Ghana’s Path to an Industrial–Led Growth: The Role of Decentralisation Policies
Bibliographic record
Abstract
Ghana’s industrial sector has evolved with the various stages of political and economic reforms since independence in 1957. Efforts to decentralize its key institutions to enhance economic growth has seen very little success especially in the area of linking industries to local institutions. Recently, the economy has been dampened by worsening macroeconomic environment, huge regional disparities and power crises. A number of policy and programme initiatives by the government have been undertaken especially in the area of revamping the local economies through the existing decentralized systems. This paper presents a critical review of the role of decentralized institutions in industrialisation in Ghana. The paper utilises annual data from the Ministry of Finance and Ghana Statistical Service from 1981 to date to show trends in growth patterns in the selected indicators.Despite key interventions, some regions in Ghana have failed to develop. The envisioned industrial geographical dispersion has not been realised as we find many Ghanaian industries concentrated in a few regions. The paper highlights the challenges facing Ghana’s decentralized institutions and identifies the opportunities that can catalyse the growth of Ghana’s industrial sector if key policy strategic reforms are undertaken. An industrial-led growth will ensure that the manufacturing sub-sector will be boosted to improve production and provide jobs. Industrialisation has been projected at the forefront of government’s development agenda. The paper provides a review that highlights the need to support decentralised institutions to enable them stimulate investment in industrial sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".