IDEAS home Printed from https://ideas.repec.org/a/spr/rrorus/v10y2020i4d10.1134_s2079970520040036.html
   My bibliography  Save this article

Assessment of the Attractiveness of Large Russian Cities for Residents, Tourists, and Business

Author

Listed:
  • R. V. Fattakhov

    (Department of Public Finance of the Financial University under the Government of the Russian Federation)

  • M. M. Nizamutdinov

    (Institute for Socioeconomic Research, Ufa Federal Research Center, Russian Academy of Sciences)

  • V. V. Oreshnikov

    (Institute for Socioeconomic Research, Ufa Federal Research Center, Russian Academy of Sciences)

Abstract

— The article proposes an approach to assessing the attractiveness of large cities of the Russian Federation for residents, businesses, and tourists. As part of the development of this approach, particular parameters of the attractiveness of cities for individual economic agents were determined and an integral indicator was calculated. The hypothesis of the study is based on the fact that high positions of a city in a particular rating do not always guarantee its leadership in other areas; however, in general, the differentiation of places in case of a particular city is more inherent to ratings outsiders than to leading cities. Systemic analysis; factorial, statistical, structural, and dynamic analysis; classification, correlation, and regression analysis; and economic and mathematical modeling were used as research tools. The approach was tested on data for large Russian cities. Both sets of particular indicators characterizing certain areas of research and the approach to forming an integral indicator of the level of attractiveness of cities necessary for a generalized assessment have been determined. The presented results generally confirm the proffered hypothesis. In modern conditions, public authorities should pay special attention not only to the development of individual cities, but to the integrated development of the spatial framework of a territory where cities play a decisive role. The proposed approach to assessing the level of attractiveness of Russian cities makes it possible to obtain logical meaningful results that can be applied to solve problems in these areas.

Suggested Citation

  • R. V. Fattakhov & M. M. Nizamutdinov & V. V. Oreshnikov, 2020. "Assessment of the Attractiveness of Large Russian Cities for Residents, Tourists, and Business," Regional Research of Russia, Springer, vol. 10(4), pages 538-548, October.
    • Handle: RePEc:spr:rrorus:v:10:y:2020:i:4:d:10.1134_s2079970520040036
    DOI: 10.1134/S2079970520040036
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1134/S2079970520040036
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1134/S2079970520040036?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gianni Amisano & John Geweke, 2017. "Prediction Using Several Macroeconomic Models," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 912-925, December.
    2. G. B. Kleiner, 2014. "Systemic Management In A Transforming Economð£," Strategic decisions and risk management, Real Economy Publishing House, issue 5.
    3. Allen J. Scott & Michael Storper, 2015. "The Nature of Cities: The Scope and Limits of Urban Theory," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 39(1), pages 1-15, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    2. Becker, Jörg & Distel, Bettina & Grundmann, Matthias & Hupperich, Thomas & Kersting, Norbert & Löschel, Andreas & Parreira do Amaral, Marcelo & Scholta, Hendrik, 2021. "Challenges and potentials of digitalisation for small and mid-sized towns: Proposition of a transdisciplinary research agenda," ERCIS Working Papers 36, University of Münster, European Research Center for Information Systems (ERCIS).
    3. Rosanna Salvia & Rares Halbac-Cotoara-Zamfir & Sirio Cividino & Luca Salvati & Giovanni Quaranta, 2020. "From Rural Spaces to Peri-Urban Districts: Metropolitan Growth, Sparse Settlements and Demographic Dynamics in a Mediterranean Region," Land, MDPI, vol. 9(6), pages 1-20, June.
    4. Warne, Anders, 2023. "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series 2768, European Central Bank.
    5. Helga Leitner & Eric Sheppard, 2018. "From Kampungs to Condos? Contested accumulations through displacement in Jakarta," Environment and Planning A, , vol. 50(2), pages 437-456, March.
    6. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    7. Paköz, Muhammed Ziya & Yaratgan, Dilara & Şahin, Aydan, 2022. "Re-mapping urban vitality through Jane Jacobs’ criteria: The case of Kayseri, Turkey," Land Use Policy, Elsevier, vol. 114(C).
    8. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    9. Oli Mould, 2016. "A Limitless Urban Theory? A Response to Scott and Storper's ‘The Nature of Cities: The Scope and Limits of Urban Theory'," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 40(1), pages 157-163, January.
    10. Kevin Ward & Timothy Bunnell, 2021. "Reflections on five years of the Summer Institute in Urban Studies," Urban Studies, Urban Studies Journal Limited, vol. 58(4), pages 863-878, March.
    11. Ilaria Zambon & Artemi Cerdà & Filippo Gambella & Gianluca Egidi & Luca Salvati, 2019. "Industrial Sprawl and Residential Housing: Exploring the Interplay between Local Development and Land-Use Change in the Valencian Community, Spain," Land, MDPI, vol. 8(10), pages 1-18, September.
    12. Florian Huber & Gregor Kastner & Martin Feldkircher, 2019. "Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 621-640, August.
    13. Hauber, Philipp, 2022. "Real-time nowcasting with sparse factor models," EconStor Preprints 251551, ZBW - Leibniz Information Centre for Economics.
    14. Frederick Guy, 2020. "Who wants their city to become a world city? Comment on “Expanding the international trade and investment policy agenda: The role of cities and services”," Journal of International Business Policy, Palgrave Macmillan, vol. 3(3), pages 224-228, September.
    15. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2020. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1092-1110, July.
    16. Alessia D’Agata & Daniele Ponza & Florin Adrian Stroiu & Ioannis Vardopoulos & Kostas Rontos & Francisco Escrivà & Francesco Chelli & Leonardo Salvatore Alaimo & Luca Salvati & Samaneh Sadat Nickyain, 2023. "Toward Sustainable Development Trajectories? Estimating Urban Footprints from High-Resolution Copernicus Layers in Athens, Greece," Land, MDPI, vol. 12(8), pages 1-17, July.
    17. Qin Ye & Xiaolei Xu, 2021. "Determining factors of cities’ centrality in the interregional innovation networks of China’s biomedical industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2801-2819, April.
    18. Yılmaz, Merve & Terzi, Fatih, 2021. "Measuring the patterns of urban spatial growth of coastal cities in developing countries by geospatial metrics," Land Use Policy, Elsevier, vol. 107(C).
    19. Tan, Fei, 2018. "A Frequency-Domain Approach to Dynamic Macroeconomic Models," MPRA Paper 90487, University Library of Munich, Germany.
    20. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).

    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:spr:rrorus:v:10:y:2020:i:4:d:10.1134_s2079970520040036. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.