arXiv 3 Oct 2018 · Econometrics · publishedThe Review of Economics and Statistics (2020) · 73 citations (OpenAlex)
arXiv:1810.01576 · PDF · DOI · OpenAlex · Extracted main text
Applied work often studies the effect of a binary variable ("treatment") using linear models with additive effects. I study the interpretation of the OLS estimands in such models when treatment effects are heterogeneous. I show that the treatment coefficient is a convex combination of two parameters, which under certain conditions can be interpreted as the average treatment effects on the treated and untreated. The weights on these parameters are inversely related to the proportion of observations in each group. Reliance on these implicit weights can have serious consequences for applied work, as I illustrate with two well-known applications. I develop simple diagnostic tools that empirical researchers can use to avoid potential biases. Software for implementing these methods is available in R and Stata. In an important special case, my diagnostics only require the knowledge of the proportion of treated units.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Angrist, J. D. and Pischke, J.-S (2009) Mostly Harmless Econometrics: An Empiricist's Companion | 0.969 | 11 | 4 | 91% |
| 2 | Humphreys, M (2009) Bounds on least squares estimates of causal effects in the presence of heterogeneous assignment probabilities | 0.965 | 10 | 3 | 90% |
| 3 | Angrist, J. D (1998) Estimating the labor market impact of voluntary military service using Social Security data on military applicants | 0.874 | 21 | 3 | 67% |
| 4 | Aizer, A., Eli, S., Ferrie, J., and Lleras-Muney, A (2016) The long-run impact of cash transfers to poor families | 0.874 | 8 | 2 | 100% |
| 5 | LaLonde, R. J (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.811 | 4 | 2 | 100% |
| 6 | Aronow, P. M. and Samii, C (2016) Does regression produce representative estimates of causal effects? | 0.763 | 9 | 3 | 44% |
| 7 | Card, D., Kluve, J., and Weber, A (2018) What works? A meta analysis of recent active labor market program evaluations | 0.737 | 3 | 2 | 100% |
| 8 | Dehejia, R. H. and Wahba, S (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs | 0.737 | 3 | 2 | 100% |
| 9 | Deaton, A (1997) The Analysis of Household Surveys: A Microeconometric Approach to Development Policy | 0.644 | 5 | 2 | 40% |
| 10 | Solon, G., Haider, S. J., and Wooldridge, J. M (2015) What are we weighting for? | 0.644 | 5 | 2 | 40% |
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