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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.    

A curated list of our postings on Measurement and Survey Design

David McKenzie's picture
This list is a companion to our curated list on technical topics. It puts together our posts on issues of measurement, survey design, sampling, survey checks, managing survey teams, reducing attrition, and all the behind-the-scenes work needed to get the data needed for impact evaluations.

Improving the Granularity of Nighttime Lights Satellite Imagery: Guest Post by Alexei Abrahams

Popular data
Nighttime lights satellite imagery (DMSP-NTL) are now a popular data source among economists. In a sentence, these imagery encompass almost all inhabited areas of the globe, and record the average quantity of light observed at each pixel (nominal size ~1km2) across cloud-free nights for every year, 1992-2012. In under-developed or conflicted regions, where survey or census data at a fine level of spatial and temporal disaggregation are seldom available or reliable or comparable over space or time, NTL and other satellite imagery can be an excellent resource. Recent economics papers have used NTL to study growth of cities in sub-Saharan Africa (Storeygard (2015)), production activity in blockaded Palestinian towns of the West Bank (Abrahams (2015), van der Weide et al (2015)), and urban form in China (Baum-Snow & Turner (2015)) and India (Harari (2015)).