Gig & Variable Income Pack

Why this Data Set exists

Variable-income clients break the budgeting and savings logic in most wealth platforms. The 'monthly savings' field assumes a stable monthly base. The 'tax-withholding' calculation assumes W-2 withholding rather than quarterly estimated payments. The 'health insurance' field assumes employer coverage rather than ACA marketplace or COBRA. The 'retirement contribution' field assumes 401k matching rather than Solo 401k or SEP-IRA self-funding. None of these defaults fit the gig / freelancer / creator client.

The production-data problem reinforces the platform-design problem. Production data from any individual platform reflects the platform's existing client base — which is biased toward W-2 clients because that's who the platform was designed for. A platform trying to expand into the variable-income segment can't validate the design choices against production data because the segment isn't in production data.

This Data Set provides 100 households with non-W-2 income patterns: gig workers (rideshare, delivery, task platforms), creators (YouTube, Substack, OnlyFans, Twitch), freelancers (graphic design, writing, consulting), royalty-income artists (musicians, authors, photographers), military with variable allowances (BAH-driven income variation), and remote-worker digital nomads. Each carries the structural data variable-income planning requires.

Use Cases

Who uses this Data Set

What's inside

The 100 households span eight archetypes covering the variable-income spectrum: F-01 new-graduate tech workers (where some are 1099 contractors); F-02 gig economy starters (rideshare, delivery); F-04 first-generation wealth builders (often gig-economy participants); F-05 active-duty enlisted (with BAH-driven variable income); F-06 international workers on visa; X-01 remote workers / digital nomads; X-02 creator economy / influencers; AR-01 artist / creative with royalty and irregular income.

Every household has structured income-stream data: each income stream identified by source, type (W-2, 1099-MISC, 1099-NEC, 1099-K, royalty, business-distribution, platform-revenue), platform identifier where applicable (Uber, DoorDash, YouTube, Patreon, Etsy, Upwork, Substack, etc.), and the realistic monthly-volatility distribution. Multi-platform income aggregation is structured for creators with 3+ revenue sources. Quarterly estimated tax payment history is documented (the dates and amounts of the prior 8 quarters of estimated payments). Self-employed retirement-plan contributions (Solo 401k, SEP-IRA, or SIMPLE-IRA) are structured with the contribution-limit calculation given the household's entity type. Self-employed health insurance is structured with the deduction calculation.

The Data Set ships as JSON and CSV. The WealthSynth Methodology PDF documents the income-stream taxonomy, the multi-platform aggregation methodology, the quarterly-estimated-tax-payment framework, the Solo 401k/SEP-IRA contribution-limit math, and the calibration source for typical variable-income profiles.

Preview a sample household

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 100 like it) ships in the ZIP.

Technical Highlights

Sample Schema Fields

{
  "income.income_streams[]": <value>,
  "income.volatility_pct": <value>,
  "income.platform_revenue": <value>,
  "taxes.quarterly_estimated_payments": <value>,
  "accounts.solo_401k_balance": <value>
}

Sample queries

households.filter(h => {
  const safeHarbor = Math.min(
    h.taxes.prior_year_tax * 1.10,
    h.taxes.estimated_current_year_tax
  );
  return h.taxes.quarterly_estimated_payments_ytd < safeHarbor;
})

households.filter(h => {
  if (!h.business.entity_type) return false;
  const maxContribution = h.business.entity_type === 'S-Corp'
    ? Math.min(23000, h.business.reasonable_salary) +
      h.business.reasonable_salary * 0.25
    : 23000 + h.business.k1_distributions * 0.20;
  return h.accounts.solo_401k_balance.ytd_contribution <
    Math.min(maxContribution, 69000);
})

households.filter(h =>
  h.income.income_streams.filter(s =>
    s.platform_identifier).length >= 3
).map(h => ({
  id: h.id,
  platforms: h.income.income_streams
    .filter(s => s.platform_identifier)
    .map(s => ({
      platform: s.platform_identifier,
      ytd_revenue: s.ytd_revenue
    }))
}))

households.filter(h => {
  const monthly = h.longitudinal.monthly.map(m => m.income);
  const cv = stddev(monthly) / mean(monthly);
  return cv > 0.30;
})

Methodology

Each household's income-stream profile is generated against archetype-specific patterns. Gig economy starters concentrate in 1-2 platforms with realistic revenue distributions per platform (Uber/Lyft drivers tend to have hourly-rate-and-hours-driven distributions; DoorDash drivers slightly different). Creators typically have 3-7 platforms with skewed distributions (one or two revenue-dominant platforms plus several small contributors). Royalty-income artists have realistic seasonal distributions reflecting the actual royalty-payment cycles of music, book, and licensing income. Quarterly estimated tax payment behaviour is calibrated against IRS data on self-employed compliance (about 30% of self-employed taxpayers under-pay quarterly estimates). Solo 401k and SEP-IRA contribution decisions are calibrated against industry data showing about 35% of eligible self-employed contribute to retirement accounts. The corpus passes the WealthSynth consistency validator (income-stream aggregation reconciles with reported income; quarterly payment math is correct; retirement contribution limits respect IRS thresholds) and the LLM-as-judge gate. Annual refresh tracks IRS rule changes and gig-platform policy changes.

Included Archetypes (8)

Frequently asked questions