The Great Divergence: Innovation, Stagnation, and the Limits of Equilibrium

Based on Broadberry and Zhai (2025) 

Historical national accounting reveals that the Great Divergence between Europe and Asia was not a sudden 19th-century event, but a slow process rooted in centuries of divergent productivity trends. While northwest Europe began a sustained climb in GDP per capita after the 14th century, the Yangzi Delta—China’s most advanced region—entered a cycle of growth and contraction. The fundamental driver of this split was differing paths of innovation, captured by trends in Total Factor Productivity (TFP).

The classic narrative points to the Black Death as a Malthusian catastrophe, but its true economic impact was more paradoxical. By decimating Europe’s population, it created a severe labor shortage, shattering the feudal system and making labor expensive. This shock provided a powerful, sustained incentive for labor-saving innovation, initiating a period of positive TFP growth in Britain. However, this framework has a limitation. It assumes that such a demographic shock is a sufficient condition for sustained growth, yet other regions experiencing similar plagues did not see the same long-term result.

Subsequent models highlight the role of specific institutional environments. The Netherlands in the 16th century, for instance, acted as a decentralized commercial hub where trade networks and financial innovation fueled a second wave of TFP growth. Empirical studies of the period, however, challenge a purely deterministic view. While Britain and the Netherlands saw sustained progress, other European powers like Spain, despite massive resource inflows, did not. This suggests that while the Black Death created the initial conditions, it was the presence of competitive states, commercialized agriculture, and emerging property rights that allowed the productivity gains to be locked in and built upon.

Some theories explore whether advanced economies can self-insure against stagnation. The case of the Yangzi Delta presents what historians call a "high-level equilibrium trap". A state where the economy was so efficient within its existing technological and resource constraints that it removed the incentive for radical innovation. With a large, cheap labor force, it was cost-effective to throw more people at a problem rather than invent a machine to solve it. This created a stable but fragile equilibrium, where the system was optimized for stability rather than growth, leaving it vulnerable to external shocks and incapable of generating its own sustained TFP growth.

A crucial missing piece in many narratives is the role of equity, in the form of human and institutional capital. The work of Rampini and Viswanathan (2009) in a different context shows that agents with limited net worth may rationally forgo hedging because the opportunity cost of capital is too high. In historical terms, China’s imperial state, with its focus on stability and revenue extraction, directed its "social equity" away from risky, disruptive entrepreneurship and into maintaining the status quo. Meanwhile, in Europe, the high "return" on scientific and commercial ventures after the Black Death made investing in innovation a rational choice, despite the risks.

The disconnect between the theoretical potential for growth and the reality of stagnation raises critical questions for understanding economic development. Why do some societies develop institutions that foster creative destruction while others optimize for stability? Can sustained TFP growth only be triggered by a catastrophic shock, or can it be engineered through policy? The story of the Great Divergence suggests that the most powerful economic forces are not just resources and labor, but the invisible architectures of incentive and risk that guide a society's capacity for change.

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References

Broadberry, Stephen, and Runzhuo Zhai. "Innovation and the Great Divergence." (2025).

Rampini, Adriano A., and Sridhar Viswanathan. "Collateral and capital structure." Journal of Financial Economics 109.2 (2013): 466-492. 

  

Why Some Companies Must Fail for the Economy to Grow

In business, we love talking about success of big companies and game changing ideas. But companies failing and shutting down is just as important. Joseph Schumpeter called this "creative destruction." It means that for new, better businesses to rise, old ones have to fall. It is about making space for progress.  

So why do companies need to fail for the economy to grow? Why do once great businesses become slow and inefficient? And can anything be done to keep them alive longer? Let us break it down.

1. Why Letting Companies Fail Helps the Economy?

Most people think growth happens when companies slowly get bigger and make more money. But Schumpeter said real growth comes from bold changes such as new inventions, better ways of doing things, and completely new industries. The growth happens when new ideas replace old ones, entrepreneurs take risks, and when old companies die. If bad companies don’t fail, they keep using up resources (like money and talent) that could be helping better businesses grow. Some weak companies keep going for years, barely surviving and these are called "zombie companies." They don’t help the economy, they just slow it down.

2. Why Do Good Companies Become Bad? 

Even the best companies can become slow, outdated, and inefficient. When companies get big, they add more managers, more meetings, and more paperwork. Decisions take forever. New ideas get stuck in approval processes. The company stops taking risks and falls behind. Some companies succeed because of one great idea, but then they refuse to change. Kodak invented the digital camera but stuck to film because it was their main business. Blockbuster ignored streaming because they made money from DVD rentals. What worked yesterday might not work tomorrow.

Young companies grow fast because they are trying new things. But older companies often run out of good ways to grow. Instead of innovating, they waste money on buying other companies just to look bigger, giving money back to shareholders instead of improving the business, and sticking to old products that people don’t want anymore. Without fresh ideas, they slowly fade away.

3. Can We Save Failing Companies?

No company lasts forever, but some can stay strong longer by making smart changes by not spending money on things that don't help the business, selling parts of the company that are not working, and focusing on what still makes money. Big companies can act like startups by letting small teams try new things without too much control, rewarding employees for creative solutions, and partnering with or buying innovative smaller companies. Some governments try to save failing companies with bailouts. But that just keeps weak businesses alive longer than they should be. Better policies would make it easier for new companies to start, help workers learn new skills when their industry changes, and let bad companies fail so better ones can take their place.

The bottom line is that change is necessary as no company can stay on top forever. The best ones adapt, but even they will eventually be replaced by something newer and better. Some companies manage their decline well such as IBM moving from computers to AI. Others spin off successful parts such as PayPal splitting from eBay. But in the end, the economy grows when old businesses make way for new ones.
That’s not a bad thing. It’s how progress works.

Banking Networks: Risk, Contagion, and the Limits of Insurance

Modern banking systems rely on intricate networks of interbank deposits to manage liquidity risk. But while these connections help stabilize individual banks, they also create pathways for financial contagion where one bank’s failure cascades through the system. The classic work of Allen and Gale (2000) illustrates this paradox. Their model assumes crises are rare and socially optimal, but this framework has limitations as it doesn’t account for banks’ equity choices, and real-world networks behave differently than theory predicts. 

Allen and Gale’s key insight is that if banks have few connections, a single failure can trigger large losses. Gai and Kapadia (2007) later confirmed this in random networks. Empirical studies, however, challenge this view. Bech and Atalay (2008) show that real interbank networks are sparse, yet Furfine (2003) and Upper & Worms (2004) find little evidence of large-scale contagion through interbank deposits. This suggests that while the theoretical risk exists, real-world banking systems may have mitigating factors such as central bank interventions or market discipline that prevent meltdowns. 

Some models explore whether banks can self-insure against counterparty risk. Babus (2009) finds that in some cases, banks hold mutual deposits as a hedge, creating a mixed equilibrium where some fully insure while others remain exposed. But in reality, centralized insurance markets often fail implying banks may not internalize systemic risks when making bilateral deals. Dasgupta (2004) introduces a positive probability of crisis, but still assumes a social planner’s solution, ignoring decentralized decision-making. Meanwhile, Kiyotaki and Moore (1997) highlight that credit chains create externalities because lenders don’t account for how their actions affect the broader network. If banks won’t renegotiate loans in distress, the system becomes brittle.

A crucial missing piece in many models is equity choice as banks don’t just optimize liquidity. Rampini and Viswanathan (2009) show that firms with limited net worth may rationally skip hedging because equity is too expensive. This helps explain why banks operate with thin buffers. If the opportunity cost of capital is high, they’ll take on more risk rather than self-insure. If banks won’t hold enough equity and can’t fully insure against contagion, the system remains vulnerable.

The disconnect between theory and reality raises critical questions for regulators. Should capital requirements account for network structure? Can central banks act as "circuit breakers" to halt contagion? Are market-based solutions better than forced equity?

The Puzzle of Bank Leverage: Between Theory and Reality

The financial accelerator model, introduced by Bernanke, Gertler, and Gilchrist (1994, 1999), revolutionized macroeconomics by showing how credit market frictions amplify economic shocks. Their core insight was that banks, constrained by capital requirements, magnify both positive and negative shocks due to high leverage, sometimes exceeding a 10:1 debt-to-equity ratio. This mechanism became the foundation of financial friction literature, with later work by Gertler, Kiyotaki, and Karadi (2010-2016) incorporating banks’ moral hazard problems and their role in crises. Yet, a critical assumption underlies these models: banks always maximize leverage within regulatory limits because doing so is optimal. But does real-world banking behavior align with this assumption?

A dominant strand of research argues that high bank leverage is economically justified. DeAngelo and Stulz (2015), Hanson et al. (2015), and Hart and Zingales (2015) contend that banks, by diversifying their loan portfolios, can minimize risk while offering low-cost, safe deposits essential for households that rely on banks for intertemporal savings. In this view, leverage is not reckless but efficient, allowing banks to intermediate funds at scale while maintaining stability. Regulatory capital requirements, then, act as a necessary backstop rather than a binding constraint.

However, critics like Admati and Hellwig (2014) warn that high leverage makes banks vulnerable to financial distress, increasing systemic risk. Their argument is bolstered by empirical evidence as Gambacorta and Shin (2018) found that even a modest increase in bank equity reduces funding costs and boosts lending, suggesting that higher capital buffers could enhance stability without sacrificing growth. If higher capital is so beneficial, why do banks still operate with such thin equity cushions? The traditional answer is profit maximization, yet recent data challenges this assumption.

Contrary to theoretical predictions, Gropp and Heider (2010) found that banks often maintain capital ratios well above regulatory minimums. This "discretionary capital" suggests that banks do not always lever up to the maximum allowed, possibly due to market discipline, risk management, or signaling strength to investors. If banks voluntarily hold extra capital, the foundational assumption of the financial accelerator that banks are tightly constrained need revisiting. This raises deeper questions. Are leverage decisions driven more by market forces than regulation? Do banks prioritize stability over short-term profits in ways models ignore?

The gap between theory and reality calls for a reassessment of financial friction models. If banks do not maximize leverage, the amplification effects of shocks may be weaker than predicted. Policymakers must consider whether capital requirements are as binding as assumed, or if market incentives already push banks toward safer buffers. Future research should explore why banks hold excess capital and how this behavior alters crisis dynamics.

Retail Investors, Behavioral Biases, and the Puzzles Shaking Up Asset Pricing

The rise of retail investors has upended traditional assumptions about market efficiency. Studies consistently show that retail traders are more speculative than institutional investors, often driven by behavioral biases rather than fundamentals. This speculative behavior helps explain several puzzling anomalies in asset pricing, particularly in consumer firms with high retail ownership. From irrational reactions to news to distorted belief updating, retail investors’ actions challenge the semi-strong form of market efficiency.

Behavioral Biases and the Puzzles They Create
Retail investors are prone to non-proportional belief updating, where they overreact to incremental information. For instance, a slight increase in the probability of an unlikely event is perceived as a much larger shift, fueling speculative trading. This aligns with Sadka’s finding of abnormal returns in the same month as news releases, even when no new fundamental data emerges. Similarly, the post-earnings announcement drift (PEAD) is amplified in retail-heavy stocks. Retail traders often misprice both cash flow implications and risk adjustments, creating sustained misalignments between price and value.

A Case Study in Irrationality
Consider the aftermath of news coverage about a pharmaceutical company. Trading volumes for both the company and the broader pharmaceutical sector surge, driving up prices despite no new information. This frenzy, followed by a correction weeks later, violates the semi-strong efficiency hypothesis, which assumes prices reflect all publicly available information. Instead, heightened information dissemination through social media hype or sensational headlines triggers herd behavior. Retail investors pile into stocks based on sentiment, not substance, creating bubbles that eventually deflate. This pattern underscores how news distribution, not just content, distorts markets.

The Post-COVID Retail Surge and Overvalued Markets
Since 2020, retail participation in equities has exploded, partly fueling stock market growth. However, this influx correlates with valuations drifting far above fundamental metrics. Retail investors, often influenced by social media and gamified trading platforms, chase momentum and meme stocks, ignoring traditional valuation models. Cross-sectional analysis reveals stark differences as sectors popular with retail traders  show higher volatility and disconnects from earnings, while institutional heavy sectors remain more stable. Over time, as retail ownership grows, these distortions intensify, raising questions about sustainability.

Diagnostic Expectations
The theory of diagnostic expectations where investors overreact to changes in beliefs explains many retail-driven anomalies. For example, if a biotech firm’s drug trial shows a 5% improved success rate, retail traders might behave as if the probability doubled. This bias amplifies price swings and creates feedback loops. These cycles are self-reinforcing until external shocks or institutional selling triggers a collapse.

The growing influence of retail investors demands a rethink of asset pricing models. Traditional frameworks struggle to account for biases like non-proportional updating or diagnostic expectations, which drive persistent mispricing. Regulators and policymakers face dual challenges of protecting retail investors from predatory practices while mitigating systemic risks from speculative bubbles. For academics, the cross-sectional variations in retail shareholding offer fertile ground to test behavioral theories.

Why Opening Up the Economy Could be Problematic?

The global push toward open economies, driven by foreign investment and trade liberalization, is often hailed as a pathway to prosperity. However, while such policies can stimulate growth, they also carry significant risks that disproportionately affect vulnerable populations. From deepening income inequality to destabilizing local industries, the downsides of rapid economic openness reveal challenges that demand careful consideration. 

Opening an economy often attracts foreign investment, but this tends to favor industries requiring specialized skills, leaving domestic sectors underfunded. As capital concentrates among a skilled minority, wage gaps widen, and inflation surges. Those excluded from high-growth sectors such as low-skilled workers or rural populations face rising costs for essentials like housing and food, despite stagnant incomes. This creates a vicious cycle in which the the wealthy invest in assets that further drive up prices, while the rest struggle to keep up. The result is a fractured society where economic gains amplify inequality rather than shared progress.

The benefits of globalization rarely spread evenly. Foreign capital often flows into urban hubs or export-oriented zones, sidelining regions reliant on traditional industries. For instance, tech centric cities might thrive, while agricultural or manufacturing areas stagnate. This geographic imbalance entrenches poverty in neglected regions and fuels migration to overcrowded cities, straining infrastructure and public services. Over time, the concentration of wealth and opportunity in specific areas can erode social cohesion, as marginalized communities grow disillusioned with a system that seems rigged against them.

Less competitive domestic businesses, unable to rival global giants, often shrink or collapse, leading to job losses and downward pressure on wages. Unemployment breeds frustration, pushing some toward crime. In densely populated regions, where job opportunities are scarce, this discontent can escalate into organized protests, destabilizing both the economy and governance. While proponents argue such disruptions are short-term, the human cost of this adjustment period is severe, especially if governments lack safety nets to retrain workers or support failing industries.

Premature economic openness can also exploit natural resources unsustainably. Foreign investors may prioritize profit over environmental stewardship, leading to deforestation, pollution, or resource depletion. Once local assets, like minerals or farmland, might be extracted for export, leaving irreversible ecological damage. Communities dependent on these resources lose not just livelihoods but also their cultural and environmental heritage. Such scenarios transform short-term economic gains into long-term crises, questioning whether growth justifies the sacrifice of environmental and social resilience.

While economic openness can spur innovation and growth, its pitfalls highlight the need for cautious, inclusive policies. Governments must regulate foreign investment to protect vulnerable sectors, redistribute gains equitably, and enforce sustainable practices. The key lies in balancing integration with safeguards, ensuring that globalization doesn’t become a race to the bottom but a tool for shared, enduring progress. The question isn’t whether to open economies, but how to do it responsibly.

Polarization, Facts, and the Weight of Perspective

We live in an age of fractured truths, where conflicts no longer have clear winners—only entrenched sides. From geopolitical disputes to social movements, polarization thrives as people cling to competing narratives. At the heart of this divide lie two phenomena: the weighting of facts (how we prioritize information) and the trusting of facts (whether we believe the source). Together, they create a labyrinth where objective reality exists but is endlessly contested. In this environment, taking a stance becomes less about evidence and more about identity, loyalty, and the stories we choose to amplify.

The Weighting of Facts 

Every conflict is underpinned by facts, but their significance depends on who’s telling the story. Consider the Israel-Hamas war: Hamas’s October 2023 terrorist attack killed over 1,200 Israelis, a fact universally acknowledged. Yet those sympathetic to Palestine emphasize Israel’s retaliatory strikes in Gaza, which have killed thousands of civilians, including children, labeling the response disproportionate. Conversely, Israel’s supporters frame the conflict through the lens of self-defense, prioritizing the state’s right to protect its citizens from terrorism. The facts are not in dispute—the death tolls, the triggers—but their weight diverges sharply. Similarly, in the India-Pakistan rivalry over Kashmir, one side highlights Pakistan’s alleged sponsorship of militants, while the other underscores India’s military crackdowns in a Muslim-majority region. What matters isn’t the absence of facts but which ones are elevated to justify moral positions.

The Trust Deficit

Even when facts are agreed upon, their origins are increasingly distrusted. Governments, media, and institutions once seen as neutral are now accused of manipulation. During the Israel-Hamas war, both sides released casualty figures, but each dismissed the other’s data as propaganda. When the UN reports on Gaza’s humanitarian crisis, pro-Israel groups question its impartiality; when Israel shares evidence of Hamas using civilian infrastructure, pro-Palestine advocates allege fabrication. This erosion of trust extends beyond geopolitics. In India and Pakistan, official narratives about cross-border terrorism or human rights violations are reflexively dismissed by the opposing side. The result? A world where fact-checking is itself viewed as a partisan act, leaving individuals to curate their truths from echo chambers that validate their biases.

A World of Parallel Realities 

The collision of weighted facts and distrust breeds paralysis. Societies fracture into tribes that no longer share a baseline reality. Debates over conflicts like Israel-Hamas or Kashmir devolve into performative shouting matches, with each side weaponizing selective data. Social media algorithms exacerbate this, amplifying extremes and burying nuance. Meanwhile, the human cost of these divisions grows. Civilians suffer in war zones, diplomatic solutions stall, and grassroots movements for peace are drowned out by absolutism. When facts cannot bridge divides, empathy and dialogue wither.

Is There a Way Forward? 

Reckoning with this fractured landscape requires humility. It starts by acknowledging that while facts are fixed, their interpretation is inherently human. This doesn’t mean comparing harm but recognizing that resolution demands more than data. It requires listening to why certain facts weigh heavier for others and rebuilding trust through transparency. Institutions must address biases, media must resist sensationalism, and individuals must question their own certainty.