Access to Credit and the Effect of Credit Constraints on the Performance of Manufacturing Firms in the East African Region: Micro Analysis
Bibliographic record
Abstract
The study set out to investigate the factors influencing manufacturing firms’ access to credit and the effect of credit constraints on firm performance in the East African Community (EAC) using the World Bank (2006) enterprise survey for 5 EAC countries. We employed simple probit, simple OLS, tobit, and a two-step probit models. Descriptively, the top five business constraints in order of severity include; electricity outages and costs, access to finance, high and volatile tax rates, corruption, and macroeconomic instability. The majority of firms within the EAC are credit constrained with only 37% of firms in the best performing sector (metal fabrications) having obtained a loan. Quantitatively, high performing firms, exporters, medium and large firms increase the probability of credit access. Findings indicate that having access to credit and a long loan duration increase firm performance, while increase in the annual interest rate reduces firm productivity. Governments in the region should tackle the business constraints rated as very severe. EAC governments should make credit access easier by lowering the annual interest rates and also negotiating for a longer pay back period for individuals in the business sector. Governments in the region should put specific attention on those sectors which are observed to have an extreme disadvantage in accessing finance.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".