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UDC 631.115.1:338.431.2.003.12
ASSESSMENT OF IMPACT OF SOCIAL FACTORS ON AGRIBUSINESS DEVELOPMENT
Rezvyakov A.V., Candidate of Economic Science Orel State Agrarian University, Orel City, Russia E-mail: [email protected]
ABSTRACT
The article describes methodical approaches to an impact assessment of social factors on development of the agribusiness as a subsidiary tool applied in development and adjustment of social and economic programs and plans for the rural territories.
KEY WORDS
Social sphere; Agribusiness; Indicator; Development; Rural territories; Labor efficiency.
In recent years, together with the stable growth of the Russian economy, and the problem of finding reserves for production development, interest in theoretical management problems of the social sphere has significantly increased, as it is one of the most important factors of economic development.
The impact assessment of social factors on agribusiness includes determining intensity of influence based on the correlation analysis, and assessing their influence on labor efficiency by calculating losses of the working days.
The author of the article uses such investigative methods information acquisition on social and economic condition of the rural territories in the Oryol region, grouping of the data in tabular forms, systematization of the data on indicators in dynamics, generalization and analysis of the received results.
Step 1. To assess the impact of social factors on the performance indicators 10 indicators were selected, which form a group of factors characterizing the level and quality of life, supply with objects of social and transport infrastructure in 24 districts of Orel region in dynamics for 2008-2010:
• population, pers.;
• the number of registered, but not employed citizens, pers.;
• the number of families with improved housing conditions, units;
• commissioning of apartment houses in the municipality territory, (one thousand sq.m);
• housing supply, (square meters of floor area per person);
• average monthly nominal wages, rub.;
• density of paved roads (km of roads per 1,000 square kilometers);
• security agencies, culture and leisure, units;
• security state and municipal educational institutions, units;
• provision with retail trade, units.
The system of indicators belongs to the author. In the study the author analyzed quite a number of social development indicators of the rural areas in the Orel region (security with social, engineering and transport infrastructure, etc.). The aim is to simplify the process of calculating their impact on the agribusiness development, that’s why using the expert method and logical analysis we selected only 10 indicators which together accurately characterize the social development of a territory. It should be noted that each of the indicators, by itself, is not of great interest. However, an integrated analysis makes it possible to get an accurate picture of current and future development of the region.
Step 2. Selection of indicators characterizing the agribusiness was carried out in the same way (Table 2). The basic principle of selection is conformity with the aims of the study, the existing statistical reporting, and access to the data and their reliability.
Therefore, sampling is based on the data of the Federal State Statistics Service of the Russian Federation, the territorial bodies of the Federal State Statistics Service, the Russia’s
executive authorities of subjects, bodies of local self-government reports on mandatory statistical reporting forms worked out in accordance with the plan of the Federal State Statistics Service of the Russian Federation, and data reporting government employment services [3, 4, 5].
Step 3. Provides calculating the correlation coefficients between the indicators that characterize groups of the social and agribusiness factors.
The problem is that most of the methods are based on a quantitative (economic and statistical) analysis of the level of the region socio-economic development. Meanwhile, a qualitative analysis is of great importance in practice. Thus, it’s appropriate to apply a scale of matching that allows assigning linguistic definitions to intervals of numerical values of the correlation coefficient [2, 6].
Table 1 - A Scale of Matching
Intervals of numerical valuesof the correlation coefficient Linguistic definition
£[0,199....] Absence of influence
[0,21?[0,499...1 Minor influence
[0,5]?[0,799...] Influence of medium intensity
[0,8]?[1] Determining influence
On the basis of the matrix analysis we revealed the social factors affecting deeply certain indicators in agribusiness, in particular, production of agricultural products on industries: population; number of registered unemployed citizens; commissioning of apartment houses in the municipality territory; provision with objects of retail trade; provision with the public and local government educational institutions.
Minor influence or its total absence of a number of social factors was also revealed: number of families with improved housing conditions, security of the population with housing, average monthly nominal accrued payroll.
According to the matrix data, population is one of the social indicators having determining impact on total output of products. The coefficient of their correlation is 0.8. To reason the received results we constructed a diagram of relation of these two indicators. The results are characterized by direct dependence: in case of decrease in population in the Oryol region total production indicators decrease also.
i i Indicators of total production, million rub.
-population. pers.
-Линейная (Indicators of total production, million rub.)
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0,0
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80000,0
70000.0
60000.0
50000.0
40000.0
30000.0
20000.0 10000,0 0,0
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Figure 1 - Interdependence of the social factor with a determining extent of influence with an
indicator of total agricultural production
Table 2 - Coefficients of correlation between social factors and productive indicators of agriculture on the example of the Oryol region for 2008-2010
Indicators of agribusiness
Output volume of agricultural products, thousand rub. Output volume of plant growing, thousand rub. Output volume of animal husbandry Gross collection of grain and leguminous crops, centner Gross collection of sugar beet (factory), centner Gross collection of potatoes, centner Gross collection of vegetables, centner Cattle and a bird on slaughter (in live weight), tons Milk production, tons Egg production, thousand pieces.
population, pers. 0,829569601 0,717887543 0,848258421 0,469104918 0,015221631 0,869341435 0,923076809 0,729755675 0,887385483 0,907776582
the number of registered,
but not employed citizens, pers. 0,883500742 0,874284184 0,791881205 0,685088804 0,622520105 0,699656252 0,718089873 0,724987465 0,770748567 0,636977796
the number of families with improved housing conditions, units 0,197031985 0,170201957 0,203986391 0,040795201 0,164919248 0,007461076 0,064092614 0,187275331 0,106269384 0,153228434
Factors of municipality social development commissioning of apartment houses in the municipality territory, (one thousand sq.m)
0,635281498 0,482101078 0,718120176 0,218543442 0,095074853 0,744108357 0,863677832 0,618436836 0,703434793 0,865542211
housing supply, (square meters of floor area per person) 0,288487593 -0,239191477 -0,306131193 -0,210269142 0,122239507 0,375756771 -0,43077952 0,281475366 0,383922041 0,337474647
density of paved roads
(km of roads per 1,000 square kilometers) 0,608052807 0,618504579 0,527927389 0,491406135 0,494438924 0,409485204 0,334490917 0,447304239 0,567640441 0,297705616
provision with
organizations of cultural and leisure type, units 0,439265996 0,391046049 0,438066212 0,39881067 0,433442342 0,546475096 0,501462011 0,384665025 0,438861167 0,454319505
provision with retail trade, units
0,634490532 0,480951573 0,718020039 0,215685094 0,106336539 0,752700326 0,89010013 0,601827491 0,735115348 0,92496061
average monthly nominal wages, rub.
0,365320588 0,24760029 0,44321175 0,033165683 0,089951669 0,016568367 0,16802071 0,35801775 0,249559416 0,274660567
security state and
municipal educational institutions, units 0,854666289 0,847875631 0,76387699 0,768116509 0,309194822 0,824994148 0,751957155 0,680901999 0,810553192 0,707369994
determining influence;
■ influence of medium intensity;
minor influence;
■ absence of influence
The analysis showed that production indicators with a different degree of intensity are influenced by such social factors as a number of the registered but not employed citizens, commissioning of apartment houses in the municipality territory, density of paved roads, provision with organizations of cultural and leisure type, objects of retail trade, and also the public and local government educational institutions. That is, the indicators characterizing the level and quality of life of rural population, its supply with objects of social and transport infrastructure.
Thus, implementation of the planned actions within the Federal Target Program "Sustainable Development of the Village for 2014-2017 and for the Period till 2020" [1] on further improvement of the rural zone, including the solution of the major social problems, undoubtedly, will have a positive impact on increase and high-quality growth of output in agribusiness.
According dta, if we reduce by 3000/4000 persons, decrease in total production is 100 million rubles on the basis of 33.3 thousand rubles of manufactured production per capita. The unfavorable demographic situation affects the main indicators of social and economic development, in particular, the decrease in growth rates of gross domestic product, provision with labor force, changes in the system of medical care taking into account increase in a share of senior citizens.
IMPACT OF SOCIAL FACTORS ON EFFICIENCY OF A LABOR ACTIVITY
While study it was revealed that the social factors in aggregate have a considerable influence on efficiency of a labor activity. Cumulative impact can have impact on agribusiness development through the worker at the expense of reduction of fund of his working hours, owing to whole-day losses on temporary disability because of diseases and the injuries connected with adverse conditions of work, maintaining a labor-consuming household as an additional source of the income and as an instrument for ensuring of activity, with absence or incomplete social services in transport, water supply, a heat supply, gas supply, road conditions, consumer services and in other indicators worsening a worker’s social position.
Calculation of the working days in a year:
Dw = Dc - Ddo - Dh - Dv - Dorg - Dd - Ddd - Ddi Dad
where Dw - number of the working days in a year;Dc - a number of calendar days; Ddo -a number of the days off; Dh- a number of holidays; Dv - a number of vacation days; Dorg -a number of the days spent by organization the worker for arrival in the organization from home and departure from the to home; Dd - number of days of disability because of unfavorable weather conditions and emergency situations (heavy rains, heavy winds, a thunder-storm, a blizzard, snowstorm, hard frosts, flood and other emergency situations); Ddd- number of days of disability due to diseases; Ddi- number of days of disability due to injuries; Dad - additional days to vacation days, by reason of working under such conditions that do not meet the state standard requirements of labor protection.
Dw = 365 - 52 - 14 -28 - 9 - 11 - 3 = 248 working days in a year
Calculation of working days losses by the reason of diseases and injuries for 20082011:
1 . According to the data "Oryoloblstat" the number of persons employed in agriculture in the Oryol region is:
2008 - 63000 pers.
2009 - 65400 pers.
2010 - 69700 pers.
2011 - 69000 pers.
2. The annual fund of working days of the workers employed in agriculture:
2008 63000 * 248 = 15624000 days.
2009 65400 * 248 = 16219200 days.
2010 69700 * 248 = 17285600 days.
2011 69000 * 248 = 17112000 days.
3. Number of working days lost by persons affected by accident:
2008 (2,3*63000*18) / 1000 = 2608,20 days.
2009 (2,5*65400*18) / 1000 = 2943,00 days.
2010 (2,6*69700*18) / 1000 = 3261,96 days.
2011 (2,5*69000*18) / 1000 = 3105,00 days.
(2.3, 2.5, 2.6, 2,5 - a number of injured people per 1000 employees by years, 18 -minimum duration of treatment, days)
4. Number of lost working days due to diseases:
2008 (892,7*63000* 14) / 1000 = 787361,40 days.
2009 (903,5*65400*14) / 1000 = 827244,60 days.
2010 (878,4*69700*14) / 1000 = 857142,72 days.
2011 (891,2*69000*14) / 1000 = 860899,20 days.
(892.7, 903.5, 878.4, 891.2 - the number of the persons who asked for medical care per 1000 of population by years, 14 - the minimum duration of treatment, days)
5. Losses of the working days as the sum of lost days by the reason of accidents and cases of diseases is characterized by the following data:
2008 100*(2608,20 + 787361,40) / 15624000 = 5,06 %
2009 100*(2943,00 + 827244,60) / 16219200 = 5,12 %
2010 100*(3261,96 + 857142,72) / 17285600 = 4,98 %
2011 100*(3105,00 + 860899,20) / 17112000 = 5,05 %
2008 2009 2010 2011
Figure 2 - Distribution of the lost working days due to social factors by years
Thus, on set of social factors companies receive annually less profit pro rata to lost the working days owing to injuries and diseases. These methodical developments on an impact assessment of social factors on agribusiness development are offered for practical use by the specialists who are engaged in resource allocation for development and support of the social sphere on the rural territories.
REFERENCES
1. The Federal Target Program Concept "Sustainable Development of the Village for 20142017 and for the Period till 2020" approved by the Government Decree of the Russian Federation of 08.11.2012 by No. 2071-r
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3. Oryol region. 2000, 2005-2010: stat. sb./Territorial body of the Federal State Statistics Service in the Oryol region. - 0 75 Oryol, 2011. - 398 p.
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5. Agricultural industry, hunting and hunting economy, forestry in Russia. 2011: Stat.sb. / Rosstat - M. 2011 . - 446 p.
6. Shumetov, V.G. Analysis of data in management: course of lectures Volume 1: Introduction to the data analysis./V. G. Shumetov. - Orel: ORASS, 2004.
