Dif-in-dif estimators of multiplicative treatment effects

Economics Discussion Papers from University of Essex, Department of Economics

Abstract: We consider a difference-in-differences setting with a continuous outcome, such as wages or expenditure. The standard practice is to take its logarithm and then interpret the results as an approximation of the multiplicative treatment effect on the original outcome. We argue that a researcher should rather focus on the non-transformed outcome when discussing causal inference. Furthermore, it is preferable to use a non-linear estimator, because running OLS on the log-linearized model might confound distributional and mean changes. We illustrate the argument with an original empirical analysis of the impact of the UK Educational Maintenance Allowance on households' expenditure.

Keywords: difference-in-differences; log-linearisation; Poisson Pseudo Maximum Likelihood (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://repository.essex.ac.uk/8973/ original version (application/pdf)

Related works:
Journal Article: Dif-in-Dif Estimators of Multiplicative Treatment Effects (2019)
Working Paper: Dif-in-dif estimators of multiplicative treatment effects (2014)
Working Paper: Dif-in-dif estimators of multiplicative treatment effects (2014)
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:esx:essedp:8973

Ordering information: This working paper can be ordered from
Discussion Papers Administrator, Department of Economics, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, U.K.

Access Statistics for this paper

More papers in Economics Discussion Papers from University of Essex, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Essex Economics Web Manager ().