This is an update to our original GiveCalc announcement from last year.
TL;DR: GiveCalc is a free, open-source calculator that computes how charitable donations affect your taxes. It runs full tax simulations to account for interactions between donations, deductions, and credits. Supports US federal/state taxes and UK Gift Aid.
What is GiveCalc?
Multiplying donations by your marginal tax rate misses interactions that affect actual tax savings:
🇺🇸 US considerations:
- Whether itemizing reduces your total tax liability
- How large donations shift your tax bracket
- State income tax interactions (all 50 states + DC + NYC)
- AGI-based deduction limits, itemized deduction phase-outs, AMT, and more
🇬🇧 UK considerations:
- Gift Aid mechanics (charity reclaims 25p per £1)
- Higher/additional rate relief (20%-25% back for 40%/45% taxpayers)
- Scottish income tax rates (19%-48%)
- Personal Allowance and Child Benefit tapering
GiveCalc uses PolicyEngine's microsimulation models to estimate these interactions. The same engine powers policy research used by policymakers, academics, think tanks, and benefit screening tools.
The key output is your marginal giving discount: how much tax you save on your next dollar/pound of giving at any donation level. This varies non-linearly, which matters for decisions about timing and amount.
Features
Multi-country support: Calculate for US (federal + all states) or UK (including Scotland).
Multiple income sources: Beyond wages, include self-employment, capital gains, dividends, and interest—each taxed differently.
Multi-year planning (US): Model 2024, 2025, or 2026. This matters given 2026 changes from HR1: a 0.5% AGI floor on charitable deductions, restored non-itemizer deduction ($1k/$2k), and 80% itemized deduction limitation for high earners.
Validated accuracy: Calculations validated against NBER's TAXSIM and the Atlanta Fed's Policy Rules Database.
Fully open source: The complete source code is public. Inspect the calculations, verify against your own analysis, or contribute improvements.
Bunching example
In some cases, bunching multiple years of donations can reduce total tax liability:
Consider the example from the above screenshot: a single person earning $200k income in California, with $20k of mortgage interest, giving $20k/year.
If they give $20k in 2025, the above section shows they will save $5,857 in taxes. If they give $20k in 2026, they will save $5,379—inflation-indexed tax policy parameters cut the savings by 8%. Between the two years, they'll give $40k and save $11,236.
If instead they give $40k in 2025, they will save $12,517, as more of their giving is in higher marginal savings rate territory due to exceeding the standard deduction. Bunching saves them $1,281 in taxes, increasing the tax savings of giving by 11% (or reducing the net cost of giving by 4.5%).
GiveCalc lets you run these dual simulations, and its marginal rate charts show where your savings rate changes, helping identify optimal bunching thresholds.
About PolicyEngine
GiveCalc is built by PolicyEngine, a 501(c)(3) nonprofit (US) and registered charity (UK, no. 1210532). We build open-source tax-benefit simulation models used by congressional offices, think tanks, and researchers for evidence-based policy analysis.
We're also investigating how public policy mediates the relationship between AI-driven economic transformation and distributional outcomes. Our research framework examines how policy interventions shape who benefits and who loses from AI-driven economic shifts.
Donations are tax-deductible (US) and Gift Aid eligible (UK).
Try it
Privacy note: GiveCalc doesn't store any of your inputs—all calculations run in your browser and our API without logging.
Need more complexity? If your situation requires inputs we don't yet support (appreciated assets, QCDs, carryforwards, etc.), comment below or email max@policyengine.org. We can add features to GiveCalc or point you to the policyengine-us Python package to run custom calculations locally.
Disclaimer: GiveCalc provides estimates for informational purposes only. Results depend on the accuracy of inputs and model assumptions. This is not tax, legal, or financial advice—consult a qualified professional for your specific situation.