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The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

Alternative Approaches to Evaluation in Empirical Microeconomics

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Blundell, Richard, Costa Dias, Monica·2009·Journal of Human Resources

Abstract

This paper reviews a range of the most popular policy evaluation methods in empirical microeconomics: social experiments, natural experiments, matching methods, instrumental variables, discontinuity design and control functions.It discusses the identification of both the traditionally used average parameters and more complex distributional parameters.In each case, the necessary assumptions and the data requirements are considered.The adequacy of each approach is discussed drawing on the empirical evidence from the education and labor market policy evaluation literature.We also develop an education evaluation model which we use to carry through the discussion of each alternative approach.A full set of STATA datasets are provided free online which contain Monte-Carlo replications of the various specifications of the education evaluation model.There are also a full set of STATA .dofiles for each of the estimation approaches described in the paper.The .do-files can be used together with the datasets to reproduce all the results in the paper.

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Metadata

Authors
Blundell, R., & Costa Dias, M.
Venue
Journal of Human Resources
Published
2009
Volume
44
Issue
3
Pages
565-640
ISSN
  • 0022-166X (ISSN-L)
  • 1548-8004
OpenAlex
W3214661451
Cited by
394
References
60
FWCI
15.85
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Alternative Approaches to Evaluation in Empirical Microeconomics. https://app.cochat.ai/papers/10.1353/jhr.2009.0009
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Blundell, R., & Costa Dias, M. (2009). Alternative Approaches to Evaluation in Empirical Microeconomics. Journal of Human Resources. https://doi.org/10.1353/jhr.2009.0009

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.