The WorkCompEdge® Blog

, by Kory Wells

Compare Your Company to Industry Standards Using Data from the Mod

by Kory Wells, WorkCompEdge Blog Editor

One of the more dreaded phrases in the English language – particularly on high school and college campuses – is probably “compare and contrast.” As in:

  • For 20 points, compare and contrast mitosis and meiosis.
  • For 30 points, compare and contrast the style and theme of Shakespeare’s “Sonnet 18″ to “A Red, Red Rose” by Robert Burns.
  • For 50 points, compare and contrast the economic, political, and social structures of ancient Athens to modern-day Iraq.

Comparing and contrasting your work comp experience to industry standards isn’t difficult with the right information and tools. (Shakespeare would insist on complete sentences, of course.)


If these questions (which I found on the Internet, by the way) give you a not-so-nostalgic pit-in-the-stomach feeling, you’re definitely not alone. If you’re like me, you’re grateful such academic gymnastics are in your past. But here’s a compare and contrast exercise that will be useful to your company today:

Compare and contrast your company’s work comp losses to the average for your industry. Use actual and expected losses on total, primary and excess amounts. Include comparisons on a policy period basis. Use complete sentences.

OK, you don’t have to use complete sentences. Even without that directive, this analysis can still sound a bit intimidating. However, if you have a mod worksheet from NCCI or another bureau, all the data you need is on the worksheet – and at least some of it is already summarized and ready to use.

The whole purpose of the mod calculation formula is to compare your company’s loss experience with the average for your industry. The code word for this in mod-speak is “expected.” On your worksheet, you see total expected losses, total expected primary losses, and total expected excess losses. If you don’t know the differences between all these expecteds yet, don’t worry. It’s enough to know that these values reflect the standard, or average, for your industry for a theoretical company that has the same payroll you do.

The mod itself tells a story, comparatively speaking: if your mod is over 1.0, your compare unfavorably to other businesses in your industry. If your mod is under 1.0, you compare favorably; you are, as we’ve said in other blog entries, “beating the average.” But the formula can be broken into components which can be analyzed for additional insight. So let’s take this exercise in pieces:

1. Compare your company’s total losses to the industry average.

Why you want to do this: This comparison provides a general indicator of your loss experience.

How to do this: Divide your total actual losses (box H on the NCCI bureau report) by total expected losses (box D).

2. Compare your company’s total primary losses to the industry average.

Why you want to do this: This comparison provides an indicator of whether too MANY losses are keeping you from reaching your minimum mod.

How to do this: Divide total primary losses (Box I) by expected primary losses (Box E).

3. Compare your company’s total excess losses to the industry average.

Why you want to do this: This comparison provides an indicator of whether the SEVERITY of your losses is keeping you from reaching your minimum mod.

How to do this: Divide total actual excess losses (Box F) by expected excess (Box C).

In all three of the comparisons above, you will get a number that’s more or less around 1.0 or, converted to a percentage, 100%. The lower the number, the better; and any percentages over 100% warrant your attention.

Now, for the trickier stuff: compare your actual versus expected losses for each policy period in the mod. This is harder to do because all of the totals that you need – by policy period – are often not shown on the bureau worksheet. So, you’ve got to haul out the slide rule, calculator, Excel workbook, or (ahem) ModMaster software to make this easier.

This seems like a good time to mention the WorkCompEdge Proposal Report that employers or (more likely) their insurance agents can print from ModMaster. We discussed the first part of this report, about what your mod is costing you, in another blog entry. Now let’s look at the second part – How Your Company Compares to Industry Standards.

Here’s a snippet of a sample WorkCompEdge Proposal Report that shows how you can use mod data to compare your company to industry standards – and identify trends that will affect your future mods.

The first 4 bullet points in the report excerpt correspond to the comparisons we discussed in items 1-3 above (note the wonderfully complete sentences), and the graph on the left, Actual vs. Expected Losses, visually shows the same information. In this sample, when so many of the percentages look so good, the 134% ratio of the primary loss comparison stands out. While this company has a pretty healthy mod, that 134% points to a clear opportunity to reduce the number of losses they’re experiencing and thus drive their mod even lower, for even more cost savings.

The graph on the right, Loss Trend, shows the actual losses and expected losses for each policy period. This graph is really helpful for two reasons:

First, it shows us the general trend of losses for our own company versus the industry average. In this particular example, we see that this company has never exceeded industry norms, and that in the most recent year they’ve beat the average by quite a bit.

That’s important information, but we can also discern more. This graph also lets us see the anomalies that a certain period may be contributing to the mod. In this case, the “blip” of increased actual losses in 2006 is probably the principal contributor to the mod. So, until 2006 comes out of the calculation (after one more year), the mod is going to stay a little higher. When policy year 2006 no longer affects the calculation, provided that the latest trend has continued, THAT’s when the mod will really decrease.

So, for real cost savings – not points on a test – compare your work comp losses with industry averages using mod analysis.

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