- WDR 2018
Here is a familiar scenario for those running field experiments: You’re conducting a study with a treatment and a comparison arm and measuring your main outcomes with surveys and/or biomarker data collection, meaning that you need to contact the subjects (unlike, say, using administrative data tied to their national identity numbers) – preferably in person. You know that you will, inevitably, lose some subjects from both groups to follow-up: they will have moved, be temporarily away, refuse to answer, died, etc. In some of these cases there is nothing more you can do, but in others you can try harder: you can wait for them to come back and revisit; you can try to track them to their new location, etc. You can do this at different intensities (try really hard or not so much), different boundaries (for everyone in the study district, region, or country, but not for those farther away), and different samples (for everyone or for a random sub-sample).
Question: suppose that you decide that you have the budget to do everything you can to find those not interviewed during the first pass through the study areas (doesn’t matter if you have enough budget for a randomly chosen sub-sample or everyone), i.e. an intense tracking exercise to reduce the rate of attrition. In addition to everything else you can do to track subjects from both groups, you have a tool that you can use for those only in the treatment arm (say, your treatment was group-based therapy for teen mums and you think that the mentors for these groups may have key contact information for subjects who moved in the treatment group. There were no placebo groups in control, i.e. no counterpart mentors). Do you use this source to track subjects – even if it is only available for the treatment group?
- Let’s start with your approach to teaching development economics at the graduate level. The class when you taught David in 1999 was heavy on the agricultural household model and understanding micro development through different types of market failures. Most classes would involve in-depth discussion of one or at most two papers, with a student assigned most weeks to lead this discussion. There was a lot of discussion of the empirical methods in different papers, but no replication tasks and the only empirical work was as part of a term paper. How has your approach to teaching development changed (or not) since this time?
Try as I might, I have made little progress on changing my basic approach to teaching. The papers and topics have changed, but the essence of my graduate teaching remains the in-depth discussion of a paper or two each class. I’ve tried to expand the use of problem sets, and had a number of years of replication assignments. The first was hindered by my own inadequate energy (it’s hard making up decent questions!). I found that replication exercises required too much time and effort in data cleaning by students relative to their learning gain. Students were spending too much time cleaning, merging and recreating variables and too little time thinking about the ideas in the paper. I’ll reassess assigning replication this year, because there may now be enough well-documented replication datasets and programs available. With these as a starting point, it would be possible to get quickly into substantive issues in the context of a replication.
In the latest JEL, Parker and Todd survey the literature on Progresa/Oportunidades: some bits of interest to me included:
- CCTs have now been used in 60+ countries;
- over 100 papers have been published using the Progresa/Oportunidades data, with at least 787 hypotheses tested – multiple testing corrections don’t change the conclusions that the program had health and education effects, but do cast doubt on papers claiming impacts on gender issues and demographic outcomes;
- FN 16 which notes that at the individual level, there are significant differences in 32% of the 187 characteristics on which baseline balance is tested, with the authors arguing that this is because the large sample size leads to a tendency to reject the null at conventional levels – a point that seems inconsistent with use of the same significant levels for measuring treatment effects;
- Two decades later, we still don’t know whether Progresa led to more learning, just more years in school;
- One of the few negative impacts is an increase in deforestation in communities which received the CCT
- Dave Evans asks whether it matters which co-author submits a paper, and summarizes responses from several editors; he also gives a short summary of a panel on how to effectively communicate results to policymakers.
- On the IPA blog, Rachel Glennerster and Claire Walsh argue that it’s time to rethink how we measure women’s household decision-making power in impact evaluations - congrats also to Rachel for being named DFID’s new chief economist.
- At VoxDev, Alaka Holla blogs about how IT training in Nigeria may have changed aspirations for women, and Natalie Bau and Jishnu Das on the market for teachers in Pakistan
- On the AfricaCan blog, Gautam Bastian and Sreelakshmi Papineni report on a test of whether quarterly or monthly cash payments work better for women in Nigeria – finding similar impacts for both.
- Tips from journal editors for young economists, summarizing a panel session at the European Economics Association.
I recently received an email from a researcher who was interested in trying to re-interview participants in one of my experiments to test several theories about whether that intervention had impacts on political participation and other political outcomes. I get these requests infrequently, but this is by no means the first. Another example in the last year was someone who had done in-depth qualitative interviews on participants in a different experiment of mine, and then wanted to be able to link their responses on my surveys to their responses on his. I imagine I am not alone in getting such requests, and I don’t think there is a one-size-fits-all response to when this can be possible, so thought I would set out some thoughts about the issues here, and see if others can also share their thoughts/experiences.
Confidentiality and Informed Consent: typically when participants are invited to respond to a survey or participate in a study they are told i) that the purpose of the survey is X ,and will perhaps involve a baseline survey and several follow-ups; and ii) all responses they provide will be kept confidential and used for research purposes only. These factors make it hard to then hand over identifying information about respondents to another researcher.
However, I think this can be addressed via the following system:
- While the rest of us took August off blogging, Dave Evans blogged about how information can improve service delivery on Let’s Talk Development.
- There was a lot of discussion about gender and economics. Rebecca Thornton helpfully has put together a list of gender and economics links.
- Marc Bellemare has good advice on how to cite intelligently.
- As more and more papers rely on large admin datasets, there are questions about who gets to use this data and under what conditions. The 74 million has an interesting discussion about this in the context of school lottery data from Louisiana.
- On the data blog – a new LSMS guidebook for using non-standard units like local tins or bunches in measuring food and agricultural quantities.