Pricing Contacts and Price Leadership in the Market For Imported Rice In Southwest Nigeria
Bibliographic record
Abstract
As Nigeria relies on importation for 56% of its national annual rice consumption, rice availability and prices are major welfare determinants in resource-poor households. Consequently, rice market integration, which will occasion price stability and pricing efficiency, are vitally important. This study examined pricing contacts in the imported rice market (IRM) in Southwest, Nigeria, using time-series data which were first subjected to stationarity tests. The major analytical tools used were growth rate model, coefficient of variation (CV) and Johansen co-integration model. Average growth in price was highest in Osun Market (33.4%), followed by Oyo Market (31.3%) and Ondo Market (29.8%). The highest average growth rate was recorded in year 2008 while negative growth rate was obtained across all IRM locations in the year 2010. This could be linked to lifting of the ban on rice imports that engendered increased importation immediately afterwards. Retail prices were more volatile in Oyo Market (35.8%) and least volatile in Ogun Market (31.1%). The generally low price variability implied that consumers can effectively plan rice expenditure. The ADF and PP tests revealed that all prices were not stationary at their levels but they attained stationarity after first-differencing. Pair-wise market integration tests revealed 14 out of 15 market pairs had prices which were spatially integrated on the long-run. Johansen’s multiple co-integration model results indicated 4 co-integration vectors out of 6 meaning that prices were stationary in 4 directions and non-stationary in 2. The Granger causality model showed that IRM locations deficient in imported rice were driving the prices in market locations with surplus of the commodity. It was recommended that the problem posed by highly inefficient and fragmented distribution and transportation systems be addressed for the rice traders and consumers to take full advantage of the high spatial market integration in the region.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".