Baby AGI is Here
The central claim of our work is that GPT-4 attains a form of general intelligence, indeed showing sparks of artificial general intelligence. This is demonstrated by its core mental capabilities (such as reasoning, creativity, and deduction), its range of topics on which it has gained expertise (such as literature, medicine, and coding), and the variety of tasks it is able to perform (e.g., playing games, using tools, explaining itself…). A lot remains to be done to create a system that could qualify as a complete AGI.
From a group of Microsoft researchers. They are correct.
The Great Digital Divide: Panic at Twitter Speed, Respond at AOL Speed
In The New Madness of Crowds I argued that SVB failed because “Greater transparency and lower transaction costs have intensified the madness of the masses and expanded their reach.” A piece by Miao, Zuckerman and Eisen in the WSJ now adds to to the other side of the problem. Depositors were working on twitter time, the regulatory apparatus was not.
Depositors were draining their accounts via smartphone apps and telling their startup networks to do the same. But inside Silicon Valley Bank, executives were trying to navigate the U.S. banking system’s creaky apparatus for emergency lending and to persuade its custodian bank to stay open late to handle a multibillion-dollar transfer.
As Matt Levine summarizes:
Instead of hearing a rumor at the coffee shop and running down to the bank branch to wait on line to withdraw your money, now you can hear a rumor on Twitter or the group chat and use an app to withdraw money instantly. A tech-friendly bank with a highly digitally connected set of depositors can lose 25% of its deposits in hours, which did not seem conceivable in previous eras of bank runs.
But the other part of the problem is that, while depositors can panic faster and banks can give them their money faster, the lender-of-last-resort system on which all of this relies is still stuck in a slower, more leisurely era. “When the user interface improves faster than the core system, it means customers can act faster than the bank can react,” wrote Byrne Hobart. You can panic in an instant and withdraw your money with an app, but the bank can’t get more money without a series of phone calls and test trades that can only happen during regular business hours.
It’s not obvious whether the right thing to do is slow down depositors, at least in some circumstances, or speed up regulators but the two systems can’t work well at different speeds.
In Praise of the Danish Mortgage System
When interest rates go up, the price of bonds goes down. As Tyler and I discuss in Modern Principles, the inverse relationship between interest rates and prices holds for any asset that pays out over time. In particular, as Patrick McKenzie points out, when interest rates go up, the value of a loan goes down. McKenzie suggests that you can use this fact to buy back your mortgage from a bank when interest rates rise.
For example, suppose you get a 500k 30-year fixed rate mortgage when interest rates are 3%–that loan obligates you to pay $2108 per month for 30 years. Now suppose that interest rates go to 6%, now that same stream of payments is only worth, in present value, about $358k. Thus, the bank should be willing to let you buy your mortgage for $358k–that is, after all, what the market would pay for such a stream of payments if your mortgage was securitized.
I am skeptical that I could find the right person at the right bank to actually authorize a deal like this but it turns out that the Danish mortgage system is built to allow this relatively easily. The Danish mortgage system is built on the match principle:
JYSKE Bank: The match-funding principle entails that for every loan made by the mortgage bank, a new bond is issued with matching cash-flow properties. This eliminates mismatches in cash-flows and refinancing risk for the mortgage bank, which also secures payments for the bondholder. In the Danish mortgage system the mortgage bank functions as an intermediary between the investor and borrower. Mortgage banks fund loans on a current basis, meaning that the bond must be sold before the loan can be given. This also entails that the market price of the bond determines the loan rate. The loan is therefore equal to the investment, which passes through the mortgage bank.
In essence, in the Danish system, mortgage banks are more like a futures clearinghouse or a platform (ala Airbnb) than a lender–they take on some credit risk but not interest rate risk.
Thus, if a Danish borrower takes out a 500k mortgage at 3% interest and then rates rise to 6%, the value of that mortgage falls to $358k and the borrower could go to the market, buy their own mortgage, deliver it to the bank, and, in this way, extinguish the loan. Since the value of homes also falls as interest rates rise this is also a neat bit of insurance. Remarkable!
The Danish mortgage market appears to be very successful and so may be a model for American reform:
JYSKE Bank: The Danish Mortgage Bond Market is one of the oldest and most stable in the world, tracing its roots all the way back to 1797 with no records of defaults since inception. Furthermore, the market value of the Danish Mortgage Bond Market is approx. EUR 402bn, making it the largest mortgage bond market in Europe.
Time Passages
Here’s an interesting idea it wouldn’t have occured to me to ask. What is the length of time described in the average 250 words of narration and how has this changed over time? Most famously James Joyce’s “Ulysses” is a long novel about single day with many pages describing brief experiences in minute detail. In contrast, Olaf Stapledon’s Last and First Men covers 2 billion years in fewer words than Joyce uses to cover a single day.
Using human readers grading 1000 passages, Underwood et al. (2018) finds that the average length of time described in a typical passage has declined substantially since the 1700s, from a day to about an hour so a decline by a factor of 24. Writers have become much more focused on describing individual experiences than events.
In a blog post, Underwood revisits his earlier work but this time comparing human readers with various statistical techniques and also GPT. To use GPT he prompts
Read the following passage of fiction. Then do five things. 1: Briefly summarize the passage. 2: Reason step by step to decide how much time is described in the passage. If the passage doesn’t include any explicit reference to time, you can guess how much time the events described would have taken. Even description can imply the passage of time by describing the earlier history of people or buildings. But characters’ references to the past or future in spoken dialogue should not count as time that passed in the scene. Report the time using units of years, weeks, days, hours, or minutes. Do not say zero or N/A. 3: If you described a range of possible times in step 2 take the midpoint of the range. Then multiply to convert the units into minutes. 4: Report only the number of minutes elapsed, which should match the number in step 3. Do not reply N/A. 5: Given the amount of speculation required in step 2, describe your certainty about the estimate–either high, moderate, or low. The passage follows: <omitting this to save space>
Here’s an example of the analysis:
ABRIDGED PASSAGE FROM Jane Eyre: And signs, for aught we know, may be but the sympathies of Nature with man. When I was a little girl, only six years old, I one night heard Bessie Leaven say to Martha Abbot that she had been dreaming about a little child … Of late I had often recalled this saying and this incident; for during the past week scarcely a night had gone over my couch that had not brought with it a dream of an infant, which I sometimes hushed in my arms, sometimes dandled on my knee, sometimes watched playing with daisies on a lawn, or again, dabbling its hands in running water. It was a wailing child this night, and a laughing one the next: now it nestled close to me, and now it ran from me; but whatever mood the apparition evinced, whatever aspect it wore, it failed not for seven successive nights to meet me the moment I entered the land of slumber.
ASSISTANT REPLIES.
1: The narrator recalls a saying about dreaming of children being a sign of trouble and remembers an incident from her childhood. She then describes her recent dreams of infants over the past week.
2: The passage mentions the narrator’s childhood memory, which happened when she was six years old, and the past week of dreams. The time elapsed in the passage is the week of dreams.
3: 1 week has elapsed. At seven days a week that’s 7 days. 24 hours in a day, so 168 hours. 60 minutes in an hour, so 10080 minutes.
4: 10080 minutes.
5: Moderate confidence, as the childhood memory is not counted as time passing in the scene.
Overall, Underwood finds that GPT-4 is more accurate than previous statistical techniques in that its judgments correlate with those of human readers nearly as much as human readers correlate with themselves.
Addendum: See also my paper with Tyler on how to use GPT models.
The New Madness of Crowds
USDC and USDT are two well-known stablecoins. USDC is fully backing by safe, liquid assets, which are verified monthly by a major U.S. accounting firm under the scrutiny of U.S. state regulators. USDT (Tether) is an unregulated stablecoin with questionable asset backing and opaque operations, founded by an actor from the Mighty Ducks and supported by a bank established by one of the creators of Inspector Gadget.
Yet, when Silicon Valley Bank (SVB) went into crisis, USDC broke the peg, and people fled to the nutty, opaque, unregulated Inspector Gadget backed coin.
(USDC is in blue and measured on the right axis and spiked below par, USDT is in red and measured on the left axis and spiked over par.)
Now, this is in some sense “explainable”. USDC kept some money at SVB and Tether (probably) did not. Matthew Zeitlin, channeling Matt Levine, put it this way:
One problem with being transparently and fully backed is that sometimes your investors can transparently see how much of your assets are in a bank that went bottom up, Tether does not have this problem.
SVB’s troubles stemmed from its investments in long-term government bonds, which dropped in value as interest rates rose. However, the bank’s fundamentals were not that dire. If no one had panicked, SVB could probably have paid off all its depositors in the ordinary course of business. The problem happened because some investors saw information they thought others might interpret negatively, prompting them to withdraw their funds. This led others to believe the information was indeed bad, validating the initial belief and causing a massive $42 billion withdrawal in a single day. Had transparency been less and transaction costs more, this wouldn’t have happened and, quite possibly, everything would have been fine.
Indeed, in the past, banks probably become insolvent on a mark-to-market basis but few people noticed. Today, a bank dips below the line and depositors are heading to the door.
SVB’s fundamentals may have been worse than I believe, poor management undoubtedly played a role. But fundamentals aren’t driving the boat; the boat is being driven by sunspots, memes, and vibes. Tether’s fundamentals are much worse than SVBs ever were. And USDC was even less imperiled than SVB, yet people ran to Tether. Why? Because there wasn’t a Tether sunspot. But be careful. Tether’s stability doesn’t mean that its fundamentals are strong. Not even close. Stability doesn’t mean good fundamentals and instability doesn’t mean bad fundamentals. The mad crowd is capricious. Tether’s time is coming, but no one knows what will spark the fire.
Greater transparency and lower transaction costs have intensified the madness of the masses and expanded their reach. From finance to politics and culture, no domain remains untouched by the new madness of crowds.
Hat tip: Connor Tabarrok and Max Tabarrok.
Chat Law Goes Global
PricewaterhouseCoopers (PWC), the global business services firm, has signed a deal with OpenAI for access to “Harvey”, OpenAI’s Chatbot for legal services.
Reuters: PricewaterhouseCoopers said Wednesday that it will give 4,000 of its legal professionals access to an artificial intelligence platform, becoming the latest firm to introduce generative AI technology for legal work.
PwC said it partnered with AI startup Harvey for an initial 12-month contract, which the accounting and consulting firm said will help lawyers with contract analysis, regulatory compliance work, due diligence and other legal advisory and consulting services.
PwC said it will also determine ways for tax professionals to use the technology.
IBM’s Watson was a failure so we will see but, yeah I will say it, this time feels different. For one, lawyers deal with text where GPTs excel. Second, GPTs have already revolutionized software coding and unlike Watson I am using GPTs every day for writing and researching and it works. The entire world of white collar work is going to be transformed over the next year. See also my paper with Tyler, How to Learn and Teach Economics with Large Language Models, Including GPT.
Teaching and Learning Economics with the AIs
Tyler and I have a new paper, How to Learn and Teach Economics with Large Language Models, Including GPT:
GPTs, such as ChatGPT and Bing Chat, are capable of answering economics questions, solving specific economic models, creating exams, assisting with research, generating ideas, and enhancing writing, among other tasks. This paper highlights how these innovative tools differ from prior software and necessitate novel methods of interaction. By providing examples, tips, and guidance, we aim to optimize the use of GPTs and LLMs for learning and teaching economics effectively.
Most of the paper is about how to use GPTs effectively but we also make some substantive points that many people are missing:
GPTs are not simply a chatty front end to the internet. Some GPTs like ChatGPT have no ability to search the internet. Others, like Bing Chat, can search the internet and might do so to aid in answering a question, but that is not fundamentally how they work. It is possible to ask a GPT questions that no one has ever asked before. For example, we asked how Fred Flintstone was like Hamlet, and ChatGPT responded (in part):
Fred Flintstone and Hamlet are two vastly different characters from different time periods, cultures, and mediums of storytelling. It is difficult to draw direct comparisons between the two.
However, one possible point of similarity is that both characters face existential dilemmas and struggles with their sense of purpose and identity. Hamlet is plagued by doubts about his ability to avenge his father’s murder, and his own worthiness as a human being. Similarly, Fred Flintstone often grapples with his place in society and his ability to provide for his family and live up to his own expectations.
Not a bad answer for a silly question and one that (as far as we can tell) cannot be found on the internet.
GPTs have “read” or “absorbed” a great amount of text but that text isn’t stored in a database; instead the text was used to weight the billions of parameters in the neural net. It is thus possible to run a GPT on a powerful home computer. It would be very slow, since computing each word requires billions of calculations, but unlike storing the internet on your home computer, it is feasible to run a GPT on a home computer or even (fairly soon) on a mobile device.
GPTs work by predicting the next word in a sequence. If you hear the phrase “the Star-Spangled”, for example, you and a GPT might predict that the word “Banner” is likely to come next. This is what GPTs are doing but it would be a mistake to conclude that GPTs are simply “autocompletes” or even autocompletes on steroids.
Autocompletes are primarily statistical guesses based on previously asked questions. GPTs in contrast have some understanding (recall the as if modifier) of the meaning of words. Thus GPTs understand that Red, Green, and Blue are related concepts that King, Queen, Man and Woman are related in a specific way such that a woman cannot be a King. It also understands that fast and slow are related concepts, such that a car cannot be going fast and slow at the same time but can be fast and red and so forth. Thus GPTs are able to “autocomplete” sentences which have never been written before, as we described earlier.2 More generally, it seems likely that GPTs are building internal models to help them predict the next word in a sentence (e.g. Li et al. 2023).
The paper is a work in progress so comments are welcome.
UK to Adopt Pharmaceutical Reciprocity!
More than twenty years ago I wrote:
If the United States and, say, Great Britain had drug-approval reciprocity, then drugs approved in Britain would gain immediate approval in the United States, and drugs approved in the United States would gain immediate approval in Great Britain. Some countries such as Australia and New Zealand already take into account U.S. approvals when making their own approval decisions. The U.S. government should establish reciprocity with countries that have a proven record of approving safe drugs—including most west European countries, Canada, Japan, and Australia. Such an arrangement would reduce delay and eliminate duplication and wasted resources. By relieving itself of having to review drugs already approved in partner countries, the FDA could review and investigate NDAs more quickly and thoroughly.
Well, it’s happening! After Brexit, there were concerns that drugs would take longer to get approved in the UK because the EU was a much larger market. To address this, the UK introduced the “reliance procedure” which recognized the EU as a stringent regulator and guaranteed approval in the UK within 67 days for any drug approved in the EU. The Reliance Procedure essentially kept the UK in the pre-Brexit situation, and was supposed to be temporary. However, recognizing the logic of recognizing the EU, the UK is now saying that it will recognize other countries.
Our aim is to extend the countries whose assessments we will take account of, increasing routes to market in the UK. We will communicate who these additional regulators are and publish detailed guidance about this new framework in due course, including any transition arrangements for applications received under existing frameworks.
The UK is already participating in a mutual recognition agreement with the FDA over some cancer drugs. Therefore, it seems likely that the FDA will be among the regulatory authorities that the UK recognizes. If the UK does recognize the FDA, then we only need the FDA to recognize the UK for my scenario from more than 20 years ago to be fulfilled.
It’s thus time to revisit the Lee-Cruz bill of 2015, which proposed the Result Act (I was an influence).
Reciprocity Ensures Streamlined Use of Lifesaving Treatments Act (S. 2388), or the RESULT Act,” which would amend the Food, Drug and Cosmetic Act to allow for reciprocal approval of drugs.
Addendum: Many previous posts on FDA reciprocity.
The Impact of AI on Productivity
We don’t yet know the impact that AI will have on productivity but some evidence is starting to come in. Peng et al. (2023) hired programmers on Upwork to write an HTTP server in Javascript; half of the programmers got access to CoPilot (this was before CoPilot was widely available) half did not.
Conditioning on completing the task, the average completion time from the treated group is 71.17 minutes and 160.89 minutes for the control group. This represents a 55.8% reduction in completion time. The p-value for the t-test is 0.0017, and a 95% confidence interval for the improvement is between [21%, 89%]. There are four outliers with time to completion above 300 min. All outliers are in the control group, however our results remain robust if these outliers are dropped. This result suggests that Copilot increases average productivity significantly in our experiment population. We also find that the treated group’s success rate is 7 percentage points higher than the control group, but the estimate is not statistically significant, with a 95% confidence interval of [-0.11, 0.25].
The authors extrapolate wildly:
In 2021, over 4.6 million people in the United States worked in computer and mathematical occupations,1 a Bureau of Labor Statistics category that includes computer programmers, data scientists, and statisticians. These workers earned $464.8 billion or roughly 2% of US GDP. If the results of this study were to be extrapolated to the population level, a 55.8% increase in productivity would imply a significant amount of cost savings in the economy and have a notable impact on GDP growth.
Still, worth thinking about.
Tabarrok on Stranded Technologies Podcast
I talk with entreprenreur Niklas Anzinger on the Stranded Technlogies Podcast. Niklas summarizes some of the discussion:
- This episode is an intellectual journey that discovers insights that can be used by entrepreneurs and city developers. We talk about the Baumol effect that Alex uses to explain the now infamous price chart.
- Alex’s recommendation to new city or governance startups like Prospera, Ciudad Morazan or the Catawba DEZ is to think of city development as a “dance between centralization and decentralization”.
- Economists have developed concepts that are waiting to be commercialized, e.g. prediction markets. In this episode, we talk about dominant assurance contracts and how they could be used in new city developments and fundraising.
The Collectivization of Innovation
In Collective Action Kills Innovation I wrote:
We have innovations like Uber and Airbnb and many others only because entrepreneurs didn’t have to ask for permission. Had we put these ideas to the vote they would have been defeated. Allow almost anyone with a car to drive customers around town? Stranger danger! Let any house be turned into a hotel? Not in my neighborhood! Once the innovations were brought into existence, the masses saw the benefits but they would not have seen those benefits if the idea had been put to a vote. Demonstration is more powerful than imagination.
More and more, however, the sphere of individual action shrinks and that of collective action grows.
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A small but sadly amusing case in point is building in San Francisco. A plan was proposed to build the apartments at right. Love it, hate it. I don’t care. But it shouldn’t be up for collective action. Instead, what we have, however, is a planning process in which the President of the SF Planning Commission, Myrna Melgar, can opposed the plan because:
….I have to just state that I hate the design. Nothing against the architect, I think that the big windows, to me, are a statement of class and privilege. …having that building, with all of those windows it’s such a statement of, to me, class privilege because you know, poor people don’t do that, they don’t you know, like, win- you know, have everything out on the street. It just, so it just, it really rankles me the wrong way. So I just have to say it is a design issue. To me, design guidelines for what’s going to come are going be really important because I do think it does say to the community – is this still our community, what are we building for?
The building was proposed as a replacement for an auto shop (!) in 2014! Building didn’t start until 2022 and as of January 2023 it still wasn’t complete, although it looks like they got most of the windows approved.
An amusing video on some of the hypocrisy involved.
Hat tip: M. Nolan Gray and twitter thread.
The Era of Planetary Defense Has Begun
In Modern Principles of Economics, Tyler and I use asteroid defense as an example of a public good (see video below). As of the 5th edition, this public good wasn’t being provided by either markets or governments. But thanks to NASA, the era of planetary defense has begun. In September of 2022 NASA smashed a spacecraft into an asteroid. A new set of five papers in Nature has now demonstrated that not only did NASA hit its target, the mission was a success in diverting the asteroid:
DART, which was the size of a golf cart, collided with a Great Pyramid-sized asteroid called Dimorphos. The impact caused the asteroid’s orbit around another space rock to shrink — Dimorphos now completes an orbit 33 minutes faster than before the impact, researchers report1 today in Nature.
…As DART hurtled towards Dimorphos at more than 6 kilometres per second, the first part that hit was one of its solar panels, which smashed into a 6.5-metre-wide boulder. Microseconds later, the main body of the spacecraft collided with the rocky surface next to the boulder — and the US$330-million DART shattered to bits….the spacecraft hit a spot around 25 metres from the asteroid’s centre, maximizing the force of its impact….large amounts of the asteroid’s rubble flew outwards from the impact. The recoil from this force pushed the asteroid further off its previous trajectory. Researchers estimate that this spray of rubble meant Dimorphos’ added momentum was almost four times that imparted by DART.
…Although NASA has demonstrated this technique on only one asteroid, the results could be broadly applicable to future hazards…if a dangerous asteroid were ever detected heading for Earth, a mission to smash into it would probably be able to divert it away from the planet.
Statement of Commitment to Academic Freedom and to Intellectual Merit
Academic freedom and intellectual merit are under attack in the United States, from both the left and the right. The norms of the university and intellectual life are fragile and need protecting because such norms are always in tension with political and economic power.
The undersigned members of the GMU Department of Economics express their commitment to academic freedom and to intellectual merit.
Addressed to the George Mason University (GMU) community and the public at large
~~~
American universities have professed allegiance to two ideals. First, the ideal of academic freedom – the right of students and faculty to express any idea in speech or writing, without fear of university punishment, and secure in the knowledge that the university will protect dissenters from threats and violence on campus.
Second, the ideal of intellectual merit – the right and duty of academic departments to hire and promote the most brilliant, creative, and productive faculty in their fields, and admit the most intellectually promising students, without pressures from the administration.
These ideals are the cornerstones of liberal education. They protect faculty and students who hold views unpopular on university campuses. Academic freedom protects existing students and faculty who dissent from current dominant academic opinion and ideology. No matter how unpopular their views, they know the university will protect them. As stated in the University of Chicago Statement on freedom of expression and as quoted in GMU’s “Free Speech at Mason” Statement:
[We must hold a fundamental commitment to] the principle that debate or deliberation may not be suppressed because the ideas put forth are thought by some or even by most members of the University community to be offensive, unwise, immoral, or wrong-headed.
Intellectual merit protects prospective students and faculty who speak and write against current dominant viewpoints. No matter how unpopular their views, they know that university administration will not obstruct or prejudice their admission, hiring, or promotion.
Recently, both of these ideals have come under attack. Pressure for conformity has intensified and universities have increasingly interfered with departments’ personnel decisions. For example, at some universities, one of the more egregious new practices is the requiring of written “diversity” statements by prospective students, staff, or faculty, then used to discriminate among candidates, often by quarters of the university with interests other than those of the department or unit. Such methods recall arrogations of the past, such as The Levering Act of 1950, used against radicals.
We strongly believe the attacks on academic freedom and intellectual merit are deeply mistaken. The classic rationales in favor of these ideals are sound. To protect them, viewpoint diversity must be celebrated and academic departments must maintain their ability to select, hire, and promote students and personnel based on intellectual merit. We insist that the degree of institutional autonomy that the GMU Department of Economics has traditionally enjoyed is vital to the health of viewpoint diversity not only within the university but within the academy writ large.
It is vital that every department in a university enjoys independence, so it can dare to be different and keep viewpoint diversity alive. George Mason University has excelled in supporting viewpoint diversity with a variety of diverse departments, centers and organizations. Viewpoint diversity at George Mason has benefited the university, the United States, and the wider intellectual world.
Indeed, some of the Department’s chief contributions have taught that all forms of authority can exert power to excess, and that guarding against such excess calls for the very ideals affirmed here, respect for dissent and intellectual merit.
We, the undersigned members of the GMU Department of Economics, look forward to continuing our independence to do good economics according to our judgment, guided by the ideals of academic freedom and intellectual merit.
Signed by the following GMU Department of Economics faculty (full-time & emeritus):
1. Jonathan P. Beauchamp
2. James T. Bennett
3. Donald J. Boudreaux
4. Bryan D. Caplan
5. Vincent J. Geloso
6. Timothy Groseclose
7. Robin D. Hanson
8. Garett Jones
9. Daniel B. Klein
10. Mark Koyama
11. David M. Levy
12. Cesar A. Martinelli
13. John V.C. Nye
14. Thomas C. Rustici
15. Vernon L. Smith
16. Alex Tabarrok
17. Karen I. Vaughn
18. Richard E. Wagner
19. Lawrence H. White
AGI is Coming
ARSTechnica: On Monday, researchers from Microsoft introduced Kosmos-1, a multimodal model that can reportedly analyze images for content, solve visual puzzles, perform visual text recognition, pass visual IQ tests, and understand natural language instructions. The researchers believe multimodal AI—which integrates different modes of input such as text, audio, images, and video—is a key step to building artificial general intelligence (AGI) that can perform general tasks at the level of a human.
In 2020 Metaculus forecasters were predicting weak general AI by around 2053. Now they are predicting weak general AI by 2028 and strong general AI which includes:
- Has general robotic capabilities, of the type able to autonomously, when equipped with appropriate actuators and when given human-readable instructions, satisfactorily assemble a (or the equivalent of a) circa-2021 Ferrari 312 T4 1:8 scale automobile model. A single demonstration of this ability, or a sufficiently similar demonstration, will be considered sufficient.
I never expected to witness the birth of aliens. It is a very strange time to be alive. If you think the world isn’t changing in a very uncertain and discontinuous way you just aren’t paying attention.
Britain’s Long Timeline of Housing Decline
In 1947 the British Town and Country Planning Act made planning permission a requirement for land development; ownership alone no longer conferred the right to develop the land. A decline in construction was predictable but housing is a durable good. Even today more than a third of the British housing stock dates to before 1947. So it has taken time but, according to a new study, the act has had a slow but long-run depressing effect on construction with the result that today the average house in England costs more than ten times the average salary.
Britain has a severe housing crisis, especially in the most prosperous places in the Greater South East. Across England, the average house costs more than ten times the average salary, vacancy rates are below 1 per cent, and space per person for private renters has dropped substantially in recent decades.
This report explores the root cause of the UK’s housing problem, how policy in this area has developed over the last 75 years, and what action policymakers need to take to deliver enough homes in the UK.
…This report uses this new data and other sources to compare British housebuilding and outcomes to that in Ireland, France, Belgium, the Netherlands, (West) Germany, Austria, Switzerland, Denmark, Sweden, Norway, and Finland from 1955 to 2015. It finds that Britain’s housing shortage began at the beginning of the post-war period…
Housebuilding rates in England and Wales have dropped by more than a third after the introduction of the Town and Country Planning Act 1947, from 2 per cent growth per year between 1856 and 1939 to 1.2 per cent between 1947 and 2019.
This has been a key factor behind the UK’s long-standing housing crisis, which has led to inflated property prices and soaring rents in recent decades.