How do strong social ties shape youth migration trajectories (using data from the Russian on-line social network www.vk.com)
It is well known that M. Granovetter wrote about the declining role of strong social ties and the rising role of weak ties. Yet the situation in Russia is different. The institutional order and a high level of transactions costs compels young Russians to use strong social ties to cut transaction cos...
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ftrepec:oai:RePEc:wiw:wiwrsa:ersa13p372 2023-05-15T15:05:50+02:00 How do strong social ties shape youth migration trajectories (using data from the Russian on-line social network www.vk.com) Nadezhda Zamyatina Alexey Yashunsky http://www-sre.wu.ac.at/ersa/ersaconfs/ersa13/ERSA2013_paper_00372.pdf unknown http://www-sre.wu.ac.at/ersa/ersaconfs/ersa13/ERSA2013_paper_00372.pdf preprint ftrepec 2020-12-04T13:32:28Z It is well known that M. Granovetter wrote about the declining role of strong social ties and the rising role of weak ties. Yet the situation in Russia is different. The institutional order and a high level of transactions costs compels young Russians to use strong social ties to cut transaction costs while they move from one city to an other. The data on migrations between cities shows us that the force of strong ties is sometimes greater than the force of a distance or of the agglomeration effect: some very distant and small cities are tied closely by migrations flows. We tend to explain the high level of migration in such cases through the existence of strong social ties not only between some individuals but also between some city communities as a whole. So we have to speak about a social proximity in addition to the well-known organizational proximity and geographical proximity. The data of our research is the personal career information contained in the personal pages of the most popular Russian on-line social network www.vk.com: the data on birthplaces, school-leaving and university-leaving places and a present-day residence places. The data was extracted by specially designed software. We use such a method because of the lack of migration data on a city level in official Russian statistics. We have collected 3000 to 14000 personal data files per city for people aging 20 to 29 years old (which is approximately 10-15% of the whole city population) for the following cities: arctic cities Noyabrsk, Norilsk, Magadan, Muravlenko, Gubkinsky; cities within an agglomeration: Gorodets, Konakovo, Nevjansk; peripheral small cities: Bezhetsk, Gagarin, Verhoturye; cities with nuclear industry or scientific centers: Obninsk, Sarov, Desnogorsk, Sosnovy Bor, Snezhinsk, Dubna, Dimitrovgrad; industrial company-towns Bratsk and Ust-Ilimsk. The largest migrations flows moved to the Russian capital and to the region centers. Yet, excluding such flows, we can see the concentration of remaining migrants in a portion of small cities. Here three types of migrations exist, which were clarified by qualitative analysis: -- migrations from the Arctic region to those places from which the parents of today's migrants came some decades ago ('over-generation coming back' migration); -- migration to the cities where a portion of former 'city-mates' have just settled; -- migration between cities of a similar specialization. social capital; strong ties; weak ties; migrations Report Arctic norilsk RePEc (Research Papers in Economics) Arctic Bor ENVELOPE(126.850,126.850,61.750,61.750) Magadan ENVELOPE(150.803,150.803,59.564,59.564) Norilsk ENVELOPE(88.203,88.203,69.354,69.354) |
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RePEc (Research Papers in Economics) |
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It is well known that M. Granovetter wrote about the declining role of strong social ties and the rising role of weak ties. Yet the situation in Russia is different. The institutional order and a high level of transactions costs compels young Russians to use strong social ties to cut transaction costs while they move from one city to an other. The data on migrations between cities shows us that the force of strong ties is sometimes greater than the force of a distance or of the agglomeration effect: some very distant and small cities are tied closely by migrations flows. We tend to explain the high level of migration in such cases through the existence of strong social ties not only between some individuals but also between some city communities as a whole. So we have to speak about a social proximity in addition to the well-known organizational proximity and geographical proximity. The data of our research is the personal career information contained in the personal pages of the most popular Russian on-line social network www.vk.com: the data on birthplaces, school-leaving and university-leaving places and a present-day residence places. The data was extracted by specially designed software. We use such a method because of the lack of migration data on a city level in official Russian statistics. We have collected 3000 to 14000 personal data files per city for people aging 20 to 29 years old (which is approximately 10-15% of the whole city population) for the following cities: arctic cities Noyabrsk, Norilsk, Magadan, Muravlenko, Gubkinsky; cities within an agglomeration: Gorodets, Konakovo, Nevjansk; peripheral small cities: Bezhetsk, Gagarin, Verhoturye; cities with nuclear industry or scientific centers: Obninsk, Sarov, Desnogorsk, Sosnovy Bor, Snezhinsk, Dubna, Dimitrovgrad; industrial company-towns Bratsk and Ust-Ilimsk. The largest migrations flows moved to the Russian capital and to the region centers. Yet, excluding such flows, we can see the concentration of remaining migrants in a portion of small cities. Here three types of migrations exist, which were clarified by qualitative analysis: -- migrations from the Arctic region to those places from which the parents of today's migrants came some decades ago ('over-generation coming back' migration); -- migration to the cities where a portion of former 'city-mates' have just settled; -- migration between cities of a similar specialization. social capital; strong ties; weak ties; migrations |
format |
Report |
author |
Nadezhda Zamyatina Alexey Yashunsky |
spellingShingle |
Nadezhda Zamyatina Alexey Yashunsky How do strong social ties shape youth migration trajectories (using data from the Russian on-line social network www.vk.com) |
author_facet |
Nadezhda Zamyatina Alexey Yashunsky |
author_sort |
Nadezhda Zamyatina |
title |
How do strong social ties shape youth migration trajectories (using data from the Russian on-line social network www.vk.com) |
title_short |
How do strong social ties shape youth migration trajectories (using data from the Russian on-line social network www.vk.com) |
title_full |
How do strong social ties shape youth migration trajectories (using data from the Russian on-line social network www.vk.com) |
title_fullStr |
How do strong social ties shape youth migration trajectories (using data from the Russian on-line social network www.vk.com) |
title_full_unstemmed |
How do strong social ties shape youth migration trajectories (using data from the Russian on-line social network www.vk.com) |
title_sort |
how do strong social ties shape youth migration trajectories (using data from the russian on-line social network www.vk.com) |
url |
http://www-sre.wu.ac.at/ersa/ersaconfs/ersa13/ERSA2013_paper_00372.pdf |
long_lat |
ENVELOPE(126.850,126.850,61.750,61.750) ENVELOPE(150.803,150.803,59.564,59.564) ENVELOPE(88.203,88.203,69.354,69.354) |
geographic |
Arctic Bor Magadan Norilsk |
geographic_facet |
Arctic Bor Magadan Norilsk |
genre |
Arctic norilsk |
genre_facet |
Arctic norilsk |
op_relation |
http://www-sre.wu.ac.at/ersa/ersaconfs/ersa13/ERSA2013_paper_00372.pdf |
_version_ |
1766337475301605376 |