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Inference with difference-in-differences with a small number of groups: a review, simulation study, and empirical application using SHARE data

Rokicki, Slawa and Cohen, Jessica and Fink, Günther and Salomon, Joshua A. and Landrum, Mary Beth. (2018) Inference with difference-in-differences with a small number of groups: a review, simulation study, and empirical application using SHARE data. Medical care, 56 (1). pp. 97-105.

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Official URL: http://edoc.unibas.ch/58381/

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Abstract

Difference-in-differences (DID) estimation has become increasingly popular as an approach to evaluate the effect of a group-level policy on individual-level outcomes. Several statistical methodologies have been proposed to correct for the within-group correlation of model errors resulting from the clustering of data. Little is known about how well these corrections perform with the often small number of groups observed in health research using longitudinal data.; First, we review the most commonly used modeling solutions in DID estimation for panel data, including generalized estimating equations (GEE), permutation tests, clustered standard errors (CSE), wild cluster bootstrapping, and aggregation. Second, we compare the empirical coverage rates and power of these methods using a Monte Carlo simulation study in scenarios in which we vary the degree of error correlation, the group size balance, and the proportion of treated groups. Third, we provide an empirical example using the Survey of Health, Ageing, and Retirement in Europe.; When the number of groups is small, CSE are systematically biased downwards in scenarios when data are unbalanced or when there is a low proportion of treated groups. This can result in over-rejection of the null even when data are composed of up to 50 groups. Aggregation, permutation tests, bias-adjusted GEE, and wild cluster bootstrap produce coverage rates close to the nominal rate for almost all scenarios, though GEE may suffer from low power.; In DID estimation with a small number of groups, analysis using aggregation, permutation tests, wild cluster bootstrap, or bias-adjusted GEE is recommended.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Household Economics and Health Systems Research > Household Economy (Fink)
UniBasel Contributors:Fink, Günther
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Lippincott Williams & Wilkins
ISSN:0025-7079
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:08 Feb 2020 14:47
Deposited On:02 Feb 2018 13:53

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