Financing Enterprises to Boost Employment in Cameroon
Bibliographic record
Abstract
Cameroon has 93969 different enterprises (NIS, 2010) operating in varied fields aimed at fostering economic growth. The enterprises confront challenges such as infrastructural weaknesses, unfavorable business climate and poor governance (World Bank, 2013), thus leading to disappointing results in terms of economic growth. Such a situation has attracted much attention from businessmen and policy-makers alike as to what to do in order to reverse the situation for favorable job creation and economic growth. The paper aims at examining the impact of external financing to enterprises in order to offer employment in Cameroon. Econometrically analyzing a sample of 180 loan recipients and 273 non-loan recipients, using the decomposition technique of Blinder-Oaxaca (1973), results show that enterprises that received external funding were more performing and creating jobs than those that did not, especially those operating in Yaoundé and Douala. A positive gap of total number of employees existed between loan and non-loan recipients estimated at 15 employees per enterprise. Also, such loans received positively amplify the actions of productive factors in Yaoundé and Douala considering the number of establishments and businesses. Equally, there exist a difference due to observable characteristics of enterprises and their coefficients, contributing 181.1 and 140.12% respectively for loan and non-loan recipients. We therefore recommend that the state, financial institutions and enterprises should work in synergy to collectively improve on enterprise financing so as to boost employment in Cameroon that can lead to economic growth.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".