International Corporate Tax Avoidance: A Review of the Channels, Magnitudes, and Blind Spots

Sebastian Beer; Ruud A. de Mooij; Li Liu

doi:10.5089/9781484363997.001.A001

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International Corporate Tax Avoidance: A Review of the Channels, Magnitudes, and Blind Spots

Author: Sebastian Beer¹, Ruud A. de Mooij, and Ms. Li Liu

¹ 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Author’s E-Mail Address: SBeer@imf.org; RdeMooij@imf.org; LLiu@imf.org

Publication Date:: 23 Jul 2018

eISBN:: 9781484363997

Language:: English

Keywords:: WP; tax rate; passive income; dependent variable

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This paper reviews the rapidly growing empirical literature on international tax avoidance by multinational corporations. It surveys evidence on main channels of corporate tax avoidance including transfer mispricing, international debt shifting, treaty shopping, tax deferral and corporate inversions. Moreover, it performs a meta analysis of the extensive literature that estimates the overall size of profit shifting. We find that the literature suggests that, on average, a 1 percentage-point lower corporate tax rate will expand before-tax income by 1 percent—an effect that is larger than reported as the consensus estimate in previous surveys and tends to be increasing over time. The literature on tax avoidance still has several unresolved puzzles and blind spots that require further research.

Abstract

I. Introduction¹

Tax avoidance by multinational corporations (MNCs) has been on top of the international tax policy agenda since the global financial crisis. The tight fiscal constraints in the aftermath of the crisis amplified long-standing concerns in many countries that large MNCs pay very low effective tax rates. Moreover, the revelation of aggressive avoidance schemes in Luxleaks and more recently the Paradise papers have reinforced the public disquiet about the unfairness of the low effective taxation of some MNCs. These concerns have led to major new international initiatives to curb international tax avoidance – most notably the G20/OECD initiative on base erosion and profit shifting (OECD, 2015). Their aim is to develop approaches that limit the opportunities for MNCs to artificially shift profits and thus to enhance revenue mobilization.

This paper offers a survey of the empirical literature on international tax avoidance. The latter is defined as the international reallocation of profits by an MNC in response to tax differences between countries, with the aim to minimize the global tax bill. Hence, we ignore reallocation of real capital in response to tax. This survey goes beyond discussing studies assessing the overall magnitude of profit shifting and include those on the importance of specific channels, especially the most commonly adopted ones such as transfer mispricing, strategic location of intellectual property (IP), international debt shifting, and treaty shopping. It also discusses tax avoidance devices that are unique to worldwide taxation systems, such as corporate inversions/headquarter relocation, and tax deferral. Moreover, it summarizes empirical evidence on the effectiveness of a range of targeted anti-avoidance measures, which have been put in place to curb tax avoidance.

This paper extends earlier literature reviews by e.g. Dharmapala (2014), Hines (2014) and OECD (2015) in three important ways. First, we place a variety of empirical tax avoidance studies into a systematic framework of the current international tax architecture. Second, within this framework, we broaden the review’s scope, as the number of studies on tax avoidance has been rapidly expanding in recent years. We survey empirical evidence on six main channels of international tax avoidance.

Finally, we perform a meta-analysis of studies estimating the magnitude of total profit shifting to derive a consensus estimate of the tax sensitivity of MNC’s reported profits to tax rates. The meta-analysis extends earlier work by Heckemeyer and Overesch (2017) by almost doubling the sample size of primary estimates,² reducing specification bias, and adopting an enhanced identification approach that corrects for within-study correlation of primary estimates. Our results suggest that a semi-elasticity of reported pre-tax profits with respect to international tax differential of unity is a good reflection of the literature. This means that a 1 percentage point larger tax rate differential reduces reported pre-tax profits of an affiliate by 1 percent. The estimate is larger than the consensus semi-elasticity of 0.8 in Heckemeyer and Overesch (2017). We further complement Heckemeyer and Overesch (2017) by providing new insights into the importance of controlling for the scale of real activities in estimating the consensus semi-elasticity estimate: excluding this control introduces upward bias in the consensus semi-elasticity by inflating it to around 1.4. Moreover, we find that the semi-elasticity has increased over time and that a value of around 1.5 applies to the most recent years. The insight is extremely important for the policy debate on profit shifting and for the calibration of models that account for tax avoidance. Using this estimate, we illustrate the revenue impact of tax avoidance for 81 countries.

The rest of this paper is organized as follows. Section II provides a broad overview of international tax avoidance, elaborating on the current international tax architecture, discussing empirical evidence on various avoidance channels, and elaborating on the impact of specific anti-avoidance measures. Section III reviews the literature on the overall size of profit shifting and provides a meta-analysis. It derives a consensus semi-elasticity and illustrates its implications for government revenue. Finally, Section IV discusses a number of puzzles and blind spots in the literature. It points to research that would be needed to further advance our understanding of international corporate tax avoidance in the future.

II. International Tax Avoidance–The Issue

A. Brief Overview of the International Tax Architecture

The tax treatment of MNCs is determined by the international tax framework, which is a myriad of domestic legislations and a wide network of bilateral and multilateral tax treaties. The framework relies largely on separate accounting, which means that taxation of an MNC group is at the level of individual subsidiaries that operate in different countries. Each country has a right to tax the income assigned, based on its domestic law and tax treaty obligations.

When can a country tax MNCs? MNCs generate income in two main ways: selling products or services, and investing their cash on hand. The former is typically known as active income, and the latter, passive. The taxing rights of a country over MNC income are based on the source of the income and the residence of the corporate taxpayer.

Source refers to where investment and production take place and is largely determined by the physical presence of labor and/or capital. Certain thresholds must be met to define a permanent establishment (PE), which determines whether a source country can tax a foreign company or not. Residence refers to the place where the company receiving the income is deemed to have its primary location. Common tests for this are where the company is incorporated or effectively managed. Following international convention, source countries have primary taxing rights over the active business income of foreign PEs while residence countries retain the right to tax passive income.

How does a country tax MNC earnings? Two alternative systems determine the taxation of active business income of MNCs. Under a territorial system (used e.g. in Europe and Japan), residence countries exempt foreign earnings of MNCs so that their active business income is only taxed in the source country. Under a worldwide system (used e.g. in the BRICS and until recently the U.S.), the residence country retains the right to tax active income from all source countries. Double taxation is typically avoided by the residence country granting a non-refundable foreign tax credit against its own tax, so that the residence tax is limited to the excess of the residence country’s effective tax rate over that in the source country.

In practice, the distinction between worldwide and territorial systems is not as clear-cut. Some provisions in existing systems, such as the deferral of dividend tax obligation until repatriation or the use of excess credits from high-tax countries to offset taxes on dividends from low-tax countries (cross-crediting), tend to soften the bite of worldwide taxation (Matheson et al, 2014). Other measures, such as Controlled Foreign Company rules (CFC), extend domestic taxation rights for both worldwide and territorial systems by taxing foreign passive income immediately.³

Withholding taxes also blur the clean allocation of passive income between source and residence countries. Many source countries impose withholding taxes on outbound income payments, such as interest, royalties and dividends. While residence countries typically provide some relief through foreign tax credits, unilateral endeavors to prevent double taxation are often imperfect and withholding taxes can “stick” on cross-border income flows. Double tax treaties (DTTs), by specifying maximum withholding tax (WHT) rates which are often lower than the domestic WHT rates and sometimes down to zero, tend to limit source country’s taxing right on cross-border passive income flows.⁴ In this context, withholding taxation becomes a bilateral instrument to divide revenues between countries.

B. Main Channels of International Tax Avoidance

Avoidance of source country taxation. Within the international tax framework, MNCs can use a wide array of techniques to shift profits between entities in the group to minimize their overall corporate tax liability. These techniques can be entirely legal, in which case they are referred to as tax avoidance—as opposed to tax evasion, which is illegal.⁵ The precise channels of tax avoidance can vary, depending on the specific features of national tax systems and treaty networks. For instance, taxation in source countries can be minimized by;⁶ (i) transfer mispricing (stretching, violating or exploiting weaknesses in the arm’s length principle); (ii) strategic location of management of intellectual property (IP) to low-tax countries to reduce taxes on associated income; (iii) debt shifting through intracompany loans (excessive borrowing in high-tax countries and lending to low-tax countries); (iv) treaty shopping (exploiting treaty networks to route income so as to avoid tax); (v) risk transfer (conducting operations in high tax jurisdictions on a contractual basis to limit profits attributable there); (vi) avoiding PE status; and (vii) locating asset sales in low-tax jurisdictions (to avoid taxes on the capital gains).

Avoidance of residence country taxation. Worldwide systems can serve as a backstop for the avoidance of source taxes, since income will ultimately be subject to repatriation taxes in the residence country. However, residence taxation can also be avoided. One way is by the artificial use of tax deferral (delaying payment to the parent, sometimes indefinitely). Alternatively, the firm can avoid resident status through corporate inversion (changing residence to escape repatriation taxes or CFC rules) or by choosing the location of a new residence in a country that operates a territorial system.

The rest of this section reviews existing empirical evidence on the main channels of international tax avoidance. The literature review is confined to channels where available data allows identifying the behavioral effects of the international tax systems. Systematic evidence on other channels is largely absent.

1. Transfer Mispricing

The valuation of intra-company transactions within an MNC affects the global allocation of the tax base between source and residence countries. Most countries use the arm’s length principle, which stipulates that internal prices between related parties should resemble prices that would prevail between independent parties. Yet, despite extensive OECD and UN Guidelines, there may be significant room for subjective interpretation. Conceptually, there may even be no “correct” arms-length price if there are no comparable third-party transactions. Given these weaknesses in the implementation of the arms-length’ principle, MNCs may be able to charge artificially low prices for exports sold from high-tax to low-tax countries, or artificially high prices for inputs coming from low-tax countries, to reduce their global tax liability.⁷

The literature provides ample evidence for the presence of tax-motivated transfer mispricing. Earlier empirical studies show that at the aggregate level, differences in the statutory corporate tax rate between the US and its trading partners substantially influence the balance and pattern of intra-firm trade in the US (Clausing, 2001, 2006). Later studies provide more direct evidence, showing that the price wedge between the arm’s-length price for unrelated transactions and the transfer price for related-party transactions varies systematically with corporate tax rate differentials faced by MNCs in the U.S. (Clausing, 2003; Bernard et al, 2006; Flaaen, 2016), Denmark (Cristea and Nguyen, 2016), France (Davies et al., 2017), Germany (Hebous and Johannesen, 2015), and the UK (Liu et al, 2017). The size of the effect, however, differs between studies, reflecting possible differences between countries, sectors and firms, as well as in the empirical sample and estimation methods. Overall, the estimated semi-elasticity (i.e. the percentage increase in the transfer price of exports in response to a 1 percentage point reduction in the tax rate differential to lower-taxed countries) ranges between 0.5 and 6.

2. Strategic Location of IP

Another way to reduce the global tax of an MNC is by strategically moving valuable IPs to low-tax affiliates. Companies can conduct their research and development (R&D) activities in one country, but transfer the ownership of the patent that is subsequently created to another country where the resulting income streams will be taxed at a lower rate.⁸ As, there is often no comparable transactions of IPs between unrelated parties, determining the arm’s length price for company’s intangible transactions is usually very difficult, leaving room for tax-induced manipulation of transfer prices (see e.g. Grubert, 2003; Desai et al, 2006).

Empirical evidence concurs that the location of valuable IP is systematically distorted toward low-tax locations, by documenting a negative association between the level of corporate tax rate and the probability of patent application and the subsidiary’s level of IPs in one country. Specifically, using a panel data set of multinational affiliates within the EU–25 between 1995 and 2005, Dischinger and Riedel (2011) find that a decrease in the average tax difference to all other affiliates by one percentage point raises the subsidiary’s level of IP by 1.6 percent. Using data on corporate patent holdings of European MNCs, Karkinsky and Riedel (2012) find that a one percent point increase in the corporate tax rate reduces the probability of patent applications by around 3.5-3.8 percent. Tax-responses of IP locations are found to be heterogeneous across European countries (for instance, due to differences in market size or enforcement)⁹, with the amount of patents held most sensitive to tax in Luxembourg (with an estimated tax semi-elasticity of 3.9) and least sensitive in Germany (with an estimated tax semi-elasticity of 0.5) (Griffith et al, 2014). Empirical evidence using data on European and US patent and trademark applications during 1996-2012 also suggests that there is substantial difference in the tax-responses of IP locations between types of IP (Dudar and Voget, 2016), where the estimated tax semi-elasticity of trademarks is -6.2 and is significantly larger than that of patents (with an average tax semi-elasticity of -1.9), as generation of the latter is likely to be tied with some real R&D activities.

3. International Debt Shifting

A third way for an MNC to reduce its tax bill is through intracompany loans. Cross-country differences in rates of CIT create opportunities for lending from low-tax countries to affiliates in high-tax countries or by locating external borrowing in high-tax countries. This debt shifting reduces the group’s tax bill without affecting the overall debt exposure of the group (and hence its bankruptcy risk).

Empirical studies confirm the presence of debt shifting, by showing that host-country taxes or international tax differentials have a positive and significant effect on internal debt of German MNCs (Weichenrieder, 1996; Ramb and Weichenrieder, 2005; Overesch and Wamser, 2006; Mintz and Weichenrieder, 2010; Schindler et al., 2013; and Buettner and Wamser, 2013), on intrafirm interest and debt ratio of U.S. MNCs (Grubert, 1998, Altshuler and Grubert 2003; Desai et al, 2007), and on internal leverage of MNCs in Europe (Huizinga et al, 2009). Synthesizing this literature in a meta analysis, De Mooij (2011) finds that the tax elasticity of intra-company debt is 0.5, which based on an average ratio of intra-company debt-to-assets of 0.24 in the meta sample, corresponds to a tax impact on the internal debt-asset ratio of 0.12. Debt shifting is found to be more pervasive in developing countries, with the effect of taxes twice as large as in developed economies (Fuest et al, 2011).

4. Tax Treaty Shopping

Considerable variation in the WHT rates in more than 3000 bilateral DTTs creates opportunities of treaty shopping. This enables MNCs to link different DTTs and divert cross-border payments through the country with the lowest WHT rate.

Empirical evidence for treaty shopping is first documented by Weichrieder and Mintz (2008), who show that higher bilateral WHTs to (from) Germany increase the probability that outward (inward) FDI is diverted via a third country. The link between lower WHT rates and re-routing of FDI is further supported in Weyzig (2013), who shows that the reduced WHT rate on dividend payments in Dutch tax treaties is the key driver of FDI diversion through Dutch Special Purpose Entities. Revenue losses associated with treaty shopping can be substantial for source countries. Beer and Loeprick (2018) find that treaty-shopping has reduced CIT revenues in Sub Saharan Africa by around 15 percent among countries having signed a treaty with an investment hub. Balabushko et al. (2017) show that reduced WHTs under the Ukraine-Cyprus DTT imply revenue losses of around US $77 million for Ukraine in 2015, or one percent of CIT revenue.

5. Tax Deferral

As worldwide taxation imposes residence tax only upon repatriation of the profit, MNCs can avoid repatriation taxes by retaining foreign earnings abroad. The tax deferral channel is different from the others in that with an unchanging tax rate, deferral affects only the present value of taxes paid instead of the total revenue. Moreover, theory predicts that, tax deferral may or may not be a relevant avoidance strategy. Analogous to the “new view” of dividend taxation, Hartman (1985) argues that dividend taxes are unavoidable costs for mature foreign subsidiaries, i.e. at some point in time they must be paid. Hence, if the subsidiary’s choice is to distribute dividends now or later and taxation remains constant, the tax rate should play no role. However, effects are expected if tax rates are anticipated to change. Several empirical papers find support for tax deferral, by showing that US MNCs increase their dividend payout in response to lower taxes on repatriation (Hines and Hubbard, 1990; Altshuler and Newlon,1993; Grubert,1998; Altshuler and Grubert, 2003). Recent studies, exploiting UK’s and Japan’s adoption of a territorial system in 2009 in a quasi-experimental setting, provide similar evidence that exemption of foreign earnings boosted dividend repatriation in the UK (Egger et al, 2015) as well as in Japan (Hasegawa and Kiyota, 2017).

6. Corporate Inversions and HQ Location

MNCs in worldwide countries can also avoid repatriation taxes by changing the residence of the corporation or, stated differently, by “inverting” roles in the corporate group.¹⁰ Corporate inversion by US parents are generally associated with substantial tax savings (Desai and Hines, 2002). Using a dataset of 60 US MNCs that restructured between 1983 and 2015, CBO (2017) finds that the average saving in each company’s global corporate taxes is around US $45 million in the year after the inversion.

More broadly, corporate inversions are a special case of cross-border mergers and acquisitions (M&As) that are influenced by tax considerations. Using data on 278 cross-border M&As between 1997 and 2007, the taxation of foreign passive income in CFC legislations and worldwide taxation of foreign active income are found to be major drivers for cross-border M&As (Voget, 2007; Huizinga and Voget, 2009).

C. How Effective are Anti-Avoidance Regulations?

During the last few decades, countries have adopted various anti-avoidance regulations to mitigate tax avoidance by MNCs. These include, for example, the adoption of transfer pricing regulations, thin capitalization rules, controlled foreign corporations (CFC) rules or a general anti-avoidance rule (GAAR). These efforts have received considerable attention in light of the G20-OECD initiative on BEPS. For instance, countries participating under what is now called the inclusive framework on BEPS, commit to four minimum standards (e.g. on treaty abuse) and adhere to the common approaches to adopt anti-avoidance legislation. The European Union has also adopted an anti-tax-avoidance directive that requires its member states to implement a common set of rules to address tax avoidance. This section reviews the studies that analyze the effectiveness of anti-avoidance rules.

Transfer Pricing Regulations offer guidance in the implementation of the arm’s length principle. They often specify the methods that can be used to calculate transfer prices, determine documentation requirements, include various specific requirements in its application needed to support the transfer prices used, and set penalties if mispricing is detected or adequate documentation is not provided. Their effectiveness in curbing international tax avoidance is supported in two concurrent studies, which show that introduction and tightening of transfer pricing rules can diminish the tax sensitivity of corporate profits by 50 percent (Riedel et al, 2015), though with much weaker effects for firms with many intangible assets or complex group structures (Beer and Loeprick, 2015). A later study shows that stricter regulations reduce reported profits of MNCs, possibly due to the combination of lower profit inflows into countries that are intermediate hubs for profit shifting and higher compliance costs of MNCs (Saunders-Scott, 2013).¹¹

Thin Capitalization Rules deny interest deductibility above a certain threshold of either net interest payment (as a ratio of income) or net debt (as a ratio to equity). The precise conditions under these rules vary between countries and over time. Empirical evidence suggests that, on average, well-designed thin capitalization rules are effective in reducing debt shifting by multinationals, using data for MNCs in Germany (Overesch and Wamser, 2010; Buettner et al, 2012) and the U.S. (Blouin et al, 2014).

CFC Rules stipulate that foreign subsidiaries’ income are subject to domestic taxation without deferral if certain conditions are met. CFC rules thus expand domestic taxation rights of territorial systems and limit the impact of deferral under worldwide systems. Initial studies have exploited the German MiDi dataset and find that CFC legislation reduces passive assets by 77-82 percent for subsidiaries where the legislation was binding, i.e. for whom the statutory tax rate in their host country falls below the safe-haven tax rate (Ruf and Weichenrieder, 2012). A recent study confirms this finding, by contrasting the financial earnings of subsidiaries in 200 countries just above and below the tax rate threshold; it reports a 20 percent difference in the level of reported profit due to binding CFCs (Clifford, 2017).

III. A New Consensus on tax-motivated profit shifting

The research reviewed in the previous session examines channels of international tax avoidance separately. In contrast, following a simple conceptual framework developed by Hines and Rice (1994), and extended by Huizinga and Leaven (2008), a wide body of empirical work has examined the overall extent of tax-motivated profit reallocation by estimating the following regression:

\begin{matrix} \log (π_{i}) = ε (τ_{i} - \bar{τ}) + γ^{'} X_{i} + ω_{i}, & (1) \end{matrix}

where π is a measure of reported profit before taxation in country $i, (τ_{i} - \bar{τ})$ is a tax rate differential, X is a vector of control variables, and u is an idiosyncratic error term. The tax rate differential measures the difference in statutory CIT rate between location i and the average CIT rate among other affiliates of the same company group. The tax coefficient (ε) captures the responsiveness of reported profits to the tax rate differential, and is expected to be negative in the presence of profit shifting. The vector of controls, X, typically includes real variables, such as capital and labor inputs, to avoid confounding effects of tax on profit shifting and real production.

The typical study estimates equation (1) by using firm-level data. However, some studies use more aggregate data to infer a relationship between taxable profits and tax rate differential. Micro studies on profit shifting only capture avoidance behavior that affects the observed profits of a MNC. While transfer mispricing, international debt shifting, and strategic location of IPs directly effects reported profitability, other avoidance channels are not necessarily captured in studies using variants of equation (1). For instance, avoidance of PE status implies that there is no profit reported in the first place, suggesting that micro studies that exploit firm-level data would neglect this channel.¹² Alternatively, avoidance of dividend taxes on repatriation would change after- but not before-tax profits, and would not be captured in either macro or micro studies. Macro studies may capture a wider range of profit shifting channels related to statutory CIT rate differential. Yet, by exploiting variation in the statutory CIT rates, all studies neglect avoidance of other corporate taxes, including those on interest, royalty and service fees, or on capital gains tax (treaty shopping and offshore indirect transfer of assets).

Empirical estimates of the semi-elasticity vary widely. Part of this dispersion is structural, as costs of profit shifting are likely to differ across MNCs of different operating scale or industry structure, and across countries with varying enforcement and administrative capacity. Given these differences, we expect variability in the estimates from different studies even with the same regression specification. Part of the dispersion in the estimated semi-elasticity, however, is study-specific. For example, early studies often use cross-sectional data at the country level, and rarely control for real determinants of economic profitability. More recent studies use micro-level panel data and control for a wide range of non-tax determinants of profits at both the macro and firm level.

This section uses meta regressions to synthesize existing empirical evidence on profit shifting, and to disentangle the drivers behind reported estimates. Specifically, we first construct a comprehensive dataset that includes 402 estimated semi-elasticities of reported profit with respect to a tax measure, associated standard errors, and a range of specification-and study-specific variables. We then use a meta regression to uncover the “consensus estimate”, which represents the most likely value of the true semi-elasticity based on all evidence currently available.¹³

A. Data and Descriptive Statistics

We identify relevant studies in a comprehensive search of the EconLit database, economic journals, and working paper platforms, such as SSRN, using the keywords “income shifting”, “profit shifting” and “tax avoidance”.¹⁴ We include studies that use a profit measure as the dependent variable and that either directly produce tax semi-elasticities or allow for their imputation. Our baseline sample has 402 semi-elasticities from 37 papers:¹⁵ the earliest study Hines and Rice (1994) uses cross-sectional data in 1982; the latest Dowd et al., (2017) uses micro-level panel data up to the year 2012.

Table 1.

Estimated Semi-Elasticity of Pre-Tax Profits

Notes: This table reports the summary statistics of the semi-elasticity estimates in four different samples in column (1). Weighted mean is computed as the average of semi-elasticity estimates weighted by the inverse squared standard error.

Table 1.

Estimated Semi-Elasticity of Pre-Tax Profits

Sample	Mean	Weighted Mean	Median	Observations	Number of underlying studies
(1)	(2)	(3)	(4)	(5)	(6)
Baseline	1.59	0.79	1.05	402	37
No conditional estimates	1.54	0.79	1.01	294	35
Heckemeyer and Overesch (2017) sample	1.47	0.79	1.00	208	26
Peer-reviewed, micro-data sample	1.38	0.72	0.99	269	22

Table 1.

Estimated Semi-Elasticity of Pre-Tax Profits

Sample	Mean	Weighted Mean	Median	Observations	Number of underlying studies
(1)	(2)	(3)	(4)	(5)	(6)
Baseline	1.59	0.79	1.05	402	37
No conditional estimates	1.54	0.79	1.01	294	35
Heckemeyer and Overesch (2017) sample	1.47	0.79	1.00	208	26
Peer-reviewed, micro-data sample	1.38	0.72	0.99	269	22

Table 1 summarizes features of the semi-elasticities in four subsamples of the main dataset (column 1, differences described below). Column 2 shows the mean semi-elasticities. The average semi-elasticities of 1.58 in the baseline sample suggests that reported profit decreases by 1.59 percent in response to a one percentage point increase in the statutory tax rate. Simple averages, however, ignore imprecisions in the primary estimates. Following HO, column 3 summarizes the weighted averages of estimated semi-elasticities by weighting each estimate with its inverse squared standard error, and shows a much smaller average response of 0.79 for the baseline sample.

Baseline sample. Our baseline sample expands the HO sample in two important ways. First, it includes more studies of profit shifting. Second, it includes more estimates from the same studies included in HO.¹⁶ Several papers investigate how profit shifting varies with some other explanatory variables (z), such as the presence of mandatory documentation requirements or the intangible intensity of the firm, typically in regressions of the form:

\begin{matrix} \log (π_{i}) = β_{1} (τ_{i} - \bar{τ}) + β_{2} z (τ_{i} - \bar{τ}) + γ^{'} X + ω_{i} . & (2) \end{matrix}

To avoid loss of information, we compute average conditional semi-elasticities as β = (β₁ + β₁z), evaluated at the mean value of z. The associated standard errors are computed using the delta method.

Sample without conditional estimates. When dropping conditional elasticities from the baseline sample, we lose around one quarter of observations. The mean estimate is 1.54 and slightly smaller to the average in the baseline sample, while the weighted average and median are almost identical.

HO (2017) sample resembles the data used in their study, with similar mean and median values of the estimated semi-elasticity.¹⁷ Minor differences occur because we use estimates based on updated or published versions of the primary studies.

Peer-reviewed micro-data sample includes 269 semi-elasticity estimates from 22 peer-reviewed papers that exploit micro-level data. The mean, weighted-average and median semi-elasticity estimate in this sample is 1.38, 0.72, and 0.99, respectively.

Figure 1 illustrates semi-elasticities of the baseline sample by the average sample year of the underlying study. On average, reported estimates (solid line) have decreased over time. This may reflect a reduced tax response over time. Alternatively, the declining trend may also reflect the more extensive use of granular data (i.e. firm-level panel instead of country-level cross-sectional data) or improved empirical strategies. We further explore the impact of these characteristics on the consensus estimate in the meta-analysis.

B. Synthesis of Primary Estimates: Meta-Regression

Benchmark Specification. Measured semi-elasticities differ due to structural differences in the underlying sample and differences in the estimation strategy. To disentangle these, we estimate a meta-regression of the form:

\begin{matrix} \hat{ε_{i j}} = ε + β^{'} X_{i j} + ω_{i j}, & (3) \end{matrix}

where $\hat{ε_{i j}}$ is the i^th semi-elasticity reported in study j, the vector X_ij comprises a set of dummy variables that take the value of one if estimate j in study i deviates from the benchmark specification, and ω_ij is an error term. The intercept ε thus captures the true semi-elasticity while the coefficients on X_ij indicate deviations from this consensus estimate due to study- and estimate-specific attributes. Specifically, X_ij includes four groups of study or estimation characteristics:

Estimation techniques and data features: Despite more use of micro-datasets in recent studies, researchers continue to exploit macro data due to its broader coverage in time and geography. Time-series of aggregate data help revealing long-run tax elasticities, as it often takes time for MNCs to restructure their transaction or financing arrangement in response to tax changes. Aggregate data also have obvious drawbacks: profit shifting incentives at the affiliate level are unobservable, factor inputs cannot be matched with reported profitability, and unobservable factors at the firm-level cannot be controlled.¹⁸

Our preferred specification uses micro-data and controls for firm- and country-specific fixed effects. The indicator Aggregate Data takes the value of one for studies using country-level data; the indicators No Firm FE and No Country FE, take the value of one if the study does not control for unobservable characteristics that differ across firms or countries, respectively.

Dependent variable characteristics: The distribution of firm profitability is heavily skewed and may not fit with a linear regressions model. Previous work addresses this issue in two ways: (1) dropping observations with negative profits and using the natural logarithm of profits as a dependent variable, or (2) using a profitability ratio, such as return on sales or return on assets, as the dependent variable. However, limiting the sample to firms with positive profitability might induce bias as loss making entities are potentially among the most aggressive tax planners (see e.g. Johannessen et al, 2017).¹⁹ While using a profitability ratio may alleviate this bias, it may capture real responses to the tax rate in the denominator, confounding tax-minimization responses with real ones.

Against this background, our preferred specification uses the logarithm of reported profit before taxation as dependent variable. The dummy variable Ratio takes the value of one for studies using profitability ratios instead of the logarithm. The expected effect of this indicator is ambiguous as it depends on the denominator’s response to tax differentials. The dummy variable EBIT takes the value of one for studies using before financing profit as the dependent variable. As before-financing profits excludes tax avoidance through internal borrowing, we expect the coefficient of EBIT to be negative.

Tax variable characteristics: From a theoretical perspective, the tax rate variable should capture the net tax savings associated with a relocation of one dollar across the corporate group. Micro-level data often allows computing statutory tax rate differentials that are likely good approximations to this theoretical concept. Absent detailed ownership information, researchers typically use the host country’s statutory tax rate. Several studies, particularly from the US, use backward looking average effective tax rates. These rates are outcomes of past profit shifting behavior and raise reverse causality concerns.

Absent special tax regimes and tax holidays, statutory corporate tax rates are precisely the rates applying to the marginal unit of profits and thus capture the true incentive for profit shifting (Devereux, 2007). Our preferred specification uses a statutory tax rate differential. The dummy SingleRate takes the value of one for studies using a single tax rate. The indicator Effective takes the value of one for studies relying on effective tax rates instead of statutory corporate tax rates. We expect the indicator SingleRate to have a negative sign due to attenuation bias. Given the reverse causality between reported profit and effective tax rates (that is, low levels of reported profits after shifting imply a low effective tax rate, generating a spurious positive correlation between the two variables), we expect a positive sign on the indicator Effective.

Controlling for real activity and leverage. Part of the variation in reported profits are due to changes in economic profits, which depend on the scale of real activities. Excluding controls for real activies, the estimated tax elasticity is the elasticity of total taxable income that encompasses both real and reporting responses.

Our benchmark study controls for real production inputs, but not for debt, as internal financing decisions are one channel of profit shifting. Accordingly, the indicator NoReal takes the value of one for studies that do not control for either assets, the number of employees, or payroll, and is expected to have a positive sign. The indicator Debt takes the value of one for studies that hold financing decisions constant (by including leverage decisions as an explanatory variable). We expect the coefficient on Debt to be negative as it excludes an important channel of profit shifting through debt shifting.

Other sample characteristics. We include the average sample year of the underlying study (Z-MIDYEAR) to investigate whether the observable extent of profit shifting has changed over time.²⁰ The expected sign of this variable is ambiguous: while growing importance of intangible assets and increasingly sophisticated tax planning strategies may indicate a larger tax sensitivity of the global tax base, strengthened anti-avoidance measures may add to the costs of profit shifting to the MNC.

C. Efficient Estimation

Researchers typically account for the varying precision of primary estimates by estimating meta-regressions with Weighted Least Squares (WLS). By using the inverse of the reported standard error as a weight, this estimation strategy gives more weight to more accurately measured observations and should thus increase estimation efficiency over OLS.

The efficiency gains of WLS depend on the correct specification of the error structure. Notably, WLS rules out correlation of primary estimates at the study level, which seems to be a restrictive assumption in our context. For instance, studies focusing on intangible intensive firms likely find larger semi-elasticities than studies focusing on publicly-owned manufacturing firms. Such differences would imply correlation of estimates at the study-level and render WLS a less efficient estimation strategy.

We allow for correlation of primary estimates at the study-level to increase the efficiency of our estimates. More specifically, we assume that the unobservable error component of semi-elasticity j in study i is given by,

\begin{matrix} ω_{i j} = u_{i} + v_{i j}, & (4) \end{matrix}

where u_i is study-specific and v_ij is estimate-specific. The study- and estimate-specific error components are independently distributed with mean zero and variances σ² and σ_ij². This formulation allows sample characteristics, such as the underlying industry-composition or strength of country-specific anti-abuse measures, to drive the tax sensitivity of reported profits.

We implement the estimations in a two-stage approach. In the first stage, we use OLS on study-specific average values of all dependent and independent variables²¹ to obtain the residuals ω_i = u_i + 1/K_i Σ_j v_ij, where K_i is the number of point-estimates provided in study i. Note that primary studies derive estimates conditional on study-specific characteristics. Reported standard errors thus directly provide information on the estimate-specific component $σ_{ij}^{2} = V a r (\hat{ε_{i j}} | X, u_{i})$ . We infer the second component using

\begin{matrix} \hat{σ^{2}} = \frac{1}{N - V} Σ_{i} ω_{i}^{2} - \frac{1}{N} Σ_{i} \frac{1}{K_{i}^{2}} Σ_{j} σ_{ij}^{2} & (5) \end{matrix}

Where V is the number of control variables included in the regression. In the second step, we combine these estimates to construct the covariance matrix for a feasible generalized least squares (GLS) estimation.

A critical advantage of the GLS is that the weight given to any observation depends on the total number of estimates in a study: each observation tends to receive less weight if the underlying study provides many estimates; it receives more weight if the underlying study provides only a few estimates. This makes intuitive sense with intra-study correlation, as the first estimate of an additional study adds more new information to the true semi-elasticity than the 45^th estimate of the same study. In contrast, WLS weighs observations solely based on the reported standard error, neglecting the underlying information source.

Figure 2 contrasts WLS (left panel) and GLS (right panel) estimates of the unconditional mean (red vertical line) in the baseline sample. Small vertical black lines depict point estimates; grey clouds around these point estimates illustrate associated 95 percent confidence bands. In the left panel, we use the standard errors which are reported in primary studies to illustrate these confidence bands, thus ignoring correlation at the study level. Many confidence intervals do not encompass the mean value in the left panel, suggesting that reported semi-elasticities and associated standard errors are from different distribution.²² In contrast, the right panel presents confidence bands that account for variation at the study-level, and show considerable more overlap.²³

The simple graphical evidence suggests that a two-component error structure is more suitable to describe the unconditional mean in the baseline sample. Similarly, we do find that an important share of the observed variation is at the study level in all conditional estimations of the mean (see regression results in the next section). Accordingly, we use GLS in all estimations to improve estimation precision over previous work.

D. Main Findings

Table 2 summarizes our regression results based on Equation (3) using the baseline sample. The dependent variable is the reported semi-elasticity of pre-tax profits, winsorized at top and bottom one percentile to remove the influence of outliers.²⁴ We account for heteroscedasticity and intra-study correlation by presenting fully robust GLS standard errors (see e.g. Arellano, 2003 and Wooldridge, 2010). Moreover, we correct for the small number of clusters by inflating residuals²⁵ and reporting p-values based on t-distributions with G-1 degrees of freedom, where G is the number of the included studies (see Cameron, Gelbach, and Miller, 2006).

Column 1 of Table 2 examines the joint impact of estimation techniques and data sources on measured semi-elasticities. The average semi-elasticity estimate is 0.72, when using micro data and including firm fixed effects. The coefficient on the indicator No Firm FE is positive and significant at the one percent level, suggesting that the assumption of no correlation between unobservable components at the firm-level and the dependent variable might be violated in these models. Omitting country fixed effects increases the estimated semi-elasticity by an average of 0.46.

Among studies using aggregate data, estimated semi-elasticities are significantly larger and more sensitive to the inclusion of country-specific fixed effects. The coefficient on the Aggregate Data indicator of 1.11, which is significant at the 5 percent level, suggests that the data sources differ beyond the ability to control for unobservable factors at the firm level. With a magnitude of 2.29 (=0.72+0.46+1.11), the implied semi-elasticity for macro-studies is much larger than the effect identified in micro-studies. Two explanations seem possible. On the one hand, macro-data potentially captures long-run responses and more channels. On the other hand, the larger effect may reflect bias, as aggregate data does not allow to match production factors with profitability measures at the firm level and precludes computation of relevant tax differentials. Country-specific fixed effects play a similar role for aggregate data as do firm-specific fixed effects for micro-data. We acknowledge this difference by interacting the No Country FE indicator with the Aggregate Data indicator. Our findings suggest that estimated semi-elasticities increase by 0.76 (=0.46+0.3) if macro-based studies do not control for country-specific fixed effects; this effect is significantly larger than the increase for micro-based studies.

Table 2.

Baseline Results

Notes: ***, **, and * depict significance at the 1%, 5%, and 10% level, respectively. Fully robust standard errors in square brackets.

Table 2.

Baseline Results

EXPLANATORY VARIABLE:	(1)	(2)	(3)	(4)
CONSTANT	0.724***	1.205***	1.017***	0.974***
	[0.127]	[0.244]	[0.286]	[0.128]
NO FIRM FE	0.121***	−0.159	−0.096	−0.211***
	[0.039]	[0.157]	[0.146]	[0.070]
NO COUNTRY FE	0.456***	0.468***	0.359***	0.472***
	[0.036]	[0.025]	[0.125]	[0.020]
AGGREGATE DATA	1.110**	1.674**	1.445**	1.082**
	[0.515]	[0.637]	[0.632]	[0.513]
AGGREGATE DATA * NO COUNTRY
FE	0.302***	0.296***	0.448***	0.354***
	[0.045]	[0.044]	[0.147]	[0.043]
SINGLE RATE		−0.22	−0.23
		[0.187]	[0.211]
EFFECTIVE		−0.536	−0.548
		[0.671]	[0.677]
RATIO		−0.341**	−0.344**	−0.374***
		[0.152]	[0.142]	[0.069]
EBIT		−0.044	−0.045
		[0.067]	[0.067]
NO REAL CONTROLS			0.469***	0.454***
			[0.104]	[0.116]
DEBT			0.02
			[0.113]
NO MARKET			0.349
			[0.319]
NO TIME FE			0.116
			[0.114]
Z-MIDYEAR	0.035***	0.033***	0.036***	0.036***
	[0.008]	[0.008]	[0.009]	[0.008]
GROUP VARIANCE/TOTAL
VARIANCE	0.34	0.362	0.412	0.367
OBSERVATIONS	402	402	402	402

Notes: ***, **, and * depict significance at the 1%, 5%, and 10% level, respectively. Fully robust standard errors in square brackets.

Table 2.

Baseline Results

EXPLANATORY VARIABLE:	(1)	(2)	(3)	(4)
CONSTANT	0.724***	1.205***	1.017***	0.974***
	[0.127]	[0.244]	[0.286]	[0.128]
NO FIRM FE	0.121***	−0.159	−0.096	−0.211***
	[0.039]	[0.157]	[0.146]	[0.070]
NO COUNTRY FE	0.456***	0.468***	0.359***	0.472***
	[0.036]	[0.025]	[0.125]	[0.020]
AGGREGATE DATA	1.110**	1.674**	1.445**	1.082**
	[0.515]	[0.637]	[0.632]	[0.513]
AGGREGATE DATA * NO COUNTRY
FE	0.302***	0.296***	0.448***	0.354***
	[0.045]	[0.044]	[0.147]	[0.043]
SINGLE RATE		−0.22	−0.23
		[0.187]	[0.211]
EFFECTIVE		−0.536	−0.548
		[0.671]	[0.677]
RATIO		−0.341**	−0.344**	−0.374***
		[0.152]	[0.142]	[0.069]
EBIT		−0.044	−0.045
		[0.067]	[0.067]
NO REAL CONTROLS			0.469***	0.454***
			[0.104]	[0.116]
DEBT			0.02
			[0.113]
NO MARKET			0.349
			[0.319]
NO TIME FE			0.116
			[0.114]
Z-MIDYEAR	0.035***	0.033***	0.036***	0.036***
	[0.008]	[0.008]	[0.009]	[0.008]
GROUP VARIANCE/TOTAL
VARIANCE	0.34	0.362	0.412	0.367
OBSERVATIONS	402	402	402	402

Notes: ***, **, and * depict significance at the 1%, 5%, and 10% level, respectively. Fully robust standard errors in square brackets.

Column 2 of Table 2 adds dependent and tax variable characteristics, reporting an average semi-elasticity of 1.2. The firm fixed effect indicator is no longer significant, suggesting the additional controls subsume variations in the firm-level unobserved heterogeneity. The coefficient on Single Rate is -0.22 but not significant at conventional statistical levels. Similarly, the coefficient on the Effective indicator is negative and statistically insignificant. Studies using profitability ratios report significantly smaller semi-elasticities (-0.34) than studies using the logarithm of reported profit. Surprisingly, we do not find systematic differences between studies using EBIT and studies using profit before taxation as a dependent variable. Debt-shifting thus plays, on average, a minor role in our baseline sample.

We examine the importance of real production controls, leverage and other covariates in column 3. The coefficient on the indicator No Real Control is close to 0.5 and significant at the one percent, indicating an omitted variable bias in studies that neglect real responses to taxation. In contrast, debt ratios, macro-controls, and time fixed effects do not seem to matter systematically in our baseline sample.

Following HO, we drop insignificant explanatory variables in column 4 to increase the estimation precision. The estimated constant of 0.98 is our preferred consensus semi-elasticity estimate. Studies yield smaller effects if they employ a profit ratio (instead of logarithm) or use a simple statutory tax rate (instead of a differential); they yield larger estimates if they do not control for unobservable country-specific effects, real activity, or use aggregate data.

Throughout all columns the coefficient for the Z-Midyear variable is around 0.03 and significant at the one percent level, suggesting that the consensus semi-elasticity estimate increases with the sample year. For example, the consensus semi-elasticity is estimated to be 0.60 in 1990 and 1.5 in 2015. The positive coefficient of the sample year also suggests that the declining trend in the primary elasticity estimates in Figure 1 is driven by important characteristics of the primary studies other than time per se.

Robustness checks

Table 3 checks the robustness of our findings by re-estimating specification in Table 2 Column 3 with alternative samples. Column 1 examines the effect of outliers by using reported observations without winsorizing. Column 2 drops the top and bottom 5 percent of primary estimates. The results change slightly: with average values of 1.03 and 1 for the consensus estimate, respectively.

Column 3 excludes studies that are not published in peer-reviewed journals, and reports a slightly larger consensus estimate of 1.25. While most indicator variables have the expected signs, two differences emerge: first, the omission of country-specific fixed effects now impacts negatively on measured semi-elasticities. Second, we find a negative and significant coefficient on the indicator EBIT. Profit before interest and taxation decreases, on average, by 0.86 (=1.25-0.39) percent in response to a one percentage point increase in the local statutory tax rate. The two estimates on the semi-elasticity of profits, before and after financial income, allow gauging the importance of debt shifting relative to other channels of profit shifting. Assuming that interest payment account for 9 percent of pre-tax profit,²⁶ if MNCs neglect financial channels to shift profit, the measured semi-elasticity of profit after interest payments should be 9 percent larger than the semi-elasticity of profit before interest payments.²⁷ We would thus expect to find a semi-elasticity of 0.94 when using profit after interest payments. However, the measured semi-elasticity for profit after interest is 1.25, indicating that MNCs also use financial channels to manipulate profit. Comparing these effects implies that financial tax avoidance strategies account for roughly one quarter (=(1.25-0.94)/1.25) of the total profit shifting response.

Table 3.

Robustness Checks

Table 3.

Robustness Checks

DEPENDENT VARIABLE: SEMI-ELASTICITY OF REPORTED PROFIT (NO WINSORIZING)
EXPLANATORY VARIABLE:	(1)	(2)	(3)	(4)
CONSTANT	1.026***	0.995***	1.250***	1.012***
	[0.292]	[0.282]	[0.256]	[0.288]
NO FIRM FE	−0.102	−0.103	0.595	0.273
	[0.146]	[0.143]	[0.486]	[0.630]
NO COUNTRY FE	0.361***	0.360***	−0.906 *	0.298
	[0.124]	[0.124]	[0.479]	[0.684]
AGGREGATE DATA	1.458**	1.461**	1.237 *	0.931
	[0.638]	[0.611]	[0.699]	[0.758]
SINGLE RATE	−0.231	−0.229	0.051	−0.434
	[0.214]	[0.212]	[0.198]	[0.353]
EFFECTIVE	−0.549	−0.558	−0.569	0.657***
	[0.679]	[0.678]	[0.694]	[0.163]
RATIO	−0.350**	−0.322**	−1.161**	−0.093
	[0.145]	[0.139]	[0.453]	[0.469]
EBIT	−0.047	0.007	−0.387 *	−0.282***
	[0.068]	[0.020]	[0.206]	[0.102]
NO REAL CONTROL	0.475***	0.426***	0.810***	0.734***
	[0.108]	[0.084]	[0.160]	[0.145]
DEBT	0.022	0.032	−0.081	−0.248**
	[0.114]	[0.124]	[0.154]	[0.114]
NO MARKET	0.348	0.356	1.034***	−0.105
	[0.322]	[0.317]	[0.353]	[0.078]
NO TIME FE	0.115	0.116	−0.169	−0.037
	[0.114]	[0.114]	[0.160]	[0.224]
AGGREGATE DATA * NO COUNTRY
FE	0.447***	0.440***	1.060**	NA
	[0.146]	[0.145]	[0.478]	NA
Z-MIDYEAR	0.036***	0.036***	0.029***	0.069**
	[0.009]	[0.009]	[0.003]	[0.031]
GROUP VARIANCE/TOTAL
VARIANCE	0.336	0.801	0.291	0.704
OBSERVATIONS	402	380	328	208

Table 3.

Robustness Checks

DEPENDENT VARIABLE: SEMI-ELASTICITY OF REPORTED PROFIT (NO WINSORIZING)
EXPLANATORY VARIABLE:	(1)	(2)	(3)	(4)
CONSTANT	1.026***	0.995***	1.250***	1.012***
	[0.292]	[0.282]	[0.256]	[0.288]
NO FIRM FE	−0.102	−0.103	0.595	0.273
	[0.146]	[0.143]	[0.486]	[0.630]
NO COUNTRY FE	0.361***	0.360***	−0.906 *	0.298
	[0.124]	[0.124]	[0.479]	[0.684]
AGGREGATE DATA	1.458**	1.461**	1.237 *	0.931
	[0.638]	[0.611]	[0.699]	[0.758]
SINGLE RATE	−0.231	−0.229	0.051	−0.434
	[0.214]	[0.212]	[0.198]	[0.353]
EFFECTIVE	−0.549	−0.558	−0.569	0.657***
	[0.679]	[0.678]	[0.694]	[0.163]
RATIO	−0.350**	−0.322**	−1.161**	−0.093
	[0.145]	[0.139]	[0.453]	[0.469]
EBIT	−0.047	0.007	−0.387 *	−0.282***
	[0.068]	[0.020]	[0.206]	[0.102]
NO REAL CONTROL	0.475***	0.426***	0.810***	0.734***
	[0.108]	[0.084]	[0.160]	[0.145]
DEBT	0.022	0.032	−0.081	−0.248**
	[0.114]	[0.124]	[0.154]	[0.114]
NO MARKET	0.348	0.356	1.034***	−0.105
	[0.322]	[0.317]	[0.353]	[0.078]
NO TIME FE	0.115	0.116	−0.169	−0.037
	[0.114]	[0.114]	[0.160]	[0.224]
AGGREGATE DATA * NO COUNTRY
FE	0.447***	0.440***	1.060**	NA
	[0.146]	[0.145]	[0.478]	NA
Z-MIDYEAR	0.036***	0.036***	0.029***	0.069**
	[0.009]	[0.009]	[0.003]	[0.031]
GROUP VARIANCE/TOTAL
VARIANCE	0.336	0.801	0.291	0.704
OBSERVATIONS	402	380	328	208

Publication Bias

In principle, estimated semi-elasticities and reported standard errors should be independently distributed. However, researchers, editors, and referees tend to prefer significant statistical results over insignificant results and previous work has indicated that selective reporting and publication of empirical findings is ubiquitous in the economics literature (see e.g. Card and Krueger, 1995; Ashenfelter, 1999; Doucouliagos and Stanely, 2013). Empirical work on profit shifting is likely no exception.

Theoretical considerations imply that profit shifting induces a negative correlation between reported profit and the tax differential. However, data sources often have insufficient detail and local statutory tax rates change infrequently. As a result, the identified effect is sometimes imprecisely measured and random errors might even lead to estimated semi-elasticities which are positive. If researchers do not report such estimates, our metaregression suffers from sample selection and the mean estimate will be upward biased. Besides selective reporting, researchers potentially re-estimate the same regression with slight perturbations (p-hacking) to provide statistically significant results. While this practice does not necessarily introduce bias in reported semi-elasticities, it does induce a correlation between standard errors and coefficient estimates, which impacts estimations using reported standard errors as weights.

Figure 3 presents two pieces of graphical evidence to explore the potential of publication bias in our sample. The left panel presents a funnel plot, where the inverse of reported standard errors is plotted against the reported semi-elasticity. Observations located at the top of the graph are accurately measured and should thus approximate the true mean. In contrast, observations at the bottom of the graph are imprecisely measured and on average expected to deviate to a larger degree from the mean. The plot clearly conveys asymmetry in the distribution of reported estimates, suggesting that the probability of ending up in our sample decreases substantially if the semi-elasticity carries the wrong sign. The right panel presents the density of t-values in our sample, where the vertical line depicts the critical value of 1.96. The spike right next to this line is consistent with p-hacking.

Table 4 column 1 examines the presence of publication bias by estimating the baseline specification, adding the reported standard error as an additional explanatory variable.²⁸ The positive effect of the standard errors on reported semi-elasticities is significant at the one percent level and consistent with publication bias. The implied publication bias is not related to the average sample year (column 2) or whether an estimate was taken from a published study or from an unpublished study (column 3).

Table 4.

Evidence on Publication Bias

Notes: ***, **, and * depict significance at the 1%, 5%, and 10% level, respectively. Fully robust standard errors in square brackets.

Table 4.

Evidence on Publication Bias

Dependent variable: semi-elasticity estimates (no winsorizing)
	(1)	(2)	(3)
Constant	0.520**	0.509**	0.548**
	[0.219]	[0.239]	[0.240]
SE	1.986***	2.033***	1.901***
	[0.385]	[0.432]	[0.404]
SE*zMidyear		0.044
		[0.050]
SE*WP			1.011
			[0.696]
Controls	Yes	Yes	Yes
Group Variance/ Total
variance	0.31	0.317	0.321
Observations	402	402	402

Notes: ***, **, and * depict significance at the 1%, 5%, and 10% level, respectively. Fully robust standard errors in square brackets.

Table 4.

Evidence on Publication Bias

Dependent variable: semi-elasticity estimates (no winsorizing)
	(1)	(2)	(3)
Constant	0.520**	0.509**	0.548**
	[0.219]	[0.239]	[0.240]
SE	1.986***	2.033***	1.901***
	[0.385]	[0.432]	[0.404]
SE*zMidyear		0.044
		[0.050]
SE*WP			1.011
			[0.696]
Controls	Yes	Yes	Yes
Group Variance/ Total
variance	0.31	0.317	0.321
Observations	402	402	402

Notes: ***, **, and * depict significance at the 1%, 5%, and 10% level, respectively. Fully robust standard errors in square brackets.

E. Simulation of Revenue Effects

This section illustrates the revenue impact of profit shifting using the consensus estimate.²⁹ Specifically, drawing on the theoretical model of Huizinga and Laeven (2008), which we elaborate in Appendix 3, the observed tax base in country i (π_t) is the sum of true profits (B_i) and shifted income S_i:

\begin{matrix} π_{i} = B_{i} + S_{i} (B, ε, τ), f o r a l l i = 1, \dots N & (6) \end{matrix}

Shifted income can be either positive or negative and is a function of the unobservable vector of true earnings before profit shifting (B), where different elements of B refer to true earnings in different countries that the MNC operates, the semi-elasticity of reported earnings (ε, negative) and the vector of tax rates in countries where the MNC operates (based on profit maximization set out in equation A. 1 in Appendix 3):

S_{i} = (\frac{B_{i}}{ε}) \frac{Σ_{k \neq i}^{n} (\frac{B_{k}}{1 - τ_{k}}) (τ_{k} - τ_{i})}{Σ_{k = 1}^{n} (\frac{B_{k}}{1 - τ_{k}})}

For a given semi-elasticity, vector of tax rates, and observed tax bases, the true tax base vector B = f(π, ε, τ) can be imputed from Equation (6) with the expression for S_i above.

For the calculations, we obtain corporate tax rates for 2015 in 81 countries from the KPMG database. We obtain corporate tax bases of these countries by dividing corporate tax revenues for 2015, obtained from the IMF’s government finance statistics, by statutory tax rates.³⁰ We then calculate for each country the tax base without profit shifting, the amount of profits shifted in or out of the country, and the associated revenue effect. In the calculations, we impute the semi-elasticity to be 1.5 for the year 2015, using the preferred semi-elasticity of 0.98 (Table 2, column 4) and the Z-Midyear coefficient of 0.036.

Table 5 summarizes the simulation results for four country groups: the US, the 15 largest countries in the sample, the 15 countries with the lowest corporate tax rates, and the average among all 81 countries. Column (1) of Table 5 suggests that reported corporate tax revenue in the US is roughly US$ 360 billion in 2015. The combined federal and state tax rate of 40 percent implies an underlying reported tax base of US$ 900 billion. The high statutory corporate tax rate relative to the rest of the world implies a net outward profit shifting from the US of US$ 198 billion, implying a true tax base of roughly US $1,095 billion. Hence, profit shifting activities eroded the US tax base by about 17 percent, or 0.4 percent of US GDP.³¹ This number is comparable to the 14.4 percent reported by Zucman (2014), but somewhat smaller than Clausing (2016) who estimates a revenue loss between 19 and 30 percent of corporate tax revenue, and Torslov et al (2018) who estimate it at 40 percent of tax revenue from MNCs.

Table 5.

Estimated Revenue Losses

Note: we calculate the variables as follows: CIT revenue is Σ_iτ π_i, reported CIT base is Σ_iπ_i, pre-shifting CIT base is Σ_iB_i, Income shifted is Σ_iS_i, tax revenue losses is

\frac{Σ_{i} τ_{i} S_{i}}{Σ_{i} τ_{i} B_{i}}

and revenues.

Table 5.

Estimated Revenue Losses

Country	US	Big 15	Low-tax 15	Global
	(1)	(2)	(3)	(4)
CIT revenue (in billion. US$)	363	1,563	49	1,888
CIT rate (federal + state, %)	40	31	15	30
Reported CIT base	907	4,983	318	6,213
True CIT base	1,095	5,0784	263	6,213
Corporate income shifted	198	102	−55	0
Revenue loss (in % of CIT revenue)	17.2	4.1	−19.8	2.6
Revenue loss (in % of GDP)	0.42	0.11	−0.43	0.07

Note: we calculate the variables as follows: CIT revenue is Σ_iτ π_i, reported CIT base is Σ_iπ_i, pre-shifting CIT base is Σ_iB_i, Income shifted is Σ_iS_i, tax revenue losses is

\frac{Σ_{i} τ_{i} S_{i}}{Σ_{i} τ_{i} B_{i}}

and revenues.

Table 5.

Estimated Revenue Losses

Country	US	Big 15	Low-tax 15	Global
	(1)	(2)	(3)	(4)
CIT revenue (in billion. US$)	363	1,563	49	1,888
CIT rate (federal + state, %)	40	31	15	30
Reported CIT base	907	4,983	318	6,213
True CIT base	1,095	5,0784	263	6,213
Corporate income shifted	198	102	−55	0
Revenue loss (in % of CIT revenue)	17.2	4.1	−19.8	2.6
Revenue loss (in % of GDP)	0.42	0.11	−0.43	0.07

Note: we calculate the variables as follows: CIT revenue is Σ_iτ π_i, reported CIT base is Σ_iπ_i, pre-shifting CIT base is Σ_iB_i, Income shifted is Σ_iS_i, tax revenue losses is

\frac{Σ_{i} τ_{i} S_{i}}{Σ_{i} τ_{i} B_{i}}

and revenues.

Columns 2 and 3 present the average effect for the largest 15 economies (Big 15), including the US, and the 15 countries with the lowest statutory tax rate (Low-tax 15). Compared to the US, the revenue consequences of profit shifting are much smaller for Big 15: corporate tax revenue declines in these countries by an average 4 percent, or by 0.11 percent of GDP. The reason is that several large countries have CIT rates below the world average. As a result, the 15 countries with the lowest statutory CIT rate gain from inward profit shifting, with corporate tax revenues increasing by almost 20 percent, or by 0.43 percent of their GDP.

Column 4 further shows the global net revenue effect of profit shifting, i.e. the balance of the gains in some countries and the losses in other countries. While the total of shifted income is equal to zero (by definition, as cross-border profit shifting does not change the total income of a MNC), the shifting from high-tax to low-tax countries reduces the total of corporate tax revenue by 2.6 percent of corporate tax revenues, or 0.07 percent of global GDP. These numbers are somewhat smaller than those reported by Crivelli et al. (2017), OECD (2015), and UNCTAD (2015).

Figure 4 presents country-specific results for a selection of simulated variables. The graph shows that seven large economies actually gain from profit shifting because their rate is lower than those prevailing elsewhere.

IV. Discussions and Conclusion

The existing literature on international tax avoidance has significantly advanced over recent years, but there remain several blind spots and puzzles that future research should address.

First, we reviewed granular evidence on tax-motivated behavioral responses of MNCs in areas such as transfer mispricing, strategic location of intangibles, intra-company debt shifting, treaty shopping, corporate inversion, and tax deferral. However, there is much less evidence on some of the other channels of profit shifting. For example, MNCs can structure their businesses to artificially avoid the creation of permanent establishments, thus escaping source country taxation at the outset. Alternatively, they can exploit mismatches in national tax rules, when different countries classify the same entity, transaction, or financial instrument differently.³² Another pressing issue for especially developing countries relates to the taxation of offshore indirect transfers of assets, where MNCs avoid taxes on significant capital gains in source countries (see, e.g. Wei (2015) and IMF (2018)). Understandably, the lack of systematic analysis on these alternative channels of profit shifting is due to both their highly technical and complex nature and the lack of suitable data. More and deeper analysis in this area is needed to advance our understanding on the anatomy of international tax avoidance.

Second, there is little evidence on the interaction between alternative modes of profit shifting. Empirical work tends to focus on specific channels in isolation, so it is difficult to infer information about substitution between alternative channels. A notable exception is Saunders-Scott (2016), who provides insight into whether transfer mispricing and debt shifting are substitutes or complements. The study examines changes in the reported EBIT following a tightening of thin-capitalization rules for multinational affiliates, using the ORBIS database. The findings suggest that MNCs use transfer mispricing and intra-company debt shifting as substitutes (see also Nicolay et.al. (2017)). More research is needed in this direction in order to shed light on how successful targeted anti-abuse rules are in restricting the overall extent of international tax avoidance by MNCs.

Third, there is limited insight into the systematic variation in tax avoidance across countries, sectors, firms and time. Indeed, the relative costs of various shifting methods are likely to differ in all these dimensions. Therefore, conclusions of certain studies are hard to generalize to all MNCs or all countries. Recently, some studies have started to focus on specific sectors or extended the scope to developing countries. For example, using tax records of US MNCs, Dowd et al (2017) find significant non-linear effect of tax differential that points to more shifting into tax havens. Findings by Johannessen, Torslov, and Wier (2017), which uses data from multinational affiliates in 142 countries, including 25,000 corporations in 94 developing countries (from ORBIS), suggest that less developed countries are more exposed to cross-border profit shifting.³³ The literature is too scattered, however, to draw firm conclusions on important sources of heterogeneity.

Fourth, there is little attention in the literature on the interaction between profit shifting and the reallocation of real activities by MNCs. Existing research has focused largely on quantifying either the scale of profit shifting or the impact of taxes on the location of FDI. Yet, profit shifting may interact with investment and thus have complex and unexpected welfare implications, with important policy implications. For instance, some have argued that profit shifting can mitigate distortions from existing tax systems on real capital (Hong and Smart, 2010; Desai, Foley and Hines, 2006). Anti-avoidance measures might then reduce welfare to the extent that they magnify distortions in real capital allocation.

Only a handful of recent studies analyze the spillover effect of anti-avoidance rules on investment. For example, De Mooij and Liu (2018) exploit introductions and tightening of transfer pricing regulation in countries as a quasi-natural experiment and find that transfer pricing regulation has a negative and significant effect on MNC investment in fixed assets. Buettner et al. (2017) provide evidence that thin-capitalization rules have a negative effect on MNC investment financed by debt. Egger and Wamser (2015) explore whether limitations to foreign income exemptions affect investment using a regression continuity design. They find that the German CFC rule decreased foreign subsidiaries’ real investments. The scarce evidence thus suggests that unilateral tightening of profit shifting through anti-avoidance rules may have negative effects on MNC investment. More evidence is needed, however, to inform the debate on effective policy design, including through international coordination.

There are also several puzzles in the literature that remain partially unresolved. One is the ‘large aggregate effects’ versus “small micro effects’. In particular, our meta regressions suggest that firm-responses reflected in aggregated data are substantially larger than in micro data, thereby controlling for several study attributes that may confound the estimated semi-elasticities in primary studies. Our interpretation of this difference is between short-term and long-term responses, e.g. due to adjustment costs and optimization frictions (i.e. fixed costs of tax planning). Indeed, micro-studies typically exploit variation across time from which the contemporaneous effect is obtained while macro-studies exploit cross-sectional variation that yield an estimate of the long-run effect. Moreover, macro studies may cover a wider array of shifting channels than micro studies. Whether this is indeed the case should be explored more deeply.

Another puzzle is our finding regarding debt shifting. The literature directly exploring the extent of debt shifting finds a robust significant impact of corporate tax differences on intracompany debt. One would expect that this would be reflected in studies on the total size of profit shifting by showing a systematic difference between studies using profit indicators that include and exclude interest costs. While our meta regressions confirm this difference, deeper analysis is needed to explain this, which might include methodological issues such as omitted variable bias or attenuation bias.

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Appendix 1. Descriptive Overview of Included Studies

Study	Total estimates	Conditional estimates	Mean	Median	Min	Max
Azemar (2010)	3	2	2.54	1.02	0.99	5.62
Barrios and d’Andria (2016)	8	0	0.49	0.37	0.23	0.95
Becker and Riedel (2012)	3	0	0.76	0.73	0.71	0.84
Beer and Loeprick (2015)	19	18	0.85	0.90	0.39	1.28
Beer and Loeprick (2017)	31	31	1.77	1.69	0.61	2.91
Beuselinck et. al (2016)	25	7	3.60	1.60	−3.25	12.54
Blouin et. al (2012)	1	0	0.54	0.54	0.54	0.54
Castillo and Lopez (2016)	1	0	2.08	2.08	2.08	2.08
Clausing (2009)	5	0	3.76	3.65	1.05	5.52
Clausing (2016)	8	0	2.92	2.84	1.85	4.61
Collins et al (1998)	1	1	0.32	0.32	0.32	0.32
Crivelli et al. (2016)	20	1	2.02	1.33	0.17	5.59
De Simone (2015)	9	9	0.83	0.91	0.25	1.29
De Simone et. al (2014)	2	0	1.00	1.00	0.66	1.34
Dharmapala and Riedel (2013)	1	0	1.13	1.13	1.13	1.13
Dischinger (2010)	16	4	1.60	1.54	0.72	3.32
Dischinger and Riedel (2011)	4	0	3.20	3.18	2.14	4.29
Dischinger et al. (2014)	28	4	0.91	0.68	−0.51	1.82
Dowd et al. (2017)	10	4	0.81	0.84	−0.03	1.44
Grubert (2003)	5	4	0.75	0.85	0.33	1.05
Grubert (2012)	18	6	1.31	1.20	0.63	2.31
Hines and Rice (1994)	6	0	5.16	3.31	2.25	12.99
Huizinga and Laeven (2008)	27	1	1.25	0.98	0.49	3.71
Johannessen et al. (2017)	8	0	0.69	0.75	0.12	1.31
Klassen and Laplante (2012a)	9	9	0.60	0.44	0.17	1.19
Klassen and Laplante (2012b)	8	8	0.91	0.88	0.69	1.28
Loretz and Mokkas (2015)	12	0	−0.07	−0.11	−0.79	0.80
Maffini and Mokkas (2011)	22	0	1.21	1.03	0.23	4.87
Markle (2016)	12	3	0.95	0.92	0.34	1.71
McDonald (2008)	20	14	1.26	1.36	0.73	1.65
Merz and Overesch (2017)	13	12	2.05	2.18	0.23	3.00
Mills and Newberry (2004)	4	0	1.94	1.03	−1.16	6.86
Riedel et al. (2015)	14	14	0.71	0.59	0.29	1.79
Rousslang (1997)	12	0	4.74	5.00	3.63	5.63
Saunder-Scott (2016)	2	0	0.87	0.87	0.66	1.08
Schwarz (2009)	9	0	1.78	2.03	−1.33	3.62
Weichenrieder (2009)	6	4	0.94	0.91	0.79	1.25

Study	Total estimates	Conditional estimates	Mean	Median	Min	Max
Azemar (2010)	3	2	2.54	1.02	0.99	5.62
Barrios and d’Andria (2016)	8	0	0.49	0.37	0.23	0.95
Becker and Riedel (2012)	3	0	0.76	0.73	0.71	0.84
Beer and Loeprick (2015)	19	18	0.85	0.90	0.39	1.28
Beer and Loeprick (2017)	31	31	1.77	1.69	0.61	2.91
Beuselinck et. al (2016)	25	7	3.60	1.60	−3.25	12.54
Blouin et. al (2012)	1	0	0.54	0.54	0.54	0.54
Castillo and Lopez (2016)	1	0	2.08	2.08	2.08	2.08
Clausing (2009)	5	0	3.76	3.65	1.05	5.52
Clausing (2016)	8	0	2.92	2.84	1.85	4.61
Collins et al (1998)	1	1

Topics

Countries

Series

About

Resources

Contributor Notes

Abstract

I. Introduction1

II. International Tax Avoidance–The Issue

A. Brief Overview of the International Tax Architecture

B. Main Channels of International Tax Avoidance

1. Transfer Mispricing

2. Strategic Location of IP

3. International Debt Shifting

4. Tax Treaty Shopping

5. Tax Deferral

6. Corporate Inversions and HQ Location

C. How Effective are Anti-Avoidance Regulations?

III. A New Consensus on tax-motivated profit shifting

A. Data and Descriptive Statistics

B. Synthesis of Primary Estimates: Meta-Regression

C. Efficient Estimation

D. Main Findings

Robustness checks

Publication Bias

E. Simulation of Revenue Effects

IV. Discussions and Conclusion

References

Appendix 1. Descriptive Overview of Included Studies

I. Introduction¹