Group contamination in Regression Discontinuity I'm an undergraduate student I have an empirical question regarding Regression Discontinuity and Diff-in-Diff methods. I'm currently evaluating the impact on fertility of a Conditional Cash Transfer in Brazil.

This is an oversimplified example of the problem we encountered.
A family will receive the benefit if their income per capita is, for example, $150, and will not receive it otherwise. We would use this fact to use a RD design to evaluate casual effects. The problem is, those above the threshold have incentives to get another child and reduce their per capita income to enter the "control" group. 

Anyone has good references (articles, textbook), where I can find something that would help me? Helpful comments are greatly appreciated.
Are you looking for general references on Diff-in-Diff (DiD) and Regression Discontinuity (RD)? 

If yes, a good starting reference at the undergraduate level is probably 


J.  Angrist  and  J.S.  Pischke, Mastering' Metrics: The Path from Cause to  Effect, Princeton University Press, 2014. Look at chapters 4 and 5 for RD and DiD.


If who want to dig deeper: 


J. Angrist and J.S. Pischke, Mostly Harmless Econometrics, Princeton University Press, 2009, chapters 5 and 6.


For recent references on RD


C. Carpenter and C. Dobkin, “The Effect of Alcohol Consumption on Mortality: Regression Discontinuity Evidence from the MLDA, American Economic Journal: Applied Economics 1 (2009), 164-182. 
A. Abdulkadiroglu, et al., “The Elite Illusion: Achievement Effects at Boston and New York Exam Schools,” Econometrica, 2014.


On Did:


D. Card and A. Krueger, “Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania,” American Economic Review 90 (1994), 1397- 420.
D. Card, “Using Regional Variation to Measure the Effect of the Federal Wage,” Industrial and Labor Relations Review (1992) 46, 22-37.
C. Carpenter and C. Dobkin, “The Minimum Legal Drinking Age and Public Health,” The Journal of Economic Perspectives 25 (2011), 133-156.


Considering your example, I guess that it could be a contribution to show that a small increase in benefit per capita may increase fertility. Is the benefit permanent? If not, I doubt that people will have a strong incentive to get another child. Moreover, it takes time to get a new child! So, there is a time dimension issue. They cannot claim the benefit if they don't have the child! If your design allow for this, you can exploit the time dimension in case some families respond to the incentive.