Spatio‑temporal Modelling of the Influence of the Number of Business Entities in Selected Urban Centres on Unemployment in the Kujawsko‑Pomorskie Voivodeship
DOI:
https://doi.org/10.18778/0208-6018.337.02Keywords:
business entities, unemployment, labour market, spatial and spatio‑temporal trends, spatial autocorrelation, spatial and spatio‑temporal autoregressive modelsAbstract
The paper presents the analysis of the spatial and spatio‑temporal tendencies and dependence in matters related to the situation in the labour market of the Kujawsko‑Pomorskie Voivodeship across municipalities in the period of 2004–2015. The aim of the investigation is to verify whether, in the presence of the dependence, investing (through a growing number of enterprises) in the development of selected urban centres with a high level of unemployment can significantly reduce the unemployment rate in the whole province. The assessment of the situation in the labour market for each of the municipalities is made with the use of two characteristics, i.e. the share of registered unemployed persons in the number of the working age population and the number of business entities per 10,000 working age population. From the methodological point of view, the values of the variables are treated as realisations of spatial and spatio‑temporal stochastic processes. The spatial and spatio‑temporal tendencies and dependence were investigated using the concept of spatial and spatio‑temporal trends and spatial autocorrelation. Additionally, spatial autoregressive models for individual processes and spatial models of the dependence of unemployment on the number of business entities, for each year of the investigated period, were estimated and verified. The specification of spatio‑temporal models of unemployment, including the model which takes into consideration spatial shifts and time lags of the dependence, was carried out. The models were used for simulating the level of unemployment in the province assuming some growth in the number of business entities in selected urban centres.
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References
Anselin L. (1988), Spatial Econometrics. Methods and Models, Kluwer Academic, Boston.
Google Scholar
Anselin L., Florax R. J.G.M., Rey S. (2004), Advances in Spatial Econometrics. Methodology, Tools and Applications, Springer‑Verlag, New York.
Google Scholar
Arbia G. (2006), Spatial Econometrics. Statistical Foundations and Applications to Regional Convergence, Springer‑Verlag, Berlin–Heidelberg.
Google Scholar
Bal‑Domańska B. (2014), Identifying Regional Diversification and Spatial Dependence of Employment in EU Regions as One of Social Cohesion Indicators, “Acta Universitatis Lodziensis. Folia Oeconomica”, vol. 5(307), pp. 7–26.
Google Scholar
Blinova T., Markow V., Rusanovskiy V. (2016), Empirical Study of Spatial Differentiation of Youth Unemployment in Russia, “Acta Oeconomica”, vol. 66(3), pp. 507–526.
Google Scholar
Cliff A. D., Ord J. K. (1973), Spatial Autocorrelation, Pion, London.
Google Scholar
Cliff A. D., Ord J. K. (1981), Spatial Processes. Models and Applications, Pion, London.
Google Scholar
Cressie N. A.C. (1993), Statistics for Spatial Data, John Wiley & Sons Inc., New York.
Google Scholar
Gawrycka M., Szymczak A. (2010), Przestrzenne zróżnicowanie rynków pracy z punktu widzenia popytu na pracę, “Współczesna Ekonomia”, no. 1(13), pp. 47–58.
Google Scholar
Haining R. (2005), Spatial Data Analysis. Theory and Practice, 3th ed., Cambridge University Press, Cambridge.
Google Scholar
LeSage J., Pace K. R. (2009), Introduction to Spatial Econometrics, Champion & Hall/CRC, Boca Raton–London‑New York.
Google Scholar
Moran P. A.P. (1950), The Interpretation of Statistical Maps, “Journal of the Royal Statistical Society”, Series B, 10, pp. 243–251.
Google Scholar
Nosek V., Netrdova P. (2014), Measuring Spatial Aspects of Variability. Comparing Spatial Autocorrelation with Regard Decomposition in Internal Unemployment Research, “Historical Social Research – Historische Socialforschung”, vol. 39(2), pp. 292–314.
Google Scholar
Perugini C., Signorelli M. (2007), Labour Market Performance Differentials and Dynamics in EU–15 Countries and Regions, “The European Journal of Comparative Economics”, vol. 4(2), pp. 209–262.
Google Scholar
Pillet F., Cañizares M. C., Ruiz A. R., Martínez H., Plaza J., Santos J. F. (2014), Applying the European Spatial Development Perspective in Low‑density Regions: a Methodology Based on Mobility and Labour Market Structure, “Urban Studies”, vol. 51(3), pp. 577–595.
Google Scholar
Schabenberger O., Gotway C. A. (2005), Statistical Methods for Spatial Data Analysis, Champion & Hall/CRC, Boca Raton–London‑New York.
Google Scholar
Semerikova E. (2015), Spatial Patterns of German Labor Market: Panel Data Analysis of Regional Unemployment, [in:] Ch. Mussida, F. Pastore (eds.), Geographical Labor Market Imbalances: Recent Explanations And Cures, Springer, Berlin–Heidelberg.
Google Scholar
Strategia rozwoju województwa kujawsko‑pomorskiego do roku 2020 – Plan modernizacji 2020+ (The 2020 Development Strategy for the Kujawsko‑Pomorskie Voivodeship and the 2020+Modernisation Plan), http://www.mojregion.eu/files/dokumenty%20rpo/konkursy_nabory/3.1_Wspieranie%20wytwarzania%20i%20dystrybucji%20energii%20pochodzacej%20ze%20zrodel%20odnawialnych_nr_102_30_05_2017/Strategia_rozwoju_wojewodztwa_kujawsko‑pomorskiego_do_roku_2020__plan_modernizacji_2020+.pdf [accessed: 10.01.2017].
Google Scholar
Szulc E. (2007), Ekonometryczna analiza wielowymiarowych procesów gospodarczych (Econometric Analysis of Multidimensional Economic Processes), UMK, Toruń.
Google Scholar
Szulc E. (2011), Identification of the Structures of Spatial and Spatio‑Temporal Processes and a Problem of Data Aggregation, “Dynamic Econometric Models”, vol. 11, pp. 4–20.
Google Scholar
Szulc E., Jankiewicz M. (2017), The Labour Market Situation in Medium‑Sized Urban Centres of the Kujawsko‑Pomorskie Voivodeship and the Problem of Unemployment in the Province, [in:] M. Papież, S. Śmiech (eds.), The 11th Professor Aleksander Zelias International Conference on Modelling and Forecasting of Socio‑Economic Phenomena. Conference Proceedings, http://pliki.konferencjazakopianska.pl/proceedings_2017/pdf/Szulc_Jankiewicz.pdf [accessed: 12.05.2017].
Google Scholar
Szulc E., Müller‑Frączek I., Pietrzak M. (2011), Modelowanie zależności między ekonomicznymi procesami przestrzennymi a poziom agregacji danych (Modelling of Dependence between Economic Spatial Processes and Level of Data Aggregation), “Acta Universitatis Lodziensis. Folia Oeconomica”, vol. 253, pp. 327–344.
Google Scholar
Tokarski T. (2005), Regionalne zróżnicowanie rynku pracy, “Wiadomości Statystyczne”, no. 11, pp. 67–88.
Google Scholar
Vega S. H., Elhorst J. P. (2014). Modelling Regional Labour Market Dynamics in Space and Time, “Papers in Regional Science”, vol. 93(4), pp. 819–841.
Google Scholar