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1.)The author adds additional structure of a linear in means model. The model is specified as:
21.)The author creates a pairs distance metric. Hypothesis is that the mean absolute distance in outcomes between section peers should be less than the distance in outcomes between class peers. This is estimated using a 2 stage procedure as described on page 17. Notice that SE need to be adjusted as individuals appear multiple times in construction the pairs.
32.)The author also creates an excess variance metric. This tests the hypothesis that peer influences should also reduce variance within peer groups relative to across groups. Trade offs between methods (1) and (2) Excess variance relies on familiar ANOVA model and results are easily comparable to previous work on peer effects. Also, estimation of elasticity does not require assumption of normality of individual fundamentals as in pairs distance measure. However, the pairs distance measure is more robust to outliers. because it relies on distance not distance squared as variance would. 3.) Finally, the author uses an "exogenous" shock or alumni reunions to test if peer effects in compensation and acquisitions are driven by contemporaneous interactions or past interactions. 4.) He also adds a robustness check in Pay for Friends Luck
→How the Author Tested Hypothesis
Executive social interactions are important determinants of managerial decision making and firm policies. The author tests to see if executive and firm outcomes are more similar among section peers than among class peers using data from HBS MBA students.
===How the Author Tested Hypothesis===
:<math>Y_{isc}=\theta\bar Y_{sc} + \phi\bar v_{sc} + \alpha_{sc} + \rho_{isc}</math>
In the baseline model, he does not differentiate between the two types of peer effects in calculating the peer elasticity.
===What Tests Achieved===
