Spatial Dynamic Modelling of Tax Gap: the Case of Italy

• Authors: Alfonso Carfora , Rosaria Vega Pansini , Stefano Pisani
• Languages of publication: EN

Abstracts

EN

This paper analyses the determinants of regional tax gap in Italy testing if tax evasion is characterised by spatial persistence. The size of spatial correlation in regional tax gaps has been tested and the role of additional determinants of evasion over the period 2001–2011 has been estimated. Using a dynamic spatial panel model, it is shown that regional tax gap is determined by tax evasion in neighbouring regions and is characterised by spatial persistence. Results make it possible to draw a taxonomy of the determinants of regional tax gap: contextual factors and operational factors linked to the relative efficacy of tax evasion contrasting policies and geography.

Keywords

EN

determinants of tax gap; spatial econometrics; panel estimation

• Publisher: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
• Journal: European Spatial Research and Policy
• Year: 2018
• Volume: 25
• Issue: 1
• Pages: 7-28

Dates

published

2018-08-14

Contributors

author

Alfonso Carfora

• Italian Revenue Agency, Via Cristoforo Colombo, 426, 00145 Rome, Italy

author

Rosaria Vega Pansini

• Italian Revenue Agency, Via Cristoforo Colombo, 426, 00145, Rome, Italy

author

Stefano Pisani

• Italian Revenue Agency, Via Cristoforo Colombo, 426, 00145, Rome, Italy

References

• Allingham, M. G. and Sandmo, A. (1972), ‘Income Tax Evasion: A Theoretical Analysis’, Journal of Public Economics, 1, pp. 323–338.
• Alm, J. (1999), ‘Tax Compliance and Administration’, Public Administration and Public Policy, 72, pp. 741–768.
• Alm, J. (2012), ‘Measuring, Explaining, and Controlling Tax Evasion: Lessons from Theory, Experiments, and Field Studies’, International Tax and Public Finance, 19 (1), pp. 54–77.
• Alm, J., McKee, M. and Beck, W. (1990), ‘Amazing Grace: Tax Amnesties and Compliance’, National Tax Journal, 43 (1), pp. 23–37.
• Alm, J. and Yunus, M. (2009), ‘Spatially and Persistence in U.S. Individual Income Tax Compliance’, National Tax Journal, 62 (1), pp. 101–124.
• Andreoni, J., Erard, B. and Feinstein, J. (1998), ‘Tax Compliance’, Journal of Economic Literature, 36 (2), pp. 818–860.
• Anselin, L. (1988), ‘Spatial Econometrics: Methods and Models’, [in:] Anselin, L., Spatial Econometrics Chapter Fourteen, Dordrecht: Kluwer Academic Publisher, pp. 310–325.
• Arellano, M. and Bond, S. (1991), ‘Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations’, Review of Economic Studies, 58, pp. 277–297.
• Baltagi, B. and Li, Q. (1995), ‘Testing AR (1) against MA (1) Disturbances in an Error Component Model’, Journal of Econometrics, 68, pp. 133–151.
• Baltagi, B. H., Song, S. H. and Koh, W. (2003), ‘Testing Panel Data Regression Models with Spatial Error Correlation’, Journal of Econometrics, 117, pp. 123–150.
• Bloomquist, K. (2003), ‘Tax Evasion, Income Inequality and Opportunity Costs of Compliance’, 96th Annual Conference of The National Tax Association, Chicago, pp. 13–15.
• Blundell, R. and Bond, S. (1998), ‘Initial Conditions and Moment Restrictions in Dynamic Panel Data Models’, Journal of Econometrics, 87 (1), pp. 115–143.
• Bordignon, M. and Zanardi, A. (1997), ‘Tax Evasion In Italy’, Giornale degli economisti e Annali di economia, 56 (3–4), pp. 169–210.
• Braiotta, A., Carfora, A., Pansini, R. V. and Pisani, S. (2015), ‘Tax Gap and Redistributive Aspects Across Italy’, Argomenti di Discussione of Italian Revenue Agency, 2, pp. 1‒27.
• Caballe, J. and Panades, J. (2000), ‘Tax Evasion and Economic Growth’, Public Finance/Finances Publiques, 52, pp. 318–340.
• Chiarini, B., Marzano, E. and Schneider, F. (2008), ‘Tax Rates and Tax Evasion: An Empirical Analysis of The Structural Aspects and Long-Run Characteristics in Italy’, IZA Discussion Papers, 3447, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1136252
• Cliff, A. D. and Ord, J. K. (1981), Spatial Processes: Models and Applications, London: Pion Ltd.
• Clotfelter, C. T. (1983), ‘Tax Evasion and Tax Rates: An Analysis of Individual Returns’, Review of Economics and Statistics, 65 (3), pp. 363–373.
• D’agosto, E., Marigliani, M. and Pisani, S. (2014), ‘Asymmetries in the Territorial Vat Gap’, Argomenti di Discussione of Italian Revenue Agency, 2.
• Dell’anno, R. and Schneider, F. G. (2006), ‘Estimating the Underground Economy by Using Mimic Models: A Response to T. Breusch’s Critique’, Economics Working Papers, 2006–07 of Department of Economics University Linz.
• Depalo, G. and Messina, G. (2011), ‘Follow the Herd. Spatial Interactions in Tax Setting Behavior of Italian Municipalitie’s’, Siecon Working Paper, http://www.siecon.org/online/wp-content/uploads/2011/04/Depalo-Messina1.pdf
• Di Caro, P. and Nicotra, G. (2014), Knowing the Unknown across Regions: Spatial Tax Evasion in Italy, Social Science Research Network, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2446803
• Eilat, Y. and Zinnes, C. (2000), ‘The Evolution of the Shadow Economy in Transition Countries: Consequences for Economic Growth and Donor Assistance’, CAER II Discussion Paper No. 83 of Harvard Institute for International Development, pp. 3–70 .
• Elhorst, J. P. (2003), ‘Specification and Estimation of Spatial Panel Data Models’, International Regional Science Review, 26, pp. 244–268.
• Fiorio, C.V. and D’amuri, F. (2006), ‘Workers’ Tax Evasion in Italy’, Giornale degli economisti e Annali di economia, 64 (2–3), pp. 247–270.
• Friedman, E., Johnson, S., Kaufmann, D., and Zoido-Lobaton, P. (2000), ‘Dodging the Grabbing Hand: The Determinants of Unofficial Activity in 69 Countries’, Journal of Public Economics, 76, pp. 459–493.
• Giles, D. E. A. (1999), ‘Measuring the Hidden Economy: Implications for Econometric Modeling’, The Economic Journal, 109 (46), pp. 370–380.
• Godfrey, L. (1978), ‘Testing against General Autoregressive and Moving Average Error Models when the Regressors Include Lagged Dependent Variables’, Econometrica, 46, pp. 1293–1302.
• Herwartz, H., Tafenau, E. and Schneider, F. (2015), ‘One Share Fits All? Regional Variations in the Extent of the Shadow Economy in Europe’, Regional Studies, 49, pp. 1575‒1587.
• ISTAT (2010), ‘La misura dell’economia sommersa secondo le statistiche ufficiali. Anni 2000‒2008’, Statistics in Brief, 13, https://www.istat.it/it/files//2011/01/testointegrale20100713.pdf
• Kapoor, M., Kelejian, H. H. and Prucha, I. R. (2007), ‘Panel Data Model with Spatially Correlated Error Components’, Journal of Econometrics, 140 (1), pp. 97–130.
• Kelejian, H. H. and Prucha, I. R. (1999), ‘A Generalised Moments Estimator for the Autoregressive Parameter in a Spatial Model’, International Economic Review, 40 (2), pp. 509–533.
• Kleven, H. J., Knudsen, M. B., Kreiner, C. T., Pedersen, S. and Saez, E. (2010), ‘Unwilling or Unable to Cheat? Evidence from a Randomised Tax Audit Experiment in Denmark’, NBER Working Paper, No. 15769.
• Marigliani, M. and Pisani, S. (2014), Una stima dell’effetto deterrenza esercitato dall’Agenzia delle Entrate, New York: Italian Revenue Agency, Mimeo.
• Marino, M. R and Zizza, R. (2012), ‘Personal Income Tax Evasion in Italy: An Estimate by Taxpayer Type’, [in:] Pickhardt, M. and Prinz, A. (eds.), Tax Evasion and The Shadow Economy, Chapter 3, Cheltenham: Edward Elgar Publishing.
• Measuring Tax Gaps (2015), HM Revenue & Customs, London, UK.
• Millo, G. (2014), ‘Narrow Replication of a Spatio-Temporal Model of House Prices in the USA Using R’, Journal of Applied Econometrics, 30 (4), pp. 703‒704.
• Moran, P. A. P. (1950), ‘Notes on Continuous Stochastic Phenomena’, Biometrika, 1/2, pp. 17–23.
• Mutl, J. and Pfaffermayr, M. (2011), ‘The Hausman Test in a Cliff and Ord Panel Model’, Econometrics Journal, 14, pp. 48–76.
• Neumann, J. V. and Morgenstern, O. (1953), Theory of Games and Economic Behavior, Princeton, NJ: Princeton University Press.
• Pesaran, M. (2004), ‘General Diagnostic Tests for Cross Section Dependence in Panels’, CESifo Working Paper, No. 1229.
• Pesaran, H. M. and Tosetti, E. (2011), ‘Large Panels with Common Factors and Spatial Correlations’, Journal of Econometrics, 161 (2), pp. 182–202.
• Richardson, G. (2006), ‘Determinants of Tax Evasion: A Cross-Country Investigation’, Journal of International Accounting, Auditing and Taxation, 15 (2), pp. 150–169.
• Santoro, A. and Fiorio, C. V. (2011), ‘Taxpayer Behavior when Audit Rules are Known: Evidence from Italy’, Public Finance Review, 39 (1), pp. 103–123.
• Schneider, F. and Enste, D. H. (2000), ‘Shadow Economies: Size, Causes, and Consequences’, Journal of Economic Literature, 38, pp. 77–114.
• Schneider, F. and Williams, C. C. (2013), ‘The Shadow Economy’, London: Institute of Economic Affairs.
• Williams, C. C. and Windebank, J. (2011), ‘Regional Variations in the Nature of the Shadow Economy: Evidence from a Survey of 27 European Union Member States’, [in:] Handbook on the Shadow Economy, Chapter 5, Cheltenham: Edward Elgar Publishing.
• Wooldridge, J. M. (2002), Econometric Analysis of Cross-Section and Panel Data, Cambridge, MA: MIT Press.
• Yitzhaki, S. (1974), ‘A Note on Income Tax Evasion: A Theoretical Analysis’, Journal of Public Economics, 3 (2), pp. 201–202.

Identifiers

DOI

10.18778/1231-1952.25.1.02