Adjusted Estimates of Worker Flows and Job Openings in JOLTS

1. Adjusted Estimates of Worker Flows and Job Openings in JOLTS May 2008 Steven Davis, University of Chicago and NBER Jason Faberman, Federal Reserve Bank of Philadelphia John Haltiwanger, University of Maryland and NBER Ian Rucker, Bureau of Labor Statistics The views expressed are solely those of the authors and do not necessarily reflect the official positions or policies of the US Bureau of Labor Statistics, the US Bureau of the Census, the Federal Reserve Bank of Philadelphia, the Federal Reserve system or the views of their staff members.

2. Introduction • The behavior of hires and separations is an important topic for labor and macroeconomics • Relevant for several classes of models • Labor search and matching, factor adjustment, etc. • Useful in understanding important margins of cyclical employment fluctuations • e.g., are recessions hires-driven or job loss-driven? • JOLTS data are the best new source on hires and separations, but JOLTS data raise new questions • Accurate measurement of magnitude, cyclicality of worker flows critical to understanding employment fluctuations

3. What is the JOLTS? • JOLTS is a monthly survey of ~16,000 establishments • Has employment, number of hires, number of separations (by quits, layoffs & discharges, and other separations) throughout month, and number of job openings at end of month • Published statistics • Begin in December 2000 • Available by major industry and region for all nonfarm establishments • Our study… • Uses both published data and micro data for Jan-01 – Dec-06 • Appeals to fact that JOLTS sample frame is the BED (QCEW) data • Focuses only on private establishments

4. JOLTS Measurement Issues • JOLTS has three notable measurement issues • Two issues are observable in the published data • JOLTS hires and separations estimates overstate growth relative to its benchmark estimates (CES)

5. Issue 1 – Overstatement of CES Growth

6. JOLTS Measurement Issues • JOLTS has three notable measurement issues • Two issues are observable in the published data • JOLTS hires and separations estimates overstate growth relative to its benchmark estimates (CES) • Magnitude of hires and separations data smaller than comparable estimates • e.g., CPS gross flows data

7. Issue 2 – Worker Flow Magnitudes • Employer-employee data from Davis-Faberman-Haltiwanger (2006) also suggest estimates are understated

8. JOLTS Measurement Issues • JOLTS has three notable measurement issues • Two issues are observable in the published data • JOLTS hires and separations estimates overstate growth relative to its benchmark estimates (CES) • Magnitude of hires and separations data smaller than comparable estimates • e.g., CPS gross flows data • Micro data show: JOLTS over-represents stable establishments and misses entry and exit • Nature of the survey frame and sample nonresponse may play a role

9. Issue 3 – JOLTS Growth Rate Distribution • BED distribution also shows greater shifts in moving between high-growth and low-growth quarters

10. Goals for this Paper • Adjust JOLTS hires, separations, and job openings to reflect the universe growth rate distribution • Adjustment uses universe quarterly growth rate distribution from BED as “true” underlying distribution in economy. • After frequency adjustment, it interacts BED distribution with JOLTS data on mean worker flows and job openings rate by growth rate to construct adjusted measures. • Compare adjusted and unadjusted JOLTS measures • Quantify change in magnitudes • Compare any differences in cyclicality

11. Summary of Results • Adjusted worker flows are about one-third larger than published statistics • Separations increase disproportionately through increase in layoff rate • Adjustment alters relative volatility of hires and separations • Volatility of hires declines, while volatility of layoffs doubles, making separations more volatile than hires overall • Little change in Beveridge Curve (unemployment and vacancies) behavior

12. Adjustment Approach • Adjustment based on identity that aggregate estimate is equal to weighted sum of values for each growth rate xt(b) = mean value over growth rate “bin” b • Can obtain from monthly JOLTS micro data ft(b) = employment density for growth rate “bin” b • Will replace JOLTS densities, after some adjustments, with BED values

13. Adjustment Step 1 – Estimating Mean Rates • Estimate mean worker flow and job opening rates for fixed growth rate intervals using JOLTS microdata • Rates show nonlinear relations to growth • Relations vary little over time

14. Hires Rate

15. Job Openings Rate

16. Layoffs Rate

17. Quits Rate

18. Adjustment Step 2 – Density Creation • Adjustment replaces JOLTS densities with BED densities by growth rate interval • Estimation of monthly-quarterly relationship: • Predicted JOLTS density based on BED data: • Issues to Consider • Only BED densities account for entry, exit • Rescale JOLTS densities; add in estimates of entry and exit • BED are quarterly, JOLTS are monthly • Adjust BED based on monthly-quarterly relations observed in JOLTS

19. Results • Magnitudes of hires, separations increase by one-third • Volatility of separations increases (through layoffs) • Net growth (H – S) decreases

20. More Results

21. Adjusted vs. Published Hires

22. Adjusted vs. Published Quits

23. Adjusted vs. Published Layoffs

24. Adjusted vs. Published Job Openings

25. Conclusions • JOLTS data have been a major innovation in the study of labor dynamics • Initial research revealed some measurement issues • Adjusted estimates provide a different picture of labor market than published estimates • Magnitudes of worker flows higher by one-third • Cyclicality of separations relatively more important • Our work stresses the importance of having a survey that is representative of both levels and growth when estimating flow statistics • Work hopefully aids statistical agencies in achieving this goal

26. Quits and Layoffs Rates

27. Time-Series Shifts in Distributions

28. Adjusted Quits and Layoffs