Why Your Health Insurance Costs a Fortune (Blame the Lemons)

 

As a student I do silly things, but I am healthy for the most part. I rarely find myself needing healthcare. But-and this is a big but- the time I needed it recently, I was shocked to find how expensive it was. 

But why is private health insurance necessary in the UK I hear you ask? Many reasons: long waits, unsatisfactory care and underfunding are seen in the NHS. Private healthcare insurance can be the answer to these issues. But we need it to be affordable…

Having investigated healthcare insurance more, I discovered there are countless issues surrounding the provision of what is presumed to be a necessary good. I found that at the centre is one idea: asymmetric information, and from there two consequences arise: adverse selection and moral hazard. I appreciate this sounds very confusing, and it did to me at first too, so let me elaborate.

 

Problems behind problems

At the heart of this issue is asymmetric information: when one party has more information than the other. Simply put, adverse selection occurs due to asymmetric information. Insurers struggle to verify applicants’ health status, costing them significant amounts of money due to unexpected costs. They recover this by increasing their insurance premiums. This creates a situation where people who are healthy are unwilling to buy insurance at higher prices. This creates an endless cycle where only unhealthy people buy insurance. This is where the market starts to break down. Scary!

A fruit market?

A simple analogy is seen through Akerlof’s work on peaches and lemons (Akerlof, 1970). Peaches are representative of healthy people who bring low risks to health insurers. Whilst lemons represent unhealthy people who constantly make expensive claims. It’s difficult to truly know someone’s health…

Akerlof creates parallels in the second-hand car market between peaches being good cars and lemons being bad cars. Like buyers of second-hand cars, health insurers also cannot tell the difference between low risk and high-risk applicants.

The asymmetric information prompts premiums being set at a price higher than the peaches are willing to pay. As this continues to happen, more peaches leave the market. Consequently, insurance prices increase because insurers need to generate profit after the losses the high-risk lemons cause them. Premiums will be forced higher … Causing disastrous effects! People are driven out of the health insurance market, unable to easily access lifesaving services.

More and more and more issues?    

The adverse selection spiral is reported by the Care Quality Commission, which speaks on the pressing issues of the “unfair provision” of healthcare. There’s a lack of access, and an underprovision in the quality of care received, and this is particularly prevalent in disadvantaged groups. Due to health insurance companies having a lot less money, the quality of their care drops immensely. (Holt and Roberts, 2023).

Let me pose a question: if you knew that any mistake you made would be covered by someone else, would you behave more rashly? This introduces the concept of moral hazard. Moral hazard is not just about people taking greater risks. It also reflects a simple economic idea: once someone is insured, the out-of-pocket cost of healthcare falls, so treatments may feel cheaper. As a result, people may visit doctors more often or accept treatment they might otherwise avoid if they had to pay themselves. A study of Alcoa Inc employees found that more generous insurance led to over $1,200 extra in healthcare spending per person annually, because it felt “free.” (Einav et al., 2013).

The NHS is a clear effort in fixing the issues of failing private health insurance in the UK, but in the NHS, healthcare and overall standards of living can feel like a postcode lottery, with those living in privileged areas receiving better overall care than those in poorer areas. Propper (2024) touches on this: finding that though health supplies are distributed across the UK relatively evenly, people from higher socioeconomic backgrounds tend to receive better treatment quicker.    

 

Solutions?

One solution is making healthcare insurance compulsory. This will prevent peaches from leaving the market. Germany enforced mandatory health insurance, whilst focusing on self-governance, ensuring there is never an underprovision of healthcare or supplies. Compulsory insurance may keep people in the market, but it does not ensure good healthcare. This is seen through the number of people reporting Germany having good healthcare being below the European average. (Zeeb et al, 2025).

Another solution? Nudge people in. Governments can exploit a quirk of human psychology called framing. Which is our tendency to stick with whatever option is presented as the default (Kahneman and Tversky, 1981). The UK proved this works: when workplace pensions switched to opt-out in 2012, participation jumped from 42% to 86% (Department for Work and Pensions, 2023). Apply the same logic to health insurance and the "peaches" stay in the market, stabilising premiums without force.

Disclosure regulations are another option. Before taking out a premium, people would have to disclose their medical history, ensuring everyone gets priced accordingly and preventing people from exiting the market. However, this could worsen inequality — those facing illnesses would be charged higher premiums than healthy people, meaning the people who need health insurance the most could end up being excluded entirely.

 

Solutions in action

In the US, the Affordable Care Act (ACA) transformed the healthcare system. With Obama making “efforts to correct failures in insurance market practices” (Johnson and Johnson, 2023). This aimed to reduce adverse selection and asymmetric information, which would’ve driven healthy individuals out of the market. The ACA implemented policies such as income-based subsidies, improving access to healthcare; however, quality improvements were limited and gaps in coverage remain.

These solutions are unlikely to be effective in isolation and should be combined. Ultimately, The NHS was Britain’s answer to a market that failed, and seeing as private insurance still operates and inequalities persist, Akerlof’s theories remain as relevant as ever. The adverse selection spiral didn’t disappear; it just changed shape.

me pose a question: if you knew that any mistake you made would be covered by someone else, would you behave more rashly? This introduces the concept of moral hazard. Moral hazard is not just about people taking greater risks. It also reflects a simple economic idea: once someone is insured, the out-of-pocket cost of healthcare falls, so treatments may feel cheaper. As a result, people may visit doctors more often or accept treatment they might otherwise avoid if they had to pay themselves. A study of Alcoa Inc employees found that more generous insurance led to over $1,200 extra in healthcare spending per person annually, because it felt “free.” (Einav et al., 2013).

The NHS is a clear effort in fixing the issues of failing private health insurance in the UK, but in the NHS, healthcare and overall standards of living can feel like a postcode lottery, with those living in privileged areas receiving better overall care than those in poorer areas. Propper (2024) touches on this: finding that though health supplies are distributed across the UK relatively evenly, people from higher socioeconomic backgrounds tend to receive better treatment quicker.    

 

Solutions?

One solution is making healthcare insurance compulsory. This will prevent peaches from leaving the market. Germany enforced mandatory health insurance, whilst focusing on self-governance, ensuring there is never an underprovision of healthcare or supplies. Compulsory insurance may keep people in the market, but it does not ensure good healthcare. This is seen through the number of people reporting Germany having good healthcare being below the European average. (Zeeb et al, 2025).

Another solution? Nudge people in. Governments can exploit a quirk of human psychology called framing. Which is our tendency to stick with whatever option is presented as the default (Kahneman and Tversky, 1981). The UK proved this works: when workplace pensions switched to opt-out in 2012, participation jumped from 42% to 86% (Department for Work and Pensions, 2023). Apply the same logic to health insurance and the "peaches" stay in the market, stabilising premiums without force.

Disclosure regulations are another option. Before taking out a premium, people would have to disclose their medical history, ensuring everyone gets priced accordingly and preventing people from exiting the market. However, this could worsen inequality — those facing illnesses would be charged higher premiums than healthy people, meaning the people who need health insurance the most could end up being excluded entirely.

 

Solutions in action

In the US, the Affordable Care Act (ACA) transformed the healthcare system. With Obama making “efforts to correct failures in insurance market practices” (Johnson and Johnson, 2023). This aimed to reduce adverse selection and asymmetric information, which would’ve driven healthy individuals out of the market. The ACA implemented policies such as income-based subsidies, improving access to healthcare; however, quality improvements were limited and gaps in coverage remain.

These solutions are unlikely to be effective in isolation and should be combined. Ultimately, The NHS was Britain’s answer to a market that failed, and seeing as private insurance still operates and inequalities persist, Akerlof’s theories remain as relevant as ever. The adverse selection spiral didn’t disappear; it just changed shape.

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. doi:10.2307/1879431.

Busse, R., Blümel, M., Knieps, F. and Bärnighausen, T. (2017). Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition. The Lancet, 390(10097), pp.882–897. doi:10.1016/s0140-6736(17)31280-1.

Claude (2026). Peaches vs lemons: Akerlof's market for lemons illustrated [Digital illustration]. Generated by Claude Sonnet 4.6 (Anthropic). Available at: https://claude.ai [Accessed: 27 March 2026].

Department for Work and Pensions (2023). Workplace pension participation and savings trends of eligible employees: 2009 to 2022. GOV.UK. Available at: Workplace pension participation and savings trends of eligible employees: 2009 to 2023 - GOV.UK

Einav, L., Finkelstein, A., Ryan, S.P., Schrimpf, P. and Cullen, M.R. (2013). Selection on moral hazard in health insurance. American Economic Review, 103(1), pp.178–219. doi:10.1257/aer.103.1.178.

Holt, A. and Roberts, M. (2023). Two-tier care crisis: People forced to pay or wait. BBC News, 20 October. Available at: https://www.bbc.co.uk/news/health-67100395 [Accessed: 29 March 2026].

Horn, P. (2023). The downward spiral [Digital image]. Medium, August. Available at: https://medium.com [Accessed: 29 March 2026].

Johnson, S.W. and Johnson, S.L. (2023). A review of the impact and evolution of the Affordable Care Act in America. International Journal of Scientific and Research Publications, 13(8), pp.25–29. doi:10.29322/ijsrp.13.08.2023.p14003.

Kelley. W.K. (2026), Asymmetric information is at the heart [Digital Image]

Mannemanna, C. (2025). Government intervention as a response to market failures in healthcare. International Journal of Science, Architecture, Technology and Environment, pp.540–544. doi:10.63680/ijsate1025054.056.

Propper, C. (2024). Socio-economic inequality in the distribution of health care in the UK. Oxford Open Economics, 3(Supplement_1), pp.i577–i581. doi:10.1093/ooec/odad090.

Tversky, A. and Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), pp.453–458. doi:10.1126/science.7455683.

 

Zeeb, H., Loss, J., Starke, D., Altgeld, T., Moebus, S., Geffert, K. and Gerhardus, A. (2025). Public health in Germany: structures, dynamics, and ways forward. The Lancet Public Health, 10(4). doi:10.1016/s2468-2667(25)00033-7.

 

Buy Now, Pay Later: The Hidden Cost of Splitting Reality

 

Figure 1: Buy Now, Pay Later (BNPL) concept

 

 

It’s 11:47 pm. You’re scrolling through an online store and spot a jacket for £200. It feels a bit expensive. Then you see the option to pay £50 today and split the rest over four months. Suddenly, it feels affordable and you click “buy.” Nothing about the price has changed, but the way it’s presented makes it easier to justify. Instead of thinking about the full £200, you focus on the £50 in front of you.

Figure 2 shows how this plays out over time. The dotted line represents what consumers feel they are paying, low at first, then rising. The solid line symbolises real cost: flat and unchanged from day one.The gap between them is where financial trouble starts.

Figure 2: Perceived vs Actual Cost over time under BNPL

 

The problem? This changes how we see cost and risk, leading to both individual debt and market failure.

In the UK, around 27% of adults have used Buy Now, Pay Later (BNPL) in the past year (Poll and Byrne, 2021, p.6). This shows how normalised this form of borrowing has become. BNPL works by shifting attention away from the total cost and towards the first instalment. This reflects present bias, where individuals place more weight on immediate payments than future ones. The £50 feels manageable, while the remaining payments are treated as less important. This can be explained by hyperbolic discounting (Laibson, 1997), where future costs are heavily discounted, leading consumers to judge affordability based only on the present.

In reality, this creates a gap between perceived affordability and actual financial commitment. Many users underestimate the risks, with 42% reporting that they did not fully understand what they were signing up for, and 2 in 5 users having used BNPL without even realising it (Poll and Byrne, 2021, p.3). As a result, payments can quickly build up. The report shows that 41% of users have struggled to make a repayment, while FCA evidence similarly highlights that many consumers do not fully understand the terms and risks of BNPL. This suggests that consumers are misjudging affordability and taking on financial obligations they cannot sustain. BNPL, therefore, does not reduce the cost of consumption; it changes how that cost is perceived, often leading to higher spending and financial strain.

The Information Gap: When 'Buy Now' Meets 'Pay Later'

This misperception is not accidental. It is rooted in a deeper economic problem: asymmetric information.

The core issue is that both sides are guessing. Unlike banks that run deep credit checks, BNPL providers approve most purchases in seconds with almost no background checks, creating an adverse selection problem. Risky borrowers are the very people attracted to BNPL services because other lenders have already said no. Meanwhile, people with excellent credit have little reason to use BNPL when their rewards credit cards offer better perks. Over time, this drives away the "good" customers and leaves BNPL platforms with borrowers who are more likely to default. This is a version of "lemons problem," described by George Akerlof in the used-car market: when buyers can't tell a good car from a bad one, the market gets flooded with bad cars, or "lemons".

At the same time, moral hazard creeps in on both sides. Consumers take more risks because a missed payment won’t immediately wreck their credit score. BNPL providers don't report most of these small loans to credit bureaus, traditional banks can't see how much BNPL debt a person is carrying. This means they can lend to someone who is already drowning in debt without other lenders knowing, and without  bearing the full cost if that borrower eventually collapses (Berg, 2025).

But the information gap isn't one-way. Many users misunderstand the penalties for missed payments or how quickly a small debt can snowball (Di Maggio et al., 2022). BNPL interfaces are designed to feel casual and low-stakes, not like a binding credit agreement. Regulators in Norway have even accused BNPL provider, klarna of hiding the true cost of borrowing (Norwegian Consumer Authority, 2022). In early 2025, Klarna's credit losses jumped 17% as more customers struggled to keep up (Chosunbiz, 2025), highlighting the risks created by information gaps and  the need for transparency.

 

Regulations and Recommendations

To address these issues, the Financial Conduct Authority (FCA) of the UK will start regulating BNPL deferred payment credit from July 15, 2026. Service providers must obtain FCA authorisation and conduct strict affordability checks to prevent irresponsible lending. At the same time, loan terms must be fully disclosed to reduce consumer misunderstandings (FCA, 2026-1).

 

Therefore, we suggest that regulators require all BNPL platforms to share users’ total debt information and present real total costs and repayment risks in a unified, clear format. This will enable platforms to assess a user’s overall indebtedness before offering new credit, and also allow consumers to understand the full financial obligation before purchasing (FCA, 2026-2).

 

These rules will bring greater transparency and fairness to the BNPL market and address market imbalances caused by information gaps.

 

 

Conclusion: Why This Matters

Present bias tricks you into focusing on the £50. Asymmetric information means BNPL doesn't know your true risk, and you don't know the true cost. Adverse selection brings risky borrowers to the front of the line. Moral hazard encourages carelessness on both sides. The lemons problem then drives good customers away.

These concepts don't exist in separate boxes. They feed into each other. A consumer with present bias takes on a BNPL plan they can't really afford. Because BNPL doesn’t run proper credit checks, they get approved anyway. Since BNPL doesn't share data with other lenders, no one sees the debt adding up. When that consumer misses a payment, their credit score stays intact, so they do it again.

The result is market failure. Resources are misallocated, debt rises, and consumers end up worse off than if BNPL had never existed. The FCA's 2026 rules and our suggested data-sharing requirement would help close the information gap. But the best protection is still the simplest. Next time you see "pay £50 today," pause and ask yourself: can I really afford £200?

References:

1.    Akerlof, G. A. (1970), The Market for 'Lemons': Quality Uncertainty and the Market Mechanism, Quarterly Journal of Economics, Vol. 84, No. 3, pp. 488-500
https://www.jstor.org/stable/1879431

2.    Berg, J. (2025), Klarna and Afterpay Keep BNPL Data From Credit Bureaus, PYMNTS.com, August 2025
https://www.pymnts.com/buy-now-pay-later/2025/klarna-afterpay-keep-bnpl-data-from-credit-bureaus/

3.    Chosunbiz (2025), Klarna's Credit Losses Jump 17% as BNPL Defaults Rise
https://biz.chosun.com/en/en-international/2026/02/20/BXF6RDDP4FB4FAZY7X25K5ZSWY/

4.    Davenport, Evans, Hurwitz & Smith, LLP (2024), Managing a Buy Now, Pay Later Loan Program, Davenport Evans

https://dehs.com/managing-a-buy-now-pay-later-loan-program/

5.    Di Maggio, M., Williams, E., & Kalda, A. (2022), Buy Now, Pay Later: Credit Supply, Consumer Demand, and Welfare, National Bureau of Economic Research (NBER) Working Paper No. 30535
https://www.nber.org/papers/w30535

6.    Financial Conduct Authority. (2026-1). Buy Now Pay Later. https://www.fca.org.uk/consumers/buy-now-pay-later

7.    Financial Conduct Authority. (2026-2). New protections confirmed for Buy Now Pay Later borrowers. https://www.fca.org.uk/news/press-releases/new-protections-confirmed-buy-now-pay-later-borrowers

8.    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.

9.    Norwegian Consumer Authority (2022), Klarna Criticized for Misleading Marketing and Hidden Costs
https://www.forbrukertilsynet.no/english/klarna/

10.                  Poll, H. and Byrne, G. (2021) Buy Now...Pain Later? Citizen’s Advice. Available at: https://assets.ctfassets.net/mfz4nbgura3g/1YG7ZltPP1JfBXDLfAIPgI/132a8617c04231d32eca733af7cdbcfe/BNPL_20report_20_FINAL_.pdf.

11.                  Protections to help Buy Now Pay Later borrowers navigate their financial lives (2025) FCA. Available at: https://www.fca.org.uk/news/press-releases/protections-help-buy-now-pay-later-borrowers-navigate-financial-lives. 

The £30 Problem: How Dupes Are Rewriting the Rules of Luxury

 

Would you still feel cool in your designer outfit if everyone around you was wearing the dupe? While you are scrolling through TikTok, you may have noticed that influencers are racing to find less expensive, look-alike knockoff products from name brands. This is what Gen Z calls the “dupe culture”. They do not feel knockoffs are uncool. Instead, the viral spread of the “dupe culture” among Gen Z even makes them prefer knockoffs more than other generations, but it is not just an economic decision.

Signalling and Differential Costs

Try to think about why you would like top brand products even though they are expensive. A Hermès Birkin can cost £10,000, with an 18-month waitlist to get one. This phenomenon is known as Veblen goods, named after American economist Thorstein Veblen. Veblen argued that in a "leisure class," consuming expensive, non-essential goods is a way of displaying social power.  This seems reasonable, but how does a Hermès Birkin display social power?

Here, we have to introduce an economic concept of signalling, which refers to when one party acts to credibly convey private information to another party to overcome asymmetric information (Spence, 1973). In our example, the leisure class are the signallers who take the Hermès Birkin as a signal to show their social power to others who are the receivers.

Can a cheap and average bag be a suitable signal? The answer is no, as an important condition of a signal is that it only works if it has differential costs. In our example, the Hermès Birkin is a small amount of spending for a leisure class person, but expensive to an average person, so the high cost screens out the latter, as they cannot genuinely afford it. However, what if someone found a cheap and similar-looking “Hermès Birkin”?

When Everyone Can Signal, Nobody Can

So, what happens when a £30 AliExpress bag becomes indistinguishable from a £10,000 Birkin? The signal loses its informational value.

Global trade in counterfeit goods and imitation products has reached $467 billion (OECD, 2025), and on TikTok alone, the hashtag #dupe has amassed over 7 billion views. Dupe culture is not a niche trend, it is a mass market phenomenon.

In economic terms, this represents a shift from a separating equilibrium to a pooling equilibrium. In a healthy luxury market, consumers sort themselves based on their ability to bear the cost of the signal: those who can genuinely afford luxury goods (high-type senders) purchase them, while those who cannot (low-type senders) are screened out by price. However, when dupes collapse this cost differential, low-type senders can mimic high-type signals at minimal cost. The result is a pooling equilibrium: nobody can tell who is who anymore. The Birkin stops being proof of wealth and becomes just another bag.

Fighting Back: How Luxury Brands Are Raising the Bar

Luxury brands are not sitting still. Faced with a pooling equilibrium, they are actively redesigning signals to restore their credibility.

First, firms intensify exclusivity. By restricting supply through waitlists and controlled distribution, brands like Hermès ensure that access (not just price) becomes the barrier. Even if a product is copied, it cannot be legitimately obtained.

Second, brands invest in non-replicable experiences. Companies such as Louis Vuitton and Chanel embed value in in-store rituals, craftsmanship, and heritage narratives. The signal shifts from the physical object to the intangible experience surrounding it.

The third, and perhaps most interesting, is digital authentication. Luxury houses are increasingly adopting Digital Product Passports, embedded microchips and blockchain-backed certificates of authenticity that verify a product's origin instantly (FashionBI, 2025). LVMH, Prada and Cartier have already piloted this through the Aura Blockchain Consortium. The signal moves somewhere a £30 knockoff cannot follow.

In effect, luxury brands are engaged in an ongoing effort to re-establish a separating equilibrium in a world where visual imitation is no longer costly.

The psychology behind the price tag

Even if brands are successful in rebuilding their signals, the way buyers and owners perceive their products still plays a crucial role. But dupe culture does not just disrupt markets, it gets inside people's heads. To understand why, we need to look beyond standard economic theory and into behavioural economics.

One key concept is the endowment effect, which describes our tendency to value items we own more highly than their market price would suggest. A £3,000 bag is more than just a bag, once it is yours, the emotional pride and social status it carries become part of its value.

However, it is at this stage where dupe culture starts to undermine the psychological value of ownership too.

Imagine paying £10,000 and waiting 18 months for an Hermès Birkin, only to see near identical versions roaming the streets. That sense of pride and ‘showing off’ that comes with owning the authentic bag is eroded, as others can no longer distinguish the real from a convincing dupe. Both the bag and its owner lose a kind of celebrity status and instead you start to blend into the crowd filled with £30 lookalikes.

This reaction is best understood through loss aversion, the idea that losses feel more significant than gains (Kahneman and Tversky, 1979). In this case, buyers who paid thousands aren't just worried about the financial loss, but also the symbolic loss of exclusivity. What was once a costly way to access the perks of owning an authentic bag is now easily mirrored through dupes, which are able to send the same signals whilst saving thousands of pounds. Due to loss aversion, that loss of exclusivity feels far more costly than the satisfaction of owning an Hermès Birkin ever did.

In other words, dupes don't destroy the market for bags like the Hermès Birkin as there will always be demand for authenticity and exclusivity. Instead, they strip away the magic of owning one. Luxury brands and dupe culture are locked in an arms race, and whether the signal survives will determine whether that £10,000 bag is ever worth the price.

References:

FashionBI (2025). How Luxury Brands Are Embracing the Digital Product Passports. Available at: https://www.fashionbi.com/insights/how-luxury-brands-are-embracing-the-digital-product-passports

Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

Ken Research (2025). Global Fast Fashion Market. Available at: https://www.kenresearch.com/global-fast-fashion-market

Mosendz, P. and Rubin, C. (2023). Gen Z Is the Dupe Generation. Business Insider. Available at: https://www.businessinsider.com/gen-z-is-the-dupe-generation-2023-12

OECD (2025). Global trade in fake goods reached USD 467 billion, posing risks to consumer safety and compromising intellectual property. Available at: https://www.oecd.org/en/about/news/press-releases/2025/05/global-trade-in-fake-goods-reached-USD-467-billion-posing-risks-to-consumer-safety-and-compromising-intellectual-property.html

Spence, M. (1973). Job Market Signaling. The Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010

Trust Place (2025). Dupes on TikTok: How Brands Are Losing Control of Their Image. Available at: https://www.trust-place.com/post/dupes-on-tiktok-how-brands-are-losing-control-of-their-image

 

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

 

Awesome Advertisements

 

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.

 

Unravelling the True Costs of Fast Fashion

 

The product that costs more than you think

Have you ever found a trendy sweater on Shein or in Zara at a surprisingly low price? You're not alone. People are buying more clothes than ever and the fast fashion industry has grown considerably to meet that demand. Shein alone churns out 10,000 new designs a day (McKinsey, 2025). You might wear that sweater a few times before it goes out of fashion or ends up in a landfill. But have you ever stopped to consider the hidden costs behind that bargain, such as the ones these companies don't show you on the price tag?

While the price tag accounts for fabric, stitching and shipping, it fails to capture the environmental and social fallout of production. Economists call these negative externalities. These are hidden costs that third parties are forced to pay, even though they did not agree to.

 

The scale of externalities is enormous

Some one million deaths annually from pollution-related illnesses (World Health Organisation, 2023); 93 billion cubic metres of water consumed each year (Oxfam, 2023; An 83% decline in freshwater species since 1970, driven in part by toxic dye runoff that turns rivers orange and poisons ecosystems. And these facts will never appear on the price tag.

To put the water figure in perspective, consider AI which is currently a hot topic for its own water consumption. AI is projected to use 6.6 billion cubic metres of water per year by 2027 (Better Planet Education, 2025). The fashion industry uses more than fourteen times that amount. Suddenly, the AI industry's water consumption looks minimal by comparison.

 

Why these externalities persist

So, if the damage is this severe, why hasn't the market corrected itself? Why don't consumers simply choose more sustainable options and punish the polluters?

The answer lies in a second form of market failure: asymmetric information. These hidden costs continue because consumers cannot see them. Producers know the true environmental cost of their clothes. However, the consumer sees only a stylish design, an attractive price and perhaps a "sustainable" label. This information imbalance prevents the market from self-correcting.

 

How bad clothes drive out good ones

Economist George Akerlof's famous "Market for Lemons" theory explains exactly what happens when this kind of information asymmetry occurs.

Imagine two shirts that look the same online. One uses real organic cotton. The other is cheap polyester with misleading ‘green’ marketing. When shopping, you see the same beautiful pattern, the same colours, the same feeling of wearing it. You cannot tell them apart.

Since you can't tell which is better, you won't pay extra for something that might be a “lemon”. Rationally, you offer an average price:

Expected value = (Q x “lemons price”) + ((1-Q) x “peaches price”)

The lemon seller is delighted. They've earned a huge profit on a cheap product. But the peach seller is losing money because their genuinely sustainable materials cost more than this average price. Eventually, peach sellers will leave the market. This is adverse selection: low-quality goods systematically drive out high-quality ones and the market declines.

 

When lemons dress up as peaches

This theoretical framework perfectly predicts actual brand behaviour in the premium high-street sector, where information asymmetry is often manipulated through clever marketing.

Consider Massimo Dutti, a premium brand within the Inditex group (Zara's parent company). A recent garment visibly featured a "100% RCS Certified Recycled Polyester" label. Sounds impressive, but is it? That recycled material was used only for the internal lining. The outer layer, comprising 13% polyamide and elastane, consisted entirely of raw petrochemicals. Most of the £150 budget went to wool. So, the brand used cheap recycled polyester for the lining just to claim a "100% Recycled" label, without hurting its profits. This is a textbook lemon disguised as a peach.

 

 

What a peach looks like

If Massimo Dutti shows how a lemon disguises itself as a peach, then TOAST demonstrates what a peach strategy looks like and how brands can survive adverse selection through radical material honesty.

According to TOAST's 2024 Materials Tracker, 92% of their products fall into high-tier categories, prioritising natural, biodegradable fibres over petrochemical derivatives. This shift to natural materials fundamentally reduces supply-chain externalities, generating significantly fewer toxic outputs and emissions during the growing process compared to synthetic manufacturing.

 

Downcycling disguised as sustainability

The greenwashing problem extends beyond individual brands to the industry's favourite sustainability concept, which is converting disposed of plastic bottles into garments. This story sounds convincing, but it is misleading.

Plastic bottles are capable of bottle-to-bottle recycling. They can be recycled back into new bottles easily. But once the plastic is turned into polyester fibre, it can never be recycled again. It ends up in a landfill or is burned. Worse still, these garments continuously shed microplastics into waterways every time they are washed.

Transferring plastic waste into the clothes supply chain is not recycling, it is downcycling. It provides a compliance rationale for fast fashion's overproduction model rather than addressing resource waste at its source. Brands get "green" labels cheaply with recycled linings. This let’s profit from green marketing pushing out of truly sustainable companies.

 

What the item really costs

Let's return to that trendy sweater. Its true cost includes polluted water, lost wildlife, and people suffering health problems caused by fashion companies' pursuit of profit. These negative externalities persist because asymmetric information shields them from consumer view, creating a market for lemons where the cheapest products dominate and sustainable brands are driven out.

True sustainability in fashion requires two shifts. Firstly, transparency over greenwashing. Requiring brands to reveal their full supply-chain environmental impact, not selective certification of a garment's cheapest component.

 

Secondly, natural fibres over downcycled synthetics. Prioritising biodegradable materials and extending garment durations, rather than turning industrial waste into disposable trends.

The next time you pick up a sweater, ask yourself: What is the true cost of this? And consider if the price is truly a bargain.

References

 

Abelvik-Lawson, H. (2023). Fashion greenwash: How companies are hiding the true environmental costs of fast fashion. [online] Greenpeace. Available at: https://www.greenpeace.org.uk/news/fashion-greenwash-report-companies-hiding-true-environmental-costs-fast-fashion/. 

Better Planet Education (2025). Water Usage. [online] Betterplaneteducation.org.uk. Available at: https://betterplaneteducation.org.uk/factsheets/water-usage. 

Hale, E. (2024). Green fashion: Why dyeing clothes has a big environmental impact. BBC News. [online] 5 Nov. Available at: https://www.bbc.co.uk/news/articles/c870j92p82wo. 

Levin, J. (2001). Information and the Market for Lemons. The RAND Journal of Economics, 32(4), p.657. doi:https://doi.org/10.2307/2696386. 

Massimodutti.com. (2026). Available at: https://www.massimodutti.com/gb/halter-neck-midi-dress-with-tie-detail-l06655508?pelement=58860507&banner=true [Accessed 23 Apr. 2026].

McKinsey & Company (2025). What Is Fast Fashion? [online] McKinsey & Company. Available at: https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-fast-fashion. 

Rice, L. (2025). Buying second hand clothes: What are the benefits? [online] Oxfam GB. Available at: https://www.oxfam.org.uk/oxfam-in-action/oxfam-blog/buying-second-hand-clothes-what-are-the-benefits/. 

Ritch, E. (2025). Fast fashion: what are the true costs? [online] Economics Observatory. Available at: https://www.economicsobservatory.com/fast-fashion-what-are-the-true-costs. 

Social Conscience Report 2024 Image by Jo Metson Scott. (n.d.). Available at: https://cdn.shopify.com/s/files/1/0282/5050/5250/files/Social_Conscience_Report_2024.pdf?v=1745483028. 

World Health Organization (2023). Drinking-water. [online] World Health Organization. Available at: https://www.who.int/news-room/fact-sheets/detail/drinking-water. 

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).