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Using a CGE Model for analyzing the Macroeconomic impact of the Grand Paris Express project on the Ile-de-France Region

Haykel Hadj-Salem, Aboulkacem El-Mehdi, Hubert Jayet, Quentin David, Hakim Hammadou and Moez Kilani,1

Abstract

This paper presents the first version of a new Computable General Equilibrium (CGE) model aiming at analyzing the expected macroeconomic impacts of a new infrastructure project, the so-called “Grand Paris Express” (GPE), on the Ile-de-France region. The GPE consists in a new high-speed circular underground train linking suburbs around Paris.

We present a new Social Accounting Matrix (SAM) developed for the model. This SAM provides a detailed representation of the transportation system and includes the land markets. This first version focuses on the public transportation sector.

The simulations focus on the impact of a shock in the transportation sector on the various sector of the economy of Ile-de-France. The objective of this analysis is to identify public policies that can improve the implementation of the Grand Paris Express. These policies will be related to transportation reforms itself, but also to real estate market and to the labor market.

Keywords: CGE model, Social Accounting Matrix, Transport sector, Ile-de-France region, monopolistic competition.

Table of Contents

Introduction.................................................................................................................................3

1. Literature review..................................................................................................................5

2. Outlines of the CGE model and objectives of the analysis.................................................5

3. The structure of the CGE model..........................................................................................8

3.1 The Social Accounting Matrix.....................................................................................8

3.1.1 The general structure of the SAM.........................................................................8

3.1.2 The structure of the SAMIF..................................................................................9

3.1.3 Balancing of the SAMIF.....................................................................................10

3.2. The current version of the CGE model for the Ile-de-France Region........................10

3.2.1 Production...........................................................................................................11

3.2.2 Incomes and saving................................................................................................11

3.2.3 Demand...................................................................................................................12

1 University of Lille, LEM UMR 9221

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3.2.4 Prices......................................................................................................................12

3.2.5 International trade...................................................................................................12

3.2.6 Transport.................................................................................................................12

4. Interpretations of the simulations......................................................................................12

Conclusion................................................................................................................................15

Appendix 1 : Data sources....................................................................................................19

Appendix 2: The balanced SAMIF.......................................................................................20

Appendix 3: The results of the simulations of the series 1 from the CGEMIF (with GAMS software)................................................................................................................................21

Appendix 4: The results of the simulations of the series 2 from the CGEMIF (with GAMS software)................................................................................................................................25

Appendix 5: The results of the simulations of the series 3 from the CGEMIF (with GAMS software)................................................................................................................................29

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Introduction2

This paper presents the first version of a Computable General Equilibrium model aiming at analyzing the expected macroeconomic impacts of a new infrastructure project, the so-called “Grand Paris Express” (GPE), on the Ile-de-France region. The GPE is a large infrastructure project, leading to the creation of new express subway lines all around Paris. The lines are expected to start receiving passengers in 2023 for the first line, in 2030 for the last line.

The size of this new infrastructure, its high speed, and its circular shape in a region where transportation is almost exclusively radial, will radically change the level and geographical structure of accessibilities within the Ile-de-France Region. The operator in charge of this infrastructure, the Société du Grand Paris (SGP) has engaged in a large research program for analyzing the impact of these changes.

Passenger transportation has an important influence on location decisions of households and firms. It also has strong impacts on everyday decisions related to urban trips an agent may, or may not, undertake. Improving public transportation, either through a better quality (less crowding, higher frequency, newer vehicles, etc.) or through transit network development, can contribute to reduce car usage but may also generate economic benefits that cover the development of some commercial activities, increased productivity in some economic sectors or better efficiency in the labor market. We have been commissioned by the SGP to develop a model aiming at quantitatively evaluate these impacts.

This new transport infrastructure is expected to impact various economic aspects of the Ile-de-France Region: extra economic growth, employment, real estate market, regional attractiveness, etc. To evaluate these impacts, we rely on a CGE model of the Region Ile-de-France where the local economy is represented through a number of representative agents and goods. The current version of the model displays several innovative features.

First, we include a detailed representation of the transportation system. This model is built with the objective to focus on transportation activities and their relationship with agents’ behavior. Since we focus on the GPE project, only passenger transportation is considered in the transportation part and freight transport is included in the activity of firms. At this stage, road congestion and crowding in public transportation are assumed to depend on passenger transportation only. It is not difficult to extend the model if some data on freight transport were made available. The Social Accounting Matrix (SAM) takes into account the public transport sector. The model includes supply and demand functions for public transport. Moreover, the demand is directly derived from micro-geographic foundations. More precisely, the determination of the demand for public transport takes account of the spatial distribution of households across locations with various levels of accessibility and transport costs measures. These micro-geographic foundations allow us to determine the impact on demand of the changes in accessibility and transport costs induced by the GPE.

Second, we study the interaction between the transportation system and the agents’ decisions. This question is more and more addressed through the development of land use and transport infrastructure models (LUTI models). Although these models provide valuable information on how human activities will evolve when a large transport project is made, they remain silent when it comes to macroeconomic impacts that cover economic growth and job creations. These issues are crucial since transportation infrastructure projects require large financial funds. The objective of the model is to provide a tool to assess the macroeconomic impacts of 2 The model presented in this paper has been elaborated for the « Société du Grand Paris » (SGP). The authors acknowkedge financial help from SGP.

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such infrastructures. Location decisions are not the focus of our analysis, but we rely on the outcome of LUTI models on this question (there are three LUTI models running on the case of the GPE). The CGE model we develop relates agents’ choice (consumers and firms) with transportation opportunities and costs. For example, with easier travel possibilities, a user may consider a new leisure activity that he couldn't consider at the initial situation. Expenditures from this agent will be revenue for other agents.

Third, the model is not a standard Walrasian model with perfectly competitive markets. Most goods and services are differentiated and produced under increasing returns, leading to monopolistically competitive markets.

Fourth, the model, in its final version, will include a representation of the regional labor market and the influence of transport infrastructure on its behavior. The Ile-de-France Region currently suffers from a high level of spatial mismatch between local labor markets, particularly for low skilled workers. The new infrastructure is expected to reduce the impact of the spatial mismatch. Our model is able to measure these frictions and their macro-economic impact. Using micro-geographic foundations, we will study the impact of the project on the aggregate equilibrium on the regional labor market (obtained from the aggregation of micro markets imperfectly connected to each other). The Grand Paris Express will impact the aggregate labor market through changes in the degree of connection between micro-markets.

Fifth, the model includes land inputs and a Real Estate sector providing housing and industrial premises. Through the inclusion of land inputs, we aim at analyzing the impact of the development of a new infrastructure on land rents and the housing market; moreover, we try to provide a tool for studying the interactions between the infrastructure and the land use policies developed by local authorities. Once again, the representation of the land market is derived from micro-geographic foundations in order to take account of the heterogeneity of land with respect to accessibility and the changes in accessibility induced by the Grand Paris Express.

The paper is organized as follows. We present a literature review in the next Section. In Section 2, we present the CGE model of the Ile-de-France Region. The structure of the model is presented in Section 3. Section 4 provides the outcome of various scenarios and simulations and we conclude in Section 5.

1. Literature reviewIf the literature on computable regional equilibrium models is large, there are much less models taking account of the regional or multi-regional dimension and even less explicitly taking account of transportation activities and their impact on the economy, particularly passengers transport.

Typical models like Monash MMRF for Australia (Giesecke and alii, 2008; Naqvi and Peter, 1995) do not explicitly include transportation activities. The impact of transportation only appear in exogenous transport of goods. The same holds for the models elaborated by Bröcker (1998; 2001; 2004; 2013). If, in the last version of his model, Bröcker makes an explicit link between transportation costs and infrastructure, transportation only appear through transportation costs without any representation of transportation activities and passagers transport is completely neglected.

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A more explicit treatment of transportation activities is provides by RAEM (ivanova and alii, 2007), which includes a sector devoted to transportation of goods. A more developed solution is proposed by Conrad and Heng (2002), who include four sectors devoted to transport activities and transport inputs used by the other sectors; however, they focus on transport of goods only. RAEM also explicitly takes account of passengers transport, and it is one of the very few models including this dimension. RAEM takes account of passengers transport through a a gravity submodel using a general transport cost.

To the best of our knowledge, the CGE model including the most explicit representation of passengers transport has been elaborated by Mayeres and Proost (2001, 2004). Their model includes a highly detailed representation of transportation activities, both for goods and passengers, with eleven transport modes. They explicitly represent the choice of transport modes and they take account of congestion and pollution.

To the best of our knowledge, there is no regional model making an explicit link between transportation, labor markets, and the real estate sector. This type of link appears in urban models (Anas and Kim, 1996; Anas and Liu, 2007), but here we are closer to the family of LUTI models than to the family of macroeconomic regional models.

2. Outlines of the CGE model and objectives of the analysisAccounting for the global impact of an infrastructure requires a framework where we show how the relevant agents interact and how monetary flows are organized. The building block of such a CGE model, as in traditional models, is the social accounting matrix (SAM). The elaboration of a SAM generally relies to the country's national accounts. As we focus on transportation, an important part of the required information is not available from national accounts and we have to infer it from distinct sources. Another difficulty relates to the local dimension of the model. Economic accounts are available at the national level and we had to estimate local aggregates for the Paris region. The details of these processes are discussed in the subsequent sections.

The main structure of the model is summarized in Figure 1. The main actors are Households, Firms, the Government and transportation operators. Land developers are also considered in our model, but we do not include them in the illustration for the sake of clarity. Investors are included to account for the transport investment decisions, but they are part of the government in our model. Transport operators manage public transportation. Both households and firms use transportation facilities. They decide on the transport mode (private of public) to use and the trip timing (in a dynamic framework) as a function of their conditions.

Households (or consumers) maximize a utility function given their budget constraint. They have to allocate their time between work, which increases income, and leisure which directly increases utility. A typical household makes a first decision on how to use public and private transport. Congestion increases with the number of road users. It depends on the number of households using private transport. Congestion reduces travel speed and increases travel distance. The generalized travel cost has a monetary part and a time part (opportunity cost of time spent in transportation). The number of users of public transport is assumed to determine the service frequency (Mohring Law) and the level of crowding in public transport. Note that, given the current situation of high crowding in main transit lines in Paris (RER A, RER B, etc.), it is unlikely that frequency decreases significantly. We should expect that the decrease in the number of passengers will mainly lead to a comfort improvement.

Firms, and in particular those providing services, are also concerned by passenger transportation. Time savings from better transportation supply can increase production since

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transport time is reduced. Our model is based on production functions that include transportation as an intermediate input. We make a distinction between economic sectors on the basis of their use of passenger transportation. For example, services rely more on the development of passenger transport than other sectors. The model we present here share some similarities with the model of Brocker and Mercenier (2012), but their paper does not proceed for an empirical analysis and remains limited to a microeconomic description of the model.

Transportation has other indirect impacts on economic activities. First, easier market access generated by an improvement in transport benefits to some activities. For example, commercial centers, are more successful when they are well connected to public transportation, well connected to the highway network and when they offer sufficient parking space. In this case, the investment in public transportation is equivalent to an increase in demand.

Second, improved transportation can reduce frictions in the labor market. This line of research has been theoretically discussed in Zenou (2006), but very few empirical studies have been undertaken on the subject. When transportation opportunities expand, job search is more efficient since workers explore job opportunities on a wider geographical area. Matching in the labor market works better. Our model takes into account this impact and includes the impact of transport facilities on the job market.

In Figure 1, a transport operator is in charge of planning and managing activities in public transportation. Making this operator distinct from the government allows us to easily account for the financial flows and cost in public transportation. Transport activities are subject to some network effects. In particular, congestion increases with the number of road users and, when the loading of public transport vehicles is high, crowding appears (comfort quality decrease). Overall, the GPE project is expected to reduce both of these external costs.

Both households and firms benefit from transport services. As we have discussed above, households benefit from better accessibility and save time in commuting. Firms benefit from the reduction in the generalized transport cost (time savings for professional trips). Households sell their labor force to the firms. In return, the firms distribute revenues (wages, profits) to households. The main part of these revenues is used to buy goods to firms. The remaining part is saved and may be used for investments in the private sector. The government supervises transport policy and may impose constraints on operators.

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Figure 1: a graphic representation of the model

3. The structure of the CGE model 3.1 The Social Accounting Matrix

3.1.1 The general structure of the SAM As every CGE model, our model rests upon a social accounting matrix, the SAM of the Ile-de-France Région (SAMIF). The general structure of the SAMIF, presented in Table 1, is a standard one for a mono regional model. Each row of this squared matrix displays the revenues received by an account, while the corresponding column displays the expenditures for the same account. The matrix has five main types or accounts, for the factors of production, the economic agents, the activities, the products and the account for savings and investment.

Investors

Government

Transport

Transport

Investment

Congestion,

Crowding

Transportation

useruser

FirmsConsumption, laborH

ouseholds

Revenue

Taxes

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Table 1: Basic Structure of the SAM.

ReceiptsExpenditures

Factors of production

Institutions Activities Products Saving-Investment

Total

Factors of production Value Added Factor returns

Institutions Revenues from factors supply

Transfers Indirect taxes Revenues of institutions

Activities Production at factor cost

Production at factor cost

Products Final consumption Intermediate inputs

Investment Absorption

Saving-Investment Savings Total savingsTotal Revenues from

factors supply Total expenditures Production at

factor costProduction at market prices

Total Investment

The SAMIF has however several original features, linked to important aspects of the model we want to develop. First of all, it includes a description of activities, factors and outputs linked to passengers transport. This choice implies that, besides the standard sectors, we add an activity devoted to passengers transport. This activity uses private inputs (e.g. vehicles and labour) and public inputs (transportation infrastructure). Second, it includes a real estate sector, which combines land, labor and capital for producing housing and business premises.

Overall, there are 23 accounts in the SAMIF: four factors of production (labor, private capital, public capital, land), four institutions (households, firms, government, Rest of the world), seven activities (real estate, manufacturing, trade, passenger transportation, business services, non-market services, and other branches), seven types of goods (one for each activity) and a Savings-Investment account for dynamic analysis.

To the best of our knowledge, this is the first SAM produced for the Ile-de-France Region. Therefore, the SAMIF has been built from scratch for the base year 2012, combining information from a large variety of sources3. When regional data were not available, we applied the Location Quotient approach (LQ) on the relevant national data using two allocators: the number of employees and the number of firms. The LQ approach is sometimes criticized, but it is still the most used method when the scarcity of regional data obliges to rest upon national data and it provides a good approximation.

3.1.2 The structure of the SAMIFThis subsection presents the main components of the SAMIF, each component corresponding to a block of the matrix.

a) Intermediate consumptions

We use the national input-output table A17 for 2012 (source INSEE) and aggregate them into seven products and seven activities. The adjustment is made applying the LQ method.

b) Value-Added

This block represents the value-added and the revenues of the four factors of production, (labor, private capital, public capital, Land) for each activity. For all the productive branches, the value-added includes the wage of employees, the Gross operating surplus (GOS) and the land rents. For the majority of the activities (real estate, manufacturing, trade, noncommercial (or public) services, services and other branches), the value-added includes the remunerations

3 See Appendix 1 for a list of the main data sources used.

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of the three factors: labor, private capital and land. For the passenger transport activity, the value-added gathers remunerations of the four factors of production.

A two stages calculation has been carried out to determine the wages and the GOS. In the first stage we apply the LQ method on national data to obtain the wages of employees and the GOS. In the second stage derive the final values of the revenues of employees and the GOS by multiplying the percentages found in the first stage with the values-added estimated for the Ile de France region by INSEE4. We use a Geographic Information System (GIS) database on land use to estimate land rents by branch.

c) Production

Production is evaluated “at market prices” and “at factors cost”. The production at market prices for each activity corresponds to the sum of the intermediate consumption and returns of factors of production. The production at factors cost is the sum of the production at market prices and of the taxes on each product. The values of the production at the market price are obtained starting from the production account per activity, whereas the Input-Output tables provides the values of taxes on products for 2012.

d) Final consumption

Final consumption by households for each product has been calculated applying the LQ method to the table of expenditures provided by the French National Accounts for the year 2012.

Final consumption by the public sector has been calculated using the administrative accounts of the Ile-de-France Regional authority for the year 2012.

e) Incomes

For households, the income includes returns from the three factors of production: labor, private capital and land, jointly with the various transfers received from the other institutions: dividends, social security contributions, social security benefits, social transfers from the government and revenues from the ROW.

For firms, the incomes are broken down between private capital factor incomes (GOS), part of the land rents and transfers from the other institutions.

For the government, this account includes a part of the capital private factor incomes (GOS), the whole income of the public capital, a part of the land rents, and taxes payed by the other institutions.

For the Rest of the world (ROW), the income (in row) gathers the transfers received from the other institutions and the imports by product. In column, we distinguish transfers paid by the ROW to other institutions, exports by product and external savings (the current balance).

f) Transfers between agents

In this block, we present the transfers between various institutions (households, firms, government and ROW). We do not take into account the transfers between the same economic agents (the diagonal of this block is empty). The various transfers were explained in the other blocks (especially the block on incomes).

g) Savings and Investment  

4 The French National Statistical Institute

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This block includes two vectors. The first vector (in row) records the savings of each agent (values drawn from the Integrated Economic Accounts (IEA) 2012). The second vector (in column) records the investments by product. Investment is calculated applying the LQ method to the national statistical data on investment provided by INSEE.

3.1.3 Balancing of the SAMIFThe construction of the SAMIF involved using a large variety of data sources, which generated different totals in rows and columns. The literature proposes several methods for balancing a SAM. We decided to use the RAS method and the Cross-Entropy method. The RAS method is simple and does not require many information. It is necessary to specify the totals of control, the rows and the columns of the unbalanced SAM. Through a series of iterations, we proportionally adjust the values of the SAM until obtaining an equality between the sum of the rows and the columns and the specified totals.

However, this method may not lead to satisfactory results, therefore we decided to carry out the Cross-Entropy method (El-Said and Robinson (2000)), which consists in minimizing the contribution of information measured by the Kullback-Leibler (1951) function under constraints we specify. We can also lock specific values of the initial SAM. By using the Cross-Entropy method, we obtain our balanced SAM for the Ile-de-France region (SAMIF) for the year 2012 (see appendix 2).

3.2. The current version of the CGE model for the Ile-de-France Region The first version of the Computable General Equilibrium Model of Ile-de-France region (CGEMIF) is static, open and multisector. It is adapted to the characteristics and specificities of the regional economy.

There are four agents: households, firms, government and the Rest of the world (ROW). Households and firms are represented in a standard way, firms being characterized by their technology and households are endowed with utility functions. Firms are profit maximizing while households are utility maximizing.

Government is represented as a unique agent collecting taxes and providing public capital and services. At this stage, we do not distinguish between government levels (national, regional and local). For the relations between the Ile-de France region and the Rest of the World, we make the standard “small economy” assumption: (international) prices are exogenous and the supply from the rest of the world is perfectly elastic.

In the following subsection, we briefly describe the main blocks of the model.

3.2.1 ProductionIn each sector, production is differentiated. There are a large number of small monopolistically competitive firms, each firm producing a specific brand. All firms share the same technology, represented by a nested production function.

In a first stage, firms combine the four factors of production (labor, public and private capital, land) using a Cobb-Douglas technology under constant returns to scale, for producing value added. In a second stage, value added is combined with intermediate inputs using a Leontief technology. Then, the product from the second stage is used for producing the final output using a technology with a constant marginal productivity and a fixed cost.

Profit maximization is used for deriving output supply and input demands under a series of assumption on the structure of markets. The labor market is assumed to be competitive, labor being mobile across activities. Capital markets are also assumed to be competitive, but capital

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is specific to each sector. Output markets are monopolistically competitive. We rest upon the Dixit and Stiglitz model and then, every individual firm faces an iso-elastic demand.

3.2.2 Incomes and saving Households receive their wages, their part of returns to private capital and of land rents, payments received from the rest of the world, and transfers from the government. From this global income, they pay taxes and social security contributions, leading to their disposable income that can be used for consumption and savings. Savings are proportional to their disposable income.

The income of firms is composed of the share of returns to private capital and land rents and the transfers from others institutions. They pay taxes and social security contributions. The government receives the whole return from public capital, its share of returns to private capital and land rents, and revenue from taxation and social security contributions (including taxes paid by the ROW). Savings by firms and the government are calculated as a residual, once all the expenditures have been paid.

3.2.3 Demand Demand for factors of production and intermediate consumption by firms are derived from profit maximization (see sub-section 3.2.1)

Demand for final consumption by households is derived from utility maximization. Households are endowed with a nested Cobb-Douglas-CES utility function: for every sector, the output of this sector is a CES composite of the brands supplied by each firm of the sector. Then, the household’s utility is a Cobb-Douglas function of these CES composites.

3.2.4 Prices For firms, the marginal cost of value added is calculated using standard results from the Cobb-Douglas production function. Then, the marginal cost of the final output may be determined knowing that the second production stage involves a Leontief technology. Last, knowing that each individual firm faces an iso-elastic demand, the price of each brand is given by the monopoly pricing rule with a constant markup. For consumers, the market price of each composite is the Dixit and Stiglitz index of the prices of all the brands, plus the taxes charged by the government. Imports and exports prices are determined by the word prices, taking account of the taxes charged by the government.

3.2.5 International trade For this block, we rest upon standard assumptions. Imports and exports are determined knowing that the world prices are exogenous and that local products are combined with products from the rest of the world using the Armington’s assumption.

3.2.6 Transport One of the main specificities of the CGEMIF is the representation of transport, at the aggregate level. The model makes a distinction between two motives and two transport modes. The two motives are transport generated by commuting home to work and all the other motives (leisure, shopping, etc…). The two transport modes are public transport (metro, bus, train,…) and private transport (mainly private car).

In the current version of the model, the repartition between public and private transport is a logistic function of the difference in generalized transport costs between the two modes. For each mode, the generalized transport cost is calculated as the sum of the monetary cost and the opportunity cost of time.

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4. Simulations In this section, we briefly present the results of simulations aiming at a better understanding the expected impact of the Grand Paris Express. We consider three scenarios combining an increase in public expenditures for passengers transport leading to an improvement in the transport infrastructure, and the result of this improvement, which is a reduction in trip durations by public transport. In the first scenario, public expenditures increase by 5% and trip durations decrease by 10%. In the second scenario, public expenditures increase by 10% and trip durations decrease by 20%. In the third scenario, public expenditures increase by 15% and trip durations decrease by 30%. Detailed simulation results are available in appendices 3 for the first scenario, 4 for the second scenario and 5 for the third scenario.

The three scenarios have similar effects on the various variables (price, volume and value) lead to similar evolutions of the macroeconomic aggregates. The main difference between scenarios lies in the intensity of changes, particularly for variables directly linked to public transport. The primary impact appears in the transport block. As one may expect, the demand for public transport increases. The decrease in the cost of public transport and in trip durations generates an increase in the demand for public transport, mainly coming from changes in the choice of the transport mode.

The increase in demand for public transport generates an increase in the value of the production of the branch devoted to passengers transport: 0.35% for the first scenario, 0.7% for the second scenario, 1.04% for the third scenario. Then, the demands for labor and land by this branch, and the returns to private and public capital invested in public transport increase in the same proportions. Impacts on the prices of other goods and on the value of production in other branches are negligible. Globally, the revenue of firms slightly increases (0.14% for the first scenario, 0.29% for the second scenario, 0.43% for the third scenario)

The changes in households’ income results from the balance of forces moving in opposite directions. The increase in the revenue of firms leads to slightly higher dividends payed to households (0.4% for the first scenario, 0.8% for the second scenario, 1.2% for the third scenario). However, the increase in public expenditures implies an increase in taxes. The households’ disposable income, their aggregate consumption and their savings fall, however by an almost negligible amount (-0.11% for the third scenario).

Conclusion This first version of the CGEMIF provides the basis for more advanced including specific features needed for a complete evaluation of the macroeconomic impact of a large infrastructure like the ‘Grand Paris Express’. There is still some progress to be made with a more detailed representation of passenger transport, a more sophisticated representation of the regional labor market making us able to explicit the link between transportation infrastructure and spatial mismatch and a more complete analysis of the behavior of the real estate market.

Using the current version of the CGEMIF shows that, without these new features, we can only analyze impacts of improvements in transportation infrastructure directly linked to the behavior of the passengers transport sector itself. Other macroeconomic impacts are generally small. Our results also highlight the importance of choices made for financing the improvement in public infrastructure. Financing this improvement through taxes has a negative impact, which may counterbalance the positive impact of better accessibility. In our simulations, the balance between these two effects is slightly negative for households.

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Modeling: A Survey and Critical Appraisal," International Regional Science Review, , vol.

21(3), pages 205-248, December.

Pyatt, G. (1988). “A SAM Approach to Modeling.” Journal of Policy Modeling 10(3): 327 –

352.

Pyatt, G. (1991). “Fundamentals of Social Accounting.” Economic Systems Research, Vol. 3

No. 3.

SNOWER, Dennis J. (1990): "New methods of updating input-output matrices", Economic

Systems Research, 2, 1, pp. 27-37.

STIF (2012), « Rapport d’activité 2012 ».

STONE R. and J.A.C. BROWN (1962): "A long-term growth model for the British

economy", in: R.C. GEARY (ed.), Europe's future in figures, North-Holland,Amsterdam.

Thissen, M. (2005), RAEM : Regional Applied General Equilibruim Model for the

Netherland. In F. van Oort, M. Thissen & L. Wissen, A survey of spatial economic planning

models in the Netherland :theory, application and evaluation (pp. 63-86). Rotterdam : NAi

Publishers.

16

Sites  :

Ile de France region : www.iledefrance.fr

National Institute of Statistics and Economic Studies (NISES) : www.insee.fr/fr/regions/idf/default.asp

The Paris Region Planning and Development Agency (Institut d’Aménagement et d’Urbanisme d’île de France) : www.iaurif.org

The Paris Chamber of Notaries (Notaires Paris île de France) : www.notaires.paris-idf.fr

Customs and rights (Douanes et droits indirects) : http://www.douane.gouv.fr/datadouane/c797-data-commerce-exterieur

Association of the regions of France : www.arf.asso.fr

Portal of the state to the service of the communities (Portail de l’Etat au service des collectivités) : http://www.collectivites-locales.gouv.fr/

17

Appendix 1 : Data sources The main data sources used for building the SAMIF are:

· The national Input-Output Table (IOT) for the year 2012 · The national Overall Economic Table (OET) for the year 2012 · The National accounts · The Economic Assessment of the Ile de France region for the year 2012· The Economic table for Ile-de-France (year 2010) · Key figures for the Ile-de-France Region (years 2011, 2012.2013 and 2014) (INSEE, Ile-de-France Region) · Foreign Trade Regional Statistics (Head office of the Customs administration) · Administrative accounts (Head office of the Regional Government)· The Geographical Information System on Land Use (The Paris Region Planning and Development Agency (IAU)). · The survey on family budgets, year 2011 (source INSEE) · The DADS (fiscal declarations by employers) · The annual survey on firms · The SIRENE database (official register of firms) · The BIEN database (housing transactions by solicitors) · Statistical data of the OLAP (Observatory of the housing rents in the urban area of Paris) · Statistical data of the observatory on employment (“Pôle Emploi”, Ile-de-France)· Statistical data of the regional observatory on land use in Ile-de-France (ORF)

18

Appendix 2: The balanced SAMIF

The Balanced of the Social Accounting Matrix of île de France Region (SAMIF) with Cross-Entropyduring the year 2012 (in billion euros)

Receipts ® Factors of production institutions (economic agents) ActivitiesExpeditures ¯ Labour private K public K Land households firms government ROW Real Estate manufacturingtrade passengers transportservices publics servicesothers activitiesLabour 5,19 26,67 14,90 16,18 162,47 41,66 6,33private capital 66,68 19,18 7,04 6,89 70,69 12,90 9,81public capital 0,26Land 21,93 3,80 0,11 4,93 0,34 1,59 113,15households 273,40 53,70 89,91 35,89 204,86 3,92firms 119,48 0,73 12,36 0,81government 20,02 0,26 55,21 113,51 11,27 0,67ROW 16,80Real estatemanufacturingtradepassenger transportservicespublic servicesothers activitiesReal estate 94,98 0,32 2,95 1,13 1,88 0,59 8,34 0,82 0,15manufacturing 113,94 0,33 88,43 0,75 68,66 2,50 3,75 15,72 4,38 10,68trade 26,15 0,34 7,46 7,63 2,37 5,34 0,58 0,91passenger transport 22,09 3,37 0,34 4,93 4,48 12,37 7,34 1,97 0,70services 137,16 1,62 3,02 12,61 32,28 11,74 8,65 135,13 11,15 9,61public services 58,13 11,94 0,07 1,96 0,40 0,84 3,05 2,26 0,31others products 26,37 0,39 2,21 1,63 16,62 0,08 0,15 3,26 1,58 14,14investment-saving 56,98 69,42 2,95 54,62Total 273,40 193,19 0,26 145,85 661,68 133,39 225,78 153,67 112,50 182,72 50,76 56,97 411,69 78,90 165,79

source : Carried out by the authors using software GAMS

Receipts ® ProductsExpeditures ¯ Real Estate manufacturingtrade passengers transportservices publics servicesothers productsinvestment-savingTotalLabour 273,40private capital 193,19public capital 0,26Land 145,85households 661,68firms 133,39government 0,32 10,26 0,03 0,62 12,95 0,08 0,58 225,78ROW 133,79 2,31 0,77 153,67Real estate 112,50 112,50manufacturing 182,72 182,72trade 50,76 50,76passenger transport 56,97 56,97services 411,69 411,69public services 78,90 78,90others activities 165,79 165,79Real estate 1,65 112,81manufacturing 17,65 326,78trade 50,80passenger transport 57,60services 63,97 426,94public services 78,97others products 100,71 167,14investment-saving 183,97Total 112,81 326,78 50,80 57,60 426,94 78,97 167,14 183,97

source : Carried out by the authors using software GAMS

19

Appendix 3: The results of the simulations of the series 1 from the CGEMIF (with GAMS software)

CGE model of the île de France region series 1 : the increase of 5% of the public spending on passenger transportTable 1 : the results from the simulations of the series 1 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

Price Block price of the land factor of the branch h IM 0,100 0,100 0,100price of the land factor of the branch h M 0,100 0,100 0,100price of the land factor of the branch h TV -0,502 -0,502 -0,502the yield of the private capital in the branch r IM -0,100 -0,100 -0,100the yield of the private capital in the branch r M 0,000 0,000 0,000the yield of the private capital in the branch r CM 0,000 0,000 0,000the yield of the private capital in the branch r TV 0,301 0,301 0,301the yield of the private capital in the branch r SR 0,000 0,000 0,000the yield of the private capital in the branch r NM -0,100 -0,100 -0,100the yield of the private capital in the branch r AP 0,000 0,000 0,000the yield of the public capital rk 0,400 0,400 0,400

Table 2 : the results from the simulations of the series 1 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

factors and production block production of the branch XS TV 0,348 0,349 0,348 0,349 0,348 0,349value-added of the branch VA TV 0,347 0,354 0,347 0,354 0,347 0,354labor demand by the branch LD TV 0,346 0,353 0,346 0,353 0,346 0,353demand for land by the branch TD TV 0,849 0,345 0,849 0,345 0,849 0,345 Table 3 : the results from the simulations of the series 1 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

incomes and savings block income of the firms YF 0,143 0,143 0,143receipts form the indirect taxation of product TI TV 0,323 0,323 0,323receipts form the direct taxation of the firms income TDE 0,142 0,142 0,142transferts from the firms to households (dividends) des entreprises vers les ménagesDIV 0,404 0,404 0,404transferts from household to the firms TME 1,631 1,631 1,631publics transferts to the household TG -0,075 -0,075 -0,075transferts from the firms to the ROW TER 0,054 0,054 0,054

20

Table 4 : the results from the simulations of the series 1 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

demand block total intermediate consumption of the branch CI TV 0,348 0,348 0,348intermediate demand of the product by the branch DI IM IM -0,034 -0,034 -0,034intermediate demand of the product by the branch DI IM M 0,000 0,000 0,000intermediate demand of the product by the branch DI IM CM 0,000 0,000 0,000intermediate demand of the product by the branch DI IM TV 0,340 0,340 0,340intermediate demand of the product by the branch DI IM SR -0,012 -0,012 -0,012intermediate demand of the product by the branch DI IM NM -0,122 -0,122 -0,122intermediate demand of the product by the branch DI IM AP 0,000 0,000 0,000intermediate demand of the product by the branch DI M IM -0,139 -0,139 -0,139intermediate demand of the product by the branch DI M M -0,021 -0,021 -0,021intermediate demand of the product by the branch DI M CM 0,000 0,000 0,000intermediate demand of the product by the branch DI M TV 0,334 0,334 0,334intermediate demand of the product by the branch DI CM TV 0,338 0,338 0,338intermediate demand of the product by the branch DI CM SR -0,019 -0,019 -0,019intermediate demand of the product by the branch DI CM NM -0,172 -0,172 -0,172intermediate demand of the product by the branch DI CM AP 0,000 0,000 0,000intermediate demand of the product by the branch DI TV IM 0,000 0,000 0,000intermediate demand of the product by the branch DI TV M -0,021 -0,021 -0,021intermediate demand of the product by the branch DI TV CM 0,000 0,000 0,000intermediate demand of the product by the branch DI TV TV 0,351 0,351 0,351intermediate demand of the product by the branch DI SR TV 0,346 0,346 0,346intermediate demand of the product by the branch DI NM CM -0,250 -0,250 -0,250intermediate demand of the product by the branch DI NM TV 0,358 0,358 0,358intermediate demand of the product by the branch DI AP TV 0,671 0,671 0,671demand of the product whose firm will cope DFtv ftv1 TV 0,348 0,348 0,348intermediate demand for product DIT TV 0,126 0,124 0,126 0,124 0,126 0,124

21

Table 5 : the results from the simulations of the series 1 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

transport block BLOC public transport demand for labor motif CMTT 0,920 1,858 2,816probability to choose public transport from z to zz alpha_tr Z1 Z1 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z1 Z2 1,107 1,845 2,952probability to choose public transport from z to zz alpha_tr Z1 Z3 0,317 0,952 1,587probability to choose public transport from z to zz alpha_tr Z1 Z4 1,311 2,623 3,934probability to choose public transport from z to zz alpha_tr Z1 Z5 0,576 1,153 2,017probability to choose public transport from z to zz alpha_tr Z1 Z6 0,875 1,458 2,041probability to choose public transport from z to zz alpha_tr Z1 Z7 0,532 1,064 1,596probability to choose public transport from z to zz alpha_tr Z1 Z8 1,042 2,431 3,472probability to choose public transport from z to zz alpha_tr Z2 Z1 1,107 1,845 2,952probability to choose public transport from z to zz alpha_tr Z2 Z2 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z2 Z3 5,848 12,865 19,298probability to choose public transport from z to zz alpha_tr Z2 Z4 2,091 3,833 5,923probability to choose public transport from z to zz alpha_tr Z2 Z5 6,299 12,598 18,898probability to choose public transport from z to zz alpha_tr Z2 Z6 6,286 12,000 18,857probability to choose public transport from z to zz alpha_tr Z2 Z7 4,464 9,375 14,286probability to choose public transport from z to zz alpha_tr Z2 Z8 8,247 15,464 24,742probability to choose public transport from z to zz alpha_tr Z3 Z1 0,317 0,952 1,587probability to choose public transport from z to zz alpha_tr Z3 Z2 5,848 12,865 19,298probability to choose public transport from z to zz alpha_tr Z3 Z3 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z3 Z4 5,941 11,881 18,317probability to choose public transport from z to zz alpha_tr Z3 Z5 0,840 1,681 2,801probability to choose public transport from z to zz alpha_tr Z3 Z6 5,189 10,377 16,509probability to choose public transport from z to zz alpha_tr Z3 Z7 4,583 9,167 14,167probability to choose public transport from z to zz alpha_tr Z3 Z8 5,288 10,577 16,346probability to choose public transport from z to zz alpha_tr Z4 Z1 1,311 2,623 3,934probability to choose public transport from z to zz alpha_tr Z4 Z2 2,091 3,833 5,923probability to choose public transport from z to zz alpha_tr Z4 Z3 5,941 11,881 18,317probability to choose public transport from z to zz alpha_tr Z4 Z4 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z4 Z5 5,729 11,458 17,708probability to choose public transport from z to zz alpha_tr Z4 Z6 6,522 13,043 20,109probability to choose public transport from z to zz alpha_tr Z4 Z7 2,389 4,437 6,826probability to choose public transport from z to zz alpha_tr Z4 Z8 7,080 15,044 23,009probability to choose public transport from z to zz alpha_tr Z5 Z1 0,576 1,153 2,017probability to choose public transport from z to zz alpha_tr Z5 Z2 6,299 12,598 18,898probability to choose public transport from z to zz alpha_tr Z5 Z3 0,840 1,681 2,801probability to choose public transport from z to zz alpha_tr Z5 Z4 5,729 11,458 17,708probability to choose public transport from z to zz alpha_tr Z5 Z5 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z5 Z6 2,181 4,673 6,854probability to choose public transport from z to zz alpha_tr Z5 Z7 2,381 4,762 7,143probability to choose public transport from z to zz alpha_tr Z5 Z8 4,898 9,388 14,694probability to choose public transport from z to zz alpha_tr Z6 Z1 0,875 1,458 2,041probability to choose public transport from z to zz alpha_tr Z6 Z2 6,286 12,000 18,857probability to choose public transport from z to zz alpha_tr Z6 Z3 5,699 11,399 17,098probability to choose public transport from z to zz alpha_tr Z6 Z4 6,522 13,043 20,109probability to choose public transport from z to zz alpha_tr Z6 Z5 2,181 4,673 6,854probability to choose public transport from z to zz alpha_tr Z6 Z6 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z6 Z7 4,428 9,225 13,653probability to choose public transport from z to zz alpha_tr Z6 Z8 5,128 10,256 15,812probability to choose public transport from z to zz alpha_tr Z7 Z1 0,532 1,064 1,596probability to choose public transport from z to zz alpha_tr Z7 Z2 4,878 9,756 15,122probability to choose public transport from z to zz alpha_tr Z7 Z3 4,545 9,545 14,545probability to choose public transport from z to zz alpha_tr Z7 Z4 2,389 4,437 6,826probability to choose public transport from z to zz alpha_tr Z7 Z5 2,381 4,762 7,143probability to choose public transport from z to zz alpha_tr Z7 Z6 4,428 9,225 13,653probability to choose public transport from z to zz alpha_tr Z7 Z7 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z7 Z8 5,528 11,055 17,085probability to choose public transport from z to zz alpha_tr Z8 Z1 1,042 2,431 3,472probability to choose public transport from z to zz alpha_tr Z8 Z2 8,247 15,464 24,742probability to choose public transport from z to zz alpha_tr Z8 Z3 5,288 10,577 16,346probability to choose public transport from z to zz alpha_tr Z8 Z4 7,080 15,044 23,009probability to choose public transport from z to zz alpha_tr Z8 Z5 4,898 9,388 14,694probability to choose public transport from z to zz alpha_tr Z8 Z6 5,128 10,256 15,812probability to choose public transport from z to zz alpha_tr Z8 Z7 5,528 11,055 17,085probability to choose public transport from z to zz alpha_tr Z8 Z8 0,000 0,000 0,000

22

Table 5 : the results from the simulations of the series 1 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

transport block BLOC

coast of public transport from z to zz PPB Z1 Z1 0,000 0,000 0,000coast of public transport from z to zz PPB Z1 Z2 -4,940 -9,881 -14,821coast of public transport from z to zz PPB Z1 Z3 -3,372 -6,744 -10,094coast of public transport from z to zz PPB Z1 Z4 -5,483 -10,950 -16,433coast of public transport from z to zz PPB Z1 Z5 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z1 Z6 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z1 Z7 -2,943 -5,905 -8,848coast of public transport from z to zz PPB Z1 Z8 -4,559 -9,119 -13,664coast of public transport from z to zz PPB Z2 Z1 -4,940 -9,881 -14,821coast of public transport from z to zz PPB Z2 Z2 0,000 0,000 0,000coast of public transport from z to zz PPB Z2 Z3 -8,715 -17,430 -26,151coast of public transport from z to zz PPB Z2 Z4 -7,198 -14,396 -21,582coast of public transport from z to zz PPB Z2 Z5 -8,116 -16,232 -24,342coast of public transport from z to zz PPB Z2 Z6 -8,164 -16,328 -24,498coast of public transport from z to zz PPB Z2 Z7 -7,899 -15,799 -23,691coast of public transport from z to zz PPB Z2 Z8 -8,344 -16,688 -25,026coast of public transport from z to zz PPB Z3 Z1 -3,372 -6,744 -10,094coast of public transport from z to zz PPB Z3 Z2 -8,715 -17,430 -26,151coast of public transport from z to zz PPB Z3 Z3 0,000 0,000 0,000coast of public transport from z to zz PPB Z3 Z4 -8,669 -17,339 -26,014coast of public transport from z to zz PPB Z3 Z5 -4,770 -9,539 -14,309coast of public transport from z to zz PPB Z3 Z6 -8,059 -16,124 -24,183coast of public transport from z to zz PPB Z3 Z7 -7,899 -15,799 -23,691coast of public transport from z to zz PPB Z3 Z8 -8,049 -16,098 -24,140coast of public transport from z to zz PPB Z4 Z1 -5,483 -10,950 -16,433coast of public transport from z to zz PPB Z4 Z2 -7,198 -14,396 -21,582coast of public transport from z to zz PPB Z4 Z3 -8,669 -17,339 -26,014coast of public transport from z to zz PPB Z4 Z4 0,000 0,000 0,000coast of public transport from z to zz PPB Z4 Z5 -8,140 -16,281 -24,421coast of public transport from z to zz PPB Z4 Z6 -8,245 -16,496 -24,741coast of public transport from z to zz PPB Z4 Z7 -6,629 -13,268 -19,896coast of public transport from z to zz PPB Z4 Z8 -8,312 -16,624 -24,936coast of public transport from z to zz PPB Z5 Z1 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z5 Z2 -8,116 -16,232 -24,342coast of public transport from z to zz PPB Z5 Z3 -4,770 -9,539 -14,309coast of public transport from z to zz PPB Z5 Z4 -8,140 -16,281 -24,421coast of public transport from z to zz PPB Z5 Z5 0,000 0,000 0,000coast of public transport from z to zz PPB Z5 Z6 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z5 Z7 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z5 Z8 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z6 Z1 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z6 Z2 -8,164 -16,328 -24,498coast of public transport from z to zz PPB Z6 Z3 -7,087 -14,179 -21,266coast of public transport from z to zz PPB Z6 Z4 -8,245 -16,496 -24,741coast of public transport from z to zz PPB Z6 Z5 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z6 Z6 0,000 0,000 0,000coast of public transport from z to zz PPB Z6 Z7 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z6 Z8 -8,672 -17,351 -26,023coast of public transport from z to zz PPB Z7 Z1 -2,943 -5,905 -8,848coast of public transport from z to zz PPB Z7 Z2 -6,877 -13,753 -20,624coast of public transport from z to zz PPB Z7 Z3 -6,877 -13,753 -20,624coast of public transport from z to zz PPB Z7 Z4 -6,629 -13,268 -19,896coast of public transport from z to zz PPB Z7 Z5 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z7 Z6 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z7 Z7 0,000 0,000 0,000coast of public transport from z to zz PPB Z7 Z8 -8,697 -17,388 -26,086coast of public transport from z to zz PPB Z8 Z1 -4,559 -9,119 -13,664coast of public transport from z to zz PPB Z8 Z2 -8,344 -16,688 -25,026coast of public transport from z to zz PPB Z8 Z3 -8,049 -16,098 -24,140coast of public transport from z to zz PPB Z8 Z4 -8,312 -16,624 -24,936coast of public transport from z to zz PPB Z8 Z5 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z8 Z6 -8,672 -17,351 -26,023coast of public transport from z to zz PPB Z8 Z7 -8,697 -17,388 -26,086coast of public transport from z to zz PPB Z8 Z8 0,000 0,000 0,000

23

Appendix 4: The results of the simulations of the series 2 from the CGEMIF (with GAMS software)

CGE model of the île de France region series 2 : the increase of 10 % of the public spending on passenger transportTable 1 : the results from the simulations of the series 2 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

Price Block price of the land factor of the branch h IM 0,201 0,201 0,201price of the land factor of the branch h M 0,100 0,100 0,100price of the land factor of the branch h TV -1,004 -1,004 -1,004price of the land factor of the branch h SR -0,115 -0,115 -0,115the yield of the private capital in the branch r IM -0,100 -0,100 -0,100the yield of the private capital in the branch r TV 0,701 0,701 0,701the yield of the private capital in the branch r NM -0,100 -0,100 -0,100the yield of the public capital rk 0,701 0,701 0,701

Table 2 : the results from the simulations of the series 2 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

factors and production block production of the branch XS IM -0,064 -0,067 -0,064 -0,067 -0,064 -0,067production of the branch XS TV 0,695 0,701 0,695 0,701 0,695 0,701production of the branch XS NM -0,052 -0,057 -0,052 -0,057 -0,052 -0,057value-added of the branch VA IM -0,064 -0,068 -0,064 -0,068 -0,064 -0,068value-added of the branch VA TV 0,694 0,708 0,694 0,708 0,694 0,708value-added of the branch VA NM -0,052 -0,059 -0,052 -0,059 -0,052 -0,059labor demand by the branch LD IM -0,077 -0,058 -0,077 -0,058 -0,077 -0,058labor demand by the branch LD M -0,052 -0,041 -0,052 -0,041 -0,052 -0,041labor demand by the branch LD TV 0,693 0,711 0,693 0,711 0,693 0,711labor demand by the branch LD NM -0,070 -0,060 -0,070 -0,060 -0,070 -0,060demand for land by the branch TD IM -0,255 -0,069 -0,255 -0,069 -0,255 -0,069demand for land by the branch TD M -0,184 -0,026 -0,184 -0,026 -0,184 -0,026demand for land by the branch TD TV 1,698 0,710 1,698 0,710 1,698 0,710demand for land by the branch TD NM 0,000 -0,063 0,000 -0,063 0,000 -0,063 Table 3 : the results from the simulations of the series 2 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

incomes and savings block disposable income of the household YDH -0,075 -0,075 -0,075income of the firms YF 0,286 0,286 0,286receipts form the indirect taxation of product TI TV 0,645 0,645 0,645receipts form the direct taxation of the firms income TDE 0,284 0,284 0,284saving of the household SH -0,075 -0,075 -0,075saving of the firms SF 0,061 0,061 0,061transferts from the firms to households (dividends) des entreprises vers les ménagesDIV 0,806 0,806 0,806transferts from household to the firms TME 3,262 3,262 3,262publics transferts to the household TG -0,150 -0,150 -0,150transferts from the firms to the ROW TER 0,107 0,107 0,107

24

Table 4 : the results from the simulations of the series 2 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

demand block household consumption of the product C IM -0,075 -0,075 -0,075 -0,075 -0,075 -0,075household consumption of the product C M -0,075 -0,075 -0,075 -0,075 -0,075 -0,075household consumption of the product C CM -0,073 -0,076 -0,073 -0,076 -0,073 -0,076household consumption of the product C TV -0,078 -0,072 -0,078 -0,072 -0,078 -0,072household consumption of the product C SR -0,075 -0,075 -0,075 -0,075 -0,075 -0,075household consumption of the product C NM -0,076 -0,076 -0,076 -0,076 -0,076 -0,076household consumption of the product C AP -0,076 -0,076 -0,076 -0,076 -0,076 -0,076household consumption of the product produced by the firm CFim fim1 IM -0,075 -0,075 -0,075 -0,075 -0,075 -0,075household consumption of the product produced by the firm CFm fm1 M -0,075 -0,075 -0,075 -0,075 -0,075 -0,075household consumption of the product produced by the firm CFcm fcm1 CM -0,073 -0,073 -0,073 -0,073 -0,073 -0,073household consumption of the product produced by the firm CFtv ftv1 TV -0,078 -0,078 -0,078 -0,078 -0,078 -0,078household consumption of the product produced by the firm CFsr fsr1 SR -0,075 -0,075 -0,075 -0,075 -0,075 -0,075household consumption of the product produced by the firm CFnm fnm1 NM -0,076 -0,076 -0,076 -0,076 -0,076 -0,076household consumption of the product produced by the firm CFap fap1 AP -0,076 -0,076 -0,076 -0,076 -0,076 -0,076total intermediate consumption of the branch CI IM -0,066 -0,066 -0,066total intermediate consumption of the branch CI TV 0,696 0,696 0,696total intermediate consumption of the branch CI NM -0,054 -0,054 -0,054intermediate demand of the product by the branch DI IM IM -0,068 -0,068 -0,068intermediate demand of the product by the branch DI IM TV 0,680 0,680 0,680intermediate demand of the product by the branch DI IM NM -0,122 -0,122 -0,122intermediate demand of the product by the branch DI M IM -0,139 -0,139 -0,139intermediate demand of the product by the branch DI M TV 0,697 0,697 0,697intermediate demand of the product by the branch DI M SR -0,027 -0,027 -0,027intermediate demand of the product by the branch DI M NM -0,072 -0,072 -0,072intermediate demand of the product by the branch DI CM TV 0,675 0,675 0,675intermediate demand of the product by the branch DI CM NM -0,172 -0,172 -0,172intermediate demand of the product by the branch DI TV TV 0,695 0,695 0,695intermediate demand of the product by the branch DI TV SR -0,028 -0,028 -0,028intermediate demand of the product by the branch DI TV NM -0,051 -0,051 -0,051intermediate demand of the product by the branch DI SR IM -0,065 -0,065 -0,065intermediate demand of the product by the branch DI SR TV 0,692 0,692 0,692intermediate demand of the product by the branch DI NM CM -0,250 -0,250 -0,250intermediate demand of the product by the branch DI NM TV 0,715 0,715 0,715intermediate demand of the product by the branch DI AP IM -0,062 -0,062 -0,062intermediate demand of the product by the branch DI AP TV 1,342 1,342 1,342intermediate demand of the product by the branch DI AP SR -0,031 -0,031 -0,031intermediate demand of the product by the branch DI AP NM -0,064 -0,064 -0,064demand of the product whose firm will cope DFim fim1 IM -0,064 -0,064 -0,064demand of the product whose firm will cope DFtv ftv1 TV 0,695 0,695 0,695demand of the product whose firm will cope DFnm fnm1 NM -0,052 -0,052 -0,052intermediate demand for product DIT TV 0,249 0,249 0,249 0,002 0,249 0,249number of firms of the branch NF IM -0,061 -0,061 -0,061number of firms of the branch NF TV 0,209 0,209 0,209

25

Table 5 : the results from the simulations of the series 2 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

transport block trasport demand for labor motif CMT TR -0,075 -0,075 -0,075transport demand for others motif CMT AU -0,075 -0,075 -0,075public transport demand for labor motif CMTT 0,920 1,858 2,816probability to choose public transport from z to zz alpha_tr Z1 Z1 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z1 Z2 1,107 1,845 2,952probability to choose public transport from z to zz alpha_tr Z1 Z3 0,317 0,952 1,587probability to choose public transport from z to zz alpha_tr Z1 Z4 1,311 2,623 3,934probability to choose public transport from z to zz alpha_tr Z1 Z5 0,576 1,153 2,017probability to choose public transport from z to zz alpha_tr Z1 Z6 0,875 1,458 2,041probability to choose public transport from z to zz alpha_tr Z1 Z7 0,532 1,064 1,596probability to choose public transport from z to zz alpha_tr Z1 Z8 1,042 2,431 3,472probability to choose public transport from z to zz alpha_tr Z2 Z1 1,107 1,845 2,952probability to choose public transport from z to zz alpha_tr Z2 Z2 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z2 Z3 5,848 12,865 19,298probability to choose public transport from z to zz alpha_tr Z2 Z4 2,091 3,833 5,923probability to choose public transport from z to zz alpha_tr Z2 Z5 6,299 12,598 18,898probability to choose public transport from z to zz alpha_tr Z2 Z6 6,286 12,000 18,857probability to choose public transport from z to zz alpha_tr Z2 Z7 4,464 9,375 14,286probability to choose public transport from z to zz alpha_tr Z2 Z8 8,247 15,464 24,742probability to choose public transport from z to zz alpha_tr Z3 Z1 0,317 0,952 1,587probability to choose public transport from z to zz alpha_tr Z3 Z2 5,848 12,865 19,298probability to choose public transport from z to zz alpha_tr Z3 Z3 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z3 Z4 5,941 11,881 18,317probability to choose public transport from z to zz alpha_tr Z3 Z5 0,840 1,681 2,801probability to choose public transport from z to zz alpha_tr Z3 Z6 5,189 10,377 16,509probability to choose public transport from z to zz alpha_tr Z3 Z7 4,583 9,167 14,167probability to choose public transport from z to zz alpha_tr Z3 Z8 5,288 10,577 16,346probability to choose public transport from z to zz alpha_tr Z4 Z1 1,311 2,623 3,934probability to choose public transport from z to zz alpha_tr Z4 Z2 2,091 3,833 5,923probability to choose public transport from z to zz alpha_tr Z4 Z3 5,941 11,881 18,317probability to choose public transport from z to zz alpha_tr Z4 Z4 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z4 Z5 5,729 11,458 17,708probability to choose public transport from z to zz alpha_tr Z4 Z6 6,522 13,043 20,109probability to choose public transport from z to zz alpha_tr Z4 Z7 2,389 4,437 6,826probability to choose public transport from z to zz alpha_tr Z4 Z8 7,080 15,044 23,009probability to choose public transport from z to zz alpha_tr Z5 Z1 0,576 1,153 2,017probability to choose public transport from z to zz alpha_tr Z5 Z2 6,299 12,598 18,898probability to choose public transport from z to zz alpha_tr Z5 Z3 0,840 1,681 2,801probability to choose public transport from z to zz alpha_tr Z5 Z4 5,729 11,458 17,708probability to choose public transport from z to zz alpha_tr Z5 Z5 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z5 Z6 2,181 4,673 6,854probability to choose public transport from z to zz alpha_tr Z5 Z7 2,381 4,762 7,143probability to choose public transport from z to zz alpha_tr Z5 Z8 4,898 9,388 14,694probability to choose public transport from z to zz alpha_tr Z6 Z1 0,875 1,458 2,041probability to choose public transport from z to zz alpha_tr Z6 Z2 6,286 12,000 18,857probability to choose public transport from z to zz alpha_tr Z6 Z3 5,699 11,399 17,098probability to choose public transport from z to zz alpha_tr Z6 Z4 6,522 13,043 20,109probability to choose public transport from z to zz alpha_tr Z6 Z5 2,181 4,673 6,854probability to choose public transport from z to zz alpha_tr Z6 Z6 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z6 Z7 4,428 9,225 13,653probability to choose public transport from z to zz alpha_tr Z6 Z8 5,128 10,256 15,812probability to choose public transport from z to zz alpha_tr Z7 Z1 0,532 1,064 1,596probability to choose public transport from z to zz alpha_tr Z7 Z2 4,878 9,756 15,122probability to choose public transport from z to zz alpha_tr Z7 Z3 4,545 9,545 14,545probability to choose public transport from z to zz alpha_tr Z7 Z4 2,389 4,437 6,826probability to choose public transport from z to zz alpha_tr Z7 Z5 2,381 4,762 7,143probability to choose public transport from z to zz alpha_tr Z7 Z6 4,428 9,225 13,653probability to choose public transport from z to zz alpha_tr Z7 Z7 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z7 Z8 5,528 11,055 17,085probability to choose public transport from z to zz alpha_tr Z8 Z1 1,042 2,431 3,472probability to choose public transport from z to zz alpha_tr Z8 Z2 8,247 15,464 24,742probability to choose public transport from z to zz alpha_tr Z8 Z3 5,288 10,577 16,346probability to choose public transport from z to zz alpha_tr Z8 Z4 7,080 15,044 23,009probability to choose public transport from z to zz alpha_tr Z8 Z5 4,898 9,388 14,694probability to choose public transport from z to zz alpha_tr Z8 Z6 5,128 10,256 15,812probability to choose public transport from z to zz alpha_tr Z8 Z7 5,528 11,055 17,085probability to choose public transport from z to zz alpha_tr Z8 Z8 0,000 0,000 0,000

26

Table 5 : the results from the simulations of the series 2 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

transport block

coast of public transport from z to zz PPB Z1 Z1 0,000 0,000 0,000coast of public transport from z to zz PPB Z1 Z2 -4,940 -9,881 -14,821coast of public transport from z to zz PPB Z1 Z3 -3,372 -6,744 -10,094coast of public transport from z to zz PPB Z1 Z4 -5,483 -10,950 -16,433coast of public transport from z to zz PPB Z1 Z5 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z1 Z6 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z1 Z7 -2,943 -5,905 -8,848coast of public transport from z to zz PPB Z1 Z8 -4,559 -9,119 -13,664coast of public transport from z to zz PPB Z2 Z1 -4,940 -9,881 -14,821coast of public transport from z to zz PPB Z2 Z2 0,000 0,000 0,000coast of public transport from z to zz PPB Z2 Z3 -8,715 -17,430 -26,151coast of public transport from z to zz PPB Z2 Z4 -7,198 -14,396 -21,582coast of public transport from z to zz PPB Z2 Z5 -8,116 -16,232 -24,342coast of public transport from z to zz PPB Z2 Z6 -8,164 -16,328 -24,498coast of public transport from z to zz PPB Z2 Z7 -7,899 -15,799 -23,691coast of public transport from z to zz PPB Z2 Z8 -8,344 -16,688 -25,026coast of public transport from z to zz PPB Z3 Z1 -3,372 -6,744 -10,094coast of public transport from z to zz PPB Z3 Z2 -8,715 -17,430 -26,151coast of public transport from z to zz PPB Z3 Z3 0,000 0,000 0,000coast of public transport from z to zz PPB Z3 Z4 -8,669 -17,339 -26,014coast of public transport from z to zz PPB Z3 Z5 -4,770 -9,539 -14,309coast of public transport from z to zz PPB Z3 Z6 -8,059 -16,124 -24,183coast of public transport from z to zz PPB Z3 Z7 -7,899 -15,799 -23,691coast of public transport from z to zz PPB Z3 Z8 -8,049 -16,098 -24,140coast of public transport from z to zz PPB Z4 Z1 -5,483 -10,950 -16,433coast of public transport from z to zz PPB Z4 Z2 -7,198 -14,396 -21,582coast of public transport from z to zz PPB Z4 Z3 -8,669 -17,339 -26,014coast of public transport from z to zz PPB Z4 Z4 0,000 0,000 0,000coast of public transport from z to zz PPB Z4 Z5 -8,140 -16,281 -24,421coast of public transport from z to zz PPB Z4 Z6 -8,245 -16,496 -24,741coast of public transport from z to zz PPB Z4 Z7 -6,629 -13,268 -19,896coast of public transport from z to zz PPB Z4 Z8 -8,312 -16,624 -24,936coast of public transport from z to zz PPB Z5 Z1 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z5 Z2 -8,116 -16,232 -24,342coast of public transport from z to zz PPB Z5 Z3 -4,770 -9,539 -14,309coast of public transport from z to zz PPB Z5 Z4 -8,140 -16,281 -24,421coast of public transport from z to zz PPB Z5 Z5 0,000 0,000 0,000coast of public transport from z to zz PPB Z5 Z6 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z5 Z7 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z5 Z8 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z6 Z1 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z6 Z2 -8,164 -16,328 -24,498coast of public transport from z to zz PPB Z6 Z3 -7,087 -14,179 -21,266coast of public transport from z to zz PPB Z6 Z4 -8,245 -16,496 -24,741coast of public transport from z to zz PPB Z6 Z5 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z6 Z6 0,000 0,000 0,000coast of public transport from z to zz PPB Z6 Z7 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z6 Z8 -8,672 -17,351 -26,023coast of public transport from z to zz PPB Z7 Z1 -2,943 -5,905 -8,848coast of public transport from z to zz PPB Z7 Z2 -6,877 -13,753 -20,624coast of public transport from z to zz PPB Z7 Z3 -6,877 -13,753 -20,624coast of public transport from z to zz PPB Z7 Z4 -6,629 -13,268 -19,896coast of public transport from z to zz PPB Z7 Z5 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z7 Z6 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z7 Z7 0,000 0,000 0,000coast of public transport from z to zz PPB Z7 Z8 -8,697 -17,388 -26,086coast of public transport from z to zz PPB Z8 Z1 -4,559 -9,119 -13,664coast of public transport from z to zz PPB Z8 Z2 -8,344 -16,688 -25,026coast of public transport from z to zz PPB Z8 Z3 -8,049 -16,098 -24,140coast of public transport from z to zz PPB Z8 Z4 -8,312 -16,624 -24,936coast of public transport from z to zz PPB Z8 Z5 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z8 Z6 -8,672 -17,351 -26,023coast of public transport from z to zz PPB Z8 Z7 -8,697 -17,388 -26,086coast of public transport from z to zz PPB Z8 Z8 0,000 0,000 0,000

27

Appendix 5: The results of the simulations of the series 3 from the CGEMIF (with GAMS software)

CGE model of the île de France region series 3 : the increase of 15% of the public spending on passenger transportTable 1 : the results from the simulations of the series 3 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

Price Block price of the land factor of the branch h IM 0,301 0,301 0,301price of the land factor of the branch h M 0,200 0,200 0,200price of the land factor of the branch h CM 0,000 0,000 0,000price of the land factor of the branch h TV -1,406 -1,406 -1,406price of the land factor of the branch h SR -0,115 -0,115 -0,115price of the land factor of the branch h NM -0,096 -0,096 -0,096price of the land factor of the branch h AP 0,000 0,000 0,000the yield of the private capital in the branch r IM -0,100 -0,100 -0,100the yield of the private capital in the branch r M -0,100 -0,100 -0,100the yield of the private capital in the branch r CM 0,000 0,000 0,000the yield of the private capital in the branch r TV 1,102 1,102 1,102the yield of the private capital in the branch r SR 0,000 0,000 0,000the yield of the private capital in the branch r NM -0,100 -0,100 -0,100the yield of the private capital in the branch r AP 0,000 0,000 0,000the yield of the public capital rk 1,101 1,101 1,101the wage rate w 0,100 0,100 0,100

Table 2 : the results from the simulations of the series 3 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

factors and production block production of the branch XS IM -0,097 -0,102 -0,097 -0,102 -0,097 -0,102production of the branch XS M -0,061 -0,061 -0,061 -0,061 -0,061 -0,061production of the branch XS CM -0,030 -0,028 -0,030 -0,028 -0,030 -0,028production of the branch XS TV 1,043 1,052 1,043 1,052 1,043 1,052production of the branch XS SR -0,038 -0,038 -0,038 -0,038 -0,038 -0,038production of the branch XS NM -0,077 -0,085 -0,077 -0,085 -0,077 -0,085production of the branch XS AP -0,028 -0,028 -0,028 -0,028 -0,028 -0,028value-added of the branch VA IM -0,096 -0,103 -0,096 -0,103 -0,096 -0,103value-added of the branch VA M -0,060 -0,060 -0,060 -0,060 -0,060 -0,060value-added of the branch VA CM -0,027 -0,023 -0,027 -0,023 -0,027 -0,023value-added of the branch VA TV 1,041 1,063 1,041 1,063 1,041 1,063value-added of the branch VA SR -0,039 -0,039 -0,039 -0,039 -0,039 -0,039value-added of the branch VA NM -0,077 -0,089 -0,077 -0,089 -0,077 -0,089value-added of the branch VA AP -0,028 -0,028 -0,028 -0,028 -0,028 -0,028labor demand by the branch LD IM -0,135 -0,097 -0,135 -0,097 -0,135 -0,097labor demand by the branch LD M -0,079 -0,060 -0,079 -0,060 -0,079 -0,060labor demand by the branch LD CM -0,040 -0,027 -0,040 -0,027 -0,040 -0,027labor demand by the branch LD TV 1,045 1,064 1,045 1,064 1,045 1,064labor demand by the branch LD SR -0,055 -0,039 -0,055 -0,039 -0,055 -0,039labor demand by the branch LD NM -0,105 -0,089 -0,105 -0,089 -0,105 -0,089labor demand by the branch LD AP -0,047 -0,032 -0,047 -0,032 -0,047 -0,032demand for land by the branch TD IM -0,383 -0,105 -0,383 -0,105 -0,383 -0,105demand for land by the branch TD M -0,263 -0,053 -0,263 -0,053 -0,263 -0,053demand for land by the branch TD CM 0,000 0,000 0,000 0,000 0,000 0,000demand for land by the branch TD TV 2,568 1,055 2,568 1,055 2,568 1,055demand for land by the branch TD SR 0,000 0,000 0,000 0,000 0,000 0,000demand for land by the branch TD NM 0,000 -0,063 0,000 -0,063 0,000 -0,063demand for land by the branch TD AP -0,029 -0,028 -0,029 -0,028 -0,029 -0,028 Table 3 : the results from the simulations of the series 3 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

incomes and savings block disposable income of the household YDH -0,113 -0,113 -0,113income of the firms YF 0,429 0,429 0,429receipts form the indirect taxation of product TI M -0,068 -0,068 -0,068receipts form the indirect taxation of product TI TV 0,968 0,968 0,968receipts form the direct taxation of the firms income TDE 0,426 0,426 0,426saving of the household SH -0,112 -0,112 -0,112saving of the firms SF 0,092 0,092 0,092saving of the government SG 0,000 0,000 0,000transferts from the firms to households (dividends) des entreprises vers les ménagesDIV 1,208 1,208 1,208transferts from household to the firms TME 4,892 4,892 4,892transferts from the ROW to the firms TRE 0,000 0,000 0,000publics transferts to the household TG -0,226 -0,226 -0,226transferts from the firms to the ROW TER 0,161 0,161 0,161

28

Table 4 : the results from the simulations of the series 3 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

demand block household consumption of the product C IM -0,113 -0,113 -0,113 -0,113 -0,113 -0,113household consumption of the product C M -0,113 -0,112 -0,113 -0,112 -0,113 -0,112household consumption of the product C CM -0,111 -0,115 -0,111 -0,115 -0,111 -0,115household consumption of the product C TV -0,114 -0,109 -0,114 -0,109 -0,114 -0,109household consumption of the product C SR -0,113 -0,113 -0,113 -0,113 -0,113 -0,113household consumption of the product C NM -0,112 -0,114 -0,112 -0,114 -0,112 -0,114household consumption of the product C AP -0,114 -0,114 -0,114 -0,114 -0,114 -0,114household consumption of the product produced by the firm CFim fim1 IM -0,113 -0,113 -0,113 -0,113 -0,113 -0,113household consumption of the product produced by the firm CFm fm1 M -0,113 -0,113 -0,113 -0,113 -0,113 -0,113household consumption of the product produced by the firm CFcm fcm1 CM -0,111 -0,111 -0,111 -0,111 -0,111 -0,111household consumption of the product produced by the firm CFtv ftv1 TV -0,114 -0,114 -0,114 -0,114 -0,114 -0,114household consumption of the product produced by the firm CFsr fsr1 SR -0,113 -0,113 -0,113 -0,113 -0,113 -0,113household consumption of the product produced by the firm CFnm fnm1 NM -0,112 -0,112 -0,112 -0,112 -0,112 -0,112household consumption of the product produced by the firm CFap fap1 AP -0,114 -0,114 -0,114 -0,114 -0,114 -0,114total intermediate consumption of the branch CI IM -0,099 -0,099 -0,099total intermediate consumption of the branch CI M -0,060 -0,060 -0,060total intermediate consumption of the branch CI CM -0,028 -0,028 -0,028total intermediate consumption of the branch CI TV 1,044 1,044 1,044total intermediate consumption of the branch CI SR -0,039 -0,039 -0,039total intermediate consumption of the branch CI NM -0,081 -0,081 -0,081total intermediate consumption of the branch CI AP -0,028 -0,028 -0,028demand of the interior product DD M -0,062 -0,061 -0,062 -0,061 -0,062 -0,061demand of the interior product DD SR -0,038 -0,038 -0,038 -0,038 -0,038 -0,038demand of the interior product DD AP -0,028 -0,029 -0,028 -0,029 -0,028 -0,029intermediate demand of the product by the branch DI IM IM -0,102 -0,102 -0,102intermediate demand of the product by the branch DI IM M -0,089 -0,089 -0,089intermediate demand of the product by the branch DI IM TV 1,020 1,020 1,020intermediate demand of the product by the branch DI IM NM -0,122 -0,122 -0,122intermediate demand of the product by the branch DI M IM -0,139 -0,139 -0,139intermediate demand of the product by the branch DI M M -0,061 -0,061 -0,061intermediate demand of the product by the branch DI M CM -0,042 -0,042 -0,042intermediate demand of the product by the branch DI M TV 1,031 1,031 1,031intermediate demand of the product by the branch DI M NM -0,095 -0,095 -0,095intermediate demand of the product by the branch DI CM IM -0,294 -0,294 -0,294intermediate demand of the product by the branch DI CM M -0,067 -0,067 -0,067intermediate demand of the product by the branch DI CM TV 1,013 1,013 1,013intermediate demand of the product by the branch DI CM NM -0,172 -0,172 -0,172intermediate demand of the product by the branch DI TV M -0,062 -0,062 -0,062intermediate demand of the product by the branch DI TV TV 1,046 1,046 1,046intermediate demand of the product by the branch DI TV NM -0,051 -0,051 -0,051intermediate demand of the product by the branch DI SR IM -0,090 -0,090 -0,090intermediate demand of the product by the branch DI SR M -0,061 -0,061 -0,061intermediate demand of the product by the branch DI SR CM -0,026 -0,026 -0,026intermediate demand of the product by the branch DI SR TV 1,037 1,037 1,037intermediate demand of the product by the branch DI SR SR -0,038 -0,038 -0,038intermediate demand of the product by the branch DI SR NM -0,074 -0,074 -0,074intermediate demand of the product by the branch DI NM M -0,051 -0,051 -0,051intermediate demand of the product by the branch DI NM CM -0,250 -0,250 -0,250intermediate demand of the product by the branch DI NM TV 1,073 1,073 1,073intermediate demand of the product by the branch DI NM SR -0,033 -0,033 -0,033intermediate demand of the product by the branch DI NM NM -0,089 -0,089 -0,089intermediate demand of the product by the branch DI AP IM -0,062 -0,062 -0,062intermediate demand of the product by the branch DI AP M -0,060 -0,060 -0,060intermediate demand of the product by the branch DI AP CM 0,000 0,000 0,000intermediate demand of the product by the branch DI AP TV 1,342 1,342 1,342intermediate demand of the product by the branch DI AP SR -0,062 -0,062 -0,062intermediate demand of the product by the branch DI AP NM -0,064 -0,064 -0,064demand of the product whose firm will cope DFim fim1 IM -0,097 -0,097 -0,097demand of the product whose firm will cope DFm fm1 M -0,061 -0,061 -0,061demand of the product whose firm will cope DFtv ftv1 TV 1,043 1,043 1,043demand of the product whose firm will cope DFsr fsr1 SR -0,038 -0,038 -0,038demand of the product whose firm will cope DFnm fnm1 NM -0,077 -0,077 -0,077intermediate demand for product DIT CM 0,061 0,061 0,061 0,061 0,061 0,061intermediate demand for product DIT TV 0,374 0,373 0,374 0,373 0,374 0,373number of firms of the branch NF IM -0,061 -0,061 -0,061number of firms of the branch NF TV 0,334 0,334 0,334

29

Table 5 : the results from the simulations of the series 3 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

transport block trasport demand for labor motif CMT TR -0,113 -0,113 -0,113transport demand for others motif CMT AU -0,113 -0,113 -0,113public transport demand for labor motif CMTT 0,920 1,858 2,816probability to choose public transport from z to zz alpha_tr Z1 Z1 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z1 Z2 1,107 1,845 2,952probability to choose public transport from z to zz alpha_tr Z1 Z3 0,317 0,952 1,587probability to choose public transport from z to zz alpha_tr Z1 Z4 1,311 2,623 3,934probability to choose public transport from z to zz alpha_tr Z1 Z5 0,576 1,153 2,017probability to choose public transport from z to zz alpha_tr Z1 Z6 0,875 1,458 2,041probability to choose public transport from z to zz alpha_tr Z1 Z7 0,532 1,064 1,596probability to choose public transport from z to zz alpha_tr Z1 Z8 1,042 2,431 3,472probability to choose public transport from z to zz alpha_tr Z2 Z1 1,107 1,845 2,952probability to choose public transport from z to zz alpha_tr Z2 Z2 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z2 Z3 5,848 12,865 19,298probability to choose public transport from z to zz alpha_tr Z2 Z4 2,091 3,833 5,923probability to choose public transport from z to zz alpha_tr Z2 Z5 6,299 12,598 18,898probability to choose public transport from z to zz alpha_tr Z2 Z6 6,286 12,000 18,857probability to choose public transport from z to zz alpha_tr Z2 Z7 4,464 9,375 14,286probability to choose public transport from z to zz alpha_tr Z2 Z8 8,247 15,464 24,742probability to choose public transport from z to zz alpha_tr Z3 Z1 0,317 0,952 1,587probability to choose public transport from z to zz alpha_tr Z3 Z2 5,848 12,865 19,298probability to choose public transport from z to zz alpha_tr Z3 Z3 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z3 Z4 5,941 11,881 18,317probability to choose public transport from z to zz alpha_tr Z3 Z5 0,840 1,681 2,801probability to choose public transport from z to zz alpha_tr Z3 Z6 5,189 10,377 16,509probability to choose public transport from z to zz alpha_tr Z3 Z7 4,583 9,167 14,167probability to choose public transport from z to zz alpha_tr Z3 Z8 5,288 10,577 16,346probability to choose public transport from z to zz alpha_tr Z4 Z1 1,311 2,623 3,934probability to choose public transport from z to zz alpha_tr Z4 Z2 2,091 3,833 5,923probability to choose public transport from z to zz alpha_tr Z4 Z3 5,941 11,881 18,317probability to choose public transport from z to zz alpha_tr Z4 Z4 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z4 Z5 5,729 11,458 17,708probability to choose public transport from z to zz alpha_tr Z4 Z6 6,522 13,043 20,109probability to choose public transport from z to zz alpha_tr Z4 Z7 2,389 4,437 6,826probability to choose public transport from z to zz alpha_tr Z4 Z8 7,080 15,044 23,009probability to choose public transport from z to zz alpha_tr Z5 Z1 0,576 1,153 2,017probability to choose public transport from z to zz alpha_tr Z5 Z2 6,299 12,598 18,898probability to choose public transport from z to zz alpha_tr Z5 Z3 0,840 1,681 2,801probability to choose public transport from z to zz alpha_tr Z5 Z4 5,729 11,458 17,708probability to choose public transport from z to zz alpha_tr Z5 Z5 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z5 Z6 2,181 4,673 6,854probability to choose public transport from z to zz alpha_tr Z5 Z7 2,381 4,762 7,143probability to choose public transport from z to zz alpha_tr Z5 Z8 4,898 9,388 14,694probability to choose public transport from z to zz alpha_tr Z6 Z1 0,875 1,458 2,041probability to choose public transport from z to zz alpha_tr Z6 Z2 6,286 12,000 18,857probability to choose public transport from z to zz alpha_tr Z6 Z3 5,699 11,399 17,098probability to choose public transport from z to zz alpha_tr Z6 Z4 6,522 13,043 20,109probability to choose public transport from z to zz alpha_tr Z6 Z5 2,181 4,673 6,854probability to choose public transport from z to zz alpha_tr Z6 Z6 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z6 Z7 4,428 9,225 13,653probability to choose public transport from z to zz alpha_tr Z6 Z8 5,128 10,256 15,812probability to choose public transport from z to zz alpha_tr Z7 Z1 0,532 1,064 1,596probability to choose public transport from z to zz alpha_tr Z7 Z2 4,878 9,756 15,122probability to choose public transport from z to zz alpha_tr Z7 Z3 4,545 9,545 14,545probability to choose public transport from z to zz alpha_tr Z7 Z4 2,389 4,437 6,826probability to choose public transport from z to zz alpha_tr Z7 Z5 2,381 4,762 7,143probability to choose public transport from z to zz alpha_tr Z7 Z6 4,428 9,225 13,653probability to choose public transport from z to zz alpha_tr Z7 Z7 0,000 0,000 0,000probability to choose public transport from z to zz alpha_tr Z7 Z8 5,528 11,055 17,085probability to choose public transport from z to zz alpha_tr Z8 Z1 1,042 2,431 3,472probability to choose public transport from z to zz alpha_tr Z8 Z2 8,247 15,464 24,742probability to choose public transport from z to zz alpha_tr Z8 Z3 5,288 10,577 16,346probability to choose public transport from z to zz alpha_tr Z8 Z4 7,080 15,044 23,009probability to choose public transport from z to zz alpha_tr Z8 Z5 4,898 9,388 14,694probability to choose public transport from z to zz alpha_tr Z8 Z6 5,128 10,256 15,812probability to choose public transport from z to zz alpha_tr Z8 Z7 5,528 11,055 17,085probability to choose public transport from z to zz alpha_tr Z8 Z8 0,000 0,000 0,000

30

Table 5 : the results from the simulations of the series 3 (variation between the simulation and the reference situation, in %)Definitions Symbol Simulation 1 : Simulation 2 : Simulation 3 : Volume value Volume value Volume value

transport block

coast of public transport from z to zz PPB Z1 Z1 0,000 0,000 0,000coast of public transport from z to zz PPB Z1 Z2 -4,940 -9,881 -14,821coast of public transport from z to zz PPB Z1 Z3 -3,372 -6,744 -10,094coast of public transport from z to zz PPB Z1 Z4 -5,483 -10,950 -16,433coast of public transport from z to zz PPB Z1 Z5 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z1 Z6 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z1 Z7 -2,943 -5,905 -8,848coast of public transport from z to zz PPB Z1 Z8 -4,559 -9,119 -13,664coast of public transport from z to zz PPB Z2 Z1 -4,940 -9,881 -14,821coast of public transport from z to zz PPB Z2 Z2 0,000 0,000 0,000coast of public transport from z to zz PPB Z2 Z3 -8,715 -17,430 -26,151coast of public transport from z to zz PPB Z2 Z4 -7,198 -14,396 -21,582coast of public transport from z to zz PPB Z2 Z5 -8,116 -16,232 -24,342coast of public transport from z to zz PPB Z2 Z6 -8,164 -16,328 -24,498coast of public transport from z to zz PPB Z2 Z7 -7,899 -15,799 -23,691coast of public transport from z to zz PPB Z2 Z8 -8,344 -16,688 -25,026coast of public transport from z to zz PPB Z3 Z1 -3,372 -6,744 -10,094coast of public transport from z to zz PPB Z3 Z2 -8,715 -17,430 -26,151coast of public transport from z to zz PPB Z3 Z3 0,000 0,000 0,000coast of public transport from z to zz PPB Z3 Z4 -8,669 -17,339 -26,014coast of public transport from z to zz PPB Z3 Z5 -4,770 -9,539 -14,309coast of public transport from z to zz PPB Z3 Z6 -8,059 -16,124 -24,183coast of public transport from z to zz PPB Z3 Z7 -7,899 -15,799 -23,691coast of public transport from z to zz PPB Z3 Z8 -8,049 -16,098 -24,140coast of public transport from z to zz PPB Z4 Z1 -5,483 -10,950 -16,433coast of public transport from z to zz PPB Z4 Z2 -7,198 -14,396 -21,582coast of public transport from z to zz PPB Z4 Z3 -8,669 -17,339 -26,014coast of public transport from z to zz PPB Z4 Z4 0,000 0,000 0,000coast of public transport from z to zz PPB Z4 Z5 -8,140 -16,281 -24,421coast of public transport from z to zz PPB Z4 Z6 -8,245 -16,496 -24,741coast of public transport from z to zz PPB Z4 Z7 -6,629 -13,268 -19,896coast of public transport from z to zz PPB Z4 Z8 -8,312 -16,624 -24,936coast of public transport from z to zz PPB Z5 Z1 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z5 Z2 -8,116 -16,232 -24,342coast of public transport from z to zz PPB Z5 Z3 -4,770 -9,539 -14,309coast of public transport from z to zz PPB Z5 Z4 -8,140 -16,281 -24,421coast of public transport from z to zz PPB Z5 Z5 0,000 0,000 0,000coast of public transport from z to zz PPB Z5 Z6 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z5 Z7 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z5 Z8 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z6 Z1 -3,502 -7,004 -10,489coast of public transport from z to zz PPB Z6 Z2 -8,164 -16,328 -24,498coast of public transport from z to zz PPB Z6 Z3 -7,087 -14,179 -21,266coast of public transport from z to zz PPB Z6 Z4 -8,245 -16,496 -24,741coast of public transport from z to zz PPB Z6 Z5 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z6 Z6 0,000 0,000 0,000coast of public transport from z to zz PPB Z6 Z7 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z6 Z8 -8,672 -17,351 -26,023coast of public transport from z to zz PPB Z7 Z1 -2,943 -5,905 -8,848coast of public transport from z to zz PPB Z7 Z2 -6,877 -13,753 -20,624coast of public transport from z to zz PPB Z7 Z3 -6,877 -13,753 -20,624coast of public transport from z to zz PPB Z7 Z4 -6,629 -13,268 -19,896coast of public transport from z to zz PPB Z7 Z5 -7,658 -15,315 -22,984coast of public transport from z to zz PPB Z7 Z6 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z7 Z7 0,000 0,000 0,000coast of public transport from z to zz PPB Z7 Z8 -8,697 -17,388 -26,086coast of public transport from z to zz PPB Z8 Z1 -4,559 -9,119 -13,664coast of public transport from z to zz PPB Z8 Z2 -8,344 -16,688 -25,026coast of public transport from z to zz PPB Z8 Z3 -8,049 -16,098 -24,140coast of public transport from z to zz PPB Z8 Z4 -8,312 -16,624 -24,936coast of public transport from z to zz PPB Z8 Z5 -8,577 -17,155 -25,732coast of public transport from z to zz PPB Z8 Z6 -8,672 -17,351 -26,023coast of public transport from z to zz PPB Z8 Z7 -8,697 -17,388 -26,086coast of public transport from z to zz PPB Z8 Z8 0,000 0,000 0,000