varieties of knowledge and economic performance evidence from canadian city-regions, 1991-2001

1. context varieties of knowledge and economic performance evidence from canadian city-regions, 1991-2001 greg spencer & tara vinodrai department of geography & munk centre for international studies university of toronto onris fall workshop, toronto, canada, november 24, 2006

2. context background: goals of quantitative analysis • social dynamics of innovation (theme I) • what is the relationship between economic performance, economic diversity and local / non-local linkages and knowledge flows? • social foundations of talent attraction/retention (theme II) • what is the relationship between economic performance and quality of place? • cultural dynamism, social diversity, openness and tolerance, social inclusion and cohesion, socio-spatial polarization • socio-economic / demographic profiles of city-regions • what are the socio-economic and demographic characteristics of the 15 city-regions included in the ISRN study? • facilitate comparison and the selection of case studies, etc.

3. context purpose of today’spresentation • present preliminary and exploratory research addressing issues related to understanding the relationship between knowledge diversity and the economic performance of city-regions • informed by three recent debates / developments in the literature on learning, innovation, and regional economic performance • talent, human capital, and the city • economic specialization vs. diversity • knowledge bases / typologies and innovation

4. context received wisdom • talent, human capital and the growth of cities • uneven geography of highly educated labour; talent attracted to, and concentrated in, particular urban centres (Florida 2002) • cities with initial higher levels of education experienced higher levels of growth in share of highly educated workers (Berry and Glaeser 2005) • focus on university-educated workers • diversity and regional economic growth • relative importance of specialization (Marshall) vs. diversity (Jacobs) to economic dynamism • presence of ‘related’ variety (knowledge spillovers) leads to economic growth whereas ‘unrelated’ variety (portfolio effect) does not influence growth (Frenken et al. 2004; Boschma and Iammorino 2006)

5. context received wisdom • knowledge bases (Asheim and Gertler 2005, Asheim et al. 2006) • innovation process of firms and industries differ substantially between various sectors, whose activities require specific ‘knowledge bases’ • analytic – science / math-based • synthetic – engineering-based • symbolic – culturally-based • industrial / sector-based approach

6. context received wisdom • combining insights from these three perspectives suggests: • need to go beyond simply noting the presence of degree/diploma holders to examine the specific knowledge assets / qualifications of workers in the local economy • the knowledge characteristics of the local labour force may be important in explaining regional economic performance • presence of specific knowledge assets individually or in combination may affect growth outcomes • ‘diverse specializations’

7. questions key questions addressed • can we characterize particular places by assessing the knowledge characteristics of the local labour force? • do city-regions with a diverse knowledge infrastructure experience higher levels of economic performance compared to cities with a more specialized knowledge infrastructure? • does the presence of particular forms of knowledge result in particular types of growth (i.e. employment vs. income growth)? • are different growth outcomes based on having different combinations of knowledge specializations?

8. methods data: sources and variables • sources of data • Census of Population, 1991 & 2001 • 136 city-regions (census metropolitan areas and census agglomerations) • key variables • educational attainment • post-secondary degree/diploma holders • knowledge specialization (field of study – 10 categories) • average years of schooling • income, income change • employment, employment change • presence of universities (ph.d. granting, other)

9. methods data: methodological approach • assign fields of study (10 categories) to knowledge base groups • identify knowledge base of city-regions • location quotients – presence of knowledge type as proportion of population 15 years or older, compare local to national • 136 city-regions can be assigned to one of 8 categories depending on knowledge specializations present in that place • None • Symbolic, Synthetic, Analytic • Symbolic-Synthetic, Symbolic-Analytic, Analytic-Synthetic • All • compare economic performance between these 8 groups • income growth, employment growth

10. methods defining knowledge bases: field of study

11. % Post-secondary Quals Post-secondary Change Employment Change % Synthetic Quals % Symbolic Quals % Analytic Quals Income Change Employment Income % Symbolic Quals 1.00 1991 % Synthetic Quals 0.06 1.00 0.73*** % Analytic Quals 0.18** 1.00 % Post-secondary Quals 0.72*** 0.44*** 0.81*** 1.00 0.35*** 0.24** 0.28*** 1991 Employment -0.04 1.00 0.45*** 0.35*** Income 0.10 -0.09 0.26*** 1.00 Post-secondary Change -0.11 -0.18 -0.13 0.07 0.19** 0.17** 1.00 1991-2001 0.23** 0.22** Employment Change 0.02 0.21** 0.14* -0.05 0.14** 1.00 0.17** Income Change -0.01 -0.11 -0.02 0.01 0.09 0.23** 0.31*** 1.00 places correlation between key variables

12. places post-secondary education and economic performance • findings confirms relationship between higher education and economic performance (employment growth and income) • presence of analytic and symbolic knowledge • tend to be geographically concentrated in fewer places and co-located • associated with larger cities and employment growth BUT not higher incomes / income growth • presence of synthetic knowledge • tends to be more geographically dispersed and not co-located with other knowledge types • associated with smaller cities, higher incomes and income growth BUT not employment growth • H: combinations of knowledge specializations may lead to better overall economic performance

13. places categorizing places: combining knowledge specializations symbolic-synthetic symbolic synthetic symbolic-synthetic- analytic symbolic-analytic analytic-synthetic no specialization analytic

14. places knowledge specializations by place symbolic synthetic Corner Brook Summerside New Glasgow Cape Breton Saint John Wetaskiwin Bathurst Baie-Comeau Alma North Bay Sept-Îles Thetford Mines Shawinigan La Tuque Sorel-Tracy Rouyn-Noranda Peterborough Belleville Oshawa Hamilton Sarnia Joliette St. Catharines-Niagara Greater Sudbury Kitchener Elliot Lake Timmins Sault Ste. Marie Thunder Bay Kenora Thompson Moose Jaw Estevan Medicine Hat Port Hope and Hope Lloydminster Cold Lake Grande Prairie Wood Buffalo Duncan Courtenay Trois-Rivières Port Alberni Campbell River Vernon Calgary Edmonton Kingston Stratford Fort St. John Prince George Whitehorse Yellowknife Regina Barrie Powell River Williams Lake Prince Rupert Quesnel Victoria Vancouver Lethbridge St. John's Cobourg Dawson Creek Sherbrooke Penticton Rimouski Terrace Toronto Ottawa-Hull Gander Red Deer Fredericton Kitimat Montréal London Guelph Kelowna Kentville Cranbrook Halifax Winnipeg Québec Brandon Brockville Kamloops Nanaimo Saskatoon Chicoutimi-Jonquière Camrose Charlottetown Portage la Prairie Truro Moncton North Battleford analytic

15. places knowledge specializations: number of places, 1991 & 2001 symbolic synthetic 1991: 2 2001: 3 1991: 47 2001: 28 1991: 6 2001: 1 1991: 11 2001: 22 1991: 12 2001: 9 1991: 12 2001: 7 1991: 40 2001: 60 1991: 6 2001: 6 analytic

16. performance knowledge specializations: employment income, 1991 symbolic synthetic $23,483 $24,835 $25,125 $25,706 $26,957 $23,489 $22,970 $20,970 analytic

17. performance employment growth by knowledge specialization

18. performance income growth by knowledge specialization

19. summary knowledge specializations: key characteristics symbolic synthetic Common Small/Mid-Sized High Income Lower Growth Rare Rare Low Growth Large Cities High Income Fastest Growth Large Cities Highest Inc. Growing Small/Mid-sized Fast Growing Common Smaller Low Income Rare Low Growth analytic

20. summary preliminary findings / arguments / hypotheses • concentration of analytic and symbolic knowledge base in larger cities suggests … • higher level of embeddedness, ‘stickiness’ • importance of face-to-face interaction, access to experts, universities, centres of excellence / star scientists / anchor firms; symbolic • importance of taking advantage of social and cultural diversity • decline in number of places with primarily synthetic knowledge base may be associated with offshoring of particular manufacturing activities, industrial restructuring

21. summary preliminary arguments / hypotheses • more inclusive and nuanced definition of highly educated/skilled workers (i.e. includes all post-secondary diploma/degree holders rather than simply university degree holders) and their field of specialization • emphasis on measuring knowledge (types of post-secondary qualifications) at the city-region level rather than via industries or occupations • potential benefit of having combinations of knowledge (variety) within particular places to overall economic performance (particularly employment growth)

22. summary next steps: analysis • hypothesis testing and multivariate analysis • what is the relationship between economic performance and the diversity of the knowledge base? • industrial structure • variations by size • social inequality • other regional effects • test other measures of ‘diversity’ • advantage to considering disaggregated categories of post-secondary qualifications? • solicit feedback and comments

23. summary thank you • we would like to acknowledge Meric Gertler and David Wolfe for their the valuable comments and insights • thank you to Deborah Huntley for her on-going help and support • we would also like to thank the Manic Monday Economic Geography Study Group (Kate Geddie, Atle Hauge, Brian Hracs, Dieter Kogler, and Josee Rekers) for pointing and laughing as appropriate • for further questions: greg.spencer@utoronto.ca or tara.vinodrai@utoronto.ca