Unemployment Clubs in Russian Regions

Danilenko, Tatiana; Demidova, Olga; Signorelli, Marcello

doi:10.1080/1540496x.2017.1281799

Cited by 2 publications

(4 citation statements)

References 61 publications

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“…In this paper, in addition to Moran plots, the "leader-outsider" approach was used ([1] Oschepkov and Kapelyushnikov (2015). Considering the above-discussed relationship between unemployment rate and employment rate it is not surprising that in this paper, we obtain empirical results different from those of other studies focused on unemployment rates (e.g., [25] Danilenko et al, 2017).…”

Section: Er Ur Prcontrasting

confidence: 93%

“…It should be stressed that these groups of regions are close to, but not identical with, the group of four unemployment groups of regions in Russia discussed in [25] Danilenko et. al (2017).…”

Section: The Splitting Of Regions By Moran Plotsupporting

confidence: 49%

“…Thus, it makes sense to try to identify such clusters. [25] Danilenko et al (2017) attempt to reveal clubs for the unemployment rate using Moran plots. In this paper, in addition to Moran plots, the "leader-outsider" approach was used ([1] Oschepkov and Kapelyushnikov (2015).…”

Section: Er Ur Prmentioning

confidence: 99%

“…In previous research (see [25] Danilenko et al, 2017), we analyzed the difference in spatial effects and the determinants of unemployment rate for three clubs of Russian regions (HH, LL, HL). In the research reported here, we decided to test similar hypotheses for three employment groups.…”

Section: The "Leader-outsider" Approachmentioning

confidence: 99%

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

Demidova¹,

Daddi²,

Medvedeva³

et al. 2018

EoR

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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.

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Section: Er Ur Prcontrasting

confidence: 93%

Section: The Splitting Of Regions By Moran Plotsupporting

confidence: 49%

Section: Er Ur Prmentioning

confidence: 99%

Section: The "Leader-outsider" Approachmentioning

confidence: 99%

See 2 more Smart Citations

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

Demidova¹,

Daddi²,

Medvedeva³

et al. 2018

EoR

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Regional Convergence or Polarization: The Case of the Russian Federation

Taymaz

2022

Reg. Res. Russ.

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— We analyse growth patterns of the Russian federal subjects, and test if all regions converge to a common growth path, or if there are many convergence clubs by using the data on regional GDP per capita for the 1996–2017 period. Our analysis shows that there are four different convergence clubs in the Russian Federation, and there is no geographical concentration of the clubs. The regions with dynamic population movements are likely to be in high-income clubs, and they attract more people. Most of the national republics are included in low-income clubs, and, after controlling for other factors, we find that the North Caucasian republics are likely to be members of low-income clubs, whereas there is not much difference between other republics and regions. The persistence of income differences across convergence clubs is a cause of concern for long term sustainable growth and stability.

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Unemployment Clubs in Russian Regions

Cited by 2 publications

References 61 publications

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

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

Regional Convergence or Polarization: The Case of the Russian Federation

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