Reasons for the decline in gross regional product. Basic research
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 the effective and factorial characteristics, the index method of analysis.
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Course work
On the topic: "Economic and statistical analysis of GRP production by a group of regions"
Plan
Introduction
Chapter 1. Identification of typical groups of enterprises
1.1 general characteristics the aggregate
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 indicators
3.1 Combination grouping
3.2 Correlation analysis
Conclusion
List of sources used
Application
Introduction
The main purpose of this term paper 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 social science and a special branch of practice.
The central macroeconomic indicator is the gross regional product. It is the most common indicator of economic activity and regional well-being.
Gross regional product - generalized indicator economic activity 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 terms of its economic content, very close to the indicator of the gross domestic product. However, there is a significant difference between the indicators of the gross domestic product (at the federal level) and the gross regional product (at the regional level). The sum of the gross regional products in Russia does not coincide with the gross domestic product, since it does not include the added value of non-market collective services (defense, public administration) provided by state institutions to society as a whole. At the moment, the calculation of the gross regional product of a constituent entity 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. Allocation of typical groups of enterprises
1.1 General characteristics of the population
Gross regional product is a generalized indicator of the economic activity of the region, which 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, a consistent policy of strengthening federalism in Russian statehood necessitate building a developed system of statistical indicators at the regional level that meet the requirements market economy... Systemic 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. A generalizing indicator of the development of regions is the gross regional product (GRP). This indicator is built on the basis of a unified methodology developed centrally in the FSGS. The results of the calculations are monitored, approved and published in a generalized form by the FSGS.
To observe the intra-annual dynamics of the development of the regional economy, the calculation of the rate of change in production volumes of the basic sectors of the economy (industry, agriculture, construction, retail and public catering, transport), which in the structure of production of the regions make up from 60% to 80%.
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 economic development the region is associated precisely with the extraction and production of "black gold".
Oil reserves located at depths of 5 to 6 kilometers are estimated at 5 billion tons of fuel equivalent. The drilling of the first prospecting well on the Caspian shelf immediately confirmed the serious "fuel" potential of this area. However, all projects require a lot of money, about 15-20 billion dollars. Oil reserves are concentrated mainly in the Volgograd and Astrakhan regions, Krasnodar.
The Southern Federal District is one of the poorest forest resources districts Russian Federation... Unique recreational resources federal district. The mild climate, the abundance of mineral springs and curative mud, warm sea waters create the richest opportunities for treatment and recreation. Mountain areas with their unique landscapes have everything 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 the non-production sphere. The basis of the economic specialization of the region is made up of mechanical engineering, chemical, light industry, flax growing, potato growing, dairy and meat cattle breeding.
In the structure of the region's industry, the dominant position is occupied by mechanical engineering, especially science-intensive, in need of qualified personnel.
The area is distinguished by transport engineering. The production of equipment for light, chemical, energy and other industries also occupies a prominent place.
The chemical industry of the region specializes in the production of plastics, chemical fibers, synthetic rubber and tires, mineral fertilizers, varnishes, paints, detergents, etc.
Light industry is the oldest in the region and the largest in the country. Textile production stands out especially: cotton (Ivanovo, Moscow, Tver, etc.), linen (Kostroma, Nerekhta, Vyazma, etc.), silk (Moscow, Tver, Naro-Fominsk), woolen (Moscow, Klintsy, etc.). The sewing, knitwear, leather and footwear, fur, and printing industries are also developed.
The fuel and energy complex, especially the production of electricity (Kostromskaya, Konakovskaya, Ryazanskaya GRES and nuclear power plants - Smolenskaya, Kalininskaya), stands out from the service industries of the region. The extraction of brown coal in the Moscow region basin has sharply decreased. Ferrous metallurgy enterprises (Tula, Elektrostal, Moscow) only partially satisfy the region's demand for metal.
The leading type of agriculture in the region is suburban, with a predominance of the production of vegetables, potatoes, milk and meat. In the northern regions of the region, dairy farming is of commercial importance. Grain farming (gray bread, spring wheat, buckwheat) is of secondary importance. Smolensk, Kostroma and Tver regions specialize in the cultivation of fiber flax. Pig and poultry farming is well developed.
The high level of development and the huge scale of transportation is distinguished transport complex district. There is a very dense network of railways, automobiles and pipelines here. The role of inland waterway and air transport is great.
Volga Federal District. The lack of access to the sea is a serious disadvantage. The country's largest reserves of potash salts (Solikamsk-Bereznyaki), deposits of oil and non-ferrous metals are distinguished from minerals. In the forest-steppe zone there are large tracts with fertile chernozem soils.
Mechanical engineering and metalworking industry is the largest branch of industrial specialization in the Volga Federal District. This is the main region of transport engineering in Russia. The most developed is the aerospace industry, and in it the production of the military-industrial complex. The main enterprises of this industry are located in Samara, Kazan, Nizhny Novgorod, Saratov, Ufa, Kumertau, Perm and Votkinsk. And their numerous subcontractors are scattered throughout the district.
The production of equipment for the oil-extracting and oil-refining industries and the chemistry of organic synthesis is also of special importance. The location of these industries is largely close to major cities districts 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 total volumes oil production in second place in the country. 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 criteria. Grouping is the division of the studied social phenomenon into qualitatively single groups according to a number of essential features.
Table 1.1 General characteristics of the population
Number of farms |
Name of areas |
Gross regional product per employee, 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 |
|||||||
Yaroslavl |
|||||||
Republic of Adygea |
|||||||
Republic of Kalmykia |
|||||||
Krasnodar region |
|||||||
Astrakhan |
|||||||
Volgograd |
|||||||
Rostov |
|||||||
Kirovskaya |
|||||||
Nizhny Novgorod |
|||||||
Orenburg |
|||||||
Penza |
|||||||
Perm Territory |
|||||||
Samara |
Gross regional product per 1 employed, thousand rubles is calculated as the ratio of the indicator of gross regional product, million rubles. as the number of people employed in the economy, thousand people:
GRP per 1 occupation = GRP / h
Gross regional product (GRP) - a generalizing indicator of the economic activity of the region, characterizing the production process goods and services.
The capital-labor ratio is calculated as the ratio of fixed assets in the economy, million rubles. to the number of people employed in the economy, thousand people:
Capital-labor ratio- the cost of fixed 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. from birth rates, mortality and population growth rates. 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
Chhigh + Chsred / 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, the highest rates 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, high average annual cost 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 technology, which may ultimately lead to a loss of competitiveness. The high employment rate of the population in all the regions under study indicates a high level of welfare of the society. The proportion of the educated population has nothing to do with the level of average wages, which indicates the demand not only for specialists, but also for workers without special education. The tallest wage RUB 17438.3 recorded in the Volgograd region, and the lowest proportion of the educated population is 2.4 in the Moscow region.
1.2 Analytical grouping
To distinguish typical groups from the characteristics shown in Table 1, it is necessary to select the most significant one. Most of the features characterize the conditions of production, and the results of activities can be judged by the indicator of production of the gross regional product. However, the direct division of regions into groups on this basis can lead to confusion different types, since, for example, a large volume of the gross product can be obtained both from a large population and other resources with poor use, and through the effective use of relatively small resources. Since the absolute indicators of the gross product are not comparable, it is advisable to use a relative indicator - GRP per 1 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 construct a ranked series of the distribution of regions by gross regional product (GRP) per 1 employed in the economy (Table 2) and depicted in the form of Galton's Ogiva (Fig. 1).
Table 1.2 - Ranked distribution of farms by GRP per 1 employed in the economy
GRP per 1 employed in the economy, thousand rubles |
||
Figure 1.1 - Ogive distribution of farms by GRP per 1 employed in the economy
When analyzing a ranked series, the intensity of the change in the value of a grouping attribute from one unit of the population to another is estimated. Table 1.2 shows that there are sharp changes and a large gap between a number of units and the entire population. Differences between the regions are visible, between the extreme ones they reach two times. But the sign in the row changes gradually, smoothly, there are no sharp deviations. and it is impossible to select groups.
In the absence of qualitative transitions in the ranked series, an interval distribution series is constructed. To construct it, we divide the population into 6 groups (K = 6). To determine the boundaries of the intervals, we find the step of the interval (h) by the formula:
h = x max -x min / K = 491.1-209.5 / 6 = 47 thousand. rub,
where x max is the maximum value of a feature in the ranked row; x min is the minimum value of a feature in the ranked row.
Table 1.3 - Interval variation series of the distribution of regions by GRP per 1 employed in the economy
Boundaries of intervals |
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 group is uneven. Regions with a GRP per employee from 209.5 to 303.3 thousand predominate. rub. Groups with higher GRP are few in number. It is required to combine 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 will compare them with each other according to the obtained indicators. The first group, which is quite large in size, differs significantly from all the others in terms of the level of education of the population; here it is several times higher than the level of education in other groups. Other indicators: average monthly salary, employment rate, capital-labor ratio are lower than in other groups. Therefore, it should be distinguished as a typical group with the lowest productivity and efficiency. Groups 4,5,6 with higher average monthly wages, higher capital-labor ratio and higher employment rates are few in number. It is advisable to combine these groups into the highest typical, most productive and effective group. Groups 2, 3 practically by all indicators occupy an intermediate position between the lower and higher typical groups, their characteristics are close to each other. They should be grouped into an average typical group.
Further, in order to characterize the three distinguished typical groups, it is necessary to calculate the average indicators for each of them.
Chapter 2. Economic and statistical analysis of the level and factors of production of BPP in typical groups of regions
2.1 AnalysisGRP production in typical groups
Data available by region: GRP per employee, capital-labor ratio, employment and activity rate, unemployment rate, average monthly salary. Let's calculate the average of these indicators and analyze them by typical groups.
Table 2.1 - Level and factors of production of GRP
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 |
|||||
Labor force participation rate |
|||||
Employment rate,% |
|||||
Unemployment rate in% |
|||||
Average monthly salary, rub |
The coefficient of economic activity is calculated using the formula:
Kek.act = Check. act / h,
where is check. act is the number of economically active population, H is the number of population.
According to table 2.1, it can be seen that, on average, the GRP per 1 employed in the economy in the upper group is more than in the lower one, by 402.1-226.4 = 175.7 thousand. 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 = 3895.6 rubles. The unemployment rate in the upper group is 2.5% lower and the employment rate is 4.5% higher than in the lower group. These differences in the results of production 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 lowest group than to the highest. The highest group differs most of all 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, high results of the highest typical group were achieved both due to the greater use of labor resources and due to the better armament of the main 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
Basic production assets - the material and technical base of social production. The production capacity of enterprises and the level of technical equipment of labor depend on their volume. The accumulation of fixed assets and an increase in the technical equipment of labor enrich the labor process, impart a creative character to labor, 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 intensification of production.
Economic and statistical analysis of fixed assets is aimed at studying changes in their volume, species composition and structure for individual industries and types of products, regions and types of enterprises.
Table 2.2 - Structure of fixed assets by industry and type of economic activity
Indicators |
Typical groups |
Average |
|||
Specific gravity of RP,%: |
|||||
Agriculture |
|||||
extractive industries |
|||||
Manufacturing industries |
|||||
Production and distribution of energy, gas and water |
|||||
construction |
|||||
transport links |
|||||
other industries |
|||||
Total OF RUB mln. |
After analyzing 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 assets (by 5524991 million rubles). As you can see, the composition of the PF is dominated by PF transport links, their share in all groups averages 28.9%, the smallest share is made up of fixed assets related to construction and agriculture , in all three typical groups it is close to the average - 1.3% and 5.4% respectively ... PF cost extractive industries of the highest group reaches 9%, which is 9 times higher than the indicator of the lowest group. The manufacturing industry in the lowest group amounted to 5.8% compared to the highest -14%. This may be due to natural conditions. , providing an opportunity for the development of the extractive industry. PFs for the production and distribution of energy, gas and water are close in terms of specific weight in the upper and lower groups - 6.6% and 5.2%, and differ significantly in the middle group - 10.3%. The rest of the indicators of the middle group are close to the average for the scoop pnosti. The largest share, on average 42.6%, is occupied by PFs of other industries. It can be: trade, catering, auto business, communications, tourism, high technologies, etc.
Let's analyze the indicators of labor resources.
Table 2.3 - Indicators of the structure of employed in the economy by industry
According to Table 2.3, the share of the employed in the three sectors presented does not differ much in typical groups. Thus, the indicators of employed in agriculture in all groups are close to the average - 50.3%. The share of people employed in construction in the highest group exceeds the indicator in the lowest, it is equal to 28%, and in the lowest - 22%, transport and communications occupies 25.9% in the highest group, 22% in the lowest. The indicators in the middle group are consistently similar to the average. Higher rates in the upper 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 the employed in the economy according to the form of ownership.
gross regional product production
Table 2.4 - Structure of 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, private enterprises employ 69% of the employed population, and state and municipal enterprises 16% and 15%. The situation is approximately the same in the regions of the middle and lower group. This suggests that one third of the population is employed in state and municipal enterprises and is provided with a stable income.
Table 2.5 - Indicators of the quality of the labor force
Analyzing the indicators of the quality of the labor force employed in the economy, we can say that approximately the same number of percent is occupied by people with secondary vocational education and higher education, which is on average 26%, the number of people with higher education is more by 0.5%, with an average age of 38.5 years.
Indicators of the condition of fixed assets include the rates of depreciation, renewal and worn-out assets.
Table 2.6 - Indicators of the state of fixed assets
Fixed assets renewal ratio.
Shows the degree of renewal of fixed assets:
TO about = F new / F con ,
where TO about - the coefficient of renewal of fixed assets;
F new - the cost of newly commissioned fixed assets for the period, thousand rubles;
F con -- the cost of fixed assets at the end of the period.
The coefficient of renewal of fixed assets in the highest group is 1% lower than in the lowest, 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 assets in all three groups is close to the average - 47%. Based on this, we conclude that fixed assets are sufficiently worn out, and the degree of fixed assets renewal 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 under conditions 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 possibilities open up for the enterprise, it will not work effectively without qualified specialists. Competently selected personnel is the basis for 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 to improve 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 upper group is higher by 402.1-226.4 = 175.7 thousand. rub., than in the lowest. Average per capita monetary incomes in the upper group are higher by 6101 thousand rubles. than in the lower typical group. Consumer spending per capita in the upper group is 5386 thousand rubles higher than in the lower group. It is possible to reveal the dependence: the higher the labor productivity, the higher the salary, and the higher the standard of living of the population.
In the Civil Code of the Russian Federation, the main organizational and legal forms are business partnerships, business societies, production cooperatives, state and municipal unitary enterprises.
The organizational and legal form of an enterprise depends on a number of characteristics: the order of formation and the minimum value authorized capital, responsibility for the obligations of the enterprise, the list and rights of founders and participants, etc.
Table 2.8 - The structure of enterprises by organizational and legal forms,%
The lowest typical group is dominated by JSCs or LLPs, accounting for 60.7%. The indicators of the middle group are close to the average; JSCs or LLPs also prevail - 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 (branches and sub-branches), 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 a region is determined by the structure of the economy and has a direct impact on the prevalence 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 industry and type of economic activity,%
Indicators |
Typical groups |
Average |
|||
Share in GRP,% |
|||||
Agriculture |
|||||
retail |
|||||
food products |
|||||
non-food products |
|||||
paid services |
|||||
Total, thousand rubles |
Table 2.9 shows that the share of agriculture in the GRP is the smallest specific weight. 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 upper group it is 12%, which is 0.4% more than in the lower group.
As the analysis of statistical data shows, at present, regions with a fuel and raw material base, export-oriented industry, with a sufficiently developed infrastructure and financial system... The regions with a significant share of the agricultural sector, light and food industries suffered the most. Since the economic space of Russia is extremely heterogeneous, the production of GRP is also distributed unevenly throughout the country. For the industry structure national economy Over the past eight years, there has been a trend towards an increase in the share of industries that provide services, and a decrease in the share of industries that produce goods. Many economists regard such a change in the structure of GDP as a progressive phenomenon, since the Russian economy is approaching the economies of developed countries.
2.4 Index method of analysis
The level of labor productivity is characterized by the ratio of the volume of products produced or work performed and the cost of working time. The rate of development of industrial production, an increase in wages and incomes, and the amount of reduction in production costs 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 of production of a unit of output are reduced under the item "Wages main production workers ", and in another - 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 from changes in qualitative indicators, and also allows us 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 averages for homogeneous populations. This index reflects the influence of two factors:
- change in the indexed indicator for individual objects (parts of a whole);
- change in the proportion of these parts in the general structure of the aggregates.
Fixed composition index - characterizes the dynamics of two averages with the same fixed structure of the population in the reporting period.
The index of structural changes is the ratio of two average values calculated for 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 a variable, fixed composition. The variable composition index will always be equal to the product of the fixed composition indices and structural breaks
Table 2.10 - Data for the index method of analysis
Yn s / x = (GRP s / x / H s / x) / GRP per 1set in the economy,
where Y ns / x is the weight of output per 1 employed in agriculture in the lowest group;
dн-specific gravity of GRP in agriculture in the lowest group, we take from the table 2.9;
Y labor productivity = Y labor productivity Y structure of variable composition of constant composition 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 the output per 1 employed in certain industries and the structure of the GRP. Therefore, a change in one indicator occurs due to a change in another.
= 0.009 + 0.819 / 0.017 + 0.757 = 0.828 / 0.774 = 1.07 or 7%,
Chapter 3. Analysis of the relationship between effective and factor indicators
3.1 Combination grouping
Combination grouping is achieved by subdividing all units of the population according to one factorial attribute, and then within the obtained groups, subgroups are distinguished according to the second factorial attribute.
The capital-labor ratio is an indicator characterizing the degree of regional armament with basic production assets.
The factor of capital-labor ratio 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 in the range from x min = 716.5 thousand. rub, up to x max = 1403.4 thousand. rub. Let's select three groups with low. average, and relatively high value grouping sign.
Determine the step of the interval 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. rubles, and in the third - from 1174.5 to 1403.5 thousand. rub.
Table 3.1 - Ranked 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 assets. 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 specific weight of depreciation of assets up to 29.4 + 15.3 = 44.7%, and the second subgroup - from 44.7 to - 60%.
Table 3.2 - Ranked distribution series by the share of completely worn-out assets
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Economic indicators conditions and results of activity of agricultural enterprises. Estimation of the parameters and nature of the distribution of the statistical population. Economic and statistical analysis of the relationship between the features of the studied phenomenon.
term paper added 03/03/2015
Concept and main goals of regional policy. Analysis of indicators regional development the Republic of Kazakhstan, namely the gross regional product of the regions, income and expenditures of the population and the investment attractiveness of the regions.
term paper, added 11/25/2010
Statistical analysis of the factors that determine the efficiency of production and sale of agricultural products. Groupings and correlation in the study of milk production growth factors. Stages of correlation and regression economic analysis.
term paper, added 02/06/2014
Characteristics and typological grouping of the aggregate of regions. Statistical analysis of economic activity, movement and reproduction of the population, employment and use of labor resources, unemployment, income and expenditures of the population.
term paper, added 06/08/2013
Dynamics of the gross regional product in the Irkutsk region in 1999-2007 in current and comparable prices. Two directions of statistical analysis of the structure: determination of the uneven distribution of units of the population by groups and assessment of changes.
laboratory work, added 05/27/2009
Development of the economy by industry, integrated ecology of the region. Raw materials and resource base, including geographic location. Dynamics of the indicator of the gross regional product of the Gomel region. Multidimensional scaling and cluster analysis methods.
term paper, added 02/21/2010
Economic indicators of the conditions and results of the activities of enterprises. Justification of the volume and assessment of the parameters of the statistical population. Economic and statistical analysis of the relationship between the features of the studied phenomenon. Analytical grouping method.
Review
On the course project on statistics on the topic: Statistical analysis of the gross regional product of the federal districts of the Russian Federation (Volga, Ural, Siberian, Far Eastern federal districts)
The course project as a whole (does not) correspond to the methodology for the implementation of course projects in statistics.
The course project is (not) completed in full.
There are the following notes:
The design (not) complies with the standard of the organization.
Theoretical substantiation of the research topic ____________________
Statistical summary and grouping ___________________________
Statistical study of dynamics __________________________
Index Analysis __________________________________________
Correlation-regression analysis __________________________
Other ____________________________________________________
The course project after revision is allowed to be defended before the commission.
Candidate of Economics, Associate Professor A.M. Ableeva __________
FGOU VPO "Bashkir State Agrarian University"
Faculty of Economics
Department of Statistics and information systems in economics
for a course project in statistics
Project topic: Statistical analysis of the gross regional product of the federal districts of the Russian Federation (Volga, Ural, Siberian, Far Eastern federal districts).
Statistical summary and grouping. The influence of the cost of fixed assets per capita on the gross regional product per capita (data for 2005).
Grouping attribute: Cost of fixed assets per capita in 2005.
Number of groups: five
A number of dynamics: Gross regional product of the Ural Federal District in 2000 - 2006. in comparable prices.
Index analysis.
a) Theoretical question. Stock index futures.
b) Determine the change in the Gross Regional Product of the Ural Federal District in 2006 compared to 2005, due to changes in the cost of fixed assets and through the use of fixed assets (return on assets).
c) Determine the change in the value of fixed assets of the Ural Federal District in 2006 compared to 2005 due to changes in GRP and capital intensity.
Correlation-regression analysis: the influence of the cost of fixed assets per capita and the average annual number of people employed in the economy on the Gross Regional Product per capita (all indicators for 2005).
The term for the course project is 37 academic week.
Head: Candidate of Economic Sciences, Associate Professor A.M. Ableeva __________
The task was accepted for execution: ____________________
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 gross regional product
3.2 Identification of trends in the development of a number of dynamics using the methods of mechanical alignment, middle level, analytical alignment
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
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 practical activity.
The central macroeconomic indicator is the gross regional product. It is the most common indicator of economic activity and regional well-being.
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 generalized indicator of the economic activity of the region, which characterizes the process of production of goods and services.
The specificity of Russian conditions, the huge role of the territorial factor in the development of socio-economic processes, a consistent policy of strengthening federalism in Russian statehood necessitate building a developed system of statistical indicators at the regional level that meet the requirements of a market economy. Systemic indicators characterizing the development of regions should be methodologically comparable and consistent with the corresponding indicators at the macro level.
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, the GRP roughly corresponds to the GDP indicator calculated by the production method at the federal level. GRP is defined as the sum of the added value of the resident units of a given region. Resident units in this case are determined on the basis of the same principles as at the federal level. That is, residents of the regional economy include all corporations, quasi-corporations or households that have a center of economic interest in economic territory this region. If an enterprise carrying out economic activity in 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 data for 1991 for 21 territories, based on the method of transition keys from calculating the net material product to gross value added. In 1993, according to the data for 1992, already all territorial bodies of state statistics participated in the experimental calculations of the gross regional product. These calculations were mainly carried out in order to familiarize the territorial statistical bodies with 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 Federal program statistical work and are mandatory for all regions of Russia. Currently, we have approved the final results of GRP calculations from 1994 to 2002. In 1998, for the first time, calculations were made of the growth (decrease) of GRP based on data for 1997 to 1996. Currently, we have the dynamics of growth (decline) rates, starting from 1997.
The information base, on the basis of which the calculation of the gross regional product is based, is practically identical to the information base of the federal level, since the consolidated statistical reporting formed on the basis of data received from regions. In this regard, the algorithm for calculating the gross regional product (GRP) coincides with the algorithm for calculating the gross domestic product.
As for the first point, conceptually accounting for these services should be carried out at the place of their production (provision), and their value should be included in the GRP of the corresponding region. The volume of these collective services is determined in the amount of the corresponding costs state budget reflected in the report on the execution of the federal budget. All federal budget expenditures in the regional context should be taken into account and reflected by the system of regional treasuries in accordance with the current unified budget classification... But the practice of accounting for some federal budget expenditures for the country as a whole continues to persist without breaking down into separate regions, which is mainly due to the inability to determine which specific region the expenditures are to be attributed to (for example, budget expenditures on international cooperation, servicing the state debt, etc.), as well as remaining shortcomings financial accounting or some political considerations (defense spending, internal affairs agencies, etc.). Thus, the presence of problems associated with the distribution of part of government expenditures across the regions of the country, as well as with overcoming the shortcomings of regional accounting (incomplete reflection of data in the treasury reports), currently force us to abandon their accounting at the regional level.
In addition, it is necessary to take into account a number of positions that determine the discrepancy between the gross domestic product as a whole and the sum of gross regional products for all territories. These primarily include indicators reflecting financial and foreign trade intermediation.
Production of services of financial intermediaries in modern conditions it is very difficult to correctly take into account the regions. Due to the specifics of banking activities, it is problematic to link its volume to one region where the bank is registered. A bank can be registered, for example, in Moscow, or have only a branch here, which, as a rule, conducts a large volume of operations, but at the same time a Moscow bank or a Moscow branch of a provincial bank today can really provide financial intermediation practically throughout Russia. As a result, the territorial statistical offices have practically no data in order to accurately assess production financial services on the territory of the region.
Another way is to estimate this volume in Russia as a whole and then distribute it on a calculated basis across the regions. But this path, firstly, requires much more detailed and reliable information for the consolidated calculation, and secondly, it is necessary to resolve the issue in proportion to what really existing indicator it would be possible to reliably distribute these services and, accordingly, the added value of banks by certain regions.
Such an element of the GDP calculation as “indirectly measured services of financial intermediaries” also does not seem to be possible to distribute among separate territories. As you know, according to the SNA methodology, the cost of these services is included in the intermediate consumption of their recipients. But the issue of attributing the cost of services of financial intermediaries to the intermediate consumption of specific consumers of these services has not yet been resolved even theoretically, their volume is measured indirectly as a whole and, accordingly, is not distributed either by industry or by territory.
At present, accounting for the interregional exchange of goods and services is a big problem in regional calculations, which makes it impossible to account for the added value of foreign trade for the region with a satisfactory degree of reliability.
It is also obvious that the volume of net import taxes in existing conditions can be assessed only for the economy as a whole, without distribution by regions. It is practically impossible to determine the territorial structure of neither taxes nor import subsidies, since there is no information on the territorial distribution of the import of goods itself.
Equally problematic is the regional accounting of net taxes on products. They are caused by insufficient information in the budget. In particular, to calculate net taxes by region, it is necessary to present the regional distribution of subsidies for products paid from the federal budget. In full, such data is not available not only in regional statistics, but also at the federal level, since a certain part of subsidies for products is distributed by the Ministry of Finance of Russia not to the regions, but is transferred to ministries and departments for the development of the relevant industry and only then through departmental distribution goes to enterprises ... It is practically impossible to trace the entire path of such subsidies to the regions, therefore, for a certain part of net taxes on products, it is possible to make only a general assessment of the economy as a whole.
Thus, due to a number of methodological and organizational reasons, a number of important positions of GDP can be calculated only at the federal level for the economy as a whole, and the amount of GRP throughout Russia is objectively less than GDP. The objectively substantiated discrepancy between GDP and GRP was 12.6 percent in 2002.
The GRP indicator as the main aggregate indicator provides for the coordination of the resulting data for all sectors of the economy.
GRP calculation is carried out in several stages. At the first stage, its volume is assessed. territorial bodies state statistics on the basis of annual statistical reports of enterprises, reports on budget execution and other available information. The second assessment is carried out by the Goskomstat of Russia after checking the calculations at the federal level and reconciling the data on GRP and GDP. Interrelation between the absolute volume and the growth (decline) rate of the aggregate GRP with the data on the gross domestic product Russia is essential condition the formation of this indicator.
The verification and analysis of indicators of output, intermediate consumption and value added in actual prices, and in prices of the previous year, carried out by the FSGS, reveals a significant number of errors made by territorial bodies of state statistics. In addition, the analysis of the quality of the initial information required to perform calculations of the added value of economic sectors reveals a large number of errors and necessitates changes in the calculation methodology for individual sectors of the economy. With the existing organization of settlements, when it is completely impossible to distribute the gross domestic product over the territories of the Russian Federation, the calculation of the gross regional product is of an estimated nature. GDP for Russia as a whole is calculated by three methods and assumes, according to the Regulations for the development and presentation of data on gross domestic product, 4 stages of clarifications, the last of which involves the introduction of adjustments caused by clarifications in the development of the input-output balance. At the regional level, it is not possible to implement the procedure for issuing GRP calculations in conjunction with the intersectoral balance, which leads to the presence of certain errors in the formation of the GRP results.
In the State Statistics Committee of Russia annual calculations GRP is coordinated by the Department of National Accounts. It, together with sectoral Departments, develops a methodology for calculating the DS of sectors of the economy. Calculation of the DS of industries that are not under the jurisdiction of individual structural units carried out by the Office of National Accounts.
Collection and processing of calculations is carried out at the GMC using software. At the same time, the compatibility of the software used by the MMC and the central office of the SCS and in the regions was ensured. According to the calculation of the DS of a number of industries, complexes for electronic data processing have been created. Layouts are spreadsheets, templates into which information is entered and the result is automatically obtained.
After processing the data, the specialists of the Goskomstat of Russia summarize the calculation results, link them with the corresponding indicators calculated at the federal level. At this stage, we are refining and bringing in methodological conformity with the calculations of the current year the calculations of the base period. An analysis of the quality of the initial information required for performing calculations of the DS of sectors of the economy reveals individual errors and necessitates a change in the calculation methodology for some sectors of the production account.
After the end of the calculations, the data are sent to the TOGS for approval. Within two weeks, the TOGS is given the opportunity to make the necessary adjustments, arguing their validity before the State Statistics Committee of Russia. After this time, the edits are not accepted and the indicators take the approved status.
The need for a responsible attitude to the calculations of the GRP is due to the importance of this indicator, since at present the GRP is used as the main unit for the distribution of funds from the Fund for Financial Support of the Subjects of the Russian Federation. On the basis of this indicator, the calculation of gross tax resources (GRR) is carried out (after isolating the ZATO, multiplying by the price index, adjusting for the fact of tax collection).
All of the above works are carried out on an annual basis. The frequency of development and submission of data on GRP is enshrined in the Regulations adopted by the Goskomstat of Russia, the Ministry of Economy of Russia and the Ministry of Finance of Russia.
To observe the intra-annual dynamics of the development of the regional economy, the calculation of the rate of change in production volumes of the basic sectors of the economy (industry, agriculture, construction, retail trade and public catering, transport) is provided, which in the structure of production of the regions make up from 60% to 80%.
The most important in the industry of the Volga region are diversified highly developed mechanical engineering and the petrochemical complex. Leader agro-industrial complex county is Saratov region... In terms of gross livestock production among the regions of the Volga Federal District, the Saratov Region ranks third in milk and meat production. The national agro-industrial complex development project is able to remove from business some of the investment risks traditionally high in agriculture. An important trend is also the enlargement of industrial complexes - new agricultural holdings are being formed, which include not only production, but also complexes for processing products, producing feed, and a high proportion of grain in feed and high prices for it lead to the attraction of agricultural holdings to the grain market.
The Siberian Federal District includes almost all regions of the West Siberian and East Siberian economic regions, with the exception of the Tyumen region. The Siberian Federal District is famous for its solid minerals. Another economic "strong point" of the region is the development of territories located in the BAM zone. This area contains gold, rare metals, copper, coal. The total investment capacity of these projects is $ 7-10 billion.
The Urals Federal District includes four regions: Kurgan, Sverdlovsk, Chelyabinsk and Tyumen, with Khanty-Mansi and Yamalo-Nenets Autonomous Districts. The Urals are a kind of economic region within Russia.
The Ural Federal District is the richest. About 27% of manganese ores, large reserves of silver, gold, and iron ores are concentrated here. Of course, gas is the leader in the region's economy, 92%.
The backbone of economic development Sverdlovsk region for the last three centuries there have been natural resources. Agriculture works for the domestic market, on the one hand, meeting the needs of the population of industrial centers, on the other hand, individual gardening and horticulture is highly developed. The crops are dominated by grain and fodder crops; animal husbandry: dairy - meat, pig, poultry.
Economically Tyumen region- this is one of the main regions - donors of the federal budget. The source of the region's economic power is the reserves of hydrocarbon fuels of world importance, the main strategic and export raw materials of Russia.
The structure of industrial production in the Khanty-Mansiysk Autonomous Okrug is very peculiar: 85% of the total volume of production falls on the fuel industry, 12% on the power industry. The largest industrial centers: Surgut, Nizhnevartovsk, Nefteyugansk, Megion, Langepas, Urai are oil production centers; Berezovo is a gas production center.
Almost 90% of the industry of the Yamalo - Nenets Autonomous Okrug is in the fuel industry. Agriculture is primitive. Fishing and fur trade with animal husbandry is of great importance.
In terms of the volume of marketable products, the Chelyabinsk region is included in the "first" ten regions. The structure of industrial production is dominated by the branches of heavy industry: ferrous metallurgy; mechanical engineering and metalworking; non-ferrous metallurgy; electric power industry. The region is in the top ten in poultry farming, in the top ten in grain harvesting, meat production and gross agricultural output.
The Far Eastern Federal District is the largest district in Russia. It occupies 36% of the country's territory. The share of the population is only 5%. The development of the Far East by Russia began in the 50s. 19th century, at about the same time as the regions of the Far West of the United States.
2 Statistical summary and grouping of the gross regional product of the federal districts of the Russian Federation
Grouping is the division of the studied social phenomenon into qualitatively single groups according to a number of essential features.
Region name | gross region, product, thousand / rub. | average year. num. population, thousand / person | ||
Republic of Bashkortostan | 381646,5 | 1797,6 | 4071,1 | 868425 |
Mari El Republic | 33350,7 | 334,4 | 714,2 | 133723 |
The Republic of Mordovia | 44267 | 399,1 | 861,8 | 183836 |
Republic of Tatarstan | 482759,2 | 1778 | 3765 | 1090879 |
Udmurtia | 139995,3 | 764,8 | 1548,6 | 368307 |
Chuvash Republic | 69391,6 | 597,5 | 1295,8 | 253775 |
Perm Territory | 327273,3 | 1318,9 | 2759 | 961938 |
Kirov region | 79800,6 | 714,6 | 1452,1 | 322973 |
Nizhny Novgorod Region | 299723,7 | 1748,9 | 3428,2 | 688092 |
Orenburg region | 213138,2 | 1020,3 | 2144,1 | 480330 |
Penza region | 74362,7 | 676,2 | 1415,4 | 262655 |
Samara Region | 401812,2 | 1579 | 3195,1 | 1056262 |
Saratov region | 170930,5 | 1169,5 | 2617 | 556180 |
Ulyanovsk region | 80584,4 | 604,9 | 1343,3 | 234805 |
Kurgan region | 50245,8 | 434,3 | 986 | 213335 |
Sverdlovsk region | 475575,5 | 2093,8 | 4419 | 1424665 |
Tyumen region | 2215584,4 | 1890,6 | 3315,4 | 5405244 |
Chelyabinsk region | 349957,2 | 1674,4 | 3541,3 | 892723 |
Altai Republic | 8805,8 | 84,9 | 204,2 | 22026 |
The Republic of Buryatia | 74912,9 | 386,6 | 966,2 | 221056 |
Tyva Republic | 11662,5 | 104,3 | 308,1 | 19490 |
The Republic of Khakassia | 41727,5 | 244,1 | 539,6 | 120518 |
Altai region | 135686,4 | 1105,1 | 2554,4 | 382472 |
Krasnoyarsk region | 439736,9 | 1424,8 | 2915,7 | 823467 |
Irkutsk region | 258095,5 | 1137,7 | 2536,1 | 651069 |
Kemerovo region | 295378,4 | 1302,7 | 2846,8 | 629492 |
Novosibirsk region | 235381,8 | 1221,7 | 2656,1 | 595609 |
Omsk region | 220686,1 | 939,1 | 2040,6 | 357195 |
Tomsk region | 159578,5 | 478,9 | 1035,4 | 319795 |
Chita region | 69647,1 | 481,8 | 1132 | 316690 |
The Republic of Sakha (Yakutia) | 183027 | 469,1 | 950,3 | 450823 |
Primorsky Krai | 186623,3 | 180,9 | 350,7 | 100939 |
Khabarovsk region | 161194,4 | 980,2 | 2027,7 | 457446 |
Amurskaya Oblast | 76861,2 | 721,3 | 1416,3 | 437286 |
Kamchatka region | 43974,3 | 424,2 | 884,3 | 384833 |
Magadan Region | 27167,8 | 93,8 | 173,1 | 93758 |
Sakhalin Region | 121014,1 | 277,8 | 529,3 | 207065 |
Jewish Autonomous Region | 14204,2 | 79,8 | 187,7 | 52480 |
Chukotka autonomous region | 12355,4 | 38,5 | 50,6 | 29615 |
The grouping should begin with studying the nature of the change in the grouping attribute; for this, a ranked series of the distribution of regions by the cost of fixed assets per capita should be built (Table 2) and depicted in the form of Galton's Ogiva (Fig. 1).
Table 2 Ranked series of the distribution of regions by the cost of fixed assets per capita
Name of regions | |
Tyva Republic | 19490 |
Altai Republic | 22026 |
Chukotka Autonomous District | 29615 |
Jewish Autonomous Region | 52480 |
Magadan Region | 93758 |
Primorsky Krai | 100939 |
The Republic of Khakassia | 120518 |
Mari El Republic | 133723 |
The Republic of Mordovia | 183836 |
Sakhalin Region | 207065 |
Kurgan region | 213335 |
The Republic of Buryatia | 221056 |
Ulyanovsk region | 234805 |
Chuvash Republic | 253775 |
Penza region | 262655 |
Chita region | 316690 |
Tomsk region | 319795 |
Kirov region | 322973 |
Omsk region | 357195 |
Udmurtia | 368307 |
Altai region | 382472 |
Kamchatka region | 384833 |
Amurskaya Oblast | 437286 |
The Republic of Sakha (Yakutia) | 450823 |
Khabarovsk region | 457446 |
Orenburg region | 480330 |
Saratov region | 556180 |
Novosibirsk region | 595609 |
Kemerovo region | 629492 |
Irkutsk region | 651069 |
Nizhny Novgorod Region | 688092 |
Krasnoyarsk region | 823467 |
Republic of Bashkortostan | 868425 |
Chelyabinsk region | 892723 |
Perm Territory | 961938 |
Samara Region | 1056262 |
Republic of Tatarstan | 1090879 |
Sverdlovsk region | 1424665 |
Tyumen region | 5405244 |
Schedule 1 Distribution of regions of the Russian Federation by the value of fixed assets
Determine the value of the equal or unequal interval according to the graph of the ranked series.
The value of the equal grouping interval is determined by the formula:
19490+1077150,8 = 1096640,8
1096640,8+1077150,8=2173791,6
2173791,6+1077150,8=3250942,4
3250942,4+1077150,8=4328093,2
4328093,2+1077150,8=5405244
The resulting distribution series is presented in the form of a table.
Table 3 Interval series of the distribution of regions of the Russian Federation by the value of fixed assets
The interval series of the distribution of the regions of the Russian Federation by the value of fixed assets has an uneven distribution by the number of regions.
Therefore, you should group the regions into groups at intervals with open borders according to the following scheme (Table 4).
Table 4 Interval series of the distribution of regions of the Russian Federation by the cost of fixed assets
Graph 2 Histogram of the distribution of regions by the value of fixed assets
We will compose a worksheet, which is necessary for the calculation average cost fixed assets (Table 5).
Table 5 Worksheet of a Simple Analytical Grouping
Groups of regions of the Russian Federation by the value of fixed assets | Region name | Gross regional product, thousand rubles | The cost of fixed assets, million rubles | Average year. number of population, thousand people | Gross region. product per capita, thousand rubles | Cost of fixed assets per capita, billion rubles |
1 group up to 130,000 | Tyva Republic | 11662,5 | 19490 | 308,1 | 37,8 | 63,3 |
Altai Republic | 8805,8 | 22026 | 204,2 | 43,1 | 107,9 | |
Chukotka Autonomous District | 12355,4 | 29615 | 50,6 | 244,2 | 585,3 | |
Jewish Autonomous Region | 14204,2 | 52480 | 187,7 | 75,7 | 279,6 | |
Magadan Region | 27167,8 | 93758 | 173,1 | 156,9 | 541,6 | |
Primorsky Krai | 186623,3 | 100939 | 350,7 | 532,1 | 287,8 | |
The Republic of Khakassia | 41727,5 | 120518 | 539,6 | 77,3 | 223,3 | |
Total for 1 group | 302546,5 | 438826 | 1814 | 166,8 | 241,9 | |
2nd group 130,000 - 260,000 | Mari El Republic | 33350,7 | 133723 | 714,2 | 46,7 | 155,2 |
The Republic of Mordovia | 44267 | 183836 | 861,8 | 51,4 | 213,3 | |
Sakhalin Region | 121014,1 | 207065 | 529,3 | 228,6 | 391,2 | |
Kurgan region | 50245,8 | 213335 | 986 | 5,1 | 216,4 | |
The Republic of Buryatia | 74912,9 | 221056 | 966,2 | 77,5 | 228,8 | |
Ulyanovsk region | 80584,4 | 234805 | 1343,3 | 60 | 174,8 | |
Chuvash Republic | 69391,6 | 253775 | 1295,8 | 53,5 | 195,8 | |
Total for group 2 | 473766,5 | 1447595 | 6696,6 | 70,7 | 216,2 | |
3rd group 260,000 - 383,000 | Penza region | 74362,7 | 262655 | 1415,4 | 52,5 | 158,6 |
Chita region | 69647,1 | 316690 | 1132 | 61,5 | 279,8 | |
Tomsk region | 159578,5 | 319795 | 1035,4 | 154,1 | 308,9 | |
Kirov region | 79800,6 | 322973 | 1452,1 | 55 | 222,4 | |
Omsk region | 220686,1 | 357195 | 2040,6 | 108,1 | 175 | |
Udmurtia | 139995,3 | 368307 | 1548,6 | 90,4 | 237,8 | |
Altai region | 135686,4 | 382472 | 2554,4 | 53,1 | 149,7 | |
Total for 3 group | 879756,7 | 2330087 | 11178,5 | 78,7 | 208,4 | |
4th group 383000 - 600000 | Kamchatka region | 43974,3 | 384833 | 884,3 | 49,7 | 435,2 |
Amurskaya Oblast | 76861,2 | 437286 | 1416,3 | 5,5 | 308,7 | |
The Republic of Sakha (Yakutia) | 183027 | 450823 | 950,3 | 192,6 | 474,4 | |
Khabarovsk region | 161194,4 | 457446 | 2027,7 | 79,5 | 225,6 | |
Orenburg region | 213138,2 | 480330 | 2144,1 | 99,4 | 224 | |
Saratov region | 170930,5 | 556180 | 2617 | 65,3 | 212,5 | |
Novosibirsk region | 235381,8 | 595609 | 2656,1 | 88,6 | 224,2 | |
Total for group 4 | 1084507,4 | 3362507 | 12695,8 | 85,4 | 264,8 | |
5 group Over 600,000 | Kemerovo region | 295378,4 | 629492 | 2846,8 | 103,7 | 221,1 |
Irkutsk region | 258095,5 | 651069 | 2536,1 | 101,8 | 256,7 | |
Nizhny Novgorod Region | 299723,7 | 688092 | 3428,2 | 87,4 | 200,7 | |
Krasnoyarsk region | 439736,9 | 823467 | 2915,7 | 150,8 | 282,4 | |
Republic of Bashkortostan | 381646,5 | 868425 | 4071,1 | 93,7 | 213,3 | |
Chelyabinsk region | 349957,2 | 892723 | 3541,3 | 98,8 | 252 | |
Perm Territory | 327273,3 | 961938 | 2759 | 118,6 | 348,6 | |
Samara Region | 401812,2 | 1056262 | 3195,1 | 125,7 | 330,6 | |
Republic of Tatarstan | 482759,2 | 1090879 | 3765 | 128,2 | 289,7 | |
Sverdlovsk region | 475575,5 | 1424665 | 4419 | 107,6 | 322,4 | |
Tyumen region | 2215584,4 | 5405244 | 3315,4 | 668,3 | 163 | |
Total for the 5th group | 5927542,8 | 14492256 | 36792,5 | 161,1 | 393,9 | |
Total: | 8668119,9 | 22071271 | 69177,4 | 562,7 | 1284,6 |
Create a pivot table (Table 6).
Table 6 Grouping of regions by the cost of fixed assets per capita
A direct dependence of the cost of fixed assets per capita on the gross regional product per capita has been revealed. The higher the gross regional product per capita, the higher the cost of fixed assets.
3 Statistical study of the dynamics of the gross regional product of the Ural Federal District in 2000 - 2006. in comparable prices
3.1 Calculation of indicators of dynamics (absolute growth, growth rate, growth rate, absolute content of 1% growth)
There is data on the gross regional product of the Ural Federal District in 2000-2006. in comparable prices. Calculate and analyze indicators of a number of dynamics.
Years | Vrp. RUB bln | Absolute gain | Growth rate, % | Rate of increase | ||||
Base. | Chain. | Base. | Chain. | Base. | Chain. | |||
886133,4 | - | - | - | - | - | - | - | |
2001 | 1120819,8 | 234686,4 | 234686,4 | 126 | 126 | 1,26 | 1,26 | 186259 |
2002 | 1335976,0 | 449842,6 | 215156,2 | 151 | 119 | 1,51 | 1,19 | 180803,5 |
2003 | 1659322,1 | 773188,7 | 323346,1 | 187 | 124 | 1,87 | 1,24 | 260763 |
2004 | 2234753,0 | 1348619,6 | 575430,9 | 252 | 135 | 2,52 | 1,35 | 426245,1 |
2005 | 3091362,9 | 2205229,5 | 856609,9 | 349 | 138 | 3,49 | 1,38 | 620731,8 |
2006 | 3772730,5 | 2886597,1 | 681367,6 | 426 | 122 | 4,26 | 1,22 | 558498 |
the average | 2014442,5 | 48109,5 | 481099,5 | 114,7 | 114,7 | 1,15 | 1,15 | - |
Absolute gains are the difference between the equations of a number of dynamics, which shows how much one level is more or less than another.
The growth rate is an indicator of the ratio of levels. The coefficient shows how many times 1 level> or< другого.
The growth rate shows how much% is 1 level compared to the other.
The growth rate shows by how many% one level> or< другого.
The absolute content of 1% increase - shows 1/100 of the absolute level of the subject period.
3.2 Identification of the trend in the development of a number of dynamics using the methods of mechanical alignment, middle level, analytical alignment
Mechanical leveling method (gross regional product of the Ural Federal District for 2000-2006 in comparable prices).
Table 8 Mechanical alignment method
Years | Gross regional product, billion rubles | Interval coarsening method | 3-year moving average method | ||||
Labor productivity | Labor productivity | ||||||
Period | Sum | The average | Period | Sum | The average | ||
2000 | 866133,4 | ||||||
2001 | 1120819,8 | 2000-2002 | 3322929,2 | 1107643 | 2000-2002 | 3322929,2 | 1107643 |
2002 | 1335976 | 2001-2003 | 4116117,9 | 1372039,3 | |||
2003 | 1659322,1 | 2002-2004 | 5230051,1 | 1743350 | |||
2004 | 2234753 | 2003-2006 | 10758168,5 | 2689542,1 | 2003-2005 | 6985439 | 2328479,7 |
2005 | 3091362,9 | 2004-2006 | 9098846,4 | 3032948,8 | |||
2006 | 3772730,5 |
Graph 3 Mechanical alignment
Average level method (according to the average growth rate, according to the average absolute growth)
Table 9 Alignment by middle-level methods
Years | GRP, billion rubles | Serial No. | Aligning values | |
Average odds growth Yt = 866133.4 * 144.7t | Average absol. growth Yt = 866133.4 + 1.15t | |||
866133,4 | 1 | 99345501 | 866134,5 | |
2001 | 1120819,8 | 2 | 198691002 | 866135,7 |
2002 | 1335976 | 3 | 298036503 | 866136,8 |
2003 | 1659322,1 | 4 | 397382004 | 866138 |
2004 | 2234753 | 5 | 496727505 | 866139,2 |
2005 | 3091362,9 | 6 | 596073005,9 | 866140,3 |
2006 | 3772730,5 | 7 | 695418506,9 | 866141,4 |
Chart 4 Medium level method
Analytical alignment method (according to the equation of a straight line by the method of least squares)
Table 10 Analytical alignment method
Years | GRP, billion rubles | t | t * 2 | Yt | Yt mean | Deviation from the trend | |
(y-yt mean) | (y-yt average) * 2 | ||||||
2000 | 866133,4 | -3 | 9 | -2598400,2 | 558765,2 | 307368,2 | 94475210371 |
2001 | 1120819,8 | -2 | 4 | -2241639,6 | 1043038,6 | 77781,2 | 6049915073 |
2002 | 1335976 | -1 | 1 | -1335976 | 1527312 | -191336 | 36609464896 |
2003 | 1659322,1 | 0 | 0 | 0 | 2011585,4 | -352263,3 | 12408943252 |
2004 | 2234753 | 1 | 1 | 2234753 | 2495858,8 | -261105,8 | 68176238794 |
2005 | 3091362,9 | 2 | 4 | 6182725,8 | 2980132,2 | 111230,7 | 12372268622 |
2006 | 3772730,5 | 3 | 9 | 11318191,5 | 3464405,6 | 308324,9 | 95064243960 |
Total | 14081097,7 | 0 | 28 | 13559654,6 | 14081097,8 | - | 3,25156 |
Graph 5 Analytical Alignment
3.3 Analysis of indicators of variability of a number of dynamics
1.Span of oscillation
R = (Y-Yt) max- (Y-Yt) min
R = 308324.9 - 111230.7 = 197094.2 billion rubles.
2. Standard deviation from the trend
yt = billion rubles
3. Coefficient of oscillation
Vyt = = 0.0001%
4. Coefficient of stability
Bush. = 100% - Vyt
Bush. = 100% - 0.0001% = 99.9%
If the oscillation coefficient does not exceed 33%, then this series of dynamics is stable and is subject to further economic analysis.
3.4 Forecasting for the future
Forecasting - determining the future size of the level of an economic phenomenon or process. It is based on identifying and characterizing the main development trends and interconnection patterns.
There are the following forecasting methods:
based on average, absolute growth;
average growth rate;
using an analytical alignment trend.
Calculate point prediction:
Forecast for 2007:
Yt = 2011585.4 + 484273.4t
Y2007 = t = 4
Y2007 t = 3948679 billion rubles.
Compared to 2006, the gross regional product increased by 175,948.5 billion rubles.
Forecast for 2008:
Yt = 2011585.4 + 484273.4t
Y2008 = t = 5
Y2008 t = 4432952.4 billion rubles.
Compared to 2006 the gross regional product increased by 660,221.9 billion rubles, compared to 2007. increased by 484273.4 billion rubles.
Muk 2007 = billion rubles
Flour. 2008 = billion rubles
1 = 2.08 * 2.7 = 5,6
2 = 2.08 * 0.9 = 1,9
1 = Y2008-2.8
3.5 Identification of the development trend in the series of dynamics using the PPP Excel
There is data on the gross regional product of the Ural Federal District in 2000-2006. in comparable prices.
Table 11 Initial data
Graph 6 Alignment of a number of dynamics
Graph 7 Linear function
Graph 8 Logarithmic function
Graph 9 Polynomial alignment grade 2
Graph 10 Power function
Graph 11 Exponential function
y = 69581x * 2 - 72378x + 909470
Y = 69581 * 49 - 72378 * 7 + 909470 = 3812293
Y = 69581 * 64 - 72378 * 8 + 909470 = 4783630
Gross Regional Product Compared to 2006 in 2007. increased by 39562.5 billion rubles. In 2008. increased by 1,010,899.5 billion rubles.
4.1 Theoretical aspects of the index method of analysis
Futures is a standard exchange-traded futures contract, according to which the parties who have entered into it undertake to deliver and receive the required amount of an exchange commodity or financial instruments at a specified time in the future at a fixed price. Futures transactions are distinguished by their impersonal nature. Sellers and buyers of contracts, as a rule, do not communicate with each other, but through their brokerage offices. That is, in this situation, the seller-buyer system does not operate, and the buyer system (plus his brokerage office), the exchange (clearing house), the seller (and his bank office operating on the exchange). The procedure for carrying out futures transactions is stipulated by the regulations of the exchange.For the buyer of a futures contract, the exchange acts as its seller, and for the seller of the contract, as its buyer. At the same time, the clearing house acts as a guarantor of performance futures contracts... In practice, commodity and financial futures are distinguished. Commodity futures are trading in futures contracts for agricultural products, energy resources, metals, etc. Financial futures are futures contracts based on financial instruments, government and other securities, stock indices, interest on bank rates as well as convertible currency and gold. Futures contracts are strongly typed and standardized. In the context of individual commodity groups and types of financial instruments, the following are agreed: their quantity, quality, delivery time, place of delivery, etc. is changing. Formally, a futures contract is a delivery contract. In this case, its seller acts as a supplier, and the buyer acts as the future purchaser of the goods (financial instrument). But futures contracts are usually not for the purpose of physical purchase or the sale of the underlying asset, but for the purpose of insuring (hedging) actual transactions with the commodity, as well as for obtaining speculative profits in the course of the resale of futures or to liquidate the transaction. So, in some futures, for example, at bank interest rates, instead of buying - receiving goods (financial instruments) or selling and delivering it, monetary compensation for its value may be provided. For a more complete definition of the concept of futures, let us compare it with such derivatives securities as a forward and an option. Forward contracts futures transactions for the supply of physical goods in deadlines at a certain price in the future. Forward contracts, unlike futures contracts, are concluded outside the exchange and only for a real product. This leads to a decrease in the reliability of forwards due to the absence in the forward trading system of a third party performing the functions of a controller and guarantor. V exchange trading It is possible to participate in futures without even having the commodity in hand, which leads to a greater proliferation of futures rather than forward transactions. The higher attractiveness of futures is also explained by the fact that futures contracts are standard, and this facilitates the process of buying and selling a contract. When concluding a forward contract, each time it is necessary to negotiate the terms of the forward, which leads to additional costs of time and money. And pay off forward contract until its expiration date is possible only through additional negotiations. At the same time, futures can be redeemed by opening an opposite position on the exchange. The ability to quickly close futures deal leads to the fact that less than 5% of futures contracts end with the delivery of real goods.
The word index itself means an indicator. Usually this term is used for some general description of changes.
First, indices measure the change in complex phenomena. For example, you need to determine how the expenses of Moscow residents for urban transport have changed over the year. To answer this question, you must have the number of passengers transported per year by each type of urban transport, calculate the average monthly number of passengers or take accurate data from monthly reports, multiply the number by the transportation tariff and get the summation values. The same should be done according to the data for the last year. Then compare the amount spent for the last year with the amount for the previous year. That is, this is not just a comparison of numbers, as when calculating the rates of dynamics or growth, but obtaining and comparing some aggregated values.
Secondly, the indices allow you to analyze the change - to reveal the role of individual factors. For example, you can determine how the amount of revenue for urban transport has changed due to changes in the number of passengers and tariffs, and finally, due to the ratio in the volume of traffic by different modes of transport.
Thirdly, the indices are indicators of comparisons not only with the previous period, but also with another territory, as well as with the standards.
The index is an indicator of comparison of two states of the same phenomenon (simple or complex, consisting of commensurate or incommensurable elements).
4.2 Index analysis of the influence of various factors on socio - economic phenomena and processes
A) Determine the change in the Gross Regional Product of the Ural Federal District in 2006 compared to 2005, due to changes in the cost of fixed assets and through the use of fixed assets (return on assets).
1. Change in the volume of production
Relative:
Ivrp = Q1 / Q0 = 3772730.5 / 3091362.9 = 1.22 = 122%
Absolute:
Δ Q = Q1 - Q0 = 681367.6
2. Change in the volume of production due to changes in capital productivity:
Ivrp / fotd = fotd1 * F1 / fotd * F1
Ivrp / fotd = 0.4 * 9209054 / 0.39 * 9209054 = 1 = 100%
ΔQ / fotd = (fotd1 - fotd0) * F1
ΔQ / fotd = (0.409676 - 0.389538) * 9209054 = 185451.9
GRP in 2006 compared to 2005 due to the change in capital productivity increased by 10%, which amounted to 185451.9
3. Change in the volume of production due to changes in the value of fixed assets.
Ivrp / f¯ = F1 * fotd0 / F0 * fotd0
Ivrp / f¯ = 9209054 * 0.39 / 7935967 * 0.39 = 1.160419 = 120%
ΔQ / ф¯ = (Ф1 - Ф0) * fotd0
ΔQ / f¯ = (9209054 - 7935967) * 0.39 = 496503.93
Ivrp = Ivrp / fotd * Ivrp / f¯
Table 12 Change in gross regional product due to changes in the value of fixed assets and through the use of fixed assets (return on assets)
GRP in 2006 compared to 2005 due to the change in the value of fixed assets increased by 20%, which amounted to 496503.93 thousand rubles.
B) Determine the change in the value of fixed assets of the Ural Federal District in 2006 compared to 2005 due to changes in GRP and capital intensity.
1. Change in the value of fixed assets
Iph¯ = Ф1 / Ф0 = 9209054/7935967 = 1.16 = 116%
ΔФ¯ = Ф1 - Ф0 = 9209054 - 7935967 = 1273087
The cost of fixed assets in 2006 compared to 2005 increased by 16%, which amounted to 1273087
2. Change in the value of fixed assets due to changes in capital intensity
Iph¯ / femk = f capac1 * Q1 / femk0 * Q1
Iph¯ / femk = 2.4 * 3772730.5 / 2.6 * 3772730.5
ΔФ¯ / f`cap = (fec1 - fec0) * Q1
ΔФ¯ / f`cap = -377273.05
The cost of fixed assets in 2006 compared to 2005 due to the change in capital intensity decreased by 8%, which is -377,273.05 thousand rubles.
3. Change in the value of fixed assets due to changes in the volume of production
Iph¯ / Q = Q1 * fc0 / Q0 * fc0
Iph¯ / Q = 3772730.5 * 2.6 / 3091362.9 * 2.6 = 1.220410098 = 122%
ΔФ¯ / Q = (Q1 - Q0) * femk0
ΔФ / Q = 1771555.76
Iph¯ = Iph¯ / femk * Iph¯ / Q
1,16 = 0,92*1,22
Table 13 Change in the value of fixed assets due to changes in GRP and capital intensity
The cost of fixed assets in 2006 compared to 2005 due to a change in the volume of production increased by 22%, which amounted to 1,771,555.76 thousand rubles.
5 Correlation - regression analysis of the influence of factors
There are data on the influence of the cost of fixed assets per capita and the average annual number of people employed in the economy on the gross regional product per capita (all indicators for 2005).
Table 14.1 Initial data
Name of regions | |||
Y | X1 | X2 | |
Republic of Bashkortostan | 1797,6 | 868425 | 93745,1 |
Mari El Republic | 334,4 | 133723 | 46696,9 |
The Republic of Mordovia | 399,1 | 183836 | 51369,8 |
Republic of Tatarstan | 1778,0 | 1090879 | 128222,0 |
Udmurtia | 764,8 | 368307 | 90401,7 |
Chuvash Republic | 597,5 | 253775 | 53552,4 |
Perm Territory | 1318,9 | 961938 | 118619,4 |
Kirov region | 714,6 | 322973 | 54954,6 |
Nizhny Novgorod Region | 1748,9 | 688092 | 87429,3 |
Orenburg region | 1020,3 | 480330 | 99405,5 |
Penza region | 676,2 | 262655 | 52540,0 |
Samara Region | 1579,0 | 1056262 | 125757,4 |
Saratov region | 1169,5 | 556180 | 65314,9 |
Ulyanovsk region | 604,9 | 234805 | 59989,2 |
Kurgan region | 434,3 | 213335 | 50959,1 |
Sverdlovsk region | 2093,8 | 1424665 | 107621,1 |
Tyumen region | 1890,6 | 5405244 | 668272,2 |
Chelyabinsk region | 1674,4 | 892723 | 98820,3 |
Altai Republic | 84,9 | 22026 | 43127,3 |
The Republic of Buryatia | 386,6 | 221056 | 77532,7 |
Tyva Republic | 104,3 | 19490 | 37856,2 |
The Republic of Khakassia | 244,1 | 120518 | 77332,8 |
Altai region | 1105,1 | 382472 | 53118,0 |
Krasnoyarsk region | 1424,8 | 823467 | 150814,0 |
Irkutsk region | 1137,7 | 651069 | 101766,6 |
Kemerovo region | 1302,7 | 629492 | 103758,5 |
Novosibirsk region | 1221,7 | 595609 | 88619,4 |
Omsk region | 939,1 | 357195 | 108147,0 |
Tomsk region | 478,9 | 319795 | 154131,1 |
Chita region | 481,8 | 316690 | 61526,8 |
The Republic of Sakha (Yakutia) | 469,1 | 450823 | 192599,0 |
Kamchatka Krai | 180,9 | 100939 | 92039,1 |
Primorsky Krai | 980,2 | 457446 | 113818,2 |
Khabarovsk region | 721,3 | 437286 | 86913,2 |
Amurskaya Oblast | 424,2 | 384833 | 125392,3 |
Magadan Region | 93,8 | 93758 | 156923,9 |
Sakhalin Region | 277,8 | 207065 | 228624,4 |
Jewish Autonomous Region | 79,8 | 52480 | 75695,8 |
Chukotka Autonomous District | 38,5 | 29615 | 244096,3 |
Table 14.2 Correlation matrix
Have | X1 | X2 | |
at | 1 | ||
X1 | 0,617107 | 1 | |
X2 | 0,262244 | 0,844487 | 1 |
The correlation matrix contains partial correlation coefficients. The coefficients of the second column of the matrix characterize the degree of closeness of the relationship between the effective (y) and factor indicators (x1, x2). The relationship between the average annual number of people employed in the economy and the cost of fixed assets (rх1 = 0.617) is direct, weak; the relationship between the average annual number of people employed in the economy and the gross regional product per capita (ryx2 = 0.262) is direct and weak.
Table 14.3 Regression Statistics
The multiple correlation coefficient R = 0.783 shows that the tightness of the relationship between the average annual number of people employed in the economy and the factors included in the model is strong. Multiple coefficient of determination (R - square) D = 0.614, i.e. 61.4% of the variation in the level of profitability is explained by the variation of the studied factors
Table 14.4 ANOVA
df | SS | MS | F | Significance of F | |
Regression | 2 | 8210529,993 | 4105264,996 | 28,69165325 | 3.5367E-08 |
Remainder | 36 | 5150959,36 | 143082,2044 | ||
Total | 38 | 13361489,35 |
Let's check the significance of the coefficient of multiple correlation, for this we use the F - criterion, for which we compare the actual value of F with the table value of Ftabl. With an error probability a = 0.05 and degrees of freedom v1 = k-1 = 2-1 = 1, v2 = nk = 39-2 = 37, where k is the number of factors in the model, n is the number of observations, Ftab. = 4 , 08. Since Ffact = 28.69> Ftab. = 4.08, the correlation coefficient means, therefore, the constructed model is generally adequate.
Table 14.5 a Regression coefficients
Using table 1.5, we compose the regression equation:
Y = 893.79 + 0.0009X1 - 0.005X2
The interpretation of the obtained parameters is as follows:
a0 = 893.79 - free term of the regression equation, meaningful interpretation is not subject;
a1 = 0.0009 - the net regression coefficient for the first factor indicates that with an increase in fixed assets per capita by 1 billion rubles. the average annual population employed in the economy will increase by 0.0009%, provided that other factors remain constant;
a2 = -0.005 - the net regression coefficient for the second factor indicates that with an increase in the gross regional product from 1 thousand rubles. for 1 thousand people the average annual number of people employed in the economy will decrease by 0.005%, provided that the factors remain constant.
We can check the significance of the regression coefficients using the Student's t-test; for this we compare the actual values of the t - criterion with the tabular value of the t - criterion. With the error probability a = 0.05 and the degree of freedom v = n-k-1 = 39-2-1 = 36, k is the number of factors in the model, n is the number of observations, ttable = 1.68. We get
t1 fact = 7.14> ttab. = 1.68
t2 fact = -4.67> ttab. = 1.68
This means that the first and second factors are statistically significant. In this case, the model is suitable for making decisions, but not making predictions.
Table 14.6 Descriptive statistics
Have | X1 | X2 | |
The average | 840,3615 | 565930 | 113525,7 |
Standard error | 94,95183 | 138158 | 16492,55 |
Median | 714,6 | 368307 | 92039,1 |
Fashion | # N / A | # N / A | # N / A |
Standard deviation | 592,974 | 862796 | 102996 |
Sample variance | 351618,1 | 7.4E + 11 | 1.06E + 10 |
Excess | -0,914121 | 27,3251 | 22,87771 |
Asymmetry | 0,480141 | 4,88112 | 4,36911 |
Interval | 2055,3 | 5385754 | 630416 |
Minimum | 38,5 | 19490 | 37856,2 |
Maximum | 2093,8 | 5405244 | 668272,2 |
Sum | 32774,1 | 2.2E + 07 | 4427504 |
Check | 39 | 39 | 39 |
The average values of the features included in the model are Y = 840.4%;
х1 = 565930 billion rubles; x2 = 113,525.7 thousand rubles.
Standard errors of the regression coefficients Sao = 351618.1; Sa1 = 7.4; Sa2 = 1.06
The standard deviations of the features σУ = 592.97%; σх1 = 862796 billion rubles; σх2 = 102996 thousand rubles.
Knowing the mean values and standard deviations of the features, we calculate the coefficients of variation to assess the homogeneity of the initial data
The variation of the factors included in the model does not exceed the permissible values (33-35%), and the level of profitability is characterized by a variation of 0.7%. In this case, it is necessary to check the initial information and exclude those values that differ significantly from the average values.
Different units of measurement make the regression coefficients incomparable, when the question arises about the relative strength of the impact on the effective indicator of each of the pure regression factors. Let us express them in a standardized form in the form of beta - coefficients and elasticity coefficients.
Each of the β - coefficients shows by how many standard deviations the average annual number of people employed in the economy will change if the corresponding factor changes by its standard deviation.
With an increase in fixed assets by 1 standard deviation, the average annual number of people employed in the economy will increase by 1.3% of its standard deviation; with an increase in the gross regional product by 1 of its standard deviation, the average annual number of people employed in the economy will decrease by 0.87 of its standard deviation.
Each of the elasticity coefficients shows the percentage of changes in the average annual number of people employed in the economy if the corresponding factor changes by 1%.
With an increase in fixed assets per capita by 1%, the average annual number of people employed in the economy will increase by 0.6%; with an increase in the gross regional product by 1%, the average annual number of people employed in the economy decreases by 0.67%.
Table 1.7 shows the calculated values of the average annual number of employed in the economy and the deviation of the actual values from the calculated ones. The calculated values were obtained by substituting the values of the factors of the average annual number of employed in the economy into the regression equation.
If the calculated value of the average annual number of people employed in the economy exceeds the actual value (residuals are negative), that is, there are reserves for increasing the average annual number of people employed in the economy due to factors included in the model, otherwise the residuals are positive) there are no reserves for increasing the average annual number of people employed in the economy due to factors, included in the model.
Table 14.7 Balances
Observation | Predicted by Y | Leftovers |
1 | -346,5332771 | 385,0332771 |
2 | 550,2069329 | -470,4069329 |
3 | 690,574439 | -605,674439 |
4 | 167,2487007 | -73,4487007 |
5 | 715,5607952 | -611,2607952 |
6 | 511,2190356 | -330,3190356 |
7 | 606,198028 | -362,098028 |
8 | -97,92013983 | 375,7201398 |
9 | 777,9102084 | -443,5102084 |
10 | 700,4655634 | -313,8655634 |
11 | 801,1335239 | -402,0335239 |
12 | 607,0256551 | -182,8256551 |
13 | 831,2316119 | -396,9316119 |
14 | 320,3533398 | 148,7466602 |
15 | 396,0356826 | 82,86431738 |
16 | 874,2950193 | -392,4950193 |
17 | 856,091577 | -258,591577 |
18 | 804,6609681 | -199,7609681 |
19 | 869,7702539 | -193,5702539 |
20 | 914,4024079 | -199,8024079 |
21 | 856,6993563 | -135,3993563 |
22 | 773,1824245 | -8,38242446 |
23 | 670,4381776 | 268,6618224 |
24 | 736,0029521 | 244,1970479 |
25 | 832,5893181 | 187,7106819 |
26 | 980,3488304 | 124,7511696 |
27 | 982,1744744 | 155,5255256 |
28 | 1081,638398 | 87,86160193 |
29 | 997,917685 | 223,782315 |
30 | 951,3697062 | 351,3302938 |
31 | 1189,29396 | 129,6060404 |
32 | 890,7347492 | 534,0652508 |
33 | 1241,618178 | 337,3818223 |
34 | 1226,563428 | 447,8365718 |
35 | 1091,772283 | 657,1277172 |
36 | 1261,626924 | 516,3730764 |
37 | 1229,902257 | 567,6977433 |
38 | 2545,203812 | -654,6038121 |
39 | 1685,092762 | 408,7072385 |
So in regions 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 38 have reserves for increasing the average annual number of employed in the economy. We use the resulting model to calculate the growth reserves of the average annual number of people employed in the economy. Let us divide the farms into two groups: the first is the regions where the average annual number of people employed in the economy is lower than the average for the entire population, and the second is the regions where the average annual number of people employed in the economy is higher than the average for the entire population. Fill in table 1.8
Table 14.8 Calculation of reserves for increasing the average annual number of employed in the economy
Factor | Average factor | Difference between groups | The coefficient of the average annual number of people employed in the economy | Influence of factors on the average annual number of people employed in the economy | ||||
1 | 2 | cumulatively | 1 | 2 | 1 | 2 | ||
A | 1 | 2 | 3 | 4=3-1 | 5=3-2 | 6 | 7=6*4 | 8=6*5 |
Fixed assets per capita, billion rubles | 258644,7 | 1257322 | 565930 | 307285,3 | -691392 | 0,0001 | 30,7 | -69,1 |
Gross regional product per capita, thousand rubles | 67248,5 | 2183515,3 | 113525,7 | 46277,2 | -2069989,6 | 0,001 | 46,3 | -2070 |
Average annual number of people employed in the economy, thousand people | 435 | 2846,2 | 840,36 | 405,36 | -2005,84 | NS | 77 | -2139,1 |
Analyzing the results of Table 1.8, we see that in the 1st group of regions there is a reserve for increasing the average annual number of employed in the economy by 77% due to the factors considered. So, if fixed assets per capita are 1 billion rubles. increase from 258644.7 billion rubles. to the average for the aggregate (565930 billion rubles), then the average annual number of people employed in the economy will increase by 30.7%; with a decrease in the gross regional product from 1 thousand rubles. up to 113,525.7 thousand rubles the average annual number of people employed in the economy will increase by 46.3%.
The total reserve for increasing the average annual number of employed in the economy is 77%. In the second group, the reserve for increasing the average annual number of employed in the economy due to the factors under consideration has been exhausted.
Conclusion
This paper considered the main goals and objectives of the gross regional product of the federal districts of the Russian Federation (Volga, Ural, Siberian, Far Eastern federal districts) and methods of its calculation.
Gross regional product is a generalized indicator of the economic activity of the region, which 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 terms of its economic content, very close to the indicator of the gross domestic product. However, there is a significant difference between the indicators of the gross domestic product (at the federal level) and the gross regional product (at the regional level). The sum of the gross regional products in Russia does not coincide with the gross domestic product, since it does not include the added value of non-market collective services (defense, public administration) provided by state institutions to society as a whole. At the moment, the calculation of the gross regional product of a constituent entity of the federation takes 28 months.
In Russia, the calculation of regional indicators is based on the methodological principles of the SNA. A generalizing indicator of the development of regions is the gross regional product (GRP). This indicator is built on the basis of a unified methodology developed centrally in the FSGS. The results of the calculations are monitored, approved and published in a generalized form by the FSGS.
In terms of its economic content, the GRP roughly corresponds to the GDP indicator calculated by the production method at the federal level. GRP is defined as the sum of the added value of the resident units of a given region. Resident units in this case are determined on the basis of the same principles as at the federal level.
At the same time, the methodology for calculating GRP differs from the methodology for calculating GDP. When calculating GRP, a number of elements that include GDP are not taken into account, therefore the total GRP of all regions of Russia is less Country GDP... These elements are:
1. Added value of industries providing collective non-market services to society as a whole (government, defense, international activities, etc.);
2. The added value of the services of financial intermediaries (primarily banks), whose activities are rarely limited to strictly separate regions;
3. The added value of foreign trade services, which in many cases can only be obtained at the federal level;
4. Part of taxes, in particular - (taxes on import and export), which cannot be taken into account at the regional level.
List of used literature
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Applications
Table 1 Initial data for 39 regions of the Russian Federation in 2005.
Region name | Gross region, product, thousand / rub. | employed in the economy, thousand / person | average year. num. population, thousand / person | article of fixed assets, mln. rub. |
Republic of Bashkortostan | 381646,5 | 1797,6 | 4071,1 | 868425 |
Mari El Republic | 33350,7 | 334,4 | 714,2 | 133723 |
The Republic of Mordovia | 44267 | 399,1 | 861,8 | 183836 |
Republic of Tatarstan | 482759,2 | 1778 | 3765 | 1090879 |
Udmurtia | 139995,3 | 764,8 | 1548,6 | 368307 |
Chuvash Republic | 69391,6 | 597,5 | 1295,8 | 253775 |
Perm Territory | 327273,3 | 1318,9 | 2759 | 961938 |
Kirov region | 79800,6 | 714,6 | 1452,1 | 322973 |
Nizhny Novgorod Region | 299723,7 | 1748,9 | 3428,2 | 688092 |
Orenburg region | 213138,2 | 1020,3 | 2144,1 | 480330 |
Penza region | 74362,7 | 676,2 | 1415,4 | 262655 |
Samara Region | 401812,2 | 1579 | 3195,1 | 1056262 |
Saratov region | 170930,5 | 1169,5 | 2617 | 556180 |
Ulyanovsk region | 80584,4 | 604,9 | 1343,3 | 234805 |
Kurgan region | 50245,8 | 434,3 | 986 | 213335 |
Sverdlovsk region | 475575,5 | 2093,8 | 4419 | 1424665 |
Tyumen region | 2215584,4 | 1890,6 | 3315,4 | 5405244 |
Chelyabinsk region | 349957,2 | 1674,4 | 3541,3 | 892723 |
Altai Republic | 8805,8 | 84,9 | 204,2 | 22026 |
The Republic of Buryatia | 74912,9 | 386,6 | 966,2 | 221056 |
Tyva Republic | 11662,5 | 104,3 | 308,1 | 19490 |
The Republic of Khakassia | 41727,5 | 244,1 | 539,6 | 120518 |
Altai region | 135686,4 | 1105,1 | 2554,4 | 382472 |
Krasnoyarsk region | 439736,9 | 1424,8 | 2915,7 | 823467 |
Irkutsk region | 258095,5 | 1137,7 | 2536,1 | 651069 |
Kemerovo region | 295378,4 | 1302,7 | 2846,8 | 629492 |
Novosibirsk region | 235381,8 | 1221,7 | 2656,1 | 595609 |
Omsk region | 220686,1 | 939,1 | 2040,6 | 357195 |
Tomsk region | 159578,5 | 478,9 | 1035,4 | 319795 |
Chita region | 69647,1 | 481,8 | 1132 | 316690 |
The Republic of Sakha (Yakutia) | 183027 | 469,1 | 950,3 | 450823 |
Primorsky Krai | 186623,3 | 180,9 | 350,7 | 100939 |
Khabarovsk region | 161194,4 | 980,2 | 2027,7 | 457446 |
Amurskaya Oblast | 76861,2 | 721,3 | 1416,3 | 437286 |
Kamchatka region | 43974,3 | 424,2 | 884,3 | 384833 |
Magadan Region | 27167,8 | 93,8 | 173,1 | 93758 |
Sakhalin Region | 121014,1 | 277,8 | 529,3 | 207065 |
Jewish Autonomous Region | 14204,2 | 79,8 | 187,7 | 52480 |
Chukotka Autonomous District | 12355,4 | 38,5 | 50,6 | 29615 |
Table 7 Calculation of indicators of dynamics
Years | Vrp. RUB bln | Absolute gain | Growth rate, % | Rate of increase | Absolute. content of 1% increase | |||
Base. | Chain. | Base. | Chain. | Base. | Chain. | |||
2000 | 886133,4 | - | - | - | - | - | - | - |
2001 | 1120819,8 | 234686,4 | 234686,4 | 126 | 126 | 1,26 | 1,26 | 186259 |
2002 | 1335976,0 | 449842,6 | 215156,2 | 151 | 119 | 1,51 | 1,19 | 180803,5 |
2003 | 1659322,1 | 773188,7 | 323346,1 | 187 | 124 | 1,87 | 1,24 | 260763 |
2004 | 2234753,0 | 1348619,6 | 575430,9 | 252 | 135 | 2,52 | 1,35 | 426245,1 |
2005 | 3091362,9 | 2205229,5 | 856609,9 | 349 | 138 | 3,49 | 1,38 | 620731,8 |
2006 | 3772730,5 | 2886597,1 | 681367,6 | 426 | 122 | 4,26 | 1,22 | 558498 |
the average | 2014442,5 | 48109,5 | 481099,5 | 114,7 | 114,7 | 1,15 | 1,15 | - |
Table 11 Initial data
Table 12.1 Initial data
Name of regions | Average annual number of people employed in the economy, thousand people | Fixed assets per capita, billion rubles | Gross regional product per capita, thousand rubles |
Y | X1 | X2 | |
Republic of Bashkortostan | 1797,6 | 868425 | 93745,1 |
Mari El Republic | 334,4 | 133723 | 46696,9 |
The Republic of Mordovia | 399,1 | 183836 | 51369,8 |
Republic of Tatarstan | 1778,0 | 1090879 | 128222,0 |
Udmurtia | 764,8 | 368307 | 90401,7 |
Chuvash Republic | 597,5 | 253775 | 53552,4 |
Perm Territory | 1318,9 | 961938 | 118619,4 |
Kirov region | 714,6 | 322973 | 54954,6 |
Nizhny Novgorod Region | 1748,9 | 688092 | 87429,3 |
Orenburg region | 1020,3 | 480330 | 99405,5 |
Penza region | 676,2 | 262655 | 52540,0 |
Samara Region | 1579,0 | 1056262 | 125757,4 |
Saratov region | 1169,5 | 556180 | 65314,9 |
Ulyanovsk region | 604,9 | 234805 | 59989,2 |
Kurgan region | 434,3 | 213335 | 50959,1 |
Sverdlovsk region | 2093,8 | 1424665 | 107621,1 |
Tyumen region | 1890,6 | 5405244 | 668272,2 |
Chelyabinsk region | 1674,4 | 892723 | 98820,3 |
Altai Republic | 84,9 | 22026 | 43127,3 |
The Republic of Buryatia | 386,6 | 221056 | 77532,7 |
Tyva Republic | 104,3 | 19490 | 37856,2 |
The Republic of Khakassia | 244,1 | 120518 | 77332,8 |
Altai region | 1105,1 | 382472 | 53118,0 |
Krasnoyarsk region | 1424,8 | 823467 | 150814,0 |
Irkutsk region | 1137,7 | 651069 | 101766,6 |
Kemerovo region | 1302,7 | 629492 | 103758,5 |
Novosibirsk region | 1221,7 | 595609 | 88619,4 |
Omsk region | 939,1 | 357195 | 108147,0 |
Tomsk region | 478,9 | 319795 | 154131,1 |
Chita region | 481,8 | 316690 | 61526,8 |
The Republic of Sakha (Yakutia) | 469,1 | 450823 | 192599,0 |
Kamchatka Krai | 180,9 | 100939 | 92039,1 |
Primorsky Krai | 980,2 | 457446 | 113818,2 |
Khabarovsk region | 721,3 | 437286 | 86913,2 |
Amurskaya Oblast | 424,2 | 384833 | 125392,3 |
Magadan Region | 93,8 | 93758 | 156923,9 |
Sakhalin Region | 277,8 | 207065 | 228624,4 |
Jewish Autonomous Region | 79,8 | 52480 | 75695,8 |
Chukotka Autonomous District | 38,5 | 29615 | 244096,3 |
The work considers the relevance of the research topic. Using bubble charts, the dependence of the gross regional product of federal districts on fixed assets and employment in 2000 and 2012 was investigated. 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 of technological innovation. A grouping of the subjects of the Russian Federation by the elasticity of output by fixed assets has been constructed. The coefficients of correlation between the per capita GRP and the share of a certain type of economic activity in the total GRP of the federal districts have been calculated. A correlation analysis has been carried out between the change in the number of employed in federal districts and the change in real wages in them. The corresponding conclusions are drawn.
real wages
type of economic activity
shower GRP
correlation coefficient
technological innovation costs
release elasticity
production functions
busyness
investments
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The current situation requires the use of various and modern tools for assessing economic development, financial balance, and conditions of competition 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 the cost of resources) as a 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 FD GRP on 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
From the data in Figures 1 and 2, it can be seen that from 2000 to 2012 the gap in the values of the FD GRP increased, there was a slight change in the number of employed in the FD and a significant uneven increase in both the FD and GRP. The production functions of the type were built (where Y is the GRP of the regions; K is the fixed assets; L is the average annual number of fixed assets;, α, β are the 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 the constituent entities of the Russian Federation. When constructing production functions of the economy Russian regions some difficulties arise: the time series are short; the available data are not accurate enough; inaccuracy of price measurement - price jumps in the Russian Federation are orders of magnitude greater than slow changes occurring in developed countries West; data on fixed assets do not correspond to their actually used part.
With the exception of certain cases, the initial data used to construct the production function can be represented by indices, i.e. relative values, at least as follows: ... The Cobb-Douglas function determines the output index Y as a weighted geometric mean of the capital K and labor L indices with weights α and β. The traditional PF is a factor averaging function or can be reduced to such a function by a simple transformation of the initial data. Since Y is an averaging function, it follows that on the graph, the time series of the output index Y should be located between the time series of capital K and labor L.
Rice. 3. Dependence of FD GRP on fixed assets and employment in 2000-2012.
It can be seen from the graph that the GRP cannot be the averaging function of the function connecting Y with K and L, i.e. factors K and L do not fully describe the dynamics of output Y.
Table 1
Calculation of the coefficients of elasticity of the production function for the calculation
Elasticity of the release by OF |
Employment Elasticity of Output |
|
Calculations show that for all federal districts it is necessary to reduce employment at the existing labor productivity, or the maximum possible increase in labor productivity is required (Table 1). It is clear that, in Russia as a whole, it is also not effective to increase the number of employed people at the existing labor productivity.
Thus, we can state the ineffective use of labor resources not only in labor-surplus, but even in labor-deficient subjects.
table 2
Grouping of subjects of the Russian Federation by elasticity of output by PF
Efficiency of issue by OF |
Number of subjects |
3 (Moscow, including the Nenets Autonomous District, the Yamalo-Nenets Autonomous District) |
|
2 (Vologda region, Murmansk region) |
|
3 (Tyumen Region, Khanty-Mansi Autonomous Okrug - Yugra, Primorsky Territory) |
|
19 (KBR, SK) |
|
2 (Kursk region, Republic of Tyva) |
|
3 (RD, KChR, Republic of Mari El) |
|
1 (Republic of Adygea) |
|
Grand total |
For the Chechen Republic in 2012, the value of the elasticity coefficient of the regions' GRP by FF is significantly less than 1, which in the future, in order to increase production efficiency or increase labor productivity, means the need to increase the accumulation rate and, accordingly, reduce the consumption rate.
In total, in 9 constituent entities of the Russian Federation, the efficiency of output in terms of fixed assets is less than 1, which means a positive elasticity of GRP in terms of employment. It is only in these 9 subjects that an increase in employment is justified in order to increase the GRP (Table 2).
One of the options for solving the problem of the absence or inadequacy of data on fixed assets is to use data on investments in fixed assets instead of data on fixed assets.
The advantages of this approach are explained by the high efficiency of investments aimed both at attracting idle funds into circulation and at acquiring new funds, thereby increasing the share of efficiently used capital.
Investment attractiveness is determined by many conditions.
Below we will consider the following conditions: the impact of investment, as well as the combined effect of investment and labor on GRP.
Rice. 4. Dependence of FD GRP on fixed assets and employment in 2000-2012.
It can be seen from the graph that Y can be the averaging function of the function connecting K and L with Y, i.e. factors K and L fully describe the dynamics of output Y (Fig. 4.).
Table 3
Calculation of the elasticity of GRP by investment
Investment elasticity of GRP |
|
Since the investment elasticity of GRP is greater than the employment elasticity of GRP (β = 1-α), it can be concluded 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, Siberian Federal District and the North Caucasus Federal District. Consider the dependence of GRP on investment and costs of technological innovation.
Costs of technological innovation (million rubles) Table 4
Labor productivity elasticity coefficient 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 investment factor plays the main role in increasing labor productivity, and the generation of innovations plays a supporting role. In the Northwestern Federal District, the Ural Federal District and the Southern Federal District, the costs of technological innovations are unreasonably high and cannot be increased. The most effective are the costs of technological innovation 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 Federal District.
Table 5
Correlation coefficients between the per capita GRP and the share of this type of economic activity in the total GRP of the Federal District in 2011
Economic activities |
Correlation coefficient between per capita GRP and the share of a certain type of economic activity in the total GRP |
Agriculture, hunting and forestry |
|
Education |
|
Health care and social services |
|
Hotels and restaurants |
|
Public administration and ensuring military security; compulsory social security |
|
Construction |
|
Wholesale and retail trade; repair of vehicles, motorcycles, household goods and personal items |
|
Production and distribution of electricity, gas and water |
|
Manufacturing industries |
|
Transport and communication |
|
Provision of other communal, social and personal services |
|
Financial activities |
|
Fishing, fish farming |
|
Operations with real estate, rental and provision of services |
|
Mining |
A high inverse relationship between the per capita GRP and the share of agriculture in the total RWP is observed for almost all countries and regions. Another thing is that the high feedback between the per capita GRP and health care and education only testifies to their overestimated share in the 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 us carry out a correlation analysis between the change in the number of employed in the Federal District and the change in real wages in them.
Table 6
Correlation analysis between changes in the number of employees in federal districts and changes in real wages in them
Correlation coefficient between changes in employment and changes in real accrued wages |
|
From the data in the table it follows that in 2010-2012. wages did not function as a stimulator of employment growth, which is largely due to the low share of wages in production costs and insufficiently high growth rates of real disposable cash incomes of the population.
Based on the above, we will draw the following conclusions.
From 2000 to 2012, there was a slight change in the number of employed in the Federal District and a significant uneven increase in both the FF and GRP. Calculations demonstrate the inefficient use of labor resources, which requires a reduction in employment with the existing labor productivity in labor-deficient entities and the maximum possible increase in labor productivity in labor-surplus entities. From 2000 to 2012, labor-saving (intensive) growth has been observed. It is most profitable to increase employment in the Far Eastern Federal District, 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 dynamics of GRP. Investments have the greatest effect in the Central Federal District, then, in decreasing order of efficiency, there are the Urals Federal District, the Southern Federal District, the Northwestern Federal District, the Volga Federal District, the North Caucasus Federal District, the Siberian Federal District, 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 investment factor plays the main role in increasing labor productivity, and the generation of innovations plays a supporting role. In the Northwestern Federal District, the Ural Federal District and the Southern Federal District, the costs of technological innovation are unreasonably high, and they cannot be increased. The most effective costs for technological innovation 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 the per capita GRP and health care and education only testifies to their overestimated share in the 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 function as a stimulator of employment growth, which is associated with low growth rates of real money incomes of the population.
Reviewers:
Gezikhanov R.A., Doctor of Economics, Professor, Head of the Department "Accounting and Audit" of the Federal State Budgetary Educational Institution of Higher Professional Education "Chechen State University", Grozny;
Yusupova S.Ya., Doctor of Economics, Professor, Head. Department of Economics and Production Management, Chechen State University, Grozny.
Bibliographic reference
Magomadov N.S., Shamilev S.R. ANALYSIS OF THE 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 accessed: 01/15/2020). We bring to your attention the journals published by the "Academy of Natural Sciences"
Page 2 of 2
The indicator - gross regional product (GRP) - is used to characterize the results of production in the region, to assess the level of economic development, economic growth and analysis of labor productivity. Total GRP is the cost of all final goods and services produced in the region for the year. According to Keynesian model, the total GRP is calculated using the following formula:
VPP = C + I + S + E - M, (1)
where, С - consumption; I - investments; S - regional and municipal expenses; E - export; M - import.
Formula (1) shows what depends on the economic growth in the country and how you can influence it. The main source of GDP growth is consumption (C) and investment (I). In order to stimulate consumer demand and investment levels, central bank reduces interest rates and the government cuts taxes. An increase in regional and municipal spending (S) also leads to an increase in GDP. To analyze labor productivity and compare regions, GRP per capita is used, which is determined by dividing the total GRP by the population of the region. We considered 80 regions of Russia for 2012-2013. ...
As a result of using the method of principal components, the greatest influence is exerted by specific factors: I, C, S, E, M, which are arranged in descending order of variations. Variation refers to variance and standard deviation. Influencing factors are independent indicators on the right in the equation.
For the total GRP, the following regression equation was constructed, which is significant at the 5% level:
GRP = exp (5.136 + 0.000001 INV_OK + 0.000076 UCH-0.000307 ACP + 0.0095 DOC-0.00008 Z_NIR +0.000013 Z_TEHN) with a correlation coefficient R = 0.82,
where INV_OK is the volume of investments in fixed assets; UCH is the number of personnel engaged in scientific research; ACP - admission and release from graduate school: DOC - admission and release from doctoral studies: Z_NIR - costs of Scientific research; Z_TEHN is the cost of technological innovation.
An increase in INV_OK, UCH, DOC, Z_TEHN gives a positive effect. The greatest effect is brought by an increase in the number of doctors of sciences.
Specific income in the regions of Russia was considered in the article. Regions were divided into clusters. All regions were divided into 4 classes. For each cluster, significant regression equations were constructed.
The influence of per capita food consumption in the regions was considered in the article. In the constructed regression equation, the factors that have a positive effect on GRP per capita are: average per capita annual consumption of meat, milk, vegetable oil, potatoes and vegetables. Factors that have a negative impact on GRP are: consumption of eggs, sugar and bread.
Comparison of the state of education and gross regional product is discussed in the article. According to the constructed regression equation, we can draw the following conclusions: with an increase in the number of students per 1 person out of 10 thousand people, the specific value of GRP per capita in the region will increase by 11.5 rubles; with an increase in investment in education by 1 ruble for each resident of the region, the specific value of the GRP will increase by 16.3 rubles. With an increase in investment in education in the "middle-income" regions by 1 ruble, the GRP per capita increases by 11.69 rubles.
Equations of demographic indicators depending on income and GRP are given in the article. The article presents the clustering of regions by the share of the active population, the unemployed, the share of employees, and per capita income. The volumes of exports and imports in the regions insignificantly affect the GRP, which can be observed by the coefficients of the constructed regression equation:
GRP = exp (5.064-0.00323 IND_P + 0.0013 IND_CX + 0.000001 E-0.000002 M-0.0112 INF +0.0244 UEA) (2)
with a correlation coefficient R = 0.75,
where IND_P is the index of industrial production; IND_CX - index of agricultural production; E - specific exports; M specific imports; INF - inflation index; UEA is the level of economic activity of the population.
Regression equation (2) is significant at the 0.05 level, but the residuals of the equation (the difference between the values of the equation and statistical data) do not correspond to the normal distribution law.
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The economic status of each constituent entity of the Russian Federation makes it relevant to use a variety of tools for assessing economic well-being, financial balance and conditions of competition not only in the domestic but also in the world market. These instruments are extremely necessary for the implementation of an effective federal policy, which is aimed at eliminating the imbalances of the interregional type, at strengthening the integrity of the economy and politics. The independence of the regions leads to the actualization of regional policy and to the importance of such an indicator as the regional gross product.
Information support using GRP
Prosperity becomes a call for the development of regional management decisions with modern approaches to information support and economic feasibility. The optimal basis for analyzing the characteristics of a complex market economy is the system of national accounts, or SNA. At the regional level, the SNA acts in the format of the CDS (system of regional accounts). The central position in the SNA belongs to gross domestic product, or GDP. The regional analogue of GDP in the SNA is the regional gross product, or RWP. This indicator shows the level of economic development, is a kind of reflection of the results of the economic activity of each of the economic entities within the region. GRP is used as the basis for the formation of regional accounts.
Why is GRP calculated?
On the territory of Russia there are about 89 administrative-territorial entities, localized in different time zones, differing in geographic location and level of economic and social development. GDP reflects only the general situation in the country, not allowing to clearly see how things are in different parts of it, which excludes the likelihood of making objective decisions. The state is interested in data that can comprehensively characterize the situation in each separate corner of the country.
Differentiated which is the regional gross product, allows you to develop a suitable economic policy and evaluate the effectiveness of decisions made not at the country level, but at the regional level. With the help of the dynamics of GRP, in combination with cost and physical indicators, it is possible to establish the direction and intensity of economic processes, which can serve as a strong impetus for development at the interregional level. GRP plays big role in the calculations of macroeconomic indicators and in the reform of interregional relations. The indicator serves as a guideline in the process of allocating funds from the Fund for Financial Support of the Subjects of the Region of the Russian Federation.
So what is GRP?
The regional gross product is, in fact, generalized characterizing the level of economic development of the region. It reflects and characterizes the process of production of goods and services. The volume of GRP indicates the value of all goods and services produced in all economic sectors in a particular region. At the first stages of implementation of the indicator in economic analysis the data were published taking into account market prices. The estimate of GRP in the format of basic prices differs significantly from the estimate in market prices exactly by the amount of net taxes on products. Subsidies are not counted. GRP in dominant shops reflects the sum of added values in basic prices with a focus on a certain type of economic activity.
GRP structure, or what is included in it
The gross regional product is calculated taking into account the basic price, which is calculated per unit of goods or services. Taxes are not taken into account, but subsidies on food are taken into account. Gross is calculated in each separate segment of economic activity as the difference between the output of goods or services and their intermediate consumption. For the total price of output of goods and services within one region and is the volume of output. The release includes already sold goods with services for market value... The average is used for the calculation. accounted for in gross output, but only at cost. Intermediate consumption includes the value of goods with services that are fully used in production during the reporting period. Fixed capital is irrelevant for calculating intermediate consumption. Expenditures for the final use of GRP include expenditures on households, on government agencies, and on collective services. Estimating the volume of the gross regional product and its structure, it is possible to determine the sources of financing for final consumption.
Calculation options
In the conditions of the modern economy, it is customary to use several options for calculating the GRP. Manufacturing method the calculation of the indicator is used at the production stage. It is, in fact, the sum of gross value added, which is formed by each institutional resident unit in the area of the economic territory of the region. The gross regional product, the calculation of which is based on the difference between the outputs of goods and services and their intermediate consumption, is formed on the basis of prices for goods and services fully consumed in production, is carried out at the level of industries and sectors of the regional economy. GRP can also be calculated based on current market prices by comparing them.
Difference between GDP and GRP
The gross regional product, which is calculated for each of the regions, has significant differences from GDP. The difference between the indicators is the amount of added value. These include:
- Non-market collective services government agencies: defense, management.
- Non-market services that are financed from the budget, but information about them is not available at the regional level.
- Services financial institutions, whose activities almost always go beyond one region.
- Services related to foreign trade, data on which are collected at the Federal level.
Gross product: features of the indicator
The difference between the indicators of GDP and GRP is formed by the costs of paying taxes in connection with imports and exports. This value is very problematic to calculate due to its specificity and uneven integration between individual regions. The gross regional product by region is calculated over 28 months. The SAC technique allows you to get a faster result. The government uses many mechanisms to track the dynamics and growth of the indicator. An interesting fact is that in total, all GRP indicators do not correspond to GDP, which is determined by the specifics of calculations and the exclusion of additional costs.
On the basis of what data is the GRP calculated?
The multifaceted structure of the gross regional product determines the use of a large number of sources at the same time for calculating parameter values. So, in the CIS countries, experts take into account the registers of enterprises and reports on the production and sale of goods with services, reports on production costs. Sample surveys and special reporting at the regional level are taken into account. The calculation is carried out on the basis of employment reports and on the basis of surveys of each separate segment of the economy, on the basis of a survey of household budgets. Significant sources of information are data tax authorities and banking statistics, reports public organizations and performance data different types budget.
GRP in Russian practice
The gross regional product by regions of Russia fully characterizes the level of development of the region and is compared with indicators of the macro level. It plays the role of a territorial factor in the development of social and economic processes. The calculation of the value is based on the methodological principles of the SNA, the development of which was carried out within the framework of the FSGS. The publication of the results after their preliminary approval is also carried out at the FSSS level.
Forecasting the gross regional product is carried out on the basis of data collected from all residents of the regional economy. These can be corporations, quasi-corporations, and households in which the center of economic interest is located directly in the region under consideration. For the first time, the calculation and analysis of the gross regional product was carried out in 1991 for 21 regions. Since 1993, all regional and territorial authorities took part in the calculations. Since 1995, the assessment and calculation of GRP has been a prerequisite for the implementation of the "Federal Program". Only in 1997, the assessment of the dynamics of the indicator began. It provides the basis for the implementation of the correct economic policy in the sphere of production and industry, which account for 60 to 80 percent of the total GRP in almost all regions.