IRS evasion: Some Preparatory Exact Discoveries

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Prof. Dr. Friedrich Schneider E-mail: friedrich.schneider@jku.at http://www.econ.jku.at/Schneider Money Laundering: Some Preliminary Empirical Findings MoneyLaundering_November2007.doc Introduction Illegal (criminal) monetary exchanges Necessity of Money Laundering Activities Quantification/Estimation of the Volume of Money Laundering Measures against Money Laundering Summary and Conclusions ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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1. Presentation The term „Money Laundering" begins from the US portraying the Mafia's endeavor to "launder" unlawful cash through trade serious washing salons out the 30s, which where controlled by criminal associations. The IMF appraises, that 2-5% of the world total national output (GDP) originates from illegal (criminal) sources. The objective of this address is to embrace a first endeavor, to reveal some insight about the size and advancement of government evasion and its strategies. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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2. Illicit (criminal) money related exchanges Apart from the "official" economy there exists an "Underground Economy", which describes an unlawful economy including a wide range of criminal exercises, which are in strife with the legitimate framework, e.g. sorted out wrongdoing or medication managing. Inverse to these traditional criminal exercises, shadow economy exercises mean the generation of (on a fundamental level) lawful products and ventures with an esteem included for the official economy and where the lawlessness originates from keeping away from duties and government managed savings installments and disregarding work advertise directions. Shadow economy and underground (criminal) economy are entirely unique exercises, which can not be summed up to one underground economy in light of the fact that the last generally creates no positive esteem included for an economy. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Table 2.2: Quantification of Money Laundering Volume – Part 1 Source: possess counts and reference list . ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Table 2.2: Quantification of Money Laundering Volume – Part 2 Source: claim counts and reference list. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Figure 2.1: Organized Crime and their principle zones in Central Europe Organized Crime – Main Fields Percentage in Central Europe ( Average 2000 - 2003) 40 10 15 5 10 20 Drugs Property Economy Violence Nightlife Weapons Drug - related Investment Armed Procuration Theft Nuclear Crime misrepresentation Robbery Prostitution Illegal Car Economic Protection Illegal Break of Narcotics Movement Subsidy Fraud Money Gambling Embargo Burglary Payment Human Kidnapping Receiving Fraud Trafficing prompts to Money Laundering Source: Siska, 1999, p. 13 and claim counts . ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Figure 2.2: Organized Crime – Main Fields (Central Europe, av. 2000-2003) Source: Siska, 1999, p. 13 and possess counts . ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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3. Need of Money Laundering Activities According to a few estimations, the aggregate turnover of sorted out wrongdoing really achieves figures between 1,200 billion and 2,1 trillion USD in 2003 and the overall volume of government evasion "from medication business" gets 810 billion in 2003. Government evasion is important, in light of the fact that 2/3 of every illicit exchange are finished with money, as money leaves no follows on data bearers like records or bank sheets. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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4. Measurement/Estimation of the Volume of Money Laundering 4.1. General Remarks Apart from a first significant trouble of veering meanings of the term „money laundering" on the national and the global level a moment one emerges, as especially the exchange concentrated layering stage can lead exceedingly to potential twofold and different numbering issues. Besides numerous assessments (or guestimates) regularly are made for particular ranges (e.g. sedate benefits) or depend on assumes that are wrongly cited or misconstrued or just designed without a logical base! ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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4. Measurement/Estimation of the Volume of Money Laundering 4.1. General Remarks (cont.) We make a refinement amongst immediate and aberrant strategies: Direct techniques concentrate on recorded ("seized"/appropriated) illicit installments from the general population powers. Be that as it may, to get a by and large/add up to figure one needs to gauge the much greater (undetected/"Dunkelziffer") volume. Strategies, which are utilized are the inconsistency investigation of worldwide adjust of installment records, or of changes in real money loads of national banks. Aberrant techniques attempt to distinguish government evasion exercises with the assistance of causes and pointers. In the first place, the different causes (e.g. the different criminal exercises) and markers (reallocated cash, indicted people) are distinguished, and second an econometric estimation is embraced. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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4. Measurement/Estimation of the Volume of Money Laundering 4.2. Econometric and DYMIMIC Procedures In the DYMIMIC estimation strategy tax evasion is dealt with as a dormant (i.e. undetectable) variable. This estimation method utilizes different reasons for tax evasion (i.e. different criminal exercises) and pointers (seized cash, indicted, people, and so forth.) to get an estimation of the dormant variable. One major trouble of this strategy is, that one gets just relative evaluated estimations of the span of IRS evasion and one needs to utilize different estimations so as to change/align the relative qualities from the DYMIMIC estimation into supreme ones. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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4. Evaluation/Estimation of the Volume of Money Laundering 4.2. Econometric and DYMIMIC Procedures – Cont. A DYMIMIC estimation of the measure of tax evasion or benefits from criminal exercises for 20 OECD nations throughout the years 1994/95, 1997/98, 2000/2001, 2002/2003 and 2003/2004 is finished. Hypothetically we expect that the more unlawful (criminal) exercises (e.g. managing drugs, unlawful weapon offering, increment in local violations, and so on.) happen, the more tax evasion exercises will occur, ceteris paribus. The more inequal the salary appropriation and the lower official GDP per capita is, the higher IRS evasion exercises will be, ceteris paribus. The better the lawful framework is working the less cash will be washed, ceteris paribus. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Figure 4.1: DYMIMIC estimation of the measure of IRS evasion for 20 exceptionally created OECD nations, 1994/95, 1997/98, 2000/2001 and 2002/2003 Functioning of the lawful System Index: 1=worst, and 9=best - 0.043* (2.10) Amount of seized cash +0.380** (2.86) Criminal exercises of illicit weapon offering +0.234** (3.41) Amount of Money Laundering or benefits from criminal exercises Lagged endogenous variable: +0.432* (2.20) Criminal exercises of unlawful medication offering +0.315** (3.26) Cash per capita +1.00 (Residuum) Criminal exercises of illicit exchange with people +0.217* (2.23) Prosecuted people (number of people) - 0.264 (*) (- 1.79) Criminal exercises of faked items +0.102 (1.51) Test-Statistics: RMSEA a) = 0.002 (p-esteem 0.884) Chi-squared b) = 16.41 (p-esteem 0.914) TMCV c) = 0.046 AGFI d) = 0.710 D.F. e) = 42 a) Steigers Root Mean Square Error of Approximation (RMSEA) for the trial of a nearby fit; RMSEA < 0.05; the RMSEA-esteem differs somewhere around 0.0 and 1.0. b) If the basic condition model is asymptotically right, then the network S (test covariance framework) will be equivalent to Σ (θ) (demonstrate inferred covariance lattice). This test has a measurable legitimacy with an expansive specimen (N ≥ 100) and multinomial disseminations; both is given for this condition utilizing a trial of multi typical conveyances. c) Test of Multivariate Normality for Continuous Variables (TMNCV); p-estimations of skewness and kurtosis. d) Test of Adjusted Goodness of Fit Index (AGFI), differing somewhere around 0 and 1; 1 = idealize fit. e) The degrees of flexibility are dictated by 0.5 (p + q) (p + q + 1) – t; with p = number of pointers; q = number of causes; t = the number with the expectation of complimentary parameters. Criminal exercises of misrepresentation, PC wrongdoing, and so forth +0.113 (1.62) Domestic wrongdoing exercises +0.156* (2.43) Income circulation Gini coefficient - 0.213(*) (1.89) Per capita salary in USD - 0.164 (1.51) ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Table 4.1: DYMIMIC Calculations of the Volume of Money Laundering Source: O wn estimations . ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Figure 4.2: DYMIMIC Calculations of the Volume of Money Laundering ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Table 4.3: Fight against government evasion in Austria and Germany Source: Own figurings (circuitous examination on premise of appraisals on shadow economy and class. criminal exercises); and Siska, Josef, 1999; BMI, 2003 and 2005; FIU 2005 und 2006. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Figure 4.3: Fight against IRS evasion in Austria and Germany - Sum of criminal income Germany Source: Own computations (circuitous examination on premise of evaluations on shadow economy and class. criminal exercises); Siska, Josef, 1999; BMI, 2003 and 2005; FIU 2005 und 2006. ©Prof. Dr. Friedrich Schneider, University of Linz, AUSTRIA

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Figure 4.4: Fight against IRS evasion in Austria and Germany - Sum of criminal income Austria and Sum of "solidified cash" Austria Source: O

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