Here, I will show that AJR’s results in two of their most famous papers were dependent on the particular choices they made in the construction of their datasets. Their work provides an illustration of how the “garden of forking paths” applies not only to specification searching in econometrics, but also to data construction, especially when it is poorly documented and research assistants are involved. AJR may have believed that proto-liberal-democratic institutions made the West rich, and therefore expected the historical statistics to demonstrate the same. In this way, they would have been led to make decisions that suited their priors. What follows, then, is not a story of intentional manipulation of data but of confirmation bias run amok.

Even when awarding the Nobel Prize in Economics, the Royal Swedish Academy of Sciences conceded that the key data in AJR’s most widely-cited article were “sometimes sketchy.” In their 2001 American Economic Review article “The Colonial Origins of Comparative Development: An Empirical Investigation,” AJR had used the mortality rates of European settlers as an instrument for good institutions. Because they could not regress modern wealth on institutions directly (since wealth also affects institutions), they needed a variable that affected wealth only through its historical effect on institutions: an exogenous source of variation to identify causation. Settler mortality was supposed to be that variable. According to the Swedish economists, AJR’s data used “rough estimates on initial settler mortality,” but they still seemed to have successfully demonstrated that favourable disease environments allowed Europeans to establish good institutions with strong protections for property rights. For this reason, AJR argued, those former colonies where settler mortality had been low are wealthy today.

Yet the Royal Swedish Academy of Sciences’ economists had not fully absorbed David Y. Albouy’s critique of AJR, which was eventually published as a comment in the American Economic Review in 2012. Three data construction issues that Albouy identified were particularly important. First, there was substantial “pseudoreplication”: AJR had taken 28 more or less genuine datapoints for particular countries and imputed them to neighbouring countries, artificially inflating their sample size to 64. Second, AJR had mixed mortality data from when European soldiers were in barracks with when they were on campaigns, as if the mortality rates between the two should be the same. Third, some mortality rates were not even for European settlers at all, but rather for African laborers who had been transported to work elsewhere.

When the regressions are corrected for these issues, the settler mortality instrument ceases to provide useful information. The extent of the debacle can be seen in my replication below. In econometrics, an instrument is considered “weak” if it doesn’t strongly predict institutions; a weak instrument produces standard errors so large that the confidence interval covers almost everything, and an F-statistic below about 10 means the instrument cannot reliably identify anything. In Table 1, we can actually see this collapse. The baseline regression produces an F-statistic of just 2.57, and the resulting confidence intervals stretch to infinity. Only when all African countries are dropped (reducing the sample to just 13 countries) does their two-stage least squares (2SLS) regression produce a confidence interval in which neither bound is infinite. The apparently useful information the settler mortality instrument had provided was an artifact of its construction.¹

AJR’s reply was unconvincing. They made, for example, inaccurate statements about Albouy’s “campaign” dummy, a 0/1 indicator variable for whether troops were on campaign. AJR’s claims about this indicator are directly contradicted by their main source of settler mortality data, Philip D. Curtin’s two books, Death by Migration: Europe’s Encounter with the Tropical World in the Nineteenth Century (1989) and Disease and Empire: The Health of European Troops in the Conquest of Africa (1998). According to AJR’s reply to Albouy, “except during times of major wars (which are excluded from the data), there is little difference in practice between what soldiers were engaged in during ‘campaigns’ and at other times. As a result, it does not in general make sense, and in fact it is not possible, to systematically distinguish campaigns and noncampaigns, and Curtin does not do so” (p. 3098). Yet the distinction is clearly made by Curtin in Death by Migration. He describes it as “one of the fundamental facts of military medical experience; troops in barracks are much healthier than troops on campaign, even disregarding losses from combat” (p. 4). Similarly, he states that campaigning “usually brought a sharp increase in deaths from disease as well as battle” (p. 13). AJR’s claim that Curtin “does not offer a systematic noncampaigns versus campaign distinction” (p. 3098n47) is incorrect. Indeed, the distinction is so fundamental to Curtin’s Disease and Empire that it features in the first paragraph of the blurb on the cover jacket:

“BEFORE THE NINETEENTH CENTURY, European soldiers serving in the tropics died from disease at a rate several times higher than that of soldiers serving at home. Then, from about 1815 to 1914, the death rates of European soldiers, both those serving at home and abroad, dropped by nearly 90 percent. But this drop apllied [sic] mainly to soldiers in barracks. Soldiers on campaign, especially in the tropics, continued to die from disease at rates as high as ever, in sharp contrast to the drop in barracks death rates.”

The campaign-barracks distinction is thus a basic framing principle for the entire book. It is obvious to Curtin that campaign rates were higher. “Troops on campaign were, of course, expected to sustain higher disease deaths rates than those in barracks—often three to ten times higher,” is how Curtin puts it in Disease and Empire (p. 43).

Having misread their principal source, AJR then engaged in an extensive specification search to salvage their results. It had three main elements. First, they arbitrarily capped the settler mortality rate at 250 per 1,000. Second, they recoded Albouy’s “campaign” dummy based on questionable criteria and an apparent lack of familiarity with or even interest in their notional sources.² Third, they excluded the Gambia, despite having noted in their 2000 NBER working paper that “doing so would help our hypothesis” (p. 19n15). Twelve years later, they finally pulled the trigger, thereby contributing to the underwhelming results shown below. After such extensive specification searching, their baseline results still only met the criteria for statistical significance by a whisker. When geographical controls were added, however, F-statistics shrank and confidence intervals crossed zero (meaning the results were no longer statistically significant).

For the economists working for the Swedish central bank, however, it was enough. In their report on the scientific background to the 2024 Nobel Prize in Economics, they showed only a cursory understanding of Albouy’s findings, while they missed the nature of AJR’s response. They seemed to believe it was significant that “the core results hold up when the authors exclude African nations,” while ignoring the complete collapse of identification when the “neo-Europes” were excluded from Albouy’s 28-country sample. In reality, AJR’s IV was a proxy for being a country like Canada or the United States, and offered no exogenous identification of institutions. All their regressions show is that Europeans sensibly settled where they were less likely to die, and those countries subsequently became wealthy and developed strong property rights. Their research design can say nothing meaningful about the relationship between the two. The story they tell is ultimately a matter of faith.

In this way, the Nobel committee’s essay on the scientific background to AJR’s award reflected the general misunderstanding of Albouy’s critique. A recent survey by the Institute for Replication (I4R) illustrates how effective the rhetorical strategies deployed by AJR in their reply proved to be. In their reply, AJR pointed toward a working paper version in which they claimed that “Different sources of data for Latin America and different benchmarking procedures lead to very similar and robust results.” In making this statement, AJR were specifically referring to Latin America; all they really did was rescale the same Curtin numbers.³ Nonetheless, the I4R told the economists surveyed that AJR “show that their results are robust to alternative assignments of mortality rates.” In this way, the I4R gave an even stronger version of AJR’s defence and framed it as a “neutral” statement of fact, helping to push the percentage of respondents who viewed the reply favourably up from 50.5 to 54.6 percent. My impression is that few have actually taken the time to understand what AJR did with their data.

Similar issues can, moreover, be found in AJR’s next major contribution to economic history, the article “Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution,” published in the Quarterly Journal of Economics in 2002. Once again, their results are a function of their data construction.

Read more

The problem is threefold.

First, the use of what D. C. M. Platt described as “Mickey Mouse numbers” has escalated due to the “credibility revolution” in economics, which has increased the demand for quantitative data. But reliable historical statistics generally do not exist. As a result, what can euphemistically be called “synthetic data” have proliferated in what would have been Platt’s nightmare.

Second, the econometric results produced using those data are extremely fragile. Timothy G. Conley and Morgan Kelly’s analysis of spatial confounding is an important example of the endemic lack of robustness: in studies that claim to find deep correlations between past and present, standard errors that ignore spatial autocorrelation can make even random spatial structure look statistically significant. Nonetheless, such “persistence” studies have become increasingly dominant in the profession.

Third, the narratives that are told in those articles are often highly simplistic due to the need to identify a clear causal effect. Good institutions make countries rich; legacies of the slave trade make Africa poor today; ancient ethnic hatreds explain the Holocaust; and so on. In reality, however, things are rarely as simple as “x causes y.”

Consequently, much of what passes for economic history today in the leading economics journals should not be taken seriously, even as it has become what economic historians who wish to write on historical issues must aspire to if they want to make it in the economics profession.

For my part, however, I really care about my discipline. I think that students should not be encouraged to follow the modalities of what is currently published in the “top five.” We should rebel, even if it means that economic historians become refugees, dependent on the kindness of business schools and history departments. They may end exiled to the wilderness or perhaps to a hill in Wales. Defending the discipline of economic history will not be easy. But, in my opinion, it must be done.

And that is why I will be posting more regularly on The Poor Rich World blog. Sellers of indulgences have been warned.

Further Reading

For an account of the decline of economic history in a major American university, see:

Peter Temin, “The Rise and Fall of Economic History at MIT,” History of Political Economy 46, annual supplement (2014): 337–350.

The rise of “persistence studies” has been a morbid symptom of that decline. See:

Martina Cioni, Giovanni Federico, and Michelangelo Vasta, “The Two Revolutions in Economic History,” in The Handbook of Historical Economics, ed. Alberto Bisin and Giovanni Federico (London: Academic Press, 2021), 17–40; “Persistence Studies: A New Kind of Economic History?” Review of Regional Research 42, no. 3 (December 2022): 227–248; and “Is Economic History Changing Its Nature? Evidence from Top Journals,” Cliometrica 17, no. 1 (January 2023): 23–48.

A critical discussion is found in:

Leticia Arroyo Abad and Noel Maurer, “History Never Really Says Goodbye: A Critical Review of the Persistence Literature,” Journal of Historical Political Economy 1, no. 1 (June 2021): 31–68.

The extent of spatial confounding is demonstrated in:

Timothy G. Conley and Morgan Kelly, “The Standard Errors of Persistence,” Journal of International Economics 153 (January 2025): 104027.

For a response to an earlier version of Kelly’s critique, see:

Hans-Joachim Voth, “Persistence – Myth and Mystery,” in Handbook of Historical Economics, ed. Alberto Bisin and Giovanni Federico (London: Academic Press, 2021), 243–267.

Notably, Voth presents results that seem to refute Kelly and claims that “code and data for regressions and simulations in this essay are freely available at http://www.jvoth.com/data/replication/handbook and at the ICSPR [sic] data repository.” Nonetheless, that link never seems to have existed and it has not been possible to locate a relevant data repository on ICPSR.

For an early warning of the rise of “synthetic data,” see:

D. C. M. Platt, Mickey Mouse Numbers in World History: The Short View (Basingstoke: Macmillan Press, 1989).

For a detailed recent example:

Joseph Francis, “Research Integrity Issues in Voigtländer and Voth (2012),” isitcredible.com, 2026.

And for case studies of some of the broader problems in economics:

Joseph Francis, “A Replication of ‘Comparative Politics and the Synthetic Control Method’ by Abadie et al. (2015),” I4R Discussion Paper Series No. 271, 2025.

Joseph Francis, “The ‘China Shock’ and the Rhetoric of Economics: A Comment on Autor, Dorn, and Hanson (2013),” 2025.

Joseph Francis, “Specification Searching in the Race between Education and Technology,” Econ Journal Watch, 23, no. 1 (March 2026): 4–26.

My adventures began with Karl Marx—or, more specifically, a “Synthetic Karl Marx.” In June 2023, the Journal of Political Economy published an article that seemed to use econometrics to demonstrate that Marx was “an occasionally acknowledged but relatively minor figure between his death and the events of 1917.” Only the Russian Revolution, the article claimed, saved him from obscurity. Knowing a little intellectual history, this seemed an odd finding, so I decided to investigate.

The result was an article in Econ Journal Watch, an online journal that I have long admired. My article on “Synthetic Karl Marx” was published in September 2024, together with a response from the two scholars whose work I was criticizing. I followed with a rejoinder in March 2025, which was accompanied by another reply from my interlocutors. Among other things, I showed that their article’s main “Synthetic Karl Marx” had used an absurd Frankenstein’s monster that mainly consisted of Abraham Lincoln and Oscar Wilde as a counterfactual for what would have happened to Marx’s Google n-gram share if there had never been a Russian Revolution. The whole thing was nonsensical, yet it had been published in the Journal of Political Economy, one of the “top five” economics journals that make or break economists’ careers.

I then began to wonder what other nonsense was being given the rubber stamp of truth by the peer review system.

The Synthetic Control Method (SCM) seemed like the obvious place to begin, given that it was the econometric technique that had been used to make the claims about Marx’s influence in the JPE. My initial assumption was that Magness and Makovi had misused a fundamentally sound method. But my doubts grew, not least when I found a weakness in the SCM that made it easy to engineer statistically significant results. So I looked harder at the existing literature.

And then I found something very silly.

It was in “Comparative Politics and the Synthetic Control Method”, an article by Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2015) that is routinely used to teach the SCM in universities. It purports to provide “precise quantitative inference” for the effect of German reunification on West Germany’s real GDP per capita from 1990 to 2003. Looking at the replication, however, I realized that Abadie et al. had actually used nominal GDP per capita but then referred to it as real GDP per capita throughout. The result would eventually be a paper released by the Institute for Replication (I4R), together with an unconvincing response from Abadie et al. In the paper, I found far more issues than expected, going beyond the use of the wrong outcome variable. My findings suggest that the SCM should not be used for real GDP per capita at all, even though it routinely is. In their response, Abadie et al. also seemed to struggle somewhat with index number theory, which is concerning, given the SCM is essentially an index number-generating algorithm and they are its architects.

I began to find more and more problems in economics articles.

“The China Syndrome: Local Labor Market Effects of Import Competition in the United States” by David H. Autor, David Dorn, and Gordon H. Hanson, came next. Published in the American Economic Review in 2013, it is arguably the most influential economics article of the past quarter century, given its use by the Trump administration. Most importantly, I tried to understand the corrections that Kirill Borusyak, Peter Hull, and Xavier Jaravel had made to Autor et al.’s econometrics. Like most people, I obsessed over how they had reduced but not eliminated the coefficient for Chinese imports’ effects on the manufacturing employment share, seemingly supporting Autor et al.’s basic findings. Finally, I wondered what the Borusyak et al. corrections did to Autor et al.’s other dependent variables: and then I found it in a table tucked away on page 42 of Borusyak et al.’s Online Appendix that showed that their corrections eliminated the major negative effects that Autor et al. had found for welfare indicators. I therefore applied the Borusyak et al. corrections to all Autor et al.’s dependent variables and found that the analysis collapsed: even if Chinese imports did lead to a reduced manufacturing employment share, they appear to have had no negative effects on welfare.

And then came the most disturbing replication of all. Published in the Quarterly Journal of Economics in 2012, Nico Voigtländer and Hans-Joachim Voth claimed to have traced the origins of Nazi antisemitism to medieval Germany. Using large language models (LLMs) to assist me, I nevertheless found major discrepancies between what Voigtländer and Voth had coded in the dataset and what their sources actually said. In a first draft of my paper, I made 171 corrections to Voigtländer and Voth’s dataset; in their response to that first draft, Voigtländer and Voth identified just 2 errors in my recoding, while also hallucinating 3 others. Subsequently, in private correspondence, Voigtländer and Voth have not been able to identify any more errors in the remaining 169 corrections that I made to their dataset. They have also offered no explanation for why there are numerous discrepancies in their dataset relating to the Gedenkbuch, the official government record of German Jews killed during the Holocaust. In my last email from them, Voth instructed me to send a revised version to the Pope and the United Nations. I therefore decided to make the paper available online, but Voigtländer and Voth have still not provided any answers to the questions that I raise.

I had also decided to automate my efforts. There was simply too much for me to replicate. I could not produce any more of these time-consuming replication studies. Instead, I thought that I could use the machines to do what I had been doing manually, albeit on a more superficial level. I became a “prompt engineer.” I spent months building a system that would use LLMs to investigate the existing literature. The result can be found at isitcredible.com, a website that produces reports by “Reviewer 2,” my automated assessment system for academic texts.

My goal was grandiose in a Borgesian way: with tongue only slightly in cheek, I wanted to produce thousands of these reports, leading to a “Total Audit of All Human Knowledge.” I hoped to use machines to provide a better answer to the question that had motivated me as a young man: What do we actually know? Eventually, I believed, isitcredible.com would tell us the limits of our knowledge. It would help us to be more like Socrates, when he famously said that he was wiser than a know-all because “what I do not know, I do not think I know, either.” It would use machines to operationalize Karl Popper’s definition of the scientific method as “the search for and the elimination of errors in the service of truth.”

Such was my belief in the capabilities of the machines that I also decided to run an experiment.

During the testing of Reviewer 2, my system found many problems in published articles. In Sarah Pierce et al.’s “Prime Editing-Installed Suppressor tRNAs for Disease-Agnostic Genome Editing,” an article on gene-editing published in Nature, it discovered an error-riddled pathologist’s report in the supplementary materials. I wrote to Nature about it, and they then allowed the authors to retrospectively alter a signed-and-dated pathologist’s report. This seemed odd, and piqued my suspicions. I therefore decided to investigate the article further. Using LLMs, I dug into the article’s replication data, and together we appeared to discover problems in the safety validation. I dug deeper using multiple models and the case seemed watertight: there was clearly something wrong with the safety data, the LLMs and I concurred. I then sought to validate my suspicions with human expertise: I made numerous appeals on social media for people with understanding of gene editing to give me their opinion; I sent a paper to the authors explaining my concerns; I posted on Pubpeer, appealing for information. But I had no luck: experts on gene-editing, it seemed, were hard to find. I was nevertheless convinced that I had discovered something important. Eventually, I submitted the paper to Nature as a Matters Arising. I saw it as a test: could the LLMs allow me to detect a major issue in an article on a scientific topic far beyond my field of expertise?

The answer was a resounding “No.”

When the authors of the article sent their response to Nature, it turned out that I had been extremely stupid: what I thought had been a failure of safety validation was in fact a sign of the drug working as expected. A random economic historian on a hill in Wales, it turns out, should not assume to know things on scientific topics far beyond his field of expertise. It was a total humiliation.

The problem, I think, was that having found such problems in major economics articles, I assumed that I would also find them in STEM articles, given that they both operate under the same peer review system. The robots then sycophantically helped me to find what I wanted to find, even though it wasn’t there. We hallucinated together, and I began to believe that I knew what I did not actually know. I had become the antithesis of Socrates’ wiseman: a sophist, taken in my own nonsense.

One of my mistakes was to assume that peer review is the same in STEM as in economics. If you look at the Pierce et al. article in Nature, however, you can see that it is quite different: two of the article’s reviewers are, for example, named. There is also a clear procedure for post-publication review: any random economic historian on a hill in Wales can submit a Matters Arising comment if they incorrectly believe they have discovered major issues in a published article.

My impression is that STEM is moving toward a system of peer review that is more transparent and continuous. The reviewers are often identified, and sometimes the journals make the reviews public. There is also a strong culture of post-publication criticism. I recently watched scientists descend like rabid dogs on an article in Nature that claimed a treatment for lung cancer was far more effective when given in the morning compared to the evening. Seeing them go to work on Pubpeer and X was a joy to behold. Their culture of criticism accords with Popper’s definition of the scientific method as “the search for and the elimination of errors in the service of truth.” Furthermore, retractions are relatively common, with clearly defined procedures to follow.

The contrast with economics is stark. While economists pride themselves on the robustness of their seminars, what actually matters is publication in just five journals. The editors have immense power. Peer review is closed and anonymous. Virtually nothing is ever retracted. Post-publication peer review is minimal. Instead, my experience suggests that there is a culture of not publicly criticizing anything that has been published. If you do, you are viewed as too aggressive, possibly due to some kind of personality defect. Meanwhile, the original authors can use their right to reply for deflection and ad hominem attacks. The fear of upsetting one’s superiors is palpable.

The machines will perhaps bring about some changes. They are massively useful for replication. Without asking a robot to explain it to me repeatedly as if I were a five-year-old, I never would have understood the SCM or Borusyak et al.’s critique of Autor et al.; I never would have done all the coding my replications required; I never would have been able to search for errors in German-language source materials. The cost of doing replication studies has dropped dramatically, even if the institutions and the culture of economics are still hostile to them. Perhaps the system will catch up.

Ultimately, however, human experts are still needed to determine the truth. The danger of the machines was demonstrated during my humiliation by Pierce et al.: using the robots, I turned myself into a know-all who actually knew nothing. For this reason, I no longer think that my Reviewer 2 system can produce “A Total Audit of All Human Knowledge,” and I have deleted the public archive from isitcredible.com. We cannot delegate the determination of truth to the machines. Hence, while I will continue to use it in my work (and I will keep it available for others for as long as the fees are paying the server costs), I do not think my system should be allowed to publicly pronounce on the credibility of research. That is ultimately for human experts to decide.

Peer review is necessary. On this, at least, I have had a Damascene conversion.

When I began my adventures in replication, I wanted to demonstrate how fundamentally corrupt the peer review system is: how it allows seemingly credible nonsense to be published with the rubber stamp of truth. In my replications of widely-cited economics articles, I discovered plenty of evidence for this. But now I think that the system can be salvaged and made into something more effective. Developments in STEM suggest that a more open and continuous system of peer review is possible. As and when I return to my critique of economics, this will be my hypothesis: the problem is not peer review per se, but the anti-scientific way in which it is applied by and to economists.

For now, however, I am returning to economic history: I am working again on my book on American capitalism. During my adventures in replication, I have learned a lot about the knowledge production system, but it has also been tiring. As Saul of Tarsus discovered, it is hard to kick against the pricks.

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Yet James D. Walsh’s article makes it clear that the new technology is not the principal cause of academia’s demise. “The ideal of college as a place of intellectual growth, where students engage with deep, profound ideas, was gone long before ChatGPT,” Walsh writes. “In a way, the speed and ease with which AI proved itself able to do college-level work simply exposed the rot at the core.” The academics who respond to his article by denouncing the large language models are therefore missing the point.

The problem is that college education has become a necessary rite of passage for getting a high-paid job. As Bryan Caplan has argued in his book Against Education, it is simply a way for young people to signal to potential employers that they have some basic competence and are, crucially, willing to obey. From this perspective, universities are little more than preparatory schools that give students the cultural capital that may, if they are lucky, provide an entry to the elite. They are not there to teach critical thinking.

The large language models have merely pushed universities towards their endpoint of becoming a simulation of what an education should, in theory, be. Students pretend to learn, while academics pretend to teach them. But now, at least, this can be automated. “Multiple AI platforms now offer tools to leave AI-generated feedback on students’ essays,” Walsh explains. “Which raises the possibility that AIs are now evaluating AI-generated papers, reducing the entire academic exercise to a conversation between two robots—or maybe even just one.” This is, then, very much the hyperreal world of Jean Baudrillard. It is the “desert of the real,” in which critical thinking only occasionally peeks through the simulacra of learning that has covered it.

And similar problems exist on the research side. Since World War II, a blind peer review system has become ever more institutionalized. It incentivizes what Thomas Kuhn called “normal science”: incremental progress through small modifications of dominant paradigms. Academics who challenge dominant paradigms will, by contrast, struggle to be published and perish. In this way, the institutions of academia also prevent the kind of critical thinking that universities are supposed to promote.

A spate of recent studies illustrates the results in the economics departments of American universities. Their faculty are increasingly likely to believe the same things—there has been a notable increase in the degree of consensus on key issues. Groupthink is taking over. Indeed, they are also far more likely to work in groups than in the past, with the share of articles being published by individual economists falling. Worryingly, they are also more likely to use their own data sources to prove their collective beliefs. And there is some evidence that they are filtering their data to get the results they expect. Confirmation bias rules supreme.

In this way, academics have themselves become like large language models, only made of flesh and bone. They have been trained to confirm what is expected from the existing literature, as a way to get published and climb the academic hierarchy. Those at the top are the ones that have mastered this game, and they ensure that its rules are enforced. Critical thinking, again, is to be discouraged.

It is likely, however, that the large language models made of silicon and various metals will soon be able to do normal science as well as, if not better than, their human equivalents. And, crucially, they will be cheaper, too. There are already signs that the most advanced models can produce research papers that are better than what is written by many academics. The slop produced by the machines is, in other words, becoming indistinguishable from the slop being produced due to the publish-or-perish imperative in academia.

Academics will therefore have to ask themselves “Why Do I Exist?” They will increasingly wonder whether androids dream of electric sheep.

The answer is, I believe, that academics should exist to promote and actively engage in critical thinking. Unlike the machines, they should not just be reiterating the existing literature. If they put their minds to it, they can do what the machines cannot. And they can train their students to do it as well. Yet many are, I suspect, out of practice due to the incentives they face. Something therefore has to change if academia is not to be made obsolete by the new technology.

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An analysis of income inequality by race and gender sheds light on the transformation of American capitalism since World War II. White men’s share of national income fell from around 73 percent in 1960 to 41 percent in 2023. Furthermore, income inequality among white men increased after a greater education premium ended the postwar era of relative egalitarianism. The origins of contemporary identity politics can be reinterpreted within this context.

In short, I have tried to make a materialist analysis of the origins of contemporary American identity politics, especially on the right. I am, in other words, trying to explain the origins of this:

“Why is the United States now run by a bunch of dumb wannabe white patriarchs?” is the question I have asked. In a single figure, this is my answer:

In a nutshell, white men have experienced a historic loss of position in the American class structure, and they are angry about it, not least because the “woke” party—the blessed Democrats—can offer them little, given its subservience to Wall Street and the donor class. The result has been a rightward shift among working class white men, leading to the emergence of the dumb wannabe white patriarchs. In less flippant terms, I explain how this happened in the paper.

My most controversial finding will probably be that the big increase in inequality is really for white men, rather than all Americans:

Please do read the paper and let me know what you think. It is front-loaded with econometrics, but you do not really need to understand them to follow my analysis. Don’t be put off! After the numbers, there is a lot of historical narrative, but I have confined technical discussion—including one or two comments on Thomas Piketty’s work—to the footnotes.

All and any comments below are welcome. I have also set up pages for them on Medium and Github. Your feedback will be especially welcome.

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Schumpeter’s Fear

Schumpeter feared academics as a class. “One of the most important features of the later stages of capitalist civilization,” Schumpeter (1942, 152) wrote, “is the vigorous expansion of the educational apparatus and particularly of the facilities for higher education.” The problem, as he saw it, was that the supply of educated people would outstrip the demand for them: “inasmuch as higher education thus increases the supply of services in professional, quasi-professional and in the end all ‘whitecollar’ lines beyond the point determined by cost-return considerations, it may create a particularly important case of sectional unemployment.” As a result, capitalism would produce a class of highly educated malcontents, who would be forced to find “employment in substandard work or at wages below those of the better-paid manual workers.” This class—the academic precariat—would then lead the opposition to capitalism, ultimately forcing it to give way to one form of socialism or another.

Looking at the context in which Schumpeter wrote, his fears were understandable. An Austrian who emigrated to the United States in 1932, he became a professor at Harvard University. Schumpeter had thereby entered a milieu in which a belief in socialism was rife. In the context of the New Deal, moreover, there was even support for some elements of socialism from the federal government. As J. Robert Oppenheimer explains to his interrogator in Christopher Nolan’s film, he was not a communist but just a “New Deal Democrat.” Schumpeter’s prognosis is therefore understandable.

In the postwar era, it seemed, moreover, that Schumpeter might be proved right. McCarthyism attempted to purge academia together with other American institutions, as dramatized in Oppenheimer. Furthermore, Nikita Khrushchev’s program of de-Stalinization made it easier to see the horrors that had occurred under actually existing socialism in Europe. Yet the expansion of higher education still brought about the rebellion against capitalism that Schumpeter had predicted. In the 1960s, radicals took over many university campuses. One of my favorite descriptions comes from Mark Naison, then a student at Columbia. In 1968, he took time out from the protests to defend his work in front of the history department’s orals board. Naison described how its members, who included Richard Hofstadter, the country’s most famous historian, “were not, as I expected, unusually hostile to me, but absolutely tickled pink at the prospect of keeping the institutional ritual alive amidst the surrounding chaos. They regarded themselves,” Naison continued, “as the carriers of the light of civilization among the depredations of the strange new barbarians who had somehow exploded into their lives” (in Novick 1988, 428–429).

In response to these protests, universities sought to placate the students by emphasizing their needs. Hence, in June 1968, a month after the protests at Columbia University had been violently suppressed by the police, Hofstadter gave a commencement address in which he deplored the radicals’ methods but admitted that their cause was just. In Hofstadter’s (1968, 587–588) words, “all our students are troubled today by two facts of the most fundamental consequence for all of us—the persistence at home of poverty and racial injustice, and abroad of the war in Vietnam.” These were the root causes of the discontent, according to Hofstadter, and the question became how the institutions of academia could respond to them. “Here at Columbia,” he continued, “we have suffered a disaster whose precise dimensions it is impossible to state, because the story is not yet finished, and the measure of our loss still depends upon what we do.” Reform, it seemed, was the only possible response. “Powers need to be redistributed,” Hofstadter explained. “Some new organs of decision and communication need to be created,” although students would, Hofstadter predicted, be repelled by “the arduous work, the sheer tedium, the high responsibilities that are always a part of administrative power.” A new kind of university administration that would be more responsive to the student experience would therefore be necessary. As Hofstadter put it, “what students need and should have is influence, not power; but they also need formal channels to assure them that their influence is in fact effective.” Only then would academia be able to prevent a recurrence of such unrest, thereby saving itself. Hofstadter thus saw a reorientation of the university administration toward improving the student experience as the best way of saving academia.

Ultimately, Hofstadter’s vision would prevail over Schumpeter’s fear. The radicalism on university campuses of the 1960s dissipated. Faculty members who had participated in the protests were slowly purged, while the administrations expanded, based on the logic that they were needed to improve the student experience. The federal government would then give them the tool to impose conformism.

The Origins of Conformism

In response to the 1960s’ radicalism, the federal government redirected research funding toward programs that it hoped would solve the problems that had inspired the unrest in the first place. Both Congress and the President, Richard Nixon, pushed for public funds to be used to make research more relevant to social problems. The National Science Foundation (NSF), most importantly, funded the Research Applied to National Needs (RANN) program, with the goal of directing academics’ research toward more practical ends. Funds were channeled especially toward the social sciences, which were supposed to find ways to address the nation’s ills.

Yet academics struggled to make themselves more relevant for society. The awarding of “silly grants” became a common critique. In 1970, a local newspaper in Indiana had derided the NSF for funding a project that it dubbed “The Study of the Sex Life of the Titmouse.”1 By the mid-1970s, such objections had reached Congress. The RANN program was criticized for wasting public money. It seemed, moreover, unresponsive to what politicians wanted. Some were, for example, concerned about the effects on children of sex and violence on television and in 1974 asked the NSF to fund research on the issue. A year later, however, it seemed to the politicians that little progress had been made. The only research group that had bid for the project came from Harvard, where, the conservatives assumed, academics’ liberal values would lead them to conclude televised sex and violence cause no harm to the young. Indeed, they claimed, the NSF had a track record of undermining public morals, such as through its funding of “Man: A Course of Study” (MACOS) project, which was supposed to improve the teaching of humanities in schools. As an advert in the Washington Post complained in April 1975, this had entailed making “ten-year-olds” watch “films featuring such fare as the torture killing of a giraffe by tribesmen and a small child eating the raw eye of a deer” (in Solovey 2020, 160). In July, the Subcommittee on Science, Research, and Technology held hearings into how a faulty peer review system was making the NSF waste public money on such dubious projects.

Academia responded to these critiques by arguing that only peer review could be used to determine what was science. As Melinda Baldwin (2018) has documented, this was a new development. Only after the hearings into the NSF’s funding in 1975 was peer review elevated to its current status. The NSF’s administrators did so, Baldwin argues, to deflect the attacks from Congress. According to the administrators’ logic, politicians could not have oversight of the NSF because politicians were not qualified to pass judgement on knowledge—only academics could do that. In the words of the Subcommittee’s chair, “experts in a scientific field are best able to judge several important factors, e.g. design and importance of proposed work, and past performance of the proposer” (Symington and Kramer 1977, 20). From then on, peer review was held up as the gold standard for academia, legitimizing its independence from political oversight.

Crucially, the number of peer-reviewed articles published by academics became a simple metric that administrators could use to evaluate faculty. The two legacies of the 1960s’ radicalism—a rapidly expanding administrative apparatus and a rigid peer review system—then combined to transform the process of scientific discovery in the United States—and, by extension, the world. It would be a new era in which ever greater numbers of administrators prioritized students’ needs, while academics would see their career trajectories determined by how many peer reviewed articles they could write.

Even at the time, the risks of this response to the politicians were obvious. They were pointed out to the Subcommittee by a group of biologists who had been recommended by John Conlan, a conservative who had decried the Old Boy’s System, which saw NSF administrators “rely on trusted friends in the academic community to review the proposals. These friends recommend their friends as reviewers,” Conlan explained. The result was “an incestuous ‘buddy system’ that frequently stifles new ideas and scientific breakthroughs” (in Committee on Science and Technology 1975, 5). To illustrate this, Conlan introduced three biochemists who claimed to have been victims of this system. At their center was Gilbert N. Ling, the director of the Department of Molecular Biology at Pennsylvania Hospital. He claimed to have refuted the “sodium pump” hypothesis of how sodium ion was transported in cells. He found it increasingly difficult, however, to obtain funding for his own attempts to formulate an alternative hypothesis because his proposals were blocked by his rivals—the members of the in group who dominated Conlan’s Old Boy’s System.

The problem was that academics deviated in practice from the ideal type of the truth-seeking scientist. As famously described by Karl Popper (1959), scientists were supposed to recognize their own fallibility in the search for the truth, leading them to reject hypotheses once they were falsified. In practice, however, they often prioritized the maintenance of the paradigms upon which they built their careers. Hence, Ling’s colleague Carlton F. Hazlewood, an associate professor at Baylor College of Medicine, described how “the existing [peer review] system is built on the expectation that in judging others’ work, scientists, unlike common mortals, are ever willing to sacrifice their own interest for the sake of moral ideals” (in Committee on Science and Technology 1975, 863). But, in reality, scientists were as human as everyone else and would therefore tend to use peer review to block research that threatened their own positions in the academic hierarchy.

Consequently, the peer review system risked making scientific revolutions far more difficult. “[A]s Thomas Kuhn has pointed out,” Hazlewood continued, “major new ideas in science have rarely, if ever, been accepted readily by the peers of the proponents even when the ideas were proposed by such distinguished scientists as Copernicus, Galileo and so forth” (in ibid., 863). In his own testimony, Ling then reiterated this point. He pointed toward Louis Pasteur’s revolutionary idea that “diseases were indeed caused by bacteria or viruses.” His peers, on the other hand, “said that everybody agreed that there were four humors and now you come along with the idea of little bugs here or there, many of which one could not even see.” As a result, any article or funding application that Pasteur had submitted would have been “unquestionably turned down” by his peers (in ibid., 875). Charles Darwin, similarly, had proposed a revolutionary new idea that would have been rejected by peer review. Indeed, in The Origin of Species, Darwin (1936, 368) had noted that his ideas would also be rejected. “I by no means expect to convince experienced naturalists whose minds are stocked with a multitude of facts all viewed, during a long course of years, from a point of view directly opposite to mine.” Darwin’s ideas could nevertheless overcome this opposition, Ling told the Committee, because the peer review system was extremely loose in the nineteenth century: “the peers did not have enough weapons in their hand to stop these mavericks, Mr. Chairman.” But that had now changed. “I am sorry to tell you that in the last 30 years in the United States, we have evolved, quite inadvertently, but surely, just such an arsenal of weapons and place them squarely in the hands of peers.” The result, he warned, would be to stultify innovation in science due to “journal editors’ power to decide what papers will be published” and senior academics’ capacity to determine “who is to continue his or her research through funding agencies.” Young researchers, in particular, were affected because the peer review system had “coerced them to follow unquestionably and meekly a certain expected opinion and certain expected way of saying the right things” (in Committee on Science and Technology 1975, 876). Generalized conformism would be the result.

These warnings were not, of course, heeded. To protect itself from politicians such as Conlan, academia placed peer review on a pedestal, portraying it as an essential part of the process of scientific discovery. The rapidly multiplying army of administrators would use the number of peer reviewed articles published as the key metric by which to measure academics’ productivity. Those academics at the top of the food chain would then use their peer review system to impose conformity on those below. Evidence then mounted that Ling’s critique of peer review may have been well founded.2

Can Capitalism Survive?

In the early twenty-first century, economists began to find that there had been a substantial decline in researchers’ productivity. Tyler Cowen (2011) was among the first to sound the alarm in The Great Stagnation. “Meaningful innovation has become harder,” Cowen wrote in 2011, “and so we must spend more money to accomplish real innovations, which means a lower and declining rate of return on technology.” His explanation for this trend was, however, unconvincing. Cowen argued that the problem was a result of innovation becoming more “geared to private goods than to public goods.” Yet the great inventions of the second industrial revolution—electricity, automobiles, pharmaceuticals—were also developed as private goods. Indeed, if anything, the slowdown in the growth of “total factor productivity” coincided with the rise of public funding for research during and after the Second World War. As such, the cause of what Robert J. Gordon (2017) described as The Rise and Fall of American Growth remains a mystery. Another explanation for the slowing rate of innovation is necessary.3

The hypothesis presented here is that the radical reorientation of academia was the root cause. It was an institutional legacy of the wartime boom in public funding for science. Under the aegis of the NSF, peer review had been established as an integral part of scientific discovery. And even as the flow of public funds was reduced in the 1970s and 1980s, the new system remained. It allowed senior academics to prevent junior researchers from threatening their entrenched positions because something was not considered knowledge if it had not successfully passed through peer review. As a result, the Popperian ideal of scientific man—the selfless pursuer of truth—became ever harder to find; Kuhn’s (2012) “normal science” instead prevailed.

From this perspective, then, Schumpeter might have been right, but for the wrong reasons. His fear of academics’ political radicalism proved to be unfounded. Following the unrest of the 1960s, universities were reoriented by their growing administrations toward improving the student experience. To defend itself from political oversight, the NSF then placed peer review on a pedestal that it had not previously occupied. In doing so, it reinforced a system that was already beginning to impede scientific discovery. After the innovations of the Second Industrial Revolution had generated capitalism’s post-war “golden age,” the supply of new ideas began to dry up, and the rate of innovation slowed because groupthink became the norm. It became ever harder to find the free interplay of opposing ideas that Paul Feyerabend (1988) identified as being key to scientific discovery.4

The question then becomes whether capitalism can survive without a high rate of innovation and the growth that it brings. If not, it may have to liberate academia from the constraints imposed by administrators whose principal concern is treating students like consumers of a product that is supposed to make them feel good. Furthermore, the peer review system will also need to be reformed, if not abolished. It has performed poorly as form of quality control, as seen most clearly in the cases of academic fraud that occasionally come to light. Fraudsters can be published, but innovators often cannot. It is unsurprising, then, that much of capitalism’s recent innovations—most notably, the Internet and large language models—have largely been based on research done outside the peer review system that the NSF made the founding stone of academia.5 Innovation has tended to come despite the peer review system.

Capitalism’s problem is that it needs the free-thinking radicals who so scared Joseph Schumpeter. In the 1940s, the atomic bomb needed J. Robert Oppenheimer, despite him being a “New Deal Democrat.” The fear of such radicalism then fed the expansion of university administrations, which exercised greater control over faculty, including through the use of the peer review system. In academia today, researchers must produce peer reviewed articles, which is most easily done by not challenging the dominant paradigms espoused by senior professors. The dull conformism of academia then tends to stultify even the most brilliant minds, resulting in a lack of innovation. Maybe academia is killing capitalism after all.

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References

Bagatolli, Luis A., Agustín Mangiarotti, and Roberto P. Stock. 2021. “Cellular Metabolism and Colloids: Realistically Linking Physiology and Biological Physical Chemistry.” Progress in Biophysics and Molecular Biology 162 (July):79–88.

Baldwin, Melinda. 2018. “Scientific Autonomy, Public Accountability, and the Rise of ‘Peer Review’ in the Cold War United States.” Isis 109 (3): 538–558.

Bloom, Nicholas, Charles I. Jones, John Van Reenen, and Michael Webb. 2020. “Are Ideas Getting Harder to Find?” American Economic Review 110 (4): 1104–1144.

Committee on Science and Technology. 1975. “National Science Foundation Peer Review: Special Oversight Hearings: Hearings before the Subcommittee on Science, Research and Technology of the Committee on Science and Technology, U.S. House of Representatives, Ninety-Fourth Congress, First Session.” Washington: U.S. Government Printing Office.

Cowen, Tyler. 2011. The Great Stagnation: How America Ate All the Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better. New York: Dutton.

Darwin, Charles. 1936. The Origin of Species by Means of Natural Selection: Or, The Preservation of Favored Races in the Struggle for Life and The Descent of Man and Selection in Relation to Sex. New York: The Modern Library.

Feyerabend, Paul. 1988. Against Method. Rev. ed. London: Verso.

Gordon, Robert J. 2017. The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil War. Princeton: Princeton University Press.

Hofstadter, Richard. 1968. “The 214th Columbia University Commencement Address.” American Scholar 37 (4): 583–589.

Jones, Benjamin F. 2010. “Age and Great Invention.” Review of Economics and Statistics 92 (1): 1–14.

Klein, Daniel B., and Charlotta Stern. 2009a. “By the Numbers: The Ideological Profile of Professors.” In The Politically Correct University: Problems, Scope, and Reforms, edited by Robert Maranto, Richard E. Redding, and Frederick M. Hess, 15–37. Washington, D.C.: AEI Press.

———. 2009b. “Groupthink in Academia: Majoritarian Departmental Politics and the Professional Pyramid.” In The Politically Correct University: Problems, Scope, and Reforms, edited by Robert Maranto, Richard E. Redding, and Frederick M. Hess, 79–98. Washington, D.C: AEI Press.

Kuhn, Thomas S. 2012. The Structure of Scientific Revolutions. 4th ed. Chicago: University of Chicago Press.

Novick, Peter. 1988. That Noble Dream: The “Objectivity Question” and the American Historical Profession. Cambridge: Cambridge University Press.

Offutt, George C. 1965. “Behavior of the Tufted Titmouse before and during the Nesting Season.” Wilson Bulletin 77 (4): 382–387.

Piore, Michael J., Phech Colatat, and Elisabeth Beck Reynolds. 2019. “NSF and DARPA as Models for Research Funding.” In The DARPA Model for Transformative Technologies, edited by William B. Bonavillian, Richard Van Atta, and Patrick Windham, 45–75. Cambridge: Open Book Publishers.

Popper, Karl R. 1959. The Logic of Scientific Discovery. New York: Basic Books.

Robinson, Joseph D. 1997. Moving Questions: A History of Membrane Transport and Bioenergetics. New York: Oxford University Press.

Schumpeter, Joseph A. 1942. Capitalism, Socialism, and Democracy. New York: Harper & Brothers.

Solovey, Mark. 2020. Social Science for What? Battles over Public Funding for the “Other Sciences” at the National Science Foundation. Cambridge, MA: MIT Press.

Symington, James W., and Thomas R. Kramer. 1977. “Does Peer Review Work? Hearings Held in July 1975 before a House Subcommittee Found Fundamental Soundness in NSF Peer Review Evaluation Systems.” American Scientist 65 (1): 17–20.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2023. “Attention Is All You Need.” arXiv.

Wallerstein, Immanuel Maurice. 2011. The Modern World-System IV: Centrist Liberalism Triumphant, 1789–1914. Berkeley: University of California Press.

These are six thoughts inspired by it:

1. Trade is good, damn it!

Mazzuca corrects an erroneous assumption that Latin American states’ dependence on taxing imports was a major source of weakness. Instead, he argues that trade was the basic path for state formation in nineteenth-century Latin America. Simply put, a booming export sector facilitated state formation because it provided the foreign exchange needed to pay for imports, which were relatively easy to tax, giving Latin American states the revenues they needed. In short, mo’ trade, mo’ state formation. Dependence on the tariff for state finance was a feature, not a bug.

This reminds me of Bill Warren’s critique of dependency theory. Unlike most of his leftist comrades, Warren (whose Wikipedia entry is wonderfully titled “Bill Warren (communist)”) argued that developing countries needed more imperialism, not less. Imperialism meant more foreign investment, which generated faster growth, which facilitated development. Similarly, Mazzuca correctly argues that more exports facilitated state formation. He is thus departing from the Latin American nationalist positions that inspired much of dependency theory in the first place.

2. Mazzuca does historical political economy

Crucially, however, Mazzuca doesn’t throw the baby out with the bathwater. He corrects some of the mistaken assumptions of the Latin American nationalists/dependency theorists while keeping what they were good at: historical political economy. It’s a good book because it uses that same methodological approach.

3. It’s all about Argentina

Notionally, Latecomer State Formation is about Latin America, but the other countries take supporting roles. Hence, Argentina gets 86 pages, Brazil 41, Mexico 36, Colombia and Uruguay 42 between them, while Central America, Venezuela, and Peru share 36. Given my own predilections as an economic historian of Argentina, I fully endorse this approach.

And I am only half joking. I do believe there is something special about Argentina that means that studying it has helped me to understand the world better.

4. Trade also made European states

A more critical thought relates to the denominator in Mazzuca’s analysis. He aims to compare Argentina’sLatin America’s “trade-led” path of state formation with the “war-led” path that Europe took, as described by Charles Tilly. The problem is that Tilly missed how trade also drove state formation in Europe and the most successful states were those that were better able to avoid war. Britain became the most powerful European state precisely because it was an island, which insulated it to a considerable degree from the threat of invasion by belligerent neighbours. Crucially, Britain did not need a large standing army, which allowed it to focus on naval power. The Royal Navy then gave it an advantage in international trade, resulting in large revenues for the government in London – revenues that have been underestimated by economic historians until recently.

War-making, moreover, tended to be expensive and often undermined growth, making it harder for states to raise more revenues without using despotic means. Spain was the major example of a war-making state that taxed too much, especially in its colonies, whereas the less belligerent British Empire benefited from what Adam Smith described as “peace, easy taxes, and a tolerable administration of justice.”

5. Where are the gringos?

The United States is largely absent from Latecomer State Formation. This is important because it, too, experienced trade-led state formation in the nineteenth century: the federal government in the United States was just as dependent upon import duties as its Latin American counterparts. Yet it didn’t suffer from the problem of “patrimonialism” – that is, personal control by local rulers over their territories through informal hierarchies and patron-client relationships.

For Mazzuca, patrimonial rule was typical of Latin America because there were few incentives for trade-led states to establish strong central governments. “Waging a battle against peripheral notables torpedoes the plan of export-led growth by pulverizing the business climate required for investment and production,” is how he puts it (p. 7). Again, the contrast with an idealized Europe is key. Mazzuca believes that in Europe war-led state formation led to “Weberian bureaucratization,” in which there was a “gradual but steady growth in the quantity, quality, and efficiency of goods and services supplied by the government across the state’s territory” (p. 2).

From my perspective, this analysis is a bit off. Export-led growth tended to empower landed elites in Latin American countries because they were exporters of primary commodities, whereas European countries didn’t have that option, given a lower land-labour ratio. Instead, European states needed capable governments that could promote industrialization. That explains how the two regions got quite different types of government despite both following paths of trade-led state formation.

The question then becomes why the United States managed to become a democracy with a capable government, even though it was also a land-abundant country that was dependent upon taxing imports for state formation. In part, the answer is high levels of literacy, which resulted in better quality government compared to Latin America – the Protestant reading ethic made a big difference. The United States had, moreover, a fairly complete set of natural resources – coal and iron ore were important ingredients for nineteenth-century industrialization. But there was something else: the United States did experience misrule by a landed elite, but it was contained. For most of the nineteenth century, cotton dominated the country’s export sector, but the power of the parasitic planter class was largely confined to the South, despite Northerners’ frequent complaints about the Slave Power. The United States was thus unusual because of its dual nature. The confinement of slavery to the South meant that the North could benefit from trade-led state formation without suffering the side effects.

6. Financial revolutions were also trade-led

Mazzuca’s focus is very much on politics – unsurprisingly, given that he is a political scientist. But Luis Felipe Zegarra has shown that export sectors were also needed for financial systems to expand in nineteenth century Latin America. In his PhD dissertation, Zegarra offers a demand side explanation: it was only when export sectors expanded that there was demand for financial services. Yet I suspect that the mechanism was more on the supply side. After independence in the 1820s, Latin American governments had attempted to promote the expansion of their financial system, but they soon discovered an almighty balance-of-payments constraint. Financial systems could not expand without export earnings.

The same, moreover, was true elsewhere. Nuno Palma has argued that Britain benefitted from inflows of Brazilian gold, making possible its financial revolution. And I make a similar argument in my work on the United States. The cotton produced by the South’s captive labor force, I argue, both allowed the American state to form and facilitated financial revolution – it made American capitalism, in other words.

This is, then, why studying Argentina is so important. I never would have understood how slavery contributed to the growth of American capitalism if I had not studied Argentina first. Balance-of-payments constraints are a persistent feature of its history, which helped me to understand why the United States’ export sector mattered in the nineteenth century. American economic historians, by contrast, are shaped by the experience of living in a country that produces the world’s reserve currency, meaning that they don’t have to worry much about the balance of payments. I have been able to make an original analysis of slavery’s contribution to growth because I had previously studied Argentina. And that’s also why I like Mazzuca’s book.

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I haven’t written anything for this blog for many months because I have been sidetracked with another project. It’s on the economic history of the United States and has monopolized my attention...

As I have been working on that project, moreover, I have realized that I’d like to blog on subjects beyond Argentina. Therefore, I have decided to turn “The Poor Rich Nation” into “The Poor Rich World.” From now on, it will be a blog that aims to use history to understand global inequality today. The question will be: How did we become such a wealthy planet with so much poverty?

Inevitably, Argentina will feature more frequently than most other countries. I intend to write a post, for example, on Sebastián Mazzuca’s Latecomer State Formation, a book that is really all about Argentina, despite notionally covering the whole of Latin America and comparing it to Europe.

But I will also be writing on other topics. After Mazzuca, there will be a post on the definition of “capitalism.” Specifically, I aim to draw on the ideas of Joseph Schumpeter and Friedrich Hayek to argue that capitalism should be seen as more a consequence than a cause of growth.

As before, you can support me in this endeavour by donating via Paypal:

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One or two of you already are contributing, so thank you!

Finally, apologies for not having written anything for so long. My ruminations on the origins of global inequality should be more forthcoming from now on.

Joe

The first is from June 1, 1973. It describes in the week after becoming president of Argentina, Héctor Cámpora gave the green light for Peronist union bosses to form vigilante groups to deal with opposition to them in the labour movement. The conclusion of the report is particularly chilling:

For me, this suggests that the death squads that began to operate in Argentina in 1973 were not simply the brainchild of José López Rega, the deeply weird aide to Perón, who became Minister of Social Welfare under Cámpora. López Rega is typically credited with having founded the Argentine Anti-communist Alliance or Triple A. But if the intelligence reported by the US Embassy is correct, the death squads were more organic to the Peronist movement than that standard narrative suggests. Specifically, they were initially established by the union bosses, who were continuing a tradition from the Peronist governments of 1946 to 1955 – albeit in a more extreme version.

The second document then further implicates the Peronist union bosses in state terrorism. It details a meeting from March 22, 1976, two days before the coup that inaugerated the military governments of 1976 to 1983. The US Embassy reported how Peronist union bosses had met with representatives of the military to agree how the labour movement should be deradicalised. It was clear that Córdoba – where opposition to the Peronist union bosses was strongest – would be targeted with the most violence:

I am not an expert on Peronism, but my impression is that the movement has not fully understood its complicity in Argentina’s experience of state terrorism in 1970s. That complicity also illustrates how complex the problem of democratisation was in Argentina in the twentieth century. Cámpora had been elected in probably the fairest elections since the 1920s, but then immediately approved the formation of death squads. The targets of those death squads often wanted to democratise Argentine society, but they often saw the overthrow of parliamentary democracy as necessary to achieve that goal. Few from the period can be clearly identified as heroes.

I am an independent scholar, so my opportunities for funding are limited. Any donation you can make to help me write The Poor Rich Nation would be fantastic.

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https://drive.google.com/drive/folders/12i1E5bkccCqGngGH5sWGr8kFwA_GrUVo?usp=sharing

Anyone can download them and use them freely.

My revision of the NARA records wasn’t systematic. I mainly looked at the folders relating to economic affairs and photographed anything that seemed mildly interesting. There is no index to the archives and I suspect they are not in any discernible order. Nonetheless, they are OCRed and searchable.

In the past, I have found the US Embassy records extremely useful. They often give a behind-the-scenes view of what was going on. Moreover, US Embassy staff were often highly intelligent with good analytical skills, especially in the decade immediately after the Second World War, when there was a strong public-service ethos.

I stopped photographing the files in 1973 because that is when the US Embassy records become available online: https://aad.archives.gov/aad/series-list.jsp?cat=WR43. However, I suspect that even those more recent years are unterutilised in research on Argentina.

If you find anything useful in the documents, please do let me know!

I am an independent scholar, so my opportunities for funding are limited. Any donation you can make to help me write The Poor Rich Nation would be fantastic.

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Giovanni Arrighi, for example, explained why he preferred to use estimates of nominal gross national product (GNP) to compare countries: it provided “is an indicator of—and a better indicator than anything else that is readily available—is the command of the inhabitants of the region or jurisdiction to which it refers over the human and natural resources of the organic core, relative to the command of the inhabitants of the organic core over the human and natural resources of that region or jurisdiction.” Rather than comparative levels of welfare, then, nominal ratios give an indication of the power of the inhabitants of one country over the another. They therefore say something different to PPP measures, but they are arguably more important.

There is, moreover, a practical reason for an economic historian to use nominal measures of income because it results in one less source of potential error. Estimating historical GDP is already difficult enough, without having to then adjust it for differences in price levels using data that don’t really exist… This is what I have been doing with Argentina’s historical statistics, but I have found that the results are very sensitive to which price-level data I use. Sticking to nominal ratios therefore seems somewhat more objective.

On top of that, in the long term, there does tend to be a correlation between nominal GDP and PPP GDP in the long run. This, for example, is Argentina relative to the United States:

And relative to Australia:

The nominal estimates are based on the free market exchange rate, while I’ve used Colin Clark’s estimates of the price level for 1946 and thereafter various estimates by international organizations for the PPP estimates. My educated guess is that Argentina’s price level was closer to the US and Australia pre-WWI, so the decline in relative PPP GDP per capita was less pronounced than suggested by the nominal series. I am, however, more interested in the timing than the level of the decline, and it’s here that the nominal series are really important.

I’m now working on the nominal GDP statistics of various countries. I’m trying to correct for the splicing problem identified by Leandro Prados de la Escosura, which means that existing series tend to overestimate past levels of nominal GDP, especially for developing countries undergoing structural transformation. These are the comparisons that I have been able to make so far:

The series are noisy due to short-term movements in exchange rates, but all point toward World War I and the interwar period as when Argentina’s relative decline began. There were several developments in these years:

• Argentina’s frontier reached it limits, as there was little more Pampean land to be brought into production.

• The terms-of-trade boom ended, first due to increased transportation costs during World War I, then as a result of the disintegration of the world market for agricultural products during the 1930s, when governments in Europe and North America began to subsidize their farmers.

• Argentina’s democratization was interrupted by the military of 1930, which led to an “infamous decade” in which the country’s traditional political class used electoral fraud to maintain its power.

• In 1932, the federal government began to print money to purchase its own bonds—a practise that would become institutionalized, driving the high rates of inflation that would become the norm in the second half of the twentieth century.

There are, then, good reasons for there to have been a structural break in Argentina’s development from 1914 to 1945. Perón then emerged as a symptom of decline.

The question is then whether Peronism and anti-Peronism made the decline worse in “positive feedback loop”. Intuitively, it seems likely that Argentina’s more or less violent swings from populism to liberalism and back again can’t have been good for the country. Maybe such instability was bad instead itself, as I previously suggested in the comparison with New Zealand. On the other hand, it might also have meant that Argentina couldn’t exploit some of the advantages that New Zealand lacked, such as the relatively large domestic market and the proximity to Brazil, an even larger market. I’m still undecided, therefore, on whether domestic or global factors should be assigned more importance when explaining Argentina’s decline. My working hypothesis is that it was initially caused by the deterioration in the terms of trade and the frontier reaching its limits, but then made worse by political instability.

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The earliest price I have for New Zealand is 1985, so I have only projected the country’s PPP GDP per capita back to the late 1950s. This is what it looks like when added to the Argentina/Australia comparison:

If anything, New Zealand’s decline relative to Australia has been even sharper than Argentina’s! And that’s why, of course, we read so many articles in the Economist about how the Kiwis’ terrible institutions led to the country’s decline…

Oh wait, that’s not right.

In fact, New Zealand has notoriously good institutions, coming above Australia in various rankings. Indeed, it is top of the World Bank’s “Ease of Doing Business” ranking, fourth in the Fraser Institute’s “Economic Freedom” index, and sixth in the Heritage Foundation’s “Index of Economic Freedom”. So, why so much decline relative to Australia?

Perhaps the second half of the twentieth century was just really bad for countries that were specialized in agricultural exports and lack the mineral deposits of Australia (or Chile, for that matter)?

If that’s correct, then what Carlos Díaz Alejandro described as Argentina’s “latent civil war” might have been irrelevant to the country’s relative GDP per capita. Even if the Argentines had been as well-behaved as the Kiwis, the country still probably would have declined in terms of that metric. The great passions of its politics—the violent swings in the pendulum between liberalism and populism—have been largely irrelevant. Instead, the origins of Argentina’s relative decline are to be found in agricultural export subsidies in the United States and Europe’s Common Agricultural Policy. These were the policies that ruined the world market for Argentina and New Zealand alike.

BUT, looking again at the graph above, it is notable how much more volatile Argentina’s relative decline was. The fluctuations around the trendline were far greater than for New Zealand. That volitality would, it seem, be a reflection of the macroeconomic mismanagement that came from Argentina’s awful politics.

Maybe we ought to consider stability a virtue in itself? It is notable, for example, that New Zealand tends to outperform its GDP per capita in international surveys of other aspects of quality of life. Here it is, for example, in the United Nations’ World Happiness Report:

New Zealand’s high-quality institutions might have other benefits that do not necessarily show up in GDP per capita…

And here, by the way, is Argentina:

What is going on in Japan and South Korea?!?

Could it be that those countries’ success in terms of GDP per capita has come at the expense of their people’s happiness?

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The results change the picture somewhat. Here you see Argentina’s current-price PPP GDP per capita relative to the United States’:

I have marked the two world wars, because I had previously pointed toward World War II as marking a break in Argentina’s relative wealth. In this version, by contrast, the relative decline really occurred from the 1930s to the 1960s, and then a little more in the 1980s and 1990s, with the trough coming during the crisis of 2001–2002.

In comparison with Australia, meanwhile, Argentina declined fairly unremittingly from the 1930s through to 2001–2002:

This comparison is particularly important because both countries prospered from exporting primary commodities in the twentieth century. It suggests, therefore, that Argentina’s domestic institutions played an important role in its relative decline. Although, notably, Australia had a lot of mineral resources that Argentina didn’t. Therefore, I am also going to work on New Zealand’s GDP per capita because it had similar institutions to Australia but, like Argentina, was far more dependent on agricultural exports.

If Argentina’s domestic institutions were to blame, these revisions of the GDP statistics point toward the 1930s as the decade when things began to go wrong. Most obviously, Argentina’s democratization was interrupted by the military coup of 1930. On top of that, the federal government began to monetize the public debt. As my sometime co-author, Carlos Newland, explains, it launched a “Patriot Loan”, to cover the fiscal deficit, even running a tango competition to advertise it:

The marketing efforts failed, however, and the federal government ended up printing money to purchase its own bonds. Correlation does not equal causation, of course, but it is notable that Argentina’s money-printing habit began at the same time as its relative decline, at least according to these revised estimates.

With these revisions, I have also made a first attempt to revise Argentina’s growth rate in constant prices by splicing a GDP deflator from the existing series. This is the result of my first attempt:

You can see that my revision shows somewhat faster growth than the most recent (2020) Maddison Project estimates. This finding should be expected because, as Leandro Prados de la Escosura has found, the standard splicing procedure used in Maddison Project-style estimates leads to a downward bias in the growth rates of developing countries experiencing structural transformation. Argentina’s twentieth century wasn’t quite as bad as is normally supposed.

The Economist, for example, claims that “Argentina’s real GDP per person was roughly the same in 2020 as it was in 1974”. Even the Maddison Project says that this is wrong: it shows Argentina’s GDP per capita growing by 21 percent over that period; for now, my revision suggests 49 percent. In 1974, my revised estimate shows Argentina’s current-price PPP GDP per capita at 36 percent of the United States’ and it fell to 33 percent in 2020.

The Economist article is a catalogue of negative tropes on Argentina. From reading it, you would have no idea that Argentina’s PPP GDP per capita had recovered considerably relative to both the United States and Australia since the crisis of 2001–2002. Indeed, the Economist even reminisces about the wonders of the macroeconomic policies that culminated in that crisis—it gushes about the “liberal government that managed to turn the country’s fortunes around for a decade”. In fact, my revisions suggest that the so-called “Convertibility” regime of a fixed exchange rate made Argentina descend to its lowest point since the late nineteenth century, in relative terms. It’s almost as if the Economist prioritizes liberal ideology over empirical reality. Who would have thunk it!

Only time will tell how sustainable Argentina’s recovery will be. I have also been catching up on the country’s recent history and I now have a better appreciation of the challenges it faces, both internal and external. But that will be the topic for another post.

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Today I signed a contract with Polity Press for my book The Poor Rich Nation: Why Argentina Fell Behind. Here’s the argument as I currently see it:

1. In the nineteenth century, there was an unprecedented boom in Argentina’s terms of trade. It was caused by three interrelated factors: (a) trade liberalisation, (b) the industrial revolution, and (c) cheaper transportation. As a result, Argentina’s terms of trade improved by roughly 2,000 percent from the 1780s to the First World War.

2. The boom had uneven impacts. It benefitted the Littoral region, especially Buenos Aires and its vast Pampean hinterland. The more densely populated Interior, on the other hand, fell behind because its cottage industries were unable to compete with cheaper imported goods. The shift from Interior to Littoral led to several decades of intermittent civil war.

3. Enormous inflows of British investment made possible the formation of a national state in the second half of the nineteenth century. Regional oligarchies in the Interior could be co-opted by the promise of railroads, which allowed them to transport their goods to markets in the Littoral. The federal government also used the railroads to send troops to support their allies in the provinces. The civil wars gradually came to an end.

4. Argentina became a wealthy country through its integration into the world market. Government policy aided growth by giving immigrants relatively easy access to the land that was being brought into production in the Pampean region. The Interior’s mestizo peasants were, however, largely excluded from the boom because they were needed to work on vineyards and sugar plantations. Land policies that discriminated against them were justified by the ruling class’ racism, which had been reinforced during the civil wars.

5. The terms-of-trade boom ended with the First World War, and Argentina thereafter declined in relative terms, much like Australia, another land-abundant country that had prospered during the nineteenth century. The small size of the domestic market limited economies of scale in industry, while labour was too expensive for manufacturers to export. Both Argentina and Australia tended, moreover, to underinvest in education due to a mentality of being “lucky countries” that were naturally wealthy thanks to their land abundance.

6. Argentina also mismanaged its relative decline. The problem was its political system. In the nineteenth century, democratisation had been limited due to the exclusion of the Interior’s mestizo population from politics. Politics then became highly conflictual once the terms-of-trade boom ended. The ruling class resisted taxation while appealing to a mythical golden age in which the country prospered thanks to laissez faire. Populists in turn funded their social programmes through inflationary deficit spending, facilitated by a subservient central bank. Financial and political instability then aggravated Argentina’s relative decline.

7. My argument, then, is that the standard narrative of Argentina’s history is only partly correct. Populism contributed to relative decline, but the (neo)liberal reforms of the populists’ opponents often did as well. More important than either was Argentina’s changing place in the world. In the nineteenth century, land abundance had allowed Argentina to prosper, but its luck ran out in the twentieth century, once the terms-of-trade boom ended. That was the most fundamental reason why Argentina fell behind.

So that’s my argument as it stands, but it is liable to change, especially in response to feedback, so please do tell me what you think. And please do consider making a donation to help me write the book if you can.

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In it, I describe my new estimates of Argentina’s GDP per capita relative to the United States. It is the first major fruit of my research since I began the project a couple of weeks ago:

In the video, I say plenty about the significance of this finding. Here, I’d just like to make a few further notes:

1. These are preliminary estimates and I will continue working on them, depending on funding (please donate!).

2. In case I seem a little unfair on the Maddison Project, their working papers do describe the problems with the database, even if they downplay them somewhat. The issue is that most users of the data ignore the working papers.

3. The price levels have been calculated using benchmarks for 1908, 1960, 1980, 2005, and 2017. The first benchmark is my own calculation, while the others are those of international organizations, especially the World Bank.

4. To interpolate between and extrapolate from those benchmarks for 1876 to 1946, I have used the new consumer price indices of Martín Cuesta and Cecilia Lanata-Briones. I also use them to convert constant-price estimates of GDP to current prices prior to 1935.

5. I haven’t been able to reconcile my results for 1950 to 2017 with those of the Penn World Table (PWT). The problem seems to come from the United Nations’ National Accounts Main Aggregates Database (NAMAD), which the PWT uses. Argentina’s GDP statistics and exchange rates in the NAMAD are unlike anywhere else, and I’m not sure why. If anyone has any ideas, please let me know.

6. Again, depending on funding (please donate!), my book will have a long online statistical appendix in which I go into a lot of details about the construction of the estimates.

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To see why Argentina and Japan stand out, it is helpful to compare the wealth per capita of my twelve countries circa 1913 with their wealth per capita a little over a hundred years later, in 2018, according to the World Bank’s estimates. The coefficient of determination (R²) was just 0.33, meaning that being wealthy in 1913 was a poor predictor of being wealthy in 2018. With Argentina and Japan excluded from the sample, however, the R² increases to 0.75. That’s why they are the only two interesting countries, at least when it comes to explaining (under)development in the twentieth century.

The reversal of fortune between Argentina and Japan was profound. Circa 1913, the average Argentine was worth about £280, whereas the average Japanese was worth £50. Yet by 2018, the average Japanese was worth $257,000 and the average Argentine $50,000. They had switched statuses as “developed” and “developing” countries—that’s what Kuznets meant.

But a hint of why this reversal occurred could already be seen early in the twentieth century. Despite its relative poverty, Japan was already better at government than Argentina. For me, probably the clearest indication comes from primary school enrollment. According to Aaron Benavot and Phyllis Riddle’s database, the Japanese government was able to get 59 percent of its young children into school in 1910; in Argentina, by contrast, only 37 percent attended. Despite its country’s wealth, the Argentine government was already failing to provide the kind of public goods that were important for growth in the twentieth century.

With hindsight, the distinct trajectories taken by Argentina and Japan were somewhat predictable. Looking again at the sample of twelve countries, the two countries’ levels of wealth per capita in 2018 were about where they should have been according to the level of primary school enrollment in 1910. For the twelve countries, the R² of the two variables is 0.74. When a broader sample of 65 countries is used, it falls somewhat, but remains quite high at 0.53—over half of the variation in the countries’ wealth per capita in 2018 can, in statistical terms, be explained by the level of primary-school enrollment in 1910.

The major caveat to this is that it is all in hindsight. As a historian, I can see that land abundance was the key to making Argentina wealthy by 1913. For twentieth-century Japan, on the other hand, government was vital. But that does not mean that government will necessarily have the same importance for growth and development in the twenty-first century. For this reason, some skepticism is warranted when people use the Argentinas and Japans of the world to sell political projects in the present. The past is not always a reliable guide to the future.

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My new book project seeks a better answer. In The Poor Rich Nation: Argentina since 1810, I will attempt to explain how Argentina became a wealthy country and why it stopped being one.

In my PhD dissertation at the LSE, I looked at the impact of globalization on Argentina in the nineteenth century. I found that there was a long boom in the terms of trade after independence. As a result of the removal of the Spanish trade monopoly, combined with the falling costs of international transportation and cheaper manufactured goods, Argentina’s terms of trade improved by upwards of 2,000 from 1810 to World War I.

This long terms-of-trade boom allowed Argentina to become one of the world’s major exporters of agricultural products. Over 31 million acres of arable land were brought into production in the Pampas grasslands. Millions more were grazed by cattle.

Argentina then became a “poor rich nation.” It was wealthy, at a similar level to France and Germany. Yet it was not developed. In terms of political institutions, education, and health, Argentina lagged behind Northern Europe. Instead, it was at the level of Italy and Spain, two poorer Southern European countries.

My hypothesis is that Argentina had prospered in a period when it was more or less enough for a country to have abundant land to become wealthy. Government was relatively unimportant. Hence, outside of Buenos Aires, the state provided few public goods, such as schooling or sanitation. Policies aimed at promoting development beyond the agricultural sector were also limited. But it did not matter because Argentina had land and the terms of trade were improving by 2 percent per year.

Only when the terms-of-trade boom ended during World War I would the lack of state capacity become a problem. The provision of public goods and the formulation and implementation of public policies then became crucial for a country’s growth. Argentina lacked that capacity, and, as a result, ended the twentieth century relatively poor.

Much of my new book project will therefore be devoted to investigating the political origins of Argentina’s decline. I will try to explain why state capacity was so limited despite its wealth in the early twentieth century. Then I’ll look at how the country’s political system struggled to cope with the deterioration in the international environment after World War I.

Ideally, it will annoy both the liberals who argue that Argentina is a case of what happens when there is too much state and the populists who haven’t always covered themselves in glory when in power. The quality rather than the quantity of government has been the problem in Argentina.

If you can support me to write this book, it would be much appreciated. I am an independent scholar, based on a hill in Wales. My first book should be published at some point soon. While I am waiting, I would like to be able to write this book. I have already done much of the research for it, but now I need to sit down and type, type, type.

I am an independent scholar, so my opportunities for funding are limited. Any donation you can make to help me write The Poor Rich Nation would be fantastic.

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