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Modelling Low Income Transitions

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  • Cappellari, Lorenzo

    (Università Cattolica del Sacro Cuore)

  • Jenkins, Stephen P.

    (London School of Economics)

Abstract

We examine the determinants of low income transitions using first-order Markov models that control for initial conditions effects (those found to be poor in the base year may be a nonrandom sample) and for attrition (panel retention may also be non-random). Our econometric model is a form of endogeneous switching regression, and is fitted using simulated maximum likelihood methods. The estimates, derived from British panel data for the 1990s, indicate that there is substantial genuine state dependence in poverty. We also provide estimates of low income transition rates and lengths of poverty and non-poverty spells for persons of different types.

Suggested Citation

  • Cappellari, Lorenzo & Jenkins, Stephen P., 2002. "Modelling Low Income Transitions," IZA Discussion Papers 504, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp504
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    References listed on IDEAS

    as
    1. Mark B. Stewart & Joanna K. Swaffield, 1999. "Low Pay Dynamics and Transition Probabilities," Economica, London School of Economics and Political Science, vol. 66(261), pages 23-42, February.
    2. Lillard, Lee A & Willis, Robert J, 1978. "Dynamic Aspects of Earning Mobility," Econometrica, Econometric Society, vol. 46(5), pages 985-1012, September.
    3. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    4. Atkinson, A B, 1987. "On the Measurement of Poverty," Econometrica, Econometric Society, vol. 55(4), pages 749-764, July.
    5. Gerard J. van den Berg & Maarten Lindeboom, 1998. "Attrition in Panel Survey Data and the Estimation of Multi-State Labor Market Models," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 458-478.
    6. Lorenzo Cappellari, 2007. "Earnings mobility among Italian low-paid workers," Journal of Population Economics, Springer;European Society for Population Economics, vol. 20(2), pages 465-482, April.
    7. Francesco Devicienti, 2011. "Estimating poverty persistence in Britain," Empirical Economics, Springer, vol. 40(3), pages 657-686, May.
    8. Ann Huff Stevens, 1999. "Climbing out of Poverty, Falling Back in: Measuring the Persistence of Poverty Over Multiple Spells," Journal of Human Resources, University of Wisconsin Press, vol. 34(3), pages 557-588.
    9. Arulampalam, Wiji & Booth, Alison L & Taylor, Mark P, 2000. "Unemployment Persistence," Oxford Economic Papers, Oxford University Press, vol. 52(1), pages 24-50, January.
    10. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    11. Stephen P. Jenkins, 2000. "Modelling household income dynamics," Journal of Population Economics, Springer;European Society for Population Economics, vol. 13(4), pages 529-567.
    12. Sloane, P J & Theodossiou, I, 1996. "Earnings Mobility, Family Income and Low Pay," Economic Journal, Royal Economic Society, vol. 106(436), pages 657-666, May.
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    More about this item

    Keywords

    state dependence; simulated maximum likelihood; first-order Markov; poverty dynamics;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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