High earner with lifestyle inflation, minimal savings despite good income, high discretionary spending, credit card debt.
B-03 captures the high-income, low-savings-rate household where every raise has been absorbed into spending. It is the testing surface for cash-flow categorisation, sinking-fund tooling, and credit-card balance-transfer eligibility against earners who look prosperous on the W-2 but thin on the balance sheet.
B-03 isolates a specific income-to-net-worth disconnect: median household income of $149k, the kind of earner most platforms treat as mass-affluent, paired with a savings rate low enough that every household carries a revolving credit-card balance and a meaningful share carry student loans into their late thirties. The diagnostic surface for budgeting and PFM tools is the difference between gross income and discretionary cash flow — earners at this band routinely exhaust take-home through housing-cost overshoot, recurring subscriptions, and FOMO-driven discretionary spend. The behavioral flag matters for tax-software too: high-bracket earners who never adjusted W-4 withholding after a raise, who under-contribute to 401(k) and HSA despite eligibility, and who routinely miss the dependent-care FSA election deadline are exactly this population.
The structural story is that this household's balance sheet looks healthier than the cash-flow behavior would predict — median net worth of $692k and a 53% home-ownership rate. The asset base exists because asset prices have risen, not because the household saved its way there. Liabilities are diversified across credit cards (all 17 households), student loans (11), mortgages (9), and auto loans (7). Retirement is a goal in every record but on-track flags lean false. Eleven of 17 carry an explicit debt-payoff goal. The household will appear to a credit underwriter as a strong applicant on DTI alone and as a marginal applicant on revolving-utilisation and savings-rate signals.
Compared to neighbouring behavioral archetypes the distinction is action versus inaction. Where B-01 defers decisions and B-02 makes too many active investment decisions, B-03 makes too many active spending decisions. The corpus is the right test population for cash-flow categorisation engines, sinking-fund and goal-bucketing UI, balance-transfer eligibility flows, and 401(k) auto-escalation campaigns that target high earners specifically rather than mass-market workers.
Aggregated across the 17 B-03 households in the shipped v3 corpus corpus. Numbers describe the corpus, not population claims.
Megan and Christopher hit the corpus income median exactly but sit below it on net worth — the household that earns at the top of the band and has not yet converted that into balance-sheet depth. Liquid net worth of $64k is thin for a $149k income; about five months of household spend if utilisation has to be paid down. Both retirement and emergency-fund goals are off-track despite ample take-home capacity, which is the canonical B-03 pattern.
Every B-03 household ships with — at minimum — these JSON fields populated. The full schema is documented in the data set you purchase.
Three buyer profiles draw on B-03 most heavily. PFM and cash-flow categorisation vendors use it to validate discretionary-spend detection, recurring-subscription identification, and sinking-fund recommendations against high-earner expense distributions rather than mass-market ones. Card issuers and consumer lenders use it for balance-transfer eligibility scoring and APR-reduction workflow testing — the underwriting question is whether income offsets utilisation behavior. Recordkeeping platforms use it for high-earner auto-escalation campaigns and HSA family-limit nudge testing; the population has the W-2 capacity to maximise contributions but does not without prompting. Tax-software teams use it for W-4 adjustment prompts and dependent-care FSA election reminders against earners who routinely under-elect.
B-03 is not a hardship archetype — median net worth of $692k means the household has accumulated assets even while spending heavily. Households where the spending behavior has tipped into structural delinquency belong in S-02 (bankruptcy recovery) or S-03 (medical-debt crisis). Households where high income is also being saved aggressively are A-03 (dual-income professional couple) or P-03 (dual high-income professionals) — reach for those when the savings rate is normal-to-high for the income band. Younger pre-family households at lower income with similar discretionary-heavy spend patterns are closer to F-03 (DINK) territory; B-03 specifically requires the income to be high enough that the savings shortfall is behavioral rather than structural. Households where the spending is on lifestyle creep tied to creator-economy income volatility belong in X-02.
Income and net-worth bands during v3 synthesis were anchored to the upper mass-affluent segments of the Survey of Consumer Finances. Expense-side shape (housing as share of take-home, discretionary share, subscription-line item density) was informed by BLS Consumer Expenditure Survey upper-quintile patterns and Federal Reserve revolving-credit utilisation distributions. Behavioral flags (FOMO-spend indicator, savings-rate flag) were synthesised as overlay attributes rather than estimated. The corpus deliberately calibrates revolving utilisation high enough to trigger most platforms' utilisation-alert thresholds. Per CLAUDE.md §9 the v3 corpus is frozen and not regenerable from current code, so calibration claims are descriptive rather than reproducible.
Avoider rather than spender. B-01 households save modestly but fail to engage; B-03 households earn more and choose to spend the difference. Different feature surfaces — engagement nudges versus cash-flow guardrails.
Overconfident DIY investor at similar mass-affluent income. B-02's failure mode is allocation and trading; B-03's failure mode is saving rate. A household can have both — overlay if you need it.
Dual-income professional couple at similar income but normal-to-high savings rate. Use A-03 when the cash-flow story is healthy and the test surface is normal accumulation rather than behavioral risk.
Creator-economy household with similar lifestyle-inflation patterns but irregular self-employment income, 1099-NEC tax surface, and platform-revenue dependencies. Reach for X-02 when the income side is the differentiator.
B-03 — Spender / Lifestyle Inflation represents a mass-affluent household (median income $148,798) whose savings rate is low enough that all 17 corpus households carry revolving credit-card balances. The diagnostic gap is between gross income and discretionary cash flow — earnings exist, the saving behavior does not.
A-03 has a similar income profile but normal-to-high savings rate and healthy emergency-fund coverage. B-03 is the behavioral counterfactual — same earning power, savings rate near zero, revolving balances on every household. Use A-03 for the well-functioning version of the same demographic.
Cash-flow categorisation engines, sinking-fund and goal-bucketing UI, balance-transfer eligibility flows, recurring-subscription detection, 401(k) auto-escalation campaigns targeted at high earners, HSA contribution-cap nudges, and W-4 withholding adjustment prompts.
Intentional. The pattern flags delayed debt paydown despite capacity — 11 of 17 households at $149k median income still carry student loans, which is materially higher than the comparable income-band base rate. Useful for testing refinance, IDR-exit, and accelerated-payment workflows against borrowers who could pay down faster.
Deterministically from a seeded sampler (Mulberry32 PRNG) in src/lib/generation/, with behavioral flags (savings-rate, FOMO-spend, revolving-utilisation) applied as overlay attributes. Per-domain version constants are surfaced in each household's _meta block.
No. The shipped 1,451-household v3 corpus is frozen and not regenerable from current code (drift confirmed 2026-05-09). Sampler improvements land in a future v4 release with per-archetype golden fixtures in CI to prevent silent drift.
Download households matching this archetype as part of a Wealth Data Set.
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