byungki-mun / CIT_2019_CUP

Cattaneo, Idrobo and Titiunik (2019/2020, CUP)

This work was supported in part by the National Science Foundation through grants SES-1357561 and SES-1459931.

Website

https://rdpackages.github.io/replication

Application: Institutions Data

• Source: Meyersson (2014): Islamic Rule and the Empowerment of the Poor and Pious, Econometrica 82(1): 229-269.

• Dataset: csv | dta

• Replication Volume I: R | Stata

• Replication Volume II: R | Stata

Application: Senate Data (canonical, useful for teaching)

• Source: Cattaneo, Frandsen and Titiunik (2015): Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate, Journal of Causal Inference 3(1): 1-24.

• Dataset: csv | dta

• Replication: R | Stata

Application: Education Data

• Source: Lindo, Sanders and Oreopoulos (2010): Ability, Gender, and Performance Standards: Evidence from Academic Probation, American Economic Journal: Applied Economics 2(2): 95-117.

• Dataset: csv | dta

• Replication: R | Stata

Application: Development Data

Source: Amarante, Manacorda, Miguel and Vigorito (2016): Do Cash Transfers Improve Birth Outcomes? Evidence from Matched Vital Statistics, Program and Social Security Data, American Economic Journal: Economic Policy 8(2): 1-43.

• Dataset: csv | dta

• Replication: R | Stata

Application: Multi-Cutoff/Multi-Score/Geographic Data

• Source: TBA.

• Dataset: csv | dta

• Replication: R | Stata

References

• Cattaneo, Idrobo and Titiunik (2020): A Practical Introduction to Regression Discontinuity Designs: Foundations.
  Cambridge Elements: Quantitative and Computational Methods for Social Science, Cambridge University Press.

• Cattaneo, Idrobo and Titiunik (2021): A Practical Introduction to Regression Discontinuity Designs: Extensions.
  Cambridge Elements: Quantitative and Computational Methods for Social Science, Cambridge University Press.