Working with Earnings Periods

Earnings periods are the building blocks of every economic loss analysis. Each period represents a span of financial years with earnings data drawn from a specific source. This article explains the two datasets, the three data sources, and how to manage your earnings periods.

Claimant vs Comparison Earnings

Every analysis uses two datasets:

• Claimant Earnings (blue on the chart) -- the claimant's actual or projected earnings. This typically includes their real earnings history before and after injury, sourced from ATO income statements, tax returns, or payslips.
• Comparison Earnings (orange on the chart) -- the earnings the claimant would have received but for the injury. This is usually sourced from ABS Average Weekly Earnings data for the relevant industry, gender, and employment type.

The difference between these two datasets is the economic loss. To work with either dataset, select it from the Dataset dropdown in the right-hand panel.

The Three Data Sources

When you click Add Earnings Period, you choose a financial year range and one of three data sources:

ABS Average Weekly Earnings (AWE)

The most common source for comparison earnings. When you select ABS AWE as the data source, configure the following parameters:

• Sex -- Male or Female
• Industry -- choose from 19 ABS industry classifications (e.g. All Industries, Construction, Health Care and Social Assistance)
• ABS Data Set -- select the earnings measure:
  • Full Time Ordinary Earnings -- full-time employees, ordinary-time earnings only (the most commonly used measure)
  • Full Time Total Earnings -- full-time employees, total earnings including overtime
  • Total Earnings -- all employees (full-time and part-time)

For example, a common configuration for a male claimant with a general employment background would be Male, All Industries, Full Time Ordinary Earnings. The chart will immediately populate once the data loads.

The system uses the ABS-published AWE figures for each financial year. For years where two survey results are available (May and November), it calculates a weighted average: (May x 4 + November x 6 + following May x 2) / 12.

Upload / Manual Entry

Use this to import the claimant's actual earnings from a spreadsheet. This is typically the first step when building an analysis -- upload the claimant's ATO data to establish their actual earnings history.

The upload process works as follows:

1. Download the Excel template by clicking the Download Excel Template button. The template includes rows for each financial year in your selected range with columns for Financial Year, Gross Yearly Income, and Tax Paid
2. Fill the template with the claimant's ATO data -- the financial year, gross earnings, and tax paid for each year
3. Drag and drop the completed file into the file upload area (or click to browse). Accepted formats are CSV, XLS, and XLSX
4. Verify column mapping -- the system reads the file and shows a preview with column dropdowns. Confirm that each column is mapped to the correct field:
   • Financial Year -- the financial year label (e.g. "2020/21")
   • Gross Yearly Income or Gross Weekly Income -- the earnings figure
   • Tax -- the tax paid for the period
5. Review the data in the editable preview table -- you can correct any values directly
6. Click Import Data to load the earnings into the analysis

Estimate Data

Use this when you need to project earnings into the future or fill gaps where no published data exists. This is particularly useful for extending your analysis beyond the range of available ABS AWE data. You provide:

• Start Amount -- the initial gross earnings figure (e.g. $109,226/year, continuing from where the ABS data left off)
• Weekly or Yearly -- whether the start amount is a weekly or yearly figure
• Increase By -- whether to increase by a dollar amount or a percentage each year (e.g. 3% yearly increase)
• Value -- the dollar or percentage increase per year
• Apply From -- whether the increase starts from the first or second year. Choose second year for a smoother transition when continuing from actual data, as the start amount already reflects the final year of the preceding period

The key with estimate data is not to overthink the initial setup. As Ashley puts it: "put something together, rough and ugly, throw it on the page and then dial it in." You can always adjust the parameters once you see how the projection looks on the chart.

Managing Earnings Periods

Each earnings period appears as a collapsible card in the right-hand panel. You can:

• Expand/collapse -- click the arrow button to view or hide the period's configuration
• Enable/disable -- use the toggle switch to include or exclude a period from calculations without deleting it
• Rename -- click the edit icon next to the label to give the period a descriptive name
• Delete -- click the delete button to remove the period (you will be asked to confirm)

Multiple earnings periods can be added to each dataset to cover different financial year ranges. Periods are sorted chronologically, and the system will warn you if periods overlap.

Scrolling Through Detailed Figures

Once earnings periods are loaded, you can expand the earnings tables at the top of the page to scroll through the detailed year-by-year figures. Each row shows the Financial Year, Gross Weekly Income, Gross Yearly Income, Tax, Net Yearly Income, Net Weekly Income, and Superannuation for that year. This lets you verify the data and spot any anomalies before proceeding to loss calculations.

Tax and Superannuation

The system automatically calculates tax for each financial year using ATO income tax tables, including:

• Marginal tax rates for each financial year
• Medicare levy (including low-income thresholds and shade-in rates)
• Low Income Tax Offset (LITO) and Low to Middle Income Tax Offset (LMITO)

You can optionally add Superannuation via the Superannuation section below the earnings periods. The system pre-fills the statutory superannuation guarantee rate for each financial year (the historical rates auto-populate for both datasets), and you can adjust individual year rates if needed.

Next Steps

Learn how modifiers adjust your comparison data in Using Modifiers.