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Comparing Small Area Techniques for Estimating Poverty Measures: the Case Study of Austria and Spain

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  • Federico Crescenzi

    (Department of Economics and Statistics, University of Siena)

  • Gianni Betti

    (Department of Economics and Statistics, University of Siena)

  • Francesca Gagliardi

    (University of Siena)

Abstract

The Europe 2020 Strategy has formulated key policy objectives or so-called “headline targets†which the European Union as a whole and Member States are individually committed to achieving by 2020. One of the five headline targets is directly related to the key quality aspects of life, namely social inclusion; within these targets, the European Union Statistics on Income and Living Condition (EU-SILC) headline indicators at risk-of-poverty or social exclusion and its components will be included in the budgeting of structural funds, one of the main instruments through which policy targets are attained. For this purpose, Directorate-General Regional Policy of the European Commission is aiming to use sub-national/regional level data (NUTS 2). Starting from this, the focus of the present paper is on the “regional dimension†of well-being. We propose to adopt a methodology based on the Empirical Best Linear Unbiased Predictor (EBLUP) with an extension to the spatial dimension (SEBLUP); moreover, we compare this small area technique with the cumulation method. The application is conducted on the basis of EU-SILC data from Austria and Spain. Results report that, in general, estimates computed with the cumulation method show standard errors which are smaller than those computed with EBLUP or SEBLUP. The gain of pooling SILC data over three years is, therefore, relevant, and may allow researchers to prefer this method.

Suggested Citation

  • Federico Crescenzi & Gianni Betti & Francesca Gagliardi, 2016. "Comparing Small Area Techniques for Estimating Poverty Measures: the Case Study of Austria and Spain," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 396-404.
    • Handle: RePEc:ura:ecregj:v:1:y:2016:i:2:p:396-404
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    References listed on IDEAS

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    1. Vijay Verma & Gianni Betti, 2011. "Taylor linearization sampling errors and design effects for poverty measures and other complex statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1549-1576, August.
    2. Tony Atkinson & Bea Cantillon & Eric Marlier & Brian Nolan, 2002. "Indicators for Social Inclusion," Politica economica, Società editrice il Mulino, issue 1, pages 7-28.
    3. Atkinson, Tony & Cantillon, Bea & Marlier, Eric & Nolan, Brian, 2002. "Social Indicators: The EU and Social Inclusion," OUP Catalogue, Oxford University Press, number 9780199253494.
    4. Gianni Betti & Francesca Gagliardi & Achille Lemmi & Vijay Verma, 2011. "Subnational indicators of poverty and deprivation in Europe: methodology and applications," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 5(1), pages 129-147.
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    More about this item

    Keywords

    small area estimation; poverty; EU “headline targets†; regional level; NUTS-2; inequality; SEBLUP; cumulation; SILC; Austria; Spain;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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