Tax burden and shadow economy growth in Russian regions

D Yu Fedotov

doi:10.15826/jtr.2021.7.3.104

Abstract

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.

Highlights

The shadow economy is, by nature, difficult to measure, as agents engaged in shadow economy activities try to remain undetected
Multiple Indicators Multiple Causes (MIMIC) estimations of the size of the shadow economy depend to a large extent on the starting values and if they are taken from other macro estimates, we have the same problem
A promising approach here is the structured hybrid approach by Dybka et al (2017), who contribute to the Currency Demand Approach (CDA) and MIMIC method in a new way avoiding a number of statistical/econometric problems

Summary

Introduction

The shadow economy is, by nature, difficult to measure, as agents engaged in shadow economy activities try to remain undetected. The request for information about the extent of the shadow economy and its developments over time is motivated by its political and economic relevance. The shadow economy is known by different names, such as the hidden economy, gray economy, black economy or lack economy, cash economy or informal economy. All these synonyms refer to some type of shadow economy activities. We use the following definition: The shadow economy includes all economic activities which are hidden from official authorities for monetary, regulatory, and institutional reasons. The shadow economy reflects mostly legal economic and productive activities that, if recorded, would contribute to national GDP, the definition of the shadow economy in our study tries to avoid illegal or criminal activities, do-it-yourself, or other household activities.

Objectives

Methods

Results

Conclusion