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)
No comments:
Post a Comment