1. Nick Bloom, Labor Topics, Spring 2010 LABOR TOPICS Nick Bloom Peers at Work

2. Nick Bloom, Labor Topics, Spring 2010 Comments on Mas and Moretti (2008) Great paper: innovative way to address an interesting topic – peer effects within workplaces Huge dataset High frequency productivity measurement Interesting setting Points to think about and learn from this I want to discuss: (A) Reflection problem (B) Bootstrap (C) Presentation (graphics and robustness test)

3. Nick Bloom, Labor Topics, Spring 2010 Reflection problems (1/2) One of the central issues in addressing spillovers is distinguishing these from: (A) Unobserved shocks (B) Sorting (selection effects) These issues are often called the “Reflection Problem” after Manski (1993). Ways to deal with this are: (A) Controlling for (instrumenting) unobserved shocks - Do this here using very long-run data, so no SR shocks (B) Having random matching by pairs - Do this here by claiming the shift matching in random (and then test this)

4. Nick Bloom, Labor Topics, Spring 2010 Reflection problems (2/2) (C) Using a distance metric to put more structure on the estimation - They do this with the facing/behind till distinction Overall I think they do a convincing job of addressing the key issue in the “peer effects” and “spillovers” literature

5. Nick Bloom, Labor Topics, Spring 2010 Bootstrap and robustness tests One issue they faced was in generating appropriate standard- errors in the regressions. They had a generated regressor (predicted Θ i ) in the second step – this has error around it so needs its SE adjusted Easiest way to do this is Bootstrap – keep re-drawing (with replacement) from the original data to look at the distribution of the coefficients. - Idea is treats the sample as the population Very computationally intensive (need to re-estimate everything 1000 times over) so they did a more complex Bayesian alternative

6. Nick Bloom, Labor Topics, Spring 2010 Presentation – graphics and robustness They used fantastic graphics to prove their results – if you can always have some graphs of results, particularly with this kind of sharp discountinuity effect They also always tested their key claims – for example that assignment of people to shifts is “random” - If you ever make a claim in a paper always try to test this as much more convincing