I once received a referee report for a journal submission that said, “In fact, in my view its contribution to science is negative…” The report continued with comments about how the paper lacked “proper and sound scientific inquiry” and was “…unsuitable for publication pretty much anywhere, I think.” Just in case the four-page assault was not sufficient, the report ended with encouraging the authors to “…move onto the next project.” It was hard to avoid the feeling that the referee was suggesting a career change for us rather than simply giving up on this paper… The paper was subsequently published in the Journal of Health Economics, but the bad taste of receiving that report lingered long afterwards…
- AEA continuing education videos and slides are now up, including the machine learning course taught by Athey and Imbens.
- One step towards fixing the leaky gender pipeline in higher education: the guardian reports on a study of UK universities which finds places with more generous maternity leave in the UK “were better able to retain qualified women who went on to become professors and receive higher pay”
Business plan competitions have increasingly become one policy option used to identify and support high-growth potential businesses. For example, the World Bank has helped design and support these programs in a number of sub-Saharan African countries, including Côte d’Ivoire, Gabon, Guinea-Bissau, Kenya, Nigeria, Rwanda, Senegal, Somalia, South Sudan, Tanzania, and Uganda. These competitions often attract large numbers of applications, raising the question of how do you identify which business owners are most likely to succeed?
In a recent working paper, Dario Sansone and I compare three different approaches to answering this question, in the context of Nigeria’s YouWiN! program. Nigerians aged 18 to 40 could apply with either a new or existing business. The first year of this program attracted almost 24,000 applications, and the third year over 100,000 applications. After a preliminary screening and scoring, the top 6,000 were invited to a 4-day business plan training workshop, and then could submit business plans, with 1,200 winners each chosen to receive an average of US$50,000 each. We use data from the first year of this program, together with follow-up surveys over three years, to determine how well different approaches would do in predicting which entrants will have the most successful businesses.
- On VoxEU, using Yelp data to track the local economy.
- Ted Miguel on plans for long-term follow-ups of child health and cash transfer programs.
- Priced out of citizenship? From Stanford News, with the cost of U.S. naturalization now $725, an experiment gave vouchers to cover these costs to low-income immigrants in NYC and found naturalization application rates rose 41%.
- David Deming in the NBER reporter on the value of soft skills in the labor market: “the very term soft skills reveals our lack of understanding of what these skills are, how to measure them, and whether and how they can be developed... Social interaction is perhaps the most necessary workplace task for which there is currently no good machine substitute... Researchers ought to stop relying on convenient, off-the-shelf measures of soft skills and start creating metrics that are theoretically sound and suitable for the task at hand”
- Duncan Green summarizes Stefan Dercon’s view of 10 top thinkers in development. E.g. on Acemoglu and Robinson “their policy advice is just ‘buy yourself a better history/don’t start from here’. Not very useful for aid”. Alice Evans responds to the lack of women on Stefan’s list with five big problems in development and female scholars to learn from on these.
- How did Chinese consumption respond to changes in the minimum wage? Dautovic and co-authors on VoxEU report that “For the period 2002-2009, we identify more than 13,874 changes in the local minimum wage across China's 2,183 counties and 285 cities…many counties experienced substantial nominal increases in their minimum wage above 20%...we show that low-income households spend their entire additional income from a higher minimum wage…for poorer households, 40% of the additional minimum wage income is spend on health care and educational expenditure”
- Looking to try out machine learning for poverty prediction? The World Bank has launched a competition (with prize money) to see how well you can predict poverty.
Often there are many steps or stages between the starting point of some intervention and its ultimate goal, and at each step, people can drop out. The result can be extremely low power to measure impacts on this end outcome, even though we might be able to detect impacts on the intermediate steps. This post illustrates this point, with the goal of making clear the importance of trying to measure intermediate outcomes, and concludes with suggestions of ways to partially overcome this problem.
Before we begin new posts next week, here are the 2017 Development Impact posts that were most popular over the last year. In this case, popular = most page views.
- 10 journals for publishing a short economics paper
- When should you cluster standard errors? New wisdom from the econometrics oracle
- What’s the latest in development economics research? A round-up of 140+ papers from NEUDC 2017
- The State of Development Journals 2017: Quality, Acceptance Rates, and Review Times
- Fact checking universal basic income: can we transfer our way out of poverty?
- What’s new in education research? Impact evaluations and measurement – March round-up
- The latest research in economics on Africa: The CSAE round-up
- IE analytics: introducing ietoolkit
- Technoskeptics pay heed: A computer-assisted learning program that delivers learning results
- A new answer to why developing country firms are so small, and how cellphones solve this problem
- top ten