『経済研究』（明治学院大学）第 164 号

2022 年

Pass-Through and Multinational Firms: Evidence fromUS Manufacturing Industries
Yoko Sakamoto

Abstract
In recent years, a greater portion of international trade is conducted by multinational firms.
In this paper, I empirically analyze the relation between firms’ multinational status and pricing behavior associating with foreign tax rate and exchange rate using data in US manufacturing industries. The results show that prices in industries with lower intrafirm share are more sensitive to
foreign tax rate change. Also, this study concludes that industries with more intrafirm transaction
show lower pass-through rate.

1．Introduction

In recent years, around 40% of all US international trade has occurred intrafirm.1 Given thissubstantial existence of multinational firms in international trade, it is important to analyze how
firms’ organizational heterogeneity affects pass-through. The goal of this paper is examining firms’
pricing behavior and discussing how it can be explained by the existence of multinational firms andother factors.
According to Engel（2003）,firms’ pricing leads to incomplete pass-through due to such factorsthat local nontraded costs, markup adjustment, and price adjusting costs. 2 If we take into account1

See introductions for Clausing（2003）and Neiman（2010）,for example.

2

There are several papers that explain incomplete pass-through using local cost although I do not refer them inthe main part of this paper since they do not directly relate to what I do in my empirical research. Corsettiand Dedola（2005）theoretically explains that local cost in final good price results in incomplete pass-throughby introducing local distribution service into the open-economy macroeconomic model. Goldberg and Verboven（2001）empirically supports this fact by finding that local costs play an important role in generating the local
currency prices stability.
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『経済研究』（明治学院大学）第 164 号

firms’ multinationality, however, we also need to be concerned with specific factors for those multinational firms to determine their price. According to Bernard, Jensen and Schott（2006）, multinationalfirms have both managerial and financial motives for setting prices in intrafirm transaction differently from arms-length transaction. Managerial motives include avoiding double marginalization in thepresence of market power, whereas financial motivations encompass the minimization of corporate
tax and tariff payments. In the empirical analysis of this paper, I will consider these multinationalfirm specific motives in the empirical model.
The data I mainly reply on in the empirical study is the import price index data from the International Price Program（IPP）of the Bureau of Labor Statistics. IPP offers pricing data in US international trade for intrafirm transaction and arms-length transaction separately. Some papers such as
Clausing（2003）and Neiman（2010）make use of this dataset to estimate exchange rate pass-throughseverally for intrafirm and arms-length transaction. Although utilizing this dataset is ideal, it is notavailable to this research. What I try to do instead in this paper is to estimate industry-level pricing
behavior and pass-through, which I compare with the intrafirm transaction share of each industry as
in Hellerstein and Villas-Boas（2010）. This approach enables me to relate intra-firm transaction to passthrough, which partially overcomes the limitation of my dataset, i.e., inseparability of intrafirm transaction from arms-length transaction.
Also, this industry-wise estimation of pass-through is the largest distinction of this paper fromother paper.3 This attempt lets us find which industry with what characteristics has higher/lower
pass-through, which is not approached by papers making use of intrafirm price data of IPP. I include
Herfindahl Hirschman Index（HHI）as well as intrafirm transaction share in the industries characteristic in order to take competitiveness of industry into account, which is another distinction of this paper. This consideration takes multinational firms’ managerial motives into account.
My empirical analysis has two objectives. The first one is testing multinational firms’ financial
motives. I will examine whether there is tax motivated income-shifting in transfer pricing by multinational firm, following Clausing（2003）. As a result, my empirical analysis shows that there is no income-shifting pricing, which is the opposite to what Clausing（2003）finds. I also compare the resultwith HHI in each industry to find that tax-motivated income shifting is more likely to be performed
when the industry is less competitive. The second one is estimating industry-level pass-through andcompare it with intrafirm transaction share. Many papers show that pass-through in intrafirm transaction is higher than arms-length transaction. Hence the natural conjecture of the result is that industry with higher share of intrafirm transaction will express higher pass-through. In my empirical
3Hellerstein and Villas-Boas（2010）try the industry-level pass-through estimation. The difference between theirpaper and my paper comes from the specification of the empirical model and dataset.
2

Pass-Through and Multinational Firms: Evidence from US Manufacturing Industries

analysis, however, we see the opposite relation between intrafirm share and pass-through: industry
with higher intrafirm share shows lower pass-through. Although I also compare the pass-throughwith HHI, observed correlation between HHI and pass-through is subtle.
The remaining of this paper proceeds as follows. In the next section, I review Clausing（2003）,
Neiman（2010）, and Hellerstein and Villas-Boas（2010）as the related literature. I will mainly overview their empirical strategy and results. In Section 3, I will show my empirical study and its result,and Section 4 concludes.

2．Literature Review
In this section, I review three papers which explain incomplete pass-through with consideringfirms’ multinationality. As mentioned in the introduction, Bernard et al.（2006）conclude that multinationals have both managerial and financial motives for setting intrafirm trans-action prices differently
from arms-length transaction. I first review Clausing（2003）which explains financial motives by finding multinational firms’ price setting can be explained by tax-motivated income shifting. Then I review Hellerstein and Villas-Boas（2010）and Neiman（2010）both of which estimate pass-through associating with firms’ multinationality using different strategies.
Clausing（2003）first theoretically hypothesize that multinational firms’ tax-minimizing behavior leads to their shifting income to more lightly-taxed locations by manipulating transfer prices byconstructing the following model.4 The home-country based multinational firm’s profit is written by

π（p）＝（1－tH）πH（p）＋（1－tF）πF（p）where tx and πx are tax rate and profit earned in country x∈｛Home; Foreign｝, respectively, and p is transfer price. Profit from each country is written by πH（p）＝revenueH－costH＋pm and πF（p）＝revenueF－costF－pm, where m is the amount of material（p）with respect to p, we have πp＝（tF－tH）m. So, if tH＞
sent to Foreign. If we take derivative of π

tF, πp is negative and the multinational firm has an incentive to underprice the transfer price whereas tF＞tH leads to overpriced transfer price for the same reason. This result shows that multinationalfirms tend to have higher transfer price when they export goods to higher tax rate country. In otherwords, by manipulating transfer pricing, they shift their income to low tax country.
Based on the model, she estimates the following equation:ln pit＝α＋β1 ln（1－τky）＋β2 ln（1－τky）×Intra dummyit＋β3 ln ekt＋β4 ln ekt×Intra dummyit＋β5 Intra dummyit＋β6 Inpute dummyit＋β7 Link dummyit＋β9 No dollarit＋βz Industry dummies＋εit.
4The model present here is different from her original model. I abstract the model but the result is qualitatively
same.
3

『経済研究』（明治学院大学）第 164 号

i indicates products, k indicates countries, t indicates months, and y indicates years. pit is the trade
price reported by Bureau of Labor Statistic, τky is tax rate of industry k in year y, which is either effective tax rate or statutory tax rate. Effective tax rate is calculated as the foreign income taxes paid
by US affiliates in a given country divided by their pre-tax net income based on the data from teh Bureau of Economic Analysis surveys on US direct investment abroad. Statutory tax rate is obtained. Inpute dummy and Link dummy are dummy
from Price Waterhouse. ekt is exchange rate（per dollar）
variables for inputed price and link price, respectively. Bureau of Labor Statistics generates inputed
prices when data are missing and also calculates a link price when survey items have changed in nature. No dollar is a dummy for goods where the price is not originally expressed in dollars.
Expected sign of coefficient based on the model and estimated coefficients are summarized inTable 1. The estimated coefficients are reported for the case of effective tax.
Table 1: Expected and estimated coefficient
Expected signln（1－τky）

ln（1－τky）
×Intra dummyit

ln ekt0a－0.7964－for exports;＋

for importsb―c?dln ekt×Intra dummyit

Estimated coefficient
Exports
Imports－1.814－1.9971.354－0.3543－0.4228－0.6869－0.1946a Prices for arms-length transaction should not be effected by tax rates
b For low tax countries, intrafirm export prices should be lower and import prices should be higher.
c As the dollar is stronger, prices should be lower.
d If intrafirm trade is more sensitive to exchange rates, this should be negative.

The result basically follows expectation. By obtaining the positive positive coefficient of ln（1－τky）×Intra dummyit for imports and negative coefficient for exports, Clausing（2003）finds the evidence of multinational firms’ income shifting to lower tax countries. The coefficient of ln ekt×Intra
dummyit is estimated to be negative, which means that intrafirm price reacts to change in exchange
rate more sensitively, which supports the higher pass-through in intrafirm transaction compared witharms-length transaction. In the empirical study in this paper, I will also test the existence of tax-motivated transfer price manipulation following her empirical model.
Hellerstein and Villas-Boas（2010）analyze the impact of vertical relationships on firms’ markup adjustment and the consequent incomplete pass-through in US auto industry. Their main analysis
employs demand estimation approach（BLP）from IO literature using mi-crolevel data, which leads
them to conclude the positive relation between degree of firms’ vertical integration and pass-through.
However, the most related part of their paper is their first look analysis of data. By performing industry level pass-though estimation, they find the positive relation between pass-through and in4

Pass-Through and Multinational Firms: Evidence from US Manufacturing Industries

trafirm transaction share. The following equation is their pass-through equation.
4βinΔ ln eit n×Δ ln ωit＋δit ln Yt＋εit，Σn

Δ ln pit＝α＋＝0－

i stands for industry and t stands for quarter. Δ ln xit＝ln xit－ln xit－1 stands for percentage difference in variable x in industry i from period t－1 to t. pit is the import price index from IPP of Bureauof Labor Statistics and ωit is import weighted foreign CPI obtained from IMF’s International Ginancial
Statistics. Yit is the US gross domestic purchase provided by Bureau of Economic Analysis. Δ ln eit－n
is the percentage change in exchange rate over the period t－n－1 to t－n. As same in Burstein
4

and Gopinath（2013）and Neiman（2010）, the exchange rate pass-through is defined aseach industry i.

βin forΣn＝0They perform the estimation for sixteen manufacturing industries and find the pass-throughgreatly varies across industries from －0.02 to 0.56. By comparing the industry level pass-throughwith intrafirm share of each industry, they find the positive relation between them. Although theiranalysis does not utilize price data which separates intrafirm and arms-length transaction, their industry-level analysis enables them to relate the pass-though and intrafirm transaction. I follow theirstrategy in my empirical study in this paper. More specifically, I will take the same sixteen industry
and estimate the pass-through equation for each industry to identify the relation between the passthrough and intrafirm transaction share. The difference of my research from theirs comes from thedifferent dataset and different specification of pass-through equation. Also, in my analysis, I estimate
the pass-through after one month, three month, and six months as well as twelve months whereas
they estimate only pass-through after twelve months. This attempt provides us the transition of passthough, which is another distinctive feature of my analysis from Hellerstein and Villas-Boas（2010）.
Neiman（2010）makes use of the same pricing data as Clausing（2003）which separates intrafirm transaction from arms-length transaction to estimate pass-through. He focus on the US import market and estimate the following pass-through equation:N

β nΔ ln eict n＋δ Δ ln πitc ＋γ Δ ln πitUS＋εit，Σn

Δ ln pict＝α＋＝0－

i stands for industries, c stands for countries, and t stands for months. pict is the price of good i
exported from country c at month t. Δ ln eict－n is the percentage change in exchange rate between US

and country c over the period t－n－1 to t－n. πitc and πitUS are CPI in country c and US, respectively.

He runs this regression for intrafirm transaction and arms-length transaction. Also, he tries
several level of N from 0 to 30 month to have the pass-through transition. As same in Burstein and
Gopinath（2013）and Hellerstein and Villas-Boas（2010）,the exchange rate pass-through is defined as
5

『経済研究』（明治学院大学）第 164 号
N

β n . The results are summarized in the Table 2 where “long run” corresponds to 3 years. TheΣn＝0reported coefficients are from regression using data after dropping large changes.
Table 2: Long-run and after 1 year exchange rate pass-throughIncluding non-dollar prices
Long run
After 1 year
IntrafirmArms-length

0.30
0.22

0.19
0.16

Excluding non-dollar prices
Long run
After 1 year
0.25
0.16

0.13
0.10

As can be seen from the Table 2, intrafirm transaction shows higher pass-through com-paredwith the arms-length transaction. From the estimated pass-transition, we observe that the passthrough from intrafirm and arms-length transaction is almost same until five months passes and thenget away so that intrafirm pass-through grows faster. In my analysis, I will try one month, threemonths, six months, and twelve months for N and examine the transition of pass-through. From myanalysis, we will see which industry shows higher grow rate of pass-through. I will relate this result
to the intrafirm transition share and conclude whether industries with higher intrafirm share show
higher growth rate of pass-through.
In this section, I reviewed three papers which explain how firms’ multinationality leads to different pass-through from arms-length transaction. Three of them support the common result that intrafirm
transaction leads to higher exchange rate pass-through. Also, Clausing（2003）shows that pricing by intrafirm transaction is distorted by the tax-motivated income shifting behavior. Following their analysis,in the next section, I will construct a dataset and examine how price is affected by the foreign tax rateand how pass-through varies across industries with different level of intrafirm transaction share.

3．Empirical Study
My empirical analysis has two objectives. First is testing whether foreign firms’ pricing shows
tax-motivated income shifting or not. The second is estimating pass-through transition to relate it withthe existence of multinational firms. Since my dataset cannot separate intra-firm transaction fromarms-length transaction, my empirical analysis employs Hellerstein and Villas-Boas（2010）’s strategy: I
will perform estimation for each of several industries with different level of intrafirm transaction
shares and then, compare the results with intrafirm share and another factor such as HHI.
There are several distinction in my empirical study from other papers. First, I estimate passthrough transition for each industry. This industry-wise attempt is not accessed by papers utilizing Bu6

Pass-Through and Multinational Firms: Evidence from US Manufacturing Industries

reau of Labor Statistics data. Second, I consider market competitiveness by introducing HHI in the analysis. Although firms’ price setting behavior should be affected by competitive environment, none of threepapers take this into account. Last, the dataset I construct employs different countries and time range.
In my empirical study, I pick up sixteen manufacturing industries in US import market withseven countries from December 2005 to December 2011. The seven countries are China, Canada, Mexico, Japan, Germany, United Kingdom, and France, who account for 60% of US import on average
from 2009 to 2013.5 The sixteen industries are determined based on 3-digit NAICS and summarized inTable 3 with their intrafirm import share and Herfindahl Hirschman Index（HHI）. I employ the resultof intrafirm import share in Bernard, Jensen, Redding and Schott（2008）and the HHI come from US
Census Bureau.6 These industries are the same as in Hellerstein and Villas-Boas（2010）except that I
use 314 for Textiles whereas Hellerstein and Villas-Boas（2010）use 313. As can be seen in Figure 1,
there is little relation between HHI and intrafirm transaction share.
Focusing on US import market enables me to make use of the monthly Import Price Index
data from IPP of Bureau of Labor Statistics（December 2005＝100）. I use monthly exchange rate（perUS dollar）data from University of British Columbia web site7 and calculate industry-level exchange
rate by taking import-weighted sum where the import share is obtained based on trade data from US
International Trade Commission.
Since multinational firms have an incentive to adjust transfer prices due to tax rate of countries they operate, I collect two types of tax data, both of which are adopted by Clausing（2003）: effective tax rate and statutory tax rate. According to Clausing（2003）,the effective tax rate is definedas the foreign income taxes paid by US affiliates in a given country divided by their pre-tax net income. I use data from surveys on US direct investment abroad by Bureau of Economic Analysis inorder to calculate the effective tax.8 As for statutory tax rate, I adopt annual tax rate（% of commercial profit）reported by the World Bank. Industry-level tax rate is obtained in the same way as having industry-level exchange rate by having import-weighted sum of them.
Table 4 shows the mean and variance of the effective tax rate and statutory tax rate for eachindustry. For every industry, effective tax rate is smaller than statutory tax rate. This tendency is5This is according to calculation based on the data from US International Trade Commission.

6

HHI from US Census Bureau is calculated for top 50 firms in each industry based on value of shipment.

7

http://pacific.commerce.ubc.ca/xr/data.html.

8

Manufacturing industries we can observe from Bureau of Economic Analysis data are: food, chemicals, primaryand fabricated metals, machinery, computers and electronic products,” electrical equipment, appliances, andcomponents, transportation equipment, and manufacturing total. When I merge data, I categorize 311 and 312
as food, 314, 315, 316, 325, 326, and 327 as chemicals, 331 and 332 as primary and fabricated metals, 333 as machinery, 334 as computers and electronic products, 335 as electrical equipment, appliances, and components,
336 as transportation equipment, and 337 and 339 as manufacturing total. When industries are categorized in
the same class, the variation of tax rate between those industries only come form the variation of import share.
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『経済研究』（明治学院大学）第 164 号
Table 3: Intrafirm transaction share and HHI for each industry
Industry
Food
Beverages and tobacco
TextilesApparelLeather goods
Chemicals
Rubber and plastics
Nonmetallic metals
Primary Metals
Fabricated metals
Industrial Machinery
Computers
Electrical equipment
Autos
Furniture
Miscellaneous

NAICS

Intrafirm import share

HHI（permillage）

311
312
314
315
316
325
326
327
331
332
333
334
335
336
337
339

0.266
0.256
0.137
0.156
0.108
0.593
0.438
0.297
0.359
0.353
0.503
0.663
0.500
0.756
0.188
0.234

102.1
555.4
418.6
44
174.8
114
31.3
89.6
180.6
9
72.7
136.6
105.3
365
61.5
52.4

Figure 1: Plotting Intrafirm transaction share and HHI

different from Clausing（2003）whose data has almost same level of effective tax rate and statutory
tax rate although the level of effective tax rate is similar to hers. This difference seems to come fromthe difference of data source.9 Figure 2 demonstrates the negative relation between foreign tax rate9

Clausing（2003）relies on data from Water House, which is not available to this research. Instead, I use the tax
rate data（% out of commercial income）from the World Bank.
8

Pass-Through and Multinational Firms: Evidence from US Manufacturing Industries
Table 4: Effective tax and Statutory tax
Industry

NAICS

Effective Tax
meanvariance

Statutory Tax
meanvariance

Food
Beverages and tobacco
TextilesApparelLeather goods
Chemicals
Rubber and plastics
Nonmetallic metals
Primary Metals
Fabricated metals
Industrial Machinery
Computers
Electrical equipment

311
312
314
315
316
325
326
327
331
332
333
334
335

0.353
0.301
0.229
0.232
0.225
0.420
0.318
0.317
0.316
0.479
0.319
0.094
0.110

0.002
0.002
0.00090.001
0.00090.009
0.0025
0.002
0.021
0.162
0.011
0.128
0.037

0.476
0.519
0.679
0.677
0.701
0.491
0.555
0.583
0.471
0.568
0.523
0.618
0.588

0.003
0.0001
0.004
0.004
0.005
0.001
0.002
0.001
0.002
0.002
0.001
0.002
0.001

Autos

336

0.302

0.318

0.482

0.0009Furniture

337

0.254

0.014

0.650

0.004

Miscellaneous

339

0.288

0.022

0.647

0.004

Figure 2: Plotting Intrafirm transaction share and Statutory Tax Rateand intrafirm import share. This tendency can be interpreted as follows. When the foreign tax rate isincreased, FDI in foreign country becomes more costly relative to importing via arms-length transaction since the affiliate need to pay more tax in foreign countries whereas cost of buying materials in
arms-length transaction is not changed.

9『経済研究』（明治学院大学）第 164 号

3.1．Foreign tax rate and price sensitivity
In this subsection, I construct an empirical model which tests the relation between foreign tax
rate and import price change discussed by Clausing（2003）. In her model, Clausing（2003）argues
that price set in intrafirm transition should become higher due to lower tax in foreign country
whereas price set in arms-length transaction should not be affected by foreign tax rate. She tested
this statement in her empirical analysis and shows that her conjecture of higher intrafirm importprice due to lower foreign tax rate is true. In this study, I will check her conjecture using my dataset. In my analysis, I cannot follow her specification of empirical model because of inseparability between intrafirm and arms-length transaction of my dataset. Instead, I estimate tax rate coefficientson price change for each industry and compare them with intrafirm share. If her statement is true, I
will have higher coefficient of tax rate in industries with higher share of intrafirm trade.
There are other differences in our datasets. First, although she adopts monthly data from January 1997 to December 1999, my monthly data covers from December 2005 to December 2011. Second, as for statutory tax data, my data source is different from hers as I mention in the previous section.
The equation to be estimated isln pit＝αi＋βi ln eit ln ωit＋γi ln（1－τit）＋δi ln Yt＋εit ,where pit is the import price index of industry i at time t, τit is either effective or statutory tax levelof industry i at time t, eit is the import-weighted exchanger rate in industry i at time t. Following Hellerstein and Villas-Boas（2010）, I let each exchange rate term be a cross term with import-weighted
foreign CPI in each industry i at time t, ωit, which is the proxy for foreign production cost. I also include gross domestic purchase in the US, Yt as a demand shifter. The CPI data comes from IMF’s International Financial Statistics while the US gross domestic purchase is provided by Bureau of Economics Analysis. This specification of equation is basically a modification of Hellerstein and Villas-Boas（2010）by adding tax term.
The estimation is performed for each industry as well as for aggregate import market. Also, I
consider two kinds of tax following Clausing（2003）: effective tax and statutory tax. The result is shown
from Table 5 to Table 8.
Although we have negative coefficient on tax rate in some cases in effective tax case, we havepositive coefficient on tax rate in most of statutory tax case and many of effective tax case. Having positive coefficient on tax rate is consistent with the optimal transfer price manipulation by multinationalfirms in her model. In Clausing（2003）, she has negative tax coefficient in arms-length transaction though
she expects this to be 0. Since my data set is aggregate in transaction arrangement, the negative coeffi10

Pass-Through and Multinational Firms: Evidence from US Manufacturing Industries
Table 5: Tax＝Effective tax, total and first 8 industries
（1）
Total

（2）
311

（3）
312

（4）
314

（5）
315

（6）
316

（7）
325

（8）
326

（9）
327

－0.00659＊ 0.0299 0.0215＊＊＊ －0.0907＊＊＊－0.0480＊＊＊ －0.184＊＊＊ －0.0128 －0.0872＊＊＊ －0.102＊＊＊（0.00386） （0.0372） （0.00363） （0.0260） （0.0169） （0.0155） （0.0102） （0.0107） （0.0233）
0.02620.422＊＊＊
0.01840.234＊＊＊ 0.237＊＊＊ 0.188＊＊＊ 0.544＊＊＊0.0678＊＊＊ －0.222
ln（1－τit）（0.00950） （0.135） （0.0375） （0.113） （0.0648） （0.0631） （0.0420） （0.0528） （0.0732）
1.215＊＊＊ 3.544＊＊＊ 0.637＊＊＊ 0.540＊＊＊ 0.344＊＊＊0.100
1.314＊＊＊ 0.376＊＊＊ 0.862＊＊＊
ln Yit（0.0605） （0.290） （0.0842） （0.176） （0.114） （0.0892） （0.196） （0.131） （0.175）
0.424
1.783
5.479＊＊＊ －7.600＊＊＊ 2.272＊－2.176－6.904＊＊＊ －29.69＊＊＊ －1.623＊
Constant（0.601） （2.868） （0.819） （1.886） （1.217） （0.966） （1.932） （1.339） （1.760）
ln eit ln ωit

Observations
R-squared
Number of NAIC

1,118
0.356
16

73
0.696

73
0.651

73
0.588

73
0.471

73
0.801

73
0.754

73
0.828

73
0.692

Standard errors in parentheses＊＊＊

p＜0.01, ＊＊ p＜0.05, ＊ p ＜ 0.1

Table 6: Tax＝effective tax, latter 8 industries（1）
331
ln eit ln ωitln（1－τit）
ln YitConstant
Observations
R-squared

（2）
332

（3）
333

（4）
334

（5）
335

（6）
336

（7）
337

（8）
339

－0.00813 －0.0857＊＊＊ －0.0478＊＊＊ 0.0643＊＊＊ －0.0708＊＊＊ －0.0167＊＊＊ －0.115＊＊＊ －0.00637（0.0137） （0.0153） （0.00305） （0.00270） （0.0112） （0.00326） （0.0324） （0.00509）
0.0246＊ －0.0998＊＊＊ －0.0769＊＊＊
0.0326－0.0456
0.0437＊＊＊ －0.00345 0.0911＊＊＊（0.0324） （0.0557） （0.0131） （0.00361） （0.0165） （0.0128） （0.0194） （0.00979）
4.462＊＊＊
0.852＊＊＊
0.488＊＊＊ －0.311＊＊＊0.261＊
0.344＊＊＊
0.562＊＊＊
1.431＊＊＊（0.289）（0.219） （0.0833） （0.0583） （0.132） （0.0567） （0.127） （0.0954）－2.3830.751
6.620＊＊＊
3.103＊＊
1.609＊＊＊
0.240－9.035＊＊＊－37.87＊＊＊（2.796）（2.269）（0.828）（0.582）（1.380）（0.554）（1.420）（0.959）
73
0.811

61
0.775

61
0.915

73
0.945

73
0.678

49
0.784

73
0.587

71
0.861

Standard errors in parentheses＊＊＊

p＜0.01, ＊＊ p＜0.05, ＊ p＜0.1

Table 7: Tax＝statutory tax, first 8 industries
（1）
Total

（2）
311

（3）
312

（4）
314

（5）
315

（6）
316

（7）
325

（8）
326

（9）
327

－0.00159 －0.0627＊＊ 0.00845＊＊ －0.0396 －0.0249 －0.0997＊＊＊－0.0343＊＊＊－0.0797＊＊＊－0.0739＊＊＊（0.00372） （0.0309） （0.00381） （0.0349） （0.0189） （0.0161） （0.0112） （0.0101） （0.0172）
0.04180.0341＊＊ 0.0634＊＊＊ 0.255＊＊ 0.180＊＊＊ 0.499＊＊＊0.125＊＊＊ 0.677＊＊＊ 0.610＊＊＊ln（1－τit）（0.0135） （0.0902） （0.108） （0.0275） （0.0145） （0.0102） （0.126） （0.0356） （0.0401）
1.069＊＊＊ 1.891＊＊＊ 0.494＊＊＊ 0.826＊＊＊ 0.390＊＊＊ 0.261＊＊＊ 1.271＊＊＊ 0.328＊＊＊ 0.749＊＊＊ln Yit（0.0595） （0.286） （0.0666） （0.178） （0.102） （0.0701） （0.252） （0.122） （0.130）－2.885
1.151
3.165＊＊＊ －6.891＊＊＊ 2.712＊＊－1.181－5.462＊＊＊ －12.44＊＊＊ 0.286
Constant（0.591） （2.918） （0.692） （1.931） （1.113） （0.756） （2.501） （1.244） （1.297）
ln eit ln ωit

Observations
R-squared
Number of NAIC

1,166
0.374
16

73
0.826

73
0.759

73
0.521

73
0.509

Standard errors in parentheses＊＊＊

p＜0.01, ＊＊ p＜0.05, ＊ p＜0.1

11

73
0.847

73
0.661

73
0.852

73
0.829

『経済研究』（明治学院大学）第 164 号
Table 8: Tax＝statutory tax, latter 8 industries（1）
331
ln eit ln ωitln（1－τit）
ln YitConstant
Observations
R-squared

（2）
332

（3）
333

（4）
334

（5）
335

（6）
336

（7）
337

（8）
339

－0.0111 －0.0741＊＊＊ －0.0435＊＊＊ 0.0460＊＊＊ －0.0346＊＊＊ －0.0177＊＊＊ －0.213＊＊＊ －0.0183＊＊＊（0.0145） （0.0110） （0.00487） （0.00445） （0.0124） （0.00271） （0.0242） （0.00417）
0.160＊＊＊ －0.112＊＊＊ 0.183＊＊＊
0.0917＊＊0.132＊＊＊
0.112＊＊＊
0.03280.140＊＊＊（0.121） （0.0456） （0.0456） （0.0248） （0.0393） （0.0445） （0.0146） （0.0116）
4.513＊＊＊
0.664＊＊＊
0.339＊＊＊ －0.387＊＊＊ 0.368＊＊＊
0.394＊＊＊－0.1450.820＊＊＊（0.349）（0.149） （0.0729） （0.0543） （0.136） （0.0620） （0.0966） （0.0752）－0.5822.229＊＊＊
7.497＊＊＊
1.758
1.191＊
8.090＊＊＊ －2.870＊＊＊－38.32＊＊＊（3.455）（1.507）（0.735）（0.549）（1.419）（0.606）（1.065）（0.752）
73
0.808

73
0.769

73
0.900

73
0.957

73
0.647

73
0.784

73
0.738

71
0.888

Standard errors in parentheses＊＊＊

p＜0.01, ＊＊ p＜0.05, ＊ p＜0.1

cient might be due to negative relation between price and（1-tax rate）in arms-length transaction.
Figure 3 and 4 show the plotting of tax coefficients and intrafirm transaction in effective tax
case and statutory tax case, respectively. If the result in Clausing（2003）holds, we should have thepositive relation between intrafirm transaction share and tax coefficient since her estimation showshigher tax coefficient in intrafirm transaction in import market.
However, as can be seen in Figure 3 and 4 , we have slightly negative relation between intrafirm transaction share and tax coefficient, which is opposite to the result in Clausing（2003）.

Figure 3: Intrafirm share vs sensitivity to effective tax change

12Pass-Through and Multinational Firms: Evidence from US Manufacturing Industries
Figure 4: Intrafirm share vs sensitivity to statutory tax change

In Figure 5 and 6, I plot the relation between tax coefficient and HHI. We observe the positive
relation between them, which shows that when the market is less competitive, taxmotivated incomeshifting pricing is observed more often.

Figure 5: Intrafirm share vs sensitivity to effective tax change

13『経済研究』（明治学院大学）第 164 号
Figure 6: Intrafirm share vs sensitivity to statutory tax change

3.2．Pass-through estimation
In this subsection, I estimate exchange rate pass-through following Neiman（2010）. In his empirical study, he shows the intrafirm transition has higher pass-through compared with arms-lengthtransaction by providing pass-through transition over 3 years for intrafirm tran-sition and armslength transition separately. In my analysis, I estimate the pass-through equation for several industries with different level of intrafirm transition share and compare the pass-through with intrafirm
share and test the result in Neiman（2010）by concluding whether there is positive relation between
intrafirm share and pass-through or not.
Following Burstein and Gopinath（2013）and Hellerstein and Villas-Boas（2010）, I specify the
pass-through equation as follows:N

βinΔ ln eit n＋δi Δ ln ωit＋δ ln Yt＋εit，Σn

Δ ln pit＝α＋

＝0

1

－

2

where Δ ln xit＝ln xit－ln xit－1 stands for percentage difference in variable x in industry i from period t－1 to t and Δ ln eit－n is the percentage change in exchange rate over the period t－n－1 to t－n.
According to Burstein and Gopinath（2013）and Neiman（2010）, the exchange rate pass-through is

14

Pass-Through and Multinational Firms: Evidence from US Manufacturing IndustriesNdefined as

βin for each industry i. I set N as 1 , 3 , 6 , or 12 and estimate pass-through in eachΣn＝0time range. Table 9 shows the results.10

As can be seen from Table 9, exchange rate pass-through greatly varies across industries andsome industries even show negative pass-through. Figure 7 shows the plotting of transition of exchange rate pass-through for each industry as well as aggregate import market. Compared with theestimated transition in Neiman（2010）, the growth rate of pass-through from my estimation is quite
high: after 1 year, the it reaches 0.33 in total manufacturing industry while it is 0.19 from Neiman（2010）.
Figure 8 shows the relation between the pass-through and intrafirm share. Expected result
from Neiman（2010）is that intrafirm share and exchange rate pass-through should have a positive
relation. However, the plotting shows the negative relation between intrafirm share and passthrough, which is opposite to previous literature’s conclusion.
Figure 9 shows the relation between the pass-through and HHI. Although we observe slightly
negative relation, degree of competitiveness seems not to explain the pass-through well.
Table 9: Estimated exchange rate pass-throughIndustry

NAICS

Total
Food
Beverages and tobacco
TextilesApparelLeather goods
Chemicals
Rubber and plastics
Nonmetallic metals
Primary Metals
Fabricated metals
Industrial Machinery
Computers
Electrical equipment
Autos
Furniture
Miscellaneous

311
312
314
315
316
325
326
327
331
332
333
334
335
336
337
339

1 month

12 months

0.00608

0.06530.15540.331480.03060.00195－0.0591
0.022
0.139－0.0120.01760.013
0.003070.00372
0.0105－0.00284
0.005020.002130.069
0.007360.20130.003747
0.00110.01140.142－0.03090.04046
0.07750.28630.032640.003808－0.02403
0.01698－0.0009892
0.32920.00557

0.26620.02080.23817－0.051173
0.148－0.07892
0.09291
0.02370.51090.085089－0.028－0.091165
0.00304
0.01333
0.435
0.01395

0.52475
0.02984
0.417215
0.14125
0.375
0.249420.34726
0.031111
0.944－0.004021－0.08439－0.107505
0.005710.03799
1.1207－0.01925


10Pass-through3 months
6 months

N

βin
Σ
n

Since exchange rate is per US dollar, pass-through is negative. In Table 9, I report－

15＝0『経済研究』（明治学院大学）第 164 号
Figure 7: Pass-through transition over 12 months

Figure 8: Pass-through and Intrafirm Share

4．Conclusion
In this paper, I tested the following two evidences from previous literature which analyzes
multinational firm’s pricing associating with foreign tax rate and exchange rate. First is that multina16

Pass-Through and Multinational Firms: Evidence from US Manufacturing Industries
Figure 9: Pass-through and HHI

tional firms manipulate the transfer price to be higher for the goods exporting to low tax countries
according to profit maximization. Second is that multinational firms’ intrafirm transaction shows thehigher exchange rate pass-through compared to arms-length transaction. In addition, I examinedboth evidences with market competitiveness by asking market competitiveness have positive relation with tax-rate sensitivity or level of pass-through.
The results are opposite to the evidence from previous literature. First, I found slightly negative relation between tax coefficient and intrafirm share. This means that industry price becomes
more sensitive when the industry has lower intrafirm share. According to the model in Clausing（2003）, intrafirm pricing of goods exporting to is affected by the change of foreign tax rate whereas
arms-length transaction is not. The expected result supporting her evidence in my study is that industry price is more sensitive to foreign tax rate change when the industry has higher intrafirm
share. However, the result shows the opposite relation.
As for exchange rate pass-through, the result from my estimation was also contrary to the evidence from previous literature. The expected result according to the previous works was that industry with higher intrafirm transaction share will show higher pass-through. As Figure 8 shows, however, the result revealed negative relation between pass-through and intrafirm transaction share. I
also estimated the transition of pass-through, which shows rapid growth rate compared to the resultin Neiman（2010）.
In addition to test the evidence from previous literature, I examined the estimated tax coefficient and pass-through with HHI. Although HHI did not explain pass-thorough well, positive relation
17

『経済研究』（明治学院大学）第 164 号

was observed between tax coefficient and HHI. This means that when the market is less competitive,
price becomes more sensitive to the change in foreign tax rate. This suggestive result might help us
understand multinationals’ pricing behavior from view point of market environment.

References
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Burstein, Ariel and Gita Gopinath, “International prices and exchange rates,” Technical Report, National Bureau
of Economic Research 2013.
Clausing, Kimberly A, “Tax-motivated transfer pricing and US intrafirm trade prices,” Journal of Public Economics, 2003, 87（9）,2207-2223.
Corsetti, Giancarlo and Luca Dedola, “A macroeconomic model of international price discrimination,” Journal ofInternational Economics, 2005, 67（1）,129-155.
Engel, Charles, “Expenditure switching and exchange-rate policy,” in “NBER Macroeconomics Annual 2002, Volume 17,” MIT Press, 2003, pp. 231-300.
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