On the Economic Impacts of Investment in Road Construction and Maintenance: New Applied CGE Analysis for Guinea‐Bissau
Bibliographic record
Abstract
ABSTRACT A general equilibrium externality approach is developed to model the economic outcomes of road construction and maintenance investment funded through government savings/debt in Guinea‐Bissau from 2014 to 2030, equivalent to 1% of current Chinese investment in this country. The model is calibrated using sector elasticities and a social accounting matrix (SAM) that includes informal activities. Additionally, workers and households are categorized as rural or urban, allowing for a flexible analysis of the investment's implications for low‐income and high‐income individuals by setting. A 1% increase in public investment in roads generates productive externalities that enhance productivity growth and influence capital accumulation and reinvestment in sectors not initially targeted by the policy. Transportation costs and intermediate input prices fall in local agricultural production markets, increasing the return on investment in these sectors. Households' income and consumption increase, but food prices decrease, which benefits the urban and rural low‐income groups the most. Chinese investments should be reallocated to competitive sectors capable of increasing value added and contributing to job and income generation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".