Journal of Geographical Systems

, Volume 8, Issue 4, pp 391–410 | Cite as

The ecological fallacy in a time series context: evidence from Spanish regional unemployment rates

  • Juan Carlos Duque
  • Manuel Artís
  • Raúl Ramos
Original Article


The ecological fallacy (EF) is a common problem regional scientists have to deal with when using aggregated data in their analyses. Although there is a wide number of studies considering different aspects of this problem, little attention has been paid to the potential negative effects of the EF in a time series context. Using Spanish regional unemployment data, this paper shows that EF effects are not only observed at the cross-section level, but also in a time series framework. The empirical evidence obtained shows that analytical regional configurations are the least susceptible to time effects relative to both normative and random regional configurations, while normative configurations are an improvement over random ones.


Ecological fallacy Time series Constrained clusters Theil index Unemployment rates 


O18 E24 C61 



The authors wish to thank three anonymous referees and Serge Rey, E. López-Bazo and E. Pons for their helpful comments and suggestions about previous versions of this paper, and Philip Stephens for editing. The usual disclaimer applies. Financial support is gratefully acknowledged from the CICYT SEJ2005-04348/ECON project.


  1. Alonso J, Izquierdo M (1999) Disparidades regionales en el empleo y el desempleo. Papeles de Economía Española 80:79–99Google Scholar
  2. Arbia G (1986) The modifiable areal unit problem and the spatial autocorrelation problem: towards a joint approach. Metron 44:391–407Google Scholar
  3. Batty M, Sikdar PK (1982) Spatial aggregation in gravity models. Environ Plan A 14:377–822CrossRefGoogle Scholar
  4. Bentolila S, Jimeno J (1995) Regional unemployment persistence: Spain 1976–1994. C.E.P.R. Discussion paper no. 1259Google Scholar
  5. Blanchard O, Jimeno JF (1995) Structural unemployment: Spain versus Portugal. Am Econ Rev 85:212–218Google Scholar
  6. Commission of the European Communities, Eurostat, Unit A4 GISCO (1997) Geographical information systems in statistics. SUP.COM 95, Lot 115Google Scholar
  7. Cressie N (1993) Statistics for spatial data. Wiley, New YorkGoogle Scholar
  8. Duque JC (2004) Design of homogeneous territorial units. A methodological proposal and applications. PhD thesis, University of Barcelona, SpainGoogle Scholar
  9. Duque JC, Church RL (2004) A new heuristic model for designing analytical regions. In: North American Meeting of the International Regional Science Association, SeattleGoogle Scholar
  10. Duque JC, Ramos R, Suriñach J (2006) Supervised regionalization methods: a survey. mimeoGoogle Scholar
  11. Eurostat (2006) Nomenclature of territorial units for statistics—NUTS. Statistical regions of Europe. (06/19/2006)Google Scholar
  12. Fischer MM (1980) Regional taxonomy—a comparison of some hierarchic and non hierarchic strategies. Reg Sci Urban Econ 10:503–537CrossRefGoogle Scholar
  13. Glover F (1977) Heuristic for integer programming using surrogate constraints. Decis Sci 8:156–166Google Scholar
  14. Glover F (1989) Tabu search. Part I. ORSA J Comput 1:190–206Google Scholar
  15. Glover F (1990) Tabu search. Part II. ORSA J Comput 2:4–32Google Scholar
  16. Gordon AD (1996) A survey of constrained classification. Comput Stat Data Anal 21:17–29CrossRefGoogle Scholar
  17. Gordon AD (1999) Classification, 2nd edn. Chapman & Hall, Boca RatonGoogle Scholar
  18. Gotway CA, Young LJ (2002) Combining incompatible spatial data. J Am Stat Assoc 97:632–648CrossRefGoogle Scholar
  19. Greenland S, Morganstern H (1989) Ecological bias, confounding, and effect modification. Int J Epidemiol 18:269–274Google Scholar
  20. Jimeno JF, Bentolila S (1998) Regional unemployment persistence (Spain, 1976–94). Labour Econ 5:25–51CrossRefGoogle Scholar
  21. López-Bazo E, del Barrio T, Artís M (2002) The regional distribution of Spanish unemployment: a spatial analysis. Pap Reg Sci 81:365–389CrossRefGoogle Scholar
  22. López-Bazo E, del Barrio T, Artís M (2005) Geographical distribution of unemployment in Spain. Reg Stud 3:305–318CrossRefGoogle Scholar
  23. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, vol. 1. University of California Press, Berkeley, pp. 281–297Google Scholar
  24. Marimon R, Zilibotti F (1998) ‘Actual’ versus ‘virtual’ employment in Europe. Is Spain different? Eur Econ Rev 42:123–153CrossRefGoogle Scholar
  25. Martin D, Nolan A, Tranmer M (2001) The application of zone-design methodology in the 2001 UK census. Environ Plan A 33:1949–1962CrossRefGoogle Scholar
  26. Moran P (1948) The interpretation of statistical maps. J R Stat Soc B 10:243–251Google Scholar
  27. Murtagh F (1985) A survey of algorithms for contiguity-constrained clustering and related problems. Comput J 28:82–88CrossRefGoogle Scholar
  28. Norman P, Rees P, Boyle P (2003) Achieving data compatibility over space and time: creating consistent geographical zones. Int J Popul Geogr 9:365–386CrossRefGoogle Scholar
  29. Openshaw S (1973) A regionalisation algorithm for large datasets. Comput Appl 3–4:136–147Google Scholar
  30. Openshaw S (1977) A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modeling. Trans Inst Br Geographers 2:459–472CrossRefGoogle Scholar
  31. Openshaw S (1984) The modifiable areal unit problem. Concepts and techniques in modern geography, vol. 38. GeoBooks, NorwichGoogle Scholar
  32. Openshaw S, Rao L (1995) Algorithms for reengineering 1991 census geography. Environ Plan A 27:425–446CrossRefGoogle Scholar
  33. Openshaw S, Wymer C (1995) Classifying and regionalizing census data. In: Openshaw S (ed) Census users handbook. Geo Information International, Cambridge, UK, pp. 239–270Google Scholar
  34. Piantadosi S, Byar DP, Green SB (1988) The ecological fallacy. Am J Epidemiol 127:893–904Google Scholar
  35. Rey S (2001) Spatial analysis of regional income inequality. REAL discussion paper 01-T9Google Scholar
  36. Richardson S (1992) Statistical methods for geographical correlation studies. In: Elliot P, Cuzick J, English D, Stern R (eds) Geographical and environmental epidemiology: methods for small area studies. Oxford University Press, New York, pp. 181–204Google Scholar
  37. Richardson S, Stucker L, Hemon D (1987) Comparison of relative risks obtained in ecological and individual studies: some methodological considerations. Int J Epidemiol 16:111–120Google Scholar
  38. Robinson WS (1950) Ecological correlations and the behavior of individuals. Am Sociol Rev 15:351–357CrossRefGoogle Scholar
  39. Theil H (1967) Economics and information theory. Rand McNally and Company, ChicagoGoogle Scholar
  40. Vickrey W (1961) On the prevention of gerrymandering. Political Sci Q 76:105–110CrossRefGoogle Scholar
  41. Wise SM, Haining RP, Ma J (1997) Regionalization tools for exploratory spatial analysis of health data. In: Fisher MM, Getis A (eds) Recent developments in spatial analysis: spatial statistics, behavioural modelling, and computational intelligence. Springer, Berlin Heidelberg New York, pp. 83–100Google Scholar
  42. Wise SM, Haining RP, Ma J (2001) Providing spatial statistical data analysis functionality for the GIS user: the SAGE project. Int J Geogr Inf Sci 15:239–254CrossRefGoogle Scholar
  43. Yule GU, Kendall MG (1950) An introduction to the theory of statistics, 14th edn. Griffin, LondonGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Juan Carlos Duque
    • 1
  • Manuel Artís
    • 2
  • Raúl Ramos
    • 2
  1. 1.Regional Analysis Laboratory (REGAL), Department of GeographySan Diego State UniversitySan DiegoUSA
  2. 2.Grup d’Anàlisi Quantitativa Regional (AQR), Departament d’Econometria, Estadística i Economia EspanyolaUniversitat de BarcelonaBarcelonaSpain

Personalised recommendations