A.3 Metatrend 3: Complex and Dynamic Innovation Ecosystems

TABLE A.2 Metatrend 2: Data-Driven and Hybrid Human-Machine Technologies for Productive Activities (continued) Metatrend 2: Potential Implications for Development

Positive

1. Increase in entrepreneurship. 2. Digitalization, upskilling, and increased human-machine interaction may accelerate innovation (see metatrend 3). 3. A young labor force enables a faster adoption of digital technologies and transition to new production processes. 4. Growing awareness about societies’ fragility may have a long-term effect on values and consumer preferences (such as a switch to green energy, mobility solutions, and local food production). 5. Digitalization of the economy may accelerate the transition from predominantly informal activities, allowing people to access markets for services and goods and to participate in new digital activities in the gig economy. 6. New opportunities emerge for home-based work, including for women (although this may reinforce socioeconomic exclusion). 1. Risk of growing economic divergence and rising inequality at the level of nations, firms, and individuals may give rise to economic nationalism and societal polarization. 2. Big tech companies solidify their dominant monopoly positions, which may slow down (local) innovation and intensify a winner-take-all dynamic. 3. The gig economy may increase the economic fragility of workers and impose additional social stress on families. 4. The loss of personal data may not enter most people’s awareness, raising fears of a permanent loss of data privacy. 5. Responsible oversight and meaningful accountability in complex technological supply chains will fragment. 6. As automation continues to displace human labor, digital have-nots will find it harder to adapt. Unless new jobs are created in large numbers, growing unemployment and unrest may erode social cohesion. 7. Global trade continues to shrink. The traditional prescription for development through economic growth could come to a halt, limiting the shift of production and jobs to emerging economies and reducing the volume of migration and remittance flows for the region.

Source: World Bank study team. Note: AI = artificial intelligence. Negative

TABLE A.3 Metatrend 3: Complex and Dynamic Innovation Ecosystems

1. Conventional R&D approaches and metrics remain out of reach for most developing countries. 2. The alternative—fostering innovation ecosystems—is viewed increasingly as offering access to diverse stakeholders, expertise networks, funding, and global knowledge as part of a long-term engagement. 3. The world over, governments and firms alike are grappling with how to connect with emerging innovation systems to unlock future drivers of productivity, employment, and competitiveness. Along the way, new forms of collaboration, skill deployment, incentives, organizational capabilities, regulatory approaches, and policies are being tested. 4. The value of a tech-enabled civic culture that relies on bottom-up information sharing, public-private partnerships,

“hacktivism” and grand challenges for quick solution testing, and participatory collective action is attracting interest from key stakeholder groups seeking to emulate these approaches. 5. Specialized knowledge institutions, especially in scientific and innovation communities, are being sought out for expert advice in anticipating, preparing for, and responding effectively to crises. 6. South Asian countries are seeking to build their domestic capabilities to participate in the global knowledge system, take advantage of opportunities offered by available technologies, adapt them to relevant domestic needs, and offset some of the risks. (Table continues on next page)

TABLE A.3 Metatrend 3: Complex and Dynamic Innovation Ecosystems (continued)

7. Diaspora communities continue to provide local innovators with critical know-how, mentoring, funding, and networks, but government regulations and overreach often stand in the way and fail to create an enabling environment for innovative business models. 8. There is growing awareness that social capital is an important complement to tapping into indigenous knowledge, scaling up grassroots innovation, and developing technologies to address specific local challenges. 9. Participation through local networks and indigenous knowledge communities provides solidarity, resilience, and targeted support at direct beneficiary levels. 10. Product innovations being developed in so-called low resource settings are finding opportunities for reverse innovation. 11. Many countries are expanding R&D and diagnostic and sourcing capabilities to build up national stockpiles of critical supplies in an effort to reduce dependencies and achieve a higher degree of self-sufficiency in, for example, vaccines and medical equipment. 12. New forms of scientific research collaboration are emerging. For example, computer modeling and big data are increasingly being used for drug discovery, which, together with advances in synthetic biology, offer prospects of more affordable new drugs and therapies. 13. Digital technologies—such as smart phones, AI, Internet of Things connectivity, digitalization of information, additive manufacturing, virtual reality and augmented reality, machine learning, blockchain, robotics, quantum computing, and synthetic biology—will accelerate granular innovation processes. 14. Innovations triggered in response to the

COVID-19 pandemic have been shown to diffuse rapidly, resulting in substantial performance and productivity improvements. 15. Transparent communication and datadriven decision-making are competing with targeted misinformation campaigns to influence public perceptions, build public support, and encourage actors to contribute to solutions. 16. The emerging lessons from how countries have prepared for and managed disasters are receiving global attention as governments seek to highlight and brand their expertise and prepare for the next crisis.

Metatrend 3: Potential Implications for Development

Positive

1. The pandemic response is viewed as a demonstration of government leadership and effectiveness. Trust matters! In some cases, effective state responses are increasing trust in government and technocratic expertise. 2. Civil society groups are mobilizing responses on the front lines of the crisis, indicating innovation capacity and democratic vitality at the local level. 3. Digital technologies can be a powerful mechanism in accelerating learning, overcoming social inequalities, and expanding access. 4. More granular innovation will lead to faster diffusion cycles, lower investment risks, more opportunities to escape lockin, higher job creation potential, and larger social returns. 1. The pandemic revealed innovation gaps in delivery systems for health, education, and social assistance, which hampered effective early response strategies and protection. 2. The “old” system is not resilient and is inadequate to respond to crisis situations.

Part of this reflects the increased inequity in income, wealth, and opportunities and lack of health care and social security benefits for all. 3. Institutional readiness matters! Unfinished federalism reforms, underfunding of public programs, and confusion over deploying available delivery systems across government agencies have revealed fundamental disconnects in countries’ ecosystems. 4. Technologies are not neutral! Digital technologies themselves are characterized by large disparities in access to, usage of, and the skills relevant to innovations. (Table continues on next page)

Negative