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I just signed my first referee report

Berk Ozler's picture

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…

Weekly links January 26: learn to machine learn, that wellness program might only help with your multiple testing correction, working beats saving, and more...

David McKenzie's picture

Can predicting successful entrepreneurship go beyond “choose smart guys in their 30s”? Comparing machine learning and expert judge predictions

David McKenzie's picture

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.

Weekly links Jan 19: soft skills and maybe a robot can’t take your job after all, the Starbucks indicator of Indian middle class growth, high fees are deterring citizenship, and more...

David McKenzie's picture
  • 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”

What do we learn from increasing teacher salaries in Indonesia? More than the students did.

David Evans's picture
Money matters in education. Recent evidence from the United States shows that increased education spending results in more completed years of schooling and higher subsequent wages for adults. Spending cuts during the Great Recession – also in the U.S. – were associated with reduced student test scores and graduation rates.

Weekly links Jan 12: Big Thinkers brought down to size, can you beat the World Bank at predicting poverty? Chinese minimum wage rises all get spent, three job openings, and more…

David McKenzie's picture
  • 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.

Six Questions with Mark Rosenzweig

David McKenzie's picture
Mark Rosenzweig is Frank Altschul Professor of International Economics at Yale University, and was one of the original leaders in bringing theory and micro-level data to addressing development questions. We caught up with him after a recent symposium, which honored his achievements, and celebrated him turning 70 and continuing to produce important new work.

Statistical Power and the Funnel of Attribution

David McKenzie's picture

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.

Top Ten Development Impact Blog Posts of 2017

David Evans's picture

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