1. Measuring the regional significance of
employment in the creative industries
Simon Freebody – Research assistant (CCI)
Peter Higgs – Senior research fellow (CCI)

2. Agglomeration and Creative industries
• Employment in the creative industries exhibits
agglomeration – i.e. Employment attracted to larger,
urbanised centres:
– Creative “Buzz” and communities
– Local stimuli
– Locality “brand”
– An absence of proclivity to do otherwise?
In light of this, how should we measure the significance of
creative employment in a given region?
• The location quotient provides the traditional method.

3. The location quotient

4. Brief history of the location quotient
• Developed in the late 1930s by Philip Sargant Florence
• Used extensively in economic base analysis to establish
regional employment multipliers
– Found to be an inaccurate estimator
– Continues to be used due to simplicity and availability of data
• Predominantly used in the past to measure manufacturing
activity
• More recently used to measure the significance of creative
industries and the “Creative Class”

5. Location quotient for manufacturing employment
30000 Each point represents a region
(statistical sub-division). The
25000
solid line represents our LQ
reference line.
Manufacturing employment
20000
The manufacturing employment
at a point divided by the
15000 corresponding point on the solid
line gives the location quotient
10000 of the region that points
represents.
5000
0
0 50000 100000 150000 200000
Total employment

6. Location quotient for CI employment
8000 Each point represents a region
(statistical sub-division). The
7000 solid line represents our LQ
reference line.
Creative industries employment
6000
5000 The creative industries
employment at a point divided
4000 by the corresponding point on
the solid line gives the location
3000
quotient of the region that
2000 points represents.
1000
0
0 50000 100000 150000 200000
Total employment

7. Location quotient for manufacturing employment
100000 By logging the scale of the axes
we can see the relationship
between manufacturing
employment and total
10000
employment.
Manufacturing employment
This relationship is reasonably
1000 well approximated by unitary
elasticity - although not
perfectly!
100
10
100 1000 10000 100000 1000000
Total employment

8. Location quotient for CI employment
100000 Conducting the same analysis for
creative industries shows a clear
departure from unitary
10000 elasticity – here the elasticity is
Creative industries employment
greater than one.
1000
What does this mean for our
location quotient?
100 - The location quotient
systematically over-estimates
the significance of creative
10 industries employment in larger
areas, i.e. larger areas will
always score better.
1
100 1000 10000 100000 1000000
Total employment

9. Do the obvious
100000 Performing simple regression
analysis using a double-log
functional form not only
10000 estimates the elasticity
Creative industries employment
mentioned in the slide above, but
the residuals provide us with a
1000
measurement of the regional
significance of creative
100 industries employment.
10
1
100 1000 10000 100000 1000000
Total employment

10. Note on the inclusion of land area
• If the intention is to partial the size of a region out of
creative employment then land area needs to be considered.
• Reasonable to assume that land area may have some impact
– population density as a measure of urbanisation
• Thus we include land area – which is also log-normally
distributed – in the regression analysis producing a density
sensitive index (DSI).
• Final regression model takes the form:

11. LQ vs. DSI
Location quotient Rank Density sensitive index
Lower Northern Sydney 1 Kimberley
Inner Sydney 2 Gold Coast Hinterland
Inner Melbourne 3 Northern Territory excl. Darwin
North Canberra 4 Tuggeranong, Canberra
Inner Brisbane 5 Lower Northern Sydney
Boroondara City, Melbourne 6 Southern Tasmania
South Canberra 7 East Barwon, Victoria
Tuggeranong, Canberra 8 North Canberra
Central Metropolitan Perth 9 Weston Creek-Stromlo, Canberra
Eastern Suburbs 10 Sunshine Coast Hinterland
Northern Beaches 11 East Central Highlands, Victoria
Eastern Adelaide 12 South Canberra
Weston Creek-Stromlo, Canberra 13 ACT excl. Canberra
Belconnen, Canberra 14 Boroondara City, Melbourne
Gungahlin-Hall, Canberra 15 Gungahlin-Hall, Canberra

12. Lets experiment...
1. Rank regions by LQ and by density sensitive index.
2. Assign regions as “under-rated” or “over-rated” thus:
– If LQ rank higher than DSI rank: “over-rated”
– If LQ rank lower than DSI rank: “under-rated”
3. Compare the two groups with key demographics.
Example:
LQ rank DSI rank
Over-rated Inner Brisbane 5 48
Under-rated Gold Coast Hinterland 20 2

13. Age: % of population by age group
9% Under-rated regions have
8% significantly less young adults
Over-rated
than over-rated regions and
7% Under-rated
significantly more
6%
children, middle and mature age
% of populaton
5% people.
4%
Under-rated regions are older
3%
2%
1%
0% 100 years and over
0-4 years
5-9 years
10-14 years
15-19 years
20-24 years
25-29 years
30-34 years
35-39 years
40-44 years
45-49 years
50-54 years
55-59 years
60-64 years
65-69 years
70-74 years
75-79 years
80-84 years
85-89 years
90-94 years
95-99 years
ABS Census 2006

14. Income: % of population by income band
25% Under-rated regions have
significantly less workers
20%
Over-rated
earning more than $800 per
Under-rated
week than over-rated regions
and significantly more workers
% of population
15%
earning less than $600 per week.
10% Under-rated regions are poorer
5%
0%
$2,000 or more
Negative income
$1-$149
$150-$249
$250-$399
$400-$599
$600-$799
$800-$999
$1,000-$1,299
$1,300-$1,599
$1,600-$1,999
ABS Census 2006

15. ABS Socio-economic index
1040 One average under-rated
regions score significantly lower
on the SES index than over-rated
1020 Over-rated
1000
Under-rated
regions.
980 Under-rated regions have lower
Socio-economic index
SES
960
940
920
900
880
1006 927
860
ABS Census 2006

16. Applications
• More accurate benchmarking of cities and suburbs
• Identifying diverse agglomeration patterns within creative
segments
• Improve understanding of:
– the determinants, economic and otherwise, of
agglomeration in the creative industries
– the causes and effects of significant employment in the
creative industries
– commuter patterns in satellite cities

17. In conclusion
• The location quotient has proved valuable for measuring
traditional industries.
• When measuring creative industries the location quotient
favours larger, urbanised regions.
• Regression analysis can provide a measure of the
agglomeration in CI and measure the significance of creative
industries employment in a given region without said bias.
• Regions that are under-rated by the location quotient tend
to be less urban: they are older, poorer and lower SES