Inference using difference-in-differences with clustered data requires care. Previous research has shown that t tests based on a cluster-robust variance estimator (CRVE) severely over-reject when there are few treated clusters, that different variants of the wild cluster bootstrap can...

Targeted employment subsidy programs are commonly employed by governments. This study examines one such initiative that rebated unemployment insurance premiums for net new insurable employment for youth aged 18 to 24. Using microdata from two datasets to estimate the labour market impacts of...

Inference based on cluster-robust standard errors is known to fail when the number of clusters is small, and the wild cluster bootstrap fails dramatically when the number of treated clusters is very small. We propose a family of new procedures called the sub- cluster wild bootstrap. In the case...

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...

Cluster-robust inference is widely used in modern empirical work in economics and many other disciplines. The key unit of observation is the cluster. We propose measures of "high-leverage" clusters and "influential" clusters for linear regression models. The measures of leverage and partial...

We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust variance matrix estimators (CRVEs) for linear regres- sion models estimated by least squares. These estimators have previously been com- putationally infeasible except for small...