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by _heimdall 274 days ago
The challenge for the second approach is that budgets are going to be spent once they have been allocated. If you improve efficiency and reduce costs that savings will just get spent somewhere else or on some new initiative.

Government work and corporate work are very much the same in that way, budgets are use it or lose it and everyone will use it this year so their budget isn't reduces next year.
1 comments
the8472 273 days ago
Some companies manage to maintain war chests, others have some insane "we had EOY leftover budget so we spent it on another redesign" craziness going on. Ditto with the "we must spend it, otherwise we'll get less next year". Where do such terrible incentives come from?
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throw10920 273 days ago
> Ditto with the "we must spend it, otherwise we'll get less next year". Where do such terrible incentives come from?

That specific one is an unfortunate side-effect of needing to allocate budgets in advance. Large organizations working with a lot of money need to statically schedule their budgets in advance - and because they don't have crystal balls, they can't actually know in advance how much they'll need, and have to make educated guesses. If a department didn't use all of its allocated budget last year, sure that's a very weak signal that it was given too large of a budget - but it is a signal, and there aren't very many other good ones.

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_heimdall 273 days ago
In my opinion this has always been a sign that an organization grew too large.

Its the way of the world now to aim for growing into a huge org, hiring as much as you can on the way up. Once those in charge know little more about a part of their company than the budget surplus at the end of the fiscal year the model just doesn't make sense.

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the8472 273 days ago
It's not a signal, no. If you're good at forecasting but don't have a crystal ball then you'll overestimate on some years and underestimates on others, averaging out.

And the budget-burning shows that this encourages making over-estimates and then spending the excess to make the actual numbers match the prediction. It's no different than research based on falsified data at that point.

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