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Statistical models for measuring job satisfaction

Author

Listed:
  • Romina Gambacorta

    () (Bank of Italy)

  • Maria Iannario

    () (University of Naples Federico II)

Abstract

In this paper we present two statistical approaches for discussing and modelling job satisfaction based on data collected in the Survey on Household Income and Wealth (SHIW) conducted by the Bank of Italy. In particular, we compare two different classes of model for ordinal data: the Ordinal Probit Model and the more recent CUB model. The aim is to establish common outcomes and differences in the estimated patterns of global job satisfaction, but also to stress the potential for curbing the effects of measurement errors on estimates by using CUB models, allowing us to control for the effect of uncertainty and shelter choices in the response process.

Suggested Citation

  • Romina Gambacorta & Maria Iannario, 2012. "Statistical models for measuring job satisfaction," Temi di discussione (Economic working papers) 852, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:wptemi:td_852_12
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    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2012/2012-0852/en_tema_852.pdf
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    References listed on IDEAS

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    1. Justina A. V. Fischer & Alfonso Sousa-Poza, 2009. "Does job satisfaction improve the health of workers? New evidence using panel data and objective measures of health," Health Economics, John Wiley & Sons, Ltd., vol. 18(1), pages 71-89.
    2. Blanchflower, David G. & Oswald, Andrew J., 2004. "Well-being over time in Britain and the USA," Journal of Public Economics, Elsevier, vol. 88(7-8), pages 1359-1386, July.
    3. Andries de Grip & Inge Sieben & Fred Stevens, 2009. "Are More Competent Workers More Satisfied?," LABOUR, CEIS, vol. 23(4), pages 589-607, December.
    4. Clark, Andrew E. & Oswald, Andrew J., 1996. "Satisfaction and comparison income," Journal of Public Economics, Elsevier, vol. 61(3), pages 359-381, September.
    5. Cornelissen, Thomas & Heywood, John S. & Jirjahn, Uwe, 2011. "Performance pay, risk attitudes and job satisfaction," Labour Economics, Elsevier, vol. 18(2), pages 229-239, April.
    6. Gabriella Conti & Stephen Pudney, 2011. "Survey Design and the Analysis of Satisfaction," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 1087-1093, August.
    7. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    8. Becker, William E. & Kennedy, Peter E., 1992. "A Graphical Exposition of the Ordered Probit," Econometric Theory, Cambridge University Press, vol. 8(01), pages 127-131, March.
    9. Andrew E. Clark, 1996. "Job Satisfaction in Britain," British Journal of Industrial Relations, London School of Economics, vol. 34(2), pages 189-217, June.
    10. Claudia Biancotti & Giovanni D'Alessio & Andrea Neri, 2008. "Measurement Error In The Bank Of Italy'S Survey Of Household Income And Wealth," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 54(3), pages 466-493, September.
    11. Ivan Faiella, 2008. "Accounting for sampling design in the SHIW," Temi di discussione (Economic working papers) 662, Bank of Italy, Economic Research and International Relations Area.
    12. Paolo Ghinetti, 2007. "The Public-Private Job Satisfaction Differential in Italy," LABOUR, CEIS, vol. 21(2), pages 361-388, June.
    13. Francesca Di Iorio & Maria Iannario, 2012. "Residual diagnostics for interpreting CUB models," Statistica, Department of Statistics, University of Bologna, vol. 72(2), pages 163-172.
    14. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653.
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    More about this item

    Keywords

    job satisfaction; ordinal data modelling; CUB models;

    JEL classification:

    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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