Today’s post is by Martha Baxter and Bathylle Missika of the OECD Development Centre
Humankind has experienced more than two centuries of almost continuous progress since the Industrial Revolution, but will this trend continue? And as we prepare to adopt a new set of Sustainable Development Goals to guide our policy choices, how can we best capitalise on progress made while anticipating challenges ahead such as the youth bulge or massive droughts? A new report by the OECD Development Centre supported by the Rockefeller Foundation cautions that that a number of global trends could slow or even reverse the progress made. Securing Livelihoods for All: Foresight for Action looks at how the world’s livelihoods may change between now and 2030. It finds that threats may come from many fronts: from global economic trends and demographic transitions to environmental change and new technologies, among others.
The report doesn’t try to forecast what will happen. It uses foresight techniques to ask “what if?” questions and develop five stories of the future, interconnecting several trends we’re concerned about, and picking up on some of the weak signals coming from new and emerging trends.
Of the five possible futures developed in the OECD report, three are dire, involving massive population movements, inequality, poverty and citizen unrest, while two involve vibrant societies that possess the skills, creativity and flexibility to thrive and stave off global crises.
The first scenario is called “Automated North”. Automation proceeds faster than expected and affects ageing societies in particular. The rapid automation in advanced and some emerging economies means that jobs in most sectors are increasingly taken over by robots and artificial intelligence systems. The process is so fast that most people whose jobs are replaced by technology cannot adapt and find it difficult to secure their livelihoods. Inequality increases faster than expected. With fewer jobs available to nationals, pressure is growing to increase barriers to immigration in developed countries. Lower fiscal revenue combined with more people in need of social security support means that government debt becomes unmanageable. Social tensions and disruptions increase. In many developing countries, the automation process is much slower, meaning that these countries are no longer competitive, even in low-cost, low-value added sectors.
In the second scenario, “Droughts and joblessness in the South”, droughts become widespread in large parts of the developing world, challenging livelihoods in regions with large youthful populations (sub-Saharan Africa, North Africa, Middle East and South Asia). Subsistence farming becomes almost impossible and even larger scale farming is seriously challenged. Famines become normal, not only for small-scale farmers but also for poor people in urban areas as food prices sky-rocket. Migration takes place primarily within countries as rural populations flood to the cities. But international migration also increases as cities reach their absorption capacities. The pace of change – in the youth population explosion as well as in the severity of droughts – is very fast. Countries, communities and individuals are unlikely to be able to adapt livelihoods or support mechanisms fast enough. The result is hunger, increasing inequality and social disruption.
In the third scenario “Global financial crash, a major financial crisis triggers a collapse of the global trading system and a shift to protectionism. A housing bubble bursts in China and some other emerging countries. High levels of corporate debt in the developing world become unsustainable and lead to capital outflows. The European Union unravels, prompting another financial crisis. Commodity prices continue to fall rapidly, creating significant challenges for currency stability in countries relying on commodity exports. These financial disruptions trigger a major global economic crisis, affecting trade, investment and consumption. Protectionist pressure re-emerges but does not help to avoid social disruptions, and governments fail to address problems of increasing inequality. In developed and developing countries alike, many people’s livelihoods come under pressure. At least one billion people fall back into extreme poverty.
The “Regenerative economies” scenario posits a more positive vision of the future. Technological innovations create enough new jobs for most people and economic activity becomes more sustainable. Many new fields flourish, including cybersecurity, environmentally resilient engineering, robot-enhanced service jobs, and jobs requiring high skills in nanotechnology and biotechnology. As the real economy becomes a virtual economy, many sectors undergo a transformation. Country borders and distance become less relevant. Markets become more international than ever before. Countries reshape their education systems so that people can perform in the knowledge economy. Technological innovation in agriculture results in migration from rural to urban areas in many developing countries, but planned, medium-sized cities with energy-efficient infrastructure contribute to sustainable urbanisation rather than slumification. While impacts on livelihoods are positive overall, certain people will still need social security, but such systems will be more affordable for nation states under this scenario. This scenario could touch all regions of the world, but would come about faster in advanced and emerging countries.
In the final scenario, “Creative societies”, diverse experiments at the local level focus on individual resilience and social well-being. Technology-induced joblessness increases in developed and developing economies alike. Societies evolve towards new ways of living and working, in which individuals and communities are the key actors of change. In the absence of secure full-time employment, individuals must put together a portfolio of work – part-time jobs, shared work with colleagues, trading skills and services. This portfolio lifestyle is made possible by three important factors: technology, which allows people to work anywhere at any time; the adoption of guaranteed minimum incomes in most developed countries, paid for by higher taxes on capital, rather than labour; and new social attitudes in which young people are not so interested in consumer culture, but contribute to what might be called “the experience economy.” Cities pursue a green agenda, retrofitting buildings and prioritising water conservation. A robust urban food movement develops, involving urban community gardens. Public-private livelihood incubators flourish in most cities, providing job counselling, the matching of skills and opportunities, start-up financing, and individually tailored aid packages for young and old. Developed countries learn from experiments in social inclusiveness and adaptive, frugal innovation pioneered in developing economies.
So what is awaiting us? Which scenario triumphs depends on the building blocks laid down today, for example a shift in values towards prioritising sustainability, as highlighted in the “regenerative economies” scenario, and on policy choices. There are also many more scenarios that could be developed beyond the ones explored in the OECD report. But what matters is that imagining different scenarios of the future can create space for strategic, often difficult, conversations in today’s policy discussions. These conversations will allow us to discuss the livelihoods we have and the ones we want by 2030. The bad news is that in spite of the use of forecasts and foresight, we still do not have anything resembling a crystal ball. The good news is that we have more than a say in how our future livelihoods will unfold.
In today’s post, Elettra Ronchi of the OECD Science, Technology and Innovation Directorate argues that our current model of innovation has so far failed to deliver the effective treatments that we urgently need for the 44 million people living with dementia worldwide, and asks if recent advances in information technology can come to the rescue.
There’s a quiet revolution afoot: health data are increasingly collected, stored and used in digital form. Doctors, nurses, researchers, and patients are all producing on a daily basis huge amounts of data, from an array of sources such as electronic health records, genomic sequencing, high-resolution medical imaging, ubiquitous sensing devices, and smart phone applications that monitor patient health. In fact the OECD predicts more medical information and health and wellness data will be generated in the next few years than ever before.
The remarkable expansion of digital health data is largely driven by technological developments, not least the expansion of broadband access, smart mobile devices and smart ICT applications. Improvements in data analytics have also played a significant role, as has the provision of super-computing resources through cloud computing.
This revolution could prove particularly helpful for neurodegenerative diseases like dementia. Because of dementia’s clinical and biological complexity, the studies needed to underpin drug discovery and develop new therapeutic strategies aimed at slowing disease progression will require massive and diverse data collection, storage and processing. And large quantities of broad and deep data are being generated across laboratories worldwide –the information is behavioural, genetic, environmental, epigenetic, clinical, administrative, and more. Harnessing this data, advocates argue, would present advantages across the board: for research, patient care, health system management, and public health.
So how can we foster this environment where data aids dementia innovation? Today, researchers’ willingness to share data is often constrained by uncertainty. Several issues are at play.
First, ethical concerns need to be accounted for. Currently, informed consent permissions, which cover the consent for the use of the participant’s data, tend to be limited to the research questions related to the primary study focus. This means they exclude potentially unrelated investigations that could follow from open access to these data in the wider research community. New tiered step-by-step or dynamic consent models are needed to meet ethical and legal requirements and at the same time accommodate the changes in data use and research practices.
Second, there are broader challenges to data sharing, related to the lack of an open data culture. Open science has an enormous potential to avoid wasteful duplication of effort, to enable the verification or scientific results and the re-analysis of data for different purposes, and to promote competition of ideas and research. In 2013, the G8 Science Ministers statement called for publicly funded scientific research data to be open.
Yet there are still considerable disincentives that researchers and scientists face with respect to the disclosure of data, particularly at the pre-publication stage. Credit sharing in the academic economy presents dilemmas for researchers. Publications by whole consortia or with numerous authors are a challenge for academics concerned about how these publications will be credited and recognised for career promotion by their institutions. This raises the question of the actions needed to promote data access and openness to boost research and innovation without discouraging data collection from individual researchers.
Third, there is a need for investment in order to harness the potential of data for dementia. The costs of collecting, storing, linking, organising, and analysing data require considerable investment and collaboration, and appropriate funding needs to be set aside. Sustaining the big data infrastructures will also require financing: for many big data projects, networks or federated research platforms, the most significant challenge once the initial funding runs out is the development of a sustainable business model, that as a bare minimum, would sustain the curation and maintenance of data in an accessible form.
Big data also requires large numbers of people who are very highly trained and in huge demand from other sectors. Data specialist skills could become the most critical enabler for big data dementia research. Incentives are needed to promote education and training of data analysts and bioinformatics experts to use big data effectively for health research.
Of course, the explosion of promising new technological opportunities and data generation will not automatically translate into new products and care solutions for dementia and other neurodegenerative diseases. In order to deliver this promise, these new developments will have to be accompanied by organisational, infrastructural and governance changes throughout the health innovation system. The current R&D process is fragmented, costly, unpredictable and inefficient. Funding for dementia and other neurodegenerative diseases accounts for less than 1% of research and development budgets in the G7. These, and other issues, will also need to be addressed.
Researchers in industry, hospitals and universities continue to make significant contributions to scientific understanding. But without better data sharing, interpretative capacity, and co-ordination of knowledge, we can make only limited progress in our understanding of the molecular basis of neurodegenerative diseases or whether treatments or interventions work. Radical improvements in information technologies and the increasing gathering and sharing of electronic health data not only make it timely to assess and improve global capacity to undertake multidisciplinary research – they make it imperative.
Big Data for Advancing Dementia Research: An Evaluation of Data Sharing Practices in Research on Age-related Neurodegenerative Diseases OECD Digital Economy Papers, No. 246
Dementia Research and Care: Can Big Data Help? Edited by G. Anderson and J. Oderkirk
Unleashing the Power of Big Data for Alzheimer’s Disease and Dementia Research: Main Points of the OECD Expert Consultation on Unlocking Global Collaboration to Accelerate Innovation for Alzheimer’s Disease and Dementia, OECD Digital Economy Papers, No. 233