Financing the Digital Public Goods Ecosystem

Curated on Posted on by Stefaan Verhulst

Blog by the Digital Public Goods Alliance (DPGA): “… we believe that digital public goods (DPGs) are essential to unlocking the full potential of digital technologies to enhance human welfare at scale. Their relevance to one or multiple sustainable development goals (SDGs), combined with their adoptability and adaptability, allows DPGs to strengthen international digital cooperation. Stakeholders can join forces to support solutions that address many of today’s greatest global challenges in critical areas such as health, education and climate change. DPGs are of particular importance for resource constrained countries looking to accelerate development through improving access to digital services.

Still, precisely due to their nature as “public goods” – which ensures that no one can prevent others from benefiting from them – DPGs can be difficult to fund through market mechanisms, and some of them should not have to prioritise generating profit….

Sustainably funded infrastructural DPGs can become a reliable core for broader ecosystems through community building:

  • For the Modular Open Source Identity Platform (MOSIP) core code management and evolution is fully funded by grants from a group of philanthropic and bilateral donors.** This enables the team responsible for managing and evolving the generic platform to focus exclusively on maximising utility for those the platform is designed to serve – in this case, countries in need of foundational digital identity systems.
  • Similarly backed by grant funding for core code development and maintenance, the team behind District Health Information Software 2 (DHIS2) has prioritised community building within and between the 70+ countries that have adopted the software, enabling countries to share improvements and related innovations. This is best exemplified by Sri Lanka, the first country in the world to use DHIS2 for COVID-19 surveillance, who shared this groundbreaking innovation with the global DHIS2 community. Today, this system is operational in 38 countries and is under development in fourteen more.
  • The data exchange layer X-Road, which is publicly funded by NIIS members (currently Estonia and Finland), demonstrates how infrastructural DPGs can use community building to advance both the core technology and the  quality of downstream deployments. The X-Road Community connects a diverse group of individuals and allows anyone to contribute to the open-source technology. This community-based support and knowledge-sharing helps local vendors around the world build the expertise needed to provide quality services to stakeholders adopting the technology….(More)”.

The speed of science

Curated on Posted on by Stefaan Verhulst

Essay by Saloni Dattani & Nathaniel Bechhofer: “The 21st century has seen some phenomenal advances in our ability to make scientific discoveries. Scientists have developed new technology to build vaccines swiftly, new algorithms to predict the structure of proteins accurately, new equipment to sequence DNA rapidly, and new engineering solutions to harvest energy efficiently. But in many fields of science, reliable knowledge and progress advance staggeringly slowly. What slows it down? And what can we learn from individual fields of science to pick up the pace across the board – without compromising on quality?

By and large, scientific research is published in journals in the form of papers – static documents that do not update with new data or new methods. Instead of sharing the data and the code that produces their results, most scientists simply publish a textual description of their research in online publications. These publications are usually hidden behind paywalls, making it harder for outsiders to verify their authenticity.

On the occasion when a reader spots a discrepancy in the data or an error in the methods, they must read the intricate details of a study’s method scrupulously, and cross-check the statistics manually. When scientists don’t share the data to produce their results openly, the task becomes even harder. The process of error correction – from scientists publishing a paper, to readers spotting errors, to having the paper corrected or retracted – can take years, assuming those errors are spotted at all.

When scientists reference previous research, they cite entire papers, not specific results or values from them. And although there is evidence that scientists hold back from citing papers once they have been retracted, the problem is compounded over time – consider, for example, a researcher who cites a study that itself derives its data or assumptions from prior research that has been disputed, corrected or retracted. The longer it takes to sift through the science, to identify which results are accurate, the longer it takes to gather an understanding of scientific knowledge.

What makes the problem even more challenging is that flaws in a study are not necessarily mathematical errors. In many situations, researchers make fairly arbitrary decisions as to how they collect their data, which methods they apply to analyse them, and which results they report – altogether leaving readers blind to the impact of these decisions on the results.

This murkiness can result in what is known as p-hacking: when researchers selectively apply arbitrary methods in order to achieve a particular result. For example, in a study that compares the well-being of overweight people to that of underweight people, researchers may find that certain cut-offs of weight (or certain subgroups in their sample) provide the result they’re looking for, while others don’t. And they may decide to only publish the particular methods that provided that result…(More)”.

How can we measure productivity in the public sector?

Curated on Posted on by Stefaan Verhulst

Ravi Somani at the World Bank: “In most economies, the public sector is a major purchaser of goods, services and labor. According to the Worldwide Bureaucracy Indicators, globally the public sector accounts for around 25% of GDP and 38% of formal employment. Generating efficiency gains in the public sector can, therefore, have important implications for a country’s overall economic performance.  

Public-sector productivity measures the rate with which inputs are converted into desirable outputs in the public sector. Measures can be developed at the level of the employee, organization, or overall public sector, and can be tracked over time. Such information allows policymakers to identify good and bad performers, understand what might be correlated with good performance, and measure the returns to different types of public expenditures. This knowledge can be used to improve the allocation of public resources in the future and maximize the impact of the public purse.

But how can we measure it?

However, measuring productivity in the public sector can be tricky because:

  • There are often no market transactions for public services, or they are distorted by subsidies and other market imperfections.
  • Many public services are complex, requiring (often immeasurable) inputs from multiple individuals and organizations.
  • There is often a substantial time lag between investments in inputs and the realization of outputs and outcomes.

This recent World Bank publication provides a summary of the different approaches to measuring productivity in the public sector, presented in the table below.  For simplicity, the approaches are separated into: ‘macro’ approaches, which provide aggregate information at the level of an organization, sector, or service as a whole; and ‘micro’ approaches, which can be applied to the individual employee, task, project, and process.   
 

Macro and Micro Approaches to measure public-sector productivity

There is no silver bullet for accurately measuring public-sector productivity – each approach has its own limitations.  For example, the cost-weighted-output approach requires activity-level data, necessitates different approaches for different sectors, and results in metrics with difficult-to-interpret absolute levels.  Project-completion rates require access to project-level data and may not fully account for differences in the quality and complexity of projects. The publication includes a list of the pros, cons, and implementation requirements for each approach….(More)”.

Using Data and Citizen Science for Gardening Success

Curated on Posted on by Stefaan Verhulst

Article by Elizabeth Waddington: “…Data can help you personally by providing information you can use. And it also allows you to play a wider role in boosting understanding of our planet and tackling the global crises we face in a collaborative way. Consider the following examples.

Grow Observatory

This is one great example of data gathering and citizen science. Grow Observatory is a European citizen’s observatory through which people work together to take action on climate change, build better soil, grow healthier food and corroborate data from the new generation of Copernicus satellites.

Twenty-four Grow communities in 13 European countries created a network of over 6,500 ground-based soil sensors and collected a lot of soil-related data. And many insights have helped people learn about and test regenerative food growing techniques.

On their website, you can explore sensor locations, or make use of dynamic soil moisture maps. With the Grow Observatory app, you can get crop and planting advice tailored to your location, and get detailed, science-based information about regenerative growing practices. Their water planner also allows small-scale growers to learn more about how much water their plants will need in their location over the coming months if they live in one of the areas which currently have available data…

Cooperative Citizen Science: iNaturalist, Bioblitzes, Bird Counts, and More

Wherever you live, there are many different ways to get involved and help build data. From submitting observations on wildlife in your garden through apps like iNaturalist to taking part in local Bioblitzes, bird counts, and more – there are plenty of ways we can collect data that will help us – and others – down the road.

Collecting data through our observations, and, crucially, sharing that data with others can help us create the future we all want to see. We, as individuals, can often feel powerless. But citizen science projects help us to see the collective power we can wield when we work together. Modern technology means we can be hyper-connected, and affect wider systems, even when we are alone in our own gardens….(More)”

The New Tech Tools in Data Sharing

Curated on Posted on by Stefaan Verhulst

Essay by Massimo Russo and Tian Feng: “…Cloud providers are integrating data-sharing capabilities into their product suites and investing in R&D that addresses new features such as data directories, trusted execution environments, and homomorphic encryption. They are also partnering with industry-specific ecosystem orchestrators to provide joint solutions.

Cloud providers are moving beyond infrastructure to enable broader data sharing. In 2018, for example, Microsoft teamed up with Oracle and SAP to kick off its Open Data Initiative, which focuses on interoperability among the three large platforms. Microsoft has also begun an Open Data Campaign to close the data divide and help smaller organizations get access to data needed for innovation in artificial intelligence (AI). Amazon Web Services (AWS) has begun a number of projects designed to promote open data, including the AWS Data Exchange and the Open Data Sponsorship Program. In addition to these large providers, specialty technology companies and startups are likewise investing in solutions that further data sharing.

Technology solutions today generally fall into three categories: mitigating risks, enhancing value, and reducing friction. The following is a noncomprehensive list of solutions in each category.

1. Mitigating the Risks of Data Sharing

Potential financial, competitive, and brand risks associated with data disclosure inhibit data sharing. To address these risks, data platforms are embedding solutions to control use, limit data access, encrypt data, and create substitute or synthetic data. (See slide 2 in the slideshow.)

Data Breaches. Here are some of the technological solutions designed toprevent data breaches and unauthorized access to sensitive or private data:

  • Data modification techniques alter individual data elements or full data sets while maintaining data integrity. They provide increasing levels of protection but at a cost: loss of granularity of the underlying data. De-identification and masking strip personal identifier information and use encryption, allowing most of the data value to be preserved. More complex encryptions can increase security, but they also remove resolution of information from the data set.
  • Secure data storage and transfer can help ensure that data stays safe both at rest and in transit. Cloud solutions such as Microsoft Azure and AWS have invested in significant platform security and interoperability.
  • Distributed ledger technologies, such as blockchain, permit data to be stored and shared in a decentralized manner that makes it very difficult to tamper with. IOTA, for example, is a distributed ledger platform for IoT applications supported by industy players such as Bosch and Software AG.
  • Secure computation enables analysis without revealing details of the underlying data. This can be done at a software level, with techniques such as secure multiparty computation (MPC) that allow potentially untrusting parties to jointly compute a function without revealing their private inputs. For example, with MPC, two parties can calculate the intersection of their respective encrypted data set while only revealing information about the intersection. Google, for one, is embedding MPC in its open-source Private Join and Compute tools.
  • Trusted execution environments (TEEs) are hardware modules separate from the operating system that allow for secure data processing within an encrypted private area on the chip. Startup Decentriq is partnering with Intel and Microsoft to explore confidential computing by means of TEEs. There is a significant opportunity for IoT equipment providers to integrate TEEs into their products….(More)”

A new approach to problem-solving across the Sustainable Development Goals

Curated on Posted on by Stefaan Verhulst

Alexandra Bracken, John McArthur, and Jacob Taylor at Brookings: “The economic, social, and environmental challenges embedded throughout the world’s 17 Sustainable Development Goals (SDGs) will require many breakthroughs from business as usual. COVID-19 has only underscored the SDGs’ central message that the underlying problems are both interconnected and urgent, so new mindsets are required to generate faster progress on many fronts at once. Our recent report, 17 Rooms: A new approach to spurring action for the Sustainable Development Goals, describes an effort to innovate around the process of SDG problem-solving itself.

17 Rooms aims to advance problem-solving within and across all the SDGs. As a partnership between Brookings and The Rockefeller Foundation, the first version of the undertaking was convened in September 2018, as a single meeting on the eve of the U.N. General Assembly in New York. The initiative has since evolved into a two-pronged effort: an annual flagship process focused on global-scale policy issues and a community-level process in which local actors are taking 17 Rooms methods into their own hands.

In practical terms, 17 Rooms consists of participants from disparate specialist communities each meeting in their own “Rooms,” or working groups, one for each SDG. Each Room is tasked with a common assignment of identifying cooperative actions they can take over the subsequent 12-18 months. Emerging ideas are then shared across Rooms to spot opportunities for collaboration.

The initiative continues to evolve through ongoing experimentation, so methods are not overly fixed, but three design principles help define key elements of the 17 Rooms mindset:

  1. All SDGs get a seat at the table. Insights, participants, and priorities are valued equally across all the specialist communities focused on individual dimensions of the SDGs
  2. Take a next step, not the perfect step. The process encourages participants to identify—and collaborate on—actions that are “big enough to matter, but small enough to get done”
  3. Conversations, not presentations. Discussions are structured around collaboration and peer-learning, aiming to focus on what’s best for an issue, not any individual organization

These principles appear to contribute to three distinct forms of value: the advancement of action, the generation of insights, and a strengthened sense of community among participants….(More)”.

Legislative Performance Futures

Curated on Posted on by Stefaan Verhulst

Article by Ben Podgursky on “Incentivize Good Laws by Monetizing the Verdict of History”….There are net-positive legislative policies which legislators won’t enact, because they only help people in the medium to far future.  For example:

  • Climate change policy
  • Infrastructure investments and mass-transit projects
  • Debt control and social security reform
  • Child tax credits

The (infrequent) times reforms on these issues are legislated — which happens rarely compared to their future value — they are passed not because of the value provided to future generations, but because of the immediate benefit to voters today:

  • Infrastructure investment goes to “shovel ready” projects, with an emphasis on short-term job creation, even when the prime benefit is to future GDP.  For example, Dams constructed in the 1930s (the Hoover Dam, the TVA) provide immense value today, but the projects only happened in order to create tens of thousands of jobs.
  • Climate change legislation is usually weakly directed.  Instead of policies which incur significant long-term benefits but short-term costs (ie, carbon taxes), “green legislation” aims to create green jobs and incentivize rooftop solar (reducing power bills today).
  • (small) child tax credits are passed to help parents today, even though the vastly larger benefit is incurred by children who exist because the marginal extra cash helped their parents afford an extra child.

On the other hand, reforms which provide nobenefit to today’s voter do not happen; this is why the upcoming Social Security Trust Fund shortfall will likely not be fixed until benefits are reduced and voters are directly impacted.

The issue is that while the future reaps the benefits or failures of today’s laws, people of the future cannot vote in today’s elections.  In fact, in almost no circumstances does the future have any ability to meaningfully reward or punish past lawmakers; there are debates today about whether to remove statues and rename buildings dedicated to those on the wrong side of history, actions which even proponents acknowledge as entirely symbolic….(More)”.

Policy 2.0 in the Pandemic World: What Worked, What Didn’t, and Why

Curated on Posted on by Stefaan Verhulst

Blog by David Osimo: “…So how, then, did these new tools perform when confronted with the once-in-a-lifetime crisis of a vast global pandemic?

It turns out, some things worked. Others didn’t. And the question of how these new policymaking tools functioned in the heat of battle is already generating valuable ammunition for future crises.

So what worked?

Policy modelling – an analytical framework designed to anticipate the impact of decisions by simulating the interaction of multiple agents in a system rather than just the independent actions of atomised and rational humans – took centre stage in the pandemic and emerged with reinforced importance in policymaking. Notably, it helped governments predict how and when to introduce lockdowns or open up. But even there uptake was limited. A recent survey showed that of the 28 models used in different countries to fight the pandemic were traditional, and not the modern “agent-based models” or “system dynamics” supposed to deal best with uncertainty. Meanwhile, the concepts of system science was becoming prominent and widely communicated. It became quickly clear in the course of the crisis that social distancing was more a method to reduce the systemic pressure on the health services than a way to avoid individual contagion (the so called “flatten the curve” project).

Open government data has long promised to allow citizens and businesses to build new services at scale and make government accountable. The pandemic largely confirmed how important this data could be to allow citizens to analyse things independently. Hundreds of analysts from all walks of life and disciplines used social media to discuss their analysis and predictions, many becoming household names and go-to people in countries and regions. Yes, this led to noise and a so-called “infodemic,” but overall it served as a fundamental tool to increase confidence and consensus behind the policy measures and to make governments accountable for their actions. For instance, one Catalan analyst demonstrated that vaccines were not provided during weekends and forced the government to change its stance. Yet it is also clear that not all went well, most notably on the supply side. Governments published data of low quality, either in PDF, with delays or with missing data due to spreadsheet abuse.

In most cases, there was little demand for sophisticated data publishing solutions such as “linked” or “FAIR” data, although particularly significant was the uptake of these kinds of solutions when it came time to share crucial research data. Experts argue that the trend towards open science has accelerated dramatically and irreversibly in the last year, as shown by the portal https://www.covid19dataportal.org/ which allowed sharing of high quality data for scientific research….

But other new policy tools proved less easy to use and ultimately ineffective. Collaborative governance, for one, promised to leverage the knowledge of thousands of citizens to improve public policies and services. In practice, methodologies aiming at involving citizens in decision making and service design were of little use. Decisions related to lockdown and opening up were taken in closed committees in top down mode. Individual exceptions certainly exist: Milan, one of the cities worst hit by the pandemic, launched a co-created strategy for opening up after the lockdown, receiving almost 3000 contributions to the consultation. But overall, such initiatives had limited impact and visibility. With regard to co-design of public services, in times of emergency there was no time for prototyping or focus groups. Services such as emergency financial relief had to be launched in a hurry and “just work.”…

Citizen science promised to make every citizen a consensual data source for monitoring complex phenomena in real time through apps and Internet-of-Things sensors. In the pandemic, there were initially great expectations on digital contact tracing apps to allow for real time monitoring of contagions, most notably through bluetooth connections in the phone. However, they were mostly a disappointment. Citizens were reluctant to install them. And contact tracing soon appeared to be much more complicated – and human intensive – than originally thought. The huge debate between technology and privacy was followed by very limited impact. Much ado about nothing.

Behavioural economics (commonly known as nudge theory) is probably the most visible failure of the pandemic. It promised to move beyond traditional carrots (public funding) and sticks (regulation) in delivering policy objectives by adopting an experimental method to influence or “nudge” human behaviour towards desired outcomes. The reality is that soft nudges proved an ineffective alternative to hard lockdown choices. What makes it uniquely negative is that such methods took centre stage in the initial phase of the pandemic and particularly informed the United Kingdom’s lax approach in the first months on the basis of a hypothetical and unproven “behavioural fatigue.” This attracted heavy criticism towards the excessive reliance on nudges by the United Kingdom government, a legacy of Prime Minister David Cameron’s administration. The origin of such criticisms seems to lie not in the method shortcomings per se, which enjoyed success previously on more specific cases, but in the backlash from excessive expectations and promises, epitomised in the quote of a prominent behavioural economist: “It’s no longer a matter of supposition as it was in 2010 […] we can now say with a high degree of confidence these models give you best policy.”

Three factors emerge as the key determinants behind success and failure: maturity, institutions and leadership….(More)”.

Open Data Day 2021: How to unlock its potential moving forward?

Curated on Posted on by Stefaan Verhulst

Stefaan Verhulst, Andrew Young, and Andrew Zahuranec at Data and Policy: “For over a decade, data advocates have reserved one day out of the year to celebrate open data. Open Data Day 2021 comes at a time of unprecedented upheaval. As the world remains in the grip of COVID-19, open data researchers and practitioners must confront the challenge of how to use open data to address the types of complex, emergent challenges that are likely to define the rest of this century (and beyond). Amid threats like the ongoing pandemic, climate change, and systemic poverty, there is renewed pressure to find ways that open data can solve complex social, cultural, economic and political problems.

Over the past year, the Open Data Policy Lab, an initiative of The GovLab at NYU’s Tandon School of Engineering, held several sessions with leaders of open data from around the world. Over the course of these sessions, which we called the Summer of Open Data, we studied various strategies and trends, and identified future pathways for open data leaders to pursue. The results of this research suggest an emergent Third Wave of Open Data— one that offers a clear pathway for stakeholders of all types to achieve Open Data Day’s goal of “showing the benefits of open data and encouraging the adoption of open data policies in government, business, and civil society.”

The Third Wave of Open Data is central to how data is being collected, stored, shared, used, and reused around the world. In what follows, we explain this notion further, and argue that it offers a useful rubric through which to take stock of where we are — and to consider future goals — as we mark this latest iteration of Open Data Day.

The Past and Present of Open Data

The history of open data can be divided into several waves, each reflecting the priorities and values of the era in which they emerged….(More)”.

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The Three Waves of Open Data

Improving Governance by Asking Questions that Matter

Curated on Posted on by Stefaan Verhulst

Fiona Cece, Nicola Nixon and Stefaan Verhulst at the Open Government Partnership:

“You can tell whether a man is clever by his answers. You can tell whether a man is wise by his questions” – Naguib Mahfouz

Data is at the heart of every dimension of the COVID-19 challenge. It’s been vital in the monitoring of daily rates, track and trace technologies, doctors appointments, and the vaccine roll-out. Yet our daily diet of brightly-coloured graphed global trends masks the maelstrom of inaccuracies, gaps and guesswork that underlies the ramshackle numbers on which they are so often based. Governments are unable to address their citizens’ needs in an informed way when the data itself is partial, incomplete or simply biased. And citizens’ in turn are unable to contribute to collective decision-making that impacts their lives when the channels for doing so in meaningful ways are largely non-existent. 

There is an irony here. We live in an era in which there are an unprecedented number of methods for collecting data. Even in the poorest countries with weak or largely non-existent government systems, anyone with a mobile phone or who accesses the internet is using and producing data. Yet a chasm exists between the potential of data to contribute to better governance and what it is actually collected and used for.

Even where data accuracy can be relied upon, the practice of effective, efficient and equitable data governance requires much more than its collection and dissemination.

And although governments will play a vital role, combatting the pandemic and its associated socio-economic challenges will require the combined efforts of non-government organizations (NGOs), civil society organizations (CSOs), citizens’ associations, healthcare companies and providers, universities, think tanks and so many others. Collaboration is key.

There is a need to collectively move beyond solution-driven thinking. One initiative working toward this end is The 100 Questions Initiative by The Governance Lab (The GovLab) at the NYU Tandon School of Engineering. In partnership with the The Asia Foundation, the Centre for Strategic and International Studies in Indonesia, and the BRAC Institute of Governance and Development, the Initiative is launching a Governance domain. Collectively we will draw on the expertise of over 100 “bilinguals”– experts in both data science and governance — to identify the 10 most-pressing questions on a variety of issues that can be addressed using data and data science. The cohort for this domain is multi-sectoral and geographically varied, and will provide diverse input on these governance challenges. 

Once the questions have been identified and prioritized, and we have engaged with a broader public through a voting campaign, the ultimate goal is to establish one or more data collaboratives that can generate answers to the questions at hand. Data collaboratives are an emerging structure that allow pooling of data and expertise across sectors, often resulting in new insights and public sector innovations.  Data collaboratives are fundamentally about sharing and cross-sectoral engagement. They have been deployed across countries and sectoral contexts, and their relative success shows that in the twenty-first century no single actor can solve vexing public problems. The route to success lies through broad-based collaboration. 

Multi-sectoral and geographically diverse insight is needed to address the governance challenges we are living through, especially during the time of COVIDd-19. The pandemic has exposed weak governance practices globally, and collectively we need to craft a better response. As an open governance and data-for-development community, we have not yet leveraged the best insight available to inform an effective, evidence-based response to the pandemic. It is time we leverage more data and technology to enable citizen-centrism in our service delivery and decision-making processes, to contribute to overcoming the pandemic and to building our governance systems, institutions and structures back better. Together with over 130 ‘Bilinguals’ – experts in both governance and data – we have set about identifying the priority questions that data can answer to improve governance. Join us on this journey. Stay tuned for our public voting campaign in a couple of months’ time when we will crowdsource your views on which of the questions they pose really matter….(More)”.