Stefaan Verhulst

Nick Dall at OZY: “Over midafternoon coffees and Fantas, Robyn-Lee Abrahams and Joyce Paulse — employees at my local supermarket in Cape Town, South Africa — tell me how their lives have changed in the past 18 months. “I never dreamed my daughter would go to college,” says Paulse. “But yesterday we went online together and started filling in the forms.”

Abrahams notes how she used to live hand to mouth. “But now I’ve got a savings account, which I haven’t ever touched.” The sacrifice? “I eat less chocolate now.”

Paulse and Abrahams are just two of thousands of beneficiaries of the Poverty Stoplight, a self-evaluation tool that’s now redefining poverty in countries as diverse as Argentina and the U.K.; Mexico and Tanzania; Chile and Papua New Guinea. By getting families to rank their own economic condition red, yellow or green based upon 50 indicators, the Poverty Stoplight gives families the agency to pull themselves out of poverty and offers organizations insight into whether their programs are working.

Social entrepreneur Martín Burt, who founded Fundación Paraguaya 33 years ago to promote entrepreneurship and economic empowerment in Paraguay, developed the first, paper-based prototype of the Poverty Stoplight in 2010 to help the organization’s microfinance clients escape the poverty cycle….Because poverty is multidimensional, “you can have a family with a proper toilet but no savings,” points out Burt. Determining questionnaires span six different aspects of people’s lives, including softer indicators such as community involvement, self-confidence and family violence. The survey, a series of 50 multiple-choice questions with visual cues, is aimed at households, not individuals, because “you cannot get a 10-year-old girl out of poverty in isolation,” says Burt. Confidentiality is another critical component….(More)”.

Laura Mann and Gianluca Lazzolino at SciDevNet: “Across the world, tech firms and software developers are embedding digital platforms into humanitarian and commercial infrastructures. There’s Jembi and Hello Doctor for the healthcare sector, for example; SASSA and Tamween for social policy; and M-farm, i-Cow, Esoko among many others for agriculture.

While such systems proliferate, it is time we asked some tough questions about who is controlling this data, and for whose benefit. There is a danger that ‘platformisation’ widens the knowledge gap between firms and scientists in poorer countries and those in more advanced economies.

Digital platforms serve three purposes. They improve interactions between service providers and users; gather transactional data about those users; and nudge them towards behaviours, activities and products considered ‘virtuous’, profitable, or valued — often because they generate more data. This data  can be extremely valuable to policy-makers interested in developing interventions, to researchers exploring socio-economic trends and to businesses seeking new markets.

But the development and use of these platforms are not always benign.

Knowledge and power

Digital technologies are knowledge technologies because they record the personal information, assets, behaviour and networks of the people that use them.

Knowledge has a somewhat gentle image of a global good shared openly and evenly across the world. But in reality, it is competitive.
Simply put, knowledge shapes economic rivalry between rich and poor countries. It influences who has power over the rules of the economic game, and it does this in three key ways.

First, firms can use knowledge and technology to become more efficient and competitive in what they do. For example, a farmer can choose to buy technologically enhanced seeds, inputs such as fertilisers, and tools to process their crop.

This technology transfer is not automatic — the farmer must first invest time to learn how to use these tools.  In this sense, economic competition between nations is partly about how well-equipped their people are in using technology effectively.

The second key way in which knowledge impacts global economic competition depends on looking at development as a shift from cut-throat commodity production towards activities that bring higher profits and wages.

In farming, for example, development means moving out of crop production alone into a position of having more control over agricultural inputs, and more involvement in distributing or marketing agricultural goods and services….(More)”.

Chapter by Stefaan Verhulst and Andrew Young in Smarter New York City:How City Agencies Innovate. Edited by André Corrêa d’Almeida: “While retail entrepreneurs, particularly those operating in the small-business space, are experts in their respective trades, they often lack access to high-quality information about social, environmental, and economic conditions in the neighborhoods where they operate or are considering operating.

The New York City Business Atlas, conceived by the Mayor’s Office of Data Analytics (MODA) and the Department of Small Business Services, is designed to alleviate that information gap by providing a public web-based tool that gives small businesses access to high-quality data to help them decide where to establish a new business or expand an existing one. e tool brings together a diversity of data, including business-fling data from the Department of Consumer Affairs, sales-tax data from the Department of Finance, demographic data from the census, and traffic data from Placemeter, a New York City startup focusing on real-time traffic information.

The initial iteration of the Business Atlas made useful and previously inaccessible data available to small-business owners and entrepreneurs in an innovative manner. After a few years, however, it became clear that the tool was not experiencing the level of use or creating the level of demonstrable impact anticipated. Rather than continuing down the same path or abandoning the effort entirely, MODA pivoted to a new approach, moving from the Business Atlas as a single information-providing tool to the Business Atlas as a suite of capabilities aimed at bolstering New York’s small-business community.

Through problem- and user-centered efforts, the Business Atlas is now making important insights available to stakeholders who can put it to meaningful use—from how long it takes to open a restaurant in the city to which areas are most in need of education and outreach to improve their code compliance. This chapter considers the open data environment from which the Business Atlas was launched, details the initial version of the Business Atlas and the lessons it generated and describes the pivot to this new approach….(More)”.

Matthew Beedham at TNW: “Walmart is asking all of its leafy greens suppliers to get on blockchain by this time next year.

With instances of E. coli on the rise, particularly in romaine lettuce, Walmart is insisting that its suppliers use blockchain to track and trace products from source to the customer.

Walmart notes that, while health officials at the Centers for Disease Control told Americans have already warned citizens to avoid eating lettuce grown in Yuma, Arizona, it’s near impossible for consumers to know where their greens are coming from.

On one hand this could be a great system for reducing waste. Earlier this year, green grocers had to throw away produce thought to be infected with E. Coli.

The announcement states, “[h]ealth officials at the Centers for Disease Control told Americans to avoid eating lettuce that was grown in Yuma, Arizona”

However, it’s near impossible for consumers to know where their lettuce was grown.

It would seem that most producers and suppliers still rely on paper-based ledgers. As a result, tracking down vital information about where a product came from can be very time consuming.

By which time, it might be too late and many customers might have purchased and consumed infected produce.

If Walmart’s plans come to fruition, it would allow customers to view the entire supply chain of a product at the point of purchase… (More)”

Toolkit: “Government leaders and staff who leverage algorithms are facing increasing pressure from the public, the media, and academic institutions to be more transparent and accountable about their use. Every day, stories come out describing the unintended or undesirable consequences of algorithms. Governments have not had the tools they need to understand and manage this new class of risk.

GovEx, the City and County of San Francisco, Harvard DataSmart, and Data Community DC have collaborated on a practical toolkit for cities to use to help them understand the implications of using an algorithm, clearly articulate the potential risks, and identify ways to mitigate them….We developed this because:

• We saw a gap. There are many calls to arms and lots of policy papers, one of which was a DataSF research paper, but nothing practitioner-facing with a repeatable, manageable process.
• We wanted an approach which governments are already familiar with: risk management. By identifing and quantifying levels of risk, we can recommend specific mitigations.. …(More)”.

Introduction to Special Issue on Urban Modeling and Simulation by Shade T. Shutters: “Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as data-driven cities.

Parallel advances in theory are needed to make sense of those novel data streams and computationally intensive decision support models are needed to guide decision makers through the avalanche of new data. Fortunately, extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modeling of complex systems.

Techniques, such as agent-based modeling and systems dynamic modeling, have taken advantage of these advances to make major contributions to diverse disciplines such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Urban systems, with dynamic webs of interacting human, institutional, environmental, and physical systems, are particularly suited to the application of these advanced modeling and simulation techniques. Contributions to this special issue highlight the use of such techniques and are particularly timely as an emerging science of cities begins to crystallize….(More)”.

Book edited by Jonas Tallberg, Karin Backstrand, and Jan Aart Scholte: “Legitimacy is central for the capacity of global governance institutions to address problems such as climate change, trade protectionism, and human rights abuses. However, despite legitimacy’s importance for global governance, its workings remain poorly understood. That is the core concern of this volume: to develop an agenda for systematic and comparative research on legitimacy in global governance. In complementary fashion, the chapters address different aspects of the overarching question: whether, why, how, and with what consequences global governance institutions gain, sustain, and lose legitimacy?

The volume makes four specific contributions. First, it argues for a sociological approach to legitimacy, centered on perceptions of legitimate global governance among affected audiences. Second, it moves beyond the traditional focus on states as the principal audience for legitimacy in global governance and considers a full spectrum of actors from governments to citizens. Third, it advocates a comparative approach to the study of legitimacy in global governance, and suggests strategies for comparison across institutions, issue areas, countries, societal groups, and time. Fourth, the volume offers the most comprehensive treatment so far of the sociological legitimacy of global governance, covering three broad analytical themes: (1) sources of legitimacy, (2) processes of legitimation and delegitimation, and (3) consequences of legitimacy…(More)”.

Christina Pena at the National Skills Coalition: “Administrative data on employment and earnings, commonly referred to as wage data or wage records, can be used to assess the labor market outcomes of workforce, education, and other programs, providing policymakers, administrators, researchers, and the public with valuable information. However, there is no single readily accessible federal source of wage data which covers all workers. Noting the importance of employment and earnings data to decision makers, the Commission on Evidence-Based Policymaking called for the creation of a single federal source of wage data for statistical purposes and evaluation. They recommended three options for further exploration: expanding access to systems that already exist at the U.S. Census Bureau or the U.S. Department of Health and Human Services (HHS), or creating a new database at the U.S. Department of Labor (DOL).

This paper reviews current coverage and allowable uses, as well as federal and state actions required to make each option viable as a single federal source of wage data that can be accessed by government agencies and authorized researchers. Congress and the President, in conjunction with relevant federal and state agencies, should develop one or more of those options to improve wage information for multiple purposes. Although not assessed in the following review, financial as well as privacy and security considerations would influence the viability of each scenario. Moreover, if a system like the Commission-recommended National Secure Data Service for sharing data between agencies comes to fruition, then a wage system might require additional changes to work with the new service….(More)”

Leslie K. John at Harvard Business Review: “…People are bad at making decisions about their private data. They misunderstand both costs and benefits. Moreover, natural human biases interfere with their judgment. And whether by design or accident, major platform companies and data aggregators have structured their products and services to exploit those biases, often in subtle ways.

Impatience. People tend to overvalue immediate costs and benefits and underweight those that will occur in the future. They want $9 today rather than $10 tomorrow. On the internet, this tendency manifests itself in a willingness to reveal personal information for trivial rewards. Free quizzes and surveys are prime examples. …

The endowment effect. In theory people should be willing to pay the same amount to buy a good as they’d demand when selling it. In reality, people typically value a goodless when they have to buy it. A similar dynamic can be seen when people make decisions about privacy….

Illusion of control. People share a misapprehension that they can control chance processes. This explains why, for example, study subjects valued lottery tickets that they had personally selected more than tickets that had been randomly handed to them. People also confuse the superficial trappings of control with real control….

Desire for disclosure. This is not a decision-making bias. Rather, humans have what appears to be an innate desire, or even need, to share with others. After all, that’s how we forge relationships — and we’re inherently social creatures…

False sense of boundaries. In off-line contexts, people naturally understand and comply with social norms about discretion and interpersonal communication. Though we may be tempted to gossip about someone, the norm “don’t talk behind people’s backs” usually checks that urge. Most of us would never tell a trusted confidant our secrets when others are within earshot. And people’s reactions in the moment can make us quickly scale back if we disclose something inappropriate….(More)”.