On the Economic Impacts of Investment in Road Construction and Maintenance: New Applied CGE Analysis for Guinea‐Bissau
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it