Measuring the regional significance of employment in the creative industries

Simon Freebody – Research assistant (CCI)

Peter Higgs – Senior research fellow (CCI)

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.

The location quotient

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”

Location quotient for manufacturing employment

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Each point represents a region (statistical sub-division). The solid line represents our LQ reference line.

The manufacturing employment at a point divided by the corresponding point on the solid line gives the location quotient of the region that points represents.

Location quotient for CI employment

Each point represents a region (statistical sub-division). The solid line represents our LQ reference line.

The creative industries employment at a point divided by the corresponding point on the solid line gives the location quotient of the region that points represents.

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Location quotient for manufacturing employment

By logging the scale of the axes we can see the relationship between manufacturing employment and total employment.

This relationship is reasonably well approximated by unitary elasticity - although not perfectly!

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Location quotient for CI employment

Conducting the same analysis for creative industries shows a clear departure from unitary elasticity – here the elasticity is greater than one.

What does this mean for our location quotient? - The location quotient systematically over-estimates the significance of creative industries employment in larger areas, i.e. larger areas will always score better.

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Do the obvious

Performing simple regression analysis using a double-log functional form not only estimates the elasticity mentioned in the slide above, but the residuals provide us with a measurement of the regional significance of creative industries employment.

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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:

LQ vs. DSI

Location quotient Rank Density sensitive indexLower Northern Sydney 1 Kimberley

Inner Sydney 2 Gold Coast HinterlandInner Melbourne 3 Northern Territory excl. DarwinNorth Canberra 4 Tuggeranong, CanberraInner Brisbane 5 Lower Northern Sydney

Boroondara City, Melbourne 6 Southern TasmaniaSouth Canberra 7 East Barwon, Victoria

Tuggeranong, Canberra 8 North CanberraCentral Metropolitan Perth 9 Weston Creek-Stromlo, Canberra

Eastern Suburbs 10 Sunshine Coast HinterlandNorthern Beaches 11 East Central Highlands, VictoriaEastern Adelaide 12 South Canberra

Weston Creek-Stromlo, Canberra 13 ACT excl. CanberraBelconnen, Canberra 14 Boroondara City, Melbourne

Gungahlin-Hall, Canberra 15 Gungahlin-Hall, Canberra

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

Age: % of population by age group

Under-rated regions have significantly less young adults than over-rated regions and significantly more children, middle and mature age people.

Under-rated regions are older

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Under-rated regions have significantly less workers earning more than $800 per week than over-rated regions and significantly more workers earning less than $600 per week.

Under-rated regions are poorer

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One average under-rated regions score significantly lower on the SES index than over-rated regions.

Under-rated regions have lower SES

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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

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