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Small Area Estimation of Latent Economic Well-being

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  • Angelo Moretti
  • Natalie Shlomo
  • Joseph W. Sakshaug

Abstract

Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany.

Suggested Citation

  • Angelo Moretti & Natalie Shlomo & Joseph W. Sakshaug, 2021. "Small Area Estimation of Latent Economic Well-being," Sociological Methods & Research, , vol. 50(4), pages 1660-1693, November.
    • Handle: RePEc:sae:somere:v:50:y:2021:i:4:p:1660-1693
    DOI: 10.1177/0049124119826160
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    References listed on IDEAS

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    1. Marchetti, Stefano & Tzavidis, Nikos & Pratesi, Monica, 2012. "Non-parametric bootstrap mean squared error estimation for M-quantile estimators of small area averages, quantiles and poverty indicators," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2889-2902.
    2. Jaya Krishnakumar & A. Nagar, 2008. "On Exact Statistical Properties of Multidimensional Indices Based on Principal Components, Factor Analysis, MIMIC and Structural Equation Models," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 86(3), pages 481-496, May.
    3. Gaston Yalonetzky, 2014. "Conditions for the most robust multidimensional poverty comparisons using counting measures and ordinal variables," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 43(4), pages 773-807, December.
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    Cited by:

    1. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.
    2. Benedetti, Ilaria & Crescenzi, Federico, 2023. "The role of income poverty and inequality indicators at regional level: An evaluation for Italy and Germany," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).

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