A Little Help: The Impact of On-Line Calculators and Financial Advisors on Setting Adequate Retirement-Savings Targets: Evidence from the 2013 Retirement Confidence Survey
Bibliographic record
Abstract
For nearly a quarter century, the Retirement Confidence Survey (RCS) has garnered a sense of American worker and retiree confidence about their financial prospects in retirement. This paper presents an analysis of the retirement savings targets set by individual respondents to the 2013 RCS, coupled with the EBRI Retirement Security Projection Model® (RSPM), and provides an assessment of how various household behaviors/beliefs are associated with the adequacy of retirement savings targets, as indicated by the modified Retirement Readiness Rating® (RRR) values. It finds that both the use of on-line calculators and seeking the advice of financial advisors result in estimated savings targets that not only increase the estimated probability of retirement income adequacy, but also result in double-digit percentage-point increases for many of the groups when analyzed by relative income quartiles and family/gender combinations. Those using an on-line calculator or asking a financial advisor appear to set more adequate savings targets, as measured by the probability of not running short of money in retirement. Those in the lowest-income quartile show a 9.1-12.6 percentage point improvement (depending on family/gender) in the probability of not running short of money in retirement if a financial advisor has been asked, and a 14.6-18.2 percentage point increase if an on-line calculator is used. On the other hand, those who “guessed” at those targets tended to underestimate their savings needs, as did the subset in this sampling that were somewhat or very confident in their prospects. The PDF for the above title, published in the March 2013 issue of EBRI Notes, also contains the fulltext of another March 2013 EBRI Notes article abstracted on SSRN: “’Post’ Script: What’s Next for Employment-Based Health Benefits?”
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".