A Dynamic Shift-Share Analysis of Economic Growth in West Virginia
Bibliographic record
Abstract
A regional economy consists of industries with a variety of economic potentials. A growth or decline in any of these sectors affects the overall growth of the economy. Analysis of economic growth by sector of a particular region helps policy makers, community leaders and researchers in better decision making and problem solving. This study analyzes the employment growth pattern and policy implications in the economic development of West Virginia using a dynamic shift share analysis. The study uses employment data for 38 years from 1970 to 2007 for the empirical analysis. Results indicate that agriculture, mining and manufacturing are no longer the backbone of the economy of West Virginia. The three sectors showed employment declined within the 38-year period. Service and financial insurance and real estate are the most robust sectors contributing 91% of employment growth from 1970 to 2007. Apart from these two sectors, the wholesale and retail and construction sectors showed positive economic growth. Identification of investment priorities within these potential sectors and implementation of a comprehensive regional development policy plan would definitely accelerate the economic growth of West Virginia. Key Words: Dynamic shift-share, employment, economic growth, West Virginia
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".