PolicyEngine

Capital gains realizations elasticity (net-of-tax-rate convention)

Simulation-facing coefficients subpanel · pooled centers and 90 percent intervals from 15 independent runs per model.

tax.capital_gains_realizations.elasticity.net_of_tax_rate

What the models were asked

Elasticity of long-term capital gains realizations with respect to the net-of-tax rate (1 - tau) on capital gains. A 1 percent increase in the net-of-tax rate changes realizations by this elasticity. Sibling of tax.capital_gains_realizations.elasticity, which uses the opposite w.r.t.-tax-rate convention; the two are related by epsilon_taxrate = -(tau / (1 - tau)) * epsilon_netoftax.

In standard notation:
ε1−τ = ∂ ln R / ∂ ln(1−τ)
(shorthand for display; the models received only the prose definition above)
Population:
Individuals with long-term capital gains in the United States
Interpretation:
Medium-run elasticity of long-term capital gains realizations with respect to the net-of-tax rate (1 - tau)
The exact prompt, verbatim
Answer from your current memory and background knowledge only.
Do not use tools, files, the web, code, or external resources.
Do not try to reconstruct a literature review or search for a consensus estimate.
Report the belief you currently endorse.

Quantity of interest:
- Name: Capital gains realizations elasticity (net-of-tax-rate convention)
- Definition: Elasticity of long-term capital gains realizations with respect to the net-of-tax rate (1 - tau) on capital gains. A 1 percent increase in the net-of-tax rate changes realizations by this elasticity. Sibling of tax.capital_gains_realizations.elasticity, which uses the opposite w.r.t.-tax-rate convention; the two are related by epsilon_taxrate = -(tau / (1 - tau)) * epsilon_netoftax.
- Target interpretation: Medium-run elasticity of long-term capital gains realizations with respect to the net-of-tax rate (1 - tau)
- Population/context: Individuals with long-term capital gains in the United States
- Units: elasticity

Sign convention for this quantity:
- An elasticity of ε means that a 1 percent increase in the net-of-tax rate (1 - τ) changes long-term realizations by ε percent; for example, if ε = 0.5, a 1 percent increase in the net-of-tax rate changes long-term realizations by 0.5 percent (not 50 percent).
- ε > 0 if and only if a higher net-of-tax rate raises realizations.
- ε < 0 if and only if a higher net-of-tax rate reduces realizations.
- This is the elasticity with respect to the net-of-tax rate, not with respect to the tax rate τ itself.

Task:
1. Use exactly the target interpretation above. In `interpretation`, restate it briefly.
2. Give your subjective quantiles p05, p25, p50, p75, and p95 for this quantity.
3. Set `point_estimate` equal to `p50`.
4. Make the quantiles weakly increasing and numerically coherent.
5. In `citations`, list up to 3 source anchors from memory that influenced your belief. These are recall anchors only. If none come to mind confidently, return `[]`.
6. Keep `reasoning_summary` brief and substantive.

Return valid JSON only with exactly this shape:
{
  "interpretation": "...",
  "point_estimate": <number>,
  "quantiles": {
    "p05": <number>,
    "p25": <number>,
    "p50": <number>,
    "p75": <number>,
    "p95": <number>
  },
  "citations": ["..."],
  "reasoning_summary": "..."
}

Read from the archived request logs; 22 of 29 models received exactly this text, and the other 7 an earlier v4 wording — every model's prompt is archived verbatim, and the two-wording comparison below shows the four models elicited under both. How the JSON response is enforced varies by provider — see the Methods harness table and the Process page.

29 of 29 models
GPT-5.4 nano
Claude Sonnet 5
GPT-5.6 Terra
Grok 4.20
Claude Opus 4.8
GPT-5.6 Luna
Claude Opus 4.7
Qwen 3.7 Max
Grok 4.5
Gemini 3.6 Flash
Kimi K2.6
Grok 4.3
Gemini 3.5 Flash
Gemini 3 Flash
DeepSeek V4 Pro
Kimi K3
Gemini 3.1 Flash-Lite
Claude Haiku 4.5
GPT-5.4
GPT-5.6 Sol
GPT-5.4 mini
MiniMax M3
GPT-5.5
GLM-5.2
Claude Opus 5
Grok 4.1 Fast
Claude Fable 5
Gemini 3.1 Pro
Claude Sonnet 4.6

Dot: pooled center (mean of run point estimates). Bar: pooled 90 percent mixture interval. Faint underlay: each run's elicited p05–p95. Models sorted by center; color = provider family. Filters change which models render; the axis stays fixed to the full panel.

Same model, two clarifier wordings

The sign clarifier for this quantity was revised two days into the April 2026 wave: plain conditionals with the conventional direction first became symmetric if-and-only-if clauses, and the definition line's conversion identity — stated backwards in the original wording — was corrected. Seven April models keep the original wording (the split disclosed above), while the four April premium models were re-elicited in full under the revision — so those four answered this quantity under both wordings. Their superseded April 19 runs remain in git history and pool to:

ModelApril 19 center (original wording)April 21 center (revised wording)Change
Claude Opus 4.70.700.700.000
Claude Sonnet 4.64.603.37-1.233
Gemini 3.1 Pro0.372.08+1.703
Grok 4.200.670.65-0.020

Pooled centers under the paper's piecewise-uniform construction, 15 runs per cell on both sides. The comparison is not a pure wording experiment — the April 21 re-elicitation also moved to the per-quantity harness that added request logging, and two days elapsed — so wording is confounded with harness path and time (paper, Appendix Tables A18–A19). Rerunning the paper's implied-tax-rate convention audit per wording moves Claude Sonnet 4.6 from 0.123 (plausible sign, outside bands) to 0.167 (LTCG-rate consistent) and Gemini 3.1 Pro from 0.545 (ordinary-income-rate consistent) to 0.259 (LTCG-rate consistent), while Claude Opus 4.7 and Grok 4.20 stay in their bands.

Alternative estimators (REML and Bayesian hierarchical)
ModelPooled 90%REML predictive 90%Bayes predictive 90%
gpt-5.4-nano[-1.01, 0.92][-0.49, 0.39][-0.53, 0.45]
claude-sonnet-5[0.16, 1.38][0.11, 1.16][0.32, 0.91]
gpt-5.6-terra[0.07, 1.93][-0.03, 1.33][0.21, 1.00]
grok-4.20[0.05, 1.95][0.01, 1.39][0.27, 1.05]
claude-opus-4.8[0.20, 1.57][0.14, 1.23][0.35, 0.97]
gpt-5.6-luna[-0.19, 2.76][-0.34, 1.99][0.05, 1.36]
claude-opus-4.7[0.13, 1.78][0.10, 1.42][0.35, 1.09]
qwen-3.7-max[0.11, 2.23][0.06, 1.47][0.32, 1.12]
grok-4.5[0.15, 2.79][-0.01, 1.54][0.28, 1.15]
gemini-3.6-flash[0.11, 1.78][0.11, 1.48][0.37, 1.14]
kimi-k2.6[0.07, 3.29][-0.15, 1.65][0.14, 1.23]
grok-4.3[0.14, 2.49][0.02, 1.71][0.32, 1.28]
gemini-3.5-flash[0.11, 1.94][0.12, 1.60][0.39, 1.23]
gemini-3-flash-preview[0.30, 1.91][0.22, 1.50][0.46, 1.18]
deepseek-v4-pro[0.11, 3.27][-0.03, 1.69][0.26, 1.28]
kimi-k3[0.03, 3.23][-0.11, 1.55][-0.10, 1.53]
gemini-3.1-flash-lite-preview[0.13, 3.55][-0.05, 1.92][0.29, 1.43]
claude-haiku-4.5[0.22, 2.87][0.15, 1.65][0.41, 1.29]
gpt-5.4[0.20, 3.19][-0.13, 2.27][0.28, 1.64]
gpt-5.6-sol[0.15, 3.57][-0.15, 1.94][0.18, 1.44]
gpt-5.4-mini[0.13, 3.89][-0.07, 2.24][0.30, 1.69]
minimax-m3[0.06, 6.14][-0.52, 2.08][-0.37, 1.81]
gpt-5.5[0.15, 5.18][-0.32, 2.54][-0.01, 2.02]
glm-5.2[0.24, 5.82][-0.25, 2.67][0.08, 2.12]
claude-opus-5[0.31, 4.37][0.12, 3.23][0.64, 2.44]
grok-4.1-fast[0.29, 5.83][-1.77, 8.59][-1.17, 5.94]
claude-fable-5[0.42, 4.29][0.32, 3.52][0.87, 2.72]
gemini-3.1-pro-preview[0.37, 4.97][0.16, 3.86][0.41, 3.53]
claude-sonnet-4.6[1.01, 8.65][-0.20, 7.34][1.05, 5.74]

The paper's headline object is the pooled mixture interval; the alternatives are robustness estimators (paper, Appendix A2).

Run-level responses

Each model answered 15 independent times. Pick a model to read every run: its elicited 90 percent interval, point estimate, and stated reasoning.

435 successful runs · elicited April and July 2026 · v4 prompts · 15 runs per model-quantity cell. Code · Raw responses · Paper (PDF)

Result directories: gpt-5.4-nano-elasticities-batch15, claude-sonnet-5-elasticities-batch15, gpt-5.6-terra-elasticities-batch15, and 26 more.

Code and dataElicited April and July 2026 · 29 models · v4 prompts