TY  - JOUR
AU  - Graham,Bryan S.
AU  - Pinto,Cristine Campos de Xavier
AU  - Egel,Daniel
TI  - Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 16928
PY  - 2011
Y2  - April 2011
UR  - http://www.nber.org/papers/w16928
L1  - http://www.nber.org/papers/w16928.pdf
N1  - Author contact info:
Bryan S. Graham
University of California - Berkeley
508-1 Evans Hall #3880
Berkeley, CA 94720-3880
Tel: (510) 642 4752
E-Mail: bgraham@econ.berkeley.edu

Cristine Campos de Xavier Pinto
Escola de Economia de São Paulo, FGV/SP
Rua Itapeva 474, sala 1200
São Paulo– SP, Brasil, 01332-000
E-Mail: cristinepinto@gmail.com

Daniel Egel
Institute on Global Conflict and Cooperation (IGCC
University of California - San Diego
9500 Gilman Drive, MC 0518
La Jolla, CA 92093-0518
E-Mail: degel@ucsd.edu
AB  - We propose a locally efficient, doubly robust, estimator for a class of semiparametric data combination problems. A leading estimand in this class is the average treatment effect on the treated (ATT). Data combination problems are related to, but distinct from, the class of missing data problems analyzed by Robins, Rotnitzky and Zhao (1994) (of which the Average Treatment Effect (ATE) estimand is a special case). Our procedure may be used to efficiently estimate, among other objects, the ATT, the two-sample instrumental variables model (TSIV), counterfactual distributions, and poverty maps. In an empirical application we use our procedure to characterize residual Black-White wage inequality after flexibly controlling for 'pre-market' differences in measured cognitive achievement as in Neal and Johnson (1996). We find that residual Black-White inequality is negligible at lower and higher quantiles of the Black wage distribution, but substantial at middle quantiles.
ER  -