The Grand Paris Express project: theoretical and practical issues - - PowerPoint PPT Presentation

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?

Findings

 General equilibrium approach  Grand Paris Express  UrbanSim / Metropolis tools  Limitations  References

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 2

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 model
• Metropolis: Dynamic model

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 3

INTRODUCTION AND SETTING

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 4

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 5

Academic objectives

Explain how the evolution of a city (Here Paris area, 11 million inhabitants) can be modelled? Special focus on agglomeration benefits.

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 6

Background about 3 major cities

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 0.45 0.50 Employment 6.0 6.2 8.7 Research 146 000 50 000 130 000

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 7

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 redistributed Difficulty to define boundary: the legal boundary of IDF (8 millions inhabitants), the geographical frontier (at least 1/3 commuters): > 14 millions!

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 8

END OF SPACE?

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 9

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?

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 10

Distribution of Facebook contacts with distance

Goldenberg J. and M Levy (2009) Distance is not dead: Social interaction and geographical distance in the internet era.

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

Distribution of Email with distances

Goldenberg & Levy (2009)

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

Impact of space on trade: CEPII, 2009

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 13

Trade : Space has not disappeared

 French trade has increased with China, but

even more with Germany!

 Accessibility matters and space still matters

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 14

about (slighty higher than) 1

r s RS

Y Y X G d

  

 

WHAT ARE AGGLOMERATION BENEFITS? THEIR MEASURE

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 15

Definition of Agglomeration economies

• A. Smith & A. Marshall: firms and workers are,
• n 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 05/11-12/2016 OBOR Conference Hong Kong 16

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 17

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 18

2 difficulties when measuring agglomeration benefits:

Correcting for selection biases 1 Disentangling different sources of agglomeration benefit 2

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 19

• 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
• f the same worker who change work location

 Panel data are needed to correct the

permanent effect of the workers

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 20

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 21

• 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 model

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 22

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 23

5 FINDINGS (AGGLO- MERATION BENEFITS)

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 24

Finding 1: Business & workers are on average more productive in large cities "Universal" phenomenon?

 "Qualitatively: yes! This relationship is

• bserved in many countries at different

periods

 “Quantitatively”: no!

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 25

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]

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 26

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 27

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%

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 28

Finding 3’: French empirical results

 Relation between density and productivity

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 29

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 bias

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 30

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 31

GENERAL EQUILIBRIUM ANALYSIS

• A. de Palma and Jean Mercenier

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 32

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.

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 33

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 34

Impact of a supply shock on the commuting route in GE

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 35

[Quantitative analysis]

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 36

GRAND PARIS EXPRESS

37

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

DéveloppementT erritorial”)

 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)

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 38

20 employment zones with their current density

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 39

What is Grand Paris ? (21 CDT)

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 40

What is Grand Paris ? (CDT), cont’

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 41

“Schéma de développement territorial” Paris-Saclay

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 42

+ Fully automated and connected + Higher frequency of service, + Inter-modality with the existing network

• No pricing strategies so far

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

Number of trains [H] /one direction Prediction used

CDT &Grand Paris Express network

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 44

Timing of the operation

45 Paris 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 trend Pessimistic trend Transport effects 17.7 17.5 Regularity 3.5 3.4 Comfort 1.6 1.5 Environmental and urban benefits 11.9 11.3 Directs relocation impacts 9.0 7.5 Agglomeration effects 6.0 5.8 Valuation of new jobs 10.3 10.3 T

• tal Advantages

59.9 57.3

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

46

URBANSIM & METROPOLIS TOOLS

A.

de Palma, Nathalie Picard, Yurii Nesterov, Paul Waddell

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 47

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.

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 48 No

Modeling agglomeration effect in UrbanSim + METROPOLIS

 Importance of firmography: the closeness

• f 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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 49

Interactions modeled in UrbanSim

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 50

Land development & infrastructure model Real estate price model Job location model Household location model Demographic model Transport model Social, economic & environmental indicators

Environmental quality indicators Social indicators (inequalities) Pollution & economic indicators Pollution & social indicators Accessibility to jobs Regional attractivity Control totals OD matrix OD matrix OD matrix Accessibility to HH Accessibilities Demand for

• ffices

Supply for

• ffices

Slow, constrained and partial adjustment of housing supply Capacity constraints reduce choice set Demand increases price Transport infrastructures Adjustment of supply of offices constrained by policies Price- elasticity to location Price- elasticity to location Wage-earners, consumers Home to work trip

METROPOLIS

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 51

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, …

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 52

Four-steps decision tree

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 53

Mode choice Destination Decision to make a trip Long term choices

hh & job location, car ownership Origin, constraint, purpose, timing D1 Motorized PC PT Other = OT (2 wheel, walking) D2 No trip

Attraction & Emission

Retour

Log-sum mode Log-sum per O & par D Prédited by UrbanSim

Population 2005–2050 (%) in the extended CBD

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 54

Sectors

1.

Agriculture

2.

Industry

3.

Construction

4.

Business

5.

Transport

6.

Financial activities

7.

Real estate activities

8.

Business services to firms

9.

Personal services

• 10. Education. Health. Social actions
• 11. Administration

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 55

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 56

Elasticities of #jobs in i to #jobs in j

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 57

i \ j

1 2 3 4 5 6 7 8 9 10 11 1 2

‐1.87 0.42 ‐0.68 ‐1.57 0.30 ‐0.12

3

1.93 0.16 2.76 0.37 0.23 0.16

4

2.73 0.51 0.67 2.38 0.55

5

‐0.85 0.06 3.00 ‐0.51

6

0.00 2.54 ‐0.24 0.46

7

3.00 0.50 ‐0.69

8

‐0.94 0.35 0.64 1.53

9

‐1.20 0.56 0.91 0.81

10

3.00 3.00

11

‐1.45 0.67

Pop

‐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)

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 58

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.

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 59

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 60

LIMITATIONS

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 61

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?

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 62

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?

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 63

Thanks for your attention

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 64

REFERENCES

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 65

Selected references

History

Smith (1776). An inquiry into the nature and causes of the wealth of nations Marshall, A. (1890). Principles of Economics

Theory

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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 66

Selected references

Agglomeration effect & transportation Venables (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

• f 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 05/11-12/2016 OBOR Conference Hong Kong 67

Selected references

Empirical studies

Combes, 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

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 68

Selected references

Surveys Melo, Graham and Noland (2009). A metaanalysis of estimates

• f urban agglomeration economies. Regional Science and Urban

Economics Rosenthal and Strange (2004). Evidence on the nature and sources of agglomeration economies. In Henderson et Thisse (eds.) Handbook of Regional and Urban Economics LUTI MODELS Bierlaire, M., A. de Palma, R Hurtubia & P. Waddell (eds.) 2015, Integrated transport and land use modeling for sustainable cities. Routledge and EPFL Press.

Paris Sacalay 05/11-12/2016 OBOR Conference Hong Kong 69