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The Estimation Of Poverty Dynamics Using Different Measurements Of Household Income

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  • Ulrich Rendtel
  • Rolf Langeheine
  • Roland Berntsen

Abstract

If surveys offer two different measurements of household income, one can use them simultaneously to identify the potential effects of measurement error on the observed‐income mobility of the poor. In this paper we investigate transition tables between subsequent income states. Latent Markov models are used to model incorrect classifications of income states. Misclassifications are interpreted as measurement error or spurious changes that are not consistent with a simple transition table model. The empirical results for the German Socio‐Economic Panel (GSOEP) show that the observed transition tables overestimate the mobility between poverty states.

Suggested Citation

  • Ulrich Rendtel & Rolf Langeheine & Roland Berntsen, 1998. "The Estimation Of Poverty Dynamics Using Different Measurements Of Household Income," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 44(1), pages 81-98, March.
    • Handle: RePEc:bla:revinw:v:44:y:1998:i:1:p:81-98
    DOI: 10.1111/j.1475-4991.1998.tb00253.x
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    Cited by:

    1. Dimitris Pavlopoulos & Ruud Muffels & Jeroen K. Vermunt, 2009. "Training and Low‐pay Mobility: The Case of the UK and the Netherlands," LABOUR, CEIS, vol. 23(s1), pages 37-59, March.
    2. Martin Ravallion, 2003. "Measuring Aggregate Welfare in Developing Countries: How Well Do National Accounts and Surveys Agree?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 645-652, August.
    3. Duclos, Jean-Yves & Araar, Abdelkrim & Giles, John, 2010. "Chronic and transient poverty: Measurement and estimation, with evidence from China," Journal of Development Economics, Elsevier, vol. 91(2), pages 266-277, March.
    4. Shaimaa Yassin & Francois Langot, 2017. "Correcting Measurement Errors in Transition Models Based on Retrospective Panel Data," IRENE Working Papers 17-04, IRENE Institute of Economic Research.
    5. Arne Bigsten & Abebe Shimeles, 2011. "The persistence of urban poverty in Ethiopia: a tale of two measurements," Applied Economics Letters, Taylor & Francis Journals, vol. 18(9), pages 835-839.
    6. Ulrich Rendtel, 2006. "The 2005 Plenary Meeting on ‘‘Missing Data and Measurement Error’’," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(4), pages 493-499, December.
    7. Jeff Larrimore & Jacob Mortenson & David Splinter, 2015. "Income and Earnings Mobility in U.S. Tax Data," Finance and Economics Discussion Series 2015-61, Board of Governors of the Federal Reserve System (U.S.).
    8. Diana Worts & Amanda Sacker & Peggy McDonough, 2010. "Re-Assessing Poverty Dynamics and State Protections in Britain and the US: The Role of Measurement Error," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 97(3), pages 419-438, July.
    9. Stefan Angel & Richard Heuberger & Nadja Lamei, 2018. "Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 575-603, July.
    10. François Langot & Shaimaa Yassin, 2015. "Reforming Employment Protection in Egypt: An Evaluation Based on Transition Models with Measurement Errors," Working Papers 918, Economic Research Forum, revised Jun 2015.
    11. Espen Villanger, 2003. "The effects of disasters on income mobility: Bootstrap inference and measurement error simulations," CMI Working Papers WP 2003:6, CMI (Chr. Michelsen Institute), Bergen, Norway.
    12. Perez, Victor, 2015. "Moving in and out of poverty in Mexico: What can we learn from pseudo-panel methods?," ISER Working Paper Series 2015-16, Institute for Social and Economic Research.

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