Determinants of shadow economy in transition countries: economic and environmental aspects

Lyulyov, Oleksii; Paliienko, Maksym; Prasol, Lesia; Vasilyeva, Tetyana; Кубатко, Олександрa Вікторівна; Kubatko, Victoria

doi:10.1504/ijgei.2021.115142

Cited by 43 publications

(13 citation statements)

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“…Lyulyov, et al [9] analyzed the driving factors for the shadow economy development in transition economies. It was found that an increase in GDP per capita in the selected countries with transition economies by 10% reduces the shadow economy by 1.2%; an increase in foreign direct investment by 10% reduces the shadow economy by 0.5%; a 10% improvement in energy efficiency correlates with a 2% growth in the shadow economy.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Dynamic stability of the financial monitoring system: Intellectual analysis

et al. 2023

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Introduction Although there is a growing number of scientific publications on financial monitoring, combating money laundering, the shadow economy, and the impact of corruption on economic development, further research needs to determine the stability of the national financial system in dynamics. The dynamic stability of the national financial monitoring system subjects will allow to adequately assess the effectiveness of the existing national financial monitoring system in each country and determine the influential factors. Materials and methods The article investigates an approach to identifying the dynamic stability of the national financial monitoring system subjects based on the calculation of the integrated indicator of the country’s financial system propensity to ALM, vector autoregression (VAR) model taking into account time lag. The proposed integrated indicator allowed to adequately assess the existing financial monitoring systems of the countries (15 countries of the European Union for 2000–2020: Austria, Belgium, Cyprus, Estonia, Finland, France, Greece, Ireland, Italy, Latvia, Malta, Netherlands, Portugal, Slovak Republic, Spain). In addition, vector autoregression models (VAR) of the dependence of the country’s financial system propensity to ALM on the regressors Government Integrity, Index of economic freedom, Monetary Sector credit to the private sector (% GDP), were built, taking into account time lags in general and for each studied country. Results According to the modeling results, the national financial monitoring systems in Austria, Belgium, Estonia, Finland, France, Ireland, Netherlands, Slovak Republic, Spain were resistant to money laundering. It is vice versa in Malta, Greece, Cyprus, Portugal, Italy, Latvia. These conclusions are also confirmed based on a binary approach. Such exogenous variables as Government Integrity (with a lag of 2 years) and the Index of economic freedom (taking into account the time delays of the regression reflection under the influence of this regressor for 1 and 2 years) have a statistically significant effect on the country’s financial system. Conclusion The general vector autoregression (VAR) model shows that the current value of the country’s financial system propensity to ALM by 92.78% is determined by its previous value. With an increase of Government Integrity by 1%, the country’s financial system’s propensity to ALM will decrease by 0.000616 units with a lag of two years. The nature of the impact made by the Index of economic freedom on the performance feature was specific—when this indicator increases by 1% for a lag delay in one year, the PFSALM value will decrease by 0.001997 units, and for a lag delay of two years it will change the trend and increase by 0.003076 units per unit, respectively.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Dynamic stability of the financial monitoring system: Intellectual analysis

et al. 2023

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“…In regard to transition economies, Lyulyov et al. (2021) found that an increase in GDP per capita in a group of transition economies by 10% decreases the shadow economy levels by 1.2%; increase in foreign direct investment by 10% shrinks the shadow economy sector by 0.5%; improvements in energy efficiency by 10% are correlated with 2% shadow economy growth. They also found that an increase in tax level by 10% increases the shadow economy by 1%.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust determinants of the shadow economy

Zhanabekov

2022

Bulletin of Econ Res

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This study examines the determinants of the shadow economy by employing Bayesian Model Averaging technique, which allows taking into account model uncertainty. Having estimated millions of combinations of models, the study revealed that while higher GDP growth rate, trade openness, and better institutional quality lead to the reduction of the informal sector, higher rate of unemployment, complicated regulations, and the large size of government are associated with a greater size of the shadow economy. Lessening bureaucratic complexity by eliminating burdensome regulations could help to reduce both wasteful spending of government on operating these regulations and the size of the underground economy.

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“…Nowadays, scientists have developed many studies, in which one of the most widely accepted approaches to increasing the country's investment attractiveness and its investment potential is to form and implement a policy of countering shadow operations (Buszko, 2022;Darchia, 2022;Fedajev et al, 2022;Hamzah et al, 2021;Kuzmenko et al, 2020Kuzmenko et al, , 2021Lyeonov et al, 2020;Lyulyov et al, 2021;Mirdamad, 2020;Pakhnenko et al, 2022;Simelyte & Tvaronaviciene, 2022;Roszko-Wójtowicz & Grzelak, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Foreign direct investment and shadow economy: One-way effect or multiple-way causality?

Tiutiunyk¹,

Cieśliński²,

Zolkover³

et al. 2022

Journal of International Studies

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The article examines the relationship between the size of the shadow economy and indicators of the investment market development. Net inflow of foreign direct investments, volume of net investments in non-financial assets, volumes of portfolio investments, and net outflow of foreign direct investment were used as parameters characterizing the development of the investment market. The dependence between the indicators was analyzed using the regression equation, Shapiro-Wilk test. Research results demonstrate that the increase in the inflow and outflow of foreign direct investments leads to an increase in the size of the shadow economy without a time lag in Ukraine, Poland, Slovenia, Romania, Croatia, Lithuania, Latvia, Estonia, and with a time lag of 1 year in Slovakia and Hungary. The largest impact on the size of the shadow economy is made by the volume of inflow and outflow of direct foreign investments, while the volume of portfolio investments has a less significant effect. Consequently, it was concluded that the processes of inflow and outflow of direct foreign investments require enhanced control by specialized state executive bodies given the scale of their potential destabilizing impact on the macroeconomic stability of the country.

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Determinants of shadow economy in transition countries: economic and environmental aspects

Cited by 43 publications

References 0 publications

Dynamic stability of the financial monitoring system: Intellectual analysis

Dynamic stability of the financial monitoring system: Intellectual analysis

Robust determinants of the shadow economy

Foreign direct investment and shadow economy: One-way effect or multiple-way causality?

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