Decumulation is harder than accumulation. The accumulation problem has one number — savings rate — and one mostly-monotonic relationship between it and outcomes. Decumulation has six interacting decisions: when to claim Social Security, how to elect a pension (lump sum or annuity), what order to draw down accounts, when to do Roth conversions, how to manage the IRMAA tier cliff, and how to cope with the RMD schedule's mechanical income spike. Each decision affects the others, and the optimal answer depends on a household's specific income, asset, and longevity profile. The Retirement Income Sequencing Pack is 210 near-retiree and retiree households built for the planning engines that try to handle this complexity correctly.
Retirement income tools used to focus on a single decision — typically the Social Security claiming age. Today's retiree client expects a tool that integrates the full decision set. The math compounds: a Roth conversion in year 65 affects the IRMAA tier in year 67 affects the RMD trajectory in year 73 affects the surviving-spouse income in year 85. A planning engine that treats these as independent gets the wrong answer.
For builders, the data problem is structural. Most retirement-data fixtures are point-in-time balance-sheet snapshots — useful for accumulation modeling, useless for decumulation modeling, which requires multi-decade trajectories. The handful of retirement-specific datasets typically focus on Social Security alone or RMD alone, not on the integrated picture. Validating a holistic income-sequencing engine against these fixtures means stitching together multiple incomplete data sources and hoping the results are calibrated.
This Data Set provides 210 households where the full decumulation picture is structurally complete: multi-source income modeling, pension election history, RMD schedules through life expectancy, annuity payout structures, IRMAA tier projections, and the planning fields that drive Roth conversion and withdrawal-sequence decisions.
Validates the engine's multi-source income coordination, withdrawal sequencing logic, and Roth conversion optimization against 210 households spanning the full pre-retirement-through-late-retirement range.
Demos the platform's retirement planning capability to advisor and prospect audiences using realistic households whose income sources and decision structures match the modal advisor client — without needing to use real client data.
Tests the firm's annuity-illustration engine against households where the annuity election interacts with Social Security, pension, and RMD income — surfacing the integrated-income scenarios where annuity choice meaningfully affects after-tax outcomes.
Validates the Roth conversion ladder logic against households where conversions happen in the 65-72 'gap years' between retirement and RMD start, ensuring the engine correctly models bracket fill, IRMAA threshold crossing, and provisional-income effects on Social Security taxation.
Tests IRMAA tier projection logic against retirees whose income trajectory crosses tier thresholds — catching the cases where a single Roth conversion creates a two-year-delayed Medicare premium spike that the planning engine should have anticipated.
The 210 households cluster around ten archetypes spanning pre-retirement-catch-up through retirement-late and the retirement-income-specific cohorts (annuity-dependent retirees, dividend-income retirees). About 25% are pre-retirees within 5 years of retirement (the planning population); 50% are retired in the 65–80 range (where most decumulation decisions are active); 25% are 80+ (where simplification, RMD compliance, and surviving-spouse planning dominate).
Every household has a complete multi-decade income picture: Social Security benefit estimates with claiming-age sensitivity, pension elections (lump sum vs. annuity, single-life vs. joint-and-survivor), RMD schedules computed from current account balances and IRS Uniform Lifetime Table, annuity payouts where present, and the structured withdrawal-sequence recommendation. IRMAA tier flags pre-populate for each year through life expectancy, showing which conversion or withdrawal decisions would push the household across a tier threshold. Roth conversion windows (typically the 65-72 gap years) are flagged with the bracket-fill capacity available without crossing IRMAA tiers.
The Data Set ships as JSON, CSV, and Parquet. The WealthSynth Methodology PDF documents the multi-source income coordination methodology, the IRMAA tier calculation (including the 2-year lookback used for Medicare premium determination), the RMD calculation methodology, and the Roth conversion bracket-fill logic. Calibration sources include Social Security Administration benefit data, IRS RMD table publications, and CMS IRMAA tier publications.
A redacted summary of one household from this Data Set — names, employers, exact balances, and metro area are stripped. Ages are bucketed, income and net worth are reported as bands. The full record (and all 210 like it) ships in the ZIP.
{
"income.social_security_benefit": <value>,
"income.pension_annual": <value>,
"income.rmd_schedule": <value>,
"planning.withdrawal_sequence": <value>,
"taxes.irmaa_tier": <value>
}Returns retiree households age 65-72 (pre-RMD) whose taxable income leaves bracket-fill capacity below the next IRMAA threshold — the planning queue for tax-efficient Roth conversions.
households.filter(h => {
const age = h.members.find(m => m.role === 'primary').age;
if (age < 65 || age >= 73) return false;
const irmaaLimit = h.taxes.next_irmaa_threshold;
const fillCapacity = irmaaLimit - h.taxes.taxable_income;
return fillCapacity > 5000;
})Returns households whose projected income next year crosses an IRMAA tier — the alert queue for advisors to plan around the 2-year-delayed Medicare premium impact.
households.filter(h =>
h.taxes.irmaa_tier_history.some((tier, i, arr) =>
i > 0 && tier > arr[i-1]
)
)Returns near-retiree households with a pension election decision pending — the structured pension data lets you compute the present-value comparison and the longevity-risk-adjusted recommendation.
households.filter(h =>
h.income.pension_decision_pending &&
h.income.pension_lump_sum_offer > 0
).map(h => ({
id: h.id,
lump_sum: h.income.pension_lump_sum_offer,
annuity_annual: h.income.pension_annuity_annual,
break_even_age: h.income.pension_lump_sum_offer /
h.income.pension_annuity_annual + 65
}))For each household, returns the breakeven age between claiming at FRA vs. delaying to 70, accounting for the spousal benefit interaction. Useful for advisor recommendations and platform-displayed comparisons.
households.map(h => {
const benefit_at_fra = h.income.social_security_benefit_at_fra;
const benefit_at_70 = benefit_at_fra * 1.32;
const benefit_diff = benefit_at_70 - benefit_at_fra;
const months_to_breakeven = benefit_at_fra / benefit_diff;
return {
id: h.id,
breakeven_age: 70 + months_to_breakeven / 12,
recommend_delay: h.members[0].life_expectancy >
70 + months_to_breakeven / 12
};
})Each household's retirement income picture is generated from a structurally complete multi-decade model. Social Security benefit estimates use the household's actual earnings history (synthesized from employment patterns) run through the SSA's PIA computation. Pension data is generated for households whose employment history includes a defined-benefit plan (about 35% of the corpus); the lump-sum-vs-annuity offer structure uses realistic actuarial assumptions. RMD schedules are computed from current tax-deferred-account balances using the IRS Uniform Lifetime Table. IRMAA tier projections compute the 2-year-look-back income and apply the current Medicare Part B and Part D surcharge brackets through each year of life expectancy. The corpus passes the WealthSynth consistency validator (income reconciles year-over-year, RMD math is correct, IRMAA tier transitions are mathematically consistent) and the LLM-as-judge gate. Annual refresh updates against current Social Security benefit formulas, RMD table changes (the SECURE Act 2.0 RMD age increase is reflected), and Medicare IRMAA tier publications.
Yes. The RMD start age is 73 for households turning 72 in 2023 or later (rising to 75 in 2033). The §401(k) catch-up provisions for high earners (Roth-only catch-up for incomes above $145K) are reflected in the contribution-history fields. The 529-to-Roth rollover provisions are structured where applicable.
Pension households with a pending lump-sum decision (typically pre-retirees within 24 months of retirement) carry a structured offer with the lump-sum amount, the equivalent single-life annuity, and the implied discount rate. About 22% of the corpus's pension-eligible households have an active lump-sum decision; the Methodology PDF documents the offer-structure calibration against actual corporate pension de-risking practice.
Yes. Married households compute both spouses' benefits with the spousal-benefit floor (50% of the higher earner's PIA) applied appropriately. Survivor benefits are pre-computed for the planning scenarios where the surviving spouse's income drops to the higher of the two prior benefits. Restricted-application strategies (limited to those born before 1954) are flagged where applicable.
IRMAA tier for year N is computed from the household's MAGI in year N-2 (the 2-year Medicare lookback). The corpus structures the year-by-year MAGI projection through life expectancy, then applies the current Medicare Part B and Part D surcharge brackets. When tier-crossing decisions are pending (Roth conversion, RMD-driven income spike), the structured projection lets your tools surface the 2-year-delayed premium impact.
Yes. About 18% of the corpus has at least one annuity position — typically a single-life immediate annuity for retirees seeking longevity protection, or a deferred annuity for pre-retirees building income. The annuity payout structures use realistic insurance-industry pricing (calibrated against current product disclosures from major carriers).
Yes. Each married household's longitudinal picture includes the projected surviving-spouse income trajectory (typically a meaningful drop due to the loss of one Social Security benefit and the conversion to single-filer tax brackets). This is structured so your tools can model the survivor-protection planning case explicitly.
The corpus structures the current Medicare election (Original Medicare with Medigap, Medicare Advantage, or unenrolled) and the IRMAA-affected premium for each. About 45% of the corpus's age-65+ population is on Medicare Advantage, in line with current CMS enrollment data.
B03 focuses on retirement income sequencing — the integrated decumulation picture. B18 focuses on healthcare benefits across the lifecycle — HSA accumulation, Medicare bridge, COBRA gaps, ACA subsidies. There's overlap at the IRMAA / Medicare planning seam, but the use cases are distinct. Retirement-income-engine builders typically buy B03; broader healthcare-planning builders typically buy B18.
250 households across the healthcare-benefits lifecycle: HDHP-with-HSA accumulators, COBRA/ACA-marketplace gap-fillers, SSDI/LTD claimants, Medicare-bridge pre-retirees, and IRMAA-exposed retirees. HSA-as-retirement-account strategies fully modeled.
130 affluent and HNW households with detailed fee structures: AUM-based advisory fees, tiered breakpoints, fund expense ratios, transaction costs, and tax-drag estimates. Includes complex fee arrangements (multi-firm, family-office, performance-based).
110 HNW and UHNW households with estate planning readiness scores, trust structures, gifting histories, charitable giving data, and GST exemption tracking. Complements B09 (Next-Gen Attrition) and B12 (Estate & Trust Planning).
Purchases are for internal use only. Redistribution or resale of data is prohibited under the WealthSchema Data License.
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