1. Regression Discontinuity (Durham, 8 April 2013) Hans Luyten (j.w.luyten@utwente.nl)j.w.luyten@utwente.nl University of Twente, Faculty of Behavioural Sciences

2. Outline of today’s session 1.RD, what is it? 2.Short history 3.Some applications 4.Sharp vs. fuzzy RD 5.RD and instrumental variables (IV)

3. RD, what is it? (I)  Research design/ technique of data analysis  Capitalizes on the existence of cut-off points

4. RD, what is it? (II)  Cut-off points mimic random assignment  Minimal differences between units (respondents) on either side of the cut-off

5. RD, what is it? An example (III)

6. RD, what is it? Another example (IV)

7. RD, what is it? (V)  Assignment of students to grades determined by date of birth (e.g. cut-off point = 1 Sept.)  Effect of one year schooling = difference in achievement between upper and lower grade minus effect of date of birth (age) Y = β 0 + β 1 AGE + β 2 GRADE

8. RD, what is it? (VI) STRENGTH  Alternative explanations largely ruled out Complications  What if the cut-off point changes?  How to deal with miss-assigned units?

9. RD, what is it? (VII) Extension with multiple cut-off points

10. RD, short history (I)  Invented/developed as a means to assess effects of scholarship programs (1960)  Cut-off criterion used for assessing the effect

11. RD, short history (II)  RD method remained obscure for decades  Rediscovered in the 1990s (by educational economists)

12. RD applications  Absolute effect of schooling  Intensive “support” of very weak schools by the school inspectorate  Extra funding for schools with disadvantaged student populations  Class size  Grade retention (fuzzy RD)

13. Sharp vs. fuzzy RD (I)  Assignment hardly 100% “correct”  Rule of thumb: 95% “correct” assignment suffices  Sharp RD special case of fuzzy RD

14. Sharp vs. fuzzy RD (II)

16. RD and Instrumental Variables (I)  Instrumental Variable (IV) has no causal relation with the outcome variable but does affect the independent variable  As such: it mimics random assignment  Cut-off points are special cases of instrumental variables

17. RD and Instrumental Variables (II)  Instrumental variables are VERY popular among economists  Earliest example (1928): estimating the effect of wheat production on wheat prices  Rainfall as IV (related to production; unrelated to prices)

18. RD and Instrumental Variables (II)  In the case of fuzzy RD: estimate probability to treatment  Use the probability as an explanatory variable (instead of actual assignment)

19. Wrapping up  RD capitalizes on cut-off points  Cut-off points mimic random assignment  Sharp RD  Fuzzy RD  Instrumental variables

20. Thank you Greetings from Twente University