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Profit shifting and corruption

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Abstract

This paper introduces heterogeneous profit shifting costs induced by corrupt tax officials to the analysis of profit shifting of multinationals. Using a theoretically derived corruption weighted tax differential, we show that corruption increases profit shifting of European firms. We use our estimates to calculate the implied tax revenue elasticities for European countries and find that countries with otherwise similar tax rates face lower tax revenue elasticities when they are more corrupt. This means that corruption negatively affects the revenue gains that countries could have from increasing their tax rates.

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Notes

  1. https://www.oecd.org/ctp/BEPSActionPlan.pdf.

  2. See for example the joint report of IMF, OECD, UN and World Bank mandated at the G-20 Seoul for the G20 Summit (https://www.oecd.org/g20/summits/seoul/48993634.pdf).

  3. See for example the United Nations. Economic Commission for Africa (2015). Illicit financial flows: report of the High-Level Panel on illicit financial flows from Africa. Addis Ababa, page 35.

  4. The case of Glencore, which is a US–Swiss company that has been involved in corruption activities in Congo, is being investigated by the US Department of Justice. For details see: https://www.bloomberg.com/news/articles/2018-07-03/glencore-gets-subpoena-from-u-s-regarding-money-laundering.

  5. For instance, World Bank Enterprise Surveys (https://www.enterprisesurveys.org/data/exploretopics/corruption) report that the average percent of firms experiencing at least one bribe payment request is 3.5%, while the average percent of firms expecting informal payment to public officials is 10.7% among the OECD countries. This implies a high level of informal bribery among the OECD countries. In contrast, for instance in Ukraine, these numbers are 50.4% and 73.1% and in Romania 9.8% and 18.7%, respectively.

  6. Here, we assume that the allocation of profits across countries is given, along with the location choice of multinationals. In the Online Appendix B.3, we relax this assumption and show that our baseline results hold.

  7. https://www.oecd.org/tax/administration/37589900.pdf.

  8. https://www.irs.gov/pub/irs-soi/17databk.pdf.

  9. This assumption is relevant in low- and middle-income European countries that we consider in our empirical analysis. The punishment for assisting in tax evasion in those countries is high and the detection risk is also high, especially when multinational companies are involved. Note that this may not hold in less developed countries.

  10. Corrupt tax officials could, despite the legality of tax avoidance, harass firms by threatening them to increase auditing costs in the case of non-cooperation. In the case of tax evasion, Marjit et al. (2000) have shown that harassment does not influence the level of tax evasion. Harassment only allows corrupt tax officials to extract more bribes from firms. Firms still profit from corruption in the tax administration. The same is true, if we allow for harassment in the case of tax avoidance. However, for simplicity of notation, we abstain from this effect in our analysis.

  11. See for instance recent article from Financial Times (https://www.ft.com/content/9cd87756-269d-11e8-b27e-cc62a39d57a0).

  12. This is a strong assumption. Introducing the risk of punishment would increase the amount of bribery. Depending on how the punishment is shared among the culprits, the rent allocation would change and, in some cases, both parties would not reach an agreement. However, none of these affect the comparative statics of the model as long as we do not assume nonlinear punishment schemes.

  13. In the Online Appendix B.1, we consider how corruption outside of the tax administration affects the generation of profits in the first place.

  14. As in the previous literature [see for example Huizinga and Laeven (2008)], we assume that profit shifting costs as well as bribery costs are not tax deductible. This assumption helps to reduce the calculus substantially and does not change the main implications of the model.

  15. The main result of the model does not depend on the assumption of the specific form of bargaining or the distribution of the bargaining power. This is the case because for the bargaining to be successful, the bribe always has to be smaller than the bureaucracy cost.

  16. For a detailed derivation, see the Online Appendix C.

  17. When we compare our model with one that does not account for corruption (\(c_{i} = 0\)), the marginal subsidiary for which the multinational is indifferent between shifting profits in or out may differ. This is the case because the firm faces a weighted tax differential. Hence, it may be that a multinational shift profits into a subsidiary with a marginally higher tax rate than the average one, because the higher level of control of corruption in that country makes it more lucrative to keep profits there.

  18. For a summary of the discussion on merits of tax competition, see Konrad and Stolper (2016).

  19. The model developed in this paper extends the basic idea that profit shifting costs occurs on both ends of the transfer by accounting for corruption in tax administrations. However, this model is by far less sophisticated than the model of Becker and Davies.

  20. Most studies use, as we do, accounting data, hence, they only indirectly study profit shifting. Two of the few noteworthy exceptions are the recent studies by Bilicka (2019) and by Davies et al. (2018) that utilize confidential corporate tax returns datasets to measure the extent of profit shifting directly.

  21. This is the case since we only have firm-level data for European firms. We know that this particular firm has affiliates in other countries, but we do not have detailed accounting information for many of those affiliates.

  22. See for example Huizinga and Laeven (2008), Dischinger and Riedel, (2011), Dharmapala and Riedel (2013) or Beer and Loeprick (2015). Similar to Huizinga and Laeven (2008), we could construct a sales-weighted or size-weighted corruption adjusted tax rate differential for a subsample of companies for which we have information on sales or assets of majority of their subsidiaries. This substantially limits our sample. However, similarly to Dischinger and Riedel (2011) when doing this, we find that the application of weighted differentials leads to qualitatively comparable results. These are available from the authors upon request.

  23. Davies et al. (2018), for example, show that the bulk of tax loss from transfer price manipulation in France is coming from the actions of a few closely linked multinational firms.

  24. The CFC rules are anti-avoidance provisions designed to prevent diversion of profits to low tax territories. For instance, if the UK profits are diverted to a CFC, those profits are apportioned and charged to a UK corporate interest holder that holds at least a 25% interest in the CFC.

  25. For a detailed discussion on the use of tax haven affiliates, see for example Desai et al. (2004) or more recently Gumpert et al. (2016).

  26. Tax rate data are taken from the CBT Tax Database.

  27. For the list of tax havens used in this paper, see Table 8 in the Appendix.

  28. Alternative robustness specifications include logs of wages instead (results available upon request from authors).

  29. Since our identification comes from differences in tax rates over time, our results are not directly comparable to Huizinga and Laeven (2008), who use a cross-sectional variation in weighted tax rate differentials to show the effects of those on profit shifting. Our results are more comparable to work of Dischinger and Riedel (2011) who use a similar firm fixed effects specification. In their estimations, the un-weighted average tax rate differential affects the ratio of intangible profits to sales negatively, which is what we find as well for profits. The magnitude of the effect is comparable as well; for the results, see Table 1, column 1.

  30. Table 3 in the Appendix presents detailed information on all data sources used.

  31. We experiment with 90% and wholly owned thresholds as well, but they do not change the main results of the paper.

  32. In the light of recently emerging evidence on the importance of reporting zero profits for the extent of multinational profit shifting (see Bilicka 2019), we will show that our results are robust to the inclusion of negative profits.

  33. The underlying definition of corruption that is used to select the different sub-indicators is: Control of Corruption, measuring the exercise of public power for private gain, including both petty and grand corruption and state capture (Kaufmann et al. 2005, p. 5).

  34. We have run the main specifications with the CPI indicators and the qualitative results remain unchanged, but the point estimates vary slightly, mostly due to a slightly different sample composition.

  35. In the Online Appendix B.2, we test the validity of these claims by including in the baseline model interactions with various governance indicators in addition to interactions with control of corruption. In majority of the specifications, the control of corruption interaction with tax rate differential is significant, while the other governance indicator interaction is not. This suggests that our main results are driven by changes in control of corruption rather than overall changes in governance quality.

  36. For the list of countries and mean values of corruption indicators, see Table 7 in the Appendix.

  37. Note that the changes in corruption are often correlated with the reforms effort. For instance, upon EU accession, Poland has reformed its Anti-Corruption Policy, which is visible in the increasing control of corruption index from 2004 onward. Further, Austria has introduced a package of anti-corruption reforms in 2012 in response to the falling levels of control of corruption; this has generated an immediate rebound in the corruption perception index in 2013.

  38. We know that the share of profits shifted should be \(\hat{\beta }_{5} \cdot {\text{CTC}}\). Hence, \({\text{CTC}}_{{{\text{AV}},{\text{C}}}} = .162\) implies a 15.7% profits inflow and \({\text{CTC}}_{{{\text{AV}},{\text{C}}}} = - .191\) a 18.6% profits outflow.

  39. Online Appendix B.1. presents the theoretical underpinning for the alternative estimation equation.

  40. We discuss additional findings in the Online Appendix. Online Appendix A talks about the role of intangible assets. Online Appendix B discusses additional factors that might influence our main results, like the corruption outside the tax administration, the level of government quality and the location choices of a multinational firm.

  41. The underlying data for Figure 3 is summarized in Table 6 in the Appendix.

  42. For a comparison of the corruption adjusted elasticities with non-corruption adjusted ones, which are standard in the literature, see Figure 5 in the Appendix. The elasticities estimated in this paper are smaller and have larger variance than the previously estimated ones.

  43. A possible concern here may be that this effect could also be the result of differences in the exposure of firms to profit shifting opportunities and not differences in the level of corruption. We show this is not the case. In Figure 5 in the Appendix, we compare tax revenue elasticities that account for corruption, with tax revenue elasticities as calculated by the previous literature. The results show that corruption decreases tax revenue elasticities.

  44. The tax revenue elasticities will differ slightly depending on the definition of the CTC parameter used. Figure 6 in the Appendix shows the mean, maximum and minimum tax revenue elasticities implied by the estimates from Table 1 Columns 2–4. The approximation of \({\text{CTC}}\) by \({\text{CTC}}_{\text{AV,C}}\) used to calculate the baseline elasticities in this paper is the lower bound estimate. For instance, tax revenue elasticities can be as low as 0.64 for Italy and 0.76 for Norway. This would imply that Italy may even be losing up to 12% of its tax revenues due to corruption.

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Acknowledgements

We would like to thank Michael Devereux, Marcel Thum, Alexander Kemnitz and participants of the IIPF 2016 meeting for their helpful comments and suggestions. Financial support from the Deutsche Forschungsgemeinschaft (Grant 759-3) is gratefully acknowledged.

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Correspondence to Katarzyna Bilicka.

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Appendix

Appendix

See Tables 3, 4, 5, 6, 7 and 8 and Figs. 4, 5 and 6.

Table 3 Data sources
Table 4 Descriptive statistics.
Table 5 Descriptive statistics on tax differentials.
Table 6 Aggregate tax revenue elasticities for 2013
Table 7 List of countries and mean values of corruption indicators
Table 8 List of tax havens
Fig. 4
figure 4

Definition of different relevant profit shifting groups: A(---), B(– – –), C(–)

Fig. 5
figure 5

Tax revenue elasticities with respect to the top statutory tax rate

Fig. 6
figure 6

Variation in the implied tax revenue elasticities (Table 1 Colum 2–4)

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Bilicka, K., Seidel, A. Profit shifting and corruption. Int Tax Public Finance 27, 1051–1080 (2020). https://doi.org/10.1007/s10797-020-09596-4

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