Co-Develop: Digital Public Infrastructure for an Equitable Recovery

Curated on Posted on by Stefaan Verhulst

A report by The Rockefeller Foundation: “Digital systems that accomplish basic, society-wide functions played a critical role in the response to the Covid-19 pandemic, enabling both public health and social protection measures. The pandemic has shown the value of these systems, but it has also revealed how they are non-existent or weak in far too many places.

As we build back better, we have an unprecedented opportunity to build digital public infrastructure that promotes inclusion, human rights, and progress toward global goals. This report outlines an agenda for international cooperation on digital public infrastructure to guide future investments and expansion of this critical tool.

6 Key Areas for International Cooperation on Digital Public Infrastructure

  1. A vision for digital public infrastructure as a whole, backed by practice, research, and evaluation.
  2. A global commons based on digital public goods.
  3. Safeguards for inclusion, trust, competition, security, and privacy.
  4. Tools that use data in digital public infrastructure for public value and private empowerment.
  5. Private and public capacity, particularly in implementing countries.
  6. Silo-busting, built-for-purpose coordinating, funding, and financing….(More)”.

Satellite Earth observation for sustainable rural development

Curated on Posted on by Stefaan Verhulst

A blog post by Peter Hargreaves: “…We find ourselves in a “golden age for satellite exploration”. ‘Big Data’ from satellite Earth observation – hereafter denoted ‘EO’ – could be an important part of the solution to the shortage of socioeconomic data required to inform several of the goals and targets that compose the United Nations (UN) Sustainable Development Goals (SDGs) [hyperlink]. In particular, the goals that pertain to socioeconomic and human wellbeing dimensions of development. EO data could play a significant role in producing the transparent data system necessary to achieve sustainable development….

Census and nationally representative household surveys are the medium through which most socioeconomic data are collected. It is impossible to understand socioeconomic conditions without them – I cannot stress this enough. But they have limitations, particularly in terms of cost and spatio-temporal coverage. In an ideal world, we would vastly upscale the spatial and temporal reporting of these surveys to cover more places and points in time. But this mass enumeration would be prohibitively expensive and *logistically impossible*. Imagine the quantity of data produced and the burden placed upon National Statistics Offices (NSOs) and governmental institutions? The 2030 end point for the SDGs would be upon us before much of the data was processed leaving very little time to use the outputs for policy.

This is where unconventional data enters the debate, and in this sphere – that of measuring socioeconomic conditions for development – EO data is unconventional. EO data has considerable potential to augment survey and census data for measuring rural poverty development in rural spaces, especially during intercensal periods, and where ground data are patchy, or non-existent. While on the subject, there is an important point to make: you can’t use EO to understand everything about a particular context. It does not matter how elaborate the model or the effort put in. Quite simply, EO cannot give you the full picture.

What EO *does* have is a five-decade temporal legacy (most platforms and data products are near continuous), and its broadly open access with low to negligible acquisition costs. EO data is also availabile across multiple spatial resolutions and is often easily comparable and complementary. When we say, ‘five-decade temporal legacy’, this means that there are roughly 50 years of EO data (if we use the Landsat program as an anchor). Not all EO platforms have operated across the whole timeline – Figure 1 below offers an idea of when different platforms were launched and for how long they were, or have been, operational. What’s more, data will be increasingly available and accessible, catalysed by technological innovation and investment in public and private ventures. A lot of this data is open access e.g. EO platforms operated by NASA or the ESA Copernicus programme, which include Landsat, MODIS, AVHRR, VIIRs, and the Sentinels amongst others. Meanwhile, the availability of EO data across multiple spatial resolutions enables disaggregation of data alongside survey and census data for subnational monitoring of socioeconomic conditions….(More)”.

The State of Global Emotions

Curated on Posted on by Stefaan Verhulst

Gallup: “Nobody was alone in feeling more sad, angry, worried or stressed last year. Gallup’s latest Negative Experience Index, which annually tracks these experiences worldwide in more than 100 countries and areas, shows that collectively, the world was feeling the worst it had in 15 years. The index score reached a new high of 32 in 2020.

Line graph. The Negative Experience Index, an annual composite index of stress, anger, worry, sadness and physical pain, continued to rise in 2020, hitting a new record of 32.

Gallup asked adults in 115 countries and areas if they had five specific negative experiences on the day preceding the survey. Four in 10 adults said they had experienced worry (40%) or stress (40%), and just under three in 10 had experienced physical pain (29%) during a lot of the previous day. About one in four or more experienced sadness (27%) or anger (24%).

Already at or near record highs in 2019, experiences of worry, stress, sadness and anger continued to gain steam and set new records in 2020. Worry and sadness each rose one percentage point, anger rose two, and stress rocketed up five. The percentage of adults worldwide who experienced pain was the only index item that declined — dropping two points after holding steady for several years at 31%.

But 2020 officially became the most stressful year in recent history. The five-point jump from 35% in 2019 to 40% in 2020 represents nearly 190 million more people globally who experienced stress during a lot of the previous day.

Line graph. Reported stress worldwide soared to a record 40% in 2020 amid the COVID-19 pandemic.

Worldwide, not everyone was feeling this stress to the same degree. Reported stress ranged from a high of 66% in Peru — which represents a new high for the country — to a low of 13% in Kyrgyzstan, where stress levels have historically been low and stayed low in 2020….(More)”

Guide on Geospatial Data Integration in Official Statistics

Curated on Posted on by Stefaan Verhulst

Report by PARIS21: “National geospatial integration agencies can provide detailed, timely and relevant data about people, businesses, buildings, infrastructures, agriculture, natural resources and anthropogenic impacts on the biosphere. There is a clear benefit to integrating geospatial data into traditional national statistical systems. Together they provide a very clear picture of the social, economic and environmental issues that underpin sustainable development and allow for more informed policy making. But the question is where to start?

geospatial data integration

This new PARIS21 publication provides a practical guide, based on five principles for national statistics offices to form stronger partnerships with national geospatial integration agencies….(More)”.

Ethiopia’s blockchain deal is a watershed moment – for the technology, and for Africa

Curated on Posted on by Stefaan Verhulst

Iwa Salami at The Conversation: “At the launch of bitcoin in 2009 the size of the potential of the underlying technology, the blockchain, was not fully appreciated.

What has not been fully exploited is the unique features of blockchain technology that can improve the lives of people and businesses. These include the fact that it is an open source software. This makes its source code legally and freely available to end-users who can use it to create new products and services. Another significant feature is that it is decentralised, democratising the operation of the services built on it. Control of the services built on the blockchain isn’t in the hands of an individual or a single entity but involves all those connected to the network.

In addition, it enables peer to peer interaction between those connected to the network. This is key as it enables parties to transact directly without using intermediaries or third parties. Finally, it has inbuilt security. Data stored on it is immutable and cannot be changed easily. New data can be added only after it is verified by everyone in the network.

Unfortunately, bitcoin, the project that introduced blockchain technology, has hogged the limelight, diverting attention from the technology’s underlying potential benefits….

But this is slowly changing.

A few companies have begun showcasing blockchain capabilities to various African countries. Unlike most other cryptocurrency blockchains which focus on private sector use in developed regions like Europe and North America, their approach has been to target the governments and public institutions in the developing world.

In April the Ethiopian government confirmed that it had signed a deal to create a national database of student and teacher IDs using a decentralised digital identity solution. The deal involves providing IDs for 5 million students across 3,500 schools which will be used to store educational records.

This is the largest blockchain deal ever to be signed by a government and has been making waves in the crypto-asset industry.

I believe that the deal marks a watershed moment for the use of blockchain and the crypto-asset industry, and for African economies because it offers the promise of blockchain being used for real socio-economic change. The deal means that blockchain technology will be used to provide digital identity to millions of Ethiopians. Digital identity – missing in most African countries – is the first step to real financial inclusion, which in turn has been shown to carry a host of benefits….(More)”.

Theories of Change

Curated on Posted on by Stefaan Verhulst

Book by Karen Wendt: “Today, it has become strikingly obvious that companies no longer operate in an environment where only risk return and volatility describe the business environment. The business has to deal with volatility plus uncertainty, plus complexity and ambiguity (VUCA): that requires new qualities, competencies, frameworks; and it demands a new mind set to deal with the VUCA environment in investment, funding and financing. This book builds on a new megatrend beyond resilience, called anti-fragility. We have had the black swan  (financial crisis) and the red swan (COVID) – the Bank for International Settlement is preparing for regenerative capitalism, block chain based analysis of financial streams and is aiming to prevent the “Green Swan” – the climate crisis to lead to the next lockdown. In the light of the UN 17 Sustainable Development Goals, what is required, is Theories of Change.

Written by experts working in the fields of sustainable finance, impact investing, development finance, carbon divesting, innovation, scaling finance, impact entrepreneurship, social stock exchanges, alternative currencies, Initial Coin Offerings (ICOs), ledger technologies, civil action, co-creation, impact management, deep learning and transformation leadership, the book begins by analysing existing Theories of Change frameworks from various disciplines and creating a new integrated model – the meta-framework. In turn, it presents insights on creating and using Theories of Change to redirect investment capital to sustainable companies while implementing the Sustainable Development Goals and the Paris Climate Agreement. Further, it discusses the perspective of planetary boundaries as defined by the Stockholm Resilience Institute, and investigates various aspects of systems, organizations, entrepreneurship, investment and finance that are closely tied to the mission ingrained in the Theory of Change. As it demonstrates, solutions that ensure the parity of profit, people and planet through dynamic change can effectively address the needs of entrepreneurs and business. By exploring these concepts and their application, the book helps create and shape new markets and opportunities….(More)”.

Bridging the data-policy gap in Africa

Curated on Posted on by Stefaan Verhulst

Report by PARIS21 and the Mo Ibrahim Foundation (MIF): “National statistics are an essential component of policymaking: they provide the evidence required to design policies that address the needs of citizens, to monitor results and hold governments to account. Data and policy are closely linked. As Mo Ibrahim puts it: “without data, governments drive blind”. However, there is evidence that the capacity of African governments for data-driven policymaking remains limited by a wide data-policy gap.

What is the data-policy gap?
On the data side, statistical capacity across the continent has improved in recent decades. However, it remains low compared to other world regions and is hindered by several challenges. African national statistical offices (NSOs) often lack adequate financial and human resources as well as the capacity to provide accessible and available data. On the policy side, data literacy as well as a culture of placing data first in policy design and monitoring are still not widespread. Thus, investing in the basic building blocks of national statistics, such as civil registration, is often not a key priority.

At the same time, international development frameworks, such as the United Nations 2030 Agenda for Sustainable Development and the African Union Agenda 2063, require that every signatory country produce and use high-quality, timely and disaggregated data in order to shape development policies that leave no one behind and to fulfil reporting commitments.

Also, the new data ecosystem linked to digital technologies is providing an explosion of data sourced from non-state providers. Within this changing data landscape, African NSOs, like those in many other parts of the world, are confronted with a new data stewardship role. This will add further pressure on the capacity of NSOs, and presents additional challenges in terms of navigating issues of governance and use…

Recommendations as part of a six-point roadmap for bridging the data-policy map include:

  1. Creating a statistical capacity strategy to raise funds
  2. Connecting to knowledge banks to hire and retain talent
  3. Building good narratives for better data use
  4. Recognising the power of foundational data
  5. Strengthening statistical laws to harness the data revolution
  6. Encouraging data use in policy design and implementation…(More)”

We’re Beating Systems Change to Death

Curated on Posted on by Stefaan Verhulst

Essay by Kevin Starr: “Systems change! Just saying the words aloud makes me feel like one of the cognoscenti, one of the elite who has transcended the ways of old-school philanthropy. Those two words capture our aspirations of lasting impact at scale: systems are big, and if you manage to change them, they’ll keep spinning out impact forever. Why would you want to do anything else?

There’s a problem, though. “Systems analysis” is an elegant and useful way to think about problems and get ideas for solutions, but “systems change” is accelerating toward buzzword purgatory. It’s so sexy that everyone wants to use it for everything. …

But when you rummage through the growing literature on systems change thinking, there are in fact a few recurring themes. One is the need to tackle the root causes of any problem you take on. Another is that a broad coalition must be assembled ASAP. Finally, the most salient theme is the notion that the systems involved are transformed as a result of the work (although in many of the examples I read about, it’s not articulated clearly just what system is being changed).

Taken individually or as a whole, these themes point to some of the ways in which systems change is a less-than-ideal paradigm for the work we need to get done:

1. It’s too hard to know to what degree systems change is or isn’t happening. It may be the case that “not everything that matters can be counted,” but most of the stuff that matters can, and it’s hard to get better at something if you’re unable to measure it. But these words of a so-called expert on systems change measurement are typical of what I’ve seen in in the literature: “Measuring systems change is about detecting patterns in the connections between the parts. It is about qualitative changes in the structure of the system, about its adaptiveness and resilience, about synergies emerging from collective efforts—and more…”

Like I said, it’s too hard to know to what is or isn’t happening.

2. “Root cause” thinking can—paradoxically—bog down progress. “Root cause” analysis is a common feature of most systems change discussions, and it’s a wonderful tool to generate ideas and avoid unintended consequences. However, broad efforts to tackle all of a problem’s root causes can turn anything into a complicated, hard-to-replicate project. It can also make things look so overwhelming as to result in a kind of paralysis. And however successful a systems change effort might be, that complication makes it hard to replicate, and you’re often stuck with a one-off project….(More)”.

Innovation in Real Places: Strategies for Prosperity in an Unforgiving World

Curated on Posted on by Stefaan Verhulst
Innovation in Real Places – Strategies for Prosperity in an Unforgiving World - Oxford Scholarship Online

Book by Dan Breznitz: “Across the world, cities and regions have wasted trillions of dollars blindly copying the Silicon Valley model of growth creation. We have lived with this system for decades, and the result is clear: a small number of regions and cities are at the top of the high-tech industry, but many more are fighting a losing battle to retain economic dynamism. But, as this books details, there are other models for innovation-based growth that don’t rely on a flourishing high-tech industry. Breznitz argues that the purveyors of the dominant ideas on innovation have a feeble understanding of the big picture on global production and innovation.

They conflate innovation with invention and suffer from techno-fetishism. In their devotion to start-ups, they refuse to admit that the real obstacle to growth for most cities is the overwhelming power of the real hubs, which siphon up vast amounts of talent and money. Communities waste time, money, and energy pursuing this road to nowhere. Instead, Breznitz proposes that communities focus on where they fit within the four stages in the global production process. Success lies in understanding the changed structure of the global system of production and then using those insights to enable communities to recognize their own advantages, which in turn allows to them to foster surprising forms of specialized innovation. All localities have certain advantages relative to at least one stage of the global production process, and the trick is in recognizing it….(More)”.

Socially Responsible Data Labeling

Curated on Posted on by Stefaan Verhulst

Blog By Hamed Alemohammad at Radiant Earth Foundation: “Labeling satellite imagery is the process of applying tags to scenes to provide context or confirm information. These labeled training datasets form the basis for machine learning (ML) algorithms. The labeling undertaking (in many cases) requires humans to meticulously and manually assign captions to the data, allowing the model to learn patterns and estimate them for other observations.

For a wide range of Earth observation applications, training data labels can be generated by annotating satellite imagery. Images can be classified to identify the entire image as a class (e.g., water body) or for specific objects within the satellite image. However, annotation tasks can only identify features observable in the imagery. For example, with Sentinel-2 imagery at the 10-meter spatial resolution, one cannot detect the more detailed features of interest, such as crop types but would be able to distinguish large croplands from other land cover classes.

Human error in labeling is inevitable and results in uncertainties and errors in the final label. As a result, it’s best practice to examine images multiple times and then assign a majority or consensus label. In general, significant human resources and financial investment is needed to annotate imagery at large scales.

In 2018, we identified the need for a geographically diverse land cover classification training dataset that required human annotation and validation of labels. We proposed to Schmidt Futures a project to generate such a dataset to advance land cover classification globally. In this blog post, we discuss what we’ve learned developing LandCoverNet, including the keys to generating good quality labels in a socially responsible manner….(More)”.