Category: Michael A. Smith

Save the Swamp

By Michael A. Smith of Emporia State University

The Trump Administration’s recent reversal on immigration policy regarding children has gotten me to thinking. What exactly does it mean to “drain the swamp?”

First, let me share a bit of background about the current situation. In 1997, a court ruling known as The Flores Settlement Agreement (Flores) set forth standards for the conditions in which children must be held, when in detention. The same standards were not set for adults. As a result, the most cost-effective way for the Immigration and Customs Enforcement (ICE) to comply was to release undocumented immigrant parents (or just one parent) with their children, with an order to appear at a specific court date. Some show up for the court dates, others do not. Trump supporters derided this policy as “catch and release” and instead attempted to put in place a new policy that such undocumented immigrants would be detained until their court date. The problem is, few facilities which meet the Flores standards can accommodate whole families, so ICE began separating the children from their parents, producing heartbreaking smartphone videos, and a grassroots movement to overturn the policy. To construct detention facilities for entire families meeting the Flores standards would cost an estimated $300 million and take time, neither of which are immediately available. There are also a lot of questions about the integrity of the contractors that bid to build and operate the facilities—oversight of private prisons and detention facilities is lacking.

Thus, acceding to public pressure to stop separating children from parents effectively means a return to the earlier policy. Currently, the implementation of the undoing is another mess. Some of the children are unaccounted-for, while others had been sent to facilities in different states from their parents. Some were being held in a converted, former department store. There even appeared to be children locked in cages. All this, because the Administration had wanted to hold the undocumented immigrants in detention until their court dates.

There are a lot of moving parts here: court rulings, campaign promises, public budgeting, public pressure, private contracting for facilities (building and management), oversight, smartphone videos and news coverage, and simple human empathy, to name a few. Oh, and it’s an election year, too.

This debacle is an excellent place to begin re-considering Trump’s oft-repeated campaign promise to “drain the swamp.”

This now-famous phrase deserves more scrutiny—Trump is effectively analogizing his plan to reform government, to the act of destroying an ecosystem. Is that really the metaphor you want, Mr. President?

Draining an actual swamp is a truly terrible idea. Swamps are teeming wetlands that support a wide variety of life. Their destruction can have ripple effects thousands of miles away—for example, by wiping out stopover sites used by migratory birds, and destroying the boundaries between fresh and salt water, just for starters. In fact, the U.S. did try to drain the swamps at least once—in the Florida Everglades, during the early and mid-twentieth century. Swamps were drained to make way for railroads, then housing developments and hotels, and later freeways. Water was diverted, while agricultural chemicals polluted what was left.

The result was unmitigated disaster. Species were driven to extinction, while human beings settled and built homes in natural-disaster prone areas. Some species reproduced out of control when their natural predators were decimated, the overflow spilling into areas populated by humans. Later, many people died and property was destroyed on developments in flood-prone areas, then rebuilt at great cost to us taxpayers- right in the path of still more disaster. Displaced from their homes, alligators and other swamp creatures still frequently appear in populated areas, for example in swimming pools.

Today, efforts are still underway to reverse the damage. An even more expensive project has allocated hundreds of millions of tax dollars to un-do the mess: trying, as best they can, to return the wilderness to this once-thriving area. Nonprofits are helping, too. At least in some places, the swamp is finally being un-drained, but there is still work to do. Florida, along with other states, still encourages and even subsidizes development in ecologically sensitive, disaster-prone areas such as coastline and floodplains, disrupting wildlife, endangering lives, and putting the taxpayers on the hook for major rebuilding expenses.

In short, draining the swamp was nothing less than a human-made disaster, the efforts to restore it cost a fortune—and still, it will still never quite be the same. The same is true of Trump’s metaphor. A key lesson of the immigrant children debacle is that “draining the swamp” of the federal government is a horrible idea.

While the metaphor is novel, Trump’s idea is not. Generations of politicians have sought office by promising to “clean up the mess” in Washington, the state capital, or city hall. The Coen Brothers’ popular movie O Brother, Where Art Thou? features a challenger candidate running for Governor of Mississippi by promising to “Clean Up for the Little Man,” complete with Vaudeville-style theatrics. (Spoiler alert: In the end, the reformer turns out to be more corrupt than the incumbent he is challenging.)

Like a real swamp, a government in a pluralistic democracy is a complicated ecosystem teeming with life. From court rulings to interest groups, election cycles to news cycles, international agreements to Gross Domestic Product, and lobbyists to lawyers, few public policy problems have easy answers. As in the case of the children, changing just one aspect of policy means changing a whole chain of interconnecting parts. Domino effects abound. Simply ending the cruel practice of separating children from parents means ending Trump’s policy initiative altogether, at least for now. There are simply too many things which affect other things which affect other things, and so on. Changing one thing—for example, the separation of children–undoes a whole policy. Public policy, like a swap, is an ecosystem.

It takes a lot of full-time professionals to oversee such a complicated government, but too often, there are not many to be found. This is the point made by John J. DiIulio, Jr. in his 2014 book Bring Back the Bureaucrats. DiIulio, a Democrat who was director of faith-based initiatives in the George W. Bush Administration, shows that federal spending has grown exponentially since the Kennedy Administration, but the federal workforce has not. Instead, the federal government has expanded its scope via entitlement payments to individuals, along with grants to for-profits, nonprofits, and state and local governments. DiIulio thinks there are far too few civil-service federal employees overseeing what is done with all this money and power, and he calls our current system “Leviathan by Proxy.” He ends by calling for an expansion of the civil-service workforce, arguing that more oversight will cost far less than one may think, and the end result of increased accountability will in fact save taxpayers’ money—a lot of it. The lax oversight of for-profit detention facilities is an excellent example, which is currently in the news.

Having more government professionals means that we can study the swamp before we go trying to drain it.

Better staffing, more professionalism, and elected officials who consult with and listen to the civil service workers we do have, can help prevent disasters like the recent one involving the immigrant children. Instead, the policy was thrown together in the same spirit as those campaign promises to “clean up the mess in Washington”—the simplistic idea that the current politicians and civil-service workers are too stupid, corrupt, or lazy to make common sense changes that will simplify and change policy. In reality, they are too smart to do this. Full-time government professionals realize that the enormous interdependence of public policies means that careful review and study of the costs and benefits of policy change are needed before seemingly-simple reforms are put in place. It would not hurt to have a few political science- and economics-trained professionals on staff to analyze the impact of things like unintended consequences, substitution effects, and ripple effects before putting these policy changes into effect. It also wouldn’t hurt to take a look at court rulings and even the Constitution itself before issuing orders.

Of course, when Trump says “drain the swamp,” he means to end a corrupt system of lawyers, lobbyists, and influence peddlers who have too much influence by comparing them to the alligators, snakes, and other reptiles that live in the swamp. No standup comedian could pass up the opportunity to point out the unfairness. Predatory alligators and snakes are just fulfilling their role in the food chain, after all—they hardly deserve to be compared to the likes of Washington lawyers and lobbyists!

Yet on a serious note, this summer’s events are a powerful reminder of the complexity and interconnectedness of policy. This is a fine time to revisit the swamp metaphor. Just as destroying an ecosystem in real life is an ecological disaster that disrupts or ends plant, animal, and human life, so draining a swamp is also a terrible way to go about governing. The diversity, complexity, and interconnectedness of governing life is as important as it is in a wetland. With actual swamps, it is time to stop the drainage. Instead, let’s hire some more wildlife biologists and park rangers and implement their recommendations. Likewise, with the metaphor, more professionals trained in political science and related disciplines working in the civil service can help show how even one seemingly small change can have a far greater impact on human lives than we ever imagined—and hopefully, next time the warnings will come sooner.

About the author: Michael A. Smith is a Professor of Political Science at Emporia State University where he teaches classes on state and local politics, campaigns and elections, political philosophy, legislative politics, and nonprofit management. Read more on the MPSA blog from Smith and follow him on Twitter.

 

More Bridging, Less Bonding: New Views of Social Capital

(or, Why I am Going to Watch Roseanne)

by Michael A. Smith of Emporia State University

MPSA-Smith-Bond-Bridge
Social capital has been a popular concept in political science, at least since the publication of Almond and Verba’s classic book The Civic Culture in 1963.  The idea waned for a while, then came roaring back in the early 2000s with the publication of Robert Putnam’s widely-cited Bowling Alone. Putnam believed that too much TV time ate away the bonds that connect communities, and he was not happy about it, arguing that it weakens ties to parties, interest groups, and other connections that sustain our political system. It also leaves us more lonely.

The basic idea of social capital is that the ties connecting each us to one another are a type of capital. Instead of money or other assets, social capital is something we can use for a variety of purposes, from finding meaning to seeking work, to being active in politics via a party, interest group, or other organization. In general, the thinking goes, the thicker the bonds of social capital, the richer the political culture and the more connected we will all be.

Of course, things never seem to quite work out so simply.

At this year’s MPSA conference, recently concluded, a lot of the buzz surrounded a distinction between bridging and bonding social capital. Bonding social capital is within-group. You build bonding capital when you connect with people that have similar religious beliefs, political views, perceptions of ethnicity—some may even be your relatives. Bonding capital can provide a sense of place and meaning, help one find a home, partner, and job, and reinforce a sense of identity, but at a price. At MPSA, I witnessed several different presentations, at multiple panels, using different datasets, all reaching similar conclusions: “thick” ties of in-group, or bonding social capital make one less trustful of those outside your social group. In diverse societies or even homogenous societies where people feel threatened by those just outside their borders, strong bonding capital can worsen tensions and deepen mistrust not within, but between groups.

The downside of bonding capital reminds me of one of my favorite works in 20th century political theory. Theologian Reinhold Niebuhr’s classic Moral Man and Immoral Society suggested that the deeper the trust and deeper the ties within a social group, the more likely members of that group will support behavior and policies that were cruel, ruthless, possibly even genocidal toward the “other.” Of course, Niebuhr, a German-American whose writings had a major impact on Dr. Martin Luther King, Jr., was alarmed by the rise of the Nazi Party in Germany, but his ideas are applicable elsewhere as well. The bottom line here is, in Niebuhr’s time, and in ours, bonding capital can have a dangerous shadow side.

Fortunately, there is an alternative. Bridging social capital is built when one makes ties with those in other social groups—other religions, ethnicities, political parties, etc. Bridging capital cuts across groups rather than reinforcing in-group identity. As always, with real-world data from real-world people, the results of many analyses presented at the conference this year were mixed. However, there were enough significant results to offer hope that bridging capital can help to reduce religious, ethnic, and political tensions instead of worsening them, while maintaining that sense of belonging.

The upshot: it turns out that it is not enough to follow the advice of Putnam by seeking to build social capital. Which kind of social capital matters—and for diverse societies, rich bridging capital ties are especially crucial to avoid deepening rivalries among groups.

While it may be a stretch, I cannot resist speculating that this has rich implications for us right here in the U.S. of A. As the norm of objective news media declines and is replaced by something akin to the partisan newspapers that drove opinion in the 18th and 19th centuries, we increasingly find Democrats and Republicans with our own news—not to mention our own neighborhoods, stores, travel destinations, and hobbies. You won’t find too many Democrats at the gun club these days—nor Republicans at the yoga studio. This is a shame. We even have our own entertainment outlets. Stephen Colbert’s Late Show, basically MSNBC with jokes, has no appeal for Republicans save the love-to-hate variety, while liberals are now boycotting new episodes of Trump-supporting Roseanne.

I cannot help but think that these separate forms of news (or “news”), entertainment, working, living, and leisure are leading to the formation of more bonding capital among Democrats and Republicans, respectively, while tearing away at what is left of our bridging capital. Why else would there be semi-serious talk of impeaching every President since Clinton—who actually was impeached—not to mention widely-varying views on just about every wedge issue imaginable, including which bathrooms people use.

Maybe we need more bridging capital here in the USA. I know that for me, as a liberal, I particularly enjoy reading serious, thoughtful conservatives such as Edmund Burke, Leo Strauss, William F. Buckley, Jr., and George F. Will. F.A. Hayek brings a thoughtful libertarian perspective, too. I rarely read liberal editorials or watch Colbert or MSNBC, because I leave with my anger aroused, having learned nothing, because the ideological assumptions involved just reinforce what I already believe. Also, and I am sorry to have to be the one to say it, but Colbert’s new show just isn’t as funny as his old one was.

Between now and the 2019 conference, I propose that we all take a vow to read and discuss the most thoughtful ideas we can find, offered up by those with different views from our own. I want to better understand the views of those who disagree with me, and I’m tired of just reinforcing my own group identity. I already know what I believe, the question is what is going to challenge me and push my thinking to the next level. Like a good workout, political theory is not much good unless it has some resistance built into it.

Let’s all build some bridging capital this year.

I think I may start by watching a couple episodes of Roseanne.

See you at #MPSA19!

About the author: Michael A. Smith is a Professor of Political Science at Emporia State University where he teaches classes on state and local politics, campaigns and elections, political philosophy, legislative politics, and nonprofit management. Read more on the MPSA blog from Smith and follow him on Twitter.

The New Political Scientists—We’re Live, We’re Nationwide, and We’re Online

By Michael A. Smith, Professor of Political Science at Emporia State University

Senior man with tattoo smiling and looking at camera

On the first day of the Midwest Political Science Conference (#MPSA18 on Twitter), I spotted both a roundtable and a vendor booth on the same topic: using Wikipedia in the classroom.

That’s right—Wikipedia may be moving from the bane of every professor to a classroom tool. The Wiki Education booth featured review copies of a new book, one professors can use to teach students the right way, and the wrong way to edit Wikipedia entries. The topic is timely. Creating or editing a Wikipedia entry is an increasingly-popular assignment in political science and other college classes. Why not? Wikipedia is here to stay, and if students are going to use the ubiquitous open-source, free encyclopedia, as they seem bound and determined to do, we may as well teach them to do it right.

Wikipedia is not the only online resource moving from “oh no, never, not in my classroom,” to useful and productive tool. For example, more and more online apps utilize smartphones as learning tools rather than classroom nuisances. Students can now use their phones to reply to flash polls or pop quizzes, with the results displayed on overhead screens in real time. I tried this teaching method a few years ago and found it kept the course fast-paced and the students involved. In the recent past, professors would warn students “if I see a phone, it’s mine.” This practice may be drawing to a close.

Professor-ing is changing in other ways, too. When I was in graduate school, there was a hierarchy of political scientists, with R1 (Carnegie Research 1) university heavy-hitters treated like pocket-protector-wearing rock stars, their panels packed at conferences while other speakers struggled to draw even a small audience. This still happens, but change is afoot. I attended a panel yesterday on the Trump election which packed the house despite featuring no big-name, “high impact factor” faculty from R1 schools. The topic itself was the draw.

Today, professors and students can make a name for ourselves almost as easily on social media as in traditional journals. George Washington University Professor John Sides has several books on the market, but he is best-known for co-founding the Monkey Cage blog, in which political scientists offer real-time analysis of current events. Once independent, the Monkey Cage has been a part of the Washington Post website for several years now. Twenty years ago, a professor making a reputation by founding a blog would have been unheard-of. Today it is increasingly common.

Twitter—the President’s Social Media of choice—is also showing strong growth among political scientists. Twitter’s algorithms make it easier to get messages to anyone with similar interests in the general public, rather than just a computer-selected group of your friends, so it makes a great resource to disseminate preliminary research findings. My colleague Patrick Miller from the University of Kansas uses Twitter particularly effectively. On tenure track at a research school, Patrick has published his share of peer-reviewed articles in traditional journals, but he also won a “Why We Love Kansas City” award from the KC area’s alternative newspaper, the Pitch. Pitch editors liked Dr. Miller’s Tweets featuring real-time political analysis, ethnic cooking, tips on making mixed drinks, and general observations about college-town life. My own grad school mentors were, and are, delightful people and fine scholars, but I cannot imagine any of them winning an award from an alternative newspaper that also features movie reviews, personal ads, and notes about the local club scene.

Finally, there is online teaching. The buzz at MPSA includes questions like, “which LMS (Learning Management System) are you using?” An online course or two per semester is now just part of a regular course rotation, and new strategies are emerging all the time to embed material and keep students engaged. Technologies like Zoom and Bluejeans (both similar to Skype) make it possible to teach “real time” online courses in which students meet face to face—almost—and hold class discussions, just like on-campus classes. These discussions are often held in evenings to accommodate nontraditional students’ work and family schedules. The earlier online era of video recording one’s own long lectures, or just having students upload work and then grading it, are giving way to a host of new approaches. Another online app offers professors the formatting tools to make every online lecture look like a TED talk– those popular, 20-minute lessons on Youtube. In fact, some political scientists have done TED talks of their own.

Professors today are trying much harder than a generation ago to make their work readable and relevant to the general public. Blogging, tweeting, and hosting a Zoom discussion are now as much a part of a professor’s day as peer review, impact factors, and literature reviews.  In the classroom, more and more faculty are coming to see Google, Wikipedia, and smartphones as resources that can be used for good or ill, rather than the bane of all good teaching.

At this point, an article like this has usually made some snarky comment about how professors no longer wear those tweed jackets with the elbow patches. In truth, many professors never wore these, while others still wear them because, well, they’re relatively comfortable, as dress clothes go. Plus, they make you look like a professor. But tweed or no tweed, it is clear that the professor’s job has shifted. Political scientists today are online, real time, and when we need to, we can say it in 280 or fewer characters– and those full-color data plots make great pictures, too. Political scientists today are live, we’re nationwide, and we’re online.

Check this blog and the Twitter hashtag #MPSA18 for more developments through the conference.

About the author: Michael A. Smith is a Professor of Political Science at Emporia State University where he teaches classes on state and local politics, campaigns and elections, political philosophy, legislative politics, and nonprofit management. Read more on the MPSA blog from Smith and follow him on Twitter.

What George Washington Really Meant About Political Parties — and Why It Matters

By Michael A. Smith of Emporia State University

Politicians Having a Beer

Did George Washington really hate political parties? For our first president’s 286th birthday, it is time for historians to set the record straight. For political scientists, a nuanced view of Washington’s stand helps us understand the modern-day Americans who also despise partisanship (or say they do).

Critics of the party system often rely on Washington’s comments to buttress their views. For example:  according to the Washington Post, Neil Simon, independent candidate for U.S. Senate from Maryland, actually pointed to a picture of Washington before paraphrasing him on the evils of parties, adding, “There are no political parties in the Constitution.”

On the surface, the case seems clear. In his 1796 farewell address, Washington talked at length about the “danger of parties in the state.” A sample quote follows:

There is an opinion that parties in free countries are useful checks upon the administration of the government and serve to keep alive the spirit of liberty. This within certain limits is probably true… [but] there being constant danger of excess, the effort ought to be by force of public opinion, to mitigate and assuage it. A fire not to be quenched, it demands a uniform vigilance to prevent its bursting into a flame…

 

There is plenty more, and readers are invited to click the hyperlink above to read the entire speech. Case closed, then?

Not so fast. First of all, Washington seems say we should reign in partisanship, not eliminate parties altogether. Furthermore, context matters, and the story—and the presidential term—that preceded Washington’s anti-party comments casts them in a remarkably different light.

From the Continental Army days onward, Washington worked closely with Alexander Hamilton, who would become a founding figure of the Federalist Party. Though Washington and Hamilton had their disagreements, Washington ultimately supported most of Hamilton’s agenda, including a strong Treasury Department, promotion of commerce, and neutrality between England and France. Thomas Jefferson, later a founder of the Democratic-Republican Party, was also in Washington’s nonpartisan cabinet, serving as Secretary of State. However, Jefferson quit in frustration as he saw the President increasingly siding with Hamilton. Later, divisions deepened over the controversial Jay Treaty, in which the U.S. sought to re-establish commercial relations with England, even making certain concessions, while staying neutral in England’s war with France. Jefferson favored an alliance with France. All of this and more is detailed in Ron Chernow’s Alexander Hamilton, the recent biography that inspired the hit Broadway musical Hamilton. Not only that, but according to historians Eric Foner and John A. Garraty, the hyper-partisan Hamilton actually helped draft Washington’s famous farewell. Spirit of party, indeed!

So, why the anti-party remarks? With Hamilton at his side, Washington denounced parties and “factions” because he saw Jefferson and Madison’s emerging, breakaway Democratic-Republican Party as a threat to national unity—especially, national unity behind the Federalist agenda. In other words, Washington and Hamilton denounced parties because if everybody would just agree with them, then there would be no need for parties. This is exactly why most Americans hate political parties today. In their 2003 book Stealth Democracy, John Hibbing and Elizabeth Theiss-Morse present the results of many focus groups conducted with nonparticipating and reluctant voters. Sure enough, most of the citizens they queried hated not only political parties, but politics itself. Yet without parties, how did these grumpy would-be voters propose to manage political conflict? Aye, there’s the rub! Hibbing and Theiss-Morse’s disengaged respondents did not propose a mechanism for managing political conflict, because they did not think there should be political conflict. If the country was simply ruled by consensus, then there would be no need for parties.

Washington, Hamilton, and the surly voters (and non-voters) studied in Stealth Democracy were skeptical of the whole idea that voters would—and should—have differing views. Instead of supporting parties as a means of managing competing ideas and interests in a pluralistic democracy, these critics proposed that all voters should simply agree with them, thus averting the need for any organized way of managing conflict. No conflict, no need to manage it: now everyone line up behind me!

This also explains another dilemma of today’s party critics. As John Sides points out, these “independent” voters overwhelmingly behave as partisans. In fact, the country is more politically polarized than it has been in a long time, with many of us even stating we would not want our children to marry someone who affiliates with the other party—and so-called “independents” are very much a part of this trend. Nor are self-identified independents necessarily more moderate: in 2016, supporters of liberal Bernie Sanders were more likely to call themselves independent. Hillary Clinton, who took more centrist positions, won most of the primary voters who self-identified as Democrats.

Today’s partisan-voting haters of political parties are not so different from their hero, Washington. Like the man from Mt. Vernon, today’s “independents” seek, not new ways of managing the tumult of political conflict, but the elimination of political conflict they imagine would occur if everyone just took the same stand on the issues—their stand. Then and now, the denunciation of parties is really an attack on people that have the audacity to have different opinions: those troublemaking factions who have the nerve to disagree with me!

As for me—like many political scientists, I like the parties. In the diverse tumult we become, we are bound to have passionate disagreements on the issues of the day. We do not all have the same values, but we can all value a system that allows us to fight, haggle, electioneer, and logroll our way toward some type of compromise instead of withdrawing or resorting to violence. The process can be messy, and parties allow for these differing opinions to coalesce into organized blocs and compete for votes. If I were king for a day, I would nudge the U.S. toward proportional representation, opening up the possibility of more than two competitive parties.  However, I harbor a deep distrust toward those that would dispense with parties altogether. As for the others, instead of denouncing the evils of “party” and “faction,” it might just be easier—and more honest—to denounce the evils of “anyone who dares to disagree with me.” It was true in 1796, and it is true today. No, thanks: I’ll stick with the parties.

Then again, perhaps Washington did have the answer. In the quote above, he suggested, not that we quench political parties, but rather that we prevent partisanship from becoming an open flame. Today, in our hyper-partisan climate, too many of us may join the cynical voters studied in Stealth Democracy, seeking only to end conflict with a win for our side, and placing no value on the system itself. We want only for our party to win, we do not nurture and celebrate the values that allow us to have political parties—all political parties, not just the one we support—to organize and negotiate our differences in the first place: country first, party second, and both are important. Hibbing and Theiss-Morse suggest that we begin by teaching children and adults alike to manage conflict productively, instead of offering only feel-good civics lessons and me-too groups of likeminded people that avoid any discussion of dissenting views. Indeed!

We owe our first president a deeper reading of his famous remarks and their context, and we owe our nation’s political parties their due. Happy Birthday, George!

About the author: Michael A. Smith is a Professor of Political Science at Emporia State University where he teaches classes on state and local politics, campaigns and elections, political philosophy, legislative politics, and nonprofit management. Read more on the MPSA blog from Smith and follow him on Twitter.

More Guns, Less Replication: The Case for Robust Research Findings

MPSA-Blog_ReplicationRobust

The meaning of the word “replication” hardly seems like the sort of thing that would land a person in court. Yet, it did. In Lott v. Levitt (2009), the U.S. District Court of Northern Illinois ruled on that very question, in a dispute between two academic authors of bestselling books.

In his book Freakonomics, Steven Levitt argued that “other researchers have failed to replicate” John Lott’s work, published in the latter’s book More Guns, Less Crime. In fact, as Lott later pointed out, he is willing to share his data, and when other researchers run the same statistical models using the same data, they successfully replicate the results. Lott did not falsify his data, fabricate results, or make errors in his reporting. This is what replication is meant to check, and Lott’s research passed the test.

Levitt countered by arguing that the word “replicate” can have a broader meaning. The courts agreed and also noted that generally, the judicial system should stay out of such disputes unless the use of the word is particularly egregious and serves to defame the target, which was not the case here. (A second part of the suit was settled in Lott’s favor, but this was unrelated to the argument over the exact meaning of replication).

I recently blundered into this whole controversy. I published a newspaper column referencing Lott’s research, and I, too, suggested that others could not replicate his research. Lott spotted the column and wrote me a friendly note offering to share some of his more recent research on Australia’s gun laws with me. I read it and found it fascinating. However, he also asked for a retraction of the replication charge. I demurred, because while I would not use the word “replication” again in this particular context, I think the way I used it is defensible, as per the Lott v. Levitt ruling and the interpretation offered in the above, hyperlinked article from Scientific American. What I had in mind were other studies, using different data, methods, and time periods, that reach different conclusions than does Lott’s book—replication in a broader sense.

Lott and Levitt are both economists, but political scientists have had our own struggles with replication. In the 1990s, Harvard Professor Gary King started a movement to push for replication in quantitative political science, but the standards for which he advocated have never become universal. King did have some success in getting participating political scientists to make their data more accessible, but not everyone is game. Enormous amounts of time and, at times, money go into data-collection, and many researchers consider their datasets proprietary. Furthermore, academic journals rely on unpaid, volunteer reviewers, who layer the responsibilities on top of their other duties as professors and researchers. With few exceptions (including the AJPS which has a third-party replication process), journals must rely on reviewers to download massive datasets into statistical programs like SPSS and R, then replicate exactly what other researchers have done. This approach is probably not realistic; editors have a hard time just getting them to complete their reviews, which are sometimes months late and only a few sentences long. By contrast, book reviewers are often paid, but book publishers increasingly look, not for the kind of detailed, technical matters involved in replication, but rather for books that will reach a broader audience. Selling books only to other political science professors is not a very lucrative market. While Lott and Levitt both have their critics (including one another), both wrote bestselling books, and this is what publishers want. They are not likely to get involved in a lengthy replication project, when what they are seeking are readability and larger audiences: the next More Guns, Less Crime or Freakonomics—and they do not much care which, as long as it sells. In short, publishers cannot be relied upon to enforce standards of replication, nor can editors, and the courts would prefer to stay out of it.

One way out of this mess is to invoke another statistical concept: robustness. Just as replication can have a narrow or broad meaning, robustness can as well. While the word always makes me think of my morning coffee, or perhaps a good merlot, robustness in the statistical sense refers to a relationship between two variables that is not driven by just a few cases or assumptions. At the risk of oversimplifying: if a few seemingly minor alterations in a data analysis result in a change in the results, then those results were not robust in the first place. For a better, more technical explanation, visit http://www.rci.rutgers.edu/~dtyler/ShortCourse.pdf.

Like replication, robustness can also be defined more broadly. Much as the results with a single dataset are robust if they hold across all (or least many) of the cases and not just a few, so research results can be said to be robust if the same finding keeps popping up in multiple studies, using different data and different ways of modeling it. Findings such as the relationship between education and political participation (those with more education are more likely to vote and to participate in other ways) hold up, no matter how you slice the data. Old data, new data, crude models, highly sophisticated analyses—again and again, the relationship appears. There is just no way around the fact that more education often pairs with more political involvement. Of course, a few individuals exist who do not fit this pattern, but these exceptions do not debunk the claim. It is solid. Within a single dataset, robustness refers to the relationship holding across a broad swath of cases. Considered more broadly, a finding can be said to be robust if it holds up across a broad swath of studies.

Conversely, the research on concealed-carry, gun ownership, and crime is not robust, in the broad sense that I am using it. Lott’s research finds that concealed-carry laws mean more crime deterrence. The research from Aneja, Donohue, and Zhang, referenced in the piece from The Washington Post above, shows that such laws increase crime—specifically aggravated assault—while having no impact on other crime rates. For his part, Levitt believes that there is little relationship at all between gun ownership and crime rates. In short, the research on this topic is highly sensitive to model specification, time periods covered, and data used. No clear, robust relationships have emerged that carry across different research by different researchers using different data and different modeling techniques, to establish clear conclusions. The most likely explanation for this, is that the relationship between gun ownership and crime is ambiguous. Whether positive or negative, the effects are small compared to the big drivers such as the percentage of poor, unemployed, young males—who commit the vast majority of street crimes, regardless of race—that exist in the population at any given time…

…which is exactly what I wrote in that newspaper column. But, by using the word “replication” loosely, I wandered into a whole new set of questions—ones which are not easily resolved, and ultimately go to the soul of social science itself.

About the author: Michael A. Smith is a Professor of Political Science at Emporia State University where he teaches classes on state and local politics, campaigns and elections, political philosophy, legislative politics, and nonprofit management. Read more on the MPSA blog from Smith and follow him on Twitter.

The Loss Lab

“Trump was an alternative to interchangeable robots whose goal is to avoid saying anything interesting.” – Molly Ball, political reporter, The Atlantic

“If you wanted to write a playbook for how to lose an election, Trump did that and he won anyway.”  – Steve Peoples, AP reporter

What rotten luck for political scientists! We had just become established as purveyors of credible data analysis that can help real-world political campaigns target their resources, and then… Well, see the quotes above.MPSAblog-MediaRoundtable

The quotes came from this year’s MPSA roundtable on the media and the 2016 election, in which Ball and Peoples were joined by CNN reporter Nia-Malika Henderson and political scientists Jennifer Lawless of American University and Danny Hayes of George Washington University. The discussion was free-wheeling and wide-ranging, but a few themes did emerge. First, Democrats are in a world of hurt. Second, Trump mobilized voters that do not normally vote, both in the primaries and in the general elections. Third, Trump opponents should be on notice — his popularity among his base is rock-solid and the Democrats offer few alternatives right now, save the simple, unconvincing negation of being not-Trump. Fourth, the news media did not take Trump’s candidacy seriously and thus did not rigorously fact-check his claims until well after he became a serious contender. Fifth, regular folks, including women, do not take Trump’s offensive rhetoric regarding gender as seriously as do those of us in the establishment bubble.

Finally comes the theme that relates to Molly Ball’s quote above; one which will trouble political scientists. Trump ran against the very political establishment which our discipline helped to create. Unless Trump’s election is a pure aberration — which these reporters doubt — the data-driven political consultancies discussed in Sasha Issenberg’s seminal book The Victory Lab lie in shambles, and the future for political scientists in campaigns is unclear.

A very brief history: while the study of politics goes back to ancient civilizations, the modern-day discipline of political science emerged here in the United States. In the early twentieth century, the discipline was associated with scholars such as W.W. Willoughby, and Woodrow Wilson, who later became U.S. President. Early political science was largely descriptive, focusing on what we now call institutions — Congress, the Supreme Court, and the Presidency (the office, that is, not just personal biographies of presidents, which are the bailiwick of our colleagues in History).

In the 1920s, scholars like Charles Merriam, who ran for mayor of Chicago, began to turn the discipline toward analysis of behavior. His classic book on non-voters was an early attempt to suggest that if the reasons why people don’t vote could be isolated, they could also be ameliorated. Happy days, and higher voter turnout, would surely follow. The belief that human behavior was subject to scientific analysis and “correction” was perfectly in keeping with the spirit of the times, where scholars in other disciplines promoted such approaches. Engineer Frederick Taylor, for example, believed that “scientific management” could bridge the gap between labor and management with a common goal of efficiency. In our discipline, public administration scholars like Wilson suggested similar goals for those working in growing bureaucracies: efficiency, not ideology. In practice, like Merriam’s failed bids for mayor, it didn’t quite work out as planned.

By the 1940s, the advent of early computers and spread of the telephone opened up new research avenues. Studies of voting behavior using polling proliferated at Columbia and Michigan, with Michigan’s iconic Institute for Political and Social Research (ICPSR) eventually emerging on top. Through the mid-to-late 20th century, data-based analyses of voting behavior were sobering, even cynical, with classic studies like The American Voter suggesting that most Americans had little understanding of political ideology, and that relatively well informed, strong partisans cancel out one anothers’ votes, leaving elections in the hands of the least informed, most fickle, undecided voters. By the 1970s, many political scientists went so far as to assert that campaigns had little impact, with fundamentals like the state of the economy and presidential approval telling us all we need to know, to predict the next election.

Next came rational choice theory, which rested on the assumption that individuals take rational actions in pursuit of goals (the goals themselves could not be judged as rational or irrational, only the actions; the goals simply were.) Rational-choice resembled economics and featured very sophisticated, mathematical models, some of which utilized no data and were purely theoretical. The goal was to make the study of human, political behavior more truly scientific, with rigor resembling the physical sciences — but then came the backlash.

The ‘90s saw Green and Shapiro’s Pathologies of Rational Choice Theory achieve great popularity by skewering “rat choice” as un-useful in predicting real-world outcomes. Meanwhile the discipline’s perestroika movement took its name from the reforms of Mikhail Gorbachev and sought to re-establish traditional approaches like area studies in comparative politics. They even ran rival candidates for the boards of political science associations, and won enough seats to make their point.

By the early 2000s, the new trend was experimental research. In particular, Yale’s Alan S. Gerber and Donald Green conducted field-based research on what approaches and messages were most effective at mobilizing voters (they concluded that old-fashioned, in-person canvassing worked best). As documented by Issenberg, political campaigners began to notice. While initially skeptical, political campaigns began to hire political scientists as consultants. Gerber and Green helped out on Rick Perry’s bid for Texas Governor, where they targeted media buys and candidate appearances. Karl Rove, George W. Bush’s right-hand man, was also a fan. Democrats noticed, too. In 2008, Barack Obama’s campaign invested heavily in these political-science-based approaches, making very precise, precinct- and even voter-level predictions and targeting appeals accordingly. Obama’s victory only encouraged them, and the consultants were back in 2012, this time working for both campaigns.

As documented by Issenberg, the data-driven revolution in campaigns was nearly complete, old political hacks having to share the back rooms with these new number-crunchers. Targeting displaced market-share-based TV ad buys as the go-to strategy for serious candidates.

And then… Trump.

As Peoples notes in the quote above, Trump’s campaign used none of these insights. There were no political scientists on staff. There was no targeting. Trump’s preferred and often his only strategy was campaign rallies, widely dismissed by our discipline as only mobilizing those who already, enthusiastically support the candidate and having no effect on undecideds or those needing mobilization. Peoples points out that Trump campaigned as if bound and determined to do the opposite of everything detailed so carefully in The Victory Lab. And he won.

Now what?

Granted, political scientists pushing for renewed relevance in our discipline did not put all of our eggs in the Victory Lab basket. The popular Monkey Cage blog features well-respected scholarship that speaks in real time to policy challenges, and serves as an inspiration to this and other political science blogs that have sprung up in its wake.

Yet, if Trump’s win is not idiosyncratic–time will tell–the data-driven microtargeting that had just emerged is now called into serious question. The approach may still work for down-ballot races, but we can only use trial and error to determine that. The first order of business, of course, is for our discipline to deploy our formidable skills to determine why we did not see this coming.

About the author: Michael A. Smith is a Professor of Political Science at Emporia State University where he teaches classes on state and local politics, campaigns and elections, political philosophy, legislative politics, and nonprofit management. Read more on the MPSA blog from Smith and follow him on Twitter.

 

Election 2016: Did New Voting Laws Tip the Balance?

Since the early 2000s, a flurry of new voting laws have passed in the states. There is a marked Democrat-Republican divide.  Democratic-leaning states, such as California, Oregon, and Massachusetts, have passed laws making access to the ballot easier.  Oregon now automatically registers citizens to vote any time they do business with the state.  California does the same for those getting driver’s licenses — a must in many parts of a car-crazy state. Massachusetts joins a few other states in allowing pre-registration of 16 year-olds, who then automatically enter the voting rolls upon turning 18: a policy that North Carolina also has, for now, depending on court rulings.

Republican-leaning states, meanwhile, have taken a different turn.  Under the auspices of curbing voter fraud, GOP legislators and governors have passed a flurry of new restrictions.  Critics, myself included, are quick to point out the dearth of evidence for those voter fraud claims. Yet the theoretical possibility, anecdotes, and research by Richman, et. al. indicating that some fraud may go undetected, all combine to make a case for new laws requiring state-issued Photo ID at the polls, proof of U.S. citizenship to register, limiting early voting, complicating third-party voter registration drives such as those once undertaken by the controversial ACORN, and so forth.

Critics, again myself included, argue that these restrictive new laws are not neutral in their effects.  Research by scholars such as Matt Barreto (.pdf) indicate that African-Americans, Latinos, the poor, the disabled, and the elderly are more likely to lack photo ID.  Stories, admittedly anecdotal, circulate about voters having trouble getting birth certificates.  Some were not born in hospitals and were never issued birth certificates, while others contain clerical errors requiring a Kafkaesque legal labyrinth to untangle.  One thing most of these voters seem to share: a tendency to vote for Democrats;  that is, they if are able to vote at all. The popularity of these new restrictions in Presidential and Senatorial battleground states like Florida, Ohio, and Wisconsin, heightens worries.

What was the bottom line in 2016?  Did the laws put a thumb on the scales and ensure Donald Trump’s victory?

I dove into this question for our roundtable, “Did It Matter in the End? Restrictive Voting Laws and the 2016 Election,” presented at this year’s MPSA conference in Chicago. Using ArcGIS mapping and multivariate regression, I traced the shift in voter turnout in each county in the U.S. I also considered the impact of various independent variables, such as percentage of white residents in a county, economic growth or decline, population (an urban-rural measure, since rural counties have smaller populations), and other factors as controls to explain vote changes between 2012 and 2016.  In addition, I put in the new voting laws.  After controlling for the “usual suspects” which explain partisan shifts and turnout change, is there anything left for these new voting laws to explain?  In other words, did they have an impact?

In terms of turnout, the evidence does suggest that restrictive new laws may have had an impact, at least in certain regions.  The most dramatic contrast is between Ohio and Pennsylvania, two states that “flipped” from Democrat to Republican in 2016.  Ohio shows marked turnout decline relative to the Keystone State. However, there is no clear partisan impact resulting from this.

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In the map above: darker green=higher turnout relative to 2012, lighter = lower turnout

To the north and west of these states lies a region containing three states which flipped from Obama to Trump (Iowa, Michigan, and Wisconsin) and one where Trump made major gains but did not win (Minnesota). In the northern Midwest, only Wisconsin had new, restrictive voting laws take effect between 2012 and 2016.  Sure enough, turnout dropped in diverse, urban, Democratic-voting Milwaukee County, by 0.3%.  Problem is, it also shifted in other area urban counties. In fact, the 3% drop in Hennepin County (Minneapolis), and 2% drop in Polk County (Des Moines) were both greater than that in Milwaukee—and Minnesota and Iowa had no new voter ID laws.  Wayne County (Detroit) and Cook County (Chicago), also in states with no new restrictive voting laws, had smaller drops at 0.2% and 0.01%, respectively.  Again, it is important to note that I am computing turnout as a percentage of the country’s adult population, as per U.S. Census population estimates from the year before the election.  Furthermore, the turnout drop in Milwaukee did not come with a Republican shift—if anything Milwaukee shifted to being more Democratic—more so than Detroit or Minneapolis.


In the map above, red=Republican shift, blue = shift away from Republican. Milwaukee is the southernmost blue county on Lake Michigan, in eastern Wisconsin

 

In other words, the results did not fit the prediction that restrictive new laws would hinder Democratic vote share.
The results were even more startling with my regression analysis.  Regarding turnout, the imposition of other new restrictions (limiting early voting days, reigning in organized voter registration drives, etc.) correlates with higher voter turnout — evidence of a possible “backlash” effect.  Democrats in affected states may use the presence of these laws to mobilize constituencies that feel targeted by the laws, a sort of “don’t let them take your vote away” message, which colleagues and I found may have boosted Democratic turnout for Pennsylvania in 2012, when that state did try to implement new voter restrictions. It probably didn’t help that a Republican leader in the state legislature boasted that the law would deliver Pennsylvania to Mitt Romney.

Even more startling was my regression for Republican vote share. Even when controlling for other factors (% white, job loss, urban-rural, etc.), there is a strong, negative relationship between the imposition of these new laws and Republican vote share.

That’s right: it appears that restrictive new voting laws hurt voter turnout for Trump.

How can this be?  A thoughtful, post-election analysis by two reporters at the Atlanta Journal-Constitution suggests an answer.  Journalists Kristina Torres and Jennifer Peebles secured the entire voter file for Georgia and analyzed it for patterns.  Their findings: counties that showed strong increases in support for Trump, mostly rural, also showed significant increases in ballots cast by new and infrequent voters in 2016.  Meanwhile, some of the voters from 2012 did not vote this time.  The result was an older, whiter, more-rural electorate.

New voting laws are likely to have the greatest impact on new and infrequent voters.  Those who vote regularly will adjust to a photo ID or proof-of-citizenship requirement, while those that do not may not be prepared for the changes.  Given that Trump appears to have mobilized new and infrequent voters, it makes sense that those voters would be the most “thrown” by new requirements.

Thus the impact of new restrictions appears to be greatest on those who are new to voting–one of Democrats’ biggest fears, but with a twist, in that it was the Republican, not the Democratic candidate, that mobilized such voters in 2016.  This is not to say that the laws won’t hurt Democrats next time, as they are the party that typically tries to bring in new voters through registration drives, election-day canvassing, “souls to the polls” drives for Sunday early voting at predominantly African-American churches, and so forth.  Yet it appears to be Trump that took the brunt this time – and it did not prevent his Electoral College victory.

Finally, a personal note: I dislike these laws.  I find scant evidence for the voter fraud claims used to substantiate them, and I see little rationale (.pdf) for them other than for the purpose of hindering those who do have a right to vote, from exercising that right.  I also think these laws, passed with few exceptions by Republican state legislatures, clearly are meant to target Democrat-leaning voters. At the same time, I am a political scientist, and facts are facts.  The fact that these laws appear to have hindered vote shifts to a (quirky, unexpected) Republican candidate this year does not mean they will do the same in the future. More importantly, it does not justify the laws. The voter-fraud premise remains as flimsy as before, while evidence of disparate impact on society’s most vulnerable remains credible.

The suggestion that the laws hinder Democratic vote share has always been an understood supposition in the debate over them, but it is not part of the actual, legal case against them. Most importantly, as political scientists we must go where the data take us. These laws do appear to be affecting elections – just not in the direction predicted… at least, not this time.

About the author: Michael A. Smith is a Professor of Political Science at Emporia State University where he teaches classes on state and local politics, campaigns and elections, political philosophy, legislative politics, and nonprofit management. Read more on the MPSA blog from Smith and follow him on Twitter.

Polling for the 2016 Presidential Election: What Went Wrong?

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As I write, Donald Trump is less than two weeks from being inaugurated as President of the United States. For political scientists, our “what the…?” moment involves the failure of most public-opinion polls to predict the results of the 2016 election. I joined numerous colleagues in assuming a Hillary Clinton victory. The news media and even Saturday Night Live took Clinton’s victory for granted. I will never in my life forget spending Election Night watching the needle on the New York Times’ prediction meter move from strongly favoring Clinton to 100% Trump.

Comparisons to the classic “Dewey Defeats Truman” headline of 1948 are inevitable, but several differences emerge. Most notably, telephone polling was in its infancy in 1948. The methodological sophistication and advanced computer programs used today were not available. Today, pollsters predict elections based not on a single poll or early returns, but rather on an amalgamation of many polls, plus other data. The methodology is so advanced, so tested, it is completely indestructible—just like the Titanic!  However, in fairness, it should be noted here that Nate Silver, the most popular proponent of this polling-amalgamation strategy, stated repeatedly that Donald Trump has a path to victory. Just before Election Day, however, even Silver’s models leaned toward a Clinton win.

What lessons can we learn from these polling-based collisions with last year’s electoral iceberg?

First, it is worth noting that political scientists were not necessarily part of the horse race frenzy. Quite a few correctly predicted the Republican victory, using various modeling techniques. Most of those who bucked the media’s conventional wisdom have one thing in common—they looked at numbers affecting partisan breakdown, not numbers for Hillary Clinton and Donald Trump specifically. The news media’s “horserace” coverage emphasizes polling respondents’ plans to vote for one candidate or another, while political scientists such as Michael Lewis-Beck and Charles Tien, Brad Lockerbie, and Alan Abramowitz, each did what political scientists (as opposed to campaign or media pollsters) usually do—they looked at fundamentals such as the state of the economy, partisan breakdown of the electorate, historical trends, approval of the current President, and voter optimism about the economy, not voters’ opinions of the candidates themselves.

Why were these models so widely ignored? That answer could be summarized as, “but… Donald Trump!” More formally, many commentators (including more than a few who were political scientists or political science-trained) assumed that Donald Trump’s quirky candidacy and high personal negatives meant that the usual partisan-breakdown models used by these political scientists and others simply did not apply this year. In fact, they were onto something. The scholars cited above all predicted a higher popular vote share for the Republican than Trump actually won, while others were even farther off, predicting percentages for the Republican nominee as high as 56% (Trump actually won just 46.1%).

If John McCain or Mitt Romney had been the Republican nominee, he might very well have gotten the 50%+ of the popular vote predicted by these models. So, in fact, the conventional wisdom was not completely wrong. Trump did underperform the expectations of these models, presumably due to his unusual personality, behavior, and candidacy. Yet he is still on the verge of becoming President. The results of another poll, in the very “red” state of Kansas where I research, write, and teach, may offer a clue as to why. According to respondents in the Kansas Speaks survey, Donald Trump was highly unpopular here, scoring particularly low with our respondents on the matters of trustworthiness and “understanding people like me.” Yet Trump won Kansas easily, and the reason is clear: not only is Kansas a heavily Republican state, but Hillary Clinton was even more unpopular here than was Trump. Her worst-scoring categories in Kansas Speaks were the same as Trump’s, and Kansans rated her lower on trustworthiness and “understands people like me” than they did Trump.

In short, outside of California, voters disliked Hillary Clinton more, but they also disliked Donald Trump. The conventional wisdom before the election had this reversed, with commentators assuming that Clinton, not Trump would be perceived as the lesser of the evils. Commentators underestimated the roles of three things: deep party ties (the vast majority of Mitt Romney’s supporters from 2012 backed Trump), the same variables that usually affect elections, such as the state of the economy and optimism about it, and finally, Hillary Clinton’s unpopularity.

While this is conjecture on my part, I cannot resist adding that in the last three elections that have been framed by the conventional wisdom as “a choice between the lesser of two evils”—2000, 2004, and 2016—Republicans have gained the White House each time.  The tiresome “lesser evil” frame appears to be toxic to Democrats, likely because their base is less reliable about turning out to vote if they do not like the candidates.

Still, I have not yet gotten to the problem with the polls themselves. Weren’t they clearly predicting a Clinton victory, not only nationwide (which was correct), but in those Great Lakes “firewall” states that put Donald Trump in the White House?

Here’s a dirty little secret of polling: no poll has a representative sample of those being studied. Polling, like scientific tests of soil or water quality, works by sampling— drawing a subset of thing being studied, testing it, and then drawing an inference (logical leap) from the results for the sample to the likely condition of the whole from which that sample was drawn. We cannot really know what the water quality is in, for example, Lake Michigan, because it is impossible to test all of it. However, water-quality experts often draw and test samples of the water, then draw inferences to the whole.

For this to work, sampling must be done with great care. Likewise, pollsters must take pains not to over-sample certain populations and under-sample others. One classic example pertains to the time, not so far back, when most households had one landline telephone. In mixed-gender households (often married heterosexual couples), the adult woman was usually the one to answer the phone. Had pollsters simply interviewed her, the result would be a sample that was heavily skewed towards women, and under-sampled men, relative to their proportions among the population. Thus, a “randomizing” technique had to be employed, such as asking to speak to the adult in the household with the next birthday.

Today, many Americans have their own cell phones, and landlines are becoming obsolete. Call “screening” is also more popular than ever.  If getting something close to a random sample was hard 20 years ago, today it is nearly impossible. It is very difficult to get proportionate numbers of complete surveys from African-Americans and from people that do not speak English as a first language, for example. Randomizing methods are still used but they are not enough.

When polling results are featured on the news, what you are hearing about are not the raw data from the poll, but rather, poll results that have been “weighted” to account for the impossibility of getting a true representative sample. Imagine that we expect 12% of the voters to be African-American, yet only 5% of the polling sample fit this description. The “weight” of each result from an African-American respondent is thus multiplied to adjust to something more representative. This process often employs “multivariate regression with post-stratification,” or, in a wonderful acronym, “Mr. P.”

Here’s where things went south in 2016. In order to weight the polling results, we have to know ahead of time who is going to vote. If we weighted the data based on a prediction that 12% of the electorate would be African-American, and it turns out that only 10% were, then our predictions were off.  It is, of course, impossible to know who is going to vote until after they have done so, therefore, the composition of the electorate is estimated, often using the composition of the electorate for the last election (in this case, the 2012 Obama-Romney race). In 2012, this worked well—the composition of the electorate was similar to 2008 and the winning candidates were also the same. Notwithstanding unnecessary media “horse race” hype, the predictions of prognosticators in 2012 were pretty much dead-on.

Then it all fell apart in 2016.

Put simply, the composition of the electorate changed. African-American turnout dropped, while Trump, like 1992 third-party candidate Ross Perot, pulled out voters who simply would not have voted at all, had Trump not been in the race. But unlike Perot, Trump also won a major-party nomination, so he was able to put the party’s base together with those infrequent voters and pull off the victory—at least in the electorally-critical states. The pollsters’ estimates of the electorate’s composition were incorrect, therefore, the weighted predictions were wrong as well.

Another possible factor in the polling inaccuracies is the “Bradley effect,”- that is, Trump voters having lied to pollsters about their intentions. This was a popular Election Night speculation.  However, subsequent analysis indicates that the Bradley effect was, at most, only one of a number of factors involved.

Taking stock of all this, it’s not yet time to invoke the famous quip about “lies, damned lies, and statistics.” In fact, many political-science-based models correctly predicted the winner, while polling data such as Kansas Speaks show how Trump could win despite relative unpopularity (because Clinton was even more unpopular). I join fellow MPSA bloggers in calling for the news media to re-orient away from “horse race” coverage. It is underlying dynamics, not the horse race, that usually decide elections—and news consumers deserve more attention and analysis of those dynamics. After all, it is things like the state of the economy and our optimism about the future, not political candidates’ personal idiosyncrasies, which are what truly affect our own lives.

About the author: Michael A. Smith is a Professor of Political Science at Emporia State University where he teaches classes on state and local politics, campaigns and elections, political philosophy, legislative politics, and nonprofit management. Read more on the MPSA blog from Smith and follow him on Twitter.

MPSA Blog: Top 10 Posts from 2016

MPSA Blog: Top 10 Posts from 2016

Regardless of your research interests, your academic (or Alt-Ac) role, or your aspirations for the new year, there is something on this list of MPSA’s most popular blog posts from 2016 that is sure to pique your interest:

MPSA would especially like to thank regular contributors Newly Paul, Adnan Rasool, Michael A. Smith, and Harry Young for sharing their research, political perspectives, and pedagogical insights with us this calendar year. We look forward to highlighting even more NSF-Funded research, conference presentations, and MPSA member interviews in the coming months. If you’re interested in sharing your work with MPSA’s members and the discipline, we’d love to hear from you.

Best wishes for a safe and productive 2017!