Offshore Profit Shifting andDomestic Productivity Measurement∗
Fatih Guvenen†, Raymond J. Mataloni Jr.‡, Dylan G. Rassier§, Kim J. Ruhl¶

Abstract
Beginning in 2004, official statistics display a slowdown in U.S. productivity
growth. We show how offshore profit shifting by U.S. multinational enterprises affects GDP and, thus, productivity measurement. Profit shifting increased in the mid1990s, resulting in lower measured productivity growth. We construct value added
adjusted for profit shifting. The adjustments raise aggregate productivity growth
rates by 0.09 percent annually for 1994–2004, 0.24 percent annually for 2004–2008,
and lower annual aggregate productivity growth rates by 0.09 percent after 2008.
The adjustments are large in R&D-intensive industries, where value added increases
by as much as 8 percent in the mid-2000s.
JEL Codes: E01, F23, O4
Keywords: Tax havens, formulary apportionment, productivity slowdown∗
Date: February 2019. First version: June 2016. We thank Jennifer K. Bruner and Alberto G. Ramon for their assistancewith the statistical analysis. For comments and suggestions, we thank Pol Antras, Nick Bloom, Carol Corrado, Arnaud
Costinot, Jason Furman, John Fernald, Sebnem Kalemli-Ozcan, Mitchell Petersen and especially our formal discussants
Molly Saunders-Scott and Dan Sichel. The statistical analysis of firm-level data on U.S. multinational companies andcompanies engaged in international transactions was conducted at the Bureau of Economic Analysis, U.S. Department ofCommerce, under arrangements that maintain legal confidentiality requirements. The views expressed in this paper are solely
those of the authors and not necessarily those of the U.S. Department of Commerce, the Bureau of Economic Analysis, theFederal Reserve Bank of Minneapolis, or the Federal Reserve System.
†

University of Minnesota, FRB of Minneapolis, and NBER; guvenen@umn.edu
Bureau of Economic Analysis; raymond.mataloni@bea.gov§
Bureau of Economic Analysis; dylan.rassier@bea.gov¶
University of Wisconsin – Madison; ruhl2@wisc.edu‡

1Introduction

Economists have long understood the pivotal role of productivity growth as the engine
of long-run economic growth. A wide range of economic policy questions hinge on theforecasts of future productivity growth, making the future path of productivity a crucial
input into policy analysis. Thus, it is not surprising that the news of a significant slowdown
in U.S. productivity growth (as measured in official statistics) that started around 2004
generated widespread concern about the growth prospects of the U.S. economy. Perhaps
more surprisingly, this slowdown took place primarily in sectors that either produce or
use information technology (IT) services intensively. This finding has raised skepticism
among some economists, who point to the major innovative products and technologies
introduced in the last decades and conjecture that the slowdown might be the result—atleast partially—of the way output is measured in official economic statistics.1 Several
recent papers have focused on official price indexes as a source of the productivity slowdown
measured in official statistics. While the jury is still out, these studies generally find pricerelated measurement to have modest effects on productivity growth.2
In this paper, we study another potential source of the productivity slowdown, one
often acknowledged but not investigated thoroughly in the existing literature: the offshore
profit shifting by domestic and foreign multinational enterprises (MNEs) operating in the
United States. Offshore profit shifting (for brevity, “profit shifting”) occurs when an MNE
structures itself so that profit that would have accrued in the United States accrues insteadin its foreign affiliate. These shifted profits are recorded in the primary income accountas a return on U.S. assets held abroad, which does not affect U.S. GDP.3 Using firm-level
data, we show that profit shifting has reduced U.S. GDP as measured in official statistics.
Further, as profit shifting has increased significantly in the last two decades, the reduction
accelerated, giving the impression of a larger slowdown in the GDP growth rate and,
consequently, the aggregate productivity growth rate. To explain the contribution of profit
shifting to the productivity slowdown and why it has worsened over time, we begin with
two important facts.
1

Some important examples include the widespread availability of broadband Internet; the rise of innovative business models and products in companies such as Apple, Google, Facebook, Amazon, Uber,and Airbnb; and new products and technologies such as smartphones, electric cars, cloud computing, andsoftware-as-a-service.
2
Aeppel (2015), Alloway (2015), Brynjolfsson and McAfee (2011), Byrne, Oliner, and Sichel (2015),
Feldstein (2015), Hatzius and Dawsey (2015), Mokyr (2014), and Smith (2015) have explored the role ofmeasurement in accounting for the dynamics of productivity. Syverson (2016) and Byrne, Fernald, andReinsdorf (2016) argue that measurement is unlikely to explain most of the productivity slowdown.
3This is the appropriate accounting under the international statistical guidelines that the Bureau of
Economic Analysis follows.

1First, the total economic activity generated by U.S. MNEs is very large and has grownsignificantly in the past 20 years. The total global value added of U.S. MNEs was $4.66
trillion in 2012, making them equivalent to the fourth-largest economy in the world, tiedwith Japan and trailing only the United States, the European Union, and China. In thesame year, their domestic value added was $3.25 trillion, accounting for 26.9 percent of
U.S. business-sector value added. Furthermore, in the last 20 years, U.S. MNEs havesubstantially increased their global operations: earnings on U.S. direct investment abroad(USDIA) averaged 12.4 percent of corporate profits from 1973 to 1993 but grew to 28.1
percent ($450 billion) of corporate profits in 2012.
Second, MNEs own significant stocks of intangible capital (e.g., intellectual property,
brands, blueprints) and have a presence in countries that vary widely in corporate tax rates.
These characteristics allow MNEs to legally take advantage of differences in national tax
regimes to shift profits from high-tax jurisdictions—such as the United States—to lowtax jurisdictions, such as Bermuda. Increasingly common profit-shifting practices include
transfer pricing and complex global structuring related to intangible capital, in which an
MNE effectively underprices intangible capital when “sold” from one of its entities in a hightax jurisdiction to another of its entities in a low-tax jurisdiction or engages in a series oftransactions among subsidiaries that are strategically located in order to reduce the MNE’s
effective global tax rate.4 For U.S. MNEs, these strategies allow them to book earnings in
low-tax foreign affiliates in ways that are disproportionate to the economic activity carriedout in those affiliates.5 These tax strategies have generated discussion among both thecompilers and users of official statistics regarding the treatment of transactions within
MNEs and their effect on national statistics.6
The effects of profit shifting on value added can be illustrated through a concrete
example. Consider the iPhone, which is developed and designed in California but assembled
by an unrelated company in China, with components manufactured in various (mostly
4

Another common strategy is to have subsidiaries in high-tax jurisdictions borrow funds from thesubsidiaries in low-tax ones, thereby reducing the profits in the former and raising them in the latter.
5
See Grubert and Mutti (1991) and the subsequent literature.
6
See, for example, Lipsey (2009; 2010), Rassier (2017), United Nations, Eurostat, and Organisation for Economic Co-operation and Development (2011), United Nations (2015), and the proceedings of a March 2018 meeting of the NBER Conference on Research in Income and Wealth (conference.nber.org/confer/2018/CRIWs18/summary.html). Although the OECD’s transfer pricing guidelines
call for an “arm’s-length principle,” which requires firms to apply market prices to related-party transactions, this is difficult to do in practice because many intrafirm transactions do not have market values.
For example, how should Apple value the intellectual property, marketing, and brand associated with theiPhone, when these intangible assets are developed in the United States but used in foreign subsidiaries?
These intangibles are not traded in organized markets, so it is very difficult to judge whether the assigned
values are correct.

2

Asian) countries. Taking some hypothetical ballpark figures, suppose the bill of materials
and labor costs of assembly amount to $250 per iPhone and the average selling price is$750, for a gross profit of $500 per phone. For simplicity, assume that there are no further
costs of retailing and that all iPhones are sold to customers outside of the United States.
Two important questions arise from this simple scenario: First, defining GDP as total
domestic value added, how much should each iPhone contribute to U.S. GDP? Second,
given the profit-shifting practices described above, how much of each iPhone’s gross profit
is actually included in U.S. GDP?
To answer the first question, note that the $250 paid to contract manufacturers andsuppliers in Asia is not part of U.S. GDP, whereas how much of the $500 gross profit
should be attributed to U.S. GDP depends on where that value is created. If consumers
are willing to pay a $500 premium over the production cost for an iPhone, it is because theyvalue the design, software, brand name, and customer service embedded in the product.
If we assume these intangibles were developed by managers, engineers, and designers at
Apple headquarters in California (Apple, U.S.), then the entire $500 should be included in
U.S. GDP. In the national accounts, the $500 would be a net export under charges for theuse of intellectual property in expenditure-based GDP, matched by an increase in Apple’s
earnings in income-based GDP. To the extent that some intangible assets were createdoutside of the United States, only the appropriate share of the gross profit and related net
export would accrue to the United States.
As to the second question, the gross profit actually included in U.S. GDP may bevery small. Suppose that Apple generates intangible assets in the United States andlegally transfers them to a foreign affiliate (e.g., one in Ireland). Payments for the use of
intellectual property will accrue in Ireland rather than in the United States, which meansthat the returns to Apple U.S.’s intangible assets are attributed to an Apple affiliate outside
the United States and not included in U.S. GDP. In this case, the returns are captured in“income on USDIA,” which is included in U.S. gross national product,
GN Pt = GDPt + income on USDIAt − income on FDIUSt + / − · · ·(1)Thus, relative to the conceptual measure, U.S. net exports and GDP are understated andearnings on USDIA are overstated.7
Profit shifting results in retained profits and other assets accumulating in foreign affiliates, particularly in affiliates located in low-tax jurisdictions (Foley, Hartzell, Titman,
7
Income on FDIUS is income earned on foreign direct investment in the United States, the foreigncounterpart to income on U.S. direct investment abroad.

3

Table I – Assets in U.S.-owned foreign affiliates, 2012
Ratio of U.S.-owned foreign affiliate total assets to
PPE

Compensation

Employment(mil. USD)

16.8
6.4

39.0
21.2

1.8
1.2

20.0
1,109.6
97.7
59.9

142.7
1,380.0
115.3
60.0

10.9
121.6
8.7
7.7

Barbados
BermudaU.K.I., Caribbean

41.8
130.8
101.2

1,444.7
1,475.5
3,330.2

43.3
155.8
199.8

Hong Kong
Singapore

40.3
18.6

39.3
50.3

2.7
3.1

World
CanadaIreland
Luxembourg
Netherlands
Switzerland

Notes: Total assets are the sum of all financial (e.g., cash, receivables) and non-financial (e.g.,property, plant, and equipment, inventories) assets on a historic cost basis—that is, amounts
reported on firms’ financial statements under U.S. generally accepted accounting principles(GAAP). United Kingdom Islands (U.K.I.), Caribbean, consist of the British Virgin Islands,
Cayman Islands, Montserrat, and Turks and Caicos Islands.

and Twitec, 2007). To illustrate the prevalence of this situation, Table I reports, by hostcountry, the total assets (cash, receivables, plant, property, equipment [PPE], etc.) ownedby the foreign affiliates of U.S. MNEs relative to several production-related measures. The
ratio of total assets to physical capital (plant, property, and equipment) for U.S.-owned
foreign affiliates in Canada is 6.4, whereas this ratio averages almost 300 for European
tax havens, Ireland, Luxembourg, the Netherlands, and Switzerland, and more than 90
for Barbados, Bermuda, and the U.K. Caribbean Islands. Measuring assets relative toemployment (number of employees) or compensation yields similar patterns.8
The goal of this paper is to provide an alternative measure of U.S. domestic productivity
growth by adjusting for the effects of MNE profit shifting on value added. For this purpose,
we use confidential MNE survey data, collected by the Bureau of Economic Analysis (BEA)

8
These cross-border strategies can wreak havoc on the official statistics of even relatively large
economies like Ireland and the Netherlands. As one example, annual Irish GDP growth in 2015 was
26 percent, compared to a consensus forecast by economists of 7.8 percent. This large discrepancy was almost entirely due to the unexpected movement of legal ownership of MNE assets to the country (Eurostat,2016).

4for the period 1982–2014.9 The survey data cover the worldwide operations of U.S. MNEs
and contain, among other key measures, information on their employment, sales, and R&D
expenditures. We also use annual data for transactions in income on direct investment
published by BEA in the International Transactions Accounts (ITAs), also available for
1982–2014.
Profit shifting distorts the relationship between the location of economic activity andthe location of reported profit. To realign reported profit and economic activity, we use
the firm-level data to reattribute earnings on USDIA among a U.S. parent and its foreign
affiliates, based on factors that reflect economic activity, under a method of formulary apportionment. Under this method, the total worldwide earnings of an MNE are attributed tolocations based on apportionment factors that aim to capture the true location of economic
activity. As apportionment factors, we use, in each geographical location, a combinationof (i) labor compensation and (ii) sales to unaffiliated parties, as they are likely to be goodproxies for the actual economic activity taking place in each location. It should be noted,
however, that formulary apportionment assumes away real factors that can create differences in the returns to productive factors in different locations. Primarily for this reason,
the results of formulary apportionment presented here should be interpreted as estimatesrather than exact measurements.
Since the aggregate earnings of U.S. MNEs are disproportionately booked to low-tax
jurisdictions with little true economic activity, our adjustment reattributes their earnings
toward the United States and other higher-tax jurisdictions, thereby increasing measured
U.S. GDP and labor productivity. We use the results of formulary apportionment tocompute an adjusted measure of domestic business-sector value added and consequent
labor productivity growth for the United States, for 1973–2014.10
For comparability with earlier work (Fernald, 2015), we use domestic business-sector
value added as our measure of output. From 1973 to 1999, our adjusted value added
series is never more than 0.6 percent larger than the official series. Starting in the late
1990s, profit shifting, and the resulting adjustments, grow rapidly. By 2012, our adjusted
value added series is 2.5 percent larger than its official counterpart. The adjustments raise
aggregate productivity growth rates by 0.09 percent annually from 1994 to 2004, by 0.24
percent annually from 2004 to 2008, and decrease aggregate productivity growth rates by
0.09 percent annually after 2008. The adjustments mitigate the slowdown found in official
9
The firm-level survey data, which by law are confidential, are collected for the purpose of producing
publicly available aggregate statistics on the activities of multinational enterprises.
10
Because the BEA surveys start in 1982, the analysis for the 1973–1981 period relies on some extrapolations discussed below.

5statistics; however, because the post-2004 slowdown is quite large, and our adjustments
raise productivity growth not only after 2004 but also before it (although to a lesser extent),
our analysis does not eliminate the slowdown in aggregate productivity growth after 2004.
That said, our adjustment to productivity growth is quite large in some important
industries. When we group industries by R&D intensity, the adjustments to the value
added of R&D-intensive industries are as large as 8 percent of the group’s value added insome years (Figure 7a). Furthermore, these adjustments raise productivity growth rates
significantly during some periods: the annual growth rate of productivity from 2000 to 2008
increases by 0.53 percentage points in R&D-intensive industries after the adjustments.
Finally, while our focus is on the profit shifting of U.S. MNEs, for some of the same
reasons, foreign MNEs are also likely to be shifting profits out of the United States, pushingmeasured U.S. GDP further downward. The BEA surveys cover the U.S. affiliates of foreign
MNEs but unfortunately do not cover (most of) their foreign operations, including theirforeign parent operations. Without this latter piece of data, constructing apportionment
factors for foreign MNEs with the BEA survey data alone is not feasible. It is possible,
however, to combine data from commercial databases to make progress on this question,and in Section 5 we construct a data set of the largest technology-intensive foreign MNEs
that have operations in the United States. Our results indicate a similar profit-shifting
behavior by these corporations, further biasing U.S. GDP and labor productivity measures.
In our admittedly incomplete analysis, we find the magnitudes of the biases for foreign
MNEs to be smaller than those for U.S. MNEs.

1.1

Related Literature

Most of the evidence of MNE profit shifting comes from cross-country regressions of MNE
profits (or income) on tax rates. Dharmapala (2014) provides a comprehensive survey.
These studies find a strong relationship between differential tax rates and income attribution. In contrast, we do not use tax rate data in our methodology, yet, consistent withthe previous literature, we find the largest effects of profit shifting in the well-known “taxhavens.” In Section 3.5, we estimate the semi-elasticity of profits to tax rates in ways
consistent with the literature and arrive at very similar results.
Of the literature on profit shifting, Clausing (2016) is closest to our work. Using
estimates of the elasticity of MNE income to tax rates, she computes the cross-country
distribution of MNE income under the counterfactual that all countries tax profits at 30
percent. The difference between the counterfactual income levels and the observed income
levels is attributed to profit shifting. Her estimates using direct investment earnings data6

are conceptually the closest to ours. Quantitatively, our measurements are similar. For
2012, she finds profit shifting to be $258 billion, compared to our measured $280 billion.
Clausing (2016) uses her estimates to compute forgone tax revenues, while our focus is on
measurement in the real economy.
Our focus is on the reduction in measured domestic productivity. In related work,
Maffini and Mokkas (2011) study the increase in measured affiliate productivity that is
the result of profit shifting. Using a panel of European manufacturing firms, the authors
regress affiliate total factor productivity on changes in statutory tax rates and find that
affiliates in lower-taxed countries are more productive. Their results are consistent with
our measurement of domestic aggregate productivity.
The formulary apportionment approach we use to reattribute earnings within the MNE
has been primarily applied in multijurisdictional tax practice. The treatment of global
income under formulary apportionment is widely explored in multidisciplinary research,and formulary apportionment has been proposed as an alternative to the complexity andsubjectivity of transfer pricing for the allocation of international tax obligations within
MNEs.11 From a tax policy perspective, a potential problem with formulary apportionmentis the endogenous response of firms to the formulary rules of the tax system (Gresik, 2001).
Formulary apportionment applied to economic accounting faces fewer challenges compared
to its use in international taxation because it is descriptive rather than fiscal.
Our approach follows Rassier and Koncz-Bruner (2015), which proposes formulary apportionment as an alternative method for attributing the profits component of incomebased value added to foreign affiliates of U.S. MNEs. The formulary adjustment reduces
the effects created by excess profits attributed to tax haven countries, as presented in
Lipsey (2010). Rassier (2014) treats the reduction in earnings on USDIA under formularyapportionment as an implied increase in U.S. GDP, which is a necessary counterpart to therelated increase in domestic income. The author, however, does not construct a time seriesof adjusted domestic business-sector value added and does not consider the consequences
for productivity measures or the differential impacts on R&D- and IT-intensive industries.
In Section 2, we present a simple model of an MNE to highlight the source of themeasured slowdown and discuss our empirical methodology. In Section 3 we report the
impact of our adjustments on aggregate productivity, and in Section 4, we show how ouradjustments matter more for R&D- and IT-intensive industries. Section 5 takes a first lookat measurement of the foreign MNEs operating in the United States, Section 6 considers
11
See, for example, Avi-Yonah (2010), Avi-Yonah and Benshalom (2011), Avi-Yonah and Clausing(2007), Fuest, Hemmelgarn, and Ramb (2007), and Runkel and Schjelderup (2011).

7other measures that might be affected by profit shifting, and Section 7 concludes.

2

Conceptual Framework

In this section, we provide a conceptual framework to demonstrate how the ownership ofintangible assets—regardless of where they are used—affects the measurement of aggregate
output and productivity. Our data cover U.S. parents and their foreign affiliates, so our
focus here is on the measurement of U.S. aggregate output and productivity.
The MNE consists of a parent (m) located in the United States and one wholly owned
foreign affiliate (a). The parent and affiliate produce nontraded final goods (y) using physical capital (k), skilled and unskilled labor (`s , `u ), and intangible capital (h).12 We assume
that physical capital and labor can be freely adjusted and are obtained from perfectlycompetitive factor markets. Intangible capital is nonrivalrous but subject to ownership.
The final-good production function for the parent is
ym = f (zm , km , `sm , `um , h),(2)where z is total factor productivity. The final-good production function for the affiliate isya = f (za , ka , `sa , `ua , h).

(3)

We assume that both production functions are homogeneous of degree one. Note that
intangible capital does not have a subscript that denotes its physical location. This isan advantageous characteristic of intangible capital: it can be used to produce in eachlocation, regardless of its location of ownership.

2.1

Accounting

We map the production framework specified above into the economic accounting frameworkused by BEA, which is based on international statistical guidelines. To do so, we need toassign economic ownership of the firm’s intangible capital. We assume that the MNE
assigns ownership share λ of the stock of intangible capital to the parent and 1 − λ to theforeign affiliate. This decision does not affect production but determines the location inwhich the returns to intangible capital are booked. The firm may assign ownership of its
intangible capital to the affiliate for several reasons, including provenance of the intangible
12
In the data, firms produce both goods and services. For simplicity, we refer to the output of firms as
goods.

8

capital, taxation, regulation, confidentiality, property rights protection, and exchange rate
management (Organisation for Economic Co-operation and Development, 2008a). For our
purposes, we do not need to model the firm’s choice of λ.
Given our assumptions, affiliate earnings are
πa = pya − ws `sa − wu `ua − rk ka − rh (1 − λ)h − rh λh + rh (1 − λ)h = rh (1 − λ)h,(4)where p is the price of the final good, ws and wu are the wage rates, rk is the returnon physical capital, and rh is the return on intangible capital. We assume that factor
and goods prices are the same in the parent and affiliate. The term −rh (1 − λ)h is thedepreciation of affiliate intangible assets; the term −rh λh is the payment from the affiliate
to the parent for the use of λh; and the term rh (1 − λ)h is the payment from the parent tothe affiliate for the use of (1 − λ)h. The second equality in (4) follows from the assumptionthat inputs are paid their marginal products and that the production function has constantreturns to scale.
Parent earnings are
πm = pym − ws `sm − wu `um − rk km − rh λh − rh (1 − λ)h + rh λh = rh λh,(5)where the term −rh λh is the depreciation of the parent intangible assets; the term −rh (1 −λ)h is the payment from the parent to the affiliate for the use of (1 − λ)h; and the termrh λh is the payment from the affiliate to the parent for the use of λh. Again, the second
equality in (5) follows from the assumption that inputs are paid their marginal products
and that the production function has constant returns to scale.

2.2

Accounting with Multinational Enterprises

How do the ownership shares of intangible capital affect aggregate output measures? In
the United States, expenditure-based GDP is
Y E = pym + rh λh − rh (1 − λ)h,(6)where the last two terms are the exports and imports of intangible capital services. Incomebased GDP is
Y I = ws `sm + wu `um + rk km + 2rh λh = pym − rh h + 2rh λh,9(7)where the second equality follows from pym = ws `sm + wu `um + rk km + rh h. The payment
from the affiliate to the parent (rh λh) is reported in the BEA surveys on transactions
in services as “payments by foreign affiliates to U.S. reporters [parents] for the use of
intellectual property.” In the aggregate accounting, these payments reduce earnings on
U.S. direct investment abroad. In our framework, earnings on USDIA is simply
Y USDIA = πa = rh (1 − λ)h.

(8)Note that rh (1−λ)h is counted as earnings on USDIA, whether or not the earnings are paid
as cash dividends to the parent. Earnings not paid as dividends are treated in economic
accounts as paid and immediately reinvested in the foreign affiliate. The earnings are notincluded in GDP.
Unadjusted aggregate labor productivity isA=

pym + 2rh λh − rh
YE
=
,
`m
`m

(9)

where `m = `sm + `um is total employment in the United States. Suppose that all of theintangible capital was created in the United States. When the parent retains all of thefirm’s intangible capital (λ = 1), the entire return to intangible capital would be counted
in GDP. When λ < 1, some of the return to intangible capital gets attributed to earnings
on USDIA rather than GDP, and the productivity measure in (9) would be smaller. This
reduction grows larger as λ decreases or intangible capital becomes more important.

2.3

Formulary Apportionment Method

The simple framework presented in the previous section made it easy to see the source ofthe measured slowdown. In this section, we present a practical solution: attribute MNE
earnings to the entities of the firm in a way that reflects the location of production. We
use a formulary apportionment approach for this attribution.
Formulary apportionment begins by constructing, for each entity in the firm, an apportionment weight, ωn , that reflects the entity’s share of the total apportionment factors.
We use sales to unaffiliated parties and labor compensation as our apportionment factors. The market presence of the entity is captured by the sales measure, and restricting
sales to unaffiliated parties mitigates problems with transfer pricing and global structuring. Compensation reflects labor’s contribution to production in the entity. To account fordifferences in labor quality across entities, we use compensation rather than employment.
Weighting the two factors equally, the apportionment weights in our framework are
10

ωn =

ws `sn + wu `un
1
pyn
1
×
+ ×
2 ws `sm + wu `um + ws `sa + wu `ua 2 pym + pya

n ∈ {a, m}.

(10)

We use these weights to allocate the MNE’s consolidated earnings across the entities,πnω = ωn (πa + πm )n ∈ {a, m},(11)which yields πnω , the earnings attributed to entity n under formulary apportionment. The
formulary adjustment to each entity is simply
n = πnω − πn .

(12)

The formulary adjustment reflects the additional earnings (which could be negative) due
to the entity. We use the formulary adjustment for the parent to adjust GDP,Ye E = Y E + m = pym + rh λh − rh (1 − λ)h + m .

(13)

The adjusted GDP in (13) is the numerator in adjusted aggregate labor productivity,hheEe = Y = pym + r λh − r (1 − λ)h + m .
A
`m
`m

(14)

While standard in the multijurisdictional tax literature, the formulary adjustment will
not generally provide an exact measure of the counterfactual payment to the parent. The
accuracy of our adjustment depends upon the extent to which inputs are substitutable:with fixed-proportions production functions, the adjustment exactly reattributes profits inproportion to output. We experiment with different apportionment factors to generate a
range of adjustments.

3

Aggregate Productivity

We begin with an analysis of U.S. aggregate productivity. In Section 4, we study productivity in industries grouped by their R&D expenditure and their use and production of
information technology.

3.1

Data and Variable Construction

We construct unadjusted labor productivity for 1973–2014 using annual domestic businesssector value added from the national income and product accounts (NIPAs) published by
11

BEA and annual total hours worked from the Labor Productivity and Costs series publishedby the U.S. Bureau of Labor Statistics (BLS). In Section 2, for clarity, we used GDP as
our measure of output. In our empirical implementation, we use the more appropriate
business-sector value added measure, which abstracts from the household, government,
and nonprofit sectors of the economy. In addition, we use the annual survey data collectedby BEA on U.S. MNEs to construct productivity measures adjusted for transactions inearnings on USDIA based on the formulary framework outlined in Section 2.3.
While the productivity series span 1973–2014, the survey-level data are only available
for 1982–2014. Furthermore, some requisite survey-level data on U.S. parents were notcollected for 1983–1988 and 1990–1993. However, aggregate statistics on transactions inincome (direct investment income and portfolio income) are available for all years. Thus, we
extrapolate backward the nominal adjustment prior to 1982 using the aggregate statistics
as an indicator. In addition, we linearly interpolate the nominal adjustment for 1983–1988
and 1990–1993.
Before turning to our results, we briefly discuss the construction of our productivity
series. More details are available in the data appendix.
3.1.1

Unadjusted productivity measures

To construct unadjusted labor productivity, we follow Fernald (2015) and take the geometric average of the income-based and expenditure-based measures of business-sector
value added, which we deflate with an implicit price deflator derived from business-sector
value added. We divide our real value added measure by total hours worked to yield theunadjusted labor productivity defined in (9).
3.1.2

Adjusted productivity measures

To construct adjusted labor productivity, we need the apportionment factors and profits
for each entity in each MNE. These data are collected in the BEA MNE surveys andused to generate the formulary adjustment in (12). The survey data include financial andoperating activities based on income statement and balance sheet information reported
under U.S. GAAP for U.S. parents and their foreign affiliates. These surveys are required
to be completed for all U.S. parents, and surveys are required to be completed for all foreign
affiliates based on thresholds for assets, sales, and net income. These surveys report, foreach parent and affiliate, compensation and sales to unaffiliated parties, which we use toconstruct the apportionment factors. The surveys are also the source of earnings reported
on U.S. parents and foreign affiliates, as well as the U.S. parent’s reported voting interest

12

in a foreign affiliate.13
Apportionment factors
We use compensation of employees and sales to unaffiliated parties as apportionment factors to construct the apportionment weights in (10) for each MNE. Each apportionment
factor has advantages and disadvantages. Compensation reflects both the number of employees and their wages. If workers are paid their value marginal product, compensationreflects variation in economic activity across industries and countries. As an apportionment
factor, compensation yields relatively more production attributable to high-margin industries and high-wage countries and relatively less production attributable to low-marginindustries and low-wage countries. In addition, compensation is based on market transactions rather than financial accounting conventions, which may affect our other apportionment factor, unaffiliated sales. Thus, apportionment weights constructed using only
compensation may provide the most objective measure of economic activity. Compensation, however, may not reflect the actual economic owner of intangible capital and may
not reflect the provision of services through means such as digital technology, which do not
require a physical presence.
While unaffiliated sales may be affected by revenue recognition rules under financial
accounting conventions, an advantage of using sales as an apportionment factor is thatit reflects activity at a location regardless of physical presence, which may be a better
indicator of economic activity for some products.14 For example, unaffiliated sales may
reflect intangible capital actually employed by a foreign affiliate. In addition, sales is ameasure of local output that results from production, whereas compensation is a measureof local inputs employed in production.
Economic profits
Our measure of profits reflects current production that is consistent with the after-tax
profits component of GDP calculated by the factor income approach. Profits of U.S.
13

Transactions in income on USDIA include earnings and net interest receivable. Earnings include a U.S.
parent’s share of its foreign affiliate’s net income less capital gains, less income from equity investments,
and plus depletion. Earnings are either distributed as dividends or reinvested as further direct investment.
Net interest is very small relative to earnings, so we do not include it in our adjustments.
14
The OECD’s work on base erosion and profit shifting recommends that the taxable presence of anentity be determined primarily by the location of “significant people functions” in the case of nonfinancial
enterprises or by the location of “key entrepreneurial risk takers” in the case of financial enterprises. In thecase of electronic commerce, the commentary to the OECD model tax convention clarifies that computerequipment at a location may constitute a taxable presence even if no personnel are required to operate
the equipment. However, the attribution of profits to the location would still depend on the performanceof “significant people functions,” which implies little or no profit would be attributed to the location(Organisation for Economic Co-operation and Development, 2008b, paragraph 95).

13

parents are calculated as net income minus capital gains and losses, minus profits of theirforeign affiliates on which the U.S. parent has a claim, plus charges for depletion of natural
resources. Profits of foreign affiliates on which the U.S. parent has a claim—our measureof foreign profits—are calculated as foreign affiliate net income minus capital gains andlosses, minus profits of other foreign affiliates on which they have a claim, plus charges
for depletion of natural resources, the result of which is multiplied by the parent’s direct
voting interest in the foreign affiliate. In the data appendix (Figure A.1), we report theadjustment using a measure of operating surplus. The two adjustments are very similar.
Adjusted output
We compute the entity-level adjustments according to (12) and aggregate the parents’ adjustments for all U.S. MNEs. We add this aggregate adjustment to nominal expenditurebased business-sector value added and to nominal income-based business-sector value
added. Let M be the set of all U.S. MNEs and Y VA be either income- or expenditure-based
nominal value added. Adjusted value added, Ye VA , isYe VA = Y VA +

X

m .

(15)

m∈M

As in the construction of unadjusted labor productivity, we take the geometric averageof the two adjusted value added series and deflate them using the implicit deflator forbusiness-sector value added. In the data appendix (Figure A.2), we show that our results
are robust to other price indexes. We divide our adjusted real value added measure bytotal hours worked to yield the adjusted labor productivity defined in (14).

3.2

Results

Figure 1 presents the aggregate formulary adjustments as a share of business-sector value
added—the sum of the m from (15). Our baseline formulary adjustment, when the apportionment weights in (10) are based on sales and employee compensation, is labeled“weighted adjustment.” We also plot the formulary adjustment when either compensationor sales is the only factor used to compute the apportionment weights. The adjustments
based on only compensation or sales are similar to each other and, thus, to the weighted
adjustment. We plot total income on USDIA for reference.
From 1973 to 2000, the adjustments grow, but very slowly, and never exceed 0.6 percent
of value added in any year. This picture changes quickly in the early 2000s as income on
USDIA surges, and the weighted formulary adjustments grow to about 2.5 percent ofvalue added. The cumulative increase in U.S. GDP from the adjustment is substantial.
14

Figure 1 – Aggregate formulary adjustments(a) As share of business-sector value added(b) Inflation-adjusted level
450

USDIA Income

4.0

350

3.5
3.0
2.5
2.0

USDIA Income

400

Compensation
Weighted
Adjustment

Sales

1.5
1.0

billion USD (2009 base year)

share of business sector value added (percent)

4.5

Compensation

300
250
200

Weighted
Adjustment

Sales

150
100

0.5

50

0.0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

From 2004 to 2014, the weighted formulary adjustment adds $2.98 trillion to GDP, thecompensation-based adjustment adds $3.16 trillion, and the sales-based adjustment adds
$2.79 trillion.
Next we turn to labor productivity. Figure 2a shows the cumulative growth in laborproductivity for the entire period (1973–2014), normalizing 1973 to zero. Unless otherwise
noted, in this paper we compute growth rates using (natural) log changes. That is, thegrowth in variable X between years t and t + k is computed as s = log(Xt+1 ) − log(Xt ),and we refer to 100 × s as “s log percent” for ease of interpretation (and drop the log
designation—s percent—when it does not cause confusion). So, for example, the cumulative growth rate in unadjusted labor productivity (solid black line) from 1973 to 2014 is
79 (log) percent, which translates to a 120 percentage points increase.
Overall, the unadjusted labor productivity series we plot here is consistent with Fernald(2015), except for 2012–2014, where we have incorporated revised NIPA data. The unadjusted labor productivity growth rate over the entire period averages 1.9 percent per year.
This period is often broken into three subperiods that displayed distinct growth patterns:
1973–1994, 1994–2004, and 2004–2014. The unadjusted labor productivity growth rate averaged 1.5 percent per year from 1973 to 1994, 3.0 percent per year from 1994 to 2004, andreverted back to a lower growth rate of 1.4 percent per year in the last period (2004–2014).
We report cumulative and average annual productivity growth rates in Table II.
In Figure 2a, we plot the cumulative growth of labor productivity unadjusted andadjusted by the weighted formulary adjustment shown in Figure 1, and in Figure 3, weplot the difference between the two series. By the end of the period, labor productivity is
15

Figure 2 – Aggregate cumulative labor productivity growth(a) 1973–2014
80(b) 1994–2014
50

Adjusted

Adjusted

70
40

Unadjusted

60

log percent

log percent

50
40
30

Unadjusted

30
20

20
10

10
0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

0

1995

2000

2005

2010

2015

Figure 3 – Increase in aggregate cumulative labor productivity due to formulary adjustment(a) 1994–2014(b) 2004–2014

2.0

1.0
0.8

1.5
log percent

log percent

0.6
1.0

0.5

0.4
0.2
0.0

0.0
1995

2000

2005

2010

2015

2004

2006

2008

2010

2012

2014

higher in the adjusted series. Adjusted cumulative labor productivity growth for 1973–2014
is 1.8 log percent higher than the unadjusted cumulative labor productivity growth.
From Figure 2a, it is clear that the formulary adjustment does not affect aggregate
productivity in a substantial way until the 1990s, with most of the adjustment occurring
after 2000. In Figure 2b, we plot aggregate cumulative productivity growth since 1994 tohighlight the period of increased productivity growth (1994–2004) and the productivity
growth slowdown (2004–2014). From 1994 to 2014, the formulary adjustment adds 1.5 log
percent to cumulative labor productivity growth. The adjustment increases the productivity growth rate during the period of increased productivity growth, but the adjustment
16

Table II – Labor productivity growth rates (log percent)

1973–2014
1973–1994
1994–2014
1994–2004
2004–2014

Cumulative growth rate

Average annual growth rate

Unadjusted

Adjusted

Unadjusted

Adjusted

76.7
32.2
44.5
30.2
14.4

78.5
32.5
46.0
31.1
14.8

1.87
1.53
2.23
3.02
1.44

1.92
1.55
2.30
3.11
1.48

has its largest effects during the productivity growth slowdown. In some years, this effectis dramatic. For example, from 2006 to 2008 adjusted productivity grew by 1.6 percent,while unadjusted productivity grew only by 0.8 percent.

3.3

The Dynamics of Aggregate Profit Shifting

In this section, we examine the factors that shaped the evolution of the aggregate adjustment, and in particular its rapid growth from the mid-1990s to late 2000s and its tapering
off thereafter.
I. Tax laws and regulations
The rapid rise in profit shifting in the 1990s spurred enormous research interest in academics(e.g., tax scholars in accounting, economics, and law) as well as in government agencies(e.g., the Department of the Treasury and Congress). The voluminous body of research
resulting from this effort strongly points to three major changes in tax regulations asimportant drivers of the rise in profit shifting in the 1990s as well as its tapering off in thelast decade. Here, we briefly summarize the most relevant parts of these tax regulations
and provide a more extensive discussion of each in Appendix C.
The rise in profit shifting seems to have been driven by two major changes in tax
regulation in the 1990s. The first one is the 1995 revision of IRS regulations on cost sharing
agreements (CSAs). In a CSA, one geographic unit of an MNE—typically a foreign affiliate
in a tax haven country—shares the cost of developing a new technology with its U.S. parent
and, in return, is granted rights to royalties on a portion of the sales of products or services
using that technology. The 1995 change made it much easier for MNEs to use CSAs totransfer their intellectual property at advantageous prices to their foreign affiliates, whichthen collect the royalties and profits accruing to those intellectual properties.
A second important change happened in 1997 when the IRS introduced what would
become known as the “check-the-box” regulation, which aimed to simplify how U.S. corpo17

rations classified their various subsidiaries. Check-the-box allowed a U.S. MNE to disregard
a foreign affiliate by “checking the box” on Form 8832. When a disregarded entity receives
a payment from another entity in the enterprise, this transaction is not considered taxable by the IRS, as the payment is viewed as occurring within a consolidated entity. Thischange made it easier for U.S. MNEs to set up chains of foreign affiliates whose payments
to each other were not taxed by the IRS. This arrangement made profit shifting an evenmore effective tax reduction strategy because now a U.S. MNE could also shift its profits
from its foreign subsidiaries in higher-tax jurisdictions to the one(s) in tax havens, without
making those transfers a taxable event from the perspective of the IRS.
The pivotal role played by CSAs and check-the-box regulation for driving profit shifting
was well understood by tax authorities, tax scholars, and MNEs.15 Starting in the mid2000s, the IRS increased its efforts to reduce the tax base erosion caused by profit shifting
and tightened the rules governing CSAs, issuing Temporary Regulations in 2008 and Final
Regulations in 2011. In testimony to the U.S. Senate in 2013, Harvard law professor
Stephen Shay described the pre-2011 regulations as“much more relaxed”than the post-2011
regulations (Senate Committee on Homeland Security and Government Affairs, 2013). This
comment suggests that the combined IRS guidance on “buy-in payments” and “platform
contributions” gradually made it much more difficult for MNEs to use CSAs to transfer
intangible assets. The timing of these tighter new rules (as well as stricter enforcement)lines up very well with the tapering off and subsequent decline in profit shifting—and hencein our adjustments—shown in Figures 1 to 3.16
II. Exchange rates
The data reported by MNEs to BEA are denominated in U.S. dollars. If foreign affiliates
keep their books in a currency other than the dollar, the data are converted at the nominal
exchange rate. In Figure 4a, we plot the trade-weighted exchange value of the U.S. dollar.
In Figure 4b, we plot the cumulative growth of our baseline aggregate adjustment (solid
black line) along with an alternative version where the nominal exchange rate is held fixed15
For example, in a 2007 report to the U.S. Congress, the Treasury Department wrote that “[t]heTreasury Department believes that CSAs under the current regulations pose significant risk of income
shifting from non-arm’s length transfer pricing (U.S. Department of the Treasury 2007).” For detailed
summaries of how these regulations have affected profit shifting, see Sullivan and Cromwell, LLP (2011)
and Senate Committee on Homeland Security and Government Affairs (2013).
16
Sullivan and Cromwell, LLP (2011) discuss various pieces of evidence on how profit shifting andtransfer pricing became a key focus for the IRS, especially after the mid-2000s. They also mention thecreation of a new director-level position as evidence of a “heightened focus on transfer pricing issues” within
the IRS.

18

Figure 4 – Exchange rate effects(a) Trade-weighted exchange value of the U.S. dollar(b) Aggregate adjustment

140

30

130

25
20

110

1973 = 1.0

index 1973 = 100120

baseline

100

15

without
exchange rate

10

90
80

5

70
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

0

1975

1980

1985

1990

1995

2000

2005

2010

2015

at its base value in 1973 (dashed blue line).17
Comparing the two series, we see that the appreciation in the U.S. dollar in the early
1980s and late 1990s depressed the baseline adjustment—the dashed line is slightly above
the solid line. The strong depreciation during 1985–1995 and during the 2000s boosted thebaseline adjustment relative to what it would have been without exchange rate effects.18
Aggregating over the entire period, exchange rate movements contributed about 24 percentof the cumulative aggregate adjustment (the ratio in Figure 4b of the 25-fold increase underthe baseline case to the 20-fold increase with fixed exchange rates). This suggests that the
weaker dollar in the last 15 years has made U.S. MNEs’ foreign earnings and sales morevaluable in dollar terms, and shifting these profits abroad led to a larger slowdown inmeasured productivity growth.
III. Petroleum
As we discuss in more detail in Sections 3.6 and 4, the oil and gas industry accounts fora large share of our adjustments to U.S. GDP. Given the large swings in oil prices during
our sample period, we examine how they have contributed to variation in our adjustments
over time.
Companies in the petroleum industry often face resource taxes and royalties (in addition
17
Specifically, letting Xt be the aggregate adjustment in dollars and et be the trade-weighted U.S. dollar
exchange rate, the aggregate adjustment fixing the exchange rate is simply XtF = Xt /et , with e1973 = 1.
18
A caveat to keep in mind is that we are holding the USDIA values denominated in foreign currency
unchanged from the baseline as we counterfactually fix the exchange rates. Depending on what drives theexchange rate fluctuations in the first place, this assumption may not hold.

19

Figure 5 – The petroleum industry(a) The aggregate adjustment(b) The price of West Texas Intermediate Crude

2.5

100

Adjustment
all industries

80

2.0

dollars per barrel

share of business sector value added (percent)

3.0

1.5
1.0

Adjustment
ex petroleum

60
40
20

0.5
0.0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01970 1975 1980 1985 1990 1995 2000 2005 2010 2015

to corporate income taxes) that create strong incentives to manage tax liabilities. Perhaps
in response to these incentives, the structure of firms in the petroleum industry lends
itself to an array of profit shifting strategies. Exploration and production affiliates may bethinly capitalized, and transportation, insurance, hedging operations, and intercompany
loans create transfer pricing opportunities. A U.S. multinational petroleum company,
for example, agreed in 2017 to pay 340 million AUD in extra taxes to the Australian
government in regard to intercompany loans that were made at above-market rates (Smyth,2017).
In Figure 5a, we plot the aggregate adjustment that excludes the petroleum sector(dashed red line) against the baseline adjustment (solid blue line) reproduced from Figure 1.
Notice that, up until 2008, excluding the petroleum sector yields slower growth in theadjustment, which is especially evident during the 2004–2008 period. The adjustment
after 2008 is flat or declining regardless of whether we exclude petroleum. The right panel(Figure 5b) plots the price of crude oil per barrel during the same time period, which was
relatively stable around $25 from about 1985 to 1999 but had a very strong run-up from2000 to 2008, during which time it quadrupled to $100 per barrel. This rise in oil prices,
coupled with the relatively inelastic demand for oil, boosted both domestic and foreign
income earned by MNEs in this sector relative to the rest of the U.S. economy. If weassume that the propensity of U.S. MNEs in this sector to shift profits was unchanged, thesector’s contribution to the low measured growth of U.S. GDP and productivity increased
along with the rise in oil prices in the last decade.
In Table III, we report cumulative labor productivity growth for the petroleum industry
20

Table III – Cumulative labor productivity growth (log changes)
Petroleum industry

1973–2014
1973–1994
1994–2014
1994–2004
2004–2014

Non-petroleum industries

Unadjusted

Adjusted

Unadjusted

Adjusted

329.0
247.2
81.8
112.1
–30.3

342.8
251.8
91.0
120.3
–29.3

66.6
29.4
37.1
26.4
10.7

68.1
29.7
38.4
27.2
11.2

and the non-petroleum industries. The non-petroleum industries behave much like the aggregate: from 1973 to 2014, the adjustment adds 1.6 percentage points to non-petroleum
productivity growth compared to 1.8 percentage points for aggregate productivity (Table II). While the petroleum industry has a modest effect on aggregate productivity, theadjustment to productivity in the petroleum industry is considerable: the adjustment adds
9.2 percentage points to productivity in the petroleum industry during 1994–2014.

3.4

Adjustments for Other Countries

Our adjustments are reattributions of earnings among the parent and foreign affiliates ofeach U.S. MNE. As evident in Figure 1, in 2012, 63 percent of earnings on USDIA are
reattributed to the United States.19 From which countries did this income originate? Almost 92 percent of the reattribution to the United States is concentrated in ten countries,
and the largest reattributions were away from countries that are considered to be low-tax
destinations for MNE profit shifting (Table IV and Figure 6). Consistent with the popularity of the “double Irish with a Dutch sandwich” tax reduction strategy (Sanchirico, 2015),
the Netherlands is the largest source (27.8 percent) and Ireland is the third-largest source(10.5 percent) of income reattributed to the United States. The top ten also includes tax
havens Bermuda, Luxembourg, Singapore, Switzerland, and the U.K. Caribbean Islands.20
19

One might note that this share is identical to the share of total FDI earnings that were reinvested in2012 ($284 b./$449 b. = 63%). We do not mean to suggest by our estimates that all reinvested earnings
on U.S. direct investment abroad are associated with profit shifting. Although the equality of these twoshares is notable, there are undoubtedly times when profits temporarily held abroad for tax reasons must
be remitted to the parent, such as in cases of domestic liquidity needs. We are grateful to Aneta Markowska
of Cornerstone Macro LLC for bringing this comparison to our attention.
20
The adjustments may be larger than GDP due to national accounting practices in some host countries
that exclude the income from certain foreign-owned companies from GDP. Bermuda, for example, does
not include exempted companies—companies that are located in Bermuda but do not do business inBermuda—in its national accounting statistics. Lipsey (2009) provides a sense of scale: in 1999, theforeign affiliates of U.S. MNEs in Bermuda reported sales of services to parties outside of Bermuda of$13,908 million; however, total service exports as reported in Bermudian official statistics are only $1,163

21

Figure 6 – Geographical reattribution of earnings of U.S. MNEs, 2012 (bil. USD)
United Kingdom: –$15.7
Ireland: –$29.5

Canada: –$13.7

Luxembourg: –$23.6
Netherlands: –$77.9
Switzerland: –$13.6

United States: +$280.1

Bermuda: –$32.4

Singapore: –$19.0
U.K.I. Caribbean: –$22.0

Notes: The United Kingdom Islands (U.K.I.), Caribbean, are made up of the British Virgin Islands,
Cayman Islands, Montserrat, and Turks and Caicos Islands.

Canada and the United Kingdom are also in the top ten sources of reattributed income;however, these countries are major hosts of U.S.-owned affiliate employment. In 2012,
Canada accounted for 8.4 percent of all employment in the foreign affiliates of U.S. parents,
and the United Kingdom accounted for 10.3 percent.
The negative adjustments for some countries do not necessarily imply that the GDP ofthose countries is overstated. Some of the larger countries are home to indigenous MNEs,
and reattribution of those companies’ global profits may very well raise the GDP of thosecountries.
Our adjustments are positive for some countries (besides the United States) as well,although the increases are small. The adjustments, in fact, are too small to be reported
given the confidentiality of the BEA survey data. The ten countries with the largest
positive reattribution of income on USDIA are, in order from highest to lowest: Japan,
France, Italy, Russia, Argentina, Greece, Turkey, Libya, Germany, and Kenya. Among thisset of countries, Japan, France, Italy, Greece, and Germany have tax rates that generally
exceed the OECD average. Overall, the reattribution pattern supports the concern thatprofit shifting affects measurement as a result of income on USDIA being shifted to affiliates
in countries with relatively low tax rates.
million.

22

Table IV – Adjustments in other countries, 2012
Country

Adjustment(share of total)

Adjustment(share of GDP)

1.00
0.28
0.12
0.11
0.08
0.08
0.07
0.06
0.05
0.05
0.12

0.10
5.91
0.14
0.43
4.76
0.07
0.01
0.01
0.02

Decrease in GDP
Netherlands
BermudaIreland
Luxembourg
U.K.I., Caribbean
Singapore
United Kingdom
CanadaSwitzerland
Remaining 176 countries

Notes: The adjustment to U.S. GDP in 2012 is an increase of $280.1 billion. This table reports
the offsetting adjustments (decreases in GDP) to the top 9 counterpart countries by size ofadjustment. The United Kingdom Islands (U.K.I.), Caribbean, include the British Virgin
Islands, Cayman Islands, Montserrat, and Turks and Caicos Islands. They are members of
CARICOM, which publishes a comprehensive set of national accounts data.

3.5

Relationship to Tax Rates

Our formulary apportionment method does not use tax rate differences across countries;
our method relies only on measures of economic activity. It is evident, however, that ourmeasures of shifted profit are correlated with tax rate differentials. Much of the literature
on profit shifting has looked for evidence of tax-motivated profit shifting by running crosscountry regressions of the formlog(πi ) = β0 + β1 τi + β2 `i + β3 ki + ui ,(16)

where πi is a measure of reported affiliate profit in country i, τi is a measure of the effective
tax rate in country i, `i is a measure of labor used by affiliates in country i, ki is a measureof the physical capital stock used by affiliates in country i, and ui is a classical error term.
This particular specification is from Hines and Rice (1994), which is estimated using dataon the foreign affiliates of U.S. multinationals for 1982, drawn from the same data that weuse.
In column 1 of Table V, we report an estimate of (16) using the 1982 data. We finda coefficient (a semi-elasticity) of –3.96 compared to –2.83 in Hines and Rice (1994). The
23

different values reflect differences in our measure of profits and a slightly different sample
of countries. In their model, the theoretical relationship between profits and taxes islog(πi ) = log(ρi ) + c1 τi + c2 ,(17)

where πi is the reported affiliate profit, ρi is the profit derived from real economic activity,and c1 and c2 are constants. In the model, ρi is derived from capital and labor, whichleads to the `i and ki terms in (16). In contrast, we have directly computed ρi , so ourtheoretically consistent version of (16) islog(πi ) − log(ρi ) = β0 + β1 τi + ui ,(18)

the estimates of which we report in the second column of Table V. We find a semi-elasticity
with respect to tax rates of –4.89. We view the similarity of our tax semi-elasticity—derived
using no data on tax rates—to that from specifications that explicitly model profit shifting
as a function of the tax rates to be an important consistency check on our results.
Since we have computed ρi directly, we can further study the relationship between tax
rates and profit derived from real economic activity. In column 3 in Table V, we regress ρi
on tax rates and measures of labor and capital inputs. Once we have removed the shifted
profit from the reported profit, the tax rate is no longer significant. Our methodology
appears to have removed the tax-motivated profit from the reported profit.

3.6

Industry Composition

How is our adjustment distributed across industries? In the next section, we group industries according to some relevant characteristics and analyze their productivity growth. In
this section, we discuss the general composition of the adjustment. In Table VI, we report
the adjustments by two-digit NAICS industry of the U.S. parent in 2012.21 We report anindustry’s adjustment as its share of the total adjustment and as its share of the industry’s
unadjusted value added. The industrial composition of the adjustment is consistent with
the idea that firms use intangible assets to shift profits to low-tax jurisdictions.
Industry group 32—a subset of manufacturing—makes up 42 percent of the total adjustment. This industry is dominated by pharmaceuticals (a part of chemicals) and petroleum,
which we have discussed above. (To avoid disclosure of confidential data, we cannot report the exact adjustments for these industries.) The pharmaceutical industry’s profits
21
The industry of the affiliate closest to the parent is often NAICS 55—holding companies—which is
not very informative about the industrial composition of the sub-affiliates held by the affiliate holding
company.

24

Table V – Shifted profits and tax rates(1)
log(unadj. profit)

profitlog( unadj)
adj. profit

(2)

(3)
log(adj. profit)

Tax rate–3.96(0.49)–4.89(0.71)

0.03(0.38)

Labor compensation

0.40(0.12)

0.90(0.09)

Physical capital

0.49(0.11)

0.02(0.08)

Constant

1.68(0.36)

2.41(0.27)-0.61(0.27)

Adj. R-squared
Observations

0.94
56

0.46
56

0.97
56

Notes: Standard errors are reported in parentheses.

are largely derived from its intellectual property, which provides ample opportunities forprofit shifting. A major U.S. pharmaceutical company, for example, has licensed the manufacturing rights to some best-selling drugs to its Irish subsidiary (Bergin and Drawbaugh,2015). Industries 51 and 54—which include software publishing, data processing and hosting, and computer system design—make up 12 percent of the aggregate adjustment. As
in the pharmaceutical industry, the “information” industry derives much of its profit from
intangible assets and low marginal production costs.
Industry group 52 is responsible for 14 percent of the total adjustment. The foreign
operations of the insurance industry, for example, are concentrated in Bermuda—a country
whose corporate tax rate is zero (Lipsey, 2010).
The industries that are not significant contributors to the aggregate adjustment tend
to be those in which it is difficult to uncouple the physical location of production fromthe point of sale. These industries include construction, transportation, education, healthcare, and other services.

25

Table VI – Adjustment by industry of U.S. parent
Adjustment (percent)Key industries
32 Petroleum, chemicals, plastics, wood, paper
52 Finance, insurance
33 Computers, electrical equip., motor vehicles
51 Publishing, information, data processing
54 Legal, computer sys. design, scientific R&D
31 Food, textiles, apparel
21 Mining
44 Retail trade
42 Wholesale trade
55 Management of companies and enterprises
53 Real estate and leasing
22 Utilities
48 Transportation and warehousing

Share oftotal adjustment

Share ofind. value added

42.04
14.32
11.95
7.14
5.34
4.91
4.16
2.81
2.33
1.75
1.00
0.72
0.61

15.99
3.51
3.38
2.71
1.33
5.37
2.83
0.84
0.68
1.61
0.48
0.47
0.37

Notes: The adjustment in construction, agriculture, education, health care, arts and entertainment, andothers that are not listed here are very small and hence omitted.

4

Productivity in R&D- and IT-intensive Industries

Attributing the return to intangible capital correctly is likely to make a bigger difference
in industries in which intangible capital is more important. To investigate this hypothesis,
we construct adjusted productivity measures for industries grouped by their R&D and IT
intensities.
We use data on value added by industry from the U.S. Industry Economic Accounts(IEAs) published by BEA and hours worked by industry from the Labor Productivity andCosts database published by BLS. As before, we use the BEA survey of MNEs to construct value added adjusted for transactions in earnings on USDIA based on the formularyframework outlined in Section 2.3.
We use the survey data to calculate R&D and IT intensity at the enterprise level —theU.S. parent and all of its foreign affiliates. We calculate R&D intensity by dividing
enterprise-level R&D expenditures, aggregated across all available years, by enterpriselevel sales to unaffiliated parties, aggregated across all available years. We consider anenterprise to be R&D intensive if its R&D intensity is at or above the 75th percentile. In
most cases, R&D-intensive enterprises include U.S. parents and foreign affiliates classified
26

into industries that are considered to be R&D intensive as defined in Moylan and Robbins(2007).
We calculate IT intensity separately for IT usage and IT production. We first categorize industries as IT using, as defined in Bloom, Sadun, and Van Reenen (2012), or as IT
producing, as defined in Fernald (2015). We consider an enterprise to be IT-using intensive
if the share of its total unaffiliated sales generated by entities classified into IT-using industries is greater than 50 percent. We consider an enterprise to be IT-producing intensive
if the share of its total unaffiliated sales generated by entities classified into IT-producing
industries is greater than 50 percent.
A complete list of industry groups is reported in appendix Table B.1. Note that a
number of industries are included in more than one group. Information industries (NAICS
51) are included in both IT-using and IT-producing groups, and computer and electronic
product manufacturing industries (NAICS 334) and computer systems design industries(NAICS 5415) are grouped with both IT-producing industries and R&D-intensive industries. Likewise, transportation and equipment manufacturing industries (NAICS 336) andscientific research and development industries (NAICS 5417) are grouped with both ITusing industries and R&D-intensive industries. IT-producing and R&D-intensive industries
overlap the most: IT-producing industries are almost a subset of R&D-intensive industries.
In contrast, IT-using industries include a number of industries that are not included in either the IT-producing industries or the R&D-intensive industries, including industries suchas wholesale trade (NAICS 42) and retail trade (NAICS 44–45).

4.1

Unadjusted Productivity Measures

The value added by industry data published in the IEAs include nominal and real measures
as well as the related price indexes (base year 2009). We group industries according totheir R&D or IT intensities and compute each group’s real value added using the relevantTörnqvist price index. We divide the group’s real value added by the group’s hours worked
to yield unadjusted labor productivity.

4.2

Adjusted Productivity Measures

We adjust nominal value added for industries grouped by R&D intensity and IT intensity.
To compute, for example, adjusted value added for the R&D-intensive industries, we begin
with the MNEs that are identified as R&D intensive. For each of these enterprises, we use
the survey data collected by BEA to compute the apportionment weights as in (10) andthe formulary adjustments as in (12).
27

We sum the formulary adjustments and add them to the nominal value added of the
R&D-intensive industries. If I RD is the set of R&D-intensive industries and M RD is the setof R&D-intensive enterprises, then the adjusted nominal value added of the R&D-intensive
industries isX
X
Ye RD =
Yi +
m ,
(19)
i∈I RDm∈M RD

where Yi is nominal value added in industry i and m is the formulary adjustment ofenterprise m defined in (12). We deflate nominal adjusted value added by a Törnqvist
index of the price indexes from the industries in I RD . We divide the industry group’s realvalue added by the group’s hours worked to yield adjusted labor productivity.
Proceeding in this way, we compute adjusted labor productivity for R&D-intensive
industries, non-R&D-intensive industries, IT-using industries, non-IT-using industries, ITproducing industries, and non-IT-producing industries.

4.3

R&D-Intensive Industries

In Figure 7a, we plot, as a share of unadjusted value added, the formulary adjustment for
R&D-intensive and non-R&D-intensive industries. The formulary adjustments for R&Dintensive industries grow rapidly during the 1990s and early 2000s before flattening out
post-2008. The formulary adjustments for R&D-intensive enterprises as a share of value
added are substantially larger than those for non-R&D-intensive enterprises, with the adjustment for R&D-intensive enterprises peaking in 2008 at 8 percent of nominal value
added.
The impact of our adjustments on productivity growth by R&D intensity is evident
in Figure 8, in which we plot cumulative labor productivity growth. From 1973 to 2014,
productivity growth in the R&D-intensive industries is about three times that in the nonR&D-intensive industries. From 1994 to 2014, adjusting for offshore profit shifting in
the R&D-intensive industries adds 4.4 log points to cumulative growth, but only a little
less than 1 log point in non-R&D-intensive industries. As we observed in the aggregate
data, the largest effects on productivity growth occurred in the 2000s. The annual growthrate from 2000 to 2008 is 0.53 log points higher in the R&D-intensive industries after the
adjustments (Table VII).

4.4

IT-Producing Industries

In Figure 9a, we plot the formulary adjustments as a share of value added for those industries that produce IT and those industries that do not. While quantitatively smaller,
the adjustment for IT-producing industries is similar to that in R&D-intensive industries.
28

Figure 7 – Formulary Adjustment and Cumulative Labor Productivity Growth, by R&D
Intensity, 1994–2014(b) Levels: Adjusted vs. Unadjusted
120

7

100
80

5
4
3
non-R&D intensive

2

Unadjusted,
R&D intensive

60
40

Adjusted, non-R&D intensive

20
Unadjusted, non-R&D intensive

10Adjusted, R&D intensive

R&D intensive

6

log percent

share of group's unadjusted value added (percent)(a) Formulary Adjustment
8

0

1995

2000

2005

2010

2015

1995

2000

2005

2010

2015

The adjustments grow strongly during the 2000s. The IT-producing industries are almost all R&D-intensive industries, so this is not surprising. From 1994 to 2014, adjustingfor offshore profit shifting adds 4.3 log points to cumulative labor productivity growth in
IT-producing industries (Figure 9b, Table VII). Our adjustments add 1.7 log points tocumulative labor productivity growth in the industries that do not produce IT.

4.5

IT-Using Industries

When we classify industries by their use of IT, we find that the formulary adjustments
in industries that use IT intensively are smaller than, but quantitatively similar to, thosein the industries that do not use IT intensively (Figure 9c, Table VII). Offshore profit
shifting is often achieved through the location of intangible assets; if industries that use IT
intensively—which include wholesale and retail trade—also have fewer intangible assets,
we would expect to see a smaller adjustment in these industries.
In Figure 9d, we plot the cumulative labor productivity growth in the industries groupedby IT usage. Since 1994, adjusting productivity for offshore profit shifting adds about 1.4
log points to productivity in the industries that use IT intensively and 1.5 log points toindustries that do not use IT intensively.

29

Figure 8 – Cumulative Labor Productivity Growth, by R&D Intensity, 1973–2014

160
Adjusted, R&D intensive

140

Unadjusted,
R&D intensive120

log percent

100
80
Adjusted,
non-R&D intensive

60
40

Unadjusted,
non-R&D intensive

20
0
1975

1980

1985

1990

30

1995

2000

2005

2010

2015

Table VII – Cumulative Labor Productivity Growth, by R&D and IT Intensity,
1994–2014
1994–2014
R&D intensive
Adjusted
Unadjusted
Non-R&D intensive
Adjusted
Unadjusted
IT producing
Adjusted
Unadjusted
Non-IT producing
Adjusted
Unadjusted
IT using
Adjusted
Unadjusted
Non-IT using
Adjusted
Unadjusted

1994–2004 2004–2014

98.9
94.5

72.3
68.9

26.5
25.6

28.5
27.6

20.8
20.3

7.7
7.3

146.0
142.3

93.7
92.0

52.4
50.4

26.8
25.5

20.5
19.6

6.3
6.0

73.9
72.5

51.0
50.6

22.9
21.9

29.0
27.5

22.5
21.2

6.6
6.3

31

Figure 9 – Industries by IT producing and using intensity(b) Productivity growth, IT producing

4.5

160

4.0

140

3.5

2.0

non-IT producing

1.5

80
60
40

1.0

Unadjusted, non-IT producing

0

1995

2000

2005

2010

1995

2015

(c) Formulary adjustment, by IT using

2000

2005

80

2015

Adjusted, IT using

70

3.0

60

non-IT using

2.5

2010

(d) Productivity growth, by IT using

3.5

Unadjusted,
IT using50

2.0

log percent

share of group's unadjusted value added (percent)

Adjusted, non-IT producing

20

0.5

1.5

40

Adjusted, non-IT using

30
20

1.0

Unadjusted, non-IT using

10

IT using
0.5
0.0

Unadjusted,
IT producing

100

2.5

0.0

Adjusted, IT producing

120

IT producing

3.0

log percent

share of group's unadjusted value added (percent)(a) Formulary adjustments, IT producing

0

1995

2000

2005

2010

2015

32

10

1995

2000

2005

2010

2015

5

Foreign MNEs in the United States

Although the primary focus of this paper is the apportionment of the worldwide profits
of U.S.-owned MNEs, profit shifting by foreign-owned MNEs that operate affiliates in the
United States can also affect measures of U.S. production and labor productivity. If foreignowned MNEs shift profit out of the United States, this will reduce U.S. GDP and income
on FDIUS. The relationship between GDP, income on USDIA, and income on FDIUS canbe seen in (1).
Ideally, we could apply the formulary apportionment method used for U.S.-owned
MNEs to foreign-owned MNEs operating in the United States and arrive at a measure of
the profit shifting by foreign MNEs. Data availability, however, poses difficulties. Whereas
the BEA direct investment surveys cover the worldwide operations of U.S.-owned MNEs,
BEA does not have legal authority to collect data on the worldwide operations of foreignowned MNEs that own U.S. affiliates. Creating a data set comparable to the one we use to
study U.S. MNEs requires combining BEA data on the U.S. operations of foreign MNEs
with data on the foreign MNEs’ worldwide operations from a commercial data set suchas Bureau van Dijk’s Orbis. A particularly challenging aspect of this work is creating amatch between companies in the two data sets. A common numeric identifier does notexist in the two data sets, so the linking must be done by name matching, which, because
of inconsistencies and ambiguities in company names, is imperfect and time consuming.
Nevertheless, to give a sense of the potential significance of profit shifting by foreignowned U.S. businesses, we develop a partial link between the BEA and Orbis data setsfor 2008, 2012, and 2015. Given our finding that R&D-intensive firms are most affectedby profit shifting, we construct a data set of large technology-intensive foreign-owned U.S.
businesses containing the data needed to conduct an apportionment exercise. These businesses account for about 40 percent of R&D spending by foreign-owned U.S. businesses
and about 13 percent of employment in all foreign-owned U.S. businesses. The scope ofour data set is summarized in Table VIII.
In the data set we have been able to construct, we find evidence that foreign-owned
MNEs operating in the United States are shifting profits out of the United States. Thisis evident in Figure 10a. On the vertical axis, we plot the reported U.S. share of worldwide profits for each foreign-owned MNE in our sample. On the horizontal axis, we plot
the apportionment weight, (10), for the U.S. operations of each foreign-owned MNE.22
In our methodology, if there was no profit shifting, the two measures would fall on the22
We use employee compensation and net plant, property, and equipment—equally weighted—as apportionment factors.

33

Table VIII – Profit shifting
FDIUS

2008
2012
2015

USDIA

Number of
firms

Share ofR&D spending

Share ofemployment

Est. shifted
profit (bil.)

Est. shifted
profit (bil.)

137
136
127

0.45
0.43
0.32

0.13
0.13
0.11

2.1
12.3
3.5

282.9
280.1
—

Notes: The column labeled “Share of R&D spending” is the share of R&D spending by foreign-owned
U.S. businesses, and the column labeled “Share of employment” is the share of total U.S. employment inforeign-owned U.S. businesses.

45-degree line. In 2012 and 2008 (not reported), we find the U.S. share of profits is significantly below the 45-degree line—particularly for MNEs with operations concentrated
in the United States. The locally weighted scatterplot (Lowess smoothed values) suggests
that, for foreign-owned businesses with less than 20 percent of their worldwide production
attributed to their U.S. operations, their U.S. profits are roughly proportional to their
attributed U.S. production. However, the U.S. profits of foreign-owned businesses with
20 percent or more of their worldwide production attributed to the United States are
significantly understated relative to their attributed production in the United States.
Altogether, if the profits of these foreign-owned businesses were reattributed according
to the weighted apportionment factors, their U.S. profits would have been $12.3 billion
higher in 2012 and $2.1 billion higher in 2008. It is difficult to judge the magnitude ofthese figures given the size of our data set, but it does suggest that foreign-owned MNEs
are shifting profit out of the United States.
In the 2015 data, we find less evidence of foreign-MNEs shifting profit out of the UnitedStates (Figure 10b). In this case, the firms in our sample have U.S. profit shares that are
roughly proportional to their attributed production shares. This result is consistent with
the increased efforts by revenue authorities in the United States to deter profit shifting,but given the incomplete nature of our data, it is difficult to know the extent to which
this reflects a reduction in profit shifting by foreign MNEs or an implication of our sample.
Despite the tighter relationship between U.S. profit shares and U.S. apportionment weights,
our reapportionment adds about $3.5 billion to U.S. GDP in 2015.
Overall, our analysis, incomplete as it is, suggests that profits of foreign-owned MNEs
are being shifted out of the United States. This implies that our findings from the U.S.
MNE data (Section 3) are the lower bounds of the measured slowdown of U.S. productivity
growth caused by offshore profit shifting.
34

Figure 10 – Foreign-owned MNEs operating in the United States(a) 2012(b) 2015

0.7

0.7
45-degree line

0.5
OLS fitted value

0.4
0.3

Lowess smoothed value

0.2

45-degree line

0.6
U.S. share of worldwide profit

U.S. share of worldwide profit

0.6

0.1

0.5
0.4

OLS fitted value

0.3

Lowess smoothed value

0.2
0.1

0.0
0.0

0.1

0.2

0.3
0.4
0.5
U.S. apportionment weight

0.6

0.0
0.0

0.7

0.1

0.2

0.3
0.4
0.5
U.S. apportionment weight

0.6

0.7

Figure 11 – Trade balances(b) Goods and services trade balances(a) Total trade balance
0.02

0.04

0.01

0.02
0.00

adjusted

0.01

share of gdp

share of gdp

0.00

0.02
0.03

0.02
0.04

0.04
0.05

0.06

unadjusted

0.06
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

6

goods unadjusted
goods adjusted
services, unadjusted
services, adjusted

0.081970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Implications for Other Measurements

We have focused on the effects of profit shifting on U.S. productivity measurement, but theadjustments we have computed will affect other measures as well. Here, we briefly discuss
some of these other measurements, which present interesting avenues for future research.

6.1

External Balances

In Section 2.2, we assumed that the income we reattribute to the United States is matchedwith a flow of exports from the United States to the rest of the world. In the aggregate,
35

the effect of our adjustment on the U.S. trade balance relative to GDP can be seen in
Figure 11a. The implied increase in exports makes the trade balance less negative. The
effect of the adjustment (the gap between the solid and dashed lines in Figure 11a) is verysmall in the 1970s and stays small in the 1980s and 1990s (less than 0.5 percent of GDP),but then grows rapidly after the turn of the 21st century. In 2014, the unadjusted tradebalance is –2.9 percent of GDP, and the adjusted trade balance is –1.3 percent of GDP.
Our assumption is that the reattributed income is the result of intangible assets createdin the United States, so the exports would be classified as “charges for the use of intellectual
property” and counted as a service export from the United States. In Figure 11b, we plot
the trade balance for goods and services separately. Our adjustment does not change
the goods trade balance, so the only differences between the adjusted and unadjusted
goods trade-balance-to-GDP ratios are from changes to GDP in the adjusted measure.
Our adjustment makes the services trade balance more positive, and, as before, the effectgrows over time and especially rapidly in the 2000s. In 2014, the unadjusted services tradebalance is 1.5 percent of GDP, and the adjusted services trade balance is 3.1 percent ofGDP.
These large effects of our adjustment on the trade balance raise a natural question:
What is the effect on the current account? As is evident from equation (1), our adjustment
decreases income on USDIA and increases exports of services by the same amount, leaving
the current account unchanged. One assumption behind this exact offsetting result thatleaves the current account intact is the treatment of interest payments between the parent
and affiliates, which are assumed to be unaffected by the adjustment in this calculation.
Bruner, Rassier, and Ruhl (2018) consider additional adjustments for these payments andfind a small, negative adjustment to the current account.23

6.2

Labor’s Share of Aggregate Income

A well-established empirical regularity during much of the 20th century is the lack of a timetrend in labor’s share of aggregate income. This relationship appeared so robust that it
became one of Kaldor’s balanced growth facts and a key feature that macroeconomic models
aimed to match. A number of recent studies, however, find that the labor share has been
declining in recent decades—especially since the early 2000s (Autor, Dorn, Katz, Patterson,
and Van Reenen, 2017; Elsby, Hobijn, and Sahin, 2013; Karabarbounis and Neiman, 2014).
The decline in the labor share has led to a surge in interest in the measurement of the23
Bruner, Rassier, and Ruhl (2018) examine the effects of profit shifting on other parts of the U.S.
national accounts and balance of payments accounts. Their analysis is limited to a single year however,whereas here we examine the entire path of adjustments going back to 1978.

36

Figure 12 – Labor share in the United States
0.76
0.74

unadjusted

share of income

0.72
0.70
adjusted
0.68
0.66
0.64
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

labor (and capital) share. Our adjustment to GDP has implications for this measurement
issue as well. It is worth noting that the income we reattribute to the United States is partof domestic capital income (corporate profits), which has the effect of reducing the labor
share and raising the capital share.
To quantify the magnitude of this effect, in Figure 12, we plot the unadjusted net
labor share of income for corporate business: employee compensation divided by net value
added.24 We also plot the adjusted net labor share: employee compensation divided by
the sum of net value added and our aggregate adjustment. As can be anticipated fromour previous findings, the adjustment grows over time and accelerates in the 2000s. The
unadjusted net labor share fell from 0.753 in 2001 to 0.662 in 2014, for a decline of 9.1
percentage points, whereas the adjusted labor share fell from 0.743 to 0.643 during the sametime, for a decline of 10.0 percentage points. Our adjustment also changes the shorter-run
dynamics of the labor share. For example, in 2008, the unadjusted series increases sharply,
suggesting that the preceding dip in 2006 was cyclical. The adjusted labor share tells
a different story: while there is an upward movement in 2008, the adjusted labor sharedisplays a strong downward trend.

24This is one of the measures used by Bridgman (2017), who also demonstrates that the treatment ofdepreciation and taxes affects the measured labor share. Our adjustment yields similar results regardlessof the measure considered.

37

6.3

Other Measures

Our reattribution of income also implies that the return on U.S. foreign direct investment
abroad is lower than current measures suggest and—to the extent that foreign MNEs are
shifting profits out of the United States—the return on direct investment in the UnitedStates is greater than current measures suggest.
Lastly, a line of research has taken up the decline in the share of C corporations in total
net business income (Cooper, McClelland, Pearce, Prisinzano, Sullivan, Yagan, Zidar, andZwick, 2016; DeBacker and Prisinzano, 2015). To the extent that profit shifting is occurringlargely in C corporations, our adjustment may offset some of the decline in the prevalenceof C corporations.

7

Concluding Remarks

Using firm-level data on U.S. MNEs, we find increasing profit shifting activity by U.S.
MNEs, leading to low measured growth in GDP. Our adjustments mitigate the productivity slowdown reflected in official statistics. The adjustments raise aggregate productivity
growth rates by 0.09 percent annually from 1994 to 2004, by 0.24 percent annually from2004 to 2008, and decrease productivity growth rates by 0.09 percent annually after 2008.25
While the effects on the measured slowdown seem to have subsided after 2008, the challenge itself still persists: from 2008 to 2014, official domestic business-sector value added in
the United States was, on average, more than 2 percent—about $260 billion—lower than
our adjusted measure. The measured slowdown is most pronounced in industries that are
R&D intensive or that produce information technology—the very industries that have been
singled out for being the most responsible for the aggregate productivity slowdown.
The upward adjustments to U.S. value added imply downward adjustments to value
added in some other countries. For small countries with very low tax rates, such as Bermuda
and U.K.I., Caribbean, the adjustment is four to five times their annual GDP. Even forsome relatively large economies, such as Ireland and the Netherlands, the adjustments areas large as 9–13 percent of their annual GDP.
We also examine the other international component of the measured slowdown: profit
shifting by the U.S. subsidiaries of foreign MNEs. The data used to analyze these companies
are not as complete as the BEA survey data that we use for U.S. MNEs, so we are notable to provide a definitive assessment. That said, our preliminary findings—especially
Figure 10—suggest a similar pattern. Further work on this topic is warranted.
25
It is not clear whether the apparent plateauing in profit shifting activity is related to corporate
inversions, which cause some firms to exit the BEA survey sample used in this paper.

38

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Times, August 18.
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42

A

Data appendix

A.1

Data and variable construction details

Data sources and productivity series
We construct annual and cumulative series of labor productivity growth for 1973–2014.
We use annual domestic business sector data published by the U.S. Bureau of Economic
Analysis (BEA) and by the U.S. Bureau of Labor Statistics (BLS) to construct unadjusted
series. The BEA data include aggregate value added in the national income and productaccounts (NIPAs) and value added by industry in the Industry Economic Accounts (IEAs)
for the entire period 1973–2014. The BLS data include, from the Labor Productivity andCosts (LPC) database, aggregate number of hours worked for 1973–2014 and number ofhours worked by industry for 1987–2014. We also use annual data for transactions inincome on direct investment published by BEA in the International Transactions Accounts(ITAs), which are available for 1982–2014. In addition, we use annual survey-level datacollected by BEA on MNEs for 1982–2014 to construct an adjustment series based on
the formulary framework outlined in Section 2.3. We construct aggregate adjusted andunadjusted productivity growth series that are consistent with Fernald (2015), and we also
construct adjusted and unadjusted productivity growth series grouped by R&D-intensive
industries and IT-intensive industries in order to more clearly observe the effects of ouradjustments for transactions in direct investment income attributable to intangible capital.
The NIPA data and the IEA data are current as of the annual revision of the NIPAs
released on July 30, 2015. The LPC data we use were released on June 4, 2015. The ITA
data are current as of the annual revision of the ITAs released on June 18, 2015. The MNE
survey data we use include surveys conducted for 1982, 1989, and 1994–2014.
The NIPA data and the IEA data that we use include real and nominal measures. We
use real measures to construct growth rates for the unadjusted series, and we use nominal
measures deflated to real measures to construct growth rates for the adjusted series. Realmeasures from both the NIPAs and the IEAs are based on a chained Fisher formula with abase year of 2009. We deflate nominal measures using either an actual or implicit deflator incases of business-sector aggregates or industry groups for which actual or implicit deflators
are available. In cases of industry groups for which actual or implicit deflators are notpublished, we calculate growth rates using a Törnqvist formula.
While the productivity series span 1973–2014, the survey-level data are only available
for 1982–2014. Furthermore, some requisite survey-level data on U.S. parents were not43

collected for 1983–1988 and 1990–1993. However, aggregate statistics on transactions inincome (i.e., direct investment income and portfolio income) are available for all years.
Thus, we extrapolate backward the nominal adjustment prior to 1982 using the aggregate
statistics as an indicator. In addition, we linearly interpolate the nominal adjustment for
1983–1988 and 1990–1993.
Aggregate unadjusted series with business-sector data
Aggregate business-sector value added published in the NIPAs includes nominal value
added published in NIPA table 1.3.5 and real value added published in NIPA table 1.3.6,
which yield an implicit deflator for business-sector value added. We derive an implied
aggregate nominal income-based measure of business-sector value added by subtracting
nominal expenditure-based value added for the non-business sector published in NIPA table 1.3.5 from aggregate nominal income-based GDP published in NIPA table 1.10. We
deflate the implied aggregate nominal income-based measure using the implicit deflator
for business-sector value added. Thus, aggregate business-sector value added includes areal expenditure-based measure and a real income-based measure from which we generate
separate growth rates and then average across the expenditure- and income-based measures. We then subtract the growth in the number of hours worked from the growthin value added to construct annual and cumulative unadjusted series of aggregate laborproductivity growth.
Aggregate adjusted series with business-sector data
We apply survey-level data collected by BEA on MNEs to (12). The survey-level data
include financial and operating activities based on income statement information and balance sheet information reported for U.S. parents and their foreign affiliates on U.S. direct
investment abroad (USDIA). Depending on the year, the data are reported on either the
Annual Survey of U.S. Direct Investment Abroad (form BE-11) or the Benchmark Surveyof U.S. Direct Investment Abroad (form BE-10).
The survey-level data provide a source for apportionment factors on U.S. parents andforeign affiliates. The apportionment factors include compensation and unaffiliated sales.
In addition, the survey-level data provide a source for earnings reported on U.S. parents
and foreign affiliates and provide a source for the U.S. parent’s reported voting interest in
a foreign affiliate. We infer a parent’s share of a foreign affiliate’s earnings under separate
accounting by multiplying earnings reported for the foreign affiliate by the U.S. parent’s
reported voting interest.
We generate an adjustment for aggregate nominal expenditure-based business-sector
value added and for our implied aggregate nominal income-based business-sector value
44

added by aggregating the survey-level adjustments obtained from (12). We then deflate
the adjusted nominal measures using the implicit deflator for business-sector value added
and construct annual and cumulative adjusted series of aggregate labor productivity growthby averaging across the adjusted expenditure- and income-based measures and then subtracting the same growth in the number of hours worked that we used in the unadjusted
aggregate series.
R&D intensity and IT intensity
The survey-level data allow us to calculate R&D intensity and IT intensity at the firm level(i.e., U.S. parents and foreign affiliates for a given U.S. MNE). We calculate R&D intensity
by dividing firm-level R&D expenditures aggregated across all available years by firmlevel unaffiliated sales aggregated across all available years. We consider R&D-intensive
firms and industries to be those with R&D intensity at or above the 75th percentile. We
calculate IT intensity separately based on IT usage and IT production. IT-using firms are
determined by the proportion of unaffiliated sales generated by entities within a firm thatare classified to industries characterized as IT using in Bloom, Sadun, and Van Reenen(2012). IT-producing firms are determined by the proportion of unaffiliated sales generated
by entities within a firm that are classified to industries characterized as IT producing in
Fernald (2015). In each case, IT-using firms and IT-producing firms include firms withan unaffiliated sales proportion greater than 50 percent. Using the survey-level data, weapply adjustments for R&D-intensive firms and IT-intensive firms to the industry-level
data to construct the adjusted labor productivity growth series for comparison with theunadjusted series.
Unadjusted series
Value added by industry published in the IEAs includes nominal and real measures as wellas the related chained Fisher price indexes. We generate the growth in unadjusted realvalue added separately for industries grouped by R&D intensity and IT intensity using
the Törnqvist formula. We also generate the growth in the number of hours worked foreach group of industries. We then subtract the growth in the number of hours worked
from the Törnqvist growth in unadjusted real value added to construct separate annualand cumulative unadjusted series of labor productivity growth for R&D industries, nonR&D industries, IT-using industries, non-IT-using industries, IT-producing industries, andnon-IT-producing industries.
Adjusted series
We adjust nominal value added for industries grouped by R&D intensity and IT intensity.
We generate separate adjustments for R&D-intensive industries and IT-intensive industries45

by first applying the survey-level data collected by BEA on MNEs to (12) and then aggregating the survey-level adjustments for R&D-intensive firms, IT-using firms, IT-producing
firms, and the respective residual firms. We then generate the growth in adjusted nominal
value added for each group of industries, and we also generate the growth in prices for eachgroup using the Törnqvist formula. We subtract the growth in prices from the growth inadjusted nominal value added to derive the growth in adjusted real value added. Finally, we
subtract the same growth in number of hours worked that we used in the unadjusted series
from the growth in adjusted real value added to construct separate annual and cumulative
adjusted series of labor productivity growth for R&D industries, non-R&D industries, ITusing industries, non-IT-using industries, IT-producing industries, and non-IT-producing
industries.

A.2

Price index adjustments

In our analysis above, we deflate the sum of nominal value added and the nominal adjustment by the value added price deflator. If the industrial composition of the nominal
adjustment varied significantly from the composition of value added, the value added pricedeflator may not be appropriate. To address this concern, we construct a Törnqvist priceindex using the domestic value added data and a Törnqvist price index using the sumof domestic value added and our adjustment. We report the difference between the two
indexes in Figure A.2.
The two price indexes are very similar. In absolute value, the two indexes do not differ
by more than five basis points. While the industrial composition of the adjustment isdifferent from the composition of aggregate value added, the difference does not change
the industry weights in the price index enough to have an appreciable impact.

46

Figure A.1 – Aggregate adjustment under alternative profit definition

300

billion USD (2009 base year)

250
200
150
Earning typeprofit measure

100
50

Value-added typeprofit measure01970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Figure A.2 – Deflator for value added minus the deflator for adjusted value added

3
2

basis points

1
0
1
2
3
4
5
1970 1975 1980 1985 1990 1995 2000 2005 2010 201547

B

Figures and tables
Table B.1 – Industry groupings

NAICS Codes
R&D Intensive 325
334
336
511
5415
5417

Description
Chemical Manufacturing
Computer and Electronic Product Manufacturing
Transportation and Equipment Manufacturing
Publishers
Computer Systems Design
Scientific R&D Services

IT Producing

334
51
5415

Computer and Electronic Product Manufacturing
Information
Computer Systems Design

IT Using

315
333
335
336
337
339
42
44-45
51
532
5417

Apparel Manufacturing
Machinery Manufacturing
Electrical Equipment, Appliance, and Component Man.
Transportation and Equipment Manufacturing
Furniture and Related Product Manufacturing
Miscellaneous Manufacturing
Wholesale Trade
Retail Trade
Information
Rental and Leasing Services
Scientific R&D Services

Notes: The IT-producing classification is from Fernald (2015). The IT-using classification is from Bloom,
Sadun, and Van Reenen (2012). The R&D classification is from Moylan and Robbins (2007). ISIC or SIC
codes have been converted to the NAICS-based ISI classification used by BEA in its surveys of MNEs.

48

Figure B.1 – Industry group shares of total gross output(a) By R&D intensity

share of total business-sector gross output

1.0
non-R&D intensive

0.8
0.6
0.4

R&D intensive

0.2
0.0
1970

1980

1990

2000

2010

(b) By IT-using intensity

share of total business-sector gross output

1.0
0.8

non-IT using

0.6
0.4
IT using

0.2
0.0
1970

1980

1990

2000

2010

(c) By IT-producing intensity

share of total business-sector gross output

1.0
non-IT producing

0.8
0.6
0.4
0.2
0.0
1970

IT producing
1980

1990

49

2000

2010

Figure B.2 – Industry group shares of total value added(a) By R&D intensity

share of total business-sector value added

1.0
non-R&D intensive

0.8
0.6
0.4

R&D intensive

0.2
0.0
1970

1980

1990

2000

2010

(b) By IT-using intensity

share of total business-sector value added

1.0
0.8
non-IT using
0.6
0.4
IT using
0.2
0.0
1970

1980

1990

2000

2010

(c) By IT-producing intensity

share of total business-sector value added

1.0
non-IT producing

0.8
0.6
0.4
0.2
0.0
1970

IT producing
1980

1990

50

2000

2010

Figure B.3 – Industry group shares of total hours worked(a) By R&D intensity
share of total business-sector hours worked

1.0
non-R&D intensive

0.8
0.6
0.4
0.2

R&D intensive
0.0
1970

1980

1990

2000

2010

(b) By IT-using intensity
share of total business-sector hours worked

1.0
0.8

non-IT using

0.6
0.4
IT using

0.2
0.0
1970

1980

1990

2000

2010

(c) By IT-producing intensity
share of total business-sector hours worked

1.0
non-IT producing

0.8
0.6
0.4
0.2
0.0
1970

IT producing
1980

1990

51

2000

2010

C

Profit shifting strategies of MNEs

Intangible assets have long been a defining feature of multinational enterprises (MNEs).
Hymer (1976, pp. 42–43) is widely acknowledged as the first scholar to recognize thatdomestic firms venture abroad to exploit their proprietary intangible assets (such as patents
and brand recognition) and that it is the market power from those assets that allows thesefirms to overcome the liability of foreignness and to operate abroad successfully. Whatis unique about the last few decades is that the importance of intangible assets in firms’balance sheets has been growing at an accelerating rate. Data in Lev and Gu (2016, p.
82) show that the contribution of investment in intangible assets to production grew atroughly a 1.1 percent annual rate in 1977–1994 compared with a 1.4 percent annual rate in
1994–2011. This rise in the importance of intangible assets has created an extra incentive
for U.S. MNEs to shift the ownership of their intangible assets to foreign affiliates in tax
haven countries to reduce their global tax liability.
U.S. tax laws regarding the intrafirm transfer of intangible assets are long-standing,but the codification and enforcement of those laws have become a major focus of boththe IRS and the corporate sector in the last few decades. Current guidelines covering theintrafirm transfer of intangible assets are covered by Section 482 of the U.S. tax code. Bose
(2002) notes that these guidelines have existed in some form ever since the creation of the
IRS in 1917 but that, in the last few decades, firms have tried to develop tax strategies
that exploit ambiguities in those guidelines and the IRS has, in turn, tried to tighten its
guidelines to eliminate ambiguity. The original guiding principle for U.S. tax treatmentof the transfer of intangible assets came out of the Tax Reform Act of 1986. That Act
amended Section 482 to require that when intangible assets are transferred between unitsof an MNE, the receiving unit must pay a price to the providing unit that is commensuratewith the expected income from that asset.

C.1

Cost sharing agreements

Firms can structure an intrafirm transfer of intangible assets in different ways, but oneof the most common legal structures is a cost sharing agreement (CSA). In a CSA, one
geographic unit of an MNE—typically a foreign affiliate in a tax haven country—shares thecost of developing a new technology with its U.S. parent and, in return, is granted rights toroyalties on a portion of the sales of products or services using that technology. Under IRS
guidelines, the foreign affiliate cannot be granted royalties on sales in the United States,
so the affiliate is typically granted rights to royalties on sales in the rest of the world.
IRS guidelines on CSAs have been updated several times since the 1986 Act. In 1995,52

the idea of a “buy-in payment” was introduced to account for the value of preexisting
technology that could be embedded in a new technology governed by a CSA. In 2005, theidea of a “platform contribution” was introduced to account for the value of other U.S.
headquarters services that could be embedded in a product or service whose technology
was governed by a CSA. These contributions could include services such as “resources, capabilities, or rights, such as expertise in decision-making concerning research and productdevelopment, manufacturing or marketing intangibles or services, and management oversight and direction.” The IRS codified the 2005 guidance on CSAs in its 2008 Temporary
Regulations and its 2011 Final Regulations. In testimony to the Senate in 2013, Harvard
law professor Stephen Shay described the pre-2011 regulations as “much more relaxed” thanthe post-2011 regulations (Senate Committee on Homeland Security and Government Affairs, 2013). This comment suggests that the combined IRS guidance on “buy-in payments”
and “platform contributions” gradually made it much more difficult for MNEs to use CSAs
to transfer intangible assets over the 2005–2011 period.

C.2

Deferral and hybrid entities

Prior to the December 2017 tax reform, U.S.-based businesses were subject to a worldwide tax system rather than a territorial tax system so that their worldwide profits were
essentially taxed at the U.S. statutory rate. On the face of it, this principle would seemto eliminate the advantage of moving intangible assets (and their associated income) totax havens. However, there were exceptions to the general principle. One exception wasthat foreign-sourced income was generally not taxed until it was repatriated to the UnitedStates, as long as it was actively invested abroad. One way for MNEs to indefinitely defer
U.S. taxation was to declare foreign earnings as “permanently reinvested” abroad.
The difference between the pre-reform 35 percent corporate statutory federal income
tax rate in the United States and the 12.5 percent rate in Ireland, for example, should havebeen incentive enough for U.S. MNEs to seek to shift profits offshore, but there could beeven further inducements, such as hybrid entities. A hybrid entity is a unit of an MNE thatis recognized by the tax authority of its home or host country but not recognized by the
tax authority of the other country. Through the creation of complex holding structures,
MNEs have, at times, been able to exploit differences in the pre-reform tax rules to create
foreign entities that were essentially stateless and therefore free from tax. One of thesestructures, spanning an ownership chain that crosses the United States, Bermuda, Ireland,
and the Netherlands, is known as the “double Irish with a Dutch sandwich” strategy. In
October of 2014, however, Ireland changed its tax laws to eliminate this loophole.

53