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| Event Study Design i Uddannelsesforskning× | Instrumentalvariabel (IV) Metoden til Kausal Inferens× | |
|---|---|---|
| Fagområde≠ | Kausal inferens | Sundhedsøkonomi |
| Familie≠ | Regression model | Process / pipeline |
| Oprindelsesår≠ | 1993 (general); 2000s–2010s (education applications) | 1990s (modern applications) |
| Ophavsperson≠ | Jacobson, LaLonde & Sullivan (1993); popularized in education by Lafortune, Rothstein & Schanzenbach (2018) and subsequent education-policy literature | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Type≠ | Quasi-experimental / causal inference | Method |
| Oprindelig kilde≠ | Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. American Economic Review, 83(4), 685-709. link ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Aliasser | event study, education event study, policy event study, dynamic difference-in-differences | IV, two-stage least squares, TSLS, causal estimation |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | An event study design tracks how educational outcomes evolve before and after a clearly defined event — such as a school finance reform, accountability policy, or curriculum change — for affected and unaffected units. By estimating period-by-period treatment effects relative to a baseline period, it delivers both a causal estimate of the policy's impact and a transparent test of the parallel-trends assumption underpinning difference-in-differences. | Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes. |
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