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The Dominant Role of Large Firms in Profit Shifting

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Abstract

Globally, the largest 0.001 per cent of firms earn one-third of all corporate profits. Nonetheless, there is little understanding of how profit shifting differs across firm size. Using the universe of South African corporate tax returns and global financial accounts, we find that profit shifting is concentrated among a few very large firms and that previous micro studies underestimate profit shifting by failing to account for firm size. This aids to explain the notable gap between micro and macro estimates of profit shifting. We revisit OECD’s micro estimate and find that this may underestimate profit shifting by 40 per cent.

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Notes

  1. Most recently, the Paradise Papers revealed the tax planning strategies of Apple, Google, Nike, and Facebook. Outside of the Western hemisphere, Glencore, SAB Miller, and Barclays are the companies that have caused public outcry in Africa.

  2. See e.g. UNCTAD (2015), IMF (2016), and OECD (2015c).

  3. From a simple back-of-an-envelope calculation: Forbes report that the largest 2,000 companies earned US$3.3 trillion in 2015. In the same year, Tørsløv et al. (forthcoming) estimate global profits were US$11.5 trillion. Finally, ORBIS have managed to identify 200 million companies globally (consolidated accounts), which in all likelihood is a lower bound as much of ORBIS coverage is based on voluntary reporting and fails to capture very small firms.

  4. See for example Jones (2015b) or Boffey (2017).

  5. In this solution the threshold for companies to be included spans from €750 million (pillar 2) to €10 billion (pillar 1) https://www.oecd.org/tax/beps/statement-on-a-two-pillar-solution-to-address-the-tax-challenges-arising-from-the-digitalisation-of-the-economy-july-2021.pdf.

  6. As noted by the OECD in their G20-mandated report on base erosion and profit shifting: “developing countries face difficulties in building the capacity needed to implement highly complex rules and to challenge well-advised and experienced MNEs” (OECD 2014).

  7. OECD (2015a: 32–37) discuss the lack of tax return usage and identify the databases in the USA, Germany, and Sweden. In addition, a recent working paper by Hopland et al. (2018) gains access to Norwegian MNE tax returns.

  8. Particularly worrying is the lack of data on activities in tax havens and the systematic lack of data on US owned multinationals, which are the most aggressive profit shifters according to macro-studies (Tørsløv et al. forthcoming)

  9. The seminal work by Grubert and Mutti (1991) and Hines and Rice (1994) introduced this methodology using macro data and it has since become its own strand of research. Heckemeyer and Overesch (2013) and Dharmapala (2014) give an overview of the later literature, which predominantly relies on micro studies. See also de Mooij and Ederveen (2008).

  10. Concerns about coverage and other shortcomings of the ORBIS database are well explained in the OECD (2015a) report that estimated the global revenue loss arising from BEPS. According to the OECD (2015a), the ORBIS database “is based upon financial account rather than tax return data.” With respect to its representativeness for the purposes of BEPS empirical analysis, Cobham and Loretz (2014) note the Eurocentric nature of the sample and its weakness in coverage of low-income. See OECD (2015a: chapter 1) for a discussion. Other issues with ORBIS relates to the absence of tax haven information (Tørsløv et al., forthcoming), which can lead to underestimates of profit shifting (Dowd et al. 2017). Finally, ORBIS faces issues of double-counting pass-through profits (Blouin and Robinson, 2020).

  11. Beer et al. (2018) do a meta study using past micro evidence and reach a “consensus” estimate of a tax loss of 2.7 per cent of corporate tax receipts.

  12. See Clausing (2016), Crivelli et al. (2015), Hines and Rice (1994), Tørsløv et al. (forthcoming), UNCTAD (2015), and Zucman (2014).

  13. This is because the propensity score matching applied in Bilicka (2019) leads to an exclusion of the very large foreign multinational subsidiaries as no comparable large domestic standalones exists in the data set used.

  14. For an in-depth description of the dataset, see Kreuser and Newman (2018).

  15. Industry codes are based on the International Standard Industrial Classification of All Economic Activities (ISIC4).

  16. In our data, we miss data on sales for 35 entities and on fixed assets for 29 entities. As the UNU-WIDER access to the data is still in its infancy we do not know whether this is an issue of data migration (the information was filled out after the download to the server) or whether the firms have failed to comply in reporting their data.

  17. Discussion with tax advisers does suggest that some companies may fill this information in if at least a 50 per cent ownership stake is met.

  18. Following Hines (2010): Andorra, Anguilla, Antigua and Barbuda, Aruba, Bahamas, Bahrain (The Kingdom of), Barbados, Belize, Bermuda, Cayman Islands, Hong Kong, Cook Islands, Costa Rica, Cyprus, Dominica, Grenada, Guernsey, Ireland, Isle of Man, Jersey, Jordan, Lebanon, Liberia, Liechtenstein, Luxembourg, Maldives, Malta, Marshall Islands, Mauritius, Micronesia, Monaco, Montserrat, Nauru, Netherlands Antilles, Niue, Panama, Samoa, San Marino, Seychelles, Singapore, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Switzerland, Tonga, Turks and Caicos Islands, and Virgin Islands (British).

  19. A company engaging in profit shifting is likely to pay for legal advice and face the probability of legal consequences (which may require legal defence costs). These legal costs are, to a large degree, country-specific; that is, tax authorities and governments are able to increase the legal costs of profit shifting by enacting effective anti-profit-shifting legislation and by distributing resources to enforce this legislation.

  20. Anecdotal interviews with managers indicate that these costs are significant enough for managers to include them in their decision making.

  21. Nielsen et al. (2008) describe how transfer mispricing strategies imply that low-level managers within the MNEs lose the ability to evaluate the true cost and value of internal transactions. Huizinga et al. (2008) describe how using increased cash flows to subsidiaries may create moral hazard implications at the subsidiary level.

  22. Denote the cost function \(C\left( {\frac{S}{\pi }} \right)\) then the optimal share of shifted profits will be independent of \(\pi\). To see this, note that in an internal optimum the marginal costs should yield the marginal tax saving \(C^{'} \left( {\frac{S}{\pi }} \right) = t~ = > C^{{' - 1}} \left( t \right) = ~\frac{S}{\pi }\).

  23. As documented e.g. by Clausing (2003), Cristea and Nguyen (2016), Davies et al. (2018), Hebous and Johannesen (2017) or Wier (2020).

  24. Most famously, the “Double-Dutch-Irish” exemplifies this (see e.g. Ting 2014).

  25. Most notably, Mills et al. (1998) use a confidential survey of 365 US firms (conducted by Slemrod and Blumenthal 1996) and establish that costs of tax planning pr. tax benefit decrease by firm size. Similarly, Wilson (2009) finds, based on news articles, that the likelihood of aggressive tax planning increases by firm size.

  26. UNCTAD (2015) use foreign direct investment (FDI) flow and stock statistics to estimate the impact of haven ownership on the return on assets. In developing countries they find that if the FDI stock was 100 per cent owned through tax havens, return on FDI assets would fall by 11–16 percentage points. In South Africa, the return on assets in haven-owned firms is 3 per cent. According to UNCTAD (2015) the return on assets of these affiliates would have been 14-19% (3%+11–16%) => 70–80% of haven-owned firms’ tax base shifted out of South Africa.

  27. Total natural resources rents (percentage of GDP) are three times the developed country average in South Africa and other middle-income countries, and twelve times as high in low-income countries (World Bank 2013).

  28. See e.g. Jones (2015b) or ActionAid (2012, 2015).

  29. See de Mooij and Ederveen, (2008), Dharmapala (2014), or Heckemeyer and Overesch (2013) for a review of this literature.

  30. Following Hines (2010): Andorra, Anguilla, Antigua and Barbuda, Aruba, Bahamas, Bahrain, (The Kingdom of), Barbados, Belize, Bermuda, Cayman Islands, Hong Kong, Cook Islands, Costa Rica, Cyprus, Dominica, Grenada, Guernsey, Ireland, Isle of Man, Jersey, Jordan, Lebanon, Liberia, Liechtenstein, Luxembourg, Maldives, Malta, Marshall Islands, Mauritius, Micronesia, Monaco, Montserrat, Nauru, Netherlands Antilles, Niue, Panama, Samoa, San Marino, Seychelles, Singapore, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Switzerland, Tonga, Turks and Caicos Islands, and Virgin Islands (British).

  31. To see this, note that \(\frac{{{\text{Tax}}~\;{\text{loss}}}}{{{\text{Current}}~\;{\text{tax}}~\;{\text{revenue}}}} = \left( {\frac{1}{{1 - \beta _{3} }} - 1} \right),{\text{which }}\;{\text{is }}\;{\text{400}}\;{\text{ per}}\;{\text{cent }}\;{\text{in }}\;{\text{the}}\;{\text{ case of}}\;\beta _{{\text{3}}} = 80\% \;{\text{and}}\;50\;{\text{per}}\;{\text{cent}}\;{\text{in}}\;{\text{the}}\;{\text{case}}\;{\text{of}}\;\beta _{{\text{3}}} = 33\%\).

  32. See Johannesen et al. (2020) and Bilicka (2019) for an elaborate discussion of this.

  33. We can add to this that some of the world’s very largest subsidiaries in tax havens (and presumably most actively profit shifting) are systematically absent in ORBIS, which again flattens the size curve. See Tørsløv et al. (forthcoming).

  34. Other concerns about using the ORBIS database are discussed in the Introduction, OECD (2015a: 32–37), Blouin and Robinson (2020) and Tørsløv et al. (forthcoming).

  35. See e.g. Zucman (2014) for examples of this.

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Acknowledgements

This study has been prepared within the UNU-WIDER SA-TIED project. An earlier version of this paper was titled: ‘Big and Unprofitable’ (Wier & Reynolds 2018). We wish to thank Alan Auerbach, Sarah Clifford, Kimberly Clausing, Maya Forstater, Johannes Hermle, Katarina Helena Jensen, Niels Johannesen, John Rand, Nadine Riedel, Emmanuel Saez, Finn Tarp, Thomas Tørsløv, Juan Carlos Suárez Serrato, Danny Yagan and Gabriel Zucman for useful comments. A very special thanks goes to Singita Rikhotso, Friedrich Kreuser and Wian Boonzair for excellent research assistance. Finally, we would like to thank seminar participants at UC Berkeley and at the UNU-WIDER conference on ‘Growth and Development Policy: New Data, New Approaches and New Evidence’.

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  1. Department of Economics, University of California, Berkeley, USA

    Ludvig Wier

  2. National Treasury of South Africa, Pretoria, South Africa

    Hayley Erasmus

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Wier, L., Erasmus, H. The Dominant Role of Large Firms in Profit Shifting. IMF Econ Rev 71, 791–816 (2023). https://doi.org/10.1057/s41308-022-00180-w

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