metadata: authors: 0: author: Avner,Paolo
1: author: Mehndiratta,Shomik Raj
2: author: Viguie,Vincent
3: author: Hallegatte,Stephane
count: Argentina
docna: 0: docna: Buses, houses or cash ? socio-economic,
            spatial and environmental consequences of reforming public
            transport subsidies in Buenos Aires
docty: Policy Research Working Paper
subtopic: Transport Services,Transport in Urban Areas,Urban Transport,Municipal Management and Reform,Urban Housing,Urban Governance and Management,Urban Housing and Land Settlements,Access of Poor to Social Services,Economic Assistance,Disability,Services & Transfers to Poor,Global Environment
teratopic: Communities and Human Settlements,Social Protections and Labor,Urban Development,Macroeconomics and Economic Growth,Poverty Reduction,Public Sector Development,Transport
abstracts: cdata!: Transit subsidies in the urban area of
            Buenos Aires are high, amounting to a total of US$5 billion
            for 2012. They have been challenged on several counts:
            suspected of driving urban sprawl and associated
            infrastructure costs, diverting resources from system
            maintenance, and failing to reach the poor among others. In
            this context, this paper examines the impacts of cost
            recovery fares under a range of different policy scenarios
            that could cushion the impact of fare increases. The
            alternative scenarios that are scrutinized are the
            uncompensated removal of the transit subsidy, its
            replacement by a lump sum transfer, and its replacement by
            two different construction subsidy schemes. Using a dynamic
            urban model (NEDUM-2D) calibrated for the urban area of
            Buenos Aires, all scenarios are assessed along four
            dimensions: (i) the efficiency/welfare impact on residents,
            (ii) the impacts on the internal structure of the urban area
            and sprawl, (iii) the impact on commuting-related carbon
            dioxide emissions, and (iv) the redistributive impacts, with
            a focus on the poorest households. A series of results
            emerge. First, there are consumption-related welfare gains
            for residents associated with replacing the transit subsidy
            by a lump sum transfer. Second, there are only moderate
            reductions in urbanization over time and thus infrastructure
            costs associated with the subsidy removal. Third, the
            replacement of the transit subsidy leads to only moderate
            increases in carbon dioxide emissions despite lower public
            transport mode shares, because households will chose to
            settle closer to jobs, thereby reducing commuting distances.
            Finally, the replacement of the transit subsidy by a lump
            sum transfer will lead to short-term harsh redistributive
            impacts for captive transit users in some areas of the urban
            area. Medium-term adjustments of land and housing prices
            will partially mitigate the negative impacts of higher
            transport costs for tenants, but will further hurt homeowners.
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repnme: repnme: Buses, houses or cash ? socio-economic,
            spatial and environmental consequences of reforming public
            transport subsidies in Buenos Aires
display_title: Buses, houses or cash
            socio-economic, spatial and environmental consequences of
            reforming public transport subsidies in Buenos Aires
listing_relative_url: /research/2017/08/27901136/buses-houses-or-cash-socio-economic-spatial-environmental-consequences-reforming-public-transport-subsidies-buenos-aires
topicv3: Transport Subsidies m1326050 1630,Transport m1326661 135,Mass Transit m1327372 1726,Environment, Natural Resources and Blue
            Economy m1327391 566,Land and Housing m1327648 817
publishtoextweb_dt: 2017-08-14T00:00:00Z
historic_topic: Communities and Human Settlements,Social Protections and Labor,Urban Development,Macroeconomics and Economic Growth,Poverty Reduction,Public Sector Development,Transport
no_of_pages: 54
author_keys: 0: author_key: 1256219
1: author_key: 658882
2: author_key: 957932
3: author_key: 1103816
model_extractions: WPS8166 Policy Research Working Paper 8166 Buses, Houses or Cash?

Socio-Economic, Spatial and Environmental Consequences of Reforming Public Transport Subsidies in Buenos Aires _Paolo Avner Shomik Raj Mehndiratta Vincent Viguié Stephane Hallegatte_ Social, Urban, Rural and Resilience Global Practice Group Transport and ICT Global Practice Group Global Facility for Disaster Reduction and Recovery August 2017
Policy Research Working Paper 8166

# **Abstract**

Transit subsidies in the urban area of Buenos Aires are high, amounting to a total of US$5 billion for 2012. They have been challenged on several counts: suspected of driving urban sprawl and associated infrastructure costs, diverting resources from system maintenance, and failing to reach the poor among others. In this context, this paper examines the impacts of cost recovery fares under a range of different policy scenarios that could cushion the impact of fare increases. The alternative scenarios that are scrutinized are the uncompensated removal of the transit subsidy, its replacement by a lump sum transfer, and its replacement by two different construction subsidy schemes. Using a dynamic urban model (NEDUM-2D) calibrated for the urban area of Buenos Aires, all scenarios are assessed along - four dimensions: (i) the efficiency/welfare impact on resi dents, (ii) the impacts on the internal structure of the urban area and sprawl, (iii) the impact on commuting-related carbon dioxide emissions, and (iv) the redistributive impacts, with a focus on the poorest households. A series of results emerge. First, there are consumption-related welfare gains for residents associated with replacing the transit subsidy by a lump sum transfer. Second, there are only moderate reductions in urbanization over time and thus infrastructure costs associated with the subsidy removal. Third, the replacement of the transit subsidy leads to only moderate increases in carbon dioxide emissions despite lower public transport mode shares, because households will chose to settle closer to jobs, thereby reducing commuting distances. Finally, the replacement of the transit subsidy by a lump sum transfer will lead to short-term harsh redistributive impacts for captive transit users in some areas of the urban area. Medium-term adjustments of land and housing prices will partially mitigate the negative impacts of higher transport costs for tenants, but will further hurt homeowners.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group, the Transport and ICT Global Practice Group, and the Global Facility for Disaster Reduction and Recovery. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pavner@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Buses, houses or cash? Socio-economic, Spatial and Environmental Consequences of Reforming Public Transport Subsidies in Buenos Aires Paolo Avner<sup>(a,b,c)</sup> , Shomik Raj Mehndiratta<sup>(a)</sup> , Vincent Viguié<sup>(b)</sup> , Stephane Hallegatte<sup>(a)1</sup> (a) The World Bank, Washington DC, USA (b) Center for International Research on the Environment and Development (CIRED) (c) Association pour la Promotion de la Recherche sur l’Economie du Carbone (APREC) JEL codes: H21, H23, O18, R14, R48, R52, C63 Keywords: Transit subsidy, Lump sum transfers, Construction subsidies, Land use – transport model, Urban form, Household welfare 1 The authors would like to thank Cynthia Goytia and Guadalupe Dorna for their time, patience and fruitful discussions as well as for accepting to share precious data and insights. Andrés Gartner was a great guide of Buenos Aires, may he be thanked for his insights as well. The crucial employment and household location data was shared by German Freiberg and Wagner Colombini Martins, we are very grateful to them. We also thank Camila Rodriguez and Santiago Ezequiel Arias for the information they provided us with on the history of subsidies in Buenos Aires and Tatiana Peralta-Quiròz for the GTFS data. Harris Selod, Daniel Rodriguez, Augustin Maria, Bernardo Alvim, Nancy Lozano Gracia as well as participants in the 2015 NARSC conference provided excellent comments on previous drafts of this paper.
# 1 Introduction

Public transport is highly subsidized in the urban area of Buenos Aires. The fares paid by the users only cover around a third of the cost of the trips with total transit subsidies amounting to more than US$ 5 billion for the urban area of Buenos Aires alone in 2012. This level of subsidization is in line with what is found in developed country cities but is high for a developing country. In light of these figures, the transit subsidies have been challenged first on a financial and fiscal basis<sup>2</sup> . Is this the best use of scarce public resources? Or could alternative policy and interventions lead to better welfare outcomes? Commentators have focused on many potential weaknesses of the subsidy – including the impact of diverting resources originally earmarked for system maintenance and investment both of which have been inadequate in the last decade; issues of financial management; as well as the lack of transparency or incentives to foster efficiency in the allocation mechanism.

There is also a debate on the impact of the low fare on the aggregate modal share of public transport in the metropolitan area. On one hand, all else equal, low fares make for more competitive public transport. But the evidence also suggests that for most riders of choice, quality trumps fare, so that when transit subsidies come at the expense of quality of service (comfort, reliability, passenger information systems), as is the case in the Buenos Aires urban area, the public transport ridership balance is unclear. In particular, Gwilliam (2017) reports evidence from London that reductions in public transport fares are less likely to bring about mode share changes in favor of transit than service quality improvements.

There are also a number of other dimensions along which the subsidies need to be assessed. First there are concerns that these artificially low public transport prices have fueled urban sprawl and related negative externalities such as excessive loss of farmland, or the need for costly infrastructure (sewage systems, roads, etc.). Second, the transit subsidy has an impact on commuting-related energy expenditures and CO2 emissions. This is closely linked to the impact of lower fares on both transit ridership – which is less carbon-intensive than travel by private cars – and in parallel on location decisions of households in the Buenos Aires urban area. Whereas higher public transport mode shares would tend to decrease emissions, lower transport costs would also enable households to settle further from job centers, thereby lengthening commuting distances and increasing CO2 emissions. The net effect is a priori ambiguous.

Finally, transit subsidies are generally believed to be pro-poor, enabling the poor to maintain a reasonable level of accessibility to jobs. Evidence however suggests that in Buenos Aires Metropolitan area, transit subsidies disproportionately benefit the middle income class rather than the poor (Bondorevsky 2007). If the aim of the transit subsidy is to provide the poor with urban transport options then the targeting is inefficient, resulting in both bad resource use and low accessibility for the poor.

In this context this paper examines the spatial development, welfare, redistributive and environmental impact of cost recovery fares under a range of different policy scenarios that could potentially cushion the impact of fare increases. The scenarios that we scrutinize are the uncompensated removal of the public transport subsidy, its replacement by a lump sum transfer, and its replacement by two different construction subsidy schemes. The first construction subsidy device assumes that a fixed share of the construction costs is subsidized wherever construction occurs, while the second device targets specific zones that benefit from high transit accessibility within the urban area. Special attention is paid to the lump sum transfer counterfactual as it is theoretically optimal and because there is a rich history of cash transfers in Argentina, in particular following the 2001 crisis. The outcomes of counterfactual scenarios

> 2 In April 2016, fare increases were introduced with the minimum bus fare doubling from AR$ 3 in 2014 to AR$ 6. This study assumes subsidies and fares before their revision in 2016.

2
are compared to the baseline and the results are assessed along four dimensions: 1/ the aggregate welfare or efficiency impact on residents, 2/ the impact on the internal structure of the urban area and on sprawl, 3/ the impact on commuting related CO2 emissions and 4/ the redistributive impact of each policy scenario with a focus on the poorest households in the urban area of Buenos Aires.

To investigate the respective impacts of cost recovery fares associated with compensatory policy options along the efficiency, spatial, environmental and redistributive dimensions, we use a simple land-use transport interaction model calibrated for the urban area of Buenos Aires, NEDUM-2D (Viguié and Hallegatte 2012), which can account for all these dimensions. Many land use – transport integrated models (LUTI) exist and the applied research community has made great progress on these in the past decades. No single LUTI model however is perfect and the choice of the most appropriate model is very dependent on the research question and the degree of detail/simplicity which is expected. A number of criteria explain the choice of NEDUM-2D: 1/ it is a simple and understandable model – not a black box; 2/ it is one of the only ones that describes the housing and land market with micro-economically founded behaviors; 3/ it is not a data greedy model which makes it usable in data scarce situations and 4/ it is a dynamic model which can capture the impacts of shocks and transitions, including adjustment costs. As we will see below, however, the model is not able to capture all the mechanisms that would be required to answer all questions, and we explore options for further developments.

A series of results emerge from our exercises which paint a complex picture of transit subsidies and alternative cost-recovery options in the urban area of Buenos Aires. First, there are efficiency/welfare gains associated with replacing the transit subsidy by a lump sum transfer. These are non-negligible when subsidization rates are high. In the case of Buenos Aires, the average gains would correspond to an extra US$ 325 per household when expressed in equivalent income or 1.75% of the average household’s annual budget in 2014. Our definition of welfare includes only consumption of housing space and other goods. For example, we do not explicitly account for distributional impacts or various externalities such as the access of the poorest to economic opportunities and the associated benefits of social inclusion. Nor do we feature specific preferences such as preferences for low density housing. For these reasons we will refer to consumption-related welfare in the rest of the paper.

Second, the removal of the transit subsidies leads to moderate reductions in urbanization over time. This indicates that the argument that low transit fares could fuel large amounts of sprawl in the future and associated infrastructure costs are likely to be overplayed. The changes in the internal structure of the urban area of Buenos Aires are much larger however with higher densities close to jobs and a decrease in the average distance to jobs triggered by the removal of the subsidy, which in turn reduces commuting distances. So, although public transport ridership decreases in the medium to long term as a result of the fare increase, the increase in commuting related CO2 emissions is expected to remain limited. In fact, if the transit subsidy is replaced by a targeted construction subsidy, the model predicts a net reduction in emissions by 0.7%. The most CO2 intensive policy alternative is the lump transfer with emissions increasing by more than 5% in 2050.

Thirdly, whereas some efficiency gains are to be expected from replacing the transit subsidy by a lump sum transfer, these will come at the expense of short term harsh redistributive impacts for the heaviest users of public transport. These negative redistributive impacts are stronger as the distance to the city center increases. For tenants, they will be partially mitigated over the medium term by land and housing markets mechanisms whereby the hardest hit areas of the urban area will see a decrease in housing rents reflecting lower accessibility. This adjustment in housing prices will benefit tenants and newcomers to the urban area but will penalize homeowners residing in these areas as they will face both higher commuting 3
costs and lower resale values of their dwellings. The high rate of home ownership, across all income groups in Buenos Aires, including the poor, increases the negative impact of higher transit prices.

The remainder of this paper is structured as follows. Section 2 provides a literature review of the welfare, spatial, environmental and distributional impacts of transport and construction subsidies. Section 3 describes the history and functioning of transit subsidies in Argentina and Buenos Aires in particular. Section 4 investigates the welfare impacts of a public transport subsidy in a very simplified urban economics framework and describes the mechanisms at play. Section 5 describes the principles and mechanisms of the land use – transport modeling framework, NEDUM-2D, used in the remainder of the paper to assess the impacts of the transit subsidy in Buenos Aires and of alternative policy options. It also shows how simulations compare to data which serves to validate the use of the model. Section 6 describes the various cost-recovery options studied and how they affect the extent of the urban area and the CO2 emissions from commuting as well as households’ welfare. Section 7 investigates the distributional impacts of removing the public transit subsidy and replacing it by a lump sum transfer for all income groups in the urban area of Buenos Aires. Finally, section 8 concludes.

# 2 Literature review

There is a relatively well established literature focusing on the link between welfare and transport subsidies starting with Mohring (1972). This seminal paper argued that in the presence of scale economies in public transport provision (for example because a train or a bus is indivisible), marginal cost pricing schemes would lead to low public transport ridership which in turn would result in low frequencies and long (and sub-optimal) waiting times for users. This Mohring effect would warrant public transport subsidies so as to reduce waiting times and increase welfare. The magnitude and even the existence of the Mohring effect is however debated (Estupiñán et al. 2007). Gwilliam (2017) in his recent review on transport pricing emphasizes that if urban transport can be considered as a “merit good”, i.e. a good which is undervalued by its consumer and the consumption of which generates positive externalities, this would provide a theoretical basis for subsidization. There are reasons to argue that urban transport indeed is a “merit good” as the accessibility for example to an increased number of jobs generates positive externalities through larger and more integrated labor markets (Rosenthal and Strange 2004). Other scholars (I. W. Parry and Small 2009; I. W. H. Parry and Timilsina 2010; Basso and Silva 2014) have built models and frameworks which aim at identifying optimal transportation policies in urban settings in the presence of externalities such as congestion, CO2 emissions contributing to climate change, air pollution and road fatalities. All these papers find positive welfare effects associated with transit subsidies. But they argue that transit subsidies are only a second best and are dominated by policies which affect congestion (the main negative externality) more directly such as congestion tolls, gasoline taxes or dedicated bus lanes. However, these models are non-spatial in essence and although they are evaluated in urban settings they do not represent households’ locational decisions explicitly. This means that they miss the tradeoff between housing and transport costs and may underestimate the benefits that policies which decrease transport costs may bring (because they can also decrease housing costs for residents) and conversely overestimate the benefits of policies that increase transport costs such as congestion tolls.

The reliance on non-spatial models also means that it is not possible to shed light on the urban expansion consequences of transit subsidies and their associated costs. To fill this gap, scholars have resorted to the classic urban economics framework based on the pioneering works of Alonso (1964), Mills (1967) and Muth (1969). In urban economic theory, a decrease in transport costs tends to flatten the land and housing rent curves thereby encouraging residents to settle further away from the city center and displacing the urban area’s boundary outwards (see for example Fujita (1989)). Whether transport subsidies lead to urban sprawl when the decrease in transport costs is financed through income taxation is however not as 4
trivial. Brueckner (2005) tackles this issue and shows that despite the higher tax burden needed to finance a transport subsidy, the transport cost reduction dominates the tax burden effect and leads to urban sprawl. He also concludes that if the transport system displays constant returns to scale, the subsidy is welfare reducing. Building on Brueckner’s work, Su and DeSalvo (2008) develop a monocentric model with two modes, cars and transit, which are characterized by different fixed and marginal costs as in LeRoy and Sonstelie (1983). The authors also employ empirical methods. They find that car subsidies tend to increase the size of urban areas while transit subsidies would reduce sprawl. In both cases the effects are limited in magnitude. Conversely, while they do not directly assess the spatial impact of any transport subsidization scheme, Gonzalez-Navarro and Turner (2014) study the impact of subway system construction in 138 cities around the world and conclude that they have caused population decentralization. It is not obvious how to make sense of these contradictory results. There are a number of subtle mechanisms at play, which would push the overall net result in one direction or another: 1/ a higher tax burden to finance the transit subsidy would tend to curb sprawl by reducing available income for housing space consumption; 2/ the lower transport cost would push the urban area’s boundaries outwards; and 3/ because public transit is by nature more localized than road (think of bus or urban rail stops for example) we could have a combination of more concentrated population with transit subsidies around transit stops and a larger urban footprint.

Scholars have also investigated the distributional impact of transit subsidies by distinguishing between income groups (Estupiñán et al. 2007; Basso and Silva 2014) but also between renters and landowners (Borck and Wrede 2005, 2008). Basso and Silva (2014) find that congestion pricing, dedicated bus lanes and, more importantly for this paper, transit subsidies unambiguously increase the welfare of the poorest. Borck and Wrede (2005, 2008) use a monocentric model with two income classes to study the reasons for popular support to transport subsidy schemes despite clear inefficiencies. Their conclusions are less straightforward. When only one transport mode is available, they show that transport subsidies can benefit all residents in the case of absentee landowners at the expense of the landowners. When conversely all land is owned by residents of the urban area, commuting subsidies lead to a distributional antagonism between central residents and suburbanites. While the rich would benefit at the expense of the poor if they reside in the periphery, benefitting proportionately more from the subsidy, the poor do not necessarily benefit if they live on the outskirts (Borck and Wrede 2005). Borck and Wrede (2008) then complete their model by introducing two distinct transport modes, cars and public transport. In this case the conclusions are much more subtle as the winners and losers depend not only on their location within the urban area and whether they are landowners or not but also on the commuting mode they use.

Finally, Estupiñan et al. (2007) conduct an in-depth review of real world transit subsidy schemes and focus on whether they are pro-poor. They find that whereas in developed countries transit subsidies mainly benefit the poor, this is mostly not the case in developing countries where transit remains a luxury good. The authors argue that, as the more wealthy urban residents benefit more than the poorest, a lump sum transfer would actually be a better device to reach the poor than a transit subsidy. In particular, they document that the transit subsidy program in the Buenos Aires region disproportionately benefits the middle income class rather than the poor, although subsidized fares are available to all. There are indeed significant errors of exclusion, defined as the share of the poor (the bottom quartile) not benefitting from transit subsidies because they do not use transit. For bus users the error of exclusion was as high as 70% in 2002 and 60% in 2006 (Bondorevsky 2007). Conversely the errors of inclusion, defined as the share of beneficiaries which are non-poor was 59% in 2002 and 75% for bus users in 2006 (Bondorevsky 2007). These figures indicate that if the aim of the transit subsidy is to provide the poor with urban transport options then the targeting is inefficient, resulting in both bad resource use and low accessibility for the poor.

5
To the best of our knowledge, the most comprehensive effort at addressing welfare, spatial, environmental and distributional impacts of transport subsidies is a paper by Tscharaktschiew and Hirte (2012). The authors develop a Computable General Equilibrium model, inspired by Anas and Xu (1999) and Anas and Rhee (2006), which includes many real world features such as congestion, polycentric urban structures, multiple household types, different trip purposes and endogenous labor supply among others. They calibrate their model so as to reproduce the main features of an ‘average’ German city and test a wide range of urban transport subsidies schemes; one of them being a variant of a transit subsidy in the form of a reduced sales tax rate on public transport fares. The authors find that transit subsidies lead to only low levels of suburbanization, are welfare enhancing (including for landowners) and decrease CO2 emissions.

The urban economics literature is surprisingly silent on the issue of construction subsidies, despite the acknowledgment that land and housing markets are central to urban development. Construction and buildings have rightfully been investigated because their durability imposes some path dependencies in cities (Duranton and Puga 2015). Other scholars have focused on the impact of building height regulations on welfare and spatial expansion (Bertaud and Brueckner 2005) and on environmental externalities such as CO2 emissions (Borck 2016). It is however surprising that in a world which faces large housing deficits (estimated to be a billion homes according to UN-Habitat), especially in the rapidly urbanizing developing countries, little attention has been paid to the potential impact of construction subsidies. This is perhaps even more surprising as the compact city concept has gained a lot of attention as a sustainable urban growth trajectory (OECD 2012). Indeed, whereas compact cities carry the potential to reduce commuting related CO2 emissions, their critics often argue that they would lead to higher housing costs (Krupp and Acharya 2014) and the actual densification of an urban area would prove difficult because taller buildings are typically too costly (Castel 2007). In this respect, construction subsidies – justified by the positive externality of higher densities – seem like an a-priori promising avenue to reduce the housing deficit while enabling the construction of taller building and therefore achieving more compact and sustainable cities without leading to strong housing price increases. As Glaeser (2009) puts it “The best thing that we can do for the planet is build more skyscrapers.” A notable exception to this silence is Helsley and Strange (2007) who develop an urban economics model where positive externalities are generated by social interactions. They find that a construction subsidy can increase the housing supply near the city center and lead to a densification of the urban area which would promote social interactions and therefore increase social welfare.

3 Public transport subsidies in the metropolitan area of Buenos Aires Following the 2001 crisis and the peso depreciation which led to spiraling inflation in Argentina, public transport fares were frozen (or increases were restrained) in an attempt to dampen the impact of rising prices and protect the incomes of the poor. In parallel, the costs of operating public transit systems increased strongly in part as oil imports were purchased using a devaluated currency. The government provided direct (supply-side) subsidies to operators in order to maintain public transport service in a postcrisis period. While buses (colectivos), which today account for 87% of the public transport ridership, were not subsidized prior to the 2001 crisis, subsidies now account for over two-thirds (71%) of their operating costs. The average commercial fare of AR$ 3.5 can be compared to the AR$ 12 average fare necessary to achieve cost recovery. On the regional railway and metro (SUBTE) systems subsidization rates today represent respectively 94% and 68% of operating costs. Their passenger market shares are however much lower than for buses at 7% and 6%. In total, the public transport subsidies in the Area Metropolitana de 6
Buenos Aires (AMBA) amount to AR$ 25 billion or around US$ 5.4 billion<sup>3</sup> in 2012 which represents close to 0.7% of Argentina’s GDP.

While public transport is subsidized in other metropolitan areas in Argentina, the subsidization rates are lower and the minimum bus fares are higher: AR$ 6 and AR$ 9.15 in Mendoza and Córdoba respectively for example compared to AR$ 3 in AMBA.

The public transit subsidies in Argentina are currently financed by the Treasury (85%) and SISTAU (Sistema Integrado de Transporte Automotor) for the remaining 15%. The SISTAU funds are based on a 18.5% tax on gasoline while the Treasury funds are channeled through the RCC (Régimen de Compensatión Complementaria). Together the Treasury funds and SISTAU finance 72% of the operating costs of public transport systems in AMBA while earnings from travel fares account for the remaining 28%.

How does this compare to other cities worldwide? These public transport subsidization levels are commonplace in developed countries; for example they reach around 70% of operational costs for the largest 20 cities in the United States (I. W. Parry and Small 2009), 55% in London, 70% in Paris (Institut Montaigne 2012) and the Millennium database reports similar figures for many developed country cities around the world (Kenworthy, Laube, and Vivier 2001). In developing country cities however, public transport subsidies are very low or non-existent. In this respect, Argentina and above all the Metropolitan area of Buenos Aires are exceptions (Basso and Silva 2014).

Beyond the general discussions about public transport subsidy merits and potential inefficiencies that are mentioned in the introduction and literature review, there are a couple of specific points that deserve to be reported here. First, public funds have an opportunity cost, meaning that when they are mobilized for financing public transit systems, they are not available for other priorities such as education, health and sanitation for example. While public funds have opportunity costs in all countries and at all times, the necessity to allocate them wisely is arguably more critical in a developing country which is recovering from a major economic crisis as occurred just above a decade ago and where fiscal resources are likely to be scarcer than elsewhere. The need to evaluate whether these resources should be sunk into public transit subsidies or not is even more pressing as Argentina is an exception in the developing world in its choice to instore a transit subsidy scheme.

Second, the design of the scheme is conducive to some inefficiencies and perverse effects. Indeed, the supply side subsidies from the government are calculated so as to cover the deficits of the private transit operators. The latter therefore do not have any incentive to rationalize their operations and seek inefficiencies in their system as this would only result in a reduction of the subsidy level received from the government.

Thirdly, the massive level of subsidies poured into the public transit systems cover only the operational costs and leave very little available fiscal resources for investments and crucial maintenance of the public transport infrastructure. The lack of investment and maintenance has led to a slow degradation of the infrastructure and to the impossibility of keeping pace with urban growth through the addition of new routes or extension of existing ones.

The Argentine government is well aware of these issues and is exploring ways in which to reduce the fiscal burden of public transport subsidies without adversely impacting the poorest 40% of households which crucially rely on transit to reach jobs and other services. In early April 2016, fare increases were

> 3 All monetary values were converted into US$ for data consistency. The exchange rate that is used throughout this exercise is AR$1 = US$0.2198. The conversion factor was retrieved from the web for the month of July 2012.

7
implemented with a doubling of the minimum fares for buses and trains (fares for colectivos now stand at min AR$ 6; trains AR$ 4); and recently fares for SUBTE (metro) also increased. Operating subsidies for buses have been reduced to 66%. Alongside this process, the government has also been offering demandside subsidies to protect the poor from these fare increases. In this study we do no account for the changes in transit fares that took place in 2016 and rely on the subsidization levels and fares prior to these increases.

# 4 What is the efficiency of public transit subsidies and other policies?

To explore the efficiency of transit subsidies in a general case we will compare these to a policy option whereby transit subsidies are removed but a lump sum redistribution of the total initial transit subsidy savings is introduced. Comparing transit subsidies to a lump sum transfer is justified because lump sum transfers are usually deemed to be a more efficient public intervention as they do not distort household choices toward the consumption of a specific good, in this case transport. The comparison of the outcomes of a transit subsidy with that of a lump sum transfer through the measurement of households welfare and landowners’ profit in an urban economy aims to provide a benchmark for measuring the (in)efficiency of a transit subsidy (through orders of magnitude). We will focus distinctly on households’ and landowners in order to untangle the impacts of removing a public transit subsidy and compensating through lump sum transfers for each group of actors in the urban economy.

## 4.1 A simple model to compare lump sum transfers to transit subsidies

To investigate the efficiency of public transport subsidies, we will first focus on a very simple and schematic case. We use a monocentric model based on the works of Alonso (1964), Mills (1967), Muth (1969) and comprehensively described in Fujita (1989). This model displays one city center which is the commuting destination of all households of the urban area (the CBD). There is only one transport mode and we run the model in a static framework, i.e. at equilibrium with only one time period. In this simple model, residents derive utility from housing space � and a composite good � : Each worker has to commute once a day to the CBD, and this commuting entails transportation costs �(�) . All households are supposed to earn the same income � and – at the equilibrium – they have the same level of satisfaction described by a utility function � . The level of rents per square meter (or, equivalently, the annualized real estate price per square meter) is �(�) _,_ at each location in the city. Each household maximizes his utility function under the budget constraint described in (1) by choosing where to settle in the urban area � , how much housing space � to consume and what to spend on other goods � . The term �<sup>�</sup> represents the annual amount accruing from land rents per household which is recycled in the urban economy in the form of increased incomes.

In parallel, developers decide how much floor space to build and where in the city. They do so by choosing the amount of capital to invest at a specific distance to the city center �(�) in order to maximize their profit function which also represents land values.

The amount of floor space built and the annual land rents depend on an exogenous specific construction technology which displays decreasing returns to capital: �(�) = �(�(�), �(�)) . These decreasing returns to capital will cap the building heights and as consequence the total amount of residential floor space. Developers will only invest large amounts of capital to build tall buildings when the anticipated rents they 8
expect will offset the extra building costs. The edge of the city is given by a transaction cost �� below which it is no longer profitable to build. At the edge of the city ( �� ) we therefore have �����= �� .

The term �<sup>�</sup> found in (1) can schematically take on one of two values depending on the type of model used: Closed City model with Absentee landowners (CCA) or Closed City model with Public ownership of land (CCP). In the closed city model with absentee landowners (CCA), landowners are supposed to live outside the urban area which means that land rents are not recycled in the local economy in the form of increased incomes. In this case �<sup>�</sup> = 0 . At the opposite end of the spectra, if all land plots are supposed to be owned by a local government (or by all households in equal shares) then �<sup>�</sup> = ���� ��(�) �� , where � is the number of households in the urban area.

The functional forms used in this paper as well as more details on the construction function can be found in appendix 10.1

# 4.2 Numeric simulations: Aggregate welfare description of results

- 4.2.1 Impacts on tenants and landowners of replacing a transport subsidy with a lump sum transfer

Solving the static model described in 4.1 with specific functional forms described in Appendix 10.1 in the closed city model with Absentee landowners (CCA), it can be shown that the impacts of replacing a transport subsidy by a lump sum transfer are complex. We run a numerical model with a number of realistic values for key parameters and data values for the model in 2012 described in Appendix 10.2, but with the assumption of a unique transport mode, linear transport costs, no land regulations and with a transaction cost of zero. We also assume, as in Parry and Timilsina (2010), that public transport displays constant returns of scale, meaning that only the variable costs of transport are accounted for and not the fixed costs linked to infrastructure provision. This is a reasonable proxy for buses which constitute the main public transport means. We run simulations for a large range of subsidization/taxation levels: from a 100 percent taxation level to a subsidization level equivalent to two-thirds of the transport costs. Impacts of these transportation policies on tenants’ welfare and landowners’ aggregate profits are depicted in Figure 1 and Figure 2.

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<!-- Start of picture text -->
1.005 es<br>——— optimal subsidy<br><!-- End of picture text -->

<!-- Start of picture text --> 1<br><!-- End of picture text --> <!-- Start of picture text --> 0.995<br><!-- End of picture text --> <!-- Start of picture text --> 0.99<br>-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 £50<br>Subsidy rate %<br><!-- End of picture text -->
<!-- Start of picture text --> Aggregate Landowner Profit level with subsidy compared to lump sum cca<br>102<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>0.98<br><!-- End of picture text --> <!-- Start of picture text --> 0.96<br><!-- End of picture text --> <!-- Start of picture text --> 0.94<br>0.92<br><!-- End of picture text --> <!-- Start of picture text --> 0.9<br><!-- End of picture text --> <!-- Start of picture text --> 0.88<br>0.86<br><!-- End of picture text --> <!-- Start of picture text --> 0.84<br><!-- End of picture text --> <!-- Start of picture text --> ° 800 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70<br>Subsidy rate %<br><!-- End of picture text -->
than a lump sum transfer. In particular transport subsidies hurt landowners quite harshly compared to a lump sum transfer.

Household utility and landowners’ profits are however difficult to compare because landowners’ total profit is measured directly in monetary terms, whereas households’ utility level is a convenient yet abstract construction which cannot be directly translated into a monetary value. We would, however, like to understand how a transit subsidy performs against a lump-sum transfer policy through one unique indicator.

# 4.2.2 Moving toward a city model with land rents recycling in the local economy

One way of achieving a unique indicator of the welfare impact of transport subsidy for landowners and households is to incorporate landowners’ proceeds into the households’ utility function by switching to a Closed City model with Public ownership of land. As detailed above, in such a model, landowners’ rents are recycled in the local urban economy through increased household incomes. This corresponds to a case whereby, �<sup>�</sup> = ���� ��(�) �� . This is equivalent to assuming that one dollar has the same value for tenants and for landowners, or that both groups have the same marginal utility of consumption. While this representation is still a simplification, it does make sense for Argentina where the rate of dwelling ownership is very high, close to 80% over all income quintiles (Fay 2005, pp 93) reported for the early 2000s. It also allows us to take into account the impacts of transit subsidies and lump sum-transfers on land rents in the form of a welfare impact.

With a fully closed city model with public ownership of land, it can be shown that the optimal subsidization/taxation level is zero (Figure 3), which is consistent with findings from Brueckner (2005) and Gusdorf and Hallegatte (2007) among others.

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<!-- Start of picture text --> Average household utility level with subsidy compared to lump sum ccp<br>1 —— ES SS<br><!-- End of picture text --> <!-- Start of picture text --> 0.99<br><!-- End of picture text --> <!-- Start of picture text --> 0.98<br><!-- End of picture text --> <!-- Start of picture text --> 0.97<br><!-- End of picture text --> <!-- Start of picture text --> 0.96<br><!-- End of picture text --> <!-- Start of picture text --> 0.94<br>-100 -90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70<br>Subsidy rate %<br><!-- End of picture text -->
||Tra<br>|nsport subsidization<br>|rate<br>|
|---|---|---|---|
||1/3|1/2|2/3|
|<br>0: Absentee<br>landowners(cca)|0.2%|1.3%|5.2%|
|Share of land<br>ld<br>0.2|0.3%|1.4%|5.4%|
|rents recyce in<br>th ll b<br>0.5|0.4%|1.6%|5.7%|
|e oca uran<br>econom<br>0.8|0.4%|1.7%|5.9%|
|y<br>1: Public ownership<br>of land(ccp)|0.5%|1.8%|6.1%|

**Table 1: Consumption-related welfare impacts of replacing a transport subsidy by a lump sum transfer as a function of subsidization rates and ratio of land rents which are recycled into the local urban economy.**

Table 1 shows that potential welfare gains are large with a lump sum transfer rather than a transport subsidy, especially if subsidization rates are high and land rents are recycled in the local urban economy, which is the case in Buenos Aires. This warrants a more in depth exploration of the welfare impact of transit subsidies in a more realistic model tailored for the urban area of Buenos Aires.

This picture omits two important dimensions of the discussion. The first is the dynamics of urbanization and in particular how each policy influences urban sprawl and its associated infrastructure costs and transport emissions. The second is the re-distributional impacts of each policy. To look into these dimensions we need a model which is spatially explicit so as to capture realistically the urbanization trajectory of the urban area of Buenos Aires and how the poor will be affected by an increase in public transport fares. The next section will present the model that we use in the rest of this paper while sections 6 and 7 will address the urban sprawl, environmental, welfare and political economy issues involved with public transit subsidies and other alternative policy options.

# 5 Dynamic model NEDUM-2D

## 5.1 General principles of NEDUM-2D

A theoretical framework that reflects the interplay between households’ localization decisions, project developers’ construction decisions and transport costs is required to evaluate the impacts of removing public transport subsidies in an urban area. The NEDUM-2D model (Non Equilibrium Dynamic Urban Model) developed in CIRED (Viguié and Hallegatte 2012) is well suited to conduct such a study.

This tool is an extension of the standard urban economic model such as defined by Fujita (1989) building on the pioneering works of Alonso, Mills and Muth at the end of the 1960s (Alonso 1964; Mills 1967; Muth 1969). It aims at explaining the variation in land costs in cities and thus the level of real estate prices together with the distribution of the households and buildings in an urban area. Classically, it is based on two very simplified, yet realistic, fundamental mechanisms. First, households, when choosing where to live, trade off between the proximity to the city center and the level of real estate prices (or equivalently between the proximity to the city center and the size of the dwelling they will occupy). Second, the project developers (or landowners<sup>4</sup> ) maximize their profits and choose to build more or less housing surface in a given place depending on the level of real estate prices. The higher the real estate prices, the denser the developers choose to build. We assume that the land is publicly owned (or equivalently owned in equal value shares by all households of the urban area) and that the annual rents generated are entirely recycled 4 A standard calculation (Fujita, 1989) proves that this hypothesis does not change the results of the model : in the model, the reasoning and the calculations that are conducted are identical whether project developers have to rent or buy the land from landowners.

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into the local economy in the form of increased income. A description of the analytical framework used to express these mechanisms can be found in Appendix 10.1 which goes into the details of the main analytic relationships but section 4.1 has already introduced these in their most basic form.

NEDUM-2D differs from the standard model developed in Fujita (1989) for three main reasons. First, it can be used dynamically, which enables to take into account the effect of a global change in income or population. As explained in Gusdorf et al. (2008), urban economics has mostly been used to explore the characteristics of long run equilibria. However, the existence of urban stationary equilibria is questionable: when population, transport prices, or income vary (sometimes in unpredictable ways), housing infrastructure cannot adapt instantaneously to changing conditions. NEDUM-2D explicitly takes these dynamics into account and describes cities as non-equilibrium systems. The model can also be run at the equilibrium for a specific year if data for a larger time period are not available.

Second, the theoretical model described by Fujita (1989) represents spatial differences solely as a function of the distance to the city center. As the name suggests NEDUM-2D is two dimensional meaning that it is an urban model that represents urbanization on a map rather than on a single axis. NEDUM-2D uses a grid with cells of variable size (classically 1km2). It can therefore account for spatial differences in land use and accessibility at a much finer scale. The model can represent differences between two cells situated at the same distance from the city center such as the amount of land that can be built upon or the proximity to public transport.

Third, the classic urban economy model only represents one means of transport. In NEDUM-2D there are three main transport options: private cars, public transport and walking. For each location in the urban area, citizens choose between walking, public transport and private vehicles, or a combination of these as a means for commuting. The competition between these modes is organized on the basis of their _generalized costs_ (i.e. the total cost including both the cost of time and the monetary costs incurred during the trip to the city center). It is assumed in this study that modal switch does not affect congestion levels and therefore leaves commuting times unchanged. In order to reflect the heterogeneity of citizens’ preferences in terms of transport modes we employ a discrete choice model (De Palma and Thisse 1987). We follow a common approach in transportation economics by using a multinomial logit model which assigns usage probabilities to each transport mode (see Salon 2009; Washbrook, Haider, and Jaccard 2006). This method ensures that even when one mode is much cheaper than the others, it will not be used by all residents in a location for all trips.

In the present study, we use this model with a certain number of simplifying hypotheses. First, we assume that all households commute every day to the center of the _Ciudad Autónoma de Buenos Aires_ (CABA), where jobs are assumed to be located. This mono-centric hypothesis is a clear simplification but one that is widely used in the literature and that finds some support in the data for the specific case of Buenos Aires. Indeed, the Buenos Aires region displays a strong mono-centric structure. For instance, in 2012 more than 40% of all jobs in the Buenos Aires region are localized in CABA even though CABA only represents 5.3% of _Region Metropolitana_ ’s area (see Appendix 10.1.5 for a more in-depth discussion about the validity of the mono-centric assumption for the Buenos Aires urban area)<sup>5</sup> .

Second, NEDUM-2D also relies on the hypothesis that all households within the urban area earn the same average income. It does not currently account for multiple household types and thus ignores income

> 5 Projections for 2012 based on Censo Nacional Economic 04/05 (CNE 04/05) and Encuesta Permanente de Hogares, 2011 (EPH, 2011).

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inequalities<sup>6</sup> . This simplification is acceptable for some indicators such as the extent of urbanization. However it can have some impacts for NEDUM-2D’s ability to capture the dispersion in rents and real estate prices around an average. Indeed, real estate prices are strongly affected by neighborhood characteristics among which social segregation can play a role. We will overcome this limitation to a certain degree below by interacting our results with a map of current income dispersion.

# 5.2 Geographic scope of the study

The study area is slightly larger than what the INDEC (Instituto Nacional de Estadística y Censos de la República Argentina) defines as the Gran Buenos Aires (GBA). GBA encompasses 24 fully or partially urbanized partidos and the City of Buenos Aires (CABA). Supplementary to the 24 partidos of GBA, our study area also includes 3 other partidos. Altogether the study area that we will name GBA+ in the rest of this report can be broken down as follows:

- The city of Buenos Aires (CABA)

- 14 fully urbanized partidos that belong to GBA:

|Avellaneda|Lanús|San Isidro|
|---|---|---|
|General San Martín|Lomas de Zamora|San Miguel|
|Hurlingham|Malvinas Argentinas|Tres de Febrero|
|Ituzaingó|Morón|Vicente López|
|José C. Paz|Quilmes||

- 10 partially urbanized partidos that belong to GBA:

|Almirante Brown|Florencio Varela|San Fernando|
|---|---|---|
|Berazategui|La Matanza|Tigre|
|Esteban Echeverría|Merlo||
|Ezeiza|Moreno||
|3 partidos that do not belo<br>Escobar|ng to GBA<br>Pilar|Presidente Perón|

- 3 partidos that do not belong to GBA

The Gran Buenos Aires covers more than 3,800 km2 of land and the GBA+ area is close to 4,000 km2.

# 5.3 Validation of NEDUM-2D on the urban area of Buenos Aires (GBA+)

With a limited amount of data describing the size of the population, households’ income, the transport system, land use, construction costs, households and developers’ behavior, the mechanisms described above can reproduce the structure of an urban area in a fairly accurate way. This section aims at demonstrating this for the Buenos Aires region<sup>7</sup> . Appendix 10.2 provides an in-depth description of the data and parameters used in the simulations both for 2012 and for the projections.

The last phase in applying the NEDUM model to a city is to run a calibration procedure. This is done by comparing results (map of urbanized area, real estate/rents, population and housing density) to observed

> 6 A version of NEDUM-2D that is able to represent multiple household types differing by annual average income is currently being developed in CIRED.

> 7 The model has been successfully applied to the urban area of Paris (Avner, Rentschler, and Hallegatte 2014; Viguié and Hallegatte 2012), London (Viguié 2012) and Toulouse (Masson et al. 2014).

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data. Overall, the NEDUM-2D model is very robust as it relies on very simple basic economic behaviors that are likely to remain true both through space and time. As a consequence the calibration process is mostly done through refining the data that are used to inform the model. However, a very limited number of parameters can then be marginally modified so as to minimize the discrepancies between observations and model results.

**Map 1 : The simulated and real urban area of the GBA+ region in 2012. The green shade corresponds to the simulation whilst the black is geographic data. The dark green shade shows areas where the model accurately predicts urbanization. Light green shows areas where the model inaccurately predicts urbanization. Black stands for areas where the model fails to reproduce urbanization. Finally where the map is yellow, there is no urbanization and the model agrees with the data.**

It can be seen from Map 1 that the simulated and the actual urban area in 2012 coincide quite well within the GBA+ region (which appears in light yellow). The model captures the general size of urbanized area but also its shape and specific urbanization directions along the transportation network. There are however some discrepancies even within the GBA+ boundaries. In particular it can be seen that the map misses some areas towards the North and the North West of the GBA+ region. This can mainly be explained either by the existence of local secondary employment centers that attract settlements or the presence of local amenities.

The shape and size is one among other features of an urban area. Below we compare data with simulations for the distribution of land values and population densities.

17
<!-- Start of picture text --> Land values (2012)<br>4000 ———————————————<br>ee Simulation boundaries<br><!-- End of picture text --> <!-- Start of picture text --> ,<br>a : |" ° Data<br>2) Average of the data<br><!-- End of picture text -->

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<!-- Start of picture text --> I * data (population||<br>| le * size)<br><!-- End of picture text -->

<!-- Start of picture text --> 5000 10 000 15.000 20 000 25 000 30 000<br>Simulated densities (households/km*)<br><!-- End of picture text --> <!-- Start of picture text --> ° 1 2 3 4 5 6<br>Simulated densities (in log (households/km*))<br><!-- End of picture text -->
endogenously in NEDUM-2D, is 56%, very close to the 57% average figure reported by the ENMODO mobility survey for the AMBA region (Ministerio del Interior y Transporte 2010). The average commuting times computed by the model is 54 minutes so slightly higher than, yet in the same magnitude order as the 44 to 49 minutes reported by ENMODO.

While we have shown some local discrepancies between model results and data, the calibration of NEDUM-2D is satisfactory overall for the main features of the internal structure of Buenos Aires urban area: size and shape of the urban footprint, population distribution, land values, average commuting times and mode shares. Improving the NEDUM-2D modelling framework so that it can account for multiple employment centers would improve the fit of the model to data. This endeavor is left for the future, with the belief that although it will add some realism, it will not modify the main conclusions of the current analysis.

# 6 Policy analysis: Urban sprawl, emissions and welfare

## 6.1 Evolution of the urban area in the baseline

In order to investigate the impact over time of the removal of public transport subsidies and alternative scenarios, our study relies on projections of various inputs to the NEDUM-2D model from 2012 to 2050; in particular the future evolution of transport prices, income and population. In the baseline scenario population and income are supposed to pursue their recent trends over the next four decades. In particular, incomes are assumed to grow at the same average annual rate as Argentina’s per capita net income over the 1970 – 2015 period (in constant 2010$), i.e. 1.24%, starting at US$ 18,140 in 2012 and reaching close to US$ 29,000 in 2050. The number of households in the urban area will evolve at the same annual pace as it did over the 2001 – 2012 period, reaching a little over 6.96 million in 2050. In the baseline scenario the prices of fuel and public transport fares increase at an identical pace to reach 120% of their 2012 levels in 2050. The evolution of these demographic and economic variables are summarized in Figure 7. It is also supposed that the current subsidization rate of public transport remains unchanged over time – around 2/3 of public transport fares. These inputs are a priori identical in the alternative counterfactual scenarios, unless the policies investigated explicitly impact some variables such as transit fares. Robustness to these assumptions on the evolution of main socio-economic variables is explored below with a high growth scenario.

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<!-- Start of picture text --> Demographic scenario Income evolution<br>7 30<br>B65 28<br><!-- End of picture text --> <!-- Start of picture text --> 25 6 26<br>3 e<br>s 55 x 24<br>6¢ 5 2<br><!-- End of picture text --> <!-- Start of picture text --> 4 18<br>2015 «2020 «2025 «22030 «20385 «2040 «2045 = 2050 2015 2020 2025 2030 2035 2040 2045 2050<br>Cost of traveling 100km by car Public transport fare for short distances<br><!-- End of picture text --> <!-- Start of picture text --> 1 aus<br>= tos 2<br>Ss = 07<br><!-- End of picture text --> <!-- Start of picture text --> 10 ”<br>9s 0.65<br>es Car cost/100km mscheapest public transport fare<br>9 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text --> <!-- Start of picture text --> Evolution of socio-demographic variables<br>70<br>60 ——= transport costs<br>income<br>50 —= population<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>s<br>30<br>20<br><!-- End of picture text --> <!-- Start of picture text --> 0 -<br>2015 2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text -->
<!-- Start of picture text --> EN ay ib<br>= cea regio<br><!-- End of picture text --> <!-- Start of picture text --> ) ie =5 ‘<br>|<br>| < ea) a "AN<br><!-- End of picture text -->
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| 4 10 20 30 km<br>KN é- SY FY Evolution ofthe urban area<br> ats 5 x in the high growth<br>amr ara i 4 baseliman scenarioi<br>% zy = = \ 2050<br><!-- End of picture text -->

<!-- Start of picture text --> —— Road ne<br>: iL i oS ss<br>: = lw A<br><<br>é & x s a . \ s \"<br><!-- End of picture text --> <!-- Start of picture text -->
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# 6.3 Policy impacts: Sprawl, distances travelled, emissions and welfare

In this section we want to investigate the impact that each policy package described above will have on 1/ the size of the urbanized area: can alternative policies limit the growth of the urbanized area over time? and how substantial can the decrease be compared to a scenario with public transit subsidies?, 2/ what are the consequences of these counterfactuals in terms of distances travelled for commuting purposes and CO2 emissions stemming from these? and 3/ what are the impacts on households’ welfare of these policy packages?

## 6.3.1 Consumption-related welfare impacts of alternative policies

The consumption-related welfare and income-equivalent impacts of alternative policies with respect to the baseline are the described in Table 2. Income equivalents are the monetary translation of household utility changes and are reported here because they are more easily understandable.

||**Without the public**<br>**transport subsidy**|**With a lump sum**<br>**transfer**|**With a generalized**<br>**construction**<br>**subsidy**|**With a targeted**<br>**construction**<br>**subsidy**|
|---|---|---|---|---|
|**Consumption-related**<br>**utility in 2050(%)**|-3.73%|+1.30%|-2.67%|-2.69%|
|**Equivalent income in**<br>**2014(US$)**|- $712|+$325|- $531|- $527|
|**Equivalent income in**<br>**2014(%)**|-3.83%|+1.75%|-2.85%|- 2.83%|
|**Equivalent income in**<br>**2050(US$)**|- $910|+$323|- $644|- $651|
|**Equivalent income in**<br>**2050(%)**|-3.14%|+1.11%|-2.22%|- 2.25%|

**Table 2: Impacts relative to the baseline on consumption-related welfare and translation into income equivalent (in US$) in each scenario for 2014 and 2050**

We draw two conclusions from these figures. First, while the lump sum dominates the transit subsidy as documented in the simple model presented in section 4, the welfare gains (utility) are lower than the schematic model previously presented pointed to (1.3% vs 6.1%). The reason for this difference resides in several realistic features of NEDUM-2D. The use of generalized costs instead of pure monetary costs means that an increase in transport fares does not translate into an equivalent increase of generalized costs because time costs remain the same. As time costs represent more than 70% of the generalized costs in the baseline, the increase in the monetary component only applies to the remaining 30%. So that the subsidy only represents around 20% (0.7*0.3) of total generalized transportation costs. This would place the transit subsidy in a regions which is much closer to its optimal level (see Figure 1). In addition, while in the schematic model only one transport mode is available, in the NEDUM-2D model, households have the choice of commuting by foot, using cars, transit or a combination of these modes. As a result when transit fares increase, a share of people will switch to an alternative and cheaper transport mode, say cars or walking, so that the actual cost of commuting will increase less than the transit fare increase. The existence of other transport modes can therefore mitigate the increase in public transport fares. So lump sum transfers can increase households’ welfare but less than the schematic model would have led us to believe.

The second take-away from the comparison of alternative policies is that the construction subsidy schemes perform better than the uncompensated removal of public transport subsidy (-2.7% for both 26
schemes vs -3.7%) but less well than the public transport subsidy. In terms of welfare, construction subsidies, targeted or generalized, are a poor substitute to transit subsidies.

# 6.3.2 Beyond consumption-related welfare: Sprawl, distance travelled and emissions

In this sub-section we look into features that are not captured by our welfare function such as the impact of alternative policies on the extent of the urban area, the average distances travelled and the emissions that stem from commuting.

Map 5 provides a visual representation of how different policy packages would modify the size of the urbanized are in 2050 in our simulations. We selected three different scenarios: the baseline in which transit subsidies exist and amount to two-thirds of the full cost of a trip, the counterfactual in which the transit subsidy is removed, resulting in a tripling of the monetary costs of riding public transport and finally a scenario whereby subsidy removal is coupled with the redistribution of the savings in the form of construction subsidies.

It can be visually seen from Map 5 that the removal (compensated or not) of public transit subsidies has an overall limited impact on the size of the urbanized area. In 2050 the urbanized area decreases by only 11 km<sup>2</sup> (2739 km<sup>2</sup> vs 2750 km<sup>2</sup> ) for the uncompensated removal of the public transport subsidy (orange in the map) and by 14 km<sup>2</sup> (2736 km<sup>2</sup> ) for the scenario in which public transport subsidy removal is accompanied by a generalized construction subsidy (green in the map). The orange shade is virtually indistinguishable because it is overlayed with the green layer.

The lump sum transfer scenario yields an urbanized area in 2050 which is slightly larger compared to the baseline: 2797 km, i.e. a 1.7% increase. And finally the targeted construction subsidy scenario results in a slight decrease of the urbanized area of -0.8% (2729 km<sup>2</sup> vs 2750 km<sup>2</sup> ). The conclusion which we draw from these simulations is that the potential to curb urban sprawl through removing the public transport subsidies exists but is limited and therefore so would the savings in terms of infrastructure costs associated to increased urbanization.

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The most notable fact in the first half of Table 3 is that whereas the reductions in urbanized area are small (or even increase with the lump sum transfer), the average distance of a household to their job center decreases much more substantially and across all counterfactual scenarios. This is the long term (2050) result of an internal restructuring of the urban area caused by the rise in public transport fares, where, in order to save on transportation costs, households choose to locate closer to the city center. Higher buildings will progressively replace smaller ones in the vicinity to the city center and residents will occupy smaller dwellings, both these mechanisms leading to higher central population densities.

This finding is important because it also means that the traveled distances to jobs will be shortened substantially – not exactly by the same amount as the distance to the city center, as roads are not straight, but not far off. This should lead to lower CO2 emissions from transport, all else equal. On the other hand public transport ridership will also decrease as the result of the increase in fares triggered by the subsidy removal. And because cars are less energy efficient<sup>10</sup> than public transport, mainly buses or _colectivos_ in the urban area of Buenos Aires, this will lead to the opposite effect on CO2 emissions with an increase, all else equal. The net effect in terms of commuting related CO2 emissions is a priori ambiguous and this is precisely where numerical simulations can be useful. The last line in Table 3 summarizes the impacts on CO2 emissions in the various counterfactual scenarios. It can be seen that the reduction in the distances travelled for commuting nearly cancel out the increase in emissions/pkm resulting from mode share switch away from public transport. The increase in emissions is indeed limited (around 1%). It can also be seen that the lump sum transfer scenario would result in a much less anecdotal increase in emissions. Part of the lump sum will be used to consume moor floor space per household, thus limiting the internal movement of households closer to the city center<sup>11</sup> . Finally the targeted construction subsidy, is able to achieve an overall reduction in commuting related CO2 emissions, although such a reduction remains limited.

It appears overall that removing the public transit subsidy would have only limited impacts in reducing sprawl over time and thus the associated costs in terms of infrastructure for example. This is disappointing because it appeared to be a reasonable motivation for removing the public transport subsidies and maybe replacing them by another policy less likely to induce sprawl. On the other hand, it is equally surprising that although the removal of the transit subsidy leads to a decrease in public transport ridership, this mode switch leads to only moderate increases in commuting related CO2 emissions (except in the scenario where transit subsidy savings are channeled to households through a lump sum transfer). Our simulations even show that alternative policies such as targeted construction subsides could decrease the level of transport related CO2 emission with respect to the baseline, although the decrease would remain limited.

It should be noted however that the switch away from public transport, triggered by the subsidy removal, could complicate future CO2 emission reductions as cities display path dependencies which make subsequent mitigation actions more expensive (Avner, Rentschler, and Hallegatte 2014).

# 7 Political economy/hotspot analysis of transit subsidy removal

An important concern for policy makers when considering the modification of public transport prices is the redistributive impacts of these changes and in particular for the poor. NEDUM-2D, in this present 10 See section 10.2.4.5 for details about respective gasoline consumption of buses and cars over time in the Buenos Aires region. In our simulations buses emit 3.5 less than cars per passenger.kilometer. If this number increased, so would emissions in alternative scenarios compared to the baseline. If buses were less than 3.5 more efficient than cars then the opposite situation would arise and a decrease in public transport ridership would be associated with a lower increase in emissions.

> 11 We do not account for larger AC/heating costs of larger dwellings.

29
version, cannot be used to perform a full study of the impact of public transport subsidy removal on the poorest. To be able to do so, the model would need to endogenously represent the location decisions of various income groups, which is beyond its current capacity. We however can document how the budget committed to transport and housing costs would be affected by location by the subsidy removal, coupled or not with lump sum transfers and other devices, if this modification did not impact the location of the households. In the short term, this assumption is valid as relocation is characterized by high inertia. Over a longer time frame however, decisions about where to live and how much living space to consume will adjust to changed transport costs.

The urban area of Buenos Aires is characterized by sizeable inequalities in terms of socio-economic levels. We capture these inequalities in Table 4 through the disaggregation of annual household income by quintile. It can be seen that the average annual income of the richest 20% of households in Buenos Aires is about eight times that of the poorest.

||**Annual household i**|**ncome by socio-econ**|**omicquintile(US$)**||
|---|---|---|---|---|
|**1**|**2**|**3**|**4**|**5**|
|5,196|10,268|15,471|22,747|41,756|

**Table 4: Annual average income for a household in Greater Buenos Aires disaggregated by socio-economic quintile in 2012 (data source: INDEC 2013)**

# 7.1 In the short run: Detrimental to transit users

With the NEDUM-2D model we assess the distributional impact of removing the public transport subsidy and replacing it with a lump sum transfer. The lump sum transfer is distributed to each household and amounts to a little over US$ 1,000 in 2012. While this amount represents around 4% of households’ average income it is nearly 20% of the poorest budget. We place ourselves in the very short term, immediately after the policy change, when it can be safely assumed that households have not had time to adjust and move to areas within the Buenos Aires area which benefit from lower transport costs.

Map 6 shows the spatial impact of the replacement of the transit subsidy by a lump sum transfer for transit users. It displays for households that commute by transit the net impact on the evolution of residual budgets after transport expenditures of increasing public transport fares (by a factor of 3 as before) but receiving a compensatory lump sum transfer.

Map 6 shows that all transit users lose from the replacement of the transit subsidy by a lump sum transfer as their budgets after transport expenditures decrease everywhere. Even in the most central locations of the Buenos Aires urban area, the annual cost of commuting daily to jobs outweighs the lump sum. The increase in transport budget for transit users is not spatially uniform: it increases with the distance to the city center (in parallel to transit budgets) and is higher for lower income quintiles. The black dots in Map 6 represent the centroid of the census tracts where the 20% poorest households reside. It can be seen that these census tracts are often hit the hardest, simply because a given monetary increases in fares has a larger impact on their budget. Some exceptions are however found when the census tract of the poorest is close to the city center.

30
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suggests that the middle income class is the heaviest user of transit so that they are likely to be hit harder than both the poorest and the richest.

# 7.2 In the longer run: Housing market adjusts

The discussion above focuses on the very short run, immediately after the replacement of transit subsidy by a lump sum transfer. As discussed, the consequences for the users of public transport are harsh, in particular if they are poor. Over a longer time frame, however, the housing market will adjust to the shock on public transport fares. In particular, areas that face strong increases in generalized transport costs will become less desirable for households and so will lead to a decrease in housing rents.

Map 7 visually depicts the change in rents per square meter of housing with a lump sum transfer compared to the situation with a transit subsidy. For tenants, the impacts on rents will mitigate in part the negative redistributive impacts of removing the transit subsidy and implementing a lump sum. Indeed, the areas that benefit most from a decrease in rents are also the ones which are hardest hit by the increase in transit fares (see Map 6) and in most instances coincide with where the poorest currently reside. Conversely, the areas with green shades will see an increase in rents because generalized transport costs will increase less than in other parts of the urban area and thus become more attractive in relative terms.

**Map 7: Variation in rents per square meter with replacement of the public transport subsidy with a lump sum transfer.**

Map 8 shows the impact of replacing transit subsidies by lump sum transfers on the combined housing and transport households’ budgets. A first important results is that in some areas of the cities the change in rents induced in the medium term by the increase in transit fares will more than compensate the price 32
<!-- Start of picture text --> 0 10 20 3gkm % change in the budget dedicated<br>aX ouYy to% change housing + transport<br>"&a SSRN — Road network<br><!-- End of picture text --> <!-- Start of picture text --> ee = i SOR * Radios with lowest socio-eco level<br>Woe<br><!-- End of picture text --> <!-- Start of picture text --> ge. Al Ran<br><!-- End of picture text -->
housing, transport and other goods to the increase in public transport fares. Our short term result should therefore be interpreted with caution and not as a long term exercise.

# 8 Conclusions and perspectives

The research presented above could benefit from modeling improvements to become more realistic and shed light on a richer set of issues. The most pressing need is to develop the NEDUM-2D model so that it can represent multiple income classes and their locational decisions within urban areas. This would allow to look at the long term redistributive impacts of various policies. A second possible direction for improvement is to enhance NEDUM-2D so that it can account for multiple job centers, going beyond the monocentric assumption, and include amenities and social segregation mechanisms. This addition to the model would help get rid of some discrepancies between simulation results and data and would therefore increase its capacity to convince. While we strongly believe that our main conclusions would be unaffected by the addition of polycentrism, it remains an empirical question. Lastly, the NEDUM-2D model does not account for congestion as it implicitly assumes that investments can be made so that additional road or public transport frequentation does not lead to higher transport times. Explicitly accounting for congestion would be an important improvement to the modeling framework.

The set of results presented in this paper paints a complex picture of public transport subsidies in the urban area of Buenos Aires. It appears that an ideal solution would be to channel lump sum transfers only to the poorest households. This solution requires detailed registries recording households’ incomes and their evolution over time. A second best alternative would be to have only the poorest households in Buenos Aires benefit from transit subsidies. This solution would protect the poor users of transit without entailing dramatic efficiency or environmental negative impacts. It would also allow for some public finance savings which could be channeled toward other issues such as investments in the public transport infrastructure or could be put towards targeted construction subsidies as discussed in this paper.

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# 10 Appendices

## 10.1 Description of the NEDUM-2D modelling framework

We use a model inspired by Von Thunen (1826), adapted by Alonso (1964), Mills (1967), and Muth (1969) and comprehensively described by Fujita (1989) and Brueckner (1987). Assuming that a city is defined by a number of jobs located in a single location, this model is based on two trades-offs, made by two categories of economic actors:

- Households choose their housing location in the agglomeration by arbitrating between larger and cheaper apartments further from the city center, and increased commuting costs to the city center where all jobs are supposed to be located.

- Landowners choose where and how much to invest (i.e., what buildings to construct), as a function of expected rents at each location.

These two trades-offs characterize a static equilibrium in the urban system.

|**Symbol**|**Signification**|
|---|---|
|_R_|Housing rents (or, equivalently, annualized real estate<br>price per square meter)|
|_R0_|Metropolitan area border rent or transaction costs|
|_L_|Available land for construction|
|_K_|Invested capital|
||Depreciation rate of capital|
|_i_|Interest rate : cost of capital|
|_p_|Unitary transport cost/km|
|_n_|Household density|
|_q_|Flat size|
|_N_|Population in the agglomeration|
|_r_|Distance to the city center|
|_rf_|Limit of the city,maximum radius to the center|

**Table 5 : Nomenclature**

### 10.1.1 Household behavior

Each household is composed of one representative worker living at a distance � from the city business district (CBD) where all jobs are located. Each worker has to commute once a day to the CBD, and this commuting entails transportation costs �(�) . All households are supposed to earn the same income � and – at the equilibrium – they have the same level of satisfaction described by a utility function � that depends on both the level of consumption of a composite good � and of the size of their apartment � . The level of rents per square meter (or, equivalently, the annualized real estate price per square meter) is �(�) _,_ at each location in the city. Each household maximizes his utility function under a budget constraint: The term �<sup>�</sup> found in (3) represents the annual amount accruing from land rents per household which is recycled in the urban economy in the form of increased incomes. It can schematically take on one of two values depending on the type of model used: Closed City model with Absentee landowners (CCA) or Closed City model with Public ownership of land (CCP). In the closed city model with absentee landowners (CCA), 38
L= { ~~ja~~ r L N K(r) H(r) = FL), K(r)) F R(r) m = [(R — Ro) F(K,L) — (i+ p)K] p I ik Ro Ro K (r) = Argmax[(R(r) — Ro) F(L(r), K(7)) —(p+i)K(r)] k=K/L

> fi) ~~=="(K,L)~~

k _ [R= Ro) FO — (@ + DK]
At the city limits we have: �����= ��

# 10.1.3 Functional forms and assumptions

We use Cobb-Douglas functional forms with constant returns to define households’ utility and the housing service construction function. This choice is widely shared in urban economics (Brueckner 1980; Fujita 1989; Mills 1972). It is possible to show analytically that there is both existence and uniqueness of an urban equilibrium (Gusdorf and Hallegatte 2007).

- �(�, �) = �<sup>�</sup> �<sup>�</sup> �h��� �, �> 0 ��� �+ �= 1

- �(�, �) = ��<sup>�</sup> �<sup>�</sup> �h��� �, �> 0 ��� �+ �= 1

# 10.1.4 Dynamics of NEDUM-2D

NEDUM-2D is a dynamic model which explicitly represents inertia and path dependence. This feature allows the model to account for the fact that housing infrastructure cannot adapt instantaneously to changing conditions (income, transport prices etc...). NEDUM-2D can therefore account for temporary imbalances between housing supply and demand.

There are two steps in defining the dynamic behavior of the NEDUM-2D model. First, developers’ decisions on how much to build and where in the urban area are determined (or equivalently how much capital to invest for residential purposes in each location). This allows the model to determine the new housing stock which accounts for inertia in building decisions. Second rents, dwelling sizes and population densities (and all other model outputs) adjust in such a manner that an equilibrium utility can emerge consistently with the static version of the model. This two-tier approach to the dynamics of NEDUM-2D differs from the one used in Viguié et al. (2014), Viguié and Hallegatte (2012) and Avner et al. (2013). We will discuss these differences and their consequences a bit further in this section.

<u>Step 1: In practice for each time step a "pseudo-equilibrium" value is computed for the housing stock. This</u> "pseudo-equilibrium" value is the value toward which the housing stock state variable will evolve. It depends on the model input, but also on the value of the other state variables (rents, population densities, dwelling sizes...). Between this time-step and the following, the housing stock state variable will evolve towards its "pseudo-equilibrium" value with some inertia. At the following time step, a new "pseudoequilibrium" value for the housing stock is computed. It can be identical to the last one, or can be different because the values of the other state variables have changed, or because model inputs are different (e.g. construction costs have changed). The housing stock state variable will now again evolve towards this new "pseudo-equilibrium" value etc. The pseudo-equilibrium value for the housing stock is computed in such a manner that, if exogenous model inputs (population, transport costs, construction costs, income...) do not evolve any more, the housing stock state variable converges towards the actual equilibrium value.

For each time step, developers when choosing whether or not and how much to build assess their expected profits using equation 4. In this version of the model the developers are considered to be myopic, meaning that they do not anticipate that housing rents from the previous time step will change as a function of different socio-economic conditions. They choose the new amount of capital that they would wish to instantaneously invest in each location (“pseudo-equilibrium”), or equivalently the capital to land ratio �′ leading to the housing to land ratio h<sup>�</sup> through: 40
Construction and building depreciation however, take time so that the capital invested does not translate immediately into new buildings or the destruction of old ones. We here follow Viguié et al. (2014) in supposing that financial investments are transformed into buildings with a time lag �� , which corresponds to the time required to achieve the construction. We also suppose that a decrease in current built floor space ratio to land ( h(�) ) can happen through depreciation only, with the timescale ��� . It follows that in a dynamic perspective housing evolution at each time step is given by: <u>Step 2: At each time step the other model variables (rents, population densities, dwelling sizes...) adjust</u> perfectly to the new socio-economic conditions and to the new housing stock. Indeed households adjust their locational and consumption choices in a way that ensures that utility levels are equal for all households throughout the urban area, consistently with the static version of the model. This second step of the dynamics of NEDUM-2D is equivalent to the maximization of households’ utility in a static framework with an exogenous housing stock.

# 10.1.5 Discussion of the mono-centric assumption

In the present study, we use this model with the assumption that all households commute every day to the center of the _Ciudad Autónoma de Buenos Aires_ (CABA), where jobs are assumed to be located. This mono-centric hypothesis is important, and can seem a strong assumption as secondary employment centers exist within the urban area of Buenos Aires. However, the Buenos Aires region displays a very strong mono-centric structure. For instance, in 2012 more than 40%<sup>14</sup> of all jobs in the Buenos Aires region are localized in CABA even though CABA only represents 5.3% of _Region Metropolitana_ ’s area. Map 9 represents the employment densities for each census tract or “radio” in the Buenos Aires region. The use of employment densities instead of absolute employment numbers can correct a statistical “radio size” bias. Indeed, as radios have different sizes, larger radios would appear as very “employment-rich” while smaller radios will display only small employment numbers.

> 14 Projections based on Censo Nacional Economic 04/05 (CNE 04/05) and Encuesta Permanente de Hogares, 2011 (EPH, 2011).

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**Map 9 : Employment densities for each census tract (Radio censal) unit within the Buenos Aires region in 2012 (data source: projections based on Censo Nacional Economic 04/05 (CNE 04/05) and Encuesta Permanente de Hogares, 2011 (EPH, 2011))**

Map 9 shows that job dispersal exists along the major public transport lines, the railway network (represented as black lines), but is nearly non-existent elsewhere. This suggests that the radial public transportation network plays a strong role in the location of employment centers which is characteristic of dominantly mono-centric urban forms.

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In its current version, NEDUM-2D also relies on the hypothesis that all households within the urban area earn the same average income. It does not currently possess the sophistication to account for multiple household types and thus ignores income inequalities<sup>15</sup> . This simplification is acceptable for some indicators such as the extent of urbanization. However it can have some impacts for NEDUM-2D’s ability to capture the dispersion in rents and real estate prices around an average. Indeed, real estate prices are strongly affected by neighborhood characteristics among which social segregation can play a role. We will overcome this limitation to a certain degree by interacting our results with a map of current income dispersion.

# 10.2 Data and choice of parameters

We will detail in this section the data used and the choice of parameters for 1/ socio-economic and demographic inputs, 2/ household utility function 3/ developers’ behavior, the housing production function and construction costs, 4/ transport data and modelling choices and 5/ land use and building regulation data.

## 10.2.1 Socio-economic and demographic inputs

- <u>Population in the Greater Buenos Aires urban area:</u> Population for the GBA+ region in 2012 is estimated to be 13,744,044 in 2012 according to projections of the Censo Nacional de Poblacion, Hogares y Vivienda 2001 (CNPHyV 01) for 2012. With an average household size of 3.3, this corresponds to 4,106,479 households. The demographic scenario used in this exercise considers that the population will increase smoothly at the same pace that it evolved over the 2001 – 2012 period, reaching a little over 6.96 million in 2050. This corresponds to nearly a 70% increase compared to 2012 with an annual demographic growth rate of 1.40%. We equally use a second demographic scenario whereby the number of households doubles in 2050 compared to 2012. This second demographic scenario corresponds to an annual growth rate of 1.84%.

- <u>Income evolution of the average household: With the current version of the model in which we only</u> consider one income class, we need an average income per household. Data from INDEC gives us this information separately for Buenos Aires and the other ‘partidos’ of the Gran Buenos Aires at trimestral intervals. We thus calculate a weighted average based on their respective household populations for all semesters of year 2012. This yields an average yearly income of AR$ 85,540 or around US$ 18,140. In the baseline scenario, average incomes will grow at the same average annual rate as Argentina’s per capita net income over the 1970 – 2015 period (in constant 2010$), i.e. 1.24%, starting at US$ 18,140 in 2012 and reaching close to US$ 29,000 in 2050. In the sprawl scenario, average income doubles over the 2012 – 2050 period, increasing at an annual rate of 1.84% and reaching close to US$ 36,300.

## 10.2.2 Household utility

The household utility function reads as follows: The widespread choice of this Cobb-Douglas functional form in urban economics is consistent with findings that households devote a near constant budget to rental housing expenditures through time and space (Davis and Ortalo-Magné 2011). In this case, households spend a share � of their income net of transportation costs on housing. Indeed, maximizing (9) with respect to � and replacing � with the budget constraint by �+ �<sup>�</sup> −�(�)�(�) −�(�) , we find: �<sup>∗</sup> (�)�(�) = �(�+ �<sup>�</sup> −�(�)) . For the purpose of the

> 15 A version of NEDUM-2D that is able to represent multiple household types differing by annual average income is currently being developed in CIRED.

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B B a F(L(r), K(r)) = H(r) = AL(r)*K(r)? where a,b > Oanda+b=1 k= K/L F _<sup><u>FKL)</u></sup> <u>(KY</u> FRO) _ ~~=F~~ = ~~(TS)~~ = Ake?

A b A=0.25 b = 0.70 a=1-—b=0.30 A Ay = Ay * (¥/Yo ~?
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roads so that they can absorb a demographic increase without leading to additional congestion. We here again assume that the number of roads will remain constant.

# _10.2.4.2 Monetary transport costs_

- <u>Private car costs: These were estimated to be 0.44 AR$/km or 0.095 US$/km in 2012. They concern</u> both cars and SUVs. It is assumed that the cost of traveling one kilometer by car increases by 20% over the 2012 – 2050 period to reach 0.114 US$/km in 2050 in the baseline scenario. In the sprawl scenario, fuel costs are assumed to decrease by 10% over the simulation period reaching US$ 0.085 in 2050.

- <u>Public transport fares: The public transport fares were retrieved from the internet for the buses or</u> _Colectivos_ . They depend on the distance travelled and we proxied this distance by a bee line distance from origin to destination. The fares do not present a large spatial variation. The ticket costs between US$ 0.65 and US$ 1. As a simplification, and because buses are the overwhelmingly used public transport means in the Buenos Aires urban area (87%), it is assumed that the fares for the metro system ( _subte_ ) and trains ( _ferrocarril_ ) are identical to those of the buses. As for the costs of traveling by car it is assumed that they evolve to reach 120% of their 2012 level by 2050 in the baseline scenario. In the sprawl scenario, public transport fares are equally assumed to follow the evolution of the price of fuel and decrease by 10% in 2050.

# _10.2.4.3 Cost of time_

NEDUM-2D uses generalized transport costs which encompass both monetary and time costs. How to value transport times is however a difficult issue which has triggered a vast number of scientific papers and reports (Boiteux and Baumstarck 2001; Mackie et al. 2003; Oort 1969; Small, Winston, and Yan 2005; Litman 2008) focusing mainly on the topic of how to quantify the benefits of transport investments which accrue in the form of travel time savings. No definite consensus has emerged from the literature; in fact estimates range from 25% (Litman 2008) to close to 180% of an hourly wage with large heterogeneities depending on the traveler (Small, Winston, and Yan 2005). Most estimates however seem to fall within the 50%-100% range of an hourly wage with Small and Verhoef (2007) arguing for the former and Glaeser, Kahn and Rappaport inclined to the latter (2008). Given the uncertainty surrounding the value that should be chosen, we retained one that fell within the 50%-100% range of hourly wage and that led to a good agreement between simulations and data for population densities and land values. We use the value of 65% of the average hourly wage in our simulations.

# _10.2.4.4 Mode competition and generalized transport costs_

<u>Mode competition: The transportation module of the NEDUM-2D model represents modal choice. For</u> each location in the urban area, citizens choose between walking, public transport and private vehicles, or a combination thereof as a means for commuting. The competition between these modes is organized on the basis of their _generalized costs_ (i.e. the total cost including both the cost of time and the monetary costs incurred during the trip to the city center). It is assumed in this study that modal switch does not affect congestion levels and therefore leaves commuting times unchanged.

In order to reflect the heterogeneity of citizens’ preferences in terms of transport modes we employ a discrete choice model (De Palma and Thisse 1987). We follow a common approach in transportation economics by using a multinomial logit model which assigns usage probabilities to each transport mode (see Salon 2009; Washbrook, Haider, and Jaccard 2006). This method ensures that even when one mode is much cheaper than the others, it will not be used by all residents in a location for all trips. This approach assumes that, for each transport mode � , the inhabitants do not take into consideration the generalized cost �� , but this cost plus an idiosyncratic random term following a Gumbel distribution. The result is that the probability of choosing mode � in a given location depends on the generalized cost of this mode 47
i ~~<u>oP</u>~~ ;Mo <u>—</u> ~~4~~ ~~<u>Pk</u>~~ Xk e <u>Pin</u>

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<u>Fuel consumption of cars:</u> For the Latin America region, the SMP spreadsheet indicates that cars will consume 8.3 liters/100km in 2010. We interpolate their data so that cars consume 8.2 liters/100km in 2012 and reach 6.5 liters/100km in 2050. This corresponds to a 20% decrease over the 2012 – 2050 period.

<u>Fuel consumption of buses: We here again draw our data from the SMP model, so that buses in the model</u> consume 26 liters/100km in 2012. We make the supplementary assumption that technological progress will allow the consumption of buses to decrease at the same pace as for cars so that consumption of buses reaches 21 liters/100km in 2050.

<u>Load factors: cars are supposed to carry 1.72 passengers/trip on average in 2012 and buses approximately</u> 19. These average load factors decrease at the same pace reaching 1.51 for cars and 16.68 for buses in 2050. This corresponds to a little more than a 12% decrease over the 2012 – 2050 period.

<u>Vehicle unitary emissions: With a CO2 content of 2416 gCO2/liter of gasoline, vehicles emit on average 114</u> and 33 grams of CO2/passenger.km for cars and buses respectively in 2012, i.e. a ratio of 3.5. These figures go down to 104 gCO2/pkm for cars and 30 gCO2/pkm for buses in 2050. The ratio between unitary emissions remains constant throughout time as gasoline consumptions and load factors evolve in parallel for cars and buses. These figures are in line with those reported by Carbontrust<sup>16</sup> .

# 10.2.5 Land use and building restrictions

<u>Land uses incompatible with urbanization: In order to be able to reproduce the location and extent of</u> urban areas in the Buenos Aires region correctly, NEDUM-2D accounts for regulations or land uses that are incompatible with the development of residential areas. NEDUM-2D therefore requires a map of land use constraints which is used as a filter to exclude some zones from potential urbanization. For example, it excludes zones occupied by water or protected natural areas such as forests. Water bodies (sea, rivers and lakes) are excluded from potential urbanization as a first step. In a second step a detailed land use map (see Map 11) is used to identify supplementary zones that need to be excluded from potential urbanization. The exclusion map encompasses any area occupied by protected natural areas, equipment such as the military zones in Campo de Mayo and infrastructure (port, railway station, Ezeiza international airport for example). While the total area of exclusion is non-negligible, it remains, by far, lower than the area that could be potentially urbanized. This is indicative that although the exclusion map can avoid major discrepancies between the simulation and the data, it is not a main driver behind the overall accuracy of our results regarding the match between simulated and observed urbanized areas.

> 16 https://www.carbontrust.com/conversionfactors

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The border rent/transaction cost is assumed to evolve over time at the same rate as household income.

- <u>Time lags for the dynamics of the housing stock: The inertia that characterizes investments in building</u> structures, �� , is set to a value of 10 years. This time lag encompasses necessary time for the actual construction to take place and other transaction costs such as possibly negotiating a bank loan, finding the appropriate location and land plot, finding a suitable construction firm and negotiating building permits. This value of 10 years means that each year only a tenth of the optimal construction can take place. In terms of the inertia that characterizes the destruction of buildings, ��� , it is set at the same value as the depreciation rate, � , meaning that without any investments, buildings would be destroyed over the course of 100 years.

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