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Weekly links September 8: career advice, measuring empowerment, is anyone reading, lumpy cash, and more…

David McKenzie's picture

Worker productivity and soft skills

Markus Goldstein's picture
There’s a lot of talk about soft skills and how they might help boost productivity and earnings.    Into this literature comes a neat new paper by Achyuta Adhvaryu, Namrata Kala, and Anant Nyshadham which looks at the returns to providing training on these skills for factory workers in India.   They provide a convincing case that it might make economic sense for firms to provide trainings for these skills.  

Is it possible to re-interview participants in a survey conducted by someone else?

David McKenzie's picture

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:

Monthly links for August: What did you miss while we were on summer break?

David McKenzie's picture

Sometimes (increasingly often times), estimating only the ITT is not enough in a RCT

Berk Ozler's picture

"In summary, the similarities between follow-up studies with and without baseline randomization are becoming increasingly apparent as more randomized trials study the effects of sustained interventions over long periods in real world settings. What started as a randomized trial may effectively become an observational study that requires analyses that complement, but go beyond, intention-to-treat analyses. A key obstacle in the adoption of these complementary methods is a widespread reluctance to accept that overcoming the limitations of intention-to-treat analyses necessitates untestable assumptions. Embracing these more sophisticated analyses will require a new framework for both the design and conduct of randomized trials."

Weekly links July 28: overpaid teachers? Should we use p=0.005? beyond mean impacts, facilitating investment in Ethiopia, and more…

David McKenzie's picture
  • Well-known blog skeptic Jishnu Das continues to blog at Future Development, arguing that higher wages will not lead to better quality or more effective teachers in many developing countries – summarizing evidence from several countries that i) doubling teacher wages had no impact on performance; ii) temporary teachers paid less than permanent teachers do just as well; and iii) observed teacher characteristics explain little of the differences in teacher effectiveness.
  • Are we now all doomed from ever finding significance? In a paper in Nature Human Behavior, a multi-discipline list of 72 authors (including economists Colin Camerer, Ernst Fehr, Guido Imbens, David Laibson, John List and Jon Zinman) argue for redefining statistical significance for the discovery of new effects from 0.05 to using a cutoff of 0.005. They suggest results with p-values between 0.005 and 0.05 now be described as “suggestive”. They claim that for a wide range of statistical tests, this would require an increase in sample size of around 70%, but would of course reduce the incidence of false positives. Playing around with power calculations, it seems that studies that are powered at 80% for an alpha of 0.05 have about 50% power for an alpha of 0.005. It implies using a 2.81 t-stat cutoff instead of 1.96. Then of course if you want to further adjust for multiple hypothesis testing…

Biased women in the I(C)T crowd

Markus Goldstein's picture
This post is coauthored with Alaka Holla

The rigorous evidence on vocational training programs is, at best, mixed.   For example, Markus recently blogged about some work looking at long term impacts of job training in the Dominican Republic.   In that paper, the authors find no impact on overall employment, but they do find a change in the quality of employment, with more folks having jobs with health insurance (for example). 

A new answer to why developing country firms are so small, and how cellphones solve this problem

David McKenzie's picture
Much of my research over the past decade or so has tried to help answer the question of why there are so many small firms in developing countries that don’t ever grow to the point of adding many workers. We’ve tried giving firms grants, loans, business training, formalization assistance, and wage subsidies, and found that, while these can increase sales and profits, none of them get many firms to grow.

Weekly links July 21: a 1930s RCT revisited, brain development in poor infants, Indonesian status cards, and more…

David McKenzie's picture

What a new preschool study tells us about early child education – and about impact evaluation

David Evans's picture
When I talk to people about impact evaluation results, I often get two reactions:
  1. Sure, that intervention delivered great results in a well-managed pilot. But it doesn’t tell us anything about whether it would work at a larger scale. 
  2. Does this result really surprise you? (With both positive results and null results, I often hear, Didn’t we already know that intuitively?)

A recent paper – “Cognitive science in the field: A preschool intervention durably enhances intuitive but not formal mathematics” – by Dillon et al., provides answers to both of these, as well as giving new insights into the design of effective early child education.