Faster Rail Scenario in Sydney: A Spatial Computable General Equilibrium Analysis

1. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Spatial computable general equilibrium analysis of faster rail in the Newcastle–Sydney–Wollongongconurbation JDS International Seminar 2019 21/01/2018 Dr James Lennox Centre of PolicyStudies Email:James.Lennox@vu.edu.au Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

2. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels Overview e Cities, both rich and poor, face many challenges,including: ) Inadequate or unaffordablehousing. ) Barriers to mobility oraccessibility. ) Energy- and carbon-intensive urbanforms. ) Vulnerability to impacts of climatechange. e Spatial computable general equilibrium (SCGE) models can show how new infrastructure or policies will reshapecities. ) An application of the ‘VU Cities’ SCGE framework to model hypothetical rail improvements in the region of Sydney, Australia illustratesthis. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

3. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels Introduction Motivation Theory andmodels VU Cities–Sydney model Modeloverview Some of the underlyingtheory Faster rail scenario Scenariodescription Spatial and regional macroeconomicimpacts Conclusions Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

4. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels Australiancitieshavegrownmainlybysprawlingoutwards e This urban form has delivered many residentshigh mobility but poor accessibility to jobs orservices. Sydney’s M4 motorway looking east towards the CBD(circled). Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

5. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels Housing has become unaffordable formany e Sydney residential price index +5.3% p.a.2003–17 e Sydney established house price index +5.9% p.a. 2003–17 e Huge spatial variation in property pricesand in landvalues Ratios of land valuations to median valuation,2017. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

6. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels Choices between competing visions and projects:1 NSW is considering a ‘Fast RailFuture’ e Can we ease Sydney’s housing affordability problems by allowing people to commute from regionaltowns? e Or at least reinvigorate struggling regions? Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

7. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels Choices between competing visions and projects:2 Victoria has announced an outer suburbanrail loop e Will this shift more jobs outwards, closer toresidents? e Or at least boost growth of secondary businessdistricts? Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

8. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels Introduction Motivation Theory andmodels VU Cities–Sydney model Modeloverview Some of the underlyingtheory Faster rail scenario Scenariodescription Spatial and regional macroeconomicimpacts Conclusions Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

9. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels How can we go about modellingcities? e Gravity models: empirically explain transport demands between fixedlocations. e Alonso-Muth-Mills circular city model: theoretical model explains bid-rent curves as a function of distance from the CBD. e Fujita–Ogawa model: theoretical model admits polycentric urban forms with both mixed and segregated landuses. e McFadden: empirical model of households’ location choices based on random utility theory =⇒ basis of much modern applied urbaneconomics. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

10. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Motivation Theory andmodels Where do SCGE modelsfit? e Many-region models focussed on interregional freight transport costs,e.g. ) Br¨ocker’s ’02 model ofEurope ) Horridge, Madden & Wittwer’s ’05 TERM model forAustralia e Models featuring commuting between regions/areasof ) Countries, e.g. RAEM for Netherlands (Oosterhaven et al.’01) ) Cities, e.g. RELU-TRAN for Chicago (Anas & Liu,’07)) ) N.B. much work in Japan: Koike, Sato, Ishikura,... e An alternative to the more ad hoc Land Use Transport Interaction (LUTI)models. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

11. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory Introduction Motivation Theory andmodels VU Cities–Sydney model Modeloverview Some of the underlyingtheory Faster rail scenario Scenariodescription Spatial and regional macroeconomicimpacts Conclusions Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

12. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory VUCities e Framework for SCGE modelling of large metroregions. ) Hundreds of land, labour, housing and product markets tightly linked via commuting flows, shoppingtrips ) Long run comparativestatics ) Endogenously determined residence locations, job locations, developmentdensities. e Implementations todate: ) Newcastle–Sydney–Wollongongconurbation ) State of New South Wales for Faster Railstudy ) State of Victoria for InfrastructureVictoria’s 30-year strategy Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

13. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory VUCities–Sydney e 363 zones cover three major metros: Sydney, Newcastle, Wollongong Detail for GreaterSydney Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

14. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory Sectoralstructure e Four sectors produce tradablegoods/services: )Primary )Manufacturing ) Construction, utilities, wholesale, storage &transport ) Business services & governmentadministration e Three sectors produce locally non-tradable (in-person) services: )Retail ) Accommodation &restauration ) Health, education & personalservices e Five skill levels of working households providelabour e Goods and services demanded by firms, workingand non-working household types, rest of the world(exports) Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

15. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory Introduction Motivation Theory andmodels VU Cities–Sydney model Modeloverview Some of the underlyingtheory Faster rail scenario Scenariodescription Spatial and regional macroeconomicimpacts Conclusions Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

16. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory Discretechoices e A individual worker-household of skill k chooses industry i , place of residence r and place of work sbased on: ) Residential amenityBr ) Housing and other local living costsPk,r ) Local skill- and industry-specific wage ratesWk,i,s ) Commuting costsτrs ) Individual idiosyncratic preferences zi,r,s,okis a random Fr´echet-distributedvariable e Indirect utility function for individual j has theform zi,r,s,ok Br(1−t)Wk,i,s = V i,r,s,ok eκτrsPk,r Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

17. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory Extension to discrete shoppingtrips e Assuming households have a ‘love of variety’ for non-tradable services from different locations, Dixit-Stiglitz-type sub-utility functions for these services can be modified to include shopping travelcosts. e Anas’ 2007 (J. Urban Econ.) model of travel and consumption explicitly generates shopping traveldemands. ) Households optimise the frequency of trips to each shopping location and the quantity of purchases pertrip. ) Longs trips are infrequent but purchaseslarger. ) Short trips are frequent and purchasessmaller. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

18. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory Land use andtransportation e Land in the region is exogenously allocated to planningzones: ) Each planning zone may permit one or severaluses ) Some land area is defined as ‘unusable’ (roads, parks,etc.) ) Allocations between (permitted) uses respond to relativeprices e Workers/households’ travel between locations entailscosts ) Generalised travel costs (GTCs) exogenously specified for every origin–destinationpair ) Modal GTCs combine various monetary and time costs for road, rail or bustrips. ) Overall GTCs combine modal GTCs reflectingtravellers’ preferences for eachmode. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

19. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Modeloverview Some of the underlyingtheory Agglomeration effects e Firms’ productivity and residential amenity increase with local agglomeration ) Measured by effective density, i.e. gravity-weighted sum of jobs orresidents ) Constant elasticity of labour productivity to effective job density ) Constant elasticity of residential amenityto effective residentialdensity Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

20. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Introduction Motivation Theory andmodels VU Cities–Sydney model Modeloverview Some of the underlyingtheory Faster rail scenario Scenariodescription Spatial and regional macroeconomicimpacts Conclusions Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

21. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts A hypothetical scenario of faster railtravel e Scenariodefinition ) In-vehicle time component of train GTC reduced by 10% for all O–Dpairs. ) No changes to bus or carGTCs. ) Overall O–D pairs GTCs recomputed using original mode shares. ) Tax rate adjusted upwards to raise A$300m p.a. tofinance improvements. e Solve for changed transport costs andtaxes ) Neglects feedbacks from locational changes to transport costs (i.e. de/congestion effects and modeswitching) ) Solving VU Cities model iteratively with a compatible transport model could endogenise thoseeffects. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

22. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Introduction Motivation Theory andmodels VU Cities–Sydney model Modeloverview Some of the underlyingtheory Faster rail scenario Scenariodescription Spatial and regional macroeconomicimpacts Conclusions Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

23. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Locationchoices Residentworkers Jobs Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

24. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Location choices: detail forSydney Resident worker numbers increase along rail corridors. Jobs increase most in CBD, inner suburbs, majorsub-centres. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

25. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Wage rates and residential rents: detail forSydney Residential rents rise increase along railcorridors. Wage rates fall in CBD, inner suburbs. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

26. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Employment by industry: detail forSydney Health, education & personal services influenced by local demand changes. Business services & administration influenced mainly by labour supply changes. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

27. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Changes in employment, GRP andcomponents Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

28. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Contributions toGRP e In aggregate, more workers are attracted to theregion. ) Lower travel times and higher amenity more than offset slight wagedeclines. e Effective labour per worker rises due to net positive compositional and spillovereffects. ) This induces more use of capital (structures,equipment) e Land is fixed resource and becomes moreexpensive ) But also allocated more efficiently, so total effective units increaseslightly. e Rises in GRP and utility per worker are much smaller than aggregate GRPrise. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

29. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Scenariodescription Spatial and regional macroeconomicimpacts Sectoralimpacts e Sectoral effects at the aggregate level are similarly modest by differentiated,e.g. ) Business & admin. benefits most from increased labour supply to innerSydney. ) Employment in primary and manufacturing sectors (also tradables) is dispersed so face lower laboursupply. ) Dwellings become more expensive so increases aremoderated by consumption substitutioneffects. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

30. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Introduction Motivation Theory andmodels VU Cities–Sydney model Modeloverview Some of the underlyingtheory Faster rail scenario Scenariodescription Spatial and regional macroeconomicimpacts Conclusions Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

31. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Recap e Governments’ decisions about transport infrastructure and land uses can ultimately reshapecities. e People trade off various costs and benefits in choosing where to live andwork. ) Housing and other localcosts ) Localamenities ) Access to different jobopportunities ) Earning higher orlower wages e Interactions between land use, transport and labour markets make the effects of infrastructure investments or transport policies difficult toassess. e SCGE models provided a (simplified) representation of peoples’ choices and local marketinteractions. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

32. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Conclusions e Many spatial impacts are intuitive but SCGE models quantify them: ) More people want to live near and use trains if they arefaster. ) Faster travel on a CBD-centric network boosts CBD labour supply and advantages sectors that agglomeratethere. e Some spatial impacts arequalitatively ambiguous,e.g. ) Where will local population-serving jobsincrease/decrease? e Aggregate impacts, esp. per capita, are oftenambiguous. ) The rail improvements in the scenario outweighed theircost ) But most of the boost to GRP was due to higherpopulation ) Such ‘dilution’ of per capita benefits is rarely even considered bydecision-makers! Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS

33. Introduction VUCities–Sydneymodel Faster railscenario Conclusions Acknowledgements anddisclaimers e Development of the VU Cities framework received financial supportfrom: ) Dept. Premier & Cabinet,NSW ) InfrastructureVictoria ) Commonwealth Scientific & Industrial ResearchOrganisation e This study of rail improvements was neither funded nor endorsed by the NSW government. The scenario design was the author’s own. It does not represent the ’Fast Rail’ project of the NSW government (see slide 6), nor any other NSW government proposal orpolicy. e Results presented here are preliminary and subject to change. A final version of this study will be submitted for publication later in2019. Dr J. Lennox, CoPS, VictoriaUniversity VU Cities–Sydney forJDS