Advertisement

Modelling Fuzzy and Multidimensional Poverty Measures in the United Kingdom with Variance Components Panel Regression

  • Gianni Betti
  • Antonella D’Agostino
  • Laura Neri
Part of the Economic Studies in Inequality, Social Exclusion and Well-Being book series (EIAP, volume 3)

Keywords

Membership Function Modelling Fuzzy Durable Good Poverty Measure Supplementary Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson TW, Hsiao C (1981) Estimation of dynamic models with error components. Journal of the American Statistical Association 76:598–606.zbMATHCrossRefMathSciNetGoogle Scholar
  2. Anderson TW, Hsiao C (1982) Formulation and Estimation of Dynamic Models Using Panel Data. Journal of Econometrics 18:47–82.zbMATHCrossRefMathSciNetGoogle Scholar
  3. Bardasi E, Jenkins SP, Rigg J (2004) Documentation for derived current and annual net household income variables. BHPS 1-12, ISER unofficial supplement to BHPS dataGoogle Scholar
  4. Betti G, Cheli B, Cambini R (2004) A statistical model for the dynamics between two fuzzy states: theory and an application to poverty analysis. Metron 62:391–411.MathSciNetGoogle Scholar
  5. Betti G, D’Agostino A, Neri L (2002) Panel regression models for measuring multi-dimensional poverty dynamics. Statistical Methods and Applications 11:359–369.zbMATHCrossRefGoogle Scholar
  6. Betti G, Verma V (1999) Measuring the degree of poverty in a dynamic and comparative context: a multi-dimensional approach using fuzzy set theory. Proceedings of the ICCS-VI, Lahore, Pakistan, August 27–31, 1999, pp 289–301.Google Scholar
  7. Betti G, Verma V (2004) A methodology for the study of multi-dimensional and longitudinal aspects of poverty and deprivation. Università di Siena, Dipartimento di Metodi Quantitativi, Working Paper 49Google Scholar
  8. Cerioli A, Zani S (1990) A fuzzy approach to the measurement of poverty. In: Dagum C, Zenga M (eds) Income and wealth distribution, inequality and poverty. Springer Verlag, Berlin, pp 272–284.Google Scholar
  9. Cheli B (1995) Totally Fuzzy and Relative Measures in Dynamics Context. Metron 53:83–205.MathSciNetGoogle Scholar
  10. Cheli B, Betti G (1999), Fuzzy Analysis of Poverty Dynamics on an Italian Pseudo Panel, 1985–1994. Metron 57:83–103.zbMATHMathSciNetGoogle Scholar
  11. Cheti B, Lemmi A (1995) A “Totally” Fuzzy and Relative Approach to the Multi-dimensional Analysis of Poverty. Economic Notes 24:115–134.Google Scholar
  12. Devicenti F (2001) Poverty persistence in Britain: a multivariate analysis using The BHPS, 1991–1997. In: Moyes P, Seidl C, Shorrocks AF (eds) Inequalities: theory, measurement and applications. Journal of Economics Suppl. 9, pp 1–34.Google Scholar
  13. Goldstein H, Healy M JR, Rasbash J (1994) Multilevel time series models with applications to repeated measures data. Statistics in Medicine 13:1643–1655.PubMedGoogle Scholar
  14. Jenkins SP (2000) Modelling household income dynamics. Journal of Population Economics 13:529–567.CrossRefGoogle Scholar
  15. Lillard LA, Willis RJ (1978) Dynamic aspect of earning mobility. Econometrica 46:985–1011.zbMATHCrossRefGoogle Scholar
  16. Littell RC, Milliken GA, Stroup WW, Wolfinger RD (1996) SAS System for Mixed Models. SAS Institute Inc., Cary, NCGoogle Scholar
  17. Mansour H, Norheim EV, Rutledge JJ (1985) Maximum likelihood estimation of variance components in repeated measure designs assuming autoregressive errors. Biometrics 41:287–294.zbMATHCrossRefMathSciNetGoogle Scholar
  18. McClements LD (1977) Equivalence scales for children. Journal of Public Economics 8:191–210.PubMedCrossRefGoogle Scholar
  19. Modigliani F (1966) The life cycle hypothesis of savings, the demand for wealth and the supply of capital. Social Research 33:160–217.Google Scholar
  20. Raghunathan TE, Lepkowski J, Van Voewyk J (2001) A multivariate technique for imputing missing values using a sequence of regression models. Survey Methodology 27:85–95.Google Scholar
  21. Stevens AH (1999) Climbing out of poverty, falling back in: measuring the persistence of poverty over multiple spells. Journal of Human Resources 34:557–588.CrossRefGoogle Scholar
  22. Trivellato U (1998) Il monitoraggio della povertà e della sua dinamica: questioni di misura e evidenze empiriche. Statistica 58:549–575.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Gianni Betti
    • 1
  • Antonella D’Agostino
    • 2
  • Laura Neri
    • 1
  1. 1.Dipartimento Metodi QuantitativiUniversity of SienaItaly
  2. 2.Dipartimento di Statistica e Matematica per la Ricerca EconomicaUniversity of Naples “Parthenope”France

Personalised recommendations