Territorial mobility of population in the context of search for happiness

Автор: Fokin Vladislav Yakovlevich

Журнал: Economic and Social Changes: Facts, Trends, Forecast @volnc-esc-en

Статья в выпуске: 4 (28) т.6, 2013 года.

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The author of the article suggests considering the territorial mobility of population as the migration, aimed at finding happiness. Such factors as ‘health’, ‘employment security’, ‘standard of living’, ‘justice’ have been examined as the components of happiness. These factors affect the psychological state of people, their positive or negative feelings. Differentiation of the given factors was revealed to influence the territorial mobility of the population of the studied territories in case of the republics and oblasts of the Volga Federal District, with the two factors, i.e. ‘employment security’ and ‘standard of living’, being decisive in shaping the direction and intensity of migration flows of population in the pursuit of happiness.

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Territorial mobility, migration, factors of happiness, emotions, health, standard of living, employment security, justice

Короткий адрес: https://sciup.org/147223497

IDR: 147223497

Текст научной статьи Territorial mobility of population in the context of search for happiness

The mobility of certain individuals, social communities and population in general, living within the boundaries of various territorial formations, is one of the social processes used in the adaptation practices of the people in response to environmental changes and differentiation.

The term ‘mobility’ has several meanings. According to a well-known English sociologist J. Urry, this term is used to define something that moves or is capable of moving (mobile man, mobile phone, etc.); in the sense of a mob, an unruly crowd; as upward or downward social mobility in a clear cut vertical hierarchy of social positions; and to define migration flows [1, p. 201].

The present article examines mobility processes, manifesting themselves as territorial movement (migration) of people.

The scale of the territorial mobility of population can be observed through migration flows resulted from the change of political forces and factors within the boundaries of the Russian Empire, later the Soviet Union throughout the 20th century.

High rates of urbanization of the Russian Empire in the beginning of the 20th century caused massive relocation of residents of Russian rural areas to cities. This led to the fact that, as Zh. A. Zayonchkovskaya notes, in the 1897–1913 period the number of citizens increased by 55%, while the total population of the country rose by 21% [2, p. 3].

Military actions and associated food shortages in the cities during the First World War and later the Civil War caused part of the urban population to resettle back to rural areas, and compelled defeated participants of the White movement, members of their families and numerous representatives of the social classes, facing repressions, to emigrate from the country.

The migration in the Soviet during the 1920s–1930s was determined by the country’s industrialization, the construction of transportation infrastructure. During the Great Patriotic War, the migration was related to the need to evacuate civilian population rearward, and in later periods it was connected with the implementation of the Party’s and Government decisions to reclamate virgin and fallow lands, Nonblack Soil Zone (the second virgin land), to build the Baikal-Amur Mainline, etc.

In modern conditions the territory of the country is sufficiently developed, but economically it is heterogeneous. Stable peaceful life is observed in most constituent entities of the Russian Federation; however, there are several problem territories, living on which is quite dangerous. Multidirectional migration flows are observed in various constituent entities of the Russian Federation due to the specified circumstances.

A.Yu. Zavalishin defines migration as a behavior constituting a ‘system of economic behavioral practices, based on the determination to use the differences of economic conditions (labour costs, living standards, etc.) in different parts of the country or world, by moving from a less favorable to a more favorable territory in socio-economic terms’ [ 3, p. 24 ] .

The author of the article shares the opinion of A.Yu. Zavalishin with regard to the fact that human behaviour, including the territorial mobility of certain individuals and social groups, is determined by their economic interests and economic way of thinking.

However, the author believes that economic interests of any subject of migration are only one of several reasons for territorial mobility. Therefore, the article suggests to examine the peculiarities of population territorial mobility, with regard to the sensory aspects of this phenomenon, on the basis of J. Urry’s point of view that certain actants depend on specific feelings, and mobility to or out of particular places is founded on certain ways of feeling [4, p. 19], namely when population searches for happiness, and is ready to change or has been changing its position in space.

Therefore, the article solves a number of tasks. Firstly, the analysis of migration flows in the republics and oblasts of the Volga Federal District (VFD) was carried out. Secondly, the intensity and direction of these flows was compared to the rate of ‘happiness indicators’, typical of comparable territories and defining migration attractiveness of the areas.

Ancient thinkers and modern philosophers, sociologists and economists expressed there opinion of happiness. Happiness is understood differently due to the religious beliefs, age, sex, well-being, cultural traditions, etc. of the people, willing to achieve happiness or trying to describe this state of mind.

The author based this study on the ancient Greek philosopher Plato’s conception of happiness, in the first place, and on the work of modern American scientist M. Argyle, in the second place. Plato considered the following five components of happiness: reasonable desires, sound feelings and body, success in activities, good reputation among people and wealth [5, p. 82-83].

When responding to the question how to study and measure happiness, M. Argyle concluded that ‘happiness is a single factor of human experience, but it consists of, at least, three somewhat independent factors: life satisfaction, positive emotions and the absence of negative emotions’ [6, p. 33].

When comparing these statements, a certain correlation can be found between these views, in spite of the huge time gap. All of the happiness components, marked out by Plato, if present in the life of a man of any time period, lead to life satisfaction and the prevalence of positive emotions over negative ones in the man’s life. Thus, an individual experiences happiness according to characteristics distinguished by M. Argyle.

Russian scientists consider the issue of human happiness both in theory and in practice. For example, V.S. Bochko believes that human happiness becomes the target vector of integrative strategic development of the territory, when governing a region. In this case, happiness is manifested in subjective satisfaction with life and is related to life quality. Noting the complexity of the concept of happiness, which includes material, moral and sociocultural components, V.S. Bochko points out the scientific category of a person’s ‘economic happiness’, which is, according to him, ‘a feeling of satisfaction and well-being, manifesting itself in the absence of anxieties about work, the certainty of more or less decent income, accessibility of professional education, guaranteed quality medical care and balanced receipt of other social benefits’ [7, p. 27].

Another representative of the Ural scientific school V.A. Antropov, arguing with V.S. Bochko, denotes the ‘circularity’ of the above definition and the impossibility of its application to diagnose the state of happiness, which, according to V.A. Antropov, is a psychological condition of an individual in the process of achieving the desired goal [8, p. 99-100].

It should be noted that in order to assess the population’s life in the RF regions, A.S. Akh-remenko uses some of the components, contributing to people’s positive emotions, such as the ability to be healthy, to live in a safe environment, etc. [9, p. 593-603].

Obviously, the territories characterized by high life quality are attractive for living and to a certain extent can be considered as areas with high happiness potential. But while A.S. Akhremenko links the ratings of the life quality of the population of the territory to successful governance, the author of the article sets another objective. The assessment of the regions in the context of potential happiness of the people, living on a specified territory, and the study of the extent to which migration activity is connected with ‘the pursuit of happiness’ is a distinctive feature of the approach suggested in this article.

The second important distinctive feature is the suggestion of A.S. Akhremenko to include the region’s migration attractiveness in the calculation to determine the rating of regions by indicators of life quality, among 12 indicators, proposed by him, equating it to coefficients of migration growth. In contrast to A.S. Akhremenko, the author of this article assumes that the rate of migration growth can not be used for the calculations to determine the integral indicator of the territory attractiveness neither in the context of the life quality of the population of the territory, nor in the context of search for happiness when moving from one territory to another. Indicator of migration growth is derived from the variables determining the territory attractiveness (indicators of health, living standards, etc.), and depends on them.

The hypothesis of the research is that high – low satisfaction degree continuum with regard to the conditions of living on a certain territory, contingent on health capacity, life quality, justice as a factor of psychological comfort and employment security, i.e. components affecting the psychological sphere of an individual, determines positive or negative emotions experienced by an individual or a group of individuals and ultimately causes the population of the territory, which is unfavourable for living due to the above characteristics, to decide in favour of relocation.

In order to confirm the suggested hypothesis, the author collected, structured and analyzed the required amount of statistical data and conducted analytical work to identify this dependence. At the first stage of the research, migration flows in the republics and oblasts of the Volga Federal District were analyzed; the average values of the indicators of the migration growth for the 2005–2011 period were calculated (tab. 1) .

All of the necessary calculations were performed afterwards and the intensity and direction of migration flows was compared to the indicators of life expectancy, unemployment rate, purchasing power rate, and the Gini coefficient, i.e. the indicators characterizing health, employment security, well-being, and the degree of psychological comfort of the population in the studied regions (tab. 2 – 6) .

The comparison of the territories is based on statistical data of the Federal State Statistics Service of the Russian Federation for the 2005–2011 period [10]. The seven-year time series is sufficient for dynamic comparisons and allows the possibility of determining trends in the development of the studied processes.

As follows from table 1, in the period under review migration population growth was observed only in 5 out of 14 subjects of the Russian Federation included into VFD, with the highest indicators of the migration growth being registered in the Samara and Nizhny Novgorod oblasts and in the Republic of Tatarstan. The Volga Federal District, as a whole, is characterized by migration decrease. The Kirov and Orenburg oblasts, Perm Krai, Udmurt and Chuvash republics are among the areas with the most negative indicators of migration growth.

The factors of ‘life expectancy’ and ‘rate of diseases (among patients with a diagnosis confirmed for the first time)’ were examined as the indicators characterizing population health in the republics and oblasts of VFD for 2005 and 2011 (tab. 2) .

According to the above data, the indicators of life expectancy have significantly increased in the period under review in all the subjects of the Russian Federation included into the Volga Federal District, without exception. On average, the expected longevity increase in the Volga Federal District makes 4 years.

Table 1. Dynamics of migration indicators in the republics and oblasts of VFD in 2005 – 2011 (persons per 10 000 population)

Territory	2005	2006	2007	2008	2009	2010	2011	Annual average	Rating position
VFD	-8	-7	2	2	5	-12	-4	-3.1
Samara Oblast	65	50	36	40	31	19	26	38.1	1
Republic of Tatarstan	22	36	31	30	32	10	32	+27.6	2
Nizhny Novgorod Oblast	13	11	21	20	15	11	21	+18.9	3
Penza Oblast	21	25	18	9	13	-3	-3	+11.4	4
Republic of Bashkortostan	-3	-6	13	14	17	2	-23	2	5
Saratov Oblast	-16	-8	-3	-5	5	-15	-4	-6.6	6
Republic of Mordovia	3	-2	-8	-21	-10	-9	-41	-12.6	7
Mari El Republic	-1	-16	-16	-18	-10	-31	-33	-17.9	8
Ulyanovsk Oblast	-23	-32	-9	-8	-9	-35	-25	-20.1	9
Chuvash Republic	-44	-37	-14	-10	-8	-27	-22	-23.1	10
Udmurt Republic	-12	-17	-23	-24	-26	-35	-25	-23.1	11
Perm Krai	-49	-46	-30	-25	-27	-46	-3	-32.3	12
Orenburg Oblast	-87	-83	-34	-37	-6	-47	-35	-47.1	13
Kirov Oblast	-86	-80	-56	-45	-37	-54	-42	-57.1	14
Source: the information was collected and classified based on [7, p. 90].

Table 2. Dynamics of health indicators in the republics and oblasts of VFD in 2005 and 2011

Territory	Life expectancy, years		Rating position		Number of diseases per 1000 people		Rating position
Territory	2005	2011	2005	2011	2005	2011	2005	2011
VFD	65.29	69.24	-	-	810.3	877.9	-	-
Republic of Tatarstan	67.93	71.30	1	1	804.5	849.8	6	5
Penza Oblast	65.61	70.23	7	2	848.8	760.9	8	3
Republic of Mordovia	66.66	70.11	2	3	681.8	710.3	1	1
Saratov Oblast	65.87	69.86	6	4	688.4	756.4	2	2
Chuvash Republic	66.34	69.66	4	5	858.6	996.9	11	13
Ulyanovsk Oblast	65.38	69.50	8	6	858.5	946.1	10	11
Kirov Oblast	64.12	69.32	11	7	739.9	788.5	4	4
Republic of Bashkortostan	66.59	69.04	3	8	807.0	860.6	7	7
Samara Oblast	66.05	69.02	5	9	855.3	1019.1	9	14
Udmurt Republic	64.43	68.88	10	10	893.7	947.7	13	12
Nizhny Novgorod Oblast	63.42	68.48	13	11	739.5	881.9	3	9
Orenburg Oblast	64.86	68.31	9	12	891.4	851.9	12	6
Mari El Republic	63.53	68.31	12	13	743.3	863.5	5	8
Perm Krai	62.28	67.52	14	14	894.4	937.3	14	10

Source: the information was collected and classified based on [7, p. 346].

However, these indicators varied considerably in 2005, as well as in 2011 in the republics and oblasts of the Volga Federal District (5.75 per year in the first case and 3.78 year in the second case, respectively between the Republic of Tatarstan and the Perm Krai, having polar values of these indicators). Comparing the rating values of the analyzed indicators, it is possible to see that the Republic of Tatarstan, the Republic of Mordovia, and the Penza Oblast were among the most prosperous territories in 2011. The indicators of the Republic of Bashkortostan, the Samara and Orenburg oblasts have worsened. Perm Krai, the Mari El Republic, the Nizhny Novgorod Oblast and the Udmurt Republic were among outsiders (demonstrated the worst indicators).

The table shows the dynamics of disease per 1000 of population. A number of authors suggest using this indicator to assess the territory competitiveness [11]. But when using this indicator, contradictions arise with regard to its estimation. On the one hand, growing number of diseases may indicate health deterioration of the region’s population. But, on the other hand, this may be the result of the improving performance of the regional health services, the advancement of diagnostic procedures, a greater number of people, covered by medical examination, etc. Therefore, this parameter of evaluating population health will not be taken into account in further calculations.

Living standards, characterized by the ability of an individual to maintain the required level of life activities, buying necessary goods and services, durables, as well as housing, transport, luxuries, high-status articles, undoubtedly affects the person’s satisfaction with life and his/her emotions. This in turn largely affects man’s happiness or unhappiness. At the same time these feelings are highly individual in each case, and sometimes ‘the rich cry too’.

In order to make an objective comparison of living standards in the studied subjects of the Russian Federation included into the Volga Federal District, the percentage ratio of the fixed market basket price1 to the nominal accrued wages of workers was determined.

As a result, the values characterizing the purchasing power of population in the studied regions have been obtained (tab. 3) .

According to the author, this indicator rather objectively reflects living standards of the population of the analyzed regions, as it takes into account the amount of wages, and prices of goods and services, sold within the boundaries of the compared territories.

When comparing the living standards of the population of the studied territories, the author assumes that the higher the ratio of fixed market basket price to wages, the lower the purchasing power, hence, the living standards of the region’s population. In such a case, people are not able to save (accumulate) money, spending it only on truly vital needs. Conversely, low percentage of the fixed market basket price indicates a higher well-being of people, the possibility of population to incur expenditures on education, purchase of real property, cars, payment of travel expenses, luxury goods, etc.

As follows from table 3, the percentage ratio of the fixed market basket price to the amount of wages for the analyzed period in the Volga

Federal District as a whole and in all constituent regions has decreased considerably, which enables to conclude that population living standards in all regions of the Volga Federal District have been rising. For example, the population of the Republic of Tatarstan (the most prosperous in terms of the given indicator) spent 56% of wages for the most necessary products and services in 2005, but only 39% in 2011.

Population expenses on the fixed market basket of goods and services decreased from 79 to 60% in the Republic of Mordovia, which is at the bottom of the rating. Thus, the reduction of the population expenditures on the fixed set of goods and services amounted to 17, and 19% respectively.

At the same time, the regional differentiation of population purchasing power practically remained the same, which is illustrated by the following facts. The gap between the Republic of Tatarstan ranking first by the purchasing power indicator, and the Republic of Mordovia which is at the bottom of the rating, made up 23% in 2005, and 21% in 2011.

Table 3. Dynamics of the purchasing power of population incomes in the republics and oblasts of VFD in 2005 and 2011

Territory	Nominal accrued wages of workers, rubles per month*		Fixed market basket price, rubles per month**		The ratio of fixed market basket price to wages, %		Rating position
Territory	2005	2011	2005	2011	2005	2011	2005	2011
VFD	6473.3	17543.6	4225.1	8180.7	65	47	-	-
Republic of Tatarstan	7067.8	20009.4	3939.9	7797.3	56	39	1	1
Republic of Bashkortostan	6612.0	18397.0	3966.6	7939.9	59	43	2	2
Orenburg Oblast	6163.5	17024.9	3898.1	7726.3	63	45	5	3
Nizhny Novgorod Oblast	6533.4	18492.4	4502.0	8608.1	69	47	7	4
Saratov Oblast	5439.3	16204.7	4056.6	7545.0	75	47	10	5
Perm Krai	7748.9	18773.3	4725.5	9215.2	61	49	3	6
Udmurt Republic	6373.3	15843.3	3945.8	7821.9	62	49	4	7
Penza Oblast	5206.8	16362.2	4054.3	8027.5	78	49	13	8
Samara Oblast	7764.9	18600.3	5086.2	9325.1	66	50	6	9
Ulyanovsk Oblast	5343.8	15008.6	4105.7	7927.8	77	52	12	10
Chuvash Republic	5073.1	14896.3	3754.4	7822.2	74	53	8	11
Mari El Republic	4938.2	14001.2	3740.0	7633.3	75	54	9	12
Kirov Oblast	5695.8	14579.0	4330.4	8559.5	76	59	11	13
Republic of Mordovia	5060.7	13305.1	4012.7	7985.0	79	60	14	14

Source: the information was collected, classified and calculated independently based on [7, p. 171*; p. 956**].

The purchasing power disparity in the regions has changed only by 2% in seven years indicating the stability of this phenomenon in Russia.

The sense of justice, felt by an individual, particularly concerning the fair income distribution in society, has a great influence on human emotions and emotional overtones, hence, on the person’s happiness or unhappiness. The Gini coefficient (index of income concentration), estimated for the region, allows the fairness of income distribution to be assessed. The Gini coefficient characterizes the deviation degree of the line of actual total income distribution from the line of equal income distribution. The coefficient value may vary from 0 to 1, the higher the value, the more uneven the distribution incomes. Obviously, an ordinary man does not survey data books and does not possess exact information on the subject. Nevertheless, he/she eagerly absorbs information concerning the surrounding reality and sharply feels income inequality in society, when assessing the quality of housing, cars, leisure time activities, etc. of other people.

As follows from table 4, the gap between the rich and the poor has been widening in all the studied territories. About one third of the regions has maintained the same rating position with regard to income inequality in the sevenyear period (the Samara and Kirov oblasts, Perm Krai, the republics of Tatarstan and Bashkortostan). The rating positions of other regions have changed insignificantly. Faster rates of social stratification by income level are observed in the regions with the initially low Gini coefficient (the Penza, Nizhny Novgorod and Kirov oblasts, the Udmurt and Mari El Republics). However, the growth rates of the Gini coefficient have decreased in the Samara, Perm and Ulyanovsk oblasts, the republics of Tatarstan and Bashkortostan, i.e. the territories with the highest income inequality.

Unemployment level has been already mentioned as one of the indicators of population employment security. Lower unemployment rate results in a higher level of employment security, as in this case the sellers of labour services reinforce their positions: when the unemployment is low, they have better chances of increasing wages and improving working conditions. Growing unemployment in the regions leads to reverse processes.

The statistical indicators characterizing general unemployment dynamics and disparity in the republics and oblasts of the Volga Federal

Table 4. Dynamics of the Gini coefficient in the republics and oblasts of VFD in 2005 and 2011

Territory	Gini coefficient		Rating position		Absolute growth 2011–2005	Rating position by absolute growth
Territory	2005	2011	2005	2011	Absolute growth 2011–2005	Rating position by absolute growth
Kirov Oblast	0,323	0,362	1	1	0,039	11
Chuvash Republic	0,335	0,363	4	2	0,028	8
Republic of Mordovia	0,337	0,367	5	3	0,030	9
Udmurt Republic	0,324	0,372	2	4	0,048	13
Saratov Oblast	0,353	0,375	7	5	0,022	6
Penza Oblast	0,330	0,378	3	6	0,048	14
Orenburg Oblast	0,357	0,382	9	7	0,025	7
Ulyanovsk Oblast	0,368	0,387	10	8	0,019	3
Mari El Republic	0,356	0,388	8	9	0,032	10
Nizhny Novgorod Oblast	0,350	0,393	6	10	0,043	12
Republic of Tatarstan	0,391	0,411	11	11	0,020	4
Republic of Bashkortostan	0,405	0,426	12	12	0,021	5
Perm Krai	0,420	0,426	13	13	0,006	2
Samara Oblast	0,438	0,442	14	14	0,004	1
Source: the information was collected, classified and calculated independently based on [7, p. 183].

District in 2005 and 2011 (tab. 5) , show that the overall unemployment rate throughout the Volga Federal District decreased by 0.7%. But region-wise the changes were extremely uneven: while in the Chuvash Republic the unemployment rate reduced by 3.7% (from 11.4 to 7.7%), in the Kirov Oblast it increased by 1.3% in absolute terms (from 7.1 to 8.4%).

The author notes that the Kirov and Nizhny Novgorod oblasts, Perm Krai, the Mari El Republic and the Republic of Bashkortostan are at the bottom of the rating of overall unemployment rate in 2011 and its decline in the 2005–2011 period, with the regions taking 9th–14th positions, except for the Chuvash Republic, which ranked 12th by the unemployment level in 2011, but has the fastest rate of unemployment decrease.

In 2011 the number of the unemployed in six regions of the Volga Federal District at the top of the rating decreased faster than in the regions, which were among outsiders, except for the Samara Oblast, in which the unemployment rate declined slowly, due to the fact that in 2005 the oblast was one of the leaders in this indicator among the republics and oblasts of VFD.

In order to prove the thesis that it is the employment security and living standards in the regions that are the crucial factors in the decision to relocate, table 6 was filled in by the certain algorithm:

1. The rating values of the republics and oblasts of VFD by the indicators of life expectancy, the ratio of the fixed market basket price to wages, the Gini coefficient and overall unemployment for 2005 and 2011 were transferred from tables 2, 3, 4 and 5 to table 6 .
2. The average rating values of the above indicators for the studied periods were estimated.
3. The average rating values of the indicators for 2011 were compared with the rating values of the indicators of the average annual population growth in the regions.

The author highlights that table 6 comprises the average rating values of the indicators both for 2011 and for 2005, making it possible to observe the dynamics of the compared indicators, and to consider its effect on the rate of population territorial mobility in the studied regions.

Table 5. Dynamics of overall unemployment in the republics and oblasts of VFD in 2005 and 2011

Territory	Overall unemployment, %		Rating position by unemployment		2011 to 2005 unemployment growth rate, %
Territory	2005	2011	2005	2011	2011 to 2005 unemployment growth rate, %
VFD	7.3	6.6	-	-	-0.7
Republic of Tatarstan	6.7	4.7	4	1	-2.0
Samara Oblast	5.3	5.1	1	2	-0.2
Republic of Mordovia	7.0	5.2	5	3	-1.8
Penza Oblast	6.5	5.3	3	4	-1.2
Saratov Oblast	9.1	6.0	11	5	-3.1
Orenburg Oblast	9.4	6.3	12	6	-3.1
Ulyanovsk Oblast	7.7	6.9	9	7	-0.8
Udmurt Republic	7.8	7.0	10	8	-0.8
Nizhny Novgorod Oblast	6.0	7.2	2	9	1.2
Perm Krai	7.0	7.6	6	10	0.6
Republic of Bashkortostan	7.1	7.7	7	11	0.6
Chuvash Republic	11.4	7.7	14	12	-3.7
Kirov Oblast	7.1	8.4	8	13	1.3
Mari El Republic	9.9	10.0	13	14	0.1

Source: the information was collected, classified and calculated independently based on [7, p. 171; p. 956].

Table 6. Bridge table of the rating values of the factors affecting population emotions to migration growth in the republics and oblasts of VFD in 2005 and 2011*

Territory	Rating position								Average values of rating positions by four indicators		Rating position
	Life expectancy		Ratio of the fixed market basket price to wages		Gini coefficient		Overall unemployment		Average values of rating positions by four indicators		Average rating values by four indicators in 2011	Annual average migration growth in 2005–2011
	2005	2011	2005	2011	2005	2011	2005	2011	2005	2011	Average rating values by four indicators in 2011	Annual average migration growth in 2005–2011
Republic of Tatarstan	1	1	1	1	11	11	4	1	4.25	3.50	1	2
Saratov Oblast	6	4	10	5	7	5	11	5	8.50	4.75	2	6
Penza Oblast	7	2	13	8	3	6	3	4	6.50	5.00	3	4
Republic of Mordovia	2	3	14	14	5	3	5	3	6.50	5.75	4	7
Orenburg Oblast	9	12	5	3	9	7	12	6	8.75	7.00	5	13
Udmurt Republic	10	10	4	7	2	4	10	8	6.50	7.25	6	11
Chuvash Republic	4	5	8	11	4	2	14	12	7.50	7.50	7	10
Ulyanovsk Oblast	8	6	12	10	10	8	9	7	9.75	7.75	8	9
Republic of Bashkortostan	3	8	2	2	12	12	7	11	6.00	8.25	9	5
Nizhny Novgorod Oblast	13	11	7	4	6	10	2	9	7.00	8.50	10	3
Kirov Oblast	11	7	11	13	1	1	8	13	7.75	8.50	11	14
Samara Oblast	5	9	6	9	14	14	1	2	6.50	8.50	12	1
Perm Krai	14	14	3	6	13	13	6	10	9.00	10.75	13	12
Mari El Republic	12	13	9	12	8	9	13	14	10.5	12.00	14	8

* The author’s calculations based on Rosstat data.

The table data shows that four out of the fourteen compared regions (the Republic of Tatarstan, the Saratov Oblast, the Penza Oblast, the Republic of Mordovia) topped the rating of the average values of the indicators characterizing the factors affecting population emotions. The same regions were included in the first half of the list with regard to the indicators of annual average migration growth in 2011 (last column of tab. 6), taking the places from the 2nd (the Republic of Tatarstan) to the 7th (Republic of Mordovia) in the corresponding rating.

Three out of four territories (the Mari El Republic, Perm Krai, the Kirov Oblast) are among outsiders, being at the bottom of the list with regard to the indicators of annual average migration growth. The exception is the Samara Oblast that occupies the twelfth place out of fourteen by the average values of the indicators characterizing the factors affecting population emotions, but is the leader in the rating of migration growth.

The Orenburg and Nizhny Novgorod oblasts are in the middle of the list. Their values of indicators contradict the theses of originally formulated hypothesis on the complex impact of such factors as health, living standards, justice, employment security (indicators of ‘happiness’ or ‘unhappiness’ on migration flows. The Orenburg Oblast ranks 5th by the average values of ‘happiness’ indicators, but next to last in the rating of migration growth. The Nizhny Novgorod Oblast, on the contrary, occupies the 10th place in the rating of ‘happiness’, but tops the rating of the annual average population growth in the studied territories.

Given that the studied correlation between ‘happiness’ factors and territorial mobility of population has not been confirmed only in three cases out of fourteen, these cases could have been considered as exceptions to the rule. However, in order to look into the causes of incomplete correlation of the analyzed values,

Table 7. Bridge table of rating positions of the ratio of the fixed market basket price to wages and overall unemployment to migration in the republics and oblasts of VFD in 2005 and 2011*

Territory	Average values of rating positions by two indicators		Rating position
	Average values of rating positions by two indicators		Average rating values by two indicators in 2011	Annual average migration growth in 2005–2011
	2005	2011	Average rating values by two indicators in 2011	Annual average migration growth in 2005–2011
Samara Oblast	3.5	5.5	4	1
Republic of Tatarstan	2.5	1.0	1	2
Nizhny Novgorod Oblast	4.5	6.5	6	3
Penza Oblast	8.0	6.0	5	4
Republic of Bashkortostan	4.5	6.5	7	5
Saratov Oblast	10.5	5.0	3	6
Republic of Mordovia	9.5	8.5	10	7
Mari El Republic	11.0	13.0	13	8
Ulyanovsk Oblast	10.5	8.5	11	9
Chuvash Republic	11.0	11.5	12	10
Udmurt Republic	7.0	7.5	8	11
Perm Krai	4.5	8.0	9	12
Orenburg Oblast	8.5	4.5	2	13
Kirov Oblast	10.5	13.0	14	14

*The author’s calculations based on Rosstat data.

the author made additional calculations, in the result of which the average rating values of only two, but not four indicators (the value of the indicator characterizing the ratio of the fixed market basket price to wages, and overall unemployment rate) have been found (tab. 7) .

As follows from the table, when considering not four, but only two factors, six regions, leading in the average rating values of indicators of the ratio of the fixed market basket price to wages and overall unemployment rate, and in the indicators of migration growth, top the list of the territories compared. Apart from the Orenburg Oblast, all the other regions from the Republic of Mordovia to the Kirov Oblast, are in the middle and in the bottom of the rating lists under review. It should be noted that the compared paired values vary by no more than three points in all other regions, except for the Mari El Republic and the Orenburg Oblast.

Certainly, the results of the adjusted calculation can not evidence the complete correlation of the analyzed values. But the dependence tendency of the migration growth upon the rate of employment security and population well-being, defined by unemployment in the first case, and by the ratio of the fixed market basket price to wages in the latter, is much more obvious.

Thus, it can be concluded that all four of the examined factors affect the territorial mobility of population. However, such factors as unemployment and living standards are decisive inn shaping the direction and intensity of population migration flows of population in the pursuit of happiness.

Список литературы Territorial mobility of population in the context of search for happiness

Urry J. Mobilites (excerpt). The monitoring of public opinion. 2012. No. 5 (111). P. 197-252.
Zayonchkovskaya Zh. A. Population migration of the USSR and Russia in the XXth century: evolution through cataclysms. Studies on Russian economic development. 2000. No. 4. P. 1-15.
Zavalishin A.Yu. Territorial behavior of the socio-territorial community (in the case of Russia’s regional communities): Dr. of Economics Thesis synopsis. Khabarovsk, 2009.
Urry J. Sociology beyond societies: mobilities for the twenty-first century. Translated by D. Kralechkin. National Research University ‘Higher School of Economics’. Мoscow: Higher School of Economics publishing house, 2012.
Bakhtin M.V. in the pursuit of happiness. Ancient religious teachings. 4th ed., revised and corrected; foreword by A.M. Pronin. Мoscow: Moscow Institute of Spiritual Culture, 2006.
Argyle M. The psychology of happiness (Masters of psychology book series). Saint Petersburg: Piter, 2003.
Bochko V.S. Theoretical and methodological aspects of integrative strategic development of territories: Dr. of Economics Thesis synopsis. Yekaterinburg, 2010.
Antropov V.A. Way to ‘happiness’, or is ‘happiness’ an economic category? Herald of the Ural State University of Railway Transport 2012. No. 4. P. 91-106.
Akhremenko A.S. Stability of political and social development of Russian regions: a view through the prism of quality of living. The materials of XI International Academic Conference on Economic and Social Development: in three volumes. Editor in chief Yasin Ye.G. Мoscow: Higher School of Economics, 2011. Volume 2. P. 593-603.
Russia’s regions. Socio-economic indicators -2011: Rosstat statistical digest. Мoscow, 2012.
Trofimov A.M. et al. Positioning and competitive ability of regions in the process of socio-economic development. Geography herald. 2009. No. 2. P. 68-77.

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