Contemporary and non-contemporary spatial dependence in unemployment rates: an attempt to empirical analysis of Italian provincial date

Journal title RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO
Author/s Massimiliano Agovino, Antonio Garofalo
Publishing Year 2014 Issue 2013/3
Language Italian Pages 38 P. 45-82 File size 1309 KB
DOI 10.3280/REST2013-003002
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The aim of this paper is to analyze the spatio-temporal trend of unemployment rates in Italian provinces by using spatial econometrics tools. Methods and Results To this purpose, we investigate the existence of contemporary and noncontemporary spatial dependence in unemployment rates by employing ESDA (Exploratory Spatial Data Analysis) and ESTDA (Exploratory Space-Time Data Analysis) techniques. Our results show that: unemployment is a phenomenon characterized by space-time persistence; a shock in the unemployment rate, which occurs in the past and in a given province, produces its effects in the present in the neighbouring provinces. Conclusions: As a consequence, economic policy measures addressed to the province where the shock is generated are not sufficient to tackle the problem since unemployment is neither a localized or a temporary phenomenon.

Keywords: Spatial model, spatial econometrics, spatial diffusion, ESDA, unemployment

Jel codes: C21, C51, C53, J08, J64

  1. Prioetti T. (2003), Forecasting the US Unemployment Rate, Computational Statistics and Data Analysis, 42, pp. 451-476.
  2. Rey S., Montouri B. (1999), US Regional Income Convergence: A Spatial Econometric Perspective, Regional Studies, 33, 2, pp. 143-156.
  3. Sweet D. (2005), Forecasting Unemployment with Spatial Correlation, PhD Thesis presented to the Faculty of the Graduate School University of Missouri-Columbia.
  4. Upton G., Fingleton B. (1985), Spatial Data Analysis by Example. Chichester: Wiley, vol. 1.
  5. Wartenberg D. (1985), Multivariate Spatial Correlation: A Method for Exploratory Geographical Analysis, Geographical Analysis, 17, pp. 263-283.
  6. Yilmaz S., Haynes K.E., Dinc M. (2002), Geographic and Network Neighbors: Spillover Effects of Telecommunications Infrastructure. Journal of Regional Science, 42, 2, pp. 339-360.
  7. Case A. (1991), Spatial Patterns in Household Demand, conometrica, 59, pp. 953-965.
  8. Arbia G. (1988), Spatial Data Configuration in Statistical Analysis of Regional Economics and related Problems. In: Advanced Statistical Theory and Applied Econometrics. Boston: Kluwer Academic Publisher.
  9. Arbia G., Basile R., Mirella M. (2002), Regional Convergence in Italy 1951-1999: A Spatial Econometric Prospective. Roma: ISAE.
  10. Anselin L. (1988), Spatial Econometrics: Methods and Models. Dordrecht: Kluwer.
  11. Anselin L. (1995), Local Indicators of Spatial Association – LISA, Geographical Analysis, 27, pp. 93-115.
  12. Anselin L. (2001a), Spatial Econometrics. In: B.H. Baltagi (ed.), A Companion to Theoretical Econometrics. Oxford: Basil Blackwell, pp. 310-330.
  13. Anselin L. (2001b), Spatial Effects in Econometric Practice in Environmental and Resource Economics, American Journal of Agricultural Economics, 83, 3: pp. 705-710.
  14. Anselin L., Bera A. (1998), Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics. In: A. Ullah, D.Giles (eds.), Handbook of Applied Economic Statistics. New York: Marcel Dekker, pp. 237-289.
  15. Anselin L., Syabri I., Smirnov O. (2002), Visualizing Multivariate Spatial Correlation with Dynamically Linked Windows. Urbana-Champaigne: Regional Economics Application Laboratory (REAL), University of Illinois. Baltagi B.H., Li D. (2003), Prediction in the Panel Data Model with Spatial Correlation.
  16. In: L. Anselin, R. Florax, S. Rey (eds.), New Advances in Spatial Econometrics. Berlin: Springer-Verlag, pp. 283-296.
  17. Baltagi B.H., Song S.H., Koh W. (2003), Testing Panel Data Regression Models with Spatial Error Correlation, Journal of Econometrics, 117, 1, November, pp. 123-150.
  18. Bronars S.G., Jansen D.W. (1987), The Geographic Distribution of Unemployment Rates, US Journal of Econometrics, 36, pp. 251-279.
  19. Chasco C., Lopez F.A. (2004), Space-time Lags: Specification Strategy in Spatial Regression Models, Econometrics 0411005, EconWPA.
  20. Chasco C., Lopez F.A. (2006), Is Spatial Dependence an Instantaneous Effect? Some Evidence in economic Series of Spanish Provinces. Germany: University Library of Munich, MPRA Paper 1777.
  21. Chasco C., Lopez F.A. (2007), Time-trend in Spatial Dependence: Specification Strategy in the First-order Spatial Autoregressive Model, Estudios de Economia Aplicada, 25, pp. 631-650.
  22. Cracolici M., Cuffaro M., Nijkamp P. (2007), Geographical Distribution of Unemployment: An Analysis of Provincial Differences in Italy, Growth and Change a Journal of Urban and Regional Policy, 38, 4, pp. 649-670.
  23. De Luna X., Genton M.G. (2004), Spatio-temporal Autoregressive Models for US Unemployment Rate, Advances in Econometrics: Spatial and Spatiotemporal Econometrics, 18, pp. 283-298.
  24. Di Giacinto V. (2006), A Generalized Space-Time ARMA Model with an Application to Regional Unemployment Analysis in Italy, International Regional Science Review, 29, 2, pp. 159-198.
  25. Elhorst J.P. (1995), Unemployment Disparities between Regions in the European Union. In: H.W. Armstrong, R.W. Vickerman (eds.), Convergence and Divergence among European Regions. London: Pion.
  26. Elhorst J.P. (2001), Dynamic Models in Space and Time, Geographical Analysis, 33, pp. 119-140.
  27. Elhorst J.P. (2003), Specification and Estimation of Spatial Panel Data Models, International Regional Science Review, 26, 3, pp. 244-268.
  28. Espa G., Arbia G., Giuliani D. (2013), Conditional versus Unconditional Industrial Agglomeration: Disentangling Spatial Dependence and Spatial Heterogeneity in the Analysis of ICT Firms’ Distribution in Milan, Journal Geographical Systems, 15, 1, pp. 31-50.
  29. Florax R., Folmer H. (1992), Specification and Estimation of Spatial Linear Regression Models: Monte Carlo Evaluation of Pre-test Estimators, Regional Science and Urban Economics, 22, 3, pp. 405-432.
  30. Getis A., Ord J.K. (1992), The Analysis of Spatial Association by Use of Distance Statistics, Geographical Analysis, 24, pp. 189-206.
  31. Giacomini R., Granger C.W.J. (2004), Aggregation of Space-Time Processes, Journal of Econometrics, 118, 1-2, pp. 7-26. Mobley L.R. (2003), Estimating Hospital Market Pricing: An Equilibrium Approach using Spatial Econometrics, Regional Science and Urban Economics, 33, pp. 489-516.
  32. Molho I. (1995), Spatial Autocorrelation in British Unemployment, Journal of Regional Science, 35, pp. 2-24.
  33. Montgomery A., Zarnowitz V., Tsay R., Tiao G. (1998), Forecasting the US Unemployment Rate, Journal of the American Statistical Association, 93, pp. 478-493.
  34. Nosvelli M. (2006) Apprendimento e conoscenze nei sistemi locali, un’analisi economica. Milano: FrancoAngeli.
  35. O’Sullivan D., Unwin D.J. (2003), Geographic Information Analysis. Hoboken: Wiley.
  36. Pace R.K., Barry R., Clapp J.M., Rodríguez M. (1998), Spatiotemporal Autoregressive Models of Neighborhood Effects, Journal of Real State Finance and Economics, 17, 1, pp. 15-33.
  37. Pace R.K., Barry R., Gilley O.W., Sirmans C.F. (2000), A Method for Spatialtemporal Forecasting with an Application to Real Estate Prices International, Journal of Forecasting, 16, pp. 229-246.
  38. Pagnini M. (2005), Spillovers geografici e dinamica dell’occupazione nell’industria italiana. In: M. Omiccioli, L.F. Signorini (a cura di), Economie locali e competizione globale. Bologna: Il Mulino.

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    DOI: 10.2139/ssrn.899976

Massimiliano Agovino, Antonio Garofalo, Dipendenza spaziale contemporanea e non contemporanea nei tassi di disoccupazione: un tentativo di analisi empirica dei dati provinciali italiani in "RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO" 3/2013, pp 45-82, DOI: 10.3280/REST2013-003002