Abstract #300885

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JSM 2003 Abstract #300885
Activity Number: 370
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract - #300885
Title: Nonparametric Balancing Methods and Complex Survey Desgin: A Study of Wage Gaps Among the Highly Educated
Author(s): Amelia M. Haviland*+
Companies: Carnegie Mellon University
Address: Dept. of Statistics, Pittsburgh, PA, 15213,
Keywords: causal inference ; bootstrap ; propensity scores
Abstract:

Wage gaps have traditionally been estimated using parametric Blinder-Oaxacca decomposition models. Recent publications have demonstrated violations of the parametric and other assumptions of these models and suggest the use of propensity score and/or kernel density matching methods. This research begins by comparing these different models using frameworks developed by Rubin and others for making causal inferences. This comparison causes us to focus on estimating what is known as "the effect of treatment on the treated." This is done using exact matching and parametrically smoothed and nonparametrically smoothed (propensity score) matching methods. The data, the 1993 National Suvey of College Graduates (NSCG), is a complex survey sample with significant rates of unit and item nonresponse. Nonparametric finite population bootstrap methods yielding random effective sample size and effective strata sizes are employed to obtain appropriate estimates of the standard errors.

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