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The Weighting Process in the SHIW

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  • 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.

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Bibliographic Info

Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 636.

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Date of creation: Jun 2007
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Handle: RePEc:bdi:wptemi:td_636_07

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Keywords: Survey Methods;

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  1. Giovanni D'Alessio & Ivan Faiella, 2002. "Non-response behaviour in the Bank of ItalyÂ’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. Vito Peragine & Flaviana Palmisano & Paolo Brunori, 2011. "Economic growth and equality of opportunity," Working Papers 232, ECINEQ, Society for the Study of Economic Inequality.
  2. Gastón Chaumont & Miguel Fuentes & Felipe Labbé & Alberto Naudon, 2011. "A Reassessment of Flexible Price Evidence Using Scanner Data: Evidence from an Emerging Economy," Working Papers Central Bank of Chile 641, Central Bank of Chile.
  3. 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.
  4. 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.
  5. Flaviana Palmisano & Dirk Van de gaer, 2013. "History dependent growth incidence: A characterisation and an application to the economic crisis in Italy," Working Papers 314, ECINEQ, Society for the Study of Economic Inequality.
  6. 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.
  7. 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.

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