In this paper, we explore whether information from firm-level data can improve forecasts of aggregate productivity growth. We generate firm-level productivity measures and aggregate them into time-series components that capture within-firm productivity and the productivity contribution of reallocation. We show that these components improve aggregate total factor productivity forecasts in a simple univariate setting, even when firm-level data are available with a time lag. Lagged firm-level information also improves aggregate productivity forecasts when we combine results from a variety of different multivariate forecasting models using Bayesian model averaging techniques.

A longstanding issue in empirical economics is the behavior of average labor productivity over the business cycle. This paper provides new insights into the cyclicality of aggregate labor productivity by examining the cyclical behavior of productivity at the plant level as well as the role of reallocation across plants over the cycle. We find that plant-level productivity is even more procyclical than aggregate productivity, because short-run reallocation yields a countercyclical contribution to labor productivity. At the plant level, we find that cyclicality of productivity varies systematically with long-run employment growth. Over the course of the cycle, plants that are long-run downsizers exhibit significantly greater procyclicality of productivity than do long-run upsizers. When we control for the direction of a cyclical shock, we find that the fall in productivity from an adverse cyclical shock for long-run downsizers is significantly larger in magnitude than is the fall in productivity from an equivalent adverse cyclical shock for long-run upsizers. We argue that these findings raise questions about one of the most popular explanations of procyclical productivity: changing factor utilization over the cycle.