Problem addressed

Poor mental health now drives larger productivity losses than physical illness: 47 percent of employer cost stems from presenteeism, 39 percent from turnover and 14 percent from absenteeism. Decision‑makers struggle to justify preventative spending because the expected ROI is hard to quantify, so companies consistently under‑invest.

Innovative solution

• Quantification engine – Converts common psychometric scores (e.g., WHO‑5, SWEMWBS) into monetary productivity impacts using peer‑reviewed cost coefficients.
• Three‑driver model – Links mental‑health scores to absenteeism, presenteeism and employee turnover.
• Scenario analysis – Shows gains from (a) an X-amount of points improvement (mental health) or (b) meeting industry benchmarks, giving management two intuitive perspectives.
• ESRS alignment – Outputs fit neatly into Social ESRS (S1–S2) disclosure tables.

Key results and benefits

• Gives executives an instant, monetary ROI figure for every proposed mental‑health programme, transforming wellbeing spend into a data‑driven investment decision.
• Enables HR to compare interventions side‑by‑side and channel resources toward those with the highest expected return on productivity.
• Tracks real‑world results post‑implementation so managers can verify whether reductions in absenteeism, presenteeism and turnover meet or exceed forecasts.
• Translates each one‑point uplift in average mental‑health score into estimated productive hours gained and bottom‑line value at department or company level.
• Supplies clear KPIs for continuous improvement and supports annual ESG/ESRS reporting on workforce wellbeing.

Potential for mainstreaming

• Universally compatible inputs – Most organisations already capture absenteeism and voluntary turnover in their HR or payroll systems, so MHAW can be deployed with data that already exist.
• One extra metric unlocks the model – Adding a brief quarterly pulse on workforce mental health (e.g., WHO‑5) plus a self‑reported presenteeism questionaire gives the three data points required to quantify productivity potential.
• Sector‑ and size‑agnostic – Standard cost coefficients can be parameterised for micro‑enterprises through to multinationals in manufacturing, services and the public sector.
• Scalable learning loop – Post‑programme results feed back into the model, refining assumptions and building benchmarks other firms can adopt.
• Regulatory fit – Outputs map directly to ESRS S1 workforce disclosures, helping companies satisfy new EU sustainability‑reporting rules without extra overhead.