nick bloom, labor topics, spring 2010 labor topics nick bloom peers at work
TRANSCRIPT
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Nick Bloom, Labor Topics, Spring 2010
LABOR TOPICS
Nick Bloom
Peers at Work
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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)
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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)
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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
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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 Θi) 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
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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