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 gross regional product (GRP) in typical groups of regions. Analysis of the relationship of productive and factor characteristics, index analysis method.
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Course work
On the topic: “Economic-statistical analysis of GRP production in a group of regions”
Plan
Introduction
Chapter 1. The allocation of typical groups of enterprises
1.1 general characteristics 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 the production of GRP 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 of productive and factor characteristics
3.1 Combination grouping
3.2 Correlation analysis
Conclusion
List of sources used
application
Introduction
The main purpose of this course work is to conduct a statistical analysis of social and economic phenomena and processes of the gross regional product of the Central, Southern and Volga Federal Districts.
Socio-economic statistics is a social science and a special branch of practical activity.
The central macroeconomic indicator is the gross regional product. It is the most general indicator of economic activity and the welfare of regions.
Gross regional product - a general indicator of economic activity in the region, characterizing the process of production of goods and services. Gross regional product is calculated in current basic and market prices (“nominal volume of gross regional product”), as well as in comparable prices (“real volume of gross regional product”). Gross regional product is the newly created value of goods and services produced in the region, and is defined as the difference between output and intermediate consumption. The indicator of gross regional product is in its economic content very close to the indicator of gross domestic product. However, there is a significant difference between the gross domestic product (at the federal level) and the gross regional product (at the regional level). The amount of gross regional products for Russia does not coincide with the gross domestic product, since it does not include the added value of non-market collective services (defense, public administration) provided by public institutions to society as a whole. Currently, the calculation of the gross regional product of a subject of the Federation takes 28 months.
The aim of this course project is to conduct a statistical analysis of the gross regional product for a group of regions.
Chapter 1. Identification of typical enterprise groups
1.1 General characteristics of the population
Gross regional product - a general indicator of economic activity in the region, characterizing the process of production of goods and services.
The specifics of Russian conditions, the huge role of the territorial factor in the development of socio-economic processes, the consistent policy of strengthening federalism in Russian statehood necessitate the construction of a developed system of regional-level statistical indicators that meet the requirements of a market economy. System indicators characterizing the development of regions should be methodologically comparable and consistent with the corresponding macro level indicators.
In Russia, the calculation of regional indicators is based on the methodological principles of the SNA. A general indicator of regional development is the gross regional product (GRP). This indicator is built on the basis of a unified methodology developed in a centralized manner in the FSGS. The calculation results are monitored, approved and published in a generalized form by the FSGS.
To observe the intra-annual dynamics of the development of the region’s economy, a calculation of the rate of change in production volumes of the basic sectors of the economy (industry, agriculture, construction, retail and 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 coal mining (Donbass), the district is in third place after the Siberian and Far Eastern regions. But the main prospects economic development The region is associated with the extraction and production of "black gold".
Oil reserves at depths of 5 to 6 kilometers are estimated at 5 billion tons of standard fuel. Drilling the first exploratory well on the Caspian shelf immediately confirmed the serious “fuel” potential of this section. 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.
Southern Federal District is one of the poorest forest resources areas Russian Federation. Are unique recreational resources federal district. The mild climate, the abundance of mineral springs and healing mud, warm sea waters create the richest opportunities for treatment and relaxation. Mountain areas with their unique landscapes have all the necessary conditions for the development of mountaineering and tourism, the organization of ski resorts of international importance here.
The central region is distinguished by a very favorable economic and geographical position, located in the center of the European part of Russia, at the intersection of the most important
The economic complex of the Central region is characterized by a complex combination of material production and non-production sectors. The basis of the region’s economic specialization is engineering, chemical, light industry, flax growing, potato growing, dairy and beef cattle breeding.
The dominant position in the structure of the industry of the region is machine-building, especially high-tech, in need of qualified personnel.
The area is distinguished by transport engineering. A prominent place is also the production of equipment for light, chemical, energy and other industries.
The chemical industry of the region specializes in the production of plastics, chemical fibers, synthetic rubber and tires, mineral fertilizers, varnishes, paints, detergents, etc.
Light industry is the oldest in the region and the largest in the country. Textile production is especially notable: cotton (Ivanovo, Moscow, Tver, etc.), linen (Kostroma, Nerekhta, Vyazma, etc.), silk (Moscow, Tver, Naro-Fominsk), woolen (Moscow, Klintsy, etc.). The sewing, knitting, leather and shoe, fur, printing industries are also developed.
The fuel and energy complex stands out from the service industries of the region, especially the production of electricity (Kostroma, Konakovskaya, Ryazan state district power plants and nuclear power plants - Smolenskaya, Kalininskaya). Brown coal production in the Moscow Region basin sharply decreased. Iron and steel enterprises (Tula, Elektrostal, Moscow) only partially satisfy the region’s metal needs.
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) has an auxiliary value. Smolensk, Kostroma and Tver regions specialize in the cultivation of flax. Pig and poultry farming are developed.
High level of development and huge scale of transportation stands out transport complex district. There is a very dense network of railways, automobiles and pipelines. The role of inland water and air transport is great.
Volga Federal District. A serious drawback is the lack of access to the sea. Of the minerals, the largest reserves of potash salts in the country (Solikamsk-Bereznyaki), oil and non-ferrous metals are distinguished. In the forest-steppe strip - large tracts with fertile chernozem soils.
Machine-building and metal-working industry is the largest branch of industrial specialization of the Volga Federal District. This is the main area of \u200b\u200btransport engineering in Russia. The most developed aerospace industry, and in it the production of military-industrial complex. The head enterprises of this industry are located in Samara, Kazan, Nizhny Novgorod, Saratov, Ufa, Kumertau, Perm and Votkinsk. And their numerous allies are dispersed throughout the district.
Of specializing importance is the production of equipment for the oil and gas refining industries and organic chemistry. The location of these industries is largely close to the largest cities of the district and regional centers (Samara, Kazan, Nizhny Novgorod, Ufa, Perm, Saratov).
Oil industry. Until the end of the 70s. VFD was the main oil-producing region of Russia.
Today, in connection with the large-scale development of the oil resources of the Tyumen region, it moved to the second place in the country in terms of total oil production. Oil production is mainly carried out on the territory of the republics of Tatarstan and Bashkiria and to a much lesser extent in the Kuibyshev, Orenburg regions, and Perm Territory.
Group the regions according to common features. A grouping is a division of the studied social phenomenon into single qualitatively groups according to a number of essential features.
Table 1.1 General characteristics of the population
Number of households |
Area Name |
Gross regional product per 1 employed, thousand rubles |
Average monthly salary, rub |
The capital ratio, thousand rubles |
Employment rate |
Higher and secondary education,% |
|
Tula |
|||||||
Bryansk |
|||||||
Moscow |
|||||||
Vladimirskaya |
|||||||
Ivanovo |
|||||||
Kaluga |
|||||||
Kostroma |
|||||||
Oryol |
|||||||
Ryazan |
|||||||
Smolenskaya |
|||||||
Tverskaya |
|||||||
Moscow city |
|||||||
Yaroslavskaya |
|||||||
Republic of Adygea |
|||||||
Republic of Kalmykia |
|||||||
Krasnodar region |
|||||||
Astrakhan |
|||||||
Volgograd |
|||||||
Rostov |
|||||||
Kirovskaya |
|||||||
Nizhny Novgorod |
|||||||
Orenburg |
|||||||
Penza |
|||||||
Perm region |
|||||||
Samara |
Gross regional product per employed, thousand rubles is calculated as the ratio of the indicator of gross regional product, million rubles. number of employees in the economy, thousand people:
GRP on 1 occupation \u003d GRP / H
Gross regional product (GRP) - a general indicator of the economic activity of the region, characterizing the production process of 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 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 economically active population, thousand people:
This coefficient shows the dependence of employment on demographic factors, i.e. from birth rates, mortality and population growth. This coefficient gives one of the characteristics of the welfare of society.
The proportion of higher and secondary education is calculated as the ratio of the number of higher and secondary education to the number of people employed in the economy, thousand people
HIGH + Chred / H * 100%
Based on the table, we can conclude: 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, the high average annual cost of real estate per worker. Low capital ratio in the Oryol, Smolensk and Yaroslavl regions may mean the lag of enterprises in the use of advanced technologies based on the introduction of new technology, which may ultimately lead to a loss of competitiveness. A high employment rate in all the studied regions indicates a high level of social welfare. The specific weight of the educated population is in no way connected with the level of average wages, which indicates the demand for not only specialists, but also workers without special education. The tallest wage RUR 17,438.3 recorded in the Volgograd region, and the lowest proportion of the educated population is 2.4 in the Moscow region.
1.2 Analytic grouping
To highlight typical groups from the characteristics shown in table 1, it is necessary to choose the most significant one. Most of the characteristics characterize the conditions of production, and the results of activity can be judged by the indicator of gross regional product production. However, the direct division of regions into groups on this basis can lead to a mixture of different types, because, for example, a large volume of gross product can be obtained both due to the large population and other resources with poor use, and through the efficient use of relatively small resources. Since the absolute indicators of the gross product are not comparable, it is advisable to use the relative indicator - GRP per 1 employed in the economy. The value of this feature, obtained by dividing the gross regional product, million rubles the number of people employed in the economy, thousand people
Grouping should begin with a study of the nature of the change in the grouping characteristic; for this, a ranked series of the distribution of regions by gross regional product (GRP) per 1 employed in the economy (Table 2) should be constructed and depicted as Ogiva Galton (Fig. 1).
Table 1.2 - Ranked series of distribution of farms by GRP per 1 employed in the economy
GRP per 1 employed in the economy, thousand rubles |
||
Figure 1.1 - Ogiva distribution of farms by GRP per 1 employed in the economy
In the analysis of the ranked series, the intensity of the change in the value of the grouping attribute from one unit of the population to another is estimated. From table 1.2 it is seen that there are sharp changes and a large separation of a number of units from the entire population. One can see the differences between the regions, between the extreme they reach a twofold value. But the sign in the series changes gradually, smoothly, there are no sharp deviations. and it’s impossible to highlight groups.
In the absence of high-quality transitions in the ranked row, an interval distribution row is constructed. To build it, we divide the population into 6 groups (K \u003d 6). To determine the boundaries of the intervals, we find the step of the interval (h) by the formula:
h \u003d x max -x min / K \u003d 491.1-209.5 / 6 \u003d 47 thousand. rub,
where x max is the maximum value of the sign in the ranked row; x min is the minimum value of the sign in the ranked row.
Table 1.3 - Interval variation series of the distribution of regions by GRP per 1 employed in the economy
Interval boundaries |
Number of holdings 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 - A 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 among groups is uneven. Regions with the size of GRP per 1 employed from 209.5 to 303.3 thousand prevail. rub. Groups with higher GRP are few. It is required to combine them.
Table 1.4 & The intermediate analytical grouping
GRP groups per 1 employed in the economy, thousand rubles |
Number of households |
The average monthly salary, rubles |
The capital ratio, thousand rub |
Employment rate |
Higher and secondary education |
|
Average |
To assess the qualitative characteristics of the groups, we compare them with each other according to the obtained indicators. The first group, rather large in number, differs significantly from all the others in terms of the level of education of the population; here it exceeds by many times the level of education in other groups. Other indicators: average monthly salary, employment rate, capital-labor ratio is lower than in other groups. Therefore, it should be distinguished as the lowest group in terms of productivity and efficiency. Groups 4,5,6 with a higher average monthly salary, greater capital-labor ratio and a large coefficient of employment are few. It is advisable to combine these groups into the highest typical, most productive and effective group. Groups 2 and 3 occupy an intermediate position between the lowest and highest typical groups in almost all indicators; their characteristics are close to each other. They should be combined in the middle typical group.
Further, to characterize the three distinguished typical groups, it is necessary to calculate 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
There is data by region: GRP per employed, capital-labor ratio, employment and population activity rate, unemployment rate, average monthly salary We calculate the average of these indicators and analyze them in typical groups.
Table 2.1 & The level and factors of production of GRP
Indicators |
Typical groups |
Average |
|||
The 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 |
|||||
The capital ratio, thousand rubles |
|||||
The coefficient of economic activity of the population |
|||||
Employment rate,% |
|||||
Unemployment rate in% |
|||||
The average monthly salary, rubles |
The economic activity coefficient is calculated by the formula:
Kak.akt \u003d Chak. Act / H
where is the check. act-the number of economically active population, H-population.
According to table 2.1, it is seen that on average the GRP per 1 employed in the economy in the higher group is more than in the lower group by 402.1-226.4 \u003d 175.7 thousand. rubles, or 175.7 / 226.4 * 100% \u003d 77.6%, while the capital-labor ratio is higher by 1131.0-771.3 \u003d 359.7 thousand rubles, the average monthly salary is higher by 16529.2- 12633.6 \u003d 3895.6 rubles. The unemployment rate in the upper group is lower by 2.5%, and the employment rate is 4.5% higher than in the lower group. These differences in production results and the situation on the labor market are due to the influence of a complex of factors, both economic and natural. It can be concluded that in the regions belonging to the highest group, intensive production is conducted, despite the average coefficient of economically active population of 0.53. The indicators of the middle group occupy an intermediate position, they are closer to the lower group than to the highest. The highest group is most different from the lowest in terms of GRP production per 1 employed in the economy, almost 2 times, and capital-labor ratio by 359.7 thousand. rubles .. Consequently, the high results of the highest typical group were achieved both due to the greater use of labor resources and due to the better armament of the basic production assets, which ensured a high output of gross output and an increase in the standard of living of the population, as evidenced by the high employment rate of the population.
2.2 analysis of production resources in typical groups
The main production assets are the material and technical base of social production. The production capacity of enterprises and the level of technical equipment of labor depend on their volume. The accumulation of fixed assets and increasing the technical equipment of labor enrich the process of labor, give work a creative character, and increase the cultural and technical level of society.
With the advent of a market economy, fixed assets are the main prerequisite for further economic growth due to all factors of intensification of production.
The economic and statistical analysis of fixed assets is aimed at studying changes in their volume, species composition and structure for individual sectors and types of products, regions and types of enterprises.
Table 2.2 - the Structure of fixed assets by industry and type of economic activity
Indicators |
Typical groups |
Average |
|||
The specific gravity of%,%: |
|||||
agriculture |
|||||
extractive industries |
|||||
Manufacturing industries |
|||||
Production and distribution of energy, gas and water |
|||||
building |
|||||
transport links |
|||||
other industries |
|||||
Total OF million rubles |
After analyzing this table, you can see that the regions higher groups have a great advantage over the regions the lowest group in terms of provision with basic production assets (by 5524991 million rubles). As can be seen in the composition of the predominant OF transport links, their specific weight in all groups is an average of 28.9%, the smallest share is made by PF related to construction and agriculture , in all three typical groups it is close to average - 1.3% and 5.4% respectively . RP cost the extractive industries of the higher group reaches 9%, which is 9 times higher than the indicator of the lower group. The processing industry in the lower group amounted to 5.8% compared with the highest -14%. This may be due to environmental conditions. providing the opportunity to develop the mining industry. The production and distribution of energy, gas and water are close in proportion to the upper and lower groups - 6.6% and 5.2%, and significantly differ in the middle group - 10.3%. The remaining indicators of the middle group are close to the average scoop of a post. The largest share, on average 42.6%, is held by the public funds of other industries. It can be: trade, catering, auto business communications, tourism, high technology, etc.
Let us 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 proportion of employees in the three sectors presented does not differ much in typical groups. Thus, the indicators of those employed in agriculture in all groups are close to the average of 50.3%. The proportion of people employed in construction in the higher group exceeds the figure in the lowest, it is 28%, and in the lower - 22%, transport and communications occupy 25.9% in the higher group, 22% - in the lower. The indicators in the middle group are stably similar to the average. Higher indicators in the higher 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 people employed in the economy by ownership.
gross regional product production
Table 2.4 - the structure of employed in the economy by ownership,%
Table 2.4 shows that most of the people employed in the economy work at 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, while state and municipal enterprises account for 16% and 15%. Approximately the same situation is developing in the regions of the middle and lower groups. This suggests that a third of the population is employed in state and municipal enterprises and is provided with stable earnings.
Table 2.5 - indicators of the quality of labor
Analyzing the quality indicators 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 0.5% more, with an average age of 38 ,5 years.
The indicators of the state of fixed assets include the coefficients of depreciation, renewal and worn-out funds.
Table 2.6 - Indicators of fixed assets
The coefficient of renewal of fixed assets.
Shows the degree of updating of fixed assets:
TO about \u003d F new / F con ,
where TO about - the coefficient of renewal of fixed assets;
F new - the cost of new fixed assets put into effect for the period, thousand rubles;
F con -- value of fixed assets at the end of the period.
The renewal coefficient of fixed assets in the upper group is lower than by 1% in the lower group, the depreciation ratio of the public assets is the lowest in the highest group of regions, it is 21.7%. The proportion of worn-out funds in all three groups is close to average - 47%. Based on what we conclude that the fixed assets are worn out enough, and the degree of renewal of public funds 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 efficient use of labor resources in enterprises is of great importance, since in conditions of market relations strong competition between firms is inevitable, which pushes them to constantly improve the quality of their products and reduce production costs. This circumstance, ultimately, changes the requirements for personnel in the direction of increasing their professionalism and creative attitude to work. No matter what technical capabilities open up for the enterprise, it will not work effectively without qualified specialists. Properly selected staff is the basis of the success of the enterprise.
To assess labor productivity, and, consequently, the quality of labor resources, an economic and statistical analysis is used to identify unused reserves and develop proposals for improving production efficiency.
Table 2.7 - Indicators of living standards of the population depending on labor productivity
Indicators |
Typical groups |
Average |
|||
Gross regional product per 1 employed in the economy |
|||||
average per capita cash income, thousand rubles |
|||||
consumer spending per capita, thousand rubles |
|||||
average monthly salary, thousand rubles |
GRP per employee in the economy in the higher group is higher by 402.1-226.4 \u003d 175.7 thousand. rub. than in the lower. The average per capita cash income in the highest group is 6101 thousand rubles higher. than in the lower typical group. Per capita consumer spending in the higher group is higher by 5386 thousand rubles than in the lower group. You can identify the dependence: the higher labor productivity, the higher the salary, and the higher the standard of living of the population.
In the Civil Code of the Russian Federation, the main legal forms are business partnerships, business companies, production cooperatives, state and municipal unitary enterprises.
The legal form of an enterprise depends on a number of features: the procedure for the formation and minimum size of the 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,%
AO or LLP prevail in the lower typical group, they make up 60.7%. The indicators of the middle group are close to average, AO or LLP also prevail - 70.8%. In the highest group, the smallest number is occupied by unitary enterprises - 0.8%, and the largest AO or LLP - 73.7%.
The sectoral structure of the national economy is understood as the totality of its parts (branches and sub-sectors), which has historically developed as a result of the social division of labor. It is characterized by fractional percentages in relation to either the employment of the economically active population or to the produced GDP. The level of socio-economic development of the region is determined by the structure of the economy and has a direct impact on the predominance of a particular sector. The gross regional product (GRP) is traditionally used as a basic indicator of the socio-economic development of individual regions of the Russian Federation, as well as of Russia as a whole, characterizing the structural and economic proportions and the quantitative result of the production of goods and services.
Table 2.9 - The composition and structure of GRP by industry and type of economic activity,%
Indicators |
Typical groups |
Average |
|||
Specific gravity in GRP,% |
|||||
agriculture |
|||||
retail |
|||||
food products |
|||||
non food items |
|||||
paid services |
|||||
Total, thousand rubles |
Table 2.9 shows that the share of agriculture in GRP is the smallest proportion. In the upper group - 9%, in the middle group - 10.3%, in the lower - 14.2%. The largest share is retail. On average, this is 38.3%. The share of trade in food products and non-food products is approximately the same in all three groups and averages about 20%. Paid services make up 12% in the upper group, which is 0.4% more than in the lower group.
As the analysis of statistics shows, at present, the regions with a fuel and raw material base, export-oriented industry, with a fairly developed infrastructure and financial system. Regions with a significant share of the agricultural sector, light and food industries suffered more than others. Since the economic space of Russia is extremely heterogeneous, GRP production is also unevenly distributed across the country. For industry structure national economy Over the past eight years, there has been a tendency towards an increase in the share of industries rendering services and a decrease in the proportion of industries producing goods. Many economists consider such a change in the structure of GDP as a progressive phenomenon, since the Russian economy is approaching the economy of developed countries.
2.4 Index analysis method
The level of labor productivity is characterized by the ratio of the volume of manufactured products or work performed and labor costs. The rate of development of industrial production, the increase in wages and incomes, and the size of the reduction in the cost of production depend on the level of labor productivity.
Under the growth of labor productivity is meant the saving of labor costs (working time) on the production of a unit of production 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 manufacturing a unit of production are reduced under the heading “Salary” main production workers, "and in another, more products are produced per unit 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 index system allows you to solve the problem of changing the structure from changes in quality indicators, and also allows you to identify the influence of factors on the indexed value. The index system is used when comparable products are produced in different areas.
The variable composition index is a relative value characterizing the dynamics of two average indicators for homogeneous populations. This index reflects the influence of two factors:
- change in the indexed indicator of individual objects (parts of the whole);
- a change in the specific gravity of these parts in the general structure of populations.
Fixed composition index - characterizes the dynamics of two averages with the same fixed structure of the population in the reporting period.
The structural shift index is the ratio of two average values \u200b\u200bcalculated for a different structure of the population, but with a constant 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 fixed composition indices and structural shifts
Table 2.10 - Data for the index analysis method
Yn s / x \u003d (GRP s / x / H s / x) / GRP at 1
where Y ns / x is the weight of output per 1 employed in agricultural in the lower group;
dn-specific gravity of GRP in agricultural in the lower group, we take from the table 2.9;
Y labor productivity \u003d Y labor productivity Y structure of variable composition of constant composition index shows. that labor productivity in the higher typical group is 7% higher than in the lower group. The variable composition index depends on the output per 1 employed in certain sectors and the structure of the GRP. Therefore, a change in one indicator occurs due to a change in another.
\u003d 0.009 + 0.819 / 0.017 + 0.757 \u003d 0.828 / 0.774 \u003d 1.07 or 7%,
Chapter 3. Analysis of the relationship of productive and factor characteristics
3.1 Combination grouping
The combination group is achieved by subdivision of all units of the population according to one factor basis, and then subgroups according to the second factor basis are distinguished within the obtained groups.
The capital-labor ratio is an indicator characterizing the degree of armament of the regions with basic production assets.
The factor of capital-labor ratio is represented by a quantitative continuously changing attribute. There are no visible qualitative transitions in its level. The construction and ranking of the series showed that the trait changes from one region to another smoothly, gradually, without sharp jumps ranging from x min \u003d 716.5 thousand rub to x max \u003d 1403.4 thousand rub. We distinguish three groups with a low. medium and relatively high grouping sign.
Define the interval step h \u003d 1403.4-716.5 / 3 \u003d 229 thousand. rub. Then the first group will include regions in the range from 716.5 to 716.5 + 229 \u003d 945.5 inclusive, the second group - from 945.5 to 945.5 + 229 \u003d 1174.5 thousand. rubles, and in the third - from 1174.5 to 1403.5 thousand. rub.
Table 3.1 - Ranked series of distribution of farms by capital-labor ratio employed in the economy
The capital ratio, thousand rubles |
|||
In the same way, two subgroups can be distinguished by the share of worn out funds. The minimum value is 29.4, the maximum is 60%. The interval step is 60-29.4 / 2 \u003d 15.3%. The first subgroup will include regions with a specific gravity of depreciation of funds up to 29.4 + 15.3 \u003d 44.7%, and the second subgroup - from 44.7 to 60%.
Table 3.2 - Ranked series of distribution by the share of completely worn out funds
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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 (not) corresponds to the methodology for the implementation of course projects on statistics.
Course project completed (not) in full.
The following comments are available:
The design is (not) consistent with the organization’s standard.
Theoretical substantiation of the research topic ____________________
Statistical Summary and Grouping ___________________________
Statistical study of dynamics __________________________
Index Analysis __________________________________________
Correlation and regression analysis __________________________
Others ____________________________________________________
The course project after completion is allowed to be defended before the commission.
Ph.D., Associate Professor A.M. Ableeva __________
FSEI HPE “Bashkir State Agrarian University”
Faculty of Economics
Department of Statistics and Information Systems in Economics
on a course project on statistics
Project theme: 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 value of fixed assets per capita on the Gross Regional Product per capita (data for 2005).
Grouping characteristic: 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 with 2005, due to changes in the value 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 with 2005 due to changes in GRP and capital intensity.
Correlation and regression analysis: the effect of the value 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 completion of the course project is 37 academic weeks.
Head: Ph.D., Associate Professor A.M. Ableeva __________
Assignment 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 development trends of a number of dynamics using methods of mechanical alignment, average level, analytical alignment
4.2 Index analysis of the influence of various factors on socio-economic phenomena and processes
5 Correlation and 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 general indicator of economic activity and the welfare of regions.
The aim 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 gross regional product of the federal districts of the Russian Federation
Gross regional product - a general indicator of economic activity in the region, characterizing the process of production of goods and services.
The specifics of Russian conditions, the huge role of the territorial factor in the development of socio-economic processes, the consistent policy of strengthening federalism in Russian statehood necessitate the construction of a developed system of regional-level statistical indicators that meet the requirements of a market economy. System indicators characterizing the development of regions should be methodologically comparable and consistent with the corresponding macro level indicators.
At the regional level, the entire system of accounts is not being built, but only its individual elements. The methodology for constructing regional macroeconomic indicators differs from the methodology for constructing similar indicators at the federal level to the extent of differences in the institutional nature and information base. For these reasons, the sum of regional indicators does not always coincide with the value of the corresponding indicator at the federal level.
In terms of its economic content, GRP approximately corresponds to the GDP indicator calculated by the production method at the federal level. GRP is defined as the sum of the value added of resident units in a given region. Resident units in this case are determined on the basis of the same principles as at the federal level. That is, all corporations, quasi-corporations or households that have a center of economic interest in the economic territory of a given region are residents of the regional economy. If an enterprise engaged in economic activity in the territory of a given region is a branch of the 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 carried out according to 1991 data for 21 territories, based on the transition key method from calculating net tangible product to gross value added. In 1993, according to 1992 data, all the territorial bodies of state statistics participated in experimental calculations of the gross regional product. These calculations were mainly carried out with the aim of introducing the territorial statistical authorities to the transition from calculation of indicators with the main provisions of the national economy balance to calculations according to the SNA. Since 1995, gross regional product calculations have been included in the implementation plan. Federal program statistical work and are mandatory for all regions of Russia. Currently, we have the approved final results of GRP calculations from 1994 to 2002. In 1998, for the first time, GRP growth (decrease) was calculated according to 1997 data by 1996. Currently, we have the dynamics of growth (decrease), since 1997.
The information base, on the basis of which the calculation of the gross regional product is based, is almost identical to the information base of the federal level, since statistical reporting It is formed on the basis of data received from the regions. In this regard, the algorithm for calculating the gross regional product (GRP) coincides with the algorithm for calculating the gross domestic product.
As for the first point, conceptually, the accounting of these services should be carried out at the place of their production (provision), and their value should be included in the volume of GRP of the corresponding region. The volume of these collective services is determined in the amount of the corresponding expenditures of the 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 in the system of regional treasuries in accordance with the current unified budget classification. But the practice of accounting for some expenses of the federal budget as a whole across the country continues to continue, without disaggregating by individual regions, which is mainly due to the inability to determine which region the carried-out expenses can be attributed to (for example, budget expenses for international cooperation, public services debt, etc.), as well as continuing weaknesses 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 by region of the country, as well as with overcoming the shortcomings of regional accounting (incomplete reflection of data in the reports of treasuries) are currently forcing them to be rejected 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 financial intermediary services in modern conditions it is very difficult to correctly take into account by region. Due to the specifics of banking, 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 usually leads a large volume of operations, but at the same time, a Moscow bank or a Moscow branch of a provincial bank can really provide financial intermediation practically throughout Russia. As a result, the territorial statistical authorities have practically no data in order to accurately assess the production of financial services in the region.
Another way is to estimate this volume as a whole for Russia and then to distribute it calculatedly by region. But this way, firstly, requires substantially more detailed and reliable information for the consolidated calculation, and secondly, it is necessary to solve the problem in proportion to what realistically existing indicator it would be possible to reliably distribute these services and, accordingly, the value added of banks by individual regions.
Such an element of calculating GDP as “indirectly measured services of financial intermediaries” is also not possible to distribute in certain territories. As is known, according to the SNA methodology, the cost of these services is included in the intermediate consumption of their recipients. But the issue of allocating the cost of financial intermediary services to the intermediate consumption of specific consumers of these services has not yet been solved even theoretically, their volume is measured indirectly as a whole and, accordingly, is not distributed either by industry or territory.
At present, accounting for the inter-regional exchange of goods and services presents a big problem in regional calculations, which makes it impossible to record the added value of foreign trade for the region with a satisfactory degree of reliability.
It is also obvious that the volume of net taxes on imports in the existing conditions can be estimated only in the whole economy without distribution by region. 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 themselves.
No less problems are associated with regional accounting for net taxes on products. They are due to lack of information in the budget. In particular, in order 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, there is no such data 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 gets through enterprises . It is practically impossible to trace the entire path of such subsidies to the regions; therefore, for a certain part of the net taxes on products, only a general assessment of the economy as a whole can be made.
Thus, for a number of methodological and organizational reasons, a number of important GDP positions 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. Objectively substantiated discrepancy between GDP and GRP amounted to 12.6 percent in 2002.
The GRP indicator as the main composite indicator provides for the coordination of the resulting data for all sectors of the economy.
The calculation of GRP is carried out in several stages. At the first stage, its volume is estimated by the territorial bodies of 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 calculations at the federal level and agreeing on data on GRP and GDP. The relationship between the absolute volume and growth rate (decrease) of the total GRP with data on Russia's gross domestic product is critical condition the formation of this indicator.
Verification and analysis of indicators of output, intermediate consumption and value added in actual prices and in prices of the previous year, conducted by the Federal State Statistics Service, reveals a significant number of errors made by the territorial bodies of state statistics. In addition, the analysis of the quality of the initial information necessary for performing calculations of the added value of economic sectors reveals a large number of errors and necessitates changes in the calculation methodology of individual economic sectors. With the existing organization of settlements, when it is completely impossible to distribute gross domestic product across the territories of the Russian Federation, the calculation of gross regional product is estimated. GDP in Russia as a whole is calculated by three methods and assumes, according to the Regulation on the development and presentation of data on gross domestic product, 4 stages of refinement, the last of which involves making adjustments caused by refinements in the development of an intersectoral balance. At the regional level, the procedure for issuing GRP calculations in conjunction with the interindustry balance is not possible, which leads to the presence of certain errors in the formation of the results of the GRP.
In the Goskomstat of Russia, GRP annual calculations are coordinated by the Office of National Accounts. It, together with sectoral Departments, is developing a methodology for calculating DS of economic sectors. The calculation of DS of industries that are not managed by individual structural units is carried out by the Office of National Accounts.
The collection and processing of calculations is carried out at the GMC using software. At the same time, the compatibility of software used by the GMC and the central apparatus of the GCS was ensured in the regions. According to the calculation of DS of a number of industries, electronic data processing complexes 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 refine and bring the calculations of the base period into methodological compliance with the calculations of the current year. An analysis of the quality of the initial information necessary for performing DS calculations of economic sectors reveals individual errors and necessitates a change in the calculation methodology for some branches of the production account.
After the calculations are completed, 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 Goskomstat of Russia. After this time, edits are not accepted and the indicators take on the status of approved.
The need for a responsible attitude to GRP calculations is caused by the importance of this indicator, since GRP is currently used as the main aggregate for the distribution of funds of the Financial Support Fund of the constituent entities of the Russian Federation. Based on this indicator, the calculation of gross tax resources (ВНР) is carried out (after isolating the closed asset, multiplying by the price index, adjusting the fact of tax collection).
All the above work is carried out on an annual basis. The frequency of development and presentation of data on GRP is fixed 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 regional economy, a calculation is made of the rate of change in production volumes of the basic sectors of the economy (industry, agriculture, construction, retail trade and public catering, transport), which in the structure of production of the regions range from 60% to 80%.
The most important in the industry of the Volga region are highly diversified engineering and the petrochemical complex. The leader agro-industrial complex District is the 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 agricultural development project is able to remove part of the investment risks, traditionally high in agriculture, from the business. An important trend is also the enlargement of production complexes - new agricultural holdings are being formed, which include not only production, but also complexes for processing products, feed production, and a high proportion of grain in the feed composition and high prices for it lead to the involvement of agricultural holdings in the grain market.
The Siberian Federal District includes almost all the regions of the West Siberian and East Siberian economic regions with the exception of the Tyumen region. The Siberian Federal District is famous for solid minerals. Another economic “horse” of the region is the development of territories located in the BAM zone. On this site there is gold, rare metals, copper, coal. The total investment capacity of these projects is 7-10 billion dollars.
The Ural Federal District includes four regions: Kurgan, Sverdlovsk, Chelyabinsk and Tyumen with the Khanty - Mansi and Yamalo-Nenets Autonomous Districts. Ural is a peculiar economic region within Russia.
The Ural Federal District is the richest. About 27% of manganese ores are concentrated here, large reserves of silver, gold, iron ores. Of course, the leader in the region’s economy is 92% gas.
Basis for economic development Sverdlovsk region over the past three centuries have been natural wealth. Agriculture works on the domestic market on the one hand, meeting the needs of the population of industrial centers, on the other hand, individual gardening and horticulture are extremely developed. In crops, cereals and fodder prevail; livestock: dairy - meat, pig, poultry.
In economic terms, the Tyumen region is one of the main regions - donors of the federal budget. The region’s source of economic power is hydrocarbon reserves of world importance of the main strategic and export raw materials of Russia.
The structure of industrial production of the Khanty-Mansiysk autonomous okrug very peculiar: 85% of the total output falls on the fuel industry, 12% on the electric 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 in the Yamalo - Nenets Autonomous Okrug is in the fuel industry. Agriculture is primitive. Of great importance is fishing and fur trade with fur farming.
By the volume of marketable products, the Chelyabinsk Region is among 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 “first” 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. The share of the population is only 5%. The development of Russia in the Far East began in the 50s. 19th century, at about the same time as the areas of the Far West of the United States.
2 Statistical summary and grouping of gross regional product of the federal districts of the Russian Federation
A grouping is a division of the studied social phenomenon into single qualitatively groups according to a number of essential features.
Name of region | 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 |
Udmurt republic | 139995,3 | 764,8 | 1548,6 | 368307 |
Chuvash Republic | 69391,6 | 597,5 | 1295,8 | 253775 |
Perm region | 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 |
Amur region | 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 Oblast | 121014,1 | 277,8 | 529,3 | 207065 |
Jewish Autonomous Region | 14204,2 | 79,8 | 187,7 | 52480 |
Chukotka Autonomous Okrug | 12355,4 | 38,5 | 50,6 | 29615 |
Grouping should begin with a study of the nature of the change in the grouping attribute; for this, a ranked series of the distribution of regions by cost of fixed assets per capita should be built (Table 2) and depicted as Ogiva Galton (Fig. 1).
Table 2 Ranked series of distribution of regions by value of fixed assets per capita
Name of the regions | |
Tyva Republic | 19490 |
Altai Republic | 22026 |
Chukotka Autonomous Okrug | 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 Oblast | 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 |
Udmurt republic | 368307 |
Altai region | 382472 |
Kamchatka region | 384833 |
Amur region | 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 region | 961938 |
Samara Region | 1056262 |
Republic of Tatarstan | 1090879 |
Sverdlovsk region | 1424665 |
Tyumen region | 5405244 |
Chart 1 Distribution of regions of the Russian Federation by the value of fixed assets
According to the schedule of the ranked series, determine the value of an equal or unequal interval.
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 table form.
Table 3 Interval series of 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, it is necessary to group the regions into groups with intervals with open borders according to the following scheme (Table 4).
Table 4 Interval series of distribution of regions of the Russian Federation by the value of fixed assets
Chart 2 Histogram of the distribution of regions by value of fixed assets
Draw up a worksheet, which is necessary to calculate the average cost of fixed assets (table 5).
Table 5 worksheet simple analytical grouping
Groups of regions of the Russian Federation by the value of fixed assets | Name of region | Gross regional product, thousand rubles | The cost of fixed assets, million rubles | Average year population, thousand people | Gross region. product per capita of us, thousand rubles | The 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 Okrug | 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 | |
2 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 Oblast | 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 | |
3 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 | |
Udmurt republic | 139995,3 | 368307 | 1548,6 | 90,4 | 237,8 | |
Altai region | 135686,4 | 382472 | 2554,4 | 53,1 | 149,7 | |
Total for group 3 | 879756,7 | 2330087 | 11178,5 | 78,7 | 208,4 | |
4 group 383000 - 600000 | Kamchatka region | 43974,3 | 384833 | 884,3 | 49,7 | 435,2 |
Amur region | 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 | |
5th 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 region | 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 group 5 | 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 value of fixed assets per capita
The direct dependence of the value of fixed assets per capita on the gross regional product per capita is 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 the performance of a number of dynamics.
Years | GRP. billion rubles | Absolute increase | Growth rate, % | Rate of increase | ||||
Baz | Chain | Baz | Chain | Baz | 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 - the difference between the equations of a number of dynamics, which shows how much one level is more or less than another.
Growth rate - an indicator of the ratio of levels. The coefficient shows how many times 1 level\u003e or< другого.
The growth rate shows how much% is 1 level compared to another.
The growth rate shows how many% one level\u003e or< другого.
Absolute content of 1% increase - shows 1/100 of the absolute level of the subject period.
3.2 Identification of the development trend of a number of dynamics using methods of mechanical alignment, average level, analytical alignment
The method of mechanical alignment (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 escalation method | 3 year moving average method | ||||
Labor productivity | Labor productivity | ||||||
Period | Amount | The average | Period | Amount | 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 |
Chart 3 Mechanical alignment
Average level method (by average growth rate, by average absolute growth)
Table 9 Alignment methods average level
Years | GRP, billion rubles | Serial number | Alignment of values | |
By average odds. growth Yt \u003d 866133.4 * 144.7t | On average abs. growth Yt \u003d 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 Mid-Level Method
The method of analytical alignment (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 average | Trend deviation | |
(y-average) | (y-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 the indicators of the oscillation of a number of dynamics
1. The swing range
R \u003d (Y-Yt) max- (Y-Yt) min
R \u003d 308324.9 - 111230.7 \u003d 197094.2 billion rubles.
2. The standard deviation from the trend
yt \u003d billion rubles
3. The coefficient of oscillation
Vyt \u003d = 0.0001%
4. Stability coefficient
Bush. \u003d 100% - Vyt
Bush. \u003d 100% - 0.0001% \u003d 99.9%
If the coefficient of oscillation 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 dimensions of the level of an economic phenomenon or process. It is based on the identification and characteristics of the main development trend and models of the relationship.
The following forecasting methods are available:
based on average, absolute growth;
average growth rate;
using the trend of analytical alignment.
Calculate point prediction:
Forecast for 2007:
Yt \u003d 2011585.4 + 484273.4t
Y2007 \u003d t \u003d 4
Y2007g.t \u003d 3948679 billion rubles.
Compared with 2006, gross regional product increased by 175948.5 billion rubles.
Forecast for 2008:
Yt \u003d 2011585.4 + 484273.4t
Y2008 \u003d t \u003d 5
Y2008g.t \u003d 4432952.4 billion rubles.
Compared to 2006 gross regional product increased by 660,221.9 billion rubles, compared with 2007. increased by 484273.4 billion rubles.
Muck 2007 \u003d billion rubles
Muk. 2008. \u003d billion rubles
1 = 2.08 * 2.7 = 5,6
2 = 2.08 * 0.9 = 1,9
1 \u003d Y2008-2.8
3.5 Identification of development trends in the ranks of dynamics using Excel IFR
There is data on the gross regional product of the Ural Federal District in 2000 - 2006. in comparable prices.
Table 11 source data
Chart 6 Alignment of a number of dynamics
Graph 7 Linear Function
Graph 8 Logarithmic Function
Graph 9 Polynomial alignment 2 degrees
Graph 10 Power Function
Graph 11 Exponential Function
y \u003d 69581x * 2 - 72378x + 909470
Y \u003d 69581 * 49 - 72378 * 7 + 909470 \u003d 3812293
Y \u003d 69581 * 64 - 72378 * 8 + 909470 \u003d 4783630
Gross regional product in comparison with 2006 in 2007 increased by 39562.5 billion rubles. In 2008 increased by 1010899.5 billion rubles.
4.1 Theoretical aspects of the index analysis method
Futures is a standard exchange futures contract, according to which the parties concluded it undertake to deliver and receive the necessary amount of exchange commodities or financial instruments at a certain 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 houses. That is, in this situation, the seller-buyer system does not work, and the buyer system (plus its brokerage office) is the exchange (settlement and clearing house) seller (and its 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 the 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 the execution of futures contracts. In practice, commodity and financial futures are distinguished. Commodity futures are trade in futures contracts for agricultural products, energy, metals, etc. Financial futures are futures contracts, which are based on financial instruments government and other securities, stock indices, interest on bank ratesas 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, their quantity, quality, delivery time, place of delivery, etc. are stipulated. The futures contract does not initially specify only the price of a standard consignment of goods (financial instrument), which is the subject of exchange trading, and further also is changing. Formally, a futures contract is a supply contract. At the same time, its seller acts as a supplier, and the buyer is a future purchaser of the goods (financial instrument). But futures contracts are usually concluded not for the purpose of physically buying or selling the underlying asset, but for the purpose of insuring (hedging) real transactions with the goods, as well as for obtaining speculative profit during the resale of futures or to liquidate the transaction. So, in some futures, for example, at bank interest rates, instead of buying - receiving a product (financial instrument) or selling and delivering it, monetary compensation for its value may be provided. For a more complete definition of the concept of futures, we compare it with such derivatives securities as a forward and an option. Forward contracts futures contracts for the delivery of physical goods on time at a certain price in the future. Forward contracts, unlike futures contracts, are concluded outside the exchange and only on the real product. This leads to a decrease in the reliability of forwards due to the absence of a third party in the forward trading system that acts as a controller and guarantor. You can participate in exchange trading in futures even without having this product in your hands, which leads to a greater spread 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. At the conclusion of the forward contract, each time it is necessary to conduct negotiations on the terms of the forward, which leads to additional costs of time and money. And to repay the forward contract before the expiration of its validity is possible only through additional negotiations. At the same time, futures can be repaid by opening the opposite position on the exchange. The ability to quickly close a futures transaction leads to the fact that less than 5% of futures contracts ends with the delivery of real goods.
The word index itself means an indicator. Usually this term is used for some generalizing characteristic of changes.
First, indices measure the change in complex phenomena. For example, you need to determine how the expenses of Moscow residents on 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 the monthly reports, multiply the number by the transportation tariff and get the total values. The same needs to be done according to last year. Then compare the amount of expenses 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 dynamics or growth rates, but obtaining and comparing some aggregated values.
Secondly, indices allow you to analyze the change - will 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, indices are indicators of comparisons not only with the previous period, but also with another territory, as well as with standards.
An index is an indicator of comparing two states of the same phenomenon (simple or complex, consisting of commensurate or disparate 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 with 2005, due to changes in the value of fixed assets and through the use of fixed assets (return on assets).
1. The change in the volume of production
Relative:
Iврп \u003d Q1 / Q0 \u003d 3772730.5 / 3091362.9 \u003d 1.22 \u003d 122%
Absolute:
Δ Q \u003d Q1 - Q0 \u003d 681 367.6
2. Change in the volume of production due to changes in capital productivity:
Ivrp / fde \u003d fde1 * F1 / fde * F1
Ivrp / fotd \u003d 0.4 * 9209054 / 0.39 * 9209054 \u003d 1 \u003d 100%
ΔQ / fdec \u003d (fdec1 - fdec0) * F1
ΔQ / fdeq \u003d (0.409676 - 0.389538) * 9209054 \u003d 185451.9
GRP in 2006 in comparison with 2005 due to changes in capital productivity increased by 10%, which amounted to 185,451.9
3. The change in the volume of production due to changes in the value of fixed assets.
Ivrp / f¯ \u003d F1 * fde0 / Ф0 * fde0
Ivrp / f¯ \u003d 9209054 * 0.39 / 7935967 * 0.39 \u003d 1.160419 \u003d 120%
ΔQ / f¯ \u003d (F1 - Ф0) * fdec0
ΔQ / f¯ \u003d (9209054 - 7935967) * 0.39 \u003d 496503.93
Ivrp \u003d Ivrp / fot * 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 in comparison with 2005 due to changes 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 with 2005 due to changes in GRP and capital intensity.
1. The change in the value of fixed assets
Iph¯ \u003d F1 / Ф0 \u003d 9209054/7935967 \u003d 1.16 \u003d 116%
ΔФ¯ \u003d Ф1 - Ф0 \u003d 9209054 - 7935967 \u003d 1273087
The cost of fixed assets in 2006 in comparison with 2005 increased by 16%, which amounted to 1273087
2. The change in the value of fixed assets due to changes in capital intensity
Iph¯ / femc \u003d f capac1 * Q1 / femc0 * Q1
IF / f \u003d 2.4 * 3772730.5 / 2.6 * 3772730.5
ΔФ¯ / f`vol \u003d (fem1 - fem0) * Q1
ΔФ¯ / f` capacitance \u003d -377273,05
The cost of fixed assets in 2006 in comparison with 2005 due to changes in capital intensity decreased by 8%, which is -377273.05 thousand rubles.
3. The change in the value of fixed assets due to changes in the volume of production
Iph¯ / Q \u003d Q1 * fv0 / Q0 * fv0
If / Q \u003d 3772730.5 * 2.6 / 3091362.9 * 2.6 \u003d 1.220410098 \u003d 122%
ΔФ¯ / Q \u003d (Q1 - Q0) * fv0
ΔФ¯ / Q \u003d 1771555.76
If¯ \u003d If¯ / fem * If¯ / 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 in comparison with 2005 due to changes in the volume of production increased by 22%, which amounted to 1771555.76 thousand rubles.
5 Correlation - regression analysis of the influence of factors
There is data on the impact of the value 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 Source data
Name of the 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 |
Udmurt republic | 764,8 | 368307 | 90401,7 |
Chuvash Republic | 597,5 | 253775 | 53552,4 |
Perm region | 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 |
Amur region | 424,2 | 384833 | 125392,3 |
Magadan Region | 93,8 | 93758 | 156923,9 |
Sakhalin Oblast | 277,8 | 207065 | 228624,4 |
Jewish Autonomous Region | 79,8 | 52480 | 75695,8 |
Chukotka Autonomous Okrug | 38,5 | 29615 | 244096,3 |
Table 14.2 Correlation Matrix
At | 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 tightness of the relationship between the resultant (y) and factor signs (x1, x2). The relationship between the average annual number of people employed in the economy and the value of fixed assets (rх1 \u003d 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 \u003d 0.262) is direct and weak.
Table 14.3 Regression Statistics
The multiple correlation coefficient R \u003d 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. The multiple coefficient of determination (R - square) D \u003d 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 Analysis of Variance
df | SS | Ms | F | Significance F | |
Regression | 2 | 8210529,993 | 4105264,996 | 28,69165325 | 3,5367Е-08 |
The remainder | 36 | 5150959,36 | 143082,2044 | ||
Total | 38 | 13361489,35 |
Check the significance of the multiple correlation coefficient, for this we use the F - criterion, for which we compare the actual value of F with the table value Ftable. With the probability of error a \u003d 0.05 and degrees of freedom v1 \u003d k-1 \u003d 2-1 \u003d 1, v2 \u003d nk \u003d 39-2 \u003d 37, where k is the number of factors in the model, n is the number of observations, Ftab. \u003d 4 08. Since Fact \u003d 28.69\u003e Ftable \u003d 4.08, the correlation coefficient means, therefore, the constructed model as a whole is adequate.
Table 14.5 a Regression coefficients
Using table 1.5 we compose the regression equation:
Y \u003d 893.79 + 0.0009X1 - 0.005X2
The interpretation of the obtained parameters is as follows:
a0 \u003d 893.79 - a free term of the regression equation, a meaningful interpretation is not subject;
a1 \u003d 0.0009 - the net regression coefficient at 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 \u003d -0.005 - the net regression coefficient for the second factor indicates that with an increase in 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.
Check the significance of the regression coefficients is carried out using t - student criterion; for this, we compare the actual values \u200b\u200bof the t - criterion with the tabular value of the t - criterion. If the probability of error is a \u003d 0.05 and the degree of freedom v \u003d n-k-1 \u003d 39-2-1 \u003d 36, k is the number of factors in the model, n is the number of observations, ttable \u003d 1.68. We get
t1 fact \u003d 7.14\u003e t \u003d 1.68
t2 fact \u003d -4.67\u003e t \u003d 1.68
Hence, the first and second factors are statistically significant. In this case, the model is suitable for making decisions, but not forecasts.
Table 14.6 Descriptive Statistics
At | 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 |
Amount | 32774,1 | 2,2E + 07 | 4427504 |
Score | 39 | 39 | 39 |
Average values \u200b\u200bof attributes included in the model Y \u003d 840.4%;
x1 \u003d 565930 billion rubles; x2 \u003d 113525.7 thousand rubles.
Standard errors of regression coefficients Sao \u003d 351618.1; Sa1 \u003d 7.4; Sa2 \u003d 1.06
Mean square deviations of signs σY \u003d 592.97%; σx1 \u003d 862796 billion rubles; σx2 \u003d 102996 thousand rubles.
Knowing the average values \u200b\u200band mean square deviations of the signs, we calculate the coefficients of variation to assess the homogeneity of the source data
The variation of the factors included in the model does not exceed the permissible values \u200b\u200b(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 \u200b\u200bthat differ significantly from the average values.
Different units of measurement make the regression coefficients incomparable when the question arises of the relative strength of the effect on the effective sign of each of the factors of pure regression. We express them in a standardized form in the form of beta - coefficients and elasticity coefficients.
Each of the β-coefficients shows how many mean square deviations the average annual number of people employed in the economy will change if the corresponding factor changes by its mean square 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 their mean square deviation; with an increase in gross regional product by 1 quadratic deviation, the average annual number of people employed in the economy will decrease by 0.87 of their quadratic deviation.
Each of the elasticity coefficients shows how many percent the average annual number of people employed in the economy will change 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 gross regional product by 1%, the average annual number of people employed in the economy decreases by 0.67%.
Table 1.7 shows the estimated values \u200b\u200bof the average annual number of people employed in the economy and the deviations of the actual values \u200b\u200bfrom the calculated ones. The calculated values \u200b\u200bare obtained by substituting the values \u200b\u200bof factors of the average annual number of people 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 (negative balances), that is, reserves for increasing the average annual number of people employed in the economy due to factors included in the model, otherwise 9 residues 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 Residues
Observation | Predicted u | 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 No. 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 38 they have reserves for increasing the average annual number of people 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. We will divide 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 aggregate, and the second are the regions where the average annual number of people employed in the economy is higher than the average for the total. Fill table 1.8
Table 14.8 the calculation of the reserves increase the average number of people employed in the economy
Factor | The average value of the factor | Difference between groups | Coefficient of average annual number of people employed in the economy | The influence of factors on the average annual number of people employed in the economy | ||||
1 | 2 | in aggregate | 1 | 2 | 1 | 2 | ||
AND | 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 |
The average annual number of people employed in the economy, thousand people | 435 | 2846,2 | 840,36 | 405,36 | -2005,84 | x | 77 | -2139,1 |
Analyzing the results of table 1.8, we see that in 1 group of regions there is a reserve for increasing the average annual number of people 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 aggregate (565930 billion rubles), the average annual number of people employed in the economy will increase by 30.7%; with a decrease in gross regional product from 1 thousand rubles. up to 113525.7 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 people employed in the economy is 77%. In the second group, the reserve for increasing the average annual number of people employed in the economy due to the factors considered has been exhausted.
Conclusion
In this paper, we 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 for its calculation.
Gross regional product - a general indicator of economic activity in the region, characterizing the process of production of goods and services. Gross regional product is calculated in current basic and market prices (“nominal volume of gross regional product”), as well as in comparable prices (“real volume of gross regional product”). Gross regional product is the newly created value of goods and services produced in the region, and is defined as the difference between output and intermediate consumption. The indicator of gross regional product is in its economic content very close to the indicator of gross domestic product. However, there is a significant difference between the gross domestic product (at the federal level) and the gross regional product (at the regional level). The amount of gross regional products for Russia does not coincide with the gross domestic product, since it does not include the added value of non-market collective services (defense, public administration) provided by public institutions to society as a whole. Currently, the calculation of the gross regional product of a subject of the Federation takes 28 months.
In Russia, the calculation of regional indicators is based on the methodological principles of the SNA. A general indicator of regional development is the gross regional product (GRP). This indicator is built on the basis of a unified methodology developed in a centralized manner in the FSGS. The calculation results are monitored, approved and published in a generalized form by the FSGS.
In terms of its economic content, GRP approximately corresponds to the GDP indicator calculated by the production method at the federal level. GRP is defined as the sum of the value added of resident units in 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 GRP calculation methodology differs from the GDP calculation methodology. When calculating the GRP, a number of elements that include the GDP are not taken into account, so the total GRP of all regions of Russia is less than the country's GDP. These elements are:
1. The added value of industries that provide collective non-market services to society as a whole (public administration, defense, international activities, etc.);
2. The added value of the services of financial intermediaries (primarily banks), whose activities are rarely limited strictly to individual 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 references
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9 Lectures on the statistics course
Applications
Table 1 Initial data for 39 regions of the Russian Federation in 2005
Name of region | Gross region, product, thousand / rub. | employed in the economy, thousand / person | average year. num. population, thousand / person | hundred fixed assets, million rubles |
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 |
Udmurt republic | 139995,3 | 764,8 | 1548,6 | 368307 |
Chuvash Republic | 69391,6 | 597,5 | 1295,8 | 253775 |
Perm region | 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 |
Amur region | 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 Oblast | 121014,1 | 277,8 | 529,3 | 207065 |
Jewish Autonomous Region | 14204,2 | 79,8 | 187,7 | 52480 |
Chukotka Autonomous Okrug | 12355,4 | 38,5 | 50,6 | 29615 |
Table 7 Calculation of indicators of dynamics
Years | GRP. billion rubles | Absolute increase | Growth rate, % | Rate of increase | Absolute. content of 1% growth | |||
Baz | Chain | Baz | Chain | Baz | 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 source data
Table 12.1 Source data
Name of the regions | The 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 |
Udmurt republic | 764,8 | 368307 | 90401,7 |
Chuvash Republic | 597,5 | 253775 | 53552,4 |
Perm region | 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 |
Amur region | 424,2 | 384833 | 125392,3 |
Magadan Region | 93,8 | 93758 | 156923,9 |
Sakhalin Oblast | 277,8 | 207065 | 228624,4 |
Jewish Autonomous Region | 79,8 | 52480 | 75695,8 |
Chukotka Autonomous Okrug | 38,5 | 29615 | 244096,3 |
The paper considers the relevance of the research topic. Using bubble diagrams, the dependence of the gross regional product of the federal districts on fixed assets and employment in 2000 and 2012 was investigated. Using the production functions, the dependence of the gross regional product of the federal districts on fixed assets and employment, on investments and employment, on investments and costs of technological innovations is calculated. A group of constituent entities of the Russian Federation by the elasticity of output by fixed assets is constructed. The correlation coefficients between the per capita GRP and the share of a certain type of economic activity in the total GRP of the federal districts are calculated. A correlation analysis was carried out between the change in the number of employees in federal districts and the change in real wages in them. The corresponding conclusions are made.
real wage
type of economic activity
shower GRP
correlation coefficient
technological innovation costs
release elasticity
production functions
employment
investments
1. Abazova R.Kh., Shamilev S.R., Shamilev R.V. Some problems of urbanization of subjects of the North-Caucasian Federal District // Modern problems of science and education. - 2012. - No. 4. - URL: www..10.2014).
2. Abusheva H.K., Shamilev S.R. Marriages and divorces in the Russian Federation and ways to reduce the latter // Modern problems of science and education. - 2013. - No. 4. - URL: www..10.2014).
3. Musaeva L.Z., Shamilev S.R. Migration to modern Russia: the need for control and optimization // Modern problems of science and education. - 2013. - No. 5. - URL: www..10.2014).
4. Musaeva L.Z., Shamilev S.R., Shamilev R.V. Features of the resettlement of the rural population of the subjects of the North-Western Federal District // Modern problems of science and education. - 2012. - No. 5; URL: October 10, 2014).
5. Regions of Russia. Socio-economic indicators. 2013: stat. Sat / Rosstat. - M., 2013 .-- 990 p.
6. Suleymanova A.Yu., Shamilev S.R. Fertility assessment in the Russian Federation and measures to increase it // Modern problems of science and education. - 2013. - No. 4. - URL: www..10.2014).
7. Shamilev R.V., Shamilev S.R. Analytical and economic rationale for increasing potato production in the Russian Federation and Federal District // Modern problems of science and education. - 2013. - No. 4. - URL: www..10.2014).
8. Shamilev S.R. The dynamics of mortality and factors of its reduction in the Russian Federation // Modern problems of science and education. - 2013. - No. 5. - URL: www..10.2014).
9. Shamilev S.R., Shamilev R.V. Analysis of per capita GRP in the subjects of the North Caucasus Federal District // Modern problems of science and education. - 2011. - No. 6. - URL: www..10.2014).
10. Edisultanova L.A., Shamilev S.R., Shamilev R.V. Problems of optimization of municipalities in the ATD of the subjects of the North-Caucasian Federal District // Modern problems of science and education. - 2012. - No. 5. - URL: www..10.2014).
The current situation requires the use of diverse and modern tools for assessing economic development, financial balance, and competitive conditions in the domestic and world markets.
From this point of view, individual scientists use the production functions (which express the dependence of the result of production on the cost of resources) as the basis for a comprehensive analysis of such macroeconomic characteristics of a market economy as GRP. This explains the relevance of this topic.
Graphically, we show the dependence of the GRP FD on PF and employment in 2000 and 2012.
Fig. 1. The dependence of the GRP FD on fixed assets and employment in 2000
Fig. 2. The dependence of the GRP FD 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 \u200b\u200bof GRF of the federal district widened, there was a slight change in the number of people employed in the federal district and a significant non-uniform increase in both the general form and the regional level of GRP. Production functions of the type were constructed (where Y is the GRP of the regions; K is the fixed assets; L is the average annual number of PFs; α, β are the coefficients) that allow us to consider the efficiency of using labor and PF both at the level of the federal budget and at the level of the constituent entities of the Russian Federation. When constructing the production functions of the economy of the Russian regions, some difficulties arise: the time series are short; the available data are not accurate enough; inaccuracy of price measurements - price jumps in the Russian Federation are orders of magnitude greater than the slow changes occurring in developed countries of the West; data on fixed assets do not correspond to their actually used part.
Except in some cases, the initial data used to construct the production function can be represented by indices, i.e. relative values, at least as follows: . The Cobb-Douglas function defines the output index Y as a weighted geometric mean of the capital indices K and labor L with weights α and β. The traditional FS is a function of averaging factors or can be reduced to such a function by simple transformation of the source data. Since Y is a function of averaging, 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.
Fig. 3. The dependence of GRP FD on fixed assets and employment in 2000-2012.
It can be seen from the graph that GRP cannot be an 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 elasticity coefficients of the production function for calculating
PF Elasticity |
Employment Release Elasticity |
|
Calculations show that for all FDs it is necessary to reduce employment with the existing labor productivity, or the maximum possible increase in labor productivity is necessary (Table 1). It is clear that, as a whole, in Russia it is also not effective to increase the number of employees with existing labor productivity.
Thus, it is possible to state the inefficient use of labor resources not only in labor-surplus, but even in labor-deficient entities.
table 2
Grouping of subjects of the Russian Federation by the elasticity of output in the OF
PF issue efficiency |
Number of subjects |
3 (Moscow, including the Nenets Autonomous Okrug, Yamalo-Nenets Autonomous Okrug) |
|
2 (Vologda Oblast, Murmansk Oblast) |
|
3 (Tyumen Oblast, Khanty-Mansiysk Autonomous Okrug - Ugra, Primorsky Krai) |
|
19 (KBR, SK) |
|
2 (Kursk region, Republic of Tuva) |
|
3 (RD, KCR, Mari El Republic) |
|
1 (Republic of Adygea) |
|
The overall result |
For the Czech Republic in 2012, the value of the coefficient of elasticity of GRP of regions in the RF is substantially 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 all, in 9 constituent entities of the Russian Federation, the effectiveness of the issue of public funds is less than 1, which means a positive GRP elasticity in employment. Only in these 9 subjects is it justified to increase employment to increase GRP (Table 2).
One solution to the problem of the lack 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 consider the following conditions: the impact of investment, as well as the combined effect of investment and labor on GRP.
Fig. 4. The dependence of the GRP FD on fixed assets and employment in 2000-2012.
It can be seen from the graph that Y can be an averaging function of the function connecting K and L with Y, i.e. factors K and L fully describe the dynamics of output Y (Fig. 4.).
Table 3
The calculation of the GRP elasticity of investments
Investment GRP elasticity |
|
Since the GRP investment elasticity is greater than the employment GRP elasticity (β \u003d 1-α), we can conclude that labor-saving (intensive) growth is observed in the period under review. It is most beneficial to increase employment in the Far Eastern Federal District, the Siberian Federal District, and the North Caucasus Federal District. Consider the dependence of GRP on investment and the cost of technological innovation.
Costs of technological innovation (million rubles) Table 4
The coefficient of elasticity of labor productivity from investment |
The coefficient of elasticity of labor productivity from the costs of technological innovation |
|
An analysis of the econometric dependence of labor productivity for the economy of the regions of the Russian Federation shows that innovation factors practically do not predetermine changes in labor productivity (labor intensity). The main role in increasing labor productivity is still played by the investment factor, and the generation of innovations plays a supporting role. In the NWFD, Ural Federal District and the Southern Federal District, the costs of technological innovations are unreasonably high and cannot be increased. The greatest efficiency is the cost 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). Production efficiency in the economy of the Federal District can be improved with the help of massive investments in fixed assets. The correlation coefficients between the per capita GRP and the share of a certain type of economic activity in the total GRF of the federal district are calculated.
Table 5
Correlation coefficients between per capita GRP and the share of this type of economic activity in the total GRF of the Federal District in 2011
Types of economic activity |
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 and social services |
|
Hotels and restaurants |
|
Public administration and military security; compulsory social security |
|
Building |
|
Wholesale and retail trade; repair of motor vehicles, motorcycles, household products and personal items |
|
Production and distribution of electricity, gas and water |
|
Manufacturing |
|
Transport and communication |
|
The provision of other utility, social and personal services |
|
Financial activities |
|
Fisheries, fish farming |
|
Real estate operations, rental and provision of services |
|
Mining |
High feedback between the per capita GRP and the share of agricultural in the total PRP is observed for almost all countries and regions. Another thing, the high feedback between the per capita GRP and healthcare and education indicates only their overestimated specific gravity in the lagging regions (other types of economic activity are absent or poorly developed), i.e. about the deformation of the regional structure of a market economy. We carry out a correlation analysis between the change in the number of people employed in the federal income and the change in real wages in them.
Table 6
Correlation analysis between the change in the number of people employed in the federal budget and the change in real wages in them
The correlation coefficient between the change in employment and the change in real accrued wages |
|
From the table it follows that in 2010-2012. wages did not serve as a stimulator of employment growth, which was largely due to the low share of wages in production costs and the insufficiently high growth rates of real disposable cash incomes of the population.
Based on the foregoing, we draw the following conclusions.
From 2000 to 2012. there was an insignificant change in the number of people employed in the federal district and a significant non-uniform increase in both the general form and the GRP. Calculations demonstrate the inefficient use of labor resources, which requires a reduction in employment with 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 was observed. It is most beneficial to increase employment in the Far Eastern Federal District, the Siberian Federal District, and the North Caucasus Federal District. Fixed assets and employment do not fully describe the dynamics of GRP. It is more correct to use investments to describe the dynamics of GRP. The investments give the greatest effect in the Central Federal District, then, as the efficiency decreases, the Ural Federal District, the Southern Federal District, the North-Western Federal District, the Northern Federal District, the Northern Federal District, the Southern Federal District, and the Far Eastern Federal District go. An analysis of the econometric dependence of labor productivity for the economy of the regions of the Russian Federation shows that innovation factors practically do not predetermine changes in labor productivity (labor intensity). The main role in increasing labor productivity is still played by the investment factor, and the generation of innovations plays a supporting role. In the NWFD, Ural Federal District and the Southern Federal District, the costs of technological innovations 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). Production efficiency in the economy of the Federal District can be improved with the help of massive investments in fixed assets. The high feedback between the per capita GRP and healthcare and education indicates only their overestimated specific gravity in the lagging regions (other types of economic activity are absent or underdeveloped), i.e. about the deformation of the regional structure of a market economy. In 2010-2012. wages did not perform the function of a stimulator of employment growth, which is associated with low growth rates of real cash incomes of the population.
Reviewers:
Gezikhanov R.A., Doctor of Economics, Professor, Head of the Department of Accounting and Audit, 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 DYNAMICS OF GRP OF 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\u003d15467 (accessed: 01/15/2020). We bring to your attention the journals published by the Academy of Natural Sciences publishing house
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 rates and analysis of labor productivity. Total GRP - the cost of all final goods and services produced in the region for the year. According to keynesian model, total GRP is calculated by the following formula:
GRP \u003d C + I + S + E - M, (1)
where, C - consumption; I - investment; S - regional and municipal expenses; E - export; M - import.
Formula (1) shows what the country's economic growth depends on and how it can be influenced. The main sources of GDP growth are consumption (C) and investment (I). In order to stimulate consumer demand and investment levels, central bank cuts 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, С, S, E, M, which are arranged in decreasing order of variations. Variation refers to variance and standard deviation. Influence factors - independent indicators in the equation on the right.
For the general GRP, the following regression equation was constructed, which is significant at the 5% level:
GRP \u003d 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 of R \u003d 0.82,
where INV_OK is the volume of investment in fixed assets; UCH - the number of personnel engaged in research; ACP - admission and graduation from postgraduate studies: DOC - admission and graduation from doctoral studies: Z_NIR - costs for scientific research; Z_TEHN - costs of technological innovation.
A positive effect is given by an increase in INV_OK, UCH, DOC, Z_TEHN. The greatest effect is brought by an increase in the number of doctors of sciences.
Specific revenues in the regions of Russia were considered in the article. The 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, 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 effect on GRP are: consumption of eggs, sugar and bread.
A comparison of the state of education and gross regional product is considered in the article. According to the constructed regression equation, we can draw conclusions: with an increase in the number of students per 1 student out of 10 thousand people, the specific value of GRP per capita in the region will increase by 11.5 rubles .; with an increase in investment in education by 1 ruble for each resident of the region, the specific value of GRP will increase by 16.3 rubles. With an increase in investment in education in the “middle-income” regions by 1 ruble, the GRP per capita increases by 11.69 rubles.
The equations of demographic indicators depending on income and GRP are given in the article. The article provides clustering of regions by the share of active population, unemployed, share of employees and per capita income. The volume of exports and imports in the regions slightly affect the GRP, which can be observed by the coefficients of the constructed regression equation:
GRP \u003d 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 of R \u003d 0.75,
where IND_P– industrial production index; IND_CX - agricultural production index; E - specific export; M specific imports; INF - inflation index; UEA - the level of economic activity of the population.
Regression equation (2) is significant at the 0.05 level, but the rest of the equation (the difference between the equation values \u200b\u200band statistical data) does 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 to assess economic well-being, financial balance and competition conditions not only in the domestic, but also in the world market. These tools are essential for the implementation of an effective federal policy, which is aimed at eliminating imbalances of the interregional type, and strengthening the integrity of the economy and politics. The independence of the regions leads to the updating of regional policy and to the significance of such an indicator as regional gross product.
Information support using GRP
Prosperity is becoming the urge to develop 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 is in the format of the CDS (regional accounts system). The central position in the SNA belongs to gross domestic product, or GDP. The regional equivalent of GDP in the SNA is the regional gross product, or RVP. This indicator shows the level of economic development, is a kind of reflection of the results of economic activity of each of the business 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 located in different time zones, differing in geographical location and level of economic and social development. GDP reflects only the general situation in the country, not allowing to see clearly how things are in its different corners, which excludes the possibility of making objective decisions. The state is interested in data that can comprehensively characterize the situation in each individual corner of the country.
Differentiated by the regional gross product, it allows you to develop a suitable economic policy and evaluate the effectiveness of decisions not at the country level, but at the regional level. With the help of GRP dynamics, in combination with cost and in-kind 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 a large role in the calculation of macroeconomic indicators and in the reform of interregional relations. The indicator serves as a guide in the process of allocating funds from the “Fund for the financial support of the regions of the Russian Federation”.
So what is GRP?
The regional gross product is, in fact, a 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 released goods and services in all economic sectors in a particular region. At the first stages of introducing the indicator into economic analysis, data were published taking into account market prices. GRP assessment in the format of basic prices differs significantly from the market assessment by exactly the amount of net taxes on products. Subsidies are not taken into account. GRP in the dominant workshops reflects the amount of value added at basic prices with a focus on a specific type of economic activity.
GRP structure, or What is included in it
Gross regional product is calculated taking into account the basic price, which is calculated per unit of product or service. Taxes are not taken into account, but subsidies for products are taken into account. Gross is calculated in each individual segment of economic activity as the difference between the output of goods or services and their intermediate consumption. For the total price of the release of goods and services within one region and is the volume of output. The issue includes already sold goods with services at market value. For calculation, the average value is used. accounted for in gross output, but only at cost. Intermediate consumption includes the cost of goods with services that are fully used in production during the reporting period. Fixed capital does not play a role in calculating intermediate consumption. The costs of the final use of GRP include expenditures on households, government agencies, and collective services. Estimating the volume of gross regional product and its structure, it is possible to determine the sources of financing of final consumption.
Calculation options
In conditions modern economy It is customary to use several options for calculating GRP. The production method for calculating the indicator is used at the production stage. It is, in fact, the sum of the gross value added that is formed by each resident institutional unit in the region's economic territory. The gross regional product, the calculation of which is based on the difference between the output of goods and services and their intermediate consumption, is formed on the basis of prices for goods and services fully consumed in production, and is carried out at the level of industries and sectors of the region’s economy. GRP can also be calculated on the basis of current market prices by comparing them.
The 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 value added. This may include:
- Non-market collective services government agencies: defense, management.
- Non-market services financed from the budget, but information about them is not available at the regional level.
- Services of financial institutions, whose activities almost always go beyond the framework of one region.
- Services related to foreign trade, data for which is collected at the Federal level.
Gross Product: Indicator Features
The difference between GDP and GRP is formed by the costs of paying taxes in connection with import and export. This value is very difficult to calculate due to its specificity and uneven integration between individual regions. Gross regional product by region is calculated over a period of 28 months. 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 sum, all GRP indicators do not correspond to GDP, which is determined by the specifics of the calculations and the exclusion of value added.
Based on what data is GRP calculated?
The multifaceted structure of gross regional product determines the simultaneous use of a large number of sources for calculating parameter values. So, in the CIS countries, experts take into account business registers 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 based on employment reports and on the basis of surveys of each individual segment of the economy, based on a survey of household budgets. Significant sources of information are data from tax authorities and banking statistics, reports public organizations and data on the implementation of different types of budgets.
GRP in the practice of Russia
The gross regional product for the regions of Russia fully characterizes the level of development of the region and is compared with macro-level indicators. It plays the role of a territorial factor in the development of social and economic processes. The value calculation uses 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 level of the FSGS.
Prediction of gross regional product is based on data collected from all residents of the regional economy. These may be corporations, quasi-corporations, and households with a center of economic interest located directly in the region in question. The first calculation and analysis of gross regional product was carried out in 1991 in 21 regions. Since 1993, all regional-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, an assessment of the dynamics of the indicator began. It provides the basis for the implementation of the right economic policy in the sphere of production and industry, which in almost all regions account for 60 to 80 percent of the total GRP.