Monthly Archives: March 2010

Where is debt headed now?

There have been a lot of posts about debt on this blog and the chart comparing government and household debt, which appeared in two of those debt posts, has proved particularly popular in discussion forums focusing on Australian property prices. Since producing the chart, the Australian government stimulus spending has continued to work its way through the economy and has been pushing up the levels of government debt. While I would still argue, as I have done many times before, that we should not follow the likes of Barnaby Joyce  in getting agitated about public debt, it does seem worth updating the chart to illustrate recent developments. The regions shaded red denote Labor party governments in power.

Chart showing changes in government and household debt

Australian Government and Household Debt (1976-2010)

As expected, government debt levels exhibit a marked up-swing (note that the government data includes Treasury projections to the end of the current financial year). What is striking, however, is that the levels of household debt have not yet fallen. While some of the weakness in the economies of countries like the US and the UK is attributed to consumers “deleveraging” (a fancy term for paying down debt rather than buying flat-screen televisions), Australian households are showing no signs yet of reducing their debt. And 90% of that debt is for housing.

While it may not be evident here, there is in fact a tight relationship between debt levels in different sectors of the economy. If I spend money then either I reduce my financial assets (drawing on my savings) or I increase my liabilities (borrow on my credit card or some other form of debt). Exactly the reverse is true of whomever I give my money to (let’s call them Joe for argument’s sake): Joe’s assets go up or his liabilities go down. Spending money is an example of a “zero sum game”. If I add the change to my net worth (assets minus liabilities) to the change of Joe’s net worth it adds to zero. My negative change offsets Joe’s positive change. Aggregating over the whole economy, the sum is still zero.

Now consider what happens if we divide the economy’s net financial worth into that of the government sector, the private sector and the foreign sector (which includes overseas governments). Any changes in net worth across all three have to add to zero. As a result, the change in the government position is the opposite of the change in the private sector and international positions combined. If the government debt is going up, debt must be going down somewhere else. Now we know the household sector is not reducing debt, but what if we look at the private sector overall, including businesses? A different picture emerges.

Australian Government and Private Sector Debt (1976-2010)

Taken as a whole, over the 12 months to the end of 2009, private sector debt fell by about 2.5% of GDP. This was almost as much as government sector debt rose (about 3% of GDP). The difference can be explained both by the role of the foreign sector as well as slight differences in data collection methods across different sectors. Keep in mind that chart includes the government debt projections out to June 2010, while the private sector debt data only extends to the end of January 2010.

Since household debt has continued to increase, what this means is that Australian businesses have in fact been reducing debt significantly. The reduction in non-household private sector debt over 2009 was almost 7% of GDP. Businesses appear far more concerned about their debt levels than home-buyers do. It will be very interesting to see what happens once the first time home buyers scheme is fully unwound.

Data sources:

Government debt to 2008: A history of public debt in Australia
Government debt for 2009: Reserve Bank of Australia – Series E10
Government debt for 2010: Australian Treasury – Budget Estimate

Private sector debt: Reserve Bank of Australia – Series D2

Gross Domestic Product: Australian Bureau of Statistics – Series 5206.0

Symbol Soup – using tags in the Mule Stable

Since the launch of the Mule Stable discussion forum three weeks ago, the number of users has been growing steadily. Some are active contributors, while others prefer just to be observers. New discussion groups are appearing, including one focusing on books, and one associated with the new Sydney-based Digital Citizens initiative. One of the more active groups at the moment is the markets group, where people have been discussing the goings on in the financial markets.  I am keen to see the Stable continue to grow, so do consider signing up yourself!

In the meantime, there have also been further developments at StatusNet, the company behind the open source software that powers the Stable. Earlier in the week, the public beta of their StatusNet hosting service was announced and shortly afterwards,  StatusNet’s CEO, Evan Prodromou, was interviewed by OStatic to explain the thinking behind StatusNet and open microblogging in general. The whole interview is worth a read, but it is really summed up by this remark:

We think microblogging is too big for any one site or company.

Evan also talked about an exciting new development known as OStatus. This is an umbrella term for a suite of technologies which will help make the open microblogging vision a reality: separate communities like the Mule Stable, which can nevertheless communicate between one another. This is in contrast to Facebook or Twitter which operate as “walled gardens”. Google Buzz, WordPress, LiveJournal and Tumblr already implement OStatus to varying degrees and, of course, so does StatusNet and hence the Mule Stable.

But back to the Mule Stable. Following on from the introductory video about getting started on the Stable, here is another video which aims to make sense of the symbol soup of microblogs. If you have been put off by seeing pages full of @, # and !, this video should help make things a little clearer. It lasts around four minutes and this time, for the benefit of speed readers and the visually impaired, I have included a transcript as well. If the video below is a bit hard to see, here is a larger format version.

Demo Video Transcript

Welcome to another Mule Stable demo video, this time it’s all about tagging.

The first time you visit the Mule Stable it can look a bit like a symbol soup, full of # symbols, @ symbols and exclamation marks. But these symbols are in fact a short-hand that can turn posting simple text messages into something a lot more powerful.

In this demo, I’ll run through all the different types of tag symbols you can use on the Mule Stable.

Even though it’s not really a tag, I’ll start with the @ symbol. Sticking an @ in front of another user’s name is a way to direct your post to that user’s attention. As a shortcut, if I click on the “reply” button next to any post, it will automatically start my post with an @, like this…

Now if I go to my Home page and click on my “replies” tab I’ll see all the posts that anyone has sent to me, in my case anything with @mule in it.

The last thing to notice about the @ replies is that they turn the username into a link. Clicking on the link takes you to that user’s profile.

Now on to hashtags. You can highlight the topic of a post by using a hash symbol, for example #music. Just as with @ replies, doing this will automatically turn your tag into a link. Clicking on the link will show you any other posts which used the same tag. Hashtags are a handy way to group discussions on a particular theme.

To get a sense of the tags other people are using, you can click on the Public timeline and the select the “Recent tags” tab. The bigger the tag, the more often it has been used.

Up next are “bang tags”, which allow you to send your post to a particular group. You can see all the Mule Stable groups by clicking the “Groups” tab on the public timeline. Now if you put an exclamation mark in front of the group’s name, it will send a post to all of the members of that group. Like hashtags, bang tags automatically create links, only this time the link takes you to the relevant group.

There is one important difference between bang tags and hashtags to be aware of. Anyone can use a hashtag at any time, but bang tags only work if you have already joined the group. If you are not a member of the group and try to use a bang tag, you’ll just have an odd-looking word, with no link and no posting to the group.

The last type of tag is a friend tag, and this one really starts looking messy! If you look at the people you subscribe to by clicking “Subscriptions” on your home page, you will see you can assign tags to other users as a way of grouping them into, say, friends, family and music buffs. Keep in mind that others will be able to see the tags you choose! Once you’ve tagged a few people you can send a message to all of them with a @ reply hashtag combo (@#). Again, this creates a link and will send the post into their “Replies” timeline.

So that’s it as far as tags are concerned….stay tuned for the next Mule Stable demo video!

Pyramid Perversion – More Junk Charts

Food pyramid charts

Knowing the reaction it would elicit, an old friend of mine sent me a link to an article entitled “Shocking Graphic Reveals Why a Big Mac Costs Less Than a Salad”, which featured the chart pictured here. I did indeed find the graphic shocking, but not for the reason the headline writer intended. The graphic in question, taken from the Consumerist which in turn had taken it from Good Medicine*, shows a pair of charts comparing the levels of subsidies different food types receive in the US compared to recommended dietary intake of corresponding food groups. Needless to say, the foods receiving the largest subsidies are the ones that should be consumed in the smallest proportions and the conclusion: no wonder Americans are getting fatter.

The idea that the US government’s agricultural policies appear to be producing decidedly unhealthy outcomes is one I have been reading about in the fascinating book The Omnivore’s Dilemma: A Natural History of Four Meals (its tale of the sex-life of corn alone makes it worth the price) and so this was not what I found shocking about the graphic. What shocked me was the travesty of data visualization used in the graphic: pyramid charts.

It should not be surprising that charts like this are becoming increasingly common since so many charting tools try to lure you into using them. The screenshot below shows the options that the current version of Microsoft Excel offers under the heading “Column” charts. I would argue that everything below “2-D Column” should be banned from the arsenal of the thinking chart-user. These variants on three-dimensional graphics all represent the trap “chart junk”: fancy extra details that, at best, add nothing to the information being conveyed and, at worst, result in distortion. Cones and pyramids fall well into the distortion category.

No doubt echoes of the “food pyramid” trope made the choice of pyramids an irresistible temptation for the Consumerist. The problem is that the data is represented by the height of each segment of the pyramid, but we tend to perceive the apparent volume of each layer. As a result, the layers near the top appear much smaller that they should relative to the lower layers**. This serves to drastically exaggerate how little government funding in the US is directed to fruits, vegetables, nuts and legumes. Using a more prosaic bar chart instead shows that, while the funding of meat and dairy is certainly far greater, the ratios are not as extreme as the pyramid suggests.

US Food Subsidies chart

The bar chart has the added advantage of making it easier to gauge the funding proportion for each category. Also, having each layer stacked one on top of another makes it harder to compare one figure with another. The bar chart eliminates the need for moving the shapes around in your mind in an attempt to make these comparisons. Note how close the funding levels are for grains compared to sugar, oil, starch and alcohol, while the pyramid chart  makes the funding of grains look significantly higher.

The original graphic compensates by quoting each of the figures, but this defeats the purpose of using a chart. If your chart does not make the numbers evident, use a table instead! The extent of the distortion that the pyramids produce is even more apparent in the case of the recommended diet data. While the recommended intake of sugar, oil and salt is certainly low, on the bar chart this category is no longer vanishingly small.

Recommended Diet Chart

Another visualisation alternative would be to use pie charts. While pie charts do have a bad reputation in statistical and scientific circles, and are often used and abused in many a business presentation, they allow more straightforward comparisons of the contributions categories make to the whole. In the pie chart it is much easier to see at a glance that vegetables and fruit should make up about a third of a regular diet, while protein combined with sugar, oil and salt should make up about a quarter. On the other hand, it is harder to use a pie chart to scan numerical values. For that purpose, the bar chart excels (no pun intended). So when choosing a chart to represent data, it is essential to first decide what aspect of the data you are aiming to highlight.

Diet Pie ChartThe pyramid charts were indeed intended to shock, but there was no need for the authors of the post to resort to misleading exaggeration. The figures should be allowed to speak for themselves. Even when using dispassionate bar charts, it remains clear that the US government is funneling a disproportionate amount of money into the types of food Americans are already over-consuming.

You might also be interested in these posts on charts.

* Thanks to Greg for the updated source.
** As a commenter on Lifehacker observed, this distortion would also occur in 2-D triangles, so it’s due to the shape rather than the 3-D nature of the charts. Having said that, the 3-D versions are far more common and indeed Excel only gives the 3-D options.

Who are the big carbon emitters?

Earlier this week, @pureandapplied brought to my attention the emissions data that has been published by the Department of Climate Change in Australia. Their report comprises data for the 2008-09 reporting year provided to the Greenhouse and Energy Data Officer by corporations whose greenhouse gas emissions exceeded 125 kilotonnes*. A few corporations are missing from the list for a number of reasons, including failure to provide their data in time for the report’s publication (a sorry excuse indeed). Nevertheless, the data makes for some interesting reading. As @pureandapplied remarked, for example, Qantas was responsible for more emissions than Shell: those air points are producing a lot of CO2-equivalent emissions!

The data is reported in two categories, “Scope 1” and “Scope 2” emissions. The definitions of the two scopes are as follows:

Scope 1 emissions are the release of greenhouse gases into the atmosphere because of activities at a facility that is controlled by the corporation. An example of this would be gases emitted by burning coal to generate electricity at an electricity production facility (i.e. a power station).

Scope 2 emissions in relation to a facility, are the release of greenhouse gases emitted at a second facility because of the electricity, heating, cooling or steam that is consumed at the facility. An example of this would be greenhouse gases emitted to generate electricity, which is then transmitted to a car factory and used there to power the car factory’s lighting. The greenhouse gas emissions are part of the factory’s scope 2 emissions. It is important to recognise that scope 2 emissions from one facility are part of the scope 1 emissions from another facility.

The report is very careful to note that these two scopes should be used warily. In fact, it warns that the two figures “should not be used individually, or added together” to estimate liabilities under any emissions abatement scheme. That is a red rag to a Mule, so I will indeed look at them individually and added together. The chart below shows the top 25 emitters in the Scope 1 category.

Top 25 Scope 1 Emitters

It should come as no surprise that the big Scope 1 emitters are primarily power generators, although there are a number of mining companies in there, along with Qantas thanks to its burning of jet fuel. Scope 2 tells a somewhat different story.

Top 25 Scope 2 Emitters

Here “poles and wires” make an appearance: Transgrid and the like, move energy from place to place that has been generated elsewhere. So, the Scope 1 emissions are counted by the generator, but the tranmission company wears the Scope 2 emissions. Woolworths manages an impressive fifth place, perhaps thanks to the lights in all of their supermarkets? Wesfarmers, the owners of the Coles supermarket chain, rank higher still.

Finally, here are the top 25 emitters by the combined total of Scope 1 and Scope 2 emissions. Not surprisingly, the generators dominate once more.

Top 25 Scope 1+2 Emitters

Also included in the data is the total amount of energy consumed by each corporation. It is in these numbers that I stumbled upon something of a puzzle. Envestra produced a reasonably sizeable 627,161 tonnes of Scope 2 CO2-equivalent, but had one of the lowest levels of total energy consumption at only 193 GJ. What have they been up to? Guesses are welcome!

* Also included are those corporations holding a reporting transfer certificate.