Christopher Newfield
The research behind this volume was funded toward the end of Barack Obama’s presidency and completed toward the end of Donald Trump’s. The transition from one to the other made the politics of the numerical a national issue. Obama embodied the rule of apparently dispassionate expertise, while Trump embodied a relentless attack on it. During one of the 2016 presidential debates between Hillary Clinton and Donald Trump, when the candidates were asked about trade deals that allowed effects like Chinese steel imports replacing US steel, Clinton pointed out that Trump had used Chinese steel in his hotels and thus given jobs to Chinese, not American, steelworkers. Trump responded, “She’s been doing this for 30 years; why the hell didn’t you [fix] it over the last 15 or 20 years? . . . You do have experience. The one thing you have over me is experience. But it’s bad experience” (Golshan 2016). Trump agreed that Clinton had massive expertise, but claimed that her expertise had made a mess of everything.
In this context, it seems that Trump, his Republican Party, and the fake newsers in his mass base must be blamed for the decline of expertise. The victims include the horrified professional middle classes who have long voted for the Democratic use of numbers to grasp larger economic and social patterns, such as racialized unemployment, so that they can be addressed (or politely finessed). In familiar contrast, Trump brought a long history of fabrication to the White House, including the “birther” claim—that Obama was born in Kenya. Once in the White House, his lying set some kind of world record for a head of state: “30,573 false or misleading claims,” according to the Washington Post fact checker (Kessler 2021).
Though Trump and his base came to dominate public discussion of the fate of expert knowledge during his presidency, they are only part of a larger story. In this chapter, I aim to tell another part. I will analyze the role and effects of the expert use of expertise, illustrated by the Barack Obama administration. We are used to complaints about the limits of technocratic governance, both in its alliance with neoliberalism and in the failure of an anti-intellectual culture to appreciate it. Here I’ll focus on the Democrats’ use of expertise to weaken the wider public’s sense of their own historical agency in relation to an economy and society that experts end up defining in fatalist quantitative terms.
The start of the 2020s will be remembered for the SARS-CoV-2 (COVID-19) global pandemic and, in the United States, for the long crescendo of the president’s public war on fact, which inspired strangely related events, such as the armed militia protest of public health mandates like mask wearing in the Michigan State Capitol on May 1, and the neo-Confederate assault on the Capitol to block the certification of the Democratic presidential victory six days into 2021. These and other events arose largely in response to falsehood propounded in the highest places: that COVID-19 was just the flu, that mask mandates were a liberal attack on liberty; that a Republican presidential victory had been stolen by massive electoral fraud. There were countless charges and grievances, and yet the events of 2020 seemed to offer a helpful simplification about the motives of Donald Trump’s supporters. While the 2016 election had produced a sustained debate about how to rank the apparently multiple issues driving his MAGA base (named for his campaign slogan, “Make America Great Again”), the 2020 election, and Trump’s denial of its result, tended to reduce these to two: authoritarianism and white supremacist views, whether formal or informal. Other motives, like “economic anxiety” (Global Strategy Group and Yang 2017) or concern about Washington corruption (“drain the swamp”), faded into the background.
I agree on this ranking of MAGA motives, in part because I’ve long held that the interaction of racism and authoritarianism shapes the core features of US democracy (Newfield 1996). But identifying constitutive features doesn’t explain why these features became more important at various times or why one political demagogue gathers a mass base while others do not. Trump’s base was not reduced but nourished by his manifest failures to fulfill his campaign promises or manage COVID-19 “like a successful businessman.” Racism and authoritarianism don’t in themselves explain the affective grievance culture that animated them—the sense in the MAGA base that their natural legitimacy and centrality had been stolen from them.
I will assert here that the major catalyst with the MAGA base was its sense of victimization—specifically by the system supposedly built by Democrats. After the 2016 election, analysts tried to identify the particular socioeconomic forms of victimization that explained Trump’s success, such as unemployment, which he had blamed on “bad trade deals” or “illegal immigration.” These efforts were largely unsuccessful. The grievance we frequently heard was that MAGA views were despised and excluded by liberals, elites, experts, and the mainstream media. Were the United States the land of liberty and fair democracy that it was supposed to be, MAGA views would be important, dominant, and plainly respected. My starting point here is my sense that white supremacist and authoritarian sentiments were catalyzed by an epistemic grievance against liberalism that adherents attributed to Democrats. The MAGA base rebelled not only against the content of liberal ideas, like immigration reform, affirmative action, or a woman’s right to choose abortion, but against the practices of evidence and argument that give liberal Democratic ideas their social centrality.
This root of MAGA grievance culture—the accepted epistemic validity of mainstream liberalism—brings us to the topic of this volume. We stated in our Introduction that the Original Critique of quantification identifies problems of both knowledge and power. Quantification tends to strip out history, context, and local knowledges, thus partially falsifying any particular situation. Quantification also weakens the power of people in particular situations either to govern themselves or to make others care about their perspectives and experiences, since governing authority easily slips from that particular situation to some other, greater authority elsewhere—a supervisor, a management team, a chief executive, a rating agency, a funding body, and so on. Numbers are both reductive and managerial. They avoid these effects only with deliberate effort.
This volume both extends and challenges this critique. In chapter 1, Elizabeth Chatterjee observes that critics of the reign of the numerical, though rightly concerned about a “democratic deficit,” too easily assume “that depoliticization through quantification is typically successful.” She goes on to show the extent to which the US and UK political systems have marshaled numbers for political ends and also the involvement of experts—the “quantocracy”—in this marshaling. Leaders of the political right, however deeply anti-intellectual their populist stance, have developed what she terms “plebiscitary numbers”—often popularity ratings supposedly confirming their mass support. Furthermore, Chatterjee outlines a genealogy of the right’s politicized use of numbers that traces it back to its political opponents, New Labour and the New Democrats, whose leaders, including Tony Blair, Bill Clinton, Al Gore, and Hillary Clinton, embraced the quantocracy for political gain. She thus deploys the Original Critique’s claim that quantification can damage democracy while extending it to conclude that this happens as readily through the politicization as through the depoliticization of numbers—and that it can be performed as readily by centrists affirming experts as by right-wingers lambasting them and the fake news they produce.
In this chapter, I join Chatterjee in focusing on the numerical sins of proexpert politicians rather than those of their anti-intellectual detractors. This means the US Democratic party in the Clinton-Obama quarter-century. During the Democrats’ prior fifty-year heyday from around 1930 to 1980, the party had two issues on which it based its mass appeal: (white) egalitarian economic development and, later, civil rights and racial justice, built also on feminist and queer insurgencies over many decades. These achievements were limited and the issues never faded in public importance. On the contrary, they gained relevance as Republicans became more militant about rolling back any progress made toward economic and racial equality, emboldened by the presidency of Ronald Reagan in the 1980s. And yet these were precisely the issues that the New Democrats downplayed, marginalized, or even opposed. After Reagan walked over prolabor and late New Dealer Walter Mondale in the 1984 presidential election, the Democrats ran technocrat Michael Dukakis, who could not summon the civil rights language to defend his candidacy against George H. W. Bush’s racial dog-whistling. (For example, Bush’s Willie Horton campaign ad implied both that Black men were inclined toward rape and that civil-rights Democrats were enabling them.) In the next election, Bill Clinton acted as though Dukakis hadn’t backpedaled either equality agenda quickly enough. His 1992 campaign featured “tough on Blacks” racial signals like the execution of a mentally incapable Black prisoner in Arkansas, Ricky Ray Rector, and a gratuitous attack on radical Black musician Sister Souljah. This continued once Clinton took office, through his abandonment of his own nominee to head the civil rights division of the Department of Justice, the Black legal academic Lani Guinier; his promotion of federal crime legislation that increased incarceration rates for Black and Latinx communities and for which Joe Biden had finally to apologize in 2020; the downsizing of welfare through his critique of welfare “dependency”; and so on (Applebome 1992; Clinton 1996; Lauter 1993; Phillips 1992; Ray and Galston 2020).
Overall, the Democratic retreat from economic and racial equality worked politically for two exceptional Democratic politicians—Bill Clinton and Barack Obama—but failed to build Democratic majorities in Congress, most state legislatures and other local jurisdictions. Donald Trump’s victory in 2016 built on Republican control of both chambers of Congress and most statehouses. Republican power, used to oppose both types of equality and then emceed so masterfully by the offensive and divisive Trump, intensified the demands of the Democratic base to build forward on the basis of the double-equality agenda—for starters—that had been neglected for about forty years.
As we made final changes to this manuscript, Joe Biden had replaced Trump in the White House, but had to work with a tied Senate, a smaller majority in the House, and Republicans controlling nearly two-thirds of statehouse chambers. Holding the spotlight on the Democratic use of numbers may be particularly useful in the 2020s. The spectacular toxicity of Trump’s final year amped up long-standing concern about his followers’ apparent contempt for facts and thinking, a contempt Trump both celebrated and capitalized on: “I love the poorly educated,” he had remarked after a string of 2016 primary victories (Stableford 2016). But that is also a distraction from the political and epistemic weaknesses of the Democrats’ modes of address.
Multitudes of voters across the spectrum want to be heard by their political leaders, don’t think they are being heard, and don’t like the various substitutes for being heard that are in common use. One of these substitutes is the collection and use of numbers to override everyday experience. The numerical can be deployed to discredit or evade democratic deliberation and the presence of lived experience in that deliberation. Using expert knowledge against experience, especially dispassionate quantified knowledge and its claim to superiority, undermines the Democrats’ historic equality agenda, as I will show. Whether or not the party leadership explicitly moves in the 2020s toward equality, it’s worth seeing how its numerical culture, so trustful of large bodies of information and rooted in professional-managerial expertise, will need to be changed for such a shift to endure.
A quick terminological note: most simply, quantification refers to descriptions of things or relations between them in the form of numbers; in contrast, qualitative, or verbal, descriptions identify features or characteristics without numbers. The numerical has two important features that the verbal in itself does not. First, it is a mode of comparison that lends itself readily to ranking (Fourcade 2016). Second, it claims objectivity. To generalize on the contrasting term, most people see qualitative description as expressive or subjective. Even when a qualitative assessment seems precise and verifiable, it remains tied to particular perceptions, contexts, or people. The numerical generally has more authority than the qualitative, given the conventional wisdom that it is more objective. The Original Critique identified the tendency of authorities to use numerical discourses, intentionally or not, to correct and exclude vernacular perspectives, rather than to explore and enhance them. This issue has not gone away.
Trump’s 2016 Electoral College victory produced a wild scramble to explain his voters. Gender bias seemed to play a role, though its explanatory power was blunted by Trump’s success with the white female vote, in spite of a long series of complaints of sexual harassment and assault, and his recorded boast of enjoying routine sexual groping of women “if they’re beautiful” (BBC Staff 2016). Perhaps the bias at work would be better understood as antifeminist.
The leading early thesis was that the white working class turned to Trump because they felt “economic anxiety” and had confidence that the smart, successful businessman could fix trade deals and the rest of the economy (Barabak and Duara 2016). This thesis did not survive detailed analysis of polling data, since Trump’s support was much stronger among whites making more than $50,000 per year than among those making less. It quickly became clear that Trump was largely a white middle-class phenomenon (Confessore and Cohn 2016).
The most durable explanation for the Trump vote has been white racism. Trump “won the white vote nationally by 21 points (one point more than Romney), and his campaign rallies were Woodstocks for bigots” (M. Davis 2017). A consensus emerged that racism fed the Trump vote (Clement 2017).1 As one pollster, Nick Gourevich, put it to Thomas Edsall,
within economically distressed communities, the individuals who found Trump appealing (or who left Obama for Trump) were the ones where the cultural and racial piece was a strong part of the reason why they went in that direction. So I guess my take is that it’s probably not economics alone that did it. Nor is it racism/cultural alienation alone that did it. It’s probably that mixture. (Edsall 2017)
Trump voters were more likely than others to tie economic problems to excessive immigration. They were more likely to rail against “illegals” and to want to build a wall along the Mexican border. They were more likely than other white voters to view everything, including economic problems, through their fear of “cultural displacement” (Cox et al. 2017).
Trump systematically stoked this race-based resentment. He did not dog-whistle racist tropes but shouted them out in the open. He spent much of 2020 suggesting that Black Lives Matter protests rested on no legitimate grievance but were borderline terrorist threats to the sanctity of the suburbs. He invited two white homeowners who had brandished assault rifles at peaceful marchers to the Republican National Convention. In addition, in July 2018, his Department of Education’s Office of Civil Rights had affirmed white grievance culture by rescinding an Obama administration guidance that allowed restricted use of race as a factor in school enrollments or university admissions (Green et al. 2018).2 Consideration of candidates’ race in enrollments or admissions, or “affirmative action,” is not a sideshow in racial politics, but a charged example of what its opponents call “reverse discrimination” against whites. These opponents are still in the majority: in one 2017 poll, 55 percent of whites said “they believe there is discrimination against white people in America today” (Gonyea 2017). Polling both before and after the 2016 election found that Trump supporters were more likely than other whites to agree that “whites losing out because of preferences for blacks and Hispanics” was a bigger problem than preference for whites. “The odds that a person who feels strongly that whites are losing out supports Trump are more than three times higher than for a demographically and financially similar person who feels blacks or Hispanics are losing out or that neither group is losing out more” (Clement 2017). Other studies also suggested that whites in similar economic and educational circumstances would break for or against Trump depending on whether they were for or against crackdowns on immigration, Black Lives Matter protests, and the like, with white racial victimization as the common thread (Craig and Richeson 2014; McElwee and McDaniel 2017).
In addition to income, higher education was a reliable breakpoint for Trump voting, with Trump favored more by people who had not gone to college than college graduates of similar incomes. The statistician Nate Silver pinpointed the “college divide” early on:
I took a list of all 981 U.S. counties with 50,000 or more people and sorted it by the share of the population that had completed at least a four-year college degree. Hillary Clinton improved on President Obama’s 2012 performance in 48 of the country’s 50 most-well-educated counties. And on average, she improved on Obama’s margin of victory in these countries by almost 9 percentage points, even though Obama had done pretty well in them to begin with. (Silver 2016)
Silver found the reverse in the counties with the lowest percentage of college graduates:
Clinton lost ground relative to Obama in 47 of the 50 counties—she did an average of 11 percentage points worse, in fact. These are really the places that won Donald Trump the presidency, especially given that a fair number of them are in swing states such as Ohio and North Carolina. (Silver 2016)
Nor was this due to that difference in income which is correlated with higher education:
I identified 22 counties where at least 35 percent of the population has bachelor’s degrees but the median household income is less than $50,000 and at least 50 percent of the population is non-Hispanic white . . . Clinton improved on Obama’s performance in 18 of the 22 counties, by an average of about 4 percentage points. (Silver 2016)
Through a series of such comparisons, Silver concluded that lower education was the more important driver of the white Trump vote than higher income—though the latter also helped. In short, the MAGA base came to be codified as the white middle class that did not finish college and read all events through a racial anxiety that this base never subjected to scrutiny.3 By the time Trump became Super-Trump in 2020 and was winding down his “stop the steal” blowout, the convergence of visible white nationalism and authoritarian insurgency seemed to confirm the 2016–17 view that the racism of the poorly educated was the Trumpian rocket fuel.
It might seem at this point that our main analytical work is done. In our reading so far, the MAGA base liked Trump’s promise to restore an America in which they were economically and culturally comfortable and also preeminent by virtue of their white race (Devega 2017). Furthermore, in MAGA America, their security was not dependent on college achievement or on any related cultural or linguistic skill, for they had no obligation to acknowledge, respect, or get along with different kinds of Americans. Nor did they need to compete with poorly paid foreigners for middle-class jobs. Women in MAGA America also would not constitute a threat of difference, or present the need to rethink anything. Women would not have abortion rights, equal pay, or experience the various benefits sought by the several waves of the feminist movement. There were no interpretive or epistemic challenges in the coming MAGA America: a hermeneutics of otherness would never be required.
This view was mostly in place by the end of 2017, and helped rally strong Democratic turnout in the 2018 elections, since this MAGA model turned the Republican base into the implacable enemy of government, affordable health care, equality, climate solutions, and the very presence of people of color. When Trump dismissed and thereby aggravated the two biggest issues of 2020, the COVID-19 public health crisis and anti-Black police racism, he reinforced the model. As 2020 Trumpism intensified the neo-Confederate ethos, the MAGA model of the noncollege white racism base was locked into place.
The 2020 election did not change this analysis of Trump’s white patriarchal appeal so much as it intensified and psychologized it. Trump increased his popular vote total from 63 to 74.2 million. As an incumbent president who had dominated the media every day of his four-year mandate, he had eliminated any shred of uncertainty about what a Trump voter was voting for.4 In 2020, Trump voters pulled the lever with eyes wide open. This fact has fed analyses of Trumpism as a mass psychological condition that pushes racism and inadequate education past previous bounds.
Discussion of MAGA authoritarianism had been building through Trump’s term (Edsall 2018; Krugman 2017; Toscano 2017). Interesting examples of book-length treatments appeared during the 2020 election season. Two in particular reinforced each other with complementary methods. In Authoritarian Nightmare, former Nixon counsel John Dean partnered with social psychologist Bob Altemeyer to tie Trump partisans to high scores on an instrument that generates a Right-Wing Authoritarian (RWA) scale, which has been in use since the 1970s. They were able to combine this scale with a Monmouth University Polling Institute survey of Trump voters. Their conclusions cast Trump voting as a symptom of cognitive impairment with a fixed authoritarian personality structure. Here is their remarkable summation:
The verdicts are in. (1) Donald Trump’s supporters are, as a group, highly authoritarian compared to most Americans. (2) They are also highly prejudiced compared to most Americans. (3) You can explain the prejudice in Trump’s supporters almost entirely by their authoritarianism. (4) Authoritarianism is a strongly organized set of attitudes in America that will prove very difficult to reduce and control. . . . The pillars of Trump’s base, white evangelicals and white undereducated males are highly authoritarian and prejudiced. . . . The connections among prejudice, authoritarianism and support for Donald Trump are so strong that no other independent factor can be as important in supporting his reelection. There just is not much left to be explained, which is a highly unusual situation in the social sciences, but that is where the data have taken us. Ask a very complicated question: Who are Trump’s staunch supporters? Get a very simple answer: Prejudiced authoritarians, and a few others. (Dean and Altemeyer 2020, 224–25)
These extremely confident pronouncements conflict with evidence of Trump-voter diversity (Global Strategy Group and Yang 2017; Olsen 2021). They are being applied to around 74 million Trump voters. They also hew so closely to hostile stereotypes of Trump supporters that I was uncomfortable crediting them. And yet they did fit with my own perception of Trumpist discourse in the context of my own research on intractable authoritarian elements in American culture. I was also persuaded by the combination of Altemeyer’s empirical work in conjunction with abundant evidence in MAGA circles of a key cognitive feature of authoritarian thinking—the preference for authority over experience. This was the foundation of the widely noted denialism of verifiable evidence such as Joe Biden’s electoral victory in November 2020. There is also growing evidence for the label of racist authoritarianism from MAGA ethnography (Hoffman 2020).
Further reinforcing this picture of an authoritarian cognitive death are the psychologists, both those who consider Trump clinically sociopathic and those who focus on his followers. The psychologist Steven Hassan argues that Trump has deployed the standard programming techniques that cult leaders always use to acquire and mentally dominate their followers. He concludes that members of the MAGA base should be treated sympathetically, as victims of techniques of control who must be led carefully back to “their true, or authentic selves” (Hassan 2020).
It may appear that we’ve lived through the following evolution: white middle-class racism, mostly of the noncollege kind, put Trump in the White House in 2016. There he used his media and propaganda apparatuses to transform racial ideology into a psychological paralysis that was in turn rooted in the underappreciated authoritarian bedrock of US culture (Newfield 1996). Hostile judgments, however, will only drive the MAGA base into deeper states of rage and dissociation, so therapeutic sympathy should be deployed before the country breaks in two. This was the tacit theory behind Joe Biden’s decency campaign in 2020: Trump was bad but his followers were not; once given a reasonable choice, they would return to good, caring, highly professional government that Biden was in early 2021 systematically putting into practice, and be naturally attracted to its practical success. The MAGA base had dealt with economic anxiety by transforming it into racist projection. Perhaps expert competence that reduces economic deprivation and injustice would allow the base to dial its racism back. Republican majorities also like the US Postal Service, Medicare, and higher taxes on the wealthy, and the achievements of the Democrat’s pros might stand them down.
Here we reencounter our question of experts and their effects on contemporary US political culture. What aspects of professional expertise could really appeal to those attracted to racist authoritarian modes of US life?
Earlier I set aside economic factors in Trump’s 2016 success. Now I’ll reintroduce them in a specific form, as a deprivation of personal agency justified by invoking quantification. I put this in a context in which racism and capitalism don’t compete for influence but interact continuously, as the concept of “racial capitalism” encourages us to see (Robinson 2007).
Pollsters found that Trump voters who earned middle-class salaries were anxious economically about threats to their standard of living. They also—and this is the key point—did not think the Democrats would help them. In one study, the group that switched from Obama to Trump held a crucial belief: congressional Democrats were even more likely to favor the rich than congressional Republicans, and so they put their faith in the supposed egalitarianism of Donald J. Trump (Carnes and Lupu 2017).
This is a startling finding. Slightly more Obama-Trump voters see congressional Democrats rather than Republicans as the party of the rich; twice as many see Democrats as pro-plutocrat compared to Trump.
The plot is thickened by the fact that many Republicans want to vote against plutocracy: nearly as many Republicans as Democrats in exit polls thought “big business has too much influence in American politics” (Edsall 2017). A study looking at historically Democratic counties that flipped to Trump found that all but one of them had voted for Obama at least once, and that in nearly all of them a factory had closed during the campaign season, forming “embittering reminders that the ‘Obama boom’ was passing them by” (M. Davis 2017). One of the authors of another study put it this way to the New York Times’s Edsall: “The biggest common denominator among Obama-Trump voters is a view that the political system is corrupt and doesn’t work for people like them” (Edsall 2017). A longtime Democratic pollster, Stanley Greenberg, elaborated on the history:
Working-class Americans pulled back from Democrats in this last period of Democratic governance because of President Obama’s insistence on heralding economic progress and the bailout of the irresponsible elites, while ordinary people’s incomes crashed and they continued to struggle financially. . . .
In what may border on campaign malpractice, the Clinton campaign chose in the closing battle to ignore the economic stress not just of the working-class women who were still in play, but also of those within the Democrats’ own base, particularly among the minorities, millennials, and unmarried women. (Greenberg 2017)
Trump seems to have made real inroads in many Obama precincts with his promise to bring jobs back. He did this specifically with people who had voted Democratic in the past and had felt betrayed by them. In this logic, having voted for Obama and seen no disruption in policies favoring the rich, they were willing to take a chance on a guy who proclaimed himself disrupter-in-chief.5
Democratic economic policy has been deeply disappointing for people who care about stability or equality. But it hasn’t been worse than Republican economic policy on those points. We are in danger of arriving back to where we started: Trump picked up those skeptics about Washington economics who were also more likely to blame immigrants and racial minorities for their problems. Trump, in other words, got the racist doubters of capitalist economics, while Clinton got the less-racist or antiracist doubters. Didn’t we already know this?
Yes, but there’s another differentiator between Democrats and Trump’s type of Republicanism. Trump describes economics as a triumph of the will, American-style. Democrats have been describing economics as the outcome of quantitative laws.
I’ll offer two examples of what I’ll dub numerical fatalism. The first is from the cornerstone text of New Democratic political economy, Robert Reich’s 1991 The Work of Nations. Reich’s book is about how the United States should fit its labor force into the global economy. It was superbly timed, appearing as the Soviet Union was collapsing and the global workforce was undergoing the Great Doubling (Freeman 2006), and it helped earn Reich an appointment from Bill Clinton as his secretary of labor.
Reich made fundamental claims that solidified an enduring New Democrat common sense. He wrote that global economics was undergoing structural changes that were beyond the reach of national policy. He wrote that eventually national economies would cease to exist (Reich 1991, 3) and there was no point in a new administration in Washington trying to control global economic forces or the offshoring of good union jobs. He wrote that the new firm would be an “enterprise web” and would thus disperse rather than concentrate economic power (88): “Instead of imposing their will over a corporate empire, [executives] guide ideas through the new webs of enterprise” (97). He wrote that power and resources would flow to skill rather than to existing wealth or ownership. He classified levels of skill into three types: routine production workers, in-person servers, and symbolic analysts. He wrote that the first group, who worked on the floor at Ford or US Steel or Carrier, belonged to the past economy; government effort could not save their jobs. He wrote that the second group were tied to a location (home care workers had to be onsite), and were not going to be highly paid but could not be offshored. He wrote that the third group contained the value creators of the new economy. Their services “include all the problem-solving, problem-identifying, and strategic-brokering activities” (177); they manipulate symbols that stand for reality, and their manipulation yields new efficiencies, new financial arrangements, new deployments of resources, new music, new designs, new ad campaigns (178). He wrote that the main job of government is to increase worker skills (and the share of symbolic analysts) by funding training and education. He admitted that the country was showing signs of a “politics of secession,” in which “growing segregation by income” (274) would encourage the wealthy to set up “homogeneous enclaves” for themselves (268). But he concluded that this inequality would be managed by a shared “positive economic nationalism” (312) in which the refusal to control trade and protect American jobs would be accompanied by generous public spending to help people raise their own productivity so they could compete and win. “In principle, all of America’s routine production workers could become symbolic analysts and let their old jobs drift overseas to developing nations” (247).
All of Reich’s key predictions were wrong, including the one about brainworkers being protected from economic turbulence in contrast to the other two categories. Over the next quarter-century, job loss and secession accelerated—deindustrialization and inequality are the hallmarks of the period, even as the economy continued to grow. Wealth and power did not become decentralized through the “network” economy. Routine production workers did not launch themselves onto the career path of symbolic analysis. Public funding did not generously support everyone’s transition to a higher level of capability. As important as Reich’s inaccuracy was his justification of change. In Reich’s model, all this shifting of employment, production, and pay happened more or less automatically. Deindustrialization wasn’t the fault of the CEOs and governing boards who made the decisions, since they were just other symbolic analysts in a network processing global economic data and doing what the numbers said they had to do. Deindustrialization wasn’t the fault of Roger Smith or his successors at the top of General Motors, or the fault of the Democrats, although they occupied the White House for two-thirds of the 1991–2016 period. It wasn’t really even the fault of the Republicans. All Democrats should do was encourage former production workers to adapt to the inevitable and give them retraining or college money in the teeth of Republican opposition. Agency, in the New Democrat model, lay with global structural forces, not with top officials in the private or public sectors. Reich got Democrats off the hook, in their own minds, for passive adaptations to forces that wreaked the most havoc on their own base.
Of course, Republican economic policy was actively rather than passively plutocratic, so Democrats stayed in national contention. Twenty-five years after Reich’s book appeared, President Barack Obama held a town hall in Indianapolis, Indiana, on June 1, 2016, in the midst of Hillary Clinton’s campaign for president. The moderator, Gwen Ifill, tried to prompt Obama to deal with people’s experiences rather than facts and figures. He struggled to do this. He said things like, “We have fewer federal employees today.” She said, “um-hmm.” He continued,
If people are feeling insecure and they’re offered a simple reason for how they can feel more secure, people are going to be tempted by it, particularly if they’re hearing that same story over and over again. The health care costs since I signed Obamacare have actually gone up slower than they were before I signed it. Twenty million more people have health insurance. So the arguments they’re making just are not borne out by the facts. (Ifill 2016).
Ifill responded by quoting Bill Clinton back to Obama: “‘Millions and millions and millions of people look at that pretty picture of America you painted,’ which you just described, ‘and they cannot find themselves in it to save their lives.’” You think the people are being seduced by a false story, she was saying, but you are giving them false statistics—true in the abstract, but false to their actual experience. “Why is there a disconnect?” she asked Obama.
He took up again with his tale of fated effects : “Well, look, here’s what has changed in the economy over the last 20 to 30 years. . . .” The striking feature of Obama’s account is the passive voice: “And what started happening is you started seeing foreign competition. Unions started getting busted.” Nobody does things, they just happen. It’s the tide of history: there’s no set of bankers, CEOs, think tank free-marketers, anti-union law firms, or Republican senators who did bad things and who now must be stopped.
Ifill gave up and said, “let’s turn to the audience and see what they think.” The first question was from a “fifth-generation fruit and vegetable grower here in Elkhart County” who was struggling under new paperwork for the Food Safety Modernization Act and Obamacare. How can we encourage younger people to enter this kind of marginal business, he asked. Obama gave a seven-paragraph answer that said he was for regulation and also against outdated regulations and that “some elements of the regulations I put in place have probably put a burden on you”—but it’s for the best.
Then he got a question that laid out the problem all over again.
Eric Cottonham: My name is Eric Cottonham and I’m representing the Steelworkers Union, Local 1999. And I’m trying to find out, what do we have left for us—all of our jobs are leaving Indianapolis. I see here you’re doing a lot of things, but in Indianapolis, there’s nothing there for us. I mean, what’s next? I mean, what can we look forward to in the future as far as jobs, employment, whatever? Because all of our jobs have left or in the process of leaving, sir.
President Obama: Well, in fact, we’ve seen more manufacturing jobs created since I’ve been president than anytime since the 1990s. That’s a fact. And you know, if you look at just the auto industry as an example, they’ve had record sales and they’ve hired back more people over the last five years than they have for a very long, long time.
We actually make more stuff, have a bigger manufacturing base today than we’ve had in most of our history. The problems have been—part of the problems have had to do with jobs going overseas and this is one of the reasons why I’ve been trying to negotiate trade deals to raise wages and environmental standards in other countries, so that they’re not undercutting us.
But frankly, part of it has had to do with automation. You go into an auto factory today that used to have 10,000 people and now they’ve got 1,000 people making the same number of cars or more. And—so what that means is even though we’re making the same amount of stuff in our manufacturing sector, we’re employing fewer people. . . .
Obama carried on like that for another seven transcript paragraphs. He overrode Cottonham’s description of local conditions by invoking the quantitative “fact” of its opposite, a manufacturing boom. This rejection of experience is point blank something people hate about experts, but it was Obama’s routine. He never came close to answering the question. He had no suggestions for things people in Indianapolis could do. He limited his own political agenda to supporting workers who adapt to job loss with retraining as opposed to addressing the job crisis directly. He praised clean tech and other new tech jobs, “like 3-D printing, or, you know, nanotechnology,” and went on to reiterate that the days when “just being willing to work hard” meant a steady job are over. So, “you cannot look backwards, and that doesn’t make folks feel good sometimes . . . but they’re going to have to retrain for the jobs of the future, not the jobs of the past.”
In short, Reich’s fatalism carried on through later Democratic policy: neither governments nor the corporate world can control economic ground rules and must instead stick with helping people adapt. By 2016, this kind of adaptation had a track record of deindustrialization and inequality: the voters who thought Democrats were twice as plutocratic as Trump in the survey I mentioned were wrong on the specific point but right about the drift of Democratic expert policy. The drift floated on a tide of economic numbers.
Facing the stereotype of a party run by and for the “the college kid who tells me how to do my job,” Hillary Clinton neither explained the thinking behind key economic policies, nor atoned for bad results (Williams 2017), nor proposed action against economic policies that had produced them. She adjusted her views around the edges of charged economic issues like the North American Free Trade Agreement, the Trans-Pacific Partnership, and other trade agreements that Trump constantly attacked. But she did not admit long-standing Clintonite mistakes in a way that people associate with basic honesty or professional integrity. She wrapped policies in a protective shield of economic science. In May 2016, Clinton told the CBS Sunday program Face the Nation, “I do believe in trade. . . . We are five percent of the world’s population. We have to trade with the other 95 percent. That has on balance been a net plus for our economy” (Perry 2016). Of course such numbers had nothing to do with the effect of trade on much of the US population or with what kind of trade deals the United States should have had, as Trump pointed out in every speech. Trump voters were wrong if they thought he would act to bring mass prosperity, but they were not wrong to doubt that Clinton would. Her party had naturalized factory closures and mass layoffs as though they reflected the quantitative laws of economic globalization. Her allies had taught two generations of nonuniversity workers that the erstwhile party of working people would come up with statistics to explain why they had no work or had gone from building trucks for union wages to unloading trucks at Wal-Mart.
The Democrats’ expert politics had three intertwined problems. One—as Chatterjee discusses in chapter 1 of this volume—was its effects: a market-based, tech-focused Democratic party, led by the Clintons and Obama, spent twenty-five years promoting an economic model that did not benefit most nonuniversity Americans and made the others feel dependent on allegedly second-class (nonknowledge) skills. The Democrats asked people to believe that the model would deliver for them in time, because that was the story the numbers told—if and only if they changed themselves into knowledge workers. Finally, they discounted the qualitative, personal, local, community-based experience that said the Democrats’ model wasn’t working. The Democrats had applied policies favored by professionals and by the finance and tech sectors to working people, had failed to help them, and had justified the policies in numerical terms.
Trump came along and embedded white supremacist politics in a rejection of the Reich-Clinton-Obama globalization enlightenment. Trump allowed the Republican base to do three things at once: reject Democratic policies; avenge themselves on their embodiment, Hillary Clinton; and affirm the only thing that seemed able to stand up to the experts and their culture of quantification, the truth-defying will of Donald Trump. They were voting for a strongman who asserted the personal power to reject the alleged facts, and who did so every day. The Clinton campaign never grasped this attraction of Trump’s lying and denial. If Trump could push back Hillary’s free-trade “quantocrats,” then, it seemed, they could push back the “truth” of deindustrialization.
Joe Biden and Kamala Harris took the White House back from Trump. Will they also democratize expertise in economic policy?
For Democrats, there’s only one right answer to that question, since nondemocratic expertise failed to keep Donald Trump out of the White House. To counter him in 2016, Democrats needed to have run against their own record of numbers-driven deindustrialization and weak responses to structural racism. They needed to engage the local knowledges that had survived or even prospered. They still need to do these things in the 2020s, for better and more successful practical politics.
Democrats should explicitly endorse and implement epistemic parity between qualitative and quantitative arguments in political life. I would say the same for professionals and researchers more generally, as parity will improve the quality of sociocultural knowledge in ways that are beyond my scope here. Parity would involve subjecting arguments claiming quantitative objectivity to democratic deliberation. The numerical would encounter qualitative retorts from people in the full range of social contexts. The process would denaturalize arguments grounded in dominant discourses like the economics of trade. Different types of arguments—qualitative and quantitative—would meet as relative equals. General principles and historical findings about economics and academic performance would come from experts; experiences of results, critiques of the paradigm, and alternative models would come from citizens. Citizens and experts would discuss the extent to which corporate tax policies, police budgets, or college admissions standards are political choices rather than laws of financial flows or biological performance. Experts would take vernacular arguments seriously rather than seeing them as methodologically inadequate or backward or in need of immediate nudging (Kuttner 2014). I posit one likely result as a stronger sense of political agency in political subjects, who would then be more regularly engaged with experts.
To illustrate this point, I’ll use this principle of epistemic parity to rewrite that exchange between Obama and Cottonham in Indianapolis in June 2016. I pick up where Cottonham concludes, “All of our jobs have left or in the process of leaving, sir,” and try to keep Obama’s voice in constructing an alternative reply that has been disquantified (see Greg Lusk, chapter 9 of this volume), with quantitative and qualitative knowledge interacting across equal footing. Some elements:
That last sentence gums things up with our key terms, but it’s on the right track. Experts will experience less political backlash and better intellectual results if they develop parity and reconnection between quantitative and qualitative discourses. This will improve relations between the experts and citizens that epistemic inequality keep apart. Epistemic parity, and the attending sense of power through knowledge, would help to redemocratize everyday political life.
1. Analysis Interpretation of the news based on evidence, including data, as well as anticipating how events might unfold based on the past.
2. The rescinded Obama policy (US Departments of Justice and Education 2011) is a useful summary of the pre-Trump limited consensus on affirmative action.
3. The press continued to feature the white middle-class Trump voter (e.g., Carnes and Lupu 2017; Sasson 2016).
4. For example, “Nearly half of the voters have seen Trump in all of his splendor—his infantile tirades, his disastrous and lethal policies, his contempt for democracy in all its forms—and they decided that they wanted more of it. His voters can no longer hide behind excuses about the corruption of Hillary Clinton or their willingness to take a chance on an unproven political novice. They cannot feign ignorance about how Trump would rule. They know, and they have embraced him” (Nichols 2020).
5. The historian Mike Davis (2017) offers a helpful example from one part of Trumpland:
The largest concentration of white poverty in North America, the Southern mountains, have been orphaned not just in Washington but also in Frankfort, Nashville, Charlestown, and Raleigh where coal lobbyists and big power companies have always dictated legislative priorities. Traditionally their henchmen were county Democratic machines and the blue faded from Appalachia only reluctantly at first. Carter won 68 percent of the vote in the region and Clinton 47 percent in 1996. . . . The United Mine Workers and Steelworkers, under the best leadership in decades, fought desperately in the 1990s and 2000s for a major political initiative to defend industrial and mining jobs in the region but were turned away at the door by the Democratic Leadership Council and the ascendant New York/California congressional leadership. Ironically, Clinton this time around did have a plan for the coal counties, although it was buried in the fine print of her website and poorly publicized. She advocated important safeguards for worker health benefits tied to failing coal companies and proposed federal aid to offset the fiscal crisis of the region’s schools. Otherwise her program was conventional boilerplate: tax credits for new investment, boutique programs to encourage local entrepreneurship, and subsidies for the cleanup and conversion of mining land into business sites (Google data centers were mentioned—talk about cargo cults). But there was no major jobs program or public-health initiative to deal with the region’s devastating opiate pandemic.