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6STATISTICAL METHODS IN THE ANALYSIS FACTORS OF WELFARE OF THE POPULATION
Abstract
The urgency of the problem under investigation due to the fact that the well-being of the population is the main objective of public policy and priority of long-term socio-economic development of the Russian Federation. The article aims to study the population welfare factors using statistical methods. The leading methods to the study of this problem are: analytical (factor) grouping and multiple correlation and regression analysis. With the help of analytical categories was found a direct correlation between incomes and indicators of socio-economic development of Russian regions. As a result, correlation and regression analysis, the most significant factors affecting the incomes of the population have been established, and a quantitative evaluation of this dependence. Article submissions may be useful for the development of programs for socio-economic development of regions with a view to improving the welfare of the population, as well as in predicting the level of income.
Keywords
well-being, incomes, wages, employment, methods of statistics, correlation, grouping
AUTHOR
Elena Vladimirovna Kabashova
PhD, Associate Professor, Bashkir State Agrarian University.
50 October Str., 34, Ufa, Rep. Bashkortostan, 450001, Russia. E-mail: wesnaele@rambler.ru
Introduction
The level of socio-economic development of a country (region) is determined, first of all, indicators of the level of life (welfare) of the population, namely the size of the incomes of the population, the presence of the living space and other property, as, ultimately, the task any state - is to ensure a decent life of its citizens.
The concept of «well-being» is quite capacious, and one of the most important socioeconomic categories.
Under the well-being refers to:
a) provision of essential material and spiritual wealth, that is, goods, services and conditions that satisfy certain human needs;
b) the availability of the necessary resources for a full life;
c) the value of assets owned by the person or group of persons.
Well-being is expressed in the system of indicators characterizing the standard of living of the population, the main ones include the following:
- Per capita income.
- The availability of real estate, consumer durables, financial assets, housing, comfort
him.
- The volume and structure of consumption of basic food and non-food non-durables and services.
- Availability of work content and working conditions, the structure of motivation and job satisfaction, and others.
- Motivation structure of family and household activities and satisfaction of its duration and structure of leisure (free time) and others.
Materials and Methods
2.1 Methods and models in the analysis
To investigate the level and dynamics of the population's welfare, as well as the factors that determine it in science, the following methods: analytical groups, correlation and regression analysis, forecasting for the regression model, the construction of variational-dynamic tables, modeling of time series, as well as the construction of the cumulative curves of Lorentz (Kabashova, 2015).
An analysis of the scientific literature shows that at the present stage formed the basic methodological position in the field of public welfare. However, not enough investigated and quantitatively comparable influence of various factors on the welfare of the population at the level of individual regions and the country as a whole.
In modern scientific literature devoted to the study of people's welfare, including the income of the population and their differentiation, there are the following models:
- A model of inter-regional differentiation of monetary income of the population of the Russian Federation; regression models depending on the size of households on the level of per capita income, depending on the structure of consumer spending on the level of per capita household income;
- Differentiated model of balance of income and consumption related to different social strata; econometric model of the relationship between differentiation and standard of living in the regions of Russia;
- Depending on the model of average disposable resources of households by type of settlement, the size and composition of the household, the number of workers in the household (Kabashova, 2014).
Research methodology of welfare in general must continuously improve, evolve, adequately reflecting all processes, from the moment of formation of incomes before they are used in real-world conditions of time and place (Kabashova, 2016).
2.2 Literature Review
In contemporary literature the results of the application of statistical methods in the assessment of well-being of the population (including the incomes and inequality), and the factors affecting it. Consider some of them.
Chernova T. in his work provides a comparative analysis of the principal components of inter-regional differentiation of monetary income of the Russian population.
For a comprehensive study of the regional impact of the environment on the differentiation of cash income was selected 23 factors, including: the number of students in higher and secondary educational institutions; the proportion of the male population of the region; specialization by industry regions, providing services and producing goods; the proportion of urban population in the region; unemployment rate; fixed assets in the region; a living wage, and others. An analysis of four major components have been identified: 1) the level of per capita incomes; 2) the concentration of productive capital; 3) the level of development of trade and services; 4) unemployment (Chernova, 2003).
Study of regional differentiation also devoted to working Gerasenko V. Proposed methodology for monitoring the socio-economic development of the region is based on the use of multi-dimensional economic and statistical analysis.
Dubyansky G. for the first time in the economic literature has analyzed the dynamics of the various forms of wages for 1991 - 2001 years. Comprehensive analysis of the history, dynamics and wage issues was held in the year on monthly and measurement, as well as in the sectoral aspect.
Much attention is paid to the analysis of dynamics of correlative indicators on the problem of wage differentiation. The main method of studying the differentiation of wages was the development of the author of the special analytical tables as a major research tool and a consistent chronological analysis.
Surinov A. in the study population (household income) income using correlation and regression and clustering techniques. So, the author reveals the dependence of the size of households on the level of per capita income, dependence on consumer spending patterns the level of per capita household income.
In the study of income differentiation Surinov A., Suvorov A. used differentiated balance of income and consumption, which gives a quantitative description of the size and structure of income, expenditure and consumption of the population belonging to different social strata, and the relationship between these characteristics. Differentiated balance shows the distribution of income by source among groups of households in areas of expenditure and consumption of specific goods and services, while the share of households across the different types of it makes it possible to show the role of different social strata in these processes.
Sheviakov A., Kiruta A. based on regression analysis evaluated the importance of factors of social stratification of the population of Russia. The differentiation of the population by income in 1995, according to their calculations, was 34,1% due to the differentiation of wages, 8,4% differentiation of social transfers and 57,5% differentiation at the expense of business, income from property and other sources.
Scientists conducted an econometric analysis of the relationships between the differentiation and the level of living of the population in the regions of Russia. The dependent variables in the construction of regression models were used indicators of living standards: the nominal per capita income, subsistence minimum, as well as the growth rates of these indicators.
The main explanatory variables considered set of indices: differentiation factor; normal differentiation factor in the case, if all the incomes of the poor have been raised to the minimum subsistence level; half the sum of the Gini-index of differentiation of income and expenditure; population with per capita income below the subsistence level as a percentage of the total population of the region.
Tamashevich V., Bokun H. using correlation analysis investigated the dependence of the average disposable resources of households by type of settlement, the size and composition of the household, the number of workers in the household.
Nikiforov O., Filippov A. offered to analyze the dynamics of the two statistical indicators that in assessing the results of development of the region traditionally emphasizes - of the dynamics of output in sectors of the economy and incomes. However, the use of traditional methods of regression analysis to study the cause-and-effect relationships of variables presented in the form of time series, can lead to false results. To solve this problem, the authors used the method deviations from the trend, which is based on the transformation of the original series of levels in the new variables that do not contain the trend.
Correlation and regression analysis of the factors of territorial differentiation in living standards is given in Chudilin G., Ryabtseva V. Taking as a characteristic of the living
standards of the population of the regions the ratio of cash income and cost of living, free from the influence of prices on consumer goods and services to regional differences, scientists have a complex multi-factor regression models of this indicator.
As arguments, factors are selected as follows: the average annual number of industrial production personnel in the regions; population density; proportion of the population of working age; the proportion of people employed in the private sector; the share of private investment; the share of investments directed to the technical re-equipment of production.
Khidirov R. in their work leads factor analysis indicators incomes. Four main factors have been identified that affect the characteristics of the population's income: the level of development of production; the presence of the country's natural, industrial, technological and financial resources; supply and demand in the labor market; sex and age factors. As a variable remuneration indicators were selected that are used in the calculation of income accounts indicators and indicators of per capita income and expenses: Xi - the wages of employees; X2 - the per capita cash expenditures and savings; X3 - the average monthly nominal wage in the economy; X4 - average per capita income of the population (Khidirov R., 2004).
Currently, estimates of cash income differentiation of the population are mainly used two indicators - assets ratio and the Gini-coefficient. Kolmakov I. offers methodology of calculation and analysis of cash income differentiation of the population on the basis of integrated cash income polarization indices of population estimates.
The standard of living and welfare of the population studied and also from a gender perspective. So, Makasheva G. by the example of regions of the Republic of Kazakhstan carries out a statistical study of the welfare of women. To characterize the regional variation value women welfare indicators used by the standard deviation and coefficient of variation. The indicators characterizing socio-economic situation of women, the author considers the following: life expectancy, the combined share of enrollment of the female population between the ages of 6 to 24 years, average nominal wages, economic activity, the level of long-term unemployment and others. Spend a rating of Kazakhstan's regions in terms of the welfare of women, allowing not only to judge the degree of gaps in the welfare of women at the regional level, but also to determine the extent of the impact of negative factors on their level and quality of life.
2.3 Methodology of the study
In our study, for in-depth study of influence of factors on the level of welfare of the population conducted by the analytical (factor) group that identified the relationship between the studied phenomena and their characteristics.
Grouping is carried out according 75 regions of the Russian Federation for 2014. To ensure uniformity of data of the plurality of regions have been excluded: Moscow, St. Petersburg, Chukotka Autonomous Okrug, Magadan and Sakhalin region.
As of the effective feature we have chosen per capita real incomes, which act as the primary indicators of welfare.
All Russian regions were divided into three groups: group 1 - low income, group 2 -middle-income, group 3 - with high incomes. As independent variables the selected indicators characterizing socio-economic development of the country: Xi - the share of the working-age population, %; X2 - the level of employment, %; X3 - the average monthly nominal accrued wages, rubles; X4 - gross regional product (GRP) per capita, rub.; X5 -investment in fixed capital per capita, rub.
The results of the analytical grouping of Russian regions by per capita money income are presented in table 1.
31 Modern European Researches No 1 / 2017 Table 1. Analytical group of Russian regions by the level of household income
Group Income, rub. Number of regions Average values of attributes
y X1 X2 X3 X4 X5
First 12398,021106,3 29 18449,2 57,3 62,3 22914,3 198591,8 56343,6
Second 21106,329814,7 37 24046,8 57,9 63,2 25915,3 269918,4 75630,0
Third 29814,738523,0 9 33709,9 60,5 67,4 41726,8 541050,1 166156,1
Average X 75 25402,0 58,5 64,3 30185,4 336520,1 99376,6
The third group with the highest income includes the following nine regions: Tatarstan (29830 rub.), The Republic of Komi (30844 rub.), Khabarovsk (31703 rub.), Sverdlovsk region (32157 rub.), Murmansk (34149 rub.), the Republic of Sakha (Yakutia) (34205 rub.), Moscow region (34948 rub.), the Kamchatka Territory (37030 rub.) and the Tyumen region (38523 rub.).
Thus, in the third group of regions compared to low-income first group: average per capita income of more than 82,7%; the proportion of the working population - by 5,6%; employment rate - by 8,2%; average nominal monthly wages - by 82,1%; gross regional product per capita - more than 2,7 times; Investments in fixed capital per capita - nearly three times.
Thus, there is a direct correlation between the level of income of the population and factors of socio-economic development of the country.
To quantify the level of income, depending on the socio-economic and demographic factors, we performed a multiple regression analysis in the whole of the Russian Federation and in the context of three formed groups, thereby expanding the analytical group of regions.
Originally based on a qualitative analysis in regression model included the following factors:
X1 - crude marriage rate, ppm;
X2 - the proportion of urban population to total population in the regions, %;
X3 - the proportion of the working age population in the total population, %;
X4 - employment rate, %;
X5 - average monthly nominal wage, rub.;
X6 - the share of wages in incomes of the population, %;
X7 - the share of income from the property incomes of the population,%;
X8 - GRP per capita, rub.;
X9 - the use of information and communication technologies in organizations, percentage of the total number of organizations surveyed;
X10 - investments in fixed capital per capita, rub.
Multiple regression analysis was performed using Statistica.
One of the conditions for a finding of multiple regression equation is the independence of the factors that disturbed the presence of collinear factors. On the basis of the matrix of paired correlation coefficients presence of collinear factors were found: the X5 and X6, X5 and X8. If factors models are collinear, they duplicate each other and one
of them is recommended to be excluded from the regression. In our case, we exclude from the model X5 factor (the average nominal monthly wage).
In addition, the assessment of the importance of value-added regression coefficients using the Student's t-test showed statistically insignificant and unimportant parameters, hence factors: X4, X7, Xs and X10 have been eliminated from the multiple regression equation.
Thus, in the whole of the Russian Federation after the multiple regression equation dropout factors is as follows:
~ = -97870,72 + 2673,64^ + 113,96x2 +1298,63x3 - 69,74x6 + 202,15x9.
Results of regression analysis by groups of regions are presented in Table 2.
Table 2. Quantitative assessment of incomes of the population depending on factors
Group Coefficient of correlation Coefficient of determination F-test Fisher
The first group 0,716 0,513 4,85
The regression equation ~ = 27990,36 + 430,20^ + 93,262 - 443,84X3 - 55,91x6 + 95,13x9
The second group 0,535 0,287 2,49
The regression equation ~ = -2285,83+2551,20x +1,542 -12,25x3 -93,21x6 + 98,39x9
The third group: Xi 0,631 0,399 4,64
X2 0,013 0,000 0,0001
X3 0,762 0,581 9,70
0,491 0,241 2,23
0,145 0,021 0,15
The regression equation ~ = -249,33 + 3768,59^ ; ~ = 33312,60 + 4,99x2 ; ~ = -39312,15 +1207,86X3 ; ~ = 27905,64 +110,84x6 ; ~ = 5,44 + 0,04x9
Results
As a result of the analytical group is a direct correlation between the level of income of the population and factors of socio-economic development of the country it has been identified. The group was supplemented by multiple regression analysis.
Let us analyze the main results obtained from the coefficients of the regression value-added. With an increase in the overall rate of marriages per mille incomes on average to increase by 2673,64 rubles, at constant other factors included in the equation. This is logical, since the family stimulates the production of higher earnings, the search for new sources of revenue.
Incomes in urban areas is higher than in rural, and therefore an increase in the share of urban population will contribute to the growth of per capita income in the region. Thus, by increasing the proportion of the urban population by 1% on the average per capita income will increase to 113,96 rubles, when other factors immutability. With the increase of the share of the working age population in the total population of 1% income increase by an average of 1298,63 rubles.
The relationship between incomes and the factors included in the regression equation, strong as multiple correlation coefficient is 0,787.
The quality of the constructed model as a whole assesses the coefficient of determination equal to 0,619, that is 61,9% of the variation of per capita income of the population due to the variation of the factors included in the equation.
In general, multiple regression model is significant, statistically reliable and can be used to analyze and forecast income levels.
Discussions
Among modern Russian researchers living standards of statistics, including the well-being of the population, it should be noted: S. Ayvazyan, I. Eliseeva, V. Zherebin, A. Kiruta, L. Nivorozhkina, A. Suvorov, A. Surinov, A. Shevyakova, T. V. Chernova and others.
In statistical science to date formed the basic methodological position in the field of public welfare. However, not enough investigated and quantitatively comparable influence of various factors on the welfare of the population of a particular region and the country as a whole.
Conclusion
Thus, in the analysis of welfare on the basis of economic and mathematical and statistical methods can identify and measure the quantitative relation between the studied parameters and factors influencing them. As a result of analysis (factor) group was found a direct correlation between incomes and indicators of socio-economic development of Russian regions. With the help of correlation and regression analysis it was established the most significant factors affecting the income of the population, and a quantitative evaluation of this dependence.
Recommendations
The results of the research can be used in the development of socio-economic development of regions of programs to improve the well-being of the population, as well as in predicting the level of income. The proposed method can also be used in the teaching of subjects "Statistics", "Econometrics", "Economic and Mathematical Modeling" in the educational process.
REFERENCES
Chernova, T. (2003) Comparative analysis of the main components of the inter-regional differentiation of monetary income of the population // Finances and Credit. №1 (115). P. 50-54.
Galiev, G. M. (2017) Structural changes household income in the region // Scientific and methodical electronic journal "Concept". № 1 (January). URL: http://e-koncept.ru/2017/170009.htm.
Kabashova, E. V. (2016) Using multiple regression models in the analysis of household incomes // Scientific Review. Actual problems and prospects of development of the economy: Russian and foreign experience: scientific review of teachers, graduate students, masters and students of Russian universities. Moscow: Buki, Vedi, P.8-10.
Kabashova, E. V. (2015) On the issues of methodology of the study population in a crisis of profitability I Strategy for sustainable development of the world science: Proceedings of the V International scientific-practical conference of the Eurasian Research Association. Moscow: Eurasian Scientific Association, № 5. P. 101-104.
Kabashova, E. V. (2014) Statistical modeling in the study of the welfare of the population II Modern technologies of management - 2Qi4: proceedings of the international scientific conference, Moscow, Russia. - Kirov: MTSNIP, 2014. P. 35б-3б4.
Khidirov, R. Z. (2004) Factor analysis indicators incomes II Questions of Statistics. № 12. P. 32-35.
Mkhitaryan, V. S. & Bakumenko, L. P. (2011 ) Integral assessment of the quality of life of the population of the Republic of Mari El II Questions of Statistics. № 6. P. 60-б7.
Regions of Russia. Socio-economic indicators (2015): Stat. Sat. I Rosstat. M., 12бб p.
Ustinova, N. V. (2014) Model of income distribution: a theoretical and practical analysis II Scientific and methodical electronic journal "Concept". V. 20. P. 3451-3455. URL: http:IIe-koncept. ruI2014I54954. htm.
PUBLIC RELATIONS IN USSR
Abstract
The relevance of the research problem due to the dynamic development of the PR industry around the world, raising public attention to PR as a science and the institutional approach to the study of PR-technologies. The purpose of this article is to describe the PR activities during the period of the Soviet Union, to distinguish methodological framework of «Soviet PR», and the formulation of the concept of "Soviet PR".A leading approach to the study of this problem was a detailed analysis of the economic ,social and political systems of the USSR and important geopolitical events of the time. In this work, we were able to identify unique Sov'et mechanism PR, to characterize the level at which realized impact on the population , to determine the status of PR objects. The materials may be useful for the history of the development and expansion of the methodological base of the Russian PR.
Keywords
PR industry, PR industry, USSR, USA, institutional development, global political processes, discharge, ideological warfare, propaganda
AUTHOR
Aleksander Vladimirovich Koretskiy
Student, Faculty of Management, Kuban State Agrarian University. 13, Kalinina Str., Krasnodar, 350004,
Russia. E-mail: saskeuchiha7777777@gmail.com
Introduction
The history of science PR in the USSR has its own unique footprint in the global system of "Public Relations" due to many circumstances. It is possible to carry the features of
