Defined benefit pension decline: the consequences for organizations and employees
Bibliographic record
Abstract
Purpose – The purpose of this paper is to use the theoretical and empirical pension literatures to question whether employers are likely to gain any competitive advantage from degrading or eliminating their employees’ defined benefit (DB) pensions. Design/methodology/approach – Critical literature review, bringing together and synthesizing the industrial relations, economics, social policy, and applied pensions literature. Findings – DB pension plans do deliver a number of potential performance benefits, most notably a decrease in turnover and establishment of longer-term employment relationships. However, benefits are more pronounced in some conditions than others, which are identified. Research limitations/implications – Most of the analysis of pension effects to date focuses primarily on DB plans. Yet, these are declining in significance. In the years ahead, more attention needs to be paid to the potential consequences of defined contribution plans and other types of pension. Practical implications – In re-evaluating DB pensions, firms have tended to focus on savings made through cost cutting. Yet, this approach tends to view a firm's people as an expense rather a potential asset. Attempts to abandon, modify, or otherwise reduce such schemes has the potential to save money in the short term, but the negative long-term consequences may be considerable, even if they are not yet obvious. Originality/value – This paper is topical in that it consolidates existing research evidence from a number of different bodies of literature to make a case for the retention of DB pension plans, when, in many contexts, they are being scaled back or discarded. It raises a number of important issues for reflection by practitioners, and highlights key agendas for future scholarly research.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".