Last June, Kevin Collins and I co-wrote a post on measuring the ideology of the Republican presidential field. At the time, many of the candidates had yet to enter the race or had recently entered. In lieu of current FEC records for these candidates, we relied on past donations raised by those candidates’ congressional or gubernatorial campaigns to estimate their positions. Thanks to the recent FEC filing deadline, there is now enough data available to re-estimate positions based solely on contributions made to the candidates’ 2012 presidential committees.
In the previous version of the figure, the y-axis measured the proportion of dollars raised from small donors, which revealed a relationship between small donors and ideological extremism. In this version, the y-axis plots the proportion of funds raised from pro-life versus pro-choice sources. I calculate this by first identifying the subset of contributors in the database that have given to one of the many PACs or ballot measures that specifically advocates a pro-life or pro-choice cause. I then classify each contributor as pro-life or pro-choice depending on whether they have donated to a pro-life or pro-choice organization. For example, anyone who has made a political contribution to Right to Life, the Susan B. Anthony List, or in favor of the South Dakota abortion ban referendum is coded as pro-life; whereas anyone who has made a political donation to NARAL, the Republican Majority for Choice, or against the South Dakota abortion ban referendum is coded as pro-choice. I then calculate the pro-life/pro-choice proportions such that,
y = ($ raised from pro-life donors) / ($ raised from pro-life donors + $ raised from pro-choice donors).
Lastly, the size of the circles scales to the percentage of a candidate’s donors that have made political contributions pro-life/pro-choice organizations. Roughly speaking, the larger the circle, the more important social conservatives are to a candidate’s fundraising operations.
Rick Santorum, who has jockeyed hard to position himself as a champion of conservative social values, has the most pro-life position on the y-axis as well as the largest percentage of abortion activists in his donor pool (all of whom are pro-life). The percentage of donors who have supported pro-life/pro-choice organizations is much smaller for candidates like Ron Paul, whose economic conservatism appears to outweigh his vocal pro-life position. This is also the case for Herman Cain who has recently come under pressure from pro-life activists.
Mitt Romney has raised a larger proportion of funds from pro-choice donors than from pro-life donors, which might not bode well for his campaign. On the other hand, despite a crowded field of bona fide pro-life candidates, many pro-life donors still opted to give to Romney’s campaign. In fact, Romney boasts a larger total number of pro-life donors than Santorum, even if they represent a much smaller percentage of Romney’s overall donor base. Equally telling is that in the absence of a single viable pro-choice candidate, the Rick Perry has attracted a sizable contingent pro-choice donors. This seems to speak to the extent to which economic issues have overshadowed social issues with respect to fundraising. We will have to wait and see whether this generalizes to voters in the primaries.
The Washington Post recently published an article on money blurts which has also been discussed at The Monkey Cage and elsewhere. I’ve done some research on this topic, so I thought I would recycle a plot of Joe Wilson’s donors before and after his outburst.
The figure shows the ideology of Joe Wilson’s contributors leading up to and following his outburst during the President’s health care speech. Each point represents the ideology of an individual contributor (higher points are more conservative) and is scaled by donation amount. What I find striking about the figure is not so much the spike in contributions following the outburst but rather that a large percentage of his post-outburst donors were more conservative than anyone who had given to him before. Many of the far-right donors that Wilson activated were the same people who later made up the core of the Tea Party’s fundraising base.
The flip side is that Wilson’s Democratic opponent, Rob Miller, also received a substantial boost from small donors, prompted Miller to develop a national donor network centered on fundraising from the professional left. We take for granted that money blurts are strategic behavior. I find it more likely that these candidates are blurting out polarizing rhetoric for its own sake. The resulting floods of ideological money serve only to encourage partisan rhetoric. To quote fundraising superstar Alan Grayson, “There’s an audience — It’s clear that people want to hear the music, and I’ll keep singing.”
I’ve been researching the effect of small donors on elections and have found their influence to be highly polarizing (I’ll be presenting a paper on this research later this year at APSA). While the proportion of campaign dollars coming from donors giving in small amounts is somewhere in the range of 15 and 25 percent (depending on how one classifies a small donor), the influence of small money on electoral outcomes is augmented by the tendency to target a select group of candidates. During the 2010 Midterms, ten percent of candidates reined in over fifty percent of the funds from small donors. A small donor network has become fashionable on the Hill and badge of honor on the campaign trail. Yet despite enjoying a reputation as protectors of democracy, small donors tend to be ideological warriors out to reward polarizing rhetoric and to punish bipartisanship.
(Co-written with Kevin Collins)
What will the field of candidates for the 2012 Republican presidential nomination look like? Presidential primaries tend to highlight divisions within parties, and the press has focused on moderates versus conservatives, social conservatives versus business conservatives, party insiders versus Tea Party insurgents, electable candidates versus long shots.
Some commentators, notably Nate Silver in his visualization of the potential Republican field, have depicted these dimensions as orthogonal. Specifically, he graphically depicts candidates ideological placement, status as insiders, and chances of winning. However, while Silver uses Intrade values as a measure of candidate chances, his placement of candidates on the other two dimensions is based on his judgment alone. Based on his judgment, these three dimensions appear to be unrelated to one another. However, drawing on data from federal and state campaign finance records, we can more reliably estimate both candidate ideology and reliance on large donors, which we take as a proxy measure of insider status. Based on this analysis, we show that these dimensions are in fact strongly correlated.
The figure below summarizes the 2012 Republican presidential field. The upper panel plots the proportion of funds raised from donations of $500 or less (including unitemized contributions) against candidate ideology. As is evident, more conservative candidates, particularly those affiliated with the tea party, raise a greater proportion of their funds from small donors. The circle sizes are proportional to the Intrade share prices for the respective candidates as of June 13th. The circles are colored coded based on candidacy status. Those who have officially announced their candidacy are red, those who have not yet announced are purple, and those that have decided not to run are green.
The bottom panel overlays two kernel densities drawn from the 2010 Election cycle. In red is the ideal point distribution of Republican candidates. This gives a sense of how the presidential candidates locate with respect to the party as a whole. In gray is the distribution of Republican donor ideal points, each weighted by the total amount donated during the 2010 Election cycle. This characterizes the fundraising landscape with respect to ideology.
The idea underlying measuring the ideology of candidates from campaign finance records is relatively straightforward. Contributors are assumed to prefer ideologically proximate candidates (i.e., those who share their views). A model then conditions on the ideology-based research conducted by the millions of political donors to provide estimates of candidate ideology.
When we compare the measures with those created by Nate Silver (link), reprinted below, we find that the rankings are correlated but exhibit a few large discrepancies. Silver ranks Ron Paul among the most moderate candidates, whereas the donors place him on the far right. (Paul may technically agree with Democrats on several key issues but has his own reasons.) He identifies Santorum as the most conservative candidate in the field, but Santorum’s donors place him nearer the center of the Republican Party. In both cases, the candidates’ DW-NOMINATE score reflects his CFscore–that is, roll call voting records consistently place Paul on the far right and place Santorum somewhere in the interior wing of the party.
Mapping candidates in this way highlights what we gain from quantitative measures of ideology. Cultural issues such as abortion and same-sex marriage happen to be strongly correlated (a pattern confirmed by looking at who donates to ballot campaigns), but they take a back seat to explaining how ideological preferences map onto political behavior and outcomes. In the end, traditional left-right economic issues best explain how a member votes and which candidates donors support.
As a point of reference, we include in the figure past Republican presidents. One of the advantages of measuring ideology from contribution records is that both candidates and contributors are typically active across multiple election cycles, which facilitates more reliable across-time comparisons (see the discussion here). If they were running today, both Bush’s would locate to the left of the mean ideal point of the Republican Party, which has become steadily more conservative over the last three decades, whereas Reagan would be slightly to its right.
Candidate positions are typically stable but are subject to change, especially for candidates seeking to reinvent themselves when competing for a new office. This certainly describes Romney predicament. Romney’s ideal point shifts dramatically over the course of his political career. Romney began his career with a score of 0.59 during his failed 1994 senate bid against Edward Kennedy, adopted a centrist position while running for governor, bringing his score to 0.48, and has currently settled near the center of his party with a score of 0.78.
These data come from the most recent available campaign finance records for each candidate, which do not include any current presidential exploratory or candidate committees but do include state records (for current and former governors) and older records, when the person in question has been out of office for a substantial period of time. These variations in source data could potentially bias the measures in a number of ways. First, if candidates have shifted their ideology since their last campaign record, their previous donation pattern may not reflect their current ideology. For example, Newt Gingrich’s fundraising as a Member of Congress may not perfectly reflect his ideology today.
Second, states have different campaign disclosure thresholds than do the federal government. While the Federal Elections Commission reports the individual records of all donations made by any individual who donates to a candidate over $200 in total, this disclosure threshold is lower in most states. In MN and IN it is $100, and in UT it is $50. Since small dollar donors tend to be more ideologically extreme than large dollar, access seeking donors, if state candidates are receiving substantial amounts of donations in between their state and the federal disclosure thresholds, these amounts could make them appear more ideologically extreme. However, since the candidates for whom state records are used are distributed roughly evenly across the space, these differences in disclosure thresholds may not be a significant source of bias here.
Our measure of proportion raised from small donors is also in part a function of campaign finance laws. This matters for past candidates for president who fundraised subject to the $1,000 limit on individual contributions in place prior to the implementation of the BCRA during the 2004 Elections. This discrepancy make past president look as though they raised slightly more funds in small amounts than they actually did.
Variation in campaign finance laws matters much more for governors turned presidential candidates. Pawlenty, Daniels, Perry and Huntsman campaigned for governor under state laws that foster very different fundraising environments. (We rely solely federal contribution records for Romney and Palin). Minnesota’s contribution limits favor fundraising from small donors whereas limits in Indiana, Texas and Utah have no limits on the amount an individual can donate to a candidate, thus favoring fundraising large amounts from a smaller set of donors.# This highlights that Pawlenty has experience fundraising in small amounts but is not particularly informative when making comparisons to other candidates. Fortunately, this will no longer be a problem as future FEC filing deadlines approach.
The New York Times published a fascinating article last week about the changing politics of physicians. The article reports that as more physicians abandon practices for salaried positions their politics have trended to the left. This trend is also present in the contribution patterns of health care professionals.
The figure below tracks the changes across time in the ideological giving patterns of four groups of health care professionals: surgeons, nurses, mental health care professionals (restricted to psychiatrists and clinical psychologists), and all other physicians. The trends track the mean ideological position (ideal point) of donors within each group. For each election cycle, I take the subset of donors from each group that gave at least once during that cycle and calculate their ideal points based on all donations made during that cycle and cycles prior. This permits the ideal point estimates to update with time.
Here are some helpful summary statistics for interpreting the scale. The mean ideal point for the entire sample of 2.5 million individual contributors (which includes health care professionals) is -0.16. The mean ideal point is -0.66 for Democratic candidates and 0.82 for Republican candidates.
Surgeons are by far the most conservative. They are also the group that was, on average, more conservative in 2010 than in 1990. The other three groups trend to the left in varying degrees.
I included nurses to emphasize that the industry has been changing as a whole. I also included psychiatrists and clinical psychologists to emphasize that differences between groups is probably better understood as a function of self-selection into a given profession than anything else. Whatever differences in pay and work conditions exist between mental health professionals and other physicians, they would not be sufficient to explain the differences in ideology. That mental health professionals are significantly to the left of nurses reinforces this point.
Although revealing, the trend line for physicians leaves out much of the story. The mean ideological position is relatively stable, but there is plenty of action in the distribution. The 1994 and 2010 midterm election cycles represent the closest thing to electoral deja vu that we can expect to see in our lifetimes. This makes these cycles useful points for comparison. The figures below show the ideological distributions for surgeons and physicians in 1994 and 2010. The distribution for surgeons fills out a little on the left over the years but remains unimodal. In contrast, the distribution for physicians changes quite a bit. Ideologically speaking, physicians have become more evenly divided and more polarized.
These figures can help address one of the paragraphs from the article that caught my attention:
“Dr. Cecil B. Wilson, the president of the A.M.A., said that changes in doctors’ practice-ownership status do not necessarily lead to changes in their politics. And some leaders of state medical associations predicted that the changes would be fleeting.”
I strongly suspect that Dr. Wilson is correct in his assessment. The link between doctors abandoning private practices for salaried jobs and their changing politics is probably overblown. I attempted to get at this question by dividing the sample into physicians who report being self-employed and physicians who report having an employer. I found only a slight difference between the groups. On the other hand, I sincerely doubt that the changes will be fleeting. The changes appear to be responding to generational shifts—in particular, the influx of women doctors.
I was curious as to whether the rightward jump observed during the 2010 midterms resulted from 1) donors that had previously given to Democrats shifting their dollars to Republicans or 2) increased giving by Republican donors relative to Democratic donors. In other words, was it a case of changing minds or changing wallets? For each year, I categorize donors into one of six categories based on their giving patterns in previous cycles.
- Strong Democrat – greater than 95% of donation dollars went to Democrats in prior elections
- Lean Democrat – between 60% and 95% of dollars went to Democrats in prior elections
- Toss Up – between 40% and 60% of dollars went to Democrats in prior elections
- Lean Republican – between 5% and 40% of dollars went to Democrats in prior elections
- Strong Republican – less than 5% of dollars went to Democrats in prior elections
- New Money – first time donors/had not given in prior elections
Each bar displays aggregate amounts donated to each party. For example, the bar labeled “Strong Dem” in the 2010 panel shows that donors who in previous cycles gave 95% or more of their dollars to Democrats, gave $41 Million to Democrats and $1.5 million to Republicans.
Partisan defections by Democratic donors had little to do with the 2010 shift in favor of Republicans. (In fact, the defection rate was greater for Republicans donors.) Increased giving by Republican donors and slightly reduced giving by Democratic donors account for the lion’s share of the swing. Republicans also received a larger proportion of dollars from first time donors than they had in the past. Republicans won 62 percent of the dollars from first donors in 2010, up from 44 percent in 2008 and 57 percent in 2006.
Lastly, it is worth noting that the overall rightward shift during the 2010 midterms is no larger than what we saw in other industries. Hence, I caution against concluding that the shift was in direct response to the Affordable Health Care Act.
The Wisconsin Supreme Court election between Justice David Prosser and Assistant Attorney General JoAnn Kloppenburg has received more media attention than any state judicial election in recent memory. The race was extremely close, but following the tide-turning discovery of 14,000 unrecorded votes from Waukesha County, a Prosser victory now appears all but certain. A challenge from Kloppenburg’s campaign will likely lead to a recount of Waukesha County, along with the possibility of a federal investigation, so the official outcome may take some time.
My co-author Michael Woodruff and I have been working on a paper that develops a quantitative measure of state supreme court ideology based on campaign finance records. I have come across several articles claiming a Kloppenburg victory would swing the court to the left. This naturally piqued my interest as something our measures can easily address. Below are the ideological measures (CFscores) for the current Wisconsin justices, as well as for Kloppenburg and a few other Wisconsin politicians. I measure Kloppenburg’s ideology based on 32 campaign contributions made by her over the last two decades. This sidesteps the problem of measuring Kloppenburg’s ideology based on a campaign that has arguably become less about her as a candidate than about opposition to Governor Walker’s policies.
Kloppenburg is indeed far more liberal than Prosser, locating near Chief Justice Shirley Abrahamson. In this sense, a Kloppenburg victory would move the court to the left. However, what really matters is the resulting position of the pivotal (median) justice. If Kloppenburg wins, Justice Roggensack will become the median justice. Judging by her donors, Roggensack is a moderate conservative, not a liberal. I should note that the uncertainty bound on Roggensack’s estimate is unusually large, indicating that her donors are not an ideologically cohesive group. In other words, she belongs to the rare breed of candidates capable of raising funds from both liberals and conservatives. That being said, she raises the bulk of her funds from conservatives. Overall, 65 percent of Roggensack’s donors are conservatives (defined as individuals with CFscores >0.5), compared with 85 percent of Prosser’s donors, 24 percent of Justice Bradley’s donors, and a mere one percent of Abrahamson’s donors.
I will refrain from speculating on Roggensack’s personal views on the union legislation or the extent to which it might influence her ruling on the matter. Yet I doubt she is eager for the union legislation to make it to the Wisconsin Supreme Court. If it does, siding with her liberal colleagues would risk gravely upsetting the majority of her donors. Unfortunately, siding with her conservative colleagues would similarly upset her liberal donors. Such is the dilemma of maintaining an ideologically diverse donor base.
Whatever the outcome, the election is an important development for the court and Wisconsin politics in general, as there is good reason to view the results as a proxy referendum on Governor Walker’s policies. But regardless of which side prevails, the evidence suggests that the Wisconsin Supreme Court is destined to remain in conservative hands.
Now that the dust from the 2010 Elections has cleared, it is time to start looking at how the 112th Congress will compare to the 111th. The figure below tracks the ideological positions of the median House member, the mean Democrat, and the mean Republican since 1988. The replacement effect of the 2010 Midterm Elections is unlike anything in recent memory. The shift in the House median is two and a half times what was observed after the 1994 Election, wiping out the effect of Democratic gains in the previous two elections and then some. The 111th was the most liberal Congress in the past three decades; the 112th will be the most conservative.
The 2010 Elections had a profound effect on congressional polarization. Not only will the 112th House be the most polarized on record; 2010 will surpass 1994 as the most polarizing election cycle.
The figure below helps place the historic jump in polarization in perspective. It displays the ideological distributions of House members continuing on from the 111th Congress, alongside the distributions of the entering class of Republicans and the sitting Democrats they defeated. Two features stand out. The first is that the mean entering Republican (1.09) is substantially more conservative than the mean continuing Republican (0.82). In total, 77 percent of freshmen Republicans in the 112th Congress will locate to the right of the party median from the 111th. In other words, nearly 8 in 10 incoming House Republicans would have been on the right wing of the party in the 111th Congress.
The second standout feature is that, contrary to many media accounts, the Republican wave was an equal opportunity un-employer for Democrats. Democrats who lost their reelection bids were slightly more moderate than those who retained their seats—0.61 compared to 0.64—but the difference is statistically insignificant. The attrition rate was greater among centrist Democrats, but there were so few Democrats remaining in the political center that the losses had to come from elsewhere.
The polarization resulting from the 2010 Midterms is fundamentally different and more worrisome than what had preceded it. By historical standards, the post-war era stands out as a period of relatively low partisan polarization. This is largely attributable to the coalition between Northern and Southern Democrats. The increase in polarization during the 103rd through 105th Congress corresponds to the tail end of the Southern partisan realignment, a period during which southern districts that had traditionally elected moderate Democrats (a.k.a. Dixiecrats) began electing conservative Republicans. As the Southern Democrats gradually disappeared throughout the 1980s and 1990s, the parties became more clearly defined, thus returning congressional polarization to the historical norm.
The hollowing out of the political center explained the momentous rise in polarization during the Southern realignment. Now that only a handful of moderates remain in the House, polarization can no longer be portrayed as a story of vanishing moderates. It appears the rise of the extremists has stepped up as the driving force behind congressional polarization.
As promised, here are my predictions for the 2010 Midterm Elections. The model parts with poll-based forecasting models in predicting that the Democrats will maintain control of the House. It predicts that Democrats will win between 217 and 238 seats, which translates into a loss of 19 to 40 seats. (The complete set of House predictions is available for downloaded as a .csv or a .xls.)
If Congress operated more like a Westminster parliamentary system, fixating on which party will win a majority of seats would be more sensible. In such a setting, after assuming power the majority party (or coalition) is free to enact legislation with as little input from the minority as it pleases. This is not the case in Congress. The past two years have been a constant reminder to Democrats that even large electoral majorities do not grant similar levels of legislative control. Current theories of congressional behavior tell us that the position of the median member of Congress can be as important to policy outcomes as which party is in the majority. An advantage of my forecasting model is that it can predict ideological quantities of interest other than seat shares. For example, I can predict the position of the median member in the next Congress and the extent to which partisan polarization will increase or decrease.
The model projects that the position of the median House member in the 112th Congress will be -0.05 with a 95 percent CI between -0.13 and .13. This represents a sizable shift to the right from the median legislator in the 111th Congress, who was located at -0.24. Yet, this will only bring the median back to where it was during the 110th Congress. To place this in perspective, the median House member in the 111th Congress was in the region of Joe Baca (D-CA) and James Oberstar (D-MN). The model predictions place the median member for the 112th Congress in the region of Arthur Davis (D-AL) and James Marshall (D-GA) but could be as far to the right as John McHugh (R-NY) or former Senator Lincoln Chaffee (R-RI), which is still very moderate. According to the model, even in the best-case scenarios, the House median will be much more moderate than what Republicans experienced during the 104th-109th Congress.
The figure below displays the trend lines for the median House member and the means for each party since 1990. Regardless of which party claims a majority after the election, the model projects an increase in partisan polarization. The mean Republican will experience its largest shift to the right ever recorded, while the mean Democrat also will move further to the left as Republican challengers pick off moderate Democratic incumbents. The general rule of thumb for this election is: the larger Republican gains, the greater the increase in polarization.
I report the model predictions faithfully here, but I remain somewhat skeptical of the model predictions for two reasons. The first is that the realm of campaign finance has undergone changes since the previous election cycle. Not accounting for independent expenditures by outside groups might have biased the model in favor of Democrats. On the other hand, the BCRA arguably represented a much larger shock to the campaign finance system than Citizens United, yet the model predictions for the 2004 Elections were right on target. Moreover, the model does not include any variables that relate to campaign expenditures; it only conditions on fundraising patterns, which remain largely unaffected by Citizens United. The second reason is that the model predicted Democrats would win about 10 seats fewer than they actually did in the 2006 Midterm Elections. It is difficult to determine whether this reflects the Mark Foley October surprise or a failure by the model to account for partisan momentum.
I suspect the model predictions are too generous to Democrats by about 8 to 12 seats. Even with the downward adjustment, I still predict the Democrats will retain their majority, but just barely. This is in line with Sandy Gordon’s forecasting model based on calibrated expert raters. Along with Sandy’s forecasts and the recent polls showing that oversampling of landlines can bias polls in favor of Republicans, my model provides additional evidence that poll-based forecasting models are overstating Republican gains. Fortunately, we won’t have to wait long to find out.
As election day nears, I thought it might be an interesting exercise to see how accurately I could forecast election outcomes using only information derived from campaign finance records. Campaign finance records represent a rich data source that speaks to many areas of U.S. politics. Elections are no exception. I’m not the first to incorporate information on fundraising into a forecasting model. However, to the best of my knowledge, I am the first to attempt to forecast election outcomes based solely on FEC records. Despite the handicap of excluding all information from polls, InTrade, expert raters, and other data sources used to forecast elections, the model’s predictions are remarkably accurate. In fact, the out-of-sample predictions for House races outperform the polls.
Contribution records contain far more useful information than what can be expressed by fundraising tallies. For instance, they provide a way to estimate a reliable set of candidate positions via CFscores. The CFscores update almost in real time as FEC records are released during the course of an election cycle. In other words, we don’t have to wait until the election is over to get ideological measures for candidates. The CFscores enable my forecasting model to account for factors that other models ignore, such as adjusting for whether the ideological extremity of Tea Party candidates will hinder Republican electoral prospects this November. In addition, I can forecast ideological quantities of interest such as how the location of the median member of Congress will change after the election. This is perhaps less useful in terms of the horse-race but is probably a better overall measure for the type of policy we should expect from the next Congress.
Also informative are the patterns of individual donors across elections. I’ve been working on assigning unique contributor IDs that link contribution records from the same donor across election cycles and across state and federal elections. This may not seem like a big deal, but the ability to track the behavior of individual donors across elections cycles unlocks a wealth of information that had previously been trapped inside the dataset. For instance, linking records across years makes it possible to calculate the proportion of a candidate’s funds that came from first-time donors as opposed to veteran contributors. At the level of campaigns, this can convey information about a candidate’s success in activating supporters. At the national level, the median CFscore across all first time donors serves as a good proxy for the enthusiasm gap.
Much of the model’s predictive power is owed to an idea I borrowed from Sandy Gordon. Sandy has an interesting paper in which he uses past performance of expert election raters to calibrate their predictions in future elections. The paper gave me the idea of treating the hundreds of thousands of donors who had given in previous election cycles as de facto expert raters by looking at the percentage of funds given to winning candidates in previous election cycles. The idea behind this is simple. Some contributors give a greater proportion of their money to candidates that go on to win, while others spend the majority of their money on losing candidates. All else equal, the more money a candidate raises from the type of donors who give to winners, the more likely he is to win. (For additional details on what goes into the model, I include at the bottom of this post a description of the other model predictors, as well as links to download the R script and dataset.)
Tom Holbrook over at Politics by the Numbers nicely overviews the accuracy of election polls. He shows that during the 2006 and 2008 election cycles approximately 85 percent of House candidates who led in the polls 45 days before the election went on to win. As a comparison, the out-of-sample predictions from my forecasting model correctly identify the winner in over 94 percent of combined 2002-2008 House elections.
It is worth noting that my sample includes a number of less competitive House races that lack polling data and hence are excluded from the poll based predictions. The larger sample accounts for some of the increase the prediction rate but not all of it.
The model’s seat share predictions are also close to the mark. The table below shows the number of seats won by Democrats that the model predicts above/below the observed outcome (e.g. a value of 4 indicates the model predicts that Democrats would win four more seats than they actually did; a value of -4 indicates the model predicts Democrats would win four fewer seats than they actually did). I ran 1000 bootstrapped simulations for each election cycle to get uncertainty estimates. The first column reports the median value from bootstrapped runs and the other two columns display the upper and lower 95 percent confidence bounds.
CI Lower Bound
CI Upper Bound
The predictions are very close to the actual outcome in all but one election cycle. In 2006 the model under predicts Democratic gains by a considerable margin. This might be a result of the Mark Foley factor but I would need more evidence to support that claim. It is just as likely that it reflects a failure of the model to adjust for the effects of partisan momentum in landslide years. (As a note, I noticed that the confidence bounds only contain the observed value in two of the four elections. This suggests that I should probably up the amount of uncertainty in the bootstrapping scheme above the software’s default.)
A major advantage of using campaign finance data to forecast elections is that it costs next to nothing. There is no need to commission polls or pay expert raters. One needs only to collect freely available data and fit a model. That being said, campaign finance based forecasting could have the greatest impact in state level elections where polling data is sparse but fundraising abounds. Although I’m not convinced that they would be, it might be interesting to seeing if the model predictions are as accurate for state elections as they are for federal elections. The catch is that not every state is quite up to speed with the FEC in releasing contribution records to the public in a timely manner, but this problem is fast solving itself as the disclosure process becomes increasingly digitized.
As much as I would like to have predictions ready for the upcoming election, I haven’t had the time to fully organize the dataset. Those forecasts will have to wait until this weekend. In the meantime, I’ve made the dataset and R script available for download for anyone who might be interested.
Candidate Positioning: I use candidate CFscores to measure ideology.
Picking Winners: For each contributor, I calculate the percentage of funds given to winning candidates in previous election cycles. Then for each candidate, I calculate the mean value of his contributors weighted by the dollar amounts received.
First-Time Donors: I calculate the percentage of a campaign’s donors that gave the first time during that election cycle. This is intended as a proxy for the enthusiasm gap.
Donor Mood: I construct an aggregate variable by calculating the CFscore of the median first-time donor to proxy public mood.
Fundraising Success: This is measured by the log total amount raised by each candidate and the log sum of unique contributors.
Source of Funds: For each candidate, I calculate the percentages of funds raised from PACs, individual donors, party committees, and self-funding.
(Maximize the video and set the quality to 720p HD for best viewing. The video is also available for download from here.)
Each senator is marked by a two-letter state code and is color coded by party membership (Democrat, Republican, Independent). The top grey bar is the distance between the mean Republican and the mean Democrat (the standard measure of partisan polarization). The size of each party’s coalition is displayed at the end of the bars. The lower grey bar is the gridlock interval, which is measured as the distance between the filibuster pivots (the 34th and 66th most conservative senators before the Senate rule change in 1975 and the 41st and 60th thereafter). The ‘M’ imposed on top of the bar marks the position of the median senator.
The ideological estimates are constructed by scaling senate voting records with dynamic optimal classification. Keith Poole’s paper on optimal classification can be found here, and the paper which explains how to extend the method to smooth legislator ideal points over time can be found here.