The spatial dimension of urban poverty

THE SPATIAL DIMENSION OF URBAN POVERTY (Paper presented at the MacArthur Research Network on Inequality and Economic

Performance and Instituto Futuro Brasil, meeting at IBMEC in Rio de Janeiro, November 19th-20th, 2004)

Haroldo Torres, Sandra Gomes,

Eduardo Marques e Maria Paula Ferreira1

Introduction

This article presents some recent results of researches carried out at the Centre

for Metropolitan Studies (CEM/Cebrap) and converges to the idea that there is a

spatial dimension of urban poverty.2 Space seems to be a constitutive element of

poverty in metropolitan regions since the existence of segregated areas contributes

to an intergenerational reproduction of poverty. In areas that concentrate poverty,

for example, school performance tends to be worst because students have to attend

to schools together with fellows that also have low socio-economic background

(César and Soares, 2001). Areas with high concentration of poverty present

different implications to poverty reduction strategies because it interferes in the

economic performance of individuals who live in, reducing their future chances of

finding a job in the labour market, for example.

The existence of residential segregation exerts particular influence on the

opportunities of poorest groups living in large cities as it isolates them from

broader social and economic circuits, therefore significantly reducing their

possibilities of social interaction and mobility. As a consequence, the study of the

processes that produce (and reproduce) poverty in metropolitan areas should bring

the spatial organization of the city to the centre of public action on poverty

1 Haroldo Torres is PhD in social sciences and a researcher at the Centre for Metropolitan Studies (Cem/Cebrap),

Sandra Gomes is a doctorate student of Political Science (University of São Paulo) and a researcher at the Centre for Metropolitan Studies (Cem/Cebrap). Eduardo Marques is PhD in social sciences, a professor at the Department of Political Science (University of São Paulo) and a researcher at the Centre for Metropolitan Studies (Cem/Cebrap). Maria Paula Ferreira is a Statistician and Head of the Methodology and Quantitative Methods Division at Fundação Seade. 2 It is worth noting that most of the analyses presented here were based on data organized within a Geographic Information System (GIS) that have been developed and organized by the Centre for Metropolitan Studies (www.centrodametropole.org.br).

Centro de Estudos da Metrópole/CEBRAP

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alleviation. The main objective of this article is to present evidence of theses

arguments.3

This paper is organized in four sections, besides this introduction, and a

conclusion. In the first section, we present some of the conceptual issues involved

in this discussion so as to specify our understanding on metropolitan poverty. In

the second section, we present a socio-economic typology of the different

neighbourhoods of the Metropolitan Region of São Paulo, identifying the most

segregated areas. The third section presents empirical results showing that the

probability of young people to conclude secondary education is not only function

of their socio-economic level but also a function of the type of area these

individuals live in. The fourth section presents a similar exercise concerning the

chances of being unemployed, the distribution of formal jobs and the levels of

payment according to different types of area. In the end, we summarize the main

findings and their consequences for public policies.

3 A more detailed approach of these issues can be found in Marques and Torres (2004).


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1. The spatial dimension of poverty

Urban poverty is a controversial issue. This is a theme full of ideological

contents and intransigent disputes among political actors. This is because the

social recognition of poverty ends up defining the distribution of benefits as well

as the definition of the costs involved in order to finance this sort of policies.

A first problem is concerned with measuring poverty. The most common way of

measuring poverty levels within a region has been the adoption of a poverty line

(Rocha, 1996 e 2000 and Hoffman, 2000). This analytical option responds to the

possibility of making ample comparisons. Poverty lines are normally defined as

absolute or relative (Mingione, 1999). They might be defined within a national

scope or in regional terms. In this last case, it takes into consideration the different

costs of living among different localities or regions. Rocha (2003), for example,

presents a range of regional poverty lines for Brazil. For the Metropolitan Region

of São Paulo (MRSP), the poverty line – in terms of per capita family income –

was calculated in 1,24 Minimum Wages in 1999 or R$ 168,00 in 19994. According

to this kind of approach, the MRSP would have 6,4 million poor people in 1999 or

something around 39% of the total population (Rocha, 2003).5

Another way of understanding urban poverty is related to the idea of “multiple

dimensions of poverty”. They could not be captured only by poverty lines and that

should be, in reality, incorporated in the analyses that aim to understand in a

broader perspective (Mingione, 1996). The idea behind this concept is that poverty

cannot be defined exclusively in terms of material need for survival. Rather, it

should take into account the individuals that, although might be able to survive

with an income above the minimum, do not have access to the most important

4 One monthly minimum wage in 1999 (R$ 136,00) corresponded to around U$ 75 according to the average

exchange rate in 1999 (Banco Central do Brasil). 5 Although they might be useful for regional or international comparative studies, poverty lines are very controversial.

Among one of the problems is the issue of not being able to consider non-monetary forms of income and access to services which are very important for survival strategies of disadvantaged social groups.


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benefits in urban societies, such as education, a basic sewage system, health,

culture and social integration (Mingione, 1999).6

The ‘multiple dimensions’ idea allow us to integrate to this issue with urban

differentials concerning the access to public services, characteristic of the

peripheral areas of Brazilian cities. Even though this idea is not completely absent

in the poverty line debate, its integration is, say, quite topic7, mostly because it is

difficult to incorporate these dimensions in wide comparisons of a quantitative

nature. Finally, this idea allows the incorporation of families that might be living

above the limits of survival but that could be living in situations of extreme

vulnerability when a negative event occurs (such as unemployment, illnesses etc.).8

A third and very interesting way of dealing analytically with poverty is related

to the idea of assets, first presented by Moser (1998). She asserts that families

living in the worst social conditions have always been defined by the elements that

they do not possess rather than by the assets that they in fact held (physical, human

and social capitals). From this point of view, the study of poverty should focus on

the analysis of the assets available to a specific family or community and on the

situations that might create “social vulnerability” as a consequence of the fragile

way these assets are managed. Therefore, social vulnerability is defined as a

situation in which there is a great “susceptibility to downward social mobility”

(Filgueiras, 1998). The most relevant strategy, thus, to combat local poverty

6 In this sense, poverty is a multidimensional phenomenon which is difficult to be captured by a single synthetic index,

therefore demanding a complex analytical strategy. We will return to this discussion later on. 7 In Rocha (2203), these dimensions appear in the last chapter of the book which presents an overall view of the main

elements discussed in the literature. The geographical dimension, by the way, is also highlighted in this chapter. In the body of the book, however, this dimension is used in the sense of regional differences (Northeastern, southeastern regions of Brazil) or differences among the main metropolis. She does not incorporate, though, the intra-urban scale. It is true that some of this emphasis is limited by the maximum desegregation possible of the data she uses, although there is also a matter of focus as the segregation patterns within an urban space are not even mentioned as a relevant dimension.

8 In other words, instead of a unique form of poverty, we can observe various and distinct forms associated to a set of different social characteristics connected to the age structure, family composition, insertion in the labour market etc. The spatial distribution of these forms of poverty increases (even more) the complexity of this phenomenon as it creates different levels of access to propriety and services and opportunities among social groups. A detail study of these elements allows us to discuss countless arguments presented by the literature on social-spatial structure of cities as well as being able to define a kind of “social conjecture” of the São Paulo metropolis.


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situations would be to facilitate the use of a full range of assets available to a

given population.

Elaborating from the proposition presented by Moser, Kaztman (1999) suggests

that the relation between assets and family vulnerability is mediated by the

“opportunity structure” – regarding the role of the market, the State and the

community – that are available in a specific location. A relatively poor area, for

example, might have important forms of access to the local labour market, raising

the chances of the local population social mobility in the long term.9 In the same

sense, a community of specialized manual workers, for instance, might become

more vulnerable when there are technological changes or a process of de-

industrialization that transforms their specific knowledge (human capital as an

asset) redundant (Kaztman, 1999). In other words, these authors understand

vulnerability as a dynamic category referred to the difficulties that some social

groups might present during the process of adjustment to changes of an exogenous

kind, such as the ones in the labour market and/or in the public policies.10

Besides all that, we also believe that another two elements should be

incorporated in this debate: residential segregation and the social network of a

given community. Segregation – understood as the degree of residential distance

among different social groups – indicates whether the assets controlled by a family

might or might not be successfully used to activate the structure of opportunities

available to this society. Although the effects of segregation will present different

levels of intensity depending on the area of the city, it is reasonable to suppose

that in the most segregated regions there will be a limitation of “outside” contacts

9 This is the case, for example, of a Favela (“shanty town”) called Paraisópolis which is located near to the rich

neighbourhood area named Morumbi, in São Paulo. The majority of the inhabitants of this Favela work as domestic workers and in the civil construction in the Morumbi area (Almeida e D´Andrea, 2004).

10 An important part of the dynamics one can see in the North-American ghettos and especially in the French quartiers dificiles, expresses this situation: although the access to policies and basic services are practically universal in these places (at least in comparison to Brazil), the access to markets is still as restricted as in the past, restricting considerably social mobility. See Wacquant (1996a and 2000) and other articles included in Mingione (1996).


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that might restrict the opportunities for social mobility and help to perpetuate the

situations of poverty and vulnerability. 11

The effect of the social network is similar, despite the fact that it tends to be

much more dynamic than segregation. In this case, the arguments favouring the

importance of social networks and the assets converge to the literature that

discusses the role of social capital (Lin, 1999 and Sunkel, 2003), understood as the

“content of certain social relations that combine trust attitudes with reciprocity and

cooperation conducts” (Durston, 2003: 147). These networks would be

fundamental not only to stimulate the connections within low income communities

but especially to build bridges that could reach the outside of that community and

would enhance easier ways of solving everyday life problems, besides being

helpful to promote social mobility and opportunities (Briggs, 2001). Hence, in the

same sense as discussed before concerning the case of segregation, the existence

and formation of social networks have a strong influence on the way the available

assets in a given community might be used.

The processes of residential segregation and social inequalities are connected to

each other and produce a separation of social groups in the space, besides an

unequal distribution of the benefits from urbanization. The combined result of all

these processes is a superposition of problems and a reduction of the opportunity

levels in specific parts of the city (Massey & Denton, 1993). As we will see below,

social indicators of poor individuals living in poor, peripheral areas are

systematically worse when compared to other individuals with similar social

characteristics but who live in areas that are mostly inhabited by wealthier groups.

This suggests that poverty presents important spatial dimensions, a point that we

explore in more detail next.

11 With respect to this point, the perspectives for the São Paulo case are not very optimistic. Torres (2004) shows that

there has been an important increase in the level of residential separation between the rich and the poor (segregation) in São Paulo from 1991 to 2000, measured by the dissimilarity index.


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2. The distribution of social groups in the São Paulo

In order to analyse empirically the spatial dimension of poverty we started by

constructing a typology of áreas de ponderação (“weighting areas”) within a GIS

database of the Metropolitan Region of São Paulo (MRSP).12 This typology was

first developed by Marques (2004). That article analysed the distribution of the

social indicators in all those areas and concluded – through a factor analysis - that

the whole set of original variables (race, education, income, migration origin, etc.)

could be described and replaced almost completely by two of the variables:

average household income and rate of population rate growth between 1991 to

2000. The combination of these two variables delimited 10 different groups of

areas, according to their average indicators. The descriptive characteristics of

these groups are presented in table 1 and map 1, below.

Table 1: Summary of the group’s characteristics

Source: Marques (2004).

12 The metropolitan region analyzed here involves 21 municipalities (cities), the most important ones in demographic

terms. They form a conurbation proper. These 21 municipalities correspond to 91,4% of the total population of the MRSP, which totalled, in 2000, 17,9 million inhabitants. The ‘areas de ponderação’ (‘weighting areas’) correspond to the smallest territorial division for the results of the sample of the 2000 Demographic Census, IBGE.

13 The Brazilian Bureau of the Census (IBGE) classifies the individual answers concerning skin color (race) in 5 categories. “Pardo” would be a choice somehow between “black” and “white”.

Groups Population Main Characteristics

1 3,130,249 Very poor, very low levels of schooling, many blacks, ‘pardos’13 and recent migrants from the Northeast, awful urban conditions and a very high rate of demographic growth (1991-2000)

2 2,519,271 Very poor, very low levels of schooling, many blacks, ‘pardos’ and recent migrantsfrom the Northeast, bad urban conditions and high rate of demographic growth

3 1,516,073 Very poor, very low levels of schooling, many women as sole head of the householdand low levels of schooling, average urban conditions and no demographic growth

4 1,019,352 Poor lower middle class with low levels of schooling, urban conditions and high demographic rates

5 1,735,361 Lower middle class, few black, ‘pardos’ or migrant population, good urbanconditions and slow demographic growth

6 3,321,056 Lower middle class, few black, ‘pardos’ or migrant population, good urbanconditions and no demographic growth

7 1,468,915 Middle class, few black, ‘pardos’ or migrant population and no demographic growth

8 826,933 Higher middle class, few black, ‘pardos’ or migrant population and negative rates ofdemographic growth (loosing population)

10 683,159 Upper class very few black, ‘pardos’ or migrant population and negative rates ofdemographic growth

11 162,895 Upper class with high presence of young individuals as household heads, very fewblack, ‘pardos’ or migrant population and positive rates of demographic growth

Total 16,383,264


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Map 1: Typology of Areas

In order to simplify the analysis, these ten types of area were re-aggregated into

three new groups. The first one is formed by the ‘peripheral’ areas of the

metropolis and includes groups number 1, 2 e 3, presenting, in general, high levels

of poverty, low levels of schooling and precarious urban conditions. It is important

to notice that this group is not present in the central area of the metropolis, being

located, in general terms, in the external fringe of the city. The second set includes

groups 4 to 7 and is formed by “middle class” areas, i.e., presents an intermediary

socio-economic profile. The third set involves the ‘elite’ areas, which can be

identified as where high-income families live (groups 8, 9 and 10). This condensed

form (with tree types) will be used in the analyses that follow.


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3. Evidences of the effects of segregation

3.1 Effects on schooling

The issue at stake here is whether ecological elements linked to the place of

residence might influence school performance of individuals. As we will present

later, the literature that discusses the relation between social inequality and years

of schooling explain the phenomenon emphasizing individual characteristics

connected to socio-economic conditions of the family as well as factors associated

with the school. We use here data from the 2000 Demographic Census, the sample

results, which for the city of São Paulo is formed by 10% of all household units.

The information in this section of the article, therefore, is restricted to the city of

São Paulo. As a statistical model, we adopted a logistic regression14, assuming as

the dependent variable the information on the conclusion (or not) of secondary

school for people aged 18 to 1915.

From the available literature we selected a set of potentially explicative

variables related to individual and neighbourhood characteristics (Barros et all,

2001). As discussed before, the idea here is that the conclusion of secondary

school would be explainable not only by particular characteristics of an individual

(income, parents’ years of schooling, etc.) but also by the characteristics of the

neighbourhood that the individual lives. We assumed as neighbourhood units the

‘areas de ponderação’ (from onwards ‘AP’).

The explicative variables representing individual and family characteristics

tested in this model are analogous to the ones used by Barros et all (2001): gender,

race, length of time living in the city, household income, years of schooling of the

14 This model is commonly used when the dependent variable is dichotomic, relating the probabilities of a given event

to happen or not to happen. We have considered in the hypothesis tests, the scheme of simple aleatory sample, following the IBGE recommendation (Fundação IBGE 2002). We used SPSS software.

15 In 2000, according to the Demographic Census data, 46% of the people between 18 and 19 years old had completed the secondary school in the city of São Paulo. You are expected to have completed the secondary level of schooling in Brazil when you are 17 years old.


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mother and the father. On the other hand, other variables related to the

neighbourhood aspect were also tested.16

We opted by the variable “type of AP”, which uses the three different types

presented in the previous section (poor, middle class and rich). The results of the

model, obtained after statistical tests, can be expressed by eight dimensions

considered significant: race in two categories (‘white’ and ‘oriental’ versus ‘black’

and ‘pardos’); gender, length of time living in the city (less than 3 years or more

than 3 years); household income in three classes (less than 3 minimum wages,

more than 3 up to 10 minimum wages and over 10 minimum wages); years of

schooling of the mother; years of schooling of the father; type of AP

(peripheral/poor, middle class or rich) and percentage of people living in the city

of São Paulo for more than 3 years in the AP.17

It is interesting to notice that some ‘ecological’ variables did not enter the

model, in particular the ones related to the conditions of the local schools (number

of students by teacher and number of computers) and the ones related to risk

situations for the young (homicide and teenage pregnancy). The exclusion of the

variables representing the infrastructure of schools suggests that the process of

universal access to schools is, in fact, taking place (at least in São Paulo) as the

schools located in the areas where the youngsters presented the worst school

performance are equally equipped and so the number of teachers per student. On

the other hand, the exclusion of the variables representing juvenile risks is quite

16 The tested variables for the AP were: percentage of people living for more that 3 years in the city of São Paulo,

homicide rates among 15 to 20 years old men, percentage of life births among mothers aged between 10 and 19, percentage of black people, rate of population growth between 1991 and 2000, average number of students per teacher in the elementary schools, number of students per employees working in the elementary schools, number of students per computer available in the elementary schools, type of AP, considering the classification presented earlier.

17 The majority of the explicative variables were expressed by indicator variables that assumed values 0 and 1, 0 indicates the reference category. The specified reference categories are: race (blacks and ‘pardos’); gender (masculine); length of time residing in SP (less than 3 years); household income (less than 3 minimum wages); place of residence (poor areas).


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surprising. Our hypothesis is that their effects might have been eliminated by the

effects of other variables, such as the different types of APs.18

From the point of view of its statistical adjustment, the model was considered

adequate, classifying 69% of the observations correctly. Among the individuals

that did not complete secondary school, the percentage of correct classification

was 75% and 61% among the ones that had completed, with variables and signals

consistent to the ones observed in the literature. 19 We present, in table 2, a

summary of the observed results.

Table 2. Results from the Logistic Regression Model

Variables Coefficient Standard Error

Descriptive Level

Odds Ratio

Marginal Effect

Household income above 10 minimum 1,095 0,060 0,000 2,989 0,266Household income between 3-10 0,544 0,0583 0,000 1,722 0,132Gender: women 0,747 0,029 0,000 2,110 0,181More than 3 years living in the city 0,595 0,123 0,000 1,814 0,144White or oriental 0,459 0,034 0,000 1,582 0,111Resident of the rich areas 0,339 0,058 0,022 1,404 0,082Resident of the middle class areas 0,076 0,034 0,024 1,079 0,018Mother’s years of schooling** 0,071 0,005 0,000 1,074 0,017Father’s years of schooling** 0,060 0,004 0,000 1,062 0,015Percentage of people living for over 3 0,050 0,010 0,000 1,051 0,012Constant 8,037 0,57 0,00

Source: Fundação IBGE. 2000 Demographic Census. Authors’ calculations. * Household income in minimum wages of July 2000 (R$ 151,00). ** In this case, the odds ratio and the marginal effect refer to the increase of one year of schooling.

Concerning the explanation related to the probabilities of completing secondary

education in the city of São Paulo, we obtained the following hierarchal variables

according to the odds ratio:20

18 In fact, the typology of the areas used here is strongly related to other social dynamics, as the ones connected to

race. In this case, the variable related to the proportion of blacks and ‘pardos’ in the APs, for example, introduces a high degree of multicolinearity in the model when this typology is considered.

19 This classification was obtained by the estimated probability in the model of completing the secondary school. The “cut point” used was 50%, a value very close to the observed in the population (46%).

20 This hierarchy is based on the values of the odds ratio, which is the most commonly used measure to indicate the level of importance of a given variable.


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• Household income, with the odds ratio increasing as the income

raises;

• The gender of the individual, with clear advantage for women;

• The mother and father’s years of schooling. In this case, the odds

ratio measures the impact of each additional year of schooling. Thus, for

example, though the odds ratio is only 1,074 for an additional year of

schooling of the mother, the odds ratio for an individual that have a mother

who studied for 11 years (i.e., completed secondary school) in comparison to

an individual whose mother studied for only 4 years is 1,646;

• Length of time living in the city, being the odds ratio for people born

in the city or living there for more than 3 years higher compared to

individuals living for less than 3 years. However, the recent migrants

represent only 2% of the studied population, which transforms this result less

relevant from the point of view of the argument developed here;

• Race, with clear advantage for whites and Orientals;

• The type of AP (‘áreas de ponderação’), with the odds ratio higher

for middle class and rich areas compared to peripheral/poor areas. As

expected, the most striking advantage is for rich areas in comparison to poor

areas;

• The proportion of recent migrants in the area of residence.

In other words, individual variables and ecological variables are both important

to explain the probability of completing secondary school, although the ecological

variables present a less significant contribution. On average, we can see that the

most well-off inhabitants of the city present a probability of completing secondary

school 0,266 (or 26,6%) higher. For the intermediary class of income the increase

is 13,2%. Among the remaining variables, we can observe that being a woman

raises the probability of completing secondary school to 18,1%, living in the city

for more than 3 years raises to 14,4%, being white raises 11,1% of the chances and


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living in a rich area raises the probability of completing secondary school to 8,2%

in comparison to individuals living in poor/peripheral areas.

In order to explore more the argument concerning the influence of the type of

area that an individual lives on the chances of completing secondary school, we

present in the tables below, the average probabilities of completing this level of

schooling according to different cross tabs of income, gender and area of

residence. In this exercise, we included the four most important explicative

dimensions. We did not incorporate the variable “years of residence in the city of

São Paulo” because the individuals living for less than 3 years represents a very

small population group (2%).

Table 3. Probabilities of 18-19 years old individuals to complete secondary

school, estimated according to income and area of residence. City of São Paulo, 2000.

Type of area that the individual lives

Household income less

than 3 minimum

wages

Household income between

3 and 10 minimum

Household income above 10 minimum

wages

Total

Probabilities Peripheral/poor areas 0,261 0,378 0,513 0,431 Middle class areas 0,275 0,396 0,532 0,450 Elite areas 0,331 0,460 0,597 0,516 Total 0,276 0,397 0,533 -

Distribution of the Population (%) Peripheral/poor areas 63,9 52,7 22,4 38,7 Middle class areas 34,0 43,7 55,6 48,7 Elite areas 2,1 3,6 22,0 12,6 Total 100,0 100,0 100,0 100,0

Source: Fundação IBGE. Censo Demográfico 2000. Author’s calculations.

The first thing we would like to notice, from table 3, is that while a young

person living in a poor/peripheral area with a monthly household income of less

than 3 minimum wages presents 0,261 (or 26,1%) probability of completing

secondary school, an individual with the same level of income but living in a rich

area has a probability of 33,1% to complete this level of schooling between 18 and

19 years old. Furthermore, the differential among the different types of areas is


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valid for all levels of income that we might consider. In other words,

independently of the level of income, to live in a peripheral area imply on an

additional disadvantage in terms of completing secondary school. An observer who

discusses this problem only from this kind of differential might argue that,

although there are differences, they do not seem to be very impressive. However,

we can add to the same elements presented before the race dimension (Table 4).

Table 4. Probabilities of 18-19 years old individuals to complete secondary

school, estimated according to race, income and area of residence. City of São Paulo, 2000.

Race and place of residence of the individuals

Household income under 3 minimum

wages

Household income between 3 and 10

minimum


wages

% of the

population Probability % of the population Probability % of the

population Probability

Whites and Orientals Peripheral/poor areas 59,2 0,288 46,4 0,410 17,4 0,547 Middle class areas 38,3 0,303 48,8 0,429 56,7 0,566 Elite areas 2,6 0,362 4,7 0,494 25,8 0,629 Total 100,0 - 100,0 - 100,0 - Blacks and ‘Pardos’ Peripheral/poor areas 69,7 0,203 62,0 0,305 44,9 0,433 Middle class areas 29,0 0,216 36,1 0,322 50,0 0,451 Elite areas 1,4 0,264 1,9 0,382 5,1 0,517 Total 100,0 - 100,0 - 100,0 -

Source: Fundação IBGE. Censo Demográfico 2000. Author’s calculations.

We can observe (table 4) that while the odds ratio of a young black person

living in a peripheral area with an income of less than 3 minimum wages to

compete secondary school in only of 20,3%, a young white man with the same

level of income but living in a rich area has a probability of 36,2%, therefore

almost doubling their chances. In other words, these dimensions sum up. A poor

black person living in poor areas has less chances than a poor black living in a rich

area and even less chances than a poor white living either in the periphery or in

central areas.


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Analogously, when we introduce the gender dimension, we notice that these

phenomena overlap (table 5). Poor man living in the periphery has also less

chances of completing secondary school (19,9% probability) than poor women

living in poor areas (34,4%) and even less than poor women living in richer area

(42,4%).

Table 5. Probabilities of 18 to 19 years old individuals of completing secondary school, estimated according to gender, income and area of

residence. (city of São Paulo)

Gender and place of residence

Household income of less than 3

minimum wages

Household income between 3 and 10

minimum


wages

% of the

population Probability % of the population Probability % of the

population Probability

Women Peripheral/poor areas 64,4 0,344 52,4 0,474 21,7 0,610 Middle class areas 33,7 0,361 43,9 0,493 55,6 0,628 Elite areas 1,9 0,424 3,7 0,559 22,6 0,687 Total 100,0 - 100,0 - 100,0 - Men Peripheral/poor areas 63,5 0,199 53,0 0,299 23,1 0,426 Middle class areas 34,2 0,211 43,5 0,315 55,5 0,444 Elite areas 2,3 0,258 3,5 0,375 21,5 0,510 Total 100,0 - 100,0 - 100,0 - Source: Fundação IBGE. Censo Demográfico 2000. Author’s calculations.

In summary, gender, race and place of residence significantly contributes -

besides income and the parent’s years of schooling - to explain worse school

performances in the city of São Paulo. Relations of this kind can be observed in all

income levels and in all types of area considered here. In fact, when we consider

only the group of young poor black man (18 to 19 years old) living in the

periphery, we can see that the probability of completing secondary school is only

15,2%, therefore forming the group of individuals with the least probability of

completing this level of schooling. On the other hand, equally poor women but

whites and living in central areas present higher chances, approximately 46%,

placing them close to the average for the population.


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In other words, although the income and parent’s years of schooling are

important, they do not exhaust the explanation regarding educational inequalities.

In the case of São Paulo, there are other intervenient factors in the explanation of

the probabilities of completing secondary school that are related to gender, race

and place of residence. Such elements present important consequences for

educational public policies.

From the point of view of the discussion in this article, these results show that

the variables related to the place of residence have substantial statistical

significance. The probability of a young man residing in the periphery to complete

secondary school is significantly lower compared to a young man living in central

areas, even when they present similar socio-economic characteristics. In other

words, the socio-spatial segregation – especially when it is combined with the

effects of gender and race – is substantially important from the point of view of

school performance.

It is possible to argue that this result could be explained by the effects of school

characteristics on the students performance (which we are not able to measure with

the available census data) as Soares et all (2001) suggests. After all, an area with a

high concentration of individuals of a low socio-economic status tends to be

strongly related to a high concentration of poor people and lower school

performance. However, the characteristics of the place of residence and the schools

located in these areas cannot be separated analytically as they are both part of the

same residential segregation dynamic.

In fact, although the assumption that people who live in the peripheral areas of

the city study, necessarily, in schools in the peripheries has not been proven here,

it is a very plausible assumption if we understand the concept of spatial friction

derived from the economic geography (Diniz, 1996). Distance implies substantial

costs of transport, particularly in a metropolis like São Paulo where the distances

are large, the traffic heavy and the public collective transport slow. In a few words,

the most plausible process is the following: to live in segregate poor areas imply


17

access to schools that have a large concentration of poor and blacks which, in turn,

implies worse school performance.

Regarding this last point, there are also arguments around the effects of

belonging to a certain group or community on the school or economic performance

of an individual. The idea is that there are different processes in operation, such as

the influence of friends and colleagues (‘peer group effect’) and the influence

exerted by adults on the choices made by the youngsters (‘role model effect’)

(Durlauf, 2001). According to this argument, the issue at stake is: to what extent

the proximity of a youngster to a gang might, for example, affect his school

performance? Typically, these two effects characterize what the economists call

externalities, namely, the way in which an external event to an individual (in this

case, the group that he belongs) affects their economic performance.21

Unfortunately, we do not have direct empirical evidences concerning the impact

of these “group affiliation effects” on school performance. Researches of this

nature are complex and part of the available evidences in the international

literature on this respect is based on ethnographic results (Durlauf, 2001). Strictly

speaking, it is impossible, with quantitative data as the ones used here, to separate

the effects produced directly by the school from the ones produced by

neighbourhood relations or by belonging to a given community or group. In reality,

the most probable thing is that theses dimensions are strongly connected.

Nonetheless, the available data clearly shows that – along with gender and race

– living in segregated areas does make a substantial difference concerning the

chances of completing secondary school. This reinforces the argument that the

educational policy, in order to promote educational equality, should consider the

characteristics of different groups and/or areas where the school is located,

21 In a way, this kind of results is a problem for the models that try to explain socio-economic performance based on

individual characteristics: “My belief is that underlying the new interest in group memberships among economists is a feeling that one cannot explain the levels of socio-economic deprivation and self-destructive behavior associated with inner cities in a framework that embodies the neoclassical assumptions of rationality, preferences defined exclusively over commodities, and complete markets for borrowing and lending. Put differently, the standard neoclassical assumptions seem better suited for explaining socio-economic success than socio-economic failure” (Durlauf, 2001: 392).


18

therefore lessening the impact that these elements have on the educational

performance of the local population.

3.2 Effects on the labour market

If the previous section seem to show the influence of the place of residence on

the level of schooling of young men, what effects it would produce on the chances

of being unemployed?22 The results presented in table 6 give us reasons to believe

that residential segregation is an important variable to explain unemployment

independent of the social group we are analysing.

Table 6. Open unemployment rate by year and social groups according to the

place of residence of the individual, City of São Paulo (PED, 1990-2000)

Rich Districts

Middle class

Districts

Poor Districts

Gap (poor-elite)

YEAR 1990 4,81 6,99 8,94 4,14 1991 5,04 7,60 9,64 4,61 1992 6,06 8,89 11,05 4,99 1993 5,05 8,39 10,51 5,45 1994 5,34 8,71 10,45 5,11 1995 5,09 8,86 10,39 5,30 1996 5,97 9,83 11,25 5,28 1997 6,09 10,00 11,92 5,84 1998 7,49 11,33 13,78 6,29 1999 7,25 11,78 13,59 6,34 2000 6,74 10,36 12,83 6,09 Variation (2000-1990) 1,93 3,37 3,88

GENDER (average of the period 1990/2000) Men 5,21 7,53 9,05 3,84 Women 6,66 11,67 14,77 8,10 Gap (man-women) 1,46 4,15 5,72

(Continues)

22 This section explores is based on data from the “Survey on Employment and Unemployment” (PED), various years,

(Fundação Seade/Dieese), the 2000 Demographic Census and the “Annual Recording of Social Information – Formal Employment, 2000” (RAIS) produced by the Brazilian Ministry of Labour. In order to test how the unemployment rate is distributed among different places of residence, we used the same typology of areas presented in section 2.


19

(Cont.)

Rich Districts

Middle class

Districts

Poor Districts

Gap (poor-elite)

RACE White 5,85 8,60 10,52 4,67 Black 6,95 11,51 12,81 5,86 “Pardo” 6,78 11,20 12,44 5,67 Oriental 3,76 4,56 6,11 2,36 Gap (blacks-oriental) 3,19 6,95 6,70

LEVEL OF SCHOOLING (average of the period 1990/2000) Illiterate 6,07 7,33 8,25 2,18 Uncompleted Primary School 6,92 10,46 11,73 4,81 Completed Primary School (8 years) 7,72 10,70 12,55 4,82 Uncompleted Secondary School 12,06 14,48 16,69 4,63 Completed Secondary School (11 years) 6,89 8,01 9,88 2,99 Uncompleted Further Education 9,16 7,61 6,99 -2,18 Completed Further Education 3,24 3,32 3,64 0,40 Gap (Complete primary – complete further) 4,48 7,38 8,90 Gap (incomplete secondary - complete further) 8,81 11,17 13,05

Source: PED, 1990 to 2000 (Dieese-Seade).

The open unemployment rate throughout the 1990s was consistently higher for

the individuals living in the districts classified as poor, followed by the middle

class ones. For example, while the unemployment rate among the people living in

rich areas was 6,7% in 2000, for the individuals living in poor areas the rate was

almost doubled, 12,8%, and for the ones living in middle class areas was slightly

lower: 10,4%.

Moreover, through the 1990s, the gap between the unemployment rate among

the population living in rich areas and poor areas increased. In other words, the

unemployment rose more intensively among the residents of poor areas than among

the ones living in richer areas. For example, while in the rich areas the growth of

the unemployment rate from 1990 to 2000 was 2 points, in the poor areas the

increase in unemployment was 3.9 points. Thus, although it is true that

unemployment rose for the whole of the population in the past decade, this was a


20

phenomenon that impacted the population living in poor, segregated areas more

intensively.

We are aware that the rate of unemployment varies according to different social

groups: by gender, race, age and years of schooling (table 6). However, when we

add the information about place of residence of an individual, new interpretations

of this phenomenon arise. Observe, for example, the case of unemployment for

men and women depending on where they live, which creates a new way of

understanding the incidence of this social problem by gender.

When we distinguish unemployment according to gender and area of residence,

we notice that the vulnerability of women to unemployment is not the same for all

of them. The unemployment rate of women living in poor areas is much higher in

comparison to women who live in rich areas. Moreover, the difference between the

rate of unemployment between men and women is also much higher in the poor

and middle class areas. For example, while the difference in the unemployment

rate between man and women who live in rich areas is 1,5 points, for the

population living in poor areas the same difference reaches almost 6 points. In

other words, women living in poor areas are more likely to be unemployed in

comparison to their pairs of gender living in richer areas, which, in turn, present a

more equal professional insertion in the labour market. Note also that the

unemployment rate for men living in middle class areas was higher than among

women living in rich areas. This suggests that equality of rights and living

conditions, including the right and access to work for men and women is a slow

process but it seems to be slower in poorer places.

The same phenomenon can be seen for other social groups. Observe the case of

the unemployment rate by race. It is unquestionable that people classified as blacks

or ‘pardos’ are more likely to be unemployed than whites and Orientals

independent of the area of residence (in this sense, this is a different process from

the gender issue discussed above). However, the place of residence has also an

important weight in the determination of the unemployment rate of the population

in terms of race. A black individual living in a poor area is almost twice more


21

vulnerable to unemployment in comparison to a (also) black individual who lives

in a rich area. The individuals classified as Orientals are the least vulnerable to

unemployment.

Being the variable ‘years of schooling’ a good proxy to the income level of

individuals in Brazil, this variable allow us to check whether unemployment is

only a matter of income or years of schooling. As one can see from table 6, this

does not seem to be the case. Observe the case of people in the ‘Uncompleted

Primary School’ category (or less than 8 years of schooling) who live in different

areas of the city. While unemployment reaches around 7% of the workers with

“Uncompleted Primary School” living in rich areas, the same figure jumps to

almost 12% of the individuals with the same level of schooling but living in a poor

area or almost 11% for the individuals living in middle class areas. Note that this

phenomenon occurs for all levels of schooling since we can observe that

independent of the years of schooling, the unemployment rate grows as the

individuals live further from the richer areas.23

Given these results, we ask ourselves what could explain a higher vulnerability

to unemployment concerning the individuals (with similar socio-economic

backgrounds) who live in poor areas. Part of the explanation might be related to a

low demand for jobs in the poorer places. We investigate this point in more detail

below. On the other hand, other important aspects that we do not explore here refer

to the costs (time and money) of transport in the metropolis and also the possible

connection between the place where an individual lives and the opportunity

structure available to him including the quality of the social relations that are

established in the different areas.

Although it seems to us a plausible hypothesis to investigate we do not have

available data to measure these effects. But the basic idea seems to make sense.

Imagine, for example, an individual who lives in a poor/peripheral community with

a high number of unemployed people. There are reasons to believe that the chances

23 It is interesting to notice that the unemployment rate for people with a “completed college/university degree” presents little variation among the different areas. In fact, as the access to this level of schooling in Brazil is still pretty much restricted, we can say that this group of the population presents higher chances of being employed comparatively.


22

of this individual to find a job will be smaller in an environment that combines

higher competition for a job and less connections from employed people that might

inform about a job.

We now observe whether the number of jobs available in a certain area might be

connected to the level of local unemployment. In order to measure this, we work

here with the RAIS database, organized by the Brazilian Ministry of Labour which

presents the total number of formally hired employees in all registered companies

in the MRSP in the year 2000.24

We must note before continuing that the observation of the distribution of the

formally registered employees cannot be taken as representative of all the

employment available in a given area as in Brazil since the number of informal

jobs is quite high. For example, in 2000 according to the sample results of the

Demographic Census, something around 22% of all employed people (i.e. people

working regularly and receiving salaries every month) was working informally.

This figure would be much higher if we had excluded the workers from the public

sector who are normally formally hired. Besides that, informal work relations are

also more common among low skilled workers. For example, while only 13% of

workers with a college/university degree are informally hired, this figure jumps to

30% if an individual has studied for less than 8 years (Gomes & Amitrano, 2004).

However, because in Brazil a formally registered employment means access to a

series of benefits and guarantees (including a future pension), we believe that

observing the distribution of formal employment in the space becomes even more

relevant as a strategy of overcoming poverty in a metropolis.

24 The RAIS (“Annual Recording of Social Information – Formal Employment”) is an administrative registry organized

by the Brazilian Ministry of Labour in which all public and private companies have to declare yearly different sort of information about their formally registered employees. The geocoding process of this database was based on the digital cartography of the ‘central registry of companies’ (IBGE, 2000) made available by the centre for metropolitan studies/Cebrap. Only the existent companies in both databases (RAIS and CCE/IBGE) were included in this analyzes.


23

The MRSP had, in 2000, 4,477,060 formal jobs (public and private sectors)25.

However, the spatial distribution of the formal jobs in the space was very

heterogeneous, not only in the region as a whole but also inside each of the

Municipalities, as it can be seen in Map 226.

In order to compare the supply of jobs among the different areas, we divided the

total amount of jobs in a given area by the number of economically active

population (EAP) living in the same area. As a result, the central areas of the cities

that form the MRSP tended to present the highest concentration of formal jobs. As

it can be seen in Map 2, there is an important concentration of formal employment

in the central areas of industrialized cities, such as the ABC area (Southeast), the

south of Guarulhos (Northeast), the northeast of Osasco (West) and the city of São

Paulo (Centre).

25 The data refers to 33 Municipalities in the MRSP, involving something around 8.9 million of economically active

population (EAP). Among them, around 7.1 million were considered occupied according to the 2000 Demographic Census (IBGE). Thus, the absolute number of unemployed people amounted to 1.7 million.

26 In order to minimize the effects of “borders” within intra-urban contiguous areas, we adopted the calculation of a moving average in Map 2. The calculation took into account the sum of jobs within in a 3km radius from the central point of each area, using for this, techniques available in a GIS.


24

Map 2: Distribution of formal employment – Metropolitan Region of São Paulo, 2000

Source: Authors’ elaboration from RAIS/MTE (2000) and IBGE (2000).

Concerning the areas with the smallest number of formal jobs and that are dense

in terms of population calls the attention an area involving more than one

municipality in the extreme East of the city of São Paulo, presenting clear spatial

continuities to the southeast of Guarulhos, Itaquaquecetuba city and part of two

municipalities in the ABC area: Mauá and Ribeirão Pires. This part of the city of

São Paulo presents an extreme absence of formal jobs. For example, if we sum all

economically active population (EAP) living in only three of the districts in that

part of the city of São Paulo (Cidade Tiradentes, Lajeado and Itaim Paulista), we

will find that there are something around 9,800 formal jobs for an EAP of 270,000.

In other words, an average of 0.04 formal jobs per residents. This sort of

phenomenon is also seen in other poor areas of the city of São Paulo, as in the

North (Brasilândia) and South (Jardim Ângela, Capão Rendondo).


25

Let’s give another example in order to clarify the difference. If we take the

number of formal jobs offered in the following richer districts: Morumbi, Butantã,

Pinheiros, Vila Mariana, Moema, Jardim Paulista and Santo Amaro (all very rich

neighbourhoods), we will find approximately 280,000 EAP living in these areas (a

figure, by the way, very close to the number used in the example above for the

poor areas) but with a number of formal jobs amounting to 623,000 or 2.24 formal

jobs per resident.

Given these discrepancies, we observe if there is any relation between the

number of jobs offered in a given area and the rate of unemployment of the

residents of the same area (graph 1). The idea is the following: if the spatial

distribution of the unemployment rate has absolutely nothing to do with the

number of local formal jobs, we will be able to say, for sure, that the number of

jobs in a certain area does not interfere with the probability of an individual being

unemployed. Note that there are two different sources of information for the two

axes: formal employment derives from the RAIS database and the unemployment

rate from the Demographic Census.

Graph 1: Unemployment rate and formal jobs in the ‘weighting areas’ by social group – Metropolitan Region of São Paulo, 2000

Source: Author’s elaboration from RAIS (2000) and IBGE (2000).


26

The results could not have been more impressive. The areas with lower levels of

unemployment are also the areas with the highest number of formal jobs and the

opposite is also true for all cities in the MRSP. The only exception is a large area

to the south of São Bernardo do Campo city. The reason is a great amount of

companies of large and medium size along important motorways that connect São

Paulo to the port of Santos (Vias Anchieta and Imigrantes), including, for example,

Volkswagen, in areas with low population density.

Various aspects could be highlighted from the observation of this graph and the

previous map. First, we can say that not only the population living in poor areas of

the MRSP presents higher levels of unemployment in comparison to the other two

groups but they live, at the same time, in areas which the number of formal jobs is

also smaller. On the other hand, in the rich areas the situation is more pulverized:

the individuals live in areas with higher and lower levels of formal jobs, although

they live in areas in a smaller concentration of unemployed people. Even though

these results are referred to areas, the data suggest that the spatial concentration

concerning the number of formal employment in the MRSP might be one of the

determinants of the economic performance of individuals27.

This data shows that there are areas inside the MRSP in which there is a

complete absence of local formal jobs. They are intra-urban spaces, densely

populated, where the local micro-economic dynamic is very poor, either because

they are areas with a high concentration of unemployed people or because there are

few formal jobs available locally. When we know that the formal jobs are normally

better remunerated and linked to other benefits, the situation becomes even more

dramatic. Given this picture, it is difficult to imagine how long it would take for

the economy to improve the number of local jobs that could benefit the locals and

maybe re-vitalize economically these areas without the interference of the State,

even if the economy was growing in a stable way.

27 Part of the more pulverized situation found in the rich areas might also be related to districts that present a high

level of social heterogeneity as it is, for example, the case of the Vila Andrade district that has one of the most expensives land values of the city of São Paulo (Morumbi) and is contiguous to the largest favela (slum) of the city (Paraisópolis).


27

From the point of view of our discussion here and based on the empirical results

found up to now, it is not only the smaller number of local formal jobs (and a

higher concentration of unemployed people living in the same area) that might

interfere with the chances of a poor individual living in a poor area of the MRSP to

find a job and, therefore, rising the chances of overcoming the poverty condition.

Further investigation of the kind of job available in the different areas has showed

that the wages in the poorest areas are also smaller.

While the average remuneration (in 2000 minimum wages – MW) of formally

hired workers was 4.3 in the companies located in the poor areas, the average

wages in the rich areas was 7.2 (and in the middle class areas 5.2)28. One might

argue that this is because the type of jobs offered in the rich areas, because of the

nature of the job, would demand more educated workers, therefore, interfering

with the average remuneration found in the rich areas. However, this is not

confirmed by analysing the data presented in table 7.

28 In July 2000, the national minimum wage was fixed at R$ 151,00 or approximately U$ 83 according to the average

exchange rate of 2000 (Banco Central do Brasil).


28

Table 7. Average remuneration* of formally hired workers by years of schooling, sector of economic activity and average size of the companies, according to the type of area where the company is located. MRSP, 2000.

Types of Areas Poor areas

Middle class areas Rich areas Total

YEARS OF SCHOOLING OF THE EMPLOYEES Uncompleted Primary School 3,64 3,93 4,29 3,86 Completed Primary School (8 years) 3,73 4,10 4,67 4,02 Completed Secondary School (11 years) 4,72 5,32 6,49 5,23 Uncompleted Further Education 7,32 8,16 10,03 8,09 Completed Further Education 10,71 12,54 16,19 12,29

SECTOR OF ECONOMIC ACTIVITY Industrial Sector 4,65 5,36 7,89 5,40 Services 4,06 5,17 7,43 5,02 Trade and Commerce 3,40 3,92 5,48 3,90 Civil Construction 3,46 3,8 4,94 3,84 Public Administration 6,39 8,81 13,48 10,29

AVERAGE SIZE OF THE COMPANY Between 1 and 4 employees 4,41 3,15 --- 4,16 5 and 9 employees 3,69 4,22 5,11 4,10

10 and 14 employees 4,47 5,82 7,02 5,75 15 and 49 employees 6,14 6,91 9,26 6,96 More than 50 employees 6,63 6,88 10,33 7,20 Total 4,71 5,36 7,25 5,35 *in minimum wages of 2000. Source: RAIS, 2000 (MTE).

It is true that average remuneration grows along with the years of schooling.

While formally registered workers with uncompleted primary school received, on

average, 3.9 MW in 2000, the ones with completed college/university degree get a

salary three times higher: 12.3 MW. However, the remuneration varies also

according to the area in which the job is offered. Whilst the average wages of a

worker with uncompleted primary school is 3.6 MW in the poor areas, another

worker with the same level of schooling but working in a rich area, earns 4.3 MW.

Besides that, the differential in the wages of workers according to where they work

is even higher for those with complete or incomplete college degree. What is


29

noticeable is that the remuneration differential varies according to the place the

company is established for all levels of schooling.

Another possible argument would say that maybe the reason is a lower number

of industrial jobs, presenting on average better wages. Yet, the data available at the

moment do not sustain such argument. Although the number of formal jobs is

much smaller in the poor areas (around 10% of all formal jobs), the industrial job

is the second most important sector employing people: 22% of all formal industrial

jobs in the MRSP are located in the poor areas. Besides that, when we distribute

the average remuneration by sector of economic activity, the differentials are kept

the same for all types of activities (table 7), including the public administration

(which, by the way, has only 1% of all its jobs in poor areas) and civil construction

(intense in low skilled workers). The same results were found for all other sector

of economic activity. Moreover, when we control the results by the size of the

company, we find similar outcomes. Although it is true that the larger the

organization is, the higher the wages are, the differentials depending on the

location of the company are still the same ones found before.29

In sum, what we can say about the labour market in the MRSP is that the

population living in the poorest areas coexist with higher rates of unemployment

that are spatially correlated to a lower number of local formal jobs and of the jobs

available the remuneration is also smaller. In order to increase the chances of

getting a job, poor individuals who live in areas with a small number of local jobs

have to bear the costs of transport (monetary or non-monetary ones) to more

central areas. Moreover, these individuals are living in ‘communities’ that present

a higher number of unemployed people and also a higher concentration of people

with low levels of schooling (in a decade that presented a higher selectivity of the

labour force concerning the level of schooling, see Gomes & Amitrano, 2004), all

factors that also have an impact on the chances of individuals to get a job and,

therefore, increasing the chances of overcoming poverty.

29 The only exception is the companies between 1 and 4 employees. The most likely reason is that the owner of a

“one-employee” company ‘gives to herself’ a higher salary.


30

4. Consequences: the case for the adoption of spatial strategies for social

policies

The results presented in the previous sections give us reasons to believe that

social policies for metropolitan areas should incorporate in their drawing and

strategy of implementation the characteristics of the territories where the

population live. The argument favouring the adoption of spatial strategies for

social policies acknowledges the existence of strong “negative externalities” when

living in neighbourhoods with a high concentration of poverty (Durlauf, 2001).

This means that individuals or families with similar socio-economic characteristics

will have different opportunities depending on the area they live. For example, the

probability of getting a formal, registered job is smaller because there is a low

proportion of individuals who are employed in the formal sector, therefore

reducing the possibilities of an individual to find a job by activating their social

relations network (discussed in section 3). Besides all that, in many cases the

population living in poor areas tends to be more exposed to other sort of risks, as

the ones related to a precarious sewage system, instability concerning property

rights and violence.

Although this accumulation of basic social problems is not complete, as it was

seen by the 1970s literature on urban sociology, there are still some areas of the

metropolitan regions of Brazil that are extremely exposed to an intense

accumulation of risks and negative situations which can be seen very clearly when

we analyse in more detail some of the so-called “hyper-peripheries” (Torres &

Marques, 2002). In other words, one of the most dramatic challenges concerning

social policies in metropolitan areas is to be able to transform these “negative

externalities” into positive ones, i.e., eliminating the factors that make the

characteristics of a place of residence determinant in the reproduction of poverty.30

30 Part of this sort of argument can be found in the urban sociology literature known as ‘area studies’ (Glennester et

all, 1999).


31

In this sense, the adoption of spatial strategies for social policies presents two

main angles31. From one perspective, such a proposition means to incorporate

spatial logics to traditional social policies – such as educational, health – which

should have a specific format in places with a high concentration of poverty. This

is the case of some existent programs as the “Health Family” program (PSF), for

example, where a doctor and trained community members visit regularly all

households in a certain neighbourhood. From a second perspective, it is also

possible to think about the adoption of integrated policies that would deal with

specific problematic areas, involving simultaneously actions from different

Secretariats and bureaucratic agencies, therefore raising the chances of improving

life conditions in a given locality in an effective manner. Initiatives like this can

be observed, for example, in some housing projects of re-urbanization of favelas32.

However, the State will only be able to implement this sort of policies if it can

correctly comprehend the processes that produce the already built environments

and if state agencies can count with a detailed description of the spatial

distribution of these elements in the urban space. In order to execute both tasks, it

is necessary to gather enough knowledge about the distribution of the social groups

in the space as well as a set of precise concepts able to describe reality.

We believe that the dominant comprehension about urban poverty, the

peripheries, segregation and socio-spatial inequalities in Brazil sometimes leads to

the execution of inefficient and badly targeted public policies, even when the

interests and the decisions for such initiatives exist. The problem lies in the fact

that the hegemonic vision tends to see the peripheries as socially homogenous

places. This is related to the consideration of a unified, one-dimensional and

31 Note that when we are talking about incorporating spatial dimensions in social policies we are not referring

necessarily to policies focused on the federative entities, as the States and Municipalities. We are also talking about territorial detailed cuts, as districts, neighbourhoods and census tracts within a metropolitan area. Strictly speaking, in metropolitan areas this cut has to be necessarily intra-urban. This argument makes sense in a country with a growing degree of urbanization and where over 40% of the whole population live in urban conglomerates with more than 1 million inhabitants, making necessary to disaggregate the public action into smaller spatial units.

32 New social policies, such as the ones in the Mexican program “Oportunidades” have combined, in an interesting way, operational initiatives of an individual character with spatial dimensions, which deserve the attention from the point of view of Brazilian policies, especially when we are talking about metropolitan regions. See www.progresa.gob.mx.


32

perfectly cumulative process of poverty production. As a consequence, the

predominant vision tends to prescribe general distributive policies for all

peripheral, poor spaces. Considering the stock of social problems accumulated in

those areas and the fiscal limitations of the State, some policies tend to be lost in

the face of evident insufficiency of what is done and the irrelevance of the

initiatives considering what should (or could) have been done instead.

These elements are important, especially when we know that in places such as

São Paulo the ‘poor among the poor’ tend to become invisible or impossible to find

even if there is well-intentioned public policies (Torres, 2002) not only because

they are normally less organized and present more difficulties to organize

themselves and express their demands or needs but also because the actual

administrative routine of state agencies hardly ever can distinct them. This might

be due to prejudices among some technical communities (Marques, 2000). But we

believe that most of the time this problem is caused by ‘ontological’ reasons,

connected to the nature of the public policies: the persons who are excluded from

the public policies are rarely target, or – in other words - not considered as part of

the demand (Torres, 2002).

Alternatively, we must develop detailed studies about segregation and spatial

inequalities that can be appropriated and used by the public authorities. The

policies, then, would be able to consider the diverse (and different) existent needs

in the peripheral spaces. Moreover, if these capacities are developed we will be

able to build spatial strategies for social policies and improve the operational

aspects of the policies that try to interfere with the role of segregation patterns.

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