Randomized controlled trials (RCTs) and regression discontinuity (RD) studies both provide estimates of causal effects. A major difference between the two is that RD only estimates local average treatment effects (LATE) near the cutoff point of the forcing variable. This has been cited as a drawback to RD designs (Cook & Wong, 2008). Comparisons of RCT estimates of average treatment effect (ATE) and RD estimates of LATE are rare because few studies have both randomized assignment and a forcing variable. Using a study which is amenable to analysis both as an RCT and an RD, the authors compare the RCT estimate of ATE to the RD estimate of LATE. They also explore how the RD estimate of average treatment effect depends on econometric specifications, including for parametric and non-parametric estimators, and for various bandwidths. Finally, they use Monte Carlo simulation based on observed data to explore the performance of RD estimators with a forcing variable which is standardized within schools by choosing a different cutoff in each school. Findings can be used to inform decisions about which estimators to use in doing regression discontinuity, as well as providing evidence of how well RD estimators approximate RCT estimators. Tables are appended.