Internal migration and youth entrepreneurship in the Democratic Republic of the Congo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it