The$Effect$of$Innovation$on$Income$Inequality$in$...

The$Effect$of$Innovation$on$Income$Inequality$in$

Canadian$Cities$

by$

Hamidreza$Bakhtiarizadeh$

M.A.Sc.,'University'of'British'Columbia,'2015'B.Sc.,'Sharif'University'of'Technology,'2011'

Research'Project'Submitted'in'Partial'Fulfillment'of'the'

Requirements'for'the'Degree'of''

Master'of'Urban'Studies'

in'the''

Urban'Studies'Program'

Faculty'of'Arts'and'Social'Sciences'

©$Hamidreza$Bakhtiarizadeh$2016$

SIMON$FRASER$UNIVERSITY$$

Spring$2016$

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Approval$

Name:$ Hamidreza$Bakhtiarizadeh$

Degree:$ Master$of$Urban$Studies$

Title:$ The$Effect$of$Innovation$on$Income$Inequality$in$Canadian$Cities$

Examining$Committee:$ Chair:' Meg'Holden''''''''''''''Associate'Professor,'Urban'Studies'Program'and'

Department'of'Geography'

Peter$V.$Hall$Senior'Supervisor'Professor'Urban'Studies'Program'and'Department'of'Geography'

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Karen$Ferguson$Supervisor'Professor'Urban'Studies'Program'and'Department'of'History'

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Sébastien$Breau$'External'Examiner'Associate'Professor'Department'of'Geography''McGill'University''

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Date$Defended/Approved:$

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January'7,'2016''

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Abstract$

This' research' explores' the' effect' of' innovation' on' income' inequality' in' Canadian'

metropolitan'areas'from'1991'to'2011.'The'analysis'has'been'done'through'regression'

analyses'on'the'income'and'employment'data'obtained'from'long'form'Canadian'census'

and'National'Household'Survey'microRdata.'The'results'show'that'the'positive'correlation'

between' innovation'and' income' inequality' in'Canadian'cityRregions'grew' from'2001' to'

2011T'however,'there'was'no'correlation'between'them'in'1990s.'Among'three'parameters'

that' were' used' as' a' measure' of' innovation' in' this' research' (ratio' of' employment' in'

KnowledgeRIntensive' Business' Services' (KIBS),' highRtech' occupations' and' highRtech'

industries),' the' ratio'of' employment' in'KIBS'has' the'most' significant' effect.'Moreover,'

cities' with' a' high' rate' of' KIBS' activities' have' a' higher' level' of' withinRindustry' income'

inequality,'that'is,'between'highRtech'occupations'and'other'employees'within'the'same'

industry.'

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Keywords:'' InnovationT'income'inequalityT'knowledge'intensive'business'servicesT'Metropolitan'areaT'Canada.''

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Acknowledgements$

By'God’s'will,'aid'and'support'the'completion'of'this'thesis'has'become'a'reality.'

I'would'like'to'acknowledge'the'help'of'those'without'whom'I'could'not'have'finished'this'

work.'Firstly,'my'deepest'gratitude'goes'to'my'senior'supervisor,'Peter'V.'Hall,'who'was'

available' to' discuss' any' and' all' elements' of' the' research' process,' and' patience'

throughout' this'work.'He'was'always'available' to' discuss'any'and'all' elements' of' the'

research'process.'I'would'also'thank'Anthony'Perl'and'Meg'Holden'for'all'I'have'learned'

from'them,'and'Terri'Evans'for'her'support,'during'the'course'of'my'studies.'

This'research'would'not'have'been'possible'without'the'generous'support'I'received'from'

Simon'Fraser'University'and'particularly'the'Urban'Studies'Program.'

I'would'also' thank'Research'Data'Centre'staffs,'Lee'Grenon'and'Lisa'Oliver,' for' their'

guidance'in'the'process'of'data'collection'for'this'research.'

Finally,'my'special'thanks'are'due'to'my'mother,'father'and'sister'for'all'their'supports.'

Most'of'all,' I'would' like' to' thank'my' lovely'wife,'Ghazal,' for'her'patience'and'sacrifice'

throughout'my'studies'as'her'words'of'kindness'and'support'were'my'constant'source'of'

motivation.''

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Table$of$Contents$

Approval'................................................................................................................'ii!Abstract'.................................................................................................................'iii!Acknowledgements'..............................................................................................'iv!Table'of'Contents'..................................................................................................'v!List'of'Tables'........................................................................................................'ix!List'of'Figures'.......................................................................................................'xi!List'of'Acronyms'...................................................................................................'xii!

Chapter$1.! Introduction$and$Thesis$Outline$..................................................$1!

Chapter$2.! Innovation,$Inequality$and$Mutual$Effects$..................................$4!2.1.! Innovation'.....................................................................................................'4!

2.1.1.! Geography'of'Innovation'.................................................................'4!2.1.2.! Innovation'Definition'and'Measures'................................................'6!

2.2.! Inequality'....................................................................................................'11!2.2.1.! Urban'Inequality'............................................................................'11!2.2.2.! Inequality'Measure'........................................................................'13!

2.3.! Relationship'between'Innovation'and'Income'Inequality'...........................'14!2.4.! Effect'of'Inequality'on'Innovation'...............................................................'18!2.5.! Patent'Policy'and'Capital'Income'Inequality'..............................................'20!

Chapter$3.! Methodology$................................................................................$22!3.1.! Census'Metropolitan'Areas'and'Census'Agglomerations'Boundaries'.......'23!3.2.! Innovation'Measure'....................................................................................'26!3.3.! Inequality'Measure'.....................................................................................'28!3.4.! Analysis'......................................................................................................'29!

3.4.1.! Control'Variables'...........................................................................'30!Economic'Variables'...................................................................................'30!SocioRdemographic'Variables'....................................................................'31!Institutional'Variables'.................................................................................'31!Geographical'Variables'..............................................................................'32!

3.4.2.! Multivariate'Regression'.................................................................'33!

Chapter$4.! Descriptive$Statistics$..................................................................$36!4.1.! Inequality'....................................................................................................'36!

4.1.1.! NationalRlevel'inequality'................................................................'36!4.1.2.! CMA/CARlevel'Inequality'...............................................................'40!

4.2.! Innovation'...................................................................................................'42!4.3.! Employment'income'...................................................................................'45!

Chapter$5.! Regression$Analysis$...................................................................$47!5.1.! 2001'to'2011'...............................................................................................'48!

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5.1.1.! The'effect'of'innovation'on'inequality'in'a'decade'(2001R2011)'....'48!5.1.2.! The'effect'of'innovation'on'inequality'in'each'year'.......................'51!

Innovation'..................................................................................................'51!Economic'Variables'...................................................................................'55!SocioRdemographic'variables'.....................................................................'55!Institutional'variables'.................................................................................'56!Geographical'variables'..............................................................................'56!

5.1.3.! Percentile'ratio'analysis'................................................................'56!5.1.4.! Income'distribution'within'or'between'industries'...........................'61!5.1.5.! Causality'test'.................................................................................'69!5.1.6.! Effect'of'parameters'on'innovation'................................................'70!

5.2.! 1991'to'2001'...............................................................................................'72!

Chapter$6.! Conclusion$...................................................................................$80!

References$ 82!Appendix'A.! ''SPSS'Syntax'for'Gini'Coefficient'Calculation'........................'87!Appendix'B.! ''Employment'in'KIBS'and'highRtech'industries'based'on'

NAICS'and'SIC,'2001,'for'130'Canadian'CMA/CAs'included'in'the'study' 89!Appendix'C.! ''The'model'summary'and'the'variance'inflation'factor'

(collinearity'test)'for'each'regression'table'presented'in'Chapter'5'...........'93!Regression'model'of'inequality'and'ratio'of'KIBS'employment'from'2001'to'

2011'('Table'5.1)'...........................................................................'93!Regression'model'of'inequality'and'ratio'of'highRtech'occupations'from'

2001'to'2011'('Table'5.1)'..............................................................'95!Regression'model'of'inequality'and'ratio'of'highRtech'industries'from'2001'

to'2011'('Table'5.1)'.......................................................................'97!Regression'model'of'inequality'and'ratio'of'KIBS'employment'in'2001'

(Table'5.2)'.....................................................................................'99!Regression'model'of'inequality'and'ratio'of'KIBS'employment'in'2006'

(Table'5.2)'...................................................................................'101!Regression'model'of'inequality'and'ratio'of'KIBS'employment'in'2011'

(Table'5.2)'...................................................................................'103!Regression'model'of'inequality'and'ratio'of'highRtech'occupations'in'2001'



(Table'5.2)'...................................................................................'109!Regression'model'of'inequality'and'ratio'of'highRtech'industries'in'2001'



(Table'5.2)'...................................................................................'115!Regression'model'of'P90/P50'and'ratio'of'KIBS'employments'from'2001'to'

2011'(Table'5.3)'..........................................................................'117!

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Regression'model'of'P90/P50'and'ratio'of'highRtech'occupations'from'2001'to'2011'(Table'5.3)'......................................................................'119!

Regression'model'of'P90/P50'and'ratio'of'highRtech'industries'from'2001'to'2011'(Table'5.3)'..........................................................................'121!

Regression'model'of'P50/P10'and'ratio'of'KIBS'employments'from'2001'to'2011'(Table'5.3)'..........................................................................'123!

Regression'model'of'P50/P10'and'ratio'of'highRtech'occupations'from'2001'to'2011'(Table'5.3)'......................................................................'125!

Regression'model'of'P50/P10'and'ratio'of'highRtech'industries'from'2001'to'2011'(Table'5.3)'..........................................................................'127!

Regression'model'of'analysis'of'ratio'of'KIBS'employment'with'different'variables'(Table'5.4)'....................................................................'129!

Regression'model'of'analysis'of'ratio'of'highRtech'occupations'with'different'variables'(Table'5.4)'....................................................................'131!

Regression'model'of'analysis'of'ratio'of'highRtech'industries'with'different'variables'(Table'5.4)'....................................................................'133!

Regression'analysis'of'Gini'coefficient'with'relative'income'and'coefficient'of'variation'of'income'in'KIBS,'highRtech'industries'and'highRtech'occupations'from'2001'to'2011'(Table'5.5)'.................................'135!

Regression'analysis'of'highRtech'occupations'average'income'as'a'function'of'ratio'of'KIBS'employments'and'other'independent'variables'from'2001'to'2011'(Table'5.6)'.............................................................'136!

Regression'analysis'of'highRtech'industries'coefficient'of'variation'of'income'as'a'function'of'ratio'of'KIBS'employments'and'other'independent'variables'(Table'5.6)'...............................................'138!

Regression'analysis'of'Gini'coefficient'as'function'of'ratio'of'KIBS'employments'and'other'independent'variables'from'1991'to'2001'(Table'5.8)'...................................................................................'140!

Regression'analysis'of'Gini'coefficient'as'function'of'ratio'of'highRtech'occupations'and'other'independent'variables'from'1991'to'2001'(Table'5.8)'...................................................................................'142!

Regression'analysis'of'Gini'coefficient'as'function'of'ratio'of'highRtech'industries'and'other'independent'variables'from'1991'to'2001'(Table'5.8)'...................................................................................'144!

Regression'model'of'inequality'and'ratio'of'KIBS'employment'in'1991'(Table'5.9)'...................................................................................'146!



Regression'model'of'inequality'and'ratio'of'highRtech'occupations'in'1991'(Table'5.9)'...................................................................................'152!



Regression'model'of'inequality'and'ratio'of'highRtech'industries'in'1991'(Table'5.9)'...................................................................................'158!

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Regression'model'of'P90/P50'and'ratio'of'KIBS'employment'from'1991'to'2001'(Table'5.10)'........................................................................'164!

Regression'model'of'P90/P50'and'ratio'of'highRtech'occupations'from'1991'to'2001'(Table'5.10)'....................................................................'166!

Regression'model'of'P90/P50'and'ratio'of'highRtech'industries'from'1991'to'2001'(Table'5.10)'........................................................................'168!

Regression'model'of'P50/P10'and'ratio'of'KIBS'employment'from'1991'to'2001'(Table'5.10)'........................................................................'170!

Regression'model'of'P50/P10'and'ratio'of'highRtech'occupations'from'1991'to'2001'(Table'5.10)'....................................................................'172!

Regression'model'of'P50/P10'and'ratio'of'highRtech'industries'from'1991'to'2001'(Table'5.10)'........................................................................'174!

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List$of$Tables$

Table'2.1! List'of'highRtech'occupations'.......................................................'10!Table'2.2! Knowledge'intensive'business'services'under'code'54'of'North'

American'Classification'System'(NAICS)'....................................'11!Table'3.1! List'of'CAs'that'are'excluded'from'the'analysis'...........................'25!Table'3.2! Provincial'minimum'wage'(hourly)'in'one'year'prior'to'Census'

years'............................................................................................'35!Table'4.1! National'employment'income'inequality'from'1991'to'2011'........'37!Table'4.2! Gini'coefficient'average'and'standard'deviation'of'Canadian'

CMAs/CAs'in'different'population'categories'in'2011'..................'40!Table'4.3! Level'of'innovation'according'to'ratio'of'employees'working'in'

KIBS,'highRtech'occupations'and'highRtech'industries'from'1991'to'2011'in'Canada'............................................................................'43!

Table'4.4! Top'five'innovative'cities'in'Canada'based'on'three'different'measures'in'2011,'share'of'total'employment'.............................'45!

Table'4.5! Average'employment'income'in'different'sectors'and'geographical'areas'in'2011'...............................................................................'46!

Table'5.1! Result'of'regression'analysis'with'three'different'innovation'measures.'Each'of'130'CMAs/CAs'in'each'year'(2001,'2006'and'2011)'is'considered'as'a'single'case'in'the'analysis'(overall,'390'cases'are'used).'..........................................................................'50!

Table'5.2! 'Result'multivariate'regression'analysis'on'in'2001,'2006'and'2011'using'three'different'measures'for'innovation'and'Gini'coefficient'as'the'inequality'measure'(dependent'variable)'.........'53!

Table'5.3! Regression'analysis'results'of'modelling'the'effect'of'innovation'and'other'independent'variables'on'percentiles'ratio'from'2001'to'2011.'............................................................................................'59!

Table'5.4! Multivariate'regression'analysis'of'innovation'measures'with'different'variables'from'2001'to'2011'..........................................'64!

Table'5.5! Regression'analysis'of'Gini'coefficient'with'relative'income'and'coefficient'of'variation'of'income'in'KIBS,'highRtech'industries'and'highRtech'occupations'from'2001'to'2011'....................................'66!

Table'5.6! Regression'analysis'of'highRtech'occupations'average'income'and'highRtech'industries'coefficient'of'variation'of'income'as'a'function'of'ratio'of'KIBS'employments'and'other'independent'variables'from'2001'to'2011'........................................................................'68!

Table'5.7! Pearson'correlation'of'the'Gini'coefficients'and'KIBS'employment'ratio'in'2001,'2006'and'2011'.......................................................'70!

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Table'5.8! Multivariate'regression'analysis'of'Gini'coefficient'as'function'of'Innovation'level'and'other'independent'variables'from'1991'to'2001.'............................................................................................'73!

Table'5.9! 'Result'multivariate'regression'analysis'on'in'1991,'1996'and'2001'using'three'different'measures'for'innovation'and'Gini'coefficient'as'the'inequality'measure'(dependent'variable)'.........'75!

Table'5.10! Regression'analysis'results'of'modelling'the'effect'of'innovation'and'other'independent'variables'on'percentiles'ratio'from'1991'to'2001,'which'represent'different'attributes'of'income'distribution.'78!

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List$of$Figures$

Figure'4.1! National'Gini'coefficient'trend'from'1991'to'2011'in'Canada'......'37!Figure'4.2! Trend'of'90R'and'50Rpercentile'over'10Rpercentile'employment'

income'in'Canada'from'1991'to'2011'..........................................'39!Figure'4.3! Gini'coefficient'of'all'Canadian'CMA/CA'in'2011'.........................'41!Figure'4.4! Trend'of'employees'in'KIBS,'highRtech'industries'and'highRtech'

occupations'from'1991'to'2011'in'Canada'..................................'42!Figure'4.5! Average'level'of'innovation'calculated'using'three'different'

measures'in'CAs'with'population'of'below'30,000,'between'30,000'and'100,000,'CMAs'and'Canada'.....................................'44!

Figure'4.6! Average'employment'income'in'different'industries'and'occupations'within'different'geographical'areas'in'2011.'............'46!

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List$of$Acronyms$

CA' Census'Agglomeration'

CMA' Census'Metropolitan'Area'

KIBS' KnowledgeRIntensive'Business'Services'

LFS' Labour'Force'Survey'

NAICS' North'American'Industrial'Classification'System'

NHS' National'Household'Survey'

NOC' National'Occupational'Classification'

OECD' Organisation'for'Economic'CoRoperation'and'Development'

RDC' Research'Data'Centre'

SBTC' SkillRBiased'Technological'Change'

SFU' Simon'Fraser'University'

SIC' Standard'Industrial'Classification'

SOC' Standard'Occupational'Classification'

TPP' Technological'Product'and'Processes'

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'1'

Chapter$1.$ Introduction$and$Thesis$Outline$

Innovation' is' an' important' factor' in' economic' growth' during' the' last' century'

(Glaeser'1991T'Porter'2000).'It'provides'the'opportunity'for'its'adopters'to'create'new'jobs,'

introduces'new'industry'sectors'and'develops'new'products.'Some'researchers'believe'

that'urban'environments'attract'firms'and'people'because'of'knowledge'spillovers,'and'at'

the' same' time' attraction' and' agglomeration' of' firms' and' people' in' cities' increase' the'

knowledge' spillovers' and' innovation' (Marshall' 1890T' Jaocbs' 1970T' Saxenian' 1996).'

Therefore,' local' governments' try' to' stimulate' innovation' through' attracting' highlyR

educated'creative'class'(Florida'2002)'or'through'supporting'the'existing'people'and'firms'

in'the'region'to'do'more'innovation'(Scott'and'Storper'2007T'and'Storper'2009).'

Most'research'into'innovation'touts'its'benefits.'However,'there'is' less'research'

on' the'negative'effects'of' innovation,' including' the'pressing' issue'of' today,' inequality.'

While'innovation'fosters'economic'growth,'it'is'important'to'understand'how'the'benefits'

of' innovation'are'distributed'in'the'society.'Around'this' issue,'here'are'some'questions'

that'remain'unanswered:'Is'the'large'value'generated'by'innovation'being'captured'by'a'

relatively'small'group'of'people?'Does'innovation'lead'to'an'increase'in'income'inequality'

or'does'it' increase'the'income'of'all'groups'of'people'equally?'What'is'the'relationship'

between'innovation'and'inequality'within'urban'economies?'

Innovative' activities' have' become' an' important' factor' in' the' contemporary'

economy,'since'they'bring'economic'growth'to'regions'through'different'mechanisms'such'

as' increasing' production' efficiency,' introduction' of' new'markets,' etc.' Therefore,' local'

governments'try'to'attract'innovative'labour'force'or'innovative'firms'to'stimulate'regional'

growth.'In'that'case,'it'is'important'for'local'government'to'be'aware'of'the'consequences'

of' having' innovative' activities' in' regions,' especially' negative' consequences' such' as'

income'inequality'(if'indeed'there'is'a'relation'between'innovation'and'income'inequality).''

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'2'

Inequality' has' increasingly' become' one' of' the' problems' in' cities.' It' can' cause'

further'problems'such'as'high'rates'of'crime,'health'problems,'etc'(Fajnzlber'et'al'2002).'

Moreover,'places'with'higher'levels'of'inequality'have'higher'rates'of'murder,'and'people'

say'that'they'are'less'happy'(Glaeser'et'al.,'2008).'There'are'two'streams'in'the'debate'

on' income' inequality.' In' the' first' approach,' income' inequality' is' understood' to' be' an'

inevitable' outcome' of' market' deregulation' and' the' drive' for' efficiency' in' the' global'

economy.'Other'economists'have'had'second' thoughts'on' this' issue.'They'argue' that'

income' inequality' is' not' the' outcome' market' efficiency,' instead,' the' rise' in' income'

inequality' is'due' to' the'power' relations'between' institutions'within' the'market' such'as'

multiRnational'companies,'workers,'customers,'etc.'In'this'approach'it'is'argued'that'gross'

inequality' is' not' efficient' in' the' market' (Atkinson' 2015T' Kuttner' 2013).' In' the' latter'

perspective,'local'governments'should'make'an'effort'to'reduce'the'income'gap'not'only'

to' overcome' its' negative' social' consequences,' but' also' to' increase'market' efficiency.'

Therefore,'local'government'should'care'about'inequality'and'be'cautious'in'supporting'

innovative'activities'when'they'take'economic'development'policies.'

Most' innovative' activities' are' concentrated' in' cities.' According' to' the' National'

Household'Survey'(NHS)'2011,'around'80%'of'Canadian'population'live'in'urban'regions,'

while'90%'of'Knowledge'Intensive'Business'Services'(KIBS)'employments'are'located'in'

urban'regions.'KIBS'employments'are'known'as'a'representative'of'innovative'activities'

(Breau'et' al.' 2014T'Wessel' 2013).'Similarly,' the' level' of' income' inequality' is' higher' in'

urban'regions'than'nonRurban'ones.'Moreover,'both' innovation'(Marshall'1890T'Jaocbs'

1970,'Glaeser'1991)'and'income'inequality'(Korpi'2008T'Baum'Snow'and'Pavan'2013)'

have'a'positive'correlation'with'population'of'urban'region.'Therefore,'the'relation'between'

innovation'and'income'inequality'is'studied'in'Canadian'urban+regions'in'this'research.'

This' research'does'not' focus'on'providing' solutions' for' local' governments'and'

policy'makers'to'overcome'the'income'inequality'problemT'however,'as'the'first'step'of'

solving'the'problem,'it'is'going'to'provide'an'investigation'into'the'mechanisms'through'

which' innovation' may' cause' urban' inequality.' Based' on' the' results' provided' in' this'

research,'it'is'hoped'that'further'studies'will'dig'into'different'possible'policies'to'overcome'

these'negative'consequences'of'innovation.'

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'3'

Based'on'the'research'objectives'the'thesis'is'organized'as'follows:'

In'Chapter'2:'Innovation,'Inequality'and'Mutual'Effects,'a'brief'review'of'literature'

on' innovation,' inequality'and'their'mutual'effect' is'presented.'The'chapter'starts'with'a'

review'of'the'literature'on'geography'of'innovation'and'the'effect'of'social'parameters'on'

innovation.' Different' ways' of' measuring' innovation,' and' their' pros' and' cons' is' also'

included.'Then'different'measures'of'inequality'are'introduced'and'a'couple'of'them'are'

selected' for' this' research.' Finally,' different' theories' explaining' the'mutual' relationship'

between'innovation'and'inequality'are'presented.'

Chapter'3'presents'the'methodology'of'the'research,'including'the'sources'of'data,'

process' of' data' collection' and' methods' of' analysis.' The' primary' source' of' data' is'

Canadian'Census'1991,'1996,'2001'and'2006'and'the'National'Household'Survey'(2011).'

In'Chapter'4:'Descriptive'Statistics,'some'descriptive'statistics'on'the'distribution'

of'innovation'and'inequality'at'the'urban'and'national'level'in'Canada'in'2011,'along'with'

their'trend'during'the'20Ryears'period'are'presented.'Moreover,'the'distribution'of'income'

in'innovative'and'nonRinnovation'employment'is'presented.'

Chapter'5:'Regression'analysis,'contains'the'main'analysis'and'discussion'of'this'

research.'Different'regression'models'are'presented'with'the'objective'of'finding'the'effect'

of' innovation' and' some' other' parameters' on' income' inequality' in' the' Canadian'

metropolitan'areas' from'1991' to'2011.'The'analysis'of' this' chapter' is'divided' into' two'

different'distinct'sections'for'the'analysis'of'two'10Ryears'periods'from'2001'to'2011'and'

1991'to'2001.'However,'the'focus'of'discussion'is'on'the'recent'decade.'

Chapter' 6' presents' concluding' remarks' and' suggestions' for' future'works.' This'

research' shows' that' there' is' a' growing' correlation' between' innovation' and' income'

inequality'in'Canadian'cityRregions'from'2001'to'2011.'Among'all'different'parameters'that'

used'for'the'innovation'measurement,'the'ratio'of'employment'in'KIBS'has'the'greatest'

influence'on'income'inequality.'

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'4'

Chapter$2.$ Innovation,$Inequality$and$Mutual$Effects$

This'chapter'discusses'geographical'distribution,'innovation'definition'and'different'

measurement'methods'of'innovation,'which'are'presented'in'academic'literature.'Then'a'

brief'overview'of'inequality'measures,'as'well'as'the'theories'that'are'linking'innovation'to'

income'inequality'in'cityRregions'are'presented.''

2.1.$ Innovation$

2.1.1.$ Geography$of$Innovation$

There'is'a'debate'in'the'literature'about'whether'economic'opportunities'are'evenly'

distributed' geographically' or' if' there' are' certain' places' that' offer' greater' economic'

opportunities.'Friedman’s'(2005)'idea'of'“the'flat'world”'focuses'on'the'global'forces'acting'

on'economic'activities,'arguing'that'economic'activities'can'freely'move'to'different'places'

without'being'confined' to' regional' forces.'However,'we'can'see' that' creative'activities'

were'concentrated'in'some'places'at'different'time'periods,'such'as'Florence'under'the'

Medici,'Paris'in'1920s,'England'during'the'industrial'revolution'and'Silicon'Valley'in'more'

recent'times'(Feldman,'2010).'

Some'economists,'geographers'and'historians'believe'that'most'innovations'are'

made'within'cities'(Marshall'1890T'Jacobs'1985T'and'Glaeser'1991).'They'believe'that'the'

agglomeration' of' people' and' firms,' and' high' levels' of' interaction' in' cities' create' an'

environment'in'which'ideas'quickly'move'from'one'person'to'another,'and'consequently'

help'people' to' innovate.'Access' to' knowledge' is'an' important' factor' that'makes'cities'

conductive'to'innovations.'Furthermore,'according'to'Malmberg'and'Maskell'(1997)'most'

innovations'come'from'the'interactive'processes'within'industrial'systems.'They'believe'

that'the'proximity'and'short'distance'between'firms'make'the'interactive'collaboration'less'

costly'and'smoother.'

Creation' and' transmission' of' knowledge' is' crucial' in' the' innovation' process.'

Philosophers'of'knowledge'make'distinction'between'knowledge'that'could'be'effectively'

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expressed'in'a'symbolic'forms'of'representation'(explicit'knowledge)'and'other'forms'of'

knowledge' that'cannot'be'expressed'or'codified'(tacit'knowledge)' (Polanyi'1966).'This'

distinction'between'explicit'and'tacit'knowledge'is'important'in'innovation'studies.'Tacit'

knowledge'has'been'known'as'the'central'component'of'the'learning'economy,'and'a'key'

to'innovation'and'value'creation'(Polanyi'1966T'and'Gertler'2003).'Howells'(2000,'p.'53)'

refers'to'tacit'knowledge'as'“knowRhow'that'is'acquired'via'the'informal'takeRup'of'learned'

behaviour'and'procedures.”'Hence,'unlike'explicit'knowledge,'which'can'be'transmitted'

through'sets'of'axioms,' rules,'algorithms'and'statements,' tacit' knowledge'can'only'be'

transmitted'through'experience.'This'idea'makes'proximity'and'faceRtoRface'interactions'

a' crucial' element' of' innovation,' even' with' the' growing' importance' of' long' distance'

interactions' through' information' technology'advances.'The' idea' that' tacit'knowledge' is'

embodied' in'human' interaction'can'help'explain'why'some'places'have'higher' rate'of'

knowledge'creation'and'innovation'than'others.'

Glaeser' et' al.' (1992)' believe' that' the' characteristics' of' the' cities' in' knowledge'

production'and'diffusion'make'them'a'desirable'place'for'most'of'the'people,'despite'the'

high'cost'of'living.'They'claim'that,'in'cities,'people'enjoy'easy'flow'of'ideas'and'improve'

their'productivity'without'paying'for'the'knowledge.'In'their'research,'they'concluded'that'

competition'and'diversity' in'cities'are' two' important' factors,'which' facilitate' innovation.'

Diversified'cities'are'more'innovative'because'ideas'from'one'industry'can'be'transmitted'

and'adopted'in'another'industry.'In'addition,'local'competition'accelerates'imitation'and'

improvement' of' innovators’' ideas.' Researchers' acknowledge' the' role' of' knowledge'

spillovers'and'innovation'in'the'economic'growth'by'defining'it'as'the'engine'of'growth.'

Therefore,' numerous' research'works' stress' the' importance' of' innovation' in' economic'

growth'in'cities.'

The'relation'between'innovation'and'places'is'beyond'only'agglomeration'of'firms'

and'people'in'cities.'Other'specific'attributes'of'different'places'(social,'institutional,'etc.)'

play'an'important'role'in'the'innovation'process'(Saxenian'1996T'Storper'2009).'There'is'

a'shift'in'innovation'analysis'approach'from'focusing'on'artefacts'and'production'side'in'

conventional' technological' systems,' to' considering' users' and' social' context' as' an'

important'factor'in'innovations'(Geels'2004).'The'broadened'approach'usually'is'known'

as' sociotechnical' systems.' Considering' all' different' aspect' of' innovation,' the'

sociotechnical' systems' approach' helps' us' to' thoroughly' understand' the' dynamic' of'

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innovation' and' generally' technological' regimes.' This' analysis' approach' suggests' that'

knowledge'production'and'innovation'systems'should'be'studied'within'the'social'norms'

and'institutions'that'are'embedded'within'the'region.'Storper'(1997)'also'believes'that'the'

knowledge'production'process'is'embedded'in'regions.'Knowledgeable'people'who'are'

embodiments'of'the'knowledge'are'not'evenly'distributed'in'the'geographical'space.'

In'summary,'innovation'is'not'evenly'or'randomly'distributed'in'the'space.'Despite'

all' technological' advances' in' longRdistance' interactions,' the' capability' of' knowledge'

creation'and'innovation'cannot'be'easily'transmitted'from'one'place'to'another.'Moreover,'

different'attributes'of'regions'(population'density,'competition,'diversity,'institutions,'social'

norms,'etc.)'affect'regional'innovation'systems.'

2.1.2.$ Innovation$Definition$and$Measures$

Researchers'have'used'different'means'to'define'the'concept'of'innovation'in'their'

research'works,'and'consequently'these'different'definitions'lead'to'different'measures'of'

innovation.'For'example,'Technological'product'and'process' innovations'are'defined'in'

Oslo'Manual'by'Organisation' for'Economic'CoRoperation'and'Development'(OECD)'as'

follows:'

Technological' product' and' process' (TPP)' innovations' comprise'implemented'technologically'new'products'and'processes'and'significant'technological'improvements'in'products'and'processes.'A'TPP'innovation'has'been' implemented' if' it' has'been' introduced'on' the'market' (product'innovation)'or'used'within'a'production'process'(process'innovation).'TPP'innovations' involve' a' series' of' scientific,' technological,' organizational,'financial'and'commercial'activities.'The'TPP'innovating'firm'is'one'that'has'implemented'technologically'new'or'significantly'technologically'improved'products'or'processes'during'the'period'under'review'(Oslo'Manual,'2005,'p.25).'

However,'a'broader'definition'is'provided'by'Schumpeter'(1934),'in'which'he'also'

included' three'additional'elements' to' the'Oslo'TPP' innovation'definition.' In'addition' to'

product'and'process'innovation,'Schumpeter’s'definition'also'includes'opening+of+a+new+

market,'development+of+new+source+of+supply'and'changes+in+an+industrial+organisation.'

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Numerous' variables' have' been' used' for' regional' innovation' measurementT'

however,'as'discussed'in'the'following'paragraphs,'none'of'them'are'able'to'include'all'

different'types'of'innovation.'Each'definition'focuses'on'a'specific'definition'and'type'of'

innovation.' Therefore,' researchers' usually' use'multiple' variables' to' measure' regional'

innovation.''

One'of'the'parameters'that'is'used'to'measure'the'level'of'innovation'in'a'region'

is'number'of'patents'registered'per'millions'of'population.'Sonn'and'Storper'(2008)'used'

the'number'patents'and'their'citation'in'the'United'States'Patent'and'Trademark'Office'

(USPTO)' as' a' measure' of' innovation' to' investigate' the' importance' of' proximity' on'

knowledge'production.'According'to'the'fact'that'a'significant'percentage'of'patents'are'

produced'by'a'group'of'inventors,'they'proposed'a'method'to'assign'a'fraction'of'patent'

to'the'city'of'each'team'member.'For'instance,'if'a'team'of'three'inventors'from'Toronto,'

Montreal'and'Ottawa'create'a'patent,'each'city'will'have'one'third'of'the'patent'in'counting'

number'of'patents.'

While'many'other'researchers'have'used'this'method'in'their'research'work,'there'

are' some' limitations' in' using' the' number' of' patents' per'million' in' the' population' as' a'

measure'of'innovation.'Despite'Sonn'and'Sorper'(2008)'having'used'patent'counts'in'their'

research,'they'have'mentioned'limitations'of'this'method.'First,'some'patents'have'never'

been'commercialized'and' therefore'have'not'had'any' impact'on' the'economic'output,'

while' they' are' counted' as' a' representative' of' innovation.' Second,' the' propensity' of'

innovators' to' patent' their' knowledge' production' varies' among' different' sectors.' For'

instance,' innovators' in' service' sectors' are' less' likely' to' patent' their' work' than' in'

manufacturing,'despite' the' fact' that'knowledge'production' in' the'service'sector'has'an'

increasing'significance'in'the'contemporary'economy.'

Moreover,'Hall'et'al.'(2013)'claim'that'the'share'of'firms,'which'do'not'patent'their'

knowledge'production,'is'significant,'because'patenting'needs'enough'time'and'money,'

and' familiarity' with' the' process,' which' makes' the' process' difficult' for' small' firms.'

Therefore,' using' number' of' patents' as' a'measure' of' innovation'will'miss' a' significant'

number'of'innovations,'which'are'not'registered'as'a'patent.'Lee'(2011)'also'stated'that'

this'measure'has'failed'to'account'for'innovations,'which'are'unlikely'to'be'patented,'such'

as'process'innovations.'

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Another'measure'of'innovation,'which'has'been'used'by'some'researchers,'is'the'

proportion'of'employees'working'in'those'industries,'which'have'a'high'rate'of'innovation'

and'knowledge'production.'This'measure'is'capable'of'capturing'the'innovations'that'are'

not'being'patented.'Lee'(2011)'used'proportion'of'such'employment'to'measure'regional'

innovation' in' European' cities.' He' used' employment' in' five' overlapping' categories' of'

knowledgeRbased'industries'provided'by'Eurostat:'knowledgeRintensive'financial'services,'

highRtech' knowledgeRintensive' services,' other' knowledgeRintensive' services' and' highR

tech'manufacturing.'However,'he'claims'that'these'classifications'are'too'broad'while'what'

is'counted'as'innovationRrelated'employment'within'each'sector'may'be'very'different.'For'

instance,'he'states'that'this'measure'is'not'able'to'distinguish'between'the'head'office'

employees'of'a'firm'and'the'manufacturing'labours,'whereas'the'former'may'gain'more'

from'innovation'than'the'latter.'In'the'Canadian'context,'this'is'more'problematic'because'

in' Canadian' Census' data' all' the' five' categories' of' knowledgeRbased' industries' are'

aggregated'in'one'category'of'knowledgeRintensive'business'services.'

Gertler'et'al.'(2002)'measured'innovation'through'location'quotient'in'the'highRtech'

industries.' They' defined' highRtech' industries' in' Canada' based' on' Standard' Industrial'

Classification' (SIC),'which' includes' employment' in' industries' related' to' aircraft,' office,'

electrical,' and' scientific' equipment' manufacturing,' bioRmedical,' telecommunications,'

computer,'engineering'and'scientific'services'and'film.'Hall'and'Khan'(2008)'also'added'

communication' equipment'manufacturing,' electrical' wholesales,' employment' agencies'

and'management'consulting'to'the'definition'of'highRtech'industries'to'capture'the'breadth'

of'highRtech'activity.'

On' the' other' hand,' Chapple' et' al.' (2004)' explored' the' possibility' of' using'

occupations' rather' than' industries' with' the' objectives' of' monitoring' and' targeting'

economic' performance.' They' introduced' a' list' of' occupations,' which' are' presented' in'

Table'2.1,' to'measure'the' level'of'highRtech'activities'of'cities'and'compare'the'results'

with'other'measures.'As'these'occupations'are'extensively'dependent'on'creativity'and'

innovation,'the'share'of'employees'working'in'these'occupations'(rather'than'highRtech'

industries)' can' be' a' parameter' that' represents' the' level' of' innovation.' As' discussed'

above,' the' problem' of' using' employment' in' highRtech' industries' as' a' measure' of'

innovation' is' that' all' employees' working' in' a' highRtech' industry' are' considered' as'

innovative'employees,'including'some'nonRinnovative'workers'who'are'doing'routine'jobs'

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in'the'industry.'Measuring'innovation'based'on'occupations'may'overcome'this'problem.'

Someone'who' has' a' nonRinnovative' job' in' a' highRtech' industry' does' not' count' as' an'

innovative'worker.'

Share'of'employment'in'KnowledgeRIntensive'Business'Services'(KIBS)'is'another'

measure'of'innovation'used'in'literature'(Breau'et'al.'2014T'Wessel'2013)'because'KIBS'

sector'produces'and'diffuses'knowledge,'which'is'crucial'to'innovation.'Muller'and'Zenker'

(2001)' studied' the' interactions' between' KIBS' and'manufacturing' smallR' and'mediumR

sized'enterprises'and'concluded'that'this'type'of'interaction'stimulates'the'generation'and'

diffusion'of'knowledge'within'innovation'systems'at'both'national'and'regional'level.'They'

showed'that'KIBS'play'an'important'role'in'innovation'systems'not'only'by'“transmitting”'

knowledge,' but' also' with' their' crucial' role' in' “knowledge' reRengineering”' (i.e.' the'

appropriation'of'knowledge).'KIBS'firms'enhance'innovation'capacity'of'their'client'firms'

and'get' stimuli' for' their'own' innovations.' In'Canada,'Breau'et'al.' (2014)'have'defined'

innovation'as'the'percentage'of'employees'in'KIBS'in'Canadian'cities.'They'have'used'

code'54'of'North'American'Industry'Classification'System'(NAICS).'Table'2.2'shows'the'

detailed'breakdown'of'this'industry'sector.'

As'discussed'above,'there'are'numerous'variables'in'measurement'of'innovation'

and'knowledge'production.'Due'to'the'limitations'of'each'of'these'methods,'it'would'be'

more'useful'to'improve'the'analysis'through'using'multiple'measures'of'innovation.'In'this'

study,'innovation'is'measured'as'ratio'of'employment'in'KIBS,'highRtech'occupations'and'

highRtech'industries'(see'Section'3.2).'

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'

Table$2.1$ List$of$high^tech$occupations$

Occupational Employment Statistics (OES) code Occupation title 13017 Engineering, math, natural science managers 22102 Aeronautical & astronautical engineers 22105 Metallurgists/metallurgical, ceramic & materials engineers 22108 Mining engineers 22111 Petroleum engineers 22114 Chemical engineers 22117 Nuclear engineers 22121 Civil engineers 22123 Agricultural engineers 22126 Electrical & electronic engineers 22127 Computer engineers 22128 Industrial engineers, except safety 22132 Safety engineers, except mining 22135 Mechanical engineers 22138 Marine engineers 22199 All other engineers 22999 Other engineers, not elsewhere classified 24102 Physical & astronomers 24105 Chemists, except biochemists 24108 Atmospheric & space scientists 24111 Geologists, geophysicists & oceanographers 24199 All other physical scientists 24308 Biological scientists 24311 Medical scientists 24399 All other life scientists 24999 All other natural scientists & related workers 25102 System analysts 25103 Database administrator 25105 Computer programmers 25111 Programmers, numerical tool & process control 25302 Operation/systems researchers and analysts, except computer 25310 Mathematical scientists 25312 Statisticians

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25319 All other mathematical scientists 25399 All other systems researchers 25999 All other computer scientists

Source: Chapple et al. 2004. $

Table$2.2$ Knowledge$intensive$business$services$under$code$54$of$North$American$Classification$System$(NAICS)$

Four-Digits NAICS Code Definition

5411 Legal Services

5412 Accounting, Tax Preparation, Bookkeeping, and Payroll Services

5413 Architectural, Engineering, and Related Services

5414 Specialized Design Services

5415 Computer Systems Design and Related Services

5416 Management, Scientific, and Technical Consulting Services

5417 Scientific Research and Development Services

5418 Advertising and Related Services

5419 Other Professional, Scientific, and Technical Services

Source: Statistics Canada (2012)

2.2.$ Inequality$

2.2.1.$ Urban$Inequality$

Over' the' last' 30' years,' income' inequality' has' increased' rapidly' in' advanced'

industrial' economies,' including' Canada.' In' the' income' inequality' ranking' of' all'

Organization' for' Economic' Cooperation' and' Development' (OECD)'members,' Canada'

stands'out' as'having' the' second' largest' surge' in' income' inequality' since' the'midR90s'

(OECD,'2008).'

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Economic' inequality' could' be' studied' at' different' international,' national,'

province/state'and'city'levels.'Inequality'at'the'city'level'could'be'as'important'as'national'

level'inequalityT'however,'researchers'paid'less'attention'to'the'former'than'the'latter.'It'is'

important'to'pay'attention'to'inequality'in'cities'because'of'its'consequences.'For'instance,'

there'is'a'connection'between'crime'and'inequality'in'cities'as'well'as'between'inequality'

in'countries'and'in'countries'(Daly'et'al.'2001,'Fajnzylber'et'al.'2002)'Moreover,'places'

with'higher'rates'of'inequality'have'higher'rates'of'murder,'and'people'say'that'they'are'

less'happy'(Glaeser'et'al.,'2008).'

Bolton'and'Breau'(2014)'conducted'research'on'the'relation'between'inequality'in'

Canadian'cities'and'city'size.'They'claim'that'larger'metropolitan'areas'experienced'more'

rapid' increases'in' income'inequality'than'the'smaller'metropolitan'areas'between'1996'

and'2006.'They'believe' that' it' is'due' to' the' fact' that'higher'density'metropolitan'areas'

experience' agglomeration' economies.' Through' this' agglomeration' process,' specific'

groups'of'people'from'specific'income'class'are'attracted'to'large'metropolitan'areas.'The'

income'distribution'within'the'people'who'are'moving'to'large'cities'is'polarized.'HighlyR

educated'workers'with'high'income'are'attracted'to'large'cities'for'better'job'opportunities'

and'amenities' to' large'cities,'as'well' as' lowRincome'or'unemployed'people' from'other'

regions'for'low'paid'service'jobs.'Therefore,'highRdensity'metropolitan'areas'experience'

greater'income'gaps'than'others.''

Moreover,'Korpi'(2008)'conducted'research'on'the'effect'of' local' labour'size'on'

wage'inequality'through'analysis'of'Swedish'local'labour'market.'He'concluded'that'there'

is'positive'link'between'wage'inequality'and'labour'market'size.'He'believes'as'population'

size' increases,' the' local' labour' market' becomes' more' diversified,' and' consequently'

earnings'will'be'distributed'more'unequally.'Baum,'Snow'and'Pavan'(2013)'also'studies'

the'wage' inequality' in' the'United'States' from'1979' to'2007,'and'concluded' that' larger'

cities'have'experienced'more'rapid'growth'in'inequality'than'the'others'in'the'same'period.'

According' to' their' research' the' important' factor' that' makes' cityRsize' an' important'

parameter' in' inequality' growth' is' that' groups' of' peoples' with' some' specific' skills' are'

disproportionately'located'in'larger'cities,'and'through'the'analysis'period,'the'demand'for'

these'skills'has'been'increased.'Therefore,' these'groups'of'workers'experienced'more'

significant'rises'in'their'wages'with'respect'to'others.'

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2.2.2.$ Inequality$Measure$

Many'different'measures'have'been'introduced'for'inequality'measurement'such'

as'Gini'coefficient,'standard'deviation,'Theil'index,'and'decile'ratio.'These'measures'are'

not'different'ways'of'measuring' inequalityT' they'are'measuring'different'definitions'and'

dimensions' of' inequalityT' however,' it' has' been' shown' that' there' is' a' high' correlation'

between'these'measures'(Allison'1978).'

De' Maio' (2007)' conducted' research' on' the' different' methods' of' inequality'

measurement.'He'believes'that'the'most'popular'parameter'in'measuring'inequality'is'the'

Gini'coefficient.'The'strength'of'the'Gini'coefficient'is'that'it'considers'different'aspects'of'

inequality' such' as' inequality' around' mean' and' at' both' ends' of' income' distribution.'

However,' its' weakness' is' that' it' cannot' differentiate' between' different' aspects' of'

inequality,'for'example,'whether'the'rich'are'richer'or'the'poor'are'poorer.'Hence,'there'

might'be'a'difference'between'characteristics'of'two'income'distributions'while'they'have'

a'same'Gini'coefficient.'Moreover,'researchers'should'be'aware'of'the'fact'that'the'Gini'

coefficient'is'the'most'sensitive'inequality'measure'to'the'distribution'around'the'middle'

part'of'the'income'distribution.'

Another' inequality' measure,' which' has' been' used' commonly' in' literature,' is'

Percentiles' ratio.' These' are' simple' measures' that' give' the' sense' of' how' income' is'

distributed'among'the'study'population.'They'help'researchers'understand'whether'the'

change'in'inequality'is'because'of'the'increase/decrease'in'the'top'or'bottom'of'the'income'

distribution.'

Most'of'the'researchers'studying'inequality,'prefer'to'use'more'than'one'measure'

of' inequality' to' be' able' to' capture' different' aspects' of' income' inequality,' such' as'

distribution'around'mean'and'at'both'ends'of'the'income'spectrum'(Breau'2014,'Lee'2011'

and'Moore'2003).'In'this'research,'I'will'use'the'Gini'coefficient'and'income'distribution'

percentiles' to' be' able' to' differentiate' the' effect' of' innovation' on' inequality' around' the'

average'income'and'at'both'ends'of'income'distribution.'

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2.3.$ Relationship$between$Innovation$and$Income$Inequality$

As' discussed' above,' innovation' is' known' as' the' engine' of' growth' for' regional'

economies.'On'the'other'hand,'innovation'and'knowledge'production'in'cities'affects'the'

distribution'of'earnings.'Although' the'discussion'on' the'effect'of' innovation'on' income'

inequality' has' been' increasingly' considered' in' the' literature' because' of' the' inequality'

growth'in'the'recent'decades,'it'is'not'a'new'concept.'It'was'documented'in'the'Communist'

Manifesto'as'follow:'

“The' bourgeoisie' cannot' exist' without' constantly' revolutionizing' the'instruments' of' production,' and' with' them' the' whole' relations' of'society...Constant'revolutionizing'of'production,'uninterrupted'disturbance'of'all'social'conditions,'everlasting'uncertainty'and'agitation'distinguish'the'bourgeois'epoch'from'all'earlier'ones”'(Marx'and'Engels,'1848)'

Numerous' theories' have' been' introduced' in' the' recent' decades' regarding' the'

distributional'effect'of' innovation'on'earnings'at'a'national'scale,'which'mostly'predicts'

that'the'innovations'increase'the'earnings'inequality.'One'of'the'most'common'theories'

is'skillRbiased'technological'change'(SBTC)'(Berman'et'al.'1997).'

According'to'SBTC,'the'introduction'of'new'technology'–especially'in'the'form'of'

computers,'information'and'communications'technology–'increases'the'demand'of'highly'

skilled' labour'and'decreases' the'demand'of' less'skilled' labour,'since'on'one'hand'the'

operation'of'new'technologies'needs'highly'skilled'and'educated'labour,'and'on'the'other'

hand'new'technologies'usually'improve'productivity'and'consequently'decrease'the'need'

for' less' skilled' labour.' Therefore,' individuals' with' higher' levels' of' education' and'

experience'will'have'more'job'opportunities'and'will'experience'an'increase'in'earnings'

while'those'who'are'not'qualified'to'work'with'the'new'technology'will'lose'their'job'or'see'

a'reduction'in'their'earnings.'

As'an'example'of'SBTC,'Wheeler'(2005)'showed'that'the'introduction'of'computer'

to' industries' increased'the' level'of' income'inequality' in' the'United'States'from'1983'to'

2002.'This'increase'in'income'inequality'was'more'related'to'dispersion'of'income'within'

industries' rather' than' between' industries.' His' findings' show' that' there' is' a' positive'

correlation'between'withinRindustry'inequality'and'frequency'of'computer'usage'and'also'

ratio' of' employees'with' a' college' degree.' His' findings' are' consistent' with' skillRbiased'

'

'15'

technological'change'theory.'The'findings'indicate'that'during'the'period'that'the'computer'

was'introduced'as'a'new'technology,'industries'which'needed'more'use'of'computers'had'

higher'income'inequality,'because'people'who'were'highly'skilled'to'work'with'a'computer'

and/or'had'a'college'degree'would'have'seen'an'increase'in'their' income'while'others'

would'have'an'earning'reduction'or'a'relative'lag'in'salaries'relative'to'those'who'have'

computer'skill,'which'leads'to'an'increase'in'income'inequality.'

Acemoglu'and'Autor'(2010)'argue'that'although'SBTC'was'notably'successful'in'

explaining' the'effect'of' innovations'and'new' technologies'on'earnings'distribution,' it' is'

silent'when'it'comes'to'the'empirical'developments'of'the'last'three'decades,'which'are'

listed'as'follows'(p.'1157):'

1.' Significant'declines'in'real'wages'of'low'skill'workers,'particularly'low'skill'malesT'

2.' NonRmonotone'changes'in'wages'at'different'parts'of'the'earnings'distribution'during'different'decadesT'

3.' BroadRbased'increases'in'employment'in'high'skill'and'low'skill'occupations'relative'to'middle'skilled'occupationsT'

4.' Rapid'diffusion'of'new'technologies'that'directly'substitute'capital'for'labor'in'tasks'previously'performed'by'moderately'skilled'workersT''

5.' Expanding'offshoring'in'opportunities,'enabled'by'technology,'which'allow'foreign'labor'to'substitute'for'domestic'workers’'specific'tasks.'

To'explain' the'above' issue,'Acemoglu'and'Autor' (2010)' introduced' taskRbased'

framework' to' effectively' explain' the' effect' of' innovations' on' earnings' inequality.' They'

consider'the'element'of'tasks'in'addition'to'technology'and'skills.'In'this'theory,'the'main'

focus'is'on'the'interaction'between'technology,'skills'and'tasks.'

Unlike' SBTC' theory,' instead' of' simply' claiming' that' people' with' a' higher'

educational'degree'will'be'able'to'work'with'new'technologies'and'be'rewarded,'the'taskR

based'framework'defines'new'tasks'that'are'introduced'by'innovation'in'an'industry.'Then'

it' relates'different' tasks' to'different'skills'and'explains'how'earnings'within'an' industry'

become' polarized' between' groups' of' workers' with' different' skills' with' respect' to' the'

changing'nature'of'the'tasks.'

'TaskRbased'framework'argue'that'innovations'and'new'technologies'will'replace'

human'labour'in'some'manual'routine'tasks'with'ICTs,'but'human'labour'is'still'needed'in'

'

'16'

nonRroutine' tasks,' namely,' (1)' complex' and' abstract' tasks' and' (2)'manual' tasks.' The'

former'includes'processing'of'information,'understanding'specialized'and'highly'technical'

subjects,'problem'solving'and'communicating,'and'the'latter'includes'tasks'that'require'

inRperson'interaction'and'physical'abilities.'Therefore,'as'a'consequence'of'diminishing'

routine'jobs'in'the'middle'of'the'wage'distribution,'the'labour'force'will'be'polarized'into'

two'tasks'at'both'ends'of'earning'distribution.'

Tacit'knowledge'is'crucial' in'these'nonRroutine'jobs,'and'their'know9how'cannot'

easily'be'codified'as'explicit'knowledge.'Hence,'the'increasing'importance'of'nonRroutine'

jobs' acknowledge' the' idea' that' innovations' are' mostly' introduced' and' used' in' cities,'

because'cities'allow' for' faceRtoRface' interactions'between'numerous' firms'and'people,'

which' is' important' in' production' and' transmission' of' tacit' knowledge' (as' discussed' in'

section'2.1.1).'

Lee' (2011)'also' looked'at' the' relation'between' regional' innovation'and' income'

inequality' through'analysis'of'European'cities.'He'described' five'mechanisms,' through'

which'innovation'leads'to'income'inequality'(p.'3R4).'

1.' Innovation'leads'to'higher'productivity'in'the'production'process'and'innovative'firms'and'workers'will'benefit'in'the'form'of'wage'gain'

2.' There'may'be'a'greater'dispersion'of'income'within'the'innovative'industry'sector,'which'may'polarize'the'pay'structure'of'the'industry'

3.' If'the'innovations'are'diffused'locally,'SBTC'process'will'affect'the'regional'inequality'

'4.' Innovative'regions'may'attract'highly'skilled'and'highly'paid'labour'from'other'regions.'This'process'will'also'increase'the'number'of'innovations'

5.' When'innovation'raises'the'wage'of'certain'group'of'people,'it'will'stimulate'the'need'for'more'lowRpaid'service'sector'jobs'

However,'his'first'four'theories'are'very'similar'to'the'aforementioned'theories,'the'

fifth'one'explains'that'the'high'wage'innovative'jobs'will'attract'workers'to'the'low'wage'

service'jobs'to'provide'services'for'highlyRpaid'innovative'people.'These'jobs'include'food'

service'workers,'security'guards,' janitors,'cleaners'and'gardeners,'home'health'aides,'

childcare'workers,'and'personal'appearance'and'recreational'occupations.'

'

'17'

Similarly,'Florida'(2002)'in'The+Rise+of+Creative+Class,'explains'that'a'significant'

growth'in'the'creative'class'necessitates'growth'in'lowRskilled'lowRwage'labours'to'take'

care'of'the'creative'class'and'“do'their'chores”'(p.71).'Therefore,'according'to'his'theory'

the'growth'of'employment' is'mainly' in'highly'paid'creative'class'occupations'and' lowR

wage' service' sectors,' which' provide' services' to' highRincome' creative' workers.' This'

pattern'of'growth'will'lead'to'polarization'and'earnings'inequality.'One'of'the'reasons'that'

his'idea'in'supporting'creative'class'has'been'criticized'by'Peck'(2005)'is'that'the'rise'of'

creative'class'neglects'the'issues'of'intraRurban'inequality.'

Through'these'different'mechanisms,' innovation'can'increase' income'inequality'

either'within'innovative'industries,'or'between'innovative'industries'and'those,'which'are'

not'heavily'dependent'on'innovation'and'knowledge'production.'Increase'in'the'income'

of'innovative'workers'can'increase'the'demand'for'lowRpaid'service'workers.'Workers'who'

are'attracted' to' the' low'paid'service'sector' jobs'could'have'been'displaced' from'other'

industrial' sectors' in' the' region,' therefore' through' this'mechanism' innovative' industries'

and' low' paid' service' sector' will' experience' economic' growth,' while' other' industries'

potentially' decline.' The'SBTC' theory' suggests' that' income' inequality' increases'within'

industries'because'workers'within'an'industry'will'experience'differential'rises'(or'fall)'in'

their'income'based'on'what'kind'of'tasks'they'are'doing'and'how'valuable'they'are.'

The'presence'of'KIBS'sectors'can'also'affect'the'income'distribution'in'economic'

regions.'KIBS'industries'can'perform'the'research'and'development'outsourced'by'other'

industries,'create'knowledge'and'innovate'new'industries.'They'also'provide'the'support'

activities' for' other' industries' in' their' innovation' progress' such' as' legal' services,'

advertising,'infrastructures'design,'etc.''

Concentration'of'KIBS'activities'can'increase'productivity'within'the'KIBS'sector'

itself,'and'within'the'firms'in'other'industry'sectors'(which'enjoy'KIBS'services'benefits),'

through'different'mechanisms'(Wessel'2011,'p.'1086):'

•' Externalization+of+activities:'reduction'in'average'cost'of'production'through'outsourcing'specific'tasks'or'services'to'other'industrial'sectors'of'the'economyT'

•' Spatial+reorganization+of+production:'reallocating'different'production'tasks'to'different'geographical'placesT'

'

'18'

•' Utilization+and+development+of+new+technologies:'introduction'of'new'technologies'through'R&D'activities'within'KIBS'sector'or'in'partnership'with'other'industriesT'

•' Participation+in+cross9border+networks:'expanding'demand'market'or'suppliers'of'an'industry'through'their'crossRborder'networkT'and'

•' Adoption+of+flexible+working+practices:'allowing'workers'to'work'remotely'or'reduced'hours.'

Wessel'(2011)'claims'the'KIBS'sector'is'an'important'distributive'force.'It'divides'

the' lowRwage' and' the' highRwage' segments' of' economy' by' specialization' and'

reorganization' of' production.' For' instance,' different' processes' of' production' that'were'

formerly' done' within' a' single' firm' will' be' separated' to' be' done' in' different' firms' and'

industries' or' different' geographical' locations.' Through' the' products' or' services' value'

chain,'processes,'which'add'a'high'value' to' the'product,'will' create'highRincome' jobs,'

whereas' ones,' which' add' a' low' value' to' the' product,' will' create' lowRincome' jobs.'

Therefore,' KIBS' activities' can' increase' the' income' inequality' in' other' industries.'

Moreover,'partly'because'of'the'use'of'flexible'labour'there'is'a'change'in'the'payment'

scheme,' union' density,' union' coverage' and' legislative' intervention' in' KIBS' sector.' A'

consequence'of'such'adjustment'is'a'growing'gap'between'income'in'KIBS'and'the'rest'

of'the'economy.'

2.4.$ Effect$of$Inequality$on$Innovation$

Among' the' literature' considering' the' relationship' between' inequality' and'

innovation,'most' researchers' do' not' empirically' investigate' to' find' out' which' of' these'

parameters' affects' the' other' one.' There' could' be' a' mutual' effect' between' income'

inequality' and' innovation,' and' it' is' an' open' question' whether' inequality' matters' for'

innovation,'or'innovation'matters'for'inequality'(or'whether'they'are'mutually'dependent'

to'each'other).'For'instance,'Tselios'(2011)'analyzed'microeconomic'data'of'102'Western'

European'regions'over'the'period'of'1995'to'2000,'to'find'out'whether'inequality'is'good'

for' innovation' or' not.' He' concluded' that' an' increase' in' a' region’s' inequality' favours'

innovation.' To' find' out' which' one' is' affecting' the' other' one,' he' has' done' a' Granger'

causality' test' between' 1995' and' 2000.' Tselios' concluded' that' the' casual' relation' of'

innovation'and'inequality'varies'across'space,'because'innovation'has'local'character'and'

'

'19'

cumulative' nature.' In' other' words,' in' some' regions,' inequality' in' 1995' caused' more'

innovation'in'2000'and'in'others'more'innovation'in'1995'caused'more'income'inequality'

in'2000.'

Some'of'the'theories'on'the'effect'of'innovation'on'income'inequality'are'explained'

above.'In'this'section,'some'of'the'theories'on'the'effect'of'inequality'on'innovation'are'

studied.'

Income'inequality'could'have'a'positive'or'negative'effect'on'innovation'through'

its'direct'impact'on'market'size'and'price'effect'(Bertola'et.'al.'2006).'On'one'hand,'the'

more' unequal' the' distribution' of' income'means' the' smaller' regional'market' for' a' new'

product,' because' when' the' income' distribution' is' unequal,' only' a' small' number' of'

consumers'can'afford'to'buy'the'newest'products.'Therefore,'the'markets'of'new'products'

–and'consequently'innovative'industries–'grow'slowly.'This'mechanism'is'valid'only'when'

the'markets'are'regional.'

On' the' other' hand,' when' the' price' effect' of' income' inequality' becomes'more'

dominant' than' the' market' size' effect,' one' can' argue' that' inequality' will' accelerate'

innovations,'since'high' income'consumers'have'a'very'high'willingness'to'pay'for'new'

products'and'services.'In'addition,'the'relatively'rich'individuals'are'more'likely'to'pay'for'

better' technologies,' therefore' it' stimulates' the' creative' class' to' innovate' more' in'

competition'with'each'other'(Vernon'1972).'

Another'theory'explaining'the'effect'of'inequality'on'innovation'is'Werner'Sombart'

Thesis' (1913),'Luxury+and+Capitalism.' It'explains' the'mechanism' through'which' luxury'

consumptions' by' ‘rich’' people' leads' to' emergence' of' new'markets' and' consequently'

innovativeness.'Therefore,' inequality'will'provide'an'appropriate'environment'–which' is'

polarized'wealth'distribution–'for'production'and'consumption'of'luxury'materials.''

Overall,' all' the' aforementioned' theories' in' this' section' implicitly' have' the'

assumption' that' local' factors'are'more'dominant' than' interRregional' factors' in' the' local'

market,'whereas'globalization'and'availability' of' transRregional'markets' through' cheap'

transportation' cost' could' reduce' the' effect' of' regional'market' size' and' price' effect' on'

'

'20'

innovation.'To' the'extent' that' innovative' firms'are'not' limited' to' their' regional'markets,'

they'will'be'able'to'sell'their'products'or'services'to'‘rich’'people'in'other'regions.'

2.5.$ Patent$Policy$and$Capital$Income$Inequality$

The'focus'of'this'research'is'on'employment'income'(or'wage)'inequalityT'however,'

another'important'aspect'of'economic'inequality'lies'in'the'capital'income'inequality.'For'

example,'Reed'et'al.'(2001)'show'that'oneRquarter'of'the'increase'in'income'inequality'in'

the'1990s' in' the'US' is'due'to' the'capital' income' inequality.' Innovation'also'affects' the'

distribution'of'capital'income.''

Chu'(2010)'conducted'a'research'on'the'effect'of'patent'policy'on'capital'income'

inequality,' and' he' concluded' that' strengthening' patent' protection' increases' economic'

growth'through'encouraging'firms'to'spend'more'on'research'and'development,'and'also'

increases'capital'income'inequality'by'increasing'the'rate'of'return'on'capital.'This'higher'

return'on'assets'leads'to'an'increase'of'the'assetRwealthy'households’'income'relative'to'

assetRpoor'households.'

On' the'other' hand,'Hatipoglu' (2008)' studied' the'effect' of' income' inequality' on'

innovation'within'different' patent' policies.'As'discussed'above' firms'are'more' likely' to'

innovate'where' the' income' inequality' is'high,'because' they'have' their'market' for'new'

products'among'highRincome'consumers.' In'addition,'Hatipoglu'believes' that'when' the'

income' inequality' is' low,' longRterm'patent'would'be'an' incentive' for' innovative' firms' to'

spend'more' on' research' and' development' because' it' increases' the' profitability' of' an'

innovation'by'increasing'the'likelihood'of'the'benefit'from'a'future'demand'jump'in'sales'

to'the'poor.'These'different'ideas'confirm'the'importance'of'policy'making'in'minimizing'

the'side'effects'of'innovation'while'the'economy'is'enjoying'its'benefits.'Specifically,'when'

policy'makers'use'patent+policy'as'an'instrument'to'increase'research'and'development'

and'consequently'economic'growth,'they'need'to'take'into'account'the'potential'negative'

effects'of'patent'policies.''

In' this' chapter,' a' brief' review'of' literature' on' innovation'was'presented.' It'was'

explained'that'innovation'is'concentrated'in'cities'and'that'some'cities'are'more'innovative'

'

'21'

than'others.'Moreover,'different'theories'on'the'mutual'effects'of'innovation'and'innovation'

presented'in'literature'were'discussed.'In'the'following'chapter,'the'methodology'of'this'

research' about' innovation' and' inequality' measurement,' and' analysis' of' correlation'

between'innovation'and'income'inequality'are'explained.'

'

'

'22'

Chapter$3.$ Methodology$

The'main'source'of'data'in'this'research'is'obtained'from'the'20Rpercent'long'form'

survey' of' Canadian' Census' in' 1991,' 1996,' 2001' and' 2006,' and' National' Household'

Survey'(NHS)'2011.'Detailed'microRdata'of'these'surveys'have'a'relatively'large'number'

of'respondent'(varies'between'5.5'to'6.8'million'per'Census/Survey'year).'Therefore,'it'is'

a' rich' data' source' for' this' research' which' needs' a' relatively' large' sample' size' for'

calculation'of'Gini'coefficient'in'Canadian'cities,'as'a'measure'of'income'inequality.'The'

detailed'microRdata'files'of'Canadian'Census'and'NHS'are'not'publicly'available.'Access'

to'these'files'needs'an'application'process'through'Research'Data'Centre'(RDC).'Most'

of'the'needed'data'in'this'research'was'obtained'from'RDC'at'Simon'Fraser'University'

(SFU).'All'the'data'that'was'released'from'RDC'required'approval'of'RDC'staff'to'make'

sure'that'released'data'meets'the'requirements'to'protect'respondent'confidentiality.''

To'conduct'the'longRform'survey,'Statistic'Canada'used'to'send'the'questionnaire'

to'a'randomly'selected'20Rpercent'of'the'population.'Responding'to'the'longRform'survey'

was'mandatory' for' the' households' who' receive' the' questionnaire1' (Statistics' Canada'

2009).'However,' the'survey'conduction'method'changed' in'2011.'To'conduct' the'NHS'

2011,'questionnaires'were'sent' to'around'50%'of' the'population,'who'could'voluntarily'

respond'to'Statistics'Canada2'(Statistics'Canada'2015).'Due'to'the'difference'in'survey'

conduction'methods'between'NHS'and'Canadian'Census,'one'should'be'cautious'when'

comparing' NHS' with' Canadian' Census' data.' Therefore,' in' this' research' other' than'

Canadian'Census'and'NHS,'Labour'Force'Survey'(LFS)'in'2001,'2006'and'2011'are'used'

to'verify'some'of'the'results'obtained'from'NHS'and'Canadian'Census.'

In'the'following'sections'of'this'chapter,'the'methodology'of'this'research'on'units'

of'analysis,'innovation'and'inequality'measurement,'and'multivariate'regression'analysis'

is'discussed.'

'1Obtained'from:'https://www12.statcan.gc.ca/censusRrecensement/2006/ref/rpRguides/rp/coverageRcouverture/covRcouv_indexReng.cfm'

2'Obtained'from:'http://www12.statcan.gc.ca/nhsRenm/2011/ref/reportsRrapports/swRep/99R002Rx2011001Reng.pdf'

'

'23'

3.1.$ Census$Metropolitan$Areas$and$Census$Agglomerations$Boundaries$

The' units' of' analysis' in' this' research' are' Canadian' cities.' These' entities' are'

defined' by' Statistics' Canada' as' Census' Metropolitan' Area' (CMA)' or' Census'

Agglomeration'(CA)'based'on'their'population.'Statistics'Canada'defines'CMA'and'CA'as'

follows:'

A' census' metropolitan' area' (CMA)' or' a' census' agglomeration' (CA)' is'formed'by'one'or'more'adjacent'municipalities'centred'on'a' large'urban'area'(known'as'the'urban'core).'A'CMA'must'have'a'total'population'of'at'least' 100,000' of' which' 50,000' or' more' must' live' in' the' urban' core.'A'CA'must'have'an'urban'core'population'of'at'least'10,000.'To'be'included'in'the'CMA'or'CA,'other'adjacent'municipalities'must'have'a'high'degree'of'integration'with'the'central'urban'area,'as'measured'by'commuting'flows'derived'from'census'place'of'work'data.'

Inclusion'of'municipalities'around' the'urban'core'depends'on' the'degree'of' integration'

with'the'central'urban'area,'which'is'measured'by'daily'commuting'flow.'Therefore,'it'is'

not'unexpected' to'see'changes' in'Canadian'CMAs/CAs'boundaries' in'20'years'of' the'

analysis'period'from'1991'to'2011.'These'changes'are'applied'every'5'years'prior'to'the'

census'years'according'to'the'commuting'patterns'in'the'previous'census'data.''

A' unique' CMA/CA' geographical' boundary' does' not' have' a' constant' identity'

independent'from'its'social'and'economic'interactions.'When'the'interactions'between'a'

CMA/CA' with' a' nearby' region' increases' between' two' census' years,' the' initial'

geographical' boundaries' do' not' represent' the' same' characteristics' at' both' instances.'

Therefore,' in' this'research,'boundaries'of'each'CMA/CA'for'crossRsectional'analysis' in'

each'census'year'is'considered'as'the'CMA/CA'boundaries'defined'by'Statistics'Canada'

at'the'year'of'analysis.'Similarly,'in'the'longitudinal'analysis'CMA/CA'boundaries'of'each'

census'year'is'used'for'that'specific'year,'in'other'words'functional'economic'regions'with'

dynamic'boundaries'over'time'are'considered'as'analysis'entities,'not'fixed'territories.'For'

instance,'as'the'boundaries'of'Edmonton'have'been'changed'slightly'from'2006'to'2011,'

when'I'perform'an'analysis'on'Edmonton'from'2006'to'2011,'I'use'boundaries'defined'by'

Statistics'Canada'in'each'census'year'for'the'corresponding'census'year.'

'

'24'

Along'with'the'changes'in'CMA/CA'boundaries,'some'CAs'has'been'merged'to'

other'CMAs/CAs'and'they'do'not'exist'as'an'independent'CA'in'some'census'years'of'

the'analysis.'In'addition,'some'CAs'are'introduced'sometime'after'1991.'Newly'emerged'

CAs' are' excluded' from' the' analysis' because' in' longitudinal' analysis'CMAs/CAs' in' all'

census' years' from' 1991' to' 2011' are' compared' with' each' other.' In' addition,' as' the'

longitudinal'analysis'should'be'consistent'with'the'crossRsectional'analysis'in'each'census'

year,'the'identical'group'of'CMAs/CAs'are'used'in'crossRsectional'analysis.'For'instance,'

Miramichi'was'introduced'as'a'CA'in'2006,'whereas'it'was'not'considered'as'a'separate'

CA'before'2001.'Therefore,'it'has'been'excluded'from'the'analysis.'

CAs/CMAs'that'existed'before'and'merged'into'another'CMA/CA'sometime'after'

1991' are' also' excluded' from' the' analysis.' One' might' consider' the' geographical'

boundaries'of'merged'CAs/CMAs'as'a'single'CA/CMA'in'all'Census'years'(even'when'

Statistics' Canada' did' not' consider' them' as' a' single' CA).' In' this' research,' these'

uncommon' CAs/CMAs' are' excluded' from' analysis' and' merged' CMAs/CAs' are' not'

considered' as' a' single' CMA/CA' in' all' years,' because' when' we' are' studying' a'

geographical'area'as'a'single'entity'the'economic'activities'of'all' the'people'within'that'

area' should' be' linked' together' to' a' specific' degree.' For' instance,' Magog' was' an'

independent' CA' in' 2001,' after' which' it' was' merged' into' Sherbrook.' It' should' not' be'

included'in'Sherbrook'for'analysis'before'2006'because'according'to'Statistics'Canada'

the'social'and'economic'relation'between'Magog'and'Sherbrook'was'not'strong'enough'

to'consider'them'as'one'CMA,'even'though'Magog'became'a'part'of'Sherbrook'CMA'after'

2006.'

According'to'the'aforementioned'selection'process'of'units'of'analysis,'among'all'

CMAs/CAs'defined'in'the'census'years'from'1991'to'2011,'29'CAs'are'excluded'from'the'

analysis'which'are'shown' in'Table'3.1'with' their' total'population' in'2011.'As' the' table'

shows,'other'than'SaintRJérôme,'which'has'the'population'of'68,456'and'has'merged'into'

Montréal'after'1991,'all'these'CAs'has'the'population'of'less'than'30,000.'A'list'of'all'130'

CMAs/CAs'that'are'included'in'the'analysis'is'provided'in'Appendix'B.''

' $

'

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Table$3.1$ List$of$CAs$that$are$excluded$from$the$analysis$

CA name Population in 2011 Reason for exclusion Saint-Jérôme 68,456 Merged with Montreal, 1996 Miramichi 28,115 Introduced as a new CA, 2006 Centre Wellington 26,693 Introduced as a new CA, 2006 Magog 25,358 Merged with Sherbrook, 2006 Okotoks 24,511 Introduced as new CA, 2006 Strathroy 20,978 Merged with London, 2001 Salmon Arm 17,464 Introduced as a new CA, 2006 Squamish 17,158 Introduced as a new CA, 2001 Amos 17,090 Introduced as a new CA, 2001 Petawawa 15,988 Introduced as a new CA, 2001 Brooks 13,676 Introduced as a new CA, 2001 Steinbach 13,524 Introduced as a new CA, 2011 High River 12,920 Introduced as a new CA, 2011 Sylvan Lake 12,327 Introduced as a new CA, 2011 Strathmore 12,305 Introduced as a new CA, 2011 Canmore 12,288 Introduced as a new CA, 2006 Ingersoll 12,146 Introduced as a new CA, 2006 Parksville 11,977 Introduced as a new CA, 2001 Lacombe 11,707 Introduced as a new CA, 2011 La Tuque 11,227 Introduced as a new CA, 2011 Gander 11,054 Introduced as a new CA, 2006 Weyburn 10,484 Introduces as a new CA, 1996 Wallaceburg 10,163 Introduces as a new CA, 1996 Selkirk 9,834 Introduces as a new CA, 1996 Smiths Falls 8,978 Eliminated, 2001 Kirkland Lake 8,493 Eliminated, 1996 Kitimat 8,335 Eliminated, 2011 Labrador City 7,367 Eliminated, 2006 Bay Roberts 5,818 Introduced as a new CA, 2006

Source: National Household Survey (NHS), Statistics Canada (2011)

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3.2.$ Innovation$Measure$

As'discussed'in'Chapter'2,'there'are'numerous'variables'used'by'researchers'to'

measure' the' level' of' regional' innovation.' Among' these' measures,' three' of' them' are'

selected' to' be' used' in' this' researchT' (1)' ratio' of' employees' in' highRtech' occupations'

(defined'by'Chapple'(2004)),'(2)'ratio'of'employees'working'in'highRtech'industries'(used'

by'Gertler'et'al.'(2002)'and'Hall'and'Khan'(2008)),'and'(3)'ratio'of'employees'working'in'

KnowledgeRIntensive'Business'Services'(KIBS)'industries.'

HighRtech' occupations' are' defined' according' to' Standard' Occupational'

Classification' (SOC)' system,' whereas' in' Canadian' Census,' occupations' are' defined'

based'on'National'Occupational'Classification'(NOC).'HighRtech'occupations'which'are'

provided'based'on'SOC'are'transformed'into'NOC'classification'system.'Moreover,'the'

NOC' classification' has' slightly' been' changed' from' 1991' to' 2011,' but' the' structure' of'

classification' has' remained' constant3.' Therefore,' the' highRtech' occupations' list' is'

consistent'among'all'analysis'census'years.'

Gertler'et'al.'(2002),'and'Hall'and'Khan'(2008)'defined'highRtech'industries'based'

on'SIC'classification,'however'the'industrial'classification'in'Canadian'Census'changed'

from' SIC' to' North' American' Industrial' Classification' System' (NAICS)' in' 2001.' This'

transition' from' SIC' to' NAICS' changed' the' structure' of' the' classification' significantly.'

Therefore,'being'consistent' in'analysis'would'be'challenging'when'using' the' industrial'

classification'within'a'period'that'includes'years'prior'and'after'2001.'But'2001'is'a'bridge'

year'that'includes'the'employment'data'based'on'both'NAICS'and'SIC'classification.'

The' ratio' of' KIBS' employment' based' on' NAICS' classification' is' defined' as'

proportion'of' people'employed' in'Professional,+ scientific+and+ technical+ services+ (under'

code'54'in'the'NAICS'classification'as'presented'in'Table'2.2).'The'transformation'of'KIBS'

industries'from'NAICS'to'SIC'classification'is'also'a'challenge'in'this'research.'

'3'http://www.statcan.gc.ca/eng/subjects/standard/noc/2011/index'

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Statistics'Canada'has'provided'concordance'tables'to' translate' industrial'codes'

between'SIC'and'NAICS'classification'systems'however,'these'tables'do'not'provide'an'

detailed' transformation' between' industry' classifications4.' Although,' using' these'

concordance'tables'may'cause'an'error'in'the'transformation'process,'this'is'the'only'way'

to'transform'the'industry'codes'between'NAICS'and'SIC.'In'2001'both'NAICS'and'SIC'

industrial'classifications'are'available'in'the'20%'long'form'Census'microRdata.'To'check'

the'accuracy'of'this'transformation'process'number'of'employees'in'highRtech'industries'

and' KIBS' are' calculated' in' 2001' based' on' both' NAICS' and' SIC' classifications.' The'

comparison'between'numbers'of'highRtech'industries’'employees'in'Canadian'CMAs/CAs'

based'on'SIC'list'of'highRtech'industries'and'its'corresponding'transformation'to'NAICS'

classification'is'presented'in'Appendix'B.'The'difference'between'these'two'classifications'

in'2001'shows'that'the'error'of'transformation'process'is'high.'The'difference'in'number'

of'KIBS'employment'based'on'two'industrial'classifications'varies'from'R35%'to'24%'in'

different' CMAs/CAs.' Also,' number' of' highRtech' industries’' employees' in' different'

CMAs/CAs' according' to' these' two' classifications' varies' from' R10%' to' 30%.' The'

concordance' tables' are' more' accurate' in' transformation' of' KIBS' industries' than' in'

transformation'of'highRtech'occupation,'since'number'of'categories'in'the'former'is'lower'

than'the'latter.'''

According'to'the'error'in'transformation'process'of'industry'codes'from'NAICS'to'

SIC'and'vice'versa,'one'should'be'cautious'in' longitudinal'analysis'within'the'20Ryears'

period'from'1991'to'2011.'Therefore,'when'the'ratio'of'employees'in'KIBS'or'highRtech'

industries' are' used' as' the' measure' of' innovation,' the' analysis' is' performed' on' two'

different'10Ryears'periodT'from'2001'to'2011,'and'from'1991'to'2001,'in'which'NAICS'and'

SIC'classifications'are'used,'respectively.'The'correlation'analysis'of'three'measures'of'

innovation'shows'that'there'is'a'statistically'significant'correlation'between'all'these'three'

variables'(ratio'of'employment'in'KIBS,'highRtech'occupations'and'highRtech'industries)'in'

all'years'(p

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3.3.$ Inequality$Measure$

Employment' income' inequality' is'calculated'based'on' the'distribution'of'annual'

employment' income' on' individuals,' reported' in' the' longRform' Canadian' Census' and'

National'Household'Survey.'According'to'Statistics'Canada,'employment'income'refers'

to'the'total'income'received'by'persons'15'years'of'age'and'over'during'the'year'prior'to'

the' survey' including' wages' and' salaries,' net' income' from' unincorporated' nonRfarm'

business'and/or'professional'practice'and'net'farm'selfRemployment'income.'

The'main' parameter' used' to'measure' inequality' level' in'CMA/CA'and'national'

level' is' the'Gini'coefficient.'This'variable' is' the'most'popular' inequality'measure' in' the'

literature.'The'Gini'coefficient'varies'from'zero'to'one.'The'higher'the'value'of'the'Gini'

coefficient' represents' the' higher' the' level' of' inequality.' The' Gini' coefficient' can' be'

calculated'using'the'following'equation:'

' 1"#

$% − $'(')*

(%)*

2,'

'(3.1)'

in'which'"'is'the'number'of'cases'whose'income'is'used'in'calculation,'$%'and'$''

are'the'employment'incomes'of'cases'number'-'and'.'in'the'sample'population,'and','is'

the'average'of'employment'income.'The'Gini'coefficients'of'all'CMAs/CAs'are'calculated'

using'the'syntax'presented'in'Appendix'A'on'the'Canadian'Census'and'NHS'2011.'

Percentile'ratios'are'also'used'as'measures'of'employment'income'inequality.+PR

Percentile'income'is'a'value'below'which'P'percent'of'the'population’s'income'fall.'For'

instance,'the'90Rpercentile'(P90)'annual'employment'income'of'Canadians'in'2011'was'

$87,000,'which'means'the'annual'employment'income'of'90'percent'of'the'population'is'

less'than'$87,000.'10R,'50R'and'90Rpercentile'incomes'are'calculated.'Then'50Rpercentile'

over' 10Rpercentile' and'90Rpercentile' over' 50Rpercentile' are' calculated' as'measures' of'

inequality.'Percentile'ratios'provide'more'accurate'information'than'Gini'coefficients'about'

how'employment'income'distributed'in'a'sample'population.'

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Errors'in'the'reported'employment'income'of'people'at'the'very'top'or'the'bottom'

of' income'distribution'can'cause'a'significant'error' in' the'Gini'coefficient.'For' instance,'

Gini'coefficient'can'be'misleading'when'the' income'data' is' topRcoded.'Fortunately,' the'

reported'annual'income'in'20Rpercent'sample'long'form'Census'and'NHS'microRdata'is'

not'top'coded'and'the'employment'income'is'reported'with'$1'precision.''

On'the'other'hand,'casual'workers'who'are'not'active'enough'in'the'labour'force'

can'cause'errors' in' the' income' inequality'analysis.'To'calculate' the' income' inequality,'

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