Datagraphics can be used to inject civility into public debates. Senate Republican leader Mitch McConnell (and others) has been quoted as saying that reconciliation has never been used to pass something like health care before. And people who are pre-disposed to believe what the right says believe him without checking further. And people pre-disposed to disbelieve what the right says ignore him. And in the middle the debate is stuck in limbo, tempers rising on both sides but no new information is forthcoming.
Now they are suggesting they might use a device which has never been used the for this kind of major systemic reform. We know it would be — the only thing bipartisan about it would be the opposition to it, because a number of Democrats have said, “Don’t do this. This is not the way to go.” — Senator McConnell on FoxNews
Recently the Sunlight Foundation ran an infographic examining the past 20 years of Senate Reconciliation bills. At a glance you can see which bills had bipartsian support and which didn’t, and the list is relatively small (13 bills) so deeper inspection can be had relatively easily. However, what’s not immediately obvious is an indication of what Senator McConnell alleges, namely the “magnitude” of these bills. Most bills seem on their face to be simple budgetary adjustments. The “Balanced Budget Act of 1997” had wide bipartsian support and a relatively simple title. However the “Jobs and Growth Tax Relief Reconciliation Act of 2001” had hardly any bipartsian support, perhaps more tax cuts were added in reconciliation than in the original bill? If the graph could be altered to show some sort of significance factor then that would be an infographic!
This significance factor or magnitude could be quantified by providing some sort of comparison of the changes against the original bill. By way of example, there are well established computerized ways to compare documents and a significance factor could be calculated by comparing the size of these changes. Bills with bigger “change files” could show up with fatter lines in the graphic above. Of course this technique would have some problems. It could give false positives if there were a lot of words to describe a relatively minor change and it could give false negatives if a massive portion of the original bill was removed (describing a removal is a fairly easy task, essentially “delete lines 1-1000”). But even a flawed mechanism in the hands of knowledgable people can be a useful tool as knowledgable people can quickly weed out the false positives, reinstate the false negatives and focus the viewers’ attention on the issues that matter.
Such a tool could be useful here if we were looking at hundreds of bills, but we’re only looking at 13. A responsible journalist would have prepared for an interview with Senator McConnell by digging into these 13 other bills and asked McConnell to choose which of them would take 2nd place behind health care for “significant bills passed through reconciliation”. McConnell could then use that as a spring board to describe how much more of a change health care is from that previous “high water mark” or the question could reveal how hollow McConnell’s talking point was. Instead, we, as consumers of news, get neither.