Trade Policy and Firm Boundaries∗
Laura Alfaro
Harvard Business School and NBER
Paola Conconi
Université Libre de Bruxelles (ECARES) and CEPR
Harald Fadinger
University of Vienna
Andrew F. Newman
Boston University and CEPR
June 2010

Abstract
We study how trade policy affects firms’ ownership structures. We embed an incomplete contracts model of vertical integration choices into a standard perfectly-competitive
international trade framework. Integration decisions are driven by a trade-off between thepecuniary benefits of coordinating production decisions and the managers’ private benefitsof operating in preferred ways. The price of output is a crucial determinant of this choice,since it affects the size of the pecuniary benefits: higher prices lead to more integration.
Because tariffs increase domestic product prices, this effect provides a novel theoreticalchannel through which trade policy can influence firm boundaries. We then examine theevidence, using a unique dataset to construct firm-level indexes of vertical integration fora large set of countries. In line with the predictions of our model, we obtain three main
results. First, higher tariffs lead to higher levels of vertical integration. Second, differencesin ownership structure across countries, measured by the difference in sectoral vertical integration indexes, are smaller in sectors with similar levels of protection. Finally, ownership
structures are more alike among members of regional trade agreements.
JEL classifications: D23, F13, F23.
Keywords: Firm Organization, Tariffs, Regional Trade Agreements.
∗

We wish to thank for their comments participants at the 2009 ETSG conference in Rome, the 2010 AEA
conference in Atlanta, the 2010 MWIEG meeting at Northwestern University, and seminar participants at the
Université Catholique de Louvain (CORE),University of Munich, University of Innsbruck, K.U. Leuven, and London School of Economics. We are particularly grateful to Holger Breinlich, Maria Guadalupe, Florian Mayneris,
Dalia Marin, Peter Neary, Emanuel Ornelas, Michael Pfaffermayr, Ray Riezman, Steve Redding, and Maurizio
Zanardi for their valuable suggestions. The usual disclaimer applies. Research funding from the FNRS andthe European Commission through the PEGGED project is gratefully acknowledged by Paola Conconi. We
thank Francisco Pino for excellent research assistance. Correspondence should be addressed to Paola Conconi,
ECARES, Université Libre de Bruxelles, CP 114, Avenue F. D. Roosevelt 50, 1050 Brussels, Belgium. E-mail:pconconi@ulb.ac.be.

1Introduction

Over the past two decades, the theory of the firm has stressed contractual incompleteness as
the key to understanding what a firm is and how it operates. Property rights over assets, which
define firm boundaries, and allocations of control over production decisions within and acrossthose boundaries are central elements of organizational design. They are chosen to mediate theway firm stakeholders trade off their collective goals and private interests.1 For instance, in ahighly integrated firm, in which a single decision maker has authority over multiple links in the
supply chain, major production decisions can be well-coordinated to accomplish organizational
goals such as profit. But this may come at the expense of imposing high costs on subordinates.
Less integrated enterprises, in which decision making is spread among several firm heads, may
economize on private costs, but may also leave the decisions ill-coordinated, harming profit.
Although the literature has identified a considerable number of trade-offs involving firmboundaries, it has largely left open the question of how such trade-offs are resolved in the market.
A few recent papers have addressed the issue.2 Our paper contributes to this recent literatureby examining theoretically and empirically the impact of trade policies on firms’ ownership
structures.
We consider a perfectly competitive environment, where the influence of the market on firmboundaries can be studied in its simplest form. Building on earlier work by Legros and Newman(2009) and Conconi, Legros and Newman (2009), we show that there is systematic relationshipbetween firm boundaries and the equilibrium price in the product market. In its starkest form,
the prediction is that the higher the market price, the more integrated firms will be. The reason
for this relationship is that the primary decision makers — the “managers”— have not only astake in the organizational goal (revenue, profit), but also a private, noncontractible benefit fromthe organization (doing things their way, or more precisely the way most consonant with theassets and workforce they manage).3 When different parts of the organization are not integrated,
1

The significance of firm boundaries was, of course, first pointed out by Coase (1937). The formal incomplete
contracts approach begins with Grossman and Hart (1986) and Hart and Moore (1990), which emphasize thehold-up problem. The trade-off described in this paper is the focus of Hart and Holmström (2009). In all these
papers, firm boundaries are identified with the extent of decision rights over assets and associated operations.
Hart (1995) provides a summary and discussion of earlier as well as more modern approaches.
2
Theoretical contributions include McLaren (2000) on hold-up and market thickness, Legros and Newman(2008) on control structures and the terms of trade in supplier markets, and Marin and Verdier (2008) on
delegation and product demand elasticity. Another literature has examined the question of whether goods are
sold within or across firm boundaries in the global economy (see, for example, Antras (2003), Antras and Helpman(2004), and Helpman (2006) for an overview).
3
As pointed out by Hart and Holmström (2009), private benefits may arise from various sources. For example,
employees’ human capital is often tied to particular technologies, with which they are familiar and like to work.
Also, future career prospects may depend on how well human capital fits firms’ needs, so strategic choices concerning technology will have significant private consequences. Differences in ways of doing things (e.g., engineers
and marketing departments) can make coordination difficult. In a similar vein, Van den Steen (2005) stresses
the importance to organization design of conflicting private benefits that stem from different corporate cultures
or managerial visions.

1managers make decisions independently, taking more account of their private benefits and lessof the organization’s, and this results in poor coordination and low output. Integration puts
decisions in the hands of a single headquarters with strong incentives to coordinate, so as tomaximize benefits to the organization. Non-integration is thus associated with high private
benefits and low coordination, integration with high coordination and high private costs.
Market price enters the trade-off because it directly affects the organizational objective —profit — but has relatively little impact on private costs. When enterprise profitability (market
price) is high, this trade-off is made in favor of integration, since the organizational goal isrelatively more valuable than private goals. At low prices, the trade-off goes the other way, infavor of non-integration. Thus, anything that affects equilibrium prices will have an indirect
effect on the degree of integration.
Of course, in an industry in which several enterprises face this organizational design trade-off,market quantity and price will depend on their choices of ownership structure: if integration ismore productive and all enterprises integrate, there may be too much produced to be taken up
by existing demand; price would then need to fall, encouraging some firms to switch to nonintegration, thus reducing the quantity supplied. Product-market clearing will therefore jointly
determine quantity, price, and ownership structures.
Trade policy provides an ideal proving ground for a model that links price levels to organizational design, since it generates a plausibly exogenous source of equilibrium price variation: thedegree of trade protection will obviously affect equilibrium prices; however, as we argue below,
it is likely to be independent of firms’ boundary choices. The first-order effect of the impositionof a tariff is to raise the price of the imported good. Thus, all else equal, the higher the tariff,the more integrated firms in the industry should be.4
By the same token, if two countries’ tariffs for the same industry are close, equilibrium prices
and ownership structures should be similar. Thus, the theory predicts convergence in ownership
structure between countries with similar levels of protection. Moreover, if two countries are
members of a regional trade agreement, all else equal, enterprises in those two economies shouldhave similar organizational structures. This effect should be more pronounced for customs
unions, since the elimination of internal trade barriers and the adoption of common externaltariffs should lead to price and organizational convergence between member countries.
Absence of an international dataset sufficiently comprehensive to support studies of firmorganization across a wide range of countries has limited empirical analysis on the effects oftrade policy on organizational choices. We overcome this limitation by using a new dataset fromDun and Bradstreet (D&B) that contains both listed and unlisted plant-level observations for alarge set of countries and territories in 2004. For each plant, the dataset includes information
4
This can be interpreted as a statement about intensive margins — more parts of the supply chain should bepart of a single firm as the price for the final good increases, or about extensive margins — a greater fraction offirms are integrated at higher prices, assuming some heterogeneity among them.

2

about its primary and secondary activities, as well as about ownership (e.g., its domestic or global
parent). By combining this information with U.S. input-output tables, we are able to constructfirm-level vertical integration indexes.5 Despite its limitations, this methodology enables us toanalyze a large set of countries and industries, and thus to overcome an important constraint in
the literature (we also do not have to worry about the value of intra-firm activities being affectedby transfer pricing).
Our empirical analysis relies on exogenous price variation induced by trade policy. In particular, we exploit the cross-country and cross-sectoral variation in most-favored-nation (MFN)
tariffs. We obtain data on applied MFN tariffs at the 4 digit SIC level for all WTO members forwhich this information is available. MFN tariffs negotiated at the GATT/WTO level over long
periods of time are less “political” than administrative measures for the regulation of imports(e.g., anti-dumping and countervailing duties).6 Firms’ ownership structures are unlikely to havea systematic impact on trade policy in general, and on MFN tariffs in particular. Our empiricalanalysis controls, however, for firm size and industry concentration, which could be correlatedwith both firms’ vertical integration decisions and the level of tariffs.7 We also collect systematic
information on all regional trade agreements (RTAs) in force in 2004. Free trade areas andcustoms unions are regulated by GATT/WTO rules and their establishment is unlikely to be
driven by firm’s ownership decisions.8
We examine first the relation between tariffs and organizational structure. In line with thepredictions of our theoretical model, we find that higher tariffs lead to more vertical integration
at the firm level. The impact of tariffs on vertical integration is sizable. In our preferred
estimation, a 100 percent tariff increase leads to a 2.15 percent increase in the vertical integrationindex, which implies that increasing tariffs from 1 percent to their mean level of around 5
percent increases vertical integration by more than 8 percent. Our results are robust to different
specifications and subsamples.
The theoretical framework also suggests that trade policy should, through its effect on prices,
affect the degree of organizational convergence across countries. That is, convergence in corporate organization — the tendency of industries to be characterized by the same ownership structure across countries — may result not only from global cultural transmission or technologicaldiffusion, but also from standard neoclassical market forces, namely, the law of one price (see5

We build on the methodology of Acemoglu, Johnson and Mitton (2009), who use the 1992 U.S. input-output
tables to produce an “ingredients list” for each industry, which they combine with 2002 WorldBase plant-level
data to derive a measure of vertical integration. Section 3.3 describes the empirical methodology in detail.
6
This is the reason why most empirical papers on the political economy of trade policy focus on non-tariffbarriers rather than MFN tariffs. For example, Goldberg and Maggi (1999) and Gawande and Bandyopadhyay(2000) use data on 1983 non-tariff barrier coverage ratios for the U.S. manufacturing sector to test Grossman
and Helpman (1994)’s lobbying model.
7
Some studies find that firm size and industry concentration affect U.S. non-tariff barriers through their impact
on lobbying contributions (e.g., Mitra, 1999; Bombardini, 2008).
8
Other studies consider RTAs as being exogenous to firms’ decisions. See, for example, Bustos (2010) on theimpact of the Southern Common Market (MERCOSUR) on Argentinean firms’ technology upgrading.

3also Conconi, Legros and Newman, 2009). In line with our predictions, we find that for a given
country-pair differences in sectoral vertical integration indexes are significantly larger (at leastat the 5 percent level) in those sectors in which differences in MFN-tariffs are larger.
We then examine the relation between the degree of sectoral organizational convergence andcommon membership in a regional trade agreement. As it is possible that countries that are moresimilar are more likely to form RTAs, we use a number of controls for common relationship. Our
theoretical model suggests that, everything else equal, liberalization of product markets between
two countries should lead to more similar product prices and, thus, more similar ownership
structures within industries. Our empirical results show that ownership structures are, indeed,more alike for members of RTAs. We find the difference in vertical integration indexes to bearound 9 percent smaller for country pairs engaged in RTAs. In line with the predictions ofour model, this effect is found to be stronger for older trade agreements, which are more likely
to have fully eliminated trade barriers among member countries. Our model also suggests thatprice and organizational convergence should be stronger for customs unions, in which members
impose common external tariffs vis-à-vis non-members, than for free trade areas. Indeed, we
find that customs unions are characterized by a lower difference (approximately 18.5 percent) invertical integration indexes.
Our paper contributes to an emerging literature on general equilibrium models with endogenous organizations and, in particular, to a nascent stream of empirical work that examines firms’organizational choices in a global economy. Like this paper, part of that literature focuses on
integration (i.e., firm boundaries/ownership structure). Acemoglu, Johnson, and Mitton (2009)(henceforth AJM) study the determinants of vertical integration using D&B data for 93 countries,
focusing on the role of contracting costs. The authors find that individually, these costs have little significant impact on vertical integration. However, they find evidence of an interaction effect(i.e., more vertical integration in countries with greater contracting costs and greater financial
development) and that contracting costs have a stronger impact on more capital-intensive industries. Acemoglu, Aghion, Griffith, and Zilibotti (2009) use data on all British manufacturing
plants to study the relationship between vertical integration and rates of innovation. Aghion,
Griffith and Howitt (2006) investigate whether the propensity for firms to vertically integratevaries systematically with the extent of competition in the product market. None of these papers
focus specifically on how industry price levels affect integration. Breinlich (2008) finds a significant increase in the level of M&A activity in Canada (but not the U.S.) following CUSFTA, butdoes not examine mechanisms.9
Another stream of the literature focuses on within-firm delegation (for a given ownership
structure). Guadalupe and Wulf (2009) investigate the effects of the 1989 CUSFTA agreement
9Single-industry studies on vertical integration include Hortaçsu and Syverson (2007) on U.S. ready-mix
concrete and Woodruff (2002) on Mexican footwear. The former specifically emphasizes that market power plays
little role in determining the extent of vertical integration.

4

that eliminated tariffs and other barriers between the U.S. and Canada and find that competition
led large U.S. firms to flatten their hierarchies. Bloom, Sadun and Van Reenen (2010), using
data on nearly 4,000 medium sized (100 to 5,000 employees) manufacturing firms across a dozen
countries, find that greater product market competition increases decentralization.
Other studies have stressed the impact of trade liberalization on the reallocation of resources
across individual plants and firms (see Goldberg and Pavcnik (2004) for an overview) or in work
practices (Schmitz, 2005).
Finally, recent studies have examined how organizational design can explain observed patterns of intra-firm trade and the location of multinational subsidiaries or suppliers (Antras,
2003; Antras and Helpman, 2004; and Grossman and Helpman, 2004). Ornelas and Turner(2008) examine how trade policy affects hold-up problems through its effect on foreign suppliers’
investment incentives.
The rest of the paper is organized as follows. Section 2 presents the theoretical frameworkand discusses the empirical implication of our model. Section 3 describes our data and themethodology used to construct vertical integration indexes. Section 4 presents and discusses theresults on tariffs and vertical integration. Section 5 analyzes the effect of trade policy (tariffs
and RTAs) on the degree of organization convergence within sectors across countries. Section 6analyzes the robustness of the results. The last section concludes.

2

Model

Our model is similar to a standard specific-factor model, in which trade among many small
countries is driven by differences in endowments of the specific factors. Before looking at international trade and the effects of trade policy, we describe the building blocks of the model in its
closed-economy form.

2.1

Setup

There are K + 1 sectors/goods, denoted by 0 and k = 1, . . . , K; good 0 is a numeraire. The
representative consumer’s utility can be written asu(c0 , . . . , cK ) ≡ c0 +KX

uk (ck ),

(1)

k=1

where c0 is consumption of the numeraire good, and ck consumption of one of the other goods.
The utility functions uk (·) are twice differentiable, increasing, strictly concave, and satisfy the
Inada conditions limci →0 u′k (ck ) = ∞ and limci →∞ u′k (ck ) = 0. Domestic demand for each goodk can then be expressed as a function Dk (pk ) of its own price.

5

Production of good k requires the cooperation of two types of input suppliers, denoted A
and Bk . Bk suppliers generate no value without being matched with an A; A suppliers caneither match with any Bk or engage in stand-alone production of the numeraire good 0. Manyinterpretations of the A and Bk firms are possible. For example, A suppliers may represent lightassembly plants or basic inputs, such as energy, or various business services (e.g., IT, retailing,logistics) that can be used to produce basic consumer goods or combined with other inputs (Bksuppliers) to produce more complex goods.
All goods are sold under conditions of perfect competition. There is a continuum of eachtype of supplier, with a measure nk of Bk ’s, and a unit measure of A’s. We assume the aggregatePsupply of A’s exceeds that of the Bk ’s (i.e., K
k=1 nk < 1) so that a positive amount of good 0 is
produced in equilibrium. The price of the numeraire good is normalized to unity.
So far, we have described a standard specific-factor model in which A suppliers represent
the mobile factor and Bk suppliers the specific factors of production. The crucial distinguishingfeature of the model is that production units are run by managers who trade off the pecuniary
benefits of coordinating their decisions with the private benefits of taking decisions that go intheir favored direction.
An equilibrium in the supplier market consists of a stable match between each Bk supplierand an A supplier: given the surplus allocation among all the managers, no (A, Bk ) pair can form
an enterprise that generates payoffs for its two managers that exceed their equilibrium levels.
All A suppliers are equally productive when matched with one of the Bk ’s. A stand-alone A
produces α units of the numeraire good. Since the price of the numeraire is equal to unity, thisalso pins down the outside option for all A’s.

2.2

Individual Firms

Our model of the firm relies on two key features. First, managers enjoy monetary returns aswell as private non-transferable benefits associated with their production perations. Because
different managers view these operations differently, their private benefits come into conflict.
For instance, a standardized production line could be convenient for the sectorally-mobile A
suppliers, but may not fit the specific design needs of the Bi suppliers. Second, some operating
decisions (e.g., choosing production techniques, deciding on marketing campaigns, etc.) cannot
be agreed upon contractually; only the right to make them can be transferred through changes
in ownership.
Once an enterprise composed of an A and a Bk has formed in the supplier market, a noncontractible decision about the way in which production is to be carried on must be made ineach unit. Denote the A and Bk decisions respectively by a ∈ [0, 1] and bk ∈ [0, 1]. Successful
firm production requires coordination between the two suppliers. More precisely, the enterprisewill succeed with a probability 1 − (a − bk )2 , in which case it generates R > 0 units of output;6otherwise it fails, yielding 0. Output realizations are independent across enterprises (A-Bk pairs).
Managers are risk-neutral and bear a private cost of the decision made in their units. The A
manager’s utility is yA −(1−a)2 , the Bk manager’s is yk −b2k , where yA , yk ≥ 0 are their respective
incomes and (1 − a)2 and b2k are the respective costs. Observe that A’s most preferred action is1, while Bk ’s is 0, so managers disagree about the direction in which decisions should be made.
Because managers’ primary function is to implement decisions and convince their workforces toagree, they bear the cost of decisions even if they don’t make them.
Assignment of decision rights via possible sale of assets is the organizational design problem
in the model. Managers may remain non-integrated and retain control over their respective
decisions. Or they can choose to integrate into a single firm by engaging a headquarters (HQ),transferring to it, in exchange for a fixed payment, a share of the realized revenue and the power
to decide a and bk . HQ is motivated only by monetary considerations, incurring no costs for aand bk , and will want to maximize the integrated firm’s income.
Before production, Bk managers match with A managers and sign contracts specifying anownership structure and payment scheme. For simplicity, we take the payment scheme to be a
fixed payment T from Bk to A.10
For each match (A,Bk ), total revenue in event of success is given by R times the productmarket price, pk , which is taken as given and correctly anticipated when managers and HQ’s sign
the contracts and make their decisions. Because A’s are in excess supply, they must all receive α
in equilibrium. Thus T will just cover A’s anticipated private cost of production together withthe opportunity cost α.
After contracts are signed, managers and HQ’s make their production decisions, output isrealized, product is sold, and revenue shares are distributed.
2.2.1

Integration

HQ’s are elastically supplied at a cost normalized to zero. After paying its acquisition fee andreceiving its compensating share of revenue, an HQ’s continuation payoff is proportional to(1 − (a − bk )2 )Rpk .11 HQs decide both a and bk , and since their incentive is to maximize heintegrated firms’ expected revenue, they choose a = bk . Among the choices in which a = bk , thePareto-dominant one is that in which a = bk = 1/2 (which minimizes the total cost of the A
and Bk managers). We assume HQs implement this choice. The private cost to each manager isthen 41 , and the payoffs to the A and Bk managers are equal to α and Rpk − α − 12 , respectively(thus T = α + 14 ).
10

In general, Bk may prefer to give A a positive contingent share of revenue. This complicates notation but
does not change any qualitative conclusion regarding the dependence of integration on price (see Legros andNewman, 2009).
11
The size of HQ’s share is indeterminate and could be pinned down in many ways not modeled here; all thatmatters for our purposes is that it is positive.

7

2.2.2

Non-integration

Under non-integration, managers retain control of their respective activities. The decisions
chosen are the (unique) Nash equilibrium of the game with payoffs T − (1 − a)2 for A, whochooses a, and (1 − (a − bk )2 ) Rpk − b2k − T for Bk , who chooses bk . These are a = 1 and1
bk = Rpk /(1 + Rpk ), with resulting expected output 1 − (1+Rp
2 . Notice that output increasesk)with price: as pk becomes larger, the revenue motive becomes more important for Bk managers,
pushing them to better coordinate with their A partners. The equilibrium transfer under non(Rpk )2
integration is T = α; the payoffs are α for A’s and 1+Rp
− α for Bk ’s.
k2.2.3

Choice of Ownership Structure

To determine managers’ choice of firm boundaries, we must compare their payoffs under integration and non-integration. A suppliers obtain α in both cases. Bk suppliers obtain a higher
2k)payoff under integration if and only if Rpk − 12 > (Rp, or pk > 1/R.
1+Rpk
Thus managers’ organizational choices depend on product prices. At low prices, revenues are
small enough that integration’s better output performance is not valuable enough to the Bk to be

worth the private cost he (and A, who would have to be compensated) would have to bear; thus,
Bk opts for the “quiet life” of non-integration, wherein both profits and costs are low. At higher
prices, the Bk manager’s revenue motive now makes higher output and therefore coordination
more valuable. Coordinating under non-integration would entail large and costly concessions
from Bk to A, who chooses a = 1 whatever the price; the compromise choice a = bk = 21 , is now
preferable, and Bk chooses to integrate.

2.3

Product Market Equilibrium and the OAS Curve

Equilibrium for the economy is a general equilibrium of the supplier and product markets. We
have already characterized the supplier markets. Some A suppliers produce by themselves α unitsof the numeraire good; others are matched with Bk ’s for the production of goods k = 1, . . . , K
and receive α.
In product market k, the large number of enterprises implies that with probability one thesupply is equal to the expected value of output given pk ; equilibrium requires that this priceadjust so that demand equals supply.
To derive industry supply, suppose R is distributed in the population according to somecontinuous c.d.f. G(R) with mean 1 and support [R, R]. Since all enterprises in industry kwith R < 1/pk remain non-integrated, and the remaining ones integrate, total supply at pricepk ∈ [1/R, 1/R] is (recall that nk is the measure of Bk suppliers)
S(pk ) = nk

"Z
1/pk
R

1
R(1 − (
)2 )dG(R) +1 + Rpk
8

Z R

1/pk

#

RdG(R) .

(2)

RR
1
)2 )dG(R); if pk > 1/R, it is nk .)(If pk < 1/R, supply is nk R R(1 − ( 1+RpkThis Organizationally Augmented Supply (OAS) curve incorporates the ownership structure
decisions of the industry’s enterprises as well as the usual price-quantity relationship. Whenpk < 1/R, the industry is entirely non-integrated, but supply increases with price, since nonintegration expected output increases. As price rises above 1/R, the most productive enterprises integrate, producing more than they would under non-integration; those that remain
non-integrated also produce more, so that industry output rises further. Once pk reaches 1/R,
all firms are integrated and industry supply is fixed at nk (the mean R being 1) for prices higher
than that threshold. Observe that, for a given market price pk , more productive enterprises(those with higher R) are more likely to be vertically integrated.
In the absence of trade, an equilibrium in the product market of good k is a price and aquantity that equate supply and demand: Dk (pk ) = S(pk ). The degree of integration of theindustry (i.e., the fraction 1 − G(1/pk ) of firms that integrate) is a nondecreasing function of theequilibrium price, strictly increasing on [R, R].

2.4

Trade Policy and Firms’ Organization

The world consists of C small countries, indexed by c, which have identical demands and technologies in the production of all goods. Trade is the result of endowment differences between
countries. In particular, we assume that the countries can be divided into two homogeneousgroups: a “Home” set H of countries relatively more endowed in the specific factors necessary
to produce goods k ∈ {m + 1, . . . , K}; and a “Foreign” set F of countries (denoted with a “*”)relatively more endowed in the specific factors necessary to produce goods k ∈ {1, . . . , m}. We
thus have nk < n∗k for k ∈ {1, . . . , m} and nk > n∗k for k ∈ {m + 1, . . . , K}. Good 0, thenumeraire, is always traded freely across countries. We choose units so that the international
market-clearing and the domestic price of good 0 in each country equal unity.
Each country c imposes an exogenously-given ad valorem tariff tck ≥ 0 on import-competing
good k. In sectors k ∈ {1, . . . , m} domestic prices are thus equal to pck = (1 + tck )Pk in Homecountries and pc∗
k = Pk in Foreign countries, where Pk denotes the international price. This is
the solution to the following market-clearing condition:
X
c

Mkc


 X
c
(1 + tk )Pk =Xkc∗(Pk ),

(3)

c∗

where Mkc = D

(1 + tck )Pk



−S



(1 + tck )Pk

denotes Home imports and Xkc∗ = S(Pk ) − D(Pk )Foreign exports. For goods k ∈ {m + 1, . . . , K} the market-clearing condition is
X
c∗


 X
)P
Xkc (Pk ).
Mkc∗ (1 + tc∗
k =
k

(4)

c

9

From (3) and (4) we can derive an expression for international equilibrium prices as a functionof the tariffs applied by all countries, that is, Pk (tk ) for k ∈ {1, . . . , m}, and Pk (t∗k ) for k ∈∗{m + 1, . . . , K}, where tk = {tck }c∈H and tk = {tck }c∗ ∈F (the separable form of demand ensuresthat the world product price in one sector depends only on tariffs imposed by importing countries
in that sector).
The trade balance condition for a Home country c requiresmX
k=1

Pk Mkc(1 + tck )Pk



−

K
X

Pk Xkc (Pk ) + Z0c = 0,

(5)

k=m+1

where Z0c denotes the net transfer of the numeraire good to settle the trade balance. A similar
condition must hold for a Foreign country.
Trade policies affect ownership structures through their impact on product prices. In particular, an increase in tck leads to an increase in the domestic price of good k; an enterprisewith productivity R will choose integration if that price exceeds 1/R. Comparing two otherwise′

identical countries c and c′ , with tck > tck , the domestic price and therefore degree of integration
in industry k will be higher in c than in c′ .
Figure 1: Tariffs and firm organizationpkc

S ( pkc )

I
1/ R

Mix
Pk (1 + t kc )

1/ R
Pk

D ( pkc )

N

nkc

10

qkc

Figure 1 above depicts the organizationally augmented supply curve of industry k in countryc. It also illustrates the price regions in which enterprises are all non-integrated (N), all integrated (I), and the middle range in which only some integrate (Mix). In this example, absent
any tariff, the domestic price would be equal to the world price Pk , and all firms in the domestic
industry would be non-integrated. Now consider a non-prohibitive tariff tck that raises the domestic price to pck = Pk (1 + tck ), which lies between 1/R and 1/R. At this price, more productive
enterprises (with R > 1/pck ) will integrate and less productive ones will remain non-integrated.
Clearly, a lower tariff would lead to fewer integrated firms, a higher one to more. Integration
thus increases with the tariff level.
Our theoretical framework can also be used to examine how trade policy affects the degreeof organizational convergence across countries. In particular, for a given country pair cc′ , thedifference in degree of integration within a sector k will depend on the differences in their applied′′
tariffs: the more similar tck and tck , the smaller the difference between pck and pck and the moresimilar firms’ organizational choices within industry k.
Finally, consider a country pair cc′ that has signed a regional trade agreement that eliminates
all tariffs between them. This implies that prices should tend to convergence across member
countries. Internal tariffs between member countries are usually reduced gradually. For example,
in the case of the CUSFTA agreement, tariffs in most industries were reduced to zero linearly
either over five years or ten years (see Trefler, 2004). We should thus expect more similarorganizational structures between members of older trade agreements. We would also expect
customs unions, in which member countries adopt common external tariffs, to be characterizedby more similar ownership structures than free trade areas, in which differences in externaltariffs and the existence of rules of origins should reduce the extent of price and organizational
convergence.
For the purpose of our empirical analysis, we can reformulate the predictions of our theoreticalmodel as follows.
1. Higher tariffs should lead to a higher degree of vertical integration within sectors.
2. Country pairs should have more similar ownership structures in sectors with closer levelsof protection.
3. RTAs should lead to organizational convergence among member countries, which effectshould be stronger for older trade agreements and for customs unions.

11

3

Data and Descriptive Statistics

3.1

The WorldBase Database

We use data for 2004 from Dun & Bradstreet’s WorldBase, a database of public and private
plant-level observations in more than 200 countries and territories.12 The leading U.S. sourceof commercial credit and marketing information since approximately 1845, Dun & Bradstreet(D&B) presently operates in different countries and territories either directly or through affiliates,
agents, and associated business partners.
WorldBase is the core database with which D&B populates its commercial data products including Who Owns WhomTM , Risk Management SolutionsTM , Sales & Marketing SolutionsTM ,and Supply Management SolutionsTM . These products provide information about the “activities, decision makers, finances, operations and markets” of the clients’ potential customers,competitors and suppliers. D&B compiles their data from a wide range of sources, including
partner firms in dozens of countries, telephone directory records, websites, and self-registration.
All information is verified centrally via a variety of manual and automated checks.
Early uses of D&B data include Caves’ (1975) analysis of size and diversification patterns
between Canadian and U.S. plants. More recently, Harrison, Love, and McMillian (2004) used
D&B’s cross-country foreign ownership information. Other studies that have used D&B data
include Black and Strahan’s (2002) study of entrepreneurial activity in the United States, Acemoglu, Johnson, and Mitton’s (2009) cross-country study of concentration and vertical integration, and Alfaro and Charlton’s (2009) analysis of vertical and horizontal activity by multinationals.
WorldBase, albeit not without problems, is best suited to our analysis having four main
advantages over most other sources. First, the data include both listed and non-listed plants,
and information that supports aggregation at the firm level. Second, Amadeus and other data
sources restricted to Europe are not useful for our purposes because they lack broad coverage ofcountries in particular developing countries, with different levels of trade barriers. WorldBase
by contrast has data in more than 200 countries and territories. Third, D&B compiles datafrom a wide range of sources, whereas other databases collect data primarily from national firm
registries. The wide variety of sources from which D&B collects data reduces the likelihood thatthe sample frame will be determined by national institutional characteristics. Finally, over its
many years in business, D&B has devised many methods of checking its data and assuring thereliability of its dataset.13
12

The dataset is not publicly available but was released to us by Dun and Bradstreet. For more information
see: http://www.dnb.com/us/about/db database/dnbinfoquality.html.
13
See Alfaro and Charlton (2009) for a more detailed discussion of the WorldBase data and comparisons withother data sources.

12

3.2

The Sample

We use data from the 2004 WorldBase file, excluding records that lack primary industry and year
started, for a total of more than 24 million observations. The unit of observation in WorldBase
is the establishment (a single physical location at which business is conducted or services orindustrial operations are performed) rather than the firm (one or more domestic establishments
under common ownership or control). Establishments, which we also refer to as plants, havetheir own addresses, business names, and managers, but might be partly or wholly owned byother firms. Plants can be linked via information on domestic and global parents using the
DUNS numbers. Our analysis is at the firm level, that is, we consider all plants connected by
the same global or domestic parent to be one unit (see discussion below).
We use four categories of data recorded by WorldBase records for each establishment:
1. Industry information: the 4-digit SIC code of the primary industry in which each establishment operates, and for most countries, the SIC codes of as many as five secondary
industries, listed in descending order of importance.14
2. Ownership information: information about the firms’ family members (number of family
members, its domestic parent and its global parent).15
3. Location information: country, state, city, and street address of each family member (usedto link establishments within a family to the relevant tariff data).
4. Basic operational information: sales and employment.
We exclude countries and territories with fewer than 80 observations and those for which
the World Bank provides no data. We further restricted the sample to Word Trade Organization (WTO) members for which we have data on tariffs/regional trading arrangements (see thediscussion below).
We focus on manufacturing firms (i.e., firms with a primary SIC code between 2000 and3999), which best fit our theory of vertical integration. We exclude government/public sector
firms, firms in the service sector (for which we have no tariff data) or agriculture (due to theexistence of many non-tariff barriers), and firms producing primary commodities (i.e., mining
and oil and gas extraction).
14

D&B uses the United States Government Department of Commerce, Office of Management and Budget,Standard Industrial Classification Manual 1987 edition to classify business establishments. The Data Universal
Numbering System — The D&B D-U-N-S Number — introduced in 1963, to identify businesses numerically fordata-processing purposes, supports the linking of plants and firms across countries and tracking of plants histories
including name changes.
15
D&B also provides information about the firm’s status (joint-venture, corporation, partnership) and its
position in the hierarchy (branch, division, headquarters).

13

We exclude firms with fewer than 20 employees, as our theory does not apply to selfemployment or small firms with little prospect of vertical integration (see also Acemoglu, Aghion,
Griffith, and Zilibotti, 2009).16
We focus on firms that operate in only one country, since this provides a cleaner analysisof the effects of tariffs and RTAs on firms’ ownership structure. This is because the degree ofvertical integration of these firms depends only on the prices of the country in which they arelocated. In the case of multinational corporations (MNCs), on the other hand, it is harder to
identify the relevant prices and tariffs. Moreover, focusing on national firms, avoids issues having
to do with the strategic behavior of multinationals across markets (e.g., transfer pricing, tariff
jumping).17 Multinationals are included in the robustness analysis. There, we split MNCs into
separate entities — one for each country — in order to link organizational structure to domestic
tariffs.
Table A-2 in the Appendix lists the countries included in our final sample and the sample
frame (the main sample is A.2).18 As a robustness check, we also exclude countries for which wehave fewer than 1,000 plants that are part of firms with at least 20 employees (see also Klapper,
Laeven, and Rajan, 2006). The countries included in this restricted sample are listed in Table
A.3.

3.3

Vertical Integration Indices

Constructing measures of vertical integration is difficult, as the exercise is highly demandingin terms of data, requiring firm-level information on sales and purchases of inputs by various
subsidiaries of a firm. Such data are generally not directly available and, to the best of ourknowledge, there is no source for such data for a wide sample of developed and developing
countries.
To measure the extent of vertical integration for a given firm, we build on the methodology
used by AJM (2009). We combine WorldBase information on plant activities and ownership
structure with input-output data to determine related industries and construct the vertical
integration coefficients Vjf,k,c in activity j, where k is the primary sector in which firm f incountry c is active. Note that the sample in AJM is restricted to a maximum of the 30,000
largest records per country in the 2002 WorldBase file (a limit imposed by cost constraints).19
16

Restricting the analysis to firms with more than 20 employees enables us to correct for possible differencesin the the collection of small firms data across countries.
17
We describe an establishment as foreign-owned if it satisfies two criteria: (1) it reports to a global parent
firm, and (2) the parent firm is located in a different country. Parents are defined in the data as entities thathave legal and financial responsibility for another firm. For purposes of matching the tariff data, we use the SIC
code of the domestic parent for multinationals.
18
Further restrictions were imposed by data availability constraints related to the control variables as explainedin the next subsections.
19
For many countries, this restriction is not binding. For countries with more than 30,000 observations, AJM
select the 30,000 largest, ranked by annual sales. They include all industries, except those operating only in

14

Having information for a broader sample of more than 24 million establishments in the 2004
WorldBase file, we are able, as discussed below, to link establishments to firms.
Given the difficulty of finding input-output matrices for all the countries in our dataset, wefollow AJM (2009) in using the U.S. input-output tables to measure vertical linkages withinfirms. As the authors note, the U.S. input-output tables should be informative about input
flows across industries to the extent that these are determined by technology.20
The input-output data are from the Bureau of Economic Analysis (BEA), Benchmark IO
Tables, which include the make table, use table, and direct and total requirements coefficientstables. We use the Use of Commodities by Industries after Redefinitions 1992 (Producers’
Prices) tables. While the BEA employs six-digit input-output industry codes, WorldBase the
SIC industry classification. The BEA website provides a concordance guide, but it is not a
one-to-one key.21 For codes for which the match was not one-to-one, we randomized between
possible matches in order not to overstate vertical linkages. The multiple matching problem,
however, is not particularly relevant when looking at plants operating only in the manufacturingsector (for which the key is almost one-to-one).
For every pair of industries, i, j, the input-output accounts support calculation of the dollar
value of i required to produce a dollar’s worth of j. We construct the input-output coefficientsfor each firm f , IOijf by combining the SIC information for each plant in each firm, the matchingcodes, and the U.S. input-output information. Here, IOijf ≡ IOij ∗ Iijf , where IOij is the inputoutput coefficient for the sector pair ij, stating the cents of output of sector i required to produce
a dollar of j, and Iijf ∈ {0, 1} is an indicator variable that equals one if and only if firm f owns
plants in both sectors i and j. A firm that produces i as well as j will be assumed to supply itself
with all the i it needs to produce j; thus, the higher IOij for an i-producing plant owned by thefirm, the more integrated in the production of j the firm will be measured to be. Adding up the
input-output coefficients IOijf for all inputs i, gives the firm’s degree of vertical integration in j.
To illustrate the procedure, consider the following example from AJM (2009) of a Japaneseestablishment with, according to WorldBase, one primary activity, automobiles (59.0301), andtwo secondary activities, automotive stampings (41.0201) and miscellaneous plastic products(32.0400). The IOij coefficients in the three activities for this plant are:“wholesale trade” and “retail trade.”
20
Note that the assumption that the U.S. IO structure carries over to other countries can potentially bias ourempirical analysis against finding a significant relationship between vertical integration and prices. On the other
hand, it also mitigates the possibility that the IO structure and control variables are endogenous.
21
This concordance is available upon request. The BEA matches its six-digit industry codes to 1987 U.S. SIC
codes http://www.bea.gov/industry/exe/ndn0017.exe.

15Output (j)Autos Stampings
0.0043 0.0000

Plastics
0.0000

Plastics

0.0780 0.0017
0.0405 0.0024

0.00000.0560SUM

0.1228 0.0041

0.0560Autos
Input (i) Stampings

The table is a restriction of the economy-wide IO table to the set of industries in which
this establishment is active (i.e., it contains all of the positive IOijf values). For example, theIOij coefficient for stampings to autos is 0.078, indicating that 7.8 cents worth of automotive
stampings are required to produce a dollar’s worth of autos. Because this plant has the internal
capability to produce stampings, we assume it produces itself all the stampings it needs.22 The
bottom row shows the sum of the IOijf for each industry. For example, given that 12.3 centsworth of the inputs required to make autos can be produced within this plant, we would saythat the degree of vertical integration for this plant is 0.123 in autos.
Our main unit of observation, however, is all plants that belong to the same firm, that is, allplants that report to the same headquarters. For example, if the plant in the example above isreported to be the headquarters of another Japanese plant (subsidiary), we consider the activities
of both plants in constructing a measure of vertical integration for the firm. In the case of multiplant firms, restricting analysis to the plant level may underestimate the number of activities
carried out within the firm’s boundaries.
We now describe the methodology used to construct the firm-level vertical integration indexes.
For a given firm f in primary sector k located in country c, we define the integration index inactivity j as
Vjf,k,c =X

IOijf,k ,(6)ithe sum of the IO coefficients for each industry in which the firm is active. Our measure ofvertical integration is based on the firm’s primary activity:
V f,k,c = Vjf,k,c, j = k.

(7)

In the case multi-plant firms (plants connected by the same global ultimate or headquarters),
we consider the main activity of the headquarters or domestic parent.
Our approach to identifying vertical integration suffers from the data limitation that we do not
observe intra-firm transactions. Instead, we infer it from information about the goods produced
22

Many industries have positive IOij coefficients with themselves; for example, miscellaneous plastic products
are required to produce miscellaneous plastic products. Any firm that produces such a product will therefore bemeasured as at least somewhat vertically integrated.

16

in each of the firm’s establishments and the aggregate input-output relationship of those goods.
The advantage of our method is that we have a large amount of data for many countries andindustries and that we do not have to worry about the value of intra-firm activities being affectedby transfer pricing. Another advantage, according to Hummels, Ishii, and Yi (2001), is that using
I-O tables avoids the arbitrariness of classification schemes that divide goods into “intermediate”
and other categories. Our index represents the opportunity for vertical integration which firms
may exercise in different ways as they may still, for example, purchase inputs from or sell themto third parties.23
Table 1 presents summary statistics for the main vertical integration index. Appendix Table
A-1 compares the index across the different samples. Our main sample consists of 196,586
domestic manufacturing firms with at least 20 employees located in 80 countries.24

3.4

Trade Policy

A further challenge in empirically assessing the impact of market prices on ownership structureis that both are determined simultaneously — prices should affect ownership structure but at thesame time ownership structure also influences market prices. We use trade policy to deal with thisendogeneity problem, arguing that most-favored-nation (MFN) tariffs and RTAs offer a plausibly
exogenous source of price variation to the boundaries of the firm. Although one might still worry
about the political economy determinants of these policies, as argued in the introduction, MFN
tariffs, being negotiated at the multilateral level over long periods of time, are less “political”
than unilateral forms of protection such as anti-dumping duties. Studies that have found thatindustry concentration and firm size may affect lobbying contributions and trade policy outcomes(e.g., Mitra, 1999; Bombardini, 2008) are based on U.S. non-tariff barriers in the manufacturingsector. Our empirical analysis controls for both firm size and industry concentration. We further
argue that ownership structure is unlikely to have a systematic impact on the determination of
trade policies in general, and on MFN tariffs in particular.25 Regional trade agreements such as
free trade areas and customs unions are also negotiated over long periods of time and regulated
by GATT/WTO rules (Article XXIV and the Enabling Clause).
23

Hortaçsu and Syverson (2009) combining U.S. Census data, the Commodity Flow Survey (a random sample ofan establishment’s shipments in each four weeks during the year, one in each quarter), and ZIP code information
to measure intra-firm trade, find that shipments from upstream units to downstream units are surprisingly low.
This result is at odds with international trade studies, which show that intra-firm trade accounts for roughly
one-third of international shipments (e.g., Bernard, Jensen, Redding, and Schott, 2008). We obtain similar results
using plant-level vertical integration measures, available upon request.
24
Differences in methodology and samples restrict comparisons with AJM. However, the authors report a meanof 0.0487 and median of 0.0334 for their vertical integration index. For our main sample, the primary sector
vertical integration index has a mean of 0.0627 and a median of 0.0437.
25
No theory relates firm boundaries to incentives to form a lobbying group. Even if one allows that lobbying
can play a role in determining MFN tariffs, it is not obvious how the direction of the political pressure (pro or anti
trade) and its extent (e.g., size of campaign contributions) could be systematically related to firms’ organizational
decisions across a large set of countries and sectors.

17

3.4.1

Tariffs

We collect applied MFN tariffs at the 4-digit SIC level for all WTO members for which thisinformation is available. We restrict the set of countries to WTO members, because they are
constrained under Article I of the GATT by the MFN principle of non-discrimination: eachcountry c must apply the same tariff tck to all imports in sector k that originate in other WTO
member countries; preferential treatment is allowed only for imports that originate in RTA
members or in developing countries.
The source for MFN tariffs is the World Integrated Trade Solution (WITS) database, which
combines information from the UNCTAD TRAINS database (default data source) with the WTO
integrated database (alternative data source). Tariffs are for 2004 unless unavailable for thatyear in which case the closest available data point in a five year window around 2004 (2002-2006)
is chosen with priority given to earlier years.26 The original classification for tariff data is the
harmonized system (HS) 6-digit classification. Tariffs are converted to the more aggregate SIC
4-digit level using internal conversion tables of WITS. Here, SIC 4-digit level MFN tariffs arecomputed as simple averages over the HS 6 digit tariffs.
We also construct for each 4-digit SIC sector and every country the fraction of imports towhich MFN tariffs apply using information on RTAs (see below) and subtracting from total
sectoral imports those that originate in countries with which the importer has a common RTA.
Bilateral import data at the 4-digit SIC level for 2004 are from the COMTRADE database.
3.4.2

RTAs

We collect information on RTAs in force in 2004 from the WTO Regional Trade Agreements
Information System (RTA-IS).27 ,28 The legal basis for the creation of RTAs can be found in
GATT/WTO Article XXIV (for agreements involving developed member countries) and the Enabling Clause (for agreements among only developing countries). Under Article XXIV, member
countries can form free trade areas (FTAs) or customs unions (CUs) covering “substantially all
trade ” that require complete duty elimination and fixed timetables for implementation. The
conditions contained in the Enabling Clause being much less stringent, RTAs between developing member countries may effectively involve less trade liberalization. Thus we construct adummy RTA that equals one whenever two countries belong to a common RTA formed under
Article XXIV. This variable does not include a number of preferential trade agreements under
the Enabling Clause that do not imply the full elimination of trade barriers. Alternatively, weconstruct separate dummy variables for customs unions and free trade agreements. because we
expect the former, which imply a common external tariff and no internal trade barriers, to have26

For example, if data are available for 2003 and 2005, but not 2004, the 2003 data are chosen.
Available online (http://rtais.wto.org/UI/PublicMaintainRTAHome.aspx).
28
Note that the dataset does not include trade preferences under the Generalized System of Preferences (GSP),
such as the U.S. African Opportunity Act program or the E.U. Everything but Arms program.
27

18

a stronger effect on organizational convergence than the latter, which permit member countries
to maintain different external tariffs. We also construct the variable Age that equals the ageof a regional trade agreement in years, since we expect older trade agreements to have a larger
impact on firms’ organizational structure.

3.5

Other Controls

We collect a number of country- and sector-specific variables to control for alternative factorsthat explain vertical integration emphasized by the literature.
In terms of country-specific variables, the empirical and theoretical literatures have studied
the roles of institutional characteristics and financial development.29 We use the variable “ruleof law” from Kaufmann, Kraay, and Mastruzzi (2003) as a measure of the Legal quality of acountry’s institutions. This is a weighted average of a number of variables (perception of incidences of crime, effectiveness and predictability of the judiciary, and enforceability of contracts)
between 1997 and 1998. The variable ranges from 0 to 1 and is increasing in the quality of
institutions.
We also use private credit by deposit money banks and other financial institutions as afraction of GDP in 2004 taken from Beck, Demigurc-Kunt, and Levine (2006) as a measure of acountry’s Financial development.
The literature stresses as well differential effects across industries. We combine these countryspecific measures with sector-specific information from the United States, to proxy for exogenous
variation in sector characteristics, such as hold-up problems.
First, we construct sectoral Capital intensity at the 4-digit-SIC level for the United States.
Data comes from the NBER-CES manufacturing industry database (Bartelsmann and Gray,
2000). In line with the literature, capital intensity is defined as the log of total capital expenditure
relative to value added averaged over the period 1993-1997.
Second, we use Nunn’s (2007) measure of Relationship dependence, which proxies for theseverity of hold-up problems. For each sector in the U.S., this variable measures the fraction of
inputs not sold on an organized exchange or reference priced. We convert the data for 1997 fromthe BEA’s input-output classification to 4-digit U.S.-SIC.30
Finally, we follow Rajan and Zingales (1998) in constructing the variable External dependence,
which measures or the United States sectoral dependence on external credit f as the fraction of
investment that cannot be financed with internal cash flows. The authors identify an industry’s
need for external finance (the difference between investment and cash generated from operations)
29

Poor legal institutions should exacerbate hold-up problems and make integration more attractive than armslength-relations. A sufficient level of financial development may be necessary for upstream and downstream firms
to be able to integrate. As AJM note, the effect of each of these variables may be ambiguous when consideredseparately and there are more robust predictions of their combined effect.
30
Nunn’s dataset is available at http://www.economics.harvard.edu/faculty/nunn.

19under two assumptions: (i) that U.S. capital markets, especially for the large, listed firms they
analyze, are relatively frictionless enabling us to identify an industry’s technological demand forexternal finance; (ii) that such technological demands carry over to other countries. Following
their methodology, we construct similar data for the period 1999-2006.31
To control for domestic industry concentration we construct Herfindahl indices for eachcountry-sector using sales of all plants in that sector.32
We also use a number of bilateral variables from CEPII: bilateral distance measured as thesimple distance between the most populated cities (in km), dummies for contiguity, for common
official or primary language, and common colonial relationship (current or past).
Finally, we use information on GDP and GNI per capita for the year 2004 obtained from theWorld Development indicators 2008.
Table 1 presents summary statistics for our control variables. Table A-4 in the Appendix isthe correlation table.

4

Tariff and Vertical Integration

In this section, we assess the empirical validity of the first prediction of our theoretical modelthat higher tariffs lead to more vertical integration at the firm level. To do so, we estimate thefollowing panel regression model:

V f,k,c = α + β1 MF Ntk,c + β2 Employmentf + β3 Xk,c + δk + δc + ǫf,c .

(8)

The dependent variable is the vertical integration index of firm f with primary sector klocated in country c, as defined in (7). We take logs of (one plus) the vertical integration index
to mitigate problems with outliers.33
Our main regressor of interest is the log (one plus) MFN tariff (already expressed in advalorem terms) applied in sector k.by country c.34
31

An industry’s external financial dependence is obtained by calculating the industry median of external financing of U.S. companies using data from Compustat calculated as: (Capex-Cashflow)/Capex, where Capexis defined as capital expenditures and Cashflow as cash flow from operations. Industries with negative externalfinance measures have cash flows that are higher than their capital expenditures.
32
These include sales by foreign-owned plants that operate in the given country-sector.
P
f,k,c
33
Alternatively, we tried using as our dependent variable average vertical integration, V= N1f j Vjf,k,c ,where Nf is the number of industries in which firm f is active. The results on tariffs are consistent, but somewhat
less significant. This is not surprising, as our regressions consider the effects of MFN tariffs applied to a firms’
primary activity not to all of its activities. We also used the log of the vertical integration index (removing
zero observations) obtaining similar results. All results not shown due to space considerations are available uponrequest.
34
In the main specifications, we use log (one plus MFN tariff) in order to be able to include zero tariffs.
Although the distribution of tariffs is extremely skewed, log tariffs are approximately normally distributed.
Using, in alternative specifications, the log of the tariff variable yields similar results.

20

The set of explanatory variables includes number of employees, Employmentf , which allows
us to control for the relation between firm size and ownership structure.35 Recall that ourmodel predicts that more productive firms, as proxied by firm size (see, for example, Bernard,
Jensen, Eaton, and Kortum (2003) for the close relation between firm size and productivity),
are more likely to be integrated for a given market price pck . We thus expect β2 to be positive.
The vector Xk,c consists of different interactions between sector and country characteristics,
previously suggested by papers on the determinants of vertical integration (e.g., interaction
between a sector’s capital intensity and a country’s level of financial development). All variables
are expressed in logs. We also include sector fixed effects at the 4-digit SIC level (δk ), whichallows us to capture cross-industry differences in technological or other determinants of vertical
integration (e.g., a sector’s capital intensity). Finally, we add country fixed effects (δc ), which
capture cross-country differences in institutional determinants of vertical integration (e.g., acountry’s level of financial development and the quality of its contracting institutions) and also
control for country-specific differences in the way firms are sampled.36 Given that tariffs vary
only at the sector-country level, while the dependent variable varies at the firm level, we cluster
standard errors at the sector-country level.
Table 2 reports the main results. Column (1) presents the results of the basic specification,
which includes the MFN tariff, firm size, and country and sector fixed effects. Consistent with
our theoretical model’s predictions, tariffs have a positive and significant effect on firm-levelvertical integration. The estimate for β1 implies that a 100 percent tariff increase leads to a 2.15
percent increase in the vertical integration index. In terms of economic magnitudes, this implies
that an increase in manufacturing tariffs from 1 percent to their mean level of 4.85 percent (a385 percent increase) increases vertical integration by 0.0215*385=8.28 percent, a sizable effect.
Note that tariffs act as a proxy variable for domestic prices. Thus, the estimate for β1 canbe interpreted as the impact of prices on vertical integration if and only if prices and tariffs vary
one to one. This would be true for a small open economy that faces a horizontal export supply
curve and imposes a specific tariff. In the case of a small country using ad-valorem tariffs, thisrelation would be weaker. More important, if a country faces a downward-sloping export supply
curve, imposing a tariff will have an impact on its terms of trade. Therefore, the elasticity ofdomestic prices with respect to tariffs is strictly smaller than one.37 Notice also that, to the35

Our dataset contains different numbers of firms from different countries. This variation in the selection ofsamples of firms could be a source of variation in vertical integration. The main source of the problem would be
potential correlation between vertical integration and firm size (combined with differential selection on firm size
across countries). Controlling for firm size alleviates this problem.
36
D&B samples establishments in the formal sector (and there are, of course, differences in the size of theformal sector across rich and poor countries). In the robustness checks, we try an alternative way to control forthis by restricting the sample to countries for which we have at least 1,000 plants that are part of firms with atleast 20 employees.
ctck∂pc tc37
k tk+
Let pck = (1 + tck )Pk . Then ∂tck pkc = ∂Pc P
c , where the first part on the right is the direct impact of
∂t
1+t
k
k
k
k
k
an ad-valorem tariff on domestic prices (< 1) and the second term is the terms of trade effect (< 0).

21

extent that countries are able to manipulate tariffs to improve their terms of trade, high tariffs
are likely to be observed precisely in sectors in which they increase domestic prices only by asmall amount.38 These arguments imply that the estimate for β1 should be interpreted as a lower
bound on the impact of prices on vertical integration. The true impact is likely to be several
orders of magnitude larger.
Turning to the effect of firm size on vertical integration, we find that, holding constantthe domestic price level, larger firms are more vertically integrated. A 100 percent increase inemployment leads to a 6 percent increase in the firm’s level of vertical integration.
In column (2), we add as controls MFN share (which corresponds to the the fraction ofimports to which MFN tariffs apply) and MFN share times the log (of one) plus the MFN tariff.
The coefficient of tariffs (first row) now measures the impact of tariffs when the MFN share iszero. This coefficient is non-significant. But when the MFN share rises, the impact of tariffs
becomes positive and significant at the one-percent level (as measured by the interaction term,
which is positive and significant).39
In columns (3) and (4) we add different sets of controls to account for other determinantsof vertical integration, as suggested by the literature. In column (3), we include two interaction
terms, one between Capital intensity and Financial development and one between Capital intensity and Legal quality. Note that the tariff coefficient remains relatively unchanged and remains
significant at the one-percent level. The estimate for the interaction term between Capital intensity and Financial development is also highly significant, indicating that more capital intensive
sectors are more integrated in countries with more developed financial markets. The interaction
term between Capital intensity and Legal quality has the expected negative sign but it is not significant. In column (4), we include two alternative interaction terms, one between Relationshipspecificity and Legal quality and one between External dependence and Financial development.
Again, tariffs are positive and highly significant and the interaction terms insignificant.40
As mentioned above, the determination of MFN tariffs is arguably “less political” than otherforms of protection (e.g., anti-dumping duties). Although there is no theory that relates firmboundaries to incentives to form a lobbying group, one might still worry, for example, thatlarger firms, which are more likely to vertically integrate, may be more effective at lobbying forprotection (leading to higher MFN tariffs). That is to say MFN tariffs may be correlated withother (omitted) firm characteristics associated with both the potential to lobby and the incentive
to vertically integrate. Our regression analysis controls for firm size (proxied by employment) as
well as controlling for industry and country effects and a number of industry-country variables
38
Broda, Limao and Weinstein (2008) provide evidence that non-WTO countries exploit their market powerin trade by setting higher tariffs on goods that are supplied inelastically. Ludema and Mayda (2010) provide
similar evidence for WTO countries.
39
A Wald test of whether both coefficients are zero is rejected at the one-percent level.
40
These results are broadly consistent with the theoretical framework described by AJM. Their empiricalanalysis finds a significant negative effect of the interaction between Capital intensity and Legal Quality, butdoes not find a significant effect for the interaction between Capital intensity and Financial development.

22

stressed in the literature.
As an additional test, columns (4)-(8) in Table 2 include Herfindahl indices to control for the
possibility of high concentration leading to both high tariffs and vertical integration. As seenin the table, point estimates for the tariff coefficient remain similar in terms of both magnitude
and significance. The Herfindahl indices, however, are not significant.41

5

Trade Policy and Organization Convergence

The theoretical framework discussed in Section 2 suggests that trade policy should, through its
effect on prices, affect the degree of organizational convergence across countries. The focus of
this section is on cross-country differences in ownership structure at the sectoral level. For eachcountry, we construct an industry measure of vertical integration by estimating the following
regression model:
V f,k,c = βEmploymentf + Vkc + ǫf,c .

(9)

The estimate for the sector-country dummy Vkc gives us a measure of the average level of vertical
integration of industry k in country c, controlling for the effect of firm size (employment) on the
average level of vertical integration in that industry-country pair. All variables are expressed in
logs.

5.1

Tariff Differences

We first examine whether cross-country differences in sectoral organizational structure are affected by differences in tariffs. Our model predicts that, for a given country-pair cc′ , organizational differences should be smaller for sectors characterized by similar levels of protection. To
verify this, we estimate the following model:|V̂k,c − V̂k,c′ | = α + β1 |MF Ntk,c − MF Ntk,c′ | + β2 |Xk,c − Xk,c′ | + δk + δcc′ + ǫk,c,c′ .

(10)

The dependent variable is the absolute difference between countries c and c′ in the estimatedvertical integration indexes for sector k (from equation (9) above). All differences are expressedin logs. The main regressor of interest is the (log of the) absolute difference between thesecountries’ MFN tariffs in sector k. The term |Xk,c − Xk,c′ | captures differences in other sectorcountry characteristics that may affect the degree of organizational convergence. Note that,
because we are including dyad fixed effects (δcc′ ), β1 is identified by the cross-sectoral variationin the tariff difference for a given country pair.
41

We obtain similar results when we use the log of the vertical integration index.

23

In the first column of Table 3, the only explanatory variable is the log-difference in MFN
tariffs. In line with our predictions, we find that, for a given country-pair differences in sectoralvertical integration indexes are significantly (at the 1 percent level) larger in sectors in which
differences in MFN tariffs are larger. A 100 percent increase in the difference in MFN tariffs
leads to a roughly 0.9 percent increase in the difference in vertical integration indexes.
The second column adds interactions between capital intensity and differences in financial
development and legal quality. The coefficient on the difference in MFN tariffs remains relatively unchanged in magnitude and is significant at the 5 percent level. The interaction term ofcapital intensity with legal quality is positive and strongly significant while the interaction termof capital intensity with the difference in financial development is positive but not significant.
The last column includes, as alternative control variables, the difference in financial development interacted with external dependence, and the difference in legal quality interacted withrelationship-dependence. Whereas the coefficient on the difference in MFN tariffs is relatively
higher and significant at the 5 percent level, both interaction terms are negative with the relation
between legal quality and relationship-dependence variables being significant.

5.2

Regional Trade Agreements

In the remainder of this section, we examine the relation between the degree of sectoral organizational convergence and common membership in a regional trade agreement. In contrast to theprevious regressions, however, a causal interpretation of these regression results is more difficult
because it is possible that countries that are generally more similar are more likely to form RTAs.
To assess the validity of our third empirical prediction, we explore how RTAs affect the extent
to which two countries have similar vertical integration structures at the industry level.
|V̂k,c − V̂k,c′ | = α + β1 RT Acc′ + β2 AgeRT Acc′ + β3 Xcc′ + δk + δc + δc′ + ǫk,c,c′ .

(11)

The dependent variable is as in model (10), expressed as before in logs. The main regressorof interest is now RT Acc′ , a dummy that equals one if countries c and c′ are members of thesame RTA. We include the age of the trade agreement to capture the effect that older RTAs arelikely to have greater impact on differences in organizational structure. The vector Xcc′ captures
a series of bilateral controls, such as dummies for contiguity, common language, and colonial
relationship, as well as variables that capture the distance between countries, and differences inlegal quality, financial development, GDP and in GNI per capita (differences expressed in logs
of absolute values). Finally, we include sector fixed effects (δk ) and country fixed effects (δc andδc′ ).
Table 3 presents the results for this regression. In the first column of the left panel, in which
we include only a dummy for regional trade agreements, the coefficient of RTA is negative and24significant at the one-percent level.42 This implies that the difference in vertical integration
indices for a country pair in an RTA is about 9.2 percent smaller than for a country pair without
an RTA. In the second column the age of the RTA is added as an additional control variable.
Coefficients for RTA and for age are both negative and significant at the 1 percent level. Thus,
as expected, country pairs with older RTAs have more similar organizational structures thancountries with young RTAs. The coefficients imply that country pairs that have an RTA withan average age (8.52 years) have a roughly 10 percent smaller difference in vertical integration
indices than country pairs without an RTA (−0.059 − 0.004 ∗ 8.52 ≈ −0.095).
The results for an alternative specification, that separates free trade agreements and custom
unions are presented in column three. Again, results remain robust and significant at the 1percent level. As expected, the quantitative impact on organizational convergence is greater for
CUs than for FTAs. Country pairs that belong to the same CU have a approximately 18.5%
smaller difference in organizational structure than country pairs without a RTA, while country
pairs that belong to the same FTA have only a 4% smaller difference in organizational structure.
The fourth column adds the age of these trade agreements. Notice that the coefficient for customs
unions remains highly significant, while FTAs are only significant if they are sufficiently old.
In the last column, we keep the coefficients for CUs and FTAs separate and add a seriesof bilateral control variables that may have an impact on similarity of organizational structure.
The coefficient for CUs is reduced somewhat in size, but remains significant at the 5 percent
level. Contiguity and common language have a significant negative effect on the difference invertical integration indices, as does distance. The dummy for common colony is insignificant.
Differences in legal quality, GDP and GNI per capita have a significant positive effect on the
difference in vertical integration. Differences in financial development are not significant.

6

Robustness Checks

We perform several additional robustness checks. We first repeat our analysis for the sample
of countries for which we observe at least 1,000 plants that are part of firms with at least 20
employees. Results for specification (8), presented in Table 5, are almost unchanged. Point estimates for the tariff coefficient remain similar in magnitude, and the significance of the estimates
is not affected by restricting the sample of countries.43
In a second set of regressions we add multinational firms to the main sample. As noted in thetext, because multinational firms have plants in different countries, the relevant product priceand what tariffs might be distorting it are unclear. We use the primary activity of the respective
42Standard errors are clustered by sector. Clustering at the country-pair level, which would be appropriate
here, is not possible because the panel is strongly unbalanced across sectors rendering the clustered variancecovariance-matrix numerically singular.
43
We also used the average integration index (not shown), in which case the coefficients for tariffs dropped
slightly in magnitude, but remained strongly significant .

25

domestic ultimate to which a plant belonging to a multinational reports.44 Table 6 reports theresults. We find that the coefficient for MFN tariffs remains significant in all specifications.
With respect to the results on organizational convergence when the sample size is restricted,
tariff differences continue to have a significant positive effect on differences in vertical integrationin all specifications using the primary vertical integration index, as can be seen in Table 7,
columns (1)-(3). Adding multinationals to the analysis we find in columns (4)-(6), that the
impact of tariff differences on differences in vertical integration remains highly significant in all
specifications.
Finally, results for regional trade agreements are also robust to restricting sample size, as
can be seen in Table 8, columns (1)-(3). In the reduced sample, having an RTA reduces differences in vertical integration by roughly 5 percent when controlling for sector and country
effects. The estimates of the coefficients for RTAs and CUs are robust across specifications andmostly significant at the one-percent level. When considering multinationals, having an RTA
continues to reduce differences in vertical integration as can be seen in Table 8, columns (4)(6). The significant impact of RTA remains robust and significant at the 1 percent level acrossspecifications.

7

Conclusions

This paper describes a simple model in which firm boundaries depend on the prices of the
products they sell: the higher the prices, the more integrated firms will be. More generally, whenequilibrium prices converge across economies, so do ownership structures. The reason behindthese predictions is that integration, although more productive than non-integration because of
its comparative advantage in the coordination of firms’ operating decisions, also imposes higher
private costs on enterprise managers. At low prices, the productivity gains from integrating
have little value, and managers choose non-integration. As prices rise, the relative value of
coordination increases, favoring integration.
To examine the validity of these predictions empirically, we use a new dataset from Dun andBradstreet (D&B) that contains both listed and unlisted plant-level observations in more than
200 countries. This dataset enables us to construct firm-level vertical integration indexes withwhich to study the link between product prices and firms’ ownership structure. In particular,
we exploit the cross-country and cross-sectoral variation in MFN tariffs and existence of regional
trade agreements, which provide a source of price variation that is plausibly exogenous to firms’
ownership decisions. Consistent with the model’s predictions, we find that higher prices, as
proxied by higher MFN tariffs, lead to more vertical integration at the firm level. Note that ourestimates should be interpreted as a lower bound of the impact of prices on vertical integration.
44

Note also that because multinationals are usually active in many sectors and the primary SIC code of theirglobal ultimate is not necessarily a good measure of their primary activity.

26

This is due to different reasons: first, ad-valorem tariffs do not have one-to-one effects on domestic
prices; second, if a country faces a downward-sloping export supply curve, increasing its levelof protection will depress the world price, weakening the effect on domestic prices; and finally,to the extent that countries can manipulate their terms of trade, high tariffs are likely to beobserved precisely in those sectors in which their effect on domestic prices is smaller (see Broda,
Limao and Weinstein, 2008).
Our results lend empirical support to a simple model of the determination of firm boundaries
in a global economy. As such they have implications beyond the positive theory of the firm.
Enterprises’ integration choices affect not only their productivity, but also aggregate economic
performance and consumer welfare. In our model, integration enhances consumer welfare, but
the unregulated market may provide too little of it (see Legros and Newman, 2009), because
managers may overvalue their private costs. Thus, to the usual welfare costs of tariffs, which wehave shown increase integration, there are additional organizational design effects to consider.

8

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29

Table 1: Summary Statistics

Vertical integration index
MFN tariffs
Employment
Herfindahl

Median0.044
2.480
38.000
0.053

Mean
0.063
4.849
98.9360.132

Std. Dev.
0.063
7.253
472.395
0.188

N196,586
196,586
196,586
196,586

Capital intensity
Relationship specificity
Financial dependence-2.857-0.456-0.756-2.902-0.526-0.524

0.458
0.356
3.058

387
387
387

Financial dependence
Legal quality
Financial development
GDP
GNI per capita

0.332
0.545
-0.234
8295
9.486

0.554
0.583
-0.478
13126
9.811

0.479
0.209
1.141
12434
0.927

80
80
80
80
80

Difference Ver. int. index
Difference Legal quality
Difference Financial development
Difference GDP
Difference GNI per capita
Distance
Regional Trade Agreement (RTA)
RTA Age
Free Trade Agreement (FTA)FTA Age
Customs Union (CU)CU AgeContiguity
Colonial relationshipCommon language-1.593-1.198-0.2340.450-0.132
9.017
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

-1.707-1.475-0.4780.201-0.467
8.629
0.263
2.045
0.148
0.892
0.115
1.153
0.041
0.020
0.122

1.614
1.134
1.141
1.812
1.230
0.965
0.440
6.333
0.355
3.790
0.319
5.273
0.139
0.178
0.328

299,649
299,649
299,649
299,649
299,649
299,649
299,649
299,649
299,649
299,649
299,649
299,649
299,649
299,649
299,649

Notes: Vertical integration indices constructed using plant level data from 2004 WorldBase, Dun & Bradstreet.
Tariff data from TRAINS/WTO. Info on regional trade agreements (RTAs) from WTO. Financial developmentfrom Beck, Demigurc-Kunt and Levine (2006). Legal quality from Kaufmann, Kraay, and Mastruzzi (2004). GDP
and GNI per capita from World Bank. Capital Intensity from NBER-CES manufacturing industry database.
Relationship specificity from Nunn (2007). Financial dependence from Compustat following Rajan and Zingales(1998). All variables are in logs except for RTA, FTA, CU, RTA Age, FTA Age, CU Age, contiguity, colonial
relationship, and common language, which are dummy variables.

30

Table 2: Tariffs and Vertical Integration, Firm Level Analysis

MFN Tariff
Employment

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.0215***(0.0061)
0.0425***(0.0045)0.0050(0.0079)
0.0426***(0.0045)-0.0170(0.0198)
0.0248***(0.0073)

0.0214***(0.0059)
0.0425***(0.0045)0.0212***(0.0060)
0.0425***(0.0045)0.0225***(0.0060)
0.0442***(0.0048)0.0225***(0.0060)
0.0442***(0.0048)0.0052(0.0077)0.0443***(0.0049)-0.0241(0.0245)0.0260***(0.0078)

0.0223***(0.0058)
0.0442***(0.0048)

MFN share
MFN share x MFN tariff
Capital int. x Financial dev.

0.0319**(0.0147)-0.0837(0.0580)

Capital int. x Legal quality

31

Relation spec. x Legal quality

0.0396**(0.0193)-0.1030(0.0731)-0.0349(0.0296)0.0000(0.0013)External dep. x Financial dev.
Herfindahl

0.0110(0.0231)0.0110(0.0231)0.0107(0.0231)0.0113(0.0229)# Observations
R2
Number of Sectors

196,5860.122
386

196,5860.122
386

196,5860.122
386

196,5860.122
386

178,1990.123
386

178,1990.123
386

178,1990.123
386

178,199
0.123
386

Sector Fixed Effect
Country Fixed Effect
Cluster

YES
YESCountrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

Notes: Robust standard errors in parentheses denoting *** 1%, **5%, and *10% significance. Dependent variable: log of one plus the vertical integrationindex of firm f in primary sector k located in country c. Sample includes firms ≥ 20 employees in the manufacturing sector, excluding MNCs. All control
variables are in logs except for MFN tariff which, corresponds to log one plus MFN tariff.

Table 3: Tariffs and Organizational Convergence

Difference MFN tariffs

(1)

(2)

(3)

0.0089***(0.0034)

0.0086**(0.0037)
0.0020(0.0066)
0.0419***(0.0062)0.0095**(0.0037)

Capital intensity x difference Financial dev.
Capital intensity x difference Legal quality
External dep. x difference Financial dev.

-0.0057(0.0009)-0.0553***(0.0061)

Relation spec. x difference Legal quality# Observations
R2# Country pairs

212,770
0.164
4392

171,908
0.164
3444

171,908
0.165
3444

Sector Fixed EffectDiadic Fixed Effect
Cluster

YES
YESCountry-pair

YES
YESCountry-pair

YES
YESCountry-pairNotes: Robust standard errors in parentheses denoting *** 1%, **5%, and *10% significance. Dependent variable:
log of the absolute difference between countries c and c′ in the estimated vertical integration index in the primarysector k. All variables are in logs. Sample includes firms ≥ 20 employees in the manufacturing sector, excluding
MNCs.

32

Table 4: Regional Trade Agreements and Organizational Convergence

RTA

(1)

(2)

-0.0921***(0.0095)-0.0593***(0.0108)-0.00412***(0.0006)

RTA x Age
Customs union
Free trade area

(3)

(4)

(5)

-0.185***(0.0150)-0.0404***(0.0099)-0.1770***(0.0178)0.0039(0.0120)-0.0005(0.0007)-0.0073***(0.0009)-0.0359**(0.0172)
0.0157(0.0128)

Customs union x Age
Free trade area x Age
Contiguity-0.204***(0.0159)
0.0322(0.0236)-0.0911***(0.0107)0.0136*(0.0070)0.0300***(0.0033)-0.0030(0.0031)0.0285***(0.0032)
0.0480***(0.0044)

Colonial relationshipCommon language
Distance
Difference Legal quality
Difference Financial dev.
Difference GDP
Difference GNI p.c.

# Observations
R2# Country pairs

299,649
0.109
101

299,649
0.109
101

299,649
0.109
101

299,6490.109
101

240,3850.122
101

Sector Fixed Effect
Country Fixed Effect
Cluster

YES
YES
SectorYES
YES
SectorYES
YES
SectorYES
YES
SectorYES
YES
SectorNotes: Robust standard errors in parentheses denoting *** 1%, **5%, and *10% significance. Dependent variable:
log of (one plus) the absolute difference between countries c and c′ in the estimated vertical integration index
in the primary sector k. Differences and distance are in logs. Sample includes firms ≥ 20 employees in the
manufacturing sector, excluding MNCs.

33

Table 5: Robustness–Tariffs and Vertical Integration, Countries ≥ 1,000 plants

MFN tariff
Employment

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.0214***(0.0059)
0.0439***(0.0046)0.0048(0.0078)0.0440***(0.0046)-0.0208(0.0216)0.0247***(0.0076)

0.0212***(0.0057)
0.0439***(0.0046)

0.0213***(0.0060)
0.0439***(0.0046)

0.0228***(0.0061)0.0449***(0.0048)0.0052(0.0078)0.0450***(0.0048)-0.0229(0.0259)
0.0261***(0.0082)

0.0225***(0.0058)
0.0449***(0.0048)0.0227***(0.0062)0.0449***(0.0048)

MFN share
MFN share x MFN tariff
Capital int. x Financial dev.

0.0368**(0.0174)-0.0998(0.0742)

Capital int. x Legal quality

34

Relation spec. x Legal quality

0.0418**(0.0213)-0.111(0.0875)-0.0210(0.0379)0.0011(0.0016)

External dep. x Financial dev.
Herfindahl

0.0089(0.0252)

0.0086(0.0253)

0.0089(0.0250)-0.0232(0.0441)0.0005(0.0019)0.0087(0.0251)# Observations
R2
Number of Sectors

189,3240.124
386

189,3240.124
386

189,3240.124
386

189,3240.124
386

174,4790.123
386

174,4790.123
386

174,4790.123
386

174,4790.123
386

Sector Fixed Effect
Country Fixed Effect
Cluster

YES
YESCountrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

Notes: Robust standard errors are in parentheses denoting *** 1%, **5%, and *10% significance. Dependent variable: log of one plus the vertical integrationindex of firm f in primary sector k located in country c. All variables are in logs except for MFN tariff which corresponds to log one plus MFN tariff. Sample
includes firms ≥ 20 employment in the manufacturing sector located in countries with at least 1,000 plants, excluding MNCs.

Table 6: Robustness–Tariffs and Vertical Integration, Multinationals

MFN tariff
Employment

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.0182***(0.0061)
0.0610***(0.0062)-0.0012(0.0084)
0.0611***(0.0062)-0.0256(0.0201)
0.0297***(0.0076)-0.0240***(0.0082)

0.0179***(0.0061)
0.0610***(0.0062)0.0179***(0.0061)
0.0610***(0.0062)0.0193***(0.0061)
0.0644***(0.0061)

0.0190***(0.0060)
0.0644***(0.0060)

0.0189***(0.0061)
0.0644***(0.0061)-0.0237***(0.0082)0.0291*(0.0149)-0.0977*(0.0571)-0.0239***(0.0082)-0.0199**(0.0089)-0.0008(0.0082)
0.0645***(0.0061)-0.0310(0.0248)0.0306***(0.0081)-0.0201**(0.0089)-0.0198**(0.0089)
0.0364*(0.0193)-0.1180*(0.0706)-0.0200**(0.0089)

MFN share
MFN share x MFN tariff
Multinational-0.0238***(0.0082)

Capital int. x Financial dev.
Capital int. x Legal quality

35

Relation spec. x Legal quality-0.0224(0.0253)-0.0006(0.0011)External dep. x Financial dev.
Herfindahl

0.0163(0.0227)0.016(0.0227)

0.0169(0.0224)-0.0303(0.0308)-0.0006(0.0015)0.0160(0.0226)# Observations
R2
Number of Sectors

215,2860.117
386

215,2860.118
386

215,2860.118
386

215,286
0.117
386

193,9380.119
386

193,9380.119
386

193,9380.119
386

193,9380.119
386

Sector Fixed Effect
Country Fixed Effect
Cluster

YES
YESCountrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

YES
YES
Countrysector

Notes: Robust standard errors are in parentheses denoting *** 1%, **5%, and *10% significance. Dependent variable: log one plus the vertical integrationindex of firm f in primary sector k located in country c. All variables are in logs except for MFN tariff, which corresponds to log one plus MFN tariff.
Sample includes firms ≥ 20 employment in the manufacturing sector, including MNCs.

Table 7: Robustness–Tariff Differences and Organizational Convergence
(1)

(2)

(3)

Countries > 1,000 plants
Difference MFN tariffs

0.0091**(0.0040)

Capital int. x difference Financial dev.
Capital int. x difference Legal quality

0.0070*(0.0043)-0.0118(0.0075)
0.0568***(0.0074)

External dep. x difference Financial dev.

36

Sector Fixed EffectDiadic Fixed Effect
Cluster(5)(6)

Including Multinationals
0.0199***(0.0030)0.0208***(0.0034)
0.0029(0.0066)
0.0477***(0.0063)-0.00582***(0.0010)-0.0650***(0.0075)

Relation specificity x difference Legal quality# Observations
R2# country pairs

0.0087**(0.0043)(4)0.0217***(0.0034)-0.00673***(0.0008)-0.0519***(0.0061)

142,573
0.171
1004

121,709
0.172
853

121,709
0.172
853

233,105
0.146
4566

187,182
0.147
3585

187,182
0.148
3585

YES
YES
Country-pair

YES
YESCountry-pair

YES
YESCountry-pair

YES
YESCountry-pair

YES
YESCountry-pair

YES
YESCountry-pairNotes: Robust standard errors in parentheses denoting *** 1%, **5%, and *10% significance. Dependent variable: log of one plus the vertical integrationindex of firm f located in country c in primary sector k. All variables are in logs. In columns (1)-(3), the sample includes firms ≥ 20 employment in the
manufacturing sector located in countries with at least 1,000 plants, excluding MNCs; in columns (4)-(6), the sample includes MNCs.

Table 8: Robustness–Organizational Convergence and RTAs
(1)

(2)

(3)

(4)

(5)

Countries > 1,000 plants
RTA-0.0474***(0.0091)

RTA x Age

Free trade area

-0.0819***(0.0089)-0.127***(0.0145)0.0094(0.0100)-0.132***(0.0170)0.0299**(0.0122)0.0007(0.0007)-0.00586***(0.0008)

Customs union x Age
Free trade area x Age

(7)

(8)

Including Multinationals-0.0239**(0.0103)-0.00281***(0.0006)

Customs union(6)-0.0476***(0.0091)-0.00435***(0.0005)-0.166***(0.0143)-0.0339***(0.0093)-0.154***(0.0154)0.0142(0.0109)(0.0009)(0.0006)-0.00788***(0.0008)37# Observations
R2
Number of Sectors

210,475
0.118
456

210,475
0.118
456

210,475
0.119
456

210,475
0.119
456

328,756
0.057
15686

328,756
0.058
15686

328,756
0.058
15686

328,756
0.058
15686

Sector Fixed Effect
Country Fixed Effect
Cluster

YES
YES
SectorYES
YES
SectorYES
YES
SectorYES
YES
SectorYES
YES
SectorYES
YES
SectorYES
YES
SectorYES
YES
SectorNotes: Robust standard errors in parentheses denoting *** 1%, **5%, and *10% significance. Dependent variable: log of (one plus) the vertical integrationindex of firm f in primary sector k located in country c. In columns (1)-(4), the sample includes firms ≥ 20 employment in the manufacturing sector locatedin countries with at least 1,000 plants, excluding MNCs; in columns (5)-(8), the sample includes MNCs.

Table A-1: Sample Comparisons: Vertical Integration Indices# of plants# of connected plants# of connected firms# of firms# of MNCs
Mean, Vertical integration index
Median, Vertical integration index
Min, Vertical integration index
Max, Vertical integration index
St. dev., Vertical integration index

firms≥ 20 empl.
no MNCs

firms≥ 20 empl.
count with
≥ 1,000 obs.,
no MNCs

firms≥ 20 empl.
MNCs

225,21229,214
6,830
196,586
00.06270.04370.00000.06290.0633

217,723
29,008
6,768
189,32400.06400.04390.00000.83330.0623279,869
64,78910,224215,28618700
0.06400.04390.00000.83330.0645

Notes: Plant- and firm-level data from 2004 WorldBase data, Dun & Bradstreet. Sample includes firms ≥ 20
employees in the manufacturing sector. Column (1) is the main sample; columns (2) and (3) are samples used inrobustness checks.

38

Table A-2: Sample Frame
WB codeFreq.

Percent

Cum.

WB code

ALB
ARG
AUS
AUT
BEL
BEN
BFA
BGD
BGR
BOL
BRA
CAN
CHE
CHL
COL
CRI
CZE
DEU
DNK
ECU
ESP
FIN
FRA
GAB
GBR
GHA
GRC
GTM
HND
HUN
IDN
IND
IRL
ISR
ITA
JAM
JOR
JPN
KEN
KOR

4
998
5,079
1,464
928
4
8
6
360
55
5,594
7,469
1,150
454
550
176
1,736
19,302
425
183
2,322
448
8,965
3
6,622
81
2,231
93
77
2,346
233
2,592
587
1,538
8,426
43
148
34,441
134
3,060

0.00
0.51
2.58
0.74
0.47
0.00
0.00
0.00
0.18
0.03
2.85
3.80
0.58
0.23
0.28
0.09
0.88
9.82
0.22
0.09
1.18
0.23
4.56
0.00
3.37
0.04
1.13
0.05
0.04
1.19
0.12
1.32
0.30
0.78
4.29
0.02
0.08
17.52
0.07
1.56

0.00
0.51
3.09
3.84
4.31
4.31
4.32
4.32
4.50
4.53
7.38
11.18
11.76
11.99
12.27
12.36
13.24
23.06
23.28
23.37
24.55
24.78
29.34
29.34
32.71
32.75
33.89
33.93
33.97
35.17
35.29
36.60
36.90
37.68
41.97
41.99
42.07
59.59
59.66
61.21

MAR
MDG
MEX
MLI
MOZ
MUS
MWI
MYS
NER
NIC
NLD
NOR
NZL
OMN
PAK
PER
PHL
PNG
POL
PRT
PRY
ROM
RWA
SAU
SEN
SGP
SLV
SWE
TGO
THA
TTO
TUN
TUR
TZA
UGA
URY
USA
VEN
ZAF
ZMB
Total

Freq.

Percent

Cum.

603
18
2,641
13
16
46
2
3,101
1
21
676
847
959
67
4
888
351
4
446
5,433
50
614
2
314
47
790
129
689
4
507
79
991
2,557
24
37
114
52,9172311
17196,586

0.31
0.01
1.34
0.01
0.01
0.02
0.00
1.58
0.00
0.01
0.34
0.43
0.49
0.03
0.00
0.45
0.18
0.00
0.23
2.76
0.03
0.31
0.00
0.16
0.02
0.40
0.07
0.35
0.00
0.26
0.04
0.50
1.30
0.01
0.02
0.06
26.92
0.12
0.00
0.01
100.00

61.52
61.53
62.87
62.88
62.89
62.91
62.91
64.49
64.49
64.50
64.84
65.27
65.76
65.80
65.80
66.25
66.43
66.43
66.66
69.42
69.45
69.76
69.76
69.92
69.94
70.35
70.41
70.76
70.76
71.02
71.06
71.57
72.87
72.88
72.90
72.96
99.87
99.99
99.99
100.00Notes: Data from 2004 WorldBase data, Dun & Bradstreet. Sample includes firms ≥ 20 employees in the
manufacturing sector, excluding MNCs.

39

Table A-3: Sample Frame: Restricted Sample

WB code
ARG
AUS
AUT
BEL
BRA
CAN
CHE
CZE
DEU
DNK
ESP
FIN
FRA
GBR
GRC
HUN
IND
IRL
ISR
ITA
JPN
KOR
MEX
MYS
NLD
NOR
NZL
PRT
SGP
SWE
TUN
TUR
USA
Total

Freq.

Percent

Cum.

998
5,079
1,464
928
5,594
7,469
1,150
1,736
19,302
425
2,322
448
8,965
6,622
2,231
2,346
2,592
587
1,538
8,426
34,441
3,060
2,641
3,101
676
847
959
5,433
790
689
991
2,557
52,917
189,324

0.53
2.68
0.77
0.49
2.95
3.95
0.61
0.92
10.2
0.22
1.23
0.24
4.74
3.5
1.18
1.24
1.37
0.31
0.81
4.45
18.19
1.62
1.39
1.64
0.36
0.45
0.51
2.87
0.42
0.36
0.52
1.35
27.95
100

0.53
3.21
3.98
4.47
7.43
11.37
11.98
12.9
23.09
23.32
24.54
24.78
29.52
33.01
34.19
35.43
36.8
37.11
37.92
42.37
60.56
62.18
63.58
65.21
65.57
66.02
66.52
69.39
69.81
70.18
70.7
72.05
100

Notes: Data from 2004 WorldBase data, Dun & Bradstreet. Sample includes firms ≥ 20 employment in the
manufacturing sector located in countries with at least 1000 plants, excluding MNCs.

40

Table A-4: Correlation Table

Vertical integration index
MFN Tariff
Employment
Herfindahl
Capital int. x Financial dev.
Capital int. x Legal quality
External dep. x Financial dev.
Relation spec. x Financial dev.

Verticalint. index
1.00000.06890.06750.00560.0231-0.0414-0.00430.0341MFN
Tariff1.00000.13510.15710.37240.47300.05400.4549

Employment

Herfindahl

1.00000.14920.18230.20800.03320.19691.00000.23340.27970.08800.2433

Cap. Int. x
Fin. dev.

Cap. Int. x
Leg. qual.

Ext. dep. x
Fin. dep.

Rel. spec. x
Fin. dev.

1.00000.77860.20910.56401.00000.16720.62481.00000.10131.0000Notes: Plant- and firm-level data from 2004 WorldBase data, Dun & Bradstreet. All variables are in logs except the Herfindahl index.

41