The Control Data Corporation and global value chains

In March 1976, Deputy Secretary of the Department of Defense (DOD), William “Bill” Clements invited William “Bill” C. Norris, CEO and Chairman of the supercomputer producer Control Data Corporation (CDC) to a closed-door meeting at the Pentagon. Secretaries and undersecretaries from the United States Army, Navy, and Air Force were to attend, as well as a selection of spokespersons from the public university system and private sector. Clements requested Norris come prepared with “any important aspect of Defense management or posture that… warrants perspective” and to be candid in his comments.

Preparing for the meeting, Norris wrote a note. The subject was “East-West trade.” The DOD was not giving enough “attention” to export administration, Norris penned. The fact that the Department was “inconsistent” in reviewing export applications for computer technology to Central and Eastern European countries created an “unhealthy,” “adversarial” relationship between industry and the military, he continued.

Living in Hyman Minsky's world

“An effective way to write the history of the last thirty years of the twentieth century,” economist Albert Hirschman wrote in 1985, “may well be to focus on the distinctive reactions of various countries to the identical issue of worldwide inflation.” Writing just as the global “great inflation” of the 1970s was abating, Hirschman could not have understood how right he was. As Claudia Sahm has recently written in the New York Times, the fear of the great inflation of the 1970s still dominates the thinking of the Federal Reserve, even as its recent messages indicate some acceptance of higher inflation.

Economists lack a good understanding of what causes inflation—and its inverse, deflation. In introductory macroeconomics curricula, Milton Friedman’s mantra “inflation is always a monetary phenomenon” remains central. By this, Friedman meant that excessive price growth happens when a state loosens the supply of money, thus over-expanding the monetary base. However, recent research

On Jairus Banaji’s A Brief History of Commercial Capitalism

Capitalism is either eternal or it isn’t. There are people who defend the first view, or something close to it—the multivolume 2014 Cambridge History of Capitalism opens in Babylonia, circa 1000 BCE—but it is much more plausible that capitalism, like most other social phenomena, has its origins in specific historical developments. The trouble is that, once you’ve got everyone to agree that capitalism has a history, you have to define what capitalism is and then explain when, where, why, and how it emerged.

Of course, no one thinks you can date the transition the way you can specify when a battle took place or a patent was filed. But even after abandoning false precision, those who’ve grappled with the problem of defining and explaining capitalism’s emergence have been unable to agree even on which centuries and continents were involved.

An interview with Amit Bhaduri

Amit Bhaduri was internationally selected professor at Pavia University and visiting Professor at the Council for Social Development, Delhi University. His six books and more than sixty journal articles have consistently scrutinized the foundations of neoclassical economic theory and presented theoretical and practical alternatives. Among his most widely cited contributions, co-authored with Stephen Marglin, is the Bhaduri-Marglin model. Distinguishing between wage-led and profit-led growth, the model has given way to a wealth of research on the relationship between the functional income distribution and effective demand. In other work, Bhaduri has studied technological change in agricultural societies, articulated and criticized the social consequences of finance-led growth, and developed just and equitable alternatives to standard models of development.

In anticipation of his forthcoming article on social democracy in PSL Quarterly, and a Phenomenal World series on the topic, we begin this interview by discussing alternatives to financial liberalization, before turning to a discussion on the future of welfare politics, development strategy, and contemporary models of economic growth.

Think of the student debt crisis as an overflowing bathtub. On the one hand, too much water is pouring in: more borrowers are taking on more debt. That is thanks to increased demand for higher education in the face of rising tuition, stagnant wages, diminishing job opportunities for those with less than a college degree, and the power of employers to dictate that would-be hires have the necessary training in advance. On the other hand, the drain is clogged and too little water is draining out: those who have taken on debt are increasingly unable to pay it off.

The last post in the Millennial Student Debt project used a new database of student debtors and their loan characteristics (matched to demographic and economic data in the American Community Survey) to document the former phenomenon, both in aggregate and particularly as it pertains to disadvantaged communities along multiple dimensions. Specifically, it showed the rapid growth of student debt levels and debt-to-income ratios in the population at large, among people of all income levels. But this growth is concentrated among non-white borrowers, who have higher debt conditional on income and whose increased indebtedness over the past decade-plus is greater than for white borrowers. That racial disparity is particularly pronounced in the middle of the income distribution. It also showed that student-debt-to-income ratios have grown fastest in the poorest communities since 2008. This post uses the same data to document the latter: non-repayment by student loan borrowers is getting worse over time, especially so for non-white debtors.

Over the last ten years, as outstanding student loan debt has mounted and been assumed by a more diverse, less affluent group of students and their families than was the case for prior cohorts, a common policy response has been to wave away its impact on wealth, both individually and in aggregate, by saying that the debt finances its own repayment. First of all, so the claim goes, student debt finances college degrees that in turn pay off in the form of higher earnings, enabling debtors to repay. Second, expanded allowance for income-driven repayment (IDR), by capping debt service as a share of disposable income, eliminates the worst forms of delinquency and default. The first claim says that repayment is inevitable, the second that it need not take place. Both claims together, however, serve to rationalize higher debt, higher tuition, higher attainment, and the forces driving all three.

IDR was designed to address a liquidity crunch: since students are graduating with more debt, they may not earn enough immediately upon entering the workforce to pay it down. That failure of earnings to align with debt service obligations means that a program to defer those obligations until earnings are realized would ameliorate delinquency and default, at the cost of capitalizing unpaid interest into a higher principal balance. The creation and expansion of IDR programs in the early 2010s did indeed serve to stop the growth of delinquency by the mid-2010s and reverse it, to the point that the share of accounts delinquent now is lower than it was before the Great Recession, despite the amount of debt and the number of debtors having increased continuously since then. For that reason, many higher education policy analysts have proposed further expanding the program.

But IDR programs will never be successful as a solution to the student debt crisis, because they’re designed to address a liquidity problem rather than the real problem—solvency. The problem with student debt is a problem of wealth—students and their families are taking on debt because they don’t have enough wealth to afford increasingly-costly, increasingly-mandatory higher education. The debt then itself exacerbates wealth disparities that the higher education it “paid” for doesn’t rectify.

The legal structure of data egalitarianism

Since the proliferation of the World Wide Web in the 1990s, critics of widely used internet communications services have warned of the misuse of personal data. Alongside familiar concerns regarding user privacy and state surveillance, a now-decades-long thread connects a group of theorists who view data—and in particular data about people—as central to what they have termed informational capitalism. Critics locate in datafication—the transformation of information into commodity—a particular economic process of value creation that demarcates informational capitalism from its predecessors. Whether these critics take “information” or “capitalism” as the modifier warranting primary concern, datafication, in their analysis, serves a dual role: both a process of production and a form of injustice.

In arguments levied against informational capitalism, the creation, collection, and use of data feature prominently as an unjust way to order productive activity. For instance, in her 2019 blockbuster The Age of Surveillance Capitalism, Shoshanna Zuboff likens our inner lives to a pre-Colonial continent, invaded and strip-mined of data by technology companies seeking profits. Elsewhere, Jathan Sadowski identifies data as a distinct form of capital, and accordingly links the imperative to collect data to the perpetual cycle of capital accumulation. Julie Cohen, in the Polanyian tradition, traces the “quasi-ownership through enclosure” of data and identifies the processing of personal information in “data refineries” as a fourth factor of production under informational capitalism.

Critiques breed proposals for reform. Thus, data governance emerges as key terrain on which to discipline firms engaged in datafication and to respond to the injustices of informational capitalism. Scholars, activists, technologists and even presidential candidates have all proposed data governance reforms to address the social ills generated by the technology industry.

An interview with Mark Blyth

Mark Blyth is William R. Rhodes Professor of International Political Economy at Brown University and a Faculty Fellow at Brown’s Watson Institute for International Studies. His research examines how the interests of states and economic actors shape ideological consensus and institutional development at the global scale. He is the author and editor of many books, including Great Transformations: Economic Ideas and Institutional Change in the Twentieth Century (2002), The Future of the Euro (2015), and Austerity: The History of a Dangerous Idea (2013).

His latest book is Angrynomics, in which he and co-author Eric Lonergan argue that the rising tide of anger dominating global politics has its roots in decades of macroeconomic policymaking. Earlier in the pandemic, I spoke with Blyth about the economics of the Covid-19 crisis, the various approaches that governments and central banks across the globe have followed in order to tame it, and what an alternative program for the global economy might look like.

On Populist programs and democratic central banking

Since Lehman collapsed in 2008, central banks have broken free of historical norms, channelling trillions into the banking system to prop up global finance and the savings of depositors from Germany to Hong Kong. The corona crash has only accelerated this emancipation. In April, the Bank of England helped the Johnson government finance an ambitious furlough scheme, while the European Central Bank stepped up their older quantitative easing program, pumping liquidity back into the bank sector. Swap lines by state banks have been set up in the United States, while the latest European recovery plan ratified an extension of the Pandemic Emergency Purchase program. Adam Tooze cast all of this as “a remarkable display of technocratic energy and imagination in western financial centers”—necessary to both “control the epidemic” and “restore the world economy.”

Amongst these institutions none possess as much luster as the American Federal Reserve. While other banks waver or bow to political leadership, the Federal Reserve’s action is swift and decisive, protecting against financial collapse at home while safeguarding assets abroad. Unsurprisingly, the Fed is often singled out as one of the greatest triumphs of capitalist statecraft, from its creation in 1913 under Woodrow Wilson to its decisive role in the neoliberal counterrevolution of the 1980s under Paul Volcker. Yet while the bank seemed deeply conservative for the intermittent period, its proactive capacity asserted itself with a vengeance in 2008, when Timothy Geithner’s New York Fed almost single-handedly saved the global financial system from collapse.

How should we measure racial discrimination?

A 2018 National Academy of Sciences report on American policing begins its section on racial bias by noting the abundance of scholarship that records disparities in the criminal justice system. But shortly thereafter, the authors make a strange clarification: “In many cases there is little informative quantitative data on whether… policing is influenced by the racial or ethnic identity of citizens in a causal sense.”

On the one hand, troves of studies demonstrating racial disparities across a range of policing situations. On the other, a lack of data showing policing to be causally influenced by race. What accounts for the gap between evidence of racial disparities and proof of race as a causal influence on those disparities? The report presents a vignette that offers some clues:

"[A] police officer may decide to stop and question or frisk a Black citizen but may decide not to question a White citizen, creating a racial disparity in stops… Based solely on measures of officer’s behavior, however, it is impossible to know whether this behavior was actually racially biased. If the Black and White pedestrians, for instance, acted differently as the officer approached (e.g., nervous versus calm), or if the officer encountered them in different surroundings (at night in an alley versus at noon in the park), or if the officer was searching for a suspect described as Black, an objective observer might conclude that the officer was simply responding to the situation at hand."

The takeaway is that robust correlations in observational data offer satisfactory descriptive statistics, but they cannot, at least not on their own, meet the gold standard of scientific explanation—causal explanation. Only social scientists pursuing causal inference methods are equipped to answer sought-after “why” questions: Why are there racial disparities in policing? Is the disparity because of race or because of something else?

Since the Great Recession, outstanding student loan debt in the United States has increased by 122% in 2019 dollars, reaching the staggering sum of \$1.66 trillion in June of this year. Student loan debt has grown faster than other debt types, including auto, credit card and mortgage debt. For many, education is the only pathway towards good employment with benefits, leading to economic and social opportunities later in life. But as college becomes more unaffordable with each passing year, student loans are bridging the ever-expanding chasm between college savings and obtaining an education. The crisis has reached the national political arena, with policymakers recently calling for debt cancellation up to \$50,000 for federal borrowers.

Our research demonstrates that the student debt crisis has exacerbated existing inequalities. We found that all young borrowers are saddled with dramatically rising debt since 2009, but low-income groups, BIPOC, and those in their 30s fare far worse than others. While richer students have higher absolute debt, low-income students experience massive and growing relative debt burdens. And students in majority-Black and Hispanic zip codes, who are more likely to attend for-profit private institutions, have seen larger debt increases than those in majority-white zip codes. Debt levels have jumped in states like Wisconsin, Michigan, Pennsylvania, and Ohio. Gaining insight into broad trends in debt accumulation, as well as details about the particular demographic or labor market characteristics that shape changes in individuals’ debt burden over time, allows us to more effectively tailor our policy recommendations. For example, our research finds that forgiving $50,000 in student loans would make 80% of young adult borrowers student debt free.