• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Of all publications in the section: 30
Sort:
by name
by year
Article
Жукова А. К., Силаев А. М., Силаева М. В. Пространственная экономика. 2016. № 4. С. 112-128.

The paper examines the influence of various socio-economic and environmental factors on the life expectancy of the Russian population using regional data in 2014. With the help of spatial econometrics the authors show that there are differences in the models of life expectancy for men and women, as well as for residents from the western and eastern Russian regions. The study reveals that air pollution negatively affects the life expectancy of the Russian population as a whole and the western regions population particularly, and water resources contamination degrades the life expectancy of the eastern regions population. The authors also compare results of spatial econometric models and regression analysis using the least squares method.

Added: Jan 3, 2017
Article
Семерикова Е. В., Демидова О. А. Пространственная экономика. 2015. № 2. С. 64-85.

This paper analyzes the regional unemployment in Russia and Germany in 2005–2010 and addresses issues of choosing the right specification of spatial-econometric models. The analysis based on data of 75 Russian and 370 German regions showed that for Germany the choice of the spatial weighting matrix has a more significant influence on the parameter estimates than for Russia. Presumably this is due to stronger linkages between regional labor markets in Germany compared to Russia. The authors also proposed an algorithm for choosing between spatial matrices and demonstrated the application of this algorithm on simulated Russian data. The authors found that 1) the deviation of the results from the true ones increases when the spatial dependence between regions is higher and 2) the matrix of inverse distances is more preferable than the boundary one for the analysis of regional unemployment in Russia (because of the lower value of the mean squared error). The authors are also planning to apply the proposed algorithm for simulated data of Germany. These results allow accounting the spatial dependence more correctly when modeling regional unemployment which is very important for making proper regional policy. 

Added: Jun 19, 2015
Article
Пилясов А. Н., Замятина Н. Ю., Гончаров Р. В. Пространственная экономика. 2019. Т. 15. № 4. С. 149-183.

The subject of this article is the new role of individual mobility in the development of the territories of Siberia and the Far East. The purpose of the study was to justify the importance of a comprehensive study of the phenomenon of mobility as the most important factor in the new economic growth of Siberia and the Far East after using a set of regional analysis methods – statistical, sociological, cartographic, and others. In the regions of Siberia and the Far East, the last thirty years there has been a ‘quiet’ revolution of mobility – when automobiles got a rapid development to compensate for exploding airfares, especially in the zone of non-alternative transport and especially in winter. Its role in the consolidation of communication, in the flow of knowledge, in the transfer of best practices and innovations is extremely significant. Since transport and local transport systems play such a decisive role in the development of the sparsely populated territories of Siberia and the Far East, it is natural to see the reserves for the growth of these territories through their modernization. There are three zones of transport arrangement: 1) alternative, 2) non-alternative and 3) off-road transport. In the first zone, we are talking about strengthening innovation centers in interregional transport hubs. In the second, about the emancipation of the forces of mobility (primarily of individuals and small businesses), in the third, about the comprehensive promotion of non-stationary forms of temporary closeness as a source of fruitful communication and places of birth of new ideas.

Added: Jan 13, 2020
Article
Семерикова Е. В., Демидова О. А. Пространственная экономика. 2016. № 3. С. 57-80.

With the help of spatial regression models and classical models of panel data the study identifies and assesses the various factors’ influence on the unemployment rate in Russian regions from 2005 to 2010. Using the spatial autoregressive lag model the authors revealed that the change (increase or decrease) in the level of unemployment in one region leads to its changes in other regions. The use of spatial regression models allowed the researchers to identify the effect of higher education on the unemployment rate in the region: the higher share of the employed with higher education corresponds to the lower unemployment rate. This can’t be revealed with the help of classical models of panel data. In addition, some regional characteristics have nonlinear functional dependence of unemployment rate, which requires the algorithm modification for finding direct, indirect and total effects and their confidence intervals using the Monte Carlo approach.

Added: Oct 2, 2016
Article
Покровский Д. А. Пространственная экономика. 2015. № 2. С. 12-30.

This paper is addressed to explanation of impact of market size on selection into entrepreneurship and inequality within two-sector economy with secluded good, populated by individuals with additively-separable preferences,  defined by power specification of utility function. Individuals are differentiated by two characteristics: productivity and  type of  variety, which can be potentially produced by given individual if he or she chooses  entrepreneurial activity instead of salaried work. Each entrepreneur can produce only one unit of  given individual characteristics specific variety.  The specific variety are produced not unique producer, but for any type of entrepreneurial ability there are individuals with given ability, who produce the same variety. Number of such producers is defined by distribution of entrepreneurial abilities. Anyone of entrepreneurs producing the given type of variety has no market power and sells the variety by market price. From the other side, under  given   type of entrepreneurial ability whole range of varieties are produced by different entrepreneurs with given entrepreneurial ability. As far cost of production depends on entreprenurial ability, the prices charged by entrepreneurs with the same ability are equal.   Such specification of market structure allows consider symmetric equilibrium in terms of prices.  The main result oа the model is nontrivial  impact of  market size on outcome:  the bigger number of population, the less share of  salaried workers, the hire prices and the economy is more unequal.This paper is addressed to explanation of impact of market size on selection into entrepreneurship and inequality within two-sector economy with secluded good, populated by individuals with additively-separable preferences,  defined by power specification of utility function. Individuals are differentiated by two characteristics: productivity and  type of  variety, which can be potentially produced by given individual if he or she chooses  entrepreneurial activity instead of salaried work. Each entrepreneur can produce only one unit of  given individual characteristics specific variety.  The specific variety are produced not unique producer, but for any type of entrepreneurial ability there are individuals with given ability, who produce the same variety. Number of such producers is defined by distribution of entrepreneurial abilities. Anyone of entrepreneurs producing the given type of variety has no market power and sells the variety by market price. From the other side, under  given   type of entrepreneurial ability whole range of varieties are produced by different entrepreneurs with given entrepreneurial ability. As far cost of production depends on entreprenurial ability, the prices charged by entrepreneurs with the same ability are equal.   Such specification of market structure allows consider symmetric equilibrium in terms of prices.  The main result oа the model is nontrivial  impact of  market size on outcome:  the bigger number of population, the less share of  salaried workers, the hire prices and the economy is more unequal.

Added: Jun 16, 2015
Article
Бондаренко К. А. Пространственная экономика. 2020. Т. 16. № 3. С. 76-108.

According to the Ministry of Employment and Labor Relations of the Republic of Uzbekistan, the number of external labor migrants corresponds to approximately 22% of the country's labor force. In 1991–2019, Uzbekistan economy went through the four stages, each determined by certain macro-level factors, i.e. demographic, economic, political and other factors. There stages may be identified as follows: i) transition from a centrally planned to a market economy, falling well-being levels of Uzbekistani population and higher migration outflows for permanent residence in 1990–2000, ii) acceleration of economic growth in 2000–2009 and subsequent formation of “migration networks” abroad, iii) a period of GDP growth slowdown in the absence of structural reforms to stimulate employment and investment in 2010–2015, which contributed to increasing labor migration, and iv) the stage of new socio-economic reforms and  deepening attention of the Uzbekistan authorities to migration processes. This study takes into account changing macroeconomic environment, but focuses mostly on sociocultural factors that affect external labor migration and reintegration of migrants back to Uzbekistan. The research is based on the reports of applied studies on external labor migration and employment conducted by the Ministry of Employment and Labor Relations of the Republic of Uzbekistan and the reports from international organizations and research centers, as well as in-depth interviews with migrants and members of their families. The study reveals that the change in the socio-cultural context in 2006–2019 amid the transformation of macroeconomic conditions contributed to the expansion of Uzbek migration in terms of geography destinations, migration “success” in terms of re-migration and the ability of migrants to reintegrate into society upon returning home as far as to the emergence of migration “feminization” and “rejuvenation” phenomena amid the development of egalitarianism in an initially patriarchal society. Micro-society in Uzbekistan has both stimulating and constraining effects in terms of transformation of migration processes.

Added: Oct 2, 2020
Article
Клочко О. А., Царева А. С. Пространственная экономика. 2020. Т. 16. № 3. С. 52-75.

The objective of the research is to identify the specifics of the global value chains (GVCs) development in the world electronics industry after the 2008 financial crisis. Since GVCs are mainly organized regionally rather than globally, this paper analyses key indicators of GVC participation of the largest electronics exporters in three regions (Europe, North America, and Asia). The methodology is based on the analysis of data extracted from the OECD – WTO Trade in Value Added Database (2018 edition). The conclusions drawn from the analysis highlight several tendencies, including strengthening of the regional character of the value chains and a decrease in the average annual growth rate of gross exports. This paper also concludes that countries increased the share of domestic value added content of gross exports after the crisis, becoming accordingly less dependent on foreign exports. In most of the reviewed countries, the upward participation index declined after the crisis. Thus, the stated above trends indicate a post-crisis GVC reduction. The paper also reveals the features of the selected countries' participation in the electronics production chains and suggests that countries in the same region show similar patterns of GVC participation. The results obtained in the study show that the Asian region plays a key role in the post-crisis transformation of the value chains in the electronics industry. The study can help Russian manufacturers to determine their target position in the global electronics industry. The results of the study are also applicable in providing policymakers with tools for building a strategy for the industry development

Added: Oct 1, 2020
Article
Кириллов А. М. Пространственная экономика. 2017. № 4. С. 41-58.
Spatial interactions among modelling economic variables observed in spatially distributed units (due to their economic and trade relations) may be considered as an additional explanatory variable in a regression model (which generally prevents from its misspecification). Usually, spatial interactions are included in a regression in the form of spatial lag. In this paper we conduct a spatial econometric analysis of consumer price indexes for foodstuffs (FCPIs) observed in Russian regions. There are 79 regions in our sample for the period of time from 2002 to 2015 (data structured in panel). Our research aims at testing hypotheses of 1) presence of spatial correlation, and 2) of its heterogeneity among regional FCPIs. We develop a spatial panel data model with two matrixes of spatial weights (which are inverse distances with the breakpoint distance of 5000 kilometers between administrative centers of regions measured by roads) to test research hypotheses. In our model, the first matrix serves to estimate spatial correlation among regions up to break point distance between them, while second matrix catches spatial interactions among regions farther than break point distance from one each other. We find strong empirical evidence that 1) there is statistically significant spatial correlation among Russian FCPIs, 2) estimated spatial correlation is heterogeneous and the degree of its heterogeneity depends on the distance. That is, spatial relation shrinks as distance between regions rise and vice versa, or alternatively the closer one region to another, the higher expected inflationary relations between them.
Added: Dec 26, 2017
Article
Иванова В. И. Пространственная экономика. 2015. № 3. С. 34-56.

Using historical data, I examine the impact of spatial relations between regions on regional disparities in rye prices. Using econometric tools and annual data on procurement and retail rye prices in provincial cities of the Russian Empire for the period from 1861 to 1915, I show that convergence in prices was taking place during the period under consideration. Studying the dynamics of the spatial autocorrelation coefficient in relative prices has revealed the need for taking into account the spatial component in the study of convergence in prices. Using spatial regression models, I estimate the role of geographical distance between provincial cities in reducing the price gap. The estimation results of prices convergence econometric models show that reduction in relative rye price growth was largely due to price changes in the neighboring provinces.

Added: Oct 10, 2015
Article
Титов С. А., Кокорина А. О., Быков П. А. и др. Пространственная экономика. 2019. Т. 15. № 3. С. 125-146.

Achievements of Chinese manufacturing industries are well known and thoroughly studied. Less analyzed are the achievements of China in advanced sectors of the post-industrial economy, namely in creative and cultural industries. Whereas the development of creative and cultural industries within the context of the modern knowledge-based and creative economy is well researched, the role and place of creative industrial parks is to great extent neglected, despite the fact that they are actively used in developed countries as tools to stimulate creative sectors of economies.

The article investigates the role of creative industrial parks in the successful development of creative and cultural industries in China. The authors show that the development of the infrastructure for creative and cultural industries in China is accompanied by the establishment of many creative clusters. In many cases, in the center of a creative cluster there are one or several industrial creative parks, which attempt to combine the focus on high technologies and creative sectors. The authors found that governmental agencies in China stimulate decentralized, open to foreign influences and private initiatives approach to creative and cultural industries regulation. The most interesting trend, identified in the article, is that regional and municipal authorities stimulate technological advancements of creative parks, which leads to the transformation of creative parks to creative technological parks. These creative technoparks encourage integration of creative and cultural industries with telecommunications and information technologies and enhance the spillover effect of the innovative potential of creative technological parks in traditional sectors of the economy.

Added: Oct 9, 2019
Article
Зюзин А. В., Демидова О. А., Долгопятова Т. Г. Пространственная экономика. 2020. Т. 16. № 2. С. 39-69.

This paper studies the influence of industry localization and region economy diversification on firm profitability in Russia and provides quantitative estimation to such an influence. In this paper two main hypotheses are tested: (a) industry localization and region economy diversification improve enterprise profitability and (b) both localization and diversification influence smaller companies rather than bigger ones. Localization effects are estimated via Ellison-Glaeser index. Diversification is measured with Herfindahl-Hirschman index (HHI). The data set consists of 650 thousand of observations and approximates the full set of commercial real sector Russian companies in 2017. The indicator ‘number of employees’ is used for Ellison-Glaeser and HHI indexes calculation but this indicator contains missing values. To avoid distortions in the indexes’ magnitudes missing values are estimated in multiple ways that given closely same results. All companies were divided into four groups by scale (big, medium, small and micro). The regression models for testing two main hypotheses are estimated separately for each group. Method of regression estimation is OLS. It was found that profitability increases with the degree of industry localization and the effect is stronger for bigger companies. An increase of Ellison-Glaeser index by 0,1 results in 0,7–7,5% rise of sales margin. The effect from region diversification was found only for small and micro companies. HHI growth of 0,1 increases sales margin by 1,5–2,6%.

Added: Jul 20, 2020
Article
Уваров Е. А. Пространственная экономика. 2020. Т. 16. № 2. С. 124-141.

The author considers a non-cash fare payment system as an effective tool to reduce the shadow income of transport organizations. The object is shadow economy in regions of Russia. The subject is the public passenger transport sphere. The author studies buses on municipal regular transport routes (city and suburbs). The category of buses also includes buses of small capacity, i.e. «Minibuses». The scientific novelty of the work is to obtain quantitative and qualitative estimates of the extent and consequences of the shadow sector of the provision in the public passenger transport sphere in regions of Russia. The work takes into account a non-cash fare system using debit, credit, transport and social (preferential) cards. It was revealed that in 2018, a non-cash system was installed in 56 regions and in 29 regions was not installed. Based on the panel data analysis, considering endogeneity, AR (1), heteroskedasticity, the work revealed, where the statistical significance at p-value <= 10%, a positive effect of a non-cash fare system on the number of passengers carried on municipal buses and commercial buses. The average annual increase in the number of passengers, as a result of the installation of a non-cash system, amounted to 5840 thousand people. The data obtained showed that from 2014 to 2017, the amount of the shadow revenue of transport organizations was 7.2 billion rubles and the amount of unpaid taxes to the budget ranged from 0.4 to 2.6 billion rubles. The author concludes that the installation of a non-cash fare system leads to a decrease in the shadow economy in the public passenger transport services.

Added: Sep 19, 2020
Article
Григорьев Л. М., Павлюшина В. А., Бриллиантова В. В. и др. Пространственная экономика. 2019. Т. 15. № 2. С. 150-168.
Added: Sep 26, 2020
Article
Шаповал А. Б., Гончаренко В. М. Пространственная экономика. 2014. Т. 3. С. 12-25.

The article deals with the theory of monopolistic competition under demand uncertainty. The authors consider the economy with labor immobility consisting of the high-tech sector with monopolistic competition and the standard sector with perfect competition. Preferences between sectors are specified by the Cobb – Douglas production function. It is assumed that companies make output decisions under preferences uncertainty and consumers’ distribution by sectors will be known by the time of realization. It means that firms are informed about consumer demand with accuracy up to a multiplicative uncertainty which is generated by random parameters in the Cobb – Douglas production function. The paper shows that demand uncertainty leads to consistent growth of prices and wages in high-tech sector in relation to salaries in the second sector. The impact of uncertainty on welfare is ambiguous. In particular, under the known expected value of uncertainty customers derive benefit from exaggerated companies’ expectations about clients’ desire to consume high-tech goods.  

Added: Sep 28, 2016
Article
Шаповал А. Б., Гончаренко В. М. Пространственная экономика. 2014. № 3. С. 12-25.

The article deals with the theory of monopolistic competition under demand uncertainty. The authors consider the economy with labor immobility consisting of the high-tech sector with monopolistic competition and the standard sector with perfect competition. Preferences between sectors are specified by the Cobb – Douglas production function. It is assumed that companies make output decisions under preferences uncertainty and consumers’ distribution by sectors will be known by the time of realization. It means that firms are informed about consumer demand with accuracy up to a multiplicative uncertainty which is generated by random parameters in the Cobb – Douglas utility function. The paper shows that demand uncertainty leads to consistent growth of prices and wages in high-tech sector in relation to salaries in the second sector. The impact of uncertainty on welfare is ambiguous. In particular, under the known expected value of uncertainty customers derive benefit from exaggerated companies’ expectations about clients’ desire to consume high-tech goods.

Added: Jan 16, 2015
Article
Васькин Д. С., Шаповал А. Б. Пространственная экономика. 2017. № 3. С. 19-39.
Added: Feb 1, 2018
Article
Федорова Е. А., Коркмазова Б., Муратов М. Пространственная экономика. 2015. № 2. С. 47-63.
Added: May 6, 2016
Article
Котов А. В. Пространственная экономика. 2017. № 1. С. 137-152.
Added: Jun 20, 2020
Article
Подколзина Е. А., Демидова О. А., Кулецкая Л. Е. Пространственная экономика. 2020. Т. 16. № 2. С. 70-100.

The main objective of this work is to assess the influence of individuals living in neighboring territorial areas on each other in decision-making on the example of presidential election in Russia in 2018 using data on 2718 territorial election commissions (TECs). Local and global indicators of spatial autocorrelation (Moran, Geary, GetisOrd indices) calculated by the authors provide empirical evidence of global positive autocorrelation (i.e. in the country as a whole voters in each TEC vote similar to their neighbors). We identify TECs that can be included in local clusters (where voters vote similar) or in local outliers (surrounded by such TECs where voters vote opposite. Using the example of Tatarstan, the region where both local cluster and outlier TECs were most common we analyzed which economic indicators together with spatial ones influence the support of the main and opposition candidates. It was shown that the willingness to vote for the main candidate is explained by the increase in salaries in the area, but at the same time the indicators of economic activity in that area and the potential mobility of citizens have a negative impact on the support of the main candidate. Salary changes have no effect on votes in favour of opposition candidates, while other indicators show an inverse correlation. We have also shown that spatial effect models are preferable to OLS models for analyzing voting results.

Added: Jul 16, 2020
Article
Гурков И. Б. Пространственная экономика. 2019. Т. 15. № 2. С. 17-36.

Factories owned by foreign corporations retain vital positions in many industries of the Russian economy, including car manufacturing, food production, machinery, construction materials production, and pharmaceuticals. Using the secondary data sources, we had identified all new factories, opened by foreign multinational corporations in Russia in 2012–2018. Almost 80% of the 261 factories opened in the last seven years are located in just 20 Russian administrative regions. Moscow (city and oblast), Kaluga oblast, St. Petersburg and Leningrad oblast, the Republic of Tatarstan, Lipetsk oblast, Nizhny Novgorod oblast and Ulyanovsk oblast are the leaders in accommodating foreign industrial investments. Although consumer goods production develops further, foreign investments increase in B2B sector, with emphasis on manufacturing of details, components, additives, outsourcing services, not the output of finished products. The majority of foreign investors preferred special economic zones and industrial parks as territories for installation of new facilities. Proximity to suppliers, availability of the local market, preferred tax regime, guaranteed infrastructure and articulated care of the local authority about the needs of foreign investors are the main factors that determine the choice of the region for industrial investments of foreign corporations. There is also a relatively large segment of ‘pioneers’ that build new factories in the regions ignored by the main portion of foreign investors. The number of such ‘pioneers’ is especially high for the companies that opened their first factory in Russia in 2012–2018.

Added: Jul 11, 2019
Article
Григорьев Л. М., Павлюшина В. А., Бондаренко К. А. и др. Пространственная экономика. 2018. № 3. С. 138-151.
Added: Oct 22, 2018
1 2