Working Paper: NBER ID: w30620
Authors: G. Jacob Blackwood; Cindy Cunningham; Matthew Dey; Lucia S. Foster; Cheryl Grim; John C. Haltiwanger; Rachel L. Nesbit; Sabrina Wulff Pabilonia; Jay Stewart; Cody Tuttle; Zoltan Wolf
Abstract: An important gap in most empirical studies of establishment-level productivity is the limited information about workers’ characteristics and their tasks. Skill-adjusted labor input measures have been shown to be important for aggregate productivity measurement. Moreover, the theoretical literature on differences in production technologies across businesses increasingly emphasizes the task content of production. Our ultimate objective is to open this black box of tasks and skills at the establishment-level by combining establishment-level data on occupations from the Bureau of Labor Statistics (BLS) with a restricted-access establishment-level productivity dataset created by the BLS-Census Bureau Collaborative Micro-productivity Project. We take a first step toward this objective by exploring the conceptual, specification, and measurement issues to be confronted. We provide suggestive empirical analysis of the relationship between within-industry dispersion in productivity and tasks and skills. We find that within-industry productivity dispersion is strongly positively related to within-industry task/skill dispersion.
Keywords: productivity; task and skill mix; establishment-level data; dispersion; labor input
JEL Codes: C81; E23; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| within-industry productivity dispersion (L69) | within-industry task-skill dispersion (L19) |
| task-skill dispersion (J29) | within-industry total factor productivity (TFP) dispersion (O49) |
| high-tech industries (L63) | elasticity of TFP dispersion with respect to task-skill dispersion (F16) |
| establishment identifiers (EINs) (E01) | between-establishment variation in task-skill indexes (J62) |