Access to Debt Finance: Which Policies Work? Empirical Evidence from Sub-Saharan Africa
Bibliographic record
Abstract
Are the structural policy reforms effective in reducing debt financing constraints on formal sector enterprises in sub-Saharan Africa? We do not know. And the reason is the relatively limited research on the effectiveness of policies in the credit market. Using policy variables from the World Bank and the Enterprise Surveys data, the analysis involves three-way error component models. The results are indicative that taken together; structural policy reforms reduce debt financing constraints, at least, as it pertains to working capital needs. There is heterogeneity in the results. Changes in the business regulatory environment benefit large firms more than small ones. Financial sector reforms affect enterprises of all sizes relatively equally. For all the twelve countries, together, trade sector reforms initially increase the likelihood of access to debt finance by 20 percent until a policy threshold, beyond which progressive reforms in the trade sector reduce the probability by as much as 13 percent. Also, not all countries experience the same effects from trade sector reforms. The result is robust to different indicators of credit constraint and measures of structural reforms. The results have implications on the World Bank's push towards reforms on trade policy across countries.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".