01Social Impact Evaluation of the Lilac Line Project: A study on the case of São Paulo
Subway System

A Thesis Submitted to the
Graduate School of Public Policy,
The University of Tokyo
By

Nicolle Aya Konai

Master of Public Policy/ Public Administration – International Dual Degree
Graduate School of Public Policy, The University of Tokyo
School of International and Public Affairs, Columbia University in the City of New YorkAdvisor: Professor Toshiro Nishizawa

May 2015

2

Abstract

The work utilizes propensity score matching and difference-in-differences statisticaltechniques to develop an evaluation study on the social impacts attributed to theconstruction and operation of the Lilac Line (Line 5) of the subway system in the city ofSão Paulo over the 1997-2007 period. The study considers the Origin Destination Survey
published by METRO in 1997 and 2007, as well as the Urban Mobility Survey 2012 as
the primary data sources. Results show that statistically significant results were foundregarding a decrease of 18.6% in the number of owned cars (-0.186, SE: 0.0892) and anincrease of the number of generated trips (8,221, SE: 3,807).

3

Acknowledgements

I would like to thank my Advisor, Professor Toshiro Nishizawa, for the encouragement,
kind comments and supervision. Also, several Professors at Columbia University who
provided me with thoughtful insights on my research, specifically Professor Noha Emara
and Professor Eric Verhoogen. I would also like to thank the Graduate School of Public
Policy (GraSPP) staff for the administrative support while I was in the US, the METRO for
providing me with further information on the surveys, the Mitsubishi UFJ Trust ScholarshipFoundation, the Go Global Scholarship (the University of Tokyo), the GoFundMe
campaign and the Lemann Foundation for the financial and career support. Finally, I thankmy family and friends for all the encouragement during my pursuit of the MPP/MPA
degree.

4

Table of Contents

Table Index ................................................................................................................................ 6
Figure Index ............................................................................................................................... 6
Introduction ................................................................................................................................ 7
Background Information ............................................................................................................. 9
Commuting Patterns .................................................................................................................11
Current Trends ..........................................................................................................................15
Considerations about Social Impact Evaluation.........................................................................17
Impact Evaluation and the Lilac Line .........................................................................................19
Data Description .......................................................................................................................22
Methodology .............................................................................................................................23
Study Design and Setting ..........................................................................................................25
Treatment and Control Groups..................................................................................................25
Dependent Variable ..................................................................................................................27
Independent Variable ................................................................................................................28
Data Analysis ............................................................................................................................28
Propensity Score Matching ................................................................................................................. 28
DID Model with Ranksum ................................................................................................................... 32
Conclusion ................................................................................................................................35
References ...............................................................................................................................38
Annex .......................................................................................................................................42
Annex 1: Variable description............................................................................................................. 42
Annex 2: Descriptive Analysis ............................................................................................................ 43
Annex 3: São Paulo Profile (maps) ................................................................................................... 47
Annex 4: Zoning ................................................................................................................................... 51
Annex 5: Zone Description (OD 2007) .............................................................................................. 52

5

Table Index

Table 1: Data Description ..........................................................................................................22
Table 3: DID Model ...................................................................................................................24
Table 4: DID Model 2 ................................................................................................................24
Table 5: Balance Checking .......................................................................................................30
Table 6: ATT .............................................................................................................................31
Table 7: Naïve Models ..............................................................................................................33
Table 8: Multivariate Models .....................................................................................................34

Figure Index

Figure 1: Metro: Daily Generated Trips (SP, 2007) ...................................................................12
Figure 2: Metro: Daily Attracted Trips (SP, 2007) ......................................................................12
Figure 3: Public Transport, Daily Generated Trips (SP, 2007) ...................................................14
Figure 4: Public Transport, Daily Attracted Trips (SP, 2007) .....................................................14
Figure 5: Public Transport, Rush Hour, Generated Trips (SP, 2007) .........................................14
Figure 6: Public Transport, Rush Hour, Attracted Trips (SP, 2007) ..........................................14
Figure 7: Lilac Line ....................................................................................................................21
Figure 9: Euclidean Distance – Subway and Lilac Areas...........................................................27
Figure 10: Region of Support ....................................................................................................29

6

Introduction

São Paulo’s urban development has been one of intense discussions over the past years.
Among the challenges the city currently faces is the mobility issue with an increasing
demand over adequate and accessible public transportation. In spite of an existing rail
network, the lack of integration between train, bus and subway lines, as well as the still
limited length of the latter contributes to the maintenance of a high rate of car ownership.
The insufficient public transportation combined with an increasing number of private cars
are responsible for heavy traffic congestion, long commuting times reaching 67 minutes
in 2007 (in comparison to 59 minutes in 1997) and harmful levels of air pollution. Also,
socioeconomic inequalities are translated into São Paulo’s spatial segregation, where
low-income people face obstacles to access work and educational opportunities, while
facing increasing fares, crowded public transportation and long journeys from theperipheral zones of São Paulo Metropolitan Region to the city center.

The excessive reliance upon motorized road transportation poses a challenge forplanners and is aggravated by the lack of resources for infrastructure and public
transportation investments. The increasing demand for an adequate and accessible
public transportation grid had also been the objective of several protests, which showed
the urgent need for substantial investments in this sector. In June 2013, São Paulo wasat the center of intense social protests, where local populations demonstrated against theapproval of an increase in the metro and bus fares, while also claiming for government
measures towards many other issues like education, health and transparency. The

7

protests in São Paulo and nationwide gathered millions of people and contributed to bringthe urban infrastructure topic to the center of the city’s debates.

São Paulo, therefore, faces a turning point in its growth process characterized by theneed for substantial structural changes that conciliate economic development and social
inclusiveness. This challenge highlights the difficulties facing emerging economies todevelop, maintain and expand modern and costly public infrastructure.

It is in this context that the assessment of the impacts of the existing infrastructure of theSão Paulo metro system is to be undertaken. The present study aims at presenting astatistical model that utilizes quantitative methods to measure social impacts associatedwith the construction of subway lines. More specifically, this work focuses on the social
impact evaluation of the Lilac Line (Line 5) in São Paulo, considering the data available
in the Origin Destination Surveys from 1997 and 2007, as well as the 2012 Urban Mobility
Survey as the main references.

In order to do so, first, the study will set the ground by analyzing the main features of thecity’s profile, commuting patterns and trends in infrastructure development. Next, the caseof the Lilac Line, as well as a brief explanation about social impact evaluation will be
discussed. Following this, two methods (propensity score matching and difference-indifferences) will be analyzed and, finally, the work will address the regression results andfinal conclusions.

8

Background Information

São Paulo is the capital city of the state of São Paulo, one of the most populated anddense cities in Latin America. According to the 2010 Census (IBGE), São Paulo has apopulation of over 11.25 million inhabitants living in a total area of 1,521.110 km2, thus
having a demographic density of nearly 7,400 people per km2. The average GDP per
capita (current prices, 2012) is R$43,890 (around USD 21,567) and the city’s GDP
amounts for R$ 499.37 billion (around USD 240 billion). Together with 39 surrounding
municipalities, the São Paulo Metropolitan Region (hereinafter referred to as SPMR) is
Brazil’s main economic and financial center, with about 10% of the national population(around 20 million people) and responsible for 20% of the Brazilian GDP.

During the first decades of the 20th century, São Paulo experienced a booming economic
activity through the expansion of coffee exports and a substantial increase in local
economic activities and trade, which led to a fast paced urban development. The needfor rapid and mass transportation, combined with the widespread nationalist ideals(STEFANI, 2007) led public authorities to invest in rapid-transit transportation systems
development in urban areas of the country. In April 24, 1968, the São Paulo Metropolitan
Company (Companhia do Metropolitano de São Paulo, METRO) a state-run company,
was established. Eight months later, the construction of the North-South line began. The
first Brazilian subway system was inaugurated in September 1974 and consisted of a
6.4km length operational line between the districts of Jabaquara and Vila Mariana.

Today, the São Paulo subway system is the city’s main rapid-transit system and thelargest in Brazil. In 2013, the metro had a total length of 74.3 km, 65.3 km of which were
9

operated by METRO and 9 km by Via Quatro, a consortium of private-sector companies
that won the bid for a private-public partnership for the operation of Line 4 – Yellow of the
subway. The current system counts with 5 lines and 64 stations (6 operated by Via
Quatro) and an annual ridership of 1.11 billion passengers. (METRO)

Although São Paulo made progress in expanding its transportation system during the past
decades, its infrastructure seems not to keep up with the growing urbanization trends ofthe city. For comparison purposes, the subway system in New York City, opened in 1904
and operated by The Metropolitan Transportation Authority (MTA), currently comprises
24 lines, totaling 468 stations distributed through 373km, totaling an annual ridership of1.75 billion passengers (MTA,2014). Likewise, Tokyo counts with 13 lines, 179 stations,
320 km and a ridership of 3.217 billion (Tokyo Metro), and Shanghai, with 11 lines, 267
stations, 410 km covered and an annual ridership of 3.410 billion passengers (Shanghai
Metro).

The reality of the São Paulo subway system is still far from the highly developed andexpanding rapid-transit systems in the previously mentioned urban centers. However,even with a comparatively small scale, the São Paulo subway system plays an importantrole in mobility and access to a diverse set of venues and services by the population.
Although limited to the municipal borders, the subway system is connected with the linesof the Metropolitan Trains Company (CPTM), and other urban transportation systems thatare crucial for the commuting processes between the city and other municipalities in themetropolitan area.

10

Commuting Patterns

According to the 2012 Urban Mobility Survey, 12.91% of the total trips (generated andattracted trips) on subway in the SPMR are made by populations living in the city’s
metropolitan area. Figure 1 and 2 show that in some metropolitan zones the demand for
subway reaches over 10 thousand daily trips.

The 2007 Origin Destination survey, a study implemented by Metro in 460 different zones
in the SPMR, points out that, considering the total supply of public transportation, the cityof São Paulo receives a daily inflow of 1.08 million commuters from other SPMR
municipalities. In contrast, almost 8 million daily trips are produced within the capital’sborders, that is, from one zone in the city to another (Figure 1 and 2)1. The zones thatreceive the greatest number of commuters are Sé (106 thousand daily trips), Parque Dom
Pedro (81 thousand daily trips) and São Bernardo do Campo, SPMR (175 thousand trips).
Also, among the zones that generate the greatest number of trips are São Bernardo doCampo (183 thousand daily trips), Santo André (141 thousand trips) and Pimentas (138.5
thousand trips).

Figures 3 and 4 below show the daily inbound and outbound movements of commutersin public transportation in the SPMR. If we consider the trips generated and attracted
during morning rush hour (6:30-8:30am) (Figure 5 and 6), São Bernardo, Granja Julieta,
Santo André and República, respectively, are the zones that receive the greatest number

1 Total trips considering public transportation: subway, train, bus, shuttle bus, school bus and vans.

11

of commuters. Likewise, Cidade Tiradentes, Alto da Boa Vista, Carapicuíba and Cocaia
are the zones from where the highest number of commuters depart.
It is noticeable that public transportation is massively utilized by the population living inthe suburban zones of São Paulo, as shown from the movements during rush hour.
According to the images, we see a high concentration of outbound movements from theseareas to the city’s central zones, as well as to a few zones in the metropolitan area.
Considering that during this period a great share of commuting trajectories are made due
to employment or school reasons, we can arguably see the importance of public
transportation for the zones located in the peripheral area of São Paulo.
Figure 1
Metro: Daily Generated Trips (SP, 2007)

Figure 2
Metro: Daily Attracted Trips (SP, 2007)

As previously explained, even with the relatively small length of the subway system incomparison to the public transportation grid as a whole, it is evident that the system is
responsible for a considerable share of the daily inbound and outbound commutes (Figure
5 and 6). Its use, as shown in the images, comprises mostly the populations living in the12central and eastern districts of São Paulo. In addition, there is a high utilization of the
subway system by commuters from other municipalities such as Guarulhos, Osasco,
Ferraz de Vaconcelos, Francisco Morato, Diadema, Franco da Rocha and Itapecerica da
Serra. (See Annex for references). The majority of the metro trips attracted and generated
per day are concentrated in the central and east areas of the city.

13

Figure 3
Public Transport, Daily Generated Trips (SP, 2007)

Figure 4
Public Transport, Daily Attracted Trips (SP, 2007)

Figure 5
Public Transport, Rush Hour, Generated Trips (SP, 2007)

Figure 6
Public Transport, Rush Hour, Attracted Trips (SP, 2007)14

Current Trends

In the past years, the State government has been putting efforts into the development of
new infrastructure investment schemes that could allow for the growth of the public
transportation grid in a faster pace. One of the major changes made by the government
consisted of the redesign of regulatory and financing mechanisms that could allow
business models to conciliate different stakeholder’s interests on infrastructure projects.
One of the initiatives that were targeted by the new legislations consisted of theestablishment of Public Private Partnerships (Parcerias Público Privadas, PPPs).

In terms of legal tools, the regulation on concessions in force until then used to impose
obstacles to the implementation of large projects due to the fact that several of them were
not self-sustainable and needed large financing. This type of cooperation agreement was
not regulated by any of the preexistent legal arrangements (LACLAU, 2008). The
approval of the new “PPP Law” by the Federal government consisted, therefore, in anadvance in terms of regulation. The new legislation made it possible for different
cooperation arrangements between the public and private sectors to be executed and, in
this sense, was more suitable to the type of initiatives aiming at expanding the São Paulo
subway system.

As briefly described above, the substantial changes in the financing and legal schemes
have the potential of boosting infrastructure development in the city of São Paulo in thefollowing years. The results or impacts of the construction of new subway lines, however,are a field that remains underexplored. The literature review made for this study showedthat most of the impact evaluation analysis performed by the academic community has
15been focusing on the economic impacts of residential property values generated by the
subway system as a whole in different locations (see VASCONCELLOS, 2005; MUNOZBOWES, 2001; RASKIN, 2010; KING, 2011; and HADDAD et al., 2013). Moreover,according to information provided by staff from METRO, technical studies on theevaluation of social impacts of the lines at the local level are still incipient.

That said, the following section aims at discussing some of the considerations of social
impact evaluation and the utilization of quantitative analysis as a tool to measure social
impacts.

16

Considerations about Social Impact Evaluation

The previous sections focused on presenting the main features and key information about
the current public transportation grid and, more specifically, the subway system in São
Paulo. The information is useful in order to understand who the users are and what typesof commuting patterns exist in the SPMR. Although the subway system has been present
in the lives of millions of citizens in the past five decades, few are the studies on the roleof the subway system in promoting changes in socioeconomic characteristics of the
populations directly served with rapid-transit systems.

According to authors such as Cloquell-Ballester et al.(2005), and Vanclay, 2001; social
impact evaluations can consider quantifiable or qualitative variables that refer to changes
in qualitative indicators such as wellbeing, cultural impacts, values ,and perceptions about
the society. The limitations regarding proper specification of all the dimensions involvedin a changing process make social impact evaluation always subject to specific contexts
and projects implemented.

That said, the concept of social impact in this study is applied only to the broad scope ofdimensions whose changes can be quantified in the form of data outputs. These outputs
are then organized in variables that are considered to be linked to the general social wellbeing of residents in a microanalysis. The well-being, in turn, considers socioeconomic
aspects of the population analyzed, which will be specified in the following sections.

17Limitations

One challenge faced by quantitative analysis applied to social impact is the scarcity ofdata produced due to budgetary and technical restrictions. Specific information oncommuting patterns in such a wide geographic area and/or lack of technical expertise
impose challenges to the public authorities and technicians in collecting accurate andsufficient information about users and their displacements.

Another challenge consists of data accessibility to the general public. Until the publicationof the present study, only three survey datasets were officially available on the METRO’s
website. The other datasets utilized in this work were acquired through an official
solicitation to the Information Service to the Citizen 2.

The last limitation is methodological. Considering the obstacles in isolating the specificimpacts of a certain policy, the conclusions made based on available data have to beanalyzed with caution. Considering the design of the study, subsequent survey data canbe added in order to make estimates more accurate. The following sections will tackle theproposal of an impact evaluation analysis taking the Lilac Line construction as a casestudy.

2 Serviço de Informação ao Cidadão, established by Federal Law n. 12.527/2011 and regulated by theState Decree Decree n. 58.052/2012, is a service that aims at providing citizens with information on the
management of activities operated by Metro.
18

Impact Evaluation and the Lilac Line

The Line 5 (Lilac) of São Paulo’s subway system was inaugurated in 2002 and currently
counts with 7 stations (Capão Redondo, Campo Limpo, Vila das Belezas, GiovanniGronchi, Santo Amaro, Largo Treze and Adolfo Pinheiro). In 2014, the passenger’s
demand for this line reached almost 80 million trips with an average of 272 thousand trips
during weekdays. The line has a length of 9.3 km and stations connected to 5 bus
terminals and 1 urban train line (CPTM).

According to data of the OD Surveys from 1997 and 2007, the stations that constitute theLilac Line are located within a total of 19 zones’ borders. As it will be discussed in thefollowing sections, the distance to a Lilac station was the criterion utilized in order to define
the subway primary area of impact or, in other words, the area that would be directly
affected by the operation of the new subway line.

The Lilac Line study case was addressed in this study due to two main reasons. The firstone consists of data availability. Considering that the OD Surveys 1997 and 2007 werethe main sources of information, the impact evaluation study had to correspond to theanalysis of an infrastructure work that was developed and started to operate between
1997 and 2007.

Another reason consisted of the socioeconomic status of the residents in the area. As
already mentioned, São Paulo is characterized by strong inequalities that are translated
into spatial segregation. While most of the SPMR revenue and employments are locatedin the central area of the city, the peripheral zones struggle with poverty and lack of19

accessibility to services and infrastructure. In 2007, zones located close to Lilac station
had an average family income of R$2,922 per month, higher than the average for themetropolitan region, but lower than the zones located close to a subway station (with the
exception of the Lilac line), which had an average of R$4,744 per month in the same
period. It is noteworthy that from 1997 to 2007, the average commuting time by residentsof Lilac zones increased from 63 to 69 minutes, while the SPMR average increased from61 to 81 minutes (see Annex 3).

20Figure 7
Lilac Line Zones

Zone
307
255
252
190
193
253
259
254
257
195
189
256
308
258
191
192
188
250
196

Name
Adventista
Capao Redondo
Centro Empresarial
Chacara Flora
Granja Julieta
Jardim Angela
Jardim Mitsutani
Jardim Sao Luis
Jardim Umarizal
Jardim Vitoria Regia
Jurubatuba
Parque Arariba
Parque Fernanda
Pirajussara
Santo Amaro
Vila Miranda
Vila Sao Pedro
Vila Socorro
Vila Suzana

21

Data Description

The case study is based on data from two surveys published by the São Paulo
Metropolitan Company (Companhia do Metropolitano de São Paulo): the Origin and
Destination Survey (Pesquisa Origem e Destino) from 1997 and 2007, and Survey on
Urban Mobility (Pesquisa de Mobilidade Urbana) from 2012. Additional information was
found on official online databases of São Paulo’s Municipal Secretariat for Urban
Development (SMDU), the Brazilian Institute of Geography and Statistics (IBGE), State
System of Data Analysis (SEADE), and São Paulo State Housing Syndicate (SECOVI).
Table 1 describes the main features of the dataset, Annex 1 contains a description ofeach variable, and Annex 2 summarizes the means for 1997 and 2007.

Table 1: Data Description

Lilac

19

Adventista, Capão Redondo, Centro Empresarial, Chácara Flora,
Granja Julieta, Jardim Ângela, Jardim Mitsutani, Jardim São Luis,
Jardim Umarizal, Jardim Vitória Régia, Jurubatuba, Parque
Arariba, Parque Fernanda, Pirajussara, Santo Amaro, Vila
Miranda, Vila São Pedro, Vila Socorro, and Vila Suzana.

Zones

389

(See Annex 4 and 5 for description)

Years

2

1997 and 2007

Observations 778
Variables

82(See Annex 1 for description)

22

Methodology

The proposed methodology to evaluate the impact of the Lilac Line consists of a“difference-in-differences” model (DID model). The DID model is a well-known statisticaltechnique implemented in order to evaluate the impacts of a specific intervention in aquasi-experiment. Unlike randomized controlled trials (RCTs), the quasi-experiments do
not present a controlled design of the treatment and control groups, which might generatebias issues in the estimates of the effects generated by the treatment. Considering this,the DID technique has the advantage of enabling the model to control for changes acrossthe different zones that do not vary over time, for instance, geographic and demographic
features (zone fixed effects) and changes that occur over time but do not impact the zones
in different ways (year fixed effects). The model, therefore, is a useful tool to examine
possible impacts of a treatment without necessarily relying on previously designed control
group that mimics a counterfactual situation, as well as to infer associations without
specifying all of the possible omitted variables in the model.

Standard DID model (example: employment rate):
𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑟𝑎𝑡𝑒 = 𝛽0 + 𝛽1 ∗ 𝑙𝑖𝑙𝑎𝑐 + 𝛽2 ∗ 𝑦2007 + 𝛽3 ∗ 𝑙𝑖𝑙𝑎𝑐_𝑦2007 + 𝜀

𝛽0= Employment rate at non-lilac in 1997
𝛽1= Difference between lilac and non-lilac in 1997
𝛽2 = Difference between employment rate in non-lilac in 1997 and 2007
𝛽3 = Interaction terms between time and zones (lilac/non-lilac)
𝛽0+ 𝛽1= Employment rate at lilac in 1997
𝛽2 + 𝛽3= Difference between lilac in 1997 and 200723

Table 2: DID Model
Treatment Group(Lilac)Control Group(non-lilac)

Difference:
Treatment-Control

Before (1997)

TB

CB

TB- CB

After (2007)

TACA

TA- CA

TA- TB

CA- CB(TA - TB ) - ( CA - CB )Difference: AfterBefore

Table 3: DID Model 2
Treatment Group(Lilac)Control Group(non-lilac)

Difference: ControlTreatment

Before (1997)

𝛽0 + 𝛽1𝛽0

𝛽1After (2007)

𝛽0 + 𝛽1 + 𝛽2 + 𝛽3

𝛽0 + 𝛽2

𝛽1 + 𝛽3

𝛽2 + 𝛽3

𝛽2

𝜷𝟑

Difference: AfterBefore

24

Study Design and Setting

In order to implement a DID analysis, some changes had to be made in the datasets
available. First, the OD surveys implemented different geographical divisions in datacollection. On one hand, the OD Survey 1997, the SPMR was divided into 389 zones. On
the other hand, the OD Survey 2007 divided the metropolitan area into 460 zones. The
author opted for utilizing the 1997 methodology because the conversion tables available
made it possible to aggregate the data from the 460 zones in the 2007 Survey into 389
observation units3. Considering that only five O-D surveys were published so far, fromwhich two are currently available online, the final dataset is comprised of data on the SP
Metropolitan area divided into 355 zones and in a time frame of two years - 1997 and2007. The observations that contained value equal to zero for major covariates were
dropped in order to reduce bias.

Treatment and Control Groups

Treatment consists of the Lilac Subway Line operation, started in 2002, and the treatmentgroup is the group of zones within a specific distance from one of the Lilac Line stations.
The control group consists of all other zones in the dataset, considering zones where asubway station other than the Lilac Line operates, as well as zones where no subway
system operates. The distance chosen was 1,500 meters (about 1 mile) and it was basedon previous studies on urban mobility (see RASKIN, 2010), which take into account the

3 It would not be possible to disaggregate the data of the Survey 1997 due to the fact that individual

observations were not available, only the aggregated values per zone.
25

average maximum time a person would be willing to spend walking from her home to theclosest subway station instead of opting for other transportation system such as bus, car,or train. The value might not take into consideration the diversity of individual utility curves
and the different motivation that influence a person to opt for a specific way of transport
over another. However, considering the limitation of the datasets to a zone-level analysis(observations on individuals were not available), the author opted for a distance thresholdto determine treatment and control groups. Another important aspect to be considered isthat no other infrastructure project effects in the zones located along the Lilac Line weretaken into account during the period from 1997 to 2007.

Finally, some of the independent variables utilized in the final dataset were converted into
log or per capita rate forms due to a great variation of the values across the different
zones. Figure 9 shows the Euclidean Distance tool (GIS) comprised of 1.5 km distancerings having the subway stations as reference. The gray area represents the zones
selected as components of the two dummy variables lilac and subway.

26

Figure 8
Euclidean Distance – Subway and Lilac Areas

Dependent Variable

Several trials were made in order to test for significance of different independent variables
associated with socioeconomic impact on the treatment group. The following resultscorrespond to the analysis of impacts of the Lilac Line on the share of the local population
comprised of employed people, the share of the local population comprised of students,
the average time spent in public transportation, the number of owned cars, and thenumber of generated trips.

27

Independent Variable

The independent variables considered are households (number of households), incpcap(income per capita), privautomob (number of private cars), the interaction termlilac_y2007, and the dummy variable subway. In addition, zone fixed effects and year
fixed effects are included in order to control for different features of Zones that do not varyover time (geography for example), and differences that vary over time but impact thezones in the same way (example, macroeconomic events, changes in overall
infrastructure, etc).

Considering that the hard railway lines were already existent when Line 5 of the subway
was constructed, its effects are not taken into consideration in the calculation of the effects
in the treatment group.

Data Analysis

Propensity Score Matching

A first impact evaluation trial was made through a Stata pre-treatment analysis with theutilization of the propensity score matching technique (Rosenbaum and Rubin, 1983).
This method was chosen in order to balance the treatment and control groups and,
therefore, to reduce bias in the estimation of treatment effects with observational datasets(nonrandomized trials). The technique, therefore, aims at balancing differences between
control and treatment groups based on the calculation of the probability to be treatedgiven observed pre-treatment characteristics x listed in Table 4. The control group, in this28

sense, approximates to a counterfactual, being as similar as possible to the treatmentgroup.

In this study, the Nearest Neighbor Matching with a noreplacement option was utilized forthe match and once tested for balance of treatment and control groups. The matchoriginated a control and treatment group comprised of 19 observations each. Figure 10
shows the distribution of probabilities and assessment of region of support (overlap)
between the two cohorts (control and treatment).

Figure 9
Region of Support

6
4
2
0

Density810

Region of Support

0

.2

.4
Pr(lilac)
Lilac

.6

.8

Other zones

29

Once developed, the treatment and control group could be comparable and further
analysis was made based on the distribution of the frequencies.

At this point, the estimation of the average treatment effect on the treated (ATT) was
achieved by the use of the “att*” command. The results, however, did not show statisticallysignificant coefficients for ATT (see example in Table 5). The major challenge faced on
this phase of the study was the small number of observations (n). The fact that manyobservations were dropped from the model after the matching created a trade-off between
bias and variance of the estimates.
Table 4: Balance Checking

Variablehouseholds
pop1997
generatedtrips

Treated
10337
40585
74354

Mean
Control
10534
41877
83766

%bias
-1.9
-3
-14.3

t-testtp>|t|-0.07 0.942-0.12 0.907-0.53 0.601

V(T)/
V(C)
0.56
0.51
0.21*

attractedtrips
avincfamincpcap
studpcap1997
employedpcap1997
timecollec

74298
4487.31248.40.242540.4297763.31683859
4480
1223.6
0.242290.4443960.947

-14.6
0.3
3.4
0.7
-24
21.7

-0.53
0.01
0.09
0.02
-0.75
0.62

0.596
0.994
0.928
0.984
0.459
0.54

0.21*
0.73
1.03
0.67
1.35
0.78

subwayprivautomobpcap1997

00.263200.25687

.
4.9

.
0.13

.
0.894

.
1.22

30

Table 5: ATT

Variable
employedp
cap2007

SampleControls

Difference

S.E.

T-stat0.414886
0.464567

0.039903-0.009780.014795
0.029355

2.7
-0.33

TreatedControls

Difference

S.E.

T-stat0.1430811

0.1667035-.02362230 0.0107381-2.2

0.1430811
9
Treated0.1629775-0.92

Controls-.01989630 0.0216684
3
Difference S.E.

69.105263

72.205240-.0999770

6.844819-0.45

69.105263

68

1.1052631

5.555442

0.2

TreatedControls

Difference

S.E.

T-statUnmatched 909.68421

763.38427

146.29993

133.00967

1.1

909.68421
1
Treated995.84210
5
Controls

Unmatched 0.454788

0.414885

ATT

0.464567-6.1578947 218.15518
3
Difference S.E.
0.0399027
0.014794
25-.00977872 0.029354
4

Unmatched 0.454788
ATT
0.454788

Variable
Samplestudpcap2
Unmatched
007
ATT
Variable
Sampletimecollec2
Unmatched
007
ATT
Variable
incpcap20
07

SampleATT
Variable
employedp
cap2007

TreatedSample0.454788

T-stat-0.39
T-stat
2.7
-0.33

31

DID Model with Ranksum

Considering the number of observations and, therefore, the high standard error of theestimates, a second trial was done with a direct comparison of the treatment and control
groups by using a Wilcoxon rank-sum test. Ranksum tests the hypothesis that two
independent samples (that is, unmatched data) are from populations with the same
distribution. The results demonstrate there is a balance between the control and treatmentgroups, which leads us to assume that the “assignment” of zones to the treatment, inother words, the construction of the Lilac Line, was made in a complex way that could beconsidered, in this context, as random.

Considering this assumption, new trials were made with a higher number of observations.
Table 8 shows the raw results in univariate regressions, while Table 9 presents the resultsin multivariate analysis. We observe that in both cases, the coefficients of number ofschool enrollments and number of jobs per capita in the zones close to a Lilac station
were not statistically significant, that is, the effects of the Lilac line cannot be assumed tohave a significant impact on generating new jobs or increasing school enrollment at the
local level. The number of cars per capita showed to be negatively affected by thepresence of the Lilac Line, which seconds the intuition of increase in public transportation
as a replacement to the use of cars.

The time spent in public transportation and the number of generated trips had their signs
flipped with the addition of other covariates. Also, the time spent, that had a significant
coefficient in the univariate model, appears not to be significant in the second case. The
multivariate model shows that the zones located close to a Lilac station had, on average,32

a decrease of 18.6% in the number of owned cars from 1997 to 2007. Also, around 8,200
new trips were originated in the districts with Lilac stations (Table 8). It is also interesting
to observe that controlling for other covariates (households, cars, and income per capita),
the group of zones with subway stations (with the exception of lilac stations) have on
average 163 thousand more trips than the rest of the zones. Also, if we consider the zones
located close to subway stations (non-Lilac), we find a strong association of zones with asubway station and number of local employments.

Table 6: Naïve Models(1)
VARIABLES Local jobs
(log)

(2)
Cars
(log)

lilac_y2007-0.0215(0.0572)-0.186** 0.0869(0.0892) (0.145)-14.35***(3.222)

5,160(4,276)

Constant

7.210***(0.182)5.560*** 8.693***(0.501) (0.308)

63.93***(5.193)

159,774***(17,823)

710
0.932
YES
YES

710
0.617
YES
YES

710
0.953
YES
YES

Observations 710
R-squared
0.964
Zone FE
YES
Year FE
YES(3)
Local school
enrollment (log)

709
0.911
YES
YES

(4)
(5)
Time: public Generated
transport
trips

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

33

Table 7: Multivariate Models(1)
VARIABLES Local jobs
(log)

(2)
Cars (log)(3)(4)
Local school Time: public
enrollment
transport
(log)

(5)
Generated
trips

lilac_y2007-0.186**(0.0892)

0.122(0.145)1.72e-05*(8.81e-06)-0.896(4.027)8,221**(3,807)3.720***(0.970)1.40e-07(9.29e-06)-0.00127***(0.000313)

0.0906(1.011)8.775***(0.0147)

2.613***(0.528)6.83e-05(4.69e-05)
6.028***(0.432)-13.91(22.48)0.0383***(0.00414)
39.71*(22.24)

163,049***(10,695)
3.303**(1.549)-3,071*(1,651)

710
0.932
YES
YES

709
0.914
YES
YES

710
0.820
YES
YES

710
0.969
YES
YES

households

0.00334(0.0442)
2.51e-05***(6.53e-06)

privautomob

1.19e-05*(6.15e-06)

subway0.494***(0.167)-4.52e-06(1.51e-05)
6.748***(0.105)incpcap
Constant

Observations 710
R-squared
0.973
Zone FE
YES
Year FE
YES-3.215***(0.501)

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

34

Conclusion

The present study focused on the current transformation that São Paulo is experiencing
regarding its infrastructure. The need for an adequate and accessible public
transportation grid is necessarily connected to the rethinking of strategies that will enablefurther developments of the city’s subway system. That said, it is imperative that the
government of São Paulo develop and consolidate proper impact evaluation
methodologies in order to assess the progress, as well as to support necessary changes
in urban policies.
This work focused on the application of two statistical techniques (propensity scorematching and difference-in-differences) in order to evaluate some of the possible social
impacts in the case of the Lilac Line construction and operation. The motivation of theauthor in analyzing the specific impacts of the subway on socioeconomic indicator at thezone level is due to the fact that a significant share of the low-income population currently
relies on public transportation in their daily commuting trajectories. Furthermore, the caseof São Paulo indicates that these segments of population are in many cases, residents atzones that do not offer proper infrastructure and services.
The results obtained through the regression models do not show statistically significant
coefficients of average number of local employments, average number of local school
enrollments and average time spent in public transportation, while average number ofcars and average generated trips had statistically significant coefficients at 5% level.
Considering the socioeconomic context and the background information on commuting
patterns of the SPMR, these results might support the idea that the operation of the Lilac
35

Line alone did not have substantial impacts in creating more jobs or more school
enrollments at the zone level, considering the zones close to Lilac stations. In addition,
the data observed cannot prove that there was a decrease in time spent in public
transportation for residents at the zones analyzed. These results can be explained fromdifferent perspectives. Considering methodological factors, the data available wasaggregated (not at the individual-level of analysis) and limited to two years ofobservations. Some possible qualitative considerations are the fact that the impacts of
the Lilac Line were not analyzed in conjugation with other major transportation systems
such as the bus and the urban train systems. Considering that commuting patterns are
made through the utilization of multimodal transports, the inclusion of other modalities
can be a good way to analyze overall impacts of the subway system in the time spent inother transportations, for example.
Also, changes in employment and school enrollment at the local level prove to be notsignificantly impacted by the operation of the Lilac Line. One possible explanation can bethat investments on these sectors were not sufficient to increase the supply of labormarket and schools at the local level. At the same time, residents might choose tocommute to other zones in order to search for jobs and enroll their children in schools
due to differences in quality of these specific services in other zones. Therefore, theinauguration of the subway line may not have been enough to improve competitivenessof local employment and education markets when compared to other zones. The sameconclusion was also found in the case of the share of population employed or enrolled atschools, which did not seem to be altered after the construction of the subway line.

36

Finally, the reduction of the number of owned cars and the increase in generated trips
might be a good indicator of possible changes in socioeconomic patterns in the following
years that could not be measured in a 10 year period (1997-2007). These results pointout to the necessity of further analysis on possible impacts that the decrease of relianceon cars and the increase in subway trips might possibly generate at the local level in the
future.
Although the models developed in this study were limited in finding positive conclusions
regarding socioeconomic changes in the priority zones of impact, they can serve as agood baseline and contribute to studies that are still incipient in the case of São Paulo. In
addition, the present work further advances the current discussions on the needs of social
impact evaluations that are consistent to the urban projects that the city yearns for in thefollowing years.

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Projects & perations of The World Bank website. Web:<www.worldbank.org/en/results/2013/04/03/Brazil-Sao-Paulo-metropublic-private-partnership-line-4>41AnnexAnnex 1: Variable description
Zone
Zone_nameYear
Households
Families
Population
Schoolenroll

Schoolenrollpcap
Employment
Employmentpcap
Privautomob
Privautomobpcap
Generatedtrips
Attractedtrips
Age_a
Age_b
Age_c
Age_d
Age_e

Zone code
Zone nameYear
Number ofhouseholds
Number offamilies
Total Population
Number of zone
schoolenrollments
Zone school
enrollments per
capitaNumber of zone
employments
Zone employment
per capita
Number of cars
Cars per capita
Number ofgenerated trips
Number of
attracted trips
Age up to 3 years
old
Age 4-6
Age 7-10
Age 11-14
Age 15-17

Age_f
Age_g
Age_h
Age_i
Age_j
Age_k

educ1

educ2

educ3
educ4
inc_a
inc_b
inc_c
inc_d

Age 18-22
Age 23-29
Age 30-39
Age 40-49
Age 50-59
Age 60 and overIlliterate/
Incomplete
Primary School
I/Complete
Primary School
I/Incomplete
Primary School II
Complete Primary
School II/
Incomplete HighschoolComplete Highschool/Incomplete
higher education
Complete higher
education
Income up to
R$760
Income R$7601520
Income R$15203040
Income R$30405700

inc_e
avincfamincpcap
student

studentrate

employed

employedrate

timecollec

Subwaylilac

Income R$5700
and above
Average Family
IncomeIncome per capita
Number of zone
residents who are
students
Share of zone
population withstudent status
Number of zone
residents who areemployed
Share of zone
population with
employed status
Time spent inpublictransportation
Dummy variable:
zones locatedclose to subway
station (nonlilac)
Dummy variable:
zones locatedclose to lilac
station42

Households

FamiliesPop

SchoolenrollEmployment

Priv.
automob

Av. Inc.
fam

Inc.percap

student

employedTimecollecGenerated
trips

Attracted
trips

Adventista

1997

19,334

18,957

77,134

20,3708,899

10,864

2,762

679

19,02327,28669

85,74583,593

2007

25,77828,17996,116

18,877

16,88012,093

1,722

505

15,05642,044

77

120,555

121,072

1997

14,062

13,98354,435

21,90913,352

9,861

2,609

670

13,283

21,79857

89,35888,695

2007

16,742

16,742

59,681

19,36813,561

10,092

2,022

567

8,001

28,93863

90,58791,565

1997

24,66826,901

95,542

20,413

26,148

8,687

2,155

607

22,191

38,775

67

137,970

137,469

2007

32,10037,958114,285

25,376

20,75915,0191,621

538

20,65452,39566

166,072

163,917

1997

2,889

3,350

11,248

5,693

13,202

6,755

9,682

2,883

2,617

5,716

79

45,52946,274

2007

3,193

3,213

10,2803,479

27,355

4,464

6,391

1,997

1,496

5,047

78

60,893

61,397

1997

7,871

9,421

31,905

14,88856,726

13,885

7,047

2,081

7,376

12,845

80

137,708

139,223

2007

8,607

8,607

25,88533,544

83,382

9,798

4,137

1,376

1,908

12,07979

179,921

180,918

Granja Julieta

Chacara FloraCentroEmpresarial

Year

Capao
RedondoAnnex 2: Descriptive Analysis

43

21,392

21,53786,22919,212

14,938

8,490

1,826

456

20,94328,87070

88,38587,091

2007

21,95826,37980,65926,591

14,017

9,807

1,563

511

12,23831,564

70

131,967

131,091

JardimMitsutani

1997

12,183

12,22646,998

14,69014,027

8,633

2,143

558

8,783

18,151

60

68,69066,9092007

18,15920,056

66,881

22,038

10,243

7,942

1,738

521

10,88329,466

69

103,932

104,334

Jardim Sao
Luis

1997

17,897

21,571

68,976

18,57021,827

15,4292,885

902

16,35534,21151

88,81187,861

2007

20,00920,421

71,461

20,44726,042

6,488

1,611

460

6,079

29,932

65

119,933

119,969

JardimUmarizal

1997

9,229

9,244

38,443

10,63013,068

6,572

2,758

663

10,254

15,21066

58,804

59,8382007

13,292

13,522

48,389

9,374

7,914

8,624

2,058

575

8,594

21,13553

69,202

70,194

1997

4,573

4,809

16,066

1,554

10,164

6,674

9,693

2,901

4,994

8,289

82

35,17034,977

2007

6,274

6,328

19,759

1,353

9,200

8,333

5,496

1,760

3,364

11,321

66

36,71036,671

1997

3,670

3,806

15,8295,103

29,106

4,911

4,595

1,105

4,677

5,907

57

55,36756,026

2007

4,978

4,978

15,905

5,766

41,872

4,520

3,350

1,048

2,277

7,377

72

83,28583,842

1997

10,851

12,03943,642

13,324

11,335

6,372

2,281

629

12,45917,341

49

75,35575,6582007

15,832

16,40357,432

9,882

10,881

7,825

1,775

507

9,503

21,59164

80,86380,660ParqueArariba

Jurubatuba

Jardim Vitoria
Regia

JardimAngela1997

44

Parque
Fernanda

17,45818,612

69,442

14,888

9,724

6,639

1,924

516

14,96528,42872

71,43772,995

2007

30,17930,821

112,606

16,762

18,120

9,426

1,258

344

23,17541,22878

137,176

136,922

Pirajussara

1997

8,621

8,982

33,572

9,725

10,159

3,357

2,369

634

9,633

12,28257

52,71951,7092007

10,623

10,81438,79627,027

11,213

7,162

2,032

566

8,139

15,35253

80,15180,207

1997

3,189

3,628

11,01021,50034,978

5,257

8,306

2,737

2,094

5,785

62

132,504

133,396

2007

3,072

3,126

9,074

26,40937,920

3,227

3,922

1,351

779

4,483

69

136,169

135,867

1997

2,245

2,283

8,826

6,127

22,578

2,496

5,431

1,405

2,038

3,387

72

51,63752,362

2007

2,183

2,239

6,855

8,999

30,155

2,208

3,156

1,031

786

3,564

81

66,154

66,951

1997

8,374

8,187

32,933

5,863

14,857

14,917

7,137

1,774

7,940

16,92443

54,97754,5462007

11,863

11,975

39,124

6,397

22,951

15,560

4,587

1,404

4,450

21,231

73

84,034

83,630

1997

2,446

2,523

8,950

6,024

26,528

2,271

3,318

935

1,757

4,588

59

52,82053,401

2007

2,558

2,558

8,091

6,273

28,551

1,839

2,897

916

751

3,129

79

59,33358,453

1997

5,443

4,980

19,929

2,600

8,616

6,799

6,337

1,584

4,688

9,663

51

29,74029,636

2007

7,663

7,872

25,125

4,355

10,091

8,096

4,173

1,307

3,749

12,492

58

48,795

48,737

Vila Suzana

Vila Socorro

Vila Sao
Pedro

Vila Miranda

Santo Amaro

1997

45

196,395

207,039

771,109

233,083

360,232

148,869

4,487

1,248

186,070

315,456

63

1,412,726

1,411,659

2007

255,063

272,191

906,404

292,317441,107

152,523

2,922

910

141,882

394,36869

1,855,732

1,856,397

1997

850,966

933,680

3,037,286 1,088,923

2,470,513

801,569

4,744

1,517

640,393

1,354,555

63

8,625,056

8,667,508

2007

925,161

946,301

2,844,566 1,075,269

3,066,032

735,094

5,018

1,750

373,839

1,347,923

101

9,299,833

9,313,807

1997

4,256,674

4,552,931 16,771,140 4,969,675

6,863,367

3,088,976

3,531

1,038

4,013,916

6,580,621

61

31,237,121

31,235,834

2007

5,500,813

5,715,051 19,512,252 5,199,908

8,884,338

3,597,453

3,320

1,072

3,132,206

8,058,968

81

37,780,430

37,781,014

SPMR

Subway

Lilac

1997

46

Annex 3: São Paulo Profile (maps)
Average Household Income (R$/month), 1997

Average Household Income (R$/month), 2007
Av Family
Income(R$)<1500
1500-3000
3000-5000>5000

Population, 1997

Population, 2007
Population(thousand)
<15
15-30
30-60
60-150>150

47Time Private Vehicle, 1997

Time Private Vehicle, 2007
Time private
vehicle<45
45 – 60
60 – 90>90

Time Collective Transport, 1997

Time Collective Transport, 2007
Time
collective
transp. (min)<45
45 – 60
60 – 90>90

48

Private Automobiles, 1997

Private Automobiles, 2007
PrivateAutomobiles<5,0005,000-10,000
10,00015,000>15,00049

Generated Trips, 1997

Generated Trips, 2007
Generated
Trips<20,000
20,00050,000
50,000100,000>100,000

Attracted Trips, 1997

Attracted Trips 2007
Attracted
Trips<20,000
20,00050,000
50,000100,000>100,00050

Annex 4: Zoning51

Annex 5: Zone Description (OD 2007)
Zone
OD
2007
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

Zone
OD
1997
1
2
3
4
5
6
7
19
19
20
20
21
22
22
22
8
8
9
10
11
12
12
13
13
14
14
15
16
16
17
17
17

Zone Name

Municipality

Sé
Parque Dom Pedro
Praça João Mendes
Ladeira da Memória
República
Santa EfigêniaLuz
Bom Retiro I
Bom Retiro II
Canindé I
Canindé II
Pari
Bresser I
Bresser II
Bresser III
Brás I
Brás II
Independência
Cambuci
Glicério
Aclimação I
Aclimação II
Liberdade I
Liberdade II
Bexiga I
Bexiga II
Bela Vista
Masp I
Masp II
Consolação I
Consolação II
Consolação III

São PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo

33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

36
36
18
38
40
23
23
24
24
24
25
26
27
55
53
53
30
30
30
30
62
64
31
31
32
63
109
111
65
66
66
116
117
67
69
121
121

Pacaembu I
Pacaembu II
Santa Cecília
Higienópolis
Barra Funda
Belenzinho I
Belenzinho II
Quarta Parada I
Quarta Parada II
Quarta Parada III
Moóca
Alto da Moóca
Parque da Moóca
Regente FeijóÁgua Rasa IÁgua Rasa II
Vila Mariana I
Vila Mariana II
Vila Mariana III
Vila Mariana IV
Santa Cruz
Vila Clementino
Lins de Vasconcelos I
Lins de Vasconcelos II
ParaísoBosque da Saúde
Miguel Estéfano
Planalto Paulista
Mirandópolis
Parque Ibirapuera I
Parque Ibirapuera II
Moema
Bandeirantes
Vila Nova Conceição
Chácara Itaim
Vila Olimpia I
Vila Olimpia II

São PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo
52

70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106

123
124
122
33
33
68
35
34
34
71
70
72
73
37
37
37
74
74
76
76
41
41
39
39
75
130
130
79
79
77
78
78
78
132
132
133
133

Brooklin
Vila Cordeiro
Berrini
Pamplona I
Pamplona II
JardinsClínicas
Trianon I
Trianon II
Jardim Paulistano
Jardim Europa
Pinheiros
Vila Madalena
Cardoso de Almeida I
Cardoso de Almeida II
Cardoso de Almeida III
Perdizes I
Perdizes II
Pompéia I
Pompéia IIÁgua Branca IÁgua Branca II
Sumaré I
Sumaré II
Vila Beatriz
Alto de Pinheiros I
Alto de Pinheiros II
Santa Marina I
Santa Marina II
Lapa
Vila Ipojuca I
Vila Ipojuca II
Vila Ipojuca III
Vila Hamburguesa I
Vila Hamburguesa II
Vila Leopoldina I
Vila Leopoldina II

São PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo

107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143

133
140
139
137
138
136
208
208
209
270
271
272
211
210
145
144
143
142
141
44
44
45
45
43
83
84
85
42
82
80
81
146
147
212
148
149
213

Vila Leopoldina III
Vila Zatt
Pirituba
São Domingos
Jardim Mutinga
JaguaraJaraguá I
Jaraguá II
Parada de Taipas
Parque Morro DoceAnhanguera
Perus
Vista Alegre
Jardim Damasceno
Vila Terezinha
Brasilândia
Vila Morro Grande
Itaberaba
Freguesia do Ó
Carandiru I
Carandiru IITietê ITietê IIParque Anhembi
Santana
Santa Terezinha
Jardim São Paulo
Casa Verde
Parque PerucheLimãoCasa Verde Alta
Cachoeirinha
Jardim Peri
Reserva da Cantareira
Mandaqui
Horto Florestal
ETA Guaraú

São PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo
53

144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179

152
151
214
215
155
86
150
153
156
154
89
87
88
49
50
48
47
46
46
52
52
51
51
91
93
164
160
92
159
159
161
161
218
162
163
219

Pq Palmas doTremembéTremembé
Cantareira
Jardim das Pedras
Jardim Guapira
Parada Inglesa
Tucuruvi
Vila Gustavo
Cohab Jova RealJaçanãParque Edu Chaves
Vila Medeiros
Jardim Brasil
Jardim JapãoParque Novo Mundo
Vila Maria
Vila Isolina Mazzei
Vila Guilherme I
Vila Guilherme II
Tatuapé I
Tatuapé II
Pq São Jorge I
Pq São Jorge II
Penha
TiquatiraVila Esperança
Rui Barbosa
Cangaíba
Eng.Goulart
USP Leste I
USP Leste II
Ermelino Matarazzo
Parque Buturussu
Ponte RasaÁguia de Haia
LimoeiroSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo

180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216

220
226
227
223
224
225
221
222
279
280
281
284
286
287
285
282
283
95
96
54
97
171
169
94
166
165
167
168
228
170
229
230
231
292
289
290
291

Vila Jacuí
Parada XV
Itaquera
Vila Campanela
Rio Verde
SaudadeSão Miguel Paulista
Cidade Nitro-Operária
Jardim Helena
Jardim Romano
Vila Curuçá
Jardim Robru
LageadoFabrica Bandeirantes
Fazenda Itaim
Itaim Paulista
Jardim das OliveirasVila California
Vila Carrão
Jardim Anália Franco
Vila Formosa
Sapopemba
Aricanduva
Vila Matilde
Vila Guilhermina
Cidade A.E.Carvalho
Artur Alvim
Cidade Lider
Santa Marcelina
Parque Savoy
Vila Carmosina
Fazenda Caguaçú
Parque do Carmo
Gleba do Pessêgo
José Bonifácio
Guaianazes
Juscelino Kubitschek

São PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo
54

217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253

293
295
294
296
236
58
58
59
59
29
28
101
103
104
106
108
107
61
61
60
57
56
100
99
98
175
176
173
174
235
233
172
232
234
120
119
118

Cidade Tiradentes
Terceira Divisão
Iguatemi
Parque São Rafael
Rodolfo Pirani
Ipiranga I
Ipiranga II
Nazaré I
Nazaré II
Vila Monumento
Vila Independência
Vila Carioca
Moinho Velho
São João Clímaco
SacomãParque do Estado
Vila Moraes
Jardim da Saúde I
Jardim da Saúde II
Jardim Previdência
Quinta Parada
Orfanato
Vila Zelina
Linhas Corrente
Vila Ema
Parque São Lucas
Parque Sta Madalena
Jardim ColoradoTeotêonio Vilela
Fazenda da Juta
São Mateus
Cidade IV Centenário
Rio Claro
Cidade Satélite
Joaquim Nabuco
Vieira de Moraes
Campo BeloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo

254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290

112
114
115
113
110
182
185
183
184
189
188
186
187
245
244
250
248
249
247
246
303
302
301
347
349
348
382
193
190
191
192
254
252
251
304
305
306

Congonhas
Jardim Aeroporto
Vila Santa Catarina
Jabaquara
Cidade Vargas
Jardim Bom Clima
Cupecê
Jardim Miriam
Vila Missionária
Jurubatuba
Vila São Pedro
Campo GrandeVila Sabará
Mar Paulista
Pedreira
Vila Socorro
Parque Interlagos
Jardim Represa
Rio Bonito
SESC Interlagos
Jardim Presidente
Grajaú
CocaiaBororé
Jaceguava
Parelheiros
Marsilac
Granja Julieta
Chácara Flora
Santo Amaro
Vila Miranda
Jardim São Luis
Centro Empresarial
Guarapiranga
Jardim Capela
RivieraM' Boi Mirim

São PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo
55

291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308

253
255
307
308
127
126
125
128
194
195
196
256
259
258
257
197
198
198

309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325

200
262
263
264
201
202
135
134
131
129
129
199
273
324
323
322
358

Jardim AngelaCapão Redondo
Adventista
Parque Fernanda
Morumbi
Joquei Clube
Fazenda Morumbi
Real Parque
Paraisópolis
Jardim Vitória Régia
Vila SuzanaParque Arariba
Jardim Mitsutani
Pirajussara
Jardim Umarizal
Portal do Morumbi
Vila Sonia I
Vila Sonia II
Jardim Maria do
Carmo
Jardim Cambará
Jardim João XXIII
Raposo Tavares
Rio Pequeno
Jardim Adalgiza
Parque Continental
JaguaréCidade Universitária
Jardim Caxingui I
Jardim Caxingui II
Jardim Bonfiglioli
Melhoramentos
Caieiras
Santa InêsCajamarAnhanguera

São PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão PauloSão Paulo
CaieirasCaieirasCaieirasCajamarCajamar

326

359

327

325

328

363

329

360

330

361

331
332
333
334
335
336
337

362
326
274
364
365
158
90

338

90

339
340
341
342
343
344
345
346

90
157
157
217
277
278
216
275

347
348
349
350
351
352
353

276
366
327
328
330
368
367

Franco da
Cristais
Rocha
Franco da
Franco da Rocha
Rocha
Franco daJuqueri
Rocha
Francisco
Sete Voltas
MoratoFranciscoFrancisco Morato
MoratoFrancisco
Cascatas
MoratoMairiporã
Mairiporã
Paiva Castro
MairiporãColinasMairiporã
Pirucaia
Mairiporã
Guarulhos
Guarulhos
Rod.Presidente Dutra I Guarulhos
Rod.Presidente Dutra
II
Guarulhos
Rod.Presidente Dutra
III
Guarulhos
Vila Galvão I
Guarulhos
Vila Galvão II
Guarulhos
Jardim América
GuarulhosCumbica
Guarulhos
Pimentas
Guarulhos
Picanço
GuarulhosMorro dos Macacos
Guarulhos
Estr.de Nazaré
Paulista
Guarulhos
Vasconcelândia
Guarulhos
Arujazinho
Arujá
Arujá
Arujá
Fazenda Velha
Arujá
Santa Isabel
Santa Isabel
Jaguari
Santa Isabel
56

354
355

Ferraz de
336 Vasconcelos
340 Paiol Velho

356
357
358
359
360
361
362
363
364
365

288
337
334
331
329
332
335
338
341
341

366

372 Mogi das Cruzes I

367

372 Mogi das Cruzes II

368

375 Mogi das Cruzes III

369

371 Brás Cubas

370

339 Jundiapeba

371

333 Jd. Graziella

372

369 Itapeti

373

374 Sabaúna

374

373 César de Souza
Reservatório de
375 Jundiaí
376 Biritiba Mirim
377 Salesópolis

375
376
377

Santos Dumont
Jardim São José
Poá
Itaquaquecetuba
Bonsucesso
Pinheirinho
Miguel Badra
SuzanoOuro Fino I
Ouro Fino II

Ferraz de
Vasconcelos
Ferraz de
Vasconcelos
Ferraz de
Vasconcelos
Poá
Poá
Itaquaquecetuba
Itaquaquecetuba
Itaquaquecetuba
SuzanoSuzanoSuzanoSuzanoMogi dasCruzesMogi dasCruzesMogi dasCruzesMogi dasCruzesMogi dasCruzesMogi dasCruzesMogi dasCruzesMogi dasCruzesMogi dasCruzesMogi dasCruzesBiritiba-Mirim
Salosópolis

378

370 Guararema

379

105 Boa Vista380

178 Vila Gerti

381

102 São Caetano do Sul I

382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398

102
179
179
177
177
177
237
239
298
380
238
297
297
342
345
343
344

399

378 Parque Sete Pontes

400

379 Rio Grande da Serra

401

241 Planalto

402
403

180 Rudge Ramos
São Bernardo do
240 Campo

404

299 Demarchi

São Caetano do Sul II
Santo André I
Santo André II
Utinga I
Utinga II
Utinga III
Parque das Nações
Jardim do Estádio
Parque do Pedroso
Paranapiacaba
Capuava
Mauá I
Mauá II
Vista Alegre
Ribeirão Pires
Jardim Santa Luzia
Ouro Fino Paulista

GuararemaSão Caetano do
SulSão Caetano do
SulSão Caetano do
SulSão Caetano do
SulSanto André
Santo André
Santo André
Santo André
Santo André
Santo André
Santo André
Santo André
Santo André
Mauá
Mauá
Mauá
Mauá
Ribeirão Pires
Ribeirão Pires
Ribeirão Pires
Rio Grande da
Serra
Rio Grande daSerraSão Bernardo
do Campo
São Bernardo
do Campo
São Bernardo
do Campo
São Bernardo
do Campo
57

405

346 Riacho Grande I

406

381 Caminho do Mar

407
408
409
410

300
181
242
243

411

260 Taboão

412
413
414
415

261
309
310
311

416

351 Itapecerica da Serra

417

352 Jardim Petrópolis

418

353 Embu-Mirim
São Lourenço da
384 Serra
350 Embu-Guaçu
383 Cipó I
383 Cipó II
385 Juquitiba
319 Barueri
269 Aphaville
268 Tamboré
317 Quitaúna
315 Jardim Silveira I
315 Jardim Silveira II
315 Jardim Silveira III
320 Jardim Belval
267 Carapicuiba I

419
420
421
422
423
424
425
426
427
428
429
430
431
432

Reservatório Billings
Diadema
Piraporinha
Eldorado

Parque Pinheiros
Santo Eduardo
Embu
RessacaSão Bernardo
do Campo
São Bernardo
do Campo
São Bernardo
do Campo
Diadema
Diadema
DiademaTaboão daSerraTaboão da
Serra
Embu
Embu
Embu
Itapecerica da
Serra
Itapecerica da
Serra
Itapecerica da
SerraSão Lourençoda Serra
Embu-Guaçu
Embu-Guaçu
Embu-Guaçu
Juquitiba
Barueri
Barueri
Barueri
Barueri
Barueri
Barueri
Barueri
Barueri
Carapicuiba

433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460

267 Carapicuiba II
Aldeia da Carapicuiba
313 I
Aldeia da Carapicuiba
313 II
204 Osasco I
204 Osasco II
203 Santo Antonio I
203 Santo Antonio II
265 Jardim das Flores
266 Jardim Veloso I
266 Jardim Veloso II
205 Jardim Piratininga I
205 Jardim Piratininga II
206 Mutinga I
206 Mutinga II
207 Três Montanhas
318 Ribeirão Itaqui
316 Jandira
314 Ribeirão das Pombas
312 Granja Viana
355 Cotia
354 Capueira
386 Caucaia
387 Caucaia do Alto
Vargem Grande
388 Paulista
389 Quatro Encruzilhadas
356 Itapeví
321 Santana de Parnaíba
Pirapora do Bom
357 Jesus

Carapicuiba
Carapicuiba
Carapicuiba
OsascoOsascoOsascoOsascoOsascoOsascoOsascoOsascoOsascoOsascoOsascoOsascoJandiraJandiraJandira
Cotia
Cotia
Cotia
Cotia
Cotia
Vargem GrandePaulista
Itapevi
Itapevi
Santana deParnaíba
Pirapora do
Bom Jesus5859