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Estimates of Household Consumption Expenditure at Provincial Level in Italy by Using Small Area Estimation Methods: “Real” Comparisons Using Purchasing Power Parities

Author

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  • Stefano Marchetti

    (University of Pisa)

  • Luca Secondi

    (University of Tuscia)

Abstract

Household consumption expenditure represents a crucial measure to be used for assessing individuals’ material living conditions and well-being. Indeed, the analysis of household conditions can provide policy makers with a clear picture of the economic and social situation of the area in which they are operating. However, official sample surveys which are generally used for this purpose, such as the Household Budget Survey in Italy carried out by the National Institute of Statistics, do not allow for reliable disaggregated estimates thus hindering appropriate and effective planning and evaluation of political interventions at local level. By referring to the 2012 Italian Household Budget Survey, this paper aims at obtaining reliable provincial estimates of household consumption expenditure in Italy. We use Small Area Estimation methods and we adjust the estimates for spatial differences in price levels by computing and using sub-national Purchasing Power Parities, thus obtaining “real” estimates of consumption expenditure to be used for intra-national comparisons.

Suggested Citation

  • Stefano Marchetti & Luca Secondi, 2017. "Estimates of Household Consumption Expenditure at Provincial Level in Italy by Using Small Area Estimation Methods: “Real” Comparisons Using Purchasing Power Parities," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(1), pages 215-234, March.
    • Handle: RePEc:spr:soinre:v:131:y:2017:i:1:d:10.1007_s11205-016-1230-8
    DOI: 10.1007/s11205-016-1230-8
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    2. Esteban Fernandez-Vazquez & Alberto Diaz Dapena & Fernando Rubiera-Morollon & Ana Viñuela, 2020. "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(2), pages 809-821, November.
    3. Roberto Benavent & Domingo Morales, 2021. "Small area estimation under a temporal bivariate area-level linear mixed model with independent time effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 195-222, March.
    4. Marek ediv & Petr Jansk, 2018. "How Do Regional Price Levels Affect Income Inequality? Household-level Evidence From 21 Countries," LIS Working papers 752, LIS Cross-National Data Center in Luxembourg.
    5. Gaia Bertarelli & Luigi Biggeri & Caterina Giusti & Stefano Marchetti & Monica Pratesi, 2020. "Intra-Country comparisons of Poverty Rate," Discussion Papers 2020/260, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    6. Dawber James & Würz Nora & Smith Paul A. & Flower Tanya & Thomas Heledd & Schmid Timo & Tzavidis Nikos, 2022. "Experimental UK Regional Consumer Price Inflation with Model-Based Expenditure Weights," Journal of Official Statistics, Sciendo, vol. 38(1), pages 213-237, March.
    7. María Dolores Esteban & María José Lombardía & Esther López‐Vizcaíno & Domingo Morales & Agustín Pérez, 2022. "Empirical best prediction of small area bivariate parameters," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1699-1727, December.
    8. Stefano Marchetti & Luca Secondi, 2022. "The Economic Perspective of Food Poverty and (In)security: An Analytical Approach to Measuring and Estimation in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(3), pages 995-1020, August.
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