On the issue of the concept of comparability: levels of application and scope of application in data taxation

Orekhov Kirill Nikolaevich Student, the Department of Taxes and Tax Administration, Financial University under the Government of the Russian Federation 49 Leningradsky Prospekt str., Moscow, 125167, Russia orekhov.kirill.n@gmail.com

Introduction. Problem statement.

The basis of any analytical process in any field of research is based on the concept of data comparability, which means the ability to make meaningful comparisons between different data sets. Without the ability to compare, differentiate, and connect objects, the ability to conduct meaningful analysis and the logical judgments that follow from it would be practically nonexistent. At the same time, the essence of the concept of comparability in analysis goes far beyond simple comparison or differentiation. It entails the study of homogeneous attributes of various objects, which, although they may be different in nature, have a common basis explaining comparative judgment.

Nevertheless, the implementation of the concept of comparability is not without a number of problems. Differences in contexts, standards, geography, or time often create significant obstacles, making comparable objects incomparable. In addition, subjective interpretations or methods adopted in the process of equating one subject with another further complicate this concept with the idea of peer review. It is necessary that not only the data be comparable, but also the analyst-practitioner has the appropriate level of knowledge and competence, choosing such methods of analysis that most accurately reflect the reality of events.

In financial analysis, comparability is ensured through the systematic use of the same accounting and tax accounting rules (hereinafter referred to as accounting and tax accounting, respectively), data adjustments when standards and legislation change to form absolute indicators reflecting the results of organizations' activities. This provides an opportunity for users of such information to form relative indicators of interest to them, which will be really useful in further study or research.

Conditionally, this can be reflected as follows (Fig. 1):

Figure 1. Levels of data comparability. Source: compiled by the author.

The accounting level of ensuring comparability of data.

The accounting level in this approach should ensure comparability of data at the information collection stage. Accounting here means a comprehensive system that involves the observation, careful recording and accurate measurement of various facts and transactions of economic activity.

Financial accounting in the context of the definition above can be interpreted as the process of registration, organization, generalization and reporting of financial transactions in which the organization participates. Thus, financial accounting includes the following components:

1. identification and registration of financial transactions;

2. development and implementation of information systems and procedures;

3. Implementation of internal controls and safeguards to ensure the accuracy of records and the safety of assets;

4. Summarizing information and preparing financial reports for internal and external users.

In the Russian Federation, there is no official definition for financial accounting and it is conducted exclusively by the Accounting Department, which is regulated by Federal Law No. 402-FZ. However, reports based on his data have a financial component, both in the name of the forms and in the description of their essence and structure. For example, according to Russian law: "Accounting (financial) statements should provide a reliable representation of the financial position of an economic entity at the reporting date, the financial result of its activities and cash flows for the reporting period, which are necessary for users of these statements to make economic decisions. Accounting (financial) statements should be prepared on the basis of data contained in accounting registers, as well as information defined by federal and industry standards" ^[1].

There are two main financial accounting statements that are prepared periodically; the balance sheet, which shows the assets and liabilities of the business; and the financial results report, which shows the profit or loss of the business for a certain period of time. Other financial accounting reports are also prepared either to meet the specific needs of internal users or to meet the general needs of external users.

External users directly include those who are not involved in the management of the organization, such as shareholders, creditors, directors, customers, suppliers, regulators, lawyers, brokers and the press. These market participants have limited access to the organization's information. However, their decisions and interpretation of the results of enterprises' work are based on reliable, relevant and comparable information generated by enterprises. Up-to-date information completely influences user decisions and risk assessment, users trust a reliable or reliable source of information that correctly reflects the results of economic activity, and comparable information allows you to analyze and compare data from different sources at different time intervals.

The demand for these qualities of information from investors, creditworthy persons, and lenders creates fundamental qualitative characteristics that are desirable in accounting information. These include the fundamental characteristics (relevance, materiality, truthful presentation) and those that enhance the usefulness of information (comparability, verifiability, timeliness and comprehensibility) ^[2].

This theory in the Russian Federation is reflected and fixed in the Concept of accounting in the market economy of Russia, approved by the Methodological Council for Accounting under the Ministry of Finance of the Russian Federation, the Presidential Council of the IPB of the Russian Federation on 12/29/1997 in paragraph 6 as a requirement of usefulness. For example, according to clause 6.4 of the concept: "Interested users should be able to compare information about an organization over different time periods in order to determine trends in its financial position and financial performance. They should also be able to compare information about different organizations in order to compare their financial position, financial performance and changes in financial position."^[3]

Based on this research approach, comparability in the context of financial analysis can be considered in two directions:

1. "inside" the organization;

2. "between" organizations.

In this case, comparability "inside" acts as a functional attribute that increases the properties of the qualitative characteristics of reporting, allowing an interested circle of people to analyze the activities of an organization for all periods presented by it and identify the strengths or weaknesses of the company, assess the risks of liquidity, bankruptcy, etc. ^[4]

In the framework of RAS, comparability in this regard means the sequence of data reflection, compliance with stability in the content and forms of reporting from one reporting period to another. So, in 2023, the balance sheet in the form approved by Order of the Ministry of Finance of the Russian Federation No. 66n dated 07/02/2010, as amended on 04/19/2019 No. 61n, includes the last 3 years of operation of the enterprise, which ensures comparability in the requirement of usefulness to the reporting provided. If in the previous periods (2021 and 2022) a different format was used, a different version of the data reflection, or the accounting method changed, then there would be a need to adjust the balance sheet of previous years to a new sample.

In the case of "between" comparability, investors and creditors should be sure that the data are not only prepared in high quality by specialists in one company, but also that these data are comparable with the prepared reports of other economic entities.

At the national level, as in the example with the form of the balance sheet, this is achieved by strict orders of the Ministry of Finance of the Russian Federation to the form of reports, regulatory legal acts. However, if there is a need to compare the performance of a Russian company with a foreign one, then a number of problems may arise that must be solved by experts at the correctional level to ensure comparability of data.

The correctional level of ensuring comparability of data

The corrective level of data comparability is generally aimed at eliminating the heterogeneity of the data obtained and further converting the collected information into a consistent, uniform format that allows direct comparisons between different data sets. At this level, we are talking about the fact that comparability remains a constant problem due to the variety of sources, formats and methodologies used for data collection and analysis, which leads to their heterogeneity, heterogeneity.

The foundation of this level is the process of data standardization. Its main purpose is to ensure that different data sources correspond to a common set of rules and indicators, which allows for meaningful and accurate statistical analysis by minimizing discrepancies arising from differences in the ways data is collected, stored and reflected. By applying consistent definitions, units of measurement, and coding schemes, it becomes simpler, more transparent, and more meaningful. Such comparability contributes to a better understanding of trends, patterns and relationships.

In practice, on the territory of the Russian Federation, the corrective approach to ensuring comparability of financial data when comparing foreign and Russian companies can be attributed:

1. adjustment of reports during the study of transfer pricing by tax authorities;

2. Formation of financial statements in accordance with International Financial Reporting Standards (IFRS).

The method of comparable profitability is associated with the analysis of financial statements from them. It involves calculating a range of profitability values through indicators such as gross margin, return on sales, costs, commercial and management expenses and assets (from minimum to maximum). This range is used to set the market price for the purposes of taxation of transactions between related parties. The rules for determining the profitability interval are set out in detail in Article 105.8 of the Tax Code of the Russian Federation. Accounting (financial) reporting data is used for calculations. If a foreign organization appears as a participant in the transaction, then all relations will be calculated through reporting according to the rules of foreign standards, "at the same time, for the purpose of ensuring comparability with the data of accounting (financial) statements, which are compiled in accordance with the legislation of the Russian Federation on accounting, such data is adjusted."^[5]

Similarly, the Organization for Economic Cooperation and Development (OECD) transfer pricing guidelines for multinational enterprises and tax administrations for 2022 state the fact that being comparable means that none of the differences (if any) between the compared situations can significantly affect the condition considered in the methodology, or that sufficiently precise adjustments can be made to eliminate the impact of any such differences. Therefore, according to paragraph 3.48 of the OECD Guidelines: "examples of comparability adjustments include adjustments to ensure accounting consistency, aimed at eliminating differences that may arise from differences in accounting methods for controlled and uncontrolled transactions; segmentation of financial data to exclude significant disparate transactions: adjustments taking into account differences in capital, functions, assets, risks" ^[6].

In the second case, it implies the fact that the use of reporting indicators of organizations compiled according to different national accounting rules is irrational and not practical without such a special reason as checking transfer pricing. They cannot be directly compared, and data adjustments would take a lot of time, not to mention the fact that you need to be a competent specialist in the theory of accounting for several states for such work. In this case, existing international standards should serve to achieve comparability of data.

The peculiarity here is that IFRS as a whole has a corrective level of ensuring comparability of data, since it is formed in most organizations due to adjustments. At its core, IFRS is a "superstructure" over national standards ^[7].

Of course, such a method as parallel accounting is also practiced for preparing IFRS reports, but the transformation of reporting and the translation of transactions, which involve the adjustment of RAS, are more popular in Russia ^[8].

The expert level of ensuring comparability of data

The expert level of ensuring comparability of data will be directed, as previously indicated, to the formation of relative indicators based on absolute ones.

Absolute values are numerical values that represent the magnitude of specific indicators or variables without taking into account other data. They provide an indication of the differences in the measured quantitative feature of the population, which makes them useful for understanding the absolute differences or similarities between populations.

In the context of financial analysis, it is possible to compare the total assets of organizations to clearly represent their relative sizes, but before that they must be unified for their full comparability. So, it can often be noticed that the financial statements of companies from different countries may reflect indicators in different currencies, respectively, they must be brought to one for a full comparison.

However, the absolute values themselves, despite the fact that they can be compared with each other, do not give a complete picture of the comparability of the data and do not allow us to draw qualitative, analytical conclusions about the objects under study.

Let's look at this in the following simple example: suppose a house is for sale, the price of which is 300,000 rubles, and the area is 2,000 m2, but potential buyers are not really sure whether it is worth such a price. Examining whether there are other houses that have been sold recently, in the process it may turn out that the house opposite was sold for 600,000 rubles, but it is larger - 4,000 m2. Comparing the difference in absolute prices is not fair, since it is obvious that the difference in size must also be estimated.

In this case, relative values should be used to express the relationship between two or more indicators characterizing different attributes of the data set, taking into account other factors. By calculating coefficients or percentages, relative values give an idea of how one variable relates to another. The figures obtained help researchers assess the proportionality or balance of the characteristics selected for analysis, which is crucial for understanding the significance of differences or similarities in the population as a whole or in the sample.

In the above example, the ratio of price to apartment size provides a more detailed comparison of the studied objects. So, the ruble/ m2 ratio will show that in fact both houses are sold for 150 rubles per square meter, based on this indicator it turns out that in fact the first house is sold at a fair price.

In financial analysis, you can point out the following reasons why it is preferable to use relative indicators (Table 1):

Table 1. The reasons for using relative indicators in practice. Source: compiled by the author.

Additionally, it should be borne in mind that most absolute indicators are extremely sensitive to changes over time. In economics, this is especially true for values expressed in monetary measures (euros, rubles, US dollars, etc.).

The concept of the time value of money states that the money available in the present is worth more than the same amount of money in the future. This is because money can generate income, such as interest or investment profits, by being invested or used for economic activities over time. Therefore, a dollar received today is more valuable than the same dollar received in the future. There are three main reasons for this (Fig. 2):

Figure 2. The main 3 factors of the concept of the time value of money. Source: compiled by the author.

Therefore, it is necessary to compare the indicators that were obtained in one time period, as in the example of buying a house, or either make additional adjustments that will offset the impact of price changes, or compare the values as part of a study of patterns in the analysis of dynamic series.

 This econometric method is used to analyze and model data that changes over time. It involves examining trends and relationships within a sequence of observations to understand the underlying dynamics and make predictions about future values. The tool in question can be applied to various fields, including finance, economics, weather forecasting and sales forecasting.

However, it is necessary to pay special attention to the study of tax statistics, both by country and in organizations in different time periods. The process of verifying individual taxes and fees, in particular receipts and accruals, is becoming difficult due to the constantly changing nature of tax legislation. Tax rates fluctuate, the elements that make up the tax base change, and tax benefits are often canceled and restored. These conditions prevent a direct comparison of the studied tax amounts, which makes the results of the analysis unreliable, and most importantly, incomparable. At the same time, there is an objective need, as mentioned earlier, to revalue indicators into constant prices in order to study the dynamics of their receipt by leveling inflation.

In order to be able to compare tax indicators that differ due to these reasons, methods such as closing the dynamic series and deflation are used. For example, evaluating the graphs below (Fig. 3), one can see the significant impact of inflation on the receipt of payments, which, as expected, does not allow a qualitative assessment of the dynamics in absolute terms. Eliminating the impact of inflation through the use of a basic GDP deflator index makes it possible to study changes in the receipt of corporate income tax through an assessment of the growth rate of this indicator. Thus, Figure 3 shows that in 2009 there was a significant decline in corporate income tax revenues to the federal budget (by 89.46%). This is due to the onset of the global economic crisis in 2008, which affected the Russian Federation as well. However, as can be seen from the presented figure, already in 2011 the economy began a new upswing, as evidenced by an increase in corporate income tax receipts to the federal budget (the growth rate was 142.95%). There are no other significant booms and dips in corporate income tax receipts, but the trend of dependence of corporate income tax receipts on the economic situation can be traced in subsequent years.

If only the unadjusted absolute figures had been used, we would also have noted strong fluctuations in receipts associated with the start of the coronavirus epidemic in 2020.

Figure 3. Graphical representation of deflation on the example of the volume of income tax receipts to the budget system of the Russian Federation. Source: compiled by the author according to the EMISS data ^[9].

Directly in accounting, the need to adjust financial statements due to inflation is indicated by certain standards in certain situations. For example, IAS 29 "Financial Reporting in Hyperinflation", which was issued by the IASB in 2001, requires that the financial statements of any enterprise whose functional currency is the currency of a hyperinflationary economy be recalculated taking into account changes in the total purchasing power of this currency, so that the financial information provided is more meaningful. The standard lists factors indicating hyperinflation in the economy. One of the indicators of hyperinflation is that aggregate inflation over a three-year period approaches or exceeds 100 percent. IAS 29 applies to the financial statements of any organization from the beginning of the reporting period in which it detects the presence of hyperinflation in the country in whose currency it reports.

An analogue of this standard in the Russian Federation is the FSB for public sector organizations "Accounting (financial) statements adjusted for inflation" approved in accordance with the order of the Ministry of Finance of the Russian Federation dated December 29, 2018 N 305n.

Indirectly, the need to take inflation into account in accounting is indicated by methods of assessing inventory values. For example, in the Russian Federation, since 2008, stocks in accounting cannot be estimated using the LIFO method ("Last in— first out"). The corresponding changes were made to PBU 5/01 (now FSB 5/2019 "Reserves" is in effect) and Methodological guidelines for the accounting of MPZ by Order of the Ministry of Finance of the Russian Federation dated 03/26/2007 No. 26n ^[10]. This method is also prohibited in IFRS, which requires the use of either the FIFO method ("First in, first out") or the weighted average cost method for estimating inventories. This is primarily due to the fact that IFRS, on which RAS is based, is aimed at presenting financial information in a fair and transparent manner, providing users with reliable and unbiased information. LIFO can distort the valuation of inventories and subsequently lead to misleading financial statements, especially in an inflationary environment.

As can be seen from these examples, partial "adjustment" of data to ensure their comparability is thought out in advance in the standards by their creators already at the accounting level.

It can also be noted that the expert level goes beyond the banal formation of relative indicators. He smoothly proceeds to the assessment of their statistical significance, where instead of systematically checking the heterogeneity of the data, evaluating their quality and usefulness, there is a finding of similarities or differences between the elements that make up a single whole, as well as the study of factors that may influence the results of research. For example, in an economy, firms may be heterogeneous in terms of productivity, industries may differ in relative factor intensity, and countries in relative abundance of factors of production. Such a statistical "sublevel" of comparability would have to indicate these differences within individual samples, between them, and between experimental results in a meta-analysis.

Traditionally, at the statistical "sublevel", the indicators calculated by experts are transformed into measures of variance. They explain the inconsistency of the data to each other by providing an accurate representation of their distribution. The variance indicators reflect and give an idea of the variation and the central value of the data under study.

The most commonly used indicator of the central trend, the average value, allows researchers to understand the overall performance of a group or identify typical characteristics of a population. Moreover, averages can also help researchers compare subgroups. By calculating and comparing the averages between them, scientists can identify differences or trends that may be invisible only in the absolute values of the entire population or sample. For example, by comparing the average performance of companies in different industries, researchers can analyze potential differences in the structure of their assets and liabilities.

To check the comparability of data (or lack thereof) within the statistical "sublevel", the method of sample separation, regression analysis and correlation coefficients are used. In the case of regression analysis, the adjusted coefficient of determination is often indicated as an indicator of the comparability of the studied factors. In the case of correlation coefficients, it is the correlation coefficient itself that measures the linear relationship between two sets of analytical results obtained during the study or from dividing the sample into different groups, subgroups.

When analyzing data over time, it is also important to check for autocorrelation in the residuals. Autocorrelation assumes that residuals at one point in time correlate with residuals at another point in time. This violates one of the many assumptions of time series models, which assumes that residuals are distributed independently and identically. If autocorrelation is present, this may indicate that the model can be improved because not all significant factors are included, or that there are basic patterns in the data that are not captured by the model. The specific methods and approaches for analyzing autocorrelation of residues may vary depending on the context and type of data being analyzed.

In the context of evaluating the activities of organizations, checking the comparability of data at the statistical "sublevel" is almost not used in practice by stakeholders. Instead, experts immediately begin to identify comparable companies through existing classifiers of economic activities and/or competing firms that can be compared without evaluating the population or sampling to directly identify groups that would be similar in their financial performance and economic factors affecting them, ensuring an appropriate level of comparability.

At the same time, there is no definition of comparable companies themselves in the economic literature, which does not allow creating a universal methodology with criteria for their selection. At the same time, the definition of comparable companies is necessary both in transfer pricing and in the assessment of business value by investors, in industry analysis. For example, typical criteria for ensuring comparability of transfer pricing are as follows:

1. ensuring similar amounts of revenue, assets and the number of employees of organizations;

2. determination of the same studied time intervals;

3. study of the specifics of markets and industries (if software development in Europe and Russia is of the same nature, this does not mean that the European industry classification KDEC (NACE) can be applied to Russian companies, however, this also does not mean that European companies will need to be excluded from the analysis);

4. Unprofitable companies are most often considered to be obviously incomparable (Clause 3, clause 5, Article 105.8 of the Tax Code of the Russian Federation);

5. Determination of the shares responsible for the interdependence and degree of influence of one organization on another.

When assessing the value of a business, investors use individual assessments of the expectations of companies through such a method as the assessment of comparable companies (CCA), which includes the following basic steps to ensure data comparability: data comparability:

1.      Target Company selection: Identify the company that the focus is on. It is necessary to make sure that the company is within the knowledge of the analyst-practitioner. Such a target company should ideally be a publicly traded company to ensure that financial data and comparable companies are available for analysis.

2.      Research of comparable companies: compiling a list of comparable companies that work in the same industry as the target company (usually 5-10 organizations must be selected) according to a number of certain characteristics (Table 2):

Table 2. CCA selection criteria. Source: compiled by the author

It is important to note that in economic theory there will never be ideal companies for this analysis. Examples of the most comparable companies in foreign practice include Pizza Hut and Domino's, Home Depot and Lowe's, or Pepsi and Coca-Cola. These companies have similar business models, products, and comparable financial characteristics. It can be added that in foreign annual reports, the practice is spreading to indicate direct competitors for whom this analysis can be carried out, because who but the organizations themselves will be able to answer more accurately the question of who their main opponents in the market are. For example, in the annual report for 2022, Nike indicates the following competitors: adidas, Anta, ASICS, Li Ning, lululemon athletica, Puma, Under Armour and V.F. Corporation ^[11].

Since using one method to evaluate companies may not be enough, CCA is often combined with methods such as the discounted cash flow method (DCF), the dividend discounting model (DDM) and precedent transaction analysis (PTA). The first two are based on the intrinsic value of companies, ways to describe the perceived or true value of their assets. PTA is similar to CCA, but is used by banks to determine the value of a target company by analyzing previous transactions of similar companies.

Conclusions

Based on the studied levels, the statistical "sublevel" and examples of ensuring comparability of financial data, we are talking about the fact that first there is a general standardization of data, and if the accounting system of different data sources is absolutely the same, then at the theoretical level there will be no need for further adjustment. After that, there is a direct study of the data: finding dependencies, trends, which boils down to checking the usefulness and uniformity of the data, where either the sample for which the calculation was carried out is typical, and the error in regression models is random for any sample (respectively, the conclusions of the study can be interpreted to the entire population), or when studying the population, the sample similarities or differences are found in individual groups or subgroups.

This conclusion can be presented and expanded in the following diagram (Fig. 4):

Figure 4. Modern scheme of comparability of financial data. Source: compiled by the author.

The systematic part of comparability of financial data refers to the consistent application of accounting and/or tax accounting principles in various organizations or time periods. This involves ensuring that financial information is prepared and presented in a consistent manner, in accordance with established guidelines, laws; using the same accounting methods, valuation methods and disclosure practices in different organizations or accounting periods.

The functional part of comparability of financial data is related to the usefulness and relevance of financial information for decision-making purposes. In this case, it means evaluating data for various organizations or time periods, taking into account their specific business activities, industry practices and economic environment. This aspect is aimed at ensuring comparability of financial information in terms of its reliability and clarity for users when directly comparing companies.

Unfortunately, the studied concept of comparability is interpreted too broadly in practice. For example, if we study in more detail the industry comparability in the Russian Federation, then users are often limited only to the codes of the all-Russian classifier of types of economic activity (hereinafter - OKVED), which is not entirely correct. Having the same OKVED code does not mean that they are actually engaged in completely comparable activities. Very often, companies can be assigned to several OKVED codes and carry out activities that do not fall under the scope of all these codes. So, according to the order of Rosstandart dated 01/31/2014 N 14-st, the assignment of codes according to OKVED is aimed only at classifying and coding types of economic activities and information about them. This is not a limitation within which organizations are required to operate. Therefore, technically speaking, analyzing the results of the work of organizations through the available data, it is convenient to use OKVED codes, however, it is worth checking beforehand how similar the organizations that were selected for study are ^[12].

It is worth noting that the types of activities within the same class, subclass and group differ significantly and should not be considered comparable. For example, in group 56.10 there are various types of activities, such as the activities of restaurants and cafes with full catering (division 56.10.1), activities for the preparation and/or sale of food ready for immediate consumption on site, from vehicles or mobile shops (division 56.10.2), as well as the activities of restaurants and bars to ensure meals in railway dining cars and on ships (subclass 56.10.3) which cannot be used as fully comparable activities. They are similar, but their business models and customer base will be different. In this case, the second of these subclasses is further divided into the following positions:

1. 56.10.21 - Activities of catering establishments with takeaway service;

2. 56.10.22 - Activities of mobile food shops for the preparation and/or sale of ready-to-eat food;

3. 56.10.23 - Activity of trailers, tents for the preparation and sale of ice cream;

4. 56.10.24 - The activity of market stalls and food stalls.

Despite this, the tax authorities practice the formation of average tax burden and profitability indicators based on the entire group for:

1. Planning of on-site tax audits (within the framework of the Order of the Federal Tax Service of Russia dated 30.05.2007 N MM-3-06/333@ (ed. dated 05/10/2012) "On approval of the Concept of the on-site tax audit planning system");

2. inspections of companies and their counterparties within the framework of the electronic service of the Federal Tax Service "Transparent Business".

If the company as a whole is being audited by the tax authorities, the opportunity to demonstrate that its financial data is consistent with industry averages can provide reliable protection, help justify its financial position and decisions, reducing the risk of penalties. This is due to the fact that within the framework of the organization of control checks in our country, a risk assessment policy has been chosen (a risk-based approach to the prevention of violations through voluntary clarifications) and if deviations from a certain level are detected during the analysis of the financial and economic activities of an organization, then the chance of conducting inspections in relation to it increases.

There are three logical problems in comparing a company only by the average indicators of an industry group:

First, in general, the existing classifications are too broad, as mentioned earlier, and cannot be fully relied upon. They also become outdated quickly and require constant revision. The last major changes in the Russian Federation were approved by the Order of Rosstandart dated 01/31/2014 No. 14-art. Foreign classifiers, for example, NACE, have prepared a new version in 2023, which will be gradually implemented in all relevant fields of statistics starting in 2025 ^[13].

Secondly, the government's expectation of industry averages from companies contradicts economic theories about sustainable development. The latter may begin to strive to minimize risks by achieving typical business results.

On the one hand, this approach can be considered correct, since in the field of statistics, the average value plays a crucial role in understanding and interpreting data. It's not just a mathematical tool; it's also an expectation. Additionally, traditionally in the long term in strategic management, microeconomics of production and competitive economics, it is indeed predicted that industries will consist of identical firms offering identical products and services at identical prices.

On the other hand, there is an imposition on competition, as a separate institution of economics, of the expectation framework for certain levels of profitability and tax burden. It is also not indicated anywhere that the calculated indicators reflect the effective operation of enterprises.

Additionally, if we talk about the contradiction of economic theories, then:

1.      According to the concept of competitive heterogeneity by G. Demsetz ^[14], in practice, it is often unprofitable for firms to be similar to others and copy the structures of competitors. Firms with better management that adapt more quickly to changes in the market (given the resources and funds available to them, rather than the successful experience of other companies that are likely to operate, albeit in similar but different conditions in various aspects), will produce better products (or similar products with lower costs) than their competitors. Such firms provide better products or lower prices (the optimal solution based on lower costs) with a higher level of demand, which leads to an increase in their revenue. These firms are getting bigger than more poorly managed, trained competitors. M. Porter ^[15] argued within the framework of the same theory that companies within a certain industry, as a rule, begin to form strategic clusters. These clusters should consist of similar companies, and the transition between different clusters can be difficult and expensive due to barriers limiting their territorial mobility. It would be illogical to compare the average performance of companies separately, without taking into account the localization of their market interests and position, role in clusters.

2.      In the theory of the innovative firm of U. Laconica ^[16], entrepreneurs themselves create new opportunities for profit, which upsets the balance in a particular industry. An entrepreneur must have specialized knowledge about the industry in which he competes so that his company can innovate. In the theory of an innovative firm, strategic decisions related to investments reflect the level of fixed costs. One of the goals of such investments is to provide the company with special production capabilities that distinguish it from competitors, which creates differences in its work and final reporting indicators. An innovative firm invests to overcome technological and market conditions that its competitors view as constraints. However, this requires higher research costs, the purchase of new technologies and the addition of the risk that such investments will not pay for themselves in the future.

3. Firms can carry out their activities at different stages of their life cycle according to I. Adizes, accordingly, it is wrong to compare a company that has just been formed with a company that is in decline in the same industry. Products also have their own life cycle, so firms should move out of the "comfort zone", the boundaries of their industry, industrial group, or subgroup, if their goal is to develop, ensure their competitiveness in the long term, and not maximize profits in the short term through aggressive strategies to optimize costs and revenues, or minimize risks by purposefully aligning their indicators with industry ones.

In terms of points 2 and 3 above, we can also recall the fact that states as a whole are trying to increase investment activity and stimulate economic development. Enterprises in priority sectors and new market participants are offered various kinds of support measures. For example, in Russia, territorial projects can be identified (special economic zones, zones of territorial development and territories of advanced development, regional investment projects), investment tax deductions, loans, tax incentives for small and medium-sized businesses (benefits for IT enterprises, tax holidays for sole proprietors, reduced late fees, etc.). Therefore organizations and enterprises have access to additional tools for their strategic development, which both allow them to gain advantages over competitors and impose a number of restrictions and requirements on taxpayers.

Thirdly, since there will always be those who become more successful and larger, and those who remain behind, these groups of firms will be considered statistical outliers that stand out from the general sample. Outliers can significantly affect average statistics because they represent extreme values that are very different from others. When calculating the average value, they can distort the result, bringing it closer to their level. Whether the same tax authorities take into account the presence of emission companies when they form their indicators for planning on-site inspections is not indicated by the Federal Tax Service. In addition, the FTS methodology assumes accounting for unprofitable companies, which, within the framework of transfer pricing, it is not always advisable to take into account when comparing.

However, it is important to note that, unlike the published information on the average industry indicators of the tax burden, profitability of goods sold, products, works, services and profitability of assets of organizations by type of economic activity, the service of the Federal Tax Service "transparent business" has clear advantages.

When checking your own indicators, you can calculate the average industry indicators of the tax burden and profitability for organizations in the general tax regime through such clarifying calculation parameters as:

1. A subject of the Russian Federation. This item is responsible for reflecting regional factors affecting the business environment.

2.      The scale of activity (microenterprise, small, medium or large).

3.      Type of economic activity (not only the general types specified according to OKVED-2, but also their groups, however, there are still no subclasses and more detailed details).

Another important functional difference between these audits in assessing the financial performance of taxpayers is the methodology for calculating the tax burden. In the first case, related to on-site inspections, the calculation of the load is made taking into account income tax receipts. This corresponds to the letter of the Federal Tax Service of Russia dated June 29, 2018 No. BA-4-1/12589@.  In the second case, "the average industry values of the tax burden and return on sales are calculated on the basis of data from the SOI PC, put into commercial operation by order dated 01/22/2016 N MMV-7-12/27@ "On the commissioning of software for individual tasks of the List of tasks of the Information and analytical subsystem AIS "Tax-3", access to which is provided to all territorial tax authorities" ^[17]. At the same time, the calculation of the tax burden excludes agency payments, which include corporate income tax on dividends and personal income tax. Insurance payments are also excluded.

To sum up, data comparability is a complex concept that requires taking into account as much detail as possible. In this paper, we have considered only some of the problems that users of financial information may face when comparing data. As you can see, even the tax authorities do not have a common position on how to functionally compare organizations. In this regard, an individual approach and/or a study of the data spread, measures of variance, correlation/regression analysis of data is necessary. In transfer pricing, it is necessary to ensure not only industry comparability, but also operational, when checking due diligence under Article 54.1 of the Tax Code of the Russian Federation, it is necessary to prove business transparency, while minimizing the risks of tax audit, similar approaches can be used, where the tax burden is calculated differently.