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Investigating the Impact of Unemployment Rate on the Romanian Shadow Economy. A Complex Approach Based on ARDL and SVAR Analysis

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  • Adriana Anamaria Davidescu (Alexandru)

    (Department of Statistics and Econometrics, Academy of Economic Studies, Bucharest)

Abstract

The paper aims to investigate the potential impact of unemployment rates (both recorded and ILO) on the Romanian shadow economy (SE) for quarterly data covering the period 2000-2013, using ARDL cointegration method in conjunction with the structural VAR (SVAR) analysis in order to provide evidence for both the long and short-run dynamics between the variables. The size of the shadow economy as % of official GDP was estimated previously using a special case of the structural equation models - the MIMIC model, recording the value of 40% at the beginning of 2000 and following a downward trend over the analyzed period. The results of ARDL approach pointed out that there is no long-run relation between unemployment rates and the Romanian shadow economy. The relationship between the two variables is further tested by imposing a long-run restriction in the Structural VAR model to analyze the effect in the size of the Romanian shadow economy to a temporary shock in unemployment rates. The impulse response function generated by the Structural VAR confirms that in the short run, a rise in the recorded unemployment rate will lead to an increase in the size of the shadow economy, meanwhile an increase in the ILO unemployment rate highlighted a decrease in the size of the shadow economy.

Suggested Citation

  • Adriana Anamaria Davidescu (Alexandru), 2014. "Investigating the Impact of Unemployment Rate on the Romanian Shadow Economy. A Complex Approach Based on ARDL and SVAR Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 109-127, December.
    • Handle: RePEc:rjr:romjef:v::y:2014:i:4:p:109-127
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    2. Meri Frig & Martin Fougère & Veronica Liljander & Pia Polsa, 2018. "Business Infomediary Representations of Corporate Responsibility," Journal of Business Ethics, Springer, vol. 151(2), pages 337-351, August.
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    6. Ligita Gasparėnienė & Rita Remeikienė & Colin C. Williams, 2022. "Unemployment and the Informal Economy," SpringerBriefs in Economics, Springer, number 978-3-030-96687-4, October.
    7. Ligita Gasparėnienė & Rita Remeikienė & Colin C. Williams, 2022. "Theorizing the Informal Economy," SpringerBriefs in Economics, in: Unemployment and the Informal Economy, chapter 0, pages 7-60, Springer.
    8. Felicia C. Abada & Charles O. Manasseh & Ifeoma C. Nwakoby & Ngozi Franca Iroegbu & Johnson I. Okoh & Felix C. Alio & Adedoyin I. Lawal & Onyinye J. Asogwa, 2021. "Relationship Between Unemployment Rate and Shadow Economy in Nigeria: A Tado-Yamamoto Approach," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 271-283, May.
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    More about this item

    Keywords

    shadow economy; unemployment rates; cointegration; ARDL Bounds Testing; SVAR model; impulse response function;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • O17 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Formal and Informal Sectors; Shadow Economy; Institutional Arrangements

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