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US payroll employment data come from a survey of nonfarm business establishments and are therefore subject to revisions. While the revisions are generally small at the national level, they can be large enough at the state level to substantially alter assessments of current economic conditions. Researchers and policymakers must therefore exercise caution in interpreting state employment data until they are "benchmarked" against administrative data on the universe of workers some 5 to 16 months after the reference period. This paper develops and tests a state space model that predicts benchmarked US 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 across the 50 US states, the model reduces the average size of benchmark revisions by about 9 percent. When we optimally average the model's predictions with those of existing models, we find that we can reduce the average size of the revisions by about 15 percent.

To date, most empirical studies of industrial agglomeration rely on data where observations are assigned an industry code based on classification systems such as NAICS in North America and NACE in Europe. This study combines industry data with occupation data to show that there are important differences in the spatial patterns of occupation groups within the widely used industry definitions. We focus on workers in manufacturing industries, whose occupations almost always fit into three groups: production, administrative, or R&D. We then employ two approaches to document the spatial distributions of each group within an industry. First, we calculate the distribution of employment shares across local labor markets and second, we calculate a version of the Duranton and Overman (2005) agglomeration index. Both approaches reveal appreciable differences in the spatial distribution of occupation groups within most manufacturing industries. These differences have important implications for our understanding of the sources of industrial agglomeration, the spatial agglomeration of innovation, the effectiveness of local economic development initiatives, and the spatial properties of particular industries.

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 article provides the public a first look at a new set of indexes constructed from the Chicago Fed’s Beige Book survey, and describes their ability to track economic activity.