Insurance Company Performance within the Framework of Trade Agreements
Bibliographic record
Abstract
In this paper, we evaluate the impact of regional integration on insurance companies' performance to assess whether integration is always favorable for the insurance market. In existing literature, the most common method of evaluating the impact of integration on insurance companies has been observing how certain indicators change over time and attributing these changes to integration, with another approach consisting of using the share of the insurance lines mostly subject to foreign competition as an explanatory variable. The results, however, are mixed. As a measure of the degree of integration, we use the share of imports from other countries that are members of the trade agreement in the country's imports of direct insurance services. The evaluation is carried out using data on 64 companies from Canada, Mexico and the United States from 2005 to 2016, then verified using data on 145 companies spanning 2005- 2018. The production function is assumed to be translog. It is shown that a higher share of other member countries in the imports of direct insurance services leads to an increase in the operating expenses incurred by life insurance companies and a decrease in the operating expenses incurred by international companies, while there is no statistically significant impact on the profits of most types of companies.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".