Nash Bargaining, Splitting Factor, and Ex-Post Outcome Testing

A research that produces know-how or other intangibles often involve highly skilled personnel, and vary in terms of importance and success. If the parties involved are independent from each other, a combination of market force, bargaining power, self-interest and profit maximizing behaviour of both parties will result in return and remuneration that are at arm’s length.

If the parties are affiliated, pricing could be distorted by other factors. One of which is tax-motivated profit shifting. If the tax rates governing the parent and the subsidiary differ, taxpayer may be tempted to apportion income beyond what is arm’s length to the entity in low-tax jurisdiction. As enterprises become globalized, and capitals increasingly morphed into intangibles (Haskel and Westlake, 2017) this problem has grown in size and complexity (Borkowski and Gaffney, 2012).

The arm’s length principle purports that if conditions are made or imposed between affiliated enterprises that differ from those that would be made between independent enterprises negotiating at arm’s length, that difference must be included in the profits of that enterprise and taxed accordingly. Further, Para 1.40 of OECD Transfer Pricing Guideline 2017 states that “All methods that apply the arm’s length principle can be tied to the concept that independent enterprises consider the options realistically available to them.”

“Options Realistically Available”

“Options realistically available” implies that independent, economically-rational enterprises would strive for Pareto optimality, i.e. only enter into a transaction if it is not expected to make them worse off than their next best option. They will only enter into the transaction if they see no alternative that offers a clearly more attractive opportunity to meet their commercial objectives.

One example of the adoption of options realistically available in domestic regulation is the US Treasury Regulation Section 482, which cites “alternatives available” to the taxpayer in determining whether the terms of the controlled transaction would be acceptable to an uncontrolled taxpayer faced with the same alternatives and operating under comparable circumstances. The changes to Section 482 by the Tax Cuts and Jobs Act (TCJA) 2017 also stipulates “realistic alternatives” as a basis of valuation of intangible property transfer.

In the Indonesian tax regulation, the Minister of Finance Regulation no. 22/PMK.03/2020 concerning Advance Pricing Agreement also contains similar clause in its transfer pricing provision. Article 14 of PMK-22/2020 considers transactions involving service, use/the rights to use intangibles, cost of debt, transfer of property, business restructuring, and cost contribution arrangement (CCA) to be special transactions that warrant a preliminary step. For the transaction involving transfer of property and business restructuring, the regulation mandates that taxpayer must demonstrate that such transaction is “the best option out of other available options”. This clause also reiterates the ex-ante application of arm’s length principle in the Indonesian transfer pricing regime.

Parekh (2015) and Amici (2020) proposed various approach to identify, value, and determining options realistically available, inter alia:

• Capital budgeting, using internal calculation to value a project based on its internal rate of return or its net present value or its payback periods, or a combination of these methods ;
• Opportunity cost, using explanation of why the other options are not realistically available, e.g. due to being incompatible with the business of taxpayer as such, commercially unattractive, not available at the time of transaction, not acceptable by the other party, or not in accordance with the regulation
• Best alternative to a negotiated agreement (BATNA), using reservation point and alternatives if negotiation fails
• Risk simulation, using the expected value of the investment (weighted average sum of all the probabilities of the possible outcomes of an investment) and the variance (deviation of possible outcomes). Other techniques such as Monte Carlo simulation could also be used.
• Bargaining power theory and game theory, using hypothetical negotiation that can be simulated based on the bargaining power, the contributions made to the marginal benefit of the transaction, and the strategy of the other party to the negotiation

In the context of bargaining power theory and game theory, Sattertwaithe (2019) highlights the similarity of “bargaining problem” (where two players negotiating over the apportionment of something desirable) to the provision of “options realistically available”. Applying John Nash’s bargaining theory, Sattertwaithe propose that seeking a Nash equilibrium strategy will maximize the utility for the player regardless of what strategy the other player adopts. This Pareto optimality will imply the most efficient allocation of utility relative to the bidding parties’ respective alternatives to entering into the transaction – hence, an arm’s length condition.

Probably owing to its mechanistic and mathematical foundation, Nash’s theory rarely enters the legal world. Even its admissibility in patent infringement case is somewhat inconsistent and vary from judge to judge, depending on its ties to the facts of the case (e.g. Compare Robocast Inc. v. Microsoft Corp. (2014), Gen-Probe Inc. v. Becton Dickinson & Co. (2012)).

Economists, on the other hand, have noted the applicability of Nash’s bargaining in transfer pricing, for instance in interdivision negotiated price (Clempner and Poznyak, 2017) or vertically integrated supply-chain (Rosenthal, 2008). There have also been studies of game theory’s application for specific taxation purpose such as in applying profit-split method (Pogorelova, 2015; Voegele, Gonnet, and Gottschling, 2008) or a BEPS-related reporting game (Dorey, 2015). Indeed, Sattertwaithe noted at least 6 elements of similarity between Nash bargaining problem with transfer pricing in general: 1) players and subject of bargaining, 2) alternatives and rationality, 3) surplus utility maximization, 4) zero-sum competing interest, 5) utility value, and 6) allocation.

Sattertwaithe further notes that the profit-split method is readily applied to intangible property primarily because it offers a solution when each party provides unique and valuable contributions but comparable data are lacking, and that an allocation of value (e.g. residual profit) should be calculated anyway under profit-split. For a tax authority’s perspective, profit-split also overcomes the deficiency of one-sided methods where they automatically assume that the residual profit earned by the “untested party” as arm’s length, even though it is literally untested (Wells and Lowell, 2014). This opens up an avenue for abuse. In the case of intangibles, an MNE group can easily designate its intellectual property holder in low-tax jurisdiction, reducing its overall tax liability on royalty income and shielding it from adjustment.

We thus borrow some parts of profit-split into our calculation.

Nash Bargaining

Nash solution for bargaining problem is:

\(\max x_{P}^{S}=(U(x_P)-V_P)(U(x_S)-V_S)\)

where U denotes utility; subscript P denotes Player P; subscript S denotes Player S; and V denotes fall-back utility, which would be gained if the player chose the second-best option to bargaining that is realistically available to her. X is possible bargaining outcome, for which if U(X) > V implies a surplus utility.

This section is again motivated by Sattertwaithe’s paper. However, we differ in the sense that we use a tax administration’s perspective in doing arm’s length outcome testing (ex post) approach when doing the audit. We thus do not assume the existence of third party firm to value the (ex ante) price of intangible. At the very least, this could serve as a sanity check in intangible transfer pricing analysis. Para. 6.113 of OECD TPG 2017 is relevant here, as transferor would not be expected to accept a price for the transfer of either all or part of its rights in an intangible that is less advantageous to the transferor than its other realistically available options which includes making no transfer at all. In a similar vein, Para. 6.79 of OECD TPG 2017 noted that “Compensation based on a reimbursement of costs plus a modest mark-up will not reflect the anticipated value of, and the arm’s length price for the contribution of the research team in all cases.”

The Setup

Assume a parent company P and subsidiary S, where – besides doing its routine functions – also conducts R&D activity to enhance P’s intangibles. (Despite its “contract research” features, Para. 7.41 OECD TPG 2017 note that the consideration of options realistically available may also prove useful in this situation.) Assume that the R&D results in a material, identifiable structural advantage in the market (thus valuable). This synergistic benefit increases the combined profit of P and S, either via increased sales and/or reduction of costs.

Following the residual approach for profit split, we first calculate the residual income of P and S after deducting the remunerations for their respective routine functions, hereinafter denoted as Π, such that:

\(\Pi=(Y_P-C_P-r_P)+(Y_S-C_S-r_S)\)

where r_P and r_S denote the routine function of P and S, respectively. Suppose S is remunerated for its R&D function using a cost-plus method, where some mark-up µ is added to R&D costs borne by S (denoted C_r). The Pareto-optimum fall-back positions of P and S are thus

\(V_P=\frac{(1+\mu)C_r}{\Pi}\)

and

\(V_S=\frac{C_r}{\Pi}\)

where there are additional profits of (1 + µ) C_r for P for not having to remunerate S; and C_r for S for not having to bear the R&D costs. The Nash bargaining problem is thus used to solve:

\(\max x_{P}^{S}=(X-V_P)-((1-X)-V_S)\)

which is done by first taking the derivative with respect to X,

\(\frac{d}{dx}(X-(\frac{(1+\mu)C_r}{\Pi})((1-X)-\frac{C_R}{\Pi})\)

then setting it to zero. Resulting in:

\(2X=1-\frac{C_r}{\Pi}+\frac{(1+\mu)C_r}{\Pi}\)

which then could be solved for X, the proportion of Π for P; and (1 – X), which is the proportion of Π for S.

The usage of Nash bargaining as “splitting factor” indeed differs from the usual asset/capital/cost-based allocation keys. We note that while R&D expense may be suitable for manufacturers, it may be insufficient given that: 1) it is only independently born by S, and 2) it may not be a reliable measure of the relative value of the transaction, taking into account options realistically available.

Illustration:

P is the parent of S, a contract manufacturing companies which sources raw material from third party suppliers. S manufactures, and subsequently sells the finished goods to P. P then sells the product to third party customers. S’s cost of goods sold and P’s sales are of independent transactions.

Assume that 100% of P’s inventory comes from S, and 100% of S’s sales are sold to P. S also conducts research for improving the products. P will remunerate S by S’s costs of doing research with a mark-up of 10%. P will also remunerate S for its contract manufacturing function with a full-cost mark-up of 10%.

Suppose the cost of research is 2, thus remuneration from P to S related to research is 2.2. The combined profit of P and S, after remunerating their respective routine functions is 16. Suppose it is sufficiently established that there is simply no justification from the taxpayer as for why the residual profit should all be automatically attributed to P, in the absence of their internal arrangement. (For example, P neither performs nor controls the research.) Hence the relative bargaining position is:

\(2X=1-\frac{2}{16}+\frac{2.2}{16},X=\frac{81}{160}\)

Therefore, out of profit of 16, 8.1 could be attributable to P, while 7.9 could be attributable to S. (This is almost similar to if the taxpayer use profit-split with 50:50 splitting factor). Nevertheless, this shows that S is inadequately remunerated since – viewed in totality – it is doing functions beyond mere contract manufacturer-contract research service provider.

A Partial Contribution Approach

The above-mentioned residual approach necessitates tax administration to calculate the routine function of P and S. We note that this does not take into account a marketing function done by P that may be similarly valuable. One obvious approach is to take into account marketing expense in calculating the relative bargaining position, which results indeed in higher proportion for P. Alternatively, we may calculate both parties’ contributions simultaneously.

Consider that the P’s sales is affected by S’s contributions in the form of R&D, or further development or enhancement of manufacturing know-how (which is accounted in S’s cost of goods sold or costs of employee). These contribution enables P to either increases price or maintains a desired profit margin given a determined price (via reduced costs). We thus propose – combining P’s effort to market the products – that a log-log function of:

\(\ln(Y_P)=\alpha+\beta_1\ln(C_r)+\beta_2\ln(C_m)+\epsilon\)

where Cm is P’s marketing expense and ε is error term, may be appropriate. The model could of course be expanded to include other costs, or to use profit in lieu of sales. The error term, which may reflect the residual, “non-routine” profit, may also be employed. (These alternatives warrant further explorations, which is the limitation of this post.)

Exploiting approximation that (1 + x)^a ≈ 1 + ax for small a, then % Δ Y_P ≈ β1% Δ C_r, i.e. a 1% change in S’s R&D cost result in β1% change in P’s sales. Π is then obtained by approximation of β1% * Y_P, and the Nash bargaining as mentioned above will follow.

Second alternative, considering that not every R&D could be expected to always increase sales – especially if the research is “blue-sky” – we may instead be interested in R&D expense variability in relation to the variability of sales. Using variability instead of correlational direction may also alleviate the drawback that using historical data would arguably constitute a hindsight.

Following Shorrocks (1982), the proportional contribution of factor C_r to the decomposition of the variance of Y_P could be computed as:

\(s_{C_r}^{*}=\frac {Cov(C_r,Y_P)}{\sigma^2(Y_P)}\)

where cov(C_r, Y_P) is the joint variability of C_r and Y_P, and σ^­2(Y_P) is the variance of Y_P. While this is not intuitively easy to associate, via Scherrer (1984), coefficient of determination R^­2 could be computed as:

\(R^2=\sum\limits_{j=1}^{k}a_jr_{xy_j}\)

where a_j is standardized regression coefficient of j-th explanatory variable, and r_y,xj is the Pearson correlation coefficient of y and x_j. Assuming we regress C_r and other variables mentioned above to Y_P, then the contribution of C_r to the variance of Y_P equals to:

\(a_{C_r}r_{{Y_P}{C_r}}=a_{C_r}\frac {Cov(C_r,Y_P)}{\sigma^2(Y_P)}\)

which sums to unity.

It should be noted that this function is not monotonic. A more exact approach would be to use Shapley-Owen decomposition. But for k parameters this requires k! combinations, hence 2^k possible models to be calculated, and this is computationally expensive.

Illustration:

Assume P as the parent of a contract manufacturing subsidiary S similar to the illustration above. The table below gives the sales of P, R&D expense of S, and marketing expense of P, all in natural log, for the last 10 fiscal years:

The log-log regression gives result:

So even though the coefficient of R&D expense is higher than marketing, given that marketing expense is more statistically significant than R&D in explaining P’s sales, the contribution of R&D is lower than marketing expense. In this case, 33% and 62% of P’s variability of sales may be approximately contributed by S’s R&D activity and P's own marketing activity, respectively (5% of sales variability is due to other factors). Subsequently, the current pricing policy could be tested against 35:65 split  (0.33:0.95 = 0.35 and 0.62:0.95 = 0.65) similar to the Nash bargaining problem as outlined above, and see if their ex-ante testing is appropriate.


Conclusion

Nash bargaining could be used in ex-post outcome testing whether a transaction involving research resulting in valuable intangibles would have been entered into by independent parties, taking into account options realistically available. It should be noted that this analysis is presumptive, and should not be taken as prima facie proof of transfer mispricing. OECD cautions that better-than-expected result may not be reasonably foreseeable ex-ante by taxpayers. Indeed, assuming taxpayers do not provide adequate justification of ex-ante pricing, insofar as the remuneration for transferor and the actual outcome do not deviate by 20% from the projection, Para. 6.194 OECD TPG 2017 discourages the usage of ex-post facto rationalization, as it is considered as hindsight.

Reference

Minister of Finance Regulation no. PMK-22/PMK.03/2020

OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations, 2017 Edition

Amici, D. (2020) In-Depth Analysis of the Concept of Options Realistically Available in Transfer Pricing. 27 Intl. Transfer Pricing J. 2, pp. 112-122

Borkowski, S. and M. A. Gaffney (2012) “Uncertainty and Transfer Pricing:(Im)Perfect Together?” J. Int’l Acct. Auditing & Taxation Vol. 32 (2012). IBFD Journal Articles & Papers

Brealey, R. A., S.C. Myers, and F. Allen. (2019) Principles of Corporate Finance ch. 5 (13th ed.). McGraw-Hill

Bullen, A. (2011) Arm’s Length Transaction Structures: Recognizing and Restructuring Controlled Transactions in Transfer Pricing. IBFD

Clempner, J. B. and A. S. Poznyak (2017) “Negotiating Transfer Pricing Using the Nash Bargaining Solution,” 27 Int’l J. Applied Mathematics & Comput. Sci. 853

Dorey, M. (2015) “To Audit or Not to Audit: Applying Game Theory to a Post-BEPS World,” 24 Transfer Pricing Rep. 404 (Aug. 6, 2015)

Haskel, J. and S. Westlake. (2017) Capitalism Without Capital: The Rise of Intangible Economy. Princeton University Press

Hafkenscheid, R. P. F. M. (2011) De bepaling van een zakelijke risicoallocatie in een business restructuring, Weekblad voor Fiscaal Recht 2011/660 (12 May 2011), at 660-668

Fisher, R. & W. Ury (1981) Getting to Yes: Negotiating Without Giving In. Penguin Group

Parekh, S. (2015) The Concept of “Options Realistically Available” under the OECD Transfer Pricing Guidelines. 22 Intl. Transfer Pricing J. 5, pp. 297-307 (2015). IBFD Journal Articles & Papers

Pogorelova, L. (2015) “Transfer-Pricing and Game Theory,” 43 Intertax 395

Rosenthal, E. C. (2008) “A Game-Theoretic Approach to Transfer Pricing in a Vertically Integrated Supply Chain,” 115 Int’l J. Production Econ. Oct. 2008

Sattertwaithe, B. M. (2019) Nash Bargaining Theory and Intangible Property Transfer Pricing. Tax Notes Federal, September 30 2019 Issue

Scherrer, B. (1984) Biostatistique. Quebec, Canada: Gaetan Morin

Shorrocks, A. F. (1982) “Inequality Decomposition by Factor Components”. Econometrica. Vol. 50 No. 1 (Jan. 1982)

Voegele A., S. Gonnet, and B. Gottschling. (2008) “Transfer Prices Determined by Game Theory,” Tax Planning Int’l Transfer Pricing

Wells, B and C. Lowell (2014) “Tax Base Erosion: Reformation of Section 482’s Arm’s Length Standard”. Florida Tax Review. Vol. 15 no. 10

Through the CbCR Looking-Glass, and the Profit Shifting Found There

Last week, the OECD published for the first time its aggregated and anonymised Country-by-country Reporting (CbCR) data. Despite its limitation, CbCR is quite a significant move in the fight against corporate tax abuse, as a part of Action Plan on Base Erosion and Profit Shifting (BEPS).

Information asymmetry has always been the bane of tax authority, especially in dealing with multinational enterprise. Prior to transfer pricing documentation regime, tax authority relies much on domestic tax return and financial statements in order to understand MNE's structure.

Those aforementioned sources, understandably, do not paint a complete picture of the functions performed, assets used, and risks assumed by each of MNE's constituent entities. The closest analogy that we often use is "blind men and elephant". A group of blind men heard that a strange animal, called an elephant, had been brought to the town, but none of them were aware of its shape and form. The first person, who touched the trunk, said, "This animal is like a snake". Another one whose hand reached its ear, it seemed like a kind of fan. Another person, whose hand was upon its leg, said that the elephant is like a tree-trunk, or a pillar. They are not entirely incorrect, of course, but they are missing the big picture.

CbCR attempts to bridge this asymmetry by requiring MNE to report its economic activities in each jurisdiction it operates. CbCR's first main forms, CbC-1, lists MNE's financial information (revenues, profit/loss, taxes, assets, capital, and employees) in per country basis. The second form, CbC-2, lists MNE's entities alongside its functions (e.g. manufactures, procurement, distribution/marketing, R&D, service, financing, etc.)

CbC-1 and CbC-2

Nevertheless, this trove of information comes with a trade off.

Firstly, CbCR's functions are limited to transfer pricing and other BEPS-related risks assessment, as well as for statistical purposes. It cannot be used as conclusive proof that there exists incorrect transfer pricing. It also cannot be used for global formulary apportionment purpose. In short, it cannot be used directly for transfer pricing correction in tax audit, but only to initiate further inquiries.

Lastly, CbCR is not public, rendering its use for public accountability limited. (Some MNEs voluntarily made their CbCR public, however, but this is not a requirement.) For the purpose of measuring BEPS risks, OECD collects aggregated and anonymised CbCR from many jurisdictions. By aggregating and anonymising the CbCRs, it make them difficult to trace back to certain MNEs, which once again serve CbCR's private characteristic. The part of OECD's report on Indonesia serves as the basis for our profit shifting measure here.

Global Profit Shifting and Revenue Loss Measures

Profit shifting to country, \(i\), denoted \(S_i\), is measured from the profit booked \(\pi_i\), and theoretical profit \(P_i\), i.e.

\(S_i = \pi_i - P_i\)

The profit booked is taken from profit (loss) before tax in CbC-1.

We employ 2 estimation strategies. First, we follow Tax Justice Network (TJN)'s formula in measuring share of economic activity.

\(P_i\) is calculated by multiplying the total profits by the share of economic activity. The share of economic activity is calculated on the basis of unrelated party sales, \(R_i\), and number of employees, \(E_i\), i.e.

\(P_i = \sum\limits_{i}{\pi_i}\cdot (\frac{\frac{1}{2}\cdot R_i}{\Sigma_i R_i}+\frac{\frac{1}{2} \cdot E_i}{\Sigma_i E_i})\)

We also employ the so-called "Massachusetts formula", where instead of using half-revenue half-employee as weights, we use weighting from unrelated party revenue, employees, and tangible assets \(A_i\), each with a third of weight.

\(P_i = \sum\limits_{i}{\pi_i}\cdot (\frac{\frac{1}{3}\cdot R_i}{\Sigma_i R_i}+\frac{\frac{1}{3}\cdot E_i}{\Sigma_i E_i}+\frac{\frac{1}{3} \cdot A_i}{\Sigma_i A_i})\)

Massachusetts formula is used in US to apportion income or cost of a corporation among US states, by placing equal weights on sales, payrolls, and assets.

TJN’s choice of two weights – revenues and employees only, but not tangible assets – follows their rationale that tangible assets are biased by profit shifting. On their analysis, tangible assets in Luxembourg are greater than Germany, France, and African countries combined, despite the former is not renowned for their real sector economy compared to the latter.

Arguably, the same reasoning could be used to suspect that revenues might be biased. For example, if the MNEs employ third party to act as distribution hub in Singapore to perform sales in Asia-Pacific region and consequently books massive unrelated party revenues in Singapore. We thus include Massachusetts formula that incorporate tangible assets to still account for MNEs that have real economic activities that have low level of labour but significant capital investment in properties, plants, or equipments.

To obtain estimates of profit shifting, \(S^c\)is then defined as the sum of positive values of \(S_i\) for countries where the effective tax rate is below 15%, such that

\(S^c = \sum\limits_{i}{S'^c_i} - P_i\), where

\[
S'^c_i =
\begin{cases}
S^c_i & \text{if } S^c_i > 0 \text{ and } ETR^c_i < 15\%\\ 0 & \text{otherwise } \end{cases} \] TJN argues that this correction allows us to remove some resource-rich countries with large profits and high tax rates, therefore obtaining a more conservative estimate of profit shifting.

The choice of break-out threshold in 15% follows TJN's methodology, based on their finding that every country with an effective tax rate above 15% loses tax revenue through profit shifting.

The global tax revenue loss, \(TRL^c\) is calculated by multiplying shifted profit \(S^c\) with the average effective tax rate in countries that is higher than 15%, weighted by the measures of real economic activities, i.e.

\(TRL^c = S^c \cdot \widehat{ETR^c}\)

Effective tax rate (ETR) is calculated as follows:

\(\widehat{ETR^c}=\sum\limits_{i\in{\{N-c\colon ETR_i \geq 15\%\}}}ETR_i(\frac{\frac{1}{2}\cdot R_i}{\Sigma_i R_i}+\frac{\frac{1}{2} \cdot E_i}{\Sigma_i E_i})\) , for TJN weight, and

\(\widehat{ETR^c}=\sum\limits_{i\in{\{N-c\colon ETR_i \geq 15\%\}}}ETR_i(\frac{\frac{1}{3}\cdot R_i}{\Sigma_i R_i}+\frac{\frac{1}{3}\cdot R_i}{\Sigma_i R_i}+\frac{\frac{1}{3} \cdot A_i}{\Sigma_i A_i})\) , for Massachusetts formula weight.

However, their definition of effective tax rate is taxes paid divided by profit before tax, which is closer to the definition of cash ETR in tax avoidance literature. We supplement this analysis with our measure of effective tax rate which is taxes accrued divided by profit before tax. From this point on, CETR will be used to denote TJN's measure, and ETR to denote "traditional" ETR.

Results

We estimate around 1-3 trillion IDR of shifting out, depending on the measure used.

To these following countries.

Plotting the distribution into box plot, there are significant variants if cash ETR is used.

How much revenue is lost? Depending on the measure of ETR and weighting, this translates to 200-600 billion IDR of global tax revenue loss.

NOTE: Due to its aggregated nature, this is not the tax revenue lost of Indonesia only, but collective tax revenue lost for some countries where Indonesian MNEs operate.

Conclusion and Further Remarks

Based on the aggregated and anonymised CbCR, there are indications that MNEs engage in global profit shifting, resulting in significant revenue loss.

Aggregated and anonymised CbC Report released by OECD is a neat tool to monitor global profit shifting lost. This type of analysis could even be conducted on MNE level, and with the indicators outlined in the OECD Handbook of Effective Tax Risk Assessment, this may be used to complement current compliance risk management analysis. Incorporating the information of CbC-2 as weights and employing different ETR threshold (e.g. using MNE's own group ETR) could also improve future analysis. It may be even useful for the purpose of Pillar 2, Global Anti-Base Erosion proposal to address digital economy and other economy of scale without mass.

Reference

INFINITE (2020), Comments on Public Consultation Document: Review of Country-by-Country Reporting (BEPS Action 13) https://www.infinite-tax.org/2020/05/19/comments-on-public-consultation-document-review-of-country-by-country-reporting-beps-action-13/ (Accessed 13 July 2020)

OECD (2020), New corporate tax statistics provide fresh insights into the activities of multinational enterprises http://www.oecd.org/tax/new-corporate-tax-statistics-provide-fresh-insights-into-the-activities-of-multinational-enterprises.htm (Accessed 13 July 2020)

OECD (2017), Country-by-Country Reporting: Handbook on Effective Tax Risk Assessment, OECD, Paris.
www.oecd.org/tax/beps/country-by-country-reporting-handbook-on-effective-tax-risk-assessment.pdf (Accessed 13 July 2020)

Tax Justice Network (2020), Watershed data indicates more than a trillion dollars of corporate profit smuggled into tax havens, https://www.taxjustice.net/2020/07/08/watershed-data-indicates-more-than-a-trillion-dollars-of-corporate-profit-smuggled-into-tax-havens/ (Accessed 13 July 2020)

Tax Justice Network (2020), Methodology: Analysis of OECD country by country reporting data, https://www.taxjustice.net/wp-content/uploads/2020/07/Methodology-Analysis-of-OECD-country-by-country-reporting-data-July-2020.pdf (Accessed 13 July 2020)

Viability of Commercial Database to Measure BEPS Risks

There have been various studies and anecdotal evidence showing that multinational enterprises (MNEs) engage in what is termed as base erosion and profit shifting (BEPS) activity. Their multinational nature afford them to utilize mismatches and gaps in domestic tax rules and tax treaties. Among other modus operandi are transfer pricing, avoidance of permanent establishment, thin capitalization, deferring tax via controlled foreign corporation, hybrid entities/instrument mismatch, et cetera.

Measuring the scale of BEPS, quantitatively speaking, proves very challenging. BEPS is complex and has numerous variations of arrangement. Corporations react to regulations, tweaking their schemes to escape taxing ambit. Current audit may not necessarily conform to its previous result. Court verdicts may be inconsistent, especially in civil law country. So BEPS is essentially irreducible to mere numbers or variables.

Nevertheless, measuring BEPS impact is of utmost importance. It gives picture to the scale of taxation abuse, as well as providing tax authorities with tool to gauge a regulation’s efficacy in preventing BEPS. This is why measurement of BEPS is included among 14 other action plans in the 2013 OECD BEPS Action Plan. Specifically, it is designated BEPS Action Plan 11.

But there is the issue of limitation of data. Available data may not be representative. There are also mismatch between real economic effect and BEPS, and between financial (accounting) and fiscal information. There are issues of timing, accessibility, and adequacy of details.

In this regard, tax authority may combine or separately analyze macro-level data and micro-data. Micro-data, in particular data sourced from published financial statements (either from public companies which are legally required to submit financial report, or from commercial database) may supplement tax authorities with information to measure BEPS. Aside from the issues mentioned above, however, financial statements data may be problematic. There is no distinction between related party and independent party transactions, effect of different accounting standards and consolidation, as well as low coverage in developing countries in particular, including Indonesia.

How well this type of data will fare in the light of BEPS Action Plan 11? Using ORBIS database, we gather data from Indonesian public and private companies. We search active companies, with known operating turnover for at least one year among 2009-2018 (this is to prevent much of “junk” data containing the name of company only but nil financial information). We further exclude financial companies (NACE code: K) following numerous past profit shifting studies. We obtain 575 companies for 10 fiscal years of 2009-2018. (n = 5750)

Testing Effective Tax Rate

We opt to test the the propensity of member of an MNE to engage in tax planning (Box 3.A1.3) and manipulation of the location of external debt (Box 3.A1.5). Our variable of interest are therefore effective tax rate and leverage. For this we need the information about profit before tax and income tax to calculate effective tax rate, as well as total equity and total liabilities and debt to calculate leverage. If all four are not available (n.a.) they are excluded them from sample, resulting in n = 4496 in unbalanced panel data.

Out of these, we extract information about their assets, employees, existence of patent and trademarks, and the locations of their global ultimate owner, controlling shareholder, immediate shareholder, headquarter, subsidiaries, and branch. These locations are of particular importance because they are used to determine whether an Indonesian entity is acting as headquarter, and whether it is a part of an MNE group. We also use these location to calculate its average headline statutory corporate income tax rate. For example, if an entity is known to have subsidiary in Singapore (statutory tax rate: 17%) and parent in Hong Kong (16.5%) then – taking into account Indonesia’s statutory tax rate of 25% – its average statutory tax rate will be (25% + 17% + 16.5%)/3 = 19.5%.

The average statutory rate of MNE, plotted against non-MNE is as follow:

On paper, it looks like Indonesian MNE indeed have higher BEPS risk on average. But if we plot their effective tax rate (calculated by dividing profit before tax with income tax as reported on ORBIS), we obtain this:

The results are widely different depending whether weighted average (sum of profit before tax divided by sum of income tax) or simple average is used. In both cases, however, MNEs often produce higher ETR compared to non-MNEs. Our unpaired t-test confirmed that in the case of weighted average, the effective tax rate of MNE is higher than non-MNE (in case of simple average, they are not statistically different). This runs in contrast to our theoretical understanding of BEPS.

Indeed, when we apply regression using equation 3.A1.3 to our data, it produce no statistically significant result. ETR is neither affected by the large size of a company nor its multinationality.

Benchmarking and Bunching the DER

Testing the debt-to-equity ratio, we also run to similar problem.

The purple line is the maximum debt-to-equity ratio (DER) to limit interest deduction as regulated by Minister of Finance Regulation no. PMK-169/2015, which is 4:1. As we can see, different counting method resulting in drastically different measurement. If we use weighted average, it seems that Indonesian entities are, on average, still within the allowed DER. But using simple average will show that in 2016-2017, MNEs are (on average) going over the 4:1 threshold. Not only that, but 2014-2015 saw double-digit level of DER. In both cases, however, t-test shows that the difference between MNE’s DER and non-MNE’s DER are not statitically significant. The result of regression using equation 3.A1.5 further confirms that difference of average statutory tax rate to Indonesian tax rate does not significantly correlate with DER.

Indeed, when evaluate whether Saezian bunching exists post-PMK 169/2015, ORBIS data did not show the evidence of debt-to-equity ratio bunching around 4 (which is the maximum allowed by PMK 169/2015). Plotting the binned frequency before and after PMK-169/2015 shows that (at least according to ORBIS data) taxpayers' behavior remain unchanged in the light of new limitation.

In fact, utilizing "bunch_count" by Chetty, et al. (2011), bunching occurs around DER = 1 instead of 4:

Probability of Being Flagged for Audit

Lastly, for a much immediate application. Suppose we flag Indonesian MNE for audit based on their ETR and DER. We flag an MNE that has lower-than-statutory tax rate ETR for BEPS-related audit. Suppose we also flag an MNE that has higher than 4:1 DER or has negative equity (which is not eligible for interest expense deduction) in the fiscal year 2016-2018 where PMK-169/2015 applies. The probability of an Indonesian MNE flagged for audit based purely from ORBIS database is:

Only 50-60% of Indonesian MNEs will be flagged for audit based on their ETR in our sample. For DER, it is even less, only around 16% will be flagged.

Conclusion

Does that mean Indonesia is safe from BEPS-related risks? Probably quite the contrary. This piece means that BEPS-related risks cannot be captured using third party commercial database alone. It requires a more holistic compendium of data from tax return, other institutions, agencies, association, and other parties, as well as data from automatic exchange of information in the form of financial accounts data and Country-by-Country Report.

References

OECD (2015a) Measuring and Monitoring BEPS, Action 11 - 2015 Final Report. OECD/G20 Base Erosion and Profit Shifting Project, OECD Publishing, Paris.

OECD (2015b) Transfer Pricing Documentation and Country-by-Country Reporting, Action 13 - 2015 Final Report. OECD/G20 Base Erosion and Profit Shifting Project, OECD Publishing, Paris.

Chetty, R., Friedman, J., Olsen, T., Pistaferri, L. (2011). “Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: Evidence from Danish Tax Records”, Quarterly Journal of Economics, 126(2).

Saez, E. (2010). “Do taxpayers bunch at kink points?" American Economic Journal: Economic Policy vol. 2, no. 3, August 2010 (pp. 180-212)

"Beggar Thy Neighbor? Or Thyself?" On Inequality and Tax Policy Spillover

Few months ago, my colleague Rizmy wrote a piece on Investor Daily about tax competition and inequality. As you may know, government sometime lowers tax rate in order to attract investments. Lower tax rate means government imposes less tax to the rich, or gets less revenue for redistributive function. Hence, it may induce inequality.

Inequality, it could be argued, could be induced even though domestic policy does not change. Lower tax rate in neighboring country may create inequality via profit shifting in the domestic country (Baker and Murphy, 2019) or by capital inflow surges to the neighboring country which can worsen inequality (Azis and Shin, 2015).

Current version of Stata (Stata 15) can seamlessly create inverse-distance weighting matrix using shapefile, which is basically a map file used in mapping software. Stata 15 also has sp- prefix to enable spatial autoregressive model to be combined nicely with regress, ivregress, or xtregress. Basically, my life would be much easier if I got a hand on this back then.

This post is intended as personal exercise in new Stata feature to answer whether neighbor’s tax policy induce inequality.

Measuring Tax Policy

Our parameter of interest is tax policy, but which one? Headline corporate income tax rate is traditionally used in tax policy research notwithstanding its shortcoming of being too uni-dimensional. Keller and Schanz (2013)'s Tax Attractiveness Index is promising, as it takes into account the existence of important regulation such as transfer pricing and controlled foreign corporation (CFC) rules. Heritage Foundation's "Fiscal Freedom" is less complex than Tax Attractiveness Index, but it takes into account total tax burden to GDP ratio as measurement of the overarching effectiveness of fiscal policy to tax. In here, we use all of them as well as headline personal tax income tax rate as addition, following Duncan and Gerrish (2014).

As you can see above, Asia-Pacific countries on average are becoming less progressive. Fiscal Freedom scores show increasing trend (more freedom = less taxed), while corporate income tax rates are decreasing. Tax Attractiveness Index and personal tax rates, however, are more or less stable.

Measuring Inequality

We use Gini index as computed by Solt (2019)'s Standardized World Income Inequality Database as a basis. Since sp- demands balanced panel data, we complete missing observations using data from UNU-WIDER's World Income Inequality Database. If there are still missing observation, data from World Bank and/or domestic statistics are added.

Plotting Gini to our tax policy measure shows the following relationship:

As is quite expected, inequality is inversely related to the progressiveness of tax policy. The more "free" or "attractive" tax policy, the higher is Gini index (and thus inequality). The reverse happens with tax rate. Whether inequality is more affected by domestic tax policy, or neighbor's, or both, are what we're trying to test here. 

Model

We slightly modify the model in Martinez-Vazquez, et al. (2012) by including neighbor's tax policy measure (headline corporate income tax rate, Tax Attractiveness Index, Fiscal Freedom, and headline personal income tax rate) instead of lagged Gini index. Population growth, percentage of young population age 0-15 to total population, percentage of old population age > 65 to total population, GDP growth, GDP per capita growth, and unemployment are used as control variables.

Results

None of the control variables are significant, except for GDP growth. So the full regression result is omitted for the sake of brevity.

Now, the question whether spillover exists:

Neighbor's tax policy indeed affects domestic inequality though not in the way we expected. Firstly, such in the case of Fiscal Freedom (FISCALFREEDOM) and corporate income tax rate (CITR), it turns out that foreign tax policy is more strongly correlate to inequality than domestic tax policy.

Secondly, it completely reversed what we can infer from the scatterplots above. For example, in the case of Tax Attractiveness Index (TAXATTRACT), the coefficient is negative. This means that the more attractive our neighbor, the less unequal is our domestic economy. In the case of corporate income tax rate (CITR), the lower our neighbor tax rate, the less unequal is our domestic.

This is still consistent with Azis and Shin (2015). If what capital inflow surges increase inequality, and if lower tax rate increase capital inflow in a country, that country's Gini index rises. For example, if Hong Kong lower its tax, capital would flow there and Hong Kong citizen become less equal. Yet this warrants further study, because this one does not test if indeed capital is mobile. As an aside disclaimer: some specification error/bias may exist that I am unaware of.


Conclusion

This post is just an exercise in STATA 15 new feature. The result, in a counterintuitive way: it appears that domestic tax rate does not correlate with GINI, but neighbors tax rates correlate negatively with GINI. That means when the neighbors lower their taxes, it reduce inequality at home insofar as domestic tax rate stays the same. Does this mean because capital flows abroad then inequality is reduced at home? Does this mean that we can lower our tax rate, but still keeping it higher than our neighbors' so as not to increase inequality? Probably. This exercise is not designed to answer that question, nor can it examine the dynamics of GINI (including the persistence of tax rate effect to GINI).

Take this with an ocean worth of salt.

References:

Azis, I. J. and Shin, H. S. (2015) Capital Flows and Income Distribution. In: Managing Elevated Risk. Springer, Singapore

Baker, A. and Murphy, R. (2019) The Political Economy of ‘Tax Spillover’: A New Multilateral Framework. Glob Policy, 10: 178-192. doi:10.1111/1758-5899.12655

Duncan, D. and Gerrish, E. (2014) "Personal Income Tax Mimicry: Evidence from International Panel Data." International Tax and Public Finance, Springer; International Institute of Public Finance, vol. 21(1), pages 119-152

Keller, S. and Schanz, D. (2013) Measuring Tax Attractiveness across Countries. arqus-Working Paper No. 143

Martinez-Vazquez, J., Moreno-Dodson, B. and Vulovic, V. (2012) The Impact of Tax and Expenditure Policies on Income Distribution: Evidence from a Large Panel of Countries. Andrew Young School of Policy Studies Research Paper Series No. 12-30

Novastria, R. O. (2019). "Kompetisi Pajak Picu Kesenjangan". Investor Daily https://investor.id/archive/kompetisi-pajak-picu-kesenjangan (accessed 10 June 2019)

Solt, F (2019) Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database. SWIID Version 8.1, May 2019.

A Time Series Analysis of Indonesian Post-Amnesty Tax Revenues

2016-2017 Indonesian tax amnesty has been an unprecedented success. There were 973,426 taxpayers participating. 61,100 new taxpayers were registering just before and during amnesty, which comprised around 6% of total participating taxpayers. 1,030,014 million asset declaration letters were reported, with 4,884.26 trillion rupiah of declared assets. Out of these, 114.54 trillion rupiah was paid as redemption money, as well as 146.70 trillion rupiah was repatriated back to Indonesia. (Directorate General of Taxes/DGT, 2017). It is, quite simply, the most successful amnesty program in terms of money to date.

However, many have noted that tax amnesty also has some risks. It may instigate public opinion of similar forgiveness in the future (Leonard and Zeckhauser, 1987). It may also reduce government credibility (Stella, 1991) as people saw the weakness and lack of authority from the government to properly collect taxes (Uchitelle, 1989). While amnesty can generate short term revenue, the aforementioned risks make the impact of amnesty to future tax revenues remain uncertain (Marchese, 2014), or outright detrimental (Das Gupta and Mookherje, 1995).

Gauging the Post-Amnesty Effect

As is known, tax amnesty granted several facilities to participating taxpayers. Article 11 para. (5) of Tax Amnesty Law stipulates the annulment of tax arrears, cancellation of administrative penalty, as well as termination of audit for participating taxpayers. While the number of terminated audits were quite numerous (21,177 audit notice were terminated, 12,365 instruction/assignment were also terminated), the amount of settlement of prior notice of tax assessment reached around 74.3% (19.4 trillion rupiah was paid to settle 26.1 trillion rupiah in unpaid taxes including the administrative penalties) (DGT, 2017). Granted, those audits may result in bigger amounts in tax collected were there no tax amnesty. Hence, ex-post testing of tax amnesty's probable counterfactual effect remains difficult.*

On the other hand, the filling compliance ratio in 2017 reached 70.98%, which is an increase from 61% in 2016 (DGT, 2017). Those who report via electronic filing (e-filing) also increased from 59% in 2016 to 70% in 2017 (DGT, 2018). Among tax amnesty participants, 870,000 tax returns were filed in subsequent fiscal year, which correspond to 89.4% of filling compliance ratio –the highest compliance rate of overall taxpayer (DGT, 2017). In terms of money, tax revenues realization grew 4.07% in 2017 compared to previous year (13.75% if non-routine revenues from amnesty is excluded). It further grew to 14.33% in 2018 (15.53% if amnesty is excluded), which is the best tax revenue realization in the last 5 years and the first double-digit growth in the last 7 years (Kemenkeu, 2019). Given this cursory view of the data, it may seem that Indonesian tax amnesty does not significantly impact post-amnesty compliance.

Alm and Beck (1993) had once tried to measure the impact of amnesty to post-amnesty tax revenues. They tested whether the 1985 Colorado tax amnesty affected subsequent years. Similar methodology is employed here.

The Models

Three models are used based on Alm and Beck (1993). First, a simple time trend analysis testing based on OLS in the form of

Where, Yt represents monthly net tax revenue collected by DGT, T is the numeric representation of the month, e represents the error term, and b0 and b are parameters. Additional specifications for a separate intercept or slope change from the amnesty based on periods within and post-amnesty is coded A and its interaction with time is coded A*T. Tests are separated in three ways: full period from January 2014 to March 2019, pre-amnesty periods from January 2014 to June 2016, and post-amnesty periods from July 2016 to March 2019.

Second model employs Autoregressive Integrated Moving Average (ARIMA) by Box and Jenkins (1976) which tests whether the process that generated the tax revenues before amnesty was the same as the process that generated the tax revenues after the amnesty. Additionally, the identified ARIMA processes from before and after the amnesty maybe used to “fit” and “forecast” the tax revenues.

I employ Augmented Dickey Fuller and DFGLS test to find the integration of tax revenues in full period, pre-amnesty period and post-amnesty periods. All three of them are not stationary at level and stationary at first-difference. So first differenced data will be used. Further ACF-PACF and Godfrey tests suggest the existence of autoregression of order 1 and no moving average. So the ARIMA specification will be (1,1,0) instead of (2,1,0) as used in Alm and Beck (1993).

Thirdly, Multivariate ARIMA (MARIMA) intervention analysis is applied. In this case, we introduce three type of intervention assuming that tax amnesty is a discrete “intervention” thus can be represented as an additive effect of the amnesty on revenues (Box and Tiao, 1975). This requires the specification both of a starting point for the intervention and of the shape of the intervention impact. Similarly, ARIMA specification of (1,1,0) will be used here.

The starting point for the intervention (or the amnesty) is simply the time at which the amnesty occurs, which in our case is June 2016. The shape may be modeled by a “step” function with zero values up to the point of the intervention and one for all periods following the intervention, by a “pulse” function where the intervention occurs at one period and the intervention variable has just one nonzero value, or by a “ramp” function in which the step is spread over some period as a ramp response. To sum up, the shapes of intervention may be crudely depicted here:

Results

Turns out amnesty does not significantly affect post-amnesty revenue. A and/or A*T are not statistically significant, as well as partial “chopping” of time in January 2014 – June 2016 and July 2016 – March 2019.

In our robustness check using ARIMA below,

it could be construed that the autoregressive process that generate tax revenues pre- and post-amnesty are quite similar, in terms of sign and coefficient. Indeed, confirming this assumption using Chow breakpoint test and forecast test at July 2016 (when amnesty started) both result in no difference of pre- and post-amnesty revenue (p-values of 0.6776 and 0.2546, respectively).

Lastly, using MARIMA:

either pulse, step, or ramp intervention does not result in statistically significant outcome.

If I were to predict post-amnesty tax revenues from pre-amnesty data, and compare the predicted outcome and actual outcome, this is the result:

You may see that the actual value is actually higher than the predicted value, which confirms the remarkable growth of 2017 and 2018 tax revenues.

Conclusion

It seems that the 2016-2017 tax amnesty in Indonesia does not lower the tax revenues of subsequent years. Yay!

Reference:

Directorate General of Taxes (DGT). (2017). Tax Amnesty Mosaic

DGT (2018). Kepatuhan dan Penerimaan Pajak 2017 Tumbuh Pesat, DJP Optimis Hadapi 2018. http://www.pajak.go.id/kepatuhan-dan-penerimaan-pajak-2017-tumbuh-pesat-djp-optimis-hadapi-2018 (Accessed March 20, 2019)

Kementerian Keuangan. (2019). APBN Kita, January 2019 edition

Alm, J. Beck, W. (1993). Tax Amnesties and Compliance in the Long Run: A Time Series Analysis. National Tax Journal, Vol. 46. Pp. 53-60

Box. G. E. and Jenkins, G. M. (1976). Time Series Analysis, Forecasting, and Control. San Francisco Holden-Day

Box. G. E. and Tiao, G. C. (1975). “Intervention Analysis with Application to Economics and Environmental Problems”. Journal of the American Statistical Association 70 (March 1975) pp. 70-79

Das-Gupta, A. and Mookherjee, D. (1995). Tax Amnesties in India: An Empirical Evaluation. IED Discussion Paper Series, 53. Boston University

Leonhard, D. Zeckhauser, R. J. (1987) “Amnesty, Enforcement, and Tax Policy”. NBER Working Paper No. 2096

Luitel, H. S. dan Sobel, R. (2007). The Revenue Impact of Repeated Tax Amnesties”. Public Budgeting and Finance 27 (Fall 2007): 19-38.

Marchesse, C. (2014). Tax Amnesties. Papers in Comparative Analysis of Institutions, Economics, and Law No. 17.

Stella, P. (1991). An Economic Analysis of Tax Amnesties. Journal of Public Economics 46 (3): 383–400.

Uchitelle, E. (1989). Amnesty Programs in Selected Countries. FRBNY Quarterly Review

Monthly tax revenues data is obtained from internal data. Net total tax revenue is used.

------

*It is possible, given microdata of corporate and self-employed taxpayers which either participated or did not participate in tax amnesty, then conducting a simple ANOVA, ANCOVA, or difference-in-differences analysis in a quasi-experimental research setting.

How Much Is the Loss from Tax Avoidance: Revisited

Interesting new paper by Tørsløv, Wier, and Zucman (2018) that came out few days ago intrigued me to revisit the question about missing profits. As you may know, corporation is driven by profits. Taxes cut the profits. So it is economically rational to try to reduce tax. One of the usual modus operandi is to establish subsidiary in low tax jurisdiction and divert the profit to it.

In previous post, I have tried to estimate this loss in macroeconomic setting. Now depending on the condition (whether companies also consider market size or proximity) Indonesia may gain 2% or loss up to 5% of GDP due to tax base spillover. In this post, inspired by an equation mentioned by Tørsløv, et al. I will try to use a much more granular data.

The Setup

Numbers of research concerning profit shifting used commercial database to extract the relevant information from the companies' financial report (mainly from income statement and balance-sheet). These are then entered into some equation. One of the equation mentioned in Tørsløv, et al. is:

where log(πic) denotes natural logarithm of pre-tax profits booked by company i in country c; τp is the tax rate in the parent company; τc is the domestic tax rate; and Firm and Country denote firm- and country-specific fixed effects.

I managed to collect the data for 904 companies for the years of 2012-2017. However, I have to modify the above equation in some ways. First, since I only concern about what happen in Indonesia, I have no use for Country fixed effect. Second, I change the tax differential to include all the subsidiaries in the multinational entities group, instead of the simple parent-subsidiary relationship. The reason is that an Indonesian company (even though it is a subsidiary, for instance) does not necessarily shift the profit to its parent. The profit can be shifted to another subsidiary within its MNE group that is a resident in low tax jurisdiction. The profit can also be "pooled" somewhere and remains unrepatriated to the parent's jurisdiction as in the case of many US companies.

Further, since the logarithmic operation can only be applied to positive numbers, then I must exclude some companies in some years because they booked 0 or loss (negative profit). This makes my panel data unbalanced and the observations are reduced. In the end only 695 companies remain with N = 3,113.

Because of the exclusion too firms may enter and leave the time series. For example, company A booked profit in 2012, 2013, 2015, and 2017 but had zero/negative profit in 2014 and 2016. Due to the log operation, company A is included in the sample for year 2012, 2013, 2015, and 2017, but it is excluded in 2014 and 2016. As a consequence, I cannot apply firm fixed effect and the model essentially turns into random-effect model. This is also confirmed by the results of Hausman and Lagrange Multiplier tests.

That being said, the model turns into:

where log(πi) denotes natural logarithm of pre-tax profits booked by company i in Indonesia; τavg is the unweighted average tax rate of the MNE group following Johansson et al. (2017); τc is the Indonesian corporate tax rate (25%); the rests are errors.

From a theoretical standpoint, choosing only companies that booked profits still has a benefit. You won't have to pay tax if you suffer loss or you have zero profit, so there is not much use to further avoid tax. Moreover, this allows the model to test whether agency theory is at play here: whether the manager tries to reduce tax without sacrificing much of the shareholder values by having zero profit or even booking loss.

The Result

Difference in tax rate negatively correlated with pre-tax profitability (p-value 0.033). If the tax rate of Indonesia is higher than the average tax rate of the MNE group, the pre-tax profit of Indonesian company belonging to said group becomes lower. For every percentage of tax rate difference, the pre-tax profit of the company drops by 4.37%. This means an Indonesian company whose other MNE group members are located in Singapore (tax rate 17%) and Vietnam (tax rate 20%) will have 18.93% less pre-tax profit than a non-MNE Indonesian company.

Extending the result to include all the companies in the sample, this is how much pre-tax profit is loss on average:

Conclusion

Again, we see evidence of tax avoidance in the samples included in this study. Of course this is not a comprehensive view of tax avoidance behavior. Loss-making companies and those who have zero profit are not covered here, although it is very likely that they engage in tax avoidance behaviors (via transfer pricing or excessive interest expense, for instance).

There are also other, more comprehensive methods to detect profit shifting explored in Tørsløv, Wier, and Zucman (2018). I suggest you read their paper if you want to know about the missing profit of nations. Indonesia, sadly, is not covered there.

References

Johansson, A, Ø. B. Skeie, S. Sorbe, and C. Menon (2017), “Tax Planning by Multinational Firms: Firm-Level Evidence from a Cross-Country Database”, OECD Economics Department working paper 1355.

Tørsløv, T, L. Wier, and G. Zucman (2018), “The Missing Profits of Nations”, NBER Working Paper 24701.

How Much Is the Ｌｏｓｓfrom Tax Avoidance?

This is a short post based on the research by Cobham and Janský (2018) which continues from Crevelli, De Mooij, and Keen (2015).

Here's the context: many countries in the world suffer from loss of tax revenues due to tax avoidance and/or tax evasion. One of the most common methods used by multinational corporations is to set up a subsidiary in the so-called tax havens non-cooperative or low-tax jurisdictions to divert their profits.

Having a subsidiary in non-cooperative/low-tax jurisdictions is not necessarily indicative of tax avoidance. Businesses may have legitimate reason such as access to market, diversification, or centralization of one of their business functions. Nevertheless, the concern of tax revenue loss cannot simply be ignored.

But how much is lost?

The study by Crevelli et al. (2015) tries to estimate how much a country loses their tax revenue from tax avoidance. Cobham and Janský (2018) tests and re-estimates this research. I'd like to try estimating the loss of Indonesia.

The Setup
A company avoid taxes in a country by diverting profit to its subsidiaries in other countries. The tax base of the aforementioned country is thus reduced and "spillover" to other countries.

But how does a company choose the jurisdiction of its subsidiaries?

In a micro-data, such as commercial database (that contains financial reports and jurisdiction of subsidiaries/headquarter), this is much easier and clearer to measure. In fact, many of the measures and monitoring tools for base erosion and profit shifting as outlined in OECD's BEPS Action Plan 11 utilize micro-data from commercial database.

Measuring in a macro (country-level) setting thus requires us to make assumptions. Since a company may create a subsidiary for access to market, we must assume that company does not pick a country purely for its low tax. A company may be hesitant to create a subsidiary in a small or faraway country. Or, given a choice of two countries with similar tax rate, a company will choose the country which is closer or has bigger market than the other one. Thus, we weight the effect of tax rate in every country of the world based on two things: distance and market size (that is represented by GDP).

The Model
We first estimate the φ and λ based on the equation:

, where bit denotes the corporate tax base in country i in time t; τit the domestic tax rate (in this case Indonesia); W-it τit a weighted average of the tax rates in countries other than Indonesia; Xit a vector of controls such as trade openness (import + export divided by GDP), share of agriculture to GDP, and log of GDP per capita in constant 2011 dollar; and μt is time specific effect. I employ LASSO (Least Absolute Shrinkage and Selection Operator) in the estimation to penalize model overfitting, as I don't have much data to begin with.

After we get φ and λ we plug them to equation:

for short term effect (L), and

for long term effect (LL), where Whτ-it denotes the average tax rates of tax haven countries as listed by Gravelle (2013).

The Results
The loss of Indonesia's tax revenue as % of GDP in year 2008-2017 is as follows:

Interestingly, this suggests that if companies actually consider GDP as the main factor to create subsidiary in a country, there is a tax base spillover to Indonesia. Meaning that Indonesia actually gains from tax avoidance.

After I examine the data, it turns out that Indonesia's tax rate is lower than the rest of the world if weighted by GDP. This is understandable as small GDP countries with zero tax rates such as British Virgin Island matters less in this scenario than, for instance, Germany (whose tax rate approximately 30% is higher than Indonesia and whose GDP is much bigger than Indonesia as well.)

On the other hand, if a company seeks to establish a subsidiary in low tax jurisdictions near Indonesia (i.e. assuming it also wants to exploit agglomeration effect or to shorten supply chains), then Indonesia loses tax base approximately 3-5% of GDP.

Translating the above result into Rupiah, this is how much Indonesia loses (gains) the tax revenue:

This is a very crude estimation due to severe data limitation. Maybe I will update this if I have the time to collect more and better data. Maybe.

References:

Crivelli E, De Mooij R, Keen M. 2016. Base erosion, profit shifting and developing countries. FinanzArchiv: Public Finance Analysis 72(3): 268–301. https://doi.org/10.1628/001522116X14646834385460

Cobham A, Janský P. 2018. Global Distribution of Revenue Loss from Corporate Tax Avoidance: Re-estimation and Country Results. Journal of International Development. UNU-WIDER.

Gravelle J G. 2013. Tax havens: international tax avoidance and evasion. Washington, DC. http://fas.org/sgp/crs/misc/R40623.pdf. Accessed 25 June 2018

OECD. 2015. Measuring and Monitoring BEPS, Action 11 - 2015 Final Report. Paris: Organisation for Economic Co-operation and Development.

Data is from World Development Indicator of World Bank; World Economics Outlook and International Finance Statistics of IMF; tax rate data is from KPMG Tax Rate Table, with some additional research for small jurisdictions; Badan Pusat Statistik; and APBN of Indonesia.