Chapter 12: Future Opportunities and Emerging Challenges

12.1 The Honest Stocktake: What the Standard Toolkit Has Been Missing

Before we look at where economic policy analysis is headed, it is worth pausing to be honest about where it currently falls short. The tools and incentives explored across this book are genuinely powerful — but no toolkit is neutral, and none is complete. Treating our methods as if they are is itself a form of analytical failure.

There is something that must be named before we even get to the gaps: political economy and power. Every piece of analysis in this book — every cost-benefit assessment, every stakeholder engagement process, every multi-criteria weighting exercise — takes place inside a political and institutional environment that shapes what gets analysed, how, and for whom. Who commissions the analysis? Who frames the problem? Whose values are embedded in the criteria? These are not questions the toolkit can answer, because they precede the toolkit. They are the water in which economic policy analysis swims. Acknowledging this does not undermine the methods — it makes their use more honest, more rigorous, and ultimately more defensible. A good policy analyst is always, at some level, also a political economist.

With that foundational reality in view, there are four structural gaps worth naming — gaps that the rest of this chapter is, in part, an answer to.

Nature Is Not a Line Item

The tools we use treat the natural environment largely as a category of costs and benefits to be monetised and weighed. We can price a tonne of carbon, estimate a willingness-to-pay for clean waterways, or assign a shadow value to biodiversity loss. These are useful — but they are not the same as accounting for how ecosystems actually behave. Ecosystems are non-linear. They have thresholds, feedback loops, and tipping points that do not announce themselves in advance and that no discount rate can adequately capture. A coral reef does not degrade gradually and proportionally; it can appear stable and then collapse. A fishery can sustain harvest levels for decades and then be gone within a season. Our standard analytical framework treats environmental impacts as though they sit neatly on a spreadsheet, when in reality they operate more like a complex adaptive system that can absorb pressure right up until the moment it cannot. This is not a problem we have entirely ignored — the evolving landscape discussion in Section 12.2 takes it up — but it is a problem we have not yet solved, and we should be clear-eyed about that.

Who Bears the Cost — and When

Economic policy analysis has always struggled with equity, but the struggle takes two distinct forms that are often conflated. The first is intragenerational equity: the question of who, among people living today, bears the costs and receives the benefits of a given policy. Our standard tools — particularly cost-benefit analysis — tend to aggregate. They produce a net present value, a benefit-cost ratio, a headline number. But averages can mask profound distributional asymmetries. A policy that is efficient in aggregate can still impose its costs disproportionately on those who are already least well-off, and our toolkit frequently does not surface this unless analysts actively seek it out.

The second is intergenerational equity: the question of what we owe people who are not yet born. This one is embedded quietly in something most analysts treat as a technical parameter — the discount rate. Applying a standard discount rate of five or seven per cent means that a significant harm occurring in fifty years is worth almost nothing in today’s analysis. That is not a neutral technical choice. It is a value judgement about the relative importance of future people, baked invisibly into our models. The debates around near-zero or declining discount rates for long-run environmental costs are, at their core, ethical debates — and they belong in the foreground of our analysis, not the footnotes. Section 12.3 takes up both dimensions of equity in depth.

Broome Is Not Brisbane — and neither is Bangladesh

The analytical toolkit presented in this book was developed primarily in, and for, OECD economies with mature institutions, functioning data systems, and policy shops staffed by economists with access to computational resources. Those assumptions travel poorly — and not just internationally.

Within Australia, the distance between a policy designed in a capital city and the community expected to implement or live under it can be enormous. Consider the difference between Dubbo and Sydney, or Broome and Brisbane. Both cities exist within the same federal policy architecture — the same CBA guidelines, the same regulatory frameworks, the same national discount rates. But Broome’s economy, labour market, housing system, service infrastructure, and cultural context are so structurally different from Brisbane’s that applying the same analytical lens without significant local adaptation is less rigorous than it appears. Indigenous knowledge systems, community governance structures, and relationships to land and country are not incidental local colour — they are economically and socially foundational in many of the places where policy intervention is most needed, and they are almost entirely absent from the standard toolkit.

Extend this further to a place like Bangladesh — facing sea level rise, extreme weather volatility, dense population, and an institutional environment that bears no resemblance to the Canberra policy context in which much of our methodological training originates — and the gap between toolkit and reality becomes starker still. Place matters. Scale matters. Context is not a footnote to good analysis; it is the analysis. Section 12.4 explores how innovation in policy design is beginning to take this seriously.

Policy Is a Process, Not a Calculation

Finally, and perhaps most fundamentally: the tools in this playbook are designed to produce outputs. A net present value. A ranked set of options. An impact estimate. An evaluation finding. Each of these is a point-in-time product of analysis conducted before or after a policy decision. What they do not natively provide is a framework for ongoing learning — for building in revision, iteration, and the institutional capacity to change course when real-world conditions diverge from modelled assumptions. Policy operates in complex adaptive systems. It rarely unfolds as designed. Feedback loops, behavioural responses, and implementation realities routinely confound even well-designed analyses. The toolkit has very little native language for treating policy as a continuous, learning-oriented process rather than a discrete analytical event. Section 12.5 explores what future-proofing this capacity might look like.

Reflection Point: Four Gaps, Four Responses
Gap	Where the chapter responds
Ecosystems behave non-linearly — nature cannot simply be priced	Section 12.2: The Evolving Landscape
Equity operates across both generations and communities today	Section 12.3: Ethical Considerations and Social Equity
Context, place, and local conditions are not incidental	Section 12.4: Innovation in Policy Design
Policy is iterative — our tools are not	Section 12.5: Future-Proofing Economic Policy Analysis

None of these gaps are fatal to the toolkit — but pretending they do not exist would be. The sections that follow explore how the field is beginning to answer each one: through better data, more honest ethics, more adaptive design, and a growing recognition that economic policy analysis is as much a craft as it is a science. The best analysts know which tool to reach for. The best analysts also know when to set the toolkit down and ask harder questions.

12.2 The Evolving Landscape of Economic Policy Analysis

The field of economic policy analysis is undergoing a profound transformation, driven by technological advancements, significant societal shifts, and pressing environmental concerns. These forces are reshaping the way we approach policy formulation, implementation, and evaluation. The tools and incentives explored throughout this book – from market-based mechanisms to regulatory approaches, from cost-benefit analysis to multi-criteria decision making – are evolving in response to these new challenges and opportunities.

Technological Advancements

The integration of Artificial Intelligence (AI) and machine learning into policy formulation is revolutionising our approach to economic governance. These technologies are enhancing our ability to process vast amounts of data, identify patterns, and generate predictive models with unprecedented accuracy. For instance, the European Commission’s Joint Research Centre has developed AI tools to analyse and predict the impacts of policy proposals across various sectors, allowing for more informed decision-making.

Consider how this might transform our use of cost-benefit analysis. AI can process a wider range of variables and stakeholder inputs, potentially reducing bias and increasing the accuracy of our projections. The UK government’s Data Science Ethical Framework provides guidelines for the responsible use of these technologies in policy-making, addressing concerns about transparency and accountability.

Big data analytics is equally transformative. The sheer volume and variety of data now available to policymakers offer new insights into economic behaviours and trends. This wealth of information allows for more granular and real-time economic impact assessments. For example, the Australian Bureau of Statistics has implemented big data techniques to produce more timely and detailed economic indicators, enhancing the responsiveness of policy decisions to economic fluctuations.

However, these technological advancements also present new challenges. Ensuring the ethical use of AI in policy-making and maintaining transparency when algorithms become increasingly complex are crucial considerations for modern policy analysts.

Societal Shifts

Demographic changes are reshaping economies worldwide, necessitating adaptive policy responses. Ageing populations in many developed countries are straining pension systems and healthcare, while younger populations in developing nations present both opportunities and challenges for economic growth.

These shifts require us to recalibrate our economic models and policy tools. For instance, Japan, facing one of the world’s most rapidly ageing populations, has implemented a range of policies to address this demographic challenge. Their Society 5.0 initiative aims to leverage technological innovations to create a more inclusive society that can thrive despite demographic pressures.

Simultaneously, we’re witnessing a complex interplay between globalisation and localisation trends. While technology continues to connect global markets, we’re also seeing a resurgence of local economic initiatives and a push for self-sufficiency in key sectors. The European Union’s Smart Specialisation strategy exemplifies this balance, encouraging regions to identify and develop their unique strengths within the global economy.

Environmental Considerations

Climate change has emerged as a defining challenge of our era, demanding a fundamental rethinking of economic policies. The traditional tools of economic analysis are being adapted to incorporate environmental externalities more comprehensively. For example, the UK’s Green Book guidance for policy appraisal now includes detailed methods for valuing environmental impacts, including greenhouse gas emissions.

The concept of a circular economy is gaining traction, challenging the linear “take-make-dispose” model of production. This shift requires new economic indicators and policy instruments. The Netherlands has been at the forefront of this transition, setting ambitious targets to become a fully circular economy by 2050. Their government’s programme includes a range of policy measures, from tax incentives for repair services to mandatory recycled content in products.

We’re seeing the emergence of innovative market-based mechanisms like cap-and-trade systems for carbon emissions. The European Union Emissions Trading System (EU ETS), the world’s first and largest carbon market, demonstrates both the potential and challenges of such approaches in addressing environmental concerns through economic policy.

As we delve deeper into this chapter, we’ll explore how these evolving landscapes are reshaping our approach to economic policy analysis, presenting both challenges and opportunities for future policy analysts. The complexity of these issues underscores the need for interdisciplinary approaches and innovative thinking in the field of economic policy analysis.

12.3 Ethical Considerations and Social Equity

As economic policy analysis evolves in response to technological advancements and societal shifts, we face new ethical challenges that our traditional tools and incentives may be ill-equipped to address. This section explores the critical issues of algorithmic bias, data privacy, and the assessment of policy impacts on inequality.

The increasing use of AI and machine learning in policy formulation brings the risk of perpetuating or even exacerbating existing biases. Ensuring fairness in AI-driven policy recommendations is crucial for maintaining public trust and achieving equitable outcomes. To address this, policy analysts must regularly audit AI systems for bias, using diverse datasets and testing across different demographic groups. Implementing ‘fairness constraints’ in algorithm design, explicitly programming for equitable outcomes, is becoming an essential practice.

Transparency and accountability in automated systems are equally important. This involves making algorithmic decision-making processes interpretable and explainable to the public, establishing clear lines of responsibility for AI-driven policy outcomes, and creating mechanisms for appeal and redress when automated systems produce unfair results.

The wealth of data available for policy analysis presents both opportunities and challenges. While it offers unprecedented insights, it also raises significant privacy concerns. Balancing data-driven insights with individual privacy requires implementing robust data anonymisation techniques, adopting differential privacy methods to protect individual data while allowing for meaningful aggregate analysis, and establishing clear consent protocols for data use in policy research.

Developing ethical frameworks for data use in public policy is crucial. This involves creating guidelines for responsible data collection, storage, and usage, establishing oversight committees to review data-driven policy proposals, and regularly updating privacy regulations to keep pace with technological advancements.

Traditional economic analysis tools often fall short in capturing the full distributional impacts of policies. Advanced econometric techniques for distributional analysis can help address this gap. Quantile regression analysis allows examination of policy effects across different income levels, while decomposition methods can identify the sources of inequality changes. Inequality indices that go beyond simple measures like the Gini coefficient to capture multidimensional aspects of inequality are increasingly valuable.

Microsimulation models for policy evaluation offer a powerful tool for assessing distributional impacts. These models can simulate policy effects at the individual or household level, providing granular insights into winners and losers. They allow for the modelling of complex policy interactions and behavioural responses. By incorporating demographic projections, they can assess long-term distributional impacts.

The challenges faced by automated unemployment benefits systems during the COVID-19 pandemic in several countries highlight the inadequacies of current tools and incentives in addressing ethical considerations and social equity. These systems, designed to streamline benefit distribution and reduce fraud, often flagged legitimate claims as potentially fraudulent, disproportionately affecting gig workers and those with non-traditional employment histories. The lack of transparency in decision-making processes made it difficult for claimants to understand why their benefits were denied. Privacy concerns were raised due to the extensive personal data required, and the systems often exacerbated inequality by disadvantaging those who lacked digital literacy or resources to navigate complex online systems.

This experience underscores why traditional tools failed: cost-benefit analyses focused primarily on fraud reduction and administrative efficiency, overlooking the social costs of false denials and delayed payments. The regulatory approach didn’t adequately account for rapid technological changes and the unique challenges of the gig economy. Behavioural insights were not sufficiently incorporated to understand how different groups might interact with the automated system.

As we continue to develop and refine our policy analysis tools, it’s crucial that we explicitly incorporate ethical considerations and social equity into every stage of the process. The challenges highlighted in this section demonstrate that our traditional approaches, while valuable, must evolve to meet the complex demands of our rapidly changing world. Future policy analysis must prioritise system transparency, establish clear accountability mechanisms, and develop more sophisticated tools for assessing distributional impacts, particularly in times of crisis.

12.4 Innovation in Policy Design and Implementation

The landscape of economic policy is undergoing a remarkable transformation. The days when policy was the exclusive domain of economists working in isolation have passed. Today, we’re witnessing a convergence of disciplines and methodologies that is reshaping our approach to societal challenges.

Behavioural economics has emerged as a powerful force in policy innovation. By acknowledging that humans aren’t always the rational actors traditional economics assumed, we’ve opened up new avenues for effective policy design. The UK’s Behavioural Insights Team, commonly referred to as the “Nudge Unit”, has been at the forefront of this movement. Their work on tax compliance is a prime example. By altering the wording in reminder letters to emphasise that most people pay their taxes on time, they increased payment rates significantly, demonstrating the power of social norms in shaping behaviour.

Our innovation extends beyond human behaviour. Ecological economics is challenging us to rethink the very foundations of how we measure economic success. The small Himalayan country of Bhutan has been a pioneer in this regard with its Gross National Happiness index. This multidimensional measure goes beyond GDP to include indicators of environmental conservation, cultural preservation, and psychological wellbeing. While not without its critics, Bhutan’s approach has sparked global discussions about more holistic ways to assess societal progress.

In the realm of policy implementation, we’re observing a shift towards more agile, iterative approaches. The Estonian government’s e-Estonia initiative is an exemplary case. Estonia has built a digital society where 99% of public services are available online 24/7. What’s truly innovative is their approach to this digital transformation. Instead of a grand, top-down plan, Estonia has adopted a flexible, iterative approach, continuously refining and expanding their digital services based on user feedback and technological advancements.

This agile mindset is facilitating bold experiments that would have been inconceivable just a few decades ago. Universal Basic Income (UBI) trials are a prime example. Finland’s 2017-2018 UBI experiment, where 2,000 unemployed individuals received €560 per month unconditionally, provided valuable insights into the potential impacts of such a policy. Contrary to some expectations, the study found that basic income recipients worked slightly more than those in the control group and reported significantly better wellbeing.

On the environmental front, we’re witnessing ambitious policy packages that aim to address climate change while stimulating economic growth. South Korea’s Green New Deal, launched in 2020, is a real-world implementation of these concepts. With substantial investments in renewable energy, green infrastructure, and clean technology, South Korea aims to create 659,000 jobs and help the country reach net-zero emissions by 2050.

These examples demonstrate that policy innovation isn’t merely about incremental improvements. It’s about reimagining our approach to societal challenges and having the resolve to test bold ideas in the real world.

However, as we navigate this new frontier of policy innovation, we must remain both ambitious and prudent. The long-term impacts of many of these innovative approaches are still uncertain. For instance, while Finland’s UBI experiment showed promising short-term results, questions remain about the long-term effects and the feasibility of scaling such a programme nationally.

Similarly, while South Korea’s Green New Deal is ambitious, its success will depend on sustained political will and effective implementation. The challenge lies in maintaining momentum and adapting the policy as circumstances change and new data emerges.

The future of economic policy is replete with possibilities we’re only beginning to explore. It’s a future where policies are as dynamic as the societies they serve, where interdisciplinary collaboration is the norm, and where we’re prepared to test novel ideas. As we continue to innovate, we must always remember that behind every policy are real people whose lives are profoundly affected by our decisions. This human-centred approach, combined with rigorous analysis and a willingness to experiment, will be key to addressing the complex challenges of the 21st century.

12.5 Future-Proofing Economic Policy Analysis

As we look towards the horizon of economic policy analysis, it’s clear that the field is evolving at an unprecedented pace. To remain effective and relevant, policy analysts must not only keep abreast of emerging challenges but also cultivate a set of essential skills and perspectives. This final section explores the competencies required for future policy analysts, examines emerging policy challenges, and synthesises key takeaways from the textbook.

Essential Skills for Future Policy Analysts

The complexity of modern economic systems demands a multifaceted skill set from policy analysts. Two areas stand out as particularly crucial:

Data Science and Systems Thinking

The proliferation of big data and advanced analytics necessitates a strong foundation in data science. Future policy analysts must be adept at handling large datasets, applying statistical techniques, and leveraging machine learning algorithms to extract meaningful insights. The UK Government’s Data Science Accelerator programme exemplifies the recognition of this need, providing intensive mentoring and training to build data science capability within the public sector.

Equally important is the ability to engage in systems thinking. Economic policies do not operate in isolation; they interact with social, environmental, and technological systems in complex ways. The Australian Public Service has framed the transition to a circular economy as a system‑wide governance challenge requiring cross‑sector leadership and coordination (DCCEEW, 2024).

Adaptive Leadership and Global Perspective

In an era of rapid change and global interconnectedness, policy analysts must exhibit adaptive leadership. This involves the ability to navigate uncertainty, facilitate collaboration across diverse stakeholders, and adjust strategies in response to emerging evidence. The New Zealand government’s response to the COVID-19 pandemic, characterised by clear communication, evidence-based decision-making, and rapid policy adjustments, offers a compelling case study in adaptive leadership.

A global perspective is equally crucial. Economic policies increasingly have international ramifications, and effective policy analysts must understand diverse economic systems and cultural contexts. Programmes like the Australian government’s New Colombo Plan, which supports Australian undergraduates to study and intern in the Indo-Pacific region, aim to cultivate this global outlook in future leaders and policy makers.

Emerging Policy Challenges

As we look to the future, several policy challenges loom large on the horizon:

Gig Economy and Future of Work Policies

The rise of the gig economy and the increasing prevalence of non-traditional employment arrangements pose significant challenges for labour market policies, social security systems, and economic stability. The UK’s Taylor Review of Modern Working Practices and subsequent policy responses highlight the complexities of adapting labour laws and social protections to these new realities.

Digital Currencies and Financial System Stability

The emergence of cryptocurrencies and the potential introduction of central bank digital currencies (CBDCs) present both opportunities and risks for financial system stability. The Reserve Bank of Australia’s research into CBDCs and the UK’s Cryptoassets Taskforce exemplify how governments are grappling with these issues, seeking to harness the benefits of financial innovation while mitigating potential risks to financial stability and consumer protection.

Key Takeaways and Future Directions

As we conclude this textbook, several core principles emerge that will continue to guide economic policy analysis in the future:

1. Interdisciplinary Approach: The most effective policy solutions often arise from the integration of insights from economics, behavioural science, data analytics, and other relevant fields.
2. Evidence-Based Policy Making: Rigorous empirical analysis, including randomised controlled trials where appropriate, should underpin policy decisions.
3. Distributional Impacts: Policy analysis must consistently consider the effects of policies across different segments of society, not just aggregate outcomes.
4. Sustainability: Long-term environmental and social sustainability must be central to economic policy formulation, not an afterthought.
5. Adaptability: Policies should be designed with built-in flexibility to adapt to changing circumstances and new evidence.

Looking ahead, embracing uncertainty in policy formulation will be crucial. Rather than seeking perfect foresight, policy analysts should focus on developing robust strategies that can perform well under a range of possible future scenarios. The UK government’s Futures Toolkit, which provides a structured approach to thinking about the future in policy development, offers a valuable resource in this regard.

As we navigate the complexities of 21st-century economic challenges, from climate change to technological disruption, the role of economic policy analysts has never been more critical. By cultivating a diverse skill set, maintaining a global and systems-level perspective, and adhering to core principles of evidence-based and equitable policy making, analysts can play a pivotal role in shaping a more prosperous, sustainable, and inclusive economic future.

The journey of economic policy analysis is ongoing, and the landscape will continue to evolve. It is our hope that this textbook has provided you with a solid foundation and the tools necessary to contribute meaningfully to this vital field. As you embark on your own journey in economic policy analysis, remember that your work has the potential to profoundly impact the lives of individuals and the trajectory of societies. Approach this responsibility with rigour, empathy, and an unwavering commitment to the public good.

This book has provided a comprehensive exploration of economic policy analysis tools and techniques. It has covered traditional approaches like Cost-Benefit Analysis and Multi-Criteria Analysis, as well as emerging methods such as behavioural economics and big data analytics.

The text has emphasized the importance of integrating multiple analytical approaches, considering ethical implications, and addressing issues of equity and sustainability in policy design. It has illustrated these concepts with real-world case studies from various countries and sectors.

Looking to the future, the book has highlighted emerging challenges in economic policy, including the gig economy, digital currencies, and climate change. It has stressed the need for interdisciplinary skills, adaptive leadership, and a global perspective in addressing these complex issues.

By providing this comprehensive toolkit and forward-looking perspective, the book aims to prepare readers to navigate the complexities of economic policy analysis in an ever-changing world, fostering more effective, equitable, and sustainable policy decisions.

Tying to Economic Policy Analysis

The field is moving fast. AI-assisted analysis, digital currencies, the gig economy, climate change — these aren’t distant developments anymore, they’re reshaping the policy landscape right now. The good news is that the foundations built throughout this book are exactly what you need to navigate them. Rigorous analysis, honest communication of uncertainty, genuine stakeholder engagement, and an uncompromising commitment to equitable outcomes don’t go out of date. As you step into practice:

• Stay interdisciplinary. The biggest policy challenges of the coming decades won’t respect disciplinary boundaries — and neither should you.
• Build evaluation in from the start. Future-proofing doesn’t mean predicting correctly; it means designing processes that can learn, adapt, and revise.
• Keep asking whether the question is right. Portugal’s drug decriminalisation succeeded largely because someone was willing to reframe a criminal justice problem as a public health one. That kind of conceptual shift is often the most valuable contribution an analyst can make.
• Remember that behind every dataset, every model, and every benefit-cost ratio are real people whose lives are shaped by these decisions. Don’t lose sight of that.

Chapter 12: Further Reading & References

Further Reading

Technology and AI in Policy Analysis

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. (Updated Edition). Crown.

Janssen, M., & Kuk, G. (2024). The Challenges and Limits of Big Data Algorithms in Public Administration and Governance. Springer.

Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press.

Behavioural Economics and Policy Innovation

Thaler, R. H., & Sunstein, C. R. (2021). Nudge: The Final Edition. Yale University Press.

Duflo, E. (2019). Good Economics for Hard Times: Better Answers to Our Biggest Problems (Revised Edition). PublicAffairs.

Social Equity and Distributional Analysis

Piketty, T. (2020). Capital and Ideology (Updated Edition). Harvard University Press.

Stiglitz, J. E. (2019). People, Power, and Profits: Progressive Capitalism for an Age of Discontent (New Edition). W. W. Norton & Company.

Environmental Economics and Sustainability

Raworth, K. (2017). Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist (Revised Edition). Chelsea Green Publishing.

Future of Work and Digital Economy

Standing, G. (2014). The Precariat Charter: From Denizens to Citizens. Bloomsbury Academic.

Systems Thinking and Complexity in Policy

Geyer, R., & Cairney, P. (2015). Handbook on Complexity and Public Policy. Edward Elgar Publishing.

Digital Governance and Global South Perspectives

Ndemo, B., & Weiss, T. (Eds.). (2017). Digital Kenya: An Entrepreneurial Revolution in the Making. Springer.

Castells, M. (2015). Networks of Outrage and Hope: Social Movements in the Internet Age (2nd ed.). Polity Press.

References

Department of Climate Change, Energy, the Environment and Water (DCCEEW). (2024). Australia’s Circular Economy Framework. Australian Government.

Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St Martin’s Press.

Hallsworth, M., List, J. A., Metcalfe, R. D., & Vlaev, I. (2017). The behaviouralist as tax collector: Using natural field experiments to enhance tax compliance. Journal of Public Economics, 148, 14–31. https://doi.org/10.1016/j.jpubeco.2017.02.003

Hughes, C. E., & Stevens, A. (2010). What can we learn from the Portuguese decriminalization of illicit drugs? British Journal of Criminology, 50(6), 999–1022. https://doi.org/10.1093/bjc/azq038

Kalvet, T. (2012). Innovation: a factor explaining e-government success in Estonia. Electronic Government, an International Journal, 9(2), 142–157. https://doi.org/10.1504/EG.2012.046266

Kangas, O., Simanainen, M., & Lillä, P. (2020). Suomen perustulokokeilun arviointi [Evaluation of the Finnish Basic Income Experiment]. Kela Research Department.

Korea Development Institute. (2020). Korean New Deal: National strategy for a great transformation. KDI.

Ura, K., Alkire, S., Zangmo, T., & Wangdi, K. (2012). A Short Guide to Gross National Happiness Index. Centre for Bhutan Studies.