Sensitivity to Missing Data Assumptions: Theory and an Evaluation of the U.S. Wage Structure
By Patrick M. Kline, Andrés Santos. 2010
76 citations recorded by the source corpus.
This paper develops methods for assessing the sensitivity of empirical conclusions regarding conditional distributions to departures from the missing at random (MAR) assumption. We index the degree of non-ignorable selection governing the missingness process by the maximal Kolmogorov-Smirnov (KS) distance between the distributions of missing and observed outcomes across all values of the covariates. Sharp bounds on minimum mean square approximations to conditional quantiles are derived as a function of the nominal level of selection considered in the sensitivity analysis and a weighted bootstrap procedure is developed for conducting inference. Using these techniques, we conduct an empirical assessment of the sensitivity of observed earnings patterns in U.S. Census data to deviations from the MAR assumption. We find that the well-documented increase in the returns to schooling between 1980 and 1990 is relatively robust to deviations from the missing at random assumption except at the lowest quantiles of the distribution, but that conclusions regarding heterogeneity in returns and changes in the returns function between 1990 and 2000 are very sensitive to departures from ignorability.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/5904d927e31d2c06c0587c6f9dd139a23c28fb74
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In Economics and Econometrics
Citations in the corpus
- Inference on breakdown frontiers (2017, methodology): “Kline and Santos (2013), on the other hand, is the first and only paper we are aware of that explicitly suggests doing inference on a breakdown point.”
- A quantile correlated random coefficients panel data model (2016, unknown): “Under the assumptions maintained in Theorem 2 above √ N ( β̂ p (·)− β p (·) ) converges in distribution to a mean zero Gaussian process Zβp (·), where Zβp (·) is defined by its covariance function, Σβp(τ, τ ) Σβp(τ, τ ) = 1 P (X ∈ XM) Υ2 (τ, τ ) + Υ3 (τ, τ ) + Υ4 (τ, τ ) fBp|X∈XM ( βM p (τ) ∣X ∈ XM ”
- Inference on Directionally Differentiable Functions (2014, methodology): “t is a curve (Arellano et al., 2012) 11This result also holds for the instrumental variables estimator of Chernozhukov and Hansen (2005). 29 or can be described by a functional lower and upper bound (Kline and Santos, 2013; Chandrasekarh et al., 2013). Escanciano and Zhu (2013) derives an estimator ”