One of the arguments in favor of more gender diversity in the economics profession is that men and women bring distinct perspectives to research and are interested in answering different research questions. We focus in on development economics in this post and examine how the research topics studied by men and women differ.
- 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.
- On the LSE impact of social sciences blog, six academic writing habits to boost productivity.
- On the Berkeley Energy Institute blog, Catherine Wolfram and co-authors summarize their RCT on rural electrification in Kenya under the heading “does solving energy poverty help solve poverty? Not quite” – “The rural electrification agency had spent more than $1,000 to connect each household. Yet eighteen months later, the households we connected seemed to be no better off”
- Paul Goldsmith-Pinkham summarizes a recent twitter thread on great figures that summarize the story of entire papers – excellent motivation to thinking hard about how to best display your data.
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 Unfortunately, papers with unclear or null results often go unpublished, even if they have rigorous research designs and good data.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.
- 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”
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:
- On VoxDev, Tanguy Bernard and co-authors on an experiment that provided quality certification for onions in Senegal, causing farmers to invest more in quality and raising farmer incomes...but with the sad post-note “In this particular case, the reform was discontinued under pressure from the long-distance middlemen who gain from the lack of transparency on markets.”
- Following on the heels of Berk’s post, Science has a story “researchers debate whether journals should publish signed peer reviews” which discusses how this debate is also taking place in other fields.