Spatial Dynamic Modelling of Tax Gap: the Case of Italy

Authors

  • Alfonso Carfora Italian Revenue Agency, Via Cristoforo Colombo, 426, 00145 Rome, Italy
  • Rosaria Vega Pansini Italian Revenue Agency, Via Cristoforo Colombo, 426, 00145, Rome, Italy
  • Stefano Pisani Italian Revenue Agency, Via Cristoforo Colombo, 426, 00145, Rome, Italy

DOI:

https://doi.org/10.18778/1231-1952.25.1.02

Keywords:

determinants of tax gap, spatial econometrics, panel estimation

Abstract

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.

Downloads

Download data is not yet available.

References

Allingham, M. G. and Sandmo, A. (1972), ‘Income Tax Evasion: A Theoretical Analysis’, Journal of Public Economics, 1, pp. 323–338.
Google Scholar DOI: https://doi.org/10.1016/0047-2727(72)90010-2

Alm, J. (1999), ‘Tax Compliance and Administration’, Public Administration and Public Policy, 72, pp. 741–768.
Google Scholar

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.
Google Scholar DOI: https://doi.org/10.1007/s10797-011-9171-2

Alm, J., McKee, M. and Beck, W. (1990), ‘Amazing Grace: Tax Amnesties and Compliance’, National Tax Journal, 43 (1), pp. 23–37.
Google Scholar

Alm, J. and Yunus, M. (2009), ‘Spatially and Persistence in U.S. Individual Income Tax Compliance’, National Tax Journal, 62 (1), pp. 101–124.
Google Scholar DOI: https://doi.org/10.17310/ntj.2009.1.05

Andreoni, J., Erard, B. and Feinstein, J. (1998), ‘Tax Compliance’, Journal of Economic Literature, 36 (2), pp. 818–860.
Google Scholar

Anselin, L. (1988), ‘Spatial Econometrics: Methods and Models’, [in:] Anselin, L., Spatial Econometrics Chapter Fourteen, Dordrecht: Kluwer Academic Publisher, pp. 310–325.
Google Scholar DOI: https://doi.org/10.1007/978-94-015-7799-1

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.
Google Scholar DOI: https://doi.org/10.2307/2297968

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.
Google Scholar DOI: https://doi.org/10.1016/0304-4076(94)01646-H

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.
Google Scholar DOI: https://doi.org/10.1016/S0304-4076(03)00120-9

Bloomquist, K. (2003), ‘Tax Evasion, Income Inequality and Opportunity Costs of Compliance’, 96th Annual Conference of The National Tax Association, Chicago, pp. 13–15.
Google Scholar

Blundell, R. and Bond, S. (1998), ‘Initial Conditions and Moment Restrictions in Dynamic Panel Data Models’, Journal of Econometrics, 87 (1), pp. 115–143.
Google Scholar DOI: https://doi.org/10.1016/S0304-4076(98)00009-8

Bordignon, M. and Zanardi, A. (1997), ‘Tax Evasion In Italy’, Giornale degli economisti e Annali di economia, 56 (3–4), pp. 169–210.
Google Scholar

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.
Google Scholar

Caballe, J. and Panades, J. (2000), ‘Tax Evasion and Economic Growth’, Public Finance/Finances Publiques, 52, pp. 318–340.
Google Scholar

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
Google Scholar

Cliff, A. D. and Ord, J. K. (1981), Spatial Processes: Models and Applications, London: Pion Ltd.
Google Scholar

Clotfelter, C. T. (1983), ‘Tax Evasion and Tax Rates: An Analysis of Individual Returns’, Review of Economics and Statistics, 65 (3), pp. 363–373.
Google Scholar DOI: https://doi.org/10.2307/1924181

D’agosto, E., Marigliani, M. and Pisani, S. (2014), ‘Asymmetries in the Territorial Vat Gap’, Argomenti di Discussione of Italian Revenue Agency, 2.
Google Scholar

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.
Google Scholar

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
Google Scholar

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
Google Scholar

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 .
Google Scholar

Elhorst, J. P. (2003), ‘Specification and Estimation of Spatial Panel Data Models’, International Regional Science Review, 26, pp. 244–268.
Google Scholar DOI: https://doi.org/10.1177/0160017603253791

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.
Google Scholar

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.
Google Scholar DOI: https://doi.org/10.1016/S0047-2727(99)00093-6

Giles, D. E. A. (1999), ‘Measuring the Hidden Economy: Implications for Econometric Modeling’, The Economic Journal, 109 (46), pp. 370–380.
Google Scholar DOI: https://doi.org/10.1111/1468-0297.00440

Godfrey, L. (1978), ‘Testing against General Autoregressive and Moving Average Error Models when the Regressors Include Lagged Dependent Variables’, Econometrica, 46, pp. 1293–1302.
Google Scholar DOI: https://doi.org/10.2307/1913829

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.
Google Scholar DOI: https://doi.org/10.1080/00343404.2013.848034

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
Google Scholar

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.
Google Scholar DOI: https://doi.org/10.1016/j.jeconom.2006.09.004

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.
Google Scholar DOI: https://doi.org/10.1111/1468-2354.00027

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.
Google Scholar

Marigliani, M. and Pisani, S. (2014), Una stima dell’effetto deterrenza esercitato dall’Agenzia delle Entrate, New York: Italian Revenue Agency, Mimeo.
Google Scholar

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.
Google Scholar

Measuring Tax Gaps (2015), HM Revenue & Customs, London, UK.
Google Scholar

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.
Google Scholar DOI: https://doi.org/10.1002/jae.2424

Moran, P. A. P. (1950), ‘Notes on Continuous Stochastic Phenomena’, Biometrika, 1/2, pp. 17–23.
Google Scholar DOI: https://doi.org/10.1093/biomet/37.1-2.17

Mutl, J. and Pfaffermayr, M. (2011), ‘The Hausman Test in a Cliff and Ord Panel Model’, Econometrics Journal, 14, pp. 48–76.
Google Scholar DOI: https://doi.org/10.1111/j.1368-423X.2010.00325.x

Neumann, J. V. and Morgenstern, O. (1953), Theory of Games and Economic Behavior, Princeton, NJ: Princeton University Press.
Google Scholar

Pesaran, M. (2004), ‘General Diagnostic Tests for Cross Section Dependence in Panels’, CESifo Working Paper, No. 1229.
Google Scholar

Pesaran, H. M. and Tosetti, E. (2011), ‘Large Panels with Common Factors and Spatial Correlations’, Journal of Econometrics, 161 (2), pp. 182–202.
Google Scholar DOI: https://doi.org/10.1016/j.jeconom.2010.12.003

Richardson, G. (2006), ‘Determinants of Tax Evasion: A Cross-Country Investigation’, Journal of International Accounting, Auditing and Taxation, 15 (2), pp. 150–169.
Google Scholar DOI: https://doi.org/10.1016/j.intaccaudtax.2006.08.005

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.
Google Scholar DOI: https://doi.org/10.1177/1091142110386214

Schneider, F. and Enste, D. H. (2000), ‘Shadow Economies: Size, Causes, and Consequences’, Journal of Economic Literature, 38, pp. 77–114.
Google Scholar DOI: https://doi.org/10.1257/jel.38.1.77

Schneider, F. and Williams, C. C. (2013), ‘The Shadow Economy’, London: Institute of Economic Affairs.
Google Scholar

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.
Google Scholar

Wooldridge, J. M. (2002), Econometric Analysis of Cross-Section and Panel Data, Cambridge, MA: MIT Press.
Google Scholar

Yitzhaki, S. (1974), ‘A Note on Income Tax Evasion: A Theoretical Analysis’, Journal of Public Economics, 3 (2), pp. 201–202.
Google Scholar DOI: https://doi.org/10.1016/0047-2727(74)90037-1

Downloads

Published

2018-08-14

How to Cite

Carfora, A., Pansini, R. V., & Pisani, S. (2018). Spatial Dynamic Modelling of Tax Gap: the Case of Italy. European Spatial Research and Policy, 25(1), 7–28. https://doi.org/10.18778/1231-1952.25.1.02

Issue

Section

Articles