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Regression Discontinuity Design Using Maimonides’ rule

Regression Discontinuity Design Using Maimonides’ rule. Marina M B ánnikova. Thistlethwaite & Campbell (1960) Goldberger (1972) Berk and Rauma (1983) Hahn (1998) Joshua D. Angrist and Victor Lavy (1999) Van der Klaauw (2002) Lemieux & Milligan (2008) Lalive (2008)

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Regression Discontinuity Design Using Maimonides’ rule

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  1. Regression Discontinuity Design Using Maimonides’ rule Marina M Bánnikova

  2. Thistlethwaite & Campbell (1960) • Goldberger (1972) • Berk and Rauma (1983) • Hahn (1998) • Joshua D. Angrist and Victor Lavy (1999) • Van derKlaauw (2002) • Lemieux & Milligan (2008) • Lalive (2008) • Card, Dobkin & Maestas (2008)

  3. Descriptive statistics

  4. The class size as a function of enrollment classsize enrollment

  5. 41 81 classsize 27 20.5 The function of Maimonides' rule enrollment

  6. Class sizes: actual and predicted by Maimonides’ rule classsize enrollment

  7. Average class size and predicted by Maimonides’ rule classsize enrollment

  8. The histogram of the differences between actual class size and predicted by the rule.

  9. OLS estimation. Regressor: absolute difference between actual class size and predicted by Maimonides' rule

  10. OLS estimation

  11. Sharp RD design probability of beingtreated enrollment

  12. Sharp design in 3 discontinuities classsize 1 2 3 enrollment

  13. Fuzzy RD design probability of beingtreated enrollment

  14. Fuzzy RD design: instrumented-variable 2SLS regression

  15. Conclusions • OLS showedusthatsmallclassesarenotalwaysbetterinscores • Followingtherulehaspositiveinfluence onthescores • Sharp RD designshowed thatthelessisthecutoff thebetterarescores • Fuzzy RD design indicateasignificant associationbetweenbeinginsmallerclassesandachievinghigherscores • Thequestion of findingtherightthresholdremainsopened

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