Why EMR Matters
EMR directly impacts your workers’ comp premium and is often used in prequalification for construction and industrial contracts.
A lower EMR signals stronger safety performance and cost control.
Key uses:
- Get a quick read on EMR trend before renewals
- Sense-check the impact of recent claims on premiums
- Share a simple benchmark with operations and safety teams
How This Calculator Works
- Enter Actual Incurred Loss Your organization’s incurred losses (claims + reserves) for the rating period.
- Enter Expected Loss
Losses your insurer/ratings bureau would expect based on payroll class codes and exposure.
- We compute EMR (simple ratio)
EMR = Actual Incurred Loss ÷ Expected Loss
- EMR < 1.00 → better than expected
- EMR = 1.00 → average
- EMR > 1.00 → worse than expected
Note: This is a simplified estimator. Official EMR calculations (NCCI/state bureaus) use weighting values, split points (primary vs. excess losses), credibility (W, G), and ballast (B). Use your Experience Rating Worksheet for the binding value.
Example
Inputs:
- Actual incurred loss: $70,000
- Expected loss: $60,000
Result:
- EMR = 70,000 ÷ 60,000 = 1.167 (rounded 1.17)
Interpretation: Premiums could be ~16.7% higher than the average account with EMR 1.00 (final impact depends on carrier and rating factors).
Pro Tips
- Focus on frequency. Many rating plans weight primary losses more than severity—reducing small, frequent claims often moves EMR fastest.
- Close reserves early. Work with your carrier to review reserves before the valuation date that feeds your next EMR.
- Track leading indicators. Near-miss reporting and modified duty programs help cut loss time and future EMR.
Benefits & Limitations
Benefits
- Instant directional EMR estimate
- Clear for non-insurance audiences (ops, finance, safety)
- Helpful during planning and bid go/no-go
Limitations
- Not a substitute for your official EMR from NCCI or a state bureau
- Doesn’t include split point, weighting, ballast, or credibility factors
- Results depend on accuracy of your input losses and expected losses
