Weekly links June 21: larger samples for mayors, experiments with policymakers, more better external validity, hardness bias in economics, and a cool snapshot

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  • So, is this all a waste of time? In a new NBER working paper, Hjort et al report results from an experiment in Brazil which they elicit mayors’ willingness to pay for impact evaluation results.  They find a positive demand for this information, which leads mayors to update their beliefs. Super cool finding is that, while larger samples are more effective, the origin of the findings (rich vs poor country) does not seem to matter. They then show that providing information on the effectiveness of a tax policy increases the probability that the policy gets implemented in the target municipality by 10 percentage points.
  • Related research from my colleague Aidan Coville and Eva Vivalt in which they run a lab-in-the-field experiment to document policymakers’ bias – they document “variance neglect” as well as a tendency to update when the news is good relative to one’s priors (RT @mosenkis)
  • Earlier this week we shared some external validity musings on the blog – we since noticed tons of cool discussions and examples:
    • this paper combines 441 estimates of the elasticity of labor force participation to fertility (RT @SylvainCF);
    • new study in Science reports results from a field experiment implemented in 355 cities across 40 countries to get at civic honesty around the globe (the news is good!) (RT @tage_rai); and
    • a recent VoxEU blog that discusses external validity in experimental economics (RT @CEGA_UC).
  • In a new paper forthcoming at the Journal of Economic Literature George Akerlof discusses a hardness bias in economics and how it may lead to sins of omission in the profession (RT @CarlGaigne)
  • ICYMI – last week, Markus blogged about Bedoya et al’s new study of the Targeting the Ultra Poor program in Afghanistan – if you don’t have time for the paper nor the blog , try this cool snapshot of the results!
  • NEUDC deadline is coming up – July 12!
  • Links from DIME Analytics (@AndradeLuizaC):
    • How do we think about loops in R vs Stata? This question always comes up in our R training -- this is a good example, by Hadley Wickham (code here)
    • Leapfrog Stata? This helps move people from excel -_- to R 😊

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