Effects of mergers on processed milk market in Kenya
Bibliographic record
Abstract
Purpose The purpose of this paper is to determine the effects of the mergers and acquisitions on market prices, consumer welfare and aggregate profit of the merging firms and those of the non-merging firms and, therefore, answer the question on the overall effect of mergers and acquisitions on different performance measures on milk market using data from all the 34 licensed and active milk processors in Kenya. Design/methodology/approach A new model of analysis as developed from the Canadian Competition Policy maker, i.e. The Canadian Competition Policy merger simulation model, was used. Findings The study found that mergers and acquisitions lead to increase in market shares of the merging firms. The study also found that mergers and acquisitions have a significant effect on product price in the processed milk market. From the findings, the study concludes that mergers and acquisition not only lead to an increase in market shares of both merging and non-merging processed milk firms but also create market dominance due to reduction in the number of market players in the industry. Research limitations/implications The study uses the data for the licensed and active milk processors in the industry. The dormant and the non-licensed processors are excluded. Future studies can use the farm-gate prices as opposed to final consumer prices for the processed milk market. Originality/value The study contributes toward providing information on the effect of buyouts on social welfares, prices, market share, profitability and other relevant market equilibrium performance measures in the processed milk market in Kenya.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".