In the 2012 election 28 percent of all disclosed political contributions came from just 31,385 people. In a nation of 313.85 million, these donors represent the 1% of the 1%, an elite class that increasingly serves as the gatekeepers of public office in the United States.
Compared to other big campaign donors, lobbyists spread their money around. And because they seek access to lawmakers to push for their clients’ interests, they give more of their contributions directly to candidates as opposed to party committees and super PACs. That’s according to a new Sunlight Foundation report on the lobbyists in the “one percent of the one percent,” the rarefied group of about 31,385 well-heeled insiders that give at least $12,950 to political campaigns. So what do these lobbysits want to get done? In particular, what about ones giving the most? Of all the players in Washington’s influence business, here is a list of the 10 who gave more than anyone else in the 2012 election. Continue reading
Are the 1% of the 1% pulling politics in a conservative direction?
In the 2012 election 28 percent of all disclosed political contributions came from just 31,385 people. In a nation of 313.85 million, these donors represent the 1% of the 1%, an elite class that increasingly serves as the gatekeepers of public office in the United States.
The more conservative the Republican, the more dependent that Republican is likely to be on the nation's biggest individual donors, a new Sunlight Foundation analysis of campaign finance data finds. By "biggest individual donors," we are referring to a group we named “the 1% of the 1%” after the share of the U.S. population that they represent. These wealthy donors may be pulling Republicans to the political right, acting as a force for a more polarized Congress. The polarizing effect for Democrats, meanwhile, is unclear. If anything, more liberal Democrats depend a little less on 1% of the 1% donors than conservative Democrats. As we explored in our big-picture look at the 1% of the 1%, the biggest donors in American politics tend to give big sums of money because they want one party to win. Approximately 85 percent of the top individual donors in U.S. politics contributed at least 90 percent of their money to one party or the other. By contrast, less than four percent of these donors spread their money roughly equally between the two parties (a 60-40 split or less).
Figure 1.
The above figure treats all Democrats and Republicans as equivalent. In reality, both parties contain some moderates and some extremists. Some -- Ezra Klein, most prominently -- have argued that while small money exerts a polarizing tug on the parties, big money is consensus-oriented and centralizing. At the time, I responded that if big money was consensus-oriented, it was doing a terrible job of building consensus. I went further to hypothesize that big money might also be polarizing. Turns out I was more right than I knew then.
Continue reading
Two principles to avoid common data mistakes
If David Brooks is correct, the “rising philosophy of the day” is “data-ism.” But you don’t have to believe David Brooks. Just look at the big data (e.g. Google Trends) on “big data.” For the political junkies, data became sexy in 2012. First, the New York Times’ Nate Silver’s meta-analyses of polling data triumphed over the pundits’ “gut feelings.” Second, the Obama campaign successfully used data analytics to increase voter turnout. This caused people to pay attention (witness, for example, David Brooks’ new devotion to the subject as prime column-fodder). Of course, for those of us in the transparency and accountability advocacy community, data has long been a prized commodity. And as governments around the world increasingly commit to open data promises, more and more data is becoming available. At its best, data allows us to transcend our personal anecdotal experiences, giving us the big picture. It allows us to detect relationships and patterns that we wouldn’t otherwise see. Using data smartly can help us to make better decisions about both our own lives and our society. But it’s important to understand that data and data analysis are merely tools. They can be used well, or they can be used poorly. It is remarkably easy both to mislead and to be misled by data. Hence the old adage: “There are three kinds of lies: lies, damned lies, and statistics.” For many people, data can quickly overwhelm and confuse. It’s easy to misinterpret data, or to use it irresponsibly. We as humans are not particularly good at intuitively grasping large numbers, and our educational system generally does a poor job of helping us to counter this problem. For that reason, I want to offer two basic principles that I think could prevent a majority of the data mistakes that I observe:
- Cherry-picking works better with fruit than data
- Correlation provokes questions better than it answers them
Quantifying Data Quality
You've already heard me complain about data quality -- how it's a bigger problem than most people realize, and a harder problem than many people hope. But let's not leave it there! Perfect datasets mostly exist in textbooks and computer simulations. We need to figure out what we can do with what we have. In this and other posts, I hope to give the developers in our community some idea of how they can deal with less-than-perfect data.
The first step is to figure out how bad things actually are. To do that, we'll use some simple statistics -- those of you with a strong stat background can skip to the next entry in your RSS reader (or better yet, correct my mistakes in comments).
59063
