Globalization, Transactions Costs and Vertical Integration: Evidence ...

Globalization, Transactions Costs and Vertical Integration: Evidence from the U.S. Manufacturing

Aekapol Chongvilaivan* and Jung Hur**

Abstract The present paper aims to empirically examine a relationship between globalization and the pattern of vertical integration using six-digit NAICS U.S. manufacturing data from 2002 to 2006. We develop an empirical framework incorporating three proxies of globalization: import tariff rates, import intensity and trade intensity on top of the traditional hypotheses of vertical integration. The empirical results reveal that globalization has no significant impacts on a motive for vertical integration even though the estimates appear with signs the theories predict. Transactions costs instead play a predominant role in tempting a firm to internalize production. A robustness check validates our main findings and further reveals that demand variability, internal costs of management and unionization have to do with motives for vertical integration only in long run. We also produce supplementary results pertaining to typology of transactionspecific assets. Keywords: Vertical Integration; Globalization; Transactions Costs; Transaction-specific Asset Typology; Unionization J.E.L. Classification: F14 ; F23 ; L22 ; L60

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Institute of Southeast Asian Studies (ISEAS), 30 Heng Mui Keng Terrace, Singapore 119614, Tel: +65-6870-4509; Email address: [email protected]. ** (Corresponding Author) Department of Economics, Sogang University, 1 Shinsu-dong, Mapo-gu, Seoul 121-742, Korea, Tel: +82-2-705-8518; Fax: +82-2-704-8599; Email address: [email protected].

1. Introduction The past few decades witnessed a remarkable change in the vertical structure across industries and regions. The phenomenon of vertical disintegration – known to business buffs and academics as “downsizing”, “subcontracting”, “production fragmentation” or “outsourcing” – has been central to an exploding amount of public and policy debates. For instance, The Economist postulated the trend toward disintegration in manufacturing that: “The whole industry is disintegrating as vehicle assemblers (and other manufacturers) try to outsource more and more of what they once did for themselves” (The Economist, February 23, 2002, p. 99). A growing number of economic inquiries pointed to globalization as a crucial driver of the disintegrating industrial structure, on top of the traditional hypotheses of vertical integration – especially transactions costs (Williamson, 1975, 1986; and Klein et al., 1978), demand fluctuations (Carlton, 1979; and Harrigan, 1983), and internal costs of management (Penrose, 1959; and Williamson, 1970). This is attributed largely to the fact that the use of foreign suppliers has increasingly become part and parcel of manufactures, particularly in industrialized economies.

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Over a third of Japanese firms’ total

manufacturing costs, for instance, goes to the costs of farming out production to foreign subcontractors (The Economist, August 31, 1991, pp. 54-56). Hummels et al. (2001) likewise provided evidence that trade in outsourced components accounts for 22 percent of U.S. exports in 1997, and this figure grew approximately 30 percent between 1970s and 1990s. There appears to be a body of theoretical studies within international economics to take in this idea. In particular, McLaren (2000) posited that international trade openness thickens the input markets, 2 mitigates the hold-up problem and ultimately tempts a firm to downsize its organizational structure. Wes (2000) in contrast related the traditional transactions costs theory to globalization with emphasis on a pro-competitive effect in the product market associated with globalization. Wes showed that an influx of final output imports makes the market more competitive and entices the upstream firms to invest more on transaction-specific assets. 1

Antràs (2003) provided cross-industry and cross-country evidence that industrialized countries where capital is relatively well endowed tends to be associated with higher shares of intra-firm trade. 2 See Footnote 7 for the definition of market thickness.

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Nevertheless, an empirical question in this subject remains unexplored critically in the literature. The present paper attempts to bridge the gap in the existing studies. We empirically investigate the effects of globalization on the pattern of vertical integration using U.S. manufacturing industry data disaggregated at the six-digit North American Industry Classification System (NAICS) level. Our empirical framework builds upon well-established traditions in industrial organization, incorporating three proxies of globalization – the import tariff rates, import intensity and trade intensity.3 It also departs from the existing literature in the other two respects. First, we break down the explanatory variables capturing the hypotheses of asset specificity into two categories: physical capital and human capital as in Masten et al. (1989) to account for the role of transaction-specific asset typology. In addition, we also examine an alternative hypothesis of unionization (Zhao, 2001) which, to the best of our knowledge, remains unexplored in the empirical literature.4 We find the following interesting results. Firstly and most importantly, although our empirical estimates reveal a negative relationship between international openness and a motive for vertical integration and are hence consistent with theoretical propositions pushed forward by McLaren (2000) and Wes (2000), we find merely weak evidence that such a negative effect is significant. Secondly, the pattern of vertical integration in the U.S. manufacturing sector is determined predominantly by fewness of firms and transaction-specific investments under the transactions cost approach. Our results based on comprehensive, disaggregated U.S. manufacturing data, therefore, put forward the existing findings by previous research which conventionally employed relatively aggregated industry-level data (MacDonald, 1985, Caves and Bradburd, 1988; and Weiss, 1992) or rather limited firm-level data (Levy, 1985; Masten et al., 1989 and Lieberman, 1991). 5 Thirdly, a breakdown of transaction-specific assets sheds further light on the

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See Sub-section 3.1 for further detailed discussions regarding their measurements, rationales, implications and limitations. 4 Zhao (2001) developed a successive monopoly model with downstream unionization. Zhao showed that the labor union appropriates the downstream firm’s rents in terms of the eliminated double marginalization problem, thereby hindering a decision of vertical integration. 5 MacDonald (1985) and Caves and Bradburd (1988) utilized the U.S. manufacturing data at the two-and four-digit levels, yielding only 79 and 83 industries, respectively. In contrast, Levy (1985) limited the sample only to 69 firms with sufficient information, and Lieberman (1991) confined the data only to American producers operating in the chemical industries.

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importance of transaction-specific investment typology since human capital investments like education and training and physical capital investments like buildings, plants, machinery and equipment demonstrate the effects on vertical integration in opposed ways.6 Last but not least, the other competing hypotheses – demand variability, internal costs of management and unionization – exhibit an imperative role in explaining a decision to internalize production only in long run. The remainder of this paper can be briefly outlined as follows. Section 2 provides a review of the theoretical and empirical literature and identifies the determinants of vertical integration. Section 3 develops the globalization measurements and empirical framework. Section 4 presents and analyzes the empirical results. Section 5 concludes.

2. Determinants of Vertical Integration 2.1 Effects of Globalization on Vertical Integration Central to our analysis are the effects of globalization on vertical integration. Indeed, a relationship between international openness and an incentive for vertical integration is not new as few studies have pushed forward how international openness deters a decision to internalize production. There are at least two mechanisms through which exposure to international markets mitigates the hold-up problem and hence undermines a motive for vertical integration, theoretically examined in the literature. First, McLaren (2000) developed a model based on the transactions cost approach to vertical integration, in which globalization opens up the economy to international trade, trims trade costs and prompts a surge in thickness of the input markets.7 In this sense, trade liberalization reduces an input supplier’s cost of searching a prospective buyer and plunges a negative externality from the hold-up problem, thereby wiping out an incentive for vertical integration. Much research has been devoted to empirically investigating the relationship between market thickness and vertical integration. For instance, Holmes (1999) found that an industry is less likely to vertically integrate in 6

In contrast to our findings, Masten et al. (1989) found that “human rather than physical assets play a more influential role in decisions to bring production within a firm”. In this sense, our results should be deemed as complements to theirs. 7 McLaren (2003) loosely defined market thickness as a firm’s ability to find an alternative interested partner. The examples of a rise in market thickness include: augmented versatility of participants and improvements in search efficiency. Ones therefore should distinguish a hike in market thickness from an increase in competition.

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geographic locations intensely concentrated, using U.S. manufacturing industry data. Several later studies, including Pirrong (1993) and Hubbard (2001), among others, confirmed analogous findings. However, McLaren’s (2000) propositions that related globalization to merger decisions are an empirical question that remains unanswered in the literature. Complementary to McLaren (2000), Wes (2000) further argued that trade in final goods also has a pivotal role to play in alleviating the hold-up problem and thus discouraging merger. In the successive monopoly structure where the upstream firm has to make a transaction-specific investment, liberalized trade in final goods entails a procompetitive effect – more drastic competition in the final output market due to an influx of imports. A boost in output in the product market induces the upstream firm to invest, reduces the hold-up problem, and therefore exacerbates an incentive for vertical integration.

2.2 Traditional Determinants of Vertical Integration - Transaction-specific Investments: Among various hypotheses that rationalize an incentive for vertical integration, transactions costs are one of the most prominent reasons that induce firms to merge (Williamson, 1975, 1986; and Klein et al., 1978). This problem, according to the transactions cost theory, emanates from either: (1) fewness of firms in the market, ex ante; or (2) asset specificity that causes a lock-in problem between buyer and seller, ex post. Consider a successive oligopolistic setting where upstream firms produce specialized, indivisible inputs for downstream firms. In the former case where the number of firms in both sides of the market is limited ex ante, vertical integration help purge opportunism – a negative externality arising from a sunk, transaction-specific investment. In the latter case where a non-contractible sunk cost the upstream firm incurs is enormous, the arm’s-length arrangement – the arrangement whereby a downstream firm procures the inputs the upstream firms produce, from the markets – is infeasible since lock-in effects and transaction-specific capital put quasi-rents at risk of being appropriated ex post by their downstream partner. This leads to an inefficient outcome 4

where a downstream firm internalizes input production vis-à-vis costly commitment technology.8 Some empirical studies provided evidence that vertical integration tends to be preferred to the arm’s-length arrangement when transactions are complex and when buyer and seller must invest in transaction-specific assets. MacDonald (1985), for instance, used two-digit NAICS U.S. manufacturing data and found that the industries, where capital is employed intensively and where buyer and seller are highly concentrated, are more likely to be characterized by high levels of vertical integration. Several subsequent studies reached analogous conclusions, including Lieberman (1991), Caves and Bradburd (1988) and Levy (1985), among many others. Masten et al. (1989) further revealed that capital typology – physical and human capital – matters for the patterns of vertical integration, using U.S. auto manufacturer data. Their results indicate that investments in human capital have a more influential effect on a vertical integration decision than do investments in physical capital. It should be highlighted that these studies typically utilized either aggregated multi-industry data (MacDonald, 1985, Caves and Bradburd, 1988, and Weiss, 1992), or firm-level surveys confined to some particular industries (Levy, 1985; Masten et al., 1989; and Lieberman, 1991). To us, a limited spectrum of industries and data coverage might produce inconsistent estimates as a result of aggregation and selection biases. 9 We attempt to taper these drawbacks in the literature by resorting to the dataset that is more disaggregated than the previously available industry-level data and more comprehensive than the conventionally employed firm-level data. We will relegate more detailed discussions regarding data sources and measurement to next section.

- Demand Variability: Two strands of theoretical literature are concerned with the linkage between demand fluctuations in the markets and vertical integration incentives. Carlton (1979) pushed forward the first strand of literature. He presented a model in which firms choose to 8

Commitment technology, like merger and a long-term contract, which ties the downstream to the upstream firms pertains to governance costs, such as costs of running more complex organizations and enforcing a contract. 9 See Shelanski and Klein (1995) for a comprehensive review of empirical research in transaction cost economics.

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internalize the production of input and obtain access to relatively stable sources of input supplies; they procure an uncertain amount of input from independent suppliers otherwise. In this sense, the use of vertical integration becomes tempting when demands are greatly fluctuated in the upstream market. Constituting the second strand, Harrigan (1983) modeled demand volatility in the final output market that induces merger incentives. He postulated that demand unpredictability in the downstream market triggers declines in internal transfer, ownership stakes and integrated activities.10 As discussed later in next section, our empirical framework confines the notion of demand variation only to the fluctuations in final output markets and therefore has to do exclusively with the second strand – the linkage between uncertainty of demand for final output and vertical integration.11 A small number of studies empirically examined the relationship between demand instability and vertical integration. The empirical results, nevertheless, are rather mixed and less clear-cut. Using a firm survey, Harrigan (1983, pp. 315-337), for instance, showed that less integrated industries are associated with greater demand variability in the downstream market and hence confirmed the theoretical prediction. Lieberman (1985) and Levy (1985), however, found merely weak evidence that demand fluctuations, measured by the log of firm sales regressed on a time trend, in the downstream market threaten vertical integration. As depicted later, we abstract from the conventional measures using fluctuation of value added as an alternative measure of demand fluctuations.

- Internal Costs of Management: The decision to merge vertically counts not only on transactions costs associated with asset specificity and the lock-in effect, but also on the same transactional inefficiency arising from the extent of internalization. A fundamental firm characteristic that affects internalization costs is firm size (or scale diseconomies) due to inefficiency of internal 10

Carlton (1979) and Harrigan (1983) assumed that demand fluctuations in the upstream and downstream markets are uncorrelated. Blair and Kaserman (1983), however, relaxed this assumption and proposed that firms are less likely to integrate when demand variation in both markets is positively correlated. 11 Even though it is undeniable that demand volatility in the upstream markets is crucial and should be taken into consideration, the absence of input price data forces us to leave the empirical investigation of Carlton (1979) beyond the scope of our empirical framework.

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management arising from cumulative loss of control (Williamson, 1970) and the fixed nature of managerial capital (Penrose, 1959). On the one hand, such diseconomies entail deteriorated effectiveness of vertical integration in terms of the internal control loss, such as ability to assess and monitor employees, the ability to disseminate information within a firm, and so forth. On the other hand, a surge in the scale of particular upstream or downstream activities comes at additional internal costs, with managerial capital fixed at a given point of time. Frank and Henderson (1992) empirically investigated a relationship between scale diseconomies and non-market coordination using U.S. food industry data. Their findings supported the theoretical predictions that the degrees of vertical coordination are negatively correlated with a firm’s scale of activities.12

- Unionization: In the successive oligopolistic structure with the absence of a labor union, it is well known that firms choose to vertically integrate to eliminate ‘double marginalization’ – the downstream firm’s failure to account for a positive externality it exerts on the upstream suppliers (Greenhut and Ohta, 1979; Waterson, 1982; Lin, 1988; and Hart and Tirole, 1990). Extending the conventional model of successive monopoly, Zhao (2001) proposed that when the market is unionized, vertical integration incentives may be dissipated since the labor union can extract surpluses arising from the elimination of double marginalization.13 Zhao’s generalization of the basic model further predicts that the strong labor union tends to deter the use of vertical integration; that is, the firm facing higher wage is less likely to integrate. Intuitively, the union with high bargaining power is more able to appropriate surpluses an integrated firm would have enjoyed in the absence of the union. If the union is sufficiently dominant in the labor-management bargaining such that the surpluses the union extracts outweigh costs of double marginalization, the firm will decide to stay disintegrated.

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Frank and Henderson (1992) focused on vertical coordination – a more comprehensive concept than vertical integration. It includes market, contractual, and ownership coordination. 13 In his basic model, Zhao (2001) assumed the successive monopoly with a non-unionized upstream monopolist and a unionized downstream monopolist, where the downstream monopolist and the labor union bargain over the wage rate with the same bargaining power.

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Even though there is a body of theoretical studies dealing with a relationship between unionization and an integration motive, whether this argument is valid empirically remains unexplored in the literature. To our knowledge, this paper is the first attempt to empirically examine unionization as a determinant of vertical integration.

3. Empirical Methodology 3.1 Globalization Measurements Having discussed a relationship between globalization and integration incentives in Section 2, we turn our attention to developing the measurements of an industry’s exposure to international markets, which can be employed in the empirical studies. An index of globalization should satisfy two criteria: First, it must be measurable. Second, it must well capture the concept of globalization McLaren (2000) and Wes (2000) examined, under which the effects of trade openness on a motive for merger pertain to two components of trade: (1) the market thickness effect that boosts trade in intermediate input; and (2) the pro-competitive effect associated with a surge in final output trade. We consider three alternative measures of globalization: import tariff rates ( TARIFF ), import intensity ( IMPORT ), and trade intensity ( TRADE ) – measured by the ratio of import duties to import values, the ratio of import values to value added and the ratio of trade values to value added, respectively. These indices are measurable and capture trade in both intermediate input and final output. The import tariff rates are perhaps the simplest measure of globalization. However, it fails to capture non-tariff barriers like anti-dumping actions, voluntary export restraints and standards, which also account for the degree to which a market is liberalized. The second index measures an industry’s access to foreign supplies and thus takes into consideration both tariff and nontariff barriers. Yet it does not reflect its access to foreign buyers. To tackle this problem, we employ the trade intensity which reveals the extent to which an industry pertains to foreign buyers and suppliers. [Insert Table 1 here] To obtain these indices of globalization, we retrieve data of import duties, exports values and general imports values at the six-digit NAICS manufacturing industries from

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the U.S. International Trade Statistics (USITS) for the period 2002-2006. 14 Table 1 reports the indices of globalization averaged at the three-digit NAICS level. It reveals that tariffs imposed on manufacturing imports are relatively low – ranging from 0.4 percent on beverage and tobacco products to approximately 3 percent on textile mills. However, they are somewhat diverse in terms of exposure to international markets measured by the import and trade intensity. This may imply that non-tariff barriers matter for measuring the degree of globalization.

3.2 The Empirical Model The empirical analysis is carried out using combined six-digit NAICS manufacturing industries data obtained primarily from the U.S. Census Bureau Annual Survey of Manufactures (ASM) for the period 2002-2006, unless otherwise stated. The sample includes firms operating in the manufacturing sector and produces five years of data (2002-2006) for 234 six-digit NAICS manufacturing industries. The firms cover 19 threedigit NAICS manufacturing industries. The estimation equation takes the following form:15 VI it = b0 + b1GLOBALIZATION it + b2 CR 4 it + b3 NFIRM it + b4 PHYSICALK it + b5 HUMANK it + b6TECH it + b7 SDit + b8 SIZEit + b9WAGEit + ε it ,

(1)

where ε it is the stochastic error term, the subscript i denotes firm i = 1,...,234 , and the subscript t refers to time period t = 2002,...,2006 . 16 Vertical integration ( VI ) is measured by the ratio of value added to total sales, as conventionally utilized in the literature (Adelman, 1955; Gort, 1962; Tucker and Wilder, 1977; and Levy, 1985, among many others). Even though this merger index is straightforwardly measurable, it is subject to measurement bias since: (1) the ratio is susceptible to other factors such as 14

The exports and imports values are based on Free Alongside Ship (F.A.S) and Cost, Insurance and Freight (C.I.F) values, respectively. The average import tariffs are measured by the ratio of import duties to CIF imports values. 15 We also include VI it −1 in the right hand side of Equation (1) to allow for partial adjustment of VI it as suggested by Maddala (1977, pp. 371-373). We find that our main results are qualitatively unchanged and therefore the results reported in this paper are based on the baseline specifications as in Equation (1). 16 The functional forms for estimating the pattern of vertical integration are varied in the existing studies, such as the linear form (Masten et al., 1989), the log-odds or logit form (MacDonald, 1985) and the BoxCox form (Levy, 1985). Our main results are qualitatively unchanged with respect to these functional forms chosen. The estimates reported later are based on the estimation results of the linear form.

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profitability; and (2) it accounts for backward and forward integration asymmetrically.17 However, this measurement bias tends to be limited since our sample is concerned primarily with manufacturing industries (Levy, 1985). Several studies proposed alternative measures of vertical integration. For instance, Maddigan (1981) and Caves and Bradburd (1988) developed a more complicated measure of vertical integration utilizing the Input-Output (IO) table. Even though this IObased measure outperforms the traditional measure and deserves further exploration, it leaves out the magnitudes of the firm’s operations and substantially data-intensive (Shelanski and Klein, 1995). 18 Davies and Morris (1995), in contrast, put forward a vertical integration measure based on the magnitude of intra-firm flows of output across industries, relative to external sales. This measure, though promising, obliges the data of intra-plant and/or intra-industry flows within the firm and is therefore unattainable when using Census-type data. [Insert Table 2 here] Table 2 portrays the definition of explanatory variables and their expected signs. These variables enter the econometric specification based on the vertical integration determinants discussed in Section 2. GLOBALIZAT ION is a proxy for an industry’s degree of international openness, including import tariffs ( TARIFF ), the ratio of C.I.F. import values to value added ( IMPORT ), and the ratio of F.A.S. export and C.I.F. import values to value added ( TRADE ). As discussed in the previous sub-section, these variables are obtained by using trade data retrieved from USITS. Two proxies for measuring fewness of firms are employed: the four-firm concentration ratio ( CR 4 ) and the number of firms ( NFIRM ). The former is a conventional proxy of small numbers bargaining, measured at the six-digit NAICS manufacturing industry level and using the 2002 Economic Census: Manufacturing. The latter, in contrast, enters the econometric specification for the reason that we are dealing with industry-level data. According to the transactions cost approach to vertical 17

This drawback is attributed to the fact that value added is usually larger at the primary production level. That is, holding the ratio constant, those firms nearer to primary production tends to be less integrated. 18 Fan and Lang (2000) employed commodity flow data from IO tables to capture inter-industry and intersegment vertical relatedness. They demonstrated that firms enhance their vertical relatedness and complementarities over time, and these results are strikingly robust with respect to accounting changes in segment definition, different weighting methods, and different IO data employed.

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integration, ones would expect that an industry with a larger number of active firms should be characterized by a less drastic lock-in problem and thus is likely to have a lower degree of vertical integration. We utilize three measures of asset specificity, including the physical capital intensity ( PHYSICALK ), human capital intensity ( HUMANK ), and industry-specific technology level ( TECH ). The first two variables are employed to accounts for the influence of transaction-specific investments in physical and human capital on the pattern of vertical integration (Masten et al., 1989). PHYSICALK is the ratio of capital assets (e.g. buildings, machinery and equipment) scaled by the number of labor whereas HUMANK is the ratio of non-production workers to total workers. Nonproduction workers (e.g. executives, accountants, engineers, product designer, and quality inspectors) as a proxy of skilled workers are commonly known in the literature (Berman et al., 1994 and Feenstra and Hanson, 1996, among others) since they pertain to skill-specific investments like work experience, on-the-job training and schooling. TECH is measured by the ratio of high-technology capital (e.g. computers and data-

processing equipment) to the total value assets as in Amiti and Wei (2009) and Chongvilaivan et al. (2009).19 SD is the standard deviation of value added, aiming to capture the effect of

demand variability on the pattern of vertical integration. This explanatory variable can be obtained directly from the ASM dataset. Of the measure of internal management cost, SIZE is the average firm size and calculated by the ratio of total industry sales to total

number of firms. Last but not least, the average wage rate, WAGE , is employed as a proxy of unionization and measured by the ratio of payroll to the total number of workers. The rationale is rather clear-cut: An industry with a strong labor union tends to pay relatively high wage.20

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TECH is also meant to be a substitute for another proxy of asset specificity – the ratio of research and development (R&D) expenditure to total sales since our dataset does not have enough information of R&D expenditure. In fact, these two proxies are likely to be highly correlated since the R&D activities are usually high-technology capital-intensive. 20 In fact, inter-industry differences in labor productivity and technological changes may also account for variation of wage rates across industries and across times. However, these factors are unlikely to affect our main results since our sample pertains merely to the manufacturing industries, with the relatively short time horizon.

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Tables A1-A2 in Appendix provide a summary of statistics and their correlation matrix, respectively.

4. Empirical Results 4.1 Main Results The econometric model we presented in the previous section is estimated using three estimation methods: the Ordinary Least Squares (OLS), Generalized Least Squares (GLS) Random Effects and Fixed Effects estimations. We attempt to take into consideration the following econometric problems. First, owing to the industry size variation in our sample, the stochastic error term ε it tends to be heteroskedastic, thereby conveying a biased variance estimator σ 2 . To tackle this problem, we employ the heteroskedasticity-robust standard error estimators. Furthermore, we account for industry- and time-specific heterogeneity, using a two-way error component model for the disturbances, with

ε it = µ i + λt + u it ,

(2)

where µ i is the unobservable industry-specific effect capturing persistent industrial differences; λt denotes the unobservable time-specific effect such as overall technological progress affecting the industries; and u it is the remaining stochastic disturbance term. In so doing, we make use of the Fixed Effects estimations21 together with the Breusch-Pagan Lagrangian Multiplier test on the Random Effects estimations under the null that there are no random effects (Baltagi, 2005, pp. 59-61).22 [Insert Tables 3-4 here] Tables 3 and 4 portray our empirical estimates corresponding to the OLS, Random Effects and Fixed Effects estimations, respectively. In each table, we employ three econometric models. The first column (Model 1) presents the model with TARIFF as a proxy of globalization. The second column (Model 2) reports the results 21

It should be noted that the Random Effects estimation reported below employs the Generalized Least Squares (GLS) estimation with the Swamy-Arora variance components estimators. We also perform a robustness check using the standard GLS and Maximum Likelihood (ML) estimations. Our results are qualitatively unchanged and are available upon requests. 22 The Breusch-Pagan Lagrangian Multiplier statistic is asymptotically distributed as a chi-squares distribution.

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when IMPORT enters the econometric specification (1). Likewise, the last column (Model 3) measures the degree of globalization by TRADE . We find that our OLS and Random Effects estimates produce qualitatively identical results across all estimation methods and models. The Breusch-Pagan Lagrangian Multiplier tests (reported in Table 4) unveil that the null of no Random Effects cannot be rejected across all specifications and hence are in favor of the Fixed Effects models. [Insert Table 5 here] Table 5 divulges the Fixed Effects results. Nevertheless, the variables of CR 4 and NFIRM , retrieved from the 2002 Economic Census: Manufacturing, are cross-sectional and therefore must be dropped in the Fixed Effects estimations. Our data limitations pose a serious econometric problem of the omitted-variable bias which potentially entails biased estimates.23 This is perhaps the reason why in Table 5 we have statistical significance of TARIFF in Model 1 and SIZE in Models 1-3 differed from that shown in Tables 3 and 4.24 To tackle this problem, we make use of the three-digit industry and time dummies to allow for industry- and time-specific effects. [Insert Table 6 here] Table 6 reports the OLS estimates with the industry and time dummies. The estimates are strikingly consistent with those revealed in Tables 3 and 4.and can be recapitulated as follows. First, an industry’s exposure to international markets exacerbates a motive for vertical integration as suggested by the theories discussed in Section 2. That is, expanded international openness trimming tariff rates and augmenting the intensity of exporting and importing activities tends to alleviate the hold-up problem through enhanced market thickness (McLaren, 2000) and the pro-competitive effect (Wes, 2000), thereby reducing merger incentives. However, we find only weak evidence that globalization has a significant effect on the pattern of vertical integration since neither of them is statistically significant. 23

As portrayed in Tables 3 and 4, CR 4 and NFIRM are both statistically significant across all specifications and estimations. 24 The indices of globalization and firm size are not statistically significant in all estimations and specifications except for TARIFF and SIZE in Table 5. As discussed above, this is attributed potentially to our data limitations that make CR 4 and NFIRM exempted from the specifications.

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The usual caveats apply, because data limitations hinder us to explicitly account for changes in market thickness – the key catalyst, as posited by McLaren (2000), through which globalization intimidates motives for vertical integration. Additionally, we cannot control for errors of globalization measurements and model misspecifications that may lead to biased estimates. These issues of econometric estimations are by all means indispensable and deserve closer examination where possible. Second, transactions costs have a prominent role to play in explaining the pattern of vertical integration in the U.S. manufacturing sector. Among the explanatory variables capturing these hypotheses are the four-firm concentration ratio ( CR 4 ), the number of firms ( NFIRM ), the physical capital intensity ( PHYSICALK ), the human capital intensity ( HUMANK ) and the industry-specific technology levels ( TECH ). Their coefficients, except for that of TECH , are statistically significant. That of CR 4 however runs counter to the small number hypothesis discussed earlier. 25 This would suggest that in the U.S. manufacturing sector industry concentration does not elicit vertical integration. In this sense, NFIRM as a proxy of firm fewness is preferred to CR 4 . The smaller number of firms aggravates opportunism, magnifies the lock-in effects and ultimately spurs an upbeat motive for vertical integration. The dominant role of firm fewness our results reveal goes along with MacDonald (1985), Lieberman (1991), and Caves and Bradburd (1988). Third, consistent with Masten et al. (1989), our estimates indicate that the typology of capital employed matters. Physical capital and human capital have contrasting effects on the decision to vertically integrate production. In particular, we find the results contradictory with the theoretical predictions that investments in physical capital (e.g. new plants and machinery) deter a merger decision. Investments in human capital (e.g. education and training) in contrast have to do closely asset specificity and thus lure vertical integration. The importance of capital typology suggests that firms may resort to ‘quasi-integration’ – ownership of relevant assets without integrating production (Monteverde and Teece, 1982) – in order to make the hold-up problem dilapidated in the case of transaction-specific physical assets. 25

The coefficient of CR 4 is negative and statistically significant at 5 percent across all specifications and estimations. An attempt to drop CR 4 from the estimations does not affect the results qualitatively.

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Last but not least, the coefficient of the standard deviation of value added ( SD ) does not appear with the expected sign and is not statistically significant, confirming the findings by Levy (1985). Similarly, neither are the coefficients of the firm size ( SIZE ) and wage rates ( WAGE ) statistically significant. These results imply that demand fluctuations, diseconomies of scale and unionization may not be an imperative factor determining the pattern of vertical integration in the U.S. manufacturing industries.

4.2 A Robustness Check Next, we attempt to carry out a robustness check of our main findings. Following Acemoglu et al. (2004),26 we construct the cross-section estimations using our pooled sixdigit NAICS manufacturing industry data averaged over the years 2002-2006. Pirotte (1999) and Baltagi (2005) demonstrated that in the static panel models in which the time dimension is short, and the cross-section dimension is large – as in our case – the crosssectional estimates approximate the long-run effects, whereas the Fixed Effect estimates as presented in the previous sub-section are concerned with their short-run counterparts. Therefore, the cross-sectional estimates of explanatory variables presented in this subsection should be interpreted as their long-run effects of vertical integration motives. [Insert Table 7 here] Table 7 reveals the cross-sectional estimates based on the OLS estimations of Equation (1) with the heteroskedasticity-robust standard error estimators. We find the following interesting results. Our main findings that globalization has no significant impacts on vertical integration remain remarkably robust since none of the three globalization indices is statistically different from zero. Those variables pertinent to transactions costs remain the crucial determinants of vertical integration since their coefficients are statistically significant with unchanged signs except for those of PHYSICALK which still exhibit a negative sign but are statistically insignificant in Model 2 and Model 3. This result may imply that in long run capital typology matters to vertical integration in a different way –

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Acemoglu et al. (2004) empirically investigated a relationship between R&D intensity and vertical integration using the UK industry data averaged over the years 1996-2001.

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merely human capital has positive, significant impacts on vertical integration in long run – and therefore sheds further light on the findings by Masten et al. (1989). From a comparison of our main results, demand variability, internal costs of management and unionization seem to play a pivotal role in explaining vertical integration in long run. In particular, the coefficients of SD is positive, statistically significant, and hence consistent with Carlton’s (1979) predictions that higher volatility of final output demands is associated with stronger merger incentives. In contrast with Frank and Henderson (1992), the positive, statistically significant coefficients of SIZE indicate that an industry with large scales of activities tends to be more integrated in long run in the U.S. manufacturing sector even though they exhibit a negative, though insignificant, relationship in the longitudinal estimations. This may be attributable to the fact that we cannot explicitly control shifts in internal managerial technology and costs which are likely to be the case especially in long run. Lastly, the cross-sectional estimates of WAGE are negative and statistically significant across all specifications and thus substantiate Zhao’s (2001) theoretical proposition that a firm is less likely to integrate when facing a stronger labor union (higher wage rates). Our empirical exercise further reveals that unionization has a crucial role in vertical integration only in long run. The weak evidence of McLaren’s (2000) argument that globalization dissipates benefits from vertical integration is perhaps attributable to the potential endogeneity bias problem. If the globalization variables are endogenously determined by other unobservable exogenous factors like political factors, sunk costs in an industry and foreign retaliation (Marks and McArthur, 1993 and Liu, 2002), our estimations, as is well known, produce biased and inconsistent estimates. To tackle this problem, we employ the standard approach using the lagged globalization variables, and thus Equation (1) must be modified as follows:27 VI it = b0 + b1GLOBALIZATION it −1 + b2 CR 4 it + b3 NFIRM it + b4 PHYSICALK it + b5 HUMANK it + b6TECH it + b7 SDit + b8 SIZEit + b9WAGEit + ε it

(3)

27

The other approach to accounting for the endogeneity bias problem is to employ the Instrumental Variable (IV) regression. It requires data of valid IVs – in particular, ones that are exogenous and strongly correlated with the globalization variables. This approach, however, is inappropriate in our case since our dataset does not provide valid IVs.

16

[Insert Table 8 here] Table 8 portrays the OLS estimates with the industry- and time-specific dummies and the heteroskedasticity-robust variance estimators based on the econometric specification (3).28 We find that our main results presented in Section 4.1 are robust and therefore potentially refrain from the endogeneity bias problem. In particular, the results in the estimations with the lagged globalization variables are qualitatively identical to those in the baseline econometric specifications except for the fact that WAGE becomes negative and statistically significant, though only at 10 percent, in Models 2 and 3. The lagged globalization variables are statistically insignificant across Models 1-3, confirming our main findings that international openness, represented by TARIFFt −1 , IMPORTt −1 and TRADEt −1 , does not explain the degrees of vertical integration in the U.S. manufacturing sector. In contrast, transactions costs

still

exhibit

predominant

roles

in

determining

a

merger

decision

since CR 4 , NFIRM , PHYSICALK and HUMANK are statistically significant across all specifications. In addition, our estimates indicate that the effects of physical and human capital on motives for vertical integration work in incongruent ways and thus conform to transaction-specific assets typology pushed forward by Masten et al. (1989).

5. Conclusion The present paper empirically investigates the impacts of globalization on an incentive for vertical integration using the six-digit NAICS U.S. manufacturing industries from 2002 to 2006. Building upon the traditional determinants of vertical integration, an empirical model is developed incorporating three proxies of globalization: import tariff rates, import intensity, and trade intensity. Our empirical results indicate that globalization debilitates vertical integration and therefore shores up theoretical predictions posited by McLaren (2000) and Wes (2000); nevertheless, that the impacts of globalization on the merger patterns are not significant – neither are those of demand fluctuations, scale diseconomies and unionization. Consistent with an overwhelming number of the existing studies (Levy, 1985; MacDonald, 1985; Caves and Bradburd, 28

We also obtain the estimates based on the OLS, Random Effects and Fixed Effects estimations. They are qualitatively unchanged in comparison with those presented in Section 4.1.

17

1988; and Lieberman, 1991, among many others), our findings point to an influential role of the transactions cost approach to vertical integration, from which several traditional determinants of vertical integration like fewness of firms and transaction-specific assets are derived. A breakdown of transaction-specific investments into two types – physical capital and human capital investments – produces the supplementary results that typology of transaction-specific assets matters in that their effects on vertical integration work in completely reversed ways. The former dissipates a merger motive perhaps through quasiintegration while the lock-in effect triggered by the latter strengthens a vertical integration decision. Our robustness check substantiates the main findings and further reveals that the roles of demand fluctuations, internal costs of management and unionization come into play only in long run. Although a number of issues such as measurement errors, causality problems and the modest goodness of fit statistics are by all means indispensable, they remain unanswered in our analyses, and therefore our findings should be contemplated as tentative. Indeed tackling these issues requires extensive discussions and analyses and hence may be far beyond the scope of this paper. We leave them for the possible avenues of future research.

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Hubbard, T.N. (2001), “Contractual Form and Market Thickness in Trucking”, RAND Journal of Economics, Vol. 32, No. 2, pp. 830-851. Hummels, D., J. Ishii, and K.-M. Yi (2001), “The Nature and Growth of Vertical Specialization in World Trade”, Journal of International Economics, Vol. 54, No. 1, pp. 75-96. Klein, B., R.G. Crawford, and A.A. Alchian (1978), “Vertical Integration, Appropriate Rents, and the Competitive Contracting Process”, Journal of Law and Economics, Vol. 21, No. 2, pp. 297-326. Levy, D.T. (1985), “The Transactions Cost Approach to Vertical Integration: An Empirical Examination”, Review of Economics and Statistics, Vol. 67, No. 3, pp. 438-445. Lieberman, M.B. (1991), “Determinants of Vertical Integration: An Empirical Test”, Journal of Industrial Economics, Vol. 39, No. 5, pp. 451-466. Lin, J. (1988), “Oligopoly and Vertical Integration: Note,” American Economic Review, Vol. 78, No. 1, pp. 251-254. Liu, M-C (2002), “Determinants of Taiwan’s Trade Liberalization: The Case of A Newly Industrialized Country”, World Development, Vol. 30, No. 6, pp. 975-989. MacDonald, J.M. (1985), “Market Exchange or Vertical Integration – An Empirical Analysis”, Review of Economics and Statistics, Vol. 67, No. 2, pp. 327-331. Maddala, G.S. (1977), Econometrics, New York: McGraw-Hill Book Company. Maddigan, R.J. (1981), “The Measurement of Vertical Integration,” Review of Economics and Statistics, Vol. 63, No. 3, pp. 328-335. Marks, S. and J. McArthur (1993), “Empirical Analyses and the Determinants of Protection: A Survey and Some New Results” in J.S. Odell and T.D. Willet (eds.), International Trade Policies: Gain from Exchange between Economics and Political Science, Ann Arbor: University of Michigan Press. Masten, S.E., J.W. Meehan, and E.S. Snyder (1989), “Vertical Integration in the U.S. Auto Industry: A Note on the Influence of Transaction Specific Assets”, Journal of Economic Behavior and Organization, Vol. 12, No. 2, pp. 265-273. McLaren, J. (2000), “‘Globalization’ and Vertical Structure”, American Economic Review, Vol. 90, No. 5, pp. 1239-1254. _____ (2003), “Trade and Market Thickness: Effects on Organization”, Journal of European Economic Association, Vol. 1, No. 2-3, pp. 328-336. 20

Monteverde, K. and D.J. Teece (1982), “Appropriable Rents and Quasi-Vertical Integration”, Journal of Law and Economics, Vol. 25, No. 2, pp. 321-328. Penrose, E. (1959), The Theory of the Growth of the Firm, Oxford: Basil Blackwell. Pirrong, S.C. (1993), “Contracting Practices in Bulk Shipping Markets: A Transaction Cost Explanation”, Journal of Law and Economics, Vol. 36, No. 2, pp. 937-976. Pirotte, A. (1999), “Convergence of the Static Estimation toward the Long-run Effects of Dynamic Panel Data Models”, Economics Letters, Vol. 63, No. 2, pp. 151-158. Shelanski, H.A. and P.G. Klein (1995), “Empirical Research in Transaction Cost Economics: A Review and Assessment”, Journal of Law, Economics and Organization, Vol. 11, No. 2, pp. 335-361. Tucker, I.B. and R.P. Wilder (1977), “Trends in Vertical Integration in the U.S. Manufacturing Sector,” Journal of Industrial Economics, Vol. 26, No. 1, pp. 8194. Waterson, M. (1982), “Vertical Integration, Variable Proportions and Oligopoly,” Economic Journal, Vol. 92, No. 365, pp. 129-144. Weiss, A. (1992), “The Role of Firm-Specific Capital in Vertical Mergers”, Journal of Law and Economics, Vol. 35, No. 1, pp. 71-88. Wes, M. (2000), “International Trade, Bargaining and Efficiency: The Holdup Problem,” Scandinavian Journal of Economics, Vol. 102, No. 1, pp. 151-162. Williamson, O.E. (1970), Corporate Control and Business Behavior, Englewood Cliffs, N.J.: Prentice-Hall. _____ (1975), Markets and Hierarchies: Analysis and Antitrust Implications, New York: Free Press. _____ (1986), “Vertical Integration and Related Variations on A Transaction-Cost Theme”, in J.E. Stiglitz and G.F. Mathewson (eds.), New Developments in the Analysis of Market Structure, Cambridge: MIT Press, pp. 149-174. Zhao,

L. (2001), “Unionization, Vertical Markets, and the Outsourcing of Multinationals,” Journal of International Economics, Vol. 55, No. 1, pp. 187-202.

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Obs. 1166 1023 1015 1010 1165 1170 1165 1170 1113 932 1170 1170

Mean .5201 .0112 2.2770 3.8151 45.809 681.96 10.870 .3154 .0759 2.4646 116831.2 45.492

S.D. .1266 .0151 7.4010 11.962 21.776 1756.9 13.564 .1336 .0651 2.2908 500122.4 11.789

TARIFF IMPORT TRADE CR 4 NFIRM PHYSICALK HUMANK TECH SD SIZE WAGE

1.00 -.083 -.095 -.265 .176 -.059 .014 .030 .014 -.053 -.007

TARIFF

1.00 .949 .033 .029 -.047 -.043 -.021 .151 -.061 -.046

IMPORT

1.00 .033 .035 -.049 -.058 -.032 .152 -.063 -.057

TRADE

1.00 -.555 .008 -.008 .004 .002 .270 .004 1.00 .037 -.005 -.038 .057 -.125 -.001

NFIRM

Minimum .1385 0 6 × 10 −6 .0002 2.2 7 .7853 .0861 .0019 1 60.759 21

CR 4

Table A2: Correlation Matrix of Independent Variables.

VI TARIFF IMPORT TRADE CR 4 NFIRM PHYSICALK HUMANK TECH SD SIZE WAGE

Variable

Table A1: Summary of Statistics.

1.00 .094 -.246 -.101 .150 .567

PHYSICALK

1.25 × 10 7 98

Maximum .8542 .1142 108.72 147.49 99.2 22148 183 .7603 .6586 28

Appendix

1.00 .479 .030 .013 .619

HUMANK

1.00 .106 -.015 .056

TECH

1.00 -.575 -.116

SD

1.00 .192

SIZE

1.00

WAGE

22

Table 1: Indices of Globalization in US Manufacturing Industries, 2002-2006. 3-Digit NAICS 311: Food Manufacturing 312: Beverage and Tobacco Products 313: Textile Mills 314: Textile Product Mills 316: Leather and Allied Products 321: Wood Products 322: Paper 324: Petroleum and Coal Products 325: Chemical Products 326: Plastics and Rubber Products 327: Non-metallic Mineral Products 331: Primary Metal 332: Fabricated Metal 333: Machinery 334: Computers and Electronics 335: Electrical Equipment, Appliance and Components 336: Transportation Equipment 337: Furniture and Related Products 339: Miscellaneous Manufacturing All Industries

No. of Firms 4881 1553 1060 2739 250 5909 720 1131 6473 3653 10765 540 29382 16374 14472 3626

Tariff (%) 1.87 0.40 3.05 2.78 0.5 0.61 0.03 0.11 0.83 1.41 1.6 0.84 1.73 1.17 0.58 1.12

9196 20109 26746 159579

0.72 0.71 1.58 1.14

Import Intensity

Export Intensity

0.3746 2.6960 1.2389 0.5749 0.5735 0.3866 0.4933 15.770 2.8797 0.4971 0.6562 1.7080 1.0579 1.4807 4.5068 1.2926

0.7181 3.1833 2.5580 0.7746 0.9541 0.4446 0.9427 20.749 5.1876 1.1949 0.9175 2.4944 1.7406 2.8426 8.2235 2.0985

4.0797 1.8975 1.4103 2.2934

6.6586 2.1127 2.1750 3.4721

Table 2: Determinants of Vertical Integration: Definition and Expected Signs. Variable Globalization:

Definition

Import Tariffs Ratio of C.I.F. Import Values to Value Added Ratio of F.A.S. Exports and C.I.F. Import Values to Value Added Transactions Costs: Four-firm Concentration Ratio CR 4 Number of Firms NFIRM PHYSICALK Physical Capital Intensity Human Capital Intensity HUMANK Industry-specific Technology Level TECH Demand Variability: Standard Deviation of Value Added SD Internal Costs of Management: Firm Size SIZE Unionization: Average Wage Rate WAGE

TARIFF IMPORT TRADE

Expected Sign

+ − − + − + + + + − −

23

Table 3: OLS Estimation with the Heteroskedasticity-Robust Variance Estimators. Variable TARIFF IMPORT TRADE CR 4 NFIRM PHYSICALK HUMANK TECH SD SIZE WAGE Constant R2 F statistic No. of Observations

Model 1 .0432 (.2881) -------.0006 (.0003) ** -.0160 (.0081) ** -.0014 (.0004) *** .2613 (.0618) *** .0392 (.0750) -.0028 (.0022) -.0048 (.0072) -.0008 (.0007) .5315 (.0252) *** .1053 12.37 *** 744

Model 2 ----.0008 (.0007) ----.0006 (.0003) ** -.0154 (.0080) * -.0014 (.0004) *** .2601 (.0622) *** .0440 (.0790) -.0025 (.0023) -.0050 (.0072) -.0008 (.0007) .5345 (.0254) *** .1074 12.40 *** 737

Model 3 -------.0002 (.0005) -.0006 (.0003) ** -.0158 (.0081) ** -.0014 (.0004) *** .2655 (.0622) *** .0430 (.0788) -.0027 (.0023) -.0046 (.0072) -.0009 (.0007) .5341 (.0255) *** .1078 12.38 *** 732

Note: Robust standard error in parentheses. * Statistically significant at 10%; ** statistically significant at 5%; *** statistically significant at 1%.

Table 4: Random Effects Estimation with the Swamy-Arora Variance Components Estimators. Variable TARIFF IMPORT TRADE CR 4 NFIRM PHYSICALK HUMANK TECH SD SIZE WAGE Constant Breusch-Pagan Test

R2 Wald χ 2 No. of Observations

Model 1 .0516 (.2939) -------.0006 (.0003) ** -.0161 (.0083) * -.0014 (.0004) *** .2610 (.0618) *** .0409 (.0751) -.0030 (.0022) -.0052 (.0073) -.0008 (.0007) .5310 (.0254) *** .05 .1053 110.42 *** 744

Model 2 ----.0008 (.0007) ----.0006 (.0003) ** -.0155 (.0082) * -.0014 (.0004) *** .2600 (.0621) *** .0459 (.0790) -.0025 (.0023) -.0055 (.0072) -.0008 (.0007) .5341 (.0256) *** .11 .1074 110.67 *** 737

Model 3 -------.0002 (.0005) -.0006 (.0003) ** -.0159 (.0082) * -.0014 (.0004) *** .2651 (.0621) *** .0449 (.0790) -.0027 (.0023) -.0051 (.0072) -.0008 (.0007) .5336 (.0257) *** .13 .1078 110.40 *** 732

Note: Robust standard error in parentheses. The Breusch-Pagan Lagrangian Multiplier test is distributed as chisquared distribution with degrees of freedom equal to unity under the null hypothesis that there are no random effects. * Statistically significant at 10%; ** statistically significant at 5%; *** statistically significant at 1%.

24

Table 5: Fixed Effects Estimation with the Heteroskedasticity-Robust Variance Estimators. Variable TARIFF IMPORT TRADE CR 4 NFIRM PHYSICALK HUMANK TECH SD SIZE WAGE Constant R2 F statistic No. of Observations

Model 1 10.91 (3.707) *** -------------.0010 (.0005) ** .2803 (.0693) *** .0652 (.0919) -.0033 (.0027) -.0195 (.0110) * -.0007 (.0008) .3739 (.0489) *** .0138 6.44 *** 744

Model 2 ----.0013 (.0009) ----------.0011 (.0005) ** .2681 (.0700) *** .0897 (.0971) -.0027 (.0029) -.0194 (.0106) * -.0008 (.0008) .4958 (.0263) *** .1007 5.73 *** 737

Model 3 -------.0005 (.0006) -------.0011 (.0005) ** .2714 (.0701) *** .0867 (.0971) -.0029 (.0029) -.0194 (.0106) * -.0008 (.0008) .4927 (.0264) *** .0999 5.68 *** 732

Note: Robust standard error in parentheses. * Statistically significant at 10%; ** statistically significant at 5%; *** statistically significant at 1%.

Table 6: OLS Estimation with the industry- and time-specific dummies and the Heteroskedasticity-Robust Variance Estimators. Variable TARIFF IMPORT TRADE CR 4 NFIRM PHYSICALK HUMANK TECH SD SIZE WAGE Constant R2 F statistic No. of Observations

Model 1 .1076 (.2825) -------.0007 (.0003) ** -.0180 (.0083) ** -.0014 (.0004) *** .2708 (.0622) *** .0443 (.0747) -.0030 (.0023) -.0048 (.0073) -.0009 (.0007) .5306 (.0300) *** .1138 7.95 *** 744

Model 2 ----.0009 (.0007) ----.0007 (.0003) ** -.0171 (.0082) ** -.0014 (.0004) *** .2696 (.0625) *** .0477 (.0785) -.0027 (.0024) -.0051 (.0072) -.0009 (.0007) .5325 (.0300) *** .1156 7.95 *** 737

Model 3 -------.0002 (.0005) -.0007 (.0003) ** -.0174 (.0082) ** -.0014 (.0004) *** .2744 (.0626) *** .0455 (.0786) -.0029 (.0023) -.0047 (.0072) -.0010 (.0007) .5316 (.0301) *** .1151 7.90 *** 732

Note: Robust standard error in parentheses. ** statistically significant at 5%; *** statistically significant at 1%.

25

Table 7: Cross-sectional OLS Estimation with the Heteroskedasticity-Robust Variance Estimators. Variable TARIFF IMPORT TRADE CR 4 NFIRM PHYSICALK HUMANK TECH SD SIZE WAGE Constant R2 F statistic No. of Observations

Model 1 -.2868 (.3209) -------.0008 (.0003) *** -.0130 (.0068) * -.0016 (.0009) * .4214 (.1224) *** -.2705 (.1843) .0020 (.0011) * .0206 (.0070) *** -.0026 (.0013) ** .5814 (.0367) *** .2032 5.12 *** 166

Model 2 ----.0006 (.0008) ----.0007 (.0003) ** -.0128 (.0069) * -.0015 (.0009) .4133 (.1235) *** -.2660 (.1844) .0019 (.0010) * .0190 (.0071) *** -.0026 (.0013) ** .5774 (.0386) *** .2032 5.68 *** 165

Model 3 -------.0002 (.0006) -.0007 (.0003) ** -.0130 (.0069) * -.0015 (.0009) .4195 (.1237) *** -.2691 (.1843) .0018 (.0010) * .0194 (.0072) *** -.0027 (.0013) ** .5794 (.0388) *** .2025 5.32 *** 164

Note: Robust standard error in parentheses. * Statistically significant at 10%; ** statistically significant at 5%; *** statistically significant at 1%.

Table 8: OLS Estimation with Lagged Globalization Variables, the industry- and timespecific dummies and the Heteroskedasticity-Robust Variance Estimators. Variable TARIFFt −1 IMPORTt −1 TRADEt −1 CR 4 NFIRM PHYSICALK HUMANK TECH SD SIZE WAGE Constant R2 F statistic No. of Observations

Model 1 -.0265 (.3265)

Model 2 ----

Model 3 ----

-------

-.0002 (.0007) ----

----.0003 (.0005)

-.0008 (.0003) *** -.0237 (.0085) *** -.0015 (.0004) *** .2768 (.0676) *** .0589 (.0735) -.0037 (.0025) .0037 (.0201) -.0013 (.0008) * .5480 (.0334) *** .1304 8.27 *** 596

-.0008 (.0003) *** -.0238 (.0085) *** -.0015 (.0004) *** .2823 (.0676) *** .0550 (.0738) -.0039 (.0025) .0039 (.0201) -.0013 (.0008) * .5487 (.0335) *** .1325 8.38 *** 592

-.0008 (.0003) *** -.0238 (.0085) *** -.0015 (.0004) *** .2742 (.0676) *** .0624 (.0742) -.0037 (.0025) .0028 (.0201) -.0012 (.0008) .5446 (.0335) *** .1305 8.34 *** 602

Note: Robust standard error in parentheses. * Statistically significant at 10%; ** statistically significant at 5%; *** statistically significant at 1%.

26