IZA DP No. 1431TheSizeoftheShadowEconomiesof145CountriesallovertheWorld:FirstResultsoverthePeriod1999to2003FriedrichSchneiderDISCUSSION PAPER SERIESForschungsinstitutzur Zukunft der ArbeitInstitute for the Studyof LaborDecember 2004 The Size of the Shadow Economies of 145 Countries all over the World: First Results over the Period 1999 to 2003 Friedrich Schneider University of Linz and IZA Bonn Discussion Paper No. 1431 December 2004 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0Fax: +49-228-3894-180 Email: iza@iza.org Anyopinionsexpressedherearethoseoftheauthor(s)andnotthoseoftheinstitute.Research disseminatedbyIZAmayincludeviewsonpolicy,buttheinstituteitselftakesnoinstitutionalpolicy positions. TheInstitutefortheStudyofLabor(IZA)inBonnisalocalandvirtualinternationalresearchcenter and a place of communication between science, politics and business. IZA is an independent nonprofit company supported by Deutsche Post World Net. 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IZA Discussion Paper No. 1431 December 2004 ABSTRACT The Size of the Shadow Economies of 145 Countries all over the World: First Results over the Period 1999 to 2003 UsingtheDYMIMICapproach,estimatesoftheshadoweconomyin145developing, transition,developedOECDcountries,SouthPacificislandsandstillcommunistcountries arepresented.Theaveragesizeoftheshadoweconomy(inpercentofofficialGDP)over 2002/2003 in developing countries is 39.1%, in transition countries 40.1%, in OECD countries 16.3%,SouthPacificislands33.4%and4remainingCommunistcountries21.8%.An increasing burden of taxation, high unemployment and low official GDP growth are the driving forces of the shadow economy. JEL Classification:O17, O5, D78, H2, H11, H26 Keywords:shadow economy, tax burden, government regulation, DYMIMIC method Friedrich Schneider Department of Economics University of Linz 4040 Linz-Auhof Austria Email: friedrich.schneider@jku.at Contents 1Introduction .......................................................................................................................3 2Some Theoretical Considerations about the Shadow Economy....................................4 2.1Defining the Shadow Economy ................................................................................................. 4 2.2The Main Causes of Determining the Shadow Economy.......................................................... 6 2.2.1Tax and Social Security Contribution Burdens ............................................................................... 6 2.2.2Intensity of Regulations .................................................................................................................. 7 2.2.3Public Sector Services..................................................................................................................... 8 3The Size of the Shadow Economies all over the World Findings for 145 Countries.9 3.1Introductionary Remarks .......................................................................................................... 9 3.2The Size of the Shadow Economies for 145 Countries for 1999/2000, 2001/2002 and 2002/2003 ................................................................................................................................. 9 3.2.1Developing Countries ................................................................................................................... 10 3.2.2Transition Countries...................................................................................................................... 23 3.2.3Highly developed OECD-Countries.............................................................................................. 28 3.2.4South West Pacific Islands............................................................................................................ 32 3.2.5Communist Countries.................................................................................................................... 36 4Summary and Conclusions .............................................................................................40 5Appendix: Methods to Estimate the Size of the Shadow Economy ............................42 5.1Direct Approaches .................................................................................................................. 42 5.2Indirect Approaches................................................................................................................ 43 5.2.1The Discrepancy between National Expenditure and Income Statistics ....................................... 43 5.2.2The Discrepancy between the Official and Actual Labor Force ................................................... 44 5.2.3The Transactions Approach .......................................................................................................... 44 5.2.4The Currency Demand Approach.................................................................................................. 45 5.2.5The Physical Input (Electricity Consumption) Method................................................................. 48 5.3The model approach ............................................................................................................... 50 5.4The Size and Development of the Shadow Economies of 145 Countries over 1999/2000 to 2002/2003 ............................................................................................................................... 54 6References.........................................................................................................................58 3 1Introduction Asshadoweconomicactivitiesareafactoflifearoundtheworld,mostsocietiesattemptto controltheseactivitiesthroughvariousmeasureslikepunishment,prosecution,economic growth or education. Gathering statistics about who is engaged in shadow economy activities, the frequencies with which these activities are occurring and the magnitude of them, is crucial for making effective and efficient decisions regarding the allocations of a countrys resources in this area. Unfortunately, it is very difficult to get accurate information about these shadow economyactivitiesonthegoodsandlabormarket,becauseallindividualsengagedinthese activitieswishnottobeidentified.Hence,theestimationoftheshadoweconomyactivities can be considered as a scientific passion for knowing the unknown. Although quite a large literature1) on single aspects of the hiddenorshadow economy exists andacomprehensivesurveyhasbeenwrittenbySchneider(theauthorofthispaper)and Enste(2000),thesubjectisstillquitecontroversial2)astherearedisagreementsaboutthe definitionofshadoweconomyactivities,theestimationproceduresandtheuseoftheir estimates in economic analysis and policy aspects.3) Nevertheless around the world, there are someindicationsforanincreaseoftheshadoweconomybutlittleisknownaboutthe development and the size of the shadow economies in transition, development and developed countries over the latest period 1999/2000 to 2002/2003. Hence,thegoalofthispaperistwofold:toundertakethechallengingtasktoestimatethe shadow economy for 145 countries all over the world and to provide some insights about the maincausesoftheshadoweconomy.Insection2anattemptismadetodefinetheshadow economy and some theoretical considerations about the reasons why the shadow is increasing areundertaken.Section3presentstheempiricalresultsofthesizeoftheshadoweconomy over145countriesallovertheworld.Insection4asummaryisgivenandsomepolicy

1)Theliteratureabouttheshadow,underground,informal,second,cash-orparallel,economyis increasing. Various topics, on how to measure it, its causes, its effect on the official economy are analyzed. See forexample,surveytypepublicationsbyFreyandPommerehne(1984);Thomas(1992);Loayza(1996);Pozo (1996);LippertandWalker(1997);Schneider(1994a,1994b,1997,1998a);Johnson,Kaufmann,andShleifer (1997),Johnson,KaufmannandZoido-Lobatn(1998a,1998b);Belev(2003);Gerxhani(2003)andPedersen (2003). For an overall survey of the global evidence of the size of the shadow economy see Schneider and Enste (2000, 2002), Schneider (2003, 2005) and Alm, Martinez and Schneider (2004). 2) Compare e.g. in the Economic Journal, vol. 109, no. 456, June 1999 the feature controversy: on the hidden economy. 3) Compare the different opinions of Tanzi (1999), Thomas (1999), Giles (1999a,b) and Pedersen (2003). 4 conclusions are drawn. Finally in the appendix (chapter 5) the various methods to estimate the shadow economy are presented and critically evaluated. 2Some Theoretical Considerations about the Shadow Economy 2.1Defining the Shadow Economy Mostauthorstryingtomeasuretheshadoweconomyfacethedifficultyofhowtodefineit. Onecommonly usedworkingdefinition is all currently unregistered economic activities that contribute to the officially calculated (or observed) Gross National Product.4) Smith (1994, p. 18) defines it as market-based production of goods and services, whether legal or illegal that escapesdetectionintheofficialestimatesofGDP.Ortoputitinanotherway,oneofthe broadestdefinitionsofit, includesthose economic activities and the income derived from themthatcircumventorotherwisegovernmentregulation,taxationorobservation.5)As these definitions still leave open a lot of questions, table 2.1 is helpful for developing a better feelingforwhatcouldbeareasonableconsensusdefinitionoftheunderground(orshadow) economy. Fromtable2.1,itbecomesclearthatabroaddefinitionoftheshadoweconomyincludes unreported income from the production of legal goods and services, either from monetary or bartertransactionsandsoincludesalleconomicactivitiesthatwouldgenerallybetaxable weretheyreportedtothestate(tax)authorities.Inthispaperthefollowingmorenarrow definition of the shadow economy is used:6) The shadow economy includes all market-based legal production of goods and services that are deliberately concealed from public authorities for the following reasons: (1) to avoid payment of income, value added or other taxes, (2) to avoid payment of social security contributions,

4)Thisdefinitionisusedforexample,byFeige(1989,1994),Schneider(1994a,2003,2005)andFreyand Pommerehne (1984). Do-it-yourself activities are not included. For estimates of the shadow economy and the do-it-yourself activities for Germany see Karmann (1986, 1990). 5)ThisdefinitionistakenfromDelAnno(2003),DelAnnoandSchneider(2004)andFeige(1989);seealso Thomas (1999), Fleming, Roman and Farrell (2000). 6) Compare also the excellent discussion of the definition of the shadow economy in Pedersen (2003, pp.13-19), 5 (3) to avoid having to meet certain legal labor market standards, such as minimum wages, maximum working hours, safety standards, etc., and (4) toavoidcomplyingwithcertainadministrativeprocedures,suchascompleting statistical questionnaires or other administrative forms. Hence, in this paper, I will not deal with typical underground, economic (classical crime) activities,whichareallillegal actionsthatfitsthecharacteristicsofclassicalcrimeslike burglary,robbery,drugdealing,etc.Ialsoincludenottheinformalhouseholdeconomy which consists of all household services and production. Also this paper does not focus on tax evasion or tax compliance, because it would get to long, and moreover tax evasion is a different subject, where already a lot of research has been underway.7) Table 2.1: A Taxonomy of Types of Underground Economic Activities1) Type of Activity Monetary Transactions Non Monetary Transactions Illegal Activities Tradewithstolengoods;drugdealing andmanufacturing;prostitution; gambling; smuggling; fraud; etc. Barterofdrugs,stolengoods, smugglingetc.Produceorgrowing drugsforownuse.Theftforown use. Tax Evasion Tax Avoidance Tax Evasion Tax Avoidance Legal Activities Unreported income from self-employment; Wages, salaries and assets from unreported work related to legal services and goods Employee discounts, fringe benefits Barter of legal services and goods All do-it-yourself work and neighbor help 1) Structure of the table is taken from Lippert and Walker (1997, p. 5) with additional remarks. who uses a similar one. 7) Compare, e.g. the survey of Andreoni, Erard and Feinstein (1998) and the paper by Kirchler, Maciejovsky and Schneider (2002). 6 2.2The Main Causes of Determining the Shadow Economy 2.2.1Tax and Social Security Contribution Burdens Inalmostallstudies8)ithasbeenfoundout,thatthetaxandsocialsecuritycontribution burdensareoneofthemaincausesfortheexistenceoftheshadoweconomy.Sincetaxes affectlabor-leisurechoices,andalsostimulatelaborsupplyintheshadoweconomy,the distortionoftheoveralltaxburdenisamajorconcernofeconomists.Thebiggerthe differencebetweenthetotalcostoflaborintheofficialeconomyandtheafter-taxearnings (fromwork),thegreateristheincentivetoavoidthisdifferenceandtoworkintheshadow economy.Sincethisdifferencedependsbroadlyonthesocialsecurityburden/paymentsand the overall tax burden, they are key features of the existence and the increase of the shadow economy.Butevenmajortaxreformswithmajortaxratedeductionswillnotleadtoasubstantial decrease of the shadow economy.9) Such reforms will only be able to stabilize the size of the shadow economy and avoid a further increase. Social networks and personal relationships, the high profit from irregular activities and associated investments in real and human capital are strong ties which prevent people from transferring to the official economy. For Canada, Spiro (1993)foundsimilarreactionsofpeoplefacinganincreaseinindirecttaxes(VAT,GST). This fact makes it even more difficult for politicians to carry out major reforms because they may not gain a lot from them. Empirical results of the influence of the tax burden on the shadow economy is provided in the studies of Schneider (1994b, 2000, 2005) and Johnson, Kaufmann and Zoido-Lobatn (1998a, 1998b);theyallfoundstatisticallysignificantevidencefortheinfluenceoftaxationonthe shadoweconomy.Thisstronginfluenceofindirectanddirecttaxationontheshadow economy is further demonstrated by discussing empirical results in the case of Austria and the Scandinavian countries. For Austria the driving force for the shadow economy activities is the

8)SeeThomas(1992);LippertandWalker(1997);Schneider(1994a,b,1997,1998a,b,2000,2003b,2005); Johnson, Kaufmann, and Zoido-Lobatn (1998a,1998b); Tanzi (1999); Giles (1999a); Mummert and Schneider (2001); Giles and Tedds (2002) and DellAnno (2003), just to quote a few recent ones. 9)See Schneider (1994b, 1998b) for a similar result of the effects of a major tax reform in Austria on the shadow economy. Schneider shows that a major reduction in the direct tax burden did not lead to a major reduction in the shadoweconomy.Becauselegaltaxavoidancewasabolishedandotherfactors,likeregulations,werenot changed;henceforaconsiderablepartofthetaxpayerstheactualtaxandregulationburdenremained unchanged. 7 directtaxburden(includingsocialsecuritypayments),ithasthebiggestinfluence,followed bytheintensityofregulationandcomplexityofthetaxsystem.Asimilarresulthasbeen achievedbySchneider(1986)fortheScandinaviancountries(Denmark,Norwayand Sweden). In all three countries various tax variables (average direct tax rate, average total tax rate (indirect and direct tax rate)) and marginal tax rates have the expected positive sign (on currencydemand)andarehighlystatisticallysignificant.Thesefindingsaresupportedby studies of Kirchgaessner (1983, 1984) for Germany and by Klovland (1984) for Norway and Sweden, too. In this study an attempt will be made to investigate the influence of the direct and indirect tax burdenaswellasthesocialsecuritypaymentsontheshadoweconomyfordeveloping, transitionandhighlydevelopedcountries.Hence,forthefirsttimethisinfluenceis investigatedfordeveloping,transitionandhighlydevelopedcountriesforthesametime period and using the same estimation technique. 2.2.2Intensity of Regulations Theincreaseoftheintensityofregulations(oftenmeasuredinthenumbersoflawsand regulations,likelicensesrequirements)isanotherimportantfactor,whichreducesthe freedom (of choice) for individuals engaged in the official economy.10) One can think of labor marketregulations,tradebarriers,andlaborrestrictionsforforeigners.Johnson,Kaufmann, and Zoido-Lobatn (1998b) find an overall significant empirical evidence of the influence of (labor) regulations on the shadow economy, the impact is clearly described and theoretically derivedinotherstudies,e.g.forGermany(DeregulationCommission1990/91).Regulations leadtoasubstantialincreaseinlaborcostsintheofficialeconomy.Butsincemostofthese costscanbeshiftedontheemployees,thesecostsprovideanotherincentivetoworkinthe shadoweconomy,wheretheycanbeavoided.Empiricalevidencesupportingthemodelof Johnson, Kaufmann, and Shleifer (1997), which predicts, inter alia, that countries withmore general regulation of their economies tend to have a higher share of the unofficial economy in totalGDP,isfoundintheirempiricalanalysis. Aone-pointincreaseoftheregulationindex (ranging from 1 to 5, with 5 = the most regulation in a country), ceteris paribus, is associated withan8.1percentagepointincreaseintheshareoftheshadoweconomy,whencontrolled for GDP per capita (Johnson et. al. (1998b), p. 18). They conclude that it is the enforcement

10)Seefora(social)psychological,theoreticalfoundationofthisfeature,Brehm(1966,1972),andfora(first) 8 of regulation, which is the key factor for the burden levied on firms and individuals, and not theoverallextentofregulation-mostlynotenforced-whichdrivefirmsintotheshadow economy. Friedman, Johnson, Kaufmann and Zoido-Lobaton (1999) reach a similar result. In their study every available measure of regulation is significantly correlated with the share of theunofficialeconomyandthesignoftherelationshipisunambiguous:moreregulationis correlatedwithalargershadoweconomy.Aonepointincreaseinanindexofregulation (rangingfrom1-5)isassociatedwitha10%increaseintheshadoweconomyfor76 developing, transition and developed countries. Thesefindingsdemonstratethatgovernmentsshouldputmoreemphasisonimproving enforcement of laws and regulations, rather than increasing their number. Some governments, however,preferthispolicyoption(moreregulationsandlaws),whentryingtoreducethe shadow economy, mostly because it leads to an increase in power of the bureaucrats and to a higherrateofemploymentinthepublicsector.Inthisstudytheeffectofgovernment regulationonthedevelopmentoftheshadoweconomywillbeinvestigatedfordeveloping, transition and highly developed countries. 2.2.3Public Sector Services An increase of the shadow economy can lead to reduced state revenues which in turn reduce the quality and quantity of publicly provided goods and services. Ultimately, this can lead to anincreaseinthetaxratesforfirmsandindividualsintheofficialsector,quiteoften combinedwithadeteriorationinthequalityofthepublicgoods(suchasthepublic infrastructure) and of the administration, with the consequence of even stronger incentives to participateintheshadoweconomy.Johnson,Kaufmann,andZoido-Lobatn(1998a,b) presentasimplemodelofthisrelationship.Theirfindingsshowthatsmallershadow economies appear in countries with higher tax revenues, if achieved by lower tax rates, fewer lawsandregulationsandlessbriberyfacingenterprises.Countrieswithabetterruleofthe law,whichisfinancedbytaxrevenues,alsohavesmallershadoweconomies.Transition countrieshavehigherlevelsofregulationleadingtoasignificantlyhigherincidenceof bribery,highereffectivetaxesonofficialactivitiesandalargediscretionaryframeworkof regulationsandconsequentlytoahighershadoweconomy.Theiroverallconclusionisthat wealthier countries of the OECD, as well as some in Eastern Europe find themselves in the good equilibrium of relatively low tax and regulatory burden, sizeable revenue mobilization, application to the shadow economy, Pelzmann (1988). 9 goodruleoflawandcorruptioncontrol,and[relatively]smallunofficialeconomy.By contrast,anumberofcountriesinLatinAmericanandtheFormerSovietUnionexhibit characteristicsconsistentwithabadequilibrium:taxandregulatorydiscretionandburden onthefirmishigh,theruleoflawisweak,andthereisahighincidenceofbriberyanda relatively high share of activities in the unofficial economy. (Johnson, Kaufmann and Zoido-Lobatn 1998a p. I). Unfortunately, due to lacking data, for example the effect of corruption on the size of the shadow economy could not be investigated. 3The Size of the Shadow Economies all over the World Findings for 145 Countries 3.1Introductionary Remarks In order to calculate the size and development of the shadow economies of 145 countries, the DYMIMICapproach(latentestimationapproachfordetailsseetheAppendix)isused. EconometricestimationshavebeenundertakenforthegroupofAfrican,SouthandMiddle American and Asian developing countries (together with the South Pacific Island countries), fortheTransitioncountries(togetherwiththestillCommunistcountries)andforthehighly developedOECDcountries.OnedisadvantageoftheDYMIMICapproachis,thatonegets only relative estimated sizes of the shadow economy and one has to use another approach (in our case, the currency demand approach) to get absolute figures. In order to calculate absolute figuresofthesizeoftheshadoweconomiesfromtheseDYMIMICestimationresultsthe authorusedthealreadyavailableestimationsfromthecurrencydemandapproachin combinationwiththeDYMIMICapproachforAustralia,Austria,Germany,Hungary,Italy, India,Peru,RussiaandtheUnitedStates(fromstudiesofChatterjee,Chaudhuryand Schneider(2003),DelAnnoandSchneider(2004),BajadaandSchneider(2003),Alexeev and Pyle (2003), Schneider and Enste (2002) and Lacko (2000)). With the help of the absolute valuesoftheshadoweconomy(in%ofGDP)forthesecountriestheabsolutevaluesofthe shadow economy for all other countries could be calculated. The results are shown in the next section. 3.2The Size of the Shadow Economies for 145 Countries for 1999/2000, 10 2001/2002 and 2002/2003 When showing the size of the shadow economies over the three periods of time (1999/2000, 2001/2002and2002/2003)forthe145countrieswhicharequitedifferentinlocationand developing stage, one should be aware that such country comparison give only a rough picture of the ranking of the size of the shadow economy over the countries and over time, because the DYMIMIC and the currency demand methods have short comings which are discussed in appendix(chapter5)seealsoThomas(1992,1999),Tanzi(1999),Pedersen(2003)and Ahumada,Alveredo,CavaneseAandP.Cavanese(2004),andSchneider(2005)).Dueto theseshortcomingsadetaileddiscussionofthe(relative)rankingofthesizeoftheshadow economies is not done. 3.2.1Developing Countries 11 TheresultsoftheshadoweconomiesforthedevelopingcountriesaregroupedforAfrica, AsiaandCentralandSouthAmerica,12)andareshownintables3.1.-3.3andfigures3.1.1-3.3.3.Theresultsfor37Africancountriesareshownintable3.1.IfIfirstconsiderthe development of the shadow economy of these 37 African countries over the three periods of time,IrealizethatshadoweconomyintheseAfricannationshasincreased.Onaverage,the sizeofthese37Africanshadoweconomieswas41.3%ofofficialGDPin1999/2000and increased to 43.2% in 2002/2003. This is an increase of 0.9 percentage points over four years (onaverage).IfInowturntothelatestresultsfor2002/2003Zimbabwe,Tanzaniaand Nigeria (with63.2,60.2 and59.4%respectively) have by far the largest shadow economies; themediancountryisMozambiquewith42.4%andthelowestshadoweconomyhasSouth Africa with 29.5%, followed by Lesotho with 33.3% and Namibia with 33.4%.

11)Foranextensiveandexcellentliteraturesurveyoftheresearchabouttheshadoweconomyindeveloping countriessee Gerxhani(2003),whostressesthorough outher paperthatthedestinationbetween developedand developing countries with respect to the shadow economy is of great importance. Due to space reasons this point is not further elaborated here also the former results and literature are not discussed here. 12) The disadvantage of these grouping is that in Asia we have also highly developed countries like Singapore. and also in Africa the state ofSouth-Africa. 11 Table 3.1: The Size of the Shadow Economy in 37 African Countries

Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method No. Country1999/002001/022002/03 1Algeria34,1 35,0 35,6 2Angola43,2 44,1 45,2 3Benin47,3 48,2 49,1 4Botswana33,4 33,9 34,6 5Burkina Faso41,4 42,6 43,3 6Burundi36,9 37,6 38,7 7Cameroon32,8 33,7 34,9 8Central African Republic44,3 45,4 46,1 9Chad46,2 47,1 48,0 10Congo, Dem. Rep.48,0 48,8 49,7 11Congo, Rep.48,2 49,1 50,1 12Cote d'Ivoire43,2 44,3 45,2 13Egypt, Arab Rep.35,1 36,0 36,9 14Ethiopia40,3 41,4 42,1 15Ghana41,9 42,7 43,6 16Guinea39,6 40,8 41,3 17Kenya34,3 35,1 36,0 18Lesotho31,3 32,4 33,3 19Madagascar39,6 40,4 41,6 20Malawi40,3 41,2 42,1 21Mali42,3 43,9 44,7 22Mauritania36,1 37,2 38,0 23Morocco36,4 37,1 37,9 24Mozambique40,3 41,3 42,4 25Namibia31,4 32,6 33,4 26Niger41,9 42,6 43,8 27Nigeria57,9 58,6 59,4 28Rwanda40,3 41,4 42,2 29Senegal45,1 46,8 47,5 30Sierra Leone41,7 42,8 43,9 31South Africa28,4 29,1 29,5 32Tanzania58,3 59,4 60,2 33Togo35,1 39,2 40,4 34Tunisia38,4 39,1 39,9 35Uganda43,1 44,6 45,4 36Zambia48,9 49,7 50,8 37Zimbabwe59,4 61,0 63,2 Unweighted Average41,3 42,3 43,2 12 Figur 3.1.1: The Size of the Shadow Economy in 37 African Countries - in % of official GDP - in 2002/032 9 , 53 3 , 33 3 , 43 4 , 63 4 , 93 5 , 63 6 , 03 6 , 93 7 , 93 8 , 03 8 , 73 9 , 94 0 , 44 1 , 34 1 , 64 2 , 14 2 , 14 2 , 24 2 , 44 3 , 34 3 , 64 3 , 84 3 , 94 4 , 74 5 , 24 5 , 24 5 , 44 6 , 14 7 , 54 8 , 04 9 , 14 9 , 75 0 , 15 0 , 85 9 , 46 0 , 26 3 , 24 3 , 2010203040506070South AfricaLesothoNamibiaBotswanaCameroonAlgeriaKenyaEgypt, Arab Rep.MoroccoMauritaniaBurundiTunisiaTogoGuineaMadagascarEthiopiaMalawiRwandaMozambiqueBurkina FasoGhanaNigerSierra LeoneMaliAngolaCote d'IvoireUgandaCentral African RepublicSenegalChadBeninCongo, Dem. Rep.Congo, Rep.ZambiaNigeriaTanzaniaZimbabweUnweighted Average 13 Figur 3.1.2: The Size of the Shadow Economy in 37 African Countries - in % of official GDP - in 2001/022 9 , 13 2 , 43 2 , 63 3 , 73 3 , 93 5 , 03 5 , 13 6 , 03 7 , 13 7 , 23 7 , 63 9 , 13 9 , 24 0 , 44 0 , 84 1 , 24 1 , 34 1 , 44 1 , 44 2 , 64 2 , 64 2 , 74 2 , 84 3 , 94 4 , 14 4 , 34 4 , 64 5 , 44 6 , 84 7 , 14 8 , 24 8 , 84 9 , 14 9 , 75 8 , 65 9 , 46 14 2 , 3010203040506070South AfricaLesothoNamibiaCameroonBotswanaAlgeriaKenyaEgypt, Arab Rep.MoroccoMauritaniaBurundiTunisiaTogoMadagascarGuineaMalawiMozambiqueEthiopiaRwandaBurkina FasoNigerGhanaSierra LeoneMaliAngolaCote d'IvoireUgandaCentral African RepublicSenegalChadBeninCongo, Dem. Rep.Congo, Rep.ZambiaNigeriaTanzaniaZimbabweUnweighted Average 14 Figur 3.1.3: The Size of the Shadow Economy in 37 African Countries - in % of official GDP - in 1999/002 8 , 43 1 , 33 1 , 43 2 , 83 3 , 43 4 , 13 4 , 33 5 , 13 5 , 13 6 , 13 6 , 43 6 , 93 8 , 43 9 , 63 9 , 64 0 , 34 0 , 34 0 , 34 0 , 34 1 , 44 1 , 74 1 , 94 1 , 94 2 , 34 3 , 14 3 , 24 3 , 24 4 , 34 5 , 14 6 , 24 7 , 34 8 , 04 8 , 24 8 , 95 7 , 95 8 , 35 9 , 44 1 , 30,010,020,030,040,050,060,070,0South AfricaLesothoNamibiaCameroonBotswanaAlgeriaKenyaEgypt, Arab Rep.TogoMauritaniaMoroccoBurundiTunisiaGuineaMadagascarEthiopiaMalawiRwandaMozambiqueBurkina FasoSierra LeoneGhanaNigerMaliUgandaAngolaCote d'IvoireCentral African RepublicSenegalChadBeninCongo, Dem. Rep.Congo, Rep.ZambiaNigeriaTanzaniaZimbabweUnweighted Average 15 In table 3.2 and figures 3.2.1-3.2.3 the results for 27 Asian countries are shown, recognizing that it is somewhatdifficulttotreatallAsiancountriesequallybecauseIsrael,SingaporeandHongkongare highly developed countries and others (like Thailand, Nepal) are more or less developing countries. If Iagaindiscussfirstthedevelopmentoftheshadoweconomyovertheperiod1999/2000and 2002/2003theaverageshadoweconomyofthese27Asiancountriesincreasedfrom28.9%in 1999/2000to30.8%ofofficialGDPin2002/2003,whichisanincreaseof1.9percentagepointsfor these four years.Table 3.2: The Size of the Shadow Economy in 27 Asian Countries

Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method No. Country1999/00 2001/022002/03 1Bangladesh35,6 36,5 37,7 2Bhutan29,4 30,5 31,7 3Cambodia50,1 51,3 52,4 4Hong Kong, China16,6 17,1 17,2 5India23,1 24,2 25,6 6Indonesia19,4 21,8 22,9 7Iran, Islamic Rep.18,9 19,4 19,9 8Israel21,9 22,8 23,9 9Jordan19,4 20,5 21,6 10Korea, Rep.27,5 28,1 28,8 11Kuwait20,1 20,7 21,6 12Lebanon34,1 35,6 36,2 13Malaysia31,1 31,6 32,2 14Nepal38,4 39,7 40,8 15Oman18,9 19,4 19,8 16Pakistan36,8 37,9 38,7 17Papua New Guinea36,1 37,3 38,6 18Philippines43,4 44,5 45,6 19Saudi Arabia18,4 19,1 19,7 20Singapore13,1 13,4 13,7 21Sri Lanka44,6 45,9 47,2 22Syrian Arab Republic19,3 20,4 21,6 23Taiwan, China25,4 26,6 27,7 24Thailand52,6 53,4 54,1 25Turkey32,1 33,2 34,3 26United Arab Emirates26,4 27,1 27,8 27Yemen, Rep.27,4 28,4 29,1 Unweighted Average28,9 29,9 30,8 If I now consider the results of the single the shadow economies of these 27 Asian countries13) for the latestperiod,2002/2003,Thailandhaswith54.1%byfarthelargestshadoweconomy,followedby Cambodia with 52.4% and Sri Lanka with 47.2% of official GDP. The median country is the Korean Republicwith28.2%ofofficialGDP,surroundedbyYemenwith29.1%andUnitedArabEmirates with 27.8%. Singapore, Hongkong and Saudi Arabia have the lowest shadow economies with 13.7%, 17.2% and 19.7% of official GDP, respectively.

13) The case of India has been extensively investigated by Chatterjee, Chaudhury and Schneider (2003). 16 Figur 3.2.1: The Size of the Shadow Economy in 27 Asian Countries - in % of official GDP - in 2002/031 3 , 71 7 , 21 9 , 71 9 , 81 9 , 92 1 , 62 1 , 62 1 , 62 2 , 92 3 , 92 5 , 62 7 , 72 7 , 82 8 , 82 9 , 13 1 , 73 2 , 23 4 , 33 6 , 23 7 , 73 8 , 63 8 , 74 0 , 84 5 , 64 7 , 25 2 , 45 4 , 13 0 , 80102030405060SingaporeHong Kong, ChinaSaudi ArabiaOmanIran, Islamic Rep.JordanKuwaitSyrian Arab RepublicIndonesiaIsraelIndiaTaiwan, ChinaUnited Arab EmiratesKorea, Rep.Yemen, Rep.BhutanMalaysiaTurkeyLebanonBangladeshPapua New GuineaPakistanNepalPhilippinesSri LankaCambodiaThailandUnweighted Average 17 Figur 3.2.2: The Size of the Shadow Economy in 27 Asian Countries - in % of official GDP - in 2001/021 3 , 41 7 , 11 9 , 11 9 , 41 9 , 42 0 , 42 0 , 52 0 , 72 1 , 82 2 , 82 4 , 22 6 , 62 7 , 12 8 , 12 8 , 43 0 , 53 1 , 63 3 , 23 5 , 63 6 , 53 7 , 33 7 , 93 9 , 74 4 , 54 5 , 95 1 , 35 3 , 42 9 , 90102030405060SingaporeHong Kong, ChinaSaudi ArabiaOmanIran, Islamic Rep.Syrian Arab RepublicJordanKuwaitIndonesiaIsraelIndiaTaiwan, ChinaUnited Arab EmiratesKorea, Rep.Yemen, Rep.BhutanMalaysiaTurkeyLebanonBangladeshPapua New GuineaPakistanNepalPhilippinesSri LankaCambodiaThailandUnweighted Average 18 Figur 3.2.3: The Size of the Shadow Economy in 27 Asian Countries - in % of official GDP - in 1999/001 3 , 11 6 , 61 8 , 41 8 , 91 8 , 91 9 , 31 9 , 41 9 , 42 0 , 12 1 , 92 3 , 12 5 , 42 6 , 42 7 , 42 7 , 52 9 , 43 1 , 13 2 , 13 4 , 13 5 , 63 6 , 13 6 , 83 8 , 44 3 , 44 4 , 65 0 , 15 2 , 62 8 , 90,010,020,030,040,050,060,0SingaporeHong Kong, ChinaSaudi ArabiaOmanIran, Islamic Rep.Syrian Arab RepublicJordanIndonesiaKuwaitIsraelIndiaTaiwan, ChinaUnited Arab EmiratesYemen, Rep.Korea, Rep.BhutanMalaysiaTurkeyLebanonBangladeshPapua New GuineaPakistanNepalPhilippinesSri LankaCambodiaThailandUnweighted Average 19 Onerealizes,thattheaveragesizeoftheAsianshadoweconomiesisconsiderablylowercompared with the ones of African and South and Latin American states partly due to the fact that in Asia there is a greater number of developed (already) industrialized countries with lower shadow economies. In table 3.3 the results of the sizes of the shadow economies for the three periods of time (1999/2000, 2001/2002, 2002/2003) for 21 Central and South American countries are shown. Discussing again first the development of the shadow economy over time in all 21 Central and South American countries it hasincreasedfrom41.1%intheyear1999/2000to43.4%ofofficialGDPintheyear2002/2003, whichisanincreaseof2.3percentagepointsoverthesefouryears.IfInowturntothesizeofthe shadow economy for single, Central and Latin American countries for the latest period 2002/2003 the largestshadoweconomyhasBoliviawith68.3%,followedbyPanamawith65.3%andPeruwith 60.9% of official GDP. The median country is Brazil with 42.3% and at the lower end are Chile with 20.9%, Costa Rica with 27.8% and Argentina with 28.9% of official GDP. In general the average sizes oftheshadoweconomiesofSouthandLatinAmericancountriesandtheonesofAfricaarequite similar. Table 3.3: The Size of the Shadow Economy in 21 Central and South American Countries

Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method No. Country1999/002001/022002/03 1Argentina25,427,1 28,9 2Bolivia67,168,1 68,3 3Brazil39,840,9 42,3 4Chile19,820,3 20,9 5Colombia39,141,3 43,4 6Costa Rica26,227,0 27,8 7Dominican Republic32,133,4 34,1 8Ecuador34,435,1 36,7 9El Salvador46,347,1 48,3 10Guatemala51,551,9 52,4 11Haiti55,457,1 58,6 12Honduras49,650,8 51,6 13Jamaica36,437,8 38,9 14Mexico30,131,8 33,2 15Nicaragua45,246,9 48,2 16Panama64,165,1 65,3 17Paraguay27,429,2 31,4 18Peru59,960,3 60,9 19Puerto Rico28,429,4 30,7 20Uruguay51,151,4 51,9 21Venezuela, RB33,635,1 36,7 Unweighted Average41,142,2 43,4 20 Figur 3.3.1: The Size of the Shadow Economy in 21 Central and South American Countries - in % of official GDP - in 2002/032 0 , 92 7 , 82 8 , 93 0 , 73 1 , 43 3 , 23 4 , 13 6 , 73 6 , 73 8 , 94 2 , 34 3 , 44 8 , 24 8 , 35 1 , 65 1 , 95 2 , 45 8 , 66 0 , 96 5 , 36 8 , 34 3 , 401020304050607080ChileCosta RicaArgentinaPuerto RicoParaguayMexicoDominican RepublicEcuadorVenezuela, RBJamaicaBrazilColombiaNicaraguaEl SalvadorHondurasUruguayGuatemalaHaitiPeruPanamaBoliviaUnweighted Average 21 Figur 3.3.2: The Size of the Shadow Economy in 21 Central and South American Countries - in % of official GDP - in 2001/022 0 , 32 7 , 02 7 , 12 9 , 22 9 , 43 1 , 83 3 , 43 5 , 13 5 , 13 7 , 84 0 , 94 1 , 34 6 , 94 7 , 15 0 , 85 1 , 45 1 , 95 7 , 16 0 , 36 5 , 16 8 , 14 2 , 201020304050607080ChileCosta RicaArgentinaParaguayPuerto RicoMexicoDominican RepublicEcuadorVenezuela, RBJamaicaBrazilColombiaNicaraguaEl SalvadorHondurasUruguayGuatemalaHaitiPeruPanamaBoliviaUnweighted Average 22 Figur 3.3.3: The Size of the Shadow Economy in 21 Central and South American Countries - in % of official GDP - in 1999/001 9 , 82 5 , 42 6 , 22 7 , 42 8 , 43 0 , 13 2 , 13 3 , 63 4 , 43 6 , 43 9 , 13 9 , 84 5 , 24 6 , 34 9 , 65 1 , 15 1 , 55 5 , 45 9 , 96 4 , 16 7 , 14 1 , 10,010,020,030,040,050,060,070,080,0ChileArgentinaCosta RicaParaguayPuerto RicoMexicoDominican RepublicVenezuela, RBEcuadorJamaicaColombiaBrazilNicaraguaEl SalvadorHondurasUruguayGuatemalaHaitiPeruPanamaBoliviaUnweighted Average 23 3.2.2Transition Countries The measurement of the size and development of the shadow economy in the transition countries has been undertaken since the late 80s starting with the work of Kaufmann and Kaliberda (1996), Johnson et.al. (1997) and Lacko (2000). They all are using the physical input (electricity) method (seeAppendix7.1.2.5)andcomeupwithquitelargefigures.IntheworkofAlexeevandPyle (2003)andBelev(2003)theabovementionedstudiesarecriticallyevaluatedarguingthatthe estimatedsizesoftheunofficialeconomiesaretoalargecontentahistoricalphenomenonand partly determined by institutional factors. In table 3.4 and figures 3.4.1-3.4.3 the size and development of the shadow economy of 25 East and Central European and Former Soviet Union countries are presented. Turning again first to the developmentofthesizeoftheshadoweconomyovertime,theaveragesizeoftheshadow economyofthese25EastandCentralEuropeancountrieswas38.1%ofofficialGDPin 1999/2000 and increased to 40.1% in 2002/2003 which is an increase of 2 percentage points over these four years. The highest shadow economies have Georgia, Azerbaijan and the Ukraine with 68.0%, 61.3% and 54.7%. The median country is Bulgaria, surrounded by Serbia and Montenegro of39.1%andRomaniaof37.4%.AtthelowerendaretheCzechRepublicwith20.1%,the Slovak Republic with 20.2% and Hungary with 26.2% of official GDP. 24 Table3.4:TheSizeoftheShadowEconomyin25EastandCentralEuropeanand Former Soviet Union Countries

Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method No. Country1999/00 2001/022002/03 1Albania33,4 34,6 35,3 2Armenia46,3 47,8 49,1 3Azerbaijan60,6 61,1 61,3 4Belarus48,1 49,3 50,4 5Bosnia and Herzegovina34,1 35,4 36,7 6Bulgaria36,9 37,1 38,3 7Croatia33,4 34,2 35,4 8Czech Republic19,1 19,6 20,1 9Estonia38,4 39,2 40,1 10Georgia67,3 67,6 68,0 11Hungary25,1 25,7 26,2 12Kazakhstan43,2 44,1 45,2 13Kyrgyz Republic39,8 40,3 41,2 14Latvia39,9 40,7 41,3 15Lithuania30,3 31,4 32,6 16Macedonia, FYR34,1 35,1 36,3 17Moldova45,1 47,3 49,4 18Poland27,6 28,2 28,9 19Romania34,4 36,1 37,4 20Russian Federation46,1 47,5 48,7 21Serbia and Montenegro36,4 37,3 39,1 22Slovak Republic18,9 19,3 20,2 23Slovenia27,1 28,3 29,4 24Ukraine52,2 53,6 54,7 25Uzbekistan34,1 35,7 37,2 Unweighted Average38,1 39,1 40,1 25 Figur 3.4.1: The Size of the Shadow Economy in 25 East and Central European and Former Soviet Union Countries - in % of official GDP - in 2002/032 0 , 12 0 , 22 6 , 22 8 , 92 9 , 43 2 , 63 5 , 33 5 , 43 6 , 33 6 , 73 7 , 23 7 , 43 8 , 33 9 , 14 0 , 14 1 , 24 1 , 34 5 , 24 8 , 74 9 , 14 9 , 45 0 , 45 4 , 76 1 , 36 8 , 04 0 , 101020304050607080Czech RepublicSlovak RepublicHungaryPolandSloveniaLithuaniaAlbaniaCroatiaMacedonia, FYRBosnia and HerzegovinaUzbekistanRomaniaBulgariaSerbia and MontenegroEstoniaKyrgyz RepublicLatviaKazakhstanRussian FederationArmeniaMoldovaBelarusUkraineAzerbaijanGeorgiaUnweighted Average 26 Figur 3.4.2: The Size of the Shadow Economy in 25 East and Central European and Former Soviet Union Countries - in % of official GDP - in 2001/021 9 , 31 9 , 62 5 , 72 8 , 22 8 , 33 1 , 43 4 , 23 4 , 63 5 , 13 5 , 43 5 , 73 6 , 13 7 , 13 7 , 33 9 , 24 0 , 34 0 , 74 4 , 14 7 , 34 7 , 54 7 , 84 9 , 35 3 , 66 1 , 16 7 , 63 9 , 101020304050607080Slovak RepublicCzech RepublicHungaryPolandSloveniaLithuaniaCroatiaAlbaniaMacedonia, FYRBosnia and HerzegovinaUzbekistanRomaniaBulgariaSerbia and MontenegroEstoniaKyrgyz RepublicLatviaKazakhstanMoldovaRussian FederationArmeniaBelarusUkraineAzerbaijanGeorgiaUnweighted Average 27 Figur 3.4.3: The Size of the Shadow Economy in 25 East and Central European and Former Soviet Union Countries - in % of official GDP - in 1999/001 8 , 91 9 , 12 5 , 12 7 , 12 7 , 63 0 , 33 3 , 43 3 , 43 4 , 13 4 , 13 4 , 13 4 , 43 6 , 43 6 , 93 8 , 43 9 , 83 9 , 94 3 , 24 5 , 14 6 , 14 6 , 34 8 , 15 2 , 26 0 , 66 7 , 33 8 , 10,010,020,030,040,050,060,070,080,0Slovak RepublicCzech RepublicHungarySloveniaPolandLithuaniaAlbaniaCroatiaMacedonia, FYRBosnia and HerzegovinaUzbekistanRomaniaSerbia and MontenegroBulgariaEstoniaKyrgyz RepublicLatviaKazakhstanMoldovaRussian FederationArmeniaBelarusUkraineAzerbaijanGeorgiaUnweighted Average 28 3.2.3Highly developed OECD-Countries The size and development of 21 highly developed OECD countries is shown in table 3.5 and figures 3.5.1-3.5.3. Table 3.5: The Size of the Shadow Economy in 21 OECD Countries

Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method Country1999/002001/022002/03 1Australia14,314,1 13,5 2Austria9,810,6 10,9 3Belgium22,222,0 21,0 4Canada16,015,8 15,2 5Denmark18,017,9 17,3 6Finland18,118,0 17,4 7France15,215,0 14,5 8Germany16,016,3 16,8 9Greece28,728,5 28,2 10Ireland15,915,7 15,3 11Italy27,127,0 25,7 12Japan11,211,1 10,8 13Netherlands13,113,0 12,6 14New Zealand12,812,6 12,3 15Norway19,119,0 18,4 16Portugal22,722,5 21,9 17Spain22,722,5 22,0 18Sweden19,219,1 18,3 19Switzerland8,69,4 9,4 20United Kingdom12,712,5 12,2 21United States8,78,7 8,4 Unweighted Average16,816,7 16,3 29 Figur 3.5.1: The Size of the Shadow Economy in 21 OECD Countries - in % of official GDP - in 2002/038 , 49 , 41 0 , 81 0 , 91 2 , 21 2 , 31 2 , 61 3 , 51 4 , 51 5 , 21 5 , 31 6 , 81 7 , 31 7 , 41 8 , 31 8 , 42 1 , 02 1 , 92 2 , 02 5 , 72 8 , 21 6 , 30 51015202530United StatesSwitzerlandJapanAustriaUnited KingdomNew ZealandNetherlandsAustraliaFranceCanadaIrelandGermanyDenmarkFinlandSwedenNorwayBelgiumPortugalSpainItalyGreeceUnweighted Average 30 Figur 3.5.2: The Size of the Shadow Economy in 21 OECD Countries - in % of official GDP - in 2001/028 , 79 , 41 0 , 61 1 , 11 2 , 51 2 , 61 3 , 01 4 , 11 5 , 01 5 , 71 5 , 81 6 , 31 7 , 91 8 , 01 9 , 01 9 , 12 2 , 02 2 , 52 2 , 52 7 , 02 8 , 51 6 , 70 51015202530United StatesSwitzerlandAustriaJapanUnited KingdomNew ZealandNetherlandsAustraliaFranceIrelandCanadaGermanyDenmarkFinlandNorwaySwedenBelgiumPortugalSpainItalyGreeceUnweighted Average 31 Figur 3.5.3: The Size of the Shadow Economy in 21 OECD Countries - in % of official GDP - in 1999/008 , 68 , 79 , 81 1 , 21 2 , 71 2 , 81 3 , 11 4 , 31 5 , 21 5 , 91 6 , 01 6 , 01 8 , 01 8 , 11 9 , 11 9 , 22 2 , 22 2 , 72 2 , 72 7 , 12 8 , 71 6 , 80,05,010,015,020,025,030,035,0SwitzerlandUnited StatesAustriaJapanUnited KingdomNew ZealandNetherlandsAustraliaFranceIrelandCanadaGermanyDenmarkFinlandNorwaySwedenBelgiumPortugalSpainItalyGreeceUnweighted Average 32 Ifwefirstconsideragainthedevelopmentofthesizeanddevelopmentoftheshadow economiesofthese21OECDcountries,werealizeforthefirsttimethatthesizeofthe shadoweconomyofthese21OECDcountrieshasdecreasedovertheperiod1999/2000to 2002/2003.Theaveragesizeoftheshadoweconomyin1999/2000ofthese21OECD countrieswas16.8%of officialGDP, it decreased to 16.3% in 2002/2003, a decrease of 0.5 percentagepoints.Ifweconsidersinglecountries,Greece,ItalyandSpainhasbyfarthe largestsizeoftheshadoweconomyin2002/2003with28.2%,25.7%and22.0%ofofficial GDP.ThemediancountryisIrelandwith15.3%,surroundedbyGermanywith16.8%and Canada with 15.2% of official GDP. At the lower end are the United States, Switzerland and Japan with a shadow economy of8.4%, 9.4% and 10.8% of official GDP. 3.2.4South West Pacific Islands Thesizeanddevelopmentoftheshadoweconomiesof10SouthWestPacificislandsis presented in table 3.6 and figures 3.6.1-3.6.3. Table 3.6.: The Size of the Shadow Economy in 10 South West Pacific Islands

Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method Country1999/00 2001/022002/03 1Fiji33,6 34,3 35,1 2Kiribati34,1 35,0 35,3 3Maldives30,3 31,4 32,0 4Marshall Islands28,1 29,0 29,6 5Micronesia, Fed. Sts.31,3 32,1 33,2 6Palau28,4 29,2 30,0 7Samoa31,4 32,6 33,5 8Solomon Islands33,4 34,5 35,3 9Tonga35,1 36,3 37,4 10Vanuatu30,9 31,7 32,5 Unweighted Average31,7 32,6 33,4 33 Figur 3.6.1: The Size of the Shadow Economy in 10 Southwest Pacific Islands - in % of official GDP - in 2002/032 9 , 63 0 , 03 2 , 03 2 , 53 3 , 23 3 , 53 5 , 13 5 , 33 5 , 33 7 , 43 3 , 40 510152025303540MarshallIslandsPalauMaldivesVanuatuMicronesia,Fed. Sts.SamoaFijiKiribatiSolomonIslandsTongaUnweightedAverage 34 Figur 3.6.2: The Size of the Shadow Economy in 10 Southwest Pacific Islands - in % of official GDP - in 2001/022 9 , 02 9 , 23 1 , 43 1 , 73 2 , 13 2 , 63 4 , 33 4 , 53 5 , 03 6 , 33 2 , 60,05,010,015,020,025,030,035,040,0MarshallIslandsPalauMaldivesVanuatuMicronesia,Fed. Sts.SamoaFijiSolomonIslandsKiribatiTongaUnweightedAverage 35 Figur 3.6.3: The Size of the Shadow Economy in 10 Southwest Pacific Islands - in % of official GDP - in 1999/002 8 , 12 8 , 43 0 , 33 0 , 93 1 , 33 1 , 43 3 , 43 3 , 63 4 , 13 5 , 13 1 , 70,05,010,015,020,025,030,035,040,0MarshallIslandsPalauMaldivesVanuatuMicronesia,Fed. Sts.SamoaSolomonIslandsFijiKiribatiTongaUnweightedAverage 36 If we again consider first the development over time the average size of the shadow economy of these 10 South West Pacific islands countries, increased from 31.7% in the year 1999/2000 to 33.4% in the year 2002/2003, which means an increase of 1.7 percentage points over these four years. The largest size of the shadow economy (the latest estimation period 2002/2003) has Tonga with 37.4%, followed by Solomon Islands with 35.3% and Kiribati with 35.3%. In themiddlefieldisMicronesiaandSamoawithashadoweconomyof33.2%and33.5%of officialGDP.ThelowestshadoweconomyhavetheMarshallIslandsandPalauwitha shadow economy of 29.6% and 30.0%. 3.2.5Communist Countries Inthislastsectionthesizeanddevelopmentoftheshadoweconomyof4communist countries(China,Laos,MongoliaandVietnam)ispresented.Theresultsareshownintable 3.7 and figures 3.7.1-3.7.3. Table 3.7: The Size of the Shadow Economy in 4 Communist Countries

Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method No. Country1999/002001/022002/03 1China13,1 14,4 15,62Lao PDR30,6 31,9 33,43Mongolia18,4 19,6 20,44Vietnam15,6 16,9 17,9Unweighted Average19,4 20,7 21,8 37 Figur 3.7.1: The Size of the Shadow Economy in 4 Communist Countries - in % of official GDP - in 2002/031 5 . 61 7 . 92 0 . 43 3 . 42 1 . 80 510152025303540ChinaVietnamMongoliaLao PDRUnweighted Average 38 Figur 3.7.2: The Size of the Shadow Economy in 4 Communist Countries - in % of official GDP - in 2001/021 4 . 41 6 . 91 9 . 63 1 . 92 0 . 70 5101520253035ChinaVietnamMongoliaLao PDRUnweighted Average 39 Figur 3.7.3: The Size of the Shadow Economy in 4 Communist Countries - in % of official GDP - in 1999/001 3 . 11 5 . 61 8 . 43 0 . 61 9 . 40.05.010.015.020.025.030.035.0ChinaVietnamMongoliaLao PDRUnweighted Average 40 If we again first consider the average development of the size of the shadow economy of these fourCommunistcountriesover time,theaveragesizeof theshadoweconomyin1999/2000 was 19.4% and increased to 21.8% in 2002/2003. This is an increase of 2.4 percentage points. Laos has the largest shadow economy with 33.4% and China the lowest with 15.6%. It should be aware that the shadow economy in these countries, and especially in China, which is partly a market economy and still a planning socialist economy, is difficult to interpret. It should be moreseenasaparalleleconomy,whereespeciallyfarmersproduceadditionalproductsto earnsomeextramoney.Itisanopenquestionwhetherthemeaningoftheseshadow economiescanbecomparedtothesizeoftheotherones.Thatisonereason,whytheyare shown in this paper in an extra section. 4Summary and Conclusions There have been many obstacles to overcome to measure the size of the shadow economy and toanalyzeitsconsequencesontheofficialeconomy,butasthispapershowsthatsome progresshasbeenmade.Iprovidedestimatesofthesizeoftheshadoweconomiesfor145 countriesforthreeperiodsoftime(1999/2000,2001/2002and2002/2003)usingthe DYMIMIC and the currency demand approach. Hence, some insights can be provided into the sizeanddevelopmentoftheshadoweconomyofdeveloping,transition,highlydeveloped OECD, Pacific Islands and Communist countries.14) The first conclusion from these results is thatforallcountriesinvestigatedtheshadoweconomyhasreachedaremarkablylargesize; the summarized results are shown in table 4.1.

14)Intheappendixsomecriticaldiscussionofthesetwomethodsisgiven,theyhavewellknownweaknesses, compare also Pedersen (2003). 41 Table 4.1: Average Size of the Shadow Economy for Developing, Transition and OECD-Countries in % of official GDP Countries/Year Average Size of the Shadow Economy Value added in % of official GDP using DYMIMIC and Currency Demand method (Number of Countries) Mostly developing countries:1999/20002000/20012002/2003 Africa33.9 (24) 37.4 (24) 41.2 (24) Central and South America34.2 (17) 37.7 (17) 41.5 (17) Asia 20.9 (25) 23.4 (25) 26.3 (25) Transition countries 31.5 (23) 34.6 (23) 37.9 (23) HighlydevelopedOECD Countries 13.2 (21) 15.7 (21) 16.8 (21) South Pacific Islands31.7 (10) 32.6 (10) 33.4 (10) Communist Countries19.4 (4) 20.7 (4) 21.8 (4) UnweightedAverageover 145 Countries 33.6 34.535.2 Source: Own calculations. Thesecondconclusionis,shadoweconomiesareacomplexphenomenon,presenttoan importantextentinalltypeofeconomies(developing,transitionanddeveloped).People engage in shadow economic activity for a variety of reasons, among most important, of which wecancountaregovernmentactions,mostnotabletaxationandregulation.Withthesetwo insights/conclusionsgoesathird,nolessimportantone:agovernmentaimingtodecrease shadoweconomicactivityhastofirstandforemostanalyzethecomplexrelationships between the official and shadow economy and even more important among consequences of its own policy decisions. 42 5Appendix: Methods to Estimate the Size of the Shadow Economy As has already been mentioned in chapters 2 and 3 estimating the size of a shadow economy is a difficult and challenging task. In this appendix I give a short but comprehensive overview on the various procedures to estimate the size of a shadow economy. Three different types of methods are most widely used, and each is briefly discussed as well as critically evaluated. 5.1Direct Approaches Thesearemicroapproachesthatemployeitherwelldesignedsurveysandsamplesbasedon voluntary replies or tax auditing and other compliance methods. Sample surveys designed to estimatetheshadoweconomyarewidelyusedinanumberofcountries15).Themain disadvantageofthismethodisthatitpresentstheflawsofallsurveys.Forexample,the average precision and results depend greatly on the respondents willingness to cooperate, it is difficulttoassestheamountofundeclaredworkfromadirectquestionnaire,most interviewershesitatetoconfessafraudulentbehavior,andresponsesareofuncertain reliability,whichmakesitdifficulttocalculatearealestimate(inmonetaryterms)ofthe extendofundeclaredwork.Themainadvantageofthismethodliesinthedetailed informationaboutthestructureoftheshadoweconomy,buttheresultsfromthesekindsof surveys are very sensitive to the way the questionnaire is formulated16). Estimatesoftheshadoweconomycanalsobebasedonthediscrepancybetweenincome declaredfortaxpurposesandthatmeasuredbyselectivechecks.Fiscalauditingprograms have been particularly effective in this regard. Since these programs are designed to measure theamountofundeclaredtaxableincome,theymayalsobeusedtocalculatetheshadow

15)The direct method of voluntary sample surveys has been extensively used for Norway by Isachsen, Klovland and Strom (1982), and Isachsen and Strom (1985). For Denmark this method is used by Mogensen et. al. (1995) in which they report estimates of the shadow economy of 2.7 percent of GDP for 1989, of 4.2 percent of GDP for 1991, of 3.0 percent of GDP for 1993 and of 3.1 percent of GDP for 1994. In Pedersen (2003) estimates of the Danish shadow economy contain the years 1995 with 3.1% up to 2001 with 3.8%. 16)The advantages and disadvantages of this method are extensively dealt by Pedersen (2003) and Mogensen et. al (1995) in their excellent and very carefully done investigations. 43 economy.17)However,anumberofdifficultiesbesetthisapproach.First,usingtax compliancedataareequivalenttousinga(possiblybiased)sampleofthepopulation.In general,theselectionoftaxpayersfortaxauditisnotrandombutbasedonpropertiesof submitted (tax) returns that indicate a certain likelihood of (tax) fraud. Consequently, such a sample is not a random one of the whole population, and estimates of the shadow based upon abiasedsamplemaynotbeaccurate.Secondestimatesbasedontaxauditsreflectonlythat portionofshadoweconomyincomethattheauthoritiessucceedindiscovering,andthisis likely to be only a fraction of hidden income. A further disadvantage of these two direct methods (surveys and tax auditing) is that they lead onlytopointestimates.Moreover,itisunlikelythattheycaptureallshadowactivities,so they can be seen as providing lower bound estimates. They are unable to provide estimates of the development and growth of the shadow economy over a longer period of time. As already argued,theyhave,howeveratleastoneconsiderableadvantage-theycanprovidedetailed information about shadow economy activities and the structure and composition of those who work in the shadow economy. 5.2Indirect Approaches Theseapproaches,whicharealsocalledindicatorapproaches,aremostlymacroeconomic onesandusevariouseconomicandotherindicatorsthatcontaininformationaboutthe developmentoftheshadoweconomy(overtime).Currentlytherearefiveindicatorsthat leave some traces of the shadow economy. 5.2.1The Discrepancy between National Expenditure and Income Statistics Thisapproachisbasedondiscrepanciesbetweenincomeandexpenditurestatistics.In national accounting the income measure of GNP should be equal to the expenditure measure ofGNP.Thus,ifanindependentestimateoftheexpendituresiteofthenationalaccountsis available, the gap between the expenditure measure and the income measure can be used as an indicatoroftheextentoftheblackeconomy.18)Sincenationalaccountsstatisticiansare

17)In the United States, IRS (1979, 1983), Simon and Witte (1982), Witte (1987), Clotefelter (1983), and Feige (1986). For a more detailed discussion, see Dallago (1990) and Thomas (1992). 18) See, e.g., Franz (1983) for Austria; MacAfee (1980) OHiggins (1989) and Smith (1985), for Great Britain; 44 anxious to minimize this discrepancy, the initial discrepancy or first estimate, rather than the publisheddiscrepancyshouldbeemployed as an estimate of the shadow economy. If allthe componentsoftheexpendituresitearemeasuredwithouterror,thenthisapproachwould indeedyieldagoodestimateofthescaleoftheshadoweconomy.Unfortunately,however, thisisnotthecase.Instead,thediscrepancyreflectsallomissionsanderrorseverywherein the national accounts statistics as well as the shadow economy activity. These estimates may therefore be very crude and of questionable reliability.19)

5.2.2The Discrepancy between the Official and Actual Labor Force Adeclineinparticipationofthelaborforceintheofficialeconomycanbeseenasan indicationofincreasedactivityintheshadoweconomy.Iftotallaborforceparticipationis assumedtobeconstant,thanadecreasingofficialrateofparticipationcanbeseenasan indicatorofanincreaseintheactivitiesintheshadoweconomy,ceterisparibus.20)One weaknessofthismethodisthatdifferencesintherateofparticipationmayalsohaveother causes.Also,peoplecanworkintheshadoweconomyandhaveajobintheofficial economy.Thereforesuchestimatesmaybeviewedasweakindicatorsofthesizeand development of the shadow economy. 5.2.3The Transactions Approach ThisapproachhasbeenmostfullydevelopedbyFeige.21)Itisbasedupontheassumption, that there is a constant relation over time between the volume of transaction and official GNP, assummarizedbythewell-knownFisherianquantityequation,orM*V=p*T(withM= money,V=velocity,p=prices,andT=totaltransactions).Assumptionsalsohavetobe madeaboutthevelocityofmoneyandabouttherelationshipsbetweenthevalueoftotal transactions(p*T)andtotal(=official+unofficial)nominalGNP.Relatingtotalnominal GNP to total transactions, the GNP of the shadow economy can be calculated by subtracting Petersen (1982) and Del Boca (1981) for Germany; Park (1979) for the United States. For a critical survey, see Thomas (1992). 19) A related approach is pursued by Pissarides and Weber (1988), who use micro data from household budget surveys to estimate the extend of income understatement by self-employed. 20) Such studies have been made for Italy, see e.g., Contini (1981) and Del Boca (1981); for the United States, see ONeill (1983), for a critical survey, see again Thomas (1992). 21) For an extended description of this approach, see Feige (1996); for a further application for the Netherlands, Boeschoten and Fase (1984), and for Germany, Langfeldt (1984). 45 theofficialGNPfromtotalnominalGNP.However,toderivefiguresfortheshadow economy,onemustalsoassumeabaseyearinwhichthereisnoshadoweconomyand thereforetheratioofp*Ttototalnominal(official=total)GNPwasnormalandwould have been constant over time, if there had been no shadow economy. Thismethod,too,hasseveralweaknesses,suchastherequiredassumptionsofabaseyear with no shadow economy, and of a normal ratio of transactions to nominal GNP. Moreover, toobtainreliableshadoweconomyestimates,precisefiguresofthetotalvolumeof transactions should be available, and this availability might be especially difficult to achieve forcashtransactions,becausetheydepend,amongotherfactors,onthedurabilityofbank notes in terms of the quality of the papers on which they are printed.22) Also, the assumption is made that all variations in the ratio between the total value of transaction and the officially measuredGNPareduetotheshadoweconomy.Thismeansthataconsiderableamountof data is required in order to eliminate financial transactions from pure cross payments, which are legal and have nothing to do with the shadow economy. In general, although this approach is theoretically attractive, the empirical requirements necessary to obtain reliable estimates are so difficult to fulfill, that its application may lead to doubtful results. 5.2.4The Currency Demand Approach The currency demand approach was first used by Cagan (1958), who calculated a correlation ofthecurrencydemandandthetaxpressure(asonecauseoftheshadoweconomy)forthe UnitedStatesovertheperiod1919to1955.20yearslater,Gutmann(1977)usedthesame approachbutwithoutanystatisticalprocedures.Cagansapproachwasfurtherdevelopedby Tanzi(1980,1983),whoeconometricallyestimatedacurrencydemandfunctionforthe UnitedStatesfortheperiod1929to1980inordertocalculatetheshadoweconomy.His approachassumesthatshadow(orhidden)transactionsareundertakenintheformofcash payments, so as to leave no observable traces for the authorities. An increase in the size of the shadoweconomywillthereforeincreasethedemandforcurrency.Toisolatetheresulting excess demand for currency, an equation for currency demand is econometrically estimated overtime.Allconventionalpossiblefactors,suchasthedevelopmentofincome,payment habits,interestrates,andsoon,arecontrolledfor.Additionally,suchvariablesasthedirect

22)ForadetailedcriticismofthetransactionapproachseeBoeschotenandFase(1984),FreyandPommerehne (1984),Kirchgaessner(1984),Tanzi(1982a,b,1986),Dallago(1990),Thomas(1986,1992,1999)andGiles 46 andindirecttaxburden,governmentregulation andthecomplexityofthetaxsystem,which areassumedtobethemajorfactorscausingpeopletoworkintheshadoweconomy,are includedintheestimationequation.Thebasicregressionequationforthecurrencydemand, proposed by Tanzi (1983), is the following: ln (C / M2)t = O + 1 ln (1 + TW)t + 2 ln (WS / Y)t + 3 ln Rt + 4 ln (Y / N)t + ut with 1 > 0, 2 > 0, 3 < 0, 4 > 0 where ln denotes natural logarithms,C / M2 is the ratio of cash holdings to current and deposit accounts,TW is a weighted average tax rate (to proxy changes in the size of the shadow economy),WS / Y is a proportion of wages and salaries in national income (to capture changing payment and money holding patterns),R is the interest paid on savings deposits (to capture the opportunity cost of holding cash) andY / N is the per capita income.23)

Any excess increase in currency, or the amount unexplained by the conventional or normal factors(mentionedabove)isthenattributedtotherisingtaxburdenandtheotherreasons leading people to work in the shadow economy. Figures for the size and development of the shadoweconomycanbecalculatedinafirststepbycomparingthedifferencebetweenthe developmentofcurrencywhenthedirectandindirecttaxburden(andgovernment regulations)areheldatitslowestvalue,andthedevelopmentofcurrencywiththecurrent (much higher) burden of taxation and government regulations. Assuming in a second step the same income velocity for currency used in the shadow economy as for legal M1 in the official economy, the size of the shadow can be computed and compared to the official GDP. Thecurrencydemandapproachisoneofthemostcommonlyusedapproaches.Ithasbeen appliedtomanyOECDcountries,24)buthasneverthelessbeencriticizedonvarious grounds.25) The most commonly raised objections to this method are several: (1999a). 23)TheestimationofsuchacurrencydemandequationhasbeencriticizedbyThomas(1999)butpartofthis criticism has been considered by the work of Giles (1999a,b) and Bhattacharyya (1999), who both use the latest econometric technics. 24)SeeKarmann(1986and1990),Schneider(1997,1998a),Johnson,KaufmannandZoido-Lobatn(1998a), and Williams and Windebank (1995).25)See Thomas (1992, 1999), Feige (1986), Pozo (1996), Pedersen (2003) and Ahumada, Alvareda, Canavese A. 47 (i)Notalltransactionsintheshadoweconomyarepaidincash.IsachsenandStrom (1985) used the survey method to find out that in Norway, in 1980, roughly 80 percent of all transactions in the hidden sector were paid in cash. The size of the total shadow economy (including barter) may thus be even larger than previously estimated. (ii)Moststudiesconsideronlyoneparticularfactor,thetaxburden,asacauseofthe shadoweconomy.Butothers(suchastheimpactofregulation,taxpayersattitudes towardthestate,taxmoralityandsoon)arenotconsidered,becausereliabledata for most countries is not available. If, as seems likely, these other factors also have an impact on the extent of the hidden economy, it might again be higher than reported in most studies.26) (iii)AsdiscussedbyGarcia(1978),Park(1979),andFeige(1996),increasesincurrency demanddepositsareduelargelytoaslowdownindemanddepositsratherthantoan increase in currency caused by activities in the shadow economy, at least in the case of the United States.(iv)Blades (1982) and Feige (1986, 1996), criticize Tanzis studies on the grounds that the US dollar is used as an international currency. Instead, Tanzi should have considered (andcontrolledfor)thepresenceofUSdollars,whichareusedasaninternational currencyandheldincashabroad.27)Moreover,FreyandPommerehne(1984)and Thomas(1986,1992,1999)claimthatTanzisparameterestimatesarenotvery stable.28) (v)Moststudiesassumethesamevelocityofmoneyinbothtypesofeconomies.As arguedbyHillandKabir(1996)forCanadaandbyKlovland(1984)forthe and P. Canavese (2004). 26)One (weak) justification for the only use of the tax variable is that this variable has by far the strongest impact on the size of the shadow economy in the studies known to the authors. The only exception is the study by Frey andWeck-Hannemann(1984)wherethevariabletaximmoralityhasaquantitativelylargerandstatistically strongerinfluencethanthe directtaxshareinthemodelapproach.InthestudyofPommerehneandSchneider (1985), for the U.S., besides various tax measures, data for regulation, tax immorality, minimum wage rates are available,thetaxvariablehasadominatinginfluenceandcontributesroughly60-70percenttothesizeofthe shadow economy. See also Zilberfarb (1986). 27)InanotherstudybyTanzi(1982,esp.pp.110-113)heexplicitlydealswiththiscriticism.Averycareful investigationoftheamountofUS-$usedabroadandtheUScurrencyusedintheshadoweconomyandto "classical" crime activities has been undertaken by Rogoff (1998), who concludes that large denomination bills aremajordrivingforceforthegrowthoftheshadoweconomyandclassicalcrimeactivitiesduelargelyto reduced transactions costs. 28)HoweverinstudiesforEuropeancountriesKirchgaessner(1983,1984)andSchneider(1986)reachthe conclusion that the estimation results for Germany, Denmark, Norway and Sweden are quite robust when using the currency demand method. Hill and Kabir (1996) find for Canada that the rise of the shadow economy varies with respect to the tax variable used; they conclude when the theoretically best tax rates are selected and a range ofplausiblevelocityvaluesisused,thismethodestimatesundergroundeconomicgrowthbetween1964and 1995 at between 3 and 11 percent of GDP. (Hill and Kabir [1996, p. 1553]).48 Scandinavian countries, there is already considerable uncertainty about the velocity of money in the official economy, and the velocity of money in the hidden sector is even moredifficulttoestimate.Withoutknowledgeaboutthevelocityofcurrencyinthe shadoweconomy,onehastoaccepttheassumptionofanequalmoneyvelocityin both sectors. (vi)Ahumada,Alvaredo,CanaveseA.andP.Canavese(2004)show,thatthecurrency approach together with the assumption of equal income velocity of money in both, the reported and the hidden transaction is only correct, if the income elasticity is 1. As this is for most countries not the case, the calculation has to be corrected. (vii)Finally,theassumptionofnoshadoweconomyinabaseyearisopentocriticism. Relaxing this assumption would again imply an upward adjustment of the size of the shadow economy. 5.2.5The Physical Input (Electricity Consumption) Method (1) The Kaufmann - Kaliberda Method29) To measure overall (official and unofficial) economic activity in an economy, Kaufmann and Kaliberda(1996)assumethatelectric-powerconsumptionisregardedasthesinglebest physicalindicatorofoverall(orofficialplusunofficial)economicactivity.Now,overall economic activity and electricity consumption have been empirically observed throughout the worldtomoveinlockstepwithanelectricitytoGDPelasticityusuallyclosetoone.This means,thatthegrowthoftotalelectricityconsumptionisanindicatorforgrowthofoverall (official and unofficial) GDP. By having this proxy measurement for the overall economy and thensubtractingfromthisoverallmeasuretheestimatesofofficialGDP,Kaufmannand Kaliberda(1996)deriveanestimateofunofficialGDP.Thismethodisverysimpleand appealing. However, it can also be criticized on various grounds: (i)Notallshadoweconomyactivitiesrequireaconsiderableamountofelectricity(e.g. personalservices),andotherenergysourcescanbeused(gas,oil,coal,etc.).Onlya part of the shadow economy will be captured.

29)This method was used earlier by Lizzeri (1979), Del Boca and Forte (1982), and then was used much later by Portes(1996),KaufmannandKaliberda(1996),Johnson,KaufmannandShleifer(1997).Foracritiquesee Lack (1998). 49 (ii)Over time, there has been considerable technical progress, so that both the production anduseofelectricityaremoreefficientthaninthepast,andthiswillapplyinboth official and unofficial uses. (iii)There may be considerable differences or changes in the elasticity of electricity/GDP across countries and over time.30) (2) The Lack Method Lack(1996,1998,1999,2000)assumesthatacertainpartoftheshadoweconomyis associatedwiththehouseholdconsumptionofelectricity.Thispartcomprisestheso-called householdproduction,do-it-yourselfactivities,andothernonregisteredproductionand services.Lackfurtherassumesthatincountrieswheretheportionoftheshadoweconomy associated with the household electricity consumption is high, the rest of the hidden economy (or the part Lack cannot measure) will also be high. Lack (1996, pp.19 ff.) assumes that in eachcountryapartofthehouseholdconsumptionofelectricityisusedintheshadow economy. Lacks approach (1998, p.133) can be described by the following two equations: ln Ei= 1 ln Ci + 2 ln PRi + 3 Gi + 4 Qi + 5 Hi + ui(1) with 1 > 0, 2 < 0, 3 > 0, 4 < 0, 5 > 0 Hi= 1 Ti + 2 (Si Ti) + 3 Di(2) with 1 > 0, 2 < 0, 3 > 0 where i: the number assigned to the country, Ei: per capita household electricity consumption in country i in Mtoe, Ci:percapitarealconsumptionofhouseholdswithouttheconsumptionofelectricityin country i in US dollars (at purchasing power parity), PRi:therealpriceofconsumptionof1kWhofresidentialelectricityinUSdollars(at purchasing power parity), Gi: the relative frequency of months with the need of heating in houses in country i, Qi: the ratio of energy sources other than electricity energy to all energy sources in household energy consumption, Hi: the per capita output of the hidden economy,

30)Johnson,KaufmannandShleifer(1997)makeanattempttoadjustforchangesintheelasticityof electricity/GDP. 50 Ti:theratioofthesumofpaidpersonalincome,corporateprofitandtaxesongoodsand services to GDP, Si: the ratio of public social welfare expenditures to GDP, and Di:thesumonnumberofdependantsover14yearsandofinactiveearners,bothper100 active earners. Inacrosscountrystudy,sheeconometricallyestimatesequation(1)substitutingHiby equation (2). The econometric estimation results can then be used to establish an ordering of thecountrieswithrespecttoelectricityuseintheirrespectiveshadoweconomies.Forthe calculation of the actual size (value added) of the shadow economy, Lack further must know howmuchGDPisproducedbyoneunitofelectricityintheshadoweconomyofeach country.Sincethesedataarenotknown,shetakestheresultofoneoftheknownshadow economy estimations, that were carried out for a market economy with another approach for the early 1990s, and she applies this proportion to the other countries. Lack used the shadow economyoftheUnitedStatesassuchabase(theshadoweconomyvalueof10.5%ofGDP taken from Morris(1993)), and then she calculates the size of the shadow economy for other countries. Lack's method is also open to criticism: (i)Notallshadoweconomyactivitiesrequireaconsiderableamountofelectricityand other energy sources can be used. (ii)Shadow economy activities do not take place only in the household sector. (iii)Itisdoubtfulwhethertheratioofsocialwelfareexpenditurescanbeusedasthe explanatoryfactorfortheshadoweconomy,especiallyintransitionanddeveloping countries. (iv)Itisquestionablewhichisthemostreliablebasevalueoftheshadoweconomyin ordertocalculatethesizeoftheshadoweconomyforallothercountries,especially, for the transition and developing countries. 5.3The model approach31 Allmethodsdescribedsofarthataredesignedtoestimatethesizeanddevelopmentofthe

31)This summary is derived from a longer study by Aigner, Schneider, and Ghosh (1988, p. 303), applying this approachfortheUnitedStatesovertime;forGermanythisapproachhasbeenappliedbyKarmann(1986and 1990).ThepioneersofthisapproachareWeck(1983),FreyandWeck-Hannemann(1984),whoappliedthis approachtocross-sectiondatafromthe24OECDcountriesforvariousyears.Beforeturningtothisapproach theydevelopedtheconceptofsoftmodeling(Frey,Weck,andPommerehne(1982),FreyandWeck(1983a and 1983b)), an approach which has been used to provide a ranking of the relative size of the shadow economy 51 shadoweconomyconsiderjustoneindicatorthatmustcapturealleffectsoftheshadow economy. However, it is obvious that shadow economy effects show up simultaneously in the production, labor, and money markets. An even more important critique is that the causes that determinethesizeoftheshadoweconomyaretakenintoaccountonlyinsomeofthe monetary approach studies that usually consider one cause, the burden of taxation. The model approachexplicitlyconsidersmultiplecausesleadingtotheexistenceandgrowthofthe shadow economy, as well as the multiple effects of the shadow economy over time. Theempiricalmethodusedisquitedifferentfromthoseusedsofar.Itisbasedonthe statisticaltheoryofunobservedvariables,whichconsidersmultiplecausesandmultiple indicators of the phenomenon to be measured. For the estimation, a factor-analytic approach isusedtomeasurethehiddeneconomyasanunobservedvariableovertime.Theunknown coefficientsareestimatedinasetofstructuralequationswithinwhichtheunobserved variable cannot be measured directly. The DYMIMIC (dynamic multiple-indicators multiple-causes)modelconsistsingeneraloftwoparts,withthemeasurementmodellinkingthe unobservedvariablestoobservedindicators.32)Thestructuralequationsmodelspecifies causalrelationshipsamongtheunobservedvariables.Inthiscase,thereisoneunobserved variable,orthesizeoftheshadoweconomy;thisisassumedtobeinfluencedbyasetof indicatorsfortheshadoweconomyssize,thuscapturingthestructuraldependenceofthe shadow economy on variables that may be useful in predicting its movement and size in the future. The interaction over time between the causes Zit (i = 1, 2, ..., k) the size of the shadow economy Xt, in time t and the indicators Yjt (j = 1, 2, ..., p) is shown in Figure 6.1. in different countries. 32) One of the latest paper dealing extensively with the DYMIMIC approach, its development and its weaknesses is from DelAnno (2003) as well as the excellent study by Giles and Tedds (2002). 52 Figure 6.1: Development of the shadow economy over time. Thereisalargebodyofliterature33)onthepossiblecausesandindicatorsoftheshadow economy, in which the following three types of causes are distinguished: Causes (i)The burden of direct and indirect taxation, both actual and perceived. A rising burden of taxation provides a strong incentive to work in the shadow economy. (ii)Theburdenofregulationasproxyforallotherstateactivities.Itisassumedthat increasesintheburdenofregulationgiveastrongincentivetoentertheshadow economy. (iii)The tax morality (citizens attitudes toward the state), which describes the readiness of individuals (at least partly) to leave their official occupations and enter the shadow economy:itisassumedthatadecliningtaxmoralitytendstoincreasethesizeofthe shadow economy.34) Indicators A change in the size of the shadow economy may be reflected in the following indicators: (i)Developmentofmonetaryindicators.Ifactivitiesintheshadoweconomyrise, additional monetary transactions are required.

33)Thomas (1992); Schneider (1994a, 1997, 2003, 2005); Pozo (1996); Johnson, Kaufmann and Zoido-Lobatn (1998a, 1998b); Giles (1997a, 1997b, 1999a, 1999b, 1999c); Giles and Tedds (2002), Giles, Tedds and Werkneh (2002), DelAnno (2003) and DelAnno and Schneider (2004). 34) When applying this approach for European countries, Frey and Weck-Hannemann (1984) had the difficulty in obtaining reliable data for the cause series, besides the ones of direct and indirect tax burden. Hence, their study wascriticizedbyHelbergerandKnepel(1988),whoarguethattheresultswereunstablewithrespectto changing variables in the model and over the years. Y1t Y2t ... Ypt Causes IndicatorsDevelopment of the shadow economy over time Xt Z1t Z2t ... Zkt Xt-1 53 (ii)Developmentofthelabormarket.Increasingparticipationofworkersinthehidden sectorresultsinadecreaseinparticipationintheofficialeconomy.Similarly, increasedactivitiesinthehiddensectormaybeexpectedtobereflectedinshorter working hours in the official economy. (iii)Developmentoftheproductionmarket.Anincreaseintheshadoweconomymeans thatinputs(especiallylabor)moveoutoftheofficialeconomy(atleastpartly),and thisdisplacementmighthaveadepressingeffectontheofficialgrowthrateofthe economy. The latest use of the model approach has been undertaken by Giles (1999a, 1999b, 1999c) and byGiles,TeddsandWerkneh(2002),GilesandTedds(2002),Chatterjee,Chaudhuryand Schneider(2002)andBajadaandSchneider(2003).Theybasicallyestimatesa comprehensive (dynamic) MIMIC model to get a time serious index of the hidden/measured outputofNewZealand,Canada,IndiaorAustralia,andthenestimateaseparatecash-demand model to obtain a benchmark for converting this index into percentage units. Unlike earlierempiricalstudiesofthehiddeneconomy,theypaidproperattentiontothenon-stationary,andpossibleco-integrationoftimeseriousdatainbothmodels.Againthis DYMIMICmodeltreatshiddenoutputasalatentvariable,andusesseveral(measurable) causalvariablesandindicatorvariables.Theformerincludemeasuresoftheaverageand marginaltaxrates,inflation,realincomeandthedegreeofregulationintheeconomy.The latterincludechangesinthe(male)laborforceparticipationrateandinthecash/money supplyratio.Intheircash-demandequationtheyallowfordifferentvelocitiesofcurrency circulation in the hidden and recorded economies. Their cash-demand equation is not used as an input to determine the variation in the hidden economy over time it is used only to obtain thelong-runaveragevalueofhidden/measuredoutput,sothattheindexforthisratio predicted by the DYMIMIC model can be used to calculate a level and the percentage units of the shadow economy. Overall, this latest combination of the currency demand and DYMIMIC approachclearlyshowsthatsomeprogressintheestimationtechniqueoftheshadow economy has been achieved and a number of critical points have been overcome. However, also against this method objections can be raised, which are(1) instability in the estimated coefficients with respect to sample size changes, (2) instability in the estimated coefficients with respect to alternative specifications, (3) difficulty to obtain reliable data on cause variables other than tax variables, and (4)thereliabilityofthevariablesgroupingintocausesandindicatorsinexplainingthe variability of the shadow economy. 54 5.4The Size and Development of the Shadow Economies of 145 Countries over 1999/2000 to 2002/2003 Finally, the results of the size and development of the shadow economies of 145 countries are shown (and the countries are listed in alphabetical order) in table 5.4 and figures 5.4.1-5.4.3. Table 5.4: The Size of the Shadow Economy of 145 Countries

Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method No.Country1999/002001/022002/03 1Albania33,4 34,6 35,3 2Algeria34,1 35,0 35,6 3Angola43,2 44,1 45,2 4Argentina25,4 27,1 28,9 5Armenia46,3 47,8 49,1 6Australia14,3 14,1 13,5 7Austria9,8 10,6 10,9 8Azerbaijan60,6 61,1 61,3 9Bangladesh35,6 36,5 37,7 10Belarus48,1 49,3 50,4 11Belgium22,2 22,0 21,0 12Benin47,3 48,2 49,1 13Bhutan29,4 30,5 31,7 14Bolivia67,1 68,1 68,3 15Bosnia and Herzegovina34,1 35,4 36,7 16Botswana33,4 33,9 34,6 17Brazil39,8 40,9 42,3 18Bulgaria36,9 37,1 38,3 19Burkina Faso41,4 42,6 43,3 20Burundi36,9 37,6 38,7 21Cambodia50,1 51,3 52,4 22Cameroon32,8 33,7 34,9 23Canada16,0 15,8 15,2 24Central African Republic44,3 45,4 46,1 25Chad46,2 47,1 48,0 26Chile19,8 20,3 20,9 27China13,1 14,4 15,6 28Colombia39,1 41,3 43,4 29Congo, Dem. Rep.48,0 48,8 49,7 30Congo, Rep.48,2 49,1 50,1 31Costa Rica26,2 27,0 27,8 32Cote d'Ivoire43,2 44,3 45,2 33Croatia33,4 34,2 35,4 34Czech Republic19,1 19,6 20,1 Table 5.4: The Size of the Shadow Economy of 145 Countries Cont. Country Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method 55 No. 1999/002001/022002/03 35Denmark18,0 17,9 17,3 36Dominican Republic32,1 33,4 34,1 37Ecuador34,4 35,1 36,7 38Egypt, Arab Rep.35,1 36,0 36,9 39El Salvador46,3 47,1 48,3 40Estonia38,4 39,2 40,1 41Ethiopia40,3 41,4 42,1 42Fiji33,6 34,3 35,1 43Finland18,1 18,0 17,4 44France15,2 15,0 14,5 45Georgia67,3 67,6 68,0 46Germany16,0 16,3 16,8 47Ghana41,9 42,7 43,6 48Greece28,7 28,5 28,2 49Guatemala51,5 51,9 52,4 50Guinea39,6 40,8 41,3 51Haiti55,4 57,1 58,6 52Honduras49,6 50,8 51,6 53Hong Kong, China16,6 17,1 17,2 54Hungary25,1 25,7 26,2 55India23,1 24,2 25,6 56Indonesia19,4 21,8 22,9 57Iran, Islamic Rep.18,9 19,4 19,9 58Ireland15,9 15,7 15,3 59Israel21,9 22,8 23,9 60Italy27,1 27,0 25,7 61Jamaica36,4 37,8 38,9 62Japan11,2 11,1 10,8 63Jordan19,4 20,5 21,6 64Kazakhstan43,2 44,1 45,2 65Kenya34,3 35,1 36,0 66Kiribati34,1 35,0 35,3 67Korea, Rep.27,5 28,1 28,8 68Kuwait20,1 20,7 21,6 69Kyrgyz Republic39,8 40,3 41,2 70Lao PDR30,6 31,9 33,4 71Latvia39,9 40,7 41,3 72Lebanon34,1 35,6 36,2 73Lesotho31,3 32,4 33,3 74Lithuania30,3 31,4 32,6 75Macedonia, FYR34,1 35,1 36,3 76Madagascar39,6 40,4 41,6 77Malawi40,3 41,2 42,1 78Malaysia31,1 31,6 32,2 79Maldives30,3 31,4 32,0 80Mali42,3 43,9 44,7 81Marshall Islands28,1 29,0 29,6 82Mauritania36,1 37,2 38,0 83Mexico30,1 31,8 33,2 84Micronesia, Fed. Sts.31,3 32,1 33,2 85Moldova45,1 47,3 49,4 Table 5.4: The Size of the Shadow Economy of 145 Countries Cont. 56 Country Shadow Economy (in % of off. GDP) using the DYMIMIC and Currency Demand Method 86Mongolia18,4 19,6 20,4 87Morocco36,4 37,1 37,9 88Mozambique40,3 41,3 42,4 89Namibia31,4 32,6 33,4 90Nepal38,4 39,7 40,8 91Netherlands13,1 13,0 12,6 92New Zealand12,8 12,6 12,3 93Nicaragua45,2 46,9 48,2 94Niger41,9 42,6 43,8 95Nigeria57,9 58,6 59,4 96Norway19,1 19,0 18,4 97Oman18,9 19,4 19,8 98Pakistan36,8 37,9 38,7 99Palau28,4 29,2 30,0 100Panama64,1 65,1 65,3 101Papua New Guinea36,1 37,3 38,6 102Paraguay27,4 29,2 31,4 103Peru59,9 60,3 60,9 104Philippines43,4 44,5 45,6 105Poland27,6 28,2 28,9 106Portugal22,7 22,5 21,9 107Puerto Rico28,4 29,4 30,7 108Romania34,4 36,1 37,4 109Russian Federation46,1 47,5 48,7 110Rwanda40,3 41,4 42,2 111Samoa31,4 32,6 33,5 112Saudi Arabia18,4 19,1 19,7 113Senegal45,1 46,8 47,5 114Serbia and Montenegro36,4 37,3 39,1 115Sierra Leone41,7 42,8 43,9 116Singapore13,1 13,4 13,7 117Slovak Republic18,9 19,3 20,2 118Slovenia27,1 28,3 29,4 119Solomon Islands33,4 34,5 35,3 120South Africa28,4 29,1 29,5 121Spain22,7 22,5 22,0 122Sri Lanka44,6 45,9 47,2 123Sweden19,2 19,1 18,3 124Switzerland8,6 9,4 9,4 125Syrian Arab Republic19,3 20,4 21,6 126Taiwan, China25,4 26,6 27,7 127Tanzania58,3 59,4 60,2 128Thailand52,6 53,4 54,1 129Togo35,1 39,2 40,4 130Tonga35,1 36,3 37,4 131Tunisia38,4 39,1 39,9 132Turkey32,1 33,2 34,3 133Uganda43,1 44,6 45,4 134Ukraine52,2 53,6 54,7 135United Arab Emirates26,4 27,1 27,8 136United Kingdom12,7 12,5 12,2 Table 5.4: The Size of the Shadow Economy of 145 Countries Cont. 57 Country Shadow Economy (in % of off. 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