Category Archives: australia

Keep the date, and Vote

James Glover is back with another guest post, this time digging into some poll figures ahead of the postal plebiscite on same sex marriage.

Hey, there is a survey/plebiscite/referendum on, in case you haven’t heard. It’s on same sex marriage or marriage equality. Leaving aside the fact that this is a survey and not at all binding on MPs, this post is not about the rights and wrongs of SSM but about how to interpret the results of a recent Newspoll. Unlike most Western democracies voting in Australian elections is compulsory but as this is voluntary we are left with the additional problem for psephologists of determining not just how people would vote but whether they feel strongly enough to vote. The Newspoll produced two sets of results. The familiar one of whether people supported SSM or not, but also whether they intended to send in their postal surveys. Strangely enough they didn’t include information on the voting intentions of those who actually intend to vote.

So I made a spreadsheet model to try to determine some possible outcomes and what were the real drivers of the result based on what we gleaned from Newpoll but with some possibilities of one side or the other getting more people out to vote and the underlying vote being skewed towards the “Yes” vote. We know from dozens of polls going back 10 years that the majority of people, when asked, support the general notion of SSM. The results are usually in the range 60-70% in favour, 15-25% against and about 15% undecided. Newspoll has the overall level of support at 65%, about in the middle of that range. And if the ABS, who are conducting the survey, were to conduct a statistically significant poll they would almost certainly (the probability theorists technical get out statement) say a clear majority support it. Game over. Surely?

But there are other factors coming into play here. Here is a table of the Newspoll results by age, probably the most significant determinant, outside political views, of whether they support, or not, SSM.

Support for SSM by Age	18-34	35-49	50-64	65+	Overall
Yes	70	64	64	49	62
No	22	28	30	47	32
Undecided	8	8	6	4	6
AEC enrolled population	4,271,289	4,271,290	4,271,291	4,271,292	17,085,162

To determine the ”overall” figure, and what I will refer to as the “voting population”, I am using the AEC’s own figures on people enrolled to vote, as of June 2017, which is the last line.

As has been noted support for SSM decreases with age. But the number of people in each age cohort is about the same. The overall figure for support of 62% is towards the bottom end of most surveys but let’s leave it at that.

The Newspoll also provide figures on whether people actually intend to return their surveys.

Intention to vote	18-34	35-49	50-64	65+	Overall
Definitely will vote	58	64	73	76	68
Probably will	19	16	11	11	14
May or may not	12	9	8	8	9
Probably won't	4	5	3	2	3
Definitely won't	3	5	3	2	3
Uncommitted	4	1	2	1	2

One obvious thing to note is that older people, who are also more likely to vote “No” are more likely to vote. That will skew the results towards the “No” case.

But polls two months out may not reflect the final vote as happened in the recent US and UK elections, and support for the “Yes” case may soften. And the “No” case is probably doing a lot more to ensure they get as high a turnout as possible. So, in my model, on a spreadsheet of course, I included some assumptions and variable inputs which are:

I assume all people who say they definitely will vote is 100%.
“Probably will vote” is an input
“May or may not vote” is an input
Probably won’t, definitely won’t and uncommitted is set at 0%
Turnout for the “No vote”. Based on the polulation figures the turnout, overall should be about 83%. So one input is the turnout for “No vote” assuming they make more of an effort to get their supporters out to vote. The turnout for the “Yes” vote is then deducted from this number to match the overall turnout, 83%, by age group so higher turnout for “No” automatically leads to a lower turnout for “Yes”.
For people who will claim that the “Yes” poll result is exaggerated and is actually lower, or will soften closer to the closing date I have included an adjustment term. So “-5” means I have reduced the polled support for “Yes” by 5% making it 57% rather than 62%.
Splitting the “undecided” vote between “Yes” and “No”. “P” means I have allocated it proportionally to the level of support, but there is a parameter which splits it, say, 25% to “Yes” and therefore 75% to “No”.

So the results? Well here they are:

Summary	BCS	Exp S	RWCS	WCS
% "probably will vote" who do vote	75%	75%	50%	25%
% "may or may not" who vote	50%	50%	32%	0%
"No" vote turnout	P	95%	95%	100%
undecided split to "Yes" vote	P	P	30%	0%
adjust yes vote	0	0	-5	-5
Vote Yes	66%	62%	50%	40%
Vote No	34%	38%	50%	60%
Support Yes	67%	67%	59%	57%
Support No	33%	33%	41%	43%
Overall turnout	84%	84%	78%	71%
Yes turnout	83%	78%	67%	50%
No turnout	85%	95%	95%	100%
Population Yes vote	56%	52%	39%	28%
Population No vote	28%	32%	39%	43%

There is good news, and bad news, depending on your viewpoint. My own view is a “Yes” vote is a good thing but if you feel otherwise feel free to substitute “Best” for “Worst” in the above table. So here are the 5 scenarios. Note that once you fix the “No vote” intention to vote at, say, 95%, you remove people who intend to vote “Yes” in order to keep the Newspoll and AEC derived figure of 83% intending to vote.

BCS – Best Case Scenario. Based on the Newspoll numbers I have split the intention to vote and undecided vote equally among “Yes” and “No” voters. I have also assumed 75% of the “Probably will vote” and 50%” of the May or may not” voters will vote. The result is a clear win 66:34 for the “Yes case”. Also the overall number of people voting “Yes” is 56% of the voting eligible population so hard to argue this isn’t a decisive result.
Expected – I am assuming that the people on the “No” case will be better at getting people out to vote than the “Yes” case, 95% of them. Here there is still a clear win for “Yes” at 62%. And overall that represents 52% of the population. A clear win for “Yes” on the vote and over 50% of the population vote “Yes” as well.
RWSC – Reasonable Worst Case Scenario. This is a term that I (and the Mule) picked up in our early days at DB to describe a scenario which assumes negative (from my point of view) parameters that could nonetheless be possible. Here I am assuming only 50% of probably wills and 33% of may or may not’s vote. Because I have fixed the “No” voting rate at 95% this leads to less “Yes” votes” to keep the overall participation rate at 83%. Here the result is a line ball at 50:50. It could go either way. The population “Yes” vote is close to 40% so people might argue that less than 50% vote “Yes” and hence conservative MPs shouldn’t take the result as definitive.
WCS – Worst case scenario. Only 25% of the maybe votes and none of the may or may nots vote and 100% of the “No” votes do. I’ve reduced the support for the “Yes” case by 5%. All undecideds get allocated to “No”. Despite the overall support being 57:43 in favour of “Yes” the actual vote goes 40:60 in favour of “No”. And the overall population vote is 43:28 in “No”s favour. Under these circumstances the PM has said the vote for SSM won’t come to parliament. Largely this is driven by the 100% turnout for “No” and only 50% turnout for “Yes” as well as softening of support for “Yes” and undecideds voting “No”. This is the result the “No” campaign will be, literally in some cases, praying for as it will be difficult for the Opposition and proponents of SSM to argue the issue hasn’t been settled for the time being.

My own guess? It will be 55:45 in favour of “Yes” with overall support at 65:35. That will be enough for the anti SSM lobby to say support was never as high as the “Yes” camp claimed. But a win is a win and only the most devout glitter sellers won’t be running out of stock by Xmas.

Extra: How do I think the ABS should actually conduct this poll? Not by post for a start. There are 150 electorates and one of the arguments against using results from 1,400 people is that it barely samples many of those, less than 10 people is some cases. In actual fact the mathematical 95% margin of error for sampling N people is (approximately) 1/sqrt(N) or for N=1400, 2.67%. So the overall sample size is sufficient if the result is 60:40. But to give everyone the feeling their voice and their neighbour’s voice is being heard how about sampling 150,000 people? That is 800 people in Australia’s smallest electorate, Kalgoorlie. The MoE by individual electorates would be better than +-3.5% and over the whole Australian voting population 0.25%. And it would only cost $10m. It might even become a regular thing.

Getting Australia Post out of the red

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John Carmody returns to the Mule in his promised second guest post and takes a close look at Australia Post’s profitability with some (ahem) back-of-the-envelope calculations.

There are many forms of communication which underpin the function and productivity of a modern society like Australia. Despite the Cassandra-commentary from Mr Ahmed Fahour (the well-paid CEO of Australia Post), regular mail delivery certainly remains one of them.

In making his tendentious, but opaque, points, he has not been entirely frank with the community. He has, for instance, claimed that 99% of our mail is electronic. That assertion is meaningless because so much e-mail is advertising, brief inter- or intra-office memos and notices, or quick substitutes for telephone calls. When these are removed from the calculation, the importance of “hard mail” becomes more obvious

The data which the Herald has published (for instance, “Please Mr Postman: snail mail doomed to disappear“, 14 June) also show how shallow or formulaic Mr Fahour’s thinking seems to be. In 2012-13 Australia Post made an after-tax profit of $312 million and if there had been no losses on the handling of letters, that would have been $530 million. Do Australians really want a profit of that magnitude from such a vital national service?

But when one looks at that “letter-loss” a little more closely and at the figure of 3.6 billion letters delivered that year, it is clear that the loss per letter was 6.5 cents. In other words, if instead of recently increasing the cost of a standard letter to 70 cents, this had been to 75 cents, the losses would have been comprehensively dealt with.

Some comparisons might be informative. The British Royal Mail currently charges about $A1.10 for delivery of a standard (20g) letter for next-day delivery within the UK (its “aim”) and $A0.95 if you’re happy for delivery within 3 days. The Deutsche Post charges the equivalent of 86 Australian cents for delivery within Germany but about $A1.08 cents to adjacent France. Given that we currently pay only 70 cents for delivery across a far larger area, my suggested price of 75 cents seems reasonable and justified.

Government spending

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Before, during and after this month’s budget, Treasurer Joe Hockey sounded dire warnings about Australia’s “budget emergency”. Amidst this fear-mongering, it was a pleasant relief to come across a dissenting view. In a recent interview on 2SER Dr Stephanie Kelton (Department of Economics at the University of Missouri in Kansas City) argued that the government budget is very different from a household budget, however appealing that analogy might be. Governments like the Australian government, with its own free-floating currency can spend more than they take in taxation without worrying about running out of money. While the economy is weak, the government can comfortably run a deficit. The constraint to worry about is the risk of inflation, which means curbing spending once the economy heats up.

I posted a link to Facebook, and immediately drew comment from a more ~~conservatively~~ libertarian-minded friend: “of course a deficit is a bad thing!”. Pressed for an explanation, he argued that government spending was inefficient and “crowded out” more productive private sector investment. This did not surprise me. Deep down, the primary concern of many fiscal conservatives is government spending itself, not a deficit. This is easy to test: ask them whether they would be happy to see the deficit closed by increased taxes rather than decreased spending. The answer is generally no, and helps explain why so many more traditional conservatives are horrified by the prospect of the Coalition’s planned tax on higher income earners….sorry, “deficit levy”.

From there, the debate deteriorated. North Korea was compared to South Korea as evidence of the proposition that government spending was harmful, while a left-leaning supporter asked whether this meant Somalia’s economy should be preferred to Sweden’s. Perhaps foolishly, I proffered a link to an academic paper (on the website of that bastion of left-wing thought, the St.Louis Fed) which presented a theoretical argument to the “crowding out” thesis. My sparring partner then rightly asked whether the thread was simply becoming a rehash of the decades old Keynes vs Hayek feud, a feud best illustrated by Planet Money’s inimitable music video.

Macroeconomic theory was never going to get us anywhere (as I should have known only too well). Instead, the answer lay in the data, with more sensible examples than North Korea and Somalia. Aiming to keep the process fair, avoiding the perils of mining data until I found an answer that suited me, here was my proposal:

I’m going to grab a broad cross-section of countries over a range of years and compare a measure of government expenditure (as % of GDP to be comparable across countries) to a measure of economic success (I’m thinking GDP per capita in constant prices).

If indeed government spending is inherently bad for an economy, we should see a negative correlation: more spending, weaker economy and vice versa. My own expectation was to see no real relationship at all. In a period of economic weakness, I do think that government spending can provide an important stimulus, but I do not think that overall government spending is inherently good or bad.

The chart below illustrates the relationship for 32 countries taken from the IMF’s data eLibrary. To eliminate short-term cyclical effects, government spending and GDP per capita (in US$ converted using purchasing power-parity) was averaged over the period 2002-2012.

The countries in this IMF data set are all relatively wealthy, with stable political structures and institutions. All but one is classified as a “democracy” by the Polity Project (the exception is Singapore, which is classified as an “anocracy” due to an assessment of a high autocracy rating). This helps to eliminate more extreme structural variances between the countries in the study, providing a better test of the impact of government spending. Even so, there are two outliers in this data set. Luxembourg has by far the highest GDP per capita and Mexico quite low GDP per capita, with the lowest rate of government spending.

The chart below removes these outliers. There is no clear pattern to the data. There is no doubt that government spending can be well-directed or wasted, but for me this chart convincingly debunks a simple hypothesis that overall government spending is necessarily bad for the economy.

Now look for the cross (+) on the chart: it is Australia (IMF does not include data for New Zealand and we are the sole representative of Oceania). Despite Hockey’s concerns about a budget emergency, Australia is a wealthy country with a relatively low rate of government spending. Among these 30 countries, only Switzerland and South Korea spend less. These figures are long run averages, so perhaps the “age of entitlement” has pushed up spending in recent years? Hardly. Spending for 2012 was 35.7% compared to the 2002-2012 average of 35.3%. The shift in the balance of government spending from surplus to deficit is the result of declining taxation revenues rather than increased spending. Mining tax anyone?

Shark season

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Summer in Australia comes with cicadas, sunburn and, in the media at least, sharks. So far, I have learned that aerial shark patrols are inefficient (or perhaps not) and that the Western Australian government plans to keep swimmers safe by shooting big sharks.

Sharks are compelling objects of fear, right up there with spiders and snakes in the package of special terrors for visitors to Australia. As good hosts, we are quick to reassure: sharks may be the stuff of nightmares and 70s horror movies, but attacks are rare.

But, exactly how rare is death by shark? Over a Boxing Day lunch, I heard an excellent ‘statistic’, designed to reassure a visiting American. Apparently, more people are killed each year in the US by falling vending machines than are killed by sharks around the world. I was skeptical, but had no data to hand. Later, with the help of Google, I discovered that this statistic is 10 years old and the source? Los Angeles life guards. The tale has, however, become taller over time. Originally, vending machine deaths in the US were compared to shark attack fatalities in the US, not the entire world.

While data on vending machine related deaths are hard to come by, subsequent attempts to validate the story concluded that it was plausible, on the basis that there were two vending machine deaths in 2005 in the US but no fatal shark attacks.

Fun though the vending machine line may be, it is not relevant to Australia and, if you are on the beach contemplating a quick dip, then the risk of a shark attack is certainly higher in the sea than death by vending machine. Local data is in order.

According to the Taronga Zoo Australian Shark Attack File (ASAF):

In the last 50 years, there have been 50 recorded unprovoked fatalities due to shark attack, which averages one per year.

Fatalities have been higher than average over the last couple of years. The ASAF recorded two deaths in 2012 and, although validated figures for 2013 are yet to be published, six deaths have been reported over the last two years, suggesting that fatalities rose further to four this year.

To compare shark fatalities to other causes of mortality, a common scale is useful. My unit of choice is the micromort. A one-in-a-million chance of death corresponds to a micromort of 1.0, a one-in-ten-million chance of death to a micromort of 0.1. Taking the recent average death rate of three per year (more conservative than the longer run average of one), and a population of 23 million in Australia leads to a figure of 0.13 micromorts for the annual risk of death for a randomly chosen Australian.

The most recent data on causes of death published by the Australian Bureau of Statistics (ABS) are for 2009. That year, three people were killed by crocodiles. Sharks are not specifically identified, but any fatal shark attacks would be included among the three deaths due to ‘contact with marine animals’. The chart below illustrates the risk of death associated with a number of ‘external causes’. None of these come close to heart disease, cancer or car accidents. Death by shark ranks well below drowning, even drowning in the bath, as well as below a variety of different types of falls, whether from stairs, cliffs or ladders.

Annual risk of death in Australia (2009 data)*

Of course, you and I are not randomly chosen Australians and our choices change the risks we face. I am far less likely to suffer death by vending machine if I steer clear of the infernal things and I am far less likely to be devoured by a shark if I stay out of the water.

So, care should be taken when interpreting the data in the chart. Drug addicts (or perhaps very serious Hendrix imitators) are far more likely to asphyxiate on their own vomit than summer beach-goers. The fairest point of comparison is drowning in natural waters. At almost 3.5 micromorts, drownings in the sea (or lakes and rivers) is more than 25 times more common than fatal shark attacks. And the risk of both can be reduced by swimming between the flags.

What does that leave us with for conversations with foreign visitors? If you are headed to the beach, the risk of shark attack would be higher than death by vending machine, but it is still very low. The drive there (at 34.3 micromorts) is almost certainly more dangerous.

I will be taking comfort from my own analysis as I am heading to Jervis Bay tomorrow and sharks were sighted there this weekend:

Bendigo Bank Aerial Patrol spotted up to 14 sharks between 50 and 100 metres from shore at various beaches in Jervis Bay. [The] crew estimated the sharks at between 2.5 and 3.5 metres in length at Nelsons, Blenheim, Greenfields, Chinaman’s Beach and Hyams Beaches.

The beaches are un-patrolled, so wish me luck…but I don’t think I’ll need it.

* The figure for ‘Shark attack’ is based on the estimate of three deaths per year rather than the ABS data.

Poll Dancing

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With elections looming, and Kevin Rudd’s return to power, it is time for our regular guest blogger, James, to pull out his beer coaster calculator and take a closer look at the polls.

It is really that time again. Australian election fever has risen. Though in this case it feels like we have been here for three years since the last election. Polls every week telling us what we think and who we will vote for. But what exactly do these polls mean? And what do they mean by “margin of error”?

So here is the quick answer. Suppose you have a two party election (which two party preferred, 2PP, effectively amounts to through Australia’s preference system). Now suppose each of those parties really has 50% of the vote. If there are 8 million voters and you poll 1,000 of them then what can you tell? Surprisingly it turns out that of these inputs the number of 8 million voters is actually irrelevant! We can all understand that if you only poll 1,000 voters out of 8 million then there is a margin of error. This margin of error turns out to be quite easy to compute (using undergraduate level Binomial probability theory) and only depends on the number of people polled, and not the total number of voters. The formula is:

MOE = k × 0.5 /√N.

where N is the number of people polled and k is the number of standard deviations for the error. The formula √1000 = 33 so 1/√1000 = 0.03 = 3%. The choice of k is somewhat arbitrary but in this case k = 2 (because for the Normal distribution 95% of outcomes lies within k=2 standard deviations of the mean) which conveniently makes k × 0.5 = 1. So MOE=1/√N is a fairly accurate formula. If N=1000 then MOE=1/33=3% (give or take). This simply means that even if the actual vote was 50:50 then 5% of the time, an unbiased poll of 1,000 voters would poll outside 47:53 due purely to random selection. And even if the actual vote is, say, 46:54, the MOE will be about the same.

Interestingly in the US where there are about 100m voters they usually poll at N = 40,000 which makes the MOE = 0.5%. In this case the economics of polling scale as the number of voters hence they can afford to poll more people. But the total number of voters, 100m or 10m, is irrelevant for the MOE. As the formula shows to improve the accuracy of the estimate by a factor of 10 (say from 3% to 0.3%) they would need to increase the sample size by a factor of 100. You simply can’t get around this.

One of the criticisms of polling is that that they don’t reach the same number of (young) people on mobile phones as older people on land lines. This is easily fixed. You just adjust the figures according to what type of phone they are using based on known percentages of who uses what type of phone. Similarly you can adjust by gender and age. The interesting thing though is that the further you get from actual phone usage/gender/age in your poll you also need to increase your MOE, but not your expected outcome.

Okay so that is it: MOE = 1/√N where N = number of people polled. If N = 1000 then MOE=3%. My all time favourite back of the beer coaster formula.

The recent jump in the 2PP polls for Labor when Kevin Rudd reassumed the PM-ship from about 45% to 49% were greeted by journalists as “Kevin Rudd is almost, but not quite, dead even”. I found this amusing as it could statistically have been 51%, within the MOE, in which case the headline would have been “Kevin Rudd is ahead!”. Indeed barely a week later he was “neck and neck” in the polls at 50:50. Next week it may be “51:49” in which case he will be declared on a certain path to victory! However within the MOE of 3% these results are statistically indistinguishable.

From my point of view, as a professional statistician, I find the way many journalists develop a narrative based on polls from week to week, without understanding the margin of error, quite annoying. Given the theory that if a politician has the “The Mo” (ie. momentum) it may end up helping them win when it is irresponsible to allow random fluctuation due to statistical sampling error to influence the outcome of an election. Unless of course it helps the party I support win.

Unfounded liability

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Today a tweet from “Australia’s most idiosyncratic economist” Christopher Joye caught my eye. I followed the link and found a scaremongering article trying to whip up concerns about Australia’s levels of government debt.

A key part of Joye’s argument is to accuse the government of creative accounting by including Future Fund assets in the calculation of net debt. Carving out these assets, along with some other tactics, leads him to assert that the true size of the government’s debt is around 40% not 11% of GDP. But it is Joye’s accounting that is flawed, not the government’s.

Joye’s argument centres on the notion that government pension obligations to public sector employees constitute an “unfunded liability”. Unlike other liabilities, i.e. government bonds, this liability is not included in the calculation of the government’s debt, thereby understating it. To remedy this, Joye argues that the calculation can be corrected by noting that the Future Fund was created with the precise purpose of funding these liabilities, so excluding them from the net debt calculation addresses the omission of the unfunded pension liabilities.

Superficially, this argument can sound plausible. But, closer scrutiny shows that Joye is cherry-picking to distort the numbers.

Analogies between government and household finances can be dangerous, but I will cautiously draw one here to illustrate the point. Imagine a family with a $300,000 house financed with a $200,000 mortgage, a net asset position of $100,000. Over time, the family works to save and pay down the mortgage. But they also want their daughter to attend a private high school and have been putting money aside into a saving fund to be able to afford the fees. A few years later, the debt has been paid down to $175,000 and they have put $25,000 into the school fund. So how does the family balance sheet look now? Assuming that property prices are unchanged, the family has assets of $325,000 (house and saving fund) and a debt of $175,00, so net assets of $150,000.

Not so fast, Christopher would argue! Those school fees are an unfunded liability! Since the school fund is there solely to fund that liability, it should be excluded, so the family only has assets of $125,000.

It’s nonsense of course. A commitment to pay pensions (or school fees) is a liability of sorts, in that in entails a commitment to making payments in the future. But why stop there? The government is also committed to making welfare payments, so there’s another unfunded liability. We can ignore the baby bonus, as that’s likely to be eliminated, but the government has a whole range of commitments for future payments.

But that ignores all the sources of future receipts for the government. If public pensions are an unfunded liability, what about the unfunded asset represented by all future income tax receipts? Corporate taxes provide another solid income stream, not factored into the governments assets.

The family’s school fees are a liability of sorts, but their capacity to earn income into the future effectively provides an even greater asset. Both are uncertain, which is why accountants stick to financial assets, like loans, bonds and deposits or even stocks, land or houses, all of which have a relatively clear value today and, more importantly, can be bought or sold for figures very close to those assessed values.

Christopher Joye drastically overstated the government’s net debt position by factoring in future government payments and ignoring future government receipts. As the less “idiosyncratic” economist Stephen Koukoulas eloquently put it:

This is like painting a red dot on a daddy long legs and telling people it is a redback spider.

NDIS and how many disabled people are there anyway?

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Regular guest writer, James Glover, returns to the Mule today to look at the figures behind the proposed NDIS.

The National Disability Insurance Scheme (NDIS) is in the news again. A welcome development for people with disability and their carers and families…and friends and pretty much anyone else who cares about their fellow humans. It is not a platitude to say that disability can strike anyone at any time in their life and the stories of these people are truly moving and shaming, especially as we live in one of the richest countries in the world. Adults who are only provided with two assisted showers a week and parents providing 24/7 care to profoundly disabled children but who cannot afford a new specialised wheelchair because there is limited funding for such things (wheelchairs cost from $500 for the basic models, of which I have two, and range up to $20,000 or more). In August 2011 The Productivity Commission reported on and recommended the NDIS and since then pretty much everyone agrees it is a good idea if we could only agree how to fund it.

So what does it replace? Currently most people with serious disabilities that prevent them from, inter alia, working, can receive the disability support pension (DSP). A small number will have insurance payouts if they were “lucky” enough to to have someone else to blame for their disability. In addition, anyone can receive a rebate on medications in excess of about $1,200 a year and, of course, access to (not quite free) public health care. On top of that, there are concession cards for public transport and a taxi card system which provides half-price taxi fares to partially make up for many disabled peoples inability to use public transport. The DSP does not depend on a specific disability and for a single adult over 21 with no children it is about $19,000 a year. For child under 18 who is living at home it is about $9,000 a year. While this would appear enough to live on (forgetting overseas holidays or a mortgage) most such people rely on additional support services for everything from basic medical equipment to respite for carers. There are currently 820,000 people, about 4% of the population, on the DSP. The Productivity Commission estimates 440,000 people on the NDIS so most of these will not be eligible for the NDIS but may still receive the DSP. People 65 and over of pensionable age are not eligible for the DSP and will not be eligible for the NDIS.

The purpose of the NDIS is to provide funding for care in line with the specific requirements of the recipients, and will mean additional support to the DSP for some. You can read more about it at ndis.gov.au. Unlike the DSP, it isn’t a fortnightly stipend or, like standard disability or employment insurance, a lump sum. The government is planning to roll out pilot programs in many regions in the next few years, aiming for a complete national program by 2018-19. I won’t go into the politics but it seems even politicians can feel shame and bipartisan support for the NDIS is emerging with a good chance of a bill through this parliament in the next few weeks. The total cost of the NDIS is often quoted as $18bn a year. Some funding is proposed from an additional 0.5% to the Medicare levy. Other funding wil come jointly from the federal government and the states. The proposed levy will raise about $3.8bn a year, so nowhere near enough for the full cost. If you subsume the half the DSP cost of $11bn a year that (only) leaves an outstanding amount of $8-10bn a year to be funded even with the Medicare Levy. Hopefully with bipartisan support the full NDIS will be implemented sooner rather than later.

So that’s the background on the NDIS. The real purpose of this article though is to consider the question “How many disabled people are there in Australia anyway?”.

Well that’s easy, just read any article on disability–for instance this one by disability advocate and media personality Stella Young–and you’ll be told the answer: 20%. 20%. 20%! I am a huge admirer of Stella Young’s work, so don’t get me wrong if I choose to disagree with her on this. The 20% figure gets quoted so frequently it must be true. Well maybe. People questioning this figure are directed to the 2009 ABS Census report on disability where the self-reported disability figure is 18.1% (+/-1.3%). So a round 20% is not too bad, right? Well like all statistics, the details are important. Firstly this includes people of all ages and, not surprisingly, many more older people have disabilites. From 40% at 65-69 to 88% at 90+. For those under 65 the figure is 13.2%. It increases with age and, in the 45-54 age group, is about the average 18%. Anyway why does it matter if the true figure is overstated? Well one reason is that while there is widespread support for the NDIS, the one concern that keeps coming up is who is eligible.

According to the Productivity Commission report they estimate 440,000 people on the NDIS of whom 330,000 would be disabled, and the rest made up of carers and people on preventative programs.

This report has a deeper analysis, which takes the figures at face value. It also includes breakdowns by disabling condition. I have paraphrased these in the following table based on some of the major causes of disability. And look, there are those perennial favourites of those who think all disabled people are really bludgers: back problems,stress and depression, making up about 18% of the total. Not quite bankrupting the country then.

But what constitutes disability? It is basically a lack of normal activity rather than a set of diseases per se. The ABS report has 5 activity based categories, four of which are based on “restrictions on core activities: communication, mobility, self care”. There are “profound”, “severe”, “moderate” and “mild” levels of disability. A fifth category is “schooling or employment restriction”, but overlaps with the first four. Here is a table with the breakdown by category and age group. Combining those with a core activity limitation with employment/school limitations the figure is 15.3%. The difference between this and the higher self-reported 18% figure I suspect comes from peope who feel a bit crap a lot of the time, but aren’t signficiantly prevented from their activities. So I would estimate the number of disabled people to be more like 15% than 20%. For those under 65 this is 11%. The NDIS has a similar definition but includes social activities as well, but don’t yet provide any breakdowns.

So much for the figures from the ABS, which I think we can all agree are definitive, right? Looking at the ABS figures for this group (under 65) they total 345,000. But wait! The figure of 15.3% is based on a total number of respondents to the census of only 9.5 million people. If the reportage rate was the same as the general population of 22m then there would be about 700,000 severe or profoundly disabled people. But the Productivity Commission only estimates 330,000 or half this number on the NDIS! The alternative to the unlikely event that less than 50% of profoundly or severely disabled people will end up on the NDIS is that the reported ABS figure for people in this category is correct but the rate is wrong. While the overall reportage rate is about 50% it looks like the reportage rate for disabled people in the severe and profound category is closer to 100%. If this was also true for the other categories of disabled people then that suggests that the real rate of disability is less than 9% and maybe as low as 7%. Assuming the reportage rate is the same as the rest of the population, ie 50%, for the other categories then the disability rate might be as high as 13%. So lets split it and say 10%. In any event the widely reported figure of 20% is well above the highest estimates based on the ABS and Productivity Commission data. The real rate of disability is closer to 10% than 20%.

Does it matter? Maybe. If you claim that 20% of the population are disabled, people start quickly calculating that the cost is unsupportable if all of those people are on the NDIS! Which of course they won’t be. Fewer than half of disabled people are already on the DSP. Less than half of those will transfer to the NDIS. Overstating the percentage of disabled people isn’t necessarily a good argument for the NDIS if it reduces support from otherwise sympathetic people.

A final thought: in the large Australian organisation I work for, there are a fair few disabled people, some of whom I think would be categorised as severe. With proper support many disabled people can gain suitable education or training and hence employment and support themselves and contribute to the economic activity of the nation. The more people with disability who are employed the fewer on the DSP or NDIS, the more money for those who really have no choice. Supporting people with disability into employment is as important, in my opinion, as supporting them in living and care through the NDIS.

[This article was rewritten following some comments and some further research. In line with all my articles on Stubbornmule this article is about estimating rough numbers from scarce data “back of the beercoaster” style rather than disability politics, it just happens I have a personal interest in this subject]

Account Keeping

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I have been digging through some family archives and came across an old bank passbook belonging to my great grandfather, William Booth. He lived in Perthville in the central west of NSW. His account was with the Bank of New South Wales, Bathurst branch.

Pasted inside the front cover is a statement of the account keeping fees. I was born after decimalisation, so 5/- was not immediately meaningful to me. It turns out that the semi-annual fee is five shillings. To complicate matters further, the first transaction in the passbook is dated 1903, so these are British shillings. Australia did not introduce its own currency until 1910.

Having worked out that much, I was interested to compare 1903 account keeping fees to account keeping fees today. So, the next step was to convert five 1903 British shillings into present day Australian dollars. The website Measuring Worth comes in handy for this purpose. The site’s banner features the following quote from Adam Smith’s The Wealth of Nations (1776).

The real price of every thing, what every thing really costs to the man who wants to acquire it, is the toil and trouble of acquiring it… But though labour be the real measure of the exchangeable value of all commodities, it is not that by which their value is commonly estimated… Every commodity, besides, is more frequently exchanged for, and thereby compared with, other commodities than with labour.

With that in mind, it provides a range of present day values for five 1903 shillings. Well, almost present day: their data series extend to 2011, so in 2011 terms five shillings is worth any one of the following

£22.00	using the retail price index
£26.00	using the GDP deflator
£86.80	using the average earnings
£134.00	using the per capita GDP
£200.00	using the share of GDP

Back in the day of William Booth, account keeping involved someone manually reconciling three columns of pounds, shillings and pence. These days the process is computer-assisted, so a retail price adjustment may be more appropriate than average earnings or any of the other measures.With UK inflation running at 2.6% over 2012, I can tweak £22.00 to £22.57. Using the current exchange rate, that amounts to A$33.33. Strictly speaking, even though Australia used British pounds in 1903, I should use an Australian retail index, but as Measuring Worth only has US, UK, Japanese and Chinese conversions at the moment, I will stick with the British approach.

So, Mr Booth was paying just over $5 per month in service fees for his banking. The Bank of New South Wales has since become Westpac. According to the Westpac website, the monthly service fee for the “Westpac Choice” transaction account is $5. Fees at other banks would be very similar. So, perhaps surprisingly, account keeping fees seem to have changed very little over the last 110 years!

Given the level of automation in banking today, it would be reasonable to expect that fees would be lower than they are today. Certainly if the five shillings were adjusted based on average wages, the cost of Mr Booth’s account keeping would be more like $20 per month. Not only that, like every other bank, Westpac also offers a basic account option with zero account keeping fees. I am sure that would not have been an option in 1903.

Mixed prediction results: Cup 0, RBA 1

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With Green Moon winning the Melbourne Cup, Fiorente in second place and Jakkalberry in third, none of the Mule’s tips even rated a place. That leaves a tipping record of one for three, and I am sure it will only get worse if I keep up this “analysis” in years to come. Fortunately, many of my readers are kind enough only to remember my success in tipping Shocking back in 2009.

The Stubborn Mule RBA poll fared somewhat better. The poll results were close, with 55% expecting no change in the cash rate and 45% looking for a 0.25% cut. As it turned out, Reserve Bank kept rates on hold, preferring to wait to see the effect of their October cut:

Further effects of actions already taken to ease monetary policy can be expected over time. The Board will continue to monitor those effects, together with information about the various other factors affecting the outlook for growth and inflation. At today’s meeting, with prices data slightly higher than expected and recent information on the world economy slightly more positive, the Board judged that the stance of monetary policy was appropriate for the time being.

By my count, this makes four correct predictions of the RBA decision from the last four Stubborn Mule polls. The moral of the story: ignore anything you read here about horses, but there might be something useful to learn about interest rates.

Mule bites horse

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The Melbourne Cup is almost here again, which means that it is time for the Mule to perform some utterly bogus analysis with which to predict a winner. So here goes.

Once again, I will look to past winners as a guide. Picking on those characteristics readily available from a Google search, I have focused on handicap weight, sex and age. Starting with handicap weight, here is a chart of the distribution of weights broken down by decade.

In the early years, handicaps were typically much lower, but things have changed in recent years. On this basis, I will focus on handicap weights from 1980 onwards.

The peak of this distribution is around 54kg, so this is where I will focus my attention. There are four horses in the 2012 field which are to carry 54kg: Cavalryman, Mount Athos, Sanagas and Ethopia. To narrow this list, we turn to sex and age. The chart below suggests favouring a gelding between 4 and 6 years old or a horse (stallion) around 4 or 5 years old, perhaps even 6.

Cavalryman and Sanagas are both 7 year old stallions: too old, although Sanagas is trained by Bart Cummings who has 12 wins under his belt. Mount Athos is a 6 year old gelding, while Ethiopia is a 4 year old gelding, either side of the 5 year peak in the distribution. Since 6 year old geldings have a slight edge, my tips are as follows:

First Choice: Mount Athos

Second Choice: Ethiopia.

Honorable Mention: Sanagas

I should point out that, despite tipping Shocking in 2009, the Mule’s track record has been terrible. You have been warned!

UPDATE: it has been pointed out that Ethopia is in fact to carry 53.5kg not 54kg. While that may not seem much of a difference, there are another nine horses carrying that weight. While one school of thought would be to scratch Ethopia from the tip list, I would prefer to think that being the only one of the ten in that weight category to slip though, it must be lucky! On that highly scientific basis, Ethiopia stays.

Now Tuesday is not just about horses, there is also a Reserve Bank meeting. So, while contemplating a flutter based on spurious tips, you can also vote in a poll on whether there will be any change in the cash rate.

Once you have voted, you will be able to see the poll results.