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Imputing Income in the CPS: Comments on "Measures of Aggregate Labor Cost in the United States"
Donald B. Rubin
- pp 333-344
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The article was published on 1983-01-01 and is currently open access. It has received 9 citations till now.read more
Citations
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Book ChapterDOI
Empirical Strategies in Labor Economics
TL;DR: In this article, the authors provide an overview of the methodological and practical issues that arise when estimating causal relationships that are of interest to labor economists, including identification, data collection, and measurement problems.
Posted Content
Match Bias in Wage Gap Estimates Due to Earnings Imputation
TL;DR: In this article, the authors derived an expression for "match bias" in which attenuation equals the sum of match error rates, in practice, attenuation can be approximated by the proportion with imputed earnings.
Journal Article
An analysis of nonignorable nonresponse to income in a survey with a rotating panel design
TL;DR: A sensitivity analysis is described to assess the impact of departures from MAR for refusals, based on SRMI for a pattern-mixture model, which avoids the well-known problems of underidentification of parameters of missing not at random models.
References
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Journal ArticleDOI
An Analysis of Transformations
George E. P. Box,David Cox +1 more
TL;DR: In this article, Lindley et al. make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's.
Journal ArticleDOI
Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.
Book ChapterDOI
Controlling Bias in Observational Studies: A Review.
TL;DR: This article reviewed the effectiveness of matched sampling and statistical adjustment, alone and in combination, in reducing bias due to confounding x-variables when comparing two populations, and the adjustment methods were linear regression adjustment for x continuous and direct standardization for x categorical.
Journal ArticleDOI
Bias Reduction Using Mahalanobis-Metric Matching
TL;DR: Monte Carlo methods are used to study the ability of nearest available Mahalanobis metric matching to make the means of matching variables more similar in matched samples than in random samples.
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Understanding Trends in Alternative Work Arrangements in the United States
Lawrence F. Katz,Alan B. Krueger +1 more