Dependence of spatial effects on the level of regional aggregation, weights matrix, and estimation method (original) (raw)

Inter- and Intra-Regional Disparities in Russia: Factors of Uneven Economic Growth

Sustainability, 2021

Despite the growing body of literature on the dependence of economic growth from different factors, the reasons for uneven growth remain unclear. Within the country, regions have different growth rates in their diverse parts. It is unclear why the same factor could influence municipalities differently. To reveal this reason, we used hierarchical linear modeling with spatial dependence, which allows us to decompose variation into regional and municipal scales and take into account spatial autocorrelation. We conducted our research on data for 2239 municipalities within 85 Russian regions in 2019. Our model incorporates 20 factors of economic growth, with 7 at the municipal scale. Cross-interaction estimates established that factors attributed to the regional level determined the relationship between dependent variables (growth rate of production, growth rate of social benefits, and taxable income) at the municipal level and predictors. The influence of initial level, investments in f...

Modeling the Employment Rate in Russia: a Spatial-Econometric Approach

Economy of Region

The purpose of this study is to identify factors that affect the level of employment in Russian regions. However, Russia is not a homogeneous country, and this effect may not be the same for all regions. That is why we split the regions of Russia into three groups, depending on the state of the labor market in this and neighboring regions. The HH (high-high) group comprises regions with a favorable situation in their labor markets, and which are also surrounded mostly by prosperous regions. Two groups of regions with a less favorable situation are located respectively in the south of Russia (LL1, low-low group 1) and southern Siberia and Zabaikalye (LL2, low-low group 2). We considered the twelve-year period from 2005 to 2016. As explanatory variables, we used variables for the attractiveness of the region, demographic characteristics of the region, and the degree of diversity of employees by economic activities. We tested hypotheses about differences in 1) the spatial effects and 2) the impact of the various explanatory variables for these groups of variables. To test our main hypotheses, we used spatial regression dynamic models estimated with the help of the generalized method of moments. Both main hypotheses received empirical confirmation. Spatial effects were different. The regions of the LL2 group are not affected by the situation in other local markets; regions of LL1 and HH groups are affected by the rest of Russia's regions, and the extent of this influence decreases with the increase in geographical distance between regions. Moreover, the regions of the LL1 group compete with neighboring regions: if the situation in one of them improves, then it draws on the resources of the others. Regarding the impact of the explanatory variables, the "group effect" was revealed for the variables: share of urban population, net migration rate, shares of people below and above working age, share of people with higher education. Our results can favor the better design of national and regional policies to improve labor market performance in Russia based on the heterogeneity of the Russian regions.

Usefulness of spatial econometrics in the regional science framework

During the last forty years, there has been an outstanding methodological development in regional and urban economics. This fact has fostered the necessity of working with cross-section data. When using this kind of data,spatial effects could arise: spatial heterogeneity and spatial dependence. Whereas the first effect could be solved by means of standard econometric techniques, spatial dependence requires a specific econometric treatment/strategy. Spatial econometrics provides appropriate techniques for testing and estimating in presence of spatial dependence. Although there has been a notable development in this field in the last two decades, spatial econometrics is far from classical econometrics in terms of its knowledge, diffusion and use in regional economic research. This work aims at contributing to the diffusion of spatial econometric techniques in our country, showing a briefly survey of the main theoretical contributions appeared in monographic and different specialised j...

Regional economic growth in Europe: A semiparametric spatial dependence approach

Papers in Regional Science, 2008

AbstractIn this article a semiparametric spatial Durbin model is employed to analyse the growth behaviour of 155 European regions in the period 1988–2000. This specification combines the semiparametric approach with the usual parametric spatial econometric technique to accommodate both spatial dependence and nonlinearities as suggested by recent neoclassical growth models with spatial technological interdependence. The results provide evidence of nonlinearities in the effect of initial per capita incomes and human capital investments. Moreover, the specification used allows identifying the effect of the interaction between the characteristics (initial conditions and structural variables) of each region and those of its neighbours. Finally, it shows some indication of global spillovers across-country and local spatial spillovers from domestic neighbours.In this article a semiparametric spatial Durbin model is employed to analyse the growth behaviour of 155 European regions in the period 1988–2000. This specification combines the semiparametric approach with the usual parametric spatial econometric technique to accommodate both spatial dependence and nonlinearities as suggested by recent neoclassical growth models with spatial technological interdependence. The results provide evidence of nonlinearities in the effect of initial per capita incomes and human capital investments. Moreover, the specification used allows identifying the effect of the interaction between the characteristics (initial conditions and structural variables) of each region and those of its neighbours. Finally, it shows some indication of global spillovers across-country and local spatial spillovers from domestic neighbours.ResumenEn este artículo se emplea un modelo semiparamétrico espacial de Durbin para analizar el comportamiento del crecimiento de 155 regiones europeas durante el periodo 1988–2000. Esta especificación combina el enfoque semiparamétrico con técnicas econométricas espaciales paramétricas habituales para reconciliar la dependencia espacial y las no linealidades, tal y como sugieren los modelos de crecimiento neoclásicos recientes con interdependencia tecnológica espacial. Los resultados ofrecen pruebas de no linealidades en el efecto de ingresos per capita iniciales e inversiones en capital humano. Además, la especificación utilizada permite utilizar el efecto de la interacción entre las características (condiciones iniciales y variables estructurales) de cada región y las de sus vecinos. Finalmente, da una idea de spillovers (efectos de derrame) globales entre países y spillovers espaciales locales de regiones vecinas dentro del país.En este artículo se emplea un modelo semiparamétrico espacial de Durbin para analizar el comportamiento del crecimiento de 155 regiones europeas durante el periodo 1988–2000. Esta especificación combina el enfoque semiparamétrico con técnicas econométricas espaciales paramétricas habituales para reconciliar la dependencia espacial y las no linealidades, tal y como sugieren los modelos de crecimiento neoclásicos recientes con interdependencia tecnológica espacial. Los resultados ofrecen pruebas de no linealidades en el efecto de ingresos per capita iniciales e inversiones en capital humano. Además, la especificación utilizada permite utilizar el efecto de la interacción entre las características (condiciones iniciales y variables estructurales) de cada región y las de sus vecinos. Finalmente, da una idea de spillovers (efectos de derrame) globales entre países y spillovers espaciales locales de regiones vecinas dentro del país.

Modelling the Folk Theorem of Spatial Economics: A Heterogeneous Regional Growth Model

SSRN Electronic Journal, 2009

During the last year, the research field of spatial economic has rapidly increased. There is consensus that the economic performance of a region depends not only on its own potential, but also on the development of their neighbouring regions. Knowledge spillovers, which are non constant over space, should influence the evolution of the region specific productivity. The so called "folk theorem of spatial economics" states, that increasing returns to scale are essential for explaining the uneven economic distribution of specific economic activity, which implies that knowledge spillover, agglomeration and distribution of per capita productivity are closely linked. Thus, the aim of this paper is, to introduce a spatial regional growth model, which links first time knowledge spillover, agglomeration, distribution of per capita productivity and the grasp of spillovers. Further, it is shown in a simulation study, how different regimes of returns to scale and grasps of knowledge affect agglomeration and distribution of per capita productivity. One of key findings is, that grasp of knowledge affects dynamic distribution of per capita productivity. Moreover, the simulation study particularly finds support for the "folk theorem of spatial economics".

The Neighbourhood Effect in Russian Regional Policies: Autocorrelation and Cluster Analysis

RUDN Journal of Political Science

Regional convergence is one of the greatest strategic challenges for the Russian Federation. Socio-economic zoning directly affects the regional policy in Russia, as most administrative and political practices are reproduced within a federal district or an economic region. This study is aimed at identifying steady clusters or, in other words, groups of Russian regions, based on quantitative data on socio-economic development. The study relies on the methods of spatial econometrics. The authors also aim to compare the results of their study to the macro-regions suggested by the Strategy of Regional Development of the Russian Federation and therefore to the current administrative practices. The paper determines 12 clusters continual in space, based on 62 regional development indicators and reflecting the statistical resemblance of the regions within a cluster. The study has not found stable macro-regions of similar values except in Siberia and the Far East. Thus, the authors conclude ...

Estimation of the Spatial Connectivity of the Economic Activity of Russian Regions

Regional Research of Russia, 2020

The article examines change in the effects of spatial connectivity of Russian regions' economic activity for 1997-2016. Quantitative estimates are obtained using spatial econometrics methods. Two specifications of the model are used: the spatial lag model and spatial error model. Relations between regions are modeled through spatial external effects, which are described in two ways: using a nearest neighbor matrix and an inverse distance matrix. The following hypotheses are tested: (1) a single macroeconomic policy and market integration stimulate growth in the spatial connectivity of economic activity; (2) Russia's western territories have closer spatial ties compared to the eastern; (3) imposition of sanctions against Russia stimulated the formation of new and strengthened existing internal ties, as well as the country's spatial connectivity. Estimates have shown that there are no distinct trends in the spatial connectivity of economic activity in Russia, nor were interregional interactions affected by international sanctions. Relations important for economic activity are supported mainly with neighboring regions. The cooperation that arises between regions is not deep spatially and fades rapidly with increasing distance. This is also confirmed by the fact that for European Russia, spatial relations are a more significant development factor than for eastern regions.

The impact of regional inequality on economic growth: a spatial econometric approach

Regional Studies

This paper investigates the relationship between economic growth and regional income inequality in a spatial econometric perspective. The role of space in the measure of inequality is discussed, and a new theoretical model that relates inequality with economic growth is introduced. The proposed model extends a spatial Mankiw-Romer-Weil specification by introducing regional income inequality as a determinant of economic growth. The measure of inequality proposed as a covariate in the model is derived by a spatial decomposition of the Gini index. An empirical analysis focused on European Union NUTS-2 regions is carried out to illustrate the model.

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

Economy of Region, 2021

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 t...