battle for citizen by MSU FMA

THE BATTLE FOR THE CITIZEN: Human Development and the Urban Environment Murmansk

Pavlovsk

Salekhard

Kursk Belgorod

Nizhniy Novgorod

Noviy Urengoy

Voronezh Borisoglebsk

Kazan Perm

Krasnodar

Samara

Ekaterinburg

Volgograd

Tyumen

Tomsk

Novosibirsk

Abakan

Biysk Gorno-Alaysk

Research prepared for IV Moscow Urban Forum

Krasnoyarsk

mosurbanforum.ru

urban.hse.ru

hse.ru

nzemlya.com

The Battle for the Citizen: Human Development and the Urban Environment has been produced for the Fourth Moscow Urban Forum

Novaya Zemlya Ltd Sabina Maslova - Senior Expert, Head of the research section ”Human development in Russian cities” Natalia Volkova, Master of Urban Planning Senior Expert, Head of the research section ”The virtual city: Internet and social networks” Olga Samovolik, Master of Urban Planning Sociologist Alena Volkova, Master of Urban Planning Analyst

Russian State Humanities University; Associate Professor, Faculty of Philology, Higher School of Economics Oksana Zaporozhets, Ph.D. (Sociology) - Associate Professor, Faculty of Social Sciences, Higher School of Economics Pauline Kolozaridi, Master of Sociology Researcher, Laboratory of Political Studies Egor Lavrenchuk, Ph.D. - Senior Analyst of Social Networks, KoRaNIS Ekaterina Kozhevina, Master of Sociology Senior Expert, Public Opinion Foundation Ramil Mukhutdinov - Developer, Yandex Sergey Zhilin - Analyst, School of Urban Transformation Research Laboratories (within the St Petersburg State University of Information Technologies, Mechanics and Optics)

The authors would like to express their gratitude to members of the IV Moscow Urban Forum organizing committee (Iya Kuzmenko, Olga Papadina and Mariika Semenenko) both for their support and for the opportunity of conducting such a fascinating study.

And implemented by the Graduate School of Urban Studies and Planning (part of the Higher School of Economics National Research University) in collaboration with Novaya zemlya Ltd Research Director Professor Alexander Vysokovsky, PhD (architecture) — Dean, Graduate School of Urban Studies and Planning (part of the Higher School of Economics National Research University) Curator Ivan Kuryachy - Managing Partner, Novaya Zemlya Ltd Research Group Graduate School of Urban Studies and Planning (Higher School of Economics NRU) Ekaterina Dyba – Researcher, Head of the research section ”Human development and the urban infrastructure” Egor Kotov - Researcher Anton Gorodnichev - Researcher Arina Miksyuk - Sociologist City Fieldwork Laboratory (within the Graduate School of Urban Studies and Planning)Peter Ivanov - Head of Laboratory, Head of the research section ”Urban environment in Russian cities” Tatiana Shvareva - Researcher Institute of Demography (within the Higher School of Economics National Research University) Nikita Mkrtchyan, PhD (Geographical Sciences) - Senior Researcher, Head of the research section ”Migration and human development” Lily Karachurina, Ph.D. - Deputy Head of Department; Associate Professor of Demography HSE

Leontief Centre for Social and Economic Research Professor Leonid Limonov - Doctor of Economics; Professor HSE (St. Petersburg); Managing Director, Leontief Centre for Social and Economic Research Arthur Batchaev, Ph.D. - Senior Researcher Tatiana Vlasova, Ph.D. - Senior Researcher Nicholas Zhunda, Ph.D. - Senior Researcher Denis Kadochnikov, Ph.D. - Senior Researcher Marina Nesena - Researcher Nina Oding, Ph.D. - Senior Researcher Olga Rusetskaya, Ph.D. - Senior Researcher Leo Savulkin, Ph.D. - Senior Researcher Daria Tabachnikova; Researcher Research Adviser for the section ”Human development in Russian cities” Dr. Natalia Zubarevich - Regional Program Director of the Independent Institute of Social Policy Advisers for the section ”The virtual city: Internet and social networks” Alexei Levinson, Ph.D. - Head of Socio-Cultural Research, «Levada-Center» Ivan Klimov, Ph.D. (Sociology) - Director, Center for Internet and Society; Associate Professor, Faculty of Social Sciences at the Higher School of Economics George Lyubarsky, Ph.D. - Senior Researcher, Zoological Museum of Moscow University Sergey Chernov - Project manager, Yandex Pavel Lebedev, Ph.D. (Sociology) - Head of Research, Superjob recruitment Boris Iomdin, Ph.D. - Senior Researcher, V.V. Vinogradov Institute of the Russian Language; Senior Lecturer, Department of Russian Language, Institute of Linguistics within the

Advisers for the section ”Belgorod. Strategies for human development” Yuri Milevsky - Managing Partner, Novaya Zemlya Ltd Glev Vitkov – Head of Project Laboratory of Graduate Higher School of Urban Studies and Planning, Managing Partner, Novaya Zemlya Ltd Design Maria Kosareva Infographics Infographer Ltd Editor Sergei Kulikov Editor of English version Oliver Carroll Proofreading Tatyana Torgovicheva

Special thanks to Sergei Bozhenov - Mayor of Belgorod Alexander Garmash - Deputy Governor of Belgorod (with responsibility for social and domestic policy) Vitaly Chekhun - Deputy Head of Belgorod City Administration, Head of the Belgorod Department of Economic Development Galina Gorozhankina - Head of Architecture and Urban Planning in the City of Belgorod; Chief Architect Konstantin Kharchenko, Ph.D. (Sociology) - Deputy Director, Institute of Municipal Development and Social Technologies Liubov Kalabin - Deputy Head, Organisational Management, Administration of the City of Belgorod; Head, Department for Risks and Analysis Professor Larissa Goncharov, Ph.D. (economic sciences), Deputy Chair, Board of Deputies of the city of Belgorod Aleksandr Scheglov - Deputy, Belgorod District Council №21 Viktor Zakharov - Director, Institute of Management, Belgorod State University

CONTENT

5 Introduction: Russian cities in the human dimension

Section 1. Human development in Russian cities 1.1. Concepts and approaches 1.2. Proposed method 1.3. Human dimension vs returns to scale 1.4. Balanced human development

Section 2. Urban environment in Russian cities 2.1 Method of sociological research 2.2. Attractiveness of the city 2.3. Features of the urban environment 2.4. Work, leisure and social capital 2.5. Four types of urban residents

Section 3. Migration and human development 3.1 “Small” cities losing out, “large” cities gaining 3.2 Migration as an indicator of human development 3.3 Migration: hypothetic and real 3.4. Interaction between population growth and human development

Section 4. Human development and the urban infrastructure 4.1. Drivers of the modern city 4.2. Urban structure and support for small businesses 4.3. Construction activity 4.4. Traditional and “new” economies of the Russian city 4.5. Airport development as a factor for increasing population mobility

Section 5. The virtual city: Internet and social networks 5.1. Internet and social media penetration 5.2. Diffusion of innovations across Russian cities 5.3. The online image of the Russian city 5.4. Towards a geography of citizens’ social connections

Section 6. Belgorod. Strategies for human development

Introduction: Russian cities in the human dimension Is it even possible to compare Moscow with other Russian cities? Common sense would say «no»: on most indicators contemporary Moscow is absolutely incompatible with all other Russian cities — even including the “second capital” of St Petersburg. Yet when the organizing committee of the Moscow Urban Forum approached us a theme of “Drivers of Megapolis Development”, this was exactly the assumption we had to work from. Whichever way we approached the study, a full examination of both the state and development potential of Russian cities in comparison with Moscow was unavoidable. In truth, as experts of the Graduate School of Urban Studies and Planning (part of the Higher School of Economics NRU), we initially considered the proposal somewhat provocative and somewhat reckless. But after a while we came to realise the correctness and even sageness of the terms. They represented both a serious professional challenge and a truism of life. 1 N.V.Zubarevich, «Social differences in Russian regions and cities», Pro et Contra № 4—5 (56), June-October 2012, p. 140

Moscow outstrips all Russian cities, including the northern capital, Saint Petersburg, on all economic indicators: on GDP, by a factor of five; on budget volume, by a factor of almost 4 (per capita budget income - 1.5 times); and on average per capita income, by 1.7 times. Per capita fiscal capacity is much higher in Moscow then in any other major Russian city. Moscow has the highest salaries, the greatest job diversity, the best welfare support, the most advanced innovation industries and research institutes. The attractiveness of Moscow promotes growth of SMEs (27-30% of total employment), which in itself supports the development of market relations. The gross regional product per capita in Moscow is incompatible with that of other cities. In 2012, this indicator amounted to $47,000 in terms of purchasing power parity, which is on par with developed countries; in St Petersburg it was less than half, $22,0001.

Is it possible to compare Moscow with other Russian cities?

Moscow and St Petersburg are the leaders of Russia’s post-industrial economy with advanced levels of development. At the same time, Moscow’s GRP represents about a quarter of the GDP of Russia. It also has the highest concentration of investments in terms of volume, or at least it did until recently. Moscow is also the country’s main centre of culture and art, and a number of public parks were reconstructed and created. Finally, Moscow is simply the largest city in terms of population (in 2013, approximately 12 million people, 16 million in the metropolitan area), which is of the same order as all of the large cities of Russia taken together. The most important thing to understand is that Moscow shares a way of life with other world capitals — it is more sophisticated, closer to international standards than other Russian cities. The variety of resources and opportunities in Moscow, together with the concentration of an enormous amount of people in one capital, means that the lifestyle of an average Muscovite is markedly different from Russians living in other cities, and more in line with international norms. Moscow, as a megapolis, creates trends, dictates fashion and leads throughout, being an example to other cities. All of this feeds into a large population growth in the capital: between the two most recent censuses (2002 and 2010), Moscow’s population increased by some 1.38 million people, while all other large cities, including St Petersburg, increased by just 0.42 million people combined, i.e. more than three times less. In short, Moscow is an indisputable leader in the eyes of Russians. Analysts engaged in the research of Russian cities have offered an explanation of the process at play. It is, they say, based on a model of agglomeration: the over-concentration of resources in Moscow; a significant concentration in St Petersburg; much less in other large cities; right down to the smallest settlements where, of course, little is left. The economic geographer Natalya Zubarevich describes this picture expertly in the following way: “Regional differences only partially explain the social differentiation of the country. The picture becomes clearer if you change the framework and look at the problem from a centre-periphery perspective. In this approach, one looks at the hierarchical system of the population points: from the largest cities to the smaller ones, down to the smallest towns and to the rural periphery. The most important criterion you use here is the population size. ‘Size matters’, in other words: the effect of concentration (the agglomeration effect) is the objective factor that accelerates modernization”.

Common sense would say «no»

“When you approach the problem from this perspective, you are able to distinguish as many as three different ‘Russias’ within one country, with varied levels and speeds of social modernization. So ‘Russia 1’ contains cities with populations above 500,000 people; ‘Russia 2’ relates to cities from 50,000 to 250,000 people (cities between 250,000 and 500,000 residents occupy an intermediate position between the first and second groups); ‘Russia 3’ takes in smaller towns, villages and rural territories. All three ‘Russias’ are roughly equal in terms of population, with approximately a third of the country’s population each2. 2 Natalya Zubarevich, «Social differences in Russian regions and cities», Pro et Contra № 4—5 (56), JuneOctober 2012, p. 135-152.

“Of course, any rigid boundaries should naturally be taken with caution. Yet at the same time, we can clearly see the effects of agglomeration — the displacement of resources to major cities — to the capitals and regional centres. The phenomenon is connected with the peculiarities of the Russian system of government, the structures of tax systems and the organization of budgets at various levels.

“Note that this model of the development of Russian cities has come about as the result of classical analytical approach, with an accent on the generalized picture, and the termination of major trends and patterns. It is the objectifying viewpoint of a researcher who looks at the city from a detached viewpoint, and who works in the main with figures and government statistics. Of course, figures and statistics are far from the whole picture. A quantitative comparison imparts an initial, superficial understanding; an expert’s experience and intuition (since experts are also citizens, knowing Moscow and other Russian cities well) can fill the gaps and create a holistic understanding of the processes at work”. The picture as presented above is absolutely correct, and it is hard to disagree with it. But the question is whether this is the only picture that can be drawn, or whether there are other possible treatments in which Moscow and other cities (apart from St Petersburg) can be compared to one another. To answer this question, it is necessary to change out initial assumptions, and to adopt a different theoretical basis in considering the processes of urban development. If you are only to consider a centre-periphery model, you might come to the conclusion that the majority of the population should already have consolidated into a small number of mega-cities. This isn’t quite the reality in Russia, or at least if it is happening, it isn’t happening quickly, which means there is also a reverse effect that is keeping people in their own cities.

The opportunities a city gives to its citizens are linked to their feelings of well-being

Even more importantly, if one were to keep to a view that the larger the city, the better life in it, it follows that the citizens of all cities apart Moscow and St Petersburg should consider themselves failures. Again, the reality is quite different. The overwhelming majority of citizens live in their cities and have no desire to leave. Of course, there is always a fraction of citizens who are unsatisfied with life in the city, and wish to leave for another place. But this doesn’t tell you much. A similar percentage of people also want to leave Moscow and St Petersburg to other cities and towns that they believe will meet their expectations of creative work, quality of life, wages, cultural life and education. If we take the well-being of a typical citizen in his particular town or city as a starting point, most likely it is not so much dependent on external, intrinsic factors, as it is rooted in the opportunities of realizing one’s interests in the current, everyday life of the city. In other words, people do not live continually comparing their place of dwelling with Moscow or any other place. They simply live in their own city. They do not fall into depression because Moscow has 150 theatres and they only have 3. Or, perhaps, if they are depressed, they are only as depressed as a typical Muscovite, who may only have been in five of them, and who dreams about Milan’s La Scala or the New York Metropolitan Opera in much the same way as a provincial Russian would dream about the Bolshoi Theatre. Continuing this line, we can make a stronger hypothesis. In our opinion, the country should boast a cohort of cities with an equally high proportion of citizens totally satisfied with their city. These would be the most attractive cities of the country, cities in which people live as fulfilling and creative lives as in Moscow, an international megapolis, or in the historically picturesque St Petersburg. It would be impossible to explain this phenomenon sticking only to a centre-periphery model. It is all a matter of scale. Of course, the size of a city matters greatly. Yet at the same time people live and feel about the same in many cities, regardless of their size. People like living in their own particular city if they feel it meets their expectations; and they do not like living in cities that destroy their expectations at every step. So while Moscow is always incomparable and beyond the reach from the point of view of size and diversity, it may very well be comparable with any well-organized Russian city on matters of general population satisfaction.

Size matters – agglomeration speeds up modernization

Clearly, both processes — that is “centre-hierarchical benefits” and “local citizen satisfaction” — are synchronized. Major cities develop quicker, and the more people to move there, the less their original cities will be able to realize their potential. Consequently, it is important to understand what exactly attracts people to their own cities, what keeps people there, and what pushes people from cities with a lower quality of urban environment. In other words, our study is aimed at finding out what can create equality between cities of different size, and how we can match levels of satisfaction and selfawareness across all cities, including Moscow. This is a good time to revisit Natalya Zubarevich’s study, which considers that “more than half of major Russian cities are similar in their demographic characteristics, economic development and in a whole range of social indicators”. Other groups include export-resource cities (that is, cities living off oil and gas deliveries or other natural resources), those that receive large financial injections from the federal budget for the organisation and holding events of national significance (Sochi, Vladivostok), and others receiving significant state support due to other factors (Grozny, Kazan). For Zubarevich, it is much harder to determine real outsiders, since the differences between them and the most other average cities are fuzzy. That said, Zubarevich considers that “the uncompetitiveness of smaller industrial cities focused on manufacturing is becoming more obvious”. These cities lose population and grow old quickly, are less attractive for investors, and have higher risks when it comes to an unemployment and low wages. The aim of our study is thus to identify groups of Russian cities comparable to the country’s first and second capitals, as measured by their attractiveness to citizens; and a second set of groups that might be considered outsiders, i.e. those who do not quite meet the expectations of citizens. We will also try to understand how cities go about becoming an attractive destination. In order to achieve this goal of differentiating cities that do not have clear differences in the fields of economics, culture and national management climate, we have proposed an approach that uses a combination of methods of sociological research and econometric analysis. It is an approach to studying cities in the “human dimension”, that involves a comparison of results obtained from population surveys with an analysis of statistics and their comparison in different cities.

People feel about the same in many different cities, regardless of their size:

The study presented in the pages that follow consisted of several sections: an evaluation of the human potential of Russian cities on the basis of statistical analysis, which revealed a group of cities that can be considered leaders with high indicators of human development, and another group of cities with lower scores. • A case study of the populations of six “leader” cities, and two “control cities” taken from the group with lower human development scores. A questionnaire was carried out by quota sampling using online panels, given that the research was oriented primarily on the economically active and mobile population. In all, 6400 people were questioned from all selected cities. The questionnaire gave us the opportunity to compare indicators of human development with the attractiveness of the urban environment, and to identify how this attractiveness increases or, conversely, decreases. Moreover, this survey allowed us to determine the relationship of citizens to quality of services, satisfaction in work, migration opportunities, leisure time and Internet usage; • An evaluation of migration as an indicator of the attractiveness and success of Russian cities, based on analysis of statistical data from censuses and current indicators, and including a detailed examination of the test group of cities with high human development; • Assessment of the conditions required for human development in Russian cities, considering it as the most significant development resource available to the city, and including impact analysis of the indicators of construction, the business environment, infrastructure, industrial production, the new economy and other factors; • A study of the Internet and social networks in Russian cities as a factor determining the human development of urban population. This section includes a study of internet and social media penetration among various age groups; looks at how innovations are distributed from the capital to second-tier cities and to remaining towns and cities; and review the image of the city as presented online (using materials from newsfeeds) and in social networks (using materials from the “vkontakte” social network) • Due to the timeframe and defined goal of the research our selection of cities was eventually limited to regional capitals (of regions, territories,

they don’t like living in places where hopes are dashed every step of the way

13 republics, autonomous districts) with populations higher than 250,000 people. In line with the idea of outrunning development of major cities, it was considered that these cities most likely demonstrate the highest levels of citizen satisfaction, most likely can be compatible with Moscow. There are 63 such cities (if we include Moscow and St Petersburg), or 86% of the cities in Russia with a population above 250,000 people3. All the selected cities are varied in their demographic structure, economic specialisation, historical/cultural/ethnic specificities, and are located in different climatic zones and economic regions, each at different distances from the capital.

3 There are 73 Russian towns and cities with a population of higher than 250,000

25 Table 5: First stratum of HDI “leader” cities including Moscow and St Petersburg

Human dimension vs returns of scale Our study identified two groups: • A group of “leaders”, comprising 15 towns and cities: large university centres (Tomsk, Yekaterinburg, Novosibirsk), cities in Siberia and the Far East (Yakutsk, Ulan-Ude), national capitals (Kazan, Makhachkala) and cities with a strong regional orientation (Tyumen, Krasnodar) (Table 3);

City

• A group of “outsiders”, comprising 14 towns and cities, which include both major and mid-size cities in European Russia (12 of 14); the two cities located East of the Ural mountains are Omsk and Kurgan (Table 4).

HDI value

Moscow and St Petersburg were compared with the sample cities by including their values into the HDI calculations as part of the first stratum of cities (750,000 residents and above). The results of this experiment (table 5) showed that both Moscow and St Petersburg make it into the group of leaders, but by no means do they take the first places (Fig. 8, 9). This is the first important result of this study, showing the Russian towns and cities can compete with Moscow and St Petersburg on human development growth. The regional distribution of groups of “leader” cities and “outsider” cities, as measured by HDI, is shown in Fig. 10, 11. It is interesting to note that there are practically no cities with a high human development index in the central parts of Russia, regardless of the fact that this area is among the most densely populated in Russia. The proximity of Moscow and St Petersburg is the significant factor, exerting as it does an overwhelming influence on surrounding towns and cities. We observe a concentration of cities with low HDI values in Moscow and St Petersburg’s zone of influence, in particular in the central, northern and northwestern economic regions. And we note that all cities with high HDI values are located outside the Moscow and St Petersburg’s immediate zone of influence. The rough borders where sustainable human development begins to emerge are the Volga region to the East, and the Black Earth region to the south.

Fig. 8. The distribution of HDI values in the first stratum (including Moscow and St Petersburg)

Population growth (2002-2012)

Yekaterinburg

5,524

9,2

Krasnodar

5,478

-2,3

Chelyabinsk

5,451

3,6

Kazan

5,427

4,6

Novosibirsk

5,405

5,3

St Petersburg

5,405

6,4

Moscow

5,379

14,2

Krasnoyarsk

5,359

9,3

Saratov

5,160

-3,9

Nizhny Novgorod

4,932

-3,5

Ufa

4,900

2,8

Rostov

4,773

2,7

Voronezh

4,731

16,7

Samara

4,721

-0,3

Perm

4,715

-0,9

Volgograd

4,273

-2,4

Omsk

4,259

-0,1

Fig. 9. “Leader” cities in the first stratum (including Moscow and St Petersburg) according to HDI value and population growth 14%

5,60

7,0

HDI score

6,5 5,54

6,0

2,5 2,0

-2% 5,30

14%

Moscow

St Petersburg

Novosibirsk

Kazan

Chelyabins k

5,36

Krasnoda r

Omsk

Volgograd

Perm

Samara

Voronezh

Rostov-on-Don

Ufa

Saratov

Krasnoyarsk

Moscow

St Petersburg

Kazan

Chelyabins k

5,42

Yekaterinburg

3,0

5,48 Nizhny Novgorod

3,5

Novosibirsk

4,0

Krasnoda r

4,5

Yekaterinburg

5,5 5,0

Population growth over 2001–2012, %

1. Human development in Russian cities Concepts and approaches The concept of human capital emerged from the work of several scholars 4, and from a recognition of the increasing role of creativity and human knowledge in economic processes. Over time, new approaches to quantify human capital were developed, but across all approaches, three main blocks of indicators remain standard: education, health and prosperity (Fig.1).

of human development. For Amartya Sen (19908) , human development growth is not connected with improved material well-being at all, but by expanded human capabilities, along with greater freedom and diversity of choice.

4 Particularly Nobel prize winners Gary Bekker (1992), Thedor Shulz (1979), Simon Kuznets (1971)

Initially, human development studies were conducted at the country level, focused on cross-country comparisons, and were aimed at understanding the regulation of macroeconomic processes in the social sphere. Beginning in the 1980s, however, the concept of human capital began to be applied to the study and planning of cities and regions. With the industrial era and “Fordist” economy fast turning into subjects of history, scholars focused on understanding that quality of life in cities depends not so much on the efficiency and volume of industry production, as much as it does on the people working and dwelling there. 8 Sen is an Indian economist and Nobel prize winner, who coined the concept of human development potential in Human Development Report, 1990

5 Florida R. Rise of the Creative Class (Moscow, Klassika-XX1, 2005)

Landry, C, The Creative City (Moscow, Klassika-XX1, 2006)

Several remarkable works (including Florida, 20055 ; Landry, 20066 ; Glaser, 20047) elaborated the idea of human capital, showing how important it is as a resource, and how much it can achieve in the areas of the urban environment, the economy and in the social sphere. Over time, the emphasis in urban studies switched from the concept of “human capital” to one of “human development potential”. The key difference between them is in the change of focus from economic benefit associated with human capital to creating the conditions that lead to the qualitative transformation

The basic measure for evaluating human development at the country level is the official Human Development Index value (HDI)9, calculated by the UN on an annual basis. The HDI uses three indicators: life expectancy at birth; literacy levels among the population and quality of life. More recently, city-by-city calculations have been produced by the London School of Economics10. These calculations use life expectancy at birth; infant deaths per 1000 live births; mean years of adult education and expectation of further education; and per capita income. Depending on the availability of statistical data, some estimates of human development include indicators of the level of vaccination amongst schoolchildren, scholar citations, the proportion of people with incomes below the poverty line, business innovation and so on. 9 Human Development Report 2013. United Nations Development Programme. http://hdr.undp. org/sites/default/ files/reports/14/ hdr2013_en_ complete.pdf

Nonetheless, it should be pointed out that there is no single methodology available to calculating indices of human development. Instead, they are based on an expert selection of indicators that summarise its various aspects as best they can with the data available. Every study develops its own approach to selecting the attributes it will use to evaluate human development.

10 LSE Cities Metropolitan HDI, 2011. http://files. lsecities.net/ files/2011/11/ LSE-CitiesMetropolitanHDI-andDensity-18-10-11. pdf

Fig. 1. Key factors in evaluating human development

rit pe

Ed uc ati

os Pr Health

7 Glaeser E. Review of Richard Florida’s the Rise of the Creative Class. Mimeo. 2004. http://www. creativeclass. com/rfcgdb/ articles/ GlaeserReview. pdf.

«Human development» views a city’s population as its main development resource

Proposed method The idea of considering people as the main development resource for cities and regions was for a long time not accepted in Russia. During Soviet times, people were perceived primarily as a labour force to increase production. Only the economic aspect of the issue was ever taken into account. Modern research based on human capital and development only began to appear in Russia at the end of the 1990s11. The contributions of Rostislav Kapelyushnikov (2010)12, Tatyana Maleva (2008)13 and Lilya Ovcharova (2010)14 and Natalya Zubarevich (2009)15 were particularly significant. Among the recent research on this topic, it is certainly worth mentioning the work of Natalya Zubarevich,16 who uses a methodology elaborated by the UN development program to calculate human development indices across the R. I. Kapelyushnikov, Russian regions. The calculation of the index uses indicators of longevity Transformatsia chelovecheskogo (life expectancy), education (literacy and youth education) and income capitala v rossiskom (GDP per capita at PPP). The “economic bias” of indicators (in the first obscheshve (Liberal Mission, instance in respect of GDP data) means that in this case the basic values Moscow, 2010) of human development index reveals little in the way of qualitative characteristics of the population, but instead demonstrates systematic T. M. Maleva, O.V Sinyavskaya features. Among the “leaders” are Russia’s capitals (Moscow, St Petersburg) Rossiya pered litsom and leading regions of oil and gas production (Tyumen region, Tatarstan, demograficheskikh vyzov. (UN Sakhalin). Human development measured in such a way is based Development Progamme, 2008) on economic development and the effects of economies of scale in the framework of an existing centrifugal-hierarchical model. 11 The UN development program began publishing yearly reports on human development growth in Russia from this time.

15 N. V. Zubarevich ‘Indeks razvitija chelovecheskogo potenciala v regionah Rossii v 2005−2006 godah’ in Doklad o razvitii chelovecheskogo potenciala v Rossijskoj Federacii 2008: Rossija pered licom demograficheskih vyzovov. (Moscow: PROON, 2009)

The usual practice in such studies is to weigh indicators per unit of population (per person or per 1000 people). However this approach is also not without its distortions: in this case medium size towns gain an advantage in comparison with large towns as a result of nonlinear quality increments (Fig. 2). In small and medium sized towns, service provision, growth in infrastructure and institutions grows steadily in a direct relationship. Once a city reaches a population of 750,000, these processes become nonlinear — even small increases of indicators require the application of significantly more resources. This has the effect of slowing the growth rate in larger cities. The other distorting factor is related to the “low base” effect, where small towns with low absolute indicator values require only small increments to achieve a significant increase in relative performance.

In order to avoid the distortions outlined above, our study employed a method of stratified comparison in analysing city case studies. What this means is that at the first stage we split cities into strata according to population size, while at the second stage quantitative comparison and cities was done exclusively within each strata on the basis of relative performance per citizen.

The current study looks to assess Russian cities in human dimensions, proposes the use of other indicators that are closer to the daily lives of citizens and reflect their lifestyles and opportunities in a much more concrete way. The main task inherent in this approach is to remove the effect of scale. A standard instrument for doing this is the use of relative values rather than absolute ones. When one compares cities using absolute values, first place always goes to those cities with a large populations, who have an advantage of size when compared to smaller towns and cities. Such comparisons undervalue smaller centres that may very well be able to provide a high standard of living for citizens.

14 L.N. Ovcharova A Burdyak ‘Dokhody naseleniya energetika i krizis’ in S. N. Bobylev (ed) Doklad o razvitii chelovecheskogo potentsiala v Rossiyskoy federatsii. Energetika i ustoychivoe razvitie (Moscow: Dizaynproekt Samolet, 2010)

Number of students, 000s

Fig. 2. The effect of scale (using the number of students as an example)

16 N. V. Zubarevich, ‘Social’naya differenciacija regionov i gorodov’ in Pro et Contra # 4−5 (56), pp. 135−152 (2012)

The way the strata were designated is shown in fig. 3. The first stratum included cities with the population of more than 750,000 citizens (a total of 15 cities); the second stratum was made up from cities between 500,000750,000 citizens (16 towns); while the third included smaller towns between 250,000 and 500,000 citizens (30 towns)

Population distribution among Russian towns and cities is irregular. There is a natural gap between cities of the first stratum (cities of near a million citizens and above) and cities of the second stratum (half a million citizens or so). Moscow and St Petersburg, as ever, stand alone with their disproportionately large populations, but they are conditionally evaluated within the framework of the first stratum (see table 1). A notable feature of the population distribution across urban centres is the absence of natural gap between cities with populations about 1 million citizens and those just below 1 million citizens. Towns with populations close to 1 million are very similar to those of 1 million in greater when it comes to development paths and processes. This is why we have chosen to use 750,000, where there is a much more natural gap, as of the cut-off point. Fig. 3. Division of urban centres into strata by population Magnitude

160

1 stratum 2 stratum

140 120 100

3 stratum

80 60 40 20

200

400

600

800

1000

1200

1400

1600

Population size, 000s

250

500

750 Population (000s)

19 The next important task in evaluating the human development in Russian cities was to select the social demographic and economic indicators that best fit the city’s population. One of the main problems encountered in research is the limited data available at the city level. In the absence of a go-to source of official data relating to municipalities, information was instead selected using multiple open source datasets (including the Multistat statistics portal, Rosstat Federal State Statistics Service, the national census, and websites of relevant ministries and municipal administrations). In contrast to region-level statistics, data at the municipal level has a very narrow set of indicators and different time series. As a result, data was collected for the period of 20112012, which provides the most complete picture for indicators used.

Based on our analysis of selected statistical data, we identified five key indicators that best display the level of human development in each city:

Source: Ministry of Education and Science of Russia

1. Number of students per thousand citizens, indexed on the average score of Unified State Exam (EGE) of high school students accepted to study full-time, and weighted by the number of high school students in a given city17. (Hereinafter “students”); 2. The number of people with higher and incomplete higher education per thousand citizens (Hereinafter “higher education”) 3. Crude death rates per thousand citizens (“total mortality”) 4. Infant mortality rate per thousand live births (“infant mortality”) 5. Average wages in roubles, indexed to the cost of a standard set of consumer goods and services in the region (“salary”) These parameters are largely independent of each other: correlation coefficients do not exceed 0.4, which indicates a weak to moderate dependence (Table 2).

Table 1: Total population by city

Group

Table 2: Correlation matrix of indicators

Total population of cities within group (people)

2002 г. Moscow Saint Petersburg More than 750,000 people From 500,000 to 750,000 From 250,000 to 500,000

10 386 900 4 656 500 16 127 900 9 011 400 10 396 720

2012 г. 11 856 578 4 953 219 16 669 879 9 390 389 10 474 943

Population growth over the last 10 years (2002-2012) %

14,15 6,37 3,36 4,21 0,75

Number of cities in group

1 1 15 16 30

Students Higher education Total mortality Infant mortality Salary

Students

1 0,366 0,081 -0,174 0,361

Higher education

1 -0,132 -0,066 0,272

Total mortality

1 -0,410 -0,110

Infant mortality

1 -0,161

Salary

21 The first two indicators represent the educational block of our human development triangle, and help us evaluate the reach and quality of higher education in cities. We introduced a correlation coefficient — exam scores of full-time students — so that our study had not only quantitative but also a qualitative basis. It was possible to include other indicators relating to preschool and school education, though the number of places in preschool education would only demonstrate the demographic burden on infrastructure (kindergartens), while the number of children in school is quite high across the whole country. At the same time, it is quite difficult to assess the quality of such education using the statistical data available.

Health and the system of health care in any given city are usually estimated using life expectancy figures. However, government statistics do not publish such indicators at the city level; instead, it is calculated only at the regional level. Because of this, a combination of indicators relating to state of health (infant mortality rates) and conditions (total mortality) were chosen as an alternative. The crude death rate reflects the age structure of the city population, which is in many respects a consequence of the historical development of the city. From the point of view of human development, the predominance of elderly people in demographic structure of the city is an inhibiting factor. In other words, the overall mortality rate can be taken as a benchmark to evaluate human development. The infant mortality rate – that is to say the number of children dying within the first year per 1000 live births — is a “quasi-indicator”, since it allows us to evaluate both of the level of healthcare and the population lifestyle. This indicator is exceptionally sensitive to changes due to the low basis effect, which is why average figures were taken for the over three years (2011-2013).

The reliability of HDI results were tested by examining various other options of calculating the index, using combinations of indicators and methods. The testing showed that independent of the choice of calculation: a) the main group of “leader” cities remains unchanged and b) only one or two positions change within the stratum. The minimum degree of fluctuation would seem to strongly support the validity of the results we obtained. Graphing the city-by-city distribution of results helps us identify a group of cities with high human development index values. This represented roughly 25% of towns and cities in each stratum. In the first stratum, 5 “leader” cities were identified; there were 4 in the second and 6 in the third. The same procedure was carried out for cities with low HDI scores. There were 4 such “outsider” cities in the first stratum; 4 in the second; and 6 in the third.

The third block that makes up the human development index — population well-being — is shown by mean salaries of workers. To smooth the disparity of incomes across cities at different stages of economic development, and to take into account the real economic well-being of residents, this indicator was indexed to the regional cost of a set of consumer goods and services. Our calculation of human development index normalises values of each indicator within the given stratum. Values are relative to the average of each stratum. Our HDI figures were calculated according to the following formula: Figure 4. Formula for calculating Human Development Index

HDI = where: S (students) M (mortality) IM (infant mortality) I (income) HE (higher education) N

(

Mix N S ix N + + ∑i=1Mi ∑i=1Si N

is the number of students per 1000 residents (indexed to the average score of high school students accepted to study full-time, and weighted by the number of students in all high schools across the city); is the crude death rate per 1000 inhabitants; is the infant mortality rate per 1000 lives births; is the mean working wage (indexed to the cost of a set of consumer goods and services in the region); is the number of people with higher and incomplete higher education per 1000 residents; is the number of towns and cities in each stratum (in the first stratum N=15, in the second N=16, and in the third N=30).

IMix N Ii x N + ∑i=1IMi ∑i=1Ii N

)

HEix N + ∑i=1HEi N

Table 3: Group of HDI “leader” cities

Groups of cities by population (000s)

Number of cities in groups

Table 4: Group of HDI “outsider” cities

Sampling unit

Sampling unit as % of overall group

Cities

Groups of cities by population (000s)

HDI value

Yekaterinburg Krasnodar Chelyabinsk Kazan Novosibirsk

5,564 5,521 5,483 5,470 5,448

Over 750

Omsk Volgograd Perm Samara

4,298 4,313 4,750 4,761

500-750

Makhachkala Tomsk Tyumen Irkutsk

6,653 6,626 6,325 5,884

500-750

Astrakhan Ulyanovsk Ryazan Lipetsk

4,065 4,368 4,466 4,511

250-500

Cheboksary Belgorod Yakutsk Ulan-Ude Tambov Stavropol

6,439 6,249 6,102 6,042 5,872 5,670

250-500

Kostroma Kurgan Kaluga Arkhangelsk Bryansk Murmansk

4,254 4,266 4,331 4,461 4,584 4,662

3,0

2,5

2,0

Kostroma

Kurgan

Kaluga

Archangelsk

Bryansk

Tver

Vologda

Tula

Ivanovo

Smolensk

Oryol

Kaliningard

Vladimir

Ioshkar-Ola

Kirov

Grozny

Nalchik

Kursk

Chita

Saransk

Tambov

Stavropol

Yakutsk

Ulan-Ude

Syktyvkar

Murmansk

3,5

Petrozavodsk

4,0

Vladikavkaz

Ulyanovsk

4,5 Astrakhan

Lipetsk

Ryazan

Yaroslavl

Izhevsk

Tyumen

Tomsk

Makhachkala

Irkutsk

Penza

Barnaul

Khabarovsk

3,5

5,0 Kemerovo

4,0

5,5

Vladivostok

Omsk

Volgograd

4,5

6,0

Orenburg

Perm

Samara

Voronezh

Ufa

5,0 Rostov-on-Don

Saratov

Nizhny Novgorod

5,5 Krasnoyarsk

5,5

Kazan

6,0

Novosibirsk

6,0

Chelyabins k

6,5

Krasnoda r

6,5

Belgorod

Fig. 7. The distribution of HDI values in the third stratum of cities

Cheboksa ry

Fig. 6. The distribution of HDI values in the second stratum of cities

6,5

Yekaterinburg

HDI value

7,0

3,5

Cities

7,0

4,0

Sampling unit as % of overall group

7,0

4,5

Sampling unit

Over 750

Fig. 5. The distribution of HDI values in the first stratum of cities

5,0

Number of cities in groups

Fig. 10. “Leader” cities according to human development index

St Petersburg

Moscow Belgor od

Yakutsk

Cheboksa ry Tambov

Kazan

Krasnoda r

Yekaterinburg Tyumen Chelyabins k

Stavropol

Tomsk

Makhachkal a

Novosibirsk

Irkutsk

Ulan- Ude

Fig. 11. “Outsider” cities according to human development index

Murmansk St Petersburg Arkhangelsk Bryansk

Kaluga

Moscow

Kostroma

Lipets k

Ryazan Ulyanovsk

Volgograd

Perm

Samara

Astrakhan

Kurgan Omsk

Strata by population size More than 750,000 .

Population density, people per sq m (2012)

500,000–750,000

Borders of Central Economic District

250,000–500,000

Core group of cities Peripher y group HDI “leader” cities HDI “outsider” cities

10-20

40-50

2-5

20-30

50-60

5-10

30-40

>60

Cities with high levels of human development are located outside the zones of influence of Moscow and St Petersburg

Our study showed that in those towns and cities with a balanced human development structure, a large proportion of people were likely to be satisfied with the quality of healthcare and education, and with the level of their pay (e.g. Yekaterinburg, Krasnodar). Those cities with an unbalanced structure of human development were more likely to reveal a disconnect between statistical analysis and a sociological survey data.

Strata based on population (000s) More than 750

Yekaterinburg

250â&#x20AC;&#x201C;500

Irkutsk

16 22

Novosibirsk

19 19

Krasnodar

19 21

Kazan

Tyumen

16 11

Chelyabinsk

1 dominant indicator

Makhachkala

17 19 21

Ulan Ude

Human development should become a priority for local administrations across Russia

500â&#x20AC;&#x201C;750

2 dominant indicators

Comparing survey and statistical analysis data show that the more balanced the human development indicators are, the likely residents are satisfied with their urban environment. Clearly, therefore, balanced human development growth should become more of a priority for city governments across Russia. Policies aimed at the sustainable development of education, health and prosperity are exceptionally important for improving the standard of living for citizens and the level of their life satisfaction. Such policies would provide additional benefits in attracting creative and professional people into the city.

Table 6: HDI balance by city

Balanced HDI structure

Balanced human development Final values of HDI are calculated from five indicators, each of which has a different contribution for each individual city, and shows which block (education, heath, prosperity) has the most weight in the city. There are cities where the contributions of all indicators are reasonably similar; in others, the contribution of one or two indicators dominates (Table 6)

Stavropol

Cheboksary

21 29

Tomsk

Belgorod

Yakutsk

21 14

Tambov

15 32

Number of students

Crude death rate

Infant mortality

Average income

Higher education

2. The urban environment of Russian cities

The study was conducted by questionnaire survey and quota sampling. Looking to determine the behavioural characteristics of the economically active and mobile population, we selected equal numbers of men and women from 18 to 60 years of age, setting quotas to reflect the age distribution in the cities being analysed. We considered the use of online panels to be the optimum means of obtaining the data we needed.18 This produces a practically unbiased selection for the given age group. In those cities with population higher than 1 million people, the sample size was 1000 respondents, and in those cities above 500,000 people, this was reduced to 800. The total sample was 5400 respondents.

Method of sociological research The starting point for this study was an idea that cities with high human development indices should be able to satisfy their residents independent of their size, and that the contrary might be true for cities with a low HDI value. The hypothesis was tested using sociological research in six selected cities: Yekaterinburg, Novosibirsk, Voronezh, Tomsk, Tyumen and Krasnodar.

18 We would like to express our gratitude to the research company Profi issledovaniya for their invaluable and timely help in collating the data

In creating our questionnaire, we considered previous research on the given theme. One question in particular, relating to the attractiveness of life in a given city, was taken directly from a 2014 survey carried out by the research portal SuperJob, which collected data from 24 towns and cities in Russia, including Moscow and St Petersburg19. The survey was interesting to us because it allowed us to compare Moscow with other cities.

The research had two objectives: • To identify how residents relate to living standards in their town/city • To verify our chosen method for calculating HDI values by determining how closely they correlate with opinions local residents themselves have about the quality of their urban environment.

Source: www.superjob. ru/research/ articles/111546/ dovolnyhzhiznyu-vmoskvei-v-sanktpeterburgestalo-bolshe/; http://www. superjob.ru/ research/ articles/111562/ rejting-gorodov2014-tyumenpo-prezhnemulidiruet-sredirossijskihmegapolisov/

The urban environment, one of the key concepts of this work, is understood as the relationship between various subjects (individuals and groups) to the physical and social environment around them, a sphere that they themselves help produce in conjunction with other city subjects. The relationship of a citizen to his or her city is the main criterion for assessing the quality of the urban environment. Apart from this, urban environment quality is linked to such indicators as the involvement of citizens in their urban communities, the opportunities of realizing oneself at work, the satisfaction citizens have the work of various institutions and service facilities, satisfaction with public spaces, and also the diversity and intensity of leisure and daily activities performed using urban infrastructure.

Attractiveness of the city The easiest way to measure the environmental health of an ordinary citizen is to ask a simple question: “In general, do you like, or not like, living in your city” (Table 7). The answers of respondents living in towns and cities with the high human development index are striking in their unanimity: 69% of respondents like living in their town or city, another 24% and so that they somewhat like living there, while only 6% of those asked were unsatisfied with their home town or city. Moreover, the differences in responses from citizens in this group were insubstantial. There were just 12% between Tomsk, which attracted the highest number of positive responses (77%) and Novosibirsk, which brought up the rear (65%).

Just over half of respondents agreed that Russia has many cities that have a good standard of living; one third believe that only larger Russian cities have it; 10% said only a few cities have it; while 3.6% of respondents were convinced that absolutely no cities in Russia fit such a bill. The range of responses is even less here that in the previous question. The most patriotic of Russian cities turned out to be 500,000 strong Tomsk; and the most critical residents were from Yekaterinburg (61% and 49% respectively) (Table 8).

Table 7. Answers to the question: «Do you like living in your city?»

City

Tomsk Tyumen Krasnodar Yekaterinburg Voronezh Novosibirsk average across cities

Yes, I like living here, %

77,4 70,8 68,5 67,4 67,0 64,8 69,0

Yes, I somewhat like living here, %

16,9 21,9 25,8 26,1 27,1 26,7 24,4

No, I somewhat don’t like living here, %

4,3 5,6 3,4 4,6 4,0 5,6 4,6

No, I absolutely do not like giving here, %

0,8 0,9 0,9 1,0 1,0 1,8 1,1

Difficult to say, %

0,8 0,9 1,5 0,9 0,9 1,1 1,0

The attitude citizens have to their city is the main criterium in assesing the quality of the urban environment

35 An overwhelming majority of population living in towns and cities with high HDI scores consider where they live to have as good a standard of living (regardless of population size) as citizens living in Moscow and St Petersburg. All of the cities have an urban environment, objectively different in terms of diversity and richness, but equally attractive and familiar. Moscow, as predicted above, is in this respect comparable to other much smaller cities boasting high HDI scores.

We now move to a comparison of the proportion of citizens who enjoy living where they do with the situation in Moscow and St Petersburg. As already mentioned, we do not have the opportunity to carry out of full scale survey in the capitals, which is why we used the results of the SuperJob online survey. It makes a lot of sense to compare these two datasets, because both studies used a similar methodology of online survey and targeting the economically active parts of the adult population. There are of course a couple of provisos: first, the SuperJob sample is made up exclusively of people actively looking for work, or offering work; and second the scale of answers to the question was constructed slightly differently (there were only two possible answers, “yes” or “no”). Nevertheless, we can conclude that the distribution of answers to the question “Do you like living in your town or city?” was similar. In the SuperJob study, the proportion of respondents answering yes to this question, and living in cities we analysed, was on average 87%; in our study, this number was 69%. If you were to combine both of the positive responses we included in our own survey — “yes” and “somewhat yes” — the numbers are even closer (93.4% against 87%)

Features of the urban environment The main reasons for giving a positive response to the question “What exactly do you like about where you live?” were: external appearance (58%), number of convenience stores (58%); diverse cultural and leisure activities, including cultural centres, museums, theatres (49%); access to markets and shopping centres (47%); high-quality eateries (47%); entertainment facilities, including bars, clubs, karaoke, bowling centres (43%); higher education opportunities (49%); and the presence of airports, railway stations and ports, making it easier to travel to other cities and countries (46%). Even where citizens were agreed in rating the standard of living highly, they often associated the high standard of living with different urban features. Some attractive features are referred to consistently, while others are specific to particular cities. So an appreciation of the architectural beauty of a city varies between 62% in Tomsk, Voronezh and Yekaterinburg and 59% in Tyumen and Krasnodar (only in Novosibirsk is it significantly lower at 43%). On another hand, the rating of convenience stores is mostly in the region of 57-67%, but falls significantly in Tomsk, where just 28% of people are satisfied with the number of such stores. When compared to the other cities, Tomsk returned the lowest values in nearly all indicators: not one indicator was higher than 40%, with the exception of architectural beauty.

If you take the dynamics of the responses to the given question over 20112012, you note that Moscow and St Petersburg do not stand out particularly over other cities, as we stated in the hypothesis of this work. Moreover, if St Petersburg relates in the group of cities with a large proportion of citizens liking where they live, then Moscow is very much mid-table: the percentage of Muscovites answering positively to this question is less than the mean average over the entire population (74% as against 78%). And finally, in line with our own study, the SuperJob survey confirmed, at least on the quantitative level, a link between HDI scores and citizens’ own opinions about the standard of living in their city. More than 70% of citizens living in cities with high HDI scores answer positively to the question about whether they like living where they do; while those citizens living in “outsider” cities, four of which were featured in the SuperJob survey, rated their cities negatively (Table 9).

Table 8: Responses to the question “which statement best reflects your view?”

61,1 58,5 57,0 56,4 50,3 49,0 55,0

Only larger cities in Russia offer a comfortable standard of living, %

27,5 26,8 31,3 32,1 32,2 36,0 31,2

There are almost no cities in Russia that offer a comfortable standard of living, %

8,4 10,6 9,1 7,6 12,0 12,7 10,1

There are absolutely no cities in Russia that offer a comfortable standard of living, %

3,0 4,1 2,6 3,9 5,5 2,3 3,6

Towns and cities with high HDI values

Above average

Tomsk Tyumen Krasnodar Voronezh Novosibirsk Yekaterinburg average across cities

There are many cities in Russia offering a comfortable standard of living, %

Table 9: Proportion of respondents answering positively to the question: “Do you like living in your town or city?” (taken as an average over 2011-2014)*

Below average

City

The most popular answers to the question about what respondents don’t like about the place where they live included: • Transport issues (traffic jams (74%), problems with parking (65%), convenience of getting around town (35%) • Accommodation issues (cost of apartments (44%), cost of renting (31%), cost of communal services (40%), quality of the communal services (40%))

Percentage of respondents, %

Towns and cities with medium HDI values

Percentage of respondents, %

Towns and cities with low HDI values

Percentage of respondents, %, %

Tyumen

Rostov

Krasnodar

Yaroslavl

St Petersburg

Krasnodar

Kazkan

Nizhny Novgorod

Yekaterinburg

Ufa

Novosibirsk

Voronezh

Chelyabinsk

Khabarovsk

Perm

Irkutsk

Tolyatti

Samara

Moscow

Vladivostok

Volgograd

Saratov

Omsk

• Other (dirt and rubbish on the streets (42%), ecology (35 %), poor healthcare (33%) and low wages (35%). Again there is variation on the local level. Citizens of Voronezh are particularly worried by unemployment (27% versus 17% average for other cities); and more than half of the working population there is unsatisfied by low wages. Three cities — Yekaterinburg, Novosibirsk and Voronezh — have a problem with dirt and rubbish on the streets. Approximately 40% of citizens in Krasnodar, Voronezh, Novosibirsk and Yekaterinburg complain about the difficulty getting around town. Yekaterinburg has a particular problem with the cost of public transport: 41% of respondents found the cost of tickets prohibitive. 46% of those living in Voronezh do not approve of the low-level of healthcare provision. High accommodation costs are especially noticed in Novosibirsk (52%) and in Yekaterinburg (61%), where there is also an issue with rental cost (44% in comparison with an average rate of 31% in other cities). 36% in Voronezh, 34% in Novosibirsk and 33% in Yekaterinburg are unsatisfied with the state of communal courtyards and children’s playgrounds. Work, leisure and social capital The results of our study show that pay levels are the crucial factor when it comes to workplace satisfaction, and workplace satisfaction, in its turn, is one of the most important factors when it comes to scoring the quality of the urban environment. The absence of clear motivations relating to professional fulfilment would suggest that the institution of professionalism and personal development lags behind the development of government and educational institutions. A belief in high-quality state education as an investment prevails over the idea of professionalism. When it comes to looking for work, citizens usually turned to their friends and acquaintances for assistance (43−52 %), while 36 - 52 % search on the Internet. State-run job centres are the least popular channel for job searching — just 4-5% of respondents use their services. That is to say that this institution is practically non-functional, and completely substituted by individual and social capital.

Transport issues are the main reasons why people don’t like living in their cities:

Almost 60% of respondents believe that doing business in the place where they live is either “difficult” or “somewhat difficult”. The situation is worst of all in Tomsk, where 15% are sure that it is practically impossible to do business, while 46% consider things “somewhat difficult” (17% couldn’t answer the question). The survey data would suggest ease of business is best in Krasnodar, though the proportion of its population who consider so is just 5.5%! Another 25% of Krasnodar citizens believe that doing business in the city is fairly straightforward. Russian citizens generally live in their own apartments within communal housing blocks (79%). The alternatives to this are poorly developed, although in specific regions there are small deviations: for example, in Krasnodar, some 18.8% of people rent, with a further 8.1% living in private developments. The vast majority of citizens spend their free time at home with family (65−77 %) or on the Internet (30−49 %). Competing with the Internet are shopping centres (26−44 %) and cinema (25−33 %). Just under a third of Russians spend free time in cafes and restaurants. A quarter dedicate leisure to parks, public squares and other green areas on the edge of town. Up to 38% prefer to spend their leisure time in the countryside. Cultural events, exhibitions underline our interest only to every tenth citizen. Leisure culture and sports infrastructure within the city attract a very limited number of people, though the overwhelming majority of these are satisfied or rather satisfied with its quality. Modern Russian cities are characterised by low level of social capital: only 35% of citizens turn to other people for moral and material assistance; and 15% have never done this, preferring to rely on themselves. In the vast majority (80%), Russians are not members of public organisations, and when they are, this is usually as members of trade union (8-12%). The most active of all cities is Krasnodar, where some 26% report being members some kind of public organisation or voluntary movement. Questions about trust in various institutions give the following picture: 18% do not trust anybody; 37% trust government and the media (trust in the government is correlated with trust in business); 21.2% trust the media, educators and health workers; while 23.7% trust only educators and health workers. We were unable to find any significant differences in the trust people have for representatives of local and national institutes.

traffic jams, problems with parking and difficulties getting from A to B

39 In other words, we can consider that the level of social capital in Russian towns and cities is exceptionally low, which ties in with the scores about doing business here. Business develops in the cities where there is a highlevel of institutional trust, where social capital is well-developed and can create pools of information and competencies. This picture has little in common with Russian regional centres today.

Four types of urban residents Cluster analysis of the pattern of relationships citizens have with their urban environments allowed us to identify four types of modern urban residents. The four groups seemed to be joined by factors that were independent of socio-demographic characteristics e.g. level of education, marital status, presence of children, sex/age composition. The groups are organised on a principally different basis that might be summarised as valueenvironmental grounds. The first group, which takes in 32.2% of the overall number of respondents from all towns and cities, is made up of people who might be called «citizens» due to their clearly formalised relationship to the urban environment, as well as their focus on socialising and community. In the main this group is interested by work, education, leisure and public services. They consider it important that the place where they live has good academic institutions and vocational-technical schools, a large number of convenience stores and large shopping centres. They enjoy going to cafes and restaurants, theatres, museums, exhibitions, ice rinks and stadiums — that is, to places where you can spend free time with friends, relatives, or alone. It is interesting that this group is the most likely to turn to other people for assistance in solving various problems (37.8%). They are also not indifferent to the external face of the city, to the condition of public squares, parks and courtyards; they support the development of pedestrian infrastructure. One third of this group spends time at home with families, on the Internet or shopping in large shopping centres. More than any other group, the “citizens» enjoy travelling and visiting cinemas, museums and shops. This group tends to gravitate to the central parts of town, actively move around the city and are highly sensitive to the urban environment: they perceive the aesthetic component of the urban environment more keenly than others; and likewise value the diversity of opportunities offered in a city. These people are least interested in the suburb environment, as they are in local government.

Almost 60% of respondents believe it is difficult to do business in their cities

This first group is relatively young and boasts the highest percentage of people with higher education (65,3%). The majority of respondents in this group earn up to 50,000 rubles per month ($10,000), but it also in this group that you find the largest percentage of people earning between 50,000 and 100,000 rubles ($10-20,000). The second group are also “citizens”, but have a different range of environmental preferences. 22.2% of our respondents could be classified as belonging to this group. Much more than citizens of the first group, they are concerned with quality of life issues, the suburban environment, housing services, rental costs and buying property. They are interested to a greater degree in the quality of courtyards, parks and public squares. This group brings together citizens of the urban periphery - they commute to work, either to the centre or to other city districts. As a result of this, the cost and quality of public transport are of great significance. This is an average group in respect of age and income per family member (from 15,000 to 25,000 rubles, $350-$550). There more likely than the first group to participate in communal moves to improve their neigbourhoods, including taking part in cleaning shared spaces, but are in many respects similar when it comes to the diversity of urban practices and understanding of the urban environment. Our third group might not be considered «citizens» as such. This group does not exhibit any clear environmental preferences, emotional attachments or strategies when it comes to urban behaviour. Their defining characteristic is instead passivity. This is the largest group, evaluated at 42% of the total. Respondents in this group value most of all employment opportunities, high salaries, and also good kindergartens, schools and clinics. This group has minimal other requirements when it comes to the quality of the urban environment, whether that be on issues of leisure opportunities or public transport. Most often they will look for work close to their home or in their own neighbourhood. Respondents in this group are rarely

Business prospers where there is a high level of social capital

41 members of public social movements, few are involved in local government or moves to improve their neighbourhoods. Representatives of this group have relatively low incomes: more than half earning less than 25,000 rubles ($550), while an additional 27% receive between 25,000 and 50,000 rubles a month per person ($550-1100).

Quite possibly, we are dealing here with people who have put in place a crisis management strategy; their habitat is limited only to their homes and work (this is how Olga Schevchenko sees it20). However they may form environmental preferences if they are particularly of trusting neighbouring people and social institutes, and also when improvement to urban infrastructure is under way. This is when they may be transformed into the first or second type.

Crisis and the Everyday in Postsocialist Moscow, Olga Shevchenko, ISBN: 978-0253-22028-8

The fourth group of respondents is particularly interesting. This is a very small group in size (only 3.8% of the total sample), but it is a very stable group, found in all towns and cities, and can be described as the city’s most active, proactive and ambitious layer of citizens. The kind of people falling into this category are most likely businessmen, students, and those who combine work with study. They appreciate a clean environment, high quality of transport and social infrastructure, but they interact with the city in an entirely consumeristic way and do not have a distinct behavioural patterns. Type IV citizens practically do not participate in local self-government, but social capital plays a significant role for them: a quarter of respondents are members of various public organisations and movements; over the last six months, 71.5% took part in some kind of voluntary action, volunteer activity or charity. Almost 40% are entirely happy with their place of work, and 32% believe that the level of their work corresponds properly to that position. In line with the fact that businessmen and working students predominate in this group, our fourth type of resident is most likely to look at business positively: 42% think that it’s easy or rather easy to do business in their city. We can assume that this is rather monolithic group when it comes to values, which are focused on individualism and personal achievement. They are not tied and are not rooted to a given city; the city is for them simply a place where they can realise their own aspirations.

54% of citizens are interested in the quality of their urban environment, 42% are indifferent, 4% are not attached to their city

The first three groups are focused on life in their own town or city. Only 10% of respondents in these groups would like to move to another city in Russia or abroad. Citizens in the first two groups do not tend to move because they are satisfied with their city, while representatives of the third group remain because they are extremely passive. The fourth group is set up quite differently: this is the group that contains the most people currently planning to leave to a new city or country (16.4%), or having already moved to this city not so long ago (42%). This group is an opinion leader when it comes to the idea of Russia having many towns and cities with a comfortable standard of living. They are ready to move to another city if there is good job offer on the table, or if there is an opportunity to realise professional interests (Table 10). In the majority of analysed cities that have a high HDI score, the first and second group were the dominant citizen types. The exception was Tomsk, where two thirds of citizens relate to the third group. By way of conclusion, we might state that the provision of varied city strategies is an important criterion when it comes to evaluating the urban environment of a given city. This study suggests improving the quality of urban environment and human development in the following ways: • Developing institutions of civil society, and involving people in the production and management of the urban environment (which will allow representatives of the third group to gain self-confidence in the urban environment, and move into the first or second group) • Improving the quality of cultural and leisure infrastructure in the city (directed at improving the level of satisfaction with the urban environment within the first group of citizens) • Developing transport systems, improving the quality and availability of accommodation (directed at satisfying the demands of citizens of the second group) • Increasing project opportunities in business, and likewise supporting social enterprise projects (to supports representatives of the fourth group)

Table 10: The distribution of citizen types by city

City Yekaterinburg Novosibirsk Voronezh Tomsk Krasnodar Tyumen Average

Type I, %

Type II, % 46,0 38,5 27,8 14,6 33,9 27,0 32,0

Type III, % 17,0 23,8 36,7 11,8 21,0 20,3 22,2

Type IV, % 34,5 35,4 31,1 69,8 40,4 47,0 42,0

2,5 2,3 4,4 3,9 4,8 5,8 3,8

3. Migration and human development

Moscow leads by some way on population growth, as with many other indicators. During the intercensal period, the population of the city rose by 13.6%. This is followed by St Petersburg, with an increase of 4.7%, after which follows a group of smaller cities. Each of the groups containing towns or cities with populations above 100,000 boasts positive growth between 1.1 and 3.1%. Conversely, in the group of towns with population less than 100,000, the population have over the eight years decreased by some 3%, with the smallest settlements contracting most of all (Fig. 13). The high population growth observed in Moscow is by no means unique for a capital city. Over roughly the same period, London grew just as phenomenally, by 13%21. Nevertheless, in ten years, Moscow grew by 10 times more all the cities of Russia combined.

“Small” cities losing out, “large” cities gaining Population migration is, in modern understanding, a critical resource for open development, for attracting people with high creative potential into a given city. Moreover, the balance of flows of migrants — arriving or leaving cities — is one of the most important indicators we have for evaluating the quality of the urban environment. Before we begin any study of migration, it is important to remember that today the urban population of Russia is distributed among more than 1100 towns and cities. The population is distributed among them very unevenly as much as 30% of the urban population of Russia is concentrated in just 13 of its major cities (Fig. 12) While the levels of internal migration are low, the dominant redistribution process is from places with smaller populations to larger towns and cities. Acorns are good until bread is found; people always look to better what they have. It is no surprise therefore that population movement continues to emulate the effects of global agglomeration. Looking at the results of the population census, the first thing that catches one’s eye is the population growth in larger cities — and the larger the city, the greater the growth (Table 11). Unfortunately this process comes at a cost to small towns and localities, where populations are declining.

Total population of towns/ cities in group, no. of people

2002

Moscow St Petersburg 1 million + 500,000-1 million 250,000-500,000 100,000-250,000 50,000-100,000 Less than 50,000

10 126 424

Increase in population over 20022010, %

Number of towns/ cities in group

2010 11 503 501

13,6

Within each of the sub-groups, there are cities with growing populations and cities with shrinking population. At the same time the share of shrinking cities among the small towns of Russia is significantly greater than among larger cities (Fig. 14). This is yet another manifestation of the trend of dominating population growth in large cities, against the background of shrinking populations in smaller towns and cities. Greater London’s population grew most of all in the inner city and in the east, reflecting the priorities of the successful London development plan. See Duncan A Smith. Overheating London and the Evolving North: Visualising Urban Growth with LuminoCity3D. org. September 27, 2014

Table 11: Population growth by group of cities

Group

The tendency to centralize population on a national scale has been observable for some time: “small” cities are losing out, and “large” cities are gaining. First is the population drains from rural to urban areas (a process of primary urbanisation), then is followed by movements from smaller towns to larger cities, then into the bigger cities and regional centres, and finally there is a push into the first and second capitals of Moscow and St Petersburg.

Number of towns/ cities with a positive growth

Proportion of towns/ cities with a positive growth (%)

100,0

4 661 219

4 879 566

4,7

100,0

12 628 430

12 830 570

1,6

90,9

14 319 743

14 763 500

3,1

58,3

12 015 335

12 146 124

1,1

52,8

13 843 326

14 105 196

1,9

46,2

10 914 159

10 854 230

-0,5

155

34,2

17 215 879

16 444 121

-4,5

781

157

20,1

The scale of spillover from small cities to larger ones leads to negative consequences, especially at the sharper extremes of this process: on the one hand, de-population of territories and desolation of rural areas; on the other hand, pressures created as a result of high inflows of people into Moscow, including the need for continuous infrastructure development, and the increased loads on transport systems both in Moscow and in the Moscow metropolitan area.

Settlement patterns in Russia continue to mirror global agglomeration tendencies

Centripetal, agglomerational tendencies limits the “horizontal” mobility of population in Russian cities. Urban centres, especially those with less than 500,000 people, find it extremely difficult to compete for human resources. Such centres can only rely on their own efforts to improve the quality of infrastructure and urban comfort, and can not rely on any kind of agglomeration effect.

The one exception is Makhachkala, which has demonstrated stubborn migration outflows over the past twenty years. As stated previously, this most likely points to inaccuracies in statistical data for calculating the index. Thus the hypothesis of a link between high human development and positive migration flows into cities is confirmed. This comparison reveals a number of interesting details. Considering relative, not absolute migration rates, i.e. weighted per 1000 residents, Belgorod, Yakutsk, Tyumen, Tomsk and Voronezh would all be considered city “leaders”. With the exception of Voronezh, which recently passed the million-citizen mark, these towns and cities are not among Russia’s largest. Conversely, Moscow and St Petersburg are on a par with cities posting average to high rates of migration growth, and compare to major cities such as Novosibirsk, Yekaterinburg, Kazan, Chelyabinsk.

Migration as an indicator of human development In this regard, the results of out study are especially important, given that they convincingly demonstrate the level of human development is associated with the growth of the population (Table 12). If one were to evaluate this link quantitatively, this is only a medium strength correlation (correlation coefficient of 0.5), with the caveat that these are complex and inadequate indicators relating to a long series of observations. The qualitative aspect of this correlation is perhaps more significant. Among the cities that our methodology considers “leaders” in human development index, 87% observed positive population growth between 2002 and 2012. Conversely, in the group of cities selected as “outsiders” in terms of human development index, only 21% boasts the same positive population growth. Note that the analysis used population growth data excluding any growth due to administrative or territorial transformations. As a result, our analysis only includes population growth/decline caused exclusively by natural increases (births-deaths) and migration. Analysing the correlation between human development indices and migrational flows provides no less interesting a picture. In the first analysis, which compared migration increase (i.e. the difference between arrivals and departures) with an index of human potential, selected “leader” cities were compared to Moscow, St. Petersburg and Voronezh. It turned out that 14 of the 15 cities considered leaders have medium or high levels of migration growth (Table 13).

Fig 13. Population growth by type of city

800

Number of cities

700

600 16

400

300 200 100 1 million+

0,5–1 million2

50,0 00– 500 000

100,000– 250,000

Groups of cities by population

50,000– 100,000

less than 50,0 00

500

3 Number of cities

+14

15%

900

Population size, millions

The final step of our analysis was to evaluate the ratio of migrants arriving into a given city. One might assume that from the point of view of competition, migrant flows from other regions and countries would represent the most significant indicator of the quality of a given city’s urban environment. Logic would suggest that these migrants have consciously chosen a better life to the one that they leave behind. It turns out, however, that this hypothesis is valid only partially (Table 14) Overall, the number of migrants arriving to Russian cities is respectively low. For example, over 2005-2013, Novosibirsk and Yekaterinburg, both major cities and regional capitals, attracted only half of the number of migrants of St Petersburg, and only a third of those of Moscow. At the same time, major cities with low human development indices — Samara, Volgograd, Omsk — attracted only half as many migrants as “leader” cities.

Fig 12. Population growth, % // Groups of city by population

Belgorod stands out with consistently elevated relative indicators (coefficients) of migration growth. In both the decades analysed, this town attracted the largest flow of migrants. This important fact, together with the city is high human development index, demanded a more detailed case study.

+3 +1

0 -1

-3 -6

-4 less than 50,000

50,000– 100,000

100,000– 250,000

250,000– 500 000

0,5–1 million

1 million+

St Petersburg

Moscow

49 Table 12. Correlation of human development index with population growth 23

There appears to be no discernible difference between “leader” cities and “outsider” cities when it comes to the proportion of migrants resettling from within a region and from outside (other Russian regions and other countries). In both groups, the proportion of people moving to the regional capital from elsewhere in the region is more or less equal and close to half of the total flow into a given city. This suggests that growth in regional centres is half fuelled by local resources, and half by open competition of free-flowing human resources, regardless of the urban environmental quality of cities.

Cities Leaders

The highest index of human development and attractiveness for the migrants are observed in cities of different sizes: Belgorod, Tomsk, Krasnodar, Ekaterinburg, Novosibirsk.

Our surveys were in line with statistical data in suggesting that the inflows of migrants are not so much connected with active interregional mobility as with more local migrants, arriving from neighbouring residential areas. Another important aspect the survey’s discovered was that citizens of modern Russian cities are not generally inclined to change their place of dwelling. When asked if they planned to move out of their city for another city in Russia or abroad, respondents answered in the following way: – Definitely or likely - 9.5% – Most unlikely or definitely not - 85.3% – Hard to say 5.3%22

22 Data was extracted from two official sources — the Multistat statistical portal and the Federal State Statistics Service

Source: Multistat

Migration: hypothetic and real Sociological surveys conducted in top-5 HDI cities (Belgorod, Tomsk, Krasnodar, Ekaterinburg, Novosibirsk) and in Voronezh confirmed some hypotheses about the motives of modern urban migration. The first point to note is that ratio of indigenous populations to migrants across the entire sample is on average 3 to 2. About half of people who identified themselves as migrants arrived in the city more than 10 years ago. In Krasnodar, for example, 30% of migrants fall into such a group, with 70% arriving within the last 10 years. In other words, the town’s urban population was renewed by almost a third in the short space of 10 years.

Index value

Population growth (2002-2012)

Ekaterinburg

5,564

9,2

Krasnodar

5,521

-2,3

Chelyabinsk

5,483

Kazan

Cities Outsiders

Index value

Population growth (2002-2012)

Omsk

4,298

-0,1

Volgograd

4,313

-2,4

3,6

Perm

4,750

-0,9

5,470

4,6

Samara

4,761

-0,3

Novosibirsk

5,448

5,3

Makhachkala

6,653

28,2

Astrakhan

4,065

4,1

Tomsk

6,626

10,8

Ulyanovsk

4,368

-2,9

Tyumen

6,325

14,4

Ryazan

4,466

0,5

Irkutsk

5,884

0,8

Lipetsk

4,511

0,4

Cheboksary

6,439

4,0

Kostroma

4,254

-3,4

Belgorod

6,249

8,4

Kurgan

4,266

-4,7

Yakutsk

6,102

30,9

Kaluga

4,331

-3,1

4,461

-1,6

Ulan-Ude

6,042

14,6

Arkhangelsk

Tambov

5,872

-4,1

Bryansk

4,584

-4,0

Stavropol

5,670

14,1

Murmansk

4,662

-8,9 Population growth over 10 years (2002-2012), excluding any growth caused by administrative boundary

Fig.14. «Growing» and «shrinking» cities by population

Moscow St Petersburg 1 million+

100% 91% 58%

42%

250,000– 500,000

53%

47%

100,000–250,000

46%

54%

50,000–100,000

100%

0.5–1 million

less than 50,000

34%

66% 20%

80% 10

Proportion of “shrinking ” cities

100 Proportion of “growing” cities

Population and human development growth are mutually dependent and cyclical

Table 13: Correlation between human development index and migration growth

City

Total migration, persons

Immigration rate per 1000 people

1989-2000 гг.

Number of arrivals 2005-2013 (thousands)

2001-2010 гг.

45926 2756 29592 69329 34539 361664 26793 22101 14282 25720 12110

39995 29234 44954 59212 67766 564956 27733 66479 202347 17197 22366

136,3 13,1 57,9 81,7 31,2 41,6 54,9 17,1 3,2 72,5 10,9

112,2 110,7 74,3 64,1 59,3 54,6 50,9 49,2 43,7 43,2 20,3

Saint Petersburg

24413

8932

54,1

19,5

13 14 15 16

Chelyabinsk Novosibirsk Tambov Irkutsk

56033 51654 15358 20943

27167 10164 -2989 -2705

39,3 79,9 42,7 67,1

18,4 12,2 -7,4 -9,4

Ulan-Ude

32658

-6960

55,0

-11,9

Makhachkala

-11088

-21742

-24,0

-30,5

High human development and migrational attractiveness scores are observed in cities of varied population size: Belgorod, Tomsk, Krasnodar, Yekaterinburg and Novosibirsk

Source: Federal State Statistics Service

Belgorod Yakutsk Tyumen Voronezh Tomsk Kazan Moscow Stavropol Cheboksary Krasnodar Ekaterinburg

City

Source: Federal State Statistics Service

1 2 3 4 5 6 7 8 9 10 11

2001-2010 гг.

Table 14: Migration structure among HDI “leader” cities

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Novosibirsk Tomsk Voronezh Krasnodar Belgorod Tambov Ekaterinburg Chelyabinsk Kazan Tyumen Stavropol Irkutsk Ulan-Ude Cheboksary Makhachkala Yakutsk Moscow St Petersburg

272,6 136,2 173,5 153,4 97,8 52,7 281,0 169,0 172,2 193,5 92,9 105,1 91,1 85,6 35,8 81,3 879,1 652,0

Share of migrants (2005-2013) Same region

30,5 37,1 41,1 42,0 46,0 46,4 51,4 52,3 54,2 57,4 58,6 58,8 64,5 69,1 76,6 82,6

Other Russian regions

53,2 46,5 41,6 52,3 43,0 27,1 40,6 35,2 32,0 25,2 35,5 35,0 33,7 24,1 21,8 13,7

C.I.S. countries Other

14,4 15,9 14,4 4,7 10,5 19,3 6,8 11,4 12,7 15,8 5,3 5,8 1,5 5,1 1,4 3,6

1,9 0,5 2,9 1,0 0,5 7,2 1,2 1,1 1,1 1,6 0,6 0,4 0,3 1,7 0,2 0,1

Table 15: Migration structure among HDI “outsider” cities

City

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Name

Astrakhan Kaluga Ulyanovsk Murmansk Lipetsk Ryazan Volgograd Samara Omsk Kostroma Kurgan Perm Bryansk Arkhangelsk

Number of arrivals 2005-2013

75,9 48,0 49,6 52,5 56,9 65,9 92,4 128,2 157,2 46,2 39,9 125,9 56,2 46,0

Same region

Other Russian regions

C.I.S. countries

Other

35,5 35,7 36,5 36,9 41,4 42,3 45,4 45,6 47,3 51,0 55,6 64,2 65,2 69,7

36,1 35,9 50,2 42,4 36,7 46,4 44,3 43,5 36,3 37,5 31,0 23,5 26,8 22,4

25,6 27,5 11,2 20,0 21,2 10,4 9,5 10,5 15,6 10,4 13,0 10,5 7,5 5,6

2,8 0,9 2,1 0,7 0,7 0,9 0,8 0,4 0,8 1,1 0,4 1,8 0,5 2,3

53 Interaction between population growth and human development The development of cities, attracting new populations to them, is closely connected to the idea of human development. The correlation between population growth (both due to natural processes as to migrational ones) human development is of a cyclical nature. Here we have a mutual influence, and not just a causal relationship (Fig. 15). The development of human potential creates an influx of new people, and an influx of new people, in turn, improves human development potential.

An even more definitive picture is uncovered by evaluating related questions concerning the motives for moving to another city. There were two questions of the survey that addressed this theme, though they were presented in various contexts. In the first instance, this was a group of questions designed to clarify where the respondent was born, currently resides, and from where and when he/she moved; the respondent was invited to give reasons for moving. In the second instance, the respondent was asked whether he/she enjoyed living in the town or city, and what exactly suited him/her, and what did not. The respondent was also asked questions about work, leisure preferences and so on. Here is where a question was put about the possibility of moving to another town. For those that answered affirmatively, a further set of questions look to establish the hypothetical motives of such a move (Table 16). Comparing real reasons and hypothetical motives for moving to another city revealed an entirely negative correlation. In the hypothetical, respondents placed a “desire to live in comfortable conditions” in first place; second was the “desire for novelty and change”; and third, “Family and personal circumstances”. Bringing up the rear of the hypothetical motives were reasons relating to work and education. In the reality, reasons of relocation were quite different. In first place are reasons of family and personal circumstances, which incorporate the whole spectrum of reasons: from parents moving, to marriage where the husband or wife moves to the home town or city of their spouse. The second most frequent reason cited was education, by which we should understand the respondents remains in the place where they went to obtain their education. The third reason is the offer of work. Consequently, we might conclude that hypothetical motives such as career opportunities, professional growth or a desire to live in a more comfortable environment are not actually all that significant factors when it comes to the reality of migration.

The correlation between human development index and population growth is independent of city size, which tells us a lot. First, we can conclude that our method of calculating human development index using statistical indicators turned out to be justified. Second, indicators relating to population growth, migration growth and migration flows are useful in assessing cities with the highest human development and most attractive urban environments. Finally, we can also state that towns and cities that want to improve must face two simultaneous challenges: a) finding new ways of stimulating migrant flows while b) improving human development to keep people in the city and improve the attractiveness of the urban environment (which, in turn, becomes the basis for a subsequent of migration to the city).

All of this points to the passivity of the majority of citizens, their reluctance to actually seek better lives for themselves, new opportunities or, at the very least, well paid work. In other words, attracting significant numbers of new, educated, enterprising citizens to a new city is an incredibly difficult and very necessary thing. Table 16: Motives for possible relocation compared to reality

Possible answers to the question (mark any number of options)

More comfortable life Novelty and change Professional growth Family and personal circumstances Offer of work Education

Fig 15. correlation between population growth and human potential

What might compel you to move from your current town/city to other cities of Russia, or abroad?

Why did you move to your current city?

% of responses

Rank

% of responses

Rank

51 40 31 30 22 6

1 2 3 4 5 6

20 14 15 44 23 34

4 6 5 1 3 2

Human potential

Population growth

4. Human development and the urban infrastructure

determining the mentality of a given place. Tomsk, for example, is a university centre without other significant drivers of economic growth. At the same time, the city is leader on many indicators, including the level of human development.

Drivers of the modern city Understanding how Russian cities can improve the quality of their environment and the level of human development seems, from a managerial perspective, to be a crucial task. In this section, we investigate the way human development is influenced by several factors and conditions that are linked to the actions of city administrations and business.

A comprehensive analysis of quantitative and qualitative parameters relating to the level of educational and medical provisions is beyond the range of this work. It is now sufficient to emphasise just how much these factors are becoming drivers for improving the standard of living in Russian cities.

Our calculations of human development index scores identified “leader” cities that generally boast relatively high average wages. These can be considered one of the key indicators of the state of the city economy, giving important information about higher education and health provision. City and university authorities located in cities with leading HDI scores look to use every opportunity they can to develop their universities — improving the quality of education, increasing institutional prestige, and by attracting the best and brightest students. It is important to note that universities and higher educational establishments are becoming increasingly significant parts of the local economy, creating jobs in the intellectual sphere, performing research and consulting work, and creating high-tech start-up businesses. Currently, there are approximately 1 million students in Moscow, and a further 300,000 teachers and higher education employees. This equates to approximately a quarter of the economically active population of the city, but it is incomparable with employment in any factory. Not only contributors to the local economy, universities are also the decisive factor in

Private medicine is playing an increasingly important role in the economies of successful cities

Modern medicine plays just as significant a role in these cities. Any given healthcare system results from a complex infrastructure of hospitals, clinics, dispensaries, modern medical equipment, medical training, admissions, and health insurance systems. Successful towns have been able to implement a whole range of measures designed to improve this complex infrastructure. Private healthcare facilities are also playing a more prominent role and the economy of the city.

Urban structure and support for small businesses This study looked to identify any link between human development scores and various other indicators relating to infrastructure and the business environment. It should be said that conducting such a study given the paucity of data available from government statistics services is quite difficult. After careful deliberation, we chose 22 indicators that give us some chance of collecting representative quantitative data on cities.24 (Table 17). 24 Data was extracted from two official sources — the Multistat statistical portal and the Federal State Statistics Service

Unfortunately, most of these indicators poorly reflect the most significant qualitative aspects of the phenomena under consideration. For example, the statistics present the average number of students per general educational institution. However, this tells us very little about the quality of educational services provided. A large number of children in one school might be evidence not only of a deficit in school places, or a two shift system of education. It might also be due to the high quality of education being offered: a large number of children might simply want to go there. An illustration of this can be found in the cases of Yekaterinburg and Chelyabinsk, which on this indicator would be close to being considered poorly performing outsiders. At the same time, however, the Ministry

Higher education institutions are key components of city economies, creating new jobs in the intellectual sphere

59 of Education recently published a rating of the best schools in Russia, which saw two Yekaterinburg schools included in the top ten, and one Chelyabinsk school make it into the top twenty five, i.e. up alongside the best schools of Moscow and St Petersburg.

Our study was conducted by means of correlation and regression analysis, i.e. identifying the presence and strength of any relationship between indicators. The data was tested for autocorrelation and completeness; extreme and false values were excluded by standardising the scores. The result of our analysis showed that, when taken in totality across all cities, these indicators have no significant correlation with the index of human development. It is only in cities with a certain population level where such blocks of indicators begin to have a discernible effect on HDI scores. (Tables 18, 19)

Table 17: List of indicators and data completeness Block of indicators

Indicator

Accommodation

Living area per resident, sq.m

100

Education

Number of children per one hundred places in preschool education

100

Average number of students in general educational institutions, per one institution

Healthcare

The most significant influence on HDI is made by the block of urban infrastructure indicators in cities with population greater than 500,000 people. Indicators relating to investment and construction activity seem to have much less impact in such cities, with the one exception of the indicator showing housing provision. Analysis of the block indicators relating to the support of SMEs showed no significant correlation with human development scores. In our opinion, this is probably due to the lack of good open source data available at the municipal level. Firstly, the data reflects only one side of the question, namely support from authorities, but business development in any given city is not only dependent on this. We also have to factor in support from federal and regional programs, bureaucratic issues (ease of registering a business, obtaining the necessary permissions for construction and so on), access to urban infrastructure, including public utilities, population characteristics (purchasing power, behaviour, habits) and much else besides.

Trade and public catering

Transport

An analysis of the significance of every indicator on a city-by-city basis in our sample (See table 6, section 1) allows us to make a series of conclusions. First, the leadership position of every concrete city is made up from a unique set of indicators. There is no single recipe for success. Second, high scores of infrastructure indicators are not the only - nor in themselves a sufficient - marker of human development. Third, it is sufficient for towns and cities, Table 18: Coefficient of determination over four blocks of indicators (all cities)

Block of indicators

Urban infrastructure Development of the construction sector Support for SMEs Security

Value R-squared

0,360 0,153 0,001 0,002

Support for SMEs

Security

Completeness of dataset

Number of doctors, per 10,000 population

100

Number of hospital beds, per 10,000 population

100

Outpatient capacity per 10,000 population, visits per shift

100

Volume of retail trade for non-SME organisations per 1,000 population, 000s of rubles

100

Public catering capacity, places per 1,000 population

Density of public paved roads per thousand square kilometres, kilometres

Average number of journeys on public transport, per 1000 population

Total area of residential developments brought into use over one year, per one resident, sq m.

Area of land allocated for construction, hectares per 10,000 people

Area of land allocated for residential development, including private development, hectares per 10,000 people

Area of land allocated for residential development before 2010, and not developed within three years, sq km

Area of land allocated for capital construction and not developed within five years, sq km.

Number of building permits issued, units

Number of permits issued for commissioning facilities, units

Share of municipal property provided on a long-term basis to SMEs, percent

Proportion of SMEs established within the last year that are supported within the framework of local government programs for SMEs

Total area of infrastructure designated to support SMEs, sq km per 100 SMEs

Local spending on developing and supporting SMEs, thousands of rubles per one SME

61 especially those with populations under 500,000, to have a basic level of urban infrastructure. Once a certain level of provision has been reached, and can be maintained at that level, city administrations can focus their resources on other areas of human development, maintaining the optimum balance between different activity sectors.

Construction activity Another important results of our study was to ascertain the relationship between the level of human development and activity in the construction sector. Such activity can be summarised using indicators showing new housing supply 25, and the average number of construction permits issued over several years per head of population. This second figure reflects both the state of the construction sector and the prevailing conditions for doing business in the city. Our sample group of cities, which included both “leaders” and outsiders” when measured against HDI, were divided into three sub-groups according to housing construction activity (high, average and low).26 The ratio of “leader” to “outsider” cities was then compared in each group. 26 “High” levels of construction activity were taken to mean more than 0.75 m² per person per year; “average” – 0,50-0,75 m² per person per year; and “low” — less than 0.50 m² per person per year

Average levels of new housing stock - public and private - over 5 years (20082012), sq. m per person

and water) in ‘000s of rubles per capita (2011 figures)

The analysis shows that HDI “leader” cities are much more active when it comes to bringing new housing stock into circulation. 33% of the cities boast high scores in this category, while more than half register average scores. As a comparison, more than half of the HDI “outsider” cities have exceptionally low scores on new housing, and only a third are in the average range (Table 20). When it comes to construction permits, however, the situation is not so obvious. There is a trend of sorts: 40% of “leader” cities have a high rate of granting permits, while the same can only be said for 14% of the “outsider” cities27 (Table 21).

From this we may conclude that both the volume of new housing supply, and the existence of bureaucratic barriers to construction work have a direct bearing on HDI scores. Cities with high levels of new housing construction and clear, organised procedures for obtaining necessary permissions, are more likely to be able to attract active and demanding people.

Traditional and “new” economies of the Russian city The relationship between industrial production and human development is an essential issue for city managers. The most suitable indicator of industrial production for our purposes is manufacturing output per capita.28 Our analysis once again ranked three groups of cities according to the level of industrial production (“low”, “average” and “high”)29. What results is the following picture: practically all the cities with leading HDI scores do not rank among the leaders when it comes to industrial production (93%); conversely, “outsider” cities often found themselves in the group of the most industrially active cities. Indeed, more than a third rank among the most industrialised cities of the country (Table 22).

27 “High” – An average of more than 450 construction permits issued per year (20112013); ”average” – 260-450 permits, “low” – less than 260

29 High - more than 300, 000 rubles per capita; Average - 100,000300,000 rubles per capita; Low - less than 100,000 rubles per capita

The indicator used to reflect the level of manufacturing output was as follows — volume of home-produced goods shipped + volume of works and services carried out by type of economic activity (mining, manufacturing, production and distribution of electricity, gas

What these figures show is that a process of transforming the structure of the economy in Russian cities is actually already underway.30 Traditional industrial activity no longer plays such a key role in the cities that are most attractive to educated, healthy, enterprising Russians. Instead, industries of the “new economy” are pushed to the forefront. Of course, this does not mean that cities should rid themselves of successful industrial enterprises. “Leader” cities should instead look to develop industrial production with high added value, generated in the first instance by the creative inputs of its citizens (high-tech production, services, academia, Enterprise and so on). In post-Fordist capitals such as Moscow, traditional industry is relocated to smaller satellite towns, while the inner city remains a core for high-tech industrial production and sectors of the new economy.

The concept of the “new economy” has not been fully settled in contemporary academic literature; there are different ways of defining it. In our study, the term «new economy» is taken to mean any kind of economic activity with high added value, where intellectual costs substantially outweigh material costs in production. The key institutions of the new economy are universities; informational, academic, cultural and medical organisations, which bring together theoretical and applied knowledge; as well as the communications industry, computer engineering, programming, services and trade, tourism,

30 Such trends have been described in works by NV Zubarevich, AS Puzanov, LE Limonov, VN The Princess, LV Smirnyagin, VV Klimanova, LM Hochberg et al.

Table 19: Degree of influence of indicator blocks on human development index

Population strata, 000s

Coefficient of determination values (R-squared) per block Urban infrastructure

More than 750 500-750 250-500

Strong correlation R-squared > 0.7

Construction sector development

0,794 0,869 0,446

Notable correlation R-squared = 0.4 - 0.7

Weak correlation R-squared = 0.2 - 0.4

Support for SMEs

0,340 0,484 0,113

No correlation R-squared < 0.2

0,064 0,066 0,030

Human development depends more on urban infrastructure than it does on general construction activity

Table 20: Housing construction HDI “Leader” cities

HDI “Outsider” cities

Cities

Proportion of all cities in group

Cities

Proportion of all cities in group

Lipetzk

Samara Ryazan Astrakhan Ulyanovsk Bryansk

Omsk Perm Volgograd

Our sample included 942 580 corporate bodies over 29 cities

Table 21: Building permits issued HDI “Outsider” cities

Cities

Proportion of all cities in group

Cities

Proportion of all cities in group

Yekaterinburg Krasnodar

Omsk Kaluga

HDI “leader” cities HDI “outsider ” cities

43 40

Krasnoda r

Novosibirsk

Yekaterinburg

Kazan

Tomsk

Tyumen

Yakutsk

Perm

Irkutsk

Stavropol

Samara

Chelyabinsk

Belgor od

Murmansk

Arkhangelsk

Kaluga

Ryazan

Kostroma

Cheboksar y

Omsk

0 Volgograd

Perm Volgograd Ulyanovsk Kostroma Kurgan Bryansk Samara Lipetsk Ryazan Astrakhan Arkhangelsk Murmansk

Ulan-Ude

Tambov

Low Irkutsk Less than 260 permits Makhachkala issued Ulan-Ude Cheboksary Yakutsk

100

Ulyanovsk

Chelyabinsk Tomsk Stavropol Belgorod Kazan Novosibirsk Tyumen Tambov

120

Bryansk

Average Between 260-450 permits issued

HDI “Leader” cities

Lipetsk

High More than 450 permits issued

Fig. 16. Number of newly established enterprises operating within the new economy, per 10000 population 32

Astrakhan

Rate of activity

1. Organisations operating in the primary and secondary sectors of the economy; government bodies and those providing basic government services, including justice, security, secondary education and so on 2. Organisations in the tertiary sector of the economy, in particular wholesale and retail trade 3. Other organisations operating in the tertiary sector, in particular hose offering services, with the exception of trade and all activities related to the “new economy” 4. Organisation operating in sectors of the new economy, in particular: • Communications (utility services, broadcasting and so on); • Financial intermediation (leasing, loans, etc); • Auxiliary financial activities (brokerage, insurance and other risk intermediation); • Insurance; • Analysis and data processing; • Software development; • Research activities; • Activities in the field of higher and vocational education; • Provision of medical services.

Kurgan

High Krasnodar more than 0.75 m² per Kazan person per year Tyuen Stavropol Tambov Average Yekaterinburg 0,50-0,75 m² per Novosibirsk person per year Chelyabinsk Tomsk Irkutsk Belgorod Yakutsk Cheboksary Low Makhachkala less than 0.50 m² per Ulan-Ude person per year

In an attempt to measure the presence of the new economy in our city case studies, we analysed data for corporate bodies officially registered and operating as of 1 September 2014.31 Limiting our analysis to operational organisations was deliberate, since we wished at only to show the dynamics of growth, but also the dynamics of their “survival”. The organisations were split into headline groups organised according to activity type:

Our data source was the SPARK (Interfax) database, which is based on data of the Federal Tax Service of Russia and the Federal State Statistics Service. Each legal body or sole-trading enterprise was assigned a main activity code according to the NACE classification system, and recorded with date and city of registration

Makhachkala

New housing supply

healthcare and eco-businesses. The new economy is playing an increasingly important role when it comes to GDP.

We performed a breakdown of new corporate registrations according to the above criteria in each of the cities we analysed, expressed as a total number of companies registered per 10,000 population The analysis allowed us to identify a whole range of patterns in the city economy as a whole, and for the new economy sector in particular. Larger cities (that is, with populations above 750,000), in the first instance HDI “leaders”, show reasonably stable growth of newly established industries, even at times of economic crisis (Novosibirsk, Yekaterinburg). On this indicator, we can safely say that HDI “leaders” significantly outperform “outsiders” with similar populations (for example Tomsk, when compared against Astrakhan) (Fig. 17). Nevertheless, it is also clear that Russia’s new economy only really exists in small clusters. Russian cities have not yet reached tipping point, and are some way off a full transition to the new economy model. Note that in order to sharpen the picture, tertiary sector organisations (services) and trade were not included in our analysis (Fig. 16). Airport development as a factor for increasing population mobility Many researchers have described how a population’s mobility — both internally, within the region and country, and internationally — directly affects the level of human development. It follows that the quality of a city’s external transport infrastructure is significant. We consider this factor to be particularly important as it is directly related to the activities of regional and city government. On the one hand, how local government manages the development of external transport facilities also determines its policies with respect to population mobility. On the other hand, it can also act as a marker for the effectiveness of local government structures, since it demonstrates their ability to resolve a complex range of issues relating to finance, federal regulation, private investment, cooperation between regional and municipal authorities, land allocation, development master-plans and architectural/ construction schemes. In the study, we chose to evaluate only airports from the whole range of external transport indicators, they are the most indicative markers of the system of regional government as a whole, while also being the most significant factor in improving the competitive advantage of a city. Table 22: Industrial production in Russian cities

Level of industrial production High

HDI “Leader” cities Proportion of all cities in group

Cities

Proportion of all cities in group

Chelyabinsk

Kaluga

Perm Omsk Volgograd 80

Ryazan

Tambov

Samara

Yekaterinburg

Ulyanovsk

Krasnodar

Kurgan

Tyumen

Bryansk

Yakutsk

Astrakhan

Kazan

Kostroma

Tomsk

Murmansk

Cheboskary Ulan-Ude Novosibirsk Irkustsk Low

Stavropol Makhachkala

Total passenger flows in cities with leading HDI scores were, according to 2013 data, three times as much as the passenger flows in “outsider” cities (Table 23). At the same time, the airports of Moscow and St Petersburg transported a significantly greater number of passengers over the same period — 71.2 million and 12.9 million people respectively. This is yet one more stark reminder of the agglomeration effect and the dominating role of Moscow and St Petersburg in the country perspective. It should not, however, take away from the leading nature of cities with high HDI scores when compared to other Russian cities. Note also that for the majority of airports in cities with leading HDI scores, the geographical coverage of services is quite broad, and in some cases includes flights to more than 100 destinations (for example, Yekaterinburg). Conversely, airport serving cities with low HDI scores usually have limited coverage, flying in the main to Moscow, St Petersburg and major cities located nearby.

City / groups of cities

HDI “Outsider” cities

Cities

Belgorod

In the group of 14 cities with the lowest HDI scores, only 30% undertook airport reconstruction projects over the same period. Indeed, at the moment of writing this study, there were no functional airports in some cities of this group (Ryazan, Kaluga). Others had only recently outlined plans the developing and reconstructing airport infrastructure (between 2013-2014). Five airports in this group serve only domestic destinations and do not have international status.

Table 23: Passenger flows of airports in HDI “leader” and “outsider” cities in comparison with Moscow and St Petersburg

Lipetsk

Average

The analysis showed that of the 15 selected cities with high HDI scores, 60% performed airport modernisation in the period between 2005 and 2012. Some of these cities carried out full-scale reconstruction, with a full upgrade of technical equipment (Belgorod, Kazan); the very fact the relatively small Belgorod undertook such a complex managerial project is significant in itself. Moreover, practically all the airports in cities with leading HDI scores (Tambov excepted) serve a number of international destinations.

Arkhangelsk

HDI “Leader” cities HDI “Outsider” cities Moscow St Petersburg

Total passenger traffic, millions of passengers 19,5 6,5 71,2 12,9

Fig. 17. Rate of new business registration

“Leader” cities

“Outsider” cities

Cities with population greater than 750,000

Novosibirsk

ед.

Chelyabinsk

Omsk 100

100

0 ’02

’03

’04

’05

’06

’07

’08

’09

’10

’11

ед.

100

’12 2013

’02

’03

’04

’05

’06

’07

’08

’09

’10

’11

’12 2013

’02 ’ 03 ’ 04 ’ 05 ’ 06 ’ 07 ’ 08 ’ 09 ’ 10 ’ 11 ’ 12 2013

Cities with populations between 500,000-750,000

Tomsk

’02

’03

100

’04

’05

’06

’07

’08

’09

’10

’11

Tyumen

100

’12 2013

’02

’03

’04

’05

’06

’07

’08

’09

’10

’11

’12 2013

Astrakhan

100 80 60 40 20 0

’02 ’ 03 ’ 04 ’ 05 ’ 06 ’ 07 ’ 08 ’ 09 ’ 10 ’ 11 ’ 12 2013

Cities with populations between 250,000-500,000

Belgorod

100

Cheboksary

100

’03

’04

’05

’06

’07

’08

’09

’10

’11

100

0 ’02

Kurgan

’12 2013

’02

’03

’04

’05

’06

’07

’08

’09

’10

’11

’12 2013

’02 ’03 ’04 ’05 ’06 ’07 ’08 ’09 ’10 ’11 ’12 2013

New economy

Trade

Services

First and second sector

5. The virtual city: Internet and social networks

The Internet gives life to the modern urban community, playing the role of a social tool while also regulating the actions of urban activists. As a major source of innovation, the Internet is at the heart of urban development, the engine behind phenomena such as Open Source Urbanism33, when grassroots initiatives are strengthened by new technologies. The extent of Internet penetration, and the diversity of the local virtual network, are two key indicators of the quality of human development in a city. The combination of social network and news feeds are able to form or destroy a city brand: they determine the desirability of a city for education, work and living. Internet and social network penetration Any study of the Internet in Russian cities should start with an analysis of the level of Internet penetration in people’s daily and professional lives, and then proceed to an analysis of the sophistication and diversity of local social networks. This, correspondingly, is the starting point of this study. Saskia Sassen, Open Source Urbanism (www. domusweb. it/en/oped/2011/06/29/ open-sourceurbanism.html).

Nationwide census, 2010

Fig. 18. City distribution by level of internet penetration and population size 85

There is no satisfactory Russian study that explains these figures. Attempts have been made to establish a link between Internet penetration figures and connection cost, the share of households with broadband connections, and the number of students in a city, but they have failed to identify statistically significant relationships.

Level of internet penetration, %

Analysis of social media penetration, on the other hand, highlights other features. The first thing to say is that headline user statistics give us only a limited idea of the real number of social media users, since any given person might have several social media accounts. Moreover, a proportion of the social media profiles are robots, created for commercial and advertising purposes. Thus, in cities with populations less than 250,000, on average there are 1.7 social media accounts per head of population; in cities with larger populations, the figure is 1.15. This discrepancy is made even clearer when analysing the number of social media accounts per active

65 Moscow 58% 11.48m people

St Petersburg 55% 4.89m people

As of 2010, the proportion of urban dwellers using the Internet in Russia was, on average, 44.3%.34 Our study showed that the geographical variation in Internet penetration is dependent on the population size of cities. In the group of cities with the average population up to 750,000, values strongly deviated from mean, both in the upper and lower segments (range from 33% до 63%). In the group of cities with populations larger than 1 million, including Moscow and St Petersburg, the range of internet penetration values observed was smaller, clustered at or above the mean (45% - 58%) (Fig. 18).

44,3

Mean level of internet penetration

Population size More than 750,000 Between 250,000–750,000

Less than 250,000

25 0

200

400

600

800

1000

1200

1400

1600

Population size, 000s

The level of internet penetration in Russia is on average 44.3%

73 Internet user. In cities with populations more than 250,000 people, there are between 2 and 2.15 social media accounts per Internet user; with populations lower than 250,000, the range is larger — from 2.2 to 4 social media accounts for each active Internet user.

Differentiating the cities using lines of best fit, you can see the group of cities with populations in excess of 750,000 — regional capitals or competing with regional capitals — are distributed some way above the average (for example, Krasnodar has 5.51 and and Samara at 3.73 accounts per one Internet user, figure 19). Nizhny Novgorod, capital of the Volga Federal District, is the one exception here, registering 1.67 accounts per one Internet user. In cities with populations lower than 500,000 people, the number of social media users per Internet user is significantly lower, and depends on the infrastructure availability and geographical location of cities (most of are located in the northern parts of Russia).

Number of social media accounts per internet user

Anadyr

Gorno-Altaysk

Krasnodar Magas

Vladimir

Ivanovo

Moscow 4.2 11.48m people

Cherkessk

The picture becomes even more complicated when you analyse accounts age groups. For example, simple profile data from social media networks would suggest there is a large group of users above 100 years of age. The 2010 Census would suggest that the number of Internet users in this age group is of the order of thousands of times smaller35 (Fig. 20). The phenomenon of such “dead souls” might theoretically be due to profiles of dead people remaining on social media systems, but is more likely to be indicative of commercial bots.36 This study was unable to uncover any explicit relationship between the degree of Internet penetration and of social media use. On the other hand, there is a demonstrable link between both these indicators and human development indices, as calculated above. In order to visualise the connection, it is necessary to once again rank the cities into HDI “leaders” and “outsiders” before comparing against indicators of Internet use and social media use (Table 24).

Fig. 19. Social media accounts vis-a-vis Internet users in Russian cities of regional significance

Russian National Census, 2010

4 Maykor

Tambov

Blagoveschensk

Rostov-on-Don

Kurgan

Samara

Vladikavkaz Chita

Khabarovsk

Yekaterinburg

Salekhard

Novosibirsk

Pskov Vologda P-Kachatsky Saransk Ryazan Syktyvkar Archangelsk Lipetsk Petrozavodsk

St Petersburg 2.25 4.89m people

Yaroslavl Izhevsk

0 0

An “internet bot” is a special programme fulfilling automatic functions through the same online interfaces that a real user would.

0,25 250000

0,50 500000

0,75 750000

The number of social media accounts registered is many times greater than the number of real users

1,00 1000000

1,25 1250000

1,50 1500000

1,75 1750000

Population size, millions

Upper deviation

Border of upper deviation

Lower deviation

Border of lower deviation

Main group

Trendline

75 Table 24: Internet and social media penetration in HDI “leader” and “outsider” cities

In the first group of cities, that is with populations greater than 750,000, both HDI “leaders” and “outsiders” had similar Internet penetration values. In both groups, the level of penetration is above average; in other words, the level of human development it not particularly dependent on the level of online development. The picture changes in cities with smaller populations. Here, we observe a direct relationship between Internet penetration figures and the levels of human development. In the group of HDI “leader” cities, 70% of cities have above-average figures on Internet usage; in second group of HDI “outsider” city, only 30% record above-average figures. It would seem reasonable to assume that increases in Internet availability and improvements of information infrastructure can contribute to the development of human potential in cities with population below 750,000 people. This factor becomes less significant in more developed cities. This is an important result, and might provide some clues for local government looking to improve human development levels. Diffusion of innovations across Russian cities Many researchers believe the Internet is gradually becoming the principal channel for diffusing all types of innovations across populations37. The present study approached analysis of such trends by observing key words connected with new technologies, goods, services and activity type. The indicator used was the search frequency of such words in various regional cities. The initial hypothesis was taken to be that Moscow services as the origin for diffusion of these new words, and that the remaining cities, over time, adopt the innovative words. FOMnibus nationwide representative survey of 1500 adult respondents conducted on 3/9/2014. The survey consisted of face-to-face interviews in 100 cities, towns and villages across 43 Russian regions. 38

Castells M. The Internet Galaxy: Reflections on the Internet, Business, and Society

Cities by population, 000s

HDI “Leader” cities

750+

Yekaterinburg

52,7

0,50

Omsk

49,8

Krasnodar

44,1

0,90

Volgorod

45,8

0,04

Chelyabinsk

49,0

-0,05

Perm

47,7

-0,18

Kazan

50,2

-0,06

Samara

46,6

0,63

Novosibirsk

52,6

0,44

500-750

Makhachkala

35,5

0,25

Astrakhan

37,4

-0,11

Tomsk

53,5

0,05

Ulyanovsk

47,0

-0,15

Tyumen

50,1

-0,07

Ryazan

43,0

-0,31

Irkutsk

48,5

0,08

Lipetsk

43,4

-0,40

250-500

Cheboksary

50,0

-0,13

Kostroma

40,4

-0,21

Belgorod

46,9

0,11

Kurgan

39,5

0,22

Yakutsk

44,2

0,26

Kaluga

42,7

-0,24

Ulan-Ude

36,0

-0,08

Arkhangelsk

55,0

-0,29

Tambov

35,9

0,04

Bryansk

39,1

-0,25

Stavropol

48,2

0,07

Murmansk

56,5

0,12

46,5

45,3

City

HDI “Outsider” cities Internet penetration, % the overall population

Sociological surveys38 and consultations with experts led to a dictionary of approximately 200 words relating to various walks of life — from hi-tech innovations to everyday leisure and household appliances. From these, 32 of the most commonly searched words over 2011-2013 were selected. 39

39 Source: Google Trends, September 2014, data for searches in the Russian Federation from 2004-2014

Average

Social media penetration, deviation from the mean

City

Internet penetration, % the overall population

Social media penetration, deviation from the mean

-0,04

Fig. 20. Age group breakdown of social media users vis-a-vis overall internet users

1000

Number of social media accounts per one internet user

800

70% of the HDI «leader» cities have above average internet penetration scores

600

400

200

0-4

5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 90-94 95-99 100- 105- date of birth 104 109 unknown Low penetration

High penetration

These 32 “innovation words” were bracketed into three groups according to search frequencies in Moscow40 (Fig. 21). Words in the first group were on average one and a half times more frequent than words in second group, and two times more frequent than words in the third group. Following this, the search frequency of these words was analysed across Russian cities. The working assumption we took was that the greater the correlation of internet searches to those observed in Moscow, the more receptive a city is to innovations. On this basis, we identified a group of the most innovationfriendly cities in Russia, those that adopt innovations simultaneously with Moscow, and then further groups of cities adopting innovations in a first wave, a second wave, and a third wave (Fig. 22).

40 Source: Yandex Wordstat, September 2014, data for Russian cities

41 Source: Yandex Wordstat, September 2014, data for Russian cities

The online image of the Russian city Virtual images of cities started to appear with the spread of the Internet a little over 20 years ago, and today the role of such online images is almost more important than the reality. Created at the junction of various informational flows, the online profile of a city reflects various strands of city life and processes — public, media and day-to-day. The study analysed publications in the “Society” section of the Yandex News service on account of it being the most comprehensive source of news over 2013.

The group of “first wave” cities comprised zonal sub-centres — in the North (Khanty-Mansiysk), Siberia and the Urals (Yekaterinburg, Perm). Some cities were located in the central part of Russia (Tula, Cheboksary) and others in the South (Rostov, Astrakhan, Krasnodar, Volgograd). The northen cities of Veliky Novgorod, Salekhard, Yakutsk made up a separate group. The majority of “second wave” cities are located further away from innovation centres, with the exception of a handful of cities in the central part of Russia (Penza, Saransk, Saratov, Kostroma). Cities of the «third wave» of the city are located on the periphery of the central part of Russia and in the Far East. We also observed another group of cities where concepts relating to innovative practices do not appear in search results frequently, irrespective of their geographical proximity to innovation centres. They constitute a kind of «inner periphery» within the country (Fig. 22).

On the one hand, the virtual image of the city forms an image of the city in the global information space. Such an external image is then amplified via mass publication: in media reports, news feeds news agencies, and leading blogs. The online information space is simultaneously a resource for citizens in their everyday communication. This online activity creates another virtual image of the city, a local one. The nature and strength of connections between the external and the local image of the city on the Internet can tell us whether a city is in the business of actively competing for new people by creating an attractive media image. Or, on the other hand, if an attractive image is the result of a successful urban development.

This study undertook an analysis of external and local online images of Novosibirsk, Yekaterinburg, Voronezh, Tomsk, Tyumen, Krasnodar, Belgorod, which, with the exception of Voronezh, all belong to the group of HDI “leader” cities. The external image of a city was studied using local news media , and the local image using social media42 feeds from the Vkontakte social network.43 Selection of Vkontakte posts was made with the assistance of Yandex Blogs. Data from 2013.

Our research indicated that innovation “waves” seem to originate from two distinct innovation hotbeds, which adopt innovation concepts simultaneously with Moscow. There is an European hotbed, which takes in territories of Central Russia and Western Siberia; and there is Asiatic hotbed, covering the Far East and Eastern Siberia. The second hotbed, in the East, is much less active — the dynamics of innovation adoption are weaker here — but nonetheless we can observe a diffusion tendencies in this direction, generally speaking from the Far East to Eastern Siberia. Fig. 21. Groups of concepts connected with innovations, according to search frequencies of Moscow Internet users 41

When comparing online activity in these cities on a seasonal basis, we can conclude that they vary considerably. Media news flows are relatively uniform throughout the year, but respond to specific events. User activity, on the other hand, is uneven and depends largely on the leisure and business activities of citizens; there are identifiable peaks in May and OctoberNovember (Fig. 23, 24). Activity in the autumn and summer months is higher than in spring.

Fig. 22. The four “waves” of innovation adoption across Russia Centres of the diffusion of innovations “First wave” cities “Second wave” cities

Group 1

Group 2

Most searched word s

chain store

verage searched word s

online payments

wet-wipes 3D-format

one-stop shop google glass

ATM

bank transfer

“Third wave” cities

Group 3

The cities irresponsive to innovation adoption

arely searched words

light panels

flat price care of the elderly self-service

beauty shop

implan t

"Waves" of innovation adoption

Kaliningrad

water meter

Saint Petersburg

paid medicine

bank loan

3D printer

baby monitor

mobile TV electronics magnetotherapy smart video conferencing artificial watches insemination instagram telemarketing 3D printing fast food videogames home delivery

minecraf t

Petrozavodsk Arkhangelsk

Veliky Novgorod Smolensk

Tver

Moscow

Oryol Kursk

Tula Ryazan

Kostroma Ivanovo Vladimir

Cheboksary Saransk Rostov-on-Don Krasnodar Maikop Stavropol Cherkessk Nalchik

Saratov Volgograd

Salekhard

Syktyvkar

Magadan

Nizhny Novgorod Penza

PetropavlovskKamchatsky

Yoshkar-Ola Kazan Perm

Samara

Yakutsk

Khanty-Mansiysk Ekaterinburg Tyumen

Orenburg Astrakhan Omsk

Tomsk Novosibirsk Barnaul Gorno-Altaisk

Khabarovsk Blagoveshchensk Chita

YuzhnoSakhalinsk

Unfortunately, an analysis of the dynamics of internet user activity and news flow only gives a limited idea of general characteristics and interactions. Understanding the nature of the interrelationships between information flows and publication content requires a further set of tests that divide information flows into two groups: “themes” and “subjects”44. The broader “theme” level will be considered here as part of the news flow, or the result of its influence; while more specific “subjects” reflect concrete urban processes and demonstrate the influence of social media users. The diversity of themes and subjects being discussed can be taken as markers of the diversity of the virtual environment. It can also indicate the level of development of social networks and urban space (Fig. 25, 26).

formed independently of urban processes and activities. The cities of Belgorod, Krasnodar and Tomsk fall into this category: here the themes of news and user-generated feeds coincide to a lesser degree (Tab. 25).

44 Words and phrases found most frequently in user-generated and mediagenerated feeds were defined as “themes”. Themes were assigned defined contexts, that is fragments of text related to these themes. The words most frequently associated

As a way of studying the link between a city’s image and its desirability as a place of education, living and employment, the content of media and user-generated feeds were compared with data on migration growth and population growth. Such a comparison brings a number of interesting findings, not least that in cities noted for a high level of migrational attractiveness, issues of city management are typically subjects of active discussion.

The results of our analysis showed that in both Krasnodar and Voronezh, media-generated feeds were dominated by issues of local government, and user-generated feeds concentrated on other themes, in a first instance sports (61%) and city life (36%). Meanwhile, the online image of Novosibirsk, Tyumen and Belgorod is fairly balanced; in both feeds, issues of local government dominate. User generated feeds in Tomsk are dominated by themes connected to everyday city life. The local profiles of Novosibirsk and Tomsk are yet more diverse. Here, the importance of any given story line does not exceed 25-40% in either mediagenerated and user-generated feeds. In Yekaterinburg and Krasnodar, however, there are dominant themes in user-generated feeds (registering up to 65% of the overall total). Belgorod’s external and internal images are to a large extent dependent on major stories. Posts on such themes overall constitute 60 to 70% of all messages contained in user-generated and media feeds.

In all the cities except Belgorod, the interaction between the external and local images of the city seems to have a direct relationship with the migration attractiveness of the city. In cities with population over 750,000, this aspect is less pronounced, but it does become particularly apparent in the example of Krasnodar. Krasnodar has both negative population growth and low migration growth, while at the same time having a very diverse external image weakly connected with local urban processes. For Tomsk and Tyumen, which fall into the category of cities with populations between 500,000-750,000, the link between external image and local urban processes is much stronger, and corresponds to increased levels of migration growth.

with these contexts (with the exception of words signifying the themes themselves) what collected in the thematic basis and defined as “subjects”

Belgorod, on another hand, boasts migration growth at a level much higher than other cities, but portrays the least diverse public image. One might suggest that the lack of diversity in the public image of the city is compensated by the presence of dominating local stories that themselves create diversity and an attractive image of the city.

Our study indicated it is possible to identify two groups of cities according to the content of media and user-generated feeds. In the first group, the external image of the city is linked to urban processes, with corresponding narratives and thematic lines. This group includes the cities of Voronezh, Novosibirsk, Yekaterinburg and Tyumen, where the themes and subjects discussed in social networks and news feeds basically coincide. In the second group of cities, the external online image of the city seems to be

A much more detailed study into the external and local images of every group of cities is required if we are to make firm conclusions. At the same time, in all the cities studied, we have observed a link between the attractiveness of a given city and the representation of different urban processes and activities in social networks. This relationship is important for both migrants and for residents. An exclusively media-generated online media image of the city does not give any clues as to its levels of human development of a city, nor does it seem to affect the levels of tourists, students or highly-qualified migrants.

Fig. 23. Dynamics of user flows by city and month

Fig. 24. Dynamics of news flows by city and month

Mean 1,0

Source: Yandex News, data for 2013

0,5

-0,5

dec

janf

mara

mayj

juna

sepo

nov

dec

Fig. 25. Thematic breakdown of media and user-generated feeds by city, % 31

Krasnoda r Tyumen

Voronezh

1 71

Novosibirsk

Belgorod

7 2

1 10

Yekaterinburg

3 46 1

1 44

Tomsk

1 76

VKontakte

Our study showed that external social media connections are in the most part with CIS countries. Krasnodar and Belgorod, as cities in our sample closest to the border with Ukraine, have the largest proportion of foreign links — 7.9% and 8.6% respectively. Ukraine accounts for more than half of foreign links in these cities. In Voronezh and Yekaterinburg, the share of foreign relations as a total of overall connections is less (6% and 6.5%, respectively), but the proportion of these connections that are with Ukraine remains considerable. The external connections of social networks in western Siberian cities are more varied. For example, in Tomsk about a quarter of all foreign connections are with Kazakhstan, due, most likely, to a number of international research programs in science and education. 45

Towards a geography of citizens’ social connections The next part of our study looks at the character, intensity and geographical spread of citizens’ social relations. For this task, we analysed “friend” lists on VKontakte social networks, taking data from users in seven cities — Novosibirsk, Yekaterinburg, Voronezh, Krasnodar, Tyumen, Tomsk and Belgorod. Two thirds of the total number of “friends» were unfortunately missing residence data, so a decision was taken to exclude them from our analysis.

News

City Administration

Culture

City Administration

Culture

Sport

Lifestyle

Sport

Lifestyle

Economy

Infrastructure

Economy

Infrastructure

City life

Media

City life

Media

Events

The other thing to note is that a city’s status has a direct effect on foreign connections. Cities of national significance, for example Novosibirsk or Yekaterinburg, or Krasnodar in the south, have stronger ties with foreign countries than the other cities selected as part of this study. Across all of the cities, the largest number of “friendly” foreign social connections are with the USA and Germany. Tyumen and Novosibirsk are the exceptions here, having a much more diverse geography of virtual connections.

Events

A small, but notable proportion of the foreign social connections of users in major cities — Novosibirsk, Yekaterinburg, Voronezh and Krasnodar — as well as in the border city of Belgorod are with tourist-friendly countries in Southern Europe and Asia.

Fig 26: Subject breakdown of media and user-generated feeds by city, %

Krasnodar

3 3 6

Tyumen

2 10 1

Voronezh

4 4

1 10

Novosibirsk

1 15

Belgorod

4 24 2 11

Yekaterinburg

1 1

-17

2 8

News

VKontakte

6 1 1

1 17

6 2 5

1 17

1 6 4

Tomsk

City management

Infrastructure

City management

Infrastructure

Events

Crime

Events

Crime

Economy

Society

Economy

Society

Sport

Internet

Sport

Internet

Culture

Achievements

Culture

Achievements

Politics

Medicine

Politics

Medicine

Security

The underworld

Security

The underworld

45 A March 2008 economic mission by the Tomsk regional government to Kazakhstan resulted in the signing of memoranda of cooperation with Kazakhstan in three regions East Kazakhstan, Karaganda region and Astana administration. The memoranda are aimed primarily at the development of cooperation in the humanitarian sphere, i.e. in the areas of family and youth, sport and tourism, culture and education. Source: official website of Tomsk Regional Administration

Social connections with users in the United Kingdom, Germany, Japan and France are as a rule concentrated in the capitals of these countries. In other countries, the majority of social connections are distributed outside the capital cities. As far as in-country distribution goes, the largest number of ÂŤfriendlyÂť foreign connections are in Moscow (13-23%), St. Petersburg (7-9%) and other cities with populations greater than 1 million (7-18%). As far as intraregional social connections go, these are strongest in cities of European Russia and the Urals, especially Yekaterinburg, Voronezh, Krasnodar and Belgorod. In these cities, 12-25% of national connections are with users who live within the same region. The cities of Siberia, Novosibirsk, Tomsk and Tyumen, observe the smallest share of such intraregional connections, at less than 10%

Table 25. Correlation of subject-theme breakdowns between media and usergenerated feeds by city Â

Voronezh

Novosibirsk

Yekaterinburg

Local government

93,4

10,3

16,9

Economics

-15,0

-14,6

54,0

Sport City life

Tyumen

Belgorod

Krasnodar

Tomsk

71,7

-11,1

-7,7

40,4

22,8

4,0

32,8

-17,7

-10,7

97,8

-14,5

0,0

17,9

1,2

86,9

0,0

49,4

37,4

Events

0,0

93,3

0,0

Culture

-7,7

48,9

0,0

Lifestyle

0,0

Urban infrastructure

-11,6

0,0

Media Average level of correlation

0,0

-12,7

0,0

22,5

20,0

15,6

12,2

8,9

6,0

5,2

Fig. 27. Strong social media connections for 7 Russian cities (Belgorod, Voronezh, Ekaterinburg, Krasnodar, Novosibirsk, Tomsk, Tyumen). Showing connections above 1% of total friends. Line weight indicates number of social contacts with the corresponding city

Saint Petersburg

Kiev Moscow

Belgorod Kharkiv Gubkin Lipetsk Shebekino Voronezh Valuiki Liski Rossosh

Kazan Perm

Anapa Novorossiysk Sochi

Rostov-on-Don Krasnodar

Nizhniy Tagil

Khanty-Mansiysk

Ekaterinburg

Ufa

Maikop

Nizhnevartovsk Tobolsk

Chelyabinsk

Tyumen Kurgan

Omsk

Seversk Tomsk Novosibirsk Berdsk

Барнаул

Kemerovo

Krasnoyarsk

Novokuznetsk

Kyzyl

Irkutsk

Ulan-Ude

9% 5% 1%

Belgorod Voronezh Ekaterinburg Krasnodar Novosibirsk Tomsk Tyumen

Fig. 28. Medium social media connections for 7 Russian cities (Belgorod, Voronezh, Ekaterinburg, Krasnodar, Novosibirsk, Tomsk, Tyumen). Showing connections between 0.5% to 1% of total friends. Line weight indicates number of social contacts with the corresponding city

Murmansk

Pavlovsk Minsk

Salekhard

Kursk

Odessa Kharkiv

Belgorov

Nizhny Novgorod

Novy Urengoy

Voronezh Donetsk

Borisoglebsk

Kazan Perm

Krasnodar

Gelendzhik Tuapse

Samara

Noyabrâ&#x20AC;&#x2122;sk

Ekaterinburg

Volgograd

Strezhevoi

Stavropol Tyumen Kolpashevo Tomsk

Kuibyshev

Novosibirsk

Krasnoyarsk

Abakan

Bijsk Gorno-Altaisk

Chita Irkutsk

Ulan-Ude

Alma-Ata

1% 0,75% 0,5%

Belgorod Voronezh Ekaterinburg Krasnodar Novosibirsk Tomsk Tyumen

29. Weak social media connections for 7 Russian cities (Belgorod, Voronezh, Ekaterinburg, Krasnodar, Novosibirsk, Tomsk, Tyumen). Showing connections between 0.25% to 0.5% of total friends. Line weight indicates number of social contacts with the corresponding city

Norilsk

Murmansk

Vorkuta Salekhard

Severodvinsk

Severouralsk Yaroslavl Kaliningrad

Bryansk Lviv Dnipropetrovsk Donetsk Sevastopol

Nyw York City

Sochi

Kirov

Kogalym Nizhnevartovsk Surgut

Yakutsk

Omsk Saratov

Pavlodar Astana

Semipalatinsk

Kyzyl

Irkutsk

Chita Khavarovsk

Alexeevka

Pyatigorsk

Vladivostok

Alma-Ata

Los Angeles

0,5% 0,375% 0,25%

Belgorod Vozonezh Ekaterinburg Krasnodar Novosibirsk Tomsk Tyumen

6. Belgorod. Strategies for human development Regardless of federal government requirements that require cities to employ strategic planning mechanisms, many Russian cities approach the idea of socio-economic planning in an entirely pro-forma fashion. In reality, the cities continue to be governed in a “hands-on” mode. This provides certain advantages in operational management, but virtually eliminates the chances of attaining long-term objectives. A notable exception to this is the case of Belgorod, which has since 2007 been implementing a multi-year strategy of socio-economic development that takes the city up to 2025. The main focus of the strategy is not so much volumes of commercial investment, nor industrial growth, nor new capital construction, but instead human development and the city’s investment in people. The higher quality of human capital resulting from this strategy has served as a driver of economic development and improving the urban environment. Belgorod’s socio-economic development strategy is based on a region-wide program to raise living standards, which has been operational since 2003 and also focuses on human capital. Both regional and municipal long-term planning strategies have set themselves synergistic tasks, and have been implementing them gradually over the last 10 years. The Belgorod development strategy is implemented through tactical interventions, “four year plans” for all the bodies of local government. A system of indices, made up of sociological and statistical data, has been developed to evaluate the effectiveness of these plans. The system takes in: human development index (HDI); an integral indicator of social well-being; a prosperity index; and seven other indicators distributed over the main strategic directions, i.e. health, intellectual capacity of the population, daily security, social prosperity, economic development, civil development and infrastructure development. The indicators are constructed in conjunction of the objectives and tasks of the city strategy, that is each objective corresponds to an integral indicator. Belgorod authorities have also established a new organisation, the Institute of Municipal Development and Social Technologies, in order to effectively monitor the implementation of its strategic and tactical initiatives, and also to adjust strategies where necessary. The institute is staffed mainly by PhD holders, many of whom are also teachers in Belgorod University, and brings together experts in the fields of municipal management, sociology, economics, education, and other fields. A large proportion of these experts was involved in developing the city strategy, and continues to work on its implementation today. The current city mayor, Dr Sergey Bozhenov, was a previous director of the institute, and contributed to work developing the city strategy.

The Institute of Municipal Development and Social Technologies is responsible for monitoring headline indicators, as set out in the city’s development strategy. It does so by collecting and processing key statistics, as well as carrying out sociological surveys of the city population. The surveys are conducted twice a year, with a sample size of 1800 people and include several blocks of questions, dedicated to determine quality of life, satisfaction with the system of city administration, social capital, security and other issues. Belgorod’s successful monitoring system was later adopted by a number of nationwide programs. The city development strategy is constantly adjusted in accordance with results of the monitoring process and newly emerging social priorities. Changes in objectives and tasks do not change the priorities of long-term planning, but instead help to develop new approaches and change the nature of tactical interventions. Several times a year, experts from all the across the country are invited to participate in theoretical and practical seminars. These meetings look to adapt and update development strategies and tactical plans. The idea of sustainable development led Belgorod to implement a number of initiatives designed to create a system of local self-government and civil society institutions. The city administration has designated this as a model of a public-private-society partnership, or a social corporation, aimed at maximising the involvement of the general population in managing city development.A two tier system of public self-government has been adopted in Belgorod since 2006. It’s basic territorial and social unit is the inner courtyard of a typical Russian apartment block. It is planned that there will be 614 constituencies of public self government in the city, corresponding exactly to the number of courtyards in the city. The city administration actively encourages residents to form housing cooperatives in order to transfer control of communal spaces directly to the owners of surrounding apartments. In turn, these public self-government structures feed into 27 city-wide councils, organised according to electoral districts. These councils are mainly constituted from local business leaders, managers of public facilities, along with leaders of housing cooperatives. With the support of local residents, the chair of each of these territorial councils has the opportunity to be elected to become a member of the City Council. More than two thirds of the chairs of such territorial councils are representatives of local business, which means that half of all members of the City Council are elected according to local constituency preferences. The remaining half are elected according to party lists. Every year, the Belgorod city administration co-finances development projects proposed by these local public self-government bodies. One supported project involved a major renovation of Eseninskaya square, which created an important new public space in a new district on the periphery of the city. This year, a new expert advisory body was established in Belgorod, constituted entirely from the city’s professional community. The main terms of reference for this new “City District Assembly” are to provide professional examination of city budgets, projects, and governmental decision-making; and to support civic initiatives and local government mechanisms. The Assembly is designed resemble urban society in miniature, and so its makeup all the socio-professional clusters of the city. Professional communities nominate 190 delegates in proportion to their age profile and membership size.

95 The city’s economic development team holds regular consultations with the local economic council, which is formed from local business leaders. A priority for the city is creating a comfortable working environment. This is the logic behind a tripartite agreement between trade unions, employers associations and the Belgorod city administration. As a result of the agreement, 97.5% of Belgorod businesses by 2011 had pledged to provide a minimum social package to all of their employees.

Source: Belgorod City Administration (2011)

The city economy, gross value added, is estimated at 230 billion rubles ($5billion), of which SMEs constitute 17%. More than half of workers are employed in SMEs. The main lines of business in Belgorod are connected with construction and production of building materials, food processing and related services, manufacturing and engineering. Thanks to a regional program supporting housing construction, Belgorod was able to get through the economic crisis of 2008 with almost no negative impact on economic indicators. The volume of new housing per head of population is more than two times higher in Belgorod than the average across Russia.46 The regional housing construction program is concentrated on developing individual homes, rather than apartment blocks. A large proportion of Belgorod’s urban population currently reside in private detached houses, located in the main in the territories adjacent to the municipal areas of Belgorod. Increasing the areas of urban construction would lead to a pendular migration back into the city centre, so in order to mitigate against this problem, local government have put into place plans to build a number of facilities of social infrastructure in the peripheral regions of the city. Belgorod’s development strategy is being implemented in accordance with general development plan approved in 2006 and running until 2025. Implementation of the general plan takes up a separate chapter of the strategy. Restrictions on land use and urban development have been in place since 2007, and are updated on an annual basis. The City Administration regularly holds public hearings with regards to all planning issues. The Belgorod city administration has also undertaken a pilot project to manage development in the Belgorod metropolitan area using instruments of the city master plan. This governor-level initiative sees the formation of a new agglomeration council, while research into the issue of agglomeration is currently being undertaken by architectural and urban planning structures at both municipal and regional level. Belgorod boasts a foundation working on matters of urban development. The foundation is funded by local business, which allocates 3% of annual turnover to it — this money is directly invested into urban improvement projects and developing public spaces. With the support of local businesses, the Belgorod city administration has initiated a new program to create a network of “third”, public spaces in the city: co-working facilities, etc. There is an academic and educational basis to Belgorod’s exceptional urban development management. Practically the entire composition of the City Administration, including specialists in the Institute of Municipal Development and Social Technologies, are graduates of the Management Institute of Belgorod State University. Founded in 2000, the Institute is now responsible for producing approximately 1000 specialists every year. The Belgorod Management Institute is a leading centre for fundamental and applied research in the fields of sociology, social technologies and people

management. It is also ranked among the top ten Russian schools preparing specialists in the field of local and federal government. The Management Institute is one of the key institutions of the university, and is the only institution in Russia’s central administrative district to have received the status of “National Research University” (universities of Moscow aside). The University was deliberately established as a driver for city development within the framework of a governor-level initiative that looked to Brazil’s success with its “university centre” program. The Belgorod State University was created practically from zero over a period of 15 years, as part of a regional initiative combining several higher education institutions in Belgorod. A new, modern University campus was constructed at beginning of the 2000s, construction paid for by the regional budget. Today, every ninth citizen of Belgorod is a student of the higher education institution. The Belgorod region boasts the highest life expectancy among Russian regions (with the exception of Moscow, St Petersburg and the republics of the North Caucasus). The average life expectancy of citizens of Belgorod itself is even higher than the region, and currently stands at 71.8 years. Development projects are undertaken with extremely limited budget resources: from the more than 21 billion rubles ($440 million) tax revenue Belgorod provides the federal budget, little more than 3 billion ($63 million) rubles returns into the city budget (a further 4 billion rubles received as subsidies from the regional budget). A federal reform of municipal government, conducted in 2013, saw hospitals and clinics transferred from municipal to regional oversight. But in order to ensure the most effective development of the health sector in the city, a regional law was adopted that transferred most of the relevant authorities back to the municipal level. Effective management of urban development has made Belgorod attractive to migrants: over the entire post-Soviet period, the city has recorded positive migrational inflows. Over the period 2005-2013, approximately 100,000 people arrived into the city, mostly for education or work. Less than half of the new arrivals were from the surrounding region; the majority was from other Russian regions and countries of the former Soviet Union. In other words, Belgorod can compete on a nationwide level when it comes to migration. The structure of migration inflows into Belgorod is balanced across all age groups. The city has become a destination for proactive migrant populations, attracting prosperous families from the oil and gas regions of the Russian North, the Far East, as well as from the western countries of the former Soviet Union. A majority of new arrivals stay on to live in Belgorod — between 2005 2013, the permanent population of the city grew by 10%, from 340,000 to 373,000 people. The successful development of Belgorod has allowed it to compete with regional capitals in both central and Volga federal administration districts, which are experiencing population outflows and economic slowdowns. Apart from Tambov, Belgorod is the only city in the central federal administration district to make it into this study’s group of leading cities, as measured by human development scores.

Chita Irkutsk

Ulan-Ude