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Who Does Not Respond in the Household Expenditure Survey


  • Schechtman, Edna
  • Yitzhaki, Shlomo
  • Artsev, Yevgeny


A nonparametric multiple regression method, based on the extended Gini that depends on one parameter, v, is investigated. The parameter v enables production of infinite alternative linear approximations to the regression curve which differ in the weighting schemes applied to the slopes of the curve. The method allows the investigator to stress different sections of one independent variable while keeping the treatment of the other independent variables intact. As an application we investigate nonresponse patterns in a survey of household expenditures to learn about the relationship between nonresponse and income. The empirical results show that the higher the income, the higher the response rate, and the larger the household, the higher the response rate. The Arab population tends to respond more than the Jewish one, whereas the ultrareligious group tends to respond less than the rest of the population. The implications on the bias in the estimates are discussed.

Suggested Citation

  • Schechtman, Edna & Yitzhaki, Shlomo & Artsev, Yevgeny, 2008. "Who Does Not Respond in the Household Expenditure Survey," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 329-344.
  • Handle: RePEc:bes:jnlbes:v:26:y:2008:p:329-344

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    References listed on IDEAS

    1. Bernard, Andrew B. & Bradford Jensen, J., 1999. "Exceptional exporter performance: cause, effect, or both?," Journal of International Economics, Elsevier, vol. 47(1), pages 1-25, February.
    2. Daron Acemoglu & Fabrizio Zilibotti, 2001. "Productivity Differences," The Quarterly Journal of Economics, Oxford University Press, vol. 116(2), pages 563-606.
    3. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    4. Richard Blundell & Stephen Bond, 2000. "GMM Estimation with persistent panel data: an application to production functions," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 321-340.
    5. Erik Brynjolfsson & Lorin M. Hitt, 2003. "Computing Productivity: Firm-Level Evidence," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 793-808, November.
    6. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    7. Aw, Bee Yan & Chen, Xiaomin & Roberts, Mark J., 2001. "Firm-level evidence on productivity differentials and turnover in Taiwanese manufacturing," Journal of Development Economics, Elsevier, vol. 66(1), pages 51-86, October.
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    Cited by:

    1. Edna Schechtman & Shlomo Yitzhaki & Taina Pudalov, 2011. "Gini’s multiple regressions: two approaches and their interaction," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 67-99.
    2. M. Grazia Pittau & Shlomo Yitzhaki & Roberto Zelli, 2011. "The make-up of a regression coefficient: An application to gender," DSS Empirical Economics and Econometrics Working Papers Series 2011/3, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.

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