2. Methodology for the study of socio-economic processes and phenomena in the regions

2. Methodology for the analysis of the gross regional product

2.4. Analysis of economic and structural proportions of the gross regional product of the Rostov region

We will conduct a study of the economic and structural proportions of the gross regional product established in Rostov region.

Calculation of GRP by the above method allows:

Analyze the dynamics of productivity (relative and absolute) of the region's economy in order to identify the structural transformation of the region's economy;

Consider the dynamics of sectoral shares of gross output and gross value added, characterizing the direction of ongoing structural and institutional shifts in the regional economy;

Express the growth trend in the sphere of production or services;

Identify the leading industries (points of economic growth) by the industry's share in the total volume of gross value added;

Determine the ratio of the share of market and non-market services in the total volume of gross value added of services produced;

Consider the dynamics of cost GRP under the influence of its constituent elements: gross output, intermediate consumption, net taxes on production.

The analysis of the dynamics of the productivity of the regional economy, the structural sectoral patterns of the economy of the Rostov region and the structural and economic proportions of the GRP production was carried out on the basis of information provided by the Rostov Regional Committee of State Statistics.

AT general view the productivity of an economic system is understood as its ability to produce a surplus of goods and services in excess of the technologically necessary volume of consumption of these goods and services in the production process. The totality of goods and services in the amount of such a surplus is called economic surplus. In value form at the macro level, it is characterized by the indicator of produced GDP, at the meso level - by produced GRP. The degree of productivity of the regional economy (relative productivity) can be estimated by the share of GRP in its gross output and is calculated by the formula

where GVA p and GVA at- gross value added, respectively, of the industries of the manufacturing sector and the service sector.

Let us analyze the dynamics of the gross output of the economy of the Rostov region in market prices in the context of its components: intermediate consumption (IC) and GRP (Fig. 2.10).

Rice. 2.10. The structure of the gross output of the Rostov region,

in % of the total

The analysis shows that the relative productivity of the region's economy as a whole decreased and by 2001 amounted to 50.7% against 51.0% in 1997. This is less than the degree of productivity of the Russian economy (in 1997 this figure was 53.3%, and in 2001 - 55.1%). There is a relationship between absolute productivity, measured by the value of real GRP, and its relative productivity (Fig. 2.11).

In 1998, absolute productivity reaches its lowest level of decline - 96.7% of the level of 1997 (100%) (the period of the financial crisis in Russia), and specific gravity GRP in EV increases to a maximum value of 54.7%. Then the economy enters a phase of economic recovery: its absolute productivity begins to grow, and its relative productivity decreases to 50.7% in 2001. This relationship indicates, firstly, that the market structural transformation of the economy of the Rostov region, aimed at , under the influence of the institutional changes made, continues. Secondly, institutional market reforms have contributed to the development in the region of a complex of industries that produce products with a high share of value added in gross output.

Rice. 2.11. Dynamics of real GRP production and its share in GDP, in %

Let us analyze the structural shifts in more detail in the context of all sectors covered by the SNA, grouping them into two sectors - production and services. To do this, first consider the structure of the produced GRP of the Rostov region (Fig. 2.12).

In the structure of the produced GRP, there is an increase in the share of the sphere of production of goods from 44.1% in 1997 to 50.8% in 2001, while the share of the service sector decreases from 50.5% to 43.4, respectively. It should be noted that significant changes occurred in the GRP structure during the period under review, which can be divided into two time periods:

From 1997 to 1998 the trend of excess of the share of production of services over the share of production of goods prevails (in 1997 - by 6.4%, in 1998 - by 8.3%);

From 1999 to 2001 there is a noticeable tendency for the share of production of goods to exceed (mainly due to the “industry” sector) over the share of production of services (in 1999 - by 2.4%, in 2000 - by 7.5%, in 2001 - by 7.4%).

Rice. 2.12. Structure of produced GRP in 1997–2001, in %

The change in the structure of GRP can be traced according to Table. 2.9.

Table 2.9

Dynamics of the structure of produced GRP by sectors of the economy

Indicators
Growth (+), decrease (-) in the share in GRP in relation to the previous year, percentage points:
production of goods
service production

So, despite the ongoing fluctuations in the material structure of the GRP, the Rostov region remains more of a “commodity” region with potential reserves for the development of both the sphere of production of goods and the sphere of production of services.

Specific gravity industries that produce goods, in GRP for the period under review (1997–2001) were constantly changing. Industry occupies the largest share in the total GVA of industries, the share of which decreased by 1.4% in 1999 compared to 1997, and in 2001 it increased by 3.1%. Such growth is explained by higher price growth for products, mainly, fuel, timber, woodworking industries. Nevertheless, the following industries show a steady positive trend over the past two years: electric power industry (228% in 2001), woodworking and pulp and paper (112.6%), light industry (115.4%), building materials (104, 8%) and food (104.9%). The share of agriculture increased by 8.1% in 1999 compared to 1997, and in 2001 it decreased by 4.1%; the share of construction decreased by 2.6% in 1999 and increased by 3.5% in 2001.

In the service sector, the largest share in the volume of GRP is occupied by market services, whose share decreased by 3.7% in 2001 compared to 1997. The preponderance of the provision of market services (35.6%) over non-market (7.8%) occurred in the following sectors of the economy: transport, communications, trade and public catering, housing and communal services. Specific gravity non-market services decreased by 3.4% in 2001 compared to 1997 due to a sharp decrease in funding from the state, regional budgets and state off-budget funds industries such as: health care, physical education and social security, culture and art, education, management.

Consideration of the dynamics of the sectoral structure of GRP production in the Rostov region in an expanded form allows us to identify the main structural shifts (Table 2.10).

Table 2.10

Shifts in the sectoral structure of the economy of the Rostov region for 1997–2001, in %

Branches of the economy	GVA structure
						Index of shifts in 1997-2001
Production of goods:	44,1	42,9	48,1	50,9	50,8	15,2
Industry
Agriculture
Forestry
Construction
Other manufacturing activities
Service production:	50,5	51,2	45,6	43,4	43,4	-14,1
Transport
Trade and commercial activities in sales of goods
blanks
Information- computing service
Geology and exploration of subsoil, geodetic and hydrometeorological services
Agricultural service
Road facilities
non-production types of consumer services for the population
Insurance
Science and scientific service
Health care, physical culture and welfare
Education
Culture and art
Control
Net taxes on products
GRP (at market prices)	100	100	100	100	100	Structural shifts towards growth occurred rapidly in the following sectors: agricultural services (by 50%), (by 44.4%), agriculture (by 34.7%), construction (by 10.6%), industry ( by 7.5%), and services (by 6.7%); Structural shifts towards a decrease in the share occurred (in descending order of the rate of decline) in the following sectors: road construction (by 80%), procurement (by 66.7%), housing and communal services (by 59.7%), culture and art (by 50%) %), education (by 37.5%), non-production types of consumer services (by 25%), communications (by 21.1%), transport (by 18.8%), healthcare, physical culture and social security (by 14.2%), management (by 8.3%); Zero structural shifts were observed in forestry, information and computing services, insurance, science and scientific services. Since some industries in the SNA provide both market and non-market services, we will bring them together by summing the corresponding indicators (BB, GVA) in each year. Net taxes on products we will distribute by branches in proportion to the volumes of their gross output. An analysis of the indicators of RR, PP, and GVA production in the selected sectoral areas shows that the degree of productivity of the economy as a whole for 1997–2001. decreased by 0.3 p.p. and amounted to 50.7%, the production sector increased by 0.9 p.p. and reached 40.5%, while the service sector increased by 1.1 p.p. and amounted to 60.7%. The change in the relative productivity of the economy as a whole and its two industry sectors is shown in Fig. 2.14. Rice. 2.14. Dynamics of the relative productivity of the regional economy Let us calculate the unit costs of the economy of the Rostov region for the production of gross output at current prices and evaluate their impact on the level of its productivity. According to Table. 2.11, the unit costs of explosives in the sectors of the production of goods increased by 7 kopecks. Accordingly, the specific VV in the service sector fell by the same amount. At the same time, both components of the specific ROI (PP and GVA) of the production sector increased, while the corresponding components of the ROI of the service sector decreased. Table 2.11 The structure of specific costs for the production of explosives (in current prices, kopecks per 1 rub. VV) Indicators Growth Production of goods Service production Economy as a whole Total GVA Growth of specific PP in the production sector by 3.4 kopecks. and a drop in the specific PP of another sphere by 3.1 kopecks. as a result, they led to an increase in the specific PP of the economy as a whole by 0.3 kopecks. (3.4–3.1=0.3). Its specific GVA decreased by the same amount, which happened due to the growth of the specific GVA of industries producing goods by 3.6 kopecks. and a decrease in the specific GVA of service industries by 3.9 kopecks. (3.6–3.9=-0.3). These changes caused a decrease in the level of relative productivity of the region's economy as a whole by 0.3%. From the analysis of indicators of relative productivity (the ratio of GRP to VV) of the sectoral structure of the region, a change in the considered indicator is observed in most sectors (Table 2.12). Moreover, the decline and growth of the productivity of industries occurred in both areas. In the sphere of production of goods, the largest increase in the degree of productivity is observed in agriculture (+7.1 p.p.), and a significant drop in construction (-2.2 p.p.). In the service sector, a high increase in the level of productivity occurred in science and scientific services (+15.1 p.p.), in healthcare (+11.3 p.p.), the largest drop was in culture and art (-25.1 p.p.). p.p.), information and computing services (-17.7 p.p.) and road sector (-16.5 p.p.). Table 2.12 Dynamics of Relative Productivity of Sectors of the Economy Rostov region Branches of the economy Growth 1997-2001 Economy as a whole Sphere of production of goods 39,6 41,5 43,3 43,0 40,5 0,9 Industry Agriculture Forestry Construction Other activities for production Services sector 59,6 64,6 61,5 60,0 60,7 1,1 Transport Trade and commercial activities for the sale of goods and services Information and Computing Services blanks Operations with real estate Geology and exploration of subsoil, geodetic and hydrometeorological services Service organizations Agriculture Road facilities Housing Utilities Non-productive types of consumer services for the population Insurance Science and scientific service Health care, physical culture and social. security Education Culture and art Control As a result of the changes that have taken place, the composition of the industries that lead in terms of production productivity has practically not changed (Table 2.13): in 2001, such industries as agricultural management and services were added. Leading real estate transactions. It should be noted that the leading sectors are mainly service industries and only one sector of goods production - forestry. High degree its productivity is ensured by reducing the cost of reforestation to almost zero. As part of industries leading in volumes production of gross value added, there have been changes (Table 2.13). Table 2.13 Leading industries in terms of relative productivity and for the production of GRP, in % Leading industries in terms of production productivity (% GVA in BB) Leading industries in terms of GRP production Operations with real estate Operations with real estate Industry Industry Information and Computing Services Insurance Trade and commercial activities for the sale of goods Trade and commercial activities for the sale of goods Insurance Forestry Agriculture Agriculture Culture and art Control Transport Construction Agricultural service Construction Transport Forestry Trade and commercial activities for the sale of goods Education Control Trade and commercial activities for the sale of goods and services Education On the Don, the following branches of economic growth remain among the leaders: industry (25.9%), in particular food (6.2%), mechanical engineering and metalworking (7.1%), electric power industry (4.4%); trade and commercial activities for the sale of goods and services (19.1%), agriculture (15.9%), construction (8.3%), transport (6.5%), management (3.3%), education (3.0%). The obtained results indicate that the Rostov region continues to be one of the major agro-industrial centers of the south of Russia. However, the sustainable functioning of the traditional branches of the regional economy is accompanied by the emergence and growth into market environment new industries-institutions: real estate transactions, insurance, information and computing services, general commercial activities. In other words, the ongoing transformations and the structural shifts that reflect them are the result (demonstration) of bringing the sectoral structure of the region's reproductive system in line with the needs of the market and general institutional changes in society and, therefore, will lead to the formation of an optimal sectoral structure. From the point of view of general economic laws governing the movement of the reproduction process, there is an intensive development of service industries against the backdrop of industrial production and agriculture. Dynamics of the structure of GRP branches of the Rostov region, grouped by reproductive sectors for 1998–2001, is given in Table. 2.14. Table 2.14 Dynamics of the reproductive structure of the GRP of the Rostov region Reproductive sector and industry Specific weight, % Change 2001 to 1998 Personal consumption sector(agriculture, housing and communal services, education, health care, physical culture and social security, culture and art) Investment sector(science and scientific service, construction, engineering and metalworking) Fuel and Raw Materials Sector(electricity, fuel, chemical and petrochemical, metallurgical, timber, woodworking, pulp and paper, industry building materials) Circulation and Services Sector(trade and commercial activities for the sale of goods and services, procurement, transport, communications, information and computing services, real estate operations, non-production types of consumer services, management, insurance) Other industries An analysis of structural shifts shows that the reproductive structure of the Rostov region is characterized by intense changes. The largest share in the structure is occupied by the sector of circulation and services (36.2%), and over the past two years it has remained virtually unchanged. This indicates the formation of the domestic service market, primarily consumer. The trend towards an increase in the share is the investment sector - from 1999 to 2001. rose by 5.6%. The reproduction sectors, working for the consumer and innovation markets, suffered the most. The sectors of the personal consumption sector tend to decrease (the sector's share in GRP fell by 2.5% in 2001 compared to 1998). The share of industries included in the fuel and raw materials sector has changed insignificantly: over the past three years it has grown by 0.9%. However, the clearly unfavorable shifts towards the reduction of those employed in science and scientific services, health care, education, culture, art, housing and communal services are cause for alarm. In addition, the sectoral shifts observed in the regional economy are closely related to the general institutional changes taking place at the macroeconomic level of the national economy of Russia. If we compare the sectoral structure of the GVA of the Rostov region with the Russian one, then in 2001 it had a significant share in agriculture (15.9 vs. 6.8%) and in sectors providing non-market services (7.8 vs. 6.6%) , a smaller share in industry (25.9 versus 31.0%) and virtually the same shares in construction (8.3 and 8.0%), transport (6.5 and 7.4%), trade and commercial activities for the sale of goods and services (19.1 and 19.4%). Based on the forecast of structural shifts in the period from 2000 to 2020, made by the Fund for Basic Research based on the use of a multidimensional reproduction-cyclic model and reporting intersectoral balances, we can conclude that the reproduction structure of Russia's GDP and the reproduction structure of the GRP of the Rostov Region are currently similar ( table 2.15). As the researchers note, the dynamics of changes in the reproductive structure of the domestic and, consequently, the regional economy will depend on the action of diverse multidirectional factors. Thus, the results of the analysis of the dynamics of the reproductive sectoral structure of the GRP of the Rostov region indicate that the regional economy as a whole has adapted to the new market conditions of management and provides expanded reproduction. In addition, the region has reserves for more effective use its economic potential (in particular, more intensive development of market services and optimization of costs for the maintenance of non-market industries). Table 2.15 Predictive assessment of the dynamics of changes in the reproductive structure of Russia's GDP reproductive sector Specific weight, % Personal consumption sector Investment sector Fuel and Raw Materials Sector Circulation and Services Sector Let us analyze the dynamics of the GRP value volume under the influence of its forming elements. The formation of the volume of GRP in terms of value is reflected in the statistical model of GRP, which shows the balance relationship of the constituent elements: gross output of goods and services (GV), intermediate consumption (IP), taxes on products (N) and subsidies on products (S). This relationship is presented in the form of a production account - the main account of the SNA (Table 2.16). Table 2.16 Production account (in current prices, thousand rubles; before 1998 - million rubles) Indicators Resources Issue at basic prices Taxes on products Subsidies for products (-) Usage Intermediate consumption GRP at market prices The change in the cost volume of GRP under the influence of the elements that form it is shown in fig. 2.15. The graphic shows: Relatively synchronous change in cost elements (VC, PP) in comparison with GRP: in 2001, GRP increased by 252% compared to 1997, VC and PP increased by 253% and 255%, respectively; Higher growth rates of GRP until 2001 compared to the constituent elements, which testify to different rates of appreciation of intermediate and final products; Dynamics of GRP cost intensity (PP per 1 ruble of GRP) isolated from changes in other factors. The dynamics of GRP elements that has developed over the period under review is characterized by the following ratio of the indices for 2001 to 1997 (see Fig. 2.15): I GRP< I ВВ < I ПП, или 3,52 < 3,53 < 3,55. Это соотношение может быть использовано при изучении последующих изменений в стоимостной структуре ВРП, например, 1% роста валового выпуска даст рост промежуточного потребления на 1,01% (3,55/3,53) и ВРП на 1% (3,52/3,53), либо при паритете цен на сырье, материалы и finished products at the level of 2000, the cost of goods and services additionally consumed in production (due to the increase in the cost intensity of GRP) could ensure an increase in GRP in the amount of 3% (101–98%). Rice. 2.15. Rates of change in the elements of GRP formation, in % to 1997 Net taxes on products (minus received subsidies on products) characterize the relationship of the region with the budgets of different levels. This factor does not have such a strong influence on the production of GRP as the previously considered elements, but it is also important for characterizing economic situation in the region. In the period under study, there is an annual excess of taxes over subsidies, which indicates the non-subsidized nature of the region's economy. However, for the period from 1999 to 2001. a decrease in the share of net taxes on products from 6.2 to 5.8% in the structure of GRP production is characteristic (Table 2.17). Table 2.17 Dynamics of the ratio of taxes and subsidies for 1997–2001 Indicators Taxes on products to GRP, in % Subsidies for products to GRP, in % Subsidies for 1 ruble of taxes, rub. Net taxes to GRP, in % This is due to a decrease in tax revenues in the GRP structure (from 8.3% in 1999 to 7.1% in 2001), mainly due to a reduction tax revenue(VAT, property tax) in the structure of the consolidated budget of the region. These changes were reflected in a sharp reduction in the provision of subsidies (from 2.1 to GRP to 1.3%). There is a tendency to improve the proportions of the relationship of the region with the budgets of different levels. Let us analyze the economic and structural proportions of the use of the gross regional product. Based on the statistical data of the Rostov Regional Committee of State Statistics, elements of the used GRP of the study region were collected, the functional structure of which is presented in Table. 2.18. Table 2.18 Functional structure of GRP use in the Rostov region, in % Final consumption expenditure accumulation fixed capital used for final consumption and accumulation, total Including households public institutions providing collective services During the period under review, there have been changes in the functional structure of the used GRP, which shows the uneven cost growth of the elements of the used GRP. The largest share in the structure of 2001 is occupied by expenditures on final consumption (78.4%), of which the actual expenditures of households account for 74.8%. However, from 1997 to 1999 there is a tendency to increase expenditures on final consumption of households at the expense of the individual budget (from 77.2% to 80.8%, respectively) and a significant drop in the share of this element in the structure of expenditures in 2000–2001. to 74.8%, which occurred mainly due to the growth in the cost of education, healthcare, culture, etc. In addition, in the structure of final consumption expenditures, there is an increase in the cost of final consumption of state institutions providing collective services to society (from 4 ,6 in 1997 to 5.8% in 1999), namely for the maintenance of the “management” sector, but in 2001 these expenses decreased to the level of 3.6%. Thus, there is an overall decline in final consumption expenditures (mainly due to a reduction in household consumption expenditures), which indicates a relative deterioration in the living standards of the population in the region. With a detailed consideration of the actual final consumption in the Rostov region for every 100 rubles. GRP used for actual consumption accounts for the costs summarized in Table. 2.19. The actual final consumption of the Rostov region by 95.4% in 2001. consisted of household expenses, in which 85.4% is the purchase of goods and services. The increase in consumption in households was due to an increase in spending on goods and services (by 5.5 percentage points) and a simultaneous decrease in the consumption of social transfers in kind (by 4.4 percentage points). Government spending on collective services changed insignificantly (decreased by 1.1 percentage points). Table 2.19 Distribution of 100 rubles of GRP used for actual final consumption, rub. Indicators Actual final consumption including: Consumed in households including on: purchase of goods and services consumption of social transfers in kind Expenditures of public institutions on collective services Uneven structural changes are noted for gross capital formation, until 1999 the share decreased by 4.7%, but over the past three years its share has increased by 8.4% and in 2001 is 21.6%, which indicates an acceleration of the reproductive process in the region. It should be noted that the volume of GRP used exceeds its production by a constant value (19.5%) during the period under consideration, i.e. there is a certain shortage of sources of financing expenses, which, undoubtedly, was accompanied by an increase in overdue wage arrears at enterprises in industry, agriculture, construction, transport, utilities, debts of enterprises and organizations on payments to the budget. In the process of further analysis of the proportions and inter-regional comparisons of the GRP of the Rostov region with other regions, it is necessary to establish how inflationary processes affected individual structural components of final consumption and accumulation. To do this, it is necessary to re-evaluate all elements of the structure of GRP use in comparable prices using the appropriate deflator indices. The task is complicated by the fact that statistical yearbooks do not contain all the necessary deflator indices. Therefore, we will use the following deflator indices: for GRP is the GRP deflator index calculated by the formula , where Idt – GRP deflator index in the region for year t; Iqt– GRP growth rate in the region for the year t; qt– the volume of GRP in the region for the year t; t= 1998 ... 2001; - for household final consumption expenditure– consumer price index (available in statistical yearbooks); - for gross capital formation– industrial price index (available in statistical yearbooks). As can be seen from the data in Table. 2.20, the elements of the used GRP of the Rostov region changed at different rates. Note that the growth rates of produced and used GRP practically coincide. Of particular note are the upward changes in the rate of accumulation of fixed capital over the past two years, which indicates an increase in investments by resident (non-resident) units of funds in fixed capital objects to create new income in the future by using them in production. In addition, in 2000 the growth rate of used GRP practically coincides with the growth rate of expenditures on final consumption of households (11.6% and 11.5%, respectively). It is advisable to analyze the degree of sensitivity (elasticity coefficient) of the growth rate of the used GRP from the growth rate of household final consumption expenditures. This will link one of key indicators the standard of living of the population (final consumption expenditures of households) with an indicator of the effectiveness of the functioning of the institutional and reproductive system of the region (GRP). In general, elasticity is understood as a measure of the response of one quantity to a change in another. Table 2.20 Deflator indices and growth rates of GRP use elements, VC previous year Indicators Deflator indices: GRP deflator index index consumer prices industry price index Growth rates (in comparable prices): Used GRP Household final consumption expenditure Gross fixed capital formation Elasticity of GRP with respect to household final consumption expenditure (E 1) shows by what percentage the value of GRP will change with a one percent change in the value of spending on final consumption of households: According to calculations, with a 1% change in household final consumption expenditures, the GRP value increased by 0.1% in 1998 and by 0.5% in 1999 (in this case, the GRP indicator is inelastic, 0<E 1<1, т.е. относительное изменение расходов домашних хозяйств превышает относительное изменение объема ВРП). В 2000–2001гг. при изменении расходов домашних хозяйств на 1% объем ВРП увеличился на 1,1 и 2,2% (E 1>1, GRP is elastic), i.e. the value of GRP is sensitive to changes in the costs under consideration. Thus, we can talk about the existing dependence (level of sensitivity) between the relative changes in the indicator of the efficiency of the functioning of the reproductive system of the region (GRP) on the indicator of the standard of living of the population of the region (final consumption expenditures of households). Let's analyze the relationship between production and consumption of the gross regional product . Let's check the statistical hypothesis about the existence of a relationship between the values ​​of per capita production (X) and GRP consumption (Y) for the Rostov region in dynamics for 1995-2001. using correlation-regression analysis. Testing this hypothesis confirms the presence of a fairly strong positive relationship between X and Y (correlation coefficient r x , y = 0.85), which gives grounds for building a linear regression model: where X(x) is the average per capita production of GRP (factorial indicator), rub.; Y(X(x)) is the theoretical (probable) value of the average per capita consumption of GRP for a given value of X (resulting indicator), rub.; A 1,– regression coefficient, which shows by how many rubles the level of per capita consumption in the region will change on average over the period with a change in average per capita production by 1 rub.; A 0,– conditional level of per capita consumption for the period at X=0 rub. The following parameters of the regression equation are obtained: Thus, for the period under review, the dependence of the growth in household consumption on the growth in GRP production amounted to 94%, or for 1 ruble of growth in per capita GRP production, consumption increased by an average of 94 kopecks. The graphical model of dependence is shown in fig. 2.16. Rice. 2.16. Production and actual final consumption of households per capita in the Rostov region in 1995-2001 So, in 2001, with the actual level of per capita GRP production of 28985.7 rubles. the theoretical (probable) value of average per capita consumption, according to the equation obtained, is 26665.6 rubles. In fact, it amounted to 26,273.2 rubles, which is 1.5% lower than the theoretical value for the year under review. Let us analyze the ratio between the produced GRP and the final consumption in the region per capita. To do this, it is necessary to calculate the coefficient (K), which characterizes the degree of sufficiency of the GRP produced in a given territory to cover the actual final consumption of households according to the formula: K=D M /S M, where D m- the value of the produced GRP per capita; Cm– actual final consumption per capita. If K>1, then the value of per capita GRP production covers the actual final costs of households. If 0<К<1, то произведенного ВРП не достаточно для возмещения потребительских расходов. Table data. 2.21 show that for the period under study, the volume of per capita produced GRP is sufficient to cover the actual final consumption of households (resident and non-resident) in the Rostov region, since K> 1. There is an upward trend in the coefficient (in 1999 - 1.06; in 2000-2001 - 1.1), which indicates the presence of the accumulation potential of this category of institutional entities. Table 2.21 The results of calculating the degree of sufficiency of the produced GRP of the Rostov region to cover the actual final consumption of households Indicators D m, rub. (before 1998 - thousand rubles) C m, rub. (before 1998 - thousand rubles) K, in fractions The analysis of the production and consumption of GRP in the Rostov region indicates that the changes in the socio-economic development taking place on the territory of the region under study are reflected in the dynamics and interconnection of the elements of the national accounting system at the regional level. Thus, the identified extensive informative and analytical capabilities of the GRP make it possible to use this most important regional economic indicator to specify and justify certain provisions of targeted programs for the economic and social development of regions. In particular, the proposed methodological tools for analyzing the economic and structural proportions of the meso-level institutional reproduction system based on the gross regional product will adequately assess, compare and track the dynamic changes in the existing economic proportions (disproportions) in order to determine effective strategies for the development of the region. Balatsky E., Potapova A. Sectoral patterns of market transformation of the Russian economy //Mirovaya ekonomika i mezhdunarodnye otnosheniya. 2000. No. 6. S. 89. The calculation of the gross capital formation deflator is considered one of the most difficult tasks in the statistical practice of deflation. Previous

1

This article examines the concept of gross regional product, its difference in the regions of the North-Western economic region, calculates and analyzes the per capita gross regional product for the constituent entities of the Russian Federation, as well as the position of the Novgorod region in comparison with other subjects of the economic region for this indicator. As a result of the study, the subjects of the Russian Federation were studied: Vologda, Kaliningrad, Leningrad, Novgorod, Pskov regions and the city of St. Petersburg. The city of St. Petersburg is the undisputed leader in terms of per capita GRP, the average value for this indicator is occupied by the Leningrad region, the outsider is the Pskov region. The Novgorod region ranks third in the region, slightly exceeding the values ​​of the Vologda and Kaliningrad regions, but lagging behind the average regional level. Based on the results of the study, recommendations are proposed to ensure stable growth of the gross regional product.

gross regional product

Northwestern economic region

population

per capita gross regional product

Novgorod

Vologda

Pskovskaya

Leningradskaya

Kaliningrad region

city ​​of St. Petersburg

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Today, under the influence of economic sanctions from the EU and the United States, the issues of ensuring the progressive development of both the country's economy as a whole and individual economic regions are becoming increasingly acute. The dynamically changing political and economic situation not only in our country, but also in a number of other industrialized countries indicates the need for economic entities to quickly adapt to unstable market conditions.

For a more successful study of the problem of regional development of the economy of the constituent entities of Russia, it is necessary to study the dynamics of the growth of the gross regional product. The dynamics of this indicator may indicate the development of the economy of a particular region of a particular country, and the results of the analysis will allow timely response to possible risks and timely use of emerging opportunities to ensure the progressive development of the relevant entities.

According to the Great Soviet Encyclopedia, an economic region is a territorially connected part of a single national economy of the country, interconnected with each other by their different specialization, constant exchange of manufactured goods and other economic relations.

That is, according to the definition, an economic region is characterized by:

1) the originality of natural and economic conditions;

2) historically developed or purposefully created specialization of the economy on the basis of the geographical division of labor;

3) the presence of intra-district stable and intensive economic ties.

There are a number of economic regions in the Russian Federation: Central, Central Black Earth, East Siberian, Far Eastern, Northern, North Caucasian, Northwestern, Volga, Ural, Volga-Vyatka and West Siberian.

The Northwestern economic region is one of the 12 economic regions of Russia, and also one of the former economic regions of the USSR. The North-Western economic region of the USSR was created in 1963, when the taxonomic grid was approved, refined in 1966. It was one of the largest districts in the Soviet Union. It occupied the entire north of the European part of the USSR. The district included: Arkhangelsk, Vologda, Leningrad, Murmansk, Novgorod, Pskov regions, Karelian and Komi Autonomous Soviet Socialist Republics. The area of ​​the district was 1662.8 thousand km² (7.4% of the territory of the Soviet Union), and the population in 1975 was 12.7 million people (5% of the population of the USSR). In 1980, the Northern economic region was separated from the North-Western region. The area of ​​the North-Western economic region began to be 0.2 million km².

According to the website of the Federal State Statistics Service of the Russian Federation, the gross regional product (GRP) is an indicator that measures gross value added, calculated by excluding the volume of its intermediate consumption from the total gross output. At the national level, GRP corresponds to the gross national product, which is one of the basic indicators of the system of national accounts.

The concept of gross regional product in terms of its economic content is located quite close to the concept of gross domestic product, which makes the study of this area relevant for studying the general development trend of the region. However, there is a significant difference between these concepts: gross domestic product (at the federal level) and gross regional product (at the regional level). The sum of indicators of gross regional products for Russia does not include value added for non-market collective services (defence, public administration, and so on) provided by state institutions to society as a whole. That is, the public sector of non-market services provided by the state to a particular region is excluded, since it is indivisible and uniform for the entire territory of the Russian Federation.

For a comparative analysis of the gross regional product of the region (Novgorod region), it is necessary to use the following regions that are similar in terms of production, economic and geographical characteristics: Vologda region, Kaliningrad region, Leningrad region, Pskov region, as well as the federal city of St. Petersburg.

According to the Federal State Statistics Service of the Russian Federation, the gross regional product is given in basic prices starting from the results for 2004 (until 2004 it was given in market prices). Basic price - the price received by the producer for a unit of a good or service, excluding taxes on products, but including subsidies on products. To eliminate the influence of different rates of taxes and subsidies in various sectors of the economy and different subjects of the federation on the structure of production and income generation, sectoral indicators are given in the assessment at basic prices.

In order to obtain a reliable picture of the ongoing changes in the areas of economic regions, it is also necessary to calculate the per capita gross regional product. This parameter is necessary for a better analysis of the GRP, since this indicator smooths out the difference in population in the constituent entities of the Russian Federation:

GRP average shower \u003d GRP / CHN,

where GRP - gross regional product, thousand rubles; CN - population, thousand people.

It is this definition of the indicator that makes it possible to more correctly display and interpret the available statistical data for various subjects that are part of the economic regions of the Russian Federation. For clarity, we present the calculation of the average per capita GRP of the subjects of the North-Western economic region of Russia (table).

Thus, based on the analysis of the regions of this economic region, we can draw the following conclusions. The leaders of the North-Western economic region in terms of gross regional product are the federal city of St. Petersburg and the Leningrad region (2496549100 and 692798600 thousand rubles, respectively), the Pskov and Novgorod regions have the lowest value of this indicator (114246500 and 177930100 thousand rubles, respectively) . The Novgorod region in terms of gross regional product is ahead of only the Pskov region by 35.79%, inferior to the Kaliningrad and Vologda regions by 55.88 and 91.73%, respectively, and is very far behind the leaders of the region of the Leningrad region (by 289.37%) and the city of St. Petersburg (by 1303.11%). The share in the GRP of the district of the Novgorod region is only 7.13% and is insignificant.

Calculation of per capita GRP of subjects of the North-Western economic region of Russia

The leaders of the economic region in terms of population are the city of St. Petersburg (5191.690 thousand people) and the Leningrad region (1775.540 thousand people), and the outsiders in this indicator are Novgorod (618.703 thousand people) and Pskov (651.108 thousand people). person) area. The Novgorod region is the smallest in terms of this demographic indicator and slightly behind the Pskov region by 5.24%, significantly behind the Kaliningrad region (by 56.61%), Vologda (by 92.50%) and Leningrad (by 186.98%) regions and is very significantly inferior in terms of population to the city of St. Petersburg (by 739.13%). The share of the Novgorod region in the population of the North-Western economic region is small and amounts to 5.95%.

The ratio of shares in the GRP and in the population of the economic region (7.13/5.95%) is 1.20:1, which allows us to conclude that the per capita gross regional product should be about 20% higher than the average for the region, which indicates the good state of the economy of the region.

The leading place in terms of the value of the per capita gross product is occupied by the city of St. Petersburg with a value of 480,874.07 rubles per person. and exceeding by 21.94% the indicator for the study area as a whole, the Leningrad region occupies a value approximately equal to the average value of the indicator (lagging behind by 1.05% from it), the Vologda, Kaliningrad and Novgorod regions seriously lag behind the average per capita value of the gross regional product, and the difference in value is 27.37; 27.41 and 27.07%, respectively, the lowest value is the region of the Pskov region, this indicator lags behind the average for the region by 55.51%. In terms of per capita GRP, the Novgorod region ranks third among the subjects of the North-Western economic region, significantly inferior to St. Petersburg (by 67.21%) and the Leningrad region (by 35.68%), surpassing the Pskov region (by 38.99%) and slightly exceeding the indicators of the Kaliningrad and Vologda regions by 0.46 and 0.40%, respectively. From the above, it follows that the Novgorod region has a slightly lower per capita gross regional product, since the sample contains the anomalous value of the city of St. Petersburg, which significantly affects the per capita GRP, increasing it.

Thus, the Novgorod region is a region for which further development of the economy is necessary to achieve the average regional value of the gross regional product. For the stable development of the economy and the achievement of this indicator, an increase in production capacities and labor productivity in the region is required.

Therefore, without detracting from the achievements in the development of small business in the Novgorod region, municipal authorities and relevant support infrastructure enterprises need to competently build work with this sector, taking into account the “bottlenecks” and existing problems, so that the region does not miss the socio-economic benefits from the functioning of small business .

It should also be noted that in most regions that are part of the Northwestern economic region, the share of the population participating in the creation of the gross regional product is quite low, which entails the problem of increasing the population. It should be noted that for regions whose strategy should be based on catching up development, it is also necessary to ensure a comparable growth in the number of economically active population in the general structure of the population. And also to achieve a corresponding increase in production capacity by increasing inter-regional and international investment in fixed capital of firms in the region in order to increase the gross regional product.

The application of the above recommendations will allow timely adaptation of the specific regional environment to changing market conditions, and will also create an opportunity to ensure the progressive development of regional entities in the medium term.

Reviewers:

Kim L.V., Doctor of Economics, Professor, Deputy Financial Director of NGazService LLC, Veliky Novgorod;

Omarova N.Yu., Doctor of Economics, Professor, Deputy Chairman, Novgorod Regional Branch of the VEO of Russia, Veliky Novgorod.

Bibliographic link

Minin I.L., Minina E.S. COMPARATIVE ANALYSIS OF GRP OF SUBJECTS OF THE NORTH-WEST ECONOMIC REGION // Fundamental research. - 2015. - No. 10-3. - S. 602-605;
URL: http://fundamental-research.ru/ru/article/view?id=39264 (date of access: 01/15/2020). We bring to your attention the journals published by the publishing house "Academy of Natural History" 1

The paper considers the relevance of the research topic. Bubble charts were used to study the dependence of the gross regional product of federal districts on fixed assets and employment in 2000 and 2012. Calculated, using production functions, the dependence of the gross regional product of the federal districts on fixed assets and employment, on investment and employment, on investment and costs for technological innovation. A grouping of subjects of the Russian Federation according to the elasticity of output by fixed assets has been constructed. The correlation coefficients between the per capita GRP and the share of a certain type of economic activity in the total GRP of the federal districts are calculated. A correlation analysis was carried out between the change in the number of employees in the federal districts and the change in real wages in them. Appropriate conclusions are drawn.

real wage

type of economic activity

per capita GRP

correlation coefficient

technological innovation costs

output elasticity

production functions

employment

investments

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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 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 various and modern tools for assessing economic development, financial balance, competitive conditions in the domestic and world markets.

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

Let us graphically reflect the dependence of the GRP of the FD on the fixed assets and employment in 2000 and 2012.

Rice. 1. Dependence of FD GRP on fixed assets and employment in 2000

Rice. 2. Dependence of FD GRP on fixed assets and employment in 2012

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

Except in some cases, the input data used to build 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 a simple transformation of the original 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 the GRP of the FD on fixed assets and employment in 2000-2012

It can be seen from the graph that the GRP cannot be an averaging function of the function that links Y to K and L, i.e. factors K and L do not fully describe the dynamics of output Y.

Table 1

Calculation of coefficients of elasticity of the production function for calculation

	Elasticity of output by OF	Elasticity of output with respect to employment

Calculations show that for all federal districts, a reduction in employment is necessary with 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 with the existing labor productivity.

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

table 2

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

Efficiency of output according to OF	Number of subjects
	3 (Moscow, including the Nenets Autonomous Okrug, Yamalo-Nenets Autonomous Okrug)
	2 (Vologda region, Murmansk region)
	3 (Tyumen region, Khanty-Mansi Autonomous Okrug - Yugra, Primorsky Territory)
	19 (CBD, SC)
	2 (Kursk region, Tyva Republic)
	3 (RD, KChR, Republic of Mari El)
	1 (Republic of Adygea)
Grand total

For the CR in 2012, the value of the GRP elasticity coefficient of the regions in terms of CF is significantly less than 1, which in the long term, in order to increase production efficiency or increase labor productivity, means the need to increase the rate of accumulation and, accordingly, reduce the rate of consumption.

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

One option to deal with the lack or inadequacy of data on fixed assets is to use fixed investment data instead of data on fixed assets.

The advantages of this approach are explained by the high efficiency of investments directed both to attract idle funds into circulation and to acquire new funds, thereby increasing the share of effectively used capital.

Investment attractiveness is determined by many conditions.

Below we consider the following conditions: the impact of investment, as well as the combined impact of investment and labor on GRP.

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

It can be seen from the graph that Y can be an averaging function of the function that relates K and L to Y, i.e. factors K and L fully describe the dynamics of output Y (Fig. 4.).

Table 3

Calculation of GRP elasticity for investments

	GRP elasticity for investment

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 in the period under review. It is most profitable to increase employment in the Far Eastern Federal District, the Siberian Federal District and the North Caucasus Federal District. Let us consider the dependence of GRP on investments and expenses for technological innovations.

Technological innovation costs (million rubles) Table 4

	Elasticity coefficient of labor productivity from investments	The coefficient of elasticity of labor productivity from the cost of technological innovation

From the analysis of the econometric dependence of labor productivity for the economy of the regions of the Russian Federation, it can be seen that innovation factors practically do not predetermine changes in labor productivity (labor intensity). The main role in increasing labor productivity is still played by the investment factor, and the generation of innovations plays a supporting role. In the NWFD, Ural Federal District and Southern Federal District, the costs of technological innovation are unreasonably high and cannot be increased. The greatest efficiency is spent on 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 FD economy can be increased with the help of massive investments in fixed assets. The paper calculates the correlation coefficients between the per capita GRP and the share of a certain type of economic activity in the total GRP of the FD.

Table 5

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

Types of economic activity	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 Delivery
Hotels and restaurants
State administration and ensuring military security; compulsory social security
Construction
Wholesale and retail trade; repair of motor vehicles, motorcycles, household and personal items
Production and distribution of electricity, gas and water
Manufacturing industries
Transport and communications
Provision of other communal, social and personal services
Financial activities
Fishing, fish farming
Operations with real estate, rent and provision of services
Mining

A high inverse relationship between per capita GRP and the share of agriculture in the total GRP is observed in almost all countries and regions. Another thing is that the high feedback between per capita GRP and health care and education only indicates their overestimated share in lagging regions (other types of economic activity are absent or underdeveloped), i.e. about the deformation of the regional structure of the market economy. Let's carry out a correlation analysis between the change in the number of people employed in the federal district and the change in real wages in them.

Table 6

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

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

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

Based on the foregoing, we draw the following conclusions.

From 2000 to 2012, there was a slight change in the number of people employed in the federal district and a significant uneven increase in both fixed assets and GRP. Calculations demonstrate the inefficient use of labor resources, which requires a reduction in employment with the existing labor productivity in labor-deficient subjects and the maximum possible increase in labor productivity in labor-surplus subjects. From 2000 to 2012, labor-saving (intensive) growth is observed. It is most profitable to increase employment in the Far Eastern Federal District, the Siberian Federal District and the North Caucasus Federal District. Fixed assets and employment of the population do not fully describe the dynamics of GRP. It is more correct to use investments to describe the GRP dynamics. Investments give the greatest effect in the Central Federal District, then, as efficiency decreases, come the Ural Federal District, the Southern Federal District, the Northwestern Federal District, the Volga Federal District, the North Caucasus Federal District, the Siberian Federal District, and the Far Eastern Federal District. From the analysis of the econometric dependence of labor productivity for the economy of the regions of the Russian Federation, it can be seen that innovation factors practically do not predetermine changes in labor productivity (labor intensity). The main role in increasing labor productivity is still played by the investment factor, and the generation of innovations plays a supporting role. In the Northwestern Federal District, the Urals Federal District and the Southern Federal District, the costs of technological innovation are unreasonably high and cannot be increased. The most effective costs for technological innovations are 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 FD economy can be increased with the help of massive investments in fixed assets. 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 underdeveloped), i.e. about the deformation of the regional structure of the market economy. In 2010-2012 wages did not fulfill the function of a stimulator of employment growth, which is associated with low growth rates of real monetary incomes of the population.

Reviewers:

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

Yusupova S.Ya., Doctor of Economics, Professor, Head. Department of "Economics and Production Management" FGBOU VPO "Chechen State University", Grozny.

Bibliographic link

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

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

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Course work

On the topic: "Economic and statistical analysis of GRP production for a 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 production of GRP 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 signs

3.1 Combination grouping

3.2 Correlation analysis

Conclusion

List of sources used

Application

Introduction

The main purpose 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 practice.

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

Gross regional product is a generalizing indicator of the region's economic activity that characterizes 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 is 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 the gross regional product is, in its economic content, very close to the indicator of the 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 value added from non-market collective services (defence, public administration) provided by state institutions to society as a whole. At the moment, the calculation of the gross regional product of a subject of the federation 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 generalizing indicator of the region's economic activity that characterizes 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 the Russian statehood necessitate the construction of a developed system of statistical indicators at the regional level that meets the requirements of a market economy. System indicators characterizing the development of regions should be methodologically comparable and consistent with the corresponding indicators at the macro level.

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

To observe the intra-annual dynamics of the development of the region's economy, the calculation of the rate of change in the production volumes of the basic sectors of the economy (industry, agriculture, construction, retail trade and public catering, transport), which in the production structure of the regions range from 60% to 80%, is provided.

Characteristics of the studied regions.

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 the economic development of the region are connected precisely with the extraction and production of "black gold".

Oil reserves lying at depths of 5 to 6 kilometers are estimated at 5 billion tons of conventional 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 very large sums 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. Are unique recreational resources federal district. The mild climate, the abundance of mineral springs and therapeutic mud, warm sea waters create the richest opportunities for treatment and recreation. The mountainous regions with their unique landscapes have all the necessary conditions for the development of mountaineering and tourism, the organization of ski resorts of international importance here.

The central region is distinguished by 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 District is characterized by a complex combination of branches of material production and non-production sphere. The basis of economic specialization of the region is mechanical engineering, chemical, light industry, flax growing, potato growing, dairy and meat animal husbandry.

In the structure of the industry of the region, the dominant position is occupied by mechanical engineering, especially science-intensive, in need of qualified personnel.

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

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 stands out especially: cotton (Ivanovo, Moscow, Tver, etc.), linen (Kostroma, Nerekhta, Vyazma, etc.), silk (Moscow, Tver, Naro-Fominsk), woolen (Moscow, Klintsy, etc.). The sewing, knitting, leather and footwear, fur, and printing industries are also developed.

Of the service industries of the region, the fuel and energy complex stands out, especially the production of electricity (Kostromskaya, Konakovskaya, Ryazanskaya GRES and nuclear power plants - Smolenskaya, Kalininskaya). The extraction of brown coal in the Moscow Basin has declined sharply. Ferrous metallurgy enterprises (Tula, Elektrostal, Moscow) only partially satisfy the needs of the region in metal.

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

The transport complex of the region is distinguished by a high level of development and a 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 massifs with fertile chernozem soils.

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

The production of equipment for the oil-producing, oil-refining industry and the chemistry of organic synthesis is also of special importance. The location of these industries 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, in connection with the large-scale development of the oil resources of the Tyumen region, he moved to the 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 the Perm Territory.

Let's group the regions according to common features. Grouping is the division of the studied social phenomenon into single groups in terms of quality according to a number of essential features.

Table 1.1 General characteristics of the population

number of farms	Name of regions	Gross regional product per 1 employed person, thousand rubles	Average monthly salary, rub	Capital-labor ratio, thousand rubles	Employment rate	Higher and secondary education,%
	Tula
	Bryansk
	Moscow
	Vladimirskaya
	Ivanovskaya
	Kaluga
	Kostroma
	Orlovskaya
	Ryazan
	Smolensk
	Tverskaya
	Moscow city
	Yaroslavskaya
	Republic of Adygea
	Republic of Kalmykia
	Krasnodar region
	Astrakhan
	Volgogradskaya
	Rostov
	Kirovskaya
	Nizhny Novgorod
	Orenburg
	Penza
	Perm region
	Samara

Gross regional product per 1 employed person, thousand rubles is calculated as the ratio of the indicator of 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 generalizing indicator of the economic activity of the region, characterizing the production process goods and services.

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 assets, which falls on one 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 birth rates, death rates and population growth. This coefficient gives 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 with higher and secondary education to the number of people employed in the economy, thousand people

HH+HM/H*100%

Based on the data in the table, we can conclude that the gross regional product per 1 employed in the economy varies from 491.1 to 209.5 thousand rubles. 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, the Republic of Kalmykia shows the technical equipment of the personnel of enterprises, 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 are lagging behind in the use of advanced technologies based on the introduction of new equipment, which may ultimately lead to a loss of competitiveness. The high employment rate of the population in all the studied regions indicates a high level of welfare of society. The proportion 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 17438.3 rubles. recorded in the Volgograd region, and the lowest share of the educated population 2.4 in the Moscow region.

1.2 Analytical grouping

To distinguish typical groups from the signs given in Table 1, it is necessary to choose the most significant one. Most of the signs characterize the conditions of production, and the results of activities can be judged by the indicator of the production of the gross regional product. However, the direct division of regions into groups on this basis can lead to a mixture of different types, since, for example, a large volume of the gross domestic product can be obtained both at the expense of a large population and other resources with poor use of them, 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 employed in the economy. The value of this feature, obtained by dividing the indicator of the gross regional product, million rubles. on the number of people employed in the economy, thousand people

The grouping should begin with studying the nature of the change in the grouping attribute, for this it is necessary to build a ranked series of the distribution of regions by gross regional product (GRP) per 1 employed in the economy (Table 2) and depict it in the form of Ogiva Galton (Fig. 1).

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

	GRP per 1 employed in the economy, thousand rubles

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

When analyzing a ranked series, the intensity of change in the value of a grouping attribute from one unit of the population to another is evaluated. Table 1.2 shows 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 extreme ones they reach a twofold value. But the sign in the series changes gradually, smoothly, there are no sharp deviations. and grouping is not possible.

If there are no qualitative transitions in the ranked series, an interval distribution series is constructed. To build it, we divide the set into 6 groups (K=6). To determine the boundaries of the intervals, we find the interval step (h) using the formula:

h \u003d x max -x min / K \u003d 491.1-209.5 / 6 \u003d 47 thousand rub,

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

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

As can be seen from Table 1.3 and Figure 1.2, the distribution of regions by groups is uneven. The regions with the size of GRP per 1 employed person from 209.5 to 303.3 thousand predominate. rub. Groups with higher GRP are few in number. You need to merge them.

Table 1.4 - Intermediate analytical grouping

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

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

Further, in order to characterize the three selected 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 regions: GRP per employee, capital-labor ratio, employment and activity rate of the population, unemployment rate, average monthly salary. We 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 employed in the economy, thousand rubles
Capital-labor ratio, thousand rubles
The coefficient of economic activity of the population
Employment rate, %
Unemployment rate in %
Average monthly salary, rub

The coefficient of economic activity is calculated by the formula:

Chek.act=Check. act/H,

where check. act-number of the economically active population, P-number of the population.

According to Table 2.1, it can be seen that, on average, GRP per 1 employed in the economy in the highest group is more than in the lowest, by 402.1-226.4 = 175.7 thousand rubles. rubles, 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 \u003d 3895.6 rubles. The unemployment rate in the top group is 2.5% lower and the employment rate is 4.5% higher than in the bottom 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. It can be concluded that intensive production is carried out on the territory of the regions belonging to the highest group, despite the average coefficient of the 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 from the lowest in terms of GRP production per 1 employed in the economy, almost 2 times, and capital-labor ratio by 359.7 thousand. rubles. 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 fixed production assets, which ensured a high output of gross output and an increase in the standard of living of the population, as evidenced by the high employment rate of the population.

2.2 Analysis of production resources in typical groups

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

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

The economic-statistical analysis of fixed production assets aims to study changes in their volume, species composition and structure by individual industries and types of products, regions and types of enterprises.

Table 2.2 - Structure of fixed assets by sectors and types of economic activity

Indicators	Typical groups	Average
Specific weight OF,%:
Agriculture
extractive industries
Manufacturing industries
Production and distribution of energy, gas and water
construction
transport links
other industries
Total fixed assets mln. rub.

Looking at this table, you can see that the regions the highest group have a great advantage over the regions the lowest group in terms of provision with fixed production assets (by 5524991 million rubles). As can be seen, the composition of the OF is dominated by OF transport links, their share in all groups is an average of 28.9%, the smallest share is PF related to construction and agriculture , in all three typical groups it is close to the average - 1.3% and 5.4% respectively . OF cost extractive industries of the highest group reaches 9%, which exceeds the figure of the lowest group by 9 times. This may be due to natural conditions. providing an opportunity for the development of the extractive industry. PF of production and distribution of energy, gas and water are similar in specific weight 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 middle group are close to the average for the scoop pnost. The largest share, on average 42.6%, is occupied by OFs of other industries. It can be: trade, public catering, auto business, communication, tourism, high technologies, etc.

Let's analyze the indicators of labor resources.

Table 2.3 - Indicators of the structure of those employed in the economy by industry

According to Table 2.3, the proportion of people employed in the three sectors presented 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 proportion of those employed in construction in the highest group exceeds the figure in the lowest group, 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 figures in the top group may be due to both a large number of jobs and a more labor-intensive type of production in these regions.

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

gross regional product production

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

Table 2.4 shows that most 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, 69% of the employed population are employed in private enterprises, and 16% and 15% in state and municipal enterprises. Approximately the same situation develops in the regions of the middle and lower groups. This suggests that a third of the population is employed at 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 state of fixed assets include coefficients of depreciation, renewal and depreciated funds.

Table 2.6 - Indicators of the state of fixed assets

The coefficient of renewal of fixed assets.

Shows the degree of renewal of fixed assets:

To about =F new /f con ,

where To about -- coefficient of renewal of fixed assets;

F new -- the 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 coefficient 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%. On the basis of which we conclude that the fixed assets are sufficiently worn out, and the degree of renewal of the OF 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 at enterprises is of great importance, since in the conditions of market relations 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. Whatever technical opportunities open up for the enterprise, it will not work effectively without qualified specialists. Competently selected personnel is the basis of the company's success.

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 improving 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 employed in the economy
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. The average per capita cash income in the highest group is higher by 6,101 thousand rubles. than in the lower typical group. Per capita consumer spending in the top group is 5,386 thousand rubles higher than in the bottom group. Dependence can be identified: the higher the productivity of labor, 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 companies, production cooperatives, state and municipal unitary enterprises.

The organizational and legal form of an enterprise depends on a number of features: the formation procedure and the minimum amount of the 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,%

The lowest typical group is dominated by JSC or LLP, they make up 60.7%. The indicators of the middle group are close to the average, JSC or LLP also predominate - 70.8%. In the highest group, the smallest number is occupied by unitary enterprises - 0.8%, and the largest JSC or LLP - 73.7%.

The sectoral structure of the national economy is understood as the totality of its parts (industries and sub-sectors), historically formed as a result of the social division of labor. It is characterized by share percentage indicators in relation to either the employment of the economically active population, or to the produced GDP. The level of socio-economic development of the region is determined by the structure of the economy and has a direct impact on the predominance of a particular sector. The 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, is traditionally used by the gross regional product (GRP).

Table 2.9 - Composition and structure of GRP by sectors and types of economic activity, %

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

Table 2.9 shows 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 - 14.2%. The largest share is retail trade. 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 top group account for 12%, which is 0.4% more than in the bottom group.

As the analysis of statistical data shows, at present, regions with a fuel and raw material base, export-oriented industry, and a fairly developed infrastructure and financial system are in a privileged position. The regions with a significant share of the agricultural sector, light and food industries suffered more than others. Since the economic space of Russia is extremely heterogeneous, the production of GRP is also unevenly distributed throughout the country. The sectoral structure of the national economy over the past eight years has been characterized by a trend towards an increase in the share of industries providing services, and a decrease in the share of industries producing goods. Many economists consider this change in the structure of GDP as a progressive phenomenon, as 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 incomes, and the size of the reduction in the cost of production depend on the level of labor productivity.

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

In the dynamic analysis of 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 system of indices allows solving 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 that characterizes the dynamics of two average indicators for homogeneous populations. This index reflects the influence of two factors:

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

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

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

The index of structural shifts is the ratio of two average values ​​calculated for a 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, fixed composition. The variable composition index will always be equal to the product of the fixed composition indices and structural shifts

Table 2.10 - Data for the index method of analysis

Yn s / x \u003d (GRP s / x / H s / x) / GRP on 1zan.v econ,

where Y ns / x is the specific weight of output per 1 employed in agriculture in the lowest group;

dн-share of GRP in agriculture in the lowest group, take from table 2.9;

Y labor productivity = Y labor productivity Y structure of the variable composition of the constant composition of the 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 employed in individual industries and the structure of GRP. Therefore, a change in one indicator occurs at the expense of 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 signs

3.1 Combination grouping

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

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

The capital-labor ratio factor is represented by a quantitative continuously changing feature. 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. We single out three groups with low medium and relatively high grouping sign.

Define 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 - from 945.5 to 945.5+229=1174.5 thousand. rub, and in the third - from 1174.5 to 1403.5 thousand. rub.

Table 3.1 - Ranked series 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 depreciation of funds up to 29.4 + 15.3 = 44.7%, and the second subgroup - from 44.7 to - 60%.

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

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Introduction

1 System of indicators and methods of gross regional product

2 Statistical summary and grouping of gross regional product

3 Statistical study of the dynamics of the gross regional product

3.1 Calculation of dynamics indicators (absolute growth, growth rate, growth rate, absolute content of 1% growth)

3.2 Identification of trends in the development of a series of dynamics using the methods of mechanical alignment, average level, analytical alignment

3.3 Analysis of the fluctuation indicators of the time series

3.4 Forecasting the future

3.5 Identification of the development trend in time series using the Excel PPP

4.1 Theoretical aspects of the index method of analysis

4.2 Index analysis of the influence of various factors on socio-economic phenomena and processes

5 Correlation-regression analysis of the influence of factors

Conclusions and offers

List of used literature

Applications

Introduction

The main purpose of this course work is to conduct a statistical analysis of socio-economic phenomena and processes of the gross regional product of the federal districts of the Russian Federation (Volga, Ural, Siberian, Far Eastern federal districts).

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

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

Gross regional product is a generalizing indicator of the economic activity of the 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 is 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 the gross regional product is, in its economic content, very close to the indicator of the 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 value added from non-market collective services (defence, public administration) provided by state institutions to society as a whole. At the moment, the calculation of the gross regional product of a subject of the federation takes 28 months.

The purpose of this course project is to conduct a statistical analysis of the gross regional product of the federal districts of the Russian Federation (Volga, Ural, Siberian, Far Eastern federal districts).

1 Indicators and methods of the gross regional product of the federal districts of the Russian Federation

Gross regional product is a generalizing indicator of the economic activity of the 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 the Russian statehood necessitate the construction of a developed system of statistical indicators at the regional level that meets the requirements of a market economy. System indicators characterizing the development of regions should be methodologically comparable and consistent with the corresponding indicators at the macro level.

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

At the regional level, the entire system of accounts is not built, but only its individual elements. The methodology for constructing regional macroeconomic indicators differs from the methodology for constructing similar indicators at the federal level to the extent of differences in the institutional nature and information base. For these reasons, the sum of regional indicators does not always coincide with the value of the corresponding indicator at the federal level.

In terms of its economic content, GRP roughly corresponds to the GDP indicator calculated by the production method at the federal level. GRP is defined as the sum of the value added of the resident units of a given region. Resident units in this case are determined based on the same principles as at the federal level. That is, residents of a regional economy include all corporations, quasi-corporations or households that have a center of economic interest in the economic territory of a given region. If an enterprise carrying out economic activity on the territory of a given region is a branch of a parent corporation located in another region, then it is a resident of this region.

For the first time, calculations at the regional level by the production method were made according to the data for 1991 for 21 territories, based on the method of transitional keys from calculating the net material product to gross value added. In 1993, according to the data for 1992, all territorial bodies of state statistics already participated in experimental calculations of the gross regional product. These calculations were mainly carried out with the aim of involving the territorial statistical bodies in the transition from the calculation of indicators with the main provisions of the balance of the national economy to the calculations according to the SNA. Since 1995, calculations of the gross regional product have been included in the implementation plan of the Federal Statistical Program and are mandatory for all regions of Russia. At present, we have approved final results of GRP calculations from 1994 to 2002. In 1998, for the first time, the GRP growth (decrease) rates were calculated based on the data for 1997 to 1996. Currently, we have the dynamics of growth (decrease) since 1997.

The information base, on the basis of which the calculation of the gross regional product is based, is almost identical to the information base of the federal level, since the summary statistical reporting is formed on the basis of data received from the regions. In this regard, the algorithm for calculating the gross regional product (GRP) coincides with the algorithm for calculating the gross domestic product.