Tax Policy and Investment in a Global Economy*
Gabriel Chodorow-Reich
Harvard and NBER
Owen Zidar
Princeton and NBER

Matthew Smith
US Treasury Department
Eric Zwick
Chicago Booth and NBER

Abstract
This paper combines administrative tax data and a model of global investment behavior
to evaluate the investment and firm valuation effects of the Tax Cuts and Jobs Act (TCJA)
of 2017, the largest corporate tax reduction in the history of the United States. We extend
the canonical model of Hall and Jorgenson (1967) to a multinational setting in which afirm produces in domestic and international locations. We use the model to characterize
and measure four determinants of domestic investment: domestic and foreign marginal tax
rates and cost-of-capital subsidies. We estimate elasticities of domestic investment with respect to each and use them to identify the structural parameters of our model, to quantify
which parts of the reform mattered most to investment, and to conduct policy counterfactuals. We have five main findings. First, the TCJA caused domestic investment of firms
with the mean tax change to increase by roughly 20% relative to firms experiencing no tax
change. Second, the TCJA created large incentives for some U.S. multinationals to increase
foreign capital, which rose substantially following the law change. Third, domestic investment also increases in response to foreign incentives, indicating complementarity between
domestic and foreign capital in production. Fourth, the general equilibrium long-run effects of the TCJA on the domestic and total capital of U.S. firms are around 6% and 9%,
respectively. Finally, in our model, the dynamic labor and corporate tax revenue feedbackin the first 10 years is less than 2% of baseline corporate revenue, as investment growthcauses both higher labor tax revenues from wage growth and offsetting corporate revenue
declines from more depreciation deductions. Consequently, the fall in total corporate tax
revenue from the tax cut is close to the static effect.

* Manuscript date: October 16, 2023. We thank Agustin Barboza, Emily Bjorkman, Walker Lewis, Anh-Huy

Nguyen, Shivani Pandey, Sarah Robinson, Francesco Ruggieri, Sam Thorpe, and Caleb Wroblewski for excellent
research assistance and seminar and conference participants for comments, ideas, and help with data. We thankAnne Moore and Laura Power for insights on multinational tax data. The views expressed here are ours and do
not necessarily reflect those of the US Treasury Office of Tax Analysis, nor the IRS Office of Research, Analysis
and Statistics. The model-implied revenue estimates are not revenue estimates of the TCJA. Chodorow-Reichgratefully acknowledges support from the Ferrante Fund and Chae fund at Harvard University. Zwick gratefully
acknowledges financial support from the Booth School of Business at the University of Chicago. Zidar thanks the
NSF for support under grant no. 1752431.

1

Introduction

The Tax Cuts and Jobs Act (TCJA) of 2017 was the largest corporate tax reduction in the historyof the United States.1 It lowered statutory corporate tax rates from 35% to 21%, changed a hostof investment incentives, and fundamentally altered the treatment of international income.
Collectively, these corporate tax changes were scored to reduce corporate tax revenue by $100
to $150 billion dollars per year (JCT, 2017; CBO, 2018). Yet, both at the time of passage and in
its aftermath, economists have not reached consensus on even ballpark estimates of its effects
on investment (Barro and Furman, 2018; Auerbach, 2018; Council of Economic Advisers, 2019;
Gale and Haldeman, 2021) or whether it would pay for itself (Goodspeed and Hassett, 2022).
This paper uses confidential, firm-level tax returns together with a model of global investment behavior to evaluate the effects of the TCJA corporate tax provisions on domestic andforeign investment, capital, and tax receipts. We have five main findings. First, the main
domestic provisions—the reduction in the corporate rate and full expensing of investment—
stimulated investment in tangible capital within the range of earlier estimates but at the lower
end. The TCJA caused domestic investment of firms with the mean tax change to increaseby roughly 20% relative to firms experiencing no tax change. Second, novel international tax
provisions, which were designed to on-shore reporting of income from intangible capital, also
created an incentive for some U.S. multinationals to increase their foreign tangible capital.
Third, we estimate within-firm complementarity between foreign and domestic capital, as theinternational tax provisions also stimulated domestic investment. Fourth, using our generalequilibrium model, the long-run effects of the TCJA on the domestic and total capital of U.S.
corporations are around 6% and 9%, respectively. Finally, the total effect on corporate tax revenue is close to the mechanical effect, because higher depreciation deductions offset additional
labor and corporate tax revenue from capital accumulation.
We begin by developing a model of firm investment behavior to structure the empiricalanalysis. Each firm operates domestic and possibly foreign production lines using domestic
and foreign capital, which may be either complements or substitutes in production, along withlabor and materials. Firms make standard investment decisions, taking account of the tax code.
Specifically, a firm pays a rate τ on domestic source income and τ̄ on foreign source income andreceives an investment subsidy Γ on domestic investment and Γ̄ on foreign investment. The
domestic terms τ and Γ incorporate TCJA changes to the corporate tax rate and expensing of
1

The official name of the act is given in Public Law 115-97, “An Act to Provide for Reconciliation Pursuant toTitles II and V of the Concurrent Resolution on the Budget for Fiscal Year 2018.” It was originally called the “TaxCuts and Jobs Act,” but this title was changed for procedural reasons.

1investment and collapse to the workhorse Hall and Jorgenson (1967) framework for domesticonly firms. The foreign tax terms allow the model to also accommodate the novel, more opaquechanges to the international tax regime. We linearize the model across steady-states to derive
an estimating equation that characterizes the investment elasticity to the TCJA changes toτ, Γ , τ̄, and Γ̄ as a function of the ratio of pre-TCJA foreign-to-domestic capital and four key
structural parameters α, σ, a, and ā that together govern the returns-to-scale in capital, theelasticity of substitution between domestic and foreign capital, and the relative importance ofeach source of capital in local profits.
Our data set consists of a representative panel of C-corporation tax returns from the U.S.
Treasury. We measure firm-level empirical counterparts to each of τ, Γ , τ̄, Γ̄ . The domestic
rate τ falls mainly because of the reduction in the statutory corporate rate from 35% to 21%.
However, this change has heterogeneous impact across firms depending on their likelihood of
having positive taxable income and their use of deductions and credits. Building on Auerbach(1983), Shevlin (1990), and Graham (1996), we use pre-TCJA firm-specific income dynamics
to simulate taxable income trajectories for each firm. We extend this work by incorporating
firm-specific use of deductions, credits, and the cap on total General Business Credits (GBCs).
We construct new firm-level marginal effective tax rates (METRs) with and without TCJA as
the additional present value of taxes paid when taxable income in a year rises by a marginalamount, taking account of the change in the statutory rate, new rules on loss carrybacks andcarryforwards, and the repeal of the Domestic Production Activities Deduction (DPAD) andAlternative Minimum Tax (AMT).
The domestic investment subsidy Γ increases mainly because of the change to full expensingof equipment. The impact of this change also varies across firms, depending on the normal
tax depreciation schedule of its investment mix as well as on whether the firm’s pre-TCJA
investment fell below the Section 179 limit. In addition to modeling these provisions, we also
incorporate the TCJA’s Foreign Deemed Intangible Income (FDII) deduction, which reduces
a firm’s domestic tax on the export share of income exceeding 10% of its domestic tangible
assets. We show that FDII effectively reduces τ and Γ for firms subject to it.
We incorporate two main foreign provisions in TCJA. First, TCJA moved the U.S. from a
global system in which a U.S. corporation would eventually have to pay taxes on all foreignsource income to a territorial system that applies the U.S. corporate rate only to domestic source
income. Second, in an effort to discourage location of intangible capital abroad, the TCJA introduced a domestic tax on deemed Global Intangible Low-Taxed Income (GILTI), which requires
firms to pay U.S. taxes on foreign income exceeding 10% of foreign tangible capital when2such income would otherwise be taxed at a low enough rate. We show that GILTI effectively
increases τ̄ and Γ̄ for firms subject to it.
We estimate the tax elasticities using the model structure and our measurement of tax
shocks in the cross-section of firms. We regress the log change in firm-level domestic investment
on the four tax policy changes τ, Γ , τ̄, and Γ̄ . Among firms that operate only domestically, we
find elasticities to the domestic tax terms in line with earlier literature (see Zwick and Mahon(2017) for a list of estimates). Firms with international operations likewise respond to thedomestic tax terms. In addition, their domestic investment responds positively to the effective
foreign subsidy Γ̄ . Our theory interprets this response as evidence of complementarity between
domestic and foreign capital; the GILTI tax incentivizes firms to increase foreign capital, which
in turn causes domestic capital to increase when domestic and foreign capital are complements
in production. We report several robustness exercises that address particular concerns with thespecification, such as testing for pretrends; including detailed industry fixed effects; or controls
for the “trade war,” firm size bins, or lagged investment.
The estimated elasticities provide moments to identify the structural parameters of ourmodel. For the domestic-only firms, the theory dictates that the coefficients on τ and Γ have
opposite signs of equal magnitude, each of which equals the inverse of 1 − α. We therefore
obtain the scale parameter for these firms using the coefficient from this restricted regression.
For multinational firms, the target moments include the regression coefficients as well as the
pre-TCJA ratios of foreign-to-domestic capital and profits. We provide analytic expressions
showing that these moments (together with an adjustment cost term that we calibrate externally) jointly identify the structural parameters. The estimated parameters have reasonablevalues: α ranges from 0.54 to 0.73, indicating substantial concavity in the production function, σ falls slightly below 1 to ensure sufficient complementarity between local and foreign
capital, and a implies that domestic earnings depend overwhelmingly on domestic rather thanforeign capital. As validation, we show that in both the data and the estimated model that
U.S. multinational firms subject to GILTI increase their foreign capital in the first two years
following the law change by an additional 10-14%.
We use the estimated model to quantify the response of corporate capital in general equilibrium when aggregate labor remains fixed and wages rise, to disentangle which parts of the reform mattered most to investment, and to assess the revenue consequences. As a starting point,
we first provide a “model-free” quantification of the effect on corporate capital in partial equilibrium. To do so, we form several “portfolios” of firms based on their domestic/multinational
status and pre and post-TCJA tax rates. We then use the regression coefficients and initial3

capital levels directly to compute fitted values of the change in capital in each portfolio due to
TCJA. This exercise suggests a long-run increase in domestic corporate capital of 15%. However, it is partial equilibrium in the sense that the fitted values omit the regression constantterm and hence omit any effects of other changes such as in wages that affect all firms. We
perform the same partial equilibrium exercise in the model and obtain a partial equilibriumincrease in capital of 12%.
The first main quantitative result from the model is a general equilibrium long-run increasein domestic corporate capital of 7.4%. To compute the general equilibrium increase, we solve
jointly for the change in capital in each portfolio of firms and a representative non-C-corporate
sector holding aggregate labor fixed, which results in a rise in the wage of roughly 0.9% as
the capital stock increases. The 95% confidence interval taking account of the estimation
uncertainty around the parameters excludes an increase in capital of less than 2.6% or greaterthan 12.2%. Total capital owned by domestic firms rises by proportionately more than domestic
capital, primarily due to the strong incentive in the GILTI rule for firms to accumulate foreign
capital.
Our second quantitative result evaluates the impact of several major provisions in isolation.
The changes to the METR by themselves deliver an increase in capital of about 3.5% after
15 years. Of course, these changes also cost the most in terms of revenue. The expensing
provisions and GILTI on their own increase capital by about 2% and 1.5%, respectively. We also
show that phase-out of expensing has relatively small short-run effects on capital accumulation
but substantially lowers the long-run level.
The third main quantiative model result is to estimate the tax revenue consequences of the
dynamic changes in capital induced by the reform.2 Corporate tax revenues fall initially due toincreased adjustment costs and larger depreciation deductions. Over time, higher profits from
capital accumulation offset these forces. However, because the negative revenue impact ofhigher depreciation deductions persists, total dynamic corporate revenue effects are substantially negative over the first 10 years and never exceed 5% of pre-TCJA revenue. We provide anenvelope argument intuition for why even the long-run dynamic revenue effects remain small.
Labor tax revenues also increase since the wage bill depends on the capital stock and generate
additional revenue of nearly 15% of pre-TCJA corporate tax revenue by year 10. Taken together, the dynamic labor and corporate tax revenue feedback in the first 10 years is less than
2% of baseline corporate revenue. Thus, in the medium term, the fall in total corporate tax
revenue from the tax cut is close to the static effect.
2This exercise is not intended to serve as a formal dynamic score of the reform because we leave unmodeled
several response margins and components of the reform (e.g., the individual tax provisions).

4

Finally, we provide auxiliary validation of these results. Since our primary empirical approach compares highly-exposed to less-exposed firms within the United States, we also validate our investment findings using an alternative approach with a non-US-based comparisongroup. We synthetically match publicly-traded U.S. firms to similar foreign firms using Compustat data and compare the evolution of investment before and after the TCJA. For each U.S.
firm in Compustat, we obtain a firm-specific synthetic counterfactual as a weighted average of
foreign firms and then aggregate across all firms and by industry. Investment at publicly-traded
U.S. firms increased by around 15% relative to the control group in the first two years after
the reform. This magnitude is smaller than but inside the confidence interval of the change inglobal investment by U.S. corporations in our estimated model and could reflect differences inmeasurement of M&A activity in Compustat. We corroborate the synthetic control results inseveral ways: backdating, using Canadian firms and the same method in a placebo analysis,
leaving out groups of foreign countries from the set of comparison countries, and controlling
directly for contemporaneous tariff shocks in the manufacturing sector.
Related literature. We provide new estimates of the effects of the largest corporate tax cut
in U.S. history. Due to its size and prominence, an early literature reported expected effects
using calibrated models (Barro and Furman, 2018; Slemrod, 2018; Gale, Gelfond, Krupkin,
Mazur and Toder, 2019; Clausing, 2020). Garcia-Bernardo, Janský and Zucman (2022) use
aggregate data and public filings to study the effect on profit shifting.3 Our measurement of the
TCJA firm-level shocks using Treasury tax returns, including specific forms that identify whichfirms deduct FDII or pay GILTI, allows us to link these provisions to firm real outcomes. Earlier
analysis of investment outcomes comes from Kennedy, Dobridge, Landefeld and Mortenson(2022), who exploit the variation in the domestic corporate rate cut across C-corporations andS-corporations of similar size. We estimate investment effects that are quite close to theirs,
despite using a different sample of firms and different tax rate variation.4
Our paper broadens this literature in three ways. First, we focus our analysis on a sample oflarge firms, including the multinational corporations exposed to the novel tax policy provisions
3

Our paper is not centrally concerned with profit shifting or the impact of the reform on this behavior. Nevertheless, we use theoretical extensions to clarify when profit shifting motives might interact with the firm’s real
investment decisions. We also confirm our main results are not driven by the small number of firms who arelikely active profit shifters. Our findings complement recent work more focused on the real implications of profit
shifting, e.g., JC’s new paper.
4
Though they focus on wage outcomes along the income distribution, in a regression of investment relative
to lagged capital on the log of the net-of-tax rate, they find a coefficient of 0.52. Our closest specification is intable 6, which shows a coefficient for domestic firms of 0.49 and a coefficient of 0.50 on the analogous tax term
for multinational firms.

5

targeting foreign and intangible income. Second, we meticulously measure for each firm theimpact of the key provisions of the TCJA on foreign and domestic tax rates and the cost ofcapital. We also integrate these different policy instruments into our structural model. Third,
we deploy a structural model to analyze long-run responses to the reform, aggregate effects ingeneral equilibrium, and policy counterfactuals.
We contribute to the theoretical and empirical literature on tax policy and investment behavior.5 We develop a structural model with multinational production and estimate the model’s
parameters. The overall profits elasticity of capital appears in the canonical Hall and Jorgenson (1967) framework and links our results to evaluations of past corporate tax policy changes.
Our estimates fall within the range of past work but at the lower end.6 The parameters governing the relationship between domestic and foreign capital within a firm have less antecedent,
although this parameter matters centrally to international tax policy (Costinot and Werning,2019). Desai, Foley and Hines Jr (2009) and Becker and Riedel (2012) are important exceptions and like us find evidence of complementarity.
Our quantitative model enables an analysis of policy counterfactuals. Indeed, many of theprovisions of TCJA remain contested in the political arena. We decompose the effect of thereform into its constituent parts, such as expensing, lower rates, and international provisions.
Future research can use our estimates to consider alternative policy proposals.

2

Policy Background

2.1

Motivation for the TCJA

The primary goal of the TCJA’s corporate provisions was to increase U.S. competitiveness andinvestment by bringing rates more in line with international levels. Specifically, policymakers
argued that the U.S. corporate tax system was not internationally competitive in terms of statutory tax rates and its worldwide rather than territorial structure (Council of Economic Advisers,
5

This literature includes Hall and Jorgenson (1967); Summers (1981); Feldstein (1982); Poterba and Summers(1983); Auerbach and Hassett (1992); Cummins, Hassett and Hubbard (1994, 1996); Hines (1996); Chirinko,
Fazzari and Meyer (1999); Devereux and Griffith (2003); Desai and Goolsbee (2004); House and Shapiro (2008);
Edgerton (2010); Dharmapala, Foley and Forbes (2011); Yagan (2015); Zwick and Mahon (2017); Ohrn (2018);
Giroud and Rauh (2019); Suárez Serrato (2018); Bilicka (2019); Curtis, Garrett, Ohrn, Roberts and Suárez Serrato(2021); Akcigit, Grigsby, Nicholas and Stantcheva (2021); Moon (2022).
6
Hassett and Hubbard (2002) propose a consensus range of 0.5 to 1 for regressions of investment relative
to capital on the tax term. In analogous specifications, we estimate coefficients of 0.42 (s.e.=0.08) and 0.61
(s.e.=0.15) for domestic and multinational firms, respectively. In Appendix A.9, we show how this specification
relates to our model parameters.

6

2018). These concerns came against the backdrop of sluggish domestic investment (Gutiérrez
and Philippon, 2017; Alexander and Eberly, 2018) and deepening cross-border investment.
Figure 1 uses aggregates from our tax return data (described in detail in section 4) andCompustat to contextualize the reform. The figure shows consistent series of domestic andglobal capital accumulation, investment, revenue, and cash holdings by U.S. publicly-tradedfirms from 1967-2019. Until the early 1990s, U.S. firms had very little foreign investment or
capital. Since that time, most of the growth in global capital by U.S. public firms has occurredabroad. This pattern along with high foreign profits and cash holdings also led to concerns
about profit-shifting. The international focus of TCJA differs from earlier corporate tax changes
in the U.S. that occurred before the period of deep globalization and that have shaped muchof our understanding of the investment effects of tax policy.

2.2

Main Corporate Provisions of the TCJA

In our model, tax policy affects firm investment through changing the marginal effective tax
rate (METR) on corporate profits and the tax term in the cost of capital. Table 1 lists the major
provisions affecting these components for either domestic or foreign activity. The last column
shows the estimated 10-year tax revenue estimate from the Joint Committee on Taxation (JCT,2017). These “static” estimates include some behavioral responses, such as income shifting
between tax bases or changes in tax credit takeup, but they assume no effect of the TCJA on
the aggregate capital stock. In section 7 we assess the effect of the dynamic changes in capitalon revenue.
The most important provision for the domestic METR was the reduction in the statutory top
corporate tax rate for C-corporations from 35% to 21%. Of course, for many firms the METR
differs from the statutory rate because of credits or deductions that make taxable income negative or otherwise modify the effective rate. The TCJA also changed some of these provisions,
including removing the ability of firms to carry back net operating losses (NOLs) to offset previous years’ taxes, limiting the deduction from carrying forward previous years’ NOLs to 80%
of taxable income, repealing the Domestic Production Activity Deduction (DPAD), which had
reduced METRs for qualifying firms, especially in the manufacturing sector, and repealing thecorporate Alternative Minimum Tax (AMT). Furthermore, the relevance of the statutory rate
reduction for the METR depends on pre-TCJA behavior, because firms without taxable income(perhaps due to high use of deductions and credits) or those facing binding limits on credit
usage do not face the statutory rate and hence do not experience the full rate reduction.7 Our
7

A firm without taxable income can still have a positive METR if the firm expects to pay taxes in the future,

7

Figure 1: Increasingly Global Investment

100

100

Aggregate Capital (Scaled)
200
300
400

Aggregate Investment (Scaled)
200
300
400

500

Global vs. US Investment, 1967–2019

500

Global vs. US Capital, 1967–2019

1970

1980

1990

2000

2010

2020

1970

1980

1990

year
Public Companies (SOI)

Public Companies (Compustat)

Public Companies (SOI)

2010

2020

Public Companies (Compustat)

Global vs. US Cash, 1967–2019

0200

Aggregate Sales (Scaled)
300
400
500

Aggregate Cash (Scaled)
500
1000
1500

600

2000

Global vs. US Revenues, 1967–2019

100

2000
year

1970

1980

1990

2000

2010

2020

1970

1980

1990

year
Public Companies (SOI)

2000

2010

2020

year
Public Companies (Compustat)

Public Companies (SOI)

Public Companies (Compustat)

Notes: These figures use merged Compustat–SOI datasets to plot aggregates for domestic variables versus global
variables for firms we are able to merge each year. We use the following Compustat variables for global measures:
PPENT for capital, CAPX for investment, SALE for revenues, and CHE+IVAO for cash. Pre-1993 SOI investment
only includes ITC-eligible basis, understating the divergence in the figure. The last year of Compustat PPENT
includes capitalized operating leases per a change in accounting rules, which explains the jump in that series.

measurement of METRs in section 4 accounts for all of these features.
The TCJA made two changes that implicate the domestic effective cost of capital. The
first directly targets the cost of capital by allowing firms to immediately expense equipment
investment. The second occurs through a new deduction for Foreign Derived Intangible Income(FDII). This provision allows firms to deduct from domestic income 37.5% of the component
deemed due to domestic intangible capital and sold abroad. The deduction is implemented
because of loss carryforwards. The leading example of binding credit usage concerns General Business Credits(GBCs), which are limited to 75% of taxable income. A firm for which this limit always binds has an effective
marginal tax rate equal to 25% of the statutory marginal rate.

8

Table 1: Main Provisions of the TCJA Affecting Investment
Provision
Domestic Provisions
1. Top corporate rate

Pre-TCJA

Post-TCJA

35%

21%

2. Accelerated depreciation

50% bonus

Full expensing for 5
years, then phase-out−86B

3. Domestic Production Activities Deduction (DPAD)

9% of qualified production activity income

None

+98B

4. Alternative Minimum Tax

Applicable if mean revenues >$7.5M

None

−40B

5. Foreign-Derived Intangible
Income (FDII)

None

37.5% deduction on export share of deemed intangible income−64B

6. Net operating losses

2 year carryback + carryforward

No carryback and limited to 80% of income+201B

Taxable when repatriatedNot taxed

−224B

None

Minimum tax of 10.5%
on foreign deemed intangible income+112B

Foreign Provisions
1. Foreign subsidiary income
2. Global Intangible Low Tax
Income (GILTI)

Cost−1.35T−1.35T

Total

Notes: The table describes the main provisions of the TCJA affecting corporate investment. The last column showsthe estimated revenue impact over 2018-2027 from JCT (2017).

as the export share of domestic income in excess of 10% of domestic tangible capital. Whileintended to encourage firms to report profits in the U.S., we show in appendix A.6 that the FDII
deduction has the same effect on investment incentives as a reduction in the domestic METR
and an increase in the cost of capital for tangible assets. The latter effect owes to the exclusionof income up to 10% of domestic tangible capital; thus, a marginal increase in domestic tangible
capital mechanically reduces the FDII deduction and increases taxes owed.
The reform also changed international corporate tax.8 Prior to the TCJA, U.S. firms paid
domestic taxes on any foreign profits repatriated as dividends to the U.S. parent. The new
system replaces this worldwide approach with a territorial tax. Firms deduct the full amount ofrepatriated dividends from their domestic income, which exempts foreign profits from domestic
income tax. The TCJA supplements this territorial system with a minimum tax on some foreign
8

See Gale, Gelfond, Krupkin, Mazur and Toder (2019) for additional detail on these provisions.

9

income, implemented via a foreign provision analogous to the FDII deduction, known as the
Global Intangible Low-Taxed Income (GILTI) tax. The TCJA defines GILTI as foreign income
in excess of 10% of foreign tangible capital. A corporation can deduct 50% of this income
and further claim credits for 80% of foreign taxes paid. The GILTI provision often is described
as a minimum tax, because a corporation with foreign income and no foreign taxes paid willpay 10.5% (= 0.5 × 21) on foreign income in excess of 10% of foreign tangible capital. We
show in appendix A.6 that GILTI has the same effect on investment incentives as an increasein the foreign METR and a decrease in the foreign cost of capital for tangible assets. The latter
effect owes to the exclusion of income up to 10% of foreign tangible capital; thus, a marginalincrease in the foreign tangible capital stock mechanically reduces GILTI tax.
The TCJA made several other changes that affect businesses but that we do not include in
our baseline analysis. Most important, the provisions for bonus depreciation are scheduled tophase out over time and the rates in FDII and GILTI change as well.9 We assume that firms
in 2018 and 2019 expected these provisions to be permanent, following Desai and Goolsbee(2004) and consistent with limited evidence of intertemporal substitution in House and Shapiro(2008) and Zwick and Mahon (2017), and explore sensitivity to this assumption through ourquantitative model.10 Other domestic provisions do not directly change the marginal incentives
for C-corporation investment in tangible capital, including those reducing the limit for interest
deductions from 50% to 30% of income and the generosity of the Research and Experimentation tax credit. We consider theoretical extensions that show how our user cost equations
change when incorporating these factors.
On the foreign side, the TCJA mandated a transition tax for firms with outstanding stocksof unrepatriated foreign earnings of 15.5% for cash and 8% for illiquid assets and gave firms
eight years to pay this tax. The TCJA also implemented a base erosion and anti-abuse tax(BEAT), which imposed a tax on payments from U.S. firms to foreign affiliates in excess of 3%
of total deductions. While important for tax revenues and profit shifting by multinationals,
these provisions are less relevant for the investment behavior of these firms.
The TCJA also reduced top individual income tax rates and created a deduction for qualify9

The TCJA allowed full expensing of equipment investment through 2022, after which the bonus amountdeclines by 20p.p. per year until it reaches zero in 2027. The FDII deduction falls from 37.5% to 21.875% and
the GILTI deduction from 50% to 37.5% beginning in 2026.
10
To preview, if firms expected the expensing provisions to expire, our estimated investment elasticities likely
overstate the investment response to a permanent change to full expensing. The short-run overreaction occurs
because standard values for discount and depreciation rates imply that the intertemporal substitution toward
investment in periods with higher expensing outweighs the lower steady-state capital value. In this sense, thepaper’s conclusions about the overall investment effects of the TCJA’s corporate provisions provide an upper boundif firms expected the expensing provisions to expire.

10

ing business income under Section 199A, which reduced the effective tax rates for pass-throughbusinesses and changed labor supply incentives. Estimating the impact of these provisions on
aggregate investment is beyond the scope of our study.

3

Model

In this section we extend the canonical Hall and Jorgenson (1967) tax-adjusted user cost framework to a multinational setting. The model relates the response of investment to four tax
terms: the METRs τ on domestic source income and τ̄ on foreign source income and the costof-capital subsidies Γ on domestic investment and Γ̄ on foreign investment. This result guides
our measurement and reduced form cross-sectional empirical specification. Furthermore, theinvestment elasticities depend on a small set of parameters governing the scale of production,
the elasticity of substitution between domestic and foreign capital, the relative importance of
foreign capital in the domestic earnings function and vice versa, and the relative size of the foreign operation. Using regression coefficients from section 5 and other moments, we estimate
these parameters in section 6 and then use them in quantitative exercises in section 7.

3.1

Setup

Time is continuous and runs forever. Atomistic firms operate up to two locations, one domestic
and the other international. Each location produces output using local and foreign capitaland local labor and materials. We denote by X and X̄ the domestic and international valuesof a variable X and describe the optimization problem of the domestic operation, with theinternational operation mirror-symmetric. We describe the decision problem of a single firmand omit firm-specific subscripts except when we discuss general equilibrium.
The domestic operation produces output Q t by combining local and foreign capital K t andK̄ t with local labor L t and materials M t :Mααα
Q t = At Kt K L t L Mt M
,
σ
 σ−1
 σ−1
σ−1
where: K = aK σ + (1 − a) K̄ σ
.

(1)
(2)

Here A t denotes (scaled) total factor productivity, K is a composite of domestic and international capital with elasticity of substitution σ > 0, a governs the relative importance of foreign
capital in determining domestic revenue, M ≥ 1 is the firm’s equilibrium markup and arises− Mfrom the demand constraint Q t ∝ Pt M −1 , and M (αK + α L + α M ) ≤ 1. At each date t, the11

firm takes the capital stocks as pre-determined and factor prices PtL and PtM as exogenous andchooses L and M to maximize operating earnings Pt Q t − PtL L t − PtM M t .
Appendix A.1 shows that this optimization problem results in an earnings function that
depends only on capital:
F K t , K̄ t ; Z t≡ Pt Q t − PtL L t − PtM M t = Z t K tα = Z tα/αKαwhere α ≡ 1−(α LK+αM ) ⊆ [0, 1] and Z t ∝ A t



σ−1
σ

aK t

σ−1
σ

+ (1 − a) K̄ t

σα
 σ−1

,

(3)

. In particular, Z is lower if TFP A is lower orthe factor cost of labor or materials is higher. Curvature in the profit function arises whenever
the revenue function features diminishing returns to scale, αK + α L + α M < 1, whether thediminishing returns result from market power (M > 1) or diminishing returns to scale in the
production of physical output. The earnings function in the international location takes asimilar form:




σ−1
σ

F̄ K̄ t , K t ; Z̄ t = Z̄ t ā K̄ tσ−1σ

+ (1 − ā) K t

σα
 σ−1

.

(4)

The scale parameter α, elasticity of substitution σ, a and ā which determine the importance of
non-local capital in generating earnings, and the relative productivity Z̄/Z collectively characterize a firm. Immediately, a domestic-only firm has a = 1 and Z̄ = 0.11
The functions F and F̄ embody all complementarities or substitution in production acrosslocations, with the elasticity of substitution σ and the curvature α determining these forces:
sign (FK K̄ ) = sign (α + 1/σ − 1). The literature on multinational firms and their location choice
has articulated reasons for either complementarity or substitution to dominate. In Helpman(1984), more foreign tangible capital increases the productivity of domestic capital, because
larger foreign scale increases brand recognition and hence demand for domestic output, andbecause it requires more managerial capacity that also benefits domestic production. Appendix A.3 incorporates such forces as part of a general accumulation of intangible capitalthat is non-rival within the firm and shows that they give rise to complementarity in the same
sense as FK K̄ > 0. Vertical integration of production, or “global value chains”, within thefirm also generates complementarity between K and K̄, because more foreign capital increases
the upstream supply of imported inputs or downstream demand for domestic output, both ofwhich raise the marginal product of domestic capital (Antrás and Chor, 2022). Alternatively,
substitution may arise if local and foreign plants of the same firm compete to serve the same11

A slight generalization would require a firm to pay a fixed cost to operate foreign capital, in which case theparameters for a domestic-only firm would not necessarily lie in the corner. This model has the same implications
as our baseline environment except that we do not allow domestic-only firms to become multinationals in responseto the TCJA.

12

destinations, as greater foreign scale then crowds out domestic production. Brainard (1993)
provides a model of this proximity-concentration trade-off. The functional form in equation (2)allows for all of these possibilities as well as the case of independence while remaining tractableenough to guide empirical work. Estimating the degree of complementarity or substitution will
be a key outcome of the analysis.12
Domestic capital evolves dynamically as K̇ t = I t − δK t , where I t is gross investment andδ is the rate of depreciation. The cost of a unit of domestic investment is (1 − Γ t ) PtK , whereΓ t contains the present value of depreciation allowances as well as any other tax provisions
such as the FDII deduction that link taxes paid to tangible capital.13 In addition, changing thecapital stock incurs an adjustment cost Φ (I t , K t ) = (φ/ (1 + γ)) (I t /K t − δ)1+γ K t paid in taxdeductible units (e.g. labor). Total domestic taxable profits consist of operating earnings net
of these adjustment costs, F K t , K̄ t ; Z t − Φ (I t , K t ), and are taxed at rate τ t . An analogous setof equations hold for international capital and profits.
The cash flow returned to equity or debt holders each period is:
Dt = (1 − τ t ) F K t , K̄ t ; Z t − Φ (I t , K t ) + (1 − τ̄ t ) F̄ K̄ t , K t , Z̄ t − Φ̄ Ī t , K̄ t− (1 − Γ t ) PtK I t − 1 − Γ̄ t PtK̄ Ī t .

(5)

The firm maximizes the present value of cash flows with a discount rate ρ, subject to initialconditions K0 and K̄0 and the dynamic evolution equations.
We make three remarks on this setup. First, for now we do not need to keep track ofwhich government collects the tax revenue generated by τ or τ̄ or the details of the subsidies
Γ and Γ̄ ; for the firm’s choice of capital, all that matters is the marginal incentives it faces.
We revisit this issue in section 7.3 when assessing the revenue effects of TCJA. Second, we
do not directly model the full system of tax credits and loss provisions, but will take accountof these in the measurement of the marginal incentives τ, Γ , τ̄, Γ̄ . Third, equation (5) makes
clear that the functions F (K, K̄; Z) and F̄ (K̄, K, Z̄) provide mappings between local and foreigntangible capital and local and foreign taxable earnings. In the presence of profit-shifting, these
mappings may differ from the physical production functions relating local and foreign capital
12

While engineered for flexibility to allow for complementarity or substitution between domestic and foreign
capital, equations (1) and (2) also implicate substitution between other factors. For example, a decline in theforeign wage can cause domestic K and hence wages to increase or decrease, depending on parameters, but
within limits imposed by the model’s structure. Since we do not directly estimate substitution between otherfactors, we avoid counterfactual exercises that involve a change in foreign factor prices.
13
For example, if a firm faces a constant tax rate τ t and can immediately deduct depreciation of θ t (“bonus”
depreciation) for an investment made at date t and subsequently deduct (1 − θ t )dh|t at horizon h (not to beR∞
confused with economic depreciation of δ), then Γ t = θ t τ t + (1 − θ t )τ t ζ t where ζ t = 0 e−rh dh|t dh.

13to true local and foreign earnings. Nonetheless, because the firm maximizes after-tax profits,
the functions F (K, K̄; Z) and F̄ (K̄, K, Z̄) determine the choice of capital. Section 3.3 discusses
an extension that explicitly incorporates profit-shifting motives.

3.2

Dynamic System and Linearization Across Steady States

Denoting by λ t and λ̄ t the costate variables associated with domestic and international capital
accumulation, the necessary conditions for domestic investment and capital can be written (seeappendix A.2):

FOC (I t ):

1
K̇ t /K t =φ

FOC(K t ) :

λ t − PtK (1 − Γ t ) γ1(1 − τ t ),λ̇ t = (ρ + δ) λ t − (1 − τ t ) (F1 − Φ2 ) − (1 − τ̄ t ) F̄2 ,

(6)
(7)


where Fn denotes the derivative of F K, K̄; Z and Φn denotes the derivative of Φ (I t , K t ) withrespect to argument n. In addition, the transversality condition requires lim T →∞ eρT λ T K T =lim T →∞ eρT λ̄ T K̄ T = 0. The analogous equations hold for foreign capital. The terminal valuesof λ and λ̄ complete the system and are given by their values in steady state:λ∗ = (1 − Γ ∗ ) P K , λ̄∗ = 1 − Γ̄ ∗ P K̄ .

(8)

A key feature of this framework is that it admits a tractable and intuitive expression forthe change in capital across the pre and post-reform steady states. Let R∗ = (ρ + δ) (1 − Γ ∗ ) P K
denote the steady state user cost of capital.14 Rearranging equation (7) and its foreign counter˙ = 0 and substituting using equation (8) gives the steadypart in the steady state with λ̇ = λ̄ttstate system:(1 − τ∗ ) F1∗ + (1 − τ̄∗ ) F̄2∗ = R∗ ,(9)(1 − τ̄∗ ) F̄1∗ + (1 − τ∗ ) F2∗ = R̄∗ .

(10)





Recognizing that F1∗ = F1 K ∗ , K̄ ∗ ; Z ∗ , F2∗ = F2 K ∗ , K̄ ∗ ; Z ∗ , F̄1∗ = F̄1 K̄ ∗ , K ∗ ; Z ∗ , F̄2∗ = F̄2 K̄ ∗ , K ∗ ; Z ∗ ,equations (9) and (10) give a system of two non-linear equations in two unknowns K ∗ and K̄ ∗ .
We next totally differentiate this system to obtain an estimating equation relating capital to tax
14
Dating back to Hall and Jorgenson (1967), most studies define the user cost as the implicit rental rate ofcapital after applying all taxes, that is, dividing the expression defining R∗ by (1 − τ). Equations (9) and (10)show that this convention does not easily extend to the multinational setting where a firm faces potentially many
corporate tax rates.

14

changes.
As a preliminary step, let χK ≡ K̄ ∗ /K ∗ denote the steady state ratio of international todomestic capital, χR = R̄∗ /R∗ the ratio of international to domestic steady state user cost, and:σ−1a (K ∗ ) σ

=
 σ−1
σ

a

⊆ [0, 1] ,(11)

⊆ [0, 1] ,
σ−1
σ
āχ
+
(1
−
ā)
ā
K


− σ1
∗
∗
a
(1
−
ā)
χ
−
āχ
R
K
(1 − τ ) F1
s F1 ≡=, ⊆ [0, 1] ,1−R∗
(1 − ā − a) χK σ


− σ1
∗
∗
(1 − a) (1 − ā) χR − āχK
(1 − τ̄ ) F̄1
s F̄1 ≡
=1−
⊆ [0, 1](1 − ā − a) χR
R̄∗

(12)

s1 ≡

a (K ∗ )

s̄1 ≡

K̄ ∗

σ−1
σ

+ (1 − a) σ−1ā K̄ ∗ σ

 σ−1
σ

K̄ ∗

σ−1

a + (1 − a) χK σ
σ−1

σ−1

+ (1 − ā) (K ∗ ) σ=āχK σ(13)(14)

denote shares of the capital inputs and marginal product terms, respectively. The second equalities show that these share terms depend on σ, a, ā and the observable ratios χK and χR .15 Letα̃ = σ (α + 1/σ − 1) ⊆ (−∞, 1].

The four tax terms central to our analysis are Γ̂ = dΓ / (1 − Γ ) , Γ̄ˆ = d Γ̄ / 1 − Γ̄ , τ̂ =
ˆ = d τ̄/ (1 − τ̄). Letting lower case k, k̄, i, ī, p K , p K̄ , r, r̄, z, z̄ denote log deviationsdτ/ (1 − τ) , τ̄of their uppercase variables, appendix A.2 gives the main result of this section:
k=



ˆ+ε
ωk,r Γ̂ + 1 − ωk,r Γ̄ˆ − ωk,τ τ̂ − 1 − ωk,τ τ̄

,
(15)
1−α

1 − (1 − s1 ) − s F̄1 (1 − s1 − s̄1 ) α̃where: ωk,r ≡,(16)
1 − 1 − s F1 − s F̄1 (1 − s1 − s̄1 ) α̃s F1 + 1 − s F1 − s F̄1 s̄1 α̃,(17)ωk,τ ≡
1 − 1 − s F1 − s F̄1 (1 − s1 − s̄1 ) α̃





 d ρ̄ + d δ̄dρ + dδKK̄ε ≡ ωk,τ z + 1 − ωk,τ z̄ − ωk,r
+p .
+ p − 1 − ωk,r
ρ+δρ̄ + δ̄(18)
15

While the second equalities in equations (11) and (12) follow immediately by dividing the numerator and
σ−1
denominator by (K ∗ ) σ , proving the second equalities in equations (13) and (14) requires using equations (9)and (10) and a substantial amount of algebra, which we detail in Appendix A.2.3. The ratio χR is directly afunction of parameters; the ratio χK is an equilibrium object that depends on α, σ, a, ā and the ratios of Z̄ ∗ /Z ∗and (1 − τ̄∗ ) / (1 − τ∗ ) . The advantage of writing the shares in terms of χR and χK is that the unobserved firmspecific ratio of productivities Z̄ ∗ /Z ∗ is replaced by observable factor quantities and prices.

15

Thus, long-run capital responds according to the elasticity 1/ (1 − α) to a weighted averageof the deviations of domestic and foreign tax rates and costs of capital. The appearance of the
returns to scale 1 − α in the denominator of the long-run elasticity is standard; in the case of adomestic-only firm, ωk,r = ωk,τ = 1 and the long-run elasticity collapses to k = (Γ̂ − τ̂)/(1−α).
Our contribution is to show that it carries over into the multinational setting with appropriatelydefined weights ωk,r and ωk,τ multiplying the domestic and foreign tax changes. These weights
are functions of the parameters α, σ, a, ā and the steady-state ratios of foreign-to-domestic
capital and user cost.16
Importantly, while the weights on domestic and international taxes sum to one, negative
weights and hence elasticities on the foreign terms are possible. In the case of Γ and Γ̄ , thedomestic weight exceeds one if and only if FK K̄ < 0, i.e. if α + 1/σ < 1.17 Intuitively, cheaper
foreign capital (Γ̄ ↑) results in higher K̄; whether this increase crowds out or in K depends
on whether FK K̄ is positive or negative. The sign of the coefficient multiplying Γ̄ therefore
reveals whether domestic and foreign capital are complements or substitutes. In the special
case where FK K̄ = 0, the domestic capital decision does not depend on foreign capital and ωk,requals one just as in the domestic-only case. The determination of whether ωk,τ exceeds one
is more complicated because foreign taxes directly affect both K and K̄; in the special case of
FK K̄ = 0 and ex ante symmetry (τ∗ = τ̄∗ , R∗ = R̄∗ , Z ∗ = Z̄ ∗ ⇒ K ∗ = K̄ ∗ ), the term ωk,τ simply
equals the domestic capital share in the production function a.

We also can characterize the responses of foreign capital k̄ and total capital k T = d ln K + K̄ :

k̄ =



ˆ − 1 − ωk̄,τ̄ τ̂ + ε̄ωk̄,r̄ Γ̄ˆ + 1 − ωk̄,r̄ Γ̂ − ωk̄,τ̄ τ̄

,


 1−α
T
T
T
T
ˆ + εTτ̄τ̂ − 1 − ωk,τΓ̄ˆ − ωk,τωk,rΓ̂ + 1 − ωk,r

(19)



kT =

1−α

,

(20)

T
T
where the expressions for ωk̄,r̄ , ωk̄,τ̄ , ωk,rand ωk,τare given in appendix A.2. The terms ε, ε̄,and ε T capture determinants of capital other than tax policy, including the foreign and domestic
profit shifters z and z̄, discount rates ρ and ρ̄, depreciation rates δ and δ̄, and market prices
Equation (15) nests several special cases: (i) the standard closed economy one factor model when a = 1,
in which case domestic profits depend only on domestic capital, ωk,r = ωk,τ = 1, and capital responds to thedomestic “tax term” (1 − Γ )/(1 − τ) with elasticity 1/ (1 − α); (ii) the closed economy two factor model whenZ̄ ∗ = s F̄1 = 0 and s F1 = 1, in which case ωk,τ = 1 and ωk,r = 1 − (1 − s1 ) α̃; and (iii) ex ante symmetry withτ∗ = τ̄∗ , R∗ = R̄∗ , and Z ∗ = Z̄ ∗ , in which case K ∗ = K̄ ∗ and hence s1 = s̄1 = s F1 = s F̄1 = a.
17
Proof: It is immediate from equation (16) that ωk,r = 1 if α̃ = σ (α + 1/σ − 1) = 0. Furthermore,∂ ωk,r /∂ α̃ ∝ − 1 − s F1 s̄1 + s F1 (1 − s1 ) . Since s F1 , s1 , s̄1 ⊆ [0, 1] the term in the parentheses is also between
zero and one and the derivative is negative.
16

16

of capital p K and p K̄ .
Equations (15) to (20) frame our empirical exercise. We use corporate tax returns to meaˆ . The possibility that the firm-specific drivers
sure k and k̄ and the policy shocks Γ̂ , τ̂, Γ̄ˆ , and τ̄
of investment contained in the residuals ε, ε̄, and ε T may be correlated with changes in taxes
motivates the measurement of ex ante tax shocks and robustness analysis.

3.3

Extensions

Appendix A.2 extends the baseline model to allow for separate investment in equipment andstructures, each with its own depreciation rate and cost-of-capital. Assuming a constant elasticity of substitution in the production function across different types of capital, equation (15)continues to hold for total capital, with the user cost terms replaced by appropriately-weighted
changes in the user costs of each type. We use this result in the measurement of changes to Γ .
Appendices A.3 to A.5 introduce three new considerations into the baseline environment.
The first explicitly models the dynamic accumulation of intangible capital. Intangible capitalis fully non-rival within the firm; it increases the productivity of both the domestic and foreign
operation. As in Helpman (1984), it therefore induces complementarity between domestic andforeign tangible capital, since cheaper foreign tangible capital results in more intangible capital
accumulation which in turn makes domestic tangible capital more profitable. Equation (15)
has two changes as a result: ωk,r now reflects the complementarity arising from intangible
capital as well as from σ and a, and a new term arises if the user cost of intangible capitalchanges.
The second change explicitly incorporates the location choice of intangible capital, as key
provisions of TCJA such as FDII and GILTI targeted this margin. Unlike equipment and structures, by definition intangible capital does not have a physical location nor does its movement
across borders leave a verifiable record in shipping or customs data, making the location of intangible capital and the associated profits in low-tax jurisdictions an attractive tax strategy. In
our framework, if firms allocate intangible capital across jurisdictions to minimize taxes without any regard to the location of physical capital, then nothing changes in the firm’s physicalinvestment decision and equations (15), (19), and (20) remain unaltered. In the case where
the relative location of physical capital constrains the firm’s location decision of intangible capital, two changes arise. First, in the realistic case of τ > τ̄, the pre-TCJA domestic user costrises and the foreign user cost falls, as the accumulation of domestic capital reduces the firm’s
ability to shift profits abroad using intangibles. Second, the reduction in the difference τ − τ̄
under TCJA has the additional effect of reducing the wedge between the user costs.
17

The third new consideration involves the tax deduction of interest payments. Once again,
if firms make their financial capital structure decision independently of their choice of physicalcapital, then nothing changes in the firm’s physical investment decision and equations (15),(19) and (20) remain unaltered. In the case where these decisions interact, perhaps becauseof a leverage constraint tying the optimal amount of debt to the quantity of physical capital,
again two changes arise. First, the pre-TCJA domestic user cost falls, as the accumulation ofdomestic capital increases the firms’ ability to issue tax-shielded debt. Second, the reductionin τ has a smaller effect on investment because it simultaneously reduces the value of the tax
shield.

3.4

General Equilibrium

While equation (15) holds firm-by-firm, the residual ε contains changes to factor prices common to all firms. In the cross-section regressions in section 5, these common changes appear in
the constant term and do not impact the identification of the parameters governing the tax elasticities. For general equilibrium questions such as the effect of TCJA on aggregate investment
or revenue, however, higher factor demand will cause factor prices to increase if supply curves
P
slope up. To model this feedback, we subscript individual firms with i and let X tD = i X i,t de∗ ν X
note aggregate demand for factor X ∈ {K, L, M }. Factor supply obeys X tS /X t∗ = PtX / PtX
and in equilibrium X tD = X tS = X t . To preview, we impose an extreme but realistic calibrationof the supply curves: we set ν M = νK = ∞ since raw materials tend to trade on international
markets and recent literature does not find an effect of investment demand on the price ofcapital goods (House, Mocanu and Shapiro, 2022), and ν L = 0 in accordance with balanced
growth path preferences.18

4

Data and Measurement of Tax Rates and Investment

This section describes the measurement of the key shock and outcome variables.
18

House, Mocanu and Shapiro (2022) show that the early and influential evidence of capital prices responding
to investment incentives in Goolsbee (1998) disappears when using more recent vintages of data on the Goolsbee
sample or extending the sample period. The factor supply function for labor can be microfounded from workers
with utility C 1−γ / (1 − γ)−v L 1+χ / (1 + χ) and no saving technology,
C = wL. Setting the wage proportional to theν L
1−γmarginal rate of substitution and solving gives L t ∝ PtL , with ν L ≡ χ+γ . Keeping L constant on a balanced
growth path requires γ = 1 and hence ν L = 0; intuitively, with balanced growth preferences the equilibrium
quantity of labor does not respond to shifts in the labor demand curve.

18

4.1

U.S. Corporate Tax Files

We measure firm-level tax rates and investment for a representative sample of C-corporations
using information reported on corporate tax returns. Our data set starts from the size-stratified
samples of roughly 100,000 C-corporation and S-corporation returns per year that are produced and cleaned by the IRS Statistics of Income (SOI) division. Firms selected into the SOI
sample remain in the sample unless they change tax identifier or fall into a size stratum witha lower sampling probability, giving us a panel (see Zwick and Mahon (2017) for details). We
drop S corporations (∼50% of the sample), financial firms (NAICS 52), firms with less than $1
million in domestic tangible assets (∼25%), and firms with insufficient history to permit measurement of each policy shock variable. These refinements leave a sample of approximately
12,000 firms. We augment the SOI Corporate Sample with variables and tax years drawn fromthe population of corporate returns. Our main analysis uses tax returns from 2011 through2019, although we use data going back to 1993 when measuring some of the policy shock
variables.19
For each firm-year, we combine data from Forms 1120, 4562, 5471, and 1118. Form 1120
is the corporate income tax return required of all domestic corporations and contains income
statement and balance sheet items, taxes, deductions, and credits, as well as basic firm characteristics such as industry. Form 4562 is required for a firm to claim depreciation and amortization and contains investment expenditure by tax duration bin. Form 5471 is required ofcorporations with ownership stakes in foreign corporations and includes the foreign subsidiary
income statement and balance sheet items as well as foreign taxes paid (see Dowd, Landefeld
and Moore (2017) for details). We define multinational firms as having positive 5471 tangible
capital.20 Form 1118 covers foreign tax credits and in particular contains information related
to GILTI obligations. Using information on these forms, we develop measures of the impact of
the reform on the tax terms (Γ , τ, Γ̄ , τ̄) and firm-level outcomes.
Domestic Cost of Capital (Γ ). The effective discount to the cost of capital for firm i, Γi,t , starts
with the time-varying present value of depreciation allowances in each of j ∈ J asset types.
R∞
Denoting the no-bonus present value of depreciation allowances as ζ j,0 = 0 e−rh d j,h|t dh, thelevel of bonus depreciation as θ t , and bonus eligibility as I{eligible}, the total present value
19

The analysis sample is approximately 10% the size of the sample in Zwick and Mahon (2017), which included
more small firms, S-corporations, and a longer panel.
20
A handful (roughly 100) of firms in our sample have positive but de minimus foreign presence, which we
define as having 5471 capital and earnings both less than 1% of their domestic counterpart. We put these firms
in the domestic group as well.

19


j
of allowances of asset type j is ζ t = I{eligible} θ t + (1 − θ t ) ζ j,0 + (1 − I{eligible}) ζ j,0 . We
calculate this present value for each depreciable life category on Form 4562 under both preTCJA bonus depreciation of θ = 0.5 and post-TCJA bonus depreciation of θ = 1.
j

We aggregate the asset-level depreciation allowances ζ t to the firm level using firm-specificinvestment shares, defined following Zwick and Mahon (2017) as the firm’s pre-2011 average
share of depreciable investment in each Form 4562 depreciable life category.21 This treatment
is consistent with the model extension to multiple types of investment in appendix A.2. Denoting by ζi,t the firm-level weighted-average present value of allowances, the present valueof tax savings is τi,t ζi,t , where τi,t is the firm’s marginal tax rate defined below.
Exposure to FDII affects Γ because the FDII deduction applies to the export share of income in excess of 10% of domestic tangible capital. As a result, increasing domestic tangible capital mechanically increases income taxes by reducing the amount of the FDII deduction. Appendix A.6 formally incorporates FDII into the firm’s optimization problem in section 3 and shows that the implications for investment are isomorphic to a lower marginal
tax rate and smaller Γ . Putting all of these elements together, using the FDII deduction of
0.375 of eligible income, the deemed intangible income threshold of 0.1, and denoting ξ
the share of domestic income from exports and τs the ex-FDII marginal tax rate, we defineΓ t = τi,t ζi,t − τsi,t × ξi × 0.375 × 0.1/(ρ + δ). To implement this formula, we apply a common
ρ = 0.06 and δ = 0.1 and obtain ξi by inverting the FDII deduction reported after TCJA on
Form 1120.22
Panel A of figure 2 plots the pre- and post-TCJA distributions of Γ . Both exhibit substantial
variation. Variation across asset types arises because equipment but not structures are bonus
eligible and because of variation in depreciation lives within each category. Variation in Γ thenreflects the firm-level investment shares in each asset type. While these shares may relate tofirm-specific technology, Curtis, Garrett, Ohrn, Roberts and Suárez Serrato (2021) argue thattax depreciation lives substantially reflect historical accident and do not closely correspond to
economic depreciation. The variation in Γ further reflects the firm-specific METR and FDIIeligibility. Panels E and F show substantial variation in Γ̂ = dΓ /(1 − Γ ), the variable that enters
into the regression, across both domestic and U.S. multinational firms.

21

We expand on Zwick and Mahon (2017) by (1) incorporating investment shares and depreciation rules forinvestment ineligible for bonus depreciation and (2) relying on firm-level rather than industry-level measures ofζ, allowing us to consider the impact of the reform on longer-lived investment and to identify causal effects using
within-industry variation in exposure to the depreciation rules.
22
Specifically, we define ξi as the section 250 (FDII) deduction reported on 1120 schedule C divided by 0.375×
taxable income (1120 line 30) less 0.1× capital (1120 schedule L line 10a less 10b).

20

Figure 2: Kernel Density Distribution of Tax Changes

B: Pre- and post-TCJA τ

.15
.05

.1

Density

.06
.04

0

0

.02

Density

.08

.2

.1

A: Pre- and post-TCJA Γ

0

10

20

30

40

0

10

Tax Rates
Post

20

30

40

Tax Rates
Pre

Post

D: Pre- and post-TCJA τ̄

.05

.1

Density

.3
.2

0

0

.1

Density

.15

.4

.2

.5

C: Pre- and post-TCJA Γ̄

Pre

0

10

20

30

40

0

10

Tax Rates
Post

20

30

40

Tax Rates
Pre

Post

Pre

ˆ
F: U.S. Multinational Firms Γ̂ , Γ̄ˆ , τ̂, τ̄

0

5

50

DensityDensity

10

100

15

150

E: Domestic Firms Γ̂ , τ̂

0

-.2
-.3

-.2

-.1

0

Tax Changes
Domestic Gamma

-.1

0

.1

.2

.3

Tax Changes.1

Domestic Gamma
Domestic Tau

Domestic Tau

Foreign Gamma
Foreign Tau

Notes: Panels A-D depict kernel density estimates for the four tax terms of interest before and after the TCJA. PanelE provides kernel density estimates for Γ̂ and τ̂ for Domestic Firms. Panel F provides kernel density estimates forˆ for U.S. multinationals.
Γ̂ , Γ̄ˆ , τ̂, and τ̄

21

Domestic Marginal Tax Rate (τ). Changes to the effective marginal tax rate, τi,t , reflect the
reduction in the statutory rate, repeal of the Domestic Production Activities Deduction (DPAD)
and corporate Alternative Minimum Tax (AMT), reform to the net operating loss (NOL) regime,
and the introduction of FDII. Our translation of these components into changes in each firm’s
METR builds on Auerbach (1983), Shevlin (1990), and Graham (1996). As in this work, we
simulate firm-level taxable income trajectories starting in year t using a firm-specific standard
deviation for income changes estimated using historical data. These trajectories determine theimpact of the net operating loss regime, since carrybacks and carryforwards make the present
value of taxes depend on past and future income in addition to current income. We go beyond
past work by also simulating the future use (if available) of the foreign tax credit, DPAD, andAMT using the historical firm-specific propensity to use each credit or deduction (conditionalon having positive taxable income) and the amount of the credit or deduction conditional on
use.
Using these simulated paths of taxable income, credits, and deductions, we define themarginal rate τsi,t as the change in the present value of taxes from an additional $1000 ofincome, divided by $1000. We compute τsi,t under both pre- and post-TCJA statutory rates,
credits, deductions, and NOL carryback and carryforward rules for income in t = 2015 andt = 2016 and average the rates for these two years to arrive at our pre- and post-TCJA τsi,t .
Changes in τsi,t therefore incorporate both the changes to the statutory rate and credits and deductions and the heterogeneous impact of these components depending on a firm’s pre-TCJA
taxes. For firms subject to FDII, the effective marginal rate further accounts for the FDII deduction and is τi,t = (1 − 0.375 × ξi ) × τsi,t . For other firms and prior to the TCJA we set
τi,t = τsi,t .
Panel B of figure 2 plots the pre- and post-TCJA distributions of τ. Both have modes aroundtheir respective statutory rates of 35 and 21%. However, both also exhibit substantial mass
below the modes, reflecting firm-specific use of deductions and credits as well as NOLs. As aresult, panels E and F show substantial variation in how much different firms’ METRs changed,with larger percent reductions for firms with higher pre-TCJA METRs and smaller reductions
for firms directly affected by the repeal of DPAD or AMT.
Foreign Cost of Capital (Γ̄ ). We measure the pre-TCJA foreign effective discount to the costof capital, Γ̄i,t , using the OECD average present value of depreciation allowances from Foertsch(2022). TCJA affects this variable through the GILTI provision because the GILTI tax applies
to foreign income in excess of 10% of foreign tangible capital. As a result, increasing foreign

22

tangible capital mechanically reduces GILTI and hence reduces U.S. income tax. Appendix A.6
formally incorporates GILTI into the firm’s optimization problem in section 3 and shows that the
implications for investment are isomorphic to a lower marginal tax rate and larger Γ . Specifically, if a firm has any GILTI tax liability in either 2018 or 2019, we lower post-TCJA Γ̄i,t by thepresent value of the savings on GILTI tax of 0.21 × 0.5 × 0.1/(ρ + δ), where 0.21 is the U.S.
post-TCJA statutory rate, 0.5 is the GILTI deduction, and 0.1 is the deemed intangible income
threshold.23 We assign GILTI liability if 0.21× GILTI income net of deductions exceeds deemed
foreign taxes paid, where each of these variables is obtained from form 1118.
Panel C of figure 2 plots the pre- and post-TCJA distributions of Γ̄ . The distribution of Γ̄
pre-TCJA is single-valued by construction since we do not have firm-level variation on foreign
depreciation schedules. The second peak in the post-TCJA distribution reflects GILTI.
Foreign Marginal Tax Rate (τ̄).

The tax rate on foreign profits, τ̄i,t has the following inputs:

the pre-reform average tax rate on foreign income t̄ i and the GILTI rate. The foreign tax ratet̄ i is defined as the average of two tax rates computed using aggregated 5471 information at
the firm level. We then take the average within firm over 2010 to 2016. The first tax rate uses
Schedule E taxes divided by Schedule C net income, and the second tax rate uses Schedule E
taxes divided by Schedule H earnings and profits. Both tax rates are censored to be between
0 and 100%. The latter rate is the rate used in Dowd, Landefeld and Moore (2017), whereas
the former rate is better populated in the data.
Our baseline specification for τ̄i,t is a myopic model, in which firms expected prior to thereform to continue paying the pre-TCJA foreign tax rate. In the post-TCJA regime, they continue to pay this rate if it exceeds the GILTI rate and otherwise pay the GILTI rate. Thus, τ̄i t
is defined as t̄ i prior to the reform and max{GI LT I, t̄ i } after the reform, where the GILTI tax
rate is 10.5%.24
Key Outcomes. Our main outcome is investment. This variable includes expenditures for all
equipment and structures investment put in place in the U.S. during the current year, obtained
from Form 4562. In some specifications, we restrict attention to the expenditures for which
23

Firms increasing foreign capital to avoid GILTI tax have a strong incentive to acquire capital with low economic
depreciation so as to avoid recurring investment outlays. We therefore set δ = 0.04 for the purpose of determining
the impact of GILTI on Γ̄ .
24
In robustness, we consider a perfect foresight model, in which firms expected prior to the reform that their
future tax burden on foreign income would be the Section 965 “toll tax” rate on liquid deferred foreign income(15.5%). They only expected to be subject to this rate if their pre-reform foreign tax rate t̄ i was lower than thisrate. In the post-TCJA regime, they pay the maximum of the GILTI rate and t̄ i . Accordingly, τ̄i t is defined asmax{15.5, t̄ i } prior to the reform and max{GI LT I, t̄ i } following the reform.

23

Billions of 2012 dollars

Figure 3: Investment Benchmark
1600

FAA non-res. E&S

1400

SOI Corp.+Pship
FAA Corp.+Pship

1200
FAA Corp.

1000

SOI broad Corp.
SOI Corp.

800

SOI C-Corp.

600

1998

2002

2006

2010

2014

2018

Notes: Fixed Asset Accounts (FAA) non-res. E&S is investment in non-residential equipment and structures (FAA
table 2.7 lines 3 and 36). FAA Corp. and Pship is private investment in non-residential equipment and structures
by C or S corporations (FAA table 4.7 lines 18 and 19) or partnerships (FAA table 4.7 lines 62 and 63). SOI
Corp.+Pship is total non-residential investment by SOI corporations or partnerships. SOI Corp. includes only
investment by corporations and SOI C-Corp. investment by C corporations. SOI broad Corp. includes the part ofpartnership investment that can be allocated to direct corporate owners of the partnership.

bonus depreciation and Section 179 incentives apply, which we refer to as equipment. Capital
includes the book value of tangible, depreciable assets net of accumulated depreciation perbooks. This measure includes the capital from consolidated domestic subsidiaries but typically
excludes that from foreign subsidiaries. Foreign capital includes the total book value of tangible, depreciable assets reported for controlled foreign corporations on all Form 5471 filings
attached to the firm’s Form 1120 corporate filing, net of accumulated depreciation.
Figure 3 shows that investment as reported on tax forms closely tracks national accounts
aggregates. The figure plots several measures of investment in non-residential equipment andstructures, all deflated using the GDP price index. In 2016, our tax-based measure of investment by C-corporations accounted for 54% of total national accounts investment. Moreover,despite the series coming from completely separate source data, the correlation of annualchanges in the logs of both series exceeds 0.7. The figure also shows that most of the gap
between SOI C-Corporate and national accounts investment occurs because of investment inother sectors identifiable in SOI, including S-corporations and partnerships. Including investment by partnerships directly owned by corporations (the line labeled “SOI broad Corp.” in the
figure) increases the 2016 corporate share in the SOI data to 78%. Fully allocating partnershipinvestment to corporate owners introduces substantial logistical hurdles due to multiple tiersof ownership and entities not in the SOI corporate sample, however, so our firm-level analysis
24

focuses on investment directly attributable to C-corporations.25
Table 2 reports summary statistics. Panel A reports statistics for the full sample and thedomestic sample, which includes firms with less than one percent of their income and capitalfrom foreign operations. Panel B provides statistics for the multinationals with high and low
levels of foreign activity. Specifically, multinational high includes U.S. multinationals that are
not in the domestic sample and had at least 15% of their capital abroad before the TJCA (i.e.,
Pre-TCJA χK >= .15). The multinational low sample are U.S. multinationals that are notdomestic but had less than 15% of their capital abroad (i.e., Pre-TCJA χK < .15).
The average change in the domestic composite tax term, which corresponds to Γ̂ − τ̂, is
4%. This number is smaller than the analogous prediction in Barro and Furman (2018). They
report a change in the user cost of capital due to the TCJA being made permanent of 10%
for equipment and 11% for structures. The difference between our estimate and theirs can be
explained by their use of the statutory corporate tax rate and by our inclusion of 50% bonus
depreciation in the pre-period.26

4.2

Compustat Data on North American and Global Firms

We use the Compustat North America and Compustat Global Databases to construct the sample
for our synthetic controls research design. We begin with all non-financial publicly-traded firms
in the Compustat North America database that are headquartered in the United States. We
focus on a window around the 2017 tax reform that starts in 2011 and ends in 2020. Appendix
Table C.2 describes the construction of the matched analysis sample. We keep firms with nonmissing assets (AT), property plan and equipment (PPENT), and sales (SALE) for each of thefour years before the tax reform. We apply analogous sample restrictions to the Compustat
Global database in addition to keeping only firms that list non-US-based headquarters (LOC).
We convert all currencies to U.S. dollars using the official exchange rates from Table 4.16 of
the World Development Indicators of the World Bank.

4.3

Other Data Sets

To address concerns about investment effects due to other policy shocks related to rising tradetensions, we include controls from Flaaen and Pierce (2019). Specifically, we use three tariff
25
Such arrangements concentrate in a few industries, including utilities (NAICS 22), pipeline transportation(NAICS 486), and real estate (NAICS 531).
26
Barro and Furman (2018) also include state corporate taxes in their user cost model and adjust the user costfor debt finance. However, accounting for these factors is not necessary to explain the difference between ourestimates.

25

Table 2: Summary Statistics by Sample
Panel A: Pooled and Domestic Samples
All Firms
Mean

Std. Dev.

Domestic Firms

Median

P10

P90

N

Mean

Std. Dev.

Median

P10

P90

N

Average Tax Rate

0.05

0.06

0.02

0.00

0.13

9287

0.05

0.06

0.02

0.00

0.14

7028

Pre-TCJA Γ

0.23

0.08

0.24

0.10

0.32

9305

0.23

0.08

0.24

0.11

0.33

7044

Pre-TCJA Γ̄

0.18

0.00

0.18

0.18

0.18

9307

Pre-TCJA τ

0.27

0.09

0.32

0.13

0.35

9307

0.28

0.09

0.33

0.14

0.35

7046

Pre-TCJA τ̄

0.06

0.13

0.00

0.00

0.29

9307

d log(Investment)

-0.04

0.99

0.04

-1.43

1.28

9307

-0.05

1.03

0.02

-1.52

1.35

7046

Γ̂ − τ̂

0.04

0.03

0.04

0.00

0.08

9305

0.04

0.03

0.03

0.00

0.08

7044

Relative Profit (EBITD)

0.11

0.37

0.00

0.00

0.26

9307

Form 5471: Rel. Profit

0.18

0.60

0.00

0.00

0.39

9305

Form 5471: Rel. Profit (EBITD)

0.13

0.42

0.00

0.00

0.31

9307

Lagged (2015-6 Average) Capital

380.9

2999.2

15.5

2.1

350.0

9307

250.8

2193.3

10.9

1.8

164.1

7046

Pre-TCJA χK

0.10

0.30

0.00

0.00

0.28

9307

Eq. 15: Γ̂
Eq. 15: Γ̄ˆ

-0.11

0.05

-0.11

-0.17

-0.03

9305

-0.11

0.05

-0.11

-0.17

-0.04

7044

0.02

0.05

0.00

0.00

0.14

9307
-0.14

0.06

-0.17

-0.20

-0.05

7046

Eq. 15: τ̂

-0.14

0.07

-0.17

-0.21

-0.05

9307

ˆ
Eq. 15: τ̄

0.10

0.05

0.13

0.00

0.13

9306

Export Share

0.07

0.20

0.00

0.00

0.28

9307

Panel B: Multinational Samples
Multinational-High

Multinational-Low

Mean

Std. Dev.

Median

P10

P90

N

Mean

Std. Dev.

Median

P10

P90

N

Average Tax Rate

0.04

0.05

0.01

0.00

0.12

1113

0.04

0.05

0.02

0.00

0.12

1146

Pre-TCJA Γ

0.21

0.08

0.23

0.08

0.31

1113

0.22

0.08

0.24

0.09

0.32

1148

Pre-TCJA Γ̄

0.18

0.00

0.18

0.18

0.18

1113

0.18

0.00

0.18

0.18

0.18

1148

Pre-TCJA τ

0.25

0.10

0.28

0.10

0.35

1113

0.26

0.10

0.30

0.11

0.35

1148

Pre-TCJA τ̄

0.23

0.15

0.23

0.03

0.43

1113

0.22

0.16

0.23

0.00

0.43

1148

d log(Investment)

0.02

0.84

0.08

-1.04

1.08

1113

-0.01

0.89

0.09

-1.18

1.11

1148

Γ̂ − τ̂

0.04

0.03

0.04

0.01

0.08

1113

0.04

0.03

0.04

0.01

0.08

1148

Relative Profit (EBITD)

0.61

0.72

0.26

0.00

1.85

1113

0.20

0.40

0.05

0.00

0.55

1148

Form 5471: Rel. Profit

0.91

1.09

0.42

0.00

2.79

1113

0.34

0.68

0.09

0.00

1.00

1147

Form 5471: Rel. Profit (EBITD)

0.69

0.76

0.36

0.00

2.00

1113

0.24

0.47

0.06

0.00

0.67

1148

Lagged (2015-6 Average) Capital

646.8

3002.672.9

4.4

1138.3

1113

921.5

5850.846.8

4.91065.51148

Pre-TCJA χK

0.66

0.56

0.48

0.08

1.66

1113

0.09

0.18

0.04

0.00

0.23

1148

Eq. 15: Γ̂
Eq. 15: Γ̄ˆ

-0.10

0.06

-0.10

-0.17

-0.02

1113

-0.11

0.05

-0.11

-0.17

-0.03

1148

0.08

0.07

0.14

0.00

0.14

1113

0.06

0.07

0.00

0.00

0.14

1148

Eq. 15: τ̂

-0.14

0.08

-0.15

-0.24

-0.03

1113

-0.14

0.07

-0.16

-0.22

-0.04

1148

ˆ
Eq. 15: τ̄

0.02

0.04

0.00

0.00

0.11

1113

0.03

0.05

0.00

0.00

0.13

1148

Export Share

0.29

0.35

0.10

0.00

0.99

1113

0.14

0.24

0.00

0.00

0.50

1148

Notes: This table provides summary statistics for four samples. Panel A includes summary statistics for All Firms(Columns 1-5), and Domestic Firms (Columns 6-10). Panel B includes summary statistics for U.S. Multinationals
with High Foreign-to-Domestic-Income (Columns 1-5), and U.S. Multinationals with Low Foreign-to-DomesticIncome (Columns 6-10). Capital is in millions of USD. We winsorize d log(Investment) from above and below at
the 5% level. For disclosure reasons, we do not report true medians (or other percentiles). Instead, we report the
average of observations in neighboring percentile bins.

26

change measures, which are only available within manufacturing industries. They are importprotection, rising input costs, and foreign retaliation measures.

5

Regression Estimates

In this section, we present our main empirical results of the effects of TCJA on investment of
U.S. C-corporations. The regression specification mirrors model equation (15):ˆ i,t + b′ × xi,t + ei,t ,Yi,t = b0 + b1 × Γ̂i,t + b2 × Γ̄ˆi,t + b3 × τ̂i,t + b4 × τ̄
5(21)

where Yi,t is an outcome, Γ , Γ̄ , τ, τ̄ are defined as in section 4, q̂ = dq/(1 − q) for a tax term q,
and x contains any controls. The main outcome is investment growth, Yi,t = d ln I i,t , measured
as the log difference between pre-TCJA average investment over 2015-2016 and post-TCJA average investment over 2018-2019.27 We winsorize Yi,t at the 5% level except where otherwise
specified.

5.1

Identification

In the next section we will use the regression coefficients from specification (21) to recover
the structural coefficients given in equation (15). Four issues merit mention now because theyaffect the empirical implementation.
First, across pre- and post-TCJA steady states where I i∗ = δKi∗ , investment growth andcapital growth coincide. We prefer investment as an outcome because of superior measurementin the tax data.
Second, our baseline specification estimates the short-run elasticities of investment to tax
changes, while equation (15) characterizes the long-run elasticities. Section 6 provides conditions under which the short-run elasticities all scale to the long-run elasticities by the same
factor, preserving equation (21) as a valid representation of the structural data generatingprocess.
Third, the elasticities in equation (15) depend on firm-specific factors. Most important,domestic-only firms have ωk,r = ωk,τ = 1, implying b2 = b4 = 0 and b1 = −b3 . We therefore
27

This specification differs from the common approach of regressing the investment-capital ratio on the levelof the tax terms and a proxy for λ (see, e.g., Desai and Goolsbee, 2004; Edgerton, 2010). Besides the obvious
fact that we cannot compute λ using the stock market capitalization for the privately held firms in our sample,
the benchmark result of Hayashi (1982) does not apply to our model with decreasing returns to scale. Moreover,
we show in appendix A.9 that the common regression does not recover structural parameters unless λ is properly
measured, because λ changes endogenously in response to a tax reform.

27

report regressions separately for domestic-only and multinational firms. Furthermore, within
multinational firms the relative values of b1 and b2 depend on the degree of foreign presence.
Intuitively, holding fixed the production function parameters, firms with very little foreign capital have a smaller domestic investment response to the foreign cost-of-capital. We therefore
also report regressions splitting multinational firms by high and low foreign presence, defined
as a ratio of foreign-to-domestic tangible capital, χK = K̄/K, above or below 15% (roughly the
median within the multinational sample). These splits also allow the other structural coefficients to vary across these sets of firms.
Fourth, the residual ei,t contains non-tax determinants of investment growth such as changes
in productivity or the price of the firm’s capital goods. Since equation (21) estimates changes ininvestment on changes in taxes, causal interpretation of the estimated coefficients requires theusual difference-in-difference assumption that firms more exposed to TCJA were otherwise on
parallel investment paths with firms less exposed. We present evidence of absence of pre-trends
and several robustness exercises that control for potential confounds to bolster the plausibilityof this assumption. Furthermore, since equation (21) contains multiple, non-binary right handside variables, in the presence of treatment effect heterogeneity the estimated coefficients are
not necessarily convex averages of the individual treatment effects. The sample splits help
along this dimension as well.

5.2

Non-Parametric Evidence

We start with a non-parametric representation of the data. Figure 4 shows means of investment
growth for different quantiles of the composite domestic tax term Γ̂ − τ̂ (“binned” scatter plots).
For domestic-only firms plotted in Panel A, this composite tax term exactly comports witheconomic theory. The tight upward slope reveals a positive investment elasticity to taxation
around TCJA. For multinational firms plotted in Panel B, our theory no longer dictates a single
elasticity to Γ̂ and τ̂. Nonetheless, the upward slope indicates a positive investment elasticity
in this sample. Furthermore, Panel B shows the investment responses separately for firms
with and without GILTI liability. For a given value of the composite domestic tax term, firms
with GILTI liability have higher investment growth. This shift up in the schedule of GILTI
versus non-GILTI firms will manifest as a positive value of b2 in the regression. Our calibrated
model will account for it by imposing complementarity between foreign and domestic capitalin production.

28

Figure 4: Tax Term Binscatters for Domestic and Multinational Firms

Panel B: Multinational Firms

-.6

-.4

Investment Growth-.20Investment Growth
-.2
0

.2

.2

.4

Panel A: Domestic

-.4

0
0

.05
.1
Percent change in Domestic Tax Term (1-Gamma)/(1-tau).05.1
Percent change in Domestic Tax Term (1-Gamma)/(1-tau)
Does not have Gilti

Has Gilti

Notes: This figure plots the binscatter for both domestic firms and U.S. multinationals where the x-axis is Γ̂ − τ̂and the y-axis is d ln I. We further categorized U.S. multinationals by whether or not they are GILTI firms.

5.3

Baseline Regressions

Table 3 reports the main regression results for the elasticities of domestic investment. Column(1) pools the entire sample and shows positive and highly statistically significant investment
elasticities to the domestic and foreign costs-of-capital Γ and Γ̄ and a statistically significant
negative elasticity to the domestic tax rate τ. Evaluated at the mean policy changes (in Γ ,Γ̄ , τ, and τ̄ of -0.10, 0.02, -0.14, and 0.10, respectively), the coefficients imply a roughly
20% increase in domestic investment relative to non-exposed firms. Motivated by our theory,
the remaining columns report results for various sub-samples. Columns (2) and (3) focus ondomestic-only firms, which comprise about three-quarters of the sample. The elasticities ofinvestment with respect to the domestic tax terms remain of similar magnitude and highly
statistically significant in this group, with column (3) imposing the restriction that the coefficients on the domestic cost-of-capital and tax rate be equal and opposite by including only thecomposite tax term Γ̂ − τ̂ on the right hand side.
Column (4) reports results for multinational firms and columns (5) and (6) for sub-samples
of multinational firms split by their degree of multinational activity. Multinational firms exhibit
positive elasticities to Γ and Γ̄ and negative elasticities to τ across all three samples. In contrast, the elasticity to τ̄ is generally not significant, which may reflect the challenges of inferringfirms’ pre-TCJA expectations of the eventual U.S. tax rate on foreign earnings and of measur-

29

Table 3: Baseline Investment Growth Regression
Dep. var.:
Sample:Log change in investment
PooledDomestic Firms
Unrestricted Restricted(1)2.65∗∗∗(0.48)

Γ̂
Γ̄ˆ
τ̂
ˆ
τ̄
Constant
Observations

0.65∗∗(0.22)−3.41∗∗∗(0.39)−0.11(0.23)−0.25∗∗∗(0.03)
9,304(2)
2.59∗∗∗(0.52)(3)
3.68∗∗∗(0.44)−3.56∗∗∗(0.44)−3.68∗∗∗(0.44)−0.29∗∗∗(0.03)
7,044−0.19∗∗∗(0.02)
7,044

Multinational firms
All

High

Low

(4)
2.96∗∗(1.11)(5)
3.74∗(1.58)(6)
1.92(1.59)

0.56∗(0.28)−3.35∗∗∗(0.85)−0.14(0.43)−0.21∗∗∗(0.04)
2,261

1.01∗(0.40)−3.50∗∗(1.20)
0.46(0.70)−0.19∗∗∗(0.06)
1,113

0.08(0.39)−3.14∗∗(1.20)−0.48(0.56)−0.25∗∗∗(0.07)
1,148

Notes: This table presents the results for regressions of the log change in investment on our tax terms acrossdifferent samples. Column 1 presents the results for our pooled sample of both domestic firms and U.S. multinationals, while columns 2 and 3 report the results for domestic firms. Column 4 provides the results for all U.S.
multinationals, while columns 4 and 5 partition U.S. multinationals into those with high and low foreign capital,
where high foreign capital firms have a ratio of foreign to domestic capital above 15%. * p < .05, ** p < .01, ***p < .001

ing foreign tax liability in U.S. tax returns.28 Consistent with the theory, firms with a larger
multinational presence display a greater response of domestic investment to Γ̄ .
Figure 5 displays the evolution of the regression coefficients as the horizon for investment
growth changes, holding the right hand side parameters fixed at their pre-to-post TCJA change.
These dynamic plots therefore trace out the impulse responses of investment using local projections. For each plot, we report separately the paths of coefficients in the domestic-only andthe multinational firm samples. Firms with larger and smaller changes in Γ or τ from TCJA
have very similar investment trajectories over the pre-TCJA period, supporting a causal interpretation of the post-TCJA responses. The coefficients for Γ̄ bounce around a little more in the
pre-TCJA period but display no evidence of pre-trends in the years immediately before passage.
Table 4 turns to another key outcome, the response of foreign tangible capital. Throughthe lens of our theory, the short-run elasticity of foreign capital to Γ̄ must be positive for complementarity to rationalize the positive Γ̄ coefficients in table 3. Panel A reports our baselineˆ does not correlate with the domestic tax terms, any classical measurement error will not contaminate
If τ̄
those coefficients. Measurement error would tend to attenuate the coefficient on Γ̄ˆ , since it implies that thedampening component of GILTI due to higher τ̄ is also attenuated.
28

30

Figure 5: Regression Coefficient Plots
B. Foreign Cost-of-Capital Γ̄ˆ

A. Domestic Cost-of-Capital Γ̂
612

Coefficient

Coefficient

4

0

0

-1
-2

-4

-2
2010

2012

2014

Year

Domestic Firms

2016

2018

2020

2010

2012

2014

Multinational Firms

Year

2016

2018

2020

2018

2020

Multinational Firms

C. Domestic Tax Rate τ̂ˆ
D. Foreign Tax Rate τ̄

4

1

2

Coefficient

Coefficient

0
0

-2

-1
-4

-6

-2
2010

2012

2014
Domestic Firms

Year

2016

2018

2020

2010

Multinational Firms

2012

2014

Year

2016

Multinational Firms

Notes: These figures plot the tax-term coefficients between 2011-2019 from the regression specified in equation
equation (21) using our firm-level corporate tax data. The outcome variable for this regression is log investment
growth, and the 2017 coefficient for each tax term is set to zero.

specification in the pooled multinational firm sample but with the log change in foreign capitalas the dependent variable. The Γ̄ coefficient of 1.0 is highly statistically significant and implies
an increase in foreign capital of roughly 14% for firms subject to GILTI. Panel B reports the location of foreign capital before and after TCJA. The foreign capital stock of U.S. multinationals
grew in all regions, but grew fastest in the G7, BRIC, and other countries. The share of foreigntangible capital in tax havens, and especially in the small island havens, already relatively low
before TCJA, fell. This geographic pattern suggests that the reported accumulation reflects
actual foreign investment and not simply accounting gimmicks in response to GILTI and hence
could plausibly complement domestic capital at home.

31

Table 4: Foreign Capital Growth
Panel A: Regression
Regressor:
log(Foreign Capital)

Γ̂
−1.10
(1.32)

Γ̄ˆ
1.00∗∗(0.36)

τ̂−0.45(1.03)

ˆτ̄
0.17(0.64)

N
2102

Panel B: Changes in Foreign Capital
Region:PrePeriod K̄

PostPeriod K̄

Share
Pre

Share
Post

Changein ShareCapitalGrowthTotal$589,132$704,082G7$106,731$131,341

18%

19%

0.5%

23%

OECD (excluding G7)$154,591$179,196

26%

25%

-0.8%

16%

BRIC

$65,938$82,624

11%

12%

0.5%

25%

Developing (Non-Bric)$24,405$30,775

4%

4%

0.2%

26%

Tax Haven Non-Islands$121,471$143,457

21%

20%

-0.2%

18%

Tax Haven Islands$73,794$79,428

13%

11%

-1.2%

8%

Other

$42,202$57,261

7%

8%

1.0%

36%

20%

Notes: Standard errors appear in parentheses. Panel A presents the results of regressing log(Foreign Capital) on
our tax terms. The sample for this regression consists of all U.S. multinational firms. Foreign capital in Panel A
is winsorized at the 1% level. Panel B summarizes how foreign capital investment (by region) changed after the
TCJA. Foreign capital investment (Columns 1-2) is in millions of USD. ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001

5.4

Robustness of Investment Results

Table 5 collects several robustness exercises designed to further support a causal interpretationof the baseline regressions. For compactness, we report coefficients for the restricted domestic
and pooled foreign specifications. The first row repeats the baseline coefficients.
The next several rows add different covariates. Rows 2 and 3 show that the estimates arevery similar with NAICS 3 or 4 digit fixed effects. These industry controls absorb many possible
alternative influences of investment. Row 4 addresses the particular concern of the “trade war”
in 2017 by including three trade war exposure measures from Flaaen and Pierce (2019), again
with little impact on the tax elasticities. Row 5 controls for size bins and row 6 for lagged
investment, again with little impact.
The remaining rows perturb the specification or sample. Row 7 weights the regression bylagged log capital. The tax elasticities remain broadly similar, albeit slightly smaller. Row 8drops industries with high investment through partnerships that our investment measure may

32

Table 5: Regression Robustness Checks
Sample:Regressor
Specification:
1. Baseline
2. Industry FE (NAICS 3D)
3. Industry FE (NAICS 4D)
4. Trade Controls
5. Size Controls
6. Lagged Investment
7. Weighted
8. Drop Industries
9. Simulated IV

Domestic

Multinational Firms

Γ̂ − τ̂

N

Γ̂

Γ̄ˆ

3.68∗∗∗(0.44)
3.32∗∗∗(0.44)
3.36∗∗∗(0.45)
3.72∗∗∗(0.44)
3.68∗∗∗(0.44)
3.31∗∗∗(0.49)
3.70∗∗∗(0.45)
3.42∗∗∗(0.41)
3.68∗∗∗(0.44)

7044

2.96∗∗(1.12)
2.35∗(1.08)
2.12(1.13)
2.90∗∗(1.12)
2.96∗∗(1.12)
2.11(1.11)
2.92∗∗(1.12)
2.68∗(1.07)
2.94∗∗(1.12)

0.56∗(0.28)
0.57∗(0.28)
0.67∗(0.30)
0.53(0.28)
0.56∗(0.28)
0.50(0.27)
0.55∗(0.28)
0.63∗(0.28)
0.55∗(0.28)

7044
7044
7044
7044
7044
6827
7044
7044

τ̂

ˆ
τ̄

−3.36∗∗∗ −0.14(0.85) (0.43)−2.86∗∗∗ −0.25(0.84) (0.40)−2.52∗∗ −0.28(0.87) (0.42)−3.31∗∗∗ −0.07(0.85) (0.43)−3.36∗∗∗ −0.13(0.85) (0.43)−2.67∗∗ −0.02(0.85) (0.46)−3.36∗∗∗ −0.14(0.85) (0.43)−3.03∗∗∗ −0.17(0.83) (0.43)−3.35∗∗∗ −0.13(0.85) (0.43)

N
2261
2261
2261
2261
2261
2261
2241
2261
2261

Notes: This table presents the results for regressions of the log change in investment on our four tax terms fordomestic firms and U.S. multinationals under different robustness specifications. Row 1 presents our baseline
results. Rows 2 and 3 include 3-digit and 4-digit NAICS fixed effects. Row 4 includes controls for trade shocks.
Row 5 controls for pre-period capital, while row 6 controls for pre-period investment. Row 7 weighs by the logof the mean capital from 2015-2016. Row 8 drops industries with high baseline investment from partnerships(2-digit NAICS 22 and 3-digit NAICS 486 and 531, which represent utilities, pipeline transportation, and realestate). Lastly, row 9 presents a simulated IV using post-TCJA tax rates. * p < .05, ** p < .01, *** p < .001

miss, with small changes in the coefficients. Row 9 drops firms that have at least 50% of theirforeign income in tax havens [awaiting disclosure].29 The results are essentially unchanged,
suggesting firms likely to be active profit-shifters are not driving the results.
The final row reports coefficients from a simulated IV regression. In our baseline regression,τ̂ comes from applying pre and post-TCJA tax law to the projected income path starting froma firm’s 2015 and 2016 tax returns to generate METRs for 2015 and 2016 with and without
TCJA. The row 10 specification instead uses this measure as an excluded instrument, with theendogenous variable the difference between the average METR in 2015 and 2016, obtained by29

For this categorization, we consider both income in dot havens like Bermuda and the Cayman Islands andnon-dot havens like Ireland and the Netherlands. Within the sample of multinational firms, these firms accountfor 7% of the sample of firms and 30% of foreign and domestic capital.

33

applying pre-TCJA tax law to the firm’s 2015 and 2016 tax returns and simulated income paths,
and in 2018 and 2019, obtained by applying post-TCJA tax law to the firm’s 2018 and 2019
tax returns and simulated income paths. Differences between the excluded instrument andendogenous variable arise because of changes in firms’ taxable income statuses or deductions
and credits between these years. In practice these inputs are very persistent and the simulated
IV yields very similar coefficients.30

5.5

Other Outcomes

Table 6, Panel A shows results for other firm outcomes: the investment to capital ratio, log
domestic capital accumulation, log investment by subcomponent, log tax revenue, log labor
compensation, log salaries and wages, and log officer compensation.31 The investment-tocapital ratio increases strongly with the tax term, Γ̂ − τ̂ in the domestic sample. The effects
on domestic capital mirror those on investment. Both equipment and structures investment
increase by a comparable magnitude, indicating that the total tangible investment responsein the main specifications comes from a combination of both types of investment. We also
report R&D investment effects for completeness although we did not include the effects ofamortization changes in the tax terms, so the full policy change and full effect is not captured
in this specification. The log tax revenue term declines with the policy change and the wage
bill measures all increase in the domestic sample. The labor market effects in the multinational
sample are less precise.

6

Structural Parameters

This section estimates the key model parameters.

6.1

Estimated Parameters

We use the method of moments to recover the parameters α, σ, a, ā and χK . We obtain separate
sets of parameters for domestic-only, multinational with high foreign presence, and multinational with low foreign presence firms. We start from six empirical moments in our data: theregression coefficients b1 , b2 , b3 , b4 , the ratio of capital at foreign subsidiaries to the domestic
30

The simulated IV also addresses the possible role of attenuation coming from constructing the METRs using
firm-level income simulations.
31
Labor compensation is the sum of the following line items on the corporate tax form: salaries and wages,
officer compensation, pension benefits and profit sharing plans, and employee benefit programs.

34

Table 6: Additional Outcomes
Sample:Domestic Firms

Regressor:
Outcome:Γ̂ − τ̂Investments
Capital0.49∗∗∗(0.08)
log(Domestic Capital)
1.72∗∗∗(0.20)
log(Equipment)
3.85∗∗∗(0.40)
log(R&D)
1.18∗(0.56)log(Structures)
3.23∗∗(1.18)
log(Tax Revenue)−2.55∗∗∗(0.72)
log(Labor Comp.)
0.73∗∗∗(0.11)log(Salaries & Wages)
0.86∗∗∗(0.14)
log(Officer Comp.)
0.45∗(0.20)

Multinational Firms (Pooled)

N

Γ̂

6963

0.34
(0.27)
1.33∗(0.60)
2.68∗(1.05)
1.31(0.92)−0.69(2.92)
1.58(1.92)−0.26(0.34)
0.17(0.39)−1.14(0.65)

6955
7008
1336
3579
4145
6029
5892
5046

Γ̄ˆ

τ̂

ˆ
τ̄

0.07
−0.50∗ −0.04(0.07)(0.20)(0.10)
∗∗∗
∗∗∗
0.59
−1.96
0.23
(0.17)(0.47)(0.26)
0.49−3.13∗∗∗ −0.16(0.26)(0.80)(0.41)
∗
0.39
−1.63
−0.53
(0.22)(0.70)(0.34)
1.57∗ −0.87
0.95(0.73)(2.20)(1.14)
2.43∗∗∗ 4.88∗∗
2.45∗(0.68)(1.66)(1.01)
0.15
−0.11
0.19
(0.09)(0.26)(0.14)
0.16
−0.51
0.31
(0.11)(0.30)(0.17)
0.03
0.89
0.32
(0.18)(0.50)(0.26)

N
2244
2214
2255
1398
1477
1336
1989
1982
1813

Notes: Standard errors appear in parentheses. This table contains coefficients from running regressions after
restricting the sample to domestic firms (Columns 1-2), and U.S. multinational firms (Columns 3-7). Outcome
variables appear as row names. Domestic capital and tax revenue are winsorized at the 1% level. All otheroutcomes are winsorized at the 5% level. ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001

parent, χK , and the ratio of after-tax profits, denoted χτ χ F . We measure χK as the ratio of foreign tangible capital from Form 5471 to domestic tangible capital from Form 1120, Schedule
L. We measure χ F by summing all foreign non-dividend income reported on Form 5471.32
If the regression coefficients had come from specifications with long-run changes in capitalor investment as the dependent variable, these moments would suffice to identify the parameters, as we show shortly. In our setting where the coefficients correspond to short-run elasticities, identification requires also determining the ratios of short-run to long-run elasticities,
which in turn depend on the capital adjustment costs.33 We proceed in two steps, first describ32

The exclusion of dividend income avoids double-counting of income generated by tiered ownership structures,
partly addressing the concerns of Blouin and Robinson (2020). Per conversations with experts, double-countingof tangible capital in these data is less of a concern due to fixed asset consolidation practices.
33
Appendix A.7 gives the linearized solution for the transition path from the old to new steady state and provides
formulas for the short-run and long-run elasticities.

35

ing our procedure for handling adjustment costs and then the identification of the parametersof interest conditional on the short-to-long-run ratios.
In the first step, we show in appendix A.7 that with no foreign adjustment costs, the four
tax term elasticities b1 , b2 , b3 , b4 all scale by approximately the same ratio of short-run to longrun investment, denoted χSR .34 This ratio therefore serves as a sufficient and portable statistic
for the effect of domestic adjustment costs on the empirical moments. Furthermore, the large
positive value of the Γ̄ elasticity effectively requires no foreign adjustment costs, because domestic investment responds to Γ̄ only through its impact on foreign capital.35 Our preferredvalue for χSR of 1.3 comes from Winberry (2021) and we next apply this value directly to each
short-run elasticity.36 Figure A.1 shows that the model-implied path of capital varies little overthe first several years across values of χSR ranging from 1 to 1.6, which highlights the difficultyof estimating this parameter internally and the benefit of calibrating it transparently.
In the second step, we choose parameters to minimize the distance between the data andmodel-implied moments. Let θ = (α, σ, a, ā, χK )′ denote the set of parameters. Using equation (15) and appendix A.2.5, equations (22) to (27) illustrate identification by giving closedform formulas for the set of model moments in terms of only θ , χSR , and χR :b1 (θ ) = χSR ωk,r / (1 − α) ,b2 (θ ) = χSR 1 − ωk,r / (1 − α) ,b3 (θ ) = χSR ωk,τ / (1 − α) ,b4 (θ ) = χSR 1 − ωk,τ / (1 − α) ,
34

(22)
(23)
(24)
(25)

Specifically, in general the dynamic system governing the transition path has two non-explosive roots that
determine the speed of convergence and the tax terms depend heterogeneously on each root. With no foreign
adjustment costs, the dynamic system has only one root, in which case the elasticities all scale by the same amount
up to terms involving third derivatives of the production function, which are small. We verify this equivalence numerically at our estimated parameter values. Intuitively, the difference between the ratio of short-run to long-run
elasticities to e.g. Γ and Γ̄ arises primarily because both ratios depend on the magnitude of domestic adjustment
costs but the short-run elasticity to Γ̄ also depends on the foreign adjustment cost. When φ̄ → 0, the only remaining difference occurs because foreign capital does not quite jump immediately to its long-run value, because of
the feedback from growing domestic capital to foreign capital. This feedback effect is small.
35
More precisely, the magnitude of the Γ̄ coefficient relative to the Γ coefficient requires much larger domestic
than foreign adjustment costs, because domestic investment responds directly to changes in Γ but only indirectly
to changes in Γ̄ through the accumulation of foreign capital and production complementarity. The prevalence ofmergers and acquisitions (M&A) rather than acquisition of newly built capital may explain low foreign adjustment
costs. Since our main model outcomes concern domestic rather than foreign capital, whether U.S. firms increase
their foreign capital stock through new investment or M&A does not matter to aggregation.
36
Winberry (2021) estimates a rich model of fixed and convex adjustment costs using moments of the investment distribution from Zwick and Mahon (2017) that come from the same SOI sample as our data set. We use
his replication code to construct impulse responses of investment in his model to nearly permanent domestic tax
changes and compute the ratio of the response at 10 years to the average response over the first two years. See
appendix A.8 for details.

36χK (θ ) = χK ,

(26)
− σ1

χτ χ F (θ ) =(1 − a) χK − aχR− σ1(1 − ā) χR − āχK

!

σ−1
σ

āχK

+ (1 − ā)
σ−1
σ

!
,

(27)

a + (1 − a) χK

where ωk,r and ωk,τ are functions of α, σ, a, ā and χK only, as given in equations (11) to (14),(16) and (17).37
Equations (22) to (27) contain the following intuition for parameter identification. For
domestic firms, ωk,r = ωk,τ = 1 and hence the coefficients on τ and Γ have opposite signsof equal magnitude, each of which equals the inverse of 1 − α. We impose this condition
already in the regression in column (3) of table 3. For multinational firms, instead the sumsof the coefficients on Γ and Γ̄ and on τ and τ̄ equate, with each sum equaling the inverseof 1 − α. Furthermore, given the profit elasticity α, the requirement that domestic capital
responds positively to a subsidy to foreign capital (the coefficients on Γ̄ in table 3 are positive)
bounds the admissible elasticity of substitution between foreign and domestic capital σ. The
magnitudes of the regression coefficients and the profit ratio inform the relative magnitudesof σ, a, and ā.
We operationalize the estimation as follows. We exclude b4 from the set of data moˆ given unobserved expectations
ments because of the difficulty in measuring the tax change τ̄about pre-TCJA unrepatriated profits and because the moment itself is redundant given b1 , b2
and b3 . Thus for domestic firms we have the set of data moments m̂ D = (b1 , b3 )′ and foreach group of multinational firms the set of data moments m̂ M = (b1 , b2 , b3 , χK , χτ χ F )′ . Letm D (θ ) = (b1 (θ ) , b3 (θ ))′ and m M (θ ) = (b1 (θ ) , b2 (θ ) , b3 (θ ) , χK (θ ) , χτ χ F (θ ))′ denote thecorresponding model-implied moments. Let V g denote the covariance matrix of the data forgroup g, where the variances of χK and χτ χ F come from the cross-sectional distribution in theappropriate sample of firms. We obtain θ̂ to minimize (m (θ ) − m̂)′ W (m (θ ) − m̂) for a weightmatrix W = (diag (V ))−1 .38
Panels A and B of table 7 list the moments and resulting parameters. For domestic firms,
the value of α follows directly from the value of b1 = −b3 and χSR . The fitted model moments
for the multinational firms with high foreign presence match their data counterparts almost
exactly, indicating that the point estimates of the data coefficients satisfy the cross-equation
restrictions imposed by the model. For the multinational firms with low foreign presence, theSince we do not have firm-by-firm information on foreign expensing, we set χR = 1 and do not discuss thisparameter further.
38
We perform the minimization in two steps to ensure a global optimum. In the first step, we find a candidate θ̂
by searching over a wide grid of values for the parameters. In the second step, we use a numerical solver startingfrom this candidate value.
37

37

Table 7: Moments and Parameters
Panel A: Momentsb1
Group:
Domestic
Multinat. highMultinat. low

b2

χK

b3

Data

Model Data

Model Data

2.83
2.88
1.48

2.83
2.88
2.14

−2.83 −2.83
0.78 −2.69 −2.69
0.05 −2.42 −2.02

0.78
0.06

Model Dataχτ χ F
Model Data

Model

0.57
0.05

0.62
0.11

0.57
0.05

0.62
0.12

Panel B: Parameters chosen to match moments
Group:
Domestic
Multinat. highMultinat. low

χK

α

σ

a

ā

0.57
0.05

0.65
0.73
0.54

0.76
0.78

0.95
1.00

0.82
0.26

Panel C: Other parameters
Symbol
ρ
δ
αL
φ

Name
Discount rate
Depreciation rate
Labor share of revenue
Adjustment cost

Value
0.06
0.1
0.65
{1.5,1.1,1.8}

model regression coefficients b1 and b3 do not exactly match the data. These firms are “almost
domestic” both in their values of χK and χτ χ F and in the small regression coefficient b2 ; as aresult, the model requires the absolute values of b1 and b3 to be closer than they are in thedata.
Turning to parameters, the multinational firms with high foreign presence have the highest
estimated α. The values of σ in the multinational high and low groups are quite similar, despite
the value of b2 being much smaller in the multinational-low group. As discussed already,the low multinational presence implies less responsiveness of domestic capital to the foreign
cost-of-capital even for the same value of σ. To interpret the values of a and ā, note thatχK = 1 ⇒ a/(1 − a) = F1 (K, K̄)/F2 (K, K̄), that is, for a firm with equal foreign and domestic
capital, a/(1 − a) equals the ratio of the marginal product of domestic earnings with respect
to domestic and foreign capital. A value of a = 0.95 thus implies that incrementing domestic
capital would increase domestic earnings by roughly 19 times as much as incrementing foreign38capital would. Interestingly, while we estimate the foreign subsidiaries of firms with a large
multinational presence to be relatively intensive in foreign capital, for those firms with a small
multinational presence their foreign productivity depends more on domestic than on foreign
capital.

6.2

Tax Changes and Other Parameters

We set several other parameters using external information, shown in panel C of table 7. We set
the discount rate ρ to 0.06 and the depreciation rate δ to 0.1, consistent with our measurementof the tax shocks. We set the labor share of revenue α L to 0.65, as in Winberry (2021). Thisparameter matters only for translating changes in capital into changes in wages and output.
We ignore materials inputs and markups for simplicity, α M = 0, M = 1. We internally set theadjustment cost parameter φ for each group to match the value of χSR (see appendix A.7).
We group firms into “portfolios” based on their domestic/multinational high/low status andtheir tax changes. Table C.6 shows these portfolios, the share of capital in each, average capitalper firm from SOI, and the pre- and post-TCJA tax rates. The “low-tax” firms had pre-TCJA
domestic rates as low as 15% while the “high-tax” firms essentially face the statutory rate.
Accordingly, the low-tax firms had smaller tax changes. For multinational firms, we further
divide by whether GILTI was binding on the firm or not. In addition to the firms representing
the SOI, we add a domestic non C-corporate sector calibrated using figure 3 to be 29% of private
sector capital.39 Since we study the effects of the provisions of TCJA affecting C-corporations,
we assign no tax changes to this sector and including it matters only for general equilibrium
labor market clearing.40
Finally, we need to assign productivities A and Ā to each firm. Given α, σ, a, χK , and α L ,the ratio χA = Ā/A follows immediately from equation (A.35). We choose A to match thecapital-per-firm shown in table C.6. This procedure assigns higher productivity to the larger
multinational firms than the domestic-only firms.
39

Since the top line in figure 3 is private investment, this calibration implicitly segments the private sector
from government. We assume the non-corporate sector has the same capital-per-firm as the domestic-only Ccorporation sector, although this assumption matters little to our results.
40
The expensing provisions applied to non C-corporations as well. However, these entities also were affected byseveral other changes in the TCJA such as reductions in personal income tax rates, making it conceptually cleaner
to consider exercises affecting taxation of C-corporations only.

39

6.3

Parameter Validation

An important untargeted moment is the response of foreign capital to changes in Γ̄ . Were themodel to imply a much larger elasticity than in the data, it would suggest the possibility offorces beyond capital complementarity in driving the relationship between domestic investment and the foreign cost of capital. We construct this elasticity in the model by perturbing
the value of post-TCJA Γ̄ around its mean value and calculating the log deviation of foreign
capital K̄ averaged over the first two years. This exercise yields elasticities of 1.00 and 0.65 formultinational-high and low firms, respectively, which compares well to the elasticity of 1.00 in
table 6.

7

Model Quantification

This section summarizes and decomposes the effects of TCJA on capital, investment, and revenue.

7.1

Capital and Investment

We start with a (nearly) “model-free” quantification. Column (1) of table 8 reports the steadystate partial equilibrium change in domestic capital (or equivalently investment), computed as
the capital-weighted fitted values using the regressions reported in table 3, adjusted by χSR .
These changes correspond to partial equilibrium because we compute the fitted values without
the constant term and hence omit any GE effects, such as changes in wages that affect all
firms. The regression coefficients directly imply capital rises by 11.8% for domestic-only firms,
18-20% for multinational firms, and 15.7% for the corporate sector as a whole. The standard
errors provide tight bounds around these values.
Imposing the model structure allows us to move from partial to general equilibrium, decompose the role of different tax changes, and explore policy counterfactuals. Column (2) oftable 8 reports the partial equilibrium effects in the model for comparison with the modelfree estimates. That is, we use the estimated parameters from table 7 and the tax changes
by group from table C.6 to compute the steady-state change for each group if wages remain
fixed. For domestic-only firms, the model imposes no additional restrictions beyond those already imposed on the data by combining the Γ̂ and τ̂ into a single regressor Γ̂ − τ̂. The partial
equilibrium responses from the data and the model therefore nearly agree by construction. For
multinational-high firms, the data and model partial equilibrium effects also nearly agree, but

40

Table 8: Long-run K and I by Group

Group:
Domestic
Multinat. highMultinat. low
Total

PE
Data

PE
Model

GE
Model

11.76
(1.41)
18.76(3.73)
20.30(4.82)
15.65(1.68)

11.08(1.41)
19.19(4.52)
6.89(6.84)
12.14(2.16)

6.01(1.74)
14.05(4.92)
3.38(6.59)
7.43(2.45)

Notes: The table shows long-run changes in domestic corporate capital (or equivalently investment) for domesticonly firms, multinational firms with high foreign presence, multinational firms with low foreign presence, and in
total. Column (1) directly applies the regression coefficients in table 3 to the tax changes by group in table C.6.
Column (2) uses the parameters estimated in table 7 to compute the model-implied change when the aggregate
economy faces perfectly elastic labor supply and the wage remains fixed. Column (3) repeats the exercise fromcolumn (2) but when the aggregate economy faces inelastic labor supply. Standard errors in parentheses arecomputed via the Delta method.

in this case because the multinational-high regression coefficients obey the additional crossequation restrictions in the model. For the multinational-low group, the data response exceeds
the model response, because the model’s cross-equation restrictions yield parameters that imply
a larger response to Γ̂ and smaller response to τ̂ than the regression coefficients (see table 7).
The total corporate sector model-implied partial equilibrium response increase in capital is12.1% with a standard error of 2.2%.41
Column (3) of table 8 shows the general equilibrium change in domestic capital in themodel, meaning when wages rise and the total supply of labor to the domestic corporate andnon-corporate sector remains fixed. In general equilibrium, the corporate provisions of the
TCJA increase total corporate capital by 7.4% in the long-run, with a standard error of 2.4p.p.
The general equilibrium dampening of 4.7p.p. relative to partial equilibrium stems from anincrease in the domestic wage of about 0.9%.42
41

We compute standard errors for the model partial and general equilibrium values using the parameter covariance matrix and the Delta method. These standard errors therefore account for sampling variation in the target
moments of the parameter estimation. Specifically, let the superscripts D, H, L refer to parameters estimatedfor the domestic, multionational-high, and multinational-low firms, respectively, and define the full parameter
vector as θ ≡ {α D , αH , σ H , a H , ā H , χKH , α L , σ L , a L , ā L , χKL }. For each parameter θ p ∈ θ , we recompute the steadystate response K/K0 replacing θ p with θ p+ = θ p + ε and with θ p− = θ p − ε for ε = 10−4 . The Jacobian is then
  
 
 
 
J(p) = K ∗ θ p+ /K0 θ p+ − K ∗ θ p− /K0 θ p− / (2ε). The variance is J(p)′ V (θ )J(p), where V (θ ) is the covariance matrix of the parameters computed as in Chamberlain (1982).
42
Roughly, an increase of 0.9% reduces the multiplicative factor in the earnings function Z by about α L /(1 −α L ) × 0.9 = 1.8% (see equation (A.3)). The capital-weighted elasticity of capital to Z or τ of roughly 2.6 (b341

Figure 6: TCJA and Model-Implied Capital

1.12
1.1
1.08
1.06
1.04
1.02
1
0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Notes: The figure shows the model-implied paths of total domestic and foreign capital of domestic corporations (solid red line), total domestic corporate capital (solid blue line), and total domestic corporate capital of
multinational (solid brown line) and domestic-only firms (solid green line). The dotted blue lines show the 95%
confidence interval for the response of domestic corporate capital.

Figure 6 plots the model-implied general equilibrium transition paths of domestic and total
corporate capital. The solid blue line shows that total domestic corporate capital achieves a 7%
increase after 15 years. For this series, we also report 95% confidence interval bands.43 The
red line shows the path of total domestic and foreign capital owned by the domestic corporate
sector. This measure corresponds to total capital in a data set such as Compustat that does
not separate domestic from foreign capital. Total capital owned by domestic firms rises by
proportionately more than domestic capital, primarily due to the strong incentive in the GILTI
rule for firms to accumulate foreign capital. Due to the complementarity between foreign anddomestic capital, total domestic capital at multinational firms (brown line) therefore also rises
by more than total domestic capital at domestic-only firms (green line).
in table 7) then accounts for the 4.7p.p. dampening. Of course, the size of the wage response, and hence thegeneral equilibrium dampening and the impact on labor tax revenue discussed shortly, depends in general on theelasticity of substitution between capital and labor, which we set to 1.
43
We compute confidence intervals using the same method to report standard errors in table 8. Specifically, for each parameter θ p ∈ θ , we recompute the impulse response of K/K0 replacing θ p with θ p+ =
θ + ε and with θ p− = θ p − ε for ε = 10−4 . The pth row of the horizon h Jacobian is then J(h, p) =
p
 
 
 
 
K t+h θ p+ /K0 θ p+ − K t+h θ p− /K0 θ p− / (2ε). The horizon h variance is J(h, p)′ V (θ )J(h, p), where V (θ )is the covariance matrix of the parameters computed as in Chamberlain (1982).

42Figure 7: Model-Implied Capital by TCJA Provision

1.035
1.03
1.025
1.02
1.015
1.01
1.005
1
0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

The figure shows the model-implied paths of domestic corporate capital applying only the TCJA changes to the
METR τ (red line), to expensing (blue line), or GILTI (green line).

7.2

Role of Different Shocks and Phase-out

Figure 7 shows the role of the different tax variables in the response of total domestic capital.
Changes in the METR τ alone would have increased capital by about 3.5% after 15 years,
or about half of the total response. Moving from 50% to 100% bonus depreciation by itself
would have increased capital by about 2%.44 Strikingly, the incentive to accumulate foreign
capital through GILTI and the induced increase in domestic capital alone account for about a
1.5% increase in domestic capital. We stress these calculations do not imply that one provision
was more effective than another because they do not account for differences in the costs. For
example, the corporate rate reduction was also expected to reduce tax revenues the most.
Figure 8 explores the importance of our baseline assumption that 100% bonus depreciation
would become a permanent feature of tax policy and that firms anticipated this change. The
green line shows the path of capital in a scenario where firms expected phase-out of expensing
as written into the TCJA law, namely a decline of 100% to 80% in 2023 and further declinesof 20p.p. per year thereafter until reaching 0. In the short-run, these expectations increase
44

Throughout this section, exercises labeled expensing-only also include the effect of the FDII 10% threshold,which amounts to an additional tax change of 37.5% × the marginal rate × the export share × 10% of domestic
tangible capital.

43

Figure 8: Role of Expensing Phase-out

1.07
1.06
1.05
1.04
1.03
1.02
1.01
1
0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Notes: The figure shows the model-implied paths of domestic corporate capital under the baseline assumptionof no phase-out or expected phase-out of the expensing provisions (blue line), when firms fully anticipate phaseout of expensing as written into the TCJA law (green line), or when firms are surprised each year that bonus
depreciation ratchets down (red line).

investment and capital relative to the permanent case. The short-run overreaction occurs because our values for discount and depreciation rates yield intertemporal substitution toward
investment in periods with higher expensing that outweighs the dampening effect of a lower
terminal capital stock.45 The red line shows the path of capital if firms did not expect phaseout but it occurs anyway. This scenario results in the same short-run behavior of capital byconstruction but a lower terminal capital stock.

7.3

Tax Revenue

The total effect of the TCJA’s corporate provisions on tax revenue combines two forces: (i) thestatic revenue effect of the tax changes holding the capital stock fixed, and (ii) the revenue
consequences of the dynamic changes in capital induced by the law. The last column of table 1
shows the JCT’s estimates of (i). Our empirical analysis and general equilibrium model of the45

In this sense, our conclusions about the overall investment effects of the TCJA’s corporate provisions provide
an upper bound if firms expected the expensing provisions to expire, since some of the short-run investment
response would then stem from the intertemporal incentive rather than the long-run tax policy changes to which
we ascribe it.

44

Figure 9: Dynamic Revenue Effects

0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
0

2

4

6

8

10

12

14

16

18

20

Notes: The figure shows the effect of changes in capital, investment, and wages on tax revenue inthe model, expressed as a share of no-TCJA corporate tax revenue. The dashed red red line shows
the change in corporate revenue dueto changes in the tax base before depreciation deductions, τ t ×
F K t , K̄ t ; Z t − Φ (I t , K t ) − F K0 , K̄0 ; Z0 . The dotted red line shows the change in corporate revenue due togreater depreciation deductions as investment rises, Γ t × (I t − I0 ). The solid red line combines these two changes
as well as the dynamic
effects of FDII and GILTI, which are small. The blue line shows the change in labor taxes,τ L × PtL L ∗ − P0L L ∗ .

dynamic changes are instead well-suited to address (ii).
The solid red line in figure 9 shows the total change in corporate taxes as a result of the
dynamic changes in capital, expressed as a ratio of pre-TCJA corporate tax revenue. The dashedand dotted red lines decompose the change in corporate tax revenue into a part coming fromchanges in the tax base before depreciation, τ t × F K t , K̄ t ; Z t − Φ (I t , K t ) − F K0 , K̄0 ; Z0 ,and a part coming from greater depreciation deductions as investment rises, Γ t × (I t − I0 ),where date 0 denotes a pre-TCJA value. (The solid red line also includes the dynamic effectsof FDII and GILTI, which are small.) The denominator calculates pre-TCJA corporate revenue as
τ0 × F K0 , K̄0 ; Z0 − B0 , where B0 denotes a lump-sum deduction that includes all credits anddeductions inframarginal for determining investment and is calibrated to match the averagetax rate in our sample. For comparison with the revenue estimates in table 1, CBO (2017)
forecast $3.9 trillion of corporate income taxes over the 10-year 2018-2027 window in theabsence of TCJA.
The dynamic response of capital reduces corporate tax revenue on impact and has a small
45

positive long-run effect. The impact reduction occurs because capital does not jump at thetime of the law change, but the immediate increase in investment incurs adjustment costs that
depress taxable income and also increases depreciation deductions. Over time, rising domestic
and foreign capital increase domestic corporate income. Higher capital quickly overcomes
the higher adjustment costs and the effect on taxable income turns positive by year 2 and byitself would result in revenue increases of 15% of pre-TCJA revenue by year 30. However,
because the negative revenue impact of higher depreciation deductions persists, total dynamic
corporate revenue effects remain negative until year 12 and never exceed 5% of pre-TCJA
revenue. While we are not aware of other estimates of the dynamic revenue effects of corporate
tax and depreciation changes in isolation, the JCT notes the offsetting revenue impacts ofhigher depreciation allowances when they discuss their dynamic scoring methodology.46
The explanation for a muted total corporate tax revenue dynamic response goes beyond thedetails of our model. To see why, it helps to consider the case of a domestic-only firm with allbonus-eligible investment, so that Γ = τ post-TCJA (see section 4.1), and to focus on the long
run. This firm chooses K to maximize (1 − τ) F (K) − (1 − Γ ) P K I = (1 − τ) F (K) − P K δK , sothat the optimal K maximizes pre-tax net income. In addition to illustrating the well-known
result (Hall and Jorgenson, 1971) that when τ = Γ changes in taxes do not distort capital,
this expression also has an envelope implication: changes in K have second-order effects on
net income and hence revenue. This result does not hold exactly in our model because someinvestment remains bonus-ineligible and because of the international provisions, but it shows
why evaluating the revenue implications of changes in K at post-TCJA tax rates yields small
effects.
The solid blue line in figure 9 shows the effect of changes in labor taxes, defined as τ L ×
PtL L ∗ − P0L L ∗ , where τ L = 0.25. Since the wage depends on the capital stock, it does not jumpat the time of the law change but instead rises over time. By year 10, the increase in wages
generates additional labor tax revenue of almost 15% of the pre-TCJA corporate tax revenue.
Any overall long-run increase in tax revenue due to changes in capital therefore largely arises
from higher labor rather than corporate tax payments. Furthermore, summing together thesolid blue and red lines, the combined dynamic revenue effects in the first 10 years averageless than 2% of pre-TCJA corporate revenue per year, or about $7 billion per year, well shortof erasing the static revenue decline shown in table 1.
46“The extension of bonus depreciation in the bill is an important contributor to increased investment incentives
created by the bill. Because of the more generous deduction created for new investment by this provision, theincreased investment reduces the taxable base during the time period when this provision is in force, thus reducing
the amount of revenue feedback associated with the increase in GDP” (JCT, 2017, footnote 8).

46

8Validation

8.1

Synthetic Controls Evidence for Publicly-traded Firms

We validate our investment effect estimates using an alternative approach that does not impose any model structure or tax shock measurement. To do so, we use synthetic controls toestimate how investment would have evolved in the United States in the absence of the TCJA.
Specifically, let Yi,tR and Yi,tN denote the reform and no-reform outcome for a variable Y for firm
PN
i in year t. We estimate Ŷi,tN = n w i Yn,t as a weighed average of outcomes Yn,t of public firms
headquartered outside the United States.
We begin with the universe of non-financial firms from Compustat North America and Compustat Global that have their headquarters in the U.S. from 2011 to 2021. The panel of firms isthen balanced by dropping firms that are missing capital expenditure (capx) data at any point
during our window, and we further restrict the sample to firms with data on total assets, sales,and property plant and equipment in at least 4 years.
For each of the remaining firms, we estimate a firm-specific set of weights w i , which are
restricted to be non-negative and sum to one. We restrict the pool of potential “donors” foreach U.S. firm to global firms in the same 4-digit NAICS industry.47 We then assign weights
to minimize the mean squared prediction error between the following variables during the
pre-reform period of 2011-2017: annual investment, annual sales, and the pre-period mean ofproperty plant and equipment (ppent).48
Appendix table C.3 provides summary statistics for four samples: matched U.S. firms,
matched global firms, closely matched U.S. firms, and closely matched global firms. We define
a firm as closely matched if the average pre-reform capital expenditure of the synthetic firm iswithin 10% of the U.S. firm. All subsequent analyses are conducted using the closely matchedsample. The rows represent different outcomes and firm characteristics of interest in either
the pre-reform years from 2011-2017 or the post-reform years 2018-2019. The table provides
means and medians for each. All values are inflation adjusted to be in 2021 dollars. Comparing the pre-reform means to the post-reform means of capital expenditures shows that thecapital expenditures of U.S. firms in both the matched and closely matched samples increased,
47

Appendix table C.2 presents a waterfall table of our sample.
See Abadie (2021) equation (7) and the surrounding discussion for additional details. We use the average
ppent to account for differences in physical assets, and annual sales to help account for growth trajectories. Our
results are not especially sensitive to the selection of these pre-period controls. We establish this assertion by
documenting raw investment effects in a simple difference-in-difference regression that uses pre-period ppent,annual sales, four-digit-industry fixed effects and other pre-period various as controls in the pooled sample ofUS-based and non-US-based public firms.
48

47

Figure 10: Investment of Public Firms in the United States and Similar Foreign Firms

2010

2012

2014

2016

Actual US Investment

2018

2020

2022

Synthetic US Investment

0

500

550

Billions of Dollars
50
100Billions of Dollars
600
650

150

Panel B: Difference in Investment

700

Panel A: Total U.S. Investment
vs Synthetic Control

2010

2014

2016

2018

2020

2022

Panel D: Difference in Investment

2010

2012

2014

2016
Year

Actual Canadian Investment

2018

2020

2022

Synthetic Canadian Investment

0

70

80

Billions of Dollars
50
100Billions of Dollars
90
100

110

150

Panel C: Total Canadian Investment
vs Synthetic Control

2012

2010

2012

2014

2016

2018

2020

2022

Notes: The figure plots aggregate investment (capital expenditure) of U.S. publicly-traded firms and comparesit to the investment of synthetically matched global firms. Panel A shows the two series separately, and Panel B
plots the difference. Panels C and D show the analogous plots when we create synthetic matches for Canadianfirms instead of U.S. firms. This figure does not contain data from tax returns.

whereas they decreased among global firms. Specifically, the average capital expenditure fora closely matched U.S. firm increased from $402 million to $435 million.
Panel A of figure 10 plots the evolution of the aggregate annual capital expenditure of U.S.
firms in our closely matched analysis sample as well as the aggregate annual capital expenditure of their synthetic controls. The aggregate capital expenditure of the matched controls

48

Table 9: DID Estimates of the Effect of TCJA on Investment (Matched Sample)
(1)

(2)

(3)

(4)

Post

0.163∗∗∗(0.026)

0.153∗∗∗(0.007)0.163∗∗∗(0.006)

0.153∗∗∗(0.007)

Observations
Controls
NAICS 4-digit FEs

13,275
NoNo

12,222
Yes
No

13,275
No
Yes

12,222
Yes
Yes

Notes: Standard errors in parentheses are clustered at the firm level. The outcome variable is ln(capx of treatedfirms) - ln(capx of synthetic firms). The controls include the mean pre-period (2011-17) values of capx, assets,
sales and property, plant and equipment (PPENT) of treated firms. All regressions are weighted by the mean
pre-period capx of treated firms. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

closely tracks that of the U.S. firms in the pre-reform period before sharply increasing in 2018.
The aggregate capital expenditure of U.S. firms in 2018 and 2019 greatly exceeds the aggregate capital expenditure of the control group.49 Panel C validates our approach by plotting
the evolution of the capital expenditures of Canadian firms relative to closely matched synthetic controls in a placebo analysis. The sharp increase around the TCJA is not present for oursample of Canadian firms and only occurs in the U.S. series.
We further validate our results with a backdating approach that uses earlier years, 2011
to 2015, in the pre-period to estimate the weights. This enables us to compare the outcomesfrom 2016-2017 to see if the treatment and control groups still evolve similarly in the prereform period. Appendix figure B.2 presents the results from this approach. The figure shows
both that the US firms evolve similarly, and that there is a still a sharp increase in the TCJA
period in the U.S. firms relative to the controls. We also conduct leave-one out analyses thatdrop countries from the donor pool of synthetic controls to ensure that foreign shocks are notdriving the results. Finally, we also include controls for other contemporaneous shocks such as
tariff changes.
Table 9 summarizes these effects by reporting difference-in-difference estimates within theanalysis sample, weighted by pre-period average capital expenditures. Capital expenditures
increase by around 15% across specifications that do and do not control for pre-reform firm
characteristics as well as four-digit industry fixed effects. For comparison, global investment
49

Appendix table C.4 decomposes these aggregate differences by two-digit NAICS industry codes. We sum
each firm and its synthetic control in a given industry, and report these totals and their difference. The tableshows that Utilities ($68 billion), Manufacturing ($21 billion), and Mining ($18 billion) were the three largest
contributors to the total difference in investment among public firms in our matched sample and their synthetic
control counterparts.

49

in the estimated model is roughly 20-25% higher over the first years following the reform andthe 95% confidence interval cannot reject a 15% increase. Furthermore, the model measureincludes mergers and acquisitions that Compustat does not include in capital expenditure,making it plausible that the difference between the synthetic control estimates and the model
are smaller than they appear.

9

Conclusion

This paper combines administrative tax data and a model of global investment behavior to investigate the effects of the TCJA—the largest corporate tax cut in U.S. history—on the level andlocation of investment and capital. We use a model to characterize and measure four channels
through which this tax policy affected investment: domestic and foreign cost-of-capital subsidies and domestic and foreign corporate tax rates. Both domestic and foreign investment of
U.S. multinationals increased due to the TCJA, with the increase in domestic investment larger
both at firms experiencing more favorable domestic tax changes as well as at firms with larger
incentives to accumulate foreign capital. Our model interprets the latter increase as evidenceof complementarity between domestic and foreign capital in production. Overall, we estimate
a long-run increase in domestic corporate capital of roughly 7.4% due to the TCJA’s corporate
provisions.
Despite the dynamic response of capital, our model contains small dynamic revenue effects. While higher investment increases corporate income and labor payments, the extra tax
revenue from this activity is offset by the higher cost of depreciation deductions, which can be
immediately expensed in the years following the enactment of the tax reform. Consequently,
the total effect on corporate tax revenue is close to the mechanical effects, which are large
given the 14-percentage-point tax rate cut and immediate expensing.
Many of the provisions of TCJA remain contested in the political arena. Our quantitativemodel enables an analysis of policy counterfactuals. We decompose the effect of the reform into
its constituent parts, such as expensing, lower rates, and international provisions, but much
more can be done. For example, future research might use our estimates and model to consider
alternative policy proposals such as the effect of global minimum tax rates, county-by-country
provisions, or other reforms. Our results highlight the potentially unintended consequencesof including deductions for the normal return to tangible capital in the GILTI and FDII provisions. Our framework is well suited to consider the impacts of policy reforms that change thisdeduction.

50

A second avenue for further research concerns the relationship between tangible capital
accumulation, our paper’s focus, and intangible capital. While we do explore the impact of
the TCJA provisions on R&D spending, much more could be done to understand what kind ofactivity is responding and the longer-term impacts. We also leave the study of the transition toamortization of R&D deductions to future work. Separately, more work could be done using
administrative data to assess whether the TCJA affected profit-shifting behavior by both U.S.
multinationals and foreign multinationals with U.S. presence.
Finally, there is considerable interest in the effects of the TCJA on the level and distributionof income for workers. We document large impacts on both domestic and foreign capital accumulation. To what extent do the productivity gains from this investment pass on to workers,
and which workers benefit most?

51

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55

Tax Policy and Investment in a Global Economy
Online Appendix
Gabriel Chodorow-Reich Matthew Smith Owen Zidar Eric Zwick

A

Model Appendix

A.1

Derivation of Profit Function

The static optimization is max L,M Pt Q t − PtL L t − PtM M t , with the demand constraint Q t =
− M

1

α

M −1

α

α

Q∗ Pt M −1 . Let Yt = Pt Q t = Q tM Q∗ M = A t K t K L t L M t M , where to keep notation simple wehave redefined A t to absorb the demand intercept Q∗ . The FOC are:α L Yt,Lt
α M YtPtM =.
Mt

FOC (L t ):PtL =FOC (M t ):By definition and substituting the FOC gives:

F (K t ; Z t ) ≡ Yt − PtL L t − PtM M t = (1 − α L − α M ) Yt .

(A.1)

Using
Mt =



αM
PtM



αL
PtL

−1
Lt

and the FOC for L t we get an expression for revenue as a function of capital:Def.:
Subst. prev. line:
Subst. FOC (L):

α

α

α

Yt = A t K t K L t L M t M
α  −αMαM M α Lαα +αAt Kt K L t L M
=
M
L
Pt
Pt
αM  −αMα L +αM

αL
αL
αM
αK
At Kt
Yt
=
PtM
PtL
PtL
 αM   α L
αα M 1−(α L +αM ) α L 1−(α L +αM ) 1−(α L1+αM ) 1−(α LK+αM )
A
=
K
.
t
t
PtM
PtL

We then have:
F (K t ; Z t ) = (1 − α L − α M ) Yt = Z t K tα , αM   α L
α M 1−(α L +αM ) α L 1−(α L +αM ) 1−(α L1+αM )where: Z t ≡ (1 − α L − α M )
At
,
PtM
PtL
αK
α≡
.
1 − (α L + α M )

A.2

(A.2)
(A.3)
(A.4)

Log-linearization details with structures and equipment

This appendix extends our model to allow for multiple types of domestic and international
capital and provides details of the log-linearization. Let Ks,t and Ke,t denote structures and1equipment capital. We assume:
K t = g Ks,t , Ke,t

and likewise for international capital. Each type of capital has its own price and depreciation
schedule and obeys its own dynamic evolution equation. The firm maximizes the present valueof dividends with a discount rate ρ, subject to initial conditions and the dynamic evolution
equations for each type of domestic and international capital.
A.2.1

First Order Conditions and Steady StateWe write the Hamiltonian:

H Is,t , Ks,t , I e,t , Ke,t , Īs,t , K̄s,t , Ī e,t , K̄e,t = Dt +Xλi,t I i,t − δ i Ki,t + λ̄i,t Ī i,t − δ̄ i K̄i,t .

i∈{s,e}Necessary conditions for i ∈ {s, e}: K(1 − τ t ) Φ1 I i,t , Ki,t + 1 − Γi,t Pi,t= λi,t ,I i,t :

(A.5)

i



K̄
(1 − τ̄ t ) Φ̄1 Ī i,t , K̄ t + 1 − Γ̄i,t Pi,t= λ̄i,t ,
(A.6)



(1 − τ t ) F1 ∂ K t /∂ Ki,t − Φ2 I i,t , Ki,t + (1 − τ̄ t ) F̄2 ∂ K t /∂ Ki,t − δ i λi,t + λ̇i,t= ρ,Ki,t :
λi,t
(A.7)



˙
(1 − τ̄ t ) F̄1 ∂ K̄ t /∂ K̄i,t − Φ̄2 Ī i,t , K̄i,t + (1 − τ t ) F2 ∂ K̄ t /∂ K̄i,t − δ̄ i λ̄i,t + λ̄i,tK̄i,t :
= ρ.
λ̄i,t
(A.8)

Ī i,t :Substituting the adjustment costs:
FOC (I i,t ): K̇i,t /Ki,t =FOC(Ki,t ) :

1
φ



K
λi,t − Pi,t
1 − Γi,t  γ1,(1 − τ t )

(A.9)





λ̇i,t = ρ + δ i λi,t − (1 − τ t ) F1 ∂ K t /∂ Ki,t − Φ2 I i,t , Ki,t − (1 − τ̄ t ) F̄2 ∂ K t /∂ Ki,t .

The analogous equations hold for foreign capital.
In steady state, K̇i,t = λ̇i,t = 0, giving:
λ∗i = (1 − Γi ) PiK .

(A.10)


Let R∗i ≡ ρ + δ i λ∗i and likewise for foreign. From FOC(K) we have the system of equations
for the steady state:(1 − τ) F1∗ + (1 − τ̄) F̄2∗ ∂ K ∗ /∂ Ks∗ = R∗s ,(A.11)2∂ K ∗ /∂ Ke∗ = R∗e ,(1 − τ̄) F̄1∗ + (1 − τ) F2∗ ∂ K̄ ∗ /∂ K̄s∗ = R̄∗s ,
(1 − τ̄) F̄1∗ + (1 − τ) F2∗ ∂ K̄ ∗ /∂ K̄e∗ = R̄∗e .

(1 − τ) F1∗ + (1 − τ̄) F̄2∗



(A.12)(A.13)(A.14)


Recognizing that F1∗ = F1 K ∗ , K̄ ∗ ; Z ∗ , F2∗ = F2 K ∗ , K̄ ∗ ; Z ∗ , F̄1∗ = F̄1 K̄ ∗ , K ∗ ; Z ∗ , F̄2∗ = F̄2 K̄ ∗ , K ∗ ; Z ∗ ,this is a system of four non-linear equations in four unknowns Ks∗ , Ke∗ , K̄s∗ , K̄e∗ .
We assume that structures and equipment combine according to:ν 1 ν−1
 ν−1
1
ν−1
ν
ν
ν
ν
K = g (Ks , Ke ) = as Ks + ae Ke(A.15)1
1−ν
1−ν  1−ν, and likewise for international capital. Standard
and define R∗ ≡ as R∗s+ ae R∗e
CES derivations give: ∗ − ν1  ∗ 
1
Ki
Ri
∂ K∗
ν
=
a
=
.
(A.16)
i
∂ Ki∗
K∗
R∗

Equation (A.16) allows us to collapse the four steady state conditions into two, as in the main
text:

A.2.2

(1 − τ) F1∗ + (1 − τ̄) F̄2∗ = R∗ ,(A.17)(1 − τ̄) F̄1∗ + (1 − τ) F2∗ = R̄∗ .

(A.18)

Log Linearization

Substituting functional forms:



1
∗
∗
∗
∗ α+1/σ−1∗∗∗ α+1/σ−1¯R = α (1 − τ ) aZ (K )+ (1 − τ̄ ) (1 − ā) Z̄ K
(K ∗ )− σ ,α+1/σ−1
− 1R̄∗ = α (1 − τ̄∗ ) ā Z̄ ∗ K¯ ∗+ (1 − τ∗ ) (1 − a) Z ∗ (K ∗ )α+1/σ−1 K̄ ∗ σ ,(A.20)

σ
 σ−1
 σ−1
σ−1
¯
σ
σ
where recall K = aK
+ (1 − a) K̄
and K = ā K̄+ (1 − ā) K.
Let α̃ ≡ σα+(1 − σ) = 1−σ (1 − α) ⊆ [1 − σ, 1] be the elasticity-adjusted returns to scale,i.e. α = 1 ⇒ α̃ = 1 and α = 0 ⇒ α̃ = 1−σ, with α̃ = α if σ = 1. Let Ew (x, y) ≡ wx +(1 − w) ydenote the weighted average of x and y.1 Defining τ̂ = dτ/ (1 − τ) and Γ̂ = dΓ / (1 − Γ ) andusing equations (11) to (14), the log-linearization around the steady state gives:



1

σ−1
σ

σ−1
σ

σ
 σ−1

(A.19)

Note the following properties which we use in the derivation that follows:
Ew (1 − x, y) = 1 − Ew (x, 1 − y) ,Ew (x, 1 − y) + Ew̄ ( y, 1 − x) − 1 = (1 − w − w̄) (1 − x − y) ,Ew̄ ( y, 1 − x) = 1 − x − w̄ (1 − x − y) ,(1 − w̄) (1 − Ew (x, 1 − y)) − wEw̄ ( y, 1 − x) = (1 − w − w̄) y.

3


 
 



α̃
α̃
ˆ
(A.19) : r + (1/σ) k = s F1 z − τ̂ +s1 k + (1 − s1 ) k̄ + 1 − s F1 z̄ − τ̄ +s̄1 k̄ + (1 − s̄1 ) k ,
σ
σ


 

ˆ − z̄ + α̃ Es (s1 , 1 − s̄1 ) k + 1 − Es (s1 , 1 − s̄1 ) k̄ ,σr + k = −σEsF τ̂ − z, τ̄
F
F1
1


1

ˆ − z̄
1 − EsF (s1 , 1 − s̄1 ) α̃k̄ − σ r + EsF τ̂ − z, τ̄1
1,(A.21)k=
1 − EsF (s1 , 1 − s̄1 ) α̃

 1


ˆ − z̄, τ̂ − z
1 − EsF̄ (s̄1 , 1 − s1 ) α̃k − σ r̄ + EsF̄ τ̄
1
1.
(A.22)(A.20) :k̄ =1 − EsF̄ (s̄1 , 1 − s1 ) α̃1

Substituting equation (A.22) into equation (A.21):
1 − EsF (s1 , 1 − s̄1 ) α̃ k
1



 
ˆ


1 − EsF̄ (s̄1 , 1 − s1 ) α̃k − σ r̄ + EsF̄ τ̄ − z̄, τ̂ − z11= 1 − EsF (s1 , 1 − s̄1 ) α̃ 
11 − EsF̄ (s̄1 , 1 − s1 ) α̃1
ˆ − z̄ .
− σ r + EsF τ̂ − z, τ̄1Grouping terms and simplifying:k=−where: ωk,r ≡ˆ − z̄
ωk,r r + 1 − ωk,r r̄ + ωk,τ (τ̂ − z) + 1 − ωk,τ τ̄
1−α
1 − EsF̄ (s̄1 , 1 − s1 ) α̃,

(A.23)1
1 − EsF (s1 , 1 − s̄1 ) + EsF̄ (s̄1 , 1 − s1 ) − 1 α̃
11
1 − (1 − s1 ) − s F̄1 (1 − s1 − s̄1 ) α̃
=,
1 − 1 − s F1 − s F̄1 (1 − s1 − s̄1 ) α̃

 

 
1 − EsF (s1 , 1 − s̄1 ) α̃ 1 − s F̄1 + 1 − EsF̄ (s̄1 , 1 − s1 ) α̃ s F111ωk,τ ≡
1 − EsF (s1 , 1 − s̄1 ) + EsF̄ (s̄1 , 1 − s1 ) − 1 α̃11s F1 + 1 − s F1 − s F̄1 s̄1 α̃
=.
1 − 1 − s F1 − s F̄1 (1 − s1 − s̄1 ) α̃Equation (15) in the main text follows from the definition of R and R̄ and re-grouping terms
to isolate the tax variables. Note in particular that r = as rs + ae re is a weighted average ofthe change in user cost of different types of capital, with the weights given by steady state
expenditure shares.

4

The expressions for k̄ follow from symmetry of the setup and are given by:
k̄ =



ˆ − 1 − ωk̄,τ̄ τ̂ + ε̄ωk̄,r̄ Γ̄ˆ + 1 − ωk̄,r̄ Γ̂ − ωk̄,τ̄ τ̄

,(A.24)1−α
1 − (1 − s̄1 ) − s F1 (1 − s1 − s̄1 ) α̃where: ωk̄,r̄ ≡,(A.25)
1 − 1 − s F1 − s F̄1 (1 − s1 − s̄1 ) α̃s F̄1 + 1 − s F1 − s F̄1 s1 α̃,(A.26)ωk̄,τ̄ ≡1 − 1 − s F1 − s F̄1 (1 − s1 − s̄1 ) α̃  dρ + dδd ρ̄ + d δ̄KK̄ε̄ ≡ ωk̄,τ̄ z̄ + 1 − ωk̄,τ̄ z − ωk̄,r̄+p .
+ p − 1 − ωk̄,r̄ρ+δρ̄ + δ̄(A.27)

Finally, let sK = K/ K + K̄ . The total capital response (scaled by the returns to scale) is:





ˆ+ε
(1 − α) sK k + (1 − sK ) k̄ = sK ωk,r Γ̂ + 1 − ωk,r Γ̄ˆ − ωk,τ τ̂ − 1 − ωk,τ τ̄ˆ − 1 − ωk̄,τ̄ τ̂ + ε̄+ (1 − sK ) ωk̄,r̄ Γ̄ˆ + 1 − ωk̄,r̄ Γ̂ − ωk̄,τ̄ τ̄




T
T
T
T
ˆ + εT ,= ωk,rΓ̂ + 1 − ωk,rΓ̄ˆ − ωk,ττ̂ − 1 − ωk,ττ̄
(A.28)



1
−
α̃
T

with: ωk,r
≡ sK ωk,r + (1 − sK ) 1 − ωk̄,r̄ = ωk,r − (1 − sK ),1 − 1 − s F1 − s F̄1 (1 − s1 − s̄1 ) α̃(A.29)
1 − s F1 − s F̄1 (1 − α̃)Tωk,τ ≡ sK ωk,τ + (1 − sK ) 1 − ωk̄,τ̄ = ωk,τ + (1 − sK ).
1 − 1 − s F1 − s F̄1 (1 − s1 − s̄1 ) α̃
(A.30)


A.2.3

Derivations of equations (11) to (14)Let χK ≡ K̄ ∗ /K ∗ denote the steady state ratio of international to domestic capital, χK ≡K¯ ∗ /K ∗ , and χτ ≡ (1 − τ̄) / (1 − τ) , χ Z ≡ Z̄ ∗ /Z ∗ , χR = R̄∗ /R∗ , χa = ā/a. Then:
s1 =

a
σ−1
σ

,

(A.31).

(A.32)a + (1 − a) χK
σ−1

s̄1 =

āχK σσ−1āχK σ + (1 − ā)Moreover:1

F1∗ = αaZ ∗ (K ∗ )− σ (K ∗ )α+1/σ−1 ,5

− σ1α+1/σ−1− 1 α+1/σ−1= χ Z χK σ χKK¯ ∗χa F1∗ ,


 1
1−a
−1
∗ α+1/σ−1∗∗∗ −σ
χK σ F1∗ ,(K )=F2 = α (1 − a) Z K̄
a


 1


1
1
−
ā
1 − ā
α+1/σ−1α+1/σ−1 ∗
∗
∗
∗
∗
∗ −σ
σ
=
χ Z χKK¯
χK F̄1 =F1 ,F̄2 = α (1 − ā) Z̄ (K )aāF̄1∗ = αā Z̄ ∗ K̄ ∗giving:s F1 =
1 − s F̄1 ==(1 − τ∗ ) F1∗a=∗α+1/σ−1(1 − τ∗ ) F1∗ + (1 − τ̄∗ ) F̄2(1 − τ∗ ) F2∗=(1 − τ̄∗ ) F̄1∗ + (1 − τ∗ ) F2∗
1−aα+1/σ−1(1 − a) + āχτ χ Z χK

a + (1 − ā) χτ χ Z χK
 −1
1−a
χK σ
a
−1

α+1/σ−1χτ χ Z χK σ χK

.

χa +

,

1−a
a

(A.33)

−1

χK σ(A.34)

Finally, multiplying equation (A.17) by χR , dividing the resulting expression and equation (A.18)
by (1 − τ), and equating, we have that χR F1∗ + χτ F̄2∗ = χτ F̄1∗ +F2∗ . Substituting the derivatives
and manipulating gives:−1(1 − a) χK σ − aχR
α+1/σ−1χτ χ Z χK=,−1(A.35)(1 − ā) χR − āχK σ

which shows that s F1 and s F̄1 are functions of a, χR , χK . Moreover, this expression implicitly
defines χK as a function of a, σ, α, χ Z , and χτ . Repeating equations (A.31) and (A.32) andsubstituting equation (A.35) into equations (A.33) and (A.34), the four share terms that enter
into the elasticity formulae are:
s1 =

a
σ−1

,

(A.36),(A.37)a + (1 − a) χK σ
σ−1

s̄1 =

s F1 =

1 − s F̄1 =A.2.4

āχK σσ−1
σāχK + (1 − ā)−1a (1 − ā) χR − āχK σ−1(1 − ā − a) χK σ

,



−1
(1 − a) (1 − ā) χR − āχK σ(1 − ā − a) χR(A.38).

(A.39)

Linearizing elastiticities

We linearize the elasticities around χK = χR = 1:
ωk,r = ωk,r (1) + ck,r,χR (χR − 1) + ck,r,χK (χK − 1) + h.o.t,
6ωk,τ = ωk,τ (1) + ck,τ,χR (χR − 1) + ck,τ,χK (χK − 1) + h.o.t
2a (1 − a) α̃ + 1 − α̃,(A.40)
where: ωk,r (1)=ωk,r (χR = χK = 1) =1 − (1 − 2a)2 α̃
(2 (1 − a) α̃ + 1 − α̃) a,(A.41)
ωk,τ (1) = ωk,τ (χR = χK = 1) =1 − (1 − 2a)2 α̃a (1 − a) α̃
ck,r,χR = ck,r,χK = −,(A.42)
1 − (1 − 2a)2 α̃
ck,r,χR,(A.43)
ck,τ,χR = −(1 − 2a) α̃(1 − 2a)2 α̃ (σ − 1) + 1
ck,τ,χK =ck,τ,χR .
(A.44)σ
A.2.5

Steady-state Relative ProfitsLet χ F = F̄ K̄ t , K t ; Z̄ t /F K t , K̄ t ; Z t denote the ratio of foreign to domestic taxable income.
Then:αχ F = χ Z χK
,
σ−1
σ

āχK

where: χK =+ (1 − ā)

σ
! σ−1

.

σ−1
σ

a + (1 − a) χK

Using this definition together with equation (A.35) gives the moment:−1χτ χ F =(1 − a) χK σ − aχR−1(1 − ā) χR − āχK σ

−1

!
1−1/σ

χK

=

(1 − a) χK σ − aχR−1(1 − ā) χR − āχK σ!

σ−1āχK σ + (1 − ā)
σ−1

!
.

a + (1 − a) χK σ

Under the interpretation that adjustment costs are paid in units of labor (so total paid labor is
split between production labor L and capital installation labor), we can associate F and F̄ withtaxable income before credits and deductions.

A.3

Dynamic Accumulation of Intangible CapitalThis extension shows that a dynamic choice of intangible capital provides one possible microfoundation for complementarity between domestic and foreign capital.
We introduce intangible capital by augmenting the domestic and foreign production functions to include the factor H t :
α

α

α

M

,

(A.45)

M
α.

(A.46)αQ t = At Ht H Kt K L t L Mt MαααQ̄ t = Ā t H t H K¯t K L̄ t L M̄ t M

Importantly, the same quantity H t enters into both the domestic and foreign production functions; the non-rivalry of H t distinguishes it as intangible capital. The domestic concentrated
7

earnings function becomes:
α α/αF K t , K̄ t , H t ; Z t = Z t H t H K K tα ,(A.47)and likewise for the foreign operation. We assume αH < αK /α = 1 − α L − α M , so that there
are not increasing returns to intangible capital in the earnings function. A natural benchmark
is that intangible capital is tangible capital-augmenting, so that αH = αK . Intangible
capitalHH
˙obeys the law of motion H t = IH ,t − δ H t , with adjustment costs Φ IH ,t , H t . We assume
for simplicity that all intangible investment (i.e. R&D) occurs domestically.2
The necessary conditions for tangible investment and capital remain unaltered in this setup.
With convex adjustment costs, the new necessary conditions relating to the accumulation of
intangible capital are:

FOC (IH ,t ):FOC(H t ) :1
H˙t /H t =φH

λH ,t − PtH 1 − ΓH ,t(1 − τ t )  γ1,Hλ̇H ,t = ρ + δH λH
−
(1
−
τ
)
F
−
Φ
− (1 − τ̄ t ) F̄3 .
t
3t2(A.48)(A.49)

Combining these equations, the steady state has the additional condition:R∗H = (1 − τ) F3∗ + (1 − τ̄) F̄3(A.50)
αH α 
(1 − τ) F K ∗ , K̄ ∗ , H ∗ ; Z ∗ + (1 − τ̄) F̄ K̄ ∗ , K ∗ , H ∗ ; Z̄ ∗ (H ∗ )−1 ,(A.51)=
αK
with R∗H = ρ + δH P H (1 − ΓH ) being the user cost of intangible capital.
As in the baseline model, we derive the long-run response of capital to changes in tax policy.
As a preliminary step, define the revenue shares:sRK =R∗ K ∗
,
(1 − τ) F K ∗ , K̄ ∗ , H ∗ ; Z ∗ + (1 − τ̄) F̄ K̄ ∗ , K ∗ , H ∗ ; Z̄ ∗R̄∗ K̄ ∗,sR̄K̄ =
(1 − τ) F K ∗ , K̄ ∗ , H ∗ ; Z ∗ + (1 − τ̄) F̄ K̄ ∗ , K ∗ , H ∗ ; Z̄ ∗and note:R∗H H ∗
αH α

.
=
αK
(1 − τ) F K ∗ , K̄ ∗ , H ∗ ; Z ∗ + (1 − τ̄) F̄ K̄ ∗ , K ∗ , H ∗ ; Z̄ ∗Let ħh = d ln H . It is straightforward to show that the numerators in the expressions for k
and k̄ in equations (A.21) and (A.22) gain the new term −αH αħ
h/αK . In addition, linearizing
equation (A.51) gives:
αH αħh/αK = ζH sRK k + sR̄K̄ k̄ − rH ,(A.52)
2This assumption is inessential to the results characterizing how the presence of intangible capital affects the
responses of domestic and foreign tangible capital to the main tax terms.

8

ζH =

where:αH α/αK
αH=⊆ [0, ∞] .
1 − αH α/αK
1 − α L − α M − αH

Substituting equation (A.52) into the augmented equations (A.21) and (A.22) and solving
gives the result for the response of tangible capital in the presence of dynamic accumulationof intangible capital:ˆ − z̄ + ζH rH
ωk,r r + 1 − ωk,r r̄ + ωk,τ (τ̂ − z) + 1 − ωk,τ τ̄
,(A.53)
k=−1 − α − ζH (sRK + sR̄K̄ )1 − ζH σsR̄K̄ − EsF̄ (s̄1 , 1 − s1 ) α̃1 ,where: ωk,r ≡1 − EsF (s1 , 1 − s̄1 ) + EsF̄ (s̄1 , 1 − s1 ) − 1 α̃1 1
s F + 1 − s F1 − s F̄1 (ζH σsR̄K̄ + s̄1 α̃)1 .
ωk,τ ≡
1 − EsF (s1 , 1 − s̄1 ) + EsF̄ (s̄1 , 1 − s1 ) − 1 α̃
11

In particular, equation (A.53) shows that intangible capital introduces a force akin to complementarity between K and K̄. Indeed, setting a = ā = s1 = s̄1 = s F1 = s F̄1 = EsF (s1 , 1 − s̄1 ) =1
EsF̄ (s̄1 , 1 − s1 ) = 1 so that foreign capital does not directly enter the domestic production func1
tion, we have:ωk,r (a = ā = 1) =1 − ζH σsR̄K̄ − α̃ 1 − α − ζH sR̄K̄=< 1.
1 − α̃
1−α(A.54)The positive response of domestic capital to the foreign cost of capital occurs because the accumulation of foreign tangible capital induces more intangible investment, which also benefits
domestic tangible capital. In addition to this force on ωk,r , the additional term in the denominator of the expression for k tends to increase the capital elasticities, because of the crowding
in of intangible investment.

A.4

Intangible Capital Location Choice Extension

This extension augments our baseline environment to allow the firm to choose the location of
intangible capital in order to shift profits into low tax jurisdictions. The firm has a stock ofintangible capital of H t , divided into intangible capital booked domestically H t and bookedabroad H̄ t . To focus on the location choice, we now take the overall stock H as exogenous.
Intangible capital is non-rival and multiplicatively scales Z t and Z̄ t ; since it is now exogenous,
the precise elasticity of earnings to intangible capital does not matter.
The firm applies a transfer price p tH to the use of intangible capital located in a different
jurisdiction. Let ∆H,t = H̄ t −H t denote the stock located abroad in excessH of the domestic stock.
H
Hence the domestic branch receives net royalties p t H t − H̄ t = −p t ∆H,t and the foreign
branch receives net royalties p tH ∆
 H,t . The firm may pay a cost from too-aggressive transfer
H
pricing, given by Ψ ∆H,t , K t , K̄ t . This cost represents the legal risk and compliance cost of
locating intangible capital differently from the location of tangible capital. Total cash flows arethus augmented by transfer pricing profits net of costs (τ t − τ̄ t ) p tH ∆H,t − Ψ H ∆H,t , K t , K̄ t .
9With this setup, equation (6) and its foreign counterpart remain unchanged. The necessary
conditions for K and K̄ and the new necessary condition for ∆H become:(A.55)Kt :λ̇ t = (ρ + δ) λ t − (1 − τ t ) (F1 − Φ2 ) − (1 − τ̄ t ) F̄2 + Ψ2H ∆H,t , K t , K̄ t ,˙ = (ρ + δ) λ̄ − (1 − τ̄ ) F̄ − Φ̄ − (1 − τ ) F + Ψ H ∆ , K , K̄ ,K̄ :λ̄
(A.56)t∆H,t :

t

t

t

1

2

t

2

3

H,t

t

t

Ψ1H = (τ t − τ̄ t ) p tH .

(A.57)The FOC (∆H,t ) says that at the margin increasing foreign intangible assets generates tax savings (τ t − τ̄ t ) p tH and increases the transfer pricing burden by Ψ1H .

Define the steady state user cost as R∗ = (ρ + δ) (1 − Γ ∗ ) P K + Ψ2H ∆H,t , K t , K̄ t . The following linearized relationship still holds with the parameters ωk,r , ωk,τ defined as in equations (16)and (17):ˆ+ε−ωk,r r − 1 − ωk,r r̄ − ωk,τ τ̂ − 1 − ωk,τ τ̄k=.
1−αImmediately, if the decision to shift profits
via the location of intangible capital does not dependH
on physical capital, Ψ2 ∆H,t , K t , K̄ t = 0, then nothing changes in the firm’s physical capitaldecision.
To understand the implications for investment when the location choice depends on phys2ical capital, we parameterize Ψ H ∆H,t , K t , K̄ t = ψ1H /2 ∆H,t − ψ2H K̄ t − K t . With thisfunctional form, we have:∆H,t − ψ2HK̄ t − K t =(τ t − τ̄ t ) p tH
ψ1H.

(A.58)The difference between the allocation of intangible and tangible capital is increasing in the
tax gap and decreasing in the intercept of the cost term ψ1H . The parameter ψ2H specifies how
the allocation of intangibles moves with tangible capital. The domestic user cost becomes:R∗ = (ρ + δ) (1 − Γ ∗ ) P K + ψ2H (τ t − τ̄ t ) p tH > (ρ + δ) (1 − Γ ∗ ) P K . The additional term arises
because an additional unit of domestic capital requires an additional ψ2H of reallocation of
intangibles, which costs (τ t − τ̄ t ) p tH of total profits. Thus, a reduction in τ reduces the user
cost and stimulates investment above the usual effect, because the lost profits from reduced
intangible-shifting that come with higher K are smaller when τ falls, so there is less disincentive
to accumulate K. At the same time, the steady state user cost is larger,
implies a larger
 which∗∗K
H
coefficient on Γ̂ . The foreign user cost becomes: R̄ = ρ̄ + δ̄ 1 − Γ̄ P − ψ2 (τ t − τ̄ t ) p tH <ρ̄ + δ̄ 1 − Γ̄ ∗ P K .
To see how these changes modify equation (15), define the share contributions of the intangible terms to the user cost:sH =ψ2H (τ − τ̄) p HR∗⊆ [0, 1] ,

s̄H =

10

ψ2H (τ − τ̄) p H
R̄∗.

Then:
r = − (1 − sH ) Γ̂ + sH

d (τ − τ̄)r̄ = − (1 + s̄H ) Γ̄ˆ − s̄Hτ − τ̄d (τ − τ̄),τ − τ̄

and hence:
k=



ˆ+
ωk,r (1 − sH ) Γ̂ + 1 − ωk,r (1 + s̄H ) Γ̄ˆ − ωk,τ τ̂ − 1 − ωk,τ τ̄ d(τ−τ̄)
1 − ωk,r s̄H − ωk,r sH τ−τ̄ + ε

1−α

A.5

(A.59)

Interest Deduction Extension

A firm with debt of B t can deduct interest i t B t from its taxable earnings. We assume the firm
also pays a cost (i.e. insurance) that is increasing in its (domestic) leverage and given byΨ B (B t , K t ). Cash flows are therefore augmented by τ t i t B t − Ψ B (B t , K t ) . The changes to thenecessary conditions are:Kt :λ̇ t = (ρ + δ) λ t − (1 − τ t ) (F1 − Φ2 ) − (1 − τ̄ t ) F̄2 + Ψ2B (B t , K t ) ,(A.60)Bt :τ t i t = Ψ1B .

(A.61)

Define the steady state user cost as R∗ = (ρ + δ) (1 − Γ ∗ ) P K − Ψ2B (B t , K t ). The following
linearized relationship still holds with the parameters ωk,r , ωk,τ defined as in equations (16)and (17):ˆ+ε−ωk,r r − 1 − ωk,r r̄ − ωk,τ τ̂ − 1 − ωk,τ τ̄.
k=1−αImmediately, if the financial capital structure decision does not depend on physical capital,
Ψ2B ∆B,t , K t = 0, then nothing changes in the firm’s physical capital decision.
To understand the implications for investment when the financial capital structure decision does depend on physical capital, we follow Barro and Furman (2018) and parame1+θ K
terize Ψ B (B t , K t ) = ψB B t / PtK K t
Pt K t / (1 + θ ). With this functional form, the steady−1/θ Kθ (ψB )
P(τ∗ i ∗ )1+1/θ . Defining
state domestic user cost becomes R∗ = (ρ + δ) (1 − Γ ∗ ) P K −1+θθτ i B /K t

sB ≡ 1+θ tR∗t tk=A.6, we have:ωk,r (1 − sB ) Γ̂ + 1 − ωk,r Γ̄ˆ − ωk,τ − sB
1−α

1+θ
θ



τ
1−τ




ˆ+ε
τ̂ − 1 − ωk,τ τ̄

.

(A.62)

FDII and GILTILet τs , τ̄s , Γ s , Γ̄ s denote the ex-FDII and ex-GILTI domestic and foreign marginal tax rates andpresent values of allowances (“s” for statutory), which we now distinguish from the GILTI and
FDII-inclusive effective marginal tax rates and costs-of-capital.
The GILTI (Global Intangible Low Taxed Income) tax applies to foreign income. The TCJA
11.

defines global deemed intangible income as foreign income in excessof θ tGILTI-T= 0.1 of foreigntangible property (“T” for tangible), i.e. GILTI = F̄ K̄ t , K t , Z̄ t − Φ Ī t , K̄ t − θ tGILTI-T K̄ t . A corporation can deduct θ tGILTI-D = 0.5 of global intangible income (“D” for deduction) and claim
a credit for θ tGILTI-C = 0.8 of foreign taxes paid (“C” for credit). The tax rate on the remainder
is set at the U.S. rate of τs . Thus, after-tax foreign profits for a GILTI-taxed firm are:Profits net of foreign taxes

}| 
{
z


1 − τ̄st F̄ K̄ t , K t , Z̄ t − Φ Ī t , K̄ t






− τst 1 − θ tGILTI-D F̄ K̄ t , K t , Z̄ t − Φ Ī t , K̄ t − θ tGILTI-T K̄ t + θ tGILTI-C τ̄st F̄ K̄ t , K t , Z̄ t − Φ Ī t , K̄ t
{z
} |
{z
}
|
GILTI tax

Foreign tax credit= (1 − τ̄ t ) F̄ K̄ t , K t , Z̄ t − Φ Ī t , K̄ twhere: τ̄ t ≡τ̄st 1 − θ tGILTI-C + τst 1 − θ tGILTI-D .

+ τst1 − θ tGILTI-Dθ tGILTI-T K̄ t ,The GILTI tax is often
described as a minimum tax because at τ̄st = 0 it nonetheless impliesτ̄ t = τst 1 − θ tGILTI-D . It ceases to apply when τ̄st ≥ τst 1 − θ tGILTI-D /θ tGILTI-C = 0.1312.
The FDII (Foreign Derived Intangible Income) deduction applies to domestic income derived from foreign sources, i.e. exports. Let ξ denote the (fixed) share of a firm’s domestic
income attributable to exports. The TCJA defines DII (deemed intangible income) as domes
tic income in excess of θ tFDII-T = 0.1 of domestic tangible property, i.e. DII = F K t , K̄ t , Z t −Φ (I t , K t )−θ tFDII-T K t , and FDII as the foreign part of DII, i.e. FDII = ξ F K t , K̄ t , Z t − Φ (I t , K t ) − θ tFDII-T K t .
A corporation can deduct θ tFDII-D = 0.375 of FDII against domestic taxable income. Thus, aftertax domestic profits for a firm with domestic income exceeding θ tFDII-T K t are:








F K t , K̄ t , Z t − Φ (I t , K t ) − τst  F K t , K̄ t , Z t − Φ (I t , K t ) − θ tFDII-D ξ F K t , K̄ t , Z t − Φ (I t , K t ) − θ tFDII-T K t |{z}
FDII deduction= (1 − τ t ) F K t , K̄ t , Z t − Φ (I t , K t ) − τst ξθ tFDII-D θ tFDII-T K t ,where: τ t = τst 1 − θ tFDII-D ξ .
Putting FDII and GILTI together, the necessary conditions become:It :λ t = (1 − τ t ) Φ1 (I t , K t ) + 1 − Γ ts PtK ,Ī t :
λ̄ t = (1 − τ̄ t ) Φ̄1 Ī t , K̄ t + 1 − Γ̄ ts PtK̄ ,Kt :K̄ t :
where:(A.63)(A.64)

λ̇ t = R t − (1 − τ t ) (F1 − Φ2 (I t , K t )) − (1 − τ̄ t ) F̄2 ,˙ = R̄ − (1 − τ̄ ) F̄ − Φ̄ Ī , K̄  − (1 − τ ) F ,
λ̄
t
t
1
2
s
FDII-D FDII-T
R t = (ρ + δ) λ t + τ t ξθ tθt,
 GILTI-T
s
GILTI-DR̄ t = (ρ + δ) λ̄ t − τ t 1 − θ t
θt
.
t

t

t

t

2

(A.65)(A.66)(A.67)(A.68)

In particular, equations (A.63) to (A.66) characterize exactly the same dynamic system as equations (6) and (7) and their foreign counterparts, but with the redefined effective marginal tax

12rates and user costs. The user cost terms can be rewritten as:
R t = (ρ + δ)

(1 − τ t ) Φ1 (I t , K t ) + (1 − Γ t ) PtKR̄ t = (ρ + δ) (1 − τ̄ t ) Φ̄1 Ī t , K̄ t + 1 − Γ̄ t





PtK̄

, Γ t ≡ Γ ts −, Γ̄ t ≡ Γ̄ ts +

τst ξθ tFDII-D θ tFDII-T,
1 − θ tGILTI-D θ GILTI-T(ρ + δ) PtKτst(ρ + δ) PtK̄.

In this sense, FDII has the same effect on investment incentives as a decline in the domestic
corporate rate and depreciation allowance, while GILTI has the same effect as an increase in
the foreign corporate rate and depreciation allowance. The impacts on the effective allowancesarise because both FDII and GILTI exempt profits up to 10% of tangible capital, which implies
that marginal changes in the tangible capital stock directly impact taxes owed.

A.7

Transition Dynamics and Short Versus Long-Run Investment Response

This appendix shows that in the case of no foreign adjustment costs, φ̄ → 0, the short-run andlong-run elasticities of investment to the four tax terms all scale by approximately the same
factor, denoted χSR . Furthermore, χSR is a sufficient statistic for the role of domestic adjustment
costs.
Linearized dynamic system. We show these results using a linear approximation of the transition dynamics with quadratic adjustment costs (γ = 1). Define:1

  1  λ − P K (1 − Γ )  γ,h λ; τ, Γ , P , φ, γ =φ(1 − τ)with: h (λ∗ ) = 0,11h′ (λ∗ ) = 0 γ −1.
φγ (1 − τ∗ )K

(A.69)(A.70)The dynamic system then takes the form:
FOC (I t ): K̇ t /K t = h λ t ; τ t , Γ t , PtK , φ, γ ,(A.71)γ

FOC (K t ):λ̇ t = (ρ + δ) λ t − (1 − τ t ) (F1 + ((γ/ (1 + γ)) h (λ t ) + δ) φh (λ t ) ) − (1 − τ̄ t ) F̄2 ,(A.72)
˙ /K̄ = h λ̄ ; τ̄ , Γ̄ , P K̄ , φ̄, γ̄ ,FOC ( Ī t ): K̄
(A.73)
t
t
t
t t
t





˙ = (ρ + δ) λ̄ − (1 − τ̄ ) F̄ + (γ̄/ (1 + γ̄)) h λ̄ + δ φ̄h λ̄ γ̄ − (1 − τ ) F .
FOC (K̄ t ):
λ̄
t
t
t
1
t
t
t
2
(A.74)
We take a Taylor expansion in the neighborhood of the steady state. Let k t,s = (K t − Ks ) /Ks ≈ln (K t /Ks ) denote the percent deviation of K t from Ks . In particular, k t,∗ = (K t − K ∗ ) /K ∗ is thedeviation from the new steady state and k∗,0 = (K ∗ − K0 ) /K0 is the long-run percent change,
simply denoted by k elsewhere in the manuscript. Note that k̇ t,∗ = K̇ t /K ∗ . The linear system13

associated with the Taylor expansion is:3k̇ t,∗k t,∗
 λ̇ t 
λ t − λ∗   = A k̄,˙k̄ t,∗ t,∗˙
λ̄ t − λ̄∗λ̄(A.75)twith:
0 h′ (λ∗ ) 00
ρ + δ a230 
aA =  21,000 h′ λ̄∗a41
0a43 ρ + δ
a21 = − (1 − τ∗ ) K ∗ F11 K ∗ , K̄ ∗ ; Z ∗ − (1 − τ̄∗ ) K ∗ F̄22 K̄ ∗ , K ∗ ; Z̄ ∗ > 0,
a23 = − (1 − τ∗ ) K̄ ∗ F12 K ∗ , K̄ ∗ ; Z ∗ − (1 − τ̄∗ ) K̄ ∗ F̄21 K̄ ∗ , K ∗ ; Z̄ ∗ ,a41 = − (1 − τ̄∗ ) K ∗ F̄12 K̄ ∗ , K ∗ ; Z̄ ∗ − (1 − τ∗ ) K ∗ F21 K ∗ , K̄ ∗ ; Z ∗ = a23 χK−1 ,
a43 = − (1 − τ̄∗ ) K̄ ∗ F̄11 K̄ ∗ , K ∗ ; Z̄ ∗ − (1 − τ∗ ) K̄ ∗ F22 K ∗ , K̄ ∗ ; Z ∗ > 0.


The two stable eigenvalues of A are:
ρ+δ
−
d1 =
2
d2 =

ρ+δ
−
2

v
u
t ρ + δ 22vut ρ + δ 2
2

+

+


 Ç

2


h′ (λ∗ ) a21 + h′ λ̄∗ a43 +h′ (λ∗ ) a21 + h′ λ̄∗ a43 − 4h′ (λ∗ ) h′ λ̄∗ a21 a43 − a23 a41
2

2



 Ç
h′ (λ∗ ) a21 + h′ λ̄∗ a43 − 4h′ (λ∗ ) h′ λ̄∗ a21 a43 − a23 a41h′ (λ∗ ) a21 + h′ λ̄∗ a43 −2with the eigenvector associated with the nth eigenvalue:


1



dn


h′ (λ∗ ).
fn = 
−1− a43 h′ λ̄∗ + (ρ + δ − dn ) dn
a41 h′ λ̄∗ −1− a43 h′ λ̄∗ + (ρ + δ − dn ) dn
a41 dnThe linearized solution is:c1ck0,∗ e d1 t + 2 k0,∗ e d2 t = sk,d e d1 t + 1 − sk,d e d2 t k0,∗ ,
k0,∗
k0,∗


c1 f1 (3)c f (3)k̄ t,∗ =k̄0,∗ e d1 t + 2 2
k̄0,∗ e d2 t = sk̄,d e d1 t + 1 − sk̄,d e d2 t k̄0,∗ ,
k̄0,∗
k̄0,∗




f2 (3) − χk0,∗
a41 h′ λ̄∗c1
a23 h′ (λ∗ )where: sk,d ≡==+ χ k0 ,k0,∗
f2 (3) − f1 (3)d2 d3 − d1 d4
a43 h′ λ̄∗ + d2 d3
k t,∗ =

(A.76)(A.77)

3
To ease notation, we omit general equilibrium terms relating to changes in Z. These do not change theconclusions of this section.

14

,

,

sk̄,d ≡c1 f1 (3)
k̄0,∗

=



f1 (3) f2 (3) χk−1−
10,∗
f2 (3) − f1 (3).

Thus, the weighted average sk,d e d1 t + 1 − sk,d e d2 t determines the speed of convergence ofdomestic capital. Furthermore:
 ∗
 ∗




K̇ t
K
K
k̇ t,0 ==k̇ t,∗ =sk,d d1 e d1 t + 1 − sk,d d2 e d2 t k0,∗ = − sk,d d1 e d1 t + 1 − sk,d d2 e d2 t k∗,0 .
K0
K0
K0
For example, the short-run response of net investment is: 
k̇0,0 = K̇0 /K0 = − sk,d d1 + 1 − sk,d d2 k∗,0 .

(A.78)

Short-run versus long-run elasticities. We now relate the tax elasticities of short-run investment, I0 /K0 , to the long-run change, dk∗,0 , where:d k∗,0 =ˆωk,r d Γ̂ + 1 − ωk,r d Γ̄ˆ + ωk,τ d τ̂ + 1 − ωk,τ d τ̄1−αˆ.
= b1 d Γ̂ + b2 d Γ̄ˆ + b3 d τ̂ + b4 d τ̄Totally differentiating I0 /K0 and letting ∗∗ denote the steady state at the base values of the tax
changes, we obtain:

 
X


 
∂
s
d
+
1
−
s
d2
k,d
1
k,d
∗∗ ∗∗
∗∗
d x.
d k̇0,0 = d I0 /K0 = δd I0 /I0 = − sk,dd1 + 1 − sk,dd2∗∗ dk∗,0 −
k∗∗,0
¦
©
∂
x
ˆ
ˆ ,Γ̄
x∈ τ̂,Γ̂ ,τ̄(A.79)


 
∗∗ ∗∗
∗∗
The first term scales the long-run change by a common factor sk,dd1 + 1 − sk,dd2∗∗ . The
second term implies possibly different short-run speeds of adjustments to different tax terms.
Equation (A.79) usefullyin the case of no foreign adjustment costs, φ̄ → 0. In
q simplifies

particular, while d1 → − h′ λ̄∗ a43 → −∞, an application of L’Hopital’s rule yields thatlimφ̄→0 sk,d d1 + 1 − sk,d d2 = d2 . Thus, the second term of equation (A.79) only involves
derivatives of d2 . These involve third derivatives of the production function and hence aresmall relative to the first term. Intuitively, the difference between the ratio of short-run tolong-run elasticities to e.g. Γ and Γ̄ arises primarily because both ratios depend on the magnitude of domestic adjustment costs but the short-run elasticity to Γ̄ also depends on the foreign
adjustment cost. When φ̄ → 0, the only remaining difference occurs because foreign capitaldoes not quite jump immediately to its long-run value, because of the feedback from growing
domestic capital to foreign capital. This feedback effect is small. In our calibration, the ratioof the short-to-long run elasticity varies by less than 10% across the tax variables.

15

Ratio χSR .
Z T
0The average deviation of of investment over period 0 to T relative to date 0 is:


Z T



K̇ t + δ (K t − K0 )1dt =δk t,0 − sk,d d1 e d1 t + 1 − sk,d d2 e d2 t k∗,0 d t
T δK0
δT 0
Z T




1
≈
δ k t,∗ + k∗,0 − sk,d d1 e d1 t + 1 − sk,d d2 e d2 t k∗,0 d t
δT 0
Z T




k∗,0
≈ k∗,0 −δ sk,d e d1 t + 1 − sk,d e d2 t + sk,d d1 e d1 t + 1 − sk,d d2 e d2 t d t
δT 0




  d1 T  d2 T

δ
δ
e −1
e −1
= k∗,0 1 − sk,d 1 +− 1 − sk,d 1 +
.
d1
δT
d2
δT

The long run deviation of investment is:δ (K ∗ − K0 )= k∗,0 .
δK0Thus, the ratio is:  d1 T  d2 T

δ
e −1
e −1
δ
χSR = 1 − sk,d 1 +− 1 − sk,d 1 +
.
d1
δT
d2
δT
  d2 T 
In particular, as φ̄ → 0, χSR → 1− 1 + dδ2 e δT−1 . Inverting this expression gives the domestic
adjustment cost as a function of χSR .

A.8

Adjustment Cost Moments

This appendix describes our analysis of the Winberry (2021) calibration. Winberry (2021)
estimates a rich model of fixed and convex adjustment costs to match interest rate dynamics
and, crucially, three targets of the firm-level investment distribution based on the SOI sample
over 1998-2010, drawn from Zwick and Mahon (2017): the average investment rate, thestandard deviation of investment rates, and the fraction of firm-years with an investment rate
above 20%.
Using the Winberry (2021) replication code, we produce impulse responses of investment
to a TFP shock and to an investment stimulus shock, similar to those shown in figures 5 and
7 of his paper. In partial equilibrium, the TFP shock has the same effect on investment as achange in 1 − τ and the investment stimulus shock the same as a shock to 1 − Γ . We start each
impulse response at the model steady state and set the quarterly persistence of each shock to0.999. We report in table A.1 the average response of investment over the first 8 quarters of
the impulse response, the same horizon over which we measure the effects of TCJA, and after
10 years, which we equate with the long-run, as well as the ratio of these responses.
Our preferred value of χSR = 1.3 falls in the middle of the ratios in partial equilibrium,
shown in the first two rows. We target the partial equilibrium impulse responses because we
do not incorporate an upward supply of capital in our model. For completeness, the next two
rows show the responses and ratios in Winberry’s general equilibrium environment. Because
16Table A.1: Ratio of Short-run to Long-run
Exercise
Winberry; TFP; PE
Winberry; Invest Stim.; PE
Winberry; TFP; GE
Winberry; Invest Stim.; GE
BCE; TFP; GE
BCE; Invest Stim.; GE; Implied rate

Quarters 0-8
13.50
7.81
3.09
1.64
2.66
1.44

Quarter 40
9.09
6.55
2.74
2.33
1.48
1.26

Ratio
1.49
1.19
1.13
0.70
1.80
1.14

his is a real business cycle model with time-to-build, output is fixed in the short-run and there is
instantaneous GE dampening of investment stimulus shocks despite the presence of adjustment
costs. The last two rows report the ratios from performing the same exercise in another leading
estimate of adjustment costs, Bachmann, Caballero and Engel (2013).
Figure A.1 shows the robustness of the response of domestic capital in the model to valuesof χSR ranging from 1 to 1.6. For each value, we re-estimate the parameters θ and set thedomestic adjustment cost parameter φ such that short-run to long-run ratio of investment
matches the value. The response of capital at year 15 varies by less than 1.5p.p. across valuesof χSR . This small difference partly reflects the larger adjustment costs required to rationalize
a smaller value of χSR = 1, as the path with χSR = 1.6 has essentially converged to its long-run
value by year 15 while the path with χSR = 1 has a steeper slope. In the short-run, the smaller
adjustment costs required to generate a larger χSR offset the smaller terminal value and the
trajectory of capital is nearly indistinguishable across a range of values of χSR .
Figure A.1: Robustness of K/K0 to χSR

1.08

1.06

1.04

1.02

1
0

1

2

3

4

5

6

7

17

8

9

10 11 12 13 14 15

A.9Interpretation of a Levels Regression

This appendix considers the common regression specification of the investment-capital ratioon the level of the “tax term” in the context of our model. For simplicity, we restrict attention
to domestic-only firms.
A common regression specification is:
I j,t= c1 T T j,t + α j + ν t + e j,t ,

K j,t



where T T j,t = 1 − Γ j,t / 1 − τ j,t denotes the “tax term.” It simplifies matters to take firstdifferences and consider the specification around a tax change at date 0:
I j,0+
K j,0

−

I j,0
K j,0



= c0 + c1 T T j∗ − T T j,0 + ∆e j,t ,(A.80)

where X j,0+ denotes the value of a variable just after the tax change. We now provide an
expression for c1 .
In the case of domestic-only firms, the system (A.75) becomes: k̇ t,∗k t,∗=A,(A.81)
λ t − λ∗
λ̇ t
with:

0 h′ (λ∗ )A=,
a21 ρ + δ

a21 = − (1 − τ∗ ) K ∗ F11 (K ∗ ; Z ∗ ) > 0.
The solution is:
k t,∗ = k0,∗ e d1 t ,λ t − λ∗ = k0,∗ d1 φ (1 − τ∗ ) e d1 t ,r(A.82)(A.83)

ρ+δ 2− φ −1 K ∗ F11 (K ∗ ; Z ∗ ) is the stable eigenvalue. Furthermore, thewhere d1 =2steadyof the) system equations (6) and (7) gives k0,∗ = state ∗of the (domestic-only version
1∗∗
1−α ln (T T /T T0 ) , λ0 = 1 − Γ0 , λ = 1 − Γ .
We now obtain an expression for c1 . Using equation (A.83) and the steady-state conditions
gives an expression for the impact change in after-tax λ:


λ0+
λ0
d1 φ
∗
−
= (T T − T T0 ) +ln (T T ∗ /T T0 ) .
(A.84)∗1−τ
1 − τ0
1−α
ρ+δ
2 −

18

FOC (6) relates equation (A.80) to the model:


I0+
I0
λ0+
λ0
1
∗
−
=
−
− (T T − T T0 ) .
K0 K0
φ 1 − τ∗ 1 − τ0(A.85)

Combining equations (A.80), (A.84) and (A.85), we find:


I +
I
C ov K00 − K00 , T T ∗ − T T0
c1 =Var (T T ∗ − T T0 )




d1 φ
ln (T T ∗ /T T0 ) − (T T ∗ − T T0 ) , T T ∗ − T T0
C ov φ1 (T T ∗ − T T0 ) + 1−α=Var (T T ∗ − T T0 )d1
C ov (ln (T T ∗ /T T0 ) , T T ∗ − T T0 )=
1−αVar (T T ∗ − T T0 )


d1
1
≈
×
.
(A.86)1−α
T T0
The final expression in equation (A.86) contains a much more complicated mapping ofparameters and policy variables into the regression coefficient than our preferred specification(see e.g. Auerbach and Hassett, 1992, for an example of this approach). Moreover, becausearound a tax reform firm-level heterogeneity in T T0 likely is correlated with T T ∗ −T T0 , a crossd1
× T1T0 .
sectional regression need not even produce an appropriate weighted-average of 1−αIncluding Tobin’s Q as a separate regressor as in Desai and Goolsbee (2004) does not resolve
the problem if the change in λ is measured with any error (e.g., because marginal Q is notobserved).

19

B

Appendix Figures
Figure B.1: Decomposing U.S. Investment of Public Firms by Sector and Firm

Notes: This treemap plots the distribution of mean capital expenditures for the preperiod (2011-2017) withineach sector using Compustat data. The squares are labelled using a company’s stock ticker and its mean capital
expenditure in the pre-period (2011-2017) in billions of USD. Firms with the largest average investment include
Chevron (CVX $27.41B), Exxon (XOM $27.13B), AT&T (T $20.95B), Verizon (VZ $16.9), General Motors (GM$16.82), Toyota Motor Credit Corp (TM $14.83), Berkshire Hathaway (BRK $12.14B), Walmart (WMT $11.98B),
Ford Motor Credit Co (F $11.93B), and ConocoPhillips (COP $11.37B). This figure does not contain data fromtax returns.

20

400

Billions of Dollars
450
500

550

Figure B.2: Investment of Public Firms in U.S. and Similar Foreign Firms: Backdated Approach

2010

2012

2014

2016

Actual US Investment

2018

2020

2022

Synthetic US Investment

Notes: The figure plots aggregate investment (capital expenditures) of US public firms and compares it to theinvestment of synthetically matched global firms. Synthetic firms were matched based on the values of firm
characteristics between 2011 and 2015, which allows the 2016 and 2017 pre-reform values to serve as a validation
test of the match. This figure does not contain data from tax returns.

21

Figure B.3: Returns for Individual Events by Tax Shock Quintile
Election Day

Outline of Reform Priorities

Release of UFTR

Senate Budget Plan Passage

TCJA Introduced in House

Senate Bill Released

Passes House & Sen Finance

Passage Through Senate

Harmonized Bill Released

Notes: This figure displays cumulative raw returns (CRSP variable ret) for the top (Q5) and bottom (Q1) quintilesof tax shock separately for each event in our event sample. Events are described in Appendix C, Narrative Historyof Key TCJA Events. This figure does not contain data from tax returns.

22

C

Appendix Tables
Table C.1: Industry-level Trends in Globalizing Investment
NAICS 2-Digit Industry
1
2
3
4
5
6
7
8
9
10

Trend (p.p.)

Information (51)
Educational Services (61)Manufacturing, Food (31)
Wholesale Trade (42)Manufacturing, Materials (32)
Accommodation and Food (72)Real Estate and Rental/Leasing (53)Manufacturing, Heavy (33)Professional, Scientific Svcs (54)Other Services (81)

NAICS 2-Digit Industry

-1.68
-1.44
-0.96
-0.84
-0.60
-0.60
-0.48
-0.48
-0.36
-0.24

11
12
13
14
15
16
17
18
19
20

Trend (p.p.)

Utilities (22)Admin Services (56)Mining and Extraction (21)Health Care (62)Transportation (48)
Retail Trade (44)Arts, Entertainment, Rec (71)Retail Trade (45)Agriculture (11)Construction (23)

-0.24
-0.12
0.00
0.00
0.00
0.00
0.48
0.60
0.84
1.80

I

on time trend from 1993 to 2019. ManufacNotes: The estimates in this table come from regressing ω I ≡ IC SOI
STAT
turing and information sectors drive the trend.

Table C.2: Coverage of the Synthetic Sample
US firms

Cleaned Compustat Firms w/o NAICS 52
Dropping Panels with any missing Year-Capx
Dropping Panels with Less than 4 Non-Missing Year-Xs
Synthetic Matches
Close Synthetic Matches

N

%

8,936
2,359
2,278
2,275
1,493

100
26
25
25
17

Capx (Avg 2011-17)
Total%(in billions USD)
1,127.8
100
833.7
74
833.6
74
830.9
74
599.4
53

Market Value (Avg 2011-17)
Total%(in billions USD)
20,673.3
100
15,723.2
76
15,460.775
15,449.975
9,168.8
44

Notes: This table shows how the sample declines as we drop firms with key variables missing in order to conductsynthetic matching. The X’s in row 3 include sales, assets, and property, plant, and equipment. The 3 firms thatare dropped in row 4 have come from NAICS industries with no no global firms. Specifically, they come from
NAICS 5313, 221, and 6222, which represent real estate, utilities, and psychiatric and substance abuse hospitals.
Close synthetic matches refer to the synthetic matches that have an average pre-reform period (2011-2017) capx
within 10% of the US firm’s average pre-period capx. This table does not contain data from tax returns.

23

Table C.3: Summary Statistics of Matched Firms
Firms:
Pre-period Characteristics (2011-17)
Capx
AssetsPPE
Sales
Market Value
Post-period Characteristics (2018-20)
Capx
AssetsPPE
Sales
Market Value
Observations

Matched: US
MeanMedian366.3(1,435)
6,918.0(26,314)2,554.4(10,039)
4,906.2(19,138)
7,474.7(29,157)
418.9(1,619)
11,791.0(61,206)
3,425.3(12,432)
5,722.2(22,571)
11,791.0(61,207)
2,275

25.0
828.6
120.5
661.7
832.5

28.8
964.6
210.6
851.6
964.6

Matched: Synthetic
MeanMedianClosely Matched: US
MeanMedianClosely Matched: Synthetic
MeanMedian304.9(1,296)
4,914.4(15,811)2,058.7(7,815)2,935.7(10,500)NA

402.6(1,519)7,426.1(27,800)
3,014.4(11,215)
4,717.7(15,951)
6,864.5(22,062)399.1(1,554)
6,164.0(18,376)
2,719.6(9,301)3,570.3(12,019)NA

287.0(1,100)6,136.7(18,048)2,487.0(8,540)3,180.6(10,243)
NA
2,275

21.2
669.5
122.0
412.5
NA

24.0
943.8
179.8
532.3
NA

435.8
(1,454)9,357.3(31,849)
3,875.8(13,314)
5,145.3(15,790)
9,640.6(28,956)
1,493

44.1
1358.2222.6
1,126.3
1,232.7

51.4
1,854.0
367.0
1348.5
1,447.9374.7(1,298)7,653.1(20,865)
3,257.7(10,027)
3,835(11,532)NA

42.1
1,132.7
231.5
788.8
NA

50.1
1,668.8
338.2
1,026.9
NA

1,493

Notes: The table uses data from Compustat North America and Global. All values are in millions of USD and
are adjusted for inflation using CPI data from the series https://fred.stlouisfed.org/series/CPIAUCSL#0 indexed
to 2021 US dollars. It shows the summary statistics for key variables in the pre-period (2011-17) as well as thepost-period (2018-19). The numbers in the brackets show the standard deviation. The first two columns show the
US headquartered firms that are in the synthetically matched sample, the next 2 columns show the syntheticallycreated global firms, and the last 4 columns represent the same but for a closely matched sample where we have
dropped all matches that are not in the 10% band of each other’s capx in the pre-reform period (2011-17). This
table does not contain tax data.

24

Table C.4: Investment of US and Synthetic Matched Firms in 2019, by Industry
Industry

US Public
Firms

Synthetic
Firms

Difference

0.2
70.1
237.1
1.8
216.1
4.4
19.9
61.2
32.3
5.3
4.1
2.6
0
2.6
1.3
3.6
0
22.4

0.2
52.4
169.4
1.5
195.6
3.7
13.6
55.2
32.0
4.0
3.3
1.7
0
2.7
1.8
2.8
0
21.6

0
17.7
67.7
0.4
20.5
0.8
6.2
6.0
0.3
1.2
0.8
0.9
0
−0.1
−0.5
0.8
0
0.8

Agriculture, Fishing
Mining, Quarrying, Oil, Gas
Utilities
Construction
Manufacturing (NAICS 31-33)
Wholesale Trade
Retail Trade (NAICS 44-45)
Transportation, Warehousing
Information
Real Estate, Rental
Professional, Science, Tech
Admin, Waste Mgmt, Remediation
Education
Health CareArts, Entertainment
Accommodation and Food
Other Services
Nonclassifiable

Notes: This table displays total investment in billions of U.S. dollars in 2019 by US firms in our Compustat analysis
sample, their synthetic counterparts from Global Compustat, and the difference between the two separately foreach two-digit NAICS industry. This table does not contain data from tax returns.

Table C.5: DID Estimates of the effect of TCJA on Investment (Backdated Matched Sample)

post
Observations
Controls
4-digit NAICS Fixed effects

(1)

(2)

(3)

(4)

0.156∗∗∗(0.047)0.156∗∗∗(0.047)0.160∗∗∗(0.047)0.160∗∗∗(0.047)23, 160
NoNo

23, 160
YesNo

23, 160
No
Yes

23, 160
Yes
Yes

Notes: Standard errors in parentheses are clustered at the firm level. The outcome variable is ln(capx of treatedfirms) - ln(capx of synthetic firms). The controls include the mean pre-period (2011-17) values of capx, assets,
sales and property, plant and equipment (PPE) of treated firms. Weighted by mean pre-period capx of treatedfirms. For the backdated matched sample, the firms are synthetically matched based on the values of firm characteristics between 2010 and 2014. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.This table does not contain data fromtax returns.

25

Table C.6: Tax Change Portfolios

Share

26

Group:
Domestic 1
Domestic 2
Domestic 3
Domestic 4
Multinat. high 1
Multinat. high 2
Multinat. high 3
Multinat. high 4
Multinat. high 5
Multinat. high 6
Multinat. high 7
Multinat. low 1
Multinat. low 2
Multinat. low 3
Multinat. low 4
Multinat. low 5
Multinat. low 6
Multinat. low 7
Multinat. low 8
Non C-corp.

18.6
1.9
2.7
12.1
4.0
4.2
0.7
0.2
0.1
8.0
1.1
4.6
2.4
0.3
0.5
0.4
0.3
3.3
5.6
29.0

K0 /
firm

142
64
94
92
288
424
557
300
111
474
160
276
344
303
388
294
256
342
399
117

100 × Γ100 × Γ̄

100 × τ100 × τ̄

Pre

Post

Pre

Pre

Post

Pre

Post

14.2
22.9
23.9
29.8
10.3
14.4
21.6
24.0
26.4
27.8
28.1
16.8
18.2
21.1
22.6
25.5
28.7
29.3
29.5
23.0

9.4
15.5
14.5
19.6
8.8
11.2
14.0
15.5
18.3
16.3
18.5
12.3
13.3
13.5
14.5
16.8
20.4
18.8
18.9
23.0

17.3
34.7
27.0
34.6
13.2
17.0
29.4
26.7
29.3
33.5
33.6
19.9
21.1
34.8
34.0
27.1
31.8
34.0
34.0
28.0

10.2
22.2
15.4
21.5
11.0
11.7
18.0
16.3
19.5
18.3
21.3
13.6
13.8
22.2
21.3
17.2
21.1
21.1
20.5
28.0

27.4
23.5
15.0
23.0
21.1
18.4
25.3
24.9
20.6
22.7
7.7
26.4
25.0
24.1
22.3

2883
639
638
2884
28.4 302
24.5 220
18.2
26
24.5
13
22.1
21
19.3 371
26.1 150
26.4 365
22.4 155
23.4
22
14.1
29
26.5
27
25.1
23
25.1 213
23.4 308

17.7
17.7
17.7
17.7
17.7
17.7
17.7
17.7
17.7
17.7
17.7
17.7
17.7
17.7
17.7

Post

17.7
29.4
29.4
29.4
17.7
29.4
17.7
17.7
29.4
17.7
29.4
17.7
29.4
17.7
29.4

N

Notes: Share is the share of domestic capital at firms in the group, in percent. K0 / firm is average domestic capital per firm in billions of dollars. Pre andpost refer to 2015-2016 and 2018-2019 averages.

Table C.7: Summary Statistics for Firms in Compustat
Mean

Std. Dev.

Median

P10

P90

N

Average Tax Rate

0.04

0.05

0.01

0.00

0.11

1910

Pre-TCJA Γ

0.21

0.09

0.22

0.08

0.31

1913

Pre-TCJA Γ̄

0.18

0.00

0.18

0.18

0.18

1914

Pre-TCJA τ

0.25

0.10

0.27

0.09

0.35

1914

Pre-TCJA τ̄

0.15

0.16

0.11

0.00

0.37

1914

d log(Investment)

-0.06

0.90

0.07

-1.32

1.03

1914

Γ̂ − τ̂

0.04

0.03

0.04

0.01

0.08

1913

Relative Profit (EBITD)

0.35

0.62

0.04

0.00

1.55

1914

Form 5471: Rel. Profit

0.45

0.88

0.02

0.00

2.01

1914

Form 5471: Rel. Profit (EBITD)

0.35

0.64

0.02

0.00

1.53

1914

Lagged (2015-6 Average) Capital

1403.06216.099.7

5.5

2412.4

1914

Pre-TCJA χK

0.28

0.47

0.04

0.00

1.06

1914

Eq. 15: Γ̂
Eq. 15: Γ̄ˆ

-0.10

0.06

-0.09

-0.16

-0.02

1913

0.05

0.07

0.00

0.00

0.14

1914

Eq. 15: τ̂ˆEq. 15: τ̄

-0.13

0.08

-0.13

-0.22

-0.03

1914

0.06

0.06

0.02

0.00

0.13

1914

Export Share

0.17

0.30

0.00

0.00

0.66

1914

Notes: This table provides summary statistics for public firms. Capital is in millions of USD. We winsorize dlog(Investment) from above and below at the 5% level. For disclosure reasons, we do not report true medians(or other percentiles). Instead, we report the average of observations in neighboring percentile bins.

27

Table C.8: Global Capital Accumulationlog(Foreign Capital), by Region:

Total
G7
OECD (Excluding G7)
BRIC
Developing (non-BRIC)Tax Havens (Islands)
Tax Havens (Non-Islands)
Other

Multinational Firms (Pooled)
Γ̂

Γ̄ˆ

τ̂

ˆ
τ̄

N

−1.10
(1.32)−0.22(1.60)
0.74(1.68)
0.11(1.70)−0.26(3.13)
5.74(5.51)
2.86(2.80)
1.59(3.28)

1.00∗∗(0.36)
1.17∗∗(0.40)
0.48(0.47)
1.34∗∗(0.46)
1.54(0.84)
0.46(1.47)
1.63∗(0.72)−0.07(0.84)−0.45(1.03)−0.57(1.23)−1.03(1.30)−0.38(1.31)
0.43(2.37)−4.36(4.07)−2.73(2.14)−2.08(2.52)

0.17(0.64)
0.34(0.71)
0.60(0.85)
0.49(0.90)
4.25∗(1.86)
0.59(2.59)
0.12(1.40)−2.30(1.27)

2102
1532
1218
1065
539
246
926
670

Notes: Standard errors appear in parentheses. This table contains coefficients from running regressions in different regions which use log(Foreign Capital) as an outcome. Regions appear as row names. Γ̂ and τ̂ were estimatedseparately. ∗ p < .05, ∗∗ p < .01, ∗∗∗ p < .001

28