Back Matter

Appendix 1. Country Listing and Classification

Low- and middle income countries: Afghanistan*, Albania, Algeria*, Antigua and Barbuda*,1/, Argentina, Armenia, Bangladesh, Barbados1/, Republic of Belarus, Belize1/, Benin*, Bhutan*, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Burkina Faso*, Burundi*, Cambodia, Cameroon, Cabo Verde*, Central African Republic*, Chad*, Chile*,2/, China, Colombia, Comoros*, Republic of Congo*, Costa Rica1/, Côte d’Ivoire, Djibouti*, Dominica*,1/, Dominican Republic, Ecuador, Egypt, El Salvador, Republic of Equatorial Guinea*, Eritrea*, Ethiopia, Fiji, Gabon, The Gambia, Georgia, Ghana, Guatemala, Grenada*,1/, Guinea, Guinea-Bissau*, Guyana, Haiti, Honduras, Hungary2/, India, Indonesia, Islamic Republic of Iran, Iraq, Jamaica, Jordan1/, Kazakhstan, Kenya*, Kyrgyz Republic, Lao P.D.R.*, Latvia, Lebanon*,1/, Lesotho*, Liberia*,1/, Libya*, Lithuania, Former Yugoslav Republic of Macedonia, Madagascar*, Malaysia, Malawi, Maldives*,1/, Mali*, Mauritania*, Mauritius1/, Mexico*,2/, Moldova, Montenegro*, Mongolia*, Montserrat*,1/, Morocco, Mozambique, Myanmar*, Namibia, Nepal*, Nicaragua*, Nigeria, Niger*, Pakistan, Panama1/, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Russian Federation, Rwanda*, São Tomé and Príncipe*, Senegal, Serbia*, Seychelles*,1/, Sierra Leone, Sri Lanka, Solomon Islands*, South Africa, St. Kitts and Nevis*,1/, St. Lucia1/, St. Vincent and the Grenadines1/, Swaziland, Syrian Arab Republic, Tajikistan*, Tanzania, Thailand, Togo*, Tonga*,1/, Tunisia, Turkey2/, Turkmenistan, Uganda, Ukraine, Uruguay Uzbekistan, Vanuatu*,1/, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe.

High income countries: Australia2/, Austria2/, The Bahamas1/, Bahrain*,1/, Belgium2/, Canada2/, Croatia, Cyprus1/, Czech Republic2/, Denmark2/, Estonia2/, Finland2/, France2/, Germany2/, Greece2/, Hong Kong SAR1/, Iceland2/, Ireland1/,2/, Israel2/, Italy2/, Japan2/, Korea2/, Kuwait, Luxemburg1/,2/, Malta1/, Netherlands2/, New Zealand2/, Norway2/, Oman, Poland2/, Portugal2/, San Marino*,1/, Saudi Arabia*, Singapore1/, Slovak Republic2/, Slovenia2/, Spain2/, Sweden2/, Switzerland1/,2/, Trinidad and Tobago*, United Arab Emirates, United Kingdom2/, United States2/.

Note: Classification by income group follows the World Bank. Data on CIT rates are available for all countries listed; * indicates that data on CIT revenue (and hence base) are not available; 1/ indicates countries labeled, following Gravelle (2013) as ‘havens’; 2/ indicates an OECD member.

Appendix 2. Results Using Average Effective Tax Rates

Focusing again only on developing countries, Appendix Table A1 presents results on base and strategic rate spillovers (in the first and last three columns respectively) using average effective tax rates (AETR) instead of the statutory rates used in the text. The sample becomes much smaller, but still contains 43 developing countries. Data limitations mean that in this case a haven-weighted average of AETRs cannot be constructed, so attention concentrates on the other three weighting schemes.

Appendix Table A1.

Base and Strategic Rate Spillovers, using AETR 1/

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Note: Dependent variable is the CIT base. Full set of year dummies and control variables in all regressions. Robust standard errors, in parenthesis; ***(**,*) indicate significance at 1 (5, 10) percent.

One step, robust, with instruments based on first lag of differences in the CIT tax base and CIT tax rates (collapsed to avoid proliferation in the number of instruments) in levels equation, and second lags of their levels in the differenced equation.

The results for base spillovers are largely consistent with the results in the text. In particular, the short-term base spillover effect when weighting ATERs abroad using GDP (column (1)) or inverse-distance (column (3)) is large, the former, for instance, taking a coefficient of 0.56. This is very similar to the result in column (1) of Table 4; as is the long-run base spillover effect of 1.33. And the significance of the effects is now somewhat higher. When using the unweighted AETR (column (2)), the short-term base spillover coefficient falls, to 0.14, but becomes more significant; this pattern is again similar to that in Table 4, though the coefficient there is somewhat higher, at 0.21. Because adjustment is more sluggish, however, the long-run effect using the AETR is larger, and also proves more significant, than the estimates in either Table 4 or column (1).

On strategic rate spillovers, columns (4) and (6) indicate no significant effect from either GDP-weighted AETRs (which is consistent with previous results for advanced economies (Albrecht and Hochgatterer, 2012)) or from inverse-distance weighted rates. This last differs from the corresponding result (using statutory rates) in Table 8. Only for the unweighted average AETR (column (5)) is the effect significant, with a magnitude in line with the findings above for statutory tax rates. Signs of strategic rate-setting interactions thus seem somewhat stronger in relation to statutory tax rates than to AETRs.

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1

This paper builds on IMF (2014). We are grateful to Tarun Narashimhan for assistance with the data, and to seminar participants and colleagues for many helpful comments and suggestions. Errors and view are ours alone.

2

Though important steps are being taken to involve developing countries in the BEPS process.

3

Civil society has also drawn attention to particular instances, as for example in Action Aid (2010) (on which see also Schatan (2012)). OECD (2014) takes stock of specific BEPS action items most relevant for developing countries.

4

Results here relating to effects on ‘developing countries’ refer to the subset of low and middle income countries (as classified by the World Bank) listed in Appendix 1 for which the necessary data are available other than those (listed in footnote 12) that are resource-rich.

5

See also the review in Leibrecht and Hochgatterer (2012).

6

Other studies, such as Clausing (2007), Brill and Hassett (2007) and Devereux (2007) have explored how corporate tax revenues (relative to GDP) vary with countries’ own statutory CIT rates. Abbas and Klemm (2013) perform a similar analysis for 50 developing countries. The empirical results reported here of course have implications for that question, too, but for brevity, this is not pursued below.

7

A richer treatment would differentiate between one tax directed to the use of capital and another on profits attributed to each jurisdiction. These would then act differently on real investment decisions and base shifting (along the lines of Keen and Konrad (2013)). Though it is somewhat artificial to think of base shifting in terms of apparent amounts of real capital employed rather than attributed profits, the single instrument specification here suffices, given limitations on the tax rate data available, for the central purpose of guiding the empirics.

8

The separation of the decisions on ki and the sij that emerges here is, of course, extreme, They would become linked if, as may be plausible, that shifting tax base into a county were easier if there is some real activity there.

9

The use of GDP as an indicator of size is not entirely clean, since, as OECD (2015) notes, measured GDP may be affected by profit shifting (through, for instance, mispricing of exports and imports). This though seems likely to be of second order importance (certainly less marked than effects on GNP) and provides another reason for the instrumenting described below.

10

The assumption in this case is thus it is equally easy to shift profits in/out of all ‘havens’, but impossible to shift profit through non-havens.

11

The average CIT rate in the ‘havens’ is around 17 percent, compared to 32 percent for the full sample (Table 1). Many havens are small, however, and a low rate is common among smaller countries more generally. Regressing the CIT rate on country size (which enters with a significant positive coefficient, as models of tax competition would predict) and a dummy for tax haven status (and the using other controls being used), it emerges that tax-havens actually have, on average, a significantly higher CIT rate than otherwise similar countries.

12

The countries for which the AETR is available are: Argentina, Botswana, Brazil, Bulgaria, Chile, China, Colombia, Costa Rica, Czech Republic, Ecuador, Egypt, Estonia, Ghana, Hong Kong SAR, Hungary, India, Indonesia, Israel, Kenya, Korea, Latvia, Lithuania, Malaysia, Mauritius, Morocco, Namibia, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Senegal, Singapore, South Africa, Sri Lanka, Tanzania, Thailand, Turkey, Uganda, Ukraine, Uruguay, and Zambia.

13

These are: Bahrain, Chad, Republic of Congo, Kazakhstan, Kuwait, Libya, Mexico, Nigeria, Norway, Oman, Russian Federation, Saudi Arabia, Syrian Arab Republic, Trinidad and Tobago, United Arab Emirates, and Venezuela.

15

The share of agriculture in aggregate value added is taken from the World Development Indicators (WDI) database; trade openness is calculated from the IMF’s International Financial Statistics (IFS) database; per capita GDP is in constant (2000) U.S. dollars, taken from the WDI; inflation is the annual change in the consumer price index, taken from the IFS.

16

The same is true for most results reported here. For cases in which the Hansen test is uncomfortably high, we also report the Sargan test which is less vulnerable to instrument proliferation, though not robust to heteroskedasticity (Roodman, 2009).

17

Calculated as (0.17/8.79) × 100.

18

Interpreted, recall, as in footnote 4.

19

Moving from column (3) to (4) reduces the number of countries in the sample because some non-OECD countries are high-income, but increase the number of observations because a few OECD countries are middle income and with a good number of observations.

20

For discussion of these wider issues, see for instance Devereux and Vella (2014) and IMF (2014).

Base Erosion, Profit Shifting and Developing Countries
Author: Ernesto Crivelli, Ruud A. de Mooij, and Mr. Michael Keen