Rowville Rail Project - City of Knox · Rowville Rail Project Economic Impact Study FINAL REPORT...

Rowville Rail Project Economic Impact Study

FINAL REPORT

Strategic and Economic Development Division

Knox City Council

May 2012

This report has been prepared for:

Strategic and Economic Development Division Knox City Council

This report has been prepared by:

SGS Economics and Planning Pty Ltd

ACN 007 437 729

Level 5 171 Latrobe Street

MELBOURNE VIC 3000

P: + 61 3 8616 0331

F: + 61 3 8616 0332

E: [email protected]

W: www.sgsep.com.au

Offices in Brisbane, Canberra, Hobart, Melbourne, Sydney

mailto:[email protected]

http://www.sgsep.com.au/

Table of Contents

Page i of 175

GLOSSARY OF TERMS .................................................................... i

EXECUTIVE SUMMARY .................................................................. i i

Objectives of Study .............................................................................................................. ii

Assessed Economic & Employment Impacts ............................................................................. ii

Estimated Accessibility & Land Use Impacts ................................................................... iii

Estimated Construction and Operation Phase Impacts (Indirect Impacts) .......................... iv

Estimated Wider Economic Benefits .............................................................................. vi

Estimated Environmental Savings ................................................................................. vi

Results Summary ...................................................................................................... vii

Upshot of Findings .............................................................................................................. vii

1 INTRODUCTION .................................................................... 1

1.1 Project in Context ........................................................................................................ 1

1.2 Project Objectives ........................................................................................................ 1

1.3 SGS Response ............................................................................................................. 2

1.4 Assumptions and Limitations ......................................................................................... 3

1.5 Report Structure ......................................................................................................... 5

2 PROPOSED ROWVILLE RAIL .................................................... 7

2.1 Project History and Background ..................................................................................... 7

2.2 Council’s Advocacy Efforts for Improved Transport........................................................... 8

2.2.1 2004 Pre Feasibility Study of the Rowville Rail ................................................... 8

2.2.2 Who’s on Board? Campaign ............................................................................. 9

2.3 2012 SKM Draft Feasibility Study ................................................................................... 9

2.3.1 Proposed Route .............................................................................................. 9

2.3.2 Community/ Stakeholder Consultation ............................................................ 11

2.3.3 Traffic Modelling Method................................................................................ 11

2.3.4 Traffic Modelling Results ................................................................................ 12

2.3.5 Construction and Operations .......................................................................... 13

2.3.6 SKM Study Recommendations ........................................................................ 14

3 BASELINE SOCIO ECONOMIC PROFILE ................................... 15

3.1 Geographical Scope ................................................................................................... 15

3.2 Resident Profile ......................................................................................................... 16

3.2.1 Population, Dwellings and Growth ................................................................... 16

3.2.2 Skills Profile ................................................................................................. 18

3.2.3 Occupation Profile ........................................................................................ 20

3.3 Employment Profile .................................................................................................... 21

3.3.1 Industry Profile ............................................................................................ 21

3.3.2 Employment Growth ..................................................................................... 25

3.3.3 Occupation Profile ........................................................................................ 26

Table of Contents

3.4 Journey to Work Patterns ........................................................................................... 27

3.4.1 Origin and Destination of Workers .................................................................. 27

3.4.2 Travel Mode ................................................................................................. 29

3.4.3 Travel Time ................................................................................................. 31

3.4.4 Accessibility ................................................................................................. 33

3.5 Section Summary ...................................................................................................... 34

4 BASELINE FORECASTS ......................................................... 36

4.1 Population Forecasts .................................................................................................. 36

4.2 Employment Forecasts ............................................................................................... 37

4.3 Section Summary ...................................................................................................... 44

5 STRATEGIC POLICY CONTEXT ............................................... 45

5.1 Metropolitan Policy Context ......................................................................................... 45

5.2 South East Region Planning Context ............................................................................ 45

5.2.1 Melbourne South East Regional Economic Strategy 2009-2030 ........................... 45

5.3 Knox City Council Planning Context .............................................................................. 46

5.3.1 Strengths and Challenges for Knox ................................................................. 46

5.3.2 Opportunities and Actions for Sustainable Growth ............................................ 47

5.3.3 Vision for City of Knox .................................................................................. 49

5.4 Section Summary ...................................................................................................... 50

6 CASE STUDY INVESTIGATIONS ............................................. 51

6.1 Introduction .............................................................................................................. 51

6.2 Short Term Construction Phase Impacts ....................................................................... 51

6.2.1 Australian Experience ................................................................................... 52

6.2.2 International Experience ............................................................................... 52

6.3 Long Term Operational Phase Impacts ......................................................................... 53

6.3.1 Australian Experience ................................................................................... 54

6.3.2 International Experience ............................................................................... 57

6.4 Section Summary ...................................................................................................... 58

7 CONSULTATION FINDINGS ................................................... 59

7.1 Respondent Profile ..................................................................................................... 59

7.2 Consultation Outcomes .............................................................................................. 62

7.3 Section Summary ...................................................................................................... 66

8 ACCESSIBILITY CHANGE ANALYSIS ....................................... 67

8.1 Land Use and Accessibility .......................................................................................... 67

8.2 Accessibility Shifts Model ............................................................................................ 69

8.2.1 Historical Relationship Analysis ...................................................................... 70

8.2.2 Accessibility Induced Land Use Shifts .............................................................. 80

8.3 Anticipated Induced Impacts ....................................................................................... 84

Table of Contents

8.3.1 Households .................................................................................................. 84

8.3.2 Employment ................................................................................................ 88

8.4 Section Summary ...................................................................................................... 93

9 ESTIMATED IMPACTS OF PROJECT ........................................ 94

9.1 Estimation Methodology ............................................................................................. 94

9.1.1 Assumptions and Limitations.......................................................................... 95

9.2 Estimated Impacts of Construction Phase ..................................................................... 96

9.3 Estimated Impacts During Operations Phase ................................................................. 98

9.3.1 Scope and Assumptions ................................................................................ 99

9.3.2 Impacts of Induced Dwellings ........................................................................ 99

9.3.3 Impacts of Induced Employment .................................................................. 104

9.3.4 Total Estimated Impacts of Project Operations ............................................... 108

9.4 Section Summary .................................................................................................... 110

10 WIDER ECONOMIC BENEFITS OF PROJECT............................ 111

10.1 Estimated Environmental Savings .............................................................................. 111

10.2 Estimated Agglomeration Effects ............................................................................... 112

10.2.1 What is Agglomeration ................................................................................ 112

10.2.2 Estimated Productivity Enhancements ........................................................... 113

11 CONCLUDING REMARKS ..................................................... 115

APPENDIX A: SGS POPULATION AND EMPLOYMENT FORECAST

METHODOLOGY ................................................................. 118

11.1 Projection Methodology ............................................................................................ 118

11.1.1 Employment .............................................................................................. 118

11.1.2 Module Description ..................................................................................... 120

11.1.3 Dwellings and Population ............................................................................. 124

11.1.4 Module Description ..................................................................................... 126

APPENDIX B: SURVEY QUESTIONNAIRE FOR SGS CONSULTATION . 131

APPENDIX C: EFFECTIVE JOB DENSITY CALCULATIONS ............... 135

APPENDIX D: SGS METHODOLOGY TO MEASURE AGGLOMERATION 137

11.2 Review of the Evidence on the Nature and Sources of Agglomeration Economies ............. 137

11.3 The Scope of Increasing Returns ............................................................................... 138

11.3.1 Industrial Scope ......................................................................................... 140

11.3.2 Geographic Scope ...................................................................................... 141

11.3.3 Temporal Scope ......................................................................................... 142

11.4 ‘Culture’ and the Transmission of Agglomeration Economies ......................................... 143

11.5 Sources of Agglomeration Economies ......................................................................... 143

Table of Contents

11.5.1 Input Sharing ............................................................................................ 144

11.5.2 Knowledge / Technological Spillovers ............................................................ 145

11.5.3 Labour Market Pooling ................................................................................ 145

11.5.4 Home Market Effect .................................................................................... 146

11.5.5 Consumption ............................................................................................. 147

APPENDIX E: DETAILED INDUSTRY WIDE BREAKDOWN OF INDUCED

IMPACTS .......................................................................... 149

REFERENCES ........................................................................... 152

Table of Contents

List of Tables

Table 1 Estimated Rail Construction Cost of Rowville Line .................................................... 13

Table 2 Growth in Population, 1996-2011 .......................................................................... 17

Table 3 Growth in Dwellings, 1996-2011 ........................................................................... 17

Table 4 Employment by Industry (Workers), 2011 .............................................................. 22

Table 5 Knox (C) Gross Regional Product by Industry, 2011 ................................................ 26

Table 6 Jobs Accessibility by Car and Public Transport, 2011 ............................................... 33

Table 7 Estimated Population Projections, 2011-46 ............................................................. 36

Table 8 Estimated Dwelling Projections, 2011-46 ............................................................... 37

Table 9 Estimated Employment Projections by 1-digit ANZSIC Industry, Knox (C) .................. 40

Table 10 Relative EJD Index, Selected SLAs, 1996-2011 ....................................................... 75

Table 11 Induced Household Changes................................................................................. 85

Table 12 Induced Employment Changes .............................................................................. 89

Table 13 Induced Broad Industry Group Changes 2021 ......................................................... 92

Table 14 Induced Broad Industry Group Changes 2046 ......................................................... 92

Table 15 Estimated Direct Construction Costs of Proposed Rowville Rail .................................. 96

Table 16 Estimated Total Impacts (Direct + Indirect) of Construction Phase of the Proposed Rowville

Line ................................................................................................................... 97

Table 17 Estimated Induced Direct Dwelling Consumption (in 2011$) ................................... 100

Table 18 Impacts of Induced Dwelling Changes in Knox (C) on Knox (C) ............................... 102

Table 19 Impacts of Induced Dwelling Changes in Knox (C) on South East Region ................. 102

Table 20 Impacts of Induced Dwelling Changes in Knox (C) on MSD ..................................... 103

Table 21 Impacts of Induced Dwelling Changes on Victoria ................................................. 103

Table 22 Impacts of Induced Employment Changes in Knox (C) on Knox (C) ......................... 105

Table 23 Impacts of Induced Employment Changes in Knox (C) on South East Region ............ 106

Table 24 Impacts of Induced Employment Changes in Knox (C) on MSD ............................... 106

Table 25 Impacts of Induced Employment Changes in Knox (C) on Victoria ........................... 107

Table 26 Impacts of Project Operations by 2046 ................................................................ 108

Table 27 Estimated Environmental Emissions Savings ......................................................... 111

Table 28 Impact of Project on GVA of Selected SLAs........................................................... 114

Table 29 Estimated Effective Job Density for South East Region ........................................... 135

Table 30 Estimated Relative Effective Job Density for South East Region ............................... 136

Table 31 Total Induced Impacts (by Industry) of Additional Households on Knox (C) Output ($

Million) ............................................................................................................ 149

Table 32 Total Induced Impacts (by Industry) of Additional Employment on Knox (C) Output .. 150

Table 33 Total Induced Impacts (by Industry) of Additional Employment on Knox (C) Employment

...................................................................................................................... 151

Table of Contents

List of Figures

Figure 1 SGS Study Method ................................................................................................ 2

Figure 2 Proposed Route of the Rowville Rail Line ................................................................ 10

Figure 3 Knox and the Surrounding South East Region ......................................................... 16

Figure 4 Education Profile of Residents, 2006 ...................................................................... 18

Figure 5 Education Profile of Residents, 1996 ...................................................................... 19

Figure 6 Residents Field of Study, 2006.............................................................................. 19

Figure 7 Occupation Profile of Resident Workers, 2006 ......................................................... 20

Figure 8 Occupation Profile of Resident Workers, 1996 ......................................................... 21

Figure 9 Employment Share by Broad Industry Category (Workers), 2011 .............................. 23

Figure 10 Knox (C) Employment and Value Added Contribution ............................................... 24

Figure 11 Knox (C) Manufacturing Employment and Value-Added, 2011................................... 24

Figure 12 Employment Growth by Broad Industry Category (Workers), 1996-2011 ................... 25

Figure 13 Occupation Profile of Workers (2006) .................................................................... 27

Figure 14 Destination LGAs of Knox (C) Resident Workers, 2006 ............................................. 28

Figure 15 Origin LGAs of Knox Workers, 2006 ....................................................................... 29

Figure 16 Mode of Travel, Origin of Resident Workers, 2006 ................................................... 30

Figure 17 Mode of Travel, Destination of Resident Workers, 2006 ............................................ 30

Figure 18 Weighted Travel Time of Destination Workers ......................................................... 32

Figure 19 Weighted Travel Time of Resident Workers ............................................................. 32

Figure 20 Industry Structure, Knox (C), 1996 - 2046 ............................................................. 41

Figure 21 Estimated Employment Growth (CAGR %) by 1-digit Industry, 2011-46 .................... 41

Figure 22 Estimated Knox (C) Employment Share in MSD ...................................................... 42

Figure 23 Declared Industrial Nodes, Urban Development Programme, 2010 ............................ 43

Figure 24 Effective Job Density Prior and Following EastLink ................................................... 56

Figure 25 Survey Respondent Profile ................................................................................... 61

Figure 26 Survey Outcome Summary .................................................................................. 64

Figure 27 Demand for Different Land Use as a Function of Accessibility .................................... 67

Figure 28 Effective Job Density MSD - 2006.......................................................................... 73

Figure 29 Relative EJD 2011 ............................................................................................... 76

Figure 30 Relative EJD Regression Coefficients ...................................................................... 78

Figure 31 Household Share Regression Coefficients ............................................................... 79

Figure 32 Change in Relative EJD 2021 ................................................................................ 81

Figure 33 Change in Relative EJD 2046 ................................................................................ 82

Figure 34 Induced Car Mode Share Changes ......................................................................... 83

Figure 35 Household Induced Changes 2021 ........................................................................ 86

Figure 36 Household Induced Changes 2046 ........................................................................ 87

Figure 37 Induced Employment Change 2021 ....................................................................... 90

Figure 38 Induced Employment Change 2046 ....................................................................... 91

Figure 39 Industry-Wide Impacts of Project Construction on Knox (C) Value Added ($ Million) .... 98

Figure 40 Industry-Wide Impacts of Project Operations on Knox (C) Value Added By 2046 ($ Million)

...................................................................................................................... 109

Figure 41 Employment Projections Methodology .................................................................. 120

Figure 42 Overview of Employment Projections Modules ...................................................... 121

Figure 43 Overview of Modules for Dwellings and Population Projections ................................ 125

Table of Contents

Figure 44 SPD to ERP by Age Method Overview ................................................................... 125

Figure 45 RAS Method ..................................................................................................... 129

Figure 46 Identified Employment Regions within Metropolitan Melbourne ............................... 148

SGS Economics & Planning ©

All rights reserved; these materials are copyright. No part may be reproduced or copied in any way form or by any

means without prior permission. The proposals, ideas, concepts and methodology set out and described in this

document are and remain the property of SGS and are provided in confidence. They may not be used or applied

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Rowville Rail Project/ Economic Impact Study

SGS Final Report – May 2012 P. i

GLOSSARY OF TERMS

ANZSIC Australia and New Zealand System of Industrial Classification

CAA Central Activity Areas

CAGR Compounded Annual Growth Rate

Council Knox City Council

DoT Department of Transport

EJD Effective Job Density

GVA Gross Value Added

JTW Journey to Work

LGA Local Government Area

MSD Melbourne Statistical Division

MSE Melbourne’s South East

MITM Melbourne Integrated Transport Model

Project Rowville Rail Project

RFP Request for Proposal

SKM Sinclair Knight and Merz

SKM Study Proposed Rowville Rail Feasibility Study

SGS SGS Economics & Planning

SLA Statistical Local Area

South East Region Combined municipalities of Knox, Monash, Casey and Greater Dandenong

TZ Travel Zone

UDP Urban Development Program

UCL Urban Centre Locality

VITM Victorian Integrated Transport Model

VKT Vehicle kilometre travelled


SGS Final Report – May 2012 P. ii

EXECUTIVE SUMMARY

Objectives of Study

The Victorian Department of Transport is currently evaluating the feasibility for a rail line from

Huntingdale to Rowville, a distance of 12 kms. This proposed Rowville rail project is expected to

benefit the residents of Knox and neighbouring municipalities. This area does not currently have

access to the primary public transport network; consequently leading to issues such as constrained

access to economic opportunities, road congestion and excessive levels of greenhouse gas

emissions.

Knox City Council is taking its own actions in support of a positive outcome for the people of Knox

and elsewhere who will benefit from the provision of a modern rail service in their communities. To

understand and support its own advocacy efforts, Council commissioned SGS Economics & Planning

to undertake an economic and employment impacts analysis of the proposed rail on Knox City

Council; the South East Region (comprised of the combined municipalities of Knox, Monash, Casey

and Greater Dandenong); the wider Melbourne Statistical Division; and the State of Victoria.

Assessed Economic & Employment Impacts

SGS assessed and determined the following four impacts of the proposed project with results

shown overleaf:

Figure A 1 Estimated Economic and Employment Impacts

Accessibility & land use impacts(induced impacts on population &

employment)

Construction & operations phases impacts

(Impacts of construction, induced dwellings & employment)

Wider economic benefits(agglomeration & productivity

enhancements)

Environmental savings(due to shorter work trips &

mode shift)

Economic & employment impacts

Direct impacts of project

Indirect impacts of project


SGS Final Report – May 2012 P. iii

Accessibility and land use impacts: Additional dwellings and employment that Knox and the

wider South East Region will host over and above the projected Base Case estimates as a result

of improved accessibility provided by the proposed rail. These constitute the direct impacts of

the operations phase of the project.

Construction and operations phase impacts: indirect impacts on the output, value added and

employment of the economies of Knox, the South East Region, the wider metropolis and

Victoria resulting from the initial change in dwellings and employment estimated for Knox at

two examined years – 2021 and 2046.

Wider economic benefits: estimated increase in metropolitan wide labour productivity of

firms due to induced changes in the density of firms; and

Environmental savings: estimated reduction in metropolitan wide greenhouse gas emissions

due to the provisioning of rail in the region, and the consequent reduction in travel time (due to

provisioning of more jobs closer to home) and reduced dependence on cars.

Estimated Accessibility & Land Use Impacts

Projections for the number of dwellings and employment levels were produced by SGS for both the

Base Case and Project Case scenarios. Base Case projections were made using official publications

and in-house modelling. When estimating the Project Case scenario, SGS made the assumption

that the estimated number of dwellings and employment levels across the metropolis would remain

constant. Shifts in households and employment would be observable across the metropolis as

residents and businesses move from other parts of the metropolis into the South East Region

following the improvements in accessibility. Refer to Table A 1 below.

Table A 1 Estimated Changes in Knox (C) Dwellings and Employment Due to

Proposed Project

1996 2011 2046

(Base Case)

2046

(Project Case)

Dwellings 43,200 55,250 71,790 72,820

Employment 60,000 75,000 99,000 100,000

The results suggest that:

Under the Base Case:

o Knox is projected to add approximately 16,000 dwellings over the 2011-46

timeframe hosting approximately ~72,000 households by 2046.

o Employment growth over the 2011-46 timeframe is expected to be just under

25,000 with the municipality hosting a total of 99,000 jobs by 2046.

o Most of this employment growth is concentrated within the commercial services

sector (~1.6% p.a.). In contrast, industrial employment is set to decline over time


SGS Final Report – May 2012 P. iv

in line with trends observed for the broader MSD economy. Albeit industrial

employment is set to fall, value creation by the sector is set to continue. The

expected decline in industrial employment is a reflection of increasing productivity

in the sector.

Under the Project Case scenario and in the long run by 2046, when the effects of the Project

would have become fully manifest:

o Knox is likely to host an additional ~1,000 households and ~1,000 additional

employees over and above its projected Base Case of ~72,000 households and

~99,000 employees respectively.

o Approximately 75% of the additional employment over and above the Base Case is

on account of commercial services and 20% on account of industry based

employment (i.e. Manufacturing, Construction, Wholesale Trade etc.).

o The South East Region in turn is likely to host an additional ~1,350 households and

~1,100 additional employees over and above its projected Base Case of ~385,000

households and ~533,000 employees respectively.

While the estimated induced employment within the City of Knox in the Project Case over and

above the Base Case may appear small, it is rather significant; the estimated induced industry

based employment alone (equating to 200 employees) is nearly half the size of the current Ford

manufacturing facility in Geelong. If the existing industry base of Knox can be strengthened further

to have a large iconic manufacturing facility, it will certainly put the municipality on the Australian,

if not the world map.

Estimated Construction and Operation Phase Impacts (Indirect Impacts)

Construction Phase Impacts

The direct and total (direct + indirect) impacts of the construction phase spanning over the four

year construction timeframe of 2016 to 2020 are shown in Table A 2 below.

Table A 2 Estimated Impacts of Construction Phase (Cumulative Over 2016-

2020)

Direct Impacts Total Impacts -

Knox Total Impacts -

Victoria

Output ($ Million) $750 mn $1,665 mn $2,341 mn

Value-Added ($ Million) $173 mn $550 mn $865 mn

Employment (FTE Jobs) 645 3,599 5,600


SGS Final Report – May 2012 P. v

Construction of the proposed project is likely to1:

Generate $750 million in direct output and $173 million in value-added, supporting

employment of nearly 650 jobs within the City of Knox.

Due to the industry’s backward and forward linkages, this direct spending and employment

generation is likely to generate a total of $1.6 billion in total output and $550 million in total

value added, supporting nearly 3,600 jobs within the Knox economy.

Support nearly 5,600 total jobs in the State of Victoria creating nearly $2.4 billion in total

output and $870 million in value added2.

Operations Phase Impacts

The estimated induced household changes and employment changes in the region over and above

the Base Case will increase the direct local expenditure in the Knox economy, which will flow

through to other sectors of the economy via economy wide linkages. The estimated induced

changes will in turn also impact expenditures in the South East Region, MSD and the Victorian

economies, because of the linkages of the Knox economy with these wider economies via

purchase/ sales. These impacts are shown in Table A 3.3

Table A 3 Estimated Impacts of Operations Phase at 2046

Direct Impacts Total Impacts –

Knox Total Impacts -

Victoria

Output ($ Million) $270 mn $440 mn $650 mn

Value-Added ($ Million) $140 mn $230 mn $320 mn

Employment (FTE Jobs) 950 1,570 2,160

1 Note that these impacts are based on the assessed direct construction stimuli in the Knox economy only, i.e.

$750 million. A larger bulk of the construction costs are likely to be spent in Monash due to the route alignment

of the proposed rail.

2 By their very scope, the estimated direct stimuli for each assessed region are equal. Because of the relatively

wider inter-linkages of metropolitan and Victorian economies compared to the Knox and South East Region

economies, the assessed indirect (and consequently, total) impacts for these former set of economies is larger

compared to those assessed for the municipality and its surrounds.

3 It should also be borne in mind that the assessment presented here only considers the impacts of the induced

changes within the City of Knox on its own regional economy and the wider economies of the South East

Region, the MSD and Victoria. A net assessment of the combined effects of the induced positive changes in

Knox and corresponding induced negative changes elsewhere (due to detractions of households and

employment away from these metropolitan locations into the South East Region) is beyond the scope of this

study due to unavailability of region specific multipliers across the MSD.


SGS Final Report – May 2012 P. vi

By 2046, total effects of the operations of the proposed rail (including the effects of increased

household spending and new employment) were estimated to be:

$270 million in direct income and $140 million in direct value added for the City of Knox,

supporting nearly 950 jobs.


generation is likely to generate a total of $440 million in total output and $230 million in total


Victorian output and value-added created would be nearly $650 million and $320 million

respectively, supporting employment for nearly 2,200 employees.

Estimated Wider Economic Benefits

Over and above the estimated direct and indirect impacts, the reorganisation of employment across

the metropolis due to improved accessibility in Knox and surrounds will increase productivity of

firms throughout the metropolis. This is because, as more economic activity starts to concentrate

in the South East Region following the opening of the proposed rail line, firm and worker interaction

and their accessibility to suppliers, customers and labour markets will increase. The net effect

(even after accounting for the fall in employment in other metropolitan locations as employment is

detracted from these locations) is estimated to be positive and significant.

Labour productivity enhancements, i.e. an increase in Gross Value Added of the South East Region,

resulting from the project were estimated at $23 million in 2021 and increasing to $133 million by

2046. This will be a significant boost to the resilience of the local economy and community.

More importantly, wider economic benefits of a similar magnitude (if not higher) are likely to

continue annually as more employment and firms move in the South East Region.

Estimated Environmental Savings

SGS estimated environmental savings by estimating the overall travel distance saving across the

metropolis between the Base Case and Project Case scenarios. These savings arise due to the

following two counts, both of which reduce the dependence on car travel:

Due to the provisioning of rail, work and leisure trips by private vehicles are substituted in

favour of public transport.

As more dwellings and employment is induced within the South East Region, resident workers

of Knox and the South East Region are able to access more jobs in close proximity rather than

travelling further for work.

SGS estimated that the distance savings equate to an annual reduction in externality costs of $3.1

million at 2021 and $4 million by 2046. The scale of these savings appears small, and indeed is. It

is because the travel modelling by SKM indicates only a less than 1% mode shift in favour of public

transport from car use once the proposed Rowville rail line becomes operational.


SGS Final Report – May 2012 P. vii

Significantly, Savings of a similar magnitude (if not higher) are likely to continue annually as more

people start using public transport over car use.

Results Summary

The SGS study shows that delivering the rail project will lead to several beneficial economic and

employment impacts for Knox, Melbourne’s South East and the Victorian economies.

First and foremost, construction of the rail line (only the section within Knox) is likely to support

nearly 5,600 total jobs in Victoria due to the backward and forward linkages of the construction

industry.

Secondly, the operational phase of the project is likely to contribute $320 million towards the

state’s value-added contribution by 2046.

However, as stated before, these estimated economic impacts only consider the impacts of the

induced changes within the City of Knox on its own regional economy and the wider economies of

the South East Region, the MSD and Victoria. A net assessment of the combined effects of the

induced positive changes in Knox and corresponding induced negative changes elsewhere (due to

detractions of households and employment away from these metropolitan locations into the South

East Region) is beyond the scope of this study due to unavailability of region specific multipliers

across the MSD.

Nonetheless, the wider economic benefits and estimated environmental cost savings have been

assessed taking into account the redistribution of population and employment across the

metropolitan economy. Aggregating annual environmental savings and wider economic benefits

over a 50 year timeframe from 2021 onwards yields a cumulative benefit to the Victorian economy

of over $5 billion to the Victorian economy, which is well above the $2.89 billion towards

constructing the project. Whilst a benefit cost ratio cannot be ascertained from these values (as

they are not net present values), it helps provide some indication of the magnitude of benefits

compared to costs.

In other words, the opportunity costs of not delivering the project are high; not only will the

economies of Knox and the South East Region lose out on the estimated impacts during the

construction phase, Victoria in turn stands to lose out on approximately $5 billion in environmental

and economic savings over a 50 year timeframe.

Upshot of Findings

These findings suggest that the proposed Rowville rail project will:

Help accelerate the growth trajectory of Knox and Melbourne’s South East. While not a

dramatic impact, the shift in dwellings and employment towards Melbourne’s South East does

begin to help to reshape the economic geography of South East Melbourne more in favour of

Knox.


SGS Final Report – May 2012 P. viii

Enable the city of Knox to meet is stated vision and objectives of: consolidating the existing

industry base and provide for a well-networked, informed and knowledge intensive business

community in the City of Knox; and providing for more employment and housing opportunities

for the people of Knox. The project will reduce the vulnerability of Knox residents to future

economic shocks and the impacts of structural adjustment within the economy.

Lead to significant economic benefits not only for the local Knox and south East economies, but

for the wider metropolitan and Victorian economies.

Be vital to delivering significant and further productivity enhancements to Victoria. Victoria and

Australia, like most developed economies are at the frontier of productivity due to the

availability of skilled and well-trained workforce as well as established technology. Further

significant enhancements are possible only due to connectivity improvements brought about by

transport projects, which this project will deliver and consequently, help lift the economic

productivity of metropolitan firms.

In the absence of any intervention, there will be a high requirement for the people of Knox and the

wider South East Region to travel outside of their municipality boundaries to access employment

opportunities. The reliance on the private vehicle for accessibility to jobs and services will heighten

the vulnerability of the residents to congestion and fuel price increases. more importantly, the state

would also not be able to capture the suggested environmental and wider economic benefits that

the project will engender.


SGS Final Report – May 2012 P. 1

1 INTRODUCTION

1.1 Project in Context

The Victorian Department of Transport (DoT) is currently evaluating the feasibility for a rail line

from Huntingdale to Rowville, a distance of 13 kms. This proposed Rowville rail project is expected

to benefit the residents of Knox and neighbouring municipalities - an area which does not have

direct access to the heavy rail network. This has implications for the economic and social prosperity

of the area, road congestion and greenhouse gas emissions.

Sinclair Knight and Merz (SKM) has been commissioned by the DoT for the “Proposed Rowville Rail

Feasibility Study”, in association with Mott MacDonald, Hassell and Phoenix Facilitation. This study

(SKM study) will be conducted in two phases across two years – 2011 and 2012, with the first

phase report having been released in March 2012. SGS Economics & Planning (SGS) understands

that the feasibility study undertook extensive consultation and research to evaluate a number of

transport options and associated construction programme. However, the first phase report released

in February 2012 assessed likely demand for only one of the most feasible considered options.

Section 2 of this report describes the project in greater detail.

1.2 Project Objectives

Knox City Council is taking its own actions in support of a positive outcome for the people of its

municipality and elsewhere who will benefit from the provision of a modern rail service in their

communities.

In addition to the transport analysis being undertaken by SKM, Council decided to undertake an

economic and employment impacts analysis of the overall Project. With this in mind, Council issued

a Request for Proposal (RFP) with the following objectives:

Define and estimate the economic and employment impacts, (direct and indirect), that could

reasonably arise from the Rowville rail project on:

o The City of Knox;

o The City of Monash to the west, and the immediately adjacent areas of the cities of

Casey and Greater Dandenong to the south east and south respectively

(collectively termed as the ‘South East Region’ for the purposes of this report;

and

o The wider Melbourne Statistical Division (MSD) and the State of Victoria.

Provide comprehensive evidence and argument for the economic and employment impacts

identified in this study to assist Council in its advocacy activities.



1.3 SGS Response

SGS was chosen as the preferred bidder and its response to the project objectives is summarised

below in Figure 1 and involved the following five stages:

Figure 1 SGS Study Method

1. Stage 1: Initialising the project at an inception meeting followed by SGS preparing a work plan

to respond to the tasks.

2. Stage 2: A desktop review which included:

o Preparation of a baseline socio-economic profile and review of the current journey

to work (JTW) patterns of Knox, with a specific focus on Rowville, to understand its

current role and performance. This profile was benchmarked against the wider

South East Region (comprising the municipalities of Knox, Monash, Casey and

Greater Dandenong) and the whole metropolitan area (i.e. the MSD).

o Development of baseline population and employment projections, out to 2046, for

Knox and the South East Region, to articulate, at a fine grained level (that of a

Statistical Local Area), the region’s development trajectory ‘without’ the Project.

o Examination of case studies from Australian and international jurisdictions to gain

an appreciation of the impacts of major transport projects on population and

employment in host regions of such projects.

o Reviewing the outputs/ inputs and assumptions used by the SKM study, in

particular, the travel time matrices ‘with’ and ‘without’ the Project out to 2046 and

the route alignment of the considered option.

o Concluding the desktop review with a Workshop with Council about the:

Lessons learnt from the socio economic profile.

Stage 1: INCEPTION

Inception meeting

Finalise work plan

Stage 2: DESKTOP REVIEW

Case study review

Prepare baseline profile and projections

Stage 3: CONSULTATION

Finalise questionnaire

design

Stakeholder workshops

Stage 4: ECONOMETRIC

ANALYSIS

SGS in-house accessibility modelling

Workshop with client

Input output modelling

Stage 5: IMPLICATIONS &

REPORTING

Draft report

Client presentation

Final reportSKM 1st stage report review

Workshop with client

Estimate impact on income,

employment, EJD and GHG



Land use impacts (i.e. population and employment at a small area level of

a Statistical Local Area (SLA) level out to 2046 ‘without’ the project, based

on SGS’ in house forecasting techniques.

3. Stage 3: preparation of a questionnaire for discussion/ consultation with regional industry

stakeholders to gain insights regarding how the Project might reshape development patterns in

Knox and the South East Region. Thoughts on short-term nuisance impacts created during the

construction phase of the Project were also gauged.

4. Stage 4: Use of SGS’ in house accessibility modelling to quantify the movement of population

and employment to and within Knox and the South East region caused by the Project. That is,

ascertaining the direct impacts of the operational phase of the Rowville rail project. The

methodology and estimated results were workshopped with Council and feedback sought.

Further, insights gained from the above tasks were converted into an assessment of the

economic and employment impacts that are likely to arise from the Project, both during the

construction and operation phases of the project. This included:

o an assessment of both the direct and indirect impacts on output, employment and

value-added using the REMPLAN input-output model of the construction and

operation phases of the project.

o Estimating changes in Effective Job Density (EJD) for the MSD associated with the

delivery of the Project. This helped measure the wider economic benefits of the

Project.

o The associated changes in greenhouse gas emissions (GHG) and mode share shifts

providing an indication of the environmental savings brought about by the Project

on a metropolitan scale.

5. Stage 5: Finally, findings from all the above tasks were brought together in a Draft report

which was presented to the Council for review and comment. Following the delivery of the Draft

report, SGS presented key findings to Council and sought feedback, which was incorporated

into this Final report.

In sum, the five stages shown above respond to the project objectives of Council in full. The first

three stages provide the background contextual and strategic information as well as employment

and population forecasts which are used to inform both Stages 4 and 5. Stage 4 responds to the

first critical objective of the study (i.e. it defines and estimates the direct and indirect economic

and employment impacts of the project. As part of this assessment, SGS estimated the wider

economic benefits of the project which provide a comprehensive evidence base to assist Council in

its advocacy activities.

1.4 Assumptions and Limitations

Before presenting the estimated results in the following sections, the major assumptions/

limitations of these results are outlined below. Other model specific assumptions are outlined in the

relevent sections.



Accessibility and land use change modelling as well as the estimation of wider economic

benefits is based on travel time matrices data provided to SGS by SKM. We understand that

these data are based on the Department of Transport’s Victorian Integrated Transport M<odel

(VITM). Whilst this model uses established techniques to estimate travel demand, it is subject

to certain limitations. For instance, the transport model assumes way any capacity constraints

on public transport. Consequently, the results produced by this study are also subject to the

same limitations as are inherent in VITM modelling.

When estimating the direct impacts of the operations phase of the project, i.e. when estimating

accessibility and land use change due to this project:

o The overall level of households and employment in metropolitan Melbourne was

held constant between the Base Case and Project Case, i.e. the ‘with’ and ‘without

project’ scenarios. That is, the accessibility and land use change analysis effectively

re-distributes households and employment across metropolitan Melbourne in favour

of Knox and Melbourne’s south-east from other locations within the MSD. In

summary, the assumption suggests that whilst this project is expected to

accelerate growth within Knox and Melbourne’s South East, it is not a trajectory

shifting project inasmuch as inducing net additional households/ employment in the

State of Victoria.

o The distribution of people and firms is purely based on the induced effects of

improved accessibility. While not captured in this analysis, in addition to these

impacts, there can often be one or more targeted intervention developments near

key locations (i.e. new stations) that may further capitalise on the improved

accessibility of the corridor.

When estimating the impacts of the construction phase of the project, we were unable to

estimate the proportion of inputs that will be sourced locally as against from inter-state/

overseas. Consequently, we have estimated that all employment generated due to construction

will be sourced locally from Knox, which indeed overstates the estimated impacts within the

Knox and south East economies. The extent of overestimation on the Victorian economy is

likely to be small, as most inputs and employment for the project is likely to be sourced from

Victoria.

Because of the imposed constraint on the number of households and employment across the

MSD in both Base and Project case scenarios, an increase in the estimated number of dwellings

and employment levels in favour of Knox and the South East Region causes a reduction in

dwellings and employment elsewhere in the metropolitan economy. When estimating the

induced effects of the operations phase of the Rowville rail project, the assessment considers

the impacts of induced changes within the City of Knox on its own regional economy and the

wider economies of the South East Region, the MSD and Victoria. A net assessment of the

combined effects of the induced positive changes in Knox and corresponding induced negative

changes elsewhere is beyond the scope of this study due to unavailability of region specific

multipliers across the MSD.



1.5 Report Structure

This report is organised as follows:

Section 2: Discusses in brief the outcomes of the pre-feasibility and feasibility studies that

have been undertaken to date for a rail line from Huntingdale to Rowville. In

particular, the outcomes of the SKM study, Stage 1 report and the DoT’s Project

Scope reports are reviewed.

In addition, key findings and lessons that were gleaned from Council’s own

advocacy efforts to date in support of the Project are highlighted.

Section 3: Provides a baseline socio-economic profile and reviews the current JTW patterns

for Knox, with a specific focus on Rowville, to understand its current role and

performance in the municipality and the wider South East Region. This profile is

compared and contrasted against the wider South East Region and the MSD.

Section 4: Presents the results of SGS’s in house baseline population and employment

projections, out to 2046, for the City of Knox and the South East Region, to

articulate, at a fine grained level (that of a SLA), the region’s development

trajectory ‘without’ the Project. These projections were finalised by SGS after

workshopping with Council.

Section 5: Showcases the existing State and local strategic planning framework supporting

multi-modal and more sustainable modes of transport provision. A specific focus is

cast here on the support for high employment growth within Knox and its

immediate surrounds. The capacity of Council to accommodate this growth, i.e.

land supply and any constrains thereof are also identified.

Section 6: Reviews relevant case studies from Australian and international jurisdictions to

assess the short, medium and long-term impacts on residents and businesses

during the construction and operation phases of a major transport project.

Section 7: Reports insights gained from consultation with regional industry stakeholders on

how the Project might reshape development patterns in Knox and the South East

Region.

Consultees views on short-term nuisance impacts created during the construction

phase of the Project are also showcased.

Section 8: Presents the results of SGS’ in house accessibility modelling to quantify the impact

of the Project on the additional population and employment that Knox and the

South East region might host because of the Project over and above that which

would become manifest in a ‘Business As Usual’/ Base Case scenario.

In effect, the outcome of this section is the estimate of the ‘direct’ impacts of the

Project on the City of Knox during the operation phase of the project.



Section 9: Presents the estimated economic and environmental impacts of the Project on

Knox, the South East Region and the wider metropolitan economy.

The estimated impacts include the direct and indirect impacts on income, value-

added and employment, both during the construction and operation phases of the

Project and environmental savings brought about by a reduction in greenhouse

gas emissions.

Section 10: Showcases the wider economic impacts via improvements in productivity

improvements to the whole metropolitan economy due to improvement in rail

services brought about by the Project.

The results of this analysis, it is hoped, will particularly help Council in its

advocacy efforts with Treasury, as this section highlights the advantages of a

transport project of a local scale on productivity improvements that might become

manifest at the metropolitan level.

Section 11: Provides concluding remarks.



2 PROPOSED ROWVILLE RAIL

This section discusses in brief the outcomes of the pre-feasibility and feasibility studies that have

been undertaken to date for a rail line from Huntingdale to Rowville. In particular, the outcomes of

the SKM study, Stage 1 report submitted to the Victorian DoT are reviewed.

In addition, key findings and lessons that were gleaned from Council’s own advocacy efforts to date

in support of the Project are highlighted.

Victoria has witnessed relatively few new major rail transport infrastructure improvements over the

past decades, including the City Loop opening in the 1980s and more recently the Altona-Westona

link (extended to Laverton to form a loop). In addition, new routes (Cranbourne and South Morang

extensions and the Regional Rail Link4) have been added to serve changing needs.

The Melbourne Metro proposal (a new rail tunnel beneath the city, linking the western suburbs lines

at South Kensington with the Dandenong, Frankston and Sandringham lines at South Yarra) are

expected to enable further growth in rail services through central Melbourne as the City Loop

reaches capacity.

2.1 Project History and Background

The proposed Rowville rail extension was first proposed in the 1969 ‘Melbourne Transportation

Plan’, with the aim of improving transport linkages to the areas of Monash University, Waverley

Park, Stud Park and Rowville. The plan showed a rail line via Rowville and Knox from Huntingdale

to Ferntree Gully as well as City-East Doncaster and Frankston-Lyndhurst links and the City Loop.

While the proposition did not proceed beyond the planning phase, the concept has remained in the

public transport realm ever since.

Completed in 1998, the ‘Scoresby Transport Corridor Environmental Effects Statement’ revisited

the need for public transport in the Rowville area. Different options in the form of light rail or bus

rapid transit service were also conceptualised. This study ultimately went on to prefer a road

option, the Scoresby Arterial Road (now EastLink) and the option of more public transport was

discounted again (Department of Transport, 2011).

4 The Regional Rail Link is currently under construction.



2.2 Council’s Advocacy Efforts for Improved Transport

2.2.1 2004 Pre Feasibility Study of the Rowville Rail

In 2004, Council commissioned an independent pre-feasibility study for the Rowville rail extension.

This Study estimated a single track line at $413 million, with an additional $66 million for rolling

stock (Russell et al, 2004). The then developed concept was for a single track line, mostly on a

viaduct in the median of Wellington Road and with passing loops at the Monash and Waverley Park

station locations, catering for 15-minute peak period service headways. The study concluded that a

Rowville line with 4 services per hour would yield the following benefits:

A reduction in public transport travel time to the centre of Melbourne, from around 80 minutes

to 30 minutes;

Improved public transport travel time to higher education institutions (notably Monash

University), employment (particularly in the Monash corridor), and leisure and entertainment

venues;

Reduced demand for car parking at Monash University, with up to 1,000 car journeys to the

university estimated to be replaced by train journeys;

Enhanced ability for the Rowville activity centre to attract and retain retail and employment

opportunities;

Improved attractiveness of the region for residents;

Reduced traffic congestion; for work trips the line was estimated to carry 2,350 passengers an

hour, which equates to an equivalent reduction in car traffic numbers;

With extended transport choice, potential avoidance of purchase of an additional car by Knox

households;

Direct and indirect economic and employment generation; and

Protection against increasing travel costs, particularly escalating costs attributed to the

consumption of oil in motor cars.

Following the release of the pre-feasibility study, community enthusiasm and Council advocacy

escalated; however the project was not cited in the 2008 Victorian Transport Plan. Nonetheless,

Council continued to advocate its efforts for a rail line, and conducted the ‘Who’s on Board?’

campaign to advocate for the Rowville Rail and the Knox Tram projects.



2.2.2 Who’s on Board? Campaign

In November 2010, Council developed the

campaign “Who’s on Board?”, aimed at

promoting two major public transport

infrastructure projects in the Knox region; the

Rowville rail extension and the Knox Tram

(Knox City Council, 2011). Postcards

containing project information were sent to all

residents within the municipality, and

dedicated web links were provided on Councils

website to advocating local and state

government members in support of public transport improvements in the region. In total over

4,500 postcards were returned to Council as a sign of support for the two projects (Knox City

Council, 2011).

2.3 2012 SKM Draft Feasibility Study

In February 2011, the Minister for Public Transport announced the Rowville Rail Feasibility Study

(Department of Transport, 2011). This study is expected to be finalised by 2013, and is being

conducted by Sinclair Knight Merz (SKM) in collaboration with Mott MacDonald, Hassell and Phoenix

Facilitation. The study is an “investigation of a 13 kilometre rail line between Huntingdale railway

station and Rowville”, that will “include engineering, architectural and operational investigations as

well as extensive consultation with the public” (Department of Transport, 2011). The trigger to

explore such projects, as stated by the Department, is Melbourne’s continued and sustained

population growth.

This study (SKM study) will be conducted in two phases across two years – 2011 and 2012, with

the first phase report having been released in March 2012. The SKM study undertook extensive

consultation and research to evaluate a number of transport options and associated construction

programme. However, the first phase report released in February 2012 assessed likely demand for

only one of the most feasible considered options. The following sections summarise the results of

the SKM study.

2.3.1 Proposed Route

The proposed route for the Rowville rail extends from Huntingdale station on the Cranbourne and

Pakenham lines, running along Wellington Road to Rowville, i.e. passing through the municipalities

of Monash and Knox.

The proposed route would be double tracked and approximately 13 kilometres in length, and

follows the central median of North Road and Wellington Road from Huntingdale to Stud Road, then

turns north to terminate at Stud Park. A secondary 12 km route option was also analysed running

along Wellington Road with a station at the corner of Stud and Wellington Roads.



Four new stations are expected to be provided at Monash University, Mulgrave, Waverley Park and

Rowville, with the possibility of a fifth in the vicinity of EastLink (Figure 2 overleaf). The proposed

stations at Monash University and Rowville are expected to be premium stations.

The rail line would link into the Dandenong line at Huntingdale; then run along the North

Road/Wellington Road corridor past Monash University, Mulgrave, Wheelers Hill and Waverley Park

to Rowville. Ultimately, train services on the Rowville line would run to and from central Melbourne,

thus reducing the need for passengers to change trains.

Figure 2 Proposed Route of the Rowville Rail Line

Source: SKM et al, 2012

The design contained in the feasibility study is intended to avoid any new level crossings, and also

preserve the existing road environment with as little traffic impact as possible. The route would

consist of a tunnel or retained cutting at the western end (from Huntingdale to Monash University),

before emerging onto a viaduct over Blackburn and Springvale Roads and return to road level

across the Monash Freeway. It would go into a tunnel again past Waverley Park, and onto an

elevated structure as it crosses the Dandenong Creek Valley and EastLink. East of EastLink, the rail

route would again go below ground, turning north and in tunnel beneath Stud Road to a station at

Stud Park.

The recommended station at Stud Park by the feasibility study would be best located underground

just north of the existing Stud Park Shopping Centre (south of Sam’s Warehouse). Redevelopment

opportunities could provide a bus interchange and surrounding transit-oriented development.



2.3.2 Community/ Stakeholder Consultation

SKM engaged the Knox community when developing its draft report. As part of this consultation

process the SKM team conducted three community workshops in July of 2011, with approximately

130 people in attendance. The main themes that arose from these workshops was the community’s

concerns for the generally poor level of public transport in the area; the need to preserve the

area’s character and amenity; and the need to further boost the employment prospects of the

area.

SKM reported that there was “very little opposition” to the project. Nonetheless, some other

concerns were noted as follows:

Concern over potential amenity impacts, including noise, visual impacts and vibrations;

Questions over the impact train stations may have on property values and security;

The alternative options to heavy rail, such as light rail or buses which may offer better value

for money; and

Questions over the possibility of future extensions beyond Rowville, e.g. to Ferntree Gully or

Lysterfield (SKM et al, 2012).

In addition to these workshops, the SKM team also conducted an online survey to gauge

stakeholder’s expectations on the level of engagement; preferred methods of communication; and

the nature of the project.

The upshot of these campaigns and survey responses is that the Knox community wants to be

engaged at every step of the process and showcases strong support for the project.

2.3.3 Traffic Modelling Method

The SKM study assessed the travel demand effects of the proposed Rowville rail line using the

Victorian Department of Transport’s Victorian Integrated Transport Model (VITM) and the

consequent effects on travel demand and road traffic.

The model includes representation of the land use (population and employment), roads and public

transport networks in and around the MSD. The model uses established techniques to estimate

travel demand, mode choice and route choice before ‘assigning’ the travel to the transport network

to provide estimated volumes of people and vehicles.

The following aspects of traffic modelling undertaken by the feasibility report are noteworthy:

Traffic modelling was undertaken for two future years: at 2021 and 2046. Key forecasts were

sourced from the Victoria in Future (VIF) 2008 publication released by the Victorian

Department of Planning and Community Development. Though, these forecasts are subject to

change with the new metropolitan planning strategy underway, They include most of the

expected ‘business-as-usual’ development in the area (including significant growth at Monash



University Clayton campus). The modelling considers future relative changes in key variables

like fuel prices, parking costs and public transport fares.

The modelling considered committed or expected improvements to transport elsewhere in

Melbourne so that the effect of the Rowville rail line can be separated from the effects of other

initiatives.

The transport model considered anticipated capacity constraints on roads, but not on public

transport. Consequently, the study could forecast more patronage than the modelled public

transport services can physically carry.

The estimated traffic and rail volumes are to be treated with reasonable confidence whilst bus

patronage is likely to be underestimated in the results presented.

2.3.4 Traffic Modelling Results

Modelling results from the SKM study indicate that:

A key finding of the study was that most of the patronage comes from diversion of trips from

other rail lines, rather than mode shift from car to rail. This could be an underestimate of

future demand, especially if future changes in fuel prices are much larger than currently

assumed in the model.

The four stations on the Rowville rail line could together attract about 68,000 users on a typical

weekday by 2046, with about 16,000 of these in the morning two-hour peak (7 am – 9 am).

On an average per-station basis, this is high compared with the modelled results on other

eastern suburbs lines It is similar to the stations on the Lilydale line east of Camberwell, about

15% higher than the Dandenong line stations between Dandenong and Caulfield, and more

than double that of Pakenham and Cranbourne lines east of Dandenong.

City bound Rowville trains in the morning two-hour peak in 2046 would carry a total of about

6,000 passengers – averaging 500 per train – as they arrive at Huntingdale station; from there

on, there is expected to be ‘standing room only’.

Similar loads are forecast in the opposite direction, because of the strong attraction of Monash

University’s Clayton campus to students and staff living in the inner suburbs.

Monash University station is expected to be the busiest on the line with around 23,000 users

on a typical weekday in 2046, followed by Rowville and Mulgrave (17,000 each) and Waverley

Park (10,000).

The redeveloped Huntingdale station would attract about 16,000 users and an additional 8,000

people transferring between trains.

Estimated activity at the proposed new stations are likely to mirror current estimated activity at

suburban stations in eastern Melbourne; Huntingdale and Monash University stations would be

comparable to Caulfield; Rowville and Mulgrave would be similar to Glen Waverley; and

Waverley Park would be similar to, say, Noble Park.

Overall the model predicts 13,000 more public transport trips and 15,000 fewer car trips on a

typical weekday in 2046. Most of the 68,000 daily users of the Rowville line would be public

transport users diverting from other services (particularly the bus services that the Rowville



line would replace, but also from Clayton, Springvale, Glen Waverley and Ferntree Gully

stations).

Consequently, the relatively small mode shift would probably not give rise to noticeable traffic

reductions on roads in the area. Though, the model probably underestimates this mode shift,

partly because of arguably conservative assumptions about changing circumstances (such as

petrol prices) in the future.

2.3.5 Construction and Operations

The construction phase is expected to last four years. The construction is likely to involve

conventional construction techniques (cut and cover, elevated structures) which are available from

Australian construction suppliers. Consequently, at this stage, requirements for imported

equipment and expertise are not envisaged.

The soonest construction commencement date is estimated to be 2016 with estimated construction

completion by 2020.

The SKM study did not carry any construction cost estimates for the option under consideration;

however, based on their experience on other projects, ball-park all-in capital costs of a twin-track

electric rail line like Rowville would be broadly as shown in the table below.

Table 1 Estimated Rail Construction Cost of Rowville Line

Length (in kms) Cost ($mn per km)

Total Cost ($ mn) by municipality of rail construction

Monash (C) Knox (C) Monash (C) Knox (C) Total

Below ground 5.5 2.0 $200 mn $1,100 mn $400 mn $1,500 mn

Above ground 4.4 1.5 $100 mn $440 mn $150 mn $590 mn

Total 9.9 3.5

$1,540 mn $550 mn $2,090 mn

Source: SKM

Construction of the railway line is expected to cost a total of $2.09 billion, with a bulk of it to be

spent in the municipality of Monash ($1.54 billion). Construction in Knox is expected to cost $550

million. This represents more than a five-fold increase in construction cost estimates from the

estimate of $413 million provided by the 2004 pre-feasibility study.

Over and above construction of the rail, construction of each station is expected to cost $200

million each, taking total construction cost of four stations to $800 million. With three stations

falling in Monash, total construction cost of stations is likely to be $600 million for Monash and

$200 million for Knox.

Although the long-run impacts observable at 2046 appear favourable, the SKM study highlights the

following short term nuisance and adverse impacts during the construction stage:



Noise and vibration will be highly sensitive to nearby residents, business and special facilities,

including the Australian Synchrotron and nearby biomedical facilities on Blackburn Road. The

feasibility study identifies techniques to mitigate these effects during construction.

Underground stations in Wellington Road would require temporary lane closures and/or

diversions to enable cut-and-cover construction, and the high water table would probably entail

waterproofed, retained temporary cuttings. A feasible staging method has been identified to

mitigate impacts.

Access to temporary land would be required given the heavy traffic in the area. Careful traffic

and access management techniques would be required, given the high levels of traffic on the

affected roads. Maintaining traffic flows on Monash Freeway and Eastlink in particular would be

a high priority given their regional significance.

2.3.6 SKM Study Recommendations

The study recommends that the feasibility of the proposed Rowville rail line would be critically

dependent on upgrading existing infrastructure. Currently, only two tracks operate between

Dandenong and Caulfield; they carry all the Dandenong, Pakenham and Cranbourne services plus

Bairnsdale V/Line trains and freight trains from the south east. During the morning peak these

services presently total 18 trains per hour in each direction. Peak period trains on the Dandenong

corridor are regularly crowded.

The SKM study estimated that if the frequency of trains is increased after extending the line from

Huntingdale to Rowville, without adding more tracks, existing express trains would have to be

reduced in number. Consequently, Cranbourne and Pakenham passengers would probably

experience longer travel times. Moreover, additional trains per hour will mean more frequent

and/or longer closures on the four level crossings between Huntingdale and Caulfield, which would

increase the already lengthy delays to road traffic (including buses), cyclists and pedestrians.

The SKM study estimates that the existing Cranbourne/ Pakenham/ Dandenong lines cannot

provide much more peak passenger capacity than it does now without allowing for longer trains,

re-signalling and/or additional tracks, and grade separation of level crossings to avoid excessive

delays to traffic.

Given that addressing the outlined critical requirements is likely to be an exhaustive exercise, the

earliest that the Rowville Rail Line can be provided is 2020.



3 BASELINE SOCIO ECONOMIC PROFILE

This section provides a baseline socio-economic profile and reviews the current journey to work

patterns of the City of Knox, with a specific focus on Rowville, to understand its current role and

performance in the municipality. This profile is benchmarked against the wider South East Region

and the whole metropolitan area (i.e. the Metropolitan Statistical Division or MSD).

3.1 Geographical Scope

For the purposes of profiling, clear statistical collection precincts have been defined. The Local

Government Area (LGA) or municipality of Knox consists of the three Statistical Local Areas (SLA)

of:

Knox – North-East,

Knox - North-West; and

Knox – South (refer Figure 3 overleaf).

Rowville falls within the SLA of Knox – South.

For the purposes of this study, the broader South East Region has been defined to consist of the

LGAs of:

Monash (C);

Knox (C);

Greater Dandenong (C); and

Casey (C).

The Melbourne Statistical Division (MSD) consists of all 31 metropolitan LGAs that make up

metropolitan Melbourne.



Figure 3 Knox and the Surrounding South East Region

Source: SGS Economics & Planning

3.2 Resident Profile

3.2.1 Population, Dwellings and Growth

The resident population of Knox was estimated at approximately 160,000 in 2011. Since 1996 the

population grew modestly, with an annual growth of 1.02%, much lower than that of the South

East Region and the MSD (Table 2 overleaf).



However, while growth across the whole of Knox was modest, growth in the southern area that

hosts Rowville has been exceptionally high since 1996, with population increasing annually at over

3%, and outpacing growth of all comparator regions. This growth was particularly strong between

the Census periods of 1996 and 2001, where 10,000 people moved to the Knox - South area.

Table 2 Growth in Population, 1996-2011

1996 2001 2006 2011 CAGR %

(1996-2011)

Knox - North-East 64,200 64,000 64,300 66,800 0.3%

Knox - North-West 45,100 46,100 45,800 48,600 0.5%

Knox - South 27,500 37,300 41,700 43,900 3.2%

Knox (C) 136,800 147,400 151,800 159,300 1.0%

South East Region 578,300 620,700 674,600 741,500 1.7%

MSD 3,283,300 3,471,600 3,743,000 4,170,600 1.6%

Source: Estimated Resident Population, Australian Bureau of Statistics 1996-2006 and SGS projections for 2011

Note: Values rounded to the nearest hundred, CAGR – Compounded Annual Growth Rate

Growth in households over the same corresponding period has outpaced population growth, a trend

exhibited across the rest of Melbourne (Table 3). This reflects a broad trend towards shrinking

household sizes. The average household size in Knox declined from 3.2 persons per dwelling in

1996 to 2.8 persons per dwelling in 2011. Notably, growth in the number of dwellings in Knox –

South has outpaced that in the South East Region and the MSD.

Table 3 Growth in Dwellings, 1996-2011

1996 2001 2006 2011 CAGR %

(1996-2011)

Knox - North-East 21,600 22,800 24,600 25,900 1.2%

Knox - North-West 13,600 14,600 15,600 16,700 1.4%

Knox - South 8,000 10,900 12,800 13,400 3.5%

Knox (C) 43,200 48,300 53,000 56,000 1.7%


MSD 1,136,900 1,247,500 1,408,500 1,527,100 2.0%

Source: Estimated Resident Population, Australian Bureau of Statistics 1996-2006 and SGS projections for 2011

Note: Values rounded to the nearest hundred, CAGR – Compounded Annual Growth Rate



3.2.2 Skills Profile

The education profile of Knox generally compares to the South East Region, but differs to the MSD

in terms of bachelor qualifications and above (Figure 4). Knox and the South East Region comprise

a high proportion of vocational and tertiary qualified residents than the MSD, with 45% and 41%

respectively of further educated residents holding certificate level qualifications.

Figure 4 Education Profile of Residents, 2006

Source: Census of Population and Housing, Australian Bureau of Statistics 2006

Interestingly though, the proportion of Knox’s residents with bachelor degrees or above has

increased since 1996, mirroring the trend in comparator regions (Figure 5 overleaf). This transition

to a more qualified workforce would increase demand for more jobs in advanced Manufacturing and

in the tertiary/ services sector.

Of the Knox residents with further education, almost a quarter have studied in the field of

Engineering (Figure 6 overleaf). A high proportion of residents are also qualified in Management

and Commerce. This presents an opportunity to develop a specialisation and clustering of

engineering, scientific services and other services related industry in Knox, sourcing local labour

and reinforcing existing local industry.

0%

25%

50%

75%

100%

Knox (C) South East Region MSD

45%41%

34%

20%20%

18%

27%29%

35%

8% 10% 13%

Certificate Level Advanced Diploma and Diploma Level

Bachelor Degree Level Graduate Dipl. / Cert. / Post Grad. Degree



Figure 5 Education Profile of Residents, 1996


Figure 6 Residents Field of Study, 2006


0%

25%

50%

75%

100%


51%47%

38%

20%20%

20%

22%25%

31%

7% 8% 11%

Certificate Level Advanced Diploma and Diploma Level

Bachelor Degree Level Graduate Dipl. / Cert. / Post Grad. Degree

0%

5%

10%

15%

20%

25%




3.2.3 Occupation Profile

The occupation profile of Knox residents is similar to that of the South East Region (Figure 7).

Contrasted to the profile of the MSD, a relatively lower proportion of Knox residents are employed

as managers and professionals.

Figure 7 Occupation Profile of Resident Workers, 2006


Over the period from 1996 to 2006, the proportion of resident workers employed in higher order

trades such as managers and professionals grew at the expense of trades (Figure 8 overleaf). This

trend also occurred in the South East Region and MSD, although to a greater degree in the MSD.

0%

25%

50%

75%

100%


36% 34%43%

34%32%

31%

22%23%

19%

8% 11% 8%

Managers and Professionals Clerical and Service Workers

Tradespersons, Labourers and Related Workers Intermediate Production and Transport Workers



Figure 8 Occupation Profile of Resident Workers, 1996


3.3 Employment Profile

3.3.1 Industry Profile

Based on SGS estimates there are approximately 75,000 jobs located in Knox in 2011 (Table 4

overleaf)5. As can be observed, employment in Knox is largely centred in Manufacturing followed

by Retail Trade, Construction, Health Care and Social Assistance and Wholesale Trade.

12% of the total jobs within the City of Knox are located within Knox – South, with higher

concentration of services oriented jobs observable.

5 Estimates for 2011 are not available from the ABS. SGS has estimated employment for 2011. The estimation methodology is shown in Appendix A.

0%

25%

50%

75%

100%


34% 32%39%

33%31%

31%

23%25%

21%

9% 12% 9%





Table 4 Employment by Industry (Workers), 2011

Knox (C) Knox - South

South East Region

MSD

Agriculture, Forestry and Fishing 150 20 1,890 11,900

Mining 80 60 240 4,700

Manufacturing 15,010 470 63,970 233,100

Electricity, Gas, Water and Waste Services 390 40 3,410 21,000

Construction 8,540 1,590 37,460 190,100

Wholesale Trade 7,100 390 26,330 94,300

Retail Trade 9,870 850 43,060 234,400

Accommodation and Food Services 3,900 420 15,320 135,900

Transport Postal and Warehousing 2,560 300 17,780 107,500

Information Media and Telecommunications 460 40 3,280 54,400

Financial and Insurance Services 1,800 130 8,160 112,600

Rental, Hiring and Real Estate Services 1,210 150 6,160 38,300

Professional, Scientific and Technical Services 3,580 570 19,040 207,500

Administrative and Support Services 2,030 310 9,820 70,600

Public Administration and Safety 1,750 110 9,160 105,500

Education and Training 4,120 960 29,790 176,500

Health Care and Social Assistance 7,790 2,100 36,360 245,700

Arts and Recreation Services 880 60 3,400 47,900

Other Services 3,530 380 14,560 85,300

Total 74,750 8,950 349,190 2,177,200

Source: SGS estimates based on Australian Bureau of Statistics, 2006 Journey to Work Data.

Notes: Values are rounded off to the nearest tenth.

Grouping industries into broad categories (primary, industry, retail and commercial), one can

observe that Knox – South mirrors employment concentrations observations for the metropolitan

average (Figure 9 overleaf). Commercial employment surpasses all other categories.

Overall though, City of Knox has a relatively high concentration of primary industry jobs compared

to the South East Region and the MSD, and a lower concentration of commercial jobs. Taken

together, primary industrial activities comprise approximately 45% of total employment within the

City of Knox with an almost equal proportion of employment centred within commercial activities.



Figure 9 Employment Share by Broad Industry Category (Workers), 2011

Source: Census of Population and Housing (ABS, 1996, 2001 and 2006), and SGS internal modelling

Notes: Primary industry includes Agriculture, forestry and fishing; and Mining. Industry group includes

Manufacturing; Wholesale trade; Transport and warehousing; Electricity, gas and water services; and

Construction. Retail comprises Retail trade; and Commercial includes all other service based industries. Total

for all broad industry groups equals 100%.

Comparing the share of employment by industry in Knox against value-added generated by

industry (derived as sum of all outputs minus sum of inputs used in production), several notable

disparities are evident (Figure 10 overleaf). The proportional contributions to value-added made by

Manufacturing; Wholesale Trade; and certain higher order services including Finance & Insurance;

and Information, Media and Telecommunications is comparatively higher to the proportional

contributions of these industries towards employment. In turn, this suggests that these sectors of

the economy are high value adding.

Given that Manufacturing contributes nearly one-fifth towards the municipality’s employment and

more than a quarter of the region’s value-added, it is beneficial to look at the composition of

manufacturing employment and value-added (shown in Figure 11 overleaf). Interestingly, much of

the value-added and employment created by Manufacturing is concentrated within higher-order

Manufacturing, including Technical Equipment & Appliances; Pharmaceutical Products; Metals &

Metal Products; Chemicals and so on and so forth. Taken together, production of technical

equipment, appliances and pharmaceuticals contribute nearly one-third of total manufacturing

value-added in the municipality.

0%

20%

40%

60%

Primary Industry Retail Commercial

Sh

are

of

Em

plo

ym

en

t

Knox (C) Knox - South SLA

South East Region MSD



Figure 10 Knox (C) Employment and Value Added Contribution

Source: Value Added contributions from REMPLAN (2011. Employment contribution from SGS internal modelling

Figure 11 Knox (C) Manufacturing Employment and Value-Added, 2011

Source: REMPLAN (2011)

0%

5%

10%

15%

20%

25%

30%

Contribution to Employment

Contriobution to Value Added

0%

4%

8%

12%

16%

20%

$0

$100

$200

$300

$400

Co

ntr

ibu

tio

n t

o M

fg. E

mp

loym

en

t (%

)

Val

ue

Ad

de

d ($

Mil

lio

n)

Value Added ($M)

Contribution to Mfg. Employment



3.3.2 Employment Growth

Over the period 1996 to 2011, the total number of jobs within the City of Knox increased by

approximately 15,000. Employment growth over this period both within Knox and Knox – South

was below the metropolitan and South East Region’s average (Figure 12), mirroring the trend

observable for population growth across these regions. Overall industrial employment has grown in

all observed regions, but within industry, Manufacturing employment has experienced an across

the board decline. Employment in the Commercial sector has been strongest over this period in all

observed regions, but employment within the sector at the wider metropolitan scale has outpaced

that in Knox and Knox – South. Retail employment in Knox – South has in fact declined over this

period.

Figure 12 Employment Growth by Broad Industry Category (Workers), 1996-

2011

Source: Census of Population and Housing (ABS, 1996, 2001 and 2006), and SGS internal modelling

In terms of GRP growth over the last 5 year period, the GRP of Knox rose 11.6% from 2005-06 to

2009-10 (Table 5 overleaf). The highest growth was seen in Health Care and Social Assistance,

followed by Financial and Insurance Services. Notably, while the Information Media and

Telecommunications industry substantially shrunk in terms of jobs since 1996, the GRP grew by

almost 4.6% annually since 2006. The value-added contribution of Manufacturing still remains the

highest growing modestly at just under 1% p.a. over this period.

-4%

-2%

0%

2%

4%

Primary Industry Retail Commercial Total

Co

mp

ou

nd

ed

An

nu

al

Gro

wth

Ra

te

Knox (C) Knox - South

South east Region MSD



Table 5 Knox (C) Gross Regional Product by Industry, 2011

2005-06

$M 2009-10

$M CAGR % (2006-

10)

Agriculture, Forestry and Fishing 12.8 13.5 1.3%

Mining 55.7 66.2 4.4%

Manufacturing 1,551.9 1,602.9 0.8%

Electricity, Gas, Water and Waste Services 70 67 -1.1%

Construction 544.8 646.9 4.4%

Wholesale Trade 916.7 1,079.3 4.2%

Retail Trade 387.7 454.1 4.0%

Accommodation and Food Services 140.9 136.4 -0.8%

Transport Postal and Warehousing 228.6 267.2 4.0%

Information Media and Telecommunications 107.2 128.5 4.6%

Financial and Insurance Services 426.5 520.9 5.1%

Rental, Hiring and Real Estate Services 196.7 201.8 0.6%

Professional, Scientific and Technical Services 343.7 336.1 -0.6%

Administrative and Support Services 164.1 184.8 3.0%

Public Administration and Safety 123.2 144.2 4.0%

Education and Training 244.6 276.8 3.1%

Health Care and Social Assistance 376.7 474.1 5.9%

Arts and Recreation Services 47 55.9 4.4%

Other Services 174 163.6 -1.5%

Total 6,112.8 6,820.2 2.8%

Source: KEFS (2011)

3.3.3 Occupation Profile

Managers and Professionals make up the largest occupation group in Knox, accounting for 35% of

jobs within the municipality (Figure 13). The occupation profile of employment located within Knox

largely mirrors that of the broader South East Region; however the municipality hosts considerably

less proportion of managers and professionals when compared with the MSD.



Figure 13 Occupation Profile of Workers (2006)

Source: Australian Bureau of Statistics 2006, Journey to Work Data

3.4 Journey to Work Patterns

3.4.1 Origin and Destination of Workers

Destination of Resident Workers

The location of employment for Knox residents at the 2006 Census is shown in Figure 14 overleaf.

The data indicates that a high proportion of resident workers from Knox are employed within the

municipality (34%) followed by surrounding LGAs of Monash (11%), Whitehorse (8%), Maroondah

and Greater Dandenong (7% each) and Casey (2%). Approximately 14% of the resident workforce

travels to the inner city municipalities of Melbourne, Stonington, Port Phillip and Yarra for work.

Origin of Destination Workers

The host LGAs of workers who were employed within Knox at 2006 (i.e. destination workers’ place

of residence) is shown overleaf in Figure 15. This does not exhibit the same relationship as the

destination of Knox residents.

Comparatively, a much higher proportion of Knox’s workforce is drawn from within the municipality

(41%); followed by Monash (5%), with only 2% of its workforce drawn from the inner city

municipalities.

0%

25%

50%

75%

100%


35% 38%45%

32%30%

32%

22% 20%

16%

11% 11%8%





Figure 14 Destination LGAs of Knox (C) Resident Workers, 2006

Source: SGS Economics & Planning & Census of Population and Housing (ABS, 2006)



Figure 15 Origin LGAs of Knox Workers, 2006

Source: SGS Economics & Planning & Census of Population and Housing (ABS, 2006)

3.4.2 Travel Mode

ABS data confirms that resident workers of Knox – South rely on cars as a form of transport far

more than other modes of transport and when compared with other municipalities across

metropolitan Melbourne. Car use to access work is pronounced compared even to growth area

municipalities of Hume and Wyndham (refer Figure 16 overleaf).

Furthermore, the use of public transport in the SLA is significantly under the MSD average and that

of surrounding and growth areas municipalities. Public transport is used to access 14% of jobs

across the MSD whereas only 5% of Knox – South residents use public transport to access

employment.

Employment within the SLA of Knox – South by destination workers is further skewed in favour of

car usage than resident workers. Over 80% of employment in the SLA is accessed by cars in the

area and the use of public transport is observed at just 2%. This compares to 78% and 14%

respectively for the overall metropolitan average (refer Figure 17 overleaf).

These data suggest that the SLA significantly suffers from accessibility problems.



Figure 16 Mode of Travel, Origin of Resident Workers, 2006

Source: Census of Population and Housing, ABS

Notes: Other modes of transport include cycling, working from home and methods not stated.

Figure 17 Mode of Travel, Destination of Resident Workers, 2006

Source: Census of Population and Housing, ABS

Notes: Other modes of transport include cycling, working from home and methods not stated.

0%

25%

50%

75%

100%

Car Public Transport Walked Other

Sha

re o

f Tr

av

el M

od

e

Knox - South SLA Monash (C) Melbourne (C)

Wyndham (C) Hume (C)

0%

25%

50%

75%

100%

Car Public Transport Walked Other

Sha

re o

f Tr

av

el M

od

e

Knox - South SLA Monash (C) Melbourne (C)

Wyndham (C) Hume (C)



3.4.3 Travel Time

SGS observed the travel time for resident and destination workers for Knox – South and compared

it with other outer area growth municipalities and also with a benchmarked well-connected region

of the MSD, i.e. Melbourne CBD, findings indicate that, on average:

Destination workers to Knox – South spend at least 35% longer commuting to work using

public transport when compared to the time spent travelling to work by employed workers in

Melbourne CBD. Travel times for destination workers to Knox - South are only a shade below

those observed for destination workers to growth area municipalities of Wyndham and Hume.

Resident workers of Knox – South spend almost 3 times longer travelling to work by car and up

to 2.8 times the time travelling by public transport to work when compared to the time spent

travelling to work by resident workers of Melbourne CBD. Yet again, travel times for resident

workers of Knox – South when taking public transport are much the same compared to the

growth area cities of Hume and Wyndham.

Both resident and destination workers travelling from and to Knox – South spend at least 3.2

times and up to 4 times travelling to their work destination when travelling by public transport

compared to driving. Consequently, the incentives to drive are high.

Compared to Monash and Melbourne CBD, travel times on public transport for Knox residents

and workers are indeed high. In fact, time taken by the residents and workers of Knox is

almost as high as those experienced by residents and workers of growth area municipalities.

When undertaking this analysis, SGS adopted the following methodology:

Using ABS’s JTW data, we observed all destination SLAs for the resident workers of Knox –

South SLA and the cities of Melbourne, Hume, Monash and Wyndham.

We used the same dataset to observe all origin SLAs for destination workers to Knox – South

SLA and the cities of Melbourne, Hume, Monash and Wyndham.

Subsequently, we estimated the:

o Average travel time from Knox – South to all SLAs across the MSD, and

subsequently repeating the same calculation for all the selected SLA’s.

o Average travel time to Knox – South to all SLAs across the MSD, and subsequently

repeating the same calculation for all the selected SLA’s.

Estimated the weighted travel time by car and public transport from and to the selected SLAs

using the proportion of workers from and to these work destinations of the total, acting as

weights.

Results of this exercise are reported below in Figure 18 and Figure 19 overleaf. It must be borne in

mind that Melbourne CBD is by far the most well-connected municipalities by public transport. Most

workers within the central municipality are also employed in close proximity to the CBD.

Consequently, all other regions within the metropolis are not as well connected compared to the

CBD and time taken by public transport to access employment from all these locations would not

be as small when compared to the CBD. Nonetheless, the results highlight that Rowville’s transport



connectivity, indeed public transport connectivity is worse compared to other regions and at par

with growth area municipalities.

Figure 18 Weighted Travel Time of Destination Workers

Source: Census of Population and Housing (ABS, 2006) and SGS estimations

Figure 19 Weighted Travel Time of Resident Workers

Source: Census of Population and Housing (ABS, 2006) and SGS estimations

0

20

40

60

80

100

120

Destination Weighted Travel Time by Car Destination Weighted Travel Time by PT

Min

ute

s o

f Tra

vel

Knox - South SLA Monash (C)

Melbourne (C) Wyndham (C)

Hume (C)

0

20

40

60

80

100

120

140

Origin Weighted Travel Time by Car Origin Weighted Travel Time by PT

Min

ute

s o

f Tra

vel

Knox - South SLA Monash (C)

Melbourne (C) Wyndham (C)

Hume (C)



3.4.4 Accessibility

The following analysis of employment accessibility was performed using geographic locations of

Rowville, Flinders Street, Monash, Werribee and the designated Central Activities Areas (CAAs)

across the MSD of Footscray, Box Hill, Dandenong, Broadmeadows, Ringwood and Frankston. The

purpose was to measure the number of jobs (i.e. employment) that could be accessed from these

nodes by public transport and car within a 30 minute timeframe. Results of this analysis are

presented below.

Table 6 Jobs Accessibility by Car and Public Transport, 2011

Car Public Transport

Number of Jobs

Proportion of

MSD jobs accessible Number of Jobs

Proportion of

MSD jobs accessible

Rowville 235,000 12.3% 4,000 0.2%

Flinders Street 1,248,000 65.4% 537,000 28.1%

Footscray 810,000 42.4% 277,000 14.5%

Monash 668,000 35.0% 21,000 1.1%

Box Hill 543,000 28.5% 53,000 2.8%

Dandenong 361,000 18.9% 27,000 1.4%

Broadmeadows 296,000 15.5% 5,000 0.3%

Ringwood 273,000 14.3% 24,000 1.2%

Werribee 131,000 6.9% 7,000 0.4%

Frankston 129,000 6.8% 13,000 0.7%

Source: SGS estimations using travel time matrices and SGS’s employment data

Note: The results for car accessibility are based on using ‘unloaded’ travel time matrices, i.e. non-peak travel times without considerations for road elevations, traffic congestion and traffic signals. Consequently, the estimated results for car accessibility appear optimistic.

The following conclusions can be drawn from this analysis:

When considering public transport accessibility, job accessibility from those closer to Flinders

Street far exceeds the small amount of jobs that residents of Rowville can access

(approximately 28% c.i.f. to a meagre 0.2% of total MSD jobs.)

Accessibility for Knox’s residents improves considerably when considering car use. Whilst

residents closer to Flinders Street can assess approximately 65% of all metropolitan jobs within

a 30 minute drive, residents of Rowville can access a much lower proportion, i.e. 12% of all

metropolitan jobs.



The difference in accessibility within a 30 minute timeframe between car and public transport

use for residents of Rowville is pronounced and considerably favours car use.6

3.5 Section Summary

The City of Knox has been a destination of choice to live and work. This is evident in the growth in

both population and employment compared to the metropolitan average. In fact, growth in

population in Knox – South (the SLA that hosts Rowville) has far outpaced growth in the

municipality and the wider metropolis.

Employment and value creation in the municipality continues to be driven by both industrial and

commercial activities. In fact, a higher share of the municipality’s employment is concentrated in

industrial activities (defined as comprising the sectors of Manufacturing; Wholesale Trade;

Transport & Logistics; Utilities and Construction) compared to the wider South East Region and the

MSD economies. Interestingly, much of the value-added and employment created by

Manufacturing within Knox is concentrated within higher-order Manufacturing, including Technical

Equipment & Appliances; Pharmaceutical Products; Metals & Metal Products; Chemicals and so on

and so forth.

Much like the wider Australian and metropolitan Melbourne economies undergoing a structural shift

over the past 15 year timeframe, i.e. a shift towards more services based employment the City of

Knox was no exception either. Indeed it is plausible that the growth in services based employment

(including growth in Financial & Insurance Services and Professional, Scientific & Technical

Services) observed in the municipality and Knox – South SLA over this period was fuelled not only

to service the needs of the growing local/ regional population but also to service the demands of a

growing sophisticated manufacturing sector.

The education and skills profile of resident workers in the municipality has also responded to the

underlying change and has notched up more toward higher education and more white collared

trades.

Whilst growth in employment opportunities within Knox has indeed been robust, it has been shy of

the rates observed in the wider comparator economies of the South East Region and the MSD.

More importantly, access to employment opportunities for resident workers of the municipality and

to employment opportunities within the municipality is constrained by longer travel times on public

transport.

Compared to a benchmarked location (Melbourne CBD) and other growth area municipalities,

resident and destination workers from and to Knox – South take longer to commute to work,

especially when taking public transport, regardless of the mode of travel. In fact, public transport

can take between 2 and nearly up to 5 times more time accessing work opportunities.

6 The results for car accessibility are based on using ‘unloaded’ travel time matrices, i.e. non-peak travel times without considerations for road elevations, traffic congestion and traffic signals. Consequently, the estimated results for car accessibility are perhaps optimistic and overestimate job accessibility.



Job accessibility on a 30 minute trip on public transport is pretty constrained too, compared to

cars. In fact, accessibility to jobs for a resident worker located in Rowville is low compared to

resident workers of growth area municipalities and other designated CAAs of the MSD. It is not

surprising then that over 90% of work trips originating from and ending in Knox – South rely on

cars compared to 5% or less on public transport.

Consequently, a high proportion of resident workers of Knox and destination workers within the

municipality travel to and from neighbouring municipalities for work as shown by the following

data:

A high proportion (~70%) of the resident workforce of Knox is employed within the

municipality and surrounding LGAs of Monash, Whitehorse, Maroondah, Greater Dandenong

and Casey.

A smaller proportion of the municipality’s residents work in the inner city municipalities of

Melbourne, Stonnington, Port Phillip and Yarra (~14%), compared to the residents of Monash

(29%) and even the growth area municipalities of Wyndham (27%) and Hume (20%).

Destination LGAs for Resident Workers Origin LGAs of destination workers

Knox (C)

Knox (C) 34% Knox (C) 41%

Inner city 14% Inner city 2%

Monash (C)

Monash (C) 32% Monash (C) 25%


Melbourne (C)

Melbourne (C) 62% Melbourne (C) 7%


Wyndham (C)

Wyndham (C) 38% Wyndham (C) 53%


Hume (C)

Hume (C) 21% Hume (C) 37%


*inner city includes the municipalities of Melbourne, Port Phillip, Stonnington and Yarra.



4 BASELINE FORECASTS

Presented in this section are baseline population and employment forecasts, out to 2046, for the

City of Knox and the South East Region. They articulate, at a fine grained level (that of a SLA), the

region’s development trajectory ‘without’ the Project, i.e. ‘Business As Usual’ scenario.

Whilst the population forecasts are based on official data releases and SGS’s estimates,

employment projections were developed using SGS in-house modelling and refined after

consultations with Council.

4.1 Population Forecasts

Future population projections are based on a combination of data sourced from the State

Government’s publication, ‘Victoria in Future (2008)’ and SGS’s estimates developed in previous

assignment experience. Appendix A outlines the methodology used in developing these forecasts.

Table 7 illustrates the projected population of Knox, the South East Region and the MSD out to

2046. To 2046, Knox is anticipated to grow relatively slower than the broader South East Region

and the MSD, adding just over 25,000 people between 2011 and 2046 with a compounded annual

growth rate (CAGR) of 0.4%.

Within Knox, the Knox - South SLA, which exhibited the most growth between 1996 and 2011, is

projected to grow modestly in the coming decades relative to other SLAs within the municipality

and the wider comparator regions. The more established areas in the north of Knox, particularly

North-West Knox which contains Knox Central are anticipated to increase in population at a greater

rate due to a degree of urban renewal and densification.

Table 7 Estimated Population Projections, 2011-46

2011 2021 2031 2046 CAGR %

(2011-2046)

Knox - North-East 66,800 70,600 72,500 76,800 0.4%

Knox - North-West 48,600 52,900 56,000 59,300 0.6%

Knox - South 43,900 45,700 47,000 50,000 0.4%

Knox (C) 159,300 169,200 175,500 186,100 0.4%

South East Region 741,500 852,000 928,900 1,014,800 0.9%

MSD 4,170,600 4,820,100 5,410,600 6,170,800 1.1%

Source: Victoria in Future 2008 and SGS Projections

Note: Values rounded to the nearest hundred



As with population growth, the anticipated dwelling growth in Knox is expected to be slow relative

to wider benchmarked regions. Nonetheless, dwellings growth in Knox is projected to increase at a

faster rate than population (Table 8), with the trend towards shrinking household sizes anticipated

to continue.

Growth in dwellings in Knox – South is likely to equate in average to the wider metropolis and is

expected be slower compared to other SLAs within the municipality. This is largely a reflection of

the age of the housing stock, most of which was constructed between 1996 and 2011, with the

relatively newer houses of Knox - South unlikely to be redeveloped before 2046.

Table 8 Estimated Dwelling Projections, 2011-46

2011 2021 2031 2046 CAGR %

(2011-2046)

Knox - North-East 26,600 28,400 30,200 32,500 0.6%

Knox - North-West 17,500 19,300 20,600 22,200 0.7%

Knox - South 13,700 14,600 15,800 17,100 0.6%

Knox (C) 57,800 62,300 66,600 71,800 0.6%


MSD 1,619,400 1,904,900 2,187,000 2,512,800 1.3%

Source: Victoria in Future 2008 and SGS Projections

Note: Values rounded to the nearest hundred

4.2 Employment Forecasts

SGS has made ‘Business As Usual’ or the ‘without project’ forecasts of employment within Knox,

South East Region and the MSD using its in house modelling techniques. The detailed methodology

describing these techniques is shown in Appendix A. Suffice here to say that SGS adopted a ‘top

down’ approach to projecting employment by industry for each SLA across Melbourne based on

data obtained from a variety of sources used to develop a set of industry projections for the entire

Australian economy. These industry projections are forecast over the short term (2016), long term

(2031) and beyond (2046), with total growth for all industries benchmarked against GDP

projections. This ensures that the projected industry growth can be resourced with the finite level

of resources at the disposal of Australia. At a State level, Victorian estimates are derived from the

current State share of Gross Value Added (GVA) and employment for each industry. Projections are

made on the future share of each industry in Victoria. Employment projections for Melbourne are

derived from these Melbourne GVA projections and projections of Melbourne’s labour productivity

growth. Employment growth is then capped using future labour force constraints.

Employment projections for SLAs are based on a combination of observable past growth rates and

expected changes in employment distribution over time. When contemplating expected changes



over time, SGS considered each sub-region’s: prospects and capacity for growth, transport

connections, resident workforce characteristics, employment lands availability and Government

spatial policy considerations. Importantly, this analysis was undertaken separately for each major

industry and occupation groups to ensure that the level of granularity appropriately reflects

respective location drivers.

These forecasts have been used by the Victorian State Departments of Transport and Planning and

Community Development in previous SGS projects.

Indeed, discussions with Knox City Council suggested that further finer adjustments were required

to the specific forecasts for the City of Knox to better reflect Council policy and its advocacy efforts

towards consolidating its existing industry base and providing support for a thriving services sector.

In addition, insights gained from the Knox Economy Future Study (KEFS 2011) – providing a

review of the Council’s economic development program and scenario analysis of possible future

economic outcomes for the municipality - was used by SGS to inform and refine its employment

estimates for the municipality.7

The final estimated employment projections for the City of Knox by 1-digit Australia and New

Zealand System of Industry Classification (ANZSIC) industry are shown overleaf in Table 9. A time-

series view of the industrial structure for the municipality is provided in Figure 20 with comparisons

of industry-wide growth between Knox and benchmarked comparator regions shown in Figure 21

overleaf.

Notable observations from these business as usual projections are as follows:

Almost 100,000 jobs are projected to be located in Knox by 2046.

Employment in the municipality is expected to grow by just under 25,000 between 2011 and

2046, nearly matching population growth over this period (refer Table 7).

The largest increases in employment are seen in Health Care and Social Assistance, followed by

Professional, Scientific and Technical Services, Education and Training and the population

servicing industries of Retail Trade and Accommodation and Food Services.

Such strong growth in services is expected to increase the share of the Knox’s services

economy observed at 42% in 2011 to 55% by 2046. This transformation could be observed

between 1996 and 2011 (refer Figure 20).

7 It must be borne in mind that employment projections made out to 2031 contained within the KEFS (2011) study were different to those produced by SGS. In projecting future

employment by industry, differing projection methodologies will invariably result in differing forecasts. This is not to discredit either of the projections, but does articulate how varying modelling methods can impact upon projection findings. The KEFS (2011) study applied the AECgroup Economic Growth Model, based on a Knox Input-Output table to model future employment by industry. The model uses population growth and industry growth estimates to forecast the economic growth of the City of Knox, utilising projections of GRP based on

expected real growth rates in production by industry, using 2008-09 as a base. Contrasted to

this ‘bottom up’ approach adopted by the KEFS study, SGS uses a ‘top down’ approach as explained in the text.



Services sector growth is due at the expense of primary and secondary industries (including

the sectors of Agriculture, Mining, Manufacturing, Utilities, Construction, Wholesale Trade and

Transport). Share of primary industries is expected to continue to fall.

Though the data indicate a fall in industrial based employment, it must be borne in mind that

this does not imply a declining industrial sector. Indeed, value created by the Manufacturing

sector will continue to rise. There has been a long term trend towards greater capital

intensiveness of most industry sectors driven by technological change underpinning the

productivity increases required to maintain competitiveness. Productivity growth (industry

value add per person employed) in Manufacturing as a whole in Victoria was 4.7% per year

from 2002 to 2006, well above the average inflation rate of 2.8% for the period, giving a real

increase in output of 1.8% per annum. Such productivity increases in Manufacturing (and

indeed, all industry sectors) are forecast to continue for Knox. Indeed, forecasts indicate that

Manufacturing sector GVA between 2021 and 2046 under the Base Case would rise by 1.2%

annually, compared to 0.8% p.a. between 2006 and 2010.

Presumably, productivity growth in industry will be driven by the already existing higher order

Manufacturing base of the region. The expanding services sector employment, especially in

Finance & Insurance and Property, Scientific & Technical Services, is expected to service the

increasing value created by this sector.

Overall, total employment growth over time is expected to slow down (Table 9) – reflection of a

trend observable across the whole of Melbourne due to slower labour force growth resulting

from an ageing population.

Compared to the wider benchmarked regions (South East Region and MSD), employment

growth within the City of Knox is low across the board (refer Figure 21). The anticipated decline

in Manufacturing is evident across all three compared regions, but lowest in the City of Knox.

This further underscores the resilience of the municipality’s manufacturing base.

Because of the changes mentioned above, Knox’s share in total metropolitan employment is

expected to remain fairly stable across most service based industries, (Figure 22). On the

contrary, Knox’s employment share in Construction and Wholesale Trade is set to fall over

time. This is on account of industrial activity likely to move away from Knox. Moreover,

Wholesale Trade employment is increasingly being concentrated in Melbourne’s west and north

where the intermodal hubs are being set up. Figure 23 overleaf in shows the declared industrial

nodes of Melbourne from the Urban Development Programme (UDP). These are the nodes

where the industrial sector of the metropolitan economy is most likely to congregate in the

future.



Table 9 Estimated Employment Projections by 1-digit ANZSIC Industry, Knox (C)

Employment Levels Growth in Employment (CAGR %)

1996 2011 2031 2046 1996-2011 2011-2031 2011-2046

Agriculture, Forestry and Fishing 263 141 74 69 -4.1% -3.2% -2.0%

Mining 20 73 59 60 9.0% -1.1% -0.6%

Manufacturing 16,840 15,010 13,111 11,495 -0.8% -0.7% -0.8%

Electricity, Gas, Water and Waste Services 238 384 545 634 3.2% 1.8% 1.4%

Construction 4,604 8,534 9,403 9,393 4.2% 0.5% 0.3%

Wholesale Trade 6,201 7,097 6,163 5,926 0.9% -0.7% -0.5%

Retail Trade 8,477 9,867 11,560 12,594 1.0% 0.8% 0.7%

Accommodation and Food Services 2,707 3,893 5,936 6,546 2.5% 2.1% 1.5%

Transport Postal and Warehousing 2,158 2,555 4,411 5,044 1.1% 2.8% 2.0%

Information Media and Telecommunications 921 458 940 933 -4.6% 3.7% 2.1%

Financial and Insurance Services 1,127 1,797 2,613 3,510 3.2% 1.9% 1.9%

Rental, Hiring and Real Estate Services 474 1,203 1,571 1,883 6.4% 1.3% 1.3%

Professional, Scientific and Technical Services 2,681 3,570 5,853 7,684 1.9% 2.5% 2.2%

Administrative and Support Services 1,249 2,022 2,119 2,183 3.3% 0.2% 0.2%

Public Administration and Safety 1,405 1,749 2,106 2,264 1.5% 0.9% 0.7%

Education and Training 2,734 4,119 5,724 7,117 2.8% 1.7% 1.6%

Health Care and Social Assistance 3,774 7,781 12,200 15,598 4.9% 2.3% 2.0%

Arts and Recreation Services 462 870 1,337 1,560 4.3% 2.2% 1.7%

Other Services 3,191 3,523 4,292 4,893 0.7% 1.0% 0.9%

Total Employment 59,525 74,647 90,017 99,385 1.5% 0.9% 0.8%



Figure 20 Industry Structure, Knox (C), 1996 - 2046

Source: ABS and SGS Projections

Figure 21 Estimated Employment Growth (CAGR %) by 1-digit Industry,

2011-46

Source: SGS Projections

0%

25%

50%

75%

100%

1996 2011 2031 2046

0% 0% 0% 0%

50%45%

37%33%

14%

13%

13%

13%

35%42%

50%55%

Primary Industry Retail Commercial

-3%

-2%

-1%

0%

1%

2%

3%

4%




Figure 22 Estimated Knox (C) Employment Share in MSD

Source: SGS Projections

0.0%

2.5%

5.0%

7.5%

10.0%

1996 2011

2031 2046



Figure 23 Declared Industrial Nodes, Urban Development Programme, 2010

Source: SGS Economics & Planning using data from Department of Planning & Community Development



4.3 Section Summary

Population and employment projections to 2046 for the SLAs of Knox, the South East Region and

the MSD were developed using official government forecasts complemented with SGS modelling.

To 2046 the City of Knox is projected to grow by just over 26,000 people, housing around 185,000

people in total. However, this future rate of growth is half that of the projected growth rate of the

South East Region and the MSD as a whole.

Levels Annual Change CAGR %

1996 2011 2046 1996-2011

2011-46 1996-2011

2011-46

Population:

Knox (C)

South East Region

MSD

137,000

578,000

3,283,000

159,000

742,000

4,171,000

186,000

1,015,000

6,171,000

1,500

11,000

59,000

765

7,800

57,000

1.0%

1.7%

1.6%

0.4%

0.9%

1.1%

Employment:

Knox (C)

South East Region

MSD

60,000

260,250

1,560,000

75,000

350,000

2,177,000

99,000

532,000

3,473,000

1,000

5,900

41,000

700

5,250

37,000

1.5%

2.0%

2.2%

0.8%

1.2%

1.3%

Note: values are rounded off

Employment growth over the 2011-46 period is expected to be just shy of population growth over

this period, i.e. just under 25,000. Whilst commercial services employment is set to rise quite

robustly (~1.6% p.a.), industrial employment is set to decline over time in line with trends

observed for the broader MSD. Albeit industrial employment is set to fall, value added creation is

set to continue reflecting increasing productivity in the sector.

Employment in Knox (C)

1996 2011 2021 2046 CAGR %

1996-2011 CAGR % 2011-46

Primary 290 220 190 130 -1.8% -1.5%

Industry 30,100 33,600 34,900 32,500 0.7% -0.1%

Retail 8,500 9,900 11,100 12,600 1.0% 0.7%

Commercial 20,800 31,000 37,600 54,200 2.7% 1.6%

Total Employment 59,690 74,720 83,790 99,430 1.5% 0.8%

As a consequence of these changes, Knox’s share in total metropolitan employment is expected to

remain fairly stable across most service based industries. On the contrary, Knox’s employment

share in Construction and Wholesale Trade is set to fall over time. This is likely because industrial

activity is likely to move away from Knox over time.



5 STRATEGIC POLICY CONTEXT

This section showcases that the existing regional and local strategic planning framework favours

high employment growth for the City of Knox and the wider South East Region, especially in higher

order services to sustain the regional economy into the future. This strategic framework also

identifies that the envisaged growth will require investments in multi-modal and more sustainable

modes of transport.

Some focus is also cast here on the capacity of Knox City Council to accommodate this growth, i.e.

land supply and any constrains thereof.

5.1 Metropolitan Policy Context

The present Victorian State Government has, at this point in time, not yet released a strategic land

use or transport plan for metropolitan Melbourne. The most recent State Government land use and

transport policies, compiled by the former Labour Government are inappropriate to review given

that the policies and strategies denoted in such plans are not necessarily endorsed by the present

Government. However, it should be noted that public transport to Rowville has, in some form or

another been in the public and political realm for several decades, regardless of the Government.

While no formal metropolitan land use or transport policy exists, the Liberal party’s pre-election

document, ‘Plan for Planning’ articulates an outcomes based approach to sustainable strategic

planning, one based on community and industry dialogue in the provision of services and

infrastructure. Council’s advocacy efforts that became manifest in community consultation as well

as SKM’s efforts at engaging the Knox community are thus in line with the government’s envisaged

policies.

5.2 South East Region Planning Context

5.2.1 Melbourne South East Regional Economic Strategy 2009-2030

Melbourne's South East (MSE) is a regional economic development alliance of eleven Local

Government municipalities, utility providers and the State and Federal Governments. The area

represented more than one million people and nearly 530,000 jobs as of 2006.

The MSE developed its Regional Economic Strategy with a forward focus over the next 20 years

and with the aim to attract more high value-adding, knowledge based and export orientated

businesses to the region. Among other things, this regional strategy identifies the following as

critical to achieve its aim:

Developing a business investment prospectus for the Region;



Accelerating the development of the Advanced Business Services sector;

Capitalising on the MSE Technology Network;

Harnessing business leadership to drive regional clusters;

Making MSE climate change ready; and

Encouraging the adoption of best practice by the Region’s business community.

The Strategy clearly identifies the services sector as leading growth and change in the South East

economy. More importantly, attracting the services sector, especially Advanced Business Services,

and initiating change, will need to be underpinned and perhaps preceded by the provisioning of

high quality and fast public transport to the region. This is because the successful formation and

operation of these businesses will rely on the ability of the region to tap into the agglomeration

benefits already generated by the central Melbourne economy. As discussed by the World Bank

(2009), attempting to create central places or ‘growth poles’ in locations which are disconnected

from existing investment magnets is likely to end in failure.

5.3 Knox City Council Planning Context

Several of Council’s plans and strategies establish Knox’s local strategic planning framework.

Primary among these that were reviewed for the purposes of this study due to their pertinence

were:

The Knox Economic Development Strategy, 2008-18;

Knox City Council Vision 2025;

Knox City Council Plan 2009-13; and

Knox Economy Futures Study (2011).

This planning framework identifies Knox’s strengths and challenges over the next few years;

establishes priority objectives for progressing with the social and economic aspirations for the

community and actions to fulfil these objectives; and foreshadows a vision for what Knox should

embody by 2025. These issues are discussed in turn below.

5.3.1 Strengths and Challenges for Knox

Taken together, the following strengths and challenges for the City of Knox are identified by the

Knox Economic Development Strategy and the Futures Study:



Strengths Challenges

A relatively diversified industry base with a

balance of sectoral employment between goods-

based, commerce-based and information and

finance-based industries.

Expected slowdown in population and household

growth over the next 15-20 years with a ageing

resident workforce.

Established industry infrastructure and linkages

with high quality business precincts, including

linkages with the National Centre for

Sustainability, Swinburne University of

Technology and TAFE, and proximity to Monash

University and the Australian synchrotron

project.

High reliance on Manufacturing with broader

metropolitan and nation-wide trends suggesting

a gradual economic transformation toward a

more services-oriented economy.

A low unemployment rate. Limited greenfield sites may constrain

development of land zoning, to accommodate

future land demand for residential and

commercial/ industrial purposes.

High purchasing power with comparatively high

levels of household income.

Lower order skill of resident workforce requires

import of skills and labour from other

municipalities.

Locational advantages including proximity to

Eastlink.

Poor provisioning of high quality public transport

services results in a high level of car

dependency, increased levels of congestion,

increased greenhouse gas emissions and more

sedentary lifestyles.

5.3.2 Opportunities and Actions for Sustainable Growth

The principal goal of the Knox Economic Development Strategy 2008-18 is to improve liveability

and wellbeing in the City through economic growth that creates high quality jobs; generates wealth

and investment; and helps ensure the City’s long term fiscal health. The strategy puts forth a

number of objectives to leverage opportunities for growth.

In addition, the Council Plan identifies a number of priority actions for Council to tackle the

challenges identified. These objectives and actions can be grouped into the following broad

categories:

Supporting and developing a network and hierarchy of integrated activity centres

o Development of Knox Central principal activity centre;



o Fully developed and highly functional major, neighbourhood and local activity

centres.

o Work in partnership with key stakeholders and the Victorian State Government to

develop Knox Central to be the ‘Capital of the Eastern Suburbs’;

o Position Knox as a centre of excellence for environmental technologies and

services;

o Deliver catalyst projects to progress Knox Central and stimulate business

investment.

Establishing industry clusters and knowledge networks supporting them

o Establishment of sustainable energy efficient industry clusters and high-tech

learning centres;

o Support the development of the Scoresby/Rowville Employment Precinct,

incorporating best practice design to provide 7,000 new jobs by 2025; and

o Support development of the Bayswater/Bayswater North Industrial precinct.

o Strong secondary, tertiary and industry linkages providing for the needs of high

tech industries (support services, technical backup and marketing);

Facilitation of entrepreneurial activity

o A more balanced business mix, including more business support services and

tertiary industries;

o A strong Knox-wide industry network promoting Knox products and services

nationally and internationally;

o Establishment of a centre for entrepreneurial development integrating education,

new start-ups and business incubators.

Availability of world-competitive infrastructure including telecommunications and supporting

services.

Enhance liveability and amenity to live and work in the City of Knox

o Support greater opportunities for residents to live and work in Knox;

o Attract businesses to Knox through the provision of enhanced local amenity and

supportive infrastructure.

Advocacy efforts for availability of better transport provision

o Engage in advocacy to encourage improved integrated transport options for the

Knox Community;

o Advocate for the construction of a heavy rail link between Huntingdale and Rowville

to achieve improved accessibility within the southern half of the municipality and to

the wider metropolitan area;

o Promote non-car based transport modes through the municipality;

o Advocate for real options for transport to and from Activity Centres and residents’

homes which are of high quality and comparable to each other;



o Facilitate the linking of public transport to employment precincts to provide

improved access for employees and movement of goods and services.

5.3.3 Vision for City of Knox

Vision 2025 consists of seven key themes that describe what Knox will be known for in 2025. These

themes and the attributes they incorporate are outlined below.

Accessible transport choices

o Excellent public transport options;

o Seamless connectivity to services and facilities within the municipality;

o Free flowing vehicular traffic.

A prosperous modern economy

o Leading edge, environmentally sustainable industry and technology;

o Contemporary business opportunities;

o Support for nationally recognised business precincts with support for

entrepreneurship;

o Retail and industrial business attraction.

Dynamic services and facilities

o Provide for quality medical, health and wellbeing services;

o Forge cutting edge partnerships with Government, public and private sectors in

education, research and knowledge sharing;

o Provide education and employment opportunities for one and all;

o Facilitate services that meet the changing demographic needs of the municipality.

Healthy, connected communities

o Supportive and inclusive communities;

o A safe haven to live;

o An abundance of community leaders;

o Motivated and valued community members;

o Communities which value diversity and provide diverse living spaces

Culturally rich and active communities

o Supporting community wide and local events, festivals and activities;

o Diverse cultural and artistic opportunities;

o Diverse leisure and recreational opportunities;

o Open spaces shared by all;

o Promote lifelong learning.



Sustainable natural environment

o Leader in environmental management;

o A healthy, green and leafy environment.

Attractive and vibrant places

o Enhanced garden ambience;

o Great spaces and public places;

o Targeted activity-centred urban development;

o Quality urban landscape and design standards;

o Neighbourhood parks and open spaces.

The future Knox economy is envisaged to have a global reputation for innovation in the emerging

high technology and knowledge based industries, particularly specialising in the design and

production of sustainability and energy efficiency products and services. The established industries

of Advanced Manufacturing and Biotechnology would be further developed, profiting from their

proximity to the Scoresby Rowville Employment Precinct and the Synchrotron. In regards to

transport accessibility, the Council would like Knox to be a place in the future where car use is a

choice, not a necessity due to the availability of high quality public transport.

5.4 Section Summary

The Australian and metropolitan Melbourne economies are and will continue to be subject to

economic and structural shifts over time. The City of Knox would not be an exception to this. In

order for the municipality to embrace this structural change, it is imperative that it capitalises on

its strengths and overcomes identified challenges.

Notable priorities that have been identified include:

Consolidating the existing industry base (Advanced Manufacturing and a rapidly growing

services base) and provide for a well-networked, informed and knowledge intensive business

community in the City of Knox; and

Building on the existing activity centre hierarchy to provide for more employment and housing

opportunities for the people of Knox.

Crucial to achieving these priorities and the vision for Knox is the ability to provide improved

integrated transport options for the Knox Community, namely faster connectivity into a well-

established agglomeration hub of metropolitan Melbourne, i.e. the city centre. If the benefits

outlined via examined case studies later in the study in section 6 are any indication, several

advantages can be gained via better public transport provisioning for the City of Knox and the

wider metropolitan economy in general.



6 CASE STUDY INVESTIGATIONS

This section reviews relevant case studies from Australian and international jurisdictions to assess

the short, medium and long-term impacts on residents and businesses during the construction and

operation phases of a major transport infrastructure project.

6.1 Introduction

Major transport projects are known to have both positive and adverse impacts, both during their

construction and operation phases.

The positive impacts of the construction phase of a project are increased trades employment as

well as transport related employment. On the contrary, there are nuisance costs, such as

construction noise and air pollution, vibration, disruption to existing services (such as road or rail

closures) and potentially reduced accessibility to homes, businesses and services that abut the

construction area. The adverse impacts are relatively easy to conceptualise, although difficult to

measure.

In contrast, the benefits and costs that derive over a longer time period from the operation of such

infrastructure are generally more difficult to capture and monetise. This is because major transport

projects yield changes to urban form, in particular, changes to population and employment

distribution which occur over a long period of time and are influenced by a range of factors. That is

to say the changes to a city are not only the result of a relative improvement in transport access,

but a result of a combination of other factors including macroeconomic forces, demographic shifts,

or targeted local initiatives. This is not to underwrite the value or necessity of transport

infrastructure. The history of cities is as good an indicator of the significant influence transport

infrastructure has on their development.

In investigating case studies to inform the costs and benefits that may flow from the proposed

Rowville rail extension, a local and international perspective has been adopted. Additionally, given

that transport projects tend to increase mobility and access, this section has reviewed both rail and

road projects It is acknowledged that while a rail project may derive slightly different spatial

outcomes to a road project, in principle some of the mobility and access benefits are comparable.

Consequently, SGS has reviewed the available evidence from both rail and road projects in gauging

potential consequences of the proposed Rowville rail extension on the local and wider metropolitan

economy.

6.2 Short Term Construction Phase Impacts

The construction of a railway or a road project, particularly in an established urban area brings

about a set of costs related to the nuisance of construction, such as noise and air pollution,

vibration, disruption to existing services (such as road or rail closures) and potentially decreased



accessibility to homes, businesses and services that abut the construction area. Indeed,

consultation held by SKM when producing their feasibility study reinforced these concerns.

The immediate upshot or the primary benefit from the construction phase of rail infrastructure, as

with any major infrastructure project, is the employment that such construction generates.

6.2.1 Australian Experience

Recent experience from Australia highlights that whilst the nuisance impacts are inevitable, some

measures can be adopted during the design and construction phase to attenuate these impacts.

Rail investments in Perth and Sydney have followed processes to ensure the nuisance of

construction is addressed in the planning phase.

The Southern Suburbs Railway in Perth, Western Australia began operation in 2007. The line

originates at Perth Railway station and terminates 72 kilometres south in Mandurah, with 9 new

stations along the route. As part of the planning process, a Public Environmental Review was

carried out by the governing body, New Metro Rail. The guidelines of the review required full

consultation with members of the public and management of the operational noise and vibration

pollution emitted from the rail line. Day time and night time maximums were set for both sound

and vibration levels. Additional vibration criteria for regenerated noise from ground borne vibration

were set, with different maximums set for various built forms around the train line (Stedman,

2007). There were several mechanisms installed across the Southern Suburbs Railway to mitigate

noise and vibration. Approximately 6 kilometres of noise walls were installed, along with 1.4

kilometres of stone mastic asphalt road surfacing, 1.3 kilometres of double track vibration isolation

in City tunnels and 680 metres of double track ballast matting.

Similarly, in Sydney the Epping to Chatswood line, which began operation in 2009, confronted

nuisance issues, both during construction and operation. The line links the North Shore Line from

Chatswood to the Main North Line at Epping. The line consists of 12.5 kilometres of new rail, three

new stations (including one at Macquarie University) and the complete redevelopment of Epping

station. Noise reduction was a key concern for the Epping to Chatswood rail line, particularly in the

form of noise during construction and vibration from trains in the tunnel section during operation.

Mechanisms were put in place to protect the local communities from the impacts of the

construction phase of the Epping to Chatswood Line outside of the tunnel section. Noise reduction

techniques included the erection and construction of hoardings and the restriction of surface work

hours (Railcorp, 2011). To mitigate operating noise, rubber bearings were installed between the

concrete track slab and the tunnel structure (Design Build Source, 2011).

6.2.2 International Experience

Looking overseas, the Crossrail project in London currently under construction, is a 21 km rail

tunnel under central and east London. The link connects commuter and suburban services and key

locations such as Heathrow Airport, the West End, the City of London and Canary Wharf.

Construction is anticipated to take approximately 7 years.



The construction of such a large tunnel creates economic stimulus on its own, with up to 14,000

people likely to be employed on the construction of Crossrail at its peak, with further jobs required

to supply the project during construction and providing services to those directly employed.

In addition, there are opportunity costs associated with delaying or failing to proceed with a

project. The cost increases can be ‘in scheme costs’ and planning and transport blight, reductions

in agglomeration benefits and delayed user benefits (Buchanan, 2007). A study into the London

Crossrail project identified that each year of delay significantly increases the costs of

implementation. Every one year delay to scheme implementation increases the scheme costs in

real terms due to expected price inflation within the construction sector at a faster rate than in the

economy as a whole. In addition changes over time in the relevant design standards and legislation

further raise the cost. The Crossrail team conclude that the cost of the project rises by some 3% in

real terms every year (Buchanan, 2007).

6.3 Long Term Operational Phase Impacts

In evaluating the costs and benefits of rail projects, a Cost Benefit Analysis (CBA) is the most

commonly used tool used to justify and prioritise projects. Traditional CBA encapsulates the cost of

infrastructure and other costs such as time, congestion and accidents, with the reductions in these

factors being the direct benefits of transport projects.

However many major projects have wider impacts. These wider impacts have not generally been

included in traditional Benefit Cost Ratios (BCR), but are often significant. Given the transformative

nature of the Rowville rail extension, wider economic benefits are of particular relevance to this

study, and as such are evaluated in detail in section 10. Wider impacts include effects on

agglomeration, productivity, competition and labour markets, as well as impacts on urban form,

and while it is widely accepted that the effects of such impacts are real, there is less agreement on

what this means for a project appraisal (Preston and Wall, 2008; OECD, 2011; Litman, 2011). By

placing rail investments in the broader context of helping to achieve wider national, regional and

local development objectives, analysis to consider the benefits that are not only attributed to

transport becomes possible (Banister and Thurstain-Goodwin, 2011). Some examples of standard

and wider economic impacts are as follows.

Standard Economic Impacts Wider Economic Impacts

Reductions in:

Travel time

Congestion

Accidents

Pollution

Noise

Waste

Improvements in:

Agglomeration

Productivity

Labour market benefits

Enhanced competition

Changes in urban form



Although including wider economic benefits in transport related CBA projects is not uncommon,

many commentators acknowledge that there is difficultly in assessing such benefits. This can be

because the wider benefits of rail vary greatly from case to case, are difficult to predict, may be

redistributive benefits rather than net additional benefits, and may be difficult to distinguish from

other developments in the area, particularly given the time span of projects (Nash, 2009; Preston

and Wall, 2008). However, excluding impacts based on the grounds that they are poorly

understood becomes problematic when these impacts are essential to the project. Not including

these benefits where they are likely to exist tends to understate the benefits of transport projects

and risks the chance of underinvestment. As such, many CBAs that include wider benefits account

explicitly for uncertainty, and give BCRs with a high and low impact range (OECD, 2011).

6.3.1 Australian Experience

The urban form of major Australian cities has undergone a sea change due to major investments in

rail and road over the years. By way of example, pre 1950s Melbourne, based on rail and tram

mobility is drastically different in job and household clustering and density compared with the post

1950s car based suburbs of middle and outer Melbourne. More broadly it is difficult to gauge the

significance of past transport infrastructure investments on the rise and continued strength of

global cities such as New York, London, Paris and Tokyo, although it is inconceivable to imagine

these cities being as strong as they are today without their respective metros, tubes, subways and

above ground rail networks.

Unfortunately, there does not exist much evidence evaluating the impacts of past major transport

investments on the present day urban form or economic productivity of the city. Part of the reason

for this is data limitations and the inability to disentangle the specific effects of rail investments

from all other significant investments taking place simultaneously. More importantly, rail

investments in Australia made in the past were that of a metropolitan scale, with significant

investments in particular suburban locations a rare occurrence.

More recently, Ernst and Young (2010) evaluated the traditional and wider economic benefits of the

Victorian Transport Plan released in 2009. The wider benefits of the plan were evaluated at $30.3

billion, more than the estimated $28.1 billion of capital expenditure needed to implement the plan.

The wider economic benefits comprise of:

Imperfect competition ($4.4 billion) - the reduction in the cost of travel over the course of work

typically resulting in firms reducing prices and increasing output;

Increased labour supply ($11.7 billion) - reducing travel times for commuters increasing gross

product by attracting new workers, allowing existing workers to use their time savings to work

longer hours, and allowing existing workers to take up more desirable and productive jobs that

are located further away; and

Agglomeration ($14.2 billion) - the benefit firms receive from being in close proximity to one

another, with transport playing a role in reducing travel times of workers and freight, therefore

increasing the effective density of firms and workers that are within reach of a certain point.



SGS’s modelling for previous assignment experience with the Victorian Departments of Planning

and Community Development and Transport, has also revealed the improvements in accessibility

brought about significant investments in road infrastructure in Victoria.

The improved road connections and the ensuing strengthening of agglomeration economies in

Melbourne has been measured by SGS by reference to Effective Job Density (EJD). SGS has used

the level of employment relative to the time taken to gain access to that employment and the

mode split that is currently experienced by those employees to better reflect the accessibility of

jobs to certain regions.

EastLink is a recent example of a change in EJD brought about by a transport project. Figure 24

overleaf presents the estimated impact on EJD before and post opening of the EastLink (and

employment growth and other minor transport improvements). The LGA of Greater Dandenong

experienced a significant uplift in EJD as a result of EastLink. Employment growth in Greater

Dandenong from 1999-2004 was 1.1 per cent. Once the construction road had commenced (pre-

empting the uplift in accessibility) employment growth increased to 2.1 per cent from 2005-2010

(which includes a period of flat employment growth related to the global economic slowdown).

There are a range of factors at play which could explain employment growth, but the changes in

accessibility appear related to improved economic outcomes.



Figure 24 Effective Job Density Prior and Following EastLink

Prior Following

Source: SGS Economics and Planning



6.3.2 International Experience

Internationally too, studies evaluating the benefits of investments in rail and road transport made

several years ago are hard to come by. Nonetheless, the available evidence points out that when

aggregating both the traditional forms of benefits of investing in transport and the wider economic

benefits, at least the new planned investments in metropolitan and suburban rail systems is

expected to yield higher benefits than planned costs.

An investigation into the benefits of improving rail services through the centre of Wales yielded a

BCR of 0.6 excluding the wider economic benefits. Including these benefits in turn boosted the BCR

in the range of 1.2 to 1.6 (Jacobs Consulting, 2010).

Wider economic impacts also contributed substantially to the viability of the Channel Tunnel Rail

Link in the UK. This rail project, linking London to the European fast rail network returned a BCR of

1.1 when excluding wider economic benefits. However, urban regeneration benefits were estimated

to be as high as £8 billion, leading to wider-economic benefits being critical to the justification of

the project (Preston and Wall, 2008).

The Crossrail project in London was identified to enable the regeneration of areas around other

stations along its route through improving accessibility with shorter journey times, and giving

employers better access to a larger, more highly skilled labour market with more choice of skilled

employees. In total, Crossrail’s wider impacts are estimated to be between £6 billion and £18

billion in welfare terms (at 2002 prices), exceeding the initial public sector funding required to build

Crossrail. Additionally, about 1,000 net additional jobs are anticipated to be created to operate and

maintain the railway once it is completed.

It has also been showcased that access increases total wealth through agglomeration, but it also

redistributes wealth through allowing relative greater access to wealth generating activities

(Levinson, 2010). By contrast poor transport is seen by business as a serious constraint on their

growth potential (Clayton et al, 2011). High car dependency and the risk of congestion are

exacerbated by limited public transport and relieving or alleviating the risk of congestion through

rail can help support future growth (Clayton et al, 2011).

However, for the effects of rail investments to have maximum impact certain conditions are stated

to be important. These include: stations being at the centre of regeneration and redevelopment;

regional services being well integrated with local and other regional transport services; and high

frequency services (Preston and Wall, 2008; Nuworsoo and Deakin, 2009).

However, improved accessibility is not necessarily sufficient to revitalise or act as a catalyst to

growth, and investments generally need to be accompanied by supporting land use policies,

transport ridership incentives, consumer acceptance and private investment (Litman, 2011a;

Banister and Thurstain-Goodwin, 2011). Private investment was identified as critical to achieve the

benefits of transport investments, but private investment was generally only found to take place

after transport funding was secured (Banister and Thurstain-Goodwin, 2011).



6.4 Section Summary

In reviewing relevant literature to the Rowville rail extension, impacts from the construction and

operation phases were investigated using both rail and road case studies from within Australia and

internationally. It is acknowledged that while a rail project may derive slightly different spatial

outcomes to a road project, in principle some of the mobility and access benefits are comparable.

Consequently, SGS has reviewed the available evidence from both rail and road projects in gauging

potential consequences of the proposed Rowville rail extension on the local and wider metropolitan

economy.

These studies suggest that the positive impacts during the construction phase of a major transport

infrastructure project include employment generation (especially trades employment). Adverse

impacts include nuisance costs such as noise, vibrations and temporary road closures. Given the

inevitability of such costs, mitigating or alleviating the extent of such costs is best addressed

during the planning stage of the project.

Evaluating the long term operational costs and benefits of transport investments are typically

investigated through Cost Benefit Analysis (CBA). CBA has traditionally evaluated the direct costs

of construction and operation with the standard benefits delivered by such projects, those being

reduced travel time, congestion and accidents. However, increasingly the wider economic benefits

of infrastructure investment are becoming commonplace in project evaluation. These wider

economic benefits, such as agglomeration, competition and labour markets, and impacts on urban

form are acknowledged as being difficult to evaluate yet critical outcomes of large scale transport

projects.

Whilst the long-term impacts of investments made in post-war infrastructure development on the

current urban form of cities is unavailable, several studies from international jurisdictions have

pointed to the significant wider economic impacts of investing in infrastructure for the metropolis

and locality under considerations.

SGS’s modelling also indicates that within Victoria, the opening of the Eastlink improved jobs

accessibility for residents along the corridor serviced by the project. A similar outcome is also

envisaged for Peninsula Link.

The available evidence has shown that improved transport and accessibility provides for better

connectivity between a firm and its suppliers and customers, which promotes healthy competition

between firms as reduced travel costs helps firms reduce prices and increase output. Such

investments also tend to benefit both Manufacturing and service oriented firms, and their workers

alike, as not only travel times are reduced promoting productivity enhancements and

agglomeration of firms in close proximity.

If the outcomes of the available evidence are any indication, the proposed rail extension to

Rowville will yield an array of benefits. The magnitude of the increases in population and

employment likely to be brought about due to the project is shown in section 8 below. In addition,

the wider economic benefits from investing in Rowville Rail project are measured in section 10.



7 CONSULTATION FINDINGS

Over and above consultations held by SKM, SGS held its own discussions with local businesses of

Knox to gauge their response on how the Project might reshape development patterns in the City

and the South East Region. Opinions were sought using both a face to face interactive forum as

well as via an online forum where the community/ businesses were asked to fill out a

questionnaire. Consultees views on short-term nuisance impacts created during the construction

phase of the Project were also gauged. This section summarises the findings from this consultation.

If the case studies are any indication, the proposed Rowville rail extension will have both positive

long term and adverse short-term impacts during the construction and operation phases of the

project. Maximising the benefits and mitigating the adverse impacts is greatly assisted by

community engagement and consultation.

The focus of community consultation held by SKM for its feasibility report was different and

perhaps more extensive compared to that held by SGS, and was to gauge among other things,

stakeholders’ opinion on best aligned rail routes, alternative transport options apart from rail and in

general, support for the project.

In contrast, the purpose of SGS’s consultation was to focus more on short-term impacts and to

gauge the extent of impacts on various industry sectors of the local/ wider region. The qualitative

analysis collected via this consultation process was used to cross-check results of the accessibility

modelling undertaken by SGS to forecast the direct impacts of the project.

Consultation with local businesses took two forms: Council and SGS held a consultation session

with businesses of Knox, and a survey was distributed to businesses within the municipality, with

responses collated by Council. In total, 117 responses were collated by Council with further

representation available at the consultation session.

The survey allowed for both qualitative and quantitative responses. Whilst the detailed survey

questionnaire is shown in Appendix B, suffice here to say that survey participants were asked to

provide details of their business, their transport requirements, and the impact the proposed rail

project may have on both their business and the broader regional economy.

7.1 Respondent Profile

Of businesses that responded to the survey, almost one third were in the Manufacturing industry.

Wholesale and Retail traders each accounted for 16% of respondents, followed by Professional and

Business Services firms, which made up 10% of respondents (refer Figure 25).

A majority of respondent businesses employed between 5 and 19 employees (43%) and have been

in continuous operation on their present site for between 10 and 20 years (33%).



Businesses surveyed drew customers from a wide and varied geographic area, with 34% primarily

drawing customers from the wider Melbourne area and 21% primarily drawing customers from

within Knox itself. Almost 10% of businesses sold primarily to overseas clients.

Suppliers were also generally sourced from across the wider metropolitan region, although a

substantial number of suppliers were primarily sourced from overseas (over 25%). This perhaps

reflects the higher order manufacturing nature of the businesses in the region requiring more

sophisticated inputs. Interestingly, the local businesses surveyed sourced employees from across

the metropolitan region, with almost an equal spread of employees being located within Knox, its

surrounding LGAs and the broader Melbourne region.

The dominance of the private vehicle as the mode of transport for customers, suppliers and

employees was quite evident. Suppliers were identified as being the most likely to travel by private

vehicle, followed by customers and finally employees. Public transport was most commonly used by

employees.

Given the high dependence on the private vehicle, not just in Knox, but more broadly in the outer

suburbs of Melbourne, road congestion and the subsequent travel delays were unsurprisingly

frequently raised as a problem in accessing businesses. However, few businesses (14%) identified

that they had considered changing locations or business operations as a result of such congestion

(please refer to the Figure 26 overleaf).



Figure 25 Survey Respondent Profile

Industry sector of survey

respondents

Employment size of survey

respondents

Mode of travel of customers,

suppliers and employees of

survey respondents

0%

10%

20%

30%

40%

Manufacturing Wholesale Trade

Other Services Retail Trade Professional & Business Services,

Construction Education and Training

Accomodation and Food Services

0%

15%

30%

45%

5-19 20-150 0-4 150+

0%

25%

50%

75%

100%

Customers Supplers Employees

Car/Private Transport Public Transport Bus/Train/Tram etc.) Walk



7.2 Consultation Outcomes

After developing a profile of local businesses and transport issues the survey turned to the impacts

of the proposed Rowville rail extension, both during the construction and operation.

Overall, local businesses were highly supportive of the proposed rail line, with 80% believing that

business and residential growth would strengthen with the construction of the rail line. That is,

respondents were of the view that population and employment growth within Knox will be over and

above business as usual trends with the construction of the project (refer Figure 26).

Over 80% of the businesses responded in the affirmative that the proposed rail line will help their

business operations. This high degree of affirmation was echoed by respondents from nearly all

industry sectors. The only two exceptions were Wholesale Trade and Education & Training with

nearly 35% of respondents from these industry sectors suggesting that the proposed train line will

not have any impact on their business.

The overwhelming view is that Manufacturing; Retail Trade; Professional, Scientific & Technical

Services; and Education and Training, will benefit most from the provision of the rail line.

Qualitatively a raft of long term benefits were identified associated with the construction and

operations of the proposed rail line. These included:

A greater ability for businesses to attract workers, drawing employees from further afield;

Improved reliability in employees getting to work, both due to transport alternatives being

available and through reduced congestion on the road network;

Improved transport efficiencies leading to reduced costs, particularly in the movement of

freight and supplies;

Increased customer base, through accessibility improvements and residential growth and

densification in the local area;

Enabling employment opportunities for people who do not own a car or cannot drive;

Improved accessibility of businesses to visiting interstate customers or employees; and

Intensification of land uses leading to a greater number of business support firms.

Additionally, some businesses saw the construction phase as a potential benefit, with construction

requiring goods and or labour that could be sourced locally. However, there were concerns

regarding the negative impacts that could be brought about during the construction phase. These

included:

Temporary delays and congestion on the road network, notably Wellington Road;

Reduced clientele due to access inconveniences; and

Reduced access to Stud Park shopping centre.



Interestingly though, while many businesses supported the construction of the rail line and

believed its provision would improve business prospects, few considered that these benefits would

persuade them to relocate closer to the proposed route to improve accessibility to either to

customers, suppliers or employees. Only between 25% and 50% of the respondents belonging to

Construction; Retail Trade; and Professional, Scientific & Business Services, suggested that they

might consider relocation. Incidentally, a large proportion of businesses within these particular

sectors had perceived the rail line to have a beneficial impact on their operations. It appears that

businesses which perceive the rail line to have any positive impacts are more likely to relocate

while others are likely to be sticky with their location choice.



Figure 26 Survey Outcome Summary

Would business & residential

growth strengthen with the

construction of the rail line

over and above business as

usual growth?

Would the proposed rail line

help your business?

0%

25%

50%

75%

100%

Wholesale Trade

Manufacturing Other Services Construction Retail Trade Education and Training


Professional & Business Services,

Total

Business & residential growth would strengthen

Business & residential growth would NOT strengthen

0%

25%

50%

75%

100%

Wholesale Trade




Total

Proposed rail line will help my business

Proposed rail line will NOT help my business



Which sectors are likely to

be benefit the most?

Are you likely to relocate

closer to a proposed rail

station to improve your

access to suppliers/

customers/ employees?

0%

25%

50%

75%

100%

Retail Trade Education and Training

Manufacturing Professional and Business

Service

Wholesale Trade

Transport and Logistics

Construciton Other Firms Utilities

0%

25%

50%

75%

100%

Wholesale Trade




Total

Likely to relocate closer to a proposed station

UNLIKELY to relocate closer to a proposed station



7.3 Section Summary

There is widespread belief that the provision of the rail line will help businesses and boost the

employment and residential growth of the corridor over and above forecast business as usual

trends.

Businesses from currently established industry sectors within the City of Knox, namely,

Manufacturing and Professional & Business sectors were highly optimistic of the positive spinoffs of

the project. On the other hand, a relatively high proportion of firms belonging to Wholesale Trade &

Education & Training (~35%) suggested that the rail line will not help improve their business

operations.

Nonetheless, it was widely perceived by respondents that businesses within the Education &

Training sector, amongst firms in other sectors, including Manufacturing; Professional & Business;

and Retail Trade firms will benefit the most from the provision of the rail line.

Whilst the belief of beneficial impacts of the project was widespread, relatively few businesses are

likely to relocate closer to a proposed rail station to improve accessibility to customers, suppliers,

employees.

There are some concerns about nuisance impacts during the construction phase but their extent or

scope could not be gauged from the responses received.

In sum, respondent views from consultation suggest positive spinoffs for the local community and

economy of Knox.



8 ACCESSIBILITY CHANGE ANALYSIS

This section presents the results of SGS’ in house accessibility modelling to quantify the impact of

the Project on the additional population and employment that the City of Knox and the South East

region might host because of the Project over and above that which would become manifest in a

business as usual/ Base Case scenario.

In effect, the outcome of this section is the estimate of the ‘direct’ impacts of the Project on the

economy of Knox during the operations phase of the project.

8.1 Land Use and Accessibility

Well established economic theory indicates that over time firms will tend to locate closer to areas

that improve their land use efficiency. Locating in areas with superior accessibility reduces

transaction costs through ease of contact with suppliers and customers. Crucially, this also

increases access to a skilled labour force.

The theoretical underpinnings of the relationship between land price and accessibility can be seen

graphically in Figure 27.

Figure 27 Demand for Different Land Use as a Function of Accessibility



Whilst all firms prefer locations of high levels of accessibility their ability to pay for locational

advantages will differ, as do their aggregate land use demands. Land use demands between

industries and the collar of the workers (Blue and White) generally differ based on the functioning

of their industries.

Land use demands by service industries are small relative to other industries, such as

Manufacturing and Wholesale Trade. Those industries require large amounts of land, relative to

service firms. Furthermore, land rents can be shared across multiple firms as office towers use the

relatively cheaper option of expanding vertically, rather than having large parcels of land. This

contributes to the ability of service firms to locate within the confines of a heavily dense area of

employment and population such as the CBD, whereas Manufacturing and Wholesale Trade tend to

locate further away from highly dense areas for their larger land requirements.

Ways to improve accessibility of firms differ across industries based on their customer and supplier

base. Generally, Manufacturing requires quality road infrastructure and as a consequence tend to

locate closer to areas where they have access to major road networks. Both their suppliers and

customers also tend to have a similar accessibility requirement and therefore efficiencies can be

gained for those industries when they locate closer to points of road infrastructure. This is not

necessarily the case for service industries.

Whilst all firms require timely access to their suppliers, employees and customers their strategic

ability to access those people differ based on the function of their business models.

Opportunities for access to employment apply in a similar way to households as they do for

industries. People, over time, will adjust their residential location due to a wide range of factors,

some of which are access to employment, education, essential services and recreation. Literature

indicates that these choices tend to be constrained due to factors such as family and historical ties

to a region or corridor.

For these reasons many people and families may tend to locate ‘within corridors’, rather than

moving ‘across town’. However, when moves are made within this context the relative accessibility

of the two areas is a key consideration.

These theoretical predications are amply borne out by recent experience in Australian cities,

particularly with respect to the Western Ring Road, CityLink, EastLink and Westlink (M7) in Sydney.



8.2 Accessibility Shifts Model

To estimate the induced change in households and employment for the new rail corridor resulting

from the introduction of the Rowville rail line a detailed analysis of changes in accessibility was

undertaken by SGS. With the introduction of the new rail line into the region two key components

related to an areas access to jobs and services will be impacted and subsequently estimated.

These are:

Changes in travel times; and/or

Changes in mode share uses (Public Transport versus Private Vehicles)

The biggest impact will be focused around the new rail corridor, which will likely receive improved

travel times to many locations right across metropolitan Melbourne. However, there will be varying

impacts right across the metropolitan landscape as the relative “rankings” of different locations

accessibility changes.

In order to quantify the level of change resulting from the Rowville rail line two key pieces of

analysis related to accessibility have been undertaken:

A historical analysis looking at changes in accessibility caused by recent major infrastructure

projects, such as Citylink, and the subsequent actual changes in the spatial distribution of

population and firms; and

Quantifying the likely changes in accessibility as a result of the Rowville rail line and applying

the historical analysis results to provide a robust evidence based estimate of the induced land

uses changes.

It should be noted that for this analysis the change in the distribution of people and firms is purely

based on the induced effects of improved accessibility. While not captured in this analysis, in

addition to these impacts, there can often be one or more targeted intervention developments near

key locations (i.e. new stations) that may further capitalise on the improved accessibility of the

corridor.

The historical analysis and subsequent application to the Rowville rail corridor is outlined in greater

detail in the following sections.



8.2.1 Historical Relationship Analysis

Over the past 15 years there have been several major road infrastructure projects that have had a

very significant impact on the accessibility of certain locations across metropolitan Melbourne, such

as:

1996 to 2001 – Western Ring Road

2001 to 2006 – Citylink

2006 to 2011 – Eastlink and major improvements to the Monash Freeway and the West Gate

Bridge

Furthermore, over this period detailed information regarding the distribution and accessibility

changes of Melbourne has been made available from the ABS Census and the DoT (Melbourne

Integrated Transport Model). This data has all been aggregated to the Statistical Local Area level

for four time periods; 1996, 2001, 2006 and 2011 for this analysis.

While all these projects are road based projects they help to provide an evidence base for

understanding the degree to which firms and households change their locational preferences as a

result of shifts in the metropolitan accessibility contours.

A detailed statistical regression analysis was undertaken using this historical data to test and

quantify this conceptual theory. This statistical analysis tests the following theory:

All else being equal, a SLAs share of metropolitan Melbourne’s total employment by industry sector

is based on two broad factors:

Share of population/dwellings8; and

Accessibility9

In addition, if all else was equal, a SLAs share of metropolitan Melbourne’s total housing stock is

based on two key factors:

the urban land supply10; and

Accessibility.

This concept suggests that the strong growth within a growth area is a result of increased land

supply, while the recent strong growth within the inner city is a result of increased accessibility.

Furthermore, if a location’s accessibility is improved it will increase the level of demand and in turn

8 The share of population/housing has been included to capture the distributional changes of underlying population serving employment.

9 Where, accessibility is defined in its broadest sense. That is, a locations access to activity centres, skilled workers, employment opportunities, services, education, transport infrastructure, restaurants, etc.

10 The amount of urban land has been included to capture the varying geographic sizes of SLAs. That is, if SLA X is 10 times larger than SLA Y, all else being equal, it should have 10 times the amount of dwellings.



growth rate, similar to releasing land within a growth area. The strength of this relationship varies

from industry to industry depending on its requirements and willingness/capacity to pay for more

accessible locations.

For this analysis these two factors have been identified as the key considerations in the distribution

of households and employment. However, it is noted that in reality the exact spatial distribution of

households and employment is far more complex, particularly at a very local level, where in fact

there is a wide range of other localised issues, historical factors and development trends that also

determine the locational choices of firms and people.

We have assumed that all these other factors bearing out of localised issues are indeed

inherent and therefore captured within the base case projections. Consequently, any

changes observed under the Project Case scenario are over and above these other

contextual issues.

In order to undertake this statistical analysis three key data variables were synthesised by SGS for

each SLA across metropolitan Melbourne from 1996 to 2011;

SLAs share of total employment by industry sector and households;

SLAs share of total urban land; and

SLAs relative accessibility.

Population and Employment

Historical employment and housing data for 1996, 2001, 2006 and 2011 by SLA has been collated

from a range of ABS data sets including the past three Censuses (1996, 2001 and 2006), the ABS

labour force survey data11 and ABS estimated resident population12. See Appendix A for a full

methodological overview of how historical household and employment estimates were developed.

Total employment by industry sector and households for each SLA is converted to a share of

overall metropolitan Melbourne levels. The share of metropolitan Melbourne was used as it was

assumed that there is a wide range of other external factors such as international and interstate

migration, economic trends etc that influence the total amount of population and employment in a

city as a whole. Furthermore, it is assumed that a project such as this will only have an influence in

the reorganisation (or locational decisions) of people/firms within Melbourne and will not be able to

influence a person from Sydney, for example. In other words, it was assumed that a project of this

scale is unlikely to affect the overall employment outcomes for the MSD with only reorganisation of

employment within the metropolitan economy envisaged.

Urban Land

There is a range of data sources that could be utilised (such as the Victorian Planning Provisions) to

calculate the amount of total urban land that an SLA has as a proportion of metropolitan

11 ABS Cat. No. 6202.0

12 ABS Cat. No 3201.0



Melbourne. However, many of these datasets are difficult to get consistently across a 15 year

historical time period (1996-2011). Therefore, a geographic unit referred to as Urban Centre

Locality (UCL) was used as a broad estimate of the amount of urban land. A UCL measures the

broad extent of urbanisation for a city/town and is published by the ABS for the 1996, 2001 and

2006 Censuses. An estimate of the Melbourne UCL for 2011 was made by adding in all future 2010

Urban Development Program broad hectare sites to the 2006 UCL. This provided a consistent

measure across all time periods.

As the UCL grows for an area (i.e. due to green-field land being released) the proportion of total

Melbourne UCL for other established locations was found to decrease. This statistic isolates dwelling

growth related to changes in accessibility from dwelling growth related to increased development

opportunities.

Accessibility

SGS has developed a measure of accessibility within a specified geographical region and the ability

to access overall economic activity across the wider MSD, known as Effective Job Density (EJD).

EJD measures the total number of jobs across the MSD and weights them according to the time

taken to access those jobs by different modes of transport, i.e. public transport and private

vehicles.

This analysis relies on travel time matrices that estimate the length of time taken to travel from

one SLA to another SLA within the MSD, both by private car and public transport.

EJD enables a more ‘real life’ representation of the proximity (in terms of travel time) of

employment and services. EJD has been estimated as follows:

Where:

= Effective Job Density for SLA i

= per cent of work trips which involve public transport for zone j

= number of jobs/employment within zone j

= time it takes to travel on public transport from zone i to zone j

= time it takes to travel by private vehicle from zone i to zone j

The equation constructs an EJD for each Statistical Local Area (SLA) across the MSD. A graphic

representation of the estimated EJD across the MSD at 2006 is shown in Figure 28.



Figure 28 Effective Job Density MSD - 2006


The EJD of an SLA reveals at least two things.

Firstly, it reveals how interconnected an area is to all the jobs in Melbourne. The smaller the

time to access other areas of the MSD the lower the level of discounting those jobs receive,

thus increasing the overall EJD score. Due to this reason the Melbourne (C) – Inner SLA

consistently receives the highest EJD score across all years analysed. This is due to a high

concentration of employment in the areas surrounding Melbourne (C) – Inner and the

significant transport infrastructure that allows speedy transport between the inner areas.

Secondly, the EJD score is also a proxy for the access to other businesses, which may

represent customers, competitors, suppliers and access to services. The tendency of businesses

to locate in areas with high EJD reveals a preference for certain characteristics of an area and

also reveals those industries that have a negative relationship with EJD.



The choice to locate in different areas of the city is driven by many factors, including accessibility.

Furthermore, absolute accessibility is not necessarily the most crucial factor in locational choice. If

this were the case there would be a relatively higher demand for residential dwellings within the

CBD. A more logical measure is relative accessibility across different areas. This allows the

comparison of areas that have similar characteristics to be compared to each other more

objectively. For example, if two growth corridors had the same distribution of services, housing etc.

and only differed in terms of accessibility then the growth corridor with relatively higher

accessibility would be expected to have stronger population and employment growth.

Quantifying changes to relative accessibility was performed through translating the absolute EJD’s

across the four years of analysis into a 0 to 1 index, i.e. a relative EJD index. The index is created

using the SLA that has the highest EJD. This was found to be Melbourne (C) – Inner consistently

across all years of analysis), which provides a ceiling for the index (a score of 1). To provide a floor

to the index the converse is performed. Consistently Yarra Ranges (C) – Central was found to have

the lowest EJD and is thus given a score of 0.

The equation used to calculate the relative EJD ranking is shown below and a ranking of selected

SLAs is shown in Table 10.

Where:

= Effective Job Density for SLA i

= the SLA found to have the lowest EJD amongst all MSD SLAs

= the SLA found to have the highest EJD amongst all MSD SLAs



Table 10 Relative EJD Index, Selected SLAs, 1996-2011

SLA 1996 2001 2006 2011

Melbourne (C) - Inner 1.00 1.00 1.00 1.00

Melbourne (C) - S'bank-D'lands 0.77 0.82 0.82 0.77

Melbourne (C) - Remainder 0.63 0.64 0.65 0.61

Knox (C) - North-East 0.23 0.23 0.24 0.22

Knox (C) - North-West 0.28 0.28 0.30 0.27

Knox (C) - South 0.28 0.27 0.29 0.27

Monash (C) - South-West 0.44 0.44 0.45 0.42

Monash (C) - Waverley East 0.36 0.35 0.37 0.34

Monash (C) - Waverley West 0.40 0.40 0.42 0.39

Gr. Dandenong (C) - Dandenong 0.33 0.33 0.35 0.32

Gr. Dandenong (C) Bal 0.32 0.31 0.32 0.30

Casey (C) - Berwick 0.17 0.17 0.22 0.18

Casey (C) - Cranbourne 0.19 0.19 0.21 0.20

Casey (C) - Hallam 0.26 0.26 0.28 0.26

Casey (C) - South 0.09 0.09 0.10 0.09


A spatial example of Relative EJD is shown in Figure 29. The inner areas of Melbourne clearly have

a significantly higher ranking on the relative EJD scale. This reduces as the distance from the CBD

increases. Melbourne south-east region with its employment concentrations, substantial road and

rail infrastructure tends to maintain a higher EJD score relative to the other areas of the MSD as

distance from the CBD increases.



Figure 29 Relative EJD 2011




Regression Analysis

The regression equation used to estimate the relationship between industry movements,

accessibility and households is shown below.

Where:

= Relative Effective Job Density for SLA i

= The share of an industry’s metropolitan employment within SLA i

= The share of metropolitan Households within SLA i

= Constant term

= Error term

Accessibility also induces locational change for households, given they can access a greater amount

of services and employment opportunities.

The regression equation that estimates accessibility induced household changes is shown below.

Where:

= Relative Effective Job Density for SLA i

= The share of Households within SLA i

= The share of overall urbanised areas within SLA i

= Constant term

= Error term

When applying this statistical relationship to the Rowville Rail project the key coefficient relates to

Relative EJD. This coefficient represents the increase in a location’s share of metropolitan

Melbourne’s total households or employment if a location was to shift from being the least

accessible location to being the most accessible location.

Figure 30 presents the relative EJD coefficient for each employment industry sector and

households. This can be interpreted as follows: If the accessibility of a location increased from 0 to

1 it would increase its share of metropolitan Melbourne’s total housing stock by 0.02 (or 2%), for

example.

Given much of the total housing and employment stock is already established, we see that the

coefficient is relatively small (i.e. 2%). However, as a share of growth within a defined period this

could potentially represent a substantial proportion of total growth.

Over the past 15 years much of Melbourne’s housing growth has been focused on two broad

regions, fringe growth areas (about half) or inner city Melbourne (about one fifth). Greenfield

growth is predominately a result of large amounts of land supply from significant areas of land

being rezoned as urban residential. While strong growth within the inner city can be predominately

attributed to a preference to be close to jobs, services and the city’s core (i.e. accessibility). As a

result Households have been estimated to have a strong propensity to relocate due to changes in

accessibility.



In relation to a firm’s locational preference, it can be seen that as previously suggested the service

sector and higher value added industries exhibit a higher preference for more accessible locations.

Information Media & Telecommunications, Professional, Scientific & Technical Services and Finance

& Insurance are some of the most susceptible industries to changes in accessibility.

Agriculture experiences the only negative coefficient with relative EJD. Agriculture uses typically

require large and relatively cheap land parcels away from major centres. Furthermore, particularly

within the fringe growth areas, farm land within the MSD has been rezoned and changed uses to

housing or other employment.

Figure 30 Relative EJD Regression Coefficients


For employment there is a secondary effect resulting from the estimated shifts in households. That

is, many industries depend on a local population as either customers or skilled workers. Therefore,

an increase in population in an area often results in a further increase in population servicing

employment. Figure 31 displays the coefficients related to a change in household share for each of

the employment industry sectors.

As expected industries that thrive on proximity to population, such as Retail Services, Health Care,

Education and Training and Accommodation and Food Services all have a strong positive

relationship with an induced change in households.

-0.01 -0.005 0 0.005 0.01 0.015 0.02 0.025

Households

Information Media and Telecommunications

Professional, Scientific and Technical Services

Public Administration and Safety

Financial and Insurance Services

Arts and Recreation Services

Administrative and Support Services

Accommodation and Food Services

Electricity, Gas, Water and Waste Services

Rental, Hiring and Real Estate Services

Other Services

Transport Postal and Warehousing

Mining

Health Care and Social Assistance

Education and Training

Wholesale Trade

Retail Trade

Construction

Manufacturing

Agriculture, Foresty and Fishing

Relative EJD Regression Coefficient



Figure 31 Household Share Regression Coefficients


-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3

Manufacturing

Wholesale Trade

Retail Trade

Health Care and Social Assistance

Other Services

Construction


Rental, Hiring and Real Estate Services

Transport Postal and Warehousing


Administrative and Support Services

Electricity, Gas, Water and Waste Services

Public Administration and Safety

Arts and Recreation Services

Professional, Scientific and Technical Services

Financial and Insurance Services

Mining

Agriculture, Foresty and Fishing

Information Media and Telecommunications

Household Share Coefficient



8.2.2 Accessibility Induced Land Use Shifts

The statistical relationships derived from the previous section (the historical analysis) have been

applied to estimated changes in accessibility (EJD) resulting from the introduction of the Rowville

rail line at 2021 and 2046. The choice of these two years was dictated by the availability of travel

time matrices provided by SKM. Consequently, any adverse impacts during the operations phase of

the project, whilst being acknowledged, were unable to be quantified.

Forecasting land use scenarios due to proposed infrastructure projects depends largely on the

accessibility changes that the proposed project will deliver. SKM have provided projected travel

times and trip shares (for car and public transport) for the Base and Project case at 2021 and

2046.

Using the provided travel time matrices and SGS base case forecasts (see Appendix C for

methodology) future EJD calculations were produced for 2021 and 2046 for both the Base and

Project cases.

Figure 32 and Figure 33 display the relative EJD changes that have been estimated.



Figure 32 Change in Relative EJD 2021




Figure 33 Change in Relative EJD 2046


EJD calculations in 2021 and 2046 not only reflect travel time savings, but also include changes to

the public transport mode shares. As the rail corridor is completed and people begin to use the

public transport network to access employment and services it adjusts the share of public transport

use. SKM provided projected trips by mode that indicated the base and project uses of the public

transport and the subsequent changes to mode share as shown below in Figure 34.

The inclusion of the rail corridor reduces the reliance of the area on car transport. Knox – South

gains the largest mode share change within the City of Knox with a reduction in car usage of 1% in

2021 and 1.4% in 2046. Monash experiences the largest reduction in car usage with the LGA as a

whole reducing car usage by 1% and 1.5% in 2021 and 2046, respectively.



Figure 34 Induced Car Mode Share Changes


The difference in the relative EJD index for each SLA between the project and base cases was

applied to the results from the historical regression analysis to estimate the induced changes to the

spatial distribution of households and employment by industry. Constraints were applied to ensure

that the total employment and population level of metropolitan Melbourne was unchanged and that

no single location saw an unrealistic reduction in employment or households relative to current

levels and base case trends. That is, the analysis removed any outliers from the analysis.

Population estimates were finally developed based on households size estimates from the base

case projections.

-1.0%

-0.3%-0.2%

-1.3% -1.3%

-0.7%

0.03%

-0.01% -0.03% -0.02%

0.17%

0.05%

-0.5%

-1.4%

-0.5%

-0.2%

-2.1%-2.0%

-0.8%

0.07%0.03% 0.07% 0.05% 0.08% 0.05%

-0.8%

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

South North-West

Knox (C) -North-East

Monash (C) - South-

West

Monash (C) - Waverley

East

Monash (C) - Waverley

West

Gr. Dandenong

(C) -

Dandenong

Gr. Dandenong

(C) BalCasey (C) - Hallam

Casey (C) - Berwick

Casey (C) - Cranbourn

e

Casey (C) - South

Knox (C) Monash (C) Greater Dandenong (C) Casey (C)

Eastern Region

Pro

ject

Ind

uce

d C

ar S

har

e C

han

ge

2021 Car Share Reduction 2046 Car Share Reduction



8.3 Anticipated Induced Impacts

8.3.1 Households

The greatest beneficiary of the project is expected to be Knox with an anticipated increase of 410

and 1,030 households respectively in 2021 and 2046 over and above Base case projections.

Overall the South East Region experiences a household increase of 450 in 2021 and 1,230 in 2046

over and above the Base Case.

Household growth rates between 2011 and 2021 are significantly smaller in absolute number and

growth rate when compared with 2021 and 2046 period. This is because households and indeed

firms are likely to respond with a lag in delivery of superior transport infrastructure. Indeed, results

from the consultation exercise held by SGS suggested that businesses were reluctant to change

location due to the provision of rail infrastructure. Consequently, induced household and

employment changes as estimated by SGS are small. The results of the consultation exercise

therefore seem to conform with estimated changes by SGS using the accessibility modelling.



Table 11 Induced Household Changes

Number of Households (#) Change in Households (#)

Average Annual Growth Rate (%)

LGA Scenario 2011 2021 2046 2011-21 2021-46 2011-21 2021-46

Knox (C)

Base Case 55,250 62,200 71,790 6,940 9,590 1.19% 0.58%

Project Case 55,250 62,610 72,820 7,360 10,210 1.26% 0.61%

Change from

Base Case

410 1,030 410 620 0.07% 0.03%

% Change

from Base

Case

0.67% 1.44% 5.98% 6.44% 5.58% 5.37%

Monash

(C)

Base Case 64,690 75,700 91,650 11,010 15,950 1.58% 0.77%

Project Case 64,690 75,740 91,960 11,050 16,220 1.59% 0.78%

Change from

Base Case

40 310 40 270 0.01% 0.01%

% Change

from Base

Case

0.05% 0.34% 0.36% 1.72% 0.34% 1.50%

Gr.

Dandenong (C)

Base Case 48,990 57,310 80,660 8,330 23,340 1.58% 1.38%

Project Case 48,990 57,300 80,660 8,310 23,360 1.58% 1.38%

Change from

Base Case

-20 0 -20 20 0.00% 0.00%

% Change

from Base

Case

-0.03% 0.00% -0.22% 0.09% -0.11% 0.05%

Casey (C)

Base Case 86,650 112,340 140,730 25,690 28,390 2.63% 0.91%

Project Case 86,650 112,330 140,740 25,680 28,410 2.63% 0.91%

Change from

Base Case

-10 10 -10 20 0.00% 0.00%

% Change

from Base

Case

-0.01% 0.01% -0.03% 0.07% -0.03% 0.07%

South East

Region

Base Case 255,580 307,550 384,820 51,970 77,280 1.87% 0.90%

Project Case 255,580 307,980 386,180 52,400 78,210 1.88% 0.91%

Change from

Base Case

430 1,360 430 930 0.01% 0.01%

% Change

from Base

Case

0.14% 0.35% 0.83% 1.20% 0.76% 0.95%


Overall MSD changes for households are displayed in Figure 35 and Figure 36 for the two examined

time periods. Households that relocate into the South East Region are centred around Knox in both

the 2021 and 2046 intervals. Initially, these households are drawn from the inner areas, and in

particular, the south eastern corridor. By 2046, established households from Melbourne’s north

east are also anticipated to move towards Knox due to better transport connections that the area is

likely to become host to.



Figure 35 Household Induced Changes 2021




Figure 36 Household Induced Changes 2046


Household and population movements are small relative to other infrastructure projects undertaken

in the recent past (Westlink, Eastlink, the Western Ring Road etc.). However, this is merely a

reflection of the relative size of the project and the use of public transport across the MSD.



8.3.2 Employment

Changes in employment are similar to the estimated induced changes in households, i.e., the

magnitude of employment changes is observed to be smaller up to 2021, but is seen to grow by

2046.

Overall, the employment impact to the South East Region raises employment levels by 280 in 2021

and by 820 in 2046.

The City of Knox experiences the largest increase to employment over and above the Base Case.

Employment increases by 260 and 420 across the two periods of analysis over and above the Base

Case, in the two examined periods of 2021 and 2046.



Table 12 Induced Employment Changes

Employment Number (#) Employment Change (#)

Average Annual Growth Rate (%)

LGA Scenario 2011 2021 2046 2011-21 2021-46 2011-21 2021-46

Knox (C)

Base Case 74,650 83,640 99,390 8,990 15,750 1.14% 0.69%

Project Case

74,650 83,900 100,060 9,250 16,170 1.18% 0.71%

Change

from Base Case

260 680 260 420 0.03% 0.01%

% Change

from Base

Case

0.31% 0.68% 2.91% 2.64% 2.75% 2.10%

Monash (C)

Base Case 117,720 137,980 173,870 20,270 35,890 1.60% 0.93%

Project

Case

117,720 138,010 174,080 20,290 36,070 1.60% 0.93%

Change

from Base

Case

20 210 20 180 0.00% 0.00%

% Change

from Base

Case

0.02% 0.12% 0.12% 0.50% 0.14% 0.43%

Gr.

Dandenong (C)

Base Case 95,430 115,050 145,310 19,620 30,260 1.89% 0.94%

Project

Case

95,430 115,040 145,310 19,610 30,270 1.89% 0.94%

Change

from Base

Case

-10 0 -10 10 0.00% 0.00%

% Change

from Base

Case

-0.01% 0.00% -0.06% 0.04% -0.05% 0.04%

Casey (C)

Base Case 61,290 71,150 113,720 9,860 42,570 1.50% 1.89%

Project

Case

61,290 71,150 113,730 9,860 42,580 1.50% 1.89%

Change

from Base

Case

0 10 0 10 0.00% 0.00%

% Change

from Base

Case

-0.01% 0.01% -0.04% 0.03% 0.00% 0.02%

South East

Region

Base Case 349,090 407,820 532,290 58,740 124,470 1.57% 1.07%

Project

Case

349,090 408,100 533,180 59,010 125,090 1.57% 1.08%

Change

from Base

Case

270 890 270 620 0.01% 0.00%

% Change

from Base

Case

0.07% 0.17% 0.46% 0.50% 0.44% 0.37%




Induced employment growth resulted in Knox contributed the majority of the employment

increases during both periods examined and comprised 92% of the overall South East Region

employment increase in the period to 2021 and then 51% in the period 2021 – 2046.

Figure 37 displays the employment changes across the MSD. The inner areas, such as Melbourne

(C), Yarra (C) and Port Phillip (C) incur the largest employment losses as these areas can be

considered within the sphere of influence of the suggested project, with the economic literature

suggesting that accessibility induced locational changes are generally fall within the corridor, rather

than across town. Employment increases are clustered within the Knox LGA and even more within

Knox - South.

Figure 37 Induced Employment Change 2021


The rate of employment change increases dramatically in the second period of analysis and

therefore further increases the employment increase for the City of Knox and the South East

Region as a whole (see Figure 38).



Figure 38 Induced Employment Change 2046


Whilst employment change in the region resulting from the project is not trajectory shifting, it is

important to note the changes across broad industrial categories. At 2021 Knox is estimated to

have an additional 280 jobs over and above the Base Case. Of these jobs 71.7% are commercial,

knowledge intensive jobs (i.e. Professional and Scientific Services, Education, Financial and

Insurance Services etc.). Growth of industry based jobs, (i.e. Manufacturing, Construction,

Wholesale Trade etc.) represents the remaining 19.9% of overall growth.

Table 13 displays the projected changes to the broad industry categories at 2021 and their

respective contribution to overall growth.



Table 13 Induced Broad Industry Group Changes 2021


Knox (C) -1 54 24 194 270

Monash (C) 0 5 2 18 26

Gr. Dandenong (C) 0 -2 -1 -8 -12

Casey (C) 0 -1 0 -3 -4

South East Region -1 56 25 201 280

Contribution to Growth % -0.5% 19.9% 8.9% 71.7% 100.0%


Between the 2021-46 period, the contribution of commercial services employment rises to 76.8%

in 2046 towards overall growth, as shown in Table 14. On the other hand, absolute industrial and

retail employment will also rise but the relative contribution of industry is expected to fall in total

employment growth to 15%.

Table 14 Induced Broad Industry Group Changes 2046


Knox (C) -2 95 52 478 622

Monash (C) -1 29 16 145 189

Gr. Dandenong (C) 0 0 0 1 2

Casey (C) 0 1 1 6 7

South East Region -3 125 68 630 820

Contribution to Growth % -0.4% 15.2% 8.3% 76.8% 100.0%




8.4 Section Summary

SGS used past observed relationships between changes in accessibility and the induced land use

changes to quantitatively model the induced land use changes, i.e. changes in the locations of

households and employment across metropolitan Melbourne following the delivery of the proposed

Rowville rail line.

This analysis suggested that whilst the Project will not alter the metropolitan scale of households

and employment, more households and employment will move into the examined South East

Region of this study, and in particular, the City of Knox.

The analysis suggested that by 2021:

An additional ~410 households will reside in Knox over and above the estimated Base Case of

62,200.

Knox will be the largest beneficiary due to the Project with total households in the South East

Region rising by ~430 by this timeframe due to the Project over and above the projected Base

Case.

Employment in Knox will be ~260 higher compared to the projected Base Case of ~84,000.

Most change will be in Commercial Services employment followed by Industry based

employment, i.e. the Project is likely to induce more industrial and commercial based

businesses in the local economy.

Employment in the South East Region would be ~280 over and above the projected Base Case.

In the long run however, by 2046, when the effects of the Project would have become fully

manifest:

Knox is likely to host an additional ~1,000 households and ~700 additional employees over

and above its projected Base Case of ~72,000 households and ~99,000 employees

respectively.

The South East Region in turn is likely to host an additional ~1,350 households and ~1,100

additional employees over and above its projected Base Case of ~385,000 households and

~533,000 employees respectively.



9 ESTIMATED IMPACTS OF PROJECT

This section presents the estimated economic impacts of the Project on the local municipality, the

South East Region and the wider metropolitan and state-wide economies.

The estimated impacts include the direct and indirect impacts on income, value-added and

employment, both during the construction and operation phases of the Project.

9.1 Estimation Methodology

SGS has used the REMPLAN model13 to estimate the indirect impacts of the initial induced changes/

stimuli. REMPLAN is a user friendly regional economic analysis package which provides detailed

region specific data for up to 111 different industry sectors. Economic data is sourced from the

ABS. The indirect impacts of the initial stimuli are estimated using input output tables and

multipliers.

In essence, the REMPLAN model takes the inter-industry relationships (buyer–supplier

transactions) that are measured by the ABS in the National Accounts,14 and scales these

relationships down to a state level initially and then, subsequently, to a regional level, i.e. using

available datasets and accepted mathematical techniques.

A regional input-output model provides several advantages. The first of these is to offer a better

understanding of the relative performance of economic sectors in the regional economy.

Information gained from the model includes estimates of regional output, regional exports, regional

imports, wages paid by each of the sectors and the value of their local interactions, which can be

viewed alongside sectoral employment figures. It is possible to examine the types of inputs used

by each sector and determine the source of supply of these inputs, be it local or from outside the

defined region of interest. The destination of outputs and whether this is to be used as an input for

other sectors in the region, for final consumption by local consumers, or whether these outputs will

go beyond the region in the form of intermediate or final goods and services for exports can also

be determined. The most important outcome of all this is to provide a picture of the interdependent

nature of the regional economy and the way all these individual pieces of data fit together. Actions

by one sector will have ramifications on many of the other sectors operating in the region.

The most useful results of the input-output model are a set of regional industry specific multipliers

which estimate how spending in a specific regional industry, via the assessed direct impacts

(stimuli), flows through to total regional:

Output (or income);

13 February 2012 release provided to SGS by Compelling Economics, the producers of REMPLAN.

14 Particularly the Australian Input-Output Tables (ABS Cat. No. 5209.0).



Value added; and

Full time equivalent employment levels.

The direct/ flow-on impacts for the region’s economy comprise industrial effects (the increased

output generated by servicing industry sectors in response to the direct change in output and

demand) and consumption effects (As output increases, so too does employment and wages and

salaries paid to local employees. Part of this additional income to households is used for

consumption in the local economy which leads to further increases in demand and output). Taken

together, the direct and indirect impacts are aggregated to estimate the overall economic impact of

the project during its construction and operational phases.

Quantification of impacts can alert regional planners to the need for additional work skills,

retraining, employment programs, infrastructure changes and further opportunities for industrial

support in the region, or indeed, more advocacy efforts. It can also help planners prioritise in terms

of competing strategies and help determine which strategy best matches the particular need of the

region in terms of output, value added and employment.

9.1.1 Assumptions and Limitations

Input output modelling has some limitations, as follows, but it is a cost effective technique,

recognising that the only feasible alternative is utilising partial or general equilibrium econometric

models, which are expensive and which are calibrated for estimation using geographic regions

which do not align well with municipal boundaries.

The input output (econometric) model assumes relationships between industries are static over

the forecast period. That is, productivity improvements are not factored in and historic

relationships are assumed to hold.

The input output (econometric) model derives relationships between industries using total

production estimates. Consequently, the relationships are ‘average’, whereas the stimulus used

as an input is ‘marginal’. Such an approach does not account for any ‘underutilised capacity’ at

the industry level or additional economies of scale that might ensue, as production expands

from its existing base. In other words, a $10 direct impact in industry X will have the same

multipliers associated with the impact as would a $10 million impact.15

As already mentioned, all of the stimuli are assumed to be ‘new’ economic activities for each

regional economy. That is, crowding out or industry substitution effects are assumed to be

negligible, meaning that key economic inputs such as labour and capital are assumed to be

unconstrained, i.e. there is sufficient slack in the economy to service these stimuli without

transferring significant resources from other productive uses. It also means that the activities

that are promoted by the subject project do not adversely affect operations elsewhere.

15 SGS understands that the REMPLAN model takes the capacity of a region to service the initial stimulus into consideration.



9.2 Estimated Impacts of Construction Phase

The estimated cumulative costs (over the 4 years construction period) and derived annual

construction costs are shown below. It should be noted that these costs do not include the costs of

procuring trains, as these carriages/ trains might be imported.

Table 15 Estimated Direct Construction Costs of Proposed Rowville Rail

Cumulative (4 year construction period)

Rail Construction Stations Total

Monash (C) $1,540 mn $600 mn $2,140 mn

Knox (C) $550 mn $200 mn $750 mn

Annual

Rail Construction Stations Total

Monash (C) $385 mn $150 mn $535 mn

Knox (C) $138 mn $50 mn $188 mn


Over the construction phase spanning from 2016 onto 2020, $188 million is likely to be spent

annually on construction in the City of Knox, which is taken to be the estimated annual direct

stimulus for the local economy. In total, $750 million is likely to be spent in Knox over the 4 year

construction period. At this stage, SGS does not have any advice whether any of the construction

will be outsourced or not. For the purposes of this assessment, we have assumed that all

construction and associated services will be sourced from within the municipality. Consequently,

the estimated indirect impacts for the Knox economy could be overstated; nonetheless, the

estimated indirect impacts for the wider MSD and Victorian economies would perhaps be more

robust.

For the purposes of this assessment, the relevant multipliers used pertain to the industry of ‘Heavy

& Civil Engineering Construction’.

The results indicate that the total construction phase of the project (over the 4 year period) will:

Generate approximately 650 jobs and $173 million in value added in Knox.

Generate a total of $1.6 billion in total output and $550 million in total value added for the

Knox economy, and generate approximately 3,600 jobs due to the flow on effects on other

industries and worker spending.

For Victoria, the total induced impacts are estimated at:

o $2.3 billion in output;

o $865 million in value added; and



o Create approximately 5,600 jobs.

Table 16 Estimated Total Impacts (Direct + Indirect) of Construction Phase

of the Proposed Rowville Line

Knox (C)

Direct Impacts Total Impacts

Output ($ Million) $750 mn $1,665 mn

Employment (Jobs) 645 3,599

Value-Added ($ Million) $173 mn $553 mn

South East Region





MSD





Victoria






The industry-wide breakdown of direct and total impacts on the value added of City of Knox is

presented in Figure 39 overleaf. The analysis shows that apart from the construction sector, the

proposal rail construction will benefit Manufacturing; Rental, Hiring & Real Estate Services;

Financial & Insurance Services; Professional, Scientific & Technical Services; Wholesale & Retail

Trade; and other industries.



Figure 39 Industry-Wide Impacts of Project Construction on Knox (C) Value

Added ($ Million)


9.3 Estimated Impacts During Operations Phase

As pointed out in the aforementioned section, construction of the Project will induce more residents

and employment within Knox. The extent of these impacts is shown in section 8.3 above (refer

Table 11 and Table 12).

Indeed, both induced household changes and induced employment changes in the region will

increase the direct local expenditure in the Knox economy, which will flow through to other sectors

of the economy via economy wide linkages. These impacts would be mutually exclusive too and

consequently, their impacts would be additive.

The estimated induced changes will in turn also impact expenditures in the South East Region, MSD

and the Victorian economies, because of the linkages of the Knox economy with these wider

economies via purchase/ sales.

$0.00

$50.00

$100.00

$150.00

$200.00

Tota

l Val

ue

Ad

de

d I

nd

uce

d (

$ M

illio

n)



9.3.1 Scope and Assumptions

It must be borne in mind that the induced dwelling and employment changes within Knox are

detracting dwellings and employment from other parts of the MSD, i.e. the metropolitan wide

number of households and employment is held constant by the analysis in the Base Case and

Project Case, i.e. with and without project scenarios. A reduction in dwellings and employment

elsewhere in the metropolitan economy as they move towards the South East Region will have

consequential adverse impacts on these respective local economies.

The assessment presented here only considers the impacts of the induced changes within the City

of Knox on its own regional economy and the wider economies of the South East Region, the MSD

and Victoria. A net assessment of the combined effects of the induced positive changes in Knox and

corresponding induced negative changes elsewhere is beyond the scope of this study due to

unavailability of region specific multipliers across the MSD.

Nonetheless, the analysis of the wider economic benefits presented in the next section (i.e. section

10 of this report) provides a net assessment of the metropolitan wide effects of changes in

productivity due to the proposed Rowville rail project.

9.3.2 Impacts of Induced Dwellings

The induced number of households in Knox at 2021 and 2046 because of the project over and

above the Base Case will induce local consumption, within the economy, too. In order to estimate

the direct/ initial stimulus on local expenditures generated by these households, SGS:

Estimated per household expenditure in 2011 by dividing total local household consumption

with the total number of households in the municipality in the year. It was found that on

average, a household in the City of Knox spent $67,000 annually on local consumption, with

total household consumption in the municipality measured at ~$3.8 billion in the year.

Estimated the total induced consumption of the additional households by multiplying the

estimated per household consumption (estimated in 2011 dollars) with the induced number of

households in the region at 2021 and 2046 over and above the Base Case.

Results of this exercise are reported in Table 17 overleaf. Assuming that consumption patterns of

households stay fairly consistent over time, these results suggest that total induced consumption

within Knox would be ~$27.6 million in 2021 and increased further to ~$69 million by 2046 over

and above the Base Case due to the induced increase in the number of dwellings in the

municipality.



Table 17 Estimated Induced Direct Dwelling Consumption (in 2011$)

Total Dwelling Consumption

in 2011 ($ Million)

Per Dwelling Consumption

in 2011

Induced Dwelling Consumption (Estimated in 2011 $ Million)

Total Induced Consumption

in 2021

Total Induced Consumption

in 2046

Agriculture, Forestry & Fishing $4 $66 $0.03 $0.07

Mining $0 $0 $0.00 $0.00

Manufacturing $708 $12,266 $5.09 $12.67

Electricity, Gas, Water & Waste Services $35 $605 $0.25 $0.62

Construction $8 $137 $0.06 $0.14

Wholesale Trade $327 $5,675 $2.35 $5.86

Retail Trade $527 $9,140 $3.79 $9.44

Accommodation & Food Services $258 $4,465 $1.85 $4.61

Transport, Postal & Warehousing $69 $1,193 $0.50 $1.23

Information Media & Telecommunications $60 $1,041 $0.43 $1.08

Financial & Insurance Services $276 $4,788 $1.99 $4.94

Rental, Hiring & Real Estate Services $1,062 $18,416 $7.64 $19.02

Professional, Scientific & Technical Services $15 $265 $0.11 $0.27

Administrative & Support Services $6 $106 $0.04 $0.11

Public Administration & Safety $5 $80 $0.03 $0.08

Education &Training $114 $1,974 $0.82 $2.04

Health Care & Social Assistance $173 $3,006 $1.25 $3.10

Arts & Recreation Services $65 $1,125 $0.47 $1.16

Other Services $137 $2,368 $0.98 $2.44

Local Expenditure $3,849 $66,717 $27.68 $68.89




The estimated total impacts (i.e. direct and indirect impacts) of the induced dwelling changes in

Knox on its own economy, and the economies of the South East Region, MSD and Victoria, due to

the estimated induced direct consumption in Knox are shown below. Refer to Table 18, Table 19,

Table 20 and Table 21 overleaf for results. More detailed data showing the industry-wise

breakdown of output, value-added and employment is provided in Appendix E.

Impacts on Knox (C)

From an estimated induced increase in household consumption of $27.6 million within the City of

Knox by 2021:

Total output, including all direct, industrial and consumption effects is estimated to increase by

up to $45.18 million, representing an aggregate output multiplier of 1.63.

The corresponding creation of direct jobs is estimated at 109 jobs. From this direct expansion

in the economy, total employment, including all direct, industrial and consumption effects is

estimated to increase by up to 167 jobs, representing an aggregate employment multiplier of

1.53.

The corresponding increase in direct value-added is estimated at $14.4 million. From this direct

expansion in the economy, total value-added, including all direct, industrial and consumption

effects is estimated to increase by up to $22.5 million, representing an aggregate value-added

multiplier of 1.56.

By 2046, total induced household consumption would have risen by $68.9 million within the

municipality over and above the Base Case. This estimated increase in consumption will support:

Total output of $112.4 million;

Creation of 272 direct jobs and gain of a further 143 jobs due to the indirect impacts; and

Direct value-added to the tune of $35.9 million and total value-added of up to $56 million

within the Knox economy.

Of course, the economy of Knox is not insulated. Any induced changes within Knox will have ripple

effects throughout the South East Region, metropolitan and Victorian economies because of income

leakages and regional imports and exports from and to Knox. These impacts are assessed below.

By its very scope, it is taken as a given that the economic activity generated in the wider

benchmarked economies will be greater than the municipality. This reflects the depth and scope of

economic linkages that the more diverse South East Region, metropolitan and Victorian economies

encompass, i.e. when compared to the City of Knox.



Table 18 Impacts of Induced Dwelling Changes in Knox (C) on Knox (C)

Effects in 2021


Output ($ Million) $28 mn $45 mn

Employment (Jobs) 109 167


Effects in 2046






Table 19 Impacts of Induced Dwelling Changes in Knox (C) on South East

Region

Effects in 2021





Effects in 2046








Table 20 Impacts of Induced Dwelling Changes in Knox (C) on MSD

Effects in 2021





Effects in 2046






Table 21 Impacts of Induced Dwelling Changes on Victoria

Effects in 2021





Effects in 2046






Impacts on South East Region

By 2046, total induced household consumption within the City of Knox would have risen by $68.9

million over and above the Base Case. This estimated increase in consumption will support:

A total output of $121 million;




Direct value-added to the tune of $36 million and total value-added of up to $59 million

within the South East Region.

Impacts on MSD

By 2046, total induced household consumption within the City of Knox would have risen by $68.9

million over and above the Base Case. This estimated increase in consumption will support:



Direct value-added to the tune of $35.9 million and total value-added of up to $73 million

within the metropolitan economy.

Impacts on Victoria

The estimated impacts on the Victorian economy by 2046 would be as follows:

$158 million in total output;

269 direct jobs with further support for 277 indirect jobs; and

$36 million in initial value-added and $77 million in total value-added.

9.3.3 Impacts of Induced Employment

The estimated total impacts (i.e. direct and indirect impacts) of the induced employment change in

Knox on its own economy and the economies of the South East Region, MSD and Victoria, due to

the estimated induced direct employment in Knox are shown below. Refer to Table 22, Table 23,

Table 24 and Table 25 overleaf for results. More detailed data showing the industry-wise

breakdown of output, value-added and employment is provided in Appendix E.

Impacts on Knox (C)

By 2021, the economy of Knox would host ~262 additional jobs over and above the Base Case due

to induced employment changes (refer Table 22 overleaf). This initial induced employment change

is expected to support:

A direct increase in output of $76 million and total output, including all direct, industrial and

consumption effects of up to $131 million, representing an output multiplier of 1.73;

Total employment, including all direct, industrial and consumption effects, of up to 447 jobs;

and

A direct increase in value-added of ~$37.6 million with total value-added, including all direct,

industrial and consumption effects, rising by up to $63 million, representing a value-added

multiplier of 1.68




By 2046, Knox would host an additional ~678 jobs over and above the Base Case in that year .

This induced employment change is expected to support:

A direct increase in output of $188 million and total output, including all direct, industrial and

consumption effects of up to $327 million;

Total employment, including all direct, industrial and consumption effects, of up to 1,151 jobs;

and

A direct increase in value-added of ~$99 million with total value-added, including all direct,

industrial and consumption effects, rising by up to $165 million


As stated before, the economy of Knox is not insulated. Any induced changes within the Knox

economy will have ripple effects throughout the South East Region, metropolitan and Victorian

economies because of income leakages and regional imports and exports from and to Knox. These

impacts are assessed below. By its very scope, it is taken as a given that the economic activity

generated in the wider benchmarked economies will be greater than the municipality. This reflects

the depth and scope of economic linkages that the more diverse South east Region, metropolitan

and Victorian economies encompass, i.e. when compared to the City of Knox.

Table 22 Impacts of Induced Employment Changes in Knox (C) on Knox (C)

Effects in 2021





Effects in 2046








Table 23 Impacts of Induced Employment Changes in Knox (C) on South

East Region

Effects in 2021





Effects in 2046






Table 24 Impacts of Induced Employment Changes in Knox (C) on MSD

Effects in 2021





Effects in 2046








Table 25 Impacts of Induced Employment Changes in Knox (C) on Victoria

Effects in 2021





Effects in 2046






Impacts on South East Region

By 2046, an increase of ~678 jobs within the City of Knox over and above the Base Case is

expected to support:

$351 million in output;

A total of 1,226 jobs; and

$175 million in total value-added

Within the south East Region.

Impacts on MSD

By 2046, an increase in ~678 jobs within the City of Knox is expected to support:


Creation of a total of 1,522 jobs (both direct and indirect); and

Total value-added of up to $226 million

within the metropolitan economy.

Impacts on Victoria

The estimated impacts on the Victorian economy by 2046 would be as follows:

$483 million in total output;

Over 1,600 total jobs; and

$238 million in value-added.



9.3.4 Total Estimated Impacts of Project Operations

Aggregating the impacts of induced households’ consumption and induced employment, it is found

that the direct impacts on output for the City of Knox are estimated at $270 million by 2046 with

approximately $140 million in additional value-added and 950 additional jobs over and above the

Base scenario (refer Table 26 below).

Table 26 Impacts of Project Operations by 2046

Knox (C)





South East Region





MSD





Victoria






Notes: Values are rounded off.

Indeed, total impacts (including both direct and indirect/ flow-on impacts) are found to be much

higher: $440 million in output, $230 million in value-added and ~1,570 jobs.



Industry-wide breakdown of direct and total impacts on the City of Knox are presented in Figure

40. The analysis shows that the total induced changes (dwellings and employment) are most likely

to benefit the higher-order services, including Finance & Insurance and Professional, Scientific &

Technical Services. These services are likely to service the local residents of the region, but more

importantly, provide support and advice to the highly sophisticated manufacturing sector, which in

turn, is likely to benefit to the tune of $14 million by then.

Figure 40 Industry-Wide Impacts of Project Operations on Knox (C) Value

Added By 2046 ($ Million)


The Victoria economy is expected to gain ~$650 million in output, $320 million in value-added and

2,160 jobs by 2046.

As mentioned before though, the assessment for the wider economies of the South East Region,

the MSD and Victoria does not take into effect the adverse effects of induced negative changes in

household consumption and employment elsewhere in the economy as households and

employment is redistributed within metropolitan Melbourne towards Knox due to the Project.

The next section considers the net impacts on changes to metropolitan environmental emissions

and productivity due to the proposed Rowville rail. It incorporates both the adverse effects of a loss

in employment locations in certain parts of the MSD at the expense of the others to consider a net

effect on the metropolitan economy.

-$10

$0

$10

$20

$30

$40

$50

Val

ue

-Ad

de

d ($

Mil

lio

n)

Direct Impacts

Total Impacts



9.4 Section Summary

Results indicate that the construction of the proposal rail spanning over the four year construction

phase is likely to;

Generate $750 million in direct output and $173 million in value-added, supporting

employment of nearly 650 jobs within the City of Knox.


generation is likely to generate a total of $1.6 billion in total output and $553 million in total


Support nearly 5,600 total jobs in the State of Victoria creating nearly $2.4 billion in total

output and $870 million in value added.

By 2046, total effects of the operations of the proposed rail (including the effects of increased

household spending and new employment) were estimated as follows:

$260 million in direct income and $140 million in direct value added for the City of Knox,

supporting nearly 950 jobs.


generation is likely to generate a total of $440 million in total output and $230 million in total


Victorian output and value-added created would be nearly $650 million and $320 million

respectively, providing employment to nearly 2,200 employees.



10 WIDER ECONOMIC BENEFITS OF PROJECT

This section presents the estimated environmental impacts and wider economic benefits of the

Project on the municipality of Knox and the wider metropolitan economy. This includes the

environmental savings brought about by reduction in greenhouse gas emissions; and wider

economic impacts showcased via improvements in effective job density.

10.1 Estimated Environmental Savings

The Victorian Department of Transport produces default costs of externalities associated with

vehicle transport. Third party costs of private vehicle transport were estimated to be 0.77 cents for

every vehicle kilometre travelled (vkt) in 2006, equating to 0.87 cents per vkt expressed in current

value dollars. These third party costs include: cost of air pollutants including carbon monoxide,

nitrous oxide, particulate matter, non methane volatile organic compounds, as well as greenhouse

gases.

Using the hypothetical trip matrices produced by SKM for years of analysis (2021 and 2046) that

detail the number of car based trips travelling from point to point across the MSD, and

complementing this with estimates of distance between travel zones, SGS estimated the difference

between the base and project case car trip matrices to yield an overall travel distance saving

across the metropolis.

In 2021 the daily travel distance is calculated to be 1.31 billion km in the base case and 1.30 in the

project case. 2046 displays a similar trend with total distance travelled in the base case amounting

to 1.45 billion km and 1.44 billion km in the project case. Multiplying the estimated distance saved

across the metropolis by the DoT default values suggests that the distance savings equate to an

annual reduction in externality costs of $3.1 million in 2021 and $3.9 million in 204616.

Table 27 Estimated Environmental Emissions Savings

Daily VKT Base Case (million

kms)

Daily VKT Project Case (million kms)

Daily VKT Change (Base Case – Project Case) (million kms)

Annual Environmental

Emissions Savings ($ Million)

2021 6,328 6,311 1.6 $3.14 million

2046 7,010 6,987 2.0 $3.94 million

Source: SKM and SGS Economics and Planning

16 This saving assumes that there are 250 working days in a year



10.2 Estimated Agglomeration Effects

Connectivity enhancements, due to the inclusion of rail based transport along the proposed train

corridor have been shown to impact on land use by households and by industry. Both the changes

to land use and connectivity have impacts on the productivity of industries, as measured by Gross

Value Added (GVA) and by the productive capacity of workers, i.e. Human Capital.

This section details the estimated benefits to both these aspects of agglomeration due to the

proposed rail extension project.

10.2.1 What is Agglomeration

Economic theory tells us that several factors contribute to productivity of firms, regardless of

locations. These include:

Scale/ geography;

Technological progress/ innovation – through own efforts or spillovers of knowledge; and

Human capital development.

However, another crucial factor is agglomeration, or the density of economic activity. This factor is

related more to the spatial organisation of the city.

Consider two cities, A and B, each with a population of five million people. Both will gain a labour

productivity premium from their size. However, economic activity in City A is poorly linked while

City B features distinct employment centres which are well integrated via a robust transport

network. In this case the labour productivity in City B is likely to be higher than City A.

Firms gain the following advantages from locating in areas of dense economic activity:

The ability to achieve economies of scale and scope through specialisation given the large

numbers of potential customers that are readily accessible;

The availability of numerous supply sources and potentially specialised infrastructure, and the

competitive environment that stems from this; and

Access to a deep and diverse pool of skilled labour, often complemented by high levels of

technological/ knowledge transfer between firms, which helps bolster innovation.

Although a well recognized factor driving productivity growth, it is only recently that availability of

data at a fine grained level which has enabled the quantification of agglomeration.

Agglomeration has historically been measured in a number of ways including city population

(Aaberg, 1973; Tabuchi, 1986), industry employment (Nakamura, 1985; Henderson, 1986), the

number of industrial plants (Henderson, 2003b) and effective job density (Graham, 2006)

depending on availability of data. But it is not density itself that leads to agglomeration.



Accessibility plays an equally important role. One does not need to be in the midst of economic

activity but should be able to access that location too.

A simple measure such as looking at the employment density of an area does not adequately

reflect the phenomenon of agglomeration. A firm in a relatively low-employment area but located

on the edge of a Central Business District (CBD) could potentially capture agglomeration benefits

through such proximity. Thus a measure of agglomeration must incorporate both the local scale

and accessibility to economic activity (as measured by employment) and be able to be calculated

for small geographical regions.

So, what happens if policy decisions can drive spatial land use change which would change the EJD

of a location? We have evaluated this relationship using statistical regression analysis.

A more detailed description of productivity enhancements and agglomeration is relegated to

Appendix D, including an explanation of the full methodology used to estimate the economic impact

of the proposed Rowville rail. Results are summarised below.

10.2.2 Estimated Productivity Enhancements

Labour productivity enhancements, i.e. an increase in GVA of the South East Region, resulting from

the project were estimated at $23 million in 2021 and $133 million in 2046 (refer Table 28

overleaf). Note that these are net impacts after accounting for employment and household shifts

across the metropolitan economy, i.e. the firm’s productivity in the South East Region increases by

$133 million by 2046 due to the Project. Some SLAs within the examined area are expected to bear

some short term adverse impacts due to structural and locational shifts. However, by 2046, after

all adjustments are taken into account, all SLAs are expected to receive a GVA uplift.

Relative to the Base Case, the Project is expected to have the strongest impact on Knox – South

SLA within the municipality of Knox (C), which is 0.63% higher in 2021 and 0.93% in 2046 over

and above the estimated Base Case GVA in those years.



Table 28 Impact of Project on GVA of Selected SLAs

GVA Impact ($) % Change to SLA GVA

SLA 2021 2046 2021 2046

Knox (C) - North-East $1,168,000 $10,049,000 0.03% 0.16%

Knox (C) - North-West $3,439,000 $26,742,000 0.17% 0.69%

Knox (C) - South $14,330,000 $45,662,000 0.63% 0.93%

Monash (C) - South-West $2,792,000 $14,266,000 0.05% 0.12%

Monash (C) - Waverley East $1,223,000 $22,157,000 0.06% 0.56%

Monash (C) - Waverley West $16,000 $6,134,000 0.00% 0.04%

Gr. Dandenong (C) - Dandenong -$114,000 $2,000,000 0.00% 0.01%

Gr. Dandenong (C) Bal $262,000 $3,762,000 0.00% 0.03%

Casey (C) - Berwick -$22,000 $343,000 0.00% 0.01%

Casey (C) - Cranbourne -$36,000 $424,000 0.00% 0.01%

Casey (C) - Hallam $76,000 1,495,000 0.00% 0.03%

Casey (C) - South -$44,000 495,000 -0.01% 0.02%

Total South East Region $23,000,000 $133,000,000




11 CONCLUDING REMARKS

The Victorian Department of Transport (DoT) is currently evaluating the feasibility for a rail line

from Huntingdale to Rowville, a distance of 13 kms. This proposed Rowville rail project is expected

to benefit the residents of Knox and neighbouring municipalities - an area which is currently devoid

of an extensive public transport network, leading to problems of road congestion and greenhouse

gas emissions.

Knox City Council is taking its own actions in support of a positive outcome for the people of its

municipality and elsewhere who will benefit from the provision of a modern rail service in their

communities. With this in mind, Council engaged SGS to undertake a study with the following

scope:

Define and estimate the economic and employment impacts, (direct and indirect), that could

reasonably arise from the proposed rail project on:

o The City of Knox;

o The City of Monash to the west, and the immediately adjacent areas of the cities of

Casey and Greater Dandenong to the south east and south respectively

(collectively termed as the ‘South East Region’ for the purposes of this report; and

o The wider Melbourne Statistical Division (MSD) and the State of Victoria.

Provide comprehensive evidence and argument for the economic and employment impacts

identified in this study to assist Council in its advocacy activities.

In its response, SGS assessed and determined the following four impacts of the proposed project:

Accessibility and land use impacts: Additional dwellings and employment that Knox and the

wider South East Region will host over and above the projected Base Case estimates as a result

of improved accessibility provided by the proposed rail. These constitute the direct impacts of

the operations phase of the project.

Construction and operations phase impacts: indirect impacts on the output, value added and

employment of the economies of Knox, the South East Region, the wider metropolis and

Victoria resulting from the initial change in dwellings and employment estimated for Knox at

two examined years – 2021 and 2046.

Wider economic benefits: estimated increase in metropolitan wide labour productivity of

firms due to induced changes in the density of firms; and

Environmental savings: estimated reduction in metropolitan wide greenhouse gas emissions

due to the provisioning of rail in the region, and the consequent reduction in travel time (due to

provisioning of more jobs closer to home) and reduced dependence on cars.

The results show that delivering the rail project will lead to several beneficial economic and

employment impacts for Knox, Melbourne’s South East and the Victorian economies.



First and foremost, construction of the rail line (only the section within Knox) is likely to support

nearly 5,600 total jobs in Victoria due to the backward and forward linkages of the construction

industry.

Secondly, the operational phase of the project is likely to contribute $320 million towards the

state’s value-added contribution by 2046.

However, as stated before, these estimated economic impacts only consider the impacts of the

induced changes within the City of Knox on its own regional economy and the wider economies of

the South East Region, the MSD and Victoria. A net assessment of the combined effects of the

induced positive changes in Knox and corresponding induced negative changes elsewhere (due to

detractions of households and employment away from these metropolitan locations into the South

East Region) is beyond the scope of this study due to unavailability of region specific multipliers

across the MSD.

Nonetheless, the wider economic benefits and estimated environmental cost savings have been

assessed taking into account the redistribution of population and employment across the

metropolitan economy. Aggregating annual environmental savings and wider economic benefits

over a 50 year timeframe from 2021 onwards yields a cumulative benefit to the Victorian economy

of over $5 billion to the Victorian economy, which is well above the $2.89 billion towards

constructing the project. This falls within the range of estimates of wider economic benefits

provided for various similar transport projects in the UK (as per the case study investigations).

Whilst a benefit cost ratio cannot be ascertained from these values (as they are not net present

values), it helps provide some indication of the magnitude of benefits compared to costs of

delivering the project.

In other words, the opportunity costs of not delivering the project are high; not only will the

economies of Knox and the South East Region lose out on the estimated impacts during the

construction phase, Victoria in turn stands to lose out on approximately $5 billion in environmental

and economic savings over a 50 year timeframe.

Findings suggest that the proposed Rowville rail project will:

Help accelerate the growth trajectory of Knox and Melbourne’s South East. While not a

dramatic impact, the shift in dwellings and employment towards Melbourne’s South East does

begin to help to reshape the economic geography of South East Melbourne more in favour of

Knox.

Enable the city of Knox to meet is stated vision and objectives of: consolidating the existing

industry base and provide for a well-networked, informed and knowledge intensive business

community in the City of Knox; and providing for more employment and housing opportunities

for the people of Knox. The project will reduce the vulnerability of Knox residents to future

economic shocks and the impacts of structural adjustment within the economy.

Lead to significant economic benefits not only for the local Knox and south East economies, but

for the wider metropolitan and Victorian economies.

Be vital to delivering significant and further productivity enhancements to Victoria. Productivity

levels in developed economies are quite high to start with due to high entrenchment of skills



and technology. Further significant enhancements are possible only due to connectivity

improvements, which this project will deliver and consequently, help lift the economic

productivity of metropolitan firms.

In the absence of any intervention, there will be a high requirement for the people of Knox and the

wider South East Region to travel outside of their municipality boundaries to access employment

opportunities. The reliance on the private vehicle for accessibility to jobs and services will heighten

the vulnerability of the residents to congestion and fuel price increases. more importantly, the state

would also not be able to capture the suggested environmental and wider economic benefits that

the project will engender.



APPENDIX A: SGS POPULATION AND EMPLOYMENT FORECAST METHODOLOGY

11.1 Projection Methodology

This section outlines the SGS methodology for employment, population and enrolments.

11.1.1 Employment

The SGS approach to projecting employment by industry and occupation for each Statistical Local

Area (SLA) and Travel Zone (TZ) in Melbourne is summarised in Figure 41 below.

In essence, the Treasury Macroeconomic Model (TRYM) and data obtained from a variety of

different sources17 was used to develop a set of industry projections for the Australian economy.

These industry projections, which include gross value added (GVA) and employment projections,

were developed for the short (2016), long term (2031) and beyond (2046), with total growth for all

industries benchmarked against GDP projections from TRYM. This ensures that the projected

industry growth can be resourced with the finite level of resources at the disposal of Australia.

At a State wide level, Victorian estimates were derived from the current State share of GVA and

employment for each industry. Projections were made on the future share of each industry in

Victoria. Employment projections for Melbourne have been derived from these Melbourne GVA

projections and projections of Melbourne’s labour productivity growth.

Employment growth was capped using future labour force constraints. The labour force was based

on the 2011 Victoria in Future (VIF 2011) and projections for labour force participation for each five

year age group. Labour force projections were made separately for men and women to account for

observed differences in their participation by age profiles. The Intergenerational Report18 was used

as a guide to workforce participation amongst various age groups into the future. A projection of

unemployment was also made to ensure a coherent picture of the future labour force.

This set of metropolitan projections were the cap to which the small area employment projections

were limited. The Australian Bureau Statistics (ABS) Census Journey to Work data has been used

to estimate employment in each SLA for 1996, 2001, and 2006. However, due to the

undercounting of this dataset, the estimates for Melbourne were benchmarked to annual average

employment estimates for each industry from the Labour Force Survey for each year. An

adjustment has been made to the Labour Force Survey to account for people who live in Regional

Victoria but travel to Melbourne for work. For 2002, 2004, 2006, 2008 and 201019, data from the

17 Including the Australian Bureau of Statistics (ABS), Australian Bureau of Agricultural & Resource Economics and the Joint Economic Forecasting Group.

18 Treasury, Australian Government, 2010

19 For 2010 only estimates for Docklands have been released.



City of Melbourne Census of Land Use and Employment (CLUE) has been used to adjust the Census

Journey to Work data industries shares for the most recent years.

These employment figures were also split into blue collar and white collar employment using

Census Journey to Work and Labour Force Survey data.

In projecting future industry employment by SLA the following process was followed:

Initially, the 2016-46 projections for each SLA’s employment by industry was assumed to

follow the growth pattern observed in Melbourne industry share between 1996 and 2011;

In 2026 and 2046 adjustments were made to this industry to share to account for known

information about the development of Melbourne;

VIF population projections for each SLA were used to adjust the projections for population

serving industries. This was done by observing the trends in population to industry employment

between 1996 and 2011;

A factor analysis of each of Melbourne’s SLA was utilised to appropriately cater for expected

changes in employment distribution over time. This factor analysis included an assessment of

each SLAs prospects and capacity for growth, transport connections, resident workforce

characteristics, employment lands availability and Government spatial policy considerations.

Importantly, this factor analysis was undertaken separately for each of major industry and to

ensure that the level of granularity appropriately reflected their respective location drivers;

For the years between 2016 and 2026, the projections were interpolated. That is, the assumed

spatial changes at 2026 were progressively introduced; and

For the 2031, 2036 and 2041 the employment projections were extrapolated using the 2026

and 2046 SLA industries employment shares.



Figure 41 Employment Projections Methodology

The 2006 Journey to Work estimates by industry and occupation at the ABS Destination Zone were

used to allocate each SLA’s total employment to the TZ in that SLA. CLUE data for 2008 and 2010

was also used as a data input. Further factor analysis was undertaken at the travel zone level to

adjust the 2006 shares for future forecast years. Finally, a detailed review of TZ employment by

industry and occupation projections was undertaken and adjustments made as necessary. This

included a review of the employment densities and a cross check against background conditions

(including known structure plans and the scale of major redevelopments).

11.1.2 Module Description

This section provides a more detailed description of each Module in the employment projection

methodology.

2006 Journey to WorkBy Industry

LGA Employment by Industry

LGA 2001 – 2006 Trends

Labour Force Survey

SGS Employment ProjectionsTRYM, Industry Analysis, Labour Force Projections

2006: ABS 2011 to 2016: Trend 2021 to 2026: Interpolated 2031: Factor Adjustment 2036 to 2046: Extrapolated

Small Areas 2006 Share of LGA

Factor Analysis:-Activity Centres-Capacity Constrained Small Areas-UDP Greenfield and Brownfield-Employment lands availability-Blue Collar Construction employment linked to residential and non-residential growth

2011 – 2046 Employment by Industry and Small Area

Factor Analysis:-Major road and rail projects-Business preferences-Leading jobs vs following jobs-Population distribution-Real income of residents-Agglomeration impacts-Other factors



Figure 42 Overview of Employment Projections Modules



Module 1: Labour Force Participation

This module estimates the total pool of labour available in Melbourne overall. It aggregates a

number of data sources that are used to generate total employment for the MSD from 2011 to

2046. These are:

ABS Labour Force Survey

ERP by age and sex from VIF2011

Intergenerational Report

Module 2: MSD Industry Projections

The forecasts of total employment by industry for the MSD are estimated in this module. Three

methods were used to estimate future employment, depending on the characteristics of each

industry. The three categories of industries were:

Population driven – Construction, Retail trade, Accommodation & food services, Education &

training, Health care & social assistance, Arts & recreation services, Other services, Electricity,

gas, water & waste services

Non-population driven – Agriculture, forestry & fishing, Mining, Manufacturing, Wholesale

trade, Transport, postal & warehousing, Rental, hiring & real estate services

Knowledge intensive services – Information, media & telecommunications, Financial &

insurance services, Professional, scientific & technical services, Administrative & support

services, Public administration & safety

Population driven industries were projected using historical data on employment by industry and

population to estimate and extrapolate the ratio between population and employment in each

industry. This trended ratio is then multiplied by the population by age projections for the MSD at

five yearly intervals from 2011 to 2046.

Non-population driven industries were projected using a moving average growth rate estimated

from the historical employment data. Given the nature of these industries it was assumed they

were likely to follow the historical trend in the long term.

A factor analysis of industry trends in the last 10 years was undertaken for the industries classified

as knowledge intensive services. A projected five yearly average annual growth rate was used to

extrapolate the base year estimates out to 2046.

The aggregate of all industries determined the split of workers into blue and white collar

employment. The number of resident workers is also estimated from the total number of workers

to account for people who live outside of the MSD but commute into the region for work.



Module 3: SLA Employment

Estimates of employment by industry for each SLA in Regional Victoria from 1996 to 2046 are

produced in this module. It compiles the ABS Census data from 1996, 2001 and 2006 for each SLA

and industry of employment by place of work. The SLA share of each industry is estimated for

these three years and interpolated for the remaining years to 2011. For 2002, 2004, 2006, 2008

and 201020, data from the CLUE has been used to adjust the Census Journey to Work data

industries shares for the most recent years. A factor analysis is then undertaken for the future

years to generate a final share for each of the forecast years. The MSD total employment by

industry from module 2 is then distributed to the SLAs based on these shares.

Module 4: TZ Employment Shares

ABS Census data on employment by industry (place of work) at a small area level from 1996 to

2011 is compiled using this module. The ABS releases the data by destination zones which are a

similar size to TZ and have been aligned to the MITM TZ for 1996, 2001 and 2006. The shares for

the remaining non-Census years have been interpolated to produce a time series from 1996 to

2011. This has been supplemented using CLUE data for the available years, to ensure that 2011 is

as accurate as possible.

Module 5: TZ Employment Projections

This module distributes the SLA employment estimates generated by module 3 to each travel zone

using the travel zone shares from module 4. Historical employment by TZ for each year from 1996

to 2011 uses the current year TZ share. Employment by TZ for the years from 2016 to 2046 is

forecast using the same method as 2011, with the shares for each future year being projected for

2026 and 2046 with factor analysis and the time periods in between interpolated.

Module 6: TZ Employment by Collar Projections

Estimates of employment (by place of work) by collar projections by TZ are produced here. The

module compiles ABS Census data (2006) on the occupation of workers at the detailed four digit

level by SLA to determine the percentage split between blue and white collar workers for each SLA.

The list of occupations by Blue and White Collar can be found in Error! Reference source not

ound. in the appendix.

The total employment by SLA estimated in module 3 is divided into blue and white collar using

shares from the Census data. The SLA totals of blue and white collar employment are then

distributed to TZs based on the TZ share of total employment from modules 4 and 5.

20 For 2010 only estimates for Docklands have been released.



11.1.3 Dwellings and Population

Figure 43 below summarises the approach used by SGS to project dwellings and population at a

small area level (travel zone). The method has been split into 6 modules which link to each other

to produce the final set of results.

SGS took the preliminary VIF 2011 projected structural private dwellings (SPD) for each LGA as the

starting point. These were combined with historical data from the Housing Development Data

(HDD) and ABS Census data to generate a time series from 1996 out to 2046. The estimates of

total ERP were then split into the MITM11 age breakdown using the ABS ERP by Age, VIF 2008

projections and 2006 Census data (ABS) to determine the appropriate share of each age cohort.

This was completed in module 1. Figure 44 illustrates how the SPD was split into its components

and age cohorts.

These LGA level projections were then apportioned to the travel zone (TZ) level over the entire

MSD. This apportionment was based on the trends in housing demand and capacity for dwellings

evident from a variety of sources. The data sets used were the 2009 Urban Development Program

(UDP), Growth Area Authority (GAA) estimates, Targeted Urban Renewal information, Housing

Development Data (HDD), 1996, 2001 and 2006 Census (from ABS) and the Housing Capacity

Assessments (HCA) draft capacity results. Assumptions around the density of Central Activity Areas

(CAAs) and the CBD were also made. This was completed in module 2.

Upon synthesising structural private dwellings for each TZ in Melbourne, SGS applied the housing

unit method to estimate the number of occupied private dwellings, persons in occupied private

dwellings and estimated resident population by TZ. This was completed in module 3.

The RAS method/ ‘iterative proportional fitting’ method was then used to map total population by

TZ into population by age cohorts that were consistent with VIF 2011 projected population by LGA.

This was completed in module 4.

SGS then examined the labour force participation rates by age group for each labour force region in

Melbourne (as defined by ABS) and projected these into the future. This dataset was combined

with the LGA projections of ERP from VIF 2011 to determine LGA estimates of the resident

workforce by both labour force status and collar of employment. This was completed in module 5.

The resident labour force by age group and collar of employment for each TZ was then estimated

via the RAS method. This was completed in module 6, with the LGA totals taken from module 5 and

the TZ distribution taken from module 4.



Figure 43 Overview of Modules for Dwellings and Population Projections

Figure 44 SPD to ERP by Age Method Overview

Module 1: LGA (SPD to ERP by age)

Inputs:VIF 2011 (DPCD), VIF2008 (DPCD), 2006 Census (ABS), ERP (ABS), Population Projections (ABS)

Outputs:SPD, UOPD, OPD, PNPD, POPD, ERP by Age

Module 3: TZ (SPD to ERP)

Inputs:M1, M2, 2006 Census (ABS)

Module 2: TZ (SPD)

Inputs:M1, 2006 Census (ABS), HDD (DPCD & SE), Previous MITM Projections, 2009 UDP, etc

Outputs:SPD

Module 4: TZ (ERP by age)

Inputs:M3, 2006 Census (ABS)

Outputs:ERP by Age

Outputs:SPD, UOPD, OPD, PNPD, POPD, ERP

Module 5: LFR (ERP by Age to LF)

Inputs:M1, LFS (ABS), Employment Projections M01

Outputs:NLF, LF, UnEmpd, Empd, Blue, White

Module 6: TZ (ERP by Age to LF)

Inputs:M5, M4, 2006 Census (ABS)

Outputs:NLF, LF, UnEmpd, Empd, Blue, White

SPD

OPDUOPD

POPD PNPD

ERP

SPD: Structural Private Dwellings

OPD: Occupied Private Dwelling

UOPD: Un-occupied Private Dwelling

ERP: Estimated Resident Population

POPD: Population in Occupied Private Dwellings

PNPD: Population in Non-Private Dwellings

NiLF: Not in Labour Force

LF: Labour Force

UnEmpd: Unemployed (residence)

Empd: Employed (residence)

0-4 5-9 15-17 18-19 10-11 20-25 26-64 65+12-14

0-4 yrs 5-11 yrs 12-17 yrs 18-25 26-64 65+

VIF08 Age Breakdown

MITM11 Age Breakdown

0-4 yrs 5-9 yrs 15-19 yrs 20-25 26-64 65+10-14 yrs

Not in Labour Force Labour Force

EmployedUnemployed

Blue White w Ties

For each MITM11 age cohort

Time Periods:1996, 2001, 2006, 2007, 2008, 2009, 2010, 2011, 2016, 2021, 2026, 2031, 2036, 2041 & 2046

White w/o Ties



11.1.4 Module Description

Module 1: Local Government Area Structural Private Dwellings to Estimated Resident Population by Age

Module 1A: Local Government Area Totals

This module estimates the structural private dwellings (SPD) and its components (OPD, UOPD,

POPD, and ERP) for each local government area (LGA) in the Melbourne Statistical Division (MSD)

for the years 1996, 2001, 2006, 2007, 2008, 2009, 2010, 2011, 2016, 2021, 2026, 2031, 2036,

2041 and 2046.

A variety of data sources are used as inputs into this module. These include:

ABS Census Data: 1996, 2001, 2006 dwellings and population in dwellings by place of usual

residence

ABS Estimated Resident Population by LGA (ERP 3218.0): 1996 to 2009

ABS Estimated Resident Population by LGA by 5 year Age Group (ERP 3235.0): 1996 to 2009

VIF2008 LGA population and household projections: 2006 to 2009

VIF2008 LGA population and household projections by Age Group: 2011, 2016, 2021, 2026

VIF 2011 LGA population and household projections: 2010 to 2046

Housing Development Data LGA dwellings: 2006 to 2009

The housing unit method was used to estimate the components of SPD using occupancy rate,

household size, ratio of people in non-private dwellings and ERP where VIF 2011 data was not

available. The following formulas and definitions were assumed to hold:

SPD(t) = SPD(t-1) + Additional PD(t)

SPD = Structural Private Dwellings; any building or structure in which people live

OPD(t) = SPD(t) * Occupancy Rate(t)

OPD = Occupied Private Dwellings; normally a house, flat, room or caravan, houseboat, tent,

shoptop

UOPD(t) = SPD(t) – OPD(t)

UOPD = Unoccupied Private Dwellings; structures built specifically for living purposes which are

habitable, but unoccupied on Census Night, eg holiday houses, vacant houses

POPD(t) = OPD(t) * Household Size(t)

POPD = Persons in Occupied Private Dwellings

PNPD(t) = POPD(t) * PNPD Ratio

PNPD = Persons in Non Private Dwellings

Non-private dwellings provide communal or transitory type accommodation, eg hotels, prisons,

boarding school, hospital, defence establishments

ERP(t) = POPD(t) + PNPD(t)

ERP = Estimated Resident Population; official measure of population in Australia based on concept

of usual residence, refers to all people regardless of nationality, citizenship or legal status who

usually live in Australia



Module 1B: Local Government Areas Age Breakdown

This module takes the LGA distribution of ERP by MITM11 age breakdown and balances it to the

Total ERP for each LGA and each age group. The VIF 2011 estimates of ERP were not yet available

at the detailed age breakdown at the time of this study. Therefore, this module uses the RAS

method to do estimate an age breakdown, which is explained in the following sections. The output

of this module is ERP by Age by LGA from 1996 to 2046. The sources of data used as input into this

module are from module 1A. These include ABS ERP by age and VIF 2008.

Module 2: Travel Zone Structural Private Dwellings

This module takes the LGA estimates of SPD and distributes down to the travel zone level using a

variety of data sources. These include:

Housing development data, dwelling estimates for 2004 to 2009

VIF 2011 projections, SPD by LGA from 2010 to 2041

Urban Development Program 2009

Growth Area Authority (GAA) Estimates

Housing Capacity Assessment (HCA) draft capacity results

Targeted Urban Renewal information from DPCD

Activity Centre density assumptions

The historical stock and growth rate of this stock was based on the ABS Census data and the HDD

data from 2004 to 2009. The 2009 HDD data was used as the pivot point for the future projections.

A comprehensive staging of capacity for dwellings at the travel zone level was estimated using

these combined data sets. The LGA level projections were then distributed to travel zones based on

the level of capacity in each time period from 2010 to 2046. Adjustments were made to the timing

of new development when demand for dwellings did not match the capacity that was available in a

particular area.

Module 3: Travel Zone Structural Private Dwellings to Estimated Resident Population

This module converts the estimates of SPD by TZ from module 2 into estimates of occupied private

dwellings (OPD) and estimated residential population (ERP) by TZ. This was done using the housing

unit method, as outlined in Section 11.1.4 previously.

This approach determines the current trends in the occupancy rate, household size and adjusts

them over time based on regional trends. The following assumptions have been made regarding

household size, occupancy rates and persons in non private dwellings to occupied private dwellings.

The occupancy rate (i.e. ratio of private dwellings to occupied private dwellings) for each

zone was initially calculated based on 2006 Census data. The rates for each zone were then



adjusted over time based on VIF 2011 projected LGA trends (i.e. if the occupancy rate was

projected to increase for the LGA then the occupancy rate for each zone within the LGA was

projected to increase at the same rate).

Zones with low occupancy rates are assumed to gradually approach the LGA average

occupancy rate.

Greenfield areas that previously had no dwellings were assumed to have the similar occupancy

rate to the average for that LGA.

The Household Ratio (i.e. average number of persons per occupied private dwelling) was

initially calculated based on 2006 Census data. Each zone’s household ratio was then adjusted

over time based on the VIF 2011 projected LGA trend.

Greenfield areas that previously had no dwellings were assumed to have the similar household

ratio to the average for that LGA.

The share of persons in non private dwellings was held constant at the 2006 Census level.

At each stage of the housing unit method all zones within each LGA were benchmarked to the

respective VIF 2011 LGA total for:

Structural Private Dwellings

Occupied Private Dwellings

Persons in Occupied Private Dwellings

Persons in Non-Private Dwellings

Estimated Residential Population

Module 4: Travel Zone Estimated Resident Population by Age Breakdown

This module disaggregates the estimates of ERP by TZ completed in module 3 into the six age

cohorts of 0-4, 5-11, 12-17, 18-25, 26-64 and 65 and over. This was done using the RAS method

outlined below.

At the time of this study VIF 2011 population by age group was not yet available. Therefore, ABS

Estimated Resident Population by Age and 2006 Census population by age were used to estimate

the TZ level age breakdown. This was undertaken using a RAS method. The RAS/‘iterative

proportional fitting’ method is commonly used for the purpose of estimating regional input-output

tables by economists21.

Through a series of iterations the RAS method adjusts the age distribution until all row and column

totals match the VIF 2011 total population by TZ and population by age group for each LGA (see

Figure 45 below). This process was repeated for each Census year between 1996 and 2046. A

rolling TZ by age group distribution has been used to estimate future years.

21 It should be noted that DSE (now DPCD) in 2006 used this approach to project population by age by TZ using VIF 2004 SLA level projections



The 2006 population by age group by TZ distribution was based on the 2006 Census data,

converted from Census collection district (CCD) to TZ based on land area apportioning. 2011

population by age group by TZ was estimated using a RAS method where row and column totals

are fixed at 2011 population by LGA by age group and 2011 total population by TZ. The internal

population by age by TZ for each LGA is determined using the 2006 distribution. 2016 population

by age group by TZ was estimated in a similar approach using the newly estimated 2011

population by age by TZ distribution in each LGA. This process of using the subsequent time

period’s distribution is repeated for each Census year to 2046.

Figure 45 RAS Method

Module 5: Local Government Area Estimated Resident Population by Age Breakdown to Labour Force Status

This module generates LGA estimates of resident workers in the labour force, not in the labour

force, employed, unemployed and white and blue collar workers from 2011 to 2046. The data

sources used in this module include the ABS Labour Force Survey and VIF 2011 projections of ERP.

The employment projections (place of work) are also used as an input.

Participation rates and unemployment rates by five year age group were estimated and applied to

the ERP by age group generated in module 1. Additionally the blue and white collar split taken from

the 2006 Census (ABS) was applied to the estimates of employed people in each forecast year to

2046.

RAS Method



Module 6: Travel Zone Estimated Resident Population by Age Breakdown to Labour Force Status & Collar

This module distributes the LGA estimates of resident workers by age (LF, NLF, Employed,

Unemployed, Blue Collar, White Collar) to the travel zone level for each forecast year. The two

sources of input data were from module 5, the LGA estimates of resident worker variables and

module 4, the TZ estimates of ERP by age.

The process of estimating these resident worker variables by age by travel zone was split into two

parts. Module 6A was concerned with the labour force status of residents (in the labour force, not

in the labour force), whilst module 6B focused on the collar of employment of the resident

workforce.

Module 6A: Travel Zone Estimated Resident Population by Age Breakdown to Labour Force Status

This module estimates the number of people employed, unemployed and not in the labour force by

age group from 1996 to 2046 at five yearly intervals. The age groups estimated were 12-17, 18-

19, 20-25, 26-64 and 65 and over. The 2006 Census distribution was used as the base year for

each age category, with each successive year out to 2046 based on the previous year’s

distribution. It was assumed that the number of people employed and unemployed equalled the

total labour force, and that the number of people in the labour force and not in the labour force

was equal to the total population.

Module 6B: Travel Zone Estimated Resident Population to Blue & White Collar Split

This module estimates the number of people employed in blue and white collar industries using the

RAS method. The 2006 Census (ABS) TZ distribution of blue and white collar is balanced to the

total employed by TZ and to the total blue and white collar resident workers in each LGA. The 2011

numbers are equal to the 2006 distribution with each following year equal to the previous period’s

distribution.



APPENDIX B: SURVEY QUESTIONNAIRE FOR SGS CONSULTATION

(Please complete the following questions)

1. Name of business/organisation

2. What is your primary business activity?

(Please tick one that applies)

Manufacturing

Wholesale Trade

Transport & Logistics

Utilities (Electricity, Water, Gas and Waste Services)

Construction

Retail Trade



Professional & Business Services (Finance, ICT, Accounting, Legal, Scientific etc.)

Other Services

3. How many people do you employ full-time?

(Please tick one that applies)

Non-employing family owned business

0-4

5-19

20-150

150+

4. How long have you been in continuous operation within the City of Knox?

Less than a year

1 to less than 5 years



More than 20 years

1/4



5. On a scale of 1 (most) to 5 (least) where are most of your customers located? (Pease assign a value of between 1 and 5 against each listed option)

Within Knox

Surrounding municipalities of Dandenong, Monash and Casey

Rest of Melbourne/Victoria

Australia inter-state

Overseas

6. On a scale of 1 (most) to 5 (least) where are most of your suppliers located?

(Please assign a value of between 1 and 5 against each listed option)

Within Knox



Australia inter-state

Overseas

7. On a scale of 1 (most) to 3 (least) where are most of your employees located?

(Please assign a value of between 1 and 3 against each listed option)

Within Knox



8. What model(s) of transport do your customers, suppliers and employees use to reach you?

(Please tick all that apply)

Customers Suppliers Employees

Car/ Private transport

Public Transport (Bus/ Train/

Tram etc)

Walk

Other

2/4



9. In the past three years, have your customers, suppliers and employees faced any of the following problems to access your services:


Congestion on roads

Congestion on public transport

Longer travel times on roads

10. Have any of these problems above prompted you to consider changing your business operation?

Yes

No

Not sure

If so what?

11. Do you have any concerns about any adverse impacts during the construction of the proposed rail line?

Yes

No

If so what are these?

12. On constructing the proposed rail line what are the potential growth opportunities for your business?

13. In the long term, will the proposed rail line help you as a business entity to expand

operations, attract more employees and reach out to other markets within the metropolitan economy?

Yes

No

Not sure

3/4 14. Considering the longer term beneficial impacts, are you likely to locate closer to a new

proposed train station to improve your access to either your customers, suppliers and

employees?

Yes

No

Not sure



15. In the long term, will the proposed rail line help attract more residents and businesses like yours to Knox and surrounding municipalities?

a. Residents Yes/No b. Businesses Yes/No

If so, why do you think so?

16. In the long term, which sectors of the Knox and surrounding economies is the proposed rail line likely to help the most and why?


Manufacturing firms

Wholesale Trade firms

Transport & Logistics firms

Utilities (Electricity, Water, Gas and Waste Services)

Construction firms

Retail firms

Education and Training Organisations

Professional & Business Services firms (Finance, ICT, Accounting, Legal, Scientific etc.)

Other firms

Why?



APPENDIX C: EFFECTIVE JOB DENSITY CALCULATIONS

Table 29 Estimated Effective Job Density for South East Region

EJD 2021 EJD 2046

SLA Base Case

Project Case

Change Base Case

Project Case

Change

Monash (C) - South-West 85,211 85,327 116 105,973 106,329 356

Monash (C) - Waverley East 78,418 78,469 51 96,461 97,028 566

Monash (C) - Waverley West 84,493 84,444 -49 104,721 104,829 108

Knox (C) - North-East 60,933 60,992 60 76,309 76,602 293

Knox (C) - North-West 71,046 71,212 166 86,244 86,932 688

Knox (C) - South 60,246 61,431 1,185 74,399 76,815 2,415

Gr. Dandenong (C) - Dandenong 73,575 73,518 -57 91,153 91,148 -4

Gr. Dandenong (C) Bal 71,921 71,886 -35 89,357 89,388 31

Casey (C) - Berwick 42,912 42,902 -10 56,404 56,408 4

Casey (C) - Cranbourne 42,846 42,837 -10 52,167 52,174 7

Casey (C) - Hallam 53,113 53,108 -5 69,075 69,100 26

Casey (C) - South 36,145 36,136 -9 42,439 42,442 3



Table 30 Estimated Relative Effective Job Density for South East Region

EJD 2021 EJD 2046

SLA Base Case

Project Case

Change Base Case

Project Case

Change

Monash (C) - South-West 0.46616 0.46706 0.00090 0.44718 0.44935 0.00217

Monash (C) - Waverley East 0.41328 0.41368 0.00040 0.38918 0.39263 0.00345

Monash (C) - Waverley West 0.46058 0.46019 -0.00039 0.43955 0.44021 0.00066

Knox (C) - North-East 0.27720 0.27766 0.00046 0.26628 0.26807 0.00179

Knox (C) - North-West 0.35591 0.35720 0.00129 0.32687 0.33107 0.00420

Knox (C) - South 0.27185 0.28107 0.00922 0.25464 0.26937 0.01473

Gr. Dandenong (C) - Dandenong 0.37559 0.37515 -0.00044 0.35681 0.35678 -0.00003

Gr. Dandenong (C) Bal 0.36272 0.36245 -0.00027 0.34585 0.34605 0.00019

Casey (C) - Berwick 0.13693 0.13685 -0.00008 0.14490 0.14493 0.00002

Casey (C) - Cranbourne 0.13642 0.13635 -0.00007 0.11906 0.11911 0.00004

Casey (C) - Hallam 0.21633 0.21629 -0.00004 0.22217 0.22233 0.00016

Casey (C) - South 0.08427 0.08420 -0.00007 0.05974 0.05976 0.00002



APPENDIX D: SGS METHODOLOGY TO MEASURE AGGLOMERATION

11.2 Review of the Evidence on the Nature and Sources of Agglomeration Economies

This section provides a short review of the literature on the topic of agglomeration. Rosenthal and

Strange (2002) provides a more comprehensive investigation of the literature.

The uneven spatial distribution of access to inputs to production provides a partial explanation for

the existence of agglomeration economies. Agglomeration economies occur specifically in some

locations which have access to raw materials and not in others – as with the concentration of US

furniture manufacturing in North Carolina and not in other states (such as Oregon) that also have

large forestry sectors. Agglomeration economies also occur in industries that do not depend on

unevenly distributed raw materials, but depend on intensive labour inputs from a relatively small

workforce – such as the agglomeration of computer software manufacturers in the San Francisco

Bay area of California. If agglomeration were solely driven by the need to locate close to raw

materials then this industry would instead seek to locate adjacent to electrical and plastic

component manufacturers.

In understanding agglomeration economies, a number of questions can be considered. What is the

nature and source of the increasing returns (external economies of scale) that lead to

agglomeration economies? Is the agglomeration economy regional or local? Is the agglomeration

economy restricted to an individual industry or does it extend across multiple industries? Are the

economies of scale impacts felt immediately or is there a lag between the agglomeration economy

being established and productivity improving? Is the agglomeration economy driven by the volume

of interactions occurring or is it driven by the nature of these interactions?

The limiting the interaction between people and business due to a lack of accessibility can impact

on the economy in three key ways; these are described by the National Cooperative Highway

Research Program as:

“By increasing business costs of current delivery operations.”

“By limiting or reducing business sales through a reduction in effective market size.”

“By increasing unit costs through loss of opportunities for scale economies in production and

delivery processes”.

The first of these impacts are effectively captured by the traditional transport benefits. The wider

economic benefits capture the improvements covered in remaining two impacts. The second two

points can also be as easily applied to households. Providing workers with an increased number of

jobs also allows them to become more productive. This has not been fully appreciated in past

studies of the economic benefits of significant land use changes and / or transport projects.



The remainder of this section attempts to answer some of these questions by providing a review of

available literature on the topic of agglomeration. At times, reference is made to the Australian

context and the method outlined in this paper.

11.3 The Scope of Increasing Returns

External economies exist where the scale of the urban environment and its component economic

activities, infrastructure and resources add to the productivity of an individual firm. External

economies are defined across three dimensions of scope:

Industrial Scope: The degree to which agglomeration economies extend across industries

rather than being confined to firms within an industry classification or boundary.

Geographic Scope: The propensity by which firms that achieve agglomeration economies

seek to cluster in a geographically confined area to increase the potential for interactions.

Temporal Scope: The extent of continuing impact from a firm or agent’s previous interactions

with another firm or agent. The concept that much business intelligence and Human Capital can

only accrue gradually and is also subject to a degree of decay over time is central to

understanding the temporal scope of agglomeration economies.

Rosenthal and Strange seek to evaluate the scope of agglomeration economies. Firstly they

consider whether external economies enhance labour or other co-determinants of productivity.

Empirical evidence from Henderson (1986) suggests that external economies affect productivity

independently of land, labour, capital and materials and are thus ‘Hicks Neutral’.

Rosenthal and Strange specify that the

aggregate agglomeration effect is the

sum of many individual external

economy effects experienced by

individual firms and agents across the

three identified dimensions of scope. If

the two firms ‘j’ and ‘k’ are considered,

then the agglomeration impact of j on k

depends on the geographic distance

between the two firm’s premises defined as dGjk; the similarity of the industrial activity that

occurs at each firm, referred to as industrial distance and defined as dIjk ; and on the length of

time since the last interaction occurred, a temporal dimension of distance defined as dTjk. An

increase in any one of these distances will diminish an agglomeration effect between j and k.

The full set of benefits that accrue from an agglomeration effect is defined as K. In addition to the

impact of the geographic, industrial and temporal distance between the two firms, the scale of

activities at j and k also help determine the scale of agglomeration benefits experienced. The

benefit accrued depending on the scale of activities at both firms is represented as q(xj,xk). If we

hold the scale of activities constant, the benefit accruing from firm j’s interaction with firm k can be

defined as a(dGjk, dIjk, dTjk).

Hicks Neutral is when a technology (or in this

case agglomeration) change does not alter the

ratio of the marginal product of capital to the

marginal product of labour. That is, after the

change, the ratio between the extra output

gained by employing one additional unit of

capital and the output gained by employing one

unit of labour remains unchanged.



The total benefit of agglomeration can be expressed as:

Aj = ∑ K E K q(xj,xk) (dGjk, dIjk, dTjk), (1.0)

The construction of the equation (1.0) indicates that A varies across different firms and agents

because each firm or agent belongs to a particular industry at a unique location and exists for a

given period of time. Rosenthal and Strange assert that most of the research on agglomeration

economies to date, has grouped industries and firms into politically defined regions. Activity in

neighbouring regions is assumed to have little or no effect on the grouped industries and firms and

productive activity in the region is assumed to be location unspecific.

SGS Conclusion: The method outlined in section Agglomeration Economies and Labour

Productivity section does take into account the surrounding geographical units.

The measure of agglomeration benefit (above) can be further adapted to form part of an

estimation of industrial output by firm j where industrial output is a function of agglomeration

benefits and the size of the firm:

Yj= g(Aj) f(xj) (1.1)

Estimates of the above equation should provide measures of the productivity effect of the

geographic, industrial and temporal dimensions of agglomeration. However, there are many

challenges to estimating 1.1. Gathering the information required to estimate all three dimensions

of agglomeration benefits presents a daunting exercise and would involve considering measuring all

forms of economic activity by industry and distance from j across a wide history of interaction.

Therefore most agglomeration models only consider one or two of the three dimensions of

agglomeration scope.

In estimating (1.1), measures of labour material and land inputs into a firm’s production process

can be made from public data sets (especially in the case of labour but also for material). Measures

of land use and other production inputs (such as capital) are more difficult to obtain where the

stock of information is not available publically. Where data is made available to the researcher at

the level of an individual plant within a firm, more accurate estimates of external economies of

scale are possible such as those made by Henderson (2003b).

Measurement error is another central challenge to estimating productivity effects and has been

surveyed considerably by Eberts and McMillen (1999). In larger cities, firms use capital and land

more intensively than in smaller cities and this can create bias in the coefficient estimates when

taken for firms operating in cities of different sizes (Moonmax 1981).

In response to the challenges presented when trying to estimate the production function of a firm

or plant, four other indirect means of investigating the scope of agglomeration economies have

been developed.

Study the growth in total employment resulting from agglomeration across a region or local

area.

Identify new firm or plant start ups and the number of jobs created.



Study the change in wages resulting from agglomeration across a region or local area.

Study the change in rents resulting from agglomeration across a region or local area.

There continues to be a debate as to whether agglomeration economies can be ascribed to the

benefits from having localised and concentrated industries (localisation) or whether agglomeration

economy benefits relate more to the size of the city in which the firms or plants are located

(urbanisation).

Clearly, the increased density of economic activity brought about by transport projects would drive

the localisation component of agglomeration.

SGS Conclusion: Although currently the analysis is only focused on a single city

(Melbourne), the method outlined in section Agglomeration & Labour Productivity does

provide the capacity for both the localisation and urbanisation effects to be accounted for.

11.3.1 Industrial Scope

It is known that related firms choosing to stay in a common location for a long period of time

benefit in terms of reduced training requirements, from the informal transfer of skills between

skilled workers and from skilled workers to their children and others in these locations (Marshall

1920). Jacobs (1969) puts forward an alternative argument that diversity rather than commonality

of spatially concentrated firms fosters innovation via a ‘cross fertilization of ideas’ process. This in

turn leads to the creation of new industry sectors with inter firm linkages that create external

economies of scale.

SGS Conclusion: The method outlined in this paper has made no assumptions about

industry interactions. That is, the uplift for a particular industry’s labour productivity

resulting from agglomeration is a function of access to total employment rather than access

to employment in other selected industries.

Various studies have sought to identify the impact of increasing city size on firm productivity - also

known as urbanisation economies. Shefer (1973) considers a cross section of municipalities and a

group of industries and concludes that doubling city size would increase firm productivity by 14 -

27%. Sveikauskas (1975) found that there would be an increase of only 6 -7%, in line with the

projections of later studies. Nakamura (1985) and Henderson (1986) examined the relative impact

of both localisation and urbanisation economies on productivity.

Nakamura looked at Japan and concluded that doubling the scale of an industry leads to a 4.5%

increase in productivity while doubling the size of a city leads to a 3.4% increase. Henderson

looked at the United States and Brazil and found almost no evidence of urbanisation economies and

a lot of evidence of localisation economies. Together with research by Moomaw (1983), Henderson

(2003) and Rosenthal and Strange (2003) these papers point to localisation effects being stronger

than urbanisation effects.

Another way of measuring agglomeration economies is to measure the extent to which a city’s

employment is specialised; measured as a share of employment by a particular industry sector by



Glaeser et al (1992) and Henderson et al (1995). Glaeser’s work found that specialisation did not

encourage growth, when the development of a city’s top six industries in 1956 was tracked over

the period 1956 – 1987. Henderson investigated eight different industries (three evolving as high

tech and five mature industries) from 1970 – 1987 and concluded that specialisation has a positive

influence on growth for mature industries but that evolving industries perform better in cities with

diverse industry profiles. Duranton and Puga (2003) use French data to show that as some

industries reach maturity they move from diverse cities to those with a less varied industrial

profile.

Theories on the origin of agglomeration economies have almost always been grounded in the

concept that increasing the absolute scale of an industrial activity always brings benefits. For

example – having more workers to choose from means workers are employed in jobs better suited

to their skills, Helsey and Strange (1990). The flip side to this argument is that diversity of

industries brings cross fertilisation of technologies and leads to the birth of new industries and

growth and innovation in existing ones, Chinitz (1961). Combes (2000) finds that specialisation

and diversity both have negative effects on growth for all but a few industry sectors within

Manufacturing. But when the same analysis is made for service industries, Coombes found that

while specialisation continued to have a negative effect on growth, diversity had a positive effect.

The question of how agglomeration economies subside as nearby activity becomes increasingly

dissimilar remains little explored in the literature. It is difficult to measure ‘industrial distance’ i.e.

the distance between the function of two industries. Cluster mapping based on supply relationships

and the similarity of production processes such as by Ellison and Glaeser (1997) is the closest

approximation available.

11.3.2 Geographic Scope

Until recently, research into agglomeration economies has defined geography on the basis of

political boundaries and not assumed the gradation of effects within and in response to firms from

outside these boundaries. Ciccone and Hall (1996) departed from this approach and measured

employment density across New York State at the local (county) level. They found that doubling

county population density led to an approximately 5% increase in productivity.

Dekle and Eaton (1999) used rents to identify minor agglomeration economies for Finance and

Manufacturing in Japanese prefectures related weakly to increasing urban size – a 1% increase in

productivity for a doubling of population. Rosenthal and Strange (2003) found that industries which

relied more heavily on manufactured or natural products inputs or which produced perishable

products were more inclined to concentrate in geographic proximity. Ellison and Glaeser (1997)

and Duranton and Overmans (2002) measured industry concentration and agglomeration effects at

different scales and concluded that the effects are localised. Duranton and Overman found that

localisation benefits dissipate beyond fifty kilometres of distance.

Graham (2006) found that an elasticity of 0.125 for the whole economy, 0.052 for Manufacturing

(with large variation within the industry) and 0.20 for services. These results are in line with the

SGS findings. Graham’s findings confirm the role that transport projects have in increasing the

density of a city and the subsequent productivity improvements.



The European Conference of Transport Minister’s Round Table 119 outlines that, by widening the

area of goods markets, transport improvements may promote competition, thereby enhancing

economic efficiency. The effect may be analogous to the removal of customs barriers. The removal

of such barriers results in higher productivity and raises the purchasing power of populations,

which benefit from the specialisation of trade.

Second, improved transport links which increase transport speeds may have the same effect as

increasing the size of the employment market, as a greater number of job-seekers will be able to

travel to more distant jobs. This will allow for greater productivity as employers are better able to

find employees qualified for the jobs they are seeking to fill.

SGS Conclusion: The method in this paper draws upon the distinct outlined by the

Minister’s Round Table that there are two distinct impacts which are at work. The first that

increase the size of the economy (in this case via improved access to jobs) will lead to

improve productivity brought about by increased economies of scale and scope. The second

is productivity improvements brought about by improved job matching.

The Commonwealth Treasury22 outlines that “The Government also has a role in investing directly

in infrastructure, innovation and Human Capital. Such direct investment may be necessary where

markets for a good or service are incomplete, goods have public good characteristics, or there are

positive spillovers associated with the production of a good or service”.

While the Treasury recognised that there are “spillovers” which come from infrastructure and

Human Capital investments, a methodological framework to measure the benefits from these

investments is not provided.

11.3.3 Temporal Scope

A key issue that is considered in the literature about agglomeration economies is the question as to

whether past economic environments (say from previous decades) can continue to impact

agglomeration economies many years later, albeit indirectly. Glaeser et al (1992) and Henderson et

al (1995) both incorporate this consideration into their growth models. A direct dynamic effect

(often referred to as ‘knowledge spill over’) involves industrial activity from many years ago

positively influencing today’s productivity.

A paper by Glaeser and Mare (2001) estimates the temporal scope of agglomeration economies by

regressing data on wage rates against a range of worker and location attributes. This analysis

shows that there is an urban wage premium of circa 20% but that this premium is enjoyed more

by long time city residents than by recent immigrants. Also, when long time urban workers leave

their city, the wages they earn in their new location are higher the larger the city they move from.

SGS Conclusion: This is no doubt reflecting the Human Capital accumulation opportunities

which these workers have had access to.

22 www.treasury.gov.au/.../4_Productivity_Growth_Submission.rtf

http://www.treasury.gov.au/.../4_Productivity_Growth_Submission.rtf



11.4 ‘Culture’ and the Transmission of Agglomeration Economies

Porter (1990) argues from case evidence that competition encourages innovation by forcing firms

to innovate or fail. Competitive pressures are therefore seen to improve productivity. Conversely,

Marshall (1920), Arrow (1962), Romer (1986) and Glaeser et al (1992) assert than competition

decreases productivity because firms in a competitive local market cannot guard their intellectual

property as effectively – they are subject to churn and transfer of staff to competitors. Saxenian

(1994) compares the performance of two centres of computer software - Silicon Valley and

Boston’s Route 128.

Saxenian argues that the extent of local technological capabilities stemming from protected

intellectual property is not the main source of differences in industry performance; instead having

an open and flexible industry culture that allows for entrepreneurialism is advanced as the main

performance driver. Rosenthal and Strange found that when smaller firms hired an additional

skilled employee this tended to have a positive effect on the entire local industry and correlated

with the birth and increased labour force of other small local businesses. The same effect was not

observed when larger firms added employees. This is consistent with Saxenian’s findings.

Another agglomeration effect comes from the different incentives that drive urban residents to

perform in the workplace. Cities may either inspire or require hard work from their residents. Cities

requiring hard work of their residents are colloquially referred to as ‘rat races’ by Rosenthal and

Strange (2002). This issue has been examined by Rosenthal and Strange who looked at the nexus

between agglomeration and work behaviour. The research found that professional workers in their

30’s and 40’s work longer hours in locations where the density of employment in their occupation is

high. Further investigation showed that both the presence of competitors and the opportunity to

advance (both agglomeration effects) motivated this behaviour. No significant effect was found for

non professional workers.

SGS Conclusion: This result is in line with the Human Capital analysis presented in section

Error! Reference source not found. Error! Reference source not found.. The analysis

ghlights that a person with a Higher Degree will reap higher benefits from increased

agglomeration than a person without qualifications.

11.5 Sources of Agglomeration Economies

Agglomeration arises both from the benefits of firms locating in an area where they can exploit a

natural advantage and from firms locating together to take advantage of agglomeration economies.

The relative contribution of each of these factors has been explored by Kim (1995, 1999) and

Ellison and Glaeser (1999). Kim looked at agglomeration between 1860 and 1987 and regressed a

location quotient – measuring the concentration of industry against plant size, natural resource

availability and dummy variables for industry sector and time. The positive coefficient returned for

the natural resources variable suggested that this was highly significant. Ellison and Glaeser

showed that 20% of agglomeration can be predicted by the presence of natural advantages.

However it is likely that over time the role of natural advantage in determining agglomeration has



been decreasing because labour has become more mobile – enabling industries to continue to

concentrate in an area and enjoy agglomeration economies by importing labour.

The research reveals little about the micro foundations of agglomeration economies.

Agglomeration economies typically involve knowledge sharing, labour pooling and input sharing.

Any one or multiple combination of these micro foundations can increase productivity which

increases profitability and leads to firm growth, Helsey and Strange (2001). This does not help

reveal the individual role of each of these micro foundations in fostering growth; a more structured

approach is necessary. Glaeser and Mare (2001) propose that by looking at the dynamic structure

of agglomeration economies, the micro foundations can be identified; the lag in wage increases in

response to urbanisation suggests that this effect is a result of knowledge spillovers. Alternatively,

Henderson (2003a) suggests that looking at the impact of the number of firms in a location on the

productivity of their neighbours will capture the impact of knowledge spillovers.

SGS Conclusion: SGS provides a clear framework for understanding productivity changes

brought about by agglomeration but also acknowledges that micro foundations still require

further more detailed research.

11.5.1 Input Sharing

The concept of input sharing depends on the existence of scale economies in purchasing production

inputs, Marshall (1920). Without scale economies, an isolated firm could request a small batch of a

production input and pay the same per unit price as a larger order from a collective of firms. Where

producers receive collective or multiple demands for an input, they can achieve the cost

advantages from an efficient scale of production and pass some of this gain on to their customers.

Holmes (1999) investigates the connection between whether a firm locates in close concentration

with similar firms and whether it engages in input sharing with other firms. Holmes uses Census

data on Manufacturing sales at the establishment (firm) level and Census data on purchased

inputs. Purchased inputs are divided by sales to give purchased input intensity which is also a

measure of vertical disintegration or input sharing.

The differences in purchased input intensity between locations of concentrated similar firms and

other locations across the USA were then examined. It was found that across all industries, moving

from an un-concentrated location (fewer than 500 employees in the same industry) to a

concentrated location (10,000 – 24,999 neighbouring employees in the same industry) resulted in

a 3% increase in purchased input intensity. It can also be expected that in the presence of input

sharing by purchasers, input suppliers would carry out more specialised functions. Because

industry classification protocols typically place vertically integrated stages of production in the

same category, it is difficult to test this theory. Holmes looks at the textile industry for which

specialised textile finishing plants are afforded a separate industry classification from the rest of

the industry.

Holmes found that where the industry was more concentrated, the ratio of specialised finishing

plants to total plants tended to be higher. Holmes and Stevens (2002) found that firm size is larger

in locations where an industry is concentrated. A ‘size coefficient’ was developed equivalent to the



ratio between mean firm size in a location and mean firm size for that industry across the USA.

This does provides indirect evidence of vertical integration.

11.5.2 Knowledge / Technological Spillovers

With knowledge / technological spillovers, information is often exchanged between firms without

being bought or sold – in contrast to input sharing, Helsey and Strange (2002). Where exchanges

do occur, these are likely to be compacted joint ventures for which data is not routinely collected.

To address the challenges of a lack of official data about knowledge spillovers, Jaffee et al (1993)

use the location of firm patent citations to create a ‘paper trail’ of knowledge / technological

spillovers. They found that patent citations are spatially concentrated, with citations five to ten

times more likely to come from the same municipal area than control patents. This effect is

expected to vary with industry scope – being most pronounced in those industries that are highly

innovative, Audretsch and Feldman (1996).

Workers are the primary carriers of knowledge / technological spillovers. Rauch (1993a) looks at

the impact of education levels on wages and rents. Where average education levels were high; this

sharing of information as a public good was shown to increase wages across the sector. Rents were

also shown to be higher, because the productivity enhanced higher wages were capitalised by the

housing market. Charlot and Duranton (2002) find that workplace communication is more

extensive in urban areas but that this accounts for only 10% of the urban education effect. This

suggests that other knowledge / technological spillovers are taking place or that improved

education levels impact other micro foundations of agglomeration economies. In summary, the

exact channel of interaction whereby increased levels of education flows through to increase

productivity across an industry sector is poorly understood.

SGS Conclusion: The method employed by SGS allows a good understanding of changing

education and experience levels have on Human Capital and the subsequent productivity

improvements are the aggregate level. But industry specific Human Capital productivity

impacts are more different to assess as Human Capital is not fixed to a particular industry

(that is workers can move between industries) but industry specific experience is strong

linked to Human Capital.

11.5.3 Labour Market Pooling

There are two related explanations for labour market pooling. One is that workers in large cities or

industrial concentrations should be better matched to their roles. This can be examined by looking

at termination rates, controlling for conditions in the local economy and industry sector. However

because employers of firms in smaller cities have fewer options with which to replace an employee

should they decide to terminate that person, the actual termination rates might not indicate the full

extent of matching unsuitability. Alternatively, rates of employee turnover could be studied to

identify labour market pooling; high employee turnover rates indicate that workers can readily

change jobs and firms can readily hire new employees. Baumgartner (1988) looks at medical

practitioners and shows that in larger markets, practitioners perform a narrower range of activities,

confirming that agglomeration can foster specialisation.



The other explanation for labour market pooling is that it is based on risk. When a new employee

commences employment with a firm, both the employee and the firm carry risks if the relationship

is unsuccessful and the employee is terminated. The worker will need to find another job and the

employer will need to find another employee. Where the worker’s skills and the firm’s requirements

are specific to an industry, both of these needs are thus more easily met where the industry is

concentrated and there are alternative firms and alternative potential employees. Worker and firm

risk are both reduced by localisation. However, industries are subject to periodic ‘shocks’ that

result in workers losing their jobs. Locating in a specialised city exposes workers to the risk of

losing their job and being unable to find alternative employment locally.

SGS Conclusion: Australia’s limited number of major cities means that there is not the level

of city specialisation which is observed in the United States or Europe.

The risk of industry shocks therefore discourages localisation in opposition to the benefits of

specialisation for firms and employees. Simon (1988) considers the relationship between the

unemployment rate and a city’s level of specialisation. Unemployment rates are shown to be

greater in cities with higher degrees of specialisation - consistent with the idea of industry shocks

being an important issue. It could therefore be expected that workers in specialised cities demand

higher wages to compensate them for this risk. Diamond and Simon (1990) show that workers

demand higher wages in more specialised cities and that these higher wages are also related to the

cyclical variability of employment by industry.

Costa and Kahn (2000) advance the argument that risk is lower and matches of employee to role

are better in larger cities. By looking at “power couples” – married couples who both have at least

a bachelor degree qualification; Costa and Kahn documented a substantial increase over time in

the population proportion of large cities that these couples comprise – from 32% in 1940 to 50% in

1990. This may result from these couples having met, married and stayed in large cities or it may

be explained by large cities being better able to offer career opportunities that closely match the

abilities of both partners than smaller cities. To test for each of these explanations, Costa and Kahn

look at the differences between the location patterns of “power couples”, other types of couples,

single persons and unmarried couples and concluded that 36% of the increase in concentration of

“power couples” in large cities can be ascribed to the dual career hypothesis.

Therefore, if the productivity of the highly educated is important for economic performance, then

large cities have a productivity advantage in so far as their ability to better match employees to

roles that meet their abilities and achievable aspirations.

11.5.4 Home Market Effect

Suppose that increasing returns result in concentration of employment into a large factory. This in

turn creates a large market for suppliers who seek to locate close to the large factory to reduce

their transport costs. This leads to a ‘magnification’ effect where the ‘home market’ (the large

factory and its suppliers) expand in a self reinforcing process of agglomeration.



Krugman (1980) and Davis & Weinstein (1996) formally investigated this phenomenon. Davis and

Weinstein (1999) looked at regional agglomeration across Japanese prefectures. They identified

substantial increasing return effects on industrial concentration in eight of nineteen prefectures.

They concluded that the home market effect is an important determinant of regional concentration

in both large cities and smaller localities. Hanson (1998a) looked at the shift in Mexican industrial

production from Mexico City to cities along the US border in response to the NAFTA trade

liberalisation agreement. In this instance, the opportunities from trade with international markets

began to outweigh home market advantages from locating in the Mexico City mega polis and the

home market effect was diluted.

11.5.5 Consumption

While it is relatively accepted that cities contribute to industry agglomeration because of their

effect on industry productivity, recent research also looks at the role of consumers in large cities in

encouraging consumption driven agglomeration economies of city firms. Glaeser (2001) argues

that there are four processes by which this can happen.

Firstly, the population of the city is large enough to create a viable local market for some goods

and services that are not available in smaller centres (such as operatic performances). Secondly, a

large city may create an aesthetic charm (‘pace’, ‘style’ or ‘mood’) which enhances citizen and

visitor’s sense of wellbeing and in doing so encourages citizens and visitors to spend more money

on leisure, food and beverage and retail goods. Thirdly, the population of the city is large enough

for the provision of public goods and services that are not available in smaller centres (such as

specialised medical services). Fourthly, the dense settlement pattern of cities allows goods and

information to be exchanged rapidly.

SGS Conclusion: Given the geographical size of Melbourne (and other Australian cities)

Glaeser thinking can be applied to the distinct regions which have formed within the city.

Figure 46 highlights the different employment / economic regions identified in Melbourne. For

example, Footscray CAD should be able to provide a range of services to western Melbourne.

But a relatively small population and relatively poor links to the central core prevent it from

being able to fulfil this role.



Figure 46 Identified Employment Regions within Metropolitan Melbourne


Glaeser presents evidence to show the importance of consumption in driving the agglomeration of

activities in cities. The phenomenon of reverse commuting indicates that some workers are willing

to live in inner city locations and drive to work in suburban locations – presumably because inner

city living – despite the high cost of dwellings – offers the resident a high amenity environment in

which to live, socialise and shop. Glaeser also regresses rates of urban growth against measures

of the four processes identified as encouraging consumption driven agglomeration in cities. The

analysis showed that having a temperate climate and/or specialised leisure facilities promoted a

cities growth.

Waldfogel (2003) takes the work of Glaeser further by investigating the concept that the larger

markets in cities enable goods to be more closely tailored to individual consumer tastes. Waldfogel

looked at radio listening patterns and identified that as a cities population increased by one million

residents, the proportion of the population who listened to radio increased by 2%. Waldfogel

identified that the number of radio stations targeting particular ethno linguistic subcultures (African

and Hispanic) also increased with city population size. This suggested that radio stations were able

to diversify their offer in larger cities and thus increase their total listening audience (market size).

Tabuchi and Yoshida (2000) found that the real wages (spending power) of city residents was

elastic (by between -7% and 12%) depending on city size. This is evidence that some city

residents are willing to forego wages to enjoy the consumption benefits of living in a large city.



APPENDIX E: DETAILED INDUSTRY WIDE BREAKDOWN OF INDUCED IMPACTS

Table 31 Total Induced Impacts (by Industry) of Additional Households on

Knox (C) Output ($ Million)

Estimated Direct Impacts of Household Consumption

Estimated Total (Direct + Indirect) Impacts of Household

Consumption

At 2021 At 2046 At 2021 At 2046

Agriculture, Forestry & Fishing $0.03 $0.07 $0.06 $0.15

Mining $0.00 $0.00 $0.02 $0.04

Manufacturing $5.09 $12.67 $9.79 $24.36

Electricity, Gas, Water & Waste Services $0.25 $0.63 $0.45 $1.13

Construction $0.06 $0.14 $0.68 $1.69

Wholesale Trade $2.35 $5.86 $3.77 $9.38

Retail Trade $3.79 $9.44 $4.95 $12.33

Accommodation & Food Services $1.85 $4.61 $2.40 $5.96

Transport, Postal & Warehousing $0.50 $1.23 $1.18 $2.93

Information Media & Telecommunications $0.43 $1.08 $0.82 $2.04

Financial & Insurance Services $1.99 $4.94 $4.18 $10.39

Rental, Hiring & Real Estate Services $7.64 $19.02 $10.09 $25.10

Professional, Scientific & Technical Services $0.11 $0.27 $1.15 $2.87

Administrative & Support Services $0.04 $0.11 $0.79 $1.97

Public Administration & Safety $0.03 $0.08 $0.13 $0.32

Education &Training $0.82 $2.04 $1.07 $2.65

Health Care & Social Assistance $1.25 $3.10 $1.54 $3.84

Arts & Recreation Services $0.47 $1.16 $0.61 $1.52

Other Services $0.98 $2.45 $1.52 $3.78

Local Expenditure $27.68 $68.89 $45.18 $112.45



Table 32 Total Induced Impacts (by Industry) of Additional Employment on

Knox (C) Output

Estimated Direct Induced Employment

Estimated Total (Direct + Indirect) Impacts of Induced

Employment on Output ($ Million)

At 2021 At 2046 At 2021 At 2046

Agriculture, Forestry & Fishing -1 -3 -$0.34 -$0.60

Mining 0 0 $0.15 $0.26

Manufacturing 16 19 $24.89 $45.01

Electricity, Gas, Water & Waste Services 2 5 $1.76 $4.30

Construction 13 29 $8.14 $19.42

Wholesale Trade 10 22 $8.12 $18.98

Retail Trade 23 56 $5.81 $14.60

Accommodation & Food Services 19 45 $4.11 $10.25

Transport, Postal & Warehousing 11 28 $5.26 $12.73

Information Media & Telecommunications 10 19 $5.90 $11.94

Financial & Insurance Services 18 56 $16.68 $49.36

Rental, Hiring & Real Estate Services 4 11 $16.07 $42.14

Professional, Scientific & Technical Services 44 153 $15.69 $51.12

Administrative & Support Services 9 18 $4.32 $9.81

Public Administration & Safety 15 30 $3.04 $6.40

Education &Training 17 39 $2.71 $6.43

Health Care & Social Assistance 35 112 $4.50 $13.77

Arts & Recreation Services 6 14 $1.38 $3.37

Other Services 10 24 $3.13 $7.76

Local Expenditure 262 678 $131.32 $327.04



Table 33 Total Induced Impacts (by Industry) of Additional Employment on

Knox (C) Employment

Estimated Direct Induced Employment

Estimated Total (Direct + Indirect) Induced Employment

At 2021 At 2046 At 2021 At 2046

Agriculture, Forestry & Fishing -1 -3 -1 -2

Mining 0 0 0 0

Manufacturing 16 19 38 70

Electricity, Gas, Water & Waste Services 2 5 3 8

Construction 13 29 24 57

Wholesale Trade 10 22 21 48

Retail Trade 23 56 60 152

Accommodation & Food Services 19 45 34 85

Transport, Postal & Warehousing 11 28 20 49

Information Media & Telecommunications 10 19 13 27

Financial & Insurance Services 18 56 28 84

Rental, Hiring & Real Estate Services 4 11 9 23

Professional, Scientific & Technical Services 44 153 61 198

Administrative & Support Services 9 18 18 42

Public Administration & Safety 15 30 17 36

Education &Training 17 39 25 59

Health Care & Social Assistance 35 112 45 136

Arts & Recreation Services 6 14 9 23

Other Services 10 24 22 55

Local Expenditure 262 678 447 1,151



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