Category Archives: economics

Banks, Central Banks and Money

One misconception about the mechanics of money that I mentioned in my last post is the idea that banks can hoard their reserves at the central bank* rather than lending them out.

Here I will explain why this idea simply does not make sense, but no more casinos and gaming chips. No more senior croupiers and casino cashiers. I will dispense with the metaphor and instead stick to a more prosaic explanation, looking at interactions between banks and central banks.

All banks have their own accounts with the central bank. Often these are called “reserve accounts”, although in Australia they are called “exchange settlement accounts” (ESAs). As the Australian terminology suggests, the primary function of these accounts is to facilitate settlement of transactions that take place between banks. To keep it simple here, I will stick to the terminology of “reserve accounts”.

To see how this works, imagine I make a $100 purchase from a shop on my credit card. If the shop banks with the same bank as I do, all that happens is that our bank increases the balance of my credit card by $100 and also increases the balance in the shop’s bank account by $100. With a couple of simple accounting entries and no movement of any physical currency, the transaction is complete. In fact, as was discussed in the casino money post, this simultaneous $100 loan advance to me and $100 deposit raising for the shop has effectively “created” an additional $100 of money in the economy that was not there before.

Continue reading →

How Money Works

31 Replies

Over the last couple of years as the global financial crisis unfolded, a subject I have spent a lot of time thinking about is the nature of money. I have been planning a blog post on the topic and the time has finally come.

The catalyst for finally writing this post was attending last week’s 16th national conference on unemployment at the University of Newcastle, hosted by the Centre of Full Employment and Equity (CofFEE). I found myself there because the centre’s director, Professor Bill Mitchell, is the author of billy blog, which I read regularly. Bill’s research and advocacy in the area of unemployment and underemployment is firmly rooted in a detailed understanding of how money works in a modern economy (hence the appeal for me) and the implications these mechanics have for government spending policy. This theme also underpinned many of the talks at the conference and the program included a panel discussion on the subject of “Modern Monetary Theory”. The panel comprised Bill Mitchell, Randy Wray and Warren Mosler, all strong advocates of what is sometimes referred to as “chartalism”. Along with another billy blog regular, Ramanan, I was invited to participate by providing a brief wrap-up at the end of the discussion.

But how hard can it really be to understand how money works? You earn it and you spend it or save it. Or, as the textbooks would have it, money serves as both a medium of exchange and a store of wealth. Is there anything more to say?

In fact there is. Most people and, indeed, many economists have not given very much thought to the mechanics of money and this leads to a number of misconceptions, all of which have made frequent appearances in the press and in political debate around the world over the course of the financial crisis. One example is the suggestion that the UK government could run out of money, an idea given further credence by the decision of rating agency Standard & Poor’s to put the UK’s rating on “negative outlook”. Even Barack Obama seems to be saying that the US is running out of money. The fact is, governments in many developed countries simply cannot run out of money. China could (but it is very unlikely) and so could member states of the European Monetary Union, but the US, UK, Japan and Australia could not. I will explain why here. In later posts I will continue the theme of the mechanics of money and will look at other misconceptions such as the idea that banks can “hoard” their reserves at central banks or that government deficits inexorably lead to high interest rates (the short answer to this one is: look at Japan).

In this post I will start with the basics of how money works and cover the following points:

how lending can “create” money
the limits to money creation
the difference between “fiat” money and money that is convertible on demand

A useful parallel to money in a real economy can be found in gaming chips in a casino. So, imagine a fairly standard sort of casino. You walk in, James Bond-style, hand over a thousand dollars to the cashier and get a pile of chips in return. The chips are marked with various denominations and total one thousand. This is an old-fashioned sort of casino: every game is played on a green felt table, there is not a poker machine in sight and, of course, you need your chips to play. To make your stay easy, you can also use your chips to buy drinks and snacks. When you have finished your evening’s play, you can redeem any chips you have not gambled away for cash.

There might be hundreds of thousands of dollars worth of chips circulating around the casino, but so far behind every chip is a corresponding amount of money sitting in the cashier’s safe. If we call this money the casino’s “reserves”, then the chip supply in circulation around the tables is equal to the casino’s dollar reserves. Of course, there might be a few cases of chips in the croupier’s office and even a chip-pressing machine in the basement, but these chips are not yet in circulation. They are just waiting to be handed over to the next patron who walks in the door with a full wallet. Under this regime, every gambler can be completely certain that they will be able to redeem their winnings at the end of the night.

While your thousand dollar stake might seem like a lot, there are a few high-rollers who frequent the place who like to play with much larger sums. Rather than producing chips with very high denominations, this casino has introduced convenient “smart chip cards”. High rollers can pay the cashier as much money as they like and the cashier will add it to the virtual chip balance on their smart cards. At every gaming table, the croupier has a card reader which can be used to debit the balance on the card in return for actual chips. This means that the total chip supply in circulation is the sum of actual chips and virtual chip balances on the smart cards. But still, this chip supply is matched by money in the cashier’s safe.

Now suppose you are a trusted regular at the casino and one night you turn up short of cash. No problem, the casino is happy to advance you your thousand dollars in return for a quickly scribbled IOU with your signature. Your credit is good. You take your $1,000-worth of chips and walk to the Blackjack table. But now something has changed. The total chip supply in the casino is $1,000 higher than the money in the cashier’s safe. In theory this could be a problem. You could immediately lose the $1,000 in chips and walk out. Then if everyone in the casino wanted to redeem their chips, there would not be enough money to go around. But, it isn’t likely to be a problem in practice. The casino operates 24 hours a day and so there are always far more than $1,000 in chips in circulation. On top of that, the house takes a decent cut on the tables, so it would not take very long for the casino to win back over $1,000-worth of chips and then $1,000 can be held back from the profits that the cashier regularly sends up to the manager’s office. In fact, the credit seems so safe, the casino decides to offer credit more widely. While they are at it, they introduce a few other innovations, like offering lucky door prizes in chips, which also adds to the supply of chips in circulation without a corresponding increase in money in the cashier’s safe.

These loans that the casino has introduced give it the ability to “create” an additional supply of chips. But not all lending creates new chips. If instead of borrowing from the house, you had offered your IOU to a high-rolling friend you would still get your $1,000 in chips for the evening, but you got them from your friend so the chip supply does not change.

The new lending arrangements are working well, but the system is limited by the fact that the cashier does not know all of the patrons very well, and is naturally being very cautious about who to lend chips to. To manage this bottleneck, the casino decides to allow senior croupiers to provide loans to gamblers they know well as long as they take responsibility for the credit-worthiness of the borrower. So now getting credit is simply a matter of providing an IOU to the senior croupier who knows you best and he or she will charge up your smart chip card. If you need actual chips, that is not a problem either as the senior croupier has a stash under the table borrowed from the cashier. Of course, the croupier is taking a bit of a risk providing you with this advance since the house expects him or her to make good any amounts you do not repay. So to make it worth their while, you give the croupier a few chips for their trouble each time you need an advance. This works so well that the cashier no longer offers loans directly to anyone other than the senior croupiers.

As successful as the new arrangements are, the casino does have to be very careful about putting strict limits on the number of chips that the senior croupiers can create through lending. Otherwise, the day may come when there are simply too many chips and not enough money in the safe and a successful gambler may walk up to the cashier to cash in their chips only to find that the cashier does not have enough money in the safe. Word will spread and everyone will want their money back, but the casino will be unable to oblige. It would be bankrupt. So while there may be no limit to the number of chips that the casino could physically manufacture (and of course it has complete control of smart chip card balances), there is a constraint on the number it can put into circulation. This constraint is a direct consequence of the fact that chips are redeemable for cash.

The analogy to the real economy should be clear here. The cashier operates like a central bank and government treasury combined. The senior croupiers are the banks. Chips are money and smart chip card balances correspond to bank account balances. In the same way that senior croupier lending effectively creates new chips, so bank lending adds to the money supply in an economy. But what is the analogy to the money in the cashier’s safe? While central banks around the world do maintain reserves of gold and foreign currencies (think of all the US dollars that the central bank of China has), for many countries the analogy breaks down in one important respect.

The casino made a commitment to redeem your chips for cash. Some central banks do make similar commitments. In the days of the gold standard, central banks in Australia, the US, the UK and elsewhere would exchange currency for gold. Of course there were times, as in war, when this convertibility was suspended, but in those days having something backing money was seen as just as important as having money backing chips in a casino. The gold standard system was abandoned after the second world war and instead, under the Bretton Woods system, domestic currencies could be exchanged at the central bank for a fixed number of US dollars. This system collapsed in turn in the 1970s. Today, some countries such as China do maintain currencies pegged to the US dollar (or some other currency) and so still make a commitment of convertibility. However, most countries have adopted so-called “fiat” money. The word fiat is Latin for “let it be” and fiat money does not derive its value from any form of backing. It is declared to be money, and so it is. Many people still assume that Australian dollars are in fact backed by something, but if you tried to take a $10 note to the Reserve Bank of Australia, you would be lucky to get two $5 notes in return. You could certainly not be assured of getting any particular amount of gold or US dollars.

Some people find the entire concept of fiat money deeply disturbing and pine for a return to the “real” money days of the gold standard. But fiat money is in fact an extremely powerful innovation. In the casino analogy, the cashier must always be careful about how many chips are put into circulation to avoid the crisis of being unable to convert chips back to cash. However, in a country with fiat money, the central bank makes no convertibility commitments, so this risk simply does not exist. It has monopoly power in the creation of currency. So, the government simply cannot run out of money. There may be very good reasons for a government to curb its spending. For example, it may not want to add too much to demand in the economy because it is concerned about inflation. But running out of money is not one of those reasons, whatever the president of the United States may think.

I will leave it there for now, as this post is long enough already. But, stay tuned for more on the macroeconomic implications of a modern fiat money system.

Which countries work the hardest?

11 Replies

Last week over dinner with friends, a debate arose as to whether Australians worked harder than Americans or not. The case for the affirmative argued that many Australians were very successful overseas and indeed Australians working abroad were highly sought after by employers. The case for the negative drew on experiences working with large US firms which exhibited far more aggressive, high-pressure work-practices than Australian firms.

Since we had more wine than data, the argument did not last very long and we instead moved on to the question of whether China now more closely resembles a fascist regime than a communist one (this debate was quickly mired in definitional issues and became rather animated). Reflecting later on the first discussion, I decided to dig up some data on hours worked and attempt to determine a winner for the debate. According to the OECD, Australia and the United States drew very close in 1979 when workers in both countries put in an average of 35 hours per week. But apart from that, over the last forty years US workers have fairly consistently worked an average of 1 to 1.5 hours more each week than Australian workers.

Total Hours Worked per head of Workforce (1950-2008)

And what of the rest of the world? Among the countries covered in the 2008 OECD data, Korea* was by far the most industrious country. Employed Koreans laboured an average of 44.5 hours each week. From there, hours worked fell quickly to Greece on 40.8 hours and then down to the Czech Republic on 38.3 hours. Australia and the United States are in a tightly packed group, ranging from Iceland in seventh place overall on 34.8 hours per week down to Australia in 16th place on 33.1 hours per week. The United States is towards the top of this group, working an average of 34.5 hours and sitting in ninth place overall. The Hanseatic League is not what it once was as Germany, Norway and the Netherlands are clustered at the bottom of the league table, all putting in around 27 hours of work each week.

National Ranking of Hours Worked in 2008*

One shortcoming of these figures is that they do not give an indication of the total effort contributed to each country. This is because the averages are calculated per head of the workforce and ignores children, the unemployed, the sick and the retired. It is conceivable that in countries with fewer workers, those workers may have to work harder to support everyone else. Indeed, recalibrating the numbers based on total hours worked per head of the total population does change the rankings somewhat. Korea still puts in a good showing, but surrenders first place to Luxembourg. Australia climbs a few places to 11th place and in the process pulls one place ahead of the United States, reflecting in part the higher unemployment rate in the United States. Coming in last place is France, which puts in an average of only 13.5 hours of labour per capita.

Two Measures of Hours Worked in 2008*

But is this data enough to resolve the debate? Unfortunately not. There are too many things that this kind of broad data does not capture. For instance, underemployment is a significant concern in many countries, including Australia and the United States. If there are many people not working as many hours as they would like to, actual hours worked may not be a good indication of the relative industriousness of different countries. Segmentation is another problem. Before our dinner-table debate moved on to China, speculation arose about possible differences in work patterns in US firms based in large cities on the East and West coasts compared to workplaces around the rest of the country. Again, aggregate statistics cannot capture any such differences.

So next time this particular group of friends meets, I will have some data to bring to the table, but not enough to carry the argument.

* Only 2007 data is available for Korea. All other data is for 2008.

Petrol Price Update

2 Replies

Another five months on since my last petrol price update and oil prices have continued to rise, but so has the value of the Australian dollar. So while crude oil prices in US dollars are up around 75% since their lows in February, they are only up 29% in Australian dollar terms.

West Texas Intermediate Oil Prices

The Australian dollar has been rising steadily for the last six months, pushed along by the Reserve Bank of Australia which has started raising their target cash rate. Higher interest rates in Australia make it more attractive for offshore investors to buy Australian securities and they have to buy Australian dollars to do so. Australian investors holding foreign assets may do the same.

Continue reading →

Does Switzerland have the world’s best universities?

2 Replies

Today @jgzebra drew my attention to the Times Higher Education league table of the top 200 univerities in the world. A quick glance at the list shows two US universities in the top three and six in the top 10. And indeed the United States dominates the results, claiming 54 spots out of the 200. The United Kingdom comes in next, taking 29 spots.

Country Count in Top 200 Universities List

Of course, this tally does not take into account the differing sizes of each country: with a population of over 300 million people, you would expect a good showing from the United States. So the obvious question is, what would the national ranking look like if population were taken into account? Rather than doing this based on the number of appearances each country makes in the list, I aggregated the overall “score” awarded to each univerity (which combines scores based on surveys of peers, employers, staff and students, citations and international staff and students) and then ranked each country by aggregate score per million population*.

Continue reading →

Curb Bonuses: They Don’t Work Anyway

7 Replies

As the G20 starts to get serious about curbing executive bonuses, we can expect banking lobbyists to get more strident in their attempts to resist these incursions into their cosy remuneration practices. This has, in fact, already begun. In a recent example, Deutsche Bank Chief Executive Josef Ackermann was resorting to cliché, claiming that “the war for talent is in full swing” (we can blame McKinsey & Co for unleashing these weasel words on an unsuspecting world). Expect to hear more.

Whether it is bankers defending bonuses or politicians frowning that bonuses contributed to excess risk-taking, what rarely seems to be questioned is whether or not bonuses actually work. That is, used as an incentive for employees, do they actually result in better performance. In most discussions, it is taken for granted that they do work, but that unwelcome side-effects can also emerge, in the form of excessive risk-taking.

However, writer Dan Pink recently challenged this basic assumption in a TED talk in August this year. He pointed to years of experimental research which suggest that while financial incentives may be very good in maximising productivity for simple tasks, they can actually result in worse performance for more complex tasks that require problem-solving or creativity. Rather than “extrinsic” motivators like financial rewards, Pink and others argue that “intrinsic” motivators like autonomy (being in control of what you do in your work environment), mastery (being good at what you do and wanting to get better) and purpose (feeling that what you are doing is worthwhile) are far better motivators.

The talk itself is under 20 minutes long and is well worth a watch (as are so many of the TED talks).

Of course, some may argue that the simplified environment of the social science laboratory does not translate to the complexities of the real business world. However, this research shows that the implicit assumption that bonuses are required in banking and finance to deliver better outcomes should not be quietly accepted. And, if the G20 are successful in initiating a change to the practices in the financial sector, it may not actually hinder staff performance. In fact, it might even help.

Fertility Declines Don’t Reverse with Development

4 Replies

In this follow-up guest post on The Stubborn Mule, Mark Lauer takes a closer look at the relationship between national development and fertility rates.

STOP PRESS: Switzerland’s population would be decimated in just two generations if it weren’t for advances in their development.

At least, that’s what the modelling in a recent Nature paper projects. The paper, widely reported in The New York Times, The Washington Post and The Economist, amongst others, was the subject of my recent Stubborn Mule guest post. In that post, I shared an animated chart and some statistical arguments that cast doubt on the paper’s conclusion. In this post, I’ll take a firmer stance: the conclusion is plain wrong. But to understand why, we’ll have to delve a little deeper into their model. Still, I’ll try to keep things as non-technical as possible.

First, let’s recap the evidence presented in the paper. It comprised three parts: a snapshot chart (republished in most of the reportage), a trajectory chart, and the results of an econometric model. As argued in my earlier post, the snapshot is misleading for several reasons, not least the distorted scales. And the trajectory chart suffers from a serious statistical bias, also explained in my earlier post. I’ll reproduce here my chart showing the same information without the bias.

That leaves the econometric model. From reading the paper, where details of the model are sketchy, I had wrongly inferred that the model suffered the same statistical bias as the trajectory chart. I have since looked at the supplementary information for the paper, and at the SAS code used to run the model. From these, it is clear that a fixed HDI threshold of 0.86 is used to define when a country’s fertility should begin to increase. So there’s no statistical bias. However, I discovered far more serious problems.

Continue reading →

Reduce, Re-use, Recycle

2 Replies

This post represents something of a milestone for the Stubborn Mule. A few months ago I passed the one year anniversary of the Mule (the first post was published on 18 May 2008). Now I have reached the 100th post. To celebrate, and in recognition of the fact that older blog posts tend to disappear under the pile of newer ones, I will take this opportunity to revisit some of these older posts.

Drivers of Australian Inflation

Back when the global financial crisis was little more than a sparkle in a sub-prime lender’s eye, outside the bond markets anyway, we were still worried about inflation. In Australia, the rate of inflation for the 12 months to March 2008 hit 4.3%, which was outside the Reserve Bank’s target range of 2-3%. Setting a pattern that was to continue, I attempted to illustrate the drivers of inflation graphically. In this case, I produced a “heatmap” (a form of “treemap”) showing the sub-categories of the Australian Consumer Price Index.

Australia and the Global Financial Crisis

A lot of the posts on the Stubborn Mule have touched on aspects of the global financial crisis, including Moody’s Colossal Bug, How Big Are Australian Banks?, AIG and DZ Bank: Dumb and Dumber and Shoots Are Greener in Australia?. But the single most popular post on the blog is Australia and the Global Financial Crisis. Written back in October 2008, not long after the collapse of Lehman Brothers, this piece aimed to explain what caused the financial crisis and why, even then, Australia seemed to be faring better than much of the rest of the world. Over the coming years there will doubtless be many millions of words written about the causes of the crisis, but in the meantime, on most days, this post still gets more hits than any other on the blog.

Olympic Medals per Capita

Another popular post, this was actually a follow up to another post which looked at the Beijing 2008 medal tally on a per capita basis and by the size of each country’s economy. The Olympics were still underway and I decided to improve the first post by having the ranking charts update live as medals were awarded. I did this with the help of the data sharing site Swivel: I wrote a little script to regularly poll the official Olympics site for medal awards, post the results to Swivel and Swivel would then update the charts embedded in the blog. A little later, I did the same thing for the Paralympics.

Sydney Petrol Prices

Back in the middle of 2008, with petrol prices soaring, there were many complaints that petrol retailers were gouging motorists with their petrol pricing. In this environment, which led to the misguided and short-lived “FuelWatch” scheme, I decided to test the relationship between crude oil prices and prices at the petrol pump. Needless to say, there was a very close relationship between the two. As oil prices fell, the sting went out of this issue, but for old time’s sake, here is an updated version of chart showing the results of my simple regression model.

Regression Model of Sydney Petrol Prices (unleaded)*

I Hate Personality Tests

Written after attending a training course at work, this post was a bit of a rant about HBDI and other similar personality tests such as Myers-Briggs, which I consider to be simplistic tools designed primarily to generate revenue for the companies that produce the tests and are closer to astrology than science. I can feel my blood pressure rising again now…

The Future of Microblogging

I was something of an early adopter of the internet phenomenon that is Twitter. When I wrote this post, I had been using Twitter for a little over a year and the total number of twitter users had just passed 2 million and looked like it might be levelling out. Now Ashton Kutcher alone has more than 3.6 million followers and overall Twitter has more than 5 million users. Although many people have become more familiar with Twitter, this post still draws in readers looking to find out more about microblogging. In the post I also look at the open microblogging platform Laconica and at identi.ca, the original example of a microblog built on Laconica. While I do still use my identi.ca account, it’s hard to escape the lure of Twitter.

Why I Always Buy the Same Sandwich

Another early post, this was inspired by my reading of Dan Ariely’s excellent book “Predictably Irrational”, which is all about the fascinating field of behavioural economics. One of the subjects Ariely discusses is the phenomenon of “self-herding”, which basically means people tend to get stuck in a rut doing the same things over and over again. In my case, I used this concept to explain why I kept buying the same sandwich. More than a year later, I still buy the same sandwich. I still plan to revisit the subject of behavioural economics at some point in the future.

So, having recycled all those electrons, I am off to start planning the next 100 posts.

* Data Sources: Sydney Petrol Prices from the Australian Automobile Association, Brent crude oil prices and A$/US$ exchange rates from Bloomberg.

Is There a Baby Bounce?

4 Replies

In this first ever guest post on The Stubborn Mule, Mark Lauer takes a careful look at the relationship between national development and fertility rates.

Recently The Economist and the Washington Post reported a research paper in Nature on the relationship between development and fertility across a large number of countries. The main conclusion of the paper is that, once countries get beyond a certain level of development, their fertility rates cease to fall and begin to rise again dramatically. In this post I’ll show an animated view of the data that casts serious doubt on this conclusion, and explain where I believe the researchers went wrong.

But first, let’s review the data. The World Bank publishes the World Development Indicators Online, which includes time series by country of the Total Fertility Rate (TFR). This statistic is an estimate of the number of children each woman would be expected to have if she bore them according to current national age-specific fertility rates throughout her lifetime. In 2005, Australia’s TFR was 1.77, while Niger’s was 7.67 and the Ukraine’s only 1.2.

The Human Development Index (HDI) is defined by the UN as a measure of development, and combines life expectancy, literacy, school enrolments and GDP. Using these statistics, again from the World Bank database, the paper’s authors construct annual time series of HDI by country from 1975 until 2005. For example, in 2005, Australia’s HDI was 0.966, the highest amongst all 143 countries in the data set. Ukraine’s HDI was 0.786, while poor old Niger’s was just 0.3.

A figure from the paper was reproduced by The Economist; it shows two snapshots of the relationship between HDI and TFR, one from 1975 and one from 2005. Both show the well-known fact that as development increases, fertility generally falls. However, the 2005 picture appears to show that countries with an HDI above a certain threshold become more fertile again as they develop further. A fitted curve on the chart suggests that TFR rises from 1.5 to 2.0 as HDI goes from 0.92 to 0.98.

Of course, this is only a snapshot. If there really is a consistent positive influence of advanced development on fertility, then we ought to see it in the trajectories through time for individual countries. So to explore this, I’ve used a Mathematica notebook to generate an animated bubble chart. The full source code is on GitHub, including a PDF version for anyone without Mathematica but still curious. After downloading the data directly from Nature’s website, the program plots one bubble per country, with area proportional to the country’s current population.

Unlike with the figure in The Economist, here it is difficult to see any turn upwards in fertility rates at high development levels. In fact, the entire shape of the figure looks different. This is because the figure in The Economist uses axes that over-emphasise changes in the lower right corner. It uses a logarithmic scale for TFR and a reflected logarithmic scale for HDI (actually the negative of the logarithm of 1.0 minus the HDI). These rather strange choices aren’t mentioned in the paper, so you’ll have to look closely at their tick labels to notice this.

To help focus on the critical region, I’ve also zoomed in on the bottom right hand corner in the following version of the bubble chart.

One interesting feature of these charts is that one large Asian country, namely Russia, and a collection of smaller European countries, dart leftwards during the period 1989 to 1997. The smaller countries are all eastern European ones, like Romania, Bulgaria and the Ukraine (within Mathematica you can hover over the bubbles to find this out, and even pause, forward or rewind the animation). In the former Soviet Union and its satellites, the transition from communism to capitalism brought a crushing combination of higher mortality and lower fertility. In Russia, this continues today. One side effect of this is to create a cluster of low fertility countries near the threshold HDI of 0.86 in the 2005 snapshot. This enhances the impression in the snapshot that fertility switches direction beyond this development level.

But the paper’s conclusion isn’t just based on these snapshots. The authors fit a sophisticated econometric model to the time series of all 37 countries that reached an HDI of 0.85, a model that is even supposed to account for time fixed-effects (changes in TFR due only to the passage of time). They find that the threshold at which fertility reverses is 0.86, and that beyond this

an HDI increase of 0.05 results in an increase of the TFR by 0.204.

This means that countries which develop from an HDI of 0.92 to 0.98 should see an increase in TFR of about 0.25. This is only about half as steep as the curve in their snapshot figure, but is still a significant rate of increase.

However, even this rate is rather surprising. Amongst all 37 countries, only two exhibit such a steep rise in fertility relative to development between the year they first reach an HDI of 0.86 and 2005, and one of these only barely. The latter country is the United States, which manages to raise TFR by 0.211 per 0.05 increase in HDI. The other is the Czech Republic, which only reaches an HDI of 0.86 in 2001, and so only covers four years. Here is a plot of the trajectories of all countries that reached an HDI of 0.86, beginning in the first year they did this. Most of them actually show decreases in TFR.

So how do the authors of the paper manage a statistically significant result (at the 0.1% level) that is so widely different from the data? The answer could well lie in their choice of the reference year, the year in which they consider each country to have passed the threshold. Rather than using a fixed threshold as I’ve done above, they express TFR

relative to the lowest TFR that was observed while a country’s HDI was within the window of 0.85–0.9. The reference year is the first year in which this lowest TFR is observed.

In other words, their definition of when a country reaches the threshold depends on its path of TFR values. In particular, they choose the year when TFR is at its lowest.

Does this choice statistically bias the subsequent trajectories of TFR upwards? I leave this question as a simple statistical exercise for the reader, but I will mention that the window of 0.85–0.9 is wider than it looks. Amongst countries that reached an HDI of 0.9, the average time taken to pass through that window is almost 15 years, while the entire data set only covers 30 years.

Finally I’d like to thank Sean for offering this space for my meandering thoughts. I hope you enjoy the charts. And remember, don’t believe everything you see in The Economist.

UPDATE:

To show that the statistical bias identified above is substantial, I’ve programmed a quick simulation to measure it. The simulation makes some assumptions about distributions, and estimates parameters from the original data. As such it gives only a rough indication of the size of the bias – there are many alternative possibilities, which would lead to larger or smaller biases, especially within a more complex econometric estimation.

In the simulation, each of the advanced countries begins exactly where it was in the year that it first reached an HDI of 0.85. Thereafter, a trajectory is randomly generated for each country, with zero mean for changes in fertility. That is, in the simulation, fertility does not increase on average at all¹. As in the paper, a threshold is found for each country based on the year with lowest TFR within the HDI window. All shifts in TFR thereafter are used to measure the impact of HDI on TFR (which is actually non-existent).

Here is a sample of the trajectories so generated, along with the fitted response from the paper.

The resulting simulations find, on average, that a 0.06 increase in HDI leads to an increase of about 0.075 in TFR, despite that fact that there is no connection whatsoever. The range of results is quite broad, with an increase of 0.12 in TFR also being a likely outcome. This is half of the value found in the paper; in other words, simulations of a simplified case where HDI does not influence TFR at all, can easily generate half of the paper’s result.

Of course, if the result is not due to statistical bias, then the authors can easily prove this. They need only rerun their analysis using a fixed HDI threshold, rather than one that depends on the path of TFR. Until they do, their conclusion will remain dubious.

¹ For the technically minded, the HDI follows a random walk with drift and volatility matching those of advanced countries, and the TFR follows an uncorrelated random walk with volatility matching the advanced countries, but with zero drift. The full source code and results have been uploaded to the Github repository.

FURTHER UPDATE:

More details can be found in the follow-up post to this one, Fertility Declines Don’t Reverse with Development.

The Muddle of Macroeconomics

10 Replies

I never formally studied any economics at school or university, but in the years since I have become increasingly interested in the subject. I am sure that is evident from many of the posts here on the Stubborn Mule. What I did study was mathematics and, although there can be internal debates within the subject of mathematics, in the end it is usually clear what is right and what is wrong. No such luck in economics, particularly when economists attempt to understand the working of the world from the broadest perspective: macroeconomics. The level of controversy, debate and antagonism in the field of macroeconomics is quite extraordinary.

In July, The Economist’s cover story asked what was wrong with the field of economics. The leader was accompanied by an article entitled The other-worldly philosophers, which narrowed in on macroeconomics. It quotes Willem Buiter of the London School of Economics describing macroeconomics as a “costly waste of time”, while prominent economist Paul Krugman described most macroeconomics of the past 30 years as “spectacularly useless at best, and positively harmful at worst”. The article goes on to explore the tensions between free-market supporting “freshwater economists” and the more interventionist “saltwater economists”. Following a detente of sorts over recent years, the global financial and economic crisis has inflamed the antagonism once more.

In the May issue of The Monthly Magazine, former banker and author of “The Two Trillion Dollar Meltdown”, Charles R Morris wrote of macroeconomics:

macroeconomics is not a science. Its methods are gross and error-prone, and its models of economic reactions bear only a distant relationship to those in the real world. The theoretical apparatus of economics – its ‘laws’ – are mostly imaginative constructs that can rarely be confirmed with any precision, and stem more often from ideologies than from careful observation.

The issue of ideology is a crucial one. Any macroeconomic theory has implications for government policy, particularly monetary and fiscal policy. Further, almost all monetary and fiscal policy, even “doing nothing” has implications for wealth transfer from one segment of society to another. All things being equal, high interest rates are bad for borrowers and good for depositors. Inflation is good for borrowers and bad for lenders (update: this is really an over-simplification: see comments below). Some policies may benefit wage earners, but create costs for businesses, others may help importers but hinder exporters. With so much real money at stake, it is no wonder that ideological biases are so significant.

Once much of this debate would have been carried to the halls of academia, only reaching the rest of us in the form of those ideas which filtered through to influence the government of the day. These days it is all readily accessible online to anyone who is interested and many of the participants engage directly with the public on their blogs. I do not pretend to have all the answers (or even very many answers), so for now I will simply list just a few of the blogs and websites I have come across in my own quest to better understand this contentious field of study.

Billy Blog

Bill Mitchell is a professor in economics at the University of Newcastle (Australia). He describes himself as a “modern monetary theorist” and focuses on the mechanics of money. On his blog, he argues forcefully that much of the thinking of mainstream macroeconomics, particular that of a neo-classical bent, has not come to terms with the implications of “fiat money” and are steeped in gold standard thinking. This position leads him to advocate strongly for the importance of government spending, particularly to support full employment, and dismiss concerns about budget deficits threatening government solvency. While this may sound Keynesian, Mitchell dislikes the The General Theory of Employment, Interest, and Money and distances himself from aspects of Keynesian thinking which can itself be caught up in misunderstandings of the mechanics of money. While Mitchell does not shy away from expressing his ideological views, he would also argue that his thinking does indeed begin with careful observation.

The Conscience of a Liberal

With the Nobel Prize for Economics in 2008 and a number of popular books under his belt, Paul Krugman is the best known of the economists in this list. Krugman’s approach to macroeconomics is more firmly in the Keynesian tradition than Mitchell’s and he has been a passionate advocate online and on television for extensive economic stimulus packages, as well as an ardent critic of much of the way that bailout of US banks was handled. Like Mitchell, Krugman dismisses concerns about escalating government debt.

Maverecon

Having been a member of theBank of England’s Monetary Policy Committee, Willem Buiter has direct experience of the real world operation of monetary policy. In his blog he criticises the whole enterprise of macroeconomics, attacking both the neo-classical and the neo-Keynsian schools of thought. Buiter has more time for “heterdox” economic thought that attempts to deal with the messier realities of the economy, such as inefficient markets, illiquidity and irrational behaviour.

Von Mises Institute

Many economists, Krugman included, dismiss the Austrian school of economics as an oddball fringe distraction from the real business of economics. However, the Austrian way of thinking has a surprisingly strong hold on the thinking of a number of people outside the economics profession. I suspect that this is due, in part, to the fact that a number of the school’s classic books such as Hazlitt’s “Economics in One Lesson”, are easily accessible to non-economists. Among the recurring themes of the Austrian school are the evils of fiat money, fractional reserve banking and the intervention of central banks in free markets. While many of their arguments that get them to these stark conclusions initially have superficial appeal, I have not found that they stand up to closer scrutiny. Presumably this is why they are not taken very seriously by most professional economists. Either that or all professional economists are either deluded fools, or swayed by vested interested or both, which I am sure would be the Austrian’s counter-argument. Indeed, another thread running through the Von Mises Institute blog and other Austrian school writings is an acrimonious tendency to ad hominem attacks on their opponents, particularly Krugman.

Econbrowser

Written by James D. Hamilton, Professor of Economics at the University of California, San Diego and Menzie Chinn, Professor of Public Affairs and Economics at the University of Wisonsin, this blog has a strong focus on data analysis, which clearly appeals to me. Nevertheless, their attitude towards government debt does show signs of the sort of gold standard thinking that Bill Mitchell criticises.

The Daily Reckoning and Money Morning

I have grouped these two blogs together as they seem to share a number of contributors and have a similar style and outlook. Many of the writers are Austrian school fellow travellers and like nothing more than a rant about the evils of fiat money, except perhaps a rant on why the banking system is a giant Ponzi scheme. I primarily visit these sites if I am looking for a bit of an adrenaline boost or an argument.

Steve Keen’s Debtwatch

Steve Keen is another iconoclastic opponent of neo-classical economics. His book Debunking Economics was an attack on the traditional underpinnings of neo-classical macroeconomics. Most of the writings on the blog focus more on his concerns about the growth in private sector debt in Australia and the US. The concerns lead him to his pessimistic view of the prospects for the Australian housing market, a view he is best known for in the mainstream press and one I have discussed elsewhere.

Photo credit: p22earl on flickr (cc licence).