Bootstrap-Based Improvements for Inference with Clustered Errors

Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (five to thirty) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo, and Mullainathan (2004). Rejection rates of 10% using standard methods can be reduced to the nominal size of 5% using our methods. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Estimating consumer lock-in effects from firm-level data

This paper proposes a practical method for estimating consumer lock-in effects from firm-level data. The method compares the behavior of already contracted consumers to the behavior of new consumers, the latter serving as a counterfactual to the former. In panel regressions on firms' incoming and quitting consumers, we look at the differential response to price changes and identify the lock-in effect from the difference between the two. We discuss the potential econometric issues and measurement problems and offer solutions to them. We illustrate our method by analyzing the market for personal loans in Hungary and find strong lock-in effects. © 2012 Springer Science+Business Media New York