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| Instrumentalne promenljive u istraživanju obrazovanja× | Metod instrumentalnih promenljivih (IV) za kauzalno zaključivanje× | |
|---|---|---|
| Oblast≠ | Kauzalno zaključivanje | Ekonomija zdravstva |
| Porodica≠ | Regression model | Process / pipeline |
| Godina nastanka≠ | 1991 (canonical education application) | 1990s (modern applications) |
| Tvorac≠ | Angrist & Krueger (canonical 1991 education application); grounded in IV theory by Wright (1928) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Tip≠ | Quasi-experimental causal identification | Method |
| Temeljni izvor≠ | Angrist, J. D., & Krueger, A. B. (1991). Does Compulsory School Attendance Affect Schooling and Earnings? Quarterly Journal of Economics, 106(4), 979-1014. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Drugi nazivi | IV in education, 2SLS in education, education IV, school IV estimation | IV, two-stage least squares, TSLS, causal estimation |
| Srodne≠ | 5 | 3 |
| Sažetak≠ | Instrumental variables (IV) estimation is a quasi-experimental strategy for isolating the causal effect of schooling or educational interventions when assignment to treatment is confounded by unobserved factors. Pioneered in education economics by Angrist and Krueger's use of quarter-of-birth as an instrument for compulsory schooling, IV finds a source of exogenous variation in exposure to education and uses only that variation to estimate outcomes such as earnings, test scores, or attainment. | 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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