Analysis of the bias of Matching and Difference-in-Difference under alternative earnings and selection processes

Sylvain Chabé-Ferret

Journal of Econometrics, 2015, vol. 185, issue 1, 110-123

Abstract: Matching and Difference in Difference (DID) are two widespread methods that use pre-treatment outcomes to correct for selection bias. I detail the sources of bias of both estimators in a model of earnings dynamics and entry into a Job Training Program (JTP) and I assess their performances using Monte Carlo simulations of the model calibrated with realistic parameter values. I find that Matching generally underestimates the average causal effect of the program and gets closer to the true effect when conditioning on an increasing number of pre-treatment outcomes. When selection bias is symmetric around the treatment date, DID is consistent when implemented symmetrically—i.e. comparing outcomes observed the same number of periods before and after the treatment date. When selection bias is not symmetric, Monte Carlo simulations show that Symmetric DID still performs better than Matching, especially in the middle of the life-cycle. These results are consistent with estimates of the bias of Matching and DID from randomly assigned JTPs. Some of the virtues of Symmetric DID extend to programs other than JTPs allocated according to a cutoff eligibility rule.

Keywords: Matching; Difference in Difference; Job Training Programs (search for similar items in EconPapers)
JEL-codes: C21 C23 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (51)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407614002437
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:185:y:2015:i:1:p:110-123

DOI: 10.1016/j.jeconom.2014.09.013

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().