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Estimation of Multidimensional Poverty in Morocco: A Small Area Estimation Approach Using Meteorological and Socio-economic Covariates

Author

Listed:
  • Gianni Betti

    (University of Siena)

  • Federico Crescenzi

    (University of Tuscia)

  • Vasco Molini

    (World Bank)

  • Lorenzo Mori

    (University of Bologna)

Abstract

In this paper we estimate the Head Count Ratio (HCR) and two fuzzy poverty measures at provincial level in Morocco using data from the Household Budget Survey (HBS). As the sample size is not always sufficient to provide reliable direct estimates, we use a Fay–Herriot model with additive logistic transformation and meteorological covariates to obtain estimates with lower mean squared errors. Among our main results, we find out that the Fuzzy Monetary measure provides more accurate estimates than the Head Count Ratio when conducting small area estimation exercises. Also, we empirically notice that the set of covariates at our disposal allows us to obtain better estimates for each supplementary poverty measure that we identify.

Suggested Citation

  • Gianni Betti & Federico Crescenzi & Vasco Molini & Lorenzo Mori, 2024. "Estimation of Multidimensional Poverty in Morocco: A Small Area Estimation Approach Using Meteorological and Socio-economic Covariates," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(2), pages 545-575, November.
    • Handle: RePEc:spr:soinre:v:175:y:2024:i:2:d:10.1007_s11205-024-03340-9
    DOI: 10.1007/s11205-024-03340-9
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