Income Inequality, Social Inclusion
and Mobility

Income Inequality, Social Inclusion

There is significant evidence across countries that lower-income populations tend
to suffer more from restricted transport options, have lower quality transport
services available to them and travel under worse conditions (safety, security,
reliability, comfort). Broad evidence also suggests that the lack of, or poor access
to, transport options is central to limitations on access to jobs, educational
institutions, health facilities, social networks, etc., which in turn generates a“poverty trap”.
This Roundtable report examines mobility policies with a focus on evaluating
their capacity to address transport-related exclusion of lower income groups.

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Roundtable Report 164

International Transport Forum

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Income Inequality, 		Social Inclusion
and Mobility

2017

164

Roundtable ReportIncome Inequality,
Social Inclusion
and Mobility

164

Roundtable report

The International Transport ForumThe International Transport Forum is an intergovernmental organisation with 57 member countries. It
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ITF Roundtable Reports
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TABLE OF CONTENTS – 5

Table of contents
Executive summary......................................................................................................................... 11
What we did .................................................................................................................................. 11
What we found .............................................................................................................................. 11
What we recommend ..................................................................................................................... 11
Chapter 1. Income inequality, social inclusion and mobility. ..................................................... 13
Introduction ................................................................................................................................... 14
Improving data and developing useful performance indicators .................................................... 16
Policy issues for deeper examination ............................................................................................ 28
Priorities for future research and discussion ................................................................................. 40
Notes ............................................................................................................................................. 43
References ..................................................................................................................................... 44
Chapter 2. National issues in the USA in economic development, mobility and income
inequality ......................................................................................................................................... 45
Introduction ................................................................................................................................... 46
The demographic challenge........................................................................................................... 47
Redistribution of population and workers ..................................................................................... 53
Consumer expenditures and the housing/transportation nexus ..................................................... 55
Transportation spending and the worker linkage .......................................................................... 61
Vehicle ownership patterns and social equity ............................................................................... 63
Growing homogeneity in travel behavior ...................................................................................... 65
Travel times, mode use and access................................................................................................ 67
The advent of new technologies and new approaches to transportation demand .......................... 73
Concluding thoughts ..................................................................................................................... 75
Notes ............................................................................................................................................. 77
References ..................................................................................................................................... 78
Chapter 3. How equitable is access to opportunities and basic services considering
the impact of the level of service? The case of Santiago, Chile ................................................... 79
Introduction ................................................................................................................................... 80Context: Santiago, Chile ............................................................................................................... 81
Accessibility measures .................................................................................................................. 86
Case study ..................................................................................................................................... 91
Discussion, conclusions and future work ...................................................................................... 97
References ................................................................................................................................... 100
Chapter 4. Housing plus transportation affordability indices: Uses, opportunities,
and challenges ............................................................................................................................... 105
Introduction and overview........................................................................................................... 106
The housing plus transportation index ........................................................................................ 106
Challenges and critiques of the H+T index ................................................................................. 112
Housing plus transportation index in Mexico City metropolitan area ........................................ 114
Conclusion and transferability to other OECD nations ............................................................... 119
References ................................................................................................................................... 121

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Chapter 5. Perspectives for integrating housing location considerations and transport
planning as a means to face social exclusion in Indian cities .................................................... 125
Urban development pattern in India ............................................................................................ 126
Slum free cities ............................................................................................................................ 129
Impact of eviction and resettlement policies ............................................................................... 133
Planning for the poor and failure of planning ............................................................................. 140
References ................................................................................................................................... 143
Chapter 6. Balancing financial sustainability and affordability in public transport.
The case of Bogota, Colombia ...................................................................................................... 147
Introduction ................................................................................................................................. 148
Subsidies in public transit - the case for supply and demand side subsidies ............................... 148
The basics of Bogota’s tariff policy & the concept of cost recovery .......................................... 150
Bogota’s pro-poor subsidy scheme ............................................................................................. 152
Conclusions ................................................................................................................................. 160
Notes ........................................................................................................................................... 161
References ................................................................................................................................... 163
Chapter 7. Is congestion pricing fair? Consumer and citizen perspectives on equity
effects............................................................................................................................................. 165
Introduction ................................................................................................................................. 166
Background and data ................................................................................................................... 167
Consumer perspectives on the fairness of congestion pricing..................................................... 168
Citizen perspectives on the fairness of congestion pricing ......................................................... 179
Conclusions ................................................................................................................................. 186
Notes ........................................................................................................................................... 188
References ................................................................................................................................... 189
Annex 7.1. Estimation Results. ................................................................................................... 191
Participants list ............................................................................................................................. 193

FiguresFigure 1.1. Scope of discussions ....................................................................................................... 14
Figure 2.1. Workers added in USA by decade.................................................................................. 47
Figure 2.2. Persons of working age and dependent ages .................................................................. 48
Figure 2.3. Working age group detail ............................................................................................... 48
Figure 2.4. Median household income in current and inflation-adjusted dollars.............................. 50
Figure 2.5. Fairfax County Virginia – 1980-2010 ............................................................................ 52
Figure 2.6. Percent of workers leaving their home county to work .................................................. 53
Figure 2.7. Why do people move? .................................................................................................... 54
Figure 2.8. Spending shares by all consumer units ........................................................................... 57
Figure 2.9. Total spending by quintile. ............................................................................................. 58
Figure 2.10. Shares of spending for housing plus transportation by income quintile ....................... 59
Figure 2.11. Spending shares to housing and rental patterns ............................................................ 60
Figure 2.12. Housing plus transport share of consumer expenditures. ............................................. 61
Figure 2.13. Transportation spending per worker (USD) ................................................................. 62
Figure 2.14. Relationship between earners and vehicles by income decile ...................................... 62
Figure 2.15. Shares of Consumer Units and population by earners .................................................. 63

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Figure 2.16. Trends in shares of households by vehicle/hh .............................................................. 64
Figure 2.17. Long term trend in households with zero vehicles by race and ethnicity ..................... 64
Figure 2.18. Workers in households without vehicles ...................................................................... 67
Figure 2.19. Long term work travel time trend US ........................................................................... 68
Figure 2.20. Recent annual travel time trend US .............................................................................. 68
Figure 2.21. Travel time distribution by mode of travel to work...................................................... 69
Figure 2.22. Components of travel time by mode of travel to work ................................................. 70
Figure 2.23. Long term trends in working at home .......................................................................... 71
Figure 2.24. Long term trends in the middle and minor modes ........................................................ 72
Figure 2.25. Long term trends in the major modes ........................................................................... 73
Figure 3.1. Activity centre evolution. ............................................................................................... 81
Figure 3.2. Car ownership in Santiago.............................................................................................. 82
Figure 3.3. Income distribution in Santiago (left) and accessibility to employment using
public transport (2012) (right) ........................................................................................ 83
Figure 3.4. Modal share evolution in Santiago, Chile ...................................................................... 85
Figure 3.5. Santiago’s public transport network ............................................................................... 85
Figure 3.6. Differences between accessibility impedance functions: Richards and Negative
Exponential ..................................................................................................................... 88
Figure 3.7. Route from San Miguel (left) and from Las Condes (right) to Santiago Centro
through walk and Metro (Line 2 and Line 1, respectively)............................................. 92
Figure 3.8. PAI results. ..................................................................................................................... 93
Figure 3.9. EUQI results. .................................................................................................................. 94
Figure 3.10. Total travel times (not including walking, transfers and waiting times) to Santiago
Centro (Morning peak, April 2013). ............................................................................ 95
Figure 3.11. Door-to-door time in our case study ............................................................................. 96
Figure 3.12. Work attractiveness (left) and education attractiveness (right) using a normalised
scale ................................................................................................................................ 97
Figure 4.1. CNT’s default online affordability maps for Philadelphia and surrounding
counties ......................................................................................................................... 107
Figure 4.2. Boxplots of monthly rent and public expenditures by 5-kilometre incrementfrom the central Zocalo. ................................................................................................ 116
Figure 4.3. Percent of rent and H+T burdened households by municipality .................................. 117
Figure 4.4. Municipal affordability indices for household earning the 25th percentile income ...... 117
Figure 4.5. Percent of housing options affordable to a household earning the 25th percentile
income........................................................................................................................... 118
Figure 5.1. Location of low income settlements ............................................................................. 133
Figure 5.2. Change in travel distance before and after relocation .................................................. 135
Figure 5.3. Change in travel time before and after relocation ........................................................ 136
Figure 5.4. Contiguous development of low-density, high income and high-density, low-income
colonies (enclosed within red boundary) in southern part of Delhi .............................. 141
Figure 6.1. Number of trips and individual income intended for the transport .............................. 153
Figure 6.2. Public transportation spending as a proportion of total household expenditure ........... 154
Figure 6.3. Evaluating financial and operational impacts: Impact of a 30% face discount forthe poor in Bogotá......................................................................................................... 156
Figure 6.4. Design and implementation of the Bogotá SISBEN Subsidy....................................... 158
Figure 6.5. Subsidized cards in use and subsidized trips as a proportion of total ........................... 159
Figure 7.1. Share of population who pay various amounts in tolls. ................................................ 169
Figure 7.2. Average toll payments per income class. Absolute numbers (left) and relative toaverage toll payment (right). ......................................................................................... 169
Figure 7.3. Average toll payments as share of income, per income class....................................... 170

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Figure 7.4. Average toll payments per income group, with company car exemption (black)and without (red). Absolute numbers (left) and as share of income (right). ................ 173
Figure 7.5. Support for congestion pricing with respect to toll payments (EUR/month). .............. 174
Figure 7.6. Support for congestion pricing in different income groups, split by those who pay
little or nothing (black) and those who pay moderate or large amounts (blue). ........... 175
Figure 7.7. Average compound self-interest per income group, relative to the meanin each city. ................................................................................................................... 178
Figure 7.8. Support for congestion charges across income groups in different cities..................... 183
Figure 7.9. Consumer and citizen components of the “utility” of congestion pricing, separated
by income group ........................................................................................................... 185
Figure 7.10. Consumer and citizen components of the “utility” of congestion pricing, with“citizen utility” separated into its subcomponents, and separated by income group .... 186
TablesTable 2.1. A new century – with limited growth .............................................................................. 49
Table 2.2. 2014 Consumer Expenditure Survey Demography ......................................................... 55
Table 2.3. 2000 Consumer Expenditure Survey Demography ......................................................... 55
Table 2.4. Ratio of highest to lowest quintile elements .................................................................... 56
Table 2.5. Spending in Major Categories by Highest and Lowest Quintiles .................................... 58
Table 2.6. Consumer Units without workers .................................................................................... 63
Table 2.7. Trends in mode to work by race and ethnicity ................................................................. 65
Table 2.8. Modal shares to work by gender ...................................................................................... 66
Table 2.9. Autonomous vehicle implications by trip purpose at the metro level.............................. 74
Table 3.1. Conditions for a housing units project to benefit from social housing subsidies............. 83
Table 3.2. Estimated parameters of Richard impedance function .................................................... 88
Table 3.3. Components of EUQI and his values ............................................................................... 89
Table 3.4. Level of service attributes considered in the accessibility indicator. ............................... 90
Table 4.1. Data and sources used to predict car ownership, car use, and transit use ...................... 108
Table 4.2. Affordability rankings for major US cities and metropolitan areas ............................... 111
Table 5.1. Provisional population 2011 by Census of India ........................................................... 127
Table 5.2. Forecast of GDP trends .................................................................................................. 127
Table 5.3. Percentage of slum population in selected cities in India .............................................. 129
Table 5.4. Number of households moved between 1997-2003....................................................... 134
Table 5.5. Percentage change in AST indicators for households relocated .................................... 135
Table 5.6. Percentage change in mobility indicators for households in the vicinity of the metroline ................................................................................................................................ 136
Table 5.7. Percentage change in MP indicators for households in the vicinity of the metro line ... 137
Table 5.8. Percentage change in MHH indicators for households relocated to expert planned
settlements .................................................................................................................... 137
Table 5.9. Percentage change in MP indicators for households relocated to expert planned
settlements .................................................................................................................... 137
Table 5.10. Travel characteristic s of residents of informal settlements ......................................... 138
Table 5.11. Travel distance of residents of informal settlements ................................................... 139
Table 7.1. Description of the surveys. ............................................................................................ 168
Table 7.2. Suits index (overall regressivity/progressivity) of the congestion charges.................... 170
Table 7.3. Share of each income class who [would] pay more than 40€/month in congestion
charges. ......................................................................................................................... 172
Table 7.4. Estimation results: impact of self-interest variables on attitude to congestion pricing.. 177
Table 7.5. Average agreement (from -3 to 3) with statements........................................................ 179

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Table 7.6. Correlation between income and agreement with the statement.................................... 181
Table 7.7. Variables affecting voting response (ordered logit). ...................................................... 184
Boxes
Box 1.1. PTAL assessment and the redevelopment of Vauxhall, Nine Elms and Battersea ............ 20
Box 1.2. TfL’s CAPITAL and ATOS indices .................................................................................. 21
Box 1.3. London’s quality incentive contracts for bus services ....................................................... 34
Box 4.1. Sample of users of the CNT’s H+T affordability index (CNT 2015) .............................. 110
Box 4.2. Applying an H+T Index in Qom, Iran .............................................................................. 115
Box 6.1. Lessons from “first generation” demand-side subsidy programs ..................................... 150
Box 6.2. Proxy means testing for targeting – Using Colombia’s SISBEN Instrument................... 155
Box 6.3. Assessing the effect of a targeted demand subsidy in Bogotá’s SITP ............................. 156

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EXECUTIVE SUMMARY. – 11

Executive summary
What we did
There is significant evidence across countries that lower-income populations tend to suffer morefrom restricted transport options, have lower quality transport services available to them and travel underworse conditions (safety, security, reliability, comfort). Broad evidence also suggests that the lack of, orpoor access to, transport options is central to limitations on access to jobs, educational institutions, healthfacilities, social networks, etc., which in turn generates a “poverty trap”.
The ITF Roundtable Report “Income Inequality, Social Inclusion and Mobility” examines mobility
policies with a focus on evaluating their capacity to address transport-related exclusion of lower income
groups. The document was prepared by the ITF Secretariat following an event that brought together anumber of international experts, academics and policy makers, and includes input papers developed forthe event and a synthesis of the debate, developed to embed conclusions in the relevant research
literature. This work is part of the OECD’s Centre for Opportunity and Equality, which is a platform
initiated in 2015 for promoting and conducting policy-oriented research on the trends, causes andconsequences of inequalities.

What we foundDespite cross-country and cross-city differences, the following priorities were identified as universal
for advancing the inclusive transport agenda. These include a) developing policies that are driven by
improved data and analysis; b) co-ordinating housing and transport policies because of their indissociable
relationship and their central role in peoples’ livelihood; and c) setting coherent pricing policies for eachtransport mode that support both sustainable mobility and social inclusion goals. In line with theseoverarching principles, the following actions are recognised as necessary.

What we recommend
Improving data and developing effective performance indicators
Authorities need to develop better performance indicators for quantifying and better understanding
the nature of exclusion. Multimodal location-based accessibility indices and housing plus transport
affordability indices have proven to be valuable tools. They improve understanding of transport poverty
and its effects on social exclusion spatially and across population segments. They also have important
potential for enabling land use, housing and transport authorities to set common goals and co-ordinate
planning. While various programmes have started incorporating such indicators, going beyond
occasional use to their systematic inclusion in transport, housing and land use policy frameworks will bevital.
Rethinking housing policies
Housing, urban development and transport authorities need to assess decisions with a comprehensive
view of the impact of location, housing quality and transport options on livelihoods. Incorporatingtransport-related indicators (travel times and costs), particularly for reaching jobs, into housing quality

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12 – EXECUTIVE SUMMARY.

definitions used in the design and evaluation of social and affordable housing programmes is particularly
important.
Readjusting urban renewal and transit-oriented development strategies
Urban renewal strategies need to shift away from clearing slums to a strong focus on seizing
opportunities for rehabilitation, and offering the population the opportunity to choose between theirpresent and alternative locations. Where TOD principles are central to urban renewal, these principles
need to be adapted to the size, mobility characteristics and purchasing power of residents, especially low
income residents.
In particular, city planners should acknowledge how informal settlements have naturally yielded
TOD characteristics adapted to the wants and needs of lower income residents. They also need toacknowledge that, while TOD principles can make urban renewal more inclusive - notably by focusing
on improving public transport and non-motorised mobility - the effect of increasing land and property
values displaces the poor even where eviction is not part of the urban renewal strategy. To address this, it
is essential to design and measure the performance of TOD projects in terms of equity and social
inclusion.
Mobilising increases in land value to deliver objectives for social inclusion
Methods of financing that incorporate land value capture mechanisms have demonstrated significant
potential for reducing the social inclusion challenge of TOD and urban renewal. But authorities need toensure that land value capture mechanisms channel part of future increases in the value of property
directly to meeting policy objectives, including social inclusion goals. Where this is not the case, themeasures could, on the contrary, accelerate the displacement of low-income populations.
Making public transport subsidies efficient and financially sustainable
Given competing needs for public funds and the limited resources, public transport subsidy schemes
need to put as little strain as possible on public finances. They need to ensure efficient resource
allocation and make sure subsidies are effectively translated into livelihood improvements (which go
beyond travel time savings). Targeted subsidies, as opposed to generalised support, are a better way of
striking a balance between financial sustainability and service affordability. An increasing number ofcities are building on the adoption of new technology, including smart cards, to put targeted subsidy
schemes into effect.
Participants also identified core issues for future discussion:•

Understanding the role, potential and limitations of shared vehicles for contributing to the
reduction of social exclusion – in particular, analysing whether gains in productivity from thisvehicle sharing through technology would allow provision of affordable services for lower
income residents, or if subsidies would still be needed.

•

Correcting policy biases and market distortions that favour car travel over more affordable
modes.

•

Attracting investment to programmes for the reduction of transport-related exclusion – inparticular, exploring ways to a) improve and complement appraisal tools; b) calculate and add
co-benefits to financial assessments; and c) shift financial and institutional resources from silostructured to multi-sector programmes and projects.

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1.INCOME INEQUALITY, SOCIAL INCLUSION AND MOBILITY – 13

Chapter 1
Income inequality, social inclusion and mobility

Aimée Aguilar Jaber
Economist, International Transport Forum
Especially in the present context of increasing levels of inequality, analyzing mobility policies witha focus on evaluating their capacity to address transport-related social exclusion is of particularimportance. This chapter builds on discussions held during a Roundtable meeting that was dedicatedto this objective and brings forward key findings and conclusions reached. A first section isdedicated to describing in detail the scope of discussions and identifies universal principles foradvancing the inclusive transport agenda. The chapter then looks at data limitations that impede
accurate analysis of transport related exclusion, followed by detailed analysis on three policy issues:
rethinking housing policy, urban renewal and transit-oriented development; making public transport
subsidies efficient and financially sustainable; and improving clarity on socio-economic issues
related to transport demand management policies. The closing section highlights priorities identified
for future research.

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Introduction
Governments across ITF member countries are concerned about increasing levels of inequality, evenas average per capita incomes rise, because of the social exclusion that results. This is reflected in thecreation in 2015 of the OECD Centre for Opportunity and Equality, which is a platform for promoting
and conducting policy-oriented research on the trends, causes and consequences of inequalities. The
Income Inequality, Social Inclusion and Mobility Roundtable was organised as part of this initiative.
Scope
Not all the socially excluded are poor and not all social exclusion is linked to limited access totransport. However, there is significant evidence across countries that a) lower income people tend tosuffer more from restricted transport options, have lower quality transport services available to them andtravel under worse conditions (safety, security, reliability, comfort), and b) poor or non-existent access totransport options is central to restrictions on access to jobs, education and health facilities, social
networks, etc., which in turn generates a “poverty trap” (Lucas et al., forthcoming).
Discussions during the roundtable were dedicated to analysing mobility policies in the light of thisphenomenon, i.e. with a focus on evaluating their capacity to address transport-related exclusion of lower
income groups. Figure 1.1 describes visually the scope of discussions.
Figure 1.1. Scope of discussions
Social exclusion

Low income

Roundtablediscussions(Passenger transport
in urban areas)

Transport poverty

Definitions for social exclusion and transport poverty, as understood in the context of this report, areas follows:
Social exclusion (Kenyon et al., 2002):[T]he unique interplay of a number of factors, whose consequence is the denial of access, to theopportunity to participate in the social, and political life of the community, resulting not only indiminished material and non-material quality of life, but also in tempered life chances, choices andreduced citizenship.

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Transport poverty (Lucas et al., 2016):
An individual is transport poor if, in order to satisfy their daily basic activity needs, at least one of thefollowing conditions apply:•

There is literally no transport option available that is suited to the individual’s physicalcondition and capabilities.

•

The existing transport options do not reach destinations where the individual can fulfil
his/her daily activity needs, in order to maintain a reasonable quality of life.

•

The necessar[y] ... amount spent on transport leaves the household with a [low] residual
income ... .

•The individual needs to spend an excessive amount of time travelling, leading to timepoverty or social isolation.

•

The prevailing travel conditions are dangerous, unsafe or unhealthy for the individual.

This definition of social exclusion was selected as sufficiently flexible to apply to conditions in the
broad range of countries and contexts analysed. Nevertheless, it was also found to reflect the key pointsof convergence highlighted during the discussions:•recognising that a multiplicity of factors needs to be considered when analysing social
exclusion•acknowledging the relevance of choice, or lack of choice, as central to identifying the sociallyexcluded•considering not only lack of access but also its incidence on life quality and opportunities asfundamental to the problem of social exclusion.

Transport poverty is the term chosen to describe poor or non-existent access to transport options. The
concept is used as developed in Lucas et al. (2016), where it incorporates multiple dimensions oftransport-related limitations faced by individuals (all considered relevant during the discussions): lack ofaffordability, non-existent or poor transport services (in particular motorised modes), difficulty inreaching necessary and desired activities through available modes of transport, and disproportionate
exposure to negative transport externalities. Consideration of transport poverty from an affordability
perspective has been slightly modified, as the approaches for determining affordability reviewed during
the roundtable explore thresholds that differ from the criteria used in the original work, i.e. residual
income after transport expenses that is below the official poverty line.
Finally, discussions were focused on the movement of people and on analysis of urban areas.
The rest of this chapter is organised in three sections. The first looks at data limitations that impede
accurate analysis of the nature and scale of transport-related exclusion. It also evaluates the potential of
location-based accessibility indices and housing plus transport affordability indices for understanding theissues and improving policy. The second section focuses on three policy issues identified as priorities fordeeper examination: a) rethinking housing policy, urban renewal and transit-oriented development; b)making public transport subsidies efficient and financially sustainable; and c) improving clarity on socioeconomic issues linked to transport demand management policies. The final section discusses
conclusions and outlines priorities identified for future research.
Debate at the roundtable highlighted the relevance of analysing cross-country and cross-city
differences and adapting policies to the different contexts. Certain priorities, however, were identified asuniversal for advancing the inclusive transport agenda: a) the need to develop policies that are driven by

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improved data and analysis, b) the urgency of co-ordinating policies between housing and transport andc) the need for coherent pricing policies for each mode of transport, in line with sustainable mobility andsocial inclusion goals. Thus these are overarching elements present throughout the chapter.

Improving data and developing useful performance indicators
Surveys are central to conducting equity-related analysis of mobility trends and policies. Among themost widely used are censuses, income-expenditure surveys, origin-destination surveys (also called
household travel surveys) and surveys characterising socio-economic conditions of the population. The
data and the basic indicators calculated based on these surveys also constitute the building blocks ofmore complex tools being developed with the aim of improving understanding of transport-related
exclusion, including those analysed later in this section. Enhancing the data available from these sources
is a priority. The following improvements were identified as necessary:•

Accounting for income transfers and savings when breaking down population by income
levels. Conducting analysis based on statistics that fail to exclude non-poor populationgroups from the lowest income categories produces misleading conclusions. It hides orminimises, for example, the disproportionately higher travel times and distances travelledby the truly poor, since these are averaged with those of residents who can afford to live
closer to their place of work and/or have access to faster modes of transport. It can also
greatly overestimate the proportion of car ownership among lower income residents. In
particular, it is necessary to make sure retirees and students who have savings or receive
allowances from parents, and thus are not in poverty, are not included in the lowest income
categories.

•

Producing data and indicators that permit analysis at the right scale and are sufficiently
updated. Adequate analysis is also hindered either because surveys are only representative
at national level or because the breakdown at local level does not coincide with functional
urban areas, leaving many peripheries unaccounted for.1 Since peripheral areas often
concentrate high proportions of lower income residents, who have the fewest and/or morecostly (in terms of money and time) transport services available, this leads to significant
underestimates of the transport-related burden in many urban areas. In particular, origindestination surveys in many cities are not conducted often enough and also tend to belimited spatially to core cities instead of metropolitan areas. Overall, survey design making
it possible to come up with indicators for functional urban areas, and to compare regions
within them (e.g. core city vs peripheries, or areas that are clearly divided in terms of socioeconomic conditions) will significantly improve analysis.

•

Separating transport expenses from impractical data groupings and re-examining
expenditures included in this category. The possibility of conducting detailed analysis ofhousehold transport expenses is limited in many countries because transport and other typeof expenditures, such as telecommunications, are grouped together. Even where this is not
the case, the transport-related expenses included do not always cover all modes and fail to
include items relevant to households (e.g. parking – especially parking at home – orpurchase of bicycles used for travel). Solving these two problems will significantly increase
the scope and depth of analysis that can be conducted with information from incomeexpenditure surveys.

•

Controlling average expenses by the proportion of population incurring them. Making
averages of various transport expenditures based on total population rather than the share of
the population actually making them is also a common cause of misleading results. In thecase of lower income groups, averaging, for instance, vehicle ownership costs using total

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population rather than only car owners can considerably minimise the shares of income
spent on fuel, maintenance, etc., by those lower income residents who may be “captive” carusers.
•

Identifying spending decisions that result from the lack of transport alternatives. Analysis
based on income-expenditure surveys tends to ignore the fact that households may be
spending too much or too little on transport due to restricted options. For example,
households located where there are limited public transport services spend high shares of
their income on car purchase and maintenance. Or lack of affordable services may result inpoor households reducing the number of motorised trips they make. Efforts to collectcomplementary data for recognising these groups is particularly important in designingpolicies aimed at reducing transport-related social exclusion.

While traditional surveys are valuable sources of information, they are costly for authorities and time
consuming for residents. Introducing technology-based procedures is important for reducing themonetary and time costs of collecting, processing, and analysing data gathered through these tools.
Authorities should increasingly look into the potential of big data sources for improving data quality andlevel of detail while bringing down costs. They could complement and in some cases replace surveys.
Making sure that data, related indicators and analysis meet the conditions listed above will, however,remain necessary even if information comes from sources other than traditional surveys.
Furthermore, authorities need better performance indicators for quantifying and understanding thenature of exclusion. Indicators need to be useful for determining whether policy interventions have beensuccessful. They also need to facilitate moving away from segmented analysis that impedes
understanding of how policies interact in different sectors (particularly between land use and transport
policies) and their ultimate effect on livelihoods. A number of indices have been developed as part of theeffort to improve indicators. Each addresses particular dimensions of transport poverty and social
exclusion, as well as factors behind them. Discussions during the roundtable included in-depth analysisof two types of indicators: location-based accessibility indices and housing plus transport affordability
indices. Insight about their value, limitations, challenges and potential to improve policies was generated.
Location-based accessibility indices
While simple to construct, performance indicators based on traffic (vehicle movement) and mobility(movement of people) have significant limitations. By narrowing down the problem to maximisingphysical movement, both fail to provide accurate information about changes in access to goods, services,
activities and destinations. In particular, they overlook the importance of non-motorised modes, locationefficient urban development patterns, and mobility substitutes (e.g. home office, delivery services), all ofwhich improve accessibility while reducing overall distance and travel. In the case of traffic-based
indicators, public transport is also given little value as it represents fewer vehicle-kilometres than private
vehicles. In sum, a significant mismatch exists between policy design and projects focusing on increasing
traffic and/or mobility and enhancing accessibility (Litman, 2011).
Accordingly, indicators directly measuring accessibility – or the opportunity of individuals andgroups of individuals to participate in activities – have proved more useful for designing and evaluating
policies. However, accessibility is more difficult to measure than mobility or traffic because it depends
on:•land-use factors: the amount, quality and spatial distribution of opportunities, demand forthese and competition for activities generated by the interaction of supply and demand in agiven place

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transport factors: time, effort and monetary resources required to cover the desired origindestination distance with the existing transport infrastructure and services•

temporal factors: changes in availability of opportunities depending on time of day•

characteristics of individuals.

A range of approaches have been developed; within these, some location-based indicators focus on
analysing accessibility to activities offered by different locations (Geurs and Ristema van Eck, 2001).
Contour accessibility indicators
Simpler indicators of this type (called contour or isochronic indices) focus on a) calculating thenumber of opportunities or facilities (e.g. transport stations) which can be reached within a given travel
time, distance or cost, or b) measuring the time/cost (average or total) required to gain access to a fixed
number of opportunities from different locations (Geurs and Ristema van Eck, 2001). Work conductedby the World Bank using such indices highlights their value for examining urban growth patterns andtheir effect on employment accessibility of different populations. The methodology developed wasrecently used to assess the case of Greater Buenos Aires (GBA), in Argentina.
The analysis focuses on employment opportunities and accessibility is defined as the number of job
opportunities that can be reached within 60 minutes of travel. Mode-specific accessibility indices arecalculated for car and public transport. In addition, a ratio of the two indices is calculated, providing
information on the number of jobs that can be accessed by car in relation to every option available whenusing public transport (given the same 60 minute commuting threshold). The three indices (caraccessibility, public transport accessibility and ratio of car to public transport accessibility) are shown inheat maps, which allow identifying spatial accessibility inequalities across GBA.2 A combination of open
data sources is used to get information on the road network and public transport characteristics, which,
together with information from the city’s mobility survey, allow estimates of average travel times forevery origin-destination pair, and the accessible employment opportunities provided by each mode indifferent areas of the city (using the Open Trip Planner Analyst engine) (Peralta Quiroz and Shomik,2014).3
Combining information on accessibility with urban growth patterns, identified by analysing remotesensing data, makes it possible to see that urban growth in GBA has generally happened in areas where
overall accessibility is poor and public transport accessibility even worse. Two underlying phenomena
highlight the links between income, spatial and employment accessibility inequalities in GBA: thesignificant development of gated communities of higher income residents (car owners) in areas where
only car accessibility is being developed (mostly in the northern corridor) and the fact that, despite highreliance of lower income groups on public transport, which accounts for 45% of total travel stages forresidents in the lowest income quintile, social housing is not located where public transport employment
accessibility is high. Some informal settlements have also developed, mostly in areas with poor public
transport employment accessibility (Peralta Quiroz and Shomik, 2014).
The analysis can be taken a step further by combining information on accessibility and density,
identifying areas that have potential for densification and already offer good employment accessibility by
public transport. This type of analysis was carried out for GBA using the accessibility ratio (car/public
transport accessibility) and an indicator that calculates the percentage of job opportunities accessible
through public transport relative to the most accessible area of the city. If linked to the urban planning
framework, the results could bring significant improvement by providing a solid tool for linking
investment to development in areas identified as offering opportunities for efficient development.
Moreover, once the larger areas for potential efficient development are identified, the same analysis canbe carried out using more granular accessibility indices. Authorities, in co-operation with developers,

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could thus identify problems and design solutions for specific cases (e.g. particular gated communities,
isolated social housing developments).
Overall, contour accessibility indicators provide an opportunity to significantly improve analysis forcities that are not data rich. Methodologies such as that described above show that the indicators can beconstructed by taking advantage of new open data sources and combining them with data from existing
mobility surveys. Other new data sources (e.g. remote sensing) provide the possibility of adding analysis
on urban growth patterns, which, together with these indicators, can a) provide improved vision on how
the distribution of opportunities (resulting from land use policy) and the availability of transport services(resulting from transport policy) play out in generating inequalities in these cities; and b) constitute abasic but solid tool for introducing accessibility as an explicit parameter to be taken into account in urban
development decisions.
Contour indices have also been useful for cities that, although relatively data rich, benefit from thefact that the indices are fairly simple to calculate and update and easy to communicate and operationalise.
An example is London, which has made them a central component of its planning framework and has set
up a publicly available tool enabling developers to calculate the indices for specific projects. The best
known of the indicators used by Transport for London (TfL), is the Public Transport Accessibility Level(PTAL), measuring accessibility to the public transport network across Greater London. The scoreprovides information on how close a selected point in the city is to public transport and how frequent
services are in that place. Calculation of the index takes into account the walking time to all public
transport service access points (SAPs) within a given distance threshold.4 This is combined with theaverage time a user has to wait in the SAP for the next service. The sum of the waiting and walking time
for all routes available from a given location is converted to levels of frequency. To calculate the final
access index (AI), routes available by each mode of public transport are compared, assigning a weight of
1 to the one presenting the highest overall frequency score and weighting all others by 0.5 (TfL, 2015).
The final AI is the sum of the access indices of all available public transport modes, which is transformed
into a PTAL on a scale from 1 to 6 (TfL, 2010).5
PTALs are used in the London Plan as an essential tool for determining suitable density ofdevelopment across Greater London. The London Plan also uses them for calculating recommended
parking capacity in housing and commercial developments, under the principle that higher PTAL areas(those better connected to the public transport network) need fewer parking spaces. A third use for
PTALs is to monitor the share of business and commercial activities taking place in areas that have goodconnections to the public transport network (i.e. that have a PTAL of 5 or above). This is used to verify
compliance with the goal set in the London Plan of having a high share of workplaces that are wellconnected to public transport.
The PTAL is also used as part of the transport assessment required for specific development projects,
evaluating the impact certain changes to the network could have in increasing accessibility to public
transport in a selected area. It is used to make the development of an area conditional upon providing asufficient level of public transport and offers the evidence base for negotiations with private developers
on their contribution to infrastructure funding. A good example of this is the analysis conducted forredevelopment of the Vauxhall, Nine Elms and Battersea (VNEB) Opportunity Area (OA) (Box 1.1).
TfL’s Web-based Connectivity Assessment Toolkit (WebCAT) allows users to create their own PTAL
maps and view PTALs for future development scenarios.

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Box 1.1. PTAL assessment and the redevelopment of Vauxhall, Nine Elms and Battersea
The VNEB OA is located in the boroughs of Wandsworth and Lambeth on the south bank of the River
Thames; its northern edges are a kilometre south of the Houses of Parliament and Victoria Station. At 227
hectares it is the largest remaining OA in London’s economic and cultural core, the Central Activities Zone,
which is the city’s most attractive and competitive business area (GLA, 2015).
The London Plan requires high density development with high trip generation patterns to be met by a highlevel of access to public transport and enough transport capacity to absorb the impact of development.
Accordingly, a detailed transport assessment was carried out as part of the VNEB OA Planning Framework. The
study, in consultation with key stakeholders, included travel demand forecasting and public transport modellingfor various development scenarios and assessed to what extent already planned transport interventions by TfL
would support the development objectives of the OA. Based on the preferred development scenario for the area,
which includes high density housing and major retail development, the assessment suggested a comprehensive
package of measures aimed at increasing transport capacity and accessibility and bring about the change inpublic transport accessibility required to make it an attractive area. Improving pedestrian, cycling and public
transport provision in the area is central to this strategy. The main components of the strategy, to becomplemented by a set of more local transport improvements, are:•extending the Northern Line of the Underground, connecting new stations at Battersea and Nine
Elms with Kennington in the north-east part of the OA•

increasing the capacity of existing bus routes and stops serving the OA and creating new ones•

improving walking and cycling, notably through a new pedestrian and cycle bridge connecting
Nine Elms with Pimlico across the river.

The PTAL was part of the transport assessment and was used in negotiations with developers on theircontributions to funding for the Northern Line extension, which, together with improvement to walking routes
around the Battersea Power Station, will raise the PTAL for parts of the area from 3 to 6.
Source: Adapted from TfL (2015), “Assessing transport connectivity in London”, Mayor of London and TfL (n.d.), Transportfor London, “Accessibility and Connectivity”, https://tfl.gov.uk/info-for/urban-planning-and-construction/transportassessment-guide/transport-assessment-inputs/accessibility-analysis

PTALs are complemented with other location-based contour accessibility indices. Among these aretravel time maps, which calculate and represent the time required to travel from zone to zone acrossLondon, based on likely public transport routes. Travel time maps can show average times to a locationfrom all other locations, average times from a location to all parts of London, or an average of both.
Analysis is based on travel time between SAPs and service egress points (SEPs); it does not account forwalking time to SAPs. A tool for calculating the TIM (as TfL calls its time mapping index) anddeveloping related heat maps is also available as part of WebCAT.
While PTALs capture the impact on local accessibility to public transport, travel time maps provide
a tool for evaluating the metropolitan-wide impact of transport improvements. Also, while PTALs arecalculated for the aggregate of available public transport modes, the TIM is available for individual
modes (e.g. step-free public transport, buses only), and thus provides information on accessibility forvarious types of users. More complex indicators are built from this analysis (Box 1.2). However, as thecalculations become complex, these are not available in the WebCAT framework and are used by TfL for
more specific analysis.

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TfL also uses catchment analysis, setting a certain time threshold and calculating the area holding a
number of opportunities at reach (e.g. jobs, colleges) or the area from which people can travel to a certain
place within the time threshold. As with travel time maps, analysis can be conducted for individual
public transport modes and can use either TIM or CAPITAL travel time (Box 1.2). Catchment area
analysis has been used to prove the benefits of investments in the transport network in terms of additional
opportunities at reach. All the TfL accessibility indicators described here are calculated for both current
and future scenarios, which makes them useful in evaluating the impact of alternative policies andinfrastructure projects.
Box 1.2. TfL’s CAPITAL and ATOS indicesA more detailed version of the TIM framework, called the Calculator of Public Transport Access(CAPITAL) index, incorporates walking time to SAPs and from SEPs. In addition, the Access to Opportunities
and Services (ATOS) index builds on the CAPITAL indicator by setting as destination the nearest employment,
education, health, food shopping and open space facility that can be reached when travelling by public transport
or walking. These indices serve to enhance the accessibility analysis by linking ease of access with attractivenessof area. Average travel times between an origin and the chosen destinations are calculated. An ATOS index is
calculated for each type of facility and has particular selection criteria (e.g. the nearest 10 000 low qualified andhigh qualified jobs or the nearest three primary schools, secondary schools and further education institutions).
Adding the ATOS index makes the analysis of accessibility even more comprehensive.
Source: TfL (2015), Assessing Transport Connectivity in London, http://content.tfl.gov.uk/connectivity-assessment-guide.pdf

While location-based contour indicators are valuable tools, they have their limitations. Theirdisadvantages are inherent in the way they are constructed. As binary indicators they treat allopportunities that are within the selected threshold (e.g. a 60 minute commute) as equally attractive andall that are not as equally unattractive. This entails ignoring user perspectives on the desirability ofopportunities that are located at different distances and that imply different travel time and cost. The
problem is more extreme when indicators simply express the total number of opportunities or facilities
that are reachable within the time or distance thresholds set. In some cases, where indicators are
represented in terms of time or frequency levels, such as PTAL or time mapping, opportunities or
facilities that lie within the threshold are differentiated to a certain extent. However, even in these cases
the indicators do not capture the way higher journey times or lower service frequencies affect thedesirability to users of opportunities or use of facilities (e.g. transport stops). Another limitation is thatthe function on which the indicators are based captures changes in land use and transport but is notdesigned to capture interaction between them (Geurs and Ristema van Eck, 2001).
Potential accessibility indicators
More complex location-based accessibility indicators, often referred to as potential or gravitational,
are better suited to address the issues described above. Such indices measure the accessibility of allopportunities in an area vis-à-vis all other areas, and a continuous basis rather than by applying a binary
function. They weight opportunities by a function that simulates the reduction in desirability of an
opportunity/facility in relation to the cost of travel (e.g. in terms of distance, time, and money). This termof the equation is often called a cost sensitivity or impedance function. In addition, changes in the land
use and transport components of this type of function weight each other and make it possible to captureinteractions between them. Solving these two issues significantly improves the accuracy of resultscompared to those obtained by using contour indicators.

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22 – 1. INCOME INEQUALITY, SOCIAL INCLUSION AND MOBILITYWork developed at the Catholic University in Chile uses these tools for the analysis of accessibility
by public transport in Santiago. In addition to the advantages offered by using potential indicators, thework takes into account information on users’ perspectives on journey comfort and convenience tocorrect travel time. It also incorporates additional analysis on elements of the urban environment thathave a relevant impact on inequality in access to opportunities and basic services across income levels.
The analysis breaks down the stages of travel into two potential accessibility indicators: one
measuring accessibility to public transport stops (Physical Accessibility Index, PAI), and one measuring
connectivity and quality of service provided by the transport system from the transport station of origin
to the station of destination (Public Transport Index, PTI). A third indicator is inspired by the ATOS
indicator developed by TfL and is used for measuring the attractiveness of destinations (AttractivenessLand Use Index, ALUI). A short description of the three indices is provided below. A detailed
explanation can be found in Chapter 3 of this report.
•

Physical Accessibility Index: the PAI calculates walking accessibility to the ten closest
transport stops, within a threshold of 400 metres. The index has an impedance function whichuses the walking time calculated for reaching each stop to weight the accessibility scoreassigned to each of them. An Environment and Urban Quality Index (EUQI) is developed inparallel, assessing sidewalk and street quality by using Santiago’s pre-census data, which
include information on public space. Four subindices are calculated and summed together tomake up the EUQI: security/safety, environment, cleanness and infrastructure quality. Given
the difficulty of calculating the effects of the EUQI on time perception, which would be neededin order to include it in the impedance function, the EUQI is left as an index that complements
the PAI when analysing walking accessibility.

•

Public Transport Index: the PTI calculates the total time between the first and last public
transport stop in a trip. It incorporates user perception by distinguishing and assigning different
weights to time spent in different types of activities during the journey (waiting, walking fortransfers and in-vehicle time). It also incorporates weights that account for differences in
comfort (e.g. crowding and standing vs. seating). Total time is expressed in terms of in-vehicle
travel. This index also uses an impedance function to weight attractiveness of routes according
to differences in the total time they require (in turn corrected by differences in comfort and timespent in different journey stages).

•

Attractiveness Land Use Index: the ALUI identifies the attractiveness of various areas of a city
in terms of the concentration of activities. It is calculated as the share of trips attracted due to arange of purposes (e.g. work, education, health).

Analysis using the three indices was carried out to evaluate and compare public transport
accessibility conditions in two areas of the city that have similar trip characteristics for reaching
Santiago’s central business district (CBD): Las Condes and San Miguel. The two present significant
income differences, having average monthly revenues per household equivalent to USD 3 260 and
USD 1 480, respectively.
Values calculated for the PAI provide very accurate scores for walking accessibility to public
transport, accounting for the reduction in the attractiveness of each stop relative to walking timeincreases. Results show that the CBD has the highest walking accessibility to public transport (0.78/1 onaverage), followed by San Miguel (0.64/1), with Las Condes having the lowest average accessibility
level of the three (0.45/1). However, EUQI results demonstrate that San Miguel has a significantly
poorer walking environment than Las Condes and, to a lower extent, the CBD.

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The contrasting PAI and EUQI results underline the importance of analysing walking accessibility to
public transport by measuring both the quantity of public transport stops accessible within a reasonable
walking time and the quality of the urban environment in which people have to walk in order to reachthem. Having this comprehensive view can make better investment decisions possible, first by bringing
attention to accessibility limitations in places like San Miguel, which on the basis of PAI-based analysis
only would probably be given low priority for investments aimed at increasing walking accessibility to
public transport; and, second, by, showing that in many cases, reducing public transport accessibility
inequality between income groups is a matter not of increasing the quantity of transport services in lower
income areas, but rather of investing in improving the urban environment.
Analysis using the PTI was also conducted in the selected areas and compared to results using an
indicator that does not account for comfort and crowding, and does not weight time spent on walking
during transfers, traveling and waiting, according to users’ perceptions. Results show that according to
the latter indicator, trips between San Miguel and the CBD would appear to be quite short, on average(18 minutes), and even slightly shorter than those made from Las Condes (21 minutes). Using the PTI
shows a very different picture, however, with both trips having significantly higher on-board time whencorrecting for crowding and comfort (adding 10 minutes to a trip from Las Condes to the CBD and 15
minutes from San Miguel), and trips from San Miguel incorporating an additional 10 minute transfer.
The final PTI result shows in-vehicle equivalent times of 44 minutes for a trip to the CBD from San
Miguel and 32 minutes from Las Condes.
Finally, calculation of the ALUI confirms that the trips evaluated connect the population to an
attractive area. The particular part of the CBD set as destination of both trips is ranked with this indicator
as the 26th most attractive of 804 transport zones, especially because of the concentration of work- andstudy-related activities.
Data requirements for developing potential location-based accessibility indices are of course
substantially higher than those for contour indices, limiting the extent to which the former can be used bycities that have poor data availability. In addition, even in cities with better data, like Santiago,
expanding methodologies like the one presented for the whole of the metropolitan area has proven atime-intensive task. In the case of Santiago, data challenges are even higher since there are significant
requirements for information about the urban environment to develop the EUQI and for data about users’
perspectives and travel conditions to calculate the PTI. The high data requirements can also limit theextent to which the indicators can be updated – an important barrier for their extended use as part ofmetropolitan-wide planning frameworks. Still, given the valuable contribution that indices such as thosedeveloped for Santiago can make, progressively adapting data collection frameworks to facilitate theirdevelopment is an important step.
Another challenge is that as these indices become more complex, they also become more challenging
to communicate. Especially as they include more information, summing results into a unique measurethat would be easier to compare carries an increasing risk of give high scores to areas where very bad
conditions in a number of variables could be compensated with very good ones in other dimensions.
These areas could, overall, be more problematic than areas with average but more homogeneousconditions, yet both would have similar ratings. With the aim of overcoming these problems, analysis of
the work developed for Santiago stresses the need to analyse separately what each index says about
accessibility in different journey stages, and different dimensions of transport poverty, rather than putting
all into an aggregate index. Even so, as the work is further developed, efforts will be required to findsimple ways to explain results and link them to the current planning framework in Santiago.

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Housing and transport affordability indices
Assessing affordability of neighbourhoods by analysing housing and transport costs borne by theirresidents is based on evidence generated across countries indicating that a) housing costs tend to go up
when housing is located closer to city centres or particularly attractive locations (with housing being
priced out by office space in some cases) and b) transport costs tend to go up the farther residents arefrom central and attractive zones. The two contrasting trends imply that households’ decisions thatenable them to pay less for either of these goods will tend to increase the costs for the other. Better
understanding and measuring this phenomenon is relevant due to the vital role these two goods have interms of livelihoods. In addition, housing and transport costs, which are often households’ two largest
expenses, are highly determinant of the budget available for other goods and services.
Housing and transport affordability indices were explored in the roundtable using the example ofmethodology developed by the Center for Neighborhood Technology (CNT). CNT’s methodology has
been used in the United States for developing a national framework that calculates neighbourhood
affordability within and across cities and generates maps that reflect results spatially. Some variations of
the CNT H+T index developed at the University of Pennsylvania were also addressed.
Description and use of the CNT H+T affordability index
CNT’s methodology is based on a simple formula that expresses the average transport and housing
expenses paid by the average household as a share of the average household’s income. Transport costs
are estimated by a model that calculates the percentage of people traveling by a given mode of transport,
and the cost incurred. Car ownership and travel as well as public transport are used as independent
variables, and are in turn estimated using publicly available data sources that contain neighbourhood andhousehold characteristics (Chapter 4 gives further detail on data sources and calculations). A
neighbourhood is considered affordable if an average household there spends 30% or less of its income
on housing and 15% or less of its income on transport.
The H+T affordability index has helped change narratives about affordability across regions andcities in the United States, bringing increased attention to the importance of considering transport costs
when determining affordable locations. New York, for example, would rank 17th on the list of mostaffordable municipalities on the basis of housing cost only, but rise to 6th if transport costs were added tothe equation. In contrast, Cincinnati would fall from 2 nd to 8th most affordable municipality in a similar
analysis. Making such rankings available has increased and enriched public debate on locations for future
urban growth and the benefits of residing in more affordable neighbourhoods. The CNT H+T rankings
are, for example, an important source of information used by the press when addressing urban
development, transport and equity issues.
The index has also had an impact on public policy, being a useful tool for city planners, housing
professionals and policy makers. Local governments (e.g. Chicago) have used it in developing regionaltransport plans and selecting optimal locations for housing development and transport corridors. It has
also been used in the development of urban revitalisation strategies that give priority to the reduction ofhouseholds’ cost of living (e.g. by Ohio State for Cincinnati, Cleveland and Columbus).
Housing professionals have used the index to determine where to locate affordable housing. The San
Francisco Bay Area, for instance, was identified as a location where housing costs put particularly highpressures on household budgets, as 50% of residents have housing costs exceeding 30% of income, while
the national average is 30%. The trade-off between affordable housing and long and costly commutes
was also identified as relatively high. The Bay Area has the country’s second largest number of people
with commutes that exceed 45 minutes, and the highest average share of household income spent ontransport plus housing (65%). As a way to improve the situation, public and private enterprises

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(including the Metropolitan Transportation Commission) established the Bay Area Transit Oriented
Affordable Housing Fund, which finances non-profit and for-profit developers, municipal agencies andjoint ventures in the development of affordable housing and other community services near public
transport (MTC, n.d.).
In some US states and cities, the index has been formally incorporated into the legal framework as
an indicator that government agencies must use in making financing and siting decisions. Examples
include the state of Illinois (using 45% of income as threshold) and the city of El Paso, Texas (using 50%
of income as threshold). The H+T index has also influenced allocation of the Low-Income Housing Tax
Credit, the primary source of funding for affordable housing in the United States. The funding is
allocated competitively and on a state by state basis. Criteria for allocation include the number ofaffordable houses to be developed as well as their affordability. In around 10 states, considerations about
the location of housing, linked to the H+T index, are incorporated into scores used for project rating, so
the index has a role in the location of subsidised housing.
At federal level, the H+T index has been used by the Partnership for Sustainable Communities, ajoint effort of the Department of Housing and Urban Development, the Department of Transportation andthe Environmental Protection Agency. The partnership’s objective is to incorporate principles of
liveability into federal funding programmes, policies and legislative proposals (Office of Safety, Energy
and the Environment, n.d.). A calculator allowing individuals to estimate locations’ housing andtransport costs or generate a list of locations that are compatible with a certain budget for housing andtransport is available within the framework of the partnership.
Challenges and limitations of housing and transport affordability indices
As with accessibility indices, understanding the limitations of housing and transport affordability
indicators is key to using them correctly. A first issue, which is shared with contour accessibility
indicators, is the somewhat arbitrary threshold. The housing cost limit has its origins in the long-time
belief that one should be able to pay for housing with around a quarter of one’s wage. The level has
progressively been raised to 30% but remains contestable, and choosing a different threshold could
significantly change decisions based on analysis of the indicator. Moreover, the use of a threshold meansneighbourhoods with transport plus housing costs that are similar, but below or above the threshold, aretreated as completely different (affordable and unaffordable), while those greatly different but below thethreshold as seen as equally desirable. As CNT’s online affordability maps show, breaking down results
into ranges of income shares spent on H+T helps reduce this problem, providing a more comprehensive
view of affordability across a metropolitan area.
Another point is that, while representing a substantial part of household expenditures, transport andhousing are not the only costs that can substantially affect a household’s finances and are linked tolocation. In the US, for instance, public school is clearly “the elephant in the room”, as suburban areas
tend to have very good public schools while many urban areas with good public transport do not. Thus,in this context, differences in tuition expenditures are also important and highly correlated to housing
location decisions along with housing and transport costs. As a general rule, conclusions reached throughanalysis based on transport and housing affordability indices need to be read with understanding of theeffects of all costs that vary directly with geography in a particular context.
A third concern is linked to limitations in capturing differences in life stages and household
preferences. For example, high shares of income spent on housing by younger households that havetaken out a loan may represent less of a problem than if the same share of income is spent by older
households, since in the former case this is an investment for future reduction of housing expenditures. In
addition, households may simply assign higher value to housing or transport (e.g. owning expensive cars)
and be spending high shares on this even when they could spend less. The need to adjust income data

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26 – 1. INCOME INEQUALITY, SOCIAL INCLUSION AND MOBILITY(accounting for transfers and savings, among other things) before using them for further analysis was
mentioned earlier. Using a corrected measure of income, adjusted to provide a better idea about wealththan simple present monetary income earned, could be an option for better capturing some of the lifestage differences when conducting H+T calculations. However, it must be acknowledged that somefactors will always be left out and that analysis based on housing and transport affordability indices needto take this into account for correct interpretation.
It is also important to recognise that further analysis concerning differences in household conditions
across regions and neighbourhoods is necessary for a correct reading of results. For example,
Washington, DC, is ranked as the most affordable place according to the H+T index both as amunicipality and as a metropolitan area. However, the result is due, to a great extent, to Washington
concentrating a high share of relatively high income residents. This might suggest that, while relatively
affordable to the type of population it concentrates (those qualified for higher paying jobs), the area is
not that affordable for the general US population.
Finally, as it focuses on average households and average prices, the H+T affordability index has anaggregation bias: a neighbourhood’s H+T affordability level could indicate that it is highly unaffordable
even though it is very heterogeneous and thus in reality has many houses that are affordable to low
income residents. A very homogenous neighbourhood could present the opposite phenomenon, being
rated as affordable while not having housing affordable to the most vulnerable.
Alternative indices and application to cities in developing countries
Work developed at the University of Pennsylvania applied and adapted the methodology forcalculating housing and transport affordability indices to the Valle de México metropolitan area.6 Results
are shown in terms of other indicators, rather than the share of income spent by the average household. A
first index shows the proportion of households burdened by housing and transport costs and is comparedto the percentage of rent-only burdened households. A second index shows municipalities that would beaffordable, on average, to a low income household (defined as one in the 25th lowest income percentile),
taking into account housing and housing plus transport costs. A third indicator calculates the percentage
of housing options that are affordable to a household in this income percentile. With this indicator it ispossible to overcome the aggregation bias problem. In all three cases, thresholds set to define whether
housing and transport are affordable or a burden are the same used for the CNT index: 30% of income
spent for housing and 45% for housing plus transport.
The study for Valle de México shows that analysis based on a range of variations of H+T
affordability indices can provide a more comprehensive picture, allowing examination of trends andanswering diverse policy questions. Similarly to results from the CNT index in the USA, the firstindicator shows that incorporating transport costs increases affordability of central locations and reduces
affordability of peripheral areas. Focusing on low income households, the second indicator highlightsthat very few municipalities are affordable to them, on average, and Mexico City itself even less so.
Nevertheless, as the third indicator shows, many municipalities that are unaffordable on average have asignificant percentage of housing options within low income residents’ means. It must still beacknowledged, though, that in most areas in Mexico City proper, less than 50% of housing is affordable
for these groups and in the municipalities with the highest concentration of economic activities the shareof housing affordable to low income residents shrinks even further – to less than 33% in Cuauhtémoc and
25% in Benito Juárez, for example. In contrast, peripheral parts of the metropolitan area have the highest
shares of affordable housing options for lower income residents, even when the high transport costs aretaken into account.
This work confirms that housing plus transport affordability indices have potential to support better
policies in countries outside the United States, including developing countries. In addition it stresses that,

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despite contextual differences, cities across countries share relevant issues. One such issue is the generallack of mass transit investment in peripheral areas and the limited efforts to develop adequate projects tointegrate suburbs into city transport networks. Another is the lack of consideration of affordability,
including transport costs, by finance and construction businesses, as well as by the government agencies
regulating these sectors. Valle de México offers multiple examples of the way these issues have led tocatastrophic results for the population, developers, government and transport concessionaires. Very large
low income housing projects in peripheral areas such as Huehuetoca and Zumpango were developed
without any co-ordination with transport planning. They are far from stations on the main transport
project linking Mexico City and the periphery (the suburban train) and lack good alternatives forcommuting to central areas of the city. The limited transport services have contributed to many
homeowners in these areas abandoning houses on which they had already started making monthly
mortgage payments, causing significant losses for construction companies. In addition, despite its highquality, the suburban rail service is operating at half the projected ridership levels, requiring the operator
to draw heavily on a contingency fund established with public resources. The federal government has
had to provide unexpected resources to support the services operation (OECD, 2015).
Developing housing plus transport accessibility indices faces challenges in terms of data availability.
In the case of Valle de México, the origin-destination survey provides information about rents and isuseful for calculating public transport vehicle miles/kilometres travelled. However, the last such survey
for Valle de México dates from 2007, which significantly undermines the accuracy of results in terms ofreflecting present conditions. Moreover, the lack of information for other urban areas limits thepossibility of developing a national framework allowing comparisons across metropolitan areas. Also,data limitations mean that calculating car ownership and car miles/kilometres travelled is time
consuming in Valle de México, though this was not a major problem for the research presented since theauthors decided to focus their analysis on public transport users, who make up two-thirds of the
population, and most lower income residents do not drive.
Discussions at the roundtable noted, however, that focusing analysis on public transport users could
result in failure to capture conditions for low income captive car users living in places with little or noaccess to public transport. Thus, future analysis including all transport users would be relevant.
Overall, as data differences across countries are high, the methodology for calculating all elements ofhousing plus transport affordability indices, especially vehicle miles/kilometres travelled, needs to beadapted to different contexts. Methodological differences would need to be acknowledged when making
cross-country comparisons.
General conclusions about indicators examined
In addition to the detailed insights about each index presented above, the following general
conclusions were reached during discussions:•

Location-based multi-modal accessibility and housing plus transport affordability indices are
particularly valuable in providing insight into transport-poverty social exclusion acrosspopulation groups and location. They have proven the value they can provide in multiple
contexts if linked to improved policy analysis and guidance. In many cases these indices arecomplementary, since although accessibility indices could theoretically incorporate monetary
cost along with time spent in journeys, having a separate index for affordability can be moreconvenient, especially as accessibility indices become more complex.

•

Authorities and institutions supporting policy makers need to acknowledge that understanding
and solving an issue as complex as transport-related exclusion requires developing acomprehensive set of indicators. Given their proven value for improving analysis, it is

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important to develop and use location-based accessibility and housing plus transport
affordability indices. These must, however, be complemented with indicators that provide
information on other dimensions of transport poverty and social exclusion, such as exposure todangerous, unsafe or unhealthy transport conditions.
•

There is a trade-off between the attributes of simple and comprehensive indices. Simpler
indices are easier to calculate, update and communicate, which makes it more feasible to linkthem to wider evaluation and planning frameworks. But they often leave out relevant
information on transport poverty and exclusion. Also, results of less sophisticatedmethodologies have shortcomings in terms of accuracy that more complex indicators canovercome. Given the pressing need for incorporating indices that can identify and measuretransport-poverty conditions into land use and transport policy evaluation, it is important for
authorities to start working with data that are already available. Even cities with limited data
availability can use very simple indicators at first, while making efforts to collect better dataand progressively improve the indicators they use. Overall, as the case of London shows,authorities may need to combine simpler and more comprehensive indices to strike a balance
between creating more general assessment frameworks that are flexible, easy to update and canbe shared with stakeholders, and having more detailed and complete tools to analyse key issues
and review policy. Continuous efforts to improve data and facilitate their collection are neededin any case to increase indicators’ accuracy and quality.

•

Beyond analysis of their value as measurement tools, finding ways to better incorporate
performance indicators into the assessment process and the framework for investment
allocation is one of the biggest challenges for progress in reducing transport-related exclusion.
While many programmes have started incorporating such indicators, going beyond occasional
use to their systematic inclusion in transport and land use policy frameworks is vital.

Policy issues for deeper examination
Rethinking housing, urban renewal and transit-oriented development policies
Meeting housing demand is a central challenge for cities, especially during phases of highimmigration from rural areas, and in particular for the lowest income residents. Authorities often respondwith a narrow approach, implementing policies that target the housing deficit in isolation rather thanformulating a wider urban development policy to ensure access to opportunities and services. The resultin many cities, particularly in developing countries, has been the location of low income groups inremote districts that are poorly connected to the main city, but where cheap land is available forproviding housing at low cost. Such policies have, to a great extent, shaped urban expansion in many
cities, and the pace at which lower income housing is built in peripheries has accelerated with a shift
from government-led to private sector construction.
Examples of the consequences for residents of these peripheries are highlighted above. Similar
conclusions can be drawn from the example of individuals being evicted from centrally located slums in
Indian cities to social housing in the peripheries. Analysis of the consequences for these residents waspresented in the roundtable and Chapter 5 presents this evaluation in detail.
Pockets of informal settlements, or settlements that are not part of any government plan, are common
to cities in many developing regions, from Asia to Latin America. The analysis in India highlights that ascities grow, the proportion of these settlements becomes higher. The trend reflects the relative
attractiveness of larger cities due to the diversity of employment available for rural migrants, the main
reason for immigration. The presence of slums in Indian cities has been recognised by authorities as a

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problem for decades, partly because of their low quality housing, but also because “clearing” slums isseen as necessary for improvement of central areas. Efforts at slum rehabilitation have been limited;policies of evicting slum residents to formal settlements in the peripheries have become the predominant
strategy.
In Delhi, one major eviction was carried out in the context of developing the metro system. Manyslum residents were relocated to the outskirts, 20-25 kilometres from their original location. Resettlement
offered them the possibility of owning a property at a low cost, and their access to health and education
was maintained or in some cases improved. However, accessibility to employment became significantly
more limited. Before eviction, a high share of this population would walk or bike to most daily activities.
The large distance between their new homes and the activities’ location made this impossible. Thus they
became reliant on public transport, which implied spending a higher share of their income oncommuting. Moreover, the accessibility of public transport in the new locations is very poor, making
their daily commutes quite time consuming. Their average distance to the nearest bus stop increased
from 200 metres to more than 1 km, and frequency of bus services near the new settlements is low.
Eviction in Delhi shows the relevance of better measuring the mobility impact of policy
interventions. It points particularly to the need for incorporating transport-related indicators (travel times
and costs), particularly for reaching jobs, into housing quality definitions used for the evaluation anddesign of social/affordable housing programmes. In several cities (e.g. Berlin, Lille), social housing
providers are increasingly acknowledging this need. Nonetheless, scaling this up to regional and national
level policies is vital.
The subject is linked to a wider discussion on the urgency of housing/urban development andtransport authorities setting common objectives, and again underlines the need to develop indicators,
such as those analysed in the previous section, which can help co-ordinate decisions in the two sectors. In
Rio de Janeiro, the Institute for Transportation and Development Policy (ITDP) has helped authorities inboth sectors to incorporate accessibility indicators into their planning framework, setting the common
goal of increasing the share of the population living within a kilometre of mass transport from 23% to
90% by 2050, and working together, along with civil society organisations, to develop plans making it
possible to reach the target. Insight from Santiago confirms, however, the need to constantly improve theindicators used and to adjust policies in both sectors according to the shared objectives. In this case,adequate accessibility levels, measured in terms of distance to public transport stops, are a condition forplanning permission for residential developments. However, the omission of service frequency has
proved misleading, since many areas have very poor frequency even when there are many bus stops
within the required walking distance. Also, many areas that are being urbanised are currently classified
as rural and thus do not have to meet public transport accessibility requirements.
Developing gap analysis tools was identified as relevant to directing actions across sectors towards
attaining social inclusion goals. These tools are composed of a set of indicators for benchmarking thecharacteristics of an area against those that would describe a community that meets transport needs forthe poor.
Discussions also questioned the slum relocation strategy, stressing that revisiting the possibilities forslum rehabilitation would be a better option. In China, for instance, a few recent programmes have given
residents the option to relocate or stay, and this has been key for the programmes’ success andacceptance. Promoting higher-rise development in the rehabilitated slum areas has also been important inmaking the strategy possible. Further discussion on the situation in India regarding past efforts at slum
rehabilitation brought attention to the importance and challenge of making housing ownership in theseareas possible. Otherwise, people are not willing to invest in upgrades even if offered low interest credit
through social programmes. In addition, the lack of property ownership becomes a barrier to credit
allocation itself.

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The case of Delhi is also a good example of the need for revisiting urban renewal and transit-oriented
development (TOD) in the light of social inclusion. In Delhi, improving central areas and introducing themetro were clearly not linked to considerations of inclusion of lower income residents. As eviction wasseen as part of the renewal strategy for central areas, the approach implied lower income residents
moving out rather than benefitting from the renewal. Moreover, the metro is not a solution for the mostvulnerable: mobility surveys show that it is mostly used by middle-class young professionals, and isunaffordable to lower income residents (even with a subsidy, the metro costs three to four times as muchas bus service). Finally, 24% of the reclaimed land, which could have been used for upgrading the slums,
remains vacant and a further 20% is dedicated to roads and parking, i.e. infrastructure used by arelatively rich minority.
Even where social inclusion is set as an objective of urban renewal projects, achieving it has proved
challenging. In many cases, the projects have been shaped under TOD principles, i.e. organisingredevelopment along mass transit corridors that serve as main transport axes, building high density
development along these corridors and fostering mixed land use. While TOD principles have thepotential to make urban renewal more inclusive, notably by focusing on improving public transport andnon-motorised mobility, the effect of increasing land and property values tends to displace the poor. The
case of the VNEB OA renewal project in London, mentioned in the previous section, illustrates thisproblem. Requirements for affordable housing were included as an indispensable component, but as the
project is located in a central zone of high value (the last available part of the Central Activities Zone),
incorporating high shares of affordable housing while making the necessary investments in infrastructureand securing attractiveness for investors has proved difficult. The painful decision was taken to fund the
public transport capacity that the desired development will need by sacrificing higher shares ofaffordable housing (GLA, 2012).
Thus, while TOD seems necessary for a compact, environment-friendly and more efficient city,
authorities need to be aware of, and address, the challenges to making TOD projects inclusive. With thisaim, the ITDP has refurbished its TOD standard – designed to support authorities in making sure TOD
projects meet a series of minimum requirements – to incorporate indicators that measure project
performance in terms of equity. To meet the new standard, projects have to prove that they a) can
minimise displacement of the poor, b) will include mixed income housing and c) incorporate mixed land
use that includes a significant offer of jobs. While meeting these objectives may be challenging (as seenin the VNEB OA case), the need for explicitly incorporating them into the planning and evaluation of
TOD performance seems unavoidable if authorities and supporting organisations are to find ways tomake projects more inclusive.
Financial schemes that incorporate land value capture mechanisms have demonstrated considerable
potential for solving, or at least reducing, the social inclusion challenge of TOD and urban renewal.
Authorities can use future land value increments to attract private developers and investors, but need toensure that a significant part of the value generated goes to meet policy objectives, including social
inclusion goals. Otherwise these could on the contrary accelerate the process of low-income population
displacement. In the case of London, resources from the Community Infrastructure Levy and tax
increment financing have helped to close the funding gap for the transport infrastructure needed, such as
the Northern Line underground extension. Without these funds, trade-offs between funding public
transport infrastructure and enlarging affordable housing areas would have been even larger and probably
a reduction of both would have resulted.
In the case of Metrovivienda, a programme developed for construction of social housing in
Colombia, these mechanisms were used to increase cost recovery of urbanisation carried out by thegovernment when transforming rural land into sites of affordable housing. This increased the margin for
keeping housing prices affordable for lower income residents. In an early stage, Metrovivienda used landbanking, but in later stages projects have been done by association with local landowners instead of by

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buying plots directly, dropping the land-bank system. Project implementation involves a planning
framework including development of specific plans (planes parciales de escala zonal). The framework
includes land-value capture mechanisms that can be used for development of the areas. First the authority
can acquire parts of the area in exchange for urbanisation services. These zones can be used for urban
amenities and social housing (among other uses) and therefore provide a way to recuperate urbanisation
costs in the form of land. In addition, if the renewal plan for the areas is approved, the municipality canrecover 30-50% of the price increase generated by the change from rural to urbanised land, theauthorisations granted for specific land uses, or increments in the value of development rights in the area
(Maldonado Copello and Smolka, 2003).
Unlike in the VNEB OA case, Metrovivienda projects have been located in more peripheral areas.
However, they also have a TOD component, since providing housing units with good accessibility to
public transport is an explicit objective. Housing location has therefore been planned in co-ordination
with future extensions of the TransMilenio bus rapid transit (BRT) system. The use of land value capture
mechanisms was also recognised as a central component of Chinese redevelopment projects.
The need to improve regulation of speculation in future land prices was acknowledged, as without
this a common result of urban redevelopment and TOD projects is that a high share of housing built
adjacent to public transport and with good amenities is acquired by investors and not necessarily by
individuals who will occupy the properties. This tends to send prices even higher, further reducing thepossibility for lower income groups to stay in the area; lower income residents who are priced out andhave to move to other areas often miss out on the improvements in transport and other facilities. The
problem is magnified when a large number of parking spaces are provided in developments, often due tominimum parking regulations, making these properties attractive to the higher end market, which
includes more car users. In the case of Metrovivienda, a mechanism that allows prices to be frozen at preproject levels was key to prevent speculation, allowing the government to acquire land at low prices in
the land-banking phase. The mechanism continues to be central to the newer arrangement in which
Metrovivienda associates with local landowners instead of buying plots directly, since non-participating
landowners thus cannot ask for compensation at future land values.
Further examination of TOD also highlighted the need to carefully adapt the concept to a range ofcity sizes and characteristics. In India, for example, local governments have a strong focus on metro
development. To a great extent this is because the national government has tended to favour this mode,
for instance through the National Urban Renewal Mission, and offers local authorities financial support
for metro projects. Hence metro has rapidly spread in India, from the biggest cities (Delhi and Mumbai)
to smaller ones such as like Bangalore, Chennai and Jaipur. However, Indian cities, especially small andmedium sized ones, have relatively short trips, high shares of walking and cycling, and high shares of
low-income population. Metro projects are therefore not an adequate solution for these cities, and do notwork as an isolated solution in bigger ones either. Rather, investing much more in adequate bus services,which make up the highest trip share after non-motorised modes even in cities like Delhi (about 20%),
would be a more suitable model for introducing TOD principles into Indian cities. Strong focus should beplaced on increasing pedestrian access to bus stops and enhancing the walking and cycling environment,
since these modes are particularly key to the mobility system. Also, as bus services need to be able to
avoid getting stuck in traffic, there is a case for considering BRT projects.
In addition, city planners need to acknowledge and incorporate many of the patterns of informal
settlements, which have naturally yielded TOD characteristics and are adapted to the wants and needs oflower income residents in particular contexts. While not very high rise, informal settlements in India arequite dense. In addition they offer mixed land use and, most notably, a large offer of jobs (both formal
and informal) for diverse skills, and important shares of public space. The widespread presence of street
vendors and residents occupying public space has resulted in low rates of street crime. Moreover, street
design is adapted to high shares of walking and cycling rather than to short car trips. As noted earlier,

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analysing the advantages of shifting towards higher rise development could be important for strategies
centred on slum rehabilitation and promotion of centrally located social and affordable housing. The
other characteristics and their advantages should be recognised as a version of TOD which is better
tailored to the reality of lower income residents in Indian cities, and used as a model when developing
urban projects. Analysis of urban patterns and social dynamics in informally planned settlements andtheir application in urban planning and design would certainly be useful for other cities, particularly
those in other developing countries.
Making public transport subsidies efficient and financially sustainable
Subsidising public transport services has a clear rationale in the light of social inclusion and equity
considerations. Among other compelling arguments is the notion that providing affordable public
transport is a form of social investment enabling the most vulnerable to increase their human and social
capital through access to jobs, medical care, schools, etc. Other arguments in favour of subsidies are
based on the notion of mobility as a basic human right and thus availability of affordable public transport
as a citizen’s prerogative (Cervero, 2011). The arguments are supported by the experience of cities,
notably in developing countries, where decisions not to subsidise public transport have resulted in thepricing out of lower income residents, isolating them from economic and social activities and/or resulting
in their reliance on insecure and low quality informal or semiformal modes.
However, discussions of the adequacy of implementing public transport subsidies need to be framedin the context of many competing needs in a context of fiscal restrictions faced by governments.
Subsidising public transport may mean not subsidising, or reducing subsidies for, education, health, etc.
Moreover, from a theoretical standpoint, a superior solution would be to offer cash transfers that people
can allocate freely to whatever they feel they need most.
There was consensus that, in practice, the possibility of offering cash transfers is limited byinadequate pricing of private modes and the consequences of granting cash transfers in such a context.
One experience illustrating this is that of Bogotá, where cash allocations were given to formal employees
for the equivalent of 20 public transport trips. Use of these resources by programme beneficiaries
confirmed that mobility needs were high in their priorities. However, the cash transfer was often used as
the first instalment on buying a motorcycle instead of being spent on public transport. The low prices ofmotorcycles in Colombia and many other countries reflect a failure to incorporate negative externalities(pollution, accidents) generated by these vehicles into the pricing framework. The impact of cash transfer
programmes on increasing motorcycle ownership and use, and their related social costs, is an importantreason why authorities in Colombia and elsewhere have shifted affordable transport strategies towards a
focus on subsidies. These are also seen as a way of securing demand for the public transport system,
which is significantly reduced due to under-pricing of private vehicles.
Given the various competing needs and limited resources, public transport subsidy schemes need tofind ways to put as little strain as possible on public finances, prove that they allocate resources
efficiently and show that subsidies are effectively translated into livelihood improvements (which go
beyond travel time savings). Programmes across countries have differed in whether subsidies are granted
to operators or directly to the public; either way, a range of schemes exists. A common challenge,
notably in the case of subsidies granted to operating companies, has been preventing subsidies frombecoming disincentives for operational efficiency rather than contributing to poverty alleviation. In thecase of schemes that grant subsidies to users, targeted subsidies are better suited than generalised ones tostrike a balance between reaching financial sustainability and addressing affordability. Targeting those
who really need the subsidy is not that straightforward, however. In many cases, schemes simply grant
subsidies to easily identifiable groups considered as vulnerable or as often having few resources (e.g. theelderly, students). Due to the frequent mismatch between these categories and lower income groups, such
schemes have resulted in errors of both inclusion and exclusion, losing significant resources on

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subsidising travel by non-poor residents while leaving high shares of the poor without support. Thus, atleast in terms of equity, programmes designed in this way are badly focused.
A targeted subsidy scheme in Bogotá addressed this problem by building on Colombia’s poverty
reduction instrument, SISBEN (explained below), combined with the city’s adoption of smart card
technology. The case study draws attention to the potential for using such tools, both increasingly
available elsewhere, to develop a scheme that can secure high quality and affordable public transport
while ensuring financial sustainability. In addition, it provides insight on issues that could put the successof such programmes at risk and offers lessons on the conditions authorities need to guarantee when
implementing them. Findings are presented below, separated into two main stages: setting tariffs andtargeting beneficiaries. A description of elements of Bogotá’s programme is provided as part of theanalysis. For a more detailed examination of the scheme and its implementation (see Chapter 6).
Setting tariffs
Tariff setting was recognised as a key element for success in reaching financial sustainability. The
following elements of this process were identified as fundamental:
Developing a solid methodology for calculating costs. Obtaining sufficient information on operating
costs so as to determine cost-recovery rates is a key but challenging process, particularly for cities indeveloping countries where, as in Bogotá, the introduction of smart cards and implementation of
integrated transport systems are part of a wider process of bus reform. In such contexts, formalising
services previously provided by semiformal operators entails changes (e.g. paying fair wages, including
safety standards on vehicles, renewing fleets) that in turn entail significant costs. In some cities thesecosts have been thought to be offset by the gains in efficiency that come with shifting towards a systemwith more rational routing, larger buses, etc. However, without solid cost estimation methodologies, it is
impossible to know whether this is so; real costs are often much higher than expected, putting financialpressure on the new systems.
Furthermore, social and political contexts can make it difficult for cities going through bus
reform to move immediately to an open, competitive tendering process for granting route concessions.
Giving preference to companies organised by incumbent operators may be seen as a social necessity sothat companies have enough time to consolidate into professionally run, competitive operators andresidents whose livelihood depended on the sector be integrated into the new system. In addition,
incumbents often have political clout. While this is understandable, it must be acknowledged that thedecision not to implement open competitive tendering reduces the extent to which efficiency gains can be
maximised to offset the costs of formalisation. Thus, especially in cases where targeted subsidy schemes
are part of a process of bus reform, there is a need for detailed scrutiny of the costs of formalisation andlimitations on efficiency gains in bus operations imposed by the social and political context. This isparticularly important as bus operating costs constitute 70-80% of the technical tariff.
A strong point in the Integrated Transport System (Sistema Integrado de Transporte, SITP) in Bogotá
has been the use of bidding for each element: trunk routes, feeder routes and fare collection agent. Thishas provided benchmarking information to calculate the average technical tariff needed to operate thesystem at cost-recovery level (the administrative costs of the public sector entity in charge of planningand managing the system are also incorporated into technical tariff calculations). While the real tariff isdetermined by the mayor, having a solid methodology behind it has been an asset in making the publicaware of the need for tariff adjustments and allowing the mayor to set a price close to the technical tariff.
Sometimes the price has even been set higher than the technical tariff, helping to offset deficits at othertimes. Bidding for routes is only open to companies formed by incumbent operators, limiting efficiency
gains. However, requiring them to compete has been a significant advantage, especially compared to

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experience in cities where payment for bus operations has been set without benchmark information (e.g.
Valle de México).
Another relevant point is the importance of estimating the effects of additional ridership on system
capacity, which could imply a need to enlarge the fleet, entailing additional costs that should be taken
into account. In Bogotá, analysis indicated that induced demand could be dealt with by operating theexisting fleet. Still, this consideration should be borne in mind.
Implementing competitive tendering and setting a suitable contract length. While there may bereasons not to use open, competitive tendering for bus concessions in the early stage in cities going
through bus reform, implementing open and competitive tendering must remain an objective in the caseof privately operated services. Thus, such cities should set clear rules and schedules for shifting into thistype of model in the medium term. Establishing a suitable length for concession contracts is an essentialcondition for ensuring this transition.
Setting an appropriate contract length is also necessary to prevent authorities from getting stuck incontractual arrangements that hinder the financial sustainability of transport systems. Even in cities
where it is decided that open, competitive tendering should be introduced only after more than one
concession period, an adequate contract length is important to make progressive adjustments to theagreed terms. In cities where concessions are already granted via open and competitive tendering,
contract length has shown to be key in incentivising efficiency and continuously adjusting the system.
For example, in London, bus concessions are granted through an open, competitive process with
contracts of five to eight years. In Bogotá, concession periods for TransMilenio (trunk) corridors are 10
to 12 years, since in this case operators need the time to recuperate investments on fleet renewal.
However, the 24-year contracts on other SITP route concessions have importantly hindered authorities’ability to reach financial balance.
Another important lesson for cities going through bus reform is that authorities should find ways toeffectively encourage companies formed by incumbent operators to become efficient during thetransition to open, competitive tendering and meet regulations that guarantee minimum quality and safety
standards. This has been an important role of the bidding process in Bogotá. Authorities could also look
into the introduction of quality incentive contracts similar to those used by TfL (Box 1.3).
Box 1.3. London’s quality incentive contracts for bus services
Concession contracts stipulate a set of bonuses and deductions on bus service remuneration,
depending on over- or under-compliance with the minimum performance standards established by theauthority. Bonuses and deductions are applied to performance on waiting time, driving quality, internal andexternal presentation of vehicles and operated mileage. The contracts also establish an extension threshold,rewarding compliance with specific performance standards with an extension of two years in theconcession.
Source: OECD, based on TfL (2008), London’s Bus Contracting and Tendering Process,www.tfl.gov.uk/cdn/static/cms/documents/uploads/forms/lbsl-tendering-and-contracting.pdf.

Accurately estimating demand. Ridership estimates have been a challenge for Bogotá, as well asother cities in Colombia using similar models. Ridership estimates are at the core of accurate estimationof cost-recovery rates and therefore of finding financial equilibrium in the concessioning process. In
Bogotá, estimates of demand in TransMilenio (trunk) corridors have been fairly accurate, but the other

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SITP routes (feeder lines) have not had the expected demand. In cities including Medellín, Barranquilla,Cali, Bucaramanga and Pereira, overall demand has been significantly underestimated.
The exponential growth of private vehicle use (cars and motorcycles) in these cities has been
identified as a central reason for the lower than expected demand for public transport services. Thisconfirms the need for adequate pricing of the use of such vehicles, since on top of the environmental andhealth consequences of not doing so, underpricing significantly hinders governments’ financial capacity
for high quality and affordable public transport services. In the meantime it is extremely important tounderstand and incorporate these effects into demand estimates.
Identifying non-fare revenue sources, especially to cover major infrastructure investment costs.
Ensuring high density along public transport corridors and implementing adequate parking control are
important for securing public transport ridership, and with this increasing fare box revenue. Nonetheless,authorities need to recognise that fare box revenues are not sufficient to cover the major capital costs thatpublic transport expansion and improvement entail. In the case of Bogotá, fare box revenues have been
expected to pay for bus depots and infrastructure investment, putting a heavy financial burden on the
SITP and underlining the need to find other sources of revenue to pay capital costs. In cases like that of
Mexico City, BRT infrastructure costs have been covered by local government, separating them fromfare box revenues.7 Even so, covering costs for further expansion has been challenging and authorities
would benefit from assigning secure funds to this. In addition, operating costs may also need subsidies,
especially in cities where transport systems are going through significant upgrading/formalisingprocesses and income inequality is high.
It was agreed that revisiting charging levels for private vehicles and introducing additional ones, ifneed be, would serve the double purpose of correcting underpricing while raising funds for investment
in public transport infrastructure and operating subsidies where needed.
Correctly targeting beneficiaries of subsidies
Once an appropriate tariff is set, developing a solid methodology for effectively targeting
beneficiaries is extremely important to ensure value for money by making sure subsidies are effectivelytranslated into life quality improvements for the poor. In this respect the following actions wereacknowledged as necessary:
Building on expertise developed for identifying beneficiaries of poverty alleviation programmes. The
pro-poor targeted subsidy programme in Bogotá was designed using Colombia’s means-tested System
for Selecting Beneficiaries of Social Spending, known as SISBEN. The system is not exclusive to thetransport subsidy scheme, but is designed as a technical and objective mechanism to select beneficiaries
for poverty alleviation programmes across distinct sectors. It is based on a variety of indicators thatmeasure socio-economic conditions from a multi-dimensional perspective, taking health, education andhousing conditions into account, as well as variables measuring vulnerability. Design of a precise
subsidy can be done by selecting specific variables to be used. The result is a score from zero to 100. In
the case of the public transport subsidy, residents having a score of 40 or below are those who canbenefit from the programme.
As data-intensive as SISBEN may seem, Colombia has not been the only country to have developed
this kind of tool. Other countries, including Brazil, Chile, Costa Rica and Mexico, could consider
developing similar schemes, building on their own systems. Nor would it be impossible to introducing
such tools in countries that do not have this kind of system. Argentina, for example, has been targeting
beneficiaries using selection processes from other programmes, which, although lacking thecomprehensive methodology, can still be useful. Using geographical targeting may also be a good option
in the absence of such tools. The fact that smart cards are increasingly being introduced across countries

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makes the model a feasible option for many cities, not only in Latin America but also in countries outsidethat region, such as India.
Taking measures to avoid fraud. Authorities in Bogotá have had to develop measures to avoid abuse
and the creation of a black market for subsidised tickets. A first measure was to personalise the smart
card with the beneficiary’s name and picture. Subsidised cards cannot be validated twice at the same
station within 30 minutes. And subsidised trips are capped at 20 a month, a level corresponding to theobjective of subsidising only commuting trips. Although authorities are studying additional measures, theones described seem to have had reasonably good results. In fact, studies have shown that fraud amongsubsidy beneficiaries is much less than general fraud in the system.
Continuously updating data used for beneficiary selection. The public transport subsidy scheme in
Bogotá has benefited considerably from the fact that SISBEN is revised every three years, so potential
new beneficiaries can be identified. A challenge, though, is that, even when updating the database, it ishard to get people off the subsidy once it has been granted.
Conducting impact analysis for assessing and adjusting program design. Impact evaluation
conducted in Bogotá has provided useful insight on the success of the scheme. Results confirm that thesubsidy has attracted new users to the SITP. In fact, 134 000 of the 160 000 cards with subsidy that havebeen validated went to new users. The evaluation also reveals that there are no effects on beneficiaries’
total transport expenses; instead, people benefit from the subsidy by increasing the number of trips they
make. Thus, the subsidy has helped to overcome the lower frequency of motorised trips made by lowerincome people in Bogotá. On average, each recipient uses the SITP system ten extra times a month.
Also, the subsidy has a significant effect on the hourly income of informal workers (not correlated withan increment of hours worked), although no effect was found in terms of employment status or education
access. The latter can probably be explained by the fact that, since the subsidy is only granted to thelowest segment of the poor, solving constraints other than transport could be crucial for them to shift
from the informal to the formal labour market or to gain access to better education.
The impact evaluation also focuses on understanding individual characteristics related to receiving
the subsidy. Results show that, within the eligible population, the employed, women and those withhigher income and education levels are more likely to get the subsidy. The role of education level
suggests that the process may be too complicated for the least educated people. Finally, word of mouth,
i.e. the fact that word about the existence of the subsidy was spread within communities of lower income
residents, also played a large part in increasing the number of eligible people applying for it.
Follow-up on the programme continues. One evaluation is reassessing the number of trips subsidised
per user as well as the percentage of the subsidy, with the purpose of readjusting both as necessary. The
point was raised that evaluations on the impact of the subsidy on the SISBEN score, as well as on
motorcycle ownership and use, would be very useful.
Identifying appropriate communication and dissemination strategies. Reaching potential
beneficiaries has been challenging, a fact that highlights the relevance of continuous analysis andadjustment of communication and dissemination strategies. At first the TransMilenio transport agency
relied on radio and television. However, it soon became obvious that this was costly and not very
effective. TransMilenio found that a better option was to partner with local health and education subsidy
programmes, leveraging their leaders’ expertise in reaching out to people. In addition, when the
programme began, people had to go to designated sites to apply for the subsidy. After it became evident
that this approach had its limitations, vans were dispatched to regions with concentrations of low income
people.
Adjusting communication and dissemination strategies has allowed the number of beneficiaries togrow significantly, but it is still marginal compared with the share of the poor in Bogotá’s population. It
was pointed out that it may be necessary to move towards a model where potential beneficiaries are

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identified through a tool such as SISBEN and given the subsidy automatically. Experience in Chile, forinstance, shows that individuals often are uncomfortable with asking for subsidies. The question was
raised whether it might be a good idea to disseminate information on the subsidy along with that on
housing rentals, making landlords marketing agents. In Bogotá and many other developing cities,
however, lower income people are often in the informal housing market. Still, further consideration ofthis model could be useful for more developed countries.
One limitation of the subsidy scheme in Bogotá is that it applies only to residents of the city proper.
This means many poor people living in peripheral areas like Soacha, from which almost
1 million residents commute daily to Bogotá, cannot benefit. Extending the subsidy to Greater Bogotá,
therefore, is an important option. Bogotá is looking into setting up a metropolitan transport authority,
which, among other benefits, would facilitate extension of the scheme. Establishing such an agency,
however, is a complex political and institutional process.
The point was also made that while public transport subsidies can certainly address the affordability
dimension of transport poverty, they do not necessarily contribute to solving time poverty, i.e. the factthat low income residents, especially women, face significant restrictions in carrying out necessary
activities due to long travel times (often because they live in remote locations) and low service
frequency. Being able to afford faster modes can help reduce time poverty for many, though improving
land use and housing policies to decrease travel distances is also crucial for solving the problem as awhole.
Finally, discussions emphasised that using targeted subsidies can enable authorities to strike abalance between choosing a tariff model that can lead to efficiency (distance-based fares) and addressing
social issues these may raise. In many cities, flat fares are chosen over distance-based fares because thelatter are seen as unfair to residents who can only afford housing in the peripheries and as penalising thepoor more generally as they tend to travel longer distances. However, flat fares have major shortcomings,
as they are not cost-effective for transport systems and imply subsidising and thus incentivising sprawl.
Applying distance-based rates and providing subsidies targeted for lower income residents can help
resolve the trade-off, allowing a better balance between efficiency, making people and activities become
more sensitive to the distance between a given location and the rest of the city, and equity considerations.
Improving clarity on social issues linked to transport demand management policies
Claims that transport demand management (TDM) policies, particularly congestion pricing, areunfair often underlie public disapproval and a related lack of political will for their implementation.
However, it is seldom clear what people mean by these policies being “fair” or “unfair, making it hard to
establish whether there is truly a valid argument for discarding or rejecting them and thus missing out on
the overall benefits their implementation could bring.
With the aim of improving understanding on this issue, work by the KTH Royal Institute ofTechnology analysed the case of four middle-sized European cities that either apply congestion charging
systems (Stockholm and Gothenburg) or have solid plans to do so (Helsinki and Lyon). The cities all
have a historical city centre surrounded by more recently developed areas.
The analysis, presented as part of the roundtable, takes as its departure point two perspectives fromwhich to examine the fairness of congestion charging and of TDM policies more generally. The first isthat of consumers, which is based on analysis of effects across income groups and relies on traditional
equity analysis. The second is that of citizens, which aims at identifying the extent to which the mainprinciples underlying these policies are aligned with societal preferences. The main findings and debate
generated by them are below, and the full evaluation can be found in Chapter 7.

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The consumer perspective
Analysis based on the consumer perspective revealed that higher income people in all four cities pay(or would pay, in the case of Lyon and Helsinki) more in congestion pricing tolls simply because they
drive more, especially in central areas. Lower income people, on the other hand, pay a higher share of
their income, making the policy regressive. In fact, the differences between the income shares paid acrossincome groups might even be greater if only car owners were considered (there are fewer of them inlower income groups), instead of total population of driving age, as in this study. Acknowledging theregressive distribution of the policies’ impact is of course important in identifying whether particulargroups carry a disproportionate burden and should be compensated. But whether this is to be judged as
unfair depends very much on the main purpose of the policies put into place.
If policies such as congestion charging or parking fees are intended to raise revenues and are foundregressive, the case for their unfairness is a valid one, as with any regressive tax. It must be stressed thatcongestion charging is a very expensive way of raising revenues, and not recommended if that is theintention. This was one of the main conclusions reached during an earlier roundtable on congestion
pricing (ITF, 2010). If, on the other hand, the main purpose is to correct pricing by better reflecting thesocial cost of driving, the case for the policies’ unfairness is rather weak. Judging price-correcting
policies as unfair is equivalent to asserting that low income car users have the right to subsidised car use.
While governments need to ensure affordable transport, it does not need to be by car. It must be notedthat the case of TDM policies aimed at price correction is in fact very similar to that of other corrective
taxes (e.g. on carbon or petrol) that are increasingly being adopted worldwide, and whose distributional
aspects have long been discarded as a reason against using them, at least by most developed countries
and transport experts.
Nevertheless, providing good transport alternatives, particularly for captive car users who would
spend a large share of their income on travel as a result of their implementation, is central to ensuringthat TDM policies are equitable and deliver their full potential. If high shares of drivers have noalternative to paying the toll, congestion levels and externalities such as pollution will simply remain,
and the only change will be the creation of a disproportionate financial burden for part of the population.
A study of Madrid discarded the policy for these reasons, since a large share of residents travel from thesouth to the north of the city and the difference between traveling by car and using public transport
would be more than an hour.
To avoid a similar problem, London accompanied its congestion charging system with significant
upgrades to bus services. In Stockholm the many city-provided alternatives – e.g. a quality public
transport system, park and ride facilities, unpriced bypasses and congestion charging with time-based
differentiated rates – have been identified as key to the success and acceptability of the policy. The caseof Stockholm also highlights the fact that transport patterns are often more flexible than authorities might
expect, thus showing that there are high margins for users’ adaptability to a policy like congestion
pricing. Studies in Stockholm showed that 75% of drivers going through the congestion charging area
were not daily users but rather individuals driving through two to three times a week, and that the biggest
reductions in congestion came from these drivers reducing their travel through the area to one or twotimes a week. The policy had a significant effect in reducing private car trips (by about 30%), half ofwhich shifted to public transport.
Re-examining the findings in the context of cities of developing countries highlights interesting
differences. Such cities have significantly higher shares of the population, and especially of lower
income residents, relying on public transport. This means TDM policies are potentially not regressive indeveloping country cities. In fact, congestion in central areas of such cities, largely caused by drivers but
reducing speeds the most for public transport, is in itself very regressive. Analysis of potential congestion
charging schemes in Beijing and Delhi confirm this, showing that it is not the poor but rather lower

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middle income residents for whom congestion charging would represent a higher cost. Of course, given
the high income inequality in most cities in developing countries, incomes of lower middle income
residents are still likely to be quite low. However, the much more limited transport alternatives,
especially in poorly connected peripheries, as well as the higher concentration of low income residents,
mean that careful analysis is particularly important to identify the extent to which mobility of individuals(whether low or lower middle income) would be hindered as a result of TDM policies. As has alreadybeen noted, such policies’ fairness and success depends on creating alternatives and analysing
compensation for individuals who do not have these alternatives while these are made available.
An additional point raised was that the design of a congestion charging system and the legal
framework to which it is subject could affect its distributional effects. In Sweden, the tax court
determined that congestion charging was part of a car’s operating costs and thus included in the “taxable
benefit value” of a company car. In other words, depending on company policy, company car users either
do not pay the charge or deduct it from their before-tax salary. Data from Gothenburg show that thisexemption significantly reduced average toll payment from the highest income groups, considerablyincreasing the scheme’s regressiveness. This example highlights the importance of carefully analysing
the effects on a policy’s impact distribution of any exemption, whether imposed by the legal system or
proposed as part of its design (e.g. for local residents or professional traffic).
Finally, analysing user attitudes towards congestion charging schemes can improve understanding ofa scheme’s potential support. Surveys in the four cities confirmed that the amount of the existing or
proposed toll is negatively correlated with individuals’ support for the system, with the biggest differencein support between those who do not pay at all and those who pay already something. Beyond this,drivers who value time are more supportive, and being in a city that already has a such scheme is a factor
in higher support (perhaps because some of the system’s benefits become evident). Finally,
unsurprisingly, not having a car is positively related to support for such a policy.
Overall, an important finding is that income in itself is not a factor that is correlated with higher
support of the system. Even in the case of a regressive system, a traditional equity assessment shows
lower income groups seem to consider themselves better off than average (analysing the self-interest
variables affecting their support of the policy).
The citizen perspective
Analysis from a citizen perspective was carried out by identifying three principles inherent in theconcept of congestion pricing and in TDM policies more generally: allocation of resources according towillingness to pay; transfer of resources from individuals to the government, which in turn is linked totrust in the government to spend any revenues wisely; and the value of environmental improvements.
The study derived a model from results of survey questions that separated respondents’ opinion of thethree principles from their opinion of the congestion charging scheme itself.
One interesting finding was that respondents in all cities rated pricing mechanisms for resource
allocation as superior to queuing, having the government decide the allocation, or using a lottery (in thatorder). Respondents in all cities also strongly agreed on the need for environmental protection.
Particularly relevant to the question of fairness was that there were no significant differences acrossincome groups in considering pricing mechanisms for resource allocations as fair, although higher
proportions of richer residents agreed with this. In addition, higher shares of poorer residents agreed that
reducing differences between rich and poor was an important priority for the government. But overall the
results gave scant support to the argument that the three principles were significantly better aligned to theutility of higher income residents.
Comparing perceptions of these principles and attitudes towards congestion charging schemes assuch reveals that the way a scheme is framed can determine its acceptance. In particular, if the scheme is

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seen by as a tax, it will be closely linked to perception of taxes. Thus, where residents think taxes are toohigh already (as is the case in most cities in the study), acceptance of the policy will probably be limited.
A scheme framed as an environmental policy would have a better chance, as residents of all four cities
strongly accepted that principle. Thus, in addition to using TDM policies to correct prices rather than
generate revenue, for the sake of “fairness”, effectively framing them as such and highlighting the
expected benefits (e.g. environmental improvements, reduced congestion) seems to be key to theiracceptance. In Stockholm, the fact that the government did not specify a particular use for the revenue
was perceived as proof that this was not the policy’s main purpose, which contributed to the city’s
success in winning public acceptance for it.
Nevertheless, even in cases where TDM policies’ main purpose is not raising revenue, they are notnecessarily revenue neutral. Citizens’ perspective on the government’s trustworthiness to allocate therevenues is therefore another determinant of support for TDM policies. In other words, while it isimportant to make clear that their main objective is not to raise revenues, it is also useful to communicatethat any revenues raised will be used in a way that makes sense to the population. In general, investmentof the revenues in public transport improvement has been acknowledged as appropriate. In London, forexample, even though legislation specifies that the main use of a congestion charging scheme cannot be
to raise revenues, surveys revealed that raising money for public transport by this means would be widely
accepted.
It should be borne in mind that, if TDM policies are not cost-effective or appropriate instruments forraising revenue, but having high quality transport alternatives is necessary for their success andacceptance, governments must find other financial sources for investing in developing these alternatives.
An interesting case from the United States shows that in a referendum in Los Angeles voters favoured
sales tax increments for making substantial investments in improving the public transport system andfacilities for walking and cycling. The agreed package would allocate 25% of the revenues raised tosupport operating costs of the public transport system. In addition, up to 20% could be reserved forconnecting local walkable roads and thus would be an incentive for redevelopment around public
transport stations. The remaining 55% would be dedicated to the construction of 13 metro lines. Overall,
the package was framed as the way to buy a public transport system in the present by committing future
payments to it (as an individual who buys a house would do). With this model a mobility system thatwould normally take around 30 years to be built would take 10-15 years.
Attempts in the United States to implement TDM policies, notably regarding parking restrictions,have cost several mayors their next election. The case of Los Angeles raises the question of whether
TDM policies would have a better chance if framed as part of a larger package that could correct pricing
while helping to significantly improve alternatives to private vehicles in the short term, both with therelatively low revenues from TDM policies themselves (if these are not revenue neutral) and with otherrevenue sources.

Priorities for future research and discussion
This last section covers subjects participants recognised as relevant to continue moving forward in
reducing transport-related exclusion. It briefly describes the main points raised as essential to consider
when addressing each topic.
Understanding the role, potential and limitations of shared vehicles for tackling social exclusion
As in other sectors, the introduction of new technologies into transport services is seen as promisingfor improving their efficiency. The assumption is that technological applications will allow demandaggregation and make services more productive. But better understanding of the extent to which thisassumption will materialise, especially in a range of contexts, is still needed. For instance, transport

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services in cities in developing and developed countries are going through distinct processes. Amongother things, the former are trying to better rationalise routes, increase wages for drivers, move towards
centrally planned systems and reduce the number of service providers in atomised markets. Developing
country cities also have high shares of low capacity vehicles that, although semiformal, have developed
rather flexible routes. In contrast, cities in developed countries have far lower shares of low capacity
vehicles and are trying to find ways to introduce more flexibility into planning and operation of transport
services, especially to reduce high labour costs. Thus, the ways shared vehicles could help advanceobjectives differ widely in different contexts, which also implies that the main benefits of theirintroduction can also vary significantly across cities and countries, as can their distribution across income
groups. It is therefore necessary to focus new research on better understanding the potential role andlimitations of these services under a range of conditions, and the policies that could best materialise theirbenefits. Impact on public transport ridership and the potential risk of increasing congestion should beanalysed.
Regarding the specific potential of shared vehicles for helping reduce social exclusion, a question yet
to be answered is whether gains in productivity brought by technology would allow provision ofaffordable services for lower income residents, or if subsidies would still be needed for this. Answering
this question will be key, especially in cities with concentrations of lower income residents in low
density developments in the peripheries, which will present the most problems in making a business casefor operators, even with applications that can optimise services.
It will also be important to make sure not only that new models and real life testing of the impact ofshared mobility look at maximising accessibility as an indicator of success, but also that the question ofwhether vulnerable residents would be left out is explicitly addressed, i.e. that what is maximised is“inclusive accessibility”. Analytical tools for understanding whether this is an outcome must be
improved, since even using common equity indicators such as the Gini coefficient could be misleading:the coefficient can improve when accessibility differences across income groups are reduced even if themost vulnerable are not necessarily better off.
Correcting policy biases and market distortions
Developing additional programmes and projects for reducing social exclusion is relevant. However,much of what needs to be done to move ahead with the inclusive transport agenda involves reforming
transport and land use policy and appraisal to remove bias towards automobile travel over more
affordable modes. Revisiting zoning codes should be a priority, especially those with minimum parking
requirements, which make it more difficult to develop affordable housing in in-fill developments.
Restructuring transport project evaluation and funding allocation frameworks is also vital where these arebased on traffic indicators and thus systematically favour road expansion over low-cost transport modes.
Another issue that needs to be addressed is that while public accounts treat public transport subsidies as
explicit, current automobile subsidies are virtually invisible to the public and generate social costs thatare being ignored.
Attracting investment to the reduction of transport-related exclusion
Finding more effective ways of attracting investment to advancing the inclusive transport agenda
should also be a priority. The following strategies were identified as promising and worthy of further
examination:•

Improving and complementing appraisal tools. While cost-benefit analysis can be improved(e.g. by including travel times corrected by user perspectives), the instrument is not well suitedfor capturing changes in the distribution of opportunities spatially. It is also difficult to adjust
weights to give preference to benefits to lower income groups within its framework. Thus, there

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is a need to explore ways to complement cost-benefit analysis to ensure that social inclusion
benefits are valued in the assessment process. The Distributional Impact Appraisal frameworkin the UK, for instance, is designed to accompany cost-benefit analysis and capture the socialand distributional impact of transport interventions. The framework uses eight indicators: user
benefits, noise, air quality, accidents, security, severance, accessibility and affordability. These
are looked at across groups of people with different socio-demographic characteristics (DfT,2015).
•

Calculating and adding co-benefits in other areas to make cases more solid. Investing in
reducing social exclusion often has benefits and/or opportunities for reducing many other socialand financial costs. Health and environmental benefits, for instance, are significant co-benefitsof investing in public and non-motorised infrastructure. Yet explicit calculation andcommunication of co-benefits are rarely linked to efforts encouraging resource allocation toprogrammes and projects that aim at reducing social exclusion.

•

Linking funding sources to transversal programmes. While there is growing consensus about
the need to co-ordinate planning on housing/land use and transport, there is little understandingof the main barriers preventing cities and countries from doing so. A large part of the answer
may be that it has not been possible to assign significant resources to programmes that set
transversal objectives (e.g. poverty reduction, improved accessibility for the most vulnerable),which are shared across sectors. Thus, while in many cases it is clear that current housing andtransport programmes are not yielding optimum results and are even creating undesiredconditions, they are still much more successful in scaling up actions than new initiatives that,
although better designed, remain marginal. Looking into ways to shift financial andinstitutional resources from silo-structured to multi-sector programmes and projects should behigh on the agenda. Capacity building for inside institutions leading the various sectors is key.

Aligning the institutional and assessment frameworks for public funding of transport investment with
policy objectives for sustainable mobility and social inclusion is always central to delivery and often the
Achilles heel of policy implementation.

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Notes

1

The term “functional urban area” refers to the core city plus peripheries that are part of the same
economic unit (OECD, 2012).

2

The maps are developed using weighted averages and based on the population of census blocks for
aggregating results into larger areas.

3

Average time takes into account both walking and in-vehicle travel time in the case of public transport.

4

SAPs group public transport stops in very close proximity (TfL, 2015). The thresholds are 640 metresfor bus and 960 metres for rail, corresponding to walking times of about eight and 12 minutes,
respectively, at a walking speed of 4.8 kph (TfL, 2015).

5

Level 1 corresponds to the lowest accessibility and 6 to the highest. Levels 1 and 6 are each subdivided
into 2 categories (TfL, 2010).

6

Valle de México is the metropolitan area containing Mexico City and municipalities in two other states,
including 59 municipalities in the state of Mexico and one in Hidalgo state (OECD, 2015).

7

This is only the case for Mexico City, not the rest of the Valle de México.

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References
Cervero, R. (2011), “State Roles in Providing Affordable Mass Transport Services for Low-Income
Residents”, International Transport Forum Discussion Papers, No. 2011/17, OECD Publishing,Paris. DOI: http://dx.doi.org/10.1787/5kg9mq4f4627-en
GLA (2015), “The London Plan: The spatial development strategy for London, consolidated withalterations
since2011”,
Greater
LondonAuthority,
London,https://s3-eu-west1.amazonaws.com/file-respository/London+Plan+March+2015+(FALP).pdf.
GLA (2012), “Vauxhall Nine Elms Battersea Opportunity Area Planning Framework”, Greater London
Authority, London.
ITF(2010),
Implementing
Congestion
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Publishing,Paris.

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Kenyon, S., G. Lyons and J. Rafferty (2002), Transport and social exclusion: Investigating the possibilityof promoting inclusion through virtual mobility. Journal of Transport Geography, 10 (3), pp. 207-19,
http://eprints.uwe.ac.uk/8903.
Litman, T. (2011), “Measuring Transportation: Traffic, Mobility and Accessibility”, Victoria Transport
Policy Institute, March, 2011.
Lucas, K., G. Mattioli, E. Verlinghieri and A. Guzmán (forthcoming), Transport poverty and its adverse
social consequences, Proceedings of the Institution of Civil Engineers – Transport.
Maldonado Copello, M. and M. Smolka (2003), Using value capture to benefit the poor: the Usme
project in Colombia, Land Lines, Vol. 15, No. 3, Lincoln Institute of Land Policy.
MTC (n.d.), Bay Area Transit Oriented Affordable Housing Fund, http://mtc.ca.gov/tags-public/bayarea-transit-oriented-affordable-housing-fund, (accessed 5 June 2016).
OECD (2015), OECD Territorial Reviews: Valle de México, Mexico, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264245174-en.
OECD (2012), Redefining Urban: A New Way to Measure Metropolitan Areas, OECD Publishing, Paris,
http://dx.doi.org/10.1787/9789264174108-en.
Peralta Quiroz, T. and R. Somik (2014), “Accessibility Analysis of growth patterns in Buenos Aires,
density, employment, spatial form”, Transport Research Board, Washington, DC.
TfL (2015), Assessing transport connectivity in London, Transport for London, London,
http://content.tfl.gov.uk/connectivity-assessment-guide.pdf
TfL (2010), Measuring Public Transport Accessibility Levels: PTALs – Summary, Transport for London,
https://files.datapress.com/london/dataset/public-transport-accessibility-levels/PTALmethodology.pdf.
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Chapter 2
National issues in the USA in economic development, mobility and income
inequality

Alan E. Pisarski
Alan Pisarski Consultancy, Falls Church Virginia, USA

This assessment of the present context for evolving socio-economic patterns and trends in the
United States is intended to support consideration of prospects for gains in income shares among thelower income population, recognising that the United States is: a very large heterogeneous
population; highly dispersed over a large geographic area; highly technologically developed; andwith relatively high incomes by world standards. All of these factors conduce to the need torecognize the characteristics of individual nations as they move toward improved incomes for their
populations. The key factor for the future in the US will be better utilisation of the underemployed
population to abet the need for skilled workers to support a large aging dependent population,
including better education, and greater mobility providing access to employment and to other socialand economic opportunities. It is hoped that this assessment adds further dimension to the important
challenges addressed here.

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Introduction
Today we live in a challenged and challenging world. In the career of this writer, it is the mostdifficult period we have ever faced in which to make sound forecasts or even to make general
assessments of the future. It is difficult to do transportation forecasting, of course, but more particularly it
is a great challenge to understand the key drivers that impact transportation. Planning and public policy
operates today in a context of serious unknowns. Among these are:•Dramatically changing demographic attributes of the population•

An erratic and unclear economic context•

Highly volatile resource costs for almost all key inputs and uses•

Shifting technologies of transportation and other sectors•

Shifting social patterns, attitudes and values

Moreover, the time constants that influence change have themselves changed. There appears to begreater volatility in almost all patterns. There are many degrees of freedom in almost all aspects of thepresent context – some factors contravening, others reinforcing expected patterns. Building scenarios of
the future has to be multi-dimensional.
Part of this is the product of the very difficult period the world, particularly the developed world, hasbeen through in this century. In America, and elsewhere, sharp declines in economic activity wereregistered in mid-decade with a severely prolonged recovery period reaching ten years, leading many tosee fundamental change rather than simply retarded recovery in the patterns since 2008. We have tocontinually be asking ourselves are the patterns we are seeing the product of a slow return to “traditional
patterns” or is this the harbinger of “a new normal”? Each day seems to confirm that the erratic state of
the world economy is the real driver of so many of these trends. The weak economy explains much andnew far-reaching technology changes add to the drama and the uncertainty.
Seeking then for a tangible and consistent thread of change therefore in this time is fraught withuncertainty. This chapter will seek to differentiate those patterns that are clearest and that establish thecontext, the stage, on which the more uncertain patterns and speculative trends will play out.
•First, demographic trends considered to be the most stable, including projected births; labour
force and employment trends, and geographic distributions, recognizing the speculative natureof trend assessments,•

Consumer spending trends and their implications for transportation and housing and theirlinkage to workers,•

Racial, ethnic and gender patterns will be considered, establishing a firmer base forconsideration of behavioural trends;•

New technologies and their influence will be assessed;•

Finally, some public policy actions will be assessed in the context of these patterns.

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The demographic challenge
The central question in this period is where will the workers come from to replace the aging baby
boomer generation now reaching retirement age, and how will we support that large aging population in
the future? The figure below shows that the great period of expanding numbers of workers added to theeconomy each decade is now behind us. It actually peaked in the eighties and has been in a steadydecline since. Part of the issue in the first decade of the new century could be attributed to poor economic
environment as much as a decline of persons coming of working age.
Figure 2.1. Workers added in USA by decade.
20

18.1

18

18.4

16
12.8

Millions

14

13.2

12
8.6

10
8

6.9

6
4
2
0
1950-1960

1960-1970

1970-1980

1980-1990

1990-2000

2000-2010

Source: US Census Bureau, Commuting in America 2013.

But the future does not promise any brightening in these trends. The following projections of the US
Census Bureau starkly depict the gaps in the work force age group in the coming years.

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Figure 2.2. Persons of working age and dependent ages
Persons of working age and dependent ages

250,000

personsin thousands

200,000
150,000

213 659

199 903working age group declines from 62% to 58% of the population in the 20
year period

100,000

79 23350,00047 830
,0

2015

2020

2025

2030

2035

.Under 18 years

73,635

74,12875,01576,27377,446.18 to 64 years

199,903

203,934

206,400

209,022

213,659.65 years and over

47,83056,441

65,92074,107

79,233

Source: US Census Bureau, Projections 2014

Examining the 18 to 64 working age group in greater detail shows that both the elder work force,those from 45 to 64 in age, and the younger work force, those in the age range of 18 to 24 actually
decline in the ten year period. Whatever growth in working age population occurs is in the mid-level age
group of 25 to 44.
Figure 2.3. Working age group detail
WORKING AGE GROUP POPULATION CHANGE (thousands)
2015
100,000
90,00080,00070,00060,00050,00040,00030,00020,00010,000,0

2020

84 65731 2142025

89 518 93 429

83 86184 03282 23530 55530 73618 to 24 years

25 to 44 years

45 to 64 years

Source: US Census Bureau, Projections 2014

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The challenges and implications are clear.
•

The working age population increases by only 13.7 million and thus falls as a share of
population from 62% in 2015 to 58% by 2035. This means greater numbers of dependents inthe society to be supported by the working age group.

•

There is an explosion in those over 65 adding over 31 million in the period, gaining 64% of the
population growth arriving at over 21% of the persons in the population up from under 15%
today. One effect, that is already happening, is that the over 65 population will have to continue
to work.
− In part because the economy’s weaknesses have forced some to postpone retirement.
− In part because the elder population are healthier than past generations and the nature ofpresent work is amenable to their skills.
− Working at home is a supporting inducement.

•

Only 4 million persons under 18 are added in 20 years, which means very few coming ofworking or driving age.

It is important to recognize that these projections were made as the nation was coming out of therecession and may be projecting the lackluster experience of the early part of the century. However, as an
example the numbers of people coming of working age in the next 10 years are already here. These
projections do include expected immigration to the USA in the period. That is perhaps the number withthe greatest potential for change. The table below summarizes some of the attributes of the new century.
The VMT values had been in decline since 2008 and have only returned to 2008 levels in the past year.
Table 2.1. A new century – with limited growth
USA

2000

2015

Change% change

Population (millions)

281.4

321.4

40

14.2%

Vehicles (millions)

221.4

260.4

39

17.6%

Road System miles* (millions)

3.936

4.177

0.241

6.1%

Lane Miles (millions)*

8.224

8.766

0.542

6.6%

Vehicle Miles of Travel (VMT)(trillions)

2.764

3.148

0.384

13.9%

VMT/ lane mile (thousands)

336

359

23

6.8%

Average Travel Time to Work(minutes)

25.5

26.0

.5

2.0%

Source: US Census Bureau; Federal Highway Administration US DOT.

More of concern, income levels in the United States as of 2014 were still well behind earlier years on
an inflation-adjusted basis. The figure shows that median incomes in 2014 were below 2009 levels andall the previous years of the century. Early indications for 2015 suggest that we are close to a return toprevious levels, but these data say nothing about the income distribution.

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Figure 2.4. Median household income in current and inflation-adjusted dollars
70,00060,000median income

50,00040,00030,00020,00010,000
,0

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

CURRENT $46,32648,201

50,23350,30349,77749,27650,05451,01751,93953,6572014 $

56,16056,59857,35755,31354,92553,507

52,69052,60552,78953,657

Source: US Census Bureau, BLS income statistics

Counties in America
While these reduced levels of worker growth and weak incomes would seem to suggest that travel
demand will weaken, or even decline, with a negative effect on infrastructure requirements, great caremust be taken in assessing these trends. A large part of the question is geographic in nature with stark
contrasts across the nation. The county system in the USA is utile in this assessment.
The sub-unit of government below the state in the United States is the county (some states give themdifferent names). For statistical purposes, the Census Bureau uses counties or county equivalents as oneof the basic units of statistical observation and presentation. One reason for this is that counties arehighly stable and their boundaries rarely change. Beyond that geographic stability, counties aretremendously variable in size, population and economic activity. There are 3 142 counties in the UnitedStates. Some of the counties, particularly in the West, are immense in size. San Bernardino county
California, the largest in the country, is larger than some states, others may be the size of small villages.
The island of Manhattan is a county. Half of the population of the country lives in 146 of the counties.
Metropolitan statistical areas are aggregates of counties and therefore may contain built areas andareas that are still quite rural in character. In a recent study of national population trends1 the stark
variations by county were revealed. Broadly, between 2000 and 2010 about one third of counties lost
population, just over a loss of two million; another third gained much of the national population change,
just over 27 million for the decade; and the last third, all rural counties, were quite stable gaining only
limited population. Therefore, many questions of economic development and sustainable population anddevelopment have very sharp variations and very different starting points in various parts of the country.
Planning and policy must be sensitive to these variations and vary with the trends and changes occurringin each area.

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A sample county
Fairfax County Virginia is a close-in suburb of Washington DC. It is large as counties go with apopulation in excess of one million and a land area of about 400 square miles, (roughly 1 000 square km)
and a population of about 1 000 per square km, typical of US suburbs. It varies from very metropolitan(Tysons Corners midway between Washington DC and Dulles airport, has multiple 20 story buildings
and is a greater job centre than many well-known large cities) and still there is extensive farmland in the
county.
One useful way to define a suburb, which will help us understand Fairfax, is that a suburb is typicallya place with more workers than jobs for them and therefore it must export workers each day. A city thencan be defined as a place with more jobs than workers which must import workers each day.
In 1980, Fairfax County was a classic case of a “bedroom suburb”. There were roughly 70 jobs forevery resident worker. Most of the jobs were the kinds of jobs that follow households – grocery stores,
department stores, car services, schools and medical services. By 2010, 30 years later, the ratio had risen
to 99 jobs for every 100 workers – effectively a job worker ratio of 1. Therefore, if all the582 000 workers filled the 574 000 jobs in the county, only 8 000 workers would need to be exported
each day. This, of course did not happen, only 52% of resident workers remained to work in their homecounty. (This is actually a rather good ratio compared to many others.) The effect was 280 000 workers
were exported each day to other jobs around the region and 272 000 workers were imported to fill theremaining jobs in the county. Therefore, 552 000 workers crossed the county boundaries each day,instead of the idealized notion of 8 000. This is really what commuting and worker access is all about.
So the concept of a balance of jobs and workers, an ideal ratio of one job per worker, answers few of
the questions of connecting jobs and workers. It would be true certainly that the potential rises for someto live near work, to walk or bike to work, but modern economic structures are not necessarily that neat.
It is important to examine why. But it is far more significant to understand that it is the skills mix of thejobs to be filled and those that the resident workers can provide that matters more than just simple
numeric balances of jobs and workers.
•

Perhaps the key reality of work – home relationships in America and other countries, as well, isthat about two-thirds of workers live in a household where there are multiple workers. The
question then, if living near work is a goal, whose job will they live near? It is possible, but not
likely, that they will both, or all, find local work that matches their disparate skills, economic
needs and career objectives.

•

When one person in the household changes jobs (especially among young workers – jobchanges occur every two or three years), will they uproot and move each time, especially when
the prospect of moving again soon is clear? There is a clear expense and mental dislocation
associated with moving – doctors, favourite stores, friends, etc.

•

One of the reasons that large metro areas are attractive to workers, and employers, is the arrayof choices of jobs (or workers), say, within a half-hours travel there. Particularly, recognizing
the growing specialization of skills in job requirements or in workers the catchment areas for
persons with the requisite skills must expand to assure adequate choices of skilled workers.

•

Growing specialization in skills seems to be an assured complement of changing work force
requirements now and into the future. Employers reaching out farther and farther to find theskills they require will be a natural outcome of this trend.

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Moreover, we must recognize that optimizing the commute trip is not necessarily very high on
many household’s preferences. Often a considerable commute distance is a price people
willingly pay to retain a preferred environment for their families – schools, amenities – whichmakes the commute an acceptable trade-off. The basis for changing homes is a complex one
discussed more fully later.
Figure 2.5. Fairfax County Virginia – 1980-2010
700,000

Jobs

Live and Work

Workers
581 976

600,000

Jobs, Workers

527 464
500,000
400,000
300,000
200,000

480 550405 000

506 272408 541
278 064285 000
205 000

574 099

302 425

238 650

100,000
,0

1980

1990

2000

2010*

Source: US Census Bureau, Commuting in America 2013

Finally, it is to be observed that by 2013 our case study area, Fairfax County Virginia, actually hadreached a stage where it had more jobs than workers, a job/worker ratio of 1.04, completing thetransmutation from a bedroom community to a job centre. In that three years from the census of 2010
there was an increase in 30 000 residents who both lived and worked in the county with the percentage
rising to 54% contrasted to 52% in 2010, and still there were now 571 000 workers, both exports andimports, crossing the borders of the county each day.
This is a trend that can be perceived around the country as suburban residence communities gain jobs
and central cities gain residents so that both types of areas are moving closer to job/worker ratios of 1
from opposite directions but still generating extensive travel between areas. The impacts on the lessskilled workers need to be carefully assessed. In some areas as employers move to be near the skilledworkers they need, the effect is to raise costs in neighbourhoods and push the lower income workers
farther away from some job sites. In the end, it may be the lower income workers with the greatercommuting requirements. The dominant commuting pattern today in the USA is from suburb to suburb
rather than focused on the centre creating very mixed patterns of flow.
A key statistic, that presents this trend in its national scope, is that since 1960 the number of workers
working in a county different from their residence county has grown astoundingly, as both housing andjobs have relocated. The number of workers traveling outside their home county quadrupled from
9.4 million in 1960 to 37.5 million in 2010, a shift from 14.5% to 27.4% of all commuters. By 2014, that
number had risen to over 40 million workers. Thus, the Fairfax county Virginia story is being told
repeatedly throughout America.

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Figure 2.6. Percent of workers leaving their home county to work

Percent of Workers Leaving their Home County to Work

50
26.7%
23.9%

Millions of Workers

40
19.2%

35

20.8%

25

34.2

20%

14.5%

15%

20.1

20
14.8

15
10

25%

37.5

27.5

30

30%

10%

Percent of Workers

45

27.4%

9.4
5%

5
0

0%
1960

1970

1980

1990

2000

2010

Source: US Census Bureau, Commuting in America 2013

Of interest is that the share of transit users rises sharply with shifts away from the residence county.
Among the 7.6 million transit users in 2014 only 4.4 million, about 58%, used transit to travel to workwithin their residence county, meaning that 42% of all transit users leave their home county to work, andwhile transit users represent only 5.2% of all commuters they represent almost 8% of workers crossing
county boundaries. Thus, while transit is often seen as the complement to a compact city the lengths oftransit lines often support extensive population dispersion and long distance job connections to workers,
even including inter-metropolitan workflows.

Redistribution of population and workers
Of the 312 million people in the US in the period of study, fewer than 12% moved between 2014 and2015, and of these the great majority simply moved elsewhere within their residence county, generally
for reasons related to housing preferences. Only about 4% of the population moved to a new county,
most often to a different county within the same state. A slightly larger share of movers is unemployed,
and they tend to be willing to move farther presumably in job searches. So worker mobility seems strong
where job search is concerned, particularly among the younger population.
There are three broad categories of why people move: the first is changes in family structure,
marriage or other changes, 31%; changes in job situations, 21%; and changes in the housing itself, 46%.

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Figure 2.7. Why do people move?
Change in marital status

Why Do People Move?

To establish own household
Other family reason2%

New job or job transfer

6%14%

To look for work or lost job

11%

1%

To be closer to work/easiercommuteRetired
Other job related reason

8%
14%
3%

15%

11%

Wanted own home, not rent
Wanted new or better home/apartment
Wanted better neighborhood/less crime
Wanted cheaper housing
Foreclosure/eviction

5%

5%
2%

1%

Other housing reason

2%

other

Source: US Census Bureau, Census table 17-1, Geographic Mobility 2014-2015.

One of the effects of the prolonged recession recovery was that it suppressed moving as persons
could not afford new housing or could not sell their present homes, or perhaps were afraid to leave jobs
for fear of lack of alternatives. It is in the nature of an economic recession that it locks people into theirhomes and jobs if only due to the uncertainty generated by trends. Current data indicate the beginnings ofa return to more normal levels of geographic mobility. One of the great strengths of America has been a
totally open market for jobs so that workers from any state can respond to job opportunities anywhere in
the country. The recent shifts of oil field workers to remote areas such as North Dakota and West Texas
are a good example of that flexibility. There are few language barriers or other social or governmental
barriers to impede worker flows across states and counties. This is a strong positive in national workforce
balances for both skilled and unskilled labour. Among the strongest growth in occupations will be in thehealth related fields, serving, in many cases, the elderly population. Both skilled professionals and lessskilled technicians and support personnel will move to where the elder populations choose to locate.
As can be seen from the chart, about 20-21% of movers give work-related reasons for their move
including job transfers, looking for work or retirements. Overall, only about 5% of movers identify
wanting to improve their commute situation as a reason for moving. It can be assumed, however, thatmany others would be delighted to see their travel to work be less time consuming, less expensive andless stressful, but accept it at the present time as the trade they make between the job and housing theywant. Comparatively, US commute times and congestion issues are relatively benign by world standards.

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Consumer expenditures and the housing/transportation nexus
The Consumer Expenditure Survey, CEX, managed by the US Bureau of Labour Statistics, BLS, isthe source of the national measures of the cost of living index. It carefully records all outlays by“consumer units”2 relatively close in definition to the typical sense of a household. The power of thissurvey is that it details expenditures for a complete array of categories and a comprehensive set of
demographic attributes. The HBS, Household Budget Survey of Eurostat is similar in function. A limited
set of these tables, focused on quintiles of income and geography, will be employed here.
Table 2.2. 2014 Consumer Expenditure Survey Demography
Averagenumber in
consumerunit:

Allconsumer
units

People

2.5

1.7

2.2

2.5

2.8

3.2

.6

.4

.5

.6

.7

.8

.4

.4

.5

.4

.3

.2

Earners

1.3

.5

.8

1.3

1.7

2.1

Vehicles

1.9

.9

1.4

1.9

2.3

2.8

AnnualExpenditures

USD 53 495

USD 23 713

USD 33 546

USD 45 395

USD 60 417

USD 104 363

Childrenunder 18
Adults65 and older

Lowest20
percent

Second20
percent

Third
20
percent

Fourth20
percent

Highest20
percent

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

Table 2.3. 2000 Consumer Expenditure Survey Demography
All

LowestSecondThird

FourthHighestAveragenumber in
consumer
unit:

consumer

20

20

20

20

20

units

percent

percent

percent

percent

percent

People

2.5

1.8

2.3

2.5

2.8

3.2

Childrenunder 18

0.7

0.4

0.6

0.7

0.8

0.9

Adults65 and older

0.3

0.4

0.5

0.3

0.2

0.1

Earners

1.4

0.7

1

1.4

1.7

2.1

Vehicles

1.9

1

1.5

1.9

2.4

2.9

AnnualExpenditures

USD 38 045

USD 17 940

USD 26 550

USD 34 716

USD 46 794

USD 75 102

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

There were about 25 million consumer units per quintile in 2014, and about 16.3 million in 2000.
Rather than examine spending against CU incomes, it is more effective to scale them against quintile
expenditures, particularly because in lower income groups older and younger people spend above theirincomes as a result of other sources of spending such as savings among older persons or family support
in the case of young students.

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Comparing 2000 and 2014
The differences are surprisingly slight between two periods 15 years apart. The key differences are:•People: the two lower income quintiles have grown slightly smaller, others are the same•

Children under 18: in each quintile, except the lowest, there were more children in 2000•

Adults over 65: there are more persons over 65 particularly in the higher income quintiles in2014•

Earners: there are fewer earners in the three lowest quintiles; the number is constant in the top
two•

Vehicles: vehicle ownership was slightly higher in almost all quintiles

To assess income inequality trends the ratios of highest to lowest quintile for each period is asfollows:
Table 2.4. Ratio of highest to lowest quintile elements
2000

2014

People1.8

1.9Children under 18
Adults 65 and
older

2.3

2.0

0.3

0.5

Earners

3.0

4.2

VehiclesAnnualexpenditures

2.9

3.1

4.2

4.4

Source: Calculation by author from US Census Bureau BLS data in tables 2.2 and 2.3 above.

There are significant changes here. The ratio of populations is up slightly; children under 18 shows asharp decline; adults over 65 increased substantially; but most significant is that the earner ratio jumped
sharply, entirely as a function of the decline in workers in the lowest quintile. Both vehicle ratios andspending ratios increased.
In assessing the population in the lowest quintile of income and their expenditures in 2014, there aremany suggestive factors to consider. Some are soundly based in statistics, others more speculative.
Overall, they do not permit a definitive picture to emerge. The first statistic to consider is that the
average expenditures of just under USD 24 000 in the lowest quintile are more than double the reported
mean income for the group of USD 10 750. Note that only 0.5 workers per household are reported,
indicating the presence of either high unemployment, high levels of retired persons, or possibly college
students in the group. With a relatively high share of persons over 65 and with over 60% of respondents
being women, a sense can emerge of a high proportion of this quintile’s population consisting of retirees,
particularly women, whose spending may be distinct from their incomes. At the same time the group
registers 50% college graduates, 39% homeownership and 63% with at least one vehicle, and at 1.8 thehighest ratio of vehicles per worker of all quintiles. It is possible that other members of this cohort can becollege students functioning as consumer units, registering limited incomes but with support fromparents or others. Finally, a third group is suggested by the fact that while African-Americans comprise

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only 13% of the US population they constitute 21% of this quintile. Clearly, the statistical picture of thisquintile transmits a very mixed image.
The trends are important here; both percent home ownership and possession of at least one vehicle
declined from 2000 to 2014: from 43% to 39% for home ownership; and from 66% to 63% with at least
one vehicle. What did rise was college degrees; from 39% in 2000 to 50% in 2014. There also was asignificant rise in the African-American share from 17% to 21%.
Figure 2.8. Spending shares by all consumer units
Spending Shares by All
Consumer
Units

food

6 759

housing

10 599transportation

1 236

apparel and services

2 728
17 7984 290

healthcare
entertainment
education

1 786

9 073

other

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014.

Aside from their incomes there are important quintile attributes revealed in the ranges from lowest to
highest quintile in the 2014 summary descriptive table:•

The number of persons in the CU’s rises with income from 1.7 to 3.2.

•

The number of children doubles from 0.4 to 0.8 per CU.

•

The number of elders halves from 0.4 to 0.2.

•

The number of vehicles owned triples from 0.9 to 2.8.

•

Most significantly, the number of earners per CU more than quadruples from 0.5 to 2.1.

•

The ratio of earners is roughly in proportion to the ratio of expenditures.

These attributes go far in explaining the significant differences in expenditures, where stage in thelife cycle as demonstrated here, may be as pertinent as incomes.

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Figure 2.9. Total spending by quintile

Total spending by quintile
foodhousingtransportation

apparel and services

healthcare

entertainment

education

other

120,000
110,000
100,000
90,00080,00070,00060,00016,78850,00040,00030,00010,844

9,073

8,475

20,000

5,696
3,555

10,000,

all unitslowest 20%

second 20%

third 20%

fourth 20%

highest 20%

Note: Other is largely contributions to insurance, pensions and social security
Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014.

Table 2.5. Spending in major categories by highest and lowest quintiles
2000
Main
Expenditure
Food

LowestQuintile
2,673

HighestQuintile
8,679

2014
Ratio
3.25

LowestQuintile
3,667

HighestQuintile
11,595

Ratio
3.16

Housing

6,509

22,611

3.47

9,643

31,812

3.30

Apparel

844

3,989

4.73

786

3,625

4.61

Transportation 3,212

13,315

4.15

3,555

16,788

4.72

Health

2,864

1.95

1,868

7,219

3.86

Entertainment 837

3,866

4.62

1,108

5,629

5.08

Other

2,395

19,778

8.26

2,909

26,646

9.16

Total

17,94075,102

4.19

23,713

104,363

4.40

1,470

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey, BLS 2000 and 2014

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Overall spending in 2014 by all units is above USD 53 000; the lowest quintile’s spending is at
USD 23 700 and the highest at USD 104 000 for a ratio of highest to lowest of 4.4. Table 2.5 identifies
the highest/lowest ratios for the main expenditure categories in 2014 and, for comparison, for the year
2000 also. The overall expenditure ratio has risen from 4.19, at the turn of the century, to 4.40 in 2014indicating a growing disparity in spending between quintiles. Of greater pertinence is that transportationis the only basic expenditure where the ratio increased between the periods, in all other basics the ratios
declined. The category of “Other” also showed an increase; this category largely consists of savings,
insurance, payments to pensions and social security. We can note that health trends showed a large rise;
this is largely a product of changes in the data collection design regarding health care that shifted somecategories and obtained improved information.
The transportation-housing trade-off
In the housing-transportation expenditure combination there is a real trade-off at work. In the lower
income quintiles, the sum of transportation and housing is above 50%. As incomes increase, the sharesdecline to below 50%. In this trend housing declines as a share of spending while transportation rises
slowly with income and then tapers off in percentage terms at the highest levels. This is reflected as wellin city vs. rural trade-offs and some other factors. Transportation is the only one of the major expenditurecategories where spending shares rise as incomes rise indicating that there is real value in transportationthat consumers pursue greatly as incomes rise. As noted above the decline in transportation spending as ashare of total expenditures does not suggest that transportation spending does not rise. It jumps
dramatically, as the figure above shows, reaching spending levels of almost USD 17 000 in the highest
quintile. Much of this expansion is related to purchases of new vehicles and expanded air travel.
Figure 2.10. Shares of spending for housing plus transportation by income quintile
Shares of spending for housing plus transportation by income quintile
share Housing

share Transportation

share Housing + Transportation

60%
50%

spending shares

40%

55.7%

54.3%

52.7%

50.3%
46.6%

40.7%

37.3%

34.0%

32.4%
30.5%

30%
18.7%

20%
10%

15.0%

17.9%
16.1%

17.0%

0%
Lowest20
percent

Second20
percent

Third
20
percent

Fourth20
percent

Highest20
percent

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

One of the key factors is the own vs. rent trade-off. The figure below shows that rentals decline
sharply with rising incomes while at the same time spending shares for housing also decline. This tradeoff will be addressed further below.

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Figure 2.11. Spending shares to housing and rental patterns
Spending shares to housing and rental patterns
share Housing% rent

70%
60%

61.0%

percent

50%
40%

47.0%
40.7%

37.3%

30%

39.0%
34.0%

32.4%
26.0%

30.5%

20%
14.0%

10%
0%
Lowest20
percent

Second20
percent

Third
20
percent

Fourth20
percent

Highest20
percent

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

Rather than focusing on quintile trade-offs based on incomes, the best mechanism to understand theinherent housing-transportation trade-off is in geographic structure. The figure below shows thattransportation rises as a share of spending in rural areas vs. central cities, even exhibiting greater absolute
spending in rural areas, despite the fact that incomes are considerably lower than centre city incomes. But
the greater spending on transportation is more than compensated for by declines in housing costs with theresult that rural total spending per CU is below 50% whereas central city spending with its much lower
transportation spending cannot overcome higher housing costs so total spending is well above 51%.
Suburban spending is at the average of almost exactly 50%. The percentage differences are modest but
have tended to be stable over time.

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Figure 2.12. Housing plus transport share of consumer expenditures

60%

HOUSING + TRANSPORTATION SHARE OF CONSUMER
EXPENDITURES
51.26%

49.93%

16.96%

15.68%

17.16%

33.27%

35.58%

32.77%

28.98%All

Central City
housing share

Suburbantrans share

Rural

50%
40%

49.28%
20.30%

30%
20%
10%
0%

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

Perhaps the key point here is that home ownership rises as shares of spending declines. Urban
residents, despite spending almost 36% of their expenditures on housing, have home ownership levels of
only 47%, which rises to 68% in suburbs and 79% in rural areas, both with significantly lower shares ofspending.
Thus, the rural resident’s payment of higher shares of expenditure for transportation is compensatedby more than equivalent declines in housing cost shares and with rising home ownership. And despite
their lower incomes, rural residents have an average ownership of 2.4 vehicles contrasted to the centre
city level of 1.4. In rural areas, many of those vehicles may have work functions. Interestingly bothsegments of the population have the same average number of 1.2 workers per CU but central city earners
have incomes after taxes of almost USD 52 000 well above the USD 46 500 in rural areas.
In short then, it may devolve into a life-style question where preferences for renting vs. owning, large
lots versus smaller space, preferences for greater access to walking opportunities vs. longer commutetimes are worked out.

Transportation spending and the worker linkage
One of the keys to understanding transportation spending in the US is through study of workers. The
figure explains the relationships. It shows that single consumers, where the consumer does not work,
spend about USD 3 000 a year for transportation; if that single consumer works it rises to about
USD 5 800. If the consumer unit has multiple members with no workers they spend about USD 7 600 per
year on transportation, about proportional to the single non-worker, on a per person basis. This value hasrisen lately with increases in CU’s with multi-person retirees. But transportation spending increases witheach additional worker. In both the single consumer and multiple consumer units the increase per
additional worker is about USD 2 800 per year reaching household spending of USD 15 500 per year inhouseholds with three or more workers. It is only in the category of three or more earners that vehicles
owned do not exceed the number of workers.

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Figure 2.13. Transportation spending per worker (USD)
Transportation spending per worker (USD)
18,000Each additional worker adds about USD 2 800 per year intransportation spending

16,00015 45814,00012 07012,00010,000

9 283

8,000

7 589

6,000

5 764

4,000

3 030

2,000
,

No earner

One earner

No earner

Single consumers

One earner Two earners

Three ormoreearners

Consumer units of two or more people

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

Figure 2.14. Relationship between earners and vehicles by income decile
Relationship between earners and vehciles by income decile
Earners

3.5

Vehicles3

2.7

number

2.5
1.8

2
1.3

1.5
1

0.8
0.5

1

0.8

1.5
1.2

2.2

2
1.4

1.6

2.9

2.4
2

2.1

1.7

0.9

0.5

0.5
0
Lowest Second
Third
FourthFifth
Sixth Seventh Eighth
Ninth Highest
10
10
10
10
10
10
10
10
10
10
percent percent percent percent percent percent percent percent percent percent

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

A major point here is that at almost every level vehicles exceed the number of workers in households.
The foregoing figure for the complete set of income deciles illustrates that point. The historical workervehicle linkage is changing, largely because of the rapid retirement trends today.

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An important characteristic of the new American demography is that there are already29 million CU’s with no workers in them, 46 million people, largely thru retirement but also job
displacement, indicating sharp declines in transportation spending. The historical national range fortransportation spending as a share of spending is about 18%. Note that in the multiple person CU’s thathome ownership, vehicle ownership and persons over 65 are all higher than those of single person CU’s.
Table 2.6. Consumer Units without workers

Consumer Units (000’s)
persons/CU
persons (000's)
One or more Vehicles
Persons > 65
% female% homeowners
Trans. spending
All spending
Trans. share

Single person
CU

Multi-personCU

15 8801
15 880
65%
0.6
61%
57%
USD 3 030
USD 25 565
11.9%

13 1072.3
30,146
86%
1.3
53%
75%
USD 7 589
USD 43 418
17.5%

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

Figure 2.15. Shares of Consumer Units and population by earners
Shares of consumer units and population by earners
no earner

one earner

two earners

8.22%

three earners
14.78%

30.52%
37.43%
38.44%
32.97%
22.82%% CU

14.81%% Pop

Source: US Bureau of Labor Statistics, Consumer Expenditure Survey BLS 2014

Vehicle ownership patterns and social equity
The figure below depicts the shares of households by the number of vehicles owned. Most notable isthat the trend has been relatively stable since the 1980’s, with only small shifts in shares over the30+ year trend. This can be labelled close to vehicle saturation. Perhaps most significant is thathouseholds without vehicles has reached about a 10%-9% share since 1990. The actual number ofhouseholds without vehicles has varied only slightly in a range of from 11 to 10 million over more than

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a 50-year period, but declining sharply in share as the number of households expanded. Moreover, themajority of those zero vehicle households are households without workers, either retired or young
unemployed. Only 4.5% of workers live in households without vehicles indicating the vast majority ofworkers have access to a vehicle for work travel.
Figure 2.16. Trends in shares of households by vehicle/hh
Trends in shares of household by vehicles/hh60%

Percent

50%
40%
30%
20%
10%
0%

1960

1970

1980

1990

2000

2010

2014

O VEHICLES

21.5%

17.5%

12.9%

11.5%

10.3%

9.1%

9.1%

1 VEHICLE

57.0%

47.7%

35.5%

33.7%

34.2%

33.8%

33.7%

2 VEHICLES

19.1%

29.3%

34.0%

37.4%

38.4%

37.6%

37.3%

3 + VEHICLES

2.5%

5.5%

17.5%

17.4%

17.1%

19.5%

19.9%

Source: US Census Bureau, US decennial Census and American Community Survey 2014

Figure 2.17. Long term trend in households with zero vehicles by race and ethnicity
Long term trend in households with zero vehicles
by race and ethnicity
50

Axis Title

40

43.1

30
20

17.5

10

32.6

30.5

21.8

20

20

19.9

12.9

23.8
17.2

11.5

10.3

12.6
9.1

11.8
9.1

0
1970

1980

1990
All

Black

2000

2010

2014

Hispanic

Source: US Census Bureau, US decennial Census and American Community Survey 2014

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Growing homogeneity in travel behavior
The above figure makes an important contribution to the relevance of a growing homogeneity inmany aspects of travel in the US. The most remarkable attribute of the figure is that African-Americanhousehold vehicle ownership has changed dramatically in the period. In 1970, 43% of black households
had no vehicles; today that level has reached below 20%. A parallel pattern is exhibited by Hispanics;
from just below 22% in 1980 to under 12% today. While this is a broadly positive trend two factors areclear: there is still a distance to go, especially for African-American households to reach comparability inhousehold attributes; and the recession has slowed but not stopped the trend.
Mode choice trends, at least in the work trip, have also become more homogeneous.
Table 2.7. Trends in mode to work by race and ethnicity
Drive alone

2000

2010

Hispanic

60.60%

67.80%

African-American

67%

72%

Total Pop.

75.70%

76.50%

Hispanic

22.70%

16%

African-American

16%

10%

Total Pop.

12.20%

9.70%

Hispanic

8.60%

7.80%

African-American

12%

10.90%

Total Pop.

4.60%

4.90%

Carpool

Transit

Source: US Census Bureau, Commuting in America 2013

The table demonstrates a closing of gaps between the national patterns and those of AfricanAmericans and Hispanics. The African-American population has long been the highest users of transit,
and Hispanics, while also using transit more than average, were most notable as mainstays of carpooling, owing to the nature of work often among the Hispanic population.
It is important, therefore, to see that car-pooling in both Hispanic and African-American populationis shifting sharply toward the national norm. A similar pattern is visible in transit usage. The effect of
this is to increase the shares in both groups of driving alone to work- the percentage changes weredramatic. These data, generated by the Census Bureau, only cover work travel. The new National
Household Travel Survey, last conducted by US DOT in 2009, and now in the field collecting new data
will tell us more about whether these shifting trends extend to other trip purposes as well, when available
in 2017-2018.
In a somewhat parallel fashion, gender differences are becoming more homogeneous as well basedon work travel data. Historically, women’s work travel patterns were significantly different from men’s;
in trip distance, in time of travel and mode choice. Many of these factors were consistent with the genderroles of the time. Fewer drivers’ licenses, less access to vehicles, jobs closer to home, etc. were part ofthe pattern. As women’s work roles became more like men’s so has their travel behavior. The
female/male ratios in most travel were sharply skewed. Today, all differences are closing in just about
every mode of travel. Most surprising is that Driving Alone to work is now a predominantly female
pattern. In 1990, women’s driving alone was in a ratio of 97% of men’s. It has now surpassed men

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at 101%. All other modes similarly have moved in the direction of homogeneity. Carpooling and transit
use ratios have moved closer to 1:1. Previously women walked to work more than men did; now it isless. The only areas where strong differences remain are in long distance commuter rail travel, bicycling,
and motorcycling, but even there the vast differences are closing somewhat.
Table 2.8. Modal shares to work by gender
Male

FemaleF/M ratio

Car, truck, or van

86.07%

86.47%

100.5

Drove alone

76.19%

77.00%

101.1

Carpooled

9.88%

9.47%

95.9

In 2-person carpool

7.51%

7.52%

100.2

In 3-person carpool

1.32%

1.21%

91.9

In 4-or-more-person carpool

1.05%

0.74%

70.3

Public transportation

4.63%

5.29%

114.4

Bus or trolley bus

2.34%

2.95%

126.4

Streetcar or trolley car

0.06%

0.07%

120.1

Subway or elevated

1.62%

1.78%

109.6

Railroad

0.58%

0.47%

80.8

Ferryboat

0.03%

0.02%

72.8

Bicycle

0.75%

0.30%

39.7

Walked

2.85%

2.68%

94.1

Taxicab, motorcycle, other
Worked at home

1.44%
4.26%

0.86%
4.40%

59.4
103.1

Source: US Census Bureau, Commuting in America 2013

None of this is to suggest that some modes are preferable and more women driving alone to work is amore desirable social outcome but it does argue that race, ethnicity and gender differences in travel arediminishing and their explanatory power in understanding travel behavior is less powerful. In the future,
it is clearer that factors such as age, education, occupation, geographic location and incomes will be themain factors of behavior differences.
Where are the zero vehicle households?
There are 10.7 million households in America without a vehicle. However, many of these households
consist of single retirees, the majority of which are women who do not have licenses. There are only 6.5
million workers who live in zero vehicle households. Of these, about one-third, 2.2 million, live in thegreater New York metropolitan area. The remainder are distributed among the top ten metros as shown in
the figure and with a remainder of 40% distributed in smaller metros, small towns and rural areas of thecountry.

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Figure 2.18. Workers in households without vehicles
Workers in households without vehicles

NY34%
ALL OTHER
40%

HOUSTON1%DALLAS
1%ATLANTA
1%PHIL
3%MIAMI SAN FRAN BOSTON
2%
3%
3%

CHI
4%

LA
4%

WASH DC
4%

Source: US Census Bureau ACS 2014

Travel times, mode use and access
It was not so long ago that the majority of American workers reached work in less than 20 minutes.
As of 2014, the following pattern existed:•

Of the 146 million workers in the US in 2014, 6.5 million worked at home, therefore with aremainder of just over 139 million who left home to work.

•

Of those, 19 million were at work in 10 minutes or less;•20 million were at work within 10 to 14 minutes;•

And 22.5 million reached work within 15 to 19 minutes•

In total over 61 million were at work in less than 20 minutes and if those who work at home areincluded, then very close to 68 million were at work in less than 20 minutes, representing 47%of the work population.

•

The trend in average travel times over time are shown in the figure below.

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America has had a very stable period in which travel times were effectively constant, largely due to
9/11 and the more recent recession, which reduced work travel, especially in that job losses were heaviest
in construction and in manufacturing, which frequently involve long distance, carpool trip making.
Between 2000 and 2009, average travel times remained constant at 25.5 minutes. As the job recovery
began after 2008, travel times slowly increased to the level of 26 minutes as of 2014 – that is a gain of ahalf a minute in this century. The two travel time figures represent two different surveys of the Census
Bureau – the decennial conducted each decade and now the American Community Survey, ACS, whichbegan as an annual survey in 2005. Their designs are structured to carry forward constant comparable
measurements.
Figure 2.19. Long term work travel time trend USLong Term Work Travel Time Trend US
26

25.5

25.3

25

MINUTES

24

23.4

23
22

21.7

21
20
19

1980

1990

2000

2010

Source: US Census Bureau, US Decennial Census

Figure 2.20. Recent annual travel time trend US
Recent Annual Travel Time Trends US
26.2

26

26
25.8

25.7

minutes

25.6

25.5

25.4
25.2
25

25.5

25.3
25.1

25

25.8

25.3
25.1

24.8
24.6
24.4

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Source: US Census Bureau, American Community Survey annual series

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The average travel time is of course a function of the modes of transportation used. The followingshows the variation in travel times by mode. Among the keys are that almost 40% of transit users take60 minutes or more to get to work; and over 60% of walkers arrive within15 minutes.
Figure 2.21. Travel time distribution by mode of travel to work
Travel Time Distribution by Mode of Travel to Work
100%
90%
80%
60 or more minutes
70%

45 to 59 minutes

60%

35 to 44 minutes
30 to 34 minutes

50%

25 to 29 minutes
40%

20 to 24 minutes
15 to 19 minutes

30%

10 to 14 minutes

20%

Less than 10 minutes
10%
0%
all

drive alone

carpooltransit

Source: US Census Bureau, American Community Survey 2014

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Figure 2.22. Components of travel time by mode of travel to work
Components of Travel Time by Mode of Travel to Work
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Less than 10 to 14
10 minutes minutes

15 to 19
minutes

drive alone

carpool

20 to 24minutestransit25 to 29
minutes

30 to 34
minuteswalk

35 to 44
minutes

45 to 59 60 or more
minutes minutes

motorcycle, bike, other

Source: US Census Bureau, American Community Survey 2014

As mentioned earlier the role of transit is in the 30 minute and beyond categories. Transit represents
almost a quarter of all trips made at 60 minutes or more. Note also that walking is a significant
component, on the order of 10%, of work trips of less than ten minutes.
Trends in mode use
This discussion has proceeded without a complete reckoning of how people get to work in America.
Just above, it was noted that 6.5 million people worked at home in 2014. The long-term trend isdramatic in nature and important in scope; it has tripled since 1980. The figure below indicates that workat home has grown from 2.2 million in 1980 to almost 6 million in 2010 (and as indicated above added
another 600 000 in the 4 years since.). Rising from a 2.3% share in 1980 to 4.3% in 2010 and holding thesame share in 2014. Of interest is that 3/4ths of those who work at home have multiple vehicles available
for their use.

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Figure 2.23. Long term trends in working at home
10000

5.0%

Workers (000's)

Share of Workers

9000

4.3%

Workers in Thousands

8000

4.5%
4.0%

7000

3.5%

3.3%
6000

5,924

3.0%

5000

2.5%

2.3%

4184

4000

1000

2.0%

3406

3000
2000

3.0%

1.5%

2180

1.0%
0.5%

Working at Home > Transit use in Metros under 5 million

0

0.0%

1980

1990

2000

2010

Source: US Census Bureau, US Decennial Census

To better frame this statistic, the following figure brings together the mode of work travel in the
middle range and in the minor ranges.

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Figure 2.24. Long term trends in the middle and minor modes
7%
6.2%

6%
5.1%

4.9%

5.6%

4.6%

Mode Share

5%

4.3%

4%
3.9%

3.3%

3%
3.0%

2%

2.8%

2.9%

2.3%
TransitWalk onlyWork at home

0.41%

0.38%

Taxi

1%
0.48%

0.53%

0%
1980

1990

2000

2010

Source: US Census Bureau, US Decennial Census

The figure above refers to the middle and minor modes. The middle modes are those in a range of 2%
to 5% of commuting; while the minor modes are those defined as less than 1% of work travel.
For statistical completeness the major modes are shown below. The decline in carpooling, discussed
earlier is shown here losing more than half its share of travel from 1980 to 2010. The Driving Alone
mode after a sharp increase in the eighties has registered modest increases per decade. The overall shareof the auto-based modes then was 86.3% showing some overall growth despite the sharp declines in
carpooling.

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Figure 2.25. Long term trends in the major modes
90%
75.7%

73.2%

76.6%

80%

Mode Share

70%

64.4%

60%
Drive alone

Carpool

50%
40%
30%
19.7%
20%

13.4%

12.2%

9.7%

1990

2000

2010

10%
0%
1980

Source: US Census Bureau, Commuting in America 2013

The advent of new technologies and new approaches to transportation demand
Many new technologies seem to be on the horizon for transportation promising substantial change.
One of the attributes of these potential changes is the time line questions expressed either in years or in
decades. The degrees of freedom and the variations in conceptual approaches vary sharply and makeplanning an almost fruitless exercise.
The one technological change that is here now and generating substantial positive and negative
disruption is the work of Transportation Network Companies, such as Uber and Lyft, which arrange
travel among private providers and users. Their immediate impact, of course, is on the regulated taxicab
industry all over the world. They have found immediate positive response from both users and providers,
but have generated complex and varying governmental responses to their approach to service. It will besome time before the balances between the heavily regulated cab industry and these new providers areresolved, and a more level playing field established. If nothing else, they have demonstrated how fastmoving, nimble technology companies can quickly out-perform government response.
However, their prospective influence goes well beyond the competition with old-line regulated cab
operators. They challenge private vehicle carpooling and transit usage, and even small package delivery
systems. American carpooling has been in decline ever since measurement began in the 1980’s.
Carpooling was responsible for moving almost 20 million workers in 1980 and was down to just above
13 million by 2010. Today in San Francisco the home of the Uber start-up, their carpooling usage
exceeds their replicated-taxicab services. Many in transportation planning, particularly in the UnitedStates, have long seen carpooling and van pooling as the greatest potential resource for responding totransportation work commute issues, recognizing that the great unused resource are all the empty seats ina car driven by a solo driver, but governmental efforts to expand vehicle occupancies have had very
limited success. Today carpooling has fallen below a 10% share of work travel in the US, for many

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complex economic and demographic reasons, some discussed above. The potential for TNC’s toresurrect vehicle pooling using real-time internet tools, as opposed to the more rigid past arrangements
can change commuting patterns dramatically drawing from taxicabs, from personal drivers and fromtransit. The implications of this seem positive but the full implications are unclear. Moreover, added tothe potential vehicle fleet are new rental models that include kiosks for cars and for bicycles such as
car2go, carshare, bike rentals, bikeshare on an hourly or daily basis. Again, given their nascent nature theroles of these semi-modes are unclear.
Given that Uber-like systems have such substantial potential, imagine a world where the Ubervehicle is driver-less – an autonomous vehicle that can provide similar services at far lower costs andprobably lower energy and environmental costs as well. If the concept of a fully autonomous vehicle isconsidered, either individually owned or available on a service to use basis, the implications are massive.
Consider, for the typical metro area the nature of travel demand as in the following broad demand
typology:
Table 2.9. Autonomous vehicle implications by trip purpose at the metro level
Demand typology
Commuting
Other Resident Travel
Tourism
Service Vehicles
Public Vehicles
Urban Goods Movement
Thru Passenger Travel
Thru Freight Travel

Impact
High
High
High
Low
Low
Low
High
High

Areas affected
Alternative use of time, stress reductions
More access for young, old, infirm
Increased opportunities
Demand mostly unaffected
Demand mostly unaffected
Delivery functions unaffected, cost changes
Increased ease
Increased ease and cost changes

These factors focus on the direct demand impacts, other than the much broader general impacts suchas increased safety, increased speed, reduced vehicle weight savings, fuel savings and ownership costs.
All of these factors are likely to induce greater ease and reduced impediments to travel resulting in
more trips, of longer lengths among an expanded user base (those too young or old, or impaired to drive).
However, there are many degrees of freedom in the potential that are open questions which will affect thepotential benefits; among these: whether trips are shared or not; whether vehicles park or move on toother uses; what mode shifts will occur in both passengers and freight; and what are the long term land
use implications.
In the broadest perspective, it should be seen as a positive step forward for a greater potential market,
providing greater access to jobs, workers, social opportunities, services and suppliers. However, there areserious questions that challenge us at this time:•

There are degrees of autonomy. How does the society function in a partly autonomous world?

•

The pace of introduction is estimated anywhere from 5 to 50 years with great transition issues.

•

Who will own the fleets: individual owners; rental agencies; manufacturers; governments?

•

Substantial legal, liability and infrastructure issues are to be resolved.

•

Are all consequences benign environmentally and socially?

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Is the shift from a high capital-low operating cost approach now in use to a low capital-highoperating cost regimen positive or negative for the economically disadvantaged? What are thetrade-offs?

•

The employment impacts could be massive; vehicle drivers such as taxis, delivery vehicles,
over the road trucking are a major occupational category. A driver’s license has been one
credential that has opened job opportunities for many, especially the less educated.

These questions and more are unanswerable, except in a very speculative sense, at this time. Theseissues will play out over the coming decades. They all raise the question as how does planning functionin this environment. How might a city plan for a transit system, or a highway, which will take 20 years tocomplete, in an environment that could obviate the need for such a system before that time?

Concluding thoughts
This limited survey of critical trends now operating in the United States, while not exhaustive does
serve to examine some of the dramatic challenges facing the nation in the years ahead. Other countries
may share some or many of these same challenges.
The primary concern has to be where will the workforce come from to replace the baby-boomer
workforce now moving off stage. They will need to be replaced in both numbers and skills. New skills
will be required as well. Among the concerns are these:•

The dependency ratio will shift from workers supporting children to supporting older
populations•

At the same time a goal for the society must be to retain large numbers of theover 65 population in the work force. Their health and work focus can help make this possible
but the work travel and other mobility questions will be a safety and economic challenge. Many
already are achieving this role by working at home – which is a modern solution to many
challenges. The work at home workforce makes no demands on the society for infrastructure orservices.

•

It is likely that the work world will be highly varied in terms of time and effort. Part time
employment and even full time employment with very varied schedules will challenge
employers, benefit travel congestion and better serve family needs.

•

In order to sustain the kinds of life styles now achieved and to extend it to those with lesser
economic means, as well as to support the growing dependent populations, greater worker
productivity will be essential. New technologies will be a great help, but the critical factor will
be education, particularly serving the lower income population and the immigrant populations.

•

Given the challenges of finding the needed skilled workers, employers will go to where theworkers are or where they want to be. The combined needs of skills and education will makeuniversities natural attractions for growth. Many, if not most, employment occupations will be“foot-loose” in that they can locate almost anywhere which has communications andtransportation support.

•

Only the resource dependent work sites requiring access to minerals or farm products ortransportation hubs will be determined by geographic constraints, others can be amenitiesbased. This will often be the larger metro areas, which as a result will get even larger filling the

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needs of employers and workers for an adequate commuter-shed in scope and scale within an
acceptable range in time, distance and cost.
•Thus, there will be a mix of close-by walkable/bikeable opportunities at the same time long
distance commutes reaching accessible jobs and affordable, desirable housing and communities
will grow.

•New technologies and new organizational arrangements that are information/communications
based will abet and at the same time challenge, traditional transport infrastructure and services.

•

With increasingly autonomous vehicles safety, speed and utility of the road system will beenhanced. In some cases, the autonomous vehicle/transit dyad will prove effective.

•

Ultimate questions of who owns what kinds of vehicles, making private and public options far
more complex and creating new cost structures that promise reduced costs but with manyquestions remaining to be resolved.

•

The transportation labour force itself – vehicle fleet drivers, taxicabs, bus drivers, truck
operators – approaching 10% of the labour force in some countries all will be operating in a
new and challenging environment with job opportunities in serious question.

•Long term infrastructure planning and development will be in question given the uncertainties
and prospects of new and revolutionary transport services and operations.

•

The challenges of planning and policy making in this environment will call for far broader
understanding of the implications of future trends. Projecting the present into the future beyond
the near term will be a hazardous undertaking.

•Will the low-income population be better off or worse off in this new world? The greater speed
and lower prospective costs of accessibility to services and opportunities should be a positive
and unifying force increasing the ability of the disadvantaged to better engage fully in thesociety. That needs to be the goal driving much of the decisions that must be addressed.

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Notes

1

Commuting in America 2013 Brief 4 Population and Worker Dynamics.

2

A CU is similar to a household except in some shared households in which each person is responsible
for their own food, housing, and other expenditures each person is considered a Consumer Unit, e.g.
college students in shared housing. There are 127 million CU’s in the 2014 Consumer Expenditure
Survey, while the Census Bureau sets 2014 households at 117 million.

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References

US Bureau of Labor Statistics (2015), Consumer Expenditure Survey, https://www.bls.gov/cex/(Accessed 6 April 2017).
US Census Bureau (2015), Geographical Mobility: 2014 to 2015,
https://www.census.gov/data/tables/2015/demo/geographic-mobility/cps-2015.html (Accessed 6 April2017).
US Census Bureau (n.d. a), American Community Survey, https://www.census.gov/acs/www/data/datatables-and-tools/index.php (Accessed 6 April 2017).
US Census Bureau (n.d. b) Historical Income Tables: Households,
https://www.census.gov/acs/www/data/data-tables-and-tools/index.php (Accessed 6 April 2017).
US Census Bureau (n.d. c), 2010 Census Data, https://www.census.gov/2010census/data/ (Accessed 6
April 2017).
US National Academy of Sciences (2013), “Commuting in America”, Transportation Research Board,
https://www.census.gov/acs/www/data/data-tables-and-tools/index.php (Accessed 6 April 2017).

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Chapter 3
How equitable is access to opportunities and basic services considering theimpact of the level of service? The case of Santiago, Chile

Ricardo Hurtubia
Pontificia Universidad Católica de Chile, School of Architecture & Department of Transport
Engineering and Logistics, Santiago, Chile
Juan Carlos Muñoz
Pontificia Universidad Católica de Chile, Department of Transport Engineering and Logistics,
Santiago, Chile
Ignacio Tiznado-Aitken
Pontificia Universidad Católica de Chile, Department of Transport Engineering and Logistics,
Santiago, Chile

Cities face the daily challenge of providing people with access to different activities through their
public transport systems. Despite its importance, there is little research on accessibility that focuseson the use of this mode and even less accounting for the impact of level of service (i.e. travel time,
waiting time, reliability, comfort and transfers). Thus, the aim of this chapter is to propose amethodology to determine how access to opportunities and basic services through public transportsystems is distributed in cities, and how the perceived level of service decreases or accentuates theexisting gaps.
Three indicators are calculated for Santiago based on data from public transport operations, smart
card validations and georeferenced information: walking accessibility to public transport stops
considering the quality of urban furniture, safety and environment; connectivity provided by thesystem in each area to the rest of the city considering the level of service through a measure ofgeneralised time (in-vehicle time); and a measure of attractiveness of the destinations, based on
number of trips attracted by purpose. The methodology is applied to a case study in Santiago, ahighly unequal and segregated city.
The results show that the accessibility gap between disadvantaged areas and more wealthyneighborhoods of the city increases if the user's perception of level of service for public transport isconsidered. We show that the three proposed indicators provide different dimensions of accessibility
suggesting how and where to intervene to effectively improve equity. Thus, the indicators could be
used to assist the prioritisation and focus of investment plans, the design process of urban policies ortransport infrastructure and become a key input for planners and decision-makers.

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Introduction
The spatial gap between the activities that people do and their homes generates travel needs andtherefore demand for transport, which in turn influences the land use system and activities distribution.
Hence, while this distance exists, accessibility, understood as how easily you can reach an activity fromone location using a particular transport mode (Dalvi and Martin, 1976), is and will remain as a central
focus of transportation research (Martellato and Nijkamp, 1998).
However, conventional transportation planning has generally focused more on mobility and thephysical journey, related to number of trips, distance and speed. This approach is more concerned aboutmass transport (how to move the most people efficiently) than social transport, which focuses on
people’s accessibility (Betts, 2007). The latter approach is particularly relevant because, in urban
settlements, lack of access to transport can mean a lack of opportunities for work, study, recreation andsocial interaction, which can profoundly impact people’s quality of life and development (Lucas, 2006).
The main objective of cities should be smart growth, with emphasis on equity, non-motorised modes
and public transport (Litman, 2003), especially considering that the majority of population with thegreatest needs is captive of this system by not having a car available. Despite its importance, Martin et al.
(2002) and Mavoa et al. (2012) note that there is little and limited research on public transport
accessibility.
In addition, Lei and Church (2010) show that most studies on this subject are focused only on
proximity to public transport stops (physical accessibility), regardless of quality of urban furniture, urban
environment and quality of public transport services. This last point is vital because, as expressed Lucaset al. (2016), there is an urgent need to consider the quality of service in people’s travel experience inpublic transport and non-motorised modes.
Thus, the main objective of this work is to develop accessibility measures accounting for quality ofservice. We compare traditional accessibility measures – that consider only travel time or an impedance
measure composed by different level of service variables with the same weight - with a new approachbased on disaggregated indicators that also take into account urban environment and level of service
impact in time perception and trip quality. A secondary objective is to use Santiago de Chile as a test
field for the proposed measures, i.e. to study how Santiago’s public transport system meets the need foraccess to opportunities and basic services.
The accessibility indicators proposed here could be used to assist the prioritisation and focus ofinvestment plans, the design process of urban policies or transport infrastructure and become a key input
for planners and decision-makers. Thus, this work attempts to contribute to the equitable development of
the city, achieving the levels of accessibility and connectivity needed for social and economic
development and reducing inequality gaps.
The chapter is organized as follows: the first section provides contextualisation of Santiago in termsof segregation, equity, mobility and access. The second section shows a brief literature review about
accessibility measures and proposes three accessibility indicators: 1) accessibility to public transport
stops taking into account the quality of urban furniture, safety and environment; 2) connectivity provided
by the system in each area considering the level of service (travel time, waiting time, reliability, comfort
and transfers) through a measure of generalized (in-vehicle time) and; 3) a measure of attractiveness ofthe destinations, based on number of trips attracted by purpose. The fourth section shows results for theapplication of these three accessibility indicators to the case study of Santiago. Finally, the last section
analyses and discusses the results in terms of land use and transport planning and equity, ending withsome conclusions and recommendations about project-prioritization and public investment.

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Context: Santiago, Chile
Santiago is the capital of Chile and its largest metropolitan region. It has a population of over
6 million people within an area of approximately 640 km2. This section addresses Santiago’s significant
social segregation, land use and inequality problems. It also provides an overview of its public transportsystem and the evolution of its modal share.
Land use, segregation and inequality
Santiago suffers from a high socioeconomic residential segregation and income inequality. This is
not surprising since Chile has one of the highest Gini coefficient within OECD countries; the income
ratio between the wealthiest and poorest 10% in the country is 26 (OECD, 2015), while 9.2% of the
population still lives below the poverty line (MDS, 2013).
The high-income elite in Santiago lives in its north-eastern area (Rodriguez, 2008). In the last
decades, this area has grown much faster than the rest of the city, attracting productive activities,
commerce and services. As shown in Figure 3.1 it has become an extension of the historic business
district located in the centre of the city. Due to weak planning instruments and a lack of integrated land
use and transport planning, the activity centre has moved chasing higher-income households (and theirpurchase power).
Figure 3.1. Activity centre evolution.

Note: In 1970 it was located in the historical city centre (Santiago) and it has moved towards Providencia and Las Condes. Thisaffects the, much larger, lower-income population by forcing longer trips

This evolution has affected the low-income population, especially those living in the periphery. Until
the 1980s, Santiago had several slums, conveniently located near the centre of the city. As a strategy toeliminate slums, their inhabitants were offered social houses, mostly located in the periphery of Santiago.
The growth and expansion of the city concentrated in these “bedroom communes” that lacked the

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diversity and quality of job and study opportunities offered in their former locations. A large part of the
low-income population still lives in these areas, which require long commutes. Rodriguez (2008) notesthat this pattern of numerous trips from poor communes (usually peripheral) to business centres(downtown and high-income areas) is common in Latin American metropolitan cities
This phenomenon is clearly observable in Santiago forcing a significant portion of the low-income
population to travel long distances. Not surprisingly, Santiago has increased its car ownership over time,
but this increment is not equally distributed: 59% of households do not have access to a car and are still
captive public transport users. Figure 3.2 shows the evolution of cars per household between 1991 and2012, and the large car ownership gap between high-income and low-income households.
Figure 3.2. Car ownership in Santiago

Note: Evolution (left) and current distribution according to income (right). 16% of households have monthly household income
above USD 2 000 while 23% is below USD 1 000
Source: Own elaboration, based on SECTRA (2015).

This urban context has important consequences in accessibility and mobility. Travel time from lowincome communes has increased due to their peripheral location (Sabatini et al., 2001; Rodriguez, 2008)
and due to trips that have grown in length. This impoverishes the accessibility of this group to activities
and urban services. As an important example, take accessibility to employment: the eastern sector of thecity is privileged due to a high concentration of jobs. A clear correlation between income and proximity
between workplaces and homes becomes evident (Figure 3.3). This generates a big impact in terms ofequity and social exclusion (Hidalgo, 2007; Rivera, 2012) and a situation that is hard to overcome just by
improving the transport system.

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Figure 3.3. Income distribution in Santiago (left) and accessibility to employment using public
transport (2012) (right)

Note: The map uses a standard measure, which depends on the number of opportunities and the generalised travel cost
Source: Niehaus et al. (2015).

Since Santiago does not have a metropolitan authority to ensure a coherent development of the city,
the prospect is not promising. Actually, each of the 37 communes has its own mayor, budget andregulations, making land use regulation something than can be easily changed and creating incentives toattract business activities to its niche. Also, this has consequences in transport infrastructure, as public
transport users do not necessarily vote in their destination communes, generating little continuity of thebasic transport structure (such as bike paths or public transport corridors).Additionally, there are loose
requirements for new real estate developments in general and social housing in particular. Table 3.1
describes the set of accessibility conditions a housing units project must fulfil to benefit from social
housing subsidies. These conditions not only ignore access to job opportunities, but also are insensitive
to the quality and capacity of the services being considered (e.g. a school within 1 000 metres becomesuseless for the new inhabitants if it is already at its capacity).
Table 3.1. Conditions for a housing units project to benefit from social housing subsidies
Basic service

Condition

EducationLess than 1 000 meters

HealthLess than 2 500 meters

Public transport

Less than 500 meters

Work, public spaces and other services

No conditions

Source: MINVU (2012).

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Thus, the regulation in Santiago is quite weak in terms of integrating land use and transport needs. As
will be shown in this chapter, this gap affects accessibility to different types of opportunities in different
areas of the city. To provide a context of the transport system in Santiago, the next section describes theevolution of its public transport system and modal split.
Transantiago and modal share evolution
Public transport in Santiago experienced significant changes during the last decade. Until 2007 thebus system consisted in the semi-formal “Micros Amarillas” (Yellow Buses), with atomised bus
property, no fare integration, intense service overlap on the main arteries of the city (meaning morecongestion), high accident rates and being overall perceived as one of the worst city services by the
population (Diaz et al., 2006 ).
In 2007 a new city-wide public transport system was implemented, named Transantiago, which
aimed to improve operator regulation, eliminate competition for passengers on the streets and reduce thenumber of accidents and environmental externalities, among other goals.
However, after its inauguration, the system had very serious problems affecting users directly in thequality of their trips. In addition, a significant financial deficit started to grow. Even though quality ofservice later improved, most citizens in Santiago consider Transantiago a failure (Muñoz and
Gschwender, 2008; Muñoz et al., 2014). An important part of this failure had to do with the overnight
operational start for the whole system (or “big bang” implementation), not leaving enough time to allow
users to adapt to the new smart-card payment and trunk-feeder structure. There were also poorly
designed contracts for the operators, who did not have strong enough incentives to comply with therequired frequencies and headway regularity. Additionally, a significant part of the infrastructure (mainly
BRT-type corridors and exclusive lanes) was not built at the time the system started operating.
This brought important consequences. Between 2001 and 2012, the use of public transport inSantiago decreased (even though public transport share has been declining systematically for decades).
Although some of this drop probably happened before 2007, it is reasonable to associate it toTransantiago not providing the level of service necessary to attract users and to the bad image acquired
during its first months of operation. The share of walking trips decreased while car trips increased by 5%(see Figure 3.4), although it is hard to tell if this is a direct consequence of Transantiago’s
implementation. Cycling increased significantly, mostly in high-income areas where it was almost nonexistent. This increment is probably due to the low service of Transantiago and the increasing congestionin that part of the city where car ownership is the highest.

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Figure 3.4. Modal share evolution in Santiago, Chile

Source: SECTRA (2015).

Nevertheless, not everything brought by the new system was bad. Before Transantiago, Metro (thesubway system) was underutilised, serving mainly those living next to Metro stations or those who could
pay double fares (mainly middle and high-income segments). Currently, Santiago’s Metro network has
103 km, and is used intensively thanks to the integrated fare and a bus system that feeds it, which has
allowed greater coverage (see Figure 3.5). In the following sections, we review accessibility measuresused in the literature and propose ways to translate this coverage into an index accounting for urban
space quality and the level of service of public transport.
Figure 3.5. Santiago’s public transport network

Source: Own elaboration, based on DTPM (2016).

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Accessibility measures
A large number of different measures for accessibility, defined as the potential to reach opportunities
that are spatially dispersed, have been proposed in the literature and used to address important questionsof policy and planning (Páez et al., 2012). According to Geurs and Van Wee (2004), there are four typesof accessibility measures:•

based on infrastructure, where infrastructure level of service is analysed (e.g., congestion andaverage speed in a transport network) (AVV, 2000; DETR, 2000);•

based on location, which describes accessibility to activities from given locations (e.g.,potential, isochrones, adapted-potential) (Ingram, 1971; Van Wee et al., 2001);•

based on the person, taking into account personal differences and time restrictions (based on
Hägerstraand’s space-time geography) (Kwan, 1998; Neutens et al., 2008);•

based on utility, related to economic benefits from accessibility (e.g. balancing factor benefit
and logsum benefit measures) (Martinez, 1995; De Jong et al., 2007).

Most of these accessibility measures have two parts: a transport component depending on distance,
travel time and/or cost, and a component related to activities, which should describe how attractive each
destination is for performing an activity like, for example, the quantity or quality of the opportunities or
facilities to perform the activity (Burns, 1979; Koenig, 1980; Handy and Niemeier, 1997).
We propose using a measure based on location, specifically Potential Accessibility measures, also
known as Gravitational Accessibility, proposed originally by Hansen (1959) and Ingram (1971). These
measurements consider all possible destinations (weighted by their importance) and use an “impedance
function” within the indicator, making the accessibility decay with the distance, cost or travel time.
Despite not being as easy to interpret and communicate as the (much more popular) isochronous
measures, they allow to evaluate the combined effects of transportation and land use, without imposing
an arbitrary threshold for distance or time in which all destinations are equally attractive. Furthermore,this type of accessibility measure is appropriate as a social indicator when used to analyse the level ofaccess for different socioeconomic groups (Geurs and van Wee, 2004).
Despite the abundant research in accessibility measures, there is little literature evaluating
accessibility, mobility and connectivity by public transport considering the perspective of the users(Cheng and Chen, 2015). One of the most relevant elements to consider should be user perceptions
regarding quality of service. Therefore, we propose to build three indicators to analyse access by public
transport on an unbundled basis.
•

Physical Accessibility Index (PAI): Access to public transport stops taking into account a proxyof walking quality (Cheng and Chen, 2015). We analyse the quality of urban furniture, safety
and environment.

•

Public Transport Index (PTI): Connectivity provided by the system based on public transport
zonification of the city accounting for perception of travel time, waiting time, reliability,
comfort and transfers, i.e., different weights for each variable within generalised travel time.

•

Attractiveness Land Use Index (ALUI): Attractiveness of the destinations, based on number oftrips that each area attracts by basic purposes, i.e., work, education, health, recreation andshopping (SECTRA, 2015).

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The methodological details of each indicator are described next, including a discussion on how they
contribute to a better and more complete analysis of city fairness in terms of access to opportunities andbasic services.
Physical Accessibility Index (PAI)
Calculating walking accessibility to public transport stops requires defining various elements:
accessibility functional form, level of disaggregation of the city, walking speed and finally, how tomeasure the travel quality during this stage.
As we pointed out before, this chapter uses measures based on location, specifically based on
potential or gravity. These measurements use the decay of accessibility based on a parameter ofresistance and can overcome the arbitrariness of the isochronous measures where there are no objective
conventions to define neither thresholds nor the ability to assess the opportunity based on theirproximity, since everything located within the isochrones limits is considered equally attractive andaccessible (Geurs and van Wee, 2004). We propose to use the indicator described in equation (1):
=

(1)
∈

Where i is localisation (origin) and j corresponds to public transport stops. Meanwhile, φ is the set of
10 public transport stops closest to i andcorresponds to impedance function, which depends onthe distanceand average walking speed . For simplicity, we use Euclidean distances and aconservative value for the speed of 3.6 km/hr . Since the actual distance that users experience when
walking through the network is greater than the Euclidean distance, a correction factor that varies
between 1.2 and 1.35 based on five values reported by Gonçalves et al. (2014) is applied to .
Most studies use Power, Gaussian, Logistic or Negative Exponential impedance functions (Handy
and Niemeier, 1997). However, the literature generally puts little attention on the functional form and on
how it relates to actual human behaviour. In this work, we use the Richards Function, tested by Martinez
and Viegas (2013), similar to a Logistic function (see equation 2).
1

=

(2)

1+
where B is the growth rate,
V affects near which asymptote growth occurs,
Q depends on the value F(0),M is the time of the maximum growth
Figure 3.6 displays graphs of the Negative Exponential and Richard functions. Martínez and Viegas(2013) argue that the second one represent better people’s perceptions by softening accessibility decay
for low travel times. To calibrate and set the maximum threshold walk to public transport we use
information from origin destination surveys in Santiago (SECTRA, 2015).

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Figure 3.6. Differences between accessibility impedance functions: Richards and Negative
Exponential

Source: Own elaboration, based on Martinez and Viegas (2013).

PAI has its maximum value (1) when travel time is 0-1 minutes because the opportunity or service isadjacent to the origin. Using the proposed method by Mamun et al. (2013), we observe that 95% of
public transport users walk less than 15 minutes to their initial stop, so we define an access value of 0.05
for 15-minute walk. Using the same methodology, we define an access value of 0.2 and 0.7 for 10minute and 5-minute walk respectively. The parameters obtained based on this calibration are shown in
Table 3.2.
Table 3.2. Estimated parameters of Richard impedance function

Parameters
Q = 5,49 E-05
B = -1,09
M = -5,09v = 4,02

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Regarding disaggregation of the city, we use 804 zones that the Metropolitan Public Transport
Directory (the entity that manages and regulates Transantiago) regularly works with. These zones are
small enough to avoid spatial aggregation errors and to allow us to calculate accessibility based onrelatively homogeneous locations.
To obtain a measure of the level of service while walking, we used georeferenced information ofSantiago pre-census (INE, 2011). In this data, each city block is evaluated by a group of trained
surveyors who rate several aspects of public space using a binary scale with 1 meaning the presence ofthe element and 0 meaning lack of the element.
Two particular aspects (sidewalk and street quality) are rated with a scale of 1 to 5, with 1 meaningpoor conditions and 5 meaning excellent conditions. Based on this, and grouping elements according totheir similarity or affinity, four indexes were created, which together form the Environment and Urban
Quality Index (EUQI) described in Table 3.3.
The aggregate EUQI values range between 2 and 19 and are analysed as a complementary indicatorof accessibility. Traditional accessibility indicators only considering walking time between locations andpublic transport stops seems insufficient since it ignores, for example, how safe or comfortable this walkmay be.
Table 3.3. Components of EUQI and his values
Index

Components

Range of values

Security/safety

Luminary, road signs and roofed

0 to 3bus stops
Environment

Gardens, seats, sport fields and

0 to 4playground
Cleanness

Garbage bins and rubble

0 to 2

Infrastructure

Sidewalks and streets quality

2 to 10

Public Transport Index (PTI)
To calculate this component, we use the same functional form from the Physical Accessibility Index(see equation 1), but instead of considering just the walking time from each origin to the closest bus stops
and/or subway stations, we account for the total (perceived) generalised travel time experienced by theuser from the initial station or stop to the final one. Thus, to calculate the connectivity and level ofservice provided by public transport, we incorporate frequency, regularity, travel time, transfers andcomfort experienced on the trip. This requires information about system operation and data from smart
card validations.
To analyse what is the impact of using this type of accessibility measure compared to traditional
indicators (which usually ignore different travel components such as walking and waiting time or do not
distinguish it from in-vehicle travel time), we use a generalised time function to account for different
dimensions of the level of service, disaggregating trip components and transforming them into equivalent

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in-vehicle time (IVT) units (Wardman, 2001). Each component has a different weight based on user’s
perception.
For example, it is not the same to travel for 20 minutes sitting than to do so standing in a context offive passengers per square meter, and it is not equivalent to wait and walk 10 minutes in a 20-minute trip
than to spend the whole 20 minutes travelling in the vehicle. Table 3.4 shows travel components
considered in this study, how we measure them and the IVT equivalence we give them.
Table 3.4. Level of service attributes considered in the accessibility indicator.
Component

MeasureWaiting time

(1 +(for a frequency

anda headway coefficientof variation

Travel time)):

expected waiting time

Equivalency (IVT)

SourceMultiplicator: 1.6 times

Marguier and(average in UK studies)

Ceder, 1984;
Wardman, 2001

including regularity
Unit of measurement is

Chen et al., 2003;

IVT

Pu (2011)

Penalty: 2 to 22 minutes

Currie, 2005;

depending on transfer

Raveau et al.,conditions

2014

Travel time is multiplied

Whelan and: passenger

by a value between 1

Crockett, 2009;

density insideand 2.2, depending on

Tirachini et al.,Metro/bus

crowding level2013; Batarce et: 90th percentile
travel time tointroduce reliability
impacts.

Transfers

Number of transfers

Comfortal., 2015

Reported values show that crowding, unreliability and transfers are highly penalised by users. The
multipliers and penalties from Table 3.4 are used to calculate an equivalent in vehicle time (IVT). We use them to build a functional form for the
between each origin destination pair of stops,
proposed access measure as follows:
=

∙
∈

(3)

∈∅

In which:corresponds to each origin destination pair of public transport stops

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3. HOW EQUITABLE IS ACCESS TO OPPORTUNITIES AND BASIC SERVICES CONSIDERING THE IMPACT OF THE LEVEL OF SERVICE? – 91

is the set of all public transport stops∅ is the set of all bus services, subway services or combination of both:and1 if there is a bus service, subway line or combination l that connects i with j
0 otherwise
corresponds to the impedance function introduced in Equation 2.

Attractiveness Land Use Index (ALUI)
Finally, we want to identify the quantity and quality of opportunities and services that can beaccessed from a given location. For this we use a methodology inspired on London’s case (Cooper et al.,2009), which develops an indicator that measures access to opportunities and essential services (ATOS)
by public transport or walk. For this work, we use the number of trips that attracts each transport area by,
travel purpose (work, education, health, personal visits, recreation and shopping, represented by, , ,divided by the maximum number of trips attracted by a zone to generate a relative
attractiveness measure (Equation 4).
=
=

,

(
,

)

∈

,

∈

(4)

,

This indicator complements the indicators presented before. Furthermore, we use it as a weight in(Equation 3) representing the attractiveness per purpose of each area to visit (Equation 5). If an area has
basic services and valuable opportunities, its accessibility indicator should have more weight, whileaccessibility to a place that has no major attractions, should have no utility for the user.

′ =

∙
∈

∙

(5)

∈∅

Case study
The motivation for this exercise is to show the impact of considering the level of service and walking
quality on accessibility analysis. Traditional accessibility analysis usually considers only physicalaccessibility, or equal weight for on-board, waiting and walking time, and does not consider comfort,
reliability, regularity and number of transfers.
To show the differences in terms of accessibility and level of service, we analysed a trip to a
destination in Santiago’s CBD attracting 17.3% of the trips (SECTRA, 2015), concentrating
employment, study, health and shopping opportunities. The idea is to compare the three proposed
indicators for trips to this destination coming from two communes of different income levels: Las
Condes (USD 3 260 per month per household on average) and San Miguel (USD 1 480 per month per
household). Both trips include an initial walk and involve the Metro, as described in Figure 3.7.

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Figure 3.7. Route from San Miguel (left) and from Las Condes (right) to Santiago Centro throughwalk and Metro (Line 2 and Line 1, respectively)

Source: 2GIS (2016))

PAI results
Santiago offers short walking times to public transport stops for most parts of the city. This isconsistent with the perception of closeness to public transport reported by 85% of the population of the
35 communes analysed in this study (MINVU, 2010). The nearly 12 000 bus stops in Santiago are part ofan extensive network that achieves wide coverage in various sectors, especially in the downtown area.
Figures 3.8 shows PAI values for Santiago and the case study communes of San Miguel, Santiago
and Las Condes. As we can see, better PAI is concentrated on the city centre. Las Condes, one of therichest parts of the city, does not have great coverage of public transport despite concentrating a large
number of service jobs that are usually performed by captive public transport users who live far from thiscommune.

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Figure 3.8. PAI results

Note: Richards function and v = 3.6 km / hr are used. Scale: 0 (35 minutes on average to 10 nearest stops) to 1 (4 minutes on
average to 10 nearest stops). The values are normalized based on the maximum value of access (8.53 of a possible 10 if thelocation had accessibility 1 to 10 stops nearest).

Meanwhile, Figure 3.9 shows EUQI values for Santiago and case study communes. As expected,
better EUQI is concentrated in the eastern sector of the city, corresponding to the high-income area.
Thus, although San Miguel has better accessibility and greater public transport modal share than Las
Condes (27% vs. 18% based on SECTRA (2015)), the latter has a better standard of urban environment(EUQI) access on the walk. One of the main reasons for this gap is the municipal expenditure per
inhabitant per year (USD 562.8 in Las Condes and USD 182.9 in San Miguel) and the financial
independence of each commune.

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Figure 3.9. EUQI results

Note: Map is constructed using information from INE (2011), with a maximum value of 19 (see Table 3.2 for more details). We
observed that north-eastern areas of the city have a better environment and urban quality, follow by city centre (12.05 score on
average, compared to 10.07 from north, south and west sectors).

It is possible to observe that inequality does not only occur between the communes of the case study.
Santiago’s north-west and south-west sectors have low levels of accessibility to public transport stops
and, at the same time, an inferior EUQI. This shows that despite the significant increment in the numberof bus stops in Santiago in recent years, this may not be enough. Focus should be placed not only on
quantity but also on quality of public spaces around public transport stops.
PTI results
To compute this indicator we calculate a generalised time measure. We want this measure to accountnot only for travel time, but also transfers, comfort, reliability and regularity. For example, if we accountonly for in-vehicle travel time, trips to the centre of Santiago look like Figure 3.10.

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Figure 3.10. Total travel times (not including walking, transfers and waiting times) to Santiago
Centro (Morning peak, April 2013)

Note: Red circles with crosses represent starting locations for the case study trips to Santiago Centro (white dot).
Source: DTPM (2013)

To account for comfort we need a measurement of crowding. Available data (Metro S.A., 2015)
describes observed in-vehicle passenger density for trains in each metro line at rush hour (8:15 and
8:30 a.m.). In the case of the trip from Las Condes, the user must travel 14 stations experimenting an
average density of 3.28 pax/m2, meanwhile the trip from San Miguel must travel seven stations on
Line 2, experimenting a density of 5.68 pax/m2. Using crowding multipliers from Tirachini (2015) for
standing conditions on the Metro subway system, on board travel time must be multiplied by 1.55 and1.95 respectively to incorporate user perception.
Regarding waiting time, we used headway information from April 2015 (Metro S.A., 2015). Thus, it
was possible to incorporate the headway regularity on user’s average waiting time. We used a 1.6 factor
from Wardman (2001) to transform waiting time to IVT. In addition, we consider a transfer penalisationfrom Raveau et al. (2014) for Metro subway system equivalent to 10.2 minutes with average conditions.
Figure 3.11 shows door-to-door time for our case study, comparing the Corrected Accessibility
Measure (CAM) with a Traditional Accessibility Measure (TAM) based only on total travel time
(formulation on Equation 3 is not calibrated for the entire city yet). The difference in the traditional
measure between San Miguel and Las Condes is just 3.26 minutes but, if we account for the level ofservice, the difference increases to 22.3 minutes. This suggests that the real differences in accessibility
levels of different locations can be significantly different when the new attributes are considered,negatively affecting lower income areas therefore having an important impact in equity of travel
conditions.

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Figure 3.11. Door-to-door time in our case study
TAM Las Condes

CAM Las Condes

TAM San Miguel

CAM San Miguel

50
45
40
35

Minutes

30
25
20
15
10
5
0

Waiting time

On board time

Transfers

Total

TAM Las Condes

1.62

19

0

20.62

CAM Las Condes

2.59

29.45

0

32.04

TAM San Miguel

2.88

15

0

17.88

CAM San Miguel

4.61

29.25

10.2

44.06

Note: If we consider the level of service perception Corrected Accessibility Measure (CAM), San Miguel increases theiraccess time by 146% regarding Traditional Accessibility Measure (TAM), unlike the 55% from Las Condes. The difference lies
mainly in transfer penalty and trip comfort, which has a bad perception from users and affects San Miguel’s population.

ALUI results
Since this is just one case study, this example does not reveal the full accessibility to work or study
from each starting point, as access to each transport zone is not considered. Since the case study focuseson morning peak hours of a working day, we will focus our analysis on accessibility to work andeducation, because more than 95% of trips made in this period have these purposes (SECTRA, 2015).
Employment and education attractiveness maps are shown on Figure 3.12. It is important to note thatthe maps only considers the number of trips attracted by purpose (SECTRA, 2015) and not the travel
density, i.e. they are not corrected by the surface of each zone. We note that most attractive zones interms of education and work are around the centre-east axis called “Alameda-Providencia”, the mostimportant avenue of Santiago. We observe that some peripheral locations also have high attractiveness towork, mainly due to industrial activity.

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Figure 3.12. Work attractiveness (left) and education attractiveness (right) using a normalised scale

The República Metro station, the final location of our case study, is located on the city centre. The
specific zone that includes this subway station has a 0.264 relative work attractiveness score,
corresponding to the 26th most attractive area between a total of 804 transport zones. Meanwhile, thisarea corresponds to the largest attractor of study trips across the city, so good accessibility to this sector
is crucial to increase educational opportunities given the existence of educational institutes, technical andtraining centres, universities and schools.
If we want to know how diverse this is zone in terms of opportunities, Shannon's equitability index isan option (TIEM, 2016) (between 0 and 1, with 1 being complete evenness). Although the predominant
purposes are work and study with 83.5% of trips, visiting someone and make purchases account for morethan 11.5% of the trips, resulting in a 0.645 index value.
It is clear that San Miguel needs to enhance comfort and transfer conditions during the trip, and walk
quality in order to improve their access level. Meanwhile, Las Condes needs to increase their access topublic transportation stops, because the commune has good public transport level of service and great
walk quality. Therefore, this case study is useful to know what kind of measures apply for each particulararea given their needs, which it is the main advantage of analysing each indicator separately.

Discussion, conclusions and future work
Santiago has developed and grown without strong land use policies during the last four decades. Lackof integrated land use and transport planning has generated urban segregation that has brought obvious
inequalities in terms of access to services. High-income communes have greater proximity to sub-centres
that concentrate activities and opportunities, and can provide better infrastructure and public spaces given
their budget. This, in turn, makes these communes even more attractive for the location of new activities(mostly commerce and services) and high-quality real estate developments, thus creating a vicious spiralof segregation and concentration of opportunities around high-income areas. One of the main problems
with this urban development pattern is the increase in travel time for lower income people, who areusually located in the periphery and are more likely to be captive public transport users.

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There is a clear need to improve this situation in Santiago, although this is not a simple problem totackle. The issue can be approached from two main dimensions: land use and transportation. First, land
use policies should focus on avoiding the excessive concentration of activities in high-income areas andon promoting the development of sub-centres that are well distributed across the territory. To achievethis, a metropolitan authority with a large-scale (and long-term) vision for the city and with attributions
to define zonings and distribute communal budgets is needed. In addition, social housing policies shouldhave stronger (and clearer) requirements in terms of accessibility to basic services and opportunities inorder to improve the inequality gap. This is, however, not easy to define because excessive requirements,that would discourage development of social housing, should also be avoided.
Second, level of service of public transport and a walking quality to public transport stops should be
improved. This chapter addresses the aforementioned problem by proposing new and more detailed
accessibility indicators and exploring their use as tools to inform decision-making and public transport
planning.
Through three proposed indicators, we analyse walking distance to public transport links, the qualityof the urban environment, the connectivity of the transport system, the level of service offered by it andthe attractiveness of land use. The indicators allow us to identify areas that have greater needs and show
where to focus policy efforts and/or infrastructure.
Therefore, the greatest challenge of this work is how to achieve integration and transition to public
policy. It is urgent for urban planners and authorities to take care of the transport inequality problem in
Santiago and to acknowledge that lack of access to opportunities can intensify this inequality, closely
related to social exclusion. In addition to generating robust and rigorous accessibility indicators, theseindexes must be able to be easily communicated to relevant authorities and stakeholders. Only in thisway, planners and decision makers can use it as an input in transport plans, projects or policies.
In most welfare policies, efficiency and equity appear simultaneously as main objectives. However,there is a clear trade-off between these two objectives, therefore triggering lengthy discussions about
how to integrate them in public policy (Le Grand, 1990; Thomopoulos et al., 2009). Murray and Davis(2001) suggest that there is a tendency to focus on issues of economic efficiency in spite of equity. Thistendency is why investment and infrastructure priorities are usually determined using CBA
methodologies rather than other methodologies, able to account for wider impacts. We suggest that in thecase of transport policy and infrastructure projects, CBA is not enough and should be complemented
with indicators of accessibility and equity, such as those proposed in this work.
One way to approach this was proposed by Martens et al. (2012), who suggests that the objective of
the authorities should be to maximise the average individual accessibility of users, setting a minimum
value, sufficient to ensure basic needs. Thus, we should prioritise projects that ensure vertical equity,
defined by Litman (2015) as compensation for other social inequalities from different areas of life.
Accessibility and equity indicators should consider the quality of service of public transport and theenvironment, road network and street furniture. These variables allow us to measure real differences interms of unequal access to opportunities. For this, we should consider these elements through a corrected
measure as proposed, which takes into account the perception of transport users over traditional
accessibility measures.
Future work will extend this case study to the entire city of Santiago, in particular the Public
Transport Indicator, and analyse what kind of measure is advisable to perform first. This is an interestingquestion that needs to be addressed from two perspectives: impact and feasibility. It is important to
identify which variables, if improved, will have the greatest impact and which are easier to implement. In
other words, we propose to identify the actions with the best cost-to-effectiveness ratio. For example,
improving transfer conditions of users could be a simpler measure to implement than reducing in-vehicle

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travel time and, maybe, perceived travel time can be greatly reduced with a relatively small investment inbetter transfer conditions.
Finally, it is important to address the issue of increase in car use, observable across several cities in
Latin America. More and more people leave public transport and become car users, which is worrying
from a sustainability point of view. This brings several challenges: first, investigate how to discourage
car use to reduce negative externalities and, second, analyse the impact of this trend on the affordabilityof housing and transportation.
A low level of service in public transport and poor quality of public spaces may trigger a shift frompublic transport and walking towards the car. Focusing on improving access by public transport anddiscouraging car use should be a priority in public policy if we want to achieve more equitable cities interms of access.

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Chapter 4
Housing plus transportation affordability indices: Uses, opportunities, andchallenges

Erick Guerra
Assistant Professor of City and Regional Planning, University of Pennsylvania, United States
Mariel Kirschen
Master’s Student in City and Regional Planning, University of Pennsylvania, United States
This chapter provides an overview of the Center for Neighborhood Technology’s (CNT) H+T
affordability index and its potential application outside of the United States (US), where it has
played a prominent role in documenting the relationship between housing and transportation and in
influencing local and national housing policies. After describing the index and its policy use, we
detail some of the challenges and opportunities of applying the index in Mexico, apply a modified
H+T index to the Mexico City metropolitan area, and examine the effect of accounting fortransportation costs on maps and measures of housing affordability.
Finally, we conclude with a discussion of some of the opportunities and challenges of applying the
H+T index in other OECD nations. The objective is to develop a better understanding of how an
H+T index or similar tool could lead to improved public policy throughout the OECD.

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Introduction and overview
This technical paper provides an overview of the Center for Neighborhood Technology’s (CNT) H+T
affordability index and its potential application outside of the United States (US). The H+T index
incorporates transportation costs into measures of neighbourhood affordability and maps these
relationships across US metropolitan areas. The central motivation for the index is to encourage more
centralized housing development and discourage urban sprawl. According the index’s supporters, thefailure to account for the higher transportation costs in remote neighbourhoods has led to policies, plans,
and regulations that exacerbate sprawl and locate households far from civic, social, and economic
amenities and opportunities. It also may harm families: After housing, transportation costs absorb thelargest share of American households’ income.
The relationship between transportation and housing costs is well-known and central to theories andmodels of urban form, suburbanization, and housing markets (Alonso 1960, 1964, Muth 1969, Mills
1972, Brueckner 1987). It also features in popular explanations of American real estate markets:
households drive far enough out from an urban centre until they can qualify for a home mortgage. Transit
plays a particularly prominent role in where transit-using households choose to live (Cervero 2007) andmay help explain part of why poor American households tend to concentrate in cities (Glaeser et al.
2008). Put simply, households often make trade-offs between how much they spend on housing and how
much they spend on transportation.
This well-established relationship has led researchers, advocates, and policy makers to argue thatmeasures of neighbourhood affordability ought to incorporate the costs of transportation as well as thecosts of housing (Bogdon and Can 1997, Belsky et al. 2005, Jewkes and Delgadillo 2010). It has also
resulted in a number of policies to try to encourage more generous mortgages and the construction of
more affordable housing construction in areas with good transit access and lower average transportation
costs. The CNT’s H+T index has played a prominent role in documenting the relationship between
housing and transportation and in influencing local and national housing policies.
Throughout the remainder of this chapter, we describe the H+T index, its policy uses, and its
critiques in greater detail. We then describe some of the challenges and opportunities of applying theindex in Mexico, apply a modified H+T index to the Mexico City metropolitan area, and examine theeffect of accounting for transportation costs on maps and measures of housing affordability. Finally, we
conclude with a discussion of some of the opportunities and challenges of applying the H+T index inother OECD nations. The objective is to develop a better understanding of how an H+T index or similar
tool could lead to improved public policy throughout the OECD.

The housing plus transportation index
The H+T index provides an estimate of the typical cost of housing and transportation in different
neighbourhoods and compares this estimate to a household or typical household’s income. The CNT
deems a neighbourhood affordable if a given household would spend 45% or less of its income on
housing and transportation costs. This number accounts for an existing rule of thumb that households
should spend 30% or less of their income on housing and adds another 15% for transportation costs.
(According to the national consumer expenditure survey, American households spend an average of 18%
of their income on transportation.)+=+

On its website, the CNT provides an interface to map neighbourhood affordability for a typicalhousehold earning the metropolitan median income in 917 US metropolitan and micropolitan areas.
Figure 4.1 shows the results of the online map for the Philadelphia region. Areas shaded in dark blues are

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increasingly less affordable, while lighter greens are increasingly more affordable. Accounting fortransportation costs (the map on the right) instead of just housing costs (the map on the left), suburbanneighbourhoods become relatively less affordable to a median-income household. However, the overallgeography of affordability does not change much. Other regions, such as St. Louis, experience notablechanges in the geography of affordability when accounting for transportation costs. Specifically,
suburban neighbourhoods become substantially less affordable.
Figure 4.1. CNT’s default online affordability maps for Philadelphia and surrounding counties

Source: Center for Neighborhood Technology’s Affordability Comparison Maps (http://htaindex.cnt.org/compare-affordability/)

Estimating transportation costs
The primary contribution of the CNT’s affordability index is the estimation of typical transportation
costs in different neighbourhoods. Building on earlier work from a 1994 Natural Resources Defense
Council study (Holtzclaw et al. 2002), the CNT estimates models of car ownership, vehicle miles
travelled (VMT), and transit expenditures using multivariate ordinary least squares regression. Table 4.1
lists the three independent variables and the predictor variables to estimate each one. Appendix A
provides additional details on the 13 publicly available data sources CNT uses to estimate travel
expenses for the H+T affordability index. The national Census and American Community Survey
provide the data on median household income and median housing prices and rents by Block Group (600
to 3 000 people) and Census Tract (1 200 to 8 000 people).

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Table 4.1. Data and sources used to predict car ownership, car use, and transit use
Prediction
Auto
Ownership

Source2013 ACS

Predictors
Fraction of single family detached housing
Commuters per household
Transit connectivity index
Median household income
Gross household density
Employment Mix
Household Size
Regional Household Intensity
Block Density
Employment Gravity

Auto Use(VMT)

Odometer readings in IL from2010-2012

Fraction of Single family detached housing
Average Available Transit Trips per Week
Commuters/Household
Gross Household Density
Regional Household Intensity
Transit connectivity index
Median household income
Average Household Size
Employment Access Index
Transit Access Shed

Transit Use

2013 ACS(% public transportation
commuters)

Regional Household Intensity
Transit Connectivity
Employment Access Index
Employment Mix Index
Fraction of single family detached housing
Transit Access Shed
Transit Access Shed Jobs
Median Household Income
Average Available Transit Trips per Week
Average Household Size

Source: Center for Neighborhood Technology’s 2015 H+T Methodology Document (accessed 1/25/2016)

The tool then applies the results of the model to estimate typical car ownership, car use, and transit
use within and across neighbourhoods and regions using publicly available data. The predictive model

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uses regional rather than local socioeconomic data to estimate local VMT, car ownership, and transit use
to estimate at the likely expenses for a typical regional household rather than a typical local household.
Finally, the CNT multiplies these predicted figures by the expected costs of car ownership, car use, andtransit use to arrive at the final estimate of transportation costs:
ℎ

=

∗

( ) +

∗

( ) +

∗

( )

where:C is the cost factor (i.e. dollars per mile)
F is a function of the independent variablesuse).

is auto ownership,

is auto use,is transit

Car ownership and vehicle travel costs
The CNT estimates the cost of car ownership and VMT using 2013 inflation-adjusted data from the2005 – 2010 Consumer Expenditure Survey. Because wealthier households tend to own more expensive
cars with different operating costs than poorer households, the CNT takes the average costs per vehicle
for five income groups (those earning USD 0-20 000; 20 000-40 000; USD 40 000-60 000; 60 000 –100 000; and 100 000 and above). For the affordability index, it applies the costs of vehicle ownership to
one of the five groups, depending on the regional median income. For example, households in greater
Philadelphia had a 2013 median income of USD 49 538, so the index applies the average cost per vehicleof households earning between USD 40 000 and 60 000 from the Consumer Expenditure Survey. To
estimate costs per vehicle mile of travel, the CNT matches total transportation expenditures by the fiveincome groups to estimates of vehicle travel for those groups. Unlike the model of car ownership andtransit use, which use nationally available date, the model of vehicle travel relies on odometer readings
from Illinois drivers in the Chicago and St. Louis metropolitan areas from 2010 to 2012.
Transit costs
To estimate transit costs, the CNT matches metropolitan areas to data from the 2013 National Transit
Database (NTD), which includes estimates of total miles of passenger travel (PMT), unlinked passenger
trips (UPT), and total fare revenues. Because the geographies of the two datasets do not match, the CNT
also estimates the proportion of fare revenues that come from each county within a transit agencies
jurisdiction, based on the proportion of total transit stations that fall within a county from publicly
available General Transit Feed Specification (GTFS) feeds. County-level fare revenues are then applied
to block groups based on the proportion of total county commute trips (taken from the ACS) thatoriginated from each block group. In counties that do not have GTFS feeds, the CNT uses national
averages. Finally, the CNT estimates average transit expenditures as the total amount of fare revenues
assigned to a block group divided by the number of households in that block group.
H+T and public policy
Since its inception, the H+T index’s emphasis has been to encourage more housing development inareas that have higher land prices but lower transportation costs. Box 4.1 summarizes some of theongoing uses of the H+T that the CNT describes on its website. These range from policies designed toincrease the availability of public housing around transit to tools to help analyse where to invest in newtransit. Of particular note, the national Department of Housing and Urban Development and Departmentof Transportation worked with the CNT to create an online location affordability index andtransportation cost calculator as part of a larger national Sustainable Communities Initiative (USDepartment of Housing and Urban Development 2016). These tools are designed to help policy makers
and individual households make informed decisions about where to encourage development, where to

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live, and how much transportation is likely to cost in a given neighbourhood or home. The Sustainable
Communities Initiative also provides grants to cities and regions in order to encourage transit-oriented
development, affordable housing construction, and sustainable development.
Box 4.1. Sample of users of the CNT’s H+T affordability index (CNT 2015)
Planners•

Chicago - Metropolitan Panning Council (MPC) used H+T index data in a “corridor selection analysis”
to determine potential BRT locations•

Chicago Metropolitan Agency for Planning (CMAP) used suggested H+T index standard as theirlivability measure in their GO TO 2040 comprehensive regional plan.

•

Ohio –Living Cities sponsored the CNT and the Ohio Governor’s office to utilize the tool for suggestions
for state urban revitalization strategies to reduce cost of living in Cincinnati, Cleveland, and Columbus.

•

Washington, DC – Office of Planning worked with CNT on a custom H+T index that integrated marketrate housing costs and local land-use and transit network data.

Housing professionals•

Minneapolis-St.Paul; Washington, DC; Boston; San Francisco Bay Area – Partnered with the Urban
Land Institute (ULI), CNT developed customized calculators that could both compare neighbourhood
costs and direct transportation choices.

•

Santa Fe, NM – Local housing non-profit uses a tailored Index platform to inform prospective
homeowners about location efficiency and how to manage transportation costs in order to save forhomeownership.

•

San Francisco, CA – The Metropolitan Transportation Commission (MTC) gave credit to the Index forthe establishment of the Bay Area Transit Oriented Affordable Housing Fund.

•Center for Housing Policy – Research with CNT concerning struggles of moderate-income households totackle hidden factors that threaten affordability of housing and transportation.

Policy makers•

Department of Housing and Urban Development (HUD) - Sustainable Communities Initiative grants to
support sustainable development projects.

•

State of Illinois – The 45% affordability measure adopted into law with bipartisan support to be used by
five government agencies for both financing and siting decisions.

•

El Paso, TX – City Council adopted 50% H+T affordability standard for City funding and policy
decisions.

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Affordability rankings
The H+T index can also help paint a substantially different picture of which parts of the country are
most affordable or expensive. These types of lists feature frequently in news stories and public policy
dialogues, but rarely feature transportation costs. Table 4.2 provides a list of cities by how affordable
they are to a typical household in that region when ignoring and considering transportation costs.
Washington D.C. is the most affordable city to its typical residents when accounting for transportation as
well as housing costs, but only the 8th most affordable when ignoring transportation costs. Other cities
like Cincinnati and Detroit are decidedly less affordable when considering transportation costs.
Table 4.2. Affordability rankings for major US cities and metropolitan areas

Source: Table generated by authors using data from the Center for Neighborhood Technology’s website

Affordable housing and mortgage policies
Lastly, the H+T index has influenced public policies about where to site of subsidized housingdevelopments and how much to lend households to buy a home. Perhaps its largest influence has been
through the Low-Income Housing Tax Credit, the largest source of funding for constructing andmaintaining subsidized housing in the US. This program requires that states develop and manage acompetitive allocation process to determine which sites and developers receive federal funds. A numberof states—notably California, Massachusetts, Colorado, Minnesota, New Jersey, Illinois, Louisiana,
Nevada, New Mexico, and Virginia—give priority to projects that are suited near transit or have goodaccessibility to job centres and other amenities (Governors’ Institute n.d., Texas Department of Housing
and Community Affairs 2010, Smart Growth America 2016). Other state and local programs, such as
California’s Transit Oriented Development (TOD) Housing Program and the Bay Area’s Transportation
for Livable Communities initiative provides subsidies to build or purchase housing units near transit(Renne 2008, California Department of Housing and Community Development 2015).

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Beginning in the late 1990s, the Federal National Mortgage Association (Fannie Mae) developed andpromoted the location-efficient and smart commute mortgage programs. The intention was to allow
homeowners to access larger loans in areas with lower commuting costs and thereby reduce auto use,
support transit use, increase the supply of housing near transit, and increase homeownership among lowand moderate-income households. Despite initial interest, lenders only used the program to make around
300 loans and the program closed in 2008 due to a lack of lender interest (Chatman and Voorhoeve2010). This lack of interest stemmed at least partially from some of the theoretical and technicalchallenges of applying an H+T index to inform housing policy.

Challenges and critiques of the H+T index
For all its benefits and policy uses, several challenges limit the use and applicability of the H+T in
the US and its transferability to other countries.
Affordability
One of the biggest challenges of the H+T index is that the concept of affordability is somewhat
arbitrary. Early measures and popular expressions of affordability considered housing affordable if it
absorbed roughly 25% of income, which came from a popular expression: a week’s wages for a month’s
rent (Feins and Lane 1981). In 1969, Congress gave legal weight to this affordability concept through the
Brooke Amendment. The amendment capped the amount that any family would spend on housing at
25% of household income with some variation based on other household characteristics. In 1983, theHousing and Urban-Rural Recovery Act increased and simplified the affordability standard to 30% for
all households (Pelletiere 2008). HUD publishes affordability guidelines that vary by region andhousehold size that are used to determine eligibility for subsidized housing and the public merit ofaffordable housing developments (US Department of Housing and Urban Development 2016).
Although easy to calculate and compare across regions (Bogdon and Can 1997), researchers andadvocates have criticized the measure for more than just its exclusion of transportation costs. Thesecriticisms extend to the H+T index:
1.

Affordability thresholds qualify something that is inherently quantitative in nature. A
household hold that spends 31% of its income on housing is not substantially more burdened
than one that spends 29% on housing. Yet the notion of affordability treats these twohouseholds similarly to households than spend 50% and 15% of their respective income on
housing.

2.

Affordability indices do not consider household size, age, or composition (Bogdon and Can
1997, Belsky et al. 2005, Jewkes and Delgadillo 2010). A family with only working-age
member and several school children will likely find it more burdensome to spend 30% ofincome on housing, yet likely needs a larger house than a single-person household with anincome.

3.

In addition to housing and transportation costs, other important expenditures like schools andfood vary substantially within and across regions (Tegeler and Chouest 2010). In practice thismay tend to offset some of the effects of the H+T index. For example, a household in thesuburbs may spend money to drive to a large grocery store with lower priced goods. Schoolingcosts almost certainly play a large role in household expenditures as well. For example, ahousehold may prefer to send their kids to free public schools in a suburban school district, butwould choose to send them to an expensive private school if the public school is in a poorly
performing urban district.

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4.

Indices ignore household preferences. Some households are willing to pay more for a larger or
high-quality house, while others might prefer to conserve housing expenditures in order tospend more on other items like food, transportation, or entertainment.

5.

By focusing on income, the indices ignore life-stage considerations, accumulated wealth, anddebt. For example a young household may elect to spend a significantly higher portion ofincome on housing in the expectation of future earnings. Many university students and recent
graduates experience high rent burdens but are financially and socially quite privileged. At theother end of the spectrum, an elderly household spending 30% of income on housing might be
extremely burdened, since houses are the largest source of wealth for most households andelderly households often rely on this accumulated wealth in retirement.

Aggregation bias
At its heart, affordability affects individuals not census block groups, cities, or metropolitan areas. A
homogenous neighbourhood where all housing costs the median price is likely much less affordable thana diverse neighbourhood with the same median housing cost but substantial variation in housing costs.
The same holds true for cities and regions. In fact, households may prefer to live in neighbourhoods withhigher median housing costs, provided that they can find a single affordable house or apartment. Whatmatters is not the median income or housing price, but the distribution of income and housing prices
along with ability to match houses and apartments to those that can afford them. By lumping all incomes
and housing costs into a single median measure, the index fails to account for this important componentof affordability. This type of aggregation bias is common when indices fail to analyse the correct unit ofanalysis, but instead aggregate up to a larger geographic area.
The aggregation of transportation expenses is perhaps even more problematic since there is so muchvariation and potential variation in how much a household spends on transportation. For example, ahousehold where every member drives to all destinations would spend substantially more than anotherwise identical household that carpooled or used transit. Some neighbourhoods may be affordable fortransit users, but not for car owners with the same income.
Even across individuals, there is a temporal component to housing and transportation affordability.
For example, transportation costs almost certainly vary with job location as well as housing location.
Changing jobs could substantially influence how much a household spends on transportation and thus
how much it can afford to spend on housing. This temporal uncertainty helped contribute to lenders’ lackof interest in Fannie Mae’s location-efficient and smart commute mortgage programs (Chatman andVoorhoeve 2010).
VMT estimate
The CNT uses odometer readings and built environment data from a single region to estimate car use
for the rest of the country in its H+T estimates. There is, however, significant variation in built
environments, travel behavior, and probably also the empirical relationship between the two throughout
the country. The CNT argues that this is not a problem because the Illinois data include a variety of built
environments from rural areas and large cities. Although there are substantial commonalities across
many studies examining the relationship between the built environment and vehicle travel (Ewing andCervero 2010), whether the model estimates are applicable to other regions remains an open question.
The existing model is not suitable for other OECD nations, where car ownership and use tend to be more
expensive than in the US.

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The high cost of new construction
Constructing new housing in transit-friendly areas is one of the CNT’s recommendations based on
the H+T Index. However, new construction by its nature is expensive and, unless highly subsidized,
unaffordable to most households. Furthermore, land prices and housing values frequently increase withproximity to transit (Knapp 2001, Cervero and Duncan 2002, Rodríguez and Targa 2004, Debrezion etal. 2007, Hess and Almeida 2007, Kahn 2007, Cervero and Kang 2009). Since wealthy households are
more likely to opt for a combination of new construction and car ownership, building market rate
housing around transit is unlikely to reduce H+T expenditures for vulnerable households.
Fair housing critique
One argument against the use of H+T measures to help determine where to locate affordable housing
is that it may encourage concentration of poverty (Tegeler and Chouest 2010). This argument isparticularly poignant in the US, where a history of racist housing discrimination has contributed toconcentrations of poor and non-white households in specific neighbourhoods, often in central
neighbourhoods with good transit access. This fair housing critique against place-based affordable
housing development recently came to a head with a ruling by the Supreme Court in the case of Texas
Department of Housing & Community Affairs v. Inclusive Communities Project, Inc. (for an overview of
the ruling, see Epstein et al. 2015). The Supreme Court ruled 5-to-4 that the Texas Department of
Housing & Community Affairs was in violation of the Fair Housing Act (Title VIII of the Civil Rights
Act of 1968) based on arguments that affordable housing allocations perpetuated and strengthened
existing patterns of racial and socioeconomic segregation. Unless carefully implemented, policies toincrease the development of affordable housing or extend credit to low income households around transit
will tend to increase socioeconomic and racial segregation and in some cases may even be in violation offederal law.

Housing plus transportation index in Mexico City metropolitan area
How readily could a housing and transportation affordability index be applied to Mexico and Mexico
City? While household expenditure data are readily available from the annual consumer expenditure
survey (INEGI 2016), the decennial census does not provide information on household income or vehicle
ownership (since 2000, the census asks where a household owns one or more vehicle, but not howmany). Furthermore, spatially refined data on vehicle travel are sparse, though estimable for Mexico City
(Guerra 2014a) using a 2007 household travel survey (INEGI 2007). With four times the population of
the next largest urban agglomeration and the most robust public transportation network, however,Mexico City’s residents’ travel behavior likely vary substantially from residents of other regions.
This section applies the housing plus transportation affordability index to the Mexico City
metropolitan area (Zona Metropolitana del Valle de México, henceforth referred to as “Mexico city”).
Given that most households rely on public transportation and driving accounts for under a third of trips,
we limit the analysis to non-driving households from the 2007 household travel survey. This excludes
wealthier households, who are the most likely to drive, but emphasizes poorer households who are mostconstrained by a lack of affordable housing or transportation options. Note that we aggregate the results
to the metropolitan area’s 76 boroughs and municipalities rather than census tracts (Agebs). This
produces clearer maps and avoids the problem of insufficient samples in many of the region’s
5 000 census tracts. Box 4.2 summarises a similar exercise conducted in Qom, Iran.

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Box 4.2. Applying an H+T Index in Qom, Iran
Unaffordable housing is a growing concern in many developing-world cities, where rapid population growth,
insufficient infrastructure, and shrinking household sizes have led to substantial increases in housing andtransportation prices. Isalou et al (2014) conducted a study in Qom, Iran, to evaluate the usefulness and findings ofthe H+T index in a developing-world context. Unlike in the US, the authors relied on a survey to collect data abouthousehold characteristics, travel behavior, transportation expenditures, and housing costs. In Qom, a typicalsuburban household spends 57% of their monthly income on housing and transportation, compared to 45% for acentrally located household. As in the US and Mexico City, accounting for travel expenditures increases theaffordability of urban neighbourhoods relative to suburban ones.

Rents and transit expenditures in Mexico City
The average non-driving household spends an estimated 33% of income on rent and 15% of incomeon transit expenditures in Mexico City (authors’ estimates using (INEGI 2007)). This suggests that theCenter for Neighborhood Technology’s measures of rent and transportation burden are at least somewhat
transferable to this metropolitan area. Across the metropolis, monthly rents decrease with distance fromthe urban centre (measured here as the network distance to the central Zocalo) while transit expenditures
increase. Figure 4.2 plots households’ transit expenditures and monthly rents by 5 kilometre incrementfrom the Zocalo. Although median household transit expenditures increase with distance into the mostremote neighbourhoods, the rent gradient flattens at around 20 kilometres from the centre. Beyond thispoint increase in travel costs do not appear to correlate with reductions in transit expenditures. Finally, it
is important to note that incorporating car ownership and use into travel expenditures would tend toincrease central households’ transportation expenditures more than peripheral households’ expenditures.
In Mexico City, as in much of the world, wealthier households tend to choose both central housing
locations and cars (Guerra 2015a). Peripheral neighbourhoods are generally poorer and more transit
reliant.

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Figure 4.2. Boxplots of monthly rent and public expenditures by 5-kilometre increment from thecentral Zocalo

Source: Compiled and calculated by author from Census Tract boundary files from the Instituto Nacional de Estadística yGeografía (INEGI, 2013), OpenStreetMap (2015), and the 2007 household travel survey (INEGI 2007). Default boxplot settings
are used with upper and lower arms representing 1.5 times the inter quartile range and outliers excluded.

Measuring affordability in Mexico City
According to data from the 2007 household travel survey, 34% of non-driving households spend
more than 30% of their income on rent and qualify as rent-burdened. This number goes about by one
percentage point to 35% when accounting for housing and transportation costs. Figure 4.3 maps thepercentage of burdened households across municipalities when accounting for rents along andaccounting for housing and transportation costs. Across municipalities (boroughs within the dark black
outline Mexico City proper), a maximum of half of households are rent-burdened. Accounting fortransportation costs changes the geography of affordability, in particular by making the centrally locatedboroughs more affordable. These areas have the best access to the 200-kilometer metro system, which

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charges flat fares and is faster and less expensive than road-based forms of transit (Guerra 2014b).
Similarly, the far periphery becomes less affordable to its residents when accounting for transit costs.
That said many peripheral neighbourhoods have relatively small percentages of H+T-burdened
households.
Figure 4.3. Percent of rent and H+T burdened households by municipality

Source: Compiled by authors from state, municipal, and locality boundary files from the Instituto Nacional de Estadística yGeografía (INEGI, 2013) and calculations from the 2007 household travel survey (INEGI 2007).

When looking at which municipalities tend to be affordable to a household earning a 25th percentile
income, a different spatial pattern emerges (Figure 4.4). The median rent in each central borough isunaffordable to a poor household, while most of the northern and eastern municipalities of the State of
Mexico remain affordable. When accounting for transportation costs, however, only a few of the poorer,more remote municipalities remain affordable. Again, there is a pattern that incorporating transportation
costs lowers the relative affordability of the periphery and increases the relative affordability of thecentre. Nevertheless, median expenditures in most municipalities would burden a household earning the25th percentile of regional income for non-driving households.
Figure 4.4. Municipal affordability indices for household earning the 25th percentile income

Source: Compiled by authors from state, municipal, and locality boundary files from the Instituto Nacional de Estadística yGeografía (INEGI, 2013) and calculations from the 2007 household travel survey (INEGI 2007).

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Households, however, probably do not care that much about median prices in an area—one of thefundamental problems of affordability indices and aggregation bias. Instead households care about
whether they are able to match to a home that is affordable and meets other needs and priorities,
including affordable transportation. Figure 4.5 tries to circumvent this aggregation problem by mapping
the percentage of households’ housing and transportation expenditures that would be affordable to a 25th
percentile income household by municipality. This gives a sense of the relative ease with which a poor
household might be able to find suitable accommodations in different parts of the metropolis. Again,
adding transportation costs makes central regions relatively more affordable. However, it also tends toincrease the affordability of many suburban neighbourhoods. These are the places where many of
Mexico City’s poor residents live and by necessity or desire are able to reduce transportation and housing
expenditures. It is also interesting to note that even some of the wealthier parts of the city sometimes
have 25% to 33% of their housing affordable to a poor household when accounting for transportation
costs.
Figure 4.5. Percent of housing options affordable to a household earning the 25th percentile income

Source: Compiled by authors from state, municipal, and locality boundary files from the Instituto Nacional de Estadística yGeografía (INEGI, 2013) and calculations from the 2007 household travel survey (INEGI 2007).

Policy implications
Taken together, these analyses demonstrate that it is possible to apply an H+T index in a place like
Mexico City with relative ease and that the findings are somewhat similar to findings from a US context,
at least when excluding car expenses. In general, more transit-friendly central locations appear relatively
more affordable when accounting for travel costs as well as housing costs. Since Mexico is the poorest of
the OECD countries and the US is one of the wealthiest, these findings likely extend to the rest of theOECD. Unfortunately, a shortage of the necessary data would make it difficult to extend this analysis
beyond Mexico City to the rest of the country. Furthermore, the data from the household travel survey
are now nearly ten years old. Nevertheless, there are two broad takeaways for public policy in Mexico
City and perhaps beyond.
1. Affordable housing policyFirst, the principal form of housing production has encouraged substantial new housing development
in remote areas with high transportation costs. Over the past two decades, Mexico City’s principal formof new housing production has shifted from an informal process, where households built their own
properties generally in informal settlements on the periphery, to a formal process where private
developers acquire large peripheral lots, build new housing speculatively, and sell completed homes in

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massive developments to households that qualify for publicly subsidized mortgages (Pardo and Velasco
Sánchez 2006, Monkkonen 2011a, 2011b). The public sector is highly involved in this process. Between
1995 and 2005, public agencies funded 75% of all housing loans by value—and even more by volume—in Mexico. Infonavit accounted for 81% of these publicly-financed loans (Monkkonen 2011b).
In addition to remote locations, these commercial housing developments have more parking, wider
streets, less-connected street-grids, and less accessible transit stops than nearby informally developed
neighbourhoods. These features encourage car ownership and use, which cost even more than transit andreinforce high peripheral transportation costs. Accounting for regional housing location, householdincome, age, and composition, a household in a commercial development is 62% more likely to own a
car and drives nearly four times as much as a household in a traditional neighbourhood (Guerra 2015b).
A consideration of H+T costs instead of just housing costs would encourage a shift in subsidies tofavour more central development. This may already be happening in the wake of the Great Recession.
Numerous homebuilders have gone bankrupt or failed to report earnings, after foreclosures of tens of
thousands of homes, particularly the most peripheral ones (Juarez 2013). President Peña Nieto’s
administration has made it a public priority reorient government loans to support vertical construction in
more central locations (La Crónica de Hoy 2013). However, until home building increases again, it
remains to be seen what the effects of the policy will be and whether increases in housing costs can beoffset by decreases in transportation costs.
2. Transit investment policy
In 2007, just 11 of Mexico City’s 192 metro stations were outside of the Federal District.
Nevertheless suburban residents of the states of Mexico and Hidalgo rely on transit for a higher share oftrips (65%) than urban residents (60%) and even use the centrally located metro for a similar share (18%vs. 19%) (INEGI 2007). These suburban residents are also poorer and more numerous than centrally
located ones. Nevertheless the most notable transit policy of the last decade has been the construction ofa new bus rapid transit network, which like the metro is a centralized system that provides only limited
service into the populous, dense, and transit-reliant suburbs. Increasing high-capacity transit investments
into the suburbs, although expensive, will likely increase housing density around suburban stations andreduce travel times and transportation expenditures for low income households as occurred around thesuburban Line B metro extension.

Conclusion and transferability to other OECD nations
Throughout this chapter, we documented the development, technical details, policy uses, andchallenges of CNT’s H+T affordability index. We then applied a modified version to Mexico City to
explore some of the opportunities and challenges of applying the index to another country. Despite someof previously described challenges and critiques of the H+T index, applying an H+T index to OECD
countries and cities has the potential to:•Strengthen the public and policy makers’ understanding of which countries, cities, and regions
are most affordable;•

Encourage bank lending and the construction of affordable housing in neighbourhoods withhigher land costs but lower transportation costs; and•Focus transit investments in a way that could help to reduce the amount that poorer households
spend on transportation.

Transferring the index will likely present two additional challenges:

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Data availability. The CNT developed the H+T index specifically to be estimable using
publicly available data. Much of this data is not available or comes in a different form in
different countries. As a result, no single methodology can or should be applied to all OECD
countries and regions. There is insufficient publicly available data to estimate an H+T index in
Mexico and applying a model built from the Mexico City region’s 2007 household travel
survey to census data for other regions would likely create substantial and systematic bias. To
estimate an H+T index in Qom, researchers had to conduct their own survey, an expensive andtime-consuming endeavour that does not lend itself to the publicly available nature of the H+T
index. Estimating the costs of vehicle travel, in particular, is likely to be problematic. Even in
the US, it remains unclear how well the model built from Illinois data predicts travel behavior
or costs outside of Illinois.

•

Context. As seen in Mexico City, some preconceived notions about the relationship between
transportation costs and housing location do not hold outside of the US. For example, wealthy
households in Mexico City generally opt to live in transit accessible areas but own and use cars.
This would tend to make central locations look less affordable to poor and moderate-income
households than they actually are. Urban economists have found similar differences whencomparing American and European cities—a difference that they attribute to the relative
desirability of central locations (Brueckner et al. 1999). The US’s local control and financing of
public school districts almost certainly also leads to substantial differences in housing markets
when compared to other countries. Many households move to the suburbs because they cannot
afford private schools but view public schools in urban locations as undesirable.

Based on these findings, we stress that no single methodology, model, or dataset can or should beapplied throughout OECD countries or regions. If there is interest in extending the index to coveradditional OECD nations, we therefore recommend that researchers identify low-hanging fruit, wheredata are readily available, and test and adopt the index to different contexts. Examples from Mexico City
and Qom show that this is possible but that additional data collection and model construction will benecessary and may in fact be specific to each OECD nation.

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Chapter 5
Perspectives for integrating housing location considerations and transport
planning as a means to face social exclusion in Indian cities

Geetam Tiwari
Transportation Research and Injury Prevention Programme,
Indian Institute of Technology Delhi, India
This chapter highlights the urban development in India and implications for low income households
living in informal settlements or slums. The chapter is divided into four sections. The first one
describes urban development pattern in India. A second section presents a summary of policies
since 1950 which have been implemented to address the housing needs of low income households incities. The chapter then presents impacts of various housing and resettlement policies in selected
cities in India. A final section summarizes key insights from self-planned low income settlements incities-the slums-, and expert planned low income settlements as part of resettlement policies.

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126 – 5. PERSPECTIVES FOR INTEGRATING HOUSING LOCATION CONSIDERATIONS AND TRANSPORT PLANNING IN INDIAN CITIESUrban development pattern in India
21st century has been termed as the urban century since majority of the world population is living inurban area since 2010. UN report (2014) notes the following three important issues regarding
urbanisation:•

Over 90% of urban growth is occurring in the developing world and an estimated 70 million
new residents are added to urban areas of developing countries each year. Over the next two
decades, the urban population of the world’s two poorest regions — South Asia and SubSaharan Africa — is expected to double, suggesting that the absolute numbers of informal
settlement and slum dwellers in these regions will dramatically grow.

•

Informal settlements, slums and other poor residential neighbourhoods are a global urban
phenomenon. They exist in urban contexts all over the world, in various forms andtypologies, dimensions, locations and by a range of names (squatter settlements, favelas,poblaciones, shacks, barrios bajos, bidonvilles). While urban informality is more present incities of the global south, housing informality and substandard living conditions can also befound in developed countries.

•

Slums affect the prosperity of cities and their sustainability. While on the one hand theseareas are acknowledged as providing much-needed mixed land use to cities and as having
an active informal economy that, in many countries, provides the majority of jobs, on theother hand, these informal jobs are unskilled, very-low-paid, and insecure livelihood
options, part of a ‘subsistence economy’ that allows inhabitants to survive but not toprogress sufficiently to change their living conditions nor to realize the full potential
contribution to urban productivity.

In the background of these three important characteristic of current urbanization we explore the urban
development and urbanization in India, formal and informal response to deal with these issues andimpact of formal policies on the lives of urban poor.
Urbanization and economic growth
India’s current population is 1.21 billion. The population is expected to grow at the rate of 1.8-1.5%
per annum by 2030. Therefore, the estimated population of India by 2040 will be in the range of 1.51.7 billion. The Indian economy has been growing at a rate of more than 7% for the last four years.
However, the urbanization process has been slow in India. The analysis of development dynamics in the
1990s shows that there has been an all-round decline in the growth of employment. Income growth andincidence of poverty have been extremely uneven across states. Thus a slowing down in the rate ofurbanization and concentrations of demographic growth in developed states seem to be the logicaloutcome. The process of urbanization has also become exclusionary in nature, as only a few large cities
with a strong economic base are able to raise resources for development, leaving out small and medium
towns (Kundu 2006). India is 30% urbanized and is expected to reach 40% by 2040. The current percapita income of USD 1 061 is expected to increase to USD 12 000 in the high growth (9% per annum)
scenario and USD 6 000 in the low growth rate (6-7% per annum) scenario.
As per Census 2011, there are 468 cities having population more than 0.1 million. These urban
agglomerations and cities are grouped into four categories as small, medium, large and mega cities(Table 5.1).

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Table 5.1. Provisional population 2011 by Census of India
Category Groups Population (million)1
Small
0.1 – 0.52
0.5 – 1.0
Medium3
1.0 – 2.04
2.0 – 4.0
Large
5
4.0 – 8.0
6
Mega
> 8.0

Total no. of
372
43
34
10
4
5

Total

468

Percentage
28%
11%
18%
9%
9%
25%

Total
73 930 414
30 235 593
46 686 245
24 265 267
23 736 923
66 037 071
264 891 513

Source: Census, 2011

Nearly 60% of urban population lives in cities which have 2 million population or less.
Five megacities which have more than 8 million population have twenty five percent of total urban
population. Substantial proportion of this population lives in slums. The recent growth of the urban
population has aggravated the crunch of urban land, which, coupled with scarcity of affordable housing,has led to proliferation of informal settlements housing the poor that have become a part of the cityscapeof Indian cities (Ahluwalia, 2011).
Economic growth
At a likely GDP growth rate of 7.5 to 8 percent per year, real per capita GDP is expected to reach
USD 2 700 by 2030, a five-fold increase over the 2005 level. India’s population is likely to reach
1.5 billion. This growth will be accompanied by increased urbanization, with well over 550 million(40%) of India’s people living in cities two decades from now. This shift towards a more urban economy
will expand demand for services like housing, power and transport. The vehicle fleet is expected toincrease from a little over 50 million today to about 380 million by 2030 (McKinsey & Company 2009).
India’s GDP (nominal) is forecast to increase from USD 1 256 billion in 2010 to USD 6 683 billion
in 2030, USD 16 510 billion in 2040 and USD 37 668 billion in 2050. Per capita GDP trend has been
forecast in terms of GDP- nominal and per capita in Table 5.2.
Table 5.2. Forecast of GDP trends
2010 2020 2030 2040
2050
GDP (nominal) USD billion 1 256 2 848 6 683 16 510 37 668
GDP (per capita)
1 061 2 091 4 360 9 802 20 836
Source: (Wilson and Stupnytska 2007)

It is clear that after twenty five years from now per capita GDP in India will remain low(<USD 1 0000), therefore presence of poor households and informal settlements in urban area willremain a challenge for urban planners.
Informal sector and urbanisation
A large proportion of urban residents in India live in self-planned informal settlements in cities.
Informality has been defined in many ways. It is outside what is official or legal or planned. It is
certainly not a synonym for criminality which is both outside the law and illegal. Squatter settlements allover the world are called informal settlements because they are not part of the official plan. Nuewirth

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128 – 5. PERSPECTIVES FOR INTEGRATING HOUSING LOCATION CONSIDERATIONS AND TRANSPORT PLANNING IN INDIAN CITIES(2005) describes the squatter as a new migrant to the city who builds a shelter with his own hands on theland that does not belong to him. Nearly one billion people who live in squatter settlements are people
who came to the city in search of jobs, needed a place to live that they could afford, and not being able tofind it on the private market, built it for themselves on land that wasn’t their own. These informal
settlements create a huge hidden economy- an unofficial system of squatter landlords and squattertenants, squatter merchants and squatter consumers, squatter builders and squatter labourers, squatter
investors and squatter brokers. The builders of informal housing are the largest builders of housing in theworld- and they are creating the cities of tomorrow. The conventional definition of informal-unofficial,illegal or unplanned denies people jobs in their home areas and denies them homes in the areas where
they have gone to get jobs.
A significant proportion of urban population in Indian cities remains outside the formal planning
process. People living in informal settlements dependent on informal economy for survival in the city are
an integral part of Indian cities. Table 5.3 shows share of slum population in selected cities in India.
Generally as the city size increases, share of population living in slums also increases.
Self-planned (informal) settlements in Indian cities
Often in Indian cities squatter settlements develop inside the city close to commercial centres,
planned housing developments, and factories. Soon these settlements demand varied services needed by
the residents- low cost food, vegetables, tailors, etc. It is a common sight in Indian cities to have street
vendors along the roads selling food, toys, flowers and various handicrafts made by family members.
Thus the informal sector provides employment opportunity to each and every family member andrewards for their creative endeavours. Growth of formal sector is accompanied with the growth of
informal sector, with the latter showing higher growth rates than the formal sector. This is not surprisingas the informal sector grows to serve the formal sector as well as to serve the informal sector.
Informal sector develops firstly because of rural-urban migration which is based on a comparison ofrural and urban opportunities. Formal sector in urban area requires unskilled or low level of skills
provided by the migrant labour. Construction labour, casual labour in factories and commercial
establishments depend on migrant labour. Higher income families require domestic help, gardener,
carpenter, plumber, electrician etc. for normal functioning.
The patterns of development in Indian mega cities are an amalgam of planned and organic (selforganising) growth. Survival compulsions force poor population to evolve as self-organising systems.
These systems rest on the innovative skills of people struggling to survive in a hostile environment andmeet their mobility and accessibility needs. Housing, employment and transport strategies adopted bythis section of the society are often termed as “informal housing, informal employment and informaltransport”. Transport solutions evolved by this section of the society do not become part of the official
policies. Their existence is mostly viewed as creating problems for the “normal traffic”. Formal plans
have no place for informal transport. Therefore, most cities face a complex situation where theinvestments are for formal plans, whereas needs of a significant section of the society is met by informaltransport. Is this desirable or sustainable?

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5. PERSPECTIVES FOR INTEGRATING HOUSING LOCATION CONSIDERATIONS AND TRANSPORT PLANNING IN INDIAN CITIES – 129

Table 5.3. Percentage of slum population in selected cities in India
Million plus cities

Percentage of slum households of total urban households (%)

Greater Mumbai

41.3

Kolkata

29.6

Chennai

28.5

Delhi

14.6

Bengaluru

8.5

Source: Census of India, GoI (2011)

Out of 13.7 million households living in slums in India, as per census 2011, 5.2 million households
live in 46 million-plus cities, or, 38% of the total slum population is situated in these million-plus cities.
The five metro cities, which attract large number of migrant population have a high share of slum
population as shown in Table 5.3.

Slum free cities
Formation of slums in cities was recognized as a serious concern in India since 1950s. Slum
eradication Act 1956 was adopted by the Indian parliament in 1956. The national housing policy of India
identified shelter as a basic human need, in 1956. However, there had been no comprehensive plan for
housing until 1988 in the country (The first National Housing Scheme was introduced in 1988).
In the First Five Years Plan, with schemes like Housing Scheme for Industrial Workers (1952),
Housing for Low Income Group (1954) and Housing Scheme for Plantation Workers (1952) the focus
was to provide housing for the LIGs (low-income group). The Second Five Year Plan recognised theneeds of slums, their improvement and resettlement. Until the fifth plan period from 1974 to 1979, theemphasis was only on certain weaker sections of the society. From the Sixth plan period onwards, thecommunity development programmes were promoted with active role of the non-profit organizations.
After liberalisation of Indian Economy in the nineties, (i.e., from the Eighth plan onwards) the market
forces gained more importance and the role of the State was transformed from a provider to that of an
enabler (Mukhija, 2004). The National Housing Policy of 1988 was more inclined towards the housing
provisions for the higher and the middle-income section. In 1994 this Housing policy was revised withthe aim to provide affordable housing for all in the country and strengthen the enabling strategies (Sivam& Karuppannan, 2002). Both public and private sector provide housing in India contributing 16 and 84%
respectively. The private sector is active in both the formal and informal categories of housing. The
formal housing stocks are unaffordable for the economically weaker section and hence they are provided
with the informal housing stocks in the form of slums. The inability of the Government to see housing asa major economic sector has resulted in the absence of any financial structure until 1970. The Housing
and Urban Development Corporation (HUDCO) was created in 1970 for providing financial assistance tothe LIGs for augmenting the housing stock. Other important contributors to housing finance are Housing
Development and Financial Corporation and Life Insurance Company. In 1988 the National Housing
Bank was set up as a subsidiary of the Reserve Bank of India to finance housing schemes (Sivam andKaruppannan, 2002).

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130 – 5. PERSPECTIVES FOR INTEGRATING HOUSING LOCATION CONSIDERATIONS AND TRANSPORT PLANNING IN INDIAN CITIESBardhan et al. (2015) have analysed four groups of policies to explain the impact on low income
households in Mumbai: i) land and housing, (ii) related to clearance and eviction, (iii) upgradation andredevelopment, and (iv) recent initiatives. The first group consists of the two major policies implemented
by the Central government – the Rent Control Act (1947) and the Urban Land Ceiling Act (1976). The
second group consists of the Slum Improvement and Clearance Act (1956), The Slum Areas(Improvement, Clearance and Redevelopment) Act (1971) and the Maharashtra Vacant Land (Prohibitionof Unauthorized Structure and Summary Eviction) Act (1975), all primarily motivated towards theremoval of slums from urban areas. The third group has the policies, which were intended to thedevelopment of slum areas, and these are the Slum Upgradation Programme (1985), the Prime Minister's
Grant Project (1985), Slum Redevelopment Scheme (1991) and the Slum Rehabilitation Scheme (1995).
Finally, the last group has the schemes like the Rajiv Awas Yojna (2013) and Cluster Redevelopment
Projects (2014), which are the recent initiatives in housing policies.
The Slum Area Improvement and Clearance Act of 1956 (Government of Maharashtra, 1956)
identified the slum areas as places where the buildings are in any respect unfit for human habitation; orare by reasons of dilapidation, overcrowding, faulty arrangement and design of such buildings,
narrowness or faulty arrangement of streets, lack of ventilation, light or sanitation facilities, or any
combination of these factors, are detrimental to safety, health or morals.
There were provisions for possible improvement and demolition of the buildings as well as that ofclearance and redevelopment of the slum areas. The act did not mention about the resettlement plans of
the evicted households. This was a major gap in the planning process as without any resettlement plan
the easiest option open to the evicted households was to settle in other slum areas or create new slums.
Hence, on one hand the Act ensured clearance of urban land; on the other hand it triggered the slum
growth further.
The Slum Areas (Improvement, Clearance and Redevelopment) Act, 1971 according to this Act, “No
person shall, on or after the appointment date, occupy any vacant land or continue to in occupation ofany vacant land in urban area or erect any shelter or enclose or other structure on such land for the
purpose of residence or otherwise without the express permission in writing of the Municipal
Commissioner in a corporation area, of the Chief in a Municipal area and elsewhere, of the Collector, or
except in accordance with any law for the time being in force in such urban area”(Government of
Maharashtra, 1975). The Act also states that in cases of contravention the Competent Authority “….mayby order requires such person to vacate the land ….and to remove all property there from and if such
person fails to comply with the order …, he may be summarily evicted from such land ….” (Governmentof Maharashtra, 1975). This Act led to wide scale eviction of slums and squatters, which were identified
as being encroachments. The Act does not mention anything about the relocation of the evicted
households and hence like the abovementioned Acts, we find a major gap in the planning process too.
Any large-scale clearance and eviction process makes a large section of low income people
houseless. These households often take shelter in the other slums or they become pavement dwellers.
These processes seriously damage their livelihood as their occupations are largely driven by the selectionof their housing location. Either they are located near to their workplaces or the slums themselves get
established as an economic centre. In case of eviction, both the occupational structures get heavily
affected. In Mumbai on an average a slum dweller family spends INR 30 000 to 50 000 in constructing
their house, due to demolition (Das, 2003). Bardhan et al. (2015) have discussed the verdict given by theSupreme Court of Indian against the eviction of illegal settlements in the Olga Tellis vs. Bombay
Municipal Corporation court case in 1985,where the Supreme court identified the relation between
habitation and livelihood (Ramanathan, 2005). This can be regarded as the initiation of the in situ slum
improvement schemes.

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Policies related to upgradation and redevelopment:
The Slum Upgradation Programme (SUP) was introduced in 1985 as part of the Bombay Urban
Development Project, in collaboration with the World Bank. It was joined by another similar programmecalled the Low Income Group Shelter Programme. The basic motive of SUP was to provide renewablelease of land to the slum cooperative societies for a period of 30 years. It would provide loans to the slum
dwellers at an interest rate of around 12% and also ensure provision of basic amenities on a cost recovery
basis. The slums on the private lands and the central government lands were out of the jurisdiction of thisprogramme. Hence the impact of the programme was not as widespread as expected. The State also
issued identity passes to the head of the household to prove their eligibility in receiving the leasehold
tenure. The impact of the programme was
In 1985 a Central grant of INR one billion was announced of which INR 600 million was used for the
upgradation and improvement of Dharavi (Barke, O' Hare, & Abbott, 1998). The funds were used toprovide housing for the slum dwellers and also to improve conditions of the existing infrastructure. It
recommended 30 000 to 35 000 families to be accommodated within Dharavi and 20 000 families to be
relocated outside Dharavi (Mukhija, 2001). Transit camps were set up to temporarily relocate thehouseholds during the construction process. There were problems like shortage of transit settlements,
delays in construction, lack of awareness among the beneficiaries, higher prices of the newly createdhousing stocks and manipulation of the beneficiary list by the outsiders. As such the housing stocks
remained limited and the idea of affordable housing was not successful.
The liberalisation of the Indian market in 1991 invited large scale foreign investment into the housing
sector as well. The ruling party introduced a new scheme called the Slum Redevelopment project,inviting the private investors to take up the job of slum redevelopment. As an incentive the FSI of theslum areas were increased up to 2.5 from 1.33, which encouraged the construction of high rise buildings.
As per the scheme the private developers should carry out in situ redevelopment of the notified slum areaby rehousing the slum dwellers into tenements measuring 180 sq. feet (16.72 m²) and the rest of the place
was available for them to build and sell in open market. This is how the builders were compensated of
their cost of construction and resettlement. However, the houses for the slum dwellers were not free, andthey had to pay a certain part of the construction cost. Also there was an added criterion of eligibility,
where the slum dweller had to establish his duration of stay in the existing from before that of 1985. This
cut-off date made a large section of the slum population ineligible for getting rehousing facility and there
was no alternative measures recommended for the ineligible section. Also the scheme failed to attract theinterest of the private developers and all in all, the scheme was criticized for inefficiency (Risbud, 2003).
The success of the scheme depended on high property rates, to benefit large developers and builders who
thus became instrumental in evacuating slums from prime lands of the city (Banerjee Guha, 2002).
A modified version of the Slum Redevelopment Scheme (SRS) was brought up by the newly elected
party in 1995. Following the recommendations of the Afzalpurkar Committee (1995), a separate body
called Slum Rehabilitation Authority was set up to exclusively look into the matters related to slum
resettlement. Thereby three types of rehabilitations were recommended, viz. In Situ, Project Affected
People (PAP) and Permanent Transit Tenement (PTT). The year of eligibility criterion was raised to1995 and recently to 2000, and the scheme also includes the pavement dwellers. The size of thetenements has been increased to 225 sq. feet (20.9 m²) to recently to 269 sq. feet (24.99 m²). The
tenements were made totally free for the slum dwellers. The private developers were provided incentives
in the form of free sale component of 7.5 sq. feet for every 10 sq. feet free rehabilitation construction.
The sale ratio varied from 1:0.75 in the inner city to 1:1 in the suburban areas. Since the FSI of theresettlement areas was fixed at 2.5, the additional area was made available in the form of generating
Transferable Development Rights.

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132 – 5. PERSPECTIVES FOR INTEGRATING HOUSING LOCATION CONSIDERATIONS AND TRANSPORT PLANNING IN INDIAN CITIESThe Slum rehabilitation Scheme is hence driven by the interest of the private developer and thestrength of the real estate forces. The scheme of rehabilitation is more inclined towards the interests of
the wealthy developers rather than the actual slum population. According to Patel (1996), it is mixture of“admirable and dubious”. The strong market forces favour active rehabilitation and vice versa. The
impact also varies with the variation in the local property prices. The developers will be more interested
in areas where the sale component can fetch higher prices. Though the SRS scheme seems to be very
lucrative, the true statistics fail to establish it as a real success (Das, 2003 and Nijman, 2008). In April
2014, the total number of completed units was 157 402 and 86 069 units are under construction (Praja,2014).
Similar policies have been followed in Delhi and Chennai and other major cities.
Bhan and Shivanand (2013) have analysed displacements in Delhi from 1990-2007. It was noted in
2000, 14.8% of the population of Delhi lived in Jhuggi Jhompri (JJ) clusters, and 12.7% lived inresettlement colonies where they had been placed post-eviction with some measure of security of tenure(Government of Delhi 2006). It is thus within negotiating and delaying, if not preventing, eviction andsubsequently fighting to access resettlement that the poor have historically claimed shelter in the city. It
is a process of settlement that is never complete, one in which the poor are constantly (un)settled,
regardless of how many decades they may have lived in the city, and one that, in turn, constantly
produces and maintains the contemporary Indian city itself as unsettled. Analyses by Bhan andShivanand (2013) and Dupont (2008), among others, suggest at least 218 evictions between 1990 and2007 in the capital, covering at least 60 000 households. Recent documentation of heightened evictions
for the Commonwealth Games in 2010 indicates the continuation and deepening of this trend post-2007(HLRN 2010). This documentation of widespread evictions is now affirmed by the Census 2011, whichstates in that “a major reason for the fall in the decadal growth rate (in Delhi) is the wide-ranging
removal of slum JJ clusters from various parts of the city”. Preliminary data further suggest “a significant
fall in the slum population despite broadening the definition of slums for the 2011 Census”. Particular
districts bear the brunt of these evictions – “It has been established that removal of slum clusters within
the NDMC (New Delhi Municipal Council) Area is the primary reason for a 25% fall in population in the
New Delhi district vis-à-vis 2001” (Government of India 2012: 44). The districts of New Delhi andCentral Delhi recorded negative decadal growth rates while North West Delhi recorded the second
highest decadal growth rate, primarily attributed to the building of resettlement colonies such as Bawana,
Bhalsawa, and Savda-Ghevra.
It is clear from the above discussion that slum problem has been understood largely as lack ofaffordable housing. While decent housing continues to remain unaffordable to a large proportion of slum
dwellers in most Indian cities, evictions continue and resettlement strategies have had major adverse
effect on livelihoods of millions of households in Indian cities.
The new central government came to power in 2014 and proposed a major shift from the earlier
policies and announced “The smart city initiatives”. Ministry of Urban Development, Government ofIndia Website states: The purpose of the Smart Cities Mission is to drive economic growth and improve
the quality of life of people by enabling local area development and harnessing technology, especially
technology that leads to Smart outcomes. Area based development will transform existing areas (retrofit
and redevelop), including slums, into better planned ones, thereby improving liveability of the whole
City. New areas (greenfield) will be developed around cities in order to accommodate the expanding
population in urban areas. Application of Smart Solutions will enable cities to use technology,
information and data to improve infrastructure and services. Comprehensive development in this way
will improve quality of life, create employment and enhance incomes for all, especially the poor and thedisadvantaged, leading to inclusive cities. There is no specific mention of policies required to address the
problems of slums or informal settlements.

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The first twenty cities have been selected to receive funding from the central government in February
2016. The emphasis is on application of IT and smart solutions. There is no mention of the existence of
informal settlements.

Impact of eviction and resettlement policies
This section presents impact of eviction and resettlement policies on low income household in Delhi,
and Chennai. Similar impacts are expected in other Indian cities.
Urban planning policies and relocation of poor households in Delhi
Delhi has witnessed large-scale evictions and resettlement since 1998. Before this large scaleevictions were carried out in 1977 during the imposition of emergency rule in India. Large numbers of
low-income households have been relocated for development projects like commercial complexes,
flyovers, recreational parks, and wide roads to improve the landscape of the city. Figure 5.1 shows thetrends of eviction of low-income settlements from the central areas of the city and relocation to theperipheral areas. Peripheral development and relocation of urban squatters has meant an increase of thespatial segregation of social groups. This has also resulted in poor access to income-generating activities.

Figure 5.1. Location of low income settlements (green dots: original location, red dots: relocation byexperts)

Source: Hazard Centre, 2001

Table 5.4 shows number of households who have been shifted to resettlement colonies. More than
40 000 households have been shifted from the central city location to the periphery of the city. This has
resulted in an increase in travel distance to work as well as to the public transport stop.

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Table 5.4. Number of households moved between 1997-2003
Site number

No. of households

Distance from original site

1

8 000

8 km

2

4 000

7 km

3

5 000

18 km

4

3 000

10 km

5

2 300

12 km

6

50

5 km

7

500

18 km

8

5 500

23 km

9

4 500

20 km

10

1 000

15 km

11

4 000

18 km

12

50

8 km

13

65

35 km

14

20

40 km

15

1 200

25 km

Source: Anand, 2007

Arora and Tiwari (2007) studied 2 000 households in Delhi to estimate the impact of relocation due
to metro construction in Delhi. The study documented accessibility and mobility conditions ofhouseholds residing in the city in slums before the construction of metro, and after they were relocated to
new locations planned by the city authorities at the outskirts.
Accessibility
Accessibility is a description of the proximity to destinations of choice and the facilitation offered by
the public transport systems to reach them (Anand, 2007).
The indicators of accessibility are defined as:•

The Public Transport Accessibility (APT) measures distance cost and time to bus stop andmetro stops.

•

The Spatio-Travel Accessibility (AST) has two components and is derived from household
surveys (both in self-planned settlements in the vicinity of the metro line, and households
relocated to expert planned settlements):− Spatial Accessibility (AS) describes the land-use components of accessibility.
− Travel Accessibility (AT) describes the accessibility of land use and transport systems forthe households surveyed.

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Mobility
Mobility is both the ability to travel to destinations of choice and the amount of movement necessary
to do so. By definition, the household’s ability to travel is seen as positive mobility from the socioeconomic perspective because it indicates that people are traveling for employment, education and otherpurposes, thus enabling value addition to the households. On the other hand, the amount of movement isseen as negative mobility from the socio-economic perspective because it uses resources of thehousehold, like time and money, which could have been better utilized to upgrade the quality of life of
the household.
Table 5.5. Percentage change in AST indicators for households relocatedChange category
Total decrease
No change
Total Increase

Deducation(diff)
40.8%
7.5%
51.7%

Dhealth(diff)
33.8%
3.5%
62.7%

Dservices(diff)
36.3%
11.9%
51.7%

Dbusstop(diff)
13.9%
14.4%
71.6%

Sbus
(diff)
1.5%
0.0%
98.5%

Source: Anand, 2007

Table 5.5 shows that, for the relocated households, the value of all the indicators have changed. The
distance to schools (Deducation) has increased for 52% of the households but decreased for 41% of thehouseholds. Similarly, the distance to health services (Dhealth) has increased for 63% of the households
and decreased for 34% of the households. Also, the distance to urban services (Dservices) has increased for
52% of the households and decreased for 36% of the households. The highest impact is seen in theindicators measuring access to bus system – the distance to the bus stop (Dbusstops) has increased for 72%
of the households and the time gap between successive buses (Sbus) has increased by more than 100% for
98% of the households. Figure 5.2 and 5.3 show change in distance and travel time before and after
relocation.
Figure 5.2. Change in travel distance before and after relocation
Daily Travel Distance per person (cumulative)
No. of people (percentage)

100%
90%
80%
70%
60%
50%
40%

Before relocation

30%
20%

After relocation

10%

Travel Distance (Km)
Source: Anand, 2007

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90

80

70

60

50

40

30

20

10

<=1

0%

136 – 5. PERSPECTIVES FOR INTEGRATING HOUSING LOCATION CONSIDERATIONS AND TRANSPORT PLANNING IN INDIAN CITIESFigure 5.3. Change in travel time before and after relocation

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Before relocation>7hrs

Travel Time (min)

390

330

270

210

150

90

45

After relocation

15

No. of people (percentage)

Daily Travel Time per person (cumulative)

Source: Anand, 2007

Table 5.6 shows that, for the households living in settlements which have not been relocated, there issome change in the indicators of per capita trip rate (PCTR) for work (there is no change for 78% of thehouseholds and it increases for 13% of the households) and other purposes (there is no change for 82%
of the households and it decreases for 14%), but little change in the PCTR for education (there is nochange for 91% of the household. The share of NMVs in the modes used for travel in households does
not change for 87% of the households, increases for 7% and decreases for 5% of the households.
Table 5.6. Percentage change in mobility indicators for households in the vicinity of the metro line
Change category

Total decrease
No change
Total increase

PCTRwork(diff)

9.4%
77.8%
12.8%

PCTRedu(diff)

3.9%
91.1%
4.9%

PCTRothers(diff)

13.8%
81.8%
4.4%

Mnmv/Mall(diff)

5.4%
87.2%
7.4%

Source: Anand, 2007

Table 5.7 shows minimum change in the mobility indicators regarding travel for education (distance,
time, cost). The distance to work, the time to work and the cost has not changed for 73%, 72% and 91%
of households respectively and has increased for 17%, 17% and 5% of households respectively. For trips
made for other purposes, the distance, time and cost indicators have not changed for 72%, 72% and 93%
households respectively, and have decreased for 15%, 16% and 4% households respectively.

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Table 5.7. Percentage change in MP indicators for households in the vicinity of the metro line
Changecategory
Total
decrease
No change
Total
increase

Dwork (diff)

Deducation(diff)

Dothers(diff)

Twork(diff)

Teducation(diff)

Tothers(diff)

Cwork(diff)

Ceducation (diff)

Cothers(diff)

10.3%
72.91%

3.9%
90.64%

15.3%
72.41%

13.8%
69.46%

4.4%
88.67%

16.3%
71.92%

3.4%
91.13%

0.0%
100.0%

4.4%
93.60%

16.7%

5.4%

12.3%

16.7%

6.9%

11.8%

5.4%

0.0%

2.0%

Source: Anand, 2007

Tables 5.8 and 5.9 show that, for the households relocated as per the land use policy, the value of all
the mobility indicators have changed for the majority of the households. Table 5.8 indicates that for 49%
households, the PCTR for work has increased and for 30% of the households it has decreased. For 71%
of households, the PCTR for education does not change – it increases for 19% and decreases for 10% of
the households. The PCTR for other purpose has increased for 35% of the households and decreased forthe same percent of households. The share of NMVs in the mode used has decreased for 59% of thehouseholds.
Table 5.8. Percentage change in MHH indicators for households relocated toexpert planned settlements
Change category
Total decrease
No change
Total increase

PCTRwork(diff)
29.9%
21.39%
48.8%

PCTRedu(diff)
10.4%
70.65%
18.9%

PCTRothers(diff)
35.3%
29.35%
35.3%

Mnmv/Mall(diff)
58.7%
21.89%
19.4%

Source: Anand, 2007

Table 5.9. Percentage change in MP indicators for households relocated toexpert planned settlements
Changecategory
Total decrease
No change
Total Increase

Dwork(diff)
14.9%
2.49%
82.6%

Deducation(diff)
22.9%
43.28%
33.8%

Dothers(diff)
58.2%
8.96%
32.8%

Twork
(diff)
14.4%
3.48%
82.1%

Teducation (diff)
21.9%
42.79%
35.3%

Tothers(diff)
52.2%
7.96%
39.8%

Cwork
(diff)
10.4%
28.36%
61.2%

Ceducation (diff)
2.5%
93.53%
4.0%

Cothers(diff)
12.4%
65.17%
22.4%

Source: Anand, 2007

Table 5.9 shows that the mobility indicators for travel to work – distance, time and cost – haveincreased for 83%, 82% and 61% of the households respectively. The distance, time and cost for
education have not changed for 43%, 43% and 94% of the households respectively and have increased
for 34%, 35% and 4% of households respectively. Regarding travel for other purposes, there is adecrease of distance and time for 58% and 52% households respectively but no change in cost for 65% ofhouseholds.
The results of the study show that for the poor households, which are not relocated to new areas,
there is no significant impact on the indicator of mobility. The construction of metro line does not change
their mobility patterns. However, since the bus routes and location of bus stops have been changed, thesehouseholds face reduced access to transport services. With regard to the accessibility of the households,
while the land use accessibility remains unchanged, the transport accessibility has changed as distance to

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the bus stops has increased for 19% of the households and bus services have become non-existent for
33% of the households.
On the other hand, for poor households relocated to the new areas, there has been a significant impact
on the indicators of accessibility and mobility. The land use accessibility has deteriorated as distance toschools, health services and other urban services have increased for 52%, 63% and 52% of thehouseholds respectively. The transport accessibility has deteriorated even more as distance to bus stop
has increased for 72% of the households and the bus frequency has decreased, on an average, from
5 minutes to 63 minutes (almost 13 times). The mobility of the households has increased significantly.
The PCTR for work has increased for 49% of the households and decreased for 30%, implying change in
the number of trips made for work in the households. The share of NMVs amongst the mode used has
decreased for 59% of the households. The mobility indicators for travel to work – distance, time and cost– have increased for 83%, 82% and 61% of the households respectively.
Sarma (2015) studied travel patterns of people living in informal settlements in Delhi in 2012. Travel
diaries of 1 700 individuals revealed the importance of quick access to employment for the inhabitants of
the informal settlements. This also explains why the informal settlements brave illegality in the face of
hostile authorities to stay close to their employment opportunities and raises questions against
rehabilitation of slums in remote corners of city with reduced access to employment and amenities(Anand and Tiwari, 2007). While planning rehabilitation of informal settlements, the authorities must
consider the proximity to their employment lest their very employment opportunities are adversely
affected. The study also found that travel cost places a burden on the travel options of the residents of
informal settlements, which accounts for the heavy dependence on walking and non-motorized transport.
The usage of even public transport has been very limited as it places a burden on their finances. Thisshows that facilities like metro rail system, which are more expensive than the city bus, havequestionable utility in providing accessibility for a large section of population that are poor.
The study also found that in settlements that came up close to the resettlement colonies significantly
reduce education and work trips. These are the settlements formed by the former dwellers of relocatedinformal settlements but were deprived of the government housing. After the forced evictions, generally
these settlers try to find a place close to their community who are rehabilitated in to government built
resettlement colonies far from their employment. We expect that those living in resettlement colonies
also are likely to have the same socioeconomic characteristics as these people and suffer similardisadvantages. The survey showed that most of the travel by the inhabitants of informal settlement isperformed by walking or bicycle, the male employees have a higher probability of performing work tours
than females. Most of the tours made by the women employees have been found to be pedestrian tours
and are short distance tours.
Table 5.10. Travel characteristics of residents of informal settlements
Type ofemployment
FormalAverage PT distance
(Km)
14.4

Average PT travel time
(Minute)70

Average PT travel cost
(INR)
28.3

Informal

9.7

62

12.3

3.3
7.1

29
47

Student
Total

1.7
8.9

Source: Sarma, 2015

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Table 5.11. Travel distance of residents of informal settlements
Type ofemployment

Percentage of employees by tour distance (km)Formal01.0%

0.4-2.0
2.1%

2.1-5.0
1.9%

5.1-10
3.1%

>10
3.9%

Total
12.0%

Informal

5.7%

7.6%

7.7%

8.1%

9.0%

38.2%

Student

14.8%

23.0%

9.4%

1.0%

1.6%

49.8%

Total

21.6%

32.8%

19.0%

12.1%

14.5%

100%

Source: Sarma, 2015

Most of the resettlement colonies have been at the periphery of the city. Bhan and Shivanand (2013)
note:“This peripheralisation has increased post-1990s, with resettlement colonies literally at theedge of the borders of Delhi. This is seen particularly in the case of Narela and Holambi
Kalan/Khurd, where more than 25 000 families from 88 evictions were offered resettlement plots.
These two resettlement colonies are at the farthest tip of the northern edge of Delhi. Similarly, thenewest resettlement colony of Savda Ghevra, where more than 9 000 families have been offeredplots since 2006, lies at the western edge of the border of Delhi. None of the resettlement colonies
since the mid-1960s fall within the NDMC and Cantonment areas even though at least 48
evictions have taken place within these boundaries, indicating the premium that space holds inthese areas. The increasingly peripheral location of resettlement colonies, along with thecontinuing practice of prioritising resettlement rather than in situ upgradation, is thus creating
precisely the spatial segregation that the Master Plan Delhi, 1962) sought to avoid”.
Bhan and Shivanand (2013) also found that the four primary uses of the land that has been madeavailable after the eviction of slum dwellers are vacant land, road and related infrastructure, parks andplaygrounds, and government infrastructure (Figure 5.1). Of former basti land, 17% has been used forroad and related infrastructure, while 15% has parks and playgrounds on them. The rest havecommercial/ office space, have been reoccupied by JJ clusters, are being used for commercial purposes,
or have metro infrastructure, landfills, concrete residences, nurseries, or drain-related infrastructure.
Importantly, 25% of the recovered land remains vacant and unused. In the vacant land category, there
are at least 51 evicted sites – accounting for the displacement of a minimum of 21 551 families – thathave remained fully or partially vacant between four and 21 years. Of these, 19 sites have remained
partially/fully vacant for at least 10 years. The presence of so many vacant land parcels that were usedproductively by the poor for housing and shelter before their “reclamation” is not just an indicator of the
futility of so much of the displacement that has taken place in Delhi, but also makes a case for re-haulingthe state’s policy towards the land it owns and redefining the basis of the priorities on use of public land.
Instead of displacing the poor to reclaim land that is not needed for any other immediate purpose, thestate could have chosen to invest in upgrading the land.
Chennai evictions and resettlement:
Large scale resettlements have taken place in Chennai between 2004 and 2014. Alberts et al.(2015)
note that although the policy was designed to improve housing conditions for slum inhabitants, it alsomeant that residents became isolated from the city in terms of geographic distance, and the social
relations on which they had depended for work and income, consumption, social, and religious activities.
Alberts et al.(2015) paper uses a relational understanding of resettlement and exclusion, by examining

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how women's spatial practices are embedded in their livelihood strategies, their decisions about efforts
and cost versus access to resources, and the extent to which they are able to counter socio-economic andspatial exclusion. Studies on the effects of resettlements on livelihoods in the Chennai area emphasize
the disruption residents face in the new locations because of the distances to former social and economic
networks (Arabindoo, 2012; Van Eerd, 2008). They highlight the resident's agency and collective
mobilization to organize basic amenities and facilities in the new areas (Van Eerd, 2008). They also
highlight the lack of facilities linking resettlement areas to the rest of the city, especially in the first
period. Choices made in seeking employment and location of employment are influenced by a wide
range of factors, as shown by the study in the resettlement area Kanagi Nagar south of Chennai 10 years
after its establishment (Coelho et al., 2013). These include household situations and combined
responsibilities within and outside the household (especially for women). Women travel up to 25 km.,and the study by Alberts et al (2015) finds that both work and social relations are maintained across these
distances, as women frequent places in the city for work, family visits, education, visiting temples andchurches. This implies that women have maintained or re-established their relations with the city. This
result places the women from the fringe firmly back into the city. However, urban accessibility isstrongly linked to transportation accessibility. The majority of women stay in and around Semmencherry,within a radius of 5 km. Timings of transportation, social safety, and health issues limit women's
possibilities to utilize transportation in physical terms. Finances limit their possibilities further inaccessing transportation. This implies that their overall time–space access is limited (Kwan, 2002). Thesefindings are in conformity with several dimensions of Fereirra's conceptualization of “access” (Fereirra
and Batey, 2007). With better transportation links in Semmencherry, women have more opportunities forextending their space-time paths. However, two groups of women with very different situations arefound to do so. The women from extended families can use their access to expand their relations with thecity, for both employment as well as for accessing other facilities and resources. Women from singleperson households have no choice but to work and access work elsewhere in the city, juggling
livelihoods demands. They find themselves overstretched, which generates health and safety issues.
Cultural norms play an important part in limiting the mobility of the large proportion of womenremaining within the immediate range of Semmencherry.
Lucas (2012) shows that transport-related exclusion is a combination of socio-economic barriers
preventing marginalized groups from using public transportation and the absence of affordable, safe, andaccessible public transportation. Alberts et al (2015) reaffirm that the removal of obstacles to travel to thecity centre combined with sufficient social-economic resources are prerequisites for inclusion. Alberts etal (2015) suggest that the choice to travel is more complex and involves weighing different factors, suchas household needs, opportunities, and preferences, against the background of the households'
composition. The choice not to travel is not always made because traveling is impossible; women may
choose not to travel because they invest their time and resources elsewhere.

Planning for the poor and failure of planning
Many issues condition planning’s ability and willingness to support the urban poor in the Global
South. Roy (2009) discusses several possible failings in the context of India. Amongst these are
inadequate planning practice, failure to accurately forecast changing need and planning policy
increasingly underpinned by the imposition of values based on neoliberal, capitalist-driven rationality,
the latter noted repeatedly by others (Du Plessis, 2005; Fernandes, 2004; Roy, 2009). Both Shatkin(2004) and Fernandes (2004) highlight the political amnesia which seeks to disregard the existence ofpoverty through an apparent abandonment of place-based poverty alleviation policies. That planning
does not simply forget the poor but, through dominant planning rationales, actually exacerbates theirdifficulties. These rationales are manifesting in the built fabric of cities through spatial policies whichprioritise the needs and desires of the affluent (Fernandes, 2004). These policies have become theguiding norms of planning. Their use as standard processes and planning tools, without regard for their

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value or impact, allows little room for accommodating Watson’s (2003) ‘conflicting rationales’ existing
between different stakeholders in the urban arena.
Low-income settlements whether self-planned or by the experts as part of the resettlement process
have similarities in some aspects and differences in many aspects. While density patterns and usage ofspace is somewhat similar, one of the most important differences is location of the settlement withrespect to the city. The self-planned low-income settlements are mostly located near the city centre,
commercial or industrial hub or formal planned residential areas. Figure 5.4 shows a Google view of a
low-income settlement in south Delhi. The land use around the low-income settlement provides bothformal and informal employment opportunities. Most of the work places can be accessed by walking or
bicycles resulting in least amount of travel expenditures. Studies conducted by Sarma (2015) show
almost 80% trips are walking trips in these settlements. The length of these trips is also less than akilometre. The travel pattern of residents of low-income settlements that are at the periphery in theresettled colonies is different. Number of walking trips reduce and distance to work increases. Locationof resettlement colonies have been decided primarily on the basis of inexpensive land. Proximity toemployment opportunities is not the prime concern.
Figure 5.4. Contiguous development of low-density, high income and high-density, low-income
colonies (enclosed within red boundary) in southern part of Delhi

Source: Sarma, 2015

The literature on transport-related exclusion provides insights into the relations between (lack of)transportation opportunities, spatial practices, and livelihoods. Its thrust is that residential locations
removed from the city centre and its job opportunities result in isolation and poverty due toinaccessibility or high costs of transportation (Anand and Tiwari, 2006; Lucas, 2012; Olvera et al., 2008;
Srinivasan et al., 2007; Srinivasan and Rogers, 2005; Ureta, 2008; Venter et al., 2007). Transport-related
exclusion occurs when social and economic disadvantages overlap with insufficient access totransportation (Lucas, 2012). Several forms of exclusion are distinguished: physical and geographicalexclusion, exclusion from facilities, economic exclusion, time-based exclusion, fear-based exclusion, andspatial exclusion (Church et al., 2000, Lucas, 2012). Especially women face limitations in their spatial
practices and access to transportation caused by the combination of productive and reproductive tasks,
geographic distances, cultural norms concerning the use of certain modes of transportation, safetyconcerns, costs, and gendered relations within the household (Anand and Tiwari, 2006; Astrop et al.,
1996; Kwan, 2002; Lucas, 2011; Venter et al., 2007).

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142 – 5. PERSPECTIVES FOR INTEGRATING HOUSING LOCATION CONSIDERATIONS AND TRANSPORT PLANNING IN INDIAN CITIESWe summarise these findings with respect to housing location, land use patterns and transport
options for inclusive urbanisation: housing of low-income households and presence of slums or informal
settlements in cities has been one of the major concerns of urban planners and policy makers since
1950s. The policies have ranged from forced evictions to in situ upgradation as the preferred option.
Most policies have focused the need for affordable decent housing and ownership of land and access tobasic amenities. Housing banks have been set up for easy financing and better rehabilitation policies.
However, these policies have missed the strong link between housing location-access to livelihoodaffordable transportation options and activity patterns of low-income households. As shown by Sarma etal (2014) employment pattern of low income households have to be understood differently. The
destinations change as per the requirement of the job. Very often the destinations are the formal planned
residential locations. Proximity to employment (formal and informal) is the most important factor
influencing the choice of slum location. Policies focusing on land ownership and affordable housing havemissed the link between location and livelihood.
Access to public transport systems like metro or bus system seems to be a weak option for addressing
the exclusion created by land use policies. A subsidized public transport does not address the timepoverty faced by many households and the flexibility required for multitasking, combining householdchores with employment. Access to employment and other activities have to be ensured by improving
pedestrian and bicycle facilities.
It is thus recommended that the new planning guidelines are framed for resettlement programs bylearning from the self-planned settlements (slums) in order that the needs and requirements of the people
are taken into account. Proximity to formal employment reduces dependency on motorized modes andreduces exclusion. This is more important for women who are involved in multitasking. More efforts are
required to understand the factors which have resulted in failure of in situ development of slums orinformal settlements.

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Chapter 6
Balancing financial sustainability and affordability in public transport:
The case of Bogotá, Colombia

Camila Rodríguez Hernández
The World Bank Group, Washington DC, United States
Tatiana Peralta-Quiros
The World Bank Group, Washington DC, United States
In order to meet the challenges of providing affordable public transit services for the urban poor andat a cost that doesn’t impinge on the system’s financial sustainability, cities can consider setting
fares at “cost recovery” levels for the majority of the population and targeting subsidies to those
who need them most. Bogotá is a case in point—the new public transport system was designed so
fares are set close to “cost recovery” levels to aim for greater financial sustainability. To provide
affordable services, the city leveraged the adoption of smartcards in its new public transit systemand the country’s poverty targeting instruments to implement a pro-poor public transit subsidy. This
chapter presents a critical analysis of Bogotá’s experience with trying to balance financial
sustainability and affordability. The chapter describes some of the features of Bogota’s tariff policy,
namely, the concept of tariff set at “cost recovery” levels and lessons learnt in trying to achievefinancial sustainability. The chapter also lays out the rationale, design and implementation of
Bogota’s pro-poor public transit subsidy, and the subsidy’s impact on its beneficiaries.

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148 – 6. BALANCING FINANCIAL SUSTAINABILITY AND AFFORDABILITY IN PUBLIC TRANSPORT: THE CASE OF BOGOTA, COLOMBIAIntroduction
Public transport is an important mode of transport, especially for low-income populations. Cities,
however, struggle to provide public transport services for fares that are both affordable and financiallysustainable. Since meeting both goals is quite difficult, transport systems, either end up relying on highlevels of subsidies or charging transit fares that are too expensive for the city’s poor.
As a result, some city’s transport systems use low fare levels (examples are Buenos Aires or the
Mexico City subway), which require high amounts of public subsidy. Other cities use higher and thus
more sustainable fares, but risk excluding the poor from public transport. The latter is the case in Bogotá,
Colombia, where fares for its new public transit system are set high, close to cost-recovery levels, which
may be unaffordable for part of the population. Bogotá is currently implementing its Integrated Public
Transport System (Sistema Integrado de Transporte Publico, SITP), which will integrate under a single
fare, operation, and infrastructure all its Transmilenio’s Bus Rapid Transit (BRT) corridors with thetraditional bus system, the bike network and, in the future, Bogotá’s first subway line. One of theprominent features of this public transit reform process—both in Transmilenio and the SITP— has been
the profound change in the business model and incentive structure for public transport service provision.
These new incentives are embedded in the concession contracts for service provision, and are linked tothe underlying concept of a tariff policy set at “cost recovery”. Against this backdrop, Bogotá has tried
to balance the needs for financial and social sustainability by setting fares at cost-recovery but thenoffering targeted subsidies for specific segments of the population.
This chapter presents a critical analysis of Bogotá’s experience with trying to balance financial
sustainability, by setting fares close to cost recovery levels, with affordability, by implementing targeted
demand side subsidies for public transit. The chapter describes some of the features of Bogotá’s tariff
policy in the context of the ongoing reforms; namely, the concept of tariff policy set at “cost recovery”
levels, and explains how a structured process that incorporates incentives for efficiency via concession
arrangements for service provision was developed. Against this backdrop, the chapter also explains how
the city tackled some of the affordability constraints of the poor, by developing a targeted public transit
subsidy that leveraged the country’s poverty targeting system and database, and the progressive adoptionof smartcards in its public transit system to design a pro-poor targeted subsidy scheme.
The first section of this chapter provides a literature review on the case for supply and demand side
subsidies in public transit, particularly for a developing country context. The second section describes
how Bogotá developed a structured process to set fares set close to “cost recovery” in its new transportsystem, and some of the lessons learnt in aiming for greater financial sustainability. The following
sections describe the design, implementation and impact of Bogotá’s pro-poor subsidy scheme. Thissection also briefly explores the results of the impact evaluation that analysed the determinants of opting
to request the subsidy and the subsidy’s impact on user’s labour market outcomes. The chapter concludes
with some recommendations in terms tariff policy, financial sustainability and design of targeted propoor subsidies.

Subsidies in public transit - the case for supply and demand side subsidies
A critical policy question is whether public transit subsidies should be given on the supply or on the
demand side. There are three primary theoretical justifications for supplyside public transit subsidies.
First, it is argued that such subsidies reduce the cost of environmental externalities; second, that they
exploit users economies of scale; and third, that they increase cost efficiency of public transit systems asa whole. The first argument is based on the idea that subsidies can reduce congestion by shifting use
from individual automobiles to public transport (here, subsidies act as a second-best alternative tomarginal social cost pricing of road use). However, this hinges on the attractiveness of public transport to

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current automobile users, and the cross-elasticity of demand has been found to be typically low (Toner
1993, Wardman 1997, Paulley et al 2004, Litman 2015). The second argument, based on the so-called
Mohring effect (Mohring, 1972) which describes the observation that there are increasing returns to scalefor urban transit services because the frequency of a service increases with demand, and so increases indemand reduce the time costs associated with waiting and transfers. This, however, relies on excess
supply side capacity. Finally, the cost-efficiency argument posits that public transit subsidies can reduce
total transport system costs through greater economies of scale due to transport system intermodality(Train 1977).
Empirically, under certain conditions supply-side subsidies that help improve the quality of service
by increasing service frequency have been found to be socially desirable (Dodgson 1987; Glaister 1987,
2001; Savage and Schupp 1997; Small and Gómez-Ibáñez 1999; Savage 2008; Parry and Small 2009).
However, some disagree that providing supply side subsidies are favorable because of the negligible
effects on the quality of service and the associated adverse effects on operational efficiency. Studies ofsubsidy impacts (Altshuler et al, 1981; Meyer and Gomez Ibanez, 1981; Hilton, 1974l Hamer, 1976;
Webber 1976) have concluded that direct benefits to transit rides have been small relative to the increasein the subsides and that alleged environmental and secondary economic benefits are negligible or no
existent. Goldman and Wachs (2003) have shown that, in the United States, transit subsidies haveresulted in system inefficiencies because of inadequate allocation mechanisms and incentives for costefficiency, and failure to improve level of service via increased frequencies or fare reduction. Pucher,Markstedt and Hirschman (1983), found that US Federal subsidies and dedicated state and local subsidies
had a slight cost-inflationary effect on the cost of providing transport services, further exacerbating
increases in cost. In the US during periods when subsidies have increased most, productivity has
declined, causing costs to grow more rapidly. Obeng (2009) finds that transport subsidies are
inadequately used, and points to the inefficiencies and risks associated with the perverse incentives
created by supply-side subsidies. A report by TRRL (1980) estimated that up to half of the costs ofsubsidies in the UK in the eighties were estimated to “leak” to operators in the form of high wages orinefficient operating arrangements.
In developing country contexts, the arguments against supply-side subsidies can further be extended
by the need to limit the fiscal burden, given limited resources and competing needs; and the need to focus
subsidies where they are most needed socially (Gwilliam 2012, Serebrisky, Gomez-Lobo, Estupinan andMunoz-Raskin, 2009). Hence, to balance the needs for economic and social sustainability, ideally cities
should try to set fares close to cost-recovery, but offer targeted demand-side subsidies for specificsegments of the population that face an affordability constraint. This, however, introduces additional
complications, and the experience thus far with demand-side subsidies is mixed; these subsidies have notalways led to the intended results because of difficulties with accurately identifying the target population,
potential abuse of the subsidy such as transferring the subsidy to an unintended recipient, and large errorsof exclusion or inclusion of the target population (Box 6.1). There is also very little evidence on theimpact that such subsidies have on the lives of target beneficiaries.

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150 – 6. BALANCING FINANCIAL SUSTAINABILITY AND AFFORDABILITY IN PUBLIC TRANSPORT: THE CASE OF BOGOTA, COLOMBIABox 6.1. Lessons from “first generation” demand-side subsidy programs
Several types of subsidies have been used to support low-income users of public transport. While wellintended, the subsidies provided through these “first generation” subsidy programs do not always reach the target
audience and may even have unintended outcomes. The following are four types of subsidy programs and lessons
that have been learned from their implementation:•

Vale-Transporte. This subsidy program in Brazil caps commuting expenses for workers in the formal
economy to 6% of their wages, with employers paying for the rest as a tax-deductible expense.
Employees could request to join the program. This makes formal workers somewhat immune to higher
transit tariffs, while providing no relief to workers in the informal sector (who constitute about 57% of
the labor force nationwide). The government paid about 35% of the cost in foregone tax revenue. Thissubsidy created a substatintial black market for reselling ‘free’ tickets.

•

Cable cars (telefericos) and feeder lines. Teleferico services in Rio de Janeiro (Brazil) and Medellin(Colombia) provide free transport to and from certain poorer neighborhoods using cable cars. In Bogotá(Colombia), free “feeder lines” connect certain neighborhoods to the city’s Transmilenio BRT system.
While providing free services to poorer neighborhoods increases access, this kind of subsidy has
significant exclusion errors in that it excludes a large number of low-income households living elsewherein the city. The subsidy may also exacerbate a city’s already existing spatial segregation.

•

The Bilete Unico and Integrated Fares that subsidize transfers. The Bilete Unico system in Brazil,
and integrated fare systems in Bogotá and Santiago, use a card to cap the fare for multi-modal trips. The
program subsidizes transfers, which (in an urban structure with most of the poor living in the city’s
periphery and often needing to transfer) overwhelmingly supports low-income households. It howeveralso reinforces tendencies for urban sprawl and is characterized by errors of inclusion.

•

Subsidized fares for the elderly, students, war veterans, and other categories. While an important
way to improve transport access for target groups, this kind of subsidy suffers from both inclusion andexclusion errors: many poor might not fall into one of these categories and people who do may not bepoor.

Source: Mehndiratta et al(2014)

Building on the experiences of these “first generation” subsidies, Bogotá has recently innovated intargeted demand-side subsidies for the poor. The following sections explain how Bogotá implemented astructured process to set fares at “cost recovery” levels and how it dealt with an affordability constraint
for the poor by designing and implementing targeted demand-side subsidies.

The basics of Bogotá’s tariff policy and the concept of cost recovery
Until the development of the Transmilenio system in 2000, all public transportation in Bogotá was
operated under a system characterized by an inadequate incentive structure that led to an oversupply of
buses, increased congestion, reduced vehicle safety and low-quality service. Bus companies owned theroutes granted to them by the city government but were not required to own the bus fleet. Individual
investors owned buses, and bus companies rented out to bus owners the right to operate a certain route.
This arrangement induced bus owners to compete against other buses, irrespective of demand patterns, as
their revenue and the wage of the bus driver was directly related to the number of passengers carried. Bus
companies’ main assets were their routes as they rent them out to bus owners, so they had the incentive
to lure as many buses as possible to operate their routes. The incorporation of a number of buses beyond

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those required to serve the market led to excessive competition, locally known as “the penny war”
(guerra del centavo) because drivers literally fought for each prospective passenger (World Bank 2012).
In response to these shortcomings, by the end of the 1990s, the government of Bogotá began to
explore alternative ownership models and incentive structures that maintained the benefits ofprivatization but improved service provision. One such ownership model was concession contracts forservice provision. In this system, a competitive bidding process is used to determine which bus operators
will have the right to operate a route or a number of buses. The concession is for a limited period of time,
which ideally coincides with the useful lifespan of the fleet, as opposed to the lifetime permits offered in
the traditional model. Bus operators, in turn, need to own the bus fleet and operate it under close
supervision and regulation from the public sector, which determines whether operators supply thescheduled service. On the infrastructure side, the system uses exclusive bus ways, high-capacity buses, acentralized fare collection system based on the use of smartcards and a fleet control system (World Bank
2011). This new ownership model, incentive structure and infrastructure provision was at the crux of theTransmilenio BRT system, implemented in Bogotá in December 2000.
There are several features of the basic tariff model developed in the context of Transmilenio and usedsubsequently in Bogotá’s SITP and in all of the other Colombian BRT systems that are worthhighlighting. The systems were designed to be operationally self-sufficient with fares set at “costrecovery” levels. At present, Colombian law (Law 86 of 1989, Article 14) requires that public transportsystems operate in this manner and that city government does not subsidize the system. In fact, the law
mandates that fares charged should be sufficient to cover the costs of operation, management,
maintenance and replacement of the bus fleet. In order to determine the appropriate cost-recovery fare a
structured process was developed that incorporated incentives for efficiency. The process is as follows,
first, the public sector administrator designs the system and the operating plan. The public sector is also
responsible for developing and maintaining the system infrastructure. The cost elements included arethose associated with bus operations, fare collection and management, a trust agent (in charge ofcollecting revenue and then distributing payments to all agents) and a public sector system administrator
in charge of planning and managing the system. Each of these elements (except the public sector
administrator costs, which are estimated and added to the other costs) is then bid out separately. In thecase of bus operations, there is a strong focus on keeping in the new system as far as possible, drivers
and operators who were participating in the old traditional system. As such, owners and drivers areencouraged to organize into formal companies that then bid against each other (often with partners that
could include operators from outside the city) for a share of the service. Lastly, a notional “technicaltariff” is then estimated by summing up the winning-low bids for each cost element (plus the per ticket
costs estimated for the administrator) and dividing that by the expected ridership. In other words, the“technical tariff” or the tariff that allows for cost recovery is an indicative tariff that captures the required
average revenue per ticket sold that is needed to guarantee that the remuneration of all of the system’s
service providers (bus operators, fare collectors, trust agent, planning agency) is covered, given apredetermined level of service.
Four features of this structure and the experience with this system in Bogotá are worth highlighting.
•First, in general, the creation of the notional “technical tariff” needed to operate the system atcost-recovery levels, constructed in a manner that incorporates incentives for efficiency for
different stakeholders has been a useful and important development. While the user tariff needs
to closely track the technical tariff, the city Mayor (who determines the user tariff) has somediscretion on timing and level of adjustments. The result has been a system whereby (i) theneed and level of user tariff adjustment needed has been important public information making
at least somewhat easier the Mayor’s task of adjusting fares; (ii) in Bogotá the system has gone
through periods when the user tariff has been higher than the technical tariff – creating a

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surplus that has helped finance deficits in periods (like the present) where adjustments in theuser tariff have lagged changes in the technical tariff.
•Second, the ridership estimates play a critical role in determining the initial technical tariff. In
the case of the initial Transmilenio corridors, demand exceeded estimates, thus providing thesystem with surpluses in initial stages. In the case of the SITP actual ridership levels have beensignificantly lower than expected, leading to important deficits for the systems in initial stages.
The experience clearly reinforces the need for high quality demand forecasts as an input into
project structure.

•

Third, it is the bidding process that guarantees efficiency in costs, and as such, the level of
efficiency obtained depends on the competitiveness of the bidding process. Bus-operating
costs constitute the bulk of the technical tariff (between 70-80%), and as described above theinitial bidding process, while competitive is not truly open. To safeguard the interest andlivelihood of the traditional and largely informal public transport sector, the tendering processincludes prerequisites that protect sector incumbents. On one hand, given the complex social
environment characterizing the traditional bus system in Bogotá, the ability to create somecompetitive dynamic is itself both difficult and commendable: in a similar environment in
Mexico, the practice is to negotiate with no elements of a competitive dynamic. Compared tothat, the practice in Bogotá provides an important incentive for efficiency in bus operating
costs. However, a truly “competitive” and open tendering process is ultimately the best
guarantor of efficiency. This suggests the need to ensure that initial contracts are of areasonable duration and that once they expire they are followed by open competitive bidding
processes. In reality, the initial contracts for Bogotá had duration of 12 to 14 years, but wereextended by Transmilenio in a recent renegotiation. The SITP contracts have duration of 24
years. In the medium and long term, moving towards a system of re-competing concessions
openly would be important to ensure system efficiency.

•

A fourth element of the tariff system is “scope creep”: more and more cost elements notdirectly related to operations have been loaded on the tariffs. In Bogotá the tariff is also paying
for planning and supervision of the systems (by way of financing the BRT agencies,
Transmilenio), bus scrapping costs (indirectly as part of bus operating costs) or infrastructure
provision. Many of these costs are capital costs— bus scrapping, fleet renewal, infrastructure—
and/or elements of the reform process –formal employment for bus drivers and maintenancepersonnel— that bring benefits not just to users but to society as a whole— pollution and GHG
emission reduction, road safety. Thus, it is not clear that public transit users should have to pay
for enhanced societal welfare or for non-operating costs. All in all, the fact is that for lack ofmore sustainable and secure sources of funding, the tariff has been used sometimes arbitrarily
to finance a number of elements that are not direct “operating costs” and are placing strains on
the financial equilibrium of the system.

Bogotá’s pro-poor subsidy scheme
In order to meet the challenge of improving affordability to public transit for the poorest, Bogotárolled-out in early 2014 a “pro-poor” public transit subsidy based on recent technical assistance provided
by the World Bank (World Bank 2013). This work provided a better understanding of the poor´s travel
patterns, the alternatives for targeting the subsidy, the implications on the system´s costs, and alternatives
on how to implement the scheme. The following sub-section presents a framework for how Bogotá
designed this scheme which is based of four key considerations: (i) understanding the current and

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expected travel patterns of potential beneficiaries; (ii) understanding who to subsidize and by how much;(iii)comparing system impacts and financial implications; and (iv) planning for implementation1.
A framework for designing and implementing a targeted transport subsidy
Understanding current and expected travel patterns.
An important part of a successfully designed subsidy program is understanding how mobility
constraints and poverty are interacting and how people, in particular the target population, currently
travel and will likely travel under a subsidized scheme. Answering these basic questions, however, is noteasy, as a significant mismatch exists between available data on travel patterns and data on poverty.
While many cities collect travel survey data as part of their broader transport planning process, most ofthese surveys do not include good information on poverty— specifically poverty as measured bymultidimensional factors. Likewise, poverty surveys rarely collect mobility data.
For Bogotá, data was analysed from the 2011 Mobility Survey, the Multi-Purpose Survey, as well as
further analysis were performed to spatially correlate quality of life indicators with mobility patterns.
According to Bogotá’s 2014 Multi-Purpose Survey, which provides welfare and socioeconomic
indicators for the city, households in the poorest areas of the city spend a greater percentage of theirincome on transport, between 16% to 27%, compared to a maximum of 4% in areas that are relatively
richer (see Figure 6.1 and 6.2). Bogotá’s 2011 Mobility Survey also corroborates this—the population in
the lowest income strata usually travels less, and walks or bikes for longer trips.

Figure 6.1. Number of trips and individual income intended for the transport

Source: Bogotá Multi-Purpose Survey, 2014.

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Figure 6.2. Public transportation spending as a proportion of total household expenditure

Source: Bogotá Multi-Purpose Survey, 2014.

Understanding who to subsidize and by how much.
Different kinds of subsidy schemes exist (including also the “first generation” schemes fromBox 6.1) and which one fits best depends on the characteristics of the city population, the transportsystem, and transit users, as well as on political decisions related to the available resources and size ofthe target populations. A systematic comparison of alternative subsidy schemes, including (i) traditionalsocio-demographic subsidies (i.e., targeting specific groups such as the elderly or students); (ii)employer-based subsidies (which generally exclude the informal sector); (iii) spatial subsidies (e.g.,
subsidies for trips that begin or end within certain pre-determined stations); and (iv) schemes that target
the poor directly (proxy-means tested), can provide a sound basis for discussion and decision-making.
Ideally, this assessment will cover both affordability benefits to the target population and financial
objectives for the transport system.
For the case of Bogotá, two targeting alternatives were analysed — proxy means tested by using
Colombia’s national targeting system, SISBEN ((Sistema Nacional de Selección de Beneficiarios, seeBox 6.2), or geographic targeting. In general, the goal was to minimize the subsidy’s errors of inclusion(percentage beneficiaries that are unintended) and exclusion (percentage of intended beneficiaries leftout) to ensure the subsidy reached the intended audience. In Bogotá, several technical and politicalreasons led to using the SISBEN as the targeting instrument. First, this instrument is used in all ofColombia’s social safety net programs, and thus provided a transparent and well-known means toadminister this new subsidy scheme; second, although Bogotá is spatially quite fragmented, and ageographic subsidy could potentially be implemented, the administration felt it could lead to further
spatial segregation and fragmentation.

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Box 6.2. Proxy means testing for targeting – Using Colombia’s SISBEN Instrument
Colombia has used two methods for targeting social spending: the geographically based Socio-economic
Stratification and the proxy means tested System for Selecting Beneficiaries of Social Spending (SISBEN, in
Spanish). The stratification instrument classifies neighborhoods and rural areas in strata based on the external
characteristics of houses and neighborhoods. It is used to target subsidies for potable water, electricity and a variety
of other small subsidies, by central and local governments. In order to more efficiency target other subsidies
including health insurance, scholarships, conditional cash transfers, Colombia’s National Planning Department(DNP) introduced the SISBEN in 1994. The general objective of SISBEN was to establish a technical, objective,
equitable and uniform mechanism for selecting beneficiaries of social programs to be used by all government
levels.
The SISBEN utilizes a proxy-means test in order to determine whether an individual is eligible for assistancebased on whether he possesses the means to do without that help. The methodology relies on several socioeconomic indicators—or proxies— (household demographic composition and marital status, education,
employment, income, possession of goods and assets, and dwelling characteristics) to estimate household welfare.
(World Bank 2005)
The SISBEN database is created in a two-step process. Initially, a statistical model is used to determine thevariables that are representative of welfare level. These are then used to create the SISBEN questionnaires. In orderconstruct the SISBEN database, municipalities identify poor areas to be surveyed using a variety of information to
produce local poverty maps. Municipalities then launch the survey operation to apply SISBEN questionnaires to all
residents in selected areas. People who are not surveyed because they do not live in the pre-selected survey areas
can apply for SISBEN application at the municipal offices. The SISBEN Index gives a continuous score from 0 to
100 (from poorest to richest) divided into six brackets or levels (1 to 6). Levels 1 and 2 are people in poverty andable to receive most national and local programs.
The SISBEN methodology is updated every three years. The newest methodology for the SISBEN III,
updated in 2015, applies a multidimensional concept of poverty and includes elements of the vulnerability
conditions of the population. Most importantly however, the new SISBEN captures a larger degree of detail andrepresentation in the ranges of scores used to deliver social programs. Whereas before, the methodology provided
generalized levels for all social programs, the SISBEN III allows each program to define its own cutting points
based on the objectives of the program, and the characteristics of the population. This allows each program to createa threshold score that best responds to the concept of poverty, deprivation, or vulnerability, of each program; inorder to include the greatest amount of the population that requires the program and prevent the entry of people that
do not need it. (DNP 2015)
Based on a 2009 World Bank report, proxy-means tested has proven to work particularly well in countries
with high levels of informality and where personal and household income is difficult to verify. Proxy-means tested
instruments are not unique to Colombia; they have been used in Countries such as Chile, Congo, Costa Rica,
Cambodia, Indonesia, Mexico and Zambia.

Determining how much to subsidize — that is, the impact of changes in fare prices on ridership, iscomplicated, as almost no empirical data exists on the response of the target population to significant
changes in fare levels, in particular reductions in fares. Moreover, most empirical estimates of fareelasticity are valid only for small changes in fare levels. In the case of Bogotá, information on the fareelasticity for different beneficiary groups (those currently using the integrated new SITP system; thoseusing the traditional public transit system; and those currently not able to afford public transport) was
needed to discuss alternatives of how much to subsidize and to assess the impact of the subsidy schemeon system revenues, costs, and operating conditions (see Box 6.3). Because only limited data wasavailable about the way potential beneficiaries would react to changes in fares (either by increasing or

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decreasing certain trips or changing modes of transport), simple correlations were estimated using thecity’s mobility survey to get a sense of the direction and magnitude of their response to fare pricechanges, which was then followed by a price elasticity analysis using seven years of data from smart cardswipes and user entries at different Transmilenio stations in one of Bogotá’s poorest neighbourhoods,
Usme. This time series included 2012, when Transmilenio applied a differential fare scheme thatprovided rebates for off-peak travel. Overall, the result of these different methodological approaches
pointed to the fact that price elasticity for potentially poor beneficiaries was rather low (World Bank2013).
Financial impacts-- comparing system impacts and financial implications
Assessing the financial sustainability of different subsidy schemes was a critical part of the planningand design process. The main objective was to determine the implications of the new subsidy scheme on
overall ridership, system revenues, operating costs, and other conditions of the public transport system.
In Bogotá, for instance the total revenue impact was a combination of the additional revenue obtained
from new trips taken by beneficiaries, the foregone revenue resulting from subsidizing beneficiaries thatare currently using the system, and any additional costs incurred by operators from increasing service
supply to cater to new trips (Box 6.3). Elasticity estimates were also used in this part of the exercise toquantify these effects.
Box 6.3. Assessing the effect of a targeted demand subsidy in Bogotá’s SITP
In Bogotá, the design of a targeted subsidy scheme required carrying out an assessment of the scheme’spotential impact on Transmilenio and SITP system conditions (operating revenues, costs, and operational
constraints) under the current contractual arrangements.
As shown in Figure 6.3, the analysis suggested three linked outcomes of the implementation of the planned
subsidy scheme. First, partial revenue would be foregone for about 650 000 trips (about 4.9 percent of ridership)currently made by the intended beneficiaries annually who paid full fare. Second, additional revenue would be
generated by 110 000 new trips a year, as a result of subsidy beneficiaries making more trips, adding 0.8% toridership and revenue annually. Third, these additional revenues from new trips would require additional system
capacity, leading to new costs — additional cost/fleet and cost/km logged —roughly equivalent (and thus cancelingout) the additional revenue. These effects are a direct result of the fare elasticity of different beneficiary cohorts.

Figure 6.3. Evaluating financial and operational impacts:
Impact of a 30% face discount for the poor in Bogotá

Source: Shomik et al (2014)

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Finally, in terms of financial impacts and system comparison, attention was paid to funding sourcesof a subsidy scheme, which ideally would not come from the public transport revenue, to not put further
financial strains on the system. Although for the case of Bogotá general city budget was used to launch
the scheme, discussion on alternative sources of financing, such as generating cross-subsidies fromprivate vehicle users (parking charges, congestion pricing, and fines) to public transit users, is critical.
Using those funds to improve public transit systems, and financing targeted subsidy schemes, is ideallythe more sustainable and economically sound financing scheme for these sorts of policies.
Planning for implementation
Several implementation arrangements and rollout strategies were analysed for Bogotá-- selfselection, dissemination and registration, subsidy delivery arrangements, sharing of databases between
government entities, etc. Furthermore, since one specific element of the Bogotá subsidy scheme was toleverage the progressive adoption of the smartcard (TuLlave Card), mechanisms to control subsidy abuse
were analysed and adopted via the modification of the smartcard card protocols (inclusion of time
windows, restriction on number of station validations, number of trips, etc.).
Figure 6.4 summarises the final design and implementation arrangements adopted. Citizens that live
in Bogotá, that do not receive other concessional fares and have a SISBEN score of 40 or less can opt torequest a public transit subsidy. The subsidy amounts to COP 900 (Colombia peso), equivalent to almost
USD 0.30 (the average fare is USD 0.55), and is capped at 40 trips per month (on average this represents
a 45% discount for trunk services, and 53% discount for feeder services). The card is also programmedwith one free “credited” trip that can be used when the card is loaded, and once validated in a station,cannot be reused within a 30-minute window. Thus, people who are eligible have to request the subsidy,
and the card is programmed with certain features to prevent subsidy abuse... Although the SISBEN
dataset has the address of the potential beneficiary and in theory cards could be mailed to the entire
potential beneficiary universe, Bogotá decided to opt for the mechanism of self- selection during initialsubsidy roll-out to manage potential subsidy abuse, get a better sense of the resources needed andmitigate fiscal impacts, as every card that is issued needs to be paid to the fare collection concessionaire.
Potential beneficiaries can obtain the personalized subsidy card in the local government’s city centres,
Transmilenio Terminal depots, or using the mobile (minivans) points of sales. Once a potential
beneficiary ID is validated against the SISBEN database, the beneficiary receives the smartcard in about
three business days.

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Figure 6.4. Design and implementation of the Bogotá SISBEN Subsidy

Source: World Bank

As of July 2015, almost 300 000 (updating info with Tranmsmilenio) people had obtained thesmartcard with the subsidy, and almost 200 000 (updating info with Tranmsmilenio) had actually used it.
However, subsidized smart card use has been substantially lower than the potential beneficiary
population (nearly 800 000 people).
Impact of targeted pro-poor public transit subsidies in Bogotá
As explored previously, there is very little evidence on the impact of targeted pro-poor subsidies on
the lives of its beneficiaries. As a part of a larger impact evaluation published in this year’s
Transportation Research Record, we explored the determinants of opting to request the smartcard withthe subsidy, the effects of the subsidy in the use of the system, and the subsidy’s impact on user’s labour
market outcomes.
This impact evaluation explored the following questions: What are the determinants of user’s “selfselection”? How did the use of the public transit system changed with the application of the subsidy?
What are the causal links between the subsidy and labour market outcomes: employment and income?
Two regressions, (linear and probabilistic) were used to identify the characteristics of individuals whochoose to request the subsidized fare. A quasi-experimental technique was used for the impact
assessment of the subsidy on the use of the system and on labour market outcomes. This research allows
us to being to understand how to design targeted subsidies for maximum labour market outcomes
Determinants of user’s “self-selection”
In terms of the determinants of “self-selection”, the analysis pointed to three important elements that
affected the probability of obtaining the subsidized cards: word of mouth, employment status and gender.
People who had a large number of neighbours in the program were more likely to enrol. Therefore adissemination campaign that focused at the neighbourhood level (significant resources have been used by
the administration to do mass media campaigns), and expands the opportunities for individuals to obtainsmartcard for the first time (scaling up for instance the minivan strategy in very poor neighbourhoods,

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6. BALANCING FINANCIAL SUSTAINABILITY AND AFFORDABILITY IN PUBLIC TRANSPORT: THE CASE OF BOGOTA, COLOMBIA – 159with low accessibility) are likely to increase the percentage of the target population that enrols.
Furthermore, people who are working are more likely to apply for the subsidy, this may reflect that thosethat are unemployed have other binding labour market conditions (eg. lack of proper skills), or that thesubsidy is not sufficient (higher percentage of the fare needs to be subsidized) to deal with their mobility
constraint. Women seem to be more resourceful The fact that women are more likely to opt for thesubsidy also might corroborate the argument that women have different travel patterns, and generally
travel more due to other household and non-employment duties, and thus would benefit more fromsubsidized travel.
Effect of the subsidy on the use of the transit system
The smartcard database provides a clear understanding of how travel patterns of the beneficiaries hadchanged with the implementation of the SISBEN subsidy.
The results demonstrated that the subsidy beneficiaries had an increase use of the public transit
system. The SISBEN subsidy recipients have an increase in monthly trips of nearly 56% when compared
to normal fare card use. This translates into 10 extra monthly uses of the system -- on average users had
eight additional trips and two additional transfers. These findings show that the subsidy helps overcome
the lower frequency of daily-motorized travel among the poor previously explored (Figure 6.5).
Secondly, the increase in transfers is also interesting, because the subsidy does not affect its cost. The
subsidy did not decrease the cost of a transfer, and therefore the increase in transfers might indicate thatthe users are learning how to take advantage of a multimodal system.
Figure 6.5. Subsidized cards in use and subsidized trips as a proportion of total

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Furthermore, we found no significant effects on the total transport expenses incurred by users being
subsidize. The proportion of income spent on transport did not change significantly, despite the subsidy.
Users appear to be using the system more.
Labour outcomes
In terms of the effects of the subsidy on labour market outcomes, the results demonstrate that thesubsidy had a significant increase on the hourly earnings of informal workers. The increase in income is
not associated with an increase in the number of hours worked per week; in other words, the effect is not,related to timesaving that allowed the individual to work for longer hours. Rather, it points to thecomplementarity between mobility and productivity of informal activities — the subsidy appears to be
increasing productivity by allowing informal workers to have better mobility and accessibility toeconomic opportunities, and thus higher earnings. Several exercises were performed to verify and further
understand these results; these exercises corroborated the effect of the subsidy on informal workers’
hourly income. Heterogeneous effects show that the effect on income of informal workers is particularlyhigh for those who, despite being informal, have a hierarchical working relationship with an employee.
This may be an indication that the channel through which the subsidy has an effect is through theincreased ease of time management.

Conclusions
In order to meet the challenges of providing affordable public transit services for the urban poor andat a cost that doesn’t impinge on the system’s financial sustainability, cities can consider setting fares atcost recovery levels for the majority of the population and targeting subsidies to those who need themmost. To this end, Bogotá, where fares are set at close to cost recovery, has designed and implemented apro-poor public transit subsidy scheme that leverages the country’s experience with poverty targeting
instruments and the gradual adoption of smartcards in its new public transit system. This chapter
presented a critical analysis of the structured process adopted in Bogotá to achieve a level of fares that isclose to “cost recovery”, and the rationale, design, implementation and impact of Bogotá’s pro-poor
public transit subsidy.
In terms of tariff policy, the experience in Bogotá suggests that tariffs set at ”cost recovery” should
be stripped down to the operating essentials, so as not to have the users pay for capital cost or societal
benefits. Furthermore, it becomes increasingly important, particularly after the initial stages of thereform, that each element or service competent included in the tariff is openly tendered to ensure
efficiency. Tariffs set at “cost recovery” levels are critical in many parts of the world, either for reasonsof law or because money from the public coffers is not available. Hence, to deal with political pressures,
it is recommended that (i) there is some assurance of cost efficiency; (ii) there are schemes in place, suchas targeted subsidies, to deal with the poor; and (iii) high quality demand forecasts are done, to aim forrealistic system equilibrium. Furthermore, as there are steep learning curves in the initial years of thereforms, it becomes critical that contractual terms are not unreasonably long, so that subsequent phases
can incorporate the lessons learnt.
In terms of targeted demand subsidies in public transit, the case of Bogotá allows us to begin tounderstand how to design targeted subsidies so that the poor and most vulnerable are able to seize theeconomic, educational, health, and cultural opportunities that greater mobility provides. However, further
research on this topic, including analysing the impacts on mobility patterns and quality of life of thetarget population is needed. A randomized impact evaluation is being considered in Bogotá to assess theimpact of different subsidy schemes (increasing the percentage of the fare being subsidized, changing thenumber of trips, etc). A randomized trial will allow us to have further understanding of labour outcomes,
mobility patterns and design optimal subsidies for the future.

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Furthermore, the case of Bogotá also demonstrates that there might be other binding constraints toimproving mobility for the urban poor. While the pro-poor subsidy in Bogotá focuses primarily ontackling increased affordability, other constraints such as the location of housing and employment
opportunities matter, issues concerning an individual’s employment accessibility. This dimension should
also be integrated in further research in order to more fully understand the impact of transport demandside subsidies.

Note

1

This section is largely based on World Bank (2013), and Mehndiratta et al (2014).

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Chapter 7
Is congestion pricing fair? Consumer and citizen perspectives on equity
effects

Jonas Eliasson
Department for Transport Science, KTH Royal Institute of Technology
Stockholm, Sweden
This chapter discusses and analyses whether congestion charges can be considered to be “fair” indifferent senses to the word. Two different perspectives are distinguished: the consumer perspective
and the citizen perspective. The consumer perspective is the traditional one in equity analyses, andincludes changes in travel costs, travel times and so on. Using data from four European cities, theanalysis shows that high-income groups pay more than low-income groups, but low-income groups
pay a higher share of their income. This chapter argues that which distributional measure is mostappropriate depends on the purpose(s) of the charging system. The citizen perspective is about
individuals’ view of social issues such as equity, procedural fairness and environmental issues. An
individual can be viewed as a “winner” from a citizen perspective if a reform (such as congestion
pricing) is aligned with his/her views of what is socially desirable. Using the same data set, theanalysis presented shows to what extent different income groups “win” or “lose” from a citizen
perspective – i.e., to what extent congestion pricing is aligned with the societal preferences of highand low-income groups.

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Introduction
Most transport economists and urban planners would agree that scarce road capacity should bepriced, and would hence support congestion pricing as a way to decrease traffic jams and use scarceurban land more efficiently. There is also substantial evidence from several cities that congestion pricing
indeed works as intended, and that the aggregate social benefits can by far exceed investment andoperating costs, provided that the system is well designed.
However, perhaps the most pervasive argument against congestion pricing is that it is unfair – astatement that can be interpreted in several different ways. The purpose of this chapter is to discuss andanalyse to what extent congestion pricing is “fair”, in several different senses of the word. The
quantitative analyses use survey data from four European cities: Stockholm and Gothenburg (Sweden),Helsinki (Finland) and Lyon (France). Stockholm and Gothenburg have operational congestion charging
systems, whereas Helsinki and Lyon do not. In the survey, respondents answered a range of questions
regarding their travel behaviour, their views of fairness and several societal/political questions, and how
they would vote in a hypothetical referendum about congestion pricing.
The purpose is to explore the fairness of congestion pricing from two perspectives, which can becalled the consumer and citizen perspectives1 (Nyborg, 2000; Sagoff, 1988). The consumer perspective
includes how an individual is affected personally: how much tolls he pays, how much travel time hesaves, his valuation of travel time and (if specified) the benefit of the recycled revenues. The citizen
perspective is about what the individual sees as “fair”, “just” or “desirable” from a social perspective,
disregarding his own self-interest. Clearly, these two perspectives are affected by each other. What anindividual thinks is “fair” is often correlated with what will benefit himself – after all, (all) humans are
not saints, at least not on the subconscious level. But just as clearly, opinions about societal issues are not
only determined by self-interest. There is abundant evidence that people’s votes and behaviour are also
affected by other concerns than self-interest, for example concerns about equity, environment andprocedural fairness.
Hence, congestion pricing may be seen as “unfair” in two senses, or both. First, they may be seen as
unfair in a “consumer” perspective, if they hurt low income groups disproportionately: if the poor pay
more in tolls than the rich, if they value their times savings less, or if they get less benefit from therevenues. Effects can either be measured in absolute terms or proportional to income; we will argue thatwhich is the most appropriate measure depends on to what extent the charges are (also) a fiscal policy.
The consumer perspective – tolls paid, time gained and revenue recycling – is the traditional perspective
on fairness in equity analyses of congestion charges, and there is abundant literature (e.g. Eliasson &Mattsson, 2006; Karlström & Franklin, 2009; Levinson, 2010; Small, 1992). This perspective is analysedand discussed in the first part of the chapter. Comparing results from the four cities show some striking
similarities, despite different system designs and travel patterns.
Second, congestion charges may be seen as unfair from a “citizen” perspective. This would be thecase if the support (or acceptability) of the fundamental underlying rationality or justification ofcongestion pricing differs across socioeconomic groups. For example, imagine a scarce resource which
can be allocated through three alternative mechanisms: pricing, queuing or by someadministrative/bureaucratic decision. Different individuals obviously prefer different mechanisms, for avariety of reasons, and the same individual may prefer different mechanisms in different contexts. Say
that an individual can be labelled a “winner”, from a citizen point of view, when her preferred allocation
mechanism is the one that is used. Similarly, citizens can be labelled “winners” when societal decisions
regarding, say, environmental regulations or tax progressivity are made in consistency with theirpreferences as citizens (which may or may not be aligned with their “consumer” interests). The question
is now whether the share of “winners” on congestion pricing is different across (socio-)economic groups.
This would be the case if congestion pricing is an “elite” project, which is more consistent with richer

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and/or more educated groups’ views of what is fair, just or socially desirable. It is known from previous
research that, ceteris paribus, support for congestion pricing is higher among individuals who rateenvironmental issues as important, and who perceive pricing as a fair allocation instrument. It is easy to
imagine that high-income groups may view pricing as a fairer allocation mechanism than, say,administrative decisions – perhaps due to education, or self-interest, or social norms. It is also
conceivable that high-income groups may place a relatively higher weight on environmental benefits.
Whatever the reason, if this is the case, it would be reasonable to conclude that rich groups are “winners”
from a citizen perspective, whether or not they are winners from a consumer perspective. These
questions are discussed and analysed in the second part of the chapter. Comparing results from the four
cities again show striking similarities, despite different political cultures in general and framing of thecongestion pricing issue in particular.

Background and data
The data in this study comes from a survey first designed by a Swedish-French-Finnish team ofresearchers, and carried out in Stockholm, Lyon and Helsinki in 2011 (Hamilton, Eliasson, BrundellFreij, Raux, & Souche, 2014). Later, two waves of the survey (with some minor modifications) werecarried out in Gothenburg in 2012 and 2013, i.e. both right before and almost one year after Gothenburg
introduced its congestion pricing system (in January 2013) (Börjesson, Eliasson, & Hamilton, 2016). The
references provide more information about the data and its collection. The survey was presented as a
general survey about several transport-related issues; to avoid policy bias, it was deliberately notpresented as a survey specifically about congestion charges.
All the four cities are medium-sized cities with fairly typical European structures and transportsystems. All have a historical city centre encircled by more recently populated areas. Around 80% ofhouseholds have access to at least one car. Trips have a predominantly radial pattern, with the main flowof commuters moving inward in the morning and outward in the evening. Public transport shares vary,
but are much higher than e.g. typical US levels in all the four cities. Transit fares are subsidized around50%. Stockholm and Gothenburg have operational congestion charging systems, whereas Helsinki and
Lyon do not.
In the survey, respondents were asked how they would vote in a (hypothetical) referendum about
congestion charges. Respondents were presented with different systems in the four cities. In Stockholm
and Gothenburg, the question referred to the actual systems. The Stockholm system was introduced in
2006, and consists of a cordon around the inner city where drivers pay EUR 1 to EUR 2 per passage(both directions) during weekdays, depending on time of day between 06.30 and 18.30. (The Stockholm
experiences are further described in e.g. Eliasson (2008) and Börjesson et. al. (2012).) The Gothenburg
system, introduced in 2013, consists of a cordon with three additional charging borders located as raysout from the cordon. Drivers pay EUR 0.8 – 1.8 per passage (in both directions) depending on the time ofday, weekdays 06:00-18:30. (Traffic effects are described in Börjesson and Kristoffersson (2015), andpublic attitudes in Börjesson, Eliasson and Hamilton (2016).)In Helsinki, the question referred to a proposed system intensively debated at the time of the survey.
The system was supposed to be based on GPS units in all vehicles, with different tariffs per kilometre in
two zones – an inner zone covering the central area in Helsinki, and an outer zone covering most of
Helsinki. Political support for congestion pricing was never widespread, and at the time of the survey, it
became clear that there was a decisive majority against its implementation. At present, there are no plansfor implementing congestion pricing in Helsinki.
In Lyon, the question referred to a hypothetical system where all cars entering the urban centre would
pay EUR 3 per day, independent of time of day or day of the week, with a maximum of EUR 50 per
month.

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Table 7.1. Description of the surveys
Stockholm

Helsinki

Lyon

Gothenburg,
2012

Gothenburg,
2013

Date

Spring 2011

Spring 2011

Spring 2011

December 2012

December 2013

MethodPostalPostalTelephone

PostalPostalNumber of
responses

1 837

1 178

1 500

1 582

1 426

Response rate

43%

39%

37%

40%

38%

The subsequent analyses are based on approximate monthly toll payments, calculated from
respondents’ own statements. In Stockholm, Lyon and Gothenburg, respondents were asked how often
they paid the congestion charge (Stockholm and Gothenburg 2013) or would pay if they drove as today(Lyon and Gothenburg 2012), and this was converted into approximate monthly payments using data on
the average payment per day. In the case of Helsinki, respondents were asked how many kilometres theydrove in each zone on an average day, which was then also converted into an approximate monthly
payment. Obviously, relying on respondents’ own estimates of their toll payments introduces someuncertainty, so numerical results need to be treated with some caution. However, the general patterns in
the results are robust enough that this is not a significant problem for the purposes of this chapter.

Consumer perspectives on the fairness of congestion pricing
The standard economic welfare effects of congestion charges are made up of four parts: paid tolls,
adaptation costs (adjusting one’s travel pattern to the charges), the value of travel time gains and finallythe benefit of the recycled revenues. In the first section, we will concentrate only on the equity effects ofthe paid tolls. This is admittedly only a partial analysis, but nonetheless a relevant one. Regarding
adaptation costs, they generally make up a small part of the total welfare effect2. The value of travel time
gains may be substantial, but unfortunately they are difficult to quantify based on our survey data;
however, an attempt is made in the next subsection. Fortunately, previous research has shown that theamount of tolls paid is a good proxy for the total welfare effect, including adaptation costs and travel
time benefits (Eliasson & Levander, 2006; Eliasson & Mattsson, 2006). The use of revenues, finally, isof absolutely crucial importance for the equity effects of congestion charging reform seen as a whole.
However, how revenues are used can be seen as a separate issue, and it is illustrative to analyse the direct
equity effects of congestion charges separately from the equity effects of the revenue use.
Hence, the first subsection analyses the incidence of toll payments across income groups in the four
cities. In the second subsection, more variables relating to self-interest are introduced: the value of travel
time savings, the number of car trips and the number of cars in the household.
Incidence of toll payments across income groups
Distributional profile of toll payments
The four systems differ considerably in their design, and in particular with respect to how muchpeople [would] pay in tolls on average. Figure 7.1 shows how many who [would] pay different amounts
in tolls, as a share of the region’s population. In Stockholm, the share of the population who pay highamounts in tolls is low, whereas in the suggested Helsinki and Lyon systems, large shares of the
population would pay rather substantial amounts. The results from the Gothenburg system are interesting
compared to Stockholm: although the charge per passage is lower than in Stockholm, the Gothenburg

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system affects a much larger share of the population, which makes the share of the population who pay
high amounts much larger than in Stockholm.
Figure 7.1. Share of population who pay various amounts in tolls

Figure 7.2 shows average toll payments per income group in the four cities. The left pane shows
results in absolute numbers, while the right pane shows the results normalised by the average toll
payment in each city, to facilitate comparison between cities.
Figure 7.2. Average toll payments per income class
Absolute numbers (left) and relative to average toll payment (right)

In all cities, high income groups pay much more than low income groups. Looking at the right pane,
the differences across income groups are surprisingly similar in the four cities, despite the differences insystem design and socioeconomic geography. In Gothenburg and Helsinki, however, the highest income
group pay less than the middle groups. In Helsinki it is because the highest income group tend to live andwork more centrally, and hence drive shorter distances on average. In Gothenburg, it is because company
cars are exempt from congestion charges (according to Swedish tax law), and high income groups haveaccess to company cars to a much larger extent. The company car exemption is discussed further below.

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However, even if the poor pay less than the rich, they actually pay more relative to their income, as
shown in Figure 7.3. The left pane shows average toll payments as a share of income3 for each income
class, while the right pane shows the same thing but normalised to allow comparisons across cities.
Figure 7.3. Average toll payments as share of income, per income class
Percentages (left), relative to average percentage (right)

The figures reveal that the congestion charges are in fact regressive in all the cities – total payments
relative to income falls with increasing income. The diagrams indicate that the regressivity seems to belargest in Lyon and smallest in Stockholm. A common measure of the overall regressivity of a tax
instrument is the Suits index (Suits, 1977). A flat-rate tax has Suits index 0, a regressive tax has anegative Suits index and a progressive tax a positive index. The index is bounded between -1 and 1.
Table 7.2 shows the Suits indices for the four systems.
Table 7.2. Suits index (overall regressivity/progressivity) of the congestion charges
City

Suits index

Stockholm-0.09

Helsinki

-0.09

Lyon

-0.16

Gothenburg-0.13

That congestion pricing is regressive in this sense is actually an expected result: a consumption tax
will be regressive if the consumption elasticity with respect to income is lower than 1. Even if driving(especially in the urban centres) increases with income, it usually increases less than proportionally toincome, which means that most taxes on car driving will be (at least slightly) regressive. The Suits
indices in Table 7.2 reveal that the analysed systems in Stockholm and Helsinki are slightly regressive,
while the Lyon and Gothenburg systems are moderately regressive. For comparison, Metcalf (1996)
calculates the Suits index of the US sales tax to -0.11; CPPP (2007) calculates Suits indices of the gas tax
and sales tax in Texas to -0.25 and -0.18, respectively; West (2004) calculate Suits indices of a US VMTINCOME INEQUALITY, SOCIAL INCLUSION AND MOBILITY — © OECD/ITF 2017

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tax and a size-differentiated vehicle tax to 0.14 and -0.30, respectively; Eliasson et al. (2016) calculate
Suits indices of the Swedish fuel tax and a differentiated vehicle tax to 0.03 and ¬ 0.09, respectively.
Are the distributional profiles fair?
What, then, is the most appropriate definition of “fairness”? Is congestion pricing fair as long as therich pay more than the poor? Or is it fair only when the poor pay a lower share of their income? This is aquestion without a clear answer, but a few things can be pointed out.
First, prices are usually the same for everyone, regardless of income or wealth (with the exception ofa few deliberate exceptions such as subsidized healthcare and social housing). Prices of gasoline, cars,
food, clothes, housing and so on do not vary with income4. The social desire for increased equality is
instead usually handled by taxation and welfare systems. The fundamental reasons for redistributing
income rather than letting prices depend on income are two: first, the government can then leave to eachindividual to choose how she wants to allocate her income on various goods, according to her own
preferences; second, the price of each good will reflect its “value” in terms of scarcity and/or production
cost, so having the same price for everyone will achieve a Pareto efficient outcome. Now, the purpose of
a congestion charge is to correct the price of car driving for external effects, to make it better reflect the
total social cost of car driving. In other words, the price for car driving with the congestion charge iswhat the price really should be; without the charge, driving is subsidised from a social point of view.
From this perspective, it can in fact be argued that the distributional effects of introducing congestion
pricing are irrelevant – that is, if one accepts that the default situation is that prices are equal foreveryone, and should reflect the true, social cost for each good. At least, one should realize that arguing
against corrective taxes with distributional arguments is logically equivalent to arguing that the good inquestion (car travel, for example) should be subsidized for distributional reasons – and this is often amuch less persuasive or intuitively appealing argument.
Second, however, congestion charges may well have a strong fiscal motivation as well. The
Gothenburg system is a good illustration: the system was designed to generate revenue of at least
1 billion kronor (EUR 100 million) per year to be used for infrastructure investments. As a secondary
purpose, the system should decrease congestion as efficiently as possible, given this revenue constraint.
In such cases, congestion pricing is clearly not only about correcting prices; the purpose is at least as
much to generate public revenues. This makes it more appropriate to compare distributional effects ofcharges against income taxation; had the revenues not come from congestion charges, they would have
had to come from the usual public tax sources. Hence, comparing toll payments relative to income is anatural default position in such cases.
Third, being aware of the distributional effects of any new policy is clearly important. Any change inprices causes transition costs which may be important to consider, at least for determining at which speeda change can be implemented. Moreover, real congestion pricing systems are not perfect – they do not, in
reality, perfectly reflect the true social cost of each car trip due to technical or cognitive constraints; somecar trips will actually be overpriced, while others will still be underpriced. In this perspective, it can berelevant to check how many drivers experience a substantial increase in travel costs, defining“substantial” in some suitable way. If this share is high, especially in low-income groups, it may be awarning signal indicating either that the policy may be too ambitious too fast, or that the design punishes
some trips disproportionately.
Looking only at averages hides the fact that the variation within each income group may besubstantial. Compare this to an income tax, which will by definition affect everyone with the same
income in the same way. In the case of congestion charges, there may be subgroups who are hurt
disproportionately relative to their income, even if the charge is progressive overall (or at least not very
regressive). This is in fact often the most important argument of those arguing against congestion charges

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using distributional arguments: not that the policy is necessarily very regressive on average, but that
there may be non-negligible subgroups who are hurt disproportionately.
With this in mind, Table 7.3 shows the share of each income group who [would] pay more than
EUR 40 /month in congestion charges. In Stockholm and Gothenburg, the shares are (very) small in thelower income groups, although they may still be non-negligible of course. In the suggested Lyon andHelsinki systems, however, the shares are quite high even in the lowest income groups, meaning that
there are quite a few people who would see their driving costs rise considerably even among the poor.
Remember, though, that these systems were never properly evaluated and re-designed. The Helsinki
system was a real suggestion, but never made it further than the initial political debate. The Lyon system
was designed for the purpose of the survey. Hence, it seems that real congestion pricing systems can be
designed to have a much smaller impact on travel costs.
Table 7.3. Share of each income class who [would] pay more than EUR 40/month in congestion
charges€/monthStockholm
Helsinki
Lyon
Gothenburg

1000
2%
15%
18%
7%

2000
7%
26%
31%
18%

3000
7%
35%
36%
29%

4000
13%
53%
37%
32%

5000
15%
47%
48%
22%

Lessons from the Swedish company car exemption
The Swedish company car exemption is an example of a generally important point, namely that the
distributional effects of congestion charges depends on the specific design of the charging system, andthat legal decisions may have unintended consequences. Some time after congestion charges had been
introduced in Stockholm, the tax court determined that congestion charges were to be considered part ofthe operating costs of the car, and as such they were included in the “taxable benefit value” of a company
car. This meant that company car owners either paid no charge at all, or could deduct the charge fromtheir before-tax salary (which implied a substantial discount), depending on the company’s policy. This
was a completely unintended consequence of how the tax law interacted with the legal definition of thecongestion charge (which, in legal terms, is a national tax) – but it had substantial effects on the
distributional profile of the charges, especially in Gothenburg with its high prevalence of company carsin high income groups (the city is dominated by the car industry).
Figure 7.4 shows the effect of the exemption. Without it, the highest income group had paid the most;with it, the richest group pay on average as little as the second-lowest income group. Further, theregressivity had been much smaller without the exemption: the Suits index had been 0.06 rather than
0.13. The effect in Stockholm seems to be smaller, but unfortunately, there is so far no data available for
Stockholm to analyse this in depth.

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Figure 7.4. Average toll payments per income group, with company car exemption (black) andwithout (red)Absolute numbers (left) and as share of income (right)

Tax rules for company cars are complicated in many countries, and the design of these rules may
have profound and substantial effects on travel patterns and equity (see for example an analysis of achange in the UK tax rules by Le Vine et al. (2013)). As the Gothenburg example shows, it may
drastically change the distributional profile of congestion pricing. More generally, the lesson is thatexemptions of various kinds – for residents, professional traffic, certain residential areas and so on – mayhave important and perhaps unintended consequences both for the effectiveness and the distributional
profile of congestion pricing.
Distributional effects in other dimensions
In addition to income, congestion pricing can have distributional effects across other socioeconomic
characteristics as well. Perhaps surprisingly, these differences are relatively small once income iscontrolled for. Detailed results are omitted to save space. The main differences across socioeconomic
groups (controlling for income) are:••••

In Lyon and Gothenburg, households with children under 18 years of age [would] pay more
tolls (controlling for income) than households without children.
In all cities except Gothenburg, men [would] pay more than women (even controlling forincome differences). The difference is largest, in relative terms, for middle income groups.
In Lyon, people older than 65 years would pay less (after controlling for income). In the other
cities, differences across age groups are negligible.
Education does not affect average toll payments in any systematic way.

Broadening the perspective: Incidence of compound self-interest
How much someone pays [would pay] affects the person’s attitude to a suggested congestion pricing
system: all else equal, people are more negative the more they [would] pay (e.g. Börjesson et al., 2016;
Eliasson, 2014; Eliasson & Jonsson, 2011; Gaunt, Rye, & Allen, 2007; Hamilton et al., 2014; Hårsman &Quigley, 2010; Jaensirisak, May, & Wardman, 2003; Schade & Schlag, 2003). Respondents were asked
how they would vote in a referendum about congestion pricing, regarding the actual schemes in
Stockholm and Gothenburg, the debated scheme in Helsinki, and a hypothetical area charging scheme in

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Lyon. The response alternatives were “Certainly yes”, “Probably yes”, “Probably no”, “Certainly no” or“No opinion/I don’t know”. Answers clearly correlated with the amount of tolls respondents paid or
would pay. Defining “support” as the share of positive responses excluding “No opinion/I don’t know”,
Figure 7.5 illustrates how support depends on the [anticipated] toll payments.
Figure 7.5. Support for congestion pricing with respect to toll payments (EUR/month)

Clearly, support falls as toll payments increase. Note, however, that the biggest drop in support is
between those who pay no toll at all (e.g. don’t own a car) and those who pay something, albeit just alittle (Lyon is an exception). It is interesting how similar the patterns are in the three cities without
congestion charges: Helsinki, Lyon and Gothenburg in 2012 (before the system started). The figure also
illustrates the “experience” effect in Gothenburg: the Gothenburg 2013 curve has moved upwards withalmost precisely the same shift for all groups, regardless of toll payments (although the effect is slightly
smaller for those who pay no toll at all).
In addition to toll payments, attitudes are also affected (as we shall see) by several other variables
which are related to self-interest, and should hence be included in the consumer perspective: how many
car trips a respondent makes, her value of travel time savings and how many cars there are in thehousehold. That the number of cars and car trips significantly affect attitudes to congestion pricing evenafter controlling for toll payments may be for several reasons: it may reflect the general car dependencyof the household or individual, for example.
The fact that there are more variables than just toll payments which affect attitudes to congestion
pricing means that an analysis which only takes toll payments into account may not fully reflect the
subjective benefits and losses of charges, as perceived by the individual. If the aim is to measure theincidence of congestion pricing as perceived by the individuals themselves, the analysis has to beextended to account for these other variables as well. After all, these variables also reflect various aspectsof the perceived personal incidence of the congestion charges. I will call this total, perceived, personal
incidence the compound self-interest, since it is the sum of several factors: tolls paid, time gains,
adaptation costs and so on.
How, then, should these variables be weighted together? A natural approach is to estimate astatistical model of how they affect respondents’ attitude to congestion charges, and take the estimatedmodel parameters as relative weights of the different variables. In this way, the variables act as indicators

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7. IS CONGESTION PRICING FAIR? CONSUMER AND CITIZEN PERSPECTIVES ON EQUITY EFFECTS – 175of or proxies for different aspects in which congestion charges affect the individuals, which in turnallows for a richer analysis of the distributional effect of congestion pricing.
In addition, it is desirable to take into account that variables may affect different income groups
differently. For example, it might be natural to expect that the same amount of toll payments might cause
more disutility for low income groups than for high income groups, simply because low income groups
might have higher marginal utility of income. Exploring the data, however, gives only limited support forthis hypothesis. Figure 7.6 shows that there are only a few cases where it seems as if (high) toll payments
affect attitudes differently across income groups.
Figure 7.6. Support for congestion pricing in different income groups, split by those who pay little
or nothing (black) and those who pay moderate or large amounts (blue)

Cross-tabulations of this kind only give rough indications, of course. To properly separate the impact
of different variables, respondents’ attitude to congestion pricing (how they would vote in a referendum)is regressed on various self-interested-related variables. A potential problem with the model estimation is

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that attitudes are affected by several other variables as well – in particular other attitudes – and if we omit
these, the parameter estimates may be biased if there are correlations between included and omitted
variables. Fortunately, however, this turns out not to be a problem: when attitude variables are introducedin the model (see next section), this does not change the parameters for the self-interest variables
appreciably. Moreover, the general conclusions are robust for various other model specifications.
Since the voting response is an ordered variable, an ordered logit model is used. A comprehensive
description of ordered models can be found in Greene (2003), but a short intuitive description isnecessary. Let y be a latent (unobservable) variable, which is a linear function of a vector of observablevariables X, a parameter vector β (to be estimated) and an idiosyncratic term ε=+

The latent variable y can be interpreted as a measure of how positive an individual is to congestion
charges, which cannot be observed (measured) directly. What is observable is the voting response z,
which has five ordered levels z=1,…,5 from most negative to most positive. We assume that this
response is determined by in which of five intervals y falls. The limits of the intervals are determined byestimated threshold parameters μ_k, so we have
= 1 if= 2 if(…)= 5 if≤≤≤≤Assuming that ε is logistically distributed, the probabilities that z=i become=1−1
1 + exp( −=11 + exp( −)=11 + exp( −

)

)
−

1
1 + exp(−)∈ {2,3,4}

Table 7.4 shows the estimation results from the ordered logit model, indicating how respondents’attitude to congestion charges (as measured by the voting response) is affected by various self-interestedrelated variables. A large number of model specifications have been tested, but details are omitted to save
space. Positive parameters indicate that support increases when the variable increases.

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Table 7.4. Estimation results: impact of self-interest variables on attitude to congestion pricing
Tolls
Tolls, add. inc.grp 1
No car
Car trips
Value of time, drivers
Stockholm
Helsinki
Lyon
Gothenburg2014

Value Std. Error t value-0.0010832 0.0001125 -9.6249-0.0007957 0.0002209 -3.6030
0.3061705 0.0610495 5.0151
0.4306097 0.0284951 15.1117
0.2389846 0.0189181 12.6326
0.9948645 0.0668150 14.8898
0.0213867 0.0740519 0.2888-0.1766564 0.0722816 -2.4440
0.5572110 0.0697637 7.9871

Intercepts:
Value
Std. Error t value
1|2 0.4265 0.1022
4.1721
2|3 1.4528 0.1034
14.05023|4 1.9839 0.1047
18.9533
4|5 3.3649 0.1098
30.6583
Residual Deviance: 19960.01
AIC: 19986.01

Toll payments affect attitudes negatively; the effect is proportional to the amount of tolls paid. Thiseffect is stronger for the lowest income group (<EUR 1 500/month) (the two “tolls” parameters areadded), but there are in fact no significant differences among the other income groups. Not owning a car
at all increases support more than can be explained merely by not having to pay tolls, so a dummy
variable for “no car in household” is significant and positive. More car trips decrease support (with alinear effect), even after controlling for toll payments. The model also includes dummy variables for thedifferent cities and for the difference in Gothenburg between 2012 and 2013; Gothenburg 2012 is taken
as the reference level. The attitude differences between Gothenburg 2012, Lyon and Helsinki (neither ofwhich had congestion pricing in place) are very small, while attitudes in Stockholm and Gothenburg
2013 (after congestion pricing was introduced) are much more positive.
Finally, the value of travel time savings has a substantial positive impact on support for those who
make at least a few car trips per week. The value of travel time savings was measured with the following
thought experiment:
You commute daily by car. On the way, you have to cross a bridge5 across a river. One day, thebridge closes for repairs for a long time. Another bridge is available further downstream, but the detour
takes an additional 20 minutes. During the time it takes to repair the bridge, the road authority has
arranged with a ferry that can take cars over the river. What is the highest amount you would be preparedto pay for a one-way ticket for the ferry, to save 20 minutes on your journey to work?
Respondents were given seven alternative answers ranging from “nothing” to “more than EUR 5”.
For our purposes, it is important to note that the impacts of the self-interest-related variables do notvary systematically across income groups (except for toll payments in the lowest income group).
Surprisingly, the effect on attitudes of making one car trip more or paying one more euro in tolls and soon seems to be the same, regardless of income. A plausible hypothesis is that for the moderate monetaryamounts we are considering, the differences in marginal utility of money are not large enough to matterfor attitudes.
With the model results in hand, we can calculate a broader measure of the perceived incidence ofcongestion charges across income groups, simply by calculating the relative differences in the latentvariable y across income groups. Note that this is an exploratory analysis where the numerical resultsmust be interpreted with caution: there is no guarantee that y can be interpreted cardinally, or in otherwords, that the absolute magnitude of y is meaningful (since the “unit” of y may not be constant if the

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distances between thresholds are very different). However, comparing average values of y across income
groups will give an indication whether some income groups can be said to be worse off6.
Figure 7.7 shows the distributional profile across income groups for the four cities. Results have beennormalized for each city, since the purpose is just to compare the relative tendency across income groups
be the latent variable for individual n. The bar in the histogram corresponding tofor each city. (Letincome group i in city j is∑ ∈( , )= ∑∈− 1. In other words, the diagram shows how much better orworse off each income group of a city is, on average, compared to the average citizen in that city.
(Remember that this measures only “self-interest” effects, weighted as individuals perceive the effects
themselves, as measured by their voting response.)
Figure 7.7. Average compound self-interest per income group, relative to the mean in each city

Except for Lyon, the pattern is the same in all cities: higher income groups are worse off, but thehighest income group is in fact a little better off than the second-highest. This is partly due to this grouphaving high values of time (on average), and partly due to average toll payments being lower than for thesecond-highest group due to central residential locations (Helsinki) or company car exemptions(Stockholm and Gothenburg). The relative differences across income groups are smaller in Stockholm
than in Gothenburg and Helsinki. For Lyon, the distributional profile of the suggested system is different:
the lowest income group is better off than the average, but after that, higher income groups are better off
than lower income groups. The primary reason for this seems to be the design of the (hypothetical) Lyon
system, where all car trips in the entire urban area is charged the same amount, regardless of time of day,destination or distance.
Broadly speaking, this shows that congestion pricing is “progressive” (with a slight abuse of theterm) in the sense that lower income groups are hurt less by direct self-interest effects – as perceived by
the individuals themselves, as measured by how self-interest variables influence voting response. The
exception is Lyon, but the reason for this seems to be the coarse design of the system: a more realistic
and efficient design may well have yielded other results. The lesson that can be drawn from Lyon is,again, that the design of the system is crucial for the distributional profile.

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Citizen perspectives on the fairness of congestion pricing
The previous section only dealt with fairness from a consumer perspective: how congestion pricing
affects different income groups in terms of self-interest variables such as money, time and so on. Thissection analyses fairness from a citizen perspective. Different allocation mechanisms, and different social
goals or benefits, can be viewed by citizens as “fair” in a more abstract sense. In this perspective, factors
such as procedural justice, equity, equal treatments, human rights and the relative weights of different
social objectives often matter (depending on the issue). Citizens’ perceptions and definitions of fairness
will vary, of course, and may well correlate with their self-interest – although the direction of causality is
not always easy to establish – but this does not change the point that “fairness” is something that people
apparently value in itself, even abstracting from their own self-interest.
Here, the analysis will focus on one particular issue, namely whether the opinions of congestion
pricing from this citizen perspective vary systematically across socioeconomic groups – in particularacross income groups. In other words, is the concept of congestion pricing – the principle of allocating
scarce road space according to willingness to pay – more consistent with, say, high-income groups’
views of what is “fair” or “just” than with low-income groups’ views? If so, one could reasonably argue
that congestion pricing is an “elite” project, since the concept would be better aligned with what highincome groups view as a “fair” or “just” society. A priori, there is no particular reason to expect neither
this nor the opposite; but the issue clearly matters for the socio-ethical or democratic justification ofcongestion pricing, and the fact that we do not know what result to expect a priori makes the question
interesting.
Disentangling this question, however, is complicated. Obviously, simply asking respondents “iscongestion pricing fair?” will elicit responses so tainted with self-interest that it is virtually pointless.
Instead, our survey contained a large number of questions about whether respondents perceived various
allocation mechanisms as “fair”, and also questions about other social issues such as environment andincome equity. Through econometric modelling (controlling for self-interest variables), we can thenreveal how perceptions of these more or less related issues correlate with the attitude to congestion
pricing, and finally measure how well aligned congestion pricing is with these related socio-politicalviews.
The question of how perceptions of fairness and other social issues vary across income groups andcities is of course interesting in itself. Therefore, the first subsection explores this, before the second
subsection turns to our main issue of citizen perspectives of congestion pricing.
Opinions about fairness and related political issues
Respondents were presented with a number of statements, and asked to indicate to what extent theyagreed on a 7-grade scale, from “completely disagree” (-3) over “neither agree nor disagree” (0) to“completely agree” (+3). Table 7.5 shows how respondents in the four cities agreed, on average, with thestatements.
Table 7.5. Average agreement (from -3 to 3) with statements
Stockholm Helsinki
12“Considerably more resources should be
used to protect the naturalenvironment.”“The government should prioritise to
reduce the differences between the poorand the rich in the society.”

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Lyon

Gothenburg

1.4

1.3

2.1

1.3

0.9

1.2

1.7

1.3

180 – 7. IS CONGESTION PRICING FAIR? CONSUMER AND CITIZEN PERSPECTIVES ON EQUITY EFFECTS

3
4
5
6

”Taxes in [country] are too high”
Pricing the ferry is fairQueuing to the ferry is fairLetting a public agency decide about
space on the ferry is fair
7 Giving out places on the ferry with alottery is fair
8 “It is fair [justified] that airplane tickets
cost more for departure during peak
hours than during off-peak”
9 “It is fair [justified] that airplane tickets
to vacation destinations cost more when
the weather in [country] is bad.”
10 “It would be fair [justified] if transit fares
were lower in off-peak hours”

0.8
1.9
1.5

1.2
1.4
2.1

1.3
0.3
-1.2

0.3
0.9
0.8

0.1

-1.1

-1.6

-0.7

-1.1

-1.3

-2.3

-2.2

0.9

0.8

-0.4

0.3

-1.0

-1.2

n/a

-1.4

1.0

0.6

0.7

0.9

Statements (1)-(3) are about general political issues: environment, social equity and taxes.
Respondents broadly agree with the statements, on average. Almost no one disagrees with theenvironment statement (2). Swedish respondents agree less with the statement that taxes are too high (3).
Comparing Stockholm and Gothenburg, more respondents in Gothenburg agree with the equity question(2) and disagree with the “taxes are high” question (3).
Statements (4)-(7) are perhaps the most interesting and relevant in the context of this chapter.
Following the thought experiment about a ferry replacing the broken bridge (see above), respondents
were given the following question:
Some people complain that it is unfair that the authority charges a price for the ferry tickets. Whenoffering the ferry for free, it turns out that there is not room on the ferry for everyone who wants to use it.
The authority now considers four different methods to choose who gets to travel with the ferry:•Price: Revert to the original policy of charging those who want to travel, and set the price so
the ferry is just filled.

•

Queue: Those who arrive first to the jetty and stand first in line get to go with the ferry.

•

Authority determines “need”: Those who want to travel with the ferry have to show someevidence to support their need. The authority then provides ferry passes based on their
judgment of the greatest need.

•

Lottery: Tickets are allocated randomly, so that everybody has an equal chance of winning.

To what extent do you consider these alternatives fair? [7 grade scale from “Completely unfair” (-3)
to “Completely fair” (+3)]
In almost all cities, respondents rate the fairness similarly: pricing comes first, followed by queueing
and then decisions by a public authority, with lottery as a distant fourth. Helsinki differs in that queueing
is ranked as fairer than pricing. However, answers vary considerably across countries, and are actually inline with some clichés about national political cultures. French respondents rate all alternatives lower
than respondents from other countries; in fact, 25% of French respondents rate all allocation mechanisms
as more or less unfair (below 0) and 17% rate all allocation mechanisms as “very” or “completely” unfair(begging the question if there is any “fair” mechanism to allocate space on the ferry). Swedish

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7. IS CONGESTION PRICING FAIR? CONSUMER AND CITIZEN PERSPECTIVES ON EQUITY EFFECTS – 181

respondents seem to put more trust in public authorities (which is consistent with many other studies),
rating decisions by a public agency as fairer than respondents from other countries do.
Statements (8)-(10) concern various forms of scarcity pricing in other contexts. Differentiating air
fares (8) and transit fares (10) with respect to peaks in demand is rated “fair” by a narrow majority of
respondents in most cities – although in all cities, there are sizeable groups who rate this as unfair.
However, commercial airlines taking advantage of increased demand due to bad weather strikes amajority of respondents as unfair. One interpretation is that there is a difference in perceived fairness ofpricing between situations when supply is “necessarily” scarce – such as airport and transit capacity inpeak hours – and situations where commercial companies simply extracts an increased willingness topay.
For the purpose of this chapter, the most interesting question is whether the answers varysystematically with income – in particular, whether views of the fairness of allocation mechanisms(including pricing) do. Table 7.6 shows correlations between income and agreeing with the statements.
Table 7.6. Correlation between income and agreement with the statement
Stockholm Helsinki“Considerably more resources should be
used to protect the naturalenvironment.”
2 “The government should prioritise to
reduce the differences between the poorand the rich in the society.”
3 ”Taxes in [country] are too high”
4 Pricing the ferry is fair
5 Queueing to the ferry is fair
6 Letting a public agency decide about
space on the ferry is fair
7 Giving out places on the ferry with alottery is fair
8 “It is fair7 [justified] that airplane tickets
cost more for departure during peak
hours than during off-peak”
9 “It is fair [justified] that airplane tickets
to vacation destinations cost more when
the weather in [country] is bad.”
10 “It would be fair [justified] if transit fares
were lower in off-peak hours”

Lyon

Gothenburg

1

-0.06

-0.10

-0.02

-0.04

-0.19

-0.27

0.00

-0.17

0.06
0.00
-0.08

-0.03
0.01
-0.03

-0.01
0.01
0.03

0.02
0.02
-0.05

0.02

0.01

-0.02

0.01

-0.05

-0.02

0.01

-0.02

0.09

0.01

0.03

0.13

0.09

0.02

n/a

0.11

0.00

-0.06

0.02

-0.03

The most striking result is that the correlation between agreement and income is generally very low.
The main exception is only that low income groups agree much more with the equity question (2) (except
in Lyon). Particularly surprising is the small differences across income groups in how respondents rate
the fairness of pricing mechanisms (4,8,9,10) and whether taxes are too high (3). In Sweden, highincome groups have a slight tendency to agree more with air pricing questions (8,9) and that taxes are too

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high, but the tendency is small. More detailed analyses, also considering the tails and asymmetries of the
distribution of answers, reveal a few more observations:•Rich tend to agree more that having higher airfares in peak hours (8) is fair/justified. In
particular, the share who agrees strongly with the statement is higher in rich groups.

•

Rich tend to agree slightly more that it is fair/justified that airfares to vacation destination are
higher when the weather is bad (9). In particular, the share who strongly disagrees is smaller
among rich groups.

•

In all cities except Helsinki, opinions about taxes (2) follows a U-shaped pattern: the twolowest income groups agree the most with that taxes are too high, but the highest income groupagree more with this statement than the two middle income groups. In Helsinki, there is nocorrelation between income and opinions about taxes.

•

Slightly fewer among the rich think that more resources should be spent on protecting theenvironment. In particular, fewer of the rich agree strongly; almost no one, in any income
group, disagrees with the statement. In Stockholm and Gothenburg this tendency is small,whereas it is considerable in Lyon and Helsinki (the share who agree strongly (≥2) falls from
80% in the poorest group to 60% in the richest group in Lyon, and from 60% to 40% in
Helsinki).

•Comparing which way to allocate space on the ferry is rated as the fairest by each individual
reveals that a higher share of the rich rates pricing highest on the fairness scale. The difference
is not big, however: in Stockholm, where this difference is largest, 50% in the richest grouprate pricing as most fair, whereas 37% in the poorest group rate pricing as most fair.

•

Conversely, slightly more in the poor groups rate allocating ferry space by a public authority as
the most fair. More of the rich strongly disagree that this is a fair allocation mechanism. (There
are no differences in how queueing is rated, however.)•As already noted, a large share of French respondents rate all alternatives to allocate ferry spaceas unfair (in the other countries, this share is negligible – less than 2% of respondents). There isno difference across income groups in this respect.

Summing up these findings, a higher share of the poor agree with the equity statement (2), and aslightly higher share among the rich regard pricing as a fair allocation mechanism. On average, slightly
fewer of the rich express strong environmental concerns (1). With these findings in hand, the next section
explores correlation between these attitudes and the support for congestion pricing.
Citizen perspectives on congestion pricing
The main purpose of this section is to explore how “citizen” perspectives of congestion pricing varyacross income groups, as explained above. An obvious place to start is to check whether the support forcongestion pricing varies across income groups. As Figure 7.8 reveals, this is not the case: there is no
particular systematic tendency in how different income groups would vote about congestion charges.

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7. IS CONGESTION PRICING FAIR? CONSUMER AND CITIZEN PERSPECTIVES ON EQUITY EFFECTS – 183

Figure 7.8. Support for congestion charges across income groups in different cities(two different years in Gothenburg)

However, these aggregate figures do not reveal enough, since voting responses are affected by a
mixture of self-interest and other considerations. Instead, we estimate a revealing how the attitude tocongestion pricing is affected both by self-interest variables and by a number of variables representing
citizen perspectives. Since several of the attitude questions (Table 7.5) measure similar things (inparticular the attitude to scarcity pricing in different contexts), a subset of these indicators has to be
selected. After testing various options, the following variables are included in the model: environment(agreement rating of statement (1) in Table 7.5), equity (statement 2), taxes are too high (statement 3),
pricing is fair (4) and agency decision is fair (6). All these variables can be expected to correlate with theattitude to congestion pricing. To what extent they do may depend on how congestion pricing isperceived or framed in the local public debate or discourse. For example, if congestion charges areperceived as “just another tax”, then the correlation with the attitude to taxes can be expected to bestrong; if congestion charging is perceived as an environmental policy, then the correlation with theattitude to environmental policy can be expected to be strong – and so on. Table 7.7 shows estimation
results from the ordered logit model (a binary logit model distinguishing only positive/negative answers
give similar results).

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Table 7.7. Variables affecting voting response (ordered logit)
Tolls
Tolls, add. inc.grp 1
No carcartrips
Value of time, drivers
Stockholm
Helsinki
Lyon
Gothenburg2014
environTaxTooHigh
PricingIsFair
AgencyIsFair
equityValue Std. Error t value-0.0010111 0.0001179 -8.5742-0.0005993 0.0002281 -2.6274
0.3297529 0.0641394
5.14120.3327746 0.0302396 11.0046
0.1825060 0.0199794
9.13471.0063928 0.0713684 14.1014
0.1555103 0.0780314
1.9929-0.0990411 0.0771785 -1.2833
0.5615992 0.0776810
7.22960.2609645 0.0166537 15.6701-0.2524060 0.0118604 -21.2813
0.1172166 0.0114416 10.2447
0.0504215 0.0098760
5.1054-0.0042233 0.0134644 -0.3137

Intercepts:
Value
Std. Error t value
1|2
1.03150.16106.40592|3
2.16870.162713.3295
3|4
2.72890.163916.65264|5
4.26950.168425.3584
Residual Deviance: 17698.47
AIC: 17734.47

The parameters of the self-interest variables are broadly unchanged by the introduction of attitude
variables. The first four are highly significant: environment and pricing is fair is associated with morepositive attitudes, while taxes are too high is associated with a more negative attitude. Authority decision
is fair is also associated with more positive attitudes, although this effect is smaller. At first this may
seem counterintuitive: after all, approving of authority decisions based on subjective “need” may seem
like the opposite to approving of market-based solutions based on willingness to pay. However, what thisquestion measures is rather the trust in government – whether the respondent believes that publicagencies are, on average, trustworthy. Previous research has shown that support for congestion pricing
correlates with trust in government and also supporting various kinds of public interventions (e.g. speed
cameras).
Two negative findings in the model estimation are interesting. First, the equity variable is not
significant (in any of the cities): there is, surprisingly, apparently no correlation between respondents’opinions about equity and their opinions about congestion pricing. In the light of this, the preoccupation
with congestion charges’ equity effects is rather remarkable – unless it is simply because supposedly
negative equity effects is a more convenient argument against congestion charges in public debate thanself-interest arguments. Second, there are no differences in the parameters for the attitude variables
across income groups. In other words, attitudes affect voting responses in the same way, regardless of
income. It might have been natural to expect that, for example, rich groups could “afford” to let, say,environmental concerns affect their attitude to congestion pricing more than poor groups – but this isapparently not the case.
The model in Table 7.7 shows how self-interest variables and the various “citizen perspective”
variables are weighted against each other. This means that we can now interpret the underlying latentvariable as an extended “utility function” comprising two parts: a consumer part (consisting of the selfinterest variables) and a citizen part (consisting of the citizen perspective variables). Note that this is an
exploratory analysis, and the same caveat applies as noted previously: there is in principle no guaranteethat the latent variable can be interpreted cardinally. However, exploring its average over income groups
will give an indication of how attitudes are affected by consumer and citizen aspects, and how this varies

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across income groups. The robustness of the conclusions has also been checked with two other methods(a binary logit model and a predicted-response method; see endnote 5).
Figure 7.9. Consumer and citizen components of the “utility” of congestion pricing,separated by income group

Figure 7.9 illustrates the citizen and consumer components of this “extended utility function” ofcongestion pricing. The blue bars (the “self-interest” or “consumer” component) are essentially the sameas in the analysis of compound self-interest (Figure 7.7) but not rescaled with the average in each city.
As was noted before, the general tendency is that low-income groups fare better compared to the averagein each city in terms of the self-interest component, although the highest income group actually farebetter than the second-to-highest.
The red bars – the “citizen” part – is the new part. Remember that this part of the “utility” function
can be interpreted as how well congestion pricing is aligned with individuals’ opinions about
environmental policy, fairness of pricing as an allocation instrument, the level of taxation and trust in
government. An individual who scores highly on these attitudes will tend to be a “winner” in a citizen
sense (controlling for the self-interest component) if congestion pricing is introduced, in an analogous
way as an individual with high value of time, low toll payments and so on will be a “winner” in a
consumer sense (controlling for the citizen component)
The citizen effects are clearly different across income groups. Except for Helsinki, the generaltendency is that the middle income group “wins” more than the low- and high-income groups. In
Helsinki, income groups simply “win” more the lower income they have.
To explain this in more detail, Figure 7.10 separates the “citizen utility” into its subcomponents.
Analysing this in detail shows that the reason that middle-income groups are “winners” from a citizen
perspective is primarily because they are the most content with current tax levels, and also rateenvironmental concerns highly (on par with the lower income groups but slightly higher than the twohighest income groups). It is also evident that the attitude variables, taken separately, have a very strong
impact on attitudes. Whether congestion pricing is perceived as, for example, mostly an environmental
policy or mostly as a tax will hence have a vast impact on support for congestion charges.

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186 – 7. IS CONGESTION PRICING FAIR? CONSUMER AND CITIZEN PERSPECTIVES ON EQUITY EFFECTS

Figure 7.10. Consumer and citizen components of the “utility” of congestion pricing, with “citizen
utility” separated into its subcomponents, and separated by income group

Is congestion pricing fair from a citizen perspective?
Given these results, can congestion pricing be considered “fair” from a citizen perspective? Our
conclusion would be a qualified “yes”. Clearly, congestion pricing is not more aligned with the citizen
preferences of the “elite”, defined as the high(est) income groups. In three of the case cities, it is the
middle income group who seem to “win” the most in a citizen perspectives; put differently, it is in thisgroup that congestion pricing most closely aligns with the group’s societal preferences. Perhaps the mostimportant result, however, is that most differences in citizen preferences are small or negligible acrossincome groups. Consequently the distribution of “citizen utility” is rather similar across groups.
The only social issue where there is a clear difference in opinions across income groups is equity.
Lower income groups agree to a much larger extent that society needs to prioritise to reduce the gap
between rich and poor. However, this turns out not to be related to the congestion pricing issue: there isno correlation between views of equity in any of the case cities.

Conclusions
The purpose of this chapter has been to discuss and analyse to which extent congestion pricing can beviewed as fair. Since “fairness” can be interpreted in several different ways, a range of different analyses
have been presented.
Starting with the consumer perspective on fairness, the incidence of toll payments is strongly
correlated with income: high-income groups pay more in tolls, on average, in all cities. However, since
car driving tends to increase less than proportionately with income, low-income groups tend to pay more
tolls relative to their income, on average. A Suits index calculation shows that all of the four analysed
congestion charging systems are regressive. Whether this is a problem depends, in my view, on the
purpose of the charges. If the purpose is truly to correct the price of car driving, to make it reflect the fullsocial cost of driving, then it is in fact doubtful whether distributional effects are relevant, at least in thelong run. Amending income inequalities through taxation and welfare systems is both more effective andefficient than subsidising goods; and allowing prices to be lower than the social cost is equivalent to asocial subsidy. However, congestion charges often have dual purposes: in addition to being a corrective

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7. IS CONGESTION PRICING FAIR? CONSUMER AND CITIZEN PERSPECTIVES ON EQUITY EFFECTS – 187

tax, the purpose is often also (and sometimes primarily) to generate revenues, often for infrastructure
investments. To the extent that the charges is a fiscal measure, the potential regressivity of congestion
charges is a serious problem. After all, it is difficult to see why the poor should contribute more thanproportionately to public tax revenues.
The distributional profile of congestion pricing depends on the design of the specific system –location of charging points or areas, exemptions, time of day and so on. An illustrative example is theSwedish (unintended) exemption for company cars, which turned the Gothenburg system from an almost
equity-neutral to a clearly regressive system.
In a second analysis, the consumer perspective was broadened from merely toll payments to a rangeof variables relating to self-interest: access to car in the household, value of time savings and the numberof car trips. The weights of the different variables were obtained by estimating their impact on
individuals’ attitudes to congestion charges (how they would vote in a congestion pricing referendum).
One interesting finding is that income hardly matters for how these variables affect attitudes tocongestion charges; their impact on attitude is almost the same across all income groups (except thelowest group). With these weights, a compound self-interest measure was calculated, and its incidence
across income groups was explored. Broadly speaking, this analysis showed that congestion pricing is“progressive” (with a slight abuse of the term) in the sense that lower income groups are hurt less than
average by the direct, self-interest effects, as perceived by the individuals themselves. The exception is
Lyon, but the reason for this seems to be the coarse design of the system: a more realistic and efficientdesign may well have yielded other results.
Fairness can also be viewed in a citizen perspective. Depending on individuals’ views of procedural
fairness, equity, environmental issues and so on, congestion pricing can be viewed as more or less “fair”
in an abstract sense, disregarding its objective, “consumer” effects. A “winner” on a congestion pricing
reform in a citizen sense is hence someone who approves of the underlying, abstract logic and rationaleof congestion pricing – whose views of fairness, environment and other societal dimensions are alignedwith what congestion pricing represents (for that individual). In order to estimate this “citizen utility”, amodel was estimated that separated how self-interest variables and attitude to various societal issues –environment, taxes, pricing as an allocation instrument, equity and trust in government – affected
attitudes to congestion pricing. This allowed for an even broader definition of the “utility” of acongestion pricing reform, capturing both consumer and citizen components. The distribution of these“utility components” across income groups can then be explored.
This analysis showed that in terms of “citizen utility”, it is in fact middle-income groups who farebetter than average, with the exception of Helsinki, where groups fare better (relative to the average) thelower their income is. Differences across income groups are relatively small, however.
In summary, it is hard to find much support of the view that congestion pricing is unfair, as long as
its purpose is to correct prices and allocate scarce resources. Both in terms of absolute payments andcompound self-interest, lower income groups fare better than average. From a citizen perspective,
differences are small, but lower income groups fare at least as well (and in some cases better) than highincome groups. This changes, however, if the purpose of a charging system is in fact to generate
revenues. In that case, the regressivity of the pricing systems – that poor pay more relative to theirincome – is a serious problem: it is difficult to defend that poor groups should contribute more thanproportionately to public revenues.

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Notes

1

Other terms for essentially the same distinction are “homo economicus” vs. “homo politicus”, or“personal well-being” vs. “subjective social welfare”.

2

It can be shown that the relative size of the adaptation cost to the tolls paid, i.e. the “triangle under thedemand curve” relative to the “rectangle under the demand curve” (ignoring changes in travel
time for the moment), given a relative price change a and demand elasticity is /2. To
illustrate magnitudes, assume that a congestion charge increases the monetary cost of an
average car trip by 25%, and that the demand elasticity is in the order of -0.5. This would givean adaptation cost which would be around 6% of the toll paid. More careful quantitative
analyses, separating welfare effects into time gains, adaptation costs and paid tolls can be foundin Eliasson (2009) and Eliasson and Levander (2006).

3

This is the average toll payment per income class divided by the average income in that class, which is
the resolution available in the data. An alternative measure would be the average of (tollpayment divided by income), but this causes problems for people with (notional) very low oreven zero incomes.

4This view is not shared by all, though. At the time I am writing this, a representative of the Swedish
Left party is quoted in a newspaper saying “Uniform pricing of trips is a necessary fairness
reform. Healthcare costs the same for everyone: travelling should, too.” However, it turns outthat the view that all trips should cost the same apparently only applies to public transport trips:
the Left party is very much in favour of congestion charges.

5

In Lyon, the hypothetical situation instead involved a closed tunnel, as this was judged to be closer toreality and easier to imagine.

6

To check the robustness of the conclusions from this exploratory analysis, two other methods are also
used. First, a binary model is estimated, grouping the responses into positive or negative,
discarding “no opinion”. This reduces the problem with different distances between thresholds,
since there will, in essence, only be one “threshold”. The parameters of this binary model turnout to be close to the ordered logit model, so conclusions stay unchanged. Second, the orderedlogit model is used to predict the responses of all the individuals, using only these self-interest
variables. This will be a measure of how individuals “should” vote if they only took selfinterest into account. (Note that the reasons that this is meaningful is that the model does notincorporate constants for each income group; in that case, the model predictions had simply
coincided with the actual average voting responses for each income class.) There is also, as
noted above, a risk that parameters are biased since attitudinal variables are omitted. To check
that, results are also compared with the model presented below where attitudinal variables areincluded. It turns out that the parameters of the “self-interest” variables hardly change.

7

The word “fair” is, it turns out, not always easy to translate. The Swedish survey used the word rimlig
which can also be translated as “reasonable”, “acceptable”, “justified”.

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Annex 7.1. Estimation results.
Ordered logit model, self-interest variables only
toll
Toll, inc.grp 1
No carcartrips
Value of time, drivers
Stockholm
Helsinki
Lyon
Gothenburg2014

Value Std. Error t value-0.0010832 0.0001125 -9.6249-0.0007957 0.0002209 -3.6030
0.3061705 0.0610495 5.0151
0.4306097 0.0284951 15.1117
0.2389846 0.0189181 12.6326
0.9948645 0.0668150 14.8898
0.0213867 0.0740519 0.2888-0.1766564 0.0722816 -2.4440
0.5572110 0.0697637 7.9871

Intercepts:
Value
Std. Error t value
1|2 0.4265 0.1022
4.1721
2|3 1.4528 0.1034
14.05023|4 1.9839 0.1047
18.9533
4|5 3.3649 0.1098
30.6583
Residual Deviance: 19960.01
AIC: 19986.01
Binary logit model, self-interest variables only
Estimate Std. Error z value Pr(>|z|)(Intercept)-1.9731613 0.1347027 -14.648 < 2e-16 ***toll-0.0009835 0.0001451 -6.778 1.22e-11 ***Toll, inc.grp 1-0.0013063 0.0003366 -3.881 0.000104 ***No car
0.3865780 0.0772495
5.004 5.61e-07 ***cartrips
0.4892545 0.0365803 13.375 < 2e-16 ***Value of time, drivers
0.2467155 0.0239912 10.284 < 2e-16 ***Stockholm
1.2330181 0.0881791 13.983 < 2e-16 ***Helsinki
0.1313171 0.0977848
1.343 0.179298
Lyon-0.0158074 0.0914899 -0.173 0.862827
Gothenburg2014
0.6557825 0.0907974
7.222 5.10e-13 ***--Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1(Dispersion parameter for binomial family taken to be 1)
Null deviance: 8367.4 on 6078 degrees of freedom
Residual deviance: 7229.9 on 6069 degrees of freedom(1011 observations deleted due to missingness)
AIC: 7249.9
Ordered logit model, self-interest and attitude variables
Tolls
Tolls, add. inc.grp 1
No car
Car trips
Value of time, drivers
Stockholm
Helsinki
Lyon
Gothenburg2014
environTaxTooHigh
PricingIsFair
AgencyIsFair
equityValue Std. Error t value-0.0010111 0.0001179 -8.5742-0.0005993 0.0002281 -2.6274
0.3297529 0.0641394
5.14120.3327746 0.0302396 11.0046
0.1825060 0.0199794
9.13471.0063928 0.0713684 14.1014
0.1555103 0.0780314
1.9929-0.0990411 0.0771785 -1.2833
0.5615992 0.0776810
7.22960.2609645 0.0166537 15.6701-0.2524060 0.0118604 -21.2813
0.1172166 0.0114416 10.2447
0.0504215 0.0098760
5.1054-0.0042233 0.0134644 -0.3137

Intercepts:
Value
Std. Error t value
1|2
1.03150.16106.40592|3
2.16870.162713.3295
3|4
2.72890.163916.65264|5
4.26950.168425.3584

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192 – 7. IS CONGESTION PRICING FAIR? CONSUMER AND CITIZEN PERSPECTIVES ON EQUITY EFFECTS

Residual Deviance: 17698.47
AIC: 17734.47
Binary logit model, self-interest and attitude variables
Estimate Std. Error z value Pr(>|z|)(Intercept)-2.9264067 0.2270337 -12.890 < 2e-16 ***Tolls-0.0009104 0.0001588 -5.735 9.77e-09 ***Tolls, add. inc.grp 1-0.0009653 0.0003484 -2.770
0.0056 **No car
0.4413697 0.0867175
5.090 3.59e-07 ***Car trips
0.3767392 0.0406581
9.266 < 2e-16 ***Value of time, drivers
0.1845074 0.0268289
6.877 6.11e-12 ***Stockholm
1.3605294 0.1002557 13.571 < 2e-16 ***Helsinki
0.3196677 0.1084253
2.948
0.0032 **Lyon
0.1205585 0.1029584
1.171
0.2416
Gothenburg2014
0.6459297 0.1068889
6.043 1.51e-09 ***environ0.3026574 0.0230508 13.130 < 2e-16 ***TaxTooHigh-0.2899107 0.0160354 -18.079 < 2e-16 ***PricingIsFair
0.1538182 0.0158089
9.730 < 2e-16 ***AgencyIsFair
0.0664233 0.0135873
4.889 1.02e-06 ***equity-0.0224911 0.0179744 -1.251
0.2108--Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1(Dispersion parameter for binomial family taken to be 1)
Null deviance: 7943.2 on 5766 degrees of freedom
Residual deviance: 6066.0 on 5752 degrees of freedom(1323 observations deleted due to missingness)
AIC: 6096

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PARTICIPANTS LIST – 193

Participants list

Chair
Mr. Peter JONES
Professor of Transport & Sustainable Development;
Director, Centre for Transport Studies
CEGE
University College London (UCL)
Chadwick Building, Gower Street
WC1E 6BT London
United Kingdom
Mr. Jonas ELIASSON
Director, City of Stockholm
and Professor, KTH Royal Institute of Technology
Wadköpingsvägen 3
168 39 Bromma
SwedenRapporteur

Mr. Erick GUERRA
Assistant Professor
City and Regional Planning
University of Pennsylvania
127 Meyerson Hall
210 S. 34th Street
PA 19104-6311 Philadelphia
United States

Rapporteur

Mr. Ricardo HURTUBIA
Assistant Professor
Department of Transport Engineering and Logistics
Pontificia Universidad Católica de Chile
Vicuña Mackenna 4860
Macul
Chile

Rapporteur

Ms. Tatiana PERALTA QUIROS
Urban Mobility and Technology
Transport & ICT, Global PracticeWorld Bank - USA
1818H St NW Washington DC
United States

Rapporteur

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194 – PARTICIPANTS LIST

Mr. Alan PISARSKI
Independent Consultant
Alan Pisarski Consulting
6501 waterway drive
22044 FALLS CHURCH
United States

Rapporteur

Ms. Camila RODRIGUEZ HERNANDEZ
Senior Infrastructure Specialist
Transport & ICT, Global Practice
The World Bank
Carrera 7 No. 71-21 Torre A, Piso 16
BogotáColombia

Rapporteur

Ms. Geetam TIWARI
Professor
Civil Engineering / Transportation Research
Indian Institute of Technology Delhi
MS 808, IIT Delhi, Hauz Khas
110016 Delhi
India

Rapporteur

Mr. Ignacio TIZNADOMSc Student
Transport and Logistic's department
Pontificia Universidad Católica de Chile
Camino Otoñal Oriente 5657
Santiago
Chile

Co-Rapporteur

Mr. Geunwon AHN
Research Fellow
Division of Transport Economics Research
Korea Transport Institute (KOTI)
370 Sicheong-Dearo
339007 Sejong-Si
Korea
Mr. Scott BERNSTEIN
President
Center for Neighborhood Technology
2125 W. North Ave
60647 Chicago
United States

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PARTICIPANTS LIST – 195

M. Akli BERRI
Researcher
Laboratory Economic and Social Dynamics of Transport(DEST)
French Institute of transport science and technologies,
regional development and networks (IFSTTAR)
14-20 Boulevard Newton, Cité Descartes, Champs-surMarne
77447 Marne-la-ValléeFranceMs. Maria CONILL
Civil Engineer
Mobility and Transport Department
Metropolitan Area of Barcelona
c/62, No. 16-18 Zona Franca
08040 Barcelona
Spain
Ms. Claudia DIAZ DIAZ
Lider de Transporte Metropolitano
Subdirección de MovilidadÁrea Metropolitana del Valle de Aburrá
Cr 53 No. 40A-31
Medellin
Colombia
Mrs. Floridea DI CIOMMO
Responsible for Research; Chair of Transport and Equity
Analysis Cost Action TU1209
Sustainable Mobility and Travel Behaviour
Universidad Politecnica de Cataluña
C. Jordi Girona, 1-3 Building C3-S120
08034 Barcelona
Spain
Mr. Onésimo FLORES DEWEY
Visiting Scholar
Center for Advanced Urbanism
Massachusetts Institute of Technology (MIT)126 Harvey Street
02140 Cambridge
United States

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196 – PARTICIPANTS LIST

Mr. Clayton LANE
Chief Executive Officer
Institute for Transportation and Development Policy
9 East, 19th Street, 1st Floor
10003 New York
United States
Mr. Todd LITMAN
Executive Director
The Victoria Transport Policy Institute1250 Rudlin Street,
V8V 3R7 Victoria
CanadaMs. Manuela LOPEZ MENENDEZ
Secretaria de Obras de Transporte
Ministerio de Transporte de la Nación
Hipólito Yrigoyen 250 of 1229
Buenos Aires
Argentina
Dr. Kylie LOVELL
Principal Research Officer
Social and Behavioural Research
Department for Transport (DfT)
Great Minster House
33 Horseferry Road
SW1P 4DR London
United Kingdom
Ms. Sofia MARTIN-PUERTA
Consultant
Polygonia
10 Rue Aristide Briand
92130 Issy les MoulineauxFranceMr. Haixiao PAN
Professor
Department of Urban Planning
Tongji University
1239 Siping Road
200092 Shanghai
People’s Republic of China

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PARTICIPANTS LIST – 197

Mr. Ivan SARMIENTO
Professor
Department of Civil engineering
Universidad Nacional de Colombia
Calle 65 No. 78-28, Oficina M1-223
05001000 Medellin
ColombiaOECD
Mme Soo-Jin KIM
Policy Analyst
GOV/RDPOECD
2 rue André-Pascal
75016 Paris
FranceMr. Alexander LEMBCKE
Economist/Policy Analyst
GOV/RDPM. Sebastian NIETO PARRA
Economist
DEV/GD/LAC
Ms. Magdalena OLCZAK
Project Manager
ITF/IRSMiss Ana Maria RUIZ
Policy analyst Junior
GOV/BUD

ITF Research and Policy
Mr. Stephen PERKINSHead of Research & Policy Analysis
International Transport Forum / OECD
2 rue André-Pascal
75016 Paris
FranceMs. Aimee AGUILAR JABER
Economist

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198 – PARTICIPANTS LIST

Mr. Guineng CHEN
Transport Analyst
Ms. Jagoda EGELAND
Economist
Ms. Wei-Shiuen NG
Transport Analyst
Mr. Christian POLLOKTransport Analyst
Mme Monserrat FONBONNATAssistant

INCOME INEQUALITY, SOCIAL INCLUSION AND MOBILITY — © OECD/ITF 2017

Income Inequality, Social Inclusion
and Mobility

Income Inequality, Social Inclusion

There is significant evidence across countries that lower-income populations tend
to suffer more from restricted transport options, have lower quality transport
services available to them and travel under worse conditions (safety, security,
reliability, comfort). Broad evidence also suggests that the lack of, or poor access
to, transport options is central to limitations on access to jobs, educational
institutions, health facilities, social networks, etc., which in turn generates a“poverty trap”.
This Roundtable report examines mobility policies with a focus on evaluating
their capacity to address transport-related exclusion of lower income groups.

2 rue André Pascal
75775 Paris Cedex 16
FranceT +33 (0)1 45 24 97 10
F +33 (0)1 45 24 13 22
Email : contact@itf-oecd.org
Web: www.itf-oecd.org

Roundtable Report 164

International Transport Forum

Photo credit: Fotos593 / Shutterstock.com

Income Inequality, 		Social Inclusion
and Mobility

2017

164

Roundtable Report