Reassessing the Impact of Financial Services on Urban Manufacturing Firms: An Impact Evaluation Method in Ethiopia
Bibliographic record
Abstract
The stock of financial literature reveals emerging and conflicting stands on the effect of finance on the performance of the economy. The previous thoughts considered finance into account as an important driving force to growth through its role in intermediation, reduction of transaction costs and risk, and efficient uses of resources. Nevertheless, the newly emerging thoughts focus on the vanishing effect of finance due to its stiff competition over resources with the rest of the economy. The dialogues also reflected in the process of industrialization and promoting the manufacturing sector. Therefore, the general objective of this study is to reexamine the impact of financial services on the performance of manufacturing firms in Ethiopia with a special focus on firms in Addis Ababa. The study used propensity score matching method. The result shows that those who access finance has increased their operating margin profit by 2.6 on average in comparison with the non-treated groups. The treated groups have 0.42 greater net return on a net asset than non-treated groups on average. Therefore, the study suggests that financial institutions should increase their involvement to expand the accessibility of financial products to manufacturing firms that are the expected engines of sustainable growth and economic transformation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".