Spatial Correlation and Distribution of the Shadow Economy in Russia
DOI:
https://doi.org/10.17059/2020-2-10Keywords:
shadow economy, spatial econometrics, spatial autocorrelation, tax evasion, tax crimes, economic crimes, economic activities, spatial correlation, spatial patterns, Russian regionsAbstract
The paper aims to examine the spatial patterns of the distribution of the shadow economy in Russia, as well as to assess the correlation between the scale of the shadow economy and crime statistics. The methodology of spatial data analysis includes three steps. Firstly, we calculated the global Moran’s index and established its criteria of relevance. Secondly, we identified clusters of regions using Moran scatterplot. Thirdly, we conducted a correlation analysis of data on the shadow economy (for the period until 2016) and crime statistics (for the period until 2017, considering a time lag between a crime and its detection). The research results confirmed the correlation between the regional shadow economies. The correlated of the spatial cooperation of regions and the scale of the shadow economy is negative: a decrease in shadow economy leads to an increase in cooperation, and vice versa. In the considered period, spatial interaction gains more importance. The shadow economy is present in more than half of the Russian regions, located amid similar entities. Only a quarter of the regions are relatively unaffected by the shadow economies of their neighbours. Thus, we have established the configuration of spatial distribution of the shadow economy in Russia, demonstrating that such economic activity prevails in the western part of the country. This area is surrounded by the “transitional” zone where there is a risk of the spread of the shadow economy. Additionally, we identified isolated centres of shadow activity in Siberia and the Far East, as well as the zones of relative well-being. The findings demonstrate the difficulty in assessing the scale of the regional shadow economy using indirect and model methods due to the regions’ interaction. Moreover, the obtained results argue for the necessity of a targeted approach for creating policies aimed at preventing the regional shadow economy.
References
Tanzi, V. (1999). Uses and abuses of estimates of the underground economy. The Economic Journal, 109(456), 338–347. DOI: 10.1111/1468–0297.00437.
Kireenko, A. P., Nevzorova, E. N., Orlova, E. N. & Polyakova, O. Yu. (2017). Tenevaya ekonomika v regionakh Rossii: otsenka na osnove MIMIC modeli [Shadow economy in Russian regions: an estimation with the MIMIC model]. Region: Ekonomika i Sotsiologiya [Region: Economics and Sociology], 1, 164–189. DOI: 10.15372/REG20170108 (In Russ.)
Ivanova, V. I. (2014). Regionalnaya konvergentsiya dokhodov naseleniya: prostranstvennyy analiz [Regional Convergence of Income: Spatial Analysis]. Prostranstvennaya Ekonomika [Spatial Economics], 4, 100–119. DOI: 10.14530/ se.2014.4.100–119. (In Russ.)
Ivanova, V. I. (2013). O dinamike prostranstvennogo vzaimodeystviya rossiyskikh regionov [On Dynamics of Spatial Interaction of Russian Regions]. In: A. P. Zaostrovtsev, L. E. Limonov. (Eds.), Ekonomika i geografiya [Economy and Geography] (pp. 96–102). St. Petersburg. (In Russ.)
Balash, O. S. (2012). Prostranstvennyy analiz konvergentsii regionov Rossii [Convergence spatial analysis of Russia’s regions]. Izvestiya Saratovskogo universiteta. Novaya seriya. Seriya Ekonomika. Upravleniye. Pravo [Izvestiya of Saratov university. New series. Series: economics. Management. Law], 12(4), 45–52. (In Russ.)
Balash, O. S. (2012). Statisticheskoe issledovanie prostranstvennoy klasterizatsii regionov Rossii [Statistical research of the spatial clustering of regions of Russia]. Izvestiya Tulskogo gosudarst- vennogo universiteta. Ekonomicheskiye i yuridicheskiye nauki [News of the Tula state university. Economic and legal sciences], 2–1, 56–65. (In Russ.)
Semerikova, E. V. & Demidova, O. A. (2016). Vzaimodeystviye regionalnykh rynkov truda v Rossii: analiz s po- moshchyu prostranstvennykh ekonometricheskikh modeley [Interaction of Regional Labour Markets in Russia: Spatial Econometric Analysis]. Prostranstvennaya Ekonomika [Spatial Economics], 3, 57–80. DOI: 10.14530/se.2016.3.057–080. (In Russ.)
Rusanovskiy, V. A. & Markov, V. A. (2016). Vliyanie prostranstvennogo faktora na regionalnuyu differentsiatsiyu bezrabotitsy v Rossiyskoy ekonomike [Influence of the spatial factor on the regional differentiation of unemployment in the Russian economy]. Problemy prognozirovaniya [Studies on Russian Economic Development], 27, 593–604. DOI: 10.1134/ S1075700716050129. (In Russ.)
Zhukova, A. K., Silaev, A. M. & Silaeva, M. V. (2016). Analiz ozhidayemoy prodolzhitelnosti zhizni s uchetom prostranstvennoy zavisimosti po regionam Rossii [Spatial Analysis of Life Expectancy in Russian Regions]. Prostranstvennaya Ekonomika [Spatial Economics], 4, 112–128. DOI: 10.14530/se.2016.4.112–128. (In Russ.)
Kireenko, A. & Nevzorova, E. (2015). Impact of shadow economy on quality of life: Indicators and model selection. Procedia Economics and Finance, 25, 559–568. DOI: 10.1016/s2212–5671(15)00770–4.
Kireyenko, A. P., Nevzorova, Ye. N., Orlova, Ye. N. & Fedotov, D. Yu. (2015). Otrazhenie tenevoy ekonomiki v pokazatelyakh kachestva zhizni naseleniya regionov [How the shadow economy impacts the quality of life in regions]. Region: ekonomika i sotsiologiya [Region: Economics and Sociology], 3(87), 213–238. DOI: 10.15372/REG20150909. (In Russ.)
Zemtsov, S. P. & Tsareva, Y. V. (2018). Predprinimatelskaya aktivnost v regionakh Rossii: naskolko prostranstvennye i vremennye effekty determiniruyut razvitie malogo biznesa [Entrepreneurial activity in the Russian regions: how spatial and temporal effects determine the development of small business]. Zhurnal Novoy ekonomicheskoy assotsiatsii [Journal of the New Economic Association], 1(37), 145–165. DOI: 10.31737/2221–2264–2018–37–1–6. (In Russ.)
Demidova, O. A. (2013). Vyyavlenie prostranstvennykh effektov dlya osnovnykh makroekonomicheskikh pokazateley rossiyskikh regionov [Identification of Spatial Effects for the Main Macroeconomic Indicators of the Russian Regions]. Moscow: Higher School of Economics, 26. (In Russ.)
Goel, R. K. & Saunoris, J. W. (2014). Global corruption and the shadow economy: spatial aspects. Public Choice, 161(1–2), 119–139. DOI: 10.1007/s11127–013–0135–1.
Goel, R. K. & Saunoris, J. W. (2016). Casting a long shadow? Cross-border spillovers of shadow economy across American states. Public Finance Review, 44(5), 610–634. DOI: 10.1177/1091142115584815.
Carfora, A., Pansini, R. V. & Pisani, S. (2018). Regional tax evasion and audit enforcement. Regional Studies, 52(3), 362–373. DOI: 10.1080/00343404.2017.1299934.
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. DOI: 10.18778/1231–1952.25.1.02.
Alm, J. & Yunus, M. (2009). Spatiality and persistence in US individual income tax compliance. National Tax Journal, 62(1), 101–124. DOI: 10.17310/ntj.2009.1.05.
García, G. A., Azorín, J. D. B. & de la Vega, M. M. S. (2018). Tax evasion in Europe: An analysis based on spatial dependence. Social Science Quarterly, 99(1), 7–23. DOI: 10.1111/ssqu.12382.
Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI: 10.2307/2332142.
Vakulenko, E. (2015). Analiz svyazi mezhdu regionalnymi rynkami truda v Rossii s ispolzovaniyem modeli Oukena [Analysis of the relationship between regional labour markets in Russia using Okun’s model]. Prikladnaya ekonometrika [Applied Econometrics], 40(4), 28–48. (In Russ.)
Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic geography. Supplement: Proceedings. International Geographical Union. Commission on Quantitative Methods, 46, 234–240. DOI: 10.2307/143141.
Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical analysis, 27(2), 93–115. DOI: 10.1111/j.1538–4632.1995.tb00338.x.
Kireenko, A. P., Nevzorova, E. N. & Fedotov, D. Y. (2019). Sector-specific characteristics of tax crime in Russia. Journal of Tax Reform, 5(3), 249–264. DOI: 10.15826/jtr.2019.5.3.071.
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Copyright (c) 2020 E.N. Nevzorova, A.P. Kireenko, I.A. Mayburov
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