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Insurance Company Performance within the Framework of Trade Agreements

2021· article· en· W4240703481 on OpenAlexaboutno aff
Валентина Демчук

Bibliographic record

VenueHigher School of Economics Economic Journal · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCompetition (biology)Market shareProduction (economics)Variable (mathematics)FinanceEconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.216
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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