The Impact of Crises on Youth Unemployment of Russian Regions: An Empirical Analysis

The main purpose of this paper is to estimate the influence of the 1998 and 2008 crises on the youth unemployment rates (age class 20-29) in Russian regions. The investigation is founded on the panel data for 78 Russian regions during 1997–2008 provided by ROSSTAT (the main Russian State statistical organization). We compare the level and dynamics of the youth unemployment in various Russian regions and try to solve three main questions. Are there any special features of the youth unemployment in comparison with overall unemployment? How the 1998 crisis did change - and how the 2008 crisis is going to change - the youth unemployment dynamics? What can we learn from the impact of 1998 crisis and what is the main difference with the impact of the 2008 crisis? With the help of the obtained results we define some preliminary policy suggestion.financial crises, regional youth unemployment, Russian labour market.

Modeling of public trust in basic social and political institutions: A comparative econometric analysis

How seriously does the degree of trust in basic social and political institutions for people from different countries depend on their individual characteristics? To answer this question, three types of models have been estimated using the data of the fifth wave of the World Value Survey: the first one based on the assumption about a generalized relationship for all countries, the second one taking into account heterogeneity of countries (using introduction of the country-level variables), the third type applying a preliminary subdivision of countries into five clusters. The obtained results have been used for suggestion of possible actions to increase public confidence in the basic institutionsinstitutions; trust; ordered logit models; cluster analysis; mixed logit models

The Role of Education in Person’s Life-long Goals Achieving

Since Francis Bacon coined the phrase "knowledge is power" in 1597, we have become acquainted with the concept. Knowledge is power is particularly true in the postindustrial society. Such a phenomenon as "high-tech" eliminates the distinction between scientific laboratory and manufacture. Nowadays, to be powerful means not only to be intelligent but also to be successful. Education systems are thus supposed to provide a person with knowledge that helps him to succeed. We think it will be productive to identify the boundaries of education systems that restrict their effectiveness. The information-synergetic approach enabled us to assess the effectiveness of the education systems

Convergence of Russian Regions: Different Patterns for Poor, Middle and Rich

The Strategy of Spatial Development of the Russian Federation until 2025 aims at the economic growth acceleration and reduction of the intra-regional socio-economic differences. Therefore, the factors affecting the economic growth of regions, convergence of regions, spillover effects from the neighbouring regions are of importance. Russian regions are very different and do not converge to a unique equilibrium path. 80 Russian regions were divided into the groups of poor, middle and rich regions. Three main hypotheses were considered, based on the differences in the 1) convergence speed, 2) influence of the same factors, 3) different mutual influence of regions. They were tested using a modified spatially autoregressive model for the three groups using the Russian regional data for 2000–2017. Beta-convergence was found only for the middle and rich regions, the rate of convergence was higher in the rich regions. The poor regions did not grow faster than the other regions, confirming the relevance of the Strategy of Spatial Development. The similarities and differences were identified in the factors ensuring the economic growth of regions belonging to the three groups. The growth in all regions is stimulated by the regional economy openness. The growth of rich regions can be achieved by increasing the investment and reducing the investment risk. However, the investments in the poor and middle regions are not effective. The poor and middle regions receive positive spillovers from the growth of the neighbouring regions. It is possible to expect reduced differences in the living standards between the poor and rich regions

Convergence of Russian Regions: Different Patterns for Poor, Middle and Rich

The Strategy of Spatial Development of the Russian Federation until 2025 aims at the economic growth acceleration and reduction of the intra-regional socio-economic differences. Therefore, the factors affecting the economic growth of regions, convergence of regions, spillover effects from the neighbouring regions are of importance. Russian regions are very different and do not converge to a unique equilibrium path. 80 Russian regions were divided into the groups of poor, middle and rich regions. Three main hypotheses were considered, based on the differences in the 1) convergence speed, 2) influence of the same factors, 3) different mutual influence of regions. They were tested using a modified spatially autoregressive model for the three groups using the Russian regional data for 2000–2017. Beta-convergence was found only for the middle and rich regions, the rate of convergence was higher in the rich regions. The poor regions did not grow faster than the other regions, confirming the relevance of the Strategy of Spatial Development. The similarities and differences were identified in the factors ensuring the economic growth of regions belonging to the three groups. The growth in all regions is stimulated by the regional economy openness. The growth of rich regions can be achieved by increasing the investment and reducing the investment risk. However, the investments in the poor and middle regions are not effective. The poor and middle regions receive positive spillovers from the growth of the neighbouring regions. It is possible to expect reduced differences in the living standards between the poor and rich regions

Dependence of spatial effects on the level of regional aggregation, weights matrix, and estimation method

Researchers have repeatedly noted that the results in spatial-econometric studies depend significantly on the level of regional aggregation (Jacobs-Crisioni et al., 2014; Kang et al., 2014, Baltagi, Li, 2014). Currently, hierarchical models can contribute a lot to the studies of spatial effects since they take into account nested structure of regions (Dong, Harris, 2014). In addition, some studies say that econometric results also depend on the choice of the weights matrix W and the estimation method used (Elhorst, Vega, 2013; Kukenova, 2008). In different studies Monte-Carlo method with specially generated data is used to justify the selection of models or estimation method and to test the goodness-of-fit criteria (Kukenova, 2008, Piras, 2012). There are not so many studies that use real data. In this work we try to fill this gap by using different models for economic growth in the Russian regions. The data for 75 Russian regions within the period between 2005 and 2011years are used. We also include two levels of data aggregation: into 12 economic regions and into 8 federal districts. We are testing three main hypotheses: H1: The estimation results of spatial-econometric models depend on the level of regional aggregation. H2: The estimation results of spatial-econometric models depend on the choice of the method of estimation. H3: The estimation results of spatial-econometric models depend on the choice of the weighs matrix. To test these hypotheses SAR models are estimated with and without hierarchical regional structure. As a dependent variable in these models we use the GRP growth in analyzing spatial units. As the spatial weighs matrix we use the binary contiguity matrix, matrix of boundaries lengths and matrix of inverse distance between the capitals of the regions by road. Methods of estimation used are ML, difference GMM and system GMM. According to the results obtained from estimated models we get the empirical support for the first and second hypotheses. This means that the level of regional aggregation and the choice of estimation method significantly influence the results of spatial analysis. Our third hypothesis has been rejected for the vast majority of cases, except for those, where system GMM and difference GMM provide different results in the significance level of the coefficients in accordance to the weights matrix used. Thus, obtained results provided by the data on Russian regions largely confirm the findings of the articles cited above (Elhorst, 2013), (Kukenova, 2008), (Piras, 2012), and other studies related to the importance of choosing the right level of aggregation, model specification and estimation method when working with spatial data. However, all of estimated models show the stable positive spatial effect at any level of aggregation, any specification and estimation method used

Chiara Mussida & Francesco Pastore (Eds.), Geographical Labor Market Imbalances: Recent Explanations and Cures (AIEL Series in Labour Economics)

Abstract. The collection of articles by 31 authors, “Geographical Labor Market Imbalances” (edited by Chiara Mussida and Francesco Pastore) belongs to the AIEL Series in Labor Economics published by Springer Verlag and impresses the readers with the broad spectrum of problems examined therein. The book consists of introduction and four parts. The structure of the book is well thought of, the material of each part is smoothly connected to the previous parts. The chapters’ distribution inside each part is well balanced. Attractive features of the book are extended number of applied econometric methods and a variety of empirical data used for the analysis.Keywords. Political economy, Institutions, Democracy, Elections.JEL. F50, D72

Marshallian vs Jacobs effects: which is stronger? Evidence for Russia unemployment dynamics

This paper studies the influence of diversification and specialization on one of the main indicators of the Russian labour market: unemployment growth. The purpose of the work is to find out which effects dominate in the Russian regions, Marshallian or Jacobs, and whether this predominance is stable for different time periods. We tested empirically the following hypotheses: 1) the dependence of the unemployment growth on the concentration or diversification is nonlinear due to possible overlapping effects of urbanization and localization; 2) the influence of the concentration or diversification on the unemployment growth depends on the time period. To test these hypotheses, we use nonparametric additive models with spatial effects. Both hypotheses found empirical confirmation, with each effect prevailing in different time periods: Marshallian effects were prevalent in 2008-2010, and 2013-2016, while Jacobs effects were prevalent in 2010-2013

Labour productivity of young and adult temporary workers and youth unemployment: a cross-country analysis

The latest crisis has exacerbated two negative macroeconomic phenomena, particularly in Southern Europe. The size and persistence of youth unemployment has become unacceptable after 2010. Stagnation in labour productivity instead goes back to the ‘90s, but it has not improved since then and even worsen with the crisis. In this paper we analysed these two macroeconomic features, using aggregate data, in relation to labour market characteristics. Reforms of regulation, in many countries over the past twenty years, introduced a set of newly designed job contracts that allowed the use of temporary work. At the same time, Employment Protection Regulation encompassed temporary workers too. The availability of new contracts and EPLT changed the incentives of firms to vary their labour needs, and to invest in new technology. Eventually, this should have an impact on labour productivity and unemployment. We distinguished between temporary young and adult workers and, conditional to the level of employment protection, we estimate their labour productivity and the correlation with the rate of youth unemployment. We use macroeconomic data for countries within groups (former Euro zone countries, Euro-zone plus Russia, OECD, G7, G8). Preliminary evidence shows that the share of adult temporary workers clearly and negatively affects labour productivity, no matter the group of countries