The cluster robust variance estimator (CRVE) relies on the number of clusters being large. The precise meaning of `large' is ambiguous, but a shorthand `rule of 42' has emerged in the literature. We show that this rule depends crucially on the assumption of equal-sized clusters. Monte Carlo...

The cluster robust variance estimator (CRVE) relies on the number of clusters being large. A shorthand "rule of 42'' has emerged, but we show that unbalanced clusters invalidate it. Monte Carlo evidence suggests that rejection frequencies are higher for datasets with 50 clusters proportional to...

We study cluster-robust inference for binary response models. Inference based on the most commonly-used cluster-robust variance matrix estimator (CRVE) can be very unreliable. We study several alternatives. Conceptually the simplest of these, but also the most computationally demanding, involves...

For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are often poor. We discuss several ways to improve...

Many empirical projects are well suited to incorporating a linear difference-in-differences research design. While estimation is straightforward, reliable inference can be a challenge. Past research has not only demonstrated that estimated standard errors are biased dramatically downwards in...

Many empirical projects are well suited to incorporating a linear difference-in-differences research design. While estimation is straightforward, reliable inference can be a challenge. Past research has not only demonstrated that estimated standard errors are biased dramatically downwards in...

Canada's Youth Hires program was a targeted employment subsidy that rebated employment insurance premiums to employers with net increases in insurable earnings for youth aged 18-24. Using a difference-in-differences approach, in each of two datasets statistically and economically significant...