How the Decision tools emergency fund sizing calculator is verified

Inputs

Input	Field	Type
Monthly essential expenses	`monthlyEssentialExpenses`	number
Current emergency savings	`currentSavings`	number
Can save toward it each month	`monthlySavingsCapacity`	number
Income stability	`incomeStability`	string
Dependents	`dependents`	integer
I have income-protection or critical-illness insurance	`hasIncomeProtectionInsurance`	boolean

The formula

Each derived value below is computed by the Valem engine from the expression shown, in this order. Nothing else runs — there is no hidden code path.

Derived value	Field	Formula (JSONata)
Recommended months	`recommendedMonths`	`$max([3 + (incomeStability = "variable" ? 2 : incomeStability = "freelance" ? 3 : 0) + $min([dependents, 3]) - (hasIncomeProtectionInsurance ? 1 : 0), 3])`
Target fund size	`targetFundSize`	`$round(monthlyEssentialExpenses * recommendedMonths, 2)`
Current Coverage Months	`currentCoverageMonths`	`$round(monthlyEssentialExpenses > 0 ? currentSavings / monthlyEssentialExpenses : 0, 2)`
Amount still needed	`shortfall`	`$round($max([0, targetFundSize - currentSavings]), 2)`
Months To Target	`monthsToTarget`	`$round(shortfall <= 0 ? 0 : (monthlySavingsCapacity > 0 ? shortfall / monthlySavingsCapacity : -1), 1)`
Percent funded	`percentFunded`	`$round(targetFundSize > 0 ? $min([100, currentSavings / targetFundSize * 100]) : 0, 1)`

Test vectors (4)

Every case is executed against this model on each build, by the Java engine and independently by a JavaScript one. A mismatch of a single cent fails the build, so the page cannot ship advertising a figure the model no longer produces.

Case 1 — freelancer with a dependent, partially funded — the baseline scenario

given `monthlyEssentialExpenses`	2500
given `currentSavings`	3000
given `monthlySavingsCapacity`	500
given `incomeStability`	freelance
given `dependents`	1
given `hasIncomeProtectionInsurance`	no
expect `recommendedMonths`	7
expect `targetFundSize`	17500
expect `currentCoverageMonths`	1.2
expect `shortfall`	14500
expect `monthsToTarget`	29
expect `percentFunded`	17.1

Case 2 — stable income, insured, no dependents, already over target — floor and cap both bind

given `monthlyEssentialExpenses`	2000
given `currentSavings`	8000
given `monthlySavingsCapacity`	200
given `incomeStability`	stable
given `dependents`	0
given `hasIncomeProtectionInsurance`	yes
expect `recommendedMonths`	3
expect `targetFundSize`	6000
expect `currentCoverageMonths`	4
expect `shortfall`	0
expect `monthsToTarget`	0
expect `percentFunded`	100

Case 3 — freelancer with several dependents starting from zero savings — the worst-case shape

given `monthlyEssentialExpenses`	3000
given `currentSavings`	0
given `monthlySavingsCapacity`	300
given `incomeStability`	freelance
given `dependents`	5
given `hasIncomeProtectionInsurance`	no
expect `recommendedMonths`	9
expect `targetFundSize`	27000
expect `currentCoverageMonths`	0
expect `shortfall`	27000
expect `monthsToTarget`	90
expect `percentFunded`	0

Case 4 — zero saving capacity with a real shortfall — the gap cannot close, monthsToTarget is the -1 sentinel rather than a false 0

given `monthlyEssentialExpenses`	2000
given `currentSavings`	1000
given `monthlySavingsCapacity`	0
given `incomeStability`	stable
given `dependents`	0
given `hasIncomeProtectionInsurance`	no
expect `recommendedMonths`	3
expect `targetFundSize`	6000
expect `shortfall`	5000
expect `monthsToTarget`	-1

Sources

Check it yourself — and reuse it

The whole model as JSON — inputs, outputs, formulas, vectors and sources in one re-runnable document.

This model is published under CC BY 4.0: use it, adapt it, build on it, with attribution. The licence covers the model — the expression of each rule and the arrangement of the catalog. The rates and thresholds are law, and nobody licenses those; the sources above are where they come from.

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