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Persisting Disadvantages: A Study of the Dynamics of Cumulative Deprivation

Author

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  • Budría, Santiago

    (Universidad Nebrija)

  • García-Gómez, César

    (University of Valladolid)

Abstract

Identifying populations at risk of deprivation is crucial for effective policy design. Yet, much existing research focuses on single aspects, such as income or material deprivation, and often abstracts from deprivation dynamics. This study addresses this gap by analyzing the dynamics and socio-economic gradient of cumulative deprivation using data from the 2005–2021 waves of the Household, Income and Labour Dynamics in Australia (HILDA) Survey. Employing copula-based techniques and two econometric approaches—a Conditional Maximum Likelihood Estimator (CMLE) estimator and a two-stage Generalized Method of Moments (GMM) procedure—the analysis reveals significant state dependence, where past cumulative deprivation strongly predicts future deprivation. Schooling, employment, and parenthood emerge as key determinants. These findings underscore the importance of adopting multidimensional and temporal perspectives on deprivation, offering critical insights for more targeted and effective policy interventions.

Suggested Citation

  • Budría, Santiago & García-Gómez, César, 2025. "Persisting Disadvantages: A Study of the Dynamics of Cumulative Deprivation," IZA Discussion Papers 17908, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17908
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    More about this item

    Keywords

    Generalized Method of Moments; energy poverty; cumulative deprivation; state dependence; Wooldridge Conditional Maximum Likelihood;
    All these keywords.

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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