Panel Regression Analysis of The Relationship Between Unemployment and Shadow Economy



Previous studies considerably discuss the rising issue of shadow economy. However, little attention has been given to the relationship between unemployment and shadow economy activity. This is why the objective of this paper is to explore the relationship, if any, between these economic terms. For this purpose, the annual panel data on 34 countries over the period 1999-2015 are collected. In order to conduct analysis, different panel data econometrics techniques are applied including: linear static panel data estimators (fixed and random effects), dynamic panel data estimators (Arellano-Bond two step) as well as ARDL approach. In order to test for the stability of the model, the impact of economic growth is controlled. The results of the linear static panel data estimators indicate a significant positive relationship between unemployment and shadow economy activity. Since both of the macroeconomic variables are expected to be highly volatile, the dynamics is taken into account. Dynamic panel data estimators support the results obtained using linear static panel data estimators. The inclusion of control variable in extended model does not significantly change the results reported in the initial model, so the initial model can be considered stable. In order to test the sensitivity of the results and avoid robust errors, we employ a panel ARDL model. The study reveals a positive and significant relationship between SE and unemployment in both the short- and the long-run. Stronger impact is reported for the long-run. In terms of the extended model, a significant positive impact of unemployment on shadow economy is reported only in the long-run. The obtained results can be of great importance for decision makers in order to foster them to reduce unemployment and consequently shadow economy activity.

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