The person and the choice
Priya Shah's decision starts before any input is entered
Priya Shah, a design lead evaluating a promotion package, is the fictional decision-maker in this worked example. Priya's package includes a $120,000 salary, target bonus, retirement match, employer health funding, paid leave, and an RSU grant that vests over four years. The goal is to turn that situation into a traceable case without pretending the assumptions describe a future that is certain.
The choice is not simply between the largest and smallest displayed values. It is whether the modeled result gives Priya Shah enough evidence to act, which number affects current cash, which number remains conditional, and which unanswered term could reverse the interpretation.
The profile is deliberately realistic rather than universal. Readers should borrow the method—document facts, normalize units, run the engine, reconcile outputs, vary one assumption, and record omitted risks—not copy Priya Shah's inputs into a different situation.
What is known
Build the evidence ledger before building the scenario
Base pay, target opportunity, benefit premiums, employer contributions, vesting schedule, paid-time policy, expected work schedule, one-time payments, and eligibility dates should be copied from authoritative compensation and plan documents. Those items anchor the base case. They receive the label “input” only when a document, record, or current policy supports them; otherwise they remain an assumption even if the value feels likely.
Priya should reconcile the offer or promotion letter, bonus plan, benefits summary, retirement match and vesting terms, equity grant notice, vesting calendar, paid-leave policy, work expectations, and one-time payment clawbacks. For the worked example, the visible input table highlights the fields that explain the result while the typed fixture supplies the calculator's complete validated object.
That distinction prevents a common reporting problem: showing six attractive inputs while hidden defaults do most of the calculation. The fixture is tested against the schema, and the displayed lead metric is recalculated by the production engine. In this a total compensation package analysis, that boundary is applied to Priya Shah's stated facts and assumptions.
| Field | Value | Role |
|---|---|---|
| Base salary | $120,000 | Input |
| Target bonus | 10% at 90% expected attainment | Assumption |
| Employer health premium | $600 monthly | Input |
| HSA funding | $1,000 annually | Input |
| RSU grant | $60,000 over four years | Input |
| Unpaid extra time | 3 hours weekly | Assumption |
Preparing the inputs
What Priya Shah has to normalize before pressing calculate
Every component needs an annual value, a recurrence label, an expected-versus-target treatment, and a year in which it becomes available. Working time must use the same calendar as compensation. The normalization step creates a common clock and compatible units, but it does not erase restrictions or uncertainty. Cash remains cash, time remains time, and conditional value remains labeled.
In this case, the three operational layers are cash certainty and attainment, benefits, retirement, and paid time, and equity vesting and recurring durability. Each is prepared separately so the engine can connect them in the right order.
A useful preflight check is to ask whether every percentage has a defined base, every annual amount has a recurrence rule, every date belongs to the correct period, and every scenario value has an owner. Ambiguous units are resolved before calculation, not explained away afterward. In this a total compensation package analysis, that boundary is applied to Priya Shah's stated facts and assumptions.
Priya Shah's result is reproducible because the fixture, engine path, and assumptions remain separate.
Why these assumptions
Choose a base case that can be defended, not one that flatters the outcome
Bonus attainment, benefit eligibility, retirement match formulas, equity vesting and value, one-time signing payments, unpaid extra hours, paid leave, and benefit replacement value can change the ranking of packages. The base fixture selects explicit values for those variables so the result can be reproduced. It does not claim they are the most likely values for every reader.
For Priya Shah, the strongest assumption is the one closest to a written term or recent observed pattern. The weakest is a future outcome controlled by a market, employer, client, schedule, or household event. Those two should never carry the same confidence label.
A conservative case should stress one credible downside without changing unrelated facts. A favorable case should do the same in the other direction. This structure shows which variable causes the spread instead of producing two opaque bundles. In this a total compensation package analysis, that boundary is applied to Priya Shah's stated facts and assumptions.
Calculation walkthrough
Follow one case through the actual PayArith engine
The engine separates guaranteed, expected, and target cash; adds employer retirement and benefits; maps equity vesting by year; distinguishes recurring and one-time value; and divides annual totals by modeled job time. The audit table below is derived from that engine result. The article does not reimplement the formulas, which prevents prose examples from drifting away from the calculator's validation, ordering, and rounding behavior.
Adding unlike components is useful only after their conditions remain visible. A recurring expected total excludes one-time items, while effective hourly value exposes a package that requires materially more time to earn. The formula block names the central relationship, while the step rows reconcile how the fixture reaches its displayed output. Each calculated value is labeled separately from the assumption that feeds it.
To audit the calculation, start with the first row and ask where its basis came from. Then carry the output into the next relevant stage. If a value cannot be traced, it should not be used as the reason for Priya Shah's choice.
| Step | Basis | Calculated result |
|---|---|---|
| Base salary | Recurring component | $120,000 |
| Fixed cash | Recurring component | $0 |
| Variable pay | Recurring component | $10,800 |
| One-time cash | One-time or vesting-dependent | $0 |
| Employer retirement | Recurring component | $4,800 |
| Other benefits | Recurring component | $8,800 |
| Vested equity | Recurring component | $15,000 |
What the outputs mean
Translate every result back into the decision
Guaranteed cash measures dependable payroll; expected cash applies stated attainment; expected total adds employer and vested value; recurring expected total removes one-time effects; effective hourly value relates the package to actual annual job time. For Priya Shah, those are not interchangeable scorecards. The metric that best describes long-term modeled value may be the wrong metric for a near-term cash constraint.
The headline strip is a navigation aid, not the whole analysis. The audit explains composition, the scenario chart explains conditional range, and the projection explains timing. A decision should cite the specific view that supports it. In this a total compensation package analysis, that boundary is applied to Priya Shah's stated facts and assumptions.
The text alternative under each chart repeats the plotted values in a table. That supports readers who cannot use the visual and also makes the numerical comparison easier to reconcile against the engine audit. In this a total compensation package analysis, that boundary is applied to Priya Shah's stated facts and assumptions.
The next view keeps the fixture constant and exposes the numerical spread. Read it to locate a decision boundary, then use the table to reconcile the plotted values without relying on color or shape.
| Scenario | Calculated value | Reference value |
|---|---|---|
| Guaranteed cash | 120,000 | — |
| Expected cash | 130,800 | — |
| Expected total | 159,400 | — |
The chart does not rank personal outcomes. It shows how the defined engine metrics move; the surrounding article explains whether the spread is liquid, recurring, sensitive, or incomplete.
The counterfactual
Change one condition and explain why the answer moves
A package can lead in Year 1 because of signing cash, lose in recurring value after that payment disappears, and lead again only if a large equity tranche vests at the assumed value. That alternative changes the relevant engine inputs while leaving the rest of the case intact. The resulting difference is therefore attributable to a named condition rather than a collection of favorable edits.
If the ranking changes, Priya Shah has found a decision boundary. The next task is to verify how plausible that condition is and whether the household can tolerate being wrong, not to average the cases into a false point estimate.
If the ranking does not change, inspect the size and timing of the remaining lead. A numerically stable result can still be impractical when its value is illiquid, delayed, reversible, or dependent on staying in the role. In this a total compensation package analysis, that boundary is applied to Priya Shah's stated facts and assumptions.
What matters now
Separate current cash, recurring economics, and later value
Salary supports monthly obligations directly. Benefits may reduce expenses and retirement contributions build restricted wealth, while equity may provide no current liquidity even when it lifts the package's modeled total. This is the part of the example most likely to affect an immediate action. A household cannot pay a current obligation with a future scenario value, even when both appear in the same long-term comparison.
Recurring salary compounds through raises and influences future negotiations; benefits protect household costs; retirement matching compounds inside an account; equity may create upside but resets with vesting, refresh grants, and employment tenure. The projection makes that sequence visible but does not predict persistence. It repeats the stated growth, schedule, vesting, cost, or availability assumptions across the chosen horizon.
A good decision memo records three numbers: the Year 1 cash consequence, the recurring annual difference after one-time effects, and the cumulative result at a horizon the person may realistically remain. That memo is more informative than one lifetime total. In this a total compensation package analysis, that boundary is applied to Priya Shah's stated facts and assumptions.
A single-year lead can disappear, widen, or reverse. The projection uses the same stated horizon so timing remains visible rather than being compressed into one lifetime total.
| Period | Primary path | Comparison path |
|---|---|---|
| Year 1 | 159,400 | 159,400 |
| Year 2 | 163,468 | 163,468 |
| Year 3 | 167,658 | 167,658 |
| Year 4 | 171,973.8 | 171,973.8 |
| Year 5 | 161,419 | 161,419 |
Use the projection to ask when value appears and which assumption repeats. Do not treat the final point as more certain merely because it is farther to the right.
Outside the output
The engine can be right while the decision is still exposed
Bonus attainment, future premiums, plan eligibility, equity price, vesting continuity, promotion timing, and workload are uncertain. Expected values should be stress-tested rather than silently promoted to guaranteed pay. The worked example isolates those uncertainties rather than hiding them inside a single expected label.
The risk is false equivalence: one dollar of salary, target bonus, health premium, restricted retirement funding, and illiquid equity do not have the same certainty, timing, or usefulness to the household. That risk is not an arithmetic defect; it is information outside or beyond the model. It belongs beside the result because it affects how much confidence Priya Shah should place in the numerical lead.
Coverage quality, provider network, leave usability, retirement access, role scope, manager quality, schedule control, promotion path, and tolerance for vesting risk can justify a choice that does not lead on expected dollars. None of those factors should be converted into invented dollars merely to force one total. A separate qualitative ledger keeps them explicit and allows Priya Shah to choose a financially lower path for a stated reason.
Decision takeaway
What Priya Shah can responsibly conclude from this worked case
This fixture proves how a validated set of a total compensation package inputs travels through PayArith's production calculation engine. It proves the arithmetic relationship and the displayed reconciliation; it does not prove that future assumptions will occur.
Before acting, Priya Shah should ask: Which components are guaranteed, recurring, vested, portable, taxable, employee-paid, subject to attainment, or lost when employment ends? The answer should update a named input or document an unsupported risk. Either outcome improves the decision more than adding another generic scenario.
The practical takeaway is to choose from the range that the household can fund and tolerate, using the metric tied to the actual objective. The highest modeled value is relevant only after its timing, availability, fragility, and nonfinancial cost are acceptable. In this a total compensation package analysis, that boundary is applied to Priya Shah's stated facts and assumptions.