Worked example: Wrongful Death Damages in Brazil

Example inputs

Below is a worked example showing how DocketMath (tool: wrongful-death-damages) can estimate wrongful death–related damages in Brazil using jurisdiction-aware rules for BR.

Scenario (facts you’ll enter)

We’ll use a simplified case with these inputs:

What you’d typically see in DocketMath input fields

In DocketMath’s workflow for wrongful-death-damages (Brazil), your screen usually covers:

• Beneficiary type (e.g., spouse, child, dependent parent)
• Monthly support / contribution (or inputs used to infer it)
• Time horizon (e.g., years until expected independence or another duration proxy)
• Dates (event date and valuation/cutoff date)
• Moral damages approach (rule-based proxy vs user-provided amount)
• Discounting / present value settings (often defaulted)

If you want, you can run the scenario without moral damages first, then toggle it on to compare the total.

Example run

Here’s a complete run using the example inputs above. The goal is to show how the numbers flow through DocketMath and which outputs you should expect to change when you adjust inputs.

Run the Wrongful Death Damages calculator using the example inputs above. Review the breakdown for intermediate steps (segments, adjustments, or rate changes) so you can see how each input moves the output. Save the result for reference and compare it to your actual scenario.

Step 1: Pecuniary-style “support stream” component

Inputs used

• Monthly net support: R$ 6,500
• Time horizon: 18 years
• Valuation date: 2024-03-01
• Event date: 2023-09-10

**Illustrative structure (what DocketMath does)

1. Compute the relevant number of months within the chosen horizon, aligned to the valuation/event timing.
2. Apply a Brazil-specific present value method (or whatever the selected/default discounting setting provides).
3. Multiply by the beneficiary allocation logic for spouse (married).

**Output (example)

• Support stream (present value): R$ 1,394,000

(The exact figure depends on DocketMath’s internal parameters for discounting and periodization.)

Step 2: Moral damages component (non-pecuniary proxy)

Inputs used

• Moral damages approach: rule-based estimate
• Beneficiary category: spouse
• Evidence confidence: standard documentation

**Output (example)

• Moral damages proxy: R$ 180,000

Again, this is a calculator-style proxy—courts can weigh factors such as degree of fault proved, severity of harm, and evidentiary support.

Step 3: Combined wrongful death damages estimate

**Output (example)

• Total estimated damages: R$ 1,574,000

Quick checklist: what this run tells you

• Changing time horizon typically moves the largest driver (support stream).
• Changing monthly support changes the scale of the pecuniary component.
• Switching beneficiary type can change both allocation and moral-damages proxy assumptions.

Sensitivity check

A worked example is most helpful when you can see what happens when you adjust inputs. Below are three controlled changes to demonstrate sensitivity, using the same baseline scenario.

Baseline (for reference)

• Support stream PV: R$ 1,394,000
• Moral damages proxy: R$ 180,000
• Total: R$ 1,574,000

Sensitivity 1: Monthly support +10%

• New monthly net support: R$ 7,150 (from R$ 6,500)
• Time horizon unchanged: 18 years

Expected effect

• Support stream scales roughly proportionally with monthly support.

Illustrative output

• Support stream PV: R$ 1,533,000
• Moral damages proxy: R$ 180,000
• Total: R$ 1,713,000

Takeaway

• In this model, pecuniary/support dominates; moral damages stays relatively stable because it’s driven more by category/proxy settings than by the monthly support figure.

Sensitivity 2: Time horizon reduced by 25%

• Time horizon: 18 → 13.5 years
• Monthly support unchanged: R$ 6,500

Expected effect

• Cutting the horizon reduces present value substantially because fewer months contribute to the support stream.

Illustrative output

• Support stream PV: R$ 1,044,000
• Moral damages proxy: R$ 180,000
• Total: R$ 1,224,000

Takeaway

• Time horizon is a high-leverage input. If your facts support a different expected duration, this is where the estimate will move the most.

Sensitivity 3: Moral damages proxy adjusted (higher confidence / alternative proxy)

• Evidence confidence: high documentation (or switch to a more flexible moral damages proxy mode, if available in the DocketMath UI)
• Monthly support unchanged: R$ 6,500
• Time horizon unchanged: 18 years

Expected effect

• Support stream stays the same.
• Moral damages proxy changes.

Illustrative output

• Support stream PV: R$ 1,394,000
• Moral damages proxy: R$ 230,000
• Total: R$ 1,624,000

Takeaway

• Moral damages is often smaller than the support stream in these scenarios, but it can still shift totals meaningfully—especially if support inputs are conservative.

Sensitivity summary table

Practical interpretation for using DocketMath

To make your DocketMath run more robust:

• Confirm the monthly support basis (e.g., net vs gross, and consistency across evidence).
• Verify the duration assumption (how the cutoff date reasoning maps to the chosen horizon).
• Log your toggles (moral damages proxy mode, discounting settings), because these can change outputs even when pecuniary inputs stay fixed.

If you want to start the calculator right away, you can use:

• Primary CTA: /tools/wrongful-death-damages

Related reading

• Wrongful Death Damages Estimator Guide for Alabama
• Wrongful Death Damages Estimator Guide for Arizona
• Wrongful Death Damages Estimator Guide for Colorado