ESTIMATING THE UNDERGROUND ECONOMY AND TAX EVASION: COINTEGRATION AND CAUSALITY EVIDENCE IN THE CASE OF CYPRUS,

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1 120 ESTIMATING THE UNDERGROUND ECONOMY AND TAX EVASION: COINTEGRATION AND CAUSALITY EVIDENCE IN THE CASE OF CYPRUS, Meryem Duygun Fehi Managemen Cener Universiy of Leiceser Sami Fehi Faculy of Business and Economics Easern Medierranean Universiy Salih Turan Kaircioglu Deparmen of Banking and Finance Easern Medierranean Universiy Absrac We empirically invesigae he size of he underground economy and he amoun of ax evasion in he ligh of Tanzi s currency demand framework for he Cyprus economy. The sudy covers he period The paper has wo purposes: (1) o find ou wheher any long run relaionship exiss for he pairs of measured GDPunderground economy, ax raes, ax evasion and underground economy-ax raes, and (2) o invesigae he direcion of causaliy beween he pairs. Our findings sugges ha (i) one co-inegraing vecor exiss beween he variables by using he Johansen coinegraion approach; (ii) he measured GDP and ax raes are he causes of underground economy and ax evasion respecively whereas bidirecional causaliy is found beween ax raes and underground economy when he FPE, Wald, Sim s LR and Holmes-Huon causaliy echniques are applied; (iii) significan underground economic aciviy Inernaional Journal of Economic Developmen Volume Six, Number 3, pp

2 121 and changes in ax raes migh simulae greaer loss of ax revenue or more ax evasion wih a larger budge defici and slower economic growh. Inroducion The size of he underground economy and ax evasion have long been of ineres and empirically invesigaed in a number of recen sudies. In he relevan lieraure, a common quesion is: Is here is any relaionship beween measured GDP and he underground economy?, in oher words, Are measured GDP and changes in ax raes causes of boh underground economy and ax evasion? I is really hard o provide precise answers for hese quesions in he firs place. However, i is widely agreed ha here exiss a relaionship beween measured GDP and he underground economy (see Giles, 1997 and Giles e al., 2002) or beween ax raes and he amoun of ax evasion (see Kesselman, 1989 and Trandel and Snow, 1999). Policy makers are also aware ha significan underground economic aciviies and changes in he ax raes are associaed wih slower economic growh, more ax evasion, greaer loss of ax revenue and higher budge deficis. I is imporan o noe ha he causaliy issue beween hese variables is sill conroversial and unresolved due o ambiguous evidence in he lieraure. Neverheless, some sudies on he underground economy provide srong evidence ha he direcion of causaliy runs from measured GDP o he underground economy (Giles, 1997) and from ax raes o measured GDP (Scully, 1996). Some evidence also suppors he presence of causaliy beween ax raes and he size of underground economy (Giles and Caragaa, 2001).

3 122 According o he European Union (EU) Repor on Undeclared Work in an Enlarged Union (2004), i is highlighed ha very lile is known abou he Cyprio siuaion regarding he underground economy. There is only one sudy by Georgoiu and Syrichas (1994) where he size of underground economy in Cyprus is measured for he period Cyprus has been a new Member of he EU since May 2004 having an esimaed amoun of $20300 GDP per capia and 3,2 per cen unemploymen rae in 2004 (The World Fac Book, 2004). Despie he low unemploymen figure, he exisence of relaively small, family size enerprises and he rapid increase of illegal immigrans may creae favorable condiions for a hriving underground economy (Cyprus Naional Acion Plan for Employmen , p.37). In his paper, we aim o esimae he size of underground economy and he amoun of ax evasion in Cyprus by conducing Tanzi s (1980, 1983) currency demand approach over he period Then, we esimae ime series daa o examine he relaionship beween measured GDP and underground economy and beween ax raes and ax evasion by employing coinegraion and causaliy echniques. Applying hese economeric echniques o he Cyprio case o deermine boh long- and shor-run causal relaionship beween he variables is he firs of is kind o he bes of our knowledge. The res of his paper is organized as follows: Secion II presens he heoreical modeling in esimaing he size of underground economy. Secion III describes he daa and he mehodology respecively. Secion IV discusses he findings. Finally, Secion V provides some concluding remarks.

4 123 Theoreical modeling in esimaing he size of underground economy The currency demand approach has been he mos influenial and widely used or cied mehod. Cagan (1958) used his mehod o calculae he correlaion beween currency demand and ax pressure for he U.S. economy. Gumann (1977) also uilized a similar approach in finding he raio beween currency and demand deposis in a simpler framework. Cagan s idea was laer developed by Tanzi (1980; 1983) o empirically invesigae he size of he US underground economy. Tanzi (1983) proposed a basic regression equaion which conains weighed average ax rae, proporion of wages and salaries in naional income, ineres rae on savings deposis, and per capia income as a funcion of currency raio in circulaion o broad money. The model assumes ha cash or currency is he main facor ha deermines underground economic aciviies. The second assumpion is ha he velociy of money in an official economy equals he velociy of money in an unofficial economy. The hird sems from a ax burden or very high ax rae ha causes he underground economy in ha individuals hus prefer o work in he underground economy o avoid high ax burden. The main idea in he model is ha a rise in he underground economy will cause an increase in demand for money. In order o find ou he size of he underground economy (i.e. excess money in money demand), he currency demand regression should be run over ime (see Thomas, 1999 and Bhaacharyya, 1999 for a deailed criicism of he assumpions of currency demand model). The modeling framework of he Currency demand approach employed in his sudy is as follows:

5 124 Using Tanzi (1980; 1983) he currency demand approach is considered in he following equaion: RCM 2 α1 α 2 α3 α 4 α5 = α o YT WSY IR INF PGNP or ln RCM 2 = α 0 + α1 lnyt + α 2 lnwsy + α 3 ln IR + α 4 ln INF + α 5 ln PGNP + ε (1) Where RCM2 is real currency in circulaion o money supply (M2) raio, TY is he average ax calculaed as direc axes on income expressed as a percenage of GNP, WSY is he share of wages and salaries in GNP, IR is one-year nominal ineres rae on saving deposis, INF is he growh rae of consumer price index and PGNP is he real per capia GNP. Ln and ε denoe naural logarihms and error erm respecively. Daa and Mehodology A. Daa The daa 1 we have employed for he Cyprio economy are annual figures covering he period The firs daa se is for he exploiaion of he currency demand approach: RCM2 is real currency in circulaion o money supply (M2) raio, TY is he average ax calculaed as direc axes on income expressed as a percenage of GNP, WSY is he share of wages and salaries in GNP, IR is one-year nominal ineres rae on saving deposis, INF is he growh rae of consumer price index and PGNP is he real per capia GNP. For he use of 1 Daa are aken from Saisical Absrac, Deparmen of Saisics and Research, Minisry of Finance, Nicosia, Cyprus, various years.

6 125 coinegraion and causaliy analyses, he variables in he second daa se, namely underground economy (UGE) and ax evasion (TEVA), are esimaed by he auhors. GDP and ax rae series are exraced from Cyprus Saisical Absrac. All variables in he second daa se are deflaed by a GDP deflaor. B. Mehodology Time series daa can be non-saionary (rended) and his kind of daa can be regarded as a poenially major problem for applied economeric sudies. I is well known ha rends may cause spurious regression problems. The presence of a rend can be deermined by examining he exisence of uni roos in ime series daa. A number of ess were widely recommended for he exisence of uni roos in he ime series daa in he relevan lieraure such as he Augmened Dickey-Fuller (ADF) (1981), Phillips and Perron, (PP) (1988) and Kwiakowski e al. (KPSS) (1992). The Augmened Dickey-Fuller (ADF) and uni roo ess are usually employed respecively in he empirical lieraure o es he saionariy of he variables for he sake of confirmaion. The mos reliable and general model of he ADF es can be reformulaed as follows 2 : y = a p 0 + y 1 + a2 + β j i= 2 γ y + (2) i 1 Where is he firs difference operaor, y is he series; = ime (rend facor); a = consan erm (drif); = Gaussian whie noise and p = he lag order. The number of lags p 2 Ender (1995) poins ou ha he mos appropriae model of ADF is o include consan erm and rend facor in he uni roo process.

7 126 in he dependen variable is chosen by he Akaike Informaion Crieria (AIC) o ensure ha he errors are whie noise. One problem wih he presence of he addiional esimaed parameers is ha i reduces he degrees of freedom and he power of he es. To confirm he es resuls obained from he ADF es, Kwiakowski Phillips, Schmid and Shin s es (1992) (KPSS) is suggesed o avoid a possible low power agains saionary near uni roo processes which occurs in he ADF es. The KPSS es complemens he ADF es in which he null hypohesis of KPSS es is ha a series is saionary. This means ha a saionary series is likely o have insignifican KPSS saisics and significan ADF saisics. The KPSS es is based on an assumpion ha a series can be invesigaed in hree pars: a ime rend, a random walk and a saionary error in he following equaion: y = ρ + + ε (3) rw Where rw = rw -1 +v and v is i.i.d (0, v 2 ). Basically, he regression above can be run in wo ways: firs wih a consan in he case of level saionary, second boh a consan and a rend in he case of rend saionary. We hen use he residuals ε from he regression and compue he LM saisics in he following equaion: LM = T T 2 i= 1 V 2 2 Vε (4) Where V 2 is he esimae of he variance of ε and V is defined as follows:

8 127 V = i= 1 ε (5) i Kwiakowski e al. (1992) provide he criical values for he disribuion of LM ha is non-sandard. Due o he assumpions of he behavior of ε, V 2 can be consruced o be more consisen esimaor as in he following equaion, similar in Phillips and Perron s (1988): V T p T ( p) = T + 2T w( v, p) ε ε k = 1 v= 1 = v+ 1 ε (6) Here w(v,p) is an opional weighing funcion as regards o he choice of a specral window. Following Newey and Wes (1987) he Barle window can be used as w(v,p)=1-v/ (v+1). Finally he es saisics of he KPSS es can be considered as follows: = T T 2 i= 1 V 2 V 2 ( p) (7) I is worh o emphasize ha he value of he es saisics depends on he choice of he lag runcaion parameer. The sample of auocorrelaion funcion of ε can be calculaed o find ou he maximum value of he lag lengh p. Wih respec o he series, here is a poenial break in 1974-he war effec. Hence, we uilize he Perron Addiive Ouliner Model (AOM) for uni roos wheher he order of inegraion is changed by he poenial srucural break. This es is carried ou in wo seps. In he firs sep, we esimae residuals using OLS as follows:

9 128 X = µ + δdu + e (8) Where DU =1 if >T b and 0 oherwise. T b is he poin where he break occurs. In he second sep, we run he following modified regression by using OLS. The es of negaiviy of γ is checked by using appropriae criical values repored in Rybinski s papers (1994; 1995): e = K K i ( DUTB) i + γ e 1 + α i e i + ε i= 0 i= 1 φ (9) Where (DUTB) =1 if =T b+1 and 0 oherwise, T b is he break year, DUTB is dummy variable for he break year, e is residual obained from equaion (8) using OLS and ε is an error erm. Afer he order of inegraion is deermined, coinegraion beween he variables should be esed o idenify any long run relaionship. There should be a leas one co-inegraing vecor for a possible co-inegraion. The Johansen (1988) and Johansen and Juselius (1990) approach allows he esimaing of all possible coinegraing vecors beween he se of variables and i is he mos reliable es o avoid he problems which sem from Engel and Granger s (1987) procedure 3. This procedure can be expressed in he following VAR model: X + =1, T) (10) = Π1 X Π K X K + µ e (for 3 See Kremers e al. (1992) and Gonzalo (1994) for commens abou disadvanages of Engel and Granger (1987) procedure wih respec o Johansen and Juselius (1990) coinegraion echnique.

10 129 Where X, X -1,, X -K are vecors of curren and lagged values of P variables which are I(1) in he model; Π 1,.,Π K are marices of coefficiens wih (PXP) dimensions; µ is an inercep vecor 4 ; and e is a vecor of random errors. The number of lagged values, in pracice, is deermined in such a way ha error erms are no significanly auocorrelaed. By adding X -1,, X -K and Π 1 X -2,, Π K-1 X -K o boh sides and rearranging he erms, he VAR model will be in he following form 5 : X = Γ X + + Γ X + ΠX + µ + e K 1 K + 1 K (11) Where Γ i =-(I-Π i -.-Π i ); (i=1, 2,, K-1); Π=-(I-Π Π 1 ) and I is he ideniy marix. The rank of he marix of coefficien, Π gives he number of long-run relaionships beween he variables of he sysem. The rank of Π is he number of coinegraing relaionship(s) (i.e. r) which is deermined by esing wheher is Eigen values ( i ) are saisically differen from zero. Johansen (1988) and Johansen and Juselius (1990) propose ha using he Eigen values of Π ordered from he larges o he smalles is for compuaion of he maximal-eigen value and race saisics. The maximal-eigen value saisics ( max ) is compued by he following formula: λ n-1 12) ( λ ) = TL, r = 0,1, 2,.., n-2, max n 1 r+1 Where T is sample size. In his es, he null hypohesis of r 4 is a vecor of I(0) variables which also represens dummy variables. This ensures ha errors e are whie noise. 5 This form of he equaion is also called Vecor Error Correcion Model (VECM). (

11 130 coinegraing vecors is esed agains he alernaive of r+1 coinegraing vecors. Alernaively, he race saisic is compued by he following formula: p λ = T i Ln(1 λ ), i = r+1,, n- race i 1 (13) Where p-r is he smalles Eigen values and he null hypohesis is esed agains he general hypohesis (i.e. H 0 : r = 0 H 1 : r 1 and so on). Having conduced boh he inegraion and coinegraion ess, we apply he Holmes-Huon (1988) causaliy procedures in a bivariae causal model. Since any kind of causaliy ess are sensiive o he choice of lag lengh, we prefer o iniiae our causaliy esing procedure by using Akaike s (1974) Minimum Final Predicion Error (FPE) crierion. This crierion alongside Hsiao s (1979) synhesis is used o choose he opimal lag-lenghs boh in lag-levels and lag-differences (see also Giles e al. 1993). Akaike s minimum FPE can be formulaed as follows: FPE ( m) ( m) T + K SRR = (14) T K T Where T is he sample, and k = m+1 if he variables under sudy are no coinegraed; k=m+2 if hey are coinegraed (he error correcion erm should be added o he equaion); SSR(m) is he sum of he squared residuals. When m=m* in Equaion (14), we change n o find ou he value n=n* so as o minimize FPE (m*,n) in which k=m*+n+2 (in he coinegraed case). If FPE (m*, n*) < FPE (m*), his means ha Y Causes X. The values of m and n are relaed o Equaion (14).

12 131 We hen apply he Holmes-Huon (HH) (1988, 1990a, 1990b) es o deermine he direcion of causaliy beween he variables raher han use he Granger causaliy esing procedure. The Granger model is premised on he mainained hypohesis of correc funcional form (i.e. linear), homoscedasiciy and normaliy of he error erm. Holmes and Huon (HH) argue ha violaion of hese condiions can influence causaliy conclusions. They sugges an alernaive procedure for causaliy esing based on rank ordering of each variable. The rank order is obained from he firs difference of each series, and each lagged variable is ranked separaely. A null hypohesis of no causaliy is rejeced if an F-saisic based on he esimaed coefficiens of he lagged causal variable is saisically significan. The Holmes-Huon (1988) procedure generaes a muliple-rank F-es and can be expressed in he following model: R m n ( DLX ) = α + βi R( DLX i ) + γ i R( DLY j ) ( X ) i= 1 j= 1 + ε Y (15) R q r ( DLY ) = c + d i R( DLY i ) + ei R( DLX j ) ( Y ) + i= 1 j= 1 X (16) Where DL is logarihm differences of he variables and R is he rank procedure. and w are serially uncorrelaed random disurbances wih zero mean respecively. In each case, H-H es is associaed wih es on he significance of he s and he e s condiional on he opimal lag lenghs, m, n, q, and r. Here, we es o see if Y HH causes X by using a muliple rank F-es and uilizing he following hypohesis: w

13 132 (i.e. H 0 = e 1 = e 2 = e 3 = = e n is rejeced agains he alernaive H 1 = no H 0 ). Empirical Resuls Esimaing he Size of Underground Economy and Tax Evasion We esimae he size of Cyprio underground economy and ax evasion by using Tanzi s currency demand model (equaion 1) for he period beween 1960 and The OLS regression resuls ha are shown in Table 1 are remarkably good. The variables used in he model are saisically significan and have he expeced signs. The adjused R 2 is very high and his indicaes ha he model is capable of explaining mos of he explanaory power in he dependen variable over he period. The Durbin Wason saisics and all diagnosic ess resuls are also a saisfacory levels. I is worh emphasizing ha ax variable (YT) and per capia income (PGNP) are a 10% significance of level compared wih he oher variables. The ax variable shows ha an increase in ax rae in erms of ax evasion effec resuls in more use of currency whereas an increase in PGNP means ha economic developmen reduces currency raio. Furhermore, WSY confirms ha a larger share of wages and salaries in naional income indicaes more use of currency. The ineres rae (IR) and he rae of inflaion (INF) are boh highly significan and depic ha an increase in he wo variables resuls in a fall in he currency raio. The addiional variable-dum74 is also highly significan and jusifies he war effec and is recovery period assigning he value 0 for he period and 1 for he period 1974-

14 Wih he aid of Table 1, we can proceed o he esimaion of he underground economy and ax evasion in Cyprus. Table 2 provides he logarihm of he esimaed currency raio for he years beween 1960 and Given M2 we can compue he esimaed currency holdings as ecc. To deermine hese values, le Y=ln(C/M2), hen Y=lnClnM2 or equally ecc=exp(y+lnm2). Columns 1 and 2 in Table 2 depic he acual and esimaed currency holdings. To esimae ecc values (Column 3), we can se he ax variable in equaion 1 equal o zero by applying he same procedure. Column 4 in he same able indicaes ha he difference beween ecc and ecc is a measure of he excess amoun of money-imon (illegal money). Column 5 calculaes legal money ha is defined as he difference beween M1 and illegal money (imon). Column 6 calculaes he income velociy of legal money as equal o GNP divided by legal money. A key assumpion here is ha he velociy of money in he underground economy is he same as in he regisered economy so ha he esimaed size of he underground economy (UGE) can be calculaed as he produc of he velociy of money and illegal money. These values are shown in Column 7 whereas column 10 demonsraes he same values as a proporion of GDP (i.e. UGE/GDP). Column 8 shows he esimaes for ax evasion (TEVA) 7 which is calculaed by muliplying he esimaed size of underground economy by esimaed raio of he oal ax gap 8 o illegal money plus underground economy. Column 9 shows he same values as a proporion of GDP (i.e. TEVA/GDP). 6 See Georgiou and Syrichas (1994) for more deails abou he use of dummy variable for he Cyprio economy. 7 See Feige (1989, p44) and Faal (2003, p20) for a similar formula. 8 See Giles (1999, p375) for he oal ax gap formula.

15 134 Currency Demand Approach Rae ugegdp% Year Figure 1 Esimaes 9 indicae ha he raio of underground economy in Cyprus varies beween 3.9% and 16.1% of GDP. Columns 7 and 10 in Table 2 depic ha he size of esimaed underground economy is he lowes in 1960 wih Cyp 9.08 million as 9.9% of GDP whereas he highes is in 2000 wih Cyp million as 5.1 percen of GDP. Figure 1 also illusraes a rend represening he raio of underground economy o GDP. The average raio for he period is 9.41 percen of GDP 10. Earlier work on Cyprus by Georgiou and Syrichas (1994) esimaed he underground economy for he period o be on average 8.8%. Coinegraion and Causaliy Esimaions 9 The relaive size of ax evasion (TEVA/GDP) in Cyprus is disclosed in Column 9 in Table 2. As i can be seen, he relaive size varies beween 0.24% and 0.41%. 10 This confirms he saemen by he Miniser of Finance who admied he exisence of underground economy and quoed is size around 10 percen of GDP (Georgiou and Syrichas 1994).

16 135 The uni roo es resuls are repored in Table 3. The ADF es shows ha all he variables are inegraed of order one, ha is I(1). This indicaes ha he firs differences of LGDP, LUGE, LTEVA and LTX are saionary. Table 4 repors he KPSS es saisics where he null hypohesis of saionary is consruced agains he alernaive of a uni roo. The resuls from he KPSS es furher confirm ha all daa series are inegraed of order one (i.e. I(1)). The wo seps are necessary for he sake of a co-inegraion procedure prior o he causaliy ess. As regards o he variables, in paricular he GDP for he period , we observe a decline afer he war effec in This siuaion may be capuring a srucural break and makes boh he ADF and KPSS es resuls unreliable. We herefore employ Perron s Addiive Oulier model (equaion 9) o show ha here are no spurious roos resuling from any srucural break or changing means (see Perron, 1990; Perron and Vogelsang, 1992). The resuls presened in Table 5 sugges ha here is no spurious uni roo creaed by an exogenous break. In oher words, he ADF and KPSS resuls recommend ha he variables used for boh coinegraing and causaliy purpose are inegraed of order one even when he break is allowed. Afer esablishing he saionariy of he daa, we use he Johansen (1988); Johansen and Juselius (1990) approaches which are very sensiive o he choice of lag lengh o explore any possible long run relaionship among he variables under consideraion (see also Chang 2002). We employ boh Akaike and Schwarz Crieria o selec he number of lags in he co-inegraion ess where he wo crieria sugges a VAR model wih 1 lag. The VAR model is used wih unresriced inercep and no rend by considering he dummy variable as I(0) (Pesaran and

17 136 Pesaran, 1997). The resuls repored in Table 6 sugges ha one coinegraing vecor exiss among he pairs of he variables namely, LGDP-LUGE, LUGE-LTX and LTEVA-LTX. These hree bivariae sysems confirm he presence of a long run equilibrium relaionship for he pairs of he official economy-he underground economy, he underground economy ax rae and ax evasion-ax rae. Our findings are consisen wih he evidence exising in he lieraure. Chrisopoulos (2003) found a long run relaionship beween he underground economy in Greece and he wo effecive ax raes. Giles (1997), on he oher hand, indicaed he exisence of a long run equilibraing relaionship beween measured and underground economies in he case of New Zealand (see also Hill and Kabir, 2000). I is also widely agreed ha here is a relaionship beween ax and he amoun of ax evasion (see Kesselman, 1989; Trandel and Snow, 1999). Since a coinegraion relaionship is found among he variables under inspecion, a vecor auoregressive (VAR) model should be consruced o deermine he direcion of he causaliy. Granger (1988) menions ha here should a leas be one direcion of causaliy among he variables if hey are co-inegraed. The causaliy VAR model is expressed as in Equaions (15) and (16) as he variables are co-inegraed (See also Engle and Granger, 1987). In he relevan empirical lieraure, a common quesion is: Does he official or measured economy cause he underground economy or is an increase in ax rae causing boh underground economy and ax evasion? To answer hese quesions, he causaliy esing procedures should be employed. Table 7 repors he opimal lag lenghs

18 137 for boh log-level (i.e. long-run) and log-difference (i.e. shor-run). In he same able, he values of FPE (m *, n * ) and FPE (m * ) are repored where hese values sugges ha here is unidirecional causaliy from measured GDP o UGE in boh long and shor-run periods. This resul is consisen wih hose found in Giles (1997) and Giles e al. (2002) for he New Zealand and he Canadian sudies respecively. They find a srong evidence of causaliy from measured GDP o he UGE. There is also bidirecional causaliy beween boh pairs of he underground economy (UGE)-ax evasion (TEVA) and ax rae (TX)-ax evasion. This is suppored by Giles and Tedds (2002) who address a clear posiive relaionship beween ax rae and underground economy (see also Giles and Caraga, 2001). Given he resuls of he FPE es, he Holmes-Huon causaliy procedure as well as he Wald and Sim s LR 11 ess are conduced in a bivariae model o confirm he earlier findings obained from he FPE es. The Wald es saisics refer o he usual asympoic x 2 disribuion and are based on a es of zero resricion on he independen variables in Equaions 15 and 16. The resuls in Table 8 show ha he causaliy is bidirecional beween UGE and TX in boh shor and long-run periods. Also here is a flow of causaliy from TX o TEVA for boh periods; however he causal relaionship beween GDP and UGE is slighly mixed. In he shor run, he unidirecional causaliy is found from GDP o UGE and a feedback beween GDP and UGE in he long run period. In general suble, he FPE resuls indicae ha here 11 A simple logarihmic ransformaion can be used o conver he Wald saisics ino Sim s LR es saisics in order o obain he resuls for he Sim s LR es. This ransformaion is also asympoically x 2 (see Giles e al., 1993 p. 202; Sims, 1980 p. 17).

19 138 is bidirecional causaliy beween TX and UGE a boh loglevel (i.e. long-run) and log-difference (i.e. shor-run) and his siuaion is suppored by he Wald es, Sims LR es and HH causaliy es. For he causal relaionship of he pair of GDP-UGE, we canno rejec he hypohesis ha he measured or official economy does no cause he underground economy. Hence, i is obvious ha causaliy runs from GDP o UGE in almos all cases excep a he log-level on he basis of he Wald, Sim s LR and HH causaliy es. There is also causaliy evidence running from ax rae (TX) o ax evasion (TEVA) a almos all cases. To sum up, he resuls in Tables 7 and 8 recommend ha he direcion of causaliy is running from GDP and TX o UGE and TEVA respecively whereas bidirecional causaliy is deermined beween TX and UGE. This general conclusion is suppored by all causaliy echniques applied in he sudy. On he basis of he available empirical evidence, i is plausible o conclude ha our findings are consisen wih he evidence found in Giles (1997) (i.e. GDP-UGE relaionship), in Giles and Tedds (2002) (i.e. UGE-TX), in Giles and Caragaa (2001) (i.e. TX-TEVA), and Giles e al. (2001) for he relaionship beween ax rae (TX) and ax evasion (TEVA). These relaionships migh need furher invesigaion o ensure wheher ax evasion is simulaed eiher by underground economic aciviy or changes in ax raes (see also Giles, 1999; Giles and Caragaa, 2001). Conclusion We used ime series daa analysis o esimae boh underground economy and ax evasion in Cyprus by conducing Tanzi s currency demand model over he period

20 Our esimaes show ha he relaive size of Cyprio underground economy (i.e. UGE/GDP) varies beween 3.9% and 16.1% whereas he relaive size of Cyprio ax evasion (i.e. TEVA/GDP) varies beween 0.21% and 0.41%. The ADF, KPSS and Perron ess were employed o examine he ime series properies and srucural break before esablishing a long-run relaionship beween he variables under inspecion. The inegraion ess resuls indicae ha he variables were non-saionary in levels bu saionary in differences, even when he break is aken ino consideraion. Having confirmed he presence of a long run relaionship by using he Johansen approach, we invesigaed he causal relaionship beween he variables by applying he FPE, he Wald, he Sim s LR and he HH causaliy echniques correspondingly. Our findings sugges ha he measured GDP and ax raes are he causes of he underground economy and ax evasion in Cyprus respecively. This means ha he direcion of causaliy runs from he measured GDP and ax raes o he underground economy and ax evasion. The resuls also indicae ha here is bidirecional causaliy beween ax raes and underground economy in boh long-run and shor-run periods. Our findings are consisen wih he available empirical evidence in he lieraure (see Giles, 1997; Giles and Caragaa, 2001; Scully, 1996; and Trandel and Snow, 1999). Finally, on he basis of available empirical evidence in he lieraure and our findings, i is plausible o conclude ha significan underground economic aciviies and changes in he ax raes in many counries migh simulae greaer loss of ax revenue wih larger budge deficis and

21 140 slower economic growh. References Akaike, H A new look a he saisical model idenificaion. IEEE ransacion on auomaic conrol. AC- 19: Bhaacharyya, D, K On he economic raionale of esimaing he hidden economy, The Economic Journal. 109: F348-F359. Cagan, P The demand for currency relaive o he oal money supply. Journal of Poliical Economy. 66: Chang, T Financial developmen and economic growh in Mainland China: a noe on esing demandfollowing or supply-leading hypohesis. Applied Economic Leers. 9: Chrisopoulos, D. K Does underground economy respond symmerically o ax changes? Evidence from Greece. Economic Modelling. 20: Dickey, D. and Fuller, W.A Likelihood raio saisics for auoregressive ime series wih a uni roo. Economerica. 49: Engle, R. F. and Granger, C. W. J Co-inegraion and error correcion: represenaion, esimaion, and esing. Economerica. 55: Ender, W Applied Economeric Time Series. Wiley,

22 141 New York. Faal, E Currency demand, he underground economy, and ax evasion: The case of Guyana. IMF Saff Papers. 7: Feige, E, L. (ed.) The underground economy, ax evasion and informaion disorion. Cambridge Universiy Press. Cambridge. Georgiou, G, M. and Syrichas, G, L The underground economy: an overview and esimaes for Cyprus. Cyprus Journal of Economics. 7 (2): Giles, D. A., Giles, J. A. and McCann, E Causaliy, uni roos, and expor-led growh: he New Zealand. Journal of Inernaional Trade and Economic Developmen. 1: Giles, D. E. A Causaliy beween he measured and underground economies in New Zealand. Applied Economics Leers. 4: Giles, D. E. A Modeling he hidden economy and he ax-gap in New Zealand. Empirical Economics. 24: Giles, D. E. A., and Caragaa, P, J The learning pah of he hidden economy: he ax and growh effecs in New Zealand. Applied Economics. 33: Giles, D. E. A., Werkneh, G. L., and Johnson B. J Asymmeric responses of underground economy o ax changes: Evidence from New Zealand Daa. The Economic Record. 77 (237):

23 142 Giles, D. E. A., and Tedds, L, M Taxes and he Canadian underground economy. Canadian Tax Foundaion. Torono. Giles, D. E. A., Tedds, L. M., and Werkneh, G The Canadian underground and measured economies: Granger Causaliy resuls. Applied Economics. 34: Gonzalo, J Five alernaive mehods of esimaing long-run equilibrium relaionships. Journal of Economerics. 60: Granger, C. W. J Some recen developmens in a concep of causaliy. Journal of Economerics. 39: Gumann, P, M Suberranean Economy. Financial Analyss Journal. 34 (1): Hill, R. and Kabir, M Currency demand and he growh of he underground economy in Canada, Applied Economics. 32: Holmes, J. M. and Huon, P. A A funcional-form disribuion free alernaive o parameric analysis of Granger causal models. Advances in Economerics. 7: Holmes, J. M. and Huon, P. A. 1990a. On he causal relaionship beween governmen, expendiures and naional income. The Review of Economics and Saisics. 72: Holmes, J. M. and Huon, P. A. 1990b. Small sample properies of he muliple rank F-es wih lagged dependen variables. Economic Leers. 33:

24 143 Hsiao, C Causaliy in economerics. Journal of Economic Dynamics and Conrol. 4: Johansen, S Saisical analysis of co-inegraion vecors. Journal of Economic Dynamics and Conrol. 12: Johansen, S. and Juselius, K. (1990) Maximum likelihood esimaion and inference on co-inegraion wih applicaion o he demand for money, Oxford Bullein of Economics and Saisics, 52, Kesselman, J. R Income ax evasion: an inersecoral analysis. Journal of Public Economics. 38: Kremers, J. M., Erisccos, N. R. and Dolado, J. J The power of coinegraion ess. Oxford Bullein of Economics and Saisics. 54: Kwiakowski, D., Phillips, C. B. P., Schmid, P. and Shin, Y Tesing he null hypohesis of Saionary agains he alernaive of a uni roo.. Journal of Economerics. 54: Newey, W.K., and Wes, K. D A simple, posiive semi-define heeroskedasiciy and auocorrelaion consisen covariance marix. Economerica. 3: Perron, P Tesing for a uni roo in a ime-series wih a changing mean. Journal of Business and Economic Saisics. 8: Perron, P. and Vogelsang, T. J Tesing for a uni roo in a ime series wih a changing mean: correcions and

25 144 exensions. Journal of Business and Economic Saisics. 10: Peseran, H. and Peseran, B Microfi 4.0: an Ineracive Economeric Sofware Package (User Manual). Oxford Universiy Press. Oxford. Phillips, P.C.B. and Perron, P Tesing for a uni roo in ime series regression. Biomerica. 75: Rybinski, K Exended abulaions for Perron addiive oulier es. Universiy of Gdansk. mimeo. Rybinski, K Coinegraion of ime series under srucural break: Mone Carlo Analysis. Universiy of Warsaw. mimeo. Scully, G.W Taxaion and economic growh in New Zealand. Pacific Economic Review. 1: Sims, C. A Macroeconomics and Realiy. Economerica. 48 (1): Saisical Absracs. Deparmen of Saisics and Research. Minisry of Finance. Nicosia. Cyprus. Various issues. Tanzi, V The underground economy in he Unied Saes: Esimaes and Implicaion. Banco Nazionnale del Lavoro Quarerly Review. 135: Tanzi, V The underground economy in he Unied Saes: Annual Esimaes, IMF Saff papers. 30:

26 145 Thomas, James, J Quanifying he black economy: Measuremen wihou heory, ye again?. The Economic Journal. 109 (455): Trandel, G. and Snow, A Progressive income axaion and he underground economy. Economics Leers. 62: Inerne sources: EU Repor on Undeclared Work in an Enlarged Union, An Analysis of Undeclared Work: an In-deph Sudy of Specific Iems, Employmen & European Social Fund, July, hp://europa.eu.in/comm/employmen_social/employmen _analysis/work/undecl_work_final_en.pdf The World Fac Book, 2004 hp:// Econ Naional Acion Plan for Employmen , Minisry of Labour and Social Insurance, Republic of Cyprus. hp://europa.eu.in/comm/employmen_social/employmen _sraegy/nap_2004/nap2004cy_en.pdf Biographical Deails Meryem Duygen Fehi is Lecurer in Finance in he Managemen Cener a he Universiy of Leiceser in he Unied Kingdom. Sami Fehi is in he Faculy of Business and Economics a he Easern Medierranean Universiy, and Salih Turan Kaircioglu is in he Deparmen of Banking and Finance a he Easern Medierranean Universiy. All have a developing ineres in invesigaing he use of indirec

27 146 mehods o measure he underground economy and an emerging ineres in comparing he oupus of direc and indirec measuremen mehods.

28 147 APPENDIX: Table 1 Regression Resuls for Equaion 1 Explanaory Variables C LYT LWSY LIR LINF LPGNP DUM74 Dependen Variable: LCM2 Currency Demand Model (-5.94) 0.13 (1.89) * 0.64 (2.03) (-4.07) (-2.54) (-1.67) * (-4.57) R R 0.97 DW 1.70 X 2 SC X 2 FF X 2 NORM X 2 HET Noes: -saisics are in parenheses and all diagnosic pass a 5% level of significance for he model. I is worh emphasising ha one sar (*) indicaes 10% level of significance and he ress pass 5% and 1% level of significance. L denoes naural logarihms (Prob=0.267) 1.17 (Prob=0.279) 2.22 (Prob=0.328) 3.60 (Prob=0.058)

29 148 Table 2: Esimaing size of Cyprios underground economy by Currency Demand Approach Year cc (1) ecc (2) ecc (3) imon (4) lmon (5) vel (6) uge (7) eva (8) evagdp% (9) ugegdp % (10) Inernaional Journal of Economic Developmen Volume Six, Number 3, pp

30

31 150 Table 3: ADF Tes for Uni Roo Tes Saisics (Levels) LGDP LUGE LTX LTEVA τ T (ADF) (1) (1) (1) (0) τ µ (ADF) (0) (1) (1) (0) Tes Saisics (Firs Difference) LGDP LUGE LTX LTEVA τ T (ADF) (0) (0) (0) (0) τ µ (ADF) (0) (0) (0) (0) Noes: 1. τ T represens he mos general model wih a drif and rend; τ µ is he model wih a drif and wihou rend. 2. Numbers in brackes are lag lenghs used in ADF es (as deermined by AIC) o remove serial correlaion in he residuals. 3. Criical values are τ µ = and τ T = a he 5% significance level respecively. 4. Tess for uni roos have been carried ou by Microfi 4.0. (Pesaran and Pesaran, 1997). Inernaional Journal of Economic Developmen Volume Six, Number 3, pp

32 151 Table 4: KPSS Tes for Uni Roo Tes Saisics (Levels) LGDP LUGE LTX LTEVA η u (KPSS) 1.54 * (2) 1.41 * (2) 1.37 * (2) 1.51 * (2) η (KPSS) 0.16 * (0) 0.31 * (2) 0.15 * (1) 0.18 * (1) Tes Saisics (Firs Difference) LGDP LUGE LTX LTEVA η u (KPSS) 0.07 (2) 0.35 (2) 0.17 (2) 0.09 (1) η (KPSS) 0.04 (2) 0.05 (2) 0.06 (2) 0.08 (1) Noes: 1. η u and η represen consan and rend in he model wih he criical values and a 5% significance level respecively. 2. Numbers in brackes are lag lenghs indicaing he lag runcaion for Barle Kernel suggesed by Newey-Wes (1987). 3. * denoe significance a he 5%. Criical values are aken from Kwiakowski e al. (1992). 4. Tess for uni roos have been carried ou by Saa 8.0.

33 152 Table 5: The Perron Uni Roo Tes for Srucural Break Variable Break Year Tes Saisics Level Criical Value (5%), λ=0.34 LGDP LUGE LTX LTEVA Noe: We use he criical value repored by Rybinski (1994; 1995) insead of he original criical value repored by Perron. The corresponding break fracion for 44 numbers of observaions is calculaed easily wih λ = (T b /T) (See Perron and Vogelsang, 1992). For 1974, he relevan break year fracion is λ =15/44=0.34. In mos cases, an augmenaion of one appeared o be sufficien o secure lack of auocorrelaion of he error erms.

34 153 Table 6: Co-inegraion Tess based on he Johansen (1988) and Johansen and Juselius (1990) Approach Coinegraion Regressions H 0 H 1 λ max C.V. a 5% λ Trace C.V. a 5% LGDP-LUGE r = 0 r = * * r <= 1 r = LUGE-LTX r = 0 r = * * r <= 1 r = LTEVA-LTX r = 0 r = * * r <= 1 r = Noes: 1. r indicaes he number of coinegraing relaionships, λ max is he maximum eigen value saisics and λ race is he race saisics. 2. VAR 1 based on boh Akaike Informaion Crierion (AIC) and Schwarz Crieria (SC) is used o selec he number of lags required in he co-inegraion es and unresriced inerceps and no rends in he VAR model are no rejeced in all cases. 3. DUM74 is considered as exogenous I(0) variable. 4. * denoes significance a 5% level and he criical values are obained from Oserwald-Lenum (1992).

35 154 Table: 7 Selecion of Lag Lenghs Using The Final Predicion Error (FPE) Dependen Variable Independen Variable m * n * FPE (m * ) FPE (m *, n * ) LOG-LEVELS LGDP LUGE 1 1 LUGE LGDP 3 1 LUGE LTX 4 1 LTX LUGE 1 1 LTEVA LTX 1 1 LTX LTEVA x 5.92 x x x x 1.69 x x 3.19 x x x x x 10-3 LOG-DIFFERENCES DLGDP DLUGE 1 3 DLUGE DLGDP 4 1 DLUGE DLTX 1 2 DLTX DLUGE 2 1 DLTEVA DLTX x 6.17 x x 1.42 x x 1.78 x x 2.53 x x x x 3.48 x DLTX DLTEVA Noes: 1. If FPE (m *, n * ) < FPE (m * ), Y causes X. 2. m * denoes maximum

36 155 lag on dependen variable. 3. n * variable. denoes minimum lag on independen

37 156 Table 8: The Wald, Sim s LR and he HH Causaliy Tess. Dependen Variable Independen Variable Degrees of freedom a Wald Tes Sim s LR Tes m * n * Muliplerank Inference Causal HH F-es LGDP LUGE 2 LOG-LEVELS * * 1 1 LUGE LGDP * * 3 1 LUGE LTX * 5.51 * 4 1 LTX LUGE * * 1 1 LTEVA LTX * 4.86 * 1 1 LTX LTEVA LOG-DIFFERENCES 6.92 * (2,39) b * (1,36) b 5.46 * (1,37) b * (1,40) b 4.79 * (1,40) b 0.10 (1,40) b UGE GDP GDP UGE TX UGE UGE TX TX TEVA NC DLGDP DLUGE DLUGE DLGDP ** 3.15 ** 4 1 DLUGE DLTX ** 3.22 ** 1 2 DLTX DLUGE ** 3.25 ** 2 1 DLTEVA DLTX ** 3.38 ** 4 1 DLTX DLTEVA (1,38) b 3.04 ** (1,36) b 3.03 ** (1,38) b 3.12 ** (1,39) b 3.19 ** (1,32) b 1.14 (1,39) b NC GDP UGE TX UGE UGE TX TX TEVA Noes: 1. * indicaes significance a he convenional levels (5% and 1%) and ** indicaes significance a he 10% level respecively. 2. a χ 2 degrees of freedom for boh he Wald NC

38 157 and he Sims s LR ess. 3. b degrees of freedom for HH muliple-rank F-es. 4. NC; no causaliy.