Local Job Multipliers in the United States: Variation with Local Characteristics and with High-Tech Shocks
Bibliographic record
Abstract
This paper provides new estimates of local job multipliers, the ratio of total jobs generated to some initial number of jobs created from a demand shock. Multipliers greatly affect benefits versus costs of local job-creation policies. These new estimates rely on improved methodology and data. The methodology better captures dynamic effects of demand shocks, specifies the model so that demand shocks are more comparable, and is more general in the types of demand shocks that are considered. The data has more industry detail than that used in previous studies. The local job multipliers estimated tend to be about one-quarter lower than typically estimated local multipliers, closer to 1.5 than to 2.0. In addition, demand shocks to all industries matter, not just to tradable industries. Multipliers are similar across different types of geographic areas, with county multipliers being only one-quarter below commuting zone multipliers and state multipliers only one-quarter above commuting zone multipliers. Multipliers are not larger for larger commuting zones, but they increase in commuting zones that have lower initial employment to population ratios. Multipliers are higher for high-tech industries, particularly in commuting zones with a larger initial high-tech share. In such high-tech local economies, high-tech multipliers may be close to 3. While our high-tech multipliers are greater than for other industries, our estimated high-tech multipliers are less than in some prior studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".