Using behavioral insights to make the most of emergency social protection cash transfers

Article by Laura Rawlings, Jessica Jean-Francois and Catherine MacLeod: “In response to the COVID-19 pandemic, countries across the globe have been adapting social assistance policies to support their populations. In fact, since March 2020, 139 countries and territories have planned, implemented, or adapted cash transfers to support their citizens. Cash transfers specifically make up about half of the social protection programs implemented to address the pandemic. Now more than ever, it’s crucial that such programs are designed to maximize impacts. Behavioral insights can be mobilized as a cost-effective way to help beneficiaries make the most out of the available support. The World Bank and ideas42 partnership on behavioral designs for cash transfer programs is helping countries achieve this goal.

Cash transfers are a key response instrument in the social protection toolkit—and for good reason. Cash transfers have been shown to generate a wide variety of positive benefits, from helping families invest in their children to promoting gender equality. However, we know from our previous work that in order to make the most out of cash transfers, recipients of any program (already facing challenging circumstances that compete for their attention) must undertake complex decisions and actions with their cash. These challenges are only magnified by the global pandemic. COVID-19 has wrought increased uncertainty around future employment and income, which makes calculations and planning to use cash transfer benefits all the more complex.

To help practitioners design programs that account for the complex thought processes and potential barriers recipients face, we mapped out their journey to effectively spend emergency social protection cash transfers. We also created simple, actionable guidance for program designers to put to use in maximizing their programs to help recipients use their cash transfer benefit to most effectively support families and reduce mid- to long-term financial volatility. 

For example, the first step is helping recipients understand what the transfer is for. For recipients who have not yet been impacted by financial instability, or indeed have never encountered a cash transfer before, such funds might seem like a gift or bonus, and recipients may spend it accordingly. Providing clear, simple framing or labelling the transfer may signal to recipients that they should use the cash not only for immediate needs, but also in ways that can help them protect investments in their family members’ human capital and jumpstart their livelihood after the crisis wanes….(More)”.

The economics of Business to Government data sharing

Paper by Bertin Martens and Nestor Duch Brown: “Data and information are fundamental pieces for effective evidence-based policy making and provision of public services. In recent years, some private firms have been collecting large amounts of data, which, were they available to governments, could greatly improve their capacity to take better policy decisions and to increase social welfare. Business-to-Government (B2G) data sharing can result in substantial benefits for society. It can save costs to governments by allowing them to benefit from the use of data collected by businesses without having to collect the same data again. Moreover, it can support the production of new and innovative outputs based on the shared data by different users. Finally, the data available to government may give only an incomplete or even biased picture, while aggregating complementary datasets shared by different parties (including businesses) may result in improved policies with strong social welfare benefits.

The examples assembled by the High Level Expert Group on B2G data sharing show that most of the current B2G data transactions remain one-off experimental pilot projects that do not seem to be sustainable over time. Overall, the volume of B2G operations still seems to be relatively small and clearly sub-optimal from a social welfare perspective. The market does not seem to scale compared to the economic potential for welfare gains in society. There are likely to be significant potential economic benefits from additional B2G data sharing operations. These could be enabled by measures that would seek to improve their governance conditions to contribute to increase the overall number of transactions. To design such measures, it is important to understand the nature of the current barriers for B2G data sharing operations. In this paper, we focus on the more important barriers from an economic perspective: (a) monopolistic data markets, (b) high transaction costs and perceived risks in data sharing and (c) a lack of incentives for private firms to contribute to the production of public benefits. The following reflections are mainly conceptual, since there is currently little quantitative empirical evidence on the different aspects of B2G transactions.

  • Monopolistic data markets. Some firms -like big tech companies for instance- may be in a privileged position as the exclusive providers of the type of data that a public body seeks to access. This position enables the firms to charge a high price for the data beyond a reasonable rate of return on costs. While a monopolistic market is still a functioning market, the resulting price may lead to some governments not being able or willing to purchase the data and therefore may cause social welfare losses. Nonetheless, monopolistic pricing may still be justified from an innovation perspective: it strengthens incentives to invest in more and better data collection systems and thereby increases the supply of data in the long run. In some cases, the data seller may be in a position to price-discriminate between commercial buyers and a public body, charging a lower price to the latter since the data would not be used for commercial purposes.
  • High transaction costs and perceived risks. An important barrier for data sharing comes from the ex-ante costs related to finding a suitable data sharing partner, negotiating a contractual arrangement, re-formatting and cleaning the data, among others. Potentially interested public bodies may not be aware of available datasets or may not be in a position to handle them or understand their advantages and disadvantages. There may also be ex-post risks related to uncertainties in the quality and/or usefulness of the data, the technical implementation of the data sharing deal, ensuring compliance with the agreed conditions, the risk of data leaks to unauthorized third-parties and exposure of personal and confidential data.
  • Lack of incentives. Firms may be reluctant to share data with governments because it might have a negative impact on them. This could be due to suspicions that the data delivered might be used to implement market regulations and to enforce competition rules that could negatively affect firms’ profits. Moreover, if firms share data with government under preferential conditions, they may have difficulties justifying the foregone profit to shareholders, since the benefits generated by better policies or public services fuelled by the private data will occur to society as a whole and are often difficult to express in monetary terms. Finally, firms might be afraid of entering into a competitive disadvantage if they provide data to public bodies – perhaps under preferential conditions – and their competitors do not.

Several mechanisms could be designed to solve the barriers that may be holding back B2G data sharing initiatives. One would be to provide stronger incentives for the data supplier firm to engage in this type of transactions. These incentives can be direct, i.e., monetary, or indirect, i.e., reputational (e.g. as part of corporate social responsibility programmes). Another way would be to ascertain the data transfer by making the transaction mandatory, with a fair cost compensation. An intermediate way would be based on solutions that seek to facilitate voluntary B2G operations without mandating them, for example by reducing the transaction costs and perceived risks for the provider data supplier, e.g. by setting up trusted data intermediary platforms, or appropriate contractual provisions. A possible EU governance framework for B2G data sharing operations could cover these options….(More)”.

The Potential of Open Digital Ecosystems

About: “Omidyar Network India (ONI), in partnership with Boston Consulting Group (BCG), has undertaken a study to reimagine digital platforms for the public good, with the aim build a shared narrative around digital platforms and develop a holistic roadmap to foster their systematic adoption.

This study has especially benefited from collaboration with the Ministry of Electronics and Information Technology (MeitY), Government of India. It builds on the thinking presented in the public consultation whitepaper on ‘Strategy for National Open Digital Ecosystems (NODEs)’ published by MeitY in February 2020, to which ONI and BCG have contributed.

This website outlines the key findings of the study and introduces a new paradigm, i.e. ODEs, which recognizes the importance of a strong governance framework as well as the community of stakeholders that make them effective….(More)”.

Algorithmic Colonisation of Africa Read

Abeba Birhane at The Elephant: “The African equivalents of Silicon Valley’s tech start-ups can be found in every possible sphere of life around all corners of the continent—in “Sheba Valley” in Addis Abeba, “Yabacon Valley” in Lagos, and “Silicon Savannah” in Nairobi, to name a few—all pursuing “cutting-edge innovations” in sectors like banking, finance, healthcare, and education. They are headed by technologists and those in finance from both within and outside the continent who seemingly want to “solve” society’s problems, using data and AI to provide quick “solutions”. As a result, the attempt to “solve” social problems with technology is exactly where problems arise. Complex cultural, moral, and political problems that are inherently embedded in history and context are reduced to problems that can be measured and quantified—matters that can be “fixed” with the latest algorithm.

As dynamic and interactive human activities and processes are automated, they are inherently simplified to the engineers’ and tech corporations’ subjective notions of what they mean. The reduction of complex social problems to a matter that can be “solved” by technology also treats people as passive objects for manipulation. Humans, however, far from being passive objects, are active meaning-seekers embedded in dynamic social, cultural, and historical backgrounds.

The discourse around “data mining”, “abundance of data”, and “data-rich continent” shows the extent to which the individual behind each data point is disregarded. This muting of the individual—a person with fears, emotions, dreams, and hopes—is symptomatic of how little attention is given to matters such as people’s well-being and consent, which should be the primary concerns if the goal is indeed to “help” those in need. Furthermore, this discourse of “mining” people for data is reminiscent of the coloniser’s attitude that declares humans as raw material free for the taking. Complex cultural, moral, and political problems that are inherently embedded in history and context are reduced to problems that can be measured and quantified Data is necessarily always about something and never about an abstract entity.

The collection, analysis, and manipulation of data potentially entails monitoring, tracking, and surveilling people. This necessarily impacts people directly or indirectly whether it manifests as change in their insurance premiums or refusal of services. The erasure of the person behind each data point makes it easy to “manipulate behavior” or “nudge” users, often towards profitable outcomes for companies. Considerations around the wellbeing and welfare of the individual user, the long-term social impacts, and the unintended consequences of these systems on society’s most vulnerable are pushed aside, if they enter the equation at all. For companies that develop and deploy AI, at the top of the agenda is the collection of more data to develop profitable AI systems rather than the welfare of individual people or communities. This is most evident in the FinTech sector, one of the prominent digital markets in Africa. People’s digital footprints, from their interactions with others to how much they spend on their mobile top ups, are continually surveyed and monitored to form data for making loan assessments. Smartphone data from browsing history, likes, and locations is recorded forming the basis for a borrower’s creditworthiness.

Artificial Intelligence technologies that aid decision-making in the social sphere are, for the most part, developed and implemented by the private sector whose primary aim is to maximise profit. Protecting individual privacy rights and cultivating a fair society is therefore the least of their concerns, especially if such practice gets in the way of “mining” data, building predictive models, and pushing products to customers. As decision-making of social outcomes is handed over to predictive systems developed by profit-driven corporates, not only are we allowing our social concerns to be dictated by corporate incentives, we are also allowing moral questions to be dictated by corporate interest.

“Digital nudges”, behaviour modifications developed to suit commercial interests, are a prime example. As “nudging” mechanisms become the norm for “correcting” individuals’ behaviour, eating habits, or exercise routines, those developing predictive models are bestowed with the power to decide what “correct” is. In the process, individuals that do not fit our stereotypical ideas of a “fit body”, “good health”, and “good eating habits” end up being punished, outcast, and pushed further to the margins. When these models are imported as state-of-the-art technology that will save money and “leapfrog” the continent into development, Western values and ideals are enforced, either deliberately or intentionally….(More)”.

Mapping socioeconomic indicators using social media advertising data

Paper by Ingmar Weber et al: “The United Nations Sustainable Development Goals (SDGs) are a global consensus on the world’s most pressing challenges. They come with a set of 232 indicators against which countries should regularly monitor their progress, ensuring that everyone is represented in up-to-date data that can be used to make decisions to improve people’s lives. However, existing data sources to measure progress on the SDGs are often outdated or lacking appropriate disaggregation. We evaluate the value that anonymous, publicly accessible advertising data from Facebook can provide in mapping socio-economic development in two low and middle income countries, the Philippines and India. Concretely, we show that audience estimates of how many Facebook users in a given location use particular device types, such as Android vs. iOS devices, or particular connection types, such as 2G vs. 4G, provide strong signals for modeling regional variation in the Wealth Index (WI), derived from the Demographic and Health Survey (DHS). We further show that, surprisingly, the predictive power of these digital connectivity features is roughly equal at both the high and low ends of the WI spectrum. Finally we show how such data can be used to create gender-disaggregated predictions, but that these predictions only appear plausible in contexts with gender equal Facebook usage, such as the Philippines, but not in contexts with large gender Facebook gaps, such as India….(More)”.

How open data could tame Big Tech’s power and avoid a breakup

Patrick Leblond at The Conversation: “…Traditional antitrust approaches such as breaking up Big Tech firms and preventing potential competitor acquisitions are never-ending processes. Even if you break them up and block their ability to acquire other, smaller tech firms, Big Tech will start growing again because of network effects and their data advantage.

And how do we know when a tech firm is big enough to ensure competitive markets? What are the size or scope thresholds for breaking up firms or blocking mergers and acquisitions?

A small startup acquired for millions of dollars can be worth billions of dollars for a Big Tech acquirer once integrated in its ecosystem. A series of small acquisitions can result in a dominant position in one area of the digital economy. Knowing this, competition/antitrust authorities would potentially have to examine every tech transaction, however small.

Not only would this be administratively costly or burdensome on resources, but it would also be difficult for government officials to assess with some precision (and therefore legitimacy), the likely future economic impact of an acquisition in a rapidly evolving technological environment.

Open data access, level the playing field

Given that mass data collection is at the core of Big Tech’s power as gatekeepers to customers, a key solution is to open up data access for other firms so that they can compete better.

Anonymized data (to protect an individual’s privacy rights) about people’s behaviour, interests, views, etc., should be made available for free to anyone wanting to pursue a commercial or non-commercial endeavour. Data about a firm’s operations or performance would, however, remain private.

Using an analogy from the finance world, Big Tech firms act as insider traders. Stock market insiders often possess insider (or private) information about companies that the public does not have. Such individuals then have an incentive to profit by buying or selling shares in those companies before the public becomes aware of the information.

Big Tech’s incentives are no different than stock market insiders. They trade on exclusively available private information (data) to generate extraordinary profits.

Continuing the finance analogy, financial securities regulators forbid the use of inside or non-publicly available information for personal benefit. Individuals found to illegally use such information are punished with jail time and fines.

They also require companies to publicly report relevant information that affects or could significantly affect their performance. Finally, they oblige insiders to publicly report when they buy and sell shares in a company in which they have access to privileged information.

Transposing stock market insider trading regulation to Big Tech implies that data access and use should be monitored under an independent regulatory body — call it a Data Market Authority. Such a body would be responsible for setting and enforcing principles, rules and standards of behaviour among individuals and organizations in the data-driven economy.

For example, a Data Market Authority would require firms to publicly report how they acquire and use personal data. It would prohibit personal data hoarding by ensuring that data is easily portable from one platform, network or marketplace to another. It would also prohibit the buying and selling of personal data as well as protect individuals’ privacy by imposing penalties on firms and individuals in cases of non-compliance.

Data openly and freely available under a strict regulatory environment would likely be a better way to tame Big Tech’s power than breaking them up and having antitrust authorities approving every acquisition that they wish to make….(More)”.

Resetting the state for the post-covid digital age

Blog by Carlos Santiso: “The COVID-19 crisis is putting our global digital resilience to the test. It has revealed the importance of a country’s digital infrastructure as the backbone of the economy, not just as an enabler of the tech economy. Digitally advanced governments, such as Estonia, have been able to put their entire bureaucracies in remote mode in a matter of days, without major disruption. And some early evidence even suggests that their productivity increased during lockdown.

With the crisis, the costs of not going digital have largely surpassed the risks of doing so. Countries and cities lagging behind have realised the necessity to boost their digital resilience and accelerate their digital transformation. Spain, for example, adopted an ambitious plan to inject 70 billion euro into in its digital transformation over the next five years, with a Digital Spain 2025 agenda comprising 10 priorities and 48 measures. In the case of Brazil, the country was already taking steps towards the digital transformation of its public sector before the COVID-19 crisis hit. The crisis is accelerating this transformation.

The great accelerator

Long before the crisis hit, the data-driven digital revolution has been challenging governments to modernise and become more agile, open and responsive. Progress has nevertheless been uneven, hindered by a variety of factors, from political resistance to budget constraints. Going digital requires the sort of whole-of government reforms that need political muscle and long-term vision to break-up traditional data silos within bureaucracies, jealous to preserve their power. In bureaucracies, information is power. Now, information has become ubiquitous and governing data, a critical challenge.

Cutting red tape will be central to the recovery. Many governments are fast-tracking regulatory simplification and administrative streamlining to reboot hard-hit economic sectors. Digitalisation is resetting the relationship between states and citizens, a Copernican revolution for our rule-based bureaucracies….(More)“.

Why real-time economic data need to be treated with caution

The Economist: “The global downturn of 2020 is probably the most quantified on record. Economists, firms and statisticians seeking to gauge the depth of the collapse in economic activity and the pace of the recovery have seized upon a new dashboard of previously obscure indicators. Investors eagerly await the release of mobility statistics from tech companies such as Apple or Google, or restaurant-booking data from OpenTable, in a manner once reserved for official inflation and unemployment estimates. Central bankers pepper their speeches with novel barometers of consumer spending. Investment-bank analysts and journalists tout hot new measures of economic activity in the way that hipsters discuss the latest bands. Those who prefer to wait for official measures are regarded as being like fans of u2, a sanctimonious Irish rock group: stuck behind the curve as the rest of the world has moved on.

The main attraction of real-time data to policymakers and investors alike is timeliness. Whereas official, so-called hard data, such as inflation, employment or output measures, tend to be released with a lag of several weeks, or even months, real-time data, as the name suggests, can offer a window on today’s economic conditions. The depth of the downturns induced by covid-19 has put a premium on swift intelligence. The case for hard data has always been their quality, but this has suffered greatly during the pandemic. Compilers of official labour-market figures have struggled to account for furlough schemes and the like, and have plastered their releases with warnings about unusually high levels of uncertainty. Filling in statisticians’ forms has probably fallen to the bottom of firms’ to-do lists, reducing the accuracy of official output measures….

The value of real-time measures will be tested once the swings in economic activity approach a more normal magnitude. Mobility figures for March and April did predict the scale of the collapse in gdp, but that could have been estimated just as easily by stepping outside and looking around (at least in the places where that sort of thing was allowed during lockdown). Forecasters in rich countries are more used to quibbling over whether economies will grow at an annual rate of 2% or 3% than whether output will shrink by 20% or 30% in a quarter. Real-time measures have disappointed before. Immediately after Britain’s vote to leave the European Union in 2016, for instance, the indicators then watched by economists pointed to a sharp slowdown. It never came.

Real-time data, when used with care, have been a helpful supplement to official measures so far this year. With any luck the best of the new indicators will help official statisticians improve the quality and timeliness of their own figures. But, much like u2, the official measures have been around for a long time thanks to their tried and tested formula—and they are likely to stick around for a long time to come….(More)”.

Coronavirus: how the pandemic has exposed AI’s limitations

Kathy Peach at The Conversation: “It should have been artificial intelligence’s moment in the sun. With billions of dollars of investment in recent years, AI has been touted as a solution to every conceivable problem. So when the COVID-19 pandemic arrived, a multitude of AI models were immediately put to work.

Some hunted for new compounds that could be used to develop a vaccine, or attempted to improve diagnosis. Some tracked the evolution of the disease, or generated predictions for patient outcomes. Some modelled the number of cases expected given different policy choices, or tracked similarities and differences between regions.

The results, to date, have been largely disappointing. Very few of these projects have had any operational impact – hardly living up to the hype or the billions in investment. At the same time, the pandemic highlighted the fragility of many AI models. From entertainment recommendation systems to fraud detection and inventory management – the crisis has seen AI systems go awry as they struggled to adapt to sudden collective shifts in behaviour.

The unlikely hero

The unlikely hero emerging from the ashes of this pandemic is instead the crowd. Crowds of scientists around the world sharing data and insights faster than ever before. Crowds of local makers manufacturing PPE for hospitals failed by supply chains. Crowds of ordinary people organising through mutual aid groups to look after each other.

COVID-19 has reminded us of just how quickly humans can adapt existing knowledge, skills and behaviours to entirely new situations – something that highly-specialised AI systems just can’t do. At least yet….

In one of the experiments, researchers from the Istituto di Scienze e Tecnologie della Cognizione in Rome studied the use of an AI system designed to reduce social biases in collective decision-making. The AI, which held back information from the group members on what others thought early on, encouraged participants to spend more time evaluating the options by themselves.

The system succeeded in reducing the tendency of people to “follow the herd” by failing to hear diverse or minority views, or challenge assumptions – all of which are criticisms that have been levelled at the British government’s scientific advisory committees throughout the pandemic…(More)”.

A Way Forward: Governing in an Age of Emergence

Paper by UNDP: “…This paper seeks to go beyond mere analysis of the spectrum of problems and risks we face, identifying a portfolio of possibilities (POPs) and articulating a new framework for governance and government. The purpose of these POPs is not to define the future but to challenge, to innovate, to expand the range of politically acceptable policies, and to establish a foundation for the statecraft in the age of risk and uncertainties.

As its name suggests, we recognise that the A Way Forward is and must be one of many pathways to explore the future of governance. It is the beginning of a journey; one on which you are invited to join us to help evolve the provocations into new paradigms and policy options that seek to chart an alternative pathway to governance and statecraft.

A Way Forward is a petition for seeding new transnational alliances based on shared interests and vulnerability. We believe the future will be built across a new constellation of governmental alliances, where innovation in statecraft and governance is achieved collaboratively. Our key objective is to establish a platform to host these transnational discussions, and move us towards the new capabilities that are necessary for statecraft in the age of risk and uncertainty….(More)”.