cost of the mtic vat fraud for european union members - an … · 2016-10-21 · cost of the mtic...

Cost of the MTIC VAT fraud for European Unionmembers

An empirical approach

FRUNZA Marius CristianSchwarzthal Kapital

European Commission, Brussels , 29th of October 2016

Agenda

1 Motivation

2 Introduction

3 Mechanism of MTIC VAT fraud

4 Macroeconomic ModelData preparationModeling approach

5 Results

6 Conclusions and next steps

7 Q&A

8 Bibliography

Cost of the MTIC VAT fraud for European Union membersSchwarzthal Kapital 2

VAT Fraud on Carbon Markets: The Carbon Connection

Fraud and Carbon Markets: The Carbon Connection [Frunza(2015a)] Carbon Crooks

https://www.amazon.co.uk/dp/B00CEG8QOU/ref=sr_1_10?ie=UTF8&qid=1475007397

http://www.amazon.com/Solving-Modern-Crime-Financial-Markets/dp/0128044942

Financial Crime

Introduction to theories and varieties of modern crimes [Frunza(2015b)] Solving modern crime in financial markets [Frunza(2015c)]


http://www.amazon.com/gp/product/B019K7CMUE?qid=1457293412

http://www.amazon.com/Solving-Modern-Crime-Financial-Markets/dp/0128044942

The Cost of Non-Europe of an incomplete Economic and Monetary Union

1 study ([Frunza(2014)]) aiming to evaluate the cost of Non-Europe.It was used by the European Parliament for preparing the 10 pointsJunker plan.

28 countries were covered by the study. The study used the Eurostatdatabase for all members of the Union

4 aspects: Macro-economic, financial , budgetary and policy relatedwere decrypted by the study.

1 framework based on panel regression was employed. It avoidedcomplex DSGE-type models


http://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_STU(2014)536365

Introduction

VAT as concept aroused originally in Germany during the post-World War I periodas a replacement to the country’s direct sales tax.

Value Added Tax was fathered in the 50’s by the then director of the French taxauthority Maurice Laure.

The efforts for unifying taxation across the new rising “Common Market”, imposedthe VAT as foundation of the fiscal policy inside the economic space of theEuropean Union. This new tax spread very quickly across all members and wasalso adopted by new joining members.

The VAT rate within EU members ranges between 18% and 27 %([Commision(2013)]).

The VAT framework from the EU is also used by the newly created EurasianEconomic Union


Focus on the VAT gap

1: Delays in VAT collection can affect the total amount of collectedtax.

2: Corporate bankruptcies during an economic downturn generateunpaid liabilities towards the Treasury

3: Cross-border litigation may have a relevant impact on the VATpayments

4: Customs clearance affects the transfer of physical goods, therebyimpacting the timing of the VAT payments.

5: Accounting and legal technicalities Inter-co. accounts, countrywhere the item is stored vs country where the item is booked...

6: VAT fraud is the intentional failure to pay the tax


VAT Gap: Estimates of the total gap: 168 billion e

Figure: 2014 VAT gap in Million e. Italy, Germany, United Kingdom and France exhibit thebiggest nominal gap


VAT Gap: Estimates of the total gap (%)

Figure: 2014 VAT gap in percentage of the collected VAT. The highest values are for EasternEuropean countries as well as for Greece and Italy


Three types of VAT fraud

The first is Missing Trader Intra-Community Fraud (MTIC-Fraud). In suchfraud there is a chain of transactions in which one company (Missing Trader)effectively charges VAT to other company from the chain but does not actuallyremit the VAT to the tax authorities. The Missing trader disappears after a whileleaving the tax authorities empty handed. The carousel fraud is the a subtype ofthe fraud whereas the same item circulates few times in a chain and thecorresponding VAT is pocketed.

The second category is black market trading, where suppliers operate that are notknown to the tax authorities and do not fulfill their obligations to charge and remitVAT. This type of fraud usually takes place in many walks of life and concerns alltype of products.

The third category concerns the trade of goods and services with Extra-EUcountries (MTEC-Fraud). In these cases goods are imported from those countriesan sole in the EU, the VAT not being remitted to the concerned authorities.


VAT fraud estimates

[Gradeva(2014)] showed that the trade gap is positively correlated with the VATrate in three of the seven Eastern European countries. Correcting for outliers andrestricting the sample to large trade leads to a positive and significant correlationin five countries. In the latter cases a one-percentage-point increase in the VATrate is associated with a 0.6% up to around 3% increase in the trade gap.

[Keen and Smith(2007)] mentioned few careful estimates of the VAT gap, for theUnited Kingdom, with a figure around 13.5 percent or, in a bad year, nearly 17percent.

[Borselli(2011)] indicates that within the EU-27, organized VAT fraud is estimatedto amount to between e20 billion and e35 billion a year.

[Barbone et al.(2013)Barbone, Belkindas, Bettendorf, Bird, Bonch-Osmolovskiy, and Smart])underlined that the total amount of VAT lost across the EU is estimated at e168billion, according to the report. This equates to 15.2% of revenue loss due to fraudand evasion, tax avoidance, bankruptcies, financial insolvencies and miscalculationin 26 Member


Mechanism of MTIC VAT fraud

Year Underlying Pocketed VAT

1991-1996 Gold 20 -50 Me1993-2000 Clothes 100-150 Me2003-2006 Mobile phones 2 000-3 000 Me2007-2010 Carbon emissions 8 000-10 000 Me2002-2006 Telecom 5 000- 7 000 Me2005-2010 VoIP 4 000- 8 000 Me2005-2012 Cars 1 000-2 000 Me2005-2012 Foods 2 000 -5 000 Me


Mechanism of MTIC VAT fraud

Figure: Basic version of the MTIC fraud. An item is bought from a differentcountry of the Union without VAT and sold domestically with VAT. The VAT isnot paid to the local tax authority.


Framework for modeling the VAT gap related to MTIC fraud

Figure: Up left: In a fraud free world the amount of VAT collected by a country would be an increasing function of Imports.Up Right: The VAT GAP or the amount of pocketed by the MTIC fraudsters increases also with the amount of Imports in acountry when this amount overpasses the thresholds of Imports that fuel the normal(legal) economy ImportsNormal Down Left:The observed VAT, the effective amount of VAT collected by a country increases linearly until the level of Import equal toImportsNormal . When this threshold is overpassed the observed VAT remains almost constant Down Right: The sensitivity ofthe Observed VAT goes towards zero when the value of Imports is much higher then the trigger value (ImportsNormal )


Data preparation

We used the Eurostat public database.

The values of collected VAT, Intra-EU trade gaps, Intra-EU importsa

and Intra-EU exports are obtained for all Union countries.

The frequency of the dataset is yearly from 1999 to 2014.

From the official statements of the EuropeanCommission(([Commision(2013)])) concerning the VAT rates acrossEurope, a set of time series is built, showing the evolution of the VATrates for each country from 1999 to 2014.

aOver the rest of the presentation the figures concerning Imports, Exportsand Trade gaps are based on the Intra EU flows.


Data preparation

QuantilesCountry Mean Std. dev. Skewness Kurtosis 0% 25% 50% 75% 100%

Belgium 0.56 0.04 -0.63 -0.1 0.46 0.54 0.56 0.6 0.63Bulgaria 0.32 0.05 -0.22 -1.1 0.22 0.29 0.32 0.36 0.39Czech Republic 0.45 0.07 -0.05 -1.21 0.34 0.4 0.46 0.5 0.57Denmark 0.2 0.01 0.33 -0.85 0.18 0.19 0.2 0.2 0.22Germany 0.18 0.02 -0.18 -1.59 0.14 0.16 0.18 0.2 0.21Estonia 0.52 0.06 -0.29 -1.49 0.41 0.45 0.53 0.57 0.6Ireland 0.23 0.05 0.95 -0.71 0.17 0.19 0.21 0.23 0.32Greece 0.14 0.01 -0.06 -1.23 0.12 0.13 0.14 0.15 0.16Spain 0.15 0.01 -0.35 -0.86 0.12 0.14 0.16 0.16 0.18France 0.16 0.01 -0.43 -1.26 0.14 0.15 0.16 0.16 0.17Croatia 0.26 0.03 -0.59 -1.36 0.2 0.23 0.28 0.29 0.3Italy 0.13 0.01 -0.77 0.47 0.11 0.12 0.13 0.13 0.13Cyprus 0.21 0.03 0.06 -1.33 0.17 0.19 0.21 0.24 0.26Latvia 0.37 0.06 0.27 -1.38 0.28 0.32 0.37 0.42 0.48Lithuania 0.35 0.07 -0.01 -1.55 0.25 0.29 0.36 0.42 0.46Luxembourg 0.41 0.04 -0.46 -1.16 0.33 0.38 0.43 0.44 0.48Hungary 0.45 0.06 0.19 -1.18 0.36 0.41 0.46 0.49 0.57

Table: Basic statistics of Imports/GDP ratio. The data-set is heterogeneous andsome countries observe high fluctuations of this ratio


Data preparation

QuantilesCountry Mean Std. dev. Skewness Kurtosis 0% 25% 50% 75% 100%

Belgium 0.068 0.001 0.063 -1.445 0.066 0.067 0.068 0.069 0.07Bulgaria 0.085 0.013 -0.192 -0.819 0.057 0.075 0.084 0.096 0.104Czech Republic 0.066 0.008 0.149 -1.869 0.057 0.059 0.065 0.074 0.076Denmark 0.095 0.003 0.522 -1.068 0.092 0.093 0.094 0.097 0.1Germany 0.062 0.003 0.147 -0.872 0.057 0.06 0.062 0.063 0.067Estonia 0.083 0.006 0.58 -0.561 0.074 0.08 0.082 0.085 0.096Ireland 0.068 0.005 -1.058 -0.126 0.056 0.067 0.07 0.07 0.074Greece 0.063 0.004 0.442 -0.536 0.057 0.06 0.063 0.067 0.073Spain 0.055 0.006 -1.004 0.388 0.039 0.052 0.057 0.06 0.063France 0.074 0.002 -0.272 -1.23 0.071 0.073 0.074 0.075 0.076Italy 0.058 0.003 -0.575 -0.401 0.052 0.057 0.059 0.06 0.063Cyprus 0.044 0.003 0.73 0.015 0.038 0.042 0.043 0.045 0.051Latvia 0.069 0.009 -0.538 -0.858 0.051 0.065 0.069 0.076 0.08Lithuania 0.073 0.006 0.111 -1.573 0.064 0.068 0.073 0.079 0.083Luxembourg 0.06 0.005 0.174 -1.418 0.053 0.057 0.06 0.063 0.068Hungary 0.084 0.008 0.305 -1.218 0.073 0.079 0.084 0.089 0.099Malta 0.059 0.005 -1.746 2.692 0.043 0.058 0.061 0.062 0.064

Table: Basic statistics of VAT/GDP ratio. The data-set is heterogeneous andsome countries observe high fluctuations of this ratio


Modeling approach

Our approach assumes that the theoretical VAT denoted VATTheoretical that should becollected by a government is the sum of the actually collected VAT (VATObserved) andthe pocketed VAT(VATGap).

VATTheoretical = VATObserved + VATGap (1)

Simplifying the framework of the MTIC mechanism, one can assume that relationshipbetween the Theoretical VAT and Imports is linear as expressed in the following equation:

VATTheoretical ≈ α · Imports (2)

The VAT gap is proportional to the difference between the actual imports and thetrigger level:

VATGap ≈ κ ·max(Imports − ImportsNormal , 0) (3)

= κ · Call(Imports, ImportsNormal) (4)

The equation (3) is similar to the value of a vanilla call option, whereas the strike isImportsNormal and the sport price is the actual level of imports. This similarity with awell known financial instrument is very useful for quantifying the sensitivity of thecollected VAT to the trade gap and for estimating the VAT gap related to MTIC fraud.


Modeling approach. Sensitives

From the initial equation

VATTheoretical = VATObserved + VATGap (5)

one can measure the sensitivity of the VAT with respect to the level of imports bydifferentiating the equation (5) :

∂VATTheoretical

∂Imports=∂VATObserved

∂Imports+∂VATGap

∂Imports(6)

Thus the sensitivity of the actual collected VAT (VATobserved) can be expressed as:

∂VATobserved

∂Imports=∂VATTheoretical

∂Imports− ∂VATGap

∂Imports(7)

∂VATobserved

∂Imports= α− κ · ∆Call(Imports,ImportsNormal ) (8)

where ∆Call(Imports,ImportsNormal ) is the Delta of an European call with a spot equal toImports,a strike equal to ImportsNormal and a one year maturity.


Modeling approach: VAT and Imports

The main driver of the VAT amount in the light of the Intra-Community trades isthe balance between the Imports and Exports.

Thus when the Imports increase relatively to the Exports in a country the collectedVAT should increase. If the reverse process happens the VAT should decrease.

Extending a set of models based on the trade gap and the gross domestic productwe introduce the following specification:

∆VAT itObserved = βImports ·∆Imports it +βExports ·∆Exports it +βGDP ·∆GDP it +εit (9)

where ∆VATit is the variation of the collected VAT in year t for the country i ,∆Importsit is the variation of the Imports, ∆Exportsit is the variation of theExports, ∆GDP it is the variation of the GDP and εit is the noise.

The model is estimated with a special method fitted for panel data that mightexhibit cross-sectional variability developed by[Croissant et al.(2008)Croissant, Millo, et al.].


Modeling approach: Initial results

As expected the VAT increases with the increase in imports and decreaseswith the increases in exports.

Factor Estimate Std. Error t-value P-value

Imports (βImports) 0.0990150 0.0254769 3.8865 0.0001197Exports (βExports) -0.0610579 0.0203287 -3.0035 0.0028429

GDP (βGDP) 0.0652438 0.0043053 15.1542 0.0

Table: Model estimation with raw VAT figures and Imports, Exports andGDP as drivers The Adjusted R2 is 55%

The panel model introduced above in equation (9) is estimated forspecification assuming that the raw VAT figures without including theeffect of VAT rates changes.



These models are estimated with the normalized VAT figures filtered forthe VAT rate changes. This way the increases of collected VAT due to anincrease in the tax rate are filtered.



GDP (βGDP) 0.0448056 0.0031739 14.1169 0.0

Table: Model estimation with normalized VAT figures and Imports, Exportsand GDP as drivers. The adjusted R2 is 62%



The GDP appears as one of the main drivers of the collected VAT. This

findings makes the task of assessing the VAT gap more difficult as it

introduces an additional variable. A straightforward way for reducing the

dimensionality is to consider the variables (VAT, Imports and Exports) as

ratios to the growth domestic product.

Thus the following variables are introduced:

VAT itGDP =

VAT itObserved

GDP it

Imports itGDP =Imports itObserved

GDP it

Exports itGDP =Exports itObserved

GDP it

Beyond the dimensionality reduction, this new specification resumed in equation (10)has many advantages as it excludes the effect of the size of the country’s economy aswell as the effect of inflation.

∆VAT itGDP = βImports · ∆Imports itGDP + βExports · ∆Exports itGDP + εit (10)


Modeling approach: Results

The results of the parameters estimation on the full data panel is exhibitedin Table 5. The adjusted R2 is only 8%, 7 underlining the strong effect ofthe GDP from the previous models.


Imports (βImports) 0.0565876 0.0098323 5.7553 1.718e-08Exports (βExports) -0.0329606 0.0107279 -3.0724 0.002268

Table: Model estimation with VAT, Imports and Exports normalized by theGDP. Data include all observations from 2000 to 20014 The adjusted R2 is 8%


Effect of the Council Directive 2006/112/EC

The directive created a legal framework for the VAT exemption on

intra-Community acquisitions and most likely was a headstone for the MTIC

fraud. To assess further the dynamics of the trade and VAT, the model is

estimated on two different time subsamples 2003-2009 and 2009-2014.


Imports (βImports) 0.097333 0.013986 6.9595 8.309e-11Exports (βExports) -0.082456 0.016934 -4.8692 2.670e-06

Table: Model estimation with VAT, Imports and Exports normalized by theGDP. Data include all observations from 2003 to 2009 The adjusted R2 is 23%



Table: Model estimation with VAT, Imports and Exports normalized by theGDP. Data include all observations from 2009 to 2014. The adjusted R2 is 2.5%


MTIC estimates: Results

Based on the specified model the VAT gap can estimated. The sensitivity of theobserved VAT with respect to the Imports corresponds to estimates from theprevious section. The figures obtained from the period previous to the economiccrisis (2003-2009) are used.

In order to measure the VAT gap, the κ and ImportsNormal estimates are requiredas an entry in the following equation:

VATGap = κ · Call(Imports, ImportsNormal) (11)

∂VATobserved

∂Imports= α− κ · ∆Call(Imports,ImportsNormal ) (12)

κ can be easily estimated from equation (12) if α approximated with the domesticVAT rate

The trigger level of normal economic level of Imports (ImportsNormal) is calibratedbased on the reported VAT gap for 2013 in the European Commission study([Barbone et al.(2013)Barbone, Belkindas, Bettendorf, Bird, Bonch-Osmolovskiy, and Smart]).



Figure: 2014 MTIC VAT Gap breakdown(%).The Eastern European countries includingRomania, Latvia, Poland, Slovakia and Hungary are in the top as well as some SouthernEuropean countries like Italy and Greece.



Figure: 2014 MTIC VAT Gap breakdown(Me). 2014 MTIC VAT Gap represents 0.67% of theGDP of the European Union. The top countries in terms of MTIC VAT fraud are the UnitedKingdom, Germany, Italy, Spain and France which are also in the top of all VAT frauds



Figure: MTIC VAT Gap/ Total VAT Gap (%) for 2013. MTIC VAT Gap represents 49% ofthe estimate of the total VAT frauds across EU as stated by[Barbone et al.(2013)Barbone, Belkindas, Bettendorf, Bird, Bonch-Osmolovskiy, and Smart]


Conclusions

The various MTIC VAT frauds and the solutions for addressing this crime were discussedby the academic literature([Ainsworth(2010)]). The link of the MTIC fraud to otherfinancial crimes like money laundering and terrorism financing was exposed in a previouspaper ([Frunza(2013)]).This paper is an empirical study aiming to assess the gravity of the MTIC VAT fraudwithin EU countries. The proposed method is a combination of econometric and optionpricing methods. The VAT gap in a country is modeled as an vanilla call on the amountof Imports in that country. The sensitivity of the collected VAT with respect to theamount of Imports is estimated using a panel regression method across all countries ofEuropean Union over the past 15 years.The first finding is that the sensitivity of collected VAT to Imports is almost zero postcrisis (2009-2014), while pre-crisis the sensitivity was much stronger. The validationthrough bootstrapping methods confirms the soundness of the model.The second finding is that the total figure of the MTIC VAT gap in 2014 is 94 eB,representing 0.67% of the EU-28 GDP. Lastly there are clusters of the MTIC fraudmainly in countries of the Eastern and Southern Europe.


Next steps

1 Refine the current model and the estimates

2 Measure the black market VAT fraud

3 Extend the current study to the newly created Eurasian Economic Union

4 Measure the MTEC VAT fraud with a focus on the trades with the EEU

5 Evaluate the effect of BREXIT upon the VAT fraud

6 Built a data repository for VAT gap assessment (Eurostat, VIES, financialstatment, sector studies...)

7 Add data from other sources: European grid, shipments, financial flows,etc....

8 Obtain more granular macro data on specific sectors

9 Assess the micro-economic impact of the VAT fraud on sectors like:Automotive in France, clothing in Italy , emissions, electricity, food inHungary...

10 Move towards a data mining bottom-up approach


Q&A

More on our website ...www.schwarzthal.com/VAT


www.schwarzthal.com/VAT

Ainsworth, R. T., 2010. Vat fraud-technological solutions. Available atSSRN 1677997.

Barbone, L., Belkindas, M., Bettendorf, L., Bird, R. M.,Bonch-Osmolovskiy, M., Smart, M., 2013. Study to quantify andanalyse the vat gap in the eu-27 member states. CASE NetworkReports (116).

Borselli, F., 2011. Organised vat fraud: features, magnitude, policyperspectives. Policy Perspectives (October 31, 2011). Bank of ItalyOccasional Paper (106).

Commision, E., 2013. Vat rates applied in the member states of theeuropean union.

Croissant, Y., Millo, G., et al., 2008. Panel data econometrics in r:The plm package. Journal of Statistical Software 27 (2), 1–43.

Frunza, M., 2014. The cost of non-europe of an incomplete economicand monetary union to prevent future crises. Available at SSRN.


Frunza, M.-C., 2013. Aftermath of the vat fraud on carbon emissionsmarkets. Journal of Financial Crime 20 (2), 222–236.

Frunza, M.-C., 2015a. Fraud and Carbon Markets. Routledge.

Frunza, M.-C., 2015b. Introduction to the Theories and Varieties ofModern Crime in Financial Markets. Academic Press.

Frunza, M. C., 2015c. Solving Modern Crime in Financial Markets:Analytics and Case Studies. Elsevier.

Gradeva, K., 2014. Vat fraud in intra-eu trade.

Keen, M., Smith, S. C., 2007. VAT Fraud and Evasion: What do weknow, and What Can be Done? No. 7-31. International MonetaryFund.


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