Making Sense of Labor Market Indicators Amid Data Imperfections

Interpreting real-time labor market conditions is challenging because commonly used indicators are noisy, revised over time, and often send conflicting signals. In practice, policymakers and market participants describe labor market developments using a shared narrative language centered on labor demand, labor supply, and matching frictions. In this paper, we show that empirical measures of these narrative concepts can be recovered from latent factors that summarize the joint movements of a broad set of high-frequency U.S. labor-market indicators. We use ninety-four labor-market indicators, over the period from 1960 to 2026, and construct measures for labor demand, long-run labor supply, short-run labor supply, and matching efficiency by selecting the factors that satisfy a limited set of restrictions on how underlying forces map into observed data. We find that labor demand and short-run labor supply account for most of the common variation in labor-market indicators. Our results also show that assigning narrow interpretations to individual indicators can lead to misleading conclusions about underlying labor market conditions. Applying the framework to the post-pandemic period reveals that although labor demand recovered briskly after the acute phase of the pandemic, it cannot account for the large rise in vacancies and quits. Instead, movements in short-run labor supply and matching efficiency play a central role. We also show that the “soft-landing” episode from 2023 through 2025 was characterized by a joint decline in labor demand and short-run labor supply, which slowed payroll growth while generating only a moderate increase in the unemployment rate.