I recently read an article in the Boston Globe discussing the unexpected rise in costs related to the state's universal healthcare plan. The program requires that every single state resident have healthcare coverage (a soon-to-be national topic based on the outcome of the presidential elections). What the article highlights is that the anticipated costs of the program could double and that the state has not budgeted for the increase in costs. The primary driver of the increased budget is that the state underestimated the number of state residents that do not have healthcare coverage. The legislature had two numbers to use to drive the budget model, one source being the state's estimate of 460,000 and the second source being the US Census Bureau estimate of 748,000. Unfortunately for the state, they used a number somewhere in between and they are now realizing that their budget will be short.Beyond the political nature of the article, what I find interesting about this situation is you can clearly see the importance of having accurate data when making a decision. More appropriately said, the state had already made a decision to provide universal healthcare for every state resident, but because of the poor quality of data, how they allocated resources (i.e. money) is being significantly impacted.
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I think this episode also highlights the value of incorporating explicit driver models into financial forecasting and budgeting processes.
At least in this case, somebody can take a financial variance and point to the specific assumption that caused it.
One client, in the restaurant business, described how the lack of a driver model for same-store-sales growth impacted results analysis: "Well, we have some ideas. 'Weather' comes up a lot, but if you ask the guys in California, it's been raining for ten years straight."
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