An Evaluation of the State of the Top-Priority and Socially Important Industries of Russian Regions: an Analysis Based on Digital Tax Calculator Data
Corresponding email: viktorova_ng@spbstu.ru
Published at : 17 May 2024
Volume : IJtech
Vol 15, No 3 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i3.6163
Victorova, N., Vylkova, E., Naumov, V., Pokrovskaia, N., 2024. An Evaluation of the State of the Top-Priority and Socially Important Industries of Russian Regions: an Analysis Based on Digital Tax Calculator Data. International Journal of Technology. Volume 15(4), pp. 732-742
| Natalia Victorova | Peter the Great St.Petersburg Polytechnic University, St. Petersburg, 195251, Russia |
| Elena Vylkova | North-West Institute of Management, branch of RANEPA, St. Petersburg, 199178, Russia |
| Vladimir Naumov | North-West Institute of Management, branch of RANEPA, St. Petersburg, 199178, Russia |
| Natalia Pokrovskaia | Saint Petersburg State University, St. Petersburg, 199034, Russia |
The
paper uses the data of digital tax burden calculator of Russia’s Federal Tax
Service to study the determinants of the total tax burden and average salary on
the example of St. Petersburg for year 2020 with enterprises broken down by
size and types of economic activity and with the focus on top-priority and
socially important industries. Therefore, this study aims to
provide a mathematical substantiation
for the need to improve state support measures provided
for top-priority or socially important types of activities in
the region using
taxation means and methods. The objectives set
were achieved using
simulation modeling.
The dependent variables that
characterize the state support of the top-priority and socially important types
of activities are the rate of tax burden (excluding mineral tax and excise
taxes) and the size of the average salary. Moreover,
two analyses were conducted
sequentially to achieve the objectives. The first was a two-way analysis of variance of the relationship
between the average salary and the size of the organization or industry as well as the dependence of tax burden
on the same factors. The second was a one-way analysis of the
variance of the tax burden and
average salary depending on different types of activities as well as the scale of the
enterprise. The results of the one-way analysis were refined through regression methods based on dummy variables. It
was observed that there were no
considerable differences between the average salary and total tax burden in
top-priority or
socially important sectors compared to the other industries in
the economy. The trend was the
evidence of insufficient support provided by the state and the
need to introduce additional
preferences.
Digital tax calculator; Priority industry; Socially important industry; Tax burden
Sustainable development of territories is the problem with many aspects (Zaborovskaya, Kudryavtseva, and Zhogova, 2019), which depend on the conditions in various regions (Shestak, Shcheka, and Klochkov, 2020; Skripnuk, 2020), including their industry specifics (Gamidullaeva et al., 2022; Rodionov, Konnikov, and Konnikova, 2018). Advancements in digital technologies (Victorova et al., 2021; Rytova et al., 2020) are observed to be significantly transforming the set of tools used for the sustainable development of regions (Kudryavtseva, Skhvediani, and Berawi, 2020). Meanwhile, the crises and global challenges (Borisov and Popova, 2022; Pinskaya, Steshenko, and Kermen, 2021) are affecting the initial objective and method to achieve the development (Leksin and Porfiryev, 2017). This shows the need to determine the appropriate methods of improving the state policy aimed at the developing any given territory. An example of relevant policy implemented is the stimulation of the top-priority or socially important industries in a region through public-private partnerships (Berawi et al., 2021). However, the measurement of the effectiveness of the support requires selecting relevant indicators and assessment criteria.
The success of
regional socioeconomic development depends on a variety of factors.
These include tax burden and average salary, which signal the economic
capabilities of the territory and its population for implementing social
projects and meeting social needs. It is generally believed that an
increase in tax burden worsens the possibility of socioeconomic development
while a higher average salary provides a positive influence.
In order to
substantiate the stated position, the findings of modern publications
concerning 1) tax burden as an
indicator of state regulation and 2) average salary to represent the success of
economic entities in a top-priority or socially important sector of the economy
are briefly reviewed and summarized.
1) Scientific studies have been discussing various aspects of tax burden since the early 20th century. In the
1970s, scholars started to profoundly compare the tax burden in
different countries. This was observed in the application of GDP (Karagianni, Pempetzoglou, and Saraidaris, 2012; Jedrzejowicz,
Kiss, and Jirsakova, 2009) and different types of effective rates (Celikay, 2020; Shevlin, Shivakumar, and Urcan, 2019) as indicators. Meanwhile, the
average effective rates are determined in taxing many factors such
as capital, consumption, labor, and others (Wu, Wang, and Peng, 2024). The general idea is to determine the ratio of the calculated taxes to
the taxation base and a specific methodology is devised to select the data for
the numerator and denominator (Nicodeme,
2007; Carey and Rabesona, 2003). Similarly, the tax burned is also estimated for territories
inside the country (Dang, Fang, and He, 2019).
Few studies were observed
to have considered tax burden as a measure of possibilities of business in a
region such as Latin America (Hallerberg,
and Scartascini, 2017),
Russia (Victorova et al., 2020), and other countries (Celikay, 2020; Park, 2020; Jedrzejowicz, Kiss, and
Jirsakova, 2009; Nicodeme, 2007). Only a few publications compared industry-specific taxation of different businesses (Rota-Graziosi, and Sawadogo, 2022; Carey and Tchilinguirian,
2000) while other studies
frequently focused on discussing the factors of production rather than a
specific industry (Karminskaya and
Islamutdinov, 2021) or
the situation of a specific company (Pan,
Huang, and Jin, 2024; Berawi et al., 2022). This leads to the conclusion that there is no
comprehensive study on the problems related to tax burden.
2) Another indicator of the socioeconomic development of a
region is the average salary size which has also been widely covered in many
publications by economists. The review of relevant literature showed two areas
considered quite problematic. Firstly, the analysis and assessment of the
impact of average monthly salary on the socioeconomic development of a
territory as a whole, and second, the effect on the development of top-priority
or socially important types of activities in the territory.
The study by Karminskaya and
Islamutdinov (2021) can be referred as an example of the first group of
research. The scholars focused on econometric analysis of the impact of human
capital, specifically in higher and vocational education, on the economic
development of a region. The results showed that a higher average monthly
salary was one of the key factors of the development. The concept has also been
proposed to represent the competitiveness of a territory. For example, Shavandina et al. (2021) discussed the context in RF
municipalities while Wojtasiak-Terech
and Majerowska (2019) analyzed the Polish case using taxonomic methods and suggested a
composite competitive indicator for each province.
An example of the second group of research is Volkov et al. (2022) which proposes to increase the average salary
indicator in the context of attracting young personnel in the agro-industrial
complex of a region. Cantillo et
al. (2022) also
showed the effect of salary on the jobs selected based on manpower in the
formal or informal sector of the economy in the Caribbean region of Columbia.
The scholars concluded conclude that the
attractiveness of a formal job depended directly on the pay raise in this
sector to a level not less than the minimum wage in the region.
The average salary of formal employees was found to be part of the
factors affecting the development of franchising in Brazilian towns (Melo et al., 2021). This was confirmed through a simulation
conducted using a multiple regression method to prove the connection between
the analyzed indicators.
Junusbekov et al. (2020) made an important conclusion concerning the
change in the form of labor remuneration in Kazakhstan using a statistical
analysis to determine the data dynamics of average salary in all regions of the
country. Chinese scholars, Liu et
al. (2021), also provided a
non-conventional and quite interesting observation in the process of discussing
the problem of market diversification in regions with private foreign
subsidiaries. The results showed that average salary was an influential but
insignificant parameter on Tobin’s Q.
The summary showed that several scientists have focused on indicators such as the size of tax burden and average salary to assess the impact of the state regulating function on the development of a region as a whole and concerning the top-priority or socially important industries. However, there was no comprehensive approach to apply these two indicators for assessment in contemporary studies. Therefore, the goal of this study was to analyze and evaluate the effect of the state policy on top-priority or socially important types of activities in a region using official digital resource data. It was also used to provide recommendations on the improvement of state support measures for the identified economic sectors using tax methods. Two hypotheses were formulated and the first was that tax burden and the average salary were influenced by the type of enterprise (its size) operating in the top-priority or socially important sectors of the region. The second was to confirm the impact on similar indicators of the industry specifics.
The
study was conducted using tax burden calculator data obtained from the RF
Federal Tax Service. This source is normally used to calculate the tax burden
for organizations based on the percentage of the earnings and the average
salary. The four measurements often used include the tax period, region in
Russia, industry, and scale of activity. Therefore, the simulation was
conducted using some factors characterizing the state support for top-priority
or socially important types of activity as the dependent variables. These
include tax burden, excluding those related to mineral and excise, and the
level as well as the average salary and the size.
1. Tax period selection: The data
covered up to the date of the study using those available for 2018, 2019, and
2020.
2. Region selection: The region
selected is St. Petersburg which is a federal city in Russia. The preference
for the region at the initial stage was due to the status as the home to
enterprises engaging in a wide range of activities and producing socially
significant products, works, and services. It was also selected due to the
presence of legally established priorities for the development of certain
sectors of the economy considered significant for both the region and the
country as a whole with a focus on the year 2020.
3. Industry selection: The activities classified as top-priority or
socially important in the economic sectors of regions in Russia. Some have been
listed in scientific literature but those identified in St. Petersburg
legislation include the production of food, clothing, machinery, and equipment
not otherwise classified, manufacture of other vehicles and equipment, erection
of buildings, specialized construction works, information technology,
employment and recruitment, as well as health care. The preference for socially
important activities was due to their critical importance in providing basic
human needs. Moreover, top-priority activities were selected for this study
based on the St. Petersburg legislation and these include others not listed
among socially important ones.
4. Scale of activity
differentiation: The types of enterprises considered independently included
micro-enterprise (revenue up to 30 million rubles), micro-enterprise (revenue
from 30 to 120 million rubles), small business (revenue from 120 to 500 million
rubles), small business (revenue from 500 to 800 million rubles), medium-sized
enterprise (revenue from 800 to 2000 million rubles), and large enterprise
(over 2000 million rubles). When the results were described, the scale of
activity was characterized based on earnings. This indicator was used in the
Tax Calculator Database and was also considered in the tax burden. Therefore,
the differentiation of the business based on the size of revenue was
specifically suitable for this study. After an observation with gaps had been
excluded, a dataset of 63 observations was considered to be used.
The summary of the results for the two-factor
dispersion analysis is presented in this section. Those related to average
salary are presented in Figure 1 while those for total tax burden are in Figure
2.
Figure 1 Average salary response
Figure 2 Tax burden response
The first model showed a
significant difference in the average salary for the two factors under
consideration based on Fisher's criterion but only found in the first factor
for the second model. The two-way analysis of variance also confirmed the
existence of a statistical difference in average wages and tax burden for the
observed groups with different types of enterprises and activities. Moreover,
significant differences were also identified in the average tax burden but only
based on the type of activity.
One-way analysis of variance results for the
tax burden related to the "type of activity" factor are presented in
Figures 3 and 5. It was observed that the
type of activity affected the average value of the cumulative load with a
significance level of < 0.001. One-factor dispersion analysis results for
the average salary on the “type of activity” factor are presented in Figures 4
and 6.
Figure 3 Assessed marginal means for tax burden based on industry
Figure 4 Assessed marginal averages for average salary based on industry
Figure 5 Criteria for intergroup effects of tax burden based on industry
Figure 6 Criteria for intergroup effects of average salary based on industry
The
results of a one-way analysis of variance for the tax burden on the
"enterprise scale" factor are presented in Figures 7 and 9. It was
observed that the statistical hypothesis about the difference in the average response
value depending on the analyzed factor.
The results of the one-factor dispersion
analysis for tax burden on the “type of activity” factor are presented in
Figures 8 and 10. It was discovered there were no statistically significant
differences in the average value of the tax burden.
Figure 7 Assessed marginal averages
of average salary based on enterprise size
Figure 8 Assessed marginal averages of tax burden based on enterprise size
Figure 9 Criteria for intergroup effects for average salary based on the scale of
activity
Figure 10 Criteria for intergroup effects for tax burden based on the scale of
activity
The summary of the results
obtained from a one-way analysis of variance is presented in the following
Table 1. The presence of the factor effect was represented by yes while the
absence was stated as no.
Table 1 Results of one-way analyses of variance
|
Factor |
Response |
|
|
Average Salary |
Tax burden |
|
|
Scale of activity (Type) |
Yes/yes |
No/no |
|
Type of activity (Industry) |
No/no |
Yes/yes |
The hypotheses were further analyzed to solve the
problems of nonparametric analysis of variance using the Kruskal-Walli’s test
and the results were presented in the table through the "/" sign. The
consistency observed in the results as well as the assumptions on the normal
law of response distribution according to the Shapiro-Wilk test for the first,
second, and fourth models were used to confirm the conclusions provided.
The table showed that the
average salary value was predetermined by the scale of the enterprise while the
tax burden for top priority or socially significant areas of activity depended
on the specific type of activity. However, there were no significant
differences in wages based on the type of activity and in total tax burden
based on the type of enterprise. This showed the provision of insufficient
state support primarily required by small and medium-sized enterprises.
The third stage focused on using regression analysis and dummy variables to investigate the changes in average response level in line with the level of the factors for the two scenarios. The results showed significant differences in the average response values depending on the scale of the enterprise as well as in tax burden based on the type of activity. This was due to the fact that the variance and regression analyses were special cases of a linear stochastic model. Moreover, the values generated for the dummy variables represented the contrast of each factor level with the base. It was also observed that the variables were only in the unit values in the generated encoding scheme. Therefore, the coefficients in the regression model were used to show the main effect of the corresponding level on the base. The results obtained from the multiple linear regression model of the dependence of average wages on the scale of the enterprise are summarized in the following Figure 11.
The "Average"
enterprise type was used as the base level through alphabetical ordering.
Therefore, the average salary level for the enterprise was recorded to be
60,567 rubles while those considered to be large had 34,195 rubles. This showed
that the average was 94,760 rubles and the lowest level was observed among
microenterprises. The results further showed that four out of the six
coefficients in the model were significantly different from zero. Moreover, the
good quality of the model was supported by the value of the coefficient of
determination, the Fisher's criterion, as well as confirmation of the normal
distribution of residuals according to the Shapiro test.
The summary of the tax burden response to the type of activity is presented in Figure 12. The results showed that clothing production was adopted as the base level and the tax burden was 8%. Therefore, the lowest was found in food production with 0.08 + 0.07 = 0.15 which was approximately 15%. The quality of the model was also confirmed by the value of the corresponding metrics and the results of the tests conducted on the hypotheses based on the normal distribution of residuals according to the Shapiro test.
Figure 12 Summary of the response to the tax burden based on
the type of activity
An
increase in average salaries based on the scale of business was substantiated
by the financial capabilities of enterprises. This showed that the earnings
from sales of products, services, and works as well as the type of activity
could increase the financial potential of an enterprise in covering all
expenses necessary for statutory activity, including wages. Moreover, official
salaries tended to grow when insurance premiums were designed for the types of
activities considered important for the state. For example, the IT field has an
insurance premium rate incentive of 7.6% compared to the usual 30% for other
categories of payers. The reduction in the insurance premium burden was also
observed to have converted all wages into official turnover.
The
lack of significant differences between the salary and total tax burden of
top-priority or socially important economic sectors compared to the other
sectors shows that the support being provided by the state is insufficient and
additional tax preferences should be introduced. It is specifically proposed
that the rate of insurance premiums be reduced and the income tax paid by
employees be reimbursed, in line with the method applied for enterprises in the
IT sector. The changes are expected to affect all the IT companies in every
region of the country. Therefore, the list of top-priority or socially
important types of economic activity is required to be selected carefully.
The
effectiveness of using only tax instruments as support for the sector by the
state compared to other forms of incentives is considered debatable. For
example, Iranian scientists (Ghazinoory
and Hashemi, 2020) proved
that direct government financing of high-tech firms, specifically those related
to small and medium-sized businesses, was more effective than tax incentives.
Meanwhile, Chinese scholars (Wang, Yuan,
and Xu, 2022) also analyzed the impact of government subsidies and preferential tax policies
on mobile phone recycling activities and concluded that tax incentives led to
an increase in production and profits. The variation in these results shows the
need for a more in-depth on the impact of both tax and other government support
instruments on the development of top-priority or socially significant sectors
of the economy in the region.
In
conclusion, the hypotheses formulated were only confirmed partially and this
was observed from the first hypothesis which was true
only for the average salary in the region which was affected by the size of the
enterprise. The second hypothesis was also confirmed for only one of the
analyzed indicators which was observed from the dependence of tax burden on
industry specifics. However, the limitations of the study included the lack of
special tax regimes for IT companies, the specifics of St. Petersburg as a
region of Russia, and the regional structure of the country as a whole. The
digital calculator of tax burden provided the data used in formulating
well-reasoned recommendations on the improvement of state support measures for
the top-priority or socially important types of activities. Moreover, tax
methods were used based on the assessment and analysis of the effects of the
state policy on the development of the selected economic sectors. Future study
on St. Petersburg can be continued in the context of longer time horizons. It
is also recommended that a similar study be extrapolated to other Russian
regions and territorial entities of different countries. Furthermore, the
methodological approach described in this study can be applied to other Russian
regions and territories of foreign states. The purpose of these individual
scientific studies is to provide detailed substantiation and concretization of
proposals for tax innovations in top-priority or socially important sectors of
a territorial entity.
The study was financed as part of
the "Development of a methodology for instrumental base formation for
analysis and modeling of the spatial socio-economic development of systems
based on internal reserves in the context of digitalization" project
(FSEG-2023-0008).
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