Networks: An Economics Approach

Book by Sanjeev Goyal: “Networks are everywhere: the infrastructure that brings water into our homes, the social networks made up of our friends and families, the supply chains connecting cities, people, and goods. These interconnections contain economic trade-offs: for example, should an airline operate direct flights between cities or route all its flights through a hub? Viewing networks through an economics lens, this textbook considers the costs and benefits that govern their formation and functioning.

Networks are central to an understanding of the production, consumption, and information that lie at the heart of economic activity. Sanjeev Goyal provides advanced undergraduate and graduate students with an accessible and comprehensive introduction to the economics research on networks of the past twenty-five years. Each chapter introduces a theoretical model illustrated with the help of case studies and formal proofs. After introducing the theoretical concepts, Goyal examines economic networks, including infrastructure, security, market power, and financial networks. He then covers social networks, with chapters on coordinating activity, communication and learning, information networks, epidemics, and impersonal markets. Finally, Goyal locates social and economic networks in a broader context covering networked markets, economic development, trust, and group networks in their relation to markets and the state…(More)”.

Valuing the U.S. Data Economy Using Machine Learning and Online Job Postings

Paper by J Bayoán Santiago Calderón and Dylan Rassier: “With the recent proliferation of data collection and uses in the digital economy, the understanding and statistical treatment of data stocks and flows is of interest among compilers and users of national economic accounts. In this paper, we measure the value of own-account data stocks and flows for the U.S. business sector by summing the production costs of data-related activities implicit in occupations. Our method augments the traditional sum-of-costs methodology for measuring other own-account intellectual property products in national economic accounts by proxying occupation-level time-use factors using a machine learning model and the text of online job advertisements (Blackburn 2021). In our experimental estimates, we find that annual current-dollar investment in own-account data assets for the U.S. business sector grew from $84 billion in 2002 to $186 billion in 2021, with an average annual growth rate of 4.2 percent. Cumulative current-dollar investment for the period 2002–2021 was $2.6 trillion. In addition to the annual current-dollar investment, we present historical-cost net stocks, real growth rates, and effects on value-added by the industrial sector…(More)”.

Knowledge monopolies and the innovation divide: A governance perspective

Paper by Hani Safadi and Richard Thomas Watson: “The rise of digital platforms creates knowledge monopolies that threaten innovation. Their power derives from the imposition of data obligations and persistent coupling on platform participation and their usurpation of the rights to data created by other participants to facilitate information asymmetries. Knowledge monopolies can use machine learning to develop competitive insights unavailable to every other platform participant. This information asymmetry stifles innovation, stokes the growth of the monopoly, and reinforces its ascendency. National or regional governance structures, such as laws and regulatory authorities, constrain economic monopolies deemed not in the public interest. We argue the need for legislation and an associated regulatory mechanism to curtail coercive data obligations, control, eliminate data rights exploitation, and prevent mergers and acquisitions that could create or extend knowledge monopolies…(More)”.

The Case for Including Data Stewardship in ESG

Article by Stefaan Verhulst: “Amid all the attention to environmental, social, and governance factors in investing, better known as ESG, there has been relatively little emphasis on governance, and even less on data governance. This is a significant oversight that needs to be addressed, as data governance has a crucial role to play in achieving environmental and social goals. 

Data stewardship in particular should be considered an important ESG practice. Making data accessible for reuse in the public interest can promote social and environmental goals while boosting a company’s efficiency and profitability. And investing in companies with data-stewardship capabilities makes good sense. But first, we need to move beyond current debates on data and ESG.

Several initiatives have begun to focus on data as it relates to ESG. For example, a recent McKinsey report on ESG governance within the banking sector argues that banks “will need to adjust their data architecture, define a data collection strategy, and reorganize their data governance model to successfully manage and report ESG data.” Deloitte recognizes the need for “a robust ESG data strategy.” PepsiCo likewise highlights its ESG Data Governance Program, and Maersk emphasizes data ethics as a key component in its ESG priorities.

These efforts are meaningful, but they are largely geared toward using data to measure compliance with environmental and social commitments. They don’t do much to help us understand how companies are leveraging data as an asset to achieve environmental and social goals. In particular, as I‘ve written elsewhere, data stewardship by which privately held data is reused for public interest purposes is an important new component of corporate social responsibility, as well as a key tool in data governance. Too many data-governance efforts are focused simply on using data to measure compliance or impact. We need to move beyond that mindset. Instead, we should adopt a data stewardship approach, where data is made accessible for the public good. There are promising signs of change in this direction…(More)”.

ESG data governance: A growing imperative for banks

Article and Report by Daniel Heller, Andreas Reiter, Sebastian Schöbl, and Henning Soller: “The banking industry is facing mounting pressure to meet fast-changing demands in environmental, social, and governance (ESG) issues. New and evolving regulations call for greater transparency and disclosure of ESG-related data (see sidebar, “ESG regulatory and disclosure requirements”). Stakeholders and investors are increasing their scrutiny of the effects investment decisions have on the climate and society. Consumers are holding banks to higher ESG standards as well—in 2019, about 14 percent of total client-driven revenues were controlled by consumers whose banking preferences were influenced by concern about purpose and sustainability.

To meet these expectations, banks must adapt their IT systems to systematically collect, aggregate, and report on a broad range of ESG data. However, many financial institutions still do not have a comprehensive approach to integrating ESG data into their existing risk reporting.

Moving toward this goal will require significant changes to the IT infrastructure, from applications to data integration, architecture, and governance. New applications include not only the management and capture of ESG data but also financed emissions models, climate risk models, ESG scorecards, climate stress tests, and climate-adjusted ratings. ESG data must be woven into existing processes, such as credit approvals and decision making. And banks will need to adjust their data architecture, define a data collection strategy, and reorganize their data governance model to successfully manage and report ESG data.

Investing in the right priorities from the beginning will enable banking IT leaders to quickly build these new capabilities and solutions without accumulating technical debt…(More)”

Data Brokers and the Sale of Americans’ Mental Health Data

Report by Joanne Kim: “This report includes findings from a two-month-long study of data brokers and data on U.S. individuals’ mental health conditions. The report aims to make more transparent the data broker industry and its processes for selling and exchanging mental health data about depressed and anxious individuals. The research is critical as more depressed and anxious individuals utilize personal devices and software-based health-tracking applications (many of which are not protected by the Health Insurance Portability and Accountability Act), often unknowingly putting their sensitive mental health data at risk. This report finds that the industry appears to lack a set of best practices for handling individuals’ mental health data, particularly in the areas of privacy and buyer vetting. It finds that there are data brokers which advertise and are willing and able to sell data concerning Americans’ highly sensitive mental health information. It concludes by arguing that the largely unregulated and black-box nature of the data broker industry, its buying and selling of sensitive mental health data, and the lack of clear consumer privacy protections in the U.S. necessitate a comprehensive federal privacy law or, at the very least, an expansion of HIPAA’s privacy protections alongside bans on the sale of mental health data on the open market…(More)”.

The Power of Citizen Science

Lauren Kirchner at ConsumerReport: “You’ve heard of Erin Brockovich, the law clerk without a science degree who exposed the existence of a dangerous contaminant polluting a town’s groundwater, a toxic hazard that otherwise might have stayed invisible.

She’s not the first person to practice “citizen science” to powerful effect, nor will she be the last.

Maybe you’ve wondered whether that plastic container you’re about to zap in the microwave is really safe to use or whether your favorite chipped coffee mug is exposing you to toxic paint. Some particularly enterprising people who’ve had similar concerns have also wondered—but then took the extra step of testing the chemical makeup of what they were concerned about and then publicized the results.

These citizen testers aren’t professional chemists or government regulators, but all of them were able to raise red flags and spark important conversations about the health hazards that can be hiding in our homes and lives…(More)”.

8 Strategies for Chief Data Officers to Create — and Demonstrate — Value

Article by Thomas H. Davenport, Richard Y. Wang, and Priyanka Tiwari: “The chief data officer (CDO) role was only established in 2002, but it has grown enormously since then. In one recent survey of large companies, 83% reported having a CDO. This isn’t surprising: Data and approaches to understanding it (analytics and AI) are incredibly important in contemporary organizations. What is eyebrow-raising, however, is that the CDO job is terribly ill-defined. Sixty-two percent of CDOs surveyed in the research we describe below reported that the CDO role is poorly understood, and incumbents of the job have often met with diffuse expectations and short tenures. There is a clear need for CDOs to focus on adding visible value to their organizations.

Part of the problem is that traditional data management approaches are unlikely to provide visible value in themselves. Many nontechnical executives don’t really understand the CDO’s work and struggle to recognize when it’s being done well. CDOs are often asked to focus on preventing data problems (defense-oriented initiatives) and such data management projects as improving data architectures, data governance, and data quality. But data will never be perfect, meaning executives will always be somewhat frustrated with their organization’s data situation. While improvements in data management may be difficult to recognize or measure, major problems such as hacks, breaches, lost or inaccessible data, or poor quality are much easier to recognize than improvements.

So how can CDOs demonstrate that they’re creating value?…(More)”

The Market Power of Technology: Understanding the Second Gilded Age

Book by Mordecai Kurz: “Since the 1980s, the United States has regressed to a level of economic inequality not seen since the Gilded Age in the late nineteenth century. At the same time, technological innovation has transformed society, and a core priority of public policy has been promoting innovation. What is the relationship between economic inequality and technological change?

Mordecai Kurz develops a comprehensive integrated theory of the dynamics of market power and income inequality. He shows that technological innovations are not simply sources of growth and progress: they sow the seeds of market power. In a free market economy with intellectual property rights, firms’ control over technology enables them to expand, attain monopoly power, and earn exorbitant profits. Competition among innovators does not eliminate market power because technological competition is different from standard competition; it results in only one or two winners. Kurz provides a pioneering analysis grounded on quantifying technological market power and its effects on inequality, innovation, and economic growth. He outlines what causes market power to rise and fall and details its macroeconomic and distributional consequences.

Kurz demonstrates that technological market power tends to rise, increasing inequality of income and wealth. Unchecked inequality threatens the foundations of democracy: public policy is the only counterbalancing force that can restrain corporate power, attain more egalitarian distribution of wealth, and make democracy compatible with capitalism. Presenting a new paradigm for understanding today’s vast inequalities, this book offers detailed proposals to redress them by restricting corporate mergers and acquisitions, reforming patent law, improving the balance of power in the labor market, increasing taxation, promoting upward mobility, and stabilizing the middle class…(More)”.

Digital Oil

Book by Eric Monteiro: “Digitalization sits at the forefront of public and academic conversation today, calling into question how we work and how we know. In Digital Oil, Eric Monteiro uses the Norwegian offshore oil and gas industry as a lens to investigate the effects of digitalization on embodied labor and, in doing so, shows how our use of new digital technology transforms work and knowing.

For years, roughnecks have performed the dangerous and unwieldy work of extracting the oil that lies three miles below the seabed along the Norwegian continental shelf. Today, the Norwegian oil industry is largely digital, operated by sensors and driven by data. Digital representations of physical processes inform work practices and decision-making with remotely operated, unmanned deep-sea facilities. Drawing on two decades of in-depth interviews, observations, news clips, and studies of this industry, Monteiro dismantles the divide between the virtual and the physical in Digital Oil.

What is gained or lost when objects and processes become algorithmic phenomena with the digital inferred from the physical? How can data-driven work practices and operational decision-making approximate qualitative interpretation, professional judgement, and evaluation? How are emergent digital platforms and infrastructures, as machineries of knowing, enabling digitalization? In answering these questions Monteiro offers a novel analysis of digitalization as an effort to press the limits of quantification of the qualitative…(More)”.