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David McKenzie's blog

Weekly links Jan 13: taking on the curse of the top 5, underpowered economics, where to for tax research, and more…

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  • A webcast of the AEA panel on “publishing in economics journals: the curse of the top 5” (h/t @DurRobert) – Heckman, Akelof, Deaton, Fudenberg and Hansen discuss. Some interesting discussion and comments – Deaton notes he didn’t have any papers rejected until he was famous; Heckman had a lot of data, including this one which shows (first column) which journals account for most dissemination of the ideas of the top development economists – with WBER number 1:

We gave Sri Lankan microenterprises wage subsidies to hire workers: 8 years after starting, here’s what happened

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In my very first experiment, Suresh de Mel, Chris Woodruff, and I gave small grants of capital to microenterprises in Sri Lanka. We found that these one-time grants had lasting impacts on firm profitability for male owners. However, despite these increases in firm profits, few owners made the leap from self-employed to hiring others.
In 2008 we therefore started a new experiment with a different group of Sri Lankan microenterprises, trying to see if we could help them make this transition to becoming employers. Eight years later, I’m delighted to finally have a working paper out with the results.

Weekly Links January 6: power calcs, dual careers, substitutes to replication, and more…

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Happy new year. I imagine many of our readers are at the annual meetings of the American Economic Association today. Good luck to all the job-seekers, and for those not there, looking through the program is always a good way to see a broad overview of current research. Some links that caught my attention over the break are:

Weekly links December 23: Bystander effects, causality, some nice profiles, and more holiday reading…

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This is our last post of the year, so happy holidays to all our readers. Thanks to everyone who contributed to our job market series, and good luck to those on the job market. We will resume again in early January. In the meantime, here is some holiday reading:

Weekly links December 9: A buyers guide to machine learning, M-Pesa and poverty, spurious correlations, and more…

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Weekly links December 2: Jane Austin does public works, unpopular truths about informal health care in India, Facebook does maps, and more…

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Weekly links November 18: doing exploratory analysis well, not so hasty in evaluating, active labor market policies and more…

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  • Andrew Gelman on how to think more seriously about the design of exploratory studies
  • Overcoming premature evaluation discussed on the From Poverty to Power blog “There is a growing interest in safe-fail experimentation, failing fast and rapid real time feedback loops…When it comes to complex setting there is a lot of merit in ‘crawling the design space’ and testing options, but I think there are also a number of concerns with this that should be getting more air time…it can simply take time for a program to generate positive tangible and measurable outcomes, and it maybe that on some measures a program that may ultimately be successful dips below the ‘its working’ curve on its way to that success…more importantly it ignores some key aspects of the complex adaptive systems in which programs are embedded…if we are serious about going beyond saying ‘context matters’ then exhortations to ‘fail fast’ need to be more thoroughly debated.”

Blogging your job market paper? Some more tips

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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…

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

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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.

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