Paper by Mert Demirer, Diego J. Jiménez Hernández, Dean Li & Sida Peng: “By regulating how firms collect, store, and use data, privacy laws may change the role of data in production and alter firm demand for information technology inputs. We study how firms respond to privacy laws in the context of the EU’s General Data Protection Regulation (GDPR) by using seven years of data from a large global cloud-computing provider. Our difference-in-difference estimates indicate that, in response to the GDPR, EU firms decreased data storage by 26% and data processing by 15% relative to comparable US firms, becoming less “data-intensive.” To estimate the costs of the GDPR for firms, we propose and estimate a production function where data and computation serve as inputs to the production of “information.” We find that data and computation are strong complements in production and that firm responses are consistent with the GDPR, representing a 20% increase in the cost of data on average. Variation in the firm-level effects of the GDPR and industry-level exposure to data, however, drives significant heterogeneity in our estimates of the impact of the GDPR on production costs…(More)”
Paper by David Autor: “While the utopian vision of the current Information Age was that computerization would flatten economic hierarchies by democratizing information, the opposite has occurred. Information, it turns out, is merely an input into a more consequential economic function, decision-making, which is the province of elite experts. The unique opportunity that AI offers to the labor market is to extend the relevance, reach, and value of human expertise. Because of AI’s capacity to weave information and rules with acquired experience to support decision-making, it can be applied to enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks that are currently arrogated to elite experts, e.g., medical care to doctors, document production to lawyers, software coding to computer engineers, and undergraduate education to professors. My thesis is not a forecast but an argument about what is possible: AI, if used well, can assist with restoring the middle-skill, middle-class heart of the US labor market that has been hollowed out by automation and globalization…(More)”.
OECD Report: “Protecting consumers when they are most vulnerable has long been a core focus of consumer policy. This report first discusses the nature and scale of consumer vulnerability in the digital age, including its evolving conceptualisation, the role of emerging digital trends, and implications for consumer policy. It finds that in the digital age, vulnerability may be experienced not only by some consumers, but increasingly by most, if not all, consumers. Accordingly, it sets out several measures to address the vulnerability of specific consumer groups and all consumers, and concludes with avenues for more research on the topic…(More)”.
Article by Nora Heffernan: “Over the last few months, I’ve asked the same question to corporate leaders from human resources, talent acquisition, learning and development, and management backgrounds. The question is this:
What kind of data needs to be included in learning and employment records to be of greatest value to you in your role and to your organization?
By data, I’m talking about credential attainment, employment history, and, emphatically, verified skills data: showing at an individual level what a candidate or employee knows and is able to do.
The answer varies slightly by industry and position, but unanimously, the employers I’ve talked to would find the greatest value in utilizing learning and employment records that include verified skills data. There is no equivocation.
And as the national conversation about skills-first talent management continues to ramp up, with half of companies indicating they plan to eliminate degree requirements for some jobs in the next year, the call for verified skill data will only get louder. Employers value skills data for multiple reasons…(More)”.
Paper by Anton Korinek: “…This article describes use cases of modern generative AI to interested economic researchers based on the author’s exploration of the space. The main emphasis is on LLMs, which are the type of generative AI that is currently most useful for research. I have categorized their use cases into six areas: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions for how to take advantage of each of these capabilities and demonstrate them using specific examples. Moreover, I classify the capabilities of the most commonly used LLMs from experimental to highly useful to provide an overview. My hope is that this paper will be a useful guide both for researchers starting to use generative AI and for expert users who are interested in new use cases beyond what they already have experience with to take advantage of the rapidly growing capabilities of LLMs. The online resources associated with this paper are available at the journal website and will provide semi-annual updates on the capabilities and use cases of the most advanced generative AI tools for economic research. In addition, they offer a guide on “How do I start?” as well as a page with “Useful Resources on Generative AI for Economists.”…(More)”
Article by Bhaskar Chakravorti, Ajay Bhalla, and Ravi Shankar Chaturvedi: “Given the high stakes of this race, which countries are in the lead? Which are gaining on the leaders? How might this hierarchy shape the future of AI? Identifying AI-leading countries is not straightforward, as data, knowledge, algorithms, and models can, in principle, cross borders. Even the U.S.–China rivalry is complicated by the fact that AI researchers from the two countries cooperate — and more so than researchers from any other pair of countries. Open-source models are out there for everyone to use, with licensing accessible even for cutting-edge models. Nonetheless, AI development benefits from scale economies and, as a result, is geographically clustered as many significant inputs are concentrated and don’t cross borders that easily….
Rapidly accumulating pools of data in digital economies around the world are clearly one of the critical drivers of AI development. In 2019, we introduced the idea of “gross data product” of countries determined by the volume, complexity, and accessibility of data consumed alongside the number of active internet users in the country. For this analysis, we recognized that gross data product is an essential asset for AI development — especially for generative AI, which requires massive, diverse datasets — and updated the 2019 analyses as a foundation, adding drivers that are critical for AI development overall. That essential data layer makes the index introduced here distinct from other indicators of AI “vibrancy” or measures of global investments, innovations, and implementation of AI…(More)”.
Article by Patrick Leblond: “On October 25, the United States announced at the World Trade Organization (WTO) that it was dropping its support for provisions meant to promote the free flow of data across borders. Also abandoned were efforts to continue negotiations on international e-commerce, to protect the source code in applications and algorithms (the so-called Joint Statement Initiative process).
According to the Office of the US Trade Representative (USTR): “In order to provide enough policy space for those debates to unfold, the United States has removed its support for proposals that might prejudice or hinder those domestic policy considerations.” In other words, the domestic regulation of data, privacy, artificial intelligence, online content and the like, seems to have taken precedence over unhindered international digital trade, which the United States previously strongly defended in trade agreements such as the Trans-Pacific Partnership (TPP) and the Canada-United States-Mexico Agreement (CUSMA)…
One pathway for the future sees the digital governance noodle bowl getting bigger and messier. In this scenario, international digital trade suffers. Agreements continue proliferating but remain ineffective at fostering cross-border digital trade: either they remain hortatory with attempts at cooperation on non-strategic issues, or no one pays attention to the binding provisions because business can’t keep up and governments want to retain their “policy space.” After all, why has there not yet been any dispute launched based on binding provisions in a digital trade agreement (either on its own or as part of a larger trade deal) when there has been increasing digital fragmentation?
The other pathway leads to the creation of a new international standards-setting and governance body (call it an International Digital Standards Board), like there exists for banking and finance. Countries that are members of such an international organization and effectively apply the commonly agreed standards become part of a single digital area where they can conduct cross-border digital trade without impediments. This is the only way to realize the G7’s “data free flow with trust” vision, originally proposed by Japan…(More)”.
Paper by Mariana Mazzucato: “To meet today’s grand challenges, economics requires an understanding of how common objectives may be collaboratively set and met. Tied to the assumption that the state can, at best, fix market failures and is always at risk of ‘capture’, economic theory has been unable to offer such a framework. To move beyond such limiting assumptions, the paper provides a renewed conception of the common good, going beyond the classic public good and commons approach, as a way of steering and shaping (rather than just fixing) the economy towards collective goals…(More)”.
Open Access Book edited by Gorgi Krlev, Dominika Wruk, Giulio Pasi, and Marika Bernhard: “Lack of progress in the area of global sustainable development and difficulties in crisis management highlight the need to transform the economy and find new ways of making society more resilient. The social economy is increasingly recognized as a driver of such transformations; it comprises traditional forms of cooperative or solidarity-based organizations alongside new phenomena such as impact investing or social tech ventures that aim to contribute to the public good. Social Economy Science provides the first comprehensive analysis of why and how social economy organizations create superior value for society. The book draws on organizational theory and transition studies to provide a systematic perspective on complex multi-stakeholder forms of action. It discusses the social economy’s role in promoting innovation for impact, as well as its role as an agent of societal change and as a partner to businesses, governments, and citizens…(More)”.
Article by Ofer Tur-Sinai: “Cars are no longer just a means of transportation. They have become rolling hubs of data communication. Modern vehicles regularly transmit information wirelessly to their manufacturers.
However, as cars grow “smarter,” the right to repair them is under siege.
As legal scholars, we find that the question of whether you and your local mechanic can tap into your car’s data to diagnose and repair spans issues of property rights, trade secrets, cybersecurity, data privacy and consumer rights. Policymakers are forced to navigate this complex legal landscape and ideally are aiming for a balanced approach that upholds the right to repair, while also ensuring the safety and privacy of consumers…
Until recently, repairing a car involved connecting to its standard on-board diagnostics port to retrieve diagnostic data. The ability for independent repair shops – not just those authorized by the manufacturer – to access this information was protected by a state law in Massachusetts, approved by voters on Nov. 6, 2012, and by a nationwide memorandum of understanding between major car manufacturers and the repair industry signed on Jan. 15, 2014.
However, with the rise of telematics systems, which combine computing with telecommunications, these dynamics are shifting. Unlike the standardized onboard diagnostics ports, telematics systems vary across car manufacturers. These systems are often protected by digital locks, and circumventing these locks could be considered a violation of copyright law. The telematics systems also encrypt the diagnostic data before transmitting it to the manufacturer.
This reduces the accessibility of telematics information, potentially locking out independent repair shops and jeopardizing consumer choice – a lack of choice that can lead to increased costs for consumers….
One issue left unresolved by the legislation is the ownership of vehicle data. A vehicle generates all sorts of data as it operates, including location, diagnostic, driving behavior, and even usage patterns of in-car systems – for example, which apps you use and for how long.
In recent years, the question of data ownership has gained prominence. In 2015, Congress legislated that the data stored in event data recorders belongs to the vehicle owner. This was a significant step in acknowledging the vehicle owner’s right over specific datasets. However, the broader issue of data ownership in today’s connected cars remains unresolved…(More)”.