Random Analytics

Charts, Infographics & Analytics. No Spinning the Data. No Juking the Stats

Random Analytics: Ebola in Nigeria (to 16 Aug 2014)

ReliefWeb is reporting more cases in Liberia over the past 24-hours with data updated to 16 August 2014. The NIGERIA Daily Situation Report (SitRep No: 17) Date: 16th August 2014 includes a new confirmed case and three suspected cases. Crawford Kilian via his excellent H5N1 site has been following reports of a suspected case from Kaduna State, although this is not reported in the latest update (noting that said update is now almost three days old). The article coming out of Nigeria in relation to Kaduna can be found here and here while the counter-article can be found here.

As Crawford correctly points out Kaduna is an 80-minute flight away from Lagos where the imported outbreak is taking place. According to Google if you wanted to drive the 775-km it would take you about 10-hours.

01 - Ebola_Nigeria_140819

The above infographic details the cases and fatalities from Ebola in Nigeria. Cases include all suspected, probable and confirmed cases and the provisional CFR is based on those total numbers (whereas ReliefWeb uses only confirmed numbers for their CFR).

The only confirmed Nigerian State to be impacted by Ebola is Lagos from an imported case back in late July where there have been 12-confirmed cases (4 deceased with some now released and some still in quarantine). The other point I wanted to make out was that the most recent articles from Nigeria have mentioned both a case from Kaduna and effectively a retraction that could be the case.

We are still in the ‘Fog of the Outbreak’ thus I have highlighted Kaduna State and await more information and evidence.


Data Sources

[1] d-maps.com. Nigeria / Federal Republic of Nigeria, boundaries, states. Accessed 19 August 2014.
[2] ReliefWeb. NIGERIA Daily Situation Report (SitRep No: 17) Date: 16th August 2014. Accessed 19 August 2014.

Random Analytics: Ebola in Sierra Leone (to 14 Aug 2014)

Sherlock Holmes was famously quoted as saying that ‘when you have eliminated the impossible, whatever remains, however improbable, must be the truth?’

Yesterday I had a Twitter conversation with virologist Ian M Mackay and currently Sierra Leone deployed health reporter Jennifer Yang about the disconnect between the official World Health Organisation figures and the Sierra Leone Ministry of Health updates. In brief, the Sierra Leone MoH figures are always lower than those presented by WHO which made no sense to me given that the MoH should be data-prime. The numbers anomaly had recently been noted by key Flublogist Crawford Kilian on his blog H5N1.

During the conversation a number of theories were put forward by various parties including a lack of capacity on the ground, data-chain promulgation/speed issues and even a conspiracy theory where-by the Ebola outbreak is linked to central government suppression of the Kailahun/Kenema districts (which border both Guinea/Liberia) as both districts are opposition strongholds.

Although there is certainly issues around ‘boots on the ground’ capacity (see Jennifer’s excellent article from Sierra Leone) the answer to the question as to why there was a significant difference between the Sierra Leone MoH and the WHO numbers turned out to be blindingly obvious.

Most of the reports coming out directly from Sierra Leone are the Ministerial statements which only include confirmed numbers whereas if you dig a little further you can find the full updates including suspected and probable cases in the Ebola Situation Reports.

Here is the look at the situation in Sierra Leone by District based on the most updated Ebola Situation Report (Vol. 78 dated 14 August 2014).

01 - Ebola_SierraLeone_140815

The above infographic details the cases and fatalities from Ebola in Sierra Leone. Cases include all suspected, probably and confirmed cases and the provisional CFR is based on those total numbers.

As you can see the Kailahun and Kenema Districts are the most impacted regions with Sierra Leone accounting for 84.7% of all cases and 95.8% of all fatalities.


Data Sources

[1] Government of Sierra Leone Ministry of Health and Sanitation. EBOLA VIRUS DISEASE – SITUATION REPORT (Sit-Rep) – 14August, 2014. Government of Sierra Leone. Accessed 15 August 2014.


The Worsening Fatality Statistics in Australian Mining

For those that closely follow the Australian Mining Sector it will come as no surprise that 2014 is emerging as one of the worst in terms of safety that we have seen in a generation. According to SafeWork Australia in the first six months of this year there were 11 notifiable fatalities in the mining sector, which according to my calculations currently equates to a Worker Fatality Rate (WFR) of 28.8 fatalities per 100,000 workers. To give that some historical context the WFR for all Australian workers in 2013 was 1.64 fatalities per 100,000 workers.

2 - MiningFatalities_2003~2014

The first chart details the amount of work related fatalities by year since 2003. Figures exclude death by iatrogenic injuries, natural causes not related to work, disease, injuries sustained while overseas or suicide. The 2014 numbers are correct to 8 August with the 2013 and 2014 numbers reflecting the more comprehensive Industry of Workplace statistics (with thanks to the statistics team at SafeWork Australia for clarifying the differences).

Since the start of 2014 I have started to closely track employment, automation and fatalities as the three key indicators on the health of the mining sector. Within a few weeks I knew that mining safety would be a big story as a number of single fatalities occurred during January and February followed up by an underground collapse in April which killed two miners at the Austar Coal Mine .

Yet, a heightened fatality count in the mining industry isn’t the only story here.

Initially out of ignorance to how the industry and SafeWork Australia tracks its work related fatalities I started to build up a personal database of mining fatalities which also included those who have died of natural causes (on-site but not work related), from suicide, fatalities in overseas Australian miners and more recently those who could be considered Lifestyle Miners.

1 - MiningRelatedFatalities_2014

The second chart looks at Mining and Mining Related deaths of Australians in 2014.The WFR is calculated only on the official SafeWork Australia figures (correct as at 8 Aug 2014).

The key data point in the chart is the inclusion of known suicides. In June The West Australian mining industry was left reeling when an onsite incident led to the death of one employee and the possible offsite death of another. This incident has been followed up by two more probable onsite suicides amongst Pilbara FIFO workers. The recent tragedies come about as the District Coroner for the Pilbara region referred a number of 2013 deaths by suicide amongst FIFO workers to the WA State Coroner for a possible inquest.

It’s not all bad news though and I was heartened by news that AngloGold Ashanti, who were at the centre of the recent multiple tragedy in the Pilbara have in the past week signed up to the FIFO Families Social Support and Education Program.

In summary, I believe the mining industry must face the issue of mental health and suicide head-on. As far as I am concerned, if a miner dies by his or her own hand onsite should be treated in exactly the same way as if it were a work related fatality including the provision of industry wide data and statistics on the subject, more education to employees and their families and if required, seeking help from appropriate resources and organisations.


If you or someone you know is thinking of suicide, phone Lifeline on 13 11 14. Help is also available via Rural Link (1800 552 002), the Suicide Call Back Line (1300 659 467) and online resources can be found at BeyondBlue.

This article was originally published on MiningIQ.
Read the original article.

Data Sources

[1] Australian Bureau of Statistics. 6291.0.55.003 – Labour Force, Australia, Detailed, Quarterly, May 2014. Accessed 11 Aug 2014.
[2] SafeWork Australia. Worker fatalities. Accessed 11 Aug 2014.
[3] SafeWork Australia. Work-related Traumatic Injury Fatalities, Australia 2013. SafeWork Australia. 2014. Pg: iii-vii.

Random Analytics: The West African Ebola Outbreak (to 4 Aug 2014)

Here are some updated charts and infographics of the 2014 Ebola Virus Disease outbreak using a number of primary sources including the most recent World Health Organisation Disease Outbreak News (DON) released 6 August 2014.

***** Please note that all EVD infographics in this series were updated with public source information to 4 August 2014 *****

01 - Ebola_Top10OutbreaksByCaseNos_140808

Ebola (Top 10 Outbreaks by Case Numbers)

The first chart displays the top 10 outbreaks in order of case numbers. Each horizontal bar is filled with the flag(s) of the country where the outbreak occurred.

With clinical cases reaching 691 in Guinea, 516 in Liberia, 495 in Liberia and nine in Nigeria the West African outbreak has now become largest Ebola outbreak in history based on both case numbers (1711) and fatalities (932). The second largest outbreak was of the Ebola Sudan strain which occurred in Uganda (2000) when 425 became infected and 224 died. The recent outbreak is the first to migrate across international land borders. The only other recording of an EVD that jumped borders prior to this outbreak was the Gabon/RSA (1996) outbreak. In that instance a doctor caught the disease in Gabon and subsequently took an international flight to South Africa where he became ill and infected other Health Care Workers (HCWs).

02 - Ebola_CasesbyClassYear_140808

Ebola (Cases by Classification and Year)

The second chart shows cases by classification (in order they are Ebola Zaire, Sudan, Bundibugyo, Reston and Ivory Coast) by year and then split into those recovered or those deceased (which follows in a red variant). The West African outbreak has become the most significant in terms of case numbers, eclipsing the 1976 dual outbreaks which saw 603 cases and 431 deaths (a combined Case Fatality Rate of 71.5%).

Currently the provisional Western African outbreak has seen 1711 cases and 932 deaths (a CFR of 54.5%).

Notes: Several years had just one case. They are 1972 (a retrospective fatality of Ebola Zaire in Zaire), 1977 (a single case of Ebola Zaire in Zaire), 1988 (an accidental infection of Ebola Zaire in Porton Down, UK) and 2011 (a single fatality of Ebola Sudan in Uganda).

 03 - WestAfrica_Cases~FatalitiesMonth_140808

The West African Outbreak – Cases & Fatalities by Month

The final chart shows both case numbers and fatalities by month. Each column is split into the current four impacted countries with data represented by the varying national flags.

The very interesting data point that springs out from this chart is that the DON I utilised for this only had data for the first four days of the month yet cases are already 271 and fatalities are 106. It should be noted that those figures are not exact as the DON that covered the month rollover between July and August had to be estimated (using a 50/50% split).


Acknowledgements: Data for this infographic was sourced from official reports from the World Health Organisation. I have also utilised resources from the CDC, CIDRAP, FluTrackers, and H5N1. I’m also a big fan of the analytical work of Virology Down Under (Ian Mackay) and Mens et Manus (Maia Majumder).

Random Analytics: Australian Mining Employment (to July 2014)

Mining continues to play an important part in the overall economy of Australia. For all of the discussion about the sector many people don’t realise that mining only employs a fraction of Australia’s workforce (currently just 264,400 source: ABS) and that many in the industry work on the construction rather than the operational side.

Each month I spend some time collating stories from a wide range of industry and media sources to build some analytics around the current state of mining employment in Australia. Here are the charts for Australian Mining Employment through to the end of July 2014.

1 - MiningJobsByState_Infographic_Jun2014_140801

The opening infographic looks at job gains and losses by State or Territory for the month of July.

Data-points: After a horror month in June when 1,592 jobs were lost Western Australia has picked up some employment this month with announcements by Atlas Iron (+200) and Transalta (LNG +250). For the rest of the country the numbers were all in the negative, even in New South Wales which gained 450 (Whitehaven Coal) but lost 915 overall including big numbers in the Hunter Valley. Overall the nation gained 920 and lost 2,043 mining jobs.

2 - MiningGainsLosses_Chart_140801

The second employment charts looks at the previous 24-months from a total mining employment gain and loss perspective. The positive employment numbers are split into those that reflect infrastructure (tan) and operational (blue) gains. Job losses are represented in red.

The biggest data-point for July 2014 was that the big numbers seen last month reduced slightly as those some of those jobs finalised in July and that we are now in the third month of 1,000+ job losses and the second consecutive month of 2,000+ job losses. The last day of the month was especially bad with 400 Hastings Deering jobs going in Queensland and another 95 coal positions being axed by BHP in NSW.

It wasn’t all bad news as some large operational announcements, like the Whitehaven Coal announcement of 450 jobs at its Maules Creek mine offset some of the big losses being announced in the Hunter Valley.

3 - MiningResourceGainsLosses_Chart_140801

The next employment chart looks in more detail at the main resource types (Iron Ore, Coal, Gold/Copper, Zinc/Lead/Nickel, CSG/LNG and Uranium) by either a job gain or a job loss.

Two key data points:

  • Although there were some gains in both coal and iron ore they were much smaller than the operational jobs lost in both resource types;
  • Coal has lost operational jobs now for 18 consecutive months (the last recording of no job losses was in Jan 2013).

4 - MiningSectorSentiment_Chart_140801

The last chart tracks employment gains and losses sentiment and is now updated back to December 2011.

Although the employment news is quite bad the sentiment is actually positive. I’ve seen this occur before. If you look at the difference between Jul 2013 (-24) and August 2013 (+3) you see a big jump in sentiment. This occurred during last year’s commodity crash as government pushed through mining approvals (which is a positive indicator even though it might not include any immediate employment outcomes). So although there are continued job cut announcements they are being largely offset (in terms of sentiment) by positive future announcements, such as the Adani announcement for the build of Carmichael Coal in Queensland.

Juking the Stats (July 2014)

Silver Lake Resources is set to cut more jobs as the gold miner closes its Lakewood Mill but didn’t announce how many would go or when.


It’s been another bad month for mining employment which came as a surprise as often the first month in a Financial Year is a chance for companies and governments to highlight some good news or to make positive announcements. This is reflected in the data where job losses outweigh job gains but sentiment is improving.

Saying that governments don’t create jobs, businesses do and with so much infrastructure finalising and the narrative squarely fixed on productivity I’ll suggest that you will see more negative than positive news in coming months.

Random Analytics: Ebola in Liberia (to 30 Jul 2014)

I have now been doing Random Analytics since October 2012 and analytics on Ebola itself was my sixth post (having been interested in the subject since I read The Hot Zone in the mid 1990’s. I’ve been doing complete posts on the subject recently, by impacted 2014 country, by African exposure, by top 10 outbreaks and by classification and year but this outbreak is moving faster than I can keep up with. I’ll now just concentrate on infographics that don’t get done in the mainstream narrative (or my flublogist colleagues don’t do). If you want to follow the detail more closely then follow Crawford Kilian, Ian Mackay or Maia Majumber (who does the dangdest infographics in the flublogist space).

Ebola in Liberia (to 30 July 2014)

01 - Ebola_Liberia_140730

***** Please note that this EVD infographic was updated with public source information to 2100hrs 30 July 2014 (EST) *****

As I go to print the overall Ebola situation can be found in the latest World Health Organisation advice (circa 27 July 2014). This infographic comes from the latest information from the Liberian Ministry of Health and Social Welfare via reliefweb. Situation Report No. 72 on the EBOLA Virus disease epidemic in Liberia as of 29th May to 28 July 2014. Detail:


Lofa County


Citizens and CHSWT have reached a compromise to allow health workers to investigate and follow up cases. Unofficial: About 15 persons are currently ill.


One of the two patients in the holding room left against medical advice and relocated to Zorzor Curran Hospital. However, specimen was taken.

Bong County

There no lab technician to collect specimen

Bomi County

There is no trained personnel to manage cases – Also, there is no lab technician to collect specimen

Nimba County

Family of confirmed case continuously refuses to allow patient to be carried to treatment facility

Random Analytics: MH17

1 - MH17_Infographic_140722

The above infographic displays the known nationalities of the 298 passengers and crew aboard Malaysian Airlines flight MH17 which departed from Amsterdam and was en-route to Kuala Lumpur. Details of the nationalities are primarily sourced by the Malaysian Airlines statement (released 18 Jul 2014) and further updates via ABC (19 Jul 2014) and the BBC (22 Jul 2014).

Depending on where you are in the World you have either awoken or are going to sleep after hearing about the terrible tragedy that has occurred near Shakhtars’k, Ukraine. Via the Australian Broadcasting Corporation. Live: Australians among dead after Malaysia Airlines jet shot down by missile over Ukraine. Excerpt:

At least 27 Australians have died on board a Malaysia Airlines passenger jet that was shot down by a ground-to-air missile over Ukraine, killing all 298 passengers and crew.

Flight MH17 was en route from Amsterdam to Kuala Lumpur when it came down in rebel-held territory near Ukraine’s border with Russia. US vice-president Joe Biden says it was “blown out of the sky” and that it was “not an accident”.

Prime Minister Tony Abbott has told Parliament it appears that Russian-backed rebels shot the plane down.

Ukrainian wire taps appear to have captured pro-Russian separatists claiming responsibility for downing the jet, but there has been no official confirmation.


To the Lost.

Random Analytics: 44th Parliament 94(a) and 94(b) ejections (to 8 Jul 2014)


Here is a look at the Standing Order 94(a) and Standing Order 94(b) ejections for the 44th Parliament of the Commonwealth of Australia House of Representatives to the end of the 14th week (8 July 2014).

2 - 94aEjectionsbyPerson_140714

The first chart details the list of those ejected from the house under Standing Orders 94(a). At the end of the 14th sitting week there were 144 ejections, of which 141 (98.0%) were from the Australian Labor Party, two (1.4%) were from the Liberal National Party and just the one (0.6%) for the National Party.

The Top-3 from the ALP:


Nicholas David “Nick” Champion MP (20-ejections) is the ALP Member representing the electorate of Wakefield (SA). He won the seat at the 2007 Australian federal election.


Mark Alfred Dreyfus QC MP (12-ejections) is the ALP Member representing the electorate of Isaacs (WA). He won the seat at the 2007 Australian federal election. He is the former Attorney-General, Minister for the Public Service and Integrity, Minister for Emergency Management, and Special Minister of State in the Second Rudd Ministry.


Pat Conroy MP (11-ejections) is the Member representing the electorate of Charlton (NSW). He won the seat at the 2013 Australian federal election.

From the Coalition:


Ewen Jones MP (2-ejections) is the Member representing the electorate of Herbert (QLD). He won the seat at the 2010 Australian federal election.

There have only been two Standing Order 94(b) ejections thus far. They are Mark Dreyfus (ALP) and Wayne Swan (ALP).

7 - 94aEjectionsbySittingDay_140714

The final chart is a look at the Standing Order 94(a) ejections by sitting day. As you can see they tend to pick up in the second last week of each large sitting period or at the end of the calendar year. In this update I’ve been able to include some colour as three ejections have come from the Coalition.

Image Sources: Bronwyn Bishop’s image care of the ABC while photos of individual members are taken from their official Australian Parliament House photo.

Random Analytics: MERS by Occupation (to >375)

Since my last Middle Eastern Respiratory Syndrome update (21 May 2014) there have been a number of key developments and even some improvements in data quality coming out of the Kingdom of Saudi Arabia.

The big announcement since my previous post was the addition of 113 legacy cases by the KSA Ministry of Health. Of the 113-cases, 42 were Health Care Workers and are included in the following charts.

Almost as importantly, the Saudi’s are now sharing their data with the World Health Organisation (WHO). The Disease Outbreak Notifications, or DON’s, are very comprehensive, light-years away from the Health Ministries updates of the past. With some irony I was pleased to see that Iran and the Kingdom featured together in the most recent DON. Perhaps we are seeing a form of MERS diplomacy occurring.

Here are the latest MERS by Occupation charts.

***** Please note that all infographics for this MERS-CoV article are using publically sourced information to 1200hrs 4 July 2014 (EST) *****


01 - MERSbyJobTitle_140704

This first chart looks at those infected with MERS by Job Title or Function.

Key Notes:

  • Retired: The largest group. There are 150-retirees (39.5% of known job titles) represented in this chart but only 2 have been confirmed (1.3%). The bulk of the retirees represented in the chart are included if they did not have a job title or function attributed to them AND if their ages are greater than the official retirement age for their home country.
  • Health Care Workers (HCW): The second largest group. Includes all types of unidentified workers in the Health sector (i.e. Nurses and Doctors).
  • Nurse: I have been able to identify 23-Nurses and in at least two cases, their speciality (ER & ICU).
  • Farmer: Includes both Owners (9/75%) and Employees (3/25%). I suspect the higher weighting toward owners is due to the fact that they are all nationals (from KSA, Qatar and the UAE). The three farm employees that have been identified are all resident workers. Just a thought here. Rich owners get to see the doctor while residents might have a range of barriers which reduce their ability to receive primary care services or choose to work through what they might believe is a bad flu.
  • Pilgrim: Of the 11-Pilgrims I have been able to identify I believe at least 8 were Umrah linked while three were potentially due to the Haj.
  • Doctor: Six identified, including one surgeon and one ICU specialist.This infographic looks at those infected with MERS-CoV by Job Family. In short I think this is a key infographic for MERS as it gives you some confidence in the key narratives (i.e. that Health Care Workers are over represented in the data as an example).

Next chart, Job Families:

02 - MERSbyJobFamily_140704

Key Notes:

  • With the inclusion of an additional 42 Health Care Workers the Non-Participatory (156/18.6%) group (Paediatrics, Students, Retiree’s and the Unemployed) move from the largest to the second largest Job Family.
  • Health Practitioners/Technical Operations (159/19.0%) or HCW as there more commonly known become the largest Job Family represented. This number includes the Nursing Assistant that was identified in Iran (but more on that later).
  • Paediatrics (18/2.1%) numbers have declined since the last update when they represented just 2.8% of the data then. Still seems low and Maia Majumder picked up on this in a recent post.
  • Pilgrim/Tourist (14/1.6%) has seen a slight increase due to some Umrah inclusions recently.
  • Healthcare Support (6/0.7%) numbers remain static so not sure if the Saudi announcement of legacy cases conflates HCW and HCSpt numbers.
  • Construction (2/0.2%) is a new inclusion from the previous update. Given the amount of building going on the in the Middle East, especially in Qatar this number seems on the very low side. I’d expect to see this number increase with more robust reporting.

Last chart.

03 - MERSbyMainJobTitle_140704

The last chart looks at those overall main job families that are most impacted by MERS, specifically Farmers, Travellers, Paediatrics, Retirees, HCW & HCSpt (combined), Other and Unknown.

Key Notes:

  • Farmer (1.7%): With only 14 confirmed cases apart from 2013 you can barely see them across an entire year, quarter or month. Numbers seem low.
  • Traveller (1.7%): Like farming, numbers seem low.
  • Paediatrics (2.1%): As suggested previously, no new paediatric cases since my last update so the numbers have declined somewhat.
  • HCW & HCSpt (19.7%): Health Care Workers and I have also included Health Care Support Workers in this grouping as well. Numbers up on previous update due to the additional 42-cases.
  • Other (2.3%): All other occupations that have been publically released. I’ve actually reduced the number in this group by one from the last update due to improved reporting from Saudi Arabia.
  • Unknown (54.7%): Unknown occupations. Up slightly but with improved reporting I’m hoping that this will reduce (over time).

Final Thoughts (on the difference between a Health Care Worker and Health Care Support

Last month I tweeted that the Iranian Nursing Assistant (FT #827) should be counted as a Health Care Support worker rather than a HCW. I then got a number of return tweets from the likes of Helen Branswell, Ian M Mackay and others who disagreed with that line of thought.

When Helen and Ian ‘guide and advise’ it’s probably worth not disregarding that advice. Upon some personal review I decided that perhaps I had taken a too hard Workforce Planning line to my job functions without fully considering the clinical implications.

I have subsequently reviewed my thinking and have re-organised my data along the following lines.

Health Practitioners/Technical Operations (nee HCW) are any job title or function that is included in the Bureau of Labor Statistics SOC Occupations 29-0000 Healthcare Practitioners and Technical Occupations PLUS any clinical function that is included within the 31-0000 Healthcare Support Occupations, such as Nursing Assistants.

I am continuing to track Health Care Support personnel (there are four job titles already identified in the MERS data including Health Clinic Admin Officer, Health Domain Worker, Hospital Employee, Hospital Receptionist) as I believe the differentiation from HCW is important but I am including their data in job family charts and infographics.

In the end, I made a bad call and I thank those of you who took the time to correct my thinking.

Flublogia is certainly a community and one I truly appreciate being involved in.

Random Analytics: Australian Mining Employment Update (to end June 2014)

Some years ago while working within the Workforce Planning fraternity I quickly understood that June was a tough month for employment, no matter what sector you either guided or worked in. It might have something to do with the seasons given that companies traditionally cut staff in the coldest months. Alternately it might be impacted on the business cycle. After more than a decade in Workforce Planning I’ve found that, whatever your view, business reflects more pragmatic ends.

Last month I suggested that June is often a horror month for mining employment.

My prediction was borne out (no matter the reason). What I didn’t guess was that the job gains versus the job losses would be the worst in more than two-years, potentially making June 2014 the worst month in mining employment since the commencement of the Global Recession (effectively Nov/Dec 2008).

Here are the charts for Australian Mining Employment through to the end of June 2014.

1 - MiningJobsByState_Infographic_Jun2014_140701


The opening infographic looks at job gains and losses by State or Territory for the month of June 2014.

Data-points: All key mining states (with the exception of the Northern Territory) have lost significant numbers of operational mining (human) capital. The only positive is that Tasmania picked up an additional 50-positions.

2 - MiningGainsLosses_Chart_140701

The first of my mining employment charts looks at the previous 24-months from a mining employment gain and loss perspective. The positive employment numbers are split into those that reflect infrastructure (tan) and operational (blue) gains. Job losses are represented in red.

The biggest data-point for June 2014 is that the losses in employment are the greatest in two-years, effectively making June 2014, officially, the worst month for two years but, in effect, the worst month for mining employment since the Global Recession which kicked off in Nov/Dec 2008.

3 - MiningSectorGainsLosses_Chart_140701


The next employment chart looks in more details at the main resource types (Iron Ore, Coal, Gold/Copper, Zinc/Lead/Nickel, CSG/LNG and Uranium) by either a job gain or a job loss.

Of the 3,939 job losses for the month of June the key points are:

  • 100% were operational job losses (effectively, zero in construction);
  • 79% were included in my tally of key resources;
  • 37.5% of the total operational losses in June were in the Coal sector, the 24th month of consecutive losses in that sector;
  • 0% of the losses were in mining construction.

It should also be noted that the figures above do not include projected job cuts from BHP Billiton who are currently reviewing their iron ore business in West Australia.

4 - MiningSectorSentiment_Chart_140701

The last chart tracks employment gains and losses sentiment.

Given that I haven’t commented on this chart for some time the key points are that August –November 2012 and Q4 FY 2012/2013 plus July 2013 were very negative times for mining employment (effectively corresponding with declining commodity prices). We are currently undergoing a similar period with soft mineral pricing from late 2013 through to even more depressed prices in the last six weeks of the financial year.

One last point on the uptick in June 2014. There were lots of positive announcements for mining in June from government (i.e. positive EIS announcements) but little in the way of immediate positive employment benefits.

Juking the Stats (June 2014)

Whitehaven Coal is cutting jobs at its coal handling and preparation plant at Gunnedah in response to “tough” coal market conditions. At the same time they are not releasing much in the way of details or numbers of the people they are putting out of work and potentially on the bench for an extended time. I look forward to a future update.

Even Money Prediction

After two years of consecutive coal mining employment losses due to depressed thermal and metallurgical coal prices the continued low iron ore pricing will see a similar (and consecutive) drip feed of employment decline in that resource group.


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