Increased Stability in Twelfth District Employment Growth
Bibliographic record
Abstract
Since the mid-1980s, virtually all states in the nation have seen nonfarm employment growth rates become much more stable than they were in the 1960s, 1970s, and early 1980s. However, the volatility of employment growth has declined by different amounts in different regions. In addition, although the general reasons for greater stability are similar across regions, the individual sectors making the greatest contribution to smoother growth differ. This Economic Letter discusses the major reasons for differences between each of the Twelfth District states and the country outside the Twelfth District in how much employment volatility has declined and in the primary causes of the declines. Smoother sailing Many kinds of economic data have shown much more stability in recent years. For example, McConnell and Perez-Quiros (2000) chronicle the dampening of GDP fluctuations in the U.S.They note that the variance (a measure of volatility) of output fluctuations during 1953–1983 was more than four times as large as the variance during 1984–1999. Using formal statistical techniques, the authors find evidence that 1984 was indeed a “break point,” indicating a one-time drop in the variance at this point, rather than a gradual downward drift. The smoothing of fluctuations also is evident in employment for U.S. regions. For example, although variance was a little higher in the Twelfth District than in the rest of the country, both before and after 1984, variance declined by more than half in both regions. The variance of employment growth for each Twelfth District state and the median of the variance of employment growth for the non-Twelfth District states (“Other Districts”) also declined. However, as seen in Figure 1, the decline in the variance of annualized quarterly employment growth
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".