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An empirical examination of the determinants of the shadow economy

Author

Listed:
  • Eduardo Acosta-Gonz�lez
  • Fernando Fern�ndez-Rodr�guez
  • Sim�n Sosvilla-Rivero

Abstract

Using a statistical methodology guided only by data and based on a genetic algorithm, we select the best econometric model for explaining the determinants of the size of the shadow economy, its main determinants being: taxes on capital gains of individuals, corporate taxes on income, profits and capital gains, domestic credit, bank secrecy, ethnic fractionalization, urban population, globalization, corruption and the socialist legal origin of country.

Suggested Citation

  • Eduardo Acosta-Gonz�lez & Fernando Fern�ndez-Rodr�guez & Sim�n Sosvilla-Rivero, 2014. "An empirical examination of the determinants of the shadow economy," Applied Economics Letters, Taylor & Francis Journals, vol. 21(5), pages 304-307, March.
    • Handle: RePEc:taf:apeclt:v:21:y:2014:i:5:p:304-307
    DOI: 10.1080/13504851.2013.856993
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    Cited by:

    1. Aysel Amir & Korhan Gökmenoğlu, 2023. "Analyzing the Drivers of the Shadow Economy for the Case of the CESEE Region," Journal of Economics / Ekonomicky casopis, Institute of Economic Research, Slovak Academy of Sciences, vol. 71(2), pages 155-181, February.
    2. Thi Hong Hanh Pham, 2022. "Shadow Economy and Poverty: What Causes What?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 861-891, December.
    3. Gennady Vasilievich Osipov & Vladimir Ivanovich Glotov & Svetlana Gennadievna Karepova, 2018. "Population in the shadow market: petty corruption and unpaid taxes," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(2), pages 692-710, December.
    4. Rajeev K. Goel & James W. Saunoris, 2017. "The nexus of white collar crimes: shadow economy, corruption and uninsured motorists," Applied Economics, Taylor & Francis Journals, vol. 49(31), pages 3032-3044, July.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    7. Korhan K. Gokmenoglu & Aysel Amir, 2023. "Investigating the Determinants of the Shadow Economy: The Baltic Region," Eastern European Economics, Taylor & Francis Journals, vol. 61(2), pages 181-198, March.
    8. Thach Ngoc Nguyen & My Ha Tien Duong & Diep Van Nguyen, 2024. "Natural resources and the underground economy: A cross-country study in ASEAN using Bayesianapproach," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, vol. 27(2), pages 1-15, June.

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