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| Regression Discontinuity Design zur Politikbewertung× | Differenz-in-Differenzen (DiD)× | |
|---|---|---|
| Fachgebiet≠ | Kausale Inferenz | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1960; policy evaluation applications widespread from 2000s | 1994 |
| Urheber≠ | Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010) | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Typ≠ | Quasi-experimental causal design | Causal inference / panel regression |
| Wegweisende Quelle≠ | Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Aliasnamen≠ | Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
| ScholarGateDatensatz ↗ |
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