No. 73: Informal Entrepreneurship and Cross-Border Trade in Maputo, Mozambique
Bibliographic record
Abstract
Cross-border trading is an essential part of Mozambique’s informal economy, with the traders playing a key role in supplying commodities that are in scarce supply nationwide. This report presents the results of a SAMP survey of informal entrepreneurs connected to cross-border trade between Johannesburg and Maputo. The study sought to enhance the evidence base on the links between migration and informal entrepreneurship in Southern African cities and to examine the implications for municipal, national and regional policy. In Mozambique, cross-border trading is primarily done by women with men mainly involved in the sale of the products brought back from South Africa. This report demonstrates the specific roles played by the cross-border traders in the economies of both Mozambique and South Africa. It shows that they contribute to the South African economy through buying goods, as well as paying for accommodation and transport costs. The cross-border traders are directly contributing to the retail, hospitality and transport sectors in South Africa, thereby creating and sustaining jobs in those sectors. In Mozambique, the traders pay import duty for the goods bought in South Africa and they play a significant role in reducing poverty and unemployment in the country. Therefore, a change in attitude of government towards cross-border traders is called for and the policy environment should encourage the operation of this trade.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".