Creative Suburban Geographies - Simon Freebody

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Creative Suburban Geographies - Simon Freebody

  1. 1. Measuring the regional significance of employment in the creative industries Simon Freebody – Research assistant (CCI) Peter Higgs – Senior research fellow (CCI)
  2. 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. 3. The location quotient
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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