Ongoing Work with US Census Bureau Data

I show that the model can reproduce the qualitative properties of worker flow data at the firm level and over the business cycle. In addition, the model has impli­cations for the cross-sectional distribution of part-time usage by firms. Firm growth causes young firms to use relatively little part-time labor, while the exit probability

of firms changes the usage of part-time employment over the firm-size distribution. However, what is lacking along these dimensions is data on the patterns of hours ad­justments or labor utilization of firms, both in the cross-section and over the business cycle. In ongoing work, I address this by documenting the cyclical and cross-sectional properties of hours and employment changes at the firm-level in US Census Bureau Data. I plan to document the patterns in employment growth and hours growth in manufacturing firms using the Longitudinal Business Database (LBD) and Census of Manufactures/Annual Survey of Manufactures (CMF/ASM). The goal is to document the correlation of hours and employment growth over the cross-sectional distribution of firms by size and age category. Also of interest is the cyclical volatility of these moments at the firm level by firm and establishment size, age and productivity. This is motivated by the findings of Moscarini and Postel-Vinay (2012), who document that the employment growth rate for large firms varies more over the business cycle than it does for small firms. Fort et al. (2013) show that young and small firms are very volatile over the cycle, while small but older firms have less cyclical employment growth. I plan to extend these facts to cover the response of hours growth rates to changes in aggregate unemployment and output by firm-level observable character­istics such as size and age. Understanding the properties of firm-level growth rates in hours and employment over the firm distribution will provide valuable insight into the dynamics of unemployment, employment, and labor utilization at the firm level.