This article discusses an issue on rounding error, specifically related to breaking down a total amount with a trend/inflation rate. It’s more of a math problem than an Excel problem, and if you work in the healthcare industry, this problem probably will look familiar to you.
Note: “Trend” here is assumed to be a compounding annual cost inflation. When “trending” an amount from one period to another, the length of time is measured between the mid-points of the two periods.
Suppose you made a projection for your client’s healthcare spendings to be $12 million in the upcoming calendar year, assuming 10% annual trend. Now the client wants to break that down to monthly figures.
The answer is not $1 million for each of the 12 months. The force of trend makes this spending an increasing function over time, i.e. February should cost slightly more than January, and so on. The industry-standard formula is to apply the 10% trend, from the mid-point of the year to the mid-point of each month, i.e.
Monthly Amount = Annual Amount / 12 * (1 + Trend) ^ ([mid month] – [mid year])
While this is a perfectly valid and defendable approach, the results always have a problem – the “rounding error”. As seen in the table below, adding the 12 months of breakdown back together produces a slightly higher sum than anticipated. This error is usually small ($4,511 in this case, < 0.1%) and people tend to overlook such insignificant imperfection.
Numbers don’t add up
Note: This type of difference is widely known in the industry as a “rounding error”, but technically it isn’t. Rather, it is a methodological inaccuracy.
But not everybody can tolerate imperfections – certain demanding bosses/clients could get very upset if your basic numbers don’t add up! To correct this situation, I’ve seen people throw that $4,511 in one of the months, break it out evenly across the 12 months, or use some GoalSeek magic to add a margin to each number until the total comes out right. None of these methods is pretty.
Take a step back and think like a Calculus student again… the trending cost can be represented as the area under curve of an exponential function, and the aforementioned approach to monthly amounts is a discrete numerical approximation of the integral!
The pink bars are the results of the previous method, which closely but not perfectly approximate the area under the blue (exponential) curve.
Now, if you can take a basic anti-derivative from point a to point b (one month to another), you’ll have a solution that’s better than a good approximation:
Monthly = Annual / Trend * ((1 + Trend)^(i/12) – (1 + Trend)^((i-1)/12))
The resulting formula is difficult to understand when you look at it for the first time. However, once you understand the concepts, it’s very easy to explain. The monthly breakdown sum up beautifully to the exact $12 million, so you won’t have to do any unsightly tweaking!
Numbers add up perfectly!
The sample file contains a hand-written derivation of the formula.
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