Transfer pricing is not necessarily done for the purpose of avoiding tax, but it is one of the most common methods to do so.
In essence, affiliated companies can set up intragroup pricing that differs from market mechanism. Suppose the cost of product P is 80, and the price of product P in the market is 100. In independent setting, selling P will result in 20 of profit.
Suppose A is selling P to B (A’s affiliate) for a transfer price of 85. B then sells it to an independent party for 100. A will pocket a profit of 5, and B 15. If the transfer price is 99, A will get 19 and B only 1; and so on.
Unlike independent parties’ behavior that each party will maximize its own interest/profit, affiliated companies can set price that may “hurt” one of them. In the example above, setting transfer price to 99 may indeed hurt B, for B only gets a meager profit of 1. The price of 99 may be too expensive for an independent party that it may not have entered such transaction.
[The above method uses price as a point of comparison. If there is no comparable transaction between independent party, for instance if the price of products comparable to product P is not available, we can compare the profit level instead. The party that conducts less complex functions – the one that only conducts routine production or distribution, bears risks that are not economically significant, and possess no unique and valuable intangibles – is designated as "tested party". The choice of simpler company as tested party is due to the consideration that the more complex a company is, the less likely it has comparable companies similar to it. If both parties assumes economically significant risks or contributes unique and valuable intangibles, then the profit is split to both parties.]
If A and B are in the same country and taxed with same rate, arguably, such arrangement does not pose a significant base erosion problem for tax authority. If tax authority failed to do a transfer pricing adjustment to A, they could do it to B with similar net gain in tax revenue.
But if A and B are in different countries with different tax rates/facilities, then the 20 profit can be arbitrarily shifted in whichever company that will pay smaller amount of tax. Or no tax at all. This is probably the reason of US, Australia, or India to deny transfer pricing benefit only in cross-border transactions. But other country, like Indonesia, allows tax authority to make a transfer pricing adjustment even when the affiliated parties are in Indonesia. There may be the case that two Indonesian companies may be affected by different tax regimes (for instance if one of them has a reduction of tax rate, tax holiday, loss carryforward, final income tax, etc.)
Tax authorities of the world thus have incentive to adjust transfer pricing done by their taxpayers. They will look up at transaction done by independent parties according to sound business practice, see how much the difference in price or profit is observed, attribute the difference, and then tax accordingly.
Accurately delineating the transaction, and then comparing affiliated transaction with independent transaction – so-called comparability analysis – is therefore the “heart” of transfer pricing adjustment. A comparable independent transaction (often called “comparable” for brevity) can be found within the company if one of the companies under scrutiny also transacts with independent party. This is called internal comparable, and generally considered to be more reliable than external comparable.
To find the appropriate comparable, there are 5 comparability factors that must be considered:
a. contractual terms;
b. functions, assets, risks;
c. characteristics of goods/services;
d. economic circumstances; and
e. business strategies.
A reliable comparable must be similar. Or, at least must not be materially different from the transaction under scrutiny so that reliable adjustment – if necessary – can be made.
Given that information in the market is imperfect – barring commodities or internal comparable, for instance – sometimes the comparables found are “inexact”. This most often happens with external comparables, such as those found via commercial database. Adjusting transfer price in Indonesian taxpayer may sometimes invoke comparables found in other countries, due to the lack of Indonesian comparables. Now recall that economic circumstances is one of comparability factor that must be considered. How to take this into account?
One of most common practice is disregarding such difference insofar as the comparables selected come from similar region. This assumes that the economic circumstances in Indonesia is more similar with other countries in Far East and Central Asia compared to, say, European Union. Of course, disagreement may arise whether this assumption holds.
The issue is therefore whether Far East and Central Asian comparables can be used, or whether there is significant difference with Indonesian comparables that, strictly speaking, only Indonesian comparables should be used in adjusting transfer price in Indonesia. There is also the issue whether some adjustments that may be implemented to improve reliability, such as working capital adjustment or country-risk adjustment, are warranted. (The latter adjustment is especially of our interest here.)
The case for European comparables has been tested by Meenan et al. (2004) and then updated by Peeters et al. (2016) as well as by Platform for Collaboration on Tax (2017). They all found that there are no material difference that comparables from other European countries can be used without adjustment.
What about Indonesia vis-a-vis Far East and Central Asian comparables? This short post is a rudimentary attempt to answer.
The Set-up
The procedure is similar to the works cited above: select companies with NACE v. 2 industry codes following Peeters, et al. (2016) and then group them into 4 industries (automotive manufacturing, electronics manufacturing, chemical distribution, and electronics distribution). We subsequently extract necessary financial information (Sales, Costs of Goods Sold/COGS, Other Operating Expenses/OPEX, Operating P/L, Tangible Fixed Assets, Stocks, Debtors, Cash and Cash Equivalents, and Total Assets) to compute some profitability ratios, which are:
a. Gross Profit Margin (GPM): (Sales – COGS) / Sales
b. Cost Plus Markup (CPM): (Sales – COGS) / COGS
c. Operating Profit Margin (OPM): Operating P/L / Sales
d. Return on Assets (ROA): Operating P/L / (Tangible Fixed Assets + Stocks + Debtors + Cash and Cash Equivalents), and
e. Full Costs Mark-up (FCMU): Operating P/L / (COGS + OPEX)
The periods used are 2014 – 2018 (5 years) using Orbis data. All are computed using five-year weighted average to smooth the earning profile that may be affected by business cycle.
Among the indicia above, only OPM and ROA are computed in the original studies by Meenan et al. (2004) and Peeters, et al. (2016). These indicators are on net profit level, which is only useful for transactional method such as Transactional Net Margin Method (TNMM). I expand the choice to include GPM and CPM to account for traditional methods such as Cost-Plus and Resale Price methods. I also include FCMU to account for the manufacturing remuneration.
We, however, relaxed some of the screening criteria because in some industries there is no Indonesian company that fits in. We, for example, do not limit based on arbitrary threshold of sales. We also do not limit the samples to only include companies whose operating profit margin is around -5% to 15% and ROA around -10% to 20% (by assuming the five-year weighted average and the usage of interquartile range will smooth out the outliers). On the other hand, we implement stricter criteria in other place, such as excluding companies that do not have financial data for at least 3 years.
After we compute the indicia, we divide the Indonesian samples and Asian samples (which include Indonesia) in each industry group by median. Then, we apply Chi-square test to each lower quartile and upper quartile.
The aggregated cumulative distribution function of comparables' profitability is denoted by πΉ(π), which gives the share of comparables with a profitability ratio of our choice smaller than or equal to r. Our interest is interquartile range, and subsequently πΉ(π) for each industry is measured using the critical values defining the 1st and 3rd quartile of the cumulative distribution as πΉ(π∗) = 0.25 and πΉ(π∗∗) = 0.75.
Given the values π∗ and π∗∗, the number of firms in Indonesia with profitability ratios below and above this benchmark profitability were recorded. Formally, the analysis defines ππ1=πΉπ(π∗)ππ, ππ2= [πΉπ(π∗∗) − πΉπ(π∗)]ππ, and ππ3= [1 − πΉπ(π∗∗)]ππ
where i denote Indonesian specific comparable. Under the null-hypothesis, 25% of the Indonesian-specific comparables in both ππ1and ππ3, and 50% in the middle group ππ2 are expected.
Accordingly, a joint test statistic defines the variable
where πππ denotes the expected number of firms in each country-specific group.
The chi-square statistic π2 increases as πππ - πππ increases, which indicates that the observed distribution of comparables is significantly different from expected.
The Result and Conclusion
No significance difference exists between Indonesian comparables' lower and upper quartiles and Far East and Central Asian comparables' lower and upper quartiles in the industries tested. We repeat the calculation with Fischer exact test which is more conservative than chi-squared test in small samples (even in sample size less than 5) and the results still hold. Changing the denominator of ROA with Total Assets also does not change the result.
What does this mean? First, this does not mean that comparables from outside of Indonesia are as reliable as Indonesian comparables. This only means that the profitability level of Indonesian and other Far East and Central Asian comparables are drawn from similar distribution function, in this case a Chi-squared. Again, a thorough comparability analysis – with consideration to all five comparability factors – is more paramount than a simple similar-country-therefore-more-reliable approach.
So, does this mean that country risk adjustment is unnecessary?
In our opinion, any kind of adjustment to increase reliability must ultimately be proven to be justifiable. If, for instance, taxpayer proposes Working Capital Adjustment, she must first demonstrate that the comparables she obtains do not have a high degree of reliability. But then again, why does she knowingly choose less reliable comparables to begin with? If, for example, the level of inventory-to-total assets of the tested party is significantly different from the comparable companies the taxpayer choose, and that difference may significantly affect price/profit, then it begs the question of why the taxpayer does not apply a quantitative screening using inventory-to-total assets as a criterion.
The similar reasoning is used in multiple-year data vs. single year data. OECD Transfer Pricing Guideline 2017 para. 3.75 does not put emphasis on the usage of multiple year data as a default or systematic requirement. But only when the usage of multiple year data adds value to the analysis (for example if there is an effect from business life cycle) then it may be justifiable.
Ultimately, referring to OECD Transfer Pricing Guideline 2017 para. 3.59, at least in the industries referred here there are no substantial deviation in the interquartile range that indicates unreliability due to country-level difference. Therefore, unadjusted comparables may be used if other comparability factors are indeed similar enough that the comparables are suitable for inclusion.
Reference:
Meenan, P., Dawid, R., and HΓΌlshorst, J. (2004) Is Europe One Market? A Transfer Pricing Economic Analysis of PanEuropean Comparables Sets. European Commission, Brussels Taxud/C1/LDH/WB
Peeters, R., Noben, S., and Laurent, I. (2016) Study on Comparable Data Used or Transfer Pricing in the EU. European Commission, Brussels TAXUD/2014/CC/126 doi: 10.2778/657328
Platform for Collaboration on Tax (2017) A Toolkit for Addressing Difficulties in Accessing Comparables Data for Transfer Pricing Analyses. IMF - OECD - UN - World Bank
OECD (2017) OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations 2017. OECD Publishing, Paris. http://dx.doi.org/10.1787/tpg-2017-en
“ I found the one my heart loves. I held, and I would not let go...” (Songs of Solomon 3:4)
In today’s vernacular, I would’ve been called a “bucin.” A slave of love. Forgetting is so long, to quote Pablo Neruda. And five years of failing to forget was very enslaving to the soul.
There were nights when I think of her, or, to be more precise, the absence of her. Thinking that I was not with her, and to feel that I had lost everything that was beautiful to me.
There were days I masochistically watched Shinkai Makoto’s film, “5 Centimeters per Second.” The protagonist, Takaki Tohno, was a reflection of mine (perhaps also shared by many others). An image cruelly reflected by the black mirror. It was not the lost of love that broke me every time I watched that film. It was the lost of the ability to love again; the lost of the ability to move on.
It made me shudder: how many years should I spend before I am free from this torment?
After all, the heart wants what the heart wants. And the heart wants nothing less.
Nothing is perfect, that is probably true. Perfection, if any, is in the eye of the beholder. And Nitha is close enough to perfection for me.
When Nitha said to me that she broke up with her ex, my immediate reaction was befuddlement, instead of joy. After all, I have spent many a year constructing a temple around the idealized, imaginary persona of her. She was the idol, beyond my horizon. I was thus unafraid of failure and heartbreak, because how can I fail a relationship that does not exist?
But now, having a relationship with her, to be disappointed (or to be a disappointment), and altogether failing the relationship seemed like an imminent possibility.
But I decided to try. I demolished my metaphorical temple, to lay ground for a new reality full of imperfections.
Nitha is a strong-willed woman, often labelled herself as egoist. She knows what she wants, the way she wants it to be. If faced with 10 options, she will comprehensively evaluate the pros and cons of all 10 of them, while I will evaluate three of them and pick the best. She’s sometimes annoyed at me for not being thorough or lacking of initiative. While I am often annoyed at her picky nature –bordering on obssessive – and inefficiency.
For the last 5 years of having this free-form arrangement, we upped the ante. We wanted to see if we could tolerate with each other’s personal quirks, habits, and difference in opinions.
We started a trial we weren’t sure how it would end. We even weren’t sure how it started. Our “anniversary” was arbitrarily chosen at 20 October because I never properly asked her to be my girlfriend, and she never properly replied a “yes”. 20-10 seemed like a cute number and we went with it for years.
We tested ourselves whether we can set aside preferences and desire. We tried giving up individuality and the convenience of being alone.
It is of my great surprise that she tolerates my jittery and erratic decision-making ability. Without admitting it, she has sacrificed her ego for far too often.
Thankfully, we pretty much survived. We then braved ourselves to be tied in engagement, and then planned to marry.
We asked our friends why they decide to marry – especially for reasons beyond mere legality and religiosity. There wasn’t very satisfying answer. To be honest, we had no particular reason to get married.
One pragmatic reason is to satisfy the state apparatus and our families, thus keeping them from intruding what should’ve been our business and our business only. Indeed, had this country been like Sweden (or had there been no unconscionable draft of Criminal Code penalizing cohabitation), we’d just probably choose to live together. Tethered by hearts but remain unattached by the law.
So, why I think I ultimately agree to marry?
“Marriage”, wrote G. W. F. Hegel, “is a contract to transcend the standpoint of all contract”. Unlike regular contract in which both parties retain their abstract freedom, marriage is instead a mutual surrender of abstract freedom and autonomy to a higher organic ethical unity.
Gracy Olmstead expounded that marriage was never meant to be a vehicle for self-fulfillment, convenience, or pleasure. It’s an ethical bond to the other, for the good of the other. Marriage was never meant to satisfy every desire and longing of the heart. On the contrary, marriage provides discomfort. Heck, even preparing for marriage is already a frustrating and tiresome endeavour. Marriage indeed goes against every individualistic instincts.
On its surface, marriage is just a declaration made on the altar, officiated by the representative of God, confirmed by the state, witnessed and recognized by family, and sometimes followed by superfluous celebration. They are the “Big Others” as Slavoj Zizek wrote when he invoked his Lacanian legerdemain.
But marriage should not simply be taken as constraining. When the knot is tied, two hearts are liberated from the transient, fickle and purely subjective aspects of love. It is a liberation through self-restraint, which is rendered substantial by the something other than the self. Marriage is not merely a union of egoists.
These – the reference of marriage to something other than me, to selflessly love someone other than me – make a marriage is at least defensible for me.
And if I have to marry, I am sure as hell wanting to marry someone close enough to my perfection.
So here we are, after all these times, plunging ourselves deeper into the unknown future.
On 28 September 2019, Nitha’s birthday, we officially start our beautiful ride. I’ve made my vow to carry her home if she falls sick. To love her at the best of times and the worst of times. To stay till the world turns to oblivion and time unwinds to apocalypse. Till death do us part.
All these would not be possible without Nitha, who has shouldered an unequal share of burdens, who makes everything more beautiful than I’ll ever do. I solemnly promised I will do the dishes and the laundry.
It is of course unfortunate, however unintentional, that we’re married in one of the bleakest moments in Indonesian democracy. I personally hope the government and its citizens may unite once again in democracy, as Nitha and I unite in holy matrimony.
Please pray for all the good things in the best possible world for us. May the good things and our happiness today also extend to you all. You have our utmost gratitude.
Lastly, wish us luck. We’re sure as hell going to need it.
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)
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.
This will be a short post.
I was inspired by I Wayan Agus Eka’s presentation at Seminar Perpajakan Nasional 2019 organized by DJP. Basically, he tested whether illicit financial flow to a country is affected by its financial secrecy, which is proxied by Financial Secrecy Index (FSI).
I was intrigued to know whether a disincentive towards financial secrecy affects illicit financial flow. For example, an expose that consequently put a pressure to secrecy jurisdiction, a la SwissLeaks, LuxLeaks, Offshore Leaks, Panama Papers, Bahamas Leaks, and Paradise Papers.
My theory, if leaks were to happen, countries affected will put some efforts in preventing money to flow from them to secrecy jurisdiction, or otherwise, pressure the secrecy jurisdiction to be more open. As an illustration, SwissLeaks implicated Swiss as destination of shady financial transactions. By making this information public, either government of other country enacts a measure to stop money to flow to Swiss, or people would stop transferring money to Swiss out of a reputational cost/fear from investigation, or that Swiss will make their country more “open” by changing its regulation.
To sum up in hypotheses, I want to know whether leaks correlate to downward pressure to countries that are implicated by the leaks. Such downward pressure may be proxied with lower illicit financial flow.
When I gather the data, however, I ran into some problems.
The latest data of illicit financial flow is 2015. I tried move to FSI as a proxy. Unfortunately, FSI data is also published bienally and the latest data is only available for 2015 as well. More unfortunately, in 2015, only SwissLeaks, LuxLeaks, Offshore Leaks can be taken into account (since they all happened in 2013-2014, unlike Panama Papers which was announced by ICIJ in 2016). Out of these three, only Offshore Leaks can be further used as variable since SwissLeaks and LuxLeaks are tied to Swiss and Luxembourg and neither of them are in Global Financial Integrity data of illicit financial flow. In the end, I get only 45 jurisdictions, year 2013 and 2015 (n = 90).
The model
I first tried to employ difference-in-differences (DID) estimation. But the pre-estimation test suggests violation of parallel trend assumption. Therefore DID cannot be used and I had to do regular regression. Consequently, the result here should be taken as correlational, not causative.
The model below is taken from my own estimation. Basically the first (to my horribly limited knowledge) that estimate such thing. (But I could indeed be mistaken). Anyway, the model:
, where i denotes country, t denotes time, log(IFFIN) is logged illicit financial inflow to country i in time t, OFFLEAKSIN is dichotomous variable equal to 0 if that country is not implicated as destination jurisdiction in the leaks, 1 if it is implicated; TAXRATE denotes statutory/headline corporate tax rate; GDPGROW is GDP growth; log(GDPPCAP) is logged GDP per capita; ΞΈ is country fixed effect; and Ξ΅ is error term.
The predicted sign of OFFLEAKSIN is negative, since we hypothesize that if leaks = 1 it correlates to lower inflow of illicit money. TAXRATE is a control variable whose sign should be negative as well, as there can be theorized that there are tax-motivated illicit financial flow to jurisdiction with low tax rate (Hearson, 2014). GDPGROW and logGDPCAP are control variables that denote inflows that are affected by domestic economy (GFI, 2015). Reuter (2012) also observe that 5 out of 7 countries with high illicit financial flows have high GDP per capita. So I assume the signs must be positive.
As robustness check, I switch illicit financial inflow with Financial Secrecy Index Score. If Financial Secrecy Index Score is lower for countries implicated in the leaks, it could signify a downward pressure. See the above reasoning. The model for robustness check:
, where FSI is Financial Secrecy Index Score and the rests are similar to the previous.
The result
In our model, OFFLEAKSIN is statistically significant with correctly predicted sign. Meaning, there may be a correlation between leaks and downward pressure to countries implicated by them. In this case, illicit money inflow to a country is negatively correlated with whether that country is mentioned in the leaks as destination of illicit flow.
If the result is hard to read, let me put in a graph:
As you can see, pre-leaks illicit financial flow is quite similar amongst all country. However, after the leaks, flow to countries implicated in the leaks is significantly reduced (the blue curve shift to the left).
Our robustness check result is as follows:
In the second model, OFFLEAKSIN is statistically significant with correctly predicted sign. Financial Secrecy Index of a country is negatively correlated with whether that country is mentioned in the leaks as destination of illicit flow. It could be surmised that some form of pressure to be more “open” did indeed exist.
Put in a graph for easier read:
There is a clear shift to the left, but not quite as pronounced as previous graph. (This is to be expected as the second model statistically significant only at 3%, compared to the first model.)
Conclusion
Leaks are good to put pressure to secrecy jurisdiction. Thank God for the whistleblowers and ICIJ.
Reference
Eka, I. W. A. (2019). Does Financial Secrecy Affect Profit Shifting? Presented on Seminar Perpajakan Nasional 2019 (forthcoming)
Global Financial Integrity (GFI). (2015). Illicit Financial Flows: The Most Damaging Economic Condition Facing the Developing World. Global Financial Integrity
Hearson, M. (2014). "Tax-motivated illicit financial flows: A guide for development practitioners." U4 Issue January 2014 no. 2. U4 Anti-Corruption Resource Centre
Reuter. P. (2012). Policy and Research Implications of Illicit Flows, in "Draining development? : Controlling Flows of Illicit Funds from Developing Countries" by P. Reuter (ed.) The World Bank: Washington DC
Data Source:
Illicit Financial Flow is from Global Financial Integrity
Countries implicated in leaked is manually gathered from International Consortium of Investigative Journalists database
Statutory Tax Rate is from KPMG
GDP growth and per capita are from IMF WEO
Financial Secrecy Index Score is from Tax Justice Network
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.
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*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.
