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Lessons from a crowdsourcing failure

Maria Jones's picture

We are working on an evaluation of a large rural roads rehabilitation program in Rwanda that relies on high-frequency market information. We knew from the get-go that collecting this data would be a challenge: the markets are scattered across the country, and by design most are in remote rural areas with bad connectivity (hence the road rehab). The cost of sending enumerators to all markets in our study on a monthly basis seemed prohibitive.
Crowdsourcing seemed like an ideal solution. We met a technology firm at a conference in Berkeley, and we liked their pitch: use high-frequency, contributor-based, mobile data capture technology to flexibly measure changes in market access and structure. A simple app, a network of contributors spanning the country, and all the price data we would need on our sample of markets.
One year after contract signing and a lot of troubleshooting, less than half of the markets were visited at the specified intervals (fortnightly), and even in these markets, we had data on less than half of our list of products. (Note: we knew all along this wasn't going well, we just kept going at it.)

 So what went wrong, and what did we learn?

The long run effects of job training

Markus Goldstein's picture
I am always on the lookout for impact evaluations that give us the long term effects of interventions.   I recently came across a paper by Pablo Ibarraran, Jochen Kluve, Laura Ripani and David Rosas Shady looking at the effects of a youth training program in the Dominican Republic.    While we have some evidence on the long term effects of these kind of programs from developed countries, this is quite possibly the first in a developing context.   

Blogging your job market paper? Some more tips

David McKenzie's picture
There is just over a week left until our deadline of Tuesday November 22 for our “blog your job market paper” series.  We have started receiving submissions, and so I thought I’d share a few more tips (in addition to those already posted) for those of you who are still planning to submit something.
  • Don’t write a big block of text with no breaks: Whether it is several subheadings, some bullet points or numbered lists, or something else, make the blog post easier for readers to read by using something to break the text up. Remember, readers might be reading this on a mobile phone or skimming it quickly to see if they think it interesting to read, so having 2 pages of solid text with nothing else will not hold reader attention.
  • Make sure to give magnitudes, not just significance: don’t just say “we found the program increased education for women”, but tell us by how much, and, where appropriate, some benchmark to help us tell whether this is a big or small effect.
  • Hyperlink any references, and spell the authors’ names correctly.
  • Get quickly to what you did, and make clear what your methods are: while general motivation for why what you are doing is important is useful, you should be able to make the case for why we should care in a paragraph or less – then we want to hear about what you did, and how you did this. Then give key details – if you do an experiment, make clear the sample sizes, unit of randomization etc.; if you do difference-in-differences, make clear why the parallel trends assumption seems reasonable and what checks you did; if you use an IV, discuss the exclusion restriction and why it seems reasonable; etc.
  • Look at previous years for examples: e.g. here is Sam Asher’s, who we hired; here is Mounir Karadja’s explanation of using an IV; and here is Paolo Abarcar’s clear explanation of an experiment he did.

Weekly links November 11: new research round-up, small sample experiments, refugee research, and more…

David McKenzie's picture

Cash transfers and health: It matters when you measure, and it matters how many health care workers are around to provide services

David Evans's picture

This post was co-authored with Katrina Kosec of IFPRI.

A whirlwind, surely incomplete tour of cash transfer impacts on health
Your run-of-the-mill conditional cash transfer (CCT) program has significant impacts on health-seeking behavior. Specifically, there are conditions (or co-responsibilities, if you prefer) that children get to school and/or that they get vaccinated or have some wellness visits. While the school enrollment effects are well established, the effects on both health seeking behavior and on health outcomes have been much more mixed. CCTs have led to better child nutritional status and improved child cognitive development in Nicaragua, better nutritional outcomes for a subset of children in Colombia, and had no impacts for child health in studies on Brazil and Honduras. CCTs conditioned only on school enrollment did not lower HIV infections among adolescent girls in South Africa; and in Indonesia CCTs increased health visits but did not translate into measurably improved health. Unconditional cash transfer programs have also had mixed results on health, with better mental health and food consumption in Kenya, better anthropometric outcomes for girls (not boys) in South Africa, no average impacts (although some for the poorest quarter) on child outcomes in Ecuador, and no average impacts on maternal health care utilization in Zambia (albeit yes effects for women with better access to such services).

Lessons from some of my evaluation failures: Part 2 of ?

David McKenzie's picture

I recently shared five failures from some of my impact evaluations. Since this is just scratching the surface of all the many ways I’ve experienced failures in attempting to conduct impact evaluations, I thought I’d share a second batch now too.

Case 4: working with a private bank in Uganda to offer business training to their clients, written up as a note here.

Weekly links November 4: education and peer effects, why the labor demand curve slopes down doesn’t explain migration impacts, and more…

David McKenzie's picture

If you want your study included in a systematic review, this is what you should report

David Evans's picture

This post is co-authored with Birte Snilstveit of 3ie
Impact evaluation evidence continues to accumulate, and policy makers need to understand the range of evidence, not just individual studies. Across all sectors of international development, systematic reviews and meta-analysis (the statistical analysis used in many systematic reviews) are increasingly used to synthesize the evidence on the effects of programmes. These reviews aim to identify all available impact evaluations on a particular topic, critically appraise studies, extract detailed data on interventions, contexts, and results, and then synthesize these data to identify generalizable and context-specific findings about the effects of interventions. (We’ve both worked on this, see here and here.)
But as anyone who has ever attempted to do a systematic review will know, getting key information from included studies can often be like looking for a needle in a haystack. Sometimes this is because the information is simply not provided, and other times it is because of unclear reporting. As a result, researchers spend a long time trying to get the necessary data, often contacting authors to request more details. Often the authors themselves have trouble tracking down some additional statistic from a study they wrote years ago. In some cases, study results can simply not be included in reviews because of a lack of information.

CCTs for Pees: Cash Transfers Halloween Edition

Berk Ozler's picture

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.”

Weekly links October 28: the platinum development intervention, super long-run cash effects, in praise of uncivil discussion, and more…

David McKenzie's picture
  • The platinum development intervention: Lant Pritchett on how the gold standard ultra-poor poverty programs don’t stack up very well against migration.
  • Cash effects after 40 years: The long-term impacts of cash transfers in the U.S. – Wonkblog covers a new working paper (and job market paper from a Stanford student David Price) on the income maintenance experiments  that took place four decades ago – they find those who received the assistance retired earlier, as a result making less money over their careers – while there appears to be no long-term impacts on children (for what they can measure using admin data).