A tale of two property rights: Knowledge, physical assets, and multinational firm boundaries

Journal of International Economics 122 (2020) 103262

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A tale of two property rights: Knowledge, physical assets, and multinational firm boundaries☆ Bohdan Kukharskyy Department of Economics and Finance, City University of New York, Baruch College, One Bernard Baruch Way, New York, NY 10010, USA

a r t i c l e

i n f o

Article history: Received 27 April 2018 Received in revised form 17 September 2019 Accepted 20 September 2019 Available online 22 October 2019 Research data related to this submission: https://data.mendeley.com/datasets/ v8t2zz58fv/draft?a=375a5146-b634-4406b776-86b3c544c515 JEL classifications: F21 F23 L23 L24 D23 O34

a b s t r a c t The theory of multinational firm boundaries has been shaped by two major paradigms: an earlier one, emphasizing the role of integration in preventing the dissipation of knowledge, and a more recent one, stressing the role of firm boundaries in mitigating underinvestments into relationship-specific assets in the face of contractual incompleteness. This paper develops a novel model encompassing both approaches in a unifying framework. The model predicts that the attractiveness of integration increases in the importance of the parent firm's knowledge capital and decreases in the importance of the affiliate's physical capital in a joint production process. Furthermore, stronger intellectual property rights (IPR) protection in the affiliate's country is predicted to mitigate the effect of knowledge intensity on the attractiveness of integration. I test these hypotheses using unique panel data on more than 100,000 firm pairs worldwide. In line with the model's predictions, knowledge-intensive parent firms choose higher ownership shares in their affiliates, yet this relationship is less pronounced the stronger the IPR protection in the affiliate's country. In addition, higher physical capital intensity of the affiliate is associated with lower ownership shares. These findings are robust to controlling for unobserved heterogeneity across countries, industries, and firms, providing strong support for the unifying theory of multinational firm boundaries. © 2019 Elsevier B.V. All rights reserved.

Keywords: Multinational firm boundaries Knowledge dissipation Hold-up Intangible assets IPR protection

1. Introduction What determines firm boundaries? This question, posed eighty years ago by Coase (1937), is more relevant than ever in the age of multinational enterprises that span their boundaries around the globe. According to UNCTAD (2011), roughly one quarter of the world GDP in 2010 was generated within multinational firms' boundaries. In 2015,

☆ I would like to thank the editor, Andrés Rodríguez-Clare, and two anonymous referees for their very helpful comments and suggestions on the earlier version of the manuscript. I am also grateful to Vanessa Alviarez, Pol Antràs, James Brander, Peter Eppinger, Keith Head, Benjamin Jung, Wilhelm Kohler, James Markusen, Gernot Müller, Alireza Naghavi, John Ries, Gabriel Smagghue, Frank Stähler, as well as participants at the European Trade Study Group meeting in Florence, and seminar participants at the UBC Sauder, CUNY, SFSU, and the University of Tübingen for their helpful suggestions. All errors are my own. E-mail address: [email protected].

https://doi.org/10.1016/j.jinteco.2019.103262 0022-1996/© 2019 Elsevier B.V. All rights reserved.

the value of cross-border mergers and acquisitions totaled $1.6 trillion (Reuters, 2016) – a number equivalent to the GDP of Canada, or 2.2% of the world GDP. Why do multinational enterprises expend managerial time and investment bankers' fees to integrate other companies into firm boundaries rather than simply cooperate with their business partners at arm's-length? The discussion of this question in the economics literature has been centered around two major paradigms. An earlier research strand emphasizes the importance of a multinational firm's knowledge (intangible assets) and sees the role of integration in preventing this knowledge from being dissipated by an affiliate.1 A more recent strand of literature suggests that integration can mitigate

1 See Rugman (1986), Ethier and Markusen (1996), Markusen (1995, 2001), Saggi (1996, 1999), Fosfuri (2000), Fosfuri et al. (2001), Glass and Saggi (2002). See also Barba Navaretti and Venables (2004) for a textbook treatment of the so-called ‘knowledgedissipation paradigm’.

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the hold-up problem arising in situations where one or both parties make investments into relationship-specific, non-contractible assets.2 Although from a practitioner's point of view both the threat of knowledge dissipation and the hold-up problem are considered to be relevant obstacles to productive cross-border collaborations (see, e.g., Midler, 2009), these two major paradigms have existed so far as disjoint narratives, with hardly any overlap. This paper bridges the ‘knowledgedissipation’ and the ‘hold-up’ paradigms of multinational firm boundaries in a unifying framework and tests its novel predictions using unique panel data on global ownership links between firm pairs. The point of departure of this paper is the seminal framework by Antràs and Helpman (2004) – the archetypal representative of the hold-up approach to multinational firm boundaries, which I suitably adapt to incorporate the knowledge-dissipation paradigm. As in the original framework, production of final goods requires two parties: a firm's headquarters (HQ) and a manufacturing producer (the affiliate). The HQ specializes in the provision of intangible assets (e.g., a blueprint or design for a new product), while the manufacturer invests in relationship-specific physical assets necessary for the production of final goods. The key distinct feature of the current model compared to Antràs and Helpman (2004) is that, rather than sourcing physical inputs from a manufacturing producer and combining them with intangible assets on her own site, the HQ has to transfer her knowledge to the manufacturer.3 The manufacturing producer can steal this knowledge and independently fabricate a ‘knock-off’ product. The threat of knowledge dissipation adversely affects the willingness of the HQ to provide intangible assets to the manufacturer. At the same time, the producer anticipates that his investment into relationship-specific physical assets may not be fully compensated by the HQ and underinvests in these assets. It is in this sense that the current paper narrates a ‘tale of two property rights’: The HQ aims to protect her intellectual property while providing sufficient incentives for the manufacturing producer to invest in his physical assets.4 What is the role of a firm's ownership structure in this context? If courts cannot fully verify and enforce the transmission of knowledge and the investment into physical capital, parties negotiate the division of surplus ex-post (i.e., after knowledge has been transferred and investment into physical capital has been sunk). Following the Property Rights Theory (PRT) by Grossman and Hart (1986) and Hart and Moore (1990), I assume that ex-post bargaining takes place both under integration and arm's-length transaction. The distribution of surplus, however, is sensitive to the ownership structure. More specifically, integration of a producer into firm boundaries has two counteracting effects. On the one hand, integration mitigates the threat of knowledge dissipation and ensures a higher transfer of intangible assets.5 On the other hand, since an integrated producer does not possess ownership rights over physical assets, he is relatively more exposed to ex-post 2 The contributions to the ‘hold-up paradigm’ can themselves be subdivided into two groups. The first group of papers builds on Williamson's (1985) Transaction-Cost Theory (TCT), which asserts that integration can fully eliminate the hold-up problem which plagues arm's-length relationships, see, e.g., McLaren (2000) and Grossman and Helpman (2002, 2003, 2005). The second strand draws on the Property-Rights Theory (PRT) by Grossman and Hart (1986) and Hart and Moore (1990), which argues that hold-up prevails even within firm boundaries, see, e.g., Antràs (2003, 2005), Antràs and Helpman (2004, 2008), Antràs and Chor (2013), and Alfaro et al. (2018). See also Antràs (2015) for a textbook treatment of the TCT and PRT of multinational firm boundaries. 3 This production process is succinctly exemplified by the famous inscription on products by Apple Inc.: “Designed by Apple in California, assembled in China”. Another notable example is Intel, whose HQ in Silicon Valley specializes on the provision of the semiconductor technology, but does not manufacture computer chips on-site. 4 For clarity, I refer henceforth to the HQ as ‘she’ and to the manufacturing producer as ‘he’. 5 This assumption lies at the heart of the knowledge-dissipation paradigm and can be justified in two possible ways. First, since an integrated producer does not possess property rights over physical assets, he is limited in his ability to make productive use of a HQ's intangible assets on the deviation path. Second, the manager of an integrating firm can choose whom to reveal her knowledge and is better able to prevent defection by employees.

hold-up by the HQ and underinvests in these assets as compared to a non-integrated manufacturer. The optimal ownership structure thus trades off the inefficiencies from the underprovision of knowledge against the underinvestment in physical assets to maximize the joint surplus from the cooperation. This theoretical framework yields two key predictions. First, the relative attractiveness of integration versus arm's-length transaction increases in the importance of a multinational firm's intangible assets in the production process (henceforth, knowledge intensity), and decreases in the importance of a manufacturing firm's tangible assets (henceforth, physical capital intensity). The intuition behind this prediction is well aligned with the general logic of the PRT: The need for incentivizing a given party's activities increases in the importance of this party's contribution to the production process. Hence, if knowledge intensity increases, the HQ has a higher incentive to integrate the producer into firm boundaries to reduce the threat of knowledge dissipation and, thereby, ensure a sufficiently high knowledge transfer. Conversely, as physical capital intensity increases, the HQ is primarily concerned with incentivizing the producer's investments into tangible assets, which can be done by relinquishing the property rights over physical capital to the manufacturer. The second key prediction suggests that the direct effect of knowledge intensity on the choice of the ownership structure systematically varies with the strength of intellectual property rights (IPR) protection in the producer's country. More specifically, I demonstrate that stronger IPR protection attenuates the positive effect of knowledge intensity on the relative attractiveness of integration. Intuitively, if the HQ's intellectual property is better protected in the producer's country, the need for integrating the manufacturer in order to minimize the threat of knowledge dissipation decreases. Motivated by the subsequent empirical setup, I further verify that the above-mentioned predictions extend to the case in which the HQ chooses a continuous ownership share in her affiliate, rather than deciding only between the two ‘extreme’ cases of integration vs. arm's-length transaction. In this extended model, a higher ownership share in the manufacturing unit strengthens the HQ's bargaining power during expost negotiations and reduces the bargaining power of the affiliate. I show that the HQ's choice whether to obtain a marginally higher ownership share in the manufacturing company is driven by the same set of explanatory factors as the binary integration decision.6 What is the value added of this unifying framework to the standalone knowledge-dissipation and hold-up paradigms?7 First, this framework can rationalize the following empirical puzzle that cannot be property explained by the two individual paradigms: Why do we observe outsourcing of knowledge-intensive production processes to countries with week IPR protection?8 The hold-up paradigm is silent on this question, as knowledge dissipation and IPR protection play no role in this paradigm. Although the knowledge-dissipation paradigm provides an answer to this question, it appears to be somewhat unsatisfactory, as it can explain the existence of outsourcing in countries with weak IPR protection only by invoking an exogenous (governance) cost of integration. In contrast, the unifying framework developed in this paper follows the PRT by endogenously deriving the costs and benefits of integration from the same set of fundamental inefficiencies. According to the present model, a multinational company may outsource its production despite the risk of knowledge dissipation in order to reduce the hold-up problem from the viewpoint of the manufacturer and,

6 In additional extensions, I show that the model's predictions remain intact if one allows for partial contractibility of inputs (similarly to Antràs and Helpman, 2008), as well as for manufacturing producer's financial constraints. 7 I refer to the two paradigms as the body of literature mentioned in footnotes 1 and 2, respectively. 8 For instance, a highly knowledge-intensive U.S. multinational corporation Apple Inc. publicly discloses the list of its 200 most important independent suppliers, see https:// www.apple.com/supplier-responsibility/. In 2017, 155 out of these suppliers had production facilities in China – a country with a relatively week IPR protection.

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Fig. 1. Knowledge intensity, physical capital intensity, and ownership shares.

thereby, incentivize the latter's investment into relationship-specific physical assets. Second, and related, the unifying framework can rationalize a positive link between IPR protection and foreign direct investment (FDI), previously reported in the literature.9 While the hold-up paradigm is once again silent on this relationship, the knowledgedissipation paradigm would predict more outsourcing (licensing), rather than FDI, in countries with better IPR protection (see, e.g., Barba Navaretti and Venables, 2004). According to the current model, an improvement in IPR protection reduces the ex-post bargaining position of the independent manufacturer and decreases his ex-ante investment into physical assets. If the associated drop in firm profits is sufficiently grave, the HQ may decide to integrate the (foreign) manufacturer into firm boundaries, leading to more (foreign) direct investment. I test the model's predictions using global data on ownership links within multinational firms from the Orbis database by Bureau van Dijk (BvD). This database is uniquely suited for investigating firm-level determinants of multinational firm's boundaries by encompassing the following three key features. First, it contains information on the outcome variable of interest – the direct ownership shares (in percent) of parent companies in their affiliates in a biannual panel from 2004 to 2014. Second, it provides yearly balance sheet information on the value of a HQ's intangible assets (such as patents and copyrights), as well as the value of the affiliate's tangible assets (such as equipment and machinery), which I use to construct proxies for knowledge and physical capital intensity, respectively. Third, it is characterized by a large international coverage, comprising headquarters and affiliates around the globe. I enrich this database with multiple proxies for IPR protection to explore the interaction between knowledge intensity and intellectual property rights protection in the affiliate's country in their impact on firm boundaries. The combination of these features allows me to provide a large-scale investigation of the effects of knowledge and physical capital intensity on multinational firms' ownership structures, while fully controlling for unobserved heterogeneity across countries, industries, and firm pairs using fixed effects (FE). Fig. 1 below provides a first glance at the data by plotting ownership shares against industry/country measures of knowledge intensity and physical capital intensity.10 More specifically, the left panel plots mean values of ownership shares by industry and country of the HQ against mean knowledge intensity in this industry/country, whereas the right panel plots mean values of ownership shares in the industry/country of the affiliate against mean physical capital intensity in this industry/ country. The regression lines and the associated estimates, presented 9 See, e.g., Lee and Mansfield (1996); Javorcik (2004); Nunnenkamp and Spatz (2004); Branstetter et al. (2006, 2007, 2011). 10 The figure depicts industry/country averages since no visual inference would be possible with the myriad of more than 100,000 firm-pairs.

in the lower right corner of the respective figure, show significant correlations in line with the first prediction: Headquarters active in knowledge-intensive industries tend to integrate their affiliates more tightly into firm boundaries, whereas ownership shares are lower for higher physical capital intensities in the affiliate's industry. Clearly, these raw correlations can be confounded by a multitude of country-, industry-, and firm-specific characteristics. I verify, however, that these relationships prevail after controlling for various observable factors and several dimensions of unobserved heterogeneity in the data. I bring the model's predictions to the data in three complementary steps. As a first pass at the data, I explore conditional correlations of ownership shares from 2014 with the lagged value of a HQ's intangible assets (as well as its interaction with IPR protection) and the lagged physical capital intensity of an affiliate in the cross-section of firm pairs. Throughout specifications, I control for all observable and unobservable characteristics of the parent's and affiliate's countries (such as economic development or institutions) and industries (e.g., relationshipspecificity) via HQ and affiliate country and industry FE. Furthermore, I include country- and industry-pair FE to account for all characteristics specific to a pair of countries (such as geographical and cultural distance) and industries (e.g., the parent's and the affiliate's down- vs. upstreamness). Conditional on the above-mentioned set of FE and a wide range of observable firm-specific factors, I find that the ownership share in a given affiliate is positively correlated with lagged knowledge intensity of the HQ and negatively associated with lagged physical capital intensity of the affiliate, in line with the model's first prediction. Furthermore, consistent with the second prediction, I find that the positive relationship between the HQ's intangible assets and her ownership share in a given affiliate is less pronounced the stronger IPR protection in the affiliate's country. In the second step, I address one of the two main challenges to identification – the issue of reverse causality. It is conceivable that some firms choose their factor intensities in anticipation of the ownership structure from the subsequent period, in which case the explanatory variables from the first empirical exercise would be the outcome, rather than the cause, of ownership decisions. To account for this possibility, I construct measures of knowledge and physical capital intensity at the aggregate level of industry/country cells. More specifically, I exploit Orbis data on more than 5 million firm-year observations within the time span of 2004–2013 to construct a leave-out median value of intangible assets by HQ's industry/country and a leave-out median value of tangible assets by affiliate's industry/country.11 Since these values are

11 The leave-out median for a given firm is defined as the median for this firm's industry/ country excluding the value corresponding to this firm in the data.

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fairly exogenous to the ownership structure of a single company, approximating knowledge and physical capital intensities with their industry/country counterparts alleviates the reverse causality concern. Controlling for unobserved heterogeneity across countryand industry-pairs using FE, I continue to find evidence supportive of the models predictions. Furthermore, the findings are confirmed in within-firm specifications with HQ firm FE. In the third step, I tackle the second major challenge to identification – the omitted variables bias. Although the first two econometric models control for observable firm characteristics, there can be unobserved firm-specific factors that confound the relationship between factor intensities and ownership shares in the crosssectional analysis. A prime example of a potential confounding factor is productivity, which is unobservable by the econometrician but has been shown theoretically to have an effect on firm boundaries (see, e.g., Kohler and Smolka, 2015). To address this concern, I exploit changes in ownership shares due to lagged variation in the HQ's intangible assets and the affiliate's tangible assets in the panel of 2004–2014. This approach allows me to control for unobserved heterogeneity across cooperating partners via HQ and affiliate firm FE. Moreover, I account for all time-invariant factors specific to a given ownership link via firm-pair FE. Controlling for firm-pair FE, I find robust evidence in line with the model's predictions: A lagged increase in the HQ's intangible assets raises her ownership share in the affiliate, while a lagged increase in the affiliate's tangible assets reduces the ownership share. Overall, the evidence uncovered in the three-pronged empirical exercise provides strong support for the unifying theory of multinational firm boundaries. In an important set of robustness checks, I verify that my results are fully robust to accounting for the importance of a parent firm's intangible assets in the production process of an affiliate using a novel measure of interindustry knowledge applicability. For the construction of this measure, I exploit information on more than 23 million patent citations from the NBER Patent Database to approximate the applicability of knowledge generated within a given industry to all other industries. I then weigh the HQ's intangible assets with this industry-pair measure of knowledge applicability to construct a refined proxy for knowledge intensity of a given production process. Throughout specifications, I continue to find a positive effect of knowledge intensity on ownership shares. This paper contributes to a body of research investigating the organization of firms across borders. In their review of this literature, Antràs and Rossi-Hansberg (2009, p.58) stress that “past research has arguably focused too much on hold-up inefficiencies as the main drivers of the internalization decision”, whereas real-world organizational decisions such as “Intel's choice to internalize fully their operations in Costa Rica may be better explained in terms of fear of technological expropriation than in terms of a double-sided hold-up problem.” This observation squares well with the recent influential study by Atalay et al. (2014), which documents that upstream units of U.S. firms ship very small shares of their output to the firms' downstream establishments, and almost one-half of upstream establishments do not conduct any shipments inside firm boundaries (cf. Ramondo et al., 2016, for further related evidence on U.S. multinationals). The authors interpret this evidence as indicating that a primary purpose of integration is to mediate efficient transfers of intangible inputs within firms rather than sourcing intermediate inputs from their affiliates. The present paper contributes to this discourse by showing that a HQ's intangible assets, as well as their interaction with IPR protection in the affiliate's country, are indeed important drivers of multinational firm boundaries. Yet, to be clear, this paper does not invalidate the conventional hold-up approach but rather shows how the latter can be reconciled with the knowledge-dissipation paradigm in a unifying framework to

obtain a more profound understanding of the organization of multinational enterprises.12 From the theoretical perspective, this paper is related to Chen et al. (2012), who consider the dissipation of knowledge and the hold-up problem in a single framework.13 As in the current paper, the authors predict that the attractiveness of integration versus arm's-length transaction increases in the knowledge intensity and decreases in the physical capital intensity of the production process. The key difference between the two approaches is that the current paper builds on the PRT, rather than the TCT (see footnote 2 for distinction). Empirically, it remains an open question whether the PRT or the TCT serves as a better model of firms' integration decisions. For instance, Corcos et al. (2013), Nunn and Trefler (2008, 2013), Kohler and Smolka (2014, 2015), and Antràs and Chor (2013) provide evidence consistent with the PRT, while Acemoglu et al. (2010) put forward some evidence pointing in the direction of the TCT. Recent work by Antràs (2015) and Eppinger and Kukharskyy (2019) attempts to discriminate between the two elemental theories of the firm and finds empirical support in favor of the PRT. Putting aside the question of the empirical precedence of the PRT vs. TCT, the value added of my unifying framework is threefold. First, the PRT-based approach delivers a novel prediction regarding the negative interaction between knowledge intensity and IPR protection on integration, which finds strong empirical support in the data. As shown in this paper, this interaction effect turns out to be ambiguous under the TCT. Second, while the direct effect of IPR protection on the prevalence of integration is unambiguously negative in Chen et al. (2012), my model allows for a positive effect of IPR protection on the integration decision. This allows me to rationalize the existing empirical evidence on the positive link between IPR and FDI (see footnote 9). Lastly, the PRT approach has a general advantage over TCT by deriving the costs and benefits of integration from the same set of inefficiencies, without resorting to the exogenous cost of integration. From the empirical perspective, this paper relates to a recent but rapidly growing literature investigating the link between headquarter intensity, defined as the relative importance of a HQ's inputs compared to inputs supplied by the manufacturing unit, and the international make-or-buy decision; see, e.g., Antràs (2003, 2015), Bernard etal. (2010), Nunn and Trefler (2008, 2013). The vast majority of this literature has approximated the relative attractiveness of integration using U.S. industry-level data on the share of intra-firm imports in total imports, while proxying headquarter intensity with various measures of R&D-, capital-, and skill-intensity of a given U.S. industry. Corcos et al. (2013) and Kohler and Smolka (2014, 2015) bring this approach to the firm level using French and Spanish import data, respectively, and approximate the headquarter intensity with capital and skill intensity of the importing firm. All above-mentioned contributions generally find a positive relationship between intra-firm imports and the employed proxies for headquarter intensity. The current paper complements these studies in four major respects. First, I exploit the firm-pair structure of my data to provide a pioneering investigation of the relationships between firm boundaries and factor intensities of both sides of the ownership link. Second, I use unique information on HQ's intangible assets to study the role of knowledge in the organization of firms. Third, I investigate how knowledge intensity interacts with IPR protection. Lastly, I make progress towards identifying the effects of both parties' factor intensities by exploring time variation in intangible and tangible assets using panel data on more than 100,000 firm pairs worldwide. 12 A recent contribution by Bolatto et al. (2019) extends the model by Antràs and Chor (2013) with the risk of knowledge appropriation to study the role of IPR protection on the organization of global supply chains. The authors show that IPR institutions have a differential effect on the integration decision depending on the position of the input along the supply chain, and whether production stages are sequential complements or substitutes. 13 In a follow-up paper, Markusen and Xie (2014) relax some of the restrictive assumptions from Chen et al. (2012) and show that the key predictions from the original framework extend to a more general setting.

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Previous studies have analyzed alternative determinants of firm boundaries using Orbis data. Eppinger and Kukharskyy (2019) uncover strong evidence that better contracting institutions in affiliates' countries favor higher ownership shares, particularly in relationship-specific industries. Kukharskyy (2016) finds that firms led by long-term oriented managers own higher shares in their affiliates. Gorodnichenko et al. (2018) find that higher cultural distance between the HQ's and the affiliate's managers is associated with lower ownership shares. The current paper fully accounts for the abovementioned alternative explanations using FE (by affiliate country/ industry, HQ firm, and firm-pair, respectively) and presents novel evidence on the effects of intangible and tangible assets. The remainder of the paper is organized as follows. Section 2 presents the model. Section 3 describes the empirical approach and the data. Section 4 reports the estimation results. Section 5 concludes. 2. Theoretical model 2.1. Set-up Consider a game between two parties: a firm's headquarters H and a manufacturing producer M. The two parties can be located in the same or in different countries and each firm is led by one manager. H and M collaborate to produce a differentiated variety of a final good. Assuming constant elasticity of substitution (CES) preferences, the demand for a single variety of the final good may be expressed by the following isoelastic function: x ¼ Dω−1=ð1−αÞ whereby x and ω denote, respectively, quantity and price of final goods, D N 0 is a demand shifter, and α ∈ (0, 1) is a parameter related to the elasticity of substitution between any two varieties, σ = 1/(1 − α). This demand function yields the following revenue: R ¼ xα D1−α

ð1Þ

Production of final goods requires three inputs: knowledge k, physical capital p, and labor l, combined into x according to the following Cobb-Douglas production function: x¼

!ηp !1−ηk −ηp
ηk k p l ηk ηp 1−ηk −ηp

ð2Þ

whereby ηk and ηp represent, respectively, the importance of knowledge and physical capital in the production process (henceforth, knowledge intensity and physical capital intensity, respectively).14 Knowledge k is supplied by the HQ.15 More specifically, I assume that H owns an exogenous stock of knowledge (intangible assets) K and decides on the endogenous amount of k ≤ K to be provided to M.16 I further assume that knowledge is perfectly divisible and let ck denote the variable cost of the provision of one unit of k. This notion of knowledge transfer can be rationalized in two different ways. In line with Helpman and Krugman (1985), one may assert that H has to adapt her general knowledge to M’s production capabilities, whereas ck may be

14

Formally, ηk and ηp denote knowledge and physical capital intensity, respectively, ∂x k ∂x p ¼ ηk and ¼ ηp . ∂k x ∂p x

since

15 This assumption is in line with the recent evidence by Antràs and Yeaple (2014), who document that parent firms account for 84–87% of the global R&D expenditures of U.S. multinational corporations. 16 The assumption that K is exogenous is for simplicity and can be relaxed in a parsimonious model of knowledge capital accumulation without altering this paper's qualitative predictions.

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interpreted as an adaptation cost per unit of relationship-specific k. Alternatively, one can assume that the transmission of knowledge occurs through training by experts, whereby ck denotes the variable training costs (see, e.g., Garicano and Rayo, 2017). Under either interpretation, the HQ is ex-post ‘locked’ into the relationship with the manufacturing producer since the cost ck cannot be recovered if the current relationship breaks down. The producer M also anticipates an ex-post lock-in since he has to invest into relationship-specific physical capital (tangible assets) p, which is assumed to have no value for a tertiary party. The unit cost of p will be denoted with cp. As it is well-known, the anticipation of future hold-up will result in the underprovision of relationship-specific inputs by the party investing into these inputs. This begs a natural follow-up question: Is it optimal for H to let M invest into physical capital, rather than accumulating p on her own and handing it to the manufacturer? In Appendix A.1, I derive a sufficient condition under which M’s investment into physical capital is a dominant strategy from H’s perspective and provide an intuitive discussion as to why this condition is assumed to hold in the current paper. Once knowledge has been transmitted and the investment into physical capital is sunk, M combines k and p to produce final goods using labor l, hired on a frictionless labor market at a unit cost cl. To keep the model as general as possible, I do not impose further assumptions on whether goods x are transported from M to H or sold by M to consumers.17 Although the production process presented above bears some resemblance to the one considered in Antràs and Helpman (2004), three differences to the original contribution are worth noting. First, rather than sourcing physical inputs from M and combining them with intangible assets on her own site, H in this model has to transfer her knowledge to M. This alternative assumption provides room for the threat of knowledge dissipation, which was not present in the original framework. Second, motivated by the subsequent empirical set-up, I impose a production technology which accommodates the intensities of H’s and M’s inputs (ηk and ηp, respectively) as separate and independent parameters.18 Third, in view of recent empirical evidence suggesting that factor intensities vary even within narrowly defined sectors, I treat ηk and ηp primarily as firm-, rather than industry-specific characteristics.19 It should be noted, however, that the latter assumption does not affect any of the model's results and is made merely for the purpose of the subsequent empirical analysis, which considers firm-level proxies for factor intensities. The parties operate in an environment of contractual incompleteness, i.e., courts cannot fully verify H’s transfer of k nor M’s investment into p. Incomplete contractibility of knowledge transfer can be justified by invoking Arrow's (1962) information paradox: In order to license know-how to M, H has to reveal it to him; yet, once the knowledge is disclosed in a comprehensive contract, she has nothing left to sell. In the benchmark model, I assume for simplicity that transfer of knowledge is fully non-contractible, but consider partial contractibility of k in Appendix A.7. Following Antràs and Helpman (2004), I also assume that M’s investment into physical inputs is non-contractible. Intuitively, if the characteristics of physical capital are not verifiable by the courts, M would have an incentive to underinvest into p but request the full 17 Furthermore, it is possible to reinterpret x as intermediate inputs either for a HQ's or a third party's (unmodeled) production process without changing the paper's predictions. 18 The technology from Eq. (2) reduces in the limit to the one from Antràs and Helpman (2004) for ηp approaching (1 − ηk), in which case ηk and ηp are interdependent factors (i.e., an increase in ηk would cause mechanically a reduction in ηp). The current paper disentangles the relative importance of both parties inputs into the independent effects of ηk and ηp. 19 Using French firm-level data, Corcos et al. (2013) document that more than 80% of variation in capital and skill intensity comes from within-industry differences across firms. Using information on more than 300,000 European firms from the Amadeus dataset, Crozet and Trionfetti (2013) report that only 30% of the total variance in firm-level capital intensity is between country-industry groups, while 70% is within the same countryindustry group.

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compensation stipulated in the ex-ante contract. Since firms cannot write ex-ante enforceable contracts specifying H’s compensation conditional on the amount of transferred knowledge, nor stipulate M’s reward contingent on the amount of accumulated physical capital, H and M bargain over the surplus ex-post, i.e., after knowledge has been transferred and investment into physical capital has been sunk. This bargaining process is modeled as a generalized Nash bargaining. That is, each party obtains his or her outside option (i.e., the payoff under no trade) plus a fraction of the ex-post gains from trade (quasirent), defined as revenue net of both parties' outside options. The fraction of the quasi-rent accruing to H and M are given by β ∈ (0, 1) and (1 − β), respectively. Against the backdrop of contractual incompleteness, the only incentive device available ex-ante is the allocation of property rights over physical assets. After H and M form a relationship, the HQ chooses whether to integrate (I) a producer into firm boundaries or transact with him at arm's-length (A). In the benchmark model, I treat the integration decision as a binary choice variable but allow H to choose from a continuum of ownership shares in the extension from Section 2.3. Following the PRT by Grossman and Hart (1986) and Hart and Moore (1990), I assume that ex-post bargaining takes place under both organizational forms, o ∈ {I, A}. The distribution of surplus, however, is sensitive to the ownership structure, which determines both parties' payoffs after the current relationship breaks down (henceforth, offthe-equilibrium path). Consider first M’s outside options. The novel feature of the current model compared to Antràs and Helpman (2004) is that an arm's-length transaction leaves M with a positive outside option. Intuitively, upon receiving H’s intangible assets, an independent producer can terminate the relationship and independently manufacture final goods. However, M can produce on his own only a fraction γ ∈ [0,1) of the output that could have been generated if the two parties had agreed and used the knowledge within the production relationship. The reason behind assuming γ b 1 is that H possesses some unique expertise regarding her intangible assets, which M cannot make use of if the current relationship breaks down. The level of γ crucially depends on the strength of IPR protection in M’s country, ν. Formally, I impose Assumption 1. If o = A, the fraction of equilibrium output that can be generated by M on the off-the-equilibrium path decreases with the ∂γ IPR protection in M’s country, b0. ∂ν Intuitively, a country with strong IPR protection secures the HQ's intellectual property and inhibits M’s attempts to independently manufacture and sell products generated with H’s intangible assets. Substituting x with γx in Eq. (1), M’s outside option under arm's-length transaction thus reads γαR, whereby R represents the revenue on the equilibrium path. In line with the knowledge-dissipation paradigm, I assume that internal organization of the production process (I) provides better protection against expropriation of intangible assets than a market-based transaction (A).20 Several theoretical justifications for this assumption have been provided in the literature. Ethier and Markusen (1996) suggest that managerial deviation incentives are more easily prohibited if M is integrated into H’s boundaries, as compared to the case when M is an independent licensee. Alternatively, Chen et al. (2012) assume that an integrated producer is limited in his ability to make use of a HQ's intangible assets on the off-the-equilibrium path, since he does

20

Anecdotal evidence corroborating this assumption is well-documented in the literature. For instance, Barba Navaretti and Venables (2004) discuss the case study of the Italian multinational corporation Pirelli, which uses a revolutionary, fully computerized technology Modular Integrated Robotized System (MIRS) to build tires. The interviews with Pirelli's managers suggest that the main rationale for using this technology exclusively in wholly owned domestic and foreign plants is to protect proprietary knowledge regarding the MIRS technology.

not possess property rights over physical assets.21 Following Chen et al. (2012), I set M’s outside option under integration equal to zero. It should be noted, however, that allowing for a positive outside option of M under I would not alter the model's qualitative results, as long as it is smaller than his outside option under A.22 The HQ's outside option iis modeled by exact analogy to Antràs and Helpman (2004). If the parties cooperate at arm's-length, H does not own physical capital and can generate zero output on the off-theequilibrium path. Hence, H’s outside option in an arm's-length relationship is equal to zero.23 In contrast, integration provides H with residual control rights over physical capital. In this case, if the bargaining fails, H can fire M’s manager and use p to manufacture final goods. However, H can produce on her own only a fraction δ ∈ [0, 1) of the output that could have been generated on the equilibrium path. That is, the HQ cannot exploit physical capital without the manufacturer as effectively as with the cooperation of M. Substituting x with δx in Eq. (1), H’s outside option under integration thus reads δαR, whereby R represents the revenue on the equilibrium path. Bearing in mind that Nash bargaining yields each party his or her outside option plus the share of the quasi-rent, H’s payoff from ex-post negotiations under integration is given by δαR + β(R − δαR − 0) and her payoff under arm's-length cooperation reads 0 + β(R − 0 − γαR). Thus, the share of revenue accruing to H under I and A, respectively, reads   βI ≡ δα þ β 1−δα ;

βA ≡ βð1−γα Þ

ð3Þ

Accordingly, M’s payoff from ex-post negotiations under integration and arm's-length transaction is given by 0 + (1 − β)(R − δαR − 0) = (1 − βI)R and γαR + (1 − β)(R − 0 − γαR) = (1 − βA)R, respectively. A simple inspection of Eq. (3) reveals that βI − βA = βγα + (1 − β)δα N 0 for all α, β ∈ (0, 1) and δ, γ b 1. That is, the HQ gets a larger share of the revenue under integration than under arm's-length transaction, βI N βA. This relationship is qualitatively similar to the one obtained in Antràs and Helpman (2004), but it follows from a different set of assumptions which allow for the threat of knowledge dissipation and provide for the role of IPR protection. The timing of events is as follows: t1 H chooses the organizational form o ∈ {I, A}. t2 H offers potential suppliers a contract which stipulates the labor employment l and the upfront transfer (participation fee) τ. A large pool of potential producers apply for this contract and H chooses one M from this pool. t3 H transmits to M knowledge k, while M invests into physical capital p. t4 The parties negotiate the division of the surplus via generalized Nash bargaining. t5 M produces final goods using knowledge k, physical capital p, and labor l. The revenue is distributed among the parties according to the sharing rule agreed upon in t4. Before turning to the analysis of the above-mentioned game, two comments are in order regarding the assumptions imposed on the contracting setup in period t2. First, since the focus of this paper lies on intangible and tangible assets, I rule out for simplicity any frictions regarding labor employment, whence assuming that l is optimally chosen by H and explicitly stipulated in the ex-ante contract. However, the model's results continue to hold if labor employment were noncontractible and M were to choose l independently and noncooperatively in period t3. Second, following Antràs and Helpman 21 This assumption presupposes that M cannot produce physical capital quickly enough to manufacture final goods in the one-shot game. Yet, even in a repeated-game context, the producer without physical assets would have to incur additional costs to accumulate anew the physical assets, which implies a lower outside option of M under I. 22 In the extension of Section 2.3, I allow for knowledge dissipation under any organizational structure and show that the model's predictions remain intact. 23 Once again, the results are qualitatively unchanged if one were to assume a positive outside option of H under A, as long as it is smaller than under I.

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

(2004), I allow for an ex-ante lump-sum transfer τ from M to H, which ensures that the organizational form is chosen so as to maximize the joint surplus from the relationship in the face of contractual incompleteness. Such a transfer is also commonly assumed in the knowledge dissipation literature, where it is interpreted as the ex-ante license fee paid by M to H for using the latter's knowledge (see, e.g., Ethier and Markusen, 1996). However, the ability of M to pay such a transfer presupposes initial cash endowments or the existence of a perfect financial market. In Appendix A.6, I show that the model's results extend to the case of a financially constrained M, who is unable (or unwilling) to conduct the ex-ante lump-sum transfer. 2.2. Equilibrium Before solving the game described above, it is instructive to first consider the hypothetical case of complete contracts. If courts could perfectly verify and enforce the transfer of knowledge and the investment into physical capital, the parties would stipulate the amounts of k, p, and l which maximize the joint surplus: ð4Þ

max R−ck k−cp p−cl l k;p;l

Using Eqs. (1) and (2), this maximization problem yields optimal amounts of inputs: k ¼ ηk

αR FB ≡k ; ck

p ¼ ηp

αR ≡ pFB ; cp

 αR FB l ¼ 1−ηk −ηp ≡l cl

ð5Þ

Note from the comparison of Eqs. (5) and (8) that, for any given level of revenue and any βo ∈ (0, 1), we have ko b kFB and po b pFB. Intuitively, both parties anticipate that the ex-post bargaining will not provide them with the full marginal revenue created by the respective asset and reduce the asset provision as compared to the first-best case. In t2, the HQ offers a contract that satisfies the producer's participation constraint: ð1−βo ÞR−cp p−cl l≥τ

n h  io max R 1−α βo ηk þ ð1−βo Þηp −cl l l

  αR l ¼ 1−ηk −ηp χ o ≡ lo cl


α −η −η −ð1−ηk −ηp Þ 1−α R ¼ αck k cp p cl D ≡ RFB

 α 1−η −η 1−α FB η R ¼ βok ð1−βo Þηp χ o k p R ≡ Ro

π ¼ ð1−α ÞRFB ≡ π FB

k

max ð1−βo ÞR−cp p p

whereby R is given by Eq. (1) and βo denotes the share of revenue obtained by H under organizational form o ∈ {I, A}, as described by Eq. (3). At this stage, each party takes as given the employment level stipulated in t2. This non-cooperative game yields optimal amounts of knowledge and physical capital, respectively: k ¼ ηk βo

αR ≡ ko ; ck

ð12Þ

ð13Þ

whereby RFB is the first-best revenue from Eq. (6), and   1−α ηk βo þ ηp ð1−βo Þ   χo ≡ 1−α ηk þ ηp

ð14Þ

ð7Þ

Consider now the relevant case of contractual incompleteness described in Section 2.1. In period t3, each party anticipates the outcome of the Nash bargaining from the subsequent period and chooses noncooperatively the investments into the respective asset which maximize his or her payoff. More specifically, H’s and M’s maximization problems in stage t3 are given, respectively, by: max βo R−ck k;

ð11Þ

whereby the term in the curly brackets represents the revenue from Eq. (9) net of the costs of knowledge transfer and physical capital accumulation. This maximization problem yields the optimal amount of labor employment:

as a function of revenue

is obtained from utilizing Eqs. (2) and (5) in Eq. (1) and solving the resulting expression for R. Plugging Eqs. (5) and (6) into Eq. (4), yields after simplification the maximum profit in the first-best case:

ð10Þ

whereby p and R are given by Eqs. (8) and (9), respectively. A competitive fringe of suppliers ensures that the equilibrium ex-ante transfer solves this participation constraint with equality. The HQ chooses in t2 the contract which maximizes her payoff βoR − ckk + τ, whereby k, R, and τ are given by Eqs. (8), (9), and (10), respectively. The ex-ante contract stipulates the amount of labor employment which solves the following maximization problem:

whereby the first-best (FB) revenue ð6Þ

7

αR p ¼ ηp ð1−βo Þ ≡ po cp

ð8Þ

as a function of revenue, which is obtained from plugging Eqs. (2) and (8) into Eq. (1) and solving the resulting expression for R: 02 1 1  !1−ηk −ηp 3α

 1−α η þ η β α ηk ð1−β o Þα ηp l 5 D1−α A R ¼ @4 o ck cp 1−ηk −ηp k

p

ð9Þ

is a parameter defined for notational simplicity. For future reference, note that χo N 1 for all α, βo, ηk, ηp ∈ (0, 1). Plugging Eqs. (12) and (13) into Eq. (11) yields after simplification the maximum profit under organizational form o ∈ {I, A}: π¼


 1 1−α ðηk þηp Þ 1−α FB η α βok ð1−βo Þηp α χ o π ≡ πo

ð15Þ

whereby πFB and χo are given by Eqs. (7) and (14), respectively. Before turning to the choice of the organizational form in period t1, it is worth pausing to contrast the expressions derived under contractual incompleteness with the ones obtained under a hypothetical case of complete contracts. I prove in Appendix A.2 that labor employment under contractual incompleteness is lower than in the first-best case, i.e., lo b lFB. Intuitively, the HQ internalizes the fact that future provision of knowledge and physical capital is below the first-best optimal level and adjusts downwards the amount of labor stipulated in the ex-ante contract. I further verify in Appendix A.2 that the maximum revenue and profit under incomplete contracts are lower than in the hypothetical case of complete contracts, i.e., Ro b RFB and πo b πFB. These results are intuitive given that contractual incompleteness leads to underprovision of all three inputs as compared to the first-best case. In t1, the HQ chooses the organizational form o ∈ {I, A} which maximizes the joint surplus from Eq. (15). Following Antràs (2003), I define the attractiveness of integration vs. arm's-length transaction

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B. Kukharskyy / Journal of International Economics 122 (2020) 103262

(henceforth, relative attractiveness of integration) as a ratio of profits πI under integration and arm's-length transaction, Θ ≡ . Using Eqs. (14) πA and (15), this ratio reads:
Θ¼

βI βA

η α
k

1−α

1−βI 1−βA

η α p

1−α

  11−αη þ η  1−α βI ηk þ ð1−βI Þηp 1−α @  A 1−α βA ηk þ ð1−βA Þηp 0

k

p

ð16Þ

As formally shown in Appendix A.3, Θ increases in knowledge intensity ηk and decreases in physical capital intensity ηp. Intuitively, as ηk increases, H has a ceteris paribus higher incentive to integrate M into firm boundaries in order to reduce the threat of knowledge dissipation and, thereby, facilitate a sufficiently high knowledge transfer. Conversely, an increase in ηp ceteris paribus decreases the relative attractiveness of integration, since the latter organizational form aggravates the hold-up problem from the viewpoint of M and leads to a stronger underinvestment into physical capital. These results are summarized in Proposition 1. (Binary organizational choice). The attractiveness of integration versus arm's-length transaction increases in knowledge intensity ηk, and decreases in physical capital intensity ηp. Proof. See Appendix A.3. This proposition reflects the general logic of the PRT by Grossman and Hart (1986) and Hart and Moore (1990), which suggests that ownership over physical assets should be allocated to a party undertaking most important non-contractible activities. It is also in line with the theoretical framework by Antràs and Helpman (2004), who build on the PRT to show that the attractiveness of integration vs. arm's-length transaction increases in the relative importance of H’s over M’s inputs.24 This paper's Proposition 1 complements the latter contribution in two major respects. First, and most importantly, it shows that the PRT logic extends to the case in which H provides to M intangible assets, facing thereby a threat of knowledge dissipation. Second, by disentangling the relative importance of both parties' inputs into the independent effects of knowledge and physical capital intensity, I generalize previous findings.25 Apart from advancing theoretical validity, this alternative approach facilitates the mapping of (the theoretical constructs of) factor intensities to observables (see Section 3.1). While Proposition 1 may be seen as a generalization of Antràs and Helpman (2004), the value-added of the unifying model featuring the threat of knowledge dissipation is that it provides an insight into the role of IPR protection in shaping the integration decision. More 2

specifically, I prove in Appendix A.3 that

∂ lnΘ b0 and summarize ∂ηk ∂ν

this interaction effect in the following: Proposition 2. (Binary organizational choice). The positive effect of knowledge intensity on the relative attractiveness of integration is mitigated by the IPR protection in the affiliate's country, ν. Proof. See Appendix A.3.

24 In Antràs (2003), the relative attractiveness of integration increases (rather than decreases) in capital intensity since capital in his model is provided by the HQ. Instead, the HQ in the current model specializes on the provision of knowledge, while the manufacturer invests into physical capital. The first part of this alternative assumption is based on the recent evidence by Antràs and Yeaple (2014), who document that parent firms account for 84–87% of the global R&D expenditures of the U.S. multinational corporations. The idea behind the second part of this assumption is that, even though the HQ may well provide the financial capital to the manufacturer, it is the latter who is generally involved in the accumulation and maintenance of physical assets used in the production process. 25 Recall from footnote 18 that the current paper encompasses the production technology from Antràs and Helpman (2004) as a limiting case – as ηp approaches (1 − ηk) – but also allows for variation in knowledge intensity that leaves physical capital intensity unchanged and vice versa.

The intuition behind this Proposition builds on the notion that stronger IPR protection in M’s country reduces the threat of knowledge dissipation by an independent producer. Hence, as ηk increases, the need for integrating the producer into firm boundaries to provide for a sufficiently high knowledge transfer is less pronounced if M is located in a country with strong IPR protection.26 Three remarks regarding Proposition 2 are in order. First, as mentioned in the Introduction, the unambiguously negative interaction between knowledge intensity and IPR protection in their impact on the relative attractiveness of integration necessitates the PRT framework. In Appendix A.4, I develop a parsimonious TCT-version of the current model and show that this alternative approach does not deliver an unambiguous prediction regarding the sign of this interaction effect. Second, note that Proposition 2 not only advances the hold-up paradigm of multinational firm boundaries but also complements the knowledge-dissipation narrative, since, to the best of my knowledge, this result has not been formally derived in the literature. Yet, a formalization of an interaction effect along this lines has been outstanding at the latest since the seminal empirical contribution by Javorcik (2004), who finds that weak IPR protection deters foreign investors in (knowledge-intensive) sectors relying heavily on protection of intellectual property. The theoretical model developed in this paper sheds some light on these findings by showing how IPR protection interacts with knowledge intensity in its impact on a multinational firm's integration decision. Third, while the interaction effect between knowledge intensity (ηk) and IPR protection (ν) on the relative attractiveness of integration (Θ) is negative, one may be wondering about the direct effect of IPR protection on integration. In the current model, the sign of the effect of ν on Θ is ambiguous, as it depends on a set of parameter values (see Appendix A.5 for derivations). This ambiguity may appear somewhat surprising if one were to look at it through the lens of the ‘stand-alone’ knowledge dissipation paradigm, which predicts a strictly negative relationship between IPR protection and the attractiveness of integration (see, e.g., Barba Navaretti and Venables, 2004). According to this paradigm, better IPR protection mitigates the threat of knowledge dissipation by the supplier and reduces the need for integration as a safeguard against imitation. Matters are more intricate in the current approach, encompassing both knowledge dissipation and the hold-up problem. While better IPR protection still reduces the threat of knowledge dissipation by an independent producer, it weakens his ex-post bargaining position and aggravates his underinvestment into physical assets. If the associated decrease in joint profits is sufficiently grave, the HQ may decide to integrate the supplier into firm boundaries instead. Arguably, the fact that the current framework allows for a positive effect of IPR protection on the attractiveness of integration can be seen as a strength of the unifying framework, as it allows to rationalize the empirical evidence on the positive link between IPR protection and FDI presented in the Introduction (see footnote 9). 2.3. Extensions This section explores the generality of Propositions 1 and 2 by discussing three extensions of the benchmark model. First, I consider the case in which M is constrained in his ability to pay the ex-ante lump sum transfer (e.g., due to credit market imperfections). Second, I allow for partial contractibility of the knowledge transfer. This alternative modeling assumption may appear to be more reflective of realworld commercial transactions in which some of the HQ's intangible assets may be explicitly defined in a ‘licensing agreement’, whereas other aspects of knowledge capital remain non-contractible. Third, I 26 Formally, ν decreases the outside option of the independent producer, γ (see Assumption 1) and, thereby, increases H’s bargaining position in an arm's-length transaction, βA (see Eq. (3)). This leads to a ceteris paribus higher knowledge transfer under A (see Eq. (8)) and a relatively lower attractiveness of integration.

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

consider a scenario in which H faces a continuous integration decision and chooses the equilibrium ownership share in M. This extension is motivated by the subsequent empirical analysis, which uses data on continuous ownership shares. In all three cases, I find that the key predictions of the benchmark model extend to a more general setting. To economize on space, I discuss only the extension on continuous integration decision in the main text and relegate the extensions on financial frictions and partial contractibility to Appendices A.6 and A.7, respectively. In the analysis so far, the choice of the ownership structure was restricted to a binary decision between integration and arm's-length transaction. Yet, in reality, a HQ may choose from a continuum of ownership shares s ∈ [0, 1] in the manufacturing producer. Motivated by the subsequent empirical analysis, I now turn to the case of the continuous integration decision. At a high level of abstraction, there are two channels through which s affects both parties' maximization problems. First, it is likely that a higher ownership share in the affiliate's company increases H’s bargaining power during ex-post negotiations, i.e., β′(s) N 0. Intuitively, a larger s yields the HQ more voting rights in the managing board of the affiliate, which allows her to obtain a higher share of the quasi-rent during ex-post bargaining. Second, a higher s may also provide H with residual control rights over a larger share of physical assets p if the relationship breaks down. Unfortunately, the second interpretation does not allow for analytically tractable solutions in the current framework and requires cumbersome numerical simulations. For this reason, I focus on the first channel in this extension and provide numerical simulations of the alternative modeling of ownership shares upon request. To introduce s ∈ [0, 1] into the benchmark model in the simplest possible manner, I impose the following set of additional assumptions. First, I assume a positive relationship between s and the bargaining power β, i.e., β′(s) N 0. To accommodate the key premise of the PRT that the ex-post bargaining takes place under any organizational form, I further assume β| s=0 N 0 and β| s=1 b 1. In words, these two conditions ensure that the bargaining power under arm's-length transaction (s = 0) and full integration (s = 1) lies strictly between zero and one, i.e., β(s) ∈ (0, 1) ∀ s ∈ [0, 1]. Furthermore, I relax the assumption of the benchmark model that H has a positive outside option only under full integration, while M has a positive outside option only under arm's-length transaction. I assume instead that, for any s ∈ [0, 1], H and M can reap on the deviation path fractions δα and γ α of the equilibrium revenue R, respectively. Intuitively, regardless of the distribution of physical assets after a failed ex-post bargaining, each party may raise (additional) physical capital to produce final goods on the off-theequilibrium path.27 Since knowledge is a non-rival input, it can be used by both parties if the current relationship breaks down. However, as in the benchmark model, the outside options depend on each party's ability to independently generate output without the respective counterpart, as well as on the strength of property rights protection in the affiliate's country (see Assumption 1). Under the above-mentioned assumptions, a HQ with an ownership share s ∈ [0, 1] can obtain the following share of revenue from ex-post bargaining:

9

Assumption 2. δα + γα b 1. Substituting βo from Eq. (15) with βH from Eq. (17) yields the maximum profit under ownership share s ∈ [0, 1]: 0

π os

1  11−α ðηk þηp Þ 11−α 0 1−α ηk β H þ ηp ð1−β H Þ B C ηk α ηp α @ A C π FB ;ð18Þ   ¼B @ðβ H Þ ð1−βH Þ A 1−α ηk þ ηp

whereby πFB is given by Eq. (7). I show in Appendix A.8 that the optimal ownership share s ∗ ∂s increases in knowledge intensity ηk, i.e., N0, and decreases in physi∂ηk  ∂s cal capital intensity ηp, i.e., b0. These results generalize the findings ∂ηp from Proposition 1 in Section 2.2 – which have been proven for a binary choice between integration and arm's-length relationship – to the case of continuous ownership shares and are summarized in Proposition 3. (Continuous organizational choice). A multinational firm's optimal ownership share in its affiliate increases in knowledge intensity ηk and decreases in physical capital intensity ηp. Proof. See Appendix A.8. Intuitively, as the importance of knowledge capital in the production process increases, the HQ chooses a higher ownership share to improve her ex-post bargaining position and ensure a relatively higher knowledge transfer. Conversely, if physical capital intensity increases, the HQ incentivizes the affiliate's investment into physical capital by choosing a lower ownership share in the affiliate's company, which reduces the hold-up from the viewpoint of the manufacturer. 2

∂ s b0 . This interaction ∂ηk ∂ν effect is isomorphic to Proposition 2 in Section 2.2 – which has been established for a binary choice of the organizational form – and is summarized in I further prove in Appendix A.8 that

Proposition 4. (Continuous organizational choice). Stronger IPR protection in the affiliate's country attenuates the positive effect of knowledge intensity on a multinational firm's ownership share in its affiliate. Proof. See Appendix A.8. As before, the intuition behind this prediction builds on the fact that stronger IPR protection reduces the threat of knowledge dissipation by the affiliate. Hence, as knowledge intensity increases, the need for integrating the affiliate more tightly into firm boundaries to ensure a sufficiently high knowledge transfer is less pronounced if the affiliate is located in a country with strong protection of intellectual property.

3. Empirical implementation 3.1. Mapping the model to observables

 βH ¼ δ þ βðsÞ 1−δα −γ α α

ð17Þ

whereby the remaining share βM = 1 − βH accrues to M. Note that participation of both parties is ensured only if the expression in square brackets is positive, which is maintained in 27 As in Section 2.1, the assumption that both party's outside options are independent of knowledge and physical capital intensities (ηk and ηp) is met to obtain analytically tractable solutions.

The benchmark model from Section 2.2 suggests that the relative attractiveness of integration versus arm's-length transactions increases in knowledge intensity and decreases in physical capital intensity of the production process (Proposition 1). Furthermore, the effect of knowledge intensity is ameliorated by the IPR protection in the affiliate's country (Proposition 2). Propositions derived in Section 2.3 further suggest that the two main predictions extend to the case in which the integration decision is treated as a continuous choice of the ownership share in the affiliate's company.

10

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

3.2. Econometric specifications

These predictions may be summarized as follows: ! Y ¼ f ηk ; ηp ; ηk  ν þ

−

−

ð19Þ

whereby Y denotes either the relative attractiveness of integration versus arm's-length transaction (for a binary choice of the ownership form), or the optimal ownership share of a HQ in its affiliate (for a continuous ownership decision). Given that arm's-length relationships are not observable in the database used in the current paper, the main focus of my empirical analysis lies on investigating continuous ownership shares of a parent company in its affiliate. However, I also consider binary choice models with the outcome variable denoting a discrete choice between majority versus minority equity stakes. Under the assumption that firm pairs with minority ownership shares are isomorphic to relationships between independent parties, these models can be interpreted as preliminary tests of Propositions 1 and 2 from Section 2.2 (for the binary organizational choice) in the absence of firm-pair data on arm'slength transactions. To map the theoretical constructs ηk and ηp to the data, I follow a large empirical literature that uses observed factor intensities as (revealed) proxies for the respective Cobb-Douglas coefficients. In particular, physical capital intensity ηp is commonly approximated by the capital-labor ratio of a given firm (see, e.g., Corcos et al., 2013; Kohler and Smolka, 2015) or mean physical capital intensity in a given industry (see, e.g., Antràs, 2003; Nunn and Trefler, 2008). In accordance with these studies, I use the ratio of (the value of) an affiliate's tangible assets over its employment to approximate physical capital intensity of a given production process, ηp.28 By the same token, I use the observed (value of) the HQ's intangible assets (such as patents and copyrights) as a baseline proxy for the importance of a HQ's knowledge capital in the production process of an affiliate, ηk.29 This approach is similar to Keller and Yeaple (2013), who use the R&D expenditures of parent firms as a proxy for the knowledge intensity of the affiliate's production process.30 Although approximating the knowledge intensity of the production process with the HQ's intangible assets or R&D is fairly standard in the literature, this approach deserves a word of caution. Clearly, not all of the HQ's intangible assets may be applicable to a given affiliate. To illustrate this point, consider a relationship between a HQ from, say, a car manufacturing industry and an affiliate which supplies windshields to the parent. While the HQ is likely to transfer her knowledge regarding windshield manufacturing to the affiliate, she may not disclose her know-how regarding other components (such as car engines or radiators). To capture the usefulness of a HQ's intangible assets in the affiliate's production process, I develop a novel proxy for interindustry knowledge applicability and utilize it to construct a refined measure of knowledge intensity. Yet, given that this measure is available only for a subset of firm pairs, I do not consider it in the baseline analysis and relegate its introduction to the robustness tests in Section 4.3.

28 It can be formally verified using Eqs. (8) and (12) that the value of an affiliate's capital pcp is directly proportional to physical capital intensity ηp, conditionally on a set over labor, l of industry- or country-specific parameters {α, βo, cl}, which will be accounted for via fixed effects (see below). 29 Formally, ηk from Eq. (12) is directly proportional to the value of knowledge transfer kck, conditionally on firm revenue R (which will be controlled for in the empirical analysis) and parameters α and βo (accounted for via fixed effects). Since the production process assumed in the current model does not necessitate any employment by the HQ, I do not divide the benchmark measure of intangible assets by the HQ's labor. I verify, however, that the results are robust to normalizing the HQ's intangible assets by the HQ's employment or revenue. 30 The dataset used in the empirical analysis does not contain information on a HQ's R&D expenditures but reports the value of the HQ's intangible assets.

The empirical implementation of this paper's theoretical hypotheses follows a three-pronged approach. In the first step, I explore conditional correlations of ownership shares with the knowledge and physical capital intensity in a cross-section of firm pairs. The econometric model in this case takes the following form: Sha2014 ¼ θ1 In tangibleh2013 þ θ2 Tangiblea2013 þ θ3 In tangibleh2013  IPRℓðaÞ þ Φ þ xXh2013 þ ϰXa2013 þ εha

ð20Þ

whereby Sha2014 denotes the ownership share of a HQ h (from industry i and country q) in an affiliate a (which may be active either in the same or in a different industry and country, j and ℓ, respectively) and year 2014.31 Intangibleh2013 represents the importance of the HQ's knowledge (intangible assets) in the production process and Tangiblea2013 captures the physical capital (tangible assets) intensity of the affiliate. The explanatory variables are lagged by one year to mitigate potential concerns regarding reverse causality. IPRℓ(a) denotes the strength of intellectual property rights protection in the affiliate's country ℓ.32 The vector Φ contains a battery of fixed effects (FE) which vary by specification. In the preferred specification, this vector includes HQ and affiliate country/industry, country-pair, and industry-pair FE. As will be discussed in detail in Section 4, the set of employed fixed effects absorbs a wide range of explanatory factors that have been identified in the existing literature as important determinants of multinational firm boundaries.33 Vectors Xh2013 and Xa2013 (with the associated coefficient vectors x and ϰ) include observable characteristics of the HQ and the affiliate, respectively, and εha is the error term. Parameters θ 1 through θ 3 denote the coefficients of interest. Based on the theoretical hypotheses summarized in Eq. (19), I expect a positive coefficient of Intangibleh2013 (θ1 N 0) and a negative coefficient of Tangiblea2013 (θ2 b 0). Moreover, the role of knowledge intensity is predicted to be mitigated if the affiliate is located in a country with better IPRℓ(a) protection (θ3 b 0). However, two main concerns prevent me from interpreting the estimates of θ1 through θ 3 in a causal manner. First, one might be worried that one-year lags of explanatory variables do not sufficiently account for the issue of reverse causality. In particular, one can envision a scenario in which the intent to integrate an affiliate more deeply into firm boundaries triggers knowledge capital accumulation by the HQ. Similarly, it is conceivable that the affiliate restructures its tangible assets before selling its equity stakes. The second main concern is due to omitted variables bias. Despite a battery of fixed effects and a range of HQ and affiliate firm-level controls, there may be unobserved firm characteristics (such as productivity), as well as firmpair specific factors (e.g., cultural differences, as in Gorodnichenko et al., 2018) that confound the estimates from specification (20). I address the above-mentioned concerns by estimating two alternative econometric models. In the second step, I tackle the issue of reverse causality by considering industry/country (rather than firm-level) measures of knowledge and physical capital intensity. More specifically, I set up the following econometric model: Sha2014 ¼ λ1 IntangibleiðhÞqðhÞ þ λ2 Tangible jðaÞℓðaÞ þ λ3 IntangibleiðhÞqðhÞ  IPRℓðaÞ þ Ψ þ ϵha

ð21Þ

31 I choose the most recent year available in the data as a baseline year due to the fact that it maximizes the number of observations. The cross-sectional estimates for other years are very similar and are available upon request. 32 As will be discussed further below, I consider a range of alternative scores for IPR protection, which are, in their vast majority, time invariant and constructed for the years within the time span of 2010–2014. 33 All econometric models in this paper are estimated by Ordinary Least Squares (OLS) using Stata routine reghdfe by Correia (2014), which absorbs high-dimensional FE and allows for multi-way clustering of standard errors.

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

whereby Intangiblei(h)q(h) represents the leave-out median knowledge intensity in the HQ's industry/country in 2004–2013, while Tangiblej(a)ℓ(a) is the leave-out median physical capital intensity in the affiliate's industry/country. Since the value corresponding to each firm in the data is excluded from the construction of the respective leave-out median, the explanatory variables in this exercise are fairly exogenous to the ownership share of a single HQ in its affiliate in 2014. By approximating knowledge and physical capital intensities with their industry/country counterparts I thus alleviate the reverse causality concern. Ψ is the vector of FE, which includes in the preferred specification country-pair and industry-pair FE. As a robustness check, I also add to this vector HQ firm FE, which account for unobserved heterogeneity across headquarters yet absorb the direct effect of Intangiblei(h)q(h). The predicted sign of the coefficients are λ1 N 0 and λ2, λ3 b 0; see Eq. (19). It is worth pausing to stress the novelty of the econometric model from Eq. (21) compared to other empirical studies of firm boundaries available to date, see Antràs (2014, 2015) for an overview. Previous research has generally approximated foreign production processes using industry-level proxies which were constructed using data from one country, usually the U.S. This approach is subject to two main caveats. First, there may be unobserved industryspecific factors that confound the effects of factor intensities on firm boundaries. Second, approximating worldwide production processes with measures taken from one country is prone to the ‘benchmarking bias’, which, under certain conditions, may lead to an upward bias in the estimates of factor intensities, see Nunn and Trefler (2014) and Ciccone and Papaioannou (2016). The current paper simultaneously addresses both concerns by considering industry/country-level measures of factor intensities. This approach allows for including industry FE to account for unobserved heterogeneity across sectors that might have confounded previous investigations of the effects of factor intensities. Furthermore, the econometric model from Eq. (21) is not prone to the benchmarking bias since it considers measures of physical capital intensity that vary by the affiliate's industry/country. Although the first two econometric models control for observable characteristics of the headquarters and their affiliates, there may be other (unobservable) firm-specific factors that confound the relationship between factor intensities and firm boundaries. For instance, the parties' idiosyncratic productivities may affect their outside options (δ and γ) and, thereby, the choice of the optimal ownership structure. Cultural backgrounds of the parent firm's managers, as well as cultural differences between managers of the HQs and their affiliates, are further examples of possible driving forces behind multinational firm boundaries (see, e.g., Kukharskyy, 2016; Gorodnichenko et al., 2018). To address the remaining concerns regarding the omitted variables bias, I return in the third step to using firm-specific measures of intangible and tangible asset intensities from specification (20) but exploit the panel structure of the data to test the following econometric model: Shat ¼ ρ1 Intangibleh;t−n þ ρ2 Tangiblea;t−n þ ρ3 Intangibleh;t−n  IPRℓðaÞ þ φha þ Φt þ ξhat

ð22Þ

whereby Shat denotes the ownership share of a HQ h in an affiliate a in year t; Intangibleh, t−n and Tangiblea, t−n represent, respectively, the knowledge and physical capital intensity in year t − n, whereby n captures the number of year-lags. The main value added of the panel specification is that it allows for the inclusion of firm-pair FE, φha. These FE mitigate the omitted variables bias by effectively controlling for heterogeneity across parent and affiliate firms (e.g., with respect to unobserved productivities), as well as firmpair specific factors (such as cultural differences, history of the relationship, and the level of the knowledge flow from the HQ to the affiliate). In addition, I control for time-specific shocks to HQ and

11

affiliate countries and industries with a vector of fixed effects, Φt . This vector includes, among other things, time-varying country/industry (henceforth, country/industry/year) and time-varying country-pair (henceforth, country/year-pair) FE. ρ1 through ρ3 are the key parameters to be estimated, with the predicted signs ρ1 N 0 and ρ2, ρ3 b 0. Before turning to the description of the data sources, two comments are in order. The first one is specific to the third econometric model from Eq. (22). Since this model exploits firm-specific variation in knowledge and physical capital intensity over time, a natural question arises with respect to the source behind this variation. While the answer to this question is certainly multi-facetted, technological progress is likely to play a major role in this context. To illustrate this point with an example, consider the transition towards renewable energy that has been under way in many developed and developing economies around the globe. Certainly, this transition has had a heterogenous impact across industries, depending on the extent to which an industry's products can either be used for generation of renewable energy, or make use thereof. However, even within narrowly defined industries, firms are differently affected by this technological change as the ensuing firmspecific innovation activity is characterized by a high degree of uncertainty. For some firms, the successful innovation process leads to an increase in intangible assets (e.g., in the form of a patent) and may require a restructuring of the affiliate's tangible assets in order to implement the novel production technology of the parent company. Yet, others fail to innovate and continue to operate under old technology. Clearly, the above-mentioned example constitutes just one of many possible demand or supply shocks that affect a firm's innovation activity and lead to a variation in knowledge and physical capital intensity of the production processes over time. The second remark relates to the issue of selection. In general, one can envision a multitude of channels through which selection bias can manifest itself in the current context: Firms may selfselect into certain industries or countries (e.g., with a good quality of IPR protection). Furthermore, one may be concerned regarding the selection of certain type of firms (e.g., large- and mediumsized) into the dataset. As will become clear further below, the unique feature of the database used in my empirical analysis is that it allows me to include an unprecedented set of fixed effects, which account for a wide range of observable and unobservable country-, industry, and firm-specific factors that may drive the selection process. 3.3. Data sources and key variables All firm-pair specific data used in the empirical analysis are taken from the Orbis database by Bureau van Dijk (BvD). This dataset provides a comprehensive picture of multinational firms' activities worldwide and is uniquely suited for investigating this paper's theoretical predictions by encompassing the following three key features. First, and most importantly, it contains information on firms' direct ownership shares (in percent) in their affiliates in a biannual panel of 2004–2014, which will be used as a measure of S hat .34 Second, it provides extensive balance sheet information for both cooperation parties, which I use to construct proxies for knowledge and physical capital intensity, as well as to control for observable firm-specific factors. Third, it reports the location and main industry affiliation of parents and their affiliates at the 4digit level of the 2012 North American Industry Classification 34 The ownership information from the Orbis data has been previously used to study the effects of institutions (Altomonte and Rungi, 2013; Eppinger and Kukharskyy, 2019), cultural differences (Kukharskyy, 2016; Gorodnichenko et al., 2018), and an affiliate's downstreamness (Del Prete and Rungi, 2017) on firm boundaries.

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B. Kukharskyy / Journal of International Economics 122 (2020) 103262

System (NAICS), and is characterized by a large international coverage, comprising headquarters and affiliates around the globe.35 The baseline estimation sample used in this paper consists of 55 countries.36 Motivated by the theoretical model, I consider only firms that are classified by BvD as ‘industrial companies’ (i.e., exclude public authorities, financial companies, and pension funds). I further restrict the sample to ownership shares of at least 10% – a conventional cutoff for direct investment. I employ the latter sample restriction to exclude firms that acquire equity stakes merely due to (short-term) portfolio considerations. However, I verify that the results are fully robust to considering the entire span of ownership shares. In the baseline estimation sample, the mean ownership share is 71%, with a standard deviation of 31. Full ownership is chosen by 39% of all firm pairs, i.e., the majority of observations are characterized by shared ownership. Within the latter group, the only noticeable ‘spike’ lies around 50–51%, accounting for 15% of observations. Against this backdrop, this paper considers two alternative dependent variables: a continuous Shat, capturing the entire spectrum of ownership shares, and a binary variable Smaj hat , which takes the value one if the HQ owns the majority (i.e., more than 50%) of the affiliate's equity and zero otherwise. The median HQ in the data has only one affiliate. Yet, the fact that some headquarters hold ownership shares in more than one affiliate is particularly advantageous in the current analysis, allowing for within-HQ estimations using HQ firm FE. To capture the importance of the HQ's knowledge capital in the production process, I use information on the value of a parent firm's intangible assets.37 This information is reported as a single variable which quantifies the total value of a HQ's patents and copyrights. Since the values of intangible assets vary vastly across firms (ranging from 0 to more than 20 million euro), I measure a firm's knowledge intensity on a logarithmic scale. More specifically, I define the baseline measure of Intangibleht as the natural logarithm of (0.01 plus) a parent firm's value of intangible assets in year t.38 In the robustness checks, I also consider three alternative measures of knowledge intensity, for which the value of the HQ's intangible assets has been normalized by the HQ's employment, the HQ's revenue, or the affiliate's employment, respectively. The size of the sample is significantly reduced by the availability of Intangibleht. Summary statistics for the main estimation sample are provided in Table B.1 of Appendix B. To construct a firm-level measure of physical capital intensity, I exploit information on the value of the affiliate's tangible assets. This information is, once again, available as a single variable which reports the total value of a firm's equipment and machinery in a given year. I define the baseline measure of Tangibleat as the natural log of the affiliate's value of tangible assets in year t divided by the affiliate's employment 35 A limitation of the data is that it does not provide information on the products manufactured by the headquarters and their affiliates and, hence, it is not possible to account for multi-product firms. 36 These countries are: Argentina, Australia, Austria, Belgium, Bulgaria, Bosnia and Herzegovina, Brazil, China, Colombia, Croatia, Czech Republic, Denmark, Ecuador, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Ireland, Israel, Italy, Japan, Latvia, Lichtenstein, Lithuania, Luxemburg, Malta, Mexico, Netherlands, Norway, Peru, Philippines, Poland, Portugal, Kazakhstan, Romania, Russia, Serbia, Singapore, Slovakia, Slovenia, South Africa, South Korea, Spain, Sweden, Switzerland, Turkey, Taiwan, Ukraine, U.K., U.S., and Vietnam. 37 Depending on a parent firm's country, balance sheet information is provided either according to the International Financial Reporting Standard (IFRS) or the U.S. Generally Accepted Accounting Principles (GAAP). Both accounting models define intangible assets as nonmonetary assets without physical substance and allow for capitalization of these assets if the following two criteria are fulfilled. First, the asset has to be related to future economic benefits that can be reliably measured (e.g., inflow of cash). Second, the asset has to be controlled by the company as a result of past events (e.g., purchase or self-creation). It should be noted that any potential differences in capitalization of intangible assets across the two accounting standards or between individual countries within a given standard are fully controlled for via country fixed effects, included in all specifications. 38 I have added a small constant (0.01) to prevent from dropping roughly one third of observations in which parent companies own zero intangible assets. This is by analogy to Antràs and Chor (2013) who construct their measure of R&D intensity by adding a small constant to firms' R&D expenditures. I verify that the results are robust to considering different values of a constant or omitting parents with zero intangible assets.

in the same year. In the robustness checks, I also consider alternative measures of Tangibleat, which either normalize the value of tangible assets with the affiliate's revenue (rather than employment) or do not employ any normalization. For the second econometric model (see Eq. (21)), I construct proxies for knowledge and physical capital intensities that vary by the HQ's and the affiliate's industry/country, respectively. To this end, I exploit the entire Orbis dataset with more than 5 million firm-year observations within the time span of 2004–2013.39 For the construction of the baseline measures, I consider only those industry/country cells in which at least 10 firms have reported more than once the value of their intangible or tangible assets, respectively.40 Intangiblei(h)q(h) is then calculated as the log of (0.01 plus) the leave-out median value of intangible assets in the HQ's industry/country, while Tangiblej(a)ℓ(a) is defined as the log of the leave-out median value of tangible assets over employment in the affiliate's industry/country. Since the leave-out proxy for a given firm excludes its own observation from the calculation of the respective industry/country measure, the explanatory variables used in this exercise are fairly exogenous to the individual firm's ownership decision (the outcome variable).41 I use four alternative measures to approximate the strength of IPR protection in the affiliate's country, IPRℓ(a).42 The first one is the index of patent protection in 2010, developed by Ginarte and Park (1997) and updated by Park (2008). This score captures the following five dimensions of IPR protection: coverage (i.e., patentability of various kinds of inventions), membership in international IPR agreements (e.g., trade-related aspects of intellectual property rights, TRIPS), absence of risks of forfeiting the patent rights (e.g., due to compulsory licensing or revocation of patents), enforcement of patent rights in case of an infringement, and duration of protection. The second measure is the index of intellectual property protection (IPP) in 2014–2015 from the Global Competitiveness Report by the World Economic Forum (WEF). This index summarizes experts' and practitioners' ratings of the IPR protection in their countries on a 7-point scale from weak and not enforced to strong and enforced. The third proxy is drawn from the 2014 Global Software Survey by the Business Software Alliance (BSA), which reports the rates of installation of unlicensed software on private and company computers. Higher values of this score reflect stronger inclination of a country's citizens towards infringing IPR and/ or weaker judicial prosecution of the intellectual property piracy. I rescale this measure such that higher values represent higher reluctance of its citizens towards the piracy of intellectual property and/or stronger revealed IPR protection in a given country. The fourth proxy is the composite intellectual property rights index (IPRI), calculated by the Property Rights Alliance (PRA) as the arithmetic mean of the abovementioned indices by Park (2008), WEF, and BSA. To facilitate the interpretation and comparability of results, I rescale all four measures of IPRℓ(a) to have a mean of zero and a standard deviation of one, whereby higher values of each measure are associated with stronger protection of intellectual property in the affiliate's country. Table B.3 in Appendix B reports the pairwise correlation between the alternative measures of IPRℓ(a) protection. Since the index by Park (2008) has been widely used in the economics literature as a proxy for the IPR protection (see, 39 In contrast to the ownership segment of the data, which is available as a biannual panel, the balance sheet information is available at a yearly basis. Furthermore, since balance sheet data includes firms that have no recorded ownership links, the underlying sample is more than ten times larger compared to the baseline estimation sample. I verify, however, that the results are fully robust to considering industry/country measures of asset intensities based solely on the subset of firms that have a recorded ownership link in the Orbis data. 40 The results are robust to considering industry/country cells with a larger number of firms and/or stricter requirements on the number of repeated observations by firm. 41 I consider leave-out medians (as opposed to means) to ensure that the results are not driven by a few large firms. 42 To the best of my knowledge, these scores constitute the entirety of country-level measures of IPR protection available to date. The definitions of these measures and the data sources are provided in Table B.2 in Appendix.

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

13

Table 1 Firm-pair cross-section estimates (baseline). Dependent variable: Sha2014

(1)

(2)

(3)

(4)

(5)

(6)

Intangibleh2013

0.517*** (0.027) −0.730*** (0.051)

0.513*** (0.026) −0.636*** (0.050)

0.456*** (0.027) −0.583*** (0.053)

0.847*** (0.103) −0.566*** (0.054) −0.337*** (0.083)

0.741*** (0.120) −0.596*** (0.066) −0.287*** (0.094) 0.665*** (0.120) 0.157 (0.148) −0.053*** (0.008) −0.002 (0.011)

yes yes yes yes no no no no 137,785 0.192

nested nested nested nested yes yes no no 135,426 0.290

nested nested nested nested yes yes yes yes 127,719 0.366

nested nested nested nested yes yes yes yes 121,078 0.362

nested nested nested nested yes yes yes yes 82,515 0.387

0.718*** (0.121) −0.416*** (0.070) −0.268*** (0.095) 0.636*** (0.122) 0.033 (0.149) −0.054*** (0.008) −0.003 (0.011) −0.606*** (0.131) 1.665*** (0.153) 0.025*** (0.008) −0.521*** (0.132) nested nested nested nested yes yes yes yes 79,349 0.402

Tangiblea2013 Intangibleh2013×IPRℓ(a) Revenueh2013 Employmenth2013 Ageh2013 #Affiliatesh2013 Revenuea2013 Employmenta2013 Agea2013 #Shareholdersa2013 HQ country FE Affiliate country FE HQ industry FE Affiliate industry FE HQ country/industry FE Affiliate country/industry FE Country-pair FE Industry-pair FE Observations R-squared

Notes: The table reports ordinary least squares (OLS) estimates of (variations of) Eq. (20). The dependent variable is the ownership share of a HQ h in affiliate a in 2014. Intangibleh2013 and Tangiblea2013 are firm-level proxies for knowledge and physical capital intensity in 2013, respectively. IPRℓ(a) is the index of patent protection by Park (2008). Robust standard errors are clustered by HQ and affiliate and presented in parentheses. ***, **, * denote 1, 5, 10% significance, respectively.

e.g., Javorcik, 2004; Keller and Yeaple, 2013; Maskus, 2000, 2012), I take it as a benchmark measure and consider the other three indices in the robustness checks. Unfortunately, none of the four measures of IPR protection vary during the considered time span of 2004–2014. This caveat should be borne in mind when interpreting the panel estimates of the econometric model from Eq. (22). The vectors Xh2013 and Xa2013 in the econometric models from Eqs. (20) and (21) include the following controls for the HQ's and affiliate's observable characteristics in 2013: Revenueh2013 and Revenuea2013 (the log of h’s and a’s operating revenue); Employmenth2013 and Employmenta2013 (the log of h’s and a’s employment – proxies for a firm's size); Ageh2013 and Agea2013 (h’s and a’s age). I further control for a firm's ownership structure by including #Affiliatesh2013 (the number of affiliates by HQ) and #Shareholdersa2013 (the number of shareholders by affiliate). 4. Estimation results 4.1. Cross-sectional analysis 4.1.1. Firm-specific measures of knowledge and physical capital intensity Table 1 develops the preferred specification of the econometric model from Eq. (20) step by step. Column 1 reports the correlations of a parent firm's ownership share Sha2014 with knowledge intensity, Intangibleh2013 and physical capital intensity, Tangiblea2013, conditional on country and industry FE by the HQ and the affiliate.43 The former fixed effects account for heterogeneity across countries with respect to time-invariant characteristics (such as history or geography), as well 43 Throughout the paper, I explore first the robustness of the direct correlation of ownership shares with knowledge and physical capital intensity to the inclusion of most stringent FE before considering the interaction effect.

as factors that are relatively stable over time (e.g., economic development or quality of institutions). Industry FE control for a wide range of industry-specific factors that have been previously identified in the literature as important determinants of the integration decision, such as relationship-specificity of the affiliate's inputs, downstreamness of the production process, and demand elasticity of final goods (see Antràs, 2015). In line with the model's predictions, ownership shares Sha2014 are positively correlated with Intangibleh2013, and negatively with Tangiblea2013. In column 2, I add more demanding FE to account for potentially confounding factors that vary by country/industry.44 For instance, Eppinger and Kukharskyy (2019) find strong evidence for the interaction of the affiliate country's contracting institutions and industrylevel relationship-specificity in their impact on firms' ownership shares. Antràs (2015) provides further evidence that country-specific factors such as contract enforcement, financial development, and labor market institutions may interact with industry-level characteristics such as contractibility, external financial dependence, and volatility, respectively, in their impact on the international make-or-buy decision.45 I fully account for any country/industry-specific determinants of firm boundaries via HQ and affiliate country/industry FE (which absorb the FE from the first column). As can be seen from column 2, the estimates of Intangibleh2013 and Tangiblea2013 remain robust to controlling for differential effects of country-level factors across industry characteristics. 44 The number of observations declines with more demanding specifications since firmpair observations are dropped if they are fully explained by a FE. 45 Previous empirical studies have identified robust interaction effects of contract enforcement and contractibility (Nunn, 2007), financial institutions and external financial dependence (Manova, 2013), and labor market institutions and volatility (Cuñat and Melitz, 2012) on international trade (see also Chor, 2010; Nunn and Trefler, 2014). Furthermore, Yeaple (2003) finds that a foreign country's skill endowment interacts with an industry's skill intensity in their impact on U.S. outward FDI.

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B. Kukharskyy / Journal of International Economics 122 (2020) 103262

Table 2 Firm-pair cross-section estimates: Alternative normalizations of firms' assets. Dep. variable: Sha2014

h’s Intangibles normalized by h’s employment

h’s Intangibles normalized by a’s employment

h’s Intangibles normalized by h’s revenue

a’s Tangibles normalized by a’s revenue

a’s Tangibles non-normalized

Intangibleh2013

0.671*** (0.133) −0.415*** (0.070) −0.228** (0.106) yes yes yes yes yes 79,349 0.402

0.738*** (0.116) −0.419*** (0.070) −0.285*** (0.090) yes yes yes yes yes 79,349 0.402

0.503*** (0.128) −0.418*** (0.070) −0.082 (0.102) yes yes yes yes yes 79,262 0.402

0.719*** (0.121) −0.414*** (0.070) −0.269*** (0.095) yes yes yes yes yes 79,339 0.402

0.718*** (0.121) −0.416*** (0.070) −0.268*** (0.095) yes yes yes yes yes 79,349 0.402

Tangiblea2013 Intangibleh2013×IPRℓ(a) HQ country/ind FE Affiliate country/ind FE Country-pair FE Industry-pair FE HQ and affiliate controls Observations R-squared

Notes: The table reports OLS estimates of Eq. (20), including all FE and control variables from column 6 of Table 1. The dependent variable is the ownership share of a HQ h in affiliate a. In columns 1,2, and 3, the HQ's intangible assets are normalized by the HQ's employment, the affiliate's employment, and the HQ's revenue, respectively. In column 4, the affiliate's tangible assets are normalized by the affiliate's revenue (instead of employment), while in column 5 a’s tangible assets are non-normalized. Robust standard errors are clustered by parent and affiliate and presented in parentheses. ***, **, * denote 1, 5, 10% significance, respectively.

In column 3, I further add country-pair and industry-pair FE. The former FE effectively control for any time-invariant factors specific to the pair of countries (such as geographic distance or historical connectedness), as well as country-pair specific factors that are relatively stable over time (e.g., cultural differences, see Gorodnichenko et al., 2018). Moreover, country-pair FE account for whether a given HQ-affiliate pair constitutes a domestic or a foreign ownership link. The idea behind the inclusion of industry-pair FE is to control for the interplay between HQ and affiliate industry characteristics in their impact on the integration decision.46 Moreover, industry-pair FE account for whether a HQ and its affiliate are active in the same or different industries.47 Apart from the above-mentioned reasons, the inclusion of country- and industry-pair FE is particular instrumental in the current context, as there may be spatial barriers to transferring knowledge from the HQ to the affiliate (Keller and Yeaple, 2013), and the feasibility of knowledge transfer may differ across industry pairs. As can be seen from column 3, the estimates of interest remain highly significant after the inclusion of country- and industry-pair FE. In column 4, I interact Intangibleh2013 with IPRℓ(a). In line with the model's prediction, this interaction effect is negative and highly significant. That is, the positive relationship between knowledge intensity and ownership shares is less pronounced if the affiliate is located in a country with strong IPRℓ(a) protection. Columns 4 and 5 of Table 1 further mitigate omitted variables bias by controlling for observable characteristics of headquarters and affiliates, respectively. Although the number of observations drops by roughly one third due to missing values of control variables, the correlations of ownership shares with Intangibleh2013 and Tangiblea2013 remain consistent with the theoretical predictions and highly significant. The estimates from column 6 further suggest that ownership shares tend to be higher for younger headquarters with higher revenues, while they are higher for older affiliates with lower revenues and higher employment. Further, the ownership share decreases, somewhat mechanically, in the number of shareholders. The quantitative interpretation of the estimates from column 6 is that ownership shares are higher by roughly 4 percentage points in relationships where knowledge intensity is higher by one standard 46 For instance, Antràs and Chor (2013) show that the downstreamness of the affiliate's industry may have a differential impact on the integration decision depending on the elasticity of demand faced by the HQ. Furthermore, Antràs and Helpman (2004) treat the relative importance of investments made by the HQ compared to those made by the manufacturing supplier (so-called headquarter intensity) as an industry-pair specific characteristic. 47 The fact that the cooperation partners are active in different industries may be interpreted as an indication of vertical (rather than horizontal) FDI, see, e.g., Alfaro and Charlton (2009) and Fajgelbaum et al. (2015).

deviation, while they are lower by roughly 1 percentage point in relationships where physical capital intensity is higher by one standard deviation. With respect to the interaction term, an increase in knowledge intensity by one standard deviation would increase the ownership share by 2 percentage points less for the affiliate located in a country with IPR protection higher by one standard deviation. To make economic sense of the estimates at hand, consider a HQ whose value of intangible assets is above the mean by the value of the average U.S. patent in 2012 (equal to $374,000 or roughly two standard deviations in the Orbis data). This HQ is predicted to own, on average, 10% higher ownership share in its affiliate, which is an economically large magnitude. Table 2 assesses the robustness of the results from Table 1 by considering alternative proxies for knowledge and physical capital intensity.48 More specifically, during the construction of Intangibleh2013 in column 1, I have normalized the value of a HQ's intangible assets by the HQ's employment. Alternatively, the measure of Intangibleh2013 in column 2 uses as a normalization the affiliate's (rather than HQ's) employment. The third proxy for Intangibleh2013 in column 3 uses as a normalization the HQ's revenue. To construct Tangiblea2013 in column 4, I have normalized the value of the affiliate's tangible assets by the affiliate's revenue (rather than employment). Lastly, column 5 considers the non-normalized value of the affiliate's tangible assets. As can be seen from Table 2, all coefficients retain the predicted sign and, apart from the interaction of Intangibleh2013 and IPRℓ(a) in the third column, are significant. It should be further noted that the magnitude of the estimates is very similar to the ones from column 6 of Table 1, which suggests that the results do not hinge on the employed definition of the explanatory variables. In Table 3, I verify that the results from Table 1 are robust to considering alternative proxies for intellectual property rights protection, introduced in Section 3.3. For each alternative measure of IPRℓ(a), I report only the preferred specification of Eq. (20), including all FE and control variables from column 6 of Table 1. As can be seen from Table 3, all coefficients of interest retain the predicted sign and are highly significant. Next, I consider a binary (rather than a continuous) outcome variable, Smaj ha2014, which takes the value one if the HQ owns the majority (i.e., more than 50%) of the affiliate's equity and zero otherwise. As mentioned in Section 3.1, the idea behind this robustness check is to provide a preliminary test of Propositions 1 and 2 derived for the binary organizational choice variable in the absence of firm-level data on arm's-length relationships. I estimate the relationship between Smaj hat and the explanatory variables from Eq. (19) using a linear probability model (LPM) 48 For each alternative measure of factor intensity, I report only the preferred specification of Eq. (20), including all FE and control variables from column 6 of Table 1. The results obtained in the larger sample (without firm-level controls) are fully robust and are provided upon request.

B. Kukharskyy / Journal of International Economics 122 (2020) 103262 Table 3 Firm-pair cross-section estimates: Alternative measures of IPR protection.

Table 4 Within firm estimates.

Dep. variable: Sha2014

IPP (WEF)

Software (BSA)

IPRI (PRA)

Intangibleh2013

0.511*** (0.042) −0.443*** (0.069) −0.218*** (0.031) yes yes yes yes yes 84,020 0.408

0.636*** (0.054) −0.446*** (0.069) −0.314*** (0.045) yes yes yes yes yes 84,243 0.408

0.650*** (0.057) −0.445*** (0.069) −0.272*** (0.042) yes yes yes yes yes 84,243 0.408

Tangiblea2013 Intangibleh2013×IPRℓ(a) HQ country/ind FE Affiliate country/ind FE Country-pair FE Industry-pair FE HQ and affiliate controls Observations R-squared

15

Notes: The table reports OLS estimates of Eq. (20), including all FE and control variables from column 6 of Table 1. The dependent variable is the ownership share of a HQ h in affiliate a in 2014. Intangibleh2013 and Tangiblea2013 are firm-level proxies for knowledge and physical capital intensity in 2013, respectively. The alternative measures of IPR protection are listed in the header, see Table B.2 for definitions. Robust standard errors are clustered by HQ and affiliate and presented in parentheses. ***, **, * denote 1, 5, 10% significance, respectively.

implemented by OLS, as above.49 As can be seen from Panel A of Table B.4, all estimates exhibit the predicted signs and are highly significant. That is, parent companies are more likely to be majority shareholders the higher knowledge intensity, but less so the stronger IPR protection in the affiliate's country. Conversely, the majority ownership share is less likely to be chosen the higher physical capital intensity of the affiliate. In the final robustness test, I add to the baseline specification HQ firm FE to explore the role of physical capital intensity across different affiliates within the same HQ.50 Moreover, since HQ firm FE implicitly restrict the sample to firms that have affiliates in different countries, this approach allows me to further corroborate the differential effect of HQ's intangible assets across subsidiary countries with different IPR protection. As can be seen from Table 4, all estimates exhibit the predicted sign and are statistically significant at least at the 5% level, both for the continuous and the binary dependent variable.51 Furthermore, the fact that the estimates are of similar order of magnitude to the respective counterparts obtained in Tables 1 and B.4 without HQ firm FE, reassures that previous results are not masked by unobserved heterogeneity across headquarters. Although robust conditional correlations observed so far are consistent with the theoretical predictions, I caution against interpreting them in a causal manner. Despite the lags of explanatory variables, one might be worried that firms' decisions regarding their assets are driven by the choice of the ownership structure in the subsequent year. To account for the issue of reverse causality, I now turn to the estimation of the second econometric model; see Eq. (21).

4.1.2. Industry/country-specific measures of knowledge and physical capital intensity In contrast to the first econometric model from Eq. (20), the second econometric model uses industry/country (rather than firm-specific) measures of knowledge and physical capital intensity, Intangiblei(h)q(h) and Tangiblej(a)ℓ(a). Since the latter proxies are calculated as leave-out 49 The plethora of fixed effects considered in Eqs. (20) through (22) render the nonlinear models (such as conditional logit) computationally infeasible. Moreover, the interpretation of interaction effects in non-linear models is prone to several caveats (see Ai and Norton, 2003). 50 The reason for not including HQ firm FE in the baseline specification is twofold. First, these FE absorb the direct effect of Intangibleh2013 – one of key variables of interest. Second, by restricting the sample to multinational firms with more than one foreign affiliate, this approach leads to a significant drop in observations. 51 The notable difference in the size of the estimates between the two columns is due to the fact that the continuous ownership share is defined in percentage terms, Sha2014 ∈ (10,100], whereas Smaj ha2014 ∈ {0,1} is a binary variable.

Tangiblea2013 Intangibleh2013×IPRℓ(a) HQ country/ind FE Affiliate country/ind FE Country-pair FE Industry-pair FE HQ firm FE Observations R-squared

Dep. variable: Sha2014

Dep. variable: Smaj ha2014

−0.507*** (0.080) −0.372** (0.189) nested yes yes yes yes 60,881 0.746

−0.007*** (0.001) −0.005** (0.002) nested yes yes yes yes 60,881 0.692

Notes: The table reports OLS estimates of Eq. (20), including all FE from column 6 of Table 1, as well as HQ firm FE. In the first column, the dependent variable is the ownership share of a HQ h in affiliate a in 2014. In the second column, the dependent variable is a binary variable which is equal to one if the HQ owns the majority of the affiliate's equity in 2014. Robust standard errors are clustered by HQ and affiliate and presented in parentheses. ***, ** denote 1 and 5% significance, respectively.

medians of knowledge and physical capital intensity over the span of 2004–2013 at the aggregate level of the HQ and the affiliate industry/ country, respectively, they are fairly exogenous to the ownership structure a given firm pair in a single year. To economize on space, Table 5 presents the results both for the continuous and the binary outcome variable, Sha2014 and Smaj ha2014, respectively.52 In each case, I report only the most stringent specifications with country-pair FE (which nest the HQ country and affiliate country FE), industry-pair FE (which nest the HQ industry and affiliate industry FE), as well as HQ firm FE (which absorb the direct effect of Intangiblei(h) q(h)). Throughout specifications, I find strong empirical support for the model's theoretical predictions. That is, the knowledge intensity in the HQ's industry/country has a positive effect on both Sha2014 and Smaj ha2014, while the effect of physical capital intensity in the affiliate's industry/ country is negative. Furthermore, I find both for the continuous and the binary ownership decision that the positive effect of knowledge intensity is mitigated by stronger IPR protection in the affiliate's country. Importantly, this interaction effect continues to hold even within HQ firms (see columns 3 and 6), providing strong support for Propositions 2 and 4. Notably, all estimates are comparable to the ones obtained in Tables 1 through 4, as well as Panel A of Table B.4, which further increases confidence in the validity of the results. 4.2. Panel data analysis In this section, I turn to the panel data analysis of the third econometric model, cf. Eq. (22). In the baseline specification, I consider one-year lags of the explanatory variables (i.e., n = 1). Table 6 presents the results from the panel regressions, developed step by step. To account for unobserved heterogeneity across countries in the panel setting, I use timevarying country FE. More specifically, column 1 includes FE for HQ and affiliate country/year and industry. The former FE not only control for all country-specific time-invariant factors, but also account for timevarying country-level shocks (such as financial crisis, changes in FDI regulations, etc.). In column 2, I further include HQ and affiliate country/ industry/year FE, which absorb the FE from the first column. These FE control for arbitrary effects of country-year specific factors across industries (e.g., China's ongoing liberalization of foreign ownership restrictions in certain sectors). Column 3 further adds industry-pair and country-year pair FE. The latter FE not only control for all bilateral time-invariant factors (such as geographical or cultural distance), but also account for shocks that are specific to a given country-pair (e.g., ratification of a bilateral investment treaty, or bilateral trade liberalization). Throughout 52 The number of observations increases compared to Table 1 due to the fact that firm pairs with missing information on intangible and/or tangible assets are now assigned the values of Intangiblei(h)q(h) and Tangiblej(a)ℓ(a) of the respective industry/country.

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Table 5 Cross-section estimates: Industry/country measures of knowledge and physical capital intensity. Dependent variable: Smaj ha2014

Dependent variable: Sha2014

Intangiblei(h)q(h) Tangiblej(a)ℓ(a) Intangiblei(h)q(h)×IPRℓ(a) Country-pair FE Industry-pair FE HQ firm FE Observations R-squared

(1)

(2)

0.919** (0.430) −0.713*** (0.230) −1.087*** (0.373) yes no no 391,632 0.187

0.680*** (0.221) −0.693*** (0.194) −0.653*** (0.181) yes yes no 381,578 0.270

(3)

(4)

(5)

(6)

−0.350*** (0.107) −0.516*** (0.110) yes yes yes 240,572 0.660

0.009* (0.005) −0.009*** (0.003) −0.011*** (0.004) yes no no 391,632 0.092

0.007*** (0.003) −0.008*** (0.002) −0.007*** (0.002) yes yes no 381,578 0.178

−0.004*** (0.001) −0.005*** (0.001) yes yes yes 240,572 0.568

Notes: The table reports OLS estimates of Eq. (21). In columns 1–3, the dependent variable is the ownership share of a HQ h in affiliate a in year 2014; in columns 4–6, the dependent variable is a binary variable which is equal to one if the HQ owns the majority of the affiliate's equity in 2014. Intangiblei(h)q(h) measures the leave-out median knowledge intensity in the HQ's industry/country, and Tangiblej(a)ℓ(a) is the leave-out median physical capital intensity in the affiliate's industry/country. Robust standard errors are clustered by HQ industry/ country and affiliate industry/country and presented in parentheses. ***, **, * denote 1, 5, 10% significance, respectively.

Table 6 Panel estimates (baseline). Dependent variable: Shat

(1)

(2)

(3)

(4)

(5)

(6)

Intangibleh, t−1

0.476*** (0.026) −0.061*** (0.021)

0.483*** (0.022) −0.078*** (0.019)

0.437*** (0.022) −0.054*** (0.019)

0.032*** (0.009) −0.080*** (0.013)

0.031*** (0.009) −0.080*** (0.013)

yes yes yes yes no no no no no no no 812,974 0.167

nested nested nested nested yes yes no no no no no 798,639 0.261

nested nested nested nested yes yes yes yes no no no 792,308 0.328

nested nested nested nested yes yes yes yes yes yes no 661,834 0.956

nested nested nested nested yes yes yes nested nested nested yes 638,061 0.958

0.032** (0.015) −0.048*** (0.018) −0.000 (0.000) nested nested nested nested yes yes yes nested nested nested yes 637,386 0.968

Tangiblea, t−1 Intangibleh, t−1×IPRℓ(a) HQ country/year FE Affiliate country/year FE HQ industry FE Affiliate industry FE HQ country/industry/year FE Affiliate country/industry/year FE Country/year-pair FE Industry-pair FE HQ firm FE Affiliate firm FE Firm-pair FE Observations R-squared

Notes: The table reports panel estimates of (variations of) Eq. (22). The dependent variable is the ownership share of a HQ h in affiliate a in year t. Intangibleh, t−1 and Tangiblea, t−1 measure, respectively, h’s intangible assets and a’s tangible assets in year t − 1. Robust standard errors are clustered by HQ and affiliate and presented in parentheses. ***, **, * denote 1, 5, 10% significance, respectively.

specifications of columns 1 to 3, the estimates of Intangibleh,t−1 and Tangiblea,t−1 exhibit the predicted signs and are highly significant. Column 4 exploits the key advantage of the panel data approach by including HQ and affiliate firm FE. These FE account for unobserved heterogeneity across firms with respect to time-invariant factors, as well as firm characteristics that are relatively stable over time (e.g., productivity). As a result, the R2 in column 4 almost triples compared to column 3 and the estimate of Intangibleh,t−1 decreases by an order of magnitude.53 The latter result is not surprising due to the fact that the majority of firms experience no change in intangible assets (e.g., due to acquisition of new patent rights or expiration of existing patents) on a yearly basis, and the stock of a firm's intangibles is fully accounted for by HQ firm FE. Nevertheless, both coefficients retain the predicted sign and are highly significant. Column 5 applies an even more stringent test by adding firm-pair FE. These FE nest the HQ and affiliate firm FE and additionally account for factors specific to a given pair of firms. It is worth pausing to briefly reflect on the value added of this within-transformation compared to the preceding cross-sectional analysis. A relevant concern

associated with approximating the knowledge intensity of the affiliate's production process with the value of the HQ's intangible assets is that, for any given value of intangibles, the headquarters may vary in their willingness and/or ability to transfer their knowledge to the affiliates. Firm-pair fixed effects control for unobserved heterogeneity regarding (the level of) knowledge flows across business partners and allow us to focus on the variation in ownership shares due to lagged changes in the HQ's knowledge (e.g., due to obtaining a patent). Another example of a potential confounding factor that is accounted for via firm-pair fixed effects is cultural distance between the firms' managers, see Gorodnichenko et al. (2018). As can be seen from column 5, the relationships of Shat with Intangibleh,t−1 and Tangiblea,t−1 remain in line with the theory and are highly significant. In the last column of Table 6, I interact Intangibleh,t−1 with IPRℓ(a). While the direct effects retain the predicted signs and are significant, the interaction effect is not significant.54 A possible explanation behind the lack of significance of this interaction is that the employed measure

53 The number of observations decreases due to the omission of observations that are fully accounted for via FE.

54 The interaction effect turns out to be insignificant for the three alternative measures of IPR protection as well.

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

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Fig. 2. Interindustry knowledge applicability matrix κij. Notes: The figure presents the interindustry knowledge applicability measure κij for NAICS manufacturing industries (codes 3311– 3399). This measure has been constructed using NBER Patent Citations Database and the crosswalk by Goldschlag et al. (2016), see Appendix C for details. The rows and columns represent the citing (j) and cited (i) industries, respectively. The number within each cell represents the likelihood of (patents associated with) industry i to cite (patents associated with) industry j, whereby a higher number (darker color) reflects a higher applicability of knowledge generated in industry i to industry j.

of IPRℓ(a) is time-invariant and serves only as an imperfect proxy for the strength of IPR protection in a given year. Table B.5 reruns the specifications from Table 6 using longer lags of Intangibleh,t−n and Tangiblea,t−n. More specifically, Panels A and B of Table B.5 report the estimates of Eq. (22) for two- and four-year lags of the explanatory variables, respectively. As can be seen from Panel A, knowledge and physical capital intensities from period t − 2 continue to have a significant effect on ownership shares in period t. The persistence of these effects over the course of two years may be rationalized by the fact that multinational companies require some time to implement the optimal ownership decision. Panel B shows that the effects of four-year lagged Intangibleh,t−4 and Tangiblea,t−4 lose significance after controlling for HQ and affiliate FE (cf. column 4 through 6). Overall, the results from Tables 6 and B.5 suggest that a firm's organization

structure reacts relatively timely (within a two-year period) to the variation in knowledge and physical capital intensities. As can be seen from Panel C of Table B.4, the variation in knowledge and physical capital intensities also has an effect on the HQ's probability to choose the majority (rather than a minority) ownership structure in a given affiliate. More specifically, a lagged increase in knowledge intensity increases the HQ's probability of becoming a majority shareholder, while a lagged increase in physical capital intensity decreases the likelihood of the majority ownership structure. The interaction of knowledge intensity with IPR protection turns out to be insignificant. Overall, the panel data analysis provides strong support for the direct effects of knowledge and physical capital intensities, which are in line with the theory. The interaction effect of knowledge intensity and IPR protection, however, is found to be insignificant. As already

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B. Kukharskyy / Journal of International Economics 122 (2020) 103262

mentioned, the lack of significance in the latter case may be explained by the fact that the existing proxies for IPR protection do not vary over time. Once time-varying proxies for IPR protection become available, the analysis of the interaction effect in the panel setting should be reconsidered.

4.3. Refined measure of knowledge intensity Recall that the benchmark proxy for η k has been constructed under the premise that a parent firm's intangible assets reflect the knowledge intensity of a given relationship. This assumption is debatable given that the HQ's intangible assets may only be partially applicable to the affiliate's production process. In this section, I revisit my results using a refined measure of knowledge intensity which explicitly accounts for the usefulness of the HQ's knowledge in the production process of a given affiliate. To this end, I develop a novel measure of interindustry knowledge applicability, described in what follows. To approximate the applicability of an industry's knowledge to another industry I exploit the NBER Patent Database, which contains information on patents issued by the U.S. Patent and Trademark Office (USPTO) within the time span of 1976–2006. The Patent Citations segment of these data reports more than 23 millions citations of the patents contained in the database (see Hall et al. (2011) for a detailed description of the data). A citation of patent A by patent B implies that patent A represents a piece of previously existing knowledge upon which patent B builds and, hence, the fact that patent B cites patent A is indicative of knowledge flowing from A to B. I exploit this notion to construct a novel measure of interindustry knowledge applicability. More specifically, I use the crosswalk provided by Goldschlag et al. (2016) to map the technology classes of both citing and cited patents to 4-digit NAICS industries (see Appendix C for details). I then calculate the likelihood of (patents associated with) industry i to quote (patents associated with) industry j and refer to this score as the measure of interindustry knowledge applicability, κij. By construction, this measure ranges between 0 and 1, whereby higher values of κij reflect greater applicability of knowledge from industry i in industry j. This bilateral measure is available for 279 NAICS 4-digit industries (77,841 industry pairs), which cover roughly three quarters of the firm-pair links observed in the Orbis database. Fig. 2 depicts as an example the matrix κij for 42 manufacturing industries with NAICS codes within the range of 3311–3399. A darker color represents a higher applicability of knowledge generated in industry i to industry j. As can be seen from this figure, knowledge applicability is generally highest on the diagonal of the matrix, i.e., if i and j represent the same industry. However, a significant fraction of citations is also made to ‘off-diagonal’ industries (i ≠ j). In fact, in most instances, these industries jointly receive the majority of industry j’s citations. I merge the κ ij -measure to the Orbis data via the NAICS industry codes of the HQ (i) and the affiliate (j).55 Weighting the value of a HQ intangible assets in year t with the knowledge applicability score κij of the respective HQ-affiliate industry-pair, I obtain the refined measure of knowledge intensity, Intangiblerht, henceforth indicated with the superscript r. To illustrate this approach with an example, the intangible assets of a HQ active in, say, ‘Motor Vehicle Manufacturing’ industry (NAICS 3361) are multiplied by a factor 0.3 if the affiliate is associated with the

55 I take thereby into account that the κij-matrix is not symmetric, in the sense that a firm-pair in which the HQ is active in, say, ‘Iron and Steel Mills and Ferroalloy Manufacturing’ industry (NAICS 3311) and the affiliate in ‘Steel Product Manufacturing from Purchased Steel’ industry (NAICS 3312), receives a different value of κij compared to a firm-pair for which the HQ and the affiliate are active in ‘Steel Product Manufacturing from Purchased Steel’ and ‘Iron and Steel Mills and Ferroalloy Manufacturing’ industry, respectively, see Figure 2.

‘Motor Vehicle Body and Trailer Manufacturing’ industry (NAICS 3362), while they are weighted with 0.01 if the affiliate is active in the ‘Forging and Stamping’ industry (NAICS 3321), see Fig. 2. Table B.6 in Appendix revisits the evidence presented so far using the refined measure of knowledge intensity, Intangiblerht, whereby panels A and B of this Table report the estimates of the econometric models from Eqs. (20) and (22), respectively. Due to the fact that knowledge applicability measure κ ij , used for the construction of Intangible rht , is available only for a subset of industry-pairs in the Orbis data, the sample is reduced by roughly one-fifth as compared to the baseline estimates from Tables 1 and 6, respectively. Nevertheless, the evidence presented in Table B.6 generally corroborates the previous findings. In particular, all estimated coefficients in Panel A exhibit the predicted signs, are highly significant, and have similar magnitudes to the ones reported in Tables 1. The estimates r of Intangibleh,t−1 in panel B exhibit throughout the predicted sign and are highly significant, even after controlling for firm-pair FE. The interaction term included in column 6 of panel B has the predicted signs, but is insignificant. Overall, Table B.6 shows that accounting for the applicability of the parent firm's knowledge in the affiliate's production process corroborates the positive effect of knowledge intensity on ownership shares, uncovered throughout the paper. 5. Conclusion This paper develops a novel unifying framework which combines two major approaches to multinational firm boundaries – the knowledge-dissipation paradigm, emphasizing the role of integration in preventing the expropriation of a parent firm's intellectual property, and the hold-up approach, highlighting the effect of the organizational form on investments into relationship-specific, noncontractible inputs. This framework suggests that the attractiveness of integration increases in knowledge intensity of the production process and decreases in the affiliate's physical capital intensity. Furthermore, it predicts that the effect of knowledge intensity on firm boundaries is ameliorated by the intellectual property protection in the affiliate's country. I test these hypotheses using unique panel data on more than 100,000 firm pairs worldwide. In line with the model's predictions, I find that parent firms with a high value of intangible assets choose, on average, higher ownership shares in their affiliates, and this relationship is less pronounced the stronger the IPR protection in the affiliate's country. I further find that higher physical capital intensity of the affiliate is associated with lower ownership shares. Exploiting the variation of asset intensities over time, I find that a lagged increase in the parent firm's intangible assets leads to an increase in the ownership share, while a lagged increase in the affiliate's physical capital intensity decreases the ownership share. These findings are robust to controlling for unobserved heterogeneity across firm-, industry-, and country-pairs, and provide strong support for the unifying theory of multinational firm boundaries. In future work, the theoretical framework developed in this paper can be extended to environments with multiple affiliates operating in countries with varying degrees of IPR protection to better understand the organization of multinational enterprises around the globe. How does a threat of knowledge dissipation by one of the affiliates affect the performance of other subsidiaries and the overall ownership structure of the parent firm? Do the effects of knowledge and physical capital intensities on firm boundaries differ depending on the relative position of a given affiliate along the global value chain? These are just two examples of research questions that can be analyzed within the extended theoretical framework. Given that these theoretical exercises and their empirical implementation would reach beyond the scope of the current paper, I relegate them to future research.

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Appendix A. Mathematical appendix A.1. Sufficient condition for M’s investment into physical capital As mentioned in the main text, it is conceivable that H internalizes M’s underinvestment into physical assets by providing p to the manufacturer. However, since parties operate in an environment of contractual incompleteness (i.e., courts cannot fully verify H’s investment into p either), this alternative scenario does not eliminate the underinvestment problem because it is now H who is held-up ex-post by the producer. Under this alternative scenario, the HQ chooses k and p to maximize βoR − ckk − cpp, while the manufacturer simply claims (1 − βo)R during ex-post bargaining, where o ∈ {I, A}. Intuitively, since investments into relationship-specific inputs are not contractible, the manufacturer may falsely claim during ex-post bargaining to have contributed physical capital to the relationship, and the HQ cannot refute this false statement in the court. Against the αR backdrop of future hold-up, H’s maximization problem yields the following investment into physical capital: p ¼ ηp βo . Replacing p from Eq. (8) cp with this expression and following otherwise unchanged steps detailed the main text yields the maximum profit under this alternative scenario: 0

 11−αðηk þηp Þ 1 1 0 1−α 1−αβ η þ η B ηk α ηp α C o k p C @ A   πFB ≡ πpo β π¼B β o @ o A 1−α ηk þ ηp

ðA:1Þ

A simple comparison of πpo from Eq. (A.1) with πo from Eq. (15) implies that πo N πpo (i.e., profit is higher when physical capital is provided by M rather than H) if and only if:   1−αðηk þηp Þ   1−αðηk þηp Þ η α F ðβo Þ ≡ ð1−βo Þηp α 1−α ηk βo þ ηp ð1−βo Þ −βop 1−αβo ηk þ ηp N0 Note first that, when evaluated at βo = 1/2, this expression is equal to zero, F(1/2) = 0. Furthermore, a tedious but straightforward analysis shows that F is strictly decreasing in βo for all permissible parameter values, F′(βo) b 0. Hence, the sufficient condition for πo N πpo reads βo b 1/2. In words, if the fraction of revenue received by the HQ during ex-post bargaining is sufficiently small, M’s investment into physical capital is a dominant strategy for H. Intuitively, if the HQ experiences a severe ex-post hold-up by the manufacturer, H’s ex-ante underinvestment into physical capital is particularly small and it becomes optimal from H’s perspective to let M accumulate p on his own. It should be noted that the sufficient condition derived above is a mirror-image of the one imposed in Antràs (2003), who shows in a slightly different framework that the HQ's share of ex-post revenue should be higher than one-half for the HQ to be willing to provide capital to the producer. The reason for assuming βo b 1/2 (rather than βo N 1/2) in the current framework is twofold. First, even though the HQ may well provide the financial capital to the manufacturer, it is the latter who is generally involved in the acquisition and maintenance of physical assets used in the production process. Second, and perhaps more importantly, the model based on βo b 1/2 is able to rationalize the robust empirical findings documented in this paper (see Proposition 1). A.2. Contractual incompleteness versus first-best scenario k+η) Note from Eqs. (5), (12), and (13) that lo b lFB if and only if βηokα(1 − βo)ηpαχ1−α(η ≡ Ω b 1, which is simultaneously the sufficient condition for πo b p o FB π to hold; see Eqs. (7) and (15). Moreover, it can be easily verified that Ω b 1 is a sufficient condition for Ro b RFB. To see this, note from Eqs. (6) and ϒ −ðη ð1−αÞþηp ð1−αÞÞ η ð1−αÞ k+η) (13) that Ro b RFB if and only if βηok(1 − βo)ηpχ1−(η ≡ ϒ b 1. Hence, since ¼ βok ð1−βo Þηp ð1−αÞ χ o k b1 for χo N 1 and α, βo, ηk, ηp ∈ p o Ω (0,1), we have ϒ b Ω. Therefore, Ω b 1 is a sufficient condition for Ro b RFB to hold. Using χo from Eq. (14), the sufficient condition for lo b lFB, Ro b RFB, and πo b πFB reads:

Ω¼

 11−α ðηk þηp Þ 0 1−α βo ηk þ ð1−βo Þηp A   b1 1−α ηk þ ηp

η α βok ð1−βo Þηp α @

To verify that this inequality is fulfilled for all parameter values, I differentiate Ω with respect to ηk to obtain after simplification: ∂Ω ¼ −αΨΩ ∂ηk whereby Ψ ≡ ln



 χo βo þ −1 βo χo

1 χ Bearing in mind that χo N 1 and βo b 1, it can be easily verified that Ψ N 0. To see this, note that lnðaÞ þ −1 is equal to zero for o ≡ a ¼ 1 and it a βo ∂Ω b0. Hence, if Ω ≤ 1 for ηk = 0, Ω b 1 holds a fortiori for all ηk ∈ (0, 1). Evaluating Ω at ηk = 0 yields: increases in a for any a N 1. Since Ψ N 0, we have ∂ηk Ωjηk ¼0 ¼ ð1−βo Þηp α

!1−αηp 1−αηp ð1−βo Þ 1−αηp

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B. Kukharskyy / Journal of International Economics 122 (2020) 103262

To verify that Ω|η k=0 b 1, I differentiate Ω|η k=0 with respect to ηp to obtain after simplification: ∂Ωjηk ¼0 ∂ηp

¼ −αZΩjηk ¼0

whereby Z ≡ ln

! 1−αηp ð1−βo Þ 1−αηp

βo − ln ð1−βo Þ− 1−αηp ð1−βo Þ

To assess the sign of Z, I differentiate it with respect to βo and obtain after simplification: ∂Z ¼ ∂βo

βo  2 N0 ð1−βo Þ 1−αηp ð1−βo Þ

Next, note that Z|β o = 0. Thus, Z N 0 for all βo ∈ (0, 1) and, therefore,

∂Ωjηk ¼0

b0. Hence, if Ω|η k=0 ≤ 1 for ηp = 0, Ω|η k=0 b 1 holds a fortiori for all ηp ∈ ∂ηp (0,1). Evaluating Ω|η k=0 at ηp = 0 yields Ω|η k=η p=0 = 1. We thus have Ω b 1 for any ηk, ηp ∈ (0,1), which implies lo b lFB, Ro b RFB, and πo b πFB for all permissible parameter values. A.3. Proof of Propositions 1 and 2 The first-order derivative of the logarithm of Θ from Eq. (16) reads after simplification:  1 2 0
 1−α βI ηk þ ð1−βI Þηp ∂ ln Θ α 4 βI  A ln ¼ − ln @ 1−α βA ∂ηk 1−α βA ηk þ ð1−βA Þηp      3 1−αηp ðβI −βA Þ 1−α ηk þ ηp    5 − 1−α βA ηk þ ð1−βA Þηp 1−α βI ηk þ ð1−βI Þηp

ðA:2Þ

To assess the sign of this derivative, I further differentiate it with respect to βA and obtain after simplification:  h i 2 α 1−αηp 1−βA −αηp ð1−2βA Þ ∂ ln Θ ¼−   2 ∂ηk ∂βA βA ð1−α Þ 1−α βA ηk þ ð1−βA Þηp

ðA:3Þ

Note that the sign of this cross-partial derivative is negative if and only if the expression in the square brackets is positive. To assess the sign ∂Φ 1 ∂Φ 1 of this expression, I define Φ ≡ 1 − βA − Λ(1 − 2βA), whereby αηp ≡ Λ ∈ (0, 1) ∀ α, ηp ∈ (0, 1). Note that ≥0 if βA ≥ , b0 if βA b , and 2 ∂Λ 2 ∂Λ 2 ∂ Φ N0 ∀ βA ∈ (0, 1). Hence, if min{Φ|β A=1, Λ=0, Φ|β A=0, Λ=1} ≥ 0, we have Φ N 0 for all βA, Λ ∈ (0, 1). It can be immediately seen that Φ|β A= ∂Λ∂βA 2

∂ ln Θ ∂ ln Θ ∂ ln Θ b0. Therefore, if ≥0 for the highest possible βA, N0 holds a fortiori ∂ηk ∂βA ∂ηk ∂ηk

∂ ln Θ

∂ lnΘ for all parameter values. Evaluating Eq. (A.2) at βA = βI yields ¼ 0. Hence, N0 for all βA b βI. ∂η

∂η 1, Λ=0

= 0 and Φ|β A=0, Λ=1 = 0. This implies Φ N 0 and

k

β A ¼β I

k

The first-order derivative of the logarithm of Θ with respect to ηp reads:  1 2 0
 1−α βI ηk þ ð1−βI Þηp ∂ ln Θ 1−βI 4 @  A− ln ¼ −α ln 1−βA ∂ηp 1−α βA ηk þ ð1−βA Þηp    3   1−αηk ðβI −βA Þ 1−α ηk þ ηp    5 − 1−α βA ηk þ ð1−βA Þηp 1−α βI ηk þ ð1−βI Þηp

ðA:4Þ

To assess the sign of this derivative, I further differentiate it with respect to βI and obtain:   2 α 1−αηk βI þ αηk ð1−2βI Þ ∂ ln Θ ¼−   2 ∂ηp ∂βI ð1−βI Þ 1−α βI ηk þ ð1−βI Þηp

ðA:5Þ

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

21 2

∂ ln Θ b0. Therefore, if ∂ηp ∂βI

∂ ln Θ ∂ ln Θ ∂ ln Θ

≤0 for the lowest possible βI, b0 holds a fortiori for all parameter values. Evaluating Eq. (A.4) at βI = βA yields ¼ 0. Hence, ∂η ∂η ∂η

As before, one can prove that the expression in square brackets is positive for all parameter values, which immediately implies

p

p

p

β I ¼βA

∂ ln Θ b0 for all βI N βA. This completes the proof of Proposition 1. ∂ηp Recall from Eq. (3) that βA decreases in γ, which, by Assumption 1, is a negative function of the IPR protection in M’s country (γ′(ν) b 0). Hence, the 2

fact that

2

∂ lnΘ ∂ ln Θ b0 according to Eq. (A.3) immediately implies b0, which is summarized in Proposition 2. ∂ηk ∂βA ∂ηk ∂ν

A.4. Transaction cost theory In contrast to the PRT approach considered in the main text, the TCT assumes that integration can fully eliminate the hold-up inefficiencies that plague arm's-length transactions. I model this alternative approach by adopting a stylized TCT-model developed in Chapter 6 of Antràs (2015). More specifically, I assume that, under integration, the HQ can enforce by fiat the manufacturer's investments into physical assets but incurs thereby an exogenous governance cost λ N 1. The HQ's maximization problem under integration thus reads: max R−ck k−λcp p−cl l k;p;l

Using Eqs. (1) and (2), this maximization problem yields the following maximum profit under integration:

πTCT I

FB

−

ηp α

¼ π λ 1−α

−

ηp α

1−α

b1 ∀α, ηp ∈ (0, 1) and, hence, πTCT b πFB. where πFB is the first-best profit from Eq. (7). Note that, since λ N 1, we have λ I Bearing in mind that the HQ's profit under arm's-length transaction, πA, is still governed by Eq. (15), the relative attractiveness of integration under πTCT the TCT, ΘTCT ≡ I , thus reads: πA

TCT

Θ

¼

λ−

ηp α 1−α

1  11−αðηk þηp Þ 11−α 0 1−α ηk βA ðν Þ þ ηp ð1−βA ðνÞÞ B C BβA ðν Þηk α ð1−βA ðνÞÞηp α @ C A   @ A 1−α ηk þ ηp 0

A straightforward analysis shows that ∂ΘTCT/∂ηk N 0 for all permissible parameter values. However, it can be easily verified that the signs of ∂2ΘTCT/ ∂ηk∂ν and ∂ΘTCT/∂ηp turn out to be ambiguous under this alternative theory.

A.5. Direct effect of IPR protection As stated in the main text, the direct effect of IPR protection on the relative attractiveness of integration is ambiguous. Bearing in mind that βA is a positive function of IPR protection ν, consider the first-order derivative of Θ with respect to ν: ∂Θ ¼ϒ ∂ν

(

) αΘβ0A ðν Þ     βA ð1−α Þð1−βA Þ 1−α βA ηk þ ð1−βA Þ 1−ηk

where by       ϒ ≡ β2A ηp −ηk þ 2βA ηk 1−αηp −ηk 1−αηp Since βA′(ν) N 0 and the expression in squared brackets is positive, the expression in curly brackets is strictly positive for all parameter values. Hence, ∂Θ ∂Θ the sign of is equal to the sign of ϒ, which may be either positive or negative, depending on the parameter values. If ϒ N 0, we have N0. ∂ν ∂ν A.6. Financial constraints Recall that the organizational form in the baseline model was chosen so as to maximize the joint surplus from the cooperation, while the ex-post gains from the relationship were distributed between the parties via ex-ante lump-sum transfers, τ. Yet, credit market imperfections and financial frictions may prevent the producers from raising the full amount of cash necessary for the ex-ante transfers. In my modeling of financial constraints, I build on Antràs (2015) and assume that M can raise from external financiers at most a share ϕ ∈ [0, 1] of the net payoff he receives from transacting with H. The parameter ϕ broadly captures the quality of financial institutions in M’s country, whereby ϕ = 1 reflects the benchmark case of

22

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

unconstrained ex-ante transfers and ϕ = 0 reflects the other extreme of no ex-ante transfers. The ex-ante transfer conducted by M in period t2 thus reads:  τ ¼ ϕ ð1−βo ÞR−cp p−cl l :

ðA:6Þ

In t2, the HQ chooses the contract which maximizes her payoff βoR − ckk + τ, subject to the ex-ante transfer from Eq. (A.6). Since the optimal amounts of k and p are not affected by the size of the transfer, one can use k and p from Eq. (8) in the expression above to formulate the HQ's optimization problem in this period: h   i  max R βo 1−αηk þ ϕð1−βo Þ 1−αηp −cl l ≡ πoϕ ;

ðA:7Þ

l

whereby R is given by Eq. (9). This maximization problem yields the optimal amount of labor  l ¼ χ oϕ

 1−ηk −ηp αR cl

≡ loϕ

ðA:8Þ

as a function of revenue  α 1−η −η 1−α FB η R ≡ Roϕ R ¼ βok ð1−βo Þηp χ oϕ k p

ðA:9Þ

whereby RFB is the first-best revenue from Eq. (6), and χoϕ is a parameter defined for notational simplicity:     βo 1−αηk þ ϕð1−βo Þ 1−αηp   χ oϕ ≡ 1−α ηk þ ηp

ðA:10Þ

Plugging Eqs. (A.8), (A.9), and (A.10) into Eq. (A.7) yields after simplification the maximum profit under organizational form o ∈ {I, A}: πoϕ ¼


 1 1−α ðη þη Þ 1−α FB η α βok ð1−βo Þηp α χ oϕ k p π

ðA:11Þ

whereby πFB is the first-best profit from Eq. (7). By analogy to the benchmark model from Section 2.2, the relative attractiveness of integration is defined asΘϕ ≡

πIϕ . Using Eqs. (A.11) and (A.10), this π Aϕ

ratio reads:
Θϕ ¼

βI βA

η α
k

1−α

1−βI 1−βA



  11−α η þ η   1−α βI 1−αηk þ ϕð1−βI Þ 1−αηp 1−α @   A  βA 1−αηk þ ϕð1−βA Þ 1−αηp

η α p

0

k



p

ðA:12Þ

It is straightforward to verify that Θϕ reduces to Θ from Eq. (16) for ϕ = 1. Yet, the expression for Θϕ is more general since it encompasses both the case of no ex-ante transfers (ϕ = 0), as well as the intermediate cases of ϕ ∈ (0, 1). The first-order derivative of the logarithm of Θϕ from Eq. (A.12) with respect to ηk reads:  1 2 0  
 βI 1−αηk þ ϕð1−βI Þ 1−αηp ∂ ln Θϕ α 4 βI  A ln ¼ − ln @   1−α βA ∂ηk βA 1−αηk þ ϕð1−βA Þ 1−αηp     3 1−αηp 1−α ηk þ ηp ðβI −βA Þ  ih   i5 −h    βA 1−αηk þ ϕð1−βA Þ 1−αηp βI 1−αηk þ ϕð1−βI Þ 1−αηp Differentiating this expression with respect to βA yields after simplification:  h   i  2 ϕα 1−αηp βA 1−αηk þ ϕð1−βA Þ 1−αηp ∂ ln Θϕ ¼− h   i2 b 0  ∂ηk ∂βA βA ð1−α Þ βA 1−αηk þ ϕð1−βA Þ 1−αηp

Hence, if

∂ ln Θϕ ∂ lnΘϕ ∂ ln Θϕ

≥0 for the highest possible βA, N0 holds a fortiori for all parameter values. It can be immediately seen that ¼0 ∂ηk ∂ηk ∂ηk βA ¼βI 2

and, therefore,

2

∂ ln Θϕ ∂ ln Θϕ ∂ ln Θϕ N0 for all βA b βI. As already established in Appendix A.3, the fact that b0 immediately implies b0. ∂ηk ∂ηk ∂βA ∂ηk ∂ν

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

23

The first-order derivative of the logarithm of Θϕ with respect to ηp reads:  1 2 0  
 ∂ lnΘϕ α 4 @ βI 1−αηk þ ϕð1−βI Þ 1−αηp A 1−βI   − ln ln ¼−   1−α 1−βA ∂ηp βA 1−αηk þ ϕð1−βA Þ 1−αηp     3 1−αηp 1−α ηk þ ηp ðβI −βA Þ  ih   i5 −h    βA 1−αηk þ ϕð1−βA Þ 1−αηp βI 1−αηk þ ϕð1−βI Þ 1−αηp To assess the sign of this derivative, I further differentiate it with respect to βI and obtain: 2

∂ ln Θϕ ¼− ∂ηp ∂βI

  α 1−αηk Γ h   i2  ð1−α Þð1−βI Þ βI 1−αηk þ ϕð1−βI Þ 1−αηp

whereby Γ ≡ βI − αηk(βI − ϕ(1 − βI)). Note that Γ is increasing in ϕ. Hence, if Γ ≥ 0 for the lowest possible ϕ, Γ N 0 holds a fortiori for all ϕ ∈ [0,1]. 2

Evaluating Γ at ϕ = 0 yields Γ|ϕ=0 = βI(1 − αηk) N 0. Since Γ N 0, we have holds a fortiori for all parameter values. Since

∂ ln Θϕ ∂ ln Θϕ ∂ ln Θϕ b0. Hence, if ≤0 for the lowest possible βI, b0 ∂ηp ∂βI ∂ηp ∂ηp

∂ lnΘϕ

∂ lnΘϕ ¼ 0; we have b0 for all βI N βA. ∂ηp β ¼β ∂ηp I

A

A.7. Partial contractibility of the knowledge transfer In this section, I relax the assumption of the benchmark model that the transmission of intangible assets is fully non-contractible and consider instead partial contractibility of the knowledge transfer. To formalize this notion, I build on Acemoglu et al. (2007) and Antràs and Helpman (2008) and assume that the input provided by H is a Cobb-Douglas aggregate of a continuum of intangible assets, indexed by points on the unit interval, i ∈ [0,1]: "Z

1

k ¼ exp

# ln kðiÞdi

ðA:13Þ

0

whereby only a fraction of intangible assets in the range [0, μ], μ ∈ [0,1), is contractible, whereas the remaining fraction (1 − μ) is non-contractible.56 There are two main determinants of the degree of contractibility μ. The first is the extent to which knowledge can be codified in an ex-ante agreement. For instance, patent licenses can be explicitly stipulated, while transfer of non-codifiable ideas or novel designs are hard to verify and contract upon. Second, μ may also depend on the ability or willingness of legal authorities to enforce the transfer of knowledge. In the baseline scenario, I assume that the latter determinant of μ is orthogonal to IPR protection in a given country (i.e., treat μ and ν as independent parameters). Intuitively, countries with weak IPR protection may very well be interested in enforcing the transfer of intangible assets from the foreign HQ but may fail to protect this knowledge from being dissipated by the local manufacturing producer. However, I prove below that the case in which μ is a positive function of the IPR protection in a given country (ν) yields qualitatively similar results. Consider both parties' maximization problems in stage t3, in which H and M solve, respectively: Z max βo R−ck

1 fkðiÞgi¼μ

1 μ

kðiÞdi;

max ð1−βo ÞR−cp p p

whereby βo is the share of revenue obtained by H under organizational form o ∈ {I, A}, as described by Eq. (3). Using Eqs. (1), (2), and (A.13), these maximization problems yield optimal amounts of non-contractible knowledge transfer and physical capital, respectively: kðiÞ ¼ ηk βo

αR ∀ i∈½μ; 1; ck

p ¼ ηp ð1−βo Þ

αR cp

ðA:14Þ

as a function of revenue, obtained from plugging Eqs. (2), (A.13), and (A.14) into Eq. (1) and solving the resulting expression for R: 02

 η β α ηk ð1−μ Þ ð1−βo Þα ηp R ¼ @4 k o cp ck

1  1  !1−ηk −ηp 3α R μ !ηk 1−α η ð1−μ Þ þ η exp 0 ln kðiÞdi l 5 D1−α A ηk 1−ηk −ηp k

p

ðA:15Þ

Under the assumption of ex-ante lump-sum transfers, H’s maximization problem in period t2 reads: maxμ

l;fkðiÞgi¼0

Z n h  io −ck R 1−α βo ηk ð1−μ Þ þ ð1−βo Þηp

μ 0

kðiÞ−cl l

ðA:16Þ

56 The model's predictions remain intact if one additionally assumes partial contractibility of physical assets p. To economize on space, I do not discuss this case in the paper, but provide analytical derivations upon request.

24

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

Using Eq. (A.15), this maximization problem yields optimal amounts of contractible knowledge transfer and labor employment, respectively: kðiÞ ¼ ηk βo χ oμ

  αR l ¼ 1−ηk −ηp χ oμ cl

αR ∀ i∈½0; μ ; ck

ðA:17Þ

as a function of revenue  α 1−η ð1−μ Þ−ηp 1−α FB η ð1−μ Þ ð1−βo Þηp χ oμ k R ≡ Ro R ¼ β ok

ðA:18Þ

whereby RFB is the first-best revenue from Eq. (6), and χoμ is defined for notational simplicity: χ oμ

  1−α βo ηk ð1−μ Þ þ ð1−βo Þηp   ≡ 1−α ηk ð1−μ Þ þ ηp

ðA:19Þ

Plugging Eqs. (A.17), (A.18), and (A.19) into Eq. (A.16) yields after simplification the maximum profit: πoμ ¼


 1 1−α ðηk ð1−μ Þþηp Þ 1−α FB η ð1−μ Þα β ok ð1−βo Þηp α χ oμ π

ðA:20Þ

whereby πFB is the first-best profit from equation. Using Eqs. (A.20) and (A.19), the relative attractiveness of integration, Θμ ≡
Θμ ¼

βI βA

η ð1−μÞα
k

1−α

1−βI 1−βA

  11−α 1−α βI ηk ð1−μ Þ þ ð1−βI Þηp 1−α @  A 1−α βA ηk ð1−μ Þ þ ð1−βA Þηp

η α p

0

πIμ , reads: πAμ

  ηk ð1−μ Þ þ ηp 1−α

ðA:21Þ

It can be immediately seen that Θμ reduces to Θ from Eq. (16) for the case of zero contractibility of knowledge transfer (μ = 0), but also encompasses more general cases of partial contractibility, μ ∈ (0, 1). Note that Θμ from Eq. (A.21) is isomorphic to Θ from Eq. (16), apart from ηk being replaced by ηk(1 − μ) ≡ η. Since the derivations in Appendix A.3 hold 2

for any ηk ∈ (0, 1), they can be reapplied for any η ∈ (0,1) to obtain

∂ lnΘμ ∂ ln Θμ ∂ ln Θμ b0. Since η increases in ηk, these relationships N0, b0, and ∂η ∂η∂ν ∂ηp 2

∂ lnΘμ ∂ lnΘμ N0 immediately implies b0. The ∂η ∂ηk ∂μ latter result suggests that, if one were to relax the assumption that μ is orthogonal to ν and assume instead that μ is a positive function of ν, the model's qualitative results would remain unchanged. A.8. Ownership shares Differentiating πos from Eq. (15) with respect to βH and solving the first-order condition for βH yields the profit-maximizing share of revenue: immediately imply Propositions 1 and 2. Moreover, using the definition of η = ηk(1 − μ), the fact that

βH ¼

  rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi    ηk 1−αηp − ηk ηp 1−αηk 1−αηp ηk −ηp

 ¼ δα þ βðs Þ 1−δα −γ α

ðA:22Þ

whereby the expression on the right-hand side implicitly defines the optimal ownership share s ∗, which ensures the optimal revenue share (see Eq. (17)). Using the implicit function theorem, differentiating s ∗ with respect to ηk yields after simplification: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi   1      ηk þ ηp −αηk ηp − ηk ηp 1−αηk 1−αηp ηp 1−αηp ∂s 2 ¼  2 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi    ∂ηk ηk ηp 1−αηk 1−αηp 1−δα −γ α β0 ðsÞ ηk −ηp 

Since the expression in square brackets is positive under Assumption 2 and β′(s) N 0, the sign of

ðA:23Þ

∂s is determined by the sign of the expression in ∂ηk

curly brackets. The latter is positive if and only if rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi     1 ηk þ ηp −αηk ηp N ηk ηp 1−αηk 1−αηp 2

ðA:24Þ

It can be easily verified that the expression on the left-hand side is strictly positive for all α, ηk, ηp ∈ (0, 1). Hence, the inequality from Eq. (A.24) holds if and only if its squared left-hand side is larger than the squared right-hand side. Raising both sides of this inequality to the power of two and 2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 1 1 ∂s N0. rearranging yields ð ðηk þ ηp Þ−αηk ηp Þ −ð ηk ηp ð1−αηk Þð1−αηp ÞÞ ¼ ðηk −ηp Þ2 N0 for all ηk, ηp ∈ (0,1). This immediately implies 2 4 ∂ηk

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

25

Using the implicit function theorem, the first-order derivative of s ∗ from Eq. (A.22) with respect to ηp yields after simplification: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi     1   η −αη η 1−αη þ η η − η η 1−αη 1−αη k k p k p k p k p ∂s 2 k ¼−  2  0 ∂ηp α 1−δ −γ α β ðsÞ ηk −ηp

ðA:25Þ

Note that the term in curly brackets is identical to the one from Eq. (A.23), which has been shown to be positive. This immediately implies

∂s b0 ∂ηp

for all permissible parameter values and completes the proof of Proposition 3 from Section 2.3. ∂s Differentiating from Eq. (A.23) with respect to ν yields after simplification: ∂ηk 2

∂ s αγ α−1 γ 0 ðν Þ ∂s b0 ¼ ∂ηk ∂ν 1−δα −γ α ∂ηk Note that the sign of this cross-partial derivative is negative since γ′(ν) b 0 under Assumption 1, 1 − δα − γα N 0 under Assumption 2, and

∂s N0 has ∂ηk

been proven above. This completes the proof of the Proposition 4 from Section 2.3. Appendix B. Appendix tables and figures Table B.1 Summary statistics for main estimation samples. Variable

Obs.

Mean

Std. Dev.

Min

Max

Firm-levelcross-section estimates: Sha2014 Intangibleh2013 Tangiblea2013 Revenueh2013 Employmenth2013 Ageh2013 #Affiliatesh2013 Revenuea2013 Employmenta2013 Agea2013 #Shareholdersa2013 Intangibleh2013 (normalized by h’s employment) Intangibleh2013 (normalized by h’s revenue) Intangibleh2013 (normalized by a’s employment) Tangiblea2013 (normalized by a’s revenue) Tangiblea2013 (non-normalized) Smaj ha2014

79,349 79,349 79,349 79,349 79,349 79,349 79,349 79,349 79,349 79,349 79,349 79,349 79,262 79,349 79,339 79,339 79,339

71.13 1.971 2.591 9.459 3.902 26.03 12.83 8.744 3.370 18.46 2.521 −2.002 −7.646 −1.446 −2.807 6.009 0.760

31.40 5.373 2.384 2.609 2.034 21.63 30.58 1.776 1.535 15.73 7.796 4.800 4.455 5.529 2.384 2.575 0.426

10 −4.605 −8.479 0 0 0 1 0 0 0 1 −17.00 −21.82 −16.26 −14.54 0 0

100 17.15 14.39 19.31 12.70 464 743 17.67 11.65 309 751 12.70 8.152 17.00 12.06 17.31 1

Industry-levelcross-section estimates: Sha2014 Intangiblei(h)q(h) Tangiblej(a)ℓ(a) Smaj ha2014

381,578 381,578 381,578 381,578

76.30 −1.391 2.706 0.824

29.68 3.536 1.722 0.380

10 −4.605 −6.604 0

100 14.07 11.50 1

Panel estimates: Shat Intangibleh, t−1 Tangiblea, t−1 Smaj hat

637,386 637,386 637,386 637,386

72.83 1.684 5.233 0.771

31.60 5.534 4.009 0.419

10 −4.605 −4.605 0

100 17.00 17.31 1

Institutional measures: IPRℓ(a) (Park) IPRℓ(a) (WEF) IPRℓ(a) (BSA) IPRℓ(a) (PRA)

100 100 100 100

0 0 0 0

1 1 1 1

−2.468 −2.129 −1.653 −2.287

1.695 2.043 1.765 1.876

Table B.2 Description of proxies for IPR protection. IPRℓ(a)

Source

Patent protection

Park (2008)

Intellectual property protection (IPP) Software piracy Intellectual property rights index (IPRI)

Description and URL (accessed July 1, 2017)

The index comprises the following five dimensions: patent coverage, membership in international IPR agreements, absence of risks of forfeiting the patent rights, enforcement of patent rights in case of an infringement, and duration of protection. URL: http://fs2.american.edu/wgp/www World Economic Forum (WEF), Global Experts' and practitioners' assessment of the IPR protection in their countries on a 7-point scale from weak Competitiveness Report and not enforced to strong and enforced. URL: http://www3.weforum.org/docs/gcr/2015-2016/GCI_Dataset_2006-2015.xlsx Business Software Alliance (BSA), The inverse of the rate of installation of unlicensed software on private and company computers. URL: Global Software Survey http://globalstudy.bsa.org/2013/downloads/studies/2013GlobalSurvey_Study_en.pdf Property Rights Alliance (PRA) The index averages the three above-mentioned scores of Patent protection by Park (2008), IPP by WEF, and Software piracy by BSA. URL: http://internationalpropertyrightsindex.org

26

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

Table B.3 Pairwise correlation between the proxies for IPR protection.

IPR (Park) IPR (WEF) IPR (BSA) IPR (PRA)

IPR (Park)

IPR (WEF)

IPR (BSA)

IPR (PRA)

1.000 0.663 0.795 0.870

1.000 0.846 0.911

1.000 0.968

1.000

Notes: The table reports pairwise correlations between measures of IPR protection used in this paper, see Table B.2 for definitions. Table B.4 Linear probability models.

Panel A. Dependent variable: Smaj ha2014 Intangibleh2013 Tangiblea2013

(1)

(2)

(3)

(4)

(5)

(6)

0.006*** (0.000) −0.009*** (0.001)

0.006*** (0.000) −0.008*** (0.001)

0.005*** (0.000) −0.007*** (0.001)

yes yes yes yes no no no no no no 137,785 0.129

nested nested nested nested yes yes no no no no 135,426 0.224

nested nested nested nested yes yes yes yes no no 127,719 0.303

0.012*** (0.001) −0.007*** (0.001) −0.006*** (0.001) nested nested nested nested yes yes yes yes no no 121,078 0.299

0.011*** (0.001) −0.008*** (0.001) −0.006*** (0.001) nested nested nested nested yes yes yes yes yes no 82,515 0.333

0.011*** (0.001) −0.006*** (0.001) −0.005*** (0.001) nested nested nested nested yes yes yes yes yes yes 79,349 0.346

0.006*** (0.000) −0.002*** (0.000)

0.006*** (0.000) −0.002*** (0.000)

0.005*** (0.000) −0.002*** (0.000)

0.001*** (0.000) −0.001*** (0.000)

0.001*** (0.000) −0.001*** (0.000)

812,974 0.117 yes yes yes yes no no no no no no no

798,639 0.206 nested nested nested nested yes yes no no no no no

792,308 0.276 nested nested nested nested yes yes yes yes no no no

661,834 0.949 nested nested nested nested yes yes yes yes yes yes no

638,061 0.954 nested nested nested nested yes yes yes nested nested nested yes

0.001*** (0.000) −0.001*** (0.000) −0.000 (0.000) 598,905 0.954 nested nested nested nested yes yes yes nested nested nested yes

Intangibleh2013×IPRℓ(a) HQ country FE Affiliate country FE HQ industry FE Affiliate industry FE HQ country/industry FE Affiliate country/industry FE Country-pair FE Industry-pair FE HQ controls Affiliate controls Observations R-squared Panel B. Dependent variable: Smaj hat Intangibleh, t−1 Tangiblea, t−1 Intangibleh, t−1×IPRℓ(a) Observations R-squared HQ country/year FE Affiliate country/year FE HQ industry FE Affiliate industry FE HQ country/industry/year FE Affiliate country/industry/year FE Country/year-pair FE Industry-pair FE HQ firm FE Affiliate firm FE Firm-pair FE

Notes: Panels A and B report OLS estimates of Eqs. (20) and (22), respectively, with binary outcome variable Smaj hat , which is equal to one if the HQ owns the majority of the affiliate's equity maj stake in year t and zero otherwise. The dependent variable is Smaj ha2014 in Panel A and Shat in Panel B. Columns 5 and 6 in Panel A include the full set of HQ and affiliate controls, included in columns 5 and 6 of Table 1. Robust standard errors are clustered by HQ and affiliate and presented in parentheses. ***, **, * denote 1, 5, 10% significance, respectively. Table B.5 Panel estimates: longer lags. Dependent variable: Shat Panel A. Two-year lags: Intangibleh, t−2 Tangiblea, t−2

(1)

(2)

(3)

(4)

(5)

(6)

0.456*** (0.029) −0.063** (0.025)

0.462*** (0.026) −0.082*** (0.024)

0.424*** (0.026) −0.054** (0.024)

0.035** (0.014) −0.056*** (0.017)

0.035*** (0.014) −0.060*** (0.017)

450,481 0.166

439,078 0.267

432,612 0.340

312,683 0.966

303,574 0.967

0.030** (0.014) −0.060*** (0.017) 0.006 (0.010) 272,353 0.968

0.439*** (0.033)

0.455*** (0.029)

0.418*** (0.029)

0.018 (0.015)

0.017 (0.015)

0.012 (0.020)

Intangibleh, t−2×IPRℓ(a) Observations R-squared Panel B. Four-year lags: Intangibleh, t−4

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

27

Table B.5 (continued) Dependent variable: Shat

(1)

(2)

(3)

(4)

(5)

(6)

Tangiblea, t−4

−0.081*** (0.030)

−0.096*** (0.028)

−0.063** (0.029)

−0.025 (0.020)

−0.026 (0.019)

260,927 0.175 yes yes yes yes no no no no no no no

252,218 0.286 nested nested nested nested yes yes no no no no no

246,653 0.368 nested nested nested nested yes yes yes yes no no no

157,544 0.969 nested nested nested nested yes yes yes yes yes yes no

154,083 0.970 nested nested nested nested yes yes yes nested nested nested yes

−0.025 (0.019) 0.005 (0.016) 154,083 0.971 nested nested nested nested yes yes yes nested nested nested yes

Intangibleh, t−4×IPRℓ(a) Observations R-squared HQ country/year FE Affiliate country/year FE HQ industry FE Affiliate industry FE HQ country/industry/year FE Affiliate country/industry/year FE Country/year-pair FE Industry-pair FE HQ firm FE Affiliate firm FE Firm-pair FE

Notes: The table reports panel estimates of (variations of) Eq. (22). The dependent variable is the ownership share of a HQ h in affiliate a in year t. Intangibleh, t−n and Tangiblea, t−n measure, respectively, h’s intangible assets and a’s tangible assets in year t − n, n ∈ {2,4}. Robust standard errors are clustered by HQ and affiliate and presented in parentheses. ***, **, * denote 1, 5, 10% significance, respectively. Table B.6 Refined measure of knowledge intensity.

Panel A. Dependent variable: Sha2014 Intangiblerh2013 Tangiblea2013

(1)

(2)

(3)

(4)

(5)

(6)

0.483*** (0.023) −0.738*** (0.056)

0.464*** (0.022) −0.636*** (0.055)

0.447*** (0.030) −0.610*** (0.058)

yes yes yes yes no no no no no no 113,623 0.193

nested nested nested nested yes yes no no no no 111,480 0.295

nested nested nested nested yes yes yes yes no no 106,235 0.362

0.819*** (0.092) −0.598*** (0.059) −0.322*** (0.073) nested nested nested nested yes yes yes yes no no 100,680 0.357

0.779*** (0.111) −0.665*** (0.072) −0.324*** (0.084) nested nested nested nested yes yes yes yes yes no 68,813 0.380

0.765*** (0.112) −0.473*** (0.076) −0.307*** (0.085) nested nested nested nested yes yes yes yes yes yes 66,185 0.395

0.417*** (0.021) −0.038* (0.023)

0.417*** (0.018) −0.057*** (0.021)

0.436*** (0.024) −0.045** (0.021)

0.037*** (0.010) −0.085*** (0.014)

0.036*** (0.010) −0.082*** (0.014)

676,830 0.167 yes yes yes yes no no no no no no no

664,126 0.263 nested nested nested nested yes yes no no no no no

659,512 0.321 nested nested nested nested yes yes yes yes no no no

548,007 0.956 nested nested nested nested yes yes yes yes yes yes no

529,872 0.958 nested nested nested nested yes yes yes nested nested nested yes

0.040*** (0.012) −0.082*** (0.014) −0.007 (0.011) 529,287 0.958 nested nested nested nested yes yes yes nested nested nested yes

Intangiblerh2013×IPRℓ(a) HQ country FE Affiliate country FE HQ industry FE Affiliate industry FE HQ country/industry FE Affiliate country/industry FE Country-pair FE Industry-pair FE HQ controls Affiliate controls Observations R-squared Panel B. Dependent variable: Shat Intangiblerh, t−1 Tangiblea, t−1 Intangiblerh, t−1×IPRℓ(a) Observations R-squared HQ country/year FE Affiliate country/year FE HQ industry FE Affiliate industry FE HQ country/industry/year FE Affiliate country/industry/year FE Country/year-pair FE Industry-pair FE HQ firm FE Affiliate firm FE Firm-pair FE

Notes: Panels A and B report OLS estimates of Eqs. (20) and (22), respectively, using the refined measure of knowledge intensity, Intangiblerht, see Appendix C for details. Robust standard errors are clustered by HQ and affiliate and presented in parentheses. ***, **, * denote 1, 5, 10% significance, respectively.

28

B. Kukharskyy / Journal of International Economics 122 (2020) 103262

Appendix C. Data appendix This appendix describes the construction of the interindustry knowledge applicability measure, κij. Patent-related information used for the construction of this measures is drawn from the NBER Patent Database.57 I utilize two segments of this dataset – Patents Granted (PG) and Patent Citations (PC). The PG segment classifies the patents according to the U.S. Patent Classification System (USPC), which assigns to each patent a single 6-digit technology class (T). Via a 7-digit patent number, I merge the PG data to citing and cited patents contained in the PC segment of the data. Using this merged database, I calculate the probability of each technology class Ta to cite any other technology class Tb and denote this probability with pab, whereby a and b can be either the same or different technology classes. To map patents to industries, I utilize the crosswalk provided by Goldschlag et al. (2016).58 From this crosswalk, I retrieve information on the probability of the citing technology class to be employed in industry j, pja and the probability of the cited technology class to be employed in industry i, pib. Combining these probabilities with pab, I calculate the joint probability of (patents associated with industry) j to cite (patents associated with industry) i, pij. The above-mentioned approach is best exemplified using a simple case of two industries {i = 1, j = 2}, and two technology classes T ∈ {a, b}, see Fig. C.1. The probability of industry 1 to cite industry 1, p11 is calculated as p11 = p1a × paa × p1a + p1a × pab × p1b + p1b × pba × p1a + p1b × pbb × p1b, while the probability of industry 1 to cite industry 2, p12 is given by p12 = p1a × paa × p2a + p1a × pab × p2b + p1b × pba × p2a + p1b × pbb × p2b. To ensure that p11 p12 and κ 12 ¼ .59 Intuitively, all citations by industry 1 add up to one, I normalize p11 and p12 by the sum of two probabilities, i.e., κ 11 ¼ p11 þ p12 p11 þ p12 κ11 and κ12 capture the reliance of industry 1 on knowledge from industries 1 and 2, respectively. I take these probabilities as the measure of interindustry knowledge applicability, κij.

Fig. C.1. Illustrative example for the construction of κij.

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