Yugoslavia

32

0.1%

31

0.1%

.

.

5

0.0%

27

0.1%

Nigeria

31

0.1%

30

0.1%

.

.

13

0.0%

18

0.1%

Papua New Guinea

31

0.1%

23

0.1%

8

0.0%

2

0.0%

29

0.1%

Bermuda

30

0.0%

24

0.1%

6

0.0%

7

0.0%

23

0.1%

Zimbabwe

30

0.0%

24

0.1%

6

0.0%

4

0.0%

26

0.1%

Bahrain

27

0.0%

25

0.1%

2

0.0%

6

0.0%

21

0.1%

Bolivia

26

0.0%

22

0.1%

4

0.0%

3

0.0%

23

0.1%

Laos

26

0.0%

24

0.1%

1

0.0%

0

0.0%

26

0.1%

Lithuania

26

0.0%

25

0.1%

1

0.0%

5

0.0%

21

0.1%

Luxembourg

26

0.0%

23

0.1%

3

0.0%

9

0.0%

17

0.1%

Tanzania

26

0.0%

25

0.1%

1

0.0%

6

0.0%

20

0.1%

Albania

24

0.0%

23

0.1%

1

0.0%

4

0.0%

20

0.1%

Morocco

24

0.0%

21

0.1%

3

0.0%

3

0.0%

21

0.1%

Oman

23

0.0%

19

0.1%

4

0.0%

9

0.0%

14

0.1%

Bangladesh

22

0.0%

20

0.1%

2

0.0%

7

0.0%

15

0.1%

East Germany

21

0.0%

21

0.1%

.

.

1

0.0%

20

0.1%

Qatar

21

0.0%

19

0.1%

2

0.0%

1

0.0%

20

0.1%

Tunisia

19

0.0%

18

0.1%

1

0.0%

3

0.0%

16

0.1%

North Korea

18

0.0%

14

0.0%

4

0.0%

5

0.0%

13

0.0%

Ecuador

17

0.0%

15

0.0%

1

0.0%

5

0.0%

12

0.0%

Ghana

17

0.0%

13

0.0%

3

0.0%

2

0.0%

15

0.1%

Latvia

17

0.0%

15

0.0%

1

0.0%

1

0.0%

16

0.1%

Trinidad and Tobago

17

0.0%

17

0.0%

.

.

5

0.0%

12

0.0%

Croatia

16

0.0%

16

0.0%

.

.

5

0.0%

11

0.0%

Kuwait

16

0.0%

14

0.0%

2

0.0%

6

0.0%

10

0.0%

Kyrgyzstan

16

0.0%

14

0.0%

2

0.0%

1

0.0%

15

0.1%

Mongolia

16

0.0%

14

0.0%

2

0.0%

2

0.0%

14

0.1%

Jordan

15

0.0%

14

0.0%

1

0.0%

2

0.0%

13

0.0%

Mozambique

15

0.0%

14

0.0%

1

0.0%

3

0.0%

12

0.0%

Belarus

13

0.0%

10

0.0%

2

0.0%

2

0.0%

11

0.0%

Brunei

12

0.0%

10

0.0%

2

0.0%

1

0.0%

11

0.0%

Dominican Republic

12

0.0%

7

0.0%

5

0.0%

3

0.0%

9

0.0%

Georgia

12

0.0%

10

0.0%

2

0.0%

2

0.0%

10

0.0%

Jamaica

12

0.0%

11

0.0%

1

0.0%

2

0.0%

10

0.0%

Abu Dhabi

11

0.0%

10

0.0%

1

0.0%

0

0.0%

11

0.0%

Turkmenistan

11

0.0%

10

0.0%

1

0.0%

4

0.0%

7

0.0%

Yemen

11

0.0%

10

0.0%

1

0.0%

2

0.0%

9

0.0%

Netherlands Antilles

10

0.0%

8

0.0%

2

0.0%

7

0.0%

3

0.0%

Cayman Islands

9

0.0%

9

0.0%

.

.

1

0.0%

8

0.0%

Costa Rica

9

0.0%

8

0.0%

1

0.0%

3

0.0%

6

0.0%

Guyana

9

0.0%

7

0.0%

2

0.0%

3

0.0%

6

0.0%

Lebanon

9

0.0%

8

0.0%

1

0.0%

2

0.0%

7

0.0%

Syria

9

0.0%

9

0.0%

.

.

0

0.0%

9

0.0%

Uruguay

9

0.0%

7

0.0%

2

0.0%

2

0.0%

7

0.0%

Angola

8

0.0%

7

0.0%

1

0.0%

1

0.0%

7

0.0%

Armenia

8

0.0%

8

0.0%

.

.

1

0.0%

7

0.0%

Guatemala

8

0.0%

5

0.0%

3

0.0%

1

0.0%

7

0.0%

Iraq

8

0.0%

7

0.0%

1

0.0%

5

0.0%

3

0.0%

Panama

8

0.0%

8

0.0%

.

.

2

0.0%

6

0.0%

British Virgin Islands (UK)

7

0.0%

5

0.0%

2

0.0%

1

0.0%

6

0.0%

Cyprus

7

0.0%

5

0.0%

2

0.0%

2

0.0%

5

0.0%

Mali

7

0.0%

7

0.0%

.

.

2

0.0%

5

0.0%

Moldova

7

0.0%

6

0.0%

1

0.0%

1

0.0%

6

0.0%

Nepal

7

0.0%

7

0.0%

.

.

0

0.0%

7

0.0%

Uganda

7

0.0%

5

0.0%

2

0.0%

0

0.0%

7

0.0%

Zambia

7

0.0%

5

0.0%

2

0.0%

1

0.0%

6

0.0%

Bahamas

6

0.0%

6

0.0%

.

.

2

0.0%

4

0.0%

Bosnia and Herzegovina

6

0.0%

5

0.0%

1

0.0%

1

0.0%

5

0.0%

Mauritania

6

0.0%

5

0.0%

1

0.0%

3

0.0%

3

0.0%

Mauritius

6

0.0%

5

0.0%

1

0.0%

2

0.0%

4

0.0%

Monaco

6

0.0%

6

0.0%

.

.

2

0.0%

4

0.0%

42

Namibia

6

0.0%

6

0.0%

.

.

1

0.0%

5

Republic of Congo

6

0.0%

5

0.0%

1

0.0%

3

0.0%

3

0.0% 0.0%

Sudan

6

0.0%

6

0.0%

.

.

1

0.0%

5

0.0%

Tajikistan

6

0.0%

6

0.0%

.

.

0

0.0%

6

0.0%

Barbados

5

0.0%

4

0.0%

1

0.0%

2

0.0%

3

0.0%

Botswana

5

0.0%

4

0.0%

1

0.0%

2

0.0%

3

0.0%

Ethiopia

5

0.0%

4

0.0%

1

0.0%

1

0.0%

4

0.0%

Gabon

5

0.0%

3

0.0%

2

0.0%

2

0.0%

3

0.0%

Iceland

5

0.0%

5

0.0%

.

.

3

0.0%

2

0.0%

Ivory Coast

5

0.0%

4

0.0%

1

0.0%

1

0.0%

4

0.0%

Libya

5

0.0%

5

0.0%

.

.

1

0.0%

4

0.0%

Macau

5

0.0%

5

0.0%

.

.

2

0.0%

3

0.0%

Malta

5

0.0%

5

0.0%

.

.

1

0.0%

4

0.0%

Puerto Rico

5

0.0%

2

0.0%

3

0.0%

4

0.0%

1

0.0%

Upper Volta (Burkina Faso)

5

0.0%

5

0.0%

.

.

2

0.0%

3

0.0%

Belize

4

0.0%

4

0.0%

.

.

3

0.0%

1

0.0%

Cameroon

4

0.0%

3

0.0%

1

0.0%

1

0.0%

3

0.0%

El Salvador

4

0.0%

3

0.0%

1

0.0%

1

0.0%

3

0.0%

Greenland

4

0.0%

3

0.0%

1

0.0%

0

0.0%

4

0.0%

Guinea

4

0.0%

4

0.0%

.

.

3

0.0%

1

0.0%

Honduras

4

0.0%

1

0.0%

3

0.0%

2

0.0%

2

0.0%

Madagascar

4

0.0%

4

0.0%

.

.

1

0.0%

3

0.0%

Nicaragua

4

0.0%

3

0.0%

1

0.0%

1

0.0%

3

0.0%

Paraguay

4

0.0%

4

0.0%

.

.

1

0.0%

3

0.0%

Slovenia

4

0.0%

3

0.0%

.

.

1

0.0%

3

0.0%

Afghanistan

3

0.0%

2

0.0%

1

0.0%

1

0.0%

2

0.0%

Aruba

3

0.0%

2

0.0%

1

0.0%

0

0.0%

3

0.0%

Kenya

3

0.0%

2

0.0%

1

0.0%

0

0.0%

3

0.0%

Malawi

3

0.0%

1

0.0%

2

0.0%

0

0.0%

3

0.0%

Surinam

3

0.0%

3

0.0%

.

.

2

0.0%

1

0.0%

Zaire

3

0.0%

3

0.0%

.

.

1

0.0%

2

0.0%

Fiji

2

0.0%

2

0.0%

.

.

1

0.0%

1

0.0%

Niger

2

0.0%

2

0.0%

.

.

1

0.0%

1

0.0%

Solomon Islands

2

0.0%

2

0.0%

.

.

0

0.0%

2

0.0%

Antigua

1

0.0%

.

.

1

0.0%

0

0.0%

1

0.0%

Cape Verde

1

0.0%

.

.

1

0.0%

1

0.0%

0

0.0%

Chad

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

Equatorial Guinea

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

French Polynesia

1

0.0%

1

0.0%

.

.

1

0.0%

0

0.0%

Gibraltar

1

0.0%

.

.

1

0.0%

0

0.0%

1

0.0%

Grenada

1

0.0%

1

0.0%

.

.

1

0.0%

0

0.0%

Lesotho

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

Liberia

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

Macedonia

1

0.0%

.

.

1

0.0%

0

0.0%

1

0.0%

Martinque

1

0.0%

1

0.0%

.

.

1

0.0%

0

0.0%

Rwanda

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

Sao Tome and Principe

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

Seychelles

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

Sierra Leone

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

Somalia

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

St Lucia

1

0.0%

1

0.0%

.

.

1

0.0%

0

0.0%

Swaziland No Country Classification, Supranational & Non-

1

0.0%

1

0.0%

.

.

0

0.0%

1

0.0%

5665

9.4%

3514

9.9%

503

2.2%

4972

14.6%

679

2.6%

22658 100%

34161

100%

26271

100%

60446 100%

35495 100%

43

Table 9 Frequency of Joint Ventures by Country and Type

USA

% JVs w/ % JVs w/ % of % of crosscrossN N all all border border partic. partic. 5825 62.3% 32.2% 6503 57.9% 38.3%

Manufacturing Marketing Agreement R&D Agreement Agreement % JVs w/ % JVs w/ % JVs w/ % JVs w/ % of % of % of % of crosscrosscrosscrossN N N N all all all all border border border border partic. partic. partic. partic. 268 14.4% 41.0% 3347 24.3% 44.0% 7361 42.8% 34.4% 5866 58.1% 33.6%

China

102

1.1%

92.2%

436

3.9%

95.4%

70

3.8%

84.3%

2407 17.5%

92.4%

895

5.2%

92.3%

196

1.9%

92.9%

58

3.4%

86.2%

3

0.3%

100.0%

Japan

578

6.2%

93.3%

929

8.3%

80.1%

3

0.2%

100.0%

835

6.1%

71.3%

1709 9.9%

84.3%

861

8.5%

60.9%

116

6.7%

75.9%

81

9.1%

85.2%

United Kingdom

253

2.7%

76.3%

304

2.7%

77.0%

58

3.1%

79.3%

299

2.2%

80.9%

479

2.8%

69.9%

275

2.7%

72.0%

45

2.6%

77.8%

15

1.7%

86.7%

Canada

304

3.3%

71.1%

290

2.6%

75.5%

247 13.2%

42.5%

243

1.8%

69.1%

365

2.1%

67.7%

183

1.8%

71.6%

34

2.0%

61.8%

22

2.5%

77.3%

Australia

81

0.9%

75.3%

91

0.8%

86.8%

276 14.8%

38.8%

165

1.2%

73.3%

232

1.4%

70.7%

100

1.0%

70.0%

20

1.2%

75.0%

7

0.8%

71.4%

Germany

130

1.4%

89.2%

201

1.8%

87.6%

4

0.2%

50.0%

381

2.8%

71.1%

345

2.0%

68.4%

182

1.8%

76.9%

32

1.9%

81.3%

10

1.1%

100.0%

India

38

0.4%

94.7%

140

1.2%

92.9%

15

0.8%

80.0%

537

3.9%

90.7%

266

1.5%

91.7%

54

0.5%

85.2%

12

0.7%

91.7%

3

0.3%

100.0%

Malaysia

21

0.2%

81.0%

81

0.7%

81.5%

17

0.9%

64.7%

357

2.6%

78.4%

165

1.0%

79.4%

53

0.5%

69.8%

12

0.7%

83.3%

1

0.1%

100.0%

France

80

0.9%

86.3%

151

1.3%

88.7%

1

0.1%

100.0%

221

1.6%

84.2%

229

1.3%

79.0%

140

1.4%

82.9%

17

1.0%

88.2%

4

0.5%

100.0%

Thailand

13

0.1%

84.6%

53

0.5%

84.9%

16

0.9%

93.8%

304

2.2%

91.1%

142

0.8%

85.9%

17

0.2%

88.2%

11

0.6%

72.7%

1

0.1%

0.0%

Singapore

28

0.3%

85.7%

47

0.4%

85.1%

3

0.2%

66.7%

134

1.0%

73.9%

130

0.8%

80.8%

53

0.5%

79.2%

13

0.8%

76.9%

1

0.1%

100.0%

Russian Federation

21

0.2%

90.5%

90

0.8%

90.0%

77

4.1%

89.6%

290

2.1%

91.7%

122

0.7%

92.6%

58

0.6%

84.5%

9

0.5%

100.0%

1

0.1%

100.0%

Indonesia

16

0.2%

100.0%

40

0.4%

82.5%

61

3.3%

77.0%

266

1.9%

91.4%

81

0.5%

86.4%

9

0.1%

100.0%

5

0.3%

80.0%

.

.

.

South Korea

99

1.1%

90.9%

110

1.0%

95.5%

2

0.1%

50.0%

250

1.8%

91.6%

157

0.9%

89.8%

58

0.6%

86.2%

12

0.7%

100.0%

2

0.2%

100.0%

Vietnam

2

0.0%

100.0%

22

0.2%

90.9%

26

1.4%

84.6%

265

1.9%

89.8%

66

0.4%

86.4%

8

0.1%

62.5%

5

0.3%

100.0%

.

.

.

Italy

45

0.5%

91.1%

71

0.6%

90.1%

4

0.2%

100.0%

137

1.0%

78.8%

125

0.7%

82.4%

43

0.4%

90.7%

14

0.8%

85.7%

1

0.1%

100.0%

Hong Kong

26

0.3%

88.5%

32

0.3%

87.5%

2

0.1%

100.0%

78

0.6%

85.9%

88

0.5%

80.7%

8

0.1%

62.5%

3

0.2%

100.0%

2

0.2%

100.0%

Mexico

23

0.2%

73.9%

32

0.3%

87.5%

39

2.1%

69.2%

129

0.9%

84.5%

96

0.6%

83.3%

2

0.0%

100.0%

7

0.4%

42.9%

3

0.3%

0.0%

Philippines

13

0.1%

76.9%

20

0.2%

85.0%

19

1.0%

57.9%

119

0.9%

90.8%

53

0.3%

96.2%

3

0.0%

100.0%

6

0.3%

83.3%

1

0.1%

100.0%

Netherlands

31

0.3%

93.5%

61

0.5%

82.0%

5

0.3%

100.0%

101

0.7%

74.3%

87

0.5%

77.0%

45

0.4%

77.8%

6

0.3%

83.3%

3

0.3%

100.0%

Taiwan

34

0.4%

88.2%

49

0.4%

93.9%

1

0.1%

100.0%

174

1.3%

88.5%

97

0.6%

87.6%

31

0.3%

87.1%

10

0.6%

100.0%

7

0.8%

100.0%

Brazil

20

0.2%

95.0%

25

0.2%

96.0%

24

1.3%

75.0%

133

1.0%

91.0%

67

0.4%

92.5%

15

0.1%

93.3%

9

0.5%

88.9%

2

0.2%

100.0%

Spain

19

0.2%

89.5%

24

0.2%

87.5%

2

0.1%

100.0%

65

0.5%

86.2%

67

0.4%

85.1%

10

0.1%

80.0%

3

0.2%

100.0%

1

0.1%

100.0%

Poland

8

0.1%

100.0%

10

0.1%

100.0%

5

0.3%

80.0%

112

0.8%

96.4%

58

0.3%

94.8%

5

0.0%

100.0%

5

0.3%

100.0%

.

.

.

Hungary

11

0.1%

81.8%

13

0.1%

100.0%

1

0.1%

100.0%

128

0.9%

93.8%

74

0.4%

91.9%

7

0.1%

100.0%

6

0.3%

100.0%

.

.

.

South Africa

15

0.2%

100.0%

16

0.1%

87.5%

26

1.4%

73.1%

79

0.6%

79.7%

73

0.4%

86.3%

6

0.1%

66.7%

3

0.2%

100.0%

.

.

.

Soviet Union

6

0.1%

100.0%

6

0.1%

100.0%

20

1.1%

95.0%

79

0.6%

98.7%

49

0.3%

93.9%

8

0.1%

100.0%

18

1.0%

100.0%

.

.

.

Czechoslovakia

4

0.0%

100.0%

9

0.1%

88.9%

3

0.2%

100.0%

125

0.9%

95.2%

57

0.3%

96.5%

10

0.1%

100.0%

9

0.5%

100.0%

.

.

.

Sweden

25

0.3%

88.0%

51

0.5%

90.2%

4

0.2%

75.0%

58

0.4%

75.9%

51

0.3%

82.4%

39

0.4%

76.9%

5

0.3%

80.0%

2

0.2%

100.0%

Switzerland

26

0.3%

92.3%

35

0.3%

85.7%

1

0.1%

100.0%

51

0.4%

76.5%

70

0.4%

68.6%

32

0.3%

78.1%

3

0.2%

100.0%

.

.

.

Belgium

11

0.1%

72.7%

17

0.2%

82.4%

.

.

.

52

0.4%

90.4%

36

0.2%

77.8%

14

0.1%

78.6%

3

0.2%

66.7%

.

.

.

Argentina

7

0.1%

85.7%

9

0.1%

88.9%

26

1.4%

73.1%

52

0.4%

90.4%

42

0.2%

85.7%

5

0.0%

80.0%

4

0.2%

75.0%

1

0.1%

100.0%

New Zealand

9

0.1%

88.9%

4

0.0%

100.0%

11

0.6%

90.9%

21

0.2%

85.7%

24

0.1%

75.0%

8

0.1%

25.0%

2

0.1%

100.0%

.

.

.

Israel

25

0.3%

88.0%

34

0.3%

85.3%

2

0.1%

100.0%

27

0.2%

88.9%

40

0.2%

80.0%

27

0.3%

74.1%

2

0.1%

50.0%

.

.

.

Saudi Arabia

12

0.1%

100.0%

22

0.2%

95.5%

5

0.3%

100.0%

75

0.5%

90.7%

26

0.2%

88.5%

5

0.0%

100.0%

3

0.2%

100.0%

.

.

.

Republic of Ireland

12

0.1%

91.7%

24

0.2%

91.7%

11

0.6%

81.8%

20

0.1%

90.0%

19

0.1%

78.9%

12

0.1%

83.3%

3

0.2%

100.0%

1

0.1%

0.0%

Chile

4

0.0%

75.0%

4

0.0%

75.0%

30

1.6%

80.0%

27

0.2%

92.6%

13

0.1%

92.3%

1

0.0%

100.0%

5

0.3%

80.0%

.

.

.

Turkey

5

0.1%

80.0%

8

0.1%

100.0%

3

0.2%

100.0%

58

0.4%

93.1%

26

0.2%

96.2%

1

0.0%

100.0%

1

0.1%

100.0%

1

0.1%

100.0%

Finland

3

0.0%

100.0%

14

0.1%

100.0%

3

0.2%

66.7%

38

0.3%

76.3%

29

0.2%

82.8%

13

0.1%

92.3%

3

0.2%

100.0%

1

0.1%

100.0%

Norway

14

0.1%

92.9%

17

0.2%

94.1%

8

0.4%

50.0%

23

0.2%

82.6%

23

0.1%

87.0%

10

0.1%

90.0%

1

0.1%

100.0%

.

.

.

Venezuela

2

0.0%

50.0%

7

0.1%

85.7%

25

1.3%

80.0%

39

0.3%

89.7%

11

0.1%

90.9%

2

0.0%

100.0%

4

0.2%

50.0%

.

.

.

Pakistan

2

0.0%

50.0%

3

0.0%

100.0%

10

0.5%

90.0%

28

0.2%

92.9%

7

0.0%

100.0%

1

0.0%

100.0%

1

0.1%

0.0%

.

.

.

Portugal

3

0.0%

100.0%

4

0.0%

100.0%

2

0.1%

100.0%

11

0.1%

81.8%

10

0.1%

70.0%

2

0.0%

100.0%

1

0.1%

100.0%

.

.

.

Romania

5

0.1%

80.0%

7

0.1%

100.0%

4

0.2%

100.0%

40

0.3%

95.0%

22

0.1%

100.0%

.

.

.

.

.

.

.

.

.

Austria

2

0.0%

100.0%

5

0.0%

60.0%

1

0.1%

.

29

0.2%

86.2%

9

0.1%

66.7%

1

0.0%

100.0%

2

0.1%

100.0%

.

.

.

Kazakhstan

2

0.0%

100.0%

6

0.1%

100.0%

40

2.1%

95.0%

11

0.1%

100.0%

12

0.1%

91.7%

2

0.0%

100.0%

2

0.1%

100.0%

.

.

.

Uzbekistan

.

.

.

2

0.0%

100.0%

14

0.8%

92.9%

40

0.3%

90.0%

2

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

Ukraine

2

0.0%

100.0%

7

0.1%

100.0%

11

0.6%

72.7%

36

0.3%

91.7%

6

0.0%

100.0%

1

0.0%

100.0%

1

0.1%

100.0%

.

.

.

Denmark

11

0.1%

100.0%

11

0.1%

100.0%

1

0.1%

100.0%

14

0.1%

85.7%

22

0.1%

81.8%

8

0.1%

87.5%

2

0.1%

100.0%

1

0.1%

100.0%

Egypt

2

0.0%

100.0%

1

0.0%

100.0%

12

0.6%

91.7%

25

0.2%

96.0%

9

0.1%

88.9%

.

.

.

1

0.1%

100.0%

.

.

.

Unt' Arab Emirates

4

0.0%

50.0%

3

0.0%

33.3%

3

0.2%

100.0%

19

0.1%

89.5%

14

0.1%

85.7%

2

0.0%

50.0%

1

0.1%

100.0%

.

.

.

Myanmar (Burma)

.

.

.

.

.

.

10

0.5%

100.0%

21

0.2%

100.0%

9

0.1%

88.9%

.

.

.

1

0.1%

100.0%

.

.

.

Peru

2

0.0%

50.0%

1

0.0%

100.0%

21

1.1%

61.9%

8

0.1%

100.0%

4

0.0%

75.0%

.

.

.

.

.

.

.

.

.

Colombia

3

0.0%

100.0%

4

0.0%

100.0%

12

0.6%

75.0%

13

0.1%

84.6%

8

0.0%

100.0%

4

0.0%

75.0%

.

.

.

.

.

.

Greece

2

0.0%

100.0%

2

0.0%

100.0%

3

0.2%

100.0%

8

0.1%

87.5%

8

0.0%

87.5%

4

0.0%

75.0%

.

.

.

.

.

.

Bulgaria

2

0.0%

100.0%

6

0.1%

100.0%

5

0.3%

80.0%

16

0.1%

100.0%

12

0.1%

83.3%

3

0.0%

66.7%

.

.

.

.

.

.

Cuba

.

.

.

3

0.0%

100.0%

8

0.4%

62.5%

15

0.1%

93.3%

6

0.0%

100.0%

.

.

.

1

0.1%

100.0%

.

.

.

Czech Republic

3

0.0%

66.7%

5

0.0%

100.0%

.

.

.

26

0.2%

92.3%

8

0.0%

87.5%

2

0.0%

100.0%

1

0.1%

100.0%

.

.

.

Sri Lanka

1

0.0%

100.0%

5

0.0%

100.0%

1

0.1%

100.0%

16

0.1%

100.0%

5

0.0%

80.0%

.

.

.

2

0.1%

100.0%

.

.

.

Licensing Agreement

1st Nation of JV's operations

Technology Agreement Exploration Agreement

44

Supply Agreement % JVs w/ % of crossN all border partic. 663 38.5% 36.3%

Equipment Manufacturing % JVs w/ % of crossN all border partic. 505 56.9% 33.9%

Iran

1

0.0%

100.0%

2

0.0%

100.0%

7

0.4%

71.4%

14

0.1%

100.0%

2

0.0%

50.0%

1

0.0%

100.0%

1

0.1%

100.0%

.

.

.

Cambodia

.

.

.

2

0.0%

100.0%

1

0.1%

100.0%

8

0.1%

87.5%

3

0.0%

100.0%

2

0.0%

50.0%

.

.

.

.

.

.

Azerbaijan

1

0.0%

.

2

0.0%

100.0%

15

0.8%

93.3%

5

0.0%

100.0%

2

0.0%

100.0%

1

0.0%

100.0%

2

0.1%

100.0%

.

.

.

Slovak Republic

1

0.0%

100.0%

5

0.0%

100.0%

1

0.1%

0.0%

23

0.2%

95.7%

8

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Algeria

.

.

.

.

.

.

10

0.5%

80.0%

13

0.1%

100.0%

6

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Estonia

1

0.0%

.

1

0.0%

100.0%

.

.

.

17

0.1%

94.1%

5

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

Yugoslavia

1

0.0%

.

1

0.0%

100.0%

1

0.1%

100.0%

11

0.1%

100.0%

10

0.1%

90.0%

.

.

.

.

.

.

.

.

.

Nigeria

2

0.0%

100.0%

.

.

.

13

0.7%

100.0%

4

0.0%

100.0%

1

0.0%

100.0%

.

.

.

1

0.1%

100.0%

.

.

.

Papua New Guinea

.

.

.

.

.

.

17

0.9%

76.5%

3

0.0%

100.0%

4

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

Bermuda

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

3

0.0%

100.0%

2

0.0%

100.0%

2

0.0%

100.0%

1

0.1%

100.0%

.

.

.

Zimbabwe

.

.

.

.

.

.

10

0.5%

70.0%

10

0.1%

80.0%

2

0.0%

50.0%

.

.

.

.

.

.

.

.

.

Bahrain

1

0.0%

100.0%

.

.

.

3

0.2%

66.7%

9

0.1%

88.9%

5

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Bolivia

1

0.0%

100.0%

1

0.0%

100.0%

15

0.8%

80.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Laos

.

.

.

.

.

.

3

0.2%

100.0%

5

0.0%

80.0%

1

0.0%

0.0%

.

.

.

.

.

.

.

.

.

Lithuania

1

0.0%

100.0%

3

0.0%

100.0%

3

0.2%

100.0%

7

0.1%

100.0%

4

0.0%

100.0%

.

.

.

2

0.1%

100.0%

.

.

.

Luxembourg

2

0.0%

100.0%

3

0.0%

100.0%

.

.

.

4

0.0%

75.0%

5

0.0%

60.0%

.

.

.

.

.

.

.

.

.

Tanzania

.

.

.

1

0.0%

100.0%

12

0.6%

91.7%

4

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Albania

.

.

.

.

.

.

7

0.4%

100.0%

3

0.0%

66.7%

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Morocco

1

0.0%

100.0%

3

0.0%

100.0%

1

0.1%

0.0%

7

0.1%

71.4%

5

0.0%

80.0%

.

.

.

1

0.1%

100.0%

.

.

.

Oman

1

0.0%

.

1

0.0%

100.0%

1

0.1%

100.0%

5

0.0%

80.0%

2

0.0%

100.0%

.

.

.

.

.

.

1

0.1%

100.0%

Bangladesh

.

.

.

.

.

.

2

0.1%

100.0%

6

0.0%

100.0%

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

East Germany

.

.

.

.

.

.

.

.

.

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

Qatar

.

.

.

1

0.0%

100.0%

1

0.1%

100.0%

12

0.1%

91.7%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Tunisia

.

.

.

1

0.0%

100.0%

4

0.2%

75.0%

6

0.0%

100.0%

4

0.0%

100.0%

.

.

.

.

.

.

.

.

.

North Korea

2

0.0%

100.0%

.

.

.

.

.

.

8

0.1%

62.5%

.

.

.

.

.

.

.

.

.

.

.

.

Ecuador

1

0.0%

100.0%

3

0.0%

100.0%

11

0.6%

90.9%

1

0.0%

0.0%

2

0.0%

50.0%

.

.

.

.

.

.

.

.

.

Ghana

.

.

.

.

.

.

11

0.6%

81.8%

1

0.0%

0.0%

.

.

.

.

.

.

.

.

.

.

.

.

Latvia

.

.

.

1

0.0%

100.0%

.

.

.

6

0.0%

66.7%

4

0.0%

75.0%

1

0.0%

100.0%

.

.

.

.

.

.

Trinidad and Tobago

.

.

.

1

0.0%

100.0%

2

0.1%

100.0%

6

0.0%

100.0%

2

0.0%

100.0%

.

.

.

1

0.1%

100.0%

.

.

.

Croatia

3

0.0%

100.0%

.

.

.

1

0.1%

100.0%

4

0.0%

100.0%

5

0.0%

100.0%

2

0.0%

100.0%

1

0.1%

100.0%

.

.

.

Kuwait

2

0.0%

100.0%

1

0.0%

100.0%

1

0.1%

100.0%

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

Kyrgyzstan

.

.

.

.

.

.

9

0.5%

88.9%

4

0.0%

75.0%

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Mongolia

.

.

.

.

.

.

6

0.3%

100.0%

4

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

Jordan

1

0.0%

100.0%

.

.

.

.

.

.

10

0.1%

90.0%

3

0.0%

66.7%

.

.

.

1

0.1%

100.0%

.

.

.

Mozambique

.

.

.

.

.

.

3

0.2%

100.0%

6

0.0%

83.3%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Belarus

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

9

0.1%

88.9%

3

0.0%

66.7%

.

.

.

.

.

.

.

.

.

Brunei

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

1

0.0%

0.0%

.

.

.

.

.

.

.

.

.

Dominican Republic

.

.

.

1

0.0%

.

1

0.1%

0.0%

2

0.0%

50.0%

2

0.0%

50.0%

.

.

.

.

.

.

.

.

.

Georgia

1

0.0%

100.0%

3

0.0%

100.0%

2

0.1%

100.0%

3

0.0%

66.7%

1

0.0%

100.0%

1

0.0%

100.0%

1

0.1%

100.0%

.

.

.

Jamaica

1

0.0%

100.0%

.

.

.

1

0.1%

100.0%

4

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Abu Dhabi

.

.

.

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Turkmenistan

.

.

.

1

0.0%

.

3

0.2%

100.0%

2

0.0%

50.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Yemen

.

.

.

.

.

.

6

0.3%

83.3%

1

0.0%

100.0%

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Netherlands Antilles

5

0.1%

100.0%

2

0.0%

100.0%

.

.

.

.

.

.

2

0.0%

100.0%

2

0.0%

100.0%

.

.

.

.

.

.

Cayman Islands

.

.

.

1

0.0%

100.0%

.

.

.

4

0.0%

100.0%

4

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

Costa Rica

1

0.0%

100.0%

.

.

.

1

0.1%

0.0%

2

0.0%

100.0%

3

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Guyana

.

.

.

.

.

.

4

0.2%

75.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Lebanon

.

.

.

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Syria

.

.

.

1

0.0%

100.0%

1

0.1%

100.0%

6

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

Uruguay

.

.

.

1

0.0%

100.0%

1

0.1%

0.0%

2

0.0%

100.0%

3

0.0%

66.7%

1

0.0%

100.0%

2

0.1%

50.0%

.

.

.

Angola

.

.

.

.

.

.

2

0.1%

100.0%

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Armenia

.

.

.

.

.

.

2

0.1%

100.0%

3

0.0%

100.0%

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

Guatemala

.

.

.

.

.

.

1

0.1%

0.0%

2

0.0%

100.0%

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Iraq

.

.

.

.

.

.

2

0.1%

100.0%

.

.

.

.

.

.

.

.

.

4

0.2%

100.0%

.

.

.

Panama

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

Brit' Virgin Isl' (UK)

.

.

.

1

0.0%

100.0%

.

.

.

2

0.0%

100.0%

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

Cyprus

.

.

.

.

.

.

2

0.1%

100.0%

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Mali

.

.

.

.

.

.

6

0.3%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Moldova

.

.

.

1

0.0%

100.0%

1

0.1%

100.0%

2

0.0%

100.0%

1

0.0%

0.0%

.

.

.

.

.

.

.

.

.

Nepal

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Uganda

.

.

.

.

.

.

.

.

.

4

0.0%

75.0%

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Zambia

.

.

.

.

.

.

1

0.1%

0.0%

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Bahamas

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

Bosnia and Herz'

.

.

.

1

0.0%

100.0%

.

.

.

4

0.0%

75.0%

.

.

.

.

.

.

.

.

.

.

.

.

Mauritania

.

.

.

.

.

.

3

0.2%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Mauritius

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

45

Monaco

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

2

0.0%

100.0%

.

.

.

.

.

.

1

0.1%

100.0%

.

.

.

Namibia

1

0.0%

100.0%

1

0.0%

100.0%

3

0.2%

100.0%

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

Republic of Congo

.

.

.

.

.

.

3

0.2%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Sudan

.

.

.

.

.

.

3

0.2%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Tajikistan

.

.

.

.

.

.

3

0.2%

100.0%

3

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Barbados

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Botswana

.

.

.

.

.

.

2

0.1%

50.0%

2

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

Ethiopia

.

.

.

.

.

.

1

0.1%

100.0%

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Gabon

.

.

.

.

.

.

3

0.2%

66.7%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Iceland

.

.

.

.

.

.

1

0.1%

100.0%

.

.

.

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

Ivory Coast

.

.

.

.

.

.

3

0.2%

66.7%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Libya

.

.

.

.

.

.

3

0.2%

100.0%

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Macau

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Malta

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Puerto Rico

.

.

.

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

1

0.1%

100.0%

.

.

.

Upper Volta

.

.

.

.

.

.

4

0.2%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Belize

.

.

.

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Cameroon

.

.

.

.

.

.

1

0.1%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

El Salvador

.

.

.

1

0.0%

100.0%

1

0.1%

0.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Greenland

.

.

.

.

.

.

3

0.2%

66.7%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Guinea

.

.

.

.

.

.

3

0.2%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Honduras

1

0.0%

.

.

.

.

1

0.1%

0.0%

1

0.0%

0.0%

1

0.0%

0.0%

.

.

.

.

.

.

.

.

.

Madagascar

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

Nicaragua

.

.

.

.

.

.

2

0.1%

50.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

Paraguay

.

.

.

.

.

.

1

0.1%

100.0%

2

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Slovenia

.

.

.

.

.

.

.

.

.

1

0.0%

0.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Afghanistan

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Aruba

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

1

0.0%

0.0%

.

.

.

.

.

.

.

.

.

Kenya

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Malawi

.

.

.

1

0.0%

100.0%

.

.

.

1

0.0%

0.0%

.

.

.

.

.

.

.

.

.

.

.

.

Surinam

1

0.0%

100.0%

.

.

.

1

0.1%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Zaire

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Fiji

.

.

.

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Niger

.

.

.

.

.

.

1

0.1%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Solomon Islands

.

.

.

.

.

.

.

.

.

2

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

Antigua

.

.

.

1

0.0%

.

.

.

.

.

.

.

.

.

.

1

0.0%

0.0%

.

.

.

.

.

.

Cape Verde

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Chad

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Equatorial Guinea

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

French Polynesia

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Gibraltar

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Grenada

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Lesotho

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Liberia

.

.

.

.

.

.

1

0.1%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Macedonia

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Martinque

.

.

.

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

.

.

.

1

0.1%

100.0%

.

.

.

Rwanda

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Sao Tome & Principe

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Seychelles

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Sierra Leone

.

.

.

.

.

.

1

0.1%

100.0%

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Somalia

.

.

.

.

.

.

.

.

.

1

0.0%

100.0%

1

0.0%

100.0%

.

.

.

.

.

.

.

.

.

St Lucia

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

Swaziland

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

88.2%

27

1.4%

66.7%

795

5.8%

71.8%

No Country Class. & Non-classifiable

1266 13.5% 9352 100%

55.1%

1008 9.0% 11241 100%

1865 100%

13780 100%

46

2720 15.8% 17183 100%

57.8%

1575 15.6% 10104 100%

53.8%

482 28.0% 1721 100%

52.3%

203 22.9% 887 100%

. 49.3%

Table 10 Summary Statistics of Partners' Equity Stake in Joint Ventures

JVs 2-participants JVs 1st participant's stake 2nd participant's stake 3-participants JVs 1st participant's stake 2nd participant's stake 3rd participant's stake 4-participants JVs 1st participant's stake 2nd participant's stake 3rd participant's stake 4th participant's stake 5-participants JVs 1st participant's stake 2nd participant's stake 3rd participant's stake 4th participant's stake 5th participant's stake

JVs 2-participants JVs 1st participant's stake 2nd participant's stake 3-participants JVs 1st participant's stake 2nd participant's stake 3rd participant's stake 4-participants JVs 1st participant's stake 2nd participant's stake 3rd participant's stake 4th participant's stake 5-participants JVs 1st participant's stake 2nd participant's stake 3rd participant's stake 4th participant's stake 5th participant's stake

All JVs Equal Mean Median N obs Stake 51.15 48.85

50 50

38.10 32.62 30.07

33.3 33.3 33.3

31.05 25.19 22.39 21.73 27.11 21.71 18.32 16.64 17.06

20785 20785

=50% =50%

N obs

%

JVs w/ Cross-border Participants Equal Mean Median N obs N obs Stake

14767 71.0% 51.18 14767 48.80

50 50

14571 14571

=50% =50%

%

9919 9919

68.1%

3012 =33.3% 1006 3012 =33.3% 1006 3012 =33.3% 1006

33.4%

3693 =33.3% 1332 3693 =33.3% 1332 3693 =33.3% 1332

36.1% 38.19 32.70 29.92

33.3 33.3 33.3

25 25 25 25

1063 1063 1063 1063

=25% =25% =25% =25%

435 435 435 435

40.9% 31.09 25.46 22.21 21.56

25 25 25 25

861 861 861 861

=25% =25% =25% =25%

320 320 320 320

37.2%

20 20 20 20 20

370 370 370 370 370

=20% =20% =20% =20% =20%

140 140 140 140 140

37.8% 26.87 21.88 18.29 16.61 17.07

20 20 20 20 20

312 312 312 312 312

=20% =20% =20% =20% =20%

107 107 107 107 107

34.3%

N obs

%

Multi-regional JVs Equal Mean Median N obs N obs Stake

%

Domestic JVs Equal Mean Median N obs Stake

51.08 48.88

50 50

1672 1672

=50% =50%

1212 1212

72.5% 51.05 49.01

50 50

5978 5978

=50% =50%

4672 4672

78.2%

37.18 32.91 30.42

33.3 33.3 33.3

239 239 239

=33.3% =33.3% =33.3%

96 96 96

40.2% 37.79 32.36 30.80

33.3 33.3 33.3

658 658 658

=33.3% =33.3% =33.3%

316 316 316

48.0%

29.98 25.38 22.59 23.34

25 25 25 25

74 74 74 74

=25% =25% =25% =25%

33 33 33 33

44.6% 30.95 24.01 23.36 22.28

25 25 25 25

193 193 193 193

=25% =25% =25% =25%

107 107 107 107

55.4%

25.28 20.82 17.93 16.87 18.94

20 20 20 20 20

29 29 29 29 29

=20% =20% =20% =20% =20%

13 13 13 13 13

44.8% 28.14 20.99 18.61 16.82 17.05

20 20 20 20 20

56 56 56 56 56

=20% =20% =20% =20% =20%

32 32 32 32 32

57.1%

47