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Building Grit in the Classroom and Measuring Changes in it

David McKenzie's picture

About a year ago I reviewed Angela Duckworth’s book on grit. At the time I noted that there were compelling ideas, but that two big issues were that her self-assessed 10-item Grit scale could be very gameable, and that there was really limited rigorous evidence as to whether efforts to improve grit have lasting impacts.

A cool new paper by Sule Alan, Teodora Boneva, and Seda Ertac makes excellent progress on both fronts. They conduct a large-scale experiment in Turkey with almost 3000 fourth-graders (8-10 year olds) in over 100 classrooms in 52 schools (randomization was at the school level, with 23 schools assigned to treatment).

List Experiments for Sensitive Questions – a Methods Bleg

Berk Ozler's picture

About a year ago, I wrote a blog post on issues surrounding data collection and measurement. In it, I talked about “list experiments” for sensitive questions, about which I was not sold at the time. However, now that I have a bunch of studies going to the field at different stages of data collection, many of which are about sensitive topics in adolescent female target populations, I am paying closer attention to them. In my reading and thinking about the topic and how to implement it in our surveys, I came up with a bunch of questions surrounding the optimal implementation of these methods. In addition, there is probably more to be learned on these methods to improve them further, opening up the possibility of experimenting with them when we can. Below are a bunch of things that I am thinking about and, as we still have some time before our data collection tools are finalized, you, our readers, have a chance to help shape them with your comments and feedback.

Skills and agricultural productivity

Markus Goldstein's picture
Do skills matter for agricultural productivity?   Rachid Laajaj and Karen Macours have a fascinating new paper out which looks at this question.   The paper is fundamentally about how to measure skills better, and they put a serious amount of work into that.    But for those of you dying to know the answer – skills do matter, with cognitive, noncognitive, and technical skills explaining about 12.1 to 16.6 of the variation in yields.   Before we delve into that

Towards a survey methodology methodology: Guest post by Andrew Dillon

When I was a graduate student and setting off on my first data collection project, my advisors pointed me to the ‘Blue Books’ to provide advice on how to make survey design choices.  The Glewwe and Grosh volumes are still an incredibly useful resource on multi-topic household survey design.  Since the publication of this volume, the rise of panel data collection, increasingly in the form of randomized control trials, has prompted a discussion abo

Issues of data collection and measurement

Berk Ozler's picture
About five years ago, soon after we started this blog, I wrote a blog post titled “Economists have experiments figured out. What’s next? (Hint: It’s Measurement)” Soon after the post, I had folks from IPA email me saying we should experiment with some important measurement issues, making use of IPA’s network of studies around the world.

What’s New in Measuring Subjective Expectations?

David McKenzie's picture

Last week I attended a workshop on Subjective Expectations at the New York Fed. There were 24 new papers on using subjective probabilities and subjective expectations in both developed and developing country settings. I thought I’d summarize some of the things I learned or that I thought most of interest to me or potentially our readers:

Subjective Expectations don’t provide a substitute for impact evaluation
I presented a new paper I have that is based on the large business plan competition I conducted an impact evaluation of in Nigeria.  Three years after applying for the program, I elicited expectations from the treatment group (competition winners) of what their businesses would be like had they not won, and from the control group of what their businesses would have been like had they won. The key question of interest is whether these individuals can form accurate counterfactuals. If they could, this would allow us a way to measure impacts of programs without control groups (just ask the treated for counterfactuals), and to derive individual-level treatment effects. Unfortunately the results show neither the treatment nor control group can form accurate counterfactuals. Both overestimate how important the program was for businesses: the treatment group thinks they would be doing worse off if they had lost than the control group actually is doing, while the control group thinks they would be doing much better than the treatment group is actually doing. In a dynamic environment, where businesses are changing rapidly, it doesn’t seem that subjective expectations can offer a substitute for impact evaluation counterfactuals.

Who in this household has the final say?

Markus Goldstein's picture
Who in the household has decision making power over various things (kids going to school, health seeking behavior of individual members) either alone or jointly with someone else in the household makes up a set of questions that often find their way into surveys (e.g. a version is included in most Demographic and Health Surveys).  An interesting new paper by Amber Peterman and coauthors takes a hard look at these questions and what they might, or might not, be telling us.