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Volume 5 • Issue 3 | November 2021

Social capital and the shadow economy: a Bayesian analysis of the BRICS

Tien Ha My Duong, Thi Anh Nhu Nguyen, and Van Diep Nguyen

09/11/2021 161 0 0
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Bayesian approach BRICS Shadow economy Social capital

Abstract:

Purpose
The paper aims to examine the impact of social capital on the size of the shadow economy in the BIRCS countries over the period 1995–2014.

Design/methodology/approach
The authors employ the Bayesian linear regression method to uncover the relationship between social capital and the shadow economy. The method applies a normal distribution for the prior probability distribution while the posterior distribution is determined using the Markov chain Monte Carlo technique.

Findings
The results indicate that the unemployment rate and tax burden positively affect the size of the shadow economy. By contrast, corruption control and trade openness are negatively associated with the development of this informal sector. Moreover, the paper's primary finding is that social capital represented by social trust and tax morale can hinder the size of the shadow economy.

Research limitations/implications
This study is limited to the case of the BRICS countries for the period 1995–2014. The determinants of the shadow economy in different groups of countries can be heterogeneous. Moreover, social capital is a multidimensional concept that may consist of various components. This difficulty of measuring the social capital calls for further research on the relationship between other dimensions of social capital and the shadow economy.

Originality/value
Many studies investigate the effect of economic factors on the size of the shadow economy. This paper applies a new approach to discover the issue. Notably, the authors use the Bayesian linear regression method to analyze the relationship between social capital and the shadow economy in the BRICS countries.

References:

  1. Aidt, T.S. (2003), “Economic analysis of corruption: a survey”, The Economic Journal, Vol. 113 No. 491, pp. F632-F652.
  2. Arrow, K.J. (1970), “Political and economic evaluation of social effects and externalities”, in Margolis, J. (Ed.), The Analysis of Public Output, National Bureau of Economic Research, Cambridge, MA, pp. 1-30.
  3. Arrow, K.J. (1973), “Social responsibility and economic efficiency”, Public Policy, Vol. 21 No. 3, pp. 303-317.
  4. Arrow, K.J. (1999), “Observations on social capital”, in Serageldin, I. and Dasgupta, P. (Eds), Social Capital: A Multifaceted Perspective, World Bank, Washington, DC, pp. 3-5.
  5. Asea, P.K. (1996), “The informal sector: baby or bath water? a comment”, Carnegie-Rochester Conference Series on Public Policy, Vol. 45, pp. 163-171.
  6. Baklouti, N. and Boujelbene, Y. (2019), “Shadow economy, corruption, and economic growth: an empirical analysis”, The Review of Black Political Economy, Vol. 47 No. 3, pp. 276-294.
  7. Birinci, S. (2013), “Trade openness, growth, and informality: panel VAR evidence from OECD economies”, Economics Bulletin, Vol. 33 No. 1, pp. 694-705.
  8. Bjørnskov, C. (2010), “How does social trust lead to better governance? An attempt to separate electoral and bureaucratic mechanisms”, Public Choice, Vol. 144 No. 1, pp. 323-346.
  9. Buehn, A. and Schneider, F. (2012), “Corruption and the shadow economy: like oil and vinegar, like water and fire?”, International Tax and Public Finance, Vol. 19 No. 1, pp. 172-194.
  10. Cowles, M.K. and Carlin, B.P. (1996), “Markov chain Monte Carlo convergence diagnostics: a comparative review”, Journal of the American Statistical Assocation, Vol. 91 No. 434, pp. 883-904.
  11. Dell'Anno, R. (2007), “The shadow economy in Portugal: an analysis with the MIMIC approach”, Journal of Applied Economics, Vol. 10 No. 2, pp. 253-277.
  12. D'Hernoncourt, J. and Méon, P.G. (2012), “The not so dark side of trust: does trust increase the size of the shadow economy?”, Journal of Economic Behavior and Organization, Vol. 81 No. 1, pp. 97-121.
  13. Engel, C. (2011), “Dictator games: a meta study”, Experimental Economics, Vol. 14 No. 4, pp. 583-610.
  14. Enste, D.H. (2010), “Shadow economy – the impact of regulation in OECD-countries”, International Economic Journal, Vol. 24 No. 4, pp. 555-571.
  15. Fehr, E. and Schmidt, K. (1999), “A theory of fairness, competition, and cooperation”, The Quarterly Journal of Economics, Vol. 114 No. 3, pp. 817-868.
  16. Feld, L.P. and Frey, S.B. (2007), “Tax compliance as the result of a psychological tax contact: the role of in-centives and responsive regulation”, Law and Policy, Vol. 29 No. 1, pp. 102-120.
  17. Fugazza, M. and Fiess, N. (2010), “Trade liberalization and informality: new stylized facts”, Working Paper [No. 44], United Nations Conference on Trade and Development, United Nations, New York and Geneva, 1 March.
  18. Gelman, A. and Rubin, D.B. (1992), “Inference from iterative simulation using multiple sequences”, Statistical Science, Vol. 7 No. 4, pp. 457-472.
  19. Goel, R.K., Saunoris, J.W. and Schneider, F. (2019), “Growth in the shadows: effect of the shadow economy on US economic growth over more than a century”, Contemporary Economic Policy, Vol. 37 No. 1, pp. 50-67.
  20. Heywood, P.M. (2014), Routledge Handbook of Political Corruption, Routledge, Abingdon, Oxon.
  21. Houser, D. and McCabe, K. (2014), “Experimental economics and experimental game theory”, in Glimcher, P.W. and Fehr, E. (Eds), Neuroeconomics: Decision Making and the Brain, 2nd ed., Academic Press, pp. 19-34.
  22. Ishak, P.W. and Farzanegan, M.R. (2020), “The impact of declining oil rents on tax revenues: does the shadow economy matter?”, Energy Economics, Vol. 92, p. 104925.
  23. Kodila-Tedika, O. and Mutascu, M. (2014), “Shadow economy and tax revenue in Africa”, Economics Bulletin, Vol. 34 No. 1, pp. 469-479.
  24. Kruschke, J.K., Aguinis, H. and Joo, H. (2012), “The time has come: Bayesian methods for data analysis in the organizational sciences”, Organizational Research Methods, Vol. 15 No. 4, pp. 722-752.
  25. Lee, D. (2013), “How does social capital reduce the size of the shadow economy?”, Global Economic Review, Vol. 42 No. 3, pp. 251-268.
  26. Lemoine, N.P. (2019), “Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses”, Oikos, Vol. 128 No. 7, pp. 912-928.
  27. Leonardi, R., Nanetti, R.Y. and Putnam, R.D. (2001), Making Democracy Work: Civic Traditions in Modern Italy, Princeton University Press, New Jersey.
  28. Mauleón, I. and Sardà, J. (2017), “Unemployment and the shadow economy”, Applied Economics, Vol. 49 No. 37, pp. 3729-3740.
  29. Mazhar, U. and Méon, P.G. (2017), “Taxing the unobservable: the impact of the shadow economy on inflation and taxation”, World Development, Vol. 90, pp. 89-103.
  30. Medina, L. and Schneider, F.G. (2019), “Shedding light on the shadow economy: a global database and the interaction with the official one”, Working Paper [No. 7981], Center for Economic Studies and ifo Institute, Munich.
  31. Nguyen, D.V. and Duong, M.T.H. (2021), “Shadow economy, corruption and economic growth: an analysis of BRICS countries”, Journal of Asian Finance, Economics and Business, Vol. 8 No. 4, pp. 665-672.
  32. Nguyen, V.D. and Duong, T.H.M. (2022), “Corruption, shadow economy, FDI, and tax revenue in BRICS: a Bayesian approach”, Montenegrin Journal of Economics, Vol. 18 No. 2, pp. 55-64.
  33. Oanh, T.T.K., Diep, N.V., Truyen, P.T. and Chau, N.X.B. (2022), “The impact of public expenditure on economic growth of provinces and cities in the Southern Key Economic Zone of Vietnam: Bayesian approach”, in Ngoc Thach, N., Ha, D.T., Trung, N.D. and Kreinovich, V. (Eds), Prediction and Causality in Econometrics and Related Topics, Springer, Cham.
  34. Roberts, G.O. and Rosenthal, J.S. (2001), “Optimal scaling for various Metropolis-Hastings algorithms”, Statistical Science, Vol. 16 No. 4, pp. 351-367.
  35. Schneider, F. (2005), “Shadow economies around the world: what do we really know?”, European Journal of Political Economy, Vol. 21 No. 3, pp. 598-642.
  36. Schneider, F., Raczkowski, K. and Mróz, B. (2015), “Shadow economy and tax evasion in the EU”, Journal of Money Laundering Control, Vol. 18 No. 1, pp. 34-51.
  37. Smith, P. (1994), “Assessing the size of the underground economy: the Canadian statistical perspectives”, Working Paper [No. 28], Statistics Canada, Ottawa.
  38. Thach, N.N. (2021), “How values influence economic progress? Evidence from South and southeast asian countries”, in Ngoc Thach, N., Kreinovich, V. and Trung, N.D. (Eds), Data Science for Financial Econometrics, Springer, Cham, pp. 207-221.
  39. Thaler, R.H. (2000), “From Homo economicus to Homo sapiens”, Journal of Economic Perspective, Vol. 14 No. 1, pp. 133-141.
  40. Torgler, B. (2003), “Tax morale, rule-governed behaviour and trust”, Constitutional Political Economy, Vol. 14 No. 2, pp. 119-140.
  41. Torgler, B. and Schneider, F. (2009), “The impact of tax morale and institutional quality on the shadow economy”, Journal of Economic Psychology, Vol. 30 No. 2, pp. 228-245.
  42. Williams, C.C. and Horodnic, I.A. (2015), “Explaining and tackling the shadow economy in Estonia, Latvia and Lithuania: a tax morale approach”, Baltic Journal of Economics, Vol. 15 No. 2, pp. 81-98.

JEL classification: C11,H26,O17,Z13

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