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Improving the Quality of Economic Data: Lessons from the HRS and AHEAD

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TLDR
Follow-up brackets as discussed by the authors represent partial responses to asset questions and apparently significantly reduce item nonresponse, which is a critical problem with economic survey data, and also provide a remedy to deal with nonignorable nonresponse bias.
Abstract
Missing data are an increasingly important problem in economic surveys, especially when trying to measure household wealth. However, some relatively simple new survey methods such as follow-up brackets appear to appreciably improve the quality of household economic data. Brackets represent partial responses to asset questions and apparently significantly reduce item nonresponse. Brackets also provide a remedy to deal with nonignorable nonresponse bias, a critical problem with economic survey data.

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References
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Working With Missing Values

TL;DR: In this paper, the effects of missing values are illustrated for a linear model, and a series of recommendations are provided for missing values can produce biased estimates, distorted statistical power, and invalid conclusions.
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