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Understanding poverty persistence in Spain

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  • Sara Ayllón

Abstract

The aim of this paper is to study the mechanisms behind poverty persistence in Spain. We examine the importance of past poverty experiences for explaining current poverty as opposed to observed and unobserved individual heterogeneity. Our results are based on the model proposed by Cappellari and Jenkins (J Appl Econometr 19:593–610, 2004a ) that estimates poverty transitions while simultaneously controlling for attrition and initial conditions. We find that about 50% of aggregate state dependence is genuine: poverty in a given year increases in itself the chances of experiencing poverty again in the future. The remainder is explained, among other characteristics, by living with a head of household who has no educational qualifications, being an immigrant or cohabiting with teenagers. Our findings call for a comprehensive and coordinated strategy against poverty that should focus equally on income-support policies and on enhancing those characteristics that best protect against economic hardship. From a methodological point of view, we learn that unobservables affecting initial conditions and sample retention are exogenous to those related to poverty transience. However, results prove to be sensitive to the choice of poverty line. Copyright The Author(s) 2013

Suggested Citation

  • Sara Ayllón, 2013. "Understanding poverty persistence in Spain," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 4(2), pages 201-233, June.
  • Handle: RePEc:spr:series:v:4:y:2013:i:2:p:201-233
    DOI: 10.1007/s13209-012-0089-4
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    Cited by:

    1. Elena Giarda & Gloria Moroni, 2018. "The Degree of Poverty Persistence and the Role of Regional Disparities in Italy in Comparison with France, Spain and the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(1), pages 163-202, February.
    2. Giuliana Parodi & Dario Sciulli, 2019. "Disability and Social Exclusion in Italian Households," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(2), pages 767-784, July.
    3. Watson, Dorothy & Maître, Bertrand & Grotti, Raffaele & Whelan, Christopher T., 2018. "Poverty Dynamics of Social Risk Groups in the EU: an analysis of the EU Statistics on Income and Living Conditions, 2005 to 2014," Research Series, Economic and Social Research Institute (ESRI), number BKMNEXT345, March.
    4. Ayşe Savaş & Julide Yildirim, 2017. "Education, Social Assistance And Employment Dynamics: A Panel Data Approach," EcoMod2017 10747, EcoMod.
    5. Fullerton Jr., Thomas M. & Macias, David R. & Walke, Adam G., 2016. "Residential Electricity Demand in El Paso," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 46(2), December.
    6. Sara Ayllón & András Gábos, 2017. "The Interrelationships between the Europe 2020 Poverty and Social Exclusion Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(3), pages 1025-1049, February.
    7. Gerhard Krug & Katrin Drasch & Monika Jungbauer-Gans, 2019. "The social stigma of unemployment: consequences of stigma consciousness on job search attitudes, behaviour and success," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 53(1), pages 1-27, December.

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    More about this item

    Keywords

    Poverty persistence; State dependence; Attrition; Initial conditions; I32; D31; C33;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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