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spillover effects

Most good you can do. But for whom?

Berk Ozler's picture

It’s hard to argue against the idea that giving cash to someone in need is the best you can do for that person in most circumstances: money maximizes your choice set and any conditions, strings attached, etc. makes that set smaller. With the advance of mobile technologies and better, bigger data, you can now send someone anywhere in the world money and make that person’s life instantly better – at least in the short run. But, what if I told you that with every dollar you send to one poor person, you’re taking away food from a few other people? How should we evaluate the impact of your transfer then?

GiveDirectly Three-Year Impacts, Explained

Berk Ozler's picture

My post earlier this week on dissipating effects of cash transfers on adults in beneficiary households has caused not only a fair amount of disturbance in the development community, but also a decent amount of confusion about the three-year impacts of GiveDirectly’s cash transfers, from a working paper by Haushofer and Shapiro (2018) – HS (18) from hereon. At least some, including GiveDirectly itself and some academics, seem to think that one can reasonably interpret the findings in HS (18) to imply that the short-term effects of GD, also by Haushofer and Shapiro (2016) – HS (16) from hereon – were sustained three years post treatment. Below, I try to clear up the confusion regarding the evidence and explain why I vigorously disagree with that interpretation.

Power Calculation Software for Randomized Saturation Experiments

Berk Ozler's picture

One of the things I get asked when people are designing experiments – when they are either interested in or worried about spillover effects – is how to divvy up the clusters into treatment and control and what share of individuals within treatment clusters to assign within-cluster controls. The answer seems straightforward – it may look intuitive to assign a third to each group and I have seen a few designs that have done this, but it turns out that it’s a bit more complicated than that. There was no software that I am aware of that helped you with such power calculations, until now...

Definitions in RCTs with interference

Berk Ozler's picture

On May 25, I attended a workshop organized by the Harvard School of Public Health, titled “Causal Inference with Highly Dependent Data in Communicable Diseases Research.” I got to meet many of the “who’s who” of this literature from the fields of biostatistics, public health, and political science, among whom was Elizabeth Halloran, who co-authored this paper with Michael Hudgens – one of the more influential papers in the field.

Designing experiments to measure spillover effects

Berk Ozler's picture

Many programs affect those who were not directly targeted by the intervention. We know this for medical interventions (e.g. deworming: Kremer and Miguel 2004); cash transfer programs (e.g. PROGRESA: Angelucci and de Giorgi 2009); and now voter awareness programs (Giné and Mansuri 2011).