Internal migration and youth entrepreneurship in the Democratic Republic of the Congo
Bibliographic record
Abstract
Abstract This paper analyzes youth internal migration in the Democratic Republic of the Congo (DRC) and its impact on entrepreneurship startup in a fresh post‐conflict context. Building on a national representative survey conducted in 2005, a recursive bivariate probit specification is used to jointly estimate the decision models of both migration and entrepreneurship. To evaluate the robustness of results, the propensity score matching method is used to test the concordance of the results after eliminating the redundant impact of unobserved factors. The two main conclusions are that youth migration increases the probability of being an entrepreneur, but in the informal sector. In addition, like secondary and post‐secondary education, the duration of stay after migrating is an important factor to being an entrepreneur in the formal sector. These conclusions are expected to enlighten policy‐makers as to the importance of promoting secondary and post‐secondary education as well as inclusive growth investments that may absorb more youth labor in formal sectors. This is the first exercise in the case of the DRC and since it focuses on youth, the paper makes a unique contribution to the literature related to the link between migration and entrepreneurship in a post‐war context.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".