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Spatial dependencies in German matching functions

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  • Lottmann, Franziska

Abstract

This paper proposes a spatial panel model for German matching functions to avoid possibly biased and inefficient estimates due to spatial dependence. We provide empirical evidence for the presence of spatial dependencies in matching data. Based on an official data set containing monthly information for 176 local employment offices, we show that neglecting spatial dependencies in the data results in upward-biased coefficients. For the incorporation of spatial information into our model, we use data on commuting relations between local employment offices. Furthermore, our results suggest that a dynamic modeling is more appropriate for matching functions.

Suggested Citation

  • Lottmann, Franziska, 2012. "Spatial dependencies in German matching functions," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 27-41.
    • Handle: RePEc:eee:regeco:v:42:y:2012:i:1:p:27-41
    DOI: 10.1016/j.regsciurbeco.2011.04.007
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    Cited by:

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    2. Vyacheslav VOLCHIK & Elena MASLYUKOVA & Wadim STRIELKOWSKI, 2020. "Spatial Effects Of Informal Employment And Precarization In Urban Centres And Regions," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 15(4), pages 21-37, November.
    3. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
    4. repec:hum:wpaper:sfb649dp2012-026 is not listed on IDEAS
    5. Parent, Olivier & LeSage, James P., 2012. "Spatial dynamic panel data models with random effects," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 727-738.
    6. Bauer, Anja, 2013. "Mismatch unemployment : evidence from Germany 2000-2010," IAB-Discussion Paper 201310, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    7. Michael Stops, 2014. "Job matching across occupational labour markets," Oxford Economic Papers, Oxford University Press, vol. 66(4), pages 940-958.
    8. Elzbieta Antczak & Ewa Galecka-Burdziak & Robert Pater, 2016. "Spatial Labour Market Matching," CERGE-EI Working Papers wp578, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Román Mínguez & María L. & Roberto Basile, 2016. "Spatio-Temporal Autoregressive Semiparametric Model for the analysis of regional economic data," Working Papers LuissLab 16126, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    10. Shi, Wenming & Lin, Kun-Chin & McLaughlin, Heather & Qi, Guanqiu & Jin, Mengjie, 2020. "Spatial distribution of job opportunities in China: Evidence from the opening of the high-speed rail," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 138-147.
    11. Tissaoui, Kais & Zaghdoudi, Taha, 2021. "Dynamic connectedness between the U.S. financial market and Euro-Asian financial markets: Testing transmission of uncertainty through spatial regressions models," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 481-492.
    12. Huang, Qiong & Chand, Satish, 2015. "Spatial spillovers of regional wages: Evidence from Chinese provinces," China Economic Review, Elsevier, vol. 32(C), pages 97-109.
    13. Nasser Dine Mohamedou, 2022. "Labor mobility within Japanese regional labor markets and spillover effects," The Japanese Economic Review, Springer, vol. 73(4), pages 625-645, October.
    14. Franziska Lottmann, 2012. "Regional Unemployment in Germany: a spatial panel data analysis," ERSA conference papers ersa12p53, European Regional Science Association.
    15. Triki, Mohamed Bilel, 2019. "The Italian wage curve revisited: A local and spatial cointegration," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 73-90.
    16. Weiwei Xie & Hongbing Deng & Zhaohui Chong, 2019. "The Spatial and Heterogeneity Impacts of Population Urbanization on Fine Particulate (PM 2.5 ) in the Yangtze River Economic Belt, China," IJERPH, MDPI, vol. 16(6), pages 1-17, March.
    17. Román Mínguez & Roberto Basile & María Durbán, 2020. "An alternative semiparametric model for spatial panel data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 669-708, December.
    18. Lottmann, Franziska, 2012. "Explaining regional unemployment differences in Germany: A spatial panel data analysis," SFB 649 Discussion Papers 2012-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. Polina A. Popova & Tatiana V. Bukina & Dmitriy V. Kashin, 2024. "Influence of Interregional Spatial Effects on the Economic Development of Russian Regions," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(3), pages 751-775.
    20. Gabriel Rodríguez & Juan Palomino, 2019. "Peru's Regional Growth and Convergence in 1979-2017: An Empirical Spatial Panel Data Analysis," Documentos de Trabajo / Working Papers 2019-478, Departamento de Economía - Pontificia Universidad Católica del Perú.
    21. Haller, Peter & Heuermann, Daniel F., 2016. "Job search and hiring in local labor markets: Spillovers in regional matching functions," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 125-138.
    22. Marcin Wozniak, 2021. "Spatial matching on the urban labor market: estimates with unique micro data," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 55(1), pages 1-17, December.
    23. Higashi, Yudai, 2018. "Spatial spillovers in job matching: Evidence from the Japanese local labor markets," Journal of the Japanese and International Economies, Elsevier, vol. 50(C), pages 1-15.

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    More about this item

    Keywords

    Empirical matching; Geographic labor mobility; Spatial dependence; Regional unemployment;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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