Random Analytica

Charts, Infographics & Analysis without the spin

Tag: Economics

Random Analytics: H7N9 Infographics (to 8 May 2013)

***** Please note that the infographics/charts of the Avian Influenza A(H7N9) virus presented were updated with public source information to 0001hrs 9 May 2013 CET/EST *****

1 - H7N9_Infographic_130508

Infographic Details

In the past 48-hours of reporting there has been one new case of H7N9 and one retrospective fatality. This brings the total for China to 131-cases including 32-deaths and Taiwan to 1-case without loss of life. Note that all totals include asymptomatic cases.

To date 24.2% of all known cases have been fatal. For context the Case Fatality Rate of SARS was 10.9%.

The Ministry of Health also confirmed that to 6 May there were 42 discharges. That keeps the recovered total to 42 (31.8%) and asymptomatic cases at one (0.8%).

The most recent fatality (with details) reported by the Chinese media was on the 3rd May 2013 via Xinhua.

Economic Impacts of H7N9: Direct Costs and The Poultry Industry

According to the World Bank, China’s Gross Domestic Product hit $7.318-trillion USD in 2011, making it the second largest economy on the globe. Back in 1982 China had a GDP of $203-million, a mere 1/36th of its current size, thus an economy that has doubled in size more than six times.

Last year China’s economy grew by 7.9%, the weakest result since 1999 and below expectations.

The first quarter of this year was expected to see growth rates return to 8% or greater. This would be driven by internal consumer consumption over exports. However the first quarter of 2013 disappointed with GDP growing by 7.7%, again under expectations.

Although the avian influenza A(H7N9) virus is not the reason the Chinese economy has been soft it is a contributing factor. Some considerations when considering direct costs:

  • Effecting flu controls on the human and fowl populations such as the additional ¥303M ($48.6M USD) which was put up by the Chinese Ministry of Finance in late April;
  • Subsidizing the poultry industry as it struggles with the outbreak, like the ¥90M ($14.6M) from the Beijing Municipal Bureau of Agriculture again in late April;
  • Additional medical costs and insurance for regional populations costed to the Ministry of Finance, and;
  • One of the biggest direct costs of the H7N9 outbreak is the loss suffered by the poultry industry. By mid-April that agriculture component stated that it has lost approximately ¥10Bn ($1.6Bn USD) as chicken consumption halved and chicken/egg prices collapsed.

If the figures are not overstated then the impact to the first and second quarter GDP figures would be at a cost of between 0.1 and 0.15% with a work-in-progress cost to the economy of between 0.2 – 0.3% over the calendar year. Although future poultry losses would not be of that magnitude (as the poultry industry ramps down production and concentrates on protecting its breeding stocks) that number will still likely increase to some degree over the coming months. The standalone poultry industry losses to mid-April alone could account for the 0.3% minimum underperformance of the Chinese economy.

Looking at this from a different angle there is also currently an unrealised loss in terms of the overall chicken population value.

In brief, the Chinese chicken population in 2009 was 4,680,000,000. Here’s a great infographic via The Economist:

2 - CountingChickens_110727

Prior to the outbreak becoming known in China the national average broiler price (chickens raised purely for meat production) was ¥9.37 (source: Beijing Shennong Kexin Agribusiness Consulting). At publication date the average price of broilers across Liaoning, Shandong, Henan, Hebei and Jiangsu was just ¥4.07 (source: www.chinafarming.com), effectively a 56% reduction. Eggs have had a similar price decline and broiler chicks around 75% as agribusiness pends new stock purchasing and waits out the virus .

Now I know that not all chickens are broilers (being an owner of nine-chickens and a rooster myself) but with a huge appetite for chicken which is on-hold and an approximate 10-week lifespan of broilers the short-term price impacts of such a decline are a huge drag on first and second quarter Chinese GDP growth numbers.

Another consideration: Taking the 2009 chicken population number of 4,680,000,000 as a base, the bulk of chickens across the country would now be worth, on average, ¥5 less. Thus the national stock, minus protected and virus free breeders could be worth as much as ¥23.4Bn (approx. $3.74Bn USD) less than it was two months ago. If not for other inputs (such as an increase in vegetable prices against the gains in chicken import prices) then the possible impact on GDP could be doubled or even tripled.

In summary, the impacts of the current H7N9 outbreak on the economy are still short-term. Prices always recover when there is a market but by the time the virus passes and confidence returns to the poultry industry the national stock will have decreased, poultry producers have gone out of business and outside of imports the industry will take at least 3-months to re-stock. This will drive up prices in the short-term and those direct impacts will flow through the economy.

Much like the medical unknowns still in play the economic impacts can only be forecast.

Unlike the medical impacts which are tracked on a daily basis and could disappear overnight the negative economic impacts of the virus could be sustained for many months.

 

Note: My previous post on H7N9 can be found at Random Analytics: H7N9 Infographic (to 6 May 2013)

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Update: (30/07/2013)

  • World Poultry published an article on 29 Jul 2013 stating that:

“The total losses of poultry-related companies all over the country up until the end of June exceeded 600 billion yuan ($92.2 billion) since the first case of H7N9 virus was discovered by the authorities at the end of March, according to the latest statistics from the National Poultry Industry Association.”

In my article I envisaged that the loss for the first and second quarters of Chinese GDP would be between 0.1 and 0.15%. The National Poultry Industry Association total would equal 1.26%. Industry bodies tend to overstate the loss in a crisis so I am still confident in my original forecast but happy that the first numbers to be discussed fell well within a reasonable range of error.

Random Analytics: Mining the Economic and Labour Data

When it comes to Economics and Labour, Australia has more in common with countries such as Chile and Russia rather than the US, UK, or the more unfortunate economies of Spain and Greece.

Yet when it comes to political and business dialogue we are constantly swamped with examples of economies that have no resemblance to Australia’s. This is especially noticeable each month as the labour figures are released and anytime a politician or business group discusses the country’s finances.

Examples of this bias can be found in almost every aspect of political dialogue.

Federally, Labor will constantly compare any economic or labour data update against developed world. Most developed countries are service based or declining manufacturing based economies. Of the developed economies only Australia and Iceland have a greater than 40% mineral export exposure (using 2010 data). Although similar to Australia in many respects the next country in that group with high exposure to mineral exports is Canada with just a 11.9% exposure to the minerals sector.

The Liberal opposition will constantly emphasise saving economies such as Norway who consistently run budget surpluses. At the same time they won’t mention Norway’s prosperous welfare state including high levels of state ownership and high public service employment levels. In his most recent speech Tony Abbott was happy to disregard the bulk of the Global Financial Crisis, except where he could talk up the growth and prosperity of Australia which as he stated ‘happened for the last five or six years of the Howard Government’. Just to jog your memory this was the period 2002-2007, the crescendo of the pre-GFC boom times and mineral exports.

Business is not immune. One of Australia’s most prominent mineral exporters, Gina Rinehart recently stated “warned Australia risks becoming another ‘Greece, Spain or Portugal’ unless it cuts government debt and lifts its competitiveness”.

If you want to understand the Australian economy in a global context you are better served by looking at OECD level (or like) countries that are predominately commodity exporters (specifically minerals).

To that end I have listed five core countries plus Australia which I believe are better indicators of the Australian economy. They are Chile, Russia (non OECD), Canada and Sweden. I have also included the limited minerals exporter Norway (it is actually a prominent oil/LNG producer but a good comparison country all the same).

Table 1: Similar countries to Australia (ranked according to ICMM contribution)

01_MineralExporters                       

To begin, let’s look at two Labour indicators.

Labour – Unemployment

While unemployment is constantly compared by Federal Labor against the developed world the Liberal opposition tends to avoid direct comparisons. The reason for the difference is that Australia’s official unemployment has been very strong, even amongst other commodity producers. Here are two different views of the global unemployment rates, the first showing the six core mineral producers over the past decade (Figure 1) and to show how bad it could have gone a comparison of my best three mineral exporters by unemployment rate against Greece and Spain (Figure 2).

Figure 1: Mineral Exporters Unemployment Rates 2003 – 2013 (as a %)

02.1_MineralExporters_Unemployment

Figure 2: Unemployment Rates 2003 – 2013 (as a %). Norway, Australia and Canada against Greece and Spain

02.2_MineralExporters_Unemployment

Labour – Participation Rate

Both sides of the political divide and almost all business sectors talk about increased productivity, yet when we talk about participation rates we don’t discuss similar commodity economies but emphasise disparities with the US, UK, Japan or Germany (outside of academia or relevant government departments of course). If we look at similar economies data we still better than a fast closing Chile but are under-par Canada by 1-2% and around 12-14% under the long-term average of European competitors. Here is a look at how Australia compares against similar mineral exporting economies.

Figure 3: Mineral Exporters Participation Rates 2003 – 2013 (as a %)

03_MineralExporters_ParticipationRates

Now, let’s consider two economic indicators.

Economy – Balance of Payments

For a country that has done so well out of the Global Financial Crisis (in GDP terms at least) you don’t see that reflected in the political discussion or business surveys. Nor do you see it in the Balance of Payments data. Even with a positive once in a generation Terms-of-Trade position Australian has still outspent it’s earnings by a considerable factor. In fact, Australia has not been able to manage a positive Balance-of-Payment number since 1972-1973!

Figure 4: Mineral Exporters Balance of Payments 1991 – 2011 (in current $USD)

04.1_MineralExporters_BoP

Another fact that is often not discussed is that, unlike Norway Australia is a debtor nation (that is, it spends more than it earns). To emphasise this point let’s look at how Australia compares against Norway (a commodity saving economy) and the USA (a debtor nation). Australia, in my view, does not compare favourably with the USA.

Figure 5: Mineral Exporters Balance of Payments 1991 – 2011 (as a % of GDP)

04.2_MineralExporters_BoP

Economy – Debt

A lot of emphasis is made of the government debt positions, especially by the Liberal opposition and business. Yet, in Australia government debt is low in comparison to globally developed nations and even in relation to other commodity exporters. What is not talked about by both sides is the level of private indebtedness. In 2011 the Australia government was approximately 14% in debt (provisional) yet the private sector (mainly via housing and commercial debt) was 181.1%. Here is a look at the mineral exporter’s data plus Spain and Greece. A good word for Greece too, they were the only country represented in this graph with lesser private than government debt.

Figure 6: Mineral Exporters: Public & Private Debt (as a % of GDP)

05_MineralExporters_Debt

Ruslan Kogan, CEO of Kogan, stated on a recent Q&A that ‘97.4% of people make up the own statistics’. In some respects he is right; many people make up facts to suit a purpose. The intent of this article is not to suggest that politicians or high-level business people make up facts. Rather they generally emphasise certain data and disregard other information which does not support their argument.

You will always gain more insight and a better understanding on the Australian economy if you concentrate on parallel economies, such as commodity (specifically mineral exporters) over manufacturing or service based economies.

What I am also suggesting, if you want to understand the Australian economy you are better served by looking a little harder at the arguments and the data presented.

Note: This is a feature article I wrote for the Australian Mining which was published on the 11 March 2013.

Random Analytics: Peak Employment (Part II): UK

I recently published a blog looking at Australian Peak Employment. Given some very good feedback and responses from the UK especially I thought it might be useful to have a similar look at the situation there.

Although the Office of National Statistics longitudinal depth was not as good as the Australian Bureau of Statistics in breaking down full-time and part-time employment pre 1992 there was some very good data not readily available in legacy ABS data captures, especially in areas like the self-employed.

Although I could only get data as far back as 1992 the picture it tells is as interesting as the Australian story (with data going back as far as 1978).

Here are the analytics.

1 - UK FT & PT Employment 1992-2012

Figure 1: UK Full-Time and Part-Time Employment (Mar – May 1992 to Oct-Dec 2012). Source: ONS.

Like the Australian example, the first graph highlights the almost identical increase in contingent (part-time) and full-time employment over the past decade. Since early 1992 full-time employment has increased by 10.3% (2.021-million) while part-time employment increased by 34.5% (2.072-million).

2 - UK FT & PT Employment Increases 1992-2012

Figure 2: Increases in UK Full-Time and Part-Time Employment (Mar-May 1992 to Oct-Dec 2012). Source: ONS.

Like the Australian example the increases in both full and part-time employment are identical. Unlike the Australian example which has not seen a downturn since 1991/92 the UK data shows a massive 1-million full-time jobs disappearing post GFC but over the past 12-months or so that has improved by around 400,000 new positions. Additionally, the start of this data series showed negative full-time employment through to early 1994 as the UK struggled out of the late 1980’s and early 1990’s downturns (oh, how I wish I had the data going back to 1987).

3 - UK Employment vs. Working Age Labour Force 1992-2012

Figure 3: Overall UK Employment versus Working Age Labour Force (Mar-May 1992 to Oct-Dec 2012). Source: ONS.

Here’s a look at the differential between total employment creation and the UK defined, economically inactive numbers. The UK created an additional 4.093-million new jobs; from 25.635-million in early 1992 to a record 29.729-million jobs at the end of 2012 (the pre-GFC high was 29.572-million in Mar-May 2008). The UK has steadily increased its working age population since 1992 (due to a combination a slightly lower than replacement fertility rate plus a higher immigration to migration ratio) which has left it with 3,734,000-million less jobs than that required to employ the 7.828-million increase in the working age labour force.

A quick point on the above. Like my previous article on this subject (avoiding wonkish angst) it should be noted that my Working Age Labour Force number in this graphic has been worked out utilising the ONS economically active rate data rather than my preferred method of using actual population statistics (you can see an example of this in my 2012 Abbott’s Promise piece).

In conclusion looking at the UK ‘employment type’ data is further confirmation of a global trend toward greater reliance on part-time employment, which on one hand is increasing employment to record levels while at the same time decreasing the amount of work available.

Has the UK reached peak employment yet? I’m not convinced it has but the more I look at the global data the more I am convinced we are reaching that point in the next decade. As I stated in my initial Australian analysis:

With an increasing working age population and a growing gap between jobs available the future is looking anything but certain, especially with the rise of labour augmentation and robotics replacing jobs quicker than they can be created.

Random Analytics: Peak Employment (Part I): Australia

One of the great aspects of research is that it often takes you in unexpected directions.

Over the past two months and previously in 2010 I conducted a series of hands-on research assignments in the area of contingent staffing. Although my current contingent research assignment has not completed (it has at least another two months to go) I started to delve into the macro level data and have made some interesting discoveries.

It has also got me thinking seriously about the following question which was not related to contingent staffing analysis.

When will Australia reach ‘Peak Jobs’?

First of all, let me explain what ‘Peak Jobs’ means.

In simple terms (via the blogosphere) ‘Peak Jobs’ is the idea that technology is replacing jobs faster than it’s creating them. For those more technically inclined it can also be attributed to the finalisation of the increased growth in average output (and income) per labour unit due to technological change since the 1820’s as put forward by Robert Solow. For another take on this subject the ABC’s online business reporter Michael Janda recently did a piece on Australia’s peak participation rate (it’s an excellent piece, however I am not a big fan of participation rate analytics, as it’s a data input which hides many sins but more on that in later articles).

Unless there is a major catastrophe and given that Australia’s population will increase significantly toward 2050 I am not going to go on the record today and say that Australia has reached the upper limit of its ability to employ more people. I will go on the record and say that within the next decade Australia will reach that number.

To commence the first in potentially many blogs about this subject here are some analytics.

1 - FTE & PTE Employment 1978-2013

Figure 1: Australian Full-Time and Part-Time Employment (Feb 1978 – Jan 2013). Source: ABS.

In the first graph I wanted to highlight the almost identical increase in contingent (part-time) employment over the past 25-years. Since February 1978 full-time employment has increased by 59.2% (3.016-million) while part-time employment increased by a massive 276.4% (2.520-million).

2 - FTE & PTE Employment Increases 1978-2013

Figure 2: Increases in Australian Full-Time and Part-Time Employment (Feb 1978 – Jan 2013). Source: ABS.

To emphasise the almost identical nature of full and part-time employment creation here is a look at the increases in both employment types since 1978. Notice the big drops in full-time employment from 1982/1983 and 1990/1991 (the last recessionary periods in Australian history) and the steady, almost linear growth of part-time employment during the past two and a half decades.

3 - Employment vs. Working Age Labour Force 1978-2013

Figure 3: Overall Employment versus Working Age Labour Force (Feb 1978 – Jan 2013). Source: ABS.

Using a similar representation I wanted to finalise the graphs with a look at the differential between total employment creation and the working age labour force. Australia has created an additional 5.536-million new full and part-time jobs, from 6.010-million in February 1978 to a record 11.546.7-million jobs as of January 2013. Contrary to populist belief the working age population of Australia has steadily increased since 1978 (due to a combination of higher than replacement fertility rates plus immigration), effectively outpacing employment creation by 2,400,000.

Two quick points in relation to the above. Firstly, to avoid wonkish angst it should be noted that my Working Age Labour Force number in this graphic has been worked out utilising the ABS participation rate data rather than my preferred method of using actual population statistics (you can see an example of this in my 2012 Abbott’s Promise piece). Secondly, although not included in the above graph from 2018 the Australian labour force grows by approximately 475,000 as the working age officially increases to 67 (as at 2011 there were 507,252 people between the age of 59 and 60 who will be 66 and 67 in 2018 and I’ve factored in a high mortality rate of approx. 6%).

In conclusion the start of my ‘Peak Jobs’ discussion is focussed on the increasing use of contingent labour in the Australian economy and a widening gap between growth in employment and the working age population. As shown in the data there is an undeniable trend over the past two and a half decades in terms to utilise part-time labour solutions rather than traditional full-time employment. In fact in 1978 the ratio of PTE to FTE jobs was 1:5.6 but this has decreased to just 1:2.4. It would only take another recessionary period to decrease this ratio further as demonstrated in the loss of FTE during previous downturns.

With an increasing working age population and a growing gap between jobs available the future is looking anything but certain, especially with the rise of labour augmentation and robotics replacing jobs quicker than they can be created.

In part two of this series I’ll be looking at similar data from comparable countries to see if the shift to part-time employment and potentially peak employment is a global phenomena.

Update 1 (25/02/2013): Initially this blog was going to be a series about Australian Peak Employment issues. However, given some excellent feedback and interest I’ve decided to look at other countries to see if this is a global issue (which I believe it is). For consistency, I have amended the original blog name from Australian Peak Employment (Part I) to Peak Employment (Part I): Australia.

Random Analytics: A Story of two Economies: China vs. DRC

If you want to look at two economies who sit as far away from each other in terms of economic prosperity in the year 2012, then you need go no further than a comparison between the China and the Democratic Republic of Congo (DRC).

But it wasn’t always so…

Figure 1: GDP per Capita (PPP $) of China and the Democratic Republic of the Congo. Data sourced from the World Bank.

Just 30-odd years before,  Zaire (as the Democratic Republic of the Congo was then known) with its 35.6 million population actually boasted a GDP of $14.39Bn USD and a GDP per capita of $533. China on the other hand with its 1.139Bn citizens had a GDP of $189.4Bn which equalled a mere $193 per citizen.

Backed by the fastest industrial revolution in history by 2011 Chinese GDP would increase to $7.3Tn (the second largest for any country after the US) and per Capita income would explode from $193 to $5,430. For its 1.344Tn citizens, roughly a fifth of the world’s population that would be a 27-fold increase in GDP per Capita and an 84% decrease in those living on less than $1.25 per day. Living on less than $1.25 per day is a standard measurement of absolute poverty adopted by the United Nations as part of its Millennium Development Goals.

As for Zaire…

From being the second most industrialised African nation in 1960 and having rich agricultural and natural resource advantages Zaire would suffer throughout the 1980’s an exponential growth in corruption, known locally as le mal Zarois, or the Zairian sickness. According to Young, C. & Turner, T. (1985) by 1984 Mobuto Sese Seko had amassed $4Bn USD in personal wealth stolen from the state (effectively odious debt). Those billions of dollars stolen by the regime were not reinvested in the country’s infrastructure which had degraded significantly prior to the First and Second Congo Wars. These wars, aka Africa’s First World War, have cost the country and region more than 5-million lives since the commencement of bloodshed in 1996 along with enormous capital and infrastructure loss.

From an economic standpoint this has left DRC and it’s almost doubled population of 67.7 million a GDP in 2011 of just $15.64Bn. The additional $1.25Bn USD growth over the previous 32-years represents an 8.65% increase or just 0.27% per annum. From a GDP per Capita perspective the $231 for each citizen is only 43% of the 1980 figure.

For the DRC it is now a story of what could have been…

Random Analytics: The Myth of Mining Employment

As a Workforce Planner I always get frustrated by almost every one’s insistence (especially government) that somehow the mining & energy sector will answer Australia’s unemployment concerns. Yet even after record FDI in recent year’s pre & post GFC these sectors continue to employ a very small percentile of Australians.

Here are some analytics that highlight the minor role that mining & energy plays in employing Australians. I’ve also added some analytics which show the recent exponential growth in employment which goes some way to explain why some get so excited about the mining and energy stories.

Figure 1: Total employed in the Mining & Energy sectors 1984-2012. Data sourced from the ABS, Aug 2012.

Mining has risen to a record high 275,200 (but just 2.4% of total employment) while Energy has risen to 153,300 (1.3% total employment). The increase in mining employment over the past 10-years has been significant, from 81,200 to 275,200 (an increase of 338.9%). Energy has increased but not to the same scale, from 83,900 to 153,300 (an increase of 182.7%). It should be noted that the Energy figures also include water & waste services employment. Although the mining data is cleaner it would also contain some infrastructure contamination, so the true mining employment figure would be lower but this won’t be quantifiable until after the current FDI deployments start to wind down (currently forecast to commence declining from next year but continuing until 2016/2017).

Figure 2: Changes in employment totals for the Mining & Energy sector 1985 – 2012. Data sourced from the ABS, Aug 2012.

I spent a lot of time travelling throughout north Queensland in the early 1990’s, when gold was under $300 per oz. and a lot of coal and gold mines closed. This is reflected in the negative numbers through most of the 1987- 1999 period. From 2000 through to 2012 the data has all been largely positive. Just this year the mining sector has put on an impressive 42,800 averaged over the first three quarters. Take a note of how Energy and Mining have increased employment every year since 2010. Mining has increased by 24,200 (2010), 33,500 (2011) and 42,800 (2012) as mentioned previously. Energy (and water & waste services) has increased but in a declining fashion with 13,300, 6,900 & 3,300 over the same period. Although not included in this data set I have been doing a Mining environment scan which has shown an increase in negative employment stories for both sectors. This should mean the first significant decrease in employment in these sectors come November.

Figure 3: Changes in employment percentiles for the Mining & Energy sector 1985 – 2012. Data sourced from the ABS, Aug 2012.

The last graph looks at the same data as represented by Figure 2 but looks at increases and decreases in sector employment as a percentile. This is one of the key reasons why so many politicians and policy makers have been sold the mining & energy sector employment myth. Even though the percentiles are impressive, with Mining averaging 11.9% increase over the past decade (13% if you remove 2009) it all comes off a very low base and is highly susceptible to commodity price movements as recent events have highlighted.

The main point here is that although mining and energy have been the boom sectors of recent years they still only employ 428,500 people or just 3.7% of the working population, although this figure is on the high side and would reduce once you factor data contamination and non-energy sector job categories. Given the recent commodity normalisation that has been occurring and other factors including (but not limited to) redeployment of FDI to other global suppliers, deferral of projects due to high CAPEX, overcapacity and a move to automation & augmentation in high-cost countries the mining & energy sectors are not going to supply the large scale employment increases many commentators, politicians and policy makers have suggested.