Vertical Production Networks in Multinational Firms*
Gordon H. Hanson
University of California, San Diego and NBER
Raymond J. Mataloni, Jr.
U.S. Bureau of Economic Analysis
Matthew J. Slaughter
Tuck School of Business at Dartmouth and NBER

February 2004
Abstract: In recent decades, growth of overall world trade has been driven in large part by the
rapid growth of trade in intermediate inputs. Much of this input trade involves multinationalfirms locating input processing in their foreign affiliates, thereby creating global verticalproduction networks. In this paper, we use firm-level data on U.S. multinationals to examine
trade in intermediate inputs for further processing between parent firms and their foreign
affiliates. We estimate affiliate demand for imported inputs as a function of host-country andindustry trade costs, factor prices, and other variables. Among our main findings are thatdemand for imported inputs is higher when affiliates face lower trade costs, lower wages for lessskilled labor (both in absolute terms and relative to wages for more-skilled labor), and lower
corporate income tax rates. These results contrast with many findings in previous research.

*Email addresses: gohanson@ucsd.edu, raymond.mataloni@bea.gov, and matthew.slaughter@dartmouth.edu. For assistancewith data we thank Bruce Blonigen, Fritz Foley, Jon Haveman, and James Rauch. For helpful comments we thank David Belli,
Lael Brainard, Fritz Foley, Ann Harrison, Jim Hines, Ned Howenstine, Tom Hubbard, Jorg Mayer, Peter Merrill, Richard
Sansing, Doug Staiger, Adrian Tschoegl, Obie Whichard, William Zeile and conference and seminar participants at the American
Economics Association, the Brookings Institution and International Tax Policy Forum, Erasmus University, the Federal Reserve
Board, the London School of Economics, the National Bureau of Economic Research, Stanford University, the U.S. Bureau of
Economic Analysis, University of Chicago Graduate School of Business, the Wharton School, and Yale University. For financial
support Hanson and Slaughter gratefully acknowledge the National Science Foundation. Views expressed in this paper are thoseof the authors and do not necessarily reflect those of the Bureau of Economic Analysis. The statistical analysis of firm-level dataon U.S. multinational companies reported in this study was conducted at the International Investment Division, U.S. Bureau of
Economic Analysis, under arrangements that maintained legal confidentiality requirements.

1. Introduction
History has seen multiple waves of “globalization.” On many measures, recent decades
constitute another such wave. All waves are not the same, however. Two novel features of thecurrent process of globalization are increased trade in intermediate inputs and increased flows offoreign direct investment (FDI).
Intermediate inputs play an important role in the recent growth of world trade. Yeats (2001)
finds that trade in inputs has grown much faster than trade in final goods, and he estimates thatintermediates now account for 30% of world trade in manufactures.1 Hummels, Ishii, and Yi(2001) identify vertical specialization, which they define as production arrangements in whichfirms make final goods via multiple stages located in multiple countries, as an important aspectof overall input trade.2 They calculate that from 1970 to 1990, the increase in exports associatedwith vertical specialization accounted for one-third of world export growth. During the early
20th century, in contrast, inputs played only a small role in U.S. trade (Feenstra, 1998).
As for FDI, the rising share of multinational enterprises in overall production helps
distinguish the current phase of globalization from previous episodes (Bordo, Eichengreen, andIrwin, 1999). Multinationals now mediate a large fraction of world trade. In the United States,
they account for over half of total exports (Slaughter, 2000). Within manufacturing, the majorityof these exports are of intermediates. In 1999, 93% of exports by U.S. parent firms to theirforeign manufacturing affiliates were inputs for further processing (U.S. BEA, 2002).
Parent-to-affiliate input trade is one element of what we shall call vertical production
networks in multinationals. The term “vertical” captures two key features of these networks: (i)within these firms the parent and each affiliate performs a narrow rather than broad range of
1 See also Campa and Goldberg (1997).

1

production activities and (ii) the parent and its affiliates are linked by intra-firm flows of inputs
and outputs. In the jargon of trade theory, vertical production networks are a form of “vertical
FDI” through which multinationals spread across different locations the different activities thatthey perform, such as R&D, input production, and input processing.
To fix ideas on vertical production networks, consider the well-known example ofautomobile production in North American Free Trade Agreement (NAFTA) countries. Canadianand Mexican auto plants have extensive intra-firm links with their U.S. counterparts, mediatedby large flows of inputs across borders. It is estimated that every day $250 million in autos andauto parts crosses the Ambassador Bridge that connects Detroit, Michigan with Windsor,
Ontario. When the U.S. government closed this bridge and other entry points after the terrorist
attacks of September 11, 2001, several NAFTA auto plants had to shut down within 48 hours
because of input shortages (Council on Foreign Relations, 2002).
The extent of vertical production networks, however, varies across both countries andindustries. In Brazil, which is protected by common external tariffs under the Common Marketof the Southern Cone (Mercosur), multinational auto manufacturers tend to set up integrated
production facilities that engage in relatively little international trade with their parent firms orwith other affiliates of the same parent (Hanson, 2001). In many countries, other industries, suchas chemicals and non-metallic minerals, also exhibit high degrees of integration.
What accounts for this variation? Theories we discuss below offer several explanations forvertical production networks, including cross-country and/or cross-industry differences in trade
costs, factor prices, and the technological separability of production. While there is anecdotal
evidence to support these theories, little work goes beyond documenting broad facts to provide a2 This phenomenon has been given various names, including de-localization, disintegration of production, fragmentation, global
production sharing, foreign outsourcing, and slicing up the value chain. See Feenstra and Hanson (2002) for a discussion.

2

micro-level empirical analysis of the importance of these explanations. In this paper we provide
such an analysis using firm-level data on U.S. multinationals.
These data come from legally mandated confidential surveys conducted by the U.S. Bureau
of Economic Analysis (BEA) of all U.S. multinationals. For majority-owned foreign affiliates inmanufacturing in 1994, we have a direct measure of input flows associated with verticalproduction networks: imports from U.S. parent firms (and other U.S. entities) of intermediate
inputs for further processing.3 We use a standard cost-minimization framework to derive thedemand for imported inputs by foreign affiliates, and we then estimate the sensitivity of thisdemand to host-country/industry trade costs, factor prices, taxes, and other variables suggestedby theory. Our estimation combines data from the BEA surveys with data from outside sources
on host-country policies and characteristics.
A problem in identifying the effects of trade costs on input flows between U.S. parents andtheir foreign affiliates is that we do not observe transaction prices for imported intermediates. To
address this issue, we exploit the fact that the typical parent has multiple foreign affiliates in eachindustry in which it operates. We model input prices as a function of a parent-and industryspecific component – which can be removed by controlling for parent-by-industry fixed effects –and country-and-industry-specific trade costs – which we observe directly. Residual variation in
input prices may reflect transfer pricing, for which we also attempt to control.
Our empirical analysis yields a number of results on the determinants of vertical production
networks in multinational firms.

One is that imports of intermediate inputs are strongly

negatively correlated with trade costs facing affiliates.
3 We examine one component of vertical production networks:

Our preferred estimates imply an

the processing by foreign affiliates of intermediate inputs
imported from their U.S. parents. Unfortunately, the BEA data do not track transactions between foreign affiliates of a given
parent or production networks involving arm’s-length inter-firm transactions. Thus, we do not observe processing trade between
foreign affiliates or between U.S. parents and foreign entities that they do not own. This implies our data are not well suited to
examine production networks in their entirety or questions of optimal firm boundaries.

3

elasticity of well over unity: a 1% fall in trade costs leads to a 2%-4% increase in the quantity ofimported intermediate inputs for further processing. This responsiveness of vertical production
networks to trade costs is suggestive of Yi (2003), who shows that small changes in tariffs may
produce large changes in input trade. Previous empirical work on multinationals (e.g., Carr,
Markusen, and Maskus, 2001), finds that the scale of foreign-affiliate operations is greater thehigher are host-country tariffs and transportation costs. These results have been interpreted tomean that higher trade costs encourage FDI. When a multinational intends to sell goods in aparticular foreign market, higher trade costs may indeed encourage locating production in thatmarket. But our results suggest that when multinationals serve various foreign markets via
vertical production networks, higher trade costs may deter FDI instead.
A second finding is that vertical production networks are sensitive to labor costs. Importedinput demand is decreasing in host-country wages for less-skilled workers and increasing in hostcountry wages for more-skilled workers. Here again, our results contrast with previous empiricalliterature (e.g., Brainard, 1997), which tends to find that, if anything, U.S. firms prefer to locate
in high-wage countries.

We find that foreign affiliates do more processing of imports incountries with relatively cheap less-skilled labor.
A third finding is that vertical production networks also depend on other host-country
policies and characteristics. Imported-input demand is higher in host countries with exportprocessing zones, and is decreasing in host-country market size and corporate tax rates. While
many studies document how taxes affect aggregate FDI (Hines, 2001), there is little work on
how taxes or other host-countries policies affect the composition of FDI.

4

Our paper has four additional sections. In section 2 we discuss related research. In section 3
we present our empirical framework. In section 4 we discuss the data and present summary
statistics. In section 5 we report our estimation results. And in section 6 we conclude.

2. Related Research
Our work is relevant to several bodies of literature on trade in intermediate inputs andmultinational enterprises. The first is research on trade costs and vertical production networks.
In an important recent paper, Yi (2003) argues that standard models of trade in final goods
cannot account for how the modest observed declines in trade barriers could have produced thedramatic observed growth in world trade. Yi shows theoretically how trade in inputs allows
declining trade barriers to trigger magnified decreases in production costs and thus dramaticincreases in total trade flows. We examine one link within global production chains – the flowof intermediate inputs from U.S. parents to their foreign affiliates – and find that within-firmtrade is indeed highly sensitive to variation in trade costs.
Related empirical work includes Feinberg and Keane (2001), who examine the determinantsof bilateral trade between U.S. parents and their Canadian affiliates over the period 1983-1992.
They find that affiliate total imports from U.S. parents have little association with Canadiantariffs, though in the reverse direction they find that parent imports from Canadian affiliates
negatively vary with Canadian tariffs. They also find that import flows have no correlation with
Canadian or U.S. wages. Our work differs from theirs by focusing on trade in intermediate
inputs, rather than total trade, and by analyzing parent-affiliate trade in a broad cross-section ofcountries, rather than just in Canada. Their finding of no wage impacts on parent-affiliate trade,for example, might simply reflect small Canadian-U.S. wage differences. In support of this idea,

5Hanson, Mataloni, and Slaughter (2001) and Borga and Zeile (2002) find that foreign affiliates of
U.S. multinationals engage in relatively more intra-firm trade in low-wage countries.4
The second body of literature to which our paper relates is empirical work on theories ofmultinational firms. Theory tends to view multinationals as the result of either horizontal
expansion (in which firms save on trade costs associated with exporting by setting up foreign
facilities whose range of production activities mirrors the operations they perform at home) or
vertical expansion (in which firms fragment different production stages across different countries
to arbitrage international differences in factor prices).5 Casual evidence suggests that horizontal
FDI is the dominant strategy of U.S. multinationals. In 1998, OECD countries accounted for
76.6% of sales by affiliates of U.S. firms (Hanson, et al, 2001), giving the impression that market
size (and not wage levels) is the host-country feature U.S. multinationals care most about.
Several recent studies test theories of FDI rigorously by using aggregate data on the total
sales of U.S. foreign affiliates by country (or by country and industry). 6 Most studies find that
affiliate sales are higher in larger countries and in countries with higher tariffs and transport costs
on U.S. goods, but not in countries with larger skill differences relative to the United States.
These results are interpreted as evidence in favor of horizontal FDI and against vertical FDI.
In our work, we do not treat horizontal and vertical FDI as mutually exclusive options. The
same U.S. multinational might choose to operate in France an integrated plant to produce for thedomestic market and might also choose to operate in China a specialized factory to assemble
components manufactured by the U.S. parent. The French affiliate would be the result of
4 For related evidence, see Kumar (1994) and Barba Navaretti, Haaland, and Venables (2002). Again using aggregated data,
Shatz (2000) and Markusen and Maskus (2001) study the exporting behavior of U.S. multinationals.
5 On the former, see Markusen (2002) and Markusen and Venables (2000). On the latter, see Helpman (1984), Helpman andKrugman (1985), and Yeaple (2001). Ekholm, Forslid, and Markusen (2003) model “export platform” FDI, in which mostaffiliate output is exported out of the host country, an affiliate activity that shares both horizontal and vertical features.

6horizontal FDI, the Chinese affiliate vertical FDI. Having firm-level data allows us to examine
the factors that determine where different affiliates fall in a continuum with pure horizontal FDI
at one extreme and pure vertical FDI at the other extreme. This goes beyond previous researchby addressing the factors that shape the composition of production inside affiliates.
Other bodies of literature to which our work relates includes studies on the labor-market
consequences of foreign outsourcing (see the survey in Feenstra and Hanson, 2002), empiricalstudies on the magnitude of spillovers associated with FDI (e.g., Haskel, Pereira, and Slaughter,2001), and recent theoretical work on how trade costs shape ownership and outsourcing
decisions in multinational firms (Grossman and Helpman, 2002a,b).

3. An Empirical ModelIn this section, we develop an empirical framework for how U.S. multinationals organize theoperations of their foreign affiliates. Consistent with our data, we focus on affiliate operations at
a given point in time (see note 7). We assume that U.S. parent firms have previously chosen inwhich countries to locate affiliates. The remaining decision is over which production activities
affiliates should perform. For simplicity, consider two production stages: input manufacturing
and input processing. Input manufacturing often involves producing sophisticated componentry,
and so is likely to be relatively skill and capital intensive. Input processing often is limited toassembly, and so is likely to be relatively labor intensive.
Consider two alternative strategies for FDI. A vertical production network would haveforeign affiliates import inputs from their U.S. parents, process these inputs into final products,
and then ship these processed inputs on to destination markets. By locating labor-intensive input
6 See Brainard (1997); Markusen and Maskus (1999); Blonigen, Davies, and Head (2002); and Helpman, Melitz, and Yeaple(2003). Yeaple (2001) finds the impact of host-country education on affiliate sales to be weaker for less-skill-intensive
industries, suggesting that multinationals in these industries prefer less-skill-abundant countries.

7

processing abroad and capital-intensive input production in the United States, this strategy could
allow the multinational to take advantage of international differences in factor prices. But
shipping inputs between countries results in high transport costs. An alternative strategy would
have foreign affiliates both produce and process inputs. In this case, the firm replicates abroad
the production activities of the U.S. parent. This strategy minimizes transport costs, since there
is no need for affiliates to import inputs, but it also prevents the multinational from taking fulladvantage of factor-price differences across countries. In reality, firms may choose a strategy
between the two extremes just described, with this choice depending on trade costs, labor costs,
and on other host-country and industry characteristics.

3.1 Empirical Specification
Consider a foreign affiliate of a U.S. parent firm. Let the cost function for the affiliate beC(w, Y). Here, Y represents affiliate total output and w is a vector of factor prices facing theaffiliate, including wage rates for labor used in input production, wage rates for labor used in
input processing, the rental price of capital, the price of manufacturing inputs imported from theparent, and the price of services imported from the parent.
There are two items to note about this cost structure. The first is its static nature. This
follows from our data being a cross-section and not a panel of affiliates.7 The second is that weassume the affiliate cost function is separable from that of the U.S. parent. This allows us to
examine activity choices of affiliates in isolation, and is appropriate so long as input production
and input processing are technologically separable activities and affiliates face output prices set
7 Data on input trade for U.S. parents do exist for a few other BEA “benchmark” years. We have not added these years to oursample because for other years we lack corresponding non-BEA data on host-country trade policies. Adding additional years
might not improve the estimation markedly. Over short to medium time horizons, the primary variation in key variables
including wages, tariffs, and tax rates is across countries and not time. Having additional years of data would allow us to control
for affiliate fixed effects, but this would come at the cost of losing much of the systematic variation in the data. Even with just a8

on the world market. In the estimation, we allow for the possibility that within a multi-affiliate
firm, the same affiliates of a given U.S. parent use similar production technology and facecommon prices for inputs or services imported from the parent.
To derive an estimating equation, we need to select a functional form for costs. A convenient
choice is the translog form, which Diewert (1974) introduced and Kohli (1978, 1991) used
initially in the international-trade literature. In our case, this function can be written as
H

ln C = α 0 + ∑ α h ln w h +
h =1

1
2

H H

∑ ∑ γ hj ln w h ln w j

h =1j =1
H

1
2

(1)

+ β y ln Y + β yy ln Y 2 + ∑ φ hy ln w h ln Y
h =1

where wh denotes the prices of the optimally chosen variable inputs h=1,…,H.
The usefulness of the translog function becomes evident when we compute first derivatives,∂lnC/∂lnwh = (∂C/∂wh)(wh/C). By Shepard’s Lemma, ∂C/∂wh equals the demand for input h. It
follows that (∂C/∂wh)(wh/C), the elasticity of total cost with respect to price h, equals the share offactor h in total costs, which we denote by the cost-share sh. Differentiating equation (1) withrespect to lnwh yields the following expression,H

s h = α h + ∑ γ hj ln w j + φ hy ln Y ,

(2)

j =1

for h=1,…,H.8 The expression in (2) relates the demand for input h, measured as its share in
total costs, to prices of variable inputs and the level of output.

single year of data, we are able to control for parent-by-industry fixed effects, which allows us to address several important
estimation issues.
8 In the event there are constant returns to scale, φ =0. We find evidence consistent with affiliate technology being constanthy
returns. That affiliates exhibit constant returns in no way implies that parents do as well (given the importance of R&D and otherfixed costs in parent operations, we expect their technology to exhibit increasing returns).

9

These cost shares are the basis for our empirical work. We estimate the cost-share equation
for the key input of imported intermediates for further processing, and we denote the share ofimported intermediate inputs in total costs by sm.9 We interpret affiliates with higher values of sm
to be affiliates that are more specialized in the activity of processing parent-produced inputs.
Accordingly, we interpret our estimates of the sm equation to inform about the determinants ofvertical production networks in multinational firms.10
Given our definition of sm, an estimating equation for affiliate a in industry i belonging to
U.S. parent p and located in host-country c is,hqmmms aipc= α ip + γ ms ln w sc + γ mu ln w cu + γ mk ln raipc + γ mm ln p ipc+ γ mo ln p ipc+ φ my ln Yaipc + ε aipc

(3)
where α ip is a parent-and-industry-specific constant term; w sc is the price of skilled labor inmcountry c; w cu is the price of unskilled labor in c; raipc is the affiliate’s rental price of capital; p ipc

hq) is the price of intermediate inputs (headquarter services) imported from parent p in
( p ipcmis a disturbance term.
industry i and country c; Yaipc is affiliate output; and ε aipc

After estimating equation (3) it is straightforward to calculate the cross- and own-priceelasticities of factor demand.

The own-price elasticity of demand for factor m, imported

intermediate inputs, is given by,

9 Given data limitations, we cannot estimate cost shares for other inputs. BEA data only report aggregate employment of capitaland labor for an affiliate. When an affiliate, say, hires labor we do not know whether it is to produce inputs, process inputs, orboth. By estimating a single cost-share equation, we cannot impose cross-equation parameter restrictions implied by thesymmetry of cross-price derivatives and by the fact that the cost shares sum to one.
10 Strictly speaking, our interpretation of sm requires the assumption that affiliates all sell the same output in terms of the locationon the value-added chain. Otherwise, differences in cost shares could reflect differences in affiliate location along the chain of
production, independent of the range of activities affiliates perform. The BEA data contain no such information on output stages,
as is the case for most if not all micro-level data. See Adelman (1955) for a discussion of these measurement issues.

10γ mm + s m (s m − 1)
PED mm =sm(4)where γ mm is a parameter to be estimated and s m is calculated at the mean for the regression

sample. The cross-price elasticity of demand between inputs m and h is given by,
PED mh =γ mh + s m s h
sm(5)

An advantage of the translog framework is that it does not impose restrictions on the pair-wiseelasticities of substitution between inputs (as do Cobb-Douglas or CES).

3.2 Estimation IssuesmSeveral important estimation issues merit attention. One is that we do not have data on p ipc

hq, the transaction prices that affiliates pay for inputs and services they import from theiror p ipc

parents. This limitation is hardly surprising. There is little price data for trade flows anywhere,
let alone for individual businesses operating in hundreds of countries and industries. We address
this problem by exploiting the structure of the BEA data; specifically, the fact that most foreign
affiliates share a U.S. parent with at least one other affiliate.
m
Suppose that p ipc
can be expressed in log terms as the sum of two parts: the f.o.b. price in themthat isUnited States for that import, and a host-country wedge between that U.S. price and p ipc

due to trade costs. For example, a U.S. parent that manufactures electrical appliances may obtaintungsten in the United States for all its light-bulb affiliates abroad, but different affiliates may
pay different prices based on the trade costs they face. In our analysis, we model this pricemwedge as the sum of import tariffs and transport costs, allowing us to write p ipc
as,mm
ln p ipc= ln p ip+ ln(1 + τ ic + f ic )

11

(6)

where p ipm is the f.o.b. price of the input in the United States, τic is the ad valorem tariff rate thatcountry c levies on imports in industry i, and fic is the ad valorem freight rate on imports fromthe United States in country c for industry i.
We have data on trade costs but not on p ipm . We do, however, observe the activities ofmultiple affiliates that operate in the same industry and that share the same parent. If the parent
charges its affiliates in the same industry the same f.o.b. price for inputs, then trade costs will bemthe only source of variation in p ipc
across affiliates in the same industry belonging to the samemin two parts: (a) a full set of parent-by-industry dummy
parent. We can then measure p ipcm~ =α +γ
variables, α
ip
ip
mm ln p ip , which captures industry-and-parent specific technology, αip,

and the unobservable f.o.b. price, p ipm ; and (b) host-country trade costs, which we measure using
data on host-country and industry tariffs, non-tariff barriers, and freight rates. Multinationals are
sometimes accused of attempting to influence host-country trade policy to their advantage.
Given this, in the estimation we allow for the possible endogeneity of trade costs.11
Allowing for parent-by-industry fixed effects, rather than just parent effects, adjusts for thefact that many multinationals are multi-product firms whose affiliates span diverse industries.
Thus, a parent firm’s light-bulb affiliates can face different imported-input prices from its
refrigerator affiliates. These parent-by-industry controls also address differences in the extent ofvertical production networks due to technological primitives or firm business practices (e.g.,
networks may be less feasible in food industries with highly perishable inputs).
11 To assign industry tariffs to imported inputs, we assume that all input trade is intra-industry (i.e., that the inputs affiliates

import from their parents have the same industry classification as the output they produce). Given that the BEA defines
industries at the three-digit level, this may not be a bad working assumption (e.g., autos and auto parts share the same three-digit
industry, as do computers and peripheral devices). To relax this assumption somewhat, we verified that our results are robust to
the sub-sample of affiliates who fall in the same three-digit industry as their parent. Within host countries, if trade costs vary12hq
By the same logic, we also control for unobservable headquarter-service prices, p ipc. The~ , capture any unobserved components of service prices thatparent-by-industry controls, αipparents charge to all affiliates in the same industry; measures of trade costs capture countryand/or industry-specific components (see note 11). Affiliates may import from parents a wide
range of headquarter services, such as use of patents, copyrights, and trademarks, analysis ofmarket conditions, or advice about management strategy. Parents may receive payment for these
services through either fees or repatriated earnings. The price parents charge may vary acrossaffiliates due to transport costs (e.g., more-remote affiliates may be more costly to service), in amanner similar to imported manufacturing inputs.
mhq
and p ipc
is transfer pricing. It is commonly
An important aspect of measuring both p ipc

asserted that multinationals price intra-firm transactions such that firm-wide pre-tax profits
accrue to affiliates in low-tax jurisdictions.12 Among the factors that affect motives for tax
avoidance are the corporate income tax rate in the affiliate’s host country and the global foreigntax-credit status of the U.S. parent (Hines, 2001). We include the host-country corporate tax rate
directly as a regressor. The incentive of a U.S.-based multinational to avoid foreign taxes isgreater for firms with excess foreign tax credits.13 By controlling for parent-by-industry fixed
effects, we implicitly control for the parent’s global tax status.
A second estimation issue is that we do not observe prices for any intermediate inputs that
affiliates may purchase locally. In principle, the price for local inputs should be an additional
regressor in equation (3). In practice, these prices may be captured in part by our just-discussed
substantially across products within three-digit industries, then our measure of trade costs may be subject to measurement error,
which would tend to bias coefficients on the variable toward zero. Our use of instrumental variables may help correct for this.
12 See Sansing (1999) for an overview of U.S. tax code regarding multinational transfer pricing and for a theoretical analysis oftransfer-pricing rules. For empirical evidence on transfer pricing, see Clausing (2001) and Grubert and Mutti (1991).

13

m
hq
measures for p ipc
and p ipc
. If local input prices are set on the world market, and so equal toworld input prices plus trade costs, then including as regressors parent-by-industry dummy
variables and trade costs controls for their presence.14
A third estimation issue involves the regressand.

In our data, the dependent variablemtakes a value of zero for many affiliates. This may be due to the technological infeasibilitys aipcof separating input production and processing, which would result in an affiliate strategy ofintegrating both activities. One approach to this problem would be to include in (3) measures of
the technological separability of production as regressors and then estimate (3) as a Tobit.
Below, we experiment with one possible industry-level proxy. The parent-by-industry dummy
variables could also proxy for these measures, but the presence of a large number of unobserved
fixed effects complicates use of the Tobit. With few observations per indicator variable category(i.e., small numbers of affiliates of the same parent firm and in the same industry), there is therisk that Tobit estimates of parameter coefficients would be inconsistent (Wooldridge, 2002).
Instead, we address zero observations in the dependent variable in two ways. First, we dropfrom the sample affiliates in industries in which imports of inputs for further processing are
rare.15 The excluded industries—e.g., food processing, pulp and paper, newspapers, soap andcleansers—appear to be primarily ones in which either there is a single stage of production or
technology or transport costs make it prohibitively expensive to geographically separate input
production and input processing. Second, we check the consistency of our OLS estimates of (3)13 All else equal, for a firm with excess foreign tax credits, a reduction in foreign tax liabilities reduces global tax liabilities(without affecting domestic tax liabilities). In contrast, for a firm with deficit foreign tax credits, a reduction in foreign tax
liabilities often implies an increase in domestic tax liabilities (without necessarily affecting global tax liabilities).
14 The coefficient that we estimate for trade costs thus captures how trade costs affect imported input demand both through theireffect on imported input prices and through their effect on domestic input prices.
15 The cutoff we apply is an industry mean of imported inputs for further processing as a share of total sales of greater than 5%.

14

by using a Tobit with a more-aggregate set of indicator variable categories. Fewer parentindustry combinations increase the observations per category and thus improve consistency.
A fourth estimation issue is how to measure wages. In equation (3) wages are assumed tovary only across countries. This would be the case if affiliates are price-takers in labor markets
and if labor markets are national in scope. In practice, wages may vary across other dimensions
as well. For instance, wages may vary by industry, consistent with evidence on inter-industry
wage differentials, and particular multinationals may offer wage premia to attract more able
workers.

In the empirical analysis, we use as a baseline wages at the country level andexperiment with measures at the industry or affiliate level.
A final estimation issue is that there may be country-level policies and characteristics that
influence how foreign affiliates organize their activities. To control for this possibility, we
include as additional regressors measures of host-country investment and exchange-rate policies
and of the quality of its financial, legal, and political institutions.

3.3 Summary of Estimation Strategy

To summarize our estimation strategy, our goal is to explain the variation in imported
intermediate inputs across foreign affiliates that are in the same industry and that share the same
U.S. parent. In controlling for parent-by-industry fixed effects, we identify the responsiveness ofprocessing imported inputs to trade costs, wages, tax rates, and other factors by exploiting thecross-country variation in these variables. Our baseline estimating equation is:m~ + γ ln w s + γ ln w u + γ ln r + γ ln(1 + τ + f )s aipc
=α
ip
ms
c
mu
c
mk
aipc
mm
ic
ic

(7)
m
+ γ mt ln(1 − t c ) + φ my ln Yaipc + βX ic + ε aipc

15

where, among the new variables relative to (3), τic is the host-country industry ad valorem tariff
rate; fic is the host-country industry ad valorem freight rate; tc is the host-country corporate tax
rate; and Xic are measures of other host-country and/or industry policies and characteristics.
Coefficient estimates from (7) will thus provide insight about the determinants of verticalproduction networks in multinational firms.

4. Data Description and Summary Statistics
4.1 Data Sources

The primary data for this project are for the operations of U.S. multinationals, both of their
U.S.-based parents and their foreign affiliates, as collected by the BEA in legally mandated
confidential surveys. An appendix describes in detail these and related non-BEA data.
We use BEA data from its 1994 “benchmark” survey, which covers the entire population of
U.S. multinationals in that year. Less data are collected for minority-owned affiliates, so we use
the population of majority-owned affiliates in manufacturing, of which there were 6,955 in 1994
(out of 8,014 total manufacturing affiliates linked to 1,456 U.S. parent enterprises). This groupof majority-owned affiliates spanned 54 manufacturing industries and 105 host countries; therelated group of parents has a median of four affiliates (with a maximum of 146). Each affiliate
is classified in a single industry of primary business based on the distribution of its sales acrossindustries. Controlling for parent-industry fixed effects require us to exclude from the sample
any affiliates that are the sole affiliate of a parent/industry.
mis the share in total
For our regression analysis in equation (7), the dependent variable s aipc

affiliate costs of imports for further processing from the United States (either from the U.S.

16

parent or from another U.S. entity).16 Total economic costs are proxied by total accounting
revenues plus inventory changes.17 We use two affiliate-level regressors: the capital rental rate
and output. Rental rates are defined in the appendix. Total output is measured as value added.
Turning to other regressors, we do not observe the true marginal prices for labor w cu and w sc
facing each affiliate. We approximate marginal prices using wage unit values (compensation
divided by employment) constructed from the United Nations’ Industrial Statistics Database. As
discussed in Section 3.2, for robustness we also try other wage measures.
Our primary trade-cost measures are tariffs, non-tariff barriers (NTBs), and transportation
costs; all are bilateral for the host country vis a vis the United States and all vary by country andindustry. Tariffs and NTBs come from the United Nations’ TRAINS (Trade Analysis andInformation System) database; transportation costs were generated from data files in Feenstra(1996). Tariffs and transport costs are ad valorem measures; NTB presence or absence is a
categorical measure. Adjacency to the United States is an additional proxy for trade barriers.18
The remaining regressors capture additional host-country or industry policies andcharacteristics.

From the University of Michigan World Tax Database, we have statutory

corporate income tax rates that measure maximum marginal tax rates facing these businesses.
To measure market size in the host-country, we use Hanson and Xiang’s (2002) measure ofmarket potential: national GDP plus a distance-weighted sum of GDP in neighboring countries.
We include an indicator variable for English as the primary language. We also include several
policy-related regressors: an indicator for the presence of export-processing zones (EPZs) and
16 The BEA tracks imported inputs from both from U.S. parents and from non-parent U.S. entities. Approximately 90-95% ofimports from the United States by foreign affiliates of U.S. multinationals are from parent firms. Even where affiliate imports
come from an entity other than the parent, the parent may still have arranged the transaction. As we discuss later, estimation
results from using just imports from parents are nearly identical to those we report.
17 Assuming that total costs equal total revenues is restrictive, but necessitated by the lack of complete data on costs.

17

the value-added tax rate, both from PricewaterhouseCoopers; an indicator for whether the hostcountry fixes the value of its currency to the U.S. dollar, from Shambaugh (2002); and measuresof “economic freedom” and of “FDI openness,” both from the Heritage Foundation and The WallStreet Journal. Finally, we include materials purchases as a share of shipments in the UnitedStates for the affiliate’s industry. This share may reflect the technological feasibility of anindustry to organize its constituent activities into global production networks.
For the sample used to estimate our initial specifications of equation (7), Table 1 reportssummary statistics for our regressand and several regressors. Relative to the full population of
6,955 majority-owned affiliates in manufacturing, our baseline sample is smaller due to missing
host-country data. This baseline sample spans 39 industries (and is linked with 632 U.S. parents,
leading to 777 parent-industry effects) across 42 host countries. For the average affiliate in our
sample, 11% of its total costs are accounted for by imports of intermediate inputs for further
processing. Average host-country/industry import tariff and freight rates are each about 5% (andthese two variables have a sample correlation of 0.22). The mean less-skilled wage is just overhalf the value of the average more-skilled wage.

4.2 Preliminary Evidence

Before showing regression results, Table 2 first shows patterns of vertical production
networks by broad industry and country groups in our data. Each cell of this table reports themean share of imported inputs for further processing as a share of total revenues.
Input processing is most prominent in regions that have low trade costs and/or low laborcosts vis a vis the United States. Mexico is the most obvious example. It is a low-wage country,

18 In unreported results, we included distance from the United States to the host-country as a regressor. After controlling fortransportation costs and adjacency to the United States, distance was insignificant in most specifications.

18and in 1994 it benefited not just from adjacency to the United States but from the start of lower
trade barriers in the NAFTA. Canada is another example. While a high-wage country, it haseven lower trade costs than Mexico. Canada’s major production centers are located quite nearindustrial regions of the United States, and the Canadian-American Free Trade Agreement was
signed in 1989. Input processing is also relatively common in East Asia. While distant from theUnited States, the region has moderate labor costs and open markets, at least in certain industries
such as electronics. Consistent with theory, poor candidates for input processing include regionswith high labor costs and moderate to high trade costs (OECD Europe, OECD Asia) and regionswith relatively closed markets (Other Latin America, with the Mercosur countries of Argentina
and Brazil; Other Asia, with India, Africa, and the Middle East).
Apart from regional patterns, certain industries appear to be good candidates for input
processing. These include machinery, transportation equipment, and electronics, of which thelargest sub-industry is computers. Several common features of these industries may make themamenable to global outsourcing. One is that production tends to involve distinct stages—design,
component production, final assembly—that are physically separable. Firms need not performthese tasks in the same location, and so can locate different stages in different countries. Anotherfeature is that these production stages exhibit different factor intensities, with design activities
and component production being more skill-intensive and assembly activities being more laborintensive. To the extent that factor costs vary across countries, firms may have an incentive tolocate labor-intensive activities in labor-abundant countries.19
In Section 1, we pointed out that the BEA does not track links in multinationals’ verticalproduction networks other than affiliate imports of inputs for further processing from U.S.

19

entities. In particular, BEA data do not measure affiliate exports of processed inputs to otheraffiliates or to their U.S. parent. Instead, the BEA reports only total affiliate exports. But if
affiliates are exporting mainly processed intermediates, then our examination of affiliate
imported inputs may be augmented by studying affiliate exports as well.
Table 3 offers some initial evidence on the export patterns of U.S. foreign affiliates.
Constructed analogous to Table 2, each cell of this table reports affiliate exports to the UnitedStates as a share of total revenues. The pattern of export intensity across regions and industries
is very similar to the pattern of imported-input intensity in the previous table. Export intensity ishigher in countries with low trade costs and/or low wages relative to the United States. For oursample of affiliates, the correlation between affiliate imports of intermediate inputs from the U.S.
as a share of revenue and affiliate exports to the U.S. as a share of revenue is 0.59. Again, Table
3 measures total affiliate exports, not just affiliate exports of processed intermediate inputs, andso may be a noisy measure of trade flows within U.S. multinationals’ vertical production
networks. Still, the strong patterns of similarity between Tables 2 and 3 suggest that affiliatesthat are more oriented towards processing imported inputs are also more oriented towards
production for export, as we would expect for affiliates that were part of such networks.
In the regression analysis, to which we turn next, we aim to uncover which factors drive thecross-affiliate variation just documented. The micro-data permit us to control for variation in theextent to which each parent chooses to operate vertical production networks.

19 Note that Table 2 broadly accords with the introductory examples in Section 1 of vertical production networks. For example,
Canadian and Mexican affiliates in transportation products have nearly half of their total sales accounted for imported
intermediate inputs—in contrast with a share of barely 3% for transportation affiliates in other Latin American countries.

205. Estimation Results
5.1 Main Results for Imported-Input Demand

Table 4 reports our baseline OLS estimates of equation (7). The dependent variable isimports for further processing as a share of affiliate revenues. Column 1 excludes additional
controls, column 2 adds in a large number of such controls, and column 3 shows our preferred
specification with a narrow set of controls chosen from the precisely estimated variables incolumn 2. All specifications include 777 parent-by-industry dummy variables.
There are three notable results in Table 4. First is the role of trade costs, which in this tableare defined as in equation (7) as the sum of both ad valorem tariffs and freight rates. Imported
intermediate inputs as a share of affiliate revenues are strongly negatively correlated with trade
costs. Recall from Section 3.2 that trade costs are a potentially important determinant of theprice of intermediate inputs that affiliates face. Our estimates suggest that affiliates do in fact
respond to trade-cost-induced changes in input prices.

The coefficient estimate from ourpreferred specification in column 3 of Table 4 implies (from equation (4)) that the own-priceelasticity of demand for imported inputs is -3.28 (standard error of 0.71): a 1% fall in input
prices due to a reduction in trade costs leads to a 3.3% increase in the quantity of imported
intermediate inputs demanded by an affiliate.20
This sensitivity of input demand to trade costs is suggestive of Yi’s (2003) model in whichsmall changes in tariffs produce large changes in production costs and thus input trade. Yi
examines the entire chain of cross-country input transactions. Our results on the parent-affiliate
link in this chain appear broadly consistent with his theory.
This evidence also differs from previous empirical work on multinationals (e.g., Carr, et al,2001), which finds that foreign-affiliate sales are higher the higher are host-country tariffs and21

transportation costs.

Our evidence suggests a more-nuanced story, in which at least one

important affiliate activity—processing imported intermediates—is inhibited, not encouraged, by
higher trade barriers. The net impact of trade barriers on total FDI is likely to vary acrossaffiliates with the scope and size of their different productive activities.
A second notable finding in Table 4 is the sensitivity of input processing to labor costs.
Imported-input demand is decreasing in host-country wages for less-skilled workers andincreasing in host-country wages for more-skilled workers, with all coefficients precisely
estimated. The estimates from column 3 of Table 4 imply (from equation (5)) that the crossprice wage elasticities of demand for imported inputs are –0.32 (standard error of 0.09) for lessskilled labor and 0.36 (standard error of 0.09) for more-skilled labor. A 1% fall in less-skilled(more-skilled) wages leads to about a 1/3% increase (decrease) in imported intermediate inputs.
This responsiveness of input processing to wages is at odds with much of the earlier
empirical work on multinationals that uses aggregate data (e.g., Brainard, 1997; Markusen and
Maskus, 1999; Blonigen et al, 2002). Consistent with models of vertical FDI, we find that
foreign affiliates do more processing of imports the lower are low-skilled wages relative to highskilled wages. This accords with our intuition that imports for further processing and less-skilledlabor are complements. That we find stronger evidence of vertical FDI than previous literature
may be due to our use of micro-level data on foreign affiliates. Previous work, in contrast, uses
data that aggregates not just across the activities of a given affiliate but also across all affiliates
in a given industry and country. Our data suggest multinationals vary in the range of activities
they perform and therefore may display both vertical and horizontal production characteristics.
The third notable result of Table 4 is the important role played by other host-country policies
and characteristics. In our preferred column (3), imported-input demand is significantly (at the
20 Standard errors for elasticities are calculated using the Delta method.

2210% level) higher in countries with lower corporate tax rates. This role for tax rates is consistent
with evidence (e.g., Hanson, 2001) that many governments give tax breaks to foreign firms thatengage in export processing within their borders—an activity that involves vertically specialized
affiliates that import large amounts of inputs. Many studies have examined how taxes affectaggregate FDI (Hines, 2001), but not on how they affect the composition of FDI.
Note that if transfer pricing were dominating the variation in our data, then we would expect
to see the opposite correlation between host-country tax rates and imported-input intensity. If
multinationals were simply shifting profits to low-tax countries via input trade, then affiliates inthese low-tax locations would be charged a below-market price for inputs. Holding affiliate
output constant, this practice would generate low values for our dependent variable in low-tax
locations, the opposite correlation from that actually revealed in our preferred specifications.
Demand for imported intermediate inputs is higher for affiliates in Canada or Mexico,consistent with these countries enjoying lower trade costs with U.S. firms due to a combinationof geographic proximity and preferential market access. Input demand is also significantly
higher in host countries with EPZs. EPZs typically offer a range of incentives to promote
exports, including tax holidays, expedited transit of goods through customs, and tariff breaks onimported equipment, which may induce firms to concentrate input processing in affiliates.21
Affiliate reliance on imported intermediate inputs is lower in countries situated in regions withlarger market potential.

This is consistent with horizontal theories of FDI, in which

multinationals located in markets with larger local demand are more oriented towards integrated
production (perhaps for local consumers) and less oriented towards operating via verticalproduction networks. Affiliate reliance on imported inputs is higher in host countries with23

higher levels of economic freedom. One interpretation is that poor governance of the economy isespecially damaging to firms engaged in high levels of input importing and exporting.
The remaining regressors in Table 4 have generally insignificant coefficient estimates.
Though insignificant, the estimates for NTBs are puzzling: imported intermediates as a share ofaffiliate revenues are positively correlated with NTBs. To the extent that NTBs are trade barriers
that raise import prices, the opposite correlation might be expected. Data problems may matter
here. NTBs do not measure whether quantitative restrictions on trade bind, but rather whether
governments have the option of imposing them.

5.2 Additional Results and Robustness Checks for Imported-Input Demand

Table 5 applies alternative estimators to our preferred specification in Table 4. Column (1)uses Tobit rather than OLS. In order to obtain consistent coefficient estimates (Wooldridge,2002), we needed to reduce the number of fixed effects in the specification from a full set ofparent-industry controls to a set of two-digit industry controls. Most coefficient estimates retain
the same sign and pattern of significance—e.g., on trade costs and wages. The coefficient on
capital is still positive but has become significantly so.

This accords with the presumed

complementarity between input processing and less-skilled labor discussed above, as there issubstantial evidence in labor economics that capital and less-skilled labor are price substitutes.
The coefficient estimate for output changes both sign and significance. As discussed in Section
2, there are concerns about the sensitivity of Tobit estimates to the included fixed effects.
Nevertheless, the broad patterns appear match those of the OLS estimates.

21 A standard feature of EPZs is tariff breaks on inputs imported from abroad (for inputs used to produce exports). One might

expect, then, that tariffs would have no impact on imported inputs in countries with EPZs. In unreported regressions weinteracted the EPZ variable with trade costs to capture this effect but found the interactions to be insignificant.

24

The second column of Table 5 re-estimates our preferred specification where we replace ourparent-industry dummy variables with a full set of country dummy variables. The cost of thesecountry controls is the need to exclude all regressors that vary by country only. The benefit isthat this checks whether estimates for the key trade-cost regressor are driven by unobserved,
country-specific factors. The coefficient on trade costs is somewhat smaller in absolute valuethat in other specifications but remains statistically significant. The implied own-price elasticity
from this country fixed-effects estimate is -2.82 (standard error of 0.97).
The third column of Table 5 uses an instrumental variables (IV) estimator. It is commonly
asserted that multinationals have a large say in trade-policy formulation in host countries (e.g.,
Blonigen and Figlio, 1998)—especially small developing countries. If this were the case, thenour negative correlation between trade costs and imported-input shares might reflect not the
response of firms to exogenously given trade costs but rather the political-economy outcome offirms with vertical production networks lobbying successfully for low tariffs. To address thispossibility we re-estimate our preferred specification instrumenting for trade costs with tariffs
from 1989, five years before our sample. These earlier-period tariffs are valid instruments ifthey are correlated with current trade costs yet are uncorrelated with the disturbance term.
Intuitively, we find it plausible that affiliate operations in 1994 have weak political-economy
links with 1989 tariffs. Our IV estimates are qualitatively unchanged from the OLS estimates,with the coefficient on trade costs remaining significantly negative.22

22 An ex ante argument for discounting the endogeneity concern may be that actual estimation sample does not contain (due todata limitations discussed in Section 4.1) many very small countries where political influence of multinationals is likely to bestrongest. The instruments used were three measures of 1989 industry-country tariffs, each of which used a different weighting
scheme for aggregating the raw tariff data (see Data Appendix for details). The correlation between trade costs and our
instruments is quite high: a regression of trade costs on the instruments obtained an R-squared of 0.60. We also note that
instrumenting for trade costs may address measurement-error bias. Our analysis as formalized in equation (7) assumes that anaffiliate classified in industry X based on the composition of its sales imports inputs in that same industry X. In principle, thisassumption need not be true: an industry-X affiliate may import inputs from another industry Y. In practice, the BEA has no
information on the industry composition of imported inputs for further processing. Note that because equation (7) conditions ouranalysis on affiliate output, we do not face measurement error due to using nominal rather than effective rates of protection.

25

Beyond what is reported in Table 4 and 5, we conducted other robustness checks. One set ofchecks involved alternative measures of key regressors and our regressand. We constructedindustry tariffs and transportation costs using simple arithmetic rather than trade-weighted
averages. We replaced statutory corporate tax rates with effective average corporate tax rates,
reported in Grubert (2001) as constructed by the IRS Statistics of Income Division from tax
returns of U.S. multinationals. We measured wages using affiliate data on average annual
salaries for production (less-skilled) and non-production (more-skilled) workers (either for agiven affiliate or for other affiliates in the same industry and country). And we also measured
our regressand using inputs imported from U.S. parents only, excluding imports from
unaffiliated U.S. third parties (see also note 14). Our estimation results were qualitatively
unchanged by these and other measurement choices.
A second set of robustness checks involved sample restrictions. One was to exclude fromour sample all affiliates in Mexico or Canada, to ensure that our findings were not driven byintra-NAFTA trade patterns.

A second was to exclude affiliates facing very high host-country/industry tariffs. One might worry that the negative correlation between trade costs andimported-input intensity reflects tax evasion: in the face of high tariffs, firms might under-reportthe value of imported inputs to lower tax liabilities. For China-Hong Kong trade, Fisman andWei (2001) find evidence of under-invoicing, but only at high tariff rates (i.e., above 35%). We
tried eliminating all observations of tariffs above various cut-offs, down to 20%. Our estimation
results were also qualitatively unchanged by these and other sampling choices.

5.3 Export Behavior and the Determinants of Vertical Production Networks

Consistent with theories of vertical FDI, we saw in Section 4.2 that affiliates with greater
reliance on imported intermediate inputs also tend to be more export-intensive. Given this, we26

can gain additional insight on vertical production networks by examining export intensity. In thepresence of vertical production networks, we would expect affiliate imports of inputs andaffiliate exports to respond similarly to trade costs, factor prices, and other variables. To seemwhether this is the case, we replace the regressand s aipc
in equation (7) with the share of affiliate

exports to the United States in affiliate revenues. Table 6 reports these final estimation results.
The five specifications are our baseline and preferred OLS specifications from Table 4, and the
Tobit, country fixed-effect, and IV specifications from Table 5.
Comparing columns 1 and 2 of Table 6 with columns 1 and 3 of Table 4, we see that every
coefficient estimate except that on capital costs retains the same sign and often the same patternof statistical significance.

Like imported-input intensity, affiliate U.S.-export intensity is

negatively correlated with host-country trade costs, less-skilled wages, and corporate taxes (incolumn 2), and positively correlated with high-skilled wages and the existence of EPZs.
Comparing columns 3, 4, and 5 of Table 6 with columns 1, 2, and 3 of Table 5, we again see
similar sign patterns and mostly similar patterns of statistical significance. In particular, trade
costs are statistically significant for two of these three alternative specifications.
These results suggest that where affiliates are more specialized in processing inputs imported
from the United States, they also export a higher fraction of their output to the United States.
Lower trade costs, lower wages for less-skilled labor, and lower corporate tax rates are all
associated with stronger export orientation, just as they are associated with stronger importintensity. While the data do not permit us to say explicitly that the exports affiliates send to theUnited States are processed inputs imported from the United States, the results are consistent
with this interpretation and with the general conclusion that vertical production networks shapetrade patterns between U.S. parents and their foreign affiliates.

27

6. Conclusion

Distinguishing features of the most recent wave of globalization have been the rise ofmultinational firms and of trade in intermediate inputs. In this paper, we have examined theforces behind both these new developments by analyzing imports of inputs for further processing
from U.S. multinational parents by their affiliates in foreign countries. These shipments ofintermediate inputs provide a direct measure of vertical production networks in multinationals.
Theory suggests that affiliate input processing depends on influences such as trade costs andfactor prices. Our results support this prediction.
Among our main findings are that affiliate demand for imported inputs is higher is hostcountries with lower trade costs, lower wages for less-skilled labor (in absolute terms andrelative to wages for more skilled labor), and lower corporate income tax rates. Demand for
inputs is also affected by other host-country policies and characteristics, including the presenceof export-processing zones and the size of the host-country market. Exports by affiliates exhibit
similar patterns of correlation with trade costs, wages, and other variables.
Our findings contrast with much earlier empirical work on multinationals, which tended tofind that the scale of activities by foreign affiliates of U.S. parent firms is larger in countries withhigher wages and higher trade costs vis a vis the United States. Our results suggest thatmultinationals tailor their foreign operations to local market conditions. They appear to focus
affiliates on processing imported inputs in countries where wages and trade costs are lower andmarkets are smaller; and they appear to focus affiliates on production for local consumers incountries where wages and trade costs are higher and markets are larger.
Our results also suggest that other host-country policies shape vertical production networks,
sometimes in unexpected ways. Lower tax rates on corporate income are associated with greater28

affiliate input processing.

While both theory and empirical work have shown that lower

corporate taxes in a country are generally associated with greater FDI inflows, there has been
little work on whether taxes affect how multinationals organize their foreign operations.
One final point is to re-emphasize what our analysis does and does not reveal about verticalproduction networks in multinational firms. Since BEA data do not track transaction prices
between parents and affiliates, we identify the sensitivity of imported input demand to trade costs
based on variation in the data across foreign affiliates that share the same parent. While we haveattempted to control for variation in input prices associated with incentives for tax avoidance andfor the endogeneity of host-country trade policies, there may still be residual sources of variation
in input transaction prices that influence the results.

Higher quality data on inter-firmtransactions would help address this issue. Additionally, we have focused on intra-firm tradeflows conditional on a parent having a particular network of foreign affiliates. In future work, it
would be interesting to extend this focus to the intertemporal decision of establishing and/or
closing foreign affiliates. It would also be interesting to study vertical production networks ininter-firm arm’s-length transactions. As we discussed, by construction the BEA data do not track
such activities. But to the extent that these inter-firm dimensions of networks respond to thesame incentives we have identified, our findings may apply more broadly.

29

Data Appendix
BEA Data

The BEA data come from legally mandated confidential surveys it conducts. A U.S.
multinational consists of one U.S. parent plus one or more foreign affiliates. A parent is a U.S.
legal entity, such as a corporation, that generally controls a business enterprise located in the
United States and that has at least one foreign affiliate. A foreign affiliate is a foreign businessenterprise (incorporated or unincorporated) in which there is U.S. direct investment; that is, it is aforeign business enterprise in which the U.S. parent has at least a 10-percent equity stake.23
Firms responding to BEA surveys are instructed to denominate revenues and costs in U.S. dollars
that are, for the most part, valued in the prices and exchange rates of the year covered by thedata.
Industry of Affiliate: Each affiliate is classified in a single industry of primary business based on
the distribution of sales across industries that it is required to report. For our 1994 data, the BEA
uses an internal industry definition, the BEA International Surveys Industry (ISI) codes, which
are closely related to the 1987 3-digit U.S. Standard Industrial Classification (SIC) codes. A
foreign affiliate generally represents the consolidation of the U.S. direct investor’s business
operations in a host country in a single three-digit industry.24
Imported Intermediate Inputs for Further Processing: Our measure of imported inputs forfurther processing capture imports both from parent and non-parent U.S. entities. Approximately
90-95% of these imports are from parent firms. Even where affiliate imports come from anentity other than the parent, the parent may still have arranged the transaction. With this in mind,
the measure of affiliate imports we use is all U.S. imports, including imports from parent andnon-parent entities. Estimation results using just imports from parents are nearly identical tothose we report. For affiliates belonging to small U.S. parents, the BEA imputes imports forfurther processing using data on total affiliate imports. Our results are robust to alternative
imputation methods and to dropping affiliates with imputed data from the sample.
Wages: Manufacturing affiliates are required by the BEA to report wage bills and employment
separately for non-production and production workers, a breakdown that tends to separate moreskilled from less-skilled workers. For each affiliate we construct wage unit values for nonproduction and production workers. In unreported results, we use these own-affiliate wage
measures and also construct “outside” wage measures defined for each affiliate as the average
wage paid by all other affiliates in the same country and industry. These outside wages may
address the potential problem of endogeneity in affiliate-level wages, in that variation in affiliate
wages may reflect variation both in true prices for given labor quality and in labor quality itself.

23 In this paper, we use the term affiliate or foreign affiliate to refer to this class of enterprises. Note that in the BEA data eachaffiliate is a business enterprise–different from other micro data sets on establishments, e.g., the U.S. Longitudinal Research
Database. Enterprises may operate any number of establishments, information that the BEA does not track.
24 The only exception to this rule is that foreign affiliates in the same host country, but in different three-digit industries, may beconsolidated if they are integral parts of the same business operation.

30

Rental Price of Capital: The rental rate of capital is defined as an affiliate's return to capital(value added less employee compensation) over its fixed capital stock (net property, plant, andequipment) averaged over the 1985-1994 period (or, for affiliates established after 1985, fromthe affiliate’s birth to 1994). Rental rates for a single year are highly volatile, and averaging
over time helps smooth this series. In unreported regressions, endogeneity concerns lead us totry as an alternative measure the rental return to capital calculated for other affiliates in the same
country and industry. Results for this measure are very similar to those that we report.
Non-BEA Data
Wages: Using data from the UNIDO Industry Database for 1994, we measure the unskilled
wage as the average annual salary per worker in that country’s apparel sector, which in mostcountries is the lowest-wage manufacturing industry. We measure the skilled wage as theaverage annual salary per worker in a country’s chemical, electronics, and electrical machineryindustries, which in most countries are the highest-wage, most skill-intensive industries.
Trade Barriers: Tariffs are from the United Nations’ TRAINS (Trade Analysis and Information
System) CD-ROM. The original source data are classified by country and by 6-digit
Harmonized System product codes.25 Aggregation of the data from a 4-digit SIC basis to an ISI
basis was obtained by weighting the disaggregated data by the value of U.S. exports to thecountry. Data on NTBs also come from TRAINS. The original source data for these were at the
4-digit SIC level, which we then concorded to BEA industries as just described. This
information is categorical, indicating presence or absence of NTBs tracked by TRAINS.

The transportation-cost data were generated from data files in Feenstra (1996). For eachobservation we constructed transportation costs as charges for insurance and freight as a share of
the customs value of imports. The original source data are classified on a 4-digit 1987 SIC basis;
they were aggregated to an ISI basis by weighting the disaggregated data by the value of total
U.S. exports to the host country. (We use exports and not imports to weight 4-digit transport
costs since the freight rates we require are those that apply to inputs that the U.S. parent exports
to its affiliates in a given country. Results using U.S. imports as weights to construct transport
costs are very similar to those that we report.)
Other Variables: From PriceWaterhouseCoopers, we use an indicator for the presence of exportprocessing zones (EPZs) that captures a range of host-country export incentives. Using datafrom Shambaugh (2002), we also construct an indicator for whether the host country fixes thevalue of its currency to the U.S. dollar. Recent research (e.g., Frankel and Rose, 2002) finds thatbilateral trade is stimulated by currency unions; we generalize from this evidence to examine
whether host-country exchange-rate policy matters along the more-general fixed-versus-float
margin. A final policy regressor is a collection of “economic freedom” measures collected by
the Heritage Foundation and The Wall Street Journal. We average country index scores forrespect for property rights, extent of government regulation, and prevalence of black-market
activities, with higher scores indicating less economic freedom.

25 These data, plus a translation of them to a 1987 4-digit U.S. SIC basis were obtained from Jon Haveman.

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Table 1: Summary Statistics
Variable

Mean (s.d.)Imported-Input Share

0.110(0.176)More-Skilled Wages($, Thousands)

28.631(17.089)

Less-Skilled Wages($, Thousands)

14.811(7.897)

Tariff Rate(ad valorem)

0.054(0.051)

Transportation Costs(ad valorem)

0.045(0.038)Trade Costs(ad valorem)

0.099(0.071)

Corporate Tax Rate

0.344
(0.056)

Adjacent to United States

0.176(0.380)English Speaking

0.317(0.465)

No. Observations

4,102

Notes: Each cell reports the variable mean and, in parentheses, its standard deviation. These statistics are for the
sample used in the baseline estimates reported in Table 4; the year of these data is 1994. See text for details on variable
definitions and construction.
Source: Authors’ calculations using data from the U.S. Bureau of Economic Analysis and other sources.

35

Table 2: Affiliate Imported Intermediate Inputs, by Industry and Region, 1994
RegionIndustry

CanadaMexicoOther
Lat.Am.

OECD
Europe

OECD
Asia

East
Asia

Other
Asia

AfricaMiddleEast

World

Chemicals

18.92

12.35

9.44

4.90

9.87

15.51

8.57

--

--

7.62

Metals

19.23

23.13

--

--

7.88

13.79

--

15.51

4.14

8.24

Machinery

37.19

44.24

9.95

8.20

7.42

8.62

6.06

4.79

10.98

10.98

Electronics

20.16

40.82

21.14

9.83

17.89

26.98

1.49

13.34

4.39

17.44

Transportation

50.36

45.67

3.36

2.33

5.11

6.21

--

--

--

21.39

Other Mfg

18.15

13.29

--

--

11.82

7.27

--

2.33

8.89

8.89

Total

38.66

36.70

8.45

5.64

10.41

15.24

4.46

11.79

4.48

13.59

Notes: This table shows means by two-digit industry and geographic region of the share of imported inputs for further processing in total revenues for foreign
affiliates of U.S. multinational enterprises. Means are weighted by affiliate total revenue. The “Other Europe” region is excluded from the table due to disclosure
restrictions (there are few affiliates per industry in the region). The same applies to entries that read, “--.” The sample is 4,285 majority-owned foreign affiliatesof U.S. multinationals that belong to a parent that has as least two foreign affiliates. The year is 1994.
Source: Authors’ calculations using data from the U.S. Bureau of Economic Analysis.

36

Table 3: Affiliate Exports to the United States, by Industry and Region, 1994
RegionIndustry

CanadaMexicoOther
Lat.Am.

OECD
Europe

OECD
Asia

East
Asia

World

Chemicals

18.96

2.05

2.71

3.64

2.76

0.77

4.82

Metals

40.86

11.76

5.71

3.44

2.73

6.19

10.29

Machinery

--

29.32

17.60

7.63

--

52.72

20.19

Electronics

24.04

53.76

--

6.14

5.52

26.55

16.16

Transportation

61.40

54.80

2.33

4.10

1.73

6.80

26.45

Other Mfg

26.96

13.64

5.24

4.01

3.02

4.36

6.95

Total

47.91

40.84

6.18

4.90

6.62

32.85

16.05

Notes: This table shows means by two-digit industry and geographic region of the share of exports to the United Statesin total revenues for foreign affiliates of U.S. multinational enterprises. Means are weighted by affiliate total sales.
Four regions are excluded from the table due to disclosure restrictions (there are few affiliates per industry in theregion): Other Europe, Other Asia, Africa, and the Middle East. The same applies to entries that read, “--.” Thesample is 4,285 majority-owned foreign affiliates of U.S. multinationals that belong to a parent that has as least two
foreign affiliates. The year is 1994.
Source: Authors’ calculations using data from the U.S. Bureau of Economic Analysis.

37

Table 4: Baseline Estimation Results for Imported Intermediate Inputs
Regressor

(1)

(2)

(3)

More-Skilled Wages

0.007(0.011)0.027(0.010)**0.028(0.010)**Less-Skilled Wages-0.046(0.010)**-0.046(0.011)**-0.045(0.009)**

Capital Rental Rate

0.003
(0.006)0.001(0.005)0.001(0.005)(1 + Trade Costs)-0.640(0.080)**-0.333(0.089)**-0.262(0.078)**(1 – Corporate Tax Rate)

0.009(0.031)0.028(0.029)0.044(0.028)Output-0.008(0.004)*-0.007(0.004)-0.007(0.004)

Adjacent to United States

0.126(0.014)**0.126(0.013)**

Export-Processing Zone

0.043
(0.015)**0.048(0.015)**

Non-Tariff Barriers

0.020(0.012)0.021(0.012)Host-Market Size

-0.043(0.010)**-0.045(0.008)**Economic Freedom-0.037(0.009)**-0.040(0.006)**

Notes: See continuation of this table on next page.

38

Table 4: Baseline Estimation Results (Continued)
Regressor

(1)

(2)

English Speaking-0.006(0.009)(1 – Value-Added Tax Rate)

0.029(0.046)

Fixed Exchange Rate w/ U.S.

0.021
(0.015)FDI Openness-0.001(0.006)

U.S. Materials Share of Sales

0.075(0.051)

Controls
No. Observations
Adjusted R-Squared

Parent-Industry
4,102
0.40

Parent-Industry
4,102
0.48

(3)

Parent-Industry
4,102
0.48

Notes: Each column of this table estimates a specification of equation (7) using the regressors of that column. The
dependent variable is imported intermediate inputs for further processing from the United States as a share of affiliate
total revenues (plus inventory changes). Cell entries are OLS parameter estimates (and standard errors that allow for
both arbitrary forms of heteroskedasticity and correlations in disturbances within parent-industry groups). * indicatesthat a parameter estimate is different from zero at the 5% significance level; ** at the 1% level. All specifications also
include 777 parent-by-industry dummy variables. All regressors in column (1), as well as Host-Market Size, (1- ValueAdded Tax Rate), and U.S. Materials Share of Sales, are in logarithms. See text for equation (7) and details on variable
definitions and construction.

39

Table 5: Estimation Results, Alternative Estimators
Regressor

(1)

(2)

(3)

More-Skilled Wages

0.068(0.013)**0.033(0.009)**Less-Skilled Wages-0.097(0.013)**-0.050(0.009)**

Capital Rental Rate

0.010
(0.005)*0.001(0.005)0.002(0.004)(1 + Trade Costs)-0.359(0.089)**-0.211(0.107)*-0.360(0.182)*(1 – Corporate Tax Rate)

0.032(0.051)

Output0.008(0.003)**

Adjacent to United States

0.133(0.012)**0.123(0.014)**

Export-Processing Zone

0.089
(0.015)**0.049(0.014)**

Non-Tariff Barriers

0.020(0.011)

Host-Market Size

-0.086(0.010)**-0.044(0.007)**

Economic Freedom-0.079(0.008)**-0.038(0.007)**

Estimator
Controls
No. of Observations
Adjusted R-Squared

Tobit
Industry
4,102

0.076
(0.036)*-0.007(0.004)0.025(0.014)

OLS
Country
4,102
0.49-0.007(0.002)**0.023(0.008)**

IV
Parent-Industry
3,977

Notes: Each column of this table estimates our preferred specification of equation (7) using the estimator and controls
indicated. The dependent variable is imported intermediate inputs for further processing from the United States as ashare of affiliate total revenues (plus inventory changes). Cell entries are parameter estimates (and standard errors thatallow for both arbitrary forms of heteroskedasticity and correlations in disturbances within parent-industry groups). *indicates that a parameter estimate is different from zero at the 5% significance level; ** at the 1% level. See text forequation (7) and details on variable definitions and construction.

40

Table 6: Estimation Results, Export-Intensity Regressand
Regressor

(1)

(2)

(3)

More-Skilled Wages

0.003

0.014

0.038

0.015

(0.010)(0.010)(0.019)*(0.010)-0.042-0.020-0.087-0.023(0.010)**(0.009)*(0.020)**(0.011)*-0.019-0.020-0.056-0.021-0.021(0.005)**(0.005)**(0.008)**(0.005)**(0.004)**-0.583-0.222-0.719-0.222-0.307(0.113)**(0.123)(0.140)**(0.152)(0.116)**-0.006

0.093

0.201

0.092

(0.035)(0.034)**(0.075)**(0.042)**-0.003-0.001

0.047-0.001-0.001(0.005)(0.004)(0.004)**(0.005)(0.003)

0.143

0.217

0.138

(0.015)**(0.017)**(0.012)**

0.057

0.139

0.059

(0.023)*(0.023)**(0.014)**

0.016

0.011

0.021

0.013

(0.018)(0.018)(0.021)(0.010)-0.0150.004-0.016(0.008)(0.016)(0.008)*-0.011-0.045-0.009(0.007)(0.013)**(0.007)Less-Skilled Wages

Capital Rental Rate

(1 + Trade Costs)(1 – Corporate Tax Rate)

OutputAdjacent to United States

Export-Processing Zone

Non-Tariff Barriers

Host-Market Size

Economic Freedom

(4)

(5)

Estimator

OLS

OLS

Tobit

OLS

IV

Controls

Parent-Ind.

Parent-Ind.

Industry

Country

Parent-Ind.

No. of Observations

4,102

4,102

4,102

4,085

4,102

Adjusted R-Squared

0.48

0.54

0.55

Notes: Each column of this table estimates a specification of equation (7) replacing equation (7)’s regressand with the share ofaffiliate sales accounted for by exports to the United States. Each specification uses the estimator and controls indicated. Cell
entries are parameter estimates (and standard errors that allow for both arbitrary forms of heteroskedasticity and correlations in
disturbances within parent-industry groups). * indicates that a parameter estimate is different from zero at the 5% significancelevel; ** at the 1% level. See text for equation (7) and details on variable definitions and construction.

41