Book review: The Corporation

I just finished reading Joel Bakan's 2004 book The Corporation. The book (and the accompanying documentary film (which is available on YouTube) outline the pathology of the corporation, given the centrality of corporations to modern life. On that point, Bakan writes that:

Today, corporations govern our lives. They determine what we eat, what we watch, what we wear, where we work, and what we do. We are inescapably surrounded by their culture, iconography, and ideology. And, like the church and the monarchy in other times, they posture as infallible and omnipotent, glorifying themselves in imposing buildings and elaborate displays. Increasingly, corporations dictate the decisions of their supposed overseers in government and control domains of society once firmly embedded within the public sphere.

This is Bakan at his most sweeping, and not always at his most convincing. It's clear that we have far more agency in our dealings with corporations than Bakan intimates. But nevertheless, corporations are and have been a big influence on consumer and government decision-making over many decades. That is why, despite being over twenty years old, the book retains its currency. The underlying incentive problems for large corporations have not really changed in that time, even if the particular corporations that might be the target of criticism may have.

Bakan's book develops a portrait of the corporation and its influence that is more than a caricature based on worn-out tropes. His work is based on exhaustive interviews (many of which can be seen in the documentary film) and research. And Bakan avoids the temptation, which many authors of similar books seem not to, to pin the blame solely on neoliberalism, laissez-faire economics, or economics in general. Instead of relying on such lazy cliches, Bakan provides a reasoned argument for questioning the place of the corporation in the modern economy.

One part of the book I appreciated in particular was the section talking about corporate social responsibility (CSR). I hadn't appreciated that CSR initiatives dated back as far as the end of World War I, where they were referred to as 'New Capitalism'. Bakan really takes issue with the double standards that corporations play, which is ably demonstrated by this passage:

Take the large and well-known energy company that once was a paragon of social responsibility and corporate philanthropy. Each year the company produced a Corporate Responsibility Annual Report; the most recent one, unfortunately its last, vowed to cut greenhouse-gas emissions and support multilateral agreements to help stop climate change. The company pledged further to put human rights, the environment, health and safety issues, biodiversity, indigenous rights, and transparency at the core of its business operations, and it created a well-staffed corporate social responsibility task force to monitor and implement its social responsibility programs... The company, which was consistently ranked as one of the best places to work in America, strongly promoted diversity in the workplace. "We believe," said the report, "that corporate leadership should set the example for community service."

That corporation was... Enron. Bakan delivers the punchline flawlessly.

This book is also more than a simple polemic against the evils of corporations. Bakan also considers potential solutions that could bring corporate behaviour more in line with social goals, and with more sincerity and depth than Enron clearly displayed. Bakan considers the potential for regulation, but is skeptical about it due to the risks of regulatory capture. In that, he rightly refers to the work of the economist George Stigler. Bakan finally alights on the prospects of charter revocation laws - laws that would allow the government to terminate a corporation. This is, obviously, a far greater penalty than has ever been imposed on a large corporation. Nevertheless, the rules do exist in many countries. 

Bakan's focus on charter revocation is interesting, but seems like an disproportionate response. If we consider the corporation as a person, which is a position that Bakan explicitly critiques, then charter revocation is the equivalent of a death sentence. While some may think that the worst actions of corporations deserve a penalty at that end, the innocent shareholders of the corporation would no doubt disagree. Aside from charter revocation, Bakan also notes that there are several things that governments can do, including improving the regulatory system, strengthening political democracy, creating a robust public sphere, and challenging international neoliberalism. Alongside that, greater penalties for senior managers and directors of corporations that break the rules would likely be an improvement, since that would focus more directly on changing the incentives of the decision-makers whose actions lead to corporate wrongdoing.

I enjoyed reading this book, especially as I didn't feel the need to be constantly defending economics in my mind while reading it. Sadly, Bakan's book could have been written today as many of the problems that he outlines are just as apparent with corporations in the 2020s as they were in the 2000s. There has been plenty of CSR language since then, as well as the rise of environmental, social, and governance (ESG) initiatives, but it is hard to see much of this as more than the same low-level box-ticking exercise that Bakan critiques. There has been far less movement on the deeper institutional reforms that Bakan favours, so it is likely that the problems will be just as apparent for corporations in the 2040s. It doesn't have to be that way, and anyone who believes in change would do well to read this book as a starting point.

Does the future of higher education look more like one-on-one mentoring?

It almost seems a cliche to say that generative AI is both the greatest threat, and the greatest opportunity, for higher education. That doesn't make the statement any less true. And there are many commentators who are trying to work out what happens next for higher education. I think one of the best is Hollis Robbins, whose Substack Anecdotal Value is well worth subscribing to.

Robbins was recently interviewed by Jay Caspian Kang of The New Yorker (paywalled, but you can find an ungated version here) about the future of higher education. The whole interview is worth reading, but I want to highlight this bit in particular, where Robbins says:

I was in Austin, Texas, a couple of times in March with a bunch of twenty-five-year-old billionaires. This is what they’re looking at. Instead of having the credential from the institution, why not have the credential from the professor? If you have a Hollis Robbins education, what would that signal? What would that credential mean as opposed to a degree from a university? There was some conversation about what that would look like, and one guy at the end of the dinner said, “Instead of OnlyFans, it’s like OnlyProfessors.”

The correct analogy here would be that it would be OnlyStudents, not OnlyProfessors (since the name contains the audience, not the performer). However, Robbins makes a good point. Higher education is, in part, an exercise in signalling (as I've noted before here and here). In fact, Bryan Caplan argued in his book The Case Against Education (which I reviewed here) that one third of the benefit of higher education is signalling (the other two-thirds is made up of genuine learning, socialisation, and transferable skills). 

The diploma that a student receives at the end of their higher education journey is a signal to employers of the student's quality as a future employee. The signal is credible to the employer because it is costly for the student to obtain, and costly in such a way that low-quality students wouldn't attempt the signal. University reputation matters here, because it is an assurance of the second of those conditions - low-quality students wouldn't attempt the signal of a Harvard degree, firstly because they wouldn't gain admission to Harvard in the first place. So, part of the signal comes from getting into Harvard. Second, low-quality students wouldn't attempt the signal of a Harvard degree because passing courses at Harvard is hard (or, at least, harder than at many other universities). The quality of the education at Harvard has traditionally been higher than elsewhere.

In the interview, Robbins makes a further important point, which is that we've spent the last several decades making higher education a commodity. Students studying a degree in a particular subject learn the same things, often using the same teaching materials, the same textbook, and the same style of assessment, regardless of which university they go to. That means that the quality of signal that arises from the education itself has reduced over time, meaning that most of the value of the signal arises from the admission process. Once a student has been admitted to Harvard, their signal is in place, and the further signalling from their education is lower than for comparable students in years past.

Robbins argues that generative AI is accelerating and expanding this commodification of education. Since generative AI has access, through its training corpus, to a large store of human knowledge, to add value over and above generative AI a professor must be a true expert in a very specific subfield. For students, learning a subject from a true expert still retains value, because generative AI cannot as easily replicate the learning that would occur from the expert. At that point, the university becomes less important as a mediator of education.

In other words, Robbins is arguing that the university could be disintermediated, with individual professors issuing credentials instead of universities. Who needs a Harvard degree, when you could have a Hollis Robbins degree? And since Robbins is an expert (in African American sonnet tradition, she says), the signal retains high value. However, that is true only to the extent that employers find the Hollis Robbins degree a credible signal.

That brings me to this Substack post by Auren Hoffman. Hoffman also argues that generative AI has diminished the value of the higher education signal. However, Hoffman argues for a different solution, this time from the perspective of the graduating student:

you have to show you can add value. that is it. that is the only thing.

the test the smart hiring manager applies in 2026 is simple. can you learn something on your own? can you finish what you started? can you do what you said you would do? these were always the skills that mattered. the difference is they are now the ONLY skills that matter, because the credential stopped doing the screening.

if you have a few years of experience, your resume can show you have these skills. if you are a new grad, you have to show what you have created and built.

Hoffman is overstating things a little, as university degrees are unlikely to disappear overnight. However, his critique is still important, as is the implication he draws. Hoffman argues that graduates (or young people, generally, since the solution doesn't depend on a student attending a university or completing a degree) need to become builders:

the most valuable thing a 22 year old can do in 2026 is create something. an app. a screenplay. a side business. an internal tool you wrote for a club you were in. a dinner series. a script that automates something annoying. a website. a chrome extension. a sculpture. a dance party. a discord bot. a substack with 32 readers and a real point of view. anything that moved from idea to working.

will the thing make money? probably not. that is not the point.

the point is that you taught yourself something. you finished it. you can describe what you learned, what broke, what you fixed, why you made the calls you made. that story is the new resume.

every hiring manager would rather interview a 22 year old with a launched app and a github full of weird side projects than a 22 year old with a 3.9 GPA from a top 50 school. it is not close. when one candidate has tangible evidence of what they can ship and the other has a transcript, the transcript will lose every time.

According to Hoffman, the best signal for young people to be sending in future is that they can build. Our students will ultimately be more successful if they can show off their skills (both technical and transferable skills) by building something. That is a signal that is costly, and costly in such a way that low-quality students will not attempt it. The signal is credible, and for the most part it retains currency even in the face of generative AI. Indeed, if the student builds something while effectively leveraging generative AI, then the signalling value to employers may be even greater. The key distinction is between the student using generative AI as a tool and the student using it as a substitute for doing the work. A student who can explain what they built, what broke, what they learned, and why they made the choices they made, is still sending a costly signal. A student who simply lets generative AI build for them is not. Employers would likely see through the latter pretty quickly.

Hoffman's idea isn't exactly new. When I think about some of my best students over the past two decades, they tend to be those that built something either while they were studying, or immediately after. For some, this was their own small business or entrepreneurial activity. For others, it was writing and publishing a research paper. Those were challenging tasks that set them apart from other students - a clear signal of quality. What Hoffman is essentially saying is that, with the signal from higher education itself being removed, the only remaining signal of quality is the signal from being a builder.

Where does that leave higher education staff? I think we can combine Robbins's and Hoffman's ideas, and chart a path forward. We don't need to start an OnlyStudents, and issue our own degrees, but we do need to cultivate closer relationships with our best students. Teach them to be builders. Encourage them to create things. Work with them and chart a path forward for their success. In other words, be a mentor.

Universities are absolutely going to hate this. Mentoring is not an activity that can be offered at scale. For example, there are simply not enough hours in the day for me to individually mentor all of the 350-plus students in my first-year economics class. Nor is mentoring easy to timetable, measure, standardise, or reward under current academic workload models. Universities are built around papers, credit points, learning outcomes, assessment rubrics, and student evaluations, all of which can be offered at scale. One-on-one mentoring doesn't work so well in that system. For example, postgraduate supervision already sits awkwardly within the system, with it being unclear whether it counts as teaching, or research. Nevertheless, mentoring may be one of the few ways that higher education can continue to offer something that is both valuable and difficult for generative AI to replicate.

If generative AI significantly reduces the signalling value of university education, and if students increasingly use it to avoid genuine learning, then the current mass higher education model looks increasingly fragile. Moreover, what remains is going to cost students a lot more. If having a high-quality mentor who can encourage a student to build is the path to their future employment, then it may be worth it. After all, high-quality signals are costly. That aspect of education, at least, won't have changed.

[HT: Marginal Revolution, for both the Hollis Robbins interview and the Auren Hoffman post]

Read more:

• Why all students need to understand adverse selection and signalling
• Signalling theory suggests that we may need two categories of university degree

This week in research #127

Here's what caught my eye in research over the past week:

• Ertl, Holb, and Bakó (with ungated version here) find, using a field experiment, that using a loss-framing in higher education assessments improves academic performance throughout the semester and on final assessments
• Donadelli, Mammi, and Paradiso (open access) investigate the impact of major wars and pandemics on supply and demand dynamics in G7 economies since the 1800s, and find that they primarily and persistently damage the supply side of the economy
• Bouchard, Yun, and Shelton (with ungated earlier version here) find that a 20-percentage point increase in the Trump two-party vote share correlates with an 8 percent increase in post-election domestic violence, following Trump's 2020 Presidential election loss

The American Economic Association papers and proceedings were published this week, which included the following papers of interest:

• Mayer, Méjean, and Thoenig find that a sufficiently strong and targeted set of sanctions, such as the 2024 sanctions imposed on Russia, would have increased Russia’s opportunity cost of war enough to deter a full-blown conflict in Ukraine
• Aksoy et al. find that younger CEOs, and younger firms, are more likely to allow their employees to work from home
• Li and Xu find that condensing a community college course into a shorter format improves course persistence, performance, and subsequent course outcomes in in-person settings, but not in online settings
• El Khoury confirms that female professors receive systematically lower teaching quality ratings than male professors, and that there is little difference in this by gender or minority status, but students who have ever posted a rating of a professor on a website penalise female professors by more than those who have not
• Underwood, Marshall, and Bilen find that redesignating economics as a STEM subject significantly increases the number of economics degrees conferred to women and non-resident students
• Ahlstrom, Asarta, and Harter investigate the use of AI in undergraduate economics courses, and find that there is a range of views about AI, but overall more experienced instructors are less inclined to integrate AI into their teaching or to adopt an open stance toward student use of AI tools to complete assignments
• Owen, Skoy, and Zhan find that easy-to-implement interventions can increase the probability that female students take additional economics courses beyond the introductory class, but that additional interventions do not have an incremental effect
• Orlov and McKee find that concurrent supplemental courses (on essential maths skills) can meaningfully improve outcomes for students at risk of underperforming in introductory microeconomics

Edmund Phelps, 1933-2026

Economics has recently lost another of the greats, Nobel Prize-winning economist Edmund Phelps, who passed away last week. Phelps was a macroeconomist, and among his many contributions he helped to formalise the concept of the natural rate of unemployment. Phelps won the Nobel Prize in 2006 for "his analysis of intertemporal tradeoffs in macroeconomic policy".

Surprisingly, for a Nobel Prize winner there have been few obituaries online so far. However, the New York Times has a good one (albeit paywalled). Phelps was 92, and had been among the oldest living economics Nobel laureates. Vernon Smith (born in 1927, so 99 years old), and Robert Aumann (born in 1930, so 95 years old) are the only economics Nobel Prize winners who are older.

The inter-temporal trade-offs that Phelps won his Nobel Prize for were not the only trade-offs that he considered in his research. Phelps also had a lot to say about one of the most famous trade-offs in macroeconomics - the short-run trade-off between inflation and unemployment that is embodied in the Phillips Curve (named after the New Zealand economist Bill Phillips). Phelps provided a keen critique of the short-run Phillips Curve, in particular noting that policymakers could not exploit the relationship permanently and, for example, 'buy' lower unemployment at a cost of higher inflation. He argued (successfully) for greater consideration of the role of inflation expectations in the relationship between inflation and unemployment (which my ECONS101 students will encounter next week). This critique contributed to our understanding that the Phillips Curve is vertical in the long run.

Phelps also contributed the 'Golden Rule' saving rate in the Solow growth model, which is the saving rate that maximises sustainable consumption per capita in the long run, as well as some of the key ideas underlying statistical discrimination (for more on that, see this post, or this one).

Phelps’s passing is a reminder that many of the ideas we now teach as standard macroeconomics were derived from hard-won arguments about expectations and the limits of policy.