Effects of mergers and acquisitions on market equilibrium performance measures for processed milk market in Kenya
Bibliographic record
Abstract
The evaluation of the mergers and acquisitions effects on the main equilibrium performance measures is essential to the understanding of the competition policy dynamics in a market with few players. There is need for adoption of a robust mergers analysis model to address the shortcoming of the traditional SSNIP model which only focuses of prices and leaving out other important aspects of mergers and acquisitions. This study aimed to determining 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 answering the question on the overall effect of mergers and acquisitions on the equilibrium performance measures on milk market using data from all the 34 licensed and active milk processors in Kenya. A new model of analysis as developed from the Canadian Competition Policy maker i.e. The Canadian Competition Policy Merger Simulation Model was used. The study found that mergers and acquisitions lead to increase in market shares of the merging firms. Their non-merging counterparts also record a significant increase in their market shares after mergers and acquisitions have taken place even though they are not directly involved in the merger or acquisition. Herfindahl-Hirschman Index, or HHI which is a measure of the size of the firms in relation to the industrial and an indicator of competition and the concentration ratio (CR4) of the four largest firms in the industry increased. The study also found that mergers and acquisitions have a significant effect on product price. From the findings, the study concludes that mergers and acquisition not only leads to increase in market shares of both merging and non-merging firms but also creates market dominance due to reduction in the number of market players in the industry. This firms ends up dictating major terms of trade affecting different equilibrium measures such as product prices, volume of output released in the market, quantity produced and the social welfare. Therefore there is need for all competition policy practitioners to carry out robust analysis for proposed mergers or acquisitions before approval. However, due to the short-comings of the Canadian Competition Policy merger Simulation Model where only companies with a market share of one percent and above can be used, a mixed model approach can be used to help arrive to near accurate conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".