Vladimir Stepanovich Bochko

Candidate of Economic Sciences, Professor, Honored Economist of the Russian Federation, Deputy Director of the Institute of Economics of the Ural Branch of the Russian Academy of Sciences

GROSS REGIONAL PRODUCT:

ASSESSMENT OF TERRITORY DEVELOPMENT

In the context of the increasing role of the constituent entities of the Russian Federation in the economic development of the country, it is necessary to more actively use modern indicators to assess the dynamics and socio-economic potential of the regions.

A logical continuation of the used system of national accounts (SNA), to which Russia is switching, is the system of regional accounts (SRA). A.G. draws attention to this. Granberg, Yu.S. Zaitseva, N.N. Mikheeva, A.A. Miroyedov, O.A. Sharamygina and other researchers.

The key indicator of the system of national accounts at the regional level is the gross regional product (GRP). The methodological principles of its construction were developed by Nobel laureate R. Stone in the 50s of the twentieth century. Currently, regional accounts are used in many countries around the world. In Russia, GRP has been calculated since 1994. At the same time, the first steps are being taken to create an SDS. At the same time, the State Statistics Committee of the Russian Federation follows the methodological provisions of the European Statistical Committee, which recommends starting work on the CDS with calculations by region of gross value added and gross capital formation.

The use of the GRP indicator is of particular importance in the context of the formation of a new scientific direction in the study of territories, which is called “spatial economics”. A significant contribution to the development of its theoretical and methodological foundations was made by E.G. Animice,

N.M. Surnina and other Ural researchers.

This article makes an attempt to analyze the gross regional product of the Sverdlovsk region from the point of view of assessing the economic development of the region.

The advantage of GRP is that with its help you can not only assess the development of a specific subject of the Federation, but also carry out

an objective comparison of the level of development of various constituent entities of the Russian Federation, as well as comparison with data for Russia as a whole.

To characterize the results of economic activity on a national scale, the gross domestic product (GDP) indicator is used.

Although GRP and GDP are very similar indicators in terms of economic content, they do not coincide with each other either quantitatively or qualitatively.

Firstly, the difference between GRP and GDP lies in the scale of coverage of activity results. GRP is limited to taking into account goods and services created in a certain territory of the country, called a region. Since a region, as a rule, is understood as a territory coinciding with the borders of a subject of the Federation, in statistical accounting GRP reflects the results of the activities of regions, republics and autonomous okrugs, which, according to the Constitution of the Russian Federation, are its subjects.

Secondly, GDP is greater than the sum of GRP for Russia, since in addition to it it includes added value that relates to the country as a whole and is not distributed among individual regions. At the federal level, GDP includes the value added of non-market collective services provided by government agencies to society as a whole (defense, public administration, etc.), added value created by financial and foreign trade intermediaries, as well as taxes on foreign economic activity.

The sectoral structure of GRP can be presented in the form of a diagram (Fig. 1), which includes two large groups of industries and the cost of net taxes on products.

Rice. 1. Structure of gross regional product

The first group of industries that ensure the creation of gross regional product includes industries producing goods. The most important among them are industry, agriculture,

construction, as well as forestry and other goods-producing activities.

The second group includes industries producing services. These include transport, communications, trade and catering, utilities, information and computing services, science, healthcare, education, management, etc. All services, in turn, are divided into market and non-market. At the same time, services in the field of healthcare, education, housing, culture and art, as well as geology and subsoil exploration can be of both a market and non-market nature, and in trade, transport, communications and some other industries - only market.

Net product taxes are product taxes minus product subsidies. As is known, a subsidy is a benefit in cash or in kind provided by the state at the expense of state or local budgets, as well as special funds to legal entities and individuals, local authorities. There are direct subsidies aimed at developing necessary sectors of the economy, and indirect subsidies, which are a system of preferential tax rates, an accelerated depreciation policy, etc.

Product subsidies are a type of subsidy paid by the government to the manufacturer per unit of good (service) produced. Most often, socially significant types of goods (services) are subsidized, the prices of which, in the absence of subsidies, would be too high for the mass consumer. With the help of subsidies, losses from the sale of products at prices that do not cover production costs and do not bring a certain amount of profit are compensated.

Since GRP represents the newly created value of goods and services produced in the territory, it is calculated as a set of added values ​​of economic sectors of the region or, in other words, as gross value added. GRP is calculated in current market and basic prices (nominal GRP volume) and in comparable prices (real GRP volume)1.

Sectoral structure of GRP of the Sverdlovsk region. The main volumetric characteristics of the structure of the gross regional product in the Sverdlovsk region are given in table. 1.

1 Market price - the price of the final buyer. It includes trade and transport margins, taxes on production and imports, and does not include subsidies on production and imports. To eliminate the impact of different tax rates and subsidies in different sectors of the economy on the structure of production and income generation, industry indicators are presented in estimates at basic prices. Basic price is the price received by a producer for a unit of a good or service, excluding product taxes but including product subsidies. Non-market goods and services are valued using the market price of similar goods and services sold on the market, if it can be determined, or by production costs if a market price is not available (in particular, this is how services of government agencies and non-profit organizations are valued).

Table 1

Sectoral structure of the gross regional product of the Sverdlovsk region, % of the gross regional product

Year Industries producing goods Of which Industries producing services Of which Net taxes on products

Industry Agriculture C o r t S Transport Communications Trade and catering

1995 53,2 36,3 10,5

1996* 51,7 36,6 5,8 8,9 40,3 10,8 1,1 9,0 8,0

1997* 47,1 34,0 6,3 6,1 44,0 11,2 1,2 10,0 8,9

1998 51,6 39,2 5,6 6,0 41,8 10,3 1,2 10,8 6,6

1999 55,6 42,2 6,6 6,3 37,7 8,3 1,0 10,8 6,7

2000 55,9 43,5 5,5 6,2 38,1 9,5 1,2 10,7 6,0

2001* 54,7 42,2 5,9 5,9 39,9 9,4 1,3 11,7 5,4

Note. * Calculated based on data from the Sverdlovsk Regional State Statistics Committee.

In first place in terms of specific gravity, as can be seen from the table. 1, there are industries that produce goods. They account for more than half of the gross regional product. Moreover, their share not only remains, but also gradually increases. Thus, in 1995 it was 53.2%, then it decreased slightly, but at the end of the 1990s it began to increase again and reached 55.9% in 2000. In 2001, it decreased to 54.7%, but the overall share of industries producing goods remains quite high and there are no signs that it will decrease.

If we compare similar processes in Russia as a whole and in highly developed industrial countries, we will have to note that, in comparison with the Sverdlovsk region, they are going in the opposite direction: the share of industries producing services is growing in them, and not vice versa.

With the intensification of market reform, the sectoral structure of Russia's GDP is gradually but steadily changing in favor of industries producing services. Thus, in 1995, the share of industries producing goods in Russia was almost the same as in the Sverdlovsk region, i.e. was 53.3%, and

by 2000 it had dropped to 47.6%. At the same time, the share of industries producing services increased from 38.1% in 1995 to 45.0% in 2000. There is an increase in the share of trade and public catering in this area (14.0% in 1998 and 19.3% in 2000), which naturally reflects the development of market relations and the focus of economic development on meeting the needs of people in accordance with the demand of the population.

So, with the initial values ​​of the share of industries producing goods almost identical for the Sverdlovsk region and Russia in 1995 (53.2% - Sverdlovsk region; 53.3% - Russia), by 2000 the situation had changed

so much so that the Sverdlovsk region overtook Russia by more than 7 percentage points (55.9% - Sverdlovsk region; 47.6% - Russia). This negative economic process from the point of view of the development of market relations continues to be consolidated by the economic and investment policies pursued in the region.

The deterioration of the GRP structure in the Sverdlovsk region is caused by an increase in the share of industry among sectors producing goods (from 36.6% in 1996 to 42.2% in 2001), including due to the metallurgical complex. In 1993, ferrous and non-ferrous metallurgy together provided 45.9% of industrial production, and in 2000 it was already 50.2%. According to the Ministry of Economy and Labor of the Sverdlovsk Region, their share in 2003 was 52.5%. At the same time, the share of agriculture, transport, communications, trade and public catering changed slightly.

The mere fact of strengthening the industrial-production direction of development does not carry anything negative. Each region must use its resources and capabilities. Focusing on them, the subjects of the Federation are looking for ways to raise the level of their economic development. Following this methodological approach, it is natural to believe that the Sverdlovsk region in modern conditions ensures its development precisely on the basis of using the existing objective prerequisites and material conditions. In other words, being an industrial region, it continues to increase primarily its industrial potential.

But such conclusions are correct only as long as we remain at the level of aggregated indicators. If we move from analyzing industry as a whole to considering its structure by industry and clarifying the role and share of each industry in the development of the regional economy, then some generally correct provisions will have to be somewhat adjusted and clarified. The most important among them will be the assertion that only such an industrial structure is optimal in which manufacturing industries occupy a worthy place, and among them the main role belongs to knowledge-intensive industries. Therefore, the raw material orientation of the industrial structure cannot be considered its best option.

A positive process in changes in the structure of GRP should be to increase the share of industries producing services. The need for such a direction of transformation of the structure of the gross regional product is associated, firstly, with the creation of a market infrastructure, especially with the development of banking, lending, insurance, real estate transactions, etc., and secondly, with the restructuring of production for the production of technical equipment. goods and services that are increasingly focused on the diverse demand of the population, both in terms of price parameters and quality characteristics.

GRP per capita. In the analysis of GRP, an important place is occupied by identifying trends in the value of gross regional product per capita. This figure, perhaps at its greatest

least, reflects the dynamics of economic activity unfolding in the region.

In statistics, data on GRP per capita are given not in comparable prices, but in current prices. This makes it difficult to carry out some calculations, for example, comparisons of GRP dynamics of the same region over a number of years, since actual data include price increases due to inflation. Depending on how different the inflation rates were in the periods being compared, the degree of error in the calculations changes.

If comparisons are made for the same year between different regions, then the level of inflation does not matter, since both in the country as a whole and in individual regions, prices in a given period of time grew approximately to the same extent. Therefore, the value of GRP per capita allows us to objectively compare the situation of some regions with others for a certain year, since in this case inflationary processes have practically no effect on the value of calculations. The slight differences in inflation rates across different regions are so small that they should only be taken into account when performing special calculations. For a general comparison of the activities of regions and establishing relationships in their development, differences in regional inflation are not of fundamental importance.

In the case where comparisons are made for different years, it is possible to compare data only “horizontally”, i.e. take different regions and compare their development for a certain year. The transition to a “vertical” comparison is possible only when the year-to-year comparison does not act as a correlation over time of the indicators of a given region to itself, but as a result of comparing different regions with each other “horizontally.”

Let us analyze the relationship between changes in the GRP of the Sverdlovsk region and the GDP of the Russian Federation. The data given in table. 2 allow us to detect two trends characteristic of the region. The first is that the GRP per capita in the region is constantly increasing. In nominal terms, it increased from 4,240.1 rubles. in 1994 to 47,028.0 rubles. in 2001, i.e. more than 11 times. Naturally, the main component of such growth was inflation. At the same time, a certain share is made up of the actual increase in GRP due to the growth of production in the second half of the 90s of the twentieth century. The second trend is less rosy and even alarming. It consists in a relative decrease in the value of the gross regional product per resident of the region, compared with the indicator for the Russian Federation as a whole.

table 2

The ratio of GRP per capita for the Sverdlovsk region and the Russian Federation,

rub., before 1998 - thousand rubles.

Year Sverdlovsk region Russian Federation Sverdlovsk region in relation to the Russian Federation, %

1994 4 240,1 3 583,7 (+) 18,3

1995 12 376,0 9 566,3 (+) 29,4

1996 14 378,4 13 230,0 (+) 8,7

1997 15 902,2 15 212,3 (+) 4,5

1998 16 832,7 16 590,8 (+) 1,5

1999 26 044,6 28 492,1 (-) 8,6

2000 36 094,1 42 902,1 (-) 15,9

2001 47 028,0 54 325,8 (-) 13,4

From the table 2 shows that from 1994 to 1998 inclusive, there was an excess of GRP per capita in the Sverdlovsk region compared to Russia. In 1994 it was 18.3%, in 1995 it increased to 29.4%. But starting from 1996, the amount of excess gradually decreased in

1998 was only 1.5%.

Since 1999, the level of GRP per capita in the Sverdlovsk region became lower than in Russia, and remained this way in subsequent years. In 2001, it was 13.4% lower than the all-Russian one.

Such a stable downward process can only indicate that the real development of the regional economy over the analyzed years is experiencing significant difficulties. One of the reasons for this situation is not just the preservation of a high share of goods-producing industries in the region, but also the growth within them of the share of raw materials-oriented industries, primarily ferrous and non-ferrous metallurgy.

The ratio of the dynamics of gross regional product per capita in the Sverdlovsk region and in the Russian Federation is clearly shown in Fig. 2. Initially, the Sverdlovsk region steadily overtook the Russian Federation, and then just as steadily began to lag behind it.

Sverdlovsk region -■-Russian Federation

Rice. 2. Ratio of GRP per capita of the Sverdlovsk region and the Russian Federation

To verify this alarming conclusion and establish its objectivity, we decided to carry out additional calculations by comparing the development of the Sverdlovsk region with neighboring regions that are located in approximately the same geographical,

climatic and economic-industrial conditions. Such regions, naturally, are primarily the Chelyabinsk and Perm regions. They are so close in terms of overall industrial potential and other development indicators that in the scientific literature all three areas are often combined with the concept of “old industrial regions.”

First look at the table. 3 shows that the Sverdlovsk region is developing better than the Chelyabinsk region, but is inferior to the Perm region.

Table 3

The ratio of GRP per capita in the Sverdlovsk, Chelyabinsk and Perm regions, rubles, until 1998 - thousand rubles.

Year Sverdlovsk region Chelyabinsk region Perm region Correlation of the indicator of the Sverdlovsk region, %

with the Chelyabinsk region with the Perm region

1994 4 240,1 3 844,5 4 436,5 (+) 10,3 (-) 4,4

1995 12 376,0 8 967,3 12 291,5 (+) 38,0 (+) 0,7

1996 14 378,4 13 193,2 14 481,8 (+) 9,0 (-) 0,7

1997 15 902,2 14 110,6 16 724,4 (+) 12,7 (-) 5,0

1998 16 832,7 12 700,5 18 615,5 (+) 32,5 (-) 9,6

1999 26 044,6 22 713,7 31 571,7 (+) 14,7 (-) 17,5

2000 36 094,1 36 908,7 43 869,7 (-) 2,2 (-) 17,7

2001 47 028,0 41 557,4 63 183,0 (+) 13,2 (-) 25,6

However, if the general assessment conclusion is correct, attention should be paid to the emerging trend of gradual deterioration in the dynamics of indicators of the Sverdlovsk region in relation to both the Chelyabinsk and Perm regions. Thus, in the mid-1990s, the Sverdlovsk region had a significant advantage over the Chelyabinsk region, reaching, for example, 32.5% in 1998. But since the late 1990s, the gap began to decrease and in 2000 it was negative.

When comparing indicators with the Perm region, the dynamics of development are also visible not in favor of the Sverdlovsk region. Thus, in the mid-1990s, the GRP per capita values ​​in both regions were almost the same: in 1995, the GRP of the Sverdlovsk region exceeded the same indicator of the Perm region by 0.7%, and in 1996 it was lower by the same amount. In other words, development in neighboring regions followed “the same scenarios.” However, since 1997, a clear gap has begun for the Perm region; it is actively moving forward, increasing the distance every year. In 1997 the difference was 5.0%, in 1998 - 9.6, in

1999 - 17.5, and in 2001 already 25.6%.

What is causing the gap to widen? Does the intensification of economic activity in the Perm region play a role here, or is the situation worsening in the Sverdlovsk region? Most likely, both occur.

If the reason for the success of the Perm region in comparison with the Sverdlovsk region were only in the factors of the Perm region itself, then when competing with such regions equal in production and economic potential, the gap in indicators would be significantly smaller, as evidenced by development data before 1996. Consequently, the lag is also associated with some negative processes occurring in the Sverdlovsk region itself. One of the reasons for this situation was the consolidation of its raw material orientation.

Dynamics of growth in the physical volume of GRP in the Sverdlovsk region. Since the cost indicators of changes in the gross regional product are largely burdened with an inflationary component, they cannot reflect the real changes that occur with GRP. The greatest difficulties arise in obtaining objective data when it is necessary to compare the indicators of the same region over a number of years. Therefore, to obtain a real picture, which should reflect the actual processes in the dynamics of GRP, the indicator of the index of the physical volume of GRP is used. In this case, the gross regional product is calculated in comparable prices and reflects the real volume.

Due to a certain faster development of Russia as a whole and its individual regions, the share of the Sverdlovsk region in the total volume of the country's gross regional product is gradually decreasing. If in 1995 the share of GRP of the Sverdlovsk region in the all-Russian volume was 4.1%, then in 2001 it was only 2.7%.

The physical volume index of the gross regional product of the Sverdlovsk region also changes unevenly (Table 4).

Table 4

Index of physical volume of GRP of the Sverdlovsk region,% compared to the previous year

Year Sverdlovsk region For reference: change in the physical volume of total GRP in the Russian Federation

1999 101,8 105,6

2000 112,2 110,7

2001 108,7 106,0

2002* 103,8 104,3

2003* 106,5 106,9

Note. * For the Sverdlovsk region - according to data from the Sverdlovsk Regional State Statistics Committee, for the Russian Federation - current data from the State Statistics Committee of the Russian Federation.

From the table Figure 4 shows that the GRP of the Sverdlovsk region in real terms began to grow since 1999. The most successful period was 2000, when GRP increased by 12.2%. Hopes arose to maintain such high rates in subsequent years. Although 2001 ended with a decline in growth rates, the latter were at such a high level that new positive economic development could be expected. These two prosperous years were also significant in that for the first time the Sverdlovsk region overtook the Russian Federation in terms of GRP growth rates. If in 2000 in the Russian Federation the GRP growth rate was 110.7%, then in the Sverdlovsk region its growth was 1.5 percentage points higher and equaled 112.2%. In 2001, a favorable outcome was again on the side of our region. It seemed that the regional economy had entered the right direction and would continue its development at the given rhythm.

However, the next year undermined hopes for sustainable advanced development of the region not only in relation to the Russian Federation. In 2002, the region's GRP grew by only 3.8%, which in itself was a low increase. In addition, this figure again became less than the all-Russian one.

There was hope that this was an accidental breakdown. But the data for 2003 again showed the result not in favor of the Sverdlovsk region. This leads to the idea that lower growth rates of GRP in the region compared to Russia may become a recurring phenomenon.

The likelihood of such consequences is evidenced by the dynamics of GRP in the Sverdlovsk region and GRP in Russia as a whole over the past 7 years, presented in Fig. 3. Except 2000 and 2001. throughout the rest of the period, the growth rate of the physical volume of GRP of the region was lower than the growth rate of the total GRP of the Russian Federation.

/1Ї0 // 105U, h. ^ %h108.7 HL0bh 106.9 104.^106.5

Ш 101.2 Г / / / > 101.8 // "Чг 103.8

*h9b\ h \ // // 93/b/

Sverdlovsk region -■---Russian Federation

Rice. 3. Comparative dynamics of the physical volume of GRP of the Sverdlovsk region and GRP of the Russian Federation as a whole

The problem of doubling the GRP of the Sverdlovsk region in relation to

2000 Since the gross regional product in a synthesized form reflects the results of the region’s work, and the gross domestic product reflects the results of the country’s economic activity, state and regional leaders began to turn to these indicators. This made it possible to focus the attention of entrepreneurs and the entire population on solving a problem that, on the one hand, would be understandable to everyone, and on the other, would not simplify the essence of the proposed guidelines.

Both GRP and GDP characterize the final result of the production activities of economic units. These indicators reflect the value of final goods and services produced by these units during the reporting period at final buyer prices. Consequently, they direct the population and economic entities to produce not just finished products and services, but exclusively those that are in effective demand.

In economic terms, GRP, like GDP, when calculated using the production method, represents the sum of the gross added value of all industries. This means that society must organize the activities of enterprises, organizations and spheres of social production in such a way that the share of added value in a product (service) tends to increase. This will result in an increase in labor efficiency and productivity. But not only that. What is important is that part of the added value comes to workers in the form of their wages and, ultimately, their income. Therefore, it becomes clear that an increase in GRP (or GDP) is equivalent to an increase in the well-being of the population of a region or country.

Based on this economic understanding of GRP (GDP), the problem of its growth is indeed the most important for both regional and national leaders, as well as for performers of any level, rank, position and qualifications. The increase in GRP (GDP) determines the success of the development of society, the individual, his material wealth and the conditions for increasing spiritual culture. Therefore, the task (and problem) of actively increasing GRP and GDP may become the main mobilizing economic slogan for the next 20-25 years both for individual regions and for Russia as a whole.

Currently, the leadership of the Sverdlovsk region has set the task of doubling the GRP by 2010. This follows the call of the President of the country to double Russia's GDP by the same date.

How possible is it to solve the named problem in the specified period of time? To answer this question, it is necessary to find out, firstly, how the region “steps” in terms of GRP increment, and secondly, how it should “step” in order to reach the specified finish line on time.

The movement of the Sverdlovsk region to increase GRP was discussed above. If we take 2000 as the base for doubling GRP, then the “step” of the region was slowing down: in 2001, the growth of GRP was 8.7%, in 2002 - 3.8%. The situation improved somewhat in 2003: the growth rate of GRP was 6.5%. The average annual growth for this period was 6.3%.

Our calculations show that if we take the level of GRP of the Sverdlovsk region in 2000 as one, then to double it in 10 years, i.e. by 2010, it is necessary to ensure an average annual increase in GRP of at least 7.5%\

If in any year the growth rate is below this figure, then in subsequent years it will be necessary to exceed 7.5% growth.

The regional government intends to end 2004 with a GRP increase of 7.5%. If this happens, then the Sverdlovsk region can enter into a rhythm of movement that will give it the opportunity to actually achieve the stated goal by 2010.

1 Calculations for the Sverdlovsk region correspond to the dynamics of gross domestic product indices for Russia as a whole. In 2000, its GDP was 66% of the 1990 level. To double this value by 2010, it is necessary to have a GDP growth rate of at least 7.5-7.7% per year. However, practice shows that Russia has not yet reached the level of 7.5% annual GDP growth. In any case, in 2001, GDP growth was 5.0%, in 2002 -4.3%, and in 2003 - 6.9%.

At the same time, from the point of view of improving the well-being of the entire population, one should not overestimate the importance of the growth of the gross regional product of the Sverdlovsk region by 2 times by 2010, since even a doubled GRP in its physical volume will only approach the level of 1990 or slightly less will exceed.

A fundamentally important point is the identification and implementation of the GRP base that will ensure the required level of growth in the gross regional product. We must proceed, firstly, from an analysis of the share of industries in the structure of GRP and their growth rates, and secondly, from the direction of economic development of the region as a whole.

Table data 5 show that over the six years analyzed, serious changes, both positive and negative, have occurred in the structure and share of individual industries.

Table 5

Dynamics of the GRP structure of the Sverdlovsk region by industry (calculated based on)

Share of gross added

Industry value sectors, %

1996 2001

Production of goods 51.75 54.73

Including by industry:

industry 36.61 42.18

agriculture 5.76 5.93

forestry 0.13 0.11

construction 8.90 5.87

other activities for the production of goods 0.34 0.63

Production of services 40.29 39.86

Market services 31.34 33.33

Including by industry:

transport 10.75 9.44

communication 1.14 1.27

trade and catering 8.97 11.69

information and computing services 0.04 0.30

real estate transactions 1.49 3.58

utilities 2.61 1.24

insurance 0.18 0.43

housing 1.39 0.87

provision 0.59 1.48

public education 0.27 0.57

culture and art 0.08 0.11

management 1.06 0.58

other market services 2.77 1.77

Non-market services 8.95 6.53

Including by industry:

housing 0.95 0.37

healthcare, physical education and social

provision 3.06 1.85

public education 3.20 2.27

culture and art 0.29 0.22

control 1.01 1.77

other non-market services 0.44 0.05

Net taxes on products 7.96 5.41

Among the positive aspects, one should mention the preservation of the share of services in the total volume of GRP. In 1996 they amounted to 40.29%, and by 2001 they decreased only slightly and amounted to 39.86%. But this is relative prosperity, since after all, the share of services should grow, not decline. In addition, it is important to note such a phenomenon as an increase in the share of market services and, accordingly, a decrease in the share of non-market services.

A more important positive shift is a significant increase in the share of trade and public catering, information and computing services, and real estate transactions among market services. This series of positive changes indicates the gradual consolidation of market relations in the economy and the creation of the necessary infrastructure for them.

There is also a significant amount of negative developments. Firstly, there was an increase in the share of industries producing goods, which does not correspond to Russian and global trends in transformations of the GRP structure. Secondly, the share of industry continues to increase. In general, this is not a negative characteristic, but on the condition that manufacturing industries will predominate among industrial sectors rather than raw materials. Thirdly, the share of construction has decreased, which may lead to a decrease in GRP growth, since construction is usually one of the drivers of the overall increase in growth rates. Fourthly, the share of transport and housing among market services is falling, although usually it is these sectors, along with communications, that rush forward with the development of market relations. Fifthly, a limiting factor in increasing the growth rate of GRP may be an increase in the share of management in the system of non-market services: from 1996 to 2001 it increased from 1.01 to 1.77%. Increasing management costs from budgetary funds indicate not only an increase in the salaries and incomes of officials, but also an increase in their number itself, which leads to the bureaucratization of the system of management of the economy and society.

The above-mentioned positive and negative trends in changes in the structure of GRP do not exhaust the full depth of changes that took place during the period from 1996 to 2001 inclusive. But they suggest ways to choose directions for improving the structure of the regional economy in order to increase the growth rate of GRP and the economic well-being of the population.

It should be understood that the focus on raw materials will not save the region. Its wealth lies not in natural resources, but in the ability to use them. Therefore, it is necessary to develop intellectual industries, primarily manufacturing, and rely on knowledge-intensive industries.

Literature

1. Granberg A., Zaitseva Yu. Production and use of gross regional product: interregional comparisons // Russian Economic Journal. 2002. No. 10.

2. Miroyedov A.A., Sharamygina O.A. Using the gross regional product indicator in assessing the economic development of the region // Questions of Statistics. 2003. No. 9.

3. Mikheeva N.N. Macroeconomic analysis based on regional accounts. Khabarovsk-Vladivostok: Dalnauka, 1998.

4. Surnina N.M. Spatial economics: problems of theory, methodology and practice / Scientific. ed. E.G. Animitsa. Ekaterinburg: Ural Publishing House. state econ. University, 2003.

5. Regions of Russia: Stat. Sat.: In 2 volumes / Goskomstat of Russia. M., 1998. T. 2.

6. Regions of Russia: Stat. Sat.: In 2 volumes / Goskomstat of Russia. M., 2001. T. 2.

7. Regions of Russia. Socio-economic indicators. 2002: Stat. Sat. / Goskomstat of Russia. M., 2002.

8. Regions of Russia. Socio-economic indicators. 2003: Stat. Sat. / Goskomstat of Russia. M., 2003.

9. Russian statistical yearbook. 2002: Stat. Sat. / Goskomstat of Russia. M., 2002.

10. Russian statistical yearbook. 2003: Stat. Sat. / Goskomstat of Russia. M., 2003.

11. “Express information” of the Sverdlovsk Regional Committee of State Statistics for 1996 and 2001.

Description of work

The purpose of this course work is to conduct a statistical analysis of the produced GRP using the example of the Vologda region.
The objectives of the work are:
study of the GRP indicator and its place in the national accounting system;

analysis of GRP dynamics for the period from 2000 to 2010

Introduction………………………………………………………………………………...…3

2. Analysis of the structure and dynamics of the produced GRP………………..……..10

2.3 Determination of the main trend of GRP by various methods……….13
3. Study of the relationship between the produced GRP and the factors influencing it………………………………………………………………..….17
3.1 Paired correlation-regression analysis………………………...17
3.2 Multiple correlation and regression analysis………………23
3.3 Forecasting the produced GRP based on the trend equation and on the basis of the regression equation………………………………………………………..…23
Conclusion……………………………………………………………………………….…30
List of references………………………………………………………...34
Applications………………………………………………………………………………...…35

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<<Вологодская государственная молочнохозяйственная

Academy named after N.V. Vereshchagina>>

Faculty of Economics

Specialty: Finance and credit

Correspondence studies

Statistics and Information Technology

COURSE WORK

on financial statistics

“Statistical analysis of produced GRP”

Completed by Yu.A. Kotova

student, code 1040041

I checked N.B. Vershinina

Art. teacher

Vologda – Dairy

Introduction…………………………………………………………………………………………3

1. Place of GRP in the national accounting system…………………………5

2. Analysis of the structure and dynamics of the produced GRP………………..…….. 10

2.1 Analysis of the GRP structure……………………………………………………………...10

2.2 Analysis of GRP dynamics…………………………………………………....12

2.3 Determination of the main trend of GRP by various methods……….13

3. Study of the relationship between the produced GRP and the factors influencing it……………………………………………………………… ………..….17

3.1 Paired correlation and regression analysis………………………...17

3.2 Multiple correlation and regression analysis………………23

3.3 Forecasting the produced GRP based on the trend equation and on the basis of the regression equation………………………………………………………..…23

3.4 Factor analysis using the index method……………………………...26

Conclusion…………………………………………………………………….…30

List of references………………………………………………………... 34

Applications……………………………………………………… …………...……35

Introduction

The modern economic status of the constituent entities of the Russian Federation requires the use of various tools for assessing economic development, financial balance, and competitive conditions in the domestic and world markets. On the other hand, such tools are necessary for pursuing an active federal policy aimed at eliminating interregional imbalances and strengthening the economic and political integrity of the country.

Strengthening the independence of regions and the development of fiscal federalism increase the importance of regional policy. In these conditions, the development of regional management decisions requires modern approaches to their information support and economic justification. From this point of view, the system of national accounts (SNA) is a universal basis for a comprehensive analysis of the general characteristics of a market economy. The logical continuation of the SNA for the regional level is the system of regional accounts (SRA). The central position in the SNA is occupied by the gross domestic product (GDP), and in the SDS its regional analogue is the gross regional product (GRP). It characterizes the level of economic development and the results of economic activity of all economic entities in the region.

Without GDP (GRP), it is impossible to construct the most important national (regional) accounts.

In Russia, the SNA began to be implemented at the federal level. However, regions also feel the need for a modern statistical generalization model. In our country, which unites 89 territorial and administrative entities with different time zones and geographical locations, there are significant differences in the levels of economic and social development of regions. Therefore, the problem of calculating the gross product for each region is especially acute.

Not only territorial authorities, but also the state as a whole are interested in information that comprehensively characterizes the economy of all regions, allowing for the development of economic policy and assessment of the effectiveness of decisions made at the regional level.

The most general quantitative indicator of regional economic development is the dynamics of the territory's gross product. Interregional comparisons on its basis, using, if necessary, additional cost and natural indicators, make it possible to determine the direction and intensity of economic processes leading to serious shifts in the interregional balance of forces.

The task of calculating regional macroeconomic indicators is of particular importance in connection with the increasing role of GRP in reforming interbudgetary relations and the use of this indicator in the distribution of funds from the Fund for Financial Support of Subjects of the Russian Federation.

The purpose of this course work is to conduct a statistical analysis of the produced GRP using the example of the Vologda region.

The objectives of the work are:

1. study of the GRP indicator and its place in the national accounting system;
2. comparative structure analysis
3. analysis of GRP dynamics for the period from 2000 to 2010;
4. determination of the main trend of GRP using the methods of enlarged intervals, moving average and analytical leveling;
5. conducting paired and multiple correlation and regression analysis;
6. forecasting of produced GRP based on the trend equation and on the basis of the regression equation.
7. conducting factor analysis of the gross regional product using the index method for 2009 and 2010.

The subject of the course work is GRP, and the object is the Vologda region.

The course work used Microsoft Word, Microsoft Excel, StatWork, as well as methods - tabular, graphical, comparisons, calculation of dynamics indicators, the method of averages, large intervals, moving average, analytical alignment and correlation-regression method.

Statistical data for the analyzed period – from 2000 to 2010 – were taken from the “Statistical Yearbook of the Vologda Region”.

1. Place of GRP in the national accounting system

Gross regional product (GRP) is a general indicator of the economic activity of a region, characterizing the process of production of goods and services. Gross regional product (GRP) is an indicator measuring gross value added, calculated by excluding the volume of intermediate consumption from the total gross output, and is defined as the sum of the newly created values ​​of the regional economic sectors.

At the national level, gross regional product (GRP) corresponds to the gross national product, which is one of the basic indicators of the system of national accounts.

GRP is calculated in current basic and market prices (nominal GRP volume), as well as in comparable prices (real GRP volume). The assessment of GRP at basic prices differs from the assessment at market prices by the amount of net (less subsidies on products) taxes on products. GRP in basic prices is the sum of added values ​​in basic prices by type of economic activity. The transition to assessing GRP in basic prices is due to information problems in determining the amount of taxes on products. GRP at market prices assumes the inclusion of net taxes on products. The procedure for collecting and processing information on taxes on products established by the Federal Tax Service does not allow obtaining information on taxes accrued and payable to the budget for the reporting period, as required by the SNA concept. In order to ensure a methodologically consistent dynamic series, GRP indicators, starting with the results of 2004, are published in basic prices.

The indicator of gross regional product is very close in content to the indicator of gross domestic product (GDP). However, there is a significant difference between the indicators of GDP (at the federal level) and GRP (at the regional level). The sum of gross regional products for Russia does not coincide with GDP, since it does not include the added value of non-market collective services provided by government agencies to society as a whole.

Just like GDP at the federal level, gross regional product at the regional level is obtained as the difference between output and intermediate consumption. [No. 7]

Currently, calculations are made at the regional level:

1. produced GRP;

2. income generation accounts:

3. individual elements: accounts for the use of disposable income, capital accounts.

GRP at the production stage is defined as the sum of newly created values ​​produced in a particular region. In market prices, it is equal to the sum of the added values ​​of economic sectors created during the reporting period by resident economic units, and calculated as the difference between output and intermediate consumption, plus net taxes on products.

The formation of GRP by source of income reflects primary income received by units directly involved in production, as well as government bodies (public sector organizations) and non-profit organizations serving households. In this method, gross profit/gross mixed income is a balancing item and is defined as the difference between GRP calculated by the production method, in market prices and wages of employees and net taxes on production and imports.

GRP calculated by the use method is the sum of expenditures of all economic sectors on final consumption, gross capital formation and net exports.

To characterize changes in GRP compared to the previous period, GRP production indicators are recalculated into comparable prices. In this case, the method of direct deflation is used (revaluation of the gross value added of industries using the output price index of each industry) or the method of extrapolation of the basic level of value added of the industry using quantitative indices of indicators that adequately reflect the dynamics of production development in a given industry. [No. 3]

The GRP deflator index is the ratio of the volume of GRP calculated in actual prices to the volume of GRP calculated in comparable prices of the base period. Unlike the price index for goods and services, the GRP deflator characterizes changes in wages, profits and consumption of fixed assets as a result of changes in prices, as well as the nominal mass of net taxes.

When calculating gross regional product (GRP), the following elements are not taken into account:

Added value of industries providing collective non-market services to society as a whole (public administration, defense, international activities, etc.);

Added value of services of financial intermediaries (primarily banks), whose activities are rarely limited strictly to certain regions;

Added value of foreign trade services, which in many cases can only be obtained at the federal level;

Part of the taxes (import and export taxes) that cannot be taken into account at the regional level.

As for the first point of the elements considered, these services should be accounted for at the place of their production (rendering), and their value should be included in the volume of GRP of the corresponding region.

The volume of these collective services is determined in the amount of the corresponding state budget expenditures reflected in the report on the execution of the federal budget. All federal budget expenditures at a regional level must be taken into account and reflected by the system of regional treasuries in accordance with the current unified budget classification. But the practice of accounting for some federal budget expenditures for the country as a whole, without breaking them down into individual regions, continues to this day.

This is mainly due to the inability to determine to which specific region expenditures can be attributed (for example, budget expenditures on international cooperation, servicing public debt, etc.), as well as persistent deficiencies in financial accounting or certain political considerations (defense expenditures). , internal affairs bodies, etc.).

Thus, the presence of problems associated with the distribution of part of public expenditures among the regions of the country, as well as with overcoming the shortcomings of regional accounting (incompleteness of data reflection in treasury reports) currently force us to abandon their accounting at the regional level.

In addition, the items that determine the discrepancy between the gross domestic product as a whole and the sum of gross regional products for all territories include indicators reflecting financial and foreign trade intermediation.

In modern conditions, it is very difficult to correctly account for the production of financial intermediary services by region. Due to the specific nature of banking activities, it is problematic to tie its volume to one region where the bank is registered. A bank may be registered, for example, in Moscow or have only a branch here, which usually conducts a large volume of transactions, but at the same time, a Moscow bank or a Moscow branch of a provincial bank today can actually provide financial intermediation almost throughout Russia. As a result, territorial statistical bodies have practically no data to accurately assess the production of financial services in the region.

General characteristics of the population. Economic and statistical analysis of the level and factors of production of gross regional product (GRP) in typical groups of regions. Analysis of the relationship between the resultant and factor characteristics, index method of analysis.

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Course work

On the topic: “Economic and statistical analysis of GRP production by group of regions”

Plan

Introduction

Chapter 1. Identification of typical groups of enterprises

1.1 General characteristics of the population

1.2 Analytical grouping

Chapter 2. Economic and statistical analysis of the level and factors of GRP production in typical groups of regions

2.1 Analysis of GRP production in typical groups

2.2 Analysis of production resources in typical groups

2.3 Analysis of GRP production in typical groups of regions

Chapter 3. Analysis of the relationship between effective and factor characteristics

3.1 Combination grouping

3.2 Correlation analysis

Conclusion

List of sources used

Application

Introduction

The main goal of this course work is to conduct a statistical analysis of socio-economic phenomena and processes of the gross regional product of the Central, Southern and Volga Federal Districts.

Socio-economic statistics is a social science and a special branch of practical activity.

The central macroeconomic indicator is the gross regional product. It is the most general indicator of economic activity and well-being of regions.

Gross regional product is a general indicator of the economic activity of a region, characterizing the process of production of goods and services. Gross regional product is calculated in current basic and market prices (“nominal volume of gross regional product”), as well as in comparable prices (“real volume of gross regional product”). Gross regional product represents the newly created value of goods and services produced in the region, and is defined as the difference between output and intermediate consumption. The indicator of gross regional product is, in its economic content, very close to the indicator of gross domestic product. However, there is a significant difference between the indicators of gross domestic product (at the federal level) and gross regional product (at the regional level). The sum of gross regional products for Russia does not coincide with the gross domestic product, since it does not include the added value of non-market collective services (defense, public administration) provided by state institutions to society as a whole. Currently, calculating the gross regional product of a federal subject takes 28 months.

The purpose of this course project is to conduct a statistical analysis of the gross regional product for a group of regions.

Chapter 1. Identification of typical groups of enterprises

1.1 General characteristics of the population

Gross regional product is a general indicator of the economic activity of a region, characterizing the process of production of goods and services.

The specifics of Russian conditions, the huge role of the territorial factor in the development of socio-economic processes, the consistent policy of strengthening federalism in Russian statehood determine the need to build a developed system of statistical indicators at the regional level that meet the requirements of a market economy. System indicators characterizing the development of regions must be methodologically comparable and consistent with the corresponding macro-level indicators.

In Russia, the calculation of regional indicators is based on the methodological principles of the SNA. A general indicator of regional development is the gross regional product (GRP). This indicator is based on a unified methodology developed centrally in the FSGS. The results of the calculations are controlled, approved and published in a summarized form by the FSGS.

To monitor the intra-annual dynamics of regional economic development, a calculation of the rate of change in production volumes of basic sectors of the economy (industry, agriculture, construction, retail trade and public catering, transport), which constitute from 60% to 80% in the regional production structure, is provided.

Characteristics of the regions under study.

The Southern Federal District ranks first in Russia in the production of mineral waters, second and third in the production of tungsten and cement raw materials. In terms of coal production (Donbass), the district is in third place after the Siberian and Far Eastern regions. But the main prospects for economic development of the region are connected precisely with the extraction and production of “black gold”.

Oil reserves, located at depths of 5 to 6 kilometers, are estimated at 5 billion tons of standard fuel. The drilling of the first exploratory well on the Caspian shelf immediately confirmed the serious “fuel” potential of this area. However, all projects require a lot of money, about 15-20 billion dollars. Oil reserves are concentrated mainly in the Volgograd and Astrakhan regions, Krasnodar.

The Southern Federal District is among the poorest forest resources regions of the Russian Federation. Unique recreational resources Federal District. The mild climate, abundance of mineral springs and healing mud, warm sea waters create rich opportunities for treatment and recreation. Mountain areas with their unique landscapes have all the necessary conditions for the development of mountaineering and tourism, and the organization of ski resorts of international importance here.

The central region has a very favorable economic and geographical position, located in the center of the European part of Russia, at the intersection of the most important

The economic complex of the Central region is characterized by a complex combination of sectors of material production and non-production spheres. The economic specialization of the region is based on mechanical engineering, chemical industry, light industry, flax growing, potato growing, and dairy and meat farming.

In the industrial structure of the region, the dominant position is occupied by mechanical engineering, especially knowledge-intensive, which requires qualified personnel.

The area is distinguished by transport engineering. The production of equipment for light, chemical, energy and other industries also occupies a prominent place.

The chemical industry of the region specializes in the production of plastics, chemical fibers, synthetic rubber and tires, mineral fertilizers, varnishes, paints, detergents, etc.

Light industry is the oldest in the region and the largest in the country. Textile production is especially prominent: cotton (Ivanovo, Moscow, Tver, etc.), linen (Kostroma, Nerekhta, Vyazma, etc.), silk (Moscow, Tver, Naro-Fominsk), wool (Moscow, Klintsy, etc.). The sewing, knitting, leather and footwear, fur, and printing industries are also developed.

Among the service industries of the region, the fuel and energy complex stands out, especially the production of electricity (Kostromskaya, Konakovskaya, Ryazan State District Power Plants and nuclear power plants - Smolenskaya, Kalininskaya). Brown coal production in the Moscow basin has decreased sharply. Ferrous metallurgy enterprises (Tula, Elektrostal, Moscow) only partially satisfy the region's needs for metal.

The leading type of agriculture in the region is suburban, with a predominance of the production of vegetables, potatoes, milk and meat. In the northern regions of the region, dairy farming is of commercial importance. Grain farming (grain bread, spring wheat, buckwheat) is of auxiliary importance. The Smolensk, Kostroma and Tver regions specialize in the cultivation of fiber flax. Pig and poultry farming are developed.

The region's transport complex stands out for its high level of development and huge scale of transportation. There is a very dense network of railways, roads and pipelines. The role of inland water and air transport is great.

Volga Federal District. A serious disadvantage is the lack of access to the sea. Mineral resources include the country's largest reserves of potassium salts (Solikamsk-Bereznyaki), oil and non-ferrous metal deposits. In the forest-steppe zone there are large tracts with fertile chernozem soils.

Mechanical engineering and metalworking industry is the largest branch of industrial specialization in the Volga Federal District. This is the main region of transport engineering in Russia. The most developed is the aerospace industry, and in it the production of military-industrial complex. The main enterprises of this industry are located in Samara, Kazan, Nizhny Novgorod, Saratov, Ufa, Kumertau, Perm and Votkinsk. And their numerous adjacent buildings are dispersed throughout the district.

The production of equipment for the oil production, oil refining industries and organic synthesis chemistry is also of special importance. The location of these production facilities is largely close to the largest cities of the district and regional centers (Samara, Kazan, Nizhny Novgorod, Ufa, Perm, Saratov).

Oil industry. Until the end of the 70s. The Volga Federal District was the main oil-producing region of Russia.

Today, due to the large-scale development of oil resources in the Tyumen region, it has moved into second place in the country in terms of total oil production. Oil production is mainly carried out on the territory of the Republics of Tatarstan and Bashkiria and, to a much lesser extent, in the Kuibyshev, Orenburg regions, and Perm Territory.

Let's group regions according to common characteristics. Grouping is the division of the social phenomenon under study into qualitatively single groups according to a number of essential characteristics.

Table 1.1 General characteristics of the population

No. of farms	Name of areas	Gross regional product per 1 employee, thousand rubles	Average monthly salary, rub	Capital-labor ratio, thousand rubles.	Occupancy rate	Higher and secondary education,%
	Tula
	Bryansk
	Moscow
	Vladimirskaya
	Ivanovskaya
	Kaluzhskaya
	Kostromskaya
	Orlovskaya
	Ryazan
	Smolenskaya
	Tverskaya
	Moscow
	Yaroslavskaya
	Republic of Adygea
	Republic of Kalmykia
	Krasnodar region
	Astrakhan
	Volgogradskaya
	Rostovskaya
	Kirovskaya
	Nizhny Novgorod
	Orenburgskaya
	Penza
	Perm region
	Samara

Gross regional product per employee, thousand rubles is calculated as the ratio of the gross regional product, million rubles. Number of people employed in the economy, thousand people:

GRP for 1 job = GRP/H

Gross regional product (GRP) - a general indicator of the economic activity of the region, characterizing the production process goods and services.

The capital-labor ratio is calculated as the ratio of fixed assets in the economy, million rubles. to the number of people employed in the economy, thousand people:

Capital-labor ratio-- the cost of fixed production assets per employee.

The employment rate is calculated as the ratio of the number of people employed in the economy, thousand people. to the number of economically active population, thousand people:

This coefficient shows the dependence of employment on demographic factors, i.e. on fertility, mortality and population growth rates. This coefficient provides one of the characteristics of the well-being of society.

The share of higher and secondary education is calculated as the ratio of the number of people with higher and secondary education to the number of people employed in the economy, thousand people

Chhigh+Chav/H*100%

Based on the data in the table, we can conclude: the gross regional product per 1 person employed in the economy varies from 491.1 to 209.5 thousand. rub., the highest figures were recorded in the southern and Volga federal districts, which is associated with active oil production in these regions. The high capital-labor ratio in the Vladimir, Penza, Volgograd regions, and the Republic of Kalmykia shows the technical equipment of enterprise personnel and the high average annual cost of real estate per employee. Low capital-labor ratio in the Oryol, Smolensk and Yaroslavl regions may mean that enterprises lag behind in the use of advanced technologies based on the introduction of new equipment, which ultimately may lead to a loss of competitiveness. The high employment rate in all studied regions indicates a high level of social well-being. The share of the educated population is in no way related to the level of average wages, which indicates the demand not only for specialists, but also for workers without special education. The highest salary is 17,438.3 rubles. recorded in the Volgograd region, and the lowest proportion of the educated population is 2.4 in the Moscow region.

1.2 Analytical grouping

To identify typical groups from the characteristics given in Table 1, it is necessary to select the most significant one. Most of the features characterize the production conditions, and the results of activity can be judged by the production indicator of the gross regional product. However, direct division of regions into groups on this basis can lead to a mixture of different types, since, for example, a large volume of gross product can be obtained both due to a large population and other resources with poor use, and through the effective use of relatively small resources. Since the absolute indicators of the gross product are not comparable, it is advisable to use a relative indicator - GRP per 1 person employed in the economy. The value of this attribute, obtained by dividing the gross regional product indicator, million rubles. on the number of people employed in the economy, thousand people.

Grouping should begin with studying the nature of changes in the grouping characteristic; for this, a ranked series of distribution of regions by gross regional product (GRP) per 1 employed in the economy should be constructed (Table 2) and depicted in the form of Galton’s Ogiva (Fig. 1).

Table 1.2 - Ranked row of distribution of farms by GRP per 1 person employed in the economy

	GRP per 1 person employed in the economy, thousand rubles.

Figure 1.1 - Ogiva of distribution of farms by GRP per 1 person employed in the economy

When analyzing a ranked series, the intensity of change in the value of a grouping characteristic from one unit of the population to another is assessed. From Table 1.2 it is clear that there are sharp changes and a large gap between a number of units and the entire population. Differences between regions are visible; between the extremes they reach a double value. But the sign in the series changes gradually, smoothly, there are no sharp deviations. and it is impossible to select groups.

In the absence of high-quality transitions in the ranked series, an interval distribution series is constructed. To construct it, we divide the population into 6 groups (K = 6). To determine the boundaries of the intervals, we find the interval step (h) using the formula:

h=x max -x min /K=491.1-209.5/6=47 thousand. rub,

where x max is the maximum value of the attribute in the ranked series; x min is the minimum value of the attribute in the ranked series.

Table 1.3 - Interval variation series of distribution of regions by GRP per 1 person employed in the economy

	Interval boundaries	Number of farms in intervals
		11(2,13,8,19.10,22,9,11,14,17,25)
		7(1,24,7,6,4,5,16)

Figure 1.2 - Histogram of the distribution of regions by gross regional product per 1 person employed in the economy

As can be seen from Table 1.3 and Figure 1.2, the distribution of regions across groups is uneven. Regions with GRP per employee from 209.5 to 303.3 thousand predominate. rub. Groups with higher GRP are small in number. It is required to combine them.

Table 1.4 - Intermediate analytical grouping

Groups by GRP per 1 person employed in the economy, thousand rubles.	Number of farms	Average monthly salary, rub	Capital-labor ratio, thousand. rub	Occupancy rate	Higher and secondary education
Average

To assess the qualitative characteristics of the groups, we compare them with each other according to the obtained indicators. The first group, which is quite large in number, differs significantly from all the others in terms of the level of education of the population; here it is several times higher than the level of education in other groups. Other indicators: average monthly salary, employment rate, capital-labor ratio are lower than in other groups. Therefore, it should be identified as the lowest typical group in terms of productivity and efficiency. Groups 4,5,6 with higher average monthly wages, higher capital-labor ratio and higher employment ratio are small in number. It is advisable to combine these groups into the highest typical, most productive and effective group. Groups 2 and 3, in almost all indicators, occupy an intermediate position between the lowest and highest typical groups; their characteristics are close to each other. They should be combined into an average typical group.

Next, to characterize the three identified typical groups, it is necessary to calculate the average indicators for each of them.

Chapter 2. Economic and statistical analysis of the level and factors of production of BPP in typical groups of regions

2.1 AnalysisGRP production in typical groups

Data available by region: GRP per employee, capital-labor ratio, employment and activity ratio, unemployment rate, average monthly salary. Let's calculate the averages of these indicators and analyze them by typical groups.

Table 2.1 - Level and factors of GRP production

Indicators	Typical groups	Average
Number of regions	11(2,13,8,19,10,22,9,11,14,17,25)	8(1,24,7,6,4,5,16,20)	6(12,3,23,21,18,15)
GRP production per 1 person employed in the economy, thousand rubles.
Capital-labor ratio, thousand rubles
Population economic activity rate
Occupancy rate, %
Unemployment rate in %
Average monthly salary, rub

We calculate the economic activity coefficient using the formula:

Kek.akt=Chek. act/H,

where is Check. act-number of economically active population, H-number of population.

According to Table 2.1, it can be seen that on average, GRP per 1 person employed in the economy in the highest group is greater than in the lowest group, by 402.1-226.4 = 175.7 thousand. rub., or by 175.7/226.4*100%=77.6%, while the capital-labor ratio is higher by 1131.0-771.3=359.7 thousand rubles, the average monthly salary is higher by 16529.2- 12633.6=3895.6 rub. The unemployment rate in the highest group is 2.5% lower, and the employment rate is 4.5% higher than in the lowest group. These differences in production results and the situation on the labor market are due to the influence of a complex of factors, both economic and natural. We can conclude that intensive production is carried out in the regions belonging to the highest group, despite the average coefficient of economically active population of 0.53. The indicators of the middle group occupy an intermediate position, they are closer to the lower group than to the highest. The highest group differs most strongly from the lowest in terms of GRP production per 1 person employed in the economy, almost 2 times, and capital-labor ratio by 359.7 thousand. rub.. Consequently, the high results of the highest typical group were achieved both due to the greater use of labor resources and due to better armament with basic production assets, which ensured high gross output and an increase in the standard of living of the population, as evidenced by the high employment rate.

2.2 Analysis of production resources in typical groups

Fixed production assets are the material and technical base of social production. The production capacity of enterprises and the level of technical equipment of labor depend on their volume. The accumulation of fixed assets and the increase in the technical equipment of labor enrich the labor process, give work a creative character, and increase the cultural and technical level of society.

In the context of the transition to a market economy, fixed assets are the main prerequisite for further economic growth due to all factors of production intensification.

Economic and statistical analysis of fixed production assets is aimed at studying changes in their volume, type composition and structure by individual industries and types of products, regions and types of enterprises.

Table 2.2 - Structure of fixed assets by industry and type of economic activity

Indicators	Typical groups	Average
Specific gravity of OF,%:
Agriculture
extractive industries
Manufacturing industries
Production and distribution of energy, gas and water
construction
transport links
other industries
Total PF million rubles

Having analyzed this table, you can see that the regions the highest groups have a great advantage over the regions the lowest group in terms of provision with fixed production assets (by 5,524,991 million rubles). As can be seen, the composition of the OF is dominated by OF transport links, their share in all groups averages 28.9%, the smallest share is made up of PF related to construction and agriculture , in all three typical groups it is close to the average - 1.3% and 5.4% respectively . PF cost extractive industries of the highest group reaches 9%, which exceeds the figure of the lowest group by 9 times. The OF of the manufacturing industry in the lowest group amounted to 5.8% compared to the highest - 14%. This may be due to natural conditions , providing the opportunity for the development of the mining industry. OF production and distribution of energy, gas and water are similar in share in the highest and lowest groups - 6.6% and 5.2%, and differ significantly in the middle group - 10.3%. The remaining indicators of the average group are close to the scoop average pnosti. The largest share, on average 42.6%, is occupied by PFs of other industries. This could be: trade, catering, auto business, communications, tourism, high technology, etc.

Let's analyze labor resource indicators.

Table 2.3 - Indicators of the structure of employees in the economy by industry

According to Table 2.3, the share of employees in the three presented industries does not differ much in typical groups. Thus, the indicators of those employed in agriculture in all groups are close to the average - 50.3%. The share of those employed in construction in the highest group exceeds the figure in the lowest, it is 28%, and in the lowest - 22%, transport and communications occupies 25.9% in the highest group, 22% in the lowest. The indicators in the middle group are consistently similar to the average. Higher rates in the top group may be due to both the larger number of jobs and the more labor-intensive type of production in these regions.

Let us analyze the structure of those employed in the economy by type of ownership.

gross regional product production

Table 2.4 - Structure of people employed in the economy by type of ownership,%

Table 2.4 shows that the majority of those employed in the economy work in private enterprises, and in all groups of regions, the situation is the same. In the highest typical group, private enterprises employ 69% of the employed population, and state and municipal enterprises employ 16% and 15%. Approximately the same situation is developing in the regions of the middle and lower groups. This suggests that a third of the population is employed in state and municipal enterprises and is provided with a stable income.

Table 2.5 - Labor force quality indicators

Analyzing the indicators of the quality of the labor force employed in the economy, we can say that approximately the same percentage is occupied by people with secondary vocational education and higher education, which is an average of 26%, the number of people with higher education is 0.5% more, with an average age of 38 ,5 years.

Indicators of the condition of fixed assets include coefficients of depreciation, renewal and worn-out assets.

Table 2.6 - Indicators of the condition of fixed assets

Fixed assets renewal ratio.

Shows the degree of renewal of fixed assets:

TO about =F new /F con ,

Where TO about -- fixed asset renewal ratio;

F new -- cost of new fixed assets put into operation for the period, thousand rubles;

F con -- value of fixed assets at the end of the period.

The rate of renewal of fixed assets in the highest group is lower than in the lowest by 1%, the degree of depreciation of fixed assets is the lowest in the highest group of regions, it is 21.7%. The share of worn-out funds in all three groups is close to the average - 47%. Based on this, we conclude that the fixed assets are quite worn out, and the degree of renewal of the PF is too low.

2.3 Analysis of GRP production in typical groups of regions

At the present stage of economic development, the problem of increasing labor productivity and the efficiency of using labor resources in enterprises is of great importance, since in market conditions strong competition between firms is inevitable, which pushes them to constantly improve the quality of their products and reduce production costs. This circumstance ultimately changes the requirements for personnel in the direction of increasing their professionalism and creative attitude to work. No matter what technical opportunities open to an enterprise, it will not work effectively without qualified specialists. Competently selected personnel are the basis for the success of an enterprise.

To assess labor productivity, and, consequently, the quality of labor resources, economic and statistical analysis is used, which makes it possible to identify unused reserves and develop proposals for increasing production efficiency.

Table 2.7 - Indicators of the standard of living of the population depending on labor productivity

Indicators	Typical groups	Average
Gross regional product per 1 person employed in the economy
average per capita cash income, thousand rubles
consumer spending per capita, thousand rubles
average monthly salary, thousand rubles

GRP per person employed in the economy in the highest group is higher by 402.1-226.4 = 175.7 thousand. rub. than in the lower. Average per capita cash income in the highest group is higher by 6,101 thousand rubles. than in the lower typical group. Consumer spending per capita in the highest group is higher by 5,386 thousand rubles than in the lowest group. It is possible to identify a relationship: the higher labor productivity, the higher the salary, and the higher the standard of living of the population.

In the Civil Code of the Russian Federation, the main organizational and legal forms are business partnerships, business societies, production cooperatives, state and municipal unitary enterprises.

The organizational and legal form of an enterprise depends on a number of characteristics: the procedure for formation and the minimum amount of authorized capital, responsibility for the obligations of the enterprise, the list and rights of founders and participants, etc.

Table 2.8 - Structure of enterprises by organizational and legal forms, %

In the lowest typical group, joint-stock companies or limited liability partnerships predominate, accounting for 60.7%. The indicators of the middle group are close to the average; joint-stock companies or limited liability partnerships also predominate - 70.8%. In the highest group, the smallest number is occupied by unitary enterprises - 0.8%, and the largest joint stock company or limited liability partnership - 73.7%.

The sectoral structure of the national economy is understood as the totality of its parts (sectors and sub-sectors), historically formed as a result of the social division of labor. It is characterized by percentage indicators in relation to either the employment of the economically active population or the produced GDP. The level of socio-economic development of a region is determined by the structure of the economy and has a direct impact on the predominance of a particular sector. Gross regional product (GRP) is traditionally used as a basic indicator of the socio-economic development of individual regions of the Russian Federation, as well as Russia as a whole, characterizing the structural and economic proportions and the quantitative result of the production of goods and services.

Table 2.9 - Composition and structure of GRP by industry and type of economic activity, %

Indicators	Typical groups	Average
Share in GRP, %
Agriculture
retail
food products
non-food products
paid services
Total, thousand rubles

From Table 2.9 it is clear that the share of agriculture in GRP is the smallest share. In the highest group - 9%, in the middle group - 10.3%, in the lowest group - 14.2%. Retail trade accounts for the largest share. On average this is 38.3%. The share of trade in food and non-food products is approximately the same in all three groups and averages about 20%. Paid services in the highest group are 12%, which is 0.4% more than in the lowest group.

As an analysis of statistical data shows, regions with a fuel and raw material base, export-oriented industry, and a fairly developed infrastructure and financial system are currently in a privileged position. Regions with a significant share of the agricultural sector, light and food industries suffered the most. Since the economic space of Russia is extremely heterogeneous, GRP production is also unevenly distributed across the country. Over the past eight years, the sectoral structure of the national economy has been characterized by a tendency to increase the share of industries providing services and a decrease in the share of industries producing goods. Many economists view this change in the structure of GDP as a progressive phenomenon, since the Russian economy is approaching the economies of developed countries.

2.4 Index method of analysis

The level of labor productivity is characterized by the ratio of the volume of products produced or work performed and the cost of working time. The rate of development of industrial production, the increase in wages and income, and the size of the reduction in production costs depend on the level of labor productivity.

An increase in labor productivity means savings in labor costs (working time) for the production of a unit of product or an additional amount of produced product per unit of time, which directly affects the increase in production efficiency, since in one case the current costs of producing a unit of product under the item “Wages” are reduced main production workers", and in the other - more products are produced per unit of time.

When dynamically analyzing average indicators, a system of indices is used, consisting of an index of variable composition, an index of fixed (constant composition) and an index of structural changes.

This index system allows you to solve the problem of changing the structure due to changes in quality indicators, and also allows you to identify the influence of factors on the indexed value. The index system is used when comparable products are produced at different sites.

The variable composition index is a relative value characterizing the dynamics of two average indicators for homogeneous populations. This index reflects the influence of two factors:

- change in the indexed indicator for individual objects (parts of the whole);

- change in the proportion of these parts in the overall structure of aggregates.

Fixed composition index - characterizes the dynamics of two average values ​​with the same fixed structure of the population in the reporting period.

The index of structural changes is the ratio of two average values ​​calculated for different structure of the population, but with a constant value of the indexed indicator in the base period.

There is a relationship between the indices of variable and fixed composition. The index of variable composition will always be equal to the product of the indices of fixed composition and structural changes

Table 2.10 - Data for the index method of analysis

Yn agricultural = (GRP agricultural /H agricultural)/GRP na1z.v econ,

where Y ns/x is the share of output per 1 person employed in agriculture in the lower group;

dn-share of GRP in agriculture in the lower group, take from table 2.9;

Y labor productivity = Y labor productivity Y structure of variable composition of constant composition index shows. that labor productivity in the highest typical group is 7% higher than in the lowest group. The index of variable composition depends on output per 1 employee in individual industries and the structure of GRP. Therefore, a change in one indicator occurs due to a change in another.

= 0.009+0.819/0.017+0.757=0.828/0.774=1.07 or 7%,

Chapter 3. Analysis of the relationship between effective and factor characteristics

3.1 Combination grouping

Combination grouping is achieved by subdividing all units of the population according to one factor characteristic, and then within the resulting groups subgroups are distinguished according to the second factor characteristic.

Capital-labor ratio is an indicator characterizing the degree of armament of regions with fixed production assets.

The capital-labor ratio is represented by a quantitative, continuously changing characteristic. There are no visible qualitative transitions in its level. The construction and ranking of the series showed that the trait changes from one region to another smoothly, gradually, without sharp jumps ranging from x min =716.5 thousand. rub, up to x max =1403.4 thousand. rub. Let's highlight three groups with low. average and relatively high value grouping sign.

Let's determine the interval step h=1403.4-716.5/3=229 thousand. rub. Then the first group will include regions in the range from 716.5 to 716.5+229=945.5 inclusive, the second group will include from 945.5 to 945.5+229=1174.5 thousand. rubles, and in the third - from 1174.5 to 1403.5 thousand. rub.

Table 3.1 - Ranked row of distribution of farms by capital-labor ratio employed in the economy

	Capital-labor ratio, thousand rubles.

In the same way, two subgroups can be distinguished according to the share of depreciation of funds. The minimum value is 29.4, the maximum is 60%. The interval step is 60-29.4/2= 15.3%. The first subgroup will include regions with a share of worn-out assets up to 29.4 + 15.3 = 44.7%, and the second subgroup will include from 44.7 to 60%.

Table 3.2 - Ranked distribution series by the share of completely worn-out funds

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1

The paper examines the relevance of the research topic. Using bubble charts, the dependence of the gross regional product of federal districts on fixed assets and employment in 2000 and 2012 was studied. Using production functions, the dependence of the gross regional product of federal districts on fixed assets and employment, on investments and employment, on investments and costs of technological innovation was calculated. A grouping of subjects of the Russian Federation has been constructed according to the elasticity of output by fixed assets. The correlation coefficients between per capita GRP and the share of a certain type of economic activity in the total GRP of federal districts were calculated. A correlation analysis was carried out between changes in the number of employees in federal districts and changes in real wages in them. The corresponding conclusions have been drawn.

real wage

type of economic activity

shower GRP

correlation coefficient

technological innovation costs

output elasticity

production functions

employment

investments

1. Abazova R.Kh., Shamilev S.R., Shamilev R.V. Some problems of urbanization of the subjects of the North Caucasus Federal District // Modern problems of science and education. – 2012. – No. 4. - URL: www..10.2014).

2. Abusheva Kh.K., Shamilev S.R. Marriages and divorces in the Russian Federation and ways to reduce them // Modern problems of science and education. – 2013. – No. 4. - URL: www..10.2014).

3. Musaeva L.Z., Shamilev S.R. Migration in modern Russia: the need for control and optimization // Modern problems of science and education. – 2013. – No. 5. - URL: www..10.2014).

4. Musaeva L.Z., Shamilev S.R., Shamilev R.V. Features of the settlement of the rural population of the subjects of the North Caucasus Federal District // Modern problems of science and education. – 2012. – No. 5; URL: www..10.2014).

5. Regions of Russia. Socio-economic indicators. 2013: stat. Sat. / Rosstat. - M., 2013. - 990 p.

6. Suleymanova A.Yu., Shamilev S.R. Assessment of the birth rate in the Russian Federation and measures to increase it // Modern problems of science and education. – 2013. – No. 4. - URL: www..10.2014).

7. Shamilev R.V., Shamilev S.R. Analytical and economic justification for increasing potato production in the Russian Federation and the Federal District // Modern problems of science and education. – 2013. – No. 4. - URL: www..10.2014).

8. Shamilev S.R. Dynamics of mortality and factors of its decline in the Russian Federation // Modern problems of science and education. – 2013. – No. 5. - URL: www..10.2014).

9. Shamilev S.R., Shamilev R.V. Analysis of per capita GRP in the subjects of the North Caucasus Federal District // Modern problems of science and education. – 2011. – No. 6. - URL: www..10.2014).

10. Edisultanova L.A., Shamilev S.R., Shamilev R.V. Problems of optimization of municipalities in the ATD of the subjects of the North Caucasus Federal District // Modern problems of science and education. – 2012. – No. 5. - URL: www..10.2014).

The current situation requires the use of diverse and modern tools for assessing economic development, financial balance, and competitive conditions in the domestic and world markets.

From this point of view, some scientists assume that the basis for a comprehensive analysis of such macroeconomic characteristics of a market economy as GRP is the use of production functions (which express the dependence of the production result on resource inputs). This explains the relevance of this topic.

Let us graphically reflect the dependence of the GRP of the federal district on the public sector and employment in 2000 and 2012.

Rice. 1. Dependence of GRP of the Federal District on fixed assets and employment in 2000.

Rice. 2. Dependence of the Federal District's GRP on fixed assets and employment in 2012.

From the data in Figures 1 and 2, it is clear that from 2000 to 2012 the gap in the values ​​of GRP of the FD increased, there was a slight change in the number of employees in the FD and a significant uneven increase in both FP and GRP. Production functions of the type were constructed (where Y is the GRP of the regions; K is fixed assets; L is the average annual number of PF; , α, β are coefficients) that allow us to consider the efficiency of the use of labor and PF both at the level of the federal district and at the level of the constituent entities of the Russian Federation. When constructing production functions of the economy of Russian regions, some difficulties arise: time series are short; the available data is not sufficiently accurate; inaccuracy of price measurement - price jumps in the Russian Federation are orders of magnitude greater than the slow changes occurring in developed Western countries; data on fixed assets do not correspond to their actually used part.

With the exception of certain cases, the initial data used to construct the production function can be represented by indices, i.e. relative values, at least as follows: . The Cobb-Douglas function defines the output index Y as the weighted geometric mean of the capital K and labor L indices with weights α and β. The traditional PF is a function of averaging factors or can be reduced to such a function by simply transforming the source data. Since Y is an averaging function, it follows that on the graph the time series of the output index Y must be located between the time series of capital K and labor L.

Rice. 3. Dependence of GRP of the Federal District on fixed assets and employment in 2000-2012.

It is clear from the graph that the GRP cannot be a function of averaging the function connecting Y with K and L, i.e. factors K and L do not fully describe the dynamics of output Y.

Table 1

Calculation of elasticity coefficients of the production function for calculation

	Elasticity of output by OF	Employment elasticity of output

Calculations show that for all federal districts a reduction in employment is necessary at the existing labor productivity, or the maximum possible increase in labor productivity is necessary (Table 1). It is clear that in Russia as a whole it is also not effective to increase the number of employees given the existing labor productivity.

Thus, we can state the ineffective use of labor resources not only in labor-surplus, but even in labor-deficient subjects.

table 2

Grouping of subjects of the Russian Federation according to the elasticity of output by PF

Efficiency of output by PF	Number of subjects
	3 (Moscow, including the Nenets Autonomous Okrug, Yamalo-Nenets Autonomous Okrug)
	2 (Vologda region, Murmansk region)
	3 (Tyumen region, Khanty-Mansiysk Autonomous Okrug - Yugra, Primorsky Territory)
	19 (KBR, SK)
	2 (Kursk region, Republic of Tyva)
	3 (RD, Karachay-Cherkessia, Republic of Mari El)
	1 (Republic of Adygea)
Grand total

For the Czech Republic in 2012, the value of the elasticity coefficient of regional GRP by PF is significantly less than 1, which in the future, in order to increase production efficiency or increase labor productivity, means the need to increase the accumulation rate and, accordingly, reduce the consumption rate.

In total, in 9 constituent entities of the Russian Federation, the output efficiency in terms of PF is less than 1, which means a positive employment elasticity of GRP. Only in these 9 regions is it justified to increase employment to increase GRP (Table 2).

One option to address the problem of missing or inadequate data on fixed assets is to use data on investment in fixed assets instead of data on fixed assets.

The advantages of this approach are explained by the high efficiency of investments directed both to the involvement of idle funds in turnover and to the acquisition of new funds, thereby increasing the share of effectively used capital.

Investment attractiveness is determined by many conditions.

Below we will consider the following conditions: the influence of investment, as well as the joint influence of investment and labor on GRP.

Rice. 4. Dependence of GRP of the Federal District on fixed assets and employment in 2000-2012.

It is clear from the graph that Y can be an averaging function of the function connecting K and L with Y, i.e. factors K and L completely describe the dynamics of output Y (Fig. 4).

Table 3

Calculation of GRP elasticity for investments

	Elasticity of GRP for investments

Since the elasticity of GRP for investment is greater than the elasticity of GRP for employment (β = 1-α), we can conclude that labor-saving (intensive) growth is observed during the period under review. It is most profitable to increase employment in the Far Eastern Federal District, Siberian Federal District and North Caucasian Federal District. Let's consider the dependence of GRP on investments and costs of technological innovation.

Costs of technological innovation (millions of rubles) Table 4

	Labor productivity elasticity coefficient from investments	Elasticity coefficient of labor productivity on the costs of technological innovation

From the analysis of the econometric dependence of labor productivity for the economies of the regions of the Russian Federation, it is clear that innovation factors practically do not predetermine changes in labor productivity (labor intensity). The investment factor still plays the main role in increasing labor productivity, and the generation of innovation plays a supporting role. In the Northwestern Federal District, Ural Federal District and Southern Federal District, the costs of technological innovation are unreasonably high and cannot be increased. The most effective costs are for technological innovations in the North Caucasus Federal District, Volga Federal District, Siberian Federal District, Central Federal District and Far Eastern Federal District (in descending order). The efficiency of production in the economy of the Federal District can be increased through massive investments in fixed assets. The work calculates the correlation coefficients between per capita GRP and the share of a certain type of economic activity in the total GRP of the Federal District.

Table 5

Correlation coefficients between per capita GRP and the share of this type of economic activity in the total GRP of the Federal District in 2011.

Types of economic activities	Correlation coefficient between per capita GRP and the share of a certain type of economic activity in total GRP
Agriculture, hunting and forestry
Education
Health and social service provision
Hotels and restaurants
Public administration and military security; compulsory social security
Construction
Wholesale and retail trade; repair of vehicles, motorcycles, household products and personal items
Production and distribution of electricity, gas and water
Manufacturing industries
Transport and communications
Provision of other utility, social and personal services
Financial activities
Fishing, fish farming
Real estate transactions, rental and provision of services
Mining

A high inverse relationship between per capita GRP and the share of agriculture in total GRP is observed for almost all countries and regions. Another thing is that the high feedback between per capita GRP and healthcare and education only indicates their overestimated share in lagging regions (other types of economic activity are absent or poorly developed), i.e. about the deformation of the regional structure of the market economy. Let us conduct a correlation analysis between the change in the number of employees in federal districts and the change in real wages in them.

Table 6

Correlation analysis between changes in the number of employees in federal districts and changes in real wages in them

	Correlation coefficient between changes in employment and changes in real accrued wages

From the table data it follows that in 2010-2012. wages did not serve as a stimulator of employment growth, which is largely due to the low share of wages in production costs and insufficiently high growth rates of real disposable income of the population.

Based on the above, we will draw the following conclusions.

From 2000 to 2012, there was a slight change in the number of employees in the federal district and a significant uneven increase in both the federal budget and GRP. Calculations demonstrate the inefficient use of labor resources, which requires a reduction in employment at the existing labor productivity in labor-insufficient subjects and the maximum possible increase in labor productivity in labor-surplus subjects. From 2000 to 2012, labor-saving (intensive) growth was observed. It is most profitable to increase employment in the Far Eastern Federal District, Siberian Federal District and North Caucasian Federal District. Fixed assets and employment do not fully describe the dynamics of GRP. It is more correct to use investments to describe the dynamics of GRP. Investments have the greatest effect in the Central Federal District, then, as their effectiveness decreases, there are the Ural Federal District, Southern Federal District, Northwestern Federal District, Volga Federal District, North Caucasus Federal District, Siberian Federal District, Far Eastern Federal District. From the analysis of the econometric dependence of labor productivity for the economies of the regions of the Russian Federation, it is clear that innovation factors practically do not predetermine changes in labor productivity (labor intensity). The investment factor still plays the main role in increasing labor productivity, and the generation of innovation plays a supporting role. In the Northwestern Federal District, Ural Federal District and Southern Federal District, the costs of technological innovation are unreasonably high, and they cannot be increased. The greatest efficiency of spending on technological innovations is in the North Caucasian Federal District, Volga Federal District, Siberian Federal District, Central Federal District and Far Eastern Federal District (in descending order). The efficiency of production in the economy of the Federal District can be increased through massive investments in fixed assets. The high inverse relationship between per capita GRP and healthcare and education only indicates their overestimated share in lagging regions (other types of economic activity are absent or poorly developed), i.e. about the deformation of the regional structure of the market economy. In 2010-2012 wages did not serve as a stimulator of employment growth, which is associated with low growth rates of real cash incomes of the population.

Reviewers:

Gezikhanov R.A., Doctor of Economics, Professor, Head of the Department of Accounting and Auditing, Chechen State University, Grozny;

Yusupova S.Ya., Doctor of Economics, Professor, Head. Department of Economics and Production Management, Federal State Budgetary Educational Institution of Higher Professional Education "Chechen State University", Grozny.

Bibliographic link

Magomadov N.S., Shamilev S.R. ANALYSIS OF THE DYNAMICS OF GRP OF REGIONS OF THE RF BY PRODUCTION FUNCTIONS // Modern problems of science and education. – 2014. – No. 6.;
URL: http://science-education.ru/ru/article/view?id=15467 (access date: 01/15/2020). We bring to your attention magazines published by the publishing house "Academy of Natural Sciences"

Page 2 of 2

The indicator - gross regional product (GRP) - is used to characterize the results of production in the region, to assess the level of economic development, the rate of economic growth and analyze labor productivity. Total GRP is the value of all final goods and services produced in the region during the year. According to the Keynesian model, the total GRP is calculated using the following formula:

VPP = C + I + S + E – M, (1)

where, C – consumption; I – investments; S – regional and municipal expenses; E – export; M – import.

Formula (1) shows what economic growth in a country depends on and how it can be influenced. The main source of GDP growth is consumption (C) and investment (I). To stimulate consumer demand and investment levels, the central bank lowers interest rates and the government reduces taxes. An increase in regional and municipal spending (S) also leads to an increase in GDP. To analyze labor productivity and compare regions, GRP per capita is used, which is determined by dividing the total GRP by the population of the region. We examined 80 regions of Russia for 2012-2013. .

As a result of using the principal component method, the greatest influence is exerted by specific factors: I, C, S, E, M, which are arranged in descending order of variations. Variation refers to dispersion and standard deviation. Influencing factors are independent indicators on the right side of the equation.

For overall GRP, the following regression equation was constructed, which is significant at the 5% level:

GRP= exp(5.136+0.000001 INV_OK+0.000076 UCH-0.000307 ACP+0.0095 DOC-0.00008 Z_NIR +0.000013 Z_TEHN) with correlation coefficient R = 0.82,

where INV_OK is the volume of investment in fixed capital; UCH – number of personnel engaged in scientific research; ACP - admission and graduation from graduate school: DOC - admission and graduation from doctoral studies: Z_NIR - expenses for scientific research; Z_TEHN – costs of technological innovation.

An increase in INV_OK, UCH, DOC, Z_TEHN has a positive effect. The greatest effect comes from increasing the number of doctors of science.

Specific incomes in the regions of Russia were discussed in the article. Regions were divided into clusters. All regions were divided into 4 classes. For each cluster, significant regression equations were constructed.

The influence of per capita food consumption in the regions was discussed in the article. In the constructed regression equation, the factors that have a positive impact on GRP per capita are: average per capita annual consumption of meat, milk, vegetable oil, potatoes and vegetables. Factors that have a negative impact on GRP are: consumption of eggs, sugar and bread.

A comparison of the state of education and gross regional product is discussed in the article. Based on the constructed regression equations, we can draw conclusions: with an increase in the number of students by 1 person out of 10 thousand people, the specific value of GRP per capita in the region will increase by 11.5 rubles; with an increase in investment in education by 1 ruble for each resident of the region, the specific value of GRP will increase by 16.3 rubles. If investments in education in “average income” regions increase by 1 ruble, then GRP per capita increases by 11.69 rubles.

Equations of demographic indicators depending on income and GRP are given in the article. The article provides a clustering of regions by the share of the active population, the unemployed, the share of employees and average per capita income. The volumes of exports and imports in the regions have a slight impact on GRP, which can be observed from the coefficients of the constructed regression equation:

GRP = exp(5.064-0.00323 IND_P+0.0013 IND_CX+0.000001 E-0.000002 M-0.0112 INF +0.0244 UEA) (2)

with correlation coefficient R = 0.75,

where IND_P is the industrial production index; IND_CX – agricultural production index; E – specific export; M specific import; INF – inflation index; UEA is the level of economic activity of the population.

Regression equation (2) is significant at the 0.05 level, but the residuals of the equation (the difference between the values ​​of the equation and the statistical data) do not correspond to the normal distribution law.

Literature

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