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Record W284941610

Oxford Handbook of Pensions and Retirement Income

2008· article· en· W284941610 on OpenAlexaboutno aff
Roland Eisen

Bibliographic record

VenueJournal of Risk & Insurance · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPensionContext (archaeology)EconomicsSociologyPolitical scienceLawHistory
DOInot available

Abstract

fetched live from OpenAlex

Oxford Handbook of Pensions Income, edited by Gordon L. Clark, Alicia H. Munnell, J. Michael Orszag with the assistance of Kate Williams, 2006, Oxford, UK: Oxford University Press, 893 pages Over the past several years the study of the economics of pensions retirement income has grown in importance, particularly outside the disciplines of insurance economics, law, mathematics. The interest is increasing mainly because of the discussion about the bomb, which is now for most of the industrialized countries a reversed one, an implosion! As international attention on retirement financing increases, it is appropriate that Clark, Munnell, Orszag have edited this Handbook. The Handbook is divided into five major parts, with each part being organized around a series of sections themes. All in all, it consists of 42 chapters with an impressive list of contributors. Besides the Introduction, a first part (entitled Retirement in Context) a final part (entitled Looking Ahead), the book is divided into sections in a manner consistent with the well-known three pillars (or layers) of the pension literature: Public Retirement, Employer-Sponsored Retirement, Individual Household Plans. In its total, this overpowering volume is impossible to review to do justice to all the contributors. Therefore, let me mention only some chapters I am especially interested in. Chapter 11, written by E. Philips Davis Yu-Wei Hu, is about Funding, Saving, Economic Growth. This is a very important question because with enough growth the demographic changes are almost negligible. The authors focus on such a shift from PAYG (i.e., unfunded pay-as-you-go) to funding is largely a matter of reallocation of the financial burden of ageing (with the risk of a generation paying twice), or whether funding improves economic performance sufficiently to generate some or all of the resources required to meet the need of an ageing population (p. 201, my emphasis). All in all, the authors weigh their arguments very carefully. David A. Wise, one of the leading specialists in this field, has written chapter 16 about Early Retirement. The public private pension plans allow workers to retire earlier; in addition, they often impose a very substantial 'implicit tax' on work (p. 310). Combining the trend of early retirement with the forthcoming demographic trends serves to magnify the increasing financial burden for the social security systems and thus contribute to their own insolvency (p. 332). As a result, changes in program provision that focus on removing the incentives to retire early can have very important implications for the labor force participation of older workers thus for the financial position of social security programs. The third part starts with chapter 18, written by Alicia H. Munnell, entitled EmployerSponsored Plans: The Shift From Defined Benefit to Defined Contribution. The chapter focuses on the United States' the United Kingdom's second tier (or layer) because supplementary employer pension plans play a major role in the retirement income system, the nature of those pensions has changed dramatically (p. 361) from defined benefit (DB) to defined contribution (DC) plans. Munnell is selective in her analysis, as she does not examine Canada or the Netherlands. The focus is also not Australia (or Switzerland) because these systems are mandatory. Munnell critically evaluates the implications of shifting from DB to 401 (k) plans, as well the shift to money purchase plans in the United Kingdom. Both place virtually all risk responsibility for retirement income on the worker in choosing [h]ow much to contribute, how to allocate contributions among stocks, bonds, fixed income securities, how to change those allocations over time (p. 377). Furthermore, at retirement, individual accounts also face further problems: how much to annuitize, how to cope with longevity inflation. …

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.143
GPT teacher head0.385
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2008
Admission routes1
Has abstractyes

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Same venueJournal of Risk & InsuranceSame topicRetirement, Disability, and EmploymentFrench-language works237,207