Partisan giving rates have long been the standard measure of contributor ideology. Measured simply as the percentage of funds a contributor gives to Republicans/Democrats, the partisan giving rate is a straightforward, easy to understand and generally useful measure of contributor ideology. Although it is a great place to start, it is a far rougher approximation of ideology than most people realize.
In their book Polarized America, McCarty, Poole and Rosenthal (MPR) introduce a method of recovering the ideology of contributors that does away with the partisan assumption without introducing a handful of more complex assumptions in its place. Rather than assume all members of each party shared a single point in ideological space, they used as starting points the ideological scores of each Congress member, as reveal from Congressional voting records via their DW-NOMINATE method. This relaxes the assumption that Tom Coburn and Olympia Snowe are ideologically indistinguishable—the same being true for Dennis Kucinich and Ben Nelson, for that matter. By first ordering Congress members along the liberal/moderate/conservative spectrum, MPR can then look at the contribution profiles of each contributor and construct an ideological score based on money-weighted averages of the recipients’ ideology. For example, if a contributor gave $1000, $500, and $1500 to three Democratic candidates with the respective ideal points of -0.5, -0.1, and -0.4, the ideal point estimate given to that contributor would be:
(-0.5*1000 + -0.1*500 + -0.4*1500)/(1000+500+1500) = -0.383.
MPR’s method of estimating the ideal points of contributors is a simple yet powerful tool. Nonetheless, its reliance on DW-NOMINATE scores limits which contributors and candidates can be included in the model as well as what we can take away from the results. Notably, it excludes contributions to unsuccessful candidates that never hold office and compile a voting record.
The Iterated Money-Weighted Averaging (IMWA) procedure takes the MPR procedure a few steps further. If we can estimate the ideological positions of contributors based on the ideology of the recipient candidates, there is no reason why we cannot simply flip the procedure on its head and recover ideological estimates of candidates based on the ideology of their financial supporters. This results in a more inclusive ideological map of campaign finance that is not reliant ideological scores of candidates recovered from voting records.
The algorithm is as follows:
1) Set the ideal points of all Republicans candidates to 1, Democrats to -1, and independents to 0.
2) Estimate contributor coordinates as a function of the money weighted averages of their contributions.
3) Estimate legislator coordinates as a function of the money weighted averages of their contributors.
4) Normalize legislators to have a mean of 0 and a standard deviation of 1.
5) Go to step 2; repeat until convergence.
To illustrate, I include results from a scaling of the 2008 Election cycle that uses data from all individuals and PACs that contributed to two or more unique candidates. The figure above compare the ideological distributions of lobbyists, an important category of individual contributors, first derived from the partisan measure and then from the IMWA measure. The partisan measure lumps most lobbyists at either 0 or 1, representing the preponderance of partisans lobbyists that give exclusively to Democrats or exclusively to Republicans.
The IMWA estimates provide a much more fleshed-out portrayal that should lead to more informed inferences. For example, while lobbyists are clearly polarized, the IMWA distribution reveals that lobbyists are not as polarized as the partisan measure suggests. In fact, lobbyists are one of the least polarized industries. It also appears to be the case that liberal lobbyists are more dispersed than their conservative counterparts on K Street. In other words, the IWMA estimates are better able to account for something that is painfully obvious to anyone who follows politics, that ideology is not uniform across members of the same party or their supporters and that it is possible for one party to be more dispersed than the other.
The “no frills” IMWA method is not the only way to construct an ideological map of campaign finance. There are plenty of alternative approaches, most some slight variation on correspondence analysis, although more sophisticated statistical methods exist as well. The take-away is that no matter how one slices it, campaign finance data is rich in ideological content waiting to be unlocked and explored.