China’s state-led financialization for tech supremacy

The financialization of Western economies has unfolded as a prolonged systemic failure. What began as a mechanism to support productive enterprise has evolved into a structural dominance of finance over the real economy. Through deregulation, the proliferation of speculative activity, and successive asset bubbles, the sector has prioritized short-term gains over long-term investment. The 2008 financial crisis underscored these dynamics, transferring the burdens of systemic risk to the broader public while financial institutions were largely shielded from the consequences. This trajectory has entrenched income inequality and contributed to the political capture of regulatory institutions, inhibiting meaningful reform.

In contrast, China presents a divergent model. Its state-led financialization exemplifies a proactive deployment of financial mechanisms in service of national industrial objectives. Unlike the market-driven financialization typical of advanced Western economies, China’s approach is characterized by strategic state intervention and institutional design. The government not only participates in markets but reconfigures them—mobilizing state-owned enterprises as venture capital vehicles, directing bank lending toward emerging technologies, and leveraging local government financing platforms to support innovation. This model represents a deliberate recalibration of financial systems to prioritize long-term technological development over immediate capital returns.

State-owned enterprises (SOEs): from asset managers to venture capitalists

Chinese SOEs have increasingly transitioned from passive asset holders to active financial agents, functioning as quasi–venture capital entities with a targeted focus on high-technology sectors such as artificial intelligence, semiconductors, and advanced manufacturing. This transformation is rooted in the 2013 reforms under Xi Jinping, which marked a shift in state asset governance from a model of “managing assets” to one of “managing capital.” Central to this new framework are state-owned capital investment and operation companies (SCIOCs)—market-oriented entities tasked with allocating state capital in alignment with national strategic objectives.

Prominent SCIOCs such as Guoxin and Chengtong exemplify this model, channeling investments into key technological domains while retaining mechanisms of state oversight. Notably, their investment strategies increasingly resemble those of global institutional investors like BlackRock, characterized by portfolio diversification and minority equity stakes across a wide range of publicly listed firms. Over time, both Guoxin and Chengtong have reduced the size of their individual holdings while broadening the scope of their portfolios, mirroring BlackRock’s index-based approach. However, unlike BlackRock, whose investment logic is primarily driven by market signals and shareholder value maximization, these Chinese entities operate within a state-directed paradigm. Their capital allocation decisions are subordinated to broader industrial policy objectives, underscoring a distinctive model of “state-capital hybridization” wherein global financial practices are repurposed to advance national technological priorities.

Banks: from conservative lenders to investment partners

China’s banking sector has undergone a significant transformation from a traditionally conservative, loan-centric model—once governed by the “separation principle” that delineated clear boundaries between lending and investment—toward a more integrated, market-oriented system. Since 2015, mechanisms such as “investment and loan linkage” have enabled commercial banks to engage in equity-related activities, particularly in support of high-technology enterprises. Institutions like the Bank of China have introduced “green channel” loans that prioritize lending to startups with venture capital backing, and in some cases have experimented with convertible instruments such as “stock option models,” allowing for the conversion of debt into equity.

This evolution has been further institutionalized through the establishment of bank wealth management companies (BWMCs), which are permitted to make direct equity investments in high-tech firms. As of the end of 2022, the China Banking and Insurance Regulatory Commission (CBIRC) had approved 29 such entities. One notable example is BOCOM International, affiliated with the Bank of Communications, which manages the BOCOM Science and Technology Innovation Fund—an investment vehicle explicitly oriented toward advancing technological innovation. These developments underscore a broader trend of financial re-engineering within the Chinese banking system, as state-affiliated financial institutions adopt quasi-investor roles to support national strategic priorities, reinforcing the architecture of state-led financialization.

Local governments: trading land speculation for innovation funding

In recent years, Chinese local governments have transitioned away from reliance on Local Government Financing Vehicles (LGFVs), traditionally used to support land-based urban development, toward the deployment of Government Guidance Funds (GGFs). This strategic reorientation marks a shift from speculative real estate-driven financing to a model of purposeful financialization aimed at fostering technological innovation. Rather than leveraging land assets to finance urban expansion, local authorities are increasingly channeling capital into science and technology sectors through state-backed investment vehicles.

A prominent example is the National Integrated Circuit Industry Investment Fund (NICIIF), with a targeted fund size of approximately USD 95.8 billion, which supports enterprises in strategically vital sectors such as semiconductors. These funds operate not merely as instruments of capital allocation but as policy tools through which local governments execute central industrial strategies. According to the Zero2IPO database, as of 2023, there were 2,086 active GGFs across China, collectively managing assets exceeding USD 1.8 trillion. This proliferation underscores a broader recalibration of subnational fiscal behavior, whereby the objectives of economic development and industrial policy are fused within a state-directed financial architecture oriented toward national technological advancement.

A coordinated push for tech supremacy

This evolving model of state-led financialization reflects a deliberate integration of financial instruments with industrial policy, positioning the state as what we termed as “financial entrepreneur.” In this capacity, the state assumes a dual function: both as a strategic investor in capital markets and as a fund manager whose objectives are shaped through a hybrid of administrative directive and market logic. The recalibration of incentives across state institutions—ranging from banks and SOEs to local governments—facilitates the targeted allocation of financial resources toward sectors deemed essential for national technological leadership.

This coordinated mobilization contrasts sharply with earlier phases of development finance in China, which were heavily reliant on infrastructure-led investment through Local Government Financing Vehicles (LGFVs). The current financial architecture instead orients capital toward innovation and industrial upgrading. As illustrated in the accompanying figure, this shift embodies a paradigmatic change in the underlying logic of state intervention. The empirical results are notable: according to a 2023 report by the Australian Strategic Policy Institute (ASPI), China now leads globally in 37 out of 44 critical technologies, including advanced batteries, quantum sensing, and 5G communications.

A growing network of state agencies in innovation finance ecosystem is to ensure ideological alignment and managerial oversight, forming a core feature of China’s model of state-led financialization. This system also serves as a reminder of the original rationale behind China’s economic reform process where the boundaries between public and private sectors, and between liberal market coordination and socialist planning, become increasingly blurred. Notwithstanding its strategic coherence, China’s model of state-led financialization faces a series of structural and operational challenges. One key risk lies in the emergence of overcapacity within state-targeted sectors such as photovoltaics and electric vehicles. In the absence of commensurate demand, excessive production may generate inefficiencies, underutilized assets, and financial losses. Furthermore, the expansive use of mechanisms like GGFs has the potential to inflate asset bubbles, as state-directed capital may push valuations beyond sustainable levels, raising concerns over long-term financial stability.

The persistence of so-called “zombie firms”—enterprises maintained through state support despite chronic unprofitability—also continues to divert capital from more productive uses, undermining allocative efficiency. Tensions emerge from the dual imperative to stimulate market-based innovation while retaining centralized Party and state control over capital flows. These competing logics often complicate investment decisions and diminish the responsiveness of the financial system. Additionally, fragmented coordination across state entities and growing international scrutiny or resistance to China’s state-capitalist practices further limit the replicability and effectiveness of this model.

For Western economies, the implications are profound. Initiatives such as the U.S. Stargate Project—reportedly valued at $500 billion over four years to support AI and semiconductor infrastructure—and the European Commission’s InvestAI scheme, backed by €20 billion in guarantees, signal a renewed policy interest in public–private coordination. However, these efforts remain constrained by political fragmentation and a reliance on market-led frameworks. China’s approach is characterized by a level of centralized state capacity and institutional discipline that would be difficult to replicate without foundational political transformation in the West.

Should China succeed in sustaining this model without triggering systemic instability, the result would extend beyond technological leadership. It would represent a paradigmatic shift in the global political economy—one that challenges prevailing liberal capitalist orthodoxy and compels a fundamental reconsideration of the relationship between the state, capital, and innovation. In this sense, China is not merely competing within existing rules but reshaping the terrain on which economic competition is conducted.

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Why economists should learn machine learning

Economists analyze data. Machine learning (ML) offers a powerful set of tools for doing just that. But while econometrics and ML share a foundation in statistics, their aims and philosophies often diverge. The questions they ask and the tools they prioritize can differ dramatically. To clarify these differences—and the reasons economists might ultimately use ML—it helps to begin by deliberately sharpening the contrast between the two.

Quantifying vs. predicting

At its core, econometrics is about explanation. The typical economist is interested in quantifying the effect of a specific variable, often within a framework of causal inference. For example: What is the effect of raising the minimum wage on employment? Do peers influence students’ academic achievements? What is the average wage gap between men and women? These questions focus on estimating one or a few key parameters, with great attention to the rigor of the identification strategy. The emphasis is on the assumptions under which we can identify the parameters and on inference—constructing confidence intervals, testing hypotheses, and, above all, establishing causality.

This approach gained prominence in recent decades, culminating in Nobel Prizes for economists like Joshua Angrist, Guido Imbens, David Card, and Esther Duflo, whose work emphasizes empirical strategies to identify causal effects in natural, field, or experimental settings.

Machine learning, by contrast, is largely concerned with prediction. The primary goal is to develop models—or more precisely, algorithms—that deliver accurate predictions for new data points. Whether it’s recommending movies, classifying elements of an image, translating text, or matching job-seekers with firms, ML prioritizes predictive performance under computational constraints. Rather than focusing on a particular parameter, the goal is to learn complex patterns from data, often using highly flexible (sometimes opaque) models.

That said, forecasting is one area where econometrics and ML converge. Econometric forecasting often imposes structure on messy data to reduce noise, while ML emphasizes complexity and flexibility. Nevertheless, many traditional econometric tasks can be reframed as prediction (sub)problems or built upon them. Estimating a treatment effect, for example, involves building a counterfactual and is inherently a predictive exercise: being able to credibly predict what would have happened to this individual had they not been treated?

Models and assumptions

Econometric models tend to be simple, theory-driven, and interpretable. They often rest on strong assumptions—like linearity or exogeneity—that are difficult to verify but motivated by behavioral or economic theory. These models aim to isolate the effect of a particular variable, not to simulate the entire system.

In ML, simplicity is often sacrificed for performance. Black-box models, such as deep neural networks, are acceptable (and even preferred) if they generate more accurate predictions. A battery of performance metrics—like precision and recall—guide model selection, depending on the stakes. For instance, in fraud detection, a model with high precision ensures that flagged cases are likely real; in cancer screening, high recall ensures few real cases are missed.

Nevertheless, within a particular defined problem, ML offers algorithms whose predictive performance often surpass the standard (non)parametric toolkit in data-rich environments. For example, when selecting a model, they allow modeling complex interactions between variables or being robust to possibly high-dimensional nuisance parameters. The issue is that the theoretical behavior of these tools is often intractable, making them difficult to use within the classic econometric framework. Fortunately, over the past fifteen years, econometric theory has advanced to incorporate ML techniques in a way that enables statistical inference—allowing researchers to understand the working assumptions and their limits, construct tests, and build confidence intervals using ML-powered estimators.

Data and deployment

Another important divergence lies in how data is used. Econometric models are typically built on a single dataset, intended for a specific study. Replication is possible, but each new dataset generally leads to a different model. The focus is on understanding a particular phenomenon using the data at hand.

In ML, models are developed to be deployed in production, where they will continuously generate predictions as new data becomes available. This makes it crucial to guard against overfitting—when a model performs well on training data but poorly on unseen data. This risk is mitigated by techniques like cross-validation, and by splitting data into training and test sets. Modern ML even grapples with new phenomena like “double descent” where larger models trained on more data can paradoxically generalize better.

Complex data, new frontiers

ML’s rise is partly fueled by its success in handling complex, unstructured data—images, text, audio—that traditional statistical approaches struggle to process. These data types don’t fit neatly into rows and columns, and extracting meaningful features from them requires sophisticated techniques from computer science. ML excels in these domains, often matching human-level performance on tasks like facial recognition or language translation. As such, ML is the key ingredient to compress or extract information from such unstructured datasets, unlocking new possibilities.

Think about it:

• classifying the sentiment of an internet review on a numerical scale to enter a regression model,
• compressing a product image into a fixed-size vector (an embedding) to analyze consumer behavior,
• measuring the tone of a central banker’s speech.

Text data is undoubtedly one of the richest sources of economic information that largely remains out-of-reach for traditional econometric approaches.

A two-way street

The distinctions above are real, but they are not absolute. Economists have long used prediction tools, and ML researchers are increasingly concerned with issues that economists know well: fairness, bias, and explainability. Recent public controversies—from racial bias in criminal risk algorithms (e.g., the COMPAS tool) to gender stereotypes in language models—have underscored the social consequences of automated decision-making.

Likewise, econometrics is not immune to methodological pitfalls. The replication crisis, “p-hacking,” and specification searching can be seen as forms of overfitting problems that ML addresses through careful validation practices. Techniques like pre-analysis plans (committing to a set of statistical tests before receiving the data in order to reduce false positives) have been adopted by economists to mitigate these risks. However, possible solutions can draw inspiration from ML’s train/test split approach.

Bridging the divide

So, should economists learn machine learning? Absolutely. ML extends the standard econometric toolkit with methods that improve predictive performance, extract insights from text and images, and enhance robustness in estimation. For economists looking to stay at the frontier of empirical research—especially in a data-rich world—ML is not just useful. It’s essential.

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Gen Z and the future of audit

As Gen Z enters the audit profession, both educators and employers need to take note of their changing expectations around careers and adapt as needed. By aligning academic preparation with the expectations of employers, educators play a critical role in shaping future auditors who are engaged, resilient, and ready to lead. But before students enter the workplace, their understanding of what audit is and what it can be is developed largely in the classroom by our activities as educators. We have the unique opportunity to shape how Gen Z perceives audit—not just as an academic subject, but as a career.

Who is Gen Z?

Born roughly between 1997 and 2012, Gen Z students are ‘digital natives’ with strong values around diversity, inclusion, mental health, and work-life balance. They want careers that offer more than a good salary and security—they’re looking for meaningful impact in their work and flexibility in how they work. In the classroom, this often translates to a desire for engaging, applied learning experiences, not just memorizing rules taught in lectures. They also value autonomy and self-direction, meaning traditional, top-down teaching styles may not resonate as effectively. As educators, adapting our teaching approach to meet these expectations can make audit more accessible, relevant, and inspiring to Gen Z.

Why this matters for audit education

For many students, their university modules on audit and related topics are the starting point for understanding audit and developing perceptions about what working in audit might be like. If students see audit as rigid, outdated, or lacking in purpose, they are less likely to pursue it. But if they encounter audit as a dynamic, evolving profession—one that plays a vital role in upholding public trust and enabling business accountability—they are more likely to engage. As educators, our influence is powerful. We can shape not only students’ technical competencies, but also their sense of what kind of career audit offers.

A purpose-driven profession

It is crucial that audit is put forward as a purpose-driven profession. Today’s students care deeply about making a difference. They are more likely to engage with audit when they understand its role in protecting stakeholders beyond investors. In my own classes, the sessions where we consider the evolution of the profession in terms of sustainability assurance resonate strongly with students as they begin to see the importance of the profession beyond the purely financial. Using real-world cases and discussions to highlight how auditors contribute to ethical business and societal well-being is a great way to engage students and show them how the profession is dynamic and responsive to global challenges.

Digital advances

Audit has sometimes been unfairly perceived as dry, mundane and repetitive. But audit practice is rapidly changing, especially with the rise of audit data analytics and artificial intelligence. Integrating these topics into the classroom helps students see the relevance of what they’re learning and prevent worries about AI threatening the existence of the audit profession. Using simulations and providing demonstrations of audit software tools where possible gives students a taste for how audit is performed in the real world. Hands-on experience with emerging audit technologies introduces exciting new learning opportunities for students.

The importance of skills and behaviours

While technical proficiency remains essential, we know that audit firms value a range of skills and behaviours. Resilience, adaptability, and the ability to apply ethical judgment and professional scepticism are sought-after skills. Embedding these into our teaching and assessments—through group work, presentations, or ethical dilemma exercises—can help students build resilience and confidence. I use a role-play based on fraud to support students in developing a behaviour of professional scepticism—an activity that goes down well in class and which students remember and discuss in job interviews. Encourage students to think beyond “what” and explore “why” and “how.” Why do auditors need professional scepticism? How do they respond to emerging challenges like sustainability reporting?

Bring the profession into your classroom

Gen Z values transparency and career clarity. Help demystify the audit career path by inviting guest speakers from audit and assurance practice, professional bodies and business. Collaborate with local firms to develop teaching and learning activities such as mock audit tenders, audit planning meetings, and reviews of audit work. But it’s not just about the audit process. Gen Z students may not be aware of the strategic role auditors play in risk management, sustainability reporting, or data assurance. Including these developments in the curriculum can help students see audit as future-oriented and intellectually rewarding.

By making the curriculum more applied, aligned with the evolution of the profession, and engaging, we as educators can help to ensure that the next generation of auditors is not only technically competent and equipped with relevant skills but is also excited at the potential to join the profession. With the right teaching tools and approaches, we can inspire students to see audit not just as a job, but a rewarding career which aligns with the core values of Gen Z.

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Organizations are our greatest achievement 

There are many contenders for the award of humanity’s greatest achievement. Some say its writing. Others say its agriculture. Electricity, space travel, and human rights are also possibilities. I disagree with them all. 

It’s not that I don’t like writing, agriculture, human rights, and all the rest. They’re fab. It’s just that I think the greatest thing we humans ever did was figure out how to form ourselves into organizations. My vote goes to the weird and wonderful social structures we humans build to get more or less complicated stuff done, from launching spaceships  to brewing craft beer. 

Nothing really happens in the world without organizations. We enter the world in an organized way, with the help of hospitals and maternity wards. We also leave the world in an organized way, with the help of funeral homes and religious ceremonies. Everything in between is stuffed full of organizations of every kind imaginable—schools, universities, social clubs, gossip groups, government agencies, banks, tech companies, and so on. Even the time I set my alarm in the morning involves an organization. I don’t want to be up at seven, but the organization I work for starts early, so I fit in with that. It seems only fair. 

It’s also worth keeping in mind that many of the things touted as our greatest achievements are intimately linked to organizations—even writing. In fact, especially writing, which was developed out of arithmetical techniques used in record keeping. The point here is that we figured out how to write, not to record the intricate beauty of human life, but to better process, store, and manage information. It is precisely because of organizations that our oldest written document is a list of ‘goods received’ at a brewery, not a love letter from some long-lost beau. 

This might feel a touch tragic, but it says something very profound about what it is to be human. Most obviously, that we’ve always had a predilection for booze—but more importantly, that fundamentally we’re an organized species. It’s who we are. Humans build organizations, of all sorts of different shapes and sizes, and for all sorts of different reasons. 

At some points in our history our capacity to build organizations has been really quite impressive. At other times, less so—the demise of the Roman Empire was a particularly dark period. The organizations we have today are arguably the most impressive that have ever existed—they’re incredibly complex and productive. The very biggest, like the UK’s National Health Service, have upwards of a million people in them. Some, like the US Department of Defense, have more than twice that, and they’re literally reshaping the world we live in right before our very eyes.  

Yet, most of the organizations we have today have distinctly ancient origins. They’re not old, as such, but they’re based on some pretty ancient innovations. For example, bureaucracy can be tracked back to the invention of writing, while the concept of organizing for the ‘public good’ similarly dates back millennia. At the very least to a 5,000-year-old chain of left-luggage offices in Syria’s Balikh River valley. 

State-run bureaucracies like the ones governments use to collect taxes today are basically a Chinese invention. The Qin Empire (c.220 BCE) gave us large large-scale public administration, as well as the concept of an HR department and entrance exams. 

The corporation was invented by the Romans, where it was at least partly responsible for the meteoric rise and fall of their empire. Did you know that pretty much all the ancient Roman monuments we goggle over today (and indeed many of Europe’s major roads) were built by corporations under contracts from the Roman state? 

Even the founding principles of the industrial revolution are not that new. Modern factories and the idea of mass-produced, standardized products can trace their linage back at least 4,000 years to the Harappans of the Indus River valley. 

Of course, another part of the story of human organization is that most people in the world do not actually work in mainstream, ‘formal’ organizations. The truth is that most people on this planet will never hold a contract of employment in organization of any kind—they’ll work informally, in shadow factories and on pop-up market stalls. Indeed, a significant number will work in organizations that are explicitly banned in the countries they live in—they’ll work in criminal organizations, like the Japanese Yakuza or the Italian ‘Ndrangheta. 

The point is that there are an infinite variety of different organizations out there, all doing different things in different ways. Sure, some of them aren’t that great, but the rest have done some pretty awesome things—like codify and disseminate the concept of human rights, put people into space, and build computers capable of outthinking us. Organizations define the world we live in, and they reflect the best and worst of what humanity is capable of. 

_{Photo by SpaceX on Unsplash.}

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Towards dynamic accountability

Accountability is a fundamental component of governance, whether the governed entity is a country, a company, or indeed any other corporate entity, including charities, cooperatives, the NHS, or universities.

What is meant by governance? Most definitions of governance, particularly corporate governance, focus on what governance does (direct and control). An alternative definition considers what governance really is:

“Corporate governance describes the way that trust is achieved, power exercised, and accountability shown, in corporate entities, for the benefit of members, other stakeholders, and society.”

Consequently, accountability is a fundamental part of governance. Yet, accountability remains the least discussed aspect of the subject.

The corporate governance matrix

The well-known corporate governance matrix suggests that the process involves strategy formulation, policy-making, monitoring/supervising management, and providing accountability.

The right-hand side of the matrix (strategy formulation and policy-making) is essentially forward-looking, covering the overall responsibility for corporate performance, whereas the left-hand demands (monitoring/supervision and accountability) are concerned with the present or past and reflect responsibility for corporate conformance and compliance with law and regulations.

On passive accountability

Many directors think of accountability as the last step in the governance process, reporting the result of recent corporate performance. The literature on accountability has focused predominantly on financial reporting, independently audited in line with the relevant accounting principles or standards. Recent calls for reporting non-financial information have added a further dimension, including, for example, reports on strategy, sustainability, and ESG (Environment, Society, and Governance).

Such reporting suggests a passive approach to accountability, which provides information in line with regulations. Passive accountability typically takes credit for good results but blames poor results on external circumstances, such as market conditions, economic developments, or geo-political issues.

Consider some examples of directors’ reports in such companies:

• “Revenues this year increased by a satisfactory 8.3%”—ignoring the fact that a large part of that increase was due to price increases reflecting inflation and that, in fact, the company had lost market share to competitors.
• “This year the company has faced shortages of imported components and supply chain difficulties, which have adversely affected results”—taking no responsibility for the board policies which relied on imported components or strategies that approved the supply chains.
• “Shareholder value, reflected in our share price, has increased substantially this year, justifying higher dividends and directors’ remuneration”—ignoring the comparable rise in the stock market overall, and failing to recognise that dividends and directors’ bonuses depend more on cash flow than share price.

Board reports showing good results under passive accountability too readily become congratulatory public relations exercises, rather than serious explanations or, worse, opportunities for self-promotion by the chief executive or board chair. However, if the results are less satisfactory, passive accountability blames unavoidable external factors, outside the board’s control.

Prior to each of their collapse, the boards of Northern Rock Bank, Enron, and Carillion produced congratulatory annual reports:

• Northern Rock took credit for its significant growth, outpacing competitors, without recognising the increased risk associated with junk-bond rated financing.
• Enron took pride in its growth, which was based not only on supplying energy, but by creating a market for energy futures, without realising that, in the process, their strategic risk profile had changed from a low-risk energy producer to a high-risk financial institution.
• Carillion gave shareholders a stream of high dividends, based on profit growth, but did not draw attention to the funding of those dividends, which was based on increasing financial leverage (debt/equity ratio), relying on interest-bearing loans, which led to corporate collapse when interest rates rose.

In other words, passive accountability tends to provide the minimum data required by reporting regulations, does not accept responsibility for poor results, and sees accountability as the final step in the corporate governance process.  

On dynamic accountability

By contrast, dynamic accountability recognises that passive accountability, routine reporting, and compliance with regulatory demands is not enough. Dynamic accountability believes that reporting performance in terms of financial profit or loss, income and expenditure, or even Key Performance Indicators (KPIs), is insufficient: a complete picture needs to show not only what the results were, but how and why they occurred.      

Overall transparency needs to provide information on the context in which the results were achieved. Only then can readers see the full picture. That might include, for example, comparing the entity’s performance with industrial sector norms, describing market conditions, even comparing competitors’ performance.

Accepting responsibility for performance follows in dynamic accountability. The governing bodies of all corporate entities should not only report their organisation’s performance, but accept responsibility for it—whether good or bad, above or below expectations—explaining why those results have occurred. Then, in dynamic accountability, boards describe how they intend to build on the reported results as the basis for future strategic developments, policies, projects, and plans.

It is apparent that the governance matrix (Figure 1) has an underlying dynamic—strategy formulation leads to policy-making, which underpins management plans, budgets, and decisions, which produce the outcomes monitored and supervised by the board, leading to accountability, which, in dynamic accountability feeds into future strategy formulation and policy-making.

In other words, dynamic accountability is concerned with transparency, making visible the entire picture. Moreover, dynamic accountability accepts responsibility for all results—good or bad. It also recognises the need to build reported outcomes into future strategies, policies, and projects.

The significance of culture in accountability

Much of the literature on accountability assumes Western accounting rules. Most countries following these Western practices are democracies, with judicial system and law courts independent of the state. Increasingly, however, economic activity, wealth creation, and associated governance practices are found in countries with autocratic or oligarchic systems of governance. In many of these countries, the law courts are not independent of the state, but respond to state demands, “in the interests of the people.”

Consequently, corporate entities operating in these states need to recognise political influences in their governance processes. Two examples from China illustrate such situations:

• The Initial Public Offering (IPO) flotation of Ant Financial Services on the Shanghai and Hong Kong Stock Exchanges would have been the world’s largest IPO. But it was withdrawn by the Shanghai Exchange, at the last moment, following state intervention.
• Ant was originally the financial services arm of the vast Alibaba Group, which provides retailing/communication platforms, similar to, but larger, than Amazon. The board of Alibaba was forced to change its organisation structure and unusual governance processes following government intervention.

China is a one-party state and companies operating there need to be sensitive to the political component of corporate governance. Similarly, companies in some countries in Africa, Latin America, and the Middle East need to be aware of political and other cultural influences in their governance processes, including accountability.

AI support for dynamic accountability

We have seen that dynamic accountability can provide higher transparency, requires boards to take responsibility for results, and feeds these results into future strategies. Each of these processes can be enhanced with artificial intelligence (AI) tools. For example, Workiva combines, on one platform, financial with non-financial records to support regulatory requirements, ESG reporting, internal audit, and risk management routines. NICE Actimize can flag compliance with both financial and non-financial reporting needs. Diligent Boards is a suite of AI tools that can support board strategic thinking through data-driven insights that identify relevant competitive, economic, environmental, or socio-political issues.

AI can monitor news sources, social media, and internal systems for early warning of stress, which could result in financial or reputational loss. AI can also assess mass data to suggest money-laundering, bribery, insider dealing, and conflicts of interest. Nevertheless, when using AI, boards should ensure that their AI systems are compatible with their corporate strategies, ethics, and accountability goals.

The way ahead for dynamic accountability

In dynamic accountability, performance reports are not seen as the final step in the governance process, but as an ongoing phase in the corporate governance cycle. Responsibility is accepted by the board for all results, good or bad, explaining how and why these results have occurred. Boards with dynamic accountability also explain how they intend to build current results into future strategies, policies, and projects. Introducing dynamic accounting will need a change of attitude in some boardrooms.

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Making economics more human

As the “official doctrine of neoclassical economics, enshrined in all respectable textbooks,” the esteemed game theorist Ken Binmore says, revealed preference theory “succeeds in accommodating the infinite variety of the human race within a single theory simply by denying itself the luxury of speculating about what is going on inside someone’s head. Instead, it pays attention only to what people do.”

So great is its reach that it doesn’t stop with the entire human race. The first papers on experimental economics I ever read as an undergraduate were about how pigeons and rats exhibit ordered preferences and downward-sloping demand curves. How absolutely thrilled was I to learn that I could apply intermediate microeconomics to the entire animal kingdom.

Economists and our students alike read Binmore’s statement and nod in agreement. It doesn’t seem to occur to us, as economists, to question what we ask of our first-year students: What are the benefits and costs of stretching a theory to accommodate all forms of human and animal life? Mid-twentieth-century economists like Paul Samuelson and Hendrik Houthakker developed the tool of revealed preferences to organize consumer behavior in markets. They were interested in the orderly way that consumer purchases change when budget constraints shift. They were not interested in why people switch from butter to margarine when the price of butter increases, just like the experimental economists Ray Battalio and John Kagel were not interested in why a rat switches from Tom Collins mix to root beer when the number of lever presses for Tom Collins mix increases.

Lest I be misunderstood, let me clearly state upfront, for the record, that utility maximization is both important and useful for understanding economics. But let’s also be clear-eyed and honest about its usefulness. Utility maximization is not a sufficient foundation for understanding economics. Economists designed the utility maximization problem to explain intelligible economic consequences—that is, outcomes and outcomes only. The tool provides answers to questions like: What happens to a consumer’s purchases when the price of a good increases? How much of the change is due to their purchasing power, and how much is due to switching to cheaper alternatives?

Economics, however, omits something rather important—namely, that which makes us human—when we do not study ourselves as purposeful beings. If a captor offers us either root beer or Tom Collins mix for pressing a lever, sure, to an observer from Mars, we would look just like a rat in a cage and prefer root beer to Tom Collins mix if and only if we choose root beer over Tom Collins mix. But to state the obvious, we, as human beings in our everyday lives, do much more than simply prefer good B to good A if and only if we choose B over A.

Human beings make meaningful decisions. We understand what one another does as intelligent conduct. We make sense of what people do by what they feel, think, know, and want. Human beings value A in the moment and value B in the future, and we imagine how good it would be for us to have B but not A in the future. We jointly imagine a better future and exchange A for B with another human being.

Human beings are the only species that routinely exchanges one thing for another thing, and all human communities do this. Primatologists have tried their hardest to create the conditions for captive chimpanzees to trade fruit they liked less for fruit they liked more. But chimpanzees would not give up something in this moment for something else they favored more in the next moment. For chimpanzees, there is no favored fruit in the future. There is only now and the fruit in their physical possession now.

Humans stand alone in nature as beings who purposefully turn natural-born enemies into exchanging friends. Humans stand alone as beings who deliberately extend their own average life expectancy and intentionally decrease their own rate of infant mortality. Humans stand alone as beings who actualize healthier and more comfortable lives for themselves. Humans are a marvel. We … are a marvel! Understanding humankind’s place in the world is key to understanding why economics is necessary to explain the wonder and surprise of the human condition. 

Our common humanity tends to get lost, however, in modern social science. We doggedly search for quantitative differences among people—statistically significant differences, of course. The very foundation of economics, however, is about what all human beings do, a point that seems to go unmentioned in every principles of economics textbook. As Adam Smith clearly recognized, the nature and causes of the wealth of nations, however, rests on “the certain propensity in human nature … to truck, barter, and exchange one thing for another.” What sets us apart is not just what we choose or trade, but that we choose to trade—purposefully, imaginatively, and with an eye toward a future that only human beings can envision.

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Beyond the paycheck

In the age of gig economy, remote work, and juggling multiple jobs, unpaid labour is no longer confined only to the domestic sphere or volunteerism. It is now an insidious undercurrent in paid employment, eroding worker rights and deepening inequality. From the creative freelancer logging extra hours to secure their next project, to the care worker stretching beyond paid duties to ensure a client’s wellbeing, unpaid labour permeates contemporary work. But why does it persist, and how can we confront the structural inequities it perpetuates?

This question lies at the heart of today’s most pressing debates on work and labour. The phenomenon of unpaid labour—work performed without direct or low compensation—is not merely a byproduct of precarious work; it can actively sustain and exacerbate it. It is time to unmask the political and structural forces behind this unpaid toil and demand systemic change.

Who benefits from unpaid labour?

Unpaid labour operates as a hidden subsidy to employers. In many industries, workers are compelled to extend their working day without additional pay to meet deadlines, maintain job security, or adhere to “ideal worker” norms. Internships, unpaid overtime, and tight employer control over schedules are just a few examples. This trend is especially pronounced in gig and platform work, where freelancers compete in a global marketplace, often shouldering risks and costs previously borne by employers.

The irony is glaring: while unpaid labour generates profits for employers and value for markets, it disproportionately harms workers. Financial insecurity, job unpredictability, and the erosion of social benefits are direct consequences. The concept of “wage theft” and “income theft” captures this injustice aptly: workers are denied the full value of their effort, trapped in a cycle of intensifying precarity.

Precarity as a process, not just a condition

To understand the entrenchment of unpaid labour, we must shift from viewing precarity as an isolated economic condition to recognizing it as a process shaped by systemic power imbalances. Neoliberal labour policies have deregulated employment, weakened unions, and heightened employer control over workers. These dynamics enable employers to extract unpaid labour under the guise of norms, even underpinning career advancements.

For instance, in care work—an industry already strained by privatization and cost-cutting measures—unpaid labour often takes the form of emotional labour and unpaid overtime to meet ethical or relational commitments to clients. Similarly, in creative sectors like dance or art, unpaid labour masquerades as “investment” in one’s career, pushing workers to accept unpaid gigs for visibility or future opportunities. Platform workers, on the other hand, endure unpredictable schedules and unpaid “waiting time” between tasks. Across these sectors, the pattern is clear: unpaid labour sustains an unequal distribution of risk and reward.

The inequities of unpaid labour

The ability to endure unpaid labour hinges on access to resources—financial, institutional, and social. Workers with familial wealth, spousal support, or robust welfare systems can buffer its impact, creating a divide between those who can afford to subsidize their work and those who cannot. This dynamic perpetuates inequality not only between individuals but also across class and identity lines, such as gender and race, as marginalized groups often lack the resources needed to sustain unpaid labour without severe consequences.

Treating unpaid labour as a personal sacrifice or a stepping stone to success obscures its broader societal harm. Instead, we must recognize it as a structural issue demanding systemic change.

Demanding systemic change: power redistribution

Unpaid labour is not an individual failing or just a symptom of precarity but a result of the ways systemic forces—like policies, cultural norms, or economic structures—create and maintain unequal power relations. This approach highlights the interconnectedness of unpaid labour and structural conditions, moving beyond individual experiences to address the root causes. Within this context, unpaid labour is a symptom of deeper structural inequalities. Tackling it requires us to rethink the politics of work, from labour laws to norms and narratives. By exposing the hidden costs of unpaid labour and advocating for systemic change, we can create a fairer, more equitable world where work is dignified, compensated, and sustainable. It is time to reclaim the ‘value’ of labour and demand justice for all workers. If unpaid labour is a political issue, then addressing it requires political solutions.

Here are four concrete steps to counter its corrosive effects:

1. Make collective bargaining effective by including workers with insecure, irregular, and informal connections to paid work.

An inclusive approach should ensure that collective agreements apply across entire sectors, covering even small or informal businesses. This would prevent companies on the fringes of the formal economy from exploiting gaps in worker protections. It also requires trade unions to mobilize and organize across traditional divides by forging alliances with grassroots organizations that have strong connections to disadvantaged workers. For example, in Germany, migrant care workers fought to have their qualifications recognized, a struggle that could benefit from union support.

2. Reduce working time in paid employment for waged labour to give workers more time to manage personal and family responsibilities, reducing their reliance on unpaid labour to meet these needs.

This approach is particularly impactful for caregivers and those juggling multiple jobs, who often bear the brunt of unpaid work. By redistributing work across more employees, reduced working hours can also create formal employment opportunities for those in precarious or informal arrangements. To ensure this policy is effective, wage adjustments and income protections must accompany shorter hours to prevent workers from losing income or facing increased workloads within reduced timeframes.

3. Introduce government policies which subsidize reduced working hours through tax incentives or direct wage supplements.

For instance, caregivers taking on fewer paid hours could receive compensatory support to ensure financial stability. Employment laws must also prevent employers from imposing unreasonable productivity targets during shorter work periods, protecting workers from intensified labour demands. Additionally, investment in public services like affordable childcare, eldercare, and healthcare can alleviate the unpaid labour burden that disproportionately falls on women and marginalized groups.

4. Promote educational initiatives and public campaigns that challenge the narratives normalizing unpaid labour.

The myth of the “ideal worker”—endlessly available, self-sacrificing, and driven by passion—legitimizes exploitation and reinforces gendered divisions of labour, where unpaid work is disproportionately carried out by women. By questioning these harmful narratives and recognizing the social and economic value of all forms of labour, we can foster a culture that values humanity and sociality over mere productivity.

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Meaningful economics

Human beings mean. We just do. Human beings contemplate the importance or significance of everything, be it a person or a place, an action or a consequence, a possession or an idea, a relationship or our well-being, an experience or our connection to something greater than ourselves. If it can be some kind of thing, we value it, inevitably and instinctively. Human life is imbued with meaning, and our conduct is guided by values and purpose. Economics is no different. It’s steeped in meaning.

Meaningful economics? The question is whether economic science is as much about purpose and human values as it is about describing and predicting the “what is” of economic events. Is economics in fact full of meaning? And what do meaning and value and purpose have to do with traditional talk about economics?

If there is something that almost all economists agree on, it’s that economics is about cost-benefit analysis, not moral human conduct. But why? Why must we separate economics and ethics such that never the twain shall meet? Both are about adjusting our actions to fit with everyone else’s in the everyday business of life. Economics and ethics, I contend, are two sides of the same coin, and, moreover, we can study both sides at the same time.

Since the late 1960s and the 1970s, economists have posed the fundamental question in the study of economics as a problem: “Economics,” they say, “is the study of how society manages its scarce resources,” or “how to arrange our scarce resources to satisfy as many of our wants as possible,” or “how agents choose to allocate scarce resources and how those choices affect society.”

In 1932, the British economist Lionel Robbins shifted the focus of economics from the study of wealth or economic welfare to examining how individuals make choices under conditions of scarcity. But the great Scottish moral philosopher Adam Smith posits a different axiom in The Wealth of Nations: “the certain propensity in human nature . . . to truck, barter, and exchange one thing for another.” It is from this other self-evident starting point that he expounds upon the nature and causes of wealth of nations. Trade makes specialization possible, and together they are the cause of a “universal opulence” that “extends” to and of a “general plenty” that “diffuses” through all ranks of people.

What’s at stake from adopting Robbins’s axiom as opposed to Smith’s in economic science? For Robbins, the exchange of things is “subsidiary to the main fact of scarcity.” The exchange relationship between two people is “a technical incident;” it derives from its connection with scarcity.

But must it be? Perhaps there is something else going on behind the rudiments of scarcity. Perhaps that something is the human mind. Perhaps the human mind is the foundation for the study of economics.

A meaningful economics would be about understanding human action in its origin rather than exclusively in its outcome. As a complement to the study of economic consequences, a meaningful economics would explain the roots of conduct, and not merely its economic effects, by going to the human capacity for moral sentiments that prompt human beings to act.

If there is something that almost everyone agrees on about the modern age, it’s that commerce is built on self-interest, if you’re being generous, or selfishness or greed, if you’re not. The credit or blame for the observation is almost universally attributed to Adam Smith in one of the most famous passages in economics:

“It is not from the benevolence of the butcher, the brewer, or the baker, that we expect our dinner, but from their regard to their own interest.”

But the evidence is not there to support such a reading, or emendation, of “self-interest,” neither in Smith’s most famous passage specifically nor in The Wealth of Nations more generally. Smith says people act in “their own interest,” not their own self-interest. The difference means that the wealth of nations is not built on a disregard for others as we pursue our own interest or advantage, which is what self-interest meant in the eighteenth century and still means now.

In the very first sentence of his first great book, Smith famously sets the tone for The Theory of Moral Sentiments, saying:

“How selfish soever man may be supposed, there are evidently some principles in his nature, which interest him in the fortune of others, and render their happiness necessary to him, though he derives nothing from it except the pleasure of seeing it.”

There is no exception for economics, even when, or just because, we receive something in return when we truck, barter, and exchange. If economic science is about human conduct, then it is as much about purposes and human values as it is about incentives.

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Dictators: why heroes slide into villainy

What do Vladimir Putin, Fidel Castro, and Kim Il-sung have in common? All took power promising change for the better: a fairer distribution of wealth, an end to internal corruption, limited foreign influence, and future peace and prosperity. To an extent, they all achieved this, but then things began to go wrong. Corruption raged in Putin’s Russia; millions fled grinding poverty and political persecution in Castro’s Cuba; and Kim’s Korea launched a disastrous war that, to this day, makes reunification with the now-wealthy South a distant dream.

Rather than admitting failure and handing over power to the next generation, each autocrat hung on, long outliving their welcome. And we can add Joseph Stalin, Benito Mussolini, Hugo Chavez, Zine El Abidine Ben Ali, Muammar Gaddafi, and Daniel Ortega to the list.

So, why does this happen so often?

From triumph to tyrant

A new model, developed by Professor Kaushik Basu from Cornell University, and published in Oxford Open Economics, simulates the decisions national leaders face. It reveals positive feedback conditions that lead to escalating acts that serve the autocrat’s self-interest, and preservation of power, rather than the best interests of the country.

“My paper was provoked by a personal encounter with Daniel Ortega of Nicaragua,” Professor Basu, former chief economist of the World Bank, recalls. “When I met him in September 2013 he still had the aura of a progressive leader, but subsequently morphed into a kind of tyrant that I would not have predicted. I wrote up the algebra to explain this transformation, and realized I had hit upon an argument that explains the behaviour of a large number of authoritarian leaders around the world.”

It can take many years of struggle for a dictator to reach office and achieve their political ambitions. Reluctance to risk this work in progress with a popular vote after a mere four- or five-year term leads to actions such as intimidating, imprisoning, or assassinating political rivals; silencing the press, financial corruption, and tax evasion; influencing or corrupting the judiciary. Such tactics work—in the short term, at least —but opponents can’t be silenced forever: truth finds a way, often supported by domestic or foreign antagonists.

As these controlling behaviours escalate, it becomes clear that relinquishing power would lead to legal persecution or imprisonment: just ask Chilean autocrat General Pinochet, arrested after leaving office; or Uganda’s Idi Amin, forced into exile in Saudi Arabia.

Basu’s algebraic model shows that, after a threshold of bad behaviour is passed, no amount of do-gooding can undo the damage: the only choice left a dictator is to tighten his grip on power especially if, beyond national justice, lie international courts and tribunals. For example, Sudan’s former leader Omar al-Bashir has been in hiding since 2009, a fugitive of the International Criminal Court.

Democratic policy matters

The model explains why two-term limits have evolved as a popular way to curb such damage as they provide a limited time for unsavoury actions to accumulate, a single opportunity for re-election, and better options for leaving office peaceably. So, could this common system be more widely employed to prevent dictatorships?

“A globally-enforced term limit is an important step, but may not be enough,” says Basu, who half-jokingly suggests that creating an easy exit for dictators, such as offering them a castle on a Pacific island, could also be effective.

Joking aside, the paper has important implications for current US politics. Former US president Donald Trump faces 91 felony counts across four states. Is the barrage of legal indictments boxing him into the corner where he has no option but to regain office, and then corrupt power to save himself, just as the model predicts? 

At a time where authoritarian regimes are on the rise—the 2024 Economist Intelligence Unit report shows that 39.4% of the world’s population is under authoritarian rule, an increase from 36.9% in 2022—the need is greater than ever to promote policies that could halt the slide.

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State supported Covid-19 nudges only really worked on the young

Who says young people never listen? A study in Sweden examining responses to state-backed nudges to get Covid-19 vaccination appointments booked has found that 16- to 17-year-olds responded much more strongly to prompts by letter, text, or email than 50- to 59-year-olds.

At first glance, the result, published in an article in Oxford Open Economics, is paradoxical. Older people who are much more vulnerable to the virus should have signed up more than younger people whose lives are much less at stake, but the reverse was the case. In fact, the study is consistent with the theory that nudges are more effective for decisions that don’t really matter to the person subjected to the nudge.

Politics, populations, and pandemic policies

‘Nudge theory’ has interested researchers and politicians for decades as a means to influence population-level behaviour such as how to get people to sign up for a donor card, or take out a pension earlier in life. And it works. Just ask any marketeer or social media influencer what the impact of a well-timed and skilfully worded notification can be. But the nudge effect is limited and often misses the target population.

Niklas Jakobsson from Karlstad University, and colleagues, saw an opportunity to empirically test nudge theories using data collected by the 21 regions of Sweden during the Covid-19 pandemic. While 20 regions sent out letters directing people to book their jabs by phone or online, one—Uppsala—nudged its citizens by summoning them to pre-booked appointments.

The researchers matched up vaccination rates in each age group in Uppsala against a synthetic control devised from a weighted combination of all the other Swedish regions. The results showed that Uppsala’s nudged, pre-booked appointments had only a small (if any) effect on older people, but boosted vaccination uptake among younger people by 10%.

“It makes sense that people who benefit a lot from vaccinations will take them even without a nudge,” Jakobsson explains. “For younger people, vaccinations were not as important and thus they were more affected by the nudge.”

When should a nudge become a push?

What is the behavioural mechanism behind such a counterintuitive finding?  One line of reasoning suggests that you can’t really nudge people into doing something they don’t want to do when their volition—that is, the power of free will—is high. However, you can change behaviour when people don’t really care either way, that is, when volition is low.

According to Kahneman’s theory of Fast and Slow Thinking, once the fast, unconscious, ‘System 1’ cognitive processes are passed, nudges get held up in the slower, deliberative, ‘System 2’ thoughts, and are less effective.

“People do actually think through their decisions and do not act randomly,” says Jakobsson. “The nudges that are most likely to work are changes of defaults that clearly decrease the costs of making a decision.”

Policymakers could take a closer look at this study to work out what they are doing right, as well as wrong, when developing future nudges. “This nudge worked, but more so for younger people,” says Jakobsson. “So, it could be a way to somewhat increase vaccinations at a low cost.”

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The rising power paradigm and India’s 2024 general elections

India, the world’s largest democracy, is holding its national elections over a six-week period starting 19 April. The elections to the 543-member lower house of the parliament (Lok Sabha) with an electorate, numbering 968 million eligible voters, assumes critical importance as India is going through both internal and external changes that are heavily linked to its rising power aspirations and achievements. The ruling Bharatiya Janata Party (BJP), led by Prime Minister Narendra Modi, has been campaigning on the claim that under his leadership, India’s global status has improved substantially and that he is determined to make India a great power and developed country by 2047, the centenary year of independence. The growing Hindu middle class seems to agree. According to a February 2023 Pew survey, Modi had a 79% favorable approval rating. More interestingly, some 85% of Indians surveyed by Pew think a strong authoritarian leader or military rule is preferable to multi-party electoral democracy, the highest for any country surveyed.

Since its economic liberalization in 1991, in terms of comprehensive national power, including both hard and soft power markers, India has made substantial progress—in some areas more than in others—even though it still lags behind China in many indicators of material power and social welfare. The critical factor is the steady economic growth rate ranging from 6 to 8% over the past three decades. The $4 trillion economy, which recently overtook previous colonial ruler Britain to reach the fifth position in the world, is poised to become number three by 2030. The tactical and strategic advantages India has made under somewhat favorable geopolitical circumstances are many, but these could easily erode if its soft power foundations, especially democracy, secularism, and federalism, decline even further.

The implications of the elections to India’s rise as an inclusive democratic state is potentially far reaching. If the BJP wins a two-thirds majority, concerns are heightened that it would amend the Indian constitution, altering its core principles of liberal democracy and secularism and declare India a majoritarian Hindu state. India’s status advancement in recent years has benefitted the ruling establishment. Modi’s achievements are built on the foundations laid by the previous Congress Party-led governments of Prime Ministers P.V. Narasimha Rao and Manmohan Singh. India’s 2005 rapprochement with the US and its opening to the world, especially to East Asia, Southeast Asia, and the Middle East, occurred during that period. It was Rao and his Finance Minister Singh who opened the Indian economy to the world through their wide-ranging economic reforms in 1991. The economic growth was also very robust during much of Singh’s tenure. Many of the social programs were started during that period, but Modi has improved on their delivery by introducing direct transfer and also adding new welfare programs guaranteeing the poor subsided rations and cooking gas. Some 300 million Indians were lifted out of extreme poverty during Singh’s term in office alone, and a similar number may have come out during BJP rule. Yet India still hosts some 12% of its 1.4 billion population below the poverty line (considered as $2 a day) while 84% have an income less than $7 a day.  

The previous Congress regime’s inability to cash in on their achievements for electoral gains is in direct contrast to Modi’s success in presenting a different image to the public on India’s economic and military achievements and general international status advancement. Skillful propaganda, especially using social media, has enabled this. India’s swing power role in the Indo-Pacific, in terms of balancing China’s rise and aggressive behavior, has helped India’s geopolitical prominence and Modi has astutely used it for his own electoral successes. He has used contentious religious nationalism, including the building of a temple in Ayodhya over a destroyed Muslim mosque, repealing the Article 370 of the Constitution which gave Jammu and Kashmir special autonomous status, and adding programs to allow citizenship to displaced minorities (excluding Muslims) from neighboring Pakistan and Bangladesh, to solidify his support among ardent Hindu-nationalist groups. The 18 million-strong Indian diaspora contains many pro-Hindu groups that have helped Modi’s efforts by offering financial and moral support.

Although the rising power claim may have helped Modi’s possible third term re-election, there is another side to this story. Some of the BJP government’s internal policies may, in the long-run, undercut the status achievement by putting its legitimacy and sustainability in question. The number one challenge is the democratic backslide that has been happening under the BJP rule. Today India is ranked at 66 as a ‘partly free country’ by Freedom House, and the rating agency V-Dem recently demoted India as an ‘electoral autocracy.’ A number of measures curtailing freedom of expression and other essential democratic rights have occurred under Modi, denting India’s democratic credentials, one of its key soft power assets. Similarly, secularism, another soft power marker of India since independence, has been reduced as there is a direct effort to assert the Hindu majoritarianism as visualized by the BJP and its militant ideological arm, the Rastriya Swayam Sevak Sangh (RSS).

The democratic backsliding presages considerable difficulties to legitimizing India’s status as a liberal democratic rising power. The major challenges to freedom of expression, the party’s increasing ideological control of India’s judiciary, and the attacks on minority rights, as well as harassment an arrest of opposition leaders using governmental agencies such as the Enforcement Directorate, all portend the emergence of an illiberal state even when elections are held periodically. While Hindutva (Hindu-ness) aimed at the hegemony of Hinduism over all other religious groups has increasing sympathy among the Hindu electorate and sections of the diaspora, it is still to obtain any international traction as an attractive ideology or model for political order. It is yet to offer a coherent and convincing agenda for the emerging world order.

The father of the nation, Mahatma Gandhi, used Hindu and Buddhist religious ideas such as Ahimsa (non-violence), among others, to develop his model of non-violent struggle. Can Modi in his third term make a conscious effort to develop India as an inclusive, democratic state, and bring peaceful and tolerant aspects of Hinduism to the fore? Or will Indian democratic exceptionalism evolve into an entrenched populist majoritarian system with all its attendant challenges for democratic freedoms, even while India makes substantial material progress? The simultaneous democratic backsliding in many countries, including the US and Europe, does not help India’s prospects in this regard. India may still receive a higher geopolitical position (in the context of China’s rise) and the steady economic growth that would allow it to emerge as a key destination for trade and foreign investment, and a source of technically qualified workforce and migrants for the next two decades or more. India’s greater inclusion in global governance is needed for reasons of equity, efforts at solving many collective action problems, and greater effectiveness of international institutions. The peaceful accommodation of India will alter the historical patterns of rise and fall of great powers through war. Whether it will be a peaceful process internally is yet to be determined. The forthcoming elections will establish India’s trajectory in a colossal way both for its domestic politics and foreign relations.

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Policy meets politics on the frontiers of world urbanization

At a time when funding for urban infrastructure and the promotion of an overarching global goal—the hard-won SDG 11—have catapulted cities up the international policy agenda, it’s hard to believe that urban issues could ever have been considered marginal.

In relation to much of Africa, however, until about 15 years ago urban development challenges were quite a fringe concern in both policy and academic research. Many governments on the continent—particularly in Eastern Africa, where a suite of countries were governed by regimes whose rebel origins lay deep in the rural peripheries—viewed city-dwellers either with indifference or active hostility. International donor agencies ploughed funds into rural development, and later into aspatial ‘social’ sectors such as education and health. Meanwhile, the renewed interest from China in Africa in the early 2000s was largely seen as being focused on natural resource extraction.

This all changed from around 2010. The intense refocusing of international attention on African cities has sometimes been taken for granted as a natural consequence of the continent’s urbanisation, and of evolving international aid and investment priorities. Yet the focus on external and demographic drivers can obscure how this reorientation has been shaped in strikingly diverse ways by different political contexts.

Against the background of this ‘urban turn’ in twenty-first century Africa, my book Politics and the Urban Frontier explores how domestic politics and power struggles harness the demographic force of urban growth, alongside diversifying flows of international finance, to produce very different kinds of cities. I explore this in a set of countries in East Africa (specifically, Ethiopia, Rwanda and Uganda), which I argue is the global urban frontier: the region with the lowest proportion of people living in urban areas, but also on average the fastest rates of urbanisation. Globally-sanctioned ideals of urban progress have been central to urban development in this region, yet their fate in any given city is partly determined by how they become entangled with other political and economic agendas.

What’s missing in ‘global’ urban policy debates?

The kinds of blueprints that have often been promoted for cities in low-income countries (think of the vogue for ‘self-help’ housing schemes in the 1970s, the prescriptive urban ‘good governance’ reforms dominating the 1990s, or the supposed panacea of land titling since the early 2000s) are rooted in certain generic assumptions about how cities work. These policy approaches are steeped in ideas of private property, infrastructure planning, and capitalist social relations that are largely rooted in industrialised countries elsewhere. African cities are often seen by foreign donors and external investors as substandard versions of the urban norm: low-income, dysfunctional nearly-cities that can be ‘lifted’ with the application of enough funds and the right regulatory frameworks.

Yet cities are not just bundles of land, regulations, and economic resources. They are also fundamentally political arrangements of people and things, and in much of the world in the twenty-first century they are shaped by three intersecting variables, about which conventional urban debates say little.

First, cities are conditioned by shifting geopolitics, both in terms of regional dynamics of trade, movement, and diplomacy, and in terms of the diversifying forms of international finance on offer from donors and creditors. These financial flows, which wax and wane in response to geopolitical conditions, provide scope for bargaining and deal-making over big investments in and around capital cities, where national government priorities jostle with urban ones.

Second, cities are increasingly subject to dynamics of resurgent authoritarianism—but the nature and impact of this varies by country, given different levels of prior democratic institutional development and very different distributions of power among urban social groups. These differences affect how easily a governing regime can crush urban social opposition and repress city-level institutions.

Third, and related, cities are sites of intensifying quests for political legitimacy by governments seeking to build and hold urban power in the context of competing socioeconomic demands. In East Africa, postcolonial legitimacy to govern has often been sought among the rural majority. This is no longer adequate in the face of rapid urban growth. Urban legitimacy is now a central concern, even for authoritarian regimes—but precisely which urban groups matter most, and what needs to be done to court their support, will differ substantially by context.

Cities, then, are geopolitical hubs in which leaders and governing coalitions draw international flows into localised bargaining processes, in pursuit of (often authoritarian) urban power and legitimacy—not just globalising sites of ‘travelling’ urban planning visions and ideals of entrepreneurialism. The latter aspects of cities—which are no doubt important—have received much greater attention than the former. Politics and the Urban Frontier is part of an attempted rebalancing.

Urban analysis as political work

Focusing on East Africa, the book develops a detailed analytical framework to explain differences in urban trajectories between three cities that face many similar socioeconomic and demographic pressures, and similar flows of ideas and finance. It aims to explain why we see a stark contrast between a sustained commitment to top-down master planning processes in Kigali, accompanied by drive towards high-end service sector-led development, in contrast with a long-term ‘anti-planning’ political culture in Kampala where major urban infrastructures are fragmented and targeted as sources of private gain. Meanwhile, Addis Ababa has seen huge investments in the kinds of large-scale mass transport and housing projects that never get off the ground in the other two cities (though these have taken on a life of their own, due to widespread use by social groups for which they were not initially designed). These differences between the cities are above all rooted in power relations, rather than economic might or depoliticised notions of ‘state capacity’.

Why does all this matter? Aside from academic rebalancing, the book’s argument aims to enhance understanding about which kinds of urban investments will be taken seriously in a given context, and which may collide with political dynamics that mean they founder or twist into radically new directions during implementation. Having the analytical tools to better understand the political agendas and conflicts that underpin urban policy in a given context is essential if the international push towards more inclusive urban futures is to produce results in actual urban places. But attention to the politics of urban development is not just about tailoring urban interventions to political contexts to enhance implementation. It’s also about recognizing how such interventions can either bolster or reconfigure existing power relations, and therefore influence politics itself in a particular place—for better or worse.

There is a lot at stake in Africa’s urban century. We have seen the forms of radical socio-economic polarisation, spatial segregation, and authoritarianism that are possible in cities across the world. Indeed, many African cities were created this way during colonialism, and have struggled to escape this shadow. As the continent becomes an increasingly urban, it is vitally important that investors, donors, analysts, and advisors, who often see themselves as providing technical assistance, realise they are doing fundamentally political work—and not always in the ways they intend.

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Less-than-universal basic income

Ten years ago, almost no one in the United States had heard of Universal Basic Income (UBI). Today, chances are that the average college graduate has not only heard of the idea, but probably holds a very strong opinion about it. From Silicon Valley elites to futurists to policy wonks, UBI is igniting passions and dividing opinions across the political spectrum.

Much of the credit for this is due to Andrew Yang, whose 2016 presidential campaign took a centuries-old academic idea and transformed it into a focal point for conversations about poverty, inequality, and the future of work in an age of increasing technological automation.

Since then, the idea of UBI has taken off. The organization Mayors for a Guaranteed Income reports that it has sponsored almost 60 pilot programs in various cities across the United States, and the results of these pilots have been largely encouraging. In Stockton, California, a guaranteed income program not only reduced income volatility and mental anxiety, but significantly boosted full-time employment among recipients—by 12 percentage points, compared to a mere 5-point increase in the control group over the same period. A more recent experiment in St. Paul, Minnesota, showed similar increases in employment as well as improvements in housing and psychological wellbeing.

And yet, for all its popularity, the idea of UBI seems stuck at the level of a temporary experiment. No government has yet to implement UBI as a permanent, large-scale policy, and none seems likely to do so in the near-term future.

The UBI That Almost Was: Nixon’s Family Assistance Plan

After all, we’ve been here before. Back in the 1960s, a similar wave of enthusiasm for UBI (or “guaranteed income,” as it was called at the time) swept the United States. Milton Friedman popularized the idea in 1962 with his proposal for a Negative Income Tax. In 1968, a letter supporting a guaranteed income garnered over 1,000 signatures from academic economists and received front-page coverage in the New York Times. Finally, by 1969, Richard Nixon proposed his “Family Assistance Plan,” which would have provided a federally guaranteed income to families with children. Between growing bipartisan support and extreme public dissatisfaction with traditional welfare, it looked like the timing might be just right for the idea to actually become a reality.

Except, it didn’t. After months of struggle and compromises that left no one happy, the Family Assistance Plan ultimately failed to make it through Congress. The full story of its defeat is a complicated one, well-documented in Brian Steensland’s masterful book, The Failed Welfare Revolution. But, in essence, its failure came down to the same two objections that always bedevil guaranteed income programs: cost and fairness.

The Two Main Objections to UBI: Cost and Fairness

The issue of cost is a serious challenge for advocates of UBI. A grant of $1,000 per month would be close to enough to bring a single individual with no other income up to the poverty level. But a fully universal grant of $1,000 per month to all the roughly 330 million people living in the United States would cost almost 4 trillion dollars – more than half the entire federal budget for 2024! A smaller grant would cost less money, but the smaller the grant, the less effective it will be at lifting people out of poverty. Fiscal constraints thus create a dilemma that is difficult to escape.

The other problem is, if anything, even more difficult to manage. One of the defining features of UBI is its universality, meaning, in this context, that everyone is eligible to receive the grant, whether they are working or not. But it is precisely this universality that strikes many people as morally unfair. It’s one thing, the argument goes, to help people who are trying to support themselves but can’t. It’s quite another thing to declare that everybody is entitled to live off the federal dole, whether they’re able and willing to work or not. The old Victorian distinction between the deserving and the undeserving poor resonates deeply with a sizable majority of the American public, liberals, and conservatives alike.

Of course, UBI advocates have responses to both objections. The cost of a UBI can be mitigated by imposing modest new taxes, consolidating existing welfare programs, or both. And claims of unfairness can be met by pointing out that just because full-time parents, artists, and caretakers aren’t working, this doesn’t make them free-riders. There are other ways of making a positive contribution to one’s community beyond participation in the paid labor market.

These responses are serious enough to merit more attention than I can devote to them here. But so far, at least, they have failed to persuade a majority of the American public. This doesn’t necessarily mean that they should give up. But it does suggest, perhaps, that it might be time to consider an alternative approach to UBI—one that avoids the main objections to the policy while retaining much of what makes it so attractive in the first place.

The Child Tax Credit as an Alternative to the UBI

We don’t have to stretch our imaginations to conceive of what such an alternative might look like. We’ve already tried it—at least briefly. In 2021, responding to the economic crisis caused by COVID-19, the United States made its Child Tax Credit (CTC) fully refundable. This meant that families whose income was too low to owe any taxes received cash payments from the government, the size of which depended on how many children they had. The results of this experiment were impressive. Childhood poverty levels fell to their lowest level on record—5.2%. When the expansion ended in 2022, child poverty more than doubled almost immediately, skyrocketing to 12.4%.

So far, efforts to make the expansion permanent have been unsuccessful. But its demonstrated success and relative popularity suggest that it might be the viable path forward for enacting a policy of large scale, no-strings-attached cash transfers.

First, because the CTC is limited to families with dependent children, its scope is far narrower than a fully universal UBI. Only about 40% of US households have children under the age of 18, so even keeping the size of the grant constant, a CTC cuts the cost of UBI by more than half.

Second, and perhaps more importantly, because the CTC is focused on families with children, it is much less vulnerable to the kind of worries about unfairness that plague UBI. Even if you think that there’s something morally objectionable about able-bodied adults being dependent on government support, surely that objection doesn’t apply to children. Children aren’t responsible for putting themselves in poverty. And they aren’t capable of getting themselves out of it. If anyone deserves a helping hand, it is children.

As Josh McCabe has recently noted, other countries such as Canada, Australia, New Zealand, and the United Kingdom all have child tax credits that are at least partially refundable. The United States not only lacks such a policy, but spends less on cash transfers to children than any other country in the Organization for Economic Cooperation and Development (OECD). No surprise, then, that the US also has the highest post-tax, post-transfer child poverty rates of any other country in the developed world.

For many of UBI’s supporters, its universality is one of its strongest appeals. And yet the objections about cost and fairness show that it might also be one of its greatest political liabilities. A permanent expansion of the Child Tax Credit has the potential to realize much of the promise of permanent, broad-based, unconditional cash transfers, while simultaneously avoiding the biggest pitfalls of UBI. In bridging ambition with practicality, expanding the Child Tax Credit could be the key to transforming the ideal of universal financial support into a sustainable reality.

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How can business leaders add value with intuition in the age of AI? [Long Read]

In a speech to the Economic Club of Washington in 2018, Jeff Bezos described how Amazon made sense of the challenge of if and how to design and implement a loyalty scheme for its customers. This was a highly consequential decision for the business; for some time, Amazon had been searching for an answer to the question: “what would loyalty program for Amazon look like?”

A junior software engineer came up with the idea of fast, free shipping. But a big problem was that shipping is expensive. Also, customers like free shipping, so much so that the big eaters at Amazon’s “buffet” would take advantage by free shipping low-cost items which would not be good for Amazon’s bottom-line. When the Amazon finance team modelled the idea of fast, free shipping the results “didn’t look pretty.” In fact, they were nothing short of “horrifying.”

But Bezos is experienced enough to know that some of his best decisions have been made with “guts… not analysis.” In deciding whether to go with Amazon Prime, the analysts’ data could only take the problem so far towards being solved. Bezos decided to go with his gut. Prime was launched in 2005. It has become one of the world’s most popular subscription services with over 100 million members who spend on average $1400 per year compared to $600 for non-prime members.

As a seasoned executive and experienced entrepreneur Bezos sensed that the Prime idea could work. And in his speech he reminded his audience that “if you can make a decision with analysis, you should do so. But it turns out in life that your most important decisions are always made with instinct and intuition, taste, heart.”

The launch of Amazon Prime is a prime example of a CEO’s informed and intelligent use of intuition paying off in decision-making under uncertainty (where outcomes are unknown and their likelihood of occurrence cannot be estimated) rather than under risk (where outcomes are known and probabilities can be estimated). The customer loyalty problem for Amazon was uncertain because probabilities and consequences could not be known at the time. No amount of analysis could reduce the fast, free shipping solution to the odds of success or failure.

Under these uncertain circumstances Bezos chose to go with this gut. This is not an uncommon CEO predicament or response. In business, decision-makers often have to act “instinctively” even though they have no way of knowing what the outcome is likely to be. The world is becoming more, not less uncertain, and “radical uncertainty” seems to have become the norm for strategic decision-making both in business and in politics. The informed and intelligent use of intuition on the part of those who have the nous and experience to be able to go with their gut is one way forward.

Human intuition meets AI

Turning to the uncertainties posed by artificial intelligence and winding the clock back to over half-a-century ago, the psychologist Paul Meehl in his book Clinical Versus Statistical Prediction (1954) compared how well the subjective predictions of trained clinicians such as physicians, psychologists, and counsellors fared when compared with predictions based on simple statistical algorithms. To many people’s surprise, Meehl found that experts’ accuracy of prediction, for example trained counsellors’ predictions of college grades, was either matched or exceeded by the algorithm.

The decision-making landscape that Meehl studied all those years ago has been transformed radically by the technological revolutions of the “Information Age” (see Jay Liebowitz, Bursting the Big Data Bubble, 2014). Computers have exceeded immeasurably the human brain’s computational capacity. Big data, data analytics, machine learning, and artificial intelligence (AI) have been described as “the new oil” (see Eugene Sadler-Smith, “Researching Intuition: A Curious Passion” in Bursting the Big Data Bubble, 2014). They have opened-up possibilities for outsourcing to machines many of the tasks that were until recently the exclusive preserve of humans. The influence of AI and machine learning is extending beyond relatively routine and sometimes mundane tasks such as cashiering in supermarkets. AI now figures prominently behind the scenes in things as diverse as social media feeds, the design of smart cars, and on-line advertising. It has extended its reach into complex professional areas such as medical diagnoses, investment banking, business consulting, script writing for advertisements, and management education (see Marcus du Sautoy, The Creativity Code, 2019).

There is nothing new in machines replacing humans: they did so in the mechanisations of the agricultural and industrial revolutions when they replaced dirty and dangerous work; dull work and decision-making work might be next. Daniel Suskind, author of World without Work thinks the current technological revolution is on a scale which is hitherto unheard of. The power with which robots and computers are able to perform tasks at high speed, with high accuracy, at scale using computational capabilities are orders of magnitude greater than those of any human or previous technology. This one reason this revolution is different and is why it has been referred to as nothing less than the “biggest event in human history” by Stuart Russell, founder of the Centre for Human-Compatible Artificial Intelligence at the University of California, Berkeley.

The widespread availability of data, along with cheap, scalable computational power, and rapid and on-going developments of new AI techniques such as machine learning and deep learning have meant that AI has become a powerful tool in business management (see Gijs Overgoor, et al). For example, the financial services industry deals with high-stakes, complex problems involving large numbers of interacting variables. It has developed AI that can be used to identify cybercrime schemes such as money laundering, fraud and ATM hacking. By using complex algorithms, the latest generation of AI can uncover fraudulent activity that is hidden amongst millions of innocent transactions and alert human analysts with easily digestible, traceable, and logged data to help them to decide, using human intuition based on their “feet on the ground” experiences, on whether activity is suspicious or not and take the appropriate action. This is just one example, and there are very few areas of business which are likely to be exempt from AI’s influence. Taking this to its ultimate conclusion Elon Musk said at the recent UK “AI Safety Summit” held at Bletchley Park (where Alan Turing worked as code breaker in World War 2) that: “There will come a point where no job is needed—you can have a job if you want one for personal satisfaction but AI will do everything.  It’s both good and bad—one of the challenges in the future will be how do we find meaning in life.”

Creativity and AI

Creativity is increasingly and vitally important in many aspects of business management. It is perhaps one area in which we might assume that humans will always have the edge. However, creative industries, such as advertising, are using AI for idea generation. The car manufacturer Lexus used IBM’s Watson AI to write the “world’s most intuitive car ad” for a new model, the strap line for which is “The new Lexus ES. Driven by intuition.” The aim was to use a computer to write the ad script for what Lexus claimed to be “the most intuitive car in the world”. To do so Watson was programmed to analyse 15 years-worth of award-winning footage from the prestigious Cannes Lions international award for creativity using its “visual recognition” (which uses deep learning to analyse images of scenes, objects, faces, and other visual content), “tone analyser” (which interprets emotions and communication style in text), and “personality insights” (using data to make inferences about consumers’ personalities) applications. Watson AI helped to “re-write car advertising” by identifying the core elements of award-winning content that was both “emotionally intelligent” and “entertaining.” Watson literally wrote the script outline. It was then used by the creative agency, producers, and directors to build an emotionally gripping advertisement.

Even though the Lexus-IBM collaboration reflects a breakthrough application of AI in the creative industries, IBM’s stated aim is not to attempt to “recreate the human mind but to inspire creativity and free-up time to spend thinking about the creative process.” The question of whether Watson’s advertisement is truly creative in the sense of being both novel and useful is open to question (it was based on rules derived from human works that were judged to be outstandingly creative by human judges at the Cannes festival). In a recent collaborative study between Harvard Business School and Boston Consulting Group, “humans plus AI” has been found to produce superior results compared to “humans without AI” when used to generate ideas by following rules created by humans. However, “creativity makes new rules, rules do not make creativity” (to paraphrase the French composer Claude Debussy). The use of generative AI which is rule-following rather than rule-making is likely to result in “creative” outputs which are homogeneous and which may ultimately fail the test of true creativity, i.e. both novel (in the actual sense of the word) and useful. Human creative intuition on the other hand adds value by:

1. going beyond conventional design processes and rules
2. drawing on human beings’ ability to think outside the box, produce innovative solutions
3. sensing what will or won’t work
4. yielding products and services that stand out in market, capture the attention of consumers, and drive business success.

—as based on suggestions offered by Chat GPT in response to the question: “how does creative intuition add value to organizations?”

Emotion intelligence and AI

Another example of area in which fourth generation AI is making in-roads is in the emotional and inter-personal domains. The US-based start-up Luka has developed the artificially intelligent journaling chatbot “Replika” which is designed to encourage people to “open-up and talk about their day.” Whilst Siri and Alexa are an emotionally “cold” digital assistants, Replika is designed to be more like your “best friend.” It injects emotion into conversations and learns from the user’s questions and answers. It’s early days, and despite the hype rigorous research is required to evaluate the claims being made on behalf of such applications.

The fact that computers are making inroads into areas that were once considered uniquely human is nothing new. Perhaps intuition is next. The roots of modern intuition research are in chess, an area of human expertise in which grand masters intuit “the good move straight away.” Nobel laureate and one of the founding figures of AI, Herbert Simon, based his classic definition of intuition (“analyses frozen into habit and the capacity for rapid response through recognition”) on his research into expertise in chess. He estimated that grandmasters have stored of the order of 50,000 “familiar patterns” in their long-term memories, the recognition and recall of which enables them to play chess intuitively at the chess board.

In 1997, the chess establishment was astonished when IBM’s Deep Blue beat Russian chess grand master and world champion Garry Kasparov. Does this mean that IBM’s AI is able to out-intuit a human chess master? Kasparov thinks not. The strategy that Deep Blue used to beat Kasparov was fundamentally different from how another human being might have attempted to do so. Deep Blue did not beat Kasparov by replicating or mimicking his thinking processes, in Kasparov’s own words:

“instead of a computer that thought and played like a chess champion, with human creativity and intuition, they [the ‘AI crowd’] got one that played like a machine, systematically, evaluating 200million chess moves on the chess board per second and winning with brute number-crunching force.”

Nobel laureate in physics, Richard Feynman, commented presciently in 1985 that it will be possible to develop a machine which can surpass nature’s abilities but without imitating nature. If a computer ever becomes capable of out-intuiting a human it is likely that the rules that the computer relies on will be fundamentally different to those used by humans and the mode of reasoning will be very different to that which evolved in the human organism over many hundreds of millennia (see Gerd Gigerenzer, Gut Feelings, 2007).

AI’s limitations

In spite of the current hype, AI can also be surprisingly ineffective. Significant problems with autonomous driving vehicles have been encountered and are well documented, as in the recent case which came to court in Arizona involving a fatality allegedly caused by an Uber self-driving car. In medical diagnoses, even though the freckle-analysing system developed at Stanford University does not replicate how doctors exercise their intuitive judgement through “gut feel” for skin diseases, it can nonetheless through its prodigious number-crunching power diagnose skin cancer without knowing anything at all about dermatology (see Daniel Susskind, A World Without Work, 2020). But as the eminent computer scientist Stuart Russell remarked, the deep learning that such AI systems rely on can be quite difficult to get right, for example some of the “algorithms that have learned to recognise cancerous skin lesions, turn out to completely fail if you rotate the photograph by 45 degrees [which] doesn’t instil a lot of confidence in this technology.”

Is the balance of how we comprehend situations and take business decisions shifting inexorably away from humans and in favour of machines?  Is “artificial intuition” inevitable and will it herald the demise of “human intuition”? If an artificial intuition is realized eventually that can match that of a human, it will be one of the pivotal outcomes of the fourth industrial revolution―perhaps the ultimate form of AI.

Chat GPT appears to be “aware” of its own limitations in this regard. In response to the question “Dear ChatGPT: What happens when you intuit?” it replied:

“As a language model I don’t have the ability to intuit. I am a machine learning based algorithm that is designed to understand and generate human language. I can understand and process information provided to me, but I don’t have the ability to have intuition or feelings.”

More apocalyptically, could the creation of an artificial intuition be the “canary in the coalmine,” signalling the emergence of Vernor Vinge’s “technological singularity” where large computer networks and their users suddenly “wake up” as “superhumanly intelligent entities” as Musk and others are warning of? Could such a development turn out to be a Frankenstein’s monster with unknown but potentially negative, unintended consequences for its makers? The potential and the pitfalls of AI are firmly in the domain of the radically uncertain and identifying the potential outcomes and how to manage them is likely to involve a judicious mix of rational analysis and informed intuition on the part of political and business leaders.

Human intuition, AI, and business management

Making any predictions about what computers will or will not be able to do in the future is a hostage to fortune. For the foreseeable future most managers will continue rely on their own rather than a computer’s intuitive judgements when taking both day-to-day and strategic decisions. Therefore, until a viable “artificial intuition” arrives that is capable of out-intuiting a human, the more pressing and practical question is “what value does human intuition add in business?” The technological advancements of the information age have endowed machines with the hard skill of “solving,” which far outstrips this capability in the human mind. The evolved capacities of the intuitive mind have endowed managers with the arguably hard-to-automate, or perhaps even impossible-to-automate, soft skill of “sensing.” This is the essence of human intuition.

Perhaps the answer lies in an “Augmented Intelligence Model (AIM),” which marries gut instinct with data and analytics. Such a model might combine three elements:

1. human analytical intelligence, which is adept in communicating, diagnosing, evaluating, interpreting, etc.
2. human intuitive intelligence, which is adept in creating, empathising, feeling, judging, relating, sensing, etc.
3. artificial intelligence, which is adept in analysing, correlating, optimising, predicting, recognizing, text-mining, etc.

The most interesting spaces in this model are in the overlaps between the three intelligences, for example when human analytical intelligence augments artificial intelligence in a chatbot with human intervention. Similar overlaps exist for human analytical and human intuitive intelligences, and for human intuitive intelligence and artificial intelligence. The most interesting space is where all three overlap and it is here that most value stands to be added by leveraging the combined strengths of human intuitive intelligence, human analytical intelligence, and artificial intelligence in an Augmented Intelligence Model which can drive success.

This blog post is adapted from Chapter 1 of Intuition in Business by Eugene Sadler-Smith.

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10 things direct reports must do to get the most out of their 1:1 meetings

1:1s are crucial in promoting positive outcomes such as increased employee engagement, higher retention rates, more innovation, and overall success for the team member, manager, and organization. A lot of focus is placed on the manager’s role in orchestrating 1:1s, where they are responsible for addressing direct reports’ practical and personal needs. However, it is also important to recognize that direct reports have agency in 1:1s and should play an active, not passive, role in the effectiveness of these meetings. When direct reports feel empowered to seek help, there are benefits to both the individual and organization.

As an employee, you need to take an active role in your 1:1s to get the most out of them. These 10 key behaviors are critical in making sure you are receiving the help that you need to grow in your career:

1. Know what you need: be ready to discuss your own needs, hopes, and goals, not just what you think you should say to your manager.
2. Be curious: do not just have a curious mindset, but also engage in curious behaviors such as asking questions, listening, and challenging yourself to discover new things.
3. Build rapport: get to know your manager on a personal and professional level by learning about their interests.
4. Actively engage: get the most out of your meeting by doing things like asking questions, expressing yourself, taking notes, and paying attention to non-verbal communication like maintaining good eye contact.
5. Communicate well: strive to be clear, concise, focused, honest and pay attention to voice infliction and tone. For difficult conversations, consider practicing before bringing them to your manager.
6. Problem solve: come to your 1:1 not only with your problems but also possible solutions. Be ready to constructively discuss counterarguments and differing viewpoints.
7. Ask for help (constructively): seek assistance from your manager that encourages independent problem solving. This includes asking to recommendations or help of others when your manager cannot assist you.
8. Ask for feedback: ask specific questions that focus on receiving suggestion on future behaviors such as “I want to improve at X, do you have any suggestions on how to get better at this?”
9. Receive feedback well: show that you are appreciative of the feedback by thanking your manager and asking further questions about issues that were raised.
10. Express gratitude: let your manager know you are grateful for their time and feedback.

Finally, as you proceed with these behaviors, it is important to keep in mind the science around asking for help. Namely, help-seeking behaviors have been categorized by social psychologists into two main types: autonomous help-seeking and dependent help-seeking.

Autonomous help-seeking can be understood as seeking information that enables individuals to be independent, accomplish tasks, and solve problems on their own. This tends to promote long-term independence—similar to the adage, “Give a person a fish and they’ll eat for a day but teach them to fish and they’ll eat for a lifetime.”

Dependent help-seeking, on the other hand, refers to searching for a “quick fix” and an “answer” from someone else. This style of help-seeking conserves time and effort and leads to immediate gratification, but typically doesn’t yield long-term self-sufficiency. Interestingly, job performance ratings have been shown to have a positive relationship with autonomous help-seeking, but a negative relationship with its counterpart—dependent help-seeking.

Bottom line: do your part in the 1:1 to maximize its value to you and approach it as an opportunity to learn to be the best you can seeking meaningful insights that enable you to thrive and grow both short-term and long-term. 

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Five unexpected things about medical debt

Debt is a subject that so many of us dread. It is a drain not only on our wallets but also on our minds, leaving us with the sense that our lives and our freedom are being slowly drained away. The consequences of medical debt, which is held by 100 million Americans, are even worse. Medical debt causes people to forgo (or be denied) necessary medical care, harms people’s credit, and leaves their property (including their bank accounts and even their homes) vulnerable to foreclosure.

In Your Money or Your Life: Debt Collection in American Medicine, I detailed some odd particularities about medical debt. Here are five:

1. Your medical debt can land you in jail

Debtors’ prisons have been illegal in the United States for centuries, but Americans are still jailed because of medical debt. If hospitals and their debt collectors decide to sue a patient for medical debt and win the judgment, a patient might be summoned to court for a hearing to “discover” where their assets are located. If a patient fails to appear in court for an oral examination as part of this process of discovery, the creditor can request that the court issues a “body attachment” directing the sheriff to arrest the debtor. Technically, the debtor is being arrested for contempt of court, not the debt itself, but the debt is the fundamental cause of the arrest.

The American Civil Liberties found that in 2020, 44 states still allowed the arrest of a debtor for failing to appear in court or failing to provide information to creditors after a judgment against them. Medical debtors are frequent victims of this practice. In Idaho, for instance, Medical Recovery Services LLC sought and obtained the arrest of more debtors than any other collector in the state between 2010 and 2016. In one case, a judge set bail at more than twice the amount of the debt. After being arrested for failing to appear for a hearing in Utah for unpaid debt for an ambulance ride, Rex Iverson was arrested and brought to the county jail. Later that day, when the police went to check the holding cell, they found Iverson dead; a subsequent investigation determined that Iverson had committed suicide by strychnine poisoning.

2. Hospitals can wipe away your bill before you see the doctor

To receive a tax exemption, every nonprofit hospital in the United States must have a financial assistance policy. These policies specify income qualifications for free and discounted care. But even if patients qualify for this “charity care,” they are usually made to complete onerous and detailed applications. But there is an easier way. Hospitals can use existing “presumptive eligibility” software to determine, at the point of care, whether a patient is likely to qualify for financial assistance. This is relatively easy to determine; for instance, if a patient is enrolled in food stamps, they meet eligibility criteria for most hospitals.

At Oregon Health and Sciences University in Portland, Oregon, the billing department started this using this software in 2017. Since instituting this process, patients have been offered free care earlier in the process, and the billing department no longer has to deal with as many incomplete applications.

Still, this effort to bring some efficiency and equity to billing is still not common practice. Hospitals must pay every time they use proprietary software to screen a patient. Most hospitals would rather not bother with the expense, so they leave the onus on patients to apply for financial assistance.

3. Medical debt is bought, sold, and collected by some of America’s wealthiest and most powerful people

The medical debt collection industry includes some of the world’s richest and most powerful people. In 2014, medical debt collector Transworld Systems was sold to a private equity firm called Platinum Equity. This firm was headquartered in Beverly Hills and owned by billionaire Tom Gores. As of July 2022, Gores was the 424^th richest person in the world, just behind Twitter founder Jack Dorsey and Star Wars creator George Lucas. Gores is best known as owner of the NBA’s Detroit Pistons, and as a philanthropist. He is a member of the Board of Directors of the UCLA Medical Center, a donor to Children’s Hospital Los Angeles and to various causes Detroit and Flint, Michigan, where he grew up. He is a giant of industry, and a fixture in the civic life of two great American cities.

Under Gores’ stewardship, Transworld Systems Inc. was not nearly so charitable as his public image. By 2017, it was the company with the most medical debt collection complaints on the CFPB’s database. One person in Georgia claimed that TSI had called a friend to find him (which is allowed), but during that call had said it was in regard to a medical debt that he owed (which is not). A resident of Illinois complained that a negative action had been filed on his credit report by TSI for a medical debt that he had never heard of, and was sure he did not owe. He said he had tried to call TSI numerous times to settle the matter but could never get anyone on the other end of the line.  Another in Missouri claimed that TSI called his work cell phone so often, and despite his pleas to them to contact them at home instead, that his supervisor became annoyed and passed him over for a pay raise.

4. Aggressive debt collection earns hospitals very little money

Not all hospitals are in financial trouble. In 2019, America’s hospitals recorded their highest average profit margin ever, at 6.7%. And while many hospitals struggled during the early days of the COVID pandemic, massive federal support led them to finish the year with similar profit margins as in 2019. Of course, there are many hospitals that do not operate with such comfortable margins; many struggle to stay afloat, and each year, some close.

But hounding patients who cannot afford to pay does precious little to help. TransUnion Healthcare reports that in 2016, 68% hospital bills of bills under $500 were not paid in full. Heftier bills were even less likely to be paid; 99% of hospital bills over $3,000 were not paid in full in 2016. This meager repayment is the reason why when hospitals sell their debt to outside buyers, they receive mere cents on the dollar. Most patients in arrears just don’t have the money to pay without risking their financial health, a problem that has given rise to a saying long in use among hospital administrators: “self-pay equals no pay.”

Suing patients does not meaningfully contribute to a hospital’s financial well-being. A study of Virginia hospitals that garnished the wages of patients found that they collected, on average, 0.1% of hospital revenue through this practice. Even the hospital that sued the most patients in the state, Mary Washington Hospital in Fredericksburg, gained only 0.2% of its revenue from wage garnishments. And most often it is not the struggling safety-net or rural access hospital filling the court dockets. Institutions that pursue patients as aggressively as possible frequently have comfortable operating margins and very well-paid executives.

5. Widows and widowers are being held responsible for repaying the debts of their late spouses

This strange fact is the result of a tangled legal history. Every state law includes a “doctrine of necessaries,” which makes parents liable for the support of their children; they must, in other words, provide what is “necessary for the health and well-being” of their children. In some states, however, this doctrine is also held to make spouses liable for the financial support of one another. This does not exist everywhere: in some states, such as Georgia, the spousal doctrine of necessaries has been repealed by the legislature, while in others, such as Florida, it has been ruled unconstitutional by the courts. But in states where the spousal doctrine remains, hospitals have used it to sue spouses when the patient has died before paying their bills. In fact, medical debt is the predominant kind of debt for which the doctrine of necessaries is invoked.

The doctrine of necessaries is a relic of early English common law in which women had no right to own property or assume debts independent of their husbands, so husbands were deemed to have an obligation to pay the necessary expenses of their wives. In an ironic interpretive move, given the burden it places on widows and widowers, the very fact that medical care is so necessary is what makes it so easy for hospitals to invoke the doctrine in court.

Widows have even been made to do manual labor to pay off the debts of their late spouses. Grieving the recent death of her husband after a prolonged hospitalization, Ms. Wilson faced medical debt she could never hope to repay on her fixed income. In 1995, Danville Regional Medical Center in southern Virginia gave her the option of entering a “Service-Credit” program, where patients owing between $300 and $20,000 were put to work typing, filing, housekeeping, lawn care and working in the print shop. For their labor, they earned $5 per hour toward settling their debts.  “Net pay is applied directly to the bill, so no cash changes hands,” explained a rather upbeat front-page article in the Richmond Times-Dispatch. Nowhere in the article was the possibility mentioned that the medical center, a non-profit, might just write off the unpaid debts of low-income patients as charity care. The journalist called the program a “working cure,” without a hint of irony.

The road ahead

The history recounted in Your Money or Your Life shows how medical debt transformed from a personal negotiation between doctor and patient into an impersonal financial instrument, bought and sold by people with no role in patient care and no social bonds to patients. Hospitals and their debt collectors have become aggressive in pursuing debts, threatening patients’ physical and financial health. There have long been people working to alleviate the suffering caused by medical debt, as well as abolitionists who aim for an America where such debt is a thing of the past. By better understanding how medical debt came to be so pervasive and so harmful to patients, we might help that day come sooner.

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Seven predictions for the biggest management trends in the next few years

In August 2023, OUP attended the Academy of Management Annual Meeting in Boston. We asked attendees what they predicted the biggest trends in management will be in the next few years and received some great answers focusing on different areas of management.

What do you think will be the next big management trend? Read on for the top seven predictions from the Annual Meeting and share your own ideas in the comments.

1. Digital transformation

Businesses are undergoing digital transformations to increase efficiency and modernise operations and workflows. New technologies are becoming increasingly popular such as automation, AI, and cloud computing.

2. Change of generations for workers and leaders

The changing of generations is an emerging management trend that recognizes the evolving dynamics within the workplace as different generations coexist and collaborate. This trend emphasises the importance of understanding and leveraging the unique strengths, preferences, and communication styles of each generation to foster a more inclusive and productive work environment.

3. The increase of Artificial Intelligence

There were a few iterations of this response, including “AI-augmented innovation and strategy” and “leveraging AI to reduce carbon emissions.” Even though these are tackling different aspects of management, it seems inevitable that business leaders will have to consider how to integrate AI into operations to help employees.

4. Sustainable green organizations

Sustainability has been a hot topic for the last few years, but businesses are now focusing on making real action. New sustainability roles are being created specifically to manage this focus, providing reporting on the status of businesses and what actionable steps need to be taken.

5. Flexible working

In the post-pandemic world, flexible working has become increasingly popular. Businesses have invested time into improving the employee experience by finding the right balance of home/office working.

6. Artificial Intuition—the ultimate AI?

As artificial intelligence becomes more and more sophisticated, it can perform many of the mental tasks that were previously carried out by humans. However, intuition is one area where humans will continue to add value to business in areas such as judgement and creativity that cannot yet be replicated by a machine. This makes intuition a valuable skill to be developed and prioritised in the age of artificial intelligence.

7. Data-driven project management

All organisations deal with copious amounts of data every day. It makes sense to use that data to drive decisions. Data analytics can help to measure success, room for improvement, optimise operations, and much more.

What do you think is the next big management trend? Share your predictions in the comments!

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The three empires of our digital world [infographic]

Today, there are three dominant technology, economic, and regulatory powers that can be viewed as digital empires, each with the ambition and capability to shape the global digital order towards their interests and values. Each jurisdiction also holds a different vision for the digital economy, which is reflected in the regulatory models they have adopted. The US has pioneered a largely market-driven model; China a state-driven model; and the EU a rights-driven model. Exploring the nuances and different values undergirding each of these models helps understand conflicts that are unfolding and others that will likely come.

The US, China, and the EU are exporting their domestic regulatory models in an effort to expand their respective spheres of influence, pulling other countries into the orbits of the American, Chinese, or European digital empires. The US’ global influence today manifests through the dominance of its tech companies that exercise private power across the global digital sphere. China’s global influence can be traced to its infrastructure power, where Chinese firms—all with close ties to the Chinese state—are building critical digital network infrastructures in countries near and far. The EU exercises global influence primarily through regulatory power that entrenches European digital norms across the global marketplace. These modes of influence have ingrained American private power, Chinese infrastructure power, and European regulatory power deep into the economic, physical, and legal foundations of foreign societies. Which digital empire will prevail in the contest for global influence remains an open question, yet their contrasting strategies are now increasingly clear.  

Digital societies are at an inflection point. In the midst of unfolding regulatory battles, governments, tech companies, and digital citizens are making important choices that will shape the future ethos of the digital society. It is crucial that we all understand the choices we face as societies and individuals along with the forces that shape those choices, and the immense stakes involved for everyone who uses digital technologies. 

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<em>Reputations at Stake</em>: 10 cautionary recommendations for leaders

There are multiple rewards and risks that stem from how we manage our reputation, from the macro level for countries and governments through to the meso level for organisations and to the micro level for leaders and managers.

Reputation has rewards for those who engage and costs for those who abscond. The virtual events company, Hopin started with four employees in 2020 and was valued at $2 billion in less than a year but has recently struggled to live up to the hype of virtual events post-pandemic. The downfall for organisations and leaders can be steep. Theranos, the American health technology corporation, was valued at $10 billion in 2013 and 2014, but when people started to question the validity of its technology, the company nosedived and was eventually dissolved. The Founder and CEO, Elizabeth Holmes, will shortly be reporting to prison for defrauding investors.

Alongside the day-to-day challenges of leading organisations, there are existential threats that leaders face, including geopolitical tensions, climate change, and unprecedented levels of inequality. Leaders have a moral duty to act responsibly and if the public sniff they are being misled, as was the media’s response to Boris Johnson’s denial of a Christmas Party in December 2020 when there was a UK-wide coronavirus lockdown, which the House of Commons Committee of Privileges found he misled the House, or Liz Truss’s infamous mini budget in 2022 that triggered financial market chaos, then the reputation implications are unforgiving, as both former UK Prime Ministers found through being ousted.

Growing emphasis on purpose 

Organisations such as the Business Roundtable, the World Economic Forum, and the British Academy are advocating businesses to be purpose led. This means leaders must ensure that their organisation’s purpose is aligned with relevant external movements like #MeToo and Black Lives Matters. 

Understanding who our different stakeholders are and reflecting on how they are important is an essential mechanism to ensure external and internal alignment. We may not always like what stakeholders say or do and at times we may decide to push back, but they are nevertheless a vital source of data that we need to understand, reflect on, and respond to. P&O’s firing of staff via videoconference showed a disconnection between the expectations of society and their actions, which created ill feeling among multiple stakeholders and undermined its reputation.

There has been a lot of rhetoric around purpose from investment in ESG to promoting diversity on leadership teams. The public are now more discerning and impatient for change, which is a reputation threat if the rhetoric leaders give is not matched by action. This was captured by Greta Thunberg’s 2021 “blah, blah, blah” statement to world leaders when their comments about building back better in response to the climate emergency were not perceived to have been matched by their policies.

The risk of reputation damage 

When we witness the likes of Sam Bankman Fried of FTX being arrested, our impression is this is another prominent leader who has gone rogue. However, my research with Navdeep Arora on white collar inmates in a United States Federal Prison suggests an uncomfortable truth: our behaviours stem from our personal circumstances, the organisational culture in which we work as well as the wider regulatory and industry environment in which we operate. It is the layering of these three levels: the individual, organisational, and environmental that escalates the risk for individuals to act unethically and unlawfully and succumb to reputation damage.

All organisations and leaders face various forms of reputation damage.

Instead. our contribution, how we propose to provide value to others in the future, is an important part of the recovery process. Different stakeholders may not like what you or your organisation has done in the past, but their future interaction will depend on their perception of and willingness to let you provide value for them in the future.

Having no engagement with reputation is a risky strategy because you run the risk of letting others control the narrative, whether you are a CEO trying to persuade investors or a politician attempting to send positive signals to the electorate. An obsession with reputation causes problems as Billy Macfarlane found with the hype around the Fyre festival in the Bahamas with the social and mass media attention causing an over-confidence with his abilities and a disconnection from the reality of the situation, with catastrophic consequences for the event and him personally.

Some cautionary recommendations

Building on the evidence and examples from Reputations at Stake, I provide 10 cautionary recommendations for leaders. The reputation stakes are high and require all leaders to take collective responsibility, whether they are government or business leaders, managers, employees, volunteers, members of communities, or many of these. While reputation is not something to unduly obsess over, leaders must attend to reputation proactively and meaningfully. If managed effectively, our reputation can have positive and sustained outcomes for wider sets of stakeholders, future generations and our planet.

10 cautionary recommendations

1. Proactive reputation management without over-engineering
2. Engaging with broader sources of information promotes innovative solutions
3. Feedback from different stakeholders makes us aware of the opportunities and risks related to our reputations
4. Multiple reputations recognises that different groups have a stake in your organisation
5. Show tangible initiatives to demonstrate commitment to responsibility and give less vacuous statements
6. Work with others to support your reputation claims
7. Aspire for building a positive reputation with multiple groups, but avoid preserving your reputation at all costs
8. External events provide clues of areas that require change in your organisation’s purpose
9. Responsible leadership can prove costly in the short-term, but will reap reputation rewards in the long-term
10. Reputation damage is a time of frustration and regret, but can mark a turning point for learning and betterment

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An inflation-proof methodology to measuring policy effects on poverty

Europe’s soaring inflation and energy prices highlight the need to measure poverty and policy responses in non-monetary ways. 

Hard times continue for those on tight budgets in Europe and elsewhere. Despite the end of COVID-19 lockdowns and a return to a new normal economy, soaring prices for necessities such as food, housing, and energy are eating up the purchasing power of households’ incomes with every purchase and bill payment. 

Non-monetary indicators of poverty such as the European Union’s (EU) Material and Social Deprivation (MSD) indicator are exceptionally well suited to register the pinches and punches delivered to the purchasing power of households with relatively low resources and/or high needs. 

The MSD indicator measures whether households cannot afford items that are considered by most people to be necessary for an adequate living standard. Examples of items are a household’s financial ability to keep their home adequately warm, to have two pairs of properly fitting shoes for every member, and to have an internet connection. A household and its members are considered deprived when they cannot afford 5 out of 13 such items.

The EU’s official income poverty indicator cannot capture the effects of the current crisis on households’ living standards. This is because inflation and soaring energy prices decrease consumption, not income. 

During the consecutive crises hitting Europe between 2007 and 2012, the MSD indicator already proved its use registering marked increases in material deprivation and, as recovery took hold, accounting for the lion’s share of the EU’s improvement towards its 2020 social inclusion target. 

The MSD indicator will therefore again be the center of political attention and it will show which households are hit hardest. When households cannot influence their income enough to make up for lost purchasing power, rising costs will force them to choose between necessities, which is exactly what the MSD indicator measures.

However, whereas measuring the effect of policies on households’ income is routine practice in governments and among researchers and interest groups, the same does not hold for assessing the effects of such policies on material deprivation. 

It is a problem when potentially substantive policy effects are going unmeasured because it could lead to less support for those in need and thus less progress on the reduction of poverty and social inclusion overall. For instance, when policies appear more costly relative to their beneficial effects, decisionmakers may not find them worthwhile of pursuit. Or policies may benefit certain groups in need (e.g., those with a low income) while excluding other groups with similar or higher needs (e.g., those with a higher income and higher needs). Or policies that have a measurable effect on income poverty may appear more effective than policies that reduce the need for households to spend money out of pocket. 

A reason why policy effects on material deprivation have gone unmeasured is that, despite the rising use of material deprivation indicators, there was no established methodology to measure such effects. A recently published study showcases a new methodology that estimates the effect of a small increase in income on material and social deprivation in 32 European countries. 

The study quantifies the impact of a small universal income transfer and shows that the transfer’s effect is considerably higher in the most deprived countries and among the most deprived persons in a country. 

Figure 1 below shows that a very modest transfer of 150 Euro per year, adjusted for differences in purchasing power across countries (PPS), would contribute to reducing MSD rates across Europe. The reductions are sizeable for countries with lower average living standards, with the largest reduction of one percentage point in Romania. 

Figure 2 below furthermore shows that the same transfer has a bigger difference for those households experiencing more deprivations in each country. The average marginal effect (AME) of the transfer reduces the average number of deprivations among households experiencing one deprivation is 0.02 in the Czech Republic whereas it is 0.03 for household experiencing five deprivations. This effect is present in all countries, regardless of their living average standard.

These results demonstrate that progressiveness should not just be thought of in terms of income, while also underlining the importance of a progressive social transfer system everywhere. 

The same research shows that the impact of extra income depends on the type of social transfers received. Compared to households not receiving transfers and those receiving pension transfers, those in receipt of non-pension transfers, on average, experience the largest decrease in the number of deprivations in Europe. Non-pension transfers include transfers such as child benefits, housing allowances, social assistance, and unemployment or disability benefits. In some Eastern European countries, however, those receiving pensions or both pensions and non-pension transfers also benefit more than average. Such results inform which parts of the social safety contribute to reducing material deprivation and where additional transfers are likely to make the biggest difference. 

The methodology developed in this study can be applied to many microdata contexts, including microsimulation datasets. A non-linear regression technique estimates the relation between the number of deprivations and predictor variables such as income, debt burden, and other household characteristics. The methodology can be used to predict effects of potential policies, as was done in the above-mentioned study. It can also be used as part of multivariate techniques that evaluate the effects of policies implemented.

In the context of the current affordability crisis, European countries have implemented policy packages involving measures such as income support, social tariffs, in-kind support, and a cap on energy prices. As many of these measures work either through increasing income or reducing expenditures, they have a “money equivalent” value. Using that monetary value, the new methodology enables estimating the effect of such measures on social and material deprivation. 

Finding out by how much such policies reduce poverty and social exclusion, who benefits (most), and by how much is an extremely relevant research effort in a policy context in which there is limited scope for expansionary fiscal policy. And now it is possible to do so.

Read the journal article in Socio-Economic Review: “Reducing poverty and social exclusion in Europe: estimating the marginal effect of income on material deprivation”

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Women in sports: Althea Gibson, Billie Jean King, and their legacies [podcast]

The world of sports has long been a contested playing field for social change. When Jackie Robinson broke the color barrier in baseball in 1947, it was widely assumed that other heroic male athletes would follow in subsequent sports. So, when Althea Gibson—a young woman who grew up in Harlem playing paddle tennis—became the first Black athlete to win a major title in 1956, she shocked the tennis world. Women’s history in sports has in fact been a long series of shocks that have reshaped the world of athletics as well as the possibilities that exist for women everywhere.

On today’s episode, we discuss the lives, careers, and lasting legacies on and off the tennis courts of two great women athletes—Althea Gibson and Billie Jean King.

First, we welcomed Ashley Brown, the author of Serving Herself: The Life and Times of Althea Gibson, to speak about the barrier breaking tennis player and golfer. We then interviewed Susan Ware, the author of American Women: A Concise History, American Women’s History: A Very Short Introduction,and Game, Set, Match: Billie Jean King and the Revolution in Women’s Sports, published by UNC Press. Susan shared with us some background on how King leveraged her career as a form of activism for gender equality and discussed how sports have changed for women athletes in the years since. 

Check out Episode 80 of The Oxford Comment and subscribe to The Oxford Comment podcast through your favourite podcast app to listen to the latest insights from our expert authors.

Recommended reading

You can read the introduction from Ashley Brown’s book, Serving Herself, which explores how gender and sexuality were essential aspects to her history of integration.

Don’t miss this interview with Ashley Brown with a deeper discussion of why she chose to write about Gibson’s life.

To learn more about the Black women athletes who broke barriers in tennis, explore the Oxford African American Studies Center profiles of Althea Gibson, Zinna Garrison, Venus Williams, and Serena Williams.

Learn how Billie Jean King was the right woman at the right moment in American history, in the introduction to Susan Ware’s Game, Set, Match.

You can also learn more about the 20^th century women’s movement in this chapter, “Modern American Women, 1920 to the present”, from Susan Ware’s American Women’s History

_{Featured image: Althea Gibson, half-length portrait, holding tennis racquet. Photograph by Fred Palumbo, 1956. Library of Congress, CC0 via Unsplash.}

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Mind the gap: the growth in economic inequality [podcast]

The world is navigating a troubling economic situation. Inflation has become a global issue, concerning policy makers and private citizens equally. Energy and supply chains woes are continuous. Interest rates, consumer prices, and cost-of-living have soared, with many economists positing that the current trajectory is indicative of a coming recession.

Amid these crises, how do we recover? How can we address such financial distress and inequity, and how might we go about enacting more permanent resolution?

On today’s episode, the first for 2023, we spoke with Chris Howard, author of Who Cares: The Social Safety Net in America, and Tom Malleson, author of Against Inequality: The Practical and Ethical Case for Abolishing the Superrich, on the social safety net, the ethical implications of extreme wealth, and what steps can be taken to achieve economic equality.

Check out Episode 79 of The Oxford Comment and subscribe to The Oxford Comment podcast through your favourite podcast app to listen to the latest insights from our expert authors.

Recommended reading

If you would like to find out more about the social safety net in America, we’ve made the introduction to Chris Howard’s new book, Who Cares: The Social Safety Net in America free for 3 months.

Before writing Against Inequality, Tom Malleson argued on behalf of economic democracy in After Occupy: Economic Democracy for the 21st Century. Its first chapter, also free for 3 months, can be found here. Tom also co-wrote Part-Time for All: A Care Manifesto with Jennifer Nedelsky, which proposed a plan to radically restructure both work and care.

We’ve freed Gøsta Esping-Andersen and John Myles’ chapter, “Economic Inequality and the Welfare State“, from The Oxford Handbook of Economic Inequality (edited by Brian Nolan et al), which discusses the difficulties of capturing the impact of the welfare state on income inequality. 

For further reading on economic inequality, social welfare, power dynamics, neoliberalism, and democratic socialism, check out these recent OUP titles:

• Transnational Social Protection: Social Welfare across National Borders by Peggy Levitt, Ken Chih-Yan Sun, Ruxandra Paul, and Erica Dobbs
• The New Power Elite by Heather Gautney
• Disorienting Neoliberalism: Global Justice and the Outer Limit of Freedom by Benjamin L. McKean
• Would Democratic Socialism Be Better? by Lane Kenworthy

Lastly, check out these Open Access journal articles and book chapters, which can all be found on Oxford Academic:

• “Introduction: Why Do Governments Struggle to Reduce Inequalities?” from Public Policy to Reduce Inequalities across Europe: Hope Versus Reality (August 2022) by Paul Cairney, Michael Keating, Sean Kippin, and Emily St Denny
• “Bringing the market in: an expanded framework for understanding popular responses to economic inequality” by Arvid Lindh and Leslie McCall in Socio-Economic Review, April 2022
• “‘It’s the value that we bring’: performance pay and top income earners’ perceptions of inequality” by Katharina Hecht from Socio-Economic Review, October 2021
• “On the Impact of Inequality on Growth, Human Development, and Governance” by Ines A Ferreira, Rachel M Gisselquist, and Finn Tarp from International Studies Review, January 2021
• “Billionaires” by Tino Sanandaji and Peter T. Leeson from Industrial and Corporate Change, January 2013 

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The intertwined history of state lotteries and convenience stores

This past summer, millions of Americans were transfixed by the prospect of becoming billionaires. After weeks with no winner, the jackpot for the multi-state lottery game Mega Millions rose to $1.3 billion before being won by an as-yet-unnamed gambler who purchased the winning ticket at a Speedway gas station in Des Plaines, Illinois. Or, more specifically, at the convenience store portion of the gas station, where customers can purchase gas, food, drinks, cigarettes, and, of course, lottery tickets.

It is unsurprising that the winning ticket was purchased at a gas station convenience store. As recently as 2020, nearly 70% of total lottery sales nationwide were made at convenience stores (including convenience stores attached to gas stations). What may be surprising is just how intertwined the history of state lotteries is with the history of the American convenience store. The two emerged at roughly the same time, convenience stores helped lotteries become a $90 billion-a-year industry, and lotteries helped convenience stores secure their place at the heart of modern American commerce. 

The first wave of state lotteries spread over the Northeast and Rust Belt in the 1960s and 1970s, where states initially strove for respectability. Tickets were sold in a variety of retail businesses and nonprofit organizations, from barbershops to union halls. This strategy was born of necessity: states wanted to meet potential players where they already gathered and to legitimize a consumer product that had long been underground and associated with organized crime. 

The rise of the convenience store changed the game for state lottery commissions. In 1959, there were just 900 convenience stores across the entire United States. By 1977 there were 27,000 and in 1990 there were 71,000. A number of factors contributed to the quick rise of quick shopping. Convenience stores opened earlier and closed later than grocery stores. As more women entered the workforce, households compensated by making fewer grocery trips and picking up goods at a corner market, even if the prices were somewhat higher. 

Another factor was the consolidation of the grocery store industry. In Chicago, the number of grocery stores declined from over 13,000 in 1954 to 3,600 by 1987. A result was entire communities with little access to healthy food, known as “food deserts.” For many, especially in minority urban neighborhoods, the choice to patronize a newly-opened convenience store was a last resort born of financial and logistical necessity.

The spread of convenience stores was especially significant for state gambling officials because these retailers shared a primary customer base with lotteries. Convenience store clientele consisted primarily of blue-collar men, many of whom were drawn to the stores’ prepared foods, their expanded operating hours, their large number of locations, and their sale of alcohol and tobacco. Nationwide, as many as 70% of 7-Eleven’s customers in the late 1970s were men. Similarly, studies show that men are more likely than women to play the lottery, and, on average, they gamble more. 

It should come as no surprise, then, that lottery tickets were a major convenience store item. By 1997, roughly 65% of convenience store customers nationwide purchased lottery tickets at least occasionally, and 55% said the availability of lottery tickets was important to their choice of shopping destination. In 1984, administrators from nine state lotteries were asked their preferences for type of lottery retailer. Liquor and convenience stores were the most preferred, followed by drug stores and newsstands

For their part, retailers were more than happy to add lottery tickets to their inventory. Stores receive a cut on each ticket sold, generally around 5%. While the margin on lottery tickets is lower than for most other products, and though periods of lottomania created havoc at overwhelmed ticket counters, lottery players typically purchased other goods when they bought their tickets. 

As a result, in some states, convenience store chains helped lobby for the passage of lottery legislation. In Virginia, 7-Eleven provided 22% of the funding for the state’s 1987 lottery referendum. 

The profits explain why. In Virginia, 7-Eleven accounted for over 30% of total lottery sales in 1991, amounting to over $14 million in commission profits. In the mid-1990s, convenience stores in the nation’s 37 lottery states sold $18 billion worth of lottery tickets. By contrast, tobacco sales at convenience stores across all 50 states totaled $17 billion.

State lotteries, then, might not be as big as they are without convenience stores, and convenience stores owe much of their continued profitability to the enduring popularity of lottery tickets. While the nation waits with bated breath to see who—if anyone—will come forward with the winning ticket, all we know about this lucky person is that they visited an Illinois Speedway in late July. Their patronage of this outlet represents another reminder of how both lottery tickets and convenience shopping have only in the last few decades become ubiquitous parts of the American commercial landscape. 

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Management in the twenty-first century [infographic]

What does a modern-day workplace look like? Now, more than ever, businesses and offices are adjusting to changes in management and working patterns. 

Explore our handy infographic, specially curated to reflect current discussions on workplaces and management techniques. Discover the latest business and management research from OUP, freely available to browse at your convenience.

Navigate the infographic by clicking on the relevant working space below to read chapters and articles on key topics including remote working, automated learning, sustainability, and business ethics. 

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Beyond the Anna Karenina principle in economic development

The opening sentence of Tolstoy’s novel Anna Karenina–All happy families are alike; each unhappy family is unhappy in its own way–is popular among development practitioners, who often offer their own version as follows: All rich economies are alike; each poor economy is poor in its own way. This idea, which we can call the Anna Karenina principle of economic development, is meant as a recognition of the value of context and local knowledge.

And it is not only practitioners that share this well-intentioned sentiment. A long-standing literature on development has usefully alerted us to the dangers of simply copying institutional best practices from developed countries, or “isomorphic mimicry” to use the term originally from evolutionary biology that has caught on in the development literature. 

But, oversimplified as in the Anna Karenina principle, it has an unintended consequence. In short, what’s wrong with it is not the punchline but its setup. Thinking all rich economies are institutionally alike is simply misguided. (In jest, it could be described as a case of “idiomorphic myopia,” or the inability to clearly see different institutional forms.) But why does this matter?

If all rich economies are seen as institutionally alike, one would want to draw inspiration from only the most successful country, or a few at most. This helps to explain, for example, the prominent role that South Korea plays in the collective imagination of development practitioners. A “winner takes all attention” disposition makes slightly less successful country cases much less studied and used in development circles. And this means that the focus is put squarely on a relatively narrow slice of the experience of successful development.

Somewhat less successful cases may not even be seen as successes to begin with. To give an example from personal experience, when sharing that I was writing about Spain’s rise to high-income status, my interlocutors would often concede that Spain was an interesting case study but that, surely, I meant to say that I was writing about economic failure not success. This about a country, Spain, that is among a handful to have graduated into high-income status in recent decades and that in 30 years had been able to catch up 30 percentage points as a share of per capita output with that of the United States.

In the view of the world captured by the Anna Karenina principle, the economic history of rich countries is of little interest. Strictly speaking, the belief that all rich economies are alike does not necessarily imply the belief that their path to their current high-income status must have been similar. But in practice these two beliefs often go together. And when they do, they imply a loss that stems from overlooking a rich set of experiences that often look little like stylized institutional trajectories. Just like with biodiversity, there is much value in understanding how specific successful adaptations work in the development space. Not seeing that rich countries got to where they are through different paths is a missed opportunity to learn from truly diverse experiences.

Why is this important? In no small part because the successful adaptations of others can provide a springboard for one’s own adaptations. The dilemma of design in development projects, as Albert Hirschman put it, is balancing between elements of the status quo that have to be taken as unchangeable and those elements of the status quo to be disrupted. Escaping this dilemma is the art of project design and, as Hirschman noted, the fate of a project becomes a wager about getting that balance right. In doing so local context and knowledge can only go so far in informing what elements to disrupt because, by definition, some elements of the status quo are to be disrupted. Hence the value of looking to how numerous other countries addressed related challenges. Often a development challenge that a country faces has been solved by another country but on a different domain.

Taking solutions from one domain and applying them to another is a fruitful methodology, common for example in design thinking. In brainstorming techniques, the quantity of ideas generated really matters for the quality of the solutions created, not because it helps in the search of stylized facts or common features, but because it helps avoid functional fixedness and brings about more creative solutions.

Not only are all rich economies not alike but their paths to how they got there may prove to be full of insights. To return to Anna Karenina, it may not be its first sentence but its very last one, uttered by Tolstoy’s own alter-ego Levin, that may be more relevant after all: “my whole life… is not only not meaningless… but has the unquestionable meaning of the good which is in my power to put into it!”

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