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Technology over the long run: zoom out to see how dramatically the world can change within a lifetime

It is easy to underestimate how much the world can change within a lifetime. considering how dramatically the world has changed can help us see how different the world could be in a few years or decades..

Technology can change the world in ways that are unimaginable until they happen. Switching on an electric light would have been unimaginable for our medieval ancestors. In their childhood, our grandparents would have struggled to imagine a world connected by smartphones and the Internet.

Similarly, it is hard for us to imagine the arrival of all those technologies that will fundamentally change the world we are used to.

We can remind ourselves that our own future might look very different from the world today by looking back at how rapidly technology has changed our world in the past. That’s what this article is about.

One insight I take away from this long-term perspective is how unusual our time is. Technological change was extremely slow in the past – the technologies that our ancestors got used to in their childhood were still central to their lives in their old age. In stark contrast to those days, we live in a time of extraordinarily fast technological change. For recent generations, it was common for technologies that were unimaginable in their youth to become common later in life.

The long-run perspective on technological change

The big visualization offers a long-term perspective on the history of technology. 1

The timeline begins at the center of the spiral. The first use of stone tools, 3.4 million years ago, marks the beginning of this history of technology. 2 Each turn of the spiral represents 200,000 years of history. It took 2.4 million years – 12 turns of the spiral – for our ancestors to control fire and use it for cooking. 3

To be able to visualize the inventions in the more recent past – the last 12,000 years – I had to unroll the spiral. I needed more space to be able to show when agriculture, writing, and the wheel were invented. During this period, technological change was faster, but it was still relatively slow: several thousand years passed between each of these three inventions.

From 1800 onwards, I stretched out the timeline even further to show the many major inventions that rapidly followed one after the other.

The long-term perspective that this chart provides makes it clear just how unusually fast technological change is in our time.

You can use this visualization to see how technology developed in particular domains. Follow, for example, the history of communication: from writing to paper, to the printing press, to the telegraph, the telephone, the radio, all the way to the Internet and smartphones.

Or follow the rapid development of human flight. In 1903, the Wright brothers took the first flight in human history (they were in the air for less than a minute), and just 66 years later, we landed on the moon. Many people saw both within their lifetimes: the first plane and the moon landing.

This large visualization also highlights the wide range of technology’s impact on our lives. It includes extraordinarily beneficial innovations, such as the vaccine that allowed humanity to eradicate smallpox , and it includes terrible innovations, like the nuclear bombs that endanger the lives of all of us .

What will the next decades bring?

The red timeline reaches up to the present and then continues in green into the future. Many children born today, even without further increases in life expectancy, will live well into the 22nd century.

New vaccines, progress in clean, low-carbon energy, better cancer treatments – a range of future innovations could very much improve our living conditions and the environment around us. But, as I argue in a series of articles , there is one technology that could even more profoundly change our world: artificial intelligence (AI).

One reason why artificial intelligence is such an important innovation is that intelligence is the main driver of innovation itself. This fast-paced technological change could speed up even more if it’s driven not only by humanity’s intelligence but also by artificial intelligence. If this happens, the change currently stretched out over decades might happen within a very brief time span of just a year. Possibly even faster. 4

I think AI technology could have a fundamentally transformative impact on our world. In many ways, it is already changing our world, as I documented in this companion article . As this technology becomes more capable in the years and decades to come, it can give immense power to those who control it (and it poses the risk that it could escape our control entirely).

Such systems might seem hard to imagine today, but AI technology is advancing quickly. Many AI experts believe there is a real chance that human-level artificial intelligence will be developed within the next decades, as I documented in this article .


Technology will continue to change the world – we should all make sure that it changes it for the better

What is familiar to us today – photography, the radio, antibiotics, the Internet, or the International Space Station circling our planet – was unimaginable to our ancestors just a few generations ago. If your great-great-great grandparents could spend a week with you, they would be blown away by your everyday life.

What I take away from this history is that I will likely see technologies in my lifetime that appear unimaginable to me today.

In addition to this trend towards increasingly rapid innovation, there is a second long-run trend. Technology has become increasingly powerful. While our ancestors wielded stone tools, we are building globe-spanning AI systems and technologies that can edit our genes.

Because of the immense power that technology gives those who control it, there is little that is as important as the question of which technologies get developed during our lifetimes. Therefore, I think it is a mistake to leave the question about the future of technology to the technologists. Which technologies are controlled by whom is one of the most important political questions of our time because of the enormous power these technologies convey to those who control them.

We all should strive to gain the knowledge we need to contribute to an intelligent debate about the world we want to live in. To a large part, this means gaining knowledge and wisdom on the question of which technologies we want.

Acknowledgments: I would like to thank my colleagues Hannah Ritchie, Bastian Herre, Natasha Ahuja, Edouard Mathieu, Daniel Bachler, Charlie Giattino, and Pablo Rosado for their helpful comments on drafts of this essay and the visualization. Thanks also to Lizka Vaintrob and Ben Clifford for the conversation that initiated this visualization.

Appendix: About the choice of visualization in this article

The recent speed of technological change makes it difficult to picture the history of technology in one visualization. When you visualize this development on a linear timeline, then most of the timeline is almost empty, while all the action is crammed into the right corner:

Linear version of the spiral chart

In my large visualization here, I tried to avoid this problem and instead show the long history of technology in a way that lets you see when each technological breakthrough happened and how, within the last millennia, there was a continuous acceleration of technological change.

The recent speed of technological change makes it difficult to picture the history of technology in one visualization. In the appendix, I show how this would look if it were linear.

It is, of course, difficult to assess when exactly the first stone tools were used.

The research by McPherron et al. (2010) suggested that it was at least 3.39 million years ago. This is based on two fossilized bones found in Dikika in Ethiopia, which showed “stone-tool cut marks for flesh removal and percussion marks for marrow access”. These marks were interpreted as being caused by meat consumption and provide the first evidence that one of our ancestors, Australopithecus afarensis, used stone tools.

The research by Harmand et al. (2015) provided evidence for stone tool use in today’s Kenya 3.3 million years ago.


McPherron et al. (2010) – Evidence for stone-tool-assisted consumption of animal tissues before 3.39 million years ago at Dikika, Ethiopia . Published in Nature.

Harmand et al. (2015) – 3.3-million-year-old stone tools from Lomekwi 3, West Turkana, Kenya . Published in Nature.

Evidence for controlled fire use approximately 1 million years ago is provided by Berna et al. (2012) Microstratigraphic evidence of in situ fire in the Acheulean strata of Wonderwerk Cave, Northern Cape province, South Africa , published in PNAS.

The authors write: “The ability to control fire was a crucial turning point in human evolution, but the question of when hominins first developed this ability still remains. Here we show that micromorphological and Fourier transform infrared microspectroscopy (mFTIR) analyses of intact sediments at the site of Wonderwerk Cave, Northern Cape province, South Africa, provide unambiguous evidence—in the form of burned bone and ashed plant remains—that burning took place in the cave during the early Acheulean occupation, approximately 1.0 Ma. To the best of our knowledge, this is the earliest secure evidence for burning in an archaeological context.”

This is what authors like Holden Karnofsky called ‘Process for Automating Scientific and Technological Advancement’ or PASTA. Some recent developments go in this direction: DeepMind’s AlphaFold helped to make progress on one of the large problems in biology, and they have also developed an AI system that finds new algorithms that are relevant to building a more powerful AI.

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How has technology changed - and changed us - in the past 20 years?

An internet surfer views the Google home page at a cafe in London, August 13, 2004.

Remember this? Image:  REUTERS/Stephen Hird

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Stay up to date:, technological transformation.

  • Since the dotcom bubble burst back in 2000, technology has radically transformed our societies and our daily lives.
  • From smartphones to social media and healthcare, here's a brief history of the 21st century's technological revolution.

Just over 20 years ago, the dotcom bubble burst , causing the stocks of many tech firms to tumble. Some companies, like Amazon, quickly recovered their value – but many others were left in ruins. In the two decades since this crash, technology has advanced in many ways.

Many more people are online today than they were at the start of the millennium. Looking at broadband access, in 2000, just half of Americans had broadband access at home. Today, that number sits at more than 90% .

More than half the world's population has internet access today

This broadband expansion was certainly not just an American phenomenon. Similar growth can be seen on a global scale; while less than 7% of the world was online in 2000, today over half the global population has access to the internet.

Similar trends can be seen in cellphone use. At the start of the 2000s, there were 740 million cell phone subscriptions worldwide. Two decades later, that number has surpassed 8 billion, meaning there are now more cellphones in the world than people

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The future of jobs report 2023, how to follow the growth summit 2023.

At the same time, technology was also becoming more personal and portable. Apple sold its first iPod in 2001, and six years later it introduced the iPhone, which ushered in a new era of personal technology. These changes led to a world in which technology touches nearly everything we do.

Technology has changed major sectors over the past 20 years, including media, climate action and healthcare. The World Economic Forum’s Technology Pioneers , which just celebrated its 20th anniversary, gives us insight how emerging tech leaders have influenced and responded to these changes.

Media and media consumption

The past 20 years have greatly shaped how and where we consume media. In the early 2000s, many tech firms were still focused on expanding communication for work through advanced bandwidth for video streaming and other media consumption that is common today.

Others followed the path of expanding media options beyond traditional outlets. Early Tech Pioneers such as PlanetOut did this by providing an outlet and alternative media source for LGBTQIA communities as more people got online.

Following on from these first new media options, new communities and alternative media came the massive growth of social media. In 2004 , fewer than 1 million people were on Myspace; Facebook had not even launched. By 2018, Facebook had more 2.26 billion users with other sites also growing to hundreds of millions of users.

The precipitous rise of social media over the past 15 years

While these new online communities and communication channels have offered great spaces for alternative voices, their increased use has also brought issues of increased disinformation and polarization.

Today, many tech start-ups are focused on preserving these online media spaces while also mitigating the disinformation which can come with them. Recently, some Tech Pioneers have also approached this issue, including TruePic – which focuses on photo identification – and Two Hat , which is developing AI-powered content moderation for social media.

Climate change and green tech

Many scientists today are looking to technology to lead us towards a carbon-neutral world. Though renewed attention is being given to climate change today, these efforts to find a solution through technology is not new. In 2001, green tech offered a new investment opportunity for tech investors after the crash, leading to a boom of investing in renewable energy start-ups including Bloom Energy , a Technology Pioneer in 2010.

In the past two decades, tech start-ups have only expanded their climate focus. Many today are focuses on initiatives far beyond clean energy to slow the impact of climate change.

Different start-ups, including Carbon Engineering and Climeworks from this year’s Technology Pioneers, have started to roll out carbon capture technology. These technologies remove CO2 from the air directly, enabling scientists to alleviate some of the damage from fossil fuels which have already been burned.

Another expanding area for young tech firms today is food systems innovation. Many firms, like Aleph Farms and Air Protein, are creating innovative meat and dairy alternatives that are much greener than their traditional counterparts.

Biotech and healthcare

The early 2000s also saw the culmination of a biotech boom that had started in the mid-1990s. Many firms focused on advancing biotechnologies through enhanced tech research.

An early Technology Pioneer, Actelion Pharmaceuticals was one of these companies. Actelion’s tech researched the single layer of cells separating every blood vessel from the blood stream. Like many other biotech firms at the time, their focus was on precise disease and treatment research.

While many tech firms today still focus on disease and treatment research, many others have been focusing on healthcare delivery. Telehealth has been on the rise in recent years , with many young tech expanding virtual healthcare options. New technologies such as virtual visits, chatbots are being used to delivery healthcare to individuals, especially during Covid-19.

Many companies are also focusing their healthcare tech on patients, rather than doctors. For example Ada, a symptom checker app, used to be designed for doctor’s use but has now shifted its language and interface to prioritize giving patients information on their symptoms. Other companies, like 7 cups, are focused are offering mental healthcare support directly to their users without through their app instead of going through existing offices.

The past two decades have seen healthcare tech get much more personal and use tech for care delivery, not just advancing medical research.

The World Economic Forum was the first to draw the world’s attention to the Fourth Industrial Revolution, the current period of unprecedented change driven by rapid technological advances. Policies, norms and regulations have not been able to keep up with the pace of innovation, creating a growing need to fill this gap.

The Forum established the Centre for the Fourth Industrial Revolution Network in 2017 to ensure that new and emerging technologies will help—not harm—humanity in the future. Headquartered in San Francisco, the network launched centres in China, India and Japan in 2018 and is rapidly establishing locally-run Affiliate Centres in many countries around the world.

The global network is working closely with partners from government, business, academia and civil society to co-design and pilot agile frameworks for governing new and emerging technologies, including artificial intelligence (AI) , autonomous vehicles , blockchain , data policy , digital trade , drones , internet of things (IoT) , precision medicine and environmental innovations .

Learn more about the groundbreaking work that the Centre for the Fourth Industrial Revolution Network is doing to prepare us for the future.

Want to help us shape the Fourth Industrial Revolution? Contact us to find out how you can become a member or partner.

In the early 2000s, many companies were at the start of their recovery from the bursting dotcom bubble. Since then, we’ve seen a large expansion in the way tech innovators approach areas such as new media, climate change, healthcare delivery and more.

At the same time, we have also seen tech companies rise to the occasion of trying to combat issues which arose from the first group such as internet content moderation, expanding climate change solutions.

The Technology Pioneers' 2020 cohort marks the 20th anniversary of this community - and looking at the latest awardees can give us a snapshot of where the next two decades of tech may be heading.

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World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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A comprehensive study of technological change

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Bar graph. On the y-axis: density, from 0.00 to 0.08. On the X-axis: estimated yearly improvement rates, from 0 to 200. There is a large spike of data going past .08 on the y-axis, in between approximately the 0 and 25 marks on the x-axis. A red vertical dotted line exists at the 36.5 mark.

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The societal impacts of technological change can be seen in many domains, from messenger RNA vaccines and automation to drones and climate change. The pace of that technological change can affect its impact, and how quickly a technology improves in performance can be an indicator of its future importance. For decision-makers like investors, entrepreneurs, and policymakers, predicting which technologies are fast improving (and which are overhyped) can mean the difference between success and failure.

New research from MIT aims to assist in the prediction of technology performance improvement using U.S. patents as a dataset. The study describes 97 percent of the U.S. patent system as a set of 1,757 discrete technology domains, and quantitatively assesses each domain for its improvement potential.

“The rate of improvement can only be empirically estimated when substantial performance measurements are made over long time periods,” says Anuraag Singh SM ’20, lead author of the paper. “In some large technological fields, including software and clinical medicine, such measures have rarely, if ever, been made.”

A previous MIT study provided empirical measures for 30 technological domains, but the patent sets identified for those technologies cover less than 15 percent of the patents in the U.S. patent system. The major purpose of this new study is to provide predictions of the performance improvement rates for the thousands of domains not accessed by empirical measurement. To accomplish this, the researchers developed a method using a new probability-based algorithm, machine learning, natural language processing, and patent network analytics.

Overlap and centrality

A technology domain, as the researchers define it, consists of sets of artifacts fulfilling a specific function using a specific branch of scientific knowledge. To find the patents that best represent a domain, the team built on previous research conducted by co-author Chris Magee, a professor of the practice of engineering systems within the Institute for Data, Systems, and Society (IDSS). Magee and his colleagues found that by looking for patent overlap between the U.S. and international patent-classification systems, they could quickly identify patents that best represent a technology. The researchers ultimately created a correspondence of all patents within the U.S. patent system to a set of 1,757 technology domains.

To estimate performance improvement, Singh employed a method refined by co-authors Magee and Giorgio Triulzi, a researcher with the Sociotechnical Systems Research Center (SSRC) within IDSS and an assistant professor at Universidad de los Andes in Colombia. Their method is based on the average “centrality” of patents in the patent citation network. Centrality refers to multiple criteria for determining the ranking or importance of nodes within a network.

“Our method provides predictions of performance improvement rates for nearly all definable technologies for the first time,” says Singh.

Those rates vary — from a low of 2 percent per year for the “Mechanical skin treatment — Hair removal and wrinkles” domain to a high of 216 percent per year for the “Dynamic information exchange and support systems integrating multiple channels” domain. The researchers found that most technologies improve slowly; more than 80 percent of technologies improve at less than 25 percent per year. Notably, the number of patents in a technological area was not a strong indicator of a higher improvement rate.

“Fast-improving domains are concentrated in a few technological areas,” says Magee. “The domains that show improvement rates greater than the predicted rate for integrated chips — 42 percent, from Moore’s law — are predominantly based upon software and algorithms.”

TechNext Inc.

The researchers built an online interactive system where domains corresponding to technology-related keywords can be found along with their improvement rates. Users can input a keyword describing a technology and the system returns a prediction of improvement for the technological domain, an automated measure of the quality of the match between the keyword and the domain, and patent sets so that the reader can judge the semantic quality of the match.

Moving forward, the researchers have founded a new MIT spinoff called TechNext Inc. to further refine this technology and use it to help leaders make better decisions, from budgets to investment priorities to technology policy. Like any inventors, Magee and his colleagues want to protect their intellectual property rights. To that end, they have applied for a patent for their novel system and its unique methodology.

“Technologies that improve faster win the market,” says Singh. “Our search system enables technology managers, investors, policymakers, and entrepreneurs to quickly look up predictions of improvement rates for specific technologies.”

Adds Magee: “Our goal is to bring greater accuracy, precision, and repeatability to the as-yet fuzzy art of technology forecasting.”

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Technology and the future of growth: Challenges of change

Subscribe to global connection, zia qureshi zia qureshi senior fellow - global economy and development.

February 25, 2020

This blog is part of a project  exploring how the agenda for economic growth is being reshaped by forces of change, particularly technological change.

Economic growth has been lackluster for more than a decade now. This has occurred at a time when economies have faced much unfolding change. What are the forces of change, how are they affecting the growth dynamics, and what are the implications for policy? A recently published book, “ Growth in a Time of Change, ” addresses these questions.

Three basic ingredients drive economic growth—productivity, capital, and labor. All three are facing new challenges in a changing context. Foremost among the drivers of change has been technology, spearheaded by digital transformation.

Slowdown in productivity and investment

Productivity is the main long-term propeller of economic growth. Technology-enabled innovation is the major spur to productivity growth. Yet, paradoxically, productivity growth has slowed as digital technologies have boomed. Among advanced economies over the past 15 years or so, it has averaged less than half of the pace of the previous 15 years. Firms at the technological frontier have reaped major productivity gains, but the impact on productivity more widely across firms has been weak. The new technologies have tended to produce winners-take-most outcomes. Dominant firms have acquired more market power, market structures have become less competitive, and business dynamism has declined.

Investment also has been weak in most major economies. The persistent weakness of investment despite historically low interest rates has prompted concerns about the risk of “secular stagnation.” Weak productivity growth and investment have reinforced each other and are linked by similar shifts in market structures and dynamics.

Shifts in labor markets

Technology is having profound effects on labor markets. Automation and digital advances are shifting labor demand away from routine low- to middle-level skills to higher-level and more sophisticated analytical, technical, and managerial skills. On the supply side, however, equipping workers with skills that complement the new technologies has lagged, hindering the broader diffusion of innovation within economies. Education and training have been losing the race with technology.

Most major economies face the challenge of aging populations. Many of them are also seeing a leveling off of gains in labor force participation rates and basic education attainments of the population. These trends put an even greater focus on productivity—and technological innovations that drive it—to deliver economic growth.

Rising inequality

Growth has also become less inclusive. Income inequality has been rising in most major economies, and the increase has been particularly pronounced in some of them, such as the United States. The new technologies favoring capital and higher-level skills have contributed to a decline in labor’s share of income and to increased wage inequality. They have also been associated with more concentrated industry structures and high economic rents enjoyed by dominant firms. Income has shifted from labor to capital and the distribution of both labor and capital income has become more unequal.

Rising inequality and mounting anxiety about jobs have contributed to increased social tensions and political divisiveness. Populism has surged in many countries. Nationalist and protectionist sentiment has been on the rise, with a backlash against international trade that, alongside technological change, is seen to have increased inequality with job losses and wage stagnation for low-skilled workers.

Changing growth pathways

While income inequality has been rising within many countries, inequality between countries has been falling as faster-growing emerging economies narrow the income gap with advanced economies. Technology poses new challenges for this economic convergence. Manufacturing-led growth in emerging economies has been the dominant driver of convergence, fueled by their comparative advantage in labor-intensive production based on their large pools of low-skill, low-wage workers. Such comparative advantage is eroding with automation of low-skill work, creating the need to develop alternative pathways to growth aligned with technological change.

AI, robotics, and the Fourth Industrial Revolution

Technological change reshaping growth will only intensify as artificial intelligence, advanced robotics, and cyber-physical systems take the digital revolution to another level. We may be on the cusp of what has been termed the “Fourth Industrial Revolution (4IR).” And globalization is going increasingly digital, a transformation that, analogous to 4IR, has been termed “Globalization 4.0.”

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Technological change recently has not delivered its full potential in boosting productivity and economic growth. It has pushed income inequality higher and generated fears about a “robocalypse”—massive job losses from automation. This should not cause despair, however.

Advances in digital technologies hold considerable potential to lift the trajectory of productivity and economic growth, and to create new and better jobs to replace old ones. As much as two-thirds of potential productivity growth in major economies over the next decade could be related to the new digital technologies. But technological change is inherently disruptive and entails difficult transitions. It also inevitably creates winners and losers—as does globalization. Policies have a crucial role to play. Unfortunately, they have been slow to adapt to the challenges of change. With improved and more responsive policies, better outcomes are possible.

An agenda to harness the potential of new technology

The core of the forward policy agenda is to better harness the potential of the new technologies. Reforms must seek to improve the enabling environment for firms and workers—to broaden access to opportunities that come from technological change and to enhance capabilities to adjust to the new challenges.

  • Policies and institutions governing markets must keep pace as technological change transforms the world of business. Competition policies should be revamped for the digital age to ensure that markets continue to provide an open and level playing field for firms, keep competition strong, and check the growth of monopolistic structures. New regulatory issues revolving around data, the lifeblood of the digital economy, must be addressed. Flexibility in markets will be key to facilitating adjustments to disruptions and structural shifts from digital transformation.
  • The innovation ecosystem should keep pushing the technological frontier but also foster wider economic impacts from the new advances. With the intangible asset of knowledge becoming an increasingly important driver of economic success, research and development systems and patent regimes should be improved to promote broader diffusion of technologies embodying new knowledge.
  • The foundation of digital infrastructure and digital literacy must be strengthened. The digital divide is narrowing but wide gaps remain.
  • Investment in education and training must be boosted and reoriented to emphasize the skills for the jobs of the future. With the old career path of “learn-work-retire” giving way to one of continuous learning, programs for worker upskilling and reskilling and lifelong learning must the scaled up. The key to winning the race with technology is not to compete against machines but to compete with machines.
  • Labor market policies should become more forward-looking, shifting the focus from seeking to protect existing jobs to improving workers’ ability to change jobs. Social protection systems, traditionally based on formal long-term employer-employee relationships, should be adapted to a more dynamic job market. Social contracts need to realign with the changing nature of work.
  • Tax systems should be reviewed in light of the new tax challenges of the digital economy, including the implications of the transformations occurring in business and work and the new income distribution dynamics. The potential tax reform agenda spans taxes on labor, capital, and wealth.

Reforms are needed at the international level as well, although the dominant part of the agenda to make technology—and globalization—work better and for all rests with policies at the national level. Not only must past gains in establishing a rules-based international trading system be shielded from protectionist headwinds, but new disciplines must be devised for the next phase of globalization led by digital flows to ensure open access and fair competition. Sensible policies on migration can complement national policies, such as pension reform and lifelong learning, in mitigating the effects of population aging.

The era of smart machines holds much promise. With smart policies, the future could be one of stronger and more inclusive growth.

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technical progress essay

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  • 1 0000000404811396 https://isni.org/isni/0000000404811396 International Monetary Fund

Advances in artificial intelligence and automation have the potential to be labor-saving and to increase inequality and poverty around the globe. They also give rise to winner-takes-all dynamics that advantage highly skilled individuals and countries that are at the forefront of technological progress. We analyze the economic forces behind these developments and delineate domestic economic policies to mitigate the adverse effects while leveraging the potential gains from technological advances. We also propose reforms to the global system of governance that make the benefits of advances in artificial intelligence more inclusive.

  • I. Introduction

Advances in artificial intelligence (AI) and related forms of automation technologies have led to growing fears about job losses and increasing inequality. This concern is widespread in high-income countries. Developing countries and emerging market economies should be even more concerned than high-income countries, as their comparative advantage in the world economy relies on abundant labor and natural resources. Declining returns to labor and natural resources as well as the winner-takes-all dynamics brought on by new information technologies could lead to further immiseration in the developing world. This could undermine the rapid gains that have been the hallmark of success in development over the past fifty years and threaten the progress made in reducing poverty and inequality.

For many decades, there was a presumption that advances in technology would benefit all— embodied by the trickle-down dogma that characterized neoliberalism. And for some time, this presumption was in fact justified. For example, for the three decades following World War II, the US economy and many other high-income and developing countries experienced broadly shared increases in living standards. However, over the past half-century, output growth and median worker incomes started to decouple.

Moreover, economic theory cautions that technological progress is likely to create both winners and losers (see Korinek and Stiglitz, 2019 , for a review). As long as the winners and losers from technological progress are located within the same country, there is at least the possibility that domestic policy measures can compensate the losers. However, when technological progress deteriorates the terms of trade and thus undermines the comparative advantage of entire countries, then entire nations may be worse off except if the winners within one country compensate the losers in other countries, which seems politically very difficult.

This paper argues that concerns about whether technological progress leads to inclusive growth are indeed justified – and that especially developing countries may face a stark new set of challenges going forward. However, we propose policies that can mitigate the adverse effects so that advances in technology lead to a world with greater shared prosperity. This will require new domestic polices and development strategies as well as strong international cooperation and a rewriting of the global rules governing the information economy.

We start by laying out the key properties of AI and related automation technologies that underlie the concerns about recent technological progress. 2 AI is likely to be labor-saving and resource-saving, devaluing the sources of comparative advantage of many developing countries and deteriorating their terms of trade. Being an information technology, AI also tends to give rise to natural monopolies, creating a small set of so-called superstar firms that are located in a few powerful countries but serve the entire world economy. Moreover, under reasonable assumptions, the rate and direction of technological progress chosen by the market are generally suboptimal ( Korinek and Stiglitz, 2019 ). This creates the possibility of steering innovation in AI and other technologies in directions that are more beneficial to humanity at large, for example, preserving the planet or creating satisfying employment opportunities, rather than substituting for labor and creating more unemployment and inequality.

Taking a step back, we evaluate to what extent the discussed concerns about technological progress are justified, given what we know at present. There is vast uncertainty about the impact of AI, even among experts in the field. Some argue that AI is less important than the big innovations of the 20 th century and will have rather limited impact on the economy, whereas others go as far as predicting that AI will lead to more rapid technological progress than mankind has ever seen before.

In this context, we discuss how to reconcile the buzz among technologists over the past decade with economic data that suggests rather modest productivity increases over the period – encapsulated by the so-called productivity puzzle. We also analyze how the forces generated by progress in AI interact with other recent developments, in particular with the recovery from COVID-19, with secular population dynamics, and with the need for a Green Transition.

Despite the uncertainties surrounding AI, its potentially dramatic consequences suggest that we should steer our own research in directions where the expected social value added of economic analysis is greatest: we need to think particularly hard about potential events that would be highly disruptive to our society.

To grasp the historical nature of what is going on, we look at the broader history of technological progress. Humanity spent much of its history at a Malthusian stage in which the vast majority of the population lived at subsistence levels. The Industrial Revolution that lifted living standards started a bit over two centuries ago, making it a mere blip in the history of human civilization. For developing countries, the era of manufacturing-based export-led growth that enabled the East Asian Miracle stretched over the past half-century – only one quarter of the history of the Industrial Revolution. It is conceivable that we are now going into another era. There is even a risk that the terms-of-trade losses generated by progress in AI may erase much of the gains that the developing countries have made in recent decades.

However, the Industrial Revolution also offers ample lessons on how to manage innovation in a positive way: technological revolutions are very disruptive, but collective action can mitigate the adverse effects and generate an environment in which the gains are shared broadly. The labor-using nature of the Industrial Revolution ushered in an Age of Labor in which the economic gains of workers also shifted political dynamics in their favor, but there is a risk that future labor-saving progress may do the opposite. The decline of manufacturing will require a new development model that follows a more multi-pronged strategy to replace the manufacturing-based export-led growth model.

The key policy question is how countries can improve the likelihood of benign outcomes from technological progress. This is especially pertinent for developing countries, but it is also a challenge for advanced economies to develop policies that ensure that technological advances lead to broadly shared prosperity and that their adverse effects are mitigated. We delineate here a number of such policies. Taxation and redistribution are a first line of defense to compensate the losers of progress, although the scope for redistribution may be limited in developing countries.

Targeted expenditure policies can serve double duty by providing both income to workers and a valuable social return – for example, investments in education or infrastructure are labor intensive and enhance human capital and the physical infrastructure of countries, both of which are important in bridging the digital divide and ensuring that all citizens can participate in the opportunities afforded by digital technologies.

To replace the manufacturing-based export-led growth model, developing countries will need to steer technological progress and technology adoption in new directions, in part by leveraging the opportunities that AI and other digital technologies afford in agriculture and services.

Finally, we describe a set of policies at the supra-national level to reform our global system of governance in a way that developing countries can benefit from advances in AI and other information technologies while addressing the downsides of these new technologies. We need to design a global tax regime for the digital age that enables countries to raise taxes on transactions that occur within their borders. Competition policy is also increasingly a question that transcends national borders as the footprint of the digital giants is global and authorities in their countries of origin do not face the correct incentives to ensure a competitive marketplace. Intellectual property regimes need to be adapted so they are attuned to the needs and circumstances of developing countries. Moreover, information policy including the regulation of data needs to be discussed at the supra-national level to provide a voice to developing countries that could otherwise not influence the design of such policies.

The remainder of this paper is organized as follows. In the second section, we provide an overview of the downside risks of technological progress, with special emphasis on potential AI-induced economic disruptions; in the third, we discuss the uncertainties surrounding the nature and level of the impacts as well as the broader context. The fourth section reviews what we can learn from the bigger historical picture of technological progress. The fifth section distills the critical role of government policy in managing the effects of technological progress and enabling the benefits of innovation to be widely shared. The sixth section analyzes how our global system of governance needs to be updated to allow developing countries to maximize the benefits and minimize the costs of advances in AI and related technologies.

II. Downside Risks of Technological Progress

Many technology optimists suggest that productivity gains go hand in hand with real wage gains. This presumption that technological progress would benefit all was also embodied by the trickle-down dogma that has characterized neoliberalism. However, the presumption was supported neither by theory nor evidence; indeed, economic theory has always held that advances in technology do not necessarily benefit all and may create winners and losers. The data ( Figure 1 ) show that in recent decades, many countries have experienced episodes during which wages lagged productivity growth. Moreover, as we argue below, even where average wages did keep up with productivity, median wages may not have, and there is a risk that any positive gains seen in the past may not continue.

Figure 1.

Productivity and Earnings Growth

Citation: IMF Working Papers 2021, 166; 10.5089/9781513583280.001.A001

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Figure 2 illustrates that the income gains associated with technological progress have been highly unevenly distributed. In the US and other high-income countries, most of the benefits of growth have gone to those at the top, resulting in widening income inequality in most advanced economies since the early 1980s, reversing an earlier downward trend in many countries.

Figure 2.

Income Inequality over Time, 1960–2015

How can we reconcile this with economic theory? In the context of a competitive economy, we can think of technological progress as moving out the production possibility frontier: one can get more of any output for a given amount of inputs. But this increase in production possibilities does not tell us how the gains from progress will be distributed. In our simplest economic models, for example, if we assume a competitive economy with a Cobb-Douglas production function, relative shares are fixed.

However, in the more general case, technical change may change the distribution of income, so that, for instance, labor gets a smaller share of a larger pie. If its share decreases enough, workers could even be worse off. Whether wages increase or decrease depends on what happens to the demand for labor at existing wages. Using the terminology first introduced by Hicks, technical change that leads to a decrease in the relative share of labor is called capital-biased; if it leads to a decrease in the share of unskilled labor, it is called skill-biased; if it leads to an outright reduction in wages, it is called labor-saving. The US, for example, has experienced routine-biased technological change that has replaced workers engaged in both manual and cognitive routine activities since the 1980s and that has contributed to the hollowing out of the middle class ( Autor et al., 2003 ).

Korinek and Stiglitz (2019) show that the distributive effects of innovations can be seen as generating quasi-rents -aside from delivering direct gains to innovators, innovations lead to changes in factor demands, for example lowering demand for unskilled labor and raising demand for skilled labor, and the affected workers experience gains or losses. The winners of progress (e.g. the skilled workers in our example) experience these gains without having contributed to the innovation, obtaining quasi-rents, whereas the losers experience losses without any fault of their own. That, in turn, has an important implication: governments can capture some of the quasi-rents by taxing the winners and redistributing it; and given the nature of the gains, governments may even be able to raise taxes in ways that have no or limited distortionary effects, for example if the winners include owners of fixed factors such as land. Thus, “managed” technological progress could allow for Pareto-improving outcomes.

However, there is a big difference between looking at the impacts of AI within a single country and from a global perspective. When the benefits are experienced in one country and the cost is borne in another, a Pareto improvement would require that the winners compensate the losers across national boundaries. Today, such cross-border transfers are voluntary and limited.

As a result, the fruits of technological progress will be unequally shared; but more troublesome is that while some countries may gain a great deal, others will lose. These differences will be reflected, respectively, in improvements and deteriorations of countries’ terms of trade. In the following, we will analyze several of the specific forms of progress that the AI revolution and related automation technologies are likely to induce, with particular focus on how they may hurt developing countries.

  • A. Labor-Saving Technological Progress

Many observers are concerned that AI may be labor-saving, that is, cause a decline in the demand for labor at existing factor prices. If this occurs, equilibrium wages will decrease and workers will be worse off.

As we have noted, over the past half-century, the US and many other countries seem to have experienced technological progress that was biased against workers with lower levels of education performing routine tasks, sufficiently biased that it may even have been labor-saving in that segment, reducing such workers’ real incomes. For example, Autor et al. (2003) observe that from the 1970s to the 1990s, while computerization was a substitute for an increasing number of routine tasks, technological change increased the productivity of workers in non-routine jobs that involved problem-solving and complex communications tasks. These changes in technology may have explained nearly 2/3 of the relative demand shift toward college-educated labor over that period. Similarly, more recently, Acemoglu and Restrepo (2020) estimated significant adverse employment and wage effects from the introduction of industrial robots in the US, concentrated in manufacturing and among routine manual, blue-collar, assembly, and related occupations, helping to explain the dramatic increase in wage dispersion across skill groups over the past five decades ( Figure 3 ).

Figure 3.

Rising Wage-Skill Premia in the US

(Real Wages of Full Time U.S. Male Workers, 1963 = 100)

This job polarization in terms of wages has also been reflected in relative employment dynamics. Employment in nonroutine jobs has continued to grow steadily in the US, while that in routine jobs has stagnated, or in some periods declined, since around 1990, contributing, as we have noted, to a “hollowing out of the middle” ( Figure 4 ). 3 OECD (2019) note that middle-skilled jobs may be the most prone to both automation and offshoring, as they most encompass routine tasks that are relatively easy to automate (or offshore). 4

Figure 4.

Employment in Routine vs. Non-Routine Jobs

(Persons, millions)

Standard models of aggregate production functions with skilled and unskilled labor-augmenting progress and capital-augmenting progress can generate the observed patterns of movements in factor prices and shares, depending on patterns of progress as well as elasticities and cross-elasticities of substitution. Acemoglu and Restrepo (2019a) formulate a particular model in which the displacement of workers by robots will reduce the labor share of income and may be labor-saving if the productivity gains from the robots are modest. Berg et al. (2018) focus on the differential effects of technological progress across worker groups and shows that technological progress may be unskilled-labor-saving because that type of labor is easily substituted for by robots; by contrast, high-skilled labor is likely complementary to robots and will benefit from technological progress; as a result, technological advances risk bringing about large increases in inequality. Automation may also worsen inequality along other dimensions—for example, in sectors where women occupy more routine jobs ( Brussevich et al., 2018 ).

Even if technological progress is labor-saving in the short run, it may also trigger additional accumulation of capital that is complementary to labor, benefiting labor in the long run. For example, Stiglitz (2015) and Caselli and Manning (2019) show that in an economy with capital and labor only, in which long-run capital accumulation is determined by an exogenous interest rate, labor will always gain. 5 Ultimately, however, impacts on inequality depend on whether there are other scarce limiting factors in the economy, for example, natural resources or land, which would benefit from technological progress and ultimately become more scarce as the factors “capital” and “machine-replacing labor” become more abundant and cheaper. Indeed, Korinek and Stiglitz (2021a) show that if this is the case, then, without government intervention, labor may lose out from technological progress even in the long run.

At a global level, similar dynamics may play out. Although labor-saving technological progress would make the world as a whole richer, it would hit developing countries that have a comparative advantage in cheap labor particularly hard. If worldwide demand for labor, or for unskilled labor, declines, such countries would experience a significant deterioration in their terms of trade and lose a substantial fraction of their export income. Labor-saving progress may not only create winners and losers within the affected developing countries, but it may make entire countries on net worse off. Alonso et al. (2020) find that improvements in the productivity of “robots” could drive divergence, as advanced countries benefit from computerization more given their higher initial capital stock.

However, it is also conceivable that other forms of advances in technology could benefit workers: intelligence-assisting devices and algorithms (IA) may be complementary to labor rather than substituting for it, thus enhancing the prospects of labor. Innovations that fall into this category may include augmented reality (AR), machine learning (ML) algorithms that help analyze complex data, and other forms of integration of AI with humans. 6 Automation technologies frequently affect particular tasks but not (entire) jobs, which consist of multiple tasks (see, e.g., Acemoglu and Autor, 2011 )—IA innovations may help workers be more productive in their jobs by taking over, or improving, certain tasks. For example, a doctor is engaged in diagnosis but also in explaining the diagnosis to the patient. AI may do a better job in diagnosis – for example, in radiology – but it may not quite replace the doctor in communicating with the patient, at least not yet.

Driverless trucks provide another example: truck driving provides significant employment opportunities for men with only a high school education so there is understandably concern for the disruption that self-driving trucks might bring about. But truck drivers also perform a number of related tasks – they fill orders, load and unload, monitor the truck, and more – not all of which may be easily automated. More generally, most jobs have multiple dimensions and consist of multiple tasks. With some tasks automated, workers will be able to devote more attention to, and perform better at, those tasks that are not. Importantly, both AI and IA imply extensive restructuring of the economy.

The central concern of this paper remains: there may be a reduction in the demand for labor, especially for unskilled labor. We will further evaluate whether or not these fears are justified below in Section 3. If, however, it turns out that AI is labor saving, and especially if it is unskilled labor saving, the consequences for developing countries would be severe. This is the “resource” which constitutes their comparative advantage and in which they are relatively rich. The convergence in standards of living between developing countries and developed that has marked the past half century would be arrested, even reversed. It would also present great challenges to domestic policy within developing countries. In many parts of the world, inequalities within developing countries are greater than in developed. AI would exacerbate those inequalities – and developing countries often lack the institutional capacities to counteract them.

  • B. Resource-Saving Technological Progress

Another type of progress that is of great concern to some developing countries is resource-saving technological progress. This has gotten less attention than labor-saving progress so far (e.g., Solow, 2009 ), but AI and other digital technologies have often been praised for their potential to produce more output with fewer natural resources. For instance, they may help reduce the demand for depletable natural resources and lower carbon emissions. Examples include algorithms that optimize efficiency in data centers or that make transportation networks more efficient. Technologies that enable telework may also reduce the carbon footprint of workers. 7 Thus, such resource-saving innovations may have adverse distributional effects on developing countries that have a comparative advantage in natural resources, and that have specialized in exporting them. The impact on exporters of different types of natural resources may be quite different – for example, exporters of carbon-based energy will fare differently from exporters of rare earth metals.

Consider oil-exporting countries, which have already experienced many developmental challenges while being resource-rich. Resource-saving AI, while saving the planet, would make them resource-poor countries that still experience the same developmental challenges. The challenges of addressing global inequality under such a scenario would be an order of magnitude larger than they are even today, posing a test for the global community. A number of oil-exporting countries rely on their export revenue to buy food and other basic essentials—if they lose their ability to export oil, the consequences would be dire. Thus, as in the case of labor-saving technological progress, the world as a whole may be better off—in this case by undoing resource scarcity and reducing climate change—but not all countries would benefit. 8

  • C. Information, Digital Monopolies and Superstars

So far we have considered the effects of technological change in a competitive environment. However, the rise of AI and other information technologies may also lead to greater concentrations of market power. As a result, the economy may move to an equilibrium that is less competitive and more distorted by market power, with greater rents for dominant firms. Actors with market power will use that power to advantage themselves. The resulting distortions may offset part of the benefits of innovation, exacerbating the adverse distributive effects of labor-saving or resource-saving innovation. With any inequality-averse social welfare function, societal welfare could decrease.

While the assumption of competitive markets often provides a useful benchmark, that model becomes less appropriate as one considers an economy that is dominated by AI. It is hard to conceive of an AI economy being competitive, or at least well-described by the standard competitive equilibrium model.

There are several reasons why advances in AI may intensify market power. First, AI is an information good, and information goods are different from other goods in that they are non-rivalrous – they can be used at close-to-zero marginal cost, implying that a single firm can serve a very large market. Moreover, the creation of AI codes or ML algorithms typically involves high sunk costs and/or fixed costs – in a private market, firms need to earn monopoly rents to recoup these costs. Moreover, even small sunk costs may result in markets not being contestable, i.e., there could be sustained rents and profits. In addition, AI applications and platforms typically involve significant network externalities. Some of these arise because firms accumulate vast amounts of data that allow them to train their algorithms better than those of the competition. All of these effects create large barriers to entry and a tendency towards creating large monopolies, sometimes also called “superstar” effects (see, e.g., Korinek and Ng, 2019 , and Stiglitz and Greenwald, 2014a ).

Some authors have identified a growing number of “superstar firms” in the economy that are “super profitable” (see, e.g., Autor et al., 2020 ). However, rather than reflecting “super-productive” technology, much of these profits may arise from the exercise of monopoly power that is derived from the nature of these information technologies. For example, in the US, a large fraction of the gains in the stock market over the past decade have been concentrated in digital giants, to an important extent driven by their market power. Moreover, algorithmic advances have also enabled digital firms to extract more consumer surplus through discriminatory pricing.

Such superstar and monopoly effects are likely to play out not only at a company level but also at a country level, and they are likely to be particularly severe in the context of AI. They may be exacerbated by agglomeration economies associated with R&D in AI. There is a risk that those countries that lead in the advancement in AI may reap all the benefits, becoming “superstar countries” and reaping all the rents associated with the development of AI. The rest of the world, and in particular most developing and emerging economies, may be left behind, with the notable exception of China – one of the leaders in AI. Moreover, to the extent that firms or countries can protect their knowledge, the resulting monopolization of knowledge may also impede the catching-up process. Importantly, even if competitors could “steal” a superstar’s knowledge, this may not necessarily be sufficient as the superstars can continuously improve their algorithms based on their users’ data, thus remaining, perhaps permanently, ahead. In the past, advances in technology were driven to an important extent by basic research that was financed by governments in high-income countries and that was freely available to all—including to developing countries. This too may change with AI.

Some observers suggest as a silver lining for developing countries in that ML technologies are reliant on data and that more diverse data contain more information. Thus, selling data might generate some income for developing countries. However, this is unlikely to make up for their lost income as the marginal return to more diverse data may be limited. Moreover, future advances in ML algorithms may make them less reliant on large quantities of data and instead require more specific, tailored data.

  • D. Misguided Technological Progress

Economic theory has illuminated why the nature of innovation (e.g., the factor bias) may not be welfare maximizing. Much of economics takes the factor bias of technological change as exogenously given, and the standard economic welfare theorems assert the efficiency of competitive market economies for a given level of technology. However, the direction and rate of technological progress are themselves economic decisions, as emphasized by the literature on induced innovation (e.g. Kennedy, 1964 ; von Weizsäcker, 1966 ; Samuelson, 1965 ; Atkinson and Stiglitz, 1969 ; Acemoglu, 1998 , 2002 ; Stiglitz, 2006 ). There is no analogue of the welfare theorems for innovation: markets on their own will not in general be efficient either in the level or direction (nature) of innovative activity and technological change. The market may even provide incentives for innovations that reduce efficiency by absorbing more resources than they create for society, as may be the case, for example, for high-frequency trading. This calls for policy to actively steer technological progress, as we will discuss further below.

The fundamental problem is that knowledge is a public good, in the Samuelsonian sense. If it is to be privately financed and produced, there must be inefficient restraints on the use of knowledge, and those restraints typically also give rise to market power. If there are no restraints on the use of knowledge, then innovators cannot appropriate the returns to their production of knowledge, and so they will have little incentive to innovate. 9 When knowledge is produced as a by-product of learning or investing, the inability to fully appropriate all the learning benefits will lead to under production or underinvestment in sectors of the economy associated with high learning and learning spillovers. As Greenwald and Stiglitz (2006) and Stiglitz and Greenwald (2014a) point out, this has important implications for developmental policy, providing a rationale for industrial and trade policies. 10

More recent literature has drawn attention not only to biases in the level and pace of innovation but also to the direction. In economies with incomplete risk markets and imperfect and/or asymmetric information (i.e., in all real-world economies), the equilibrium is not constrained Pareto efficient, and prices do not necessarily give the “correct” signal to innovators on the direction of innovation. There are pecuniary externalities that matter. 11 For instance, in the Shapiro-Stiglitz (1984) efficiency wage model, where unemployment acts as a disciplining device to discourage shirking in the context of a labor market with imperfect and costly monitoring, there will be too much labor-augmenting technological progress, resulting in too high a level of unemployment ( Stiglitz, 2006 ). There are multiple other biases, for example, towards innovative activities in which intellectual property rights are more easily secured, and in drugs, to me-too innovations, where private returns can markedly exceed social returns.

Markets do not care about income distribution. Market forces may drive economic decisions towards efficiency—in the narrow, microeconomic sense— but will not give any consideration to the distributive consequences. Recent contributions, however, have emphasized that overall economic performance can be affected by inequality ( Ostry et al., 2019 ; Stiglitz, 2013 ); obviously, individual entrepreneurs will not take into account this macroeconomic externality, and accordingly the market will be biased towards producing too much labor-saving innovation, creating a role for redistributive policies. In addition, Korinek and Stiglitz (2020) show that in the presence of constraints on redistribution, policy can improve welfare by steering innovation to take into account its distributive implications.

There are some self-correcting forces: for example, if labor is getting cheaper, innovators face smaller incentives to save on labor, providing a corrective mechanism within the market economy to an ever-decreasing share of labor, but this mechanism no longer works when wages are set by efficiency wage considerations or reach subsistence levels. 12

What is most relevant for developing countries is that these distributive implications extend across borders, and so decisions made in one country have effects on other countries that the innovating country and the innovators within that country have no incentive to consider. Even if markets were efficient in the choice of technology for the conditions of the country in which the innovation occurs, those conditions are markedly different from the conditions in other countries. In developing countries, a key question is about adopting appropriate technologies rather than innovating, but the same kind of analysis that argues for the need for government intervention in steering technological innovation also provides arguments for intervention in steering technology adoption. This is especially so if, after the initial adoption of technology from abroad, there is further adaptation to local circumstances, and the benefits and costs of the technological evolution are not fully appropriated, for example, in the process of learning by doing. These concerns have long been at the center of concern of industrial policy.

  • E. Broader Harms Associated with AI

There are also a number of broader harms associated with AI that have recently received a lot of attention—the ways in which new technology can affect security (including cybersecurity), privacy, incitement to “bad” behavior, including through hate speech, political manipulation, and, in the economic arena, price discrimination, sometimes exacerbating pre-existing societal divides.

While these matters affect both high-income and developing economies, an important concern is that the international community may address them in a way that does not reflect the priorities and needs of developing countries. Policymakers in many countries are beginning to discuss appropriate regulatory regimes and a set of rules to address these potential harms. It is unclear whether developing countries and emerging markets will be sufficiently represented at the table when these discussions take place. In fact, many of the standards, rules and regulations are likely to be set by high-income countries and China (e.g., Ding et al., 2018 ; Sacks, 2018 ), even though the impacts may be larger, and potentially different, on developing countries and emerging markets.

Moreover, the institutional capacity of developing countries to counter these harms may be more limited—especially when facing off against the technology giants. Weaker institutional foundations may make some countries more prone to abuses of autocratic and totalitarian leaders using mis-/disinformation and surveillance technologies. Less educated populations may suffer more from the consequences of mis-/disinformation, such as those associated with the anti-vaccine movement.

III. Evaluating the Uncertainties and Opportunities

  • A. Uncertainty about the Pace and Scale of Progress

The impact of technological change depends heavily on its pace and scale. If it occurs slowly, there is time to adjust. If automation is limited to a few tasks or sectors at a time, the impacts will be limited. However, there is a great degree of uncertainty about the pace of change and the magnitude of the coming disruption, even among experts in this area. Some economists (e.g., Gordon, 2016 ) assert that we are not in an era of unprecedented innovation, and that economic growth will be less rapid in the future than it has been over the past century. In fact, Gordon (2016) argues that indoor toilets and electricity had far bigger consequences on people’s standards of living than more recent innovations. Another view is that AI is a truly transformative technology—a General-Purpose Technology (GPT)—that has the potential to revolutionize every sector of the economy (e.g., Trajtenberg, 2019 ). Like steam engines or electricity in previous technological revolutions, this view predicts that AI will lead to significant productivity gains and structural changes across the entire economy.

An even more radical perspective that goes back to John von Neumann is that AI may eventually advance to a point where AI systems reach human levels of general intelligence. This may imply that they can also do research, design better versions of themselves and thereby recursively self-improve, giving rise to accelerating technological progress and, in the words of von Neumann, “the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue” (see Ulam, 1958 ). 13 The concept of such a singularity has been popularized by Good (1965) , Vinge (1993) and Kurzweil (2005) , and is being increasingly discussed among economists (e.g. Nordhaus, 2015 ; Aghion et al., 2017 ). Predictions of when such a chain of events might occur, however, continue to be perpetually revised— Armstrong et al. (2014) note that over the past six decades or so analysts have continued to expect “the development of [general] AI [to occur] within 15–25 years from whenever the prediction is made.” 14

This last perspective emphasizes that AI-driven machines may not only be physically stronger than humans and better and faster at processing information, but in an increasing number of domains, they may also learn better and faster than humans. 15 Thus, AI may be much more disruptive than a “mere” GPT; AI programs are increasingly replacing tasks previously performed by humans. If machines can engage in all tasks that have traditionally been performed by labor, and if they can do so at ever lower cost, then traditional labor would eventually become redundant, with the marginal product of human labor possibly falling so low that it no longer covers the subsistence cost necessary to keep a human alive ( Korinek and Stiglitz, 2019 ). This would represent the extreme case of labor-saving innovation: it is in fact labor-replacing innovation – employing labor would become a strictly dominated technology. 16

We discussed earlier some studies examining which jobs may be replaced by automation and AI in coming decades, typically based on job- or task-level data. The predictions in these studies vary widely, ranging from a relatively small percentage of 14% of all jobs ( OECD, 2019 ) to an estimate of 20–25% (Bain and Company, 2018) and almost 50% by Frey and Osborne (2017) and McKinsey Global Institute (2017) . Even the lower numbers suggest a significant effect, especially because the impact may be concentrated in certain industries and among certain groups of workers, specifically among unskilled and routine jobs. Knowing what fraction of all jobs will be lost to AI therefore does not necessarily provide a good metric of the impact on income distribution, and especially so in the short run.

Applying our earlier insights on steering innovation to economic research, economists should steer their research in directions where the expected social value added of economic analysis is greatest, that is, where it has the highest welfare impact.

Even if some of the described scenarios have a relatively low probability, it is important to think particularly hard about events that will be highly disruptive to society, to think through the consequences, and to prepare for how we might ameliorate some of the more adverse effects. Extensive labor replacing innovation would be such an event. Even if one places a relatively low probability on such an event—and one may argue that it is not actually a low-probability event—the associated social repercussions would be sufficiently large that it makes sense to focus attention on such an event. Studying scenarios that pose the most adverse social impacts would better prepare economies to deal with them when they occur— and they also provide valuable lessons for scenarios in which the impact is less stark.

  • B. The Productivity Puzzle: Are We Really in an Era of Unprecedented Innovation?

In relating the debate about the economic significance of AI-based innovation to recent economic data, we encounter a well-known puzzle: if we are really living in an era of significant technological disruption, why are the increases in innovation not showing up in GDP data? This is analogous to the puzzle of missing productivity growth from computerization that Bob Solow described in the 1980s when the GPT of the time – computers – spread throughout the economy ( Solow, 1987 ). It took until the following decade for US national accounts to show a pickup in productivity growth. Part of the explanation for the productivity puzzle is that there are long lags, as was the case for computerization. At present, AI is influential in a limited number of sectors, like inventing better ways of advertising. Even if AI is transforming advertising, this will not transform our overall standard of living. (In this particular case, it may actually lower overall efficiency, as it may undermine the price system by enabling pervasive discriminatory pricing.) Going forward, many sectors of the economy will require complementary investments and changes in processes and organization as well as new skills among their workers to take full advantage of AI (see e.g. Brynjolfsson et al., 2019 ).

Another part of the explanation of the productivity puzzle is that there are difficult measurement problems. Many recent technologies may have led to increases in societal welfare that are not captured by GDP (see e.g. Brynjolfsson, 2020 ). For example, when online services are exchanged against “eyeballs,” i.e., when users are exposed to advertisements instead of paying for services, the benefits to consumers are not included. 17

C. Putting AI in the Broader Context of Development

There are several other important factors that are relevant when it comes to managing the potential adverse effects of AI on developing countries in coming decades.

The COVID-19 pandemic has imposed an extra shadow cost on physical interaction with humans, which is likely to accelerate the automation of jobs that require physical interaction (see e.g. Korinek and Stiglitz, 2021b ). The resulting changes will have long-lasting effects on the economy, even after the pandemic is overcome. The new technologies that are introduced now will reduce the demand for labor worldwide for some time to come. 18

  • Population Dynamics

Population dynamics will interact in important ways with labor-saving or -replacing technologies (see e.g. Varian, 2020 ). In countries with rapidly growing working-age populations, such as in many African countries, lots of new jobs will have to be created to maintain a given employment rate. Advances in automation that are developed in high-income countries and easily deployed around the world will make this more difficult. However, the large supply of labor may slow down the development and adoption of automation technologies within such countries (although the evidence in several countries suggests that at least in large export-oriented manufacturing, the technologies employed are remarkably similar to those in advanced countries; see Rodrik, 2011 ). 19 Moreover, young populations also generate significant demand for education, which in turn creates jobs. Overall, even countries like India face difficulties in creating enough formal sector jobs to keep pace with the growing working age population. The faster growth of population makes capital deepening more difficult, slowing the pace of growth in income per capita.

Conversely, in countries in which the working-age population is declining, such as China, the impact of job automation on the workforce is mitigated as workers that are replaced by technological progress can simply retire. Moreover, aging populations create large service sector needs, particularly in healthcare. Many of these service sector jobs are unlikely to be replaced by automation or AI in the near future. Overall, the evidence suggests that aging societies adopt new technologies and automate ( Acemoglu and Restrepo, 2019b and Figure 5 ). 20

Figure 5.

Population Aging and Automation

  • The Green Transition

A third important force affecting developing countries in coming decades is the threat of global warming, which calls for significant public policy interventions to facilitate the Green Transition, i.e., the transition away from an economy that is dependent on fossil fuels to one that is more environmentally sustainable and relies more on renewable energy. Without global policies to save our planet, developing countries will experience some of the largest losses from global warming.

There are many similarities between the effects of AI and the Green Transition. Both involve large changes in relative prices and generate significant redistributions, and many developing countries will be strongly affected. The Green Transition is similar to resource-saving innovation and risks undermining the standard of living of oil-exporting countries, among which there are a number of low-income countries.

There is also an important complementarity between the Green Transition and AI: the Green Transition is likely to increase the demand for labor which could offset some of the negative effects on labor demand of automation and AI. Indeed, given the labor needed for the Green Transition, the labor replacement due to automation and AI in many activities, including manufacturing, could be considered a fortunate development enabling countries to better address the challenges of climate change. There is thus an inherent tension in frequent claims that on the one hand economies cannot afford to mitigate climate change (i.e., that there are insufficient resources), and on the other hand concerns over a potential crisis with a surplus of labor arising from labor-saving AI. 21, 22

However, we do face challenges in how to channel surplus resources into what is required for the Green Transition. Some of the skill sets of those labor resources freed up by technological progress will differ from those needed in the Green Transition, although Louie and Pearce (2016) argue that the retraining costs would be moderate , and many of the investments (such as installing solar panels) require only limited skills.

There may be institutional constraints that make it difficult to reallocate capital towards green investment. While many sources of savings are long term (pension funds and sovereign wealth funds) and the investments needed for the Green Transition are long term, standing in between are short-term financial markets. Local, national, and multilateral Green Development Banks may be helpful in financing the private green transition. Better disclosure to investors of risks associated with “brown” investments (i.e., ones that contribute to pollution) and changes in fiduciary standards for asset managers towards their investors, would help move resources into green investments. Of course, without strong incentives, provided by price signals and environmental regulatory constraints, incentives for green investments and innovation will be greatly attenuated.

IV. Lessons from Past Technological Transformations

To grasp the historical nature of what is going on, it is necessary to put the advent of AI and related technologies in the context of the broader history of technological progress. Humanity spent much of its history at a Malthusian stage. The Industrial Revolution started a little over two centuries ago, and was but a blip in the history of mankind. The era of manufacturing-based export-led growth that enabled the East Asian Miracle stretched over the past half-century – one quarter of the history of the Industrial Revolution. It is easily conceivable that we are now going into another era.

Many are far more sanguine than we are about the disruptive potential of AI. They point to the automobile and other innovations at the end of the nineteenth century. Jobs were lost, making buggy whips and horse carriages obsolete, but overall, labor demand increased, and more jobs were created. Our analytical discussion made clear that there is no inherent reason that innovation has these effects. This time could well be different. Looking at the time before the Industrial Revolution and the early decades of the revolution itself serves as a reminder.

  • A. Pre-Industrial Revolution

Before the Industrial Revolution, innovation proceeded at a far slower pace than today. There were still many innovations, but the actual living standard of the vast majority of people was stagnant ( Maddison, 2003 ). The interpretation of Malthus (1798) was that every time an innovation took place, the population started to grow and absorbed the surplus that was generated.

This pre-industrial state of affairs may be still relevant in the least developed countries and is particularly problematic in some African countries, where the death rate has been greatly reduced by medical innovations, but reproductive rates have continued to be very high. The affected countries have been slow to go through the demographic transition that marked the rise of living standards in Asia. As a result, several countries are facing a difficult-to-manage explosion in population combined with stagnant living standards.

There is a risk that poor countries may see a return to Malthusian dynamics if technological progress undermines the source of their comparative advantage. Consider a country that exports manufacturing goods produced using cheap labor but is not very productive in agriculture, for example because of a shortage of land and a high population density. The country uses its export revenues to import food for workers in the manufacturing sector, granting them a living standard that is above subsistence levels. If a new technology produces the manufacturing goods more cheaply, the wages of the manufacturing workers will fall, and they may well fall below the subsistence cost of workers. If that is the case, the country may return to a Malthusian state of affairs in which part of the population suffers from hunger and deprivation. Increasing agricultural productivity may mitigate this dire state of affairs but the question is, would they be sufficient to support a population that was previously supported by imported food? Thus, populations may decline not as a result of choice, as in many developed countries, but from Malthusian dynamics. In today’s globally connected world, that presents ugly alternatives: Will the rich countries simply look away, as they see this suffering and near-starvation in poorer countries? Will they create ever-increasing barriers to stave off the inevitable pressures of migration?

B. Industrial Revolution

The Industrial Revolution marked the beginning of rapid growth in high-income countries. After centuries in which standards of living had been stagnant, growth started to increase markedly. It transformed the world. The Industrial Revolution thus provides us with a number of lessons that are very relevant today:

  • Innovation Can Be Very Disruptive

Even when an innovation ultimately proves to be beneficial for society at large, not everyone benefits. It can give rise to very large disruptions during the transition. In the short run, there was significant social upheaval from the industrial revolution—Charles Dickens’ novels make it clear that not everyone prospered. In the UK, some people were living under much worse conditions in the cities of the mid-19 th century than they had been in the rural areas prior to that. Even indicators such as life expectancy initially went down. Looking at those who suffered, the Industrial Revolution was clearly not a Pareto improvement.

  • Collective Action Can Mitigate the Adverse Effects

The onset of the industrial revolution posed many challenges that required collective action. However, it took time for societies to put in place the collective mechanisms to respond to these challenges. This is why the industrial revolution had significant negative effects on the masses for some time. Eventually, governments played an important role in mitigating the adverse effects, including the problems posed by urbanization, such as challenges in sanitation, environmental degradation, public health, infrastructure, and congestion.

Government took a strong role too in advancing the positive effects of the new economy. Education was an important element in creating a productive workforce – it was therefore also in the interests of capitalists, and public education received broad public support.

In high-income countries, institutions related to labor legislation, unionization, and social safety nets were not created until the end of the 19 th century and beginning of the 20 th century. In the United States, the ready availability of land implied that labor was relatively scarce, limiting the extent to which labor could be exploited. Nonetheless, in the early years of the 20 th century, labor was not doing very well. It was only dramatic events like the 1911 Triangle Shirtwaist Factory fire in New York City that led to labor legislation that really protected workers. In most high-income countries, labor legislation today is taken for granted, but in 1900, it was not obvious if meaningful labor legislation would ever be enacted. Strikingly, some of the tough political battles that made the adoption of such legislation problematic a century ago are playing out once again in the United States, where there has been an erosion of protections, for example, those associated with minimum wages, health and safety standards, or overtime pay, among others.

These labor market reforms helped support the structural transformation that occurred with the rise of manufacturing, and they showed that equality and growth are complementary (e.g., Ostry et al., 2019 ). At a basic level, they were necessary to sustain social peace and democracy. And they ushered in what might be called an “Age of Labor.” Most developing countries have not gone through this process yet.

This Age of Labor may not last forever. In the US, minimum wages have declined in real terms in recent decades, below the level of fifty years ago ( Figure 6 ), and many protections on hours and working conditions have been eviscerated. Advances in AI may further contribute to undermining labor’s bargaining position and thus these social protections. And in developing countries, they may do so before workers have ever acquired similar levels of rights and protections as they have in high-income countries.

Figure 6.

Federal Minimum Wage (adjusted for inflation) in the US, 1938–2020

  • Politics and Political Economy

The Age of Labor conferred not only unprecedented economic returns upon workers in the form of rising wages, but also, in parallel, unprecedented political power. However, this power has been eroded more recently (see e.g. Boix, 2019 ). In simple models of democracy, the median voter (or more broadly, the “majority”) determines political outcomes. But the evidence is that that model provides a poor description of the outcomes of the political process. For instance, the majority of voters want a more egalitarian society (see, e.g., chapter 1 in Cerra et al., forthcoming ). But in recent decades, in many countries, the political and economic rules have evolved in the opposite direction, giving more influence to the power of “money”. 23 , 24

  • C. Manufacturing-Based Export-Led Growth

In developing countries, there has been a single model of development that has proved enormously successful over the past fifty years: manufacturing-based export-led growth (see Stiglitz, 2018a ). It enabled many East Asian countries to close the gap between themselves and high-income countries, increasing per capita incomes in these countries multifold.

One big change inherent in this development strategy was moving from discussions of static comparative advantage to more dynamic comparative advantage. This was central to the East Asia “Miracle.” Half a century ago, South Korea was seen by many to have a comparative advantage in agriculture. It instead pursued a strategy of creating its own dynamic comparative advantage via an industrial policy that led it towards industrialization. That model served most of East Asia remarkably well, in a way few had anticipated (e.g., Myrdal (1968) who predicted that Asia would never develop). See also Aghion et al. (2021) .

The path to development in East Asia has been via exports of cheap labor-intensive manufactured goods. This development strategy combined learning, the provision of employment opportunities, foreign exchange, tax revenue—everything that was needed for a quick developmental transition.

While their development trajectory began with taking advantage of their static comparative advantage in cheap labor, and especially cheap unskilled labor, over time, many East Asian countries moved up the “value” chain, producing higher value added and more complex products and developing their dynamic comparative advantage.

Earlier advances in technology have already reduced the importance of cheap labor; but now advances in AI may erode it further still. Going forward, growth led solely by exports of labor-intensive manufacturing goods will no longer be available as a strategy of development. Indeed, the share of manufacturing employment is decreasing globally. Moreover, the jobs that can be outsourced may be more easily automated. There may be reshoring of production that had previously been outsourced, using highly automated production processes, and the process may have been accelerated by the Covid pandemic.

The forces that facilitated the development in East Asia may thus be going in reverse, making it difficult for other developing countries to follow the strategy.

One of the critical reasons for the success of the export-led growth model based on manufacturing goods was that it enabled developing countries to catch up in multiple domains. 25 Developing countries are poorer than developed countries not only because there is a gap in material resources but also because of a gap in knowledge ( World Bank, 1998 ). A quarter century ago, the World Bank began thinking of itself as a knowledge bank, not only helping countries to catch up in resources but also to catch up in knowledge.

AI may have characteristics that will increase the gap in knowledge and make it more difficult to catch up. While technology adoption lags have declined over the past centuries ( Comin and Hobijn, 2010 and Figure 7 ), the specific nature of AI may reverse that. Cutting-edge AI technology is highly specialized, and improvements are driven to a large degree by learning from large datasets, creating a winner-takes-all dynamic, as we noted earlier. In addition, a disproportionate share of the people working in AI are in private companies, and a significant share of the knowledge is not in the public domain and therefore not easily accessible to developing and emerging economies. (This contrasts with many past technologies, when publicly financed knowledge production was more central, so access to knowledge was more easily available to developing and emerging economies.) Moreover, an important resource input to AI is data, and access to data is concentrated and not globally public. The implication is that the nature of AI technology and how these advances are generated will make it more difficult to catch up than in the past. In fact, the exponential nature of growth in AI technology may imply that laggards not only cannot catch up, but that the gap between them and the front runners may grow, compounding the potential adverse effects that developing countries may suffer from labor-saving or resource-saving technological progress. 26

Figure 7.

Technology Adoption Lags

  • D. What is Different This Time

Not only may the AI revolution make it more difficult for developing countries to catch up, the AI revolution may also be more difficult to manage for economic policymakers than earlier technological transitions. The structural transformation from an agrarian rural economy to an industrial urban economy eventually led to a more egalitarian society. As we have noted, the reasons included that innovation associated with that transition overall was unskilled-biased, i.e., it increased the relative productivity of unskilled labor. Moreover, industrial production provided a strong force towards mass education. Furthermore, industrial production typically involved large establishments that could be unionized relatively easily, and the unions advocated for wage compression. All these forces led to greater equality. In the current transition, what risks becoming our “destination”—a service sector economy, marked by greater inequality, with less support for public education and more concentrations of market power—may be less attractive in many ways than the current situation, and the process of getting there may be more disruptive; that is, unless countervailing policy interventions are made.

AI may be labor-saving and resource-saving, and it is likely more biased towards ever-higher skills so that general education becomes less important. 27 This may reduce support for equality-enhancing public education, which has been one of the strong forces for more equalitarian outcomes in the past. Moreover, the service sector which is becoming an increasingly important part of the economy is marked by smaller establishments. In addition, worker tenure has declined, making it harder to unionize the workforce ( Choi and Spletzer, 2012 ). Digital technologies are likely to create more barriers to entry and give rise to more monopoly power and winner-takes-all dynamics, with rents going to a small number of extremely wealthy individuals and enterprises, disproportionately located in high-income countries.

Although for many developing countries, average income per capita may increase, large fractions of society may be left behind. Moreover, some developing countries may experience declines in income per capita as innovation erodes their comparative advantage. Unskilled workers in these countries may suffer the most.

Although greater inequality would increase the need for social protection, it may result in a less egalitarian politico-economic equilibrium, as the new concentrations of economic and political power may reduce support for the critical role of government in mitigating the adverse distributional consequences of technological change. (See, e.g., Gilens, 2005.)

V. Domestic Policy Responses

We have seen how economic policy played a critical role in shaping economic outcomes in previous eras of innovation; the same will be true in the case of AI. In this section, we discuss what policy levers can be employed to address the effects of technological disruption, both in developing countries and to protect vulnerable segments in advanced economies. Some of these are similar to what worked in earlier periods of technological change; some are attuned to the special problems posed by AI and labor-replacing innovation. In section 6, we will discuss changes in global policies, norms and rules that would assist developing countries in their response to technological change. In this short paper, we can only touch on a few of the more salient policies.

A. Taxation, Redistribution, and Government Expenditures

Among the critical policies to combat rising inequality are those of taxation and redistribution, with a particularly important role for progressive taxation. However, in recent years, a number of countries have actually made their tax systems more and more regressive. For example, many countries tax the returns to capital and rents (such as land rents, monopoly rents, and other forms of exploitation rents) at lower rates than workers. In the US, the rich pay a lower fraction of their income in taxes than the majority of the population ( Saez and Zucman, 2019 ).

Raising taxes is a particular challenge for developing countries, in which the informal sector is typically much larger than in high-income economies. However, this also means that there is significant scope for developing economies to enhance their tax structures and expenditure systems (e.g., scrapping harmful subsidies and tax exemptions) to build fiscal space for public spending, and improvements in tax capacity. Also, new digital tools and new data may actually give governments new policy tools to increase tax compliance. For example, when an activity becomes intermediated via centralized digital platforms, it becomes easier for governments to access business transactions and levy taxes on them. For example, governments have long found it difficult to monitor and tax the earnings of taxi drivers. But if driving is intermediated via digital platforms, all their earnings – including most tips – are recorded. 28

One of the dilemmas when it comes to taxation and redistribution is that labor-saving technological progress reduces tax revenue from labor – traditionally the most highly taxed factor in the economy – precisely at the time when the need for redistribution rises (see e.g. Korinek, 2020 ). This necessitates that taxation increasingly shifts towards other factors and rents. From the perspective of efficiency, the taxation of rents is particularly desirable ( George, 1879 ). Imposing taxes on fixed factors, such as land, acts like a lump sum tax, and taxing rents generated by market power and political activity may discourage such rent-seeking, enhancing efficiency.

We have argued earlier that technological progress creates winners and losers, and the gains of the winners are quasi-rents that governments may be able to tax without introducing distortions. In particular, some of the monopoly rents of digital giants can be taxed without introducing major distortions into the economy.

In designing tax systems, an important concern is about incidence: the possibility that general equilibrium effects imply that taxes are ultimately borne by other factors and agents than those on whom they are levied, undermining the desired redistributive objectives. For example, a common result in simple models is that capital taxation discourages capital accumulation by capitalists. However, the adverse effects may be more than offset by public investments in human and physical capital (see e.g. Stiglitz, 2018b ). High on the list of what is desirable to tax are “bads” rather than goods, i.e., Pigouvian taxes on activities and goods that create negative externalities, for example, polluting or carbon-emitting goods. This would contribute to the Green Transition in a dual way, not only by providing tax revenue for public investments but also by correcting market prices to reflect the negative externalities. 29

  • Social Protection

If individuals could obtain insurance against the adverse effects of disruptive innovations, then it would be more likely that these innovations would be Pareto improvements ( Korinek and Stiglitz, 2019 ). But such insurance is not available. One of the functions of social insurance is to socialize these risks that otherwise would have been borne by individuals. But in developing countries, systems of social protection are typically less developed, making it even more likely that there be significant groups that are worse off.

  • Universal Basic Income

Many commentators have responded to concerns about the impact of technological progress on employment by advocating a universal basic income (UBI). While proposals differ in their detail, they typically entail that all individuals are paid a UBI independent of their employment or wealth status, and with a level of UBI payments geared above the poverty line. While such programs would imply formidable fiscal costs, and with it, possibly large distortionary taxes, those could be contained if a UBI replaced other social safety programs (such as social security, welfare, or unemployment insurance systems). By doing so, it would also reduce the overall administration costs.

From a global welfare perspective, a global UBI that was truly “universal” as the name suggests, i.e., that covers all citizens of the world equally, would be most desirable, given the potentially large global implications of AI. Currently, access to prototypes of a UBI is exclusive to people who were lucky to be born in specific locations that have the fiscal capacity to afford such programs (e.g., in Alaska where oil revenue is collected in the Alaska Permanent Fund and distributed to the residents of the state). But given the limitations on cross-border transfers that have been the center of attention of this paper, a global UBI is clearly still in the realm of fantasy. 30 , 31

However, in the short- to medium-run, the focus should be on creating jobs for everyone who is able and willing to work, especially in light of the earlier discussion of how much labor will be needed for the Green Transition, to provide services to the young, the sick, and the elderly, and to invest in infrastructure. Governments may have a role to play in helping match the need for work and people willing and able to work. However, while a clear need for a UBI may be in a more distant future, there are other policies that may achieve similar objectives to a UBI. For example, one approach to ensuring a modicum of income for all over the long run, with co-benefits of perhaps increasing social cohesion and solidarity, is shared capital ownership (e.g., Solow, 2009 ): as part of government assistance programs (such as those enacted in the wake of COVID-19 in 2020), firms receiving government help should contribute shares to a sovereign wealth fund—owned by everyone within the nation. Similarly, firms that build on or employ innovations that are based in part on government-funded research should be required to do the same. 32

Starting with Keynes (1931), economists have argued that technological progress and automation would in principle enable people to work less and spend more time on more meaningful activities rather than tedious and repetitive tasks – a point also emphasized, for example, by Varian (2020) . However, this requires either that wages go up in tandem with productivity growth, unlike in recent decades, or that the fruits of progress are shared more widely using transfers. If these questions of distribution can be solved satisfactorily, then individuals could indeed respond to productivity growth by working less without experiencing material losses. There is considerable evidence that many workers would prefer to work less and with more flexible work sessions. The Dutch model, which provides all workers with a right to part-time work (at pro-rated wages) could serve as an example, assuming that wages are sufficiently high. 33 , 34

  • Expenditure and Infrastructure Policy

Expenditure policy can be as important in offsetting the adverse effects of AI as taxation and direct redistribution, and it carries several benefits over transfers that are particularly relevant in developing countries: government expenditures may be easier to target based on need, and for whom the social returns of those expenditures may be high. For instance, expenditures on human well-being, such as on education and health, are naturally targeted to those who need education and healthcare, rather than being spent on those who already are educated or on those who are healthy. Expenditures to protect the environment help those who bear the brunt of environmental degradation, including climate change, which disproportionately affect the poor. 35

Expenditure policies that increase the demand for unskilled labor may serve double duty: they raise demand for unskilled labor, increasing the equality of market income (what is often now called pre-distribution), and sometimes they can be targeted so that the benefits of the expenditure go disproportionately to the less well-off. One important example is infrastructure investments in poorer neighborhoods, which are a labor-intensive expenditure that can be designed to be pro-egalitarian.

Of particular importance are investments in digital infrastructure that reduce the “digital divide” and allow citizens to access the vast services provided by the Internet. Recent advances in network technology allow developing countries to leapfrog older technologies in which high-income countries have invested fortunes, for example by using wireless 5G technologies instead of laying vast networks of cables.

Other infrastructure investments include public transportation systems that connect especially lower income workers with jobs and enhance the opportunities available to them. Another example of labor-demand increasing public expenditures is creating service sector jobs, for example in healthcare, caring for the elderly, and some aspects of education, which can again be designed to serve double duty – disproportionately benefiting the poor and needy as they increase wages by increasing the demand for labor.

B. Pre-Distribution

Our concern here is the distribution of consumption (or more broadly, of well-being) among the citizens of a country. That is affected by inequalities in market incomes and the extent of redistribution. The previous subsection discussed redistribution through tax and expenditure policies. But a society with a more equalitarian market distribution needs to place less burden on redistribution. Good policy entails an optimal mix of “pre-distribution”—actions to increase the equality of market income—and redistribution. This is especially so because some of the actions to increase the equality of market distribution are actually efficiency-enhancing, i.e., have a negative cost. For instance, actions which reduce market power, the ability of firms to exploit information asymmetries, or to engage in a variety of other exploitive practices.

There are two categories of policies which affect the distribution of market incomes: (1) Policies that affect individuals endowments of assets—human capital (education) and financial assets. These are affected by the public provision of education and more broadly, policies which affect the intergenerational transmission of advantage and disadvantage (such as inheritance taxes.) And (2) policies that affect the returns on factors, which include the laws and regulations that determine the “rules of the game.” These include competition laws, labor legislation, and rules governing globalization, the financial sector, and corporate governance. These rules affect simultaneously efficiency and distribution. 36

Education Regarding the first set of such policies, the fact that more educated workers receive higher incomes than less educated ones may invite the conclusion that education is the solution to inequality. While providing more equal access to high-quality education especially for the poor may reduce inequality—and is absolutely essential to avoid an education-based digital divide whereby some simply do not know how to access and benefit from the resources and opportunities offered by the Internet and related digital technologies—education is far from a panacea. Indeed, if there are large innate differences in ability, education can identify and amplify these differences, actually increasing inequalities in market income. ( Stiglitz, 1975b ). Moreover, education cannot address the problems arising from the declining share of labor income overall.

  • Steering Innovation in AI in High-income Countries

The overall direction of innovation in AI will be set to a large extent by high-income countries plus China. This implies that the direction of technological progress in those countries – how labor-saving it is – also matters for developing countries that will be exposed to the new technologies.

Korinek and Stiglitz (2020) make the case for actively steering technological progress so that it is more labor-using. They show that whenever lump-sum transfers are not available, it is desirable to encourage technological progress that leads to higher demand for those types of workers with the lowest incomes. This can be done by nudging entrepreneurs, by considering the labor market implications of government-sponsored research, or by explicit incentives provided to the private sector. Klinova and Korinek (2021) and Partnership on AI (2021) describe how to develop and how to operationalize frameworks for steering advances in AI towards greater shared prosperity.

Many governmental policies have indirect effects on incentives for innovation. For example, at least in the short run, the cost of capital is influenced by monetary policy, with the goal of stabilizing aggregate demand. In recent years, monetary authorities in many countries have set interest rates such that real returns on safe assets have been very low or even negative, likely below the social shadow price of capital. Stiglitz (2014) shows that this encourages excessive automation in high-income countries. Acemoglu et al (2020) observe that tax policies that favor capital over labor also distort the direction of progress towards saving labor.

And there are immediate implications for developing countries: Once the cost of developing a labor-saving innovation has been incurred in high-income countries, it can frequently be rolled out globally at comparatively low cost, potentially imposing significant welfare costs on workers in developing countries. Examples include self-checkout kiosks that harm workers, whatever their benefits or costs may be for consumers and global corporates.

Pritchett (2019) observes that migration policies in high-income countries restrict labor supply and lead to comparatively high wages that do not reflect the abundance of labor, and in particular of unskilled labor, at the global level. The high wages then provide innovators in high-income countries with excessive incentives to invest in the automation of tasks that are performed by unskilled labor compared to what is desirable from the perspective of developing countries (or from the perspective of global efficiency). 37

Economists are also becoming increasingly aware of the importance of regional heterogeneity. Unlike in stylized models in which only national borders exist, labor does not move seamlessly across regions within countries. Even in high-income countries, large disparities between regions or between rural and urban areas persist, as illustrated, for example, by the case of northern and southern Italy or by the rural/urban differential in the United States and many other countries. Looking ahead, the “good” jobs of the future (e.g., those hard to automate and/or complementary to AI) might be located in major urban centers (such as non-routine manual occupations and personal services). Such geographical disparities call for location-based policies in fostering development, although the details of such policies can be complex. For example, they may entail a trade-off between income growth (which benefits from geographical concentration) and geographical inequality (which does not).

  • New Development Strategies

Developing countries will need a new multi-pronged development strategy to replace the manufacturing-led export-based growth model. Industrial policies have traditionally been among the most important aspects of countries’ development strategies—interventions that shape the direction in which the economy is moving, with particular emphasis on the secondary sector. However, in an age of increasing automation in manufacturing, development strategies have to broaden their focus beyond manufacturing and the secondary sector to other sectors of the economy, including agriculture and services. 38

Greenwald and Stiglitz (2014b) point out that every country has, in effect, a sectoral development policy—shaped by infrastructure and education investments and tax and regulatory policy. It is only that some countries do not know (or admit) that they have such policies. The danger then is that such policies can be more easily captured by special interests. 39 In developing countries development policies are much more at the center of economic policy. They need to be designed to manage innovations and mitigate the effects of and adapt to the disruptions that innovations may engender, to ensure that the net societal benefits, broadly defined, are maximized.

A lot of innovation in developing countries focuses on technology adoption and adaptation rather than developing entirely novel technologies. Whereas high-income countries focus on “steering innovation,” developing countries need to pay attention to “steering the adoption of technologies.” Their development strategy should intentionally focus on steering the adoption of labor-using technologies that have already been developed in high-income countries, adapting them to their own circumstances and needs, redesigning them, and building on them. Decisions on what type of inward FDI to encourage should also be informed by these objectives.

In designing the new development strategies, developing countries will need to think carefully about the rationale for public interventions: how can government improve upon the decisions made by decentralized agents? Of particular importance is that the direction of technological progress and technology adoption is endogenous, and there is no presumption that market decisions in this area are socially desirable. Decisions made at one date have effects in later periods, with firms making the decisions appropriating only a fraction of the benefits and bearing only part of the costs of their decisions. For example, this is clearly manifest when there are knowledge spillovers to other firms and when technology evolves over time, e.g., through learning by doing. Firms acting on their own will not fully consider the dynamic implications of their decisions today on others.

There are also market failures beyond the ability to appropriate the returns from current choices—for instance, imperfections of risk and capital markets. The capital market imperfections that impede the reallocation of labor in high-income countries in response to innovation—and that can result in innovations which decrease welfare —are even more important in developing countries, making it imperative to combine industrial policies with active labor market policies (see, e.g., delli Gati et al., 2012a, 2012b).

Relatedly, part of the problem is that market prices do not adequately reflect social shadow values. A well-known example is that, in the absence of appropriate regulation, the price of carbon in the market is zero, but this does not reflect the social cost of carbon. Similarly, market prices do not reflect the social value of an equitable distribution of resources and do not guide innovation in that direction. Given the constraints on redistribution, this leaves an important role for the government to steer innovation and foster economic development in a socially desirable direction ( Korinek and Stiglitz, 2020 ). For example, much could be gained from encouraging innovators to shift their focus from labor-saving towards more labor-using technologies.

Fortunately, while the new technologies necessitate a change away from the old and highly successful development strategies of the past half century, they also open up new opportunities. In agriculture, AI offers the potential for large productivity increases based on algorithms that help farmers fine-tune and optimize a range of decisions that increase their yield. Such algorithms depend on crops, soil and weather conditions and need to be customized to local conditions. Just as agricultural extension services, which extended general knowledge about agriculture to local farmers, played a critical role in the development of the US, there is an important role for government agricultural extension services today in developing countries. 40 Digital platforms can also enhance the ability of small farmers to trade their products at fair market prices, reducing the market power of middle men that frequently absorb a significant fraction of the surplus generated in agriculture.

Developing the service sector is crucial for economic development as the role of the primary and secondary sectors is declining. Many developing countries may carve out new areas of comparative advantage in services that will, however, depend on good internet connections and a certain degree of education of the workforce. For example, call centers and similar business and consumer services rely on requisite language skills. There is also a growing market for simple human services that can be broken down into small components and fed into AI systems (e.g., labeling images). However, as we noted earlier, services that can be outsourced are often also more easily automated. Other services such as tourism have proven a more automation-resistant (although not pandemic-resistant) source of export revenue for countries that have managed to fashion themselves into desirable tourist destinations. Exporting services offers many of the potential growth benefits of the manufacturing-based export-led growth model.

Services that are aimed at a domestic audience, for example, healthcare, caring for the elderly, as well as education, may not deliver much export revenue but are important for economic development and welfare. There is much scope for employing AI to improve the delivery and efficiency of these services, and it requires government policy to do so since private service providers are frequently small in size and cannot afford the necessary investments. And even in these areas, there may be significant opportunities for cross border trade, for example, via medical tourism and via retirees from advanced countries relocating to warmer climates, if adequate health care is available.

VI. Global Governance

In a globally integrated economy—from which developing countries and emerging markets have benefited enormously in many ways—global rules matter. The global rules have always been set to favor high-income countries; they are, to a large extent, set by the large powerful countries, and frequently by powerful special interests within them, whereas developing countries do not have a seat at the table, or are at least underrepresented.

The global rules have large effects on the ability of these countries to levy taxes in the digital era, on high-income countries’ ability to extract rents from the developing countries (say through market power and intellectual property rights), and more broadly on the global terms of trade and distribution of income. 41 While developing countries may realize these inequities—and the inefficiencies—of our global economic system, it often seems that there is little they can do.

AI has provided a new arena in which rules need to be set, at the same time that it may exacerbate the imbalances in economic power, as our earlier discussion emphasized. However, there are reasons for cautious hope when it comes to the rules governing information and AI. First, the rules in this area are still in the process of being set so there is hope that international institutions and civil society may have a positive impact on the shape of these rules. Still, the fact that recent trade agreements between the US and other countries have contained provisions reflecting the interests of big-tech companies—with limited open debate and limiting the scope for these trading partners to design regimes that reflect a broader public interest—is of concern.

Secondly, it should be in the self-interest of high-income countries to avoid the possibility of a strong backlash to globalization in developing countries. The possibility of such a backlash is considerable: The United States and a number of other high-income countries, which have been big beneficiaries of globalization, have experienced such a backlash – in part because they have not ensured that the losers of globalization were compensated. In the past, there was at least some sense that globalization created mutual gains for high-income and developing countries. The backlash in developing countries would be even greater if they come to see globalization as a mechanism of rent extraction from their economies (even if the truth may be that technological change is making them lose some of the earlier gains from globalization).

Moreover, international institutions, some of which are less and less dominated by high-income countries, may play a role in ensuring that the rules are set in a way that more adequately reflects the interests and concerns of all countries, including developing countries. As the rules for new technologies are being written, there are several areas of particular concern in which reforms in global governance would help developing countries better adapt to advances in AI.

  • A. A Global Tax Regime for the Digital Age

The inadequacies in the global tax regime make it difficult for developing countries to capture much of the rents that the global digital giants earn within their borders, even as their activities take away business from domestic firms and thereby reduce the domestic tax base. Indeed, even high-income countries have had difficulty with adequately taxing global tech giants. Some of the issues are now being discussed at the OECD in an attempt to establish a global tax regime.

The current global tax regime allows multinational firms to avoid much taxation—often paying taxes at rates markedly lower than local small businesses. It also impairs the ability of developing countries and emerging markets to tax the economic activity which occurs within their territories. This system is both inefficient and inequitable.

The controversy over digital taxation has exposed the deeper problems of multinational corporate taxation based on transfer prices, which are easily manipulated. The issue could be addressed by moving to a formulary apportionment system, whereby the worldwide profits of a corporation are apportioned to different countries according to a formula (see, e.g., Clausing and Avi-Yonah, 2007 ). The exact formula could have large distributive effects across countries. For instance, a simple formula based just on sales, while less manipulatable than other formulae, may disadvantage developing countries. A particular controversy associated with the digital economy is the value assigned to the data that are collected in the process of economic transactions and how and whether that value should be taxed.

The broader debate over international taxation has also led to renewed attention on closing down fiscal paradises, on international initiatives for transparency in capital ownership, which would help developing countries to increase their tax base, and on creating a global minimum multinational corporate tax rate, to prevent a race to the bottom.

  • B. Global Competition Policy

The tendency of digital technologies to give rise to natural monopolies makes competition policy especially important. One challenge is that the countries in which tech giants are based have incentives to protect their own tech firms since they share in the rents that these firms earn globally. For example, when the European Union investigated Google for anticompetitive practices or when Germany investigated the privacy practices of Facebook, the US treated it as a political question rather than a matter of economic policy and responded by accusing Europe of being anti-American. While the policy remedies suggested by the Europeans may have reduced the rents the companies could earn in Europe, their purported aim was to ascertain that these firms’ practices did not violate the norms on competition and privacy established in Europe. The tendency for matters of competition policy to turn into arguments over rents may get worse, given the global concentration of market power in AI in two countries, China and the United States.

Individual developing countries and emerging market economies stand little chance in reining in the behaviors of powerful global corporations on their own – in many instances, the corporations have a higher market capitalization than the GDP of the countries in question. This makes it important for developing countries to coordinate and develop competition policy together, for example, via a common competition authority for developing and emerging economies that can exert sufficient power over large global corporations, just as the countries of Europe would not be able to police the competitive behavior of American corporations on their own but are able to do so through the European Union.

Given the breadth and reach of the new digital giants, there is a need for stronger rules preventing conflicts of interest for companies that simultaneously own a marketplace and participate in it, and stronger rules preventing pre-emptive mergers, i.e., mergers and acquisitions designed to stifle the threat of a competitive marketplace in the future. There will also be a need for more ex-post remediation: breaking up mergers when they prove to be anti-competitive. 42 As the experiences cited above have shown, the countries in which digital giants are based may not have the correct incentives to police these companies’ competitive practices, given the large global rents that are at stake.

  • C. Intellectual Property Rights

The current system of intellectual property (IP) rights is designed to give (temporary) monopoly rents to innovators to compensate and reward them for their innovative activities. There has been much concern in recent years that the prevailing IP system gives excessive protection to innovators, with particularly adverse effects on developing countries. As the World Commission on the Social Dimensions of Globalization (2004) emphasized, there is a need to rebalance the international IP regime to ensure an equitable distribution of the gains from technological progress. Korinek and Stiglitz (2019) demonstrated that reducing the length of patent protection can ensure that the gains from AI-based innovations are better shared among society and can thus lead to a welfare improvement.

The most efficient way of distributing technological advances is to keep them in the public domain, financed via governments, international organizations, donors or charities. This avoids restrictions in access to new technologies and the creation of monopolies that concentrate rents and power. There is much scope for publicly financed research and development to benefit developing countries, for example, in the areas of agriculture where new technologies increase the productivity of crops, or in healthcare where developing countries face unique challenges that do not attract sufficient research by private corporations in high-income countries.

When research and development is financed privately, there is a strong case for granting different patent protection in developing countries than in high-income economies. The length of patent protection trades off how much surplus to allocate to innovators to compensate them for their efforts versus how much to let the broader public benefit from an innovation. Most patents are developed in high-income countries and are financed by the surplus that innovators extract from the patent protection there; innovators would not incur significant losses if developing countries could use their technology for free before their patents expire in high-income countries. Indeed, in many sectors, including pharmaceuticals, there is extensive cross-border price discrimination; drug companies could offer life-saving drugs to some of the poorest countries at steeply discounted prices. Compulsory licenses (part of TRIPS and other international agreements) give the right to access such life-saving IP at appropriate royalties, but many developing countries do not have the capacity to exercise those rights; and those that do have the capacity are intimidated from doing so by threats from developed countries. Trade agreements have done everything they can to impede access to generic medicines, forcing developing countries to pay high prices for drugs.

Before the advent of AI, it was clear that there was a need for a developmentally oriented IP regime—in some ways markedly different from that currently prevailing ( Cimoli et al 2014 ). But AI has made the challenge of access to knowledge even greater. Part of the nature of AI is that it may not even need much protection by the patent system. Algorithms can be kept proprietary, and they are always evolving. Requiring disclosure of certain key algorithms is imperative to ascertain whether algorithms are discriminatory, for example, by engaging in price discrimination. 43

  • D. Data and Information Policy

Data is a critical input underlying the new AI economy. That is why information policy – the rules governing the control over and use of data – has moved to the top of the policy agenda. Global tech firms are setting the data regulatory agenda in their interest without sufficient public oversight. This has already happened in recent trade agreements. For instance, while the new trade agreement between Canada, Mexico and the US had stronger provisions protecting labor and access to healthcare as well as better investor-state dispute settlement provisions, rules on the digital economy moved in the opposite direction, providing better protection for the tech giants. Being part of an international agreement, it may be difficult to change the data regulation regime in the future. This is particularly important for developing countries: the rules are currently being set with little concern for the views of citizens in the high-income countries, let alone those in the rest of the world.

Moreover, the monopolization of data by global AI firms also makes it more difficult for developing countries to catch up and develop their own AI-based companies. Global firms can use their access to vast troves of data from across the world to refine their products and offerings to consumers ever further. This makes it more and more difficult for newcomers in developing countries to close the gap between themselves and the leading firms.

Europe has actively worked on rules to ensure that the benefits of new digital technologies are shared and the harms are minimized. For instance, the EU has put forward proposals to require data sharing, with the goal of preventing accretion of monopoly power by monopolizing data. But giving control rights over data to individuals will not suffice; without proper regulation, individuals turn their data over to the digital giants and internet providers, receiving but a pittance: asymmetries in information and power are just too great to ensure an equitable outcome.

New transparency regulations, for example, regarding the algorithms and targeting of advertising, are necessary, but again not sufficient. Policymakers must be able to address the discriminatory impacts of pricing and advertising.

There is also a need for stronger rules protecting privacy and the rapid spread of misinformation and messages that promote violence and hate as well as other harmful messaging, even when conducted as part of a political campaign. In the US, the Section 230 provision which reduces the accountability of internet companies—unlike other publishers— is an example of a regulation that should be reconsidered.

As in the case of competition policy, the countries in which tech giants are based may not face the correct incentives to police the worldwide behavior of their companies since they share in the rents that these companies earn around the world. Developing countries need to cooperate and band together to have sufficient clout to impose regulation on global giants that reflects their developmental interests.

  • VII. Conclusion

Advances in AI and related technologies may, like the Industrial Revolution, represent a critical turning point in history. Increasing automation in manufacturing may lead to increases in wage inequality, declining labor demand, and increased skill premia in most countries; as well as to the demise of the manufacturing-export-led developmental model, which has historically had profound positive effects on many emerging market economies. The worst-case scenario is the unravelling of much of the gains in development and poverty reduction that could be observed over the last half century.

While earlier technological advances were associated with more shared prosperity and increasing equality between and within countries, the new advances may result in increasing inequality along both dimensions unless policies are designed to counterbalance them.

The new era will be governed by different rules and will require a different kind of economic analysis. Just as the production functions that Ricardo used to analyze agrarian and rural economies are very different from those in the models of a manufacturing economy that dominated the mid-20 th century, current economic frameworks must be adjusted and updated to think about the models that will describe the next 50 years. For instance, the competitive equilibrium model may be even less relevant to the 21 st century AI economy than it was to the 20 th century manufacturing economy.

There is a particular high degree of uncertainty across the possible scenarios of technological development and their impact, but what we do know is that there are large potential downside risks that should not be ignored. Economic analysis, based on models appropriate to this new era, has the potential to help in the development of policies—both at the global and national level—that can mitigate these adverse effects, to ensure that this new era of innovation will lead to increased standards of living for all, including the billions living in developing countries.

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This paper is an extended version of a chapter that has been accepted for publication by Oxford University Press in the forthcoming book on “How to Achieve Inclusive Growth,” edited by V. Cerra, B. Eichengreen, A. El-Ganainy, and M. Schindler due for publication in 2021. Parts of this paper subsume ideas presented in previous work by Korinek and Stiglitz (2021a) under the title “Artificial Intelligence, Globalization, and Strategies for Economic Development.” We thank Avital Balwit, Andy Berg, Valerie Cerra, Sharmini Coorey, Romain Duval, Barry Eichengreen, Katya Klinova, Nikola Spatafora, Jeromin Zettelmeyer, participants in the IMF IG seminar series, and numerous IMF colleagues for insightful comments and suggestions; David Autor, Adrian Peralta-Alva and Agustin Roitman for helpful data and charts; and Jaime Sarmiento for excellent research assistance. Financial support from the Institute for New Economic Thinking is gratefully acknowledged.

AI is frequently defined as the ability of machines to perform tasks that previously required human intelligence. This implies that AI carries by its nature the potential to replace human labor. A number of arguments put forward in this paper are applicable not only to AI but also to other forms of technological progress.

As can be seen in the figure, the COVID shock in 2020 has clearly accelerated the trend, at least temporarily, giving rise to a large decline in employment in routine manual jobs but only a modest dip in nonroutine cognitive jobs.

See also WEO (2018) for a broader review of employment and earnings dynamics across countries and sectors.

The result is intuitive: the dual to the production function is the factor price frontier. Technological change shifts out the factor price frontier, implying that if the interest rate is unchanged, wages must increase.

One extreme example is Elon Musk’s Neuralink which aims to achieve a symbiosis of humans and AI by surgically implanting technology into the brain.

As always, calculating the full consequences of a new technology on the demand for any natural resource, or carbon emissions, is complex. It must be done on a full life-cycle basis, incorporating initial investment, maintenance, as well as day-to-day operations. That said, for instance, data centers running cutting-edge AI applications are typically energy-intensive and may lead to increases in demand for electricity and depletable natural resources. Still, on net, it is likely that the demand for carbon-based energy sources will decrease. Some natural-resource-rich economies may benefit, such as those rich in rare earths or other metals that are inputs in the production of batteries, microchips, solar panels, wind turbines etc.

In addition, many fossil-fuel-dependent countries have not yet diversified their export base, and may face limited options to diversify into job-rich manufacturing growth given this sector’s vulnerability to automation. See, e.g., Peszko et al. (2020) .

There is a large literature on the welfare economics of innovation, dating back to Arrow (1962a) . Stiglitz (1975a , 1987 ) drew attention explicitly to the public good aspects of knowledge, and the similarity between the economics of information and the economics of knowledge. See also Romer (1986) .

The inefficiencies in economies with learning by doing was first noted by Arrow (1962b) .

See Greenwald and Stiglitz (1986) . These, in turn, give rise to macroeconomic externalities, the consequences in the context of innovation have been studied by Korinek and Stiglitz (2019) .

More generally, the direction of innovation is affected by the share of the factor. If the elasticity of substitution is high, a lower factor price will be associated with an increased factor share, and this can induce greater efforts at increasing the productivity of that factor. In that case, the equilibrating force just described does not arise, and the opposite occurs ( Stiglitz, 2014 ).

As Vinge (1993) noted: “Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended.” However, it should be noted that general AI does not in itself imply the singularity (e.g., Walsh, 2016 ).

Responding to Kurzweil’s (2005) thesis that “The Singularity is Near,” Walsh (2016) provides arguments for why “The Singularity May Never Be Near.”

There is even a perspective that holds that AI-powered machines could become agents of their own ( Korinek, 2019 ).

Note that this is in contrast to a long tradition in the traditional economics literature that viewed labor as an essential input for any production process.

The measurement problems are still more complicated: advertising is an “intermediate” product and does not directly enter into the value of the final goods and service that constitutes GDP. If advertising were a normal input, and markets were competitive, an increase in the efficiency of production of an intermediate good would be reflected in a lowering of the final goods price, and that in turn would be associated with an increase in GDP. Better advertising engines may, as we noted earlier, actually increase market power and decrease overall economic efficiency. Moreover, they may induce an adverse redistribution, lowering welfare still more.

Any innovations to deal with Covid-19 will still be available in the post-Covid-19 world. Moreover, the development of research strategies in response to Covid-19 may set in motion a process of “learning to learn,” learning better how to innovate in human-replacing dimensions. See Atkinson and Stiglitz (1969) and Stiglitz (1987b ).

This would not, of course, be true if the factor price equalization theorem held. More generally, differences in domestic factor ratios do not necessarily align well with differences in factor prices.

There are countervailing forces to the scarcity of labor associated with a declining working age population. A younger population may be more tech savvy, better able to pick up, adopt and adapt to new technologies. The figure suggests that the scarcity effect dominates. There are other factors too that play a role in robotization.

There is a similar dissonance between those who argue that the economy faces secular stagnation and those who say there are not the resources required for a rapid green transition.

Over the long run, the effect of the green transition on the demand for labor is more problematic. While many of the green technologies have higher upfront costs, maintenance costs are markedly lower, and not only are life-cycle carbon emissions lower, but so is labor usage.

For example, based on data for 1981–2002, Gilens (2005) finds that in the US actual policy outcomes strongly reflected the preferences of higher-income groups, with little relationship to the preferences of the poor or middle-income citizens. For a broader discussion of the interplay of economic and political inequality, see Stiglitz (2013 , 2019 ).

Harari (2017) also explores the implications of super-human artificial intelligence on society and politics.

The emphasis here is on (traded) goods rather than (non-traded) services—while learning by doing could occur in both, it is the former that drives export-led development. See, e.g., McMillan and Rodrik (2011) who note that non-traded service sector development on its own typically has not had a substantial impact on overall productivity.

Stiglitz (2015) models the relationship between technological leaders and followers.

We emphasize that the focus here is on the more adverse scenarios, to help prepare policies; should they not materialize, so much the better. We noted countervailing forces—the need for labor for the green transition, that even within advanced economies, people may still be needed for service jobs requiring physical proximity and/or the “human touch” (such as elderly care, housekeeping, etc.). Most important, these outcomes are not inevitable: we can steer innovation in a different direction and, as the discussion below will hopefully make clear, there are multiple actions that can be taken to mitigate some of the adverse effects.

Some are justifiably concerned that digital platforms are in fact very efficient at exploiting workers. But platforms can also provide information on whether workers are exploited and, with proper regulation, make it easier to address such exploitation than it used to be before the digital age.

See also Pouokam (2021) .

Some countries have started to experiment with schemes that have some characteristics of a UBI. E.g., Spain introduced in early 2020 a “minimum vital income” to ensure a guaranteed minimum income for the poorest. However, it is not unconditional, but instead tops up incomes below the minimum income, which may create disincentive effects to continue work in jobs that pay below that threshold. Several other countries have run pilot programs, often on a small scale and/or for a limited time. Overall, these programs appear to indicate that such schemes tend to have little impact on labor supply (see, e.g., https://www.vox.com/future-perfect/2020/2/19/21112570/universal-basic-income-ubi-map ). Earlier research on a negative income tax in the US suggested that by enabling individuals to search more for a better matching job may actually enhance productivity.

UBI programs may turn out to be important policies in a future in which labor truly becomes redundant ( Korinek and Juelfs, 2021 ). There is uncertainty over when that future may arise, as the earlier discussions indicated—but given the complexities of transitioning to such a new regime, there may be a rationale for countries to start experimenting with UBI systems.

Notably, some have discussed a “robot tax” that could help finance redistributive fiscal measures (e.g., Rubin, 2020 ). However, such a robot tax may be difficult to implement (e.g., what distinguishes a “robot” from traditional capital?) and may discourage innovation (e.g., Summers, 2017 ). Conceptually, government ownership of capital is equivalent to taxes on capital with exemptions on new investment that avoid any negative incentive effects of capital taxation, although it may be insufficient to provide funding for large-scale redistributive programs that may be needed in a long-term equilibrium with low employment levels. See also Korinek (2020) .

The reduced labor supply may itself help sustain higher wages.

For a broader view of how to achieve inclusivity in the labor market beyond the challenges posed by technological advances, see El-Ganainy et al. (2021) .

For example, Colmer et al. (2020) find that while fine-particle air pollution has decreased overall in the US over the past four decades, whiter and richer neighborhoods have become relatively less polluted, while poor and minority communities are (still) the most polluted.

For an extensive discussion of some of the critical “rules,” see Stiglitz et al (2015 , 2019 ). Later, we discuss a particularly important set of policies that can affect the returns to factors—those associated with steering the development and adoption of technologies.

The alternative – to allow for greater migration – would of course also put downward pressure on wages in advanced countries and might increase inequality within those countries.

Curiously, such policies have continued to be referred to as “industrial policies” even when they move the economy away from the industrial sector. We use the more generic term sectoral policies , but they are broader: they can also be used to change technology within a sector (e.g., towards green or more labor-intensive technologies).

For example, US bankruptcy provisions favoring derivatives can be thought of a sectoral policy encouraging the growth of derivatives; but until the 2008 financial crisis, few outside of that sector were even aware of the favorable treatment that derivatives have received.

In 1914, the U.S. Department of Agriculture created a system of “extension” services, with the aim of providing farmers with expert advice on agriculture and farming. See, e.g., https://www.almanac.com/cooperative-extension-services .

For a discussion of how this plays out in trade rules, see, e.g., Charlton and Stiglitz (2005) .

There is by now a large literature describing the new competition policies that may be required. See Stiglitz (2019) and Wu (2018) , as well as Akcigit et al. (2021) for an overview of emerging issues and complexities in competition policy.

It is sometimes argued that such disclosure is not possible because algorithms are always evolving. While they are always changing, they could still be disclosed as of a particular moment in time. There are other (often costly) ways of monitoring the behavior of algorithms at any point in time.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

image of Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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The list of new technologies grows every day. Robots, Augmented Reality, algorithms, and machine-to-machine communications help people with a range of different tasks.(1) These technologies are broad-based in their scope and significant in their ability to transform existing businesses and personal lives. They have the potential to ease people’s lives and improve their personal and business dealings.(2) Technology is becoming much more sophisticated and this is having a substantial impact on the workforce.(3)

BBVA, OpenMind. Technological Progress and Potential Future Risks. WEst. Workers walk among shelves lined with goods at an Amazon warehouse, in Brieselang, Germany. Germany is online retailer Amazon’s second largest market after the USA.

In this paper, I explore the impact of robots, artificial intelligence, and machine learning on the workforce and public policy. If society needs fewer workers due to automation and robotics, and many social benefits are delivered through jobs, how are people outside the workforce for a lengthy period of time going to get health care and pensions? These are profound questions for public policy and we need to figure out how to deliver social benefits in the new digital economy.

Emerging Technologies

Industrial robots are expanding in magnitude around the developed world. In 2013, for example, there were an estimated 1.2 million robots in use. This total rose to around 1.5 million in 2014 and is projected to increase to about 1.9 million in 2017.(4) Japan has the largest number with 306,700, followed by North America (237,400), China (182,300), South Korea (175,600), and Germany (175,200). Overall, robotics is expected to rise from a $15-billion sector now to $67 billion by 2025.(5)

According to an RBC Global Asset Management study, the costs of robots and automation have fallen substantially. It used to be that the “high costs of industrial robots restricted their use to few high-wage industries like the auto industry. However, in recent years, the average costs of robots have fallen, and in a number of key industries in Asia, the cost of robots and the unit costs of low-wage labor are converging… Robots now represent a viable alternative to labor.”(6)

In the contemporary world, there are many robots that perform complex functions. According to a presentation on robots:

The early 21st century saw the first wave of companionable social robots. They were small cute pets like AIBO, Pleo, and Paro. As robotics become more sophisticated, thanks largely to the smart phone, a new wave of social robots has started, with humanoids Pepper and Jimmy and the mirror-like Jibo, as well as Geppetto Avatars’ software robot, Sophie. A key factor in a robot’s ability to be social is their ability to correctly understand and respond to people’s speech and the underlying context or emotion.(7)

These machines are capable of creative actions. Anthropologist Eitan Wilf of Hebrew University of Jerusalem says that sociable robots represent “a cultural resource for negotiating problems of intentionality.”(8) He describes a “jazz-improvising humanoid robot marimba player” that can interpret music context and respond creatively to improvisations on the part of other performers. Designers can put it with a jazz band, and the robot will ad lib seamlessly with the rest of the group. If someone were listening to the music, that person could not discern the human from the robot performer.

Amazon has organized a “picking challenge” designed to see if robots can “autonomously grab items from a shelf and place them in a tub.” The firm has around 50,000 people working in its warehouses and it wants to see if robots can perform the tasks of selecting items and moving them around the warehouse. During the competition, a Berlin robot successfully completed ten of the twelve tasks. To move goods around the facility, the company already uses 15,000 robots and it expects to purchase additional ones in the future.(9)

In the restaurant industry, firms are using technology to remove humans from parts of food delivery. Some places, for example, are using tablets that allow customers to order directly from the kitchen with no requirement of talking to a waiter or waitress. Others enable people to pay directly, obviating the need for cashiers. Still others tell chefs how much of an ingredient to add to a dish, which cuts down on food expenses.(10) Other experimentalists are using a robot known as Nao to help people deal with stress. In a pilot project called “Stress Game,” Thi-Hai-Ha Dang and Adriana Tapus subject people to a board game where they have to collect as many hand objects as they can. During the test, stress is altered through game difficulty and noises when errors are made. The individuals are wired to a heart monitor so that Nao can help people deal with stress. When the robot feels human stress levels increasing, it provides coaching designed to decrease the tension. Depending on the situation, it can respond in empathetic, encouraging, or challenging ways. In this way, the “robot with personality” is able to provide dynamic feedback to the experimental subjects and help them deal with tense activities.(11)

Computerized Algorithms

There are computerized algorithms that have taken the place of human transactions. We see this in the stock exchanges, where high-frequency trading by machines has replaced human decision making. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials at a very small scale and execute trades that make money for people.(12)

Some individuals specialize in arbitrage trading, whereby the algorithms see the same stocks having different market values. Humans are not very efficient at spotting price differentials but computers can use complex mathematical formulas to determine where there are trading opportunities. Fortunes have been made by mathematicians who excel in this type of analysis.(13)

Artificial Intelligence

Artificial intelligence refers to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.”(14) It incorporates critical reasoning and judgment into response decisions. Long considered a visionary advance, AI is now here and being incorporated in a variety of different areas. It is being used in finance, transportation, aviation, and telecommunications. Expert systems “make decisions which normally require a human level of expertise.”(15) These systems help humans anticipate problems or deal with difficulties as they come up.

There is growing applicability of artificial intelligence in many industries.(16) It is being used to take the place of humans in a variety of areas. For example, it is being used in space exploration, advanced manufacturing, transportation, energy development, and health care. By tapping into the extraordinary processing power of computers, humans can supplement their own skills and improve productivity through artificial intelligence.

Impact on the Workforce

The rapid increase in emerging technologies suggests that they are having a substantial impact on the workforce. Many of the large tech firms have achieved broad economic scale without a large number of employees. For example, Derek Thompson writes: “Google is worth $370 billion but has only about 55,000 employees—less than a tenth the size of AT&T’s workforce in its heyday [in the 1960s].”(17) According to economist Andrew McAfee: “We are facing a time when machines will replace people for most of the jobs in the current economy, and I believe it will come not in the crazy distant future.”(18)

In a number of fields, technology is substituting for labor, and this has dramatic consequences for middle-class jobs and incomes. Cornell University engineer Hod Lipson argues that “for a long time the common understanding was that technology was destroying jobs but also creating new and better ones. Now the evidence is that technology is destroying jobs and indeed creating new and better ones but also fewer ones.”(19)

Martin Ford issues an equally strong warning. In his book, The Lights in the Tunnel, he argues that “as technology accelerates, machine automation may ultimately penetrate the economy to the extent that wages no longer provide the bulk of consumers with adequate discretionary income and confidence in the future. If this issue is not addressed, the result will be a downward economic spiral.”(20) Continuing, he warns that “at some point in the future—it might be many years or decades from now—machines will be able to do the jobs of a large percentage of the ‘average’ people in our population, and these people will not be able to find new jobs.”

Firms have discovered that robotics, machine learning, and artificial intelligence can replace humans and improve accuracy, productivity, and efficiency of operations. During the Great Recession of 2008–09, many businesses were forced to downsize their workforce for budgetary reasons. They had to find ways to maintain operations through leaner workforces. One business leader I know had five hundred workers for his $100 million business and now has the same size workforce even though the company has grown to $250 million in revenues. He did this by automating certain functions and using robots and advanced manufacturing techniques to operate the firm.

The US Bureau of Labor Statistics (BLS) compiles future employment projections. In its most recent analysis, the agency predicts that 15.6 million new positions will be created between 2012 and 2022. This amounts to growth of about 0.5 percent per year in the labor force.

The health-care and social assistance sector is expected to grow the most with an annual rate of 2.6 percent. This will add around five million new jobs over that decade. That is about one-third of all the new jobs expected to be created.(21) Other areas that are likely to experience growth include professional services (3.5 million), construction (1.6 million), leisure and hospitality (1.3 million), state and local government (929,000), finance (751,000), and education (675,000).

Interestingly, in light of technology advances, the information sector is one of the areas expected to shrink in jobs. BLS projections anticipate that about 65,000 jobs will be lost there over the coming decade. Even though technology is revolutionizing many businesses, it is doing this by transforming operations, not increasing the number of jobs. Technology can boost productivity and improve efficiency, but does so by reducing the number of employees needed to generate the same or even higher levels of production.

Manufacturing is another area thought to lose jobs. The BLS expects the United States to lose 550,000 jobs, while the federal government will shed 407,000 positions, and agriculture, forestry, fishing, and hunting will drop 223,000 jobs.(22) These sectors are the ones thought to be least likely to generate new positions in the coming decade.

Since BLS projections make few assumptions about emerging technologies, it is likely that their numbers underestimate the disruptive impact of these developments. It is hard to quantify the way that robots, artificial intelligence, and sensors will affect the workforce because we are in the early stages of the technology revolution. It is hard to be definitive about emerging trends because it is not clear how new technologies will affect various jobs.

But there are estimates of the likely impact of computerization on many occupations. Oxford University researchers Carl Frey and Michael Osborn claim that technology will transform many sectors of life. They studied 702 occupational groupings and found that “forty-seven percent of US workers have a high probability of seeing their jobs automated over the next twenty years.”(23) According to their analysis, telemarketers, title examiners, hand sewers, mathematical technicians, insurance underwriters, watch repairers, cargo agents, tax preparers, photographic process workers, new accounts clerks, library technicians, and data entry keyers have a ninety-nine percent of having their jobs computerized. At the other end of the spectrum, recreational therapists, mechanic supervisors, emergency management directors, mental health social workers, audiologists, occupational therapists, health-care social workers, oral surgeons, supervisors of fire fighters, and dieticians have less than a one percent chance of having their tasks computerized. They base their analysis of improving levels of computerization, wage levels, and education required in different fields.(24) In addition, we know that fields such as health care and education have been slow to embrace the technology revolution, but are starting to embrace new models. Innovations in personalized learning and mobile health mean that many schools and hospitals are shifting from traditional to computerized service delivery. Educators are using massive, open, online courses (MOOCs) and tablet-based instruction, while health-care providers are relying on medical sensors, electronic medical records, and machine learning to diagnose and evaluate health treatments.

Hospitals used to be staffed with people who personally delivered the bulk of medical treatment. But health providers now are storing information in electronic medical records and data-sharing networks are connecting lab tests, clinical data, and administration information in order to promote greater efficiency. Patients surf the web for medical information and supplement professional advice with online resources. Both education and health-care sectors are seeing the disruption that previously has transformed other fields.

Given the uncertainties surrounding job projections, it is not surprising that experts disagree over the impact of emerging technologies. For example, in their highly acclaimed book, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies , economists Erik Brynjolfsson and Andrew McAfee argue that technology is producing major changes in the workforce. According to them:

Technological progress is going to leave behind some people, perhaps even a lot of people, as it races ahead. As we will demonstrate, there has never been a better time to be a worker with special skills or the right education because these people can use technology to create and capture value. However, there has never been a worse time to be a worker with only “ordinary” skills and abilities to offer, because computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate.(25)

Former US Treasury Secretary Lawrence Summers is equally pessimistic about the employment impact. He argues that “if current trends continue, it could well be that a generation from now a quarter of middle-aged men will be out of work at any given moment.” From his standpoint, “providing enough work” will be the major economic challenge facing the world. (26)

However, some economists dispute these claims. They recognize that many jobs will be lost through technology improvements, but say that new ones will be created. There may be fewer people sorting items in a warehouse because machines can do that better than humans. But jobs analyzing big data, mining information, and managing data-sharing networks will be created. According to those individuals, the job gains and losses will even out over the long run. In future decades, work will be transformed but humans will still be needed to manage the digital world.

For example, MIT economist David Autor has analyzed data on jobs and technology but “doubts that technology could account for such an abrupt change in total employment […] The sudden slowdown in job creation is a big puzzle, but there is not a lot of evidence it is linked to computers.”(27) In the same vein, Harvard economist Richard Freeman is “skeptical that technology would change a wide range of business sectors fast enough to explain recent job numbers.”(28)

Northwestern economist Robert Gordon takes an even stronger stance. He argues that:

Recent progress in computing and automation is less transformative than electrification, cars, and wireless communication, and perhaps even indoor plumbing. Previous advances that enabled people to communicate and travel rapidly over long distances may end up being more significant to society’s advancement than anything to come in the twenty-first century.(29)

Based on this reasoning, he does not anticipate dramatic workforce effects from emerging technologies, even though many other experts already see the substitution of technology for labor.

A Pew Research Center study asked 1,896 experts about the impact of emerging technologies. Its researchers found that:

Half of these experts (48%) envision a future in which robots and digital agents have displaced significant numbers of both blue- and white-collar workers—with many expressing concern that this will lead to vast increases in income inequality, masses of people who are effectively unemployable, and breakdowns in the social order.(30)

Implications for Public Policy

In the classic Edward Bellamy book, Looking Backwards, protagonist Julian West wakes up from a 113-year slumber and finds that the United States in 2000 is completely different from 1887. People stop working at age forty-five and devote their lives to mentoring other people and contributing to the overall sense of community.(31) There are shorter work weeks for ordinary people and everyone receives full benefits, food, and housing.

Similar to our time period, new technologies at that time enabled people to be very productive in a short period of time. Society did not need a large number of workers so people could devote much of their lives to education, volunteerism, and community development. In conjunction with these employment trends, public policy shifted to encourage new lifestyles and ways of living.

In flash-forwarding to the current era, we may be on the verge of a similar technology transition. Robotics and machine learning have improved productivity and enhanced the overall economy of developed nations. Countries that have invested in innovation have seen tremendous growth in overall economic performance. In the future, it may be possible that society will not need as many workers as seen today.

Yet unlike Bellamy’s utopia, there has been little public discussion regarding the economic or policy impact of emerging technologies. Observers worry that knowledge societies are destroying industrial and manufacturing jobs, and exacerbating social and economic divisions. In its most pointed form, skeptics fear that technology will eliminate jobs, reduce incomes, and create a permanent underclass of unemployable people. As argued by Nicolas Colin and Bruno Palier: “Employment is becoming less routine, less steady, and generally less well remunerated. Social policy will therefore have to cover the needs of not just outside the labor market but even many inside it.”(32)

If technology innovation allows businesses to provide goods and services with far fewer employees, what will that mean for workers? A significant increase in the number of people without full-time jobs would exacerbate divisions within society and complicate the distribution of benefits such as pensions, health care, and insurance. Most benefits are tied to employment so if the economy requires fewer workers due to technological advancement, we need to consider how this will affect social benefit delivery.

In this section, I review short- and long-term steps we should consider to deal with emerging technologies. This includes thinking about how to deliver benefits outside of jobs, considering a basic income guarantee, revamping the earned income tax credit, providing activity accounts for lifetime learning and job retraining, encouraging corporate profit-sharing, providing benefit credits for volunteerism, making curricular reforms to assure that students have the skills they need for a twenty-first-century economy, encouraging adult education and continuing learning, expanding arts and culture for leisure time, and avoiding a permanent underclass suffering the ill effects of income inequality.

Benefits Outside of Jobs

If we end up in a situation with many people unemployed or underemployed for significant periods of time, we need a way to provide health care, disability, and pension benefits outside of employment. Called “flexicurity” or flexible security, this idea “separate(s) the provision of benefits from jobs.”(33) It offers health care, education, and housing assistance on a universal basis.

Currently, people must work sixty percent of the time (around twenty-four hours a week) in order to qualify for full-time benefits. When they are fully employed, they are eligible for company-sponsored health-care plans and pensions. During the period since World War II, jobs have been a primary distribution system for social benefits. Except for the poor and elderly, this keeps benefits outside of the public sector and places the onus on private companies.

That approach worked well in an era when most of the people who wanted jobs were able to get them. People with limited skills were able to get well-paying jobs with benefits in factories, warehouses, and production facilities. They could educate their children, achieve a reasonable standard of living, and guard against disabling illnesses.

The complication came when the economy shifted, wages stagnated, and technology made it possible for companies to get by with fewer workers. The advent of robotics, machine learning, artificial intelligence, and machine-to-machine communications eliminated a number of jobs and put a number of people outside the typical workforce.

For health care, people need access to quality care through plans outside of employment. It is possible through commercial insurers to purchase catastrophic insurance for extraordinary health claims. Or if people are poor or elderly, there are government programs that guarantee access to medical care. The recent expansion of health insurance through the Affordable Care Act has extended insurance to millions of people who previously lacked coverage.

In regard to retirement planning, many employers have moved to 401-style pension plans. Employees contribute to their own funds and sometimes get a match from the employer. But this does not help those outside the workforce who need retirement assistance. Even Social Security is tied to employment. People who have not worked are not eligible for retirement benefits so we need to figure out ways to take care of those people in the newly emerging economy.

Provide Activity Accounts for Lifetime Learning and Job Retraining

We should consider the establishment of activity accounts for lifetime learning and job retraining. In an era of fast technology innovation and job displacement, there needs to be a means for people to gain new skills throughout their adulthood. When people are employed, their companies could contribute a set amount to an individual’s fund. This account could be augmented by contributions from the person him or herself as well as the government. Similar to a retirement account, money in the fund could be invested tax-free in investment options including cash reserves, stocks, and bonds. The owner of the account could draw on it to finance lifetime learning and job retraining expenses. It would be portable, meaning that if the person moved or switched jobs, the account would migrate with that individual.

The goal of this account is to provide incentives for continuing education. Under virtually any scenario, people are going to have to continue their education beyond the first twenty years of their lives. Emerging jobs are going to require different skills than what people gain in school. There will be new jobs created that may not exist today. As pointed out by Brookings Institution scholar Kemal Dervis, it will be crucial as technology innovation continues in the future to provide people with a means to upgrade their skills and knowledge levels.(34) He notes that France has established “individual activity accounts” that provide social benefits.

With the expected increase in leisure time, adults need time and financial support for continued learning. We should not envision education merely as a time for young people to learn new skills or pursue areas of interest. Instead, we need to think about education as a continuing activity that broadens people’s horizons over the course of their entire lives. Education is an enrichment activity and we need to view it as a general benefit for the individual as well as the society as a whole.

Incentives for Volunteerism

The trends cited in this analysis suggest that we need to consider income supplements or benefit eligibility through vehicles other than full-time jobs. The workforce ramifications of emerging technologies mean that many people in the future may not be able to provide for their families through regular employment.

One possibility comes through volunteer activities. Even if people have limited employment options, many participate in a wide range of public-minded organizations. They help other people, train the next generation, or provide assistance for the less fortunate in society.

A variety of survey evidence demonstrates that young people are particularly interested in volunteerism. In general, they have different attitudes toward work and leisure time, and many say they want time to pursue outside activities. For example, a survey of American students found that they want “a job that focuses on helping others and improving society.” In addition, they value quality of life considerations, not just financial well-being.(35)

A number of them value volunteer activities outside of their work experience. They have varied interests and want extra-curricular activities that fulfill them. This may involve tutoring in after-school programs, helping English as a Second Language pupils, stopping domestic violence, protecting the environment, or encouraging entrepreneurship. According to a Deloitte study, “63 percent of Millennials donate to charities and 43 percent actively volunteer or are a member of a community organization.”(36)

In a digital world where there may be less work and more leisure time, it makes sense to think about incentives and work credits for volunteerism. This could include credits toward social benefits or public rewards that acknowledge community contributions. In the United Kingdom, for example, volunteers get reimbursed for expenses or earn credits for job training programs through participation in worthy causes. In addition, volunteering counts as “looking for work” so people can use those activities to qualify for national insurance credits.(37)

Going forward, the United States should consider those types of incentives. In the future, people are likely to have more time outside of employment so it makes sense to encourage them toward community engagement and give them incentives to volunteer for nonprofit organizations or charitable causes. This will benefit the overall community and give people purposeful activities in which to engage.

Expanding Arts and Culture for Leisure Time

The so-called “end of work” may create a new kind of economy. According to Harvard economist Lawrence Katz: “It is possible that information technology and robots [will] eliminate traditional jobs and make possible a new artisanal economy […] an economy geared around self-expression, where people would do artistic things with their time.”(38) From his standpoint, this transition would move the world from one of consumption to creativity.

People will use their leisure time to pursue interests in arts and culture, or special areas that they follow. This could include reading, poetry, music, or woodworking. Depending on their background, they could have more time for family and friends. A study of family time found that macroeconomic conditions affect how much time people spend together. When employment problems rise, “fathers spend more time engaging in enriching childcare activities” and “mothers are less likely to work standard hours.”(39) As long as there are opportunities for people to pursue broader interests, reduction in work does not have to eliminate chances for cultural pursuits.

To summarize, advanced societies are at a major turning point in terms of how we think about work, leisure, and social benefit delivery. If these economies need fewer workers to complete needed tasks and benefits are delivered mainly through full-time jobs, there is a danger that many people will have difficulties getting health care, pensions, and the income maintenance they need to sustain their lives. This is of particular concern at a time of large income inequality and highly skewed economic distributions.(40)

The contrast between the age of scarcity in which we have lived and the upcoming age of abundance through new technologies means that we need to pay attention to the social contract. We need to rewrite it in keeping with the dramatic shifts in employment and leisure time that are taking place. People have to understand we are witnessing a fundamental interruption of the current cycle where people are paid for their work and spend their money on goods and services. When a considerable portion of human labor is no longer necessary to run the economy, we have to rethink income generation, employment, and public policy. Our emerging economic system means we will not need all the workers that we have. New technologies will make these individuals obsolete and unemployable.

In this situation, it is important to address the policy and leisure time issues raised by persistent unemployment or underemployment. There is a danger of disruptions and unrest from large groups of people who are not working. That creates poverty and social dissatisfaction and runs the risk of instability for the society as a whole. Stability cannot be enforced through a police presence or having wealthy individuals live in gated communities.

There needs to be ways for people to live fulfilling lives even if society needs relatively few workers. We need to think about ways to address these issues before we have a permanent underclass of unemployed individuals. This includes a series of next steps for society. There needs to be continuous learning avenues, opportunities for arts and culture, and mechanisms to supplement incomes and benefits other than through full-time jobs. Policies that encourage volunteerism and reward those who contribute to worthy causes make sense from the standpoint of society as a whole. Adoption of these steps will help people adapt to the new economic realities.


I wish to thank Hillary Schaub for outstanding research assistance on this project.

1. James Manyika, Michael Chui, Jacques Bughin, Richard Dobbs, Peter Bisson, and Alex Marrs, Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy (McKinsey Global Institute, May, 2013).

2. Daniela Rus, “How technological breakthroughs will transform everyday life,” Foreign Affairs, July/August, 2015.

3. A more extended discussion of these issues can be found in Darrell M. West, What Happens If Robots Take the Jobs? (Brookings Institution Policy Report, October, 2015).

4. James Hagerty, “Meet the new generation of robots for manufacturing,” Wall Street Journal, June 2, 2015.

5. Alison Sander and Meldon Wolfgang, “The rise of robotics,” The Boston Consulting Group, August 27, 2014. https://www.bcgperspectives.com/content/articles/business_unit_strategy_innovation_rise_of_robotics.

6. RBC Global Asset Management, Global Megatrends: Automation in Emerging Markets (2014).

7. Cynthia Breazeal, “The personal side of robots,” South by Southwest, March 13, 2015.

8. Eitan Wilf. “Sociable robots, jazz music, and divination: contingency as a cultural resource for negotiating problems of intentionality,” American Ethnologist: Journal of the American Ethnological Society, November 6, 2013: 605. http://onlinelibrary.wiley.com/doi/10.1111/amet.12041/abstract.

9. Mike Murphy, “Amazon tests out robots that might one day replace warehouse workers,” Quartz, June 1, 2015.

10. Lydia DePillis, “Minimum-wage offensive could speed arrival of robot-powered restaurants,” Washington Post, August 16, 2015.

11. Thi-Hai-Ha Dang and Adriana Tapus, “Stress game: the role of motivational robotic assistance in reducing user’s task stress,” International Journal of Social Robotics, April, 2015.

12. Michael Lewis, Flash Boys: A Wall Street Revolt (New York: Norton, 2015).

13. Andrei A. Kirilenko and Andrew W. Lo, “Moore’s Law versus Murphy’s Law: algorithmic trading and its discontents,” Journal of Economic Perspectives, 2013. http://www.jstor.org/stable/pdf/23391690.pdf?acceptTC=true.

14. Shukla Shubhendu and Jaiswal Vijay, “Applicability of artificial intelligence in different fields of life,” International Journal of Scientific Engineering and Research, September, 2013.

17. Derek Thompson, “A world without work,” The Atlantic, July/August, 2015.

18. Dawn Nakagawa, “The second machine age is approaching,” Huffington Post, February 24, 2015.

19. MIT Technology Review, “Who will own the robots,” September, 2015.

20. Martin Ford, The Lights in the Tunnel: Automation, Accelerating Technology, and the Economy of the Future (CreateSpace Independent Publishing Platform, 2009). Also see his more recent book, Rise of the Robots: Technology and the Threat of a Jobless Future (New York: Basic Books, 2015).

21. US Bureau of Labor Statistics, “Employment projections: 2012–2022 summary,” December 19, 2013. http://www.bls.gov/news.release/ecopro.nr0.htm

23. Quoted in Harold Meyerson, “Technology and trade policy is pointing America toward a job apocalypse,” Washington Post, March 26, 2014. The original paper is by Carl Benedikt Frey and Michael Osborne, “The future of employment: how susceptible are jobs to computerisation,” Oxford University Programme on the Impacts of Future Technology, September 17, 2013.

24. Frey and Osborne, “The future of employment,” op. cit.: 57–72.

25. Erik Brynjolfsson and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (New York: W. W. Norton, 2014), 11.

26. Lawrence Summers, “The economic challenge of the future: jobs,” Wall Street Journal, July 7, 2014.

27. Quoted in David Rotman, “How technology is destroying jobs,” MIT Technology Review, June 12, 2013. http://www.technologyreview.com/featuredstory/515926/how-technology-is-destroying-jobs/

29. Quoted in Melissa Kearney, Brad Hershbein, and David Boddy, “The future of work in the age of the machine,” Brookings Institution Hamilton Project, February, 2015.

30. Aaron Smith and Janna Anderson, “AI, robotics, and the future of jobs,” Pew Research Center, August 6, 2014.

31. Edward Bellamy, Looking Backward 2000–1887 (Boston: Ticknor & Co., 1888).

32. Nicolas Colin and Bruno Palier, “Social policy for a digital age,” Foreign Affairs, July/August, 2015.

34. Kemal Dervis, “A new birth for social democracy,” Brookings Institution Project Syndicate, June 10, 2015.

35. The Griffith Insurance Education Foundation, “Millennial generation attitudes about work and the insurance industry,” February 6, 2012.

36. Lindsey Pollack, “Attitudes and attributes of millennials in the workplace,” September 12, 2014.

37. Job Centre Plus, “Volunteering while getting benefits,” UK Department for Work and Pensions, October, 2010. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/264508/dwp1023.pdf

38. Quoted in Thompson, “A world without work,” op. cit.

39. Melinda Sandler Morill and Sabrina Wulff Pabilonia, “What effects do macroeconomic conditions have on families’ time together?” Leibniz Information Centre for Economics, 2012. http://hdl.handle.net/10419/58561

40. Darrell M. West, Billionaires: Reflections on the Upper Crust (Washington DC: Brookings Institution Press, 2014).

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  • Knowledge, Technology and Complexity in Economic Growth

Co-Chairs: Ricardo Hausmann (Director, Center for International Development and Professor of the Practice of Economic Development, Harvard Kennedy School of Government) and José Domínguez (Professor of Structural Engineering, School of Engineering, University of Seville). 

The application of complexity science tools to the study of society allows for the analysis of phenomena that have been hard to identify and analyze with more traditional tools, especially in the field of Economics, which in the absence of these tools has tended to work with relatively low dimensional representations of reality. But the increasing availability of more detailed information of social phenomena makes it particularly useful to use tools that can exploit this informational richness. This opens up fascinating new horizons on almost all fields of knowledge in the social sciences.

In economics, it is widely accepted that technology is the key driver of economic growth of countries, regions and cities. Technological progress allows for  the more efficient production of more and better goods and services, which is what prosperity depends on.

However, the mechanisms through which technology is developed, adopted and used in production are complex. Their more detailed analysis can allow for new findings that could have important impacts in many areas of policy, including science policy, research and development, industrial policy, and both national and regional development policies. In fact, the concept of technology itself as well as the individual and social capabilities required for its development ca now be studied at a much more fine-grained level leading to potential contributions that may impact higher education, job creation and economic growth. Clearly, there are links between education, research and development, innovation and economic activity that are part of the process we aim to uncover.


The recent shift towards open innovation has resulted in increased flows of knowledge and new types of cooperation between education institutions, research organizations and business. Top corporate R&D investors worldwide lead the development of many emerging technologies. This is evident from an examination of the technology fields in which these companies intensified their inventive activities in the recent years and the contribution of top R&D investors to the overall development of these fields. Top corporate R&D investors accelerated their inventive activities in areas such as engines, automated driving systems, big data, artificial intelligence, 3-D printing and information and communication technologies.

It is necessary to remember that the two main ingredients for the development of new technology are codified knowledge in the form of theories, frameworks, scientific papers, patents, recipes, protocols, routines and instruction manuals and tacit knowledge or knowhow, which is acquired through learning by doing in a long process of imitation and repetition and which exists only in brains . The development of science, technology, innovation and production require both codified and tacit knowledge but the codifiable component of science and technology get registered, respectively, in the form of scientific publications and patents, and these are grouped into categories. Scientific publications, patents, industries, occupations and products are proxies of scientific knowledge, technological development, economic activity and human skills.

They form what is known as a multiplex network with six kinds of nodes where geographic location represents the last one. Understanding relations within each layer, e.g., the knowledge space, the patent space, the industry space, the occupation space, the product space and the country (or location space); and across layers should shed light on the foundations of countries economic development and the policies to be implemented in all of these areas to promote it.

Policies towards science, technology and innovation (STI) would benefit enormously from a deeper and more detailed understanding of the 6-fold multiplex that this research wants to uncover. What are the detailed connections between areas of science, as captured by journal publications and patents. What forms of human collaboration are necessary for the authorship of papers and patents? And for national and local authorities: What are the connections between a local effort in science and local innovation? What are the backward linkages of industrial diversification on the local capacity to create patents and scientific papers? Which occupations are key to facilitate the diffusion of industries to particular locations and what are the educational profiles of those occupations? What is the role of human mobility and the attraction of talent in the successful development of STI?

To sum up, this study group pursues three purposes:

  • To analyze empirically the nature of the partnerships and ecosystem relations that underpin scientific and technological progress and its manifestation in the development of new industries, the appearance of new products and the formation of new teams of people with different and complementary occupations. To do this, we will carry out a research agenda to uncover these connections.  
  • To develop tools in order to assess the position of each country and region in the multiplex and its evolution over time in order to evaluate their “adjacent possible” in a way that can help them plan their efforts towards progress.  
  • To expand the dimensions that the quantitative tools of CID’s Atlas of Economic Complexity can offer its worldwide users, thus allowing policymakers, corporations, STI participants and the broader public to benefit from the results of the research.
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Industrial Revolution and Technology

Whether it was mechanical inventions or new ways of doing old things, innovations powered the Industrial Revolution.

Social Studies, World History

Steam Engine Queens Mill

The use of steam-powered machines in cotton production pushed Britain’s economic development from 1750 to 1850. Built more than 100 years ago, this steam engine still powers the Queens Mill textile factory in Burnley, England, United Kingdom.

Photograph by Ashley Cooper

The use of steam-powered machines in cotton production pushed Britain’s economic development from 1750 to 1850. Built more than 100 years ago, this steam engine still powers the Queens Mill textile factory in Burnley, England, United Kingdom.

It has been said that the Industrial Revolution was the most profound revolution in human history, because of its sweeping impact on people’s daily lives. The term “industrial revolution” is a succinct catchphrase to describe a historical period, starting in 18th-century Great Britain, where the pace of change appeared to speed up. This acceleration in the processes of technical innovation brought about an array of new tools and machines. It also involved more subtle practical improvements in various fields affecting labor, production, and resource use. The word “technology” (which derives from the Greek word techne , meaning art or craft) encompasses both of these dimensions of innovation. The technological revolution, and that sense of ever-quickening change, began much earlier than the 18th century and has continued all the way to the present day. Perhaps what was most unique about the Industrial Revolution was its merger of technology with industry. Key inventions and innovations served to shape virtually every existing sector of human activity along industrial lines, while also creating many new industries. The following are some key examples of the forces driving change. Agriculture Western European farming methods had been improving gradually over the centuries. Several factors came together in 18th-century Britain to bring about a substantial increase in agricultural productivity. These included new types of equipment, such as the seed drill developed by Jethro Tull around 1701. Progress was also made in crop rotation and land use, soil health, development of new crop varieties, and animal husbandry . The result was a sustained increase in yields, capable of feeding a rapidly growing population with improved nutrition. The combination of factors also brought about a shift toward large-scale commercial farming, a trend that continued into the 19th century and later. Poorer peasants had a harder time making ends meet through traditional subsistence farming. The enclosure movement, which converted common-use pasture land into private property, contributed to this trend toward market-oriented agriculture. A great many rural workers and families were forced by circumstance to migrate to the cities to become industrial laborers. Energy Deforestation in England had led to a shortage of wood for lumber and fuel starting in the 16th century. The country’s transition to coal as a principal energy source was more or less complete by the end of the 17th century. The mining and distribution of coal set in motion some of the dynamics that led to Britain’s industrialization. The coal-fired steam engine was in many respects the decisive technology of the Industrial Revolution. Steam power was first applied to pump water out of coal mines. For centuries, windmills had been employed in the Netherlands for the roughly similar operation of draining low-lying flood plains. Wind was, and is, a readily available and renewable energy source, but its irregularity was considered a drawback. Water power was a more popular energy source for grinding grain and other types of mill work in most of preindustrial Europe. By the last quarter of the 18th century, however, thanks to the work of the Scottish engineer James Watt and his business partner Matthew Boulton, steam engines achieved a high level of efficiency and versatility in their design. They swiftly became the standard power supply for British, and, later, European industry. The steam engine turned the wheels of mechanized factory production. Its emergence freed manufacturers from the need to locate their factories on or near sources of water power. Large enterprises began to concentrate in rapidly growing industrial cities. Metallurgy In this time-honored craft, Britain’s wood shortage necessitated a switch from wood charcoal to coke, a coal product, in the smelting process. The substitute fuel eventually proved highly beneficial for iron production. Experimentation led to some other advances in metallurgical methods during the 18th century. For example, a certain type of furnace that separated the coal and kept it from contaminating the metal, and a process of “puddling” or stirring the molten iron, both made it possible to produce larger amounts of wrought iron. Wrought iron is more malleable than cast iron and therefore more suitable for fabricating machinery and other heavy industrial applications. Textiles The production of fabrics, especially cotton, was fundamental to Britain’s economic development between 1750 and 1850. Those are the years historians commonly use to bracket the Industrial Revolution. In this period, the organization of cotton production shifted from a small-scale cottage industry, in which rural families performed spinning and weaving tasks in their homes, to a large, mechanized, factory-based industry. The boom in productivity began with a few technical devices, including the spinning jenny, spinning mule, and power loom. First human, then water, and finally steam power were applied to operate power looms, carding machines, and other specialized equipment. Another well-known innovation was the cotton gin, invented in the United States in 1793. This device spurred an increase in cotton cultivation and export from U.S. slave states, a key British supplier. Chemicals This industry arose partly in response to the demand for improved bleaching solutions for cotton and other manufactured textiles. Other chemical research was motivated by the quest for artificial dyes, explosives, solvents , fertilizers, and medicines, including pharmaceuticals. In the second half of the 19th century, Germany became the world’s leader in industrial chemistry. Transportation Concurrent with the increased output of agricultural produce and manufactured goods arose the need for more efficient means of delivering these products to market. The first efforts toward this end in Europe involved constructing improved overland roads. Canals were dug in both Europe and North America to create maritime corridors between existing waterways. Steam engines were recognized as useful in locomotion, resulting in the emergence of the steamboat in the early 19th century. High-pressure steam engines also powered railroad locomotives, which operated in Britain after 1825. Railways spread rapidly across Europe and North America, extending to Asia in the latter half of the 19th century. Railroads became one of the world’s leading industries as they expanded the frontiers of industrial society.

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Related Resources

Technological Progress and Economic Growth | Economics

technical progress essay

Technological change is the most important factor that determine rate of economic growth. It plays a important role than the capital formation. It is the technological change which can bring about continued increase in output per head of the population. Thus it is the prime-mover of economic growth.

Technological change or progress refers to the discovery of the new and improved methods of producing goods. Sometimes technological advances result in the increase in available supplies of natural resources. But more generally technological changes result in increasing the productivity of labour, capital and other resources. The productivity of combined inputs of all factors is called total factor productivity. Thus technological progress means increase in total factor productivity. As a result of technological advance, it becomes possible to produce more output with same resources or the same amount of product with less resource.

But the question arises as to how the technological progress takes place. The technological progress takes place through inventions and innovations. The word invention is used for the new scientific discoveries, whereas the innovations are said to take place only when the new scientific discoveries are commercially used for actual production of goods. Some inventions may not be economically profitable to be used for actual production.

It follows from above that technological change brings about an increase in output per head. Thus technological change, or more precisely technological progress, is the change in the production process which results in an increased output per unit of labour. Technological change causes a shift in the production function embodying all known techniques. Technological change must be distinguished from a change in technique. While by technological change we mean advance in knowledge resulting in improved methods of production, change in technique refers to the use of a different but already known method of production.


The process of economic growth involves the increase in the production of goods and services. Increase in production can be achieved either through the use of more resources and/or through the realization of higher productivity by means of using the resources of labour, capital and land more efficiently. Technological change helps to promote growth in both these ways. It can help in the discovery of new natural resources in the country and thereby enhances the productive potential of the country. Technological change also increases the productivity of available resources.

For instance, it can find out the productive uses of land that hitherto has been regarded as infertile or it can discover new economic use of a raw material that had previously been considered as useless. But, as explained above, technological change more generally results in higher productivity of resources. Technological change raises the productivity of worker through the provision of better machines, better methods and superior skills.

Table 8.1 gives the percentage increase in labour productivity in a number of countries during the period 1970-1989. By bringing about increase in productivity of resources the progress in technology makes it possible to produce more output with the same resources or the same amount of output with less resource. Progress in technology causes improvement in technology through the provision of better machines, better methods and enhanced skills.

It is technology which underlies the process of producing new things with the existing resources or using the existing resources in new ways. This is what Schumpeter means when he says that, “the slow and continuous increase in the national supply of productive means and savings is obviously an important factor in explaining the course of economic history through the centuries, but it is completely overshadowed by the fact that development consists primarily in employing existing resources in a different way, in doing new things with them, irrespective of whether those resources increase or not. It is important to emphasize that newly discovered techniques lead to the increase in output per worker.”

Productivity of workers depends upon the quantity and quality of capital tools with which they work. For higher productivity the instruments of production have to be technologically efficient and superior. The technological options open to an economy determine the input mix of production. A commodity can be produced by various technologies. The quantity and quality of capital, skills and other factors required for production is directly dependent on the efficiency of the technique of production being used. Also, the managerial and organisational expertise has to be in line with the technological requirements of production. Thus, technology in the present stage of economic development is an indispensable factor of production.

This is the age of technology. The developing countries are obsessed by the desire to make rapid progress in technology so as to catch up with the present-day developed countries. Frantic efforts are being made to install improved technology in agriculture, industries, health, sanitation and education; in fact in all walks of human life. Indeed, the newly emerging nations have come to regard technology as a bastion of national autonomy and as a status symbol in the international community.

technical progress essay

  • Charles Kennedy &
  • A. P. Thirlwall  

50 Accesses

1 Citations

Over the years the term technical progress has been given a wide range of meanings and interpretations. Here we shall use the term in two main senses which will subsequently form the subject-matter of the two main sections of this Survey. First we shall use the term to refer to the effects of changes in technology, or more specifically the role of technical progress in the growth process. Secondly, we shall use the term to refer to changes in technology itself, defining technology as useful knowledge pertaining to the art of production. In this context, we shall be concerned with the knowledge-creating activities of research, invention and development, together with the process of absorption of new knowledge into the productive system. These two interpretations of the term technical progress correspond broadly to the division in the economic literature between “macro”-studies which attempt to quantify the rate of technical progress as a determinant of the growth of output, and “micro”-studies which seek to explain the process of technical change—usually in a disaggregated way in firms and industries. In some places our Survey will overlap with the recent theoretical survey of growth by Hahn and Matthews [112], but in the main it is its complement, except that to emulate its thoroughness and masterly exposition would be a technological feat in itself! Our instructions were to make this Survey Anglo-centric. But for reasons that economists will appreciate it has come inevitably to stride the Atlantic with by far the larger foot in North America.

“The annual produce of the land and labour of any nation can be increased in its value by no other means but by increasing either the number of its productive labourers or the productive powers of those labourers who had before been employed … in consequence either of some addition and improvement to those machines and instruments which facilitate and abridge labour or of a more proper division and distribution of labour.” (A. Smith)

We are grateful to the Committee in charge of this Survey series for their comments on an early draft of the Survey, and also to numerous colleagues at the University of Kent especially Mr. R. Dixon who checked the references and who has subsequently pointed out two small errors in the text as originally published, which are now corrected. They refer to equation (10) and the conclusions of Johansen’s study.

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Kennedy, C., Thirlwall, A.P. (1973). Technical Progress. In: Surveys of Applied Economics. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-01860-4_3

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Writing Progress Reports

Stacey Corbitt

Chapter Overview

It may seem like technical writing – indeed, many kinds of professional business writing – must be huge undertakings involving much effort and endless detail. With all the emphasis on being complete, accurate, and collaborative, do you wonder whether you can develop enough skill during college to compete as a writer in a technical or other business position? You may be hoping there’s an engineering or other professional position out there where you can stay under the radar and do your job without having to write anything important.

There is good news on this matter, and then there is great news.

First, the good news: virtually all entry-level professional positions present opportunities to practice writing in a variety of situations and for multiple types of readers. Writing in technical fields, as you may now realize, can require significant time commitment and collaboration, as well as various other “soft skills.” As a result, employees working to gain experience in the field may be tapped frequently to complete writing tasks.

Do you wonder exactly how the preceding paragraph is good news? Consider the great news: the day-to-day business of technical writing is largely short, direct reporting for specific purposes and audiences. While short reports aren’t necessarily easy to write, they do offer opportunities to practice crafting clear and concise documents. The progress report is one of several standard forms of short reports. This chapter aims to help students understand how to plan and write progress reports that meet the needs of their assignments as well as the standards of professionalism required by their fields of study and work.

What is the audience and purpose of a progress report?

Progress reports are typically requested and reviewed by one or more stakeholders in a project. Stakeholders is a general term for people who have a business interest in the subject project and may need progress reports because of fiscal, legal, financial, or other responsibility for the work in question. While progress reports may be required by the person or group at the next level of responsibility above your own, the readership and reach of your periodic progress reports can be greater than you know, sometimes applying to the top tier of an organization.

Put simply, stakeholders use progress reports to communicate about work on projects, including levels of completion and delays alike. These reports provide a number of opportunities for communication, including but not limited to

  • reporting early research findings
  • notifying stakeholders about problems
  • discussing potential changes in planned work, schedule, and other project factors
  • evaluating work completed

As with all technical writing opportunities, careful characterization of the audience and the context in which the report will be used is crucial to successfully achieving the purpose of a progress report. In addition to these standard considerations, other specific questions a writer should ask in preparation for writing a progress report include the following:

  • Has the requestor specified a form you must use? If so, do you have the most up-to-date form and specifications to follow?
  • What is the date of expected delivery for this report? What is the expected frequency of reporting? For example, do you need to report once weekly, or more or less frequently?
  • Is supporting documentation necessary? If so, how should you include it?
  • Is there an oral presentation component required with this report?
  • Have you set aside enough time to complete this report and obtain a peer review?

In a word, the key to writing efficient, clear progress reports is preparation . Always take the time needed to ask these practical questions about the rhetorical situation in which you will be writing a progress report for any project.

What is the necessary content for a progress report?

Depending upon the information you collect through the questioning activity outlined in the previous section, the specific content your project progress report will need can vary. In general, though, you might think about the content required in a progress report in a specific way: that is, part of the content comes from the past; part of it discusses the work you are doing today; and the third part of the content represents the project’s future.

Activity: begin drafting a progress report

Begin with an individual or group project in which you are currently involved, whether for your writing course or another class. Proceed by making notes in response to the following directions.

  • Next, a brief discussion of the work you are doing today or this week will address the present tense portion of your discussion.
  • Third, from the same point of view in the present moment, look ahead of you at all the project-related work you want to address between now and the next reporting milestone. Write a quick description of what plans you have for the project’s future, using the future tense to describe what you and your team will begin, what you will complete, and so on.
  • Finally, build a draft timeline that displays the entire list of tasks for your project, whether completed, ongoing, or to begin at a point in the future. You may consider developing a Gantt chart , like the one presented in Figure 11.7, shown below and adapted from Exploring Business, published by University of Minnesota (2016) .

Gantt Chart for Vermont Teddy Bear feautring the activities of cut fur, stuff and sew fur, cut material, sew clothes, embroider T-shirt, cut accessories, sew accessories, dress bears, package bears, and ship bears

Use the notes you have prepared in this activity to complete the Homework at the end of this chapter.

What are the important stylistic considerations for a progress report?

If you put yourself in the position of the typical audience for a progress report, you can identify the characteristics that are most important for that reader’s use of the document. As you know, writing that is clear, concise, complete, and correct is vital to the success of any technical document in reaching its audience and accomplishing its purpose. With regard to progress reports, particularly those written in business, one additional quality critical to success is brevity . The progress report is an ideal demonstration of writing that should include only significant details and nothing extraneous. To the extent a progress report for your work can be accomplished in one single-spaced page, do not make it longer.

Use active construction

Because they constitute a direct communication from the writer to one or more identified readers, progress reports are frequently presented in one of the common business correspondence formats: namely, an email, memo, or letter report. Correspondence is a genre of writing that lends itself to the use of personal pronouns like I , we , and you in particular. Being able to use a first-person voice with personal pronouns gives writers an advantage toward writing progress reports: personal pronouns make it easier to use active constructions.

Using the active voice, or active construction , essentially means that you construct sentences and passages in which the following characteristics are evident:

  • The subject performs the action of the verb rather than receiving the action of the verb.
  • The use of forms of “to be,” also known as state of being verbs, is minimized.
  • The emphasis of an active sentence is on the subject and verb, rather than on an object.

Consider the following examples:

Notice that the nouns first written in each sentence – my sister, the carpool, and my glasses – are all receiving the action of the verbs in the sentences.

Notice also that each of those verb phrases includes a form of to be : was bitten, is being organized, and have…been seen .

Finally, notice that the same word follows the verb phrase in each sentence – by – creating a prepositional phrase that indicates the noun or pronoun performing the action in each sentence.

Now examine the same three statements below, written in the active voice:

Notice the change in arrangement of words in each statement. You can identify the subject that appears at the beginning of each sentence; followed by the verb or verb phrase that indicates the action being performed by the subject; and finally the direct object of the sentence that receives the action of the verb. The numbers in parentheses in both sets of examples indicate the total number of words in each sentence.

What are your thoughts about converting sentence construction from passive to active for purposes of being clear in a progress report? Discuss the question with a partner in class and make some notes about your observations. Do you think the active construction has advantages over passive construction? Does active construction have disadvantages?

Near the beginning of this section, you read “… personal pronouns make it easier to use active constructions.” Do you think that statement is true? Discuss why or why not.

Stick to the facts

Your goal is to write an excellent progress report by making your work clear and complete while keeping the document brief. In the previous section, you practiced revising sentences from passive to active construction, a tactic that increases clarity while usually decreasing overall sentence length. Another useful practice in writing short reports – particularly those for the workplace – is to resist sharing your opinions, suggestions, and other unrequested content. Concentrate on reporting the facts and responding to exactly what the reader has requested.

What organizational structure should be used for a progress report?

Recall that one of your earliest tasks in preparing to write a progress report is to discover what information you must report and whether a specific form is required. In the event these details are not part of the assignment you receive, you may need to determine the clearest and most efficient way to organize the body of your report. Consider the following possibilities.

As is the case with structural considerations for any technical report, the most important point in choosing an organizational pattern is to make that pattern clear to the reader. Keep in mind that the structures delineated in the previous table are intended to guide the development of the body of your report in the event you do not receive specific guidance from the project manager or your instructor. Similarly, you may have to decide whether the report should be submitted as a letter, a memo, an email, a presentation, or another format that may be preferred by your reader.

In her 2019 book Technical Writing Essentials: Introduction to Professional Communications in the Technical Fields , author Suzan Last provides the following suggested outline of elements to include in a progress report generally (pp. 178-179):

Progress Reports: a Structural Overview

1. Introduction

Review the details of your project’s purpose, scope, and activities. The introduction may also contain the following:

  • date the project began; date the project is scheduled to be completed
  • people or organization working on the project
  • people or organization for whom the project is being done
  • overview of the contents of the progress report.

2. Project status

This section (which could have sub-sections) should give the reader a clear idea of the current status of your project.  It should review the work completed, work in progress, and work remaining to be done on the project, organized into sub-sections by time, task, or topic. These sections might include

  • Direct reference to milestones or deliverables established in previous documents related to the project
  • Timeline for when remaining work will be completed
  • Any problems encountered or issues that have arisen that might affect completion, direction, requirements, or scope.

3.  Conclusion

The final section provides an overall assessment of the current state of the project and its expected completion, usually reassuring the reader that all is going well and on schedule. It can also alert recipients to unexpected changes in direction or scope, or problems in the project that may require intervention.

4.  References section if required.

Chapter conclusion

Progress reports are an ideal example of workplace technical writing for science and engineering students to study. Progress reports represent short, clear documents with a specific purpose. These reports use typical business correspondence formats to communicate detailed technical information to a known audience. A successful progress report’s other characteristics include

  • sentences constructed in the active voice
  • factual information without opinions, speculation, or extraneous content
  • an appropriate pattern of organization

Use the notes and project schedule you prepared in the Activity earlier in this chapter to write a progress report for your current research project. Address all of the following considerations, but do not use this list to organize your report:

  • Confirm with your instructor the required report format – email, letter, memorandum, or presentation
  • Determine the appropriate organizational pattern – chronological, priority, or topic – for the body of the report
  • summarize and evaluate research findings to date
  • present the project schedule
  • problems, changes, delays, and questions

Last, S. (2019, January 1). Technical writing essentials . BCcampus OpenEd: University of Victoria. License: CC-BY-4.0 . https://pressbooks.bccampus.ca/technicalwriting

University of Minnesota. (2016, April 8). Exploring business . University of Minnesota Libraries Publishing. License: CC BY-NC-SA 4.0 . https://open.lib.umn.edu/exploringbusiness/

Mindful Technical Writing Copyright © 2020 by Stacey Corbitt is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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On Jan. 31, 2024, researchers from the Fraunhofer Institute for Solar Energy Systems (Fraunhofer ISE) announced that, alongside perovskite developer Oxford PV, they had produced a full-sized perovskite tandem module with a conversion efficiency of 25%. At 421 W, the dual-glass module’s power output is far from that achieved by the large-format modules manufactured by solar industry giants. Nonetheless, the result was a powerful demonstration of the steps being made toward commercializing what is widely considered the next generation of solar cell technology.

When announcing the result, the Fraunhofer ISE team noted that scientists from its CalLab PV Modules’ calibration laboratory used a “multispectral solar simulator” to measure both the crystalline silicon solar cell and perovskite cells. It allowed for different light spectra to be applied to the cell while under continuous illumination. This required specialized measurement equipment based on LED light sources that were able to provide illumination evenly across the module’s 1.68 m 2 surface.

“The continuous intensity and spectral stability of the light source is of particular importance especially for tandem devices,” said Johnson Wong, general manager for the Americas at equipment provider Wavelabs. The researchers from Fraunhofer ISE used Wavelabs’ Sinus-3000 Advanced LED module I-V tester for the Oxford PV module.

“Thanks to its optimized light distribution over a long working distance, the tester light source is designed to cast a light field that very closely mimics the sun at every point over the large module area,” Wong added. He said the Sinus-3000 LED tester exceeds A+ class in terms of “spectrum, light uniformity, and stability over time, which play a critical role in the measurement accuracy.”

Accurate characterization

The accurate characterization of perovskite solar devices requires not only new equipment but also novel processes. Longer illumination times are needed; the temperature impact of the light source must be controlled or corrected for; I-V sweeps should be significantly slower than in crystalline silicon cells; and, in tandem cells, their current must be aligned so that the combined power output is not limited.

The PV research community, prospective manufacturers, and equipment suppliers are making strides in overcoming the formidable challenges posed by perovskite solar devices. New, collaborative research projects are being launched and measurement routines are becoming more sophisticated. As a result, confidence is growing that as the prospective PV perovskite manufacturers develop their devices toward maturity, the equipment and processes will be ready.

Sunny prospects

Karl Melkonyan, PV technology analyst with S&P Global Commodity Insights, said that perovskite tandems have “the best chances for commercialization” among next-generation solar cell technologies. Perovskite PV cells can be coupled with either crystalline silicon (c-Si) or thin-film solar cells.

Early perovskite PV devices achieved conversion efficiencies in the low single digits – 3.8% was recorded in 2008. Record efficiencies are now set at regular intervals and are well beyond 25%.

Perovskite tandem devices are extremely promising, primarily because the thin-film perovskite cell plus the “base” c-Si, cadmium telluride, or copper indium gallium selenide layer can capture different light wavelengths, resulting in small-scale research cells with efficiencies beyond 30%.

Translating lab efficiency to larger cells and modules is difficult, however. “While there are many record efficiency achievements of perovskite solar cells reaching 20% and above, the total efficiency of a tandem structure can be much lower than the sum of those individual efficiencies,” said Melkonyan. He noted that the reason for this is often a current mismatch between bottom and top cells.

Measurement challenges

For a PV device to prove its worth, its power output must be able to be measured in a highly accurate, replicable, and standardized fashion. At the end of the day, if a PV module is to be purchased and installed, it is vital that its nameplate power output can be trusted.

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Here, as noted in the recent Fraunhofer ISE and Oxford PV result, perovskite PV devices present a host of new challenges. “Yes, the power measurement of a perovskite tandem or multi-junction cell presents challenges and could be quite difficult because very specific spectrally-adjustable solar simulators are required,” said Melkonyan. “Apart from appropriate stabilization methods for different perovskite materials, the processes should include standardized protocols to measure under standard test conditions.”

In late April 2024, Fraunhofer ISE, Oxford PV, Wavelabs, and the University of Freiburg wound up an 11-month investigation into how large-format perovskite tandem PV cells can be accurately characterized. Fraunhofer ISE’s Martin Schubert led the project – abbreviated to “Katana” in German. He said there are two major differences between the characterization of perovskite tandem devices and regular PV modules.

Two factors

“One is that the efficiency may change during illumination,” said Schubert, who leads the quality assurance, characterization and simulation team. “The reason for that is that there is an ion migration in the perovskite cell in which some ions are moving. The second complication is the tandem architecture. By itself, that means we have two solar cells – one on top of the other and with different spectral sensitivity. We need to take care that the top cell gets the right amount of current and the bottom cell gets the right amount of current.”

Ion migration within the perovskite device while under continuous illumination means that the measured efficiency can either increase or decrease over time. This “metastability” necessitates the long illumination time needed for stabilized power output to be ascertained. Complicating things further, different perovskite PV compositions demonstrate varying levels of metastability.

The need for long light exposure, to accommodate metastability, brings heat, even when using LEDs. This means that the measurement of perovskite devices is often carried out at temperatures higher than standard test conditions (STC).

The power output of a photovoltaic device declines as its temperature increases, a factor described as a device’s temperature coefficient. Different PV technologies mean differing temperature coefficients. c-Si solar products, for example, have a larger temperature coefficient than thin film devices. If that is not controlled and accounted for, the result is measurement uncertainty.

Testing equipment with temperature control – essentially a chamber with air conditioning – can reduce this uncertainty in best-case scenarios. Such sophisticated devices, particularly with sufficient scale to accommodate full modules, come at a cost.

The impact of temperature can be corrected for using mathematical models based on accurate temperature readings and can account for the uncertainty higher temperatures can bring. With tandem devices, the temperature sensitivity of both the top and bottom cell must be accounted for – a complex, if not impossible, equation.

Commercial implications

At present, the testing of perovskite devices is carried out within minutes, to account for metastability related to ion migration in the perovskite cell, so that slower I-V sweeps, with multiple power point tracking (MPPT), can be carried out. This is unsuitable for mass production, as many modules need to be rolling off production lines every minute.

Wavelabs’ Wong said that a “more pragmatic test routine” would likely first involve a preconditioning of the module using light soaking, from mass-production light sources. That could then be followed by “a fast I-V sweep using high quality illumination that must fit within the specifications of spectral match, uniformity, and stability,” said Wong. “The fast I-V sweep will likely be done in the order of 100 milliseconds to one second, during which the ions are ‘frozen in’ to their preconditioned distribution and do not significantly redistribute.”

Fraunhofer ISE will be launching a three-year research project in May 2024 that will investigate how “fast and precise measurements” can be developed and executed for perovskite devices, including tandems. The project, abbreviated to “PERLE” in German, will be funded by Germany’s Federal Ministry of Economic Affairs and Climate Action. Fraunhofer ISE’s Schubert said that it is possible that the first findings from the project will be published by May 2025.

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Jonathan Gifford

technical progress essay

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technical progress essay

MLB says robot home plate umpires unlikely for 2025

NEW YORK (AP) — Major League Baseball says robot home plate umpires are unlikely for 2025.

“We still have some technical issues,” baseball Commissioner Rob Manfred said Thursday at a news conference following an owners meeting. “We haven’t made as much progress in the minor leagues this year as we sort of hoped at this point. I think it’s becoming more and more likely that this will not be a go for ’25.”

MLB has been experimenting with the automated ball-strike system in minor leagues since 2019. It is being used at all Triple-A parks this year for the second straight season, the robot alone for the first three games of each series and a human with a challenge system in the final three.

“There’s a growing consensus in large part based on what we’re hearing from players that the challenge form should be the form of ABS if and when we bring it to the big leagues, at least as a starting point,” Manfred said. “I think that’s a good decision.”

After instituting a pitch clock in 2023, MLB slowed innovation this year, with only small rules adjustments.

“One thing we did learn with the changes that we went through last year: taking the extra time to make sure you have it right is definitely the best approach,” Manfred said. “I think we’re going to use that same approach here.”

Manfred said discussions have not taken place with the players' association on the shape of an automated strike zone. There is little desire to call the strike zone as defined in the rule book as a cube. The ABS currently calls strikes solely based on where the ball crosses the midpoint of the plate, 8.5 inches from the front and the back.

“We have not started those conversations because we haven’t settled on what we think about it,” Manfred said.

MLB's meetings with players revealed a preference for a challenge system in order to continue to incentivize catcher framing skills.

“Originally we thought everybody was going to be wholeheartedly in favor of the idea if you can get it right every single time, that’s a great idea,” Manfred said. “One thing we’ve learned in these meetings is the players feel there could be other effects on the game that would be negative if you use it full-blown.

“Players feel that a catcher that frames is part of the — if you’ll let me use the word — art of the game, and that if in fact framing is no longer important, the kind of players that would occupy that position might be different than they are today," Manfred said. “You could hypothesize a world where instead of a framing catcher who’s focused on defense, the catching position becomes a more offensive player. That alters people’s careers. Those are real, legitimate concerns that we need to think all the way through before we jump off that bridge.”

John Slusher, Nike's executive vice president of global sports marketing, spoke to owners about the company's much-criticized new uniforms , which will be altered for 2025. MLB and Nike announced on May 3 that uniforms will have larger lettering on the back of jerseys and individual pants customization will be available to all players beginning in 2025.

“I think they appropriately took responsibility for the issues with respect to the new uniforms and the rollout of those uniforms,” Manfred said. “It’s the first time the owners had had heard this directly from Nike. They had been consistent with me about taking responsibility.”

Manfred said Nike said it will address "the letters, the non-customized pants, the sweat through and the lack of matching of the grays."

Manfred said Houston, Miami, San Juan and Tokyo will be sites for the 2026 World Baseball Classic . The final will be in Miami for the second tournament in a row, after 2023.

MLB has switched efforts to develop a tackier baseball and now is working with Rawlings, its supplier since 1977. MLB had been collaborating with Dow Chemical.

“Dow has kind of cried uncle,” Manfred said. “They spent a ton of money and worked with us. They were great partners, had a lot of good ideas and we just were not able to come up with a ball that was playable. We’re now focusing our efforts on a tacky ball with the Rawlings people.”

MLB is working on a broadcast strategy to cope with the decline in cable television.

Diamond Sports, which has been in bankruptcy proceedings since March 2023, has rights to 12 MLB teams.

“If I were a betting man, I think that Diamond continues to operate and pay our teams through the ’24 season,” Manfred said.

Manfred said there are two issues: whether MLB should take control of local rights from clubs and if that occurs, how to distribute revenue to clubs.

“For it to have any steam, the conversation about nationalization, I think it’s dependent on getting in the relatively short term some body of rights: 14, 15, 16, 17 clubs, and you’d start down the path from there,” Manfred said. "I’m not so naive as to believe two weeks from tomorrow I’m going to have all 30.”

The strikeout rate of 8.38 per team per game is down from 8.61 last year and the .240 big league batting average is down from .248 for last season and on track to be the lowest since 1968.

“Strikeout rate was down a little bit. That’s a positive,” Manfred said. “Batting average was down a little bit. That’s not necessarily a good thing if you’re looking for action in the game. But, again, it’s a part of a season and you just don’t know whether it means anything at this point."

Manfred said the attention given to the debuts of Baltimore second baseman Jackson Holliday and Pittsburgh pitcher Paul Skenes was partly due to the decision to hold the amateur draft in conjunction with the All-Star Game starting in 2021 and broadcasting minor league games on MLB.tv.

“Over the long haul for players in terms of developing their individual brands, it’s a real improvement,” he said.

After moving the 2021 All-Star Game from Atlanta to Denver because of Georgia enacting a more restrictive voting rights law, MLB in November awarded the 2025 game to Truist Park . “One of the things we’ve learned over time is that the more we stay out of political issues, the better off we are,” Manfred said. “People like their sports separate from their politics. We got a fan base that’s all over the political spectrum and the safest thing for us to do is focus on baseball.”

AP MLB: https://apnews.com/hub/MLB

Major League Baseball Commissioner Rob Manfred speaks at a press conference following an owners meeting, Thursday, May 23, 2024, in New York. (AP Photo/Julia Nikhinson)


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