An EU Strategy on Standardisation

Curated on Posted on by Stefaan Verhulst

Press Release: “Today, the Commission is presenting a new Standardisation Strategy outlining our approach to standards within the Single Market as well as globally. The Strategy is accompanied by a proposal for an amendment to the Regulation on standardisation, a report on its implementation, and the 2022 annual Union work programme for European standardisation. This new Strategy aims to strengthen the EU’s global competitiveness, to enable a resilient, green and digital economy and to enshrine democratic values in technology applications.

Standards are the silent foundation of the EU Single Market and global competitiveness. They help manufacturers ensure the interoperability of products and services, reduce costs, improve safety and foster innovation. Standards are an invisible but fundamental part of our daily life: from Wi-Fi frequencies, to connected toys or ski bindings, just to mention a few. Standards give confidence that a product or a service is fit for purpose, is safe and will not harm people or the environment. Compliance with harmonised standards guarantees that products are in line with EU law.

The fast pace of innovation, our green and digital ambitions and the implications of technological standards for our EU democratic values require an increasingly strategic approach to standardisation. The EU’s ambitions towards a climate neutral, resilient and circular economy cannot be delivered without European standards. Having a strong global footprint in standardisation activities and leading the work in key international fora and institutions will be essential for the EU to remain a global standard-setter. By setting global standards, the EU exports its values while providing EU companies with an important first-mover advantage.

Executive Vice-President for a Europe Fit for the Digital Age, Margrethe Vestager, said: “Ensuring that data is protected in artificial intelligence or ensuring that mobile devices are secure from hacking, rely on standards and must be in line with EU democratic values. In the same way, we need standards for the roll-out of important investment projects, like hydrogen or batteries, and to valorise innovation investment by providing EU companies with an important first-mover advantage.”…(More)”.

Leveraging Non-Traditional Data For The Covid-19 Socioeconomic Recovery Strategy

Curated on Posted on by Stefaan Verhulst

Article by Deepali Khanna: “To this end, it is opportune to ask the following questions: Can we harness the power of data routinely collected by companies—including transportation providers, mobile network operators, social media networks and others—for the public good? Can we bridge the data gap to give governments access to data, insights and tools that can inform national and local response and recovery strategies?

There is increasing recognition that traditional and non-traditional data should be seen as complementary resources. Non-traditional data can bring significant benefits in bridging existing data gaps but must still be calibrated against benchmarks based on established traditional data sources. These traditional datasets are widely seen as reliable as they are subject to established stringent international and national standards. However, they are often limited in frequency and granularity, especially in low- and middle-income countries, given the cost and time required to collect such data. For example, official economic indicators such as GDP, household consumption and consumer confidence may be available only up to national or regional level with quarterly updates…

In the Philippines, UNDP, with support from The Rockefeller Foundation and the government of Japan, recently setup the Pintig Lab: a multidisciplinary network of data scientists, economists, epidemiologists, mathematicians and political scientists, tasked with supporting data-driven crisis response and development strategies. In early 2021, the Lab conducted a study which explored how household spending on consumer-packaged goods, or fast-moving consumer goods (FMCGs), can been used to assess the socioeconomic impact of Covid-19 and identify heterogeneities in the pace of recovery across households in the Philippines. The Philippine National Economic Development Agency is now in the process of incorporating this data for their GDP forecasting, as additional input to their predictive models for consumption. Further, this data can be combined with other non-traditional datasets such as credit card or mobile wallet transactions, and machine learning techniques for higher-frequency GDP nowcasting, to allow for more nimble and responsive economic policies that can both absorb and anticipate the shocks of crisis….(More)”.

How digital transformation is driving economic change

Curated on Posted on by Stefaan Verhulst

Blog (and book) by Zia Qureshi: “We are living in a time of exciting technological innovations. Digital technologies are driving transformative change. Economic paradigms are shifting. The new technologies are reshaping product and factor markets and profoundly altering business and work. The latest advances in artificial intelligence and related innovations are expanding the frontiers of the digital revolution. Digital transformation is accelerating in the wake of the COVID-19 pandemic. The future is arriving faster than expected.

A recently published book, “Shifting Paradigms: Growth, Finance, Jobs, and Inequality in the Digital Economy,” examines the implications of the unfolding digital metamorphosis for economies and public policy agendas….

Firms at the technological frontier have broken away from the rest, acquiring dominance in increasingly concentrated markets and capturing the lion’s share of the returns from the new technologies. While productivity growth in these firms has been strong, it has stagnated or slowed in other firms, depressing aggregate productivity growth. Increasing automation of low- to middle-skill tasks has shifted labor demand toward higher-level skills, hurting wages and jobs at the lower end of the skill spectrum. With the new technologies favoring capital, winner-take-all business outcomes, and higher-level skills, the distribution of both capital and labor income has tended to become more unequal, and income has been shifting from labor to capital.

One important reason for these outcomes is that policies and institutions have been slow to adjust to the unfolding transformations. To realize the promise of today’s smart machines, policies need to be smarter too. They must be more responsive to change to fully capture potential gains in productivity and economic growth and address rising inequality as technological disruptions create winners and losers.

As technology reshapes markets and alters growth and distributional dynamics, policies must ensure that markets remain inclusive and support wide access to the new opportunities for firms and workers. The digital economy must be broadened to disseminate new technologies and opportunities to smaller firms and wider segments of the labor force…(More)”.

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)”