Assessment of the impact of socio-economic indicators of the Volga Federal District of the Russian Federation on the number of payers of the Professional income tax

Kustov Nikita Postgraduate student; Department of Accounting, Audit and Economic Analysis; Perm State National Research University 14 Kuibyshev str., Perm, 614000, Russia parolaks@yandex.ru

Gorodilov Mikhail Anatol'evich Doctor of Economics Associate Professor; Department of Accounting, Audit and Economic Analysis; Perm State National Research University 614000, Russia, Kuibyshev 14 region, Perm, Kuibyshev str., 14 gorodilov59@yandex.ru

introduction

The special tax regime "Tax on professional income" (hereinafter referred to as the NPD) is established by the Federal Law "On conducting an experiment to establish a special tax regime "Tax on professional Income" dated November 27, 2018 No. 422—FZ^[1], and today has become familiar to most citizens of the Russian Federation who have decided to become self-employed. Introduced in 2019, the special tax regime, which was used until recently only as a tax experiment, set its main goal to provide self-employed citizens with favorable conditions for carrying out their activities by offering these persons an optimal tax burden. This goal was outlined in the explanatory note to the draft law "On conducting an experiment to establish a special tax regime "Tax on professional income" in the federal city of Moscow, in the Moscow and Kaluga regions, as well as in the Republic of Tatarstan (Tatarstan)"^[2]. The steady positive dynamics of the number of self-employed and tax revenues from the budget shows that this goal has been successfully implemented.

The number of NAP payers in the subjects of the Volga Federal District according to the register of small and medium-sized businesses as of September 30, 2023 was 1,404,956 people^[3], and the total population of this federal district as of January 01, 2023 was 28,683,247 people.^[4], therefore, approximately every 20th citizen of the Russian Federation living in the Volga Federal District had the status of a NAP payer. And this ratio is constantly growing. The question of whether the socio-economic indicators of the subjects of the Volga Federal District have an impact on the number of NAP payers becomes relevant. The practical purpose of this study is to identify and evaluate the indicators that determine the growth of income from the specified tax.

THEORETICAL FOUNDATIONS OF THE STUDY

The global trend in the development of self-employment is becoming the foundation for various studies and assessments of the impact of various methods of taxation of self-employment on the economy of various regions and countries. For example, in the work of Getz S., Fleming D. and Rupasinga A., the positive impact of the development of self-employment on the growth of per capita income and poverty reduction is studied ^[1], and in the work of Juan Herreno and Sergio Ocampo, on the contrary, the negative impact of self-employment on the development of lending is assessed, due to the high risks of reducing the income of self-employed persons ^[2]. There are also many studies on the topic of taxation of the self-employed, among which one can note Ogembo D., who studied the specifics of taxation of self-employed specialists in Africa ^[3], Luo B., Chong T., touching on the study of the specifics of regional taxation of the self-employed in China ^[4], Batrancha L.M., Nikita A., De Agostini R. et al., who conducted assessment of compliance with tax legislation by self-employed persons in eleven countries ^[5], and Ken E., who devoted his work to the study of changes in tax rates on the overall activity of self-employment development ^[6].

Also, in foreign practice, much attention is focused on studies of the consequences of the development of dependent self-employment, this area was studied by Bozzon R. And Murgia A., who came to the conclusion that the development of dependent self-employment leads to a transformation of the labor market ^[7], Williams, K.S., Gorodnich, A.V., who concluded that more than 4% of the labor force in Europe are dependent self-employed ^[8], as well as Slavina Spasova, Denis Bouget, Dalila Gailani, and Bart Vanherke, who studied the social security of dependent self-employed people in European countries ^[9].

The evolution of self-employment in the Russian Federation from its inception to the introduction and expansion of the NAP to all subjects of the Russian Federation is described in the author's previous work ^[10]. Within the framework of the current study, the assessment of the impact of the development of the NPD, expressed in an increase in the number of payers of this tax regime, depending on the dynamics of regional socio-economic indicators, should be considered as a tax policy tool that affects the economy of a region, district or the whole country.

In domestic practice, the analysis of the mutual influence of the tax policy of the Russian Federation and the economy of the regions is a promising area for research, the mechanisms of tax policy can have a significant impact on the socio-economic development of the subjects of the Russian Federation, and vice versa. Thus, in the work of N.S. Trusova, a methodological toolkit of tax policy aimed at stimulating the economic development of regions was proposed ^[11]. Current trends in tax policy in the Russian Federation with respect to individuals are aimed at reducing the shadow economy sector, developing small businesses and digitalizing the tax industry, which is confirmed by the active introduction of special tax regimes ^[12].

The assessment of special tax regimes and the consequences of their implementation on the territory of the Russian Federation has always been an urgent topic and the basis for various studies. The results of these studies are used to adjust tax policy and are taken into account when introducing new tax regimes. Among the authors involved in the study of special tax regimes, one can note A.A. Bochkova and A.V. Yushko, who conducted a legal assessment of special tax regimes ^[13], A.G. Kazmina and I.V. Lolenko, who assessed the fiscal function of tax regimes and concluded that special tax regimes do not have high fiscal significance, but their The main goal is to stimulate the development of entrepreneurial activity ^[14], Khizhak N.P., who was engaged in forecasting budget tax revenues from special tax regimes ^[15].

The topic of NAP and self–employment is also a popular area for research, a number of the following studies can be noted on this issue: Pavlova E.V. – she assessed the impact of NAP on the development of small businesses in the Tver region ^[16]; Chekmarev O.P. - he investigated the place of the NAP payer in the labor market ^[17]; Zhukova T.V. – She analyzed the international experience of pension provision for the self-employed with a view to possible application in the Russian Federation ^[18]. The authors of this article also conducted a number of scientific studies on this topic: a study of approaches and methods for assessing the budgetary effectiveness of the introduction of NAPS in the subjects of the Russian Federation ^[19], highlighting the features of taxation of the self-employed in the near and far abroad ^[20], an analysis of the positive effects and risks of the introduction of NAPS in the subjects of the Russian Federation ^[21], etc.

In the context of the study of the impact of the NAP on regional socio-economic indicators, one of the authors conducted a study, and in the article^[29] hypotheses were put forward reflecting the relationship of changes in a number of regional socio-economic indicators depending on the dynamics of the number of NAP payers.

Other authors who have previously studied this area include Abramova E.A., in whose work a theorized analysis of the positive impact of the NAP on reducing unemployment during the crisis period was carried out ^[22], and Zvereva T.V., who studied the role of digitalization in the taxation of NAP payers ^[23]. Doroshenko S.V. also studied the emerging effects of the NAP, assessing with the help of econometric methods the impact of the introduction of the NAP on the share of income from entrepreneurial activity in the income structure of the population in the subjects of the Russian Federation ^[24]. However, a quantitative assessment of the mutual influence of socio-economic indicators and the number of NAP payers in a particular region of the Russian Federation is being carried out for the first time.

The hypothesis of the study: the change in socio-economic indicators of the subjects of the Volga Federal District has an impact on the change in the number of NAP payers.

THE RESULTS OF THE STUDY

To assess the impact of regional socio-economic indicators on the change in the number of NAP payers in the subjects of the Volga Federal District, we will determine the dependent and explanatory variables. We have taken the indicator of the number of NAP payers as a dependent variable.

Explanatory variables consist of regional socio-economic indicators, which are presumably influenced by changes in the number of NAP payers. This choice is due to the selection of explicit indicators from groups for assessing tax efficiency (budgetary, social and economic).

Regional socio-economic indicators of the budget efficiency group include: professional income tax – budget execution by income (billion rubles), the number of individual entrepreneurs – small and medium-sized businesses (people); the second group of social indicators includes: gross regional product by subjects of the Volga Federal District (in current prices; million rubles), the number of unemployed aged 15 years and older (people); the third group of economic indicators includes: the average monthly nominal accrued wages of employees for a full range of organizations in the economy as a whole by subjects of the Volga Federal District (R.).

The choice of regional socio-economic indicators that influence the change in the number of NAP payers is due to the choice of the most obvious indicators from the groups for assessing the effectiveness of the introduction of tax benefits (budgetary, social and economic). Previously, the author conducted a study on the assessment of the special tax regime "Professional income tax" as a preferential tax regime ^[25].

To assess the impact of socio-economic indicators of the subjects of the Volga Federal District on the number of NAP payers, we use a sample of panel data consisting of observed statistical data for the period of the special tax regime "Tax on professional income" from 2020 to 2022. The main advantages of using panel data are: a large number of observations, the ability to track dynamics for many objects and the ability to eliminate endogeneity ^[26]. The collected data contain the regional socio-economic indicators of the subjects of the Volga Federal District necessary for the study.

In Table 1, we present a description of the variables, these data, and their indicators, as well as the sources of information.

Table 1

Initial data

All data were collected for the period 2020-2022 for 14 subjects of the Volga Federal District. This period is explained by the fact that the experiment on the introduction of a special tax regime "Tax on professional income" extended its effect to the entire territory of the Russian Federation starting from the second half of 2020. Thus, we have panel data containing information on 252 observations. It is also important to note that all data is collected for all time periods and has no gaps, which will allow us to conclude that the panel is balanced.

Next, for each variable, we calculate descriptive statistics (mean, median, mode) and variances (standard deviation, span). This provides an overview of the value distribution for each variable and allows you to identify obvious patterns or outliers for the variables under study. The results are presented in Table 2.

Table 2

Descriptive statistics

Based on the results of descriptive statistics, several conclusions can be drawn. According to the self-employed indicator, the average number of NAP payers in the subjects of the Volga Federal District is 48,100.00 people, the median is 31,546.00 people, according to the individual entrepreneurs indicator, the average number of sole proprietors in the subjects of the Volga Federal District is 43,917.00 people, the median is 35,113.00, which tells us about the superiority of the number of NAP payers over the number of sole proprietors in some subjects of the Volga Federal District. The median number of unemployed in the subjects of the Volga Federal District is 38,493.00 people. The average GRP (GRP) and the average monthly nominally accrued salary (nominal salary) amount to 1,118,100.25 million rubles and 39,848.00 rubles, respectively. The median income of the regional budgets of the Volga Federal District from the NPD (professional income tax) is 0.055 billion rubles.

To study the multiple relationships between the variables under study and determine the strength of this relationship, we will use the method of correlation analysis.

Correlation analysis is a method that allows us to determine the closeness of the relationship between the effective and factorial features, in our case, between the dependent and the group of explanatory variables. The Pearson correlation coefficient takes values from -1 to +1. The closer to 1, the stronger the linear relationship between the variables. The Student's criterion is used to assess the significance of the correlation coefficient. To do this, it is necessary to compare the correlation coefficient with a critical value, the value of which depends on the sample size and the level of significance. If the actual value of the correlation coefficient is greater than the critical value, it can be concluded that the correlation coefficient is reliable (or significant).

The correlation matrix illustrates the relationship between variables. The correlation matrix also has the following properties: symmetry (its elements on the main diagonal are equal to 1, and the remaining elements are symmetrical relative to the main diagonal), correlation values (ranging from -1 to +1), independence of variables (if the correlation value between a pair of matrix elements is 0, then they are independent of each other).

Let's build a correlation matrix based on the available group of indicators using the Gretl software product.

Table 3

The correlation matrix

5% critical value (double-sided) = 0.3044

Let's present the indicators and their values indicated in Table 3 in the form of a heat map (see Fig. 1).

Fig. 1. Heat map (compiled by the author according to Table 3)

According to Table 3 and Fig. 1, the following conclusion can be drawn: since the critical value for checking the significance of the correlation coefficient is 0.3044, therefore, the values of the correlation matrix that are above the critical value are reliable. The correlation values between the nominalsalary and unemployed indicators and the professionalincometax and unemployed indicators are not reliable. Further analysis of the correlation matrix shows that the greatest relationship between self-employed and professional income tax is 0.9611, as well as between GRP and individual entrepreneurs - 0.9534. The least reliable relationship can be traced between the indicators of unemployed and self-employed - 0.3322. At this stage of the study, it can already be concluded that there is a close linear relationship between the estimated socio-economic indicators and the change in the number of NAP payers.

Multiple regression models

Next, let's move on to building a model of multiple linear regression on panel data. The model has the form:

selfemployed – dependent variable;

; ; ; ; – explanatory variables;

 – unknown model parameters;

i – the PFD region;

t – year;

 – an accidental mistake.

Let's build a model of multiple linear regression using the least squares method (combined regression model or pooled model).

Model 1: Pooled

Dependent variable: selfemployed

The coefficient is significant "..." * at the level of 5%; ** at the level of 1%, *** at the level of 0.1%.

According to this model, a number of the following conclusions can be drawn:

1)      According to the individual entrepreneurs indicator, regions with a large number of individual entrepreneurs (for each unit) have a large number of NAP payers (by 1.25 units), this is also due to the fact that sole proprietors can combine a special tax regime "Professional income tax" with other tax regimes;

2)      According to the unemployed indicator, regions with a large number of unemployed people have a smaller number of NAP payers (each additional unemployed person reduces the number of NAP payers by 0.64 units).;

3)      According to the GRP indicator, (GRP) – it does not significantly affect the change in the number of NAP payers and, in general, is not significant;

4)      According to the nominal salary indicator, regions with a higher average nominal salary have a large number of NAP payers;

5)      According to the professional income tax indicator, it is expected that the growth in the number of NAP payers occurs in proportion to the increase in the amount of regional budget revenues.

The analysis of the R-squared coefficient shows that the analyzed variables in total have (explain) 98% influence on the change in the number of NAP payers.

An analysis of the F value of statistics shows us that at a 1% significance level, the equation as a whole is significant, which allows us to reject the hypothesis that there is no connection between the variables.

To reduce the variation in values, we prolog the variables: GRP and professional income tax, and re-build the model with updated data.

Model 2: Pooled

Dependent variable: selfemployed (with logarithms)

The coefficient is significant "..." * at the level of 5%; ** at the level of 1%, *** at the level of 0.1%.

Based on the results of the construction of model 2, we see that the R-squared coefficient is less than in model 1, 0.95<0.98, therefore, we can conclude that model 1 has a high degree of reliability.

The economic meaning of the results obtained from the review of Model 1 and model 2 contains a number of important conclusions. Firstly, in the subjects of the Russian Federation included in the Volga Federal District, in which the development of the sphere of microenterprise is observed, there is a synergistic effect of increasing the number of NAP payers. Secondly, the decrease in the number of unemployed in these subjects of the Russian Federation is also interconnected with an increase in the number of NAP payers. Thirdly, in regions with higher average nominal wages, there is also a noticeable increase in the number of NAP payers. Fourth, the economic situation of the subjects of the Volga Federal District, estimated by the GRP indicator, does not affect the change in the number of NAP payers. Fifthly, the growth of revenues of the regional budgets of the subjects of the Russian Federation included in the Volga Federal District in the form of income from the NAP has a close positive correlation with the number of NAP payers.

Further, to clarify and further verify the obtained conclusions, we will construct models with random and fixed effects ^[27]. Fixed-effect models (fixedeffects) are usually used to estimate the average influence of a variable in a group, in this type of model, the error characterizes unobservable temporary effects, and is considered as fixed effects (temporal or spatial). In models with random effects, the error is considered as random effects (temporal or spatial).

In accordance with econometric practice, to build models and study regions, a fixed-effect model is built and evaluated, where each region has its own individual characteristics. A fixed-effects model allows you to estimate the effects of past values of a variable on future values of that variable. In addition, since the subjects of the PFD in our sample are not homogeneous, and it is impossible to fully take into account these differences, therefore, the presence of certain fixed effects should be assumed. Fixed effects in this model will be indicated for and presumably correlated with explanatory variables.

The model with fixed effects has the following form:

selfemployed – dependent variable;

; ; ; ; – explanatory variables;

 – unknown model parameters;

i – the PFD region;

t – year;

 – an accidental error;

– fixed effects of regions.

The results of constructing a model with fixed effects

Model 3: Fixed Effects

Dependent variable: selfemployed

The coefficient is significant "..." * at the level of 5%; ** at the level of 1%, *** at the level of 0.1%.

According to the above model, several important conclusions can be drawn:

1) the nominalsalary coefficient in the fixed-effects model turned out to be statistically insignificant, therefore, the size of the average monthly nominal accrued salary in the subjects of the Volga Federal District does not significantly affect the change in the number of NAP payers;

2) the GRP coefficient in the fixed-effects model begins to influence the number of NAP payers in the subjects of the Volga Federal District at the level of 5%.

This study also considered models with fixed and random effects with time effects included in the form of dummy variables (dummyvariable), the Wald criterion was used for their significant effect, and the choice between models with fixed and random effects was carried out using the Hausman criterion ^[27].

Taking into account the fact that there are individual fixed regional characteristics of each studied subject of the Russian Federation, which is part of the Volga Federal District, we need to adjust the conclusions obtained based on the combined assessment of model 1, model 2 and model 3, changing our opinion about the importance of socio-economic indicators of average nominal accrued wages and GRP on the number of NAP payers. The decrease in the influence of one indicator and the increase in the influence of another in the model with fixed effects confirms the presence of regional features.

To compare model 1 (pooled regression model or pooled model) and model 3 (fixed effects model), it is necessary to conduct the Wald test ^[28] for linear constraints, which includes the following hypotheses:

 - null hypothesis H0 – select a (pooled) model;

- an alternative hypothesis FOR – choosing a model with fixed effects.

In our case: test statistics: F(13, 23) = 20.5294, p-value = P(F(13, 23) > 2,05294) = 0,00636977.

Since p= 0.00636977 is less than the significance level of 0.05, we accept an alternative hypothesis, therefore, the hypothesis is confirmed, which allows us to conclude that there are individual effects of model 3.

Next, we will build and test a model with random effects. This model assumes that the unobservable effects are not correlated with the explanatory variables. Also, in this model, it is possible to evaluate regressors that do not change over time, which could not be done in a model with fixed effects. In fact, the model uses the fact that some factors affecting the result may randomly differ from one object to another.

The model with random effects has the following form:

selfemployed – dependent variable;

; ; ; ; – explanatory variables;

– unknown model parameters;

i – the PFD region;

t – year;

 – an accidental error;

– random effects.

To build a model with random effects based on the available panel data, we will use the Gretl software product.

Model 4: Random Effects (GLS)

Dependent variable: selfemployed

The coefficient is significant "..." * at the level of 5%; ** at the level of 1%, *** at the level of 0.1%.

According to this model, it can be concluded that the GRP and nominalsalary coefficients in the model with random effects are statistically significant.

Assuming the presence of unobservable effects that are not correlated with the analyzed socio-economic indicators in the subjects of the Volga Federal District, we see that in model 4 the significance of the influence of average nominal accrued wages on the change in the number of NAP payers has been restored, and the influence of GRP on the dependent variable has also become noticeable.

         We will perform the Broich-Pagan test ^[28] to check the presence or absence of heteroskedasticity in the above model. This test allows you to compare Model 1 (Pooled model) and model 4 (Random Effects (GLS)), and the following null and alternative hypotheses are used in this test:

- null hypothesis H0 – select a (pooled) model;

- an alternative hypothesis FOR – choosing a model with random effects.

In our example: asymptotic test statistics: Chi-squared(1) = 2.93929, p-value = 0.0864489 is greater than the significance level of 0.05, therefore, we accept the hypothesis H0. According to the final assessment of the Broich-Pagan test, it can be concluded that the null hypothesis is confirmed, the variance of observation-specific errors is 0, therefore, model 4 is less significant than model 1.

Also, to compare models with fixed and random effects, we will use the Hausmann test ^[28]. The following null and alternative hypotheses are used in this test:

- null hypothesis H0 – choosing a model with random effects;

- an alternative hypothesis FOR – choosing a model with fixed effects.

Asymptotic test statistics: Chi-squared (5) = 23.3015, p-value = 0.0801828 is less than the significance level of 0.1, therefore, the alternative hypothesis is confirmed, the model with fixed effects is more reliable.

Based on the results of the conducted comparative tests, it can be concluded that the best model is a model with fixed effects, which characterizes the individual characteristics of regions that distinguish regions from each other.

conclusion

Based on the results of the econometric study, it can be concluded that the socio-economic indicators of the subjects of the Volga Federal District have a noticeable impact on the number of NAP payers in these regions, namely: individualentrepreneurs, unemployed and professionalincometax. The hypothesis of the study is confirmed.

1) individualentrepreneurs – the number of individual entrepreneurs (people) has a positive relationship with the number of NAP payers, which allows us to conclude that in regions with a large number of sole proprietors, the number of NAP payers is also growing;

2) unemployed – the number of unemployed aged 15 years and older (people) has an inverse relationship, therefore, the fewer unemployed there are in the subject of the Volga Federal District, the more NAP payers there are;

3) professionalincometax – tax on professional income, budget execution by income (billion rubles), is expected to have a direct relationship with the number of NAP payers in the subjects of the Volga Federal District;

4)                Such indicators as GRP – gross regional product by subjects of the Russian Federation (PFD) (in current prices; million rubles) and nominalsalary – average monthly nominal accrued wages of employees for a full range of organizations in the economy as a whole by subjects of the PFD (R.) do not significantly affect the number of NAP payers.

In general, the approach to quantifying the mutual impact of socio-economic indicators on the number of NAP payers in the subjects of the Russian Federation included in the Volga Federal District can be used in the formation of a budget and tax policy program, both at the federal and regional levels in the Russian Federation, as well as in countries near and far abroad.

A similar approach to assessing the effects of the introduction of special tax regimes can also be used to assess the achievement of goals under other special tax regimes applicable to individuals aimed at stimulating and developing entrepreneurial activity. In addition, with the help of various macroeconomic instruments, it is possible to exert targeted influence on various socio-economic indicators in order to increase the positive effects of the introduced tax regimes. 

^[1] Federal Law "On conducting an experiment to establish a special tax regime "Tax on professional income" dated 11/27/2018 No. 422-FZ.

^[2] [Electronic resource] URL: https://sozd.duma.gov.ru/bill/551845-7 The official website of the Legislative Support System / Bill No. 551845-7/ Explanatory note to the bill / (date of reference: 10/29/2023).

^[3] [Electronic resource] URL: https://59.rosstat.gov.ru/folder/160975 # Official website of the Federal Tax Service of Russia/Unified Register of Small and medium-sized Businesses/ (date of application: 10/29/2023)

^[4] [Electronic resource] URL: https://rosstat.gov.ru/folder/12781 Official website of the Federal State Statistics Service/Demography/Population size and composition/ (accessed: 10/29/2023)