IDEAS home Printed from https://ideas.repec.org/a/spr/cejnor/v29y2021i3d10.1007_s10100-020-00714-5.html
   My bibliography  Save this article

Spatial econometric approach to the EU regional employment process

Author

Listed:
  • Andrea Furková

    (University of Economics in Bratislava)

  • Michaela Chocholatá

    (University of Economics in Bratislava)

Abstract

This paper deals with the estimation of spatial econometric models of employment rate across 259 NUTS 2 (Nomenclature of Units for Territorial Statistics) regions of the European Union in 2018 regarding different region-specific factors. Since, spatial autocorrelation and spatial heterogeneity often occur jointly, the paper is oriented at verification of two hypotheses. Hypothesis 1 related to the existence of the spatial autocorrelation, i.e., that the regional employment process is not a spatially isolated process, was confirmed. Based on the estimation of Spatial Durbin Model, direct, indirect and total spatial impacts were quantified and verified. The results proved the significant impact of neighbouring regions for GDP and compensation of employees variables in explaining regional employment rate. Significant influence of factors like educational attainment level and population density seems to be limited only to the particular region. Hypothesis 2 reflected the existence of the spatial heterogeneity. Based on the geographically weighted regression the assumption of spatial variability of the model parameters was also verified. The regional employment in the EU seems to be affected by both spatial effects and the presented approaches thus represent two different insights into the complex spatial character of the modelled process.

Suggested Citation

  • Andrea Furková & Michaela Chocholatá, 2021. "Spatial econometric approach to the EU regional employment process," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(3), pages 1037-1056, September.
  • Handle: RePEc:spr:cejnor:v:29:y:2021:i:3:d:10.1007_s10100-020-00714-5
    DOI: 10.1007/s10100-020-00714-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10100-020-00714-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10100-020-00714-5?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. Thomas De Graaff & Raymond J.C.M. Florax & Peter Nijkamp & Aura Reggiani, 2001. "A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity," Journal of Regional Science, Wiley Blackwell, vol. 41(2), pages 255-276, May.
    2. Cristiano Perugini & Marcello Signorelli, 2004. "Employment Performance and Convergence in the European Countries and Regions," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 1(2), pages 243-278, December.
    3. Geniaux, Ghislain & Martinetti, Davide, 2018. "A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 74-85.
    4. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
    5. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model," Environment and Planning A, , vol. 32(1), pages 9-32, January.
    6. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
    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. Li, Deng-Kui & Mei, Chang-Lin & Wang, Ning, 2019. "Tests for spatial dependence and heterogeneity in spatially autoregressive varying coefficient models with application to Boston house price analysis," Regional Science and Urban Economics, Elsevier, vol. 79(C).
    2. Yaxiong Ma & Sucharita Gopal, 2018. "Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework," Sustainability, MDPI, vol. 10(4), pages 1-27, March.
    3. Shaoming Cheng & Huaqun Li, 2011. "Spatially Varying Relationships of New Firm Formation in the United States," Regional Studies, Taylor & Francis Journals, vol. 45(6), pages 773-789.
    4. Elżbieta Antczak & Katarzyna M. Miszczyńska, 2021. "Causes of Sickness Absenteeism in Europe—Analysis from an Intercountry and Gender Perspective," IJERPH, MDPI, vol. 18(22), pages 1-22, November.
    5. María D. Illescas-Manzano & Sergio Martínez-Puertas & Gema M. Marín-Carrillo & María B. Marín-Carrillo, 2023. "Dynamics of agglomeration and competition in the hotel industry: A geographically weighted regression analysis based on an analytical hierarchy process and geographic information systems (GIS) data," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 213-252, March.
    6. Dongwoo Kang & Sandy Dall’erba, 2016. "Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach," Journal of Geographical Systems, Springer, vol. 18(2), pages 125-157, April.
    7. Marques, Samuel de França & Pitombo, Cira Souza, 2023. "Local modeling as a solution to the lack of stop-level ridership data," Journal of Transport Geography, Elsevier, vol. 112(C).
    8. Elżbieta Antczak, 2020. "Regionally Divergent Patterns in Factors Affecting Municipal Waste Production: The Polish Perspective," Sustainability, MDPI, vol. 12(17), pages 1-25, August.
    9. Miryam S. Merk & Philipp Otto, 2022. "Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross‐sectional resampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
    10. Yu, Haijing & Devece, Caarlos & Martinez, José Manuel Guaita & Xu, Bing, 2021. "An analysis of the paradox in R&D. Insight from a new spatial heterogeneity model," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    11. Carosi, Andrea, 2016. "Do local causations matter? The effect of firm location on the relations of ROE, R&D, and firm SIZE with MARKET-TO-BOOK," Journal of Corporate Finance, Elsevier, vol. 41(C), pages 388-409.
    12. Yan Kestens & Marius Thériault & François Des Rosiers, 2006. "Heterogeneity in hedonic modelling of house prices: looking at buyers’ household profiles," Journal of Geographical Systems, Springer, vol. 8(1), pages 61-96, March.
    13. Kevin McNamara, 2005. "Analysis of Manufacturing Growth in Indiana," ERSA conference papers ersa05p827, European Regional Science Association.
    14. Chang-Lin Mei & Feng Chen & Wen-Tao Wang & Peng-Cheng Yang & Si-Lian Shen, 2021. "Efficient estimation of heteroscedastic mixed geographically weighted regression models," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(1), pages 185-206, February.
    15. Hans R. A. Koster & Jos N. van Ommeren & Piet Rietveld, 2016. "Historic amenities, income and sorting of households," Journal of Economic Geography, Oxford University Press, vol. 16(1), pages 203-236.
    16. Bethany Everett & David Rehkopf & Richard Rogers, 2013. "The Nonlinear Relationship Between Education and Mortality: An Examination of Cohort, Race/Ethnic, and Gender Differences," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 32(6), pages 893-917, December.
    17. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    18. Tsimpanos, Apostolos & Tsimbos, Cleon & Kalogirou, Stamatis, 2018. "Assessing spatial variation and heterogeneity of fertility in Greece at local authority level," MPRA Paper 100406, University Library of Munich, Germany.
    19. Diana Gutiérrez Posada & Fernando Rubiera Morollón & Ana Viñuela, 2018. "Ageing Places in an Ageing Country: The Local Dynamics of the Elderly Population in Spain," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 109(3), pages 332-349, July.
    20. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.

    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:cejnor:v:29:y:2021:i:3:d:10.1007_s10100-020-00714-5. 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.