Australian Jobs profile for May Qtr. 2014 Jobs Profile May Qtr 2014.pdf · in public service growth and cuts to protected blue collar jobs in the private sector. These six industries

©Copyright Australian Development Strategies 2014 1

Australian Jobs

profile for

May Qtr. 2014

Prepared by Australian

Development Strategies

Pty Ltd

This report has been prepared as an educational and public relations exercise and has not been designed as

an advisory tool for business and we take no responsibility for those who use it for these purposes. The

sampling errors for smaller Labour Force regions are often large and the raw figures used cannot be easily

adjusted for seasonal trends. The statistical significance of the profiles also need to be considered. We

repeat, caution is urged in any interpretation of these statistics. We acknowledge and thank the Australian

Bureau of Statistics for the provision of original data, Dr Otto Hellwig of MDS for the HES

microsimulation modelling and Phil Henry of Business Geographics for the mapping.


Executive Summary

During the 12 months to May 2014, YoY employment growth was 99,400, unemployment growth was 43,000 and Not in the Labour Force

growth was 197,500. Given our long term participation and unemployment rates of about 65 percent and 5 percent respectively, we should have

seen employment growth of 210,000, unemployment growth of 10,000 and Not in the Labour Force growth of 120,000. So our employment

growth was about 110,000 too low, employment growth was 30,000 too high and Not in the Labour Force was 80,000 too high.

Year on Year increases in those Not in the Labor Force includes students and retirees, but also discouraged workers – the Hidden Unemployed.

These are persons who have lost their jobs but had not been actively seeking work during the survey period or were not ready to start work

immediately. They could have a working partner, some savings or supportive parents and find it difficult to relocate a short notice to a different

work region.

At the end of the GFC in May 2010, 64.8 percent of full time jobs were held by men, but in the four years since then, only 54.6 percent of

additional full time jobs were won by men. When we include part time jobs, in the past four years, men have gained only 46.3 percent of all new

jobs, pulling down the percentage of males in the total workforce from 54.5 percent to 54.1 percent.

Despite this disproportionate loss of job by men, women dominate those who are unemployed but actively seeking work, while those men losing

jobs are increasingly sliding into the ranks of the hidden unemployed. In the last 12 months twice as many women (96,600) as men (45,800)

joined the labour force and almost twice as many men (124,100) as women (73,300) left the labour market.

Since May 2010 the participation rate for women has increased by 0.4 percent, from 58.4 percent to 58.8 percent, while the participation rate for

men has dropped by 1.2 percent, from 72.1 percent to 70.9 percent. This represents a loss of about 75,000 male jobs in the past year.

Drilling down into Industries.

During the past 12 months, we have seen 153,880 more men hired in Agriculture, Mining, Construction, Real Estate, Prof. Consulting,

Education, Health and Services, while we have seen 120,700 men leaving jobs in Utilities, Wholesale, Retail, Hospitality, Transport, Media,

Finance, Public Admin and Arts & Recreation, producing a net 33,100 extra jobs for men in 12 months.

Women won fewer jobs in the past year than men (116,200 compared to 153,880) in industries like Real Estate, Professional Consulting, Public

Admin and Services, but lost far fewer jobs than men (50,700 compared to 120,700) in Media, Health and Arts & Rec. In net terms, women won


two out of every three jobs created in the past year (65,500 compared to 33,100) due to their success in winning Public Admin and Real Estate

jobs and by not losing as many private sector jobs as men in Wholesale, Retail and Hospitality.

Workers in health, education and public admin industries are more likely to be taxpayer funded, members of a white collar union, female, and

vote Labor or Green. Under Labor and the Greens, between November 2007 and September 2013, these industries gained more than half a

million new jobs, moving from 23.7 percent of the workforce to 26.5 percent.

Workers in manufacturing, construction and transport industries tend to be private sector, belong to ALP affiliated unions, male, and vote Labor.

Under Labor's protectionist industry polices, these industries lost 25,000 workers in absolute terms and fell from 24.1 percent of the workforce in

2007 to 22.2 percent in September 2013.

In recent months the workforce proportion of both of these female white collar and male blue collar industry groups has fallen, with a slowdown

in public service growth and cuts to protected blue collar jobs in the private sector. These six industries make up nearly half the total Australian

workforce, they often comprise both parents of the same working families and they completely dominate Australia's commuter belt.

The Spatial Dimension

Regions which lost the most jobs in the 12 months to May 14 cover a cross section of Australia, from selected inner suburbs of Melbourne and

North Sydney, to middle class commuter regions in Sydney and Perth, to blue collar outer suburbs of Brisbane, Adelaide and Perth and further

out from the CBD, to Provincial cities in NSW, Victoria and Queensland.

We are looking at pockets of older empty nesters in the inner cities, retail salespersons and self-drive commuters in the middle class commuter

suburbs, low income males driving a truck to work in the outer suburbs and older home owners in the provincial cities. These are big,

mainstream demographics, led by the six out of ten workers who drive themselves to work.

The regions doing well are biased towards Queensland, with six out of the State’s 19 regions performing strongly. We have some higher income

inner city regions from Brisbane and Perth and some outer suburban regions, provincial city regions and remote and rural regions from most

states.

Demographically we see young, mobile voters, prepared to travel to find work. This includes travel to mining regions where they join

Australians speaking Indigenous Languages and practising traditional Aboriginal religions.


The younger groups are heavily biased towards migrants from Ireland and other Anglophone countries not shown here, such as Canada, the

USA, Singapore and New Zealand. Their homes are rented, their web access comes from their mobile phone or tablet, their relationships are

informal and there are no kids in sight.

The political dimension

The 13 regional and outer urban mortgage belt electorates losing the most jobs could be considered to be in a serious economic downturn over

the past year, with significant contraction of their local employment bases and flow on effects on industries such as retail and hospitality. All 13

seats are in New South Wales or Victoria and ten of the 13 are represented by the Coalition, with five of them in Vic. Of the 10 seats represented

by the Coalition, five are marginal, three in NSW and two in Victoria.

The 13 electorates with strongly expanding local job markets over the past 12 months include six from NSW, one from Vic, two from Qld, three

from WA and the NT. Nine of the 13 seats are represented by the Coalition, three of them marginal.

The unemployed hiding in plain view

We define the current Hidden Jobless demographics by profiling regions where the past 12 months have seen employment numbers fall, but

unemployment rates fall or remain neutral. So jobs have been lost, but those losses have not shown up as unemployed.

We are looking at three distinct stereotypes here. The first is mainstream Australian working families, forty something and married. Dad has just

lost his full time job in wholesale or retail and mum with a background in management and commerce studies is working in a senior clerical or

admin job, probably in public admin, real estate, professional consulting or services.

Mum has taken over as the principal breadwinner, driving herself to work, and dad would be looking after their outer suburban McMansion,

keeping a stressed eye on both the jobs market and the mortgage offset account, walking the two kids to school and networking with the other

dads about play dates after the first bell.

The family was earning $80,000 to $135,000 per annum when both parents were working and the mortgage would be $17,000 to $30,000 per

month, meaning they were in some mortgage stress before dad lost his job. This story does not have a happy ending – especially for

Governments.


The second group is working mums 55 and over, with a family and fully paid off house. They had a part time job, then lost virtually all their

working hours and now they’ve moved on, chasing a job with more hours and living off savings or their husband’s income.

The third group is single mums retraining at TAFE and navigating their way through the welfare maze.

Those making a go of it.

If there is a positive score for a measure of Hidden Unemployment, then there must also be a negative score, for where things are on the

improve. We find this in the demographic profile of regions where in the past 12 months, employment numbers have risen and unemployment

rates have also risen. This means Hidden Jobless numbers have been falling, as formerly discouraged workers have been moving from the Not in

the Labour Force group and back into the Labour Force, either in a job, or actively hunting for a job and able to start work straight away.

There are a number of points which stand out here. Firstly this group is young, unattached and highly mobile. They rent their house and get their

access to the web via their phone or iPad. If there is a new job available in another region picking fruit or in coal mining, exploration or mining

services, then they are on their way. They are prepared to take jobs where they have to live away from home or live in special tenancy

arrangements on farms.

They aren’t big on filling in census forms, especially when it comes to telling someone from the Government how much they earn or what

religion they follow, although some follow the rural religions of Uniting or Lutheran.

A small number also follow the Traditional Aboriginal religions, which is a sign of increasing employment in real mining or agriculture jobs for

remote Indigenous Australians.

There are a lot of young migrants in the group from English speaking countries, such as Ireland and New Zealand.


Method and Presentation

This profile is based on jobs data collected by the Australian Bureau of Statistics. The primary source is the monthly Labour Force survey by

Regions (6291.0.55.001), but includes national data from 6202.0, detailed quarterly data from 6291.0.55.003, earnings by industry 6302.0 Job

Vacancies Australia 6354.0 and Demographic Statistics 3101.0. We have also made some use of ABS data on underemployment and labour

underutilisation. As detailed data is only available in Original form, we use this, unless otherwise stated to be Trend or Seasonally Adjusted.

The modelling used by ADS compares this cross section of data with our Elaborate database and uses inferential statistics to project these sample

results onto all Australian postcodes and Commonwealth Electoral Divisions. Material in the ADS Elaborate database is based on data from the

ABS Census, Electoral Commissions, Household Expenditure Surveys modelled by MDS Data Systems, data on home loan arrears and a wide

range of published data on political and economic behaviour.

We present here, in Correlation Tables, the correlation between our database and the relevant jobs figures by region. The correlations have been

ranked to typically show those which are normally significant to 95 percent or more (0.21). In other words, there’s a five percent probability the

correlations in the table are due to chance. The higher the correlation, plus or minus, the lower the probability it is due to chance.

The Correlation Charts should be read the same way as the worm debating chart – the zero line is neutral and the score heightens as the

correlation increases its distance above or below the zero line. A positive correlation shown well above the zero line means that the demographic

group in question is concentrated in regions or suburbs with high levels of the relevant jobs figure. A negative correlation well below the zero

line means they’re concentrated in regions or suburbs where the jobs figure is lower.

Growth in hidden unemployment across labour market regions after the GFC led us to rely more on workforce participation and employment

growth as a stronger guide to the health of regional labour markets than the traditional unemployment rate. In this report we make some attempts

to infer a demographic picture of the hidden unemployed, using the differences between jobs lost and unemployment growth. If jobs are being

lost in some regions, but unemployment is not rising in the same regions, then it is reasonable to assume we have a lot of retirements happening

in these regions or we have a rise in hidden unemployment. This is a judgement call based the demographics of these regions and national

underemployment trends by age, with the latter indicating many older persons are chasing more hours of work when interest rates are low.

Labour Market data provided by the Australian Bureau of Statistics has undergone a major overhaul leading up to the preparation of the

February Quarter figures. With adjustments for a new Census, new boundaries, a new sample and a new collection system, the figures will need

caution in their interpretation for the first six months of the year.


Table 1. National labour market summary (shown in 000s). The monthly figures for each year are in the left hand columns (green

headings) and the Year on Year figures (YoY) are in the right hand columns (blue headings).

During the 12 months to May 2014, YoY employment growth was 99,400, unemployment growth was 43,000 and Not in the Labour Force

growth was 197,500. Given our long term participation and unemployment rates of about 65 percent and 5 percent respectively, we should

have seen employment growth of 210,000, unemployment growth of 10,000 and Not in the Labour Force growth of 120,000. So our

employment growth was about 110,000 too low, employment growth was 30,000 too high and Not in the Labour Force was 80,000 too

high.

Year on Year increases in those Not in the Labor Force includes students and retirees, but also discouraged workers. These are persons who

have lost their jobs but had not been actively seeking work during the survey period or were not ready to start work immediately. They

could have a working partner, some savings or supportive parents and find it difficult to relocate a short notice to a different work region.

At the end of the GFC in May 2010, 64.8 percent of full time jobs were held by men, but in the four years since then, only 54.6 percent of

additional full time jobs were won by men. When we include part time jobs, in the past four years, men have gained only 46.3 percent of all

new jobs, pulling down the percentage of males in the total workforce from 54.5 percent to 51.1 percent.

Despite this disproportionate loss of job by men, women dominate those who are unemployed but actively seeking work, while those men

losing jobs are increasingly sliding into the ranks of the hidden unemployed. In the last 12 months twice as many women (96,600) as men

(45,800) joined the labour force and almost twice as many men (124,100) as women (73,300) left the labour market.

Since May 2010 the participation rate for women has increased by 0.4 percent, from 58.4 percent to 58.8 percent, while the participation

rate for men has dropped by 1.2 percent, from 72.1 percent to 70.9 percent. This is a loss of about 75,000 male jobs in the past year.

Month &

Year

Employed

total

Persons

Unemployed

total

Persons

Labour

Force

Persons

Not in the

Labour

Force

Persons

Civilian

population

Persons

Unemploym

ent rate

Persons

Participati

on rate

Persons

Employmen

t to

population

ratio

Persons

12 month

change

Employed

Persons

12 month

change

Unemploye

d Persons

12 month

change

Labour

Force

Persons

12 month

change Not

in the Labour

Force

Persons

12 month

change

Civilian

population

Persons

12 month

change

Unemploym

ent rate

Persons

12 month

change

Participation

rate

Persons

12 month

change

Employment

to population

ratio Persons

Nov-2007 10535.0 451.4 10986.4 5876.8 16863.2 4.1 65.1 62.5 338.2 6.0 344.3 -0.2 344.0 -0.1 0.7 0.7

May-2008 10690.3 478.3 11168.6 5896.4 17065.0 4.3 65.4 62.6 280.3 15.0 295.3 75.0 370.2 0.0 0.3 0.3

May-2009 10786.8 667.5 11454.3 5995.0 17449.4 5.8 65.6 61.8 93.6 192.1 285.7 107.7 393.4 1.5 0.2 -0.9

May-2010 10957.4 604.6 11562.0 6191.1 17753.2 5.2 65.1 61.7 243.1 -60.9 182.1 211.6 393.8 -0.6 -0.4 0.0

May-2011 11196.2 589.8 11786.0 6238.2 18024.2 5.0 65.4 62.1 255.4 -13.7 241.7 39.1 280.7 -0.2 0.3 0.4

May-2012 11389.0 620.2 12009.2 6330.1 18339.3 5.2 65.5 62.1 192.8 30.4 223.2 91.9 315.1 0.2 0.1 0.0

May-2013 11495.6 675.9 12171.5 6502.0 18673.5 5.6 65.2 61.6 106.6 55.7 162.3 171.9 334.2 0.4 -0.3 -0.5

Apr-2014 11588.7 714.6 12303.3 6680.1 18983.4 5.8 64.8 61.0 106.5 32.2 138.7 198.3 337.0 0.2 -0.4 -0.5

May-2014 11595.0 718.9 12313.9 6699.4 19013.3 5.8 64.8 61.0 99.4 43.0 142.3 197.5 339.8 0.3 -0.4 -0.6







Table 2. This table shows (top rows) quarterly employment levels by industry in 000’s for employed persons in column two of Table 1.

Middle rows show breakdowns for each major industry group for employment changes for the last 12 months, then the last quarter and

the last row shows longer term trends between November 2007 and May 2014.

Bottom rows show gender break downs for the past 12 months for each Industry. For example, Agriculture grew by 28,100 persons in

the past 12 months and 20,400 were men, with 7,700 women. In May 14, 71.7 percent of persons employed in Agriculture were men.

The industry basket cases since November 2007 have been the male dominated industries of Agriculture, Manufacturing, and the Media.

The big job drivers over the same period have been in Mining, Accommodation and Food, Professional Consulting and the

overwhelmingly female public sector dominated trio of Public Admin, Education and Health. This public sector growth meant female

jobs growth outnumbered male jobs growth by 50,000 since November 2007.

During the past 12 months, we have seen 153,880 more jobs for men in Agriculture, Mining, Construction, Real Estate, Prof.

Consulting, Education, Health and Services, while we have seen 120,700 job losses for men in Utilities, Wholesale, Retail, Hospitality,

Transport, Media, Finance, Public Admin and Arts & Recreation, producing a net 33,100 extra jobs for men in 12 months.

Women won fewer jobs in the past year than men (116,200 compared to 153,880) in industries like Real Estate, Professional

Consulting, Public Admin and Services, but lost far fewer jobs than men (50,700 compared to 120,700) in Media, Health and Arts &

Rec. In net terms, women won two out of every three jobs created in the past year (65,500 compared to 33,100) due to their success in

winning Public Admin and Real Estate jobs and by not losing as many private sector jobs as men in Wholesale, Retail and Hospitality.

Quarter & Year

Agricultur

e\

forestry &

f ishing Mining

Manufactur

ing Utilities

Constructi

on

Wholesal

e Retail

Accom &

Food Transport Media Finance

Real

Estate

Professio

nal

consulting

Admin

consulting

Public

admin

Educatio

n

Health &

social

assist

Arts &

recreact

ion

Other

Service

s

Total

Employe

d

Nov-2007 342.9 137.1 1038.8 109.6 953.6 371.9 1240.9 679.2 553.4 234.2 400.5 193.3 763.7 335.5 628.9 785.7 1084.7 199.5 481.9 10535.1

May-2008 352.4 163.7 1062.2 124.4 973.2 397.9 1223.9 707.8 566.6 222.3 406.9 199.4 778.6 339.5 626.7 821.6 1101.1 176.7 445.6 10690.3

May-2009 338.1 154.2 991.3 135.4 987.7 401.3 1205.1 721.2 597.8 223.3 394.9 181.6 767.6 343.2 692.0 811.7 1192.0 204.6 443.9 10786.8

May-2010 358.9 179.2 965.1 140.1 1001.9 404.6 1172.7 753.9 590.2 219.7 388.1 195.6 834.6 365.7 692.3 832.1 1210.9 194.8 457.0 10957.4

May-2011 319.6 214.1 952.6 150.3 1030.4 392.2 1202.4 777.4 574.0 214.6 426.8 202.3 839.6 390.7 710.1 870.4 1266.9 217.4 444.5 11196.2

May-2012 339.2 275.5 939.6 155.7 982.9 400.3 1190.9 766.2 544.9 236.7 431.6 216.0 922.4 394.2 701.5 896.7 1330.9 218.6 445.3 11389.0

May-2013 299.9 260.3 916.7 148.8 990.2 426.0 1236.3 801.5 583.6 219.5 423.8 193.5 907.5 390.7 730.7 915.8 1371.4 221.9 457.2 11495.6

May-2014 327.9 264.6 921.5 146.4 1033.0 385.6 1234.8 780.5 585.7 203.4 408.5 229.5 937.6 397.1 730.2 924.2 1385.1 190.6 508.7 11595.0

May 14 minus May 13 28.1 4.2 4.8 -2.4 42.8 -40.5 -1.5 -21.0 2.1 -16.1 -15.4 36.0 30.1 6.4 -0.6 8.4 13.7 -31.3 51.5 99.4

May 14 minus Feb 14 8.6 -6.6 -28.3 -9.7 -4.4 10.0 26.7 54.5 -5.0 -0.1 -13.7 29.0 47.1 17.7 -11.3 41.8 -49.1 -17.9 12.9 101.9

May 14 minus Nov 07 -15.0 127.5 -117.4 36.8 79.4 13.6 -6.1 101.3 32.3 -30.8 8.0 36.3 174.0 61.6 101.3 138.5 300.5 -8.9 26.9 1059.9

May 14 Percentage Males 71.7 86.0 73.3 78.5 89.0 66.3 43.9 44.0 77.0 61.9 48.0 48.2 56.4 47.2 51.2 30.2 23.2 52.6 56.2 54.2

Extra Males since May 13 20.4 7.6 5 -1.9 38.2 -31.2 -3.9 -25.2 -4.4 -5.1 -14.3 10.8 12.3 0.3 -16.6 9.7 24.6 -18.1 24.9 33.1

Extra Females since May 13 7.7 -3.3 -0.2 -0.5 3.4 -9.3 2.4 4.2 6.6 -11.0 -1.0 25.2 17.9 6.7 15.5 -1.3 -10.8 -13.3 26.6 65.5

Fem minus Male since May 13 -12.7 -10.9 -5.2 1.4 -34.8 21.9 6.3 29.4 11 -5.9 13.3 14.4 5.6 6.4 32.1 -11 -35.4 4.8 1.7 32.4



Table 3. Labour Force Regions with the biggest percentage loss of jobs in the year to May 2014 are shown above left. Above right are the

demographic groups disproportionately represented in these regions. The regions at left are colour coded for each state and the

demographic groups at right are also colour coded, with shades of red referring to lower income and mortgages groups.

Regions which lost the most jobs in the 12 months to May 14 cover a cross section of Australia, from the rich inner suburbs of Melbourne

and North Sydney, to middle class suburban regions in Sydney and Perth, to blue collar outer suburbs of Brisbane, Adelaide and Perth, to

Provincial cities in NSW, Victoria and Queensland. So what does such a broad cross section of regions have in common?

The answer is seen in the demographic groups at top right. We are looking at older Australians in the inner cities, retail salespersons and

self-drive commuters in the middle class suburbs, low income males in the outer suburbs and older home owners in the provincial cities.

Of interest here is the fact that the sales industry and occupation groups include males and females, but all of the age groups are female.

These are big, mainstream demographics, led by the six out of ten workers who drive themselves to work.

Variables

Emp May 14

over May 13

Aust

Means

persons 55-64 Married -0.30 9.7

Retail -0.30 8.3

Fem 55-59 two kids -0.30 2.9

Fem Aged 50-54 -0.29 6.8

persons 65-74 Married -0.29 6.2

Fully Owned Home -0.28 32.1

Fem Retail -0.28 13.0

Fem 60-64 two kids -0.28 2.7

Fem Aged 60-64 -0.28 5.6

Fem Aged 65-69 -0.27 4.3

Self Drive Car Commuters -0.27 60.2

Fem 50-54 two kids -0.27 3.2

Fem 65-69 two kids -0.27 1.9

Median age -0.27 37.00

Inc $400-599 -0.26 9.2

Aged 60-64 -0.26 5.6

Aged 75-79 -0.25 2.4

Fem Aged 55-59 -0.25 6.1

Fem Salespersons -0.25 12.4

Labour Force Regions

Emp May

13

Emp May

14

Emp May

14 over

May 13

Murray 54.8 44.1 80.5

Logan - Beaudesert 150.8 124.9 82.8

Shepparton 64.7 54.2 83.9

Townsville 123.9 106.6 86.1

Adelaide - South 191.8 165.3 86.1

New England and North West 87.0 77.2 88.7

Melbourne - Inner East 188.2 167.8 89.1

Sydney - South West 166.1 150.5 90.6

Warrnambool and South West 62.2 56.4 90.7

Richmond - Tweed 100.9 92.1 91.3

Wide Bay 118.8 110.0 92.6

Hunter Valley exc Newcastle 125.3 116.5 93.0

Far West and Orana 57.1 53.1 93.1

Coffs Harbour - Grafton 59.1 55.4 93.6

Central Coast 156.8 147.7 94.2

Sydney - North Sydney and Hornsby 231.0 218.7 94.7

Mandurah 43.0 40.8 94.9

Perth - South West 209.7 199.0 94.9


Table 4. Labour Force Regions with the greatest percentage increases in employment in the year to May 2014 are shown at top left and

above right are the demographic groups located disproportionately in these regions. The average increase in employment across all regions

was 101.1 percent. The reader should be away that high population growth regions will be favoured in this table.

The regions here look biased towards Queensland, with six out of the State’s 19 regions performing strongly. We have some inner city

regions from Brisbane and Perth, some outer suburban regions, provincial city regions and remote and rural regions from most states. But

we aren’t seeing many middle class commuter regions, which is to be expected with self-drive commuters in the opposing table above.

Demographically we see young, mobile voters, prepared to travel to find work. This includes travel to mining regions where they join

Australians speaking Indigenous Languages and practising traditional Aboriginal religions. The younger groups are heavily biased towards

migrants from Ireland and other Anglophone countries not shown here, such as Canada, the USA, Singapore and New Zealand. Their homes

are rented, their web access comes from their mobile phone or tablet, their relationships are informal and there are no kids in sight.

Variables

Emp May 14

over May 13

Aust

Means

Rented Total 0.30 29.6

Renter via State 0.28 4.1

Walk to work 0.27 3.7

Aus Indig Lang 0.26 0.3

Moved past year 0.26 15.1

Fem Aged 25-29 0.26 7.0

Aged 25-29 0.25 7.1

Semi Detached Bedrooms 1 0.25 0.7

Internet Other 0.25 3.9

Fem Mining 0.25 0.7

Fem Aged 20-24 0.25 6.6

Aged 20-24 0.24 7.0

No Cars at home 0.23 8.6

Fem Aged 30-34 0.23 6.8

Aged 30-34 0.23 6.8

persons 25-34 De Facto 0.22 3.3

Fem Ireland 0.21 0.3

Fem Aust Ab Trad 0.21 0.0

Labour Force Regions

Emp May

13

Emp May

14

Emp May

14 over

May 13

Moreton Bay - North 89.6 108.9 121.6

Perth - Inner 89.0 107.9 121.2

Northern Territory - Outback 47.4 56.2 118.5

Illawarra 117.1 137.5 117.4

Bunbury 81.9 95.7 116.8

Riverina 77.0 89.3 116.1

Queensland - Outback 39.4 45.3 114.9

Brisbane - North 108.7 123.7 113.8

Brisbane - East 110.8 125.1 112.9

Brisbane Inner City 142.6 160.9 112.9

North West 68.0 76.3 112.1

Fitzroy 111.7 121.5 108.8

Hume 76.0 82.2 108.2

Melbourne - West 336.2 363.2 108.0

Bendigo 71.1 76.7 107.9

Sydney - Ryde 86.0 92.5 107.6

Newcastle and Lake Macquarie 167.3 178.0 106.4

Sydney - Parramatta 194.2 206.2 106.2


Table 5. We used statistical modelling to project an estimate of the regional Labour market figures onto 2013 election boundaries and

Table 5 above, shows the 13 electorates with the biggest falls in Participation Rates at top left, and the 13 electorates with the biggest

rises in Participation Rates at top right. Participation Rates are an excellent measure of the employment shown above in Tables 3 and 4.

They are controlled for growth and include those actively seeking work.

The 13 regional and outer urban electorates at top left could be considered to be in a serious economic downturn over the past year,

with significant contraction of their local employment bases, with flow on effects on industries such as retail and hospitality. All 13

seats are in New South Wales or Victoria and ten of the 13 are represented by the Coalition, with five of them in Vic. These seats

contain the demographics in Table 3 above – older female workers in part time jobs chasing more hours of work to make up for falling

investment incomes and men driving themselves to work in a high street sales job which is now under pressure from falling consumer

confidence.

The 13 electorates at top right have had strongly expanding local job markets over the past 12 months. Some six of the seats are in

NSW, one in Vic, two in Qld, three in WA and the NT. Nine of the 13 seats are represented by the Coalition. These seats tend to

contain the demographics we saw in Table 4, above – young singles or de facto couples, lots of Kiwis and Anglophone migrants with

rented homes, mobiles and tablets, prepared to travel to earn the best money, even to remote parts of the country. In these remote seats

we also see – finally – some original Australians making a dollar from the Mining Industry.

Seat STATE

ALP 2013

2PP Sept

8

Part Rate

May 13

Part Rate

May 14

Part Rate

May 14 min

May 13 Seat STATE

ALP 2013

2PP Sept

8

Part Rate

May 13

Part Rate

May 14

Part Rate

May 14 min

May 13

Menzies VIC 36.3 66.0 60.7 -5.4 Reid NSW 49.6 66.4 71.4 5.0

Flinders VIC 38.9 61.9 57.1 -4.8 Gellibrand VIC 66.8 64.1 69.1 5.0

Aston VIC 42.1 70.3 65.5 -4.8 Wentworth NSW 32.4 70.2 74.3 4.1

Paterson NSW 40.3 58.1 53.6 -4.5 Blaxland NSW 62.3 56.6 60.2 3.7

Lyne NSW 34.9 53.8 49.5 -4.3 Warringah NSW 34.5 69.0 72.6 3.6

Shortland NSW 57.0 59.3 55.1 -4.2 Swan WA 44.1 67.4 70.5 3.2

Robertson NSW 47.9 62.2 58.1 -4.1 Griffith QLD 53.0 73.2 76.1 3.0

Corio VIC 57.2 62.5 58.4 -4.0 Solomon NT 49.1 74.4 77.3 2.9

Lindsay NSW 47.3 68.7 64.7 -4.0 Petrie QLD 49.5 61.1 63.7 2.6

Dobell NSW 49.3 61.9 58.1 -3.8 O'Connor WA 30.0 67.6 70.2 2.6

Charlton NSW 59.9 62.3 58.8 -3.5 North Sydney NSW 34.4 70.4 72.9 2.5

Corangamite VIC 46.3 65.8 62.6 -3.2 Forrest WA 38.1 65.1 67.6 2.5

Dunkley VIC 45.4 64.1 60.9 -3.1 Parramatta NSW 50.8 64.3 66.7 2.4


Labour Market Change Charts The charts above deal with descriptive statistics. Where they use original data, we compare a given month with the same month in the preceding

years, typically using Year on Year figures, to remove the problem of seasonal adjustment. For the female underemployment chart we use

national trend figures.

The following charts rely on inferential statistics and show correlations between some of the variables in our database and changes in the Year

on Year levels of employment across Australian Labour Force regions. In the chart directly below, we see interest rates over the period

November 2008 to May 14 shown by the green line. The red line shows the correlation between rises in employment and increases in the

average age of Labour Force Regions. As employment rises in older regions, the red line rises, as jobs are lost in older regions, the red line falls.

When interest rates are low for an extended period jobs seem to be lost in older regions and gained in younger regions.

For the section below on hidden unemployment, from Page 21 onwards, we make some attempts to infer a demographic picture of the hidden

unemployed, using the differences between jobs lost and unemployment growth. If jobs are being lost in some regions, but unemployment is not

rising in the same regions, then it is reasonable to assume we have a lot of retirements happening in these regions or we have a rise in hidden

unemployment. We know retirement ages are being push upwards, not least of all because the Government plans to lift the retirement age. We

can also see in both the descriptive and inferential statistics that many older people can’t afford to retire when interest rates are too low. And we

know there has been an abnormal 80,000 increase in those Not in the Labour force during the past 12 months.

In the Age Male Chart on Page 24 below we see men aged 45-49 years and their teenage children tend to live in mid urban Labour Force regions

where jobs have been lost over the past year, but Unemployment Rates have not risen to a similar degree. The dark line represents the extent to

which jobs lost have exceeded growth in Unemployment rates and hence is our best inferred demographic match up to the numbers from the

descriptive statistics in tables 1 and 2.

Regions dominated by men in their late twenties have been recording negative jobs loss (they have gained jobs), negative unemployment growth

(Unemployment Rates have risen) and negative Hidden Unemployment rates (Hidden Unemployment has fallen). In employment growth

regions, the lure of jobs attracts younger, more mobile workers and unemployment rates increase in the short term. In older regions jobs are

being lost, but being added straight onto the Unemployment Rates as there are no jobs in these areas and older workers find it harder to relocate.

Any correlation more than plus or minus 0.18 from the zero line is significant to 90 percent confidence interval, which means there is a ten

percent chance the result is due to random factors and doesn’t mean anything at all. If the correlation is .21 the confidence interval is 95 percent,

0.27 is 99 percent and 0.34 is 99.9 percent.








Variables

Aust

Means

Jobs lost

in past

year

Unem

growth

in past

year

Hidden

Jobless

rise in past

year

Fem 70-74 one kid 0.3 0.15 -0.18 0.33

Home Being Purchased 34.9 0.18 -0.13 0.31

Single Parent kids over 15 7.5 0.11 -0.17 0.28

Family kids over 15 13.9 0.12 -0.16 0.28

TAFE Students 2.2 0.19 -0.09 0.28

Self Drive Car Commuters 60.2 0.27 0.01 0.26

Fem Clerical & Admin 24.2 0.04 -0.21 0.25

Wholesale 4.9 0.17 -0.08 0.25

Rent $225-274 11.7 0.19 -0.06 0.25

Retail 8.3 0.30 0.06 0.25

Three Person Home 16.0 0.02 -0.22 0.24

Fem 60-64 no kids 0.8 0.06 -0.19 0.24

Fem 65-69 one kid 0.5 0.19 -0.06 0.24

Mort $1400-1799 14.8 0.25 0.01 0.24

Fem 45-49 one kid 1.2 -0.03 -0.27 0.23

Four Person Home 15.7 0.05 -0.17 0.22

Fem fos Management/Commerce 22.3 0.06 -0.17 0.22

Fem 40-44 two kids 3.5 0.09 -0.14 0.22

Fem Aged 45-49 7.0 0.18 -0.04 0.22

Fam $1500-1999 12.5 0.12 -0.09 0.21

Fem 60-64 one kid 0.7 0.15 -0.05 0.21

Fem 75-79 two kids 0.8 0.19 -0.02 0.21

persons 45-54 Married 11.1 0.20 -0.02 0.21

Fem 55-59 two kids 2.9 0.30 0.09 0.21

Mort $1800-2399 20.2 -0.01 -0.21 0.20

Aged 45-49 7.0 0.02 -0.18 0.20

Fem 55-59 one kid 0.9 0.05 -0.15 0.20

Mortgage stress 33.7 0.18 -0.02 0.20

Two Cars at home 36.1 0.22 0.02 0.20

Table 6. Here we see in the Hidden Jobless column at far

right the demographic profile of regions where the past 12

months have seen employment numbers fall, but

unemployment rates fall or remain neutral.

We are looking at three distinct stereotypes here. The first is

mainstream Australian working families, forty something

and married. Dad has just lost his full time job in wholesale

or retail and mum has a background in management and

commerce studies and is working in a senior clerical or

admin job, probably in public admin, real estate, professional

consulting or services (see also Table 2).

Mum has taken over as the principal breadwinner, driving

herself to work, and dad would be looking after their outer

suburban McMansion, keeping a stressed eye on both the

jobs market and the mortgage offset account, walking the

two kids to school and networking with the other dads about

play dates after the first bell.

The family was earning $80,000 to $135,000 per annum

when both parents were working and the mortgage would be

$17,000 to $30,000 per month, meaning they were in some

mortgage stress before dad lost his job. This story does not

have a happy ending – especially for Governments.

The second group is working mums 55 and over, with a

family and fully paid off house. They had a part time job,

then lost virtually all their working hours and now they’ve

moved on, chasing a job with more hours and living off

savings or their husband’s income.

The third group is single mums retraining at TAFE and

navigating their way through the welfare maze.


Table 7. Here we see in the Hidden Jobless column at far

right, the demographic profile of regions where in the past

12 months, employment numbers have risen and

unemployment rates have also risen. This means Hidden

Jobless numbers have been falling, as formerly discouraged

workers have been moving from the Not in the Labour Force

group and back into the Labour Force, either in a job, or

actively hunting for a job and able to start work straight

away.

There are a number of points which stand out here. Firstly

this group is young, unattached and highly mobile. They rent

their house and get their access to the web via their phone or

iPad. If there is a new job available in another region picking

fruit or in coal mining, exploration or mining services, then

they are on their way. They are prepared to take jobs where

they have to live away from home or live in special tenancy

arrangements on farms.

They aren’t big on filling in census forms, especially when it

comes to telling someone from the Government how much

they earn or what religion they follow, although some follow

the rural religions of Uniting or Lutheran.

A small number also follow the Traditional Aboriginal

religions, which is a sign of increasing employment in real

mining or agriculture jobs for remote Indigenous

Australians.

There are a lot of young migrants in the group from English

speaking countries, such as Ireland and New Zealand.

Finally they don’t look the sort of workers who would

belong to unions or be easy to sign up into unions. They are

the sort of self-motivated kids who can look after

themselves.

Variables

Aust

Means

(RHS)

Jobs lost

in past

year

Unem

growth

in past

year

Hidden

Jobless

rise in

past year

Other Tenure 0.9 -0.12 0.27 -0.40

Walk to work 3.7 -0.27 0.09 -0.36

Fem Mining 0.7 -0.25 0.10 -0.35

Rented Total 29.6 -0.30 -0.01 -0.29

Rel Not Stated 9.2 -0.19 0.08 -0.27

Lutheran 1.1 -0.06 0.21 -0.26

Fem Lutheran 1.2 -0.07 0.19 -0.26

Rent Not Stated 3.5 -0.11 0.15 -0.26

persons 20-24 De Facto 1.2 -0.15 0.11 -0.26

Fem Rel Not Stated 7.9 -0.16 0.09 -0.25

Mining 2.7 -0.16 0.08 -0.25

Uniting 4.5 0.09 0.33 -0.24

Fem Employment NS 5.2 -0.22 0.01 -0.23

Moved past year 15.1 -0.26 -0.03 -0.23

Rent $0-74 6.8 -0.14 0.09 -0.23

Inc NS_Inc 8.5 -0.20 0.03 -0.22

Fem Uniting 5.4 0.09 0.32 -0.22

Employment NS 6.0 -0.24 -0.02 -0.22

Not Stated Schooling 9.0 -0.18 0.03 -0.21

Fem Managers & Farmers 9.7 0.07 0.28 -0.21

Renter Other 1.4 -0.20 0.01 -0.21

Home Lang NS 5.6 -0.26 -0.05 -0.21

persons 25-34 De Facto 3.3 -0.22 -0.01 -0.21

Ireland 0.3 -0.21 0.00 -0.20

Fem_NS_Inc 7.3 -0.15 0.04 -0.19

Aust Ab Trad 0.0 -0.21 -0.01 -0.19

Internet Other 3.9 -0.25 -0.06 -0.19

Fem Agric\ forestry & fishing 1.6 0.07 0.26 -0.19

Employed/away from work 2.1 -0.10 0.09 -0.19








Documents

Australian Jobs profile for May Qtr. 2014 Jobs Profile May Qtr 2014.pdf · in public service growth and cuts to protected blue collar jobs in the private sector. These six industries