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Record W3143427066

Securing Lifelong Retirement Income: Global Annuity Markets and Policy

2011· article· en· W3143427066 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOUP Catalogue · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLongevity riskLongevityPublic policyLife insuranceGovernment (linguistics)AnnuityLife annuityPopulationEconomicsBusinessPensionEconomic growthFinanceActuarial scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

Interest in longevity and longevity risk management is burgeoning, as government and regulatory agencies are increasingly conscious of the potential risks and benefits of longer lifespans. Commercial and industrial organizations, especially within the financial sector, are awakening to the opportunities presented by population aging, along with the new array of financial insurance instruments to manage longevity risk, which more sophisticated markets are making possible. This volume explores three main themes: the need for products to manage longevity risk; the structure and safety of financial products on the market that help manage longevity risk; and the role of policy in stimulating and strengthening longevity insurance products. This edited volume brings together leading international experts to evaluate the challenge posed by trends in longevity risk and draws out the implications and constraints of this new reality for insurance companies and annuity providers. It discusses both emerging economies (India, Chile) and many of the older nations (Sweden, Canada, the US, Australia, Japan, the UK and Switzerland). It aims to instigate new thinking among retirement planners, plan sponsors, academics, and industry leaders seeking to manage retirement payouts and longevity risk. Contributors to this volume - Mukul G. Asher, Professor of Public Policy, the Lee Kuan Yew School of Public Policy, the National University of Singapore Hazel Bateman, Associate Professor of Economics and the Director of the Centre for Pensions and Superannuation, the University of New South Wales, Sydney, Australia Monika Butler, Professor of Economics and Public Policy, St. Gallen University, Switzerland and Managing Director, the Swiss Institute for Empirical Economic Research SEW-HSG Edmund Cannon, Professor of Economics, Finance, and Management. the University of Bristol Barbara Kaschutzke, Researcher, the Finance Department, the Goethe University Frankfurt, and Chair of Investment, Portfolio Management, and Pension Finance Bo Larsson, Analyst, the Swedish Pensions Agency and Assistant Professor, Dalarna University College Raimond Maurer, Endowed Chair of Investment, Portfolio Management, and Pension Finance, the Finance Department, the Goethe University of Frankfurt Moshe A. Milevsky, Professor of Finance, the Schulich School of Business, York University, Toronto, and Executive Director, The IFID Centre Olivia S. Mitchell, Executive Director of the Pension Research Council, Wharton School, University of Pennsylvania Edward Palmer, Professor of Social Insurance Economics, Uppsala University, Sweden, and Senior Advisor to the Swedish Social Insurance Agency John Piggott, Professor of Economics in the Australian School of Business, University of New South Wales Jose Ruiz, Professor of Finance, the University of Chile Business School Junichi Sakamoto, Chief Adviser to the Pension Management Research Group of the Nomura Research Institute and Lecturer at the University of Tokyo, Nihon University, and Sophia University Stefan Staubli, Research Associate, the Swiss Institute for Empirical Economic Research, the University of St. Gallen Noriyuki Takayama, Professor at the Institute of Economic Research, the Hitotsubashi University, Tokyo Ian Tonks, Professor of Finance, the School of Management, the University of Bath Deepa Vasudevan, Researcher Anthony Webb, Associate Director of Research, the Center for Retirement Research, Boston College Ling-wu Shao, doctoral student in Finance, the Schulich School of Business, York University

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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