The good Artificial Intelligence

David Autor analyzes in this article the use of Artificial Intelligence (AI), in the context of the economic history of technological change in the last three hundred years. The analysis goes from artisans prior to the Industrial Revolution to generative Artificial Intelligence, passing through factory machinery and the role of computers.

A type of knowledge is expert when it is necessary and scarce at the same time. It is what, for example, artisans had in the stage prior to the Industrial Revolution, a knowledge that required a long training process, which only a minority could afford. These experts developed each product creatively, as a complete and differentiated unit.

The industrial revolution displaced artisans, producing a great increase in productivity, but relegating the majority of industrial workers to extremely hard work for miserable wages for several decades. Productivity increased because from then on each worker was in charge of a small part of the process, in a repetitive and specialized way, on a mechanized assembly line.

The Luddites (who protested the mechanization that wiped out artisans) were correct in their protest that it took five decades for industrial workers to see their real wages grow significantly, requiring the power of unions and the expansion of democracy, as well as additional technological changes. Then a middle class of mass experts did emerge (intermediate workers doing administrative tasks), but they followed rules and lacked discretion (they were not the ones who made the decisions), so they were vulnerable to the automation that computers brought from the second half of the 20th century. Until personal computing and the Internet arrived, these intermediate workers saw their real wages increase and began to swell an abundant middle class in developed societies.

Computers are very effective with routine tasks, but not with those that require tacit knowledge, such as improvised language, or recognizing the face of a child in an adult. AI is the opposite, much more effective with tacit knowledge than with routine tasks.

Already before the advent of AI, and also with it, it is important to start from the basis that tools are levers that allow us to improve human work, not substitutes for it. Think of the examples of calculators, electric saws or drills. These three examples have two characteristics in common: first, they make the task of those who work with these tools much easier; secondly, to be used they require some training.

In common with other stages of accelerated technological change, AI will not eliminate human work. Employment has not stopped growing with the emergence of new technologies, despite the fact that many professions have become obsolete. But other professions have been created and professions that already existed have been able to develop in a different way. The improvement in productivity that technological change allows generates new demand for new products and services that did not exist before or that were enjoyed by a small minority. The challenge is that the new jobs created contribute to improving the dignity and living conditions of working people. In this sense, David Autor speaks out against the “inevitabilism” of thinking that AI will make human work redundant (something that he does not consider desirable, as perhaps some supporters of basic income do, as is said in passing in the article).

Unlike other technological changes, however, AI can be complementary to decision-making (and not just routine tasks), which can make it easier for many more people to participate in it, eroding the monopoly power of some specialized professions, such as doctors or university professors (or football coaches, see this article in Nature). The existing AI already helps make decisions, although the final responsibility lies with the human being, for example accepting or not a suggestion to complete a sentence, or accepting or not the “smart car” warning about its speed and direction.

The text compares computers with classical music, which follows a series of rules reproducible in each concert, and AI with jazz, which allows improvisation and adaptation to changing circumstances. David Autor suggests that AI will allow what has happened to people who work in nursing to become widespread, a portion of whom have been enabled in recent years to assume functions (for example, prescribing) that could only be performed by people before who had a medical degree. This requires additional training, but not the same as was traditionally required for a medical degree, and this expansion of employment responsibility has been made possible by technological developments such as the connection and digitization of medical records. Analogous developments can occur in education.

In this way, AI can facilitate more affordable healthcare and education (or football quality), which are not in the hands of elites who monopolize the knowledge necessary to make decisions, whether in an operating room or a classroom. If we combine this with the demographic trends that are occurring, in the future there will not be a shortage of jobs, but rather there will be a lack of people who can work, although as in the past, jobs will disappear and new ones will emerge.

The problem is not the disappearance of work, but the dignity and remuneration of working people. The human decision will be irreplaceable. That is why self-driving cars have failed, because they do not know how to make quick decisions when reality is changing. The role of AI is not to drive a car, but to assist in driving.

The unique opportunity that AI offers humanity is to reverse the shrinking trend of the mass of decent-wage workers: to expand the relevance, reach and value of human experience to a broader set of tasks. Not only could this reduce income inequality and the costs of key services such as healthcare and education, but it could also help restore the quality, prestige and prominence that too many people and jobs have lost. This alternative path is not an inevitable or intrinsic consequence of AI development. However, for David Autor (in line with other economists such as Dani Rodrik or Daron Acemoglu) it is technologically plausible, economically coherent and morally convincing. Recognizing this potential, we should not ask what AI will do for us, but what we want it to do for us.

The article does not make a prediction, but rather points out a possibility. The same technology can have different uses depending on how institutions and incentives develop. Just as nuclear energy can be used to make atomic bombs or to produce energy without contributing to climate change, AI can be used to enrich a small minority, or to pit elites against each other, or to improve the life and work experience of the vast majority.

Lessons from the good populism

In this crucial electoral year of 2024 in Europe and the US, it is important that progressive candidates get the message right, and fine-tune their narrative with the objective of maximizing the number of votes, and stop the rise of a eurosceptic right and far right. Not everything is lost, as polls suggest in the UK (unfortunately, not anymore in the EU) and Catalonia (where the federalist Socialist Party comes ahead in all recent polling, after more than 10 years of a strong pro-independence revolt).

Those of us who are in academia and keep an active interest (and an interested activity) in politics, have also a duty to learn with a critical and cautious eye from all the existing and growing research and literature on "populism," and to contribute to it if possible.

I have certainly learned from reading the 2020 book by Thomas Frank, "The People, No." The message in the book, as well and in this article in The Guardian (also, this one) from the book's author, is that there is a good and positive "populist" tradition in the US, which has its origins in the "People's Party" of poor farmers at the end of the XIXth century. Frank claims that this progressive, egalitarian tradition should claim the property rights to the word "populist," as they were the first to use it as something positive, democratic and egalitarian. In Europe, it may come as a surprise that such a positive tradition exists, but it is one that has been claimed several times by the economist Paul Krugman as well. Frank reveals that some politicians (like President Obama) have used the term in a positive sense and in a negative sense on different occasions. But the book is more than a crusade to reclaim a word: it is a crusade to reclaim a popular movement that was anti-elitist but was against demagoguery and bigotry, and that was focused on income inequality above everything else. The author, who shows little patience for the recent social scientific literature on populism and its associated psychological biases and irrational voters and voting outcomes (with authors such as Mounk, Müller, Levitsky, or Mudde, from which I have also learned) argues that "Populist" has become shorthand for racist authoritarianism. But the first populists were progressive labor activists who fought for democracy.  According to Frank, genuine populism is neither new nor right-wing. 

The book is a very interesting history of ideas and facts. In general, it strongly criticizes the centrist wing of the Democratic Party and academic liberal orthodoxy for having forgotten that rich progressive populist tradition.

This progressive populism of the late XIXth century is the one that gave the US independent regulatory agencies, and that tried to spread education and culture to the masses, and unite white and black workers. That populism did not go against science and knowledge, but it went against orthodoxy. That tradition was followed in the XXth century by Franklin D. Roosevelt and by Martin Luther King (MLK), and therefore is an important ingredient of the New Deal and the fight for Civil Rights. Thomas Frank points out that the elite of academia (and especially prestigious economists, such as Schumpeter) did not endorse the policies of Roosevelt at the beginning, because they thought that they went against the established consensus. In the XXIst century the politician that better reflects this tradition is Bernie Sanders (Elizabeth Warren is also mentioned in the book).

In the past, Frank has been criticized for not being careful with data. For example, political scientist Larry Bartels (also an egalitarian, in my view) said in the past that his claim that the working class had abandoned the Democratic Party was an exaggeration. In an article in The Guardian about Paul Krugman, Frank mentioned Bartels.

Not all left wing populisms are like Sanders, Roosevelt or MLK: Corbin in the UK, AMLO in México, Iglesias in Spain, Maduro in Venezuela, Kirschner in Argentina, are not mentioned in a book that is only about the US.

Not everything Obama or other centrist Democrats did was wrong: Obamacare, gay rights... Not all non-income progressive causes (feminism, ecologism?) should be relativized as woke or culture wars. Democracy has problems (Kenneth Arrow cannot just be forgotten) and voters do have psychological biases. There are no simple recipes. But Thomas Frank has a point: there has been such a thing as good populism, and we can learn from it.

Coaches in my course on soccer and economics

What is the role of coaches in soccer and what analogies can be established with economics? That is the subject of an interesting literature that is summarized by Peeters and Van Ours in their contribution to an IEB Report that I recently coordinated.

Sarina Wiegman, Jurgen Klopp and Pep Guardiola play the role of intermediate principals in a three-tier agency structure, similarly to referees. Upper principals are club or governing body officials, and agents are players. A problem they may have is that some agents may be more powerful than coaches. This weakness may be related to the extreme quick turnover of head coaches in soccer leagues. A graph for The Economist with data in 2016 showed that the median time in the job for a head coach (manager) in the English Premier League was less than one year. From coordination games (as for example in David Sumter's "Soccermatics"), we know that coaches could play an important motivational role to promote trust and team spirit, like Al Pacino in the movie "Any Given Sunday" in American Football. From empirical work in other branches of economics (like from Bloom and Van Reenen), we know that managerial practices are an important determinant of productivity differences among firms that produce similar goods. 

Intermediate executives can contribute to preventing organizational failure by allocating talent and wisely splitting resources between the short-run and the long-run. But it's not easy to see the impact of coaches in soccer data. In the book "Soccernomics," Kuper and Szymanski are very skeptical that coaches play any significant role (they quote Arrigo Sacchi criticizing some former players becoming coaches: "good horses do not make good jockeys"). Although they have to decide on a complex vector of multidimensional tasks, player talent seems to be much more important, and soccer coaches intervene less during the game than, say, basketball coaches. That irrelevance is confirmed by most empirical work on managerial dismissals mid-season, if those coaches that are fired are compared to coaches that experience a similarly bad streak but do not get fired. The teams of both types of coaches experience a rebound, but it is so similar that most probably it is because of regression to the mean. 

Peeters and Van Ours reach a similar conclusion in their contribution to the IEB Report when they compare the dismissal of Ronald Koeman in FC Barcelona with the non-dismissal of Valverde two coaches before. However, very recent research (which I found in Palacios-Huerta's article "The Beautiful Dataset") shows that distinguishing (imperfectly, using the notion of expected goals) merit from luck, wise dismissals really have a positive impact on performance, even after comparing with a control group of similar coaches that are not fired. It is just that there are not many wise dismissals. Peeters and Szymanski also have an article where they show that one of the reasons of the mediocrity of most managers, is that clubs are reluctant to hire new coaches with high potential due to credit constraints (new coaches may be better on average, but it is risky to hire one), and due to the fact that if they turn out to be good, they will be poached by better teams. Peeters and Van Ours argue that nevertheless, a few coaches may have as much as one goal difference impact on performance. Good coaches seem to exist, but they are rare. Perhaps artificial intelligence and big data will democratize the profession, but we are probably at a primitive stage on this.

Appointing the right coach in very popular clubs is as much a political decision as it is a business decision. That's why probably FC Barcelona tends to appoint famous former players that contributed to the past glories of the team (Guardiola, Luis Enrique, Koeman, Xavi), some of which are better as coaches than others. In general most former famous coaches do not have a good track record as managers (Guardiola is an exception: think of Rooney or Maradona), but clubs keep valuing player experience. When this experience is rich enough and is combined with a smart individual, then perhaps we have part of the secret to relevant impact (Xabi Alonso?). What surely empirical evidence shows is that coaches contribute to spreading successful tactical styles through networks of influence and evolution, as we've seen in the past with Dutch and Italian coaches and today with Spanish coaches. This season, the favourites to the English, French and German leagues are Spanish coaches. 

China and us

Political scientist Yuen Yuen Ang, from Michigan University, has written two books that analyze the incredible economic growth of China in the recent decades, and relate it to two phenomena that in theory have played a role in the economic development of rich countries: institutions and corruption. In doing so, she demolishes the conventional wisdom of mainstream political economy.

In “How China Escaped the Poverty Trap,” she shows that the institutions that facilitated the expansion of markets and economic prosperity in China after Deng’s reforms had nothing to do with the high quality institutions that are supposed to facilitate economic growth in mainstream political economy (an impersonal and specialized administration, secure property rights, etc.). China’s rulers gave a high degree of freedom to non specialized and not impersonal officials at all levels, in a climate of confusing property rights.

Acemoglu and Robinson in “Why Nations Fail” claimed that China is an anomaly that should soon collapse because it lacks “inclusive” institutions. However, more than ten years after they wrote their book, China refuses to collapse –or to adopt inclusive institutions. If anything, secure property rights and a neutral administration may be useful to preserve markets, but not necessarily to create them, according to Ang.

She proposes an evolutionary interpretation of history, where causality does not only run in one linear direction, but in multidimensional non-linear feedbacks among several domains, such as state and market. If one applies this approach not only to China, but also to Europe and the US, one can see that their economic takeoff was not preceded at all by the type of institutions that the international organizations try to promote for developing countries nowadays.

In “China’s Gilded Age,” Ang focuses on one aspect of institutional quality, namely corruption. In doing that, she criticizes the use of unbundled measures of corruption, such as the Corruption Perception Index (CPI) of Transparency Internacional. She argues that such an unbundled index gives a misleading perception of a multidimensional phenomenon.

She proposes four types of corruption mechanisms along two dimensions. One dimension reflects whether corruption affects elites or non-elites. And the other reflects whether corruption involves theft or exchange. Combining the two dimensions results into four types of corruption, which can be further decomposed. It turns out that when corruption involves exchanges among the elites, we obtain what she calls “Access money,” a sophisticated set of corrupt deals that may even be legal. This could be regulatory capture, revolving doors, or similar deals, and has a lower negative effect on economic growth than the other, less sophisticated and more harmful, types of corruption. Of more concern is the effect of Access money on inequality.

Today’s growth in China is compatible with corruption, as the growth of the US after the Civil War in the XIXth century was compatible with corruption, and today’s economic power of the US is compatible with large scale, sophisticated access money.

What can be done? Ang suggests that, consistently with her multifaceted perspective on corruption, one-size-fits-all solutions will not work. Rather, remedies must be adapted to local context, must be incentive compatible (you should not ask officials something that goes against their incentives), and must combine a top-down approach with a bottom-up approach where civil society can make a positive contribution.

What seems clear is that “the rise of capitalism is accompanied not by the eradication of corruption, but rather by the evolution of the quality of corruption from thuggery and theft to influence peddling.”