In a workshop on cost estimates at a conference on paid family leave last winter, one presenter said that in his model he chose assumptions that would result in a total cost figure at the high end of the possible range in costs for a new program. That way, if his state adopted the program, it would be funded at a high enough level to be certain of not running out of money. The next presenter, in contrast, said that he chose assumptions that would put his cost estimates at the lower end of the possible range. That way, legislators would be far more likely to consider and ultimately pass a program for paid family leave. He reasoned that once the program was in place, its political popularity and far reaching benefits would assure continued funding at whatever level proved necessary.

Both of these presenters were serious about making realistic cost estimates. Both were careful to consider a broad range of issues that would affect need, take-up rates, average lengths of leave, administrative costs, etc., and to use the best data available in making their estimates. Both were also supporters of paid family leave, and thus not trying to sabotage enactment of the policies. Paid family leave has not been adopted in either of their states – or any state – to date, so we do not have an ending to the story. However, this example does illustrate what we all know to be true at some level – that cost and benefit analyses are not neutral, no matter how scientific our approach, and that our analyses often are used in very political ways.

As policy analysts and scholars, most of us devote a great deal of time and thought to the details and assumptions behind the bottom line numbers. We know that the assumptions we choose to make, the costs and benefits we decide to include or choose not to count hugely affect our final figures. But my experience in dealing with politicians, the media, and citizens groups on issues ranging from Social Security to paid family leave is that the bottom line is the only number most people have the time and inclination to deal with. Once we have produced and published a number, it takes on a life of its own. The bottom line number gets reported and used by people on all sides of an issue, severed from the multiple assumptions and careful qualifications that tempered and grounded it.

In this paper I in no way intend to suggest that we should cook our numbers. We need to be rational, nuanced, thorough, and scholarly in our approaches to making projections and cost estimates. But as the people who produce those bottom line numbers, we cannot ignore the political and policy consequences. And as people who care about policy outcomes, we have to become more adept at using and packaging our numbers to achieve our policy objectives. My recent work on both the issues of paid family leave and Social Security has highlighted how little attention gets paid to the assumptions, and the power that a single number has to focus and shape public debate on an issue.