What goes into setting your annual salary & wage budget or long-term forecasting?

Cathy Fitzsimons
Jun 19, 2024
2 min read

This post explains the differences between a budget and a forecast, it identifies data you can use and some ideas for doing each analysis.

BUDGETS

The budget for salary and wages is determined as part of the annual budgeting process. Typically it is fixed as a set limit above the current total salary and wage budget for example, +$500,000 or, +3% on top of the current total. To establish the budget, organisations consider factors including:

Internal data:

▫ Labour costs, turnover, recruitment

▫ Forecasts, strategy and goals for the next 12-18 months: growth, downsizing, M&A…

▫ Desired level of competitiveness

▫ And critically, affordability

External data:

▫ Labour Cost Index - Stats NZ primary wage inflation metric

▫ Projected market movements for the next 12 months - market data provider(s)

▫ Historical market movements - market data providers

▫ Projected regulatory changes including minimum wage

▫ Industry or sector trends / pressures

▫ Projected labour market supply and demand

▫ Other applicable metrics; CPI, Living Wage, OCR

TIPS: Be data driven and prioritise the budget outcomes. Align the budget with your Employee Value Proposition goals. Consider modelling costs with a whole organisation remuneration model to reflect outcomes on a granular employee level.

FORECASTS

Operating over a longer time horizon, forecasts use statistical models to predict future costs based on historical data and defined assumptions. They guide and inform the long-term strategic vision of the organisation. Effective forecasting involves both quantitative analysis and qualitative judgment. The models are dynamic, and most variables can change to test scenarios.

A forecast needs:

▫ Clarity on strategic vision and goals

▫ Insight into economic, sector, industry, technological and demographic trends

▫ Additional long-term historical data beyond the budgeting process. Noting for trend identification, you need to look back twice as far as you look forward.

▫ Most forecasts create multiple scenarios with probabilities, e.g., a 20% chance of an optimistic scenario, a 60% chance of a realistic outcome and 20% for a pessimistic result.

▫ The integration and mitigation of risk

▫ The willingness to be honest and see what is likely, not just what you want

▫ And lastly, great data analysts able to generate rolling averages or linear regression models

TIPS: Work to leverage business intelligence and AI LLM’s (large language models) to gain data insights into likely scenarios with outcomes and costs. Critically, it is important to build great communication channels, collaborate with key stakeholders and work closely with your new BFF’s; the finance team and analysts, to create powerful and insightful tools.

Cathy Fitzsimons

19 June 2024