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The State of Development Journals 2018: Quality, Acceptance Rates, Review Times, and Representation

David McKenzie's picture
Last year I published an inaugural “state of development journals” in which I put together information about different development journals that is not otherwise publicly available. Seeing as there seemed to be interest in this from readers and many of the editors, I thought I would do it again this year and see how much things have changed, as well as investigate a few more topics not covered last year.  Many thanks to the editors and editorial staff at different journals for the information they shared.
  1. Is this a good quality, high visibility journal to publish my work?

Since these were collected last year as well, I provide the impact factor of the journals. The standard impact factor is the mean number of citations in the last year of papers published in the journal in the past 2 years, while the 5-year is the mean number of cites in the last year of papers published in the last 5. This is complemented with RePec’s journal rankings which take into account article downloads and abstract views in addition to citations. The impact factors and RePec ranks are reasonably stable over the two years – with the World Bank Research Observer seeing the biggest jump in impact factor. It publishes the smallest number of articles, so the mean is more likely to be influenced by one or two papers.

Weekly links March 16: write more productively or fake it, null power, great figures, and more...

David McKenzie's picture

Having an impact as a development economist outside of a research university: Interview with Alix Zwane

David McKenzie's picture
When you study for a Ph.D. in economics, the pathway to success and happiness as a development economist seems very straight and narrow. The implicit (or explicit) metric of success is to publish lots of articles and become a professor in a research university, and you are taught by people who have done this, and surrounded by lots of classmates aspiring to do the same. But there are many other ways to use the skills of your Ph.D., contribute to the world as a development economist, and have a great job and happy life following different paths. Since Ph.D.

Registered Reports: Piloting a Pre-Results Review Process at the Journal of Development Economics

This is a guest post by Andy Foster, Dean Karlan, and Ted Miguel.

The world is a messy place. What happens when the results of an empirical study are mushy or inconsistent with prevailing theories? Unfortunately, papers with unclear or null results often go unpublished, even if they have rigorous research designs and good data. In such cases, the research community is typically only left to consider the papers that tell a “neat” and clean story. When economic and social policy relies on academic knowledge, this publication bias can be costly to society.

Weekly links March 9: export super-stars, poor stats on poor women, psychosocial interventions for refugees, psychologists up their game, and more...

David McKenzie's picture
  • Among the many posts on international women’s day, I thought our readers might find most useful this one on measurement of poverty and gender by Carolina Sanchez and Ana-Maria Munoz-Boudet “No, 70% of the world’s poor aren’t women, but this doesn’t mean poverty isn’t sexist
  • Emergency loans that are automatically given out when disaster hits as a substitute for microinsurance – summarized by Feed the Future – “Results ... show that the availability of emergency loans has had a big effect on how these farmers manage risk. Households who knew they were pre-qualified planted about 25 percent more rice than households who were not offered the emergency loan” (h/t Mushfiq Mobarak).
  • Video and slides from Ana Fernandes’ policy research talk on exporter dynamics, superstar firms, and trade policy – it is stunning how large a share of exports from many developing countries comes from the top 1% or even top 5 exporters.
  • Have you questioned your life choices enough lately? If not, Video of Lant Pritchett’s talk last month at NYU’s DRI on “The Debate about RCTs in Development is over. We won. They lost”

How can machine learning and artificial intelligence be used in development interventions and impact evaluations?

David McKenzie's picture

Last Thursday I attended a conference on AI and Development organized by CEGA, DIME, and the World Bank’s Big Data groups (website, where they will also add video). This followed a World Bank policy research talk last week by Olivier Dupriez on “Machine Learning and the Future of Poverty Prediction” (video, slides). These events highlighted a lot of fast-emerging work, which I thought, given this blog’s focus, I would try to summarize through the lens of thinking about how it might help us in designing development interventions and impact evaluations.

A typical impact evaluation works with a sample S to give them a treatment Treat, and is interested in estimating something like:
Y(i,t) = b(i,t)*Treat(i,t) +D’X(i,t) for units i in the sample S
We can think of machine learning and artificial intelligence as possibly affecting every term in this expression:

Weekly links March 2: quality onions, don’t just try to prove something you already know, jobs cost a lot to create, and more...

David McKenzie's picture

How to attract and motivate passionate public service providers

David Evans's picture

In Gaile Parkin's novel Baking Cakes in Kigali, two women living in Kigali, Rwanda – Angel and Sophie – argue over the salary paid to a development worker: "Perhaps these big organisations needed to pay big salaries if they wanted to attract the right kind of people; but Sophie had said that they were the wrong kind of people if they would not do the work for less. Ultimately they had concluded that the desire to make the world a better place was not something that belonged in a person's pocket. No, it belonged in a person's heart."
It's not a leap to believe – like Angel and Sophie – that teachers should want to help students learn, health workers who want help people heal, and other workers in service delivery should want to deliver that service. But how do you attract and motivate those passionate public servants? Here is some recent research that sheds light on the topic.

Facility-based data collection: a data methods bleg

Berk Ozler's picture

Today, I come to our readers with a request. I have a ton of experience with household and individual survey data collection. Ditto with biomarkers, assessments/tests at home, etc. However, I have less experience with facility-based data collection, especially when it is high frequency. For example, we do have a lot of data from the childcare centers in our study in Malawi, but we had to visit each facility once at each round of data collection and spend a day to collect all the facility-level data, including classroom observations, etc. What would you do if you needed high frequency data (daily, weekly, or monthly) that is a bit richer that what the facility collects themselves for their own administrative purposes that would not break the bank?