Monthly Archives: September 2009

Curb Bonuses: They Don’t Work Anyway

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

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.

FertilityNullTrajectories

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

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.

Petrol Model (Sep 2009)

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.


Posterous: the next big thing?

A few months ago, a new site arrived on the increasingly crowded web 2.0 scene. Posterous offers a medium that fits somewhere between a blog and a microblog (the canonical example of the microblog being, of course, the juggernaut that is Twitter). Maybe it should be called a “miniblog”.

Posterous is not the only site to target the miniblog niche. Tumblr has been been around for a few years and has been reasonably successful in building a base of users who like the ability it provides to easily share photos, links and assorted random scribblings. As an obsessive early-adopter of most things web 2.0, I have a tumblr account (the “Raw Prawn” identity pre-dates the “Stubborn Mule”), but  I have not been very active there of late.

Although Posterous launched only about six months ago, it has already seen healthy growth in traffic since then and has already reached the traffic rank that tumblr had six months ago.

Posterous

Posterous.com Traffic Rank (September 2009)

Part of the reason for its success is that it is extraordinarily easy to use. There is no need to sign up or create an account, as you would on twitter, tumblr or any other web 2.0 site. Instead, simply send an email to post@posterous.com. Give it a try! Send a snippet of text or, better still, a photo, music file or a link to a youtube video and Posterous will work its magic to send back to you a link to a web page with your content that you can easily share with anyone and everyone. Here is one I prepared earlier. If you live in the US, you can also send posts via SMS from your phone.

Posterous has a raft of other features that put it on a level above tumblr. For a start, it tracks the number of times that a post has been viewed (the power user can even track traffic using Google Analytics). Also, like any good web 2.0 application, it supports tags which can easily be added, edited or deleted after creating your post. There is also an iPhone application that allows you to take a photo and immediately send it to Posterous (to be fair, tumblr has an iPhone application too).

To take full advantage of Posterous, you should “claim” your email address (ok, so at this point you are effectively signing up for the service, but you don’t have to take this step). One of the features this will allow you to access is the ability to “auto-post” to an increasing range of other sites, including Twitter, Identica, Facebook, Flickr and Delicious. Turning on these services is straightforward once you have claimed your address signed up.

What exactly auto-posting does varies with each service. In the case of Twitter, Posterous will send the title of each post with a shortened link to the post. If you auto-post to Flickr, any photos you sent to Posterous will be added to your Flickr account. If you have a blog, the chances are you can repost the entire content of your Posterous post.

Posterous also shares with tumblr and any good web 2.0 a social networking feature that allows you to subscribe to other people’s Posterous accounts. You can see posts you have subscribed to through the “My Subscriptions” link on Posterous as well as receiving regular email updates. Posterous also allows the creation of multiple miniblogs (up to three) within the one account.

Unlike Twitter, Posterous even has a business model in mind, with plans to offer premium services for a fee at some point in the future. This “freemium” service approach has already been adopted by the likes of Flickr, Dropbox and a number of other web 2.0 services. Even for users who never take up these premium services, any means of revenue generation should help the site to stick around for longer than some of the more fleeting web 2.0 sites.

I have only been experimenting with Posterous for the last couple of weeks, but with the combination of extreme ease of use, smooth handling of multiple media types and the auto-posting feature I expect that it has a bright future ahead. In the meantime, keep an eye on the Mule’s Posterous account for posts that do not quite warrant appearing here on the blog.

Posterous Tips

  • Add tags to your posts using this short-hand in your email  subject line: ((tag: food, photos)) – of course, you don’t have to use “food” or “photos”.
  • Email to twitter@posterous.com if you only want to auto-post to Twitter. Similar email addresses work for other services.
  • Email to posterous@posterous.com if you do not want to auto-post anywhere.
  • Email to private@posterous.com if you want to create a private post.
  • Type #end in the email and no subsequent text (signatures, etc) will be included in the post.
  • If you use gmail, you can use gmail’s hyperlink creator to create links in your post (you will need to be using “Rich Formatting”).

Crime Around The Corner

Observant visitors to this blog may have noticed the recent appearance of a “wiki” button at the top of the page. This links to the recently established Stubborn Mule wiki, which I plan to use as a repository of information relevant in some way to the blog. Since so many of the posts here focus on data analysis, I have started with a collection of links to useful sources of data online, particularly economics and finance data.

The latest link I have added is to the New South Wales Bureau of Crime Statistics & Research (while I did not include it in the economics and finance section, maybe it does belong there). This site includes a research data set which provides monthly crime data going back to 1995 broken down by local government (council) area and offence type.  Needless to say, the first thing I was interested to learn was the level of criminality in my own local area, particularly as I moved here only very recently.

The chart below shows the total number of crimes in the various offence categories for 2008 in my local government area of Marrickville. While I was not surprised to see theft coming in at the top of the list, there were a few oddities further down. I was initially surprised to see driving offences at the bottom of the list. My driving is, of course, impeccable but I do not know if the same is true of all of my neighbours, not to mention visitors to the area. Digging further, I discovered that from 2003 onwards*, the figures for driving offences have been zero for all areas and transport regulatory offences have leapt up. So, presumably there has been a classification change. One mystery solved.

Crime in Marrickville (III)

Marrickville Crime Count (2008)

More intriguing is blackmail and extortion. Until 2008, the highest rate this crime had reached in Marrickville was four cases per year and in three years, the figure was zero. Yet, in 2008, there were nine cases of blackmail and extortion. What lies behind this wave of blackmail around the corner? Mystery not solved.

This led me to examine other trends through time. Starting with theft, I was gratified to learn that 2008 was the lowest year for theft since these records began. I am hoping 2009 will be lower still.

Theft in Marrickville (II)

Occurrences of Theft in Marrickille

A look at prostitution also suggests the area has become more law-abiding after a significant spike in offences in 2001.

Prostitution in Marrickville (II)

Occurrences of Prostitution Offences in Marrickville

As for serious crime, Marrickville experienced three homicides in 2008. The total number of homicides in the area since 1995 is 66, putting Marrickville in a somewhat disturbing 14th place out of 155 local government areas, although these two have been reducing over recent years. For those interested in the most murderous areas in New South Wales, here is a list of the top five areas in terms of total homicides since 1995. Any country readers will note that all of these local government areas are in Sydney (the area in the table labelled “Sydney” encompasses only the central business district and some inner-city suburbs).

Area Homicides
Fairfield 242
Sydney 327
Blacktown 136
Liverpool 102
Parramatta 82

* The historical data for Marrickville is in the “Files” section of the blog.

Is There a Baby Bounce?

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.

FertilityTrajectories

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.

FertilitySimulationExample

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

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).