Adaptation and the Boundary of Multinational Firms
Arnaud Costinot

Lindsay Oldenski

James Rauch

MIT

UCSD

UCSD

November 24, 2008

Abstract
What determines the boundary of multinational firms? According to Williamson(1975), a potential rationale for vertical integration is to facilitate adaptation in a world
where uncertainty is resolved over time. This paper offers the first empirical analysisof the impact of adaptation on the boundary of multinational firms. To do so, we firstdevelop a ranking of sectors in terms of their “routineness” by merging two sets ofdata: (i) ratings of occupations by their intensities in “problem solving” from the U.S.
Department of Labor’s Occupational Information Network; and (ii) U.S. employment
shares of occupations by sectors from the Bureau of Labor Statistics Occupational Employment Statistics. Using U.S. Census trade data, we then demonstrate that, in linewith adaptation theories of the firm, the share of intrafirm trade tends to be higher
in less routine sectors. This result is robust to inclusion of other variables known to
influence the U.S. intrafirm import share such as capital intensity, R&D intensity, relationship specificity, intermediation and productivity dispersion. Our most conservative
estimate suggests that a one standard deviation decrease in average routineness raises
the share of intrafirm imports by 0.26 standard deviations, or an additional 7% ofimports that are intrafirm.

Adaptation and Multinational Firms

1

2

Introduction

Multinational firms choosing to import intermediates from abroad can manufacture themin subsidiaries or buy them from third parties. If this procurement process were free ofcontractual frictions, this would be a distinction without a difference. Many aspects of contractual incompleteness have been analyzed in the theoretical international trade literature
as explanations for why multinationals should strictly prefer one mode of procurement over
the other,1 but just two strands of this literature have dominated empirical application. The
older strand (e.g., Ethier 1986, Markusen 1995) emphasizes difficulty in enforcing intellectual property rights in the countries that host the multinational subsidiaries. Employing the“knowledge capital” model of multinational firms, these papers argue that when multinationals have important trade secrets to protect, this is done more easily if the manufacturing
process is kept within the firm. The newer strand (e.g., Antras 2003, Antras and Helpman
2004, 2008) emphasizes the holdup problem that arises when the multinational headquarters
and its supplier have to make noncontractible relationship-specific investments. Applying
the insight of Grossman and Hart (1986), these papers argue that property rights in the
output of the relationship should be held by the party whose incentive to invest is more
important, hence supply should be kept within the multinational firm when its headquarters
makes the larger contribution to the relationship.2
In this paper we emphasize a different source of contractual frictions that arises dueto the nonroutine quality of many activities a supplier must undertake for a multinational
headquarters. The premise of our analysis is that some activities are more likely than others
to give rise to problems the nature of which cannot be fully specified in a contract ex ante.
When these unspecifiable situations arise the headquarters and its supplier must adapt, andthis adaptation is more efficiently carried out within a firm because incentives for opportunistic behavior are lower, because ex post renegotiation is less costly or because of internal
communications infrastructure. By emphasizing adaptation in an uncertain environment, webuild on fundamental contributions by Simon (1951) and Williamson (1975) and on the recent synthesizing work of Tadelis (2002) and Gibbons (2005).3 In section 2 below we describe
in more detail the theoretical arguments for why nonroutine activities are more likely to be1

See Helpman (2006) and Antras and Rossi-Hansberg (2008) for recent surveys of this literature.
Recent empirical tests of the property rights model of the multinational include Feenstra and Hanson(2005), Yeaple (2006), Defever and Toubal (2007), Tomiura (2007), Bernard et al. (2008), Carluccio and
Fally (2008) and Nunn and Trefler (2008).
3
For an application of the adaptation approach to vertical integration in the U.S. airline industry, see
Forbes and Lederman (2008).
2

Adaptation and Multinational Firms

3

supplied internally, but we will not take a stand on which argument is the most important.
To investigate whether or not “routineness” is an important determinant of the boundaryof multinational firms, we first need data on multinational activities. Following Antras(2003), Yeaple (2006), Nunn and Trefler (2007), and Bernard et al. (2008), we use sector
level data on the intrafirm imports of U.S. multinationals. The United States is the world’s
biggest foreign direct investor, with subsidiaries abroad worth $2.9 trillion in 2006. The
share of U.S. imports that is intrafirm is both remarkably high, 47% in 2006, and widely
varying across industries, from 4% in footwear to 92% in motor vehicles. It is not surprisingthat these data have proven to be a rich source of insight into multinational behavior.
To give empirical content to the notion of “routineness” we build on the work of Autor,
Levy, and Murnane (2003). They used the U.S. Department of Labor’s Dictionary of Occupational Titles (DOT) to classify occupations as routine or nonroutine. We use the Departmentof Labor’s successor to the DOT, the Occupational Information Network (O*NET), to order
occupations from lowest to highest intensity in “problem solving.” 4 We then interpret theseoccupations as activities or “tasks” in a simple Ricardian model of imports of tasks by U.S.
multinational firms, and intensity in “problem solving” as a measure of the need for ex post
adaptation by a headquarters and a supplier, to which we refer as “task routineness.” Accordingly, we say that a sector is less routine than another if its distribution of employment
over the ranked tasks is first-order stochastically dominant.5 The main prediction of oursimple trade model is that if vertical integration increases productivity ex post, but reduces
it ex ante, then less routine sectors should have a higher share of intrafirm trade.
Keeping as close to our theory as possible for our first empirical test, we consider sign
tests for all pairs of sectors that can be ranked in terms of routineness. Sign tests offer mild,
but encouraging support for our prediction: in 67% of all cases, the less routine sector has ahigher share of intrafirm trade. This should not be too surprising since they do not control
for any other determinant of the boundary of multinational firms.
In order to control for these other determinants, we then turn to cross-sector regressions
with country fixed effects. Within chains of sectors that can be ranked in terms of routineness, we find that average task routineness is a strong predictor of the intrafirm share ofimports. According to our most conservative estimate, a one standard deviation decrease in
the average task routineness of a sector leads to a 0.26 standard deviation increase in theshare of intrafirm imports, or an additional 7% of imports that are intrafirm. This result
4
5

O*NET has also been used by Blinder (2007) and Jensen and Kletzer (2007).
We come back to the role of this definition, which is central to our empirical strategy, in Section 2.

Adaptation and Multinational Firms

4is robust to inclusion of the other variables shown by previous studies to influence the U.S.
intrafirm import share.
As a robustness check, we also rerun these regressions using alternative samples of sectors,
including the full sample of 4-digit NAICS industries, and an alternative sample of countries.
In all cases, we obtain qualitatively similar results: less routine sectors have a higher share of
intrafirm trade. Overall, we view these results as strongly supportive of the main hypothesisof our paper: adaptation is an important determinant of the boundary of multinational firms.
In the next section of this paper we develop a simple theoretical model of the determinantsof industry variation in the intrafirm share of U.S. imports to guide our empirical analysis.
Section 3 describes our data sources and provides some descriptive statistics. We present
our empirical results in section 4 and robustness checks of these results in section 5. Our
conclusions are in section 6.

2

Theoretical Framework

2.1

Basic environment

Consider a world economy with c = 1, ..., C countries; s = 1, ..., S goods or sectors; t =1, ..., T tasks; and one factor of production, labor, immobile across countries. We denoteby wc the wage per efficiency unit in country c. There are two types of firms, intermediate
suppliers and final good producers. Intermediate suppliers are present in all countries. Theytransform labor into tasks using a constant-returns-to-scale technology. The total output oftask t in sector s and country c is given by
Ycs (t) =Lsc (t)ac (t, X)(1)

where Lsc (t) ≥ 0 is the amount of labor allocated to task t in sector s and country c; andac (t, X) > 0 is the amount of labor necessary to perform task t once in country c. The
role of X will be described in detail in a moment. Final good producers only are present
in country 1, the United States. They transform tasks into goods using a Cobb-Douglas
technology. The total amount of good s produced with tasks from country c is given by
Ycs =

YT
t=1

s

[Ycs (t)]b (t)(2)

Adaptation and Multinational Firms

5

P
where 1 ≥ bs (t) ≥ 0 and Tt=1 bs (t) = 1. We refer to bs (t) as the intensity of task t in sectors. All markets are perfectly competitive. Tasks and goods are freely traded.

2.2

Adaptation and the make-or-buy decision

For each task, there exist two states of the world, “routine” and “problematic”. Tasks only
differ in their probabilities µ(t) of being in the routine state. µ(t) ≥ 0 is an exogenous
characteristic of a task, to which we refer as its routineness. Without loss of generality, we
index tasks such that higher tasks are less routine, µ0 (t) < 0.
For each task and each country, final good producers in the United States can choose
between two organizations, X ∈ {O, I}. Under organization I (Integration), US final good
producers own their intermediate suppliers at home or abroad, whereas under organization O(Outsourcing), intermediate suppliers are independently owned. The premise of our analysis
is that firms’ organizational choices affect productivity at the task level both ex ante andex post. Let ac (t, X) > 0 denote the amount of labor necessary to perform task t once incountry c under organization X. We assume that ac (t, X) can be decomposed intoac (t, X) = αc (X) + [1 − µ (t)] β c (X)(3)

where αc (X) > 0 is the ex ante unit labor requirement, and β c (X) > 0 is an additional
ex post unit labor requirement capturing the amount of labor necessary to deal with the
problematic state.
The central hypothesis of our paper is that:
H0 . In any country c = 1, ..., C, integration lowers productivity ex ante, αc (I) > αc (O), but
increases productivity ex post, β c (I) < β c (O) .
According to H0 , the basic trade-off associated with the make-or-buy decision is that
integrated parties are less productive ex ante, but more productive ex post. Though H0admittedly is reduced form, there are many theoretical reasons, as we briefly mention in the
introduction, why it may hold in practice:
1. Opportunism. It is standard to claim that external suppliers have stronger incentives
to exert effort than internal suppliers (e.g., Alchian and Demsetz 1972, Holmstrom 1982),
so that contracting out yields a cost advantage to headquarters ex ante. When problems
require the parties to go beyond the contract ex post, however, opportunities for suppliersAdaptation and Multinational Firms

6

to “cut corners” may open up and their stronger incentives to reduce costs can backfire on
headquarters (Tadelis 2002).6
2. Renegotiation. Although contracting out reduces cost ex ante, an arm’s length contract
between headquarters and a supplier can lead to costly delays ex post when problems force
renegotiation (Bajari and Tadelis 2001). Exercise of command and control within the firm
avoids renegotiation costs.
3. Communication. Cremer, Garicano, and Prat (2007) argue that agents within the boundary of a firm develop a common “code” or “language” to facilitate communication.7 Building
up this communications infrastructure is a superfluous expense when a standard contract
can convey all necessary information to a supplier ex ante, but if problems arise ex post thata contract does not cover, a common language shared by the headquarters and the supplier
will reduce the cost of the communication necessary to resolve them.

2.3

Testable implications

Let Xc∗ (t) ∈ {O, I} denote the organization chosen by final good producers (if any) purchasing task t from country c. Profit maximization requiresXc∗ (t) = argmin ac (t, X)(4)

X∈{O,I}The first implication of our theory can be stated as follows.
Lemma 1 Suppose that H0 holds. Then for any country c = 1, ..., C, there exists t∗c ∈{0, ..., N } s.t. task t is outsourced if and only if t ≤ t∗c .
Proof. Let ∆c (t) ≡ ac (t, O) − ac (t, I). By Equation (3), we have∆c (t) = [αc (O) − αc (I)] + [1 − µ (t)] [β c (O) − β c (I)]
Since µ0 (t) < 0, H0 implies that ∆c (t) is strictly increasing in t. Therefore, if Xc∗ (t0 ) = I fort0 ∈ {1, ..., N }, then Equation (4) implies Xc∗ (t) = I for all t ≥ t0 . Lemma 1 directly derives
from this observation.
6

Tadelis in turn cites Williamson (1985, p. 140), who wrote that “low powered incentives have well known
adaptability advantages.”
7
Their model is based on the Arrow (1974) conception of the firm as a community specialized in thecreation and transfer of knowledge. Azoulay (2004) finds that pharmaceutical firms assign “knowledgeintensive” projects to internal teams and outsource “data-intensive” projects.

Adaptation and Multinational Firms

7

Although Lemma 1 offers a simple way to test H0 on task-level data, such disaggregateddata unfortunately are not available. In our empirical analysis, we only have access to sectorlevel data. With this in mind, we now derive sufficient conditions under which one can relate
H0 to sector-level data. We introduce the following definition.
Definition 1 A sector s is less routine than another sector s0 if
Xt0
t=1

bs (t) ≤

Xt0
t=1

0

bs (t) for all 1 ≤ t0 ≤ T.

Broadly speaking, we say that a sector s is less routine than another sector s0 if it isrelatively more intensive in the less routine tasks. Formally, s is less routine than s0 if the
distribution of task intensities in s first-order stochastically dominates the distribution oftask intensities in s0 .8 This implies that if s is less routine than s0 in the sense of DefinitionX
1, then the average routineness of tasks in sector s, µs ≡bs (t) µ(t), is lower than the
average routineness of tasks in s0 . Of course, the converse is not true. Hence, our notion of
a sector being “less routine” is a stronger one.
Let χsc denote the share of intra-firm trade from country c in sector s.
Proposition 1 Suppose that H0 holds. Then for any country c = 1, ..., C, the share of
intrafirm trade is higher in less routine sectors.
Proof. Consider two sectors s and s0 such that s is less routine than s0 . By Lemma 1, weknow thatPTst=t∗ +1 p(t)Yc (t)χsc = PT cs
t=1 p(t)Yc (t)where p (t) is the price of task t under free trade. By Equation (2), we can rearrange theprevious expression asPχsc = Tt=t∗c +1 bs (t)(5)
P
Since Tt=1 bs (t) = 1, Definition 1 impliesPTst=t∗c +1 b (t) ≥PT

t=t∗c +1 b

s0

(t)

(6)

Equation (5) and Inequality (6) imply that for any country c = 1, ..., C, the share of intrafirm
trade is higher in less routine sectors.
8

Recall that 1 ≥ bs (t) ≥ 0 and

PT

t=1 b

s

(t) = 1 for all s.

Adaptation and Multinational Firms

8

Before we turn to our empirical analysis, a few comments are in order. First, Equation (2)rules out fragmentation across countries. Although multiple tasks are combined to produce
one unit of the final good, all tasks must be coming from the same country. Thus we ignore
situations, potentially important in practice, where countries specialize in different sets oftasks.9 In addition, Equation (2) requires that tasks always are combined with the same
technology: task intensity, bs (t), does not vary with c. These two features of the model—nofragmentation and no differences in task intensity across countries—allow us to infer the task
composition of US imports from US (rather than Foreign) data on employment across tasks.
Second, it is worth emphasizing that H0 does not imply that the share of intrafirm trade
should be higher in sectors with lower average routineness of tasks.10 This is an important
observation, which will be at the core of our empirical strategy. In order to test H0 withsector level data, one needs to restrict the sample of sectors to those whose routineness canbe ranked in the sense of Definition 1.
Finally, we wish to point out that the fact that any task is either always outsourced oralways performed in house is not crucial for Proposition 1. In a generalized version of ourmodel where less routine tasks are less likely to be outsourced—because of other unspecified
sector characteristics—Proposition 1 would still hold.11

3

Data

To investigate empirically whether adaptation is an important determinant of the boundaryof multinationals, we first need measures of: (i) routineness at the task level, µ(t); (ii) task
intensity at the sector level, bs (t); and (iii) share of intra-firm trade at the sector and country
level, χsc .
9

See e.g. Feenstra and Hanson (1996) and Grossman and Rossi-Hansberg (2008) for trade models developed along those lines.
10
To see this, consider the following example with three tasks, t = 1, 2, 3, and two sectors, s = 1, 2. Supposethat the levels of task routineness are such that µ (1) = 1, µ (2) = 0.5, and µ (3) = 0; task intensities in
sector 1 are such that b1 (1) = 0, b1 (2) = 1, and b1 (3) = 0; and task intensities in sector 2 are such thatb2 (1) = 0.9, b2 (2) = 0, and b2 (3) = 0.1. By construction, the average task routineness in sector 2 (0.9) isstrictly higher than the average task routineness in sector 1 (0.5). Yet, if t∗c = 2, the share of intra-firm tradeis strictly higher in sector 2.
11This directly derives from the fact that if a distribution F first-order stochastically dominates another
distribution G, then the expected value of any increasing function is higher under F than under G.

Adaptation and Multinational Firms

9

Table 1: Ranking of Tasks from Least to Most Routine

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

3.1

Task
Computer and mathematicalProtective service
Management
Healthcare
Architecture and engineering
Business and financial operations
Life, physical, and social sciences
Arts, design, entertainment, sports, and media
Construction and extraction
Installation, maintenance, and repair
Office and administrative support
Farming, fishing, and forestry
Production
Transportation and material moving
Sales and related occupations
Food preparation and serving
Cleaning and maintenance

Task Data

We define a task t as a 2-digit occupation in the Standard Occupational Classification (SOC)system. To measure how routine each of these tasks is, we use the U.S. Department ofLabor’s Occupational Information Network (O*NET). This database includes measures of
the importance of more than 200 worker and occupational characteristics in about 800 6digit occupations. Such characteristics include finger dexterity, oral expression, thinking
creatively, operating machines, general physical activities, analyzing data, and interactingwith computers. In this paper, we use the importance of “making decisions and solving
problems” as our main index of how routine a task is. Formally, we measure the routinenessµ(t) of a task t asµ(t) = 1 −Xτ.
α (τ , t) P (τ ) 100

where τ is a 6-digit occupation; α (τ , t) ∈ [0, 1] is the employment share of occupations τ intask t in 2006; and P (τ ) ∈ [0, 100] is the importance of problem solving for occupation τ in
O*NET. Table 1 ranks the 17 tasks in our sample from least to most routine.

Adaptation and Multinational Firms

3.2

10

Sector Data

We define a sector as a 4-digit industry in the North American Industry Classification System(NAICS). Equation (1), Equation (2), and perfect competition imply
wc Ls (t)Ls (t)
bs (t) = PT c
= PT css
t=1 wc Lc (t)t=1 Lc (t)(7)

Since task intensity, bs (t), does not vary with c, we can measure it using data on the shareof employment of that 2-digit occupation in any country. In the rest of this paper, we use
U.S. data from the Bureau of Labor Statistics Occupational Employment Statistics 2006.
By Definition 1 and Equation (7), a sector s is less routine than a sector s0 if and only if
its distribution of employment across tasks dominates the distribution in s0 in terms of firstorder stochastic dominance. Table 2 summarizes the 77 sectors in our sample ranked by
X
their average routineness µs =bs (t) µ(t). Asterisks denote chains of sectors that can beranked in the sense of Definition 1.

3.3

Trade data

All trade data are from the U.S. Census Bureau Related Party Trade database and cover theyears 2000 though 2006.12 Variables reported in this database include the total value of all
U.S. imports and the value of related party, or intrafirm, U.S. imports. Imports are classified
as intrafirm if one of the parties owns at least 6% of the other. The data originate with a
Customs form that accompanies all shipments entering the U.S. and asks for the value ofthe shipment and whether or not the transaction is with a related party. These data arecollected at the 10-digit HS level and reported at the 2 though 6-digit level for both HS and
NAICS codes. We use the 4-digit NAICS data for our baseline analysis. Table 3 gives aranking of these sectors by share of intrafirm imports in total U.S. imports for 2006. We
constrain our sample to include only the largest exporters to the U.S., comprising 99 percent
12

The Bureau of Economic Analysis (BEA) also collects data on intrafirm imports in its benchmark surveys
of U.S. direct investment abroad and of foreign direct investment in the US. We use the Census data rather
than the BEA data for several reasons. First, the Census data are publicly available. A subset of the BEA
data is public, however the full dataset is restricted. Second, when reporting intrafirm trade between foreign
owned multinationals and their US affiliates the BEA uses the country of ownership rather than the country
in which the shipment originated. This is problematic for imports by U.S affiliates of foreign parents fromother foreign affiliates of the same parent that are located in different countries. Finally, BEA conducts
benchmark surveys approximately every 5 years and smaller annual surveys in non-benchmark years, withthe firm size cutoff for inclusion in these surveys changing over time. However, for robustness, we also test
our model using the BEA data and get similar results.

Adaptation and Multinational Firms

Table 2: Ranking of Sectors from Lowest to Highest Average Routineness1 Computer equipment
40 Aluminium
2 Basic chemicals
41 Nonferrous (exc alum)
3 Pharmaceuticals
42 Household appliances
4 Pulp, paper, etc.
43 Office furniture*
5 Other chemical
44 Transport equip, nesoi
6 Communications equip
45 Other fabricated metal
7 Converted paper
46 Lime & gypsum
8 Pesticides, etc.
47 Tobacco products
9 Paints & adhesives
48 Ships & boats
10 Crowns/closures/seals
49 Dairy
11 Magnetic & optical media
50 Grain & oilseed milling
12 Aerospace*
51 Boilers & containers
13 Audio & video
52 Foods, nesoi
14 Syn rubber & fibers
53 Purchased steel products
15 Engines & turbines
54 Plastics
16 Cutlery & handtools
55 Fruit & veg preserves
17 Petroleum & coal
56 Other nonmetallic
18 Medical equip & supplies
57 Architect & struct metals
19 Hardware
58 Fabrics
20 Elec equip, nesoi
59 Other textiles
21 Foundries
60 Springs & wire**
22 Clay & refractory
61 Motor vehicles
23 Semiconductors, etc.**
62 Textile furnishings
24 Cement and concrete
63 Sugar & confectionary
25 Electric lighting equipment 64 Finished fabrics*
26 Electrical equipment*
65 Fibers, yarns & threads
27 Sawmill & wood
66 Furniture, nesoi**
28 Ag & cnstrct machinery*
67 Railroad rolling stock
29 Engineered wood
68 Apparel
30 Industrial machinery
69 Bakeries & tortillas*
31 Other wood
70 Apparel accessories
32 Motor vhcle bodies
71 Glass & glass products
33 Household furniture
72 Animal foods
34 Other machinery
73 Other leather
35 Rubber
74 Leather & hide tanning
36 Iron & steel
75 Footwear
37 Beverages
76 Seafood*
38 Motor vehicle parts
77 Meat products
39 Bolts, nuts, screws, etc.
*Chain of sectors that can be ranked in the sense of Definition 1 (Small Sample)**Chain of sectors that can be ranked in the sense of Definition 1 (Large Sample)

11

12

Adaptation and Multinational Firms

Table 3: Ranking of Sectors by Share of Intrafirm Imports in 2006
SectorMotor vehicles
Pharmaceuticals
Magnetic & optical media
Semiconductors, etc.
Transportation equip, nesoi
Computer equipment
Audio & video equip
Rubber products
Medical equip & supplies
Electrical equipment
Syn rubber & fibers
Engines & turbines
Communications equip
Pesticides, fertilizers, etc.
Petroleum & coal
Other chemical products
Paints & adhesives
Ag & cnstrct machinery
Motor vehicle parts
Basic chemicals
Aluminium
Elec components, nesoi
Railroad rolling stock
Motor vhcle bodies
Other machinery
Sugar & confectionary
Pulp, paper & paperboard
Industrial machinery
Hardwareother fabricated metal
Household appliances
Iron & steel
Animal foods
Tobacco products
Dairy
Finished textile fabrics
Leather tanningShips & boats
Foods, nesoi

Share
0.92
0.80
0.71
0.69
0.68
0.67
0.64
0.64
0.64
0.63
0.63
0.61
0.60
0.60
0.60
0.59
0.59
0.59
0.57
0.56
0.55
0.50
0.49
0.48
0.46
0.45
0.43
0.42
0.40
0.40
0.40
0.39
0.39
0.38
0.38
0.37
0.36
0.36
0.36

SectorGlass & glass products
Bolts, nuts, screws, etc.
Bakeries & tortillas
Fruit &veg preserves
Converted paper
Boilers & containers
Products from purchased steel
Cutlery & handtools
Cement and concrete
Aerospace
Office furniture
Springs & wire
Electric lighting equipment
Crowns/closures/seals
Beverages
Plastics
Grain & oilseed milling
Foundries
Lime & gypsum
Clay & refractory
Architech & struct metals
Nonferrous (exc alum)
Furniture related, nesoi
Other woodEngineered wood
Other nonmetallic mineral
Fabrics
Other textiles
Meat prdcts & packaging
Sawmill & wood
Seafood
ApparelApparel accessories
Other leather
Household furniture
Fibers, yarns & threads
Textile furnishings
Footwear

Share
0.35
0.35
0.35
0.34
0.33
0.33
0.32
0.32
0.32
0.32
0.29
0.28
0.28
0.28
0.28
0.27
0.27
0.27
0.26
0.26
0.24
0.24
0.23
0.23
0.22
0.20
0.20
0.19
0.18
0.18
0.17
0.14
0.13
0.13
0.12
0.11
0.10
0.04

13

Adaptation and Multinational Firms

Table 4: Correlation of Sector Characteristics
rtne ln(K/L)
routine1ln(K/L)-0.3901ln(S/L)-0.581
0.427
ln(R&D)-0.553
0.195
specificity-0.126 -0.409
intermediation 0.495-0.485dispersion-0.1830.470

ln(S/L)ln(RD)

spcfcty

intrmddsprsn

1
0.466
0.178
-0.447
0.279

1
0.415
-0.4850.194

1-0.036
0.06691-0.2501

of all U.S. imports. This results in a set of 55 exporters in 77 sectors over 7 years.

3.4

Controls

We use data on capital intensity, skill intensity, R&D intensity, relationship specificity, thedistribution of firm size, and the level of intermediation to control for other known determinants of the boundary of multinationals. Data on the relative capital and skilled labor
intensities of industries are from the NBER Manufacturing Database. Capital intensity ismeasured as the ratio of the total capital stock to total employment. Skill intensity is measured as the ratio of nonproduction workers to production workers in a given industry. As
in Antras (2003), data on the ratio of research and development spending to sales are fromthe 1977 U.S. Federal Trade Commission (FTC) Line of Business Survey. To control forvariations in the importance of relationship specific investments, we use the index developedby Nunn (2007) based on the Rauch (1999) classification. In the spirit of Yeaple (2006), we
also use Compustat data on the standard deviation of sales of firms in an industry to control
for productivity dispersion within an industry. Finally, we follow Bernard, Jensen, Redding,and Schott (2008) and use the weighted average of retail and wholesale employment sharesof importing firms in an industry as a control for intermediation. Table 4 gives correlations
for all of the variables described above as well as sector average routineness.

Adaptation and Multinational Firms

4

Estimation and Results

4.1

Sign tests

14

Proposition 1 offers a simple way to test H0 . For any pair of sectors, if one is less routine
than the other in the sense of Definition 1, then exporter by exporter, it should have ahigher share of intrafirm trade. 67 of the 77 industries in our sample exhibit stochastic
dominance over at least one other sector and 67 industries are dominated by at least one
other sector. Only two sectors (motor vehicles and railroad rolling stock) neither dominate
nor are dominated by any other sector. Out of the 27,081 possible comparisons in our datafor 2006 (pair sectors*countries), 18,002 have the right signs. In other words, in 67% of allcases, the less routine sector has a higher share of intrafirm trade. Overall, we view this firstlook at the data as surprisingly encouraging. Recall that Proposition 1 assumes away anyother determinant of the boundary of US multinationals!
Tables 5 and 6 present the results of our sign tests using 2006 data broken down by
countries and sectors. Each sector-level sign test includes all observations for which a given
industry is either the dominant or the dominated sector in the pair. There is a substantial
amount of variation across countries. Success rates of the sign tests range from 30% in Peru
to 100% in Algeria. Based on these results, there is little evidence that fragmentation andtechnological differences are a major issue for our approach. Algeria, Cambodia and China
are among the most successful countries. There also is a substantial amount of variation
across sectors. Success rates range from 43% for tobacco products to 86% for petroleum
and coal products. The poor performance of our theory for tobacco products clearly suggests that other sector characteristics, such as capital intensity, also affect the boundary ofmultinational firms. In order to address this issue, we now turn to cross-sector regressions.

4.2

Cross-sector regressions

We consider linear regressions of the form
χsc = αc + βµs + γZ s + εsc(8)where αc is a country fixed effect; µs is the average routineness of sector s; and Z s is a vector
of controls. Holding Z s fixed, Proposition 1 predicts that under H0 , less routine sectors

Adaptation and Multinational Firms

Table 5: Sign Tests, Country by Country, 2006
Country (N † )Algeria (13)Argentina (557)
Australia (567)
Austria (501)
Bangladesh (169)
Belgium (295)Brazil (580)
Cambodia (60)Canada (581)
Chile (505)
China (581)Columbia (545)
Costa Rica (546)
Denmark (581)
Dominican Republic (507)
Egypt (359)
El Salvador (314)Finland (536)France (568)Germany (581)
Guatemala (369)
Honduras (284)
Hong Kong (365)
Hungary (473)
India (565)Indonesia (565)
Ireland (560)Israel (561)
Italy (568)*Significant at the 5% level†
Number of sector pairs

Sign
Test
1.00*
0.50
0.79*
0.70*
0.67*
0.53*
0.67*
0.87*
0.50
0.52
0.85*
0.54*
0.65*
0.58*
0.65*
0.67*
0.45*
0.72*
0.70*
0.67*
0.50
0.60*
0.78*
0.61*
0.80*
0.67*
0.76*
0.75*
0.84*

Country (N † )

Sign
Test
Jamaica (228)0.46Japan (581)0.72*
Korea (581)0.72*
Macao (134)0.81*
Malaysia (562)0.67*
Mexico (581)0.64*Netherlands (581)0.65*Netherlands Antilles (136) 0.77*
New Zealand (553)0.43*
Norway (543)
0.68*
Pakistan (337)0.42*Peru (442)0.30*
Philippines (558)0.79*Poland (545)0.62*Portugal (516)0.71*
Saudi Arabia (211)0.85*
Singapore (550)0.82*South Africa (568)0.82*Spain (581)
0.78*
Sri Lanka (295)0.54
Sweden (581)
0.69*
Switzerland (577)
0.79*Thailand (581)0.83*
Trinidad (342)0.55*Turkey (553)0.57*
United Kingdom (581)0.68*
Venezuela (477)0.47
Vietnam (532)
0.66*

15

Adaptation and Multinational Firms

Table 6: Sign Tests, Sector by Sector, 2006
Sector(N † )Test Sector(N † )TestAnimal foods (686)0.70* Lime & gypsum (74)0.61*
Grain & oilseed 418)
0.64* Other nonmetallic (346)0.65*Sugar & confectionary (2014) 0.64* Iron & steel (398)0.69*
Fruit & veg preserves (1535) 0.68* Steel products (583)
0.61*
Dairy (1284)
0.71* Aluminium (484)
0.56*
Meat products (623)
0.79* Other nonferrous (571)
0.66*
Seafood (2112)
0.80* Foundries (204)0.54
Bakeries & tortillas (3097)
0.67* Closures & seals (221)
0.52
Foods, nesoi (980)
0.58* Cutlery & handtools (587)0.67*Beverages (1049)
0.74* Structural metals (746)0.60*
Tobacco products (373)
0.43* Boilers & containers (768)0.62*Fibers, yarns & threads (177) 0.67* Hardware (441)0.68*
Fabrics (302)
0.64* Springs & wire (596)
0.69*
Finished fabrics (878)0.64* Bolts, nuts, etc. (222)
0.68*
Textile furnishings (409)
0.58* Other fabr metal (559)
0.69*
Other textiles (469)
0.63* Ag & cnstrct machinery (940) 0.74*
Apparel (558)0.67* Industrial machinery (489)0.62*Apparel accessories (898)0.67* Engines & turbines (1356)
0.68*
Leather tanning (291)0.79* Other machinery (575)0.72*Footwear (355)
0.65* Computer equip (678)0.77*
Other leather (167)
0.71* Comm equip (505)0.72*
Sawmill & wood (373)
0.77* Audio & video (573)0.70*
Engineered wood (541)0.62* Semiconductors, etc. (2484)
0.75*
Other wood (666)
0.65* Mag & optical media (331)0.53
Pulp & paper (1010)
0.54* Lighting equipment (151)0.61*
Converted paper (373)0.66* Appliances (486)0.65*Petroleum & coal (42)0.86* Electrical equip (1569)
0.78*
Basic chemicals (1570)
0.73* Electrical, nesoi (675)0.74*
Syn rubber & fibers (1432)
0.68* Motor vhcle bodies (231)
0.71*
Fertilizers, etc. (404)0.64* Motor vehicle parts (766)0.78*Pharmaceuticals (278)0.67* Aerospace (3025)
0.46
Paints & adhesives (379)
0.63* Ships & boats (326)0.49
Other chemical (638)0.64* transportation, nesoi (990)
0.56*
Plastics (523)0.55* Household furniture (512)0.69*
Rubber (298)0.59* Office furniture (619)
0.67*Clay & refractory (815)0.63* Furniture, nesoi (966)0.75*
Glass (327)
0.56* Medical equipment (638)0.72*
Cement & concrete (133)0.68**Significant at the 5% level†
N=(number of sectors each industry either dominates or is dominated by)*(number of countries)

16

17

Adaptation and Multinational Firms

Table 7: Regressions for Chain of Sectors Ranked in the Sense of Definition 1 (Small Sample)
Model :
1
2
3
4
N:
2697
2697
2697
2697
Dependent variable is the share of intrafirm imports
routine-0.257*** -0.314*** -1.276*** -1.425***(-7.87)(-5.45)(-10.57)(-7.42)ln(K/L)0.095**
0.914*** 1.092***(2.02)(8.52)(5.30)
ln(S/L)-0.263*** -1.023*** -1.153***(-3.93)(-9.50)(-6.96)ln(R&D)
0.217*** 0.416*** 0.454***(5.53)(9.17)(7.55)
specificity-1.008*** -1.080***(-8.44)(-7.68)intermediation
0.124(1.02)
dispersion

52697-1.310***(-10.38)
0.942***(8.50)-1.089***(-8.75)0.389***(7.56)-1.026***(-8.46)0.058(1.02)
fixed effects
country
country
country
country
country
year
year
year
year
year
R-sq
0.243
0.311
0.364
0.365
0.365
Standardized beta coefficients reported for pooled data from 2000 to 2006.
*,** and *** indicate significance at the 10, 5 and 1 percent levels.
Standard errors are clustered by country-industry.
T-statistics are in parentheses.

should have a higher share of intra-firm trade.13 In other words, if we restrict ourselves to achain of sectors that can be ranked in the sense of Definition 1, then we should observe thatβ < 0.14
Table 7 presents the OLS estimates of Equation (8) for the 7 industries that can beranked in the sense of Definition 1 for all years in our sample. In order to allow for comparison
across right-hand-side variables, we report beta coefficients, which have been standardized torepresent the change in the intrafirm import share that results from a one standard deviation
change in each independent variable. In all specifications, the OLS estimate of β is negative
13

Formally, if ex ante productivity can be written as αc (X, Z s ), then ceteris paribus, less routine sectors
have a higher share of intra-firm trade.
14
Recall that, in general, H0 has no implications for the impact of µs on the share of intra-firm trade.

Adaptation and Multinational Firms

18and statistically significant, implying that less routine sectors have a higher share of intrafirm
imports. Regarding the impact of other sector characteristics, our results are consistent withthe empirical findings of Antras (2003). Capital intensity and R&D intensity tend to increase
the share of intrafirm trade, whereas skill intensity tends to decrease it. By contrast, in thissample of seven sectors the impact of the dispersion of firm size, relationship specificity
and intermediation differ from the results of Yeaple (2006), Nunn and Trefler (2008), andBernard, Jensen, Redding, and Schott (2008), respectively. In Section 5, we return to these
regressions using the full set of manufacturing industries, as in the aforementioned studies.
As we will see, the qualitative results from all of these previous studies are replicated in thiscase.
In terms of magnitude, the impact of routineness is larger than that of capital intensity,
specificity, intermediation, and dispersion in all specifications reported in Table 7. Using thespecification with the smallest coefficient on routineness as a lower bound, we find that a
one standard deviation decrease in the routineness level of a sector leads to a 0.26 standard
deviation increase in the share of intrafirm imports, or an additional 7% of total importsthat are within firm. We view these results as strongly supportive of the main hypothesis of
our paper: adaptation is an important determinant of the boundary of multinational firms.

5

Robustness checks

5.1

Alternative sample of sectors

An obvious drawback of the results presented in Section 4 is that they rely on a sample ofonly seven sectors. In order to increase the size of our sample, we now weaken the criteria
under which two sectors can be ranked in the sense of Definition 1. Instead of requiring
the distribution of task intensities in a given sector to dominate the distribution of task
intensities in another sector for all years, we only require that this ranking holds for atleast one year in our sample. The broad rationale for this alternative criterion is that theabsence of dominance from one year to the next may simply be due to measurement errors
in occupation shares. By following this approach, we extend our sample to ten sectors; see
Table 2 for details. Our OLS estimates using this new sample are reported in Table 8. As
in Section 4, the impact of routineness is negative, statistically significant, and larger inabsolute value than the impacts of the other control variables.
As an additional robustness check, we reestimate Equation (8) using the full set of 4-

19

Adaptation and Multinational Firms

Table 8: Regressions for Chain of Sectors Ranked in the Sense of Definition 1 (Large Sample)
Model :
1
2
3
4
N:
3887
3887
3887
3887
Dependent variable is the share of intrafirm imports
routine-0.256*** -0.282*** -0.406*** -0.486***(-9.57)(-6.00)(-7.15)(-8.98)ln(K/L)
0.193*** 0.252*** 0.231***(4.73)(5.76)(5.38)
ln(S/L)-0.267*** -0.317*** -0.367***(-5.10)(-6.02)(-7.01)ln(R&D)
0.257*** 0.297*** 0.327***(6.88)(7.73)(8.52)
specificity-0166*** -0.486***(-3.57)(-7.53)intermediation-0.322***(-6.25)dispersion

53887-0.610***(-9.63)0.332***(6.50)-0.600***(-7.21)0.223***(4.54)-0.678***(-8.26)-0.484***(-7.36)0.210***(3.31)
fixed effects
country
country
country
country
country
year
year
year
year
year
R-sq
0.211
0.313
0.32
0.342
0.349
Standardized beta coefficients reported for pooled data from 2000 to 2006.
*,** and *** indicate significance at the 10, 5 and 1 percent levels.
Standard errors are clustered by country-industry.
T-statistics are in parentheses.

20

Adaptation and Multinational Firms

Table 9: Regressions for 4-Digit NAICS Manufacturing Sectors
Model :
1
2
3
4
N:
29505
29505
29505
29505
Dependent variable is the share of intrafirm imports
routine-0.179*** -0.066*** -0.067*** -0.066***(-17.75)(-4.78)(-4.98)(-4.83)ln(K/L)0.031**
0.081*** 0.077***(2.43)(4.82)(4.41)
ln(S/L)
0.007-0.013-0.014(0.46)(-0.87)(-0.91)ln(R&D)
0.174*** 0.139*** 0.136***(12.56)(9.51)(9.02)
specificity
0.082*** 0.082***(5.15)(5.15)
intermediation-0.01(-0.75)dispersion

5
29505

-0.069***(-5.07)0.055***(2.99)-0.015(-0.97)0.137***(9.12)
0.070***(4.33)-0.012(-0.88)0.032**(2.48)
fixed effects
country
country
country
country
country
year
year
year
year
year
R-sq
0.213
0.235
0.238
0.238
0.24
Standardized beta coefficients reported for pooled data from 2000 to 2006.
*,** and *** indicate significance at the 10, 5 and 1 percent levels.
Standard errors are clustered by country-industry.
T-statistics are in parentheses.

Adaptation and Multinational Firms

21

digit NAICS industries. The results are presented Table 9.15 The coefficient on routinenessremains negative and statistically significant in all of these specifications. In line with ourtheory, the results using all manufacturing industries are not quite as strong as those using
chains of sectors satisfying Definition 1. Nevertheless, the impact of routineness is still more
important than capital intensity, skill intensity, specificity, intermediation and dispersion.
Finally, note that unlike in previous regressions, these last results also are consistent withprevious empirical findings of Yeaple (2006), Nunn and Trefler (2008), and Bernard, Jensen,
Redding and Schott (2008). Productivity dispersion and relationship specificity tend toincrease the share of intrafirm trade, whereas intermediation tends to decrease it.

5.2

Alternative sample of countries

One drawback of the Census data is that they do not distinguish between imports by U.S.owned multinationals from their foreign affiliates and imports by U.S. affiliates of foreignowned multinationals.16 Since our theoretical framework focuses on the former case, we also
run our regressions using the restricted sample of countries proposed by Nunn and Trefler(2008). A country is included in the restricted sample if at least two-thirds of intrafirm U.S.
imports from that country are imported by U.S.-owned firms. Nunn and Trefler constructthis sample using data on intrafirm U.S. imports by country and parent in 1997 from Zeile(2003). The results using this restricted set of countries are presented in Table 10. In linewith the results using the full sample of countries, the coefficient on routineness is negativeand statistically significant in all specifications.17

6

Conclusion

Nonroutine activities a supplier must undertake for a multinational headquarters are more
likely than routine activities to give rise to problems the nature of which cannot be fully
specified in a contract ex ante. A strand of the literature stretching back to Simon (1951)
15

In yet another robustness check, we also considered 6-digit NAICS level sectors instead of 4-digit NAICS
in our regressions using the industry-level weighted average problem solving score. The results were qualitatively similar.
16
A second drawback is that we only have data on intrafirm imports relative to total imports by all U.S.
firms, not relative to U.S. imports by multinationals, which would do a better job of capturing the share of
inputs imported by multinationals that are intrafirm. This drawback, unfortunately, is common to both the
U.S. Census and BEA data.
17
We also re-run the regressions presented in Tables 8 and 9 using this restricted set of countries andobtain results that are similar to those using the full sample of countries.

22

Adaptation and Multinational Firms

Table 10: Regressions for Chain of Sectors Ranked in the Sense of Definition 1 (Small Sample)
for Restricted Set of Countries
Model :
1
2
3
4
N:
1323
1323
1323
1323
Dependent variable is the share of intrafirm imports
routine-0.210*** -0.203*** -0.988*** -1.03***(-4.72)(-2.77)(-5.69)(-4.02)ln(K/L)
0.094
0.765*** 0.819***(1.49)(5.09)(2.93)
ln(S/L)-0.201** -0.815*** -0.854***(-2.40)(-5.38)(-3.88)ln(R&D)
0.261*** 0.420*** 0.432***(4.86)(6.68)(5.18)
specificity-0.820*** -0.842***(-5.01)(-4.40)intermediation
0.038(0.24)
dispersion

51323-0.997***(-5.60)0.773***(4.98)-0.834***(-4.92)0.412***(5.92)-0.825***(-4.96)0.017(0.24)
fixed effects
country
country
country
country
country
year
year
year
year
year
R-sq
0.229
0.302
0.337
0.337
0.337
Standardized beta coefficients reported for pooled data from 2000 to 2006.
*,** and *** indicate significance at the 10, 5 and 1 percent levels.
Standard errors are clustered by country-industry.
T-statistics are in parentheses.

Adaptation and Multinational Firms

23

and Williamson (1975) that we refer to as “adaptation theories” of the firm implies thatmultinationals are more likely to supply nonroutine than routine activities internally. We
tested this prediction using sector level data on the intrafirm imports of U.S. multinationals
from the Census and occupation level data from the U.S. Department of Labor’s Occupational Information. Using both non parametric sign tests and cross-sector regressions, wefound that less routine sectors tend to have a higher share of intrafirm trade. This result isrobust to inclusion of other variables known to influence the U.S. intrafirm import share suchas capital intensity, R&D intensity, relationship specificity, intermediation and productivity
dispersion. Our most conservative estimate suggests that a one standard deviation decreasein average routineness raises the share of intrafirm imports by 0.26 standard deviations, oran additional 7% of imports that are intrafirm. To us, these results indicate that routinenessis a key determinant of the boundary of multinational firms, and that “adaptation theories”
of the firm merit further development and empirical application in the multinational context.

7

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