Roles of Political and Economic Environments on Agricultural Commodity Import Demand in Developing Economy: A Case Study of Rice Sub-Sector in Nigeria
Bibliographic record
Abstract
Nigeria is currently the largest importer of milled rice in the world. The country has implemented several trade policies, set up institutions and incentives to boost domestic production with the intention to meet both domestic and international demands. Despite these attempts and favorable climatic, manpower and edaphic conditions in the country, Nigeria still spent millions of dollars on annual basis on rice imports. Based on this assertion, the study rather examined the roles of political and economic environments on rice import demand from 1960 to 2014 in Nigeria. Time series data were obtained from FAO, Central Bank of Nigeria and National Bureau of Statistics as well as World Bank. Augmented Dickey-Fuller-GLS unit root test showed that all series were integrated of order one. The long-run and short-run elasticity of rice import demand were determined using the techniques of co-integration and error correction models. The trend in rice import revealed that, the country had witnessed significant average positive exponential growth rate of about 15.975% in rice import from 1960 to 2014. The empirical results revealed that, the long run import demand function of rice responded negatively to the world price, industrial capacity utilization, nominal exchange rate, and the value of gross domestic production; whereas, it reacted positively to period of civilian rule, nominal value of external reserve, period of liberalization and the net volume of credit to the entire economy. The symmetric adjustment coefficient of rice import demand to a long run equilibrium stood at 39.65% per annum. In the short run, rice import had a significant negative and elastic relationship with the domestic and world price of rice; while it has significant positive inelastic association with external reserve and net credit to the economy. Based on these results; it is recommended that, the Nigeria government should designed programmes and incentives to boost industrial capacity utilization in the country. Markets determine nominal exchange rate should prevail in the economy. The country should regulate its foreign reserve policy by setting a threshold, above which excess deposit should be plough back to the domestic economy inform of investments rather than support excessive importation.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".