Available online at ScienceDirect. Transportation Research Procedia 16 (2016 )

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1 Avalable onlne at ScenceDrect Transportaton Research Proceda 16 (2016 ) nd Internatonal Conference "Green Ctes - Green Logstcs for Greener Ctes", 2-3 March 2016, Szczecn, Poland Predctng market allocatons, user benefts and wder economc mpacts of large nfrastructure nvestments for freght transportaton Bjørn Gjerde Johansen*, Wljar Hansen Insttute of Transport Economcs, Gaustadalleen 21, 0349 OSLO, Norway Abstract Conventonal cost-beneft analyses of nfrastructure projects are often partal analyses n whch only the transport market s assessed whle adjacent markets are neglected. For large nfrastructure nvestments, however, ths s more often than not an unrealstc assumpton, especally n the case of nvestments affectng urban areas where markets adjacent to the transport markets are beleved to exhbt a large number of externaltes. Ths paper suggests an alternatve approach to tacklng the problem; namely, modelng the entre Norwegan economy n a general equlbum settng. We have developed a Spatal Computable General Equlbum (SCGE) model for Norway that takes nto account urban dynamcs through New Economc Geography features. Ths means modelng centpetal and centfugal economc forces that produce changes n urban clusters. We use t to calculate drect benefts and the wder economc mpacts of nfrastructure nvestments for freght transport n selected case studes. For one partcular case study, we fnd wder economc mpacts of 17.0% of the drect benefts for passenger transport and 3.7% for freght transport. Ths suggests, frst, that apprasal of nfrastructure projects s to underestmate the real benefts and, second, decsons based on conventonal wder economc mpact analyses that only take nto account passenger transport wll tend to overemphasze passenger transport relatve to freght transport. We argue that goods and labor demand effects,.e. effects n markets adjacent to the transport market, are mportant f we are fully to realze the effects of nfrastructure nvestments The Authors. Publshed by Elsever B.V. Ths s an open access artcle under the CC BY-NC-ND lcense 2015 The Authors. Publshed by Elsever B.V. ( Peer-revew under responsblty of the organzng commttee of Green Ctes Peer-revew under responsblty of the organzng commttee of Green Ctes Keywords: Spatal computable general equlbum modelng; SCGE; general equlbum; wder economc mpacts; transport apprasal. * Correspondng author. Tel.: ; fax: E-mal address: bgj@to.no The Authors. Publshed by Elsever B.V. Ths s an open access artcle under the CC BY-NC-ND lcense ( Peer-revew under responsblty of the organzng commttee of Green Ctes do: /j.trpro

2 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) Introducton In most countes, dsaggregated four-step passenger transport models wth a detaled road network are used when nfrastructure nvestments are beng apprased, and Norway s no excepton n ths regard (.e. partal market analyses n whch only the transport market s taken nto account). Norway also utlzes a transport model for freght, one n whch goods demand s held constant and the logstcs chan s adapted to mnmze logstcs costs gven the new nfrastructure (de Jong and Ben-Akva, 2007). These models calculate (drect monetary) the benefts of larger nfrastructure projects, ther man strength beng the extremely dsaggregated representaton of the transport market and nfrastructure networks. However, ths approach has two shortcomngs: Frst, snce demand s held constant for freght transport, t s mpossble to predct how the demand for goods adapts to the lower transport costs. Second, t s generally beleved that larger nfrastructure projects wll have repercussons throughout the economy. Hence, wthout takng the rest of the economy nto account, t s not possble to assess how these repercussons wll manfest, nor the effect they may have on the benefts of the nfrastructure nvestment. Ths paper suggests an alternatve approach n a general equlbum settng where all transactons n the Norwegan economy are taken nto account. We have developed a Spatal Computable General Equlbum (SCGE) model for Norway of the New Economc Geography (NEG) class (Krugman, 1991). Ths class of models effectvely takes nto account urban dynamcs through centpetal and centfugal economc forces that produce changes n urban clusters. The model s nspred by, among others, RAEM (Ivanova, Heyndckx et al., 2007) and RHOMOLO (Brandsma, Ivanova et al. 2011), and s drectly based on an updated verson of the ognal PINGO model (Hansen and Ivanova, 2012). The man reason ths partcular model stands out s the qualty of nput data; data regardng natonal accounts from the central statstcs bureau and of passenger and freght transport from the natonal transport models are of hgh qualty. Ths approach not only makes t possble to calculate the drect welfare benefts n standard project apprasal, but also the wder economc mpacts. Moreover, t makes t possble to forecast the general effect of an nfrastructure project on supply, demand, pces and allocatons through the repercussons t has on the economy. Our SCGE model s effectvely lnked to the Norwegan natonal transport models for both passenger transport and freght transport. Runs wth these transport models produce LoS data and tp matces that are used as nput data for the SCGE model. Thus, the SCGE model combnes the best of two worlds: the detaled transport network from the network models at the lower level and macroeconomc effects n a general equlbum settng at the upper level. In ths paper we dscuss urban dynamcs n lght of drect welfare effects and wder economc mpacts; we present the man features of the model and, through a case study, show how these can be calculated for freght transport. Frst, we present the background to cost beneft analyses and the wder economc mpacts, and, second, the prototype of the model. Thrd, we conduct the case study for freght transport apprasal. 2. Background: Cost beneft analyses and wder economc mpacts Conventonal cost beneft analyses (CBA) consder the pced consequences of projects, typcally the drect user benefts of a project n addton to drect external effects on emssons or accdent rates. CBAs are usually conducted as partal market analyses n whch the effect n the pmary market (the transport market) s assessed partally, whle all pces n secondary (adjacent) markets are assumed to reman constant. In realty, the effect of a large nfrastructure project wll have repercussons throughout an entre economy. Economc theory states that f markets adjacent to the transport market are complete, the benefts asng from pce and allocaton changes n secondary markets wll only be a redstbuton of the user beneft calculated n a partal CBA (Jara-Daz, 1986). Hence, consdeng effects n adjacent markets n such a perfect compettve envronment wll lead to double countng of welfare effects (Mohng, 1993). A central assumpton behnd the economcs of conventonal CBAs s that of complete markets. If t s no longer realstc to assume that the secondary markets are complete, there may be addtonal benefts to those calculated n the CBA, benefts known as wder economc mpacts (WEI). The two most mportant mperfectons n secondary markets effectvely leadng to WEIs are market power and agglomeraton effects. Market power charactezes a stuaton n whch a producer can earn postve revenues by settng the pce hgher than t would have been n a perfect competton scenao. Ths leads to a lower number of traded commodtes n equlbum and thus to neffcent

3 148 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) resource allocaton. Agglomeraton effects manly denote the fact that larger or denser labor markets ncrease the amount of potental nteracton between agents. Ths tends to reduce fctons n the labor market, thus reducng transacton costs of partcpatng n the labor marketplace. For nstance, larger labor markets tend to map specalzed human captal to the ght specalzed job more quckly. Other agglomeraton ncludes knowledge spllovers and shang effects. When agents nteract n a market, they do not take nto account the externaltes they nflct upon others when ncreasng the densty of the market. Ths leads to less effcent labor allocaton than under the assumpton of perfect competton. 3. Data and metadata The model s calbrated for the base year 2012 and ncludes 25 sectors (three agcultural, two related to ol, gas and mnng, thrteen commodty producng ndustry sectors and seven servce sectors). Producton takes place by means of the two producton factors captal and labor and ntermedate nputs from other sectors. The spatal dmenson of the model s mplemented as consstng of 90 zones, as can be seen from the llustraton below. Furthermore, seven foregn sectors are ncluded for mports, exports and net transfers abroad. The man data sources used for calbraton are lsted below the fgure. Fg. 1. The spatal dmenson of the model: The set of 90 zones. The socal accountng matx (SAM): Ths s the man data source for the SCGE model. A SAM s a complete lst of all transactons n the economy for a gven year (the base year of the model) at the natonal level. Rows represent use, whle columns represent supply, and there s one row/column par for each sector, each commodty, and for other transactons such as captal supply/use, government consumpton/producton, household consumpton/producton (labor), mports/exports, transfers, etc. The most fundamental underlyng assumpton n SCGE modelng s that the economy s n a state of equlbum represented by transactons n the SAM. Ths s formalzed through row sums beng equal to column sums,.e. there s nether excess demand nor supply n the economy. The SAM s compled of data from the natonal statstcs bureau for the base year 2012 and balanced through a least squares mnmzaton procedure. Sectoral growth rates: In order to run the model startng n other years (than 2012), sectoral growth rates are appled to each element of the SAM, and then the balancng procedure s repeated. It s therefore possble to forecast changes resultng from nfrastructure projects mplemented n the future as well. The growth rates are collected from MSG, the equlbum model owned by the Mnstry of Fnance and admnstered by the Natonal Statstcs Bureau.

4 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) Regon-specfc sectoral producton statstcs: Snce the SAM s at a natonal level, regon-specfc sectoral producton statstcs for producton, gross value added, captal nvestments, wages and employees are used to dstbute transactons, captal, labor, producton, consumpton, etc., from the SAM to the zones of the model. The model s calbrated to 90 zones correspondng to Norwegan regons. The regon-specfc sectoral producton statstcs are collected from the Natonal Statstcs Bureau. Runs wth the natonal transport models: Results from the natonal freght model are used to calbrate nter-zonal trade of commodtes, whle results from natonal passenger models calbrate the travellng and commutng pattern wthn and between zones. Furthermore, scenao runs wth the freght and passenger models are used as nput for scenao runs for the SCGE model by shockng the economy wth new LoS data and predctng the macroeconomc repercussons. Elastctes and econometc estmates: It s not possble to calbrate all model elastctes based on the avalable data, so some are collected from the lterature. Ths ncludes ncome elastcty, a frm s substtuton elastctes between captal and labor, the Fsch elastcty, the Armngton elastctes wth respect to natonal producton and mport, elastctes for export and the agglomeraton elastcty. These are wthn reasonable bounds. Furthermore, the mgraton part of the model s mplemented based on econometc estmates conducted on a panel of mgraton between countes, wth wages and unemployment as explanatory vaables. Senstvty tests wth respect to these parameters and estmates can be found n Hansen and Johansen (2016). Informaton regardng choce of specfc model elastctes may be obtaned from the authors on request. 4. The model The model s based on the noton of representatve agents, e.g. households, frms and the government. It s assumed that these agents act by predefned optmzaton ctea; frms maxmze profts accordng to the producton functon, whle households and the government maxmze a utlty functon. Accordng to economc theory, the compettve equlbum s defned as the pce vector that maxmzes the optmzaton ctea for each agent, such that budget constrants hold and markets clear,.e. the pces that mpose equalty between supply and demand (Arrow and Debreu, 1954). The structure of the model s shown n the fgure below: Bass data Reference data Growth rates Reference equlbum Functonal equatons Exogenous parameters Scenao analyses New equlbum Senstvty analyses Neccessary changes Modeller s judgement EXIT Fg. 2. Runnng the SCGE model for scenao analyses (adapted from Hansen, 2015).

5 150 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) The above fgure llustrates the process of runnng the model. Frst, a reference dataset s produced based on the SAM, the regonal-specfc sectoral producton statstcs and the MSG growth rates, as explaned n the prevous secton. Ths reference dataset, along wth numerous exogenous parameters and elastctes, s then used to calbrate the analytcal equatons of the model. The SCGE model s calbrated correctly when the agents of the model maxmze ther optmzaton ctea n such a way that the reference data are replcated n a reference equlbum. Ths means the agents of the model act n a way that s consstent wth the nput data,.e. that they produce a reference equlbum equvalent to the natonal accounts n the base year as reported by the natonal statstcs bureau and all the transactons as reported n the SAM take place. To run a scenao analyss, the economy s shocked wth changes n some of the baselne values. For an nfrastructure nvestment, ths means that new LoS data for passenger and/or freght data from the transport model are read n. Agents then have to re-optmze ther ctea functons subject to the changes, and ths produces the new equlbum. Note that the model does not explan the transtory changes between the equlba; the only output s the new equlbum, whch may be nterpreted as the long-term state after the economy has settled down. The equlbum s thus the set of pces and allocatons that satsfes all agents wth ther own choces gven the choce of everyone else n the economy. To assess the effects of the nfrastructure nvestment, the reference equlbum and the new equlbum are compared. In ths way, the relatve changes n pces, allocatons and agents welfare may be calculated. When the new equlbum has been produced, senstvty analyses wll be conducted and the modeler wll make a subjectve judgement regardng the realsm of the results. If the modeler s not satsfed, the necessary changes must be conducted. Ths procedure wll be repeated untl the modeler s satsfed. The model s a set of 251,920 endogenous vaables and 251,920 analytcal equatons. In the followng subsectons, certan central equaton sets are lsted explctly, although most are omtted. A full lst of model equatons can be seen n Hansen and Johansen (2016). In the followng, subscpts denote zones, whle subscpts denote sectors. The superscpt wll denote baselne values,.e. values from the ntal equlbum Households The economy s modeled so that each zone entals one representatve household, whch chooses consumpton of goods and servces n a way that the consumpton bundle maxmzes a utlty functon subject to a budget constrant. The ncome of each household s the sum of wages, captal rents and transfers from the government. Labor ncome ncludes the ncome from workng n the home zone, as well as from commutng to and workng n other zones. The commutng pattern between zones s dscussed n subsecton 4.6. Captal ncome s calculated as the sum of captal nputs over all regonal sectors multpled by the sector-specfc rate of return on captal. The consumpton budget of a household s calculated as after-tax ncome mnus savngs and transport costs plus socal transfers. Snce the model does not nclude a bankng agent (.e. households are not allowed to borrow money), the savngs cannot exceed the total ncome and the consumpton budget s stctly postve. The utlty functons are of the Stone-Geary form (Stone, 1954): U r A r C H where s utlty, s consumpton, s a scalng constant, s the household s subsstence consumpton level and s the budget share of commodty for the household n zone. Stone Geary utlty functons ensure that the expendture system s lnear, and may be nterpreted as a Cobb Douglas functon wth as nputs (utlty deves from consumpton level above subsstence level). We make a sharp dstncton between utlty and welfare. Utltes are abstract measures used only as optmzaton ctea for households. Welfare s used to measure the monetary benefts of an nfrastructure nvestment, and s calculated based on the noton of compensatng vaaton (CV). CV s the amount households would need to pay to be ndfferent between the nvestment and the baselne scenao,.e. the transfer that would be necessary to make aggregate utlty the same as n the baselne scenao gven the new pce vector. H

6 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) Market power We assume that frms n ndustry and the servce sectors have market power of the Dxt Stgltz type (Dxt and Stgltz, 1975). These frms face fxed and vaable costs of producton, mplyng ncreasng returns. All frms n each sector and zone are dentcal n sze and producton technology, but they produce dfferent vaetes that act as mperfect substtutes. Consumers prefer to have a bundle of vaetes and wll therefore demand a postve amount of each vaety even f pces are dfferent. Frms are therefore able to set pces hgher than vaable costs to cover the losses of the fxed costs and stll be able to sell ther commodtes. Snce we assume free market entrance, the number of frms s determned endogenously, such that profts are equal to zero (when frms start makng profts new frms enter the market and dve pces down agan, such that the revenue s always equal to the frms fxed costs). The mark-up on the pce due to market power s determned accordng to the equaton: where superscpt denotes values from the baselne equlbum, s the fnal pce for the commodty, s the pce under the assumpton of perfect competton, s the number of frms, s the elastcty of substtuton between vaetes and s a dummy equal to 1 f the sector exhbts market power and 0 otherwse. Thus, the markup on the perfect competton pce s hgh when the number of frms and the elastcty of substtuton are low (.e. when consumers are not able to substtute between vaetes). Snce market power of the Dxt Stgltz type mples that vaous vaetes are produced n each frm n each zone, cross-haulng of close substtutes s allowed between regons. Ths means that the model can be calbrated such that t reproduces the nterregonal trade flows we observe n the data Frms PDC ( PD ( PD 0 NF NF 1 1 ) 1 o 1 ) d PD (1 d ) Frms mnmze producton costs for each output level gven producton technology, output pces, wages, captal rent and the pces of ntermedate consumpton goods. Ths gves the cost-mnmzng levels of labor, captal and ntermedate consumpton unts that s requred to produce one unt of the composte sectoral output. Output s then determned accordng to the no-proft condton. The producton functon s a nested functon where the trade-off between labor and captal s determned by a constant elastcty of substtuton (CES) producton technology at the lower level. The proporton of ntermedate nput factors from other sectors and the composte captal labor nput s assumed to be fxed, determned through nput output tables and operatonalzed through Leontef producton technologes at the upper level. The composte captal labor bundle and the ntermedate nputs from sector to sector are thus determned by: II o XD KL okl XD where and are fxed nput-output shares and are producton levels. The pces of the captal labor bundles are determned as weghted after-tax averages of wages and captal rents. Thus, demand for captal and labor can be calculated from cost-mnmzaton of vaable costs from the CES functon plus fxed costs. Fxed costs of labor and captal are calculated as fxed costs per frm multpled by the number of frms n each zone and each sector. The producton level s determned so that the no-proft condton holds for each frm. The resultng captal demand gven the pce vector wll be: K j j K KL (1 tk r ) RK rj PI KL rj PKL KL rj akl rj ( KL 1) NF fck

7 152 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) where and are parameters of the CES functon, s the elastcty of substtuton between captal and labor, s the tax rate on captal, s the captal rent, s the deprecaton rate, s the pce of the nvestment good, s the pce of the labor captal bundle, s the number of frms and s the average fxed cost of captal. The demand functon for labor s defned smlarly (usng the after-tax wage rate as the pce of labor), but not lsted here owng to space restctons Government The federal government s ncluded n the model as a sngle agent who collects taxes, consumes commodtes accordng to a utlty functon and dstbutes socal benefts. These elements add up to the governmental budget constrant from whch the government chooses a consumpton bundle to maxmze ts utlty. The utlty functon s n the Cobb Douglas form: s the maxmzaton cteon of the government, are sector and zone specfc consumpton shares and s the government s consumpton of commodty n zone. Hence, the consumpton bundle resultng from ths optmzaton procedure dstbutes governmental consumpton between vaous regonal commodtes Freght transport and nterregonal trade Accordng to the Armngton specfcaton, goods mported and goods produced n domestc zones have slghtly dfferent specfcatons, such that households wll prefer to consume goods from more than one locaton (Armngton, 1969). Ths ensures that we are able to replcate the trade pattern between countes and regons that we observe n the data. Consumers wll substtute between the vaous specfcatons based on changes n relatve specfcaton pce accordng to a CES functon, where the level of substtuton s defned by Armngton elastctes: A s A1 A 1 XDDE s XDD PDDT aa PDC PTM trmvs where are the sector freght flows from zone to zone, s the composte domestc commodty of type consumed by zone, are Armngton elastctes and and are share parameters of the CES functon calbrated from freght flow data. s the natonal pce of transport margns and the transport margn for movng goods of type from zone to zone. The pce of the composte domestc commodty consumed s calculated as the weghted average pce of commodtes consumed from all zones accordng to the equatons: 4.6. Commutng and mgraton UG PDDT XDD CG r, s G XDDE s Indvduals lvng n zone wll dstbute themselves between workng n zone and commutng to zone. Commuters wll earn ther wages n zone based on ths zone s specfc salary, and spend the wages on consumpton n zone. Thus, commutng s an effectve way of transferng values between zones. To beneft from the predcted commutng pattern from the transport models, commutng preferences are calbrated such that the commutng pattern n the SCGE model replcates the commutng pattern from the transport model. The labor supply s defned as: PDC LS r LCOMM sr s A1 PTM trmv s

8 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) Where s the labor supply (to be splt between sectors) n zone and s the sum of commuters from zone to zone, the commutng preferences are mplemented as: where are commutng preferences calbrated accordng to the transport model, s sectoral labor supply, s the cost coeffcent for generalzed travel cost and s the generalzed travel cost for commutng between zones and. In other words, commutng tps are dstbuted accordng to a gravty model n whch a correcton term s ncluded to ft commutng to the data from the transport model. When scenao analyses are conducted, the generalzed travel cost between zone pars can be changed and wll produce a new commutng pattern. Note that and can be the same zone,.e. the commutng preference model also calculates ntra-zonal commutng. We do not go nto detal on the mgraton part of the model here due to space lmtatons. However, a short, descpton follows. We allow mgraton to change between model runs. Hstoc mgraton data from the Natonal Statstcs Bureau are read n for the baselne equlbum. In the scenao equlbum, the baselne mgraton s changed by a percentage determned based on the relatve wage changes between zones,.e. mgraton wll ncrease n zones where the wage ncreases. The parameters that dve ths effect were estmated n a panel data regresson on hstoc data of mgraton and wage levels between countes Agglomeraton effects Agglomeraton effects are wdely recognzed n the WEI lterature and are modeled takng nto account that large labor markets produce postve externaltes. Ths ncludes both productvty gans due to urban clusters, and reduced fctons n the labor market due to better matches between employers and employees n ndustes where specalzed human captal s requred. Agglomeraton effects are assumed to drectly nfluence the total factor productvty (TFP) of the producton functon of the frms, thus makng both labor and captal more productve. Agglomeraton effects are mplemented through the factor, such that an ncrease n ths factor of ncreases TFP by percent. The factor s modeled as: ext LCOMM s sr 0 LCOMM s sr where superscpt denotes values from the baselne equlbum and s the agglomeraton elastcty. We follow the WEI lterature and have adapted the value. Ths expresson connects the productvty of each worker n zone to the total sze of the zone s labor market, and allows for productvty gans across sectors Market cleang LCOMM sr comm _ pref sr L q exp( exp( TCOMM TCOMM The market cleang condton ensures that demand s equal to supply n all markets. Snce the model only predcts relatve pce changes, one pce must be chosen as the numerare n our case the exchange rate. In lne wth Walras s law, gven equlba n all other markets, the exchange rate must also be the equlbum exchange rate. The model assumes trade balance, so the values of exports and net transfers abroad are restcted to beng equal to the value of mports. Furthermore, pces clear both captal and labor markets n all zones, whch means that frms captal stock must be equal to households total savngs, and that number of workers must be equal to the workforce. To ensure that zonal supply of a commodty s equal to zonal demand of a commodty, the followng equaton must hold: X C CG I SV L q TMX tme sr tme sq o j j XD rj sr ) sq )

9 154 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) where s sectoral and regonal supply, s households consumpton, s governmental consumpton, s nvestments, s changes n stocks, are the commodtes consumed for trade and transport margns; the last term s the consumpton of as an ntermedate good for producton of the other goods Centpetal and centfugal forces The SCGE model contans both centpetal and centfugal forces. Centfugal forces weaken spatal concentraton and urbanzaton, whle centpetal forces work towards further agglomeraton and urban clusters. It s the balance between the two that defnes the urban dynamcs at work between the baselne and scenao equlba. Centfugal forces nclude: Congeston: Congeston s measured through the generalzed transportaton costs for passenger and freght transport. These costs are used as nput drectly from the transport models and therefore are not the outcome of the equlbum model, but taken as gven. Urban clusters ncrease congeston, thus ncreasng the cost of transport. Therefore, frms and ndvduals may deve benefts from localzng n and commutng to zones wth less congeston. Market power: Frms wth market power wll fnd t benefcal to localze n areas where the markets are relatvely small. Ths leads to fewer vaetes for consumers to substtute between, thus ncreasng the market power of each frm and allowng frms to set a hgher mark-up on ther products. Centpetal forces nclude: Shorter transport dstances for households: Households locatng n urban areas wll have more work opportuntes close to where they lve. Ths can reduce transport costs for both commutng and lesure tps. In the model, ths s mplemented by ncreasng ther consumpton budget accordngly. Agents are therefore effectvely able to substtute between transportaton and consumpton. Increased vaety: Households wll prefer to lve n areas where markets are as large as possble. Ths leads to ncreased vaety n ther consumpton bundles through both the Dxt Stgltz market power and Armngton assumptons. Reduced costs of consumpton: When households buy consumpton goods they are faced wth a pce that s nfluenced by the transport costs from the frm to the retaler. Beng located close to the producng frms reduces ths cost, whch agan reduces the pce of the consumpton goods. Shorter transport dstances for frms: Frms located close to other frms wll pay a lower pce for ntermedate consumpton goods through lower transportaton costs. Furthermore, frms located close to the workforce may be able to reduce salaes, snce lower commutng costs for the workers ncrease ther consumpton budget. Increased market potental for frms: Frms wll prefer to be located as close to as many markets as possble, thus ncreasng ther market potental. The fact that frms produce vaetes that are mperfect substtutes ensures that they are able to sell ther products n nearby zones. Moreover, frms less dstant to the retaler wll be able to sell ther products at a more compettve pce snce freght costs are lower. Agglomeraton effects: As dscussed earler, large labor markets n urban agglomeratons mpose postve externaltes on TFP. Ths makes producton more effcent; frms can pay hgher wages, and product pces decrease. Consumpton s cheaper. The ncrease n TFP also allows for more frms gven the zero-proft condton. Ths produces more vaetes, whch ncreases utlty of the consumpton bundle. 5. Model applcaton In ths secton we conduct a case study of an nfrastructure mprovement project between two Norwegan ctes Ålesund and Molde. It s part of the Natonal Transport Plan for Norway and means the man road (E39) between these two ctes becomng ferry-free after constructon of one tunnel and one bdge, ther total length beng 22 km. In

10 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) addton, the general qualty of the remanng road network wll be mproved and the entre nvestment s forecast to be completed by Whle the urban areas of Molde and Ålesund have 23,710 and 38,855 nhabtants respectvely, the correspondng zones of the model consstng of some of the nearby muncpaltes have 60,781 and 82,933 nhabtants. The fgure below shows the model zones Molde and Ålesund, as well as the new road between the urban areas. The road wll be approxmately 85 km n length. Fg. 3. Model zones for Møre og Romsdal county. Runs wth the Natonal Transport Models ndcate that general transport costs between these two ctes wll be reduced by approxmately 8.8%. The changes n transport costs for both passenger and freght transport between all the zones of the model are used as nput data n the SCGE model, and the equlba before and after the nfrastructure change are compared. Results of the analyss are presented n the table below. The frst column dsplays the drect benefts,.e. socal welfare under the compettve market assumpton, wthout market power and agglomeraton effects; the second, the socal welfare of the full model,.e. drect benefts plus wder economc mpacts; the thrd, the wder economc mpacts as a percentage of the drect benefts. Table 1. The dscounted net present value of socal welfare over 40 years. Socal welfare s calculated as the sum of CV over all zones. Monetary values are dsplayed n 2016 currency (mll. NOK). Molde Ålesund Socal welfare Drect benefts Socal welfare Drect benefts + WEI WEI (%) Passenger transport 11,770 mll. NOK 13,770 mll. NOK 17.0 Freght transport 4,359 mll. NOK 4,520 mll. NOK 3.7 Total 16,129 mll. NOK 18,290 mll. NOK 13.4 Even though the WEIs are notceably hgher for passenger transport (17.0%), the SCGE model produces sgnfcant WEIs for freght transport as well (3.7%). The total WEIs (13.4%) sgnfes that there are n fact large external effects n secondary markets that alter the allocatons of workers and commodtes, leadng CBA to sgnfcantly underestmatng the real benefts of the nfrastructure nvestments. The man mechansms governng magntude of the WEIs are the nterplay between centpetal and centfugal forces and the changes n agglomeraton effects and market power these produce. Changes for passenger transport lead to hgher WEIs snce passenger costs affect the labor market to a greater extent through drect effects on the commutng pattern.

11 156 Bjørn Gjerde Johansen and Wljar Hansen / Transportaton Research Proceda 16 ( 2016 ) It s not possble to generalze based on the numbers from ths analyss. WEIs wll always be dependent on the specfc markets that are nfluenced, the frm structure n the relevant zones, the nearby commutng opportuntes and the wage dfferences compared to adjacent labor markets. Nevertheless, the analyss shows that for ths specfc nfrastructure nvestment, WEIs are sgnfcant. Furthermore, analyses of 14 other cases wth the same model gve WEIs n the range 10 30% for passenger transport and 2 6% for freght transport (Hansen and Johansen, 2016). The conventonal WEI lterature only consders passenger transport effects, often through partal analyss of the labor market only. These results have two major mplcatons. Frst, conventonal CBA wll underestmate the total effect of nfrastructure nvestments. Second, decsons based on conventonal WEI analyses that only take nto account passenger transport wll tend to overemphasze the mpact of passenger transport relatve to freght transport. 6. Concludng remarks We have formulated and mplemented an SCGE model based on the New Economc Geography lterature, one that calculates the long-term general equlbum n the Norwegan economy resultng from changes n nfrastructure. By takng nto account urban dynamcs through centpetal and centfugal forces and mperfectons n markets adjacent to the transport market, we are able to calculate the drect user benefts as well as the wder economc mpacts of nfrastructure changes. We have mplemented the model for a specfc case study; namely, an mprovement of the European hghway between Molde and Ålesund n the Møre og Romsdal regon. We present the results for freght transport as well as passenger transport. The model ndcates that there are sgnfcant wder economc mpacts for passenger as well as freght transport and ths has two major mplcatons. Frst, conventonal CBA wll underestmate the total effect of nfrastructure nvestments. Second, decsons based on conventonal WEI analyses that only take nto account the wder economc mpacts of passenger transport wll tend to overemphasze passenger transport relatve to freght transport. However, we emphasze that t s not possble to generalze based on the numbers from ths analyss. WEIs wll always depend on the specfc markets that are nfluenced, the frm structure and sector composton n the relevant zones, the nearby commutng opportuntes and the wage dfferences compared to adjacent labor markets. The results from an SCGE model should therefore be used as supplementary to those from conventonal CBAs f decson-makers are to be well nformed of the total effects of vaous nvestments. Moreover, ths framework enables predctons of how nfrastructure nvestments wll change relatve pces and market allocatons through repercussons n the economy. Metculous senstvty analyses and more elaborate numecal results can be found n Hansen and Johansen (2016). Acknowledgements We are grateful to Dr. Olga Ivanova for helpng out wth the programmng and specfcaton of the model, and wthout whose gudance we would not have been able to mplement t. We are grateful also to the Natonal Statstcs Bureau for provdng natonal accounts data and to the Norwegan transport agences for provdng nput data from the transport models for smulaton purposes. The model s an extenson of PINGO, descbed n Hansen and Ivanova (2012). We agan make reference to the SCGE models RHOMOLO and RAEM (Brandsma et al., 2011; Ivanova et al., 2007), on whch much of ths model s based. RHOMOLO and RAEM contan many of the same mechansms descbed here. References Armngton, P. (1969). "A Theory of Demand for Products Dstngushed by Place of Producton " Staff Papers-Internatonal Monetary Fund: Arrow, K. J. and G. Debreu (1954). "Exstence of an equlbum for a compettve economy." Econometca: Journal of the Econometc Socety: Brandsma, A., O. Ivanova, et al. (2011). RHOMOLO, a dynamc spatal general equlbum model. 19th nternatonal nput-output conference. Alexanda, Vrgna, USA.

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