Reorienting retirement risk management
Bibliographic record
Abstract
Retirement risk management must be dramatically overhauled if workers and retirees are to better prepare themselves to meet future retirement challenges. Recent economic events including the global financial crisis have upended expectations about what pension and endowment fund managers can do. Employers and employees have found it difficult to make pension contributions, despite drops in retirement plan funding. In many countries, government social security systems are also facing insolvency. These factors, coupled with an aging population and rising longevity, are giving rise to serious questions about the future of retirement in America and around the world. This volume explores how workers and firms can reassess the risks associated with retirement saving and dissaving, to identify creative adjustments to adapt to these new risks and realities. One area explored is the key role for financial literacy and education programs. In addition, those acting as plan sponsors and fiduciaries must reconsider pension design to help them better address the new realities. Also novel financial products are described that can help with the design of retirement plans. Experts provide new research and offer policy recommendations, illustrating how retirement plans can be amended to better meet the retirement needs of workers and firms. This volume is an important addition to the Pensions Research Council / Oxford Univeristy Press series and to the current debate on retirement security. Contributors to this volume - Steven G. Allen, Associate Dean for Graduate Programs and Research, the College of Management at NC State University John Ameriks, Vanguard Principal and Head of Vanguard's Investment Counseling and Research Group Igor Balevich, Director in the Pension Solutions Group, Barclays Capital Aaron Bernstein, Senior Research Fellow at the Labor and Worklife Program, Harvard Law School Robert L. Clark, Professor of Management, Innovation, and Entrepreneurship and Professor of Economics, North Carolina State University Craig Copeland, Senior Research Associate, the Employee Benefit Research Institute Joelle H.Y. Fong, doctoral candidate in the Department of Insurance and Risk Management, The Wharton School, University of Pennsylvania Sterling Gunn, CPP Investment Board P. Brett Hammond, Managing Director and Chief Investment Strategist, TIAA-CREF Asset Management Michael Hess, Project Manager in the Investment Counseling and Research department , Vanguard Group R. Hall Kesmodel, Jr., Senior Manager at Ernst and Young, LLP Benedict S. K. Koh, Associate Professor of Finance and Director of the MSc in Applied Finance, the Singapore Management University Tracy Livingstone, Senior Advisor at the Investment Risk Management Group, the Canada Pension Plan Raimond Maurer, Chair of Investment, Portfolio Management, and Pension Finance, Finance Department, Goethe University of Frankfurt Olivia S. Mitchell, Executive Director of the Pension Research Council, Wharton School, University of Pennsylvania Melinda S. Morrill, Research Assistant Professor, the Department of Economics, North Carolina State University Lynn Pettus, Director for the Employee Financial Services practice, Ernst and Young, LLP Anna M. Rappaport, actuary, consultant, author, and speaker, Anna Rappaport Consulting Liqian Ren, Investment Analyst in the Investment Counseling and Research department, Vanguard Group David P. Richardson, Principal Research Fellow, the TIAA-CREF Institute Ralph Rogalla, Assistant Professor for Investments, Portfolio Management, and Pension Finance, the Goethe University of Frankfurt Damon Silvers, Associate General Counsel, the AFL-CIO John A. Turner, social security and pension policy consultant Jack VanDerhei, Research Director, the Employee Benefit Research Institute
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 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".