Do Firm Effects Drift? Evidence from Washington Administrative Data
Bibliographic record
Abstract
We study the time-series properties of firm effects in the two-way fixed effects model popularized by Abowd, Kramarz, and Margolis (1999) (AKM) using two approaches. The firstthe rolling AKM approach (R-AKM)-estimates AKM models separately for successive twoyear intervals. The second-the time-varying AKM approach (TV-AKM)-is an extension of the original AKM model that allows for unrestricted interactions of year and firm indicators. We apply to both approaches the leave-out methodology of Kline, Saggio and Slvsten (2020) to correct for biases in the estimated variance components. Using administrative wage records from Washington State, we find, first, that firm effects for hourly wage rates are highly persistent with an autocorrelation coefficient between firm effects in 2002 and 2014 of 0.74. Second, the R-AKM approach reveals cyclicality in firm effects and worker-firm sorting. During the Great Recession the variability in firm effects increased, while the degree of worker-firm sorting decreased. Third, misspecification of standard AKM models resulting from restricting firm effects to be fixed over time appears to be minimal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".