Regulation
Random Walk: Memoir of an Itinerant – review
In Random Walk: Memoir of an Itinerant, economist Richard Dale reflects on his life and career, tracking his intellectual shift from a believer in free-market economics to a proponent of more stringent regulation. An accessible and engaging read, Dale’s autobiography shares significant insights for those interested in the complexities of financial markets, writes Nicholas Barr.
Random Walk: Memoir of an Itinerant. Richard Dale. Tricorn Books. 2023.
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Richard Dale’s autobiography raises an interesting conundrum. He describes jobs in financial markets and academia (many, often multiple), homes (I lost count), properties contemplated (uncountable), academic disciplines explored (economics, law, finance), books authored (nine, including law and finance, and in retirement history and fiction).
The conundrum is whether the story is the “random walk” of the book’s title or something more deliberate. An early chapter describes Dale’s undergraduate days at LSE. Then, as now, LSE was about analytical training, aiming to give students broad, flexible skills applicable to problem solving in whichever areas they ended up. At the time, unlike now, there was relatively little teaching support – in some courses students were given a book list, ie, a list of books, in which they were encouraged to forage to complement lectures.
Dale used the resulting analytical self-sufficiency [from his undergraduate degree at LSE] to qualify as a barrister via self-study, posing the question of whether his account is less random than an early example of a portfolio career.
Dale used the resulting analytical self-sufficiency to qualify as a barrister via self-study, posing the question of whether his account is less random than an early example of a portfolio career. His early career was in financial markets, including working for the Moscow Narodny Bank, Cripps Warburg, and Rothschild’s, a combination of hard work and high living. Partly for health reasons, the second part was primarily academic, initially at the University of Kent, later at the University of Southampton. And threading throughout were entrepreneurial activities such as establishing the International Currency Review, setting up a credit rating service sponsored by the Financial Times, and suggesting and then editing the FT Financial Regulation Report – a life of career success and latterly of financial comfort.
That said, Dale is open about the role of luck (on which see Robert Frank’s excellent book). He describes a childhood heavily financially constrained, but as the book makes clear, the family had solid social capital, so his early life was eased by advice from family contacts and financial help from relatives for school fees (like his father, he went to Marlborough College). Luck also included legendary teachers at LSE, notably the economist Richard Lipsey and political philosopher Michael Oakeshott. As it turned out, a further piece of luck was the departure of his sponsor at Kent University just after Dale arrived, leaving him with an unstructured two years of funding, which he used to write his first well-received book (a reminder of the famous golfer Gary Player’s dictum that “he harder you work, the luckier you get”). Also lucky was the new appointment at Kent University of the eminent lawyer, Rosalyn Higgins, who supported Dale’s attempt to start an academic career, and sponsored him for a two-year Rockefeller Foundation Fellowship. A third view of Dale’s journey, therefore, is as a rolling stone (Mick Jagger was one of his fellow students).
Given my own work on the role of markets – when they work well, and when they don’t – I was particularly interested in Dale’s intellectual journey. In his words,
“Since LSE days I had always had a great admiration for Milton Friedman and the free-market economics of the Chicago School. However, over the years I became increasingly sceptical about the periodic boom-bust cycles of financial markets and the propensity of both equity and credit markets to succumb to bouts of euphoria and panic… I experienced for myself as a fund manager the mad boom-bust years of 1973/76 and I observed the absurd stock market valuations of dot.com and technology companies in the late 1990s which was followed by a spectacular collapse” (200).
That change of view, based on practical experience, was supported by academic research on market failures – imperfect information, behaviour different from narrow economic rationality, search frictions (eg, the fact that it takes time to find a new job) and incomplete contracts – recognised by multiple Nobel prizes this century. Thus, over time Dale moved from a view based on what economists call a rational expectations model, to the more recent emphasis on behavioural finance.
A further reinforcement of Dale’s views is the distinction between risk (where the likelihood of different outcomes is well known, eg, the probability of breaking a leg during a skiing holiday) and uncertainty (where there is a clear risk but little knowledge of its likelihood, eg, future rates of inflation) or whether, when and how artificial intelligence will be beneficial or harmful.
A further reinforcement of Dale’s views is the distinction between risk (where the likelihood of different outcomes is well known, eg, the probability of breaking a leg during a skiing holiday) and uncertainty (where there is a clear risk but little knowledge of its likelihood, eg, future rates of inflation) or whether, when and how artificial intelligence will be beneficial or harmful. It is a fundamental error to conflate risk and uncertainty when analysing financial markets.
Dale became convinced of the need for more stringent regulation, and was prescient in predicting the 2008 financial and economic crisis.
Thus, Dale became convinced of the need for more stringent regulation, and was prescient in predicting the 2008 financial and economic crisis. In doing so, as one of very few experts to sound a warning, he faced considerable – at times personal – pushback, both from finance academics and from practitioners.
During his academic career, Dale straddled the worlds of scholarship and practice. He established a successful MSc in International Banking and Financial Studies at Southampton. In parallel was policy work, including talks at the World Bank and International Monetary Fund, testifying before US Congressional Committees, membership of the European Shadow Financial Regulatory Committee, specialist adviser to the Treasury and Civil Service Committee, and writing books and policy papers (on the last – to my great envy – he developed an ability to write fast with no need for drafts, a skill he shared with his LSE mentor Alan Day who was his tutor and subsequently supported some of his policy activities).
Which brings the story to the third part of Dale’s career, so-called retirement, giving him freedom to pursue a long-standing interest in history, writing a series of books, including on Walter Raleigh, those writings being sufficiently acclaimed to bring him election to a Fellowship of the Royal Historical Society.
Running through the career narrative is Dale’s personal life: a pre-university spell on a kibbutz, influenced by his father, a man with strong socialist views (which made for interesting subsequent conversations with a son working in finance); a long first marriage with children, including “too many jobs [and] too many house moves” and a long, happy second marriage in which he had, “only one employer … and owned only one house (plus a share in another)” (246). He had a very active social life, including meeting friends abroad, sometimes for shared holidays, often with lifelong friends from his student days and early career.
So, a career straddling economics, law and finance, retirement as historian with considerable holiday travel, and a full personal and social life – what, if anything, might be missing?
So, a career straddling economics, law and finance, retirement as historian with considerable holiday travel, and a full personal and social life – what, if anything, might be missing? Some readers might wish to see more context around external events. Dale recounts childhood memories of the 1952 Great London Fog and 1953 coronation of Queen Elizabeth II, but makes little mention of other events relevant to the economy and financial markets such as the collapse of the communist economic system in the USSR and Central and Eastern Europe and the highly consequential Deng Xiaoping economic reforms in the 1970s that underpinned the economic rise of China.
Also relevant are the dramatic changes in technology. Around the time Dale was an undergraduate, LSE installed a new machine; it was called a photocopier. Staff were sent on training courses on how to use and maintain it; students were not allowed anywhere near it. The timeline from there to Facetime (or listening to Test Match Special on a transatlantic flight) is also directly relevant to the operation of financial markets, for example the possibility of high-speed trading.
An engaging and non-technical read, accessible to anyone with an interest in financial markets.
All in all, this is an engaging and non-technical read, accessible to anyone with an interest in financial markets. For me, the core message of the book, which comes through loud and clear, is that financial market regulation matters big time. With complex products, sellers are often better-informed than buyers, creating space for misselling (think 19th century snake-oil salesmen). Precisely for that reason, products like pharmaceutical drugs are heavily regulated. With analogous complexities, the case for regulating financial products is equally compelling.
This post gives the views of the author, and not the position of the LSE Review of Books blog, or of the London School of Economics and Political Science. The LSE RB blog may receive a small commission if you choose to make a purchase through the above Amazon affiliate link. This is entirely independent of the coverage of the book on LSE Review of Books.
Image Credit: dgcampillo on Shutterstock.
More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech – review
In More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech, Meredith Broussard scrutinises bias encoded into a range of technologies and argues that their eradication should be prioritised as governments develop AI regulation policy. Broussard’s rigorous analysis spotlights the far-reaching impacts of invisible biases on citizens globally and offers practical policy measures to tackle the problem, writes Fabian Lütz.
More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech. Meredith Broussard. MIT Press. 2023.
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As the world witnesses advancements in the use of Artificial Intelligence (AI) and new technologies, governments around the world such as the UK and US the EU and international organisations are slowly starting to propose concrete measures, regulation and AI bodies to mitigate any potential negative effects of AI on humans. Against this background, More than a Glitch offers a timely and relevant contribution to the current AI regulatory debate. It provides a balanced look at biases and discriminatory outcomes of technologies, focusing on race, gender and ability bias, topics that tend to receive less attention in public policy discussions. The author’s academic and computer sciences background as well as her previous book Artificial Unintelligence – How Computers Misunderstand the World make her an ideal author to delve into this important societal topic. The book addresses algorithmic biases and algorithmic discrimination which not only receives increasing attention in academic circles but is of practical relevance due to its potential impacts on citizens and considering the choice of regulation in the coming months and years.
[More than a Glitch] provides a balanced look at biases and discriminatory outcomes of technologies, focusing on race, gender and ability bias, topics that tend to receive less attention in public policy discussions
The book’s cornerstone is that technology is not neutral, and therefore racism, sexism and ableism are not mere glitches, but are coded into AI systems.
Broussard argues that “social fairness and mathematical fairness are different. Computers can only calculate mathematical fairness” (2). This paves the way to understand that biases and discriminatory potential are encoded in algorithmic systems, notably by those who have the power to define the models, write the underlying code and decide which datasets to use. She argues that rather than just making technology companies more inclusive, the exclusion of some demographics in the conceptualisation and design of frameworks needs to stop. The main themes of the book, which spans eleven short chapters, are machine bias, facial recognition, fairness and justice systems, student grading by algorithms, ability bias, gender, racism, medical algorithms, the creation of public interest technology and options to “reboot” the system and society.
Biases and discriminatory potential are encoded in algorithmic systems, notably by those who have the power to define the models, write the underlying code and decide which datasets to use.
Two chapters stand out in Broussard’s attempt to make sense of the problems at hand: Chapter Two, “Understanding Machine Bias” and more specifically Chapter Seven “Gender Rights and Databases”. Both illustrate the author’s compelling storytelling skills and her ability to explain complex problems and decipher the key issues surrounding biases and discrimination.
Chapter Two describes one of the major applications of AI: machine learning which Broussard defines as to take
“..a bunch of historical data and instruct a computer to make a model. The model is a mathematical construct that allows us to predict patterns in the data based on what already exists. Because the model describes the mathematical patterns in the data, patterns that humans can’t easily see, you can use that model to predict or recommend something similar” (12).
The author distinguishes between different forms of training a model and discusses the so called “black box problem” – the fact that AI systems are very often opaque – and explainability of machine decisions. Starting from discriminatory treatment of bank loan applications, for example credit score assessment on the basis of length of employment, income or debt, the author explains with illustrative graphs how algorithms find correlations in datasets which could lead to certain discriminatory outcomes. She explains that contrary to humans, machines have the capacity to analyse huge amounts of datasets with data points which enable for example banks to make predictions on the probability of loan repayment. The mathematics underlying such predictions are based on what similar groups of people with similar variables have done in the past. The complex process often hides underlying biases and potential for discriminations. As Broussard points out,
“Black applicants are turned away more frequently than white applicants [and] are offered mortgages at higher rates than white counterparts with the same data […]” (25).
The book also demonstrates convincingly that the owners or designers of the model wield a powerful tool to shape decisions for society. Broussard sums up the chapter and provides crucial advice for AI developers when she states, advice for AI developers when she states,
“If training data is produced out of a system of inequality, don’t use it to build models that make important social decisions unless you ensure the model doesn’t perpetuate inequality” (28).
Chapter Seven looks at how databases impact gender rights, starting with the example of gender transition which is registered in Official Registers. This example illustrates the limitations of algorithmic systems as compared to humans, not only in light of the traditional binary system for assigning gender as male and female, but more generally the binary system that lies at the heart of computing. Both in the gender binary and computer binary framework, choices need to be made between one or the other leaving no flexibility. Broussard describes the binary system as follows:
“Computers are powered by electricity, and the way they work is that there is a transistor, a kind of gate, through which electricity flows. If the gate is closed, electricity flows through, and that is represented by a 1. If the gate is open, there is no electricity, and that is represented by a 0” (107).
When programmers design an algorithm, they “superimpose human social values onto a mathematical system.” Broussard urges us to ask ourselves, “Whose values are encoded in the system?” (109).
The resulting choices that need to be made within AI systems or forms used in administration often do not adequately represent reality. For people who do not feel represented by the options of male and female, such as gender non-conforming people, they are asked to make the choice in which category they fall even though this would not reflect their gender identity. Here again, Broussard reminds us of the importance of design choices and assumptions of coders which impact people’s everyday life. When programmers design an algorithm, they “superimpose human social values onto a mathematical system.” Broussard urges us to ask ourselves, “Whose values are encoded in the system?” (109). The chapter concludes with the challenge of making “technological systems more inclusive” (116) and argues that computers constitute not only mathematical but sociotechnical systems that need to be updated regularly in order to reflect societal change.
Computers constitute not only mathematical but sociotechnical systems that need to be updated regularly in order to reflect societal change.
The book successfully describes the invisible dangers and impacts of these rapidly advancing technologies in terms of race, gender and ability bias, making these ideas accessible through concrete examples. Ability bias is discussed in Chapter Seven, “Ability and Technology”, where she gives several examples, how technology companies try to provide technology to serve the disabled community in their daily jobs or lives. She gives the example of Apple shops where either sign language interpreters are available or where Apple equips employees with an iPad to communicate with customers. For consumers, she also highlights Voiceover screen reader software, auto-captioning and transcripts of audio or read-aloud functions of newspaper sites. Broussard points both to the advantages and the limitations of those technological solutions.
She also introduces the idea of tackling biases and discrimination with the help of audit systems
Readers are invited to reflect on concrete policy proposals and suggestions, on the basis of some ideas sketched out in last chapter, “Potential Reboot” where she shows her enthusiasm for the EU’s proposed AI Act and the US Algorithmic Accountability Act. She also introduces the idea of tackling biases and discrimination with the help of audit systems and presents a project for one such system based on the regulatory sandbox idea, which is a “safe space for testing algorithms or policies before unleashing them on the world” (175). The reader might wish that Broussard‘s knowledge of technology and awareness of discrimination issues could have informed the ongoing policy debate even further.
In sum, the book will be of interest and use to a wide range of readers, from students, specialised academics, policy makers and AI experts to those new to the field who want to learn more about the impacts of AI on society.
This post gives the views of the author, and not the position of the LSE Review of Books blog, or of the London School of Economics and Political Science. The LSE RB blog may receive a small commission if you choose to make a purchase through the above Amazon affiliate link. This is entirely independent of the coverage of the book on LSE Review of Books.
Image Credit: Vintage Tone on Shutterstock.
Even crypto mixing deserves a threshold
Many of you may not realize this, but in most parts of the developed world, banks automatically record and report our transactions to law enforcement. The logic behind this is that by giving up our personal data, we get more security, albeit at the cost of 1) losing our privacy, and 2) adding an extra layer of costly red tape into financial life.
It's a pragmatic compromise, and one hopes that the benefits outweigh the costs. The way that we've been balancing this compromise up till now is by using thresholds, so as to reduce the cost side of the equation. Below a certain dollar threshold (i.e. $10,000 for cash), transactions don't get reported. The folks making these sub-threshold transactions thus enjoy the dignity of not having their privacy invaded, nor
do they add to the financial sector's administrative burden. However, they also don't contribute to the effort to improve security and safety.
Anyways, last month, the U.S. government announced a new anti-money laundering reporting requirement, one for crypto mixing. In doing so it broke with a long tradition of not including a threshold. That got my hackles up. Thresholds have always been key to balancing the costs and benefits of automatic reporting requirements.
In short, the government thinks that mixing of cryptocurrency is of primary money laundering concern.
Any U.S. financial institution that knows, suspects, or has reason to
suspect that a customer's incoming or outgoing crypto transaction, in any amount,
involves the use of a mixer will have to flag it and send a report to
the government. That report must include information like the customer's
name, date of birth, address, and tax ID.
I submitted the following comment on the proposed rule for crypto mixing. If you agree, feel free to copy it and add your own comment to the growing pile.
Dear sir/madam,
Re: Proposal of Special Measure Regarding
Convertible Virtual Currency Mixing, as a Class of Transactions of
Primary Money Laundering Concern
Historically, all U.S.
anti-money laundering recordkeeping and reporting requirements have been
accompanied by a monetary threshold. The current proposal to impose
recordkeeping and reporting requirements for crypto mixing is the sole
exception. This should be fixed.
When Treasury Secretary Henry
Morgenthau published an executive order to implement the U.S.'s first
large cash transaction reporting regime all the way back in 1945, for
instance, he established a $1,000 reporting requirement for transactions
in which only bills in denominations over $50 were present. He also set
a $10,000 reporting threshold when small and large denomination bills
were involved in the transaction.
Morgenthau's thresholds
remained in place through the 1950s and 1960s. They were eventually
ratified in 1972 with the implementation of a $10,000 cash reporting
threshold for the purposes of implementing the Bank Secrecy Act.
When
suspicious activity reports were introduced in 1996, the government's
initial proposal did not include a reporting threshold. But after
receiving public comments, the government admitted that its first
version of the rule would impose a "burden of reporting." In its final
version it introduced a $5,000 threshold for filing a suspicious
activity report, which remains to this day.
In addition to
reporting thresholds for cash transactions and suspicious activity, the
government has set a number of thresholds for recordkeeping
requirements. For instance, financial institutions are required to keep a
log of all cash purchases of monetary instruments between $3,000 and
$10,000.
The government's long history of twinning reporting and
recordkeeping requirements with thresholds is a pragmatic compromise. It
balances law enforcement's need for information against the
administrative burden imposed on the private sector as well the invasion
of privacy imposed on civil society. It only seems fair and prudent to
extend this pragmatic compromise to cryptocurrency mixing recordkeeping
and reporting requirements, especially in light of the fact that, as
FinCEN admits, there are "legitimate purposes" for mixing.
I would suggest a threshold of at least $10,000, which is in-line with the cash transaction reporting threshold.
Sincerely,
JP Koning
Moneyness Blog
Robert Solow on 'Why Economies Grow'
As a follow-up and companion piece to my previous post, I decided to publish a transcription of a lecture on economic growth by Robert Solow that I transcribed originally as an aid for friends and colleagues who were studying economics. Although the lecture was given by Prof. Solow a few years ago during the height of the financial crisis, it contains loads of timeless insights, some of which is useful to be reminded of in the current situation, as discussions about the output gap resume in the next few years (see chart).
However, it's extremely important to keep in mind that in our current predicament as a result of covid potential GDP will also likely take a huge hit, as businesses and employees require some catching up in terms of business practices (misaligned with changing consumer preferences) and job training (due to skills entropy from employees being on furlough), to name only a few aspects that are likely to be impacted. In many ways, the post-covid period will bring us back to the type of economic analysis that used to occur a long time ago when natural catastrophes had significant and frequent impacts on economies' productive capacities.
The video of the lecture is included down below, though the sound quality is very bad, which is why I recommend reading the transcription instead (and you'll get through the transcript much faster by reading it).
Key insights are highlighted in bold font. Enjoy!
The business of this course is the long run. What are the
sources of economic growth in the national economy or in the larger economy?
Where does growth come from? And the policy implication – well, not implication,
but policy question – is ‘How do you get an economy to grow rapidly and to have
that growth widely shared in the nation?’
But there is a problem – it is a problem that appeared in
the slides that Prof Newstone showed. It is a problem about getting there from
here. So I’m going to start by talking a little bit about right now – this is
not going to be the usual stuff about the financial crisis and all that – I
have something else in mind.There is something very odd about our economic situation in
the US today. I read just recently an estimate from the Federal Reserve that
about $7 trillion worth of wealth has been destroyed in the last year or year
in a half (in 2008-2009). The country, so to speak, is $7 trillion poorer than it
was.When I wasn’t having a conversation with Cathy in the car, I
was trying to divide 7 trillion by 300 million--the population of the US--in my
head. It comes to about $23,000 for every man, woman and child in the country. Some,
of course, have lost more, some have lost less.What I want to point out is how strange that is: $7 trillion
of wealth has gone down the drain but the productive capacity of the US economy
– the capacity of our system to produce goods and service for its people –
hasn’t diminished at all. In fact, it is undoubtedly higher than it was a year
ago or 18 months ago: the labour force is a couple percent larger, the skills
and education and training of the population is certainly not deteriorating and
have probably gained. The net investment in capital has been positive – it’s
been declining – but has been positive.So we have a bigger stock of productive capital in the
economy now than we did a year ago or 18 months ago. So the productive capacity
of this economy is bigger than it was, despite of this $7 trillion of
disappearance of wealth. If you are thinking of buying the US economy as a gift
for your boyfriend or girlfriend, it would be worth just as much as it was
worth – you know, like a used car – it would be worth just about as much as it
was worth a year ago.So in that sense we haven’t lost anything at all. But, of
course, the point is we are in a recession. It is one year old according to
pundits. And according to other pundits, or the same pundits, it’ll continue
for at least until the second half of this year and maybe beyond. And the point
is we are not using the productive capacity that we have.You saw the unemployment numbers that Professor Newstone
showed you. It is a lot harder to measure excess capacity in industry than it
is to measure unemployment, but there are such figures, and they show an
increase in unused capacity. So we have this machine for producing the goods
and services for the population and we are not making full use of it. And that
under-use of economic capacity, of productive capacity will go on for a long
time. Even if the economy turns up in the second half of this year we will
undoubtedly finish 2010 still with some slack in the economy because the slack
disappears only gradually.
So if you are interested – now, this is the point, this is
why I started this way – if we are thinking about the long run growth of the
economy (which means the long run growth of its capacity to produce), it’s not
a separate but it’s an analytically slightly different problem to make sure
that that capacity is used.As long as we are not using all of the capacity that we
have, the economy and the decision-makers in the economy are not likely to be
motivated to do the things that increase potential output, that increase the
productive capacity very rapidly.So the short-run order of business – policy business – for
us and every other rich country in Europe or Asia right now is to close that
gap or narrow that gap between productive capacity and actual output, which
means fundamentally trying to increase the demand for goods and services. And
to do that in a way that at least doesn’t create obstacles to the long-run
growth of the economy once the gap is closed, and maybe does some things that
will help it.So, imagine it is now January 2011 and the American economy
and the economies of the other rich countries – developed countries of the
world – are prospering reasonably well, are using their capacity, have closed
that gap. Then the question is: What makes them grow? What economic activities
that take place have the effect of increasing the capacity of the economy to
produce useful goods and services?
Now, you won’t be surprised – in fact, I’m staring at this
monitor here and it says: so what determines the rate of economic growth in the
economy? And that’s the question that I want to come to now, and it becomes
relevant after we have done the short run task of closing that gap. There isn’t
any one word or two word answer to that question.
And I should make it explicit that I am thinking now about
what determines the rate of economic growth in a rich economy, in an advanced
industrial economy. I am not thinking about developing economies where the
answers are related but the answers are somewhat different.And the truth is that for an advanced economy the answers to
that question – what are the sources of growth of national output, of productive
capacity – are really the usual suspects. They are things we have known about
now for quite a long time. And basically, what matters is what you might
describe as investment in a very broad sense. I have to emphasize “in a very
broad sense”.What increases the productive of an economy like ours is
investment in physical capital, in machinery, in computers and all the rest of
that, investment in what economists call human capital, meaning skills and
capacities of workers and people who work in the economy, and investment in new
technology.And here there is a slight difference between the US and
even most of the countries in Europe. Not quite across the board but in most
branches of industry the US is the technological leader. The gap was very big
at the end of the Second World War and has closed considerably. But still, if
you look at sector by sector, with some exceptions, the US is the technological
leader.Other countries of the world, that were even fairly rich
countries have the luxury of being able to acquire technology by innovation,
essentially by adopting, using what is already known. This country (i.e., the US)
is in the position of having – so to speak – to invent its own future.So basically, if we are looking now at the US, the things we
have to look after in order to have a successful fairly high rate of growth (we
can talk about the equity issues later) are a high rate of savings and investment
in plant and equipment. I’d rather have the saving done here than abroad so
that, in effect, the capital equipment that is built by investment in this
country is owned in this country, and the returns to it stay in this country.
It’s not necessary but it’s probably desirable.
We need an extraordinary amount of emphasis – and we’ll talk
more about this later – on investment in human capital, on producing the labour
force that has the skills that are necessary to successfully operate that plant
and equipment. And that is especially important because a country like this
also has to invest in new technology. There is no place it can copy from – it
has to in most cases create it itself.Now, when I say new technology, the phrase tends to have a
“high tech” air about it. But I don’t mean it that way. New technology needn’t be high tech. It turns
out that – in many ways – the most important contributors to productivity in
the US over the last decade or two have been the application of information
technology to wholesale trade, retail trade and financial services.In fact, there are studies trying to understand why the
major, big European economies, Germany, France, UK and Italy have lagged behind
the US in productivity terms, general productivity terms. And the common answer
seems to be that they have been slow to adapt the information technology to the
service sectors. In manufacturing, there is very little gap, if any. But the
gap is in the service sectors.
So, this is extremely important. And I want to emphasize it,
even at the risk of some repetition. One of the standard, valid, almost
universal generalizations about the way people behave economically is that
technically the income elasticity of the demand for services is high. All over
the world, as incomes rise, personal incomes rise, people want to spend, [and] choose
to spend a larger fraction of that income on services rather than goods. And
you can understand why that should be so.So this means that most of the rapidly growing advanced
economies grow more rapidly in the service-producing sectors than in the
goods-producing sector. There are exceptions to that. A country like Germany – to
a lesser extent Japan, or formally Japan, not so much anymore – has a strong bias
toward trying to make its living from simply exporting high quality
manufactured goods. You notice I said exporting because the population of
Germany, like the population of anywhere else, wants to consume services as it
gets rich, not goods.So those are the things, the essentially important things
that a country like the US needs to do to generate long-run growth of
productive capacity.
I should say, in terms of policy, that you should beware of
any universal advice like “well, the market will take care of that”. You know,
if the alternative to the free-market economy is some kind of central planning,
there is no question to where the advantage lies. But there is absolutely no
evidence in the historical record of the advanced economies that zero
regulation or weak regulation of industry is somehow conducive to rapid growth,
or that minimal involvement of the government in the economy is conducive to
rapid growth.The functions of the government in terms of long run growth
are just what you would deduce from what I have already said: promoting
research and development, providing incentives for investment when they are
lacking, taking care of education, and looking after mobility. By the way, it
is probably also true that a country – there is less evidence for this
generalization, but it’s probably also true – that business cycle instability
is bad for economic growth.For countries that are given to wide fluctuations like the
ones we were looking at a few minutes ago, that’s not helpful for long-run
growth because it adds to uncertainty. The likelihood of broad fluctuations
adds to uncertainty is bad for all forward looking activities, like investment,
like mobility, like education.I wanted to say one more thing about the issue of mobility. When
I say mobility, I mean industrial mobility and occupational mobility. In a
rapidly growing, technologically-based economy, people have to change the
nature of their jobs frequently and capital has to flow freely from obsolescent
industries to new industries.It is very important when you come in this course to talk
about issues of equity. I think it is very important to find ways so that the
burdens that are associated with necessary mobility don’t fall on workers and
other people who are ill-equipped to prepare them [for that eventuality].Dislocation and sometimes dislocation is probably an
inevitable part of fast, mainly technologically-based growth. But it is the
task of economic policy to find ways of combining that with income security, up
to now, where it’s mostly below the median for incomes.
Is there still a role for validation?
Yes, answers the OU's Phil Berry, who argues that a validation arrangement can benefit alternative HE and established universities - serving to build a better quality sector.
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Time to open the door on sector diversity
The sector is diverse, but it could offer more choices of delivery methods to support the needs of a wider range of learners. Paul Feldman of Jisc, a member of the Higher Education Commission, introduces their recent report.
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On senior pay, the ball is in the sector’s court
OfS Chair Sir Michael Barber encourages the sector to get their house in order regarding value for money, as he looks towards the formal existence of the new sector regulator in the new year.
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Can one size fit all? OfS and the future of regulation
The Higher Education Commission has launched a new report on the challenges facing the OfS in fostering a diverse higher education sector. Wonkhe's Arthi Nachiappan and Catherine Boyd digest the key findings.
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The OfS should make university governance a top priority
Many of the criticism's recently levelled at universities could be fixed with improved governance, but will the new regulator be sufficiently ambitious to ensure reform? Jim Dickinson suggests some ways forward.
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Contracts, complaints and unintended consequences
Jim Dickinson reflects on former OIA chief Rob Behrens' new book, in the context of Jo Johnson's latest pledge to further students' rights.
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