Digitally Invisible: How the Internet is Creating the New Underclass

Curated on Posted on by Stefaan Verhulst

Book by Nicol Turner Lee: “President Joe Biden has repeatedly said that the United States would close the digital divide under his leadership. However, the divide still affects people and communities across the country. The complex and persistent reality is that millions of residents live in digital deserts, and many more face disproportionate difficulties when it comes to getting and staying online, especially people of color, seniors, rural residents, and farmers in remote areas.

Economic and health disparities are worsening in rural communities without available internet access. Students living in urban digital deserts with little technology exposure are ill prepared to compete for emerging occupations. Even seniors struggle to navigate the aging process without access to online information and remote care.

In this book, Nicol Turner Lee, a leading expert on the American digital divide, uses personal stories from individuals around the country to show how the emerging digital underclass is navigating the spiraling online economy, while sharing their joys and hopes for an equitable and just future.

Turner Lee argues that achieving digital equity is crucial for the future of America’s global competitiveness and requires radical responses to offset the unintended consequences of increasing digitization. In the end, “Digitally Invisible” proposes a pathway to more equitable access to existing and emerging technologies, while encouraging readers to weigh in on this shared goal…(More)”.

(Almost) 200 Years of News-Based Economic Sentiment

Curated on Posted on by Stefaan Verhulst

Paper by Jules H. van Binsbergen, Svetlana Bryzgalova, Mayukh Mukhopadhyay & Varun Sharma: “Using text from 200 million pages of 13,000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country and state levels, that expands existing measures in both the time series (by more than a century) and the cross-section. Our measure predicts GDP (both nationally and locally), consumption, and employment growth, even after controlling for commonly-used predictors, as well as monetary policy decisions. Our measure is distinct from the information in expert forecasts and leads its consensus value. Interestingly, news coverage has become increasingly negative across all states in the past half-century…(More)”.

A new index is using AI tools to measure U.S. economic growth in a broader way

Curated on Posted on by Stefaan Verhulst

Article by Jeff Cox: “Measuring the strength of the sprawling U.S. economy is no easy task, so one firm is sending artificial intelligence in to do the job.

The Zeta Economic Index, launched Monday, uses generative AI to analyze what its developers call “trillions of behavioral signals,” largely focused on consumer activity, to score growth on both a broad level of health and a separate measure on stability.

At its core, the index will gauge online and offline activity across eight categories, aiming to give a comprehensive look that incorporates standard economic data points such as unemployment and retail sales combined with high-frequency information for the AI age.

“The algorithm is looking at traditional economic indicators that you would normally look at. But then inside of our proprietary algorithm, we’re ingesting the behavioral data and transaction data of 240 million Americans, which nobody else has,” said David Steinberg, co-founder, chairman and CEO of Zeta Global.

“So instead of looking at the data in the rearview mirror like everybody else, we’re trying to put it out in advance to give a 30-day advanced snapshot of where the economy is going,” he added…(More)”.

Building an AI ecosystem in a small nation: lessons from Singapore’s journey to the forefront of AI

Curated on Posted on by Stefaan Verhulst

Paper by Shaleen Khanal, Hongzhou Zhang & Araz Taeihagh: “Artificial intelligence (AI) is arguably the most transformative technology of our time. While all nations would like to mobilize their resources to play an active role in AI development and utilization, only a few nations, such as the United States and China, have the resources and capacity to do so. If so, how can smaller or less resourceful countries navigate the technological terrain to emerge at the forefront of AI development? This research presents an in-depth analysis of Singapore’s journey in constructing a robust AI ecosystem amidst the prevailing global dominance of the United States and China. By examining the case of Singapore, we argue that by designing policies that address risks associated with AI development and implementation, smaller countries can create a vibrant AI ecosystem that encourages experimentation and early adoption of the technology. In addition, through Singapore’s case, we demonstrate the active role the government can play, not only as a policymaker but also as a steward to guide the rest of the economy towards the application of AI…(More)”.

The Simple Macroeconomics of AI

Curated on Posted on by Stefaan Verhulst

Paper by Daron Acemoglu: “This paper evaluates claims about large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest—no more than a 0.66% increase in total factor productivity (TFP) over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.53%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. Instead, AI is predicted to widen the gap between capital and labor income. Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value…(More)”.

Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution, and in the Age of AI

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Paper by Daron Acemoglu & Simon Johnson: “David Ricardo initially believed machinery would help workers but revised his opinion, likely based on the impact of automation in the textile industry. Despite cotton textiles becoming one of the largest sectors in the British economy, real wages for cotton weavers did not rise for decades. As E.P. Thompson emphasized, automation forced workers into unhealthy factories with close surveillance and little autonomy. Automation can increase wages, but only when accompanied by new tasks that raise the marginal productivity of labor and/or when there is sufficient additional hiring in complementary sectors. Wages are unlikely to rise when workers cannot push for their share of productivity growth. Today, artificial intelligence may boost average productivity, but it also may replace many workers while degrading job quality for those who remain employed. As in Ricardo’s time, the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages…(More)”.

Potential competition impacts from the data asymmetry between Big Tech firms and firms in financial services

Curated on Posted on by Stefaan Verhulst

Report by the UK Financial Conduct Authority: “Big Tech firms in the UK and around the world have been, and continue to be, under active scrutiny by competition and regulatory authorities. This is because some of these large technology firms may have both the ability and the incentive to shape digital markets by protecting existing market power and extending it into new markets.
Concentration in some digital markets, and Big Tech firms’ key role, has been widely discussed, including in our DP22/05. This reflects both the characteristics of digital markets and the characteristics and behaviours of Big Tech firms themselves. Although Big Tech firms have different business models, common characteristics include their global scale and access to a large installed user base, rich data about their users, advanced data analytics and technology, influence over decision making and defaults, ecosystems of complementary products and strategic behaviours, including acquisition strategies.
Through our work, we aim to mitigate the risk of competition in retail financial markets evolving in a way that results in some Big Tech firms gaining entrenched market power, as seen in other sectors and jurisdictions, while enabling the potential competition benefits that come from Big Tech firms providing challenge to incumbent financial services firms…(More)”.

The Future Data Economy

Curated on Posted on by Stefaan Verhulst

Report by the IE University’s Center for the Governance of Change: “…summarizes the ideas and recommendations of a year of research into the possibilities of creating a data economy that is fair, competitive and secure, carried out together with experts in the field such as Andrea Renda and Stefaan Verhulst.

According to the report, the data economy represents “a fundamental reconfiguration of how value is generated, exchanged, and understood in our world today” but it remains deeply misunderstood:

  • The authors argue that data’s particular characteristics make it different from other commodities and therefore more difficult to regulate.
  • Optimizing data flows defies the sort of one-size-fits-all solutions that policymakers tend to search for in other domains, requiring instead a more nuanced, case-by-case approach. 
  • Policymakers need to strike a delicate balance between making data sufficiently accessible to foster innovation, competition, and economic growth, while regulating its access and use to protect privacy, security, and consumer rights.

The report identifies additional overarching principles that lay the groundwork for a more coherent regulatory framework and a more robust social contract in the future data economy:

  • A paradigm shift towards greater collaboration on all fronts to address the challenges and harness the opportunities of the data economy.
  • Greater data literacy at all levels of society to make better decisions, manage risks more effectively, and harness the potential of data responsibly.
  • Regaining social trust, not only a moral imperative but also a prerequisite for the long-term sustainability and viability of data governance models.

To realize this vision, the report advances 15 specific recommendations for policymakers, including:

  • Enshrining people’s digital rights through robust regulatory measures that empower them with genuine control over their digital experiences.
  • Investing in data stewards to increase companies’ ability to recognize opportunities for collaboration and respond to external data requests. 
  • Designing liability frameworks to properly identify responsibility in cases of data misuse…(More)”

Technological Progress and Rent Seeking

Curated on Posted on by Stefaan Verhulst

Paper by Vincent Glode & Guillermo Ordoñez: “We model firms’ allocation of resources across surplus-creating (i.e., productive) and surplus-appropriating (i.e., rent-seeking) activities. Our model predicts that industry-wide technological advancements, such as recent progress in data collection and processing, induce a disproportionate and socially inefficient reallocation of resources toward surplus-appropriating activities. As technology improves, firms rely more on appropriation to obtain their profits, endogenously reducing the impact of technological progress on economic progress and inflating the price of the resources used for both types of activities. We apply our theoretical insights to shed light on the rise of high-frequency trading…(More)”,

The impact of generative artificial intelligence on socioeconomic inequalities and
policy making

Curated on Posted on by Stefaan Verhulst

Paper by Valerio Capraro et al: “Generative artificial intelligence, including chatbots like ChatGPT, has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the probable impacts of generative AI on four critical domains: work, education, health, and information. Our goal is to warn about how generative AI could worsen existing inequalities while illuminating directions for using AI to resolve pervasive social problems. Generative AI in the workplace can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning but may widen the digital divide. In healthcare, it improves diagnostics and accessibility but could deepen pre-existing inequalities. For information, it democratizes content creation and access but also dramatically expands the production and proliferation of misinformation. Each section covers a specific topic, evaluates existing research, identifies critical gaps, and recommends research directions. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while
mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We contend that these policies should promote shared prosperity through the advancement of generative AI. We suggest several concrete policies to encourage further research and debate. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI…(More)”.