Bizarre Bakeries in the Heart of Manchester

 

bakery in Chinatown. Just when we think we’ve found a good one, the next visit disappoints. Recently, a new bakery opened, adding more uncertainty. We cannot observe the true quality of bread before purchase, while producers have full information!

 

#ManchesterFoodie #ChinatownMcr #BakeryGamble

 

Information Inequality

 

In the streets of Chinatown, asymmetric information is the silent guest at every bakery encounter. This occurs when the seller possesses significantly more information about a transaction than the buyer (Akerlof, 1970). As hungry students, we stand in front of two bakeries, unable to distinguish a “peach”—a bun made with premium, fresh ingredients—from a “lemon”, which might use low grade flour, for example. While the baker knows exactly when that custard bun left the oven, we face total uncertainty until that very first bite. In economic terms, the quality of the bakery products, categorized as experience goods, is an unobservable characteristic at the time of purchase (Nelson, 1970).

 

#HiddenQuality #PeachOrLemon? #TheDailyBake

 

We can’t tell the difference through the glass window and are forced to take a mental gamble based on expected value. If you believe half the bakeries in Chinatown are high quality and half are low quality, you won’t be willing to pay the premium price. Instead, you’ll only offer a price somewhere in the middle—an average of what both buns are worth to you!

 

This leads to a pooling equilibrium, where both the premium and budget bakery end up selling their products at the same mid-range price (Cadsby, Frank and Maksimovic, 1990). While this may seem fair to us as students, it creates a massive win for the “lemon” bakery. They are essentially overcharging for an inferior product, benefitting from the fact that their true quality is hidden behind a sugary glaze.

 

#MiddleGround #SoggyBuns

 

Bad Buns

 

The real tragedy happens behind the scenes for the high-quality baker. Producing a “peach” bun is expensive, with premium ingredients and skilled labour. If the market price is dragged down by the presence of “lemons”, the higher quality baker may find it impossible to cover costs. This triggers adverse selection, a process where low-quality goods eventually drive high-quality goods out of the market (Akerlof, 1970). As the “peach” sellers exit because they can’t negotiate a price that reflects their value, us students quickly realise that only bad bakeries are left to buy from. This unravelling of the market means that the high-quality loaves that we really want are no longer available. As we’ll see below, we are saved from this worst-case scenario, but this really does happen! In Benin, farmers have realized that market prices for maize are low because most of it has pesticides (Kadjo et al., 2019). That’s why even when farmers produce non-pesticide maize, they don’t sell it on the market where they would only earn a little, instead keeping it for their own family to eat. This makes high-quality maize almost impossible to buy for market-goers.

 

#MarketFail #WheresTheGoodStuff

 

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Now, what about us? Are there no good bakeries anymore? Well, not quite, and that’s thanks to market signalling. The better bakery can signal to us consumers that they sell better goods, by advertising their delicious-looking pastries on posters around town. Of course, the worse bakery can try to do the same, but because it has less appealing pastries and bread, even if they advertise in the same way, it won’t make us crave it as much as the better bakery. In other words, the worse bakery would have to spend more money to advertise as effectively as the better bakery, using professional camera crews and editing to make the products look as good. And so, when the two bakeries decide how much to advertise to make the most profit, the better bakery ends up advertising more; it’s just not worth it for the worse bakery because of the high cost (Archibald, Haulman and Moody, Jr., 1983). So even though both bakeries are allowed to advertise, the difference in how much advertising they do tells us that one sells better pastries than the other.

 

And as the advertising increases demand for one bakery but not the other, the worse bakery must lower prices to compete, while we will still buy at the more expensive one as we are more confident now about the quality (Milgrom and Roberts, 1986). And we really see this! Comparing the Chinatown bakeries of Lottie’s Bakehouse and Wong Wong Bakery through our observations (no shade, both have their advantages), Lottie’s has 11000+ followers on Instagram compared to under 5000 for Wong Wong. Safe to say one has been advertising more! Then, looking at their prices, Lottie’s sells buns for about £3, while Wong Wong even has bun + drink deals for just £1! So the economics tell us that Lottie’s might be the high-quality bakery we’ve been looking for!

 

#SpotTheDifference #RealDeal

Images: Lottie’s Bakehouse Instagram and Interior

Internet Improvement

 

In the future, new signals may emerge for Chinatown. For example, online reviews could become the ultimate weapon against asymmetric information. They act as a market signal, where past consumers reveal the unobservable characteristics of experience goods (Luca, 2016). While a "lemon" bakery might try to manipulate the system, faking a good reputation would be costly when against a tide of disappointed students. This transparency minimizes asymmetry, preventing the market from unravelling. Instead of a gamble, we would get a separating equilibrium as “lemons" get exposed. Market failure is prevented and we get efficient (and tasty!) economic outcomes.

 

Next time we walk through Chinatown looking for something to eat, we’re not just buying bread. We’re making a decision under uncertainty. Asymmetric information makes us more cautious and means good bakeries don’t always get rewarded. That’s why signalling is so important. Things like reputation, presentation, and especially online reviews help us figure out which places are actually worth it. As students, a simple way to make better choices is to rely on these signals—checking reviews, noticing busy shops, or even sticking to places we trust. In the end, the best bakery isn’t just the one that makes the tastiest bread, but the one that can convince us it’s worth taking the risk.

 

#ChinatownEats #AsymmetricInformation #AdverseSelection #Uncertainty #MarketSignalling #EconomicEfficiency

References:

Akerlof, G.A. (1970) ‘The Market for “Lemons”: Quality Uncertainty and the Market Mechanism’, The Quarterly Journal of Economics, 84(3), pp. 488–500. Available at: https://doi.org/10.2307/1879431.

Archibald, R.B., Haulman, C.A. and Moody, Jr., C.E. (1983) ‘Quality, Price, Advertising, and Published Quality Ratings’, Journal of Consumer Research, 9(4), p. 347. Available at: https://doi.org/10.1086/208929.

Cadsby, C.B., Frank, M. and Maksimovic, V. (1990) ‘Pooling, Separating, and Semiseparating Equilibria in Financial Markets: Some Experimental Evidence’, The Review of Financial Studies, 3(3), pp. 315–342. Available at: https://doi.org/10.2307/2962073.

Kadjo, D. et al. (2019) ‘Food Safety and Adverse Selection in Rural Maize Markets’, Journal of Agricultural Economics, 71(2), pp. 412–438. Available at: https://doi.org/10.1111/1477-9552.12350.

Luca, M. (2016) Reviews, Reputation, and Revenue: the Case of Yelp.com, Hbs.edu. Available at: https://www.hbs.edu/faculty/Pages/item.aspx?num=41233 (Accessed: 23 April 2026).

Milgrom, P. and Roberts, J. (1986) ‘Price and Advertising Signals of Product Quality’, Journal of Political Economy, 94(4), pp. 796–821. Available at: https://doi.org/10.2307/1833203.

Nelson, P. (1970) ‘Information and Consumer Behavior’, Journal of Political Economy, 78(2), pp. 311–329. Available at: https://doi.org/10.2307/1830691.

 

Why can’t we stop scrolling? The Economics of attention in the age of Algorithms

 

Perhaps we all have had such an experience: we intended to look up a word but ended up watching videos for an hour without realizing it. Is it the content itself or the continuous anticipation for "the next more interesting content" that makes people addicted? During this process, the algorithm continuously learns user behaviour, makes precise recommendations, gradually shapes our information environment, and influences our attention and decision-making. It is like an "invisible hand", guiding our choices imperceptibly. This raises a key question: In the AI era, are our decisions autonomous or driven by algorithms? This blog explores that algorithms are influencing individual decisions by taking advantage of uncertainty and human behavioural biases and may even deviate from the optimal outcome. Therefore, we need to view algorithms rationally, regarding them as auxiliary tools rather than being dominated by them, and become the true "decision-making agents". 

Image 1 (AI-generated using ChatGPT-4o, OpenAI, 2026)

 

You think you are just "swiping casually", but in fact, you are participating in a carefully designed economic system. Each swipe seems like an independent choice, but it occurs in an environment full of uncertainties: you don't know what the next content will be, but you always feel - perhaps the next one will be better. So, you continue. This kind of behaviour is not about maximizing the average return, but rather about chasing after "possible better outcomes". In other words, you are not calculating the expected value but gambling on luck. 

 

Behavioural economics predicted this long ago. Prospect theory tells us that people overreact to "possible good things" (Kahneman, Tversky, 1979), while hyperbolic discounting indicates that we overestimate the immediate attractiveness of "one more" (Laibson, 1997). In short: It's certain to stop, but there's hope to keep scrolling - humans would choose the latter. But the key point is that this kind of "uncertainty" does not exist naturally but is deliberately created. The algorithmic goal of the platform is not to make you happier, but to make you stay a little longer. By constantly providing fresh and unpredictable content, it keeps you in a state of "the next one might be better". This is precisely the application of information design in reality (Kamenica, Gentzkow, 2011). Even better (and even more terrifying) is that all of this will reinforce itself. Every time you scroll and every second you stay, it will be recorded to train the algorithm, making it better understand you - and then more accurately get you to keep scrolling. This is a typical feedback loop: behaviour → data → algorithm → more behaviour. Over time, the system will converge to a stable state: you keep scrolling, and it keeps feeding. Does it sound like balanced? Indeed. But the problem is that this equilibrium may not be good. According to the bounded rationality viewpoints of Herbert A. Simon and Reinhard Selten, people do not make optimal decisions in the first place, and the system happens to take advantage of this. 

 

Add a classic economic issue: information asymmetry. As George Akerlof (1970) pointed out, when one party has more information than the other, the result will be systematically distorted. Here, the platform knows everything about you, while you have almost no idea what the algorithm is doing. This information advantage enables it not only to predict you but also to "shape you". What's more interesting is that having more information doesn't necessarily mean better. Jonathan Levin (2001) pointed out that an increase in information sometimes reduces overall efficiency instead. On the platform, more data about you doesn't necessarily mean better results; instead, it might make it harder for you to stop. So, the question has never been "Why can't you stop?", but rather: In such a system, do you really have a chance to stop? This is not a matter of individual willpower, but rather a systematic outcome composed of uncertainty, behavioural bias, algorithmic incentives, feedback loops and information asymmetry. You are scrolling through your phone, but the system is also "scrolling through you". 

 

This system brings up a deeper issue: market failure caused by externalities. As mentioned earlier, behavioural biases - hyperbolic discounting - can cause individuals to underestimate future costs when making decisions under uncertainty. When people decide to keep scrolling, they often prioritize the immediate entertainment benefits and overlook long-term costs such as reduced productivity, weakened attention and decreased happiness (Laibson, 1997). Therefore, the perceived cost of watching videos is lower than its actual cost. Excessive consumption occurs when private costs are lower than social costs.

 

More importantly, this influence is not limited to the individual level. When a large number of users systematically allocate their time to low-value content, it will have a broader impact on human capital, productivity and information quality. However, these social costs are not reflected in the platform's incentive mechanism. On the contrary, because attention can be monetized, platforms tend to maximize user engagement, thereby causing a deviation between private incentives and social welfare. Therefore, the final equilibrium formed is inefficient: the consumption of attention exceeds the social optimum level, although every individual decision seems rational

Image 2 (AI-generated using ChatGPT-4o, OpenAI, 2026)

 

If the problem stems from the system itself, then the answer is not merely to enhance self-control. AI-driven platforms are not neutral tools; they reinforce behaviours and maintain usage habits. Under the combined effect of uncertainty, behavioural bias and algorithmic incentives, seemingly simple choices accumulate continuously, eventually forming a stable yet inefficient equilibrium - attention is overly consumed.

 

Against such a backdrop, a more effective approach might start from system design. On the one hand, the "choice architecture" can be redesigned. By adding moderate "friction" (such as using reminders or natural stop points), behaviour can be transformed from automation to conscious decision-making. On the other hand, it can enhance transparency, enabling users to have a clearer understanding of their own actions and the operational logic of the platform. Given the scale of screen usage time (Barber, 2025), this problem is difficult to be alleviated naturally and consciously by individuals alone. Therefore, the key issue is not whether we use technology, but whether we still hold the initiative in the process of using it.

 

 

Reference List:

Akerlof, G.A. (1970) ‘The market for “lemons”: Quality uncertainty and the market mechanism’, The Quarterly Journal of Economics, 84(3), pp. 488–500. Available at: https://doi.org/10.2307/1879431(Accessed: 5 April 2026). 

Barber, S. (2025) Mobile phone and internet usage statistics in the UK. Finder. Available at: Mobile internet statistics UK (Accessed: 23 April 2026). 

Image 1 (AI-generated using ChatGPT-4o, OpenAI, 2026)

Image 2 (AI-generated using ChatGPT-4o, OpenAI, 2026)

Kahneman, D. and Tversky, A. (1979) ‘Prospect theory: An analysis of decision under risk’, Econometrica, 47(2), pp. 263–291. Available at:https://doi.org/10.2307/1914185 (Accessed: 7 April 2026). 

Kamenica, E. and Gentzkow, M. (2011) ‘Bayesian persuasion’, American Economic Review, 101(6), pp. 2590–2615. Available at: https://www.nber.org/papers/w15540 (Accessed: 6 April 2026)

Laibson, D. (1997) ‘Golden eggs and hyperbolic discounting’, The Quarterly Journal of Economics, 112(2), pp. 443–478. Available at: https://doi.org/10.1162/003355397555253(Accessed: 3 April 2026)

Levin, J. (2001) ‘Information and the market for lemons’, The RAND Journal of Economics, 32(4), pp. 657–666. Available at: https://doi.org/10.2307/2696386 (Accessed: 3 April 2026). 

Selten, R. (1990) ‘Bounded rationality’, Journal of Institutional and Theoretical Economics, 146(4), pp. 649–658. Available at:https://www.jstor.org/stable/40751353 (Accessed: 5 April 2026). 

Simon, H.A. (1955) ‘A behavioral model of rational choice’, The Quarterly Journal of Economics, 69(1), pp. 99–118. Available at: https://doi.org/10.2307/1884852 (Accessed: 5 April 2026). 

When “It Probably Won’t Happen” Isn’t Enough: Why People Still Choose Insurance?

 

Every day, individuals make decisions under uncertainty. From buying travel insurance for a short holiday to purchasing health or car insurance, people routinely pay for protection against events that may never happen. This raises an important economic question: why do individuals choose to buy insurance even when the probability of needing it is relatively low?

Real-world behaviour strongly reflects this pattern. In the UK, car insurance is legally required for drivers, contributing to near-universal coverage. Beyond necessity, voluntary insurance is also widespread: millions of travellers purchase travel insurance each year despite relatively low claim rates. Globally, the insurance market was valued at over $6 trillion in 2023 (Swiss Re, 2024). In the UK alone, insurers paid out approximately £11 billion in general insurance claims in 2022, highlighting both the scale of risk and the demand for financial protection (Association of British Insurers, 2023).

Students frequently ensure laptops or phones, even though the probability of theft or damage is relatively small. Similarly, many individuals pay for extended warranties on electronics, despite evidence that such warranties often cost more than the expected repair value. These decisions suggest that individuals are motivated by more than just simple cost-benefit calculations.

Isn’t this violating economic intuition? At first glance, this behavior may appear irrational. Based on expected value alone, insurance premiums often exceed the expected monetary loss. Sometimes our loss even cannot be valued by the monetary compensation from insurance. However, as highlighted in microeconomic theory, individuals evaluate uncertain outcomes using expected utility rather than expected value. In particular, humans are born to be risk-hated, preferring a certain outcome over a risky one with a similar or even higher expected return.

This blog explores why individuals continue to purchase insurance despite low probabilities of loss. Drawing on concepts such as risk aversion and expected utility, this blog examines how decision-making under uncertainty often departs from standard profit-maximizing assumptions.

 

Expected Utility Theorem: The Microeconomic Logic Behind Insurance Demand

Insurance decision-making is influenced by Uncertainty and Risk Aversion (People’s dislike of risk). In daily life, there are many unexpected situations that like accidents, illness or injury. Although they occur rarely at once, the financial loss can be considerable. As a result, most people have been afraid to take risks, being content with just something safe and certain (Varian, 2014). What matters is not just how much money we may lose, but also how that loss would affect our life.

This is where microeconomics gives an important explanation through Expected Utility Theory. People evaluate outcomes not only by money itself but also by how beneficial or costly the result feels to them personally (Von Neumann and Morgenstern, 1944).

If a person’s wealth is , and there is a chance  of losing $, then without insurance the future situation they face is uncertainty. However, with insurance, they pay a fixed premium p and avoid the risk of a great loss in the future.

We can easily get:

Without insurance, the expected utility is:

 

With insurance, the expected utility is:

 

A risk-averse person will choose to buy insurance whenever:

 

In simple terms, uninsured individuals are exposed to wealth outcome uncertainty: they face a probability p of incurring economic losses, and a probability of 1-p to maintain their original wealth level. By purchasing insurance, individuals surrender a fixed sum of wealth, namely the insurance premium, to obtain fully predictable wealth status. For risk-averse economic agents, this wealth trade-off is highly favorable. The utility loss brought by massive wealth shrinkage is far more severe than the utility decline caused by paying a fixed, low premium.

The result depends on the assumption that the utility function is concave, which implies diminishing marginal utility of wealth. In other words, for a risk-averse person, the utility loss from a large fall in wealth is greater than the utility gain from an equally large increase. This is also emphasized in behavioural economics. Because of loss aversion, people are usually more sensative to a loss than to receive a benefit of the same amount (Kahneman and Tversky, 1979).

 

What Role Does Insurance Play In This?

Economically, insurance functions as a mechanism for Risk Pooling and Risk Transfer. In other words, insurance helps people transfer risks to the insurer in exchange for stable financial situation in the future. By paying a fixed, predictable amount, people avoid the possibility of a significant loss later.

This is especially important when the loss would generate a sharp fall in welfare. For many households, a sudden expense such as medical treatment, phone replacement, or travel disruption may create liquidity pressure even if the probability of that event is low. After all this, insurance provides not only financial protection but also peace of mind, which many people consider highly valuable.

Insurance markets are also shaped by information asymmetries. In particular, Adverse Selection arises when higher-risk individuals are more likely to purchase insurance, which may drive up premiums and reduce market efficiency (Akerlof, 1970). This means that insurance demand is not only about personal preferences toward risk, but also about the structure of the market itself.

 

New Insurance Appearing

There are also more and more new and flexible types of insurance appearing nowadays. Compared with traditional insurance, these are often more suitable for daily life. Parametric Insurance provides compensation according to an objective condition. For example, it may pay out depending on how long your flight is delayed. Usage-Based Insurance is a more flexible type of insurance that sets prices according to your previous behavior. The most common example is car insurance. In this case, the insurance company does not only evaluate your age or car type, but also considers your driving style and driving experience in order to see whether you drive safely.

 

Reference:

Varian, H.R. (2014) Intermediate Microeconomics: A Modern Approach. 9th edn. New York: W.W. Norton & Company.

Kahneman, D. and Tversky, A. (1979) ‘Prospect Theory: An Analysis of Decision under Risk’, Econometrica, 47(2), pp. 263–291.

Von Neumann, J. and Morgenstern, O. (1944) Theory of Games and Economic Behavior. Princeton: Princeton University Press.

Akerlof, G.A. (1970) ‘The Market for Lemons: Quality Uncertainty and the Market Mechanism’, The Quarterly Journal of Economics, 84(3), pp. 488–500.

Swiss Re (2024) World insurance: Strengthening global resilience. Sigma Report. Available at: https://www.swissre.com

Association of British Insurers (2023) General insurance claims statistics. Available at: https://www.abi.org.uk