Nonparametric Bounds in the Presence of Item Nonresponse, Unfolding Brackets and Anchoring
Abstract
Household surveys often suffer from nonresponse on variables such as income, savings or wealth.Recent work by Manski shows how bounds on conditional quantiles of the variable of interest can be derived, allowing for any type of nonrandom item nonresponse.The width between these bounds can be reduced using follow up questions in the form of unfolding brackets for initial item nonrespondents.Recent evidence, however, suggests that such a design is vulnerable to anchoring effects.In this paper Manski's bounds are extended to incorporate the information provided by the bracket respondents allowing for different forms of anchoring.The new bounds are applied to earnings in the 1996 wave of the Health and Retirement Survey.The results show that the categorical questions can be useful to increase precision of the bounds, even if anchoring is allowed for.Download Info
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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2001-67.Length:
Date of creation: 2001
Date of revision:
Handle: RePEc:dgr:kubcen:200167
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Web page: http://center.uvt.nl
Related research
Keywords:Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
- C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-10-16 (All new papers)
- NEP-ECM-2001-10-16 (Econometrics)
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Christian Bontemps & Thierry Magnac & Eric Maurin, 2012.
"Set Identified Linear Models,"
Econometrica,
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"Estimating Income Poverty in the Presence of Missing Data and Measurement Error,"
CEIS Research Paper
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