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The Weighting Process in the SHIW

Author

Listed:
  • Ivan Faiella

    (Bank of Italy)

  • Romina Gambacorta

    (Bank of Italy)

Abstract

The design of a probability sample jointly determines the method used to select sampling units from the population and the estimator of the population parameter. If the sampling fraction is constant for all the units in the sample, then the unweighted sampling mean is an unbiased estimator. In the Survey on Household Income and Wealth (SHIW), units included in the sample have unequal probabilities of selection and each observation is weighted using the inverse of the proper sampling fraction (design weight) adjusted for the response mechanism (nonresponse weight) and for other factors such as imperfect coverage. In this paper we present the weighting scheme of the SHIW and assess its impact on bias and variance of selected estimators. Empirical evidences show that the increasing variability induced by using weighted estimators is compensated by the bias reduction even when performing analysis on sample domains. A set of longitudinal weights is also proposed to account for the selection process and the attrition of the SHIW panel component. These weights, giving their enhanced description of the �panel population�, should be better suited to perform longitudinal analysis; nevertheless their higher variance implies that they wouldn�t always be preferable in terms of mean square error.

Suggested Citation

  • Ivan Faiella & Romina Gambacorta, 2007. "The Weighting Process in the SHIW," Temi di discussione (Economic working papers) 636, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_636_07
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    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2007/2007-0636/en_tema_636.pdf
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    References listed on IDEAS

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    1. Giovanni D'Alessio & Ivan Faiella, 2002. "Non-response behaviour in the Bank of Italy�s Survey of Household Income and Wealth," Temi di discussione (Economic working papers) 462, Bank of Italy, Economic Research and International Relations Area.
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    Cited by:

    1. Flaviana Palmisano & Dirk Van de gaer, 2016. "History-dependent growth incidence: a characterization and an application to the economic crisis in Italy," Oxford Economic Papers, Oxford University Press, vol. 68(2), pages 585-603.
    2. Eurosystem Household Finance and Consumption Network, 2013. "The Eurosystem Household Finance and Consumption Survey - Methodological report," Statistics Paper Series 1, European Central Bank.
    3. David Loschiavo, 2021. "Household debt and income inequality: Evidence from Italian survey data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(1), pages 61-103, March.
    4. Giuseppe Cappelletti & Giovanni Guazzarotti & Pietro Tommasino, 2011. "What determines annuity demand at retirement?," Temi di discussione (Economic working papers) 805, Bank of Italy, Economic Research and International Relations Area.
    5. Paolo Del Giovane & Silvia Fabiani & Roberto Sabbatini, 2008. "What�s behind �inflation perceptions�? A survey-based analysis of Italian consumers," Temi di discussione (Economic working papers) 655, Bank of Italy, Economic Research and International Relations Area.
    6. Carlos Madeira, 2019. "Computing population weights for the EFH survey," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(1), pages 004-026, April.
    7. Vito Peragine & Flaviana Palmisano & Paolo Brunori, 2014. "Economic Growth and Equality of Opportunity," The World Bank Economic Review, World Bank, vol. 28(2), pages 247-281.
    8. Andrea Neri & Roberta Zizza, 2010. "Income reporting behaviour in sample surveys," Temi di discussione (Economic working papers) 777, Bank of Italy, Economic Research and International Relations Area.
    9. Bertocchi, Graziella & Brunetti, Marianna & Torricelli, Costanza, 2014. "Who holds the purse strings within the household? The determinants of intra-family decision making," Journal of Economic Behavior & Organization, Elsevier, vol. 101(C), pages 65-86.
    10. Andrea Camilli & Pedro Gomes, 2023. "Public employment and homeownership dynamics," Public Choice, Springer, vol. 194(1), pages 101-155, January.
    11. Giuseppe Cappelletti, 2012. "Do wealth fluctuations generate time-varying risk aversion? Italian micro-evidence on household asset allocation," Temi di discussione (Economic working papers) 845, Bank of Italy, Economic Research and International Relations Area.
    12. Marianna Brunetti & Costanza Torricelli, 2017. "Second homes in Italy: every household’s dream or (un)profitable investments?," Housing Studies, Taylor & Francis Journals, vol. 32(2), pages 168-185, February.
    13. Stefania Capecchi & Romina Gambacorta & Rosaria Simone & Domenico Piccolo, 2024. "Modelling cognitive response patterns to survey questions using the class of CUB models," Questioni di Economia e Finanza (Occasional Papers) 885, Bank of Italy, Economic Research and International Relations Area.
    14. Ivan Faiella, 2010. "The use of survey weights in regression analysis," Temi di discussione (Economic working papers) 739, Bank of Italy, Economic Research and International Relations Area.
    15. Daniele Di Giulio & Carlo Milani, 2013. "Plastic Money Diffusion and Usage: An Empirical Analysis on Italian Households," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 42(1), pages 47-74, February.

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    More about this item

    Keywords

    Survey Methods;

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods

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