GDP’s Days Are Numbered

Curated on Posted on by Stefaan Verhulst

Essay by Diane Coyle: “How should we measure economic success? Criticisms of conventional indicators, particularly gross domestic product, have abounded for years, if not decades. Environmentalists have long pointed out that GDP omits the depletion of natural assets, as well as negative externalities such as global warming. And its failure to capture unpaid but undoubtedly valuable work in the home is another glaring omission. But better alternatives may soon be at hand.

In 2009, a commission led by Joseph Stiglitz, Amartya Sen, and Jean-Paul Fitoussi spurred efforts to find alternative ways to gauge economic progress by recommending a “dashboard” of indicators. Since then, economists and statisticians, working alongside natural scientists, have put considerable effort into developing rigorous wealth-based prosperity metrics, particularly concerning natural assets. The core idea is to create a comprehensive national balance sheet to demonstrate that economic progress today is illusory when it comes at the expense of future living standards.

In an important milestone in March of this year, the United Nations approved a statistical standard relating to the services that nature provides to the economy. That followed the UK Treasury’s publication of a review by the University of Cambridge’s Partha Dasgupta setting out how to integrate nature in general, and biodiversity in particular, into economic analysis. With the consequences of climate change starting to become all too apparent, any meaningful concept of economic success in the future will surely include sustainability.

The next steps in this statistical endeavor will be to incorporate measures of social capital, reflecting the ability of communities or countries to act collectively, and to extend measurement of the household sector. The COVID-19 pandemic has highlighted how crucial this unpaid work is to a country’s economic health. For example, the US Bureau of Labor Statistics intends to develop a more comprehensive concept of living standards that includes the value of such activity….(More)”.

Improving Consumer Welfare with Data Portability

Curated on Posted on by Stefaan Verhulst

Report by Daniel Castro: “Data protection laws and regulations can contain restrictive provisions, which limit data sharing and use, as well as permissive provisions, which increase it. Data portability is an example of a permissive provision that allows consumers to obtain a digital copy of their personal information from an online service and provide this information to other services. By carefully crafting data portability provisions, policymakers can enable consumers to obtain more value from their data, create new opportunities for businesses to innovate with data, and foster competition….(More)”.

Articulating Value from Data

Curated on Posted on by Stefaan Verhulst

Report by the World Economic Forum: “The distinct characteristics and dynamics of data – contextual, relational and cumulative – call for new approaches to articulating its value. Businesses should value data based on cases that go beyond the transactional monetization of data and take into account the broader context, future opportunities to collaborate and innovate, and value created for its ecosystem stakeholders. Doing so will encourage companies to think about the future value data can help generate, beyond the existing data lakes they sit on, and open them up to collaboration opportunities….(More)”.

Strengthening international cooperation on AI

Curated on Posted on by Stefaan Verhulst

Report by Cameron F. Kerry, Joshua P. Meltzer, Andrea Renda, Alex Engler, and Rosanna Fanni: “Since 2017, when Canada became the first country to adopt a national AI strategy, at least 60 countries have adopted some form of policy for artificial intelligence (AI). The prospect of an estimated boost of 16 percent, or US$13 trillion, to global output by 2030 has led to an unprecedented race to promote AI uptake across industry, consumer markets, and government services. Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025.

At the same time, the work on developing global standards for AI has led to significant developments in various international bodies. These encompass both technical aspects of AI (in standards development organizations (SDOs) such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE) among others) and the ethical and policy dimensions of responsible AI. In addition, in 2018 the G-7 agreed to establish the Global Partnership on AI, a multistakeholder initiative working on projects to explore regulatory issues and opportunities for AI development. The Organization for Economic Cooperation and Development (OECD) launched the AI Policy Observatory to support and inform AI policy development. Several other international organizations have become active in developing proposed frameworks for responsible AI development.

In addition, there has been a proliferation of declarations and frameworks from public and private organizations aimed at guiding the development of responsible AI. While many of these focus on general principles, the past two years have seen efforts to put principles into operation through fully-fledged policy frameworks. Canada’s directive on the use of AI in government, Singapore’s Model AI Governance Framework, Japan’s Social Principles of Human-Centric AI, and the U.K. guidance on understanding AI ethics and safety have been frontrunners in this sense; they were followed by the U.S. guidance to federal agencies on regulation of AI and an executive order on how these agencies should use AI. Most recently, the EU proposal for adoption of regulation on AI has marked the first attempt to introduce a comprehensive legislative scheme governing AI.

In exploring how to align these various policymaking efforts, we focus on the most compelling reasons for stepping up international cooperation (the “why”); the issues and policy domains that appear most ready for enhanced collaboration (the “what”); and the instruments and forums that could be leveraged to achieve meaningful results in advancing international AI standards, regulatory cooperation, and joint R&D projects to tackle global challenges (the “how”). At the end of this report, we list the topics that we propose to explore in our forthcoming group discussions….(More)”

Data Science for Social Good: Philanthropy and Social Impact in a Complex World

Curated on Posted on by Stefaan Verhulst

Book edited by Ciro Cattuto and Massimo Lapucci: “This book is a collection of insights by thought leaders at first-mover organizations in the emerging field of “Data Science for Social Good”. It examines the application of knowledge from computer science, complex systems, and computational social science to challenges such as humanitarian response, public health, and sustainable development. The book provides an overview of scientific approaches to social impact – identifying a social need, targeting an intervention, measuring impact – and the complementary perspective of funders and philanthropies pushing forward this new sector.

TABLE OF CONTENTS


Introduction; By Massimo Lapucci

The Value of Data and Data Collaboratives for Good: A Roadmap for Philanthropies to Facilitate Systems Change Through Data; By Stefaan G. Verhulst

UN Global Pulse: A UN Innovation Initiative with a Multiplier Effect; By Dr. Paula Hidalgo-Sanchis

Building the Field of Data for Good; By Claudia Juech

When Philanthropy Meets Data Science: A Framework for Governance to Achieve Data-Driven Decision-Making for Public Good; By Nuria Oliver

Data for Good: Unlocking Privately-Held Data to the Benefit of the Many; By Alberto Alemanno

Building a Funding Data Ecosystem: Grantmaking in the UK; By Rachel Rank

A Reflection on the Role of Data for Health: COVID-19 and Beyond; By Stefan E. Germann and Ursula Jasper….(More)”

Economic Data Engineering

Curated on Posted on by Stefaan Verhulst

Paper by Andrew Caplin: “Economic data engineering deliberately designs novel forms of data to solve fundamental identification problems associated with economic models of choice. I outline three diverse applications: to the economics of information; to life-cycle employment, earnings, and spending; and to public policy analysis. In all three cases one and the same fundamental identification problem is driving data innovation: that of separately identifying appropriately rich preferences and beliefs. In addition to presenting these conceptually linked examples, I provide a general overview of the engineering process, outline important next steps, and highlight larger opportunities…(More)”.

Keeping labour data flowing during the COVID-19 pandemic

Curated on Posted on by Stefaan Verhulst

Blog by ILO: “The availability of data tends to be taken for granted by the vast majority of people. The COVID-19 pandemic illustrates this vividly: estimates of case numbers and deaths have been widely quoted throughout and assumed by most to be available on demand.

However, those responsible for compiling official statistics know all too well that, even at the best of times, providing high-quality data to meet even just a small part of user needs is incredibly challenging and, on the whole, very resource-intensive. That said, the world has, in general, been steadily moving in the right direction, with more and better data being produced over time.

At the end of 2019, most users and producers of statistics would have predicted, with good reason, that the trend of increasing data availability would continue in the new decade, not least in the field of labour statistics. What no one could foresee then is that one of the cornerstones of data collection for surveys, namely the ability to visit and interview respondents, could be undermined so rapidly and drastically as was the case in 2020 owing to the COVID-19 pandemic.

Various organizations and specialized agencies in the United Nations system, including the ILO and collectively through the Intersecretariat Working Group on Household Surveys, have sought to track the impact of COVID-19 on data collection. In March 2021, the ILO launched a global survey to understand better the extent to which the crisis had affected the compilation of official labour market statistics. Information was received from 110 countries, of which 97 had planned to complete a labour force survey (LFS) in 2020. The findings point to both the tremendous challenges faced and the remarkable efforts undertaken to provide information on the world of work during the pandemic.

Nearly half of countries had to suspend interviewing at some point in 2020

Close to half (46.4 per cent) of the countries with plans to conduct a LFS in 2020 had to suspend interviews at some point in the year.The highest levels of suspensions were reported by countries in Africa and the Arab States (70.6 per cent) and in the Americas (66.7 per cent). While some countries were able to attempt to recover those interviews later on, the majority were not, which means they completely lost data that had been expected to be available, creating a risk of gaps in data series for key labour market indicators, among others…(More)”

Big data for big issues: Revealing travel patterns of low-income population based on smart card data mining in a global south unequal city

Curated on Posted on by Stefaan Verhulst

Paper by Caio Pieroni, Mariana Giannotti, Bianca B.Alves, and Renato Arbex: “Smart card data (SCD) allow analyzing mobility at a fine level of detail, despite the remaining challenges such as identifying trip purpose. The use of the SCD may improve the understanding of transit users’ travel patterns from precarious settlements areas, where the residents have historically limited access to opportunities and are usually underrepresented in surveys. In this paper, we explore smart card data mining to analyze the temporal and spatial patterns of the urban transit movements from residents of precarious settlements areas in São Paulo, Brazil, and compare the similarities and differences in travel behavior with middle/high-income-class residents. One of our concerns is to identify low-paid employment travel patterns from the low-income-class residents, that are also underrepresented in transportation planning modeling due to the lack of data. We employ the k-means clustering algorithm for the analysis, and the DBSCAN algorithm is used to infer passengers’ residence locations. The results reveal that most of the low-income residents of precarious settlements begin their first trip before, between 5 and 7 AM, while the better-off group begins from 7 to 9 AM. At least two clusters formed by commuters from precarious settlement areas suggest an association of these residents with low-paid employment, with their activities placed in medium / high-income residential areas. So, the empirical evidence revealed in this paper highlights smart card data potential to unfold low-paid employment spatial and temporal patterns….(More)”.

The Battle for Digital Privacy Is Reshaping the Internet

Curated on Posted on by Stefaan Verhulst

Brian X. Chen at The New York Times: “Apple introduced a pop-up window for iPhones in April that asks people for their permission to be tracked by different apps.

Google recently outlined plans to disable a tracking technology in its Chrome web browser.

And Facebook said last month that hundreds of its engineers were working on a new method of showing ads without relying on people’s personal data.

The developments may seem like technical tinkering, but they were connected to something bigger: an intensifying battle over the future of the internet. The struggle has entangled tech titans, upended Madison Avenue and disrupted small businesses. And it heralds a profound shift in how people’s personal information may be used online, with sweeping implications for the ways that businesses make money digitally.

At the center of the tussle is what has been the internet’s lifeblood: advertising.

More than 20 years ago, the internet drove an upheaval in the advertising industry. It eviscerated newspapers and magazines that had relied on selling classified and print ads, and threatened to dethrone television advertising as the prime way for marketers to reach large audiences….

If personal information is no longer the currency that people give for online content and services, something else must take its place. Media publishers, app makers and e-commerce shops are now exploring different paths to surviving a privacy-conscious internet, in some cases overturning their business models. Many are choosing to make people pay for what they get online by levying subscription fees and other charges instead of using their personal data.

Jeff Green, the chief executive of the Trade Desk, an ad-technology company in Ventura, Calif., that works with major ad agencies, said the behind-the-scenes fight was fundamental to the nature of the web…(More)”

Social welfare gains from innovation commons: Theory, evidence, and policy implications

Curated on Posted on by Stefaan Verhulst

Paper by Jason Potts, Andrew W. Torrance, Dietmar Harhoff and Eric A. von Hippel: “Innovation commons – which we define as repositories of freely-accessible, “open source” innovation-related information and data – are a very significant resource for innovating and innovation-adopting firms and individuals: Availability of free data and information reduces the innovation-specific private or open investment required to make the next innovative advance. Despite the clear social welfare value of innovation commons under many conditions, academic innovation research and innovation policymaking have to date focused almost entirely on enhancing private incentives to innovate by enabling innovators to keep some types of innovation-related information at least temporarily apart from the commons, via intellectual property rights.


In this paper, our focus is squarely on innovation commons theory, evidence, and policy implications. We first discuss the varying nature of and contents of innovation commons extant today. We summarize what is known about their functioning, their scale, the value they provide to innovators and to general social welfare, and the mechanisms by which this is accomplished. Perhaps somewhat counterintuitively, and with the important exception of major digital platform firms, we find that many who develop innovation-related information at private cost have private economic incentives to contribute their information to innovation commons for free access by free riders. We conclude with a discussion of the value of more general support for innovation commons, and how this could be provided by increased private and public investment in innovation commons “engineering”, and by specific forms of innovation policymaking to increase social welfare via enhancement of innovation commons….(More)”.