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Record W2163007319

Defined Benefi tt o Defined Contribution and Back: Valuation of the Florida Pension Election

2003· article· en· W2163007319 on OpenAlexaff
Moshe A. Milevsky, S. David Promislow

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsEconomicsActuarial sciencePensionValuation (finance)Entitlement (fair division)Pension planFinanceBusinessMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

During the year 2002, The State of Florida’s 600,000 public employees were given the choice of converting their traditional Defined Benefit (DB) pension plan into an individual-account Defined Contribution (DC) plan with full control over asset allocation and investment decisions. To mitigate some of the risk and uncertainty in the decision, the State granted each employee electing the DC plan an additional option to switch back into the DB plan at any point prior to retirement. This option has been labeled the 2nd election by the State and the cost of re-entry is fixed at the accumulated benefit obligation (ABO) of their pension entitlement. Our paper presents some original analytic insights relating to the optimal time and financial value of this unique 2nd election. We start with a simple deterministic model to provide intuition and conclude with a stochastic model that derives a formal upper bound for the economic value. The conclusions from our analysis differ from the results of Lachance, Mitchell and Smetters (JRI, 2003). We argue that the 2nd election behaves less like a traditional downside-protected put option and more like a linear forward contract. We estimate that the value of this 2nd election is at most 30% of the DC contribution rate and only when exercised at the optimal time. Furthermore, for most State employees above the age of 45, the 2nd election has little economic value since the DB plan dominates the DC plan from day one. Of course, it remains to be seen what percent of Florida’s 600,000 employees will elect to behave rationally with their newfound pension autonomy. 1 The Florida Pension Election: During the year 2002, The State of Florida’s 600,000 public employees were given the choice of converting their traditional Defined Benefit (DB) pension plan into an individual-account Defined Contribution (DC) plan with full control over asset allocation and investment decisions. This new Public Employee Optional Retirement Program (PEORP) has been the focus of intense scrutiny by local and national media because it is the largest such pension conversion in the history of the U.S. and is being viewed by some observers as a potential laboratory for Social Security reform. Interestingly, to mitigate some of the risk associated with this decision, the State granted each employee electing the DC plan an option to switch back into the DB plan at any point prior to retirement. This option has been called the 2nd election by the State authorities and we will adopt this name. The cost of getting back into the DB plan is the accumulated benefit obligation (ABO) of their pension entitlement. The ABO is effectively the present value of that portion of the life annuity (pension) to be received at retirement, based on the number of years of service and salary at the time of computation. For future employees — i.e. those not in the plan at the time the PEORP was initiated — the buy back price will be the accumulated actuarial liability (AAL). Our paper presents some original analytic insights relating to the optimal time and financial value of this unique 2nd election. We start with a simple deterministic model to provide intuition and conclude with a stochastic model that derives a formal upper bound for the economic value. We are careful to distinguish between the financial economic value of the 2nd election — which is the focus of this paper — versus the more vague and controversial pension ‘funding cost’ of providing the 2nd election to the employee. While the former is related to portfolio replication and dynamic hedging of guarantees, the latter depends on various actuarial standards of practice, assumptions and cost methods that are beyond the scope (and interest) of this analysis. We refer the interested reader to the work by Haberman and Sung (1994) as well as O’Brien (1986) for stochastic models of pension plans that are focused on actuarial funding methods. Most importantly, the conclusions from our analysis differ from the results of Lachance,

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.016
GPT teacher head0.213
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2003
Admission routes1
Has abstractyes

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