Political scientists envision elections as sets of classic puzzles: Can party elites decide primary elections? Why do people vote at all given the high costs and low benefits? Why don’t more women run for office? Debates over answering these questions sustain many researchers’ careers, although as a specialist in foreign policy I mostly keep up with them by skimming articles and reading the Monkey Cage. But these puzzles suddenly became of more than academic interest to me when my mother (hereafter “M”) decided to run for a seat on the three-member county commission that runs
Middletown County, a community of just under 200,000 people in Indiana.
As the family’s most accomplished (and only) political scientist, I felt an obligation to contribute to her campaign. I felt confident that I could be useful. As a grad student teaching research methodology, I had used the experiments of Gerber and Green (2000) and Nickerson (Nickerson 2008) and the stories collected in Issenberg (2012) as examples of the real-world impact of the best political science research. And so I set about
summarizing the newest research and applying its lessons to the myriad tasks of a campaign: developing and maintaining a voter database;
designing mailers and Facebook ads; and coordinating calling campaigns— all while also performing my duties as a first-year assistant professor.
I want to share what I learned from this unusual experience. This article is not “research” in a standard hypothesis-testing or even
politics and often hostile to campaigning. Put another way, my experience validated the insights both of works like Eitan Hersh’s Hacking the
Electorate (2015), a sophisticated quantitative book that argues that modern campaigns perceive voters as data, and of those like Kathy Cramer’s The Politics of Resentment (2016), an ethnographic work that argues that how citizens see themselves shapes how they regard politics and governance.
This article speaks to three distinct audiences. To specialists in the campaigns-and-elections literature, I want to provide a reminder of how ordinary campaigns work, an example of how research findings can (and can’t) be applied, and to point out research questions that need addressing. For consumers of the campaigns-and-election literature, I want to add an informed perspective showing the importance—and limitations—of
specialists’ contributions. And for the discipline as a whole, I want to argue for the importance of integrating theory with broader observations about the health of American democracy. The lessons of 2016 include not just what I learned from my mother’s campaign but those we need to process from Donald Trump’s victory—including, perhaps, an appreciation of why research programs and campaign strategies that assume the long-term stability of institutions might prove to be self-negating prophecies.
Running Scared in the Dark: Uncertainty During the Primary
lost by fewer than 200 votes). Her general-election opponent seemed formidable. A Democrat and the incumbent, he would be seeking his third four-year term on the commission. Before that, he had served several terms as a district City Council member for Middletown City, the largest city in Middletown County (and the third-largest city in Indiana). It would be a tough campaign: the county went for Romney over Obama 54-44 but Obama over McCain by 50.6-48.2. Even with favorable baselines, winning the
Republican nomination did not mean a cakewalk in the general election.
The paragraph above lays out most of the salient data points that political scientists would normally ask about. Given the conventional wisdom about turnout and partisan identification, it’s likely that the predicted probabilities favored M’s victory. But, of course, the map is not the territory and the predicted distribution is only a suggestion of what the real one looks like. In fact, the most important lesson I took from advising my mother’s campaign was that we never quite knew what was going on. Even as the campaign hummed along seemingly from strength to strength, persistent doubts
nagged: how much more money could the campaign raise? Would M get the newspaper’s endorsement? Did a phone call from a television reporter
presage a good story for M or for her opponent? How could the campaign respond to opponents’ strategies—and how could it disrupt their plans? After all, regardless of the longstanding debate of whether “campaigns matter”, any campaign more or less has to act on the presumption that it does. These ordinary uncertainties were a lived contradiction to the well-identified strategies we relied on from the turnout literature.
In response, the county party chairman had symbolically expelled her from the party. (The local Republican women’s club followed suit via a
handwritten note on expensive stationery.) Not coincidentally, several days after M’s announcement, A, a young White male attorney and political
neophyte, suddenly announced that he would run against her in the primary. We learned that the mayor’s team—who had raised over a million dollars to support his 2015 re-election—was fundraising for A in earnest. Another complication arose when a third candidate, B, emerged; B was a retired White woman and Tea Party activist who had appeared on Glenn Beck’s show.
All of these complicated the campaign’s decisions. As a devoted student of Bawn et al’s theory of parties (2012), I was nervous about how M would fare if partisans coalesced around the mayor’s signals.1 Would it be good or bad
for her if the election turned into a referendum on the mayor instead of a focus on M’s record? (Indeed, the local media bought into the “referendum” narrative early on, to my irritation.) But even beyond that, uncertainties loomed: what would matter more in the primary: the fact that M had vastly higher name recognition or the fact that A would enjoy a massive
fundraising advantage? Similarly, given the dynamics of the Republican presidential primary, would M’s record of public service hurt her relative to A’s youth and B’s conservative activism? Moderate Indiana Senator Richard Lugar’s 2012 primary defeat by conservative insurgent Richard Mourdock loomed over the primary (Hershey 2012). Finally, a quirk of the alphabet added one last worry: ballot-order effect. Meredith and Salant’s (2013) find that the first-positioned candidate gains (in this case, B) at the expense of the median-ranked candidate (in this case, M). In a close election, every vote could matter (Ho, & Imai 2008).
We couldn’t afford polling to answer these questions. (Our total budget for the primary was only a few thousand dollars; fundraising dried up suddenly after the mayor’s candidate entered the race.) Nor is it obvious that polling would have helped. What we needed to know was how voters would change their mind as they got to know the new candidates. Perhaps A and B would flame out; perhaps they would display previously unknown political talents. It is likely that polls at the beginning of that process would have been as informative as the mid-2015 polls that showed Hillary easily defeating Bernie or Jeb handily winning the Republican nomination.
Macropolitical factors compounded these uncertainties. Indiana’s primary comes late (in 2016, it was held on May 3), and so it had not hosted a contested Republican presidential race since Ronald Reagan defeated
Gerald Ford in 1976, 51%-49%. We had expected that 2016 would be similar —that Jeb Bush or Marco Rubio would wrap up the nomination by early March. The campaign prepared for a turnout in line with historical
experiences of about 7,000 to 10,000 primary voters, an assumption that guided our targeting for months. By mid-April, however, it became clear that Indiana would feature Ted Cruz’s last stand against Donald Trump. As one of the largest cities in the state, candidates and surrogates (including former Indiana University basketball coach Bob Knight) descended on Middletown County.2 That suddenly threw into question all of our
fundamental assumptions.
I drew on the 2008 Indiana Democratic primary turnout surge3 and surges
in other 2016 Republican primaries to estimate the turnout surge that a competitive Republican primary could bring. With only a handful of data
2 I suspect that the “surprisingly competitive” primaries like Indiana’s 2008 and 2016 primaries could prove a boon to students seeking as-if random assignments for salience and turnout’s effect on down-ballot races.
points, I guesstimated that Republican turnout would range from between 14,000 and 40,000 voters, most likely nearer 20,000 to 25,000 voters. Despite all of my graduate training’s emphasis on getting the most precise
estimate possible, adjusting ourselves for a big range proved Tukey’s
dictum about the value of an approximate answer to the right question. This was a broad range, but it spurred changes in outreach strategies to reach a less well-identified electorate. Our data vendor relied on primary voting history to identify partisans—exactly as Hersh (2015) describes.4 One
consequence of low Republican primary turnout over four decades was that we had poor data to guide us about who was a Republican. We “knew” only 20,000 identified Republican voters going into an election in which as much as twice as many might turn out. 5(In the event, Republican turnout totaled
26,772.) We therefore shifted tactics from reliance on pure voter contact, including buying ads on conservative talk radio (apparently grabbing
timeslots the Cruz campaign wanted a few days later), and sending mailers to a much larger population.
In the end, M received 49.35 percent of the vote to A’s 38.32 percent and B’s 12.33 percent. Figure 1 displays M’s precinct-level share of the top-two candidates’ votes plotted against Trump’s share of the Republican vote (Trump carried the county with 54.71 percent, trailed by Cruz’s 34.97 percent and 8.16 percent for Kasich). Bivariate OLS regression suggests that every additional percentage point of Trump turnout predicted a 0.34 percentage point increase in M’s vote share ( .096; p <0.001). The
elections results suggested that M’s campaign benefitted somewhat from
4 One rather direct implication of running as an ‘insurgent’ was that we couldn’t get access to VoterVault.
Trump’s turnout, although probably not enough to change the election results compared to a normal year.
30 40 50 60 70 80
30 40 50 60 70
Primary Trump Vote Share
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M
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Number of R Ballots Cast 100
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Figure 2. Map of 2016 Primary Election showing M’s Share of Top-Two Candidate Vote Share by Precinct. For ease of presentation, I have subtracted 50 percent from M’s vote share, so that positive values represent winning each precinct, negative figures represent losing that precinct, and 0 represents breaking even. Middletown City boundaries in Red.
Figure 2 maps M’s share of the two-candidate primary vote share by precinct. Two areas stand out as weak spots for her: the county’s “West Side” and the Downtown of Middletown City. A’s personal ties were
strongest on the West Side; his family had resided there for six generations. As for the Downtown area, the mayor and a surprising share of his
administration live there. In local elections, the sorts of concentric circles that Fenno described are almost visible even in aggregate data.
Presidential politics continued to yield surprises for the campaign
nationally, I also hoped that the Republican ticket would carry Indiana by a solid margin to avoid any potential negative coattails.
Thus, when Trump chose Indiana Governor Mike Pence as his running mate, I was relieved. The findings in Heersink & Petersen (2016) convinced me that any downside to Trump’s candidacy would thereby be mitigated by the boost Pence would give Trump in the state. (We received another boost when Pence’s replacement as a candidate for governor, Lt. Gov. Eric
Holcomb, chose a Middletown County resident as his running-mate.) But my fears of reverse coattails returned with a vengeance after the Access
Hollywood tape was released in early October.
Throughout, my uncertainty was heightened by the fact that the political science literature assessing coattail effects on local candidates is all but nonexistent, despite an urgent theoretical and practical need for such understanding. Local politics is simply neglected out of all proportion to its importance. As Baybeck (2014) notes, review articles on local politics start with a lament that the field is “woefully understudied”. There is no
particularly justifiable reason for this, either; there are as many as 500,000 local elected officials in the United States, a population greater than that of Iceland, and the ways in which their politics configure across a number of institutional forms (from mayor-council cities to boll weevil eradication districts) should prove ample fodder for theorizing.6 Although not as
glamorous as presidential or legislative studies, I can assure the doubters that the marginal importance of any new piece of work in this field would be high.
Viewed retrospectively, the narrative about the primary campaign became one of inevitability—a practiced candidate making a triumphant candidate. But it did not feel like that at the time. More precisely, I usually believed in victory but often doubted my confidence in my belief. And the range of counterfactuals suggests skepticism would have been justified. If turnout had been lower or A more capable a challenger, “inevitability” could have become a “squeaker” or worse.
Strangely, despite the prominence that uncertainty played in my
perceptions of this campaign, I find that open discussions of doubt and bewilderment are almost absent from most political science analyses. Sides and Vavreck’s (2014) The Gamble hardly mentions “uncertainty” and almost never in the sense of strategists’ uncertainty about decision-making. Oliver (2012, p. 33) recognizes that uncertainty matters, but dismisses its
importance by saying that “exogenous shocks [will] upset the standard operating procedure.” If one is describing systems at a highly abstract level, this explanation suffices. But it occludes the question of what kind of
exogenous shocks occur, how much they matter, and when they take place— all questions ripe for empirical examination. Indeed, studies that have
exploited earthquakes, hurricanes, floods, natural disasters, and football games as sources of exogenous shocks have found that such events suggest that voters are myopically retroscopic, blaming incumbents for issues
Why, then, are campaigns—and political scientists—so confident that they downplay uncertainty? Enos and Hersh (2015) provide one explanation for this absence of doubt: campaign managers and others may simply be overly confident in whatever strategy they hit upon. (Moreover, any campaign manager who did express doubt would quickly find herself turned into an
ex-campaign manager.) None of these were predictable, but all of them had some impact on how campaigns presented themselves to voters. Typical studies of elections downplay these uncertainties by concentrating on average treatment effects or relegating exogenous shocks to the error term —but how campaigns anticipate and prepare for shocks (or why they
systematically fail to do so!) deserves more attention.
Turning Out The Vote: Running the Standard Playbook
The most directly useful parts of political science tell people how to run campaigns—or run them better. In the nineteenth century, the rise of democratic politics yielded urban political organizations now familiar to political scientists mostly, if at all, through classics like Plunkitt of Tammany Hall (1995). Since World War II, labor-intensive ‘machines’ have been
displaced by campaigns that represent their precise opposite: capital-intensive, white-collar, and technologically mediated. More recently still, a focus on experimentation has disrupted previously unchallenged campaign strategies (Badwin-Philippi 2016). So pervasive has the turn toward
analytics become that journalists’ profiles of campaigns’ data teams are now the subject of parodies.7
7 For instance: “[Campaign] gave [Gullible Reporter’s Political Rag] an exclusive look behind the scenes of their digital and data operation. Buried in a nondescript office building on the outskirts of [State Capital], [Candidate]’s digital and data team (internally referred to as Team [Nerd Cliche]) pounds away at their keyboards. Their office is an open floor plan plastered with posters of [Candidate]’s face overlaid with ‘[Pop culture
reference]”’ Its occupants are crammed into tight rows of tables; many are sitting on workout balls instead of chairs. Clad in jeans and t-shirts, an army of 20-somethings are glued to their screens, hacking away at new cutting-edge apps designed to engage [Candidate]’s supporters online.”
Nick Marcelli, “Every political reporter’s campaign tech article ever,”
https://medium.com/soapbox-dc/every-political-reporters-campaign-tech-article-ever-The promise of the experimental revolution for social scientists was cleaner theory-testing and –generation. (For campaigns, the payoff was more direct: winning.) Normatively, the literature often hoped for positive spillovers, such as generating more competitive campaigns by lowering perceived barriers to entry by challengers or, as Gerber and Green (2000, p. 662) wrote, reversing the longstanding decline in voting rates. Many individual political scientists also found the revolution to be beneficial personally (suddenly political scientists were in-demand consultants) and ideologically (since the Democratic Party and progressive causes were faster to catch on; Kreiss, & Jasinski 2016).
Research on campaigns and campaigns’ use of that research is broadly understood to have been good for political campaigns and social science alike. As Pons (2016, p. 34) writes, “in no other fields are the
recommendations of social scientists followed so closely and so rapidly.” Sometimes, the mimicry is unusually direct: the 2016 Ted Cruz campaign sent a threatening mailer to Iowa Republican voters in Iowa; the mailer’s text, design, and strategy was derived from the “social pressure” turnout experiment of Gerber, Green, and Larimer (2008).8 (The script I used for our
final week’s GOTV phone calls closely followed Nickerson and Rogers’s.) Indeed, the field is now both “hot” enough and established enough that it has generated its own scandals—the 2015 saga of UCLA graduate student Michael LaCour9 and the 2014 Stanford-Dartmouth field experiment in
Montana.10 Moreover, there is a literal playbook on how to incorporate
political-science’s findings into campaigns: Gerber and Green’s Get Out The Vote (2015). It is not an elementary primer for the first-time candidate, but
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read in combination with another how-to manual for running a campaign it certainly helps to suss out which strategies are effective and which are just rent-extraction by consultants.
Yet as celebrated as these findings have been, news of the credibility
revolution has yet to reach Middletown County. Our opponent spent lavishly on radio and newspaper advertising. We targeted conservatively: I guessed that the risks of targeting appeals based on demographic characteristics were greater than the possible benefits, and so we relied on vote history to identify our audience. Reading Hersh’s description of how campaigns
perceive voters after the election felt like reading a transcript of my thoughts as I sat with our database. Despite lots of hype about
“microtargeting”, I found that the most useful variables were vote history and party ID; anything beyond that largely seemed to be either spuriously correlated or overly niche. Designing and printing campaign materials takes time, and even if we could have identified particular niches it wasn’t clear that the marginal benefit was worth the cost of pursuing them. Moreover, Hersh and Schaffner (2013) find that if a campaign microtargets to one audience but sends the message to members of a different audience—all but inevitable, if the data I saw was any indication—this can spark voter
backlash. (There was one exception: we sent a letter from my sister
describing our mother as a role model for women. This went to households with women who were likely to be mothers or grandmothers—which seemed like a safe choice, given that gender and age were well-identified in the data.)
My experience confirms the literature’s consistent finding that direct
voters who lived along high-traffic streets.11 The response was
overwhelmingly positive, and at the end of the process the campaign placed more than 600 signs. Anecdotal evidence from fundraising and other
indicators suggests that Green et al’s hypothesis that signs serve as
indicators of a campaign’s viability seems more than plausible. Because of the literature’s consensus on direct contact as a mobilization tool, investing effort into validating well-attested findings seemed to be inefficient. Unlike Alvarez, Hopkins, and Sinclair’s (2010) work with Pasadena Democrats or the “eggheads” who consulted for Rick Perry’s 2006 re-election campaign (Issenberg 2012), I did not reserve some voters as a control group.
Consequently, although I cannot directly evaluate the hypothesis that our GOTV efforts had a positive effect, then, I do think it is justifiable to believe that the more than 30,000 phone calls the campaign placed was able to sway a few votes.12
11 I learned practical data-cleaning lessons, including the importance of removing
apartment buildings and retirement homes—something that didn’t matter creating mailing lists or phone lists, but was vital when trying to find yards for signs.
12 However, even the most aggressive phone bank cannot reach every targeted voter directly. We broadened our outreach by sending several mailers. Our mailings were
Figure 3. M's vote share by precinct, adjusted so that negative numbers represent deficits and positive numbers represent surpluses. On the left, vote share including straight-ticket votes; on the right, straight-ticket voters have been removed from the totals to focus on ticket-splitters. ”Missing” precincts are uninhabited.
M won, 56%-44%. Figure 3 maps the distribution of her vote share (just under 45% of voters cast straight-ticket Republican, Libertarian, or Democratic ballots). As one might expect, the urban core (within the Middletown City boundaries, as shown) voted more strongly Democratic and the suburban and rural parts of the county voted more strongly
Republican—although those differences are mostly attenuated among ticket-splitters.
outperformed Trump in strongly Democratic areas and slightly
underperformed him in rural and suburban areas, suggesting that there was at least some ticket-splitting based on local factors. Yet bivariate regression shows a tight correlation between M’s vote share and Trump’s: every
additional percentage point in Trump’s share of the non-straight-ticket vote predicted a 0.5 percentage point increase in M’s vote share (se = 0.041, p < 0.001). And the fact that just under half of voters cast straight-ticket ballots —overwhelmingly for Republicans—made party identification valuable. Figure 4 reinforces the notion that the election proceeded as we would expect if partisanship mattered quite a bit.
20
Figure 4. Vote share net of straight ticket votes, general election, 2016, showing M's vote share as a function of Trump's vote share. Straight black line is the 45-degree line; the blue line with shaded area is the bivariate OLS line.
Making Democracy Work? Why Middletown Hates Politics And Why It Matters
Of course, I was happy that the campaign was successful. But what I
learned in helping to run it made me a little less cheerful about the health of seemingly robust institutions. One implication of running a campaign that emphasizes voter contact is that one is constantly in contact with voters— even those who don’t want to be contacted. Accordingly, the campaign taught me that some voters really hate politics. True, when contacted by canvassers, most people were polite, and some were even enthusiastic. But others were not. “Who I vote for is none of your damn business,” the most polite of these irate citizens expostulated before slamming down their
phone. Had I known that people would be so irritated by an elementary part of democracy, I would have kept statistics on this sort of incident. However, even the anecdotal regularities were profound enough that I began to worry that we needed to adjust our targeting and scripts to avoid blowback.
Unlike researchers trying to pin down a theoretical effect, I wanted to generate a desired outcome, not get a surprising result. I was even more worried after reading Bailey, Hopkins, and Rogers (2013), who convincingly argue that persuasion attempts have markedly counterproductive effects for low-information voters—turning them against the candidate on whose
behalf canvassers were working.
For a political scientist from a political family who relaxes by watching television shows about politics, the depth of this backlash came as a personal, felt surprise—and not just because I was trying to use best practices to win an election. As Klar and Krupnikov (2016) establish, the resistance the campaign encountered was not a local phenomenon: Americans loathe partisans. Klar and Krupnikov’s findings sit well with earlier work by Hibbing and Theiss-Moore (2002) about Americans’ preference for “stealth democracy”. For Hibbing and Theiss-Moore,
compromise with a strong preference for being left alone. If they are right, then an emphasis on reaching voters might be both effective in raising turnout and as a nuisance to the very voters who are successfully mobilized.
In one sense, I found that these disaffected voters’ cynicism about some parts of politics was justifiable. The irony of personalized campaigning, of course, is how much of it could be conducted remotely and impersonally. High-touch campaigns require nothing like the volunteer labor-intensive local campaigns I remember firsthand from the 1980s and 1990s, to say nothing of Tammany-style system of ward heelers and precinct workers. Although much of the campaign still involved face-to-face meetings with donors, voters, and opinion leaders, the Internet and cheap long-distance dialing let the campaign organize its activities for optimal cost-efficiency. The majority of the voter contacts were conducted by a team of workers located in another state.13
What is the end point of these processes? It seems to be toward steadily disintermediating campaigns as fully as possible. Much more campaign drudgery can, and will, be outsourced to vendors or automated altogether. Services such as NationBuilder have already begun to tap into this market, but far more messaging and design services could be automated or semi-automated than currently are. (If a candidate for county dogcatcher uses the same mailer as a candidate in a neighboring county, who would know or care?) Although we did not do so in this campaign, it is now possible to run a local campaign from almost anywhere else in the United States, with outside vendors and networked teams supplying everything but the
candidate. These developments will make volunteer labor—and even entry-level campaign positions—steadily less necessary.
One might expect that such automation and standardization will lower barriers to entry for candidates, making it easier for people to participate directly. But I think the opposite is more likely true. The proliferation of channels necessary to reach voters more than offsets the decreasing marginal costs of phone calls and direct mail. Contacting younger voters and households without landlines was far more challenging than it would have been twenty-five years ago. Soon, if not already, the generalist training of political scientists will be just as generally (ir)relevant to campaigning as academics’ skills were before the analytic revolution.14
Any novice contemplating a run for even local offices in mid-sized
communities, then, would be at a massive disadvantage unless they already possessed an array of specialized skills. No longer is it enough to
understand phone calling, door knocking, and the US Postal Service (and don’t underestimate how hard it is to ask people to talk to strangers or navigate postal regulations). Now, one would need access to a network with money and expertise that I suspect most citizens simply do not possess. In reviewing the most avant-garde literature on analytics, I am astonished by how specialized and extensive the media toolkit available to well-funded, experienced campaigns is—far outstripping what we could assemble, even though the basic data with which we operated was not all that much
different. And note that I have not even mentioned basic candidate skills like asking for money or speaking about The Issues, much less any
qualifications for governance (a skillset increasingly orthogonal to campaigning), which will remain significant barriers. Furthermore, the steady erosion of intermediary institutions, from unions to churches to local parties to (most relevant) local media, has greatly increased the barriers to entry. These phenomena are hard to quantify because all the campaigns
researchers can observe today have passed through a selection filter they do not recognize (although some scholars are working on the issue, e.g. (Broockman et al 2014)). In the same way as Hersh describes the
“perceived voter model”, then, there is a “perceived campaign model” that drives, and biases, theorizing about politics in the United States. But having seen “what it takes” for even a modest campaign, we should not be blasé about assuming that barriers to entry are low.
One impact of the advent of campaign professionalization is more widely appreciated. If specialized, well-funded campaigns can simply outperform less well trained opponents, the implications are far from cheering. First, there is no reason to suspect that well-organized candidates will be more representative of the popular will than less-organized ones. Indeed, I rather suspect the opposite is true, given current campaign funding realities. And representation matters, even at the local level. Although Oliver (2012, p. 65) downplays worries over whether systematic biases in local elections affects policy outcomes, since (he believes) the homeowning electorate is voting based on competence, Farris and Holman (2015) document how sheriffs’ biases against women affect how they address domestic violence cases—just one piece of evidence suggesting how systematic biases in local politics can affect public well-being. If incumbents, professionals, or others are
asymmetrically more able to adopt better campaign techniques in ways that correlate with unrepresentative traits or views, then responsiveness and representation may well suffer as a result of improvements in campaign technology.
mail, phone calls, and social media only makes this process easier. Yet this is a recipe in which, at the extreme, nonvoters are never contacted and supporters never hear opposing arguments. As Hersh (2015, p. 208) notes,
Fine-grained segmentation strategies may be troubling because they reduce the portion of the electorate that a politician needs to care about. If politicians can focus their campaigns just on the 51 percent of the electorate that is likely to support them, then they might begin to see their job as representing the 51 percent and not the entire electorate.
As marginally or individually effective as each tool in the new toolkit is, then, the aggregate effect of this revolution seems at least as likely to further alienate much of the citizenry from the political class. Pons (2016) develops this concern at greater length, with particular regard for the ways that more efficient and intrusive campaigns might cement the nonvoting population as nonvoters alienated from politics altogether. Little I
experienced in this campaign, or that I have subsequently read, has convinced me that this conclusion could not follow from current
electioneering research or practice. Nor does any individual actor besides, perhaps, a presidential candidate or a large-state statewide elected official have a strong enough incentive to break out of this tragedy of the
democratic commons. For ordinary, workaday politicians and their campaigns, broadening the electorate would cost too much and only increase the risks they face.
Lessons From 2016
process to the final push to impose an Electoral College remedy. The scale of that surprise, and the likely consequences of his victory for democratic norms, should lead to introspection about what the discipline studies. The irony, of course, is that Trump won just as political science had grown accustomed to thinking of elections as well-understood phenomena. Hersh, for instance, wrote in 2015 that “Political elites are no longer making
strategic decisions based on gut feelings or intuitions; they are looking at quantitative data to inform their decisions.” Although the Trump campaign was not quite run by luddites, this still seems an inapt description of the winning 2016 ticket.
How well positioned are American political scientists to evaluate these possibilities? I worry. Much of the finely-tuned deductive apparatus of the American politics literature rests on an unrecognized selection bias:
because major failures in American institutions have never been observed, research assumes that those institutions cannot fail. For all its progress in studying turnout and campaigns, then, the discipline may not be taking seriously enough the sorts of major ruptures in U.S. institutions that history, theory, and comparative perspectives suggests are always possible. In seeking to identify causal effects within existing institutions, that is, the discipline might be picking up pennies in front of a steamroller. To put it another way, political science might soon face the intellectual equivalent of the 2008 financial crash.
strategy for someone or some group who stands outside that system is not to invest heavily in it but rather to say that it is time to burn it down—or to drain the swamp.
Despite the discipline’s success in understanding existing institutions, then, we should take more seriously the possibility that the foundations on which that success rests could weaken of collapse. For instance, we might want to investigate the idea that mass attachments to democracy are weaker than we have assumed. Doing so would require taking seriously phenomena like political nonparticipation. Achen & Bartels (2016) argue that political scientists should view democratic politics less as a process by which rational individuals cast votes for optimal programs and competence and more as a process of expressing group solidarity. But if a large fraction of Americans chooses not to cast votes, then we should contemplate whether that is not a failure to make a decision or a rational weighing of the cost of voting versus its trivial benefits but the result of an active decision to refrain from participation. That, in turn, suggests that non-voters also
constitute a group who refrain from taking action in line with their identity —a “third party” of non-voters who are not be indifferent to but resistant or even hostile to democratic processes and institutions. That resonates with exactly the phenomena that, qualitatively, Cramer (2016) and Hochschild (2016) describe and that, quantitatively, Klar and Krupnikov as well as Hibbing and Theiss-Moore recognize.
My experience, and the recent scholarship that I engaged with during and after that experience, suggests that the discipline should feel rightly
seemingly clinical precision of these new methods. In general, the worlds of confusion and resentment described by Hochshild and Cramer co-existed with the well-identified world of turnout and persuasion described by
Gerber and Green—and, I think, not just because this campaign principally engaged with conservative Republicans in the Midwest.
Discussion and Conclusion
Although political scientists often fret about their relevance, in at least one key area of American politics they have made sustained progress toward the Holy Grail of theoretically-important, methodologically rigorous, and
practically useful knowledge. Studies of turnout and campaigning proved useful, even if less complete than advertised, and, I think, effective not just in general but in my application of them. Indeed, over the next ten or fifteen years, I suspect that these seeds may come to fruition in an applied
discipline of electioneering that will gradually take on its own professional identity distinct from its academic and practical forebears.
However, much work on American politics assumes away not only variations across political scale but also assumes that institutional forms—and civic norms—are more widespread and immutable than they are. In broadening their scope to account for the wider varieties of political strategies and the broader roots of uncertainty that 2016 displayed, many Americanists will find that they have much to learn from working with their comparativist and theorist colleagues.15 As Milner (1998) envisioned, a consilience of political
scientists might enable bridging observations across seemingly disparate realms of politics—although that collaboration might not be based on the precise foundation Milner assumed.
Yet this is not a call to discard the role of causal identification and large-N
Works Cited
Achen, C.H. & Bartels, L.M., 2016, Democracy for Realists: Why Elections Do Not Produce Responsive Government, Princeton University Press,. Alvarez, R.M., Hopkins, A. & Sinclair, B., 2010, Mobilizing Pasadena
democrats: Measuring the effects of partisan campaign contacts, The Journal of Politics, 72(01), pp. 31-44.
Badwin-Philippi, J. 2016, The Cult(ure) of Analytics in 2014, in
Communication and Midterm Elections, Palgrave Macmillan US, New York, pp. 25-42.
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