IDEAS home Printed from https://ideas.repec.org/p/rnp/wpaper/w20220295.html
   My bibliography  Save this paper

Development Of An Approach To Assessing The Relative Strength Of Agglomeration Effects Mechanisms In Russia Based On Microdata On Russian Producers And Municipalities
[Разработка Подхода К Оценке Относительной Силы Механизмов Агломерационных Эффектов В России На Основе Микроданных О Российских Производителях И Муниципальных Образованиях]

Author

Listed:
  • Rostislav, Konstantin (Ростислав, Константин)

    (The Russian Presidential Academy of National Economy and Public Administration)

  • Ponomarev, Yuriy (Пономарев, Юрий)

    (The Russian Presidential Academy of National Economy and Public Administration)

  • Radchenko, Darina (Радченко, Дарина)

    (The Russian Presidential Academy of National Economy and Public Administration)

Abstract

The development of agglomerations in Russia is a priority of spatial policy. To enhance agglomeration effects and accelerate the growth of the Russian economy it is necessary to understand the mechanisms of agglomeration effects. To compare the strength of Marshall agglomeration effects using the Ellison-Glaser-Kerr approach, the degree of concentration of Russian industries was measured using data on all organizations without exception as of January 1, 2020. The estimates show that pairs of industries in Russia tend to be dispersed relative to each other: most industries have significantly lower concentration than would be expected based on the overall location of these industries. On average, of the three external benefits of concentration according to Marshall, Russia's large labor market is the most important. Proximity to suppliers/buyers, their diversity is least related to the placement of industries in the same areas. The example of Kaliningrad region shows that regardless of the method of selection of organizations for comparison, there is no truncation of the distribution traits. Although the choice of the geographical unit of observation determines the estimation of the strength or even direction of the net agglomeration effects, the general conclusion about the lack of selection of enterprises, which we could take for the benefit of concentration, was unchanged. To verify this conclusion, we used various methods of territorial grouping of enterprises and the boundaries of clusters (agglomerations) of enterprises were estimated using the DBSCAN method. The resulting estimates of the relationship of concentration to various sources of its external benefits support those public policies that seek to encourage the development of large urban agglomerations with large and constant markets for skilled labor. When forming particularly dense clusters, it is advisable to set activity requirements for areas with a special entrepreneurial regime, which would be consistent with estimates of the intensity of possible knowledge exchange between industries.

Suggested Citation

  • Rostislav, Konstantin (Ростислав, Константин) & Ponomarev, Yuriy (Пономарев, Юрий) & Radchenko, Darina (Радченко, Дарина), 2022. "Development Of An Approach To Assessing The Relative Strength Of Agglomeration Effects Mechanisms In Russia Based On Microdata On Russian Producers And Municipalities [Разработка Подхода К Оценке О," Working Papers w20220295, Russian Presidential Academy of National Economy and Public Administration.
  • Handle: RePEc:rnp:wpaper:w20220295
    as

    Download full text from publisher

    File URL: https://repec.ranepa.ru/rnp/wpaper/w20220295.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    agglomerations; agglomeration effects; mechanisms; boundary delimitation; machine learning;
    All these keywords.

    JEL classification:

    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rnp:wpaper:w20220295. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: RANEPA maintainer (email available below). General contact details of provider: https://edirc.repec.org/data/aneeeru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.