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  "subfield_secondary": "asset_pricing (option valuation); executive compensation",
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   "estimand": "Conditional daily rate at which employees voluntarily exercise vested in-the-money options, as a function of option, stock, firm and holder characteristics, and the implied option cost to the firm.",
   "strategy": "Reduced-form fractional-logistic exercise function (hazard-like) estimated by GMM that is robust to heteroskedasticity and within-employee correlation. The estimated function is then fed into simulation-based valuation.",
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    "Employee termination and cancellation hazards are modeled separately."
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    "Correlation across exercises by the same employee",
    "Heteroskedasticity",
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   "convention_notes": "Table notes define the dependent variable and specifications, and report coefficients with standard errors in parentheses. One table translates coefficients into percentage changes in the exercise rate for a one-SD change in each factor. Valuation tables state their base-case parameters (strike 100, 5% rate, 10% termination, SIC 3). Summary tables are grouped by one-digit SIC.",
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   {
    "claim": "Moving from annual or cliff vesting to monthly vesting lowers option value by roughly 11-16%.",
    "evidence_type": "table",
    "evidence_ref": "Table VIII"
   },
   {
    "claim": "Men exercise faster, lowering option value by 2-4%.",
    "evidence_type": "table",
    "evidence_ref": "Tables IV, VIII"
   },
   {
    "claim": "Top-ranked employees exercise more slowly, raising option value by 2-7%.",
    "evidence_type": "table",
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   {
    "claim": "Black-Scholes approximations used in practice produce valuation errors, and the new analytic approximation is more accurate.",
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    "evidence_ref": "Table IX"
   },
   {
    "claim": "Omitting portfolio and additional factors distorts exercise estimates.",
    "evidence_type": "table",
    "evidence_ref": "Table IV specs (1)-(2)"
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   {
    "claim": "ESOs are a major share of CEO pay and their valuation is controversial in practice.",
    "evidence_type": "prior_literature",
    "evidence_ref": "Murphy (2013); Meridian (2018); ISS/IBM example"
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  "structural_summary": "The introduction motivates the topic with the economic importance of ESOs and a practical valuation controversy. It argues that standard theory and Black-Scholes modifications are inadequate, then previews a GMM fractional-logistic exercise model applied to proprietary data. A separate literature section contrasts utility-based theoretical models with empirical work. Next come the methodology, a long data section, estimation results with rank interactions, and a valuation section that quantifies cost effects, critiques practitioner approximations and proposes a new analytic approximation. The conclusion restates the method and findings.",
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