Structural reforms, debt financing and the formal and informal sector in sub-Saharan Africa--an empirical analysis
Bibliographic record
Abstract
The study is about enterprises in the formal and informal sectors in sub-Saharan Africa and consists of three separate but connected essays. The first essay examines whether or not structural reforms in the business regulatory environment, trade sector, and the financial sector, can influence access to debt financing for investment by enterprises in sub-Sahara Africa. The data is from the World Bank Enterprise Surveys. The analyses involve panel data models. The results are indicative that taken together; structural policy reforms reduce debt-financing constraints. Reforms in the business regulatory environment and the financial sector increase the likelihood of access to debt financing. However, for trade, beyond a threshold, further reductions in the tariff and non-tariff barriers make it harder for enterprises to obtain debt financing. These results have implications for the type of reforms pursued in various countries. The second essay examines how social capital, education, and liquidity constraints influence the decision of an entrepreneur to operate either in the formal or informal sector. For enterprises that did not register and operating for less than five years, there is evidence that the likelihood of running in the informal sector, as opposed to the formal sector, decreases with the entrepreneurial level of education while social capital increases this likelihood. However, for enterprises in the informal sector, operating for over five years, liquidity constraints impedes formalisation. In the long run, the decision to stay informal or formalise depends on funding constraints, the incidence of taxes in the formal sector and the perception that there are no benefits from operating in the formal sector. The third essay is about the relationship between enterprises in the formal and informal sector and aims to uncover, at least in part, whether or not social and human capitals are important in facilitating the linkages between enterprises in the formal and the informal sectors. The analysis involves flexible binary generalised extreme value models. The results are indicative that for both male and female entrepreneurs, social and human capitals have significant positive real effects on the likelihood of linkages.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".