The Grand Paris Express project: theoretical and practical ...KS-B... · Paris London New York GNP 588 505 960 GNP (per head) 49 800 38 200 43 600 Population 11.8 13.2 22 Gini 0.35

The Grand Paris Express project: theoretical and practical issues

André de Palma, ENS-Cachan, University Paris-Saclay

“Logistics and Maritime Studies on One Belt One Road” Conference - The Hong Kong Polytechnic University

Hong Kong, May 10-11, 2016

OUTLINE Introduction, and setting End of space?What are agglomeration benefits?

FindingsGeneral equilibrium approach Grand Paris ExpressUrbanSim / Metropolis tools LimitationsReferences

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Issues (extended list) What is Grand Paris Express? Which costs are involved; how are they covered? What are the benefits, and how could they be

measured? What are the equity issues: within Paris/France What are the implementation phrases? What are the local/(inter)national dimension?UrbanSim: Partial equilibrium modelMetropolis: Dynamic model


INTRODUCTION AND SETTING


Modelling

Spontaneous/induced spatial organization Modelling approaches so far in…◦ Physics◦ Geography◦ Regional science◦ Transportation◦ New Economic Geography.

Here: Combination of economic, OR, econometric, planning tools, political economy


Academic objectives

Explain how the evolution of a city (Here Paris area, 11 million inhabitants) can be modelled?

Special focus on agglomeration benefits.


Background about 3 major citiesParis London New York

GNP 588 505 960

GNP (per head) 49 800 38 200 43 600

Population 11.8 13.2 22

Gini 0.35 0.45 0.50

Employment 6.0 6.2 8.7

Research 146 000 50 000 130 000


2010 Data from AT Kearney Global Cities Index, 2014 in Le grand Paris Express: Investissement pour le XXI sciècle, SGP

Can we make predictions and evaluate costs and benefits over the life span of a large scale project ?

Role of large Metropolitan area Paris area (10 millions) produces 30 % of

French GNP, but get 22% of disposable income Great London (9 millions) produces 23% of UK

GNP, but get 17% of disposable income. Brussels Region produces 21% of Belgian GNP,

but get 10% of Belgian disposable income. Resources generated by large cities are redistributedDifficulty to define boundary: the legal boundary of IDF (8 millions inhabitants), the geographical frontier (at least 1/3 commuters): > 14 millions!


END OF SPACE?


Technology suggests

Transport costs have decreased historically:Some authors argue that space does not matter, and so just local amenities play a role in structuring the space.

Is that true?


Distribution of Facebook contacts with distanceGoldenberg J. and M Levy (2009) Distance is not dead: Social interaction and geographical distance in the internet era.


Distribution of Email with distances

Goldenberg & Levy (2009)


Impact of space on trade: CEPII, 2009


Trade : Space has not disappeared

French trade has increased with China, but even more with Germany!

Accessibility matters and space still matters


about (slighty higher than) 1

r sRS

Y YX Gd

WHAT ARE AGGLOMERATION BENEFITS? THEIR MEASURE


Definition of Agglomeration economiesA. Smith & A. Marshall: firms and workers are, on average, more productive in larger cities

Graham approach in the UK

E. Glaeser: “Agglomeration economies are the benefits that come when firms and people locate near one another together in cities and industrial clusters.”Paris Sacalay

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Typology of agglomeration benefits Reduced transportation costs◦ inputs: raw material / suppliers / subcontractors◦ outputs: other companies, consumers

Sharing infrastructure, amenities local public goods

Sharing experience, learning Better matching on the job market; division of

labor, specialization, reduced friction, face-to-face Dissemination of knowledge and innovations;

radical innovations: new technologies Headquarter near government, lobbies


Typology of agglomeration diseconomies

Exacerbated competition in the markets for goods, labor, customers◦ Rising production costs, wages, land rent

Systemic risk in sectoral group (diversification)◦ E.g. Textile and steel industry in the North of France

Congestion in transport, ubiquitous queues Rental or purchase prices of offices and

housing units raises with demand Pollution, degradation of the living environment Diseconomies of scale: Bell function with

agglom. Size: Optimal city size


2 difficulties when measuring agglomeration benefits:

Correcting for selection biases 1

Disentangling different sources of agglomeration benefit 2


1. Correction of selection biases Most productive workers choose to work in

most dense areas [Only the most productive firms survive in

dense areas] Productivity gains due to agglomeration

effects :…

◦ Is not the difference of productivity between 2 workers who decided to work in different places, ◦ but it is measured by the difference of productivity

of the same worker who change work location Panel data are needed to correct the

permanent effect of the workers


Correction of selection bias using individual data (panel)

Selection bias generally lead to over-estimate the agglomeration benefits: ◦ typically 20% of over-estimation◦ more than 50% with better econometric

techniques


2. Difficulty to empirically disentangle different sources of agglomeration

Difficulty to separately quantify the impacts of different sources such as:◦ Wages ◦ Local growth◦ Local employment and unemployment◦ Random event, chances, black swans.

Need to do a structural modelParis Sacalay


Some orders of magnitude (literature)

Doubling density increases productivity and wages by 1.4% to 2.5%

Composition of the local workforce explains 50 % of agglomeration benefits ! Should subtract > 20% for endogeneity


5 FINDINGS (AGGLO-MERATION BENEFITS)


Finding 1: Business & workers are on average more productive in large cities "Universal" phenomenon? "Qualitatively: yes! This relationship is

observed in many countries at different periods

“Quantitatively”: no!


Finding 2 : Who benefits from agglomeration economies? The most innovative sectors The most productive firms The largest firms (>100 workers) The most educated workers The top managers [Inequality issues]


Finding 2’: Sectorial differences

Combes et al (2011) for period 1860-2000 compute the elasticity of productivity to population density◦ - 11% for the agricultural sector◦ +13% for industry◦ +7% for services


Finding 3: By how much ?

Elasticity of productivity to density is between 2% and 8%. Depends on:◦ location◦ sectors◦ qualification◦ + methodology used!

Combes, Gobillon (2014) : 3% to 7%


Finding 3’: French empirical results

Relation between density and productivity


Relation between log-productivity and log-density in the French Regions: Combes et al. 2012.

Finding 4: Why ? (summary)

Concentration of firms and population in cities :

◦ … decreases transport costs (inputs et outputs)◦ … favours interactions, which increase

productivity ◦ … favours sharing the experiences, the

innovations◦ … improves matching between firms and workers ◦ … triggers competition between firms,…but

Selection biasParis Sacalay


Finding 5: How does one measure productivity ? Accounting of the firms (?), Production functions (?),… Better measure: Wages: Wage = marginal productivity (theory),

but…Rigidity of wages, minimum wage


GENERAL EQUILIBRIUM ANALYSIS

A. de Palma and Jean Mercenier


Why GE model

Hypothesis at a more fundamental level: e.g. cost elasticity to transport.

Consistent estimates: e.g. no need for exogenous value of the marginal cost of public funds, etc.

Suited to study the range of impacts of a policy (e.g.) toll pricing.


Setting: residence, manufacturers, warehouse/delivery via Internet, malls


Impact of a supply shock on the commuting route in GE


[Quantitative analysis]


GRAND PARIS EXPRESS

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Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong

Objectives of Grand Paris Express

Develop an ambitious 200Km network of automatic public transport around Paris (4 lines, 72 stations) to: Promote 21 development clusters (CDT: “Contrat de

Développement Territorial”) Polycentric city (Limit urban Sprawl) Reduce congestion on existing public (private) network Generate agglomeration effects (at different scales)◦ 685 000 jobs (do nothing), with 115 000 additional jobs

created by the Grand Paris by 2030◦ Concentration of jobs in dense zones

Investment of more than 35 billion € (2020-2030)


20 employment zones with their current density


What is Grand Paris ? (21 CDT)


What is Grand Paris ? (CDT), cont’


“Schéma de développement territorial” Paris-Saclay


+ Fully automated and connected+ Higher frequency of service, + Inter-modality with the existing network - No pricing strategies so far

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Number of trains [H] /one directionPrediction used

CDT &Grand Paris Express network


Timing of the operation

45Paris Sacalay

05/11-12/2016 OBOR Conference Hong Kong

2010 present value of the socio‐economic advantages (CGI estimates)

Scenario of the main project

2010 Present Value in 2010 billion € Reference trendPessimistic

trend

Transport effects 17.7 17.5Regularity 3.5 3.4Comfort 1.6 1.5

Environmental and urban benefits 11.9 11.3

Directs relocation impacts 9.0 7.5Agglomeration effects 6.0 5.8Valuation of new jobs 10.3 10.3

Total Advantages 59.9 57.3


URBANSIM & METROPOLIS TOOLS

A. de Palma, Nathalie Picard,

Yurii Nesterov, Paul Waddell


Need for LUTI models. What are they?

LUTI: required in US for CBA of large infrastructures.

Elasticities are useful, but incomplete local measures (necessary step): location of firms and households, explain agglomeration benefits.


No

Modeling agglomeration effect in UrbanSim + METROPOLIS

Importance of firmography: the closeness of complementary jobs favors … The creation of new firms and relocation The survival of existent firms The development of existing firms

Snow ball effects triggered by transport


Interactions modeled in UrbanSim


Land development& infrastructure

model

Real estate pricemodel

Job location model

Household location model

Demographicmodel

TransportmodelSocial, economic

& environmentalindicators

Environmentalquality

indicators

Social indicators

(inequalities)

Pollution & economicindicatorsPollution & social

indicators

Accessibilityto jobsRegional

attractivity Control totals

OD matrix OD matrix

OD matrix Accessibility

to HH

Accessibilities

Demand for offices

Supply for offices

Slow, constrainedand partial

adjustment of housing supply

Capacityconstraints reduce

choice setDemandincreases

price

Transport infrastructures

Adjustment of supply of offices constrained by

policies

Price-elasticity to

locationPrice-

elasticityto location

Wage-earners, consumers

Home to work trip

METROPOLIS


The main link between US & METRO

Accessibility measure (better than connectivity):◦ Definition: welfare measure◦ Need for heterogeneity ◦ Useful to explain wage, residential location, work

location, …


Four-steps decision tree


Mode choice

Destination

Decision to make a trip

Long term choiceshh & job location,

car ownership

Origin,constraint, purpose,timing

D1

Motorized

PC PT

Other = OT(2 wheel, walking)

D2

No tripAttraction & Emission

Retour

Log-sum mode

Log-sum per O & par D

Prédited by UrbanSim

Population 2005–2050 (%) in the extended CBD


Sectors1. Agriculture2. Industry3. Construction4. Business5. Transport6. Financial activities7. Real estate activities8. Business services to firms9. Personal services10. Education. Health. Social actions11. Administration


Next table: empirical measures of elasticities Elasticities of #jobs in Sector i at time t, wrt

#jobs in Sector j at time t-1. Last line: partial derivative of log(Jobs in

Sector i at time t) w.r.t. density of population at time t-1.

These figures are used to explain why jobs agglomerate over time: US+METRO produce dynamics of agglomeration benefits


Elasticities of #jobs in i to #jobs in j


i \ j 1 2 3 4 5 6 7 8 9 10 11

12 ‐1.87 0.42 ‐0.68 ‐1.57 0.30 ‐0.123 1.93 0.16 2.76 0.37 0.23 0.164 2.73 0.51 0.67 2.38 0.555 ‐0.85 0.06 3.00 ‐0.516 0.00 2.54 ‐0.24 0.467 3.00 0.50 ‐0.698 ‐0.94 0.35 0.64 1.539 ‐1.20 0.56 0.91 0.8110 3.00 3.0011 ‐1.45 0.67Pop ‐0.03 0.06 0.05 0.05 0.04 0.06 0.04 0.06 0.03 0.03

Empirical measure of agglomeration effects in UrbanSim-METROPOLIS Particularly strong synergies within◦ Construction (3)◦ Business (4)◦ Transportation (5)◦ Finance (6)

Positive synergies :◦ Business on industry (2), construction (3) and finance (6) ◦ Business services to firms (8) on real estate (7)

Negative synergies (non-symmetric examples)◦ From real estate (7) to business services to firms (8)◦ From industry (2) to business services to firms (8)


Firmographics structural models

Logit / Probit transition probabilities: Creation and destruction of firms.

Location of newly created firms: MNL Relocation is equivalent to death and birth

(and location) Jobs growing / shrinking or stable in firms Model of disequilibrium (supply and

demand): rental prices do not necessarily clear the market.


Expected benefit of the Grand Paris Express: URBANSIM & METROPOLIS Anticipated agglomeration benefits computed

from US+METRO outputs with different Pop&Jobs scenarios : NPV: 6 to 10 billion €This corresponds an elasticity of 5% for agglomeration benefits (5% discount rate) [Matthew Turner]

80 % of the new jobs will concentrate in dense zone (trend growth + induced jobs)Less than 60 % without Grand Paris Express


LIMITATIONS


Limitations of the current literature Elasticity should a local (not global) measure Density is a1st-order aggregate explanatory

variable Develop a structural model based on

accessibility (not on densities)? Distance should play + or - in accessibility?


Limitations of the current literature Engine is missing (Lowry model) currently, but:◦ Jobs and Population are endogenous in the CBA◦ Non-linearity (bifurcation) matters in the long-run

GE & LUTI models are needed. Current tools implicitly incorporate agglomeration benefits

New technologies matter: ◦ Supply (Automated car, telemedicine, robots, etc.)◦ Demand (Teleworking, teleshopping, MOOC, ..)

Big Data: The end of modelling?


Thanks for your attention


REFERENCES


Selected referencesHistorySmith (1776). An inquiry into the nature and causes of the wealth of nations

Marshall, A. (1890). Principles of Economics

TheoryFujita et Thisse (2013). Economics of Agglomeration: Cities, Industrial Location, and Globalization? Cambridge Uni. Press


Selected references Agglomeration effect & transportationVenables (2007). Evaluating Urban Transport Improvements: Cost-Benefit Analysis in the Presence of Agglomeration and Income Taxation Journal of Transport Economics and Policy

Mackie, Graham and Laird (2011), The direct and wider impacts of transport projects: a review, in A Handbook of Transport Economics, de Palma, Lindsey, Quinet, et Vickerman Eds.

Glaeser, Joshua, Gottlieb (2009). "The Wealth of Cities: Agglomeration Economies and Spatial Equilibrium in the United States"Paris Sacalay


Selected references

Empirical studiesCombes, Duranton, Gobillon, Puga and Roux (2012). The productivity advantages of large cities: Distinguishing agglomeration from firm selection, Econometrica

Combes, P-P. and L. Gobillon (2014) The Empirics of Agglomeration Economies, ISA DP 8508


Selected references SurveysMelo, Graham and Noland (2009). A metaanalysis of estimates of urban agglomeration economies. Regional Science and Urban EconomicsRosenthal and Strange (2004). Evidence on the nature and sources of agglomeration economies. In Henderson et Thisse (eds.) Handbook of Regional and Urban EconomicsLUTI MODELSBierlaire, M., A. de Palma, R Hurtubia & P. Waddell (eds.) 2015, Integrated transport and land use modeling for sustainable cities. Routledge and EPFL Press.


Documents

The Grand Paris Express project: theoretical and practical ...KS-B... · Paris London New York GNP 588 505 960 GNP (per head) 49 800 38 200 43 600 Population 11.8 13.2 22 Gini 0.35