Shadow Economies Around the World: Evidence from Metropolitan Areas

Abstract

In this paper, I use a calibrated two-sector dynamic general equilibrium model to construct annual estimates of shadow economy size (as a percentage of GDP) for 57 metropolitan areas from 31 countries throughout the world from 2001 to 2016. In addition to fully describing and characterizing the constructed dataset, I also provide some stylized facts regarding the trends of these estimates and some of their correlates. I also use the model to evaluate the effects of two policy tools: Changing the tax burden and tax enforcement.

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Notes

  1. 1.

    This model is mostly adapted from Roca et al. (2001), Ihrig and Moe (2004), and Busato and Chiarini (2004), Elgin and Oztunali (2012), Orsi et al. (2014) and more recently from Elgin et al. (2019).

  2. 2.

    The link to the metropolitan area database of OECD is https://stats.oecd.org/Index.aspx?DataSetCode=CITIES

  3. 3.

    Therefore, the assumption we make regarding the growth of \(\theta _{S_t}\) is not unrealistic. Moreover, in the calculation of the informal productivity, the reported results in the next section specifically use equal weight for formal productivity and capital; however, the results are qualitatively unaffected when this weight changes within a range of 20%. These additional sensitivity analyses are available upon request from the author.

  4. 4.

    Moreover, Tables 7 and 8 in the appendix report results of two sensitivity analyses conducted for robustness checks. In the former, we use different values of \(\gamma\) and in the latter we choose \(\theta _{S_{2001}}\) to match the informal sector size (as \(\%\) of GDP for 2001 reported in Elgin and Oztunali (2012), instead of informal employment from Elgin et al. (2019). Noticeably, regional averages reported in these two tables for a robustness check are highly similar to the ones reported in Table 1.

  5. 5.

    Surely, there are other significant correlates (determinants or indicators) of informality such as tax burden, monetary base, life expectancy, education (or human capital), and institutional quality indices; however, as these series are not available at the city-level, they could not be used in the correlation or regression analysis.

  6. 6.

    Among different parameters, \(\beta\) is calibrated directly from the Euler equation and \(\alpha\) and \(\delta\) are taken directly from the bPWT.

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Correspondence to Ceyhun Elgin.

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Appendix: Dataset and Robustness Checks

Appendix: Dataset and Robustness Checks

This appendix includes the whole dataset in Tables 7 and 8.

Table 7 Shadow economy size estimates (as % of GDP)
Table 8 Shadow economy size estimates (cont’d) (as % of GDP)

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Elgin, C. Shadow Economies Around the World: Evidence from Metropolitan Areas. Eastern Econ J 46, 301–322 (2020). https://doi.org/10.1057/s41302-019-00161-4

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Keywords

  • Informal sector
  • Metropolitan areas
  • Dynamic general equilibrium models
  • Panel data
  • Shadow economy
  • Two-sector dynamic general equilibrium models

JEL Classification

  • E26
  • J46
  • R12