arXiv 12 Nov 2024 · Econometrics
arXiv:2411.07952 · PDF · DOI · OpenAlex · Extracted main text
Since LaLonde's (1986) seminal paper, there has been ongoing interest in estimating treatment effects using pre- and post-intervention data. Scholars have traditionally used experimental benchmarks to evaluate the accuracy of alternative econometric methods, including Matching, Difference-in-Differences (DID), and their hybrid forms (e.g., Heckman et al., 1998b; Dehejia and Wahba, 2002; Smith and Todd, 2005). We revisit these methodologies in the evaluation of job training and educational programs using four datasets (LaLonde, 1986; Heckman et al., 1998a; Smith and Todd, 2005; Chetty et al., 2014a; Athey et al., 2020), and show that the inequality relationship, Matching $\leq$ Hybrid $\leq$ DID, appears as a consistent norm, rather than a mere coincidence. We provide a formal theoretical justification for this puzzling phenomenon under plausible conditions such as negative selection, by generalizing the classical bracketing (Angrist and Pischke, 2009, Section 5). Consequently, when treatments are expected to be non-negative, DID tends to provide optimistic estimates, while Matching offers more conservative ones.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dube, A., Girardi, D., Jorda, O., and Taylor, A. M (2023) A local projections approach to difference-in-differences event studies | 1.000 | 9 | 3 | 100% |
| 2 | Dehejia, R. H. and Wahba, S (2002) Propensity score-matching methods for nonexperimental causal studies | 1.000 | 8 | 5 | 100% |
| 3 | Chetty, R., Friedman, J. N., and Rockoff, J. E (2014) Measuring the impacts of teachers i: Evaluating bias in teacher value-added estimates | 1.000 | 6 | 6 | 100% |
| 4 | Smith, J. A. and Todd, P. E (2005) Does matching overcome lalonde's critique of nonexperimental estimators? | 0.986 | 24 | 6 | 96% |
| 5 | LaLonde, R. J (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.971 | 12 | 6 | 92% |
| 6 | Heckman, J., Ichimura, H., Smith, J., and Todd, P (1998) Characterizing selection bias using experimental data | 0.958 | 25 | 8 | 88% |
| 7 | Chabé-Ferret, S (2017) Should we combine difference in differences with conditioning on pre-treatment outcomes? | 0.928 | 5 | 3 | 80% |
| 8 | Dehejia, R. H. and Wahba, S (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs | 0.928 | 4 | 4 | 100% |
| 9 | Imai, K., Kim, I. S., and Wang, E. H (2023) Matching methods for causal inference with time-series cross-sectional data | 0.928 | 4 | 3 | 100% |
| 10 | Athey, S., Chetty, R., and Imbens, G (2020) Combining experimental and observational data to estimate treatment effects on long term outcomes | 0.909 | 8 | 5 | 75% |
Showing the top 10 of 35 scored citations.