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Berk Ozler's blog

Power Calculation Software for Randomized Saturation Experiments

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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...

CCTs for Pees: Cash Transfers Halloween Edition

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Subsidies to increase utilization are used in all sorts of fields and I have read more than my fair share of CCT papers. However, until last week, I had not come across a scheme that paid people to purchase their urine. Given that I am traveling and the fact that I am missing Halloween, I thought I’d share (I hope it’s not TMI)…
Here is the abstract of an article by Tilley and Günther (2016), published in Sustainability:
In the developing world, having access to a toilet does not necessarily imply use: infrequent or non-use limits the desired health outcomes of improved sanitation. We examine the sanitation situation in a rural part of South Africa where recipients of novel, waterless “urine-diverting dry toilets” are not regularly using them. In order to determine if small, conditional cash transfers (CCT) could motivate families to use their toilets more, we paid for urine via different incentive-based interventions: two were based on volumetric pricing and the third was a flat-rate payment (irrespective of volume). A flat-rate payment (approx. €1) resulted in the highest rates of regular (weekly) participation at 59%. The low volumetric payment (approx. €0.05/L) led to regular participation rates of only 12% and no increase in toilet use. The high volumetric payment (approx. €0.1/L) resulted in lower rates of regular participation (35%), but increased the average urine production per household per day by 74%. As a first example of conditional cash transfers being used in the sanitation sector, we show that they are an accepted and effective tool for increasing toilet use, while putting small cash payments in the hands of poor, largely unemployed populations in rural South Africa.”
 

Did Peru’s CCT program halve its stunting rate?

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On September 30, the Guardian ran several articles (see here, here, and an editorial here) linking the halving of Peru’s stunting rate (from 28 to 14% between mid-2000s and 2015) to its CCT program Juntos. Of course, it is great to hear that the share of stunted children in Peru declined dramatically over a short period. However, as I know that while CCT programs (conditional or not) have been successful in improving various outcomes including child health, the effect sizes are never this dramatic, I was curious to see whether the decline was part of a secular trend in Peru or actually could be attributed primarily to Juntos

Weekly Links: Capabilities and Skills, More on RDD, Elite capture, Real-time price tracking in South Sudan, and more...

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  • Heckman turned one of his lectures from last year into an NBER WP titled "Capabilities and Skills." Looks really interesting - here's a quote from the abstract: "We address measurement problems common to both the economics of human development and the capability approach. The economics of human development analyzes the dynamics of preference formation, but is silent about which preferences should be used to evaluate alternative policies. This is both a strength and a limitation of the approach."

  • Advances in Econometrics has a special issue on Regression Discontinuity Design, including many papers by prominent statisticians and econometricians in the field and edited by Cattaneo and Escanciano.

  • Do poor people want more redistributive programs and less public goods? Latest issue of the Journal of the European Economic Association has a paper by Bursztyn that challenges elite capture as the explanation for low levels of investment in public education. Here is the abstract: "A large literature has emphasized elite capture of democratic institutions as the explanation for the low levels of spending on public education in many low-income democracies. This paper provides an alternative to that longstanding hypothesis. Motivated by new cross-country facts and evidence from Brazilian municipalities, we hypothesize that many democratic developing countries might invest less in public education spending because poor decisive voters prefer the government to allocate resources elsewhere. One possible explanation is that low-income voters could instead favor redistributive programs that increase their incomes in the short run, such as cash transfers. To test for this possibility, we design and implement an experimental survey and an incentivized choice experiment in Brazil. The findings from both interventions support our hypothesis."

  • My brilliant former research assistant Utz Pape has a blog post titled: "What did we learn from real-time tracking of market prices in South Sudan?" Read it if you're into innovative use of technology in development.

  • Private Enterprise Development in Low-Income Countries (PEDL), a joint research initiative of the Centre for Economic Policy Research (CEPR) and the Department For International Development (DFID), is offering a competitive research grants scheme for projects related to the behaviour of firms in Low-Income Countries (LICs) that aim to better understand what determines the strength of market forces driving efficiency in these countries. Round 21 of their new Exploratory Research Grants is now open:

Papers that caught my attention last week

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A lot of interesting papers came across my desk this past week. Here are a few that I think may be useful to you too (and why):

Practical advice on robust standard errors in (not so small) samples: Imbens and Kolesár have an old working paper just published in REStat that tells you to do three things:

You ran a field experiment. Should you then run a regression?

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Recently, a colleague came over for dinner and made the following statement: “Person X told me that Imbens is now saying that we should not be running regressions to estimate average treatment effects in experiments.” When I showed some sympathy for this statement while focusing more on making tortillas, she was resistant: it was clear she did not want to give up on regression models…

Weekly Links, July 15 -- U.S. Edition: what does police bias have to do with colliders, questions on POTUS publishing in JAMA.

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  • What’s JAMA’s new impact factor now that POTUS has published a paper there? As you probably heard, Mr. Obama published a paper in the Journal of the American Medical Association this week, describing the progress to date of the US Health Care Reform and outlining the next steps. I have so many questions: was the review process (if there was one) double blind? Was he first rejected from NEJM? Was there a revise and resubmit? Was Obama totally nice to that rude referee #2, so that his paper could get published without further hassle? If you’re a handling editor or a referee, we want to hear from you (anonymously or not)...

Definitions in RCTs with interference

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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.

Weekly Links, May 27: Conscious insects, good data collection practices, all male panels, and more...

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  • On selecting what variables to gather data for in your impact evaluation: Carneiro et al. have a new paper out – “Optimal Data Collection for Randomized Control Trials” – which argues that if you have a household survey or census in advance, you can use an algorithm to select the right covariates, potentially reducing data collection costs or improving precision substantially.

Issues of data collection and measurement

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About five years ago, soon after we started this blog, I wrote a blog post titled “Economists have experiments figured out. What’s next? (Hint: It’s Measurement)” Soon after the post, I had folks from IPA email me saying we should experiment with some important measurement issues, making use of IPA’s network of studies around the world.

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