scholarly journals Tax burden and shadow economy growth in Russian regions

2021 ◽  
Vol 7 (3) ◽  
pp. 284-301
Author(s):  
D. Yu. Fedotov ◽  
Keyword(s):  
Shadow Economy ◽  
Inverse Correlation ◽  
Informal Employment ◽  
Tax Burden ◽  
Russian Regions ◽  
Informal Workers ◽  
Industrial Sectors ◽  
Per Capita ◽  
The Relationship ◽  
Social Sectors

The study is devoted to verification the hypothesis that a higher tax burden does not necessarily lead to the growth in the shadow economy in Russian regions. The cross-regional comparative analysis was undertaken to measure the influence of the tax burden on the shadow economy. The analysis used Rosstat workforce surveys data about the number of informal workers nationwide and by sector from 2007 to 2019. Stochastic factor analysis was used to examine the relationship between the share of informal workers and such factors as the tax burden, GRP per capita, advanced production technologies, innovation activities of organizations, industrial sectors’ and social sectors’ contribution to GRP. To determine the strength of the relationship between the factors and the resultant indicator, a correlation and cluster analysis were conducted. It has shown that there is an inverse correlation between the tax burden and informal employment. Regions with a lower tax burden tend to have higher rates of informal employment (in 2019, the correlation coefficient was –0.4274). A similar inverse correlation is observed for the level of informal employment and the macro-economic indicators – GRP per capita, innovation, and the contribution of industrial sectors to GRP. There is a direct correlation between informal employment and the contribution of social sectors to GRP. These findings shed light on the key factors conducive to the growth in the shadow economy: what matters most is the economic and innovation lag in the development of certain regions. The results of this research can be useful for policy-makers seeking to address the problem of the shadow economy in regions.

scholarly journals Classification of Russian regions taking into account the structure of informal employment and the level of socio-economic development

2020 ◽  
Vol 17 (6) ◽  
pp. 31-43
Author(s):  
E. I. Dubravskaya
Keyword(s):  
Economic Development ◽  
Labor Market ◽  
Informal Employment ◽  
Federal State ◽  
Russian Regions ◽  
Homogeneous Groups ◽  
Managerial Decisions ◽  
Socio Economic Development ◽  
The Relationship

Purpose of the study. The possibilities for the implementation of national and regional strategic objectives depend on the compliance of the measures taken with the chosen development path. The set of measures includes managerial decisions in the field of labor market regulation and concerning the legalization of informal employment. To make managerial decisions on the regulation of the labor market at the regional level, an objective statistical assessment of the relationship between informal employment and indicators of socio-economic development is required.The information basis for the regulation of informal employment should be quantitatively assessed stable regularities of the relationship between the parameters of informal employment and the structural and dynamic characteristics of economic growth and development. To identify and evaluate these statistical patterns, based on data from the Federal State Statistics Service in a regional context, a system of statistical indicators has been developed and significant factors of informal employment and socio-economic development have been identified. Given the heterogeneity of the constituent entities of the Russian Federation in terms of the scale and structure of informal employment, the distribution of regions into homogeneous groups is required. The article describes the approaches to the classification of Russian regions, taking into account the level of socio-economic development and the structure of informal employment.Materials and methods. The article examines an approach to the distribution of regions into homogeneous groups using the methods of cluster analysis based on a group of indicators characterizing those employed in the informal sector, which is based on the assumption that the indicators of informal employment are causally related to indicators of socio-economic development.Results. Five groups of regions are obtained, homogeneous in terms of the structural characteristics of informal employment and generalized factors of socio-economic development. For the purposes of further interpretation, the selected groups are assessed and ranked relative to the average Russian level of socio-economic development: low level (8 regions), below average (26 regions), average (41 regions), above average (8 regions), high level (2 regions) ...Conclusion. The resulting classification of Russian regions is a transitional stage to the construction of an econometric model of the relationship between informal employment and indicators of socio-economic development. Further analysis will allow us to assess which indicators have the greatest multiplier effect on the regional economy and to obtain a quantitative assessment of this impact on its growth.

Informal Workers in the Russian Economy: Who Are They and How Many?

2011 ◽  
pp. 53-76 ◽  
Author(s):  
V. Gimpelson ◽  
A. Zudina
Keyword(s):  
Cross Section ◽  
Descriptive Analysis ◽  
Informal Employment ◽  
Federal State ◽  
Russian Regions ◽  
Informal Workers ◽  
Russian Economy ◽  
Marginal Effects ◽  
Informal Groups ◽  
Over Time

The paper discusses the evolution of the informal employment in the Russian labor market over the last decade. It uses all consecutive waves of the Labor Force Survey conducted by the Russian Federal State Statistics Service in 1999-2009. Looking inside the informality and tracing its evolution over time, we pay special attention to heterogeneity of the informal employment and distinguish salaried informal workers and informal self-employed as two major informal groups. Simple descriptive analysis is complemented by estimating marginal effects from multinomial logit regressions. Additionally, we use cross-section estimates of informality for all Russian regions for 1999-2009 and build a new hand-made panel database with regions as observations.

Assessing the impact of banking intermediationon the economy of Russian regions: The post-crisis trends

2017 ◽  
pp. 103-122 ◽  
Author(s):  
K. Krinichansky ◽  
A. Fatkin
Keyword(s):  
Positive Relationship ◽  
Composite Indicator ◽  
Regional Level ◽  
Russian Regions ◽  
Banking Services ◽  
Significant Positive Relationship ◽  
Per Capita ◽  
Relationship Of ◽  
The Impact ◽  
The Relationship

The paper explores the changes in relative levels of banking services availability in Russia’s regions in 2007-2015 and the relationship between the regional financial development characteristics and the indicators of GRP and investments on the regional level in 2002-2014. For 2013-2015, the paper demonstrates the divergence of regions by the composite indicator - the composite banking services density index by region and its separate components. A significant positive relationship of indicators characterizing the regions’ financial mediator services density with the indicators of GRP has been revealed. This relationship is non-linear which is demonstrated in different sensitivity of GRP per capita by groups of regions to the indicators of financial mediation, so that regions with relatively lower or, on the contrary, higher GRP per capita show weaker or no relationship between finance and growth, whereas regions of “central” groups with medium values of GRP demonstrate stronger connection.

scholarly journals Determinants of Shadow Economy in Eastern European Countries

2019 ◽  
Vol 66 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Mykolas Navickas ◽  
Vytautas Juščius ◽  
Valentinas Navickas
Keyword(s):  
Regression Models ◽  
Shadow Economy ◽  
Scientific Literature ◽  
European Countries ◽  
Eastern European ◽  
Per Capita ◽  
Eastern European Countries ◽  
The Relationship ◽  
The Eu ◽  
Business Freedom

Abstract In this article the relationship between shadow economy and its’ determinants has been examined. Ten Eastern countries from European Union were chosen due to specific particularities, which may cause higher shadow economy levels in the investigated countries compared with the EU average. Time span of 2003-2016 was selected, as 2017 data has yet to be released at the time of the analysis. Article consists of examination of the current situation and shadow economy trends in Eastern European countries; overview of shadow economy scientific literature followed by hypothesis, which are examined by constructing regression models. Models aim to distinguish the relationship between selected determinants and shadow economy size. Scientific literature analysis revealed that increase of tax burden on labor is seen as a primary reason for the increase of shadow economy, however, such relation has not been identified. Furthermore, results show that unemployment and self-employed people ratio affect shadow economy insignificantly. This suggests that further analysis is needed. Nonetheless, regression model has not rejected the hypotheses of corruption level, income inequality, business freedom and GDP per capita effect on shadow economy. Thus, it can be stated that these variables are determinants of shadow economy in Eastern European countries.

scholarly journals Is the Tax Burden a Generating Factor of Fiscal Evasion in South-East Europe?

2020 ◽  
Author(s):  
Loredana Andreea Cristea ◽  
Alina Daniela Vodă ◽  
Bianca Ciocanea ◽  
Mihaela Luca
Keyword(s):  
Tax Evasion ◽  
Shadow Economy ◽  
Underground Economy ◽  
Tax Burden ◽  
The European Union ◽  
East Europe ◽  
South East Europe ◽  
South Eastern Europe ◽  
Fiscal Pressure ◽  
The Relationship

Tax evasion is a pernicious phenomenon, very widespread in the world, which is closely linked to the system of taxes and fees. This is considered to be a response to the excessive fiscal pressure exerted on taxpayers. Bypassing the law is also closely related to the phenomenon of corruption and its removal is a difficult target, under the existing conditions. The main purpose of this paper is to study the influence of corruption and fiscal pressure on the phenomenon of tax evasion, materialized by the shadow economy indicator. The analysis is carried out over a period of 18 years, namely 1999-2016, for six countries of South-Eastern Europe, member states of the European Union, divided into two categories, namely developed and emerging states. The research methodology requires a comparative analysis of existing situations in the research countries. Also, we will use the econometric analysis, with the help of statistical package for social sciences, of the relationship between the underground economy and corruption, as well as the correlation between tax burden exerted by the budget revenues and tax evasion. Being difficult to quantify, the level of shadow economy has been taken from Friedrich Schneider’s studies, through which he measured underground economy in 157 countries over the period 1999-2013; but also in 36 states, between 2003 and 2016. The level of corruption is measured using the corruption perception index at the international level and the tax burden is calculated as the ratio between tax revenue and gross domestic product. Keywords: tax burden, shadow economy, corruption, South-East Europe.

ASSESSMENT OF THE LEVEL OF LOCALIZATION OF INDUSTRIAL PRODUCTION OF THE CENTRAL FEDERAL DISTRICT

2021 ◽  
pp. 117-132
Author(s):  
Anna V. Eshchenko ◽  
◽  
Darina A. Kosyakova ◽  
Keyword(s):  
Industrial Production ◽  
Federal District ◽  
Favorable Effect ◽  
Building Industry ◽  
Industry Clusters ◽  
Central Federal District ◽  
Industrial Sectors ◽  
Perfect Form ◽  
Per Capita ◽  
The Relationship

This article is devoted to assessing the level of specialization by means of localization and per capita production of industrial products by industry in the Central Federal District. The article considers the concept of "localization" and concludes that the increase in the number of localized production facilities in the country, has a favorable effect on the functioning of the economy. The approaches and methods currently used to determine the localization of industrial production are described. The article presents an example of calculating the coefficients of specialization of industrial sectors on the example of the subjects of the Central Federal District with the interpretation of the obtained indicators. On the basis of the calculated coefficients of specialization we assess the possibility of building industry clusters on the territory of the regions of the CFD. The relationship between the level of specialization and clusters is described. A comparison is presented and the relationship between clusters and ecosystems as the most perfect form of clusters is shown.

scholarly journals The Relationship of Energy and CO2 Emissions with GDP per Capita in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 778
Author(s):  
Lourdes Isabel Patiño ◽  
Emilio Padilla ◽  
Vicent Alcántara ◽  
Josep Lluís Raymond
Keyword(s):  
Co2 Emissions ◽  
Primary Energy ◽  
Seemingly Unrelated Regression ◽  
Gdp Per Capita ◽  
Regression Equations ◽  
Environmental Pressures ◽  
Industrial Sectors ◽  
Per Capita ◽  
Relationship Of ◽  
The Relationship

We analyze the relationship of CO2 emissions per capita and primary energy per capita with gross domestic product (GDP) per capita and other relevant variables in Colombia for the period 1971–2017. Two partial adjustment models are estimated through the seemingly unrelated regression equations method. There is a decrease in these environmental pressures during some years of the period. However, the results reject the environmental Kuznets curve hypothesis and indicate that economic growth is still linked to an increase in these environmental pressures in Colombia. Moreover, a linear relationship between both indicators and GDP per capita is not rejected. Several factors explain the changes in energy consumption and emissions over time, the policies applied being crucial. Some determinants that helped to control these environmental pressures are the change in primary energy source composition, which entailed primary energy savings and a reduction in CO2 emissions, as well as the regulations aimed at controlling CO2 emissions from the transport and industrial sectors.

scholarly journals The impact of the institutional environment on the shadow economy

2018 ◽  
Vol 68 (1) ◽  
pp. 115-133 ◽  
Author(s):  
Tural Makhmudov ◽  
Maria Konovalova ◽  
Olga Kuzmina ◽  
Natalia Persteneva
Keyword(s):  
Shadow Economy ◽  
Institutional Environment ◽  
Tax Burden ◽  
Economic Policies ◽  
Development Levels ◽  
The One ◽  
Relationship Of ◽  
The Impact ◽  
The Relationship

This article aims to explore the relationship of the shadow economy with the institutional environment and develop practical recommendations for government policies around the world, and particularly in Russia. The urgency of the issue under research is caused by the existing need to study the shadow economy in order to find ways to reduce its scale and level out its negative externalities. Despite the fact that most of the papers focus on tax burden as a fundamental determinant of the shadow economy, the authors of this article believe that institutional tools can expand the boundaries of research on the content of the shadow economy as an economic category. Statistical analysis of 105 countries with different development levels revealed a stronger correlation between the quality of institutions and the size of the shadow economy than the one between total tax burden and the size of the shadow economy. The findings of this article can be useful in developing state strategies for combating the shadow economy and carrying out economic policies of the state as a whole.

Atlas of economic complexity, Russian regional pages

2018 ◽  
pp. 71-91 ◽  
Author(s):  
I. L. Lyubimov ◽  
M. V. Lysyuk ◽  
M. A. Gvozdeva
Keyword(s):  
Value Added ◽  
Growth Strategy ◽  
Gdp Per Capita ◽  
Export Diversification ◽  
Russian Regions ◽  
Economic Complexity ◽  
Diversification Strategies ◽  
Central Regions ◽  
Per Capita ◽  
Regions Of Russia

Well-established results indicate that export diversification might be a better growth strategy for an emerging economy as long as its GDP per capita level is smaller than an empirically defined threshold. As average incomes in Russian regions are likely to be far below the threshold, it might be important to estimate their diversification potential. The paper discusses the Atlas of economic complexity for Russian regions created to visualize regional export baskets, to estimate their complexity and evaluate regional export potential. The paper’s results are consistent with previous findings: the complexity of export is substantially higher and diversification potential is larger in western and central regions of Russia. Their export potential might become larger if western and central regions, first, try to join global value added chains and second, cooperate and develop joint diversification strategies. Northern and eastern regions are by contrast much less complex and their diversification potential is small.

Retrospective analysis of the resilience of Russian regions as socio-economic systems

2019 ◽  
pp. 46-64 ◽  
Author(s):  
Vladimir V. Klimanov ◽  
Sofiya М. Kazakova ◽  
Anna A. Mikhaylova
Keyword(s):  
Panel Data ◽  
Economic Systems ◽  
Financial Indicators ◽  
Russian Regions ◽  
Integral Index ◽  
Negative Effects ◽  
Economic Resilience ◽  
Correlation Dependence ◽  
The Impact ◽  
The Relationship

The article examines the impact of various socio-economic and financial indicators on the resilience of Russian regions. For each region, the integral index of resilience is calculated, and its correlation dependence with the selected indicators is revealed. The study confirms the relationship between fiscal resilience and socio-economic resilience of the regions. The analysis of panel data for 75 regions from 2007 to 2016 shows that there are significant differences in the dynamics of indicators in different periods. In particular, the degree of exposure to the negative effects of the crises of 2008—2009 and 2014—2015 in non-resilient regions is higher than in resilient ones.

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