Effects of Industrial Diversity on Economic Stability: A Panel GARCH Process to Predict Economic Stability
Bibliographic record
Abstract
This paper studies the relationship between industry diversity and economic stability. The economic stability has been estimated using a panel-GARCH model. Our sample consists of US county-level data for the period 2003 to 2017. The results suggest that industry diversity improves economic stability and reduces a region’s unemployment rate. However, this study finds a negative relationship between industry diversity and economic growth. Although diversity negatively affects economic growth, it minimizes income loss when the nation falls into an economic recession. Therefore, we cannot conclude a tradeoff between economic stability and economic growth. It is possible that a region can achieve both stability and growth together through industry diversification. This paper also explores that the effects of diversity on economic stability, unemployment rate, and economic growth may vary between different counties depending on their metro or non-metro status. Thus, this paper suggests that policymakers may choose industry diversification as a strategy to achieve long-run economic stability and precaution against an unexpected economic downturn.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".