Effects of Financial Liberalization on the Productivity Growth of Agriculture Sector in the Presence of Structural Breaks: Evidence from Ghana
Bibliographic record
Abstract
The study examined the relationship between financial liberalization and productivity growth of the Agriculture Sector in Ghana using the annual (yearly) data over the period 1970-2013. In the econometric analysis, the credits provided to private sector, investment, trade liberalization and capital account openness are considered as financial liberalization index while sector level value added as a percentage of GDP represented productivity growth. The stationarity of the series and the long run relationship were analyzed using Zivot-Andrews (1992) and Clemente, Montanes and Reyes (1998) test and Gregory Hansen tests in which structural breaks are considered. The findings of the study revealed that, opening up the economy will yield a positive result of sustainable productivity growth at sector levels. It behooves on the government to ensure that any structural reform programs that are initiated is comprehensively and completely implemented and also accompanied by sound macroeconomic policies to maintained a lasting effect, because the effect of such structural reforms in the long run growth path are prone to be thrown out of gear by other external shocks. The favorable influence of financial liberalization on productivity growth of agriculture sector is confirmed in Ghana. Future studies could be focused on whether financial liberalization will yield the expected effect on agriculture and other economic sectors’ productivity growth using primary data from the various sectors of the economy in a survey study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".