Abstract
We use official time series of the Italian evaded VAT base (Ministry of Finance) for the period 1980:1–2006:4 to investigate empirically the long-run characteristics of tax evasion and the relationship with the tax burden. Three hitherto unexplored issues are addressed. First, using different measures of aggregate economic activity as reference variables in estimating the average tax burden, we examine the size and dynamics of the overburden traceable back to tax evasion. Second, exploiting cointegration techniques, we quantify the elasticity between tax evasion and the average tax rate in Italy. We then comment on the complex dynamic interaction between the tax burden and tax evasion to ascertain whether in the Italian experience there is evidence for any “vicious circle” between them.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
We are aware that part of VAT evasion is generated by VAT “carousel frauds”, which do not necessarily entail other forms of tax evasion, and, in particular, the under-reporting of production.
Of course in discussing our results it should be borne in mind that we are dealing with: (a) estimates of unobservable data; (b) aggregate realizations rather than observable or theoretical behaviours. Spanos (1999), Lutkepohl and Kratzig (2004), amongst others, stress these aspects with relation to time series.
In this paper, in order to quantify the dimension of the hidden activities, we will use the size of the unreported VAT liabilities, as explained in Sect. 3.
An analysis of the assumptions which characterize the standard model and the main criticisms may be found in Sandmo (2005).
Interestingly, in its annual report on tax evasion, the Italian ministry of economy (MEF 2009) claimed that the main causes of the low level of compliance in Italy were: the economic structure of the productive system, characterized by a very strong presence of small and medium sized firms and self-employed; the organization of the revenue collection system; the social pact between the tax payer and the State. One of the strategies adopted to pursue a reduction in tax evasion by small firms is investigated in Arachi and Santoro (2007), who focus on a major innovation in the field of tax auditing of self-employed workers and small firms: “Studi di settore” or “business sector analyses”.
See for instance, Busato et al. (2005b).
Detailed analysis of the quarterly time series estimate for the hidden VAT base may be found in Basile et al. (2010) and is available upon request.
More than 80% of tax evasion is generated by the service sectors (trade and services to households and firms). Typically these sectors consist of small firms which are most likely to evade and are subject to a presumptive taxation mechanism: in Italy about 96% of all firms employ 1–9 workers, about 60% of which are individual firms. See Pisani and Polito (2006) and Convenevole (2006).
More insights about the reform may be found in the special issue of Politica Economica (2002).
It is essential to identify the non-stationary nature of our series in order to avoid problems of spurious regressions when calculating the elasticity of tax evasion to fiscal pressure.
See, for instance, the Economic Journal symposium on the Hidden Economy and Schneider and Enste (2002).
Marigliani and Pisani (2007) compare their estimate of tax evasion (here exploited) with the ISTAT estimate of the underground economy for the years in common, i.e. 1992–2004, finding no large differences.
In order to analyze the tax burden we use the average fiscal pressure and not marginal tax rates. In any case, the closest definition to a legal tax rate is, in this paper, our measure for the effective tax rate, that is the ratio between total tax revenues and the production in the regular economy.
However, a different point of view would suggest that each taxpayer acts as a moonlighter, deciding how much to comply/declare according to the utility maximization process. In this case the higher tax rate depicted in Fig. 3 would represent the statutory tax rate, whereas the share of tax revenues to total GDP is the smoothed tax rate resulting from optimal evasion choice.
See Bernasconi and Lapecorella (2007).
This is related to the Laffer Curve as modified by Gutmann (1981) to account for shadow economy activities.
The econometric analysis is carried out only for the apparent tax rate (the tax burden is compared with the total GDP), since when controlling for the effective tax rate the estimated residuals display too large a correlation with the size of tax evasion, weakening the interpretation of the statistical model.
See, for instance, Johansen (1995).
In the Appendix we show the time series graph for a further measure of tax evasion, the ratio of uncollected VAT revenues -calculated by multiplying the unreported VAT base by the marginal VAT rate- to total GDP. The graph clearly shows that there is only a difference in the scale of measurement between VAT evasion and the proxy of tax evasion used in the VECM. The results of a different model, using as endogenous variables VAT evasion and VAT tax burden (measured as the ratio of VAT revenues to total GDP), though qualitatively similar to those displayed in the text, cannot be trusted, since the correlations between the estimated residuals are too large.
As in Johansen (1995), \( \alpha \bot \) is orthogonal to α and serves to define and distinguish the (unrelated) constants from the cointegration space and the constants from the data.
Introducing dummies eliminates outliers and induces residuals to be normal, providing more Gaussian-like residuals. See Juselius (2006) amongst others.
The critical values provided by Johansen and Osterwald-Lenum (1992) are known to be only indicative in such a situation (small sample, dummy variables and trends). We also perform unit root tests on the cointegrating residuals.
Cointegrating tests indicates the existence of one stationary equation. With regard to the VEC residual tests: the LM-test does not reject the Null of no serial correlation (lag 5 prob 0.72) and the Jarque–Bera tests applied to the individual residual series do not reject the Null hypothesis of normality (prob. 0.89 and 0.44), and a similar test result (joint test statistic 2.37 and p-value 0.67) is provided by the multivariate test proposed by Doornik and Hansen (1994).
This aspect is not investigated in this paper and it has much to do with the audit selection mechanisms purported to detect firms which are most likely to evade, i.e. Studi di Settore (see Convenevole et al. 2007). This instrument is far from reflecting the theory of optimal auditing for several reasons. In particular Studi di Settore (roughly, “Sector Studies”) focus on reported turnover rather than reported profits (turnover minus costs), but the real problem is that firm representatives are deeply involved in all the phases of construction of the Studi di Settore. As stressed by many (see, for instance, Santoro 2007, 2008), this presumed level reflects a political compromise between the government (or the Tax Agency) and these unions. Thus, the level of deterrence of the Studi di Settore is directly related to this political compromise rather than the probability of being audited.
See, for instance, Lutkepohl (1991).
The residuals’ correlation is 0.25.
These elasticities are computed from the numerical impulse responses depicted in the plots and the estimated standard error of the VECM regression. Lutkepohl (1991) discusses the caveats of impulse response analyses. Using absolute values, we find that a one percentage point shock to the tax rate provides, on impact, a positive tax evasion reaction of 0.22% points, peaking to 0.67 points after 2 years and, finally, approaching an equilibrium level of 0.43 points after 4 years. Conversely, a shock of one percentage point to tax evasion produces an immediate decline in fiscal pressure of 0.127 percentage points, followed by a gradual increase up to 2 percentage points in the long run.
References
Alesina, A., & Marè, M. (1996). Evasione e debito. In A. Monorchio (Ed.), La Finanza Pubblica Italiana dopo la Svolta del 1992. Il Mulino Bologna.
Allingham, M., & Sandmo, A. (1972). Income tax evasion: A theoretical analysis. Journal of Public Economics, 1, 323–338.
Andreoni, J., Erard, B., & Feinstein, J. (1998). Tax compliance. Journal of Economic Literature, 36(2), 818–860.
Arachi, G., & Santoro, A. (2007). Tax enforcement for SMEs: Lessons from the Italian experience? eJournal of Tax Research, 5(2), 225–243. (Michigan Issue).
Basile, R., Chiarini, B., & Marzano, E. (2010). Can we rely upon fiscal policy estimates in countries with a tax evasion of 15 per cent (or more) of GDP?, University of Naples, Parthenope, Dept. of Economic Studies, mimeo.
Bernasconi, M., & Lapecorella, F. (2007). I condoni nel sistema tributario italiano. In M. C. Guerra & A. Zanardi (Eds.), La Finanza Pubblica Italiana. Rapporto 2006. Il Mulino Bologna.
Bovi, M. (1999). Un miglioramento del metodo di Tanzi per la stima dell’economia sommersa in Italia. ISTAT, Quaderni di Ricerca, 2, 5–51.
Busato, F., Chiarini, B., & Di Maro, V. (2005a). Using theory for measurement. An analysis of behavior of undeground economy. University of Aarhus Working paper.
Busato, F., Chiarini, B., & Rey, G. M. (2005b). Equilibrium implications of fiscal policy with tax evasion. University of Aarhus Working paper.
Busato, F., & Chiarini, B. (2004). Market and underground activities in a two sector dynamic equilibrium model. Economic Theory, 23, 831–861.
Busato, F., & Chiarini, B. (2009). Steady state Laffer curve with the underground economy. University of Naples, Parthenope, Discussion paper, No. 2.
Chiarini, B., Di Domizio, M., & Marzano, E. (2009). Why do underground reducing policies often fail their scope? Some answers from the Italian experience. Economics and Politics, 21, 308–318.
Chiarini, B., & Marzano, E. (2004). Dimensione e dinamica dell’economia sommersa: Un approfondimento del Currency Demand Approach. Politica Economica, 3, 303–334.
Chow, G. C., & Lin, A. (1971). Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. Review of Economics and Statistics, 53, 372–375.
Convenevole, R. (2006). Come accrescere il gettito IVA. Analisi comparata dei sistemi italiano e francese, Documenti di Lavoro dell’Ufficio Studi Agenzia delle Entrate, May, No. 5.
Convenevole, R., Farina, A., Perinetti, A. R., & Pisani, S. (2007). Gli effetti dell’applicazione degli studi di settore in termini di ampliamento delle basi imponibili, Ministero dell’Economia e delle Finanze, Documenti di Lavoro dell’Ufficio Studi, 2007/1.
Davis, S. J., & Henrekson, M. (2004). Tax effects on work activity, industry mix and shadow economy size: Evidence from rich-country comparisons. NBER Working Paper 10509.
Dell’Anno, R. (2003), Estimating the shadow economy in Italy: A structural equation approach. University of Aarhus, Department of Economics, DK. Working paper 2003-7.
Dell’Anno, R., & Schneider, F. (2003). The shadow economy of Italy and other OECD countries: What do we know? Journal of Public Finance and Public Choice (PFPC)/Economia delle scelte pubbliche, XXI(2–3), 97–120.
Doornik, J. A., & Hansen, H. (1994). A practical test of multivariate normality, unpublished paper. UK: Nuffield College.
Feige, E. L. (Ed.). (1989). The underground economies. Tax evasion and informal distortion. Cambridge: Cambridge University Press.
Feld, L. P., & Frey, B. S. (2007). Tax compliance as the result of a psychological tax contract: The role of incentives and responsive regulation. Law and Policy, 29, 102–120.
Feld, L. P., & Schneider, F. (2010). Survey on the shadow economy and undeclared earnings in OECD countries. German Economic Review, 11(2), 109–149.
Fiorio, C., & Zanardi, A. (2006). L’evasione fiscale: Cosane pensano gli italiani? In M. C. Guerra & A. Zanardi (Eds.), La finanza pubblica italiana, rapporto. il Mulino.
Giannini, S., & Guerra, M. C. (2000). Dove eravamo e dove siamo: Il sistema tributario dal 1990 al 2000. In L. Bernardi (Ed.), La Finanza Pubblica Italiana. Rapporto 2000. Il Mulino Bologna.
Giles, D. E. A., & Tedds, L. M. (2002). Taxes and the Canadian underground economy. Toronto: Canadian tax foundation.
Gutmann, P. (1981). Implications of subterranean economy. In R. X. Bove & R. D. Klingenstein (Eds.), Wertheim’s underground economy conference (pp. 31–58). Wertheim, Inc.
Hodrick, R., & Prescott, E. (1997). Post-war US business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29, 1–16.
ISTAT. (2008), La misura dell’economia sommersa secondo le statistiche ufficiali, Statistiche in Breve, 18 June 2008, Rome.
Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Oxford: Oxford University Press.
Juselius, K. (2006). The cointegrated VAR Model. Methodology and applications. Oxford: Oxford University Press.
Kirchler, E. (2007). The economic psychology of tax behaviour. Cambridge: Cambridge University Press.
Lippert, O., & Walzer, M. (Eds.). (1997). The underground economy: Global evidences of its size and impact. Vancouver: The Fraser Institute.
Lutkepohl, H. (1991). Introduction to multiple time series analysis. Berlin: Springer-Verlag.
Lutkepohl, H. (1994). Interpretation of cointegration relations. Econometric Reviews, 13, 391–394.
Lutkepohl, H., & Kratzig, M. (2004). Applied time series econometrics. Cambridge: Cambridge University Press.
Marigliani, M., & Pisani, S. (2007). Le basi imponibili IVA. Aspetti generali e principali risultati per il periodo 1980–2004, Documenti di lavoro dell’Ufficio Studi Agenzia delle Entrate, no.7.
MEF. (2009), Relazione concernente i risultati derivanti dalla lotta all’evasione fiscale. Senato della Repubblica, XVI Legislatura, Doc. LXVII no. 1.
Osterwald-Lenum, M. (1992). A note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics. Oxford Bulletin of Economics and Statistics, 54, 461–471.
Papp, T. K., & Takáts, E. (2008), Tax rate cuts and tax compliance—The laffer curve revisited. IMF Working Paper, January.
Pisani, S., & Polito, C. (2006), Analisi dell’evasione fondata sui dati IRAP—anni 1998–2002. Documenti di Lavoro dell’Ufficio Studi Agenzia delle Entrate, maggio, No. 2.
Politica Economica. (2002), Il disegno di legge delega per la riforma fiscale. Quali Problemi? Quali Prospettive?, Politica Economica, no.3, December 2002.
Proietti, T. (2006). Temporal disaggregation by State Space methods: Dynamic regression methods revisited. Econometric Journal, 9, 357–372.
Sandmo, A. (2005). The theory of tax evasion: A retrospective view. National Tax Journal, LVIII(4), 643–663.
Santoro, A. (2007). Evasione e studi di settore. Quali risultati? Quali prospettive? In M. C. Guerra & A. Zanardi (Eds.), La Finanza Pubblica Italiana. Rapporto 2006. Il Mulino Bologna.
Santoro, A. (2008). Taxpayers’ choices under Studi di settore: What do we know and how we can interpret it? il Giornale degliEconomisti e Annali di Economia, 67(2), 161–184.
Schneider, F. (2002). The size and development of shadow economies of 22 transition and 21 OECD countries. IZA Discussion Paper 514.
Schneider, F., & Enste, D. H. (2002). The shadow economy. An international survey. Cambridge: Cambridge University Press.
Slemrod, J., & Yitzhaki, S. (2000). Tax avoidance, evasion and administration. NBER Working Paper N. 7473.
Spanos, A. (1999). Probability theory and statistical inference. Cambridge: Cambridge University Press.
Tanzi, V. (1982). The underground economy in the United States and abroad. Lexington: D.C. Heath.
Thomas, J. J. (1992). Informal economy activity, LSE handbooks in economics. London: Harvester Wheatsheaf.
Torgler, B. (2007). Tax compliance and tax morale. A theoretical and empirical analysis. Cheltenham, UK: Edward Elgar.
Yitzaky, S. (1974). A note on income tax evasion: A theoretical analysis. Journal of Public Economics, 4, 201–220.
Zizza R. (2002) Metodologie di stima dell’economia sommersa: un’applicazione al caso italiano. Banca d’Italia, Temi di Discussione 4.
Acknowledgments
We are indebted to Douglas Hibbs, Eelke de Jong, Stefano Pisani, Roberto Golinelli and Roberta Zizza for their helpful comments. We thank participants at the Italian Treasury BBL Meeting held in Rome in 2008, and the participants at the 2008 Annual Meeting of the European Public Choice Society (Jena, Germany). We thank an anonymous referee for helpful suggestions. Bruno Chiarini and Elisabetta Marzano gratefully acknowledge the funding from PRIN 2008, “Tassazione, tax morale ed evasione fiscale: una analisi delle determinanti e delle implicazioni di policy”. Most of this material has been discussed within the Study Centre of the Revenues Agency.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Here we show the results of stationary tests for the two tax rates and their difference, and the ratio of the unreported VAT base to total GDP. It is essential to identify the non-stationary nature of our series and avoid problems of spurious regressions. Statistical inference about a stochastic trend is often combined with a deterministic trend, and distinguishing between them when several breaks are present in the variables is not straightforward. Furthermore, the analysis is complicated by the weakness of the unit root tests when small samples are used (See Table 2).
Note that as the calculated ADF statistics for tax rates and VAT evasion exceed the critical values when considering the first differences, we reject the null (variables have a unit root) at conventional test sizes for the differenced variables. The tests confirm that we are dealing with non-stationary series (Fig. 5).
Tax evasion (left axes) and VAT evasion (right axes), percentage, quarterly data. Tax Evasion is defined as the ratio of concealed VAT base to total GDP, whereas VAT Evasion is defined as the ratio of uncollected VAT revenues—calculated by multiplying the unreported VAT base by the marginal VAT rate- to total GDP
Rights and permissions
About this article
Cite this article
Chiarini, B., Marzano, E. & Schneider, F. Tax rates and tax evasion: an empirical analysis of the long-run aspects in Italy. Eur J Law Econ 35, 273–293 (2013). https://doi.org/10.1007/s10657-011-9247-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10657-011-9247-6