“ 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; θ is country fixed effect; and ε 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
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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.
They say 27 is the age when rockstars often die. We are not rockstars, although life has probably fucked us harder than any drugs imaginable. Most importantly, we aren't died yet. I think we are still blessed in that matter.
Getting older has its bane. These days, I forget easily. There are much to think, but there are less time to commit everything to memory. And in the process I threw away what is essential and kept what is irrelevant instead. Long gone are those times when we stop and ponder and marvel at the banality of our lives. But I am trying.
I know that in the eve of your 27th birthday you wanted to celebrate it in your own way – in solitude, trying to recover some quiet moments. So this time, in your absence, I try to mythologize all the strange roads that we once walked – supplanting the half-remembered nostalgia with an allegory of my own making.
I remember the walk we walked on one night on that alley where we found a kitten, lost and alone in the darkness of the bushes. You told be to throw a cat food that I bring in my pocket. You were worried despite my insistence that it would be fine.
Sometimes I think of us as the kitten. Astray, wailing for its mom. We think it won't survive. But help did come. It survived. The kitten turned out strong despite its apparent helplessness. And so as it survived, we survive.
You survive.
Because you are strong.
Sometimes stubbornly so.
I also remember that time when you once walked for a couple miles, under the scorching sun and the heat waves. Few hours later you were still able to dance it all away, with multitudes of strangers, on the dancefloor in the night market.
I once likened you to the bristlecone pine. A tree that thrives under the cold, harsh gale, yet lives longer than many civilizations. I still think that you are.
A bristlecone pine won't die at 27. 27 is the age when rockstars often die. I think I kinda know why. 27 is the forking path where you can no longer using youthfulness as an excuse. It's a prelude for a full-fledged, well-functioning adulthood. Things are getting more pressing. Societies are more demanding. New expectations are coming. The road that we walk will be harder, perhaps even more punishing. Some people try to resist this, and they crash themselves so hard they destroy themselves in the process.
But I know you are strong, strong enough to walk any road.
That's why I want to walk with you, to watch your figure from behind.
It does not have to be in a faraway land, or places that we dream to walk. I don't care if it's Coburg, or Shimogyo-Ku, or Ubud, or Santorini, or Setiabudi, or Gang Gloria, or Pasar Klewer. I don't care if blisters and cramps come as they may. As long as I walk with you. As long as I walk with you forever. As long as you are happy walking with me.
So, happy 27th birthday, N. May you always be happy on your every journey. I promise I will remember this.
The world gets older, N, and so must we. But if the world gets colder, we must not.
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:
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:
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.
