- If you could go back to the time you did not have any children and could choose exactly the number of children to have in your whole life, how many would that be?
- How many of these children would you like to be boys, how many would you like to be girls, and for how many would it not matter if it’s a boy or a girl?
- Women in Economics at Berkeley has a great summer reading list of recent papers which look at the gender earnings gap in different ways, including short summaries of some very recently published papers in the AER, QJE, and JPE on this issue.
- The NYTimes on how business schools are trying to teach fintech, although with no agreement on what this means or how to do it.
Suppose that you’re at your doctor’s office, discussing an important health issue that may become a concern in the near future. There are multiple drugs available in the market that you can use to prevent unwanted outcomes. Some of them are so effective that there is practically no chance you will have a negative event if you start taking them. Effectiveness of the other options range from 94% to much lower, with the most commonly used drug failing about 10% of the time for the typical user. Somehow, you go home with the drug that has a one in 10 failure rate: worse, you’re not alone; most people end up in the same boat…
Gonzalo was part of a panel with David McKenzie at a recent meeting of the Impact Evaluation Network (IEN). One of the questions during this discussion was whether there were good examples of cases where impact evaluations had found null or negative results, and policymakers had actually changed policy as a result. We thought others would be interested in hearing his examples from Mexico.
It feels like a cold water shower when impact evaluations (IEs) do not show positive impacts. Those studies are neither sexy for academic publication nor for public policy use. But the fact that some IEs show no impact of certain programs or projects, it’s an important piece of information!
I would like to suggest here that if a country has an institutional and relatively credible Monitoring and Evaluation System (M&E), the chances of using IEs with no impact increase.
- Milli Lake and Sarah Parkinson on the ethics of fieldwork preparedness – “It’s one of the discipline’s worst kept secrets that graduate students, in particular, feel practically unprepared for their fieldwork… We worry about an intellectual trend that increasingly rewards researchers for “out-dangering” one another (often with dubious scholarly gain). This doesn’t mean scholars should abandon fieldwork; it means that we should take the practical and ethical components of its planning and implementation more seriously. We can start by asking simple questions about first aid, check-ins, transport safety, and data protection”
Good principals can make a big difference
“It is widely believed that a good principal is the key to a successful school.” So say Branch, Hanushek, and Rivkin in their study of school principals on learning productivity. But how do you measure this? Using a database from Texas in the United States, they employ a value-added approach analogous to that used to measure performance among teachers. They control for basic information on student backgrounds (gender, ethnicity, and an indicator of poverty) as well as student test scores from the previous year. Then they ask, What happens to student learning when a school changes principals? They find that increasing principal quality by one standard deviation increases student learning by 0.11 standard deviations. Even after additional adjustments, their most conservative estimates show that “a 1-standard-deviation increase in principal quality translates into roughly 0.05 standard deviations in average student achievement gains, or nearly two months of additional learning.”
Notably, while improving teacher effectiveness affects the average performance of all of the students in his class, improving principal effectiveness affects average performance of the entire school, so the potential gains are high.
- WDR 2018
This post is joint with Niklas Buehren and Muthoni Ngatia
You can find the entire conference schedule here. In the summaries below we link to papers and videos (where applicable).
- Cyrus Samii discusses how to think about multiple hypothesis testing in pre-specifying your analysis. He links to this excellent post on multiple testing by Daniel Lakens with the great title “Why you don’t need to adjust your alpha level for all the tests you’ll do in your lifetime”.
- The Elusive Entrepreneur - From EconJournalWatch – examining the content of macro, micro, and IO classes in top PhD programs sees hardly any mention made of the entrepreneur. My work with Anna Luisa Paffhausen on how development economics is taught also found entrepreneurship to be scarcely mentioned in undergraduate and masters classes.
- Big ideas are getting harder to find – SIEPR summarizes work by Nick Bloom and co-authors: “the number of Americans engaged in R&D has jumped by more than twentyfold since 1930 while their collective productivity has dropped by a factor of 41”…It’s getting harder and harder to make new ideas, and the economy is more or less compensating for that,” Bloom said. “The only way we’ve been able to roughly maintain growth is to throw more and more scientists at it.”
- Pre-registration should be a plan, not a prison – from the Center for Open Science
- the Atlantic on how female mentors help female engineering students based on a paper forthcoming in PNAS – study only has n=150 at one college, assigned to male mentors, female mentors, or no mentors: 100% of women with female mentors remained in engineering majors at the end of year 1 compared with 82% with male mentors, and 89% without mentors
- Eva Vivalt gives four reasons your study should collect priors