EL IMPACTO DE LAS FUSIONES Y ADQUISICIONES EN LA DISTRIBUCIÓN DEL INGRESO Y EL PIB: AMÉRICA LATINA, 1990-2014
Bibliographic record
Abstract
RESUMENEl artículo presenta los factores explicativos de las fusiones y adquisiciones (F&A), mostrando la evolución, importancia relativa y sectores de destino de esas operaciones en 16 economías de América Latina en el periodo 1990-2014. Con base en un modelo integral, se evalúan los impactos de las F&A en el producto interno bruto (PIB), la vinculación entre el valor de las operaciones de F&A y las ganancias y con ello se determinan los impactos en el consumo privado, la inversión privada y las exportaciones. Se concluye que la elasticidad del PIB respecto al valor de las F&A es negativa en los casos de Chile, Perú y México. Más fusiones reconcentran el ingreso a favor de las ganancias e impactan de forma negativa en el producto. En el resto de las economías de la región estos resultados son ligeramente positivos o inciertos. THE IMPACT OF MERGERS AND ACQUISITIONS ON INCOME DISTRIBUTION AND GROWTH IN LATIN AMERICA, 1990-2014ABSTRACTThe paper reviews the factors accounting for Mergers and Acquisitions (M&As). It then discusses the evolution of M&A trades, their relative importance and the economic sectors that have been influenced by M&As in sixteen Latin-American economies during 1990-2014. An integral model is developed with the aim of assessing both the impact of M&As on Gross Domestic Product (GDP) and the link between the value of M&A transactions and profits so as to determine the effects of profit share changes on private consumption, private investment and exports. The conclusion is reached that the elasticity of GDP with respect to the value of M&As is negative in countries such as Chile, Peru and Mexico. A larger value of M&As tend to redistribute income towards profits and impart a negative impact on output. The impact for other Latin American economies in our sample is either slightly positive or uncertain.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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; both teacher heads agree on what is shown here.
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".