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The impact of local cost-of-living differences on relative poverty incidence: an application using retail scanner data and small area estimation models

Author

Listed:
  • Stefano Marchetti

    (Università di Pisa)

  • Caterina Giusti

    (Università di Pisa)

  • Francesco Schirripa Spagnolo

    (Università di Pisa)

  • Gaia Bertarelli

    (Università Ca’ Foscari)

  • Luigi Biggeri

    (Università degli Studi di Firenze)

Abstract

Estimating economic poverty indicators at the local level is essential for well-targeted data-driven welfare policies. However, Italy is a country characterized by strong geographical heterogeneity represented by unequal price levels among different areas, and computing poverty indicators with a national monetary poverty threshold can be misleading. This work proposes a novel approach to estimate monetary poverty incidence at the provincial level in Italy considering the different price levels within national boundaries. To account for local price variation, Spatial Price Indices (SPIs) are computed using scanner data on retail prices. The SPIs are estimated in two ways, referring to the mean local prices and using the 20th percentile of the prices. These two kinds of SPIs are used to adjust the national poverty line when computing the poverty incidence at the provincial level using Small Area Estimation (SAE) models. Our findings suggest that adjusting the national poverty line using the SPIs to compute a monetary poverty index can modify the poverty mapping results from the map produced with the traditional national poverty line that ignores price differences.

Suggested Citation

  • Stefano Marchetti & Caterina Giusti & Francesco Schirripa Spagnolo & Gaia Bertarelli & Luigi Biggeri, 2024. "The impact of local cost-of-living differences on relative poverty incidence: an application using retail scanner data and small area estimation models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1117-1143, September.
  • Handle: RePEc:spr:stmapp:v:33:y:2024:i:4:d:10.1007_s10260-023-00742-w
    DOI: 10.1007/s10260-023-00742-w
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    References listed on IDEAS

    as
    1. Sugasawa, Shonosuke & Kubokawa, Tatsuya, 2017. "Transforming response values in small area prediction," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 47-60.
    2. repec:bla:jorssa:v:180:y:2017:i:4:p:1163-1190 is not listed on IDEAS
    3. Monica Pratesi & Stefano Marchetti & Caterina Giusti & Gaia Bertarelli & Francesco Schirripa Spagnolo & Luigi Biggeri, 2021. "Estimations of local spatial price indices using scanner data, to be used for the comparisons of economic poverty measures," Discussion Papers 2021/281, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    4. Suits, Daniel B, 1984. "Dummy Variables: Mechanics v. Interpretation," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 177-180, February.
    5. repec:csb:stintr:v:17:y:2016:i:1:p:41-66 is not listed on IDEAS
    6. Tonutti, Giovanni & Bertarelli, Gaia & Giusti, Caterina & Pratesi, Monica, 2022. "Disaggregation of poverty indicators by small area methods for assessing the targeting of the “Reddito di Cittadinanza” national policy in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
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