Business Environment Effect on Formalization Willingness and Registration Decision of SMEs in Ivory Coast
Bibliographic record
Abstract
This paper analyses the effects of the business environment on the formalisation provision and the decision to register small and medium enterprises (SMEs) in Ivory Coast, based on informal sector survey data collected in the cities of Abidjan, San-Pedro and Daloa. The study is based on descriptive analyses and estimation of a Probit model with selection. The analysis reveals that procedural complexity, information asymmetry and geographical location are the factors that significantly determine both the formalisation disposition and the decision to register businesses. It appears that, in addition to the business environment, subcontracting and the size of SMEs explain the decision to register them, while the possession of a business plan, access to infrastructures and markets are the determinants of formalisation. Thus, it appears that, in an integrated approach, the strengthening of tax incentives for SMEs operating in low-profit localities, the formalisation of subcontracting relationships, the dematerialization of procedures and the popularisation of reforms are proving to be a guarantee of the formalisation of informal activities.
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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 | 0.005 |
| 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 | 0.001 |
| 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".