WHAT DRIVES SOUTH AFRICA’S DISAGGREGATED IMPORT DEMAND FUNCTION WITH TANZANIA? AN EMPIRICAL ANALYSIS
Bibliographic record
Abstract
This empirical study investigates the behavior of South African imports from Tanzania during the period 1980-2010 utilizing cointegration analysis and the Error Correction Model developed by Pesaran, Shin, and Smith (2001). Results indicate that a long run stable relationship between imports and its independent variables exists. Estimates of the long-run and short-run partial elasticities of imports with respect to relative prices, real foreign reserves, exchange rate volatility, consumption expenditure, investment, and exports meet theoretical expectations and are mostly significant. The results of two dummy variables employed to capture the impact of apartheid (1980-1994) and the post-apartheid commitment to increase trade with other African countries (1996-2010) reveal that apartheid negatively drove imports, while the policy to increase trade has had a positive but inelastic impact on imports from Tanzania. To increase south-south trade and strengthen its own economy and that of Tanzania, we suggest that South Africa increases trade with Tanzania.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".