We describe the new command margte, which computes marginal and average treatment effects for a model with a binary treatment and a continuous outcome given selection on unobservables and returns. Marginal treatment effects differ from average treatment effects in instances where the impact of treatment varies within a population in correlation with unobserved characteristics. Both parametric and semiparametric estimation methods can be used with margte, and we provide evidence from a Monte Carlo simulation for when each is preferable.

This study uses worker‐level data on industry, occupation, and place of work to explore differences in the spatial properties of production, administrative, and R&D occupation groups within industries. To measure differences, we calculate location quotients at the local labor market level and the Duranton and Overman (2005) agglomeration index for each group. We find appreciable differences in the spatial distribution of occupation groups within most manufacturing industries, with R&D occupations consistently exhibiting the highest degree of spatial concentration. Our results are consistent with the core theoretical and empirical results in the agglomeration literature.

US payroll employment data come from a survey and are subject to revisions. While revisions are generally small at the national level, they can be large enough at the state level to alter assessments of current economic conditions. Users must therefore exercise caution in interpreting state employment data until they are "benchmarked" against administrative data 5-16 months after the reference period. This paper develops a state-space model that predicts benchmarked state employment data in real time. The model has two distinct features: 1) an explicit model of the data revision process and 2) a dynamic factor model that incorporates real-time information from other state-level labor market indicators. We find that the model reduces the average size of benchmark revisions by about 11 percent. When we optimally average the model's predictions with those of existing models, the model reduces the average size of the revisions by about 14 percent.

US payroll employment data come from a survey and are subject to revisions. While revisions are generally small at the national level, they can be large enough at the state level to alter assessments of current economic conditions. Users must therefore exercise caution in interpreting state employment data until they are "benchmarked" against administrative data 5-16 months after the reference period. This paper develops a state-space model that predicts benchmarked state employment data in realtime. The model has two distinct features: 1) an explicit model of the data revision process and 2) a dynamic factor model that incorporates realtime information from other state-level labor market indicators. We find that the model reduces the average size of benchmark revisions by about 11 percent. When we optimally average the model's predictions with those of existing models, the model reduces the average size of the revisions by about 14 percent.